From ecb51e423599e0ef6fc711680f4577899487a8fb Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 21:25:50 +0200
Subject: [PATCH 001/106] Add Bayesian analysis design note

---
 docs/dev/bayesian-analysis-design.md | 410 +++++++++++++++++++++++++++
 1 file changed, 410 insertions(+)
 create mode 100644 docs/dev/bayesian-analysis-design.md

diff --git a/docs/dev/bayesian-analysis-design.md b/docs/dev/bayesian-analysis-design.md
new file mode 100644
index 00000000..d3097e46
--- /dev/null
+++ b/docs/dev/bayesian-analysis-design.md
@@ -0,0 +1,410 @@
+# Bayesian Analysis Design
+
+**Status:** Draft
+**Date:** 2026-05-07
+
+## Goal
+
+Add Bayesian analysis to the existing fitting workflow with the smallest
+user-facing API change possible, reusing the current `Analysis`,
+`Fit`, `Fitter`, minimizer factory, result reporting, and plotting
+surfaces.
+
+The primary first target is BUMPS DREAM sampling for diffraction
+refinement with parameter bounds taken from each parameter's
+`fit_min` and `fit_max` attributes.
+
+## User-Facing API
+
+The intended workflow stays aligned with the current fitting API:
+
+```python
+project.analysis.fit.minimizer_type = 'bumps (lm)'
+project.analysis.fit()
+
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+project.analysis.fit()
+
+project.analysis.display.fit_results()
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+project.display.plotter.plot_param_correlations()
+```
+
+Additional Bayesian-only plotting methods should be added rather than
+overloading every existing method with different semantics:
+
+```python
+project.display.plotter.plot_posterior_pairs()
+project.display.plotter.plot_param_distribution(param)
+```
+
+For the first implementation, these Bayesian plotting methods may use
+ArviZ with the Plotly backend internally.
+
+### Explicit Two-Step Workflow
+
+`'bumps (dream)'` should **not** implicitly run a deterministic
+Levenberg-Marquardt pre-fit. Users should call `'bumps (lm)'` first when
+they want a better starting point.
+
+Reasons:
+
+- It keeps `fit()` behavior explicit and predictable.
+- It avoids hidden extra runtime.
+- It keeps deterministic fit failures separate from sampling failures.
+- It matches the current minimizer-selection model.
+
+## Core Architectural Decision
+
+### Keep Full Results in `analysis.fit_results`
+
+Bayesian results should be stored in a new runtime result object,
+**not** in a new heavy `Analysis` category.
+
+Recommended model:
+
+- `FitResults` remains the base deterministic result container.
+- `BayesianFitResults` extends `FitResults` for posterior-specific data.
+- `analysis.fit_results` stores either `FitResults` or
+  `BayesianFitResults`.
+
+This keeps the existing public access pattern unchanged:
+
+```python
+project.analysis.fit_results
+```
+
+### Why Not a New Results Category Under `Analysis`
+
+Current `Analysis` categories are lightweight, structured,
+project-owned configuration or control objects:
+
+- `fit`
+- `aliases`
+- `constraints`
+- `joint_fit_experiments`
+
+These are serialized with the project. Full Bayesian results are a poor
+fit for this category model because they are:
+
+- runtime products rather than project configuration
+- potentially large arrays
+- not natural CIF content
+- often disposable or recomputable
+
+Posterior chains, posterior predictive draws, and pair-plot input data
+should therefore remain runtime-only objects attached to
+`BayesianFitResults`.
+
+## Optional Future Category
+
+A **small** `Analysis` category may still be useful later, but only for
+Bayesian configuration, not for heavy result storage.
+
+Possible future category name:
+
+- `analysis.sampling`
+- or `analysis.bayesian`
+
+Possible responsibilities:
+
+- sampler type
+- point estimate policy
+- default HDI levels
+- exposed DREAM hyperparameters if they become public later
+
+This category should stay lightweight and serializable. It should not
+store raw chains.
+
+## Proposed New Runtime Types
+
+### `BumpsDreamMinimizer`
+
+Add a new minimizer implementation registered through
+`MinimizerFactory` with tag:
+
+```python
+'bumps (dream)'
+```
+
+Responsibilities:
+
+- build bounded BUMPS parameters from EasyDiffraction parameters
+- require finite `fit_min` and `fit_max` for every sampled parameter
+- run DREAM with internal default settings
+- collect posterior samples and sampler state
+- compute posterior summaries
+- set project parameters to the chosen point estimate after sampling
+
+### `BayesianFitResults`
+
+Subclass `FitResults` and add Bayesian-specific fields.
+
+Recommended fields:
+
+- `sampler_name`
+- `point_estimate_name`
+- `posterior_samples`
+- `posterior_parameter_summaries`
+- `posterior_predictive`
+- `credible_interval_levels`
+- `engine_result`
+
+### Supporting Value Objects
+
+Recommended helper containers:
+
+- `PosteriorSamples`
+- `PosteriorParameterSummary`
+- `PosteriorPredictiveSummary`
+
+These keep `BayesianFitResults` structured and reduce pressure to hide
+plotting-specific arrays inside experiment data categories.
+
+## Point Estimate Policy
+
+After a DREAM run, project parameters should be updated to the **MAP**
+or best posterior sample, not to independent marginal medians.
+
+Reason:
+
+- per-parameter medians can produce a parameter vector that was never
+  sampled jointly
+- MAP corresponds to a coherent sampled state
+- the calculated profile shown after Bayesian fitting should correspond
+  to one actual parameter set
+
+Posterior tables can still report median, standard deviation, and HDIs.
+
+## Parameter Bounds
+
+DREAM should require finite bounds for every sampled free parameter.
+
+Behavior:
+
+- use `fit_min` and `fit_max`
+- if any sampled parameter is unbounded, stop with a clear error
+- the error should list the offending parameter names
+
+This is stricter than some deterministic minimizers, but appropriate for
+bounded posterior sampling.
+
+## Hidden DREAM Defaults
+
+For the first implementation, sampling parameters should stay internal
+and use library defaults.
+
+Examples:
+
+- `n_steps`
+- `n_burn`
+- `thin`
+- `pop`
+- `alpha`
+
+These can become user-configurable later through a small sampling
+configuration category or explicit keyword API.
+
+## Display Behavior
+
+### `project.analysis.display.fit_results()`
+
+Keep the existing entry point and dispatch by result type.
+
+Deterministic fit:
+
+- keep the current summary
+- keep the current fitted-parameter table
+
+Bayesian fit:
+
+- show a sampling summary section
+- show posterior parameter summaries instead of only fitted values and
+  covariance-derived uncertainties
+
+Recommended Bayesian table columns:
+
+- datablock
+- category
+- entry
+- parameter
+- start
+- MAP
+- median
+- posterior std
+- 68% interval
+- 95% interval
+- units
+
+## Plotting Behavior
+
+### `plot_meas_vs_calc(expt_name=...)`
+
+Keep the existing method name.
+
+Deterministic fit:
+
+- current measured vs calculated behavior unchanged
+
+Bayesian fit:
+
+- measured pattern
+- MAP calculated line
+- shaded credible band around prediction
+
+Recommended default:
+
+- one shaded 95% credible interval band
+
+This is the clearest default for diffraction patterns. A full posterior
+predictive distribution at every x value is useful but visually denser,
+so it should not be the default first view.
+
+### `plot_param_correlations()`
+
+Keep the current method name and purpose.
+
+Deterministic fit:
+
+- covariance- or engine-derived correlation matrix
+
+Bayesian fit:
+
+- posterior-sample correlation matrix
+
+This preserves the meaning of the method.
+
+### New `plot_posterior_pairs()`
+
+Add a new Bayesian-specific method for pairwise posterior exploration.
+
+Intended behavior:
+
+- ArviZ-backed implementation first
+- Plotly backend
+- pairwise scatter or density panels
+- marginal distributions on the diagonal
+- optional parameter subset support later
+
+This method should serve the role of an interactive corner or pair plot.
+
+### New `plot_param_distribution(param)`
+
+Add a one-dimensional marginal posterior plot for a selected parameter.
+
+Intended behavior:
+
+- ArviZ-backed implementation first
+- posterior histogram or density
+- MAP marker
+- median marker
+- HDI overlay
+
+This is more natural than starting with a model-comparison distribution
+plot because the current library stores one active fit result, not a set
+of compared Bayesian models.
+
+### Possible Later Addition: `plot_posterior_predictive(...)`
+
+A dedicated method can be added later for richer predictive views.
+
+Possible API:
+
+```python
+project.display.plotter.plot_posterior_predictive(
+    expt_name='hrpt',
+    style='band',
+)
+```
+
+Possible styles:
+
+- `band`
+- `dist`
+
+For the first implementation, these views may also be delegated to
+ArviZ where practical.
+
+## Data Ownership
+
+Experiment `data` categories should remain responsible for:
+
+- measured pattern
+- current calculated pattern
+- current background pattern
+
+Posterior-specific data should remain analysis-owned through
+`BayesianFitResults`, because it is:
+
+- fit-result scoped
+- potentially joint across experiments
+- used mainly for reporting and plotting
+
+## Persistence and Serialization
+
+For the first implementation:
+
+- do not serialize full posterior chains to CIF
+- do not force posterior arrays into project save files
+
+Possible future export surfaces:
+
+- explicit posterior export methods
+- CSV summaries
+- NumPy or NetCDF export if needed later
+
+## Dependency Direction
+
+For the first implementation, add ArviZ as a runtime dependency and use
+it for Bayesian plotting and summary-oriented data transformations where
+that reduces implementation cost.
+
+Recommended direction:
+
+- keep Plotly as the interactive rendering backend
+- use ArviZ-backed plotting internally for Bayesian views where it fits
+- keep the public EasyDiffraction plotting API independent of ArviZ
+
+Future note:
+
+- once Bayesian functionality stabilizes, EasyDiffraction may replace
+  some or all ArviZ-backed plots with project-native implementations to
+  improve control over styling, interactivity, and domain-specific
+  diffraction presentation
+
+## Recommended First Implementation Scope
+
+### Phase 1
+
+- add `MinimizerTypeEnum.BUMPS_DREAM`
+- add `BumpsDreamMinimizer`
+- add `BayesianFitResults`
+- keep `analysis.fit_results` as the single public result slot
+- make `analysis.display.fit_results()` dispatch by result type
+
+### Phase 2
+
+- extend `plot_meas_vs_calc()` with Bayesian credible bands
+- extend `plot_param_correlations()` to use posterior samples when
+  available
+- add `plot_posterior_pairs()`
+- add `plot_param_distribution(param)`
+
+### Phase 3
+
+- consider adding a lightweight `analysis.sampling` category for saved
+  Bayesian configuration
+- consider explicit posterior export APIs
+- consider richer predictive plotting modes
+
+## Summary
+
+The recommended design is:
+
+- add `'bumps (dream)'` as a normal minimizer type
+- keep Bayesian analysis inside the existing `fit()` workflow
+- store heavy posterior results in `BayesianFitResults`
+- keep `analysis.fit_results` as the main public result entry point
+- avoid a new heavy `Analysis` category for results
+- only add a small new `Analysis` category later if Bayesian settings
+  need to become persistent and user-configurable

From b3a5462669b4c007b36c6c881ec4d754156211ab Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 21:26:05 +0200
Subject: [PATCH 002/106] Add arviz for Bayesian analysis

---
 pixi.lock      | 254 ++++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |   1 +
 2 files changed, 253 insertions(+), 2 deletions(-)

diff --git a/pixi.lock b/pixi.lock
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       - pypi: https://files.pythonhosted.org/packages/db/d9/c495884c6e548fce18a8f40568ff120bc3a4b7b99813081c8ac0c936fa64/watchdog-6.0.0-py3-none-win_amd64.whl
       - pypi: https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a4/f5/10b68b7b1544245097b2a1b8238f66f2fc6dcaeb24ba5d917f52bd2eed4f/wsproto-1.3.2-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/dc/83/6d810a8a9ebc9c307989b418840c20e46907c74d707beb67ab566773e6fc/xarray-2026.4.0-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/19/d4/225027a913621a879b429a043674aa35220e6ce67785acad4f7bd0c4ff33/xarray_einstats-0.10.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/38/8b/7ec325b4e9e78beefc2d025b01ee8a2fde771ef7c957c3bff99b9e1fbffa/xraydb-4.5.8-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/a9/5b/9b92f54c784c26e2a422e55a8d2607ab15b7ea3349e28359282f84f01d43/yarl-1.23.0-cp314-cp314-win_amd64.whl
       - pypi: ./
@@ -4089,6 +4143,125 @@ packages:
   - pkg:pypi/arrow?source=hash-mapping
   size: 113854
   timestamp: 1760831179410
+- pypi: https://files.pythonhosted.org/packages/64/a8/c09fbe44b12fa919c5bfe0afb71e60d1231a7dc93405e54c30496c57c9d3/arviz-1.1.0-py3-none-any.whl
+  name: arviz
+  version: 1.1.0
+  sha256: 87ebd21ce052f30d21f932b4166fc31b91c0bc7443f8da7fed3518b342267010
+  requires_dist:
+  - arviz-base>=1.1.0,<1.2.0
+  - arviz-stats[xarray]>=1.1.0,<1.2.0
+  - arviz-plots>=1.1.0,<1.2.0
+  - arviz-plots[bokeh] ; extra == 'bokeh'
+  - build ; extra == 'check'
+  - pre-commit ; extra == 'check'
+  - h5netcdf ; extra == 'doc'
+  - h5py ; extra == 'doc'
+  - jupyter-sphinx ; extra == 'doc'
+  - matplotlib ; extra == 'doc'
+  - myst-parser[linkify] ; extra == 'doc'
+  - myst-nb ; extra == 'doc'
+  - pydata-sphinx-theme>=0.13 ; extra == 'doc'
+  - sphinx>=5 ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - sphinx-design ; extra == 'doc'
+  - sphinx-notfound-page ; extra == 'doc'
+  - sphinxcontrib-youtube ; extra == 'doc'
+  - sphinx-togglebutton ; extra == 'doc'
+  - sphobjinv ; extra == 'doc'
+  - arviz-base[h5netcdf] ; extra == 'h5netcdf'
+  - arviz-plots[matplotlib] ; extra == 'matplotlib'
+  - arviz-base[netcdf4] ; extra == 'netcdf4'
+  - arviz-plots[plotly] ; extra == 'plotly'
+  - pytest ; extra == 'test'
+  - arviz-base[zarr] ; extra == 'zarr'
+  requires_python: '>=3.12'
+- pypi: https://files.pythonhosted.org/packages/69/d1/705e6c19b437a4105bf3b9ae7945fcfc3ad2abb73d14bae0a3f2d58b305b/arviz_base-1.1.0-py3-none-any.whl
+  name: arviz-base
+  version: 1.1.0
+  sha256: 4f97016f697751038f45d144331a1830c921f0ebc2739d5df343120fba453e83
+  requires_dist:
+  - numpy>=2
+  - xarray>=2024.11.0
+  - typing-extensions>=3.10
+  - lazy-loader>=0.4
+  - build ; extra == 'check'
+  - pre-commit ; extra == 'check'
+  - docstub==0.4 ; extra == 'check'
+  - mypy ; extra == 'check'
+  - pre-commit ; extra == 'ci'
+  - cloudpickle ; extra == 'ci'
+  - sphinx-book-theme ; extra == 'doc'
+  - myst-parser[linkify] ; extra == 'doc'
+  - myst-nb ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - sphinx>=5 ; extra == 'doc'
+  - sphinx-design ; extra == 'doc'
+  - jupyter-sphinx ; extra == 'doc'
+  - h5netcdf[h5py] ; extra == 'doc'
+  - h5netcdf[h5py] ; extra == 'h5netcdf'
+  - netcdf4 ; extra == 'netcdf4'
+  - xarray!=2025.8.0 ; extra == 'test'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - scipy ; extra == 'test'
+  - zarr ; extra == 'zarr'
+  requires_python: '>=3.12'
+- pypi: https://files.pythonhosted.org/packages/2f/c8/005d1de3af80f54411703d1263a0b9d31276411ec9f273d9432c59b17299/arviz_plots-1.1.0-py3-none-any.whl
+  name: arviz-plots
+  version: 1.1.0
+  sha256: 5c7ab5b0c7c29cda6ddb5e04c699c70285fe68a76d2b1b42302c69a85742adde
+  requires_dist:
+  - arviz-base>=1.1,<1.2
+  - arviz-stats[xarray]>=1.1,<1.2
+  - bokeh>=3.4 ; extra == 'bokeh'
+  - sphinx-book-theme ; extra == 'doc'
+  - myst-parser[linkify] ; extra == 'doc'
+  - myst-nb ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - sphinx>=6 ; extra == 'doc'
+  - sphinx-design ; extra == 'doc'
+  - jupyter-sphinx ; extra == 'doc'
+  - h5netcdf[h5py] ; extra == 'doc'
+  - plotly<6 ; extra == 'doc'
+  - matplotlib>=3.9 ; extra == 'matplotlib'
+  - plotly>=5.19 ; extra == 'plotly'
+  - webcolors ; extra == 'plotly'
+  - hypothesis ; extra == 'test'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - h5netcdf[h5py] ; extra == 'test'
+  - kaleido ; extra == 'test'
+  requires_python: '>=3.12'
+- pypi: https://files.pythonhosted.org/packages/0d/1f/d398de1612f7a611e22d743280339c9af4903675635e41be3370091c704b/arviz_stats-1.1.0-py3-none-any.whl
+  name: arviz-stats
+  version: 1.1.0
+  sha256: ed47334ccff8670a0b90a50e1a37e7257268084eb3436e6b7b15e623f1001947
+  requires_dist:
+  - numpy>=2
+  - scipy>=1.13
+  - sphinx-book-theme ; extra == 'doc'
+  - myst-parser[linkify] ; extra == 'doc'
+  - myst-nb ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - sphinx<9 ; extra == 'doc'
+  - sphinx-design ; extra == 'doc'
+  - jupyter-sphinx ; extra == 'doc'
+  - h5netcdf[h5py] ; extra == 'doc'
+  - sphinx-autosummary-accessors ; extra == 'doc'
+  - numba ; extra == 'numba'
+  - xarray-einstats[einops,numba] ; extra == 'numba'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest ; extra == 'test-xarray'
+  - pytest-cov ; extra == 'test-xarray'
+  - h5netcdf[h5py] ; extra == 'test-xarray'
+  - arviz-base>=1.1,<1.2 ; extra == 'xarray'
+  - xarray-einstats ; extra == 'xarray'
+  - xarray>=2024.11.0 ; extra == 'xarray'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/3f/d0/7b958df957e4827837b590944008f0b28078f552b451f7407b4b3d54f574/asciichartpy-1.5.25-py2.py3-none-any.whl
   name: asciichartpy
   version: 1.5.25
@@ -5388,9 +5561,10 @@ packages:
   requires_python: '>=3.12'
 - pypi: ./
   name: easydiffraction
-  version: 0.16.0+devdirty3
-  sha256: f7b222922aa1bbe773d6384ee96acc456820490c46139a8f2598129cb6c4717e
+  version: 0.16.0+devdirty7
+  sha256: 0b4085f9386dbebf4b7c5a85d5f98b35d8cf1fc69f791bd0759d20995c534a33
   requires_dist:
+  - arviz
   - asciichartpy
   - asteval
   - bumps
@@ -12697,6 +12871,82 @@ packages:
   requires_dist:
   - h11>=0.16.0,<1
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/dc/83/6d810a8a9ebc9c307989b418840c20e46907c74d707beb67ab566773e6fc/xarray-2026.4.0-py3-none-any.whl
+  name: xarray
+  version: 2026.4.0
+  sha256: d43751d9fb4a90f9249c30431684f00c41bc874f1edccd862631a40cbc0edf08
+  requires_dist:
+  - numpy>=1.26
+  - packaging>=24.2
+  - pandas>=2.2
+  - scipy>=1.15 ; extra == 'accel'
+  - bottleneck ; extra == 'accel'
+  - numbagg>=0.9 ; extra == 'accel'
+  - numba>=0.62 ; extra == 'accel'
+  - flox>=0.10 ; extra == 'accel'
+  - opt-einsum ; extra == 'accel'
+  - xarray[accel,etc,io,parallel,viz] ; extra == 'complete'
+  - netcdf4>=1.6.0 ; extra == 'io'
+  - h5netcdf[h5py]>=1.5.0 ; extra == 'io'
+  - pydap ; extra == 'io'
+  - scipy>=1.15 ; extra == 'io'
+  - zarr>=3.0 ; extra == 'io'
+  - fsspec ; extra == 'io'
+  - cftime ; extra == 'io'
+  - pooch ; extra == 'io'
+  - sparse>=0.15 ; extra == 'etc'
+  - dask[complete] ; extra == 'parallel'
+  - cartopy>=0.24 ; extra == 'viz'
+  - matplotlib>=3.10 ; extra == 'viz'
+  - nc-time-axis ; extra == 'viz'
+  - seaborn ; extra == 'viz'
+  - pandas-stubs ; extra == 'types'
+  - scipy-stubs ; extra == 'types'
+  - types-colorama ; extra == 'types'
+  - types-decorator ; extra == 'types'
+  - types-defusedxml ; extra == 'types'
+  - types-docutils ; extra == 'types'
+  - types-networkx ; extra == 'types'
+  - types-openpyxl ; extra == 'types'
+  - types-pexpect ; extra == 'types'
+  - types-psutil ; extra == 'types'
+  - types-pycurl ; extra == 'types'
+  - types-pygments ; extra == 'types'
+  - types-python-dateutil ; extra == 'types'
+  - types-pytz ; extra == 'types'
+  - types-pyyaml ; extra == 'types'
+  - types-requests ; extra == 'types'
+  - types-setuptools ; extra == 'types'
+  - types-xlrd ; extra == 'types'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/19/d4/225027a913621a879b429a043674aa35220e6ce67785acad4f7bd0c4ff33/xarray_einstats-0.10.0-py3-none-any.whl
+  name: xarray-einstats
+  version: 0.10.0
+  sha256: fa3169b46cee29092db820d8bbc203148bada4fc970ee75e62cbf3dd7c5a8945
+  requires_dist:
+  - numpy>=2.0
+  - scipy>=1.13
+  - xarray>=2024.2.0
+  - furo ; extra == 'doc'
+  - myst-parser[linkify] ; extra == 'doc'
+  - myst-nb ; extra == 'doc'
+  - sphinx-copybutton ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - sphinx>=5 ; extra == 'doc'
+  - jupyter-sphinx ; extra == 'doc'
+  - sphinx-design ; extra == 'doc'
+  - watermark ; extra == 'doc'
+  - matplotlib ; extra == 'doc'
+  - sphinx-togglebutton ; extra == 'doc'
+  - einops ; extra == 'einops'
+  - numba>=0.55 ; extra == 'numba'
+  - hypothesis ; extra == 'test'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - packaging ; extra == 'test'
+  - scipy>=1.15 ; extra == 'test'
+  - preliz>=0.19 ; extra == 'test'
+  requires_python: '>=3.12'
 - pypi: https://files.pythonhosted.org/packages/38/8b/7ec325b4e9e78beefc2d025b01ee8a2fde771ef7c957c3bff99b9e1fbffa/xraydb-4.5.8-py3-none-any.whl
   name: xraydb
   version: 4.5.8
diff --git a/pyproject.toml b/pyproject.toml
index 5faffe6b..3bab31f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,6 +47,7 @@ dependencies = [
   'darkdetect',     # Detecting dark mode (system-level)
   'pandas',         # Displaying tables in Jupyter notebooks
   'plotly',         # Interactive plots
+  'arviz',          # Bayesian analysis summaries and posterior plotting
   'py3Dmol',        # Visualisation of crystal structures
 ]
 

From 252ee56acfc217c4563f92f6700679d74870f2b7 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 23:46:22 +0200
Subject: [PATCH 003/106] Remove old package structure documentation

---
 .github/copilot-instructions.md              |   6 +-
 docs/architecture/package-structure-full.md  | 425 -------------------
 docs/architecture/package-structure-short.md | 224 ----------
 tools/generate_package_docs.py               |   4 +-
 4 files changed, 5 insertions(+), 654 deletions(-)
 delete mode 100644 docs/architecture/package-structure-full.md
 delete mode 100644 docs/architecture/package-structure-short.md

diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 18b01c47..5613d742 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -77,7 +77,7 @@
 ## Testing
 
 - Every new module, class, or bug fix ships with tests. See
-  `docs/architecture/architecture.md` §10 for the full strategy.
+  `docs/dev/architecture.md` §10 for the full strategy.
 - Unit tests mirror the source tree:
   `src/easydiffraction/<pkg>/<mod>.py` →
   `tests/unit/easydiffraction/<pkg>/test_<mod>.py`. Verify with
@@ -102,8 +102,8 @@
 
 - Before any structural/design change (new categories, factories,
   switchable-category wiring, datablocks, CIF serialisation), read
-  `docs/architecture/architecture.md` and follow documented patterns.
-  Localised bug fixes or test updates need only this file.
+  `docs/dev/architecture.md` and follow documented patterns. Localised
+  bug fixes or test updates need only this file.
 - Project is in beta: no legacy shims, no deprecation warnings — update
   tests and tutorials to the current API.
 - Minimal diffs; don't reformat working code. Fix only what's asked;
diff --git a/docs/architecture/package-structure-full.md b/docs/architecture/package-structure-full.md
deleted file mode 100644
index be35bb07..00000000
--- a/docs/architecture/package-structure-full.md
+++ /dev/null
@@ -1,425 +0,0 @@
-# Package Structure (full)
-
-```
-📦 easydiffraction
-├── 📁 analysis
-│   ├── 📁 calculators
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   │   ├── 🏷️ class PowderReflnRecord
-│   │   │   └── 🏷️ class CalculatorBase
-│   │   ├── 📄 crysfml.py
-│   │   │   └── 🏷️ class CrysfmlCalculator
-│   │   ├── 📄 cryspy.py
-│   │   │   └── 🏷️ class CryspyCalculator
-│   │   ├── 📄 factory.py
-│   │   │   └── 🏷️ class CalculatorFactory
-│   │   └── 📄 pdffit.py
-│   │       └── 🏷️ class PdffitCalculator
-│   ├── 📁 categories
-│   │   ├── 📁 aliases
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   ├── 🏷️ class Alias
-│   │   │   │   └── 🏷️ class Aliases
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class AliasesFactory
-│   │   ├── 📁 constraints
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   ├── 🏷️ class Constraint
-│   │   │   │   └── 🏷️ class Constraints
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class ConstraintsFactory
-│   │   ├── 📁 fit
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   └── 🏷️ class Fit
-│   │   │   ├── 📄 enums.py
-│   │   │   │   └── 🏷️ class FitModeEnum
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class FitFactory
-│   │   ├── 📁 joint_fit_experiments
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   ├── 🏷️ class JointFitExperiment
-│   │   │   │   └── 🏷️ class JointFitExperiments
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class JointFitExperimentsFactory
-│   │   └── 📄 __init__.py
-│   ├── 📁 fit_helpers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 metrics.py
-│   │   ├── 📄 reporting.py
-│   │   │   └── 🏷️ class FitResults
-│   │   └── 📄 tracking.py
-│   │       ├── 🏷️ class _TerminalLiveHandle
-│   │       └── 🏷️ class FitProgressTracker
-│   ├── 📁 minimizers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   │   └── 🏷️ class MinimizerBase
-│   │   ├── 📄 bumps.py
-│   │   │   ├── 🏷️ class _EasyDiffractionFitness
-│   │   │   └── 🏷️ class BumpsMinimizer
-│   │   ├── 📄 bumps_amoeba.py
-│   │   │   └── 🏷️ class BumpsAmoebaMinimizer
-│   │   ├── 📄 bumps_de.py
-│   │   │   └── 🏷️ class BumpsDEMinimizer
-│   │   ├── 📄 bumps_lm.py
-│   │   │   └── 🏷️ class BumpsLmMinimizer
-│   │   ├── 📄 dfols.py
-│   │   │   └── 🏷️ class DfolsMinimizer
-│   │   ├── 📄 enums.py
-│   │   │   └── 🏷️ class MinimizerTypeEnum
-│   │   ├── 📄 factory.py
-│   │   │   └── 🏷️ class MinimizerFactory
-│   │   ├── 📄 lmfit.py
-│   │   │   └── 🏷️ class LmfitMinimizer
-│   │   ├── 📄 lmfit_least_squares.py
-│   │   │   └── 🏷️ class LmfitLeastSquaresMinimizer
-│   │   └── 📄 lmfit_leastsq.py
-│   │       └── 🏷️ class LmfitLeastsqMinimizer
-│   ├── 📄 __init__.py
-│   ├── 📄 analysis.py
-│   │   ├── 🏷️ class AnalysisDisplay
-│   │   └── 🏷️ class Analysis
-│   ├── 📄 fitting.py
-│   │   └── 🏷️ class Fitter
-│   └── 📄 sequential.py
-│       └── 🏷️ class SequentialFitTemplate
-├── 📁 core
-│   ├── 📄 __init__.py
-│   ├── 📄 category.py
-│   │   ├── 🏷️ class CategoryItem
-│   │   └── 🏷️ class CategoryCollection
-│   ├── 📄 collection.py
-│   │   └── 🏷️ class CollectionBase
-│   ├── 📄 datablock.py
-│   │   ├── 🏷️ class DatablockItem
-│   │   └── 🏷️ class DatablockCollection
-│   ├── 📄 diagnostic.py
-│   │   └── 🏷️ class Diagnostics
-│   ├── 📄 factory.py
-│   │   └── 🏷️ class FactoryBase
-│   ├── 📄 guard.py
-│   │   └── 🏷️ class GuardedBase
-│   ├── 📄 identity.py
-│   │   └── 🏷️ class Identity
-│   ├── 📄 metadata.py
-│   │   ├── 🏷️ class TypeInfo
-│   │   ├── 🏷️ class Compatibility
-│   │   └── 🏷️ class CalculatorSupport
-│   ├── 📄 singleton.py
-│   │   ├── 🏷️ class SingletonBase
-│   │   └── 🏷️ class ConstraintsHandler
-│   ├── 📄 validation.py
-│   │   ├── 🏷️ class DataTypeHints
-│   │   ├── 🏷️ class DataTypes
-│   │   ├── 🏷️ class ValidationStage
-│   │   ├── 🏷️ class ValidatorBase
-│   │   ├── 🏷️ class TypeValidator
-│   │   ├── 🏷️ class RangeValidator
-│   │   ├── 🏷️ class MembershipValidator
-│   │   ├── 🏷️ class RegexValidator
-│   │   └── 🏷️ class AttributeSpec
-│   └── 📄 variable.py
-│       ├── 🏷️ class GenericDescriptorBase
-│       ├── 🏷️ class GenericStringDescriptor
-│       ├── 🏷️ class GenericNumericDescriptor
-│       ├── 🏷️ class GenericParameter
-│       ├── 🏷️ class StringDescriptor
-│       ├── 🏷️ class NumericDescriptor
-│       └── 🏷️ class Parameter
-├── 📁 crystallography
-│   ├── 📄 __init__.py
-│   ├── 📄 crystallography.py
-│   └── 📄 space_groups.py
-│       └── 🏷️ class _RestrictedUnpickler
-├── 📁 datablocks
-│   ├── 📁 experiment
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 background
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   │   └── 🏷️ class BackgroundBase
-│   │   │   │   ├── 📄 chebyshev.py
-│   │   │   │   │   ├── 🏷️ class PolynomialTerm
-│   │   │   │   │   └── 🏷️ class ChebyshevPolynomialBackground
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   │   └── 🏷️ class BackgroundTypeEnum
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class BackgroundFactory
-│   │   │   │   └── 📄 line_segment.py
-│   │   │   │       ├── 🏷️ class LineSegment
-│   │   │   │       └── 🏷️ class LineSegmentBackground
-│   │   │   ├── 📁 calculation
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class Calculation
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class CalculationFactory
-│   │   │   ├── 📁 data
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   │   ├── 🏷️ class PdDataPointBaseMixin
-│   │   │   │   │   ├── 🏷️ class PdCwlDataPointMixin
-│   │   │   │   │   ├── 🏷️ class PdTofDataPointMixin
-│   │   │   │   │   ├── 🏷️ class PdCwlDataPoint
-│   │   │   │   │   ├── 🏷️ class PdTofDataPoint
-│   │   │   │   │   ├── 🏷️ class PdDataBase
-│   │   │   │   │   ├── 🏷️ class PdCwlData
-│   │   │   │   │   └── 🏷️ class PdTofData
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class DataFactory
-│   │   │   │   └── 📄 total_pd.py
-│   │   │   │       ├── 🏷️ class TotalDataPoint
-│   │   │   │       ├── 🏷️ class TotalDataBase
-│   │   │   │       └── 🏷️ class TotalData
-│   │   │   ├── 📁 diffrn
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class DefaultDiffrn
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class DiffrnFactory
-│   │   │   ├── 📁 excluded_regions
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class ExcludedRegion
-│   │   │   │   │   └── 🏷️ class ExcludedRegions
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ExcludedRegionsFactory
-│   │   │   ├── 📁 experiment_type
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class ExperimentType
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ExperimentTypeFactory
-│   │   │   ├── 📁 extinction
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 becker_coppens.py
-│   │   │   │   │   └── 🏷️ class BeckerCoppensExtinction
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ExtinctionFactory
-│   │   │   ├── 📁 instrument
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   │   └── 🏷️ class InstrumentBase
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   │   ├── 🏷️ class CwlInstrumentBase
-│   │   │   │   │   ├── 🏷️ class CwlScInstrument
-│   │   │   │   │   └── 🏷️ class CwlPdInstrument
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class InstrumentFactory
-│   │   │   │   └── 📄 tof.py
-│   │   │   │       ├── 🏷️ class TofScInstrument
-│   │   │   │       └── 🏷️ class TofPdInstrument
-│   │   │   ├── 📁 linked_crystal
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class LinkedCrystal
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class LinkedCrystalFactory
-│   │   │   ├── 📁 linked_phases
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class LinkedPhase
-│   │   │   │   │   └── 🏷️ class LinkedPhases
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class LinkedPhasesFactory
-│   │   │   ├── 📁 peak
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   │   └── 🏷️ class PeakBase
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   │   ├── 🏷️ class CwlPseudoVoigt
-│   │   │   │   │   ├── 🏷️ class CwlPseudoVoigtEmpiricalAsymmetry
-│   │   │   │   │   └── 🏷️ class CwlThompsonCoxHastings
-│   │   │   │   ├── 📄 cwl_mixins.py
-│   │   │   │   │   ├── 🏷️ class CwlBroadeningMixin
-│   │   │   │   │   ├── 🏷️ class EmpiricalAsymmetryMixin
-│   │   │   │   │   └── 🏷️ class FcjAsymmetryMixin
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   │   └── 🏷️ class PeakFactory
-│   │   │   │   ├── 📄 tof.py
-│   │   │   │   │   ├── 🏷️ class TofPseudoVoigt
-│   │   │   │   │   ├── 🏷️ class TofJorgensen
-│   │   │   │   │   ├── 🏷️ class TofJorgensenVonDreele
-│   │   │   │   │   └── 🏷️ class TofDoubleJorgensenVonDreele
-│   │   │   │   ├── 📄 tof_mixins.py
-│   │   │   │   │   ├── 🏷️ class TofGaussianBroadeningMixin
-│   │   │   │   │   ├── 🏷️ class TofLorentzianBroadeningMixin
-│   │   │   │   │   ├── 🏷️ class TofBackToBackExponentialMixin
-│   │   │   │   │   └── 🏷️ class TofDoubleExponentialMixin
-│   │   │   │   ├── 📄 total.py
-│   │   │   │   │   └── 🏷️ class TotalGaussianDampedSinc
-│   │   │   │   └── 📄 total_mixins.py
-│   │   │   │       └── 🏷️ class TotalBroadeningMixin
-│   │   │   ├── 📁 refln
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   │   ├── 🏷️ class PowderReflnBase
-│   │   │   │   │   ├── 🏷️ class PowderCwlRefln
-│   │   │   │   │   ├── 🏷️ class PowderTofRefln
-│   │   │   │   │   ├── 🏷️ class PowderReflnDataBase
-│   │   │   │   │   ├── 🏷️ class PowderCwlReflnData
-│   │   │   │   │   └── 🏷️ class PowderTofReflnData
-│   │   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   │   ├── 🏷️ class Refln
-│   │   │   │   │   └── 🏷️ class ReflnData
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class ReflnFactory
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   │   ├── 🏷️ class ExperimentBase
-│   │   │   │   ├── 🏷️ class ScExperimentBase
-│   │   │   │   └── 🏷️ class PdExperimentBase
-│   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   └── 🏷️ class BraggPdExperiment
-│   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   ├── 🏷️ class CwlScExperiment
-│   │   │   │   └── 🏷️ class TofScExperiment
-│   │   │   ├── 📄 enums.py
-│   │   │   │   ├── 🏷️ class SampleFormEnum
-│   │   │   │   ├── 🏷️ class ScatteringTypeEnum
-│   │   │   │   ├── 🏷️ class RadiationProbeEnum
-│   │   │   │   ├── 🏷️ class BeamModeEnum
-│   │   │   │   ├── 🏷️ class CalculatorEnum
-│   │   │   │   ├── 🏷️ class PeakProfileTypeEnum
-│   │   │   │   └── 🏷️ class ExtinctionModelEnum
-│   │   │   ├── 📄 factory.py
-│   │   │   │   └── 🏷️ class ExperimentFactory
-│   │   │   └── 📄 total_pd.py
-│   │   │       └── 🏷️ class TotalPdExperiment
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   │       └── 🏷️ class Experiments
-│   ├── 📁 structure
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 atom_site_aniso
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class AtomSiteAniso
-│   │   │   │   │   └── 🏷️ class AtomSiteAnisoCollection
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class AtomSiteAnisoFactory
-│   │   │   ├── 📁 atom_sites
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   ├── 🏷️ class AtomSite
-│   │   │   │   │   └── 🏷️ class AtomSites
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   │   └── 🏷️ class AdpTypeEnum
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class AtomSitesFactory
-│   │   │   ├── 📁 cell
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class Cell
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class CellFactory
-│   │   │   ├── 📁 space_group
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   │   └── 🏷️ class SpaceGroup
-│   │   │   │   └── 📄 factory.py
-│   │   │   │       └── 🏷️ class SpaceGroupFactory
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   │   └── 🏷️ class Structure
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class StructureFactory
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   │       └── 🏷️ class Structures
-│   └── 📄 __init__.py
-├── 📁 display
-│   ├── 📁 plotters
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 ascii.py
-│   │   │   └── 🏷️ class AsciiPlotter
-│   │   ├── 📄 base.py
-│   │   │   ├── 🏷️ class BraggTickSet
-│   │   │   ├── 🏷️ class PowderMeasVsCalcSpec
-│   │   │   ├── 🏷️ class XAxisType
-│   │   │   └── 🏷️ class PlotterBase
-│   │   └── 📄 plotly.py
-│   │       ├── 🏷️ class PowderCompositeRows
-│   │       └── 🏷️ class PlotlyPlotter
-│   ├── 📁 tablers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   │   └── 🏷️ class TableBackendBase
-│   │   ├── 📄 pandas.py
-│   │   │   └── 🏷️ class PandasTableBackend
-│   │   └── 📄 rich.py
-│   │       └── 🏷️ class RichTableBackend
-│   ├── 📄 __init__.py
-│   ├── 📄 base.py
-│   │   ├── 🏷️ class RendererBase
-│   │   └── 🏷️ class RendererFactoryBase
-│   ├── 📄 plotting.py
-│   │   ├── 🏷️ class PlotterEngineEnum
-│   │   ├── 🏷️ class _MeasVsCalcPlotOptions
-│   │   ├── 🏷️ class _PowderMeasVsCalcSeries
-│   │   ├── 🏷️ class Plotter
-│   │   └── 🏷️ class PlotterFactory
-│   ├── 📄 tables.py
-│   │   ├── 🏷️ class TableEngineEnum
-│   │   ├── 🏷️ class TableRenderer
-│   │   └── 🏷️ class TableRendererFactory
-│   └── 📄 utils.py
-│       └── 🏷️ class JupyterScrollManager
-├── 📁 io
-│   ├── 📁 cif
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 handler.py
-│   │   │   └── 🏷️ class CifHandler
-│   │   ├── 📄 parse.py
-│   │   └── 📄 serialize.py
-│   ├── 📄 __init__.py
-│   └── 📄 ascii.py
-├── 📁 project
-│   ├── 📁 categories
-│   │   ├── 📁 display
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   │   └── 🏷️ class Display
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class DisplayFactory
-│   │   └── 📄 __init__.py
-│   ├── 📄 __init__.py
-│   ├── 📄 project.py
-│   │   └── 🏷️ class Project
-│   └── 📄 project_info.py
-│       └── 🏷️ class ProjectInfo
-├── 📁 summary
-│   ├── 📄 __init__.py
-│   └── 📄 summary.py
-│       └── 🏷️ class Summary
-├── 📁 utils
-│   ├── 📁 _vendored
-│   │   ├── 📁 jupyter_dark_detect
-│   │   │   ├── 📄 __init__.py
-│   │   │   └── 📄 detector.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 theme_detect.py
-│   ├── 📄 __init__.py
-│   ├── 📄 enums.py
-│   │   └── 🏷️ class VerbosityEnum
-│   ├── 📄 environment.py
-│   ├── 📄 logging.py
-│   │   ├── 🏷️ class IconifiedRichHandler
-│   │   ├── 🏷️ class ConsoleManager
-│   │   ├── 🏷️ class LoggerConfig
-│   │   ├── 🏷️ class ExceptionHookManager
-│   │   ├── 🏷️ class Logger
-│   │   └── 🏷️ class ConsolePrinter
-│   └── 📄 utils.py
-├── 📄 __init__.py
-└── 📄 __main__.py
-```
diff --git a/docs/architecture/package-structure-short.md b/docs/architecture/package-structure-short.md
deleted file mode 100644
index ba2a0b33..00000000
--- a/docs/architecture/package-structure-short.md
+++ /dev/null
@@ -1,224 +0,0 @@
-# Package Structure (short)
-
-```
-📦 easydiffraction
-├── 📁 analysis
-│   ├── 📁 calculators
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   ├── 📄 crysfml.py
-│   │   ├── 📄 cryspy.py
-│   │   ├── 📄 factory.py
-│   │   └── 📄 pdffit.py
-│   ├── 📁 categories
-│   │   ├── 📁 aliases
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📁 constraints
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📁 fit
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   ├── 📄 enums.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📁 joint_fit_experiments
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   └── 📄 __init__.py
-│   ├── 📁 fit_helpers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 metrics.py
-│   │   ├── 📄 reporting.py
-│   │   └── 📄 tracking.py
-│   ├── 📁 minimizers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   ├── 📄 bumps.py
-│   │   ├── 📄 bumps_amoeba.py
-│   │   ├── 📄 bumps_de.py
-│   │   ├── 📄 bumps_lm.py
-│   │   ├── 📄 dfols.py
-│   │   ├── 📄 enums.py
-│   │   ├── 📄 factory.py
-│   │   ├── 📄 lmfit.py
-│   │   ├── 📄 lmfit_least_squares.py
-│   │   └── 📄 lmfit_leastsq.py
-│   ├── 📄 __init__.py
-│   ├── 📄 analysis.py
-│   ├── 📄 fitting.py
-│   └── 📄 sequential.py
-├── 📁 core
-│   ├── 📄 __init__.py
-│   ├── 📄 category.py
-│   ├── 📄 collection.py
-│   ├── 📄 datablock.py
-│   ├── 📄 diagnostic.py
-│   ├── 📄 factory.py
-│   ├── 📄 guard.py
-│   ├── 📄 identity.py
-│   ├── 📄 metadata.py
-│   ├── 📄 singleton.py
-│   ├── 📄 validation.py
-│   └── 📄 variable.py
-├── 📁 crystallography
-│   ├── 📄 __init__.py
-│   ├── 📄 crystallography.py
-│   └── 📄 space_groups.py
-├── 📁 datablocks
-│   ├── 📁 experiment
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 background
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   ├── 📄 chebyshev.py
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   └── 📄 line_segment.py
-│   │   │   ├── 📁 calculation
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 data
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   └── 📄 total_pd.py
-│   │   │   ├── 📁 diffrn
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 excluded_regions
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 experiment_type
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 extinction
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 becker_coppens.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 instrument
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   └── 📄 tof.py
-│   │   │   ├── 📁 linked_crystal
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 linked_phases
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 peak
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 base.py
-│   │   │   │   ├── 📄 cwl.py
-│   │   │   │   ├── 📄 cwl_mixins.py
-│   │   │   │   ├── 📄 factory.py
-│   │   │   │   ├── 📄 tof.py
-│   │   │   │   ├── 📄 tof_mixins.py
-│   │   │   │   ├── 📄 total.py
-│   │   │   │   └── 📄 total_mixins.py
-│   │   │   ├── 📁 refln
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   ├── 📄 bragg_pd.py
-│   │   │   ├── 📄 bragg_sc.py
-│   │   │   ├── 📄 enums.py
-│   │   │   ├── 📄 factory.py
-│   │   │   └── 📄 total_pd.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   ├── 📁 structure
-│   │   ├── 📁 categories
-│   │   │   ├── 📁 atom_site_aniso
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 atom_sites
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   ├── 📄 enums.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 cell
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   ├── 📁 space_group
-│   │   │   │   ├── 📄 __init__.py
-│   │   │   │   ├── 📄 default.py
-│   │   │   │   └── 📄 factory.py
-│   │   │   └── 📄 __init__.py
-│   │   ├── 📁 item
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 base.py
-│   │   │   └── 📄 factory.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 collection.py
-│   └── 📄 __init__.py
-├── 📁 display
-│   ├── 📁 plotters
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 ascii.py
-│   │   ├── 📄 base.py
-│   │   └── 📄 plotly.py
-│   ├── 📁 tablers
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 base.py
-│   │   ├── 📄 pandas.py
-│   │   └── 📄 rich.py
-│   ├── 📄 __init__.py
-│   ├── 📄 base.py
-│   ├── 📄 plotting.py
-│   ├── 📄 tables.py
-│   └── 📄 utils.py
-├── 📁 io
-│   ├── 📁 cif
-│   │   ├── 📄 __init__.py
-│   │   ├── 📄 handler.py
-│   │   ├── 📄 parse.py
-│   │   └── 📄 serialize.py
-│   ├── 📄 __init__.py
-│   └── 📄 ascii.py
-├── 📁 project
-│   ├── 📁 categories
-│   │   ├── 📁 display
-│   │   │   ├── 📄 __init__.py
-│   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
-│   │   └── 📄 __init__.py
-│   ├── 📄 __init__.py
-│   ├── 📄 project.py
-│   └── 📄 project_info.py
-├── 📁 summary
-│   ├── 📄 __init__.py
-│   └── 📄 summary.py
-├── 📁 utils
-│   ├── 📁 _vendored
-│   │   ├── 📁 jupyter_dark_detect
-│   │   │   ├── 📄 __init__.py
-│   │   │   └── 📄 detector.py
-│   │   ├── 📄 __init__.py
-│   │   └── 📄 theme_detect.py
-│   ├── 📄 __init__.py
-│   ├── 📄 enums.py
-│   ├── 📄 environment.py
-│   ├── 📄 logging.py
-│   └── 📄 utils.py
-├── 📄 __init__.py
-└── 📄 __main__.py
-```
diff --git a/tools/generate_package_docs.py b/tools/generate_package_docs.py
index 671a4f0e..1d860557 100644
--- a/tools/generate_package_docs.py
+++ b/tools/generate_package_docs.py
@@ -3,7 +3,7 @@
 
 """Generate project package structure markdown files.
 
-Outputs two docs under docs/architecture/:
+Outputs two docs under docs/dev/:
  - package-structure-short.md  (folders/files only)
  - package-structure-full.md   (folders/files and classes)
 
@@ -21,7 +21,7 @@
 
 REPO_ROOT = Path(__file__).resolve().parents[1]
 SRC_ROOT = REPO_ROOT / 'src' / 'easydiffraction'
-DOCS_OUT_DIR = REPO_ROOT / 'docs' / 'architecture'
+DOCS_OUT_DIR = REPO_ROOT / 'docs' / 'dev'
 
 
 IGNORE_DIRS = {

From 7849de32de0dd83b5767ba92a0c1ca8b2939cf2a Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 23:48:03 +0200
Subject: [PATCH 004/106] Update package structure docs with refln
 reorganization

---
 docs/dev/package-structure-full.md  | 19 ++++++++++++++++---
 docs/dev/package-structure-short.md |  6 +++++-
 2 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index ae233b21..be35bb07 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -6,6 +6,7 @@
 │   ├── 📁 calculators
 │   │   ├── 📄 __init__.py
 │   │   ├── 📄 base.py
+│   │   │   ├── 🏷️ class PowderReflnRecord
 │   │   │   └── 🏷️ class CalculatorBase
 │   │   ├── 📄 crysfml.py
 │   │   │   └── 🏷️ class CrysfmlCalculator
@@ -169,9 +170,6 @@
 │   │   │   │   │   ├── 🏷️ class PdDataBase
 │   │   │   │   │   ├── 🏷️ class PdCwlData
 │   │   │   │   │   └── 🏷️ class PdTofData
-│   │   │   │   ├── 📄 bragg_sc.py
-│   │   │   │   │   ├── 🏷️ class Refln
-│   │   │   │   │   └── 🏷️ class ReflnData
 │   │   │   │   ├── 📄 factory.py
 │   │   │   │   │   └── 🏷️ class DataFactory
 │   │   │   │   └── 📄 total_pd.py
@@ -257,6 +255,20 @@
 │   │   │   │   │   └── 🏷️ class TotalGaussianDampedSinc
 │   │   │   │   └── 📄 total_mixins.py
 │   │   │   │       └── 🏷️ class TotalBroadeningMixin
+│   │   │   ├── 📁 refln
+│   │   │   │   ├── 📄 __init__.py
+│   │   │   │   ├── 📄 bragg_pd.py
+│   │   │   │   │   ├── 🏷️ class PowderReflnBase
+│   │   │   │   │   ├── 🏷️ class PowderCwlRefln
+│   │   │   │   │   ├── 🏷️ class PowderTofRefln
+│   │   │   │   │   ├── 🏷️ class PowderReflnDataBase
+│   │   │   │   │   ├── 🏷️ class PowderCwlReflnData
+│   │   │   │   │   └── 🏷️ class PowderTofReflnData
+│   │   │   │   ├── 📄 bragg_sc.py
+│   │   │   │   │   ├── 🏷️ class Refln
+│   │   │   │   │   └── 🏷️ class ReflnData
+│   │   │   │   └── 📄 factory.py
+│   │   │   │       └── 🏷️ class ReflnFactory
 │   │   │   └── 📄 __init__.py
 │   │   ├── 📁 item
 │   │   │   ├── 📄 __init__.py
@@ -353,6 +365,7 @@
 │   ├── 📄 plotting.py
 │   │   ├── 🏷️ class PlotterEngineEnum
 │   │   ├── 🏷️ class _MeasVsCalcPlotOptions
+│   │   ├── 🏷️ class _PowderMeasVsCalcSeries
 │   │   ├── 🏷️ class Plotter
 │   │   └── 🏷️ class PlotterFactory
 │   ├── 📄 tables.py
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure-short.md
index 30b4daf7..ba2a0b33 100644
--- a/docs/dev/package-structure-short.md
+++ b/docs/dev/package-structure-short.md
@@ -85,7 +85,6 @@
 │   │   │   ├── 📁 data
 │   │   │   │   ├── 📄 __init__.py
 │   │   │   │   ├── 📄 bragg_pd.py
-│   │   │   │   ├── 📄 bragg_sc.py
 │   │   │   │   ├── 📄 factory.py
 │   │   │   │   └── 📄 total_pd.py
 │   │   │   ├── 📁 diffrn
@@ -128,6 +127,11 @@
 │   │   │   │   ├── 📄 tof_mixins.py
 │   │   │   │   ├── 📄 total.py
 │   │   │   │   └── 📄 total_mixins.py
+│   │   │   ├── 📁 refln
+│   │   │   │   ├── 📄 __init__.py
+│   │   │   │   ├── 📄 bragg_pd.py
+│   │   │   │   ├── 📄 bragg_sc.py
+│   │   │   │   └── 📄 factory.py
 │   │   │   └── 📄 __init__.py
 │   │   ├── 📁 item
 │   │   │   ├── 📄 __init__.py

From 9a880810c0054b2438842c02b415c9175b098296 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 23:48:15 +0200
Subject: [PATCH 005/106] Plan Bayesian DREAM implementation

---
 docs/dev/bayesian-analysis-design.md | 616 ++++++++++++++++++++-------
 docs/dev/plan_bayesian-analysis.md   | 334 +++++++++++++++
 2 files changed, 806 insertions(+), 144 deletions(-)
 create mode 100644 docs/dev/plan_bayesian-analysis.md

diff --git a/docs/dev/bayesian-analysis-design.md b/docs/dev/bayesian-analysis-design.md
index d3097e46..1ca66fdb 100644
--- a/docs/dev/bayesian-analysis-design.md
+++ b/docs/dev/bayesian-analysis-design.md
@@ -1,18 +1,49 @@
 # Bayesian Analysis Design
 
-**Status:** Draft
-**Date:** 2026-05-07
+**Status:** Design proposal **Date:** 2026-05-07
 
 ## Goal
 
 Add Bayesian analysis to the existing fitting workflow with the smallest
-user-facing API change possible, reusing the current `Analysis`,
-`Fit`, `Fitter`, minimizer factory, result reporting, and plotting
-surfaces.
-
-The primary first target is BUMPS DREAM sampling for diffraction
-refinement with parameter bounds taken from each parameter's
-`fit_min` and `fit_max` attributes.
+user-facing API change possible. The first implementation target is the
+BUMPS DREAM sampler for diffraction refinement, with sampled parameters
+bounded by each parameter's `fit_min` and `fit_max` attributes.
+
+The word `DREAM` in this document means the BUMPS Markov-chain Monte
+Carlo sampler. It does not refer to the ESS DREAM instrument.
+
+## Design Principles
+
+- Keep `project.analysis.fit()` as the single execution entry point.
+- Add `'bumps (dream)'` as a normal minimizer tag.
+- Store large posterior outputs in runtime result objects, not in CIF
+  categories.
+- Commit one coherent sampled parameter vector back to the project after
+  sampling.
+- Keep deterministic fitting and Bayesian sampling behavior explicit.
+- Add Bayesian display and plotting behavior without changing existing
+  deterministic semantics.
+
+## Current Code Anchors
+
+The design should extend the existing seams instead of adding a parallel
+analysis stack:
+
+- `src/easydiffraction/analysis/categories/fit/default.py` owns
+  `fit.minimizer_type` and `fit.mode`.
+- `src/easydiffraction/analysis/fitting.py` collects free parameters,
+  builds the residual objective, and calls `MinimizerBase.fit()`.
+- `src/easydiffraction/analysis/minimizers/base.py` defines the
+  minimizer lifecycle and returns a `FitResults` instance.
+- `src/easydiffraction/analysis/minimizers/bumps.py` already adapts an
+  EasyDiffraction residual function into BUMPS through
+  `_EasyDiffractionFitness`.
+- `src/easydiffraction/analysis/fit_helpers/reporting.py` owns
+  `FitResults` display behavior.
+- `src/easydiffraction/display/plotting.py` currently builds parameter
+  correlations from `analysis.fit_results.engine_result`.
+- `pyproject.toml` already declares `arviz`, so Bayesian plotting does
+  not need a new dependency decision.
 
 ## User-Facing API
 
@@ -30,36 +61,104 @@ project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
 project.display.plotter.plot_param_correlations()
 ```
 
-Additional Bayesian-only plotting methods should be added rather than
-overloading every existing method with different semantics:
+Additional Bayesian-specific plotting methods should be added rather
+than forcing every existing plotting method to accept Bayesian-specific
+options:
 
 ```python
 project.display.plotter.plot_posterior_pairs()
 project.display.plotter.plot_param_distribution(param)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
 ```
 
-For the first implementation, these Bayesian plotting methods may use
-ArviZ with the Plotly backend internally.
-
 ### Explicit Two-Step Workflow
 
-`'bumps (dream)'` should **not** implicitly run a deterministic
+`'bumps (dream)'` must not implicitly run a deterministic
 Levenberg-Marquardt pre-fit. Users should call `'bumps (lm)'` first when
 they want a better starting point.
 
 Reasons:
 
-- It keeps `fit()` behavior explicit and predictable.
-- It avoids hidden extra runtime.
-- It keeps deterministic fit failures separate from sampling failures.
-- It matches the current minimizer-selection model.
+- `fit()` stays explicit and predictable.
+- Hidden pre-fitting can add significant runtime.
+- Deterministic failures stay separate from sampling failures.
+- The behavior matches the existing minimizer-selection model.
+
+## Scope
+
+### In Scope For The First Implementation
+
+- Register `MinimizerTypeEnum.BUMPS_DREAM` with tag `'bumps (dream)'`.
+- Add `BumpsDreamMinimizer`.
+- Add `BayesianFitResults` and small posterior value objects.
+- Require finite bounds for every sampled free parameter.
+- Use bounded uniform priors implied by `fit_min` and `fit_max`.
+- Use the existing residual objective and BUMPS likelihood convention.
+- Store posterior chains runtime-only in `analysis.fit_results`.
+- Display posterior summaries from `analysis.display.fit_results()`.
+- Add posterior correlation support to `plot_param_correlations()`.
+- Add posterior marginal and posterior pair plots.
+- Add posterior predictive plotting with capped draw evaluation.
+
+### Out Of Scope Initially
+
+- Automatic deterministic pre-fitting.
+- Persistent Bayesian configuration categories.
+- CIF serialization of posterior chains.
+- Export of posterior chains.
+- Model comparison across multiple Bayesian fits.
+- User-facing DREAM hyperparameter controls.
+- Unbounded posterior predictive storage for every draw and every
+  experiment.
+- Sequential Bayesian fitting.
+
+## Resolved Design Decisions
+
+- `plot_posterior_predictive(...)` is required for the first useful
+  Bayesian implementation.
+- Posterior chains remain runtime-only and do not need an export API in
+  phase 1.
+- `project.analysis.fit(random_seed=...)` should be supported for
+  `'bumps (dream)'` because stochastic scientific workflows need
+  reproducibility. If omitted, the minimizer should generate a seed and
+  record it in `BayesianFitResults.sampler_settings`.
+- Deterministic minimizers should reject a non-`None` `random_seed` with
+  a clear error rather than silently ignoring it.
+- `BayesianFitResults.success` should mean that DREAM completed and
+  produced usable posterior samples. Convergence quality should be
+  stored separately in diagnostics, displayed prominently, and warned on
+  when poor. It should set `success=False` only when there are no usable
+  samples or the sampler itself fails.
+- Posterior predictive plots should evaluate a capped subset of
+  posterior draws. Start with an internal default cap of 200 draws,
+  record the effective draw count in the result, and expose the cap only
+  later if users need control.
+
+## Statistical Semantics
+
+The existing residual objective returns the residual vector consumed by
+deterministic minimizers. The first Bayesian implementation should treat
+the BUMPS negative log likelihood as:
+
+```python
+nllf = 0.5 * sum(residuals**2)
+```
+
+This assumes residuals are already scaled consistently with the
+experimental uncertainty model. If an experiment does not provide valid
+uncertainties, the posterior is only as meaningful as the residual
+weighting currently used by deterministic fitting.
+
+Initial priors are uniform inside finite `fit_min` and `fit_max` bounds
+and zero outside those bounds. No Gaussian, log-normal, or
+domain-specific priors are part of the first implementation.
 
 ## Core Architectural Decision
 
-### Keep Full Results in `analysis.fit_results`
+### Keep Full Results In `analysis.fit_results`
 
-Bayesian results should be stored in a new runtime result object,
-**not** in a new heavy `Analysis` category.
+Bayesian results should be stored in a new runtime result object, not in
+a new heavy `Analysis` category.
 
 Recommended model:
 
@@ -74,10 +173,10 @@ This keeps the existing public access pattern unchanged:
 project.analysis.fit_results
 ```
 
-### Why Not a New Results Category Under `Analysis`
+### Why Not A New Results Category Under `Analysis`
 
-Current `Analysis` categories are lightweight, structured,
-project-owned configuration or control objects:
+Current `Analysis` categories are lightweight, structured, project-owned
+configuration or control objects:
 
 - `fit`
 - `aliases`
@@ -92,36 +191,39 @@ fit for this category model because they are:
 - not natural CIF content
 - often disposable or recomputable
 
-Posterior chains, posterior predictive draws, and pair-plot input data
+Posterior chains, posterior predictive draws, and pair-plot inputs
 should therefore remain runtime-only objects attached to
 `BayesianFitResults`.
 
 ## Optional Future Category
 
-A **small** `Analysis` category may still be useful later, but only for
+A small `Analysis` category may still be useful later, but only for
 Bayesian configuration, not for heavy result storage.
 
 Possible future category name:
 
 - `analysis.sampling`
-- or `analysis.bayesian`
+- `analysis.bayesian`
 
 Possible responsibilities:
 
 - sampler type
 - point estimate policy
-- default HDI levels
-- exposed DREAM hyperparameters if they become public later
+- default highest-density interval levels
+- default posterior predictive draw cap
+- public DREAM hyperparameters
+- persistent default random seed policy, if users need saved sampling
+  preferences later
 
-This category should stay lightweight and serializable. It should not
+This category should stay lightweight and serializable. It must not
 store raw chains.
 
-## Proposed New Runtime Types
+## Runtime Type Contracts
 
 ### `BumpsDreamMinimizer`
 
-Add a new minimizer implementation registered through
-`MinimizerFactory` with tag:
+Add a new minimizer implementation registered through `MinimizerFactory`
+with tag:
 
 ```python
 'bumps (dream)'
@@ -129,18 +231,37 @@ Add a new minimizer implementation registered through
 
 Responsibilities:
 
-- build bounded BUMPS parameters from EasyDiffraction parameters
-- require finite `fit_min` and `fit_max` for every sampled parameter
-- run DREAM with internal default settings
-- collect posterior samples and sampler state
-- compute posterior summaries
-- set project parameters to the chosen point estimate after sampling
+- Reuse `_EasyDiffractionFitness` or a small subclass of it.
+- Build bounded BUMPS parameters from EasyDiffraction parameters.
+- Preserve parameter order from `Fitter.fit()` through all arrays.
+- Validate finite bounds before starting the sampler.
+- Run DREAM with internal defaults.
+- Collect posterior samples, log likelihood values, and sampler
+  diagnostics where BUMPS exposes them.
+- Choose and commit the MAP or best posterior sample after sampling.
+- Build `BayesianFitResults`.
+
+The existing `BumpsMinimizer` handles BUMPS parameter ordering carefully
+because `FitProblem` can sort parameters internally. DREAM must preserve
+the same guarantee: result arrays and summaries must be mapped back to
+the original EasyDiffraction parameter order.
 
 ### `BayesianFitResults`
 
-Subclass `FitResults` and add Bayesian-specific fields.
+Subclass `FitResults` and add Bayesian-specific fields. Inherited fields
+should keep deterministic-compatible meanings:
 
-Recommended fields:
+- `success`: sampler completed and produced usable posterior samples
+- `parameters`: EasyDiffraction parameters updated to the committed MAP
+  vector
+- `starting_parameters`: an immutable snapshot of starting values, not
+  the same mutable parameter objects after fitting
+- `reduced_chi_square`: reduced chi-square at the committed MAP vector,
+  when available
+- `engine_result`: raw or lightly wrapped BUMPS DREAM result
+- `fitting_time`: elapsed sampling time
+
+Recommended Bayesian fields:
 
 - `sampler_name`
 - `point_estimate_name`
@@ -148,51 +269,154 @@ Recommended fields:
 - `posterior_parameter_summaries`
 - `posterior_predictive`
 - `credible_interval_levels`
-- `engine_result`
+- `diagnostics`
+- `sampler_settings`
+
+`sampler_settings` should record the internal defaults actually used.
+That keeps a runtime audit trail even before the settings are public
+configuration.
+
+For DREAM, `sampler_settings` must include `random_seed`, whether the
+seed was user-provided or generated, and the posterior predictive draw
+cap used for summaries.
+
+### `PosteriorSamples`
+
+Recommended fields:
+
+- `parameter_names`: minimizer-safe names, matching sampled array order
+- `parameter_labels`: user-facing labels for tables and plots
+- `values`: NumPy array shaped `(chain, draw, parameter)`
+- `flat_values`: optional cached array shaped `(sample, parameter)`
+- `log_likelihood`: optional NumPy array shaped `(chain, draw)`
+- `log_posterior`: optional NumPy array shaped `(chain, draw)`
+- `bounds`: mapping from parameter name to `(fit_min, fit_max)`
+- `start_values`: starting values in parameter order
+- `map_values`: committed MAP values in parameter order
+- `map_chain`: chain index of the MAP sample, if known
+- `map_draw`: draw index of the MAP sample, if known
+
+The object should provide one conversion helper:
+
+```python
+posterior_samples.to_arviz()
+```
 
-### Supporting Value Objects
+This keeps public EasyDiffraction methods independent of ArviZ while
+letting plotting and summaries use ArviZ internally.
 
-Recommended helper containers:
+### `PosteriorParameterSummary`
 
-- `PosteriorSamples`
-- `PosteriorParameterSummary`
-- `PosteriorPredictiveSummary`
+Recommended fields:
 
-These keep `BayesianFitResults` structured and reduce pressure to hide
-plotting-specific arrays inside experiment data categories.
+- `parameter_name`
+- `parameter_label`
+- `datablock`
+- `category`
+- `entry`
+- `parameter`
+- `start`
+- `map`
+- `mean`
+- `median`
+- `std`
+- `hdi`
+- `fit_min`
+- `fit_max`
+- `units`
+
+`hdi` should be a mapping keyed by interval probability, for example:
+
+```python
+{
+    0.68: (lower_68, upper_68),
+    0.95: (lower_95, upper_95),
+}
+```
+
+### `PosteriorPredictiveSummary`
+
+Posterior predictive data can be expensive for diffraction patterns, so
+the first implementation should store summaries rather than every draw.
+
+Recommended fields:
+
+- `experiment_name`
+- `x`
+- `y_observed`
+- `y_map`
+- `intervals`
+- `draw_count`
+
+`intervals` should be keyed by interval probability:
+
+```python
+{
+    0.95: (lower_y, upper_y),
+}
+```
+
+Posterior predictive summaries are required for
+`plot_posterior_predictive(...)`, but they should be based on a capped
+subset of posterior draws. The default cap is an internal implementation
+constant, initially 200 draws.
 
 ## Point Estimate Policy
 
-After a DREAM run, project parameters should be updated to the **MAP**
-or best posterior sample, not to independent marginal medians.
+After a DREAM run, project parameters should be updated to the MAP or
+best posterior sample, not to independent marginal medians.
 
-Reason:
+Reasons:
 
-- per-parameter medians can produce a parameter vector that was never
-  sampled jointly
-- MAP corresponds to a coherent sampled state
-- the calculated profile shown after Bayesian fitting should correspond
-  to one actual parameter set
+- Per-parameter medians can produce a parameter vector that was never
+  sampled jointly.
+- MAP corresponds to one coherent sampled state.
+- The calculated profile shown after Bayesian fitting should correspond
+  to one actual parameter set.
 
-Posterior tables can still report median, standard deviation, and HDIs.
+Posterior tables should still report mean, median, standard deviation,
+and HDIs.
+
+If the sampler fails before producing usable samples, the minimizer
+should restore starting parameter values instead of leaving the project
+at an arbitrary last sampled state.
 
 ## Parameter Bounds
 
 DREAM should require finite bounds for every sampled free parameter.
 
-Behavior:
+Validation rules:
+
+- Use `fit_min` and `fit_max`.
+- Bounds must be finite.
+- `fit_min` must be strictly less than `fit_max`.
+- The starting value must be inside the bounds.
+- Validate only sampled parameters: free, unconstrained parameters
+  collected by `Fitter.fit()`.
+
+Failure behavior:
 
-- use `fit_min` and `fit_max`
-- if any sampled parameter is unbounded, stop with a clear error
-- the error should list the offending parameter names
+- Stop before running BUMPS.
+- Raise a clear `ValueError`.
+- List every offending parameter and the specific problem.
 
 This is stricter than some deterministic minimizers, but appropriate for
 bounded posterior sampling.
 
-## Hidden DREAM Defaults
+### Interaction With Physical Limits
+
+`Analysis.fit(use_physical_limits=True)` currently allows deterministic
+minimizers to fill unbounded `fit_min` and `fit_max` from parameter
+value spec physical limits. DREAM should use the same pre-processing
+path.
+
+If `use_physical_limits=True` still leaves any sampled parameter
+unbounded, DREAM must fail with the same finite-bound error.
+
+## DREAM Defaults And Reproducibility
 
 For the first implementation, sampling parameters should stay internal
-and use library defaults.
+and use library defaults or small wrapper constants in `bumps_dream.py`.
 
 Examples:
 
@@ -202,8 +426,17 @@ Examples:
 - `pop`
 - `alpha`
 
-These can become user-configurable later through a small sampling
-configuration category or explicit keyword API.
+These should not be user-facing API initially. They should be recorded
+in `BayesianFitResults.sampler_settings` so users can inspect how a
+runtime result was produced.
+
+The exception is `random_seed`, which should be accepted as an optional
+keyword by `project.analysis.fit(...)` when the active minimizer is
+`'bumps (dream)'`. This keeps stochastic results reproducible without
+creating a persistent sampling category in phase 1.
+
+Tests may monkeypatch the internal defaults to keep unit and integration
+tests fast. That test hook should not become public API.
 
 ## Display Behavior
 
@@ -219,6 +452,8 @@ Deterministic fit:
 Bayesian fit:
 
 - show a sampling summary section
+- show sampler settings that materially affect the result
+- show diagnostics when available
 - show posterior parameter summaries instead of only fitted values and
   covariance-derived uncertainties
 
@@ -230,12 +465,16 @@ Recommended Bayesian table columns:
 - parameter
 - start
 - MAP
+- mean
 - median
 - posterior std
-- 68% interval
-- 95% interval
+- 68% HDI
+- 95% HDI
 - units
 
+Display code should not require ArviZ at render time if summaries were
+already computed by the minimizer.
+
 ## Plotting Behavior
 
 ### `plot_meas_vs_calc(expt_name=...)`
@@ -250,11 +489,12 @@ Bayesian fit:
 
 - measured pattern
 - MAP calculated line
-- shaded credible band around prediction
-
-Recommended default:
+- one shaded 95% credible interval band when `posterior_predictive` is
+  available
 
-- one shaded 95% credible interval band
+If predictive intervals are unavailable, the method should plot the MAP
+calculated line and warn that posterior predictive intervals were not
+computed.
 
 This is the clearest default for diffraction patterns. A full posterior
 predictive distribution at every x value is useful but visually denser,
@@ -270,23 +510,33 @@ Deterministic fit:
 
 Bayesian fit:
 
-- posterior-sample correlation matrix
+- posterior-sample correlation matrix computed from flattened posterior
+  samples
+
+Implementation detail:
+
+- Check for `BayesianFitResults.posterior_samples` first.
+- Fall back to the existing covariance path for deterministic results.
+- Keep the current thresholding and lower-triangle display behavior.
 
-This preserves the meaning of the method.
+This preserves the meaning of the method while making it work for
+posterior samples.
 
 ### New `plot_posterior_pairs()`
 
-Add a new Bayesian-specific method for pairwise posterior exploration.
+Add a Bayesian-specific method for pairwise posterior exploration.
 
 Intended behavior:
 
 - ArviZ-backed implementation first
-- Plotly backend
+- Plotly backend where practical
 - pairwise scatter or density panels
 - marginal distributions on the diagonal
-- optional parameter subset support later
+- optional parameter subset support
 
 This method should serve the role of an interactive corner or pair plot.
+When the active result is deterministic, it should log a clear warning
+instead of trying to infer a posterior from covariance.
 
 ### New `plot_param_distribution(param)`
 
@@ -300,15 +550,23 @@ Intended behavior:
 - median marker
 - HDI overlay
 
-This is more natural than starting with a model-comparison distribution
-plot because the current library stores one active fit result, not a set
-of compared Bayesian models.
+Parameter selection should accept the same identifiers users see in
+fit-result tables where possible:
 
-### Possible Later Addition: `plot_posterior_predictive(...)`
+- parameter object
+- unique parameter name
+- user-facing label
 
-A dedicated method can be added later for richer predictive views.
+Ambiguous string matches should fail with a clear error listing matching
+parameters.
 
-Possible API:
+### New `plot_posterior_predictive(...)`
+
+Add a dedicated method for posterior predictive views. This is required
+for phase 1 because `plot_meas_vs_calc(...)` should stay a concise
+measured-versus-current-calculation view.
+
+Initial API:
 
 ```python
 project.display.plotter.plot_posterior_predictive(
@@ -320,10 +578,21 @@ project.display.plotter.plot_posterior_predictive(
 Possible styles:
 
 - `band`
-- `dist`
+- `draws`
+- `distribution`
+
+Initial behavior:
+
+- default to `style='band'`
+- show measured pattern
+- show MAP calculated line
+- show a 95% posterior predictive band by default
+- support capped individual posterior predictive draws with
+  `style='draws'`
 
-For the first implementation, these views may also be delegated to
-ArviZ where practical.
+For the first implementation, richer views may be delegated to ArviZ
+where practical, but diffraction-specific x/y plotting should remain
+clear and domain-oriented.
 
 ## Data Ownership
 
@@ -340,71 +609,130 @@ Posterior-specific data should remain analysis-owned through
 - potentially joint across experiments
 - used mainly for reporting and plotting
 
-## Persistence and Serialization
+When the MAP vector is committed, normal category update behavior should
+make the current calculated pattern correspond to the MAP values.
+Posterior predictive summaries should not mutate experiment data
+categories for every posterior draw.
+
+## Persistence And Serialization
 
 For the first implementation:
 
-- do not serialize full posterior chains to CIF
-- do not force posterior arrays into project save files
+- Do not serialize full posterior chains to CIF.
+- Do not force posterior arrays into project save files.
+- Let project auto-save persist the current MAP parameter values and
+  `fit.minimizer_type`, as it already does for deterministic fitting.
+- Treat `analysis.fit_results` as runtime-only.
 
 Possible future export surfaces:
 
 - explicit posterior export methods
 - CSV summaries
-- NumPy or NetCDF export if needed later
-
-## Dependency Direction
-
-For the first implementation, add ArviZ as a runtime dependency and use
-it for Bayesian plotting and summary-oriented data transformations where
-that reduces implementation cost.
-
-Recommended direction:
-
-- keep Plotly as the interactive rendering backend
-- use ArviZ-backed plotting internally for Bayesian views where it fits
-- keep the public EasyDiffraction plotting API independent of ArviZ
-
-Future note:
-
-- once Bayesian functionality stabilizes, EasyDiffraction may replace
-  some or all ArviZ-backed plots with project-native implementations to
-  improve control over styling, interactivity, and domain-specific
-  diffraction presentation
-
-## Recommended First Implementation Scope
-
-### Phase 1
-
-- add `MinimizerTypeEnum.BUMPS_DREAM`
-- add `BumpsDreamMinimizer`
-- add `BayesianFitResults`
-- keep `analysis.fit_results` as the single public result slot
-- make `analysis.display.fit_results()` dispatch by result type
-
-### Phase 2
-
-- extend `plot_meas_vs_calc()` with Bayesian credible bands
-- extend `plot_param_correlations()` to use posterior samples when
-  available
-- add `plot_posterior_pairs()`
-- add `plot_param_distribution(param)`
-
-### Phase 3
-
-- consider adding a lightweight `analysis.sampling` category for saved
-  Bayesian configuration
-- consider explicit posterior export APIs
-- consider richer predictive plotting modes
+- ArviZ NetCDF export
+- NumPy archive export
+
+## Failure And Warning Policy
+
+Hard failures should stop before expensive sampling when possible:
+
+- missing finite bounds
+- invalid bound order
+- start value outside bounds
+- no sampled parameters
+- BUMPS DREAM backend unavailable
+
+Soft warnings should allow a result to be returned:
+
+- convergence diagnostics unavailable
+- convergence diagnostics outside recommended thresholds
+- posterior predictive intervals not computed
+- MAP value close to a fit bound
+
+`success` should mean that the sampler completed and usable posterior
+samples were produced. Convergence diagnostics should be shown and
+stored separately. Poor convergence should set a diagnostic flag and log
+a warning, but it should not turn the result into a hard failure unless
+there are no usable samples.
+
+## Testing Requirements
+
+Unit tests should cover:
+
+- `MinimizerTypeEnum.BUMPS_DREAM` and factory registration.
+- Finite-bound validation with all offending parameters reported.
+- Start-value-inside-bounds validation.
+- `random_seed` threading and recording for DREAM.
+- Non-`None` `random_seed` rejection for deterministic minimizers.
+- Preservation of parameter order between EasyDiffraction parameters,
+  BUMPS parameters, posterior arrays, and summaries.
+- MAP commit behavior.
+- Restore-start-values behavior on sampler failure.
+- `BayesianFitResults.display_results()` table content.
+- Posterior correlation calculation from flattened posterior samples.
+- Posterior predictive summary generation with capped draw evaluation.
+- Deterministic correlation behavior remaining unchanged.
+- Project serialization not including posterior arrays.
+
+Integration tests should cover:
+
+- A small bounded synthetic refinement with `'bumps (dream)'`.
+- Optional deterministic pre-fit followed by DREAM as two explicit
+  `fit()` calls.
+- `analysis.display.fit_results()` after DREAM.
+- `plot_posterior_predictive(...)` after DREAM.
+- Bayesian plotting smoke tests using a small posterior fixture.
+
+## Implementation Plan
+
+The detailed implementation plan is
+`docs/dev/plan_bayesian-analysis.md`. It follows
+`.github/copilot-instructions.md`: phase 1 is implementation only, phase
+2 is verification, and every completed phase 1 implementation step must
+be staged with explicit paths and committed locally before the next step
+starts.
+
+High-level implementation order:
+
+- register the DREAM minimizer
+- add Bayesian result models
+- thread optional `random_seed` through fitting
+- implement DREAM sampling and MAP commit behavior
+- add Bayesian result display
+- add posterior correlations
+- add posterior distribution plots
+- add posterior predictive summaries and plots
+
+## Acceptance Criteria
+
+The first useful implementation is complete when:
+
+- `project.analysis.fit.show_minimizer_types()` lists `'bumps (dream)'`.
+- `project.analysis.fit.minimizer_type = 'bumps (dream)'` runs through
+  the same `project.analysis.fit()` path as other minimizers.
+- Missing finite bounds fail before sampling with a clear parameter
+  list.
+- Successful sampling stores `BayesianFitResults` in
+  `project.analysis.fit_results`.
+- Project parameters are left at the MAP sample after a successful run.
+- `project.analysis.display.fit_results()` renders posterior summaries.
+- `plot_param_correlations()` works from posterior samples.
+- `plot_posterior_predictive(expt_name=...)` shows measured data, MAP
+  calculation, and a posterior predictive band.
+- Saving a project does not serialize posterior chains.
+
+## Implementation Discovery
+
+During implementation, inspect which BUMPS DREAM result object fields
+are stable enough to store directly in `engine_result`. Normalize
+posterior samples, log-likelihood values, diagnostics, and summaries
+into EasyDiffraction-owned value objects first; keep `engine_result` as
+an opaque backend escape hatch.
 
 ## Summary
 
-The recommended design is:
-
-- add `'bumps (dream)'` as a normal minimizer type
-- keep Bayesian analysis inside the existing `fit()` workflow
-- store heavy posterior results in `BayesianFitResults`
-- keep `analysis.fit_results` as the main public result entry point
-- avoid a new heavy `Analysis` category for results
-- only add a small new `Analysis` category later if Bayesian settings
-  need to become persistent and user-configurable
+The recommended design is to add `'bumps (dream)'` as a normal minimizer
+type, keep Bayesian analysis inside the existing `fit()` workflow, store
+heavy posterior data in `BayesianFitResults`, and keep
+`analysis.fit_results` as the main public result entry point. A
+lightweight serialized `Analysis` category can be added later if
+Bayesian settings need to become persistent and user-configurable.
diff --git a/docs/dev/plan_bayesian-analysis.md b/docs/dev/plan_bayesian-analysis.md
new file mode 100644
index 00000000..76be62c3
--- /dev/null
+++ b/docs/dev/plan_bayesian-analysis.md
@@ -0,0 +1,334 @@
+# Bayesian Analysis Implementation Plan
+
+**Status:** Planned **Feature name:** `bayesian-analysis` **Suggested
+branch:** `feature/bayesian-analysis` **Design doc:**
+`docs/dev/bayesian-analysis-design.md`
+
+## Context
+
+This plan follows `.github/copilot-instructions.md`. The instructions
+refer to `docs/architecture/architecture.md`, but this checkout does not
+contain that file. The matching living architecture document used for
+this plan is `docs/dev/architecture.md`.
+
+Relevant current seams:
+
+- `src/easydiffraction/analysis/categories/fit/default.py`
+- `src/easydiffraction/analysis/analysis.py`
+- `src/easydiffraction/analysis/fitting.py`
+- `src/easydiffraction/analysis/minimizers/base.py`
+- `src/easydiffraction/analysis/minimizers/bumps.py`
+- `src/easydiffraction/analysis/minimizers/enums.py`
+- `src/easydiffraction/analysis/minimizers/factory.py`
+- `src/easydiffraction/analysis/minimizers/__init__.py`
+- `src/easydiffraction/analysis/fit_helpers/reporting.py`
+- `src/easydiffraction/display/plotting.py`
+
+## Decisions
+
+- Add Bayesian sampling as a normal minimizer tag: `'bumps (dream)'`.
+- Keep `project.analysis.fit()` as the execution entry point.
+- Keep posterior chains runtime-only in `BayesianFitResults`.
+- Do not add posterior chain export in phase 1.
+- Add `plot_posterior_predictive(...)` in phase 1.
+- Add optional `random_seed` support for `'bumps (dream)'`.
+- Generate and record a seed when the user does not provide one.
+- Reject non-`None` `random_seed` for deterministic minimizers.
+- Treat sampler completion and convergence quality separately.
+- Use `success=False` only when DREAM fails or no usable posterior
+  samples exist.
+- Store and display convergence diagnostics; warn on poor convergence.
+- Start with an internal posterior predictive draw cap of 200.
+
+## Scope
+
+Phase 1 delivers implementation only. Do not add or run tests in phase 1
+unless the user explicitly asks. Stop after phase 1 and request review.
+
+Phase 2 delivers verification only. Add/update tests, then run the
+commands listed in this plan.
+
+## Agent Execution Rule
+
+When an AI agent follows this plan, every completed phase 1
+implementation step must be staged with explicit paths and committed
+locally before moving to the next implementation step or the phase 1
+review gate. Commits must be atomic, single-purpose, and aligned with
+the step just completed.
+
+If implementation uncovers a serious requirement, risk, design issue, or
+scope change not covered by this plan, stop and ask for clarification or
+approval before proceeding.
+
+## Status Checklist
+
+- [x] Repository context gathered.
+- [x] Design decisions recorded.
+- [ ] Phase 1 implementation complete.
+- [ ] Phase 1 review approved.
+- [ ] Phase 2 tests added.
+- [ ] Phase 2 verification commands completed.
+
+## Phase 1: Implementation
+
+### Step 1: Register The DREAM Minimizer
+
+- [ ] Add `BUMPS_DREAM = 'bumps (dream)'` to `MinimizerTypeEnum`.
+- [ ] Add a human-readable enum description.
+- [ ] Add `src/easydiffraction/analysis/minimizers/bumps_dream.py`.
+- [ ] Register `BumpsDreamMinimizer` with `MinimizerFactory`.
+- [ ] Import `BumpsDreamMinimizer` from
+      `src/easydiffraction/analysis/minimizers/__init__.py`.
+- [ ] Update `docs/dev/architecture.md` minimizer tag tables if the
+      implementation changes the documented supported tags.
+
+Likely files:
+
+- `src/easydiffraction/analysis/minimizers/enums.py`
+- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
+- `src/easydiffraction/analysis/minimizers/__init__.py`
+- `docs/dev/architecture.md`
+
+Suggested commit message:
+
+```text
+Register BUMPS DREAM minimizer
+```
+
+### Step 2: Add Bayesian Result Models
+
+- [ ] Add `BayesianFitResults`.
+- [ ] Add `PosteriorSamples`.
+- [ ] Add `PosteriorParameterSummary`.
+- [ ] Add `PosteriorPredictiveSummary`.
+- [ ] Keep public constructors explicit; do not use `**kwargs`.
+- [ ] Add NumPy-style docstrings for public classes and methods.
+- [ ] Provide `PosteriorSamples.to_arviz()` for plotting integration.
+
+Likely files:
+
+- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
+- `src/easydiffraction/analysis/fit_helpers/reporting.py`
+- `src/easydiffraction/analysis/fit_helpers/__init__.py`
+
+Suggested commit message:
+
+```text
+Add Bayesian fit result models
+```
+
+### Step 3: Thread Random Seed Through Fit Execution
+
+- [ ] Add optional `random_seed: int | None = None` to `Fit.run(...)`
+      and `Fit.__call__(...)`.
+- [ ] Thread `random_seed` through `Analysis._run_fit(...)`,
+      `_fit_single(...)`, `_fit_joint(...)`, and `Fitter.fit(...)`.
+- [ ] Thread `random_seed` through `MinimizerBase.fit(...)`.
+- [ ] Reject non-`None` `random_seed` in deterministic minimizers with a
+      clear `ValueError`.
+- [ ] Let `BumpsDreamMinimizer` generate a seed when `random_seed` is
+      `None`.
+- [ ] Record the user-provided or generated seed in
+      `BayesianFitResults.sampler_settings`.
+
+Likely files:
+
+- `src/easydiffraction/analysis/categories/fit/default.py`
+- `src/easydiffraction/analysis/analysis.py`
+- `src/easydiffraction/analysis/fitting.py`
+- `src/easydiffraction/analysis/minimizers/base.py`
+- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
+
+Suggested commit message:
+
+```text
+Thread random seed through fitting
+```
+
+### Step 4: Implement DREAM Sampling
+
+- [ ] Reuse `_EasyDiffractionFitness` or extract a shared BUMPS helper
+      only if needed.
+- [ ] Build BUMPS parameters from sampled EasyDiffraction parameters.
+- [ ] Validate finite bounds, bound ordering, and start values before
+      sampling.
+- [ ] Preserve parameter order across BUMPS parameters, posterior
+      arrays, summaries, and MAP commit.
+- [ ] Run BUMPS DREAM with internal defaults.
+- [ ] Commit the MAP or best posterior sample to project parameters.
+- [ ] Restore starting values if the sampler fails before producing
+      usable samples.
+- [ ] Store raw BUMPS output as `engine_result` only after stable fields
+      have been normalized into EasyDiffraction-owned result objects.
+
+Likely files:
+
+- `src/easydiffraction/analysis/minimizers/bumps.py`
+- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
+- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
+
+Suggested commit message:
+
+```text
+Implement BUMPS DREAM sampling
+```
+
+### Step 5: Add Bayesian Display
+
+- [ ] Make `BayesianFitResults.display_results(...)` render sampler
+      status, diagnostics, settings, and posterior parameter summaries.
+- [ ] Keep deterministic `FitResults.display_results(...)` unchanged.
+- [ ] Ensure `analysis.display.fit_results()` continues to dispatch
+      through the active result object.
+- [ ] Display poor convergence prominently without hiding usable
+      posterior summaries.
+
+Likely files:
+
+- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
+- `src/easydiffraction/analysis/fit_helpers/reporting.py`
+
+Suggested commit message:
+
+```text
+Display Bayesian fit summaries
+```
+
+### Step 6: Add Posterior Correlations
+
+- [ ] Extend `plot_param_correlations()` to detect
+      `BayesianFitResults.posterior_samples`.
+- [ ] Compute the correlation matrix from flattened posterior samples.
+- [ ] Preserve deterministic covariance and engine-derived behavior.
+- [ ] Preserve current thresholding and lower-triangle display behavior.
+
+Likely files:
+
+- `src/easydiffraction/display/plotting.py`
+- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
+
+Suggested commit message:
+
+```text
+Plot posterior parameter correlations
+```
+
+### Step 7: Add Bayesian Posterior Plots
+
+- [ ] Add `plot_posterior_pairs(...)`.
+- [ ] Add `plot_param_distribution(param, ...)`.
+- [ ] Use `PosteriorSamples.to_arviz()` internally where practical.
+- [ ] Accept parameter objects, unique names, and user-facing labels for
+      one-parameter selection.
+- [ ] Fail ambiguous string matches with a clear list of matches.
+- [ ] Warn clearly when Bayesian-only plots are called on deterministic
+      results.
+
+Likely files:
+
+- `src/easydiffraction/display/plotting.py`
+- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
+
+Suggested commit message:
+
+```text
+Add posterior distribution plots
+```
+
+### Step 8: Add Posterior Predictive Plots
+
+- [ ] Generate posterior predictive summaries from a capped subset of
+      posterior draws.
+- [ ] Store summaries in `BayesianFitResults.posterior_predictive`.
+- [ ] Add `plot_posterior_predictive(expt_name, style='band', ...)`.
+- [ ] Support `style='band'` and `style='draws'` initially.
+- [ ] Extend `plot_meas_vs_calc(...)` to show a 95% credible band when
+      posterior predictive summaries are available.
+- [ ] Keep experiment data categories responsible only for measured,
+      current calculated, and current background patterns.
+
+Likely files:
+
+- `src/easydiffraction/display/plotting.py`
+- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
+- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
+
+Suggested commit message:
+
+```text
+Add posterior predictive plotting
+```
+
+## Phase 1 Review Gate
+
+- [ ] Inspect `git status --short`.
+- [ ] Inspect the diff for unrelated changes.
+- [ ] Confirm every phase 1 step has an atomic local commit.
+- [ ] Present the implementation summary for review.
+- [ ] Do not start phase 2 until the user approves verification work.
+
+## Phase 2: Verification
+
+### Test Additions
+
+- [ ] Add unit tests for enum and factory registration.
+- [ ] Add unit tests for finite-bound validation.
+- [ ] Add unit tests for start-value-inside-bounds validation.
+- [ ] Add unit tests for `random_seed` behavior.
+- [ ] Add unit tests for deterministic minimizer seed rejection.
+- [ ] Add unit tests for parameter order preservation.
+- [ ] Add unit tests for MAP commit and restore-on-failure behavior.
+- [ ] Add unit tests for Bayesian display output.
+- [ ] Add unit tests for posterior sample correlation calculation.
+- [ ] Add unit tests for posterior predictive summary generation.
+- [ ] Add plotting smoke tests using small posterior fixtures.
+- [ ] Add integration coverage for a small bounded DREAM refinement.
+- [ ] Add integration coverage for explicit LM pre-fit followed by
+      DREAM.
+- [ ] Add serialization coverage proving posterior chains are not saved.
+
+Likely test files:
+
+- `tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py`
+- `tests/unit/easydiffraction/analysis/minimizers/test_enums.py`
+- `tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py`
+- `tests/unit/easydiffraction/display/test_plotting.py`
+- `tests/integration/fitting/test_bayesian_dream.py`
+- `tests/integration/fitting/test_project_load.py`
+
+Suggested commit message:
+
+```text
+Test Bayesian DREAM workflow
+```
+
+### Verification Commands
+
+- [ ] `pixi run test-structure-check`
+- [ ] `pixi run fix`
+- [ ] `pixi run check`
+- [ ] `pixi run unit-tests`
+- [ ] `pixi run integration-tests`
+- [ ] `pixi run script-tests`
+
+If `pixi run fix` regenerates package-structure docs, accept the
+generated changes and include them only in the verification commit where
+they were produced.
+
+## Non-Blocking Implementation Discovery
+
+The BUMPS DREAM return object must be inspected during implementation.
+If the stable public fields are insufficient for posterior samples,
+log-likelihood values, or diagnostics, normalize the available fields
+into EasyDiffraction-owned value objects and keep `engine_result`
+opaque. If this prevents a required feature, stop and ask before
+changing scope.
+
+## Suggested Pull Request
+
+**Title:** Add Bayesian DREAM sampling and posterior plots
+
+**Description:** Adds Bayesian refinement through the BUMPS DREAM
+sampler, including reproducible runs, posterior parameter summaries, and
+posterior predictive plots so users can inspect uncertainty in fitted
+diffraction models.

From e34c278a56e50c7815a8a2e7608072ed7a4b543e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 23:54:00 +0200
Subject: [PATCH 006/106] Register BUMPS DREAM minimizer

---
 docs/dev/architecture.md                      | 45 ++++++++++---------
 .../analysis/minimizers/__init__.py           |  1 +
 .../analysis/minimizers/bumps_dream.py        | 35 +++++++++++++++
 .../analysis/minimizers/enums.py              |  2 +
 4 files changed, 61 insertions(+), 22 deletions(-)
 create mode 100644 src/easydiffraction/analysis/minimizers/bumps_dream.py

diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index 390aa77b..dbfe59c8 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -527,28 +527,28 @@ from .line_segment import LineSegmentBackground
 
 ### 5.5 All Factories
 
-| Factory                      | Domain                 | Tags resolve to                                                                                                                                                             |
-| ---------------------------- | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `BackgroundFactory`          | Background categories  | `LineSegmentBackground`, `ChebyshevPolynomialBackground`                                                                                                                    |
-| `PeakFactory`                | Peak profiles          | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, …                                                                                                                |
-| `InstrumentFactory`          | Instruments            | `CwlPdInstrument`, `TofPdInstrument`, …                                                                                                                                     |
-| `DataFactory`                | Data collections       | `PdCwlData`, `PdTofData`, `TotalData`                                                                                                                                       |
-| `ReflnFactory`               | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData`                                                                                                                     |
-| `ExtinctionFactory`          | Extinction models      | `BeckerCoppensExtinction`                                                                                                                                                   |
-| `LinkedCrystalFactory`       | Linked-crystal refs    | `LinkedCrystal`                                                                                                                                                             |
-| `ExcludedRegionsFactory`     | Excluded regions       | `ExcludedRegions`                                                                                                                                                           |
-| `LinkedPhasesFactory`        | Linked phases          | `LinkedPhases`                                                                                                                                                              |
-| `ExperimentTypeFactory`      | Experiment descriptors | `ExperimentType`                                                                                                                                                            |
-| `CellFactory`                | Unit cells             | `Cell`                                                                                                                                                                      |
-| `SpaceGroupFactory`          | Space groups           | `SpaceGroup`                                                                                                                                                                |
-| `AtomSitesFactory`           | Atom sites             | `AtomSites`                                                                                                                                                                 |
-| `AtomSiteAnisoFactory`       | Anisotropic ADPs       | `AtomSiteAnisoCollection`                                                                                                                                                   |
-| `AliasesFactory`             | Parameter aliases      | `Aliases`                                                                                                                                                                   |
-| `ConstraintsFactory`         | Parameter constraints  | `Constraints`                                                                                                                                                               |
-| `FitModeFactory`             | Fit-mode category      | `FitMode`                                                                                                                                                                   |
-| `JointFitExperimentsFactory` | Joint-fit weights      | `JointFitExperiments`                                                                                                                                                       |
-| `CalculatorFactory`          | Calculation engines    | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator`                                                                                                                 |
-| `MinimizerFactory`           | Minimisers             | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
+| Factory | Domain | Tags resolve to |
+| --- | --- | --- |
+| `BackgroundFactory` | Background categories | `LineSegmentBackground`, `ChebyshevPolynomialBackground` |
+| `PeakFactory` | Peak profiles | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, … |
+| `InstrumentFactory` | Instruments | `CwlPdInstrument`, `TofPdInstrument`, … |
+| `DataFactory` | Data collections | `PdCwlData`, `PdTofData`, `TotalData` |
+| `ReflnFactory` | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData` |
+| `ExtinctionFactory` | Extinction models | `BeckerCoppensExtinction` |
+| `LinkedCrystalFactory` | Linked-crystal refs | `LinkedCrystal` |
+| `ExcludedRegionsFactory` | Excluded regions | `ExcludedRegions` |
+| `LinkedPhasesFactory` | Linked phases | `LinkedPhases` |
+| `ExperimentTypeFactory` | Experiment descriptors | `ExperimentType` |
+| `CellFactory` | Unit cells | `Cell` |
+| `SpaceGroupFactory` | Space groups | `SpaceGroup` |
+| `AtomSitesFactory` | Atom sites | `AtomSites` |
+| `AtomSiteAnisoFactory` | Anisotropic ADPs | `AtomSiteAnisoCollection` |
+| `AliasesFactory` | Parameter aliases | `Aliases` |
+| `ConstraintsFactory` | Parameter constraints | `Constraints` |
+| `FitModeFactory` | Fit-mode category | `FitMode` |
+| `JointFitExperimentsFactory` | Joint-fit weights | `JointFitExperiments` |
+| `CalculatorFactory` | Calculation engines | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator` |
+| `MinimizerFactory` | Minimisers | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsDreamMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
 
 > **Note:** `ExperimentFactory` and `StructureFactory` are _builder_
 > factories with `from_cif_path`, `from_cif_str`, `from_data_path`, and
@@ -675,6 +675,7 @@ the choice.
 | `dfols`                 | `DfolsMinimizer`             |
 | `bumps`                 | `BumpsMinimizer`             |
 | `bumps (lm)`            | `BumpsLmMinimizer`           |
+| `bumps (dream)`         | `BumpsDreamMinimizer`        |
 | `bumps (amoeba)`        | `BumpsAmoebaMinimizer`       |
 | `bumps (de)`            | `BumpsDEMinimizer`           |
 
diff --git a/src/easydiffraction/analysis/minimizers/__init__.py b/src/easydiffraction/analysis/minimizers/__init__.py
index 7dddb75e..7639dd8a 100644
--- a/src/easydiffraction/analysis/minimizers/__init__.py
+++ b/src/easydiffraction/analysis/minimizers/__init__.py
@@ -4,6 +4,7 @@
 from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
 from easydiffraction.analysis.minimizers.bumps_amoeba import BumpsAmoebaMinimizer
 from easydiffraction.analysis.minimizers.bumps_de import BumpsDEMinimizer
+from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
 from easydiffraction.analysis.minimizers.bumps_lm import BumpsLmMinimizer
 from easydiffraction.analysis.minimizers.dfols import DfolsMinimizer
 from easydiffraction.analysis.minimizers.lmfit import LmfitMinimizer
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
new file mode 100644
index 00000000..7a951d81
--- /dev/null
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bumps minimizer variant using the DREAM sampler."""
+
+from __future__ import annotations
+
+from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
+from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
+from easydiffraction.analysis.minimizers.factory import MinimizerFactory
+from easydiffraction.core.metadata import TypeInfo
+
+DEFAULT_METHOD = 'dream'
+DEFAULT_MAX_ITERATIONS = 1000
+
+
+@MinimizerFactory.register
+class BumpsDreamMinimizer(BumpsMinimizer):
+    """Bumps minimizer using the DREAM Bayesian sampler."""
+
+    type_info = TypeInfo(
+        tag=MinimizerTypeEnum.BUMPS_DREAM,
+        description='Bumps library with DREAM Bayesian sampling',
+    )
+
+    def __init__(
+        self,
+        name: str = MinimizerTypeEnum.BUMPS_DREAM,
+        method: str = DEFAULT_METHOD,
+        max_iterations: int = DEFAULT_MAX_ITERATIONS,
+    ) -> None:
+        super().__init__(
+            name=name,
+            method=method,
+            max_iterations=max_iterations,
+        )
\ No newline at end of file
diff --git a/src/easydiffraction/analysis/minimizers/enums.py b/src/easydiffraction/analysis/minimizers/enums.py
index cdfe4ecb..5d8ad218 100644
--- a/src/easydiffraction/analysis/minimizers/enums.py
+++ b/src/easydiffraction/analysis/minimizers/enums.py
@@ -16,6 +16,7 @@ class MinimizerTypeEnum(StrEnum):
     DFOLS = 'dfols'
     BUMPS = 'bumps'
     BUMPS_LM = 'bumps (lm)'
+    BUMPS_DREAM = 'bumps (dream)'
     BUMPS_AMOEBA = 'bumps (amoeba)'
     BUMPS_DE = 'bumps (de)'
 
@@ -45,6 +46,7 @@ def description(self) -> str:
                 'BUMPS library using the default Levenberg-Marquardt method'
             ),
             MinimizerTypeEnum.BUMPS_LM: ('BUMPS library with Levenberg-Marquardt method'),
+            MinimizerTypeEnum.BUMPS_DREAM: ('BUMPS library with DREAM Bayesian sampling'),
             MinimizerTypeEnum.BUMPS_AMOEBA: ('BUMPS library with Nelder-Mead simplex method'),
             MinimizerTypeEnum.BUMPS_DE: ('BUMPS library with differential evolution method'),
         }

From 303dc2708df22491105dfa032a641b09a5202260 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 23:55:42 +0200
Subject: [PATCH 007/106] Add Bayesian fit result models

---
 .../analysis/fit_helpers/__init__.py          |   6 +
 .../analysis/fit_helpers/bayesian.py          | 234 ++++++++++++++++++
 2 files changed, 240 insertions(+)
 create mode 100644 src/easydiffraction/analysis/fit_helpers/bayesian.py

diff --git a/src/easydiffraction/analysis/fit_helpers/__init__.py b/src/easydiffraction/analysis/fit_helpers/__init__.py
index 4e798e20..c7c53862 100644
--- a/src/easydiffraction/analysis/fit_helpers/__init__.py
+++ b/src/easydiffraction/analysis/fit_helpers/__init__.py
@@ -1,2 +1,8 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
+
+from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+from easydiffraction.analysis.fit_helpers.reporting import FitResults
diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
new file mode 100644
index 00000000..811b8ecb
--- /dev/null
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -0,0 +1,234 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian fit result models and posterior data containers."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+from easydiffraction.analysis.fit_helpers.reporting import FitResults
+
+
+@dataclass(slots=True)
+class PosteriorParameterSummary:
+    """Posterior summary statistics for one fitted parameter.
+
+    Parameters
+    ----------
+    unique_name : str
+        Unique parameter name used across EasyDiffraction.
+    display_name : str
+        Human-readable label used in plots and tables.
+    map_value : float
+        Maximum-a-posteriori or best-sampled parameter value.
+    median : float
+        Posterior median value.
+    standard_deviation : float
+        Posterior standard deviation.
+    interval_68 : tuple[float, float]
+        Central 68% interval.
+    interval_95 : tuple[float, float]
+        Central 95% interval.
+    ess_bulk : float | None, default=None
+        Bulk effective sample size when available.
+    r_hat : float | None, default=None
+        Rank-normalized split-$\hat{R}$ when available.
+    """
+
+    unique_name: str
+    display_name: str
+    map_value: float
+    median: float
+    standard_deviation: float
+    interval_68: tuple[float, float]
+    interval_95: tuple[float, float]
+    ess_bulk: float | None = None
+    r_hat: float | None = None
+
+
+@dataclass(slots=True)
+class PosteriorPredictiveSummary:
+    """Posterior predictive summaries for one experiment.
+
+    Parameters
+    ----------
+    experiment_name : str
+        Experiment identifier.
+    x : np.ndarray
+        X-axis values for the predictive curves.
+    map_prediction : np.ndarray
+        Prediction corresponding to the committed point estimate.
+    lower_95 : np.ndarray | None, default=None
+        Lower bound of the 95% credible interval.
+    upper_95 : np.ndarray | None, default=None
+        Upper bound of the 95% credible interval.
+    lower_68 : np.ndarray | None, default=None
+        Lower bound of the 68% credible interval.
+    upper_68 : np.ndarray | None, default=None
+        Upper bound of the 68% credible interval.
+    draws : np.ndarray | None, default=None
+        Optional capped predictive draws with shape ``(n_draws, n_x)``.
+    """
+
+    experiment_name: str
+    x: np.ndarray
+    map_prediction: np.ndarray
+    lower_95: np.ndarray | None = None
+    upper_95: np.ndarray | None = None
+    lower_68: np.ndarray | None = None
+    upper_68: np.ndarray | None = None
+    draws: np.ndarray | None = None
+
+
+@dataclass(slots=True)
+class PosteriorSamples:
+    """Posterior samples and sample statistics from a Bayesian fit.
+
+    Parameters
+    ----------
+    parameter_names : list[str]
+        Parameter names in the preserved EasyDiffraction order.
+    parameter_samples : np.ndarray
+        Sample array with shape ``(n_draws, n_chains, n_parameters)``.
+    log_posterior : np.ndarray | None, default=None
+        Log-posterior values with shape ``(n_draws, n_chains)`` when
+        available.
+    draw_index : np.ndarray | None, default=None
+        Optional draw or generation indices associated with the first
+        axis of ``parameter_samples``.
+    """
+
+    parameter_names: list[str]
+    parameter_samples: np.ndarray
+    log_posterior: np.ndarray | None = None
+    draw_index: np.ndarray | None = None
+
+    def flattened(self) -> np.ndarray:
+        """Return flattened posterior samples by parameter.
+
+        Returns
+        -------
+        np.ndarray
+            Array with shape ``(n_draws * n_chains, n_parameters)``.
+        """
+        return np.asarray(self.parameter_samples).reshape(-1, len(self.parameter_names))
+
+    def to_arviz(self) -> object:
+        """Convert posterior samples to an ArviZ ``InferenceData`` object.
+
+        Returns
+        -------
+        object
+            ArviZ ``InferenceData`` instance built from the stored
+            posterior samples.
+
+        Raises
+        ------
+        ValueError
+            If the stored arrays do not have the expected shapes.
+        """
+        import arviz as az
+
+        posterior_array = np.asarray(self.parameter_samples, dtype=float)
+        if posterior_array.ndim != 3:
+            msg = 'Posterior sample array must have shape (n_draws, n_chains, n_parameters).'
+            raise ValueError(msg)
+
+        n_draws, n_chains, n_parameters = posterior_array.shape
+        if n_parameters != len(self.parameter_names):
+            msg = 'Posterior sample array does not match the parameter name list length.'
+            raise ValueError(msg)
+
+        posterior_dict = {
+            name: np.transpose(posterior_array[:, :, index], (1, 0))
+            for index, name in enumerate(self.parameter_names)
+        }
+
+        sample_stats: dict[str, np.ndarray] | None = None
+        if self.log_posterior is not None:
+            log_posterior = np.asarray(self.log_posterior, dtype=float)
+            if log_posterior.shape != (n_draws, n_chains):
+                msg = 'Log-posterior array must match the first two posterior sample axes.'
+                raise ValueError(msg)
+            sample_stats = {'lp': np.transpose(log_posterior, (1, 0))}
+
+        return az.from_dict(posterior=posterior_dict, sample_stats=sample_stats)
+
+
+class BayesianFitResults(FitResults):
+    """Container for Bayesian fit results and posterior summaries.
+
+    Parameters
+    ----------
+    success : bool, default=False
+        Whether the Bayesian fit produced usable posterior results.
+    parameters : list[object] | None, default=None
+        Final committed parameter objects.
+    reduced_chi_square : float | None, default=None
+        Reduced chi-square evaluated at the committed point estimate.
+    engine_result : object | None, default=None
+        Opaque backend result object.
+    starting_parameters : list[object] | None, default=None
+        Starting parameter objects or snapshots.
+    fitting_time : float | None, default=None
+        Total fitting time in seconds.
+    sampler_name : str, default='dream'
+        Sampler identifier.
+    point_estimate_name : str, default='map'
+        Name of the point estimate committed back to the project.
+    posterior_samples : PosteriorSamples | None, default=None
+        Stored posterior samples.
+    posterior_parameter_summaries : list[PosteriorParameterSummary] | None, default=None
+        Posterior summaries for each sampled parameter.
+    posterior_predictive : dict[str, PosteriorPredictiveSummary] | None, default=None
+        Posterior predictive summaries keyed by experiment name.
+    credible_interval_levels : tuple[float, ...], default=(0.68, 0.95)
+        Interval levels available in the summaries.
+    sampler_settings : dict[str, object] | None, default=None
+        Sampler settings recorded for reproducibility.
+    convergence_diagnostics : dict[str, object] | None, default=None
+        Convergence diagnostics and status metadata.
+    """
+
+    def __init__(
+        self,
+        *,
+        success: bool = False,
+        parameters: list[object] | None = None,
+        reduced_chi_square: float | None = None,
+        engine_result: object | None = None,
+        starting_parameters: list[object] | None = None,
+        fitting_time: float | None = None,
+        sampler_name: str = 'dream',
+        point_estimate_name: str = 'map',
+        posterior_samples: PosteriorSamples | None = None,
+        posterior_parameter_summaries: list[PosteriorParameterSummary] | None = None,
+        posterior_predictive: dict[str, PosteriorPredictiveSummary] | None = None,
+        credible_interval_levels: tuple[float, ...] = (0.68, 0.95),
+        sampler_settings: dict[str, object] | None = None,
+        convergence_diagnostics: dict[str, object] | None = None,
+    ) -> None:
+        super().__init__(
+            success=success,
+            parameters=parameters,
+            reduced_chi_square=reduced_chi_square,
+            engine_result=engine_result,
+            starting_parameters=starting_parameters,
+            fitting_time=fitting_time,
+        )
+        self.sampler_name = sampler_name
+        self.point_estimate_name = point_estimate_name
+        self.posterior_samples = posterior_samples
+        self.posterior_parameter_summaries = (
+            posterior_parameter_summaries if posterior_parameter_summaries is not None else []
+        )
+        self.posterior_predictive = (
+            posterior_predictive if posterior_predictive is not None else {}
+        )
+        self.credible_interval_levels = credible_interval_levels
+        self.sampler_settings = sampler_settings if sampler_settings is not None else {}
+        self.convergence_diagnostics = (
+            convergence_diagnostics if convergence_diagnostics is not None else {}
+        )
\ No newline at end of file

From cd8d233ed0e8351ab946b369435c00d9a77ea02b Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Thu, 7 May 2026 23:57:13 +0200
Subject: [PATCH 008/106] Thread random seed through fitting

---
 src/easydiffraction/analysis/analysis.py      | 32 +++++++++++++++--
 .../analysis/categories/fit/default.py        | 16 +++++++--
 src/easydiffraction/analysis/fitting.py       |  4 +++
 .../analysis/minimizers/base.py               | 34 +++++++++++++++++++
 .../analysis/minimizers/bumps_dream.py        | 24 ++++++++++++-
 5 files changed, 104 insertions(+), 6 deletions(-)

diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index c132e409..dd4de8c0 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -513,7 +513,13 @@ def joint_fit_experiments(self) -> object:
         """Per-experiment weight collection for joint fitting."""
         return self._joint_fit_experiments
 
-    def _run_fit(self, verbosity: str | None = None, *, use_physical_limits: bool = False) -> None:
+    def _run_fit(
+        self,
+        verbosity: str | None = None,
+        *,
+        use_physical_limits: bool = False,
+        random_seed: int | None = None,
+    ) -> None:
         """
         Execute fitting for all experiments.
 
@@ -542,6 +548,8 @@ def _run_fit(self, verbosity: str | None = None, *, use_physical_limits: bool =
             When ``True``, fall back to physical limits from the value
             spec for parameters whose ``fit_min``/``fit_max`` are
             unbounded.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
         """
         verb = VerbosityEnum(verbosity if verbosity is not None else self.project.verbosity)
 
@@ -563,10 +571,20 @@ def _run_fit(self, verbosity: str | None = None, *, use_physical_limits: bool =
         # Run the fitting process
         mode = FitModeEnum(self._fit.mode.value)
         if mode is FitModeEnum.JOINT:
-            self._fit_joint(verb, structures, experiments, use_physical_limits=use_physical_limits)
+            self._fit_joint(
+                verb,
+                structures,
+                experiments,
+                use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
+            )
         elif mode is FitModeEnum.SINGLE:
             self._fit_single(
-                verb, structures, experiments, use_physical_limits=use_physical_limits
+                verb,
+                structures,
+                experiments,
+                use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
             )
         elif mode is FitModeEnum.SEQUENTIAL:
             log.error(
@@ -585,6 +603,7 @@ def _fit_joint(
         experiments: object,
         *,
         use_physical_limits: bool,
+        random_seed: int | None,
     ) -> None:
         """
         Run joint fitting across all experiments with weights.
@@ -599,6 +618,8 @@ def _fit_joint(
             Project experiments collection.
         use_physical_limits : bool
             Whether to use physical limits as fit bounds.
+        random_seed : int | None
+            Optional random seed passed to stochastic minimizers.
         """
         mode = FitModeEnum.JOINT
         # Auto-populate joint_fit_experiments if empty
@@ -622,6 +643,7 @@ def _fit_joint(
             analysis=self,
             verbosity=verb,
             use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
         )
 
         # After fitting, get the results
@@ -634,6 +656,7 @@ def _fit_single(
         experiments: object,
         *,
         use_physical_limits: bool,
+        random_seed: int | None,
     ) -> None:
         """
         Run single-mode fitting for each experiment independently.
@@ -648,6 +671,8 @@ def _fit_single(
             Project experiments collection.
         use_physical_limits : bool
             Whether to use physical limits as fit bounds.
+        random_seed : int | None
+            Optional random seed passed to stochastic minimizers.
         """
         mode = FitModeEnum.SINGLE
         expt_names = experiments.names
@@ -666,6 +691,7 @@ def _fit_single(
                 analysis=self,
                 verbosity=verb,
                 use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
             )
 
             # After fitting, snapshot parameter values before
diff --git a/src/easydiffraction/analysis/categories/fit/default.py b/src/easydiffraction/analysis/categories/fit/default.py
index c002fe39..7a9ebcb1 100644
--- a/src/easydiffraction/analysis/categories/fit/default.py
+++ b/src/easydiffraction/analysis/categories/fit/default.py
@@ -148,6 +148,7 @@ def run(
         verbosity: str | None = None,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> None:
         """
         Execute fitting for the owning analysis.
@@ -158,6 +159,8 @@ def run(
             Console output verbosity override.
         use_physical_limits : bool, default=False
             Whether to fall back to physical limits as fit bounds.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
 
         Raises
         ------
@@ -168,16 +171,25 @@ def run(
         if parent is None:
             msg = 'Fit category is not attached to an Analysis object.'
             raise RuntimeError(msg)
-        parent._run_fit(verbosity=verbosity, use_physical_limits=use_physical_limits)
+        parent._run_fit(
+            verbosity=verbosity,
+            use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
+        )
 
     def __call__(
         self,
         verbosity: str | None = None,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> None:
         """Execute :meth:`run` for convenience."""
-        self.run(verbosity=verbosity, use_physical_limits=use_physical_limits)
+        self.run(
+            verbosity=verbosity,
+            use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
+        )
 
     def from_cif(self, block: object, idx: int = 0) -> None:
         """
diff --git a/src/easydiffraction/analysis/fitting.py b/src/easydiffraction/analysis/fitting.py
index 9b036014..db5590e1 100644
--- a/src/easydiffraction/analysis/fitting.py
+++ b/src/easydiffraction/analysis/fitting.py
@@ -39,6 +39,7 @@ def fit(
         verbosity: VerbosityEnum = VerbosityEnum.FULL,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> None:
         """
         Run the fitting process.
@@ -65,6 +66,8 @@ def fit(
             When ``True``, fall back to physical limits from the value
             spec for parameters whose ``fit_min``/``fit_max`` are
             unbounded.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
         """
         # Enforce symmetry constraints (e.g. ADP) before collecting
         # free parameters so that components fixed by site symmetry are
@@ -118,6 +121,7 @@ def objective_function(engine_params: dict[str, Any]) -> np.ndarray:
             objective_function,
             verbosity=verbosity,
             use_physical_limits=use_physical_limits,
+            random_seed=random_seed,
         )
 
     def _process_fit_results(
diff --git a/src/easydiffraction/analysis/minimizers/base.py b/src/easydiffraction/analysis/minimizers/base.py
index c0b39eb3..93b811ae 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -41,6 +41,7 @@ def __init__(
         self._best_chi2: float | None = None
         self._best_iteration: int | None = None
         self._fitting_time: float | None = None
+        self._resolved_random_seed: int | None = None
         self.tracker: FitProgressTracker = FitProgressTracker()
 
     def _start_tracking(
@@ -231,6 +232,32 @@ def _warn_physical_limit_violations(parameters: list[object]) -> None:
     def _check_success(self, raw_result: object) -> bool:
         """Determine whether the fit was successful."""
 
+    def _resolve_random_seed(self, random_seed: int | None) -> int | None:
+        """Validate or normalize the random seed for this minimizer.
+
+        Parameters
+        ----------
+        random_seed : int | None
+            User-provided random seed.
+
+        Returns
+        -------
+        int | None
+            Seed accepted by the minimizer, or ``None`` when not used.
+
+        Raises
+        ------
+        ValueError
+            If this minimizer does not support ``random_seed``.
+        """
+        if random_seed is None:
+            self._resolved_random_seed = None
+            return None
+
+        minimizer_name = self.name or self.__class__.__name__
+        msg = f"Minimizer '{minimizer_name}' does not support random_seed."
+        raise ValueError(msg)
+
     def fit(
         self,
         parameters: list[object],
@@ -238,6 +265,7 @@ def fit(
         verbosity: VerbosityEnum = VerbosityEnum.FULL,
         *,
         use_physical_limits: bool = False,
+        random_seed: int | None = None,
     ) -> FitResults:
         """
         Run the full minimization workflow.
@@ -255,6 +283,8 @@ def fit(
             When ``True``, fall back to physical limits from the value
             spec for parameters whose ``fit_min``/``fit_max`` are
             unbounded.
+        random_seed : int | None, default=None
+            Optional random seed passed to stochastic minimizers.
 
         Returns
         -------
@@ -264,6 +294,8 @@ def fit(
         if use_physical_limits:
             self._apply_physical_limits(parameters)
 
+        resolved_random_seed = self._resolve_random_seed(random_seed)
+
         minimizer_name = self.name or 'Unnamed Minimizer'
         if self.method is not None and f'({self.method})' not in minimizer_name:
             minimizer_name += f' ({self.method})'
@@ -271,6 +303,8 @@ def fit(
         self._start_tracking(minimizer_name, verbosity=verbosity)
 
         solver_args = self._prepare_solver_args(parameters)
+        if resolved_random_seed is not None:
+            solver_args['random_seed'] = resolved_random_seed
         raw_result = self._run_solver(objective_function, **solver_args)
 
         self._stop_tracking()
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 7a951d81..c989ffcc 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -4,6 +4,8 @@
 
 from __future__ import annotations
 
+import numpy as np
+
 from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
 from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 from easydiffraction.analysis.minimizers.factory import MinimizerFactory
@@ -32,4 +34,24 @@ def __init__(
             name=name,
             method=method,
             max_iterations=max_iterations,
-        )
\ No newline at end of file
+        )
+
+    def _resolve_random_seed(self, random_seed: int | None) -> int:
+        """Return a user-provided or generated random seed.
+
+        Parameters
+        ----------
+        random_seed : int | None
+            User-provided random seed.
+
+        Returns
+        -------
+        int
+            Seed to use for the DREAM run.
+        """
+        if random_seed is None:
+            generator = np.random.default_rng()
+            random_seed = int(generator.integers(0, np.iinfo(np.int32).max))
+
+        self._resolved_random_seed = int(random_seed)
+        return self._resolved_random_seed
\ No newline at end of file

From 3e4d09e9f38fac93e07e79e7c6a6e954c606d7dd Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 00:09:36 +0200
Subject: [PATCH 009/106] Implement DREAM sampling

---
 .../analysis/fit_helpers/bayesian.py          | 157 ++++++++-
 .../analysis/minimizers/base.py               |  33 +-
 .../analysis/minimizers/bumps_dream.py        | 308 +++++++++++++++++-
 3 files changed, 492 insertions(+), 6 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 811b8ecb..516a1cb9 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -6,10 +6,14 @@
 
 from dataclasses import dataclass
 
+import arviz as az
 import numpy as np
 
 from easydiffraction.analysis.fit_helpers.reporting import FitResults
 
+R_HAT_CONVERGENCE_THRESHOLD = 1.01
+ESS_BULK_CONVERGENCE_THRESHOLD = 400.0
+
 
 @dataclass(slots=True)
 class PosteriorParameterSummary:
@@ -34,7 +38,7 @@ class PosteriorParameterSummary:
     ess_bulk : float | None, default=None
         Bulk effective sample size when available.
     r_hat : float | None, default=None
-        Rank-normalized split-$\hat{R}$ when available.
+        Rank-normalized split-$\\hat{R}$ when available.
     """
 
     unique_name: str
@@ -154,7 +158,11 @@ def to_arviz(self) -> object:
                 raise ValueError(msg)
             sample_stats = {'lp': np.transpose(log_posterior, (1, 0))}
 
-        return az.from_dict(posterior=posterior_dict, sample_stats=sample_stats)
+        data = {'posterior': posterior_dict}
+        if sample_stats is not None:
+            data['sample_stats'] = sample_stats
+
+        return az.from_dict(data)
 
 
 class BayesianFitResults(FitResults):
@@ -190,6 +198,10 @@ class BayesianFitResults(FitResults):
         Sampler settings recorded for reproducibility.
     convergence_diagnostics : dict[str, object] | None, default=None
         Convergence diagnostics and status metadata.
+    sampler_completed : bool, default=False
+        Whether the sampler completed a run and returned posterior data.
+    best_log_posterior : float | None, default=None
+        Best log-posterior value reported by the sampler.
     """
 
     def __init__(
@@ -209,6 +221,8 @@ def __init__(
         credible_interval_levels: tuple[float, ...] = (0.68, 0.95),
         sampler_settings: dict[str, object] | None = None,
         convergence_diagnostics: dict[str, object] | None = None,
+        sampler_completed: bool = False,
+        best_log_posterior: float | None = None,
     ) -> None:
         super().__init__(
             success=success,
@@ -231,4 +245,141 @@ def __init__(
         self.sampler_settings = sampler_settings if sampler_settings is not None else {}
         self.convergence_diagnostics = (
             convergence_diagnostics if convergence_diagnostics is not None else {}
-        )
\ No newline at end of file
+        )
+        self.sampler_completed = sampler_completed
+        self.best_log_posterior = best_log_posterior
+
+
+def compute_convergence_diagnostics(posterior_samples: PosteriorSamples) -> dict[str, object]:
+    """Compute convergence diagnostics from posterior samples.
+
+    Parameters
+    ----------
+    posterior_samples : PosteriorSamples
+        Posterior samples container.
+
+    Returns
+    -------
+    dict[str, object]
+        Convergence metrics keyed by diagnostic name.
+    """
+    inference_data = posterior_samples.to_arviz()
+    rhat_dataset = az.rhat(inference_data)
+    ess_dataset = az.ess(inference_data, method='bulk')
+
+    r_hat_by_parameter = _dataset_to_scalar_dict(rhat_dataset)
+    ess_bulk_by_parameter = _dataset_to_scalar_dict(ess_dataset)
+
+    max_r_hat = max(r_hat_by_parameter.values(), default=None)
+    min_ess_bulk = min(ess_bulk_by_parameter.values(), default=None)
+
+    converged = True
+    if max_r_hat is not None and max_r_hat > R_HAT_CONVERGENCE_THRESHOLD:
+        converged = False
+    if min_ess_bulk is not None and min_ess_bulk < ESS_BULK_CONVERGENCE_THRESHOLD:
+        converged = False
+
+    return {
+        'converged': converged,
+        'r_hat_by_parameter': r_hat_by_parameter,
+        'ess_bulk_by_parameter': ess_bulk_by_parameter,
+        'max_r_hat': max_r_hat,
+        'min_ess_bulk': min_ess_bulk,
+        'n_draws': int(posterior_samples.parameter_samples.shape[0]),
+        'n_chains': int(posterior_samples.parameter_samples.shape[1]),
+        'n_parameters': len(posterior_samples.parameter_names),
+    }
+
+
+def summarize_posterior_parameters(
+    parameter_names: list[str],
+    posterior_samples: PosteriorSamples,
+    map_values: np.ndarray,
+    convergence_diagnostics: dict[str, object] | None = None,
+) -> list[PosteriorParameterSummary]:
+    """Build posterior parameter summaries in EasyDiffraction order.
+
+    Parameters
+    ----------
+    parameter_names : list[str]
+        Sampled parameter names in EasyDiffraction order.
+    posterior_samples : PosteriorSamples
+        Posterior sample container.
+    map_values : np.ndarray
+        MAP or best-sampled parameter values in the same order.
+    convergence_diagnostics : dict[str, object] | None, default=None
+        Optional convergence diagnostics keyed by parameter name.
+
+    Returns
+    -------
+    list[PosteriorParameterSummary]
+        Summary rows matching the input parameter order.
+
+    Raises
+    ------
+    ValueError
+        If the posterior sample array is incompatible with the
+        parameter name list.
+    """
+    flattened = posterior_samples.flattened()
+    if flattened.shape[1] != len(parameter_names):
+        msg = 'Posterior samples do not match the sampled parameter name list length.'
+        raise ValueError(msg)
+
+    r_hat_by_parameter = {}
+    ess_bulk_by_parameter = {}
+    if convergence_diagnostics is not None:
+        r_hat_by_parameter = convergence_diagnostics.get('r_hat_by_parameter', {})
+        ess_bulk_by_parameter = convergence_diagnostics.get('ess_bulk_by_parameter', {})
+
+    summaries: list[PosteriorParameterSummary] = []
+    for index, parameter_name in enumerate(parameter_names):
+        values = flattened[:, index]
+        interval_68 = tuple(np.quantile(values, [0.16, 0.84]).tolist())
+        interval_95 = tuple(np.quantile(values, [0.025, 0.975]).tolist())
+        summaries.append(
+            PosteriorParameterSummary(
+                unique_name=parameter_name,
+                display_name=parameter_name,
+                map_value=float(map_values[index]),
+                median=float(np.median(values)),
+                standard_deviation=float(np.std(values, ddof=1)),
+                interval_68=(float(interval_68[0]), float(interval_68[1])),
+                interval_95=(float(interval_95[0]), float(interval_95[1])),
+                ess_bulk=_maybe_scalar(ess_bulk_by_parameter.get(parameter_name)),
+                r_hat=_maybe_scalar(r_hat_by_parameter.get(parameter_name)),
+            )
+        )
+
+    return summaries
+
+
+def standard_deviations_from_summaries(
+    summaries: list[PosteriorParameterSummary],
+) -> np.ndarray:
+    """Return posterior standard deviations in summary order.
+
+    Parameters
+    ----------
+    summaries : list[PosteriorParameterSummary]
+        Posterior summaries in parameter order.
+
+    Returns
+    -------
+    np.ndarray
+        Standard deviations in the same order.
+    """
+    return np.array([summary.standard_deviation for summary in summaries], dtype=float)
+
+
+def _dataset_to_scalar_dict(dataset: object) -> dict[str, float]:
+    values: dict[str, float] = {}
+    for name, data_array in dataset.data_vars.items():
+        values[name] = float(np.asarray(data_array).reshape(-1)[0])
+    return values
+
+
+def _maybe_scalar(value: object) -> float | None:
+    if value is None:
+        return None
+    return float(value)
\ No newline at end of file
diff --git a/src/easydiffraction/analysis/minimizers/base.py b/src/easydiffraction/analysis/minimizers/base.py
index 93b811ae..46da77c5 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -125,7 +125,37 @@ def _finalize_fit(
         self._warn_boundary_parameters(parameters)
         self._warn_physical_limit_violations(parameters)
         success = self._check_success(raw_result)
-        self.result = FitResults(
+        self.result = self._build_fit_results(
+            parameters=parameters,
+            raw_result=raw_result,
+            success=success,
+        )
+        return self.result
+
+    def _build_fit_results(
+        self,
+        *,
+        parameters: list[object],
+        raw_result: object,
+        success: bool,
+    ) -> FitResults:
+        """Build the final fit-result object for this minimizer.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            Parameters after the solver finished.
+        raw_result : object
+            Backend-specific solver output object.
+        success : bool
+            Whether the minimizer considers the run successful.
+
+        Returns
+        -------
+        FitResults
+            Aggregated outcome of the fit.
+        """
+        return FitResults(
             success=success,
             parameters=parameters,
             reduced_chi_square=self.tracker.best_chi2,
@@ -133,7 +163,6 @@ def _finalize_fit(
             starting_parameters=parameters,
             fitting_time=self.tracker.fitting_time,
         )
-        return self.result
 
     @staticmethod
     def _warn_boundary_parameters(parameters: list[object]) -> None:
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index c989ffcc..28a2f4de 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -4,15 +4,35 @@
 
 from __future__ import annotations
 
+import random
+
 import numpy as np
+from bumps.fitproblem import FitProblem
+from bumps.fitters import FITTERS
+from bumps.fitters import FitDriver
+from scipy.optimize import OptimizeResult
 
+from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+from easydiffraction.analysis.fit_helpers.bayesian import compute_convergence_diagnostics
+from easydiffraction.analysis.fit_helpers.bayesian import standard_deviations_from_summaries
+from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
 from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
 from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 from easydiffraction.analysis.minimizers.factory import MinimizerFactory
 from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.utils.logging import log
 
 DEFAULT_METHOD = 'dream'
 DEFAULT_MAX_ITERATIONS = 1000
+DEFAULT_BURN_FRACTION = 0.2
+DEFAULT_MIN_BURN = 50
+DEFAULT_THIN = 1
+DEFAULT_POP = 4
+DEFAULT_ALPHA = 0.0
+DEFAULT_OUTLIER_TEST = 'none'
+DEFAULT_TRIM = False
 
 
 @MinimizerFactory.register
@@ -54,4 +74,290 @@ def _resolve_random_seed(self, random_seed: int | None) -> int:
             random_seed = int(generator.integers(0, np.iinfo(np.int32).max))
 
         self._resolved_random_seed = int(random_seed)
-        return self._resolved_random_seed
\ No newline at end of file
+        return self._resolved_random_seed
+
+    def _prepare_solver_args(
+        self,
+        parameters: list[object],
+    ) -> dict[str, object]:
+        """Prepare DREAM solver arguments in EasyDiffraction order.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            List of parameters to be sampled.
+
+        Returns
+        -------
+        dict[str, object]
+            BUMPS parameters plus EasyDiffraction parameter metadata.
+        """
+        solver_args = super()._prepare_solver_args(parameters)
+        solver_args['parameter_names'] = [parameter.unique_name for parameter in parameters]
+        solver_args['parameter_uids'] = [parameter._minimizer_uid for parameter in parameters]
+        solver_args['starting_uncertainties'] = [parameter.uncertainty for parameter in parameters]
+        return solver_args
+
+    def _run_solver(
+        self,
+        objective_function: object,
+        **kwargs: object,
+    ) -> object:
+        """Run the DREAM sampler and normalize its posterior outputs.
+
+        Parameters
+        ----------
+        objective_function : object
+            Objective function returning residuals.
+        **kwargs : object
+            Solver arguments including BUMPS parameters and random seed.
+
+        Returns
+        -------
+        object
+            Normalized DREAM result stored in an ``OptimizeResult``.
+        """
+        bumps_params = kwargs.get('bumps_params')
+        parameter_names = kwargs.get('parameter_names')
+        parameter_uids = kwargs.get('parameter_uids')
+        random_seed = kwargs.get('random_seed')
+        starting_uncertainties = kwargs.get('starting_uncertainties')
+        fitness = _EasyDiffractionFitness(bumps_params, objective_function)
+        fitness.nllf()
+        problem = FitProblem(fitness)
+
+        fitclass = next(cls for cls in FITTERS if cls.id == self.method)
+        proposed_burn = max(DEFAULT_MIN_BURN, int(self.max_iterations * DEFAULT_BURN_FRACTION))
+        burn = min(proposed_burn, max(self.max_iterations - 1, 0))
+        samples = self.max_iterations * DEFAULT_POP * len(bumps_params)
+        driver = FitDriver(
+            fitclass=fitclass,
+            problem=problem,
+            monitors=[],
+            steps=self.max_iterations,
+            burn=burn,
+            thin=DEFAULT_THIN,
+            pop=DEFAULT_POP,
+            samples=samples,
+            alpha=DEFAULT_ALPHA,
+            outliers=DEFAULT_OUTLIER_TEST,
+            trim=DEFAULT_TRIM,
+        )
+        driver.clip()
+
+        starting_values = np.array([parameter.value for parameter in bumps_params], dtype=float)
+        if starting_uncertainties is None:
+            starting_uncertainties = [None] * len(bumps_params)
+
+        numpy_state = np.random.get_state()
+        python_state = random.getstate()
+        np.random.seed(random_seed)
+        random.seed(random_seed)
+        try:
+            best_values, best_nllf = driver.fit()
+        except Exception as error:  # pragma: no cover - defensive runtime path
+            return OptimizeResult(
+                x=starting_values,
+                dx=None,
+                fun=None,
+                success=False,
+                status=-1,
+                message=f'DREAM sampling failed: {error}',
+                var_names=parameter_names,
+                posterior_samples=None,
+                posterior_parameter_summaries=[],
+                convergence_diagnostics={},
+                sampler_settings={'random_seed': int(random_seed)},
+                sampler_completed=False,
+                raw_state=None,
+                best_log_posterior=None,
+                starting_values=starting_values,
+                starting_uncertainties=starting_uncertainties,
+            )
+        finally:
+            np.random.set_state(numpy_state)
+            random.setstate(python_state)
+
+        state = getattr(driver.fitter, 'state', None)
+        if best_values is None or state is None:
+            return OptimizeResult(
+                x=starting_values,
+                dx=None,
+                fun=None,
+                success=False,
+                status=-1,
+                message='DREAM sampling did not produce usable posterior samples.',
+                var_names=parameter_names,
+                posterior_samples=None,
+                posterior_parameter_summaries=[],
+                convergence_diagnostics={},
+                sampler_settings={'random_seed': int(random_seed)},
+                sampler_completed=False,
+                raw_state=state,
+                best_log_posterior=None,
+                starting_values=starting_values,
+                starting_uncertainties=starting_uncertainties,
+            )
+
+        draw_index, parameter_samples_array, log_posterior = state.chains()
+        if parameter_samples_array.ndim != 3 or parameter_samples_array.size == 0:
+            return OptimizeResult(
+                x=starting_values,
+                dx=None,
+                fun=None,
+                success=False,
+                status=-1,
+                message='DREAM sampling did not return a usable posterior sample array.',
+                var_names=parameter_names,
+                posterior_samples=None,
+                posterior_parameter_summaries=[],
+                convergence_diagnostics={},
+                sampler_settings={'random_seed': int(random_seed)},
+                sampler_completed=True,
+                raw_state=state,
+                best_log_posterior=None,
+                starting_values=starting_values,
+                starting_uncertainties=starting_uncertainties,
+            )
+
+        state_best_values, best_log_posterior = state.best()
+        best_by_name = dict(zip(state.labels, state_best_values, strict=True))
+        label_to_index = {label: index for index, label in enumerate(state.labels)}
+        ordered_indices = [label_to_index[uid] for uid in parameter_uids]
+        ordered_samples = np.asarray(parameter_samples_array, dtype=float)[:, :, ordered_indices]
+        map_values = np.array([best_by_name[uid] for uid in parameter_uids], dtype=float)
+
+        posterior_samples = PosteriorSamples(
+            parameter_names=parameter_names,
+            parameter_samples=ordered_samples,
+            log_posterior=np.asarray(log_posterior, dtype=float),
+            draw_index=np.asarray(draw_index, dtype=float),
+        )
+        convergence_diagnostics = compute_convergence_diagnostics(posterior_samples)
+        posterior_parameter_summaries = summarize_posterior_parameters(
+            parameter_names=parameter_names,
+            posterior_samples=posterior_samples,
+            map_values=map_values,
+            convergence_diagnostics=convergence_diagnostics,
+        )
+        posterior_standard_deviations = standard_deviations_from_summaries(
+            posterior_parameter_summaries
+        )
+
+        sampler_settings = {
+            'random_seed': int(random_seed),
+            'steps': int(self.max_iterations),
+            'burn': int(burn),
+            'thin': int(DEFAULT_THIN),
+            'pop': int(DEFAULT_POP),
+            'samples': int(samples),
+            'alpha': float(DEFAULT_ALPHA),
+            'outliers': DEFAULT_OUTLIER_TEST,
+            'trim': DEFAULT_TRIM,
+        }
+
+        if not convergence_diagnostics.get('converged', True):
+            log.warning(
+                'DREAM sampling completed, but convergence diagnostics indicate '
+                'the posterior may be poorly mixed.'
+            )
+
+        return OptimizeResult(
+            x=map_values,
+            dx=posterior_standard_deviations,
+            fun=float(best_nllf),
+            success=True,
+            status=0,
+            message='DREAM sampling completed',
+            var_names=parameter_names,
+            posterior_samples=posterior_samples,
+            posterior_parameter_summaries=posterior_parameter_summaries,
+            convergence_diagnostics=convergence_diagnostics,
+            sampler_settings=sampler_settings,
+            sampler_completed=True,
+            raw_state=state,
+            best_log_posterior=float(best_log_posterior),
+            starting_values=starting_values,
+            starting_uncertainties=starting_uncertainties,
+        )
+
+    def _sync_result_to_parameters(
+        self,
+        parameters: list[object],
+        raw_result: object,
+    ) -> None:
+        """Commit MAP values on success and restore starts on failure.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            Parameters being optimized.
+        raw_result : object
+            DREAM result object.
+        """
+        if getattr(raw_result, 'success', False):
+            values = raw_result.x
+            uncertainties = getattr(raw_result, 'dx', None)
+        else:
+            values = getattr(raw_result, 'starting_values', None)
+            uncertainties = getattr(raw_result, 'starting_uncertainties', None)
+
+        if values is None:
+            return
+
+        for index, parameter in enumerate(parameters):
+            parameter._set_value_from_minimizer(float(values[index]))
+            if uncertainties is None:
+                parameter.uncertainty = None
+                continue
+
+            uncertainty = uncertainties[index]
+            parameter.uncertainty = None if uncertainty is None else float(uncertainty)
+
+    def _build_fit_results(
+        self,
+        *,
+        parameters: list[object],
+        raw_result: object,
+        success: bool,
+    ) -> BayesianFitResults:
+        """Build the Bayesian fit result container.
+
+        Parameters
+        ----------
+        parameters : list[object]
+            Parameters after the solver finished.
+        raw_result : object
+            Normalized DREAM solver output.
+        success : bool
+            Whether DREAM produced usable posterior samples.
+
+        Returns
+        -------
+        BayesianFitResults
+            Bayesian result object for the finished run.
+        """
+        fit_results = BayesianFitResults(
+            success=success,
+            parameters=parameters,
+            reduced_chi_square=self.tracker.best_chi2,
+            engine_result=getattr(raw_result, 'raw_state', raw_result),
+            starting_parameters=parameters,
+            fitting_time=self.tracker.fitting_time,
+            sampler_name='dream',
+            point_estimate_name='map',
+            posterior_samples=getattr(raw_result, 'posterior_samples', None),
+            posterior_parameter_summaries=getattr(
+                raw_result, 'posterior_parameter_summaries', []
+            ),
+            posterior_predictive={},
+            credible_interval_levels=(0.68, 0.95),
+            sampler_settings=getattr(raw_result, 'sampler_settings', {}),
+            convergence_diagnostics=getattr(raw_result, 'convergence_diagnostics', {}),
+            sampler_completed=getattr(raw_result, 'sampler_completed', False),
+            best_log_posterior=getattr(raw_result, 'best_log_posterior', None),
+        )
+        fit_results.message = getattr(raw_result, 'message', '')
+        fit_results.iterations = int(self.max_iterations)
+        fit_results.result = raw_result
+        return fit_results
\ No newline at end of file

From 448887b51c37d6978dd89331dd6079b589b8cf48 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 00:12:55 +0200
Subject: [PATCH 010/106] Add Bayesian fit result display

---
 .../analysis/fit_helpers/bayesian.py          | 249 +++++++++++++++++-
 .../analysis/fit_helpers/reporting.py         |  32 ++-
 2 files changed, 278 insertions(+), 3 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 516a1cb9..e5e85fbf 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -9,7 +9,15 @@
 import arviz as az
 import numpy as np
 
+from easydiffraction.analysis.fit_helpers.metrics import calculate_r_factor
+from easydiffraction.analysis.fit_helpers.metrics import calculate_r_factor_squared
+from easydiffraction.analysis.fit_helpers.metrics import calculate_rb_factor
+from easydiffraction.analysis.fit_helpers.metrics import calculate_weighted_r_factor
+from easydiffraction.analysis.fit_helpers.reporting import _build_parameter_row
+from easydiffraction.analysis.fit_helpers.reporting import _format_optional_float
 from easydiffraction.analysis.fit_helpers.reporting import FitResults
+from easydiffraction.utils.logging import console
+from easydiffraction.utils.utils import render_table
 
 R_HAT_CONVERGENCE_THRESHOLD = 1.01
 ESS_BULK_CONVERGENCE_THRESHOLD = 400.0
@@ -249,6 +257,82 @@ def __init__(
         self.sampler_completed = sampler_completed
         self.best_log_posterior = best_log_posterior
 
+    def display_results(
+        self,
+        y_obs: list[float] | None = None,
+        y_calc: list[float] | None = None,
+        y_err: list[float] | None = None,
+        f_obs: list[float] | None = None,
+        f_calc: list[float] | None = None,
+    ) -> None:
+        """Render a Bayesian fit summary with posterior diagnostics.
+
+        Parameters
+        ----------
+        y_obs : list[float] | None, default=None
+            Observed intensities for pattern R-factor metrics.
+        y_calc : list[float] | None, default=None
+            Calculated intensities for pattern R-factor metrics.
+        y_err : list[float] | None, default=None
+            Standard deviations of observed intensities for wR.
+        f_obs : list[float] | None, default=None
+            Observed structure-factor magnitudes for Bragg R.
+        f_calc : list[float] | None, default=None
+            Calculated structure-factor magnitudes for Bragg R.
+        """
+        status_icon = '✅' if self.success else '❌'
+        rf = rf2 = wr = br = None
+        if y_obs is not None and y_calc is not None:
+            rf = calculate_r_factor(y_obs, y_calc) * 100
+            rf2 = calculate_r_factor_squared(y_obs, y_calc) * 100
+        if y_obs is not None and y_calc is not None and y_err is not None:
+            wr = calculate_weighted_r_factor(y_obs, y_calc, y_err) * 100
+        if f_obs is not None and f_calc is not None:
+            br = calculate_rb_factor(f_obs, f_calc) * 100
+
+        console.paragraph('Bayesian fit results')
+        console.print(f'{status_icon} Success: {self.success}')
+        if self.message:
+            console.print(f'ℹ️ Status: {self.message}')
+        console.print(f'🧪 Sampler: {self.sampler_name}')
+        console.print(f'🎯 Committed point estimate: {self.point_estimate_name}')
+        console.print(f'🔁 Sampler completed: {self.sampler_completed}')
+        console.print(f'⏱️ Fitting time: {_format_optional_float(self.fitting_time, suffix=" seconds")}')
+        console.print(
+            '📏 Goodness-of-fit (reduced χ²): '
+            f'{_format_optional_float(self.reduced_chi_square)}'
+        )
+        if self.best_log_posterior is not None:
+            console.print(f'📉 Best log-posterior: {self.best_log_posterior:.2f}')
+
+        sampler_settings = _format_sampler_settings(self.sampler_settings)
+        if sampler_settings is not None:
+            console.print(f'⚙️ Sampler settings: {sampler_settings}')
+
+        convergence_summary = _format_convergence_summary(self.convergence_diagnostics)
+        if convergence_summary is not None:
+            console.print(f'📊 Convergence: {convergence_summary}')
+
+        if rf is not None:
+            console.print(f'📏 R-factor (Rf): {rf:.2f}%')
+        if rf2 is not None:
+            console.print(f'📏 R-factor squared (Rf²): {rf2:.2f}%')
+        if wr is not None:
+            console.print(f'📏 Weighted R-factor (wR): {wr:.2f}%')
+        if br is not None:
+            console.print(f'📏 Bragg R-factor (BR): {br:.2f}%')
+
+        console.print('📈 Committed parameters:')
+        _render_committed_parameter_table(self.parameters)
+
+        console.print('📊 Posterior parameter summaries:')
+        _render_posterior_summary_table(
+            parameters=self.parameters,
+            posterior_parameter_summaries=self.posterior_parameter_summaries,
+        )
+
+        self._print_table_notes()
+
 
 def compute_convergence_diagnostics(posterior_samples: PosteriorSamples) -> dict[str, object]:
     """Compute convergence diagnostics from posterior samples.
@@ -382,4 +466,167 @@ def _dataset_to_scalar_dict(dataset: object) -> dict[str, float]:
 def _maybe_scalar(value: object) -> float | None:
     if value is None:
         return None
-    return float(value)
\ No newline at end of file
+    return float(value)
+
+
+def _format_sampler_settings(sampler_settings: dict[str, object]) -> str | None:
+    if not sampler_settings:
+        return None
+
+    parts: list[str] = []
+    for key in ('random_seed', 'steps', 'burn', 'thin', 'pop', 'samples'):
+        if key in sampler_settings:
+            parts.append(f'{key}={sampler_settings[key]}')
+    return ', '.join(parts) if parts else None
+
+
+def _format_convergence_summary(convergence_diagnostics: dict[str, object]) -> str | None:
+    if not convergence_diagnostics:
+        return None
+
+    parts: list[str] = []
+    converged = convergence_diagnostics.get('converged')
+    if converged is not None:
+        status = 'yes' if converged else '[yellow]check diagnostics[/yellow]'
+        parts.append(f'converged={status}')
+
+    max_r_hat = _maybe_scalar(convergence_diagnostics.get('max_r_hat'))
+    if max_r_hat is not None:
+        parts.append(f'max_r_hat={_format_r_hat(max_r_hat)}')
+
+    min_ess_bulk = _maybe_scalar(convergence_diagnostics.get('min_ess_bulk'))
+    if min_ess_bulk is not None:
+        parts.append(f'min_ess_bulk={_format_ess_bulk(min_ess_bulk)}')
+
+    n_draws = convergence_diagnostics.get('n_draws')
+    n_chains = convergence_diagnostics.get('n_chains')
+    if n_draws is not None and n_chains is not None:
+        parts.append(f'draws={n_draws}, chains={n_chains}')
+
+    return ', '.join(parts) if parts else None
+
+
+def _render_committed_parameter_table(parameters: list[object]) -> None:
+    headers = [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'start',
+        'map',
+        'uncertainty',
+        'units',
+        'change',
+    ]
+    alignments = [
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'left',
+        'right',
+    ]
+    rows = [_build_parameter_row(parameter) for parameter in parameters]
+    render_table(
+        columns_headers=headers,
+        columns_alignment=alignments,
+        columns_data=rows,
+    )
+
+
+def _render_posterior_summary_table(
+    *,
+    parameters: list[object],
+    posterior_parameter_summaries: list[PosteriorParameterSummary],
+) -> None:
+    if not posterior_parameter_summaries:
+        console.print('No posterior parameter summaries available.')
+        return
+
+    parameters_by_name = {parameter.unique_name: parameter for parameter in parameters}
+    headers = [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'median',
+        'std',
+        '68% interval',
+        '95% interval',
+        'r_hat',
+        'ess_bulk',
+        'units',
+    ]
+    alignments = [
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+        'right',
+        'right',
+        'left',
+    ]
+    rows = [
+        _build_posterior_summary_row(summary, parameters_by_name)
+        for summary in posterior_parameter_summaries
+    ]
+    render_table(
+        columns_headers=headers,
+        columns_alignment=alignments,
+        columns_data=rows,
+    )
+
+
+def _build_posterior_summary_row(
+    summary: PosteriorParameterSummary,
+    parameters_by_name: dict[str, object],
+) -> list[str]:
+    parameter = parameters_by_name.get(summary.unique_name)
+    identity = getattr(parameter, '_identity', None)
+    datablock = getattr(identity, 'datablock_entry_name', 'N/A')
+    category = getattr(identity, 'category_code', 'N/A')
+    entry = getattr(identity, 'category_entry_name', '') or ''
+    units = getattr(parameter, 'units', 'N/A')
+
+    return [
+        datablock,
+        category,
+        entry,
+        summary.display_name,
+        f'{summary.median:.4f}',
+        f'{summary.standard_deviation:.4f}',
+        _format_interval(summary.interval_68),
+        _format_interval(summary.interval_95),
+        _format_r_hat(summary.r_hat),
+        _format_ess_bulk(summary.ess_bulk),
+        units,
+    ]
+
+
+def _format_interval(interval: tuple[float, float]) -> str:
+    return f'[{interval[0]:.4f}, {interval[1]:.4f}]'
+
+
+def _format_r_hat(value: float | None) -> str:
+    if value is None:
+        return 'N/A'
+    formatted = f'{value:.3f}'
+    if value > R_HAT_CONVERGENCE_THRESHOLD:
+        return f'[yellow]{formatted}[/yellow]'
+    return formatted
+
+
+def _format_ess_bulk(value: float | None) -> str:
+    if value is None:
+        return 'N/A'
+    formatted = f'{value:.1f}'
+    if value < ESS_BULK_CONVERGENCE_THRESHOLD:
+        return f'[yellow]{formatted}[/yellow]'
+    return formatted
\ No newline at end of file
diff --git a/src/easydiffraction/analysis/fit_helpers/reporting.py b/src/easydiffraction/analysis/fit_helpers/reporting.py
index a83ad7bf..329f77e6 100644
--- a/src/easydiffraction/analysis/fit_helpers/reporting.py
+++ b/src/easydiffraction/analysis/fit_helpers/reporting.py
@@ -108,8 +108,11 @@ def display_results(
 
         console.paragraph('Fit results')
         console.print(f'{status_icon} Success: {self.success}')
-        console.print(f'⏱️ Fitting time: {self.fitting_time:.2f} seconds')
-        console.print(f'📏 Goodness-of-fit (reduced χ²): {self.reduced_chi_square:.2f}')
+        console.print(f'⏱️ Fitting time: {_format_optional_float(self.fitting_time, suffix=" seconds")}')
+        console.print(
+            '📏 Goodness-of-fit (reduced χ²): '
+            f'{_format_optional_float(self.reduced_chi_square)}'
+        )
         if rf is not None:
             console.print(f'📏 R-factor (Rf): {rf:.2f}%')
         if rf2 is not None:
@@ -248,3 +251,28 @@ def _compute_relative_change(param: object) -> str:
     change = ((param.value - param._fit_start_value) / param._fit_start_value) * 100
     arrow = '↑' if change > 0 else '↓'
     return f'{abs(change):.2f} % {arrow}'
+
+
+def _format_optional_float(
+    value: float | None,
+    *,
+    suffix: str = '',
+) -> str:
+    """Format an optional float for console output.
+
+    Parameters
+    ----------
+    value : float | None
+        Value to format.
+    suffix : str, default=''
+        Optional suffix appended to formatted numeric values.
+
+    Returns
+    -------
+    str
+        ``'N/A'`` when the value is ``None``; otherwise a formatted
+        string with two decimal places.
+    """
+    if value is None:
+        return 'N/A'
+    return f'{value:.2f}{suffix}'

From 3d44796be3eb1e1b2ae966e136345c2b18bee6e4 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 00:14:16 +0200
Subject: [PATCH 011/106] Plot Bayesian parameter correlations

---
 src/easydiffraction/display/plotting.py | 73 +++++++++++++++++++------
 1 file changed, 57 insertions(+), 16 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 61e93f68..09b8d5a8 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -739,10 +739,27 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
             Square correlation matrix labeled by parameter unique names,
             or ``None`` if unavailable.
         """
-        result = self._get_fit_result_for_correlation()
-        if result is None:
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is not None:
+            corr_df = self._correlation_from_posterior_samples(posterior_samples)
+            if corr_df is not None:
+                return corr_df
+
+        raw_result = getattr(fit_results, 'result', None)
+        if raw_result is None:
+            raw_result = getattr(fit_results, 'engine_result', None)
+        if raw_result is None:
+            log.warning('No raw fit result available. Correlation matrix cannot be plotted.')
+            return None
+
+        var_names = getattr(raw_result, 'var_names', None)
+        if not var_names:
+            log.warning('Fit result does not expose variable names for a correlation matrix.')
             return None
-        raw_result, var_names, fit_results = result
 
         covar = getattr(raw_result, 'covar', None)
         if covar is not None:
@@ -757,21 +774,20 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
 
         log.warning(
             'Correlation matrix is unavailable for this fit. '
-            'Use the lmfit minimizer and ensure covariance estimation succeeds.'
+            'Use a minimizer that returns covariance information or posterior samples.'
         )
         return None
 
     def _get_fit_result_for_correlation(
         self,
-    ) -> tuple[object, list[str], object] | None:
+    ) -> object | None:
         """
-        Validate and return the raw fit result for correlation.
+        Validate and return the fit result for correlation.
 
         Returns
         -------
-        tuple[object, list[str], object] | None
-            A tuple of ``(raw_result, var_names, fit_results)`` when all
-            required data is present, or ``None`` otherwise.
+        object | None
+            Fit result object when available, or ``None`` otherwise.
         """
         if self._project is None:
             log.warning('Plotter is not attached to a project.')
@@ -781,18 +797,43 @@ def _get_fit_result_for_correlation(
         if fit_results is None:
             log.warning('No fit results available. Run fit() first.')
             return None
+        return fit_results
 
-        raw_result = getattr(fit_results, 'engine_result', None)
-        if raw_result is None:
-            log.warning('No raw fit result available. Correlation matrix cannot be plotted.')
+    @staticmethod
+    def _correlation_from_posterior_samples(
+        posterior_samples: object,
+    ) -> pd.DataFrame | None:
+        """Convert posterior samples into a correlation DataFrame.
+
+        Parameters
+        ----------
+        posterior_samples : object
+            Posterior sample container exposing ``flattened()`` and
+            ``parameter_names``.
+
+        Returns
+        -------
+        pd.DataFrame | None
+            Correlation matrix labeled by posterior parameter names, or
+            ``None`` if the sample array is invalid.
+        """
+        parameter_names = getattr(posterior_samples, 'parameter_names', None)
+        if not parameter_names:
+            log.warning('Posterior samples do not expose parameter names.')
             return None
 
-        var_names = getattr(raw_result, 'var_names', None)
-        if not var_names:
-            log.warning('Fit result does not expose variable names for a correlation matrix.')
+        flattened = np.asarray(posterior_samples.flattened(), dtype=float)
+        if flattened.ndim != 2 or flattened.shape[1] != len(parameter_names):
+            log.warning('Posterior sample array has an invalid shape for correlations.')
+            return None
+        if flattened.shape[0] < 2:
+            log.warning('At least two posterior draws are required for correlations.')
             return None
 
-        return raw_result, var_names, fit_results
+        corr = np.corrcoef(flattened, rowvar=False)
+        corr = np.nan_to_num(corr, nan=0.0, posinf=0.0, neginf=0.0)
+        np.fill_diagonal(corr, 1.0)
+        return pd.DataFrame(corr, index=parameter_names, columns=parameter_names)
 
     @staticmethod
     def _correlation_from_covariance(

From 391699df62f7d736a01f836a9a02993819baaa2e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 00:16:32 +0200
Subject: [PATCH 012/106] Add posterior ArviZ plots

---
 src/easydiffraction/display/plotting.py | 182 ++++++++++++++++++++++++
 1 file changed, 182 insertions(+)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 09b8d5a8..1c9e0afb 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -623,6 +623,40 @@ def plot_param_correlations(
             )
         )
 
+    def plot_posterior_pairs(
+        self,
+        parameters: list[object] | None = None,
+    ) -> None:
+        """Plot posterior pair relationships for sampled parameters.
+
+        Parameters
+        ----------
+        parameters : list[object] | None, default=None
+            Optional subset of sampled parameters to include. When
+            ``None``, all sampled parameters are shown.
+        """
+        plot = self._build_posterior_pairs_plot(parameters=parameters)
+        if plot is None:
+            return
+        plot.show()
+
+    def plot_param_distribution(
+        self,
+        param: object,
+    ) -> None:
+        """Plot the posterior distribution for one sampled parameter.
+
+        Parameters
+        ----------
+        param : object
+            Parameter descriptor whose posterior distribution should be
+            plotted.
+        """
+        plot = self._build_param_distribution_plot(param)
+        if plot is None:
+            return
+        plot.show()
+
     @staticmethod
     def _filter_correlation_dataframe(
         corr_df: pd.DataFrame,
@@ -778,6 +812,154 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
         )
         return None
 
+    def _build_posterior_pairs_plot(
+        self,
+        *,
+        parameters: list[object] | None,
+    ) -> object | None:
+        """Build an ArviZ posterior pair-plot object.
+
+        Parameters
+        ----------
+        parameters : list[object] | None
+            Optional subset of sampled parameters to include.
+
+        Returns
+        -------
+        object | None
+            ArviZ plot object, or ``None`` when posterior plotting is
+            unavailable.
+        """
+        inference_data, fit_results = self._get_posterior_inference_data()
+        if inference_data is None or fit_results is None:
+            return None
+
+        parameter_names = self._resolve_posterior_parameter_names(
+            fit_results=fit_results,
+            parameters=parameters,
+        )
+        if parameter_names is None:
+            return None
+        if len(parameter_names) < 2:
+            log.warning('Posterior pair plots require at least two sampled parameters.')
+            return None
+
+        import arviz as az
+
+        plot = az.plot_pair(
+            inference_data,
+            var_names=parameter_names,
+            backend='plotly',
+            marginal=True,
+        )
+        if hasattr(plot, 'add_title'):
+            plot.add_title('Posterior pair plot')
+        return plot
+
+    def _build_param_distribution_plot(
+        self,
+        param: object,
+    ) -> object | None:
+        """Build an ArviZ posterior distribution plot for one parameter.
+
+        Parameters
+        ----------
+        param : object
+            Parameter descriptor to plot.
+
+        Returns
+        -------
+        object | None
+            ArviZ plot object, or ``None`` when posterior plotting is
+            unavailable.
+        """
+        inference_data, fit_results = self._get_posterior_inference_data()
+        if inference_data is None or fit_results is None:
+            return None
+
+        parameter_names = self._resolve_posterior_parameter_names(
+            fit_results=fit_results,
+            parameters=[param],
+        )
+        if parameter_names is None:
+            return None
+
+        import arviz as az
+
+        plot = az.plot_dist(
+            inference_data,
+            var_names=parameter_names,
+            backend='plotly',
+            point_estimate='median',
+        )
+        if hasattr(plot, 'add_title'):
+            plot.add_title(f'Posterior distribution: {parameter_names[0]}')
+        return plot
+
+    def _get_posterior_inference_data(
+        self,
+    ) -> tuple[object | None, object | None]:
+        """Return posterior inference data for the current Bayesian fit.
+
+        Returns
+        -------
+        tuple[object | None, object | None]
+            ``(inference_data, fit_results)`` when posterior samples are
+            available, otherwise ``(None, None)``.
+        """
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            log.warning('Posterior plots currently require the Plotly plotting backend.')
+            return None, None
+
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None, None
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            log.warning('Posterior samples are unavailable. Run a Bayesian fit first.')
+            return None, None
+
+        return posterior_samples.to_arviz(), fit_results
+
+    @staticmethod
+    def _resolve_posterior_parameter_names(
+        *,
+        fit_results: object,
+        parameters: list[object] | None,
+    ) -> list[str] | None:
+        """Resolve posterior parameter names from descriptors.
+
+        Parameters
+        ----------
+        fit_results : object
+            Bayesian fit result exposing posterior samples.
+        parameters : list[object] | None
+            Optional parameter subset.
+
+        Returns
+        -------
+        list[str] | None
+            Posterior parameter names in plotting order, or ``None`` if
+            the selection cannot be resolved.
+        """
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        available_names = getattr(posterior_samples, 'parameter_names', None)
+        if not available_names:
+            log.warning('Posterior samples do not expose parameter names.')
+            return None
+
+        if parameters is None:
+            return list(available_names)
+
+        requested_names = [getattr(parameter, 'unique_name', None) for parameter in parameters]
+        missing_names = [name for name in requested_names if name not in available_names]
+        if missing_names:
+            missing = ', '.join(str(name) for name in missing_names)
+            log.warning(f'Posterior samples do not contain the selected parameters: {missing}')
+            return None
+        return [str(name) for name in requested_names if name is not None]
+
     def _get_fit_result_for_correlation(
         self,
     ) -> object | None:

From 8add02835fc59453eed40ba41261909dfcd6d8a3 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 00:20:26 +0200
Subject: [PATCH 013/106] Add posterior predictive plots

---
 .../analysis/fit_helpers/bayesian.py          |   3 +
 src/easydiffraction/display/plotters/ascii.py |   2 +
 src/easydiffraction/display/plotters/base.py  |   2 +
 .../display/plotters/plotly.py                |  40 ++
 src/easydiffraction/display/plotting.py       | 381 ++++++++++++++++++
 5 files changed, 428 insertions(+)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index e5e85fbf..3615a0ed 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -68,6 +68,8 @@ class PosteriorPredictiveSummary:
     ----------
     experiment_name : str
         Experiment identifier.
+    x_axis_name : str
+        Name of the x-axis used for the predictive arrays.
     x : np.ndarray
         X-axis values for the predictive curves.
     map_prediction : np.ndarray
@@ -85,6 +87,7 @@ class PosteriorPredictiveSummary:
     """
 
     experiment_name: str
+    x_axis_name: str
     x: np.ndarray
     map_prediction: np.ndarray
     lower_95: np.ndarray | None = None
diff --git a/src/easydiffraction/display/plotters/ascii.py b/src/easydiffraction/display/plotters/ascii.py
index a0c95fe1..4aeb5577 100644
--- a/src/easydiffraction/display/plotters/ascii.py
+++ b/src/easydiffraction/display/plotters/ascii.py
@@ -131,6 +131,8 @@ def plot_powder_meas_vs_calc(
             title=plot_spec.title,
             height=plot_spec.height,
         )
+        if plot_spec.predictive_lower_95 is not None and plot_spec.predictive_upper_95 is not None:
+            console.print('Posterior predictive bands are available with the Plotly engine only.')
         if plot_spec.bragg_tick_sets:
             console.print('Bragg peak subplot rows are available with the Plotly engine only.')
 
diff --git a/src/easydiffraction/display/plotters/base.py b/src/easydiffraction/display/plotters/base.py
index b1a19a27..85d09434 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -60,6 +60,8 @@ class PowderMeasVsCalcSpec:
     bragg_peaks_height_fraction: float
     height: int | None = None
     y_bkg: np.ndarray | None = None
+    predictive_lower_95: np.ndarray | None = None
+    predictive_upper_95: np.ndarray | None = None
 
 
 class XAxisType(StrEnum):
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 887879dd..e219ffa9 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -65,6 +65,7 @@
 COMPOSITE_MARGIN_RIGHT = 30
 COMPOSITE_MARGIN_TOP = 40
 COMPOSITE_MARGIN_BOTTOM = 45
+PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.18)'
 
 
 @dataclass(frozen=True)
@@ -1233,6 +1234,15 @@ def plot_powder_meas_vs_calc(
             row_heights=layout.row_heights,
         )
 
+        if plot_spec.predictive_lower_95 is not None and plot_spec.predictive_upper_95 is not None:
+            lower_trace, upper_trace = self._get_predictive_band_traces(
+                x=plot_spec.x,
+                lower=plot_spec.predictive_lower_95,
+                upper=plot_spec.predictive_upper_95,
+            )
+            fig.add_trace(lower_trace, row=1, col=1)
+            fig.add_trace(upper_trace, row=1, col=1)
+
         main_traces = (
             (
                 ('meas', plot_spec.y_meas),
@@ -1370,6 +1380,36 @@ def plot_powder_meas_vs_calc(
 
         self._show_figure(fig)
 
+    @staticmethod
+    def _get_predictive_band_traces(
+        *,
+        x: np.ndarray,
+        lower: np.ndarray,
+        upper: np.ndarray,
+    ) -> tuple[go.Scatter, go.Scatter]:
+        """Return Plotly traces for a filled predictive interval band."""
+        lower_trace = go.Scatter(
+            x=x,
+            y=lower,
+            mode='lines',
+            line={'color': 'rgba(0, 0, 0, 0)'},
+            hoverinfo='skip',
+            showlegend=False,
+            legendgroup='predictive_band',
+        )
+        upper_trace = go.Scatter(
+            x=x,
+            y=upper,
+            mode='lines',
+            line={'color': 'rgba(0, 0, 0, 0)'},
+            fill='tonexty',
+            fillcolor=PREDICTIVE_BAND_COLOR,
+            name='Posterior predictive 95% CI',
+            hoverinfo='skip',
+            legendgroup='predictive_band',
+        )
+        return lower_trace, upper_trace
+
     def plot_single_crystal(
         self,
         x_calc: object,
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 1c9e0afb..9a5fc56e 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -69,6 +69,8 @@ def description(self) -> str:
 DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION = 0.10
 DEFAULT_RESID_HEIGHT = DEFAULT_RESIDUAL_HEIGHT_FRACTION
 DEFAULT_BRAGG_ROW = DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION
+DEFAULT_POSTERIOR_PREDICTIVE_DRAWS = 200
+DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP = 50
 
 
 @dataclass(frozen=True)
@@ -657,6 +659,70 @@ def plot_param_distribution(
             return
         plot.show()
 
+    def plot_posterior_predictive(
+        self,
+        expt_name: str,
+        style: str = 'band',
+    ) -> None:
+        """Plot posterior predictive curves for a powder experiment.
+
+        Parameters
+        ----------
+        expt_name : str
+            Experiment name to plot.
+        style : str, default='band'
+            Either ``'band'`` for a 95% credible interval or
+            ``'draws'`` for sampled predictive curves.
+        """
+        if style not in {'band', 'draws'}:
+            msg = "style must be either 'band' or 'draws'."
+            raise ValueError(msg)
+
+        if self._project is None:
+            log.warning('Plotter is not attached to a project.')
+            return
+
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            log.warning('Posterior predictive plots currently require the Plotly backend.')
+            return
+
+        experiment = self._project.experiments[expt_name]
+        x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(experiment.type, None)
+        if sample_form != SampleFormEnum.POWDER:
+            log.warning('Posterior predictive plots currently support powder experiments only.')
+            return
+
+        summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+        )
+        if summary is None:
+            return
+
+        pattern = intensity_category_for(experiment)
+        y_meas = getattr(pattern, 'intensity_meas', None)
+        if y_meas is None:
+            log.warning(f'No measured data available for experiment {expt_name}.')
+            return
+
+        axes_labels = self._get_axes_labels(sample_form, scattering_type, x_axis)
+        if style == 'band':
+            self._plot_posterior_predictive_band(
+                expt_name=expt_name,
+                summary=summary,
+                y_meas=np.asarray(y_meas, dtype=float),
+                axes_labels=axes_labels,
+            )
+            return
+
+        self._plot_posterior_predictive_draws(
+            expt_name=expt_name,
+            summary=summary,
+            y_meas=np.asarray(y_meas, dtype=float),
+            axes_labels=axes_labels,
+        )
+
     @staticmethod
     def _filter_correlation_dataframe(
         corr_df: pd.DataFrame,
@@ -896,6 +962,183 @@ def _build_param_distribution_plot(
             plot.add_title(f'Posterior distribution: {parameter_names[0]}')
         return plot
 
+    def _get_or_build_posterior_predictive_summary(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+    ) -> object | None:
+        """Return a cached or newly built posterior predictive summary."""
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None
+
+        posterior_predictive = getattr(fit_results, 'posterior_predictive', None)
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_predictive is None or posterior_samples is None:
+            return None
+
+        x_axis_name = getattr(x_axis, 'value', x_axis)
+        cache_key = self._posterior_predictive_key(expt_name, str(x_axis_name))
+        summary = posterior_predictive.get(cache_key)
+        if summary is not None:
+            return summary
+
+        summary = self._build_posterior_predictive_summary(
+            fit_results=fit_results,
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+        )
+        if summary is None:
+            return None
+
+        posterior_predictive[cache_key] = summary
+        return summary
+
+    def _build_posterior_predictive_summary(
+        self,
+        *,
+        fit_results: object,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+    ) -> object | None:
+        """Build posterior predictive summaries from posterior draws."""
+        from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            return None
+
+        flattened_samples = np.asarray(posterior_samples.flattened(), dtype=float)
+        parameter_names = getattr(posterior_samples, 'parameter_names', None)
+        if flattened_samples.ndim != 2 or not parameter_names:
+            log.warning('Posterior samples are unavailable for predictive summaries.')
+            return None
+
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter for parameter in fit_results.parameters
+        }
+        sampled_parameters = []
+        for name in parameter_names:
+            parameter = parameters_by_name.get(name)
+            if parameter is None:
+                log.warning(
+                    'Posterior predictive summaries require matching fitted parameters for '
+                    f"'{name}'."
+                )
+                return None
+            sampled_parameters.append(parameter)
+
+        original_values = np.array([parameter.value for parameter in sampled_parameters], dtype=float)
+        original_uncertainties = [parameter.uncertainty for parameter in sampled_parameters]
+
+        map_prediction = None
+        x_values = None
+        predictive_draws: list[np.ndarray] = []
+        draw_indices = self._posterior_predictive_draw_indices(flattened_samples.shape[0])
+        try:
+            map_prediction, x_values = self._evaluate_posterior_predictive_state(
+                sampled_parameters=sampled_parameters,
+                values=original_values,
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=x_axis,
+            )
+            if map_prediction is None or x_values is None:
+                return None
+
+            for index in draw_indices:
+                prediction, current_x = self._evaluate_posterior_predictive_state(
+                    sampled_parameters=sampled_parameters,
+                    values=flattened_samples[index],
+                    experiment=experiment,
+                    expt_name=expt_name,
+                    x_axis=x_axis,
+                )
+                if prediction is None or current_x is None:
+                    return None
+                if prediction.shape != map_prediction.shape or current_x.shape != x_values.shape:
+                    log.warning('Posterior predictive draws returned inconsistent array shapes.')
+                    return None
+                predictive_draws.append(prediction)
+        finally:
+            for parameter, value, uncertainty in zip(
+                sampled_parameters,
+                original_values,
+                original_uncertainties,
+                strict=True,
+            ):
+                parameter._set_value_from_minimizer(float(value))
+                parameter.uncertainty = uncertainty
+            self._update_project_categories(expt_name)
+
+        predictive_draw_array = np.asarray(predictive_draws, dtype=float)
+        lower_68, upper_68 = np.quantile(predictive_draw_array, [0.16, 0.84], axis=0)
+        lower_95, upper_95 = np.quantile(predictive_draw_array, [0.025, 0.975], axis=0)
+        x_axis_name = getattr(x_axis, 'value', x_axis)
+
+        return PosteriorPredictiveSummary(
+            experiment_name=expt_name,
+            x_axis_name=str(x_axis_name),
+            x=np.asarray(x_values, dtype=float),
+            map_prediction=np.asarray(map_prediction, dtype=float),
+            lower_95=np.asarray(lower_95, dtype=float),
+            upper_95=np.asarray(upper_95, dtype=float),
+            lower_68=np.asarray(lower_68, dtype=float),
+            upper_68=np.asarray(upper_68, dtype=float),
+            draws=predictive_draw_array,
+        )
+
+    def _evaluate_posterior_predictive_state(
+        self,
+        *,
+        sampled_parameters: list[object],
+        values: np.ndarray,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+    ) -> tuple[np.ndarray | None, np.ndarray | None]:
+        """Evaluate one posterior predictive state for an experiment."""
+        for parameter, value in zip(sampled_parameters, values, strict=True):
+            parameter._set_value_from_minimizer(float(value))
+
+        self._update_project_categories(expt_name)
+        pattern = intensity_category_for(experiment)
+        x_name = getattr(x_axis, 'value', x_axis)
+        x_values = getattr(pattern, x_name, None)
+        y_calc = getattr(pattern, 'intensity_calc', None)
+        if x_values is None or y_calc is None:
+            log.warning(
+                f'Posterior predictive data is unavailable for experiment {expt_name}. '
+                'Ensure calculated intensities and the selected x axis are available.'
+            )
+            return None, None
+
+        return np.asarray(y_calc, dtype=float), np.asarray(x_values, dtype=float)
+
+    @staticmethod
+    def _posterior_predictive_draw_indices(n_draws: int) -> np.ndarray:
+        """Select evenly spaced posterior draws for predictive summaries."""
+        if n_draws <= DEFAULT_POSTERIOR_PREDICTIVE_DRAWS:
+            return np.arange(n_draws, dtype=int)
+
+        return np.unique(
+            np.linspace(
+                0,
+                n_draws - 1,
+                num=DEFAULT_POSTERIOR_PREDICTIVE_DRAWS,
+                dtype=int,
+            )
+        )
+
+    @staticmethod
+    def _posterior_predictive_key(expt_name: str, x_axis_name: str) -> str:
+        """Return the cache key for a posterior predictive summary."""
+        return f'{expt_name}:{x_axis_name}'
+
     def _get_posterior_inference_data(
         self,
     ) -> tuple[object | None, object | None]:
@@ -922,6 +1165,120 @@ def _get_posterior_inference_data(
 
         return posterior_samples.to_arviz(), fit_results
 
+    def _plot_posterior_predictive_band(
+        self,
+        *,
+        expt_name: str,
+        summary: object,
+        y_meas: np.ndarray,
+        axes_labels: list[str],
+    ) -> None:
+        """Render a posterior predictive band plot using Plotly."""
+        import plotly.graph_objects as go
+
+        fig = go.Figure()
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=summary.lower_95,
+                mode='lines',
+                line={'color': 'rgba(0, 0, 0, 0)'},
+                hoverinfo='skip',
+                showlegend=False,
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=summary.upper_95,
+                mode='lines',
+                line={'color': 'rgba(0, 0, 0, 0)'},
+                fill='tonexty',
+                fillcolor='rgba(214, 39, 40, 0.18)',
+                name='Posterior predictive 95% CI',
+                hoverinfo='skip',
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=summary.map_prediction,
+                mode='lines',
+                line={'color': 'rgb(214, 39, 40)', 'width': 2},
+                name='MAP prediction',
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=y_meas,
+                mode='lines+markers',
+                line={'color': 'rgb(31, 119, 180)', 'width': 1.5},
+                name='Measured',
+            )
+        )
+        fig.update_layout(
+            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
+            xaxis_title=axes_labels[0],
+            yaxis_title=axes_labels[1],
+        )
+        fig.show()
+
+    def _plot_posterior_predictive_draws(
+        self,
+        *,
+        expt_name: str,
+        summary: object,
+        y_meas: np.ndarray,
+        axes_labels: list[str],
+    ) -> None:
+        """Render posterior predictive draws using Plotly."""
+        import plotly.graph_objects as go
+
+        fig = go.Figure()
+        draws = getattr(summary, 'draws', None)
+        if draws is None:
+            log.warning('Posterior predictive draws are unavailable for plotting.')
+            return
+
+        draw_cap = min(len(draws), DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP)
+        for index in range(draw_cap):
+            fig.add_trace(
+                go.Scatter(
+                    x=summary.x,
+                    y=draws[index],
+                    mode='lines',
+                    line={'color': 'rgba(120, 120, 120, 0.18)', 'width': 1},
+                    name='Posterior draw' if index == 0 else None,
+                    showlegend=index == 0,
+                )
+            )
+
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=summary.map_prediction,
+                mode='lines',
+                line={'color': 'rgb(214, 39, 40)', 'width': 2},
+                name='MAP prediction',
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=y_meas,
+                mode='lines+markers',
+                line={'color': 'rgb(31, 119, 180)', 'width': 1.5},
+                name='Measured',
+            )
+        )
+        fig.update_layout(
+            title=f"Posterior predictive draws for experiment 🔬 '{expt_name}'",
+            xaxis_title=axes_labels[0],
+            yaxis_title=axes_labels[1],
+        )
+        fig.show()
+
     @staticmethod
     def _resolve_posterior_parameter_names(
         *,
@@ -1506,6 +1863,28 @@ def _plot_powder_bragg_meas_vs_calc(
                 x_min=ctx['x_min'],
                 x_max=ctx['x_max'],
             )
+
+        predictive_summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=ctx['x_axis'],
+        )
+        predictive_lower_95 = None
+        predictive_upper_95 = None
+        if predictive_summary is not None:
+            predictive_lower_95 = self._filtered_y_array(
+                predictive_summary.lower_95,
+                predictive_summary.x,
+                ctx['x_min'],
+                ctx['x_max'],
+            )
+            predictive_upper_95 = self._filtered_y_array(
+                predictive_summary.upper_95,
+                predictive_summary.x,
+                ctx['x_min'],
+                ctx['x_max'],
+            )
+
         plot_spec = PowderMeasVsCalcSpec(
             x=ctx['x_filtered'],
             y_meas=series.y_meas,
@@ -1518,6 +1897,8 @@ def _plot_powder_bragg_meas_vs_calc(
             bragg_peaks_height_fraction=DEFAULT_BRAGG_ROW,
             height=self._composite_plot_height(),
             y_bkg=series.y_bkg,
+            predictive_lower_95=predictive_lower_95,
+            predictive_upper_95=predictive_upper_95,
         )
         self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
 

From 100678ed501c0c090b355ac816bc3f6bdc053fca Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 00:55:20 +0200
Subject: [PATCH 014/106] Refine Bayesian fit UX

---
 .../analysis/fit_helpers/bayesian.py          |  13 +-
 .../analysis/fit_helpers/tracking.py          |  88 +++++-
 .../analysis/minimizers/bumps_dream.py        |  69 ++++-
 .../display/plotters/plotly.py                |   7 +-
 src/easydiffraction/display/plotting.py       | 252 ++++++++++++++++--
 5 files changed, 389 insertions(+), 40 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 3615a0ed..62d6cab3 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -382,6 +382,7 @@ def summarize_posterior_parameters(
     parameter_names: list[str],
     posterior_samples: PosteriorSamples,
     map_values: np.ndarray,
+    parameter_display_names: list[str] | None = None,
     convergence_diagnostics: dict[str, object] | None = None,
 ) -> list[PosteriorParameterSummary]:
     """Build posterior parameter summaries in EasyDiffraction order.
@@ -394,6 +395,8 @@ def summarize_posterior_parameters(
         Posterior sample container.
     map_values : np.ndarray
         MAP or best-sampled parameter values in the same order.
+    parameter_display_names : list[str] | None, default=None
+        Human-readable parameter names in the same order.
     convergence_diagnostics : dict[str, object] | None, default=None
         Optional convergence diagnostics keyed by parameter name.
 
@@ -412,6 +415,9 @@ def summarize_posterior_parameters(
     if flattened.shape[1] != len(parameter_names):
         msg = 'Posterior samples do not match the sampled parameter name list length.'
         raise ValueError(msg)
+    if parameter_display_names is not None and len(parameter_display_names) != len(parameter_names):
+        msg = 'Posterior display-name list must match the sampled parameter name list length.'
+        raise ValueError(msg)
 
     r_hat_by_parameter = {}
     ess_bulk_by_parameter = {}
@@ -424,10 +430,15 @@ def summarize_posterior_parameters(
         values = flattened[:, index]
         interval_68 = tuple(np.quantile(values, [0.16, 0.84]).tolist())
         interval_95 = tuple(np.quantile(values, [0.025, 0.975]).tolist())
+        display_name = (
+            parameter_display_names[index]
+            if parameter_display_names is not None
+            else parameter_name
+        )
         summaries.append(
             PosteriorParameterSummary(
                 unique_name=parameter_name,
-                display_name=parameter_name,
+                display_name=display_name,
                 map_value=float(map_values[index]),
                 median=float(np.median(values)),
                 standard_deviation=float(np.std(values, ddof=1)),
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 99f9c8b6..97aeeec9 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -29,6 +29,8 @@
 from easydiffraction.utils.logging import ConsoleManager
 
 SIGNIFICANT_CHANGE_THRESHOLD = 0.01  # 1% threshold
+SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0
+SAMPLER_PROGRESS_STATUS = 'sampling...'
 DEFAULT_HEADERS = ['iteration', 'χ²', 'improvement [%]']
 DEFAULT_ALIGNMENTS = ['center', 'center', 'center']
 
@@ -101,6 +103,7 @@ def __init__(self) -> None:
         self._best_iteration: int | None = None
         self._fitting_time: float | None = None
         self._verbosity: VerbosityEnum = VerbosityEnum.FULL
+        self._last_progress_time: float | None = None
 
         self._df_rows: list[list[str]] = []
         self._display_handle: object | None = None
@@ -115,6 +118,7 @@ def reset(self) -> None:
         self._best_chi2 = None
         self._best_iteration = None
         self._fitting_time = None
+        self._last_progress_time = None
 
     def track(
         self,
@@ -185,6 +189,75 @@ def track(
 
         return residuals
 
+    def track_sampler_progress(
+        self,
+        *,
+        iteration: int,
+        reduced_chi2: float,
+        elapsed_time: float,
+        status: str = SAMPLER_PROGRESS_STATUS,
+    ) -> None:
+        """Update progress from a sampler monitor.
+
+        Parameters
+        ----------
+        iteration : int
+            Sampler iteration or generation index.
+        reduced_chi2 : float
+            Best reduced chi-square implied by the current best sample.
+        elapsed_time : float
+            Elapsed wall time in seconds.
+        status : str, default='sampling...'
+            Text shown for periodic progress rows without a chi-square
+            improvement.
+        """
+        self._iteration = iteration
+
+        row: list[str] = []
+        if self._previous_chi2 is None or self._best_chi2 is None:
+            self._previous_chi2 = reduced_chi2
+            self._best_chi2 = reduced_chi2
+            self._best_iteration = iteration
+            self._last_progress_time = elapsed_time
+            row = [
+                str(iteration),
+                f'{reduced_chi2:.2f}',
+                '',
+            ]
+        else:
+            if reduced_chi2 < self._best_chi2:
+                self._best_chi2 = reduced_chi2
+                self._best_iteration = iteration
+
+            change = 0.0
+            if self._previous_chi2 > 0:
+                change = (self._previous_chi2 - reduced_chi2) / self._previous_chi2
+
+            if change > SIGNIFICANT_CHANGE_THRESHOLD:
+                row = [
+                    str(iteration),
+                    f'{reduced_chi2:.2f}',
+                    f'{change * 100:.1f}% ↓',
+                ]
+                self._previous_chi2 = reduced_chi2
+                self._last_progress_time = elapsed_time
+            elif (
+                self._last_progress_time is None
+                or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
+            ):
+                row = [
+                    str(iteration),
+                    f'{self._best_chi2:.2f}',
+                    status,
+                ]
+                self._last_progress_time = elapsed_time
+
+        if row:
+            self.add_tracking_info(row)
+
+        self._last_chi2 = reduced_chi2
+        self._last_iteration = iteration
+
     @property
     def best_chi2(self) -> float | None:
         """Best recorded reduced chi-square value or None."""
@@ -266,13 +339,14 @@ def add_tracking_info(self, row: list[str]) -> None:
 
     def finish_tracking(self) -> None:
         """Finalize progress display and print best result summary."""
-        # Add last iteration as last row
-        row: list[str] = [
-            str(self._last_iteration),
-            f'{self._last_chi2:.2f}' if self._last_chi2 is not None else '',
-            '',
-        ]
-        self.add_tracking_info(row)
+        if self._last_iteration is not None:
+            row: list[str] = [
+                str(self._last_iteration),
+                f'{self._last_chi2:.2f}' if self._last_chi2 is not None else '',
+                '',
+            ]
+            if not self._df_rows or self._df_rows[-1][:2] != row[:2]:
+                self.add_tracking_info(row)
 
         if self._verbosity is not VerbosityEnum.FULL:
             return
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 28a2f4de..9f2ade75 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -10,6 +10,7 @@
 from bumps.fitproblem import FitProblem
 from bumps.fitters import FITTERS
 from bumps.fitters import FitDriver
+from bumps.fitters import monitor as bumps_monitor
 from scipy.optimize import OptimizeResult
 
 from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
@@ -35,6 +36,60 @@
 DEFAULT_TRIM = False
 
 
+class _DreamProgressMonitor(bumps_monitor.Monitor):
+    """Progress monitor translating DREAM updates into chi-square rows."""
+
+    def __init__(
+        self,
+        *,
+        tracker: object,
+        n_points: int,
+        n_parameters: int,
+        n_chains: int,
+    ) -> None:
+        self._tracker = tracker
+        self._n_points = n_points
+        self._n_parameters = n_parameters
+        self._n_chains = max(1, n_chains)
+
+    def config_history(self, history: object) -> None:
+        """Declare the history fields needed for progress updates."""
+        history.requires(time=1, step=1, value=1)
+
+    def __call__(self, history: object) -> None:
+        """Forward sampler progress to the shared fit tracker."""
+        step = int(history.step[0]) if history.step else 0
+        generation = max(1, step // self._n_chains)
+        reduced_chi2 = self._reduced_chi_square_from_nllf(float(history.value[0]))
+        self._tracker.track_sampler_progress(
+            iteration=generation,
+            reduced_chi2=reduced_chi2,
+            elapsed_time=float(history.time[0]),
+        )
+
+    def final(self, history: object, best: dict[str, object]) -> None:
+        """Record the final DREAM state in the shared fit tracker."""
+        if not history.time or best.get('value') is None:
+            return
+        step = int(history.step[0]) if history.step else 0
+        generation = max(1, step // self._n_chains)
+        reduced_chi2 = self._reduced_chi_square_from_nllf(float(best['value']))
+        self._tracker.track_sampler_progress(
+            iteration=generation,
+            reduced_chi2=reduced_chi2,
+            elapsed_time=float(history.time[0]),
+            status='',
+        )
+
+    def _reduced_chi_square_from_nllf(self, nllf: float) -> float:
+        """Convert DREAM's negative log-likelihood to reduced chi-square."""
+        dof = self._n_points - self._n_parameters
+        chi_square = 2.0 * nllf
+        if dof <= 0:
+            return chi_square
+        return chi_square / dof
+
+
 @MinimizerFactory.register
 class BumpsDreamMinimizer(BumpsMinimizer):
     """Bumps minimizer using the DREAM Bayesian sampler."""
@@ -94,6 +149,9 @@ def _prepare_solver_args(
         """
         solver_args = super()._prepare_solver_args(parameters)
         solver_args['parameter_names'] = [parameter.unique_name for parameter in parameters]
+        solver_args['parameter_display_names'] = [
+            getattr(parameter, 'name', parameter.unique_name) for parameter in parameters
+        ]
         solver_args['parameter_uids'] = [parameter._minimizer_uid for parameter in parameters]
         solver_args['starting_uncertainties'] = [parameter.uncertainty for parameter in parameters]
         return solver_args
@@ -119,6 +177,7 @@ def _run_solver(
         """
         bumps_params = kwargs.get('bumps_params')
         parameter_names = kwargs.get('parameter_names')
+        parameter_display_names = kwargs.get('parameter_display_names')
         parameter_uids = kwargs.get('parameter_uids')
         random_seed = kwargs.get('random_seed')
         starting_uncertainties = kwargs.get('starting_uncertainties')
@@ -130,10 +189,17 @@ def _run_solver(
         proposed_burn = max(DEFAULT_MIN_BURN, int(self.max_iterations * DEFAULT_BURN_FRACTION))
         burn = min(proposed_burn, max(self.max_iterations - 1, 0))
         samples = self.max_iterations * DEFAULT_POP * len(bumps_params)
+        n_chains = DEFAULT_POP * len(bumps_params)
+        progress_monitor = _DreamProgressMonitor(
+            tracker=self.tracker,
+            n_points=fitness.numpoints(),
+            n_parameters=len(bumps_params),
+            n_chains=n_chains,
+        )
         driver = FitDriver(
             fitclass=fitclass,
             problem=problem,
-            monitors=[],
+            monitors=[progress_monitor],
             steps=self.max_iterations,
             burn=burn,
             thin=DEFAULT_THIN,
@@ -238,6 +304,7 @@ def _run_solver(
             parameter_names=parameter_names,
             posterior_samples=posterior_samples,
             map_values=map_values,
+            parameter_display_names=parameter_display_names,
             convergence_diagnostics=convergence_diagnostics,
         )
         posterior_standard_deviations = standard_deviations_from_summaries(
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index e219ffa9..59bab8db 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -65,7 +65,8 @@
 COMPOSITE_MARGIN_RIGHT = 30
 COMPOSITE_MARGIN_TOP = 40
 COMPOSITE_MARGIN_BOTTOM = 45
-PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.18)'
+PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.26)'
+PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
 
 
 @dataclass(frozen=True)
@@ -1392,7 +1393,7 @@ def _get_predictive_band_traces(
             x=x,
             y=lower,
             mode='lines',
-            line={'color': 'rgba(0, 0, 0, 0)'},
+            line={'color': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1},
             hoverinfo='skip',
             showlegend=False,
             legendgroup='predictive_band',
@@ -1401,7 +1402,7 @@ def _get_predictive_band_traces(
             x=x,
             y=upper,
             mode='lines',
-            line={'color': 'rgba(0, 0, 0, 0)'},
+            line={'color': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1},
             fill='tonexty',
             fillcolor=PREDICTIVE_BAND_COLOR,
             name='Posterior predictive 95% CI',
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 9a5fc56e..bb0ecb4b 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -883,7 +883,7 @@ def _build_posterior_pairs_plot(
         *,
         parameters: list[object] | None,
     ) -> object | None:
-        """Build an ArviZ posterior pair-plot object.
+        """Build a Plotly posterior pair plot.
 
         Parameters
         ----------
@@ -893,11 +893,11 @@ def _build_posterior_pairs_plot(
         Returns
         -------
         object | None
-            ArviZ plot object, or ``None`` when posterior plotting is
+            Plotly figure, or ``None`` when posterior plotting is
             unavailable.
         """
-        inference_data, fit_results = self._get_posterior_inference_data()
-        if inference_data is None or fit_results is None:
+        posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
+        if posterior_samples is None or fit_results is None:
             return None
 
         parameter_names = self._resolve_posterior_parameter_names(
@@ -910,23 +910,87 @@ def _build_posterior_pairs_plot(
             log.warning('Posterior pair plots require at least two sampled parameters.')
             return None
 
-        import arviz as az
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Histogram', 'Scattergl'])
+        make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
 
-        plot = az.plot_pair(
-            inference_data,
-            var_names=parameter_names,
-            backend='plotly',
-            marginal=True,
+        samples = self._selected_posterior_samples(posterior_samples, parameter_names)
+        if samples is None:
+            return None
+        samples = self._thin_posterior_samples(samples)
+        labels = self._posterior_plot_labels(fit_results, parameter_names)
+
+        n_parameters = len(parameter_names)
+        fig = make_subplots(
+            rows=n_parameters,
+            cols=n_parameters,
+            shared_xaxes='columns',
+            shared_yaxes='rows',
+            horizontal_spacing=0.03,
+            vertical_spacing=0.03,
         )
-        if hasattr(plot, 'add_title'):
-            plot.add_title('Posterior pair plot')
-        return plot
+
+        for row_index in range(n_parameters):
+            for col_index in range(n_parameters):
+                row = row_index + 1
+                col = col_index + 1
+                if col_index > row_index:
+                    fig.update_xaxes(visible=False, row=row, col=col)
+                    fig.update_yaxes(visible=False, row=row, col=col)
+                    continue
+
+                x_values = samples[:, col_index]
+                y_values = samples[:, row_index]
+                if row_index == col_index:
+                    fig.add_trace(
+                        go.Histogram(
+                            x=x_values,
+                            nbinsx=40,
+                            histnorm='probability density',
+                            marker={'color': 'rgb(99, 110, 250)'},
+                            showlegend=False,
+                            hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+                        ),
+                        row=row,
+                        col=col,
+                    )
+                else:
+                    fig.add_trace(
+                        go.Scattergl(
+                            x=x_values,
+                            y=y_values,
+                            mode='markers',
+                            marker={
+                                'color': 'rgba(99, 110, 250, 0.18)',
+                                'size': 4,
+                            },
+                            showlegend=False,
+                            hovertemplate=(
+                                f'{labels[col_index]}: %{{x:.4f}}<br>'
+                                f'{labels[row_index]}: %{{y:.4f}}<extra></extra>'
+                            ),
+                        ),
+                        row=row,
+                        col=col,
+                    )
+
+                fig.update_xaxes(showticklabels=(row_index == n_parameters - 1), row=row, col=col)
+                fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
+                if row_index == n_parameters - 1:
+                    fig.update_xaxes(title_text=labels[col_index], row=row, col=col)
+                if col_index == 0 and row_index > 0:
+                    fig.update_yaxes(title_text=labels[row_index], row=row, col=col)
+
+        fig.update_layout(
+            title='Posterior pair plot',
+            bargap=0.05,
+        )
+        return fig
 
     def _build_param_distribution_plot(
         self,
         param: object,
     ) -> object | None:
-        """Build an ArviZ posterior distribution plot for one parameter.
+        """Build a Plotly posterior distribution plot for one parameter.
 
         Parameters
         ----------
@@ -936,11 +1000,11 @@ def _build_param_distribution_plot(
         Returns
         -------
         object | None
-            ArviZ plot object, or ``None`` when posterior plotting is
+            Plotly figure, or ``None`` when posterior plotting is
             unavailable.
         """
-        inference_data, fit_results = self._get_posterior_inference_data()
-        if inference_data is None or fit_results is None:
+        posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
+        if posterior_samples is None or fit_results is None:
             return None
 
         parameter_names = self._resolve_posterior_parameter_names(
@@ -950,17 +1014,64 @@ def _build_param_distribution_plot(
         if parameter_names is None:
             return None
 
-        import arviz as az
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Histogram'])
+
+        parameter_name = parameter_names[0]
+        samples = self._selected_posterior_samples(posterior_samples, [parameter_name])
+        if samples is None:
+            return None
+        values = samples[:, 0]
+        label = self._posterior_plot_labels(fit_results, [parameter_name])[0]
+        summary = self._posterior_summary_by_name(fit_results).get(parameter_name)
+
+        fig = go.Figure()
+        if summary is not None:
+            fig.add_vrect(
+                x0=summary.interval_95[0],
+                x1=summary.interval_95[1],
+                fillcolor='rgba(99, 110, 250, 0.08)',
+                line_width=0,
+            )
+            fig.add_vrect(
+                x0=summary.interval_68[0],
+                x1=summary.interval_68[1],
+                fillcolor='rgba(99, 110, 250, 0.18)',
+                line_width=0,
+            )
+
+        fig.add_trace(
+            go.Histogram(
+                x=values,
+                nbinsx=50,
+                histnorm='probability density',
+                marker={'color': 'rgb(99, 110, 250)'},
+                opacity=0.85,
+                name='Posterior samples',
+            )
+        )
+
+        median = float(np.median(values))
+        fig.add_vline(
+            x=median,
+            line={'color': 'rgb(99, 110, 250)', 'width': 2},
+            annotation_text='median',
+            annotation_position='top',
+        )
+        if summary is not None:
+            fig.add_vline(
+                x=summary.map_value,
+                line={'color': 'rgb(214, 39, 40)', 'width': 2, 'dash': 'dash'},
+                annotation_text='MAP',
+                annotation_position='top right',
+            )
 
-        plot = az.plot_dist(
-            inference_data,
-            var_names=parameter_names,
-            backend='plotly',
-            point_estimate='median',
+        fig.update_layout(
+            title=f'Posterior distribution: {label}',
+            xaxis_title=label,
+            yaxis_title='Probability density',
+            bargap=0.05,
         )
-        if hasattr(plot, 'add_title'):
-            plot.add_title(f'Posterior distribution: {parameter_names[0]}')
-        return plot
+        return fig
 
     def _get_or_build_posterior_predictive_summary(
         self,
@@ -1165,6 +1276,25 @@ def _get_posterior_inference_data(
 
         return posterior_samples.to_arviz(), fit_results
 
+    def _get_posterior_samples_and_fit_results(
+        self,
+    ) -> tuple[object | None, object | None]:
+        """Return posterior samples and fit results for plotting."""
+        if self.engine != PlotterEngineEnum.PLOTLY.value:
+            log.warning('Posterior plots currently require the Plotly plotting backend.')
+            return None, None
+
+        fit_results = self._get_fit_result_for_correlation()
+        if fit_results is None:
+            return None, None
+
+        posterior_samples = getattr(fit_results, 'posterior_samples', None)
+        if posterior_samples is None:
+            log.warning('Posterior samples are unavailable. Run a Bayesian fit first.')
+            return None, None
+
+        return posterior_samples, fit_results
+
     def _plot_posterior_predictive_band(
         self,
         *,
@@ -1174,7 +1304,7 @@ def _plot_posterior_predictive_band(
         axes_labels: list[str],
     ) -> None:
         """Render a posterior predictive band plot using Plotly."""
-        import plotly.graph_objects as go
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
 
         fig = go.Figure()
         fig.add_trace(
@@ -1233,7 +1363,7 @@ def _plot_posterior_predictive_draws(
         axes_labels: list[str],
     ) -> None:
         """Render posterior predictive draws using Plotly."""
-        import plotly.graph_objects as go
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
 
         fig = go.Figure()
         draws = getattr(summary, 'draws', None)
@@ -1317,6 +1447,72 @@ def _resolve_posterior_parameter_names(
             return None
         return [str(name) for name in requested_names if name is not None]
 
+    @staticmethod
+    def _selected_posterior_samples(
+        posterior_samples: object,
+        parameter_names: list[str],
+    ) -> np.ndarray | None:
+        """Return flattened posterior samples in the selected order."""
+        available_names = getattr(posterior_samples, 'parameter_names', None)
+        if not available_names:
+            return None
+
+        name_to_index = {name: index for index, name in enumerate(available_names)}
+        try:
+            indices = [name_to_index[name] for name in parameter_names]
+        except KeyError:
+            return None
+
+        flattened = np.asarray(posterior_samples.flattened(), dtype=float)
+        if flattened.ndim != 2:
+            return None
+        return flattened[:, indices]
+
+    @staticmethod
+    def _thin_posterior_samples(
+        samples: np.ndarray,
+        *,
+        max_points: int = 4000,
+    ) -> np.ndarray:
+        """Downsample posterior samples for interactive plotting."""
+        if samples.shape[0] <= max_points:
+            return samples
+
+        indices = np.linspace(0, samples.shape[0] - 1, num=max_points, dtype=int)
+        return samples[indices]
+
+    @staticmethod
+    def _posterior_plot_labels(
+        fit_results: object,
+        parameter_names: list[str],
+    ) -> list[str]:
+        """Return readable posterior plot labels for selected parameters."""
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter for parameter in fit_results.parameters
+        }
+        labels: list[str] = []
+        for parameter_name in parameter_names:
+            parameter = parameters_by_name.get(parameter_name)
+            if parameter is None:
+                labels.append(parameter_name)
+                continue
+
+            entry_name = getattr(getattr(parameter, '_identity', None), 'category_entry_name', '')
+            short_name = getattr(parameter, 'name', parameter_name)
+            if entry_name:
+                labels.append(f'{entry_name} {short_name}')
+            else:
+                labels.append(short_name)
+        return labels
+
+    @staticmethod
+    def _posterior_summary_by_name(
+        fit_results: object,
+    ) -> dict[str, object]:
+        """Return posterior summaries keyed by unique parameter name."""
+        summaries = getattr(fit_results, 'posterior_parameter_summaries', [])
+        return {summary.unique_name: summary for summary in summaries}
+
     def _get_fit_result_for_correlation(
         self,
     ) -> object | None:

From d17fc0d632ca2432b83f2a6406216d572a639928 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 01:16:00 +0200
Subject: [PATCH 015/106] Fix DREAM sampler progress reporting

---
 .../analysis/fit_helpers/tracking.py          |  9 +++++++-
 .../analysis/minimizers/bumps_dream.py        | 23 ++++++++++++++-----
 2 files changed, 25 insertions(+), 7 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 97aeeec9..f9789b88 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -31,7 +31,7 @@
 SIGNIFICANT_CHANGE_THRESHOLD = 0.01  # 1% threshold
 SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0
 SAMPLER_PROGRESS_STATUS = 'sampling...'
-DEFAULT_HEADERS = ['iteration', 'χ²', 'improvement [%]']
+DEFAULT_HEADERS = ['iteration', 'χ²', 'change / status']
 DEFAULT_ALIGNMENTS = ['center', 'center', 'center']
 
 
@@ -99,6 +99,7 @@ def __init__(self) -> None:
         self._previous_chi2: float | None = None
         self._last_chi2: float | None = None
         self._last_iteration: int | None = None
+        self._last_reported_iteration: int | None = None
         self._best_chi2: float | None = None
         self._best_iteration: int | None = None
         self._fitting_time: float | None = None
@@ -115,6 +116,7 @@ def reset(self) -> None:
         self._previous_chi2 = None
         self._last_chi2 = None
         self._last_iteration = None
+        self._last_reported_iteration = None
         self._best_chi2 = None
         self._best_iteration = None
         self._fitting_time = None
@@ -242,8 +244,11 @@ def track_sampler_progress(
                 self._previous_chi2 = reduced_chi2
                 self._last_progress_time = elapsed_time
             elif (
+                iteration != self._last_reported_iteration
+                and (
                 self._last_progress_time is None
                 or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
+                )
             ):
                 row = [
                     str(iteration),
@@ -325,6 +330,8 @@ def add_tracking_info(self, row: list[str]) -> None:
         row : list[str]
             Columns corresponding to DEFAULT_HEADERS.
         """
+        if row and row[0].isdigit():
+            self._last_reported_iteration = int(row[0])
         self._df_rows.append(row)
         if self._verbosity is not VerbosityEnum.FULL:
             return
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 9f2ade75..0f1cf08c 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -45,12 +45,14 @@ def __init__(
         tracker: object,
         n_points: int,
         n_parameters: int,
-        n_chains: int,
+        total_generations: int,
+        burn_steps: int,
     ) -> None:
         self._tracker = tracker
         self._n_points = n_points
         self._n_parameters = n_parameters
-        self._n_chains = max(1, n_chains)
+        self._total_generations = max(1, total_generations)
+        self._burn_steps = max(0, burn_steps)
 
     def config_history(self, history: object) -> None:
         """Declare the history fields needed for progress updates."""
@@ -59,12 +61,13 @@ def config_history(self, history: object) -> None:
     def __call__(self, history: object) -> None:
         """Forward sampler progress to the shared fit tracker."""
         step = int(history.step[0]) if history.step else 0
-        generation = max(1, step // self._n_chains)
+        generation = max(1, step)
         reduced_chi2 = self._reduced_chi_square_from_nllf(float(history.value[0]))
         self._tracker.track_sampler_progress(
             iteration=generation,
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
+            status=self._status_text(generation),
         )
 
     def final(self, history: object, best: dict[str, object]) -> None:
@@ -72,7 +75,7 @@ def final(self, history: object, best: dict[str, object]) -> None:
         if not history.time or best.get('value') is None:
             return
         step = int(history.step[0]) if history.step else 0
-        generation = max(1, step // self._n_chains)
+        generation = max(1, step)
         reduced_chi2 = self._reduced_chi_square_from_nllf(float(best['value']))
         self._tracker.track_sampler_progress(
             iteration=generation,
@@ -81,6 +84,13 @@ def final(self, history: object, best: dict[str, object]) -> None:
             status='',
         )
 
+    def _status_text(self, generation: int) -> str:
+        """Return a human-readable DREAM progress string."""
+        clamped_generation = min(generation, self._total_generations)
+        progress = 100.0 * clamped_generation / self._total_generations
+        phase = 'burn-in' if clamped_generation <= self._burn_steps else 'sampling'
+        return f'{phase} {progress:.1f}% ({clamped_generation}/{self._total_generations})'
+
     def _reduced_chi_square_from_nllf(self, nllf: float) -> float:
         """Convert DREAM's negative log-likelihood to reduced chi-square."""
         dof = self._n_points - self._n_parameters
@@ -189,12 +199,13 @@ def _run_solver(
         proposed_burn = max(DEFAULT_MIN_BURN, int(self.max_iterations * DEFAULT_BURN_FRACTION))
         burn = min(proposed_burn, max(self.max_iterations - 1, 0))
         samples = self.max_iterations * DEFAULT_POP * len(bumps_params)
-        n_chains = DEFAULT_POP * len(bumps_params)
+        total_generations = int(self.max_iterations + burn + 1)
         progress_monitor = _DreamProgressMonitor(
             tracker=self.tracker,
             n_points=fitness.numpoints(),
             n_parameters=len(bumps_params),
-            n_chains=n_chains,
+            total_generations=total_generations,
+            burn_steps=int(burn),
         )
         driver = FitDriver(
             fitclass=fitclass,

From a33f65750d4f731b9767d7aa9382aa46e8507f2b Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 01:29:24 +0200
Subject: [PATCH 016/106] Fix posterior pair plot axis sharing

---
 src/easydiffraction/display/plotting.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index bb0ecb4b..3c4567b2 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -924,7 +924,6 @@ def _build_posterior_pairs_plot(
             rows=n_parameters,
             cols=n_parameters,
             shared_xaxes='columns',
-            shared_yaxes='rows',
             horizontal_spacing=0.03,
             vertical_spacing=0.03,
         )

From 55adea8846d047381b1db5d73ab76b8c7232ff3d Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 01:45:07 +0200
Subject: [PATCH 017/106] Improve Bayesian demo parameter choices

---
 tmp/bumps_dream/ed-2-bayesian-new-API.py | 207 +++++++++++++++++++++++
 1 file changed, 207 insertions(+)
 create mode 100644 tmp/bumps_dream/ed-2-bayesian-new-API.py

diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
new file mode 100644
index 00000000..680a2302
--- /dev/null
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -0,0 +1,207 @@
+# %% [markdown]
+# # Structure Refinement: LBCO, HRPT
+#
+# This minimalistic example is designed to show how Rietveld refinement
+# can be performed when both the crystal structure and experiment are
+# defined directly in code. Only the experimentally measured data is
+# loaded from an external file. It also shows how to switch calculation
+# engine.
+#
+# For this example, constant-wavelength neutron powder diffraction data
+# for La0.5Ba0.5CoO3 from HRPT at PSI is used.
+#
+# It does not contain any advanced features or options, and includes no
+# comments or explanations — these can be found in the other tutorials.
+# Default values are used for all parameters if not specified. Only
+# essential and self-explanatory code is provided.
+#
+# The example is intended for users who are already familiar with the
+# EasyDiffraction library and want to quickly get started with a simple
+# refinement. It is also useful for those who want to see what a
+# refinement might look like in code. For a more detailed explanation of
+# the code, please refer to the other tutorials.
+
+# %% [markdown]
+# ## Import Library
+
+# %%
+import easydiffraction as ed
+
+# %% [markdown]
+# ## Step 1: Define Project
+
+# %%
+project = ed.Project()
+
+# %% [markdown]
+# ## Step 2: Define Structure
+
+# %%
+project.structures.create(name='lbco')
+
+# %%
+structure = project.structures['lbco']
+
+# %%
+structure.space_group.name_h_m = 'P m -3 m'
+structure.space_group.it_coordinate_system_code = '1'
+
+# %%
+structure.cell.length_a = 3.8909
+
+# %%
+structure.atom_sites.create(
+    label='La',
+    type_symbol='La',
+    fract_x=0,
+    fract_y=0,
+    fract_z=0,
+    wyckoff_letter='a',
+    adp_type='Biso',
+    adp_iso=0.5,
+    occupancy=0.5,
+)
+structure.atom_sites.create(
+    label='Ba',
+    type_symbol='Ba',
+    fract_x=0,
+    fract_y=0,
+    fract_z=0,
+    wyckoff_letter='a',
+    adp_type='Biso',
+    adp_iso=0.5,
+    occupancy=0.5,
+)
+structure.atom_sites.create(
+    label='Co',
+    type_symbol='Co',
+    fract_x=0.5,
+    fract_y=0.5,
+    fract_z=0.5,
+    wyckoff_letter='b',
+    adp_type='Biso',
+    adp_iso=0.5,
+)
+structure.atom_sites.create(
+    label='O',
+    type_symbol='O',
+    fract_x=0,
+    fract_y=0.5,
+    fract_z=0.5,
+    wyckoff_letter='c',
+    adp_type='Biso',
+    adp_iso=0.5,
+)
+
+# %% [markdown]
+# ## Step 3: Define Experiment
+
+# %%
+data_path = ed.download_data(id=3, destination='data')
+
+# %%
+project.experiments.add_from_data_path(
+    name='hrpt',
+    data_path=data_path,
+    sample_form='powder',
+    beam_mode='constant wavelength',
+    radiation_probe='neutron',
+)
+
+# %%
+experiment = project.experiments['hrpt']
+
+# %%
+experiment.instrument.setup_wavelength = 1.494
+experiment.instrument.calib_twotheta_offset = 0.6226
+
+# %%
+experiment.peak.broad_gauss_u = 0.0816
+experiment.peak.broad_gauss_v = -0.1159
+experiment.peak.broad_gauss_w = 0.1204
+experiment.peak.broad_lorentz_y = 0.0844
+
+# %%
+experiment.background.create(id='1', x=10, y=168.5585)
+experiment.background.create(id='2', x=30, y=164.3357)
+experiment.background.create(id='3', x=50, y=166.8881)
+experiment.background.create(id='4', x=110, y=175.4006)
+experiment.background.create(id='5', x=165, y=174.2813)
+
+# %%
+experiment.excluded_regions.create(id='1', start=0, end=20)
+experiment.excluded_regions.create(id='2', start=160, end=180)
+
+# %%
+experiment.linked_phases.create(id='lbco', scale=9.1351)
+
+# %% [markdown]
+# ## Step 4: Perform Analysis
+
+# %%
+structure.cell.length_a.free = True
+experiment.peak.broad_gauss_w.free = True
+experiment.linked_phases['lbco'].scale.free = True
+
+# %%
+project.analysis.fit.show_minimizer_types()
+project.analysis.fit.minimizer_type = 'bumps (lm)'
+
+# %%
+project.analysis.fit()
+
+# %%
+project.analysis.display.fit_results()
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+
+# %% [markdown]
+# ## Step 5: Perform Bayesian Analysis
+
+# %%
+length_a = structure.cell.length_a.value
+structure.cell.length_a.fit_min = length_a - 0.01
+structure.cell.length_a.fit_max = length_a + 0.01
+
+scale = experiment.linked_phases['lbco'].scale.value
+experiment.linked_phases['lbco'].scale.fit_min = scale * 0.8
+experiment.linked_phases['lbco'].scale.fit_max = scale * 1.2
+
+broad_gauss_w = experiment.peak.broad_gauss_w.value
+experiment.peak.broad_gauss_w.fit_min = max(0.001, broad_gauss_w * 0.5)
+experiment.peak.broad_gauss_w.fit_max = broad_gauss_w * 1.5
+
+# %%
+project.analysis.display.free_params()
+
+# %%
+project.analysis.fit.show_minimizer_types()
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+
+# %%
+project.analysis.fit()
+
+# %%
+project.analysis.display.fit_results()
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+
+# %%
+project.display.plotter.plot_posterior_pairs()
+
+# %%
+project.display.plotter.plot_param_distribution(structure.cell.length_a)
+project.display.plotter.plot_param_distribution(experiment.linked_phases['lbco'].scale)
+project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_w)
+
+# %%
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt', style='band')
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt', style='draws')

From 4c07c18b9b002d4134ba8194302687e1f4c6c095 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 01:47:41 +0200
Subject: [PATCH 018/106] Expose DREAM sampler settings

---
 .../analysis/minimizers/bumps_dream.py        | 149 +++++++++++++++---
 tmp/bumps_dream/ed-2-bayesian-new-API.py      |   6 +
 2 files changed, 131 insertions(+), 24 deletions(-)

diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 0f1cf08c..147828fb 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -120,6 +120,48 @@ def __init__(
             method=method,
             max_iterations=max_iterations,
         )
+        self._burn: int | None = None
+        self._thin: int = DEFAULT_THIN
+        self._pop: int = DEFAULT_POP
+
+    @property
+    def steps(self) -> int:
+        """Number of DREAM generations retained after burn-in."""
+        return self._validated_positive_integer('steps', self.max_iterations)
+
+    @steps.setter
+    def steps(self, value: int) -> None:
+        self.max_iterations = self._validated_positive_integer('steps', value)
+
+    @property
+    def burn(self) -> int | None:
+        """Explicit DREAM burn-in generations or ``None`` for auto."""
+        return self._burn
+
+    @burn.setter
+    def burn(self, value: int | None) -> None:
+        if value is None:
+            self._burn = None
+            return
+        self._burn = self._validated_non_negative_integer('burn', value)
+
+    @property
+    def thin(self) -> int:
+        """DREAM thinning interval."""
+        return self._thin
+
+    @thin.setter
+    def thin(self, value: int) -> None:
+        self._thin = self._validated_positive_integer('thin', value)
+
+    @property
+    def pop(self) -> int:
+        """DREAM population multiplier."""
+        return self._pop
+
+    @pop.setter
+    def pop(self, value: int) -> None:
+        self._pop = self._validated_positive_integer('pop', value)
 
     def _resolve_random_seed(self, random_seed: int | None) -> int:
         """Return a user-provided or generated random seed.
@@ -166,6 +208,68 @@ def _prepare_solver_args(
         solver_args['starting_uncertainties'] = [parameter.uncertainty for parameter in parameters]
         return solver_args
 
+    @staticmethod
+    def _validated_positive_integer(name: str, value: int | float) -> int:
+        """Validate a DREAM setting that must be a positive integer."""
+        if isinstance(value, bool):
+            msg = f"DREAM setting '{name}' must be a positive integer."
+            raise ValueError(msg)
+
+        integer_value = int(value)
+        if integer_value != value or integer_value < 1:
+            msg = f"DREAM setting '{name}' must be a positive integer."
+            raise ValueError(msg)
+        return integer_value
+
+    @staticmethod
+    def _validated_non_negative_integer(name: str, value: int | float) -> int:
+        """Validate a DREAM setting that must be a non-negative integer."""
+        if isinstance(value, bool):
+            msg = f"DREAM setting '{name}' must be a non-negative integer."
+            raise ValueError(msg)
+
+        integer_value = int(value)
+        if integer_value != value or integer_value < 0:
+            msg = f"DREAM setting '{name}' must be a non-negative integer."
+            raise ValueError(msg)
+        return integer_value
+
+    def _resolved_burn(self, steps: int) -> int:
+        """Return the configured or automatic DREAM burn-in length."""
+        if self.burn is None:
+            proposed_burn = max(DEFAULT_MIN_BURN, int(steps * DEFAULT_BURN_FRACTION))
+            return min(proposed_burn, max(steps - 1, 0))
+
+        burn = self.burn
+        if burn >= steps:
+            msg = "DREAM setting 'burn' must be smaller than 'steps'."
+            raise ValueError(msg)
+        return burn
+
+    def _sampler_settings(
+        self,
+        *,
+        random_seed: int,
+        steps: int,
+        burn: int,
+        thin: int,
+        pop: int,
+        n_parameters: int,
+    ) -> dict[str, object]:
+        """Build the sampler settings dictionary recorded in results."""
+        samples = steps * pop * n_parameters
+        return {
+            'random_seed': int(random_seed),
+            'steps': int(steps),
+            'burn': int(burn),
+            'thin': int(thin),
+            'pop': int(pop),
+            'samples': int(samples),
+            'alpha': float(DEFAULT_ALPHA),
+            'outliers': DEFAULT_OUTLIER_TEST,
+            'trim': DEFAULT_TRIM,
+        }
+
     def _run_solver(
         self,
         objective_function: object,
@@ -196,10 +300,19 @@ def _run_solver(
         problem = FitProblem(fitness)
 
         fitclass = next(cls for cls in FITTERS if cls.id == self.method)
-        proposed_burn = max(DEFAULT_MIN_BURN, int(self.max_iterations * DEFAULT_BURN_FRACTION))
-        burn = min(proposed_burn, max(self.max_iterations - 1, 0))
-        samples = self.max_iterations * DEFAULT_POP * len(bumps_params)
-        total_generations = int(self.max_iterations + burn + 1)
+        steps = self.steps
+        burn = self._resolved_burn(steps)
+        thin = self.thin
+        pop = self.pop
+        sampler_settings = self._sampler_settings(
+            random_seed=int(random_seed),
+            steps=steps,
+            burn=burn,
+            thin=thin,
+            pop=pop,
+            n_parameters=len(bumps_params),
+        )
+        total_generations = int(steps + burn + 1)
         progress_monitor = _DreamProgressMonitor(
             tracker=self.tracker,
             n_points=fitness.numpoints(),
@@ -211,11 +324,11 @@ def _run_solver(
             fitclass=fitclass,
             problem=problem,
             monitors=[progress_monitor],
-            steps=self.max_iterations,
+            steps=steps,
             burn=burn,
-            thin=DEFAULT_THIN,
-            pop=DEFAULT_POP,
-            samples=samples,
+            thin=thin,
+            pop=pop,
+            samples=sampler_settings['samples'],
             alpha=DEFAULT_ALPHA,
             outliers=DEFAULT_OUTLIER_TEST,
             trim=DEFAULT_TRIM,
@@ -244,7 +357,7 @@ def _run_solver(
                 posterior_samples=None,
                 posterior_parameter_summaries=[],
                 convergence_diagnostics={},
-                sampler_settings={'random_seed': int(random_seed)},
+                sampler_settings=sampler_settings,
                 sampler_completed=False,
                 raw_state=None,
                 best_log_posterior=None,
@@ -268,7 +381,7 @@ def _run_solver(
                 posterior_samples=None,
                 posterior_parameter_summaries=[],
                 convergence_diagnostics={},
-                sampler_settings={'random_seed': int(random_seed)},
+                sampler_settings=sampler_settings,
                 sampler_completed=False,
                 raw_state=state,
                 best_log_posterior=None,
@@ -289,7 +402,7 @@ def _run_solver(
                 posterior_samples=None,
                 posterior_parameter_summaries=[],
                 convergence_diagnostics={},
-                sampler_settings={'random_seed': int(random_seed)},
+                sampler_settings=sampler_settings,
                 sampler_completed=True,
                 raw_state=state,
                 best_log_posterior=None,
@@ -322,18 +435,6 @@ def _run_solver(
             posterior_parameter_summaries
         )
 
-        sampler_settings = {
-            'random_seed': int(random_seed),
-            'steps': int(self.max_iterations),
-            'burn': int(burn),
-            'thin': int(DEFAULT_THIN),
-            'pop': int(DEFAULT_POP),
-            'samples': int(samples),
-            'alpha': float(DEFAULT_ALPHA),
-            'outliers': DEFAULT_OUTLIER_TEST,
-            'trim': DEFAULT_TRIM,
-        }
-
         if not convergence_diagnostics.get('converged', True):
             log.warning(
                 'DREAM sampling completed, but convergence diagnostics indicate '
@@ -436,6 +537,6 @@ def _build_fit_results(
             best_log_posterior=getattr(raw_result, 'best_log_posterior', None),
         )
         fit_results.message = getattr(raw_result, 'message', '')
-        fit_results.iterations = int(self.max_iterations)
+        fit_results.iterations = int(fit_results.sampler_settings.get('steps', self.steps))
         fit_results.result = raw_result
         return fit_results
\ No newline at end of file
diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index 680a2302..6c819a6f 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -182,6 +182,12 @@
 project.analysis.fit.show_minimizer_types()
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
+dream = project.analysis.fit.minimizer
+dream.steps = 1000
+dream.burn = 200
+dream.thin = 1
+dream.pop = 4
+
 # %%
 project.analysis.fit()
 

From c1d6e8399a3649cf4c44234454a3707c1d317ac8 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 07:23:52 +0200
Subject: [PATCH 019/106] Render posterior marginals as KDE curves

---
 src/easydiffraction/display/plotting.py | 191 +++++++++++++++++++++---
 1 file changed, 167 insertions(+), 24 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 3c4567b2..f63b5b7a 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -916,7 +916,7 @@ def _build_posterior_pairs_plot(
         samples = self._selected_posterior_samples(posterior_samples, parameter_names)
         if samples is None:
             return None
-        samples = self._thin_posterior_samples(samples)
+        samples = self._thin_posterior_samples(samples, max_points=1500)
         labels = self._posterior_plot_labels(fit_results, parameter_names)
 
         n_parameters = len(parameter_names)
@@ -940,18 +940,27 @@ def _build_posterior_pairs_plot(
                 x_values = samples[:, col_index]
                 y_values = samples[:, row_index]
                 if row_index == col_index:
-                    fig.add_trace(
-                        go.Histogram(
-                            x=x_values,
-                            nbinsx=40,
-                            histnorm='probability density',
-                            marker={'color': 'rgb(99, 110, 250)'},
-                            showlegend=False,
-                            hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
-                        ),
-                        row=row,
-                        col=col,
+                    density_trace = self._posterior_density_trace(
+                        fit_results=fit_results,
+                        parameter_name=parameter_names[col_index],
+                        values=x_values,
+                        trace_name=labels[col_index],
                     )
+                    if density_trace is None:
+                        fig.add_trace(
+                            go.Histogram(
+                                x=x_values,
+                                nbinsx=40,
+                                histnorm='probability density',
+                                marker={'color': 'rgb(99, 110, 250)'},
+                                showlegend=False,
+                                hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+                            ),
+                            row=row,
+                            col=col,
+                        )
+                    else:
+                        fig.add_trace(density_trace, row=row, col=col)
                 else:
                     fig.add_trace(
                         go.Scattergl(
@@ -959,8 +968,8 @@ def _build_posterior_pairs_plot(
                             y=y_values,
                             mode='markers',
                             marker={
-                                'color': 'rgba(99, 110, 250, 0.18)',
-                                'size': 4,
+                                'color': 'rgba(99, 110, 250, 0.22)',
+                                'size': 3,
                             },
                             showlegend=False,
                             hovertemplate=(
@@ -1038,16 +1047,25 @@ def _build_param_distribution_plot(
                 line_width=0,
             )
 
-        fig.add_trace(
-            go.Histogram(
-                x=values,
-                nbinsx=50,
-                histnorm='probability density',
-                marker={'color': 'rgb(99, 110, 250)'},
-                opacity=0.85,
-                name='Posterior samples',
-            )
+        density_trace = self._posterior_density_trace(
+            fit_results=fit_results,
+            parameter_name=parameter_name,
+            values=values,
+            trace_name='Posterior density',
         )
+        if density_trace is None:
+            fig.add_trace(
+                go.Histogram(
+                    x=values,
+                    nbinsx=50,
+                    histnorm='probability density',
+                    marker={'color': 'rgb(99, 110, 250)'},
+                    opacity=0.85,
+                    name='Posterior samples',
+                )
+            )
+        else:
+            fig.add_trace(density_trace)
 
         median = float(np.median(values))
         fig.add_vline(
@@ -1068,10 +1086,135 @@ def _build_param_distribution_plot(
             title=f'Posterior distribution: {label}',
             xaxis_title=label,
             yaxis_title='Probability density',
-            bargap=0.05,
         )
         return fig
 
+    def _posterior_density_trace(
+        self,
+        *,
+        fit_results: object,
+        parameter_name: str,
+        values: np.ndarray,
+        trace_name: str,
+    ) -> object | None:
+        """Return a filled KDE trace for one posterior marginal."""
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+
+        lower_bound, upper_bound = self._posterior_parameter_bounds(
+            fit_results=fit_results,
+            parameter_name=parameter_name,
+        )
+        density_curve = self._posterior_density_curve(
+            values,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+        )
+        if density_curve is None:
+            return None
+
+        grid, density = density_curve
+        return go.Scatter(
+            x=grid,
+            y=density,
+            mode='lines',
+            line={'color': 'rgb(99, 110, 250)', 'width': 2},
+            fill='tozeroy',
+            fillcolor='rgba(99, 110, 250, 0.22)',
+            name=trace_name,
+            showlegend=False,
+            hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+        )
+
+    @staticmethod
+    def _posterior_parameter_bounds(
+        *,
+        fit_results: object,
+        parameter_name: str,
+    ) -> tuple[float | None, float | None]:
+        """Return finite fit bounds for a posterior parameter when available."""
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter for parameter in fit_results.parameters
+        }
+        parameter = parameters_by_name.get(parameter_name)
+        if parameter is None:
+            return None, None
+
+        lower_bound = getattr(parameter, 'fit_min', None)
+        upper_bound = getattr(parameter, 'fit_max', None)
+        lower = (
+            float(lower_bound)
+            if lower_bound is not None and np.isfinite(float(lower_bound))
+            else None
+        )
+        upper = (
+            float(upper_bound)
+            if upper_bound is not None and np.isfinite(float(upper_bound))
+            else None
+        )
+        return lower, upper
+
+    @classmethod
+    def _posterior_density_curve(
+        cls,
+        values: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+        grid_size: int = 256,
+    ) -> tuple[np.ndarray, np.ndarray] | None:
+        """Estimate a boundary-aware posterior density curve."""
+        gaussian_kde = __import__('scipy.stats', fromlist=['gaussian_kde']).gaussian_kde
+
+        data = np.asarray(values, dtype=float)
+        data = data[np.isfinite(data)]
+        if data.size < 2:
+            return None
+
+        data_min = float(np.min(data))
+        data_max = float(np.max(data))
+        data_range = data_max - data_min
+        padding = 0.05 * data_range if data_range > 0 else max(abs(data_min), 1.0) * 0.05
+        if padding == 0:
+            padding = 1e-6
+
+        grid_lower = lower_bound if lower_bound is not None else data_min - padding
+        grid_upper = upper_bound if upper_bound is not None else data_max + padding
+        if grid_upper <= grid_lower:
+            return None
+
+        grid = np.linspace(grid_lower, grid_upper, num=grid_size)
+        if np.allclose(data, data[0]):
+            bandwidth = max(abs(data[0]) * 0.01, 1e-6)
+            density = np.exp(-0.5 * ((grid - data[0]) / bandwidth) ** 2)
+            density /= bandwidth * np.sqrt(2.0 * np.pi)
+        else:
+            reflected_data = cls._reflected_density_samples(
+                data,
+                lower_bound=lower_bound,
+                upper_bound=upper_bound,
+            )
+            density = np.asarray(gaussian_kde(reflected_data)(grid), dtype=float)
+
+        area = np.trapezoid(density, grid)
+        if area <= 0:
+            return None
+        return grid, density / area
+
+    @staticmethod
+    def _reflected_density_samples(
+        values: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+    ) -> np.ndarray:
+        """Reflect posterior samples across finite bounds for KDE stability."""
+        reflected = [values]
+        if lower_bound is not None:
+            reflected.append(2.0 * lower_bound - values)
+        if upper_bound is not None:
+            reflected.append(2.0 * upper_bound - values)
+        return np.concatenate(reflected)
+
     def _get_or_build_posterior_predictive_summary(
         self,
         *,

From 5ec9def96d648bd6a304ba81ac78d0f6af94b07e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 07:35:08 +0200
Subject: [PATCH 020/106] Add posterior pair density contours

---
 src/easydiffraction/display/plotting.py | 152 +++++++++++++++++++++++-
 1 file changed, 151 insertions(+), 1 deletion(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index f63b5b7a..c1075107 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -910,7 +910,10 @@ def _build_posterior_pairs_plot(
             log.warning('Posterior pair plots require at least two sampled parameters.')
             return None
 
-        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Histogram', 'Scattergl'])
+        go = __import__(
+            'plotly.graph_objects',
+            fromlist=['Figure', 'Histogram', 'Scattergl', 'Contour'],
+        )
         make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
 
         samples = self._selected_posterior_samples(posterior_samples, parameter_names)
@@ -962,6 +965,15 @@ def _build_posterior_pairs_plot(
                     else:
                         fig.add_trace(density_trace, row=row, col=col)
                 else:
+                    contour_trace = self._posterior_contour_trace(
+                        fit_results=fit_results,
+                        x_parameter_name=parameter_names[col_index],
+                        y_parameter_name=parameter_names[row_index],
+                        x_values=x_values,
+                        y_values=y_values,
+                    )
+                    if contour_trace is not None:
+                        fig.add_trace(contour_trace, row=row, col=col)
                     fig.add_trace(
                         go.Scattergl(
                             x=x_values,
@@ -994,6 +1006,52 @@ def _build_posterior_pairs_plot(
         )
         return fig
 
+    def _posterior_contour_trace(
+        self,
+        *,
+        fit_results: object,
+        x_parameter_name: str,
+        y_parameter_name: str,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+    ) -> object | None:
+        """Return a 2D KDE contour trace for posterior pair plots."""
+        go = __import__('plotly.graph_objects', fromlist=['Contour'])
+
+        bounds = self._posterior_pair_bounds(
+            fit_results=fit_results,
+            x_parameter_name=x_parameter_name,
+            y_parameter_name=y_parameter_name,
+            x_values=x_values,
+            y_values=y_values,
+        )
+        density_surface = self._posterior_pair_density_surface(
+            x_values=x_values,
+            y_values=y_values,
+            x_bounds=bounds[0],
+            y_bounds=bounds[1],
+        )
+        if density_surface is None:
+            return None
+
+        x_grid, y_grid, density = density_surface
+        return go.Contour(
+            x=x_grid,
+            y=y_grid,
+            z=density,
+            contours={
+                'coloring': 'none',
+                'showlabels': False,
+                'start': float(np.max(density) * 0.15),
+                'end': float(np.max(density) * 0.95),
+                'size': float(np.max(density) * 0.16),
+            },
+            line={'color': 'rgba(99, 110, 250, 0.55)', 'width': 1.2},
+            hoverinfo='skip',
+            showscale=False,
+            showlegend=False,
+        )
+
     def _build_param_distribution_plot(
         self,
         param: object,
@@ -1153,6 +1211,51 @@ def _posterior_parameter_bounds(
         )
         return lower, upper
 
+    @classmethod
+    def _posterior_pair_bounds(
+        cls,
+        *,
+        fit_results: object,
+        x_parameter_name: str,
+        y_parameter_name: str,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+    ) -> tuple[tuple[float, float], tuple[float, float]]:
+        """Return plotting bounds for a posterior pair panel."""
+        x_lower, x_upper = cls._posterior_parameter_bounds(
+            fit_results=fit_results,
+            parameter_name=x_parameter_name,
+        )
+        y_lower, y_upper = cls._posterior_parameter_bounds(
+            fit_results=fit_results,
+            parameter_name=y_parameter_name,
+        )
+        return (
+            cls._posterior_axis_bounds(x_values, lower_bound=x_lower, upper_bound=x_upper),
+            cls._posterior_axis_bounds(y_values, lower_bound=y_lower, upper_bound=y_upper),
+        )
+
+    @staticmethod
+    def _posterior_axis_bounds(
+        values: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+    ) -> tuple[float, float]:
+        """Return finite plotting bounds for one posterior axis."""
+        data = np.asarray(values, dtype=float)
+        data = data[np.isfinite(data)]
+        data_min = float(np.min(data))
+        data_max = float(np.max(data))
+        data_range = data_max - data_min
+        padding = 0.05 * data_range if data_range > 0 else max(abs(data_min), 1.0) * 0.05
+        if padding == 0:
+            padding = 1e-6
+
+        resolved_lower = lower_bound if lower_bound is not None else data_min - padding
+        resolved_upper = upper_bound if upper_bound is not None else data_max + padding
+        return float(resolved_lower), float(resolved_upper)
+
     @classmethod
     def _posterior_density_curve(
         cls,
@@ -1200,6 +1303,53 @@ def _posterior_density_curve(
             return None
         return grid, density / area
 
+    @classmethod
+    def _posterior_pair_density_surface(
+        cls,
+        *,
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+        x_bounds: tuple[float, float],
+        y_bounds: tuple[float, float],
+        grid_size: int = 96,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray] | None:
+        """Estimate a 2D KDE surface for a posterior pair panel."""
+        gaussian_kde = __import__('scipy.stats', fromlist=['gaussian_kde']).gaussian_kde
+
+        x_data = np.asarray(x_values, dtype=float)
+        y_data = np.asarray(y_values, dtype=float)
+        mask = np.isfinite(x_data) & np.isfinite(y_data)
+        x_data = x_data[mask]
+        y_data = y_data[mask]
+        if x_data.size < 2 or y_data.size < 2:
+            return None
+
+        if np.allclose(x_data, x_data[0]) and np.allclose(y_data, y_data[0]):
+            return None
+
+        reflected_x = cls._reflected_density_samples(
+            x_data,
+            lower_bound=x_bounds[0],
+            upper_bound=x_bounds[1],
+        )
+        reflected_y = cls._reflected_density_samples(
+            y_data,
+            lower_bound=y_bounds[0],
+            upper_bound=y_bounds[1],
+        )
+        if reflected_x.shape != reflected_y.shape:
+            return None
+
+        x_grid = np.linspace(x_bounds[0], x_bounds[1], num=grid_size)
+        y_grid = np.linspace(y_bounds[0], y_bounds[1], num=grid_size)
+        mesh_x, mesh_y = np.meshgrid(x_grid, y_grid)
+        positions = np.vstack([mesh_x.ravel(), mesh_y.ravel()])
+        density = gaussian_kde(np.vstack([reflected_x, reflected_y]))(positions)
+        density = np.asarray(density, dtype=float).reshape(mesh_x.shape)
+        if not np.any(np.isfinite(density)):
+            return None
+        return x_grid, y_grid, density
+
     @staticmethod
     def _reflected_density_samples(
         values: np.ndarray,

From 0f2731c1547469fbc9d9e0c0414a38981c85a1f7 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 07:38:17 +0200
Subject: [PATCH 021/106] Switch Bayesian demo to correlated pair

---
 tmp/bumps_dream/ed-2-bayesian-new-API.py | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index 6c819a6f..7c8dc20b 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -140,8 +140,7 @@
 
 # %%
 structure.cell.length_a.free = True
-experiment.peak.broad_gauss_w.free = True
-experiment.linked_phases['lbco'].scale.free = True
+experiment.instrument.calib_twotheta_offset.free = True
 
 # %%
 project.analysis.fit.show_minimizer_types()
@@ -164,16 +163,16 @@
 
 # %%
 length_a = structure.cell.length_a.value
-structure.cell.length_a.fit_min = length_a - 0.01
-structure.cell.length_a.fit_max = length_a + 0.01
+length_a_sigma = structure.cell.length_a.uncertainty or 0.001
+length_a_window = max(8.0 * length_a_sigma, 0.001)
+structure.cell.length_a.fit_min = length_a - length_a_window
+structure.cell.length_a.fit_max = length_a + length_a_window
 
-scale = experiment.linked_phases['lbco'].scale.value
-experiment.linked_phases['lbco'].scale.fit_min = scale * 0.8
-experiment.linked_phases['lbco'].scale.fit_max = scale * 1.2
-
-broad_gauss_w = experiment.peak.broad_gauss_w.value
-experiment.peak.broad_gauss_w.fit_min = max(0.001, broad_gauss_w * 0.5)
-experiment.peak.broad_gauss_w.fit_max = broad_gauss_w * 1.5
+twotheta_offset = experiment.instrument.calib_twotheta_offset.value
+twotheta_offset_sigma = experiment.instrument.calib_twotheta_offset.uncertainty or 0.01
+twotheta_offset_window = max(8.0 * twotheta_offset_sigma, 0.01)
+experiment.instrument.calib_twotheta_offset.fit_min = twotheta_offset - twotheta_offset_window
+experiment.instrument.calib_twotheta_offset.fit_max = twotheta_offset + twotheta_offset_window
 
 # %%
 project.analysis.display.free_params()
@@ -205,8 +204,7 @@
 
 # %%
 project.display.plotter.plot_param_distribution(structure.cell.length_a)
-project.display.plotter.plot_param_distribution(experiment.linked_phases['lbco'].scale)
-project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_w)
+project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
 
 # %%
 project.display.plotter.plot_posterior_predictive(expt_name='hrpt', style='band')

From c3ad035aa30026e5dd25e7d8315f290d43e7757e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 08:58:19 +0200
Subject: [PATCH 022/106] Add Bayesian tracking and posterior plot updates

---
 .../analysis/fit_helpers/tracking.py          | 129 +++++---
 .../analysis/minimizers/base.py               |   6 +-
 .../analysis/minimizers/bumps_dream.py        |  27 +-
 src/easydiffraction/display/plotting.py       | 293 ++++++++++--------
 4 files changed, 275 insertions(+), 180 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index f9789b88..68ce2543 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -30,9 +30,12 @@
 
 SIGNIFICANT_CHANGE_THRESHOLD = 0.01  # 1% threshold
 SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0
-SAMPLER_PROGRESS_STATUS = 'sampling...'
+TRACKING_MODE_FIT = 'fit'
+TRACKING_MODE_SAMPLER = 'sampling'
 DEFAULT_HEADERS = ['iteration', 'χ²', 'change / status']
 DEFAULT_ALIGNMENTS = ['center', 'center', 'center']
+SAMPLER_HEADERS = ['iteration', 'progress', 'phase']
+SAMPLER_ALIGNMENTS = ['center', 'center', 'center']
 
 
 class _TerminalLiveHandle:
@@ -105,6 +108,10 @@ def __init__(self) -> None:
         self._fitting_time: float | None = None
         self._verbosity: VerbosityEnum = VerbosityEnum.FULL
         self._last_progress_time: float | None = None
+        self._tracking_mode: str = TRACKING_MODE_FIT
+        self._sampler_total_iterations: int | None = None
+        self._last_sampler_phase: str | None = None
+        self._last_sampler_progress_percent: float | None = None
 
         self._df_rows: list[list[str]] = []
         self._display_handle: object | None = None
@@ -121,6 +128,10 @@ def reset(self) -> None:
         self._best_iteration = None
         self._fitting_time = None
         self._last_progress_time = None
+        self._tracking_mode = TRACKING_MODE_FIT
+        self._sampler_total_iterations = None
+        self._last_sampler_phase = None
+        self._last_sampler_progress_percent = None
 
     def track(
         self,
@@ -195,9 +206,11 @@ def track_sampler_progress(
         self,
         *,
         iteration: int,
+        total_iterations: int,
+        phase: str,
+        progress_percent: float,
         reduced_chi2: float,
         elapsed_time: float,
-        status: str = SAMPLER_PROGRESS_STATUS,
     ) -> None:
         """Update progress from a sampler monitor.
 
@@ -205,15 +218,26 @@ def track_sampler_progress(
         ----------
         iteration : int
             Sampler iteration or generation index.
+        total_iterations : int
+            Total sampler generations configured for the run.
+        phase : str
+            Current sampler phase, e.g. ``'burn-in'`` or ``'sampling'``.
+        progress_percent : float
+            Completed fraction expressed in percent.
         reduced_chi2 : float
             Best reduced chi-square implied by the current best sample.
         elapsed_time : float
             Elapsed wall time in seconds.
-        status : str, default='sampling...'
-            Text shown for periodic progress rows without a chi-square
-            improvement.
         """
         self._iteration = iteration
+        self._tracking_mode = TRACKING_MODE_SAMPLER
+        self._sampler_total_iterations = max(1, total_iterations)
+
+        clamped_iteration = min(max(1, iteration), self._sampler_total_iterations)
+        clamped_progress = min(max(progress_percent, 0.0), 100.0)
+        previous_phase = self._last_sampler_phase
+        self._last_sampler_phase = phase
+        self._last_sampler_progress_percent = clamped_progress
 
         row: list[str] = []
         if self._previous_chi2 is None or self._best_chi2 is None:
@@ -222,38 +246,28 @@ def track_sampler_progress(
             self._best_iteration = iteration
             self._last_progress_time = elapsed_time
             row = [
-                str(iteration),
-                f'{reduced_chi2:.2f}',
-                '',
+                f'{clamped_iteration}/{self._sampler_total_iterations}',
+                f'{clamped_progress:.1f}%',
+                phase,
             ]
         else:
             if reduced_chi2 < self._best_chi2:
                 self._best_chi2 = reduced_chi2
                 self._best_iteration = iteration
 
-            change = 0.0
-            if self._previous_chi2 > 0:
-                change = (self._previous_chi2 - reduced_chi2) / self._previous_chi2
-
-            if change > SIGNIFICANT_CHANGE_THRESHOLD:
-                row = [
-                    str(iteration),
-                    f'{reduced_chi2:.2f}',
-                    f'{change * 100:.1f}% ↓',
-                ]
-                self._previous_chi2 = reduced_chi2
-                self._last_progress_time = elapsed_time
-            elif (
+            if (
                 iteration != self._last_reported_iteration
                 and (
-                self._last_progress_time is None
-                or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
+                    previous_phase != phase
+                    or self._last_progress_time is None
+                    or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
+                    or clamped_iteration >= self._sampler_total_iterations
                 )
             ):
                 row = [
-                    str(iteration),
-                    f'{self._best_chi2:.2f}',
-                    status,
+                    f'{clamped_iteration}/{self._sampler_total_iterations}',
+                    f'{clamped_progress:.1f}%',
+                    phase,
                 ]
                 self._last_progress_time = elapsed_time
 
@@ -292,7 +306,7 @@ def stop_timer(self) -> None:
         self._end_time = time.perf_counter()
         self._fitting_time = self._end_time - self._start_time
 
-    def start_tracking(self, minimizer_name: str) -> None:
+    def start_tracking(self, minimizer_name: str, *, mode: str = TRACKING_MODE_FIT) -> None:
         """
         Initialize display and headers and announce the minimizer.
 
@@ -300,14 +314,23 @@ def start_tracking(self, minimizer_name: str) -> None:
         ----------
         minimizer_name : str
             Name of the minimizer used for the run.
+        mode : str, default='fit'
+            Tracking mode for the run.
         """
         if self._verbosity is VerbosityEnum.SILENT:
             return
         if self._verbosity is VerbosityEnum.SHORT:
             return
 
+        self._tracking_mode = (
+            TRACKING_MODE_SAMPLER if mode == TRACKING_MODE_SAMPLER else TRACKING_MODE_FIT
+        )
+
         console.print(f"🚀 Starting fit process with '{minimizer_name}'...")
-        console.print('📈 Goodness-of-fit (reduced χ²) change:')
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            console.print('📈 Bayesian sampling progress:')
+        else:
+            console.print('📈 Fit progress:')
 
         # Reset rows and create an environment-appropriate handle
         self._df_rows = []
@@ -315,8 +338,8 @@ def start_tracking(self, minimizer_name: str) -> None:
 
         # Initial empty table; subsequent updates will reuse the handle
         render_table(
-            columns_headers=DEFAULT_HEADERS,
-            columns_alignment=DEFAULT_ALIGNMENTS,
+            columns_headers=self._headers(),
+            columns_alignment=self._alignments(),
             columns_data=self._df_rows,
             display_handle=self._display_handle,
         )
@@ -328,25 +351,43 @@ def add_tracking_info(self, row: list[str]) -> None:
         Parameters
         ----------
         row : list[str]
-            Columns corresponding to DEFAULT_HEADERS.
+            Columns corresponding to the active tracking headers.
         """
-        if row and row[0].isdigit():
-            self._last_reported_iteration = int(row[0])
+        if row:
+            iteration_cell = row[0].split('/', maxsplit=1)[0]
+            if iteration_cell.isdigit():
+                self._last_reported_iteration = int(iteration_cell)
         self._df_rows.append(row)
         if self._verbosity is not VerbosityEnum.FULL:
             return
         # Append and update via the active handle (Jupyter or
         # terminal live)
         render_table(
-            columns_headers=DEFAULT_HEADERS,
-            columns_alignment=DEFAULT_ALIGNMENTS,
+            columns_headers=self._headers(),
+            columns_alignment=self._alignments(),
             columns_data=self._df_rows,
             display_handle=self._display_handle,
         )
 
     def finish_tracking(self) -> None:
         """Finalize progress display and print best result summary."""
-        if self._last_iteration is not None:
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            if self._last_iteration is not None and self._sampler_total_iterations is not None:
+                final_progress = self._last_sampler_progress_percent
+                if final_progress is None:
+                    final_progress = 100.0 * min(
+                        self._last_iteration,
+                        self._sampler_total_iterations,
+                    ) / self._sampler_total_iterations
+                row = [
+                    f'{min(self._last_iteration, self._sampler_total_iterations)}/'
+                    f'{self._sampler_total_iterations}',
+                    f'{final_progress:.1f}%',
+                    self._last_sampler_phase or 'sampling',
+                ]
+                if not self._df_rows or self._df_rows[-1] != row:
+                    self.add_tracking_info(row)
+        elif self._last_iteration is not None:
             row: list[str] = [
                 str(self._last_iteration),
                 f'{self._last_chi2:.2f}' if self._last_chi2 is not None else '',
@@ -363,9 +404,25 @@ def finish_tracking(self) -> None:
             with suppress(Exception):
                 self._display_handle.close()
 
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            console.print('✅ Bayesian sampling complete.')
+            return
+
         # Print best result
         console.print(
             f'🏆 Best goodness-of-fit (reduced χ²) is {self._best_chi2:.2f} '
             f'at iteration {self._best_iteration}'
         )
         console.print('✅ Fitting complete.')
+
+    def _headers(self) -> list[str]:
+        """Return column headers for the active tracking mode."""
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            return SAMPLER_HEADERS
+        return DEFAULT_HEADERS
+
+    def _alignments(self) -> list[str]:
+        """Return column alignments for the active tracking mode."""
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            return SAMPLER_ALIGNMENTS
+        return DEFAULT_ALIGNMENTS
diff --git a/src/easydiffraction/analysis/minimizers/base.py b/src/easydiffraction/analysis/minimizers/base.py
index 46da77c5..84d5371d 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -61,7 +61,7 @@ def _start_tracking(
         """
         self.tracker.reset()
         self.tracker._verbosity = verbosity
-        self.tracker.start_tracking(minimizer_name)
+        self.tracker.start_tracking(minimizer_name, mode=self._tracking_mode())
         self.tracker.start_timer()
 
     def _stop_tracking(self) -> None:
@@ -69,6 +69,10 @@ def _stop_tracking(self) -> None:
         self.tracker.stop_timer()
         self.tracker.finish_tracking()
 
+    def _tracking_mode(self) -> str:
+        """Return the tracker mode for the current minimizer."""
+        return 'fit'
+
     @abstractmethod
     def _prepare_solver_args(self, parameters: list[Any]) -> dict[str, Any]:
         """
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 147828fb..801ab385 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -65,9 +65,11 @@ def __call__(self, history: object) -> None:
         reduced_chi2 = self._reduced_chi_square_from_nllf(float(history.value[0]))
         self._tracker.track_sampler_progress(
             iteration=generation,
+            total_iterations=self._total_generations,
+            phase=self._phase_name(generation),
+            progress_percent=self._progress_percent(generation),
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
-            status=self._status_text(generation),
         )
 
     def final(self, history: object, best: dict[str, object]) -> None:
@@ -79,17 +81,24 @@ def final(self, history: object, best: dict[str, object]) -> None:
         reduced_chi2 = self._reduced_chi_square_from_nllf(float(best['value']))
         self._tracker.track_sampler_progress(
             iteration=generation,
+            total_iterations=self._total_generations,
+            phase=self._phase_name(generation),
+            progress_percent=self._progress_percent(generation),
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
-            status='',
         )
 
-    def _status_text(self, generation: int) -> str:
-        """Return a human-readable DREAM progress string."""
+    def _phase_name(self, generation: int) -> str:
+        """Return the current sampler phase name."""
         clamped_generation = min(generation, self._total_generations)
-        progress = 100.0 * clamped_generation / self._total_generations
-        phase = 'burn-in' if clamped_generation <= self._burn_steps else 'sampling'
-        return f'{phase} {progress:.1f}% ({clamped_generation}/{self._total_generations})'
+        if clamped_generation <= self._burn_steps:
+            return 'burn-in'
+        return 'sampling'
+
+    def _progress_percent(self, generation: int) -> float:
+        """Return DREAM progress as a percentage."""
+        clamped_generation = min(generation, self._total_generations)
+        return 100.0 * clamped_generation / self._total_generations
 
     def _reduced_chi_square_from_nllf(self, nllf: float) -> float:
         """Convert DREAM's negative log-likelihood to reduced chi-square."""
@@ -183,6 +192,10 @@ def _resolve_random_seed(self, random_seed: int | None) -> int:
         self._resolved_random_seed = int(random_seed)
         return self._resolved_random_seed
 
+    def _tracking_mode(self) -> str:
+        """Use sampler-style progress reporting for DREAM runs."""
+        return 'sampling'
+
     def _prepare_solver_args(
         self,
         parameters: list[object],
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index c1075107..4235f0c5 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -71,6 +71,13 @@ def description(self) -> str:
 DEFAULT_BRAGG_ROW = DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION
 DEFAULT_POSTERIOR_PREDICTIVE_DRAWS = 200
 DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP = 50
+POSTERIOR_DENSITY_LINE_COLOR = 'rgb(99, 110, 250)'
+POSTERIOR_DENSITY_FILL_COLOR = 'rgba(99, 110, 250, 0.22)'
+POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(140, 140, 140)'
+POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
+POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
+POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
+POSTERIOR_CONTOUR_LINE_COLOR = 'rgba(99, 110, 250, 0.50)'
 
 
 @dataclass(frozen=True)
@@ -662,7 +669,7 @@ def plot_param_distribution(
     def plot_posterior_predictive(
         self,
         expt_name: str,
-        style: str = 'band',
+        style: str = 'band+draws',
     ) -> None:
         """Plot posterior predictive curves for a powder experiment.
 
@@ -670,12 +677,13 @@ def plot_posterior_predictive(
         ----------
         expt_name : str
             Experiment name to plot.
-        style : str, default='band'
-            Either ``'band'`` for a 95% credible interval or
-            ``'draws'`` for sampled predictive curves.
+        style : str, default='band+draws'
+            ``'band'`` shows the 95% credible interval,
+            ``'draws'`` shows sampled predictive curves, and
+            ``'band+draws'`` shows both together.
         """
-        if style not in {'band', 'draws'}:
-            msg = "style must be either 'band' or 'draws'."
+        if style not in {'band', 'draws', 'band+draws'}:
+            msg = "style must be 'band', 'draws', or 'band+draws'."
             raise ValueError(msg)
 
         if self._project is None:
@@ -707,20 +715,13 @@ def plot_posterior_predictive(
             return
 
         axes_labels = self._get_axes_labels(sample_form, scattering_type, x_axis)
-        if style == 'band':
-            self._plot_posterior_predictive_band(
-                expt_name=expt_name,
-                summary=summary,
-                y_meas=np.asarray(y_meas, dtype=float),
-                axes_labels=axes_labels,
-            )
-            return
-
-        self._plot_posterior_predictive_draws(
+        self._plot_posterior_predictive_summary(
             expt_name=expt_name,
             summary=summary,
             y_meas=np.asarray(y_meas, dtype=float),
             axes_labels=axes_labels,
+            show_band=style in {'band', 'band+draws'},
+            show_draws=style in {'draws', 'band+draws'},
         )
 
     @staticmethod
@@ -949,6 +950,7 @@ def _build_posterior_pairs_plot(
                         values=x_values,
                         trace_name=labels[col_index],
                     )
+                    diagonal_y_axis_range = None
                     if density_trace is None:
                         fig.add_trace(
                             go.Histogram(
@@ -964,6 +966,11 @@ def _build_posterior_pairs_plot(
                         )
                     else:
                         fig.add_trace(density_trace, row=row, col=col)
+                        diagonal_y_axis_range = self._posterior_density_axis_range(
+                            np.asarray(density_trace.y)
+                        )
+                    if diagonal_y_axis_range is not None:
+                        fig.update_yaxes(range=list(diagonal_y_axis_range), row=row, col=col)
                 else:
                     contour_trace = self._posterior_contour_trace(
                         fit_results=fit_results,
@@ -980,7 +987,7 @@ def _build_posterior_pairs_plot(
                             y=y_values,
                             mode='markers',
                             marker={
-                                'color': 'rgba(99, 110, 250, 0.22)',
+                                'color': POSTERIOR_SCATTER_MARKER_COLOR,
                                 'size': 3,
                             },
                             showlegend=False,
@@ -1040,13 +1047,20 @@ def _posterior_contour_trace(
             y=y_grid,
             z=density,
             contours={
-                'coloring': 'none',
+                'coloring': 'fill',
                 'showlabels': False,
                 'start': float(np.max(density) * 0.15),
                 'end': float(np.max(density) * 0.95),
                 'size': float(np.max(density) * 0.16),
             },
-            line={'color': 'rgba(99, 110, 250, 0.55)', 'width': 1.2},
+            colorscale=[
+                [0.0, 'rgba(99, 110, 250, 0.00)'],
+                [0.35, 'rgba(99, 110, 250, 0.00)'],
+                [0.60, 'rgba(99, 110, 250, 0.10)'],
+                [0.80, 'rgba(99, 110, 250, 0.18)'],
+                [1.0, 'rgba(99, 110, 250, 0.30)'],
+            ],
+            line={'color': POSTERIOR_CONTOUR_LINE_COLOR, 'width': 1.0},
             hoverinfo='skip',
             showscale=False,
             showlegend=False,
@@ -1111,40 +1125,62 @@ def _build_param_distribution_plot(
             values=values,
             trace_name='Posterior density',
         )
+        y_axis_range = None
         if density_trace is None:
+            histogram_density, _ = np.histogram(values, bins=50, density=True)
+            y_axis_range = self._posterior_density_axis_range(histogram_density)
             fig.add_trace(
                 go.Histogram(
                     x=values,
                     nbinsx=50,
                     histnorm='probability density',
-                    marker={'color': 'rgb(99, 110, 250)'},
+                    marker={'color': POSTERIOR_DENSITY_LINE_COLOR},
                     opacity=0.85,
-                    name='Posterior samples',
+                    name='Posterior density',
                 )
             )
         else:
+            density_trace.name = 'Posterior density'
+            density_trace.showlegend = True
             fig.add_trace(density_trace)
+            y_axis_range = self._posterior_density_axis_range(np.asarray(density_trace.y))
 
         median = float(np.median(values))
-        fig.add_vline(
-            x=median,
-            line={'color': 'rgb(99, 110, 250)', 'width': 2},
-            annotation_text='median',
-            annotation_position='top',
-        )
-        if summary is not None:
-            fig.add_vline(
-                x=summary.map_value,
-                line={'color': 'rgb(214, 39, 40)', 'width': 2, 'dash': 'dash'},
-                annotation_text='MAP',
-                annotation_position='top right',
+        if y_axis_range is not None:
+            fig.add_trace(
+                self._posterior_reference_line_trace(
+                    x_value=median,
+                    y_axis_range=y_axis_range,
+                    trace_name='Median',
+                    color=POSTERIOR_MEDIAN_LINE_COLOR,
+                    dash='dash',
+                )
             )
+            if summary is not None:
+                fig.add_trace(
+                    self._posterior_reference_line_trace(
+                        x_value=summary.map_value,
+                        y_axis_range=y_axis_range,
+                        trace_name='Max posterior',
+                        color=POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
+                        dash='dot',
+                    )
+                )
 
         fig.update_layout(
             title=f'Posterior distribution: {label}',
             xaxis_title=label,
             yaxis_title='Probability density',
+            legend={
+                'bgcolor': 'rgba(0, 0, 0, 0)',
+                'xanchor': 'right',
+                'x': 1.0,
+                'yanchor': 'top',
+                'y': 1.0,
+            },
         )
+        if y_axis_range is not None:
+            fig.update_yaxes(range=list(y_axis_range))
         return fig
 
     def _posterior_density_trace(
@@ -1175,14 +1211,54 @@ def _posterior_density_trace(
             x=grid,
             y=density,
             mode='lines',
-            line={'color': 'rgb(99, 110, 250)', 'width': 2},
+            line={'color': POSTERIOR_DENSITY_LINE_COLOR, 'width': 2},
             fill='tozeroy',
-            fillcolor='rgba(99, 110, 250, 0.22)',
+            fillcolor=POSTERIOR_DENSITY_FILL_COLOR,
             name=trace_name,
             showlegend=False,
             hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
         )
 
+    @staticmethod
+    def _posterior_density_axis_range(
+        density_values: np.ndarray,
+    ) -> tuple[float, float] | None:
+        """Return a padded y-axis range for posterior density plots."""
+        data = np.asarray(density_values, dtype=float)
+        data = data[np.isfinite(data)]
+        if data.size == 0:
+            return None
+
+        data_min = float(np.min(data))
+        data_max = float(np.max(data))
+        data_range = data_max - data_min
+        padding = 0.08 * data_range if data_range > 0 else max(abs(data_max), 1.0) * 0.05
+        if padding == 0:
+            padding = 1e-6
+        return data_min - padding, data_max + padding
+
+    @staticmethod
+    def _posterior_reference_line_trace(
+        *,
+        x_value: float,
+        y_axis_range: tuple[float, float],
+        trace_name: str,
+        color: str,
+        dash: str,
+    ) -> object:
+        """Return a named vertical reference line for posterior plots."""
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+
+        return go.Scatter(
+            x=[x_value, x_value],
+            y=[y_axis_range[0], y_axis_range[1]],
+            mode='lines',
+            line={'color': color, 'width': 2, 'dash': dash},
+            name=trace_name,
+            showlegend=True,
+            hovertemplate=f'{trace_name}: %{{x:.4f}}<extra></extra>',
+        )
+
     @staticmethod
     def _posterior_parameter_bounds(
         *,
@@ -1587,104 +1663,77 @@ def _get_posterior_samples_and_fit_results(
 
         return posterior_samples, fit_results
 
-    def _plot_posterior_predictive_band(
+    def _plot_posterior_predictive_summary(
         self,
         *,
         expt_name: str,
         summary: object,
         y_meas: np.ndarray,
         axes_labels: list[str],
+        show_band: bool,
+        show_draws: bool,
     ) -> None:
-        """Render a posterior predictive band plot using Plotly."""
+        """Render posterior predictive summaries using Plotly."""
         go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
 
         fig = go.Figure()
-        fig.add_trace(
-            go.Scatter(
-                x=summary.x,
-                y=summary.lower_95,
-                mode='lines',
-                line={'color': 'rgba(0, 0, 0, 0)'},
-                hoverinfo='skip',
-                showlegend=False,
-            )
-        )
-        fig.add_trace(
-            go.Scatter(
-                x=summary.x,
-                y=summary.upper_95,
-                mode='lines',
-                line={'color': 'rgba(0, 0, 0, 0)'},
-                fill='tonexty',
-                fillcolor='rgba(214, 39, 40, 0.18)',
-                name='Posterior predictive 95% CI',
-                hoverinfo='skip',
-            )
-        )
-        fig.add_trace(
-            go.Scatter(
-                x=summary.x,
-                y=summary.map_prediction,
-                mode='lines',
-                line={'color': 'rgb(214, 39, 40)', 'width': 2},
-                name='MAP prediction',
-            )
-        )
-        fig.add_trace(
-            go.Scatter(
-                x=summary.x,
-                y=y_meas,
-                mode='lines+markers',
-                line={'color': 'rgb(31, 119, 180)', 'width': 1.5},
-                name='Measured',
+        if show_band:
+            fig.add_trace(
+                go.Scatter(
+                    x=summary.x,
+                    y=summary.lower_95,
+                    mode='lines',
+                    line={'color': 'rgba(0, 0, 0, 0)'},
+                    hoverinfo='skip',
+                    showlegend=False,
+                )
             )
-        )
-        fig.update_layout(
-            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
-            xaxis_title=axes_labels[0],
-            yaxis_title=axes_labels[1],
-        )
-        fig.show()
-
-    def _plot_posterior_predictive_draws(
-        self,
-        *,
-        expt_name: str,
-        summary: object,
-        y_meas: np.ndarray,
-        axes_labels: list[str],
-    ) -> None:
-        """Render posterior predictive draws using Plotly."""
-        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
-
-        fig = go.Figure()
-        draws = getattr(summary, 'draws', None)
-        if draws is None:
-            log.warning('Posterior predictive draws are unavailable for plotting.')
-            return
-
-        draw_cap = min(len(draws), DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP)
-        for index in range(draw_cap):
             fig.add_trace(
                 go.Scatter(
                     x=summary.x,
-                    y=draws[index],
+                    y=summary.upper_95,
                     mode='lines',
-                    line={'color': 'rgba(120, 120, 120, 0.18)', 'width': 1},
-                    name='Posterior draw' if index == 0 else None,
-                    showlegend=index == 0,
+                    line={'color': 'rgba(0, 0, 0, 0)'},
+                    fill='tonexty',
+                    fillcolor='rgba(214, 39, 40, 0.18)',
+                    name='Posterior predictive 95% CI',
+                    hoverinfo='skip',
                 )
             )
 
+        if show_draws:
+            draws = getattr(summary, 'draws', None)
+            if draws is None:
+                log.warning('Posterior predictive draws are unavailable for plotting.')
+                return
+
+            draw_cap = min(len(draws), DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP)
+            for index in range(draw_cap):
+                fig.add_trace(
+                    go.Scatter(
+                        x=summary.x,
+                        y=draws[index],
+                        mode='lines',
+                        line={'color': POSTERIOR_DRAW_LINE_COLOR, 'width': 1},
+                        name='Posterior draw' if index == 0 else None,
+                        showlegend=index == 0,
+                    )
+                )
+
         fig.add_trace(
             go.Scatter(
                 x=summary.x,
                 y=summary.map_prediction,
                 mode='lines',
-                line={'color': 'rgb(214, 39, 40)', 'width': 2},
-                name='MAP prediction',
+                line={'color': POSTERIOR_POINT_ESTIMATE_LINE_COLOR, 'width': 2},
+                name='Max posterior prediction',
             )
         )
+        fig.update_layout(
+            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
+            xaxis_title=axes_labels[0],
+            yaxis_title=axes_labels[1],
+        )
         fig.add_trace(
             go.Scatter(
                 x=summary.x,
@@ -1694,11 +1743,6 @@ def _plot_posterior_predictive_draws(
                 name='Measured',
             )
         )
-        fig.update_layout(
-            title=f"Posterior predictive draws for experiment 🔬 '{expt_name}'",
-            xaxis_title=axes_labels[0],
-            yaxis_title=axes_labels[1],
-        )
         fig.show()
 
     @staticmethod
@@ -2352,27 +2396,6 @@ def _plot_powder_bragg_meas_vs_calc(
                 x_max=ctx['x_max'],
             )
 
-        predictive_summary = self._get_or_build_posterior_predictive_summary(
-            experiment=experiment,
-            expt_name=expt_name,
-            x_axis=ctx['x_axis'],
-        )
-        predictive_lower_95 = None
-        predictive_upper_95 = None
-        if predictive_summary is not None:
-            predictive_lower_95 = self._filtered_y_array(
-                predictive_summary.lower_95,
-                predictive_summary.x,
-                ctx['x_min'],
-                ctx['x_max'],
-            )
-            predictive_upper_95 = self._filtered_y_array(
-                predictive_summary.upper_95,
-                predictive_summary.x,
-                ctx['x_min'],
-                ctx['x_max'],
-            )
-
         plot_spec = PowderMeasVsCalcSpec(
             x=ctx['x_filtered'],
             y_meas=series.y_meas,
@@ -2385,8 +2408,6 @@ def _plot_powder_bragg_meas_vs_calc(
             bragg_peaks_height_fraction=DEFAULT_BRAGG_ROW,
             height=self._composite_plot_height(),
             y_bkg=series.y_bkg,
-            predictive_lower_95=predictive_lower_95,
-            predictive_upper_95=predictive_upper_95,
         )
         self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
 

From 6eb6725af44a7970f5d061d98c4e23999f8862da Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 09:01:46 +0200
Subject: [PATCH 023/106] Add posterior draws and log-posterior tracking

---
 .../analysis/fit_helpers/bayesian.py          |  14 +-
 .../analysis/fit_helpers/tracking.py          |  37 +++-
 .../analysis/minimizers/bumps_dream.py        |   8 +-
 src/easydiffraction/display/plotters/base.py  |   2 +
 .../display/plotters/plotly.py                |  85 +++++++--
 src/easydiffraction/display/plotting.py       | 164 ++++++++++++++++--
 tmp/bumps_dream/ed-2-bayesian-new-API.py      |  19 +-
 7 files changed, 282 insertions(+), 47 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 62d6cab3..2da05935 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -298,7 +298,9 @@ def display_results(
         if self.message:
             console.print(f'ℹ️ Status: {self.message}')
         console.print(f'🧪 Sampler: {self.sampler_name}')
-        console.print(f'🎯 Committed point estimate: {self.point_estimate_name}')
+        console.print(
+            f'🎯 Committed point estimate: {_format_point_estimate_name(self.point_estimate_name)}'
+        )
         console.print(f'🔁 Sampler completed: {self.sampler_completed}')
         console.print(f'⏱️ Fitting time: {_format_optional_float(self.fitting_time, suffix=" seconds")}')
         console.print(
@@ -494,6 +496,14 @@ def _format_sampler_settings(sampler_settings: dict[str, object]) -> str | None:
     return ', '.join(parts) if parts else None
 
 
+def _format_point_estimate_name(point_estimate_name: str) -> str:
+    """Return a user-facing label for the committed point estimate."""
+    normalized_name = point_estimate_name.strip().lower().replace('_', ' ')
+    if normalized_name == 'map':
+        return 'Max posterior'
+    return point_estimate_name.replace('_', ' ').title()
+
+
 def _format_convergence_summary(convergence_diagnostics: dict[str, object]) -> str | None:
     if not convergence_diagnostics:
         return None
@@ -527,7 +537,7 @@ def _render_committed_parameter_table(parameters: list[object]) -> None:
         'entry',
         'parameter',
         'start',
-        'map',
+        'max posterior',
         'uncertainty',
         'units',
         'change',
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 68ce2543..24d7ad5d 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -34,8 +34,8 @@
 TRACKING_MODE_SAMPLER = 'sampling'
 DEFAULT_HEADERS = ['iteration', 'χ²', 'change / status']
 DEFAULT_ALIGNMENTS = ['center', 'center', 'center']
-SAMPLER_HEADERS = ['iteration', 'progress', 'phase']
-SAMPLER_ALIGNMENTS = ['center', 'center', 'center']
+SAMPLER_HEADERS = ['iteration', 'progress', 'log posterior', 'phase']
+SAMPLER_ALIGNMENTS = ['center', 'center', 'center', 'center']
 
 
 class _TerminalLiveHandle:
@@ -112,6 +112,7 @@ def __init__(self) -> None:
         self._sampler_total_iterations: int | None = None
         self._last_sampler_phase: str | None = None
         self._last_sampler_progress_percent: float | None = None
+        self._last_sampler_log_posterior: float | None = None
 
         self._df_rows: list[list[str]] = []
         self._display_handle: object | None = None
@@ -132,6 +133,7 @@ def reset(self) -> None:
         self._sampler_total_iterations = None
         self._last_sampler_phase = None
         self._last_sampler_progress_percent = None
+        self._last_sampler_log_posterior = None
 
     def track(
         self,
@@ -157,6 +159,16 @@ def track(
 
         reduced_chi2 = calculate_reduced_chi_square(residuals, len(parameters))
 
+        if self._tracking_mode == TRACKING_MODE_SAMPLER:
+            if self._previous_chi2 is None:
+                self._previous_chi2 = reduced_chi2
+                self._best_chi2 = reduced_chi2
+            elif self._best_chi2 is None or reduced_chi2 < self._best_chi2:
+                self._best_chi2 = reduced_chi2
+
+            self._last_chi2 = reduced_chi2
+            return residuals
+
         row: list[str] = []
 
         # First iteration, initialize tracking
@@ -192,7 +204,7 @@ def track(
             self.add_tracking_info(row)
 
         # Update best chi-square if better
-        if reduced_chi2 < self._best_chi2:
+        if self._best_chi2 is None or reduced_chi2 < self._best_chi2:
             self._best_chi2 = reduced_chi2
             self._best_iteration = self._iteration
 
@@ -209,6 +221,7 @@ def track_sampler_progress(
         total_iterations: int,
         phase: str,
         progress_percent: float,
+        log_posterior: float,
         reduced_chi2: float,
         elapsed_time: float,
     ) -> None:
@@ -224,6 +237,8 @@ def track_sampler_progress(
             Current sampler phase, e.g. ``'burn-in'`` or ``'sampling'``.
         progress_percent : float
             Completed fraction expressed in percent.
+        log_posterior : float
+            Current best log-posterior implied by the sampler state.
         reduced_chi2 : float
             Best reduced chi-square implied by the current best sample.
         elapsed_time : float
@@ -238,6 +253,7 @@ def track_sampler_progress(
         previous_phase = self._last_sampler_phase
         self._last_sampler_phase = phase
         self._last_sampler_progress_percent = clamped_progress
+        self._last_sampler_log_posterior = log_posterior
 
         row: list[str] = []
         if self._previous_chi2 is None or self._best_chi2 is None:
@@ -248,6 +264,7 @@ def track_sampler_progress(
             row = [
                 f'{clamped_iteration}/{self._sampler_total_iterations}',
                 f'{clamped_progress:.1f}%',
+                f'{log_posterior:.2f}',
                 phase,
             ]
         else:
@@ -267,6 +284,7 @@ def track_sampler_progress(
                 row = [
                     f'{clamped_iteration}/{self._sampler_total_iterations}',
                     f'{clamped_progress:.1f}%',
+                    f'{log_posterior:.2f}',
                     phase,
                 ]
                 self._last_progress_time = elapsed_time
@@ -317,15 +335,15 @@ def start_tracking(self, minimizer_name: str, *, mode: str = TRACKING_MODE_FIT)
         mode : str, default='fit'
             Tracking mode for the run.
         """
+        self._tracking_mode = (
+            TRACKING_MODE_SAMPLER if mode == TRACKING_MODE_SAMPLER else TRACKING_MODE_FIT
+        )
+
         if self._verbosity is VerbosityEnum.SILENT:
             return
         if self._verbosity is VerbosityEnum.SHORT:
             return
 
-        self._tracking_mode = (
-            TRACKING_MODE_SAMPLER if mode == TRACKING_MODE_SAMPLER else TRACKING_MODE_FIT
-        )
-
         console.print(f"🚀 Starting fit process with '{minimizer_name}'...")
         if self._tracking_mode == TRACKING_MODE_SAMPLER:
             console.print('📈 Bayesian sampling progress:')
@@ -383,6 +401,11 @@ def finish_tracking(self) -> None:
                     f'{min(self._last_iteration, self._sampler_total_iterations)}/'
                     f'{self._sampler_total_iterations}',
                     f'{final_progress:.1f}%',
+                    (
+                        f'{self._last_sampler_log_posterior:.2f}'
+                        if self._last_sampler_log_posterior is not None
+                        else ''
+                    ),
                     self._last_sampler_phase or 'sampling',
                 ]
                 if not self._df_rows or self._df_rows[-1] != row:
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 801ab385..68fd3264 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -62,12 +62,14 @@ def __call__(self, history: object) -> None:
         """Forward sampler progress to the shared fit tracker."""
         step = int(history.step[0]) if history.step else 0
         generation = max(1, step)
-        reduced_chi2 = self._reduced_chi_square_from_nllf(float(history.value[0]))
+        nllf = float(history.value[0])
+        reduced_chi2 = self._reduced_chi_square_from_nllf(nllf)
         self._tracker.track_sampler_progress(
             iteration=generation,
             total_iterations=self._total_generations,
             phase=self._phase_name(generation),
             progress_percent=self._progress_percent(generation),
+            log_posterior=-nllf,
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
         )
@@ -78,12 +80,14 @@ def final(self, history: object, best: dict[str, object]) -> None:
             return
         step = int(history.step[0]) if history.step else 0
         generation = max(1, step)
-        reduced_chi2 = self._reduced_chi_square_from_nllf(float(best['value']))
+        best_nllf = float(best['value'])
+        reduced_chi2 = self._reduced_chi_square_from_nllf(best_nllf)
         self._tracker.track_sampler_progress(
             iteration=generation,
             total_iterations=self._total_generations,
             phase=self._phase_name(generation),
             progress_percent=self._progress_percent(generation),
+            log_posterior=-best_nllf,
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
         )
diff --git a/src/easydiffraction/display/plotters/base.py b/src/easydiffraction/display/plotters/base.py
index 85d09434..6f041161 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -62,6 +62,8 @@ class PowderMeasVsCalcSpec:
     y_bkg: np.ndarray | None = None
     predictive_lower_95: np.ndarray | None = None
     predictive_upper_95: np.ndarray | None = None
+    predictive_draws: np.ndarray | None = None
+    y_calc_name: str | None = None
 
 
 class XAxisType(StrEnum):
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 59bab8db..15e1ac11 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -67,6 +67,8 @@
 COMPOSITE_MARGIN_BOTTOM = 45
 PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.26)'
 PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
+PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
+PREDICTIVE_DRAW_PLOT_CAP = 50
 
 
 @dataclass(frozen=True)
@@ -463,12 +465,13 @@ def _powder_meas_vs_calc_hover_template(plot_spec: PowderMeasVsCalcSpec) -> str:
         """
         Return a shared hover template for composite powder traces.
         """
+        calc_label = plot_spec.y_calc_name or 'Icalc'
         if plot_spec.y_bkg is None:
             return (
                 'x: %{x:,.2f}<br>'
                 'Imeas: %{customdata[0]:,.2f}<br>'
-                'Icalc: %{customdata[1]:,.2f}<br>'
-                'Imeas - Icalc: %{customdata[2]:,.2f}'
+                f'{calc_label}: %{{customdata[1]:,.2f}}<br>'
+                f'Imeas - {calc_label}: %{{customdata[2]:,.2f}}'
                 '<extra></extra>'
             )
 
@@ -476,8 +479,8 @@ def _powder_meas_vs_calc_hover_template(plot_spec: PowderMeasVsCalcSpec) -> str:
             'x: %{x:,.2f}<br>'
             'Imeas: %{customdata[0]:,.2f}<br>'
             'Ibkg: %{customdata[1]:,.2f}<br>'
-            'Icalc: %{customdata[2]:,.2f}<br>'
-            'Imeas - Icalc: %{customdata[3]:,.2f}'
+            f'{calc_label}: %{{customdata[2]:,.2f}}<br>'
+            f'Imeas - {calc_label}: %{{customdata[3]:,.2f}}'
             '<extra></extra>'
         )
 
@@ -1172,6 +1175,18 @@ def _get_main_intensity_range(cls, plot_spec: PowderMeasVsCalcSpec) -> tuple[flo
             y_bkg = np.asarray(plot_spec.y_bkg)
             if y_bkg.size > 0:
                 main_series.append(y_bkg)
+        if plot_spec.predictive_lower_95 is not None:
+            lower_95 = np.asarray(plot_spec.predictive_lower_95)
+            if lower_95.size > 0:
+                main_series.append(lower_95)
+        if plot_spec.predictive_upper_95 is not None:
+            upper_95 = np.asarray(plot_spec.predictive_upper_95)
+            if upper_95.size > 0:
+                main_series.append(upper_95)
+        if plot_spec.predictive_draws is not None:
+            predictive_draws = np.asarray(plot_spec.predictive_draws)
+            if predictive_draws.ndim == 2 and predictive_draws.size > 0:
+                main_series.extend(predictive_draws)
 
         main_y_min = float(min(np.min(series) for series in main_series))
         main_y_max = float(max(np.max(series) for series in main_series))
@@ -1244,24 +1259,24 @@ def plot_powder_meas_vs_calc(
             fig.add_trace(lower_trace, row=1, col=1)
             fig.add_trace(upper_trace, row=1, col=1)
 
-        main_traces = (
-            (
-                ('meas', plot_spec.y_meas),
-                ('bkg', plot_spec.y_bkg),
-                ('calc', plot_spec.y_calc),
-            )
-            if plot_spec.y_bkg is not None
-            else (
-                ('meas', plot_spec.y_meas),
-                ('calc', plot_spec.y_calc),
-            )
+        fig.add_trace(
+            self._get_powder_trace(
+                plot_spec.x,
+                plot_spec.y_meas,
+                'meas',
+                customdata=hover_data,
+                hovertemplate=hover_template,
+            ),
+            row=1,
+            col=1,
         )
-        for label, y_values in main_traces:
+
+        if plot_spec.y_bkg is not None:
             fig.add_trace(
                 self._get_powder_trace(
                     plot_spec.x,
-                    y_values,
-                    label,
+                    plot_spec.y_bkg,
+                    'bkg',
                     customdata=hover_data,
                     hovertemplate=hover_template,
                 ),
@@ -1269,6 +1284,40 @@ def plot_powder_meas_vs_calc(
                 col=1,
             )
 
+        if plot_spec.predictive_draws is not None:
+            predictive_draws = np.asarray(plot_spec.predictive_draws)
+            if predictive_draws.ndim == 2 and predictive_draws.size > 0:
+                draw_cap = min(predictive_draws.shape[0], PREDICTIVE_DRAW_PLOT_CAP)
+                for index in range(draw_cap):
+                    fig.add_trace(
+                        go.Scatter(
+                            x=plot_spec.x,
+                            y=predictive_draws[index],
+                            mode='lines',
+                            line={'color': PREDICTIVE_DRAW_COLOR, 'width': 1},
+                            name='Posterior draw' if index == 0 else None,
+                            showlegend=index == 0,
+                            hovertemplate=(
+                                'Posterior draw<br>'
+                                'x: %{x:,.2f}<br>'
+                                'y: %{y:,.2f}<extra></extra>'
+                            ),
+                        ),
+                        row=1,
+                        col=1,
+                    )
+
+        calc_trace = self._get_powder_trace(
+            plot_spec.x,
+            plot_spec.y_calc,
+            'calc',
+            customdata=hover_data,
+            hovertemplate=hover_template,
+        )
+        if plot_spec.y_calc_name is not None:
+            calc_trace.name = plot_spec.y_calc_name
+        fig.add_trace(calc_trace, row=1, col=1)
+
         if layout.bragg_row is not None:
             for idx, tick_set in enumerate(plot_spec.bragg_tick_sets):
                 color = BRAGG_TICK_COLORS[idx % len(BRAGG_TICK_COLORS)]
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 4235f0c5..93d0b570 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -73,6 +73,8 @@ def description(self) -> str:
 DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP = 50
 POSTERIOR_DENSITY_LINE_COLOR = 'rgb(99, 110, 250)'
 POSTERIOR_DENSITY_FILL_COLOR = 'rgba(99, 110, 250, 0.22)'
+POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(140, 140, 140, 0.08)'
+POSTERIOR_INTERVAL_68_FILL_COLOR = 'rgba(140, 140, 140, 0.16)'
 POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(140, 140, 140)'
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
@@ -670,6 +672,11 @@ def plot_posterior_predictive(
         self,
         expt_name: str,
         style: str = 'band+draws',
+        x_min: float | None = None,
+        x_max: float | None = None,
+        *,
+        show_residual: bool | None = None,
+        x: object | None = None,
     ) -> None:
         """Plot posterior predictive curves for a powder experiment.
 
@@ -681,6 +688,14 @@ def plot_posterior_predictive(
             ``'band'`` shows the 95% credible interval,
             ``'draws'`` shows sampled predictive curves, and
             ``'band+draws'`` shows both together.
+        x_min : float | None, default=None
+            Lower bound for the x-axis range.
+        x_max : float | None, default=None
+            Upper bound for the x-axis range.
+        show_residual : bool | None, default=None
+            Whether to include the residual row in the composite plot.
+        x : object | None, default=None
+            Optional explicit x-axis data to override stored values.
         """
         if style not in {'band', 'draws', 'band+draws'}:
             msg = "style must be 'band', 'draws', or 'band+draws'."
@@ -694,12 +709,30 @@ def plot_posterior_predictive(
             log.warning('Posterior predictive plots currently require the Plotly backend.')
             return
 
+        self._update_project_categories(expt_name)
         experiment = self._project.experiments[expt_name]
-        x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(experiment.type, None)
+        x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(experiment.type, x)
         if sample_form != SampleFormEnum.POWDER:
             log.warning('Posterior predictive plots currently support powder experiments only.')
             return
 
+        plot_options = _MeasVsCalcPlotOptions(
+            x_min=x_min,
+            x_max=x_max,
+            show_residual=show_residual,
+            x=x,
+        )
+
+        if scattering_type == ScatteringTypeEnum.BRAGG:
+            self._plot_posterior_predictive_data(
+                experiment=experiment,
+                expt_name=expt_name,
+                plot_options=plot_options,
+                x_axis=x_axis,
+                style=style,
+            )
+            return
+
         summary = self._get_or_build_posterior_predictive_summary(
             experiment=experiment,
             expt_name=expt_name,
@@ -1109,14 +1142,16 @@ def _build_param_distribution_plot(
             fig.add_vrect(
                 x0=summary.interval_95[0],
                 x1=summary.interval_95[1],
-                fillcolor='rgba(99, 110, 250, 0.08)',
+                fillcolor=POSTERIOR_INTERVAL_95_FILL_COLOR,
                 line_width=0,
+                layer='below',
             )
             fig.add_vrect(
                 x0=summary.interval_68[0],
                 x1=summary.interval_68[1],
-                fillcolor='rgba(99, 110, 250, 0.18)',
+                fillcolor=POSTERIOR_INTERVAL_68_FILL_COLOR,
                 line_width=0,
+                layer='below',
             )
 
         density_trace = self._posterior_density_trace(
@@ -1720,6 +1755,15 @@ def _plot_posterior_predictive_summary(
                     )
                 )
 
+        fig.add_trace(
+            go.Scatter(
+                x=summary.x,
+                y=y_meas,
+                mode='lines+markers',
+                line={'color': 'rgb(31, 119, 180)', 'width': 1.5},
+                name='Measured',
+            )
+        )
         fig.add_trace(
             go.Scatter(
                 x=summary.x,
@@ -1734,16 +1778,114 @@ def _plot_posterior_predictive_summary(
             xaxis_title=axes_labels[0],
             yaxis_title=axes_labels[1],
         )
-        fig.add_trace(
-            go.Scatter(
-                x=summary.x,
-                y=y_meas,
-                mode='lines+markers',
-                line={'color': 'rgb(31, 119, 180)', 'width': 1.5},
-                name='Measured',
+        fig.show()
+
+    def _plot_posterior_predictive_data(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        plot_options: _MeasVsCalcPlotOptions,
+        x_axis: object,
+        style: str,
+    ) -> None:
+        """Render posterior predictive curves on the composite powder layout."""
+        pattern = intensity_category_for(experiment)
+        expt_type = experiment.type
+        ctx = self._prepare_powder_context(
+            pattern,
+            expt_name,
+            expt_type,
+            plot_options.x_min,
+            plot_options.x_max,
+            plot_options.x,
+        )
+        if ctx is None:
+            return
+
+        summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+        )
+        if summary is None:
+            return
+
+        y_meas = self._filtered_y_array(
+            pattern.intensity_meas,
+            ctx['x_array'],
+            ctx['x_min'],
+            ctx['x_max'],
+        )
+        y_bkg_raw = getattr(pattern, 'intensity_bkg', None)
+        y_bkg = (
+            self._filtered_y_array(y_bkg_raw, ctx['x_array'], ctx['x_min'], ctx['x_max'])
+            if y_bkg_raw is not None
+            else None
+        )
+        y_calc = self._filtered_y_array(summary.map_prediction, summary.x, ctx['x_min'], ctx['x_max'])
+        show_residual = True if plot_options.show_residual is None else plot_options.show_residual
+        y_resid = y_meas - y_calc if show_residual else None
+
+        predictive_lower_95 = None
+        predictive_upper_95 = None
+        if style in {'band', 'band+draws'}:
+            predictive_lower_95 = self._filtered_y_array(
+                summary.lower_95,
+                summary.x,
+                ctx['x_min'],
+                ctx['x_max'],
             )
+            predictive_upper_95 = self._filtered_y_array(
+                summary.upper_95,
+                summary.x,
+                ctx['x_min'],
+                ctx['x_max'],
+            )
+
+        predictive_draws = None
+        if style in {'draws', 'band+draws'}:
+            draws = getattr(summary, 'draws', None)
+            if draws is None:
+                log.warning('Posterior predictive draws are unavailable for plotting.')
+                return
+            predictive_draws = np.asarray(
+                [
+                    self._filtered_y_array(draw, summary.x, ctx['x_min'], ctx['x_max'])
+                    for draw in draws
+                ],
+                dtype=float,
+            )
+
+        if np.asarray(ctx['x_filtered']).size == 0:
+            bragg_tick_sets = ()
+        else:
+            bragg_tick_sets = self._extract_bragg_tick_sets(
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=ctx['x_axis'],
+                x_min=ctx['x_min'],
+                x_max=ctx['x_max'],
+            )
+
+        plot_spec = PowderMeasVsCalcSpec(
+            x=ctx['x_filtered'],
+            y_meas=y_meas,
+            y_calc=y_calc,
+            y_resid=y_resid,
+            bragg_tick_sets=bragg_tick_sets,
+            axes_labels=ctx['axes_labels'],
+            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
+            residual_height_fraction=DEFAULT_RESID_HEIGHT,
+            bragg_peaks_height_fraction=DEFAULT_BRAGG_ROW,
+            height=self._composite_plot_height(),
+            y_bkg=y_bkg,
+            predictive_lower_95=predictive_lower_95,
+            predictive_upper_95=predictive_upper_95,
+            predictive_draws=predictive_draws,
+            y_calc_name='Max posterior prediction',
         )
-        fig.show()
+        self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
 
     @staticmethod
     def _resolve_posterior_parameter_names(
diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index 7c8dc20b..a2c0986e 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -58,7 +58,7 @@
     fract_z=0,
     wyckoff_letter='a',
     adp_type='Biso',
-    adp_iso=0.5,
+    adp_iso=0.5151,
     occupancy=0.5,
 )
 structure.atom_sites.create(
@@ -69,7 +69,7 @@
     fract_z=0,
     wyckoff_letter='a',
     adp_type='Biso',
-    adp_iso=0.5,
+    adp_iso=0.5151,
     occupancy=0.5,
 )
 structure.atom_sites.create(
@@ -80,7 +80,7 @@
     fract_z=0.5,
     wyckoff_letter='b',
     adp_type='Biso',
-    adp_iso=0.5,
+    adp_iso=0.2190,
 )
 structure.atom_sites.create(
     label='O',
@@ -90,7 +90,7 @@
     fract_z=0.5,
     wyckoff_letter='c',
     adp_type='Biso',
-    adp_iso=0.5,
+    adp_iso=1.3916,
 )
 
 # %% [markdown]
@@ -130,7 +130,7 @@
 
 # %%
 experiment.excluded_regions.create(id='1', start=0, end=20)
-experiment.excluded_regions.create(id='2', start=160, end=180)
+experiment.excluded_regions.create(id='2', start=105, end=180)
 
 # %%
 experiment.linked_phases.create(id='lbco', scale=9.1351)
@@ -158,6 +158,9 @@
 # %%
 project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
 
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=100, x_max=102)
+
 # %% [markdown]
 # ## Step 5: Perform Bayesian Analysis
 
@@ -199,6 +202,9 @@
 # %%
 project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
 
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=100, x_max=102)
+
 # %%
 project.display.plotter.plot_posterior_pairs()
 
@@ -207,5 +213,4 @@
 project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt', style='band')
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt', style='draws')
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')

From 2f592c87b1c2dde4b36a06be2176a5042b491af1 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 10:32:14 +0200
Subject: [PATCH 024/106] Use population mean log-posterior for progress
 tracking

---
 .../analysis/minimizers/bumps_dream.py        | 34 ++++++++++++++-----
 tmp/bumps_dream/ed-2-bayesian-new-API.py      |  5 +++
 2 files changed, 31 insertions(+), 8 deletions(-)

diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 68fd3264..f3f33f4d 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -56,7 +56,7 @@ def __init__(
 
     def config_history(self, history: object) -> None:
         """Declare the history fields needed for progress updates."""
-        history.requires(time=1, step=1, value=1)
+        history.requires(time=1, step=1, value=1, population_values=1)
 
     def __call__(self, history: object) -> None:
         """Forward sampler progress to the shared fit tracker."""
@@ -64,12 +64,13 @@ def __call__(self, history: object) -> None:
         generation = max(1, step)
         nllf = float(history.value[0])
         reduced_chi2 = self._reduced_chi_square_from_nllf(nllf)
+        log_posterior = self._population_mean_log_posterior(history)
         self._tracker.track_sampler_progress(
             iteration=generation,
             total_iterations=self._total_generations,
             phase=self._phase_name(generation),
             progress_percent=self._progress_percent(generation),
-            log_posterior=-nllf,
+            log_posterior=log_posterior,
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
         )
@@ -87,7 +88,7 @@ def final(self, history: object, best: dict[str, object]) -> None:
             total_iterations=self._total_generations,
             phase=self._phase_name(generation),
             progress_percent=self._progress_percent(generation),
-            log_posterior=-best_nllf,
+            log_posterior=self._population_mean_log_posterior(history),
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
         )
@@ -104,6 +105,19 @@ def _progress_percent(self, generation: int) -> float:
         clamped_generation = min(generation, self._total_generations)
         return 100.0 * clamped_generation / self._total_generations
 
+    @staticmethod
+    def _population_mean_log_posterior(history: object) -> float:
+        """Return the current mean log-posterior across the walker population."""
+        population_values = history.population_values[0] if history.population_values else None
+        if population_values is None:
+            return -float(history.value[0])
+
+        nllf_values = np.asarray(population_values, dtype=float)
+        finite_mask = np.isfinite(nllf_values)
+        if not np.any(finite_mask):
+            return -float(history.value[0])
+        return float(np.mean(-nllf_values[finite_mask]))
+
     def _reduced_chi_square_from_nllf(self, nllf: float) -> float:
         """Convert DREAM's negative log-likelihood to reduced chi-square."""
         dof = self._n_points - self._n_parameters
@@ -491,12 +505,16 @@ def _sync_result_to_parameters(
         raw_result : object
             DREAM result object.
         """
-        if getattr(raw_result, 'success', False):
-            values = raw_result.x
-            uncertainties = getattr(raw_result, 'dx', None)
+        if hasattr(raw_result, 'x'):
+            if getattr(raw_result, 'success', False):
+                values = raw_result.x
+                uncertainties = getattr(raw_result, 'dx', None)
+            else:
+                values = getattr(raw_result, 'starting_values', raw_result.x)
+                uncertainties = getattr(raw_result, 'starting_uncertainties', None)
         else:
-            values = getattr(raw_result, 'starting_values', None)
-            uncertainties = getattr(raw_result, 'starting_uncertainties', None)
+            values = raw_result
+            uncertainties = None
 
         if values is None:
             return
diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index a2c0986e..6ebdb441 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -214,3 +214,8 @@
 
 # %%
 project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+
+# %%
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=100, x_max=102)
+
+# %%

From 4dbc84def07f58cf1a3d37c92240be33fd994ed4 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 11:26:41 +0200
Subject: [PATCH 025/106] Polish Bayesian progress and posterior plots

---
 .../analysis/fit_helpers/tracking.py          | 89 +++++++++++++++--
 src/easydiffraction/display/plotting.py       | 99 ++++++++++++-------
 2 files changed, 148 insertions(+), 40 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 24d7ad5d..39328dcc 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -32,10 +32,10 @@
 SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0
 TRACKING_MODE_FIT = 'fit'
 TRACKING_MODE_SAMPLER = 'sampling'
-DEFAULT_HEADERS = ['iteration', 'χ²', 'change / status']
-DEFAULT_ALIGNMENTS = ['center', 'center', 'center']
-SAMPLER_HEADERS = ['iteration', 'progress', 'log posterior', 'phase']
-SAMPLER_ALIGNMENTS = ['center', 'center', 'center', 'center']
+DEFAULT_HEADERS = ['iteration', 'time (s)', 'χ²', 'change / status']
+DEFAULT_ALIGNMENTS = ['center', 'center', 'center', 'center']
+SAMPLER_HEADERS = ['iteration', 'progress', 'time (s)', 'log posterior', 'phase']
+SAMPLER_ALIGNMENTS = ['center', 'center', 'center', 'center', 'center']
 
 
 class _TerminalLiveHandle:
@@ -106,6 +106,8 @@ def __init__(self) -> None:
         self._best_chi2: float | None = None
         self._best_iteration: int | None = None
         self._fitting_time: float | None = None
+        self._start_time: float | None = None
+        self._end_time: float | None = None
         self._verbosity: VerbosityEnum = VerbosityEnum.FULL
         self._last_progress_time: float | None = None
         self._tracking_mode: str = TRACKING_MODE_FIT
@@ -113,6 +115,7 @@ def __init__(self) -> None:
         self._last_sampler_phase: str | None = None
         self._last_sampler_progress_percent: float | None = None
         self._last_sampler_log_posterior: float | None = None
+        self._last_sampler_elapsed_time: float | None = None
 
         self._df_rows: list[list[str]] = []
         self._display_handle: object | None = None
@@ -128,12 +131,15 @@ def reset(self) -> None:
         self._best_chi2 = None
         self._best_iteration = None
         self._fitting_time = None
+        self._start_time = None
+        self._end_time = None
         self._last_progress_time = None
         self._tracking_mode = TRACKING_MODE_FIT
         self._sampler_total_iterations = None
         self._last_sampler_phase = None
         self._last_sampler_progress_percent = None
         self._last_sampler_log_posterior = None
+        self._last_sampler_elapsed_time = None
 
     def track(
         self,
@@ -179,6 +185,7 @@ def track(
 
             row = [
                 str(self._iteration),
+                self._format_elapsed_time(),
                 f'{reduced_chi2:.2f}',
                 '',
             ]
@@ -193,6 +200,7 @@ def track(
 
                 row = [
                     str(self._iteration),
+                    self._format_elapsed_time(),
                     f'{reduced_chi2:.2f}',
                     f'{change_in_percent:.1f}% ↓',
                 ]
@@ -254,6 +262,7 @@ def track_sampler_progress(
         self._last_sampler_phase = phase
         self._last_sampler_progress_percent = clamped_progress
         self._last_sampler_log_posterior = log_posterior
+        self._last_sampler_elapsed_time = elapsed_time
 
         row: list[str] = []
         if self._previous_chi2 is None or self._best_chi2 is None:
@@ -264,6 +273,7 @@ def track_sampler_progress(
             row = [
                 f'{clamped_iteration}/{self._sampler_total_iterations}',
                 f'{clamped_progress:.1f}%',
+                self._format_elapsed_time(elapsed_time),
                 f'{log_posterior:.2f}',
                 phase,
             ]
@@ -284,6 +294,7 @@ def track_sampler_progress(
                 row = [
                     f'{clamped_iteration}/{self._sampler_total_iterations}',
                     f'{clamped_progress:.1f}%',
+                    self._format_elapsed_time(elapsed_time),
                     f'{log_posterior:.2f}',
                     phase,
                 ]
@@ -318,9 +329,13 @@ def fitting_time(self) -> float | None:
     def start_timer(self) -> None:
         """Begin timing of a fit run."""
         self._start_time = time.perf_counter()
+        self._end_time = None
 
     def stop_timer(self) -> None:
         """Stop timing and store elapsed time for the run."""
+        if self._start_time is None:
+            self._fitting_time = None
+            return
         self._end_time = time.perf_counter()
         self._fitting_time = self._end_time - self._start_time
 
@@ -401,6 +416,11 @@ def finish_tracking(self) -> None:
                     f'{min(self._last_iteration, self._sampler_total_iterations)}/'
                     f'{self._sampler_total_iterations}',
                     f'{final_progress:.1f}%',
+                    self._format_elapsed_time(
+                        self._fitting_time
+                        if self._fitting_time is not None
+                        else self._last_sampler_elapsed_time
+                    ),
                     (
                         f'{self._last_sampler_log_posterior:.2f}'
                         if self._last_sampler_log_posterior is not None
@@ -408,15 +428,24 @@ def finish_tracking(self) -> None:
                     ),
                     self._last_sampler_phase or 'sampling',
                 ]
-                if not self._df_rows or self._df_rows[-1] != row:
+                if not self._df_rows:
+                    self.add_tracking_info(row)
+                elif self._rows_match_on_columns(self._df_rows[-1], row, (0, 1, 3, 4)):
+                    self._replace_last_tracking_row(row)
+                elif self._df_rows[-1] != row:
                     self.add_tracking_info(row)
         elif self._last_iteration is not None:
             row: list[str] = [
                 str(self._last_iteration),
+                self._format_elapsed_time(self._fitting_time),
                 f'{self._last_chi2:.2f}' if self._last_chi2 is not None else '',
                 '',
             ]
-            if not self._df_rows or self._df_rows[-1][:2] != row[:2]:
+            if not self._df_rows:
+                self.add_tracking_info(row)
+            elif self._rows_match_on_columns(self._df_rows[-1], row, (0, 2)):
+                self._replace_last_tracking_row(row)
+            elif self._df_rows[-1][:3] != row[:3]:
                 self.add_tracking_info(row)
 
         if self._verbosity is not VerbosityEnum.FULL:
@@ -449,3 +478,51 @@ def _alignments(self) -> list[str]:
         if self._tracking_mode == TRACKING_MODE_SAMPLER:
             return SAMPLER_ALIGNMENTS
         return DEFAULT_ALIGNMENTS
+
+    def _current_elapsed_time(self) -> float | None:
+        """Return elapsed run time in seconds when timing is active."""
+        if self._start_time is None:
+            return None
+
+        end_time = self._end_time if self._end_time is not None else time.perf_counter()
+        return max(end_time - self._start_time, 0.0)
+
+    def _format_elapsed_time(self, elapsed_time: float | None = None) -> str:
+        """Format elapsed time in seconds with two decimal places."""
+        resolved_time = elapsed_time
+        if resolved_time is None:
+            resolved_time = self._current_elapsed_time()
+        if resolved_time is None:
+            return ''
+        return f'{resolved_time:.2f}'
+
+    @staticmethod
+    def _rows_match_on_columns(
+        current_row: list[str],
+        new_row: list[str],
+        column_indices: tuple[int, ...],
+    ) -> bool:
+        """Return whether two tracking rows match on selected columns."""
+        return all(
+            len(current_row) > index
+            and len(new_row) > index
+            and current_row[index] == new_row[index]
+            for index in column_indices
+        )
+
+    def _replace_last_tracking_row(self, row: list[str]) -> None:
+        """Replace the last rendered tracking row and refresh the view."""
+        if not self._df_rows:
+            self.add_tracking_info(row)
+            return
+
+        self._df_rows[-1] = row
+        if self._verbosity is not VerbosityEnum.FULL:
+            return
+
+        render_table(
+            columns_headers=self._headers(),
+            columns_alignment=self._alignments(),
+            columns_data=self._df_rows,
+            display_handle=self._display_handle,
+        )
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 93d0b570..ba592bda 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -573,6 +573,7 @@ def plot_param_correlations(
         self,
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
         precision: int = 2,
+        show_diagonal: bool = False,
     ) -> None:
         """
         Plot the parameter correlation matrix from the latest fit.
@@ -581,8 +582,10 @@ def plot_param_correlations(
         the active engine is Plotly, an interactive heatmap is shown.
         Otherwise, a rounded correlation table is rendered.
 
-        Only the lower triangle is shown (without the diagonal), since
-        the matrix is symmetric and diagonal values are always ``1``.
+        By default only the lower triangle is shown (without the
+        diagonal), since the matrix is symmetric and diagonal values are
+        always ``1``. Set ``show_diagonal=True`` to keep blank diagonal
+        cells for a square lower-triangle layout.
 
         Parameters
         ----------
@@ -594,6 +597,9 @@ def plot_param_correlations(
             matrix.
         precision : int, default=2
             Number of decimal places to show in the table fallback.
+        show_diagonal : bool, default=False
+            Whether to retain blank diagonal cells in the displayed
+            lower-triangle matrix.
         """
         corr_df = self._get_param_correlation_dataframe()
         if corr_df is None:
@@ -612,6 +618,7 @@ def plot_param_correlations(
         display_corr_df, row_numbers, col_numbers = self._trim_correlation_display_dataframe(
             corr_df,
             preserve_all_rows=not is_graphical,
+            show_diagonal=show_diagonal,
         )
 
         if is_graphical:
@@ -649,7 +656,7 @@ def plot_posterior_pairs(
         plot = self._build_posterior_pairs_plot(parameters=parameters)
         if plot is None:
             return
-        plot.show()
+        self._show_plot_figure(plot)
 
     def plot_param_distribution(
         self,
@@ -666,7 +673,7 @@ def plot_param_distribution(
         plot = self._build_param_distribution_plot(param)
         if plot is None:
             return
-        plot.show()
+        self._show_plot_figure(plot)
 
     def plot_posterior_predictive(
         self,
@@ -831,6 +838,7 @@ def _trim_correlation_display_dataframe(
         corr_df: pd.DataFrame,
         *,
         preserve_all_rows: bool,
+        show_diagonal: bool,
     ) -> tuple[pd.DataFrame, list[int], list[int]]:
         """
         Trim empty outer rows/columns from the lower-triangle view.
@@ -845,6 +853,8 @@ def _trim_correlation_display_dataframe(
         preserve_all_rows : bool
             Whether to keep the full row list so row labels continue to
             identify all numeric column headers in tabular output.
+        show_diagonal : bool
+            Whether blank diagonal cells should remain visible.
 
         Returns
         -------
@@ -856,7 +866,7 @@ def _trim_correlation_display_dataframe(
         row_numbers = list(range(1, num_rows + 1))
         col_numbers = list(range(1, num_cols + 1))
 
-        if min(num_rows, num_cols) <= 1:
+        if show_diagonal or min(num_rows, num_cols) <= 1:
             return corr_df, row_numbers, col_numbers
 
         if preserve_all_rows:
@@ -1136,8 +1146,13 @@ def _build_param_distribution_plot(
         values = samples[:, 0]
         label = self._posterior_plot_labels(fit_results, [parameter_name])[0]
         summary = self._posterior_summary_by_name(fit_results).get(parameter_name)
+        title = f'Posterior distribution: {label}'
+        layout_factory = getattr(self._backend, '_get_layout', None)
+        if callable(layout_factory):
+            fig = go.Figure(layout=layout_factory(title, [label, 'Probability density']))
+        else:
+            fig = go.Figure()
 
-        fig = go.Figure()
         if summary is not None:
             fig.add_vrect(
                 x0=summary.interval_95[0],
@@ -1154,31 +1169,36 @@ def _build_param_distribution_plot(
                 layer='below',
             )
 
+        histogram_density, _ = np.histogram(values, bins=50, density=True)
+        fig.add_trace(
+            go.Histogram(
+                x=values,
+                nbinsx=50,
+                histnorm='probability density',
+                marker={
+                    'color': 'rgba(140, 140, 140, 0.28)',
+                    'line': {'color': 'rgba(140, 140, 140, 0.18)', 'width': 1},
+                },
+                opacity=0.65,
+                name='Posterior histogram',
+                hovertemplate='sample=%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+            )
+        )
+
         density_trace = self._posterior_density_trace(
             fit_results=fit_results,
             parameter_name=parameter_name,
             values=values,
             trace_name='Posterior density',
         )
-        y_axis_range = None
-        if density_trace is None:
-            histogram_density, _ = np.histogram(values, bins=50, density=True)
-            y_axis_range = self._posterior_density_axis_range(histogram_density)
-            fig.add_trace(
-                go.Histogram(
-                    x=values,
-                    nbinsx=50,
-                    histnorm='probability density',
-                    marker={'color': POSTERIOR_DENSITY_LINE_COLOR},
-                    opacity=0.85,
-                    name='Posterior density',
-                )
-            )
-        else:
+        density_sources = [histogram_density]
+        if density_trace is not None:
             density_trace.name = 'Posterior density'
             density_trace.showlegend = True
             fig.add_trace(density_trace)
-            y_axis_range = self._posterior_density_axis_range(np.asarray(density_trace.y))
+            density_sources.append(np.asarray(density_trace.y, dtype=float))
+
+        y_axis_range = self._posterior_density_axis_range(np.concatenate(density_sources))
 
         median = float(np.median(values))
         if y_axis_range is not None:
@@ -1202,22 +1222,33 @@ def _build_param_distribution_plot(
                     )
                 )
 
-        fig.update_layout(
-            title=f'Posterior distribution: {label}',
-            xaxis_title=label,
-            yaxis_title='Probability density',
-            legend={
-                'bgcolor': 'rgba(0, 0, 0, 0)',
-                'xanchor': 'right',
-                'x': 1.0,
-                'yanchor': 'top',
-                'y': 1.0,
-            },
-        )
+        if callable(layout_factory):
+            fig.update_layout(title={'text': title})
+        else:
+            fig.update_layout(
+                title=title,
+                xaxis_title=label,
+                yaxis_title='Probability density',
+                legend={
+                    'bgcolor': 'rgba(0, 0, 0, 0)',
+                    'xanchor': 'right',
+                    'x': 1.0,
+                    'yanchor': 'top',
+                    'y': 1.0,
+                },
+            )
         if y_axis_range is not None:
             fig.update_yaxes(range=list(y_axis_range))
         return fig
 
+    def _show_plot_figure(self, figure: object) -> None:
+        """Display a figure through the active backend when possible."""
+        show_figure = getattr(self._backend, '_show_figure', None)
+        if callable(show_figure):
+            show_figure(figure)
+            return
+        figure.show()
+
     def _posterior_density_trace(
         self,
         *,

From 16f9fc65ee3b253ac9c1bb79de40550277e8d8a2 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 12:09:14 +0200
Subject: [PATCH 026/106] Fix DREAM diagnostics and posterior KDEs

---
 .../analysis/fit_helpers/bayesian.py          |  41 +++++-
 .../analysis/fit_helpers/tracking.py          |   6 +-
 .../analysis/minimizers/bumps_dream.py        |  70 ++++++++++
 src/easydiffraction/display/plotting.py       | 125 ++++++++++++++----
 4 files changed, 210 insertions(+), 32 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 2da05935..a85b3fbc 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -17,6 +17,7 @@
 from easydiffraction.analysis.fit_helpers.reporting import _format_optional_float
 from easydiffraction.analysis.fit_helpers.reporting import FitResults
 from easydiffraction.utils.logging import console
+from easydiffraction.utils.logging import log
 from easydiffraction.utils.utils import render_table
 
 R_HAT_CONVERGENCE_THRESHOLD = 1.01
@@ -338,6 +339,12 @@ def display_results(
 
         self._print_table_notes()
 
+    def _print_table_notes(self) -> None:
+        """Print parameter and posterior-diagnostic notes below tables."""
+        super()._print_table_notes()
+        for note in _posterior_table_notes(self.posterior_parameter_summaries):
+            log.warning(note)
+
 
 def compute_convergence_diagnostics(posterior_samples: PosteriorSamples) -> dict[str, object]:
     """Compute convergence diagnostics from posterior samples.
@@ -511,7 +518,7 @@ def _format_convergence_summary(convergence_diagnostics: dict[str, object]) -> s
     parts: list[str] = []
     converged = convergence_diagnostics.get('converged')
     if converged is not None:
-        status = 'yes' if converged else '[yellow]check diagnostics[/yellow]'
+        status = 'yes' if converged else '[red]failed[/red]'
         parts.append(f'converged={status}')
 
     max_r_hat = _maybe_scalar(convergence_diagnostics.get('max_r_hat'))
@@ -643,7 +650,7 @@ def _format_r_hat(value: float | None) -> str:
         return 'N/A'
     formatted = f'{value:.3f}'
     if value > R_HAT_CONVERGENCE_THRESHOLD:
-        return f'[yellow]{formatted}[/yellow]'
+        return f'[red]{formatted}[/red]'
     return formatted
 
 
@@ -652,5 +659,31 @@ def _format_ess_bulk(value: float | None) -> str:
         return 'N/A'
     formatted = f'{value:.1f}'
     if value < ESS_BULK_CONVERGENCE_THRESHOLD:
-        return f'[yellow]{formatted}[/yellow]'
-    return formatted
\ No newline at end of file
+        return f'[red]{formatted}[/red]'
+    return formatted
+
+
+def _posterior_table_notes(
+    posterior_parameter_summaries: list[PosteriorParameterSummary],
+) -> list[str]:
+    """Return warning notes for posterior summary diagnostics."""
+    if not posterior_parameter_summaries:
+        return []
+
+    has_failed_r_hat = any(
+        summary.r_hat is not None and summary.r_hat > R_HAT_CONVERGENCE_THRESHOLD
+        for summary in posterior_parameter_summaries
+    )
+    has_failed_ess_bulk = any(
+        summary.ess_bulk is not None and summary.ess_bulk < ESS_BULK_CONVERGENCE_THRESHOLD
+        for summary in posterior_parameter_summaries
+    )
+
+    if not has_failed_r_hat and not has_failed_ess_bulk:
+        return []
+
+    return [
+        '[red]Convergence warning:[/red] posterior diagnostics failed '
+        '(r_hat > 1.01 or ess_bulk < 400). Consider longer sampling, '
+        'tighter bounds, or reparameterization.'
+    ]
\ No newline at end of file
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index 39328dcc..d85a705e 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -232,6 +232,7 @@ def track_sampler_progress(
         log_posterior: float,
         reduced_chi2: float,
         elapsed_time: float,
+        force_report: bool = False,
     ) -> None:
         """Update progress from a sampler monitor.
 
@@ -251,6 +252,8 @@ def track_sampler_progress(
             Best reduced chi-square implied by the current best sample.
         elapsed_time : float
             Elapsed wall time in seconds.
+        force_report : bool, default=False
+            Whether to render the row regardless of heartbeat timing.
         """
         self._iteration = iteration
         self._tracking_mode = TRACKING_MODE_SAMPLER
@@ -285,7 +288,8 @@ def track_sampler_progress(
             if (
                 iteration != self._last_reported_iteration
                 and (
-                    previous_phase != phase
+                    force_report
+                    or previous_phase != phase
                     or self._last_progress_time is None
                     or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
                     or clamped_iteration >= self._sampler_total_iterations
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index f3f33f4d..2ea64fe1 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -34,6 +34,8 @@
 DEFAULT_ALPHA = 0.0
 DEFAULT_OUTLIER_TEST = 'none'
 DEFAULT_TRIM = False
+BURN_IN_PROGRESS_POINTS = 5
+SAMPLING_PROGRESS_POINTS = 20
 
 
 class _DreamProgressMonitor(bumps_monitor.Monitor):
@@ -53,6 +55,18 @@ def __init__(
         self._n_parameters = n_parameters
         self._total_generations = max(1, total_generations)
         self._burn_steps = max(0, burn_steps)
+        self._burn_targets = self._progress_targets(
+            start=1,
+            stop=self._burn_steps,
+            target_count=BURN_IN_PROGRESS_POINTS,
+        )
+        self._sampling_targets = self._progress_targets(
+            start=self._burn_steps + 1,
+            stop=self._total_generations,
+            target_count=SAMPLING_PROGRESS_POINTS,
+        )
+        self._next_burn_target_index = 0
+        self._next_sampling_target_index = 0
 
     def config_history(self, history: object) -> None:
         """Declare the history fields needed for progress updates."""
@@ -62,6 +76,8 @@ def __call__(self, history: object) -> None:
         """Forward sampler progress to the shared fit tracker."""
         step = int(history.step[0]) if history.step else 0
         generation = max(1, step)
+        if not self._should_report(generation):
+            return
         nllf = float(history.value[0])
         reduced_chi2 = self._reduced_chi_square_from_nllf(nllf)
         log_posterior = self._population_mean_log_posterior(history)
@@ -73,6 +89,7 @@ def __call__(self, history: object) -> None:
             log_posterior=log_posterior,
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
+            force_report=True,
         )
 
     def final(self, history: object, best: dict[str, object]) -> None:
@@ -91,8 +108,61 @@ def final(self, history: object, best: dict[str, object]) -> None:
             log_posterior=self._population_mean_log_posterior(history),
             reduced_chi2=reduced_chi2,
             elapsed_time=float(history.time[0]),
+            force_report=True,
+        )
+
+    @staticmethod
+    def _progress_targets(
+        *,
+        start: int,
+        stop: int,
+        target_count: int,
+    ) -> list[int]:
+        """Return monotonically increasing reporting targets for one phase."""
+        if target_count < 1 or stop < start:
+            return []
+
+        targets = np.linspace(start, stop, num=target_count)
+        rounded = np.rint(targets).astype(int)
+        unique_targets = sorted(set(int(value) for value in rounded if start <= value <= stop))
+        if start not in unique_targets:
+            unique_targets.insert(0, start)
+        if stop not in unique_targets:
+            unique_targets.append(stop)
+        return unique_targets
+
+    def _should_report(self, generation: int) -> bool:
+        """Return whether the current generation should be rendered."""
+        clamped_generation = min(max(1, generation), self._total_generations)
+        if self._phase_name(clamped_generation) == 'burn-in':
+            return self._consume_progress_target(
+                clamped_generation,
+                phase_targets=self._burn_targets,
+                target_index_name='_next_burn_target_index',
+            )
+
+        return self._consume_progress_target(
+            clamped_generation,
+            phase_targets=self._sampling_targets,
+            target_index_name='_next_sampling_target_index',
         )
 
+    def _consume_progress_target(
+        self,
+        generation: int,
+        *,
+        phase_targets: list[int],
+        target_index_name: str,
+    ) -> bool:
+        """Advance a phase target pointer when the generation reaches it."""
+        target_index = getattr(self, target_index_name)
+        should_report = False
+        while target_index < len(phase_targets) and generation >= phase_targets[target_index]:
+            target_index += 1
+            should_report = True
+        setattr(self, target_index_name, target_index)
+        return should_report
+
     def _phase_name(self, generation: int) -> str:
         """Return the current sampler phase name."""
         clamped_generation = min(generation, self._total_generations)
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index ba592bda..38ddc174 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1168,6 +1168,18 @@ def _build_param_distribution_plot(
                 line_width=0,
                 layer='below',
             )
+            fig.add_trace(
+                self._posterior_interval_legend_trace(
+                    trace_name='95% credible interval',
+                    color=POSTERIOR_INTERVAL_95_FILL_COLOR,
+                )
+            )
+            fig.add_trace(
+                self._posterior_interval_legend_trace(
+                    trace_name='68% credible interval',
+                    color=POSTERIOR_INTERVAL_68_FILL_COLOR,
+                )
+            )
 
         histogram_density, _ = np.histogram(values, bins=50, density=True)
         fig.add_trace(
@@ -1298,10 +1310,30 @@ def _posterior_density_axis_range(
         data_min = float(np.min(data))
         data_max = float(np.max(data))
         data_range = data_max - data_min
-        padding = 0.08 * data_range if data_range > 0 else max(abs(data_max), 1.0) * 0.05
-        if padding == 0:
-            padding = 1e-6
-        return data_min - padding, data_max + padding
+        upper_padding = 0.08 * data_range if data_range > 0 else max(abs(data_max), 1.0) * 0.05
+        if upper_padding == 0:
+            upper_padding = 1e-6
+        lower = 0.0 if data_min >= 0.0 else data_min
+        return lower, data_max + upper_padding
+
+    @staticmethod
+    def _posterior_interval_legend_trace(
+        *,
+        trace_name: str,
+        color: str,
+    ) -> object:
+        """Return a legend-only proxy trace for a credible interval."""
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+
+        return go.Scatter(
+            x=[None],
+            y=[None],
+            mode='markers',
+            marker={'size': 12, 'symbol': 'square', 'color': color},
+            name=trace_name,
+            showlegend=True,
+            hoverinfo='skip',
+        )
 
     @staticmethod
     def _posterior_reference_line_trace(
@@ -1433,12 +1465,13 @@ def _posterior_density_curve(
             density = np.exp(-0.5 * ((grid - data[0]) / bandwidth) ** 2)
             density /= bandwidth * np.sqrt(2.0 * np.pi)
         else:
-            reflected_data = cls._reflected_density_samples(
-                data,
+            kde = gaussian_kde(data)
+            density = cls._evaluate_reflected_1d_kde(
+                kde,
+                grid,
                 lower_bound=lower_bound,
                 upper_bound=upper_bound,
             )
-            density = np.asarray(gaussian_kde(reflected_data)(grid), dtype=float)
 
         area = np.trapezoid(density, grid)
         if area <= 0:
@@ -1469,43 +1502,81 @@ def _posterior_pair_density_surface(
         if np.allclose(x_data, x_data[0]) and np.allclose(y_data, y_data[0]):
             return None
 
-        reflected_x = cls._reflected_density_samples(
-            x_data,
-            lower_bound=x_bounds[0],
-            upper_bound=x_bounds[1],
-        )
-        reflected_y = cls._reflected_density_samples(
-            y_data,
-            lower_bound=y_bounds[0],
-            upper_bound=y_bounds[1],
-        )
-        if reflected_x.shape != reflected_y.shape:
-            return None
-
         x_grid = np.linspace(x_bounds[0], x_bounds[1], num=grid_size)
         y_grid = np.linspace(y_bounds[0], y_bounds[1], num=grid_size)
         mesh_x, mesh_y = np.meshgrid(x_grid, y_grid)
-        positions = np.vstack([mesh_x.ravel(), mesh_y.ravel()])
-        density = gaussian_kde(np.vstack([reflected_x, reflected_y]))(positions)
-        density = np.asarray(density, dtype=float).reshape(mesh_x.shape)
+        density = cls._evaluate_reflected_2d_kde(
+            gaussian_kde(np.vstack([x_data, y_data])),
+            mesh_x=mesh_x,
+            mesh_y=mesh_y,
+            x_bounds=x_bounds,
+            y_bounds=y_bounds,
+        )
         if not np.any(np.isfinite(density)):
             return None
         return x_grid, y_grid, density
 
     @staticmethod
-    def _reflected_density_samples(
+    def _reflection_positions_1d(
         values: np.ndarray,
         *,
         lower_bound: float | None,
         upper_bound: float | None,
-    ) -> np.ndarray:
-        """Reflect posterior samples across finite bounds for KDE stability."""
+    ) -> list[np.ndarray]:
+        """Return mirrored evaluation positions for boundary-corrected KDEs."""
         reflected = [values]
         if lower_bound is not None:
             reflected.append(2.0 * lower_bound - values)
         if upper_bound is not None:
             reflected.append(2.0 * upper_bound - values)
-        return np.concatenate(reflected)
+        return reflected
+
+    @classmethod
+    def _evaluate_reflected_1d_kde(
+        cls,
+        kde: object,
+        grid: np.ndarray,
+        *,
+        lower_bound: float | None,
+        upper_bound: float | None,
+    ) -> np.ndarray:
+        """Evaluate a 1D KDE using mirrored-boundary correction."""
+        density = np.zeros_like(grid, dtype=float)
+        for reflected_grid in cls._reflection_positions_1d(
+            grid,
+            lower_bound=lower_bound,
+            upper_bound=upper_bound,
+        ):
+            density += np.asarray(kde(reflected_grid), dtype=float)
+        return density
+
+    @classmethod
+    def _evaluate_reflected_2d_kde(
+        cls,
+        kde: object,
+        *,
+        mesh_x: np.ndarray,
+        mesh_y: np.ndarray,
+        x_bounds: tuple[float, float],
+        y_bounds: tuple[float, float],
+    ) -> np.ndarray:
+        """Evaluate a 2D KDE using mirrored-boundary correction."""
+        x_positions = cls._reflection_positions_1d(
+            mesh_x.ravel(),
+            lower_bound=x_bounds[0],
+            upper_bound=x_bounds[1],
+        )
+        y_positions = cls._reflection_positions_1d(
+            mesh_y.ravel(),
+            lower_bound=y_bounds[0],
+            upper_bound=y_bounds[1],
+        )
+        density = np.zeros(mesh_x.size, dtype=float)
+        for reflected_x in x_positions:
+            for reflected_y in y_positions:
+                positions = np.vstack([reflected_x, reflected_y])
+                density += np.asarray(kde(positions), dtype=float)
+        return density.reshape(mesh_x.shape)
 
     def _get_or_build_posterior_predictive_summary(
         self,

From 1dde15b864d718aaff1b81f8879b11628e231d2f Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 13:42:53 +0200
Subject: [PATCH 027/106] Refine posterior plot styling

---
 src/easydiffraction/display/plotting.py | 156 ++++++++++++++----------
 1 file changed, 91 insertions(+), 65 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 38ddc174..cc7530bd 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -73,13 +73,15 @@ def description(self) -> str:
 DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP = 50
 POSTERIOR_DENSITY_LINE_COLOR = 'rgb(99, 110, 250)'
 POSTERIOR_DENSITY_FILL_COLOR = 'rgba(99, 110, 250, 0.22)'
+POSTERIOR_HISTOGRAM_FILL_COLOR = 'rgba(120, 120, 120, 0.38)'
+POSTERIOR_HISTOGRAM_LINE_COLOR = 'rgba(120, 120, 120, 0.24)'
 POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(140, 140, 140, 0.08)'
 POSTERIOR_INTERVAL_68_FILL_COLOR = 'rgba(140, 140, 140, 0.16)'
-POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(140, 140, 140)'
+POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(80, 80, 80)'
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
 POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
-POSTERIOR_CONTOUR_LINE_COLOR = 'rgba(99, 110, 250, 0.50)'
+POSTERIOR_CONTOUR_LINE_COLOR = 'rgba(65, 85, 225, 0.85)'
 
 
 @dataclass(frozen=True)
@@ -960,10 +962,10 @@ def _build_posterior_pairs_plot(
         )
         make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
 
-        samples = self._selected_posterior_samples(posterior_samples, parameter_names)
-        if samples is None:
+        density_samples = self._selected_posterior_samples(posterior_samples, parameter_names)
+        if density_samples is None:
             return None
-        samples = self._thin_posterior_samples(samples, max_points=1500)
+        scatter_samples = self._thin_posterior_samples(density_samples, max_points=1500)
         labels = self._posterior_plot_labels(fit_results, parameter_names)
 
         n_parameters = len(parameter_names)
@@ -984,20 +986,22 @@ def _build_posterior_pairs_plot(
                     fig.update_yaxes(visible=False, row=row, col=col)
                     continue
 
-                x_values = samples[:, col_index]
-                y_values = samples[:, row_index]
+                x_density_values = density_samples[:, col_index]
+                y_density_values = density_samples[:, row_index]
+                x_scatter_values = scatter_samples[:, col_index]
+                y_scatter_values = scatter_samples[:, row_index]
                 if row_index == col_index:
                     density_trace = self._posterior_density_trace(
                         fit_results=fit_results,
                         parameter_name=parameter_names[col_index],
-                        values=x_values,
+                        values=x_density_values,
                         trace_name=labels[col_index],
                     )
                     diagonal_y_axis_range = None
                     if density_trace is None:
                         fig.add_trace(
                             go.Histogram(
-                                x=x_values,
+                                x=x_density_values,
                                 nbinsx=40,
                                 histnorm='probability density',
                                 marker={'color': 'rgb(99, 110, 250)'},
@@ -1015,19 +1019,19 @@ def _build_posterior_pairs_plot(
                     if diagonal_y_axis_range is not None:
                         fig.update_yaxes(range=list(diagonal_y_axis_range), row=row, col=col)
                 else:
-                    contour_trace = self._posterior_contour_trace(
+                    contour_traces = self._posterior_contour_traces(
                         fit_results=fit_results,
                         x_parameter_name=parameter_names[col_index],
                         y_parameter_name=parameter_names[row_index],
-                        x_values=x_values,
-                        y_values=y_values,
+                        x_values=x_density_values,
+                        y_values=y_density_values,
                     )
-                    if contour_trace is not None:
-                        fig.add_trace(contour_trace, row=row, col=col)
+                    if contour_traces is not None:
+                        fig.add_trace(contour_traces[0], row=row, col=col)
                     fig.add_trace(
                         go.Scattergl(
-                            x=x_values,
-                            y=y_values,
+                            x=x_scatter_values,
+                            y=y_scatter_values,
                             mode='markers',
                             marker={
                                 'color': POSTERIOR_SCATTER_MARKER_COLOR,
@@ -1042,6 +1046,8 @@ def _build_posterior_pairs_plot(
                         row=row,
                         col=col,
                     )
+                    if contour_traces is not None:
+                        fig.add_trace(contour_traces[1], row=row, col=col)
 
                 fig.update_xaxes(showticklabels=(row_index == n_parameters - 1), row=row, col=col)
                 fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
@@ -1056,7 +1062,7 @@ def _build_posterior_pairs_plot(
         )
         return fig
 
-    def _posterior_contour_trace(
+    def _posterior_contour_traces(
         self,
         *,
         fit_results: object,
@@ -1064,8 +1070,8 @@ def _posterior_contour_trace(
         y_parameter_name: str,
         x_values: np.ndarray,
         y_values: np.ndarray,
-    ) -> object | None:
-        """Return a 2D KDE contour trace for posterior pair plots."""
+    ) -> tuple[object, object] | None:
+        """Return filled and line contour traces for posterior pair plots."""
         go = __import__('plotly.graph_objects', fromlist=['Contour'])
 
         bounds = self._posterior_pair_bounds(
@@ -1085,29 +1091,49 @@ def _posterior_contour_trace(
             return None
 
         x_grid, y_grid, density = density_surface
-        return go.Contour(
+        contour_start = float(np.max(density) * 0.20)
+        contour_end = float(np.max(density) * 0.95)
+        contour_size = float(np.max(density) * 0.15)
+        fill_trace = go.Contour(
             x=x_grid,
             y=y_grid,
             z=density,
             contours={
                 'coloring': 'fill',
                 'showlabels': False,
-                'start': float(np.max(density) * 0.15),
-                'end': float(np.max(density) * 0.95),
-                'size': float(np.max(density) * 0.16),
+                'showlines': False,
+                'start': contour_start,
+                'end': contour_end,
+                'size': contour_size,
             },
             colorscale=[
                 [0.0, 'rgba(99, 110, 250, 0.00)'],
                 [0.35, 'rgba(99, 110, 250, 0.00)'],
-                [0.60, 'rgba(99, 110, 250, 0.10)'],
-                [0.80, 'rgba(99, 110, 250, 0.18)'],
-                [1.0, 'rgba(99, 110, 250, 0.30)'],
+                [0.60, 'rgba(99, 110, 250, 0.12)'],
+                [0.80, 'rgba(99, 110, 250, 0.22)'],
+                [1.0, 'rgba(99, 110, 250, 0.34)'],
             ],
-            line={'color': POSTERIOR_CONTOUR_LINE_COLOR, 'width': 1.0},
             hoverinfo='skip',
             showscale=False,
             showlegend=False,
         )
+        line_trace = go.Contour(
+            x=x_grid,
+            y=y_grid,
+            z=density,
+            contours={
+                'coloring': 'lines',
+                'showlabels': False,
+                'start': contour_start,
+                'end': contour_end,
+                'size': contour_size,
+            },
+            line={'color': POSTERIOR_CONTOUR_LINE_COLOR, 'width': 1.4},
+            hoverinfo='skip',
+            showscale=False,
+            showlegend=False,
+        )
+        return fill_trace, line_trace
 
     def _build_param_distribution_plot(
         self,
@@ -1153,64 +1179,59 @@ def _build_param_distribution_plot(
         else:
             fig = go.Figure()
 
-        if summary is not None:
-            fig.add_vrect(
-                x0=summary.interval_95[0],
-                x1=summary.interval_95[1],
-                fillcolor=POSTERIOR_INTERVAL_95_FILL_COLOR,
-                line_width=0,
-                layer='below',
-            )
-            fig.add_vrect(
-                x0=summary.interval_68[0],
-                x1=summary.interval_68[1],
-                fillcolor=POSTERIOR_INTERVAL_68_FILL_COLOR,
-                line_width=0,
-                layer='below',
-            )
+        histogram_density, _ = np.histogram(values, bins=50, density=True)
+        density_trace = self._posterior_density_trace(
+            fit_results=fit_results,
+            parameter_name=parameter_name,
+            values=values,
+            trace_name='Posterior density',
+        )
+        density_sources = [histogram_density]
+        if density_trace is not None:
+            density_trace.name = 'Posterior density'
+            density_trace.showlegend = True
+            density_sources.append(np.asarray(density_trace.y, dtype=float))
+
+        y_axis_range = self._posterior_density_axis_range(np.concatenate(density_sources))
+
+        if summary is not None and y_axis_range is not None:
             fig.add_trace(
-                self._posterior_interval_legend_trace(
+                self._posterior_interval_band_trace(
+                    x0=summary.interval_95[0],
+                    x1=summary.interval_95[1],
+                    y_axis_range=y_axis_range,
                     trace_name='95% credible interval',
                     color=POSTERIOR_INTERVAL_95_FILL_COLOR,
                 )
             )
             fig.add_trace(
-                self._posterior_interval_legend_trace(
+                self._posterior_interval_band_trace(
+                    x0=summary.interval_68[0],
+                    x1=summary.interval_68[1],
+                    y_axis_range=y_axis_range,
                     trace_name='68% credible interval',
                     color=POSTERIOR_INTERVAL_68_FILL_COLOR,
                 )
             )
 
-        histogram_density, _ = np.histogram(values, bins=50, density=True)
         fig.add_trace(
             go.Histogram(
                 x=values,
                 nbinsx=50,
                 histnorm='probability density',
                 marker={
-                    'color': 'rgba(140, 140, 140, 0.28)',
-                    'line': {'color': 'rgba(140, 140, 140, 0.18)', 'width': 1},
+                    'color': POSTERIOR_HISTOGRAM_FILL_COLOR,
+                    'line': {'color': POSTERIOR_HISTOGRAM_LINE_COLOR, 'width': 1},
                 },
-                opacity=0.65,
+                opacity=0.82,
                 name='Posterior histogram',
                 hovertemplate='sample=%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
             )
         )
-
-        density_trace = self._posterior_density_trace(
-            fit_results=fit_results,
-            parameter_name=parameter_name,
-            values=values,
-            trace_name='Posterior density',
-        )
-        density_sources = [histogram_density]
         if density_trace is not None:
             density_trace.name = 'Posterior density'
             density_trace.showlegend = True
             fig.add_trace(density_trace)
-            density_sources.append(np.asarray(density_trace.y, dtype=float))
-
-        y_axis_range = self._posterior_density_axis_range(np.concatenate(density_sources))
 
         median = float(np.median(values))
         if y_axis_range is not None:
@@ -1317,19 +1338,24 @@ def _posterior_density_axis_range(
         return lower, data_max + upper_padding
 
     @staticmethod
-    def _posterior_interval_legend_trace(
+    def _posterior_interval_band_trace(
         *,
+        x0: float,
+        x1: float,
+        y_axis_range: tuple[float, float],
         trace_name: str,
         color: str,
     ) -> object:
-        """Return a legend-only proxy trace for a credible interval."""
+        """Return a hideable credible-interval band trace."""
         go = __import__('plotly.graph_objects', fromlist=['Scatter'])
 
         return go.Scatter(
-            x=[None],
-            y=[None],
-            mode='markers',
-            marker={'size': 12, 'symbol': 'square', 'color': color},
+            x=[x0, x1, x1, x0, x0],
+            y=[y_axis_range[0], y_axis_range[0], y_axis_range[1], y_axis_range[1], y_axis_range[0]],
+            mode='lines',
+            fill='toself',
+            fillcolor=color,
+            line={'color': color, 'width': 0},
             name=trace_name,
             showlegend=True,
             hoverinfo='skip',

From 8cf5f052ceca77938bf8229c66ed13d31d3976ee Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 14:02:49 +0200
Subject: [PATCH 028/106] Improve posterior pair plot UX

---
 src/easydiffraction/display/plotting.py | 69 ++++++++++++++++++++++---
 1 file changed, 61 insertions(+), 8 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index cc7530bd..27f394ed 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -82,6 +82,17 @@ def description(self) -> str:
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
 POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
 POSTERIOR_CONTOUR_LINE_COLOR = 'rgba(65, 85, 225, 0.85)'
+POSTERIOR_CONTOUR_FILL_COLORSCALE = [
+    [0.0, 'rgba(68, 1, 84, 0.00)'],
+    [0.35, 'rgba(68, 1, 84, 0.00)'],
+    [0.58, 'rgba(59, 82, 139, 0.16)'],
+    [0.75, 'rgba(33, 145, 140, 0.28)'],
+    [0.88, 'rgba(94, 201, 98, 0.38)'],
+    [1.0, 'rgba(253, 231, 37, 0.48)'],
+]
+PAIR_PLOT_CELL_SIZE_PIXELS = 190
+PAIR_PLOT_MIN_SIZE_PIXELS = 680
+PAIR_PLOT_MARGIN_PIXELS = 120
 
 
 @dataclass(frozen=True)
@@ -967,6 +978,9 @@ def _build_posterior_pairs_plot(
             return None
         scatter_samples = self._thin_posterior_samples(density_samples, max_points=1500)
         labels = self._posterior_plot_labels(fit_results, parameter_names)
+        show_density_legend = True
+        show_scatter_legend = True
+        show_contour_legend = True
 
         n_parameters = len(parameter_names)
         fig = make_subplots(
@@ -1012,7 +1026,11 @@ def _build_posterior_pairs_plot(
                             col=col,
                         )
                     else:
+                        density_trace.name = 'Marginal density'
+                        density_trace.legendgroup = 'posterior-marginal-density'
+                        density_trace.showlegend = show_density_legend
                         fig.add_trace(density_trace, row=row, col=col)
+                        show_density_legend = False
                         diagonal_y_axis_range = self._posterior_density_axis_range(
                             np.asarray(density_trace.y)
                         )
@@ -1027,7 +1045,13 @@ def _build_posterior_pairs_plot(
                         y_values=y_density_values,
                     )
                     if contour_traces is not None:
+                        contour_traces[0].name = 'Posterior contours'
+                        contour_traces[0].legendgroup = 'posterior-contours'
+                        contour_traces[0].showlegend = show_contour_legend
+                        contour_traces[1].legendgroup = 'posterior-contours'
+                        contour_traces[1].showlegend = False
                         fig.add_trace(contour_traces[0], row=row, col=col)
+                        show_contour_legend = False
                     fig.add_trace(
                         go.Scattergl(
                             x=x_scatter_values,
@@ -1037,7 +1061,9 @@ def _build_posterior_pairs_plot(
                                 'color': POSTERIOR_SCATTER_MARKER_COLOR,
                                 'size': 3,
                             },
-                            showlegend=False,
+                            name='Posterior samples',
+                            legendgroup='posterior-samples',
+                            showlegend=show_scatter_legend,
                             hovertemplate=(
                                 f'{labels[col_index]}: %{{x:.4f}}<br>'
                                 f'{labels[row_index]}: %{{y:.4f}}<extra></extra>'
@@ -1046,9 +1072,28 @@ def _build_posterior_pairs_plot(
                         row=row,
                         col=col,
                     )
+                    show_scatter_legend = False
                     if contour_traces is not None:
                         fig.add_trace(contour_traces[1], row=row, col=col)
 
+                fig.update_xaxes(
+                    showline=True,
+                    mirror=True,
+                    zeroline=False,
+                    tickformat=',.6~g',
+                    separatethousands=True,
+                    row=row,
+                    col=col,
+                )
+                fig.update_yaxes(
+                    showline=True,
+                    mirror=True,
+                    zeroline=False,
+                    tickformat=',.6~g',
+                    separatethousands=True,
+                    row=row,
+                    col=col,
+                )
                 fig.update_xaxes(showticklabels=(row_index == n_parameters - 1), row=row, col=col)
                 fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
                 if row_index == n_parameters - 1:
@@ -1056,9 +1101,23 @@ def _build_posterior_pairs_plot(
                 if col_index == 0 and row_index > 0:
                     fig.update_yaxes(title_text=labels[row_index], row=row, col=col)
 
+        figure_size = max(
+            PAIR_PLOT_MIN_SIZE_PIXELS,
+            PAIR_PLOT_CELL_SIZE_PIXELS * n_parameters + PAIR_PLOT_MARGIN_PIXELS,
+        )
         fig.update_layout(
             title='Posterior pair plot',
             bargap=0.05,
+            width=figure_size,
+            height=figure_size,
+            legend={
+                'bgcolor': 'rgba(0, 0, 0, 0)',
+                'xanchor': 'right',
+                'x': 1.0,
+                'yanchor': 'top',
+                'y': 1.0,
+                'groupclick': 'togglegroup',
+            },
         )
         return fig
 
@@ -1106,13 +1165,7 @@ def _posterior_contour_traces(
                 'end': contour_end,
                 'size': contour_size,
             },
-            colorscale=[
-                [0.0, 'rgba(99, 110, 250, 0.00)'],
-                [0.35, 'rgba(99, 110, 250, 0.00)'],
-                [0.60, 'rgba(99, 110, 250, 0.12)'],
-                [0.80, 'rgba(99, 110, 250, 0.22)'],
-                [1.0, 'rgba(99, 110, 250, 0.34)'],
-            ],
+            colorscale=POSTERIOR_CONTOUR_FILL_COLORSCALE,
             hoverinfo='skip',
             showscale=False,
             showlegend=False,

From 74930c1bacb4d084c976f315428c347e43858377 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 14:09:37 +0200
Subject: [PATCH 029/106] Add uncertainty-based fit bounds method

---
 src/easydiffraction/core/variable.py | 69 ++++++++++++++++++++++++++++
 1 file changed, 69 insertions(+)

diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 84bbd2f4..6ae427f8 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -427,6 +427,75 @@ def fit_max(self, v: float) -> None:
             v, name=f'{self.unique_name}.fit_max', current=self._fit_max
         )
 
+    def set_fit_bounds_from_uncertainty(
+        self,
+        multiplier: float = 8.0,
+        *,
+        clip_to_limits: bool = True,
+    ) -> tuple[float, float]:
+        """Set fit bounds from the current standard uncertainty.
+
+        Parameters
+        ----------
+        multiplier : float, default=8.0
+            Positive finite factor applied symmetrically to the current
+            parameter uncertainty.
+        clip_to_limits : bool, default=True
+            Whether to clip the resolved fit bounds to the parameter's
+            physical lower and upper limits when those are finite.
+
+        Returns
+        -------
+        tuple[float, float]
+            The resolved ``(fit_min, fit_max)`` bounds.
+
+        Raises
+        ------
+        ValueError
+            If the current value, uncertainty, or multiplier is
+            missing, invalid, or produces non-increasing bounds.
+        """
+        name = self.unique_name
+        value = self.value
+        uncertainty = self.uncertainty
+
+        if value is None or not np.isfinite(float(value)):
+            msg = f'Cannot set fit bounds for {name}: current value is missing or invalid.'
+            raise ValueError(msg)
+
+        if isinstance(multiplier, bool) or not np.isfinite(float(multiplier)):
+            msg = 'multiplier must be a positive finite number.'
+            raise ValueError(msg)
+        if float(multiplier) <= 0:
+            msg = 'multiplier must be a positive finite number.'
+            raise ValueError(msg)
+
+        if uncertainty is None or uncertainty <= 0 or not np.isfinite(float(uncertainty)):
+            msg = f'Cannot set fit bounds for {name}: uncertainty is missing or invalid.'
+            raise ValueError(msg)
+
+        lower = float(value) - float(multiplier) * float(uncertainty)
+        upper = float(value) + float(multiplier) * float(uncertainty)
+
+        if clip_to_limits:
+            physical_lower = float(self._physical_lower_bound())
+            physical_upper = float(self._physical_upper_bound())
+            if np.isfinite(physical_lower):
+                lower = max(lower, physical_lower)
+            if np.isfinite(physical_upper):
+                upper = min(upper, physical_upper)
+
+        if lower >= upper:
+            msg = (
+                f'Cannot set fit bounds for {name}: resolved lower bound {lower} '
+                f'is not below upper bound {upper}.'
+            )
+            raise ValueError(msg)
+
+        self.fit_min = lower
+        self.fit_max = upper
+        return lower, upper
+
 
 # ======================================================================
 

From 46d2d813ca9c7236f50ff3fc1318c6e13970562a Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 14:18:12 +0200
Subject: [PATCH 030/106] Tighten posterior pair plot layout

---
 src/easydiffraction/display/plotting.py | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 27f394ed..51e61520 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -81,18 +81,19 @@ def description(self) -> str:
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
 POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
-POSTERIOR_CONTOUR_LINE_COLOR = 'rgba(65, 85, 225, 0.85)'
+POSTERIOR_CONTOUR_LINE_COLOR = 'rgba(58, 86, 224, 0.96)'
 POSTERIOR_CONTOUR_FILL_COLORSCALE = [
-    [0.0, 'rgba(68, 1, 84, 0.00)'],
-    [0.35, 'rgba(68, 1, 84, 0.00)'],
-    [0.58, 'rgba(59, 82, 139, 0.16)'],
-    [0.75, 'rgba(33, 145, 140, 0.28)'],
-    [0.88, 'rgba(94, 201, 98, 0.38)'],
-    [1.0, 'rgba(253, 231, 37, 0.48)'],
+    [0.0, 'rgba(224, 233, 255, 0.62)'],
+    [0.35, 'rgba(183, 203, 255, 0.70)'],
+    [0.60, 'rgba(138, 169, 252, 0.78)'],
+    [0.82, 'rgba(96, 131, 242, 0.84)'],
+    [1.0, 'rgba(58, 86, 224, 0.90)'],
 ]
 PAIR_PLOT_CELL_SIZE_PIXELS = 190
 PAIR_PLOT_MIN_SIZE_PIXELS = 680
 PAIR_PLOT_MARGIN_PIXELS = 120
+PAIR_PLOT_SUBPLOT_SPACING = 0.015
+PAIR_PLOT_MAJOR_TICKS = 3
 
 
 @dataclass(frozen=True)
@@ -987,8 +988,8 @@ def _build_posterior_pairs_plot(
             rows=n_parameters,
             cols=n_parameters,
             shared_xaxes='columns',
-            horizontal_spacing=0.03,
-            vertical_spacing=0.03,
+            horizontal_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+            vertical_spacing=PAIR_PLOT_SUBPLOT_SPACING,
         )
 
         for row_index in range(n_parameters):
@@ -1080,6 +1081,7 @@ def _build_posterior_pairs_plot(
                     showline=True,
                     mirror=True,
                     zeroline=False,
+                    nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
                     separatethousands=True,
                     row=row,
@@ -1089,6 +1091,7 @@ def _build_posterior_pairs_plot(
                     showline=True,
                     mirror=True,
                     zeroline=False,
+                    nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
                     separatethousands=True,
                     row=row,

From c68035573dfce2ef0cafe9d5d833daa50bfda96a Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 14:23:54 +0200
Subject: [PATCH 031/106] Make fit bounds helper a setter

---
 src/easydiffraction/core/variable.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 6ae427f8..cc5b9ec9 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -432,7 +432,7 @@ def set_fit_bounds_from_uncertainty(
         multiplier: float = 8.0,
         *,
         clip_to_limits: bool = True,
-    ) -> tuple[float, float]:
+    ) -> None:
         """Set fit bounds from the current standard uncertainty.
 
         Parameters
@@ -444,11 +444,6 @@ def set_fit_bounds_from_uncertainty(
             Whether to clip the resolved fit bounds to the parameter's
             physical lower and upper limits when those are finite.
 
-        Returns
-        -------
-        tuple[float, float]
-            The resolved ``(fit_min, fit_max)`` bounds.
-
         Raises
         ------
         ValueError
@@ -494,7 +489,6 @@ def set_fit_bounds_from_uncertainty(
 
         self.fit_min = lower
         self.fit_max = upper
-        return lower, upper
 
 
 # ======================================================================

From 7fd16ced2e664ffa84d891125b3999627d45d81b Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 14:32:57 +0200
Subject: [PATCH 032/106] Refine posterior contour layering

---
 src/easydiffraction/display/plotting.py | 29 ++++++++++++++++---------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 51e61520..27c85976 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -81,7 +81,6 @@ def description(self) -> str:
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
 POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
-POSTERIOR_CONTOUR_LINE_COLOR = 'rgba(58, 86, 224, 0.96)'
 POSTERIOR_CONTOUR_FILL_COLORSCALE = [
     [0.0, 'rgba(224, 233, 255, 0.62)'],
     [0.35, 'rgba(183, 203, 255, 0.70)'],
@@ -89,6 +88,16 @@ def description(self) -> str:
     [0.82, 'rgba(96, 131, 242, 0.84)'],
     [1.0, 'rgba(58, 86, 224, 0.90)'],
 ]
+POSTERIOR_CONTOUR_LINE_COLORSCALE = [
+    [0.0, 'rgba(183, 203, 255, 0.94)'],
+    [0.35, 'rgba(183, 203, 255, 0.94)'],
+    [0.35, 'rgba(138, 169, 252, 0.95)'],
+    [0.60, 'rgba(138, 169, 252, 0.95)'],
+    [0.60, 'rgba(96, 131, 242, 0.96)'],
+    [0.82, 'rgba(96, 131, 242, 0.96)'],
+    [0.82, 'rgba(58, 86, 224, 0.98)'],
+    [1.0, 'rgba(58, 86, 224, 0.98)'],
+]
 PAIR_PLOT_CELL_SIZE_PIXELS = 190
 PAIR_PLOT_MIN_SIZE_PIXELS = 680
 PAIR_PLOT_MARGIN_PIXELS = 120
@@ -1045,14 +1054,6 @@ def _build_posterior_pairs_plot(
                         x_values=x_density_values,
                         y_values=y_density_values,
                     )
-                    if contour_traces is not None:
-                        contour_traces[0].name = 'Posterior contours'
-                        contour_traces[0].legendgroup = 'posterior-contours'
-                        contour_traces[0].showlegend = show_contour_legend
-                        contour_traces[1].legendgroup = 'posterior-contours'
-                        contour_traces[1].showlegend = False
-                        fig.add_trace(contour_traces[0], row=row, col=col)
-                        show_contour_legend = False
                     fig.add_trace(
                         go.Scattergl(
                             x=x_scatter_values,
@@ -1075,7 +1076,14 @@ def _build_posterior_pairs_plot(
                     )
                     show_scatter_legend = False
                     if contour_traces is not None:
+                        contour_traces[0].name = 'Posterior contours'
+                        contour_traces[0].legendgroup = 'posterior-contours'
+                        contour_traces[0].showlegend = show_contour_legend
+                        contour_traces[1].legendgroup = 'posterior-contours'
+                        contour_traces[1].showlegend = False
+                        fig.add_trace(contour_traces[0], row=row, col=col)
                         fig.add_trace(contour_traces[1], row=row, col=col)
+                        show_contour_legend = False
 
                 fig.update_xaxes(
                     showline=True,
@@ -1184,7 +1192,8 @@ def _posterior_contour_traces(
                 'end': contour_end,
                 'size': contour_size,
             },
-            line={'color': POSTERIOR_CONTOUR_LINE_COLOR, 'width': 1.4},
+            colorscale=POSTERIOR_CONTOUR_LINE_COLORSCALE,
+            line={'width': 0.9},
             hoverinfo='skip',
             showscale=False,
             showlegend=False,

From 0aaf2e9b5a8f14b46ba81f5a6755d9b9a7eb8f79 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 14:45:43 +0200
Subject: [PATCH 033/106] Fix posterior contour layering

---
 src/easydiffraction/display/plotting.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 27c85976..2f91fb09 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -979,7 +979,7 @@ def _build_posterior_pairs_plot(
 
         go = __import__(
             'plotly.graph_objects',
-            fromlist=['Figure', 'Histogram', 'Scattergl', 'Contour'],
+            fromlist=['Figure', 'Histogram', 'Scatter', 'Contour'],
         )
         make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
 
@@ -1055,7 +1055,7 @@ def _build_posterior_pairs_plot(
                         y_values=y_density_values,
                     )
                     fig.add_trace(
-                        go.Scattergl(
+                        go.Scatter(
                             x=x_scatter_values,
                             y=y_scatter_values,
                             mode='markers',
@@ -1164,10 +1164,12 @@ def _posterior_contour_traces(
         contour_start = float(np.max(density) * 0.20)
         contour_end = float(np.max(density) * 0.95)
         contour_size = float(np.max(density) * 0.15)
+        fill_density = np.array(density, copy=True)
+        fill_density[fill_density < contour_start] = np.nan
         fill_trace = go.Contour(
             x=x_grid,
             y=y_grid,
-            z=density,
+            z=fill_density,
             contours={
                 'coloring': 'fill',
                 'showlabels': False,
@@ -1177,6 +1179,9 @@ def _posterior_contour_traces(
                 'size': contour_size,
             },
             colorscale=POSTERIOR_CONTOUR_FILL_COLORSCALE,
+            zmin=contour_start,
+            zmax=contour_end,
+            connectgaps=False,
             hoverinfo='skip',
             showscale=False,
             showlegend=False,
@@ -1193,6 +1198,8 @@ def _posterior_contour_traces(
                 'size': contour_size,
             },
             colorscale=POSTERIOR_CONTOUR_LINE_COLORSCALE,
+            zmin=contour_start,
+            zmax=contour_end,
             line={'width': 0.9},
             hoverinfo='skip',
             showscale=False,

From b4a00c38477377bd0c700035d1d9ee7b1c5bb275 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 14:58:32 +0200
Subject: [PATCH 034/106] Preserve pair plot sample hover

---
 src/easydiffraction/display/plotting.py | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 2f91fb09..0d41e9f3 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1054,6 +1054,10 @@ def _build_posterior_pairs_plot(
                         x_values=x_density_values,
                         y_values=y_density_values,
                     )
+                    sample_hovertemplate = (
+                        f'{labels[col_index]}: %{{x:.4f}}<br>'
+                        f'{labels[row_index]}: %{{y:.4f}}<extra></extra>'
+                    )
                     fig.add_trace(
                         go.Scatter(
                             x=x_scatter_values,
@@ -1066,10 +1070,7 @@ def _build_posterior_pairs_plot(
                             name='Posterior samples',
                             legendgroup='posterior-samples',
                             showlegend=show_scatter_legend,
-                            hovertemplate=(
-                                f'{labels[col_index]}: %{{x:.4f}}<br>'
-                                f'{labels[row_index]}: %{{y:.4f}}<extra></extra>'
-                            ),
+                            hoverinfo='skip',
                         ),
                         row=row,
                         col=col,
@@ -1084,6 +1085,21 @@ def _build_posterior_pairs_plot(
                         fig.add_trace(contour_traces[0], row=row, col=col)
                         fig.add_trace(contour_traces[1], row=row, col=col)
                         show_contour_legend = False
+                    fig.add_trace(
+                        go.Scatter(
+                            x=x_scatter_values,
+                            y=y_scatter_values,
+                            mode='markers',
+                            marker={
+                                'color': 'rgba(0, 0, 0, 0)',
+                                'size': 6,
+                            },
+                            showlegend=False,
+                            hovertemplate=sample_hovertemplate,
+                        ),
+                        row=row,
+                        col=col,
+                    )
 
                 fig.update_xaxes(
                     showline=True,

From c7de06201c2cdb6fb30820538205b2aad4d8dc60 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:04:45 +0200
Subject: [PATCH 035/106] Enforce pair plot trace z-order

---
 src/easydiffraction/display/plotting.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 0d41e9f3..356c617c 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1071,6 +1071,7 @@ def _build_posterior_pairs_plot(
                             legendgroup='posterior-samples',
                             showlegend=show_scatter_legend,
                             hoverinfo='skip',
+                            zorder=0,
                         ),
                         row=row,
                         col=col,
@@ -1096,6 +1097,7 @@ def _build_posterior_pairs_plot(
                             },
                             showlegend=False,
                             hovertemplate=sample_hovertemplate,
+                            zorder=3,
                         ),
                         row=row,
                         col=col,
@@ -1201,6 +1203,7 @@ def _posterior_contour_traces(
             hoverinfo='skip',
             showscale=False,
             showlegend=False,
+            zorder=1,
         )
         line_trace = go.Contour(
             x=x_grid,
@@ -1220,6 +1223,7 @@ def _posterior_contour_traces(
             hoverinfo='skip',
             showscale=False,
             showlegend=False,
+            zorder=2,
         )
         return fill_trace, line_trace
 

From 2b501c975b196f7e05d61727ce0c35547d2521b0 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:10:37 +0200
Subject: [PATCH 036/106] Restore pair plot axis frames

---
 src/easydiffraction/display/plotting.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 356c617c..3b144c35 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1107,6 +1107,7 @@ def _build_posterior_pairs_plot(
                     showline=True,
                     mirror=True,
                     zeroline=False,
+                    layer='above traces',
                     nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
                     separatethousands=True,
@@ -1117,6 +1118,7 @@ def _build_posterior_pairs_plot(
                     showline=True,
                     mirror=True,
                     zeroline=False,
+                    layer='above traces',
                     nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
                     separatethousands=True,

From a71b706792cae7746a28ed5949137b051328a0db Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:16:47 +0200
Subject: [PATCH 037/106] Split posterior diagnostic warnings

---
 .../analysis/fit_helpers/bayesian.py            | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index a85b3fbc..064c7e4e 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -682,8 +682,15 @@ def _posterior_table_notes(
     if not has_failed_r_hat and not has_failed_ess_bulk:
         return []
 
-    return [
-        '[red]Convergence warning:[/red] posterior diagnostics failed '
-        '(r_hat > 1.01 or ess_bulk < 400). Consider longer sampling, '
-        'tighter bounds, or reparameterization.'
-    ]
\ No newline at end of file
+    notes: list[str] = []
+    if has_failed_r_hat:
+        notes.append(
+            f'[red]r_hat[/red]: exceeds {R_HAT_CONVERGENCE_THRESHOLD:.2f} '
+            '(consider longer sampling, tighter bounds, or reparameterization).'
+        )
+    if has_failed_ess_bulk:
+        notes.append(
+            f'[red]ess_bulk[/red]: less than {ESS_BULK_CONVERGENCE_THRESHOLD:.0f} '
+            '(consider longer sampling, tighter bounds, or reparameterization).'
+        )
+    return notes
\ No newline at end of file

From 62590849a0440e9a7874e044010e31b33ba11ee5 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:19:41 +0200
Subject: [PATCH 038/106] Strengthen pair plot axis borders

---
 src/easydiffraction/display/plotting.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 3b144c35..96fefb84 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -103,6 +103,8 @@ def description(self) -> str:
 PAIR_PLOT_MARGIN_PIXELS = 120
 PAIR_PLOT_SUBPLOT_SPACING = 0.015
 PAIR_PLOT_MAJOR_TICKS = 3
+POSTERIOR_PAIR_AXIS_LINE_COLOR = 'rgba(112, 129, 163, 0.88)'
+POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
 
 
 @dataclass(frozen=True)
@@ -1108,6 +1110,8 @@ def _build_posterior_pairs_plot(
                     mirror=True,
                     zeroline=False,
                     layer='above traces',
+                    linecolor=POSTERIOR_PAIR_AXIS_LINE_COLOR,
+                    linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
                     nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
                     separatethousands=True,
@@ -1119,6 +1123,8 @@ def _build_posterior_pairs_plot(
                     mirror=True,
                     zeroline=False,
                     layer='above traces',
+                    linecolor=POSTERIOR_PAIR_AXIS_LINE_COLOR,
+                    linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
                     nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
                     separatethousands=True,

From bdcb0955a58c69261c6698e8e633f7c7c8aa2222 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:20:23 +0200
Subject: [PATCH 039/106] Free broad_gauss and set bounds from uncertainty

---
 tmp/bumps_dream/ed-2-bayesian-new-API.py | 42 ++++++++++--------------
 1 file changed, 18 insertions(+), 24 deletions(-)

diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index 6ebdb441..13951a38 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -129,8 +129,8 @@
 experiment.background.create(id='5', x=165, y=174.2813)
 
 # %%
-experiment.excluded_regions.create(id='1', start=0, end=20)
-experiment.excluded_regions.create(id='2', start=105, end=180)
+experiment.excluded_regions.create(id='1', start=0, end=30)
+experiment.excluded_regions.create(id='2', start=90, end=180)
 
 # %%
 experiment.linked_phases.create(id='lbco', scale=9.1351)
@@ -140,6 +140,8 @@
 
 # %%
 structure.cell.length_a.free = True
+experiment.peak.broad_gauss_u.free = True
+experiment.peak.broad_gauss_v.free = True
 experiment.instrument.calib_twotheta_offset.free = True
 
 # %%
@@ -153,29 +155,25 @@
 project.analysis.display.fit_results()
 
 # %%
-project.display.plotter.plot_param_correlations()
+project.display.plotter.plot_param_correlations(show_diagonal=True)
 
 # %%
 project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=100, x_max=102)
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=83, x_max=85)
 
 # %% [markdown]
 # ## Step 5: Perform Bayesian Analysis
 
 # %%
-length_a = structure.cell.length_a.value
-length_a_sigma = structure.cell.length_a.uncertainty or 0.001
-length_a_window = max(8.0 * length_a_sigma, 0.001)
-structure.cell.length_a.fit_min = length_a - length_a_window
-structure.cell.length_a.fit_max = length_a + length_a_window
+project.analysis.display.free_params()
 
-twotheta_offset = experiment.instrument.calib_twotheta_offset.value
-twotheta_offset_sigma = experiment.instrument.calib_twotheta_offset.uncertainty or 0.01
-twotheta_offset_window = max(8.0 * twotheta_offset_sigma, 0.01)
-experiment.instrument.calib_twotheta_offset.fit_min = twotheta_offset - twotheta_offset_window
-experiment.instrument.calib_twotheta_offset.fit_max = twotheta_offset + twotheta_offset_window
+# %%
+structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=5)
+experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=5)
+experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=5)
+experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=5)
 
 # %%
 project.analysis.display.free_params()
@@ -185,8 +183,8 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 dream = project.analysis.fit.minimizer
-dream.steps = 1000
-dream.burn = 200
+dream.steps = 200 #1000
+dream.burn = 50 #200
 dream.thin = 1
 dream.pop = 4
 
@@ -197,25 +195,21 @@
 project.analysis.display.fit_results()
 
 # %%
-project.display.plotter.plot_param_correlations()
-
-# %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
-
-# %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=100, x_max=102)
+project.display.plotter.plot_param_correlations(show_diagonal=True)
 
 # %%
 project.display.plotter.plot_posterior_pairs()
 
 # %%
 project.display.plotter.plot_param_distribution(structure.cell.length_a)
+project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)
+project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)
 project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
 
 # %%
 project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=100, x_max=102)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=83, x_max=85)
 
 # %%

From 60bac37b3263e98072af19b7218fd72f3667b985 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:26:25 +0200
Subject: [PATCH 040/106] Draw explicit pair plot borders

---
 src/easydiffraction/display/plotting.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 96fefb84..4dfb51b7 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -995,6 +995,7 @@ def _build_posterior_pairs_plot(
         show_contour_legend = True
 
         n_parameters = len(parameter_names)
+        subplot_border_shapes: list[dict[str, object]] = []
         fig = make_subplots(
             rows=n_parameters,
             cols=n_parameters,
@@ -1138,6 +1139,25 @@ def _build_posterior_pairs_plot(
                 if col_index == 0 and row_index > 0:
                     fig.update_yaxes(title_text=labels[row_index], row=row, col=col)
 
+                subplot = fig.get_subplot(row, col)
+                subplot_border_shapes.append(
+                    {
+                        'type': 'rect',
+                        'xref': 'paper',
+                        'yref': 'paper',
+                        'x0': subplot.xaxis.domain[0],
+                        'x1': subplot.xaxis.domain[1],
+                        'y0': subplot.yaxis.domain[0],
+                        'y1': subplot.yaxis.domain[1],
+                        'line': {
+                            'color': POSTERIOR_PAIR_AXIS_LINE_COLOR,
+                            'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+                        },
+                        'fillcolor': 'rgba(0, 0, 0, 0)',
+                        'layer': 'above',
+                    }
+                )
+
         figure_size = max(
             PAIR_PLOT_MIN_SIZE_PIXELS,
             PAIR_PLOT_CELL_SIZE_PIXELS * n_parameters + PAIR_PLOT_MARGIN_PIXELS,
@@ -1147,6 +1167,7 @@ def _build_posterior_pairs_plot(
             bargap=0.05,
             width=figure_size,
             height=figure_size,
+            shapes=subplot_border_shapes,
             legend={
                 'bgcolor': 'rgba(0, 0, 0, 0)',
                 'xanchor': 'right',

From ca7bddef82546078e8592a5e5348ce36886da3b6 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:34:16 +0200
Subject: [PATCH 041/106] Align pair plot y-axis titles

---
 src/easydiffraction/display/plotting.py | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 4dfb51b7..0b1be094 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -105,6 +105,7 @@ def description(self) -> str:
 PAIR_PLOT_MAJOR_TICKS = 3
 POSTERIOR_PAIR_AXIS_LINE_COLOR = 'rgba(112, 129, 163, 0.88)'
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
+POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 56
 
 
 @dataclass(frozen=True)
@@ -995,6 +996,7 @@ def _build_posterior_pairs_plot(
         show_contour_legend = True
 
         n_parameters = len(parameter_names)
+        subplot_title_annotations: list[dict[str, object]] = []
         subplot_border_shapes: list[dict[str, object]] = []
         fig = make_subplots(
             rows=n_parameters,
@@ -1136,10 +1138,23 @@ def _build_posterior_pairs_plot(
                 fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
                 if row_index == n_parameters - 1:
                     fig.update_xaxes(title_text=labels[col_index], row=row, col=col)
-                if col_index == 0 and row_index > 0:
-                    fig.update_yaxes(title_text=labels[row_index], row=row, col=col)
 
                 subplot = fig.get_subplot(row, col)
+                if col_index == 0 and row_index > 0:
+                    subplot_title_annotations.append(
+                        {
+                            'x': subplot.xaxis.domain[0],
+                            'xref': 'paper',
+                            'xanchor': 'right',
+                            'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
+                            'y': 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1]),
+                            'yref': 'paper',
+                            'yanchor': 'middle',
+                            'text': labels[row_index],
+                            'textangle': -90,
+                            'showarrow': False,
+                        }
+                    )
                 subplot_border_shapes.append(
                     {
                         'type': 'rect',
@@ -1167,6 +1182,7 @@ def _build_posterior_pairs_plot(
             bargap=0.05,
             width=figure_size,
             height=figure_size,
+            annotations=subplot_title_annotations,
             shapes=subplot_border_shapes,
             legend={
                 'bgcolor': 'rgba(0, 0, 0, 0)',

From 659c30ae3757e140ab8678568d05b5c4ba8ebc77 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 15:39:17 +0200
Subject: [PATCH 042/106] Unify plot axis border colors

---
 src/easydiffraction/display/plotters/plotly.py |  9 +++++++++
 src/easydiffraction/display/plotting.py        | 15 +++++++++++----
 2 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 15e1ac11..49d567af 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -161,6 +161,11 @@ def _correlation_grid_color(cls) -> str:
             return 'rgba(110, 145, 190, 0.35)'
         return 'rgba(120, 140, 160, 0.28)'
 
+    @classmethod
+    def _axis_frame_color(cls) -> str:
+        """Return the shared axis-frame color for Plotly figures."""
+        return cls._correlation_grid_color()
+
     @classmethod
     def _legend_background_color(cls) -> str:
         """Return a half-transparent legend background color."""
@@ -916,12 +921,14 @@ def _get_layout(
             xaxis={
                 'title_text': axes_labels[0],
                 'showline': True,
+                'linecolor': cls._axis_frame_color(),
                 'mirror': True,
                 'zeroline': False,
             },
             yaxis={
                 'title_text': axes_labels[1],
                 'showline': True,
+                'linecolor': cls._axis_frame_color(),
                 'mirror': True,
                 'zeroline': False,
             },
@@ -1367,6 +1374,7 @@ def plot_powder_meas_vs_calc(
             x_axis_kwargs = {
                 'matches': 'x',
                 'showline': True,
+                'linecolor': self._axis_frame_color(),
                 'mirror': True,
                 'zeroline': False,
                 'tickformat': ',.6~g',
@@ -1377,6 +1385,7 @@ def plot_powder_meas_vs_calc(
             fig.update_xaxes(row=row_idx, col=1, **x_axis_kwargs)
             fig.update_yaxes(
                 showline=True,
+                linecolor=self._axis_frame_color(),
                 mirror=True,
                 zeroline=False,
                 tickformat=',.6~g',
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 0b1be094..f104e22a 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -103,7 +103,6 @@ def description(self) -> str:
 PAIR_PLOT_MARGIN_PIXELS = 120
 PAIR_PLOT_SUBPLOT_SPACING = 0.015
 PAIR_PLOT_MAJOR_TICKS = 3
-POSTERIOR_PAIR_AXIS_LINE_COLOR = 'rgba(112, 129, 163, 0.88)'
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
 POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 56
 
@@ -994,6 +993,7 @@ def _build_posterior_pairs_plot(
         show_density_legend = True
         show_scatter_legend = True
         show_contour_legend = True
+        axis_frame_color = self._plot_axis_frame_color()
 
         n_parameters = len(parameter_names)
         subplot_title_annotations: list[dict[str, object]] = []
@@ -1113,7 +1113,7 @@ def _build_posterior_pairs_plot(
                     mirror=True,
                     zeroline=False,
                     layer='above traces',
-                    linecolor=POSTERIOR_PAIR_AXIS_LINE_COLOR,
+                    linecolor=axis_frame_color,
                     linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
                     nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
@@ -1126,7 +1126,7 @@ def _build_posterior_pairs_plot(
                     mirror=True,
                     zeroline=False,
                     layer='above traces',
-                    linecolor=POSTERIOR_PAIR_AXIS_LINE_COLOR,
+                    linecolor=axis_frame_color,
                     linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
                     nticks=PAIR_PLOT_MAJOR_TICKS,
                     tickformat=',.6~g',
@@ -1165,7 +1165,7 @@ def _build_posterior_pairs_plot(
                         'y0': subplot.yaxis.domain[0],
                         'y1': subplot.yaxis.domain[1],
                         'line': {
-                            'color': POSTERIOR_PAIR_AXIS_LINE_COLOR,
+                            'color': axis_frame_color,
                             'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
                         },
                         'fillcolor': 'rgba(0, 0, 0, 0)',
@@ -1195,6 +1195,13 @@ def _build_posterior_pairs_plot(
         )
         return fig
 
+    def _plot_axis_frame_color(self) -> str:
+        """Return the shared axis-frame color for Plotly-backed plots."""
+        axis_frame_color = getattr(self._backend, '_axis_frame_color', None)
+        if callable(axis_frame_color):
+            return axis_frame_color()
+        return PlotlyPlotter._axis_frame_color()
+
     def _posterior_contour_traces(
         self,
         *,

From a1ad6c32d54b2c875a54711e3dc5a6b3b8b15e4c Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 20:42:51 +0200
Subject: [PATCH 043/106] Lock posterior pair axis ranges

---
 src/easydiffraction/display/plotting.py | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index f104e22a..733232a6 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -994,6 +994,20 @@ def _build_posterior_pairs_plot(
         show_scatter_legend = True
         show_contour_legend = True
         axis_frame_color = self._plot_axis_frame_color()
+        parameter_axis_ranges = [
+            self._posterior_axis_bounds(
+                density_samples[:, index],
+                lower_bound=self._posterior_parameter_bounds(
+                    fit_results=fit_results,
+                    parameter_name=parameter_names[index],
+                )[0],
+                upper_bound=self._posterior_parameter_bounds(
+                    fit_results=fit_results,
+                    parameter_name=parameter_names[index],
+                )[1],
+            )
+            for index in range(len(parameter_names))
+        ]
 
         n_parameters = len(parameter_names)
         subplot_title_annotations: list[dict[str, object]] = []
@@ -1111,6 +1125,7 @@ def _build_posterior_pairs_plot(
                 fig.update_xaxes(
                     showline=True,
                     mirror=True,
+                    range=list(parameter_axis_ranges[col_index]),
                     zeroline=False,
                     layer='above traces',
                     linecolor=axis_frame_color,
@@ -1134,6 +1149,8 @@ def _build_posterior_pairs_plot(
                     row=row,
                     col=col,
                 )
+                if row_index != col_index:
+                    fig.update_yaxes(range=list(parameter_axis_ranges[row_index]), row=row, col=col)
                 fig.update_xaxes(showticklabels=(row_index == n_parameters - 1), row=row, col=col)
                 fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
                 if row_index == n_parameters - 1:

From e87055e51971c09bf80256d8ae18620e05756094 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 21:13:58 +0200
Subject: [PATCH 044/106] Align pair plot title with modebar

---
 src/easydiffraction/display/plotting.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 733232a6..c2b111f7 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1195,7 +1195,13 @@ def _build_posterior_pairs_plot(
             PAIR_PLOT_CELL_SIZE_PIXELS * n_parameters + PAIR_PLOT_MARGIN_PIXELS,
         )
         fig.update_layout(
-            title='Posterior pair plot',
+            margin={
+                'autoexpand': True,
+                'r': 30,
+                't': 40,
+                'b': 45,
+            },
+            title={'text': 'Posterior pair plot'},
             bargap=0.05,
             width=figure_size,
             height=figure_size,

From 2d58e561eadc2747836c8921e54cded07e9fbb0c Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 21:23:42 +0200
Subject: [PATCH 045/106] Update diagonal subplot axis styling

---
 src/easydiffraction/display/plotting.py | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index c2b111f7..21b1eeaa 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1033,7 +1033,8 @@ def _build_posterior_pairs_plot(
                 y_density_values = density_samples[:, row_index]
                 x_scatter_values = scatter_samples[:, col_index]
                 y_scatter_values = scatter_samples[:, row_index]
-                if row_index == col_index:
+                is_diagonal_subplot = row_index == col_index
+                if is_diagonal_subplot:
                     density_trace = self._posterior_density_trace(
                         fit_results=fit_results,
                         parameter_name=parameter_names[col_index],
@@ -1149,10 +1150,17 @@ def _build_posterior_pairs_plot(
                     row=row,
                     col=col,
                 )
-                if row_index != col_index:
+                if not is_diagonal_subplot:
                     fig.update_yaxes(range=list(parameter_axis_ranges[row_index]), row=row, col=col)
                 fig.update_xaxes(showticklabels=(row_index == n_parameters - 1), row=row, col=col)
-                fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
+                fig.update_yaxes(
+                    showticklabels=(col_index == 0 and not is_diagonal_subplot),
+                    ticks='' if is_diagonal_subplot else None,
+                    row=row,
+                    col=col,
+                )
+                if row_index == 0 and col_index == 0:
+                    fig.update_yaxes(title_text='Probability density', row=row, col=col)
                 if row_index == n_parameters - 1:
                     fig.update_xaxes(title_text=labels[col_index], row=row, col=col)
 

From 1da1f2e5cb63ec37b8ba8dc5ec0b4864a829f66f Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 21:35:54 +0200
Subject: [PATCH 046/106] Expose DREAM init and align pair labels

---
 .../analysis/minimizers/__init__.py           |  1 +
 .../analysis/minimizers/bumps_dream.py        | 31 +++++++++++++++++++
 .../analysis/minimizers/enums.py              |  9 ++++++
 src/easydiffraction/display/plotting.py       | 22 +++++++------
 4 files changed, 54 insertions(+), 9 deletions(-)

diff --git a/src/easydiffraction/analysis/minimizers/__init__.py b/src/easydiffraction/analysis/minimizers/__init__.py
index 7639dd8a..1006eefb 100644
--- a/src/easydiffraction/analysis/minimizers/__init__.py
+++ b/src/easydiffraction/analysis/minimizers/__init__.py
@@ -7,6 +7,7 @@
 from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
 from easydiffraction.analysis.minimizers.bumps_lm import BumpsLmMinimizer
 from easydiffraction.analysis.minimizers.dfols import DfolsMinimizer
+from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
 from easydiffraction.analysis.minimizers.lmfit import LmfitMinimizer
 from easydiffraction.analysis.minimizers.lmfit_least_squares import LmfitLeastSquaresMinimizer
 from easydiffraction.analysis.minimizers.lmfit_leastsq import LmfitLeastsqMinimizer
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 2ea64fe1..0ad88ef9 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -20,6 +20,7 @@
 from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
 from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
 from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
+from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
 from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 from easydiffraction.analysis.minimizers.factory import MinimizerFactory
 from easydiffraction.core.metadata import TypeInfo
@@ -31,6 +32,7 @@
 DEFAULT_MIN_BURN = 50
 DEFAULT_THIN = 1
 DEFAULT_POP = 4
+DEFAULT_INIT = DreamPopulationInitializationEnum.EPS
 DEFAULT_ALPHA = 0.0
 DEFAULT_OUTLIER_TEST = 'none'
 DEFAULT_TRIM = False
@@ -220,6 +222,7 @@ def __init__(
         self._burn: int | None = None
         self._thin: int = DEFAULT_THIN
         self._pop: int = DEFAULT_POP
+        self._init: DreamPopulationInitializationEnum = DEFAULT_INIT
 
     @property
     def steps(self) -> int:
@@ -260,6 +263,15 @@ def pop(self) -> int:
     def pop(self, value: int) -> None:
         self._pop = self._validated_positive_integer('pop', value)
 
+    @property
+    def init(self) -> DreamPopulationInitializationEnum:
+        """DREAM population initializer."""
+        return self._init
+
+    @init.setter
+    def init(self, value: DreamPopulationInitializationEnum | str) -> None:
+        self._init = self._validated_init(value)
+
     def _resolve_random_seed(self, random_seed: int | None) -> int:
         """Return a user-provided or generated random seed.
 
@@ -335,6 +347,20 @@ def _validated_non_negative_integer(name: str, value: int | float) -> int:
             raise ValueError(msg)
         return integer_value
 
+    @staticmethod
+    def _validated_init(
+        value: DreamPopulationInitializationEnum | str,
+    ) -> DreamPopulationInitializationEnum:
+        """Validate a DREAM population initializer."""
+        try:
+            return DreamPopulationInitializationEnum(value)
+        except ValueError:
+            valid_values = ', '.join(
+                initialization.value for initialization in DreamPopulationInitializationEnum
+            )
+            msg = f"DREAM setting 'init' must be one of: {valid_values}."
+            raise ValueError(msg) from None
+
     def _resolved_burn(self, steps: int) -> int:
         """Return the configured or automatic DREAM burn-in length."""
         if self.burn is None:
@@ -355,6 +381,7 @@ def _sampler_settings(
         burn: int,
         thin: int,
         pop: int,
+        init: DreamPopulationInitializationEnum,
         n_parameters: int,
     ) -> dict[str, object]:
         """Build the sampler settings dictionary recorded in results."""
@@ -365,6 +392,7 @@ def _sampler_settings(
             'burn': int(burn),
             'thin': int(thin),
             'pop': int(pop),
+            'init': init.value,
             'samples': int(samples),
             'alpha': float(DEFAULT_ALPHA),
             'outliers': DEFAULT_OUTLIER_TEST,
@@ -405,12 +433,14 @@ def _run_solver(
         burn = self._resolved_burn(steps)
         thin = self.thin
         pop = self.pop
+        init = self.init
         sampler_settings = self._sampler_settings(
             random_seed=int(random_seed),
             steps=steps,
             burn=burn,
             thin=thin,
             pop=pop,
+            init=init,
             n_parameters=len(bumps_params),
         )
         total_generations = int(steps + burn + 1)
@@ -429,6 +459,7 @@ def _run_solver(
             burn=burn,
             thin=thin,
             pop=pop,
+            init=init.value,
             samples=sampler_settings['samples'],
             alpha=DEFAULT_ALPHA,
             outliers=DEFAULT_OUTLIER_TEST,
diff --git a/src/easydiffraction/analysis/minimizers/enums.py b/src/easydiffraction/analysis/minimizers/enums.py
index 5d8ad218..0d5bb086 100644
--- a/src/easydiffraction/analysis/minimizers/enums.py
+++ b/src/easydiffraction/analysis/minimizers/enums.py
@@ -51,3 +51,12 @@ def description(self) -> str:
             MinimizerTypeEnum.BUMPS_DE: ('BUMPS library with differential evolution method'),
         }
         return descriptions.get(self, '')
+
+
+class DreamPopulationInitializationEnum(StrEnum):
+    """Supported DREAM population initializers."""
+
+    EPS = 'eps'
+    COV = 'cov'
+    LHS = 'lhs'
+    RANDOM = 'random'
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 21b1eeaa..08785cfe 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1153,19 +1153,23 @@ def _build_posterior_pairs_plot(
                 if not is_diagonal_subplot:
                     fig.update_yaxes(range=list(parameter_axis_ranges[row_index]), row=row, col=col)
                 fig.update_xaxes(showticklabels=(row_index == n_parameters - 1), row=row, col=col)
-                fig.update_yaxes(
-                    showticklabels=(col_index == 0 and not is_diagonal_subplot),
-                    ticks='' if is_diagonal_subplot else None,
-                    row=row,
-                    col=col,
-                )
-                if row_index == 0 and col_index == 0:
-                    fig.update_yaxes(title_text='Probability density', row=row, col=col)
+                if is_diagonal_subplot:
+                    fig.update_yaxes(
+                        showticklabels=False,
+                        ticks='',
+                        ticklen=0,
+                        showgrid=False,
+                        title_text=None,
+                        row=row,
+                        col=col,
+                    )
+                else:
+                    fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
                 if row_index == n_parameters - 1:
                     fig.update_xaxes(title_text=labels[col_index], row=row, col=col)
 
                 subplot = fig.get_subplot(row, col)
-                if col_index == 0 and row_index > 0:
+                if col_index == 0:
                     subplot_title_annotations.append(
                         {
                             'x': subplot.xaxis.domain[0],

From 9cc934388d3d8e3e9d04698d8f5bf498344b0165 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 21:43:28 +0200
Subject: [PATCH 047/106] Tighten fit bounds multiplier from 5 to 4

---
 tmp/bumps_dream/ed-2-bayesian-new-API.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index 13951a38..d4c6a910 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -170,10 +170,10 @@
 project.analysis.display.free_params()
 
 # %%
-structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=5)
-experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=5)
-experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=5)
-experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=5)
+structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=4)
+experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=4)
+experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)
+experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)
 
 # %%
 project.analysis.display.free_params()

From f4aa5ef331e6f0dca09ae7c42287fa78fad2881e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 21:54:28 +0200
Subject: [PATCH 048/106] Match pair plot axis title font sizes

---
 src/easydiffraction/display/plotting.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 08785cfe..35b0b33b 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -104,6 +104,7 @@ def description(self) -> str:
 PAIR_PLOT_SUBPLOT_SPACING = 0.015
 PAIR_PLOT_MAJOR_TICKS = 3
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
+POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = 14
 POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 56
 
 
@@ -1180,6 +1181,7 @@ def _build_posterior_pairs_plot(
                             'yref': 'paper',
                             'yanchor': 'middle',
                             'text': labels[row_index],
+                            'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
                             'textangle': -90,
                             'showarrow': False,
                         }

From ae98ffe918b7f550728ba3a10a8c5793987751d8 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 22:00:01 +0200
Subject: [PATCH 049/106] Disable Rich highlighting in Bayesian summaries

---
 src/easydiffraction/analysis/fit_helpers/bayesian.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 064c7e4e..4815a2c2 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -8,6 +8,7 @@
 
 import arviz as az
 import numpy as np
+from rich.text import Text
 
 from easydiffraction.analysis.fit_helpers.metrics import calculate_r_factor
 from easydiffraction.analysis.fit_helpers.metrics import calculate_r_factor_squared
@@ -313,11 +314,11 @@ def display_results(
 
         sampler_settings = _format_sampler_settings(self.sampler_settings)
         if sampler_settings is not None:
-            console.print(f'⚙️ Sampler settings: {sampler_settings}')
+            console.print(Text(f'⚙️ Sampler settings: {sampler_settings}'))
 
         convergence_summary = _format_convergence_summary(self.convergence_diagnostics)
         if convergence_summary is not None:
-            console.print(f'📊 Convergence: {convergence_summary}')
+            console.print(Text.from_markup(f'📊 Convergence: {convergence_summary}'))
 
         if rf is not None:
             console.print(f'📏 R-factor (Rf): {rf:.2f}%')

From e5dd8b6f02aff87201c6a43ee8579b52a515e246 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 22:12:27 +0200
Subject: [PATCH 050/106] Tune DREAM and model parameters in fit

---
 tmp/bumps_dream/ed-2-bayesian-new-API.py | 26 +++++++++++++-----------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index d4c6a910..31c8a182 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -47,7 +47,7 @@
 structure.space_group.it_coordinate_system_code = '1'
 
 # %%
-structure.cell.length_a = 3.8909
+structure.cell.length_a = 3.88
 
 # %%
 structure.atom_sites.create(
@@ -113,11 +113,11 @@
 
 # %%
 experiment.instrument.setup_wavelength = 1.494
-experiment.instrument.calib_twotheta_offset = 0.6226
+experiment.instrument.calib_twotheta_offset = 0.0
 
 # %%
-experiment.peak.broad_gauss_u = 0.0816
-experiment.peak.broad_gauss_v = -0.1159
+experiment.peak.broad_gauss_u = 0.1
+experiment.peak.broad_gauss_v = -0.1
 experiment.peak.broad_gauss_w = 0.1204
 experiment.peak.broad_lorentz_y = 0.0844
 
@@ -130,7 +130,7 @@
 
 # %%
 experiment.excluded_regions.create(id='1', start=0, end=30)
-experiment.excluded_regions.create(id='2', start=90, end=180)
+experiment.excluded_regions.create(id='2', start=70, end=180)
 
 # %%
 experiment.linked_phases.create(id='lbco', scale=9.1351)
@@ -161,7 +161,7 @@
 project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=83, x_max=85)
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
 
 # %% [markdown]
 # ## Step 5: Perform Bayesian Analysis
@@ -180,13 +180,15 @@
 
 # %%
 project.analysis.fit.show_minimizer_types()
+
+# %%
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
-dream = project.analysis.fit.minimizer
-dream.steps = 200 #1000
-dream.burn = 50 #200
-dream.thin = 1
-dream.pop = 4
+# %%
+project.analysis.fit.minimizer.steps = 200 #1000
+project.analysis.fit.minimizer.burn = 40 #200
+project.analysis.fit.minimizer.thin = 1
+project.analysis.fit.minimizer.pop = 4
 
 # %%
 project.analysis.fit()
@@ -210,6 +212,6 @@
 project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=83, x_max=85)
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
 
 # %%

From 29f9db74f6cd9143ee4e0f88e594a57afd59f259 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Fri, 8 May 2026 22:13:53 +0200
Subject: [PATCH 051/106] Expand Bayesian tutorial walkthrough

---
 tmp/bumps_dream/ed-2-bayesian-new-API.py | 161 +++++++++++++++++++----
 1 file changed, 137 insertions(+), 24 deletions(-)

diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index 31c8a182..8472c994 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -1,25 +1,25 @@
 # %% [markdown]
-# # Structure Refinement: LBCO, HRPT
+# # Deterministic and Bayesian Refinement: LBCO, HRPT
 #
-# This minimalistic example is designed to show how Rietveld refinement
-# can be performed when both the crystal structure and experiment are
-# defined directly in code. Only the experimentally measured data is
-# loaded from an external file. It also shows how to switch calculation
-# engine.
+# This tutorial demonstrates a practical two-stage workflow for powder
+# diffraction analysis with EasyDiffraction.
 #
-# For this example, constant-wavelength neutron powder diffraction data
-# for La0.5Ba0.5CoO3 from HRPT at PSI is used.
+# In the first stage, we run a fast local refinement to obtain a sensible
+# point estimate and parameter uncertainties. In the second stage, we use
+# these refined values to define fit bounds and then sample the posterior
+# distribution with DREAM.
 #
-# It does not contain any advanced features or options, and includes no
-# comments or explanations — these can be found in the other tutorials.
-# Default values are used for all parameters if not specified. Only
-# essential and self-explanatory code is provided.
+# The example uses constant-wavelength neutron powder diffraction data
+# for La0.5Ba0.5CoO3 measured on HRPT at PSI.
 #
-# The example is intended for users who are already familiar with the
-# EasyDiffraction library and want to quickly get started with a simple
-# refinement. It is also useful for those who want to see what a
-# refinement might look like in code. For a more detailed explanation of
-# the code, please refer to the other tutorials.
+# The goal is not only to obtain a good fit, but also to answer Bayesian
+# questions such as:
+#
+# - Which parameter values are most probable?
+# - How broad are the credible intervals?
+# - Which parameters are strongly correlated?
+# - How much uncertainty propagates into the calculated diffraction
+#   pattern?
 
 # %% [markdown]
 # ## Import Library
@@ -28,13 +28,21 @@
 import easydiffraction as ed
 
 # %% [markdown]
-# ## Step 1: Define Project
+# ## Step 1: Create a Project Container
+#
+# The project object keeps structures, experiments, fit settings, and
+# plotting utilities together in a single place. We will build the full
+# workflow inside this object.
 
 # %%
 project = ed.Project()
 
 # %% [markdown]
-# ## Step 2: Define Structure
+# ## Step 2: Build the Structural Model
+#
+# We define a simple cubic perovskite model for LBCO. La and Ba share the
+# same crystallographic site with equal occupancy, while Co and O occupy
+# the remaining ideal perovskite positions.
 
 # %%
 project.structures.create(name='lbco')
@@ -49,6 +57,11 @@
 # %%
 structure.cell.length_a = 3.88
 
+# %% [markdown]
+# The atom-site definitions below form the starting structural model. The
+# parameters are intentionally reasonable rather than fully optimized,
+# because the refinement step will improve them.
+
 # %%
 structure.atom_sites.create(
     label='La',
@@ -94,11 +107,21 @@
 )
 
 # %% [markdown]
-# ## Step 3: Define Experiment
+# ## Step 3: Define the Diffraction Experiment
+#
+# Next we download the measured powder pattern, create a neutron powder
+# experiment, and configure the instrument, profile, background, and
+# excluded regions.
+
+# %% [markdown]
+# #### Download the Measured Data
 
 # %%
 data_path = ed.download_data(id=3, destination='data')
 
+# %% [markdown]
+# #### Create the Experiment Object
+
 # %%
 project.experiments.add_from_data_path(
     name='hrpt',
@@ -111,6 +134,12 @@
 # %%
 experiment = project.experiments['hrpt']
 
+# %% [markdown]
+# #### Set Instrument and Peak-Profile Parameters
+#
+# These values provide the initial instrument description for the local
+# refinement. Later, a subset of them will be refined.
+
 # %%
 experiment.instrument.setup_wavelength = 1.494
 experiment.instrument.calib_twotheta_offset = 0.0
@@ -121,6 +150,12 @@
 experiment.peak.broad_gauss_w = 0.1204
 experiment.peak.broad_lorentz_y = 0.0844
 
+# %% [markdown]
+# #### Add Background Points and Excluded Regions
+#
+# The line-segment background is defined by a few anchor points. We also
+# exclude regions that are not intended to contribute to the fit.
+
 # %%
 experiment.background.create(id='1', x=10, y=168.5585)
 experiment.background.create(id='2', x=30, y=164.3357)
@@ -132,11 +167,25 @@
 experiment.excluded_regions.create(id='1', start=0, end=30)
 experiment.excluded_regions.create(id='2', start=70, end=180)
 
+# %% [markdown]
+# #### Link the Structural Phase to the Experiment
+
 # %%
 experiment.linked_phases.create(id='lbco', scale=9.1351)
 
 # %% [markdown]
-# ## Step 4: Perform Analysis
+# ## Step 4: Run an Initial Local Refinement
+#
+# Before Bayesian sampling, it is useful to run a deterministic fit. This
+# gives us:
+#
+# - a good point estimate near the best-fit region,
+# - uncertainties from the local optimizer,
+# - a quick check that the model and experiment are configured
+#   sensibly.
+#
+# In this tutorial we refine only a small set of parameters that are easy
+# to interpret in the later Bayesian stage.
 
 # %%
 structure.cell.length_a.free = True
@@ -144,6 +193,11 @@
 experiment.peak.broad_gauss_v.free = True
 experiment.instrument.calib_twotheta_offset.free = True
 
+# %% [markdown]
+# We choose the BUMPS Levenberg-Marquardt minimizer as a fast local
+# optimizer. Its main purpose here is to provide a stable starting point
+# and uncertainty estimates for the Bayesian run.
+
 # %%
 project.analysis.fit.show_minimizer_types()
 project.analysis.fit.minimizer_type = 'bumps (lm)'
@@ -154,6 +208,12 @@
 # %%
 project.analysis.display.fit_results()
 
+# %% [markdown]
+# The correlation plot shows how strongly the fitted parameters move
+# together in the local refinement. The measured-vs-calculated plots show
+# how well the refined model reproduces the data globally and in a zoomed
+# region.
+
 # %%
 project.display.plotter.plot_param_correlations(show_diagonal=True)
 
@@ -164,7 +224,15 @@
 project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
 
 # %% [markdown]
-# ## Step 5: Perform Bayesian Analysis
+# ## Step 5: Prepare for Bayesian Sampling
+#
+# DREAM requires finite bounds for the free parameters. Instead of
+# setting them manually, we derive them from the uncertainties estimated
+# in the local refinement.
+#
+# The helper method `set_fit_bounds_from_uncertainty` centers the bounds
+# on the current parameter value and expands them by a chosen multiple of
+# the reported uncertainty.
 
 # %%
 project.analysis.display.free_params()
@@ -175,9 +243,25 @@
 experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)
 experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)
 
+# %% [markdown]
+# Displaying the free parameters again is a convenient way to confirm
+# that the fit bounds have been assigned as expected before launching the
+# sampler.
+
 # %%
 project.analysis.display.free_params()
 
+# %% [markdown]
+# ## Step 6: Configure and Run DREAM
+#
+# We now switch from the local minimizer to the Bayesian DREAM sampler.
+#
+# The settings below are intentionally small so the tutorial runs
+# quickly. For production analysis you would usually increase the number
+# of steps and often the burn-in as well. When needed, the DREAM API
+# also lets you tune how chains are initialized through the `init`
+# setting.
+
 # %%
 project.analysis.fit.show_minimizer_types()
 
@@ -193,25 +277,54 @@
 # %%
 project.analysis.fit()
 
+# %% [markdown]
+# ## Step 7: Inspect Bayesian Results
+#
+# The fit-results display now includes sampler settings, convergence
+# diagnostics, committed parameter values, and posterior summary
+# statistics.
+
 # %%
 project.analysis.display.fit_results()
 
+# %% [markdown]
+# The correlation and posterior-pair plots are complementary:
+#
+# - `plot_param_correlations` summarizes pairwise structure in a compact
+#   matrix.
+# - `plot_posterior_pairs` shows marginal densities on the diagonal and
+#   posterior contours off-diagonal.
+
 # %%
 project.display.plotter.plot_param_correlations(show_diagonal=True)
 
 # %%
 project.display.plotter.plot_posterior_pairs()
 
+# %% [markdown]
+# The one-dimensional posterior distributions below make it easier to
+# inspect individual parameters in isolation, including asymmetry or
+# multimodality.
+
 # %%
 project.display.plotter.plot_param_distribution(structure.cell.length_a)
 project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)
 project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)
 project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
 
+# %% [markdown]
+# Finally, the posterior predictive plot propagates the sampled parameter
+# uncertainty into the calculated diffraction pattern. Comparing this to
+# the zoomed measured-vs-calculated view helps assess whether the sampled
+# model family explains the data in the region of interest.
+
 # %%
 project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
 
-# %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
+# %% [markdown]
+# A final zoomed measured-vs-calculated plot is useful for checking how
+# the posterior-supported model behaves in a narrow region of the pattern
+# after the Bayesian run.
 
 # %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)

From af27c2f4a1d97c3a271879214d4b008d5514915e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sat, 9 May 2026 00:04:41 +0200
Subject: [PATCH 052/106] Apply linting rules and formatting

---
 docs/dev/architecture.md                      |   44 +-
 docs/dev/package-structure-full.md            |   17 +-
 docs/dev/package-structure-short.md           |    2 +
 .../analysis/fit_helpers/bayesian.py          |  270 ++--
 .../analysis/fit_helpers/reporting.py         |   12 +-
 .../analysis/fit_helpers/tracking.py          |  359 +++--
 .../analysis/minimizers/base.py               |    9 +-
 .../analysis/minimizers/bumps_dream.py        |  377 ++++--
 src/easydiffraction/core/variable.py          |    7 +-
 .../display/plotters/plotly.py                |  415 ++++--
 src/easydiffraction/display/plotting.py       | 1156 +++++++++++------
 .../fitting/test_bayesian_dream.py            |  178 +++
 .../analysis/fit_helpers/test_bayesian.py     |  188 +++
 .../analysis/minimizers/test_bumps.py         |   12 +
 .../analysis/minimizers/test_bumps_dream.py   |  236 ++++
 .../analysis/minimizers/test_enums.py         |    7 +
 .../analysis/minimizers/test_factory.py       |   12 +-
 .../easydiffraction/core/test_parameters.py   |   65 +
 .../easydiffraction/display/test_plotting.py  |  168 +++
 tmp/bumps_dream/ed-2-bayesian-new-API.py      |    3 +
 20 files changed, 2645 insertions(+), 892 deletions(-)
 create mode 100644 tests/integration/fitting/test_bayesian_dream.py
 create mode 100644 tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
 create mode 100644 tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py

diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index dbfe59c8..8c30e378 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -527,28 +527,28 @@ from .line_segment import LineSegmentBackground
 
 ### 5.5 All Factories
 
-| Factory | Domain | Tags resolve to |
-| --- | --- | --- |
-| `BackgroundFactory` | Background categories | `LineSegmentBackground`, `ChebyshevPolynomialBackground` |
-| `PeakFactory` | Peak profiles | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, … |
-| `InstrumentFactory` | Instruments | `CwlPdInstrument`, `TofPdInstrument`, … |
-| `DataFactory` | Data collections | `PdCwlData`, `PdTofData`, `TotalData` |
-| `ReflnFactory` | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData` |
-| `ExtinctionFactory` | Extinction models | `BeckerCoppensExtinction` |
-| `LinkedCrystalFactory` | Linked-crystal refs | `LinkedCrystal` |
-| `ExcludedRegionsFactory` | Excluded regions | `ExcludedRegions` |
-| `LinkedPhasesFactory` | Linked phases | `LinkedPhases` |
-| `ExperimentTypeFactory` | Experiment descriptors | `ExperimentType` |
-| `CellFactory` | Unit cells | `Cell` |
-| `SpaceGroupFactory` | Space groups | `SpaceGroup` |
-| `AtomSitesFactory` | Atom sites | `AtomSites` |
-| `AtomSiteAnisoFactory` | Anisotropic ADPs | `AtomSiteAnisoCollection` |
-| `AliasesFactory` | Parameter aliases | `Aliases` |
-| `ConstraintsFactory` | Parameter constraints | `Constraints` |
-| `FitModeFactory` | Fit-mode category | `FitMode` |
-| `JointFitExperimentsFactory` | Joint-fit weights | `JointFitExperiments` |
-| `CalculatorFactory` | Calculation engines | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator` |
-| `MinimizerFactory` | Minimisers | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsDreamMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
+| Factory                      | Domain                 | Tags resolve to                                                                                                                                                                                    |
+| ---------------------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `BackgroundFactory`          | Background categories  | `LineSegmentBackground`, `ChebyshevPolynomialBackground`                                                                                                                                           |
+| `PeakFactory`                | Peak profiles          | `CwlPseudoVoigt`, `TofJorgensen`, `TofJorgensenVonDreele`, …                                                                                                                                       |
+| `InstrumentFactory`          | Instruments            | `CwlPdInstrument`, `TofPdInstrument`, …                                                                                                                                                            |
+| `DataFactory`                | Data collections       | `PdCwlData`, `PdTofData`, `TotalData`                                                                                                                                                              |
+| `ReflnFactory`               | Reflection collections | `ReflnData`, `PowderCwlReflnData`, `PowderTofReflnData`                                                                                                                                            |
+| `ExtinctionFactory`          | Extinction models      | `BeckerCoppensExtinction`                                                                                                                                                                          |
+| `LinkedCrystalFactory`       | Linked-crystal refs    | `LinkedCrystal`                                                                                                                                                                                    |
+| `ExcludedRegionsFactory`     | Excluded regions       | `ExcludedRegions`                                                                                                                                                                                  |
+| `LinkedPhasesFactory`        | Linked phases          | `LinkedPhases`                                                                                                                                                                                     |
+| `ExperimentTypeFactory`      | Experiment descriptors | `ExperimentType`                                                                                                                                                                                   |
+| `CellFactory`                | Unit cells             | `Cell`                                                                                                                                                                                             |
+| `SpaceGroupFactory`          | Space groups           | `SpaceGroup`                                                                                                                                                                                       |
+| `AtomSitesFactory`           | Atom sites             | `AtomSites`                                                                                                                                                                                        |
+| `AtomSiteAnisoFactory`       | Anisotropic ADPs       | `AtomSiteAnisoCollection`                                                                                                                                                                          |
+| `AliasesFactory`             | Parameter aliases      | `Aliases`                                                                                                                                                                                          |
+| `ConstraintsFactory`         | Parameter constraints  | `Constraints`                                                                                                                                                                                      |
+| `FitModeFactory`             | Fit-mode category      | `FitMode`                                                                                                                                                                                          |
+| `JointFitExperimentsFactory` | Joint-fit weights      | `JointFitExperiments`                                                                                                                                                                              |
+| `CalculatorFactory`          | Calculation engines    | `CryspyCalculator`, `CrysfmlCalculator`, `PdffitCalculator`                                                                                                                                        |
+| `MinimizerFactory`           | Minimisers             | `LmfitMinimizer`, `LmfitLeastsqMinimizer`, `LmfitLeastSquaresMinimizer`, `DfolsMinimizer`, `BumpsMinimizer`, `BumpsLmMinimizer`, `BumpsDreamMinimizer`, `BumpsAmoebaMinimizer`, `BumpsDEMinimizer` |
 
 > **Note:** `ExperimentFactory` and `StructureFactory` are _builder_
 > factories with `from_cif_path`, `from_cif_str`, `from_data_path`, and
diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index be35bb07..d1901351 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -49,10 +49,16 @@
 │   │   └── 📄 __init__.py
 │   ├── 📁 fit_helpers
 │   │   ├── 📄 __init__.py
+│   │   ├── 📄 bayesian.py
+│   │   │   ├── 🏷️ class PosteriorParameterSummary
+│   │   │   ├── 🏷️ class PosteriorPredictiveSummary
+│   │   │   ├── 🏷️ class PosteriorSamples
+│   │   │   └── 🏷️ class BayesianFitResults
 │   │   ├── 📄 metrics.py
 │   │   ├── 📄 reporting.py
 │   │   │   └── 🏷️ class FitResults
 │   │   └── 📄 tracking.py
+│   │       ├── 🏷️ class SamplerProgressUpdate
 │   │       ├── 🏷️ class _TerminalLiveHandle
 │   │       └── 🏷️ class FitProgressTracker
 │   ├── 📁 minimizers
@@ -66,12 +72,18 @@
 │   │   │   └── 🏷️ class BumpsAmoebaMinimizer
 │   │   ├── 📄 bumps_de.py
 │   │   │   └── 🏷️ class BumpsDEMinimizer
+│   │   ├── 📄 bumps_dream.py
+│   │   │   ├── 🏷️ class _DreamRunContext
+│   │   │   ├── 🏷️ class _DreamDriverResult
+│   │   │   ├── 🏷️ class _DreamProgressMonitor
+│   │   │   └── 🏷️ class BumpsDreamMinimizer
 │   │   ├── 📄 bumps_lm.py
 │   │   │   └── 🏷️ class BumpsLmMinimizer
 │   │   ├── 📄 dfols.py
 │   │   │   └── 🏷️ class DfolsMinimizer
 │   │   ├── 📄 enums.py
-│   │   │   └── 🏷️ class MinimizerTypeEnum
+│   │   │   ├── 🏷️ class MinimizerTypeEnum
+│   │   │   └── 🏷️ class DreamPopulationInitializationEnum
 │   │   ├── 📄 factory.py
 │   │   │   └── 🏷️ class MinimizerFactory
 │   │   ├── 📄 lmfit.py
@@ -366,6 +378,9 @@
 │   │   ├── 🏷️ class PlotterEngineEnum
 │   │   ├── 🏷️ class _MeasVsCalcPlotOptions
 │   │   ├── 🏷️ class _PowderMeasVsCalcSeries
+│   │   ├── 🏷️ class _PosteriorDistributionContext
+│   │   ├── 🏷️ class _PosteriorPairsContext
+│   │   ├── 🏷️ class _PosteriorPairsLegendState
 │   │   ├── 🏷️ class Plotter
 │   │   └── 🏷️ class PlotterFactory
 │   ├── 📄 tables.py
diff --git a/docs/dev/package-structure-short.md b/docs/dev/package-structure-short.md
index ba2a0b33..9ce283eb 100644
--- a/docs/dev/package-structure-short.md
+++ b/docs/dev/package-structure-short.md
@@ -31,6 +31,7 @@
 │   │   └── 📄 __init__.py
 │   ├── 📁 fit_helpers
 │   │   ├── 📄 __init__.py
+│   │   ├── 📄 bayesian.py
 │   │   ├── 📄 metrics.py
 │   │   ├── 📄 reporting.py
 │   │   └── 📄 tracking.py
@@ -40,6 +41,7 @@
 │   │   ├── 📄 bumps.py
 │   │   ├── 📄 bumps_amoeba.py
 │   │   ├── 📄 bumps_de.py
+│   │   ├── 📄 bumps_dream.py
 │   │   ├── 📄 bumps_lm.py
 │   │   ├── 📄 dfols.py
 │   │   ├── 📄 enums.py
diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 4815a2c2..243ce872 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -14,22 +14,29 @@
 from easydiffraction.analysis.fit_helpers.metrics import calculate_r_factor_squared
 from easydiffraction.analysis.fit_helpers.metrics import calculate_rb_factor
 from easydiffraction.analysis.fit_helpers.metrics import calculate_weighted_r_factor
+from easydiffraction.analysis.fit_helpers.reporting import FitResults
 from easydiffraction.analysis.fit_helpers.reporting import _build_parameter_row
 from easydiffraction.analysis.fit_helpers.reporting import _format_optional_float
-from easydiffraction.analysis.fit_helpers.reporting import FitResults
 from easydiffraction.utils.logging import console
 from easydiffraction.utils.logging import log
 from easydiffraction.utils.utils import render_table
 
 R_HAT_CONVERGENCE_THRESHOLD = 1.01
 ESS_BULK_CONVERGENCE_THRESHOLD = 400.0
+POSTERIOR_SAMPLE_NDIM = 3
+DEFAULT_CI_LEVELS = (0.68, 0.95)
+DEFAULT_CREDIBLE_INTERVAL_LEVELS = DEFAULT_CI_LEVELS
+IntervalLevels = tuple[float, ...]
+SettingsMap = dict[str, object] | None
+DiagnosticsMap = dict[str, object] | None
 
 
 @dataclass(slots=True)
 class PosteriorParameterSummary:
-    """Posterior summary statistics for one fitted parameter.
+    r"""
+    Posterior summary statistics for one fitted parameter.
 
-    Parameters
+    Attributes
     ----------
     unique_name : str
         Unique parameter name used across EasyDiffraction.
@@ -48,7 +55,7 @@ class PosteriorParameterSummary:
     ess_bulk : float | None, default=None
         Bulk effective sample size when available.
     r_hat : float | None, default=None
-        Rank-normalized split-$\\hat{R}$ when available.
+        Rank-normalized split-$\hat{R}$ when available.
     """
 
     unique_name: str
@@ -64,9 +71,10 @@ class PosteriorParameterSummary:
 
 @dataclass(slots=True)
 class PosteriorPredictiveSummary:
-    """Posterior predictive summaries for one experiment.
+    """
+    Posterior predictive summaries for one experiment.
 
-    Parameters
+    Attributes
     ----------
     experiment_name : str
         Experiment identifier.
@@ -101,9 +109,10 @@ class PosteriorPredictiveSummary:
 
 @dataclass(slots=True)
 class PosteriorSamples:
-    """Posterior samples and sample statistics from a Bayesian fit.
+    """
+    Posterior samples and sample statistics from a Bayesian fit.
 
-    Parameters
+    Attributes
     ----------
     parameter_names : list[str]
         Parameter names in the preserved EasyDiffraction order.
@@ -123,7 +132,8 @@ class PosteriorSamples:
     draw_index: np.ndarray | None = None
 
     def flattened(self) -> np.ndarray:
-        """Return flattened posterior samples by parameter.
+        """
+        Return flattened posterior samples by parameter.
 
         Returns
         -------
@@ -133,7 +143,8 @@ def flattened(self) -> np.ndarray:
         return np.asarray(self.parameter_samples).reshape(-1, len(self.parameter_names))
 
     def to_arviz(self) -> object:
-        """Convert posterior samples to an ArviZ ``InferenceData`` object.
+        """
+        Convert posterior samples to an ArviZ ``InferenceData`` object.
 
         Returns
         -------
@@ -146,10 +157,8 @@ def to_arviz(self) -> object:
         ValueError
             If the stored arrays do not have the expected shapes.
         """
-        import arviz as az
-
         posterior_array = np.asarray(self.parameter_samples, dtype=float)
-        if posterior_array.ndim != 3:
+        if posterior_array.ndim != POSTERIOR_SAMPLE_NDIM:
             msg = 'Posterior sample array must have shape (n_draws, n_chains, n_parameters).'
             raise ValueError(msg)
 
@@ -178,10 +187,16 @@ def to_arviz(self) -> object:
         return az.from_dict(data)
 
 
+SummaryList = list[PosteriorParameterSummary] | None
+PredictiveMap = dict[str, PosteriorPredictiveSummary] | None
+
+
+@dataclass(kw_only=True)
 class BayesianFitResults(FitResults):
-    """Container for Bayesian fit results and posterior summaries.
+    """
+    Container for Bayesian fit results and posterior summaries.
 
-    Parameters
+    Attributes
     ----------
     success : bool, default=False
         Whether the Bayesian fit produced usable posterior results.
@@ -201,15 +216,15 @@ class BayesianFitResults(FitResults):
         Name of the point estimate committed back to the project.
     posterior_samples : PosteriorSamples | None, default=None
         Stored posterior samples.
-    posterior_parameter_summaries : list[PosteriorParameterSummary] | None, default=None
+    posterior_parameter_summaries : SummaryList, default=None
         Posterior summaries for each sampled parameter.
-    posterior_predictive : dict[str, PosteriorPredictiveSummary] | None, default=None
+    posterior_predictive : PredictiveMap, default=None
         Posterior predictive summaries keyed by experiment name.
-    credible_interval_levels : tuple[float, ...], default=(0.68, 0.95)
+    credible_interval_levels : IntervalLevels, default=DEFAULT_CI_LEVELS
         Interval levels available in the summaries.
-    sampler_settings : dict[str, object] | None, default=None
+    sampler_settings : SettingsMap, default=None
         Sampler settings recorded for reproducibility.
-    convergence_diagnostics : dict[str, object] | None, default=None
+    convergence_diagnostics : DiagnosticsMap, default=None
         Convergence diagnostics and status metadata.
     sampler_completed : bool, default=False
         Whether the sampler completed a run and returned posterior data.
@@ -217,50 +232,47 @@ class BayesianFitResults(FitResults):
         Best log-posterior value reported by the sampler.
     """
 
-    def __init__(
-        self,
-        *,
-        success: bool = False,
-        parameters: list[object] | None = None,
-        reduced_chi_square: float | None = None,
-        engine_result: object | None = None,
-        starting_parameters: list[object] | None = None,
-        fitting_time: float | None = None,
-        sampler_name: str = 'dream',
-        point_estimate_name: str = 'map',
-        posterior_samples: PosteriorSamples | None = None,
-        posterior_parameter_summaries: list[PosteriorParameterSummary] | None = None,
-        posterior_predictive: dict[str, PosteriorPredictiveSummary] | None = None,
-        credible_interval_levels: tuple[float, ...] = (0.68, 0.95),
-        sampler_settings: dict[str, object] | None = None,
-        convergence_diagnostics: dict[str, object] | None = None,
-        sampler_completed: bool = False,
-        best_log_posterior: float | None = None,
-    ) -> None:
+    success: bool = False
+    parameters: list[object] | None = None
+    reduced_chi_square: float | None = None
+    engine_result: object | None = None
+    starting_parameters: list[object] | None = None
+    fitting_time: float | None = None
+    sampler_name: str = 'dream'
+    point_estimate_name: str = 'map'
+    posterior_samples: PosteriorSamples | None = None
+    posterior_parameter_summaries: SummaryList = None
+    posterior_predictive: PredictiveMap = None
+    credible_interval_levels: IntervalLevels = DEFAULT_CI_LEVELS
+    sampler_settings: SettingsMap = None
+    convergence_diagnostics: DiagnosticsMap = None
+    sampler_completed: bool = False
+    best_log_posterior: float | None = None
+
+    def __post_init__(self) -> None:
+        """
+        Initialize inherited FitResults state and normalize containers.
+        """
         super().__init__(
-            success=success,
-            parameters=parameters,
-            reduced_chi_square=reduced_chi_square,
-            engine_result=engine_result,
-            starting_parameters=starting_parameters,
-            fitting_time=fitting_time,
+            success=self.success,
+            parameters=self.parameters,
+            reduced_chi_square=self.reduced_chi_square,
+            engine_result=self.engine_result,
+            starting_parameters=self.starting_parameters,
+            fitting_time=self.fitting_time,
         )
-        self.sampler_name = sampler_name
-        self.point_estimate_name = point_estimate_name
-        self.posterior_samples = posterior_samples
         self.posterior_parameter_summaries = (
-            posterior_parameter_summaries if posterior_parameter_summaries is not None else []
+            list(self.posterior_parameter_summaries)
+            if self.posterior_parameter_summaries is not None
+            else []
         )
         self.posterior_predictive = (
-            posterior_predictive if posterior_predictive is not None else {}
+            dict(self.posterior_predictive) if self.posterior_predictive is not None else {}
         )
-        self.credible_interval_levels = credible_interval_levels
-        self.sampler_settings = sampler_settings if sampler_settings is not None else {}
+        self.sampler_settings = dict(self.sampler_settings) if self.sampler_settings else {}
         self.convergence_diagnostics = (
-            convergence_diagnostics if convergence_diagnostics is not None else {}
+            dict(self.convergence_diagnostics) if self.convergence_diagnostics is not None else {}
         )
-        self.sampler_completed = sampler_completed
-        self.best_log_posterior = best_log_posterior
 
     def display_results(
         self,
@@ -270,7 +282,8 @@ def display_results(
         f_obs: list[float] | None = None,
         f_calc: list[float] | None = None,
     ) -> None:
-        """Render a Bayesian fit summary with posterior diagnostics.
+        """
+        Render a Bayesian fit summary with posterior diagnostics.
 
         Parameters
         ----------
@@ -285,30 +298,53 @@ def display_results(
         f_calc : list[float] | None, default=None
             Calculated structure-factor magnitudes for Bragg R.
         """
+        metrics = _calculate_fit_quality_metrics(
+            y_obs=y_obs,
+            y_calc=y_calc,
+            y_err=y_err,
+            f_obs=f_obs,
+            f_calc=f_calc,
+        )
+
+        self._display_summary_header()
+        _print_fit_quality_metrics(metrics)
+
+        console.print('📈 Committed parameters:')
+        _render_committed_parameter_table(self.parameters)
+
+        console.print('📊 Posterior parameter summaries:')
+        _render_posterior_summary_table(
+            parameters=self.parameters,
+            posterior_parameter_summaries=self.posterior_parameter_summaries,
+        )
+
+        self._print_table_notes()
+
+    def _print_table_notes(self) -> None:
+        """
+        Print parameter and posterior-diagnostic notes below tables.
+        """
+        super()._print_table_notes()
+        for note in _posterior_table_notes(self.posterior_parameter_summaries):
+            log.warning(note)
+
+    def _display_summary_header(self) -> None:
+        """Render the high-level Bayesian fit summary."""
         status_icon = '✅' if self.success else '❌'
-        rf = rf2 = wr = br = None
-        if y_obs is not None and y_calc is not None:
-            rf = calculate_r_factor(y_obs, y_calc) * 100
-            rf2 = calculate_r_factor_squared(y_obs, y_calc) * 100
-        if y_obs is not None and y_calc is not None and y_err is not None:
-            wr = calculate_weighted_r_factor(y_obs, y_calc, y_err) * 100
-        if f_obs is not None and f_calc is not None:
-            br = calculate_rb_factor(f_obs, f_calc) * 100
+        fitting_time = _format_optional_float(self.fitting_time, suffix=' seconds')
+        goodness_of_fit = _format_optional_float(self.reduced_chi_square)
 
         console.paragraph('Bayesian fit results')
         console.print(f'{status_icon} Success: {self.success}')
         if self.message:
-            console.print(f'ℹ️ Status: {self.message}')
+            console.print(f'i Status: {self.message}')
         console.print(f'🧪 Sampler: {self.sampler_name}')
         console.print(
             f'🎯 Committed point estimate: {_format_point_estimate_name(self.point_estimate_name)}'
         )
         console.print(f'🔁 Sampler completed: {self.sampler_completed}')
-        console.print(f'⏱️ Fitting time: {_format_optional_float(self.fitting_time, suffix=" seconds")}')
-        console.print(
-            '📏 Goodness-of-fit (reduced χ²): '
-            f'{_format_optional_float(self.reduced_chi_square)}'
-        )
+        console.print(f'⏱️ Fitting time: {fitting_time}')
+        console.print(f'📏 Goodness-of-fit (reduced χ²): {goodness_of_fit}')
         if self.best_log_posterior is not None:
             console.print(f'📉 Best log-posterior: {self.best_log_posterior:.2f}')
 
@@ -320,35 +356,10 @@ def display_results(
         if convergence_summary is not None:
             console.print(Text.from_markup(f'📊 Convergence: {convergence_summary}'))
 
-        if rf is not None:
-            console.print(f'📏 R-factor (Rf): {rf:.2f}%')
-        if rf2 is not None:
-            console.print(f'📏 R-factor squared (Rf²): {rf2:.2f}%')
-        if wr is not None:
-            console.print(f'📏 Weighted R-factor (wR): {wr:.2f}%')
-        if br is not None:
-            console.print(f'📏 Bragg R-factor (BR): {br:.2f}%')
-
-        console.print('📈 Committed parameters:')
-        _render_committed_parameter_table(self.parameters)
-
-        console.print('📊 Posterior parameter summaries:')
-        _render_posterior_summary_table(
-            parameters=self.parameters,
-            posterior_parameter_summaries=self.posterior_parameter_summaries,
-        )
-
-        self._print_table_notes()
-
-    def _print_table_notes(self) -> None:
-        """Print parameter and posterior-diagnostic notes below tables."""
-        super()._print_table_notes()
-        for note in _posterior_table_notes(self.posterior_parameter_summaries):
-            log.warning(note)
-
 
 def compute_convergence_diagnostics(posterior_samples: PosteriorSamples) -> dict[str, object]:
-    """Compute convergence diagnostics from posterior samples.
+    """
+    Compute convergence diagnostics from posterior samples.
 
     Parameters
     ----------
@@ -395,7 +406,8 @@ def summarize_posterior_parameters(
     parameter_display_names: list[str] | None = None,
     convergence_diagnostics: dict[str, object] | None = None,
 ) -> list[PosteriorParameterSummary]:
-    """Build posterior parameter summaries in EasyDiffraction order.
+    """
+    Build posterior parameter summaries in EasyDiffraction order.
 
     Parameters
     ----------
@@ -418,14 +430,16 @@ def summarize_posterior_parameters(
     Raises
     ------
     ValueError
-        If the posterior sample array is incompatible with the
-        parameter name list.
+        If the posterior sample array is incompatible with the parameter
+        name list.
     """
     flattened = posterior_samples.flattened()
     if flattened.shape[1] != len(parameter_names):
         msg = 'Posterior samples do not match the sampled parameter name list length.'
         raise ValueError(msg)
-    if parameter_display_names is not None and len(parameter_display_names) != len(parameter_names):
+    if parameter_display_names is not None and len(parameter_display_names) != len(
+        parameter_names
+    ):
         msg = 'Posterior display-name list must match the sampled parameter name list length.'
         raise ValueError(msg)
 
@@ -465,7 +479,8 @@ def summarize_posterior_parameters(
 def standard_deviations_from_summaries(
     summaries: list[PosteriorParameterSummary],
 ) -> np.ndarray:
-    """Return posterior standard deviations in summary order.
+    """
+    Return posterior standard deviations in summary order.
 
     Parameters
     ----------
@@ -497,13 +512,52 @@ def _format_sampler_settings(sampler_settings: dict[str, object]) -> str | None:
     if not sampler_settings:
         return None
 
-    parts: list[str] = []
-    for key in ('random_seed', 'steps', 'burn', 'thin', 'pop', 'samples'):
-        if key in sampler_settings:
-            parts.append(f'{key}={sampler_settings[key]}')
+    parts = [
+        f'{key}={sampler_settings[key]}'
+        for key in ('random_seed', 'steps', 'burn', 'thin', 'pop', 'samples')
+        if key in sampler_settings
+    ]
     return ', '.join(parts) if parts else None
 
 
+def _calculate_fit_quality_metrics(
+    *,
+    y_obs: list[float] | None,
+    y_calc: list[float] | None,
+    y_err: list[float] | None,
+    f_obs: list[float] | None,
+    f_calc: list[float] | None,
+) -> dict[str, float | None]:
+    """Compute optional fit-quality metrics for summary rendering."""
+    metrics: dict[str, float | None] = {
+        'rf': None,
+        'rf2': None,
+        'wr': None,
+        'br': None,
+    }
+    if y_obs is not None and y_calc is not None:
+        metrics['rf'] = calculate_r_factor(y_obs, y_calc) * 100
+        metrics['rf2'] = calculate_r_factor_squared(y_obs, y_calc) * 100
+    if y_obs is not None and y_calc is not None and y_err is not None:
+        metrics['wr'] = calculate_weighted_r_factor(y_obs, y_calc, y_err) * 100
+    if f_obs is not None and f_calc is not None:
+        metrics['br'] = calculate_rb_factor(f_obs, f_calc) * 100
+    return metrics
+
+
+def _print_fit_quality_metrics(metrics: dict[str, float | None]) -> None:
+    """Render any available fit-quality metrics."""
+    metric_labels = (
+        ('📏 R-factor (Rf)', metrics['rf']),
+        ('📏 R-factor squared (Rf²)', metrics['rf2']),
+        ('📏 Weighted R-factor (wR)', metrics['wr']),
+        ('📏 Bragg R-factor (BR)', metrics['br']),
+    )
+    for label, value in metric_labels:
+        if value is not None:
+            console.print(f'{label}: {value:.2f}%')
+
+
 def _format_point_estimate_name(point_estimate_name: str) -> str:
     """Return a user-facing label for the committed point estimate."""
     normalized_name = point_estimate_name.strip().lower().replace('_', ' ')
@@ -694,4 +748,4 @@ def _posterior_table_notes(
             f'[red]ess_bulk[/red]: less than {ESS_BULK_CONVERGENCE_THRESHOLD:.0f} '
             '(consider longer sampling, tighter bounds, or reparameterization).'
         )
-    return notes
\ No newline at end of file
+    return notes
diff --git a/src/easydiffraction/analysis/fit_helpers/reporting.py b/src/easydiffraction/analysis/fit_helpers/reporting.py
index 329f77e6..0e867156 100644
--- a/src/easydiffraction/analysis/fit_helpers/reporting.py
+++ b/src/easydiffraction/analysis/fit_helpers/reporting.py
@@ -108,11 +108,10 @@ def display_results(
 
         console.paragraph('Fit results')
         console.print(f'{status_icon} Success: {self.success}')
-        console.print(f'⏱️ Fitting time: {_format_optional_float(self.fitting_time, suffix=" seconds")}')
-        console.print(
-            '📏 Goodness-of-fit (reduced χ²): '
-            f'{_format_optional_float(self.reduced_chi_square)}'
-        )
+        fitting_time = _format_optional_float(self.fitting_time, suffix=' seconds')
+        goodness_of_fit = _format_optional_float(self.reduced_chi_square)
+        console.print(f'⏱️ Fitting time: {fitting_time}')
+        console.print(f'📏 Goodness-of-fit (reduced χ²): {goodness_of_fit}')
         if rf is not None:
             console.print(f'📏 R-factor (Rf): {rf:.2f}%')
         if rf2 is not None:
@@ -258,7 +257,8 @@ def _format_optional_float(
     *,
     suffix: str = '',
 ) -> str:
-    """Format an optional float for console output.
+    """
+    Format an optional float for console output.
 
     Parameters
     ----------
diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py
index d85a705e..b5cfb11a 100644
--- a/src/easydiffraction/analysis/fit_helpers/tracking.py
+++ b/src/easydiffraction/analysis/fit_helpers/tracking.py
@@ -3,6 +3,7 @@
 
 import time
 from contextlib import suppress
+from dataclasses import dataclass
 
 import numpy as np
 
@@ -38,6 +39,22 @@
 SAMPLER_ALIGNMENTS = ['center', 'center', 'center', 'center', 'center']
 
 
+@dataclass(frozen=True, slots=True)
+class SamplerProgressUpdate:
+    """
+    Normalized sampler progress payload forwarded by monitor hooks.
+    """
+
+    iteration: int
+    total_iterations: int
+    phase: str
+    progress_percent: float
+    log_posterior: float
+    reduced_chi2: float
+    elapsed_time: float
+    force_report: bool = False
+
+
 class _TerminalLiveHandle:
     """
     Adapter that exposes update()/close() for terminal live updates.
@@ -222,93 +239,45 @@ def track(
 
         return residuals
 
-    def track_sampler_progress(
-        self,
-        *,
-        iteration: int,
-        total_iterations: int,
-        phase: str,
-        progress_percent: float,
-        log_posterior: float,
-        reduced_chi2: float,
-        elapsed_time: float,
-        force_report: bool = False,
-    ) -> None:
-        """Update progress from a sampler monitor.
+    def track_sampler_progress(self, update: SamplerProgressUpdate) -> None:
+        """
+        Update progress from a sampler monitor.
 
         Parameters
         ----------
-        iteration : int
-            Sampler iteration or generation index.
-        total_iterations : int
-            Total sampler generations configured for the run.
-        phase : str
-            Current sampler phase, e.g. ``'burn-in'`` or ``'sampling'``.
-        progress_percent : float
-            Completed fraction expressed in percent.
-        log_posterior : float
-            Current best log-posterior implied by the sampler state.
-        reduced_chi2 : float
-            Best reduced chi-square implied by the current best sample.
-        elapsed_time : float
-            Elapsed wall time in seconds.
-        force_report : bool, default=False
-            Whether to render the row regardless of heartbeat timing.
+        update : SamplerProgressUpdate
+            Sampler iteration, phase, timing, and fit-quality payload.
         """
-        self._iteration = iteration
+        self._iteration = update.iteration
         self._tracking_mode = TRACKING_MODE_SAMPLER
-        self._sampler_total_iterations = max(1, total_iterations)
+        self._sampler_total_iterations = max(1, update.total_iterations)
 
-        clamped_iteration = min(max(1, iteration), self._sampler_total_iterations)
-        clamped_progress = min(max(progress_percent, 0.0), 100.0)
+        clamped_iteration = min(max(1, update.iteration), self._sampler_total_iterations)
+        clamped_progress = min(max(update.progress_percent, 0.0), 100.0)
         previous_phase = self._last_sampler_phase
-        self._last_sampler_phase = phase
+        self._last_sampler_phase = update.phase
         self._last_sampler_progress_percent = clamped_progress
-        self._last_sampler_log_posterior = log_posterior
-        self._last_sampler_elapsed_time = elapsed_time
+        self._last_sampler_log_posterior = update.log_posterior
+        self._last_sampler_elapsed_time = update.elapsed_time
 
-        row: list[str] = []
-        if self._previous_chi2 is None or self._best_chi2 is None:
-            self._previous_chi2 = reduced_chi2
-            self._best_chi2 = reduced_chi2
-            self._best_iteration = iteration
-            self._last_progress_time = elapsed_time
-            row = [
-                f'{clamped_iteration}/{self._sampler_total_iterations}',
-                f'{clamped_progress:.1f}%',
-                self._format_elapsed_time(elapsed_time),
-                f'{log_posterior:.2f}',
-                phase,
-            ]
-        else:
-            if reduced_chi2 < self._best_chi2:
-                self._best_chi2 = reduced_chi2
-                self._best_iteration = iteration
-
-            if (
-                iteration != self._last_reported_iteration
-                and (
-                    force_report
-                    or previous_phase != phase
-                    or self._last_progress_time is None
-                    or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
-                    or clamped_iteration >= self._sampler_total_iterations
-                )
-            ):
-                row = [
-                    f'{clamped_iteration}/{self._sampler_total_iterations}',
-                    f'{clamped_progress:.1f}%',
-                    self._format_elapsed_time(elapsed_time),
-                    f'{log_posterior:.2f}',
-                    phase,
-                ]
-                self._last_progress_time = elapsed_time
+        row = self._initial_sampler_progress_row(
+            update=update,
+            clamped_iteration=clamped_iteration,
+            clamped_progress=clamped_progress,
+        )
+        if not row:
+            row = self._continued_sampler_progress_row(
+                update=update,
+                previous_phase=previous_phase,
+                clamped_iteration=clamped_iteration,
+                clamped_progress=clamped_progress,
+            )
 
         if row:
             self.add_tracking_info(row)
 
-        self._last_chi2 = reduced_chi2
-        self._last_iteration = iteration
+        self._last_chi2 = update.reduced_chi2
+        self._last_iteration = update.iteration
 
     @property
     def best_chi2(self) -> float | None:
@@ -351,7 +320,7 @@ def start_tracking(self, minimizer_name: str, *, mode: str = TRACKING_MODE_FIT)
         ----------
         minimizer_name : str
             Name of the minimizer used for the run.
-        mode : str, default='fit'
+        mode : str, default=TRACKING_MODE_FIT
             Tracking mode for the run.
         """
         self._tracking_mode = (
@@ -367,7 +336,7 @@ def start_tracking(self, minimizer_name: str, *, mode: str = TRACKING_MODE_FIT)
         if self._tracking_mode == TRACKING_MODE_SAMPLER:
             console.print('📈 Bayesian sampling progress:')
         else:
-            console.print('📈 Fit progress:')
+            console.print('📈 Goodness-of-fit progress:')
 
         # Reset rows and create an environment-appropriate handle
         self._df_rows = []
@@ -409,62 +378,204 @@ def add_tracking_info(self, row: list[str]) -> None:
     def finish_tracking(self) -> None:
         """Finalize progress display and print best result summary."""
         if self._tracking_mode == TRACKING_MODE_SAMPLER:
-            if self._last_iteration is not None and self._sampler_total_iterations is not None:
-                final_progress = self._last_sampler_progress_percent
-                if final_progress is None:
-                    final_progress = 100.0 * min(
-                        self._last_iteration,
-                        self._sampler_total_iterations,
-                    ) / self._sampler_total_iterations
-                row = [
-                    f'{min(self._last_iteration, self._sampler_total_iterations)}/'
-                    f'{self._sampler_total_iterations}',
-                    f'{final_progress:.1f}%',
-                    self._format_elapsed_time(
-                        self._fitting_time
-                        if self._fitting_time is not None
-                        else self._last_sampler_elapsed_time
-                    ),
-                    (
-                        f'{self._last_sampler_log_posterior:.2f}'
-                        if self._last_sampler_log_posterior is not None
-                        else ''
-                    ),
-                    self._last_sampler_phase or 'sampling',
-                ]
-                if not self._df_rows:
-                    self.add_tracking_info(row)
-                elif self._rows_match_on_columns(self._df_rows[-1], row, (0, 1, 3, 4)):
-                    self._replace_last_tracking_row(row)
-                elif self._df_rows[-1] != row:
-                    self.add_tracking_info(row)
-        elif self._last_iteration is not None:
-            row: list[str] = [
-                str(self._last_iteration),
-                self._format_elapsed_time(self._fitting_time),
-                f'{self._last_chi2:.2f}' if self._last_chi2 is not None else '',
-                '',
-            ]
-            if not self._df_rows:
-                self.add_tracking_info(row)
-            elif self._rows_match_on_columns(self._df_rows[-1], row, (0, 2)):
-                self._replace_last_tracking_row(row)
-            elif self._df_rows[-1][:3] != row[:3]:
-                self.add_tracking_info(row)
+            self._finalize_sampler_tracking_row()
+        else:
+            self._finalize_fit_tracking_row()
 
         if self._verbosity is not VerbosityEnum.FULL:
             return
 
-        # Close terminal live if used
+        self._close_display_handle()
+        self._print_completion_summary()
+
+    def _initial_sampler_progress_row(
+        self,
+        *,
+        update: SamplerProgressUpdate,
+        clamped_iteration: int,
+        clamped_progress: float,
+    ) -> list[str]:
+        if self._previous_chi2 is not None and self._best_chi2 is not None:
+            return []
+
+        self._previous_chi2 = update.reduced_chi2
+        self._best_chi2 = update.reduced_chi2
+        self._best_iteration = update.iteration
+        self._last_progress_time = update.elapsed_time
+        return self._sampler_progress_row(
+            clamped_iteration=clamped_iteration,
+            clamped_progress=clamped_progress,
+            log_posterior=update.log_posterior,
+            phase=update.phase,
+            elapsed_time=update.elapsed_time,
+        )
+
+    def _continued_sampler_progress_row(
+        self,
+        *,
+        update: SamplerProgressUpdate,
+        previous_phase: str | None,
+        clamped_iteration: int,
+        clamped_progress: float,
+    ) -> list[str]:
+        if self._best_chi2 is not None and update.reduced_chi2 < self._best_chi2:
+            self._best_chi2 = update.reduced_chi2
+            self._best_iteration = update.iteration
+
+        if not self._should_render_sampler_row(
+            iteration=update.iteration,
+            previous_phase=previous_phase,
+            phase=update.phase,
+            elapsed_time=update.elapsed_time,
+            force_report=update.force_report,
+            clamped_iteration=clamped_iteration,
+        ):
+            return []
+
+        self._last_progress_time = update.elapsed_time
+        return self._sampler_progress_row(
+            clamped_iteration=clamped_iteration,
+            clamped_progress=clamped_progress,
+            log_posterior=update.log_posterior,
+            phase=update.phase,
+            elapsed_time=update.elapsed_time,
+        )
+
+    def _should_render_sampler_row(
+        self,
+        *,
+        iteration: int,
+        previous_phase: str | None,
+        phase: str,
+        elapsed_time: float,
+        force_report: bool,
+        clamped_iteration: int,
+    ) -> bool:
+        if iteration == self._last_reported_iteration:
+            return False
+
+        return (
+            force_report
+            or previous_phase != phase
+            or self._last_progress_time is None
+            or elapsed_time - self._last_progress_time >= SAMPLER_PROGRESS_UPDATE_SECONDS
+            or clamped_iteration >= self._sampler_total_iterations
+        )
+
+    def _sampler_progress_row(
+        self,
+        *,
+        clamped_iteration: int,
+        clamped_progress: float,
+        log_posterior: float,
+        phase: str,
+        elapsed_time: float,
+    ) -> list[str]:
+        return [
+            self._sampler_iteration_label(clamped_iteration),
+            f'{clamped_progress:.1f}%',
+            self._format_elapsed_time(elapsed_time),
+            f'{log_posterior:.2f}',
+            phase,
+        ]
+
+    def _finalize_sampler_tracking_row(self) -> None:
+        row = self._final_sampler_tracking_row()
+        if row is None:
+            return
+
+        if not self._df_rows:
+            self.add_tracking_info(row)
+            return
+
+        if self._rows_match_on_columns(self._df_rows[-1], row, (0, 1, 3, 4)):
+            self._replace_last_tracking_row(row)
+            return
+
+        if self._df_rows[-1] != row:
+            self.add_tracking_info(row)
+
+    def _final_sampler_tracking_row(self) -> list[str] | None:
+        if self._last_iteration is None or self._sampler_total_iterations is None:
+            return None
+
+        final_progress = self._resolved_final_sampler_progress()
+        elapsed_time = self._resolved_final_sampler_elapsed_time()
+        log_posterior = (
+            f'{self._last_sampler_log_posterior:.2f}'
+            if self._last_sampler_log_posterior is not None
+            else ''
+        )
+        return [
+            self._sampler_iteration_label(self._last_iteration),
+            f'{final_progress:.1f}%',
+            self._format_elapsed_time(elapsed_time),
+            log_posterior,
+            self._last_sampler_phase or 'sampling',
+        ]
+
+    def _finalize_fit_tracking_row(self) -> None:
+        row = self._final_fit_tracking_row()
+        if row is None:
+            return
+
+        if not self._df_rows:
+            self.add_tracking_info(row)
+            return
+
+        if self._rows_match_on_columns(self._df_rows[-1], row, (0, 2)):
+            self._replace_last_tracking_row(row)
+            return
+
+        if self._df_rows[-1][:3] != row[:3]:
+            self.add_tracking_info(row)
+
+    def _final_fit_tracking_row(self) -> list[str] | None:
+        if self._last_iteration is None:
+            return None
+
+        return [
+            str(self._last_iteration),
+            self._format_elapsed_time(self._fitting_time),
+            f'{self._last_chi2:.2f}' if self._last_chi2 is not None else '',
+            '',
+        ]
+
+    def _resolved_final_sampler_progress(self) -> float:
+        if self._last_sampler_progress_percent is not None:
+            return self._last_sampler_progress_percent
+
+        if self._last_iteration is None or self._sampler_total_iterations is None:
+            msg = 'Sampler progress is unavailable without final iteration counts.'
+            raise RuntimeError(msg)
+        return (
+            100.0
+            * min(self._last_iteration, self._sampler_total_iterations)
+            / self._sampler_total_iterations
+        )
+
+    def _resolved_final_sampler_elapsed_time(self) -> float | None:
+        if self._fitting_time is not None:
+            return self._fitting_time
+        return self._last_sampler_elapsed_time
+
+    def _sampler_iteration_label(self, iteration: int) -> str:
+        if self._sampler_total_iterations is None:
+            msg = 'Sampler iteration labels require a configured total iteration count.'
+            raise RuntimeError(msg)
+        clamped_iteration = min(iteration, self._sampler_total_iterations)
+        return f'{clamped_iteration}/{self._sampler_total_iterations}'
+
+    def _close_display_handle(self) -> None:
         if self._display_handle is not None and hasattr(self._display_handle, 'close'):
             with suppress(Exception):
                 self._display_handle.close()
 
+    def _print_completion_summary(self) -> None:
         if self._tracking_mode == TRACKING_MODE_SAMPLER:
             console.print('✅ Bayesian sampling complete.')
             return
 
-        # Print best result
         console.print(
             f'🏆 Best goodness-of-fit (reduced χ²) is {self._best_chi2:.2f} '
             f'at iteration {self._best_iteration}'
@@ -506,7 +617,9 @@ def _rows_match_on_columns(
         new_row: list[str],
         column_indices: tuple[int, ...],
     ) -> bool:
-        """Return whether two tracking rows match on selected columns."""
+        """
+        Return whether two tracking rows match on selected columns.
+        """
         return all(
             len(current_row) > index
             and len(new_row) > index
@@ -515,7 +628,9 @@ def _rows_match_on_columns(
         )
 
     def _replace_last_tracking_row(self, row: list[str]) -> None:
-        """Replace the last rendered tracking row and refresh the view."""
+        """
+        Replace the last rendered tracking row and refresh the view.
+        """
         if not self._df_rows:
             self.add_tracking_info(row)
             return
diff --git a/src/easydiffraction/analysis/minimizers/base.py b/src/easydiffraction/analysis/minimizers/base.py
index 84d5371d..142ed569 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -69,7 +69,8 @@ def _stop_tracking(self) -> None:
         self.tracker.stop_timer()
         self.tracker.finish_tracking()
 
-    def _tracking_mode(self) -> str:
+    @staticmethod
+    def _tracking_mode() -> str:
         """Return the tracker mode for the current minimizer."""
         return 'fit'
 
@@ -143,7 +144,8 @@ def _build_fit_results(
         raw_result: object,
         success: bool,
     ) -> FitResults:
-        """Build the final fit-result object for this minimizer.
+        """
+        Build the final fit-result object for this minimizer.
 
         Parameters
         ----------
@@ -266,7 +268,8 @@ def _check_success(self, raw_result: object) -> bool:
         """Determine whether the fit was successful."""
 
     def _resolve_random_seed(self, random_seed: int | None) -> int | None:
-        """Validate or normalize the random seed for this minimizer.
+        """
+        Validate or normalize the random seed for this minimizer.
 
         Parameters
         ----------
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 0ad88ef9..d2fb1773 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -5,6 +5,7 @@
 from __future__ import annotations
 
 import random
+from dataclasses import dataclass
 
 import numpy as np
 from bumps.fitproblem import FitProblem
@@ -18,8 +19,9 @@
 from easydiffraction.analysis.fit_helpers.bayesian import compute_convergence_diagnostics
 from easydiffraction.analysis.fit_helpers.bayesian import standard_deviations_from_summaries
 from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
-from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
+from easydiffraction.analysis.fit_helpers.tracking import SamplerProgressUpdate
 from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
+from easydiffraction.analysis.minimizers.bumps import _EasyDiffractionFitness
 from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
 from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 from easydiffraction.analysis.minimizers.factory import MinimizerFactory
@@ -38,10 +40,39 @@
 DEFAULT_TRIM = False
 BURN_IN_PROGRESS_POINTS = 5
 SAMPLING_PROGRESS_POINTS = 20
+DREAM_SAMPLE_ARRAY_NDIM = 3
+DREAM_DRIVER_FAILURES = (ArithmeticError, RuntimeError, TypeError, ValueError)
+
+
+@dataclass(slots=True)
+class _DreamRunContext:
+    """Prepared driver state and metadata for one DREAM run."""
+
+    driver: FitDriver
+    parameter_names: list[str]
+    parameter_display_names: list[str]
+    parameter_uids: list[str]
+    sampler_settings: dict[str, object]
+    starting_values: np.ndarray
+    starting_uncertainties: list[float | None]
+
+
+@dataclass(slots=True)
+class _DreamDriverResult:
+    """
+    Raw driver outcome captured before EasyDiffraction normalization.
+    """
+
+    best_values: object | None
+    best_nllf: float | None
+    raw_state: object | None
+    error: Exception | None = None
 
 
 class _DreamProgressMonitor(bumps_monitor.Monitor):
-    """Progress monitor translating DREAM updates into chi-square rows."""
+    """
+    Progress monitor translating DREAM updates into chi-square rows.
+    """
 
     def __init__(
         self,
@@ -70,7 +101,8 @@ def __init__(
         self._next_burn_target_index = 0
         self._next_sampling_target_index = 0
 
-    def config_history(self, history: object) -> None:
+    @staticmethod
+    def config_history(history: object) -> None:
         """Declare the history fields needed for progress updates."""
         history.requires(time=1, step=1, value=1, population_values=1)
 
@@ -84,14 +116,16 @@ def __call__(self, history: object) -> None:
         reduced_chi2 = self._reduced_chi_square_from_nllf(nllf)
         log_posterior = self._population_mean_log_posterior(history)
         self._tracker.track_sampler_progress(
-            iteration=generation,
-            total_iterations=self._total_generations,
-            phase=self._phase_name(generation),
-            progress_percent=self._progress_percent(generation),
-            log_posterior=log_posterior,
-            reduced_chi2=reduced_chi2,
-            elapsed_time=float(history.time[0]),
-            force_report=True,
+            SamplerProgressUpdate(
+                iteration=generation,
+                total_iterations=self._total_generations,
+                phase=self._phase_name(generation),
+                progress_percent=self._progress_percent(generation),
+                log_posterior=log_posterior,
+                reduced_chi2=reduced_chi2,
+                elapsed_time=float(history.time[0]),
+                force_report=True,
+            )
         )
 
     def final(self, history: object, best: dict[str, object]) -> None:
@@ -103,14 +137,16 @@ def final(self, history: object, best: dict[str, object]) -> None:
         best_nllf = float(best['value'])
         reduced_chi2 = self._reduced_chi_square_from_nllf(best_nllf)
         self._tracker.track_sampler_progress(
-            iteration=generation,
-            total_iterations=self._total_generations,
-            phase=self._phase_name(generation),
-            progress_percent=self._progress_percent(generation),
-            log_posterior=self._population_mean_log_posterior(history),
-            reduced_chi2=reduced_chi2,
-            elapsed_time=float(history.time[0]),
-            force_report=True,
+            SamplerProgressUpdate(
+                iteration=generation,
+                total_iterations=self._total_generations,
+                phase=self._phase_name(generation),
+                progress_percent=self._progress_percent(generation),
+                log_posterior=self._population_mean_log_posterior(history),
+                reduced_chi2=reduced_chi2,
+                elapsed_time=float(history.time[0]),
+                force_report=True,
+            )
         )
 
     @staticmethod
@@ -120,13 +156,15 @@ def _progress_targets(
         stop: int,
         target_count: int,
     ) -> list[int]:
-        """Return monotonically increasing reporting targets for one phase."""
+        """
+        Return monotonically increasing reporting targets for one phase.
+        """
         if target_count < 1 or stop < start:
             return []
 
         targets = np.linspace(start, stop, num=target_count)
         rounded = np.rint(targets).astype(int)
-        unique_targets = sorted(set(int(value) for value in rounded if start <= value <= stop))
+        unique_targets = sorted({int(value) for value in rounded if start <= value <= stop})
         if start not in unique_targets:
             unique_targets.insert(0, start)
         if stop not in unique_targets:
@@ -156,7 +194,9 @@ def _consume_progress_target(
         phase_targets: list[int],
         target_index_name: str,
     ) -> bool:
-        """Advance a phase target pointer when the generation reaches it."""
+        """
+        Advance a phase target pointer when the generation reaches it.
+        """
         target_index = getattr(self, target_index_name)
         should_report = False
         while target_index < len(phase_targets) and generation >= phase_targets[target_index]:
@@ -179,7 +219,7 @@ def _progress_percent(self, generation: int) -> float:
 
     @staticmethod
     def _population_mean_log_posterior(history: object) -> float:
-        """Return the current mean log-posterior across the walker population."""
+        """Return the mean log-posterior across the population."""
         population_values = history.population_values[0] if history.population_values else None
         if population_values is None:
             return -float(history.value[0])
@@ -191,7 +231,9 @@ def _population_mean_log_posterior(history: object) -> float:
         return float(np.mean(-nllf_values[finite_mask]))
 
     def _reduced_chi_square_from_nllf(self, nllf: float) -> float:
-        """Convert DREAM's negative log-likelihood to reduced chi-square."""
+        """
+        Convert DREAM's negative log-likelihood to reduced chi-square.
+        """
         dof = self._n_points - self._n_parameters
         chi_square = 2.0 * nllf
         if dof <= 0:
@@ -273,7 +315,8 @@ def init(self, value: DreamPopulationInitializationEnum | str) -> None:
         self._init = self._validated_init(value)
 
     def _resolve_random_seed(self, random_seed: int | None) -> int:
-        """Return a user-provided or generated random seed.
+        """
+        Return a user-provided or generated random seed.
 
         Parameters
         ----------
@@ -292,7 +335,8 @@ def _resolve_random_seed(self, random_seed: int | None) -> int:
         self._resolved_random_seed = int(random_seed)
         return self._resolved_random_seed
 
-    def _tracking_mode(self) -> str:
+    @staticmethod
+    def _tracking_mode() -> str:
         """Use sampler-style progress reporting for DREAM runs."""
         return 'sampling'
 
@@ -300,7 +344,8 @@ def _prepare_solver_args(
         self,
         parameters: list[object],
     ) -> dict[str, object]:
-        """Prepare DREAM solver arguments in EasyDiffraction order.
+        """
+        Prepare DREAM solver arguments in EasyDiffraction order.
 
         Parameters
         ----------
@@ -322,11 +367,11 @@ def _prepare_solver_args(
         return solver_args
 
     @staticmethod
-    def _validated_positive_integer(name: str, value: int | float) -> int:
+    def _validated_positive_integer(name: str, value: float) -> int:
         """Validate a DREAM setting that must be a positive integer."""
         if isinstance(value, bool):
             msg = f"DREAM setting '{name}' must be a positive integer."
-            raise ValueError(msg)
+            raise TypeError(msg)
 
         integer_value = int(value)
         if integer_value != value or integer_value < 1:
@@ -335,11 +380,13 @@ def _validated_positive_integer(name: str, value: int | float) -> int:
         return integer_value
 
     @staticmethod
-    def _validated_non_negative_integer(name: str, value: int | float) -> int:
-        """Validate a DREAM setting that must be a non-negative integer."""
+    def _validated_non_negative_integer(name: str, value: float) -> int:
+        """
+        Validate a DREAM setting that must be a non-negative integer.
+        """
         if isinstance(value, bool):
             msg = f"DREAM setting '{name}' must be a non-negative integer."
-            raise ValueError(msg)
+            raise TypeError(msg)
 
         integer_value = int(value)
         if integer_value != value or integer_value < 0:
@@ -373,8 +420,8 @@ def _resolved_burn(self, steps: int) -> int:
             raise ValueError(msg)
         return burn
 
+    @staticmethod
     def _sampler_settings(
-        self,
         *,
         random_seed: int,
         steps: int,
@@ -404,7 +451,8 @@ def _run_solver(
         objective_function: object,
         **kwargs: object,
     ) -> object:
-        """Run the DREAM sampler and normalize its posterior outputs.
+        """
+        Run the DREAM sampler and normalize its posterior outputs.
 
         Parameters
         ----------
@@ -418,47 +466,114 @@ def _run_solver(
         object
             Normalized DREAM result stored in an ``OptimizeResult``.
         """
+        context = self._prepare_run_context(objective_function=objective_function, kwargs=kwargs)
+        driver_result = self._execute_driver(
+            driver=context.driver,
+            random_seed=int(context.sampler_settings['random_seed']),
+        )
+        if driver_result.error is not None:
+            return self._failure_result(
+                context=context,
+                message=f'DREAM sampling failed: {driver_result.error}',
+                raw_state=driver_result.raw_state,
+                sampler_completed=False,
+            )
+        if driver_result.best_values is None or driver_result.raw_state is None:
+            return self._failure_result(
+                context=context,
+                message='DREAM sampling did not produce usable posterior samples.',
+                raw_state=driver_result.raw_state,
+                sampler_completed=False,
+            )
+
+        return self._build_success_result(
+            context=context,
+            raw_state=driver_result.raw_state,
+            best_nllf=driver_result.best_nllf,
+        )
+
+    def _prepare_run_context(
+        self,
+        *,
+        objective_function: object,
+        kwargs: dict[str, object],
+    ) -> _DreamRunContext:
+        """Prepare a driver and metadata for one DREAM solver run."""
         bumps_params = kwargs.get('bumps_params')
         parameter_names = kwargs.get('parameter_names')
         parameter_display_names = kwargs.get('parameter_display_names')
         parameter_uids = kwargs.get('parameter_uids')
-        random_seed = kwargs.get('random_seed')
+        random_seed = int(kwargs.get('random_seed'))
         starting_uncertainties = kwargs.get('starting_uncertainties')
+
         fitness = _EasyDiffractionFitness(bumps_params, objective_function)
         fitness.nllf()
-        problem = FitProblem(fitness)
-
         fitclass = next(cls for cls in FITTERS if cls.id == self.method)
         steps = self.steps
         burn = self._resolved_burn(steps)
-        thin = self.thin
-        pop = self.pop
         init = self.init
         sampler_settings = self._sampler_settings(
-            random_seed=int(random_seed),
+            random_seed=random_seed,
+            steps=steps,
+            burn=burn,
+            thin=self.thin,
+            pop=self.pop,
+            init=init,
+            n_parameters=len(bumps_params),
+        )
+        driver = self._build_driver(
+            fitclass=fitclass,
+            fitness=fitness,
             steps=steps,
             burn=burn,
-            thin=thin,
-            pop=pop,
             init=init,
+            sampler_settings=sampler_settings,
             n_parameters=len(bumps_params),
         )
+        starting_values = np.array([parameter.value for parameter in bumps_params], dtype=float)
+        resolved_uncertainties = (
+            list(starting_uncertainties)
+            if starting_uncertainties is not None
+            else [None] * len(bumps_params)
+        )
+        return _DreamRunContext(
+            driver=driver,
+            parameter_names=parameter_names,
+            parameter_display_names=parameter_display_names,
+            parameter_uids=parameter_uids,
+            sampler_settings=sampler_settings,
+            starting_values=starting_values,
+            starting_uncertainties=resolved_uncertainties,
+        )
+
+    def _build_driver(
+        self,
+        *,
+        fitclass: object,
+        fitness: object,
+        steps: int,
+        burn: int,
+        init: DreamPopulationInitializationEnum,
+        sampler_settings: dict[str, object],
+        n_parameters: int,
+    ) -> FitDriver:
+        """Build and clip the BUMPS DREAM driver."""
         total_generations = int(steps + burn + 1)
         progress_monitor = _DreamProgressMonitor(
             tracker=self.tracker,
             n_points=fitness.numpoints(),
-            n_parameters=len(bumps_params),
+            n_parameters=n_parameters,
             total_generations=total_generations,
             burn_steps=int(burn),
         )
         driver = FitDriver(
             fitclass=fitclass,
-            problem=problem,
+            problem=FitProblem(fitness),
             monitors=[progress_monitor],
             steps=steps,
             burn=burn,
-            thin=thin,
-            pop=pop,
+            thin=self.thin,
+            pop=self.pop,
             init=init.value,
             samples=sampler_settings['samples'],
             alpha=DEFAULT_ALPHA,
@@ -466,101 +581,105 @@ def _run_solver(
             trim=DEFAULT_TRIM,
         )
         driver.clip()
+        return driver
 
-        starting_values = np.array([parameter.value for parameter in bumps_params], dtype=float)
-        if starting_uncertainties is None:
-            starting_uncertainties = [None] * len(bumps_params)
-
-        numpy_state = np.random.get_state()
+    @staticmethod
+    def _execute_driver(*, driver: FitDriver, random_seed: int) -> _DreamDriverResult:
+        """
+        Run the DREAM driver under a deterministic RNG-state guard.
+        """
+        numpy_rng = np.random.mtrand._rand
+        numpy_state = numpy_rng.get_state()
         python_state = random.getstate()
-        np.random.seed(random_seed)
+        numpy_rng.seed(random_seed)
         random.seed(random_seed)
         try:
             best_values, best_nllf = driver.fit()
-        except Exception as error:  # pragma: no cover - defensive runtime path
-            return OptimizeResult(
-                x=starting_values,
-                dx=None,
-                fun=None,
-                success=False,
-                status=-1,
-                message=f'DREAM sampling failed: {error}',
-                var_names=parameter_names,
-                posterior_samples=None,
-                posterior_parameter_summaries=[],
-                convergence_diagnostics={},
-                sampler_settings=sampler_settings,
-                sampler_completed=False,
-                raw_state=None,
-                best_log_posterior=None,
-                starting_values=starting_values,
-                starting_uncertainties=starting_uncertainties,
+        except DREAM_DRIVER_FAILURES as error:  # pragma: no cover - backend-specific
+            return _DreamDriverResult(
+                best_values=None,
+                best_nllf=None,
+                raw_state=getattr(driver.fitter, 'state', None),
+                error=error,
             )
         finally:
-            np.random.set_state(numpy_state)
+            numpy_rng.set_state(numpy_state)
             random.setstate(python_state)
 
-        state = getattr(driver.fitter, 'state', None)
-        if best_values is None or state is None:
-            return OptimizeResult(
-                x=starting_values,
-                dx=None,
-                fun=None,
-                success=False,
-                status=-1,
-                message='DREAM sampling did not produce usable posterior samples.',
-                var_names=parameter_names,
-                posterior_samples=None,
-                posterior_parameter_summaries=[],
-                convergence_diagnostics={},
-                sampler_settings=sampler_settings,
-                sampler_completed=False,
-                raw_state=state,
-                best_log_posterior=None,
-                starting_values=starting_values,
-                starting_uncertainties=starting_uncertainties,
-            )
+        return _DreamDriverResult(
+            best_values=best_values,
+            best_nllf=float(best_nllf),
+            raw_state=getattr(driver.fitter, 'state', None),
+        )
+
+    @staticmethod
+    def _failure_result(
+        *,
+        context: _DreamRunContext,
+        message: str,
+        raw_state: object,
+        sampler_completed: bool,
+    ) -> OptimizeResult:
+        """
+        Build a normalized failure result for an incomplete DREAM run.
+        """
+        return OptimizeResult(
+            x=context.starting_values,
+            dx=None,
+            fun=None,
+            success=False,
+            status=-1,
+            message=message,
+            var_names=context.parameter_names,
+            posterior_samples=None,
+            posterior_parameter_summaries=[],
+            convergence_diagnostics={},
+            sampler_settings=context.sampler_settings,
+            sampler_completed=sampler_completed,
+            raw_state=raw_state,
+            best_log_posterior=None,
+            starting_values=context.starting_values,
+            starting_uncertainties=context.starting_uncertainties,
+        )
 
-        draw_index, parameter_samples_array, log_posterior = state.chains()
-        if parameter_samples_array.ndim != 3 or parameter_samples_array.size == 0:
-            return OptimizeResult(
-                x=starting_values,
-                dx=None,
-                fun=None,
-                success=False,
-                status=-1,
+    def _build_success_result(
+        self,
+        *,
+        context: _DreamRunContext,
+        raw_state: object,
+        best_nllf: float | None,
+    ) -> OptimizeResult:
+        """Normalize a completed DREAM run into an OptimizeResult."""
+        draw_index, parameter_samples_array, log_posterior = raw_state.chains()
+        if (
+            parameter_samples_array.ndim != DREAM_SAMPLE_ARRAY_NDIM
+            or parameter_samples_array.size == 0
+        ):
+            return self._failure_result(
+                context=context,
                 message='DREAM sampling did not return a usable posterior sample array.',
-                var_names=parameter_names,
-                posterior_samples=None,
-                posterior_parameter_summaries=[],
-                convergence_diagnostics={},
-                sampler_settings=sampler_settings,
+                raw_state=raw_state,
                 sampler_completed=True,
-                raw_state=state,
-                best_log_posterior=None,
-                starting_values=starting_values,
-                starting_uncertainties=starting_uncertainties,
             )
 
-        state_best_values, best_log_posterior = state.best()
-        best_by_name = dict(zip(state.labels, state_best_values, strict=True))
-        label_to_index = {label: index for index, label in enumerate(state.labels)}
-        ordered_indices = [label_to_index[uid] for uid in parameter_uids]
+        state_best_values, best_log_posterior = raw_state.best()
+        best_by_name = dict(zip(raw_state.labels, state_best_values, strict=True))
+        label_to_index = {label: index for index, label in enumerate(raw_state.labels)}
+        ordered_indices = [label_to_index[uid] for uid in context.parameter_uids]
         ordered_samples = np.asarray(parameter_samples_array, dtype=float)[:, :, ordered_indices]
-        map_values = np.array([best_by_name[uid] for uid in parameter_uids], dtype=float)
-
+        map_values = np.array([best_by_name[uid] for uid in context.parameter_uids], dtype=float)
         posterior_samples = PosteriorSamples(
-            parameter_names=parameter_names,
+            parameter_names=context.parameter_names,
             parameter_samples=ordered_samples,
             log_posterior=np.asarray(log_posterior, dtype=float),
             draw_index=np.asarray(draw_index, dtype=float),
         )
         convergence_diagnostics = compute_convergence_diagnostics(posterior_samples)
         posterior_parameter_summaries = summarize_posterior_parameters(
-            parameter_names=parameter_names,
+            parameter_names=context.parameter_names,
             posterior_samples=posterior_samples,
             map_values=map_values,
-            parameter_display_names=parameter_display_names,
+            parameter_display_names=context.parameter_display_names,
             convergence_diagnostics=convergence_diagnostics,
         )
         posterior_standard_deviations = standard_deviations_from_summaries(
@@ -580,24 +699,25 @@ def _run_solver(
             success=True,
             status=0,
             message='DREAM sampling completed',
-            var_names=parameter_names,
+            var_names=context.parameter_names,
             posterior_samples=posterior_samples,
             posterior_parameter_summaries=posterior_parameter_summaries,
             convergence_diagnostics=convergence_diagnostics,
-            sampler_settings=sampler_settings,
+            sampler_settings=context.sampler_settings,
             sampler_completed=True,
-            raw_state=state,
+            raw_state=raw_state,
             best_log_posterior=float(best_log_posterior),
-            starting_values=starting_values,
-            starting_uncertainties=starting_uncertainties,
+            starting_values=context.starting_values,
+            starting_uncertainties=context.starting_uncertainties,
         )
 
+    @staticmethod
     def _sync_result_to_parameters(
-        self,
         parameters: list[object],
         raw_result: object,
     ) -> None:
-        """Commit MAP values on success and restore starts on failure.
+        """
+        Commit MAP values on success and restore starts on failure.
 
         Parameters
         ----------
@@ -636,7 +756,8 @@ def _build_fit_results(
         raw_result: object,
         success: bool,
     ) -> BayesianFitResults:
-        """Build the Bayesian fit result container.
+        """
+        Build the Bayesian fit result container.
 
         Parameters
         ----------
@@ -662,9 +783,7 @@ def _build_fit_results(
             sampler_name='dream',
             point_estimate_name='map',
             posterior_samples=getattr(raw_result, 'posterior_samples', None),
-            posterior_parameter_summaries=getattr(
-                raw_result, 'posterior_parameter_summaries', []
-            ),
+            posterior_parameter_summaries=getattr(raw_result, 'posterior_parameter_summaries', []),
             posterior_predictive={},
             credible_interval_levels=(0.68, 0.95),
             sampler_settings=getattr(raw_result, 'sampler_settings', {}),
@@ -675,4 +794,4 @@ def _build_fit_results(
         fit_results.message = getattr(raw_result, 'message', '')
         fit_results.iterations = int(fit_results.sampler_settings.get('steps', self.steps))
         fit_results.result = raw_result
-        return fit_results
\ No newline at end of file
+        return fit_results
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index cc5b9ec9..a09f502f 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -433,7 +433,8 @@ def set_fit_bounds_from_uncertainty(
         *,
         clip_to_limits: bool = True,
     ) -> None:
-        """Set fit bounds from the current standard uncertainty.
+        """
+        Set fit bounds from the current standard uncertainty.
 
         Parameters
         ----------
@@ -447,8 +448,8 @@ def set_fit_bounds_from_uncertainty(
         Raises
         ------
         ValueError
-            If the current value, uncertainty, or multiplier is
-            missing, invalid, or produces non-increasing bounds.
+            If the current value, uncertainty, or multiplier is missing,
+            invalid, or produces non-increasing bounds.
         """
         name = self.unique_name
         value = self.value
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 49d567af..fcc07fa9 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -69,6 +69,7 @@
 PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
 PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
 PREDICTIVE_DRAW_PLOT_CAP = 50
+PREDICTIVE_DRAW_ARRAY_NDIM = 2
 
 
 @dataclass(frozen=True)
@@ -1177,23 +1178,7 @@ def _get_main_intensity_range(cls, plot_spec: PowderMeasVsCalcSpec) -> tuple[flo
         if min(y_meas.size, y_calc.size) == 0:
             return 0.0, 1.0
 
-        main_series = [y_meas, y_calc]
-        if plot_spec.y_bkg is not None:
-            y_bkg = np.asarray(plot_spec.y_bkg)
-            if y_bkg.size > 0:
-                main_series.append(y_bkg)
-        if plot_spec.predictive_lower_95 is not None:
-            lower_95 = np.asarray(plot_spec.predictive_lower_95)
-            if lower_95.size > 0:
-                main_series.append(lower_95)
-        if plot_spec.predictive_upper_95 is not None:
-            upper_95 = np.asarray(plot_spec.predictive_upper_95)
-            if upper_95.size > 0:
-                main_series.append(upper_95)
-        if plot_spec.predictive_draws is not None:
-            predictive_draws = np.asarray(plot_spec.predictive_draws)
-            if predictive_draws.ndim == 2 and predictive_draws.size > 0:
-                main_series.extend(predictive_draws)
+        main_series = cls._main_intensity_series(plot_spec, y_meas=y_meas, y_calc=y_calc)
 
         main_y_min = float(min(np.min(series) for series in main_series))
         main_y_max = float(max(np.max(series) for series in main_series))
@@ -1204,6 +1189,49 @@ def _get_main_intensity_range(cls, plot_spec: PowderMeasVsCalcSpec) -> tuple[flo
 
         return main_y_min - 1.0, main_y_max + 1.0
 
+    @classmethod
+    def _main_intensity_series(
+        cls,
+        plot_spec: PowderMeasVsCalcSpec,
+        *,
+        y_meas: np.ndarray,
+        y_calc: np.ndarray,
+    ) -> list[np.ndarray]:
+        main_series = [y_meas, y_calc]
+        for values in (
+            plot_spec.y_bkg,
+            plot_spec.predictive_lower_95,
+            plot_spec.predictive_upper_95,
+        ):
+            cls._append_non_empty_series(main_series, values)
+
+        predictive_draws = cls._predictive_draw_array(plot_spec.predictive_draws)
+        if predictive_draws is not None:
+            main_series.extend(predictive_draws)
+        return main_series
+
+    @staticmethod
+    def _append_non_empty_series(
+        main_series: list[np.ndarray],
+        values: np.ndarray | None,
+    ) -> None:
+        if values is None:
+            return
+
+        array = np.asarray(values)
+        if array.size > 0:
+            main_series.append(array)
+
+    @staticmethod
+    def _predictive_draw_array(values: object | None) -> np.ndarray | None:
+        if values is None:
+            return None
+
+        predictive_draws = np.asarray(values)
+        if predictive_draws.ndim != PREDICTIVE_DRAW_ARRAY_NDIM or predictive_draws.size == 0:
+            return None
+        return predictive_draws
+
     @classmethod
     def _get_residual_limit(cls, plot_spec: PowderMeasVsCalcSpec) -> float:
         """Return a symmetric residual limit matched to the main row."""
@@ -1245,11 +1273,40 @@ def plot_powder_meas_vs_calc(
         layout = self._get_powder_composite_rows(plot_spec)
         x_min, x_max = self._composite_x_range(np.asarray(plot_spec.x))
         main_y_min, main_y_max = self._get_main_intensity_range(plot_spec)
-        residual_limit = None
         hover_data = self._powder_meas_vs_calc_hover_data(plot_spec)
         hover_template = self._powder_meas_vs_calc_hover_template(plot_spec)
+        fig = self._create_powder_composite_figure(layout)
+        self._add_predictive_band_traces(fig=fig, plot_spec=plot_spec)
+        self._add_main_intensity_traces(
+            fig=fig,
+            plot_spec=plot_spec,
+            hover_data=hover_data,
+            hover_template=hover_template,
+        )
+        self._add_predictive_draw_traces(fig=fig, plot_spec=plot_spec)
+        self._add_bragg_tick_traces(fig=fig, plot_spec=plot_spec, layout=layout)
+        residual_limit = self._add_residual_trace(
+            fig=fig,
+            plot_spec=plot_spec,
+            layout=layout,
+            hover_data=hover_data,
+            hover_template=hover_template,
+        )
+        self._configure_powder_composite_layout(fig=fig, plot_spec=plot_spec, layout=layout)
+        self._configure_powder_composite_axes(
+            fig=fig,
+            plot_spec=plot_spec,
+            layout=layout,
+            x_range=(x_min, x_max),
+            main_y_range=(main_y_min, main_y_max),
+            residual_limit=residual_limit,
+        )
+
+        self._show_figure(fig)
 
-        fig = make_subplots(
+    @staticmethod
+    def _create_powder_composite_figure(layout: PowderCompositeRows) -> object:
+        return make_subplots(
             rows=layout.row_count,
             cols=1,
             shared_xaxes=True,
@@ -1257,62 +1314,49 @@ def plot_powder_meas_vs_calc(
             row_heights=layout.row_heights,
         )
 
-        if plot_spec.predictive_lower_95 is not None and plot_spec.predictive_upper_95 is not None:
-            lower_trace, upper_trace = self._get_predictive_band_traces(
-                x=plot_spec.x,
-                lower=plot_spec.predictive_lower_95,
-                upper=plot_spec.predictive_upper_95,
-            )
-            fig.add_trace(lower_trace, row=1, col=1)
-            fig.add_trace(upper_trace, row=1, col=1)
+    def _add_predictive_band_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+    ) -> None:
+        if plot_spec.predictive_lower_95 is None or plot_spec.predictive_upper_95 is None:
+            return
 
-        fig.add_trace(
-            self._get_powder_trace(
-                plot_spec.x,
-                plot_spec.y_meas,
-                'meas',
-                customdata=hover_data,
-                hovertemplate=hover_template,
-            ),
-            row=1,
-            col=1,
+        lower_trace, upper_trace = self._get_predictive_band_traces(
+            x=plot_spec.x,
+            lower=plot_spec.predictive_lower_95,
+            upper=plot_spec.predictive_upper_95,
         )
+        fig.add_trace(lower_trace, row=1, col=1)
+        fig.add_trace(upper_trace, row=1, col=1)
+
+    def _add_main_intensity_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        hover_data: object,
+        hover_template: str,
+    ) -> None:
+        meas_trace = self._get_powder_trace(
+            plot_spec.x,
+            plot_spec.y_meas,
+            'meas',
+            customdata=hover_data,
+            hovertemplate=hover_template,
+        )
+        fig.add_trace(meas_trace, row=1, col=1)
 
         if plot_spec.y_bkg is not None:
-            fig.add_trace(
-                self._get_powder_trace(
-                    plot_spec.x,
-                    plot_spec.y_bkg,
-                    'bkg',
-                    customdata=hover_data,
-                    hovertemplate=hover_template,
-                ),
-                row=1,
-                col=1,
+            bkg_trace = self._get_powder_trace(
+                plot_spec.x,
+                plot_spec.y_bkg,
+                'bkg',
+                customdata=hover_data,
+                hovertemplate=hover_template,
             )
-
-        if plot_spec.predictive_draws is not None:
-            predictive_draws = np.asarray(plot_spec.predictive_draws)
-            if predictive_draws.ndim == 2 and predictive_draws.size > 0:
-                draw_cap = min(predictive_draws.shape[0], PREDICTIVE_DRAW_PLOT_CAP)
-                for index in range(draw_cap):
-                    fig.add_trace(
-                        go.Scatter(
-                            x=plot_spec.x,
-                            y=predictive_draws[index],
-                            mode='lines',
-                            line={'color': PREDICTIVE_DRAW_COLOR, 'width': 1},
-                            name='Posterior draw' if index == 0 else None,
-                            showlegend=index == 0,
-                            hovertemplate=(
-                                'Posterior draw<br>'
-                                'x: %{x:,.2f}<br>'
-                                'y: %{y:,.2f}<extra></extra>'
-                            ),
-                        ),
-                        row=1,
-                        col=1,
-                    )
+            fig.add_trace(bkg_trace, row=1, col=1)
 
         calc_trace = self._get_powder_trace(
             plot_spec.x,
@@ -1325,33 +1369,89 @@ def plot_powder_meas_vs_calc(
             calc_trace.name = plot_spec.y_calc_name
         fig.add_trace(calc_trace, row=1, col=1)
 
-        if layout.bragg_row is not None:
-            for idx, tick_set in enumerate(plot_spec.bragg_tick_sets):
-                color = BRAGG_TICK_COLORS[idx % len(BRAGG_TICK_COLORS)]
-                fig.add_trace(
-                    self._get_bragg_tick_trace(
-                        tick_set=tick_set,
-                        row_y=float(idx + 1),
-                        color=color,
+    def _add_predictive_draw_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+    ) -> None:
+        predictive_draws = self._predictive_draw_array(plot_spec.predictive_draws)
+        if predictive_draws is None:
+            return
+
+        draw_cap = min(predictive_draws.shape[0], PREDICTIVE_DRAW_PLOT_CAP)
+        for index in range(draw_cap):
+            fig.add_trace(
+                go.Scatter(
+                    x=plot_spec.x,
+                    y=predictive_draws[index],
+                    mode='lines',
+                    line={'color': PREDICTIVE_DRAW_COLOR, 'width': 1},
+                    name='Posterior draw' if index == 0 else None,
+                    showlegend=index == 0,
+                    hovertemplate=(
+                        'Posterior draw<br>x: %{x:,.2f}<br>y: %{y:,.2f}<extra></extra>'
                     ),
-                    row=layout.bragg_row,
-                    col=1,
-                )
+                ),
+                row=1,
+                col=1,
+            )
+
+    def _add_bragg_tick_traces(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+    ) -> None:
+        if layout.bragg_row is None:
+            return
 
-        if layout.residual_row is not None and plot_spec.y_resid is not None:
-            residual_limit = self._get_residual_limit(plot_spec)
+        for idx, tick_set in enumerate(plot_spec.bragg_tick_sets):
+            color = BRAGG_TICK_COLORS[idx % len(BRAGG_TICK_COLORS)]
             fig.add_trace(
-                self._get_powder_trace(
-                    plot_spec.x,
-                    plot_spec.y_resid,
-                    'resid',
-                    customdata=hover_data,
-                    hovertemplate=hover_template,
+                self._get_bragg_tick_trace(
+                    tick_set=tick_set,
+                    row_y=float(idx + 1),
+                    color=color,
                 ),
-                row=layout.residual_row,
+                row=layout.bragg_row,
                 col=1,
             )
 
+    def _add_residual_trace(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+        hover_data: object,
+        hover_template: str,
+    ) -> float | None:
+        if layout.residual_row is None or plot_spec.y_resid is None:
+            return None
+
+        residual_limit = self._get_residual_limit(plot_spec)
+        fig.add_trace(
+            self._get_powder_trace(
+                plot_spec.x,
+                plot_spec.y_resid,
+                'resid',
+                customdata=hover_data,
+                hovertemplate=hover_template,
+            ),
+            row=layout.residual_row,
+            col=1,
+        )
+        return residual_limit
+
+    def _configure_powder_composite_layout(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+    ) -> None:
         fig.update_layout(
             height=self._composite_figure_height(plot_spec, layout),
             margin={
@@ -1370,11 +1470,58 @@ def plot_powder_meas_vs_calc(
             },
         )
 
-        for row_idx in range(1, layout.row_count + 1):
+    def _configure_powder_composite_axes(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+        x_range: tuple[float | None, float | None],
+        main_y_range: tuple[float, float],
+        residual_limit: float | None,
+    ) -> None:
+        self._configure_shared_composite_axes(
+            fig=fig,
+            row_count=layout.row_count,
+            x_min=x_range[0],
+            x_max=x_range[1],
+        )
+        fig.update_xaxes(showticklabels=(layout.row_count == 1), row=1, col=1)
+        fig.update_yaxes(
+            title_text=plot_spec.axes_labels[1],
+            range=list(main_y_range),
+            row=1,
+            col=1,
+        )
+
+        if layout.bragg_row is not None:
+            self._configure_bragg_axes(fig=fig, plot_spec=plot_spec, layout=layout)
+        if layout.residual_row is not None and residual_limit is not None:
+            self._configure_residual_axes(
+                fig=fig,
+                plot_spec=plot_spec,
+                layout=layout,
+                residual_limit=residual_limit,
+            )
+            return
+
+        terminal_row = layout.bragg_row if layout.bragg_row is not None else 1
+        fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=terminal_row, col=1)
+
+    def _configure_shared_composite_axes(
+        self,
+        *,
+        fig: object,
+        row_count: int,
+        x_min: float | None,
+        x_max: float | None,
+    ) -> None:
+        axis_frame_color = self._axis_frame_color()
+        for row_idx in range(1, row_count + 1):
             x_axis_kwargs = {
                 'matches': 'x',
                 'showline': True,
-                'linecolor': self._axis_frame_color(),
+                'linecolor': axis_frame_color,
                 'mirror': True,
                 'zeroline': False,
                 'tickformat': ',.6~g',
@@ -1385,7 +1532,7 @@ def plot_powder_meas_vs_calc(
             fig.update_xaxes(row=row_idx, col=1, **x_axis_kwargs)
             fig.update_yaxes(
                 showline=True,
-                linecolor=self._axis_frame_color(),
+                linecolor=axis_frame_color,
                 mirror=True,
                 zeroline=False,
                 tickformat=',.6~g',
@@ -1394,50 +1541,48 @@ def plot_powder_meas_vs_calc(
                 col=1,
             )
 
-        fig.update_xaxes(showticklabels=(layout.row_count == 1), row=1, col=1)
+    @staticmethod
+    def _configure_bragg_axes(
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+    ) -> None:
         fig.update_yaxes(
-            title_text=plot_spec.axes_labels[1],
-            range=[main_y_min, main_y_max],
-            row=1,
+            tickmode='array',
+            tickvals=[float(idx + 1) for idx in range(len(plot_spec.bragg_tick_sets))],
+            ticktext=[tick_set.phase_id for tick_set in plot_spec.bragg_tick_sets],
+            range=[float(len(plot_spec.bragg_tick_sets)) + 0.5, 0.5],
+            showgrid=False,
+            row=layout.bragg_row,
+            col=1,
+        )
+        fig.update_xaxes(
+            showticklabels=layout.residual_row is None,
+            row=layout.bragg_row,
             col=1,
         )
 
-        if layout.bragg_row is not None:
-            fig.update_yaxes(
-                # title_text='Bragg peaks',
-                tickmode='array',
-                tickvals=[float(idx + 1) for idx in range(len(plot_spec.bragg_tick_sets))],
-                ticktext=[tick_set.phase_id for tick_set in plot_spec.bragg_tick_sets],
-                range=[float(len(plot_spec.bragg_tick_sets)) + 0.5, 0.5],
-                showgrid=False,
-                row=layout.bragg_row,
-                col=1,
-            )
-            fig.update_xaxes(
-                showticklabels=layout.residual_row is None,
-                row=layout.bragg_row,
-                col=1,
-            )
-
-        if layout.residual_row is not None and plot_spec.y_resid is not None:
-            residual_tick_limit = self._get_display_tick_limit(residual_limit)
-            fig.update_yaxes(
-                # title_text='Residual',
-                range=[-residual_limit, residual_limit],
-                tickmode='array',
-                tickvals=[-residual_tick_limit, 0.0, residual_tick_limit],
-                scaleanchor='y',
-                scaleratio=1,
-                zeroline=False,
-                row=layout.residual_row,
-                col=1,
-            )
-            fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=layout.residual_row, col=1)
-        else:
-            terminal_row = layout.bragg_row if layout.bragg_row is not None else 1
-            fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=terminal_row, col=1)
-
-        self._show_figure(fig)
+    def _configure_residual_axes(
+        self,
+        *,
+        fig: object,
+        plot_spec: PowderMeasVsCalcSpec,
+        layout: PowderCompositeRows,
+        residual_limit: float,
+    ) -> None:
+        residual_tick_limit = self._get_display_tick_limit(residual_limit)
+        fig.update_yaxes(
+            range=[-residual_limit, residual_limit],
+            tickmode='array',
+            tickvals=[-residual_tick_limit, 0.0, residual_tick_limit],
+            scaleanchor='y',
+            scaleratio=1,
+            zeroline=False,
+            row=layout.residual_row,
+            col=1,
+        )
+        fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=layout.residual_row, col=1)
 
     @staticmethod
     def _get_predictive_band_traces(
@@ -1446,7 +1591,9 @@ def _get_predictive_band_traces(
         lower: np.ndarray,
         upper: np.ndarray,
     ) -> tuple[go.Scatter, go.Scatter]:
-        """Return Plotly traces for a filled predictive interval band."""
+        """
+        Return Plotly traces for a filled predictive interval band.
+        """
         lower_trace = go.Scatter(
             x=x,
             y=lower,
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 35b0b33b..2fee1031 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -16,6 +16,7 @@
 import numpy as np
 import pandas as pd
 
+from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
 from easydiffraction.datablocks.experiment.item.base import intensity_category_for
 from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
 from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
@@ -71,6 +72,9 @@ def description(self) -> str:
 DEFAULT_BRAGG_ROW = DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION
 DEFAULT_POSTERIOR_PREDICTIVE_DRAWS = 200
 DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP = 50
+POSTERIOR_FLATTENED_SAMPLE_NDIM = 2
+MIN_POSTERIOR_PARAMETER_COUNT = 2
+MIN_POSTERIOR_SAMPLE_COUNT = 2
 POSTERIOR_DENSITY_LINE_COLOR = 'rgb(99, 110, 250)'
 POSTERIOR_DENSITY_FILL_COLOR = 'rgba(99, 110, 250, 0.22)'
 POSTERIOR_HISTOGRAM_FILL_COLOR = 'rgba(120, 120, 120, 0.38)'
@@ -127,6 +131,45 @@ class _PowderMeasVsCalcSeries:
     y_bkg: np.ndarray | None = None
 
 
+@dataclass(frozen=True)
+class _PosteriorDistributionContext:
+    """Inputs needed to build a posterior distribution plot."""
+
+    fit_results: object
+    parameter_name: str
+    values: np.ndarray
+    label: str
+    title: str
+    summary: object | None
+
+
+@dataclass(frozen=True)
+class _PosteriorPairsContext:
+    """Inputs needed to build a posterior pair plot."""
+
+    fit_results: object
+    parameter_names: list[str]
+    labels: list[str]
+    density_samples: np.ndarray
+    scatter_samples: np.ndarray
+    axis_frame_color: str
+    axis_ranges: list[tuple[float, float]]
+
+    @property
+    def n_parameters(self) -> int:
+        """Return the number of plotted parameters."""
+        return len(self.parameter_names)
+
+
+@dataclass(slots=True)
+class _PosteriorPairsLegendState:
+    """Legend-visibility state for posterior pair plots."""
+
+    show_density: bool = True
+    show_scatter: bool = True
+    show_contour: bool = True
+
+
 class Plotter(RendererBase):
     """User-facing plotting facade backed by concrete plotters."""
 
@@ -599,6 +642,7 @@ def plot_param_correlations(
         self,
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
         precision: int = 2,
+        *,
         show_diagonal: bool = False,
     ) -> None:
         """
@@ -671,7 +715,8 @@ def plot_posterior_pairs(
         self,
         parameters: list[object] | None = None,
     ) -> None:
-        """Plot posterior pair relationships for sampled parameters.
+        """
+        Plot posterior pair relationships for sampled parameters.
 
         Parameters
         ----------
@@ -688,7 +733,8 @@ def plot_param_distribution(
         self,
         param: object,
     ) -> None:
-        """Plot the posterior distribution for one sampled parameter.
+        """
+        Plot the posterior distribution for one sampled parameter.
 
         Parameters
         ----------
@@ -711,16 +757,17 @@ def plot_posterior_predictive(
         show_residual: bool | None = None,
         x: object | None = None,
     ) -> None:
-        """Plot posterior predictive curves for a powder experiment.
+        """
+        Plot posterior predictive curves for a powder experiment.
 
         Parameters
         ----------
         expt_name : str
             Experiment name to plot.
         style : str, default='band+draws'
-            ``'band'`` shows the 95% credible interval,
-            ``'draws'`` shows sampled predictive curves, and
-            ``'band+draws'`` shows both together.
+            ``'band'`` shows the 95% credible interval, ``'draws'``
+            shows sampled predictive curves, and ``'band+draws'`` shows
+            both together.
         x_min : float | None, default=None
             Lower bound for the x-axis range.
         x_max : float | None, default=None
@@ -729,6 +776,12 @@ def plot_posterior_predictive(
             Whether to include the residual row in the composite plot.
         x : object | None, default=None
             Optional explicit x-axis data to override stored values.
+
+        Raises
+        ------
+        ValueError
+            If ``style`` is not one of ``'band'``, ``'draws'``, or
+            ``'band+draws'``.
         """
         if style not in {'band', 'draws', 'band+draws'}:
             msg = "style must be 'band', 'draws', or 'band+draws'."
@@ -913,12 +966,40 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
         if fit_results is None:
             return None
 
+        corr_df = self._posterior_correlation_dataframe(fit_results)
+        if corr_df is not None:
+            return corr_df
+
+        raw_result = self._raw_fit_result_for_correlation(fit_results)
+        if raw_result is None:
+            return None
+
+        corr_df = self._correlation_dataframe_from_engine_result(
+            raw_result=raw_result,
+            parameters=fit_results.parameters,
+        )
+        if corr_df is not None:
+            return corr_df
+
+        log.warning(
+            'Correlation matrix is unavailable for this fit. '
+            'Use a minimizer that returns covariance information or posterior samples.'
+        )
+        return None
+
+    def _posterior_correlation_dataframe(
+        self,
+        fit_results: object,
+    ) -> pd.DataFrame | None:
+        """Return posterior-sample correlations when available."""
         posterior_samples = getattr(fit_results, 'posterior_samples', None)
-        if posterior_samples is not None:
-            corr_df = self._correlation_from_posterior_samples(posterior_samples)
-            if corr_df is not None:
-                return corr_df
+        if posterior_samples is None:
+            return None
+        return self._correlation_from_posterior_samples(posterior_samples)
 
+    @staticmethod
+    def _raw_fit_result_for_correlation(fit_results: object) -> object | None:
+        """Return raw fit results for correlation fallback."""
         raw_result = getattr(fit_results, 'result', None)
         if raw_result is None:
             raw_result = getattr(fit_results, 'engine_result', None)
@@ -930,30 +1011,34 @@ def _get_param_correlation_dataframe(self) -> pd.DataFrame | None:
         if not var_names:
             log.warning('Fit result does not expose variable names for a correlation matrix.')
             return None
+        return raw_result
 
+    def _correlation_dataframe_from_engine_result(
+        self,
+        *,
+        raw_result: object,
+        parameters: list[object],
+    ) -> pd.DataFrame | None:
+        """Return correlations derived from engine result fields."""
         covar = getattr(raw_result, 'covar', None)
         if covar is not None:
-            return self._correlation_from_covariance(covar, var_names, fit_results.parameters)
-
-        corr_df = self._get_param_correlation_dataframe_from_engine_params(
+            return self._correlation_from_covariance(
+                covar,
+                getattr(raw_result, 'var_names', None),
+                parameters,
+            )
+        return self._get_param_correlation_dataframe_from_engine_params(
             raw_result=raw_result,
-            parameters=fit_results.parameters,
-        )
-        if corr_df is not None:
-            return corr_df
-
-        log.warning(
-            'Correlation matrix is unavailable for this fit. '
-            'Use a minimizer that returns covariance information or posterior samples.'
+            parameters=parameters,
         )
-        return None
 
     def _build_posterior_pairs_plot(
         self,
         *,
         parameters: list[object] | None,
     ) -> object | None:
-        """Build a Plotly posterior pair plot.
+        """
+        Build a Plotly posterior pair plot.
 
         Parameters
         ----------
@@ -966,6 +1051,47 @@ def _build_posterior_pairs_plot(
             Plotly figure, or ``None`` when posterior plotting is
             unavailable.
         """
+        context = self._posterior_pairs_context(parameters)
+        if context is None:
+            return None
+
+        make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
+        subplot_title_annotations: list[dict[str, object]] = []
+        subplot_border_shapes: list[dict[str, object]] = []
+        legend_state = _PosteriorPairsLegendState()
+        fig = make_subplots(
+            rows=context.n_parameters,
+            cols=context.n_parameters,
+            shared_xaxes='columns',
+            horizontal_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+            vertical_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+        )
+
+        for row_index in range(context.n_parameters):
+            for col_index in range(context.n_parameters):
+                self._populate_posterior_pair_panel(
+                    fig=fig,
+                    context=context,
+                    row_index=row_index,
+                    col_index=col_index,
+                    legend_state=legend_state,
+                    subplot_title_annotations=subplot_title_annotations,
+                    subplot_border_shapes=subplot_border_shapes,
+                )
+
+        self._finalize_posterior_pairs_figure(
+            fig=fig,
+            context=context,
+            subplot_title_annotations=subplot_title_annotations,
+            subplot_border_shapes=subplot_border_shapes,
+        )
+        return fig
+
+    def _posterior_pairs_context(
+        self,
+        parameters: list[object] | None,
+    ) -> _PosteriorPairsContext | None:
+        """Return the resolved inputs for a posterior pair plot."""
         posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
         if posterior_samples is None or fit_results is None:
             return None
@@ -976,245 +1102,343 @@ def _build_posterior_pairs_plot(
         )
         if parameter_names is None:
             return None
-        if len(parameter_names) < 2:
+        if len(parameter_names) < MIN_POSTERIOR_PARAMETER_COUNT:
             log.warning('Posterior pair plots require at least two sampled parameters.')
             return None
 
-        go = __import__(
-            'plotly.graph_objects',
-            fromlist=['Figure', 'Histogram', 'Scatter', 'Contour'],
-        )
-        make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
-
         density_samples = self._selected_posterior_samples(posterior_samples, parameter_names)
         if density_samples is None:
             return None
-        scatter_samples = self._thin_posterior_samples(density_samples, max_points=1500)
-        labels = self._posterior_plot_labels(fit_results, parameter_names)
-        show_density_legend = True
-        show_scatter_legend = True
-        show_contour_legend = True
-        axis_frame_color = self._plot_axis_frame_color()
-        parameter_axis_ranges = [
-            self._posterior_axis_bounds(
-                density_samples[:, index],
-                lower_bound=self._posterior_parameter_bounds(
-                    fit_results=fit_results,
-                    parameter_name=parameter_names[index],
-                )[0],
-                upper_bound=self._posterior_parameter_bounds(
-                    fit_results=fit_results,
-                    parameter_name=parameter_names[index],
-                )[1],
+
+        return _PosteriorPairsContext(
+            fit_results=fit_results,
+            parameter_names=parameter_names,
+            labels=self._posterior_plot_labels(fit_results, parameter_names),
+            density_samples=density_samples,
+            scatter_samples=self._thin_posterior_samples(density_samples, max_points=1500),
+            axis_frame_color=self._plot_axis_frame_color(),
+            axis_ranges=self._posterior_pair_axis_ranges(
+                fit_results=fit_results,
+                parameter_names=parameter_names,
+                density_samples=density_samples,
+            ),
+        )
+
+    def _posterior_pair_axis_ranges(
+        self,
+        *,
+        fit_results: object,
+        parameter_names: list[str],
+        density_samples: np.ndarray,
+    ) -> list[tuple[float, float]]:
+        """Return per-parameter axis ranges for a pair plot."""
+        axis_ranges: list[tuple[float, float]] = []
+        for index, parameter_name in enumerate(parameter_names):
+            lower_bound, upper_bound = self._posterior_parameter_bounds(
+                fit_results=fit_results,
+                parameter_name=parameter_name,
             )
-            for index in range(len(parameter_names))
-        ]
+            axis_ranges.append(
+                self._posterior_axis_bounds(
+                    density_samples[:, index],
+                    lower_bound=lower_bound,
+                    upper_bound=upper_bound,
+                )
+            )
+        return axis_ranges
 
-        n_parameters = len(parameter_names)
-        subplot_title_annotations: list[dict[str, object]] = []
-        subplot_border_shapes: list[dict[str, object]] = []
-        fig = make_subplots(
-            rows=n_parameters,
-            cols=n_parameters,
-            shared_xaxes='columns',
-            horizontal_spacing=PAIR_PLOT_SUBPLOT_SPACING,
-            vertical_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+    def _populate_posterior_pair_panel(
+        self,
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row_index: int,
+        col_index: int,
+        legend_state: _PosteriorPairsLegendState,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Populate one panel in the posterior pair plot grid."""
+        row = row_index + 1
+        col = col_index + 1
+        if col_index > row_index:
+            self._hide_posterior_pair_panel(fig=fig, row=row, col=col)
+            return
+
+        if row_index == col_index:
+            self._add_posterior_pair_diagonal(
+                fig=fig,
+                context=context,
+                row=row,
+                col=col,
+                parameter_index=col_index,
+                legend_state=legend_state,
+            )
+        else:
+            self._add_posterior_pair_off_diagonal(
+                fig=fig,
+                context=context,
+                row=row,
+                col=col,
+                row_index=row_index,
+                col_index=col_index,
+                legend_state=legend_state,
+            )
+
+        self._configure_posterior_pair_panel_axes(
+            fig=fig,
+            context=context,
+            row=row,
+            col=col,
+            row_index=row_index,
+            col_index=col_index,
+        )
+        self._collect_posterior_pair_panel_decorations(
+            fig=fig,
+            context=context,
+            row_index=row_index,
+            col_index=col_index,
+            subplot_title_annotations=subplot_title_annotations,
+            subplot_border_shapes=subplot_border_shapes,
         )
 
-        for row_index in range(n_parameters):
-            for col_index in range(n_parameters):
-                row = row_index + 1
-                col = col_index + 1
-                if col_index > row_index:
-                    fig.update_xaxes(visible=False, row=row, col=col)
-                    fig.update_yaxes(visible=False, row=row, col=col)
-                    continue
+    @staticmethod
+    def _hide_posterior_pair_panel(
+        *,
+        fig: object,
+        row: int,
+        col: int,
+    ) -> None:
+        """Hide an upper-triangle panel in the pair plot grid."""
+        fig.update_xaxes(visible=False, row=row, col=col)
+        fig.update_yaxes(visible=False, row=row, col=col)
 
-                x_density_values = density_samples[:, col_index]
-                y_density_values = density_samples[:, row_index]
-                x_scatter_values = scatter_samples[:, col_index]
-                y_scatter_values = scatter_samples[:, row_index]
-                is_diagonal_subplot = row_index == col_index
-                if is_diagonal_subplot:
-                    density_trace = self._posterior_density_trace(
-                        fit_results=fit_results,
-                        parameter_name=parameter_names[col_index],
-                        values=x_density_values,
-                        trace_name=labels[col_index],
-                    )
-                    diagonal_y_axis_range = None
-                    if density_trace is None:
-                        fig.add_trace(
-                            go.Histogram(
-                                x=x_density_values,
-                                nbinsx=40,
-                                histnorm='probability density',
-                                marker={'color': 'rgb(99, 110, 250)'},
-                                showlegend=False,
-                                hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
-                            ),
-                            row=row,
-                            col=col,
-                        )
-                    else:
-                        density_trace.name = 'Marginal density'
-                        density_trace.legendgroup = 'posterior-marginal-density'
-                        density_trace.showlegend = show_density_legend
-                        fig.add_trace(density_trace, row=row, col=col)
-                        show_density_legend = False
-                        diagonal_y_axis_range = self._posterior_density_axis_range(
-                            np.asarray(density_trace.y)
-                        )
-                    if diagonal_y_axis_range is not None:
-                        fig.update_yaxes(range=list(diagonal_y_axis_range), row=row, col=col)
-                else:
-                    contour_traces = self._posterior_contour_traces(
-                        fit_results=fit_results,
-                        x_parameter_name=parameter_names[col_index],
-                        y_parameter_name=parameter_names[row_index],
-                        x_values=x_density_values,
-                        y_values=y_density_values,
-                    )
-                    sample_hovertemplate = (
-                        f'{labels[col_index]}: %{{x:.4f}}<br>'
-                        f'{labels[row_index]}: %{{y:.4f}}<extra></extra>'
-                    )
-                    fig.add_trace(
-                        go.Scatter(
-                            x=x_scatter_values,
-                            y=y_scatter_values,
-                            mode='markers',
-                            marker={
-                                'color': POSTERIOR_SCATTER_MARKER_COLOR,
-                                'size': 3,
-                            },
-                            name='Posterior samples',
-                            legendgroup='posterior-samples',
-                            showlegend=show_scatter_legend,
-                            hoverinfo='skip',
-                            zorder=0,
-                        ),
-                        row=row,
-                        col=col,
-                    )
-                    show_scatter_legend = False
-                    if contour_traces is not None:
-                        contour_traces[0].name = 'Posterior contours'
-                        contour_traces[0].legendgroup = 'posterior-contours'
-                        contour_traces[0].showlegend = show_contour_legend
-                        contour_traces[1].legendgroup = 'posterior-contours'
-                        contour_traces[1].showlegend = False
-                        fig.add_trace(contour_traces[0], row=row, col=col)
-                        fig.add_trace(contour_traces[1], row=row, col=col)
-                        show_contour_legend = False
-                    fig.add_trace(
-                        go.Scatter(
-                            x=x_scatter_values,
-                            y=y_scatter_values,
-                            mode='markers',
-                            marker={
-                                'color': 'rgba(0, 0, 0, 0)',
-                                'size': 6,
-                            },
-                            showlegend=False,
-                            hovertemplate=sample_hovertemplate,
-                            zorder=3,
-                        ),
-                        row=row,
-                        col=col,
-                    )
+    def _add_posterior_pair_diagonal(
+        self,
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row: int,
+        col: int,
+        parameter_index: int,
+        legend_state: _PosteriorPairsLegendState,
+    ) -> None:
+        """Add the diagonal marginal-density panel."""
+        go = __import__('plotly.graph_objects', fromlist=['Histogram'])
+        density_values = context.density_samples[:, parameter_index]
+        density_trace = self._posterior_density_trace(
+            fit_results=context.fit_results,
+            parameter_name=context.parameter_names[parameter_index],
+            values=density_values,
+            trace_name=context.labels[parameter_index],
+        )
+        if density_trace is None:
+            fig.add_trace(
+                go.Histogram(
+                    x=density_values,
+                    nbinsx=40,
+                    histnorm='probability density',
+                    marker={'color': 'rgb(99, 110, 250)'},
+                    showlegend=False,
+                    hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+                ),
+                row=row,
+                col=col,
+            )
+            return
 
-                fig.update_xaxes(
-                    showline=True,
-                    mirror=True,
-                    range=list(parameter_axis_ranges[col_index]),
-                    zeroline=False,
-                    layer='above traces',
-                    linecolor=axis_frame_color,
-                    linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
-                    nticks=PAIR_PLOT_MAJOR_TICKS,
-                    tickformat=',.6~g',
-                    separatethousands=True,
-                    row=row,
-                    col=col,
-                )
-                fig.update_yaxes(
-                    showline=True,
-                    mirror=True,
-                    zeroline=False,
-                    layer='above traces',
-                    linecolor=axis_frame_color,
-                    linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
-                    nticks=PAIR_PLOT_MAJOR_TICKS,
-                    tickformat=',.6~g',
-                    separatethousands=True,
-                    row=row,
-                    col=col,
-                )
-                if not is_diagonal_subplot:
-                    fig.update_yaxes(range=list(parameter_axis_ranges[row_index]), row=row, col=col)
-                fig.update_xaxes(showticklabels=(row_index == n_parameters - 1), row=row, col=col)
-                if is_diagonal_subplot:
-                    fig.update_yaxes(
-                        showticklabels=False,
-                        ticks='',
-                        ticklen=0,
-                        showgrid=False,
-                        title_text=None,
-                        row=row,
-                        col=col,
-                    )
-                else:
-                    fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
-                if row_index == n_parameters - 1:
-                    fig.update_xaxes(title_text=labels[col_index], row=row, col=col)
-
-                subplot = fig.get_subplot(row, col)
-                if col_index == 0:
-                    subplot_title_annotations.append(
-                        {
-                            'x': subplot.xaxis.domain[0],
-                            'xref': 'paper',
-                            'xanchor': 'right',
-                            'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
-                            'y': 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1]),
-                            'yref': 'paper',
-                            'yanchor': 'middle',
-                            'text': labels[row_index],
-                            'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
-                            'textangle': -90,
-                            'showarrow': False,
-                        }
-                    )
-                subplot_border_shapes.append(
-                    {
-                        'type': 'rect',
-                        'xref': 'paper',
-                        'yref': 'paper',
-                        'x0': subplot.xaxis.domain[0],
-                        'x1': subplot.xaxis.domain[1],
-                        'y0': subplot.yaxis.domain[0],
-                        'y1': subplot.yaxis.domain[1],
-                        'line': {
-                            'color': axis_frame_color,
-                            'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
-                        },
-                        'fillcolor': 'rgba(0, 0, 0, 0)',
-                        'layer': 'above',
-                    }
-                )
+        density_trace.name = 'Marginal density'
+        density_trace.legendgroup = 'posterior-marginal-density'
+        density_trace.showlegend = legend_state.show_density
+        fig.add_trace(density_trace, row=row, col=col)
+        legend_state.show_density = False
+        y_axis_range = self._posterior_density_axis_range(np.asarray(density_trace.y))
+        if y_axis_range is not None:
+            fig.update_yaxes(range=list(y_axis_range), row=row, col=col)
+
+    def _add_posterior_pair_off_diagonal(
+        self,
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row: int,
+        col: int,
+        row_index: int,
+        col_index: int,
+        legend_state: _PosteriorPairsLegendState,
+    ) -> None:
+        """Add one off-diagonal pair-relationship panel."""
+        go = __import__('plotly.graph_objects', fromlist=['Scatter'])
+        x_density_values = context.density_samples[:, col_index]
+        y_density_values = context.density_samples[:, row_index]
+        x_scatter_values = context.scatter_samples[:, col_index]
+        y_scatter_values = context.scatter_samples[:, row_index]
+        contour_traces = self._posterior_contour_traces(
+            fit_results=context.fit_results,
+            x_parameter_name=context.parameter_names[col_index],
+            y_parameter_name=context.parameter_names[row_index],
+            x_values=x_density_values,
+            y_values=y_density_values,
+        )
+        sample_hovertemplate = (
+            f'{context.labels[col_index]}: %{{x:.4f}}<br>'
+            f'{context.labels[row_index]}: %{{y:.4f}}<extra></extra>'
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=x_scatter_values,
+                y=y_scatter_values,
+                mode='markers',
+                marker={'color': POSTERIOR_SCATTER_MARKER_COLOR, 'size': 3},
+                name='Posterior samples',
+                legendgroup='posterior-samples',
+                showlegend=legend_state.show_scatter,
+                hoverinfo='skip',
+                zorder=0,
+            ),
+            row=row,
+            col=col,
+        )
+        legend_state.show_scatter = False
+        if contour_traces is not None:
+            contour_traces[0].name = 'Posterior contours'
+            contour_traces[0].legendgroup = 'posterior-contours'
+            contour_traces[0].showlegend = legend_state.show_contour
+            contour_traces[1].legendgroup = 'posterior-contours'
+            contour_traces[1].showlegend = False
+            fig.add_trace(contour_traces[0], row=row, col=col)
+            fig.add_trace(contour_traces[1], row=row, col=col)
+            legend_state.show_contour = False
+        fig.add_trace(
+            go.Scatter(
+                x=x_scatter_values,
+                y=y_scatter_values,
+                mode='markers',
+                marker={'color': 'rgba(0, 0, 0, 0)', 'size': 6},
+                showlegend=False,
+                hovertemplate=sample_hovertemplate,
+                zorder=3,
+            ),
+            row=row,
+            col=col,
+        )
 
+    @staticmethod
+    def _configure_posterior_pair_panel_axes(
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row: int,
+        col: int,
+        row_index: int,
+        col_index: int,
+    ) -> None:
+        """Apply axis styling and labels to one pair-plot panel."""
+        is_diagonal = row_index == col_index
+        fig.update_xaxes(
+            showline=True,
+            mirror=True,
+            range=list(context.axis_ranges[col_index]),
+            zeroline=False,
+            layer='above traces',
+            linecolor=context.axis_frame_color,
+            linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+            nticks=PAIR_PLOT_MAJOR_TICKS,
+            tickformat=',.6~g',
+            separatethousands=True,
+            row=row,
+            col=col,
+        )
+        fig.update_yaxes(
+            showline=True,
+            mirror=True,
+            zeroline=False,
+            layer='above traces',
+            linecolor=context.axis_frame_color,
+            linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+            nticks=PAIR_PLOT_MAJOR_TICKS,
+            tickformat=',.6~g',
+            separatethousands=True,
+            row=row,
+            col=col,
+        )
+        if not is_diagonal:
+            fig.update_yaxes(range=list(context.axis_ranges[row_index]), row=row, col=col)
+        fig.update_xaxes(showticklabels=(row_index == context.n_parameters - 1), row=row, col=col)
+        if is_diagonal:
+            fig.update_yaxes(
+                showticklabels=False,
+                ticks='',
+                ticklen=0,
+                showgrid=False,
+                title_text=None,
+                row=row,
+                col=col,
+            )
+        else:
+            fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
+        if row_index == context.n_parameters - 1:
+            fig.update_xaxes(title_text=context.labels[col_index], row=row, col=col)
+
+    @staticmethod
+    def _collect_posterior_pair_panel_decorations(
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        row_index: int,
+        col_index: int,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Collect annotations and frame shapes for one pair panel."""
+        row = row_index + 1
+        col = col_index + 1
+        subplot = fig.get_subplot(row, col)
+        if col_index == 0:
+            subplot_title_annotations.append({
+                'x': subplot.xaxis.domain[0],
+                'xref': 'paper',
+                'xanchor': 'right',
+                'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
+                'y': 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1]),
+                'yref': 'paper',
+                'yanchor': 'middle',
+                'text': context.labels[row_index],
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'textangle': -90,
+                'showarrow': False,
+            })
+        subplot_border_shapes.append({
+            'type': 'rect',
+            'xref': 'paper',
+            'yref': 'paper',
+            'x0': subplot.xaxis.domain[0],
+            'x1': subplot.xaxis.domain[1],
+            'y0': subplot.yaxis.domain[0],
+            'y1': subplot.yaxis.domain[1],
+            'line': {
+                'color': context.axis_frame_color,
+                'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+            },
+            'fillcolor': 'rgba(0, 0, 0, 0)',
+            'layer': 'above',
+        })
+
+    @staticmethod
+    def _finalize_posterior_pairs_figure(
+        *,
+        fig: object,
+        context: _PosteriorPairsContext,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Apply final layout settings to the posterior pair plot."""
         figure_size = max(
             PAIR_PLOT_MIN_SIZE_PIXELS,
-            PAIR_PLOT_CELL_SIZE_PIXELS * n_parameters + PAIR_PLOT_MARGIN_PIXELS,
+            PAIR_PLOT_CELL_SIZE_PIXELS * context.n_parameters + PAIR_PLOT_MARGIN_PIXELS,
         )
         fig.update_layout(
-            margin={
-                'autoexpand': True,
-                'r': 30,
-                't': 40,
-                'b': 45,
-            },
+            margin={'autoexpand': True, 'r': 30, 't': 40, 'b': 45},
             title={'text': 'Posterior pair plot'},
             bargap=0.05,
             width=figure_size,
@@ -1230,10 +1454,11 @@ def _build_posterior_pairs_plot(
                 'groupclick': 'togglegroup',
             },
         )
-        return fig
 
     def _plot_axis_frame_color(self) -> str:
-        """Return the shared axis-frame color for Plotly-backed plots."""
+        """
+        Return the shared axis-frame color for Plotly-backed plots.
+        """
         axis_frame_color = getattr(self._backend, '_axis_frame_color', None)
         if callable(axis_frame_color):
             return axis_frame_color()
@@ -1248,7 +1473,9 @@ def _posterior_contour_traces(
         x_values: np.ndarray,
         y_values: np.ndarray,
     ) -> tuple[object, object] | None:
-        """Return filled and line contour traces for posterior pair plots."""
+        """
+        Return filled and line contour traces for posterior pair plots.
+        """
         go = __import__('plotly.graph_objects', fromlist=['Contour'])
 
         bounds = self._posterior_pair_bounds(
@@ -1320,7 +1547,8 @@ def _build_param_distribution_plot(
         self,
         param: object,
     ) -> object | None:
-        """Build a Plotly posterior distribution plot for one parameter.
+        """
+        Build a Plotly posterior distribution plot for one parameter.
 
         Parameters
         ----------
@@ -1333,6 +1561,58 @@ def _build_param_distribution_plot(
             Plotly figure, or ``None`` when posterior plotting is
             unavailable.
         """
+        context = self._posterior_distribution_context(param)
+        if context is None:
+            return None
+
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Histogram'])
+        fig, layout_factory = self._posterior_distribution_figure(
+            go=go,
+            title=context.title,
+            label=context.label,
+        )
+        density_trace = self._posterior_density_trace(
+            fit_results=context.fit_results,
+            parameter_name=context.parameter_name,
+            values=context.values,
+            trace_name='Posterior density',
+        )
+        y_axis_range = self._posterior_distribution_y_axis_range(
+            values=context.values,
+            density_trace=density_trace,
+        )
+
+        self._add_posterior_distribution_interval_traces(
+            fig=fig,
+            summary=context.summary,
+            y_axis_range=y_axis_range,
+        )
+        self._add_posterior_distribution_histogram(
+            fig=fig,
+            go=go,
+            values=context.values,
+        )
+        self._add_posterior_distribution_density_trace(fig=fig, density_trace=density_trace)
+        self._add_posterior_distribution_reference_traces(
+            fig=fig,
+            summary=context.summary,
+            values=context.values,
+            y_axis_range=y_axis_range,
+        )
+        self._apply_posterior_distribution_layout(
+            fig=fig,
+            layout_factory=layout_factory,
+            title=context.title,
+            label=context.label,
+            y_axis_range=y_axis_range,
+        )
+        return fig
+
+    def _posterior_distribution_context(
+        self,
+        param: object,
+    ) -> _PosteriorDistributionContext | None:
+        """Return the context for a posterior distribution plot."""
         posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
         if posterior_samples is None or fit_results is None:
             return None
@@ -1344,57 +1624,86 @@ def _build_param_distribution_plot(
         if parameter_names is None:
             return None
 
-        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Histogram'])
-
         parameter_name = parameter_names[0]
         samples = self._selected_posterior_samples(posterior_samples, [parameter_name])
         if samples is None:
             return None
-        values = samples[:, 0]
+
         label = self._posterior_plot_labels(fit_results, [parameter_name])[0]
-        summary = self._posterior_summary_by_name(fit_results).get(parameter_name)
-        title = f'Posterior distribution: {label}'
+        return _PosteriorDistributionContext(
+            fit_results=fit_results,
+            parameter_name=parameter_name,
+            values=samples[:, 0],
+            label=label,
+            title=f'Posterior distribution: {label}',
+            summary=self._posterior_summary_by_name(fit_results).get(parameter_name),
+        )
+
+    def _posterior_distribution_figure(
+        self,
+        *,
+        go: object,
+        title: str,
+        label: str,
+    ) -> tuple[object, object | None]:
+        """Return the figure and optional backend layout factory."""
         layout_factory = getattr(self._backend, '_get_layout', None)
         if callable(layout_factory):
-            fig = go.Figure(layout=layout_factory(title, [label, 'Probability density']))
-        else:
-            fig = go.Figure()
+            figure = go.Figure(layout=layout_factory(title, [label, 'Probability density']))
+            return figure, layout_factory
+        return go.Figure(), layout_factory
 
+    def _posterior_distribution_y_axis_range(
+        self,
+        *,
+        values: np.ndarray,
+        density_trace: object | None,
+    ) -> tuple[float, float] | None:
+        """Return the y-axis range for a posterior distribution plot."""
         histogram_density, _ = np.histogram(values, bins=50, density=True)
-        density_trace = self._posterior_density_trace(
-            fit_results=fit_results,
-            parameter_name=parameter_name,
-            values=values,
-            trace_name='Posterior density',
-        )
         density_sources = [histogram_density]
         if density_trace is not None:
-            density_trace.name = 'Posterior density'
-            density_trace.showlegend = True
             density_sources.append(np.asarray(density_trace.y, dtype=float))
+        return self._posterior_density_axis_range(np.concatenate(density_sources))
 
-        y_axis_range = self._posterior_density_axis_range(np.concatenate(density_sources))
+    def _add_posterior_distribution_interval_traces(
+        self,
+        *,
+        fig: object,
+        summary: object | None,
+        y_axis_range: tuple[float, float] | None,
+    ) -> None:
+        """Add credible-interval bands to the distribution plot."""
+        if summary is None or y_axis_range is None:
+            return
 
-        if summary is not None and y_axis_range is not None:
-            fig.add_trace(
-                self._posterior_interval_band_trace(
-                    x0=summary.interval_95[0],
-                    x1=summary.interval_95[1],
-                    y_axis_range=y_axis_range,
-                    trace_name='95% credible interval',
-                    color=POSTERIOR_INTERVAL_95_FILL_COLOR,
-                )
+        fig.add_trace(
+            self._posterior_interval_band_trace(
+                x0=summary.interval_95[0],
+                x1=summary.interval_95[1],
+                y_axis_range=y_axis_range,
+                trace_name='95% credible interval',
+                color=POSTERIOR_INTERVAL_95_FILL_COLOR,
             )
-            fig.add_trace(
-                self._posterior_interval_band_trace(
-                    x0=summary.interval_68[0],
-                    x1=summary.interval_68[1],
-                    y_axis_range=y_axis_range,
-                    trace_name='68% credible interval',
-                    color=POSTERIOR_INTERVAL_68_FILL_COLOR,
-                )
+        )
+        fig.add_trace(
+            self._posterior_interval_band_trace(
+                x0=summary.interval_68[0],
+                x1=summary.interval_68[1],
+                y_axis_range=y_axis_range,
+                trace_name='68% credible interval',
+                color=POSTERIOR_INTERVAL_68_FILL_COLOR,
             )
+        )
 
+    @staticmethod
+    def _add_posterior_distribution_histogram(
+        *,
+        fig: object,
+        go: object,
+        values: np.ndarray,
+    ) -> None:
+        """Add the histogram trace for a posterior distribution plot."""
         fig.add_trace(
             go.Histogram(
                 x=values,
@@ -1409,33 +1718,65 @@ def _build_param_distribution_plot(
                 hovertemplate='sample=%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
             )
         )
-        if density_trace is not None:
-            density_trace.name = 'Posterior density'
-            density_trace.showlegend = True
-            fig.add_trace(density_trace)
 
-        median = float(np.median(values))
-        if y_axis_range is not None:
-            fig.add_trace(
-                self._posterior_reference_line_trace(
-                    x_value=median,
-                    y_axis_range=y_axis_range,
-                    trace_name='Median',
-                    color=POSTERIOR_MEDIAN_LINE_COLOR,
-                    dash='dash',
-                )
+    @staticmethod
+    def _add_posterior_distribution_density_trace(
+        *,
+        fig: object,
+        density_trace: object | None,
+    ) -> None:
+        """Add the KDE trace for a posterior distribution plot."""
+        if density_trace is None:
+            return
+
+        density_trace.name = 'Posterior density'
+        density_trace.showlegend = True
+        fig.add_trace(density_trace)
+
+    def _add_posterior_distribution_reference_traces(
+        self,
+        *,
+        fig: object,
+        summary: object | None,
+        values: np.ndarray,
+        y_axis_range: tuple[float, float] | None,
+    ) -> None:
+        """Add posterior median and MAP reference lines."""
+        if y_axis_range is None:
+            return
+
+        fig.add_trace(
+            self._posterior_reference_line_trace(
+                x_value=float(np.median(values)),
+                y_axis_range=y_axis_range,
+                trace_name='Median',
+                color=POSTERIOR_MEDIAN_LINE_COLOR,
+                dash='dash',
             )
-            if summary is not None:
-                fig.add_trace(
-                    self._posterior_reference_line_trace(
-                        x_value=summary.map_value,
-                        y_axis_range=y_axis_range,
-                        trace_name='Max posterior',
-                        color=POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
-                        dash='dot',
-                    )
-                )
+        )
+        if summary is None:
+            return
+
+        fig.add_trace(
+            self._posterior_reference_line_trace(
+                x_value=summary.map_value,
+                y_axis_range=y_axis_range,
+                trace_name='Max posterior',
+                color=POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
+                dash='dot',
+            )
+        )
 
+    @staticmethod
+    def _apply_posterior_distribution_layout(
+        *,
+        fig: object,
+        layout_factory: object | None,
+        title: str,
+        label: str,
+        y_axis_range: tuple[float, float] | None,
+    ) -> None:
+        """Apply layout settings to the distribution plot."""
         if callable(layout_factory):
             fig.update_layout(title={'text': title})
         else:
@@ -1453,7 +1794,6 @@ def _build_param_distribution_plot(
             )
         if y_axis_range is not None:
             fig.update_yaxes(range=list(y_axis_range))
-        return fig
 
     def _show_plot_figure(self, figure: object) -> None:
         """Display a figure through the active backend when possible."""
@@ -1515,7 +1855,7 @@ def _posterior_density_axis_range(
         upper_padding = 0.08 * data_range if data_range > 0 else max(abs(data_max), 1.0) * 0.05
         if upper_padding == 0:
             upper_padding = 1e-6
-        lower = 0.0 if data_min >= 0.0 else data_min
+        lower = min(0.0, data_min)
         return lower, data_max + upper_padding
 
     @staticmethod
@@ -1532,7 +1872,13 @@ def _posterior_interval_band_trace(
 
         return go.Scatter(
             x=[x0, x1, x1, x0, x0],
-            y=[y_axis_range[0], y_axis_range[0], y_axis_range[1], y_axis_range[1], y_axis_range[0]],
+            y=[
+                y_axis_range[0],
+                y_axis_range[0],
+                y_axis_range[1],
+                y_axis_range[1],
+                y_axis_range[0],
+            ],
             mode='lines',
             fill='toself',
             fillcolor=color,
@@ -1551,7 +1897,9 @@ def _posterior_reference_line_trace(
         color: str,
         dash: str,
     ) -> object:
-        """Return a named vertical reference line for posterior plots."""
+        """
+        Return a named vertical reference line for posterior plots.
+        """
         go = __import__('plotly.graph_objects', fromlist=['Scatter'])
 
         return go.Scatter(
@@ -1570,9 +1918,10 @@ def _posterior_parameter_bounds(
         fit_results: object,
         parameter_name: str,
     ) -> tuple[float | None, float | None]:
-        """Return finite fit bounds for a posterior parameter when available."""
+        """Return finite fit bounds for a posterior parameter."""
         parameters_by_name = {
-            getattr(parameter, 'unique_name', ''): parameter for parameter in fit_results.parameters
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
         }
         parameter = parameters_by_name.get(parameter_name)
         if parameter is None:
@@ -1651,7 +2000,7 @@ def _posterior_density_curve(
 
         data = np.asarray(values, dtype=float)
         data = data[np.isfinite(data)]
-        if data.size < 2:
+        if data.size < MIN_POSTERIOR_SAMPLE_COUNT:
             return None
 
         data_min = float(np.min(data))
@@ -1703,7 +2052,7 @@ def _posterior_pair_density_surface(
         mask = np.isfinite(x_data) & np.isfinite(y_data)
         x_data = x_data[mask]
         y_data = y_data[mask]
-        if x_data.size < 2 or y_data.size < 2:
+        if x_data.size < MIN_POSTERIOR_SAMPLE_COUNT or y_data.size < MIN_POSTERIOR_SAMPLE_COUNT:
             return None
 
         if np.allclose(x_data, x_data[0]) and np.allclose(y_data, y_data[0]):
@@ -1730,7 +2079,7 @@ def _reflection_positions_1d(
         lower_bound: float | None,
         upper_bound: float | None,
     ) -> list[np.ndarray]:
-        """Return mirrored evaluation positions for boundary-corrected KDEs."""
+        """Return mirrored positions for boundary-corrected KDEs."""
         reflected = [values]
         if lower_bound is not None:
             reflected.append(2.0 * lower_bound - values)
@@ -1792,7 +2141,9 @@ def _get_or_build_posterior_predictive_summary(
         expt_name: str,
         x_axis: object,
     ) -> object | None:
-        """Return a cached or newly built posterior predictive summary."""
+        """
+        Return a cached or newly built posterior predictive summary.
+        """
         fit_results = self._get_fit_result_for_correlation()
         if fit_results is None:
             return None
@@ -1829,22 +2180,73 @@ def _build_posterior_predictive_summary(
         x_axis: object,
     ) -> object | None:
         """Build posterior predictive summaries from posterior draws."""
-        from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+        sampling_inputs = self._posterior_predictive_sampling_inputs(fit_results)
+        if sampling_inputs is None:
+            return None
 
+        flattened_samples, parameter_names = sampling_inputs
+        sampled_parameters = self._posterior_predictive_parameters(
+            fit_results=fit_results,
+            parameter_names=parameter_names,
+        )
+        if sampled_parameters is None:
+            return None
+
+        predictive_data = self._evaluate_posterior_predictive_draws(
+            flattened_samples=flattened_samples,
+            sampled_parameters=sampled_parameters,
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+        )
+        if predictive_data is None:
+            return None
+
+        map_prediction, x_values, predictive_draw_array = predictive_data
+        lower_68, upper_68 = np.quantile(predictive_draw_array, [0.16, 0.84], axis=0)
+        lower_95, upper_95 = np.quantile(predictive_draw_array, [0.025, 0.975], axis=0)
+        x_axis_name = getattr(x_axis, 'value', x_axis)
+
+        return PosteriorPredictiveSummary(
+            experiment_name=expt_name,
+            x_axis_name=str(x_axis_name),
+            x=np.asarray(x_values, dtype=float),
+            map_prediction=np.asarray(map_prediction, dtype=float),
+            lower_95=np.asarray(lower_95, dtype=float),
+            upper_95=np.asarray(upper_95, dtype=float),
+            lower_68=np.asarray(lower_68, dtype=float),
+            upper_68=np.asarray(upper_68, dtype=float),
+            draws=predictive_draw_array,
+        )
+
+    @staticmethod
+    def _posterior_predictive_sampling_inputs(
+        fit_results: object,
+    ) -> tuple[np.ndarray, list[str]] | None:
+        """Return predictive-sampling arrays and parameter names."""
         posterior_samples = getattr(fit_results, 'posterior_samples', None)
         if posterior_samples is None:
             return None
 
         flattened_samples = np.asarray(posterior_samples.flattened(), dtype=float)
         parameter_names = getattr(posterior_samples, 'parameter_names', None)
-        if flattened_samples.ndim != 2 or not parameter_names:
+        if flattened_samples.ndim != POSTERIOR_FLATTENED_SAMPLE_NDIM or not parameter_names:
             log.warning('Posterior samples are unavailable for predictive summaries.')
             return None
+        return flattened_samples, list(parameter_names)
 
+    @staticmethod
+    def _posterior_predictive_parameters(
+        *,
+        fit_results: object,
+        parameter_names: list[str],
+    ) -> list[object] | None:
+        """Return fitted parameters in posterior sample order."""
         parameters_by_name = {
-            getattr(parameter, 'unique_name', ''): parameter for parameter in fit_results.parameters
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
         }
-        sampled_parameters = []
+        sampled_parameters: list[object] = []
         for name in parameter_names:
             parameter = parameters_by_name.get(name)
             if parameter is None:
@@ -1854,14 +2256,26 @@ def _build_posterior_predictive_summary(
                 )
                 return None
             sampled_parameters.append(parameter)
+        return sampled_parameters
 
-        original_values = np.array([parameter.value for parameter in sampled_parameters], dtype=float)
+    def _evaluate_posterior_predictive_draws(
+        self,
+        *,
+        flattened_samples: np.ndarray,
+        sampled_parameters: list[object],
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray] | None:
+        """Return MAP and sampled predictive curves."""
+        original_values = np.array(
+            [parameter.value for parameter in sampled_parameters],
+            dtype=float,
+        )
         original_uncertainties = [parameter.uncertainty for parameter in sampled_parameters]
-
-        map_prediction = None
-        x_values = None
         predictive_draws: list[np.ndarray] = []
         draw_indices = self._posterior_predictive_draw_indices(flattened_samples.shape[0])
+
         try:
             map_prediction, x_values = self._evaluate_posterior_predictive_state(
                 sampled_parameters=sampled_parameters,
@@ -1888,33 +2302,38 @@ def _build_posterior_predictive_summary(
                     return None
                 predictive_draws.append(prediction)
         finally:
-            for parameter, value, uncertainty in zip(
-                sampled_parameters,
-                original_values,
-                original_uncertainties,
-                strict=True,
-            ):
-                parameter._set_value_from_minimizer(float(value))
-                parameter.uncertainty = uncertainty
-            self._update_project_categories(expt_name)
-
-        predictive_draw_array = np.asarray(predictive_draws, dtype=float)
-        lower_68, upper_68 = np.quantile(predictive_draw_array, [0.16, 0.84], axis=0)
-        lower_95, upper_95 = np.quantile(predictive_draw_array, [0.025, 0.975], axis=0)
-        x_axis_name = getattr(x_axis, 'value', x_axis)
+            self._restore_posterior_predictive_parameters(
+                sampled_parameters=sampled_parameters,
+                original_values=original_values,
+                original_uncertainties=original_uncertainties,
+                expt_name=expt_name,
+            )
 
-        return PosteriorPredictiveSummary(
-            experiment_name=expt_name,
-            x_axis_name=str(x_axis_name),
-            x=np.asarray(x_values, dtype=float),
-            map_prediction=np.asarray(map_prediction, dtype=float),
-            lower_95=np.asarray(lower_95, dtype=float),
-            upper_95=np.asarray(upper_95, dtype=float),
-            lower_68=np.asarray(lower_68, dtype=float),
-            upper_68=np.asarray(upper_68, dtype=float),
-            draws=predictive_draw_array,
+        return (
+            np.asarray(map_prediction, dtype=float),
+            np.asarray(x_values, dtype=float),
+            np.asarray(predictive_draws, dtype=float),
         )
 
+    def _restore_posterior_predictive_parameters(
+        self,
+        *,
+        sampled_parameters: list[object],
+        original_values: np.ndarray,
+        original_uncertainties: list[float | None],
+        expt_name: str,
+    ) -> None:
+        """Restore parameter state after predictive sampling."""
+        for parameter, value, uncertainty in zip(
+            sampled_parameters,
+            original_values,
+            original_uncertainties,
+            strict=True,
+        ):
+            parameter._set_value_from_minimizer(float(value))
+            parameter.uncertainty = uncertainty
+        self._update_project_categories(expt_name)
+
     def _evaluate_posterior_predictive_state(
         self,
         *,
@@ -1944,7 +2363,9 @@ def _evaluate_posterior_predictive_state(
 
     @staticmethod
     def _posterior_predictive_draw_indices(n_draws: int) -> np.ndarray:
-        """Select evenly spaced posterior draws for predictive summaries."""
+        """
+        Select evenly spaced posterior draws for predictive summaries.
+        """
         if n_draws <= DEFAULT_POSTERIOR_PREDICTIVE_DRAWS:
             return np.arange(n_draws, dtype=int)
 
@@ -1965,7 +2386,8 @@ def _posterior_predictive_key(expt_name: str, x_axis_name: str) -> str:
     def _get_posterior_inference_data(
         self,
     ) -> tuple[object | None, object | None]:
-        """Return posterior inference data for the current Bayesian fit.
+        """
+        Return posterior inference data for the current Bayesian fit.
 
         Returns
         -------
@@ -2007,8 +2429,8 @@ def _get_posterior_samples_and_fit_results(
 
         return posterior_samples, fit_results
 
+    @staticmethod
     def _plot_posterior_predictive_summary(
-        self,
         *,
         expt_name: str,
         summary: object,
@@ -2098,13 +2520,12 @@ def _plot_posterior_predictive_data(
         x_axis: object,
         style: str,
     ) -> None:
-        """Render posterior predictive curves on the composite powder layout."""
+        """Render posterior predictive curves on the powder layout."""
         pattern = intensity_category_for(experiment)
-        expt_type = experiment.type
         ctx = self._prepare_powder_context(
             pattern,
             expt_name,
-            expt_type,
+            experiment.type,
             plot_options.x_min,
             plot_options.x_max,
             plot_options.x,
@@ -2132,7 +2553,9 @@ def _plot_posterior_predictive_data(
             if y_bkg_raw is not None
             else None
         )
-        y_calc = self._filtered_y_array(summary.map_prediction, summary.x, ctx['x_min'], ctx['x_max'])
+        y_calc = self._filtered_y_array(
+            summary.map_prediction, summary.x, ctx['x_min'], ctx['x_max']
+        )
         show_residual = True if plot_options.show_residual is None else plot_options.show_residual
         y_resid = y_meas - y_calc if show_residual else None
 
@@ -2202,7 +2625,8 @@ def _resolve_posterior_parameter_names(
         fit_results: object,
         parameters: list[object] | None,
     ) -> list[str] | None:
-        """Resolve posterior parameter names from descriptors.
+        """
+        Resolve posterior parameter names from descriptors.
 
         Parameters
         ----------
@@ -2251,7 +2675,7 @@ def _selected_posterior_samples(
             return None
 
         flattened = np.asarray(posterior_samples.flattened(), dtype=float)
-        if flattened.ndim != 2:
+        if flattened.ndim != POSTERIOR_FLATTENED_SAMPLE_NDIM:
             return None
         return flattened[:, indices]
 
@@ -2273,9 +2697,12 @@ def _posterior_plot_labels(
         fit_results: object,
         parameter_names: list[str],
     ) -> list[str]:
-        """Return readable posterior plot labels for selected parameters."""
+        """
+        Return readable posterior plot labels for selected parameters.
+        """
         parameters_by_name = {
-            getattr(parameter, 'unique_name', ''): parameter for parameter in fit_results.parameters
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
         }
         labels: list[str] = []
         for parameter_name in parameter_names:
@@ -2325,7 +2752,8 @@ def _get_fit_result_for_correlation(
     def _correlation_from_posterior_samples(
         posterior_samples: object,
     ) -> pd.DataFrame | None:
-        """Convert posterior samples into a correlation DataFrame.
+        """
+        Convert posterior samples into a correlation DataFrame.
 
         Parameters
         ----------
@@ -2345,10 +2773,12 @@ def _correlation_from_posterior_samples(
             return None
 
         flattened = np.asarray(posterior_samples.flattened(), dtype=float)
-        if flattened.ndim != 2 or flattened.shape[1] != len(parameter_names):
+        if flattened.ndim != POSTERIOR_FLATTENED_SAMPLE_NDIM or flattened.shape[1] != len(
+            parameter_names
+        ):
             log.warning('Posterior sample array has an invalid shape for correlations.')
             return None
-        if flattened.shape[0] < 2:
+        if flattened.shape[0] < MIN_POSTERIOR_SAMPLE_COUNT:
             log.warning('At least two posterior draws are required for correlations.')
             return None
 
diff --git a/tests/integration/fitting/test_bayesian_dream.py b/tests/integration/fitting/test_bayesian_dream.py
new file mode 100644
index 00000000..23e0de58
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_dream.py
@@ -0,0 +1,178 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import tempfile
+
+import numpy as np
+
+from easydiffraction import ExperimentFactory
+from easydiffraction import Project
+from easydiffraction import StructureFactory
+from easydiffraction import download_data
+
+TEMP_DIR = tempfile.gettempdir()
+
+
+def _create_lbco_project() -> Project:
+    model = StructureFactory.from_scratch(name='lbco')
+    model.space_group.name_h_m = 'P m -3 m'
+    model.cell.length_a = 3.88
+    model.atom_sites.create(
+        label='La',
+        type_symbol='La',
+        fract_x=0,
+        fract_y=0,
+        fract_z=0,
+        wyckoff_letter='a',
+        occupancy=0.5,
+        adp_iso=0.1,
+    )
+    model.atom_sites.create(
+        label='Ba',
+        type_symbol='Ba',
+        fract_x=0,
+        fract_y=0,
+        fract_z=0,
+        wyckoff_letter='a',
+        occupancy=0.5,
+        adp_iso=0.1,
+    )
+    model.atom_sites.create(
+        label='Co',
+        type_symbol='Co',
+        fract_x=0.5,
+        fract_y=0.5,
+        fract_z=0.5,
+        wyckoff_letter='b',
+        adp_iso=0.1,
+    )
+    model.atom_sites.create(
+        label='O',
+        type_symbol='O',
+        fract_x=0,
+        fract_y=0.5,
+        fract_z=0.5,
+        wyckoff_letter='c',
+        adp_iso=0.1,
+    )
+
+    data_path = download_data(id=3, destination=TEMP_DIR)
+    experiment = ExperimentFactory.from_data_path(name='hrpt', data_path=data_path)
+    experiment.instrument.setup_wavelength = 1.494
+    experiment.instrument.calib_twotheta_offset = 0.0
+    experiment.peak.broad_gauss_u = 0.1
+    experiment.peak.broad_gauss_v = -0.1
+    experiment.peak.broad_gauss_w = 0.2
+    experiment.peak.broad_lorentz_x = 0.0
+    experiment.peak.broad_lorentz_y = 0.0
+    experiment.linked_phases.create(id='lbco', scale=5.0)
+    experiment.background.create(id='1', x=10, y=170)
+    experiment.background.create(id='2', x=165, y=170)
+
+    project = Project(name='lbco_bayesian')
+    project.structures.add(model)
+    project.experiments.add(experiment)
+    return project
+
+
+def _dream_parameters(project: Project) -> tuple[object, object, object]:
+    structure = project.structures['lbco']
+    experiment = project.experiments['hrpt']
+    return (
+        structure.cell.length_a,
+        experiment.linked_phases['lbco'].scale,
+        experiment.instrument.calib_twotheta_offset,
+    )
+
+
+def _configure_small_dream(project: Project) -> None:
+    project.analysis.fit.minimizer_type = 'bumps (dream)'
+    minimizer = project.analysis.fit.minimizer
+    minimizer.steps = 20
+    minimizer.burn = 5
+    minimizer.thin = 1
+    minimizer.pop = 4
+    minimizer.init = 'lhs'
+
+
+def test_small_bounded_dream_refinement_produces_posterior_results():
+    project = _create_lbco_project()
+    length_a, scale, offset = _dream_parameters(project)
+    for parameter in (length_a, scale, offset):
+        parameter.free = True
+
+    length_a.fit_min = 3.84
+    length_a.fit_max = 3.92
+    scale.fit_min = 1.0
+    scale.fit_max = 12.0
+    offset.fit_min = -1.0
+    offset.fit_max = 1.0
+
+    _configure_small_dream(project)
+    project.analysis.fit(verbosity='silent', random_seed=11)
+
+    results = project.analysis.fit_results
+    assert results.success is True
+    assert results.sampler_completed is True
+    assert results.sampler_name == 'dream'
+    assert results.posterior_samples is not None
+    assert results.posterior_samples.parameter_samples.ndim == 3
+    assert results.sampler_settings['random_seed'] == 11
+    assert results.sampler_settings['init'] == 'lhs'
+    assert len(results.posterior_parameter_summaries) == 3
+
+
+def test_lm_prefit_followed_by_dream_uses_uncertainty_based_bounds():
+    project = _create_lbco_project()
+    length_a, scale, offset = _dream_parameters(project)
+    for parameter in (length_a, scale, offset):
+        parameter.free = True
+
+    project.analysis.fit.minimizer_type = 'bumps (lm)'
+    project.analysis.fit(verbosity='silent')
+
+    for parameter in (length_a, scale, offset):
+        assert parameter.uncertainty is not None
+        parameter.set_fit_bounds_from_uncertainty(multiplier=4)
+        assert np.isfinite(parameter.fit_min)
+        assert np.isfinite(parameter.fit_max)
+
+    _configure_small_dream(project)
+    project.analysis.fit(verbosity='silent', random_seed=13)
+
+    results = project.analysis.fit_results
+    assert results.success is True
+    assert results.posterior_samples is not None
+    assert results.sampler_settings['random_seed'] == 13
+    assert len(results.posterior_parameter_summaries) == 3
+
+
+def test_bayesian_fit_results_are_runtime_only_after_save_load(tmp_path):
+    project = _create_lbco_project()
+    length_a, scale, offset = _dream_parameters(project)
+    for parameter in (length_a, scale, offset):
+        parameter.free = True
+
+    length_a.fit_min = 3.84
+    length_a.fit_max = 3.92
+    scale.fit_min = 1.0
+    scale.fit_max = 12.0
+    offset.fit_min = -1.0
+    offset.fit_max = 1.0
+
+    _configure_small_dream(project)
+    project.analysis.fit(verbosity='silent', random_seed=17)
+
+    assert project.analysis.fit_results.posterior_samples is not None
+
+    proj_dir = tmp_path / 'dream_project'
+    project.save_as(str(proj_dir))
+
+    analysis_cif = proj_dir / 'analysis' / 'analysis.cif'
+    assert analysis_cif.is_file()
+    assert 'posterior' not in analysis_cif.read_text().lower()
+
+    loaded = Project.load(str(proj_dir))
+    assert loaded.analysis.fit_results is None
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
new file mode 100644
index 00000000..58fee092
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -0,0 +1,188 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+
+class Identity:
+    def __init__(self) -> None:
+        self.datablock_entry_name = 'db'
+        self.category_code = 'cat'
+        self.category_entry_name = 'entry'
+
+
+class Param:
+    def __init__(self, unique_name: str, start: float, value: float, uncertainty: float) -> None:
+        self._identity = Identity()
+        self._fit_start_value = start
+        self.unique_name = unique_name
+        self.name = unique_name
+        self.value = value
+        self.uncertainty = uncertainty
+        self.units = 'arb'
+
+
+def test_module_import():
+    import easydiffraction.analysis.fit_helpers.bayesian as MUT
+
+    assert MUT.__name__ == 'easydiffraction.analysis.fit_helpers.bayesian'
+
+
+def test_posterior_samples_flatten_and_to_arviz():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a', 'b'],
+        parameter_samples=np.array(
+            [
+                [[1.0, 10.0], [2.0, 20.0]],
+                [[3.0, 30.0], [4.0, 40.0]],
+            ],
+            dtype=float,
+        ),
+        log_posterior=np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float),
+    )
+
+    flattened = posterior_samples.flattened()
+    inference_data = posterior_samples.to_arviz()
+
+    assert flattened.shape == (4, 2)
+    np.testing.assert_allclose(flattened[:, 0], np.array([1.0, 2.0, 3.0, 4.0]))
+    np.testing.assert_allclose(flattened[:, 1], np.array([10.0, 20.0, 30.0, 40.0]))
+    assert set(inference_data.posterior.data_vars) == {'a', 'b'}
+    assert inference_data.posterior['a'].shape == (2, 2)
+    assert inference_data.sample_stats['lp'].shape == (2, 2)
+
+
+def test_posterior_samples_to_arviz_validates_shapes():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.array([1.0, 2.0]),
+    )
+
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior sample array must have shape \(n_draws, n_chains, n_parameters\)\.',
+    ):
+        posterior_samples.to_arviz()
+
+
+def test_summarize_posterior_parameters_preserves_order_and_display_names():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['beta', 'alpha'],
+        parameter_samples=np.array(
+            [
+                [[2.0, 1.0], [2.2, 1.2]],
+                [[1.8, 0.8], [2.1, 1.1]],
+            ],
+            dtype=float,
+        ),
+    )
+
+    summaries = summarize_posterior_parameters(
+        parameter_names=['beta', 'alpha'],
+        posterior_samples=posterior_samples,
+        map_values=np.array([2.05, 1.05]),
+        parameter_display_names=['Beta width', 'Alpha shift'],
+        convergence_diagnostics={
+            'r_hat_by_parameter': {'beta': 1.02, 'alpha': 1.0},
+            'ess_bulk_by_parameter': {'beta': 120.0, 'alpha': 800.0},
+        },
+    )
+
+    assert [summary.unique_name for summary in summaries] == ['beta', 'alpha']
+    assert [summary.display_name for summary in summaries] == ['Beta width', 'Alpha shift']
+    assert summaries[0].r_hat == pytest.approx(1.02)
+    assert summaries[0].ess_bulk == pytest.approx(120.0)
+    assert summaries[1].r_hat == pytest.approx(1.0)
+    assert summaries[1].ess_bulk == pytest.approx(800.0)
+
+
+def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(capsys, monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.utils.logging import Logger
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
+
+    results = BayesianFitResults(
+        success=True,
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        reduced_chi_square=1.2345,
+        fitting_time=0.9876,
+        sampler_name='dream',
+        sampler_completed=True,
+        sampler_settings={
+            'random_seed': 1313900679,
+            'steps': 200,
+            'burn': 50,
+            'thin': 1,
+            'pop': 4,
+            'samples': 3200,
+        },
+        convergence_diagnostics={
+            'converged': False,
+            'max_r_hat': 1.107,
+            'min_ess_bulk': 125.9,
+            'n_draws': 200,
+            'n_chains': 16,
+        },
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+        best_log_posterior=-12.34,
+    )
+
+    results.display_results(y_obs=[10.0, 20.0], y_calc=[9.5, 19.5])
+
+    out = capsys.readouterr().out
+    assert 'Bayesian fit results' in out
+    assert 'Sampler: dream' in out
+    assert 'random_seed=1313900679' in out
+    assert 'steps=200' in out
+    assert 'max_r_hat=1.107' in out
+    assert 'min_ess_bulk=125.9' in out
+    assert 'Posterior parameter summaries:' in out
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
+
+
+def test_posterior_table_notes_split_failed_diagnostics():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _posterior_table_notes
+
+    notes = _posterior_table_notes([
+        PosteriorParameterSummary(
+            unique_name='a',
+            display_name='a',
+            map_value=1.0,
+            median=1.0,
+            standard_deviation=0.1,
+            interval_68=(0.9, 1.1),
+            interval_95=(0.8, 1.2),
+            r_hat=1.02,
+            ess_bulk=100.0,
+        )
+    ])
+
+    assert len(notes) == 2
+    assert 'r_hat' in notes[0]
+    assert 'ess_bulk' in notes[1]
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
index 668524ea..1a73fdbc 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py
@@ -38,6 +38,18 @@ def test_default_max_iterations():
     assert m.max_iterations == 1000
 
 
+def test_bumps_minimizer_rejects_random_seed():
+    from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
+
+    m = BumpsMinimizer()
+
+    with pytest.raises(
+        ValueError,
+        match=r"Minimizer 'bumps' does not support random_seed\.",
+    ):
+        m._resolve_random_seed(17)
+
+
 def test_is_subclass_of_base():
     from easydiffraction.analysis.minimizers.base import MinimizerBase
     from easydiffraction.analysis.minimizers.bumps import BumpsMinimizer
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
new file mode 100644
index 00000000..783b37aa
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -0,0 +1,236 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+
+class FakeParam:
+    """Minimal stand-in for an EasyDiffraction parameter."""
+
+    def __init__(self, uid: str, value: float, uncertainty: float | None = None) -> None:
+        self._minimizer_uid = uid
+        self.unique_name = uid
+        self.name = uid.upper()
+        self.value = value
+        self.uncertainty = uncertainty
+
+    def _set_value_from_minimizer(self, value: float) -> None:
+        self.value = value
+
+
+def test_module_import():
+    import easydiffraction.analysis.minimizers.bumps_dream as MUT
+
+    assert MUT.__name__ == 'easydiffraction.analysis.minimizers.bumps_dream'
+
+
+def test_type_info_and_default_init():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+    from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer.type_info.tag == MinimizerTypeEnum.BUMPS_DREAM
+    assert minimizer.init is DreamPopulationInitializationEnum.EPS
+    assert minimizer.steps == 1000
+
+
+def test_init_accepts_enum_or_string_and_rejects_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
+
+    minimizer.init = 'random'
+    assert minimizer.init is DreamPopulationInitializationEnum.RANDOM
+
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'init' must be one of: eps, cov, lhs, random\.",
+    ):
+        minimizer.init = 'bad-init'
+
+
+def test_resolve_random_seed_returns_provided_or_generated(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolve_random_seed(17) == 17
+    assert minimizer._resolved_random_seed == 17
+
+    generator = SimpleNamespace(integers=lambda *args, **kwargs: 123456)
+    monkeypatch.setattr(np.random, 'default_rng', lambda: generator)
+
+    assert minimizer._resolve_random_seed(None) == 123456
+    assert minimizer._resolved_random_seed == 123456
+
+
+def test_resolved_burn_uses_auto_or_explicit_and_validates():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolved_burn(steps=200) == 50
+
+    minimizer.burn = 20
+    assert minimizer._resolved_burn(steps=200) == 20
+
+    minimizer.burn = 200
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'burn' must be smaller than 'steps'\.",
+    ):
+        minimizer._resolved_burn(steps=200)
+
+
+def test_sampler_settings_include_init_and_sample_count():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+    settings = minimizer._sampler_settings(
+        random_seed=7,
+        steps=10,
+        burn=2,
+        thin=1,
+        pop=4,
+        init=DreamPopulationInitializationEnum.LHS,
+        n_parameters=3,
+    )
+
+    assert settings['random_seed'] == 7
+    assert settings['init'] == 'lhs'
+    assert settings['samples'] == 120
+
+
+def test_sync_result_to_parameters_restores_starting_values_on_failure():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [FakeParam('a', 10.0, uncertainty=0.7), FakeParam('b', 20.0, uncertainty=0.8)]
+    raw_result = SimpleNamespace(
+        x=np.array([99.0, 88.0]),
+        success=False,
+        starting_values=np.array([1.5, 2.5]),
+        starting_uncertainties=[0.1, None],
+    )
+
+    minimizer._sync_result_to_parameters(parameters, raw_result)
+
+    assert parameters[0].value == 1.5
+    assert parameters[0].uncertainty == 0.1
+    assert parameters[1].value == 2.5
+    assert parameters[1].uncertainty is None
+
+
+def test_run_solver_preserves_parameter_order_and_forwards_init():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.steps = 4
+    minimizer.burn = 1
+    minimizer.thin = 1
+    minimizer.pop = 2
+    minimizer.init = 'lhs'
+
+    draw_index = np.array([0.0, 1.0])
+    parameter_samples = np.array(
+        [
+            [[1.0, 10.0], [2.0, 20.0]],
+            [[3.0, 30.0], [4.0, 40.0]],
+        ],
+        dtype=float,
+    )
+    log_posterior = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float)
+
+    class FakeState:
+        labels = ['uid_a', 'uid_b']
+
+        def chains(self):
+            return draw_index, parameter_samples, log_posterior
+
+        def best(self):
+            return np.array([11.0, 22.0]), 3.5
+
+    fake_fitter = SimpleNamespace(id='dream')
+
+    with (
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitProblem'),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FITTERS', [fake_fitter]),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.compute_convergence_diagnostics',
+            return_value={'converged': True},
+        ),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.summarize_posterior_parameters',
+            return_value=[
+                PosteriorParameterSummary(
+                    unique_name='beta',
+                    display_name='Beta',
+                    map_value=22.0,
+                    median=21.0,
+                    standard_deviation=0.4,
+                    interval_68=(20.5, 21.5),
+                    interval_95=(20.0, 22.0),
+                ),
+                PosteriorParameterSummary(
+                    unique_name='alpha',
+                    display_name='Alpha',
+                    map_value=11.0,
+                    median=10.5,
+                    standard_deviation=0.3,
+                    interval_68=(10.0, 11.0),
+                    interval_95=(9.5, 11.5),
+                ),
+            ],
+        ) as summarize_mock,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.standard_deviations_from_summaries',
+            return_value=np.array([0.4, 0.3]),
+        ),
+    ):
+        driver_instance = mock_driver_cls.return_value
+        driver_instance.clip = MagicMock()
+        driver_instance.fit.return_value = (np.array([22.0, 11.0]), 0.25)
+        driver_instance.fitter = SimpleNamespace(state=FakeState())
+
+        result = minimizer._run_solver(
+            lambda values: np.array([0.0, 0.0]),
+            bumps_params=[
+                SimpleNamespace(name='uid_a', value=1.0),
+                SimpleNamespace(name='uid_b', value=2.0),
+            ],
+            parameter_names=['beta', 'alpha'],
+            parameter_display_names=['Beta', 'Alpha'],
+            parameter_uids=['uid_b', 'uid_a'],
+            random_seed=17,
+            starting_uncertainties=[0.01, 0.02],
+        )
+
+    assert mock_driver_cls.call_args.kwargs['init'] == 'lhs'
+    np.testing.assert_allclose(result.x, np.array([22.0, 11.0]))
+    np.testing.assert_allclose(result.dx, np.array([0.4, 0.3]))
+    assert result.posterior_samples.parameter_names == ['beta', 'alpha']
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 0], parameter_samples[:, :, 1]
+    )
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 1], parameter_samples[:, :, 0]
+    )
+    assert result.sampler_settings['init'] == 'lhs'
+    assert result.sampler_settings['random_seed'] == 17
+    assert summarize_mock.call_args.kwargs['parameter_names'] == ['beta', 'alpha']
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_enums.py b/tests/unit/easydiffraction/analysis/minimizers/test_enums.py
index 23e44e5b..5551b9b7 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_enums.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_enums.py
@@ -9,12 +9,19 @@ def test_module_import():
 
 
 def test_enum_members():
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
     from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 
     assert MinimizerTypeEnum.LMFIT == 'lmfit'
     assert MinimizerTypeEnum.LMFIT_LEASTSQ == 'lmfit (leastsq)'
     assert MinimizerTypeEnum.LMFIT_LEAST_SQUARES == 'lmfit (least_squares)'
     assert MinimizerTypeEnum.DFOLS == 'dfols'
+    assert MinimizerTypeEnum.BUMPS_DREAM == 'bumps (dream)'
+
+    assert DreamPopulationInitializationEnum.EPS == 'eps'
+    assert DreamPopulationInitializationEnum.COV == 'cov'
+    assert DreamPopulationInitializationEnum.LHS == 'lhs'
+    assert DreamPopulationInitializationEnum.RANDOM == 'random'
 
 
 def test_enum_default():
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_factory.py b/tests/unit/easydiffraction/analysis/minimizers/test_factory.py
index 7351d84b..406a5e09 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_factory.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_factory.py
@@ -9,11 +9,12 @@ def test_minimizer_factory_list_and_show(capsys):
 
     lst = MinimizerFactory.supported_tags()
     assert isinstance(lst, list)
-    assert len(lst) >= 4
+    assert len(lst) >= 5
     assert 'lmfit' in lst
     assert 'lmfit (leastsq)' in lst
     assert 'lmfit (least_squares)' in lst
     assert 'dfols' in lst
+    assert 'bumps (dream)' in lst
     MinimizerFactory.show_supported()
     out = capsys.readouterr().out
     assert 'Supported types' in out
@@ -75,3 +76,12 @@ def test_minimizer_factory_create_lmfit_least_squares():
     m = MinimizerFactory.create('lmfit (least_squares)')
     assert isinstance(m, LmfitLeastSquaresMinimizer)
     assert m.method == 'least_squares'
+
+
+def test_minimizer_factory_create_bumps_dream():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.factory import MinimizerFactory
+
+    m = MinimizerFactory.create('bumps (dream)')
+    assert isinstance(m, BumpsDreamMinimizer)
+    assert m.method == 'dream'
diff --git a/tests/unit/easydiffraction/core/test_parameters.py b/tests/unit/easydiffraction/core/test_parameters.py
index e7d102d9..0a4c95cc 100644
--- a/tests/unit/easydiffraction/core/test_parameters.py
+++ b/tests/unit/easydiffraction/core/test_parameters.py
@@ -103,6 +103,71 @@ def test_parameter_fit_bounds_assign_and_read():
     assert np.isclose(p.fit_max, 10.0)
 
 
+def test_parameter_set_fit_bounds_from_uncertainty_sets_bounds_and_returns_none():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='d',
+        value_spec=AttributeSpec(default=0.0),
+        cif_handler=CifHandler(names=['_param.d']),
+    )
+    p.value = 2.0
+    p.uncertainty = 0.25
+
+    result = p.set_fit_bounds_from_uncertainty(multiplier=4)
+
+    assert result is None
+    assert np.isclose(p.fit_min, 1.0)
+    assert np.isclose(p.fit_max, 3.0)
+
+
+def test_parameter_set_fit_bounds_from_uncertainty_clips_to_physical_limits():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.validation import DataTypes
+    from easydiffraction.core.validation import RangeValidator
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='bounded',
+        value_spec=AttributeSpec(
+            data_type=DataTypes.NUMERIC,
+            default=1.0,
+            validator=RangeValidator(ge=0.5, le=1.5),
+        ),
+        cif_handler=CifHandler(names=['_param.bounded']),
+    )
+    p.value = 1.0
+    p.uncertainty = 0.3
+
+    p.set_fit_bounds_from_uncertainty(multiplier=4)
+
+    assert np.isclose(p.fit_min, 0.5)
+    assert np.isclose(p.fit_max, 1.5)
+
+
+def test_parameter_set_fit_bounds_from_uncertainty_requires_valid_uncertainty():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='invalid',
+        value_spec=AttributeSpec(default=0.0),
+        cif_handler=CifHandler(names=['_param.invalid']),
+    )
+    p.value = 2.0
+    p.uncertainty = None
+
+    with pytest.raises(
+        ValueError,
+        match=r'Cannot set fit bounds for invalid: uncertainty is missing or invalid\.',
+    ):
+        p.set_fit_bounds_from_uncertainty(multiplier=4)
+
+
 def _make_param() -> object:
     from easydiffraction.core.validation import AttributeSpec
     from easydiffraction.core.variable import Parameter
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 5e1cc4da..6117b5cb 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -2,6 +2,10 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import re
+from types import MethodType
+from types import SimpleNamespace
+
+import numpy as np
 
 import pytest
 
@@ -233,6 +237,170 @@ class Experiment:
     assert tick_sets == ()
 
 
+def _make_bayesian_plotter_fixture():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.display.plotting import Plotter
+
+    samples = np.array(
+        [
+            [[3.8900, 0.0760, -0.1170, 0.6290], [3.8908, 0.0775, -0.1185, 0.6300]],
+            [[3.8912, 0.0785, -0.1190, 0.6310], [3.8916, 0.0790, -0.1200, 0.6320]],
+        ],
+        dtype=float,
+    )
+    parameter_names = ['length_a', 'broad_gauss_u', 'broad_gauss_v', 'twotheta_offset']
+    posterior_samples = PosteriorSamples(
+        parameter_names=parameter_names,
+        parameter_samples=samples,
+        log_posterior=np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float),
+    )
+    parameters = [
+        SimpleNamespace(unique_name='length_a', name='length_a', fit_min=3.8895, fit_max=3.8920),
+        SimpleNamespace(
+            unique_name='broad_gauss_u', name='broad_gauss_u', fit_min=0.05, fit_max=0.11
+        ),
+        SimpleNamespace(
+            unique_name='broad_gauss_v', name='broad_gauss_v', fit_min=-0.14, fit_max=-0.10
+        ),
+        SimpleNamespace(
+            unique_name='twotheta_offset', name='twotheta_offset', fit_min=0.625, fit_max=0.64
+        ),
+    ]
+    summaries = [
+        PosteriorParameterSummary(
+            unique_name=name,
+            display_name=name,
+            map_value=float(samples[-1, -1, index]),
+            median=float(np.median(samples[:, :, index])),
+            standard_deviation=float(np.std(samples[:, :, index], ddof=1)),
+            interval_68=tuple(np.quantile(samples[:, :, index], [0.16, 0.84]).tolist()),
+            interval_95=tuple(np.quantile(samples[:, :, index], [0.025, 0.975]).tolist()),
+        )
+        for index, name in enumerate(parameter_names)
+    ]
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        posterior_parameter_summaries=summaries,
+        posterior_predictive={},
+        parameters=parameters,
+    )
+    plotter = Plotter()
+    plotter._get_posterior_samples_and_fit_results = MethodType(
+        lambda self: (posterior_samples, fit_results),
+        plotter,
+    )
+    plotter._get_fit_result_for_correlation = MethodType(lambda self: fit_results, plotter)
+    return plotter, fit_results, posterior_samples
+
+
+def test_correlation_from_posterior_samples_returns_labeled_dataframe():
+    from easydiffraction.display.plotting import Plotter
+
+    _, _, posterior_samples = _make_bayesian_plotter_fixture()
+
+    corr_df = Plotter()._correlation_from_posterior_samples(posterior_samples)
+
+    assert list(corr_df.index) == posterior_samples.parameter_names
+    assert list(corr_df.columns) == posterior_samples.parameter_names
+    np.testing.assert_allclose(np.diag(corr_df.to_numpy()), np.ones(len(corr_df)))
+
+
+def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    assert figure.layout.title.text == 'Posterior pair plot'
+    assert [annotation.text for annotation in figure.layout.annotations] == [
+        'length_a',
+        'broad_gauss_u',
+        'broad_gauss_v',
+        'twotheta_offset',
+    ]
+    subplot = figure.get_subplot(1, 1)
+    assert subplot.yaxis.showticklabels is False
+    assert subplot.yaxis.ticks == ''
+    assert subplot.yaxis.ticklen == 0
+    assert subplot.yaxis.title.text is None
+
+
+def test_build_param_distribution_plot_returns_plotly_figure():
+    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    parameter = fit_results.parameters[0]
+
+    figure = plotter._build_param_distribution_plot(parameter)
+
+    assert figure.layout.title.text == 'Posterior distribution: length_a'
+    assert {trace.name for trace in figure.data} >= {
+        'Posterior histogram',
+        'Posterior density',
+        '68% credible interval',
+        '95% credible interval',
+        'Median',
+        'Max posterior',
+    }
+
+
+def test_build_posterior_predictive_summary_restores_parameter_state(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+    from easydiffraction.display.plotting import Plotter
+
+    class FakePredictiveParameter:
+        def __init__(self, unique_name, value, uncertainty):
+            self.unique_name = unique_name
+            self.value = value
+            self.uncertainty = uncertainty
+
+        def _set_value_from_minimizer(self, value):
+            self.value = value
+
+    sampled_parameters = [
+        FakePredictiveParameter('a', 1.0, 0.1),
+        FakePredictiveParameter('b', 2.0, 0.2),
+    ]
+    posterior_samples = SimpleNamespace(
+        parameter_names=['a', 'b'],
+        flattened=lambda: np.array(
+            [
+                [1.0, 2.0],
+                [1.1, 2.1],
+                [0.9, 1.9],
+                [1.2, 2.2],
+            ],
+            dtype=float,
+        ),
+    )
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        parameters=sampled_parameters,
+    )
+    plotter = Plotter()
+
+    def fake_evaluate(self, *, sampled_parameters, values, experiment, expt_name, x_axis):
+        x = np.array([0.0, 1.0], dtype=float)
+        y = np.array([values[0] + values[1], values[0] - values[1]], dtype=float)
+        return y, x
+
+    monkeypatch.setattr(Plotter, '_evaluate_posterior_predictive_state', fake_evaluate)
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+
+    summary = plotter._build_posterior_predictive_summary(
+        fit_results=fit_results,
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+    )
+
+    assert isinstance(summary, PosteriorPredictiveSummary)
+    assert summary.experiment_name == 'hrpt'
+    assert summary.x_axis_name == 'two_theta'
+    assert summary.draws.shape == (4, 2)
+    np.testing.assert_allclose(summary.map_prediction, np.array([3.0, -1.0]))
+    np.testing.assert_allclose([parameter.value for parameter in sampled_parameters], [1.0, 2.0])
+    assert [parameter.uncertainty for parameter in sampled_parameters] == [0.1, 0.2]
+
+
 def test_extract_bragg_tick_sets_uses_derived_d_spacing_for_cwl_ticks():
     import numpy as np
 
diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
index 8472c994..571cd01f 100644
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ b/tmp/bumps_dream/ed-2-bayesian-new-API.py
@@ -191,6 +191,7 @@
 structure.cell.length_a.free = True
 experiment.peak.broad_gauss_u.free = True
 experiment.peak.broad_gauss_v.free = True
+experiment.peak.broad_gauss_w.free = True
 experiment.instrument.calib_twotheta_offset.free = True
 
 # %% [markdown]
@@ -241,6 +242,7 @@
 structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=4)
 experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=4)
 experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)
+experiment.peak.broad_gauss_w.set_fit_bounds_from_uncertainty(multiplier=4)
 experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)
 
 # %% [markdown]
@@ -310,6 +312,7 @@
 project.display.plotter.plot_param_distribution(structure.cell.length_a)
 project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)
 project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)
+project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_w)
 project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
 
 # %% [markdown]

From 506e5f474e80a7378e3b2198448f71e4ddb2ec11 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sat, 9 May 2026 00:09:45 +0200
Subject: [PATCH 053/106] Add LBCO Bayesian tutorial

---
 docs/docs/tutorials/ed-21.py | 330 +++++++++++++++++++++++++++++++++++
 docs/docs/tutorials/index.md |   9 +
 docs/mkdocs.yml              |   2 +
 3 files changed, 341 insertions(+)
 create mode 100644 docs/docs/tutorials/ed-21.py

diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
new file mode 100644
index 00000000..9e3f62e9
--- /dev/null
+++ b/docs/docs/tutorials/ed-21.py
@@ -0,0 +1,330 @@
+# %% [markdown]
+# # Deterministic and Bayesian Refinement: LBCO, HRPT
+#
+# This tutorial demonstrates a practical two-stage workflow for powder
+# diffraction analysis with EasyDiffraction.
+#
+# In the first stage, we run a fast local refinement to obtain a sensible
+# point estimate and parameter uncertainties. In the second stage, we use
+# these refined values to define fit bounds and then sample the posterior
+# distribution with DREAM.
+#
+# The example uses constant-wavelength neutron powder diffraction data
+# for La0.5Ba0.5CoO3 measured on HRPT at PSI.
+#
+# The goal is not only to obtain a good fit, but also to answer Bayesian
+# questions such as:
+#
+# - Which parameter values are most probable?
+# - How broad are the credible intervals?
+# - Which parameters are strongly correlated?
+# - How much uncertainty propagates into the calculated diffraction
+#   pattern?
+
+# %% [markdown]
+# ## Import Library
+
+# %%
+import easydiffraction as ed
+
+# %% [markdown]
+# ## Step 1: Create a Project Container
+#
+# The project object keeps structures, experiments, fit settings, and
+# plotting utilities together in a single place. We will build the full
+# workflow inside this object.
+
+# %%
+project = ed.Project()
+
+# %% [markdown]
+# ## Step 2: Build the Structural Model
+#
+# We define a simple cubic perovskite model for LBCO. La and Ba share the
+# same crystallographic site with equal occupancy, while Co and O occupy
+# the remaining ideal perovskite positions.
+
+# %%
+project.structures.create(name='lbco')
+
+# %%
+structure = project.structures['lbco']
+
+# %%
+structure.space_group.name_h_m = 'P m -3 m'
+structure.space_group.it_coordinate_system_code = '1'
+
+# %%
+structure.cell.length_a = 3.88
+
+# %% [markdown]
+# The atom-site definitions below form the starting structural model. The
+# parameters are intentionally reasonable rather than fully optimized,
+# because the refinement step will improve them.
+
+# %%
+structure.atom_sites.create(
+    label='La',
+    type_symbol='La',
+    fract_x=0,
+    fract_y=0,
+    fract_z=0,
+    wyckoff_letter='a',
+    adp_type='Biso',
+    adp_iso=0.5151,
+    occupancy=0.5,
+)
+structure.atom_sites.create(
+    label='Ba',
+    type_symbol='Ba',
+    fract_x=0,
+    fract_y=0,
+    fract_z=0,
+    wyckoff_letter='a',
+    adp_type='Biso',
+    adp_iso=0.5151,
+    occupancy=0.5,
+)
+structure.atom_sites.create(
+    label='Co',
+    type_symbol='Co',
+    fract_x=0.5,
+    fract_y=0.5,
+    fract_z=0.5,
+    wyckoff_letter='b',
+    adp_type='Biso',
+    adp_iso=0.2190,
+)
+structure.atom_sites.create(
+    label='O',
+    type_symbol='O',
+    fract_x=0,
+    fract_y=0.5,
+    fract_z=0.5,
+    wyckoff_letter='c',
+    adp_type='Biso',
+    adp_iso=1.3916,
+)
+
+# %% [markdown]
+# ## Step 3: Define the Diffraction Experiment
+#
+# Next we download the measured powder pattern, create a neutron powder
+# experiment, and configure the instrument, profile, background, and
+# excluded regions.
+
+# %% [markdown]
+# #### Download the Measured Data
+
+# %%
+data_path = ed.download_data(id=3, destination='data')
+
+# %% [markdown]
+# #### Create the Experiment Object
+
+# %%
+project.experiments.add_from_data_path(
+    name='hrpt',
+    data_path=data_path,
+    sample_form='powder',
+    beam_mode='constant wavelength',
+    radiation_probe='neutron',
+)
+
+# %%
+experiment = project.experiments['hrpt']
+
+# %% [markdown]
+# #### Set Instrument and Peak-Profile Parameters
+#
+# These values provide the initial instrument description for the local
+# refinement. Later, a subset of them will be refined.
+
+# %%
+experiment.instrument.setup_wavelength = 1.494
+experiment.instrument.calib_twotheta_offset = 0.0
+
+# %%
+experiment.peak.broad_gauss_u = 0.1
+experiment.peak.broad_gauss_v = -0.1
+experiment.peak.broad_gauss_w = 0.1204
+experiment.peak.broad_lorentz_y = 0.0844
+
+# %% [markdown]
+# #### Add Background Points and Excluded Regions
+#
+# The line-segment background is defined by a few anchor points. We also
+# exclude regions that are not intended to contribute to the fit.
+
+# %%
+experiment.background.create(id='1', x=10, y=168.5585)
+experiment.background.create(id='2', x=30, y=164.3357)
+experiment.background.create(id='3', x=50, y=166.8881)
+experiment.background.create(id='4', x=110, y=175.4006)
+experiment.background.create(id='5', x=165, y=174.2813)
+
+# %%
+experiment.excluded_regions.create(id='1', start=0, end=30)
+experiment.excluded_regions.create(id='2', start=70, end=180)
+
+# %% [markdown]
+# #### Link the Structural Phase to the Experiment
+
+# %%
+experiment.linked_phases.create(id='lbco', scale=9.1351)
+
+# %% [markdown]
+# ## Step 4: Run an Initial Local Refinement
+#
+# Before Bayesian sampling, it is useful to run a deterministic fit. This
+# gives us:
+#
+# - a good point estimate near the best-fit region,
+# - uncertainties from the local optimizer,
+# - a quick check that the model and experiment are configured
+#   sensibly.
+#
+# In this tutorial we refine only a small set of parameters that are easy
+# to interpret in the later Bayesian stage.
+
+# %%
+structure.cell.length_a.free = True
+experiment.peak.broad_gauss_u.free = True
+experiment.peak.broad_gauss_v.free = True
+experiment.instrument.calib_twotheta_offset.free = True
+
+# %% [markdown]
+# We choose the BUMPS Levenberg-Marquardt minimizer as a fast local
+# optimizer. Its main purpose here is to provide a stable starting point
+# and uncertainty estimates for the Bayesian run.
+
+# %%
+project.analysis.fit.show_minimizer_types()
+project.analysis.fit.minimizer_type = 'bumps (lm)'
+
+# %%
+project.analysis.fit()
+
+# %%
+project.analysis.display.fit_results()
+
+# %% [markdown]
+# The correlation plot shows how strongly the fitted parameters move
+# together in the local refinement. The measured-vs-calculated plots show
+# how well the refined model reproduces the data globally and in a zoomed
+# region.
+
+# %%
+project.display.plotter.plot_param_correlations(show_diagonal=True)
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
+
+# %% [markdown]
+# ## Step 5: Prepare for Bayesian Sampling
+#
+# DREAM requires finite bounds for the free parameters. Instead of
+# setting them manually, we derive them from the uncertainties estimated
+# in the local refinement.
+#
+# The helper method `set_fit_bounds_from_uncertainty` centers the bounds
+# on the current parameter value and expands them by a chosen multiple of
+# the reported uncertainty.
+
+# %%
+project.analysis.display.free_params()
+
+# %%
+structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=4)
+experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=4)
+experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)
+experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)
+
+# %% [markdown]
+# Displaying the free parameters again is a convenient way to confirm
+# that the fit bounds have been assigned as expected before launching the
+# sampler.
+
+# %%
+project.analysis.display.free_params()
+
+# %% [markdown]
+# ## Step 6: Configure and Run DREAM
+#
+# We now switch from the local minimizer to the Bayesian DREAM sampler.
+#
+# The settings below are intentionally small so the tutorial runs
+# quickly. For production analysis you would usually increase the number
+# of steps and often the burn-in as well. When needed, the DREAM API
+# also lets you tune how chains are initialized through the `init`
+# setting.
+
+# %%
+project.analysis.fit.show_minimizer_types()
+
+# %%
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+
+# %%
+project.analysis.fit.minimizer.steps = 200  # 1000
+project.analysis.fit.minimizer.burn = 40  # 200
+project.analysis.fit.minimizer.thin = 1
+project.analysis.fit.minimizer.pop = 4
+
+# %%
+project.analysis.fit()
+
+# %% [markdown]
+# ## Step 7: Inspect Bayesian Results
+#
+# The fit-results display now includes sampler settings, convergence
+# diagnostics, committed parameter values, and posterior summary
+# statistics.
+
+# %%
+project.analysis.display.fit_results()
+
+# %% [markdown]
+# The correlation and posterior-pair plots are complementary:
+#
+# - `plot_param_correlations` summarizes pairwise structure in a compact
+#   matrix.
+# - `plot_posterior_pairs` shows marginal densities on the diagonal and
+#   posterior contours off-diagonal.
+
+# %%
+project.display.plotter.plot_param_correlations(show_diagonal=True)
+
+# %%
+project.display.plotter.plot_posterior_pairs()
+
+# %% [markdown]
+# The one-dimensional posterior distributions below make it easier to
+# inspect individual parameters in isolation, including asymmetry or
+# multimodality.
+
+# %%
+project.display.plotter.plot_param_distribution(structure.cell.length_a)
+project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)
+project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)
+project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
+
+# %% [markdown]
+# Finally, the posterior predictive plot propagates the sampled parameter
+# uncertainty into the calculated diffraction pattern. Comparing this to
+# the zoomed measured-vs-calculated view helps assess whether the sampled
+# model family explains the data in the region of interest.
+
+# %%
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+
+# %% [markdown]
+# A final zoomed measured-vs-calculated plot is useful for checking how
+# the posterior-supported model behaves in a narrow region of the pattern
+# after the Bayesian run.
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md
index 69b9d0ca..18c2e143 100644
--- a/docs/docs/tutorials/index.md
+++ b/docs/docs/tutorials/index.md
@@ -98,6 +98,15 @@ The tutorials are organized into the following categories:
 - [BEER McStas](ed-20.ipynb) – Rietveld refinement based on the data
   simulated with McStas for the BEER instrument at ESS.
 
+## Bayesian Analysis
+
+- [LBCO Bayesian](ed-21.ipynb) – Demonstrates how to perform a Bayesian
+  analysis of the La0.5Ba0.5CoO3 crystal structure using constant
+  wavelength neutron powder diffraction data from HRPT at PSI. This
+  tutorial covers the use of Markov Chain Monte Carlo (MCMC) sampling to
+  explore the posterior distribution of the refined parameters,
+  providing insights into parameter uncertainties and correlations.
+
 ## Workshops & Schools
 
 - [DMSC Summer School](ed-13.ipynb) – A workshop tutorial that
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 45c2ce5f..6ad6b770 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -214,6 +214,8 @@ nav:
       - Simulated Data:
           - LBCO+Si McStas: tutorials/ed-9.ipynb
           - BEER McStas: tutorials/ed-20.ipynb
+      - Bayesian Analysis:
+          - LBCO Bayesian: tutorials/ed-21.ipynb
       - Workshops & Schools:
           - DMSC Summer School: tutorials/ed-13.ipynb
   - API Reference:

From 9cf4ffec9142995e390d3ec5f72ee52d13d67c6e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sat, 9 May 2026 00:22:13 +0200
Subject: [PATCH 054/106] Make posterior pair plots responsive

---
 .../display/plotters/plotly.py                | 133 +++++++++++++++++-
 src/easydiffraction/display/plotting.py       |  64 ++++++++-
 .../display/plotters/test_plotly.py           | 117 +++++++++++++++
 .../easydiffraction/display/test_plotting.py  |  20 +++
 4 files changed, 324 insertions(+), 10 deletions(-)

diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index fcc07fa9..c514b743 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -70,6 +70,7 @@
 PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
 PREDICTIVE_DRAW_PLOT_CAP = 50
 PREDICTIVE_DRAW_ARRAY_NDIM = 2
+RESPONSIVE_PAIR_PLOT_META_KEY = 'responsive_pair_plot'
 
 
 @dataclass(frozen=True)
@@ -789,6 +790,134 @@ def _modebar_legend_toggle_post_script() -> str:
 window.requestAnimationFrame(installLegendToggleButton);
 """
 
+    @staticmethod
+    def _responsive_pair_plot_post_script() -> str:
+        """
+        Return client-side code for responsive posterior pair plots.
+        """
+        return r"""
+const graphDiv = document.getElementById('{plot_id}');
+if (!graphDiv || !window.Plotly || !graphDiv.layout || !graphDiv.layout.meta) {
+    return;
+}
+
+const responsivePairPlot = graphDiv.layout.meta.responsive_pair_plot;
+if (!responsivePairPlot) {
+    return;
+}
+
+const readPositiveNumber = function (value, fallback) {
+    const numberValue = Number(value);
+    return Number.isFinite(numberValue) && numberValue > 0 ? numberValue : fallback;
+};
+
+const nParameters = Math.max(
+    1,
+    Math.round(readPositiveNumber(responsivePairPlot.n_parameters, 1))
+);
+const marginPx = readPositiveNumber(responsivePairPlot.margin_px, 0);
+const minCellSizePx = readPositiveNumber(responsivePairPlot.min_cell_size_px, 90);
+const maxCellSizePx = readPositiveNumber(responsivePairPlot.max_cell_size_px, 190);
+
+let lastHeight = null;
+let resizeFrame = null;
+
+const applyResponsivePairPlotSize = function () {
+    const containerWidth = graphDiv.clientWidth || graphDiv.getBoundingClientRect().width;
+    if (!Number.isFinite(containerWidth) || containerWidth <= 0) {
+        return;
+    }
+
+    const plotWidth = Math.max(minCellSizePx, containerWidth - marginPx);
+    const cellSizePx = Math.min(maxCellSizePx, Math.max(minCellSizePx, plotWidth / nParameters));
+    const nextHeight = Math.round(cellSizePx * nParameters + marginPx);
+
+    if (lastHeight === nextHeight) {
+        window.Plotly.Plots.resize(graphDiv);
+        return;
+    }
+
+    lastHeight = nextHeight;
+    window.Plotly.relayout(graphDiv, {autosize: true, height: nextHeight});
+};
+
+const scheduleResponsivePairPlotSize = function () {
+    if (resizeFrame !== null) {
+        return;
+    }
+
+    resizeFrame = window.requestAnimationFrame(function () {
+        resizeFrame = null;
+        applyResponsivePairPlotSize();
+    });
+};
+
+if (typeof ResizeObserver === 'function') {
+    const resizeObserver = new ResizeObserver(scheduleResponsivePairPlotSize);
+    resizeObserver.observe(graphDiv);
+}
+else {
+    window.addEventListener('resize', scheduleResponsivePairPlotSize);
+}
+
+if (graphDiv.on) {
+    graphDiv.on('plotly_afterplot', scheduleResponsivePairPlotSize);
+}
+
+scheduleResponsivePairPlotSize();
+"""
+
+    @staticmethod
+    def _figure_meta(fig: object) -> dict[str, object] | None:
+        """Return figure layout metadata when available."""
+        layout = getattr(fig, 'layout', None)
+        if layout is None:
+            return None
+
+        meta = getattr(layout, 'meta', None)
+        if isinstance(meta, dict):
+            return meta
+
+        layout_kwargs = getattr(layout, 'kwargs', None)
+        if isinstance(layout_kwargs, dict):
+            meta = layout_kwargs.get('meta')
+            if isinstance(meta, dict):
+                return meta
+        return None
+
+    @classmethod
+    def _has_responsive_pair_plot(cls, fig: object) -> bool:
+        """
+        Return whether a figure requests responsive pair-plot sizing.
+        """
+        meta = cls._figure_meta(fig)
+        if not isinstance(meta, dict):
+            return False
+
+        responsive_pair_plot = meta.get(RESPONSIVE_PAIR_PLOT_META_KEY)
+        if not isinstance(responsive_pair_plot, dict):
+            return False
+        return bool(responsive_pair_plot.get('n_parameters'))
+
+    @classmethod
+    def _html_post_script(cls, fig: object) -> str | None:
+        """Return concatenated HTML post scripts for a Plotly figure."""
+        scripts: list[str] = []
+        if cls._has_visible_legend(fig):
+            scripts.append(cls._modebar_legend_toggle_post_script())
+        if cls._has_responsive_pair_plot(fig):
+            scripts.append(cls._responsive_pair_plot_post_script())
+        if not scripts:
+            return None
+        return '\n'.join(cls._scoped_html_post_script(script) for script in scripts)
+
+    @staticmethod
+    def _scoped_html_post_script(script: str) -> str:
+        """
+        Return one HTML post script wrapped in its own block scope.
+        """
+        return '{\n' + script.strip() + '\n}'
+
     @staticmethod
     def _get_figure(
         data: object,
@@ -866,9 +995,7 @@ def _show_figure(
         if in_pycharm() or display is None or HTML is None:
             fig.show(config=config)
         else:
-            post_script = None
-            if self._has_visible_legend(fig):
-                post_script = self._modebar_legend_toggle_post_script()
+            post_script = self._html_post_script(fig)
             html_fig = pio.to_html(
                 fig,
                 include_plotlyjs='cdn',
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 2fee1031..98f718de 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -103,8 +103,10 @@ def description(self) -> str:
     [1.0, 'rgba(58, 86, 224, 0.98)'],
 ]
 PAIR_PLOT_CELL_SIZE_PIXELS = 190
+PAIR_PLOT_MIN_CELL_SIZE_PIXELS = 90
 PAIR_PLOT_MIN_SIZE_PIXELS = 680
 PAIR_PLOT_MARGIN_PIXELS = 120
+PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS = 980
 PAIR_PLOT_SUBPLOT_SPACING = 0.015
 PAIR_PLOT_MAJOR_TICKS = 3
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
@@ -1424,8 +1426,8 @@ def _collect_posterior_pair_panel_decorations(
             'layer': 'above',
         })
 
-    @staticmethod
     def _finalize_posterior_pairs_figure(
+        self,
         *,
         fig: object,
         context: _PosteriorPairsContext,
@@ -1433,18 +1435,16 @@ def _finalize_posterior_pairs_figure(
         subplot_border_shapes: list[dict[str, object]],
     ) -> None:
         """Apply final layout settings to the posterior pair plot."""
-        figure_size = max(
-            PAIR_PLOT_MIN_SIZE_PIXELS,
-            PAIR_PLOT_CELL_SIZE_PIXELS * context.n_parameters + PAIR_PLOT_MARGIN_PIXELS,
-        )
+        figure_height = self._posterior_pair_figure_height_pixels(context.n_parameters)
         fig.update_layout(
+            autosize=True,
             margin={'autoexpand': True, 'r': 30, 't': 40, 'b': 45},
             title={'text': 'Posterior pair plot'},
             bargap=0.05,
-            width=figure_size,
-            height=figure_size,
+            height=figure_height,
             annotations=subplot_title_annotations,
             shapes=subplot_border_shapes,
+            meta=self._posterior_pair_layout_meta(context.n_parameters),
             legend={
                 'bgcolor': 'rgba(0, 0, 0, 0)',
                 'xanchor': 'right',
@@ -1455,6 +1455,56 @@ def _finalize_posterior_pairs_figure(
             },
         )
 
+    @staticmethod
+    def _posterior_pair_layout_meta(n_parameters: int) -> dict[str, object]:
+        """
+        Return responsive layout metadata for posterior pair plots.
+        """
+        return {
+            'responsive_pair_plot': {
+                'n_parameters': int(n_parameters),
+                'margin_px': PAIR_PLOT_MARGIN_PIXELS,
+                'min_cell_size_px': PAIR_PLOT_MIN_CELL_SIZE_PIXELS,
+                'max_cell_size_px': PAIR_PLOT_CELL_SIZE_PIXELS,
+            }
+        }
+
+    @staticmethod
+    def _posterior_pair_cell_size_pixels(
+        n_parameters: int,
+        *,
+        available_width_pixels: float,
+    ) -> int:
+        """Return an estimated square cell size for a pair plot."""
+        if n_parameters < 1:
+            return PAIR_PLOT_CELL_SIZE_PIXELS
+
+        plot_width = max(
+            PAIR_PLOT_MIN_CELL_SIZE_PIXELS,
+            available_width_pixels - PAIR_PLOT_MARGIN_PIXELS,
+        )
+        cell_size = plot_width / n_parameters
+        return round(
+            min(
+                PAIR_PLOT_CELL_SIZE_PIXELS,
+                max(PAIR_PLOT_MIN_CELL_SIZE_PIXELS, cell_size),
+            )
+        )
+
+    @classmethod
+    def _posterior_pair_figure_height_pixels(cls, n_parameters: int) -> int:
+        """
+        Return the initial figure height for a responsive pair plot.
+        """
+        cell_size = cls._posterior_pair_cell_size_pixels(
+            n_parameters,
+            available_width_pixels=PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS,
+        )
+        return max(
+            PAIR_PLOT_MIN_SIZE_PIXELS,
+            cell_size * n_parameters + PAIR_PLOT_MARGIN_PIXELS,
+        )
+
     def _plot_axis_frame_color(self) -> str:
         """
         Return the shared axis-frame color for Plotly-backed plots.
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index 3905ee27..cdd847db 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -278,6 +278,123 @@ def __init__(self, html):
     assert captured['displayed_html'] == '<div>plot</div>'
 
 
+def test_show_figure_adds_responsive_pair_plot_script(monkeypatch):
+    import easydiffraction.display.plotters.plotly as pp
+
+    monkeypatch.setattr(pp, 'in_pycharm', lambda: False)
+
+    captured = {}
+
+    class DummyLayout:
+        def __init__(self):
+            self.meta = {
+                'responsive_pair_plot': {
+                    'n_parameters': 4,
+                    'margin_px': 120,
+                    'min_cell_size_px': 90,
+                    'max_cell_size_px': 190,
+                }
+            }
+            self.showlegend = False
+
+    class DummyFig:
+        def __init__(self):
+            self.data = []
+            self.layout = DummyLayout()
+
+        def show(self, **kwargs):
+            captured['show_called'] = True
+
+    class DummyPIO:
+        @staticmethod
+        def to_html(fig, include_plotlyjs=None, full_html=None, config=None, post_script=None):
+            captured['post_script'] = post_script
+            return '<div>plot</div>'
+
+    def dummy_display(obj):
+        captured['displayed_html'] = obj.html
+
+    class DummyHTML:
+        def __init__(self, html):
+            self.html = html
+
+    monkeypatch.setattr(pp, 'pio', DummyPIO)
+    monkeypatch.setattr(pp, 'display', dummy_display)
+    monkeypatch.setattr(pp, 'HTML', DummyHTML)
+
+    plotter = pp.PlotlyPlotter()
+    plotter._show_figure(DummyFig())
+
+    assert captured.get('show_called') is not True
+    assert 'responsive_pair_plot' in captured['post_script']
+    assert 'ResizeObserver' in captured['post_script']
+    assert 'window.Plotly.relayout' in captured['post_script']
+    assert 'containerWidth' in captured['post_script']
+    assert captured['displayed_html'] == '<div>plot</div>'
+
+
+def test_show_figure_scopes_multiple_post_scripts(monkeypatch):
+    import easydiffraction.display.plotters.plotly as pp
+
+    monkeypatch.setattr(pp, 'in_pycharm', lambda: False)
+
+    captured = {}
+
+    class DummyTrace:
+        def __init__(self):
+            self.name = 'Posterior samples'
+            self.showlegend = True
+            self.visible = True
+
+    class DummyLayout:
+        def __init__(self):
+            self.meta = {
+                'responsive_pair_plot': {
+                    'n_parameters': 4,
+                    'margin_px': 120,
+                    'min_cell_size_px': 90,
+                    'max_cell_size_px': 190,
+                }
+            }
+            self.showlegend = True
+
+    class DummyFig:
+        def __init__(self):
+            self.data = [DummyTrace()]
+            self.layout = DummyLayout()
+
+        def show(self, **kwargs):
+            captured['show_called'] = True
+
+    class DummyPIO:
+        @staticmethod
+        def to_html(fig, include_plotlyjs=None, full_html=None, config=None, post_script=None):
+            captured['post_script'] = post_script
+            return '<div>plot</div>'
+
+    def dummy_display(obj):
+        captured['displayed_html'] = obj.html
+
+    class DummyHTML:
+        def __init__(self, html):
+            self.html = html
+
+    monkeypatch.setattr(pp, 'pio', DummyPIO)
+    monkeypatch.setattr(pp, 'display', dummy_display)
+    monkeypatch.setattr(pp, 'HTML', DummyHTML)
+
+    plotter = pp.PlotlyPlotter()
+    plotter._show_figure(DummyFig())
+
+    assert captured.get('show_called') is not True
+    assert (
+        captured['post_script'].count("const graphDiv = document.getElementById('{plot_id}');")
+        == 2
+    )
+    assert '\n}\n{\n' in captured['post_script']
+    assert captured['displayed_html'] == '<div>plot</div>'
+
+
 def test_plotly_single_crystal_trace_and_plot(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 6117b5cb..d0f23651 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -307,11 +307,22 @@ def test_correlation_from_posterior_samples_returns_labeled_dataframe():
 
 
 def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
+    from easydiffraction.display.plotting import PAIR_PLOT_CELL_SIZE_PIXELS
+    from easydiffraction.display.plotting import PAIR_PLOT_MARGIN_PIXELS
+
     plotter, _, _ = _make_bayesian_plotter_fixture()
 
     figure = plotter._build_posterior_pairs_plot(parameters=None)
 
     assert figure.layout.title.text == 'Posterior pair plot'
+    assert figure.layout.autosize is True
+    assert figure.layout.width is None
+    assert figure.layout.meta['responsive_pair_plot']['n_parameters'] == 4
+    assert (
+        figure.layout.meta['responsive_pair_plot']['max_cell_size_px']
+        == PAIR_PLOT_CELL_SIZE_PIXELS
+    )
+    assert figure.layout.meta['responsive_pair_plot']['margin_px'] == PAIR_PLOT_MARGIN_PIXELS
     assert [annotation.text for annotation in figure.layout.annotations] == [
         'length_a',
         'broad_gauss_u',
@@ -325,6 +336,15 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
     assert subplot.yaxis.title.text is None
 
 
+def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():
+    from easydiffraction.display.plotting import PAIR_PLOT_CELL_SIZE_PIXELS
+    from easydiffraction.display.plotting import Plotter
+
+    cell_size = Plotter._posterior_pair_cell_size_pixels(8, available_width_pixels=980)
+
+    assert cell_size < PAIR_PLOT_CELL_SIZE_PIXELS
+
+
 def test_build_param_distribution_plot_returns_plotly_figure():
     plotter, fit_results, _ = _make_bayesian_plotter_fixture()
     parameter = fit_results.parameters[0]

From 3443981846d9bbb95494f5a624efd6e025618883 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sat, 9 May 2026 00:46:58 +0200
Subject: [PATCH 055/106] Add DREAM bound validation and flexible parameter
 selection

---
 .github/copilot-instructions.md               |    7 +-
 docs/dev/plan_bayesian-analysis.md            |    6 +-
 docs/docs/tutorials/ed-13.ipynb               |    2 +-
 docs/docs/tutorials/ed-21.ipynb               | 2284 +++++++++++++++++
 docs/docs/tutorials/ed-21.py                  |    4 +-
 .../analysis/minimizers/bumps_dream.py        |   75 +
 src/easydiffraction/display/plotting.py       |  132 +-
 .../analysis/minimizers/test_bumps_dream.py   |   52 +-
 .../easydiffraction/display/test_plotting.py  |   72 +
 9 files changed, 2615 insertions(+), 19 deletions(-)
 create mode 100644 docs/docs/tutorials/ed-21.ipynb

diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 5613d742..0a54cc94 100644
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -146,13 +146,12 @@ Non-trivial changes use a two-phase workflow:
 
 Notes:
 
-- `pixi run fix` regenerates `docs/architecture/package-structure-*.md`
+- `pixi run fix` regenerates `docs/dev/package-structure-*.md`
   automatically — never edit those by hand. Don't review auto-fixes;
   accept and move on. Then `pixi run check` until clean.
 - Open issues / design questions / planned improvements live in
-  `docs/architecture/issues_open.md` (priority-ordered). On resolution,
-  move to `docs/architecture/issues_closed.md` and update
-  `architecture.md` if affected.
+  `docs/dev/issues_open.md` (priority-ordered). On resolution, move to
+  `docs/dev/issues_closed.md` and update `architecture.md` if affected.
 
 ### Planning
 
diff --git a/docs/dev/plan_bayesian-analysis.md b/docs/dev/plan_bayesian-analysis.md
index 76be62c3..4a5697b8 100644
--- a/docs/dev/plan_bayesian-analysis.md
+++ b/docs/dev/plan_bayesian-analysis.md
@@ -7,9 +7,9 @@ branch:** `feature/bayesian-analysis` **Design doc:**
 ## Context
 
 This plan follows `.github/copilot-instructions.md`. The instructions
-refer to `docs/architecture/architecture.md`, but this checkout does not
-contain that file. The matching living architecture document used for
-this plan is `docs/dev/architecture.md`.
+refer to `docs/dev/architecture.md`, but this checkout does not contain
+that file. The matching living architecture document used for this plan
+is `docs/dev/architecture.md`.
 
 Relevant current seams:
 
diff --git a/docs/docs/tutorials/ed-13.ipynb b/docs/docs/tutorials/ed-13.ipynb
index 4d605962..69eae9aa 100644
--- a/docs/docs/tutorials/ed-13.ipynb
+++ b/docs/docs/tutorials/ed-13.ipynb
@@ -2665,7 +2665,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "tags,title,-all",
+   "cell_metadata_filter": "title,tags,-all",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   }
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
new file mode 100644
index 00000000..c9b1236e
--- /dev/null
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -0,0 +1,2284 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0",
+   "metadata": {
+    "tags": [
+     "hide-in-docs"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Check whether easydiffraction is installed; install it if needed.\n",
+    "# Required for remote environments such as Google Colab.\n",
+    "import importlib.util\n",
+    "\n",
+    "if importlib.util.find_spec('easydiffraction') is None:\n",
+    "    %pip install easydiffraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Deterministic and Bayesian Refinement: LBCO, HRPT\n",
+    "\n",
+    "This tutorial demonstrates a practical two-stage workflow for powder\n",
+    "diffraction analysis with EasyDiffraction.\n",
+    "\n",
+    "In the first stage, we run a fast local refinement to obtain a sensible\n",
+    "point estimate and parameter uncertainties. In the second stage, we use\n",
+    "these refined values to define fit bounds and then sample the posterior\n",
+    "distribution with DREAM.\n",
+    "\n",
+    "The example uses constant-wavelength neutron powder diffraction data\n",
+    "for La0.5Ba0.5CoO3 measured on HRPT at PSI.\n",
+    "\n",
+    "The goal is not only to obtain a good fit, but also to answer Bayesian\n",
+    "questions such as:\n",
+    "\n",
+    "- Which parameter values are most probable?\n",
+    "- How broad are the credible intervals?\n",
+    "- Which parameters are strongly correlated?\n",
+    "- How much uncertainty propagates into the calculated diffraction\n",
+    "  pattern?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import easydiffraction as ed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create a Project Container\n",
+    "\n",
+    "The project object keeps structures, experiments, fit settings, and\n",
+    "plotting utilities together in a single place. We will build the full\n",
+    "workflow inside this object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project = ed.Project()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Step 2: Build the Structural Model\n",
+    "\n",
+    "We define a simple cubic perovskite model for LBCO. La and Ba share the\n",
+    "same crystallographic site with equal occupancy, while Co and O occupy\n",
+    "the remaining ideal perovskite positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.structures.create(name='lbco')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure = project.structures['lbco']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.space_group.name_h_m = 'P m -3 m'\n",
+    "structure.space_group.it_coordinate_system_code = '1'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.cell.length_a = 3.88"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11",
+   "metadata": {},
+   "source": [
+    "The atom-site definitions below form the starting structural model. The\n",
+    "parameters are intentionally reasonable rather than fully optimized,\n",
+    "because the refinement step will improve them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.atom_sites.create(\n",
+    "    label='La',\n",
+    "    type_symbol='La',\n",
+    "    fract_x=0,\n",
+    "    fract_y=0,\n",
+    "    fract_z=0,\n",
+    "    wyckoff_letter='a',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=0.5151,\n",
+    "    occupancy=0.5,\n",
+    ")\n",
+    "structure.atom_sites.create(\n",
+    "    label='Ba',\n",
+    "    type_symbol='Ba',\n",
+    "    fract_x=0,\n",
+    "    fract_y=0,\n",
+    "    fract_z=0,\n",
+    "    wyckoff_letter='a',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=0.5151,\n",
+    "    occupancy=0.5,\n",
+    ")\n",
+    "structure.atom_sites.create(\n",
+    "    label='Co',\n",
+    "    type_symbol='Co',\n",
+    "    fract_x=0.5,\n",
+    "    fract_y=0.5,\n",
+    "    fract_z=0.5,\n",
+    "    wyckoff_letter='b',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=0.2190,\n",
+    ")\n",
+    "structure.atom_sites.create(\n",
+    "    label='O',\n",
+    "    type_symbol='O',\n",
+    "    fract_x=0,\n",
+    "    fract_y=0.5,\n",
+    "    fract_z=0.5,\n",
+    "    wyckoff_letter='c',\n",
+    "    adp_type='Biso',\n",
+    "    adp_iso=1.3916,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13",
+   "metadata": {},
+   "source": [
+    "## Step 3: Define the Diffraction Experiment\n",
+    "\n",
+    "Next we download the measured powder pattern, create a neutron powder\n",
+    "experiment, and configure the instrument, profile, background, and\n",
+    "excluded regions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {},
+   "source": [
+    "#### Download the Measured Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "15",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mGetting data\u001b[0m\u001b[1;34m...\u001b[0m\n",
+      "Data #\u001b[1;36m3\u001b[0m: La0.5Ba0.5CoO3, HRPT \u001b[1m(\u001b[0mPSI\u001b[1m)\u001b[0m, \u001b[1;36m300\u001b[0m K\n",
+      "✅ Data #\u001b[1;36m3\u001b[0m already present at \u001b[32m'data/ed-3.xye'\u001b[0m. Keeping existing file.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_path = ed.download_data(id=3, destination='data')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "#### Create the Experiment Object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "17",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mData loaded successfully\u001b[0m\n",
+      "Experiment 🔬 \u001b[32m'hrpt'\u001b[0m. Number of data points: \u001b[1;36m3098\u001b[0m.\n"
+     ]
+    }
+   ],
+   "source": [
+    "project.experiments.add_from_data_path(\n",
+    "    name='hrpt',\n",
+    "    data_path=data_path,\n",
+    "    sample_form='powder',\n",
+    "    beam_mode='constant wavelength',\n",
+    "    radiation_probe='neutron',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment = project.experiments['hrpt']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19",
+   "metadata": {},
+   "source": [
+    "#### Set Instrument and Peak-Profile Parameters\n",
+    "\n",
+    "These values provide the initial instrument description for the local\n",
+    "refinement. Later, a subset of them will be refined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.instrument.setup_wavelength = 1.494\n",
+    "experiment.instrument.calib_twotheta_offset = 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.peak.broad_gauss_u = 0.1\n",
+    "experiment.peak.broad_gauss_v = -0.1\n",
+    "experiment.peak.broad_gauss_w = 0.1204\n",
+    "experiment.peak.broad_lorentz_y = 0.0844"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22",
+   "metadata": {},
+   "source": [
+    "#### Add Background Points and Excluded Regions\n",
+    "\n",
+    "The line-segment background is defined by a few anchor points. We also\n",
+    "exclude regions that are not intended to contribute to the fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.background.create(id='1', x=10, y=168.5585)\n",
+    "experiment.background.create(id='2', x=30, y=164.3357)\n",
+    "experiment.background.create(id='3', x=50, y=166.8881)\n",
+    "experiment.background.create(id='4', x=110, y=175.4006)\n",
+    "experiment.background.create(id='5', x=165, y=174.2813)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.excluded_regions.create(id='1', start=0, end=30)\n",
+    "experiment.excluded_regions.create(id='2', start=70, end=180)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "#### Link the Structural Phase to the Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_phases.create(id='lbco', scale=9.1351)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27",
+   "metadata": {},
+   "source": [
+    "## Step 4: Run an Initial Local Refinement\n",
+    "\n",
+    "Before Bayesian sampling, it is useful to run a deterministic fit. This\n",
+    "gives us:\n",
+    "\n",
+    "- a good point estimate near the best-fit region,\n",
+    "- uncertainties from the local optimizer,\n",
+    "- a quick check that the model and experiment are configured\n",
+    "  sensibly.\n",
+    "\n",
+    "In this tutorial we refine only a small set of parameters that are easy\n",
+    "to interpret in the later Bayesian stage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.cell.length_a.free = True\n",
+    "experiment.peak.broad_gauss_u.free = True\n",
+    "experiment.peak.broad_gauss_v.free = True\n",
+    "experiment.instrument.calib_twotheta_offset.free = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29",
+   "metadata": {},
+   "source": [
+    "We choose the BUMPS Levenberg-Marquardt minimizer as a fast local\n",
+    "optimizer. Its main purpose here is to provide a stable starting point\n",
+    "and uncertainty estimates for the Bayesian run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mMinimizer types\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <th id=\"T_db54b_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
+       "      <th id=\"T_db54b_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
+       "      <th id=\"T_db54b_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_db54b_row0_col0\" class=\"data row0 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
+       "      <td id=\"T_db54b_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_db54b_row1_col0\" class=\"data row1 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
+       "      <td id=\"T_db54b_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_db54b_row2_col0\" class=\"data row2 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
+       "      <td id=\"T_db54b_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_db54b_row3_col0\" class=\"data row3 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
+       "      <td id=\"T_db54b_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_db54b_row4_col0\" class=\"data row4 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
+       "      <td id=\"T_db54b_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_db54b_row5_col0\" class=\"data row5 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
+       "      <td id=\"T_db54b_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_db54b_row6_col0\" class=\"data row6 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
+       "      <td id=\"T_db54b_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_db54b_row7_col0\" class=\"data row7 col0\" ></td>\n",
+       "      <td id=\"T_db54b_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
+       "      <td id=\"T_db54b_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db54b_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_db54b_row8_col0\" class=\"data row8 col0\" >*</td>\n",
+       "      <td id=\"T_db54b_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
+       "      <td id=\"T_db54b_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
+       "    </tr>\n",
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+       "</table>\n"
+      ],
+      "text/plain": [
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mCurrent minimizer changed to\u001b[0m\n",
+      "bumps \u001b[1m(\u001b[0mlm\u001b[1m)\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()\n",
+    "project.analysis.fit.minimizer_type = 'bumps (lm)'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mStandard fitting\u001b[0m\n",
+      "📋 Using experiment 🔬 \u001b[32m'hrpt'\u001b[0m for \u001b[32m'single'\u001b[0m fitting\n",
+      "🚀 Starting fit process with \u001b[32m'bumps \u001b[0m\u001b[32m(\u001b[0m\u001b[32mlm\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[33m...\u001b[0m\n",
+      "📈 Goodness-of-fit progress:\n"
+     ]
+    },
+    {
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+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_c89b0_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
+       "      <th id=\"T_c89b0_level0_col1\" class=\"col_heading level0 col1\" >time (s)</th>\n",
+       "      <th id=\"T_c89b0_level0_col2\" class=\"col_heading level0 col2\" >χ²</th>\n",
+       "      <th id=\"T_c89b0_level0_col3\" class=\"col_heading level0 col3\" >change / status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_c89b0_row0_col0\" class=\"data row0 col0\" >1</td>\n",
+       "      <td id=\"T_c89b0_row0_col1\" class=\"data row0 col1\" >0.14</td>\n",
+       "      <td id=\"T_c89b0_row0_col2\" class=\"data row0 col2\" >736.04</td>\n",
+       "      <td id=\"T_c89b0_row0_col3\" class=\"data row0 col3\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_c89b0_row1_col0\" class=\"data row1 col0\" >7</td>\n",
+       "      <td id=\"T_c89b0_row1_col1\" class=\"data row1 col1\" >0.25</td>\n",
+       "      <td id=\"T_c89b0_row1_col2\" class=\"data row1 col2\" >315.22</td>\n",
+       "      <td id=\"T_c89b0_row1_col3\" class=\"data row1 col3\" >57.2% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_c89b0_row2_col0\" class=\"data row2 col0\" >12</td>\n",
+       "      <td id=\"T_c89b0_row2_col1\" class=\"data row2 col1\" >0.34</td>\n",
+       "      <td id=\"T_c89b0_row2_col2\" class=\"data row2 col2\" >93.97</td>\n",
+       "      <td id=\"T_c89b0_row2_col3\" class=\"data row2 col3\" >70.2% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_c89b0_row3_col0\" class=\"data row3 col0\" >17</td>\n",
+       "      <td id=\"T_c89b0_row3_col1\" class=\"data row3 col1\" >0.43</td>\n",
+       "      <td id=\"T_c89b0_row3_col2\" class=\"data row3 col2\" >40.33</td>\n",
+       "      <td id=\"T_c89b0_row3_col3\" class=\"data row3 col3\" >57.1% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_c89b0_row4_col0\" class=\"data row4 col0\" >21</td>\n",
+       "      <td id=\"T_c89b0_row4_col1\" class=\"data row4 col1\" >0.51</td>\n",
+       "      <td id=\"T_c89b0_row4_col2\" class=\"data row4 col2\" >39.06</td>\n",
+       "      <td id=\"T_c89b0_row4_col3\" class=\"data row4 col3\" >3.1% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_c89b0_row5_col0\" class=\"data row5 col0\" >22</td>\n",
+       "      <td id=\"T_c89b0_row5_col1\" class=\"data row5 col1\" >0.54</td>\n",
+       "      <td id=\"T_c89b0_row5_col2\" class=\"data row5 col2\" >28.50</td>\n",
+       "      <td id=\"T_c89b0_row5_col3\" class=\"data row5 col3\" >27.0% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_c89b0_row6_col0\" class=\"data row6 col0\" >26</td>\n",
+       "      <td id=\"T_c89b0_row6_col1\" class=\"data row6 col1\" >0.61</td>\n",
+       "      <td id=\"T_c89b0_row6_col2\" class=\"data row6 col2\" >24.69</td>\n",
+       "      <td id=\"T_c89b0_row6_col3\" class=\"data row6 col3\" >13.4% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_c89b0_row7_col0\" class=\"data row7 col0\" >27</td>\n",
+       "      <td id=\"T_c89b0_row7_col1\" class=\"data row7 col1\" >0.63</td>\n",
+       "      <td id=\"T_c89b0_row7_col2\" class=\"data row7 col2\" >16.23</td>\n",
+       "      <td id=\"T_c89b0_row7_col3\" class=\"data row7 col3\" >34.2% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_c89b0_row8_col0\" class=\"data row8 col0\" >31</td>\n",
+       "      <td id=\"T_c89b0_row8_col1\" class=\"data row8 col1\" >0.71</td>\n",
+       "      <td id=\"T_c89b0_row8_col2\" class=\"data row8 col2\" >14.77</td>\n",
+       "      <td id=\"T_c89b0_row8_col3\" class=\"data row8 col3\" >9.0% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
+       "      <td id=\"T_c89b0_row9_col0\" class=\"data row9 col0\" >32</td>\n",
+       "      <td id=\"T_c89b0_row9_col1\" class=\"data row9 col1\" >0.73</td>\n",
+       "      <td id=\"T_c89b0_row9_col2\" class=\"data row9 col2\" >2.74</td>\n",
+       "      <td id=\"T_c89b0_row9_col3\" class=\"data row9 col3\" >81.4% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
+       "      <td id=\"T_c89b0_row10_col0\" class=\"data row10 col0\" >37</td>\n",
+       "      <td id=\"T_c89b0_row10_col1\" class=\"data row10 col1\" >0.82</td>\n",
+       "      <td id=\"T_c89b0_row10_col2\" class=\"data row10 col2\" >1.45</td>\n",
+       "      <td id=\"T_c89b0_row10_col3\" class=\"data row10 col3\" >47.1% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
+       "      <td id=\"T_c89b0_row11_col0\" class=\"data row11 col0\" >42</td>\n",
+       "      <td id=\"T_c89b0_row11_col1\" class=\"data row11 col1\" >0.91</td>\n",
+       "      <td id=\"T_c89b0_row11_col2\" class=\"data row11 col2\" >1.37</td>\n",
+       "      <td id=\"T_c89b0_row11_col3\" class=\"data row11 col3\" >5.6% ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c89b0_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
+       "      <td id=\"T_c89b0_row12_col0\" class=\"data row12 col0\" >69</td>\n",
+       "      <td id=\"T_c89b0_row12_col1\" class=\"data row12 col1\" >1.39</td>\n",
+       "      <td id=\"T_c89b0_row12_col2\" class=\"data row12 col2\" >1.37</td>\n",
+       "      <td id=\"T_c89b0_row12_col3\" class=\"data row12 col3\" ></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🏆 Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m1.37\u001b[0m at iteration \u001b[1;36m68\u001b[0m\n",
+      "✅ Fitting complete.\n"
+     ]
+    }
+   ],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mFit results\u001b[0m\n",
+      "✅ Success: \u001b[3;92mTrue\u001b[0m\n",
+      "⏱️ Fitting time: \u001b[1;36m1.39\u001b[0m seconds\n",
+      "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.37\u001b[0m\n",
+      "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.27\u001b[0m%\n",
+      "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.42\u001b[0m%\n",
+      "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m3.78\u001b[0m%\n",
+      "📈 Fitted parameters:\n"
+     ]
+    },
+    {
+     "data": {
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+       "<table id=\"T_de5f7\" class=\"dataframe\">\n",
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+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_de5f7_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
+       "      <th id=\"T_de5f7_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
+       "      <th id=\"T_de5f7_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_de5f7_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_de5f7_level0_col4\" class=\"col_heading level0 col4\" >start</th>\n",
+       "      <th id=\"T_de5f7_level0_col5\" class=\"col_heading level0 col5\" >fitted</th>\n",
+       "      <th id=\"T_de5f7_level0_col6\" class=\"col_heading level0 col6\" >uncertainty</th>\n",
+       "      <th id=\"T_de5f7_level0_col7\" class=\"col_heading level0 col7\" >units</th>\n",
+       "      <th id=\"T_de5f7_level0_col8\" class=\"col_heading level0 col8\" >change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_de5f7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_de5f7_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_de5f7_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_de5f7_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_de5f7_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_de5f7_row0_col4\" class=\"data row0 col4\" >3.8800</td>\n",
+       "      <td id=\"T_de5f7_row0_col5\" class=\"data row0 col5\" >3.8907</td>\n",
+       "      <td id=\"T_de5f7_row0_col6\" class=\"data row0 col6\" >0.0002</td>\n",
+       "      <td id=\"T_de5f7_row0_col7\" class=\"data row0 col7\" >Å</td>\n",
+       "      <td id=\"T_de5f7_row0_col8\" class=\"data row0 col8\" >0.27 % ↑</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_de5f7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_de5f7_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_de5f7_row1_col1\" class=\"data row1 col1\" >peak</td>\n",
+       "      <td id=\"T_de5f7_row1_col2\" class=\"data row1 col2\" ></td>\n",
+       "      <td id=\"T_de5f7_row1_col3\" class=\"data row1 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_de5f7_row1_col4\" class=\"data row1 col4\" >0.1000</td>\n",
+       "      <td id=\"T_de5f7_row1_col5\" class=\"data row1 col5\" >0.0333</td>\n",
+       "      <td id=\"T_de5f7_row1_col6\" class=\"data row1 col6\" >0.0163</td>\n",
+       "      <td id=\"T_de5f7_row1_col7\" class=\"data row1 col7\" >deg²</td>\n",
+       "      <td id=\"T_de5f7_row1_col8\" class=\"data row1 col8\" >66.71 % ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_de5f7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_de5f7_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_de5f7_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_de5f7_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_de5f7_row2_col3\" class=\"data row2 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_de5f7_row2_col4\" class=\"data row2 col4\" >-0.1000</td>\n",
+       "      <td id=\"T_de5f7_row2_col5\" class=\"data row2 col5\" >-0.0934</td>\n",
+       "      <td id=\"T_de5f7_row2_col6\" class=\"data row2 col6\" >0.0091</td>\n",
+       "      <td id=\"T_de5f7_row2_col7\" class=\"data row2 col7\" >deg²</td>\n",
+       "      <td id=\"T_de5f7_row2_col8\" class=\"data row2 col8\" >6.57 % ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_de5f7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_de5f7_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_de5f7_row3_col1\" class=\"data row3 col1\" >instrument</td>\n",
+       "      <td id=\"T_de5f7_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_de5f7_row3_col3\" class=\"data row3 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_de5f7_row3_col4\" class=\"data row3 col4\" >0.0000</td>\n",
+       "      <td id=\"T_de5f7_row3_col5\" class=\"data row3 col5\" >0.6221</td>\n",
+       "      <td id=\"T_de5f7_row3_col6\" class=\"data row3 col6\" >0.0029</td>\n",
+       "      <td id=\"T_de5f7_row3_col7\" class=\"data row3 col7\" >deg</td>\n",
+       "      <td id=\"T_de5f7_row3_col8\" class=\"data row3 col8\" >N/A</td>\n",
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+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33",
+   "metadata": {},
+   "source": [
+    "The correlation plot shows how strongly the fitted parameters move\n",
+    "together in the local refinement. The measured-vs-calculated plots show\n",
+    "how well the refined model reproduces the data globally and in a zoomed\n",
+    "region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "34",
+   "metadata": {},
+   "outputs": [
+    {
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+   "id": "35",
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+       "                            {\n",
+       "const graphDiv = document.getElementById('aaad5bbe-c49b-44ce-8aab-9c1248cea2c0');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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\"white\",\"polar\":{\"bgcolor\":\"white\",\"angularaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"white\",\"aaxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#A2B1C6\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"#EBF0F8\",\"linecolor\":\"#EBF0F8\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"#EBF0F8\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"white\",\"gridcolor\":\"#DFE8F3\",\"linecolor\":\"#EBF0F8\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"#EBF0F8\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"white\",\"subunitcolor\":\"#C8D4E3\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"matches\":\"x\",\"showticklabels\":false,\"showline\":true,\"linecolor\":\"rgba(120, 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0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"tickmode\":\"array\",\"tickvals\":[1.0],\"ticktext\":[\"lbco\"],\"range\":[1.5,0.5],\"showgrid\":false},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0.0,1.0],\"matches\":\"x\",\"showline\":true,\"linecolor\":\"rgba(120, 140, 160, 0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"range\":[65.0,68.0],\"title\":{\"text\":\"2\\u03b8 (degree)\"}},\"yaxis3\":{\"anchor\":\"x3\",\"domain\":[0.0,0.18108737864077668],\"showline\":true,\"linecolor\":\"rgba(120, 140, 160, 0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"range\":[-408.95177858045844,408.95177858045844],\"tickmode\":\"array\",\"tickvals\":[-400.0,0.0,400.0],\"scaleanchor\":\"y\",\"scaleratio\":1},\"margin\":{\"autoexpand\":true,\"r\":30,\"t\":40,\"b\":45},\"title\":{\"text\":\"Measured vs Calculated data for experiment \\ud83d\\udd2c 'hrpt'\"},\"legend\":{\"bgcolor\":\"rgba(255, 255, 255, 0.5)\",\"xanchor\":\"right\",\"x\":1.0,\"yanchor\":\"top\",\"y\":1.0},\"height\":600.0},                        {\"displayModeBar\": true, \"displaylogo\": false, \"modeBarButtonsToRemove\": [\"select2d\", \"lasso2d\", \"zoomIn2d\", \"zoomOut2d\", \"autoScale2d\"], \"responsive\": true}                    ).then(function(){\n",
+       "                            {\n",
+       "const graphDiv = document.getElementById('9c43452a-8746-45f7-a4d0-c7677c6da1df');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
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+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
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+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37",
+   "metadata": {},
+   "source": [
+    "## Step 5: Prepare for Bayesian Sampling\n",
+    "\n",
+    "DREAM requires finite bounds for the free parameters. Instead of\n",
+    "setting them manually, we derive them from the uncertainties estimated\n",
+    "in the local refinement.\n",
+    "\n",
+    "The helper method `set_fit_bounds_from_uncertainty` centers the bounds\n",
+    "on the current parameter value and expands them by a chosen multiple of\n",
+    "the reported uncertainty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\n"
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+       "</style>\n",
+       "<table id=\"T_2fe4a\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_2fe4a_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
+       "      <th id=\"T_2fe4a_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
+       "      <th id=\"T_2fe4a_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_2fe4a_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_2fe4a_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
+       "      <th id=\"T_2fe4a_level0_col5\" class=\"col_heading level0 col5\" >uncertainty</th>\n",
+       "      <th id=\"T_2fe4a_level0_col6\" class=\"col_heading level0 col6\" >min</th>\n",
+       "      <th id=\"T_2fe4a_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
+       "      <th id=\"T_2fe4a_level0_col8\" class=\"col_heading level0 col8\" >units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2fe4a_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_2fe4a_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_2fe4a_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_2fe4a_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_2fe4a_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_2fe4a_row0_col4\" class=\"data row0 col4\" >3.89066</td>\n",
+       "      <td id=\"T_2fe4a_row0_col5\" class=\"data row0 col5\" >0.00021</td>\n",
+       "      <td id=\"T_2fe4a_row0_col6\" class=\"data row0 col6\" >-inf</td>\n",
+       "      <td id=\"T_2fe4a_row0_col7\" class=\"data row0 col7\" >inf</td>\n",
+       "      <td id=\"T_2fe4a_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2fe4a_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_2fe4a_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_2fe4a_row1_col1\" class=\"data row1 col1\" >peak</td>\n",
+       "      <td id=\"T_2fe4a_row1_col2\" class=\"data row1 col2\" ></td>\n",
+       "      <td id=\"T_2fe4a_row1_col3\" class=\"data row1 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_2fe4a_row1_col4\" class=\"data row1 col4\" >0.03329</td>\n",
+       "      <td id=\"T_2fe4a_row1_col5\" class=\"data row1 col5\" >0.01633</td>\n",
+       "      <td id=\"T_2fe4a_row1_col6\" class=\"data row1 col6\" >-inf</td>\n",
+       "      <td id=\"T_2fe4a_row1_col7\" class=\"data row1 col7\" >inf</td>\n",
+       "      <td id=\"T_2fe4a_row1_col8\" class=\"data row1 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2fe4a_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_2fe4a_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_2fe4a_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_2fe4a_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_2fe4a_row2_col3\" class=\"data row2 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_2fe4a_row2_col4\" class=\"data row2 col4\" >-0.09343</td>\n",
+       "      <td id=\"T_2fe4a_row2_col5\" class=\"data row2 col5\" >0.00911</td>\n",
+       "      <td id=\"T_2fe4a_row2_col6\" class=\"data row2 col6\" >-inf</td>\n",
+       "      <td id=\"T_2fe4a_row2_col7\" class=\"data row2 col7\" >inf</td>\n",
+       "      <td id=\"T_2fe4a_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2fe4a_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_2fe4a_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_2fe4a_row3_col1\" class=\"data row3 col1\" >instrument</td>\n",
+       "      <td id=\"T_2fe4a_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_2fe4a_row3_col3\" class=\"data row3 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_2fe4a_row3_col4\" class=\"data row3 col4\" >0.62207</td>\n",
+       "      <td id=\"T_2fe4a_row3_col5\" class=\"data row3 col5\" >0.00294</td>\n",
+       "      <td id=\"T_2fe4a_row3_col6\" class=\"data row3 col6\" >-inf</td>\n",
+       "      <td id=\"T_2fe4a_row3_col7\" class=\"data row3 col7\" >inf</td>\n",
+       "      <td id=\"T_2fe4a_row3_col8\" class=\"data row3 col8\" >deg</td>\n",
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+    }
+   ],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=4)\n",
+    "experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=4)\n",
+    "experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)\n",
+    "experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40",
+   "metadata": {},
+   "source": [
+    "Displaying the free parameters again is a convenient way to confirm\n",
+    "that the fit bounds have been assigned as expected before launching the\n",
+    "sampler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\n"
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+       "</style>\n",
+       "<table id=\"T_a35b7\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_a35b7_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
+       "      <th id=\"T_a35b7_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
+       "      <th id=\"T_a35b7_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_a35b7_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_a35b7_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
+       "      <th id=\"T_a35b7_level0_col5\" class=\"col_heading level0 col5\" >uncertainty</th>\n",
+       "      <th id=\"T_a35b7_level0_col6\" class=\"col_heading level0 col6\" >min</th>\n",
+       "      <th id=\"T_a35b7_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
+       "      <th id=\"T_a35b7_level0_col8\" class=\"col_heading level0 col8\" >units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a35b7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_a35b7_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_a35b7_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_a35b7_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_a35b7_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_a35b7_row0_col4\" class=\"data row0 col4\" >3.89066</td>\n",
+       "      <td id=\"T_a35b7_row0_col5\" class=\"data row0 col5\" >0.00021</td>\n",
+       "      <td id=\"T_a35b7_row0_col6\" class=\"data row0 col6\" >3.88983</td>\n",
+       "      <td id=\"T_a35b7_row0_col7\" class=\"data row0 col7\" >3.89148</td>\n",
+       "      <td id=\"T_a35b7_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a35b7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_a35b7_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_a35b7_row1_col1\" class=\"data row1 col1\" >peak</td>\n",
+       "      <td id=\"T_a35b7_row1_col2\" class=\"data row1 col2\" ></td>\n",
+       "      <td id=\"T_a35b7_row1_col3\" class=\"data row1 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_a35b7_row1_col4\" class=\"data row1 col4\" >0.03329</td>\n",
+       "      <td id=\"T_a35b7_row1_col5\" class=\"data row1 col5\" >0.01633</td>\n",
+       "      <td id=\"T_a35b7_row1_col6\" class=\"data row1 col6\" >-0.03205</td>\n",
+       "      <td id=\"T_a35b7_row1_col7\" class=\"data row1 col7\" >0.09863</td>\n",
+       "      <td id=\"T_a35b7_row1_col8\" class=\"data row1 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a35b7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_a35b7_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_a35b7_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_a35b7_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_a35b7_row2_col3\" class=\"data row2 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_a35b7_row2_col4\" class=\"data row2 col4\" >-0.09343</td>\n",
+       "      <td id=\"T_a35b7_row2_col5\" class=\"data row2 col5\" >0.00911</td>\n",
+       "      <td id=\"T_a35b7_row2_col6\" class=\"data row2 col6\" >-0.12988</td>\n",
+       "      <td id=\"T_a35b7_row2_col7\" class=\"data row2 col7\" >-0.05699</td>\n",
+       "      <td id=\"T_a35b7_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a35b7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_a35b7_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_a35b7_row3_col1\" class=\"data row3 col1\" >instrument</td>\n",
+       "      <td id=\"T_a35b7_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_a35b7_row3_col3\" class=\"data row3 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_a35b7_row3_col4\" class=\"data row3 col4\" >0.62207</td>\n",
+       "      <td id=\"T_a35b7_row3_col5\" class=\"data row3 col5\" >0.00294</td>\n",
+       "      <td id=\"T_a35b7_row3_col6\" class=\"data row3 col6\" >0.61031</td>\n",
+       "      <td id=\"T_a35b7_row3_col7\" class=\"data row3 col7\" >0.63383</td>\n",
+       "      <td id=\"T_a35b7_row3_col8\" class=\"data row3 col8\" >deg</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x16b8b8910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42",
+   "metadata": {},
+   "source": [
+    "## Step 6: Configure and Run DREAM\n",
+    "\n",
+    "We now switch from the local minimizer to the Bayesian DREAM sampler.\n",
+    "\n",
+    "The settings below are intentionally small so the tutorial runs\n",
+    "quickly. For production analysis you would usually increase the number\n",
+    "of steps and often the burn-in as well. When needed, the DREAM API\n",
+    "also lets you tune how chains are initialized through the `init`\n",
+    "setting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mMinimizer types\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
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+       "</style>\n",
+       "<table id=\"T_90fe9\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_90fe9_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
+       "      <th id=\"T_90fe9_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
+       "      <th id=\"T_90fe9_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_90fe9_row0_col0\" class=\"data row0 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
+       "      <td id=\"T_90fe9_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_90fe9_row1_col0\" class=\"data row1 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
+       "      <td id=\"T_90fe9_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_90fe9_row2_col0\" class=\"data row2 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
+       "      <td id=\"T_90fe9_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_90fe9_row3_col0\" class=\"data row3 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
+       "      <td id=\"T_90fe9_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_90fe9_row4_col0\" class=\"data row4 col0\" >*</td>\n",
+       "      <td id=\"T_90fe9_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
+       "      <td id=\"T_90fe9_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_90fe9_row5_col0\" class=\"data row5 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
+       "      <td id=\"T_90fe9_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_90fe9_row6_col0\" class=\"data row6 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
+       "      <td id=\"T_90fe9_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_90fe9_row7_col0\" class=\"data row7 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
+       "      <td id=\"T_90fe9_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_90fe9_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_90fe9_row8_col0\" class=\"data row8 col0\" ></td>\n",
+       "      <td id=\"T_90fe9_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
+       "      <td id=\"T_90fe9_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x16b8b8910>"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mCurrent minimizer changed to\u001b[0m\n",
+      "bumps \u001b[1m(\u001b[0mdream\u001b[1m)\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "project.analysis.fit.minimizer_type = 'bumps (dream)'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer.steps = 2000  # 1000\n",
+    "project.analysis.fit.minimizer.burn = 400  # 200\n",
+    "project.analysis.fit.minimizer.thin = 1\n",
+    "project.analysis.fit.minimizer.pop = 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mStandard fitting\u001b[0m\n",
+      "📋 Using experiment 🔬 \u001b[32m'hrpt'\u001b[0m for \u001b[32m'single'\u001b[0m fitting\n",
+      "🚀 Starting fit process with \u001b[32m'bumps \u001b[0m\u001b[32m(\u001b[0m\u001b[32mdream\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[33m...\u001b[0m\n",
+      "📈 Bayesian sampling progress:\n"
+     ]
+    },
+    {
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+       "</style>\n",
+       "<table id=\"T_f1d86\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_f1d86_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
+       "      <th id=\"T_f1d86_level0_col1\" class=\"col_heading level0 col1\" >progress</th>\n",
+       "      <th id=\"T_f1d86_level0_col2\" class=\"col_heading level0 col2\" >time (s)</th>\n",
+       "      <th id=\"T_f1d86_level0_col3\" class=\"col_heading level0 col3\" >log posterior</th>\n",
+       "      <th id=\"T_f1d86_level0_col4\" class=\"col_heading level0 col4\" >phase</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_f1d86_row0_col0\" class=\"data row0 col0\" >1/2401</td>\n",
+       "      <td id=\"T_f1d86_row0_col1\" class=\"data row0 col1\" >0.0%</td>\n",
+       "      <td id=\"T_f1d86_row0_col2\" class=\"data row0 col2\" >0.30</td>\n",
+       "      <td id=\"T_f1d86_row0_col3\" class=\"data row0 col3\" >-543.37</td>\n",
+       "      <td id=\"T_f1d86_row0_col4\" class=\"data row0 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_f1d86_row1_col0\" class=\"data row1 col0\" >101/2401</td>\n",
+       "      <td id=\"T_f1d86_row1_col1\" class=\"data row1 col1\" >4.2%</td>\n",
+       "      <td id=\"T_f1d86_row1_col2\" class=\"data row1 col2\" >27.35</td>\n",
+       "      <td id=\"T_f1d86_row1_col3\" class=\"data row1 col3\" >-545.75</td>\n",
+       "      <td id=\"T_f1d86_row1_col4\" class=\"data row1 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_f1d86_row2_col0\" class=\"data row2 col0\" >200/2401</td>\n",
+       "      <td id=\"T_f1d86_row2_col1\" class=\"data row2 col1\" >8.3%</td>\n",
+       "      <td id=\"T_f1d86_row2_col2\" class=\"data row2 col2\" >53.82</td>\n",
+       "      <td id=\"T_f1d86_row2_col3\" class=\"data row2 col3\" >-545.34</td>\n",
+       "      <td id=\"T_f1d86_row2_col4\" class=\"data row2 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_f1d86_row3_col0\" class=\"data row3 col0\" >300/2401</td>\n",
+       "      <td id=\"T_f1d86_row3_col1\" class=\"data row3 col1\" >12.5%</td>\n",
+       "      <td id=\"T_f1d86_row3_col2\" class=\"data row3 col2\" >80.18</td>\n",
+       "      <td id=\"T_f1d86_row3_col3\" class=\"data row3 col3\" >-545.22</td>\n",
+       "      <td id=\"T_f1d86_row3_col4\" class=\"data row3 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_f1d86_row4_col0\" class=\"data row4 col0\" >400/2401</td>\n",
+       "      <td id=\"T_f1d86_row4_col1\" class=\"data row4 col1\" >16.7%</td>\n",
+       "      <td id=\"T_f1d86_row4_col2\" class=\"data row4 col2\" >106.78</td>\n",
+       "      <td id=\"T_f1d86_row4_col3\" class=\"data row4 col3\" >-545.39</td>\n",
+       "      <td id=\"T_f1d86_row4_col4\" class=\"data row4 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_f1d86_row5_col0\" class=\"data row5 col0\" >401/2401</td>\n",
+       "      <td id=\"T_f1d86_row5_col1\" class=\"data row5 col1\" >16.7%</td>\n",
+       "      <td id=\"T_f1d86_row5_col2\" class=\"data row5 col2\" >107.05</td>\n",
+       "      <td id=\"T_f1d86_row5_col3\" class=\"data row5 col3\" >-545.35</td>\n",
+       "      <td id=\"T_f1d86_row5_col4\" class=\"data row5 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_f1d86_row6_col0\" class=\"data row6 col0\" >506/2401</td>\n",
+       "      <td id=\"T_f1d86_row6_col1\" class=\"data row6 col1\" >21.1%</td>\n",
+       "      <td id=\"T_f1d86_row6_col2\" class=\"data row6 col2\" >134.74</td>\n",
+       "      <td id=\"T_f1d86_row6_col3\" class=\"data row6 col3\" >-545.28</td>\n",
+       "      <td id=\"T_f1d86_row6_col4\" class=\"data row6 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_f1d86_row7_col0\" class=\"data row7 col0\" >612/2401</td>\n",
+       "      <td id=\"T_f1d86_row7_col1\" class=\"data row7 col1\" >25.5%</td>\n",
+       "      <td id=\"T_f1d86_row7_col2\" class=\"data row7 col2\" >164.08</td>\n",
+       "      <td id=\"T_f1d86_row7_col3\" class=\"data row7 col3\" >-545.50</td>\n",
+       "      <td id=\"T_f1d86_row7_col4\" class=\"data row7 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f1d86_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_f1d86_row8_col0\" class=\"data row8 col0\" >717/2401</td>\n",
+       "      <td id=\"T_f1d86_row8_col1\" class=\"data row8 col1\" >29.9%</td>\n",
+       "      <td id=\"T_f1d86_row8_col2\" class=\"data row8 col2\" >192.06</td>\n",
+       "      <td id=\"T_f1d86_row8_col3\" class=\"data row8 col3\" >-546.77</td>\n",
+       "      <td id=\"T_f1d86_row8_col4\" class=\"data row8 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47",
+   "metadata": {},
+   "source": [
+    "## Step 7: Inspect Bayesian Results\n",
+    "\n",
+    "The fit-results display now includes sampler settings, convergence\n",
+    "diagnostics, committed parameter values, and posterior summary\n",
+    "statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49",
+   "metadata": {},
+   "source": [
+    "The correlation and posterior-pair plots are complementary:\n",
+    "\n",
+    "- `plot_param_correlations` summarizes pairwise structure in a compact\n",
+    "  matrix.\n",
+    "- `plot_posterior_pairs` shows marginal densities on the diagonal and\n",
+    "  posterior contours off-diagonal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_correlations(show_diagonal=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_pairs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52",
+   "metadata": {},
+   "source": [
+    "The one-dimensional posterior distributions below make it easier to\n",
+    "inspect individual parameters in isolation, including asymmetry or\n",
+    "multimodality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_distribution(structure.cell.length_a)\n",
+    "project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)\n",
+    "project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)\n",
+    "project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54",
+   "metadata": {},
+   "source": [
+    "Finally, the posterior predictive plot propagates the sampled parameter\n",
+    "uncertainty into the calculated diffraction pattern. Comparing this to\n",
+    "the zoomed measured-vs-calculated view helps assess whether the sampled\n",
+    "model family explains the data in the region of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56",
+   "metadata": {},
+   "source": [
+    "A final zoomed measured-vs-calculated plot is useful for checking how\n",
+    "the posterior-supported model behaves in a narrow region of the pattern\n",
+    "after the Bayesian run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 9e3f62e9..543c3085 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -269,8 +269,8 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 200  # 1000
-project.analysis.fit.minimizer.burn = 40  # 200
+project.analysis.fit.minimizer.steps = 2000  # 1000
+project.analysis.fit.minimizer.burn = 400  # 200
 project.analysis.fit.minimizer.thin = 1
 project.analysis.fit.minimizer.pop = 4
 
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index d2fb1773..7eab3f23 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -357,6 +357,7 @@ def _prepare_solver_args(
         dict[str, object]
             BUMPS parameters plus EasyDiffraction parameter metadata.
         """
+        self._validate_sampled_parameter_bounds(parameters)
         solver_args = super()._prepare_solver_args(parameters)
         solver_args['parameter_names'] = [parameter.unique_name for parameter in parameters]
         solver_args['parameter_display_names'] = [
@@ -366,6 +367,80 @@ def _prepare_solver_args(
         solver_args['starting_uncertainties'] = [parameter.uncertainty for parameter in parameters]
         return solver_args
 
+    @classmethod
+    def _validate_sampled_parameter_bounds(
+        cls,
+        parameters: list[object],
+    ) -> None:
+        """
+        Validate finite ordered bounds for sampled DREAM parameters.
+        """
+        issues: list[str] = []
+        for parameter in parameters:
+            parameter_name = cls._parameter_name_for_bound_validation(parameter)
+            parameter_issues = cls._parameter_bound_issues(parameter)
+            if parameter_issues:
+                issues.append(f'- {parameter_name}: {"; ".join(parameter_issues)}')
+
+        if not issues:
+            return
+
+        message = 'DREAM requires finite valid bounds for every sampled parameter:\n' + '\n'.join(
+            issues
+        )
+        raise ValueError(message)
+
+    @staticmethod
+    def _parameter_name_for_bound_validation(parameter: object) -> str:
+        """Return the user-facing name for DREAM bound validation."""
+        unique_name = getattr(parameter, 'unique_name', None)
+        if unique_name:
+            return str(unique_name)
+
+        parameter_name = getattr(parameter, 'name', None)
+        if parameter_name:
+            return str(parameter_name)
+        return '<unknown parameter>'
+
+    @classmethod
+    def _parameter_bound_issues(
+        cls,
+        parameter: object,
+    ) -> list[str]:
+        """Return bound-validation issues for one sampled parameter."""
+        lower_bound = getattr(parameter, 'fit_min', None)
+        upper_bound = getattr(parameter, 'fit_max', None)
+        value = getattr(parameter, 'value', None)
+        issues: list[str] = []
+
+        lower_is_finite = cls._is_finite_bound_value(lower_bound)
+        upper_is_finite = cls._is_finite_bound_value(upper_bound)
+        value_is_finite = cls._is_finite_bound_value(value)
+
+        if not lower_is_finite:
+            issues.append(f'fit_min must be finite (got {lower_bound!r})')
+        if not upper_is_finite:
+            issues.append(f'fit_max must be finite (got {upper_bound!r})')
+
+        bounds_are_ordered = lower_is_finite and upper_is_finite and lower_bound < upper_bound
+        if lower_is_finite and upper_is_finite and not bounds_are_ordered:
+            issues.append(f'fit_min ({lower_bound}) must be smaller than fit_max ({upper_bound})')
+
+        if not value_is_finite:
+            issues.append(f'starting value must be finite (got {value!r})')
+        elif bounds_are_ordered and not lower_bound <= value <= upper_bound:
+            issues.append(f'starting value {value} is outside [{lower_bound}, {upper_bound}]')
+
+        return issues
+
+    @staticmethod
+    def _is_finite_bound_value(value: object) -> bool:
+        """Return whether a bound-validation value is finite."""
+        try:
+            return bool(np.isfinite(value))
+        except TypeError:
+            return False
+
     @staticmethod
     def _validated_positive_integer(name: str, value: float) -> int:
         """Validate a DREAM setting that must be a positive integer."""
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 98f718de..8d8a99bd 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -741,8 +741,9 @@ def plot_param_distribution(
         Parameters
         ----------
         param : object
-            Parameter descriptor whose posterior distribution should be
-            plotted.
+            Parameter descriptor or string identifier selecting the
+            posterior to plot. Strings may be unique names or
+            user-facing labels.
         """
         plot = self._build_param_distribution_plot(param)
         if plot is None:
@@ -2700,13 +2701,128 @@ def _resolve_posterior_parameter_names(
         if parameters is None:
             return list(available_names)
 
-        requested_names = [getattr(parameter, 'unique_name', None) for parameter in parameters]
-        missing_names = [name for name in requested_names if name not in available_names]
-        if missing_names:
-            missing = ', '.join(str(name) for name in missing_names)
-            log.warning(f'Posterior samples do not contain the selected parameters: {missing}')
+        resolved_names: list[str] = []
+        for parameter in parameters:
+            resolved_name = Plotter._resolve_posterior_parameter_name(
+                fit_results=fit_results,
+                available_names=list(available_names),
+                parameter=parameter,
+            )
+            if resolved_name is None:
+                return None
+            resolved_names.append(resolved_name)
+        return resolved_names
+
+    @staticmethod
+    def _resolve_posterior_parameter_name(
+        *,
+        fit_results: object,
+        available_names: list[str],
+        parameter: object,
+    ) -> str | None:
+        """
+        Resolve one posterior parameter selection into a unique name.
+        """
+        if isinstance(parameter, str):
+            return Plotter._resolve_posterior_parameter_name_from_string(
+                fit_results=fit_results,
+                available_names=available_names,
+                selection=parameter,
+            )
+
+        unique_name = getattr(parameter, 'unique_name', None)
+        if unique_name is None:
+            log.warning(
+                'Posterior parameter selection expects parameter objects '
+                'or strings matching a unique name or label.'
+            )
             return None
-        return [str(name) for name in requested_names if name is not None]
+        if unique_name not in available_names:
+            log.warning(f'Posterior samples do not contain the selected parameter: {unique_name}')
+            return None
+        return str(unique_name)
+
+    @staticmethod
+    def _resolve_posterior_parameter_name_from_string(
+        *,
+        fit_results: object,
+        available_names: list[str],
+        selection: str,
+    ) -> str | None:
+        """Resolve a string posterior parameter selection."""
+        stripped_selection = selection.strip()
+        if not stripped_selection:
+            log.warning('Posterior parameter selection cannot use an empty string.')
+            return None
+        if stripped_selection in available_names:
+            return stripped_selection
+
+        selection_candidates = Plotter._posterior_parameter_selection_candidates(
+            fit_results=fit_results,
+            available_names=available_names,
+        )
+        matching_names = [
+            unique_name
+            for unique_name, candidates in selection_candidates.items()
+            if stripped_selection in candidates
+        ]
+        if len(matching_names) == 1:
+            return matching_names[0]
+        if len(matching_names) > 1:
+            matches = ', '.join(matching_names)
+            log.warning(
+                f"Posterior parameter selection '{stripped_selection}' is ambiguous. "
+                f'Matches: {matches}'
+            )
+            return None
+
+        log.warning(
+            f'Posterior samples do not contain the selected parameter or label: '
+            f'{stripped_selection}'
+        )
+        return None
+
+    @staticmethod
+    def _posterior_parameter_selection_candidates(
+        *,
+        fit_results: object,
+        available_names: list[str],
+    ) -> dict[str, set[str]]:
+        """
+        Return string identifiers accepted for posterior selection.
+        """
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
+        }
+        summaries_by_name = Plotter._posterior_summary_by_name(fit_results)
+        plot_labels = dict(
+            zip(
+                available_names,
+                Plotter._posterior_plot_labels(fit_results, available_names),
+                strict=True,
+            )
+        )
+        candidates_by_name: dict[str, set[str]] = {}
+        for unique_name in available_names:
+            candidates = {unique_name}
+            parameter = parameters_by_name.get(unique_name)
+            if parameter is not None:
+                parameter_name = getattr(parameter, 'name', None)
+                if isinstance(parameter_name, str) and parameter_name.strip():
+                    candidates.add(parameter_name.strip())
+
+            summary = summaries_by_name.get(unique_name)
+            summary_label = getattr(summary, 'display_name', None)
+            if isinstance(summary_label, str) and summary_label.strip():
+                candidates.add(summary_label.strip())
+
+            plot_label = plot_labels.get(unique_name)
+            if isinstance(plot_label, str) and plot_label.strip():
+                candidates.add(plot_label.strip())
+
+            candidates_by_name[unique_name] = candidates
+        return candidates_by_name
 
     @staticmethod
     def _selected_posterior_samples(
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index 783b37aa..9fa85263 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -14,12 +14,22 @@
 class FakeParam:
     """Minimal stand-in for an EasyDiffraction parameter."""
 
-    def __init__(self, uid: str, value: float, uncertainty: float | None = None) -> None:
+    def __init__(
+        self,
+        uid: str,
+        value: float,
+        uncertainty: float | None = None,
+        *,
+        fit_min: float | None = 0.0,
+        fit_max: float | None = 1.0,
+    ) -> None:
         self._minimizer_uid = uid
         self.unique_name = uid
         self.name = uid.upper()
         self.value = value
         self.uncertainty = uncertainty
+        self.fit_min = fit_min
+        self.fit_max = fit_max
 
     def _set_value_from_minimizer(self, value: float) -> None:
         self.value = value
@@ -115,6 +125,46 @@ def test_sampler_settings_include_init_and_sample_count():
     assert settings['samples'] == 120
 
 
+@pytest.mark.parametrize(
+    ('fit_min', 'fit_max', 'value', 'message'),
+    [
+        (None, 1.0, 0.5, r'fit_min must be finite'),
+        (0.0, np.inf, 0.5, r'fit_max must be finite'),
+        (2.0, 1.0, 1.5, r'fit_min \(2\.0\) must be smaller than fit_max \(1\.0\)'),
+        (0.0, 1.0, 2.0, r'starting value 2\.0 is outside \[0\.0, 1\.0\]'),
+    ],
+)
+def test_prepare_solver_args_rejects_invalid_dream_bounds(
+    fit_min,
+    fit_max,
+    value,
+    message,
+):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameter = FakeParam('alpha', value, fit_min=fit_min, fit_max=fit_max)
+
+    with pytest.raises(ValueError, match=message):
+        minimizer._prepare_solver_args([parameter])
+
+
+def test_prepare_solver_args_lists_all_offending_dream_parameters():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [
+        FakeParam('alpha', 2.0, fit_min=0.0, fit_max=1.0),
+        FakeParam('beta', 0.5, fit_min=None, fit_max=1.0),
+    ]
+
+    with pytest.raises(
+        ValueError,
+        match=r'alpha: .*outside \[0\.0, 1\.0\][\s\S]*beta: .*fit_min',
+    ):
+        minimizer._prepare_solver_args(parameters)
+
+
 def test_sync_result_to_parameters_restores_starting_values_on_failure():
     from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
 
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index d0f23651..076db1f5 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -362,6 +362,78 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     }
 
 
+def test_build_param_distribution_plot_accepts_unique_name_string():
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    figure = plotter._build_param_distribution_plot('length_a')
+
+    assert figure.layout.title.text == 'Posterior distribution: length_a'
+
+
+def test_build_param_distribution_plot_accepts_user_facing_label_string():
+    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    fit_results.posterior_parameter_summaries[0].display_name = 'Cell a'
+
+    figure = plotter._build_param_distribution_plot('Cell a')
+
+    assert figure.layout.title.text == 'Posterior distribution: length_a'
+
+
+def test_resolve_posterior_parameter_names_warns_on_ambiguous_label(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.display.plotting import Plotter
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['phase_a.length_a', 'phase_b.length_a'],
+        parameter_samples=np.ones((2, 2, 2), dtype=float),
+    )
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='phase_a.length_a',
+                display_name='length_a',
+                map_value=1.0,
+                median=1.0,
+                standard_deviation=0.1,
+                interval_68=(0.9, 1.1),
+                interval_95=(0.8, 1.2),
+            ),
+            PosteriorParameterSummary(
+                unique_name='phase_b.length_a',
+                display_name='length_a',
+                map_value=2.0,
+                median=2.0,
+                standard_deviation=0.1,
+                interval_68=(1.9, 2.1),
+                interval_95=(1.8, 2.2),
+            ),
+        ],
+        parameters=[
+            SimpleNamespace(unique_name='phase_a.length_a', name='length_a'),
+            SimpleNamespace(unique_name='phase_b.length_a', name='length_a'),
+        ],
+    )
+    warning_messages: list[str] = []
+
+    monkeypatch.setattr(
+        'easydiffraction.display.plotting.log.warning',
+        lambda message: warning_messages.append(message),
+    )
+
+    result = Plotter._resolve_posterior_parameter_names(
+        fit_results=fit_results,
+        parameters=['length_a'],
+    )
+
+    assert result is None
+    assert warning_messages
+    assert 'ambiguous' in warning_messages[0]
+    assert 'phase_a.length_a' in warning_messages[0]
+    assert 'phase_b.length_a' in warning_messages[0]
+
+
 def test_build_posterior_predictive_summary_restores_parameter_state(monkeypatch):
     from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
     from easydiffraction.display.plotting import Plotter

From 83019002ca403daab7dea12dcb0e295c43eefe4a Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 00:30:04 +0200
Subject: [PATCH 056/106] Adopt fixed-aspect CSS for posterior pair plots

---
 .../display/plotters/plotly.py                | 156 ++++++---------
 src/easydiffraction/display/plotting.py       | 177 ++++++++++++------
 .../display/plotters/test_plotly.py           | 135 +------------
 .../easydiffraction/display/test_plotting.py  |  22 ++-
 4 files changed, 198 insertions(+), 292 deletions(-)

diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index c514b743..9186b166 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -70,7 +70,8 @@
 PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
 PREDICTIVE_DRAW_PLOT_CAP = 50
 PREDICTIVE_DRAW_ARRAY_NDIM = 2
-RESPONSIVE_PAIR_PLOT_META_KEY = 'responsive_pair_plot'
+FIXED_ASPECT_WRAPPER_META_KEY = 'fixed_aspect_wrapper'
+FIXED_ASPECT_WRAPPER_CLASS_NAME = 'ed-fixed-aspect-plotly-wrapper'
 
 
 @dataclass(frozen=True)
@@ -790,82 +791,22 @@ def _modebar_legend_toggle_post_script() -> str:
 window.requestAnimationFrame(installLegendToggleButton);
 """
 
+    @classmethod
+    def _html_post_script(cls, fig: object) -> str | None:
+        """Return concatenated HTML post scripts for a Plotly figure."""
+        scripts: list[str] = []
+        if cls._has_visible_legend(fig):
+            scripts.append(cls._modebar_legend_toggle_post_script())
+        if not scripts:
+            return None
+        return '\n'.join(cls._scoped_html_post_script(script) for script in scripts)
+
     @staticmethod
-    def _responsive_pair_plot_post_script() -> str:
+    def _scoped_html_post_script(script: str) -> str:
         """
-        Return client-side code for responsive posterior pair plots.
+        Return one HTML post script wrapped in its own block scope.
         """
-        return r"""
-const graphDiv = document.getElementById('{plot_id}');
-if (!graphDiv || !window.Plotly || !graphDiv.layout || !graphDiv.layout.meta) {
-    return;
-}
-
-const responsivePairPlot = graphDiv.layout.meta.responsive_pair_plot;
-if (!responsivePairPlot) {
-    return;
-}
-
-const readPositiveNumber = function (value, fallback) {
-    const numberValue = Number(value);
-    return Number.isFinite(numberValue) && numberValue > 0 ? numberValue : fallback;
-};
-
-const nParameters = Math.max(
-    1,
-    Math.round(readPositiveNumber(responsivePairPlot.n_parameters, 1))
-);
-const marginPx = readPositiveNumber(responsivePairPlot.margin_px, 0);
-const minCellSizePx = readPositiveNumber(responsivePairPlot.min_cell_size_px, 90);
-const maxCellSizePx = readPositiveNumber(responsivePairPlot.max_cell_size_px, 190);
-
-let lastHeight = null;
-let resizeFrame = null;
-
-const applyResponsivePairPlotSize = function () {
-    const containerWidth = graphDiv.clientWidth || graphDiv.getBoundingClientRect().width;
-    if (!Number.isFinite(containerWidth) || containerWidth <= 0) {
-        return;
-    }
-
-    const plotWidth = Math.max(minCellSizePx, containerWidth - marginPx);
-    const cellSizePx = Math.min(maxCellSizePx, Math.max(minCellSizePx, plotWidth / nParameters));
-    const nextHeight = Math.round(cellSizePx * nParameters + marginPx);
-
-    if (lastHeight === nextHeight) {
-        window.Plotly.Plots.resize(graphDiv);
-        return;
-    }
-
-    lastHeight = nextHeight;
-    window.Plotly.relayout(graphDiv, {autosize: true, height: nextHeight});
-};
-
-const scheduleResponsivePairPlotSize = function () {
-    if (resizeFrame !== null) {
-        return;
-    }
-
-    resizeFrame = window.requestAnimationFrame(function () {
-        resizeFrame = null;
-        applyResponsivePairPlotSize();
-    });
-};
-
-if (typeof ResizeObserver === 'function') {
-    const resizeObserver = new ResizeObserver(scheduleResponsivePairPlotSize);
-    resizeObserver.observe(graphDiv);
-}
-else {
-    window.addEventListener('resize', scheduleResponsivePairPlotSize);
-}
-
-if (graphDiv.on) {
-    graphDiv.on('plotly_afterplot', scheduleResponsivePairPlotSize);
-}
-
-scheduleResponsivePairPlotSize();
-"""
+        return '{\n' + script.strip() + '\n}'
 
     @staticmethod
     def _figure_meta(fig: object) -> dict[str, object] | None:
@@ -886,37 +827,53 @@ def _figure_meta(fig: object) -> dict[str, object] | None:
         return None
 
     @classmethod
-    def _has_responsive_pair_plot(cls, fig: object) -> bool:
-        """
-        Return whether a figure requests responsive pair-plot sizing.
-        """
+    def _fixed_aspect_wrapper_aspect_ratio(cls, fig: object) -> str | None:
+        """Return the fixed aspect ratio requested for inline HTML."""
         meta = cls._figure_meta(fig)
         if not isinstance(meta, dict):
-            return False
+            return None
 
-        responsive_pair_plot = meta.get(RESPONSIVE_PAIR_PLOT_META_KEY)
-        if not isinstance(responsive_pair_plot, dict):
-            return False
-        return bool(responsive_pair_plot.get('n_parameters'))
+        wrapper = meta.get(FIXED_ASPECT_WRAPPER_META_KEY)
+        if not isinstance(wrapper, dict):
+            return None
 
-    @classmethod
-    def _html_post_script(cls, fig: object) -> str | None:
-        """Return concatenated HTML post scripts for a Plotly figure."""
-        scripts: list[str] = []
-        if cls._has_visible_legend(fig):
-            scripts.append(cls._modebar_legend_toggle_post_script())
-        if cls._has_responsive_pair_plot(fig):
-            scripts.append(cls._responsive_pair_plot_post_script())
-        if not scripts:
+        aspect_ratio = wrapper.get('aspect_ratio')
+        if not isinstance(aspect_ratio, str):
             return None
-        return '\n'.join(cls._scoped_html_post_script(script) for script in scripts)
 
-    @staticmethod
-    def _scoped_html_post_script(script: str) -> str:
-        """
-        Return one HTML post script wrapped in its own block scope.
-        """
-        return '{\n' + script.strip() + '\n}'
+        aspect_ratio = aspect_ratio.strip()
+        if not aspect_ratio:
+            return None
+        return aspect_ratio
+
+    @classmethod
+    def _wrap_html_figure(cls, fig: object, html_fig: str) -> str:
+        """Wrap inline Plotly HTML in a fixed-aspect container."""
+        aspect_ratio = cls._fixed_aspect_wrapper_aspect_ratio(fig)
+        if aspect_ratio is None:
+            return html_fig
+
+        return (
+            '<style>\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} {{\n'
+            '    width: 100%;\n'
+            f'    aspect-ratio: {aspect_ratio};\n'
+            '}\n\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} > div,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .plotly-graph-div,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .js-plotly-plot,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .plotly,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .plot-container,\n'
+            f'.{FIXED_ASPECT_WRAPPER_CLASS_NAME} .svg-container {{\n'
+            '    display: block;\n'
+            '    width: 100% !important;\n'
+            '    height: 100% !important;\n'
+            '}\n'
+            '</style>\n\n'
+            f'<div class="{FIXED_ASPECT_WRAPPER_CLASS_NAME}">\n'
+            f'{html_fig}\n'
+            '</div>'
+        )
 
     @staticmethod
     def _get_figure(
@@ -1003,6 +960,7 @@ def _show_figure(
                 config=config,
                 post_script=post_script,
             )
+            html_fig = self._wrap_html_figure(fig, html_fig)
             display(HTML(html_fig))
 
     @classmethod
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 8d8a99bd..a26d97a4 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -107,11 +107,20 @@ def description(self) -> str:
 PAIR_PLOT_MIN_SIZE_PIXELS = 680
 PAIR_PLOT_MARGIN_PIXELS = 120
 PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS = 980
-PAIR_PLOT_SUBPLOT_SPACING = 0.015
-PAIR_PLOT_MAJOR_TICKS = 3
+PAIR_PLOT_SUBPLOT_SPACING = 0.01
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
 POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = 14
-POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 56
+POSTERIOR_PAIR_TITLE_FONT_SIZE = 16
+POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 16
+POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS = 10
+POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS = 2
+POSTERIOR_PAIR_GUIDE_LINE_COLOR = 'rgba(125, 140, 173, 0.18)'
+POSTERIOR_PAIR_FIXED_ASPECT_RATIO = '1 / 1'
+POSTERIOR_PAIR_FIXED_ASPECT_META_KEY = 'fixed_aspect_wrapper'
+POSTERIOR_PAIR_LEFT_MARGIN_PIXELS = 58
+POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS = 10
+POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 26
+POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 42
 
 
 @dataclass(frozen=True)
@@ -1084,7 +1093,6 @@ def _build_posterior_pairs_plot(
 
         self._finalize_posterior_pairs_figure(
             fig=fig,
-            context=context,
             subplot_title_annotations=subplot_title_annotations,
             subplot_border_shapes=subplot_border_shapes,
         )
@@ -1343,12 +1351,14 @@ def _configure_posterior_pair_panel_axes(
             mirror=True,
             range=list(context.axis_ranges[col_index]),
             zeroline=False,
+            showgrid=False,
             layer='above traces',
             linecolor=context.axis_frame_color,
             linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
-            nticks=PAIR_PLOT_MAJOR_TICKS,
-            tickformat=',.6~g',
-            separatethousands=True,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
             row=row,
             col=col,
         )
@@ -1356,32 +1366,26 @@ def _configure_posterior_pair_panel_axes(
             showline=True,
             mirror=True,
             zeroline=False,
+            showgrid=False,
             layer='above traces',
             linecolor=context.axis_frame_color,
             linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH,
-            nticks=PAIR_PLOT_MAJOR_TICKS,
-            tickformat=',.6~g',
-            separatethousands=True,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
             row=row,
             col=col,
         )
         if not is_diagonal:
             fig.update_yaxes(range=list(context.axis_ranges[row_index]), row=row, col=col)
-        fig.update_xaxes(showticklabels=(row_index == context.n_parameters - 1), row=row, col=col)
         if is_diagonal:
             fig.update_yaxes(
-                showticklabels=False,
-                ticks='',
-                ticklen=0,
-                showgrid=False,
                 title_text=None,
                 row=row,
                 col=col,
             )
-        else:
-            fig.update_yaxes(showticklabels=(col_index == 0), row=row, col=col)
-        if row_index == context.n_parameters - 1:
-            fig.update_xaxes(title_text=context.labels[col_index], row=row, col=col)
+        fig.update_xaxes(title_text=None, row=row, col=col)
 
     @staticmethod
     def _collect_posterior_pair_panel_decorations(
@@ -1397,6 +1401,8 @@ def _collect_posterior_pair_panel_decorations(
         row = row_index + 1
         col = col_index + 1
         subplot = fig.get_subplot(row, col)
+        x_mid = 0.5 * (subplot.xaxis.domain[0] + subplot.xaxis.domain[1])
+        y_mid = 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1])
         if col_index == 0:
             subplot_title_annotations.append({
                 'x': subplot.xaxis.domain[0],
@@ -1411,65 +1417,126 @@ def _collect_posterior_pair_panel_decorations(
                 'textangle': -90,
                 'showarrow': False,
             })
-        subplot_border_shapes.append({
-            'type': 'rect',
+        if row_index == context.n_parameters - 1:
+            subplot_title_annotations.append({
+                'x': x_mid,
+                'xref': 'paper',
+                'xanchor': 'center',
+                'y': subplot.yaxis.domain[0],
+                'yref': 'paper',
+                'yanchor': 'top',
+                'yshift': -POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS,
+                'text': context.labels[col_index],
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'showarrow': False,
+            })
+        subplot_border_shapes.extend([
+            {
+                'type': 'line',
+                'xref': 'paper',
+                'yref': 'paper',
+                'x0': x_mid,
+                'x1': x_mid,
+                'y0': subplot.yaxis.domain[0],
+                'y1': subplot.yaxis.domain[1],
+                'line': {
+                    'color': POSTERIOR_PAIR_GUIDE_LINE_COLOR,
+                    'width': 1,
+                },
+                'layer': 'above',
+            },
+            {
+                'type': 'line',
+                'xref': 'paper',
+                'yref': 'paper',
+                'x0': subplot.xaxis.domain[0],
+                'x1': subplot.xaxis.domain[1],
+                'y0': y_mid,
+                'y1': y_mid,
+                'line': {
+                    'color': POSTERIOR_PAIR_GUIDE_LINE_COLOR,
+                    'width': 1,
+                },
+                'layer': 'above',
+            },
+            {
+                'type': 'rect',
+                'xref': 'paper',
+                'yref': 'paper',
+                'x0': subplot.xaxis.domain[0],
+                'x1': subplot.xaxis.domain[1],
+                'y0': subplot.yaxis.domain[0],
+                'y1': subplot.yaxis.domain[1],
+                'line': {
+                    'color': context.axis_frame_color,
+                    'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+                },
+                'fillcolor': 'rgba(0, 0, 0, 0)',
+                'layer': 'above',
+            },
+        ])
+
+    @staticmethod
+    def _posterior_pair_title_annotation() -> dict[str, object]:
+        """Return the outer title annotation for the pair plot."""
+        return {
+            'x': 0.0,
             'xref': 'paper',
+            'xanchor': 'left',
+            'y': 1.0,
             'yref': 'paper',
-            'x0': subplot.xaxis.domain[0],
-            'x1': subplot.xaxis.domain[1],
-            'y0': subplot.yaxis.domain[0],
-            'y1': subplot.yaxis.domain[1],
-            'line': {
-                'color': context.axis_frame_color,
-                'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
-            },
-            'fillcolor': 'rgba(0, 0, 0, 0)',
-            'layer': 'above',
-        })
+            'yanchor': 'bottom',
+            'yshift': POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS,
+            'text': 'Posterior pair plot',
+            'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+            'showarrow': False,
+        }
+
+    @staticmethod
+    def _posterior_pair_layout_meta() -> dict[str, object]:
+        """Return layout metadata used by the Plotly HTML wrapper."""
+        return {
+            POSTERIOR_PAIR_FIXED_ASPECT_META_KEY: {
+                'aspect_ratio': POSTERIOR_PAIR_FIXED_ASPECT_RATIO,
+            }
+        }
 
     def _finalize_posterior_pairs_figure(
         self,
         *,
         fig: object,
-        context: _PosteriorPairsContext,
         subplot_title_annotations: list[dict[str, object]],
         subplot_border_shapes: list[dict[str, object]],
     ) -> None:
         """Apply final layout settings to the posterior pair plot."""
-        figure_height = self._posterior_pair_figure_height_pixels(context.n_parameters)
         fig.update_layout(
             autosize=True,
-            margin={'autoexpand': True, 'r': 30, 't': 40, 'b': 45},
-            title={'text': 'Posterior pair plot'},
+            margin={
+                'autoexpand': False,
+                'l': POSTERIOR_PAIR_LEFT_MARGIN_PIXELS,
+                'r': POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS,
+                't': POSTERIOR_PAIR_TOP_MARGIN_PIXELS,
+                'b': POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS,
+            },
             bargap=0.05,
-            height=figure_height,
-            annotations=subplot_title_annotations,
+            annotations=[
+                self._posterior_pair_title_annotation(),
+                *subplot_title_annotations,
+            ],
             shapes=subplot_border_shapes,
-            meta=self._posterior_pair_layout_meta(context.n_parameters),
+            meta=self._posterior_pair_layout_meta(),
             legend={
                 'bgcolor': 'rgba(0, 0, 0, 0)',
                 'xanchor': 'right',
-                'x': 1.0,
+                'x': 0.995,
                 'yanchor': 'top',
-                'y': 1.0,
+                'y': 0.995,
                 'groupclick': 'togglegroup',
             },
+            paper_bgcolor='white',
+            plot_bgcolor='white',
         )
 
-    @staticmethod
-    def _posterior_pair_layout_meta(n_parameters: int) -> dict[str, object]:
-        """
-        Return responsive layout metadata for posterior pair plots.
-        """
-        return {
-            'responsive_pair_plot': {
-                'n_parameters': int(n_parameters),
-                'margin_px': PAIR_PLOT_MARGIN_PIXELS,
-                'min_cell_size_px': PAIR_PLOT_MIN_CELL_SIZE_PIXELS,
-                'max_cell_size_px': PAIR_PLOT_CELL_SIZE_PIXELS,
-            }
-        }
-
     @staticmethod
     def _posterior_pair_cell_size_pixels(
         n_parameters: int,
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index cdd847db..54c2d5d6 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -13,62 +13,9 @@ def test_module_import():
     assert expected_module_name == actual_module_name
 
 
-def test_default_template_name_prefers_jupyter_theme(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp, 'in_jupyter', lambda: True)
-    monkeypatch.setattr(pp, 'is_dark', lambda: True)
-    monkeypatch.setattr(pp.darkdetect, 'isDark', lambda: False)
-
-    assert pp.PlotlyPlotter._default_template_name() == 'plotly_dark'
-
-
-def test_correlation_colorscale_uses_black_center_in_dark_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: True))
-
-    assert pp.PlotlyPlotter._correlation_colorscale()[1] == (0.5, '#000000')
-
-
-def test_default_template_name_uses_system_theme_outside_jupyter(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp, 'in_jupyter', lambda: False)
-    monkeypatch.setattr(pp, 'is_dark', lambda: False)
-    monkeypatch.setattr(pp.darkdetect, 'isDark', lambda: False)
-
-    assert pp.PlotlyPlotter._default_template_name() == 'plotly_white'
-
-
-def test_correlation_colorscale_uses_white_center_in_light_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: False))
-
-    assert pp.PlotlyPlotter._correlation_colorscale()[1] == (0.5, '#f7f7f7')
-
-
-def test_legend_background_color_uses_light_overlay_in_light_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: False))
-
-    assert pp.PlotlyPlotter._legend_background_color() == 'rgba(255, 255, 255, 0.5)'
-
-
-def test_legend_background_color_uses_dark_overlay_in_dark_mode(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp.PlotlyPlotter, '_is_dark_mode', staticmethod(lambda: True))
-
-    assert pp.PlotlyPlotter._legend_background_color() == 'rgba(0, 0, 0, 0.5)'
-
-
 def test_get_trace_and_plot(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
-    # Arrange: force non-PyCharm branch and stub fig.show/HTML/display so nothing opens
     monkeypatch.setattr(pp, 'in_pycharm', lambda: False)
 
     shown = {'count': 0}
@@ -83,7 +30,6 @@ def update_yaxes(self, **kwargs):
         def show(self, **kwargs):
             shown['count'] += 1
 
-    # Patch go.Scatter and go.Figure to minimal dummies
     class DummyScatter:
         def __init__(self, **kwargs):
             self.kwargs = kwargs
@@ -122,7 +68,6 @@ def __init__(self, html):
 
     plotter = pp.PlotlyPlotter()
 
-    # Exercise _get_powder_trace
     x = [0, 1, 2]
     y = [1, 2, 3]
     trace = plotter._get_powder_trace(x, y, label='calc')
@@ -278,7 +223,7 @@ def __init__(self, html):
     assert captured['displayed_html'] == '<div>plot</div>'
 
 
-def test_show_figure_adds_responsive_pair_plot_script(monkeypatch):
+def test_show_figure_wraps_fixed_aspect_html(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
     monkeypatch.setattr(pp, 'in_pycharm', lambda: False)
@@ -288,11 +233,8 @@ def test_show_figure_adds_responsive_pair_plot_script(monkeypatch):
     class DummyLayout:
         def __init__(self):
             self.meta = {
-                'responsive_pair_plot': {
-                    'n_parameters': 4,
-                    'margin_px': 120,
-                    'min_cell_size_px': 90,
-                    'max_cell_size_px': 190,
+                'fixed_aspect_wrapper': {
+                    'aspect_ratio': '1 / 1',
                 }
             }
             self.showlegend = False
@@ -326,73 +268,10 @@ def __init__(self, html):
     plotter._show_figure(DummyFig())
 
     assert captured.get('show_called') is not True
-    assert 'responsive_pair_plot' in captured['post_script']
-    assert 'ResizeObserver' in captured['post_script']
-    assert 'window.Plotly.relayout' in captured['post_script']
-    assert 'containerWidth' in captured['post_script']
-    assert captured['displayed_html'] == '<div>plot</div>'
-
-
-def test_show_figure_scopes_multiple_post_scripts(monkeypatch):
-    import easydiffraction.display.plotters.plotly as pp
-
-    monkeypatch.setattr(pp, 'in_pycharm', lambda: False)
-
-    captured = {}
-
-    class DummyTrace:
-        def __init__(self):
-            self.name = 'Posterior samples'
-            self.showlegend = True
-            self.visible = True
-
-    class DummyLayout:
-        def __init__(self):
-            self.meta = {
-                'responsive_pair_plot': {
-                    'n_parameters': 4,
-                    'margin_px': 120,
-                    'min_cell_size_px': 90,
-                    'max_cell_size_px': 190,
-                }
-            }
-            self.showlegend = True
-
-    class DummyFig:
-        def __init__(self):
-            self.data = [DummyTrace()]
-            self.layout = DummyLayout()
-
-        def show(self, **kwargs):
-            captured['show_called'] = True
-
-    class DummyPIO:
-        @staticmethod
-        def to_html(fig, include_plotlyjs=None, full_html=None, config=None, post_script=None):
-            captured['post_script'] = post_script
-            return '<div>plot</div>'
-
-    def dummy_display(obj):
-        captured['displayed_html'] = obj.html
-
-    class DummyHTML:
-        def __init__(self, html):
-            self.html = html
-
-    monkeypatch.setattr(pp, 'pio', DummyPIO)
-    monkeypatch.setattr(pp, 'display', dummy_display)
-    monkeypatch.setattr(pp, 'HTML', DummyHTML)
-
-    plotter = pp.PlotlyPlotter()
-    plotter._show_figure(DummyFig())
-
-    assert captured.get('show_called') is not True
-    assert (
-        captured['post_script'].count("const graphDiv = document.getElementById('{plot_id}');")
-        == 2
-    )
-    assert '\n}\n{\n' in captured['post_script']
-    assert captured['displayed_html'] == '<div>plot</div>'
+    assert captured['post_script'] is None
+    assert 'aspect-ratio: 1 / 1;' in captured['displayed_html']
+    assert 'ed-fixed-aspect-plotly-wrapper' in captured['displayed_html']
+    assert '<div>plot</div>' in captured['displayed_html']
 
 
 def test_plotly_single_crystal_trace_and_plot(monkeypatch):
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 076db1f5..a049fdaa 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -307,33 +307,35 @@ def test_correlation_from_posterior_samples_returns_labeled_dataframe():
 
 
 def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
-    from easydiffraction.display.plotting import PAIR_PLOT_CELL_SIZE_PIXELS
-    from easydiffraction.display.plotting import PAIR_PLOT_MARGIN_PIXELS
-
     plotter, _, _ = _make_bayesian_plotter_fixture()
 
     figure = plotter._build_posterior_pairs_plot(parameters=None)
 
-    assert figure.layout.title.text == 'Posterior pair plot'
+    assert figure.layout.title.text is None
     assert figure.layout.autosize is True
     assert figure.layout.width is None
-    assert figure.layout.meta['responsive_pair_plot']['n_parameters'] == 4
-    assert (
-        figure.layout.meta['responsive_pair_plot']['max_cell_size_px']
-        == PAIR_PLOT_CELL_SIZE_PIXELS
-    )
-    assert figure.layout.meta['responsive_pair_plot']['margin_px'] == PAIR_PLOT_MARGIN_PIXELS
+    assert figure.layout.height is None
+    assert figure.layout.meta['fixed_aspect_wrapper']['aspect_ratio'] == '1 / 1'
     assert [annotation.text for annotation in figure.layout.annotations] == [
+        'Posterior pair plot',
+        'length_a',
+        'broad_gauss_u',
+        'broad_gauss_v',
+        'twotheta_offset',
         'length_a',
         'broad_gauss_u',
         'broad_gauss_v',
         'twotheta_offset',
     ]
     subplot = figure.get_subplot(1, 1)
+    bottom_subplot = figure.get_subplot(4, 1)
     assert subplot.yaxis.showticklabels is False
     assert subplot.yaxis.ticks == ''
     assert subplot.yaxis.ticklen == 0
     assert subplot.yaxis.title.text is None
+    assert bottom_subplot.xaxis.showticklabels is False
+    assert bottom_subplot.xaxis.title.text is None
+    assert len(figure.layout.shapes) == 30
 
 
 def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():

From 1282c95ff835a57e872506082410bf3b81151474 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 12:34:15 +0200
Subject: [PATCH 057/106] Add posterior pair plot rendering modes

---
 src/easydiffraction/display/plotting.py       | 143 ++++++++++++++++--
 .../easydiffraction/display/test_plotting.py  |  75 +++++++++
 2 files changed, 205 insertions(+), 13 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index a26d97a4..2f199574 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -64,6 +64,14 @@ def description(self) -> str:
         return ''
 
 
+class PosteriorPairPlotStyleEnum(StrEnum):
+    """Available posterior pair-plot rendering modes."""
+
+    AUTO = 'auto'
+    FAST = 'fast'
+    FULL = 'full'
+
+
 DEFAULT_CORRELATION_THRESHOLD = 0.7
 EXPECTED_COVAR_NDIM = 2
 DEFAULT_RESIDUAL_HEIGHT_FRACTION = 0.25
@@ -102,6 +110,14 @@ def description(self) -> str:
     [0.82, 'rgba(58, 86, 224, 0.98)'],
     [1.0, 'rgba(58, 86, 224, 0.98)'],
 ]
+POSTERIOR_PAIR_SCATTER_MAX_POINTS = 1500
+POSTERIOR_PAIR_MAX_DENSITY_SAMPLES = 4000
+POSTERIOR_PAIR_MIN_DENSITY_SAMPLES = 800
+POSTERIOR_PAIR_TARGET_DENSITY_SAMPLE_BUDGET = 24000
+POSTERIOR_PAIR_MAX_CONTOUR_GRID_SIZE = 96
+POSTERIOR_PAIR_MIN_CONTOUR_GRID_SIZE = 56
+POSTERIOR_PAIR_TARGET_CONTOUR_GRID_POINT_BUDGET = 73728
+POSTERIOR_PAIR_AUTO_MAX_CONTOUR_PARAMETERS = 6
 PAIR_PLOT_CELL_SIZE_PIXELS = 190
 PAIR_PLOT_MIN_CELL_SIZE_PIXELS = 90
 PAIR_PLOT_MIN_SIZE_PIXELS = 680
@@ -163,6 +179,8 @@ class _PosteriorPairsContext:
     labels: list[str]
     density_samples: np.ndarray
     scatter_samples: np.ndarray
+    show_contours: bool
+    contour_grid_size: int
     axis_frame_color: str
     axis_ranges: list[tuple[float, float]]
 
@@ -725,6 +743,7 @@ def plot_param_correlations(
     def plot_posterior_pairs(
         self,
         parameters: list[object] | None = None,
+        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
     ) -> None:
         """
         Plot posterior pair relationships for sampled parameters.
@@ -734,8 +753,12 @@ def plot_posterior_pairs(
         parameters : list[object] | None, default=None
             Optional subset of sampled parameters to include. When
             ``None``, all sampled parameters are shown.
+        style : PosteriorPairPlotStyleEnum | str, default='auto'
+            ``'auto'`` keeps contours for compact plots and disables
+            them for wide grids. ``'fast'`` always skips contours.
+            ``'full'`` always renders contours.
         """
-        plot = self._build_posterior_pairs_plot(parameters=parameters)
+        plot = self._build_posterior_pairs_plot(parameters=parameters, style=style)
         if plot is None:
             return
         self._show_plot_figure(plot)
@@ -1048,6 +1071,7 @@ def _build_posterior_pairs_plot(
         self,
         *,
         parameters: list[object] | None,
+        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
     ) -> object | None:
         """
         Build a Plotly posterior pair plot.
@@ -1056,6 +1080,8 @@ def _build_posterior_pairs_plot(
         ----------
         parameters : list[object] | None
             Optional subset of sampled parameters to include.
+        style : PosteriorPairPlotStyleEnum | str, default='auto'
+            Posterior pair-plot rendering mode.
 
         Returns
         -------
@@ -1063,7 +1089,7 @@ def _build_posterior_pairs_plot(
             Plotly figure, or ``None`` when posterior plotting is
             unavailable.
         """
-        context = self._posterior_pairs_context(parameters)
+        context = self._posterior_pairs_context(parameters, style=style)
         if context is None:
             return None
 
@@ -1101,12 +1127,16 @@ def _build_posterior_pairs_plot(
     def _posterior_pairs_context(
         self,
         parameters: list[object] | None,
+        *,
+        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
     ) -> _PosteriorPairsContext | None:
         """Return the resolved inputs for a posterior pair plot."""
         posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
         if posterior_samples is None or fit_results is None:
             return None
 
+        plot_style = self._validated_posterior_pair_plot_style(style)
+
         parameter_names = self._resolve_posterior_parameter_names(
             fit_results=fit_results,
             parameters=parameters,
@@ -1117,21 +1147,44 @@ def _posterior_pairs_context(
             log.warning('Posterior pair plots require at least two sampled parameters.')
             return None
 
-        density_samples = self._selected_posterior_samples(posterior_samples, parameter_names)
-        if density_samples is None:
+        selected_samples = self._selected_posterior_samples(posterior_samples, parameter_names)
+        if selected_samples is None:
             return None
 
+        n_parameters = len(parameter_names)
+        show_contours = self._posterior_pair_show_contours(
+            n_parameters=n_parameters,
+            style=plot_style,
+        )
+        if plot_style is PosteriorPairPlotStyleEnum.AUTO and not show_contours:
+            log.warning(
+                'Posterior pair plot auto mode disabled contours for '
+                f'{n_parameters} parameters. Use style="full" to force '
+                'contours.'
+            )
+
+        density_samples = self._thin_posterior_samples(
+            selected_samples,
+            max_points=self._posterior_pair_density_max_points(n_parameters),
+        )
+        scatter_samples = self._thin_posterior_samples(
+            selected_samples,
+            max_points=POSTERIOR_PAIR_SCATTER_MAX_POINTS,
+        )
+
         return _PosteriorPairsContext(
             fit_results=fit_results,
             parameter_names=parameter_names,
             labels=self._posterior_plot_labels(fit_results, parameter_names),
             density_samples=density_samples,
-            scatter_samples=self._thin_posterior_samples(density_samples, max_points=1500),
+            scatter_samples=scatter_samples,
+            show_contours=show_contours,
+            contour_grid_size=self._posterior_pair_contour_grid_size(n_parameters),
             axis_frame_color=self._plot_axis_frame_color(),
             axis_ranges=self._posterior_pair_axis_ranges(
                 fit_results=fit_results,
                 parameter_names=parameter_names,
-                density_samples=density_samples,
+                density_samples=selected_samples,
             ),
         )
 
@@ -1284,13 +1337,16 @@ def _add_posterior_pair_off_diagonal(
         y_density_values = context.density_samples[:, row_index]
         x_scatter_values = context.scatter_samples[:, col_index]
         y_scatter_values = context.scatter_samples[:, row_index]
-        contour_traces = self._posterior_contour_traces(
-            fit_results=context.fit_results,
-            x_parameter_name=context.parameter_names[col_index],
-            y_parameter_name=context.parameter_names[row_index],
-            x_values=x_density_values,
-            y_values=y_density_values,
-        )
+        contour_traces = None
+        if context.show_contours:
+            contour_traces = self._posterior_contour_traces(
+                fit_results=context.fit_results,
+                x_parameter_name=context.parameter_names[col_index],
+                y_parameter_name=context.parameter_names[row_index],
+                x_values=x_density_values,
+                y_values=y_density_values,
+                grid_size=context.contour_grid_size,
+            )
         sample_hovertemplate = (
             f'{context.labels[col_index]}: %{{x:.4f}}<br>'
             f'{context.labels[row_index]}: %{{y:.4f}}<extra></extra>'
@@ -1573,6 +1629,65 @@ def _posterior_pair_figure_height_pixels(cls, n_parameters: int) -> int:
             cell_size * n_parameters + PAIR_PLOT_MARGIN_PIXELS,
         )
 
+    @staticmethod
+    def _posterior_pair_contour_panel_count(n_parameters: int) -> int:
+        """Return the number of lower-triangle contour panels."""
+        if n_parameters < MIN_POSTERIOR_PARAMETER_COUNT:
+            return 1
+        return n_parameters * (n_parameters - 1) // 2
+
+    @classmethod
+    def _posterior_pair_density_max_points(cls, n_parameters: int) -> int:
+        """Return a KDE sample cap for interactive pair plots."""
+        panel_count = cls._posterior_pair_contour_panel_count(n_parameters)
+        estimated_limit = round(
+            POSTERIOR_PAIR_TARGET_DENSITY_SAMPLE_BUDGET / panel_count,
+        )
+        return max(
+            POSTERIOR_PAIR_MIN_DENSITY_SAMPLES,
+            min(POSTERIOR_PAIR_MAX_DENSITY_SAMPLES, estimated_limit),
+        )
+
+    @classmethod
+    def _posterior_pair_contour_grid_size(cls, n_parameters: int) -> int:
+        """Return contour grid size for current pair-plot width."""
+        panel_count = cls._posterior_pair_contour_panel_count(n_parameters)
+        estimated_grid_size = round(
+            np.sqrt(POSTERIOR_PAIR_TARGET_CONTOUR_GRID_POINT_BUDGET / panel_count),
+        )
+        return max(
+            POSTERIOR_PAIR_MIN_CONTOUR_GRID_SIZE,
+            min(POSTERIOR_PAIR_MAX_CONTOUR_GRID_SIZE, estimated_grid_size),
+        )
+
+    @staticmethod
+    def _validated_posterior_pair_plot_style(
+        style: PosteriorPairPlotStyleEnum | str,
+    ) -> PosteriorPairPlotStyleEnum:
+        """Return a validated posterior pair-plot rendering mode."""
+        try:
+            return PosteriorPairPlotStyleEnum(style)
+        except ValueError as exc:
+            supported_styles = ', '.join(item.value for item in PosteriorPairPlotStyleEnum)
+            msg = (
+                'style must be one of '
+                f'{supported_styles} for posterior pair plots.'
+            )
+            raise ValueError(msg) from exc
+
+    @staticmethod
+    def _posterior_pair_show_contours(
+        *,
+        n_parameters: int,
+        style: PosteriorPairPlotStyleEnum,
+    ) -> bool:
+        """Return whether contours should be rendered."""
+        if style is PosteriorPairPlotStyleEnum.FULL:
+            return True
+        if style is PosteriorPairPlotStyleEnum.FAST:
+            return False
+        return n_parameters <= POSTERIOR_PAIR_AUTO_MAX_CONTOUR_PARAMETERS
+
     def _plot_axis_frame_color(self) -> str:
         """
         Return the shared axis-frame color for Plotly-backed plots.
@@ -1590,6 +1705,7 @@ def _posterior_contour_traces(
         y_parameter_name: str,
         x_values: np.ndarray,
         y_values: np.ndarray,
+        grid_size: int,
     ) -> tuple[object, object] | None:
         """
         Return filled and line contour traces for posterior pair plots.
@@ -1608,6 +1724,7 @@ def _posterior_contour_traces(
             y_values=y_values,
             x_bounds=bounds[0],
             y_bounds=bounds[1],
+            grid_size=grid_size,
         )
         if density_surface is None:
             return None
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index a049fdaa..1840388f 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -336,6 +336,15 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
     assert bottom_subplot.xaxis.showticklabels is False
     assert bottom_subplot.xaxis.title.text is None
     assert len(figure.layout.shapes) == 30
+    assert any(trace.name == 'Posterior contours' for trace in figure.data)
+
+
+def test_build_posterior_pairs_plot_fast_mode_skips_contours():
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None, style='fast')
+
+    assert all(trace.name != 'Posterior contours' for trace in figure.data)
 
 
 def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():
@@ -347,6 +356,72 @@ def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():
     assert cell_size < PAIR_PLOT_CELL_SIZE_PIXELS
 
 
+def test_posterior_pair_density_budget_scales_with_parameter_count():
+    from easydiffraction.display.plotting import Plotter
+
+    assert Plotter._posterior_pair_density_max_points(8) < Plotter._posterior_pair_density_max_points(4)
+    assert Plotter._posterior_pair_contour_grid_size(8) < Plotter._posterior_pair_contour_grid_size(4)
+
+
+def test_posterior_pairs_context_thins_kde_samples_and_preserves_axis_ranges():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_SCATTER_MAX_POINTS
+    from easydiffraction.display.plotting import Plotter
+
+    parameter_count = 8
+    sample_count = 5000
+    parameter_names = [f'param_{index}' for index in range(parameter_count)]
+    samples = np.zeros((1, sample_count, parameter_count), dtype=float)
+    samples[0, 1, 0] = 100.0
+    samples[0, 2, 1] = -50.0
+    for index in range(2, parameter_count):
+        samples[0, :, index] = np.linspace(index, index + 1, sample_count, dtype=float)
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=parameter_names,
+        parameter_samples=samples,
+        log_posterior=np.zeros((1, sample_count), dtype=float),
+    )
+    parameters = [
+        SimpleNamespace(unique_name=name, name=name, fit_min=None, fit_max=None)
+        for name in parameter_names
+    ]
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        posterior_parameter_summaries=[],
+        posterior_predictive={},
+        parameters=parameters,
+    )
+    plotter = Plotter()
+    plotter._get_posterior_samples_and_fit_results = MethodType(
+        lambda self: (posterior_samples, fit_results),
+        plotter,
+    )
+
+    context = plotter._posterior_pairs_context(parameters=None)
+
+    assert context is not None
+    assert context.density_samples.shape == (
+        Plotter._posterior_pair_density_max_points(parameter_count),
+        parameter_count,
+    )
+    assert context.scatter_samples.shape == (POSTERIOR_PAIR_SCATTER_MAX_POINTS, parameter_count)
+    assert context.show_contours is False
+    assert context.contour_grid_size == Plotter._posterior_pair_contour_grid_size(parameter_count)
+    assert context.axis_ranges[0][1] > 100.0
+    assert context.axis_ranges[1][0] < -50.0
+
+
+def test_build_posterior_pairs_plot_rejects_unknown_style():
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    with pytest.raises(
+        ValueError,
+        match=r'style must be one of auto, fast, full for posterior pair plots\.',
+    ):
+        plotter._build_posterior_pairs_plot(parameters=None, style='slow')
+
+
 def test_build_param_distribution_plot_returns_plotly_figure():
     plotter, fit_results, _ = _make_bayesian_plotter_fixture()
     parameter = fit_results.parameters[0]

From 65c4a28e4500be0c48f086061d76d39adfda7c49 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 16:04:15 +0200
Subject: [PATCH 058/106] Add multiline axis titles to posterior pair plots

---
 src/easydiffraction/analysis/analysis.py      |  4 +-
 src/easydiffraction/display/plotting.py       | 72 ++++++++++++++++---
 src/easydiffraction/project/project.py        |  5 ++
 .../easydiffraction/display/test_plotting.py  | 26 +++++++
 .../easydiffraction/project/test_project.py   | 14 ++++
 5 files changed, 108 insertions(+), 13 deletions(-)

diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index dd4de8c0..d389fd27 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -184,9 +184,7 @@ def free_params(self) -> None:
         """Print only currently free (varying) parameters."""
         project = self._analysis.project
         self._flush_structure_categories()
-        structures_params = project.structures.free_parameters
-        experiments_params = project.experiments.free_parameters
-        free_params = structures_params + experiments_params
+        free_params = project.free_parameters
 
         if not free_params:
             log.warning('No free parameters found.')
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 2f199574..8f7ea759 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -125,7 +125,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS = 980
 PAIR_PLOT_SUBPLOT_SPACING = 0.01
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
-POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = 14
+POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = 12
 POSTERIOR_PAIR_TITLE_FONT_SIZE = 16
 POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 16
 POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS = 10
@@ -137,6 +137,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS = 10
 POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 26
 POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 42
+POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
 
 
 @dataclass(frozen=True)
@@ -177,6 +178,7 @@ class _PosteriorPairsContext:
     fit_results: object
     parameter_names: list[str]
     labels: list[str]
+    annotation_labels: list[str]
     density_samples: np.ndarray
     scatter_samples: np.ndarray
     show_contours: bool
@@ -1119,6 +1121,7 @@ def _build_posterior_pairs_plot(
 
         self._finalize_posterior_pairs_figure(
             fig=fig,
+            context=context,
             subplot_title_annotations=subplot_title_annotations,
             subplot_border_shapes=subplot_border_shapes,
         )
@@ -1176,6 +1179,7 @@ def _posterior_pairs_context(
             fit_results=fit_results,
             parameter_names=parameter_names,
             labels=self._posterior_plot_labels(fit_results, parameter_names),
+            annotation_labels=self._posterior_pair_axis_title_labels(parameter_names),
             density_samples=density_samples,
             scatter_samples=scatter_samples,
             show_contours=show_contours,
@@ -1468,7 +1472,8 @@ def _collect_posterior_pair_panel_decorations(
                 'y': 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1]),
                 'yref': 'paper',
                 'yanchor': 'middle',
-                'text': context.labels[row_index],
+                'text': context.annotation_labels[row_index],
+                'align': 'center',
                 'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
                 'textangle': -90,
                 'showarrow': False,
@@ -1482,7 +1487,8 @@ def _collect_posterior_pair_panel_decorations(
                 'yref': 'paper',
                 'yanchor': 'top',
                 'yshift': -POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS,
-                'text': context.labels[col_index],
+                'text': context.annotation_labels[col_index],
+                'align': 'center',
                 'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
                 'showarrow': False,
             })
@@ -1561,19 +1567,14 @@ def _finalize_posterior_pairs_figure(
         self,
         *,
         fig: object,
+        context: _PosteriorPairsContext,
         subplot_title_annotations: list[dict[str, object]],
         subplot_border_shapes: list[dict[str, object]],
     ) -> None:
         """Apply final layout settings to the posterior pair plot."""
         fig.update_layout(
             autosize=True,
-            margin={
-                'autoexpand': False,
-                'l': POSTERIOR_PAIR_LEFT_MARGIN_PIXELS,
-                'r': POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS,
-                't': POSTERIOR_PAIR_TOP_MARGIN_PIXELS,
-                'b': POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS,
-            },
+            margin=self._posterior_pair_layout_margin(context.annotation_labels),
             bargap=0.05,
             annotations=[
                 self._posterior_pair_title_annotation(),
@@ -1593,6 +1594,37 @@ def _finalize_posterior_pairs_figure(
             plot_bgcolor='white',
         )
 
+    @staticmethod
+    def _posterior_pair_layout_margin(annotation_labels: list[str]) -> dict[str, int | bool]:
+        """Return outer margins sized for multiline pair-plot labels."""
+        extra_margin = Plotter._posterior_pair_extra_axis_title_margin(annotation_labels)
+        return {
+            'autoexpand': False,
+            'l': POSTERIOR_PAIR_LEFT_MARGIN_PIXELS + extra_margin,
+            'r': POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS,
+            't': POSTERIOR_PAIR_TOP_MARGIN_PIXELS,
+            'b': POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS + extra_margin,
+        }
+
+    @staticmethod
+    def _posterior_pair_extra_axis_title_margin(annotation_labels: list[str]) -> int:
+        """Return extra margin needed for multiline axis labels."""
+        if not annotation_labels:
+            return 0
+
+        max_line_count = max(
+            Plotter._posterior_pair_axis_title_line_count(label)
+            for label in annotation_labels
+        )
+        return max(0, max_line_count - 1) * POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
+
+    @staticmethod
+    def _posterior_pair_axis_title_line_count(label: str) -> int:
+        """Return the number of display lines in one axis title."""
+        if not label:
+            return 1
+        return label.count('<br>') + 1
+
     @staticmethod
     def _posterior_pair_cell_size_pixels(
         n_parameters: int,
@@ -3069,6 +3101,26 @@ def _posterior_plot_labels(
                 labels.append(short_name)
         return labels
 
+    @staticmethod
+    def _posterior_pair_axis_title_labels(
+        parameter_names: list[str],
+    ) -> list[str]:
+        """Return compact multiline labels for pair-plot axes."""
+        return [Plotter._posterior_pair_axis_title_label(name) for name in parameter_names]
+
+    @staticmethod
+    def _posterior_pair_axis_title_label(unique_name: str) -> str:
+        """Return one compact multiline axis title for a pair plot."""
+        normalized_name = unique_name.strip()
+        if not normalized_name or '.' not in normalized_name:
+            return normalized_name
+
+        name_parts = [part.strip() for part in normalized_name.split('.') if part.strip()]
+        if not name_parts:
+            return normalized_name
+
+        return '<br>'.join([*(f'{part}.' for part in name_parts[:-1]), name_parts[-1]])
+
     @staticmethod
     def _posterior_summary_by_name(
         fit_results: object,
diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py
index 7dc42cf6..58d7e97f 100644
--- a/src/easydiffraction/project/project.py
+++ b/src/easydiffraction/project/project.py
@@ -171,6 +171,11 @@ def parameters(self) -> list:
         """Return parameters from all structures and experiments."""
         return self.structures.parameters + self.experiments.parameters
 
+    @property
+    def free_parameters(self) -> list:
+        """Return free parameters from structures and experiments."""
+        return self.structures.free_parameters + self.experiments.free_parameters
+
     @property
     def as_cif(self) -> str:
         """Export whole project as CIF text."""
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 1840388f..1eb130b4 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -347,6 +347,32 @@ def test_build_posterior_pairs_plot_fast_mode_skips_contours():
     assert all(trace.name != 'Posterior contours' for trace in figure.data)
 
 
+def test_build_posterior_pairs_plot_formats_dotted_axis_titles_multiline():
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_LEFT_MARGIN_PIXELS
+
+    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    dotted_parameter_names = [
+        'lbco.cell.length_a',
+        'hrpt.peak.broad_gauss_u',
+        'hrpt.peak.broad_gauss_v',
+        'hrpt.instrument.twotheta_offset',
+    ]
+    fit_results.posterior_samples.parameter_names = dotted_parameter_names
+    for index, unique_name in enumerate(dotted_parameter_names):
+        fit_results.parameters[index].unique_name = unique_name
+        fit_results.posterior_parameter_summaries[index].unique_name = unique_name
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    annotation_texts = [annotation.text for annotation in figure.layout.annotations]
+    assert 'hrpt.<br>peak.<br>broad_gauss_u' in annotation_texts
+    assert 'hrpt.<br>instrument.<br>twotheta_offset' in annotation_texts
+    assert all('None broad_gauss_u' not in text for text in annotation_texts)
+    assert figure.layout.margin.l > POSTERIOR_PAIR_LEFT_MARGIN_PIXELS
+    assert figure.layout.margin.b > POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
+
+
 def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():
     from easydiffraction.display.plotting import PAIR_PLOT_CELL_SIZE_PIXELS
     from easydiffraction.display.plotting import Plotter
diff --git a/tests/unit/easydiffraction/project/test_project.py b/tests/unit/easydiffraction/project/test_project.py
index 92907f66..090ce540 100644
--- a/tests/unit/easydiffraction/project/test_project.py
+++ b/tests/unit/easydiffraction/project/test_project.py
@@ -1,6 +1,8 @@
 # SPDX-FileCopyrightText: 2025 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
+from types import SimpleNamespace
+
 
 def test_module_import():
     import easydiffraction.project.project as MUT
@@ -49,3 +51,15 @@ def test_project_verbosity_invalid():
     p = Project()
     with pytest.raises(ValueError, match="'verbose' is not a valid VerbosityEnum"):
         p.verbosity = 'verbose'
+
+
+def test_project_free_params_aggregate_structures_and_experiments():
+    from easydiffraction.project.project import Project
+
+    project = Project()
+    structure_param = object()
+    experiment_param = object()
+    project._structures = SimpleNamespace(free_parameters=[structure_param])
+    project._experiments = SimpleNamespace(free_parameters=[experiment_param])
+
+    assert project.free_parameters == [structure_param, experiment_param]

From 46a9cb697b17ff3aaecb5465fe4f7087eecd34a9 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 22:35:02 +0200
Subject: [PATCH 059/106] Redesign correlation heatmap display

---
 src/easydiffraction/analysis/analysis.py      |   6 +-
 src/easydiffraction/display/plotting.py       | 422 ++++++++++++++++--
 .../easydiffraction/display/test_plotting.py  | 124 +++--
 3 files changed, 494 insertions(+), 58 deletions(-)

diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index d389fd27..23b9fcf3 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -184,7 +184,11 @@ def free_params(self) -> None:
         """Print only currently free (varying) parameters."""
         project = self._analysis.project
         self._flush_structure_categories()
-        free_params = project.free_parameters
+        free_params = getattr(project, 'free_parameters', None)
+        if free_params is None:
+            structures_params = project.structures.free_parameters
+            experiments_params = project.experiments.free_parameters
+            free_params = structures_params + experiments_params
 
         if not free_params:
             log.warning('No free parameters found.')
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 8f7ea759..4f35e498 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -72,7 +72,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
     FULL = 'full'
 
 
-DEFAULT_CORRELATION_THRESHOLD = 0.7
+DEFAULT_CORRELATION_THRESHOLD = 0.0
 EXPECTED_COVAR_NDIM = 2
 DEFAULT_RESIDUAL_HEIGHT_FRACTION = 0.25
 DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION = 0.10
@@ -85,6 +85,9 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 MIN_POSTERIOR_SAMPLE_COUNT = 2
 POSTERIOR_DENSITY_LINE_COLOR = 'rgb(99, 110, 250)'
 POSTERIOR_DENSITY_FILL_COLOR = 'rgba(99, 110, 250, 0.22)'
+POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR = 'rgb(44, 160, 44)'
+POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR = 'rgba(44, 160, 44, 0.22)'
+POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH = 1
 POSTERIOR_HISTOGRAM_FILL_COLOR = 'rgba(120, 120, 120, 0.38)'
 POSTERIOR_HISTOGRAM_LINE_COLOR = 'rgba(120, 120, 120, 0.24)'
 POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(140, 140, 140, 0.08)'
@@ -100,6 +103,13 @@ class PosteriorPairPlotStyleEnum(StrEnum):
     [0.82, 'rgba(96, 131, 242, 0.84)'],
     [1.0, 'rgba(58, 86, 224, 0.90)'],
 ]
+POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE = [
+    [0.0, 'rgba(255, 224, 224, 0.62)'],
+    [0.35, 'rgba(250, 188, 188, 0.70)'],
+    [0.60, 'rgba(245, 148, 148, 0.78)'],
+    [0.82, 'rgba(237, 104, 104, 0.84)'],
+    [1.0, 'rgba(215, 48, 39, 0.90)'],
+]
 POSTERIOR_CONTOUR_LINE_COLORSCALE = [
     [0.0, 'rgba(183, 203, 255, 0.94)'],
     [0.35, 'rgba(183, 203, 255, 0.94)'],
@@ -110,6 +120,16 @@ class PosteriorPairPlotStyleEnum(StrEnum):
     [0.82, 'rgba(58, 86, 224, 0.98)'],
     [1.0, 'rgba(58, 86, 224, 0.98)'],
 ]
+POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE = [
+    [0.0, 'rgba(250, 188, 188, 0.94)'],
+    [0.35, 'rgba(250, 188, 188, 0.94)'],
+    [0.35, 'rgba(245, 148, 148, 0.95)'],
+    [0.60, 'rgba(245, 148, 148, 0.95)'],
+    [0.60, 'rgba(237, 104, 104, 0.96)'],
+    [0.82, 'rgba(237, 104, 104, 0.96)'],
+    [0.82, 'rgba(215, 48, 39, 0.98)'],
+    [1.0, 'rgba(215, 48, 39, 0.98)'],
+]
 POSTERIOR_PAIR_SCATTER_MAX_POINTS = 1500
 POSTERIOR_PAIR_MAX_DENSITY_SAMPLES = 4000
 POSTERIOR_PAIR_MIN_DENSITY_SAMPLES = 800
@@ -192,6 +212,27 @@ def n_parameters(self) -> int:
         return len(self.parameter_names)
 
 
+@dataclass(frozen=True)
+class _CorrelationHeatmapContext:
+    """Inputs needed to build a correlation matrix plot."""
+
+    corr_df: pd.DataFrame
+    row_labels: list[str]
+    col_labels: list[str]
+    threshold: float | None
+    precision: int
+
+    @property
+    def n_rows(self) -> int:
+        """Return the number of displayed rows."""
+        return self.corr_df.shape[0]
+
+    @property
+    def n_cols(self) -> int:
+        """Return the number of displayed columns."""
+        return self.corr_df.shape[1]
+
+
 @dataclass(slots=True)
 class _PosteriorPairsLegendState:
     """Legend-visibility state for posterior pair plots."""
@@ -674,7 +715,7 @@ def plot_param_correlations(
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
         precision: int = 2,
         *,
-        show_diagonal: bool = False,
+        show_diagonal: bool = True,
     ) -> None:
         """
         Plot the parameter correlation matrix from the latest fit.
@@ -683,10 +724,9 @@ def plot_param_correlations(
         the active engine is Plotly, an interactive heatmap is shown.
         Otherwise, a rounded correlation table is rendered.
 
-        By default only the lower triangle is shown (without the
-        diagonal), since the matrix is symmetric and diagonal values are
-        always ``1``. Set ``show_diagonal=True`` to keep blank diagonal
-        cells for a square lower-triangle layout.
+        By default the lower triangle is shown with blank diagonal cells
+        so the grid stays square, like posterior pair plots. Set
+        ``show_diagonal=False`` to trim the empty outer row and column.
 
         Parameters
         ----------
@@ -698,7 +738,7 @@ def plot_param_correlations(
             matrix.
         precision : int, default=2
             Number of decimal places to show in the table fallback.
-        show_diagonal : bool, default=False
+        show_diagonal : bool, default=True
             Whether to retain blank diagonal cells in the displayed
             lower-triangle matrix.
         """
@@ -745,7 +785,7 @@ def plot_param_correlations(
     def plot_posterior_pairs(
         self,
         parameters: list[object] | None = None,
-        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
+        style: PosteriorPairPlotStyleEnum | str = 'auto',
     ) -> None:
         """
         Plot posterior pair relationships for sampled parameters.
@@ -756,9 +796,10 @@ def plot_posterior_pairs(
             Optional subset of sampled parameters to include. When
             ``None``, all sampled parameters are shown.
         style : PosteriorPairPlotStyleEnum | str, default='auto'
-            ``'auto'`` keeps contours for compact plots and disables
-            them for wide grids. ``'fast'`` always skips contours.
-            ``'full'`` always renders contours.
+            Pair-plot rendering mode. Defaults to ``'auto'``. ``'auto'``
+            keeps contours for compact plots and disables them for wide
+            grids. ``'fast'`` always skips contours. ``'full'`` always
+            renders contours.
         """
         plot = self._build_posterior_pairs_plot(parameters=parameters, style=style)
         if plot is None:
@@ -1073,7 +1114,7 @@ def _build_posterior_pairs_plot(
         self,
         *,
         parameters: list[object] | None,
-        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
+        style: PosteriorPairPlotStyleEnum | str = 'auto',
     ) -> object | None:
         """
         Build a Plotly posterior pair plot.
@@ -1083,7 +1124,7 @@ def _build_posterior_pairs_plot(
         parameters : list[object] | None
             Optional subset of sampled parameters to include.
         style : PosteriorPairPlotStyleEnum | str, default='auto'
-            Posterior pair-plot rendering mode.
+            Posterior pair-plot rendering mode. Defaults to ``'auto'``.
 
         Returns
         -------
@@ -1131,7 +1172,7 @@ def _posterior_pairs_context(
         self,
         parameters: list[object] | None,
         *,
-        style: PosteriorPairPlotStyleEnum | str = PosteriorPairPlotStyleEnum.AUTO,
+        style: PosteriorPairPlotStyleEnum | str = 'auto',
     ) -> _PosteriorPairsContext | None:
         """Return the resolved inputs for a posterior pair plot."""
         posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
@@ -1306,7 +1347,7 @@ def _add_posterior_pair_diagonal(
                     x=density_values,
                     nbinsx=40,
                     histnorm='probability density',
-                    marker={'color': 'rgb(99, 110, 250)'},
+                    marker={'color': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR},
                     showlegend=False,
                     hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
                 ),
@@ -1315,6 +1356,7 @@ def _add_posterior_pair_diagonal(
             )
             return
 
+        self._style_posterior_pair_marginal_density_trace(density_trace)
         density_trace.name = 'Marginal density'
         density_trace.legendgroup = 'posterior-marginal-density'
         density_trace.showlegend = legend_state.show_density
@@ -1539,21 +1581,39 @@ def _collect_posterior_pair_panel_decorations(
         ])
 
     @staticmethod
-    def _posterior_pair_title_annotation() -> dict[str, object]:
-        """Return the outer title annotation for the pair plot."""
+    def _square_matrix_title_annotation(
+        title: str,
+        annotation_labels: list[str],
+    ) -> dict[str, object]:
+        """Return a top-left title annotation for matrix plots."""
         return {
             'x': 0.0,
             'xref': 'paper',
             'xanchor': 'left',
+            'xshift': -Plotter._square_matrix_title_left_shift(annotation_labels),
             'y': 1.0,
             'yref': 'paper',
             'yanchor': 'bottom',
             'yshift': POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS,
-            'text': 'Posterior pair plot',
+            'text': title,
             'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
             'showarrow': False,
         }
 
+    @staticmethod
+    def _posterior_pair_title_annotation(annotation_labels: list[str]) -> dict[str, object]:
+        """Return the outer title annotation for the pair plot."""
+        return Plotter._square_matrix_title_annotation(
+            'Posterior pair plot',
+            annotation_labels,
+        )
+
+    @staticmethod
+    def _square_matrix_title_left_shift(annotation_labels: list[str]) -> int:
+        """Return the title shift that cancels left margin."""
+        extra_margin = Plotter._posterior_pair_extra_axis_title_margin(annotation_labels)
+        return POSTERIOR_PAIR_LEFT_MARGIN_PIXELS + extra_margin
+
     @staticmethod
     def _posterior_pair_layout_meta() -> dict[str, object]:
         """Return layout metadata used by the Plotly HTML wrapper."""
@@ -1577,7 +1637,7 @@ def _finalize_posterior_pairs_figure(
             margin=self._posterior_pair_layout_margin(context.annotation_labels),
             bargap=0.05,
             annotations=[
-                self._posterior_pair_title_annotation(),
+                self._posterior_pair_title_annotation(context.annotation_labels),
                 *subplot_title_annotations,
             ],
             shapes=subplot_border_shapes,
@@ -1613,8 +1673,7 @@ def _posterior_pair_extra_axis_title_margin(annotation_labels: list[str]) -> int
             return 0
 
         max_line_count = max(
-            Plotter._posterior_pair_axis_title_line_count(label)
-            for label in annotation_labels
+            Plotter._posterior_pair_axis_title_line_count(label) for label in annotation_labels
         )
         return max(0, max_line_count - 1) * POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
 
@@ -1701,10 +1760,7 @@ def _validated_posterior_pair_plot_style(
             return PosteriorPairPlotStyleEnum(style)
         except ValueError as exc:
             supported_styles = ', '.join(item.value for item in PosteriorPairPlotStyleEnum)
-            msg = (
-                'style must be one of '
-                f'{supported_styles} for posterior pair plots.'
-            )
+            msg = f'style must be one of {supported_styles} for posterior pair plots.'
             raise ValueError(msg) from exc
 
     @staticmethod
@@ -1762,6 +1818,10 @@ def _posterior_contour_traces(
             return None
 
         x_grid, y_grid, density = density_surface
+        fill_colorscale, line_colorscale = self._posterior_pair_contour_colorscales(
+            x_values,
+            y_values,
+        )
         contour_start = float(np.max(density) * 0.20)
         contour_end = float(np.max(density) * 0.95)
         contour_size = float(np.max(density) * 0.15)
@@ -1779,7 +1839,7 @@ def _posterior_contour_traces(
                 'end': contour_end,
                 'size': contour_size,
             },
-            colorscale=POSTERIOR_CONTOUR_FILL_COLORSCALE,
+            colorscale=fill_colorscale,
             zmin=contour_start,
             zmax=contour_end,
             connectgaps=False,
@@ -1799,7 +1859,7 @@ def _posterior_contour_traces(
                 'end': contour_end,
                 'size': contour_size,
             },
-            colorscale=POSTERIOR_CONTOUR_LINE_COLORSCALE,
+            colorscale=line_colorscale,
             zmin=contour_start,
             zmax=contour_end,
             line={'width': 0.9},
@@ -2070,6 +2130,45 @@ def _show_plot_figure(self, figure: object) -> None:
             return
         figure.show()
 
+    @staticmethod
+    def _style_posterior_pair_marginal_density_trace(density_trace: object) -> None:
+        """Apply pair-plot-specific styling to a marginal KDE trace."""
+        density_trace.line = {
+            'color': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR,
+            'width': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH,
+        }
+        density_trace.fillcolor = POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+
+    @staticmethod
+    def _posterior_pair_correlation_value(
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+    ) -> float | None:
+        """Return the sample correlation for one contour panel."""
+        finite_mask = np.isfinite(x_values) & np.isfinite(y_values)
+        if np.count_nonzero(finite_mask) < MIN_POSTERIOR_SAMPLE_COUNT:
+            return None
+
+        correlation_matrix = np.corrcoef(x_values[finite_mask], y_values[finite_mask])
+        correlation_value = float(correlation_matrix[0, 1])
+        if not np.isfinite(correlation_value):
+            return None
+        return correlation_value
+
+    @staticmethod
+    def _posterior_pair_contour_colorscales(
+        x_values: np.ndarray,
+        y_values: np.ndarray,
+    ) -> tuple[list[list[float | str]], list[list[float | str]]]:
+        """Return sign-aware contour palettes for one panel."""
+        correlation_value = Plotter._posterior_pair_correlation_value(x_values, y_values)
+        if correlation_value is not None and correlation_value < 0:
+            return (
+                POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE,
+                POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE,
+            )
+        return POSTERIOR_CONTOUR_FILL_COLORSCALE, POSTERIOR_CONTOUR_LINE_COLORSCALE
+
     def _posterior_density_trace(
         self,
         *,
@@ -3320,7 +3419,7 @@ def _plot_correlation_heatmap(
         precision: int,
     ) -> None:
         """
-        Delegate correlation heatmap rendering to the backend.
+        Render a Plotly correlation matrix with pair-plot styling.
 
         Parameters
         ----------
@@ -3333,12 +3432,277 @@ def _plot_correlation_heatmap(
         precision : int
             Number of decimals to show in plot labels and hover text.
         """
-        self._backend.plot_correlation_heatmap(
+        figure = self._build_correlation_heatmap_plot(
             corr_df,
             title,
             threshold=threshold,
             precision=precision,
         )
+        self._show_plot_figure(figure)
+
+    def _build_correlation_heatmap_plot(
+        self,
+        corr_df: pd.DataFrame,
+        title: str,
+        *,
+        threshold: float | None,
+        precision: int,
+    ) -> object:
+        """Build a Plotly correlation matrix with pair-plot geometry."""
+        make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
+
+        context = _CorrelationHeatmapContext(
+            corr_df=corr_df,
+            row_labels=self._posterior_pair_axis_title_labels(corr_df.index.tolist()),
+            col_labels=self._posterior_pair_axis_title_labels(corr_df.columns.tolist()),
+            threshold=threshold,
+            precision=precision,
+        )
+        subplot_title_annotations: list[dict[str, object]] = []
+        subplot_border_shapes: list[dict[str, object]] = []
+        fig = make_subplots(
+            rows=context.n_rows,
+            cols=context.n_cols,
+            shared_xaxes='columns',
+            shared_yaxes='rows',
+            horizontal_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+            vertical_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+        )
+
+        for row_index in range(context.n_rows):
+            for col_index in range(context.n_cols):
+                self._populate_correlation_heatmap_panel(
+                    fig=fig,
+                    context=context,
+                    row_index=row_index,
+                    col_index=col_index,
+                    subplot_title_annotations=subplot_title_annotations,
+                    subplot_border_shapes=subplot_border_shapes,
+                )
+
+        fig.update_layout(
+            autosize=True,
+            margin=self._posterior_pair_layout_margin([
+                *context.row_labels,
+                *context.col_labels,
+            ]),
+            annotations=[
+                self._square_matrix_title_annotation(
+                    title,
+                    [*context.row_labels, *context.col_labels],
+                ),
+                *subplot_title_annotations,
+            ],
+            shapes=subplot_border_shapes,
+            meta=self._posterior_pair_layout_meta(),
+            showlegend=False,
+            paper_bgcolor='white',
+            plot_bgcolor='white',
+        )
+        return fig
+
+    def _populate_correlation_heatmap_panel(
+        self,
+        *,
+        fig: object,
+        context: _CorrelationHeatmapContext,
+        row_index: int,
+        col_index: int,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Populate one panel in the correlation-matrix grid."""
+        row = row_index + 1
+        col = col_index + 1
+        if col_index > row_index:
+            self._hide_posterior_pair_panel(fig=fig, row=row, col=col)
+            return
+
+        value = context.corr_df.iat[row_index, col_index]
+        if not pd.isna(value):
+            self._add_correlation_heatmap_value_panel(
+                fig=fig,
+                context=context,
+                row_index=row_index,
+                col_index=col_index,
+                value=float(value),
+            )
+
+        self._configure_correlation_heatmap_panel_axes(fig=fig, row=row, col=col)
+        self._collect_correlation_heatmap_panel_decorations(
+            fig=fig,
+            context=context,
+            row_index=row_index,
+            col_index=col_index,
+            subplot_title_annotations=subplot_title_annotations,
+            subplot_border_shapes=subplot_border_shapes,
+        )
+
+    def _add_correlation_heatmap_value_panel(
+        self,
+        *,
+        fig: object,
+        context: _CorrelationHeatmapContext,
+        row_index: int,
+        col_index: int,
+        value: float,
+    ) -> None:
+        """Add one colored cell and optional text label."""
+        go = __import__('plotly.graph_objects', fromlist=['Heatmap'])
+        row = row_index + 1
+        col = col_index + 1
+        hovertemplate = (
+            f'x: {context.corr_df.columns[col_index]}<br>'
+            f'y: {context.corr_df.index[row_index]}<br>'
+            f'corr: %{{z:.{context.precision}f}}<extra></extra>'
+        )
+        fig.add_trace(
+            go.Heatmap(
+                z=[[value]],
+                x=[0.0, 1.0],
+                y=[0.0, 1.0],
+                zmin=-1.0,
+                zmax=1.0,
+                zmid=0.0,
+                colorscale=self._plot_correlation_colorscale(),
+                showscale=False,
+                hoverongaps=False,
+                hovertemplate=hovertemplate,
+            ),
+            row=row,
+            col=col,
+        )
+
+        if (
+            context.threshold is not None
+            and context.threshold > 0
+            and abs(value) < context.threshold
+        ):
+            return
+
+        fig.add_trace(
+            go.Scatter(
+                x=[0.5],
+                y=[0.5],
+                mode='text',
+                text=[f'{value:.{context.precision}f}'],
+                textposition='middle center',
+                textfont={'color': PlotlyPlotter._correlation_label_color()},
+                hoverinfo='skip',
+                showlegend=False,
+            ),
+            row=row,
+            col=col,
+        )
+
+    @staticmethod
+    def _configure_correlation_heatmap_panel_axes(
+        *,
+        fig: object,
+        row: int,
+        col: int,
+    ) -> None:
+        """Hide ticks and titles for one correlation-matrix panel."""
+        fig.update_xaxes(
+            range=[0.0, 1.0],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            row=row,
+            col=col,
+        )
+        fig.update_yaxes(
+            range=[1.0, 0.0],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            row=row,
+            col=col,
+        )
+
+    def _collect_correlation_heatmap_panel_decorations(
+        self,
+        *,
+        fig: object,
+        context: _CorrelationHeatmapContext,
+        row_index: int,
+        col_index: int,
+        subplot_title_annotations: list[dict[str, object]],
+        subplot_border_shapes: list[dict[str, object]],
+    ) -> None:
+        """Collect labels and frames for one correlation cell."""
+        row = row_index + 1
+        col = col_index + 1
+        subplot = fig.get_subplot(row, col)
+        x_mid = 0.5 * (subplot.xaxis.domain[0] + subplot.xaxis.domain[1])
+        y_mid = 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1])
+
+        if col_index == 0:
+            subplot_title_annotations.append({
+                'x': subplot.xaxis.domain[0],
+                'xref': 'paper',
+                'xanchor': 'right',
+                'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
+                'y': y_mid,
+                'yref': 'paper',
+                'yanchor': 'middle',
+                'text': context.row_labels[row_index],
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'textangle': -90,
+                'showarrow': False,
+            })
+        if row_index == context.n_rows - 1:
+            subplot_title_annotations.append({
+                'x': x_mid,
+                'xref': 'paper',
+                'xanchor': 'center',
+                'y': subplot.yaxis.domain[0],
+                'yref': 'paper',
+                'yanchor': 'top',
+                'yshift': -POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS,
+                'text': context.col_labels[col_index],
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'showarrow': False,
+            })
+
+        subplot_border_shapes.append({
+            'type': 'rect',
+            'xref': 'paper',
+            'yref': 'paper',
+            'x0': subplot.xaxis.domain[0],
+            'x1': subplot.xaxis.domain[1],
+            'y0': subplot.yaxis.domain[0],
+            'y1': subplot.yaxis.domain[1],
+            'line': {
+                'color': self._plot_axis_frame_color(),
+                'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+            },
+            'fillcolor': 'rgba(0, 0, 0, 0)',
+            'layer': 'above',
+        })
+
+    def _plot_correlation_colorscale(self) -> list[tuple[float, str]]:
+        """Return the active correlation colorscale."""
+        correlation_colorscale = getattr(self._backend, '_correlation_colorscale', None)
+        if callable(correlation_colorscale):
+            return correlation_colorscale()
+        return PlotlyPlotter._correlation_colorscale()
 
     @staticmethod
     def _format_correlation_table_dataframe(
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 1eb130b4..5afe289f 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -327,6 +327,9 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
         'broad_gauss_v',
         'twotheta_offset',
     ]
+    assert figure.layout.annotations[0].xshift == -plotter._square_matrix_title_left_shift(
+        ['length_a', 'broad_gauss_u', 'broad_gauss_v', 'twotheta_offset']
+    )
     subplot = figure.get_subplot(1, 1)
     bottom_subplot = figure.get_subplot(4, 1)
     assert subplot.yaxis.showticklabels is False
@@ -347,6 +350,44 @@ def test_build_posterior_pairs_plot_fast_mode_skips_contours():
     assert all(trace.name != 'Posterior contours' for trace in figure.data)
 
 
+def test_build_posterior_pairs_plot_sign_colors_contours_and_marginals():
+    from easydiffraction.display.plotting import POSTERIOR_CONTOUR_FILL_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_CONTOUR_LINE_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
+
+    plotter, _, _ = _make_bayesian_plotter_fixture()
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    marginal_traces = [trace for trace in figure.data if trace.name == 'Marginal density']
+    assert marginal_traces
+    assert all(trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR for trace in marginal_traces)
+    assert all(trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH for trace in marginal_traces)
+    assert all(trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR for trace in marginal_traces)
+
+    fill_contours = [
+        trace
+        for trace in figure.data
+        if trace.type == 'contour' and trace.contours.coloring == 'fill'
+    ]
+    line_contours = [
+        trace
+        for trace in figure.data
+        if trace.type == 'contour' and trace.contours.coloring == 'lines'
+    ]
+    fill_end_colors = {trace.colorscale[-1][1] for trace in fill_contours}
+    line_end_colors = {trace.colorscale[-1][1] for trace in line_contours}
+
+    assert POSTERIOR_CONTOUR_FILL_COLORSCALE[-1][1] in fill_end_colors
+    assert POSTERIOR_NEGATIVE_CONTOUR_FILL_COLORSCALE[-1][1] in fill_end_colors
+    assert POSTERIOR_CONTOUR_LINE_COLORSCALE[-1][1] in line_end_colors
+    assert POSTERIOR_NEGATIVE_CONTOUR_LINE_COLORSCALE[-1][1] in line_end_colors
+
+
 def test_build_posterior_pairs_plot_formats_dotted_axis_titles_multiline():
     from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_LEFT_MARGIN_PIXELS
@@ -385,8 +426,12 @@ def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():
 def test_posterior_pair_density_budget_scales_with_parameter_count():
     from easydiffraction.display.plotting import Plotter
 
-    assert Plotter._posterior_pair_density_max_points(8) < Plotter._posterior_pair_density_max_points(4)
-    assert Plotter._posterior_pair_contour_grid_size(8) < Plotter._posterior_pair_contour_grid_size(4)
+    assert Plotter._posterior_pair_density_max_points(
+        8
+    ) < Plotter._posterior_pair_density_max_points(4)
+    assert Plotter._posterior_pair_contour_grid_size(
+        8
+    ) < Plotter._posterior_pair_contour_grid_size(4)
 
 
 def test_posterior_pairs_context_thins_kde_samples_and_preserves_axis_ranges():
@@ -1004,7 +1049,7 @@ class Project:
     p = Plotter()
     p.engine = 'asciichartpy'
     p._set_project(Project())
-    p.plot_param_correlations(threshold=0.1, precision=3)
+    p.plot_param_correlations(threshold=0.1, precision=3, show_diagonal=False)
 
     df = captured['df']
     assert [column.strip() for column in df.columns.get_level_values(0)] == [
@@ -1057,7 +1102,7 @@ class Project:
     p = Plotter()
     p.engine = 'plotly'
     p._set_project(Project())
-    p.plot_param_correlations(threshold=0.1)
+    p.plot_param_correlations()
 
     fig = captured['fig']
     assert len(fig.data) == 2
@@ -1066,10 +1111,11 @@ class Project:
     assert list(fig.data[0].y) == [0.0, 1.0]
     assert fig.data[0].xgap in (None, 0)
     assert fig.data[0].ygap in (None, 0)
-    assert fig.data[0].colorbar.lenmode == 'fraction'
-    assert fig.data[0].colorbar.len == 1.0
-    assert fig.data[0].colorbar.title.text == ''
-    assert fig.data[0].hovertemplate == 'x: %{x}<br>y: %{y}<br>corr: %{z:.2f}<extra></extra>'
+    assert fig.data[0].showscale is False
+    assert (
+        fig.data[0].hovertemplate
+        == 'x: phase.scale<br>y: phase.cell.length_c<br>corr: %{z:.2f}<extra></extra>'
+    )
     assert pytest.approx(fig.data[0].z[0][0], rel=1e-9) == -0.5
     assert fig.data[1].type == 'scatter'
     assert fig.data[1].mode == 'text'
@@ -1078,23 +1124,32 @@ class Project:
     assert list(fig.data[1].text) == ['-0.50']
     assert fig.data[1].textposition == 'middle center'
     assert fig.data[1].hoverinfo == 'skip'
-    assert fig.layout.xaxis.side == 'bottom'
-    assert fig.layout.xaxis.tickangle < 0
-    assert list(fig.layout.xaxis.tickvals) == [0.5]
-    assert list(fig.layout.xaxis.ticktext) == ['phase.scale']
+    assert [annotation.text for annotation in fig.layout.annotations] == [
+        'Refined parameter correlation matrix',
+        'phase.<br>scale',
+        'phase.<br>cell.<br>length_c',
+        'phase.<br>scale',
+        'phase.<br>cell.<br>length_c',
+    ]
+    assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift(
+        ['phase.<br>scale', 'phase.<br>cell.<br>length_c']
+    )
+    assert fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio'] == '1 / 1'
+    assert fig.layout.xaxis.domain[1] < fig.layout.xaxis2.domain[0]
     assert fig.layout.xaxis.showline is False
     assert fig.layout.xaxis.mirror is False
     assert fig.layout.xaxis.layer == 'above traces'
-    assert list(fig.layout.yaxis.tickvals) == [0.5]
-    assert list(fig.layout.yaxis.ticktext) == ['phase.cell.length_c']
+    assert fig.layout.xaxis.showticklabels is False
+    assert fig.layout.xaxis.title.text is None
     assert fig.layout.yaxis.showline is False
     assert fig.layout.yaxis.mirror is False
     assert fig.layout.yaxis.layer == 'above traces'
-    assert fig.layout.yaxis.ticklabelstandoff == 8
-    assert len(fig.layout.shapes) == 1
-    assert fig.layout.shapes[-1].type == 'rect'
-    assert fig.layout.shapes[-1].xref == 'paper'
-    assert fig.layout.shapes[-1].yref == 'paper'
+    assert fig.layout.yaxis.showticklabels is False
+    assert fig.layout.yaxis.title.text is None
+    assert len(fig.layout.shapes) == 3
+    assert all(shape.type == 'rect' for shape in fig.layout.shapes)
+    assert all(shape.xref == 'paper' for shape in fig.layout.shapes)
+    assert all(shape.yref == 'paper' for shape in fig.layout.shapes)
 
 
 def test_plot_param_correlations_plotly_labels_respect_threshold(monkeypatch):
@@ -1148,17 +1203,20 @@ class Project:
     p = Plotter()
     p.engine = 'plotly'
     p._set_project(Project())
-    p.plot_param_correlations()
+    p.plot_param_correlations(threshold=0.7)
 
     fig = captured['fig']
-    assert len(fig.data) == 2
-    assert fig.data[0].type == 'heatmap'
-    assert fig.data[1].type == 'scatter'
-    assert fig.data[1].mode == 'text'
-    assert list(fig.data[1].text) == ['-0.91', '0.83', '-0.89', '0.82']
+    heatmap_traces = [trace for trace in fig.data if trace.type == 'heatmap']
+    text_traces = [
+        trace
+        for trace in fig.data
+        if trace.type == 'scatter' and trace.mode == 'text'
+    ]
+    assert len(heatmap_traces) == 10
+    assert [trace.text[0] for trace in text_traces] == ['-0.91', '0.83', '-0.89', '0.82']
 
 
-def test_plot_param_correlations_filters_by_default_threshold(monkeypatch):
+def test_plot_param_correlations_shows_full_table_by_default(monkeypatch):
     from easydiffraction.display.plotting import Plotter
     from easydiffraction.display.tables import TableRenderer
 
@@ -1212,12 +1270,22 @@ class Project:
     assert [column.strip() for column in df.columns.get_level_values(0)] == [
         'parameter',
         '1',
+        '2',
+        '3',
     ]
-    assert list(df.index) == [0, 1]
+    assert list(df.index) == [0, 1, 2]
     assert df.iloc[0, 0] == 'phase.scale'
     assert df.iloc[0, 1] == ''
+    assert df.iloc[0, 2] == ''
+    assert df.iloc[0, 3] == ''
     assert df.iloc[1, 0] == 'phase.cell.length_a'
     assert _strip_markup(df.iloc[1, 1]).strip() == '0.82'
+    assert df.iloc[1, 2] == ''
+    assert df.iloc[1, 3] == ''
+    assert df.iloc[2, 0] == 'phase.background'
+    assert _strip_markup(df.iloc[2, 1]).strip() == '0.25'
+    assert _strip_markup(df.iloc[2, 2]).strip() == '0.00'
+    assert df.iloc[2, 3] == ''
 
 
 def test_plot_param_correlations_hides_subthreshold_table_values(monkeypatch):
@@ -1272,7 +1340,7 @@ class Project:
     p = Plotter()
     p.engine = 'asciichartpy'
     p._set_project(Project())
-    p.plot_param_correlations()
+    p.plot_param_correlations(threshold=0.7, show_diagonal=False)
 
     df = captured['df']
     assert [column.strip() for column in df.columns.get_level_values(0)] == [

From e97f04980f133d381f1c4db463e528c4277e3a04 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 22:35:33 +0200
Subject: [PATCH 060/106] Simplify tutorial parameter loops and bounds

---
 docs/docs/tutorials/ed-21.py | 42 +++++++++++++++++++-----------------
 1 file changed, 22 insertions(+), 20 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 543c3085..72eb8967 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -161,11 +161,11 @@
 experiment.background.create(id='2', x=30, y=164.3357)
 experiment.background.create(id='3', x=50, y=166.8881)
 experiment.background.create(id='4', x=110, y=175.4006)
-experiment.background.create(id='5', x=165, y=174.2813)
+#experiment.background.create(id='5', x=165, y=174.2813)
 
 # %%
-experiment.excluded_regions.create(id='1', start=0, end=30)
-experiment.excluded_regions.create(id='2', start=70, end=180)
+experiment.excluded_regions.create(id='1', start=0, end=10)
+experiment.excluded_regions.create(id='2', start=100, end=180)
 
 # %% [markdown]
 # #### Link the Structural Phase to the Experiment
@@ -189,6 +189,8 @@
 
 # %%
 structure.cell.length_a.free = True
+
+experiment.linked_phases['lbco'].scale.free = True
 experiment.peak.broad_gauss_u.free = True
 experiment.peak.broad_gauss_v.free = True
 experiment.instrument.calib_twotheta_offset.free = True
@@ -200,6 +202,8 @@
 
 # %%
 project.analysis.fit.show_minimizer_types()
+
+# %%
 project.analysis.fit.minimizer_type = 'bumps (lm)'
 
 # %%
@@ -233,15 +237,16 @@
 # The helper method `set_fit_bounds_from_uncertainty` centers the bounds
 # on the current parameter value and expands them by a chosen multiple of
 # the reported uncertainty.
+#
+# Default multiplier is 8 to give a wide range for the sampler to
+# explore, but here we use 3 to speed up the tutorial.
 
 # %%
 project.analysis.display.free_params()
 
 # %%
-structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=4)
-experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=4)
-experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)
-experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)
+for param in project.free_parameters:
+    param.set_fit_bounds_from_uncertainty(multiplier=3)
 
 # %% [markdown]
 # Displaying the free parameters again is a convenient way to confirm
@@ -258,9 +263,11 @@
 #
 # The settings below are intentionally small so the tutorial runs
 # quickly. For production analysis you would usually increase the number
-# of steps and often the burn-in as well. When needed, the DREAM API
-# also lets you tune how chains are initialized through the `init`
-# setting.
+# of steps (`steps`) and often the burn-in (`burn`) as well. When
+# needed, the DREAM API also lets you tune how chains are initialized
+# through the `init` setting. Other sampler settings such as `thin` and
+# `pop` can be adjusted  as well, but here we keep them at their
+# defaults.
 
 # %%
 project.analysis.fit.show_minimizer_types()
@@ -269,10 +276,7 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 2000  # 1000
-project.analysis.fit.minimizer.burn = 400  # 200
-project.analysis.fit.minimizer.thin = 1
-project.analysis.fit.minimizer.pop = 4
+project.analysis.fit.minimizer.steps = 100  # 1000
 
 # %%
 project.analysis.fit()
@@ -296,7 +300,7 @@
 #   posterior contours off-diagonal.
 
 # %%
-project.display.plotter.plot_param_correlations(show_diagonal=True)
+project.display.plotter.plot_param_correlations(threshold=0, show_diagonal=True)
 
 # %%
 project.display.plotter.plot_posterior_pairs()
@@ -307,10 +311,8 @@
 # multimodality.
 
 # %%
-project.display.plotter.plot_param_distribution(structure.cell.length_a)
-project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)
-project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)
-project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
+for param in project.free_parameters:
+    project.display.plotter.plot_param_distribution(param)
 
 # %% [markdown]
 # Finally, the posterior predictive plot propagates the sampled parameter
@@ -327,4 +329,4 @@
 # after the Bayesian run.
 
 # %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=65, x_max=68)

From ffbc0331aecff58ffd3d52e563e3ec97fb1db403 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 22:48:13 +0200
Subject: [PATCH 061/106] Improve hover template readability

---
 docs/docs/tutorials/ed-21.py                  |  6 +-
 .../display/plotters/plotly.py                |  2 +-
 src/easydiffraction/display/plotting.py       | 37 ++++++++---
 .../easydiffraction/display/test_plotting.py  | 65 ++++++++++++++-----
 4 files changed, 82 insertions(+), 28 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 72eb8967..364abeaf 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -161,7 +161,6 @@
 experiment.background.create(id='2', x=30, y=164.3357)
 experiment.background.create(id='3', x=50, y=166.8881)
 experiment.background.create(id='4', x=110, y=175.4006)
-#experiment.background.create(id='5', x=165, y=174.2813)
 
 # %%
 experiment.excluded_regions.create(id='1', start=0, end=10)
@@ -190,6 +189,7 @@
 # %%
 structure.cell.length_a.free = True
 
+# %%
 experiment.linked_phases['lbco'].scale.free = True
 experiment.peak.broad_gauss_u.free = True
 experiment.peak.broad_gauss_v.free = True
@@ -219,7 +219,7 @@
 # region.
 
 # %%
-project.display.plotter.plot_param_correlations(show_diagonal=True)
+project.display.plotter.plot_param_correlations()
 
 # %%
 project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
@@ -300,7 +300,7 @@
 #   posterior contours off-diagonal.
 
 # %%
-project.display.plotter.plot_param_correlations(threshold=0, show_diagonal=True)
+project.display.plotter.plot_param_correlations()
 
 # %%
 project.display.plotter.plot_posterior_pairs()
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 9186b166..764c5a0f 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -220,7 +220,7 @@ def plot_correlation_heatmap(
                 'yanchor': 'middle',
             },
             hoverongaps=False,
-            hovertemplate=f'x: %{{x}}<br>y: %{{y}}<br>corr: %{{z:.{precision}f}}<extra></extra>',
+            hovertemplate=f'%{{x}}<br>%{{y}}<br>correlation: %{{z:.{precision}f}}<extra></extra>',
         )
         label_trace = self._get_correlation_label_trace(
             corr_df,
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 4f35e498..6242b404 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1349,7 +1349,9 @@ def _add_posterior_pair_diagonal(
                     histnorm='probability density',
                     marker={'color': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR},
                     showlegend=False,
-                    hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+                    hovertemplate=self._posterior_pair_density_hovertemplate(
+                        context.parameter_names[parameter_index]
+                    ),
                 ),
                 row=row,
                 col=col,
@@ -1357,6 +1359,9 @@ def _add_posterior_pair_diagonal(
             return
 
         self._style_posterior_pair_marginal_density_trace(density_trace)
+        density_trace.hovertemplate = self._posterior_pair_density_hovertemplate(
+            context.parameter_names[parameter_index]
+        )
         density_trace.name = 'Marginal density'
         density_trace.legendgroup = 'posterior-marginal-density'
         density_trace.showlegend = legend_state.show_density
@@ -1393,9 +1398,9 @@ def _add_posterior_pair_off_diagonal(
                 y_values=y_density_values,
                 grid_size=context.contour_grid_size,
             )
-        sample_hovertemplate = (
-            f'{context.labels[col_index]}: %{{x:.4f}}<br>'
-            f'{context.labels[row_index]}: %{{y:.4f}}<extra></extra>'
+        sample_hovertemplate = self._posterior_pair_scatter_hovertemplate(
+            x_parameter_name=context.parameter_names[col_index],
+            y_parameter_name=context.parameter_names[row_index],
         )
         fig.add_trace(
             go.Scatter(
@@ -2042,7 +2047,7 @@ def _add_posterior_distribution_histogram(
                 },
                 opacity=0.82,
                 name='Posterior histogram',
-                hovertemplate='sample=%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+                hovertemplate='sample=%{x:.4f}<br>density: %{y:.2f}<extra></extra>',
             )
         )
 
@@ -2139,6 +2144,20 @@ def _style_posterior_pair_marginal_density_trace(density_trace: object) -> None:
         }
         density_trace.fillcolor = POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
 
+    @staticmethod
+    def _posterior_pair_density_hovertemplate(parameter_name: str) -> str:
+        """Return the hover template for a marginal density trace."""
+        return f'{parameter_name}: %{{x:.4f}}<br>density: %{{y:.4f}}<extra></extra>'
+
+    @staticmethod
+    def _posterior_pair_scatter_hovertemplate(
+        *,
+        x_parameter_name: str,
+        y_parameter_name: str,
+    ) -> str:
+        """Return the hover template for pair-plot sample points."""
+        return f'{x_parameter_name}: %{{x:.4f}}<br>{y_parameter_name}: %{{y:.4f}}<extra></extra>'
+
     @staticmethod
     def _posterior_pair_correlation_value(
         x_values: np.ndarray,
@@ -2202,7 +2221,7 @@ def _posterior_density_trace(
             fillcolor=POSTERIOR_DENSITY_FILL_COLOR,
             name=trace_name,
             showlegend=False,
-            hovertemplate='%{x:.4f}<br>density=%{y:.4f}<extra></extra>',
+            hovertemplate='%{x:.4f}<br>density: %{y:.2f}<extra></extra>',
         )
 
     @staticmethod
@@ -3552,9 +3571,9 @@ def _add_correlation_heatmap_value_panel(
         row = row_index + 1
         col = col_index + 1
         hovertemplate = (
-            f'x: {context.corr_df.columns[col_index]}<br>'
-            f'y: {context.corr_df.index[row_index]}<br>'
-            f'corr: %{{z:.{context.precision}f}}<extra></extra>'
+            f'{context.corr_df.columns[col_index]}<br>'
+            f'{context.corr_df.index[row_index]}<br>'
+            f'correlation: %{{z:.{context.precision}f}}<extra></extra>'
         )
         fig.add_trace(
             go.Heatmap(
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 5afe289f..bdadc9e8 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -327,9 +327,12 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
         'broad_gauss_v',
         'twotheta_offset',
     ]
-    assert figure.layout.annotations[0].xshift == -plotter._square_matrix_title_left_shift(
-        ['length_a', 'broad_gauss_u', 'broad_gauss_v', 'twotheta_offset']
-    )
+    assert figure.layout.annotations[0].xshift == -plotter._square_matrix_title_left_shift([
+        'length_a',
+        'broad_gauss_u',
+        'broad_gauss_v',
+        'twotheta_offset',
+    ])
     subplot = figure.get_subplot(1, 1)
     bottom_subplot = figure.get_subplot(4, 1)
     assert subplot.yaxis.showticklabels is False
@@ -365,9 +368,15 @@ def test_build_posterior_pairs_plot_sign_colors_contours_and_marginals():
 
     marginal_traces = [trace for trace in figure.data if trace.name == 'Marginal density']
     assert marginal_traces
-    assert all(trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR for trace in marginal_traces)
-    assert all(trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH for trace in marginal_traces)
-    assert all(trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR for trace in marginal_traces)
+    assert all(
+        trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR for trace in marginal_traces
+    )
+    assert all(
+        trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH for trace in marginal_traces
+    )
+    assert all(
+        trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR for trace in marginal_traces
+    )
 
     fill_contours = [
         trace
@@ -414,6 +423,35 @@ def test_build_posterior_pairs_plot_formats_dotted_axis_titles_multiline():
     assert figure.layout.margin.b > POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
 
 
+def test_build_posterior_pairs_plot_uses_full_names_in_hovertemplates():
+    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    dotted_parameter_names = [
+        'lbco.cell.length_a',
+        'hrpt.peak.broad_gauss_u',
+        'hrpt.peak.broad_gauss_v',
+        'hrpt.instrument.twotheta_offset',
+    ]
+    fit_results.posterior_samples.parameter_names = dotted_parameter_names
+    for index, unique_name in enumerate(dotted_parameter_names):
+        fit_results.parameters[index].unique_name = unique_name
+        fit_results.posterior_parameter_summaries[index].unique_name = unique_name
+
+    figure = plotter._build_posterior_pairs_plot(parameters=None)
+
+    hovertemplates = {
+        trace.hovertemplate
+        for trace in figure.data
+        if getattr(trace, 'hovertemplate', None) is not None
+    }
+
+    assert (
+        'lbco.cell.length_a: %{x:.4f}<br>hrpt.peak.broad_gauss_u: %{y:.4f}<extra></extra>'
+    ) in hovertemplates
+    assert (
+        'hrpt.instrument.twotheta_offset: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
+    ) in hovertemplates
+
+
 def test_posterior_pair_figure_height_shrinks_cells_for_many_parameters():
     from easydiffraction.display.plotting import PAIR_PLOT_CELL_SIZE_PIXELS
     from easydiffraction.display.plotting import Plotter
@@ -1114,7 +1152,7 @@ class Project:
     assert fig.data[0].showscale is False
     assert (
         fig.data[0].hovertemplate
-        == 'x: phase.scale<br>y: phase.cell.length_c<br>corr: %{z:.2f}<extra></extra>'
+        == 'phase.scale<br>phase.cell.length_c<br>correlation: %{z:.2f}<extra></extra>'
     )
     assert pytest.approx(fig.data[0].z[0][0], rel=1e-9) == -0.5
     assert fig.data[1].type == 'scatter'
@@ -1131,9 +1169,10 @@ class Project:
         'phase.<br>scale',
         'phase.<br>cell.<br>length_c',
     ]
-    assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift(
-        ['phase.<br>scale', 'phase.<br>cell.<br>length_c']
-    )
+    assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift([
+        'phase.<br>scale',
+        'phase.<br>cell.<br>length_c',
+    ])
     assert fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio'] == '1 / 1'
     assert fig.layout.xaxis.domain[1] < fig.layout.xaxis2.domain[0]
     assert fig.layout.xaxis.showline is False
@@ -1207,11 +1246,7 @@ class Project:
 
     fig = captured['fig']
     heatmap_traces = [trace for trace in fig.data if trace.type == 'heatmap']
-    text_traces = [
-        trace
-        for trace in fig.data
-        if trace.type == 'scatter' and trace.mode == 'text'
-    ]
+    text_traces = [trace for trace in fig.data if trace.type == 'scatter' and trace.mode == 'text']
     assert len(heatmap_traces) == 10
     assert [trace.text[0] for trace in text_traces] == ['-0.91', '0.83', '-0.89', '0.82']
 

From 78261ca928f87ae772b981844fc5c69bdefde509 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 23:04:46 +0200
Subject: [PATCH 062/106] Improve posterior plot binning and styling

---
 docs/dev/package-structure-full.md            |   2 +
 src/easydiffraction/display/plotting.py       | 107 +++++++++++++++++-
 .../easydiffraction/display/test_plotting.py  |  15 ++-
 3 files changed, 118 insertions(+), 6 deletions(-)

diff --git a/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md
index d1901351..8083f39b 100644
--- a/docs/dev/package-structure-full.md
+++ b/docs/dev/package-structure-full.md
@@ -376,10 +376,12 @@
 │   │   └── 🏷️ class RendererFactoryBase
 │   ├── 📄 plotting.py
 │   │   ├── 🏷️ class PlotterEngineEnum
+│   │   ├── 🏷️ class PosteriorPairPlotStyleEnum
 │   │   ├── 🏷️ class _MeasVsCalcPlotOptions
 │   │   ├── 🏷️ class _PowderMeasVsCalcSeries
 │   │   ├── 🏷️ class _PosteriorDistributionContext
 │   │   ├── 🏷️ class _PosteriorPairsContext
+│   │   ├── 🏷️ class _CorrelationHeatmapContext
 │   │   ├── 🏷️ class _PosteriorPairsLegendState
 │   │   ├── 🏷️ class Plotter
 │   │   └── 🏷️ class PlotterFactory
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 6242b404..59262e3a 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1903,15 +1903,27 @@ def _build_param_distribution_plot(
             title=context.title,
             label=context.label,
         )
+        histogram_bin_edges = self._posterior_distribution_histogram_bin_edges(context.values)
         density_trace = self._posterior_density_trace(
             fit_results=context.fit_results,
             parameter_name=context.parameter_name,
             values=context.values,
-            trace_name='Posterior density',
+            trace_name='Marginal density',
+        )
+        if density_trace is not None:
+            self._style_posterior_pair_marginal_density_trace(density_trace)
+            density_trace.hovertemplate = self._posterior_pair_density_hovertemplate(
+                context.parameter_name
+            )
+        x_axis_range = self._posterior_distribution_x_axis_range(
+            values=context.values,
+            density_trace=density_trace,
+            histogram_bin_edges=histogram_bin_edges,
         )
         y_axis_range = self._posterior_distribution_y_axis_range(
             values=context.values,
             density_trace=density_trace,
+            histogram_bin_edges=histogram_bin_edges,
         )
 
         self._add_posterior_distribution_interval_traces(
@@ -1923,6 +1935,7 @@ def _build_param_distribution_plot(
             fig=fig,
             go=go,
             values=context.values,
+            histogram_bin_edges=histogram_bin_edges,
         )
         self._add_posterior_distribution_density_trace(fig=fig, density_trace=density_trace)
         self._add_posterior_distribution_reference_traces(
@@ -1936,6 +1949,7 @@ def _build_param_distribution_plot(
             layout_factory=layout_factory,
             title=context.title,
             label=context.label,
+            x_axis_range=x_axis_range,
             y_axis_range=y_axis_range,
         )
         return fig
@@ -1990,14 +2004,82 @@ def _posterior_distribution_y_axis_range(
         *,
         values: np.ndarray,
         density_trace: object | None,
+        histogram_bin_edges: np.ndarray | None,
     ) -> tuple[float, float] | None:
         """Return the y-axis range for a posterior distribution plot."""
-        histogram_density, _ = np.histogram(values, bins=50, density=True)
-        density_sources = [histogram_density]
+        density_sources = []
+        histogram_density = self._posterior_distribution_histogram_density(
+            values,
+            histogram_bin_edges,
+        )
+        if histogram_density is not None:
+            density_sources.append(histogram_density)
         if density_trace is not None:
             density_sources.append(np.asarray(density_trace.y, dtype=float))
+        if not density_sources:
+            return None
         return self._posterior_density_axis_range(np.concatenate(density_sources))
 
+    def _posterior_distribution_x_axis_range(
+        self,
+        *,
+        values: np.ndarray,
+        density_trace: object | None,
+        histogram_bin_edges: np.ndarray | None,
+    ) -> tuple[float, float] | None:
+        """Return the x-axis range for a posterior distribution plot."""
+        if density_trace is not None:
+            return self._posterior_axis_bounds(
+                np.asarray(density_trace.x, dtype=float),
+                lower_bound=None,
+                upper_bound=None,
+            )
+
+        if histogram_bin_edges is not None:
+            return (
+                float(histogram_bin_edges[0]),
+                float(histogram_bin_edges[-1]),
+            )
+
+        finite_values = np.asarray(values, dtype=float)
+        finite_values = finite_values[np.isfinite(finite_values)]
+        if finite_values.size == 0:
+            return None
+        return self._posterior_axis_bounds(
+            finite_values,
+            lower_bound=None,
+            upper_bound=None,
+        )
+
+    @staticmethod
+    def _posterior_distribution_histogram_bin_edges(values: np.ndarray) -> np.ndarray | None:
+        """Return histogram bin edges used by the distribution plot."""
+        finite_values = np.asarray(values, dtype=float)
+        finite_values = finite_values[np.isfinite(finite_values)]
+        if finite_values.size == 0:
+            return None
+
+        bin_edges = np.histogram_bin_edges(finite_values, bins='auto')
+        if bin_edges.size >= MIN_POSTERIOR_SAMPLE_COUNT:
+            return np.asarray(bin_edges, dtype=float)
+        return None
+
+    @staticmethod
+    def _posterior_distribution_histogram_density(
+        values: np.ndarray,
+        histogram_bin_edges: np.ndarray | None,
+    ) -> np.ndarray | None:
+        """Return densities matching the rendered histogram bins."""
+        if histogram_bin_edges is None:
+            return None
+
+        histogram_density, _ = np.histogram(
+            np.asarray(values, dtype=float),
+            bins=histogram_bin_edges,
+            density=True,
+        )
+        return np.asarray(histogram_density, dtype=float)
+
     def _add_posterior_distribution_interval_traces(
         self,
         *,
@@ -2034,12 +2116,23 @@ def _add_posterior_distribution_histogram(
         fig: object,
         go: object,
         values: np.ndarray,
+        histogram_bin_edges: np.ndarray | None,
     ) -> None:
         """Add the histogram trace for a posterior distribution plot."""
+        histogram_kwargs: dict[str, object] = {}
+        if (
+            histogram_bin_edges is not None
+            and histogram_bin_edges.size >= MIN_POSTERIOR_SAMPLE_COUNT
+        ):
+            histogram_kwargs['xbins'] = {
+                'start': float(histogram_bin_edges[0]),
+                'end': float(histogram_bin_edges[-1]),
+                'size': float(histogram_bin_edges[1] - histogram_bin_edges[0]),
+            }
+
         fig.add_trace(
             go.Histogram(
                 x=values,
-                nbinsx=50,
                 histnorm='probability density',
                 marker={
                     'color': POSTERIOR_HISTOGRAM_FILL_COLOR,
@@ -2048,6 +2141,7 @@ def _add_posterior_distribution_histogram(
                 opacity=0.82,
                 name='Posterior histogram',
                 hovertemplate='sample=%{x:.4f}<br>density: %{y:.2f}<extra></extra>',
+                **histogram_kwargs,
             )
         )
 
@@ -2061,7 +2155,7 @@ def _add_posterior_distribution_density_trace(
         if density_trace is None:
             return
 
-        density_trace.name = 'Posterior density'
+        density_trace.name = 'Marginal density'
         density_trace.showlegend = True
         fig.add_trace(density_trace)
 
@@ -2106,6 +2200,7 @@ def _apply_posterior_distribution_layout(
         layout_factory: object | None,
         title: str,
         label: str,
+        x_axis_range: tuple[float, float] | None,
         y_axis_range: tuple[float, float] | None,
     ) -> None:
         """Apply layout settings to the distribution plot."""
@@ -2124,6 +2219,8 @@ def _apply_posterior_distribution_layout(
                     'y': 1.0,
                 },
             )
+        if x_axis_range is not None:
+            fig.update_xaxes(range=list(x_axis_range))
         if y_axis_range is not None:
             fig.update_yaxes(range=list(y_axis_range))
 
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index bdadc9e8..047a05b6 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -532,6 +532,10 @@ def test_build_posterior_pairs_plot_rejects_unknown_style():
 
 
 def test_build_param_distribution_plot_returns_plotly_figure():
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
+
     plotter, fit_results, _ = _make_bayesian_plotter_fixture()
     parameter = fit_results.parameters[0]
 
@@ -540,12 +544,21 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert figure.layout.title.text == 'Posterior distribution: length_a'
     assert {trace.name for trace in figure.data} >= {
         'Posterior histogram',
-        'Posterior density',
+        'Marginal density',
         '68% credible interval',
         '95% credible interval',
         'Median',
         'Max posterior',
     }
+    marginal_trace = next(trace for trace in figure.data if trace.name == 'Marginal density')
+    histogram_trace = next(trace for trace in figure.data if trace.name == 'Posterior histogram')
+    assert marginal_trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
+    assert marginal_trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
+    assert marginal_trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
+    assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
+    assert histogram_trace.xbins.size is not None
+    assert figure.layout.xaxis.range is not None
+    assert figure.layout.yaxis.range is not None
 
 
 def test_build_param_distribution_plot_accepts_unique_name_string():

From 0f79235948cc81d0b5738e56bdea9dc5f0990b41 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 23:06:34 +0200
Subject: [PATCH 063/106] Refactor posterior x-axis range to static method

---
 src/easydiffraction/display/plotting.py           | 15 ++++-----------
 .../unit/easydiffraction/display/test_plotting.py |  1 +
 2 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 59262e3a..0b3a2cb6 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -2020,8 +2020,8 @@ def _posterior_distribution_y_axis_range(
             return None
         return self._posterior_density_axis_range(np.concatenate(density_sources))
 
+    @staticmethod
     def _posterior_distribution_x_axis_range(
-        self,
         *,
         values: np.ndarray,
         density_trace: object | None,
@@ -2029,11 +2029,8 @@ def _posterior_distribution_x_axis_range(
     ) -> tuple[float, float] | None:
         """Return the x-axis range for a posterior distribution plot."""
         if density_trace is not None:
-            return self._posterior_axis_bounds(
-                np.asarray(density_trace.x, dtype=float),
-                lower_bound=None,
-                upper_bound=None,
-            )
+            density_x = np.asarray(density_trace.x, dtype=float)
+            return float(density_x[0]), float(density_x[-1])
 
         if histogram_bin_edges is not None:
             return (
@@ -2045,11 +2042,7 @@ def _posterior_distribution_x_axis_range(
         finite_values = finite_values[np.isfinite(finite_values)]
         if finite_values.size == 0:
             return None
-        return self._posterior_axis_bounds(
-            finite_values,
-            lower_bound=None,
-            upper_bound=None,
-        )
+        return float(np.min(finite_values)), float(np.max(finite_values))
 
     @staticmethod
     def _posterior_distribution_histogram_bin_edges(values: np.ndarray) -> np.ndarray | None:
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 047a05b6..8f731513 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -558,6 +558,7 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
     assert histogram_trace.xbins.size is not None
     assert figure.layout.xaxis.range is not None
+    assert tuple(figure.layout.xaxis.range) == (float(marginal_trace.x[0]), float(marginal_trace.x[-1]))
     assert figure.layout.yaxis.range is not None
 
 

From dc38184ac8bb32ed7a3c808fa187bf47a9c4b0a0 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 23:44:06 +0200
Subject: [PATCH 064/106] Change posterior predictive default style to band

---
 src/easydiffraction/display/plotting.py       |  4 +-
 .../easydiffraction/display/test_plotting.py  | 50 +++++++++++++++++++
 2 files changed, 52 insertions(+), 2 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 0b3a2cb6..c0a53092 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -828,7 +828,7 @@ def plot_param_distribution(
     def plot_posterior_predictive(
         self,
         expt_name: str,
-        style: str = 'band+draws',
+        style: str = 'band',
         x_min: float | None = None,
         x_max: float | None = None,
         *,
@@ -842,7 +842,7 @@ def plot_posterior_predictive(
         ----------
         expt_name : str
             Experiment name to plot.
-        style : str, default='band+draws'
+        style : str, default='band'
             ``'band'`` shows the 95% credible interval, ``'draws'``
             shows sampled predictive curves, and ``'band+draws'`` shows
             both together.
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 8f731513..f90d6585 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -693,6 +693,56 @@ def fake_evaluate(self, *, sampled_parameters, values, experiment, expt_name, x_
     assert [parameter.uncertainty for parameter in sampled_parameters] == [0.1, 0.2]
 
 
+def test_plot_posterior_predictive_defaults_to_band_for_bragg(monkeypatch):
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.BRAGG})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Experiment:
+        type = ExptType()
+
+    class Project:
+        experiments = {'hrpt': Experiment()}
+
+    plotter = Plotter()
+    plotter.engine = 'plotly'
+    plotter._set_project(Project())
+
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+
+    def fake_plot_posterior_predictive_data(
+        self,
+        *,
+        experiment,
+        expt_name,
+        plot_options,
+        x_axis,
+        style,
+    ):
+        captured['experiment'] = experiment
+        captured['expt_name'] = expt_name
+        captured['style'] = style
+        captured['x_axis'] = x_axis
+        captured['show_residual'] = plot_options.show_residual
+
+    monkeypatch.setattr(Plotter, '_plot_posterior_predictive_data', fake_plot_posterior_predictive_data)
+
+    plotter.plot_posterior_predictive('hrpt')
+
+    assert captured['experiment'] is Project.experiments['hrpt']
+    assert captured['expt_name'] == 'hrpt'
+    assert captured['style'] == 'band'
+    assert captured['show_residual'] is None
+
+
 def test_extract_bragg_tick_sets_uses_derived_d_spacing_for_cwl_ticks():
     import numpy as np
 

From 6d8ce0f920d628ab7850807d5589ec2a8daa106d Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 23:44:33 +0200
Subject: [PATCH 065/106] Add separate caching for band summaries

---
 src/easydiffraction/display/plotting.py       |  41 +++++--
 .../easydiffraction/display/test_plotting.py  | 114 +++++++++++++++++-
 2 files changed, 143 insertions(+), 12 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index c0a53092..105976c6 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -849,7 +849,7 @@ def plot_posterior_predictive(
         x_min : float | None, default=None
             Lower bound for the x-axis range.
         x_max : float | None, default=None
-            Upper bound for the x-axis range.
+            include_draws=style in {'draws', 'band+draws'},
         show_residual : bool | None, default=None
             Whether to include the residual row in the composite plot.
         x : object | None, default=None
@@ -901,6 +901,7 @@ def plot_posterior_predictive(
             experiment=experiment,
             expt_name=expt_name,
             x_axis=x_axis,
+            include_draws=style in {'draws', 'band+draws'},
         )
         if summary is None:
             return
@@ -2615,10 +2616,9 @@ def _get_or_build_posterior_predictive_summary(
         experiment: object,
         expt_name: str,
         x_axis: object,
+        include_draws: bool = True,
     ) -> object | None:
-        """
-        Return a cached or newly built posterior predictive summary.
-        """
+        """Return a cached or built predictive summary."""
         fit_results = self._get_fit_result_for_correlation()
         if fit_results is None:
             return None
@@ -2629,8 +2629,20 @@ def _get_or_build_posterior_predictive_summary(
             return None
 
         x_axis_name = getattr(x_axis, 'value', x_axis)
-        cache_key = self._posterior_predictive_key(expt_name, str(x_axis_name))
+        draw_cache_key = self._posterior_predictive_key(
+            expt_name,
+            str(x_axis_name),
+            include_draws=True,
+        )
+        band_cache_key = self._posterior_predictive_key(
+            expt_name,
+            str(x_axis_name),
+            include_draws=False,
+        )
+        cache_key = draw_cache_key if include_draws else band_cache_key
         summary = posterior_predictive.get(cache_key)
+        if summary is None and not include_draws:
+            summary = posterior_predictive.get(draw_cache_key)
         if summary is not None:
             return summary
 
@@ -2639,6 +2651,7 @@ def _get_or_build_posterior_predictive_summary(
             experiment=experiment,
             expt_name=expt_name,
             x_axis=x_axis,
+            include_draws=include_draws,
         )
         if summary is None:
             return None
@@ -2653,6 +2666,7 @@ def _build_posterior_predictive_summary(
         experiment: object,
         expt_name: str,
         x_axis: object,
+        include_draws: bool = True,
     ) -> object | None:
         """Build posterior predictive summaries from posterior draws."""
         sampling_inputs = self._posterior_predictive_sampling_inputs(fit_results)
@@ -2691,7 +2705,7 @@ def _build_posterior_predictive_summary(
             upper_95=np.asarray(upper_95, dtype=float),
             lower_68=np.asarray(lower_68, dtype=float),
             upper_68=np.asarray(upper_68, dtype=float),
-            draws=predictive_draw_array,
+            draws=predictive_draw_array if include_draws else None,
         )
 
     @staticmethod
@@ -2854,9 +2868,15 @@ def _posterior_predictive_draw_indices(n_draws: int) -> np.ndarray:
         )
 
     @staticmethod
-    def _posterior_predictive_key(expt_name: str, x_axis_name: str) -> str:
+    def _posterior_predictive_key(
+        expt_name: str,
+        x_axis_name: str,
+        *,
+        include_draws: bool = True,
+    ) -> str:
         """Return the cache key for a posterior predictive summary."""
-        return f'{expt_name}:{x_axis_name}'
+        key_suffix = 'draws' if include_draws else 'band'
+        return f'{expt_name}:{x_axis_name}:{key_suffix}'
 
     def _get_posterior_inference_data(
         self,
@@ -3012,6 +3032,7 @@ def _plot_posterior_predictive_data(
             experiment=experiment,
             expt_name=expt_name,
             x_axis=x_axis,
+            include_draws=style in {'draws', 'band+draws'},
         )
         if summary is None:
             return
@@ -3896,8 +3917,8 @@ def _plot_meas_data(
             Object with x-axis arrays (``two_theta``,
             ``time_of_flight``, ``d_spacing``) and ``meas`` array.
         expt_name : str
-            Experiment name for the title.
-        expt_type : object
+            Experiment name for the title. *,
+        include_draws : bool,
             Experiment type with scattering/beam enums.
         x_min : object, default=None
             Optional minimum x-axis limit.
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index f90d6585..a26fb9ec 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -558,7 +558,10 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
     assert histogram_trace.xbins.size is not None
     assert figure.layout.xaxis.range is not None
-    assert tuple(figure.layout.xaxis.range) == (float(marginal_trace.x[0]), float(marginal_trace.x[-1]))
+    assert tuple(figure.layout.xaxis.range) == (
+        float(marginal_trace.x[0]),
+        float(marginal_trace.x[-1]),
+    )
     assert figure.layout.yaxis.range is not None
 
 
@@ -693,6 +696,111 @@ def fake_evaluate(self, *, sampled_parameters, values, experiment, expt_name, x_
     assert [parameter.uncertainty for parameter in sampled_parameters] == [0.1, 0.2]
 
 
+def test_build_posterior_predictive_summary_omits_draws_when_not_requested(monkeypatch):
+    from easydiffraction.display.plotting import Plotter
+
+    class FakePredictiveParameter:
+        def __init__(self, unique_name, value, uncertainty):
+            self.unique_name = unique_name
+            self.value = value
+            self.uncertainty = uncertainty
+
+        def _set_value_from_minimizer(self, value):
+            self.value = value
+
+    sampled_parameters = [
+        FakePredictiveParameter('a', 1.0, 0.1),
+        FakePredictiveParameter('b', 2.0, 0.2),
+    ]
+    posterior_samples = SimpleNamespace(
+        parameter_names=['a', 'b'],
+        flattened=lambda: np.array(
+            [
+                [1.0, 2.0],
+                [1.1, 2.1],
+                [0.9, 1.9],
+                [1.2, 2.2],
+            ],
+            dtype=float,
+        ),
+    )
+    fit_results = SimpleNamespace(
+        posterior_samples=posterior_samples,
+        parameters=sampled_parameters,
+    )
+    plotter = Plotter()
+
+    def fake_evaluate(self, *, sampled_parameters, values, experiment, expt_name, x_axis):
+        x = np.array([0.0, 1.0], dtype=float)
+        y = np.array([values[0] + values[1], values[0] - values[1]], dtype=float)
+        return y, x
+
+    monkeypatch.setattr(Plotter, '_evaluate_posterior_predictive_state', fake_evaluate)
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+
+    summary = plotter._build_posterior_predictive_summary(
+        fit_results=fit_results,
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+        include_draws=False,
+    )
+
+    assert summary is not None
+    assert summary.draws is None
+    np.testing.assert_allclose(summary.lower_95.shape, (2,))
+    np.testing.assert_allclose(summary.upper_95.shape, (2,))
+
+
+def test_get_or_build_posterior_predictive_summary_rebuilds_draws_after_band_cache(
+    monkeypatch,
+):
+    from easydiffraction.display.plotting import Plotter
+
+    fit_results = SimpleNamespace(
+        posterior_predictive={},
+        posterior_samples=object(),
+    )
+    band_summary = SimpleNamespace(draws=None)
+    draw_summary = SimpleNamespace(draws=np.ones((2, 2), dtype=float))
+    build_calls: list[bool] = []
+    plotter = Plotter()
+
+    monkeypatch.setattr(Plotter, '_get_fit_result_for_correlation', lambda self: fit_results)
+
+    def fake_build(
+        self,
+        *,
+        fit_results,
+        experiment,
+        expt_name,
+        x_axis,
+        include_draws=True,
+    ):
+        del fit_results, experiment, expt_name, x_axis
+        build_calls.append(include_draws)
+        return draw_summary if include_draws else band_summary
+
+    monkeypatch.setattr(Plotter, '_build_posterior_predictive_summary', fake_build)
+
+    summary_band = plotter._get_or_build_posterior_predictive_summary(
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+        include_draws=False,
+    )
+    summary_draws = plotter._get_or_build_posterior_predictive_summary(
+        experiment=object(),
+        expt_name='hrpt',
+        x_axis='two_theta',
+        include_draws=True,
+    )
+
+    assert summary_band is band_summary
+    assert summary_draws is draw_summary
+    assert build_calls == [False, True]
+
+
 def test_plot_posterior_predictive_defaults_to_band_for_bragg(monkeypatch):
     from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
     from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
@@ -733,7 +841,9 @@ def fake_plot_posterior_predictive_data(
         captured['x_axis'] = x_axis
         captured['show_residual'] = plot_options.show_residual
 
-    monkeypatch.setattr(Plotter, '_plot_posterior_predictive_data', fake_plot_posterior_predictive_data)
+    monkeypatch.setattr(
+        Plotter, '_plot_posterior_predictive_data', fake_plot_posterior_predictive_data
+    )
 
     plotter.plot_posterior_predictive('hrpt')
 

From 19815afaf184b6f53b21588f141a23209f5488d7 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 23:45:01 +0200
Subject: [PATCH 066/106] Reduce sampling steps and adjust plot ranges

---
 docs/docs/tutorials/ed-13.ipynb |    2 +-
 docs/docs/tutorials/ed-21.ipynb | 3677 +++++++++++++++++++++++++------
 docs/docs/tutorials/ed-21.py    |   14 +-
 3 files changed, 3031 insertions(+), 662 deletions(-)

diff --git a/docs/docs/tutorials/ed-13.ipynb b/docs/docs/tutorials/ed-13.ipynb
index 69eae9aa..4d605962 100644
--- a/docs/docs/tutorials/ed-13.ipynb
+++ b/docs/docs/tutorials/ed-13.ipynb
@@ -2665,7 +2665,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "title,tags,-all",
+   "cell_metadata_filter": "tags,title,-all",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   }
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index c9b1236e..2a938413 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -340,8 +340,7 @@
     "experiment.background.create(id='1', x=10, y=168.5585)\n",
     "experiment.background.create(id='2', x=30, y=164.3357)\n",
     "experiment.background.create(id='3', x=50, y=166.8881)\n",
-    "experiment.background.create(id='4', x=110, y=175.4006)\n",
-    "experiment.background.create(id='5', x=165, y=174.2813)"
+    "experiment.background.create(id='4', x=110, y=175.4006)"
    ]
   },
   {
@@ -351,8 +350,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "experiment.excluded_regions.create(id='1', start=0, end=30)\n",
-    "experiment.excluded_regions.create(id='2', start=70, end=180)"
+    "experiment.excluded_regions.create(id='1', start=0, end=10)\n",
+    "experiment.excluded_regions.create(id='2', start=100, end=180)"
    ]
   },
   {
@@ -399,7 +398,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure.cell.length_a.free = True\n",
+    "structure.cell.length_a.free = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_phases['lbco'].scale.free = True\n",
     "experiment.peak.broad_gauss_u.free = True\n",
     "experiment.peak.broad_gauss_v.free = True\n",
     "experiment.instrument.calib_twotheta_offset.free = True"
@@ -407,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29",
+   "id": "30",
    "metadata": {},
    "source": [
     "We choose the BUMPS Levenberg-Marquardt minimizer as a fast local\n",
@@ -417,8 +426,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "30",
+   "execution_count": 19,
+   "id": "31",
    "metadata": {},
    "outputs": [
     {
@@ -432,124 +441,135 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_db54b   {\n",
+       "#T_c23e7   {\n",
        "  border: 1px solid #dadada;\n",
        "  border-collapse: collapse;\n",
        "  margin-top: 0.5em;\n",
        "  margin-left: 0.5em;\n",
        "}\n",
-       "#T_db54b thead {\n",
+       "#T_c23e7 thead {\n",
        "  border-bottom: 1px solid #dadada;\n",
        "}\n",
-       "#T_db54b th {\n",
+       "#T_c23e7 th {\n",
        "  border: none;\n",
        "  padding-top: 0.25em;\n",
        "  padding-bottom: 0.25em;\n",
        "  line-height: 1.15em;\n",
        "}\n",
-       "#T_db54b  td {\n",
+       "#T_c23e7  td {\n",
        "  border: none;\n",
        "  padding-top: 0.25em;\n",
        "  padding-bottom: 0.25em;\n",
        "  line-height: 1.15em;\n",
        "}\n",
-       "#T_db54b th.row_heading {\n",
+       "#T_c23e7 th.row_heading {\n",
        "  color: #dadada;\n",
        "  font-weight: normal;\n",
        "}\n",
-       "#T_db54b tbody tr:nth-child(odd) {\n",
+       "#T_c23e7 tbody tr:nth-child(odd) {\n",
        "  background-color: transparent;\n",
        "}\n",
-       "#T_db54b  tbody tr:nth-child(even) {\n",
+       "#T_c23e7  tbody tr:nth-child(even) {\n",
        "  background-color: transparent;\n",
        "}\n",
-       "#T_db54b th.col0 {\n",
+       "#T_c23e7 th.col0 {\n",
        "  text-align: left;\n",
        "}\n",
-       "#T_db54b th.col1 {\n",
+       "#T_c23e7 th.col1 {\n",
        "  text-align: left;\n",
        "}\n",
-       "#T_db54b th.col2 {\n",
+       "#T_c23e7 th.col2 {\n",
        "  text-align: left;\n",
        "}\n",
-       "#T_db54b_row0_col0, #T_db54b_row0_col1, #T_db54b_row0_col2, #T_db54b_row1_col0, #T_db54b_row1_col1, #T_db54b_row1_col2, #T_db54b_row2_col0, #T_db54b_row2_col1, #T_db54b_row2_col2, #T_db54b_row3_col0, #T_db54b_row3_col1, #T_db54b_row3_col2, #T_db54b_row4_col0, #T_db54b_row4_col1, #T_db54b_row4_col2, #T_db54b_row5_col0, #T_db54b_row5_col1, #T_db54b_row5_col2, #T_db54b_row6_col0, #T_db54b_row6_col1, #T_db54b_row6_col2, #T_db54b_row7_col0, #T_db54b_row7_col1, #T_db54b_row7_col2, #T_db54b_row8_col0, #T_db54b_row8_col1, #T_db54b_row8_col2 {\n",
+       "#T_c23e7_row0_col0, #T_c23e7_row0_col1, #T_c23e7_row0_col2, #T_c23e7_row1_col0, #T_c23e7_row1_col1, #T_c23e7_row1_col2, #T_c23e7_row2_col0, #T_c23e7_row2_col1, #T_c23e7_row2_col2, #T_c23e7_row3_col0, #T_c23e7_row3_col1, #T_c23e7_row3_col2, #T_c23e7_row4_col0, #T_c23e7_row4_col1, #T_c23e7_row4_col2, #T_c23e7_row5_col0, #T_c23e7_row5_col1, #T_c23e7_row5_col2, #T_c23e7_row6_col0, #T_c23e7_row6_col1, #T_c23e7_row6_col2, #T_c23e7_row7_col0, #T_c23e7_row7_col1, #T_c23e7_row7_col2, #T_c23e7_row8_col0, #T_c23e7_row8_col1, #T_c23e7_row8_col2 {\n",
        "  text-align: left;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_db54b\" class=\"dataframe\">\n",
+       "<table id=\"T_c23e7\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_db54b_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
-       "      <th id=\"T_db54b_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_db54b_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+       "      <th id=\"T_c23e7_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
+       "      <th id=\"T_c23e7_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
+       "      <th id=\"T_c23e7_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_db54b_row0_col0\" class=\"data row0 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
-       "      <td id=\"T_db54b_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
+       "      <th id=\"T_c23e7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_c23e7_row0_col0\" class=\"data row0 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
+       "      <td id=\"T_c23e7_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_db54b_row1_col0\" class=\"data row1 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
-       "      <td id=\"T_db54b_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
+       "      <th id=\"T_c23e7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_c23e7_row1_col0\" class=\"data row1 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
+       "      <td id=\"T_c23e7_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_db54b_row2_col0\" class=\"data row2 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
-       "      <td id=\"T_db54b_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
+       "      <th id=\"T_c23e7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_c23e7_row2_col0\" class=\"data row2 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
+       "      <td id=\"T_c23e7_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_db54b_row3_col0\" class=\"data row3 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
-       "      <td id=\"T_db54b_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
+       "      <th id=\"T_c23e7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_c23e7_row3_col0\" class=\"data row3 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
+       "      <td id=\"T_c23e7_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_db54b_row4_col0\" class=\"data row4 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
-       "      <td id=\"T_db54b_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
+       "      <th id=\"T_c23e7_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_c23e7_row4_col0\" class=\"data row4 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
+       "      <td id=\"T_c23e7_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_db54b_row5_col0\" class=\"data row5 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
-       "      <td id=\"T_db54b_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
+       "      <th id=\"T_c23e7_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_c23e7_row5_col0\" class=\"data row5 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
+       "      <td id=\"T_c23e7_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_db54b_row6_col0\" class=\"data row6 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
-       "      <td id=\"T_db54b_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
+       "      <th id=\"T_c23e7_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_c23e7_row6_col0\" class=\"data row6 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
+       "      <td id=\"T_c23e7_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_db54b_row7_col0\" class=\"data row7 col0\" ></td>\n",
-       "      <td id=\"T_db54b_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
-       "      <td id=\"T_db54b_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
+       "      <th id=\"T_c23e7_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_c23e7_row7_col0\" class=\"data row7 col0\" ></td>\n",
+       "      <td id=\"T_c23e7_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
+       "      <td id=\"T_c23e7_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_db54b_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_db54b_row8_col0\" class=\"data row8 col0\" >*</td>\n",
-       "      <td id=\"T_db54b_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
-       "      <td id=\"T_db54b_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
+       "      <th id=\"T_c23e7_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_c23e7_row8_col0\" class=\"data row8 col0\" >*</td>\n",
+       "      <td id=\"T_c23e7_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
+       "      <td id=\"T_c23e7_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x16b153620>"
+       "<pandas.io.formats.style.Styler at 0x36992fe00>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
+    }
+   ],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "32",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
@@ -560,14 +580,13 @@
     }
    ],
    "source": [
-    "project.analysis.fit.show_minimizer_types()\n",
     "project.analysis.fit.minimizer_type = 'bumps (lm)'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "31",
+   "execution_count": 21,
+   "id": "33",
    "metadata": {},
    "outputs": [
     {
@@ -584,154 +603,133 @@
      "data": {
       "text/html": [
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-       "#T_c89b0 th.col3 {\n",
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-       "#T_c89b0_row0_col0, #T_c89b0_row0_col1, #T_c89b0_row0_col2, #T_c89b0_row0_col3, #T_c89b0_row1_col0, #T_c89b0_row1_col1, #T_c89b0_row1_col2, #T_c89b0_row1_col3, #T_c89b0_row2_col0, #T_c89b0_row2_col1, #T_c89b0_row2_col2, #T_c89b0_row2_col3, #T_c89b0_row3_col0, #T_c89b0_row3_col1, #T_c89b0_row3_col2, #T_c89b0_row3_col3, #T_c89b0_row4_col0, #T_c89b0_row4_col1, #T_c89b0_row4_col2, #T_c89b0_row4_col3, #T_c89b0_row5_col0, #T_c89b0_row5_col1, #T_c89b0_row5_col2, #T_c89b0_row5_col3, #T_c89b0_row6_col0, #T_c89b0_row6_col1, #T_c89b0_row6_col2, #T_c89b0_row6_col3, #T_c89b0_row7_col0, #T_c89b0_row7_col1, #T_c89b0_row7_col2, #T_c89b0_row7_col3, #T_c89b0_row8_col0, #T_c89b0_row8_col1, #T_c89b0_row8_col2, #T_c89b0_row8_col3, #T_c89b0_row9_col0, #T_c89b0_row9_col1, #T_c89b0_row9_col2, #T_c89b0_row9_col3, #T_c89b0_row10_col0, #T_c89b0_row10_col1, #T_c89b0_row10_col2, #T_c89b0_row10_col3, #T_c89b0_row11_col0, #T_c89b0_row11_col1, #T_c89b0_row11_col2, #T_c89b0_row11_col3, #T_c89b0_row12_col0, #T_c89b0_row12_col1, #T_c89b0_row12_col2, #T_c89b0_row12_col3 {\n",
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-       "<table id=\"T_c89b0\" class=\"dataframe\">\n",
+       "<table id=\"T_c22af\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_c89b0_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
-       "      <th id=\"T_c89b0_level0_col1\" class=\"col_heading level0 col1\" >time (s)</th>\n",
-       "      <th id=\"T_c89b0_level0_col2\" class=\"col_heading level0 col2\" >χ²</th>\n",
-       "      <th id=\"T_c89b0_level0_col3\" class=\"col_heading level0 col3\" >change / status</th>\n",
+       "      <th id=\"T_c22af_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
+       "      <th id=\"T_c22af_level0_col1\" class=\"col_heading level0 col1\" >time (s)</th>\n",
+       "      <th id=\"T_c22af_level0_col2\" class=\"col_heading level0 col2\" >χ²</th>\n",
+       "      <th id=\"T_c22af_level0_col3\" class=\"col_heading level0 col3\" >change / status</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_c89b0_row0_col0\" class=\"data row0 col0\" >1</td>\n",
-       "      <td id=\"T_c89b0_row0_col1\" class=\"data row0 col1\" >0.14</td>\n",
-       "      <td id=\"T_c89b0_row0_col2\" class=\"data row0 col2\" >736.04</td>\n",
-       "      <td id=\"T_c89b0_row0_col3\" class=\"data row0 col3\" ></td>\n",
+       "      <th id=\"T_c22af_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_c22af_row0_col0\" class=\"data row0 col0\" >1</td>\n",
+       "      <td id=\"T_c22af_row0_col1\" class=\"data row0 col1\" >0.19</td>\n",
+       "      <td id=\"T_c22af_row0_col2\" class=\"data row0 col2\" >377.51</td>\n",
+       "      <td id=\"T_c22af_row0_col3\" class=\"data row0 col3\" ></td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_c89b0_row1_col0\" class=\"data row1 col0\" >7</td>\n",
-       "      <td id=\"T_c89b0_row1_col1\" class=\"data row1 col1\" >0.25</td>\n",
-       "      <td id=\"T_c89b0_row1_col2\" class=\"data row1 col2\" >315.22</td>\n",
-       "      <td id=\"T_c89b0_row1_col3\" class=\"data row1 col3\" >57.2% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_c22af_row1_col0\" class=\"data row1 col0\" >8</td>\n",
+       "      <td id=\"T_c22af_row1_col1\" class=\"data row1 col1\" >0.37</td>\n",
+       "      <td id=\"T_c22af_row1_col2\" class=\"data row1 col2\" >56.31</td>\n",
+       "      <td id=\"T_c22af_row1_col3\" class=\"data row1 col3\" >85.1% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_c89b0_row2_col0\" class=\"data row2 col0\" >12</td>\n",
-       "      <td id=\"T_c89b0_row2_col1\" class=\"data row2 col1\" >0.34</td>\n",
-       "      <td id=\"T_c89b0_row2_col2\" class=\"data row2 col2\" >93.97</td>\n",
-       "      <td id=\"T_c89b0_row2_col3\" class=\"data row2 col3\" >70.2% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_c22af_row2_col0\" class=\"data row2 col0\" >14</td>\n",
+       "      <td id=\"T_c22af_row2_col1\" class=\"data row2 col1\" >0.50</td>\n",
+       "      <td id=\"T_c22af_row2_col2\" class=\"data row2 col2\" >39.54</td>\n",
+       "      <td id=\"T_c22af_row2_col3\" class=\"data row2 col3\" >29.8% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_c89b0_row3_col0\" class=\"data row3 col0\" >17</td>\n",
-       "      <td id=\"T_c89b0_row3_col1\" class=\"data row3 col1\" >0.43</td>\n",
-       "      <td id=\"T_c89b0_row3_col2\" class=\"data row3 col2\" >40.33</td>\n",
-       "      <td id=\"T_c89b0_row3_col3\" class=\"data row3 col3\" >57.1% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_c22af_row3_col0\" class=\"data row3 col0\" >21</td>\n",
+       "      <td id=\"T_c22af_row3_col1\" class=\"data row3 col1\" >0.66</td>\n",
+       "      <td id=\"T_c22af_row3_col2\" class=\"data row3 col2\" >37.66</td>\n",
+       "      <td id=\"T_c22af_row3_col3\" class=\"data row3 col3\" >4.7% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_c89b0_row4_col0\" class=\"data row4 col0\" >21</td>\n",
-       "      <td id=\"T_c89b0_row4_col1\" class=\"data row4 col1\" >0.51</td>\n",
-       "      <td id=\"T_c89b0_row4_col2\" class=\"data row4 col2\" >39.06</td>\n",
-       "      <td id=\"T_c89b0_row4_col3\" class=\"data row4 col3\" >3.1% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_c22af_row4_col0\" class=\"data row4 col0\" >26</td>\n",
+       "      <td id=\"T_c22af_row4_col1\" class=\"data row4 col1\" >0.77</td>\n",
+       "      <td id=\"T_c22af_row4_col2\" class=\"data row4 col2\" >34.14</td>\n",
+       "      <td id=\"T_c22af_row4_col3\" class=\"data row4 col3\" >9.4% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_c89b0_row5_col0\" class=\"data row5 col0\" >22</td>\n",
-       "      <td id=\"T_c89b0_row5_col1\" class=\"data row5 col1\" >0.54</td>\n",
-       "      <td id=\"T_c89b0_row5_col2\" class=\"data row5 col2\" >28.50</td>\n",
-       "      <td id=\"T_c89b0_row5_col3\" class=\"data row5 col3\" >27.0% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_c22af_row5_col0\" class=\"data row5 col0\" >27</td>\n",
+       "      <td id=\"T_c22af_row5_col1\" class=\"data row5 col1\" >0.80</td>\n",
+       "      <td id=\"T_c22af_row5_col2\" class=\"data row5 col2\" >23.47</td>\n",
+       "      <td id=\"T_c22af_row5_col3\" class=\"data row5 col3\" >31.3% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_c89b0_row6_col0\" class=\"data row6 col0\" >26</td>\n",
-       "      <td id=\"T_c89b0_row6_col1\" class=\"data row6 col1\" >0.61</td>\n",
-       "      <td id=\"T_c89b0_row6_col2\" class=\"data row6 col2\" >24.69</td>\n",
-       "      <td id=\"T_c89b0_row6_col3\" class=\"data row6 col3\" >13.4% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_c22af_row6_col0\" class=\"data row6 col0\" >33</td>\n",
+       "      <td id=\"T_c22af_row6_col1\" class=\"data row6 col1\" >0.93</td>\n",
+       "      <td id=\"T_c22af_row6_col2\" class=\"data row6 col2\" >8.74</td>\n",
+       "      <td id=\"T_c22af_row6_col3\" class=\"data row6 col3\" >62.7% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_c89b0_row7_col0\" class=\"data row7 col0\" >27</td>\n",
-       "      <td id=\"T_c89b0_row7_col1\" class=\"data row7 col1\" >0.63</td>\n",
-       "      <td id=\"T_c89b0_row7_col2\" class=\"data row7 col2\" >16.23</td>\n",
-       "      <td id=\"T_c89b0_row7_col3\" class=\"data row7 col3\" >34.2% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_c22af_row7_col0\" class=\"data row7 col0\" >39</td>\n",
+       "      <td id=\"T_c22af_row7_col1\" class=\"data row7 col1\" >1.06</td>\n",
+       "      <td id=\"T_c22af_row7_col2\" class=\"data row7 col2\" >1.85</td>\n",
+       "      <td id=\"T_c22af_row7_col3\" class=\"data row7 col3\" >78.9% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_c89b0_row8_col0\" class=\"data row8 col0\" >31</td>\n",
-       "      <td id=\"T_c89b0_row8_col1\" class=\"data row8 col1\" >0.71</td>\n",
-       "      <td id=\"T_c89b0_row8_col2\" class=\"data row8 col2\" >14.77</td>\n",
-       "      <td id=\"T_c89b0_row8_col3\" class=\"data row8 col3\" >9.0% ↓</td>\n",
+       "      <th id=\"T_c22af_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_c22af_row8_col0\" class=\"data row8 col0\" >45</td>\n",
+       "      <td id=\"T_c22af_row8_col1\" class=\"data row8 col1\" >1.19</td>\n",
+       "      <td id=\"T_c22af_row8_col2\" class=\"data row8 col2\" >1.30</td>\n",
+       "      <td id=\"T_c22af_row8_col3\" class=\"data row8 col3\" >29.9% ↓</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
-       "      <td id=\"T_c89b0_row9_col0\" class=\"data row9 col0\" >32</td>\n",
-       "      <td id=\"T_c89b0_row9_col1\" class=\"data row9 col1\" >0.73</td>\n",
-       "      <td id=\"T_c89b0_row9_col2\" class=\"data row9 col2\" >2.74</td>\n",
-       "      <td id=\"T_c89b0_row9_col3\" class=\"data row9 col3\" >81.4% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
-       "      <td id=\"T_c89b0_row10_col0\" class=\"data row10 col0\" >37</td>\n",
-       "      <td id=\"T_c89b0_row10_col1\" class=\"data row10 col1\" >0.82</td>\n",
-       "      <td id=\"T_c89b0_row10_col2\" class=\"data row10 col2\" >1.45</td>\n",
-       "      <td id=\"T_c89b0_row10_col3\" class=\"data row10 col3\" >47.1% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
-       "      <td id=\"T_c89b0_row11_col0\" class=\"data row11 col0\" >42</td>\n",
-       "      <td id=\"T_c89b0_row11_col1\" class=\"data row11 col1\" >0.91</td>\n",
-       "      <td id=\"T_c89b0_row11_col2\" class=\"data row11 col2\" >1.37</td>\n",
-       "      <td id=\"T_c89b0_row11_col3\" class=\"data row11 col3\" >5.6% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c89b0_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
-       "      <td id=\"T_c89b0_row12_col0\" class=\"data row12 col0\" >69</td>\n",
-       "      <td id=\"T_c89b0_row12_col1\" class=\"data row12 col1\" >1.39</td>\n",
-       "      <td id=\"T_c89b0_row12_col2\" class=\"data row12 col2\" >1.37</td>\n",
-       "      <td id=\"T_c89b0_row12_col3\" class=\"data row12 col3\" ></td>\n",
+       "      <th id=\"T_c22af_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
+       "      <td id=\"T_c22af_row9_col0\" class=\"data row9 col0\" >77</td>\n",
+       "      <td id=\"T_c22af_row9_col1\" class=\"data row9 col1\" >1.87</td>\n",
+       "      <td id=\"T_c22af_row9_col2\" class=\"data row9 col2\" >1.29</td>\n",
+       "      <td id=\"T_c22af_row9_col3\" class=\"data row9 col3\" ></td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
@@ -747,7 +745,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "🏆 Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m1.37\u001b[0m at iteration \u001b[1;36m68\u001b[0m\n",
+      "🏆 Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m1.29\u001b[0m at iteration \u001b[1;36m76\u001b[0m\n",
       "✅ Fitting complete.\n"
      ]
     }
@@ -758,8 +756,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "32",
+   "execution_count": 22,
+   "id": "34",
    "metadata": {},
    "outputs": [
     {
@@ -768,11 +766,11 @@
      "text": [
       "\u001b[1;34mFit results\u001b[0m\n",
       "✅ Success: \u001b[3;92mTrue\u001b[0m\n",
-      "⏱️ Fitting time: \u001b[1;36m1.39\u001b[0m seconds\n",
-      "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.37\u001b[0m\n",
-      "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.27\u001b[0m%\n",
-      "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.42\u001b[0m%\n",
-      "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m3.78\u001b[0m%\n",
+      "⏱️ Fitting time: \u001b[1;36m1.87\u001b[0m seconds\n",
+      "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.29\u001b[0m\n",
+      "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.65\u001b[0m%\n",
+      "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.92\u001b[0m%\n",
+      "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m4.08\u001b[0m%\n",
       "📈 Fitted parameters:\n"
      ]
     },
@@ -780,140 +778,152 @@
      "data": {
       "text/html": [
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-       "#T_de5f7 tbody tr:nth-child(odd) {\n",
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        "  background-color: transparent;\n",
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-       "#T_de5f7  tbody tr:nth-child(even) {\n",
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        "</style>\n",
-       "<table id=\"T_de5f7\" class=\"dataframe\">\n",
+       "<table id=\"T_9c9a1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_de5f7_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_de5f7_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
-       "      <th id=\"T_de5f7_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
-       "      <th id=\"T_de5f7_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
-       "      <th id=\"T_de5f7_level0_col4\" class=\"col_heading level0 col4\" >start</th>\n",
-       "      <th id=\"T_de5f7_level0_col5\" class=\"col_heading level0 col5\" >fitted</th>\n",
-       "      <th id=\"T_de5f7_level0_col6\" class=\"col_heading level0 col6\" >uncertainty</th>\n",
-       "      <th id=\"T_de5f7_level0_col7\" class=\"col_heading level0 col7\" >units</th>\n",
-       "      <th id=\"T_de5f7_level0_col8\" class=\"col_heading level0 col8\" >change</th>\n",
+       "      <th id=\"T_9c9a1_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
+       "      <th id=\"T_9c9a1_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
+       "      <th id=\"T_9c9a1_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_9c9a1_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_9c9a1_level0_col4\" class=\"col_heading level0 col4\" >start</th>\n",
+       "      <th id=\"T_9c9a1_level0_col5\" class=\"col_heading level0 col5\" >fitted</th>\n",
+       "      <th id=\"T_9c9a1_level0_col6\" class=\"col_heading level0 col6\" >uncertainty</th>\n",
+       "      <th id=\"T_9c9a1_level0_col7\" class=\"col_heading level0 col7\" >units</th>\n",
+       "      <th id=\"T_9c9a1_level0_col8\" class=\"col_heading level0 col8\" >change</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_de5f7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_de5f7_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_de5f7_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_de5f7_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_de5f7_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_de5f7_row0_col4\" class=\"data row0 col4\" >3.8800</td>\n",
-       "      <td id=\"T_de5f7_row0_col5\" class=\"data row0 col5\" >3.8907</td>\n",
-       "      <td id=\"T_de5f7_row0_col6\" class=\"data row0 col6\" >0.0002</td>\n",
-       "      <td id=\"T_de5f7_row0_col7\" class=\"data row0 col7\" >Å</td>\n",
-       "      <td id=\"T_de5f7_row0_col8\" class=\"data row0 col8\" >0.27 % ↑</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_de5f7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_de5f7_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_de5f7_row1_col1\" class=\"data row1 col1\" >peak</td>\n",
-       "      <td id=\"T_de5f7_row1_col2\" class=\"data row1 col2\" ></td>\n",
-       "      <td id=\"T_de5f7_row1_col3\" class=\"data row1 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_de5f7_row1_col4\" class=\"data row1 col4\" >0.1000</td>\n",
-       "      <td id=\"T_de5f7_row1_col5\" class=\"data row1 col5\" >0.0333</td>\n",
-       "      <td id=\"T_de5f7_row1_col6\" class=\"data row1 col6\" >0.0163</td>\n",
-       "      <td id=\"T_de5f7_row1_col7\" class=\"data row1 col7\" >deg²</td>\n",
-       "      <td id=\"T_de5f7_row1_col8\" class=\"data row1 col8\" >66.71 % ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_de5f7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_de5f7_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_de5f7_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_de5f7_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_de5f7_row2_col3\" class=\"data row2 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_de5f7_row2_col4\" class=\"data row2 col4\" >-0.1000</td>\n",
-       "      <td id=\"T_de5f7_row2_col5\" class=\"data row2 col5\" >-0.0934</td>\n",
-       "      <td id=\"T_de5f7_row2_col6\" class=\"data row2 col6\" >0.0091</td>\n",
-       "      <td id=\"T_de5f7_row2_col7\" class=\"data row2 col7\" >deg²</td>\n",
-       "      <td id=\"T_de5f7_row2_col8\" class=\"data row2 col8\" >6.57 % ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_de5f7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_de5f7_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_de5f7_row3_col1\" class=\"data row3 col1\" >instrument</td>\n",
-       "      <td id=\"T_de5f7_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_de5f7_row3_col3\" class=\"data row3 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_de5f7_row3_col4\" class=\"data row3 col4\" >0.0000</td>\n",
-       "      <td id=\"T_de5f7_row3_col5\" class=\"data row3 col5\" >0.6221</td>\n",
-       "      <td id=\"T_de5f7_row3_col6\" class=\"data row3 col6\" >0.0029</td>\n",
-       "      <td id=\"T_de5f7_row3_col7\" class=\"data row3 col7\" >deg</td>\n",
-       "      <td id=\"T_de5f7_row3_col8\" class=\"data row3 col8\" >N/A</td>\n",
+       "      <th id=\"T_9c9a1_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_9c9a1_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_9c9a1_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_9c9a1_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_9c9a1_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_9c9a1_row0_col4\" class=\"data row0 col4\" >3.8800</td>\n",
+       "      <td id=\"T_9c9a1_row0_col5\" class=\"data row0 col5\" >3.8913</td>\n",
+       "      <td id=\"T_9c9a1_row0_col6\" class=\"data row0 col6\" >0.0001</td>\n",
+       "      <td id=\"T_9c9a1_row0_col7\" class=\"data row0 col7\" >Å</td>\n",
+       "      <td id=\"T_9c9a1_row0_col8\" class=\"data row0 col8\" >0.29 % ↑</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9c9a1_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_9c9a1_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_9c9a1_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
+       "      <td id=\"T_9c9a1_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
+       "      <td id=\"T_9c9a1_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
+       "      <td id=\"T_9c9a1_row1_col4\" class=\"data row1 col4\" >9.1351</td>\n",
+       "      <td id=\"T_9c9a1_row1_col5\" class=\"data row1 col5\" >9.1329</td>\n",
+       "      <td id=\"T_9c9a1_row1_col6\" class=\"data row1 col6\" >0.0333</td>\n",
+       "      <td id=\"T_9c9a1_row1_col7\" class=\"data row1 col7\" ></td>\n",
+       "      <td id=\"T_9c9a1_row1_col8\" class=\"data row1 col8\" >0.02 % ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9c9a1_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_9c9a1_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_9c9a1_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_9c9a1_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_9c9a1_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_9c9a1_row2_col4\" class=\"data row2 col4\" >0.1000</td>\n",
+       "      <td id=\"T_9c9a1_row2_col5\" class=\"data row2 col5\" >0.0817</td>\n",
+       "      <td id=\"T_9c9a1_row2_col6\" class=\"data row2 col6\" >0.0078</td>\n",
+       "      <td id=\"T_9c9a1_row2_col7\" class=\"data row2 col7\" >deg²</td>\n",
+       "      <td id=\"T_9c9a1_row2_col8\" class=\"data row2 col8\" >18.33 % ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9c9a1_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_9c9a1_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_9c9a1_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
+       "      <td id=\"T_9c9a1_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_9c9a1_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_9c9a1_row3_col4\" class=\"data row3 col4\" >-0.1000</td>\n",
+       "      <td id=\"T_9c9a1_row3_col5\" class=\"data row3 col5\" >-0.1169</td>\n",
+       "      <td id=\"T_9c9a1_row3_col6\" class=\"data row3 col6\" >0.0057</td>\n",
+       "      <td id=\"T_9c9a1_row3_col7\" class=\"data row3 col7\" >deg²</td>\n",
+       "      <td id=\"T_9c9a1_row3_col8\" class=\"data row3 col8\" >16.91 % ↑</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9c9a1_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_9c9a1_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
+       "      <td id=\"T_9c9a1_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
+       "      <td id=\"T_9c9a1_row4_col2\" class=\"data row4 col2\" ></td>\n",
+       "      <td id=\"T_9c9a1_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_9c9a1_row4_col4\" class=\"data row4 col4\" >0.0000</td>\n",
+       "      <td id=\"T_9c9a1_row4_col5\" class=\"data row4 col5\" >0.6306</td>\n",
+       "      <td id=\"T_9c9a1_row4_col6\" class=\"data row4 col6\" >0.0019</td>\n",
+       "      <td id=\"T_9c9a1_row4_col7\" class=\"data row4 col7\" >deg</td>\n",
+       "      <td id=\"T_9c9a1_row4_col8\" class=\"data row4 col8\" >N/A</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x16b048690>"
+       "<pandas.io.formats.style.Styler at 0x369840cd0>"
       ]
      },
      "metadata": {},
@@ -926,7 +936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33",
+   "id": "35",
    "metadata": {},
    "source": [
     "The correlation plot shows how strongly the fitted parameters move\n",
@@ -937,15 +947,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "34",
+   "execution_count": 23,
+   "id": "36",
    "metadata": {},
    "outputs": [
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@@ -956,22 +986,22 @@
     }
    ],
    "source": [
-    "project.display.plotter.plot_param_correlations(show_diagonal=True)"
+    "project.display.plotter.plot_param_correlations()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "35",
+   "execution_count": 24,
+   "id": "37",
    "metadata": {},
    "outputs": [
     {
      "data": {
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ATwRxHk4AZUCCzM6i9\\u002f9kQLSULCeh\\u002f2RA5lyKq0r\\u002fZEAYJegv9P5kQErtRbSd\\u002fmRAfbWjOEf+ZECvfQG98P1kQOFFX0Ga\\u002fWRAEw69xUP9ZEBF1hpK7fxkQHieeM6W\\u002fGRAqmbWUkD8ZEDcLjTX6ftkQA73kVuT+2RAQL\\u002fv3zz7ZEBzh01k5vpkQKVPq+iP+mRA1xcJbTn6ZEAJ4Gbx4vlkQDuoxHWM+WRAbnAi+jX5ZECgOIB+3\\u002fhkQNIA3gKJ+GRABMk7hzL4ZEA2kZkL3PdkQGlZ94+F92RAmyFVFC\\u002f3ZEDN6bKY2PZkQP+xEB2C9mRAMXpuoSv2ZEBkQswl1fVkQJYKKqp+9WRAyNKHLij1ZED6muWy0fRkQCxjQzd79GRAXyuhuyT0ZECR8\\u002f4\\u002fzvNkQMO7XMR382RA9YO6SCHzZEAnTBjNyvJkQFoUdlF08mRAjNzT1R3yZEC+pDFax\\u002fFkQPBsj95w8WRAIjXtYhrxZEBV\\u002fUrnw\\u002fBkQIfFqGtt8GRAuY0G8BbwZEDrVWR0wO9kQB0ewvhp72RAUOYffRPvZECCrn0Bve5kQLR224Vm7mRA5j45ChDuZEAYB5eOue1kQEvP9BJj7WRAfZdSlwztZECvX7AbtuxkQOEnDqBf7GRAE\\u002fBrJAnsZEBGuMmosutkQHiAJy1c62RAqkiFsQXrZEDcEOM1r+pkQA7ZQLpY6mRAQaGePgLqZEBzafzCq+lkQKUxWkdV6WRA1\\u002fm3y\\u002f7oZEAJwhVQqOhkQDyKc9RR6GRAblLRWPvnZECgGi\\u002fdpOdkQNLijGFO52RABKvq5ffmZEA3c0hqoeZkQGk7pu5K5mRAmwMEc\\u002fTlZEDNy2H3neVkQP+Tv3tH5WRAMlwdAPHkZEBkJHuEmuRkQJbs2AhE5GRAyLQ2je3jZED6fJQRl+NkQC1F8pVA42RAXw1QGuriZECR1a2ek+JkQMOdCyM94mRA9WVpp+bhZEAoLscrkOFkQFr2JLA54WRAjL6CNOPgZEC+huC4jOBkQPBOPj024GRAIxecwd\\u002ffZEBV3\\u002flFid9kQIenV8oy32RAuW+1TtzeZEDrNxPThd5kQB4AcVcv3mRAUMjO29jdZECCkCxggt1kQLRYiuQr3WRA5iDoaNXcZEAZ6UXtftxkQEuxo3Eo3GRAfXkB9tHbZECvQV96e9tkQOEJvf4k22RAFNIag87aZEBGmngHeNpkQHhi1osh2mRAqio0EMvZZEDc8pGUdNlkQA+77xge2WRAQYNNncfYZEBzS6shcdhkQKUTCaYa2GRA19tmKsTXZEAKpMSubddkQDxsIjMX12RAbjSAt8DWZECg\\u002fN07atZkQNLEO8AT1mRABY2ZRL3VZEA3VffIZtVkQGkdVU0Q1WRAm+Wy0bnUZEDNrRBWY9RkQAB2btoM1GRAMj7MXrbTZEBkBirjX9NkQJbOh2cJ02RAyJbl67LSZED7XkNwXNJkQC0nofQF0mRAX+\\u002f+eK\\u002fRZECRt1z9WNFkQMN\\u002fuoEC0WRA9kcYBqzQZEAoEHaKVdBkQFrY0w7\\u002fz2RAjKAxk6jPZEC+aI8XUs9kQPEw7Zv7zmRAI\\u002flKIKXOZEBVwaikTs5kQIeJBin4zWRAuVFkraHNZEDsGcIxS81kQB7iH7b0zGRAUKp9Op7MZECCctu+R8xkQLQ6OUPxy2RA5wKXx5rLZEAZy\\u002fRLRMtkQEuTUtDtymRAfVuwVJfKZECvIw7ZQMpkQOLra13qyWRAFLTJ4ZPJZEBGfCdmPclkQHhEhermyGRAqgzjbpDIZEDd1EDzOchkQA+dnnfjx2RAQWX8+4zHZEBzLVqANsdkQKX1twTgxmRA2L0ViYnGZEAKhnMNM8ZkQDxO0ZHcxWRAbhYvFobFZECg3oyaL8VkQNOm6h7ZxGRABW9Io4LEZEA3N6YnLMRkQGn\\u002fA6zVw2RAm8dhMH\\u002fDZEDOj7+0KMNkQABYHTnSwmRAMiB7vXvCZEBk6NhBJcJkQJawNsbOwWRAyXiUSnjBZED7QPLOIcFkQC0JUFPLwGRAX9Gt13TAZECRmQtcHsBkQMRhaeDHv2RA9inHZHG\\u002fZEAo8iTpGr9kQFq6gm3EvmRAjILg8W2+ZEC\\u002fSj52F75kQPESnPrAvWRAI9v5fmq9ZEBVo1cDFL1kQIdrtYe9vGRAujMTDGe8ZEDs+3CQELxkQB7EzhS6u2RAUIwsmWO7ZECCVIodDbtkQLUc6KG2umRA5+RFJmC6ZEAZraOqCbpkQEt1AS+zuWRAfT1fs1y5ZECwBb03BrlkQOLNGryvuGRAFJZ4QFm4ZEBGXtbEArhkQHgmNEmst2RAq+6RzVW3ZEDdtu9R\\u002f7ZkQA9\\u002fTdaotmRAQUerWlK2ZEBzDwnf+7VkQKbXZmOltWRA2J\\u002fE5061ZEAKaCJs+LRkQDwwgPChtGRAbvjddEu0ZEChwDv59LNkQNOImX2es2RABVH3AUizZEA3GVWG8bJkQGnhsgqbsmRAnKkQj0SyZEDOcW4T7rFkQAA6zJeXsWRAMgIqHEGxZEBkyoeg6rBkQJeS5SSUsGRAyVpDqT2wZED7IqEt569kQC3r\\u002frGQr2RAX7NcNjqvZECSe7q6465kQMRDGD+NrmRA9gt2wzauZEAo1NNH4K1kQFqcMcyJrWRAjWSPUDOtZEC\\u002fLO3U3KxkQPH0SlmGrGRAI72o3S+sZEBVhQZi2atkQIhNZOaCq2RAuhXCaiyrZEDs3R\\u002fv1apkQB6mfXN\\u002fqmRAUG7b9yiqZECDNjl80qlkQLX+lgB8qWRA58b0hCWpZEAZj1IJz6hkQEtXsI14qGRAfh8OEiKoZECw52uWy6dkQOKvyRp1p2RAFHgnnx6nZEBGQIUjyKZkQHkI46dxpmRAq9BALBumZEDdmJ6wxKVkQA9h\\u002fDRupWRAQSlauRelZEB08bc9waRkQKa5FcJqpGRA2IFzRhSkZEAKStHKvaNkQDwSL09no2RAb9qM0xCjZEChoupXuqJkQNNqSNxjomRABTOmYA2iZEA3+wPltqFkQGrDYWlgoWRAnIu\\u002f7QmhZEDOUx1ys6BkQAAce\\u002fZcoGRAMuTYegagZEBlrDb\\u002fr59kQJd0lINZn2RAyTzyBwOfZED7BFCMrJ5kQC3NrRBWnmRAYJULlf+dZECSXWkZqZ1kQMQlx51SnWRA9u0kIvycZEAotoKmpZxkQFt+4CpPnGRAjUY+r\\u002fibZEC\\u002fDpwzoptkQPHW+bdLm2RAI59XPPWaZEBWZ7XAnppkQIgvE0VImmRAuvdwyfGZZEDsv85Nm5lkQB6ILNJEmWRAUVCKVu6YZECDGOjal5hkQLXgRV9BmGRA56ij4+qXZEAZcQFolJdkQEw5X+w9l2RAfgG9cOeWZECwyRr1kJZkQOKReHk6lmRAFFrW\\u002feOVZEBHIjSCjZVkQHnqkQY3lWRAq7LviuCUZEDdek0PipRkQA9Dq5MzlGRAQgsJGN2TZEB002achpNkQKabxCAwk2RA2GMipdmSZEAKLIApg5JkQD303a0skmRAb7w7MtaRZEChhJm2f5FkQNNM9zopkWRABRVVv9KQZEA43bJDfJBkQGqlEMglkGRAnG1uTM+PZEDONczQeI9kQAD+KVUij2RAM8aH2cuOZEBljuVddY5kQJdWQ+IejmRAyR6hZsiNZED75v7qcY1kQC6vXG8bjWRAYHe688SMZECSPxh4boxkQMQHdvwXjGRA9s\\u002fTgMGLZEApmDEFa4tkQFtgj4kUi2RAjSjtDb6KZECoctpT8opkQMO8x5kmi2RA3ga131qLZED4UKIlj4tkQBObj2vDi2RALuV8sfeLZEBJL2r3K4xkQGR5Vz1gjGRAf8NEg5SMZECZDTLJyIxkQLRXHw\\u002f9jGRAz6EMVTGNZEDq6\\u002fmaZY1kQAU25+CZjWRAIIDUJs6NZEA6ysFsAo5kQFUUr7I2jmRAcF6c+GqOZECLqIk+n45kQKbydoTTjmRAwTxkygePZEDbhlEQPI9kQPbQPlZwj2RAERssnKSPZEAsZRni2I9kQEevBigNkGRAYvnzbUGQZEB8Q+GzdZBkQJeNzvmpkGRAste7P96QZEDNIamFEpFkQOhrlstGkWRAA7aDEXuRZEAdAHFXr5FkQDhKXp3jkWRAU5RL4xeSZEBu3jgpTJJkQIkoJm+AkmRApHITtbSSZEC+vAD76JJkQNkG7kAdk2RA9FDbhlGTZEAPm8jMhZNkQCrltRK6k2RARS+jWO6TZEBgeZCeIpRkQHrDfeRWlGRAlQ1rKouUZECwV1hwv5RkQMuhRbbzlGRA5usy\\u002fCeVZEABNiBCXJVkQBuADYiQlWRANsr6zcSVZEBRFOgT+ZVkQGxe1VktlmRAh6jCn2GWZECi8q\\u002fllZZkQLw8nSvKlmRA14aKcf6WZEDy0He3MpdkQA0bZf1ml2RAKGVSQ5uXZEBDrz+Jz5dkQF35LM8DmGRAeEMaFTiYZECTjQdbbJhkQK7X9KCgmGRAySHi5tSYZEDka88sCZlkQP61vHI9mWRAGQCquHGZZEA0Spf+pZlkQE+UhETamWRAat5xig6aZECFKF\\u002fQQppkQJ9yTBZ3mmRAurw5XKuaZEDVBiei35pkQPBQFOgTm2RAC5sBLkibZEAm5e5zfJtkQEEv3Lmwm2RAW3nJ\\u002f+SbZEB2w7ZFGZxkQJENpItNnGRArFeR0YGcZEDHoX4XtpxkQOLra13qnGRA\\u002fDVZox6dZEAXgEbpUp1kQDLKMy+HnWRATRQhdbudZEBoXg67751kQIOo+wAknmRAnfLoRlieZEC4PNaMjJ5kQNOGw9LAnmRA7tCwGPWeZEAJG55eKZ9kQCRli6Rdn2RAPq946pGfZEBZ+WUwxp9kQHRDU3b6n2RAj41AvC6gZECq1y0CY6BkQMUhG0iXoGRA32sIjsugZED6tfXT\\u002f6BkQBUA4xk0oWRAMErQX2ihZEBLlL2lnKFkQGbequvQoWRAgCiYMQWiZECbcoV3OaJkQLa8cr1tomRA0QZgA6KiZEDsUE1J1qJkQAebOo8Ko2RAIuUn1T6jZEA8LxUbc6NkQFd5AmGno2RAcsPvptujZECNDd3sD6RkQKhXyjJEpGRAw6G3eHikZEDd66S+rKRkQPg1kgThpGRAE4B\\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\\u002fJKr2RAbEPFOH+vZECHjbJ+s69kQKHXn8Tnr2RAvCGNChywZEDXa3pQULBkQPK1Z5aEsGRADQBV3LiwZEAoSkIi7bBkQEKUL2ghsWRAXd4crlWxZEB4KAr0ibFkQJNy9zm+sWRArrzkf\\u002fKxZEDJBtLFJrJkQONQvwtbsmRA\\u002fpqsUY+yZEAZ5ZmXw7JkQDQvh933smRAT3l0IyyzZEBqw2FpYLNkQIQNT6+Us2RAn1c89cizZEC6oSk7\\u002fbNkQNXrFoExtGRA8DUEx2W0ZEALgPEMmrRkQCbK3lLOtGRAQBTMmAK1ZEBbXrneNrVkQHaopiRrtWRAkfKTap+1ZECsPIGw07VkQMeGbvYHtmRA4dBbPDy2ZED8GkmCcLZkQBdlNsiktmRAMq8jDtm2ZEBN+RBUDbdkQGhD\\u002fplBt2RAgo3r33W3ZECd19glqrdkQLghxmvet2RA02uzsRK4ZEDutaD3RrhkQAkAjj17uGRAI0p7g6+4ZEA+lGjJ47hkQFneVQ8YuWRAdChDVUy5ZECPcjCbgLlkQKq8HeG0uWRAxAYLJ+m5ZEDfUPhsHbpkQPqa5bJRumRAFeXS+IW6ZEAwL8A+urpkQEt5rYTuumRAZcOayiK7ZECADYgQV7tkQJtXdVaLu2RAtqFinL+7ZEDR60\\u002fi87tkQOw1PSgovGRAB4Aqbly8ZEAhyhe0kLxkQDwUBfrEvGRAV17yP\\u002fm8ZEByqN+FLb1kQI3yzMthvWRAqDy6EZa9ZEDChqdXyr1kQN3QlJ3+vWRA+BqC4zK+ZEATZW8pZ75kQC6vXG+bvmRASflJtc++ZEBjQzf7A79kQH6NJEE4v2RAmdcRh2y\\u002fZEC0If\\u002fMoL9kQM9r7BLVv2RA6rXZWAnAZEAEAMeePcBkQB9KtORxwGRAOpShKqbAZEBV3o5w2sBkQHAofLYOwWRAi3Jp\\u002fELBZEClvFZCd8FkQMAGRIirwWRA21Axzt\\u002fBZED2mh4UFMJkQBHlC1pIwmRALC\\u002f5n3zCZEBGeeblsMJkQGHD0yvlwmRAfA3BcRnDZECXV663TcNkQLKhm\\u002f2Bw2RAzeuIQ7bDZEDoNXaJ6sNkQAKAY88exGRAHcpQFVPEZEA4FD5bh8RkQFNeK6G7xGRAbqgY5+\\u002fEZECJ8gUtJMVkQKM883JYxWRAvobguIzFZEDZ0M3+wMVkQPQau0T1xWRAD2WoiinGZEAqr5XQXcZkQET5ghaSxmRAX0NwXMbGZEB6jV2i+sZkQJXXSugux2RAsCE4LmPHZEDLayV0l8dkQOW1ErrLx2RAAAAAAADIZEAbSu1FNMhkQDaU2otoyGRAUd7H0ZzIZEBsKLUX0chkQIZyol0FyWRAobyPoznJZEC8Bn3pbclkQNdQai+iyWRA8ppXddbJZEAN5US7CspkQCcvMgE\\u002fymRAQnkfR3PKZEBdwwyNp8pkQHgN+tLbymRAk1fnGBDLZECuodReRMtkQMjrwaR4y2RA4zWv6qzLZED+f5ww4ctkQBnKiXYVzGRANBR3vEnMZEBPXmQCfsxkQGqoUUiyzGRAhPI+jubMZECfPCzUGs1kQLqGGRpPzWRA1dAGYIPNZEDwGvSlt81kQAtl4evrzWRAJa\\u002fOMSDOZEBA+bt3VM5kQFtDqb2IzmRAdo2WA73OZECR14NJ8c5kQKwhcY8lz2RAxmte1VnPZEDhtUsbjs9kQPz\\u002fOGHCz2RAF0omp\\u002fbPZEAylBPtKtBkQE3eADNf0GRAZyjueJPQZECCctu+x9BkQJ28yAT80GRAuAa2SjDRZEDTUKOQZNFkQO6akNaY0WRACOV9HM3RZEAjL2tiAdJkQD55WKg10mRAWcNF7mnSZEB0DTM0ntJkQI9XIHrS0mRAqaENwAbTZEDE6\\u002foFO9NkQN816Etv02RA+n\\u002fVkaPTZEAVysLX19NkQDAUsB0M1GRASl6dY0DUZEBlqIqpdNRkQIDyd++o1GRAmzxlNd3UZEC2hlJ7EdVkQNHQP8FF1WRA7BotB3rVZEAGZRpNrtVkQCGvB5Pi1WRAPPn02BbWZEBXQ+IeS9ZkQHKNz2R\\u002f1mRAjde8qrPWZECnIarw59ZkQMJrlzYc12RA3bWEfFDXZED4\\u002f3HChNdkQBNKXwi512RALpRMTu3XZEBI3jmUIdhkQGMoJ9pV2GRAfnIUIIrYZECZvAFmvthkQLQG76vy2GRAz1Dc8SbZZEDpmsk3W9lkQATltn2P2WRAHy+kw8PZZEA6eZEJ+NlkQFXDfk8s2mRAcA1slWDaZECKV1nblNpkQKWhRiHJ2mRAwOszZ\\u002f3aZEDbNSGtMdtkQPZ\\u002fDvNl22RAEcr7OJrbZEArFOl+zttkQEZe1sQC3GRAYajDCjfcZEB88rBQa9xkQK36XG2l3GRA3gIJit\\u002fcZEAPC7WmGd1kQEETYcNT3WRAchsN4I3dZECjI7n8x91kQNQrZRkC3mRABTQRNjzeZEA2PL1Sdt5kQGhEaW+w3mRAmUwVjOreZEDKVMGoJN9kQPtcbcVe32RALGUZ4pjfZEBdbcX+0t9kQI51cRsN4GRAwH0dOEfgZEDxhclUgeBkQCKOdXG74GRAU5YhjvXgZECEns2qL+FkQLWmecdp4WRA5q4l5KPhZEAYt9EA3uFkQEm\\u002ffR0Y4mRAescpOlLiZECrz9VWjOJkQNzXgXPG4mRADeAtkADjZEA\\u002f6NmsOuNkQHDwhcl042RAofgx5q7jZEDSAN4C6eNkQAMJih8j5GRANBE2PF3kZEBlGeJYl+RkQJchjnXR5GRAyCk6kgvlZED5MeauReVkQCo6kst\\u002f5WRAW0I+6LnlZECMSuoE9OVkQL5SliEu5mRA71pCPmjmZEAgY+5aouZkQFFrmnfc5mRAgnNGlBbnZECze\\u002fKwUOdkQOSDns2K52RAFoxK6sTnZEBHlPYG\\u002f+dkQHicoiM56GRAqaROQHPoZEDarPpcrehkQAu1pnnn6GRAPL1SliHpZEBuxf6yW+lkQJ\\u002fNqs+V6WRA0NVW7M\\u002fpZEAB3gIJCupkQDLmriVE6mRAY+5aQn7qZECV9gZfuOpkQMb+snvy6mRA9wZfmCzrZEAoDwu1ZutkQFkXt9Gg62RAih9j7trrZEC7Jw8LFexkQO0vuydP7GRAHjhnRInsZEBPQBNhw+xkQIBIv3397GRAsVBrmjftZEDiWBe3ce1kQBRhw9Or7WRARWlv8OXtZEB2cRsNIO5kQKd5xyla7mRA2IFzRpTuZEAJih9jzu5kQDqSy38I72RAbJp3nELvZECdoiO5fO9kQM6qz9W272RA\\u002f7J78vDvZEAwuycPK\\u002fBkQGHD0ytl8GRAk8t\\u002fSJ\\u002fwZEDE0ytl2fBkQPXb14ET8WRAJuSDnk3xZEBX7C+7h\\u002fFkQIj029fB8WRAufyH9PvxZEDrBDQRNvJkQBwN4C1w8mRATRWMSqryZEB+HThn5PJkQK8l5IMe82RA4C2QoFjzZEARNjy9kvNkQEM+6NnM82RAdEaU9gb0ZEClTkATQfRkQNZW7C979GRAB1+YTLX0ZEA4Z0Rp7\\u002fRkQGpv8IUp9WRAm3ecomP1ZEDMf0i\\u002fnfVkQP2H9NvX9WRALpCg+BH2ZEBfmEwVTPZkQJCg+DGG9mRAwqikTsD2ZEDzsFBr+vZkQCS5\\u002fIc092RAVcGopG73ZECGyVTBqPdkQLfRAN7i92RA6dms+hz4ZEAa4lgXV\\u002fhkQEvqBDSR+GRAfPKwUMv4ZECt+lxtBflkQN4CCYo\\u002f+WRADwu1pnn5ZEBBE2HDs\\u002flkQHIbDeDt+WRAoyO5\\u002fCf6ZEDUK2UZYvpkQAU0ETac+mRANjy9Utb6ZEBnRGlvEPtkQJlMFYxK+2RAylTBqIT7ZED7XG3FvvtkQCxlGeL4+2RAXW3F\\u002fjL8ZECOdXEbbfxkQMB9HTin\\u002fGRA8YXJVOH8ZEAijnVxG\\u002f1kQFOWIY5V\\u002fWRAhJ7Nqo\\u002f9ZEC1pnnHyf1kQOauJeQD\\u002fmRAGLfRAD7+ZEBJv30deP5kQHrHKTqy\\u002fmRAq8\\u002fVVuz+ZEDc14FzJv9kQA3gLZBg\\u002f2RAP+jZrJr\\u002fZEBw8IXJ1P9kQKH4MeYOAGVA0gDeAkkAZUADCYofgwBlQDQRNjy9AGVAZRniWPcAZUCXIY51MQFlQMgpOpJrAWVA+THmrqUBZUAqOpLL3wFlQFtCPugZAmVAjErqBFQCZUC9UpYhjgJlQO9aQj7IAmVAIGPuWgIDZUBRa5p3PANlQIJzRpR2A2VAs3vysLADZUDkg57N6gNlQBaMSuokBGVAR5T2Bl8EZUB4nKIjmQRlQKmkTkDTBGVA2qz6XA0FZUALtaZ5RwVlQDy9UpaBBWVAbsX+srsFZUCfzarP9QVlQNDVVuwvBmVAAd4CCWoGZUAy5q4lpAZlQGPuWkLeBmVAlfYGXxgHZUDG\\u002frJ7UgdlQPcGX5iMB2VAKA8LtcYHZUBZF7fRAAhlQIofY+46CGVAuycPC3UIZUDtL7snrwhlQB44Z0TpCGVAT0ATYSMJZUCASL99XQllQLFQa5qXCWVA4lgXt9EJZUATYcPTCwplQEVpb\\u002fBFCmVAdnEbDYAKZUCneccpugplQNiBc0b0CmVACYofYy4LZUA6kst\\u002faAtlQGyad5yiC2VAnaIjudwLZUDOqs\\u002fVFgxlQP+ye\\u002fJQDGVAMLsnD4sMZUBhw9MrxQxlQJLLf0j\\u002fDGVAxNMrZTkNZUD129eBcw1lQCbkg56tDWVAV+wvu+cNZUCI9NvXIQ5lQLn8h\\u002fRbDmVA6wQ0EZYOZUAcDeAt0A5lQE0VjEoKD2VAfh04Z0QPZUCvJeSDfg9lQOAtkKC4D2VAETY8vfIPZUBDPujZLBBlQHRGlPZmEGVApU5AE6EQZUDWVuwv2xBlQAdfmEwVEWVAOGdEaU8RZUBqb\\u002fCFiRFlQJt3nKLDEWVAzH9Iv\\u002f0RZUD9h\\u002fTbNxJlQC6QoPhxEmVAX5hMFawSZUCQoPgx5hJlQMKopE4gE2VA87BQa1oTZUAkufyHlBNlQFXBqKTOE2VAhslUwQgUZUC30QDeQhRlQOjZrPp8FGVAGuJYF7cUZUBL6gQ08RRlQHzysFArFWVArfpcbWUVZUDeAgmKnxVlQA8LtabZFWVAQRNhwxMWZUByGw3gTRZlQKMjufyHFmVA1CtlGcIWZUAFNBE2\\u002fBZlQDY8vVI2F2VAZ0Rpb3AXZUCZTBWMqhdlQMpUwajkF2VA+1xtxR4YZUAsZRniWBhlQF1txf6SGGVAjnVxG80YZUDAfR04BxllQPGFyVRBGWVAIo51cXsZZUBTliGOtRllQISezarvGWVAtaZ5xykaZUDmriXkYxplQBi30QCeGmVASb99HdgaZUB6xyk6EhtlQKvP1VZMG2VA3NeBc4YbZUAN4C2QwBtlQD7o2az6G2VAcPCFyTQcZUCh+DHmbhxlQNIA3gKpHGVAAwmKH+McZUA0ETY8HR1lQGUZ4lhXHWVAliGOdZEdZUDIKTqSyx1lQPkx5q4FHmVAKjqSyz8eZUBbQj7oeR5lQIxK6gS0HmVAvVKWIe4eZUDvWkI+KB9lQCBj7lpiH2VAUWuad5wfZUCCc0aU1h9lQLN78rAQIGVA5IOezUogZUAVjErqhCBlQEeU9ga\\u002fIGVAeJyiI\\u002fkgZUCppE5AMyFlQNqs+lxtIWVAC7WmeachZUA8vVKW4SFlQG7F\\u002frIbImVAn82qz1UiZUDQ1VbsjyJlQAHeAgnKImVAMuauJQQjZUBj7lpCPiNlQJT2Bl94I2VAxv6ye7IjZUD3Bl+Y7CNlQCgPC7UmJGVAWRe30WAkZUCKH2PumiRlQLsnDwvVJGVA7S+7Jw8lZUAeOGdESSVlQE9AE2GDJWVAgEi\\u002ffb0lZUCxUGua9yVlQOJYF7cxJmVAE2HD02smZUBFaW\\u002fwpSZlQHZxGw3gJmVAp3nHKRonZUDYgXNGVCdlQAmKH2OOJ2VAOpLLf8gnZUBsmnecAihlQJ2iI7k8KGVAzqrP1XYoZUD\\u002fsnvysChlQDC7Jw\\u002frKGVAYcPTKyUpZUCSy39IXyllQMTTK2WZKWVA9dvXgdMpZUAm5IOeDSplQFfsL7tHKmVAiPTb14EqZUC5\\u002fIf0uyplQOoENBH2KmVAHA3gLTArZUBNFYxKaitlQH4dOGekK2VAryXkg94rZUDgLZCgGCxlQBE2PL1SLGVAQz7o2YwsZUB0RpT2xixlQKVOQBMBLWVA1lbsLzstZUAHX5hMdS1lQDhnRGmvLWVAaW\\u002fwhektZUCbd5yiIy5lQMx\\u002fSL9dLmVA\\u002fYf025cuZUAukKD40S5lQF+YTBUML2VAkKD4MUYvZUDBqKROgC9lQPOwUGu6L2VAJLn8h\\u002fQvZUBVwaikLjBlQIbJVMFoMGVAt9EA3qIwZUDo2az63DBlQBriWBcXMWVAS+oENFExZUB88rBQizFlQK36XG3FMWVA3gIJiv8xZUAPC7WmOTJlQEATYcNzMmVAchsN4K0yZUCjI7n85zJlQNQrZRkiM2VABTQRNlwzZUA2PL1SljNlQGdEaW\\u002fQM2VAmUwVjAo0ZUDKVMGoRDRlQPtcbcV+NGVALGUZ4rg0ZUBdbcX+8jRlQI51cRstNWVAv30dOGc1ZUDxhclUoTVlQCKOdXHbNWVAU5YhjhU2ZUCEns2qTzZlQLWmeceJNmVA5q4l5MM2ZUAYt9EA\\u002fjZlQEm\\u002ffR04N2VAescpOnI3ZUCrz9VWrDdlQNzXgXPmN2VADeAtkCA4ZUA+6NmsWjhlQHDwhcmUOGVAofgx5s44ZUDSAN4CCTllQAMJih9DOWVANBE2PH05ZUBlGeJYtzllQJYhjnXxOWVAyCk6kis6ZUD5MeauZTplQCo6ksufOmVAW0I+6Nk6ZUCMSuoEFDtlQL1SliFOO2VA71pCPog7ZUAgY+5awjtlQFFrmnf8O2VAgnNGlDY8ZUCze\\u002fKwcDxlQOSDns2qPGVAFYxK6uQ8ZUBHlPYGHz1lQHicoiNZPWVAqaROQJM9ZUDarPpczT1lQAu1pnkHPmVAPL1SlkE+ZUBtxf6yez5lQJ\\u002fNqs+1PmVA0NVW7O8+ZUAB3gIJKj9lQDLmriVkP2VAY+5aQp4\\u002fZUCU9gZf2D9lQMb+snsSQGVA9wZfmExAZUAoDwu1hkBlQFkXt9HAQGVAih9j7vpAZUC7Jw8LNUFlQOwvuydvQWVAHjhnRKlBZUBPQBNh40FlQIBIv30dQmVAsVBrmldCZUDiWBe3kUJlQBNhw9PLQmVARWlv8AVDZUB2cRsNQENlQKd5xyl6Q2VA2IFzRrRDZUAJih9j7kNlQDqSy38oRGVAa5p3nGJEZUCdoiO5nERlQM6qz9XWRGVA\\u002f7J78hBFZUAwuycPS0VlQGHD0yuFRWVAkst\\u002fSL9FZUDE0ytl+UVlQPXb14EzRmVAJuSDnm1GZUBX7C+7p0ZlQIj029fhRmVAufyH9BtHZUDqBDQRVkdlQBwN4C2QR2VATRWMSspHZUB+HThnBEhlQK8l5IM+SGVA4C2QoHhIZUARNjy9skhlQEM+6NnsSGVAdEaU9iZJZUClTkATYUllQNZW7C+bSWVAB1+YTNVJZUA4Z0RpD0plQGlv8IVJSmVAm3ecooNKZUDMf0i\\u002fvUplQP2H9Nv3SmVALpCg+DFLZUBfmEwVbEtlQJCg+DGmS2VAwaikTuBLZUDzsFBrGkxlQCS5\\u002fIdUTGVAVcGopI5MZUCGyVTByExlQLfRAN4CTWVA6Nms+jxNZUAa4lgXd01lQEvqBDSxTWVAfPKwUOtNZUCt+lxtJU5lQN4CCYpfTmVADwu1pplOZUBAE2HD005lQHIbDeANT2VAoyO5\\u002fEdPZUDUK2UZgk9lQAU0ETa8T2VANjy9UvZPZUBnRGlvMFBlQJhMFYxqUGVAylTBqKRQZUD7XG3F3lBlQCxlGeIYUWVAXW3F\\u002flJRZUCOdXEbjVFlQL99HTjHUWVA8YXJVAFSZUAijnVxO1JlQFOWIY51UmVAhJ7Nqq9SZUC1pnnH6VJlQOauJeQjU2VAF7fRAF5TZUBJv30dmFNlQHrHKTrSU2VAq8\\u002fVVgxUZUDc14FzRlRlQA3gLZCAVGVAPujZrLpUZUBw8IXJ9FRlQKH4MeYuVWVA0gDeAmlVZUADCYofo1VlQDQRNjzdVWVAZRniWBdWZUCWIY51UVZlQMgpOpKLVmVA+THmrsVWZUAqOpLL\\u002f1ZlQFtCPug5V2VAjErqBHRXZUC9UpYhrldlQO9aQj7oV2VAIGPuWiJYZUBRa5p3XFhlQIJzRpSWWGVAs3vysNBYZUDkg57NClllQBWMSupEWWVAR5T2Bn9ZZUB4nKIjuVllQKmkTkDzWWVA2qz6XC1aZUALtaZ5Z1plQDy9UpahWmVAbcX+sttaZUCfzarPFVtlQNDVVuxPW2VAAd4CCYpbZUAy5q4lxFtlQGPuWkL+W2VAlPYGXzhcZUDG\\u002frJ7clxlQPcGX5isXGVAKA8LteZcZUBZF7fRIF1lQIofY+5aXWVAuycPC5VdZUDsL7snz11lQB44Z0QJXmVAT0ATYUNeZUCASL99fV5lQLFQa5q3XmV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BlQIt28lZeIGVAekNs8dsfZUDI1HEeWh9lQM48y9vYHmVAV3JMJ1geZUApCdX+1x1lQL3sT2BYHWVA8hyzSdkcZUC3bP+4WhxlQKdCQKzcG2VAc1uLIV8bZUAdjgAX4hplQPKRyYplGmVAKsYZe+kZZUA8+y3mbRllQLs9TMryGGVAxqLDJXgYZUD4Fez2\\u002fRdlQMgoJjyEF2VAX+Pa8woXZUDHlnsckhZlQHKwgbQZFmVAGI9uuqEVZUDNWMssKhVlQGXSKAqzFGVAADgfUTwUZUDLFk4AxhNlQOcnXBZQE2VAZSz3kdoSZUBxytNxZRJlQHZrrbTwEWVAXxtGWXwRZUDYaGZeCBFlQIxG3cKUEGVAau1\\u002fhSEQZUDTvymlrg9lQMYtvCA8D2VA75ke98kOZUCjPz4nWA5lQMcZDrDmDWVAlMqGkHUNZUA3hKbHBA1lQFLycFSUDGVAUiTvNSQMZUCgeC9rtAtlQKSIRfNEC2VAlRVKzdUKZUAn9lr4ZgplQPUEm3P4CWVA0A8yPooJZUDLx0xXHAllQBqyHL6uCGVAvxnYcUEIZUD+Abpx1AdlQK0ZAr1nB2VARa\\u002f0UvsGZUDGpdoyjwZlQHNqAVwjBmVAVuu6zbcFZUCrjl2HTAVlQBQrRIjhBGVArwDOz3YEZUAPs15dDARlQBNEXjCiA2VArA85SDgDZUCLyF+kzgJlQMt1R0RlAmVAi3FpJ\\u002fwBZUCQaENNkwFlQPNaV7UqAWVA2J0rX8IAZUBJ3kpKWgBlQC8lRHby\\u002f2RAfdyq4or\\u002fZECf1RaPI\\u002f9kQCRRJHu8\\u002fmRAyQd0plX+ZEDqNKsQ7\\u002f1kQF+ic7mI\\u002fWRA87V7oCL9ZEBmgHbFvPxkQCvOGyhX\\u002fGRA5DkoyPH7ZEDBQF2ljPtkQNxYgb8n+2RAowlgFsP6ZEB7BsqpXvpkQLVLlXn6+WRA\\u002fj2dhZb5ZEBtzMLNMvlkQF6V7FHP+GRAPA4HEmz4ZEB4rgQOCfhkQNcd3kWm92RAYGaSuUP3ZEAPKidp4fZkQK3cqFR\\u002f9mRA\\u002fQErfB32ZECdcMjfu\\u002fVkQPWZo39a9WRAedfmW\\u002fn0ZEDFvcR0mPRkQOZ1eMo39GRAXB1GXdfzZEBLLXstd\\u002fNkQH3pbjsX82RAs9eCh7fyZEAZQCMSWPJkQG+3x9v48WRA1rPz5JnxZEAULTcuO\\u002fFkQDdJL7jc8GRAxBaHg37wZEB+VfiQIPBkQCJPTOHC72RAa8FcdWXvZED72hROCO9kQMxMcmyr7mRAHnKG0U7uZEDokHd+8u1kQBg1gnSW7WRAOan6tDrtZEBFj05B3+xkQMycBhuE7GRA8n3IQynsZEAV41i9zutkQHS9nYl062RAta+gqhrrZECTt5EiwepkQNYWyvNn6mRAPoLPIA\\u002fqZEAFnlestulkQG7QS5le6WRA+HPN6gbpZEARdDqkr+hkQFtgMslY6GRAZwScXQLoZEB0k6tlrOdkQO956eVW52RA4Og54wHnZEAmMuVireZkQL8PoWpZ5mRABPSaAAbmZEAhhYMrs+VkQEBrm\\u002fJg5WRAh57BXQ\\u002flZEBYaYN1vuRkQAhbLkNu5GRAEXHk0B7kZEDwx7Ip0ONkQDMyq1mC42RAFCQBbjXjZEC7dCp16eJkQMOMBH+e4mRAV7T+nFTiZEByU0riC+JkQJ4dEmTE4WRAZlG5OX7hZEBkbCR9OeFkQET6DEv24GRAV3liw7TgZEBOt7oJdeBkQISI1EU34GRAcFowpPvfZEC\\u002f5sJWwt9kQFFCx5WL32RADLi2oFffZEDmZG6\\u002fJt9kQLx4jEP53mRAMXYRis\\u002feZEDr9FT9qd5kQBJ\\u002fYReJ3mRASnHQZG3eZECUtkWIV95kQBZhtD5I3mRAuEihZEDeZED+Han8QN5kQJdUpTdL3mRAfi3pfmDeZECROTmBgt5kQPGkWEOz3mRAy3BXNfXeZEBY3z1OS99kQPa9UzC532RAl24wWUPgZECPpDBi7+BkQDvUfFjE4WRAcmu\\u002fOcviZECsee60D+RkQD8\\u002f+Iyh5WRAomIJx5fnZEDIFe8YGepkQL6JKY9y7WRA5mwnuU\\u002fyZEBy9jFFNPpkQMfrMgZXCGVAuCYTh9EiZUBt+hbgnFNlQK7U0\\u002fIkqGVAFn5MluktZkAWZF9rlOtmQKKFwK9L2GdAfGOGSjDVaED6idrIv6xpQPoXCd5fHWpA5KBxClv6aUBLigCMe1FpQAVGAu\\u002fNXmhAb8gX2uJhZ0DI0DjG0YZmQEef5qjp4WVAHNMGZK1zZUA8zNApCjFlQFlnZzm1C2VA4SIMIoz3ZECoPiT1aexkQH\\u002f3cDe05WRAm2AUryPhZEBfE039sN1kQKNWiJTo2mRAfJShY5TYZECSZNj4ltZkQFZ8oG7d1GRAZdH7PlrTZEDetbUyA9JkQMPEBF\\u002fQ0GRAHgDeibvPZECcpL+\\u002fv85kQIajMwfZzWRA3TpkKATNZECOIeeCPsxkQLEU0O2Fy2RAF8VLn9jKZEC7lc8ZNcpkQJW8dh2ayWRAhlmHnAbJZEBZc1+yechkQE1nO5zyx2RAJC9rs3DHZEBsBqZo88ZkQBo1P0B6xmRA8Q0NzwTGZECI3923ksVkQHumXqkjxWRARm9dXLfEZEAJBlaSTcRkQH0uOxTmw2RAvmpxsYDDZEAshvI+HcNkQAfQkpa7wmRA70pillvCZEDLKCUg\\u002fcFkQA7F3xigwWRAUP5yaETBZEA3XUb56cBkQFfo\\u002fbeQwGRAXuE5kzjAZEAL81574b9kQFCVZWKLv2RAlZ+vOza\\u002fZEDjKuP74b5kQHYIypiOvmRAyS01CTy+ZEBIj+NE6r1kQNT2a0SZvWRA\\u002f3MpAUm9ZEAkEip1+bxkQFqMH5uqvGRAXsFRbly8ZEAvspLqDrxkQEbdMwzCu2RAqc78z3W7ZEBiwiIzKrtkQGk7QTPfumRAJXVTzpS6ZEDWmK4CS7pkQGmj\\u002fM4BumRAjuo3Mrm5ZEBKMqcrcblkQCdG2ropuWRAuwqn3+K4ZED5\\u002fSaanLhkQOQdtepWuGRAeC7s0RG4ZEDGWKVQzbdkQP8d92eJt2RAS5o0GUa3ZEDJE+1lA7dkQBHT60\\u002fBtmRAGkM42X+2ZEADVxYEP7ZkQPE0B9P+tWRAuyXKSL+1ZECiyV1ogLVkQAOTATVCtWRASYg3sgS1ZEAsT8bjx7RkQLqDu82LtGRAO15udFC0ZEDFq4LcFbRkQMId7Arcs2RAjPXxBKOzZEDsETPQarNkQA5lqnIzs2RAcNqz8vyyZEAytRFXx7JkQEJw8qaSsmRA\\u002fyr36V6yZEA4rjooLLJkQN0WWWr6sWRAMzV4ucmxZEAtsVAfmrFkQJEGOKZrsWRAnm0rWT6xZECSx9tDErFkQACpunLnsGRATJ8I872wZEAI0uTSlbBkQP4kXiFvsGRAYASG7kmwZEC4CYVLJrBkQDKusUoEsGRAm0Wp\\u002f+OvZEBVhWt\\u002fxa9kQEDjeOCor2RAASP0Oo6vZEC0c8eoda9kQN+NzUVfr2RAAVH\\u002fL0uvZECIcqaHOa9kQNzklW8qr2RAGLZoDR6vZEBtQsiJFK9kQPO3uhAOr2RA7xD70QqvZECx11sBC69kQL09NtcOr2RAPVDnkBavZEDLX11xIq9kQEsMuMEyr2RASNH90UevZEA7cOr5Ya9kQMQw2pmBr2RAwajWG6evZEDonsr00q9kQGKq46UFsGRA7nwpvj+wZED\\u002fWFTcgbBkQDUk7rDMsGRAKujLACGxZEBrBoBpCrJkQIYADCD\\u002fsmRAZOCFOQC0ZEDN3TTuDrVkQNmtGJ8stmRAifx63Fq3ZEBgscVtm7hkQCGD51rwuWRArJCj91u7ZEAynEHx4LxkQJUNNV+CvmRAH6t710PAZECxrK2HKcJkQL+JBFQ4xGRACt4D\\u002fnXGZEBtRPpU6chkQLfgTXOay2RA+wiPDJPOZECGvbrR3tFkQFOAHfOL1WRA77osyqvZZECTRd+5U95kQAN\\u002fQ1qe42RAIY69Da3pZECbkq0vqvBkQLM+SDXM+GRA3DCPmloCZUBV9i45tw1lQAHCA4F0G2VA1q+L6pEsZUCVQQPvH0NlQMDB3h7UY2VAvIqgJF+ZZUBvexflI\\u002fplQB6oJh1Vr2ZAvDuhP+P4Z0A5DkMQiyVqQDDs9DWbdm1ApHT7L2X4cECahM33wZVzQNL7daifG3ZACtzjdTbZd0C1R114UCV4QB9glAnj3HZASjcekniGdEBz7iYBtN1xQO4pD1x75m5AirB2Iooma0Ba\\u002fpHua5hoQGFtz6VwCWdABQzgLGQqZkA0iCgTwrNlQN7TdkkrdGVA3Yq++2VPZUB0pV3keTdlQHzeLnUlJmVAgoZtYrMYZUDcQSuH4w1lQN0CCewIBWVAHgml+LT9ZEBu6CfKmPdkQDBYRQp58mRAz8Yj9SfuZECN1iC5gepkQOyrFv1p52RABZOKGcrkZED+NdrKj+JkQES2xTis4GRAswZkOxPfZEBZ\\u002ftzMut1kQOUSJpya3GRAj\\u002ftWuKvbZECzAn9O6NpkQIzshnVL2mRAkpbVBNHZZEBE4UNzddlkQBooiLw12WRAHKezSw\\u002fZZEDxi7Dp\\u002f9hkQEtZ7q4F2WRAZYuZ9x7ZZEBUO99ZStlkQFvP2J2G2WRAjsHQttLZZECMvqG9LdpkQCv9\\u002feuW2mRAB5F2mA3bZEABQSIzkdtkQGCTyUIh3GRAFKiCYr3cZEAtZqs\\u002fZd1kQCOpM5gY3mRAF6grOdfeZEDA44z9oN9kQJOZNc114GRAlB4QnFXhZECcqmBpQOJkQMgQNT8242RAWK7yMTfkZED3hP9fQ+VkQF8HhPFa5mRAZZxDGH7nZEBsSIoPrehkQB5MLhzo6WRAaNukjC\\u002frZEBwWym5g+xkQA\\u002fc9gPl7WRAUsKT2VPvZEDe1y+x0PBkQG8vFQ1c8mRATIsse\\u002fbzZECCL5aVoPVkQENJWANb92RAc1ckeSb5ZEDIRzW6A\\u002ftkQBRJSJnz\\u002fGRAF6iy+fb+ZEDMaZfQDgFlQJe+PyY8A2VAEd+ZF4AFZUDEZ+LX2wdlQJHffLJQCmVADcAADeAMZUDCG4Fpiw9lQMffFWlUEmVAXbKuzjwVZUAKnTiCRhhlQEsNIZRzG2VAZExCQcYeZUCgZ0f3QCJlQKaql1nmJWVAhlXcRrkpZUA8MTbfvC1lQB8nPIv0MWVAOSTgA2Q2ZUCMe1xbDztlQHPRUQf7P2VALZJE7CtFZUDUS7Fqp0plQPhF+W1zUGVAC790fZZWZUDwyQbQF11lQOFyoGL\\u002fY2VAC1k2ElZrZUDUOca5JXNlQIk2KlV5e2VAoWufKV2EZUAluRb13o1lQK7moyUOmGVAFOWrGvyiZUCYINNxvK5lQGtEJmJlu2VADPCRKBDJZUCgm4KJ2ddlQOSGgmzi52VAFvbxl1D5ZUBNfp6UTwxmQK78KcMRIWZAf+4OsNE3ZkBJFN6201BmQJ07agpobGZAgat5Pu2KZkC3pfZ406xmQA4wgH2g0mZATkop2PT8ZkCQNl2FkixnQM41kphlYmdAfZ60lo+fZ0DktMeDduVnQDRu5BHZNWhAXQt9C+qSaECWtOxAdv9oQGSGr\\u002fgaf2lA7hgpV5sWakD77IsvgcxqQNlGdz+bqmtAesXttRXCbECKa3BstzZuQP5BGfVoLXBAPEOkXTP1cUBcSvm7uTd1QHhHCnsMXXtAOHm0SAtFg0BIidnBn6SMQCasrYuqX5VAZDUFo\\u002fqxnkC6zFL5F56kQP4rNtpRaalA0jeluD08rEBCztmBs+WrQJx3tTaLi6hAfurprgqGo0BOHMfOrJScQOb++DnPppNA5jZWIFowikCvGX3Xt7KBQG8Tq0YgfnlATsmsqtsgdEAb5t19E0txQKGaGYg9dG9AlOO4MFaHbUCMxxuQOTFsQGFSvJCFLWtA9zicfTJeakAaUopPUrRpQFXmeKkEJ2lALKo8bDewaEBhUpb9cUtoQDUlGTNM9WdA8jlobSGrZ0BMXJgf3mpnQJ+n4ELcMmdAGJeljMkBZ0Cp\\u002f4hqlNZmQBmxWdBdsGZABAIngW6OZkC9EsTjLnBmQMMtxrggVWZARSsPONo8ZkC+03E9AidmQB1ynkNNE2ZAGmZf\\u002fXoBZkCY3VFpVPFlQAqjHEWq4mVAzfyjy1PVZUDyN4iuLcllQKHQ0z4ZvmVAwmdyu\\u002fuzZUCU4ha+vaplQGxZyMBKomVAKTKCt5CaZUDhz0K6f5NlQICDnrwJjWVAm2Z+UCKHZUAx3yVyvoFlQE7C9FvUfGVAErukYFt4ZUAD0vrKS3RlQHDMFMKecGVAcQKgMU5tZUCrGGa1VGplQE40tYetZ2VA4uY9clRlZUBEGRLBRWNlQF5ffjd+YWVA2uiCBvtfZUCBKrvEuV5lQOm8i2e4XWVA8BN1PfVcZUDE3G\\u002fpblxlQNwxPV8kXGVA3peZ4BRcZUBdAUb7P1xlQMII3oelXGVA8kR2qUVdZUD4LwHOIF5lQE6gfK83X2VAnG3tVYtgZUDlmjIaHWJlQJBjvanuY2VAoO0\\u002fCwJmZUCPSWmkWWhlQH33ykD4amVATo4KGuFtZUB7k4nhF3FlQGJTtsugdGVAzetCnYB4ZUBiIYy6vHxlQH+CiDlbgWVAMWOq9mKGZUA8LTWt24tlQOpOoRPOkWVA1wPL\\u002fEOYZUDg3tB+SJ9lQNtyuSDopmVA0Gc2EDGvZUDVLiphM7hlQH\\u002fz+FgBwmVA2WYryK\\u002fMZUDCjop1VthlQNibqZ4Q5WVAY03ckv3yZUApKvFuQQJmQD17yQEGE2ZAiUM65HslZkCRarTR2zlmQKJyTlNoUGZARmdU1G9pZkBccgs\\u002fT4VmQCyqrUp1pGZAXUj3sWbHZkDEMa+dw+5mQMyN+6pOG2dA9ff9HfZNZ0DD+igM4IdnQBvVi6B6ymdAZNJ4H5IXaEBhrCkjb3FoQDUTmScB22hAI1A9xh1YaUCDV4Gn6+1pQFideGTEo2pA+iAtKIGFa0ADxdh97qlsQIAcb0\\u002fpQ25AQhkf4\\u002fxkcED3MJrA1aByQPaTD0++1XZA8WXe8zutfkAsSZOmgS6GQAzM8HJQfZBAyN9Do7MCmECiKId2xIWgQO6vgCXQ\\u002faRArF8YZxclqECXGzq5A7aoQDDtB6uiaaZArMBCIu9CokBJyAvDj0CbQEnrFz6KBZNALCjhBUebiUBBRBb2fGGBQJUeXgUnBnlA8AgtHha2c0D0mBBzxehwQOMMZuAfwm5AcKHrFX7obECOxrKgcaRrQJGAwoqzsGpATrxkGifvaUBCoqF7GFFpQLdoxuLvzWhA3hYpUN1faEDs++0GoAJoQNJOZjv+smdAij\\u002ffs3luZ0B+TlS7HjNnQP277MVh\\u002f2ZARjK2oAbSZkD3h504DqpmQBIeswyphmZASBI0\\u002fCxnZkBj97SGDUtmQALJc9nVMWZAoLfnMyQbZkBrglVOpgZmQJQHKIQW9GVAFvoflDnjZUA\\u002fEyfU3NNlQDUnFL\\u002fUxWVA\\u002fJznyPu4ZUCHgaFqMa1lQCnrP1pZomVA+XgH5lqYZUAajypsII9lQJw9YOmWhmVAcLEfmq1+ZUDWpharVXdlQDDBIPaBcGVAN+eOySZqZUC8ufm3OWRlQO+kL2+xXmVACfgSlYVZZUCGuHKprlRlQDx\\u002fFOwlUGVA\\u002ftlIRuVLZUCHLoA350dlQJNObcQmRGVAM+NVaJ9AZUCQZEAITT1lQPUjvOcrOmVADYcKnzg3ZUCkXnkScDRlQM2WxWrPMWVAaJ5jDlQvZUAGCJCb+yxlQNA6D+PDKmVAOrSG46ooZUAqeF3FriZlQHn2E9fNJGVAiPUFigYjZUBADItvVyFlQB7waza\\u002fH2VAX2KkqDweZUBj5WqpzhxlQFCieDN0G2VAehCNVywaZUApDSo79hhlQPwrhhfRF2VAyii0OLwWZUBvlv\\u002f8thVlQPE3gNTAFGVAQvPmQNkTZUA8E4nV\\u002fxJlQBO0sDc0EmVAxt07H3YRZUBwKpdXxRBlQLgiJcEhEGVAlhEpU4sPZUD5a1MeAg9lQINy1iaMDmVASm1+5CMOZUBg8YfMyQ1lQItnIIJ+DWVA1WPM30INZUBW8aYDGA1lQGnrb1\\u002f\\u002fDGVAi4vBzfoMZUCLGlSuDA1lQJXsCQw4DWVACCi\\u002f0IANZUBooOcM7A1lQM5\\u002fIF2ADmVAEAgqg0YPZUDurQ9vShBlQIHReoGdEWVAiDlHj1wTZUAtLMXLwBVlQGl6NRNMGWVA8klj3SwfZUChL0a99CllQIFc28iKPmVAOaOJWMNkZUCLlOEAaKZlQL8d9mN4C2ZAfebnGNSTZkD9W+VCvzBnQHh6GqB9wWdANFHFwCcXaECQtyX4jQtoQFQDVNEjpGdAhuZQgVkMZ0C39yz9SXFmQEyyH3LF72VA4AtXis2SZUCTaV4nA1hlQLD0NmhzNmVAVHSJOYMkZUCtbGD1ABtlQK6Za02gFWVA9qhxOzUSZUDN86AWxg9lQAllnSroDWVArw+UTmsMZUCfMkoUNgtlQPBnMHw4CmVAdg\\u002f4PWcJZUAJELHxuQhlQGbCEioqCGVAZFC66bIHZUCfLBxFUAdlQJx9ph\\u002f\\u002fBmVAwcYq+7wGZUAMmA3UhwZlQMtFjwZeBmVAt0OmOj4GZUDBY6ZUJwZlQC84dGkYBmVAR61etRAGZUAuOfiUDwZlQHCfdX8UBmVAHBQ4Ah8GZUDlcz+9LgZlQIXyUWBDBmVAfd+yqFwGZUCPNUtfegZlQLhzLVecBmVAS15jbMIGZUBqLfiC7AZlQAitNIYaB2VAbiUFaEwHZUAdtYIgggdlQP41m627B2VAwfDTEvkHZUA\\u002fUyRZOghlQMio5o5\\u002fCGVAQn\\u002fdx8gIZUB1+0wdFgllQEjoJ65nCWVAUs5Qn70JZUBn4O8bGAplQGML31V3CmVAtAAuhtsKZUCxqMDtRAtlQMERC9azC2VAmLHukSgMZUAbrr1+owxlQAHtaQUlDWVAXebmm60NZUCntcbGPQ5lQOm2HBvWDmVA2DiyQHcPZUDCjJz0IRBlQEMmRwzXEGVAVb0IeZcRZUCnul9MZBJlQHjv+Lw+E2VA2SGsLCgUZUBV16QvIhVlQAC\\u002f+5QuFmVATSUXcU8XZUBZT0AqhxhlQDJO+ofYGWVA9VXPxUYbZUB7bX+q1RxlQIhUw6SJHmVAd8k372cgZUB26om9diJlQGXIs3W9JGVA244V+UQnZUCiM5sCGCplQMtAF6FDLWVAge7P19cwZUC+d1lz6DRlQLBQ6yaOOWVAGtyHD+g+ZUBS3efIHUVlQHY9MVtiTGVAeChQjPhUZUCcZIoBOl9lQNWCtc2la2VAvDeioAV7ZUCrgF9w1o5lQECfoTZiqmVA48WvM1fVZUBhamwYyCBmQCJ2TUplrmZAviYrlku2Z0D8PrXIuoJpQOpP311mV2xA9J4mEG0hcECyZwL2\\u002fnRyQDo72glns3RA\\u002fvLLDG4rdkAI86+M7jh2QPxOxuqH1HRAWAWMgUScckAgJALN7ERwQLsTUmpjjWxAxncZsoimaUBpWh2Hp8tnQC5V9al8umZAdOHUTvQnZkBAkNm8Y9plQHaqJWnKrmVADatarEiTZUAtxDmGy39lQBHB9W7ecGVAavt01PNkZUAFvRRaO1tlQO\\u002fySdEyU2VAzq1e2H5MZUDCa4OR20ZlQK\\u002fA8DoVQmVARkzEuwM+ZUBHRHCihzplQLTZ\\u002fAKIN2VAxr1I6\\u002fA0ZUBJLdk\\u002fsjJlQIAG9+K+MGVAjbJTEQwvZUBCILflkC1lQHzwdvlFLGVAujZhGiUrZUDZGcwQKSplQE666HFNKWVAz4J7e44oZUC0x9b26CdlQA40fCFaJ2VA+Ogsmt8mZUAxtnlRdyZlQBWmG30fJmVAN0+GjdYlZUCJl0QlmyVlQPVoyBFsJWVA6yVoRUglZUAa1VLSLiVlQGoAT+YeJWVAnuMgxxclZUAXUnvPGCVlQIUbZGwhJWVADP\\u002f4GjElZUBrqIVmRyVlQOLy3eZjJWVA\\u002fuXxPoYlZUBqrZIbriVlQEhEYTLbJWVAfb\\u002fgQA0mZUBMHqcLRCZlQGhYqF1\\u002fJmVABAyYB78mZUBVvV7fAidlQFoPoL9KJ2VAJ8FPh5YnZUDujVMZ5idlQA5UMFw5KGVAbCLBOZAoZUAx\\u002ffae6ihlQI1VoHtIKWVADVQ3wqkpZUAMNLZnDiplQPkIcWN2KmVAAl30ruEqZUAxLOhFUCtlQM\\u002fP9iXCK2VAx3y3TjcsZUBIBJzBryxlQEOR4YErLWVA\\u002fSaElKotZUDMrTQALS5lQIZiUc2yLmVAL4TgBTwvZUB2IY21yC9lQDDtpelYMGVABQYesewwZUApoo8chDFlQEuUQD4fMmVA76IoKr4yZUA4r\\u002fn1YDNlQMurKbkHNGVAHGj+jLI0ZUDUN5uMYTVlQI+BENUUNmVAo0Nthcw2ZUAyodK+iDdlQHaNiaRJOGVA8q4aXA85ZUA9mGgN2jllQEh4zOKpOmVAb2g1CX87ZUCKg0qwWTxlQDf2jwo6PWVAUD6PTSA+ZUCH1QKyDD9lQLqJBXT\\u002fP2VA\\u002fsxF0\\u002fhAZUAvTz0T+UFlQNo7bXsAQ2VA+YCfVw9EZUDOjS34JUVlQDgFTbJERmVAGuxi4GtHZUCB613im0hlQINOGB7VSWVAvnbC\\u002fxdLZUDZllb6ZExlQAqaFoi8TWVAljkVKx9PZUAHX8ttjVBlQPESu+MHUmVAtl0hKo9TZUBNqrjoI1VlQAxrjdLGVmVAPvfmpnhYZUAf1kYyOlplQMDygE8MXGVApYTv6O9dZUAl1cX55V9lQMhyhY\\u002fvYWVAvNyZyw1kZUAiOx\\u002flQWZlQJpY2SqNaGVA3clgBfFqZUCuBI35bm1lQEgeI6sIcGVAawfS379yZUD6aIaClnVlQGDIIaeOeGVAe2Cijqp7ZUBFL8ur7H5lQM0oXqhXgmVAUF\\u002f9au6FZUBUVMsdtIllQOOq5jWsjWVAzzfie9qRZUBKG2AVQ5ZlQFJY\\u002fY\\u002fqmmVARYHD7dWfZUA32V+zCqVlQEcXa\\u002fiOqmVANkUbemmwZUCqkcqwobZlQNIh0+g\\u002fvWVAB+VZX03EZUD0XcJj1MtlQOLIrH7g02VAZ\\u002fyQn37cZUCEakVSveVlQFEeDv6s72VAFdkvMGD6ZUCST3306wVmQKWo7z5oEmZAXfkhavAfZkBCTofPoy5mQAR3fn+mPmZAy5oXISJQZkBddJgDR2NmQBJBvW5NeGZAWnKhQnePZkDGT4P9EalmQNiYwER5xWZAAw87GBrlZkAgaObkdghnQInNNr4sMGdAAW\\u002fsIPpcZ0BKovTHx49nQOJlXlO0yWdAHF5W0iMMaEDPEq+51FhoQPHOgYn8sWhArH6YmG8aaUCbf5z12ZVpQGKPbVQWKWpAmxSfrcjaakCn9LFQubRrQPRvCNF7x2xAPhUXSpo0bkAXUn27QCRwQKYiIQ5K2XFAVsqlT4nydEA8tvuGsMJ6QChwzsa+o4JAc3jlC1hii0CR1eNwiiuUQAN\\u002fZjAfhZxA5CfVw6zHokC7CSldUIOmQFt8bG69CKhAtOHeSdaWpkAYPB2Ye+SiQFoZD6ECvpxAvKqLBEFZlEAz+Y1O1KCLQGj+YVkOyIJAvfEqZUHlekAhrOzd4P10QLjGk2x72HFApDlrRPQdcEASJBbA0CNuQPh2YGKUtWxAAjrsTQGja0BtcfwzzslqQMs0NHIaGWpACNZN2vyGaUCoqctPwQxpQPNjxf+DpWhAwRg6R5NNaECJMR5RFwJoQKMQjXPYwGdAQq6F7xaIZ0AcYOL\\u002fbVZnQE9037C+KmdAFlWmNCAEZ0Ay4sEl1OFmQD1F7aI9w2ZAqP7hgdqnZkC1pdoOPo9mQMIZQPUMeWZArWYTCfpkZkAoONa7w1JmQOFJbRUyQmZAAYRjExUzZkAaaTFYQyVmQHIMgRmZGGZA9HBcQPcMZkAeaCuxQgJmQIkeo7Nj+GVAvlZ9dEXvZUCAGhuc1eZlQEkGOPYD32VAuwmbJ8LXZUA9Zk1vA9FlQG8uW3G8ymVABUaBCePEZUB2tnkkbr9lQJOG1J5VumVAgIF8KJK1ZUD6Fi8sHbFlQFO8TrrwrGVA04SRdgepZUDoQiOIXKVlQOFi5IvroWVAm5p9iLCeZUBHIg3kp5tlQEP2PFvOmGVAGhWb+SCWZUDkLBUTnZNlQK4agT5AkWVA0l4jUQiPZUADqSxb84xlQMtdMKX\\u002fimVAmhqgriuJZUDMn2QtdodlQKBZvA7ehWVApLWceWKEZUDqBfDSAoNlQNycMcS+gWVAA6wiRZaAZUCG+KSpiX9lQJOTQrWZfmVAYgmktsd9ZUDyimmuFX1lQDrlRYeGfGVAJx\\u002frax58ZUAsuYle43tlQKwURIfee2VApQyVrR98ZUApx7HaxnxlQGG4M\\u002fkcfmVAkQ8\\u002fgcmAZUAEwmOHL4ZlQAD8YuvdkGVAidGc\\u002fLqkZUBKOthYUMZlQNE4Tim8+GVA9kmdn5k6ZkBPzYmSH4NmQHTzNA\\u002fLwGZAmC4q95rcZkC1\\u002fxTsvMhmQILf0ar0jmZAh8HmnRZGZkDINLhGPwFmQPC2yHHVymVAS6Okhm6lZUCipwybhI5lQOSMeQOggWVA8nihyqR6ZUDRkhbFwXZlQJ\\u002flQmdidGVA9rm82sJyZUAckB6AjHFlQGU5UTeZcGVA8\\u002f\\u002fWXtZvZUAJXU4mOW9lQIybyUu6bmVAwHVHiFRuZUBmXzPgA25lQN5MSkPFbWVAKCGmTpZtZUAKXK8hdW1lQD4ZPD9gbWVAjxoKd1ZtZUC7ThzVVm1lQB0iUJVgbWVAlcMCGnNtZUAV2PbkjW1lQDxM55GwbWVAypFd0tptZUDnun5qDG5lQCKOly5FbmVAq4I+AYVuZUB\\u002fXO3Ry25lQK8S\\u002fJsZb2VAN5zsZW5vZUDix\\u002ftAym9lQBzL7kgtcGVAKQcYpJdwZUD424+DCXFlQDJnoCODcWVAjeZkzARyZUCLOp\\u002fSjnJlQNvTx5ghc2VAsUFekL1zZUAWvII7Y3RlQGF+4y4TdWVAf7QLFM51ZUDZSSWslHZlQJ9EQ9Nnd2VADcVPhEh4ZUDdkb\\u002fdN3llQNLANSc3emVArRdN2Ed7ZUDj\\u002fcqga3xlQGROknKkfWVAunrEjfR+ZUAaN5+PXoBlQAwRz4TlgWVALbknAI2DZUAC3\\u002f42WYVlQIYLziRPh2VA0ChOuHSJZUCavgQN0YtlQDeNT7VsjmVAIDqDGlKRZUDJ0OP6jZRlQBG+bRAwmGVAxRwX8EucZUDrRj44+qBlQBryFDBapmVAZuDBDZSsZUAC\\u002fvBH3LNlQGJ7Yex4vGVAc0qTA8zGZUCKGaLabNNlQKwdkDtt42VAtjUwHBL5ZUDJsmqUhxlmQPpaQww0UGZAF+7qm6SzZkAD2hio1WlnQMv8ALIypmhA2tMVyy2aakDMxE16dVZtQGrv1I4KU3BA\\u002fVbrHoH5cUDWnlqp0yJzQIQ3fE3fT3NA5vTGOcFpckDp9nA5hdtwQOlT+fR6VW5AJtymz9dka0ASzY\\u002f1PjVpQIaVnJlQx2dArr06ONrvZkDk\\u002fLuP0XlmQLr9iKaIOmZAJc0ygw8XZkD7LXWtIQFmQKYL4vQT8mVAf8Z5UffmZUBeUnm4dd5lQGwxk9Pc12VAJml1xr7SZUAxl71sz85lQBBsG8rWy2VA7\\u002ff+larJZUDCKahuKshlQNeCXU89x2VAHI1Sxs\\u002fGZUB+p+up0sZlQNVAlyY6x2VAAcadC\\u002f3HZUClWGJFFMllQIGP4nh6ymVAfHvdtyvMZUDOZINHJc5lQG6iSHVl0GVA4hitduvSZUAUiahRt9VlQD1rDczJ2GVATEOrYCTcZUCO+VI5yd9lQEu7JC2742VA95PFwv3nZUBu7Ec2lexlQCjfuIKG8WVAnohnb9f2ZUBiYx2hjvxlQNBQoa+zAmZA3AoEQE8JZkBkV14kaxBmQGCc2YESGGZArfQU\\u002flEgZkCOvz71NylmQMDIkLrUMmZAtqVI5To9ZkA7Wr2sf0hmQDh\\u002f31a7VGZA+adNvQliZkDJSUPuinBmQK8\\u002fGvBjgGZA\\u002fEX7r7+RZkA4qtgm0KRmQNviH8PPuWZAFGsBKgPRZkCTnzFpu+pmQOAbObpYB2dAxlW2A04nZ0Dk86ZUJUtnQGS34auFc2dAyaRGfTqhZ0DpTEKTPdVnQGVS6izEEGhAX8AamlBVaEAACLMqyqRoQNftnTeeAWlA\\u002fQ3f6O5uaUAY8JlY2fBpQPgj7nXtjGpAWAp65itLa0AkY3P7kDhsQC6Tzef8bW1A3Omwsr8hb0B0d8H4DOdwQLaIVEHEOnNAejknRxR9d0ASBfnDYCd\\u002fQECOPxeRBIZA7fFOLS7Lj0DGsnlww3CWQJzWKF3r9J1APUjnn7VzokAOT6wcQYGkQOb1BMpWM6RAIhroqjCxoUBeWO4jPhycQKPmTaRNupRAMHQ2Xz8ejUBCaJbevjGEQCohI+FN7nxASZC\\u002fmhE9dkDxTPS1pIxyQInwJ6pNhXBA8NOKpu2pbkAmkglRwRxtQP96MQx5\\u002fWtAZ1Yb2Nsea0CkYsDdsWtqQB5nxJN82GlA5Ro60u5daUBRspEM4PZoQDWjn1R4n2hA2u0sWcdUaEBESvXlhBRoQNIP4ebl3GdA5Zgm2n2sZ0DuQi6ZKIJnQA3ZA\\u002fX5XGdAsmXCeTE8Z0DeQBUyMR9nQONsaJx2BWdAJLdtP5XuZkB6Gfd0MtpmQHPa\\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\\u002fzJJnCM3NAwmeqycdWeEA1N7LS6TaAQGujEpzT54VAqxhGhsXljEAIyurbOg2SQE8576fP0JRAy\\u002f3d6gColUDW4ouBcimUQMFKnSI6CZFAF3FrRbnFikDcqIC5fBeEQHIWJMCEwX1Ajiv9LyOXdkCYFU8CXChyQJaNDGAdPW9A5dMZtTFubEBojcAma9ZqQCYMNzzz2mlA5Qqi5UguaUBxgspdzqxoQLcNFvRNRmhA+XB237nyZ0CVmNbMZq1nQN55C203c2dAxsqJhulBZ0D+YW9LyBdnQFbwq76C82ZA44AqSBHUZkDc+99mo7hmQJbyCIaSoGZAnAbGVViLZkCpyMeeh3hmQCXhJd7GZ2ZASUsdLcxYZkAiKQ8dWktmQF6YKEk9P2ZAFB9\\u002fcEo0ZkBKGqP3XCpmQPtIVblVIWZABn9FFBoZZkB4J0MokxFmQNUVlTitCmZAKOedK1cEZkAhpsIhgv5lQGMJ5h4h+WVA3lvSwij0ZUDNErUNj+9lQHJHaS5L62VAm2bFWFXnZUBxAX2ipuNlQEYacOU44GVAfUh6pgbdZUABLgEAC9plQMampY9B12VAPOOXZqbUZUC50yX8NdJlQEgjLiLtz2VAgG8x+8jNZUA3McjxxstlQBGDTrHkyWVAOR+gHyDIZUDu\\u002fMZXd8ZlQBtOhqXoxGVAfaGygXLDZUAJCk+PE8JlQGnfbpnKwGVA6pXkkZa\\u002fZUARKNGQdr5lQKjNNdVpvWVAQgi9xm+8ZUBlEgv5h7tlQJPIDDGyumVAzi\\u002f3bO65ZUDbAw3wPLllQFOtEVSeuGVAPqujpBO4ZUD13G6QnrdlQC7xRdVBt2VAReo\\u002fVAK3ZUBAnrfk6bZlQOHpjCgOt2VADmC5yp63ZUCd4T+R\\u002fbhlQL1Ppa\\u002fbu2VA7beZqkjBZUDy9n34jsplQDHiNZu92GVAE6apD+brZUDIDgv2TQJmQL8GErLeF2ZAT7L78UAmZkDn6VQ+XidmQMD3iJmNGmZAZ+rVWUsFZkBoft8tMO5lQLILet\\u002fC2WVAj78n8R7KZUCfj7grd79lQOPhZVPduGVAtX0Lmwm1ZUBemFni3rJlQJjYigWbsWVA3lXV586wZUC8gEaVQrBlQLf9Hfnbr2VAcE\\u002f\\u002f946vZUCtbelwVa9lQMfPEc0rr2VA0hixpw+vZUA2ctZH\\u002f65lQLpIZ2X5rmVAPjVyCf2uZUBZliF6Ca9lQAPF5ywer2VABQmfvDqvZUC36GLiXq9lQPcOXHCKr2VABZf4Tb2vZUAG4DN1969lQNHurPA4sGVA87Nd2oGwZUBoKtNa0rBlQDIP0KgqsWVAoCdLCYuxZUDKgb\\u002fP87FlQKBkyV5lsmVAvisOKeCyZUBKW3CyZLNlQAENlJHzs2VAi6C6cY20ZUCRgf8UM7VlQJUFA1fltWVA\\u002fgUUMKW2ZUAQH+24c7dlQJCbHy9SuGVAAlRM+kG5ZUBOf1KyRLplQGYRpiZcu2VAB14LZ4q8ZUDrCQXO0b1lQMLjVA01v2VAmV0JPbfAZUDj3LHtW8JlQP9lfj4nxGVAoaNG+B3GZUAul72uRchlQOLTeOmkymVAPhv1VkPNZUD1bHMMKtBlQDGce9Zj02VAsQIfn\\u002f3WZUAmH+bwBttlQNu94J+S32VAI43wpbfkZUCGc55EkuplQLKpcIZF8WVAZdRTRf34ZUD8Vgvv8AFmQDCjoWhnDGZA5\\u002fUh37wYZkBSJv6zbCdmQHM77xomOWZAL8TzlP5OZkCyCqZ382pmQAVP8NwikWZApzjGdWPKZkB8R8r80yhnQDr67N7vzWdAARMoQhrvaECMe736KNJqQKSEqDOGum1A0t3FeY3icECkFF2fKV5zQChBaKbE+XVAdBb66eMbeECj2Yn\\u002f\\u002fgZ5QFAGfaS5UXhAgxj+KVNPdkC8sLhYObtzQGyjGBurNnFAGXngVytAbkB9xPetsjFrQHwL0hrKLWlAg1eQwsj0Z0C0pIc3okBnQKhfdwfB2WZALhgvuTCcZkAtai2M8HNmQPdiNJ0FV2ZAlbWnpbtAZkAbcwLO0C5mQO+RTjISIGZAVQbQrcMTZkAx3wRkYwlmQDpkUkuRAGZAaacT3gP5ZUAANabDgfJlQGbD76\\u002fd7GVAR6Q+fPPnZUAlwxQKpuNlQHDS0rHd32VAdh9ZFYfcZUATR0k7ktllQBNtst\\u002fx1mVAwIJy7JrUZUBz9WYPhNJlQKIkNGel0GVAgF9RQfjOZUDnIGTlds1lQHEd8moczGVAh6sql+TKZUAVixDBy8llQPunsrrOyGVA66huvurHZUC\\u002funFfHcdlQBTp13xkxmVAtfrrNr7FZUAnwSPmKMVlQLP3iBOjxGVAXJJOcivEZUCTwV7awMNlQLy6t0Niw2VAgR11wg7DZUCCBGqDxcJlQBnMNMmFwmVA9Z656U7CZUAAGfRLIMJlQL72Emb5wWVAQfDSu9nBZUB7pA\\u002fdwMFlQLzugmSuwWVAnDSs9qHBZUASOtpAm8FlQBveUviZwWVAudOU2Z3BZUD1\\u002f6+npsFlQBaisSu0wWVA4dQhNMbBZUCAUJCU3MFlQEygLiX3wWVA7z92whXCZUAlStlMOMJlQIeSfKhewmVA3Sv6vIjCZUCffyt1tsJlQDI5+r7nwmVAuGE3ixzDZUA0IHjNVMNlQNCl93uQw2VAyN99j8\\u002fDZUA9mEoDEsRlQFG9BNVXxGVAiZKtBKHEZUAfnJeU7cRlQGMeYYk9xWVAYxXy6ZDFZUAgkn2\\u002f58VlQLBzhhVCxmVAMXvn+Z\\u002fGZUDhwd58AcdlQCqhHLFmx2VAGSXWq8\\u002fHZUBJKtuEPMhlQLhRsFatyGVAff6sPiLJZUC9mx1dm8llQGN0atUYymVALXJDzprKZUAHJ9FxIctlQEOS6+2sy2VAmiNXdD3MZUArkAg708xlQAIicHxuzWVA7kHNdw\\u002fOZUBXFYtxts5lQJQnp7Njz2VAsjcjjhfQZUBwaoNX0tBlQPNLWm2U0WVAiD\\u002fkNF7SZUDFNbQbMNNlQLfEc5gK1GVAJAu5K+7UZUAUIvVg29VlQDpIfs\\u002fS1mVApGy5G9XXZUAqTGf44thlQHv7GSj92WVA+3rYfiTbZUA21\\u002fbjWdxlQOdnKlSe3WVANgDj4\\u002fLeZUBSX\\u002fPBWOBlQGT1lDrR4WVAay3Vul3jZUDT83zU\\u002f+RlQB07hkK55mVAUOk27ovoZUCqBv30eeplQFtmLa+F7GVASI\\u002fMt7HuZUCzrpH1APFlQGI2XaV282VAmOxoZhb2ZUC5PoZI5PhlQL5f0tzk+2VAICRfSR3\\u002fZUCb9mxgkwJmQI9o97tNBmZAHA+F3lMKZkDhnmdarg5mQO6f5gBnE2ZAOB4zG4kYZkCr4ISvIR5mQF4jbNU\\u002fJGZApaFDHfUqZkD6dtkPVjJmQB5n9cx6OmZAgzh\\u002f0X9DZkAcSOfwhk1mQGgaZpG4WGZA2ZohQEVlZkAvlfO5Z3NmQM7kcJBng2ZASdGSopyVZkDXRju4c6pmQA==\"},\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"},{\"hoverlabel\":{\"bordercolor\":\"white\",\"font\":{\"color\":\"white\"}},\"hovertemplate\":\"%{text}\",\"marker\":{\"color\":\"rgb(255, 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2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (2 2 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (3 0 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 75.38\\u003cbr\\u003eMiller indices: (3 1 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 79.72\\u003cbr\\u003eMiller indices: (3 1 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 83.99\\u003cbr\\u003eMiller indices: (2 2 2)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 88.23\\u003cbr\\u003eMiller indices: (3 2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 92.45\\u003cbr\\u003eMiller indices: (3 2 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xxAULaYee+RK8DASgxiWmsuQFAKcyKWZypAAMj5vCS1yD+ALshLSLoYQLDS379uqSvA4CDSDrNhG8Dw79vN7EYqQABQNpte7\\u002fE\\u002fAGC\\u002f6usUuj8wnUkFrCkiQICR1BlMDP+\\u002fIP2K2hglHEAAeJyxH\\u002fQXwNBmpiyj+CVAAFv4+5iqB0AATlWwNCDxvwDnXRk4mRdAgOUqH3NDIsCAe8L\\u002fr6Hyv0Ao\\u002fp5DaQZAoIxzCMPvFMBAeVsHL3UvQCA+Gz1+YSFAACbJ1hw0DUAAktTaiG8aQBA\\u002fksCvISdA4NrDZsIKIUAQQYF2enk5QBAJrS4\\u002f2ihAoKWbJkxgNEDgB52m9qUiQNBre8pPiiBAQCPqtmG2CUDAwVUu4icxQMBue2vGMCRAcIEqm2\\u002fvL8DAnHYJ7RAgwEgLAs4e5jJAgIjvzAvA8r\\u002fAg9mBQoIpQEDkN58slALAQAMwQAM4C8AAH3I\\u002fSzr4P8gBUbdcbTxAMNoUFTOsJECAvdhxX+4JQFAr773GSChAgHO21zcnFEAA9OzoHkgcwICp6mWWeQnAYFjFbZSbJsAgdk4GGZI9QAjHHqZbcDJA4Hj7g38zHUAA4x7X1sMGwDAXHXWw\\u002fyvAwHTb7gS1DkCAhdapEqsWQMAvym+bgzLAqAdaFExLNECATzaAvugzwPho\\u002fOoE4D5AqDdNbk+lM0BAnWOcWzIlwLhLrlY1JDtAwBclmLXoBkAoybWo9246wCCVwaUdfyBAsGtQlQ\\u002f0R0BA+2ZxHIo0QLBGZCJWti\\u002fAAAN8JApECsAA6KZgYvYFwIDcUJBN\\u002fBzAUE9wQHs1MEAwGxi2BjshQCA1YM3j7i9AAAIj4p9c0D+4XQ9JYnkwQKBzR00BjRHAQOBqnk+7AsBwW2qPN80gwCD\\u002f\\u002fCf41ShAIFtrfbIwJEAALllUr20WQOBcI4lZJiLAoM+T1vLxE8CAnPZ40K4UwMAcueFuoiBAwCi9IH1FC0CgtjTxmwgkQIDM8BVf4xfAkDi3RiVLMEBgn33xJZAVwOAkFxGYrB5A6BW311aIQMBQXINju7U1wPAPyqhH60XA4Bki7ZqpRsAASGsNiIzgPwBE3HrCeQlAgGy4dEuvQkCAa2fiA7ZGQKA9JUVXRVtAgFNZn6vAR0AAdJml8i8UwOC\\u002fYqMdNWHAIH1TittjYsCAkHN24GxDwECGqJRMZ2hAQLDCIqr+V0AgO44TM3VcQADm\\u002fvg5zxrAgLmNFYgWJMDwmtF3fatEwPA2sWoE4j7A4JTMqroNPMDYMO\\u002fum9hFwAjVzEDsoTLAYOM4ek7FMECgwyF\\u002fM3Y7QCBMiheysBHAcI\\u002fD2SfjLcAwb62DbV0wQKgyx0slOD3AoK4ankR+NEDw2Zom4EgnQMCytjnzrApAcDC+lPSmM0CAdvQMPKQSQBCG9dE70iZAADqa1pyND0C4ArirXEszQDh3Mn0RfTpAAH8\\u002ftl9kDEBArwTxuAsEwOiRzjn6VjdA2KWGPy4ZNEAQYnEU7sc5QBhvDOOVZTBAYJ4JKlDoJ0CgibharrpBQGDPNa5b1StAmBngomFVOUBwQL6LkrY3QPh6YQk2Dz1A8MFsJCJgN0Bg60gPEqowQFCa3vzUdkNAcN3ch\\u002fRWKEB8wPQWAbJLQABw0JM3oRXAlGUGvnZjQUDwDKLdGOQtQJDtWSBdGTJAcCfa+iQ9NEDQ31JQQ7sqQADHTCHsCSXA8Nj\\u002f5RwrL0CoOs9UWq0zQOBEU3vYeijAAHHzHjmq8r\\u002fAa97uo8slQCAKGogc6C1AMLnoyycANUDgr5poxygcQHAxRId5JC5AAMGuKKwwIkCAZwTSInIcQEDiLAy6PSZAIIJm9rE+IECM+ucSAA9AQPC5D8HTGjRAOOwmWukKQkCAAO0f8x0oQCD7NQiFDCJAIJOUUKX7M0CwUdZcLt4vQADHKWQVwSlAACYBTN+fJUAgztZyy\\u002fQaQEDfvTIQvgLA4OSooBHeKsBQyGbnge4gQIAnaxLN1wJAMEPRK\\u002fZ3KEDA03uuaGgYQCCw786Y1B9AiBlTtRczPMAAT0tN6\\u002fYKwMAi1os9xhFAuB5Y5h\\u002fCMMAA94b2Q\\u002fo4wLAtlwuCbizAgMFMfneGNUCwEl3tyXwoQCCQ4eDUvxdAOMmkvoGaMUDgE1enmHomQACFzCrsXRNAgLEmbsl++b\\u002fgutoh0rcTQIBhQhv\\u002ffPY\\u002fcMdbvEGoJ0B4q1xyITE8QMCrMZb18hFAMDqjon5LMsDAJbfw81QkwKCukqpV3DVA0IV0H2t2LMDA4wwlxhMvQIBGjqAqlhxAYAFBQDxBFkDQU3A9\\u002f4EnQFCuQvdVWC9AADhbDsHb0D8gjDVl5C0ewIBldjNsf\\u002fI\\u002fMALVY9yBI8Bg8CrwdL0RwACsTjwy4vG\\u002f0AdpQQksN8AwUIzd554kwADkeHtKHzfAEMr4GOcnQsBwD6MJXA0wwMB+8uHJtyjAEF\\u002fmPb\\u002fTNcCA2pYtyy8\\u002fQEA\\u002f89D4Kl5AAAeEl4spREAAvK7xEnccQIAE1J\\u002f2i0NAgPUL4+wiWcBwuaGTO4BlwACm\\u002fWBVNFLAANVPbzdEQUAAtzf0PHAfQICk9QsfxSDAAOprD31cEkAQRGRhzxdGwKj08Co4sUfAgEdo8bAwRcCAHhNirhgnwDDOYAb+ISrAABe6XuGHJsBALqfpy3EbQHj766ljejFAIDu0WY4NJUDAt8uL8NwdQJB0mdMBIStAwNsiBar7H8BAQSCR\\u002f9UtQHCJzSQOaDZAAHbAIEyG4T\\u002fAz4lq12MewMAA0YSOJwBAoNuc9JXfIkCQgCd2HF8lQMA9nGneKhdAwG1fHpjGPEDoRFjKk6c0QCCQPJddHSnAgCFhMG+TQUBQ2acam5UtQMCGf72XviJAwEL\\u002fgzJnEMCAYHbslRH2P2AZex1oRRNAQDCGcUNwKkAgz8+r0lkeQABIWf\\u002fRyrK\\u002fIB\\u002fvwFwLF8BgDlc9+Q0jQADOHrB0S\\u002fO\\u002fAOkEXiYVKkBAJSudShUVQIAJBrEPhDHASMaIs8muPECAyMgAyXgXQBCxBQ3pFCpAIAFforIwE8Cgd9RoFbUkQBADrk9ov0JAIGBym6tBL0Dg0AXw5vwYwGiLldzJ3jlAgJnxKrRP8L8A3ObvvawIQFjgHsKgLj5AoOFQ4RPnGsAAV6hh++MKQJhJ06mEcTFAADOMM34VHkDQv38jI5g+QACYYngOnvo\\u002fMF7K46i6NkBgfSiqE5UhQADk2U9cN8O\\u002fgFhfoGgwIsAAPE8HieopQED8gTIZCBBAgNDOravh8D\\u002fQqVFpFTMoQADYrkPmRfI\\u002fQA+i8YpFE8BASoPKjj0GwICAOg\\u002fdBRVAwOZ66XgoEUB4s\\u002fznt60\\u002fwED8DSA1MAXA8GXI+DVhOUDAM5FY\\u002fS8hwJighb2hkTDAoCNCDJ7oJkAAp3J5VQwhwMAVKQkOgTPAgJq7NSsdDECAA0rW8H74P6CvMSV7LhpAQKnNj3zEGcAAwOIPmZPbv6jD\\u002fO4LlDFAsMI5A9IrJUCAVbzpAEYLwJC2\\u002fvb1zyrAgDo6H5mC+T9gUYPKlaEZwFDJnsCA0ybAAP8a6OVPEkBAAz1nYG8zwADnP5AuQPe\\u002fAASCyqDRC8Dg3vrwpgQnwPDnUugn5iBAwDPGhRtlMkDQwlK8DFwrwLgsVGafNTlAkE8a62b5NcAAQl+MeunTv0Ag1zayog\\u002fAOOayO+XZNUBgkTIcAM0UwBAknsB5SCdAgMEMjPMV9j+gf6tc6BcWwICnpwHaFyrAgCaqGPjkEsAA8rYEv3EVwEA1QBU9QibAwNAcKjCLHcAgCWFawNYRQACT7Idc8QvAAEKPWifTLkBoswzF5j83QAC1kHzJmCJAgHXRzphvG0AwNfmp8E8mQACkzChlv9Q\\u002foHDlSKzcIkBA8zyohfELwEgo61RBbzPAAF4fKJ3W2L\\u002fwKaNEYbMmwIA\\u002fg+HDUTHAoAh\\u002fIIxpKUDA\\u002fbuTg9ENQKDZ015dfSNAwPlaZNGEJUBA2PS47isGQHD2mX78m0PA8JNTTTCUJ0DAgNlAfWgswFCjC5cfmitAwO2QqY6PAcAQPGZKdWIowOAFhy4NwxDAwFGrVy2EAcBA2vhgDT4fQAAJpgi4nitAgHz9uB88E0BQPvfT62IgwAB7Da6fvOk\\u002foO9bFpXLGMDgsWbpS88QwADuXMsVpwnAUBvKOdmTIUDAUvqgbyITQIDQSmOocSTAwK2kAI3pEMBgXKnvuw8bQIB\\u002fzWbr7gHAIBh+WiL\\u002fHMCAnymSLB30vyCjmjv+IUDAAKG\\u002f7mNk9L+o22fqsEgwwICEfuJ6liLAgGvMK5iQDUCQ+QFBfl4lQACd9CCqiOU\\u002fAC2Ai+G2CsBALCpwO9ECwCA4YsF6dhnAAM0pwu16GkBgw7XXb5QZwAhoTysoaTfA6DI332xtNMBwWmts3OMmwOAiaZw8JR5AABtHFujuGcAgzMhJHwIvwNCbjTuP8iJAqOpFyLuMNcA41f1iIpM2wABEiIOhYPY\\u002fkB3CyoJCK8CAVZQt6G71PwD43ad0sTrA0FRyEfd0LcDIegJSOcQywJBx0j\\u002fEzjfAqK96LjbaMcBgr+1PtZobwIBINUya6C\\u002fAAIk2yBCY7z+wU0\\u002fstRMwwFCGPFvXSyrAYNuxIp\\u002foGMAQndkUwFA0wCwDXYQONUHAEOj+fH0NJ8DwvcuaQ6c5wICFWjdxzDHAQIP78AHdE8BAqvscuaMYwEBR2Jm0cxJAwEOBYsSnNMAAJ5ligM4HwFAtWNtcuijAQITc9aVPLkDAKuAXnLUDwAD0GWojpizAGC9+nbmqOsAIU2PDQAYywEDErkmpzB3AQCibOo02FUBAEKx0U9QtQKD+9N1ldiXAgCEs+13SKUBwwuxPCNhEQBgTl9hjclVAEGigmY+kQUDAZ4CaizhNQCCIzanAW0tAgKo\\u002f57c7M0AA3+6XmVhHQChib63klDVAABjfZcgiyr9I0+o4PF03wIBLVX0qnwbAoAundqI\\u002fMsAACDKbd0wTwMBOtSRN2RXAYJ6qdOqWHUBoIc4xXP47wODdRyEy5BlAsCRYXGHYP0BQ0EuhtbMnwOBdPilaZhbAwLnVC9uPGcAIrw1GbptBwPAKDK9TISrAwIiJeHfAH8Agd\\u002fexC68UQECbzS+AeCHAQEfoluJhEUC4lTYBbmoxQADQ4F7cFxbAmGtidZ+HMkBACOS2HLIZwIDwIdFAdx5AYJTsWa5NJ0CQncEMkaIqwMBJFz2uKRnAiOkjJIy7NECA9tjaHv0cwJBfHvQuxTBAAOCjs2h+y78QY5sXdWciwID1LHJB8Pw\\u002fcPNk2GhdIsDgDm1Xm0wTQKAeDTmCrRjAUKF9qXurJUDAXKW1JVobQMDyHzYjXBNAQIzjGwddH0BIbyWEOdcwQFC4QcYWUijAoAc4KHfWMkBgDbXQ7KgcwCAu320+ViLAENBwCM7TOkBgKdVGHqUhQMDd9Wwm0TJA0PcLDtRgIMDA5HG6QGQowIB5eiUKmCtAABAwYB78yr+o6vX6FjgzwDCF\\u002fwKqxTlAINsHFi8NF0Dg3jiVYX4iwAAHKhiuHPS\\u002foOz2MX67NUDwLJAQVnEnQIDGKtA9pPS\\u002fML+K3GNlJ0DAQGN75qAmwJCPEDdmpyLAEOiidw1XMsCA5+kL3Vf6P8Dzs32J8ALAcMx363TDIsCARMAZ\\u002fMoowECvm4cftQRA6DchpFRtPcAAjdTyv3QEQDAUi2qWdTPAEL33oT8MI0CwC2msRX4wwACgJbQsTt8\\u002fsJ8f7jTxLECQIvrIQRgnwKB27TcYvB1A8C6KouIrJcCA85pfDzYpwIDl1ZTcA\\u002fq\\u002fwIHmzyhLL8AAWJDJ5jzVP3gMhKimsDbAMDrUVshsIcDA2asl6A4VQAAoytlt2c4\\u002fkHfV4HrIMMCgBxOtQTsTwAAJj1k8VRfAIPJS2R6QEECgOKgEm8UjwEhOgKsvFjJAABsMJ\\u002fsdKEDIU+lvmgc7QEA3sYDu\\u002fxvAQGBGl+XgF0Dw8tQzkR8qwEB7p20PYBDAwMEfGCr8BkCAc2MLfkkJwABZX7ZpNxNA0GAXA187O8AIXO8S30Q2wKAv9jxcxR5AIJTYA4BOI0DItrTSJ2M9wLAvS78bkj9AADVjdWpw+D+Ag6\\u002fltkb4v4iX2B0\\u002fkDvAsO0KUXmcOMCAJlVc9JH6v2BYa5Q2tjjA4HNvboqHLcD4sDaS0dE3wAAsD\\u002fjEwCnAYGsP4gJCHECgpLo4v\\u002fwfwACyXOHjewjAgJc7c7mBHMDgTQyu4ZshQDA7NamWuzFAoDsZrCBeHcAgtS9nECkjQAA3nJvq\\u002fh1AWDuQ\\u002fv6VOMDA4DKvL00dQABDDjl1xDXAwB6YqKPqDsBQ9LK8yu4vwDBzHXqF2iFAANRXv5p7AcDAukojvmQhQNBRlaE8JSdAeL4R39OOM8BAaQOsYsgawEAKq\\u002f9RXRvAQFHgcHv1D8CAZBNQpnr9Pzi6WMN3VDDAYFO0oZcGIcBANVIZNtQSwCAdMo3+0yPAIF\\u002fDFFR2G0DAINrDuUItQMDjZirwgxFAOOOH\\u002fPTjMcDAV6nbKqQrQBDl4eDP+iDAwLoJ9PNnAUBIPvXRrxdAwMDWFUTwZQtAYNTAu\\u002fIBFEDwjcWD4Yo1QIDgzu\\u002fP1BfAUFOG7t39KEDQRYc5cDQqwJAI6nVrwjbAMG306kWEMEDQgeUTcLc2QAAoZz+7huo\\u002foB+e+LxcF0AA\\u002fDJjI\\u002fESQJDYbOPLAi\\u002fAgH\\u002fIie+C8T+gJEp\\u002ffPwowBAv85qiTTLAwOFlJT3CJMAQ0w5ltHIkQCDeOTCRPxrAwOqHifmmIcCw+cdZn10twCD22EZlkS7AwCbFZyuyBsAAp\\u002fSxULnkvwD9G0L03gnA4FNxoaRJLcBwIdW3C0QzwGAqEuKglDjAsFIufUoUQcDAY7tvCCszwIAC52zsu0LAMIdXvoAlTMCAyOpxOMUtwGDgzwzmo0jAgPykeph1VMAA2E1I6YL6v4AtPja3XjnAgMkjxDYlVkCAPix+RjhjQGBqDu\\u002fVX15AAKIqun1fKUDQbCAXu3JBQIDmH5blgErA0BuvUhZUOcAIYWXvBm9CwICUwzZJeDdAwGVMubsgNUC4b6f\\u002fvPBDQAAh8bNzTRVA8C+ekPXnMUBAWxz\\u002fjHMCwKhZXm4sNzNAwE+RLRnIOkAAauryUzADwGjg1AHg0zlA4Oe8GJdNGkBw5xzuit4vQOh6l2M8+TBA8I3Sg0i\\u002fPEBA\\u002ftkBIJkiQMAT3TesrwrAgEWVKQ0IAcCAc4eP9ooHQKCnqF20ZxjAgBXg4VeCJUAsaZXEB0NBQADhDKB6hvy\\u002fMJXJbl+wJ0AAKDjWu8Piv+DDVlK9uRtAIIBwbKJiGsCARloM1lAJwHCc9zM3OzBAgOFx9BdhFkAA8Utqp97+P7gL52LiPDRAgFCq4KIuBEAAoMYGZ7UBwADgNv84gZ8\\u002fQJE9GXYPCkDAcKYsJL8DQIDIaFLHofo\\u002f2M\\u002f1s+fYOUCAYpLhdiQIQNCWtxKmWiZAADCQD0WyGsCgb\\u002fHC0hEvwEDrUGzRwwRAaNlzS8S3MkAAGL3cd6PqPwgXI19cDzPA2NTsQ4T1M8DgSKSB\\u002fHQXwGDInmfLGRPAQKNiMx\\u002fEFsBAzlIx0TkjwOCqEegDFC\\u002fA4BLK7XoPKUAYSGXZmmc1wECN6LMWQA1AYFb+FUVtLUAA8ydZi90BwACaxevgXSDA4FIaM+zcP0CAegG8tMAIwCAZc94J8jJAoH+qWzftE0BgiE+amvgtwIA2tl4ls\\u002fA\\u002f8LTfSsIRO0CAvGKaa0UPwDDW0MXLGzNA4ORjfc2DG8DAkpvoNb4vwMBKQXTovSHAKGWobP3gNsC4xnEqyRk7QBCGO5PP4SvAEPnwE5gMLMAgEB47fDE2wAAg6KQQeThAIDGak1+XGMDAtvjk9DUXQPByHGs9dCZAoGArBmZWKEDw1Jwo+TEmwGBkWIan+T9AAC26GqFsFEBYAlifGLoxQMAPxKVqIRZAcBhs3mlhIsCATINr9BMkwAAQSTeOKuU\\u002fwNIoqaFbCcDwwpon24syQMAxZ8j\\u002f5SlAcDj0qtkqM0Dw0pau45MiQAjT6MXsXDBAwD6XV1tYHsBA\\u002fhb+N+cqQEBZTtRNJgzAwPy\\u002ftZf1C8AgocvJJOcRwMC4mbyk6yrAACBF3LvV+D9AE5T5gz8WQBCZreXVkTVAwPbOik2TMkCA\\u002fSiUt6IEQOCTwfMD1iTAeNRQuLNqOMBg14mj2qoZwBjvflX7lDBAAGs8\\u002feWM4j9g\\u002fSRhQ04SQICH6TwSthHAYOMm2lIiLUAgoyiwcj4aQCDieOlS5CrAwKqgT0ChC8AQOCrhQhMhQHiV4N\\u002fMeDHAcMPJXub2IsCA+PE+kPILwCCnibiVfjZAIOUAg\\u002fQSHsAA\\u002fvbjYEIEwCBzBjoyGCfAoNGcjJlAHsAAizqf0o7iv8D29DFmyATAIDbIC7J3EsCw4BXcGaM1wIDPQI5odvU\\u002fELhEvx6jJkBAdlIJK6UFwBC2y8uCiSBAgHkd2L3b8z8YcQMSQTY3QMDlRxkbuRhAmEKXPcElNUCgQ6Dmi3IcQCBzkpQj7TLAgK4ecSO9B8BA4+bz0QscwADAEPFMWK6\\u002fgJbcE4DG8b\\u002fAHyQg2VQXQECPPmYbFh9AABrFCZcuEcCApi9BQ\\u002fQKwEiWg1WajDFAAIjOoIZUBMDg5oWjQG4VQBDh2wYBgy3AsDOO\\u002fkA7KECgIjf4uOAfQABoyFPAaMm\\u002fYAYe3EDJJMDgH\\u002fCAO8IkQMCT0HPicBxAaKxl45\\u002fJMUCgmCGqzTYhwIC1A3KnbRzAUFJ+Hm83OEBw01S5hmcmQGBQyju\\u002f\\u002fCZASI+oINtiPUAwoI2BWp0uwAA1A\\u002f9Nzek\\u002fMGFRp5EuMUCg2ZEtVEw9QFB7p3ZUGEHAAAOpFOF+9j9g7amVOi0wwEB4w9f0\\u002fTjAMKc3zglORsBIt4fLKVxCwED6VD69Xg3A0B7JgkHZM0CAuwycQrACQGBYyZkJdSjAQEqo+LgEFkDgGCCCczE2QICRwF3WXQlAsC3TzAePKkAABdKKUt7uP9Ci71hgbzdACMwxdEXIMUDASyoPt84TwIAtZnKa+xLAYJJWZ+wrI8B4RhOahIkxQAAE24ay9QrA4P3ev1I1FcBAOAXVTQUZwJDSJworjCFAAI5GKeNn87+AIBaFVUsOQAArv2jZxeg\\u002f+M8RoxfAMEBQiiVWRJEgwPAH13FYWClAICQUQaKeMEAgs8mHqx0jQMBNFKmuDBrAgMe5VCJFA0Bgt7tyRVI+QDBMKT9jcypAwDS0CkbCIcBAzqDV2AYUQEAt0bY0EQ9AQAKbTo8ADkDgxn5kU8kowIANctSX5yhAwBPx7O3aGsCAJ6e4V9wIQAAaKvR1VhzAIKnf\\u002f4W1OECAEzV0m\\u002fIdQAA6ZuLXfiRAmFpYD3D9PUDA8den\\u002fiYdwAB081a00i5AoKR1q1EsJkAoLhWaArw6QAAZECSVaBFAAMg\\u002fbepNsL9wY8sbUfcgQBAFXP7A+StAEOgSQAG5Q0CAXHWS\\u002fbIrQGBb\\u002fGP4ORfAYKf0r+Z3OUCANjBnS6L6P4CQGxedYkBA0FFN4o\\u002fFMUDAwZC1qk0rQMCJ5EGlxyBA4GYrV7DYH0C4mO1lE1Y6QFAmpc5CDCvA8AWsoQlVJcAAgDCrokwkwEiREEMO8zJA0N\\u002fwje7uJMAA\\u002fcHs1OQZQAB\\u002fxL2unRXAwFLQM19ZM8AAkrG5fUj8vyDNJoMxICFAcKfDyv4GQMAA3V0wPsMRwGCLSBVErDrAMDTGxl2XSEDgrm\\u002fA84k8QAA4DhjdbT5AMOEQKx3NQ0BAp8nwkWdHQIAs5VqU4kJAQA\\u002fS+WdtR8DA4YmVIyhSwMC8nkDZ6lPAAIhooKvCDMBA9DSCO3hFQAB0ZchuyfY\\u002fAIbO5uvHNMAghGbp7RtLwFAJGQlvckDASIL81YzoRcAgnom+lpQpwHD3gF0lqzRAAAiWuiwJ+z\\u002fQbrN39RkxQCCg0HneUBxAZKaSn5CES0Dg6vwRcaI3QPA4I57kwzLA2CgvbokQNEB4bXKb\\u002f0gyQFjmAls9BDhAYCIxbYqzLkCAt1ZBY28ZQHCB98jQGDBA2DjLLhGcMkDgLuSSiSIkwGDehF\\u002fBYBxAcNLsMXQeMECI\\u002fKo3O4NEQOiYvBxL1DhAwI0kCawWJUCAXsY9nYwOwACNJQHh\\u002fec\\u002fQMP3HX4HFUCI+LdJ8LUwQPBC5OqRHTFA8BAywf15PEAggnCG+TAbQOBq8IM2FT9AGHSBmKZVMkBAhuVhneMuwLgM+j9fvzRAaB7y7tB0QEAgQr7MVQ0yQFDSpWmJVShASHxWoiZCMECcFcB+0SlDQDBELUaiviRAAIiaDcUpC0DgsduNu2oSwECGC4gO\\u002fApAaEvTuKBTNEAAgDiFT1zQP7BR9XGiKDJAxEV1WGaESECQNmzQIj0iwFB64CPpsTdAsPr2Zb08SkAAjkUDa4z8v3QEhPME9UFA0HCtWZeQOECAWtGaFaUQQAhuyqQgsj5ACEeKvckoMUCQbZ8RS+sqwMCHdJm9LDLAKOrQQxj\\u002fMkBAUraVWwg+QMAK2vVA0ClAYHqlmzgrKUDQE4uF9\\u002fQtwADRJ98SkvI\\u002fgEQJ3aDZKUBAXNf3tCsTwAAM7pGEuw5AQN0+CAgeGkBAzON4G7QuwDBMrZdy0DpAgMRAVX4ZIkCAUUOrdbIRwIBQhm2zHgZA4INZZYOTMkCg5kZWOt00wKCSq32MBTxAQNV5bp6OIkAAQVl9WydBwICnnPfTZyjAUCMgUvF\\u002fVkAAa\\u002fHFQLkswOCnVqy7WENAIN2RUtdIPkAAgOTqz2iwP8AxfftnzytA4LjS\\u002fzJyMsAAZsWTABcawKAmN\\u002f8pLipAgA0WcyXn+D\\u002fAlPvJpkw0QGjPIw8zlENAwEZ9l8ZtDEB47KsRjZkzQEiST1+QzTZA4OAxEcSoFUBg7SxlJlMWQIgY0ksiM0ZAwFg50TA9EMBAwBOS9gYdQGAFv+orOCfA0PFXfeu+IEAgIQDJ5DoyQECt4T6v7RNAQJafo3pKI0C4usEJwzs\\u002fQGDbQzskBxNAgFtacemZ8z+AtzW8eCkNQED07NpWGBhAAMXHH5wSBcAAJY3Rey71v7CPVJVbFSbAgD6g7nq5CUBAxOa9ZnMyQGBFXe1YqhpAABtDjBp1F0AgxFQ4RNAfwIBO+4xwb\\u002fM\\u002fIELa0nO9LEDATLuSPBwEQMAR1zKalhJAoCxT51QVF0CAR3\\u002fBriz+P8BIA64cBxDAGLwtzMoXMkDgP9r\\u002fn74YQEAnSwvOFxVAIOZAy8xaN0DAEYv2gHIDQIA04iLS\\u002firAAPgWs4\\u002fcIMCAZOcbhxz6P\\u002fDOF+u0YS9AcAz+Bf59JUAAd37jqzvzv4Dy2Dyti\\u002f0\\u002f0BcqGyg3LsBwXw8Lx94rQLgEiTSr+ThAwEJtwy0NGEAQgRINaecjwABLRlnFUhhA8L0XyoEcMEBgs53eAI8UQEDbzbxzUwlAIAONMBlhIkAAlPDLPwzLPzDVGkHliSPA4LM6SjqBIcCwMBL5FoYmQJB20FXxRTFAYLcCeLUfHUAA8rwlT7c+wEB4OiOKww3AcADLVfa8N8Aw5gHsJnAqQGAIVguKhBxAQPttpipSCMDAIUHwIK4SQIgRr9nsxTzAACZMUx\\u002fM178AJ54+QbYXQAC\\u002fBhKy3ue\\u002f8CS7Hw9kKsA4T6fy8To5wACEcGcmVyZAOFOvzloqOMAAmt93C4wLwMgu6H0LZ0HAgDggbIfw+j\\u002fg8xF35e4XwOjw0GQVOTdAWBKkJ7NXMECA6JlpR0gDQAATW7FgMxrAIKKqghHzIEAAor9ctd73P7jt78jffjPAAO7p\\u002f97xFcAwJWnxqAorQHD8HNG88iTAAKQxYk8f3r9gI2d99O8owIAu0llZvgvAQFi50V4gEkAAp2nehfbgP4CB4nMWj\\u002fe\\u002foCDhk1fnGcDgFl1M3OsVwHDvwAWn+CLAsI92ICsEI0AYgM\\u002fU3T9AwADR7qzwOOC\\u002faGASLfF7MEAAs5qltOUZQCDzAI2qEifAAPRkKWExHsCgHPj7PB8jwACULNmrTB7AgO+KQwq4DMCALelj0yYDQOBffs2NgR1AgLqvIke59T8QGfhPMVMpwEBlwcBVeQ3AwL+AAqbUHsAAeHCJI0b0PwDk5seLFAbAAAEqJv2g\\u002fL8gC9jatFsiQGAWYWrCtSHAID+UHrNuHEBgPaHAOboXwMCHY1VeGRRAyOxK6LkFMcDAOHds+5YAwBCgGRiMvSlA4Isr90A8G0DgctnrWoQmwCA+jZdOqibA4LNRbzXUIMBQzzbHoAIrwCCGc896bB7AiBX4+Oy3OsAAaINnPOTDPygR+gBrAzRAoFvuQQtpF0CA4ZwnkaQCwCCbCmdYFC\\u002fAYHOFVVfgEECAIJUe4sEbQIAZDUU7YzTAcF4f8s2LK8CAlIn\\u002fLNsBwNBIDFGebSZAQPQOw1pABEDAqT4LkaEaQMA+7MNItwtAIOs4R5GzEkCAELdmgOUWQGBcMBECISlAIGACRUjjFcCgIxQquG42QIBr74o5rxBAcBHRMZRrQkCAlKLrM8UlwAAlCM2UOCfAQGehnz6+PMAYzU789zdHwABl0EeaGzPA0HceYM0KMkCAFxYXK2cpwIBtFGNj1SbAAOeGH6jU7z\\u002fQR3zfKhslwADfgrSGcgPAMHgFKYxMJ8DAtIWH3PUvQAALFBHfxxRAgPQ5tNHzAEDQotbCCD8jwICEzmUxffQ\\u002fwJoSlhbRD0BQztgf8xIhQIhwi21u\\u002fzJAAFbzX6R42D94Btnf5LQ1QMB4s5XW7RdA4BKLPYTfHEAAsJzF49cgQECTBwJKZBZAkLsVPMiALUAA2zzr9VnsP4BsaXESATZAAHYfWRWH7L8wcZS0I5kvwMBEm+x3vA3AACwox65JL8Bgrt7sgVASwIAoCc1ZKQTAAPgVFYTvLsBwDkJWbtcswIC4Dnk1Dva\\u002fOFxVuSRXO8CoWIQIXk4\\u002fwNg\\u002fldV1RjjAWEd181U\\u002fM8Dwqxv31XEswKBIv+YjMzPAgKUCiuQg\\u002fT\\u002fI9uHOuNY3QKAJ4Y6daxNAwCXpJLdCKsAAbT6hJT\\u002fsP+DVvR0SGzHAgMCOOmGH8b9AkEBtsFgYwEAGM01yoQjAgHrP3HQn8b8AcL5A+90hQPC1lzDLDzrAEAQvvZsdLMDAHulDN3AIwIDXXZDMNRzAIFueSvDyNEAA7sUlv2TuPygPaT0w8zlAgCOWNRMx\\u002fz8ACwBQWFnuP1DvcqJe8jNA8LHivZzjL0AAhIKk5A40wMAE6lJyHyDA8P5jJ1whJMBQopTNhCMgwHAoyYfqJSjAMEJdMHTXIUD4\\u002fFupsxU5wIBMjr7vuQjAgBt2s8gxIsCA+ftQZpUbQACjhUD4xitAgAMiCAfDI0DQg6k0IEEowBDVS1B9RSbAkCjZShBKIsAQPoa2JrEhQKAz3NNOWB9AGKuQT4csMsAAItn3e14uwMDOGVYImUHAELN3nv9pIsAQHuzNF3AkwGDtNe6UiSNAkFFivfp8LsCQpLJNyIMuwMCNgrVqRTPA0OfP6iOSKMDo3ezo2kwxwDBGp1aNoSzAoEVuyFlTGcDg4CQ6LmQZwBiSXG9nXTfAoDlyRdfDKMCwAomwM80mwCAgAsfn1iDA4B7UfPfgKsC4qliMs3UwwKA8OZ0dezfAIHsz4ngBJ8AAspIPteURQDC\\u002fpNVGGSHAQPwhp\\u002fGSM8BQXEO7ATMtwLglnsNUoDbAwE1vRB2xIkAA2LsVPknUP6CD5PcsbSvAQMqWu1F9IcBQYTrDF8c4wKCQwPxawBBAUFB4ErhNIkCwubYfz+I3wIDzMxUqz\\u002f6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-       "const graphDiv = document.getElementById('aaad5bbe-c49b-44ce-8aab-9c1248cea2c0');\n",
+       "const graphDiv = document.getElementById('3fcd604a-e315-4a23-9958-6cd1bd442d04');\n",
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@@ -1190,17 +1220,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "36",
+   "execution_count": 25,
+   "id": "38",
    "metadata": {},
    "outputs": [
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@@ -1419,7 +1449,7 @@
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+   "id": "39",
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    "source": [
     "## Step 5: Prepare for Bayesian Sampling\n",
@@ -1430,13 +1460,16 @@
     "\n",
     "The helper method `set_fit_bounds_from_uncertainty` centers the bounds\n",
     "on the current parameter value and expands them by a chosen multiple of\n",
-    "the reported uncertainty."
+    "the reported uncertainty.\n",
+    "\n",
+    "Default multiplier is 8 to give a wide range for the sampler to\n",
+    "explore, but here we use 3 to speed up the tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "38",
+   "execution_count": 26,
+   "id": "40",
    "metadata": {},
    "outputs": [
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-       "      <th id=\"T_2fe4a_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_2fe4a_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
-       "      <th id=\"T_2fe4a_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
-       "      <th id=\"T_2fe4a_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
-       "      <th id=\"T_2fe4a_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
-       "      <th id=\"T_2fe4a_level0_col5\" class=\"col_heading level0 col5\" >uncertainty</th>\n",
-       "      <th id=\"T_2fe4a_level0_col6\" class=\"col_heading level0 col6\" >min</th>\n",
-       "      <th id=\"T_2fe4a_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
-       "      <th id=\"T_2fe4a_level0_col8\" class=\"col_heading level0 col8\" >units</th>\n",
+       "      <th id=\"T_2d608_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
+       "      <th id=\"T_2d608_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
+       "      <th id=\"T_2d608_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_2d608_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_2d608_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
+       "      <th id=\"T_2d608_level0_col5\" class=\"col_heading level0 col5\" >uncertainty</th>\n",
+       "      <th id=\"T_2d608_level0_col6\" class=\"col_heading level0 col6\" >min</th>\n",
+       "      <th id=\"T_2d608_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
+       "      <th id=\"T_2d608_level0_col8\" class=\"col_heading level0 col8\" >units</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_2fe4a_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_2fe4a_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_2fe4a_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_2fe4a_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_2fe4a_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_2fe4a_row0_col4\" class=\"data row0 col4\" >3.89066</td>\n",
-       "      <td id=\"T_2fe4a_row0_col5\" class=\"data row0 col5\" >0.00021</td>\n",
-       "      <td id=\"T_2fe4a_row0_col6\" class=\"data row0 col6\" >-inf</td>\n",
-       "      <td id=\"T_2fe4a_row0_col7\" class=\"data row0 col7\" >inf</td>\n",
-       "      <td id=\"T_2fe4a_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2fe4a_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_2fe4a_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_2fe4a_row1_col1\" class=\"data row1 col1\" >peak</td>\n",
-       "      <td id=\"T_2fe4a_row1_col2\" class=\"data row1 col2\" ></td>\n",
-       "      <td id=\"T_2fe4a_row1_col3\" class=\"data row1 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_2fe4a_row1_col4\" class=\"data row1 col4\" >0.03329</td>\n",
-       "      <td id=\"T_2fe4a_row1_col5\" class=\"data row1 col5\" >0.01633</td>\n",
-       "      <td id=\"T_2fe4a_row1_col6\" class=\"data row1 col6\" >-inf</td>\n",
-       "      <td id=\"T_2fe4a_row1_col7\" class=\"data row1 col7\" >inf</td>\n",
-       "      <td id=\"T_2fe4a_row1_col8\" class=\"data row1 col8\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2fe4a_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_2fe4a_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_2fe4a_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_2fe4a_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_2fe4a_row2_col3\" class=\"data row2 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_2fe4a_row2_col4\" class=\"data row2 col4\" >-0.09343</td>\n",
-       "      <td id=\"T_2fe4a_row2_col5\" class=\"data row2 col5\" >0.00911</td>\n",
-       "      <td id=\"T_2fe4a_row2_col6\" class=\"data row2 col6\" >-inf</td>\n",
-       "      <td id=\"T_2fe4a_row2_col7\" class=\"data row2 col7\" >inf</td>\n",
-       "      <td id=\"T_2fe4a_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2fe4a_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_2fe4a_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_2fe4a_row3_col1\" class=\"data row3 col1\" >instrument</td>\n",
-       "      <td id=\"T_2fe4a_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_2fe4a_row3_col3\" class=\"data row3 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_2fe4a_row3_col4\" class=\"data row3 col4\" >0.62207</td>\n",
-       "      <td id=\"T_2fe4a_row3_col5\" class=\"data row3 col5\" >0.00294</td>\n",
-       "      <td id=\"T_2fe4a_row3_col6\" class=\"data row3 col6\" >-inf</td>\n",
-       "      <td id=\"T_2fe4a_row3_col7\" class=\"data row3 col7\" >inf</td>\n",
-       "      <td id=\"T_2fe4a_row3_col8\" class=\"data row3 col8\" >deg</td>\n",
+       "      <th id=\"T_2d608_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_2d608_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_2d608_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_2d608_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_2d608_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_2d608_row0_col4\" class=\"data row0 col4\" >3.89134</td>\n",
+       "      <td id=\"T_2d608_row0_col5\" class=\"data row0 col5\" >0.00011</td>\n",
+       "      <td id=\"T_2d608_row0_col6\" class=\"data row0 col6\" >-inf</td>\n",
+       "      <td id=\"T_2d608_row0_col7\" class=\"data row0 col7\" >inf</td>\n",
+       "      <td id=\"T_2d608_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d608_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_2d608_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_2d608_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
+       "      <td id=\"T_2d608_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
+       "      <td id=\"T_2d608_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
+       "      <td id=\"T_2d608_row1_col4\" class=\"data row1 col4\" >9.13288</td>\n",
+       "      <td id=\"T_2d608_row1_col5\" class=\"data row1 col5\" >0.03329</td>\n",
+       "      <td id=\"T_2d608_row1_col6\" class=\"data row1 col6\" >-inf</td>\n",
+       "      <td id=\"T_2d608_row1_col7\" class=\"data row1 col7\" >inf</td>\n",
+       "      <td id=\"T_2d608_row1_col8\" class=\"data row1 col8\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d608_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_2d608_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_2d608_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_2d608_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_2d608_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_2d608_row2_col4\" class=\"data row2 col4\" >0.08167</td>\n",
+       "      <td id=\"T_2d608_row2_col5\" class=\"data row2 col5\" >0.00783</td>\n",
+       "      <td id=\"T_2d608_row2_col6\" class=\"data row2 col6\" >-inf</td>\n",
+       "      <td id=\"T_2d608_row2_col7\" class=\"data row2 col7\" >inf</td>\n",
+       "      <td id=\"T_2d608_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d608_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_2d608_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_2d608_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
+       "      <td id=\"T_2d608_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_2d608_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_2d608_row3_col4\" class=\"data row3 col4\" >-0.11691</td>\n",
+       "      <td id=\"T_2d608_row3_col5\" class=\"data row3 col5\" >0.00566</td>\n",
+       "      <td id=\"T_2d608_row3_col6\" class=\"data row3 col6\" >-inf</td>\n",
+       "      <td id=\"T_2d608_row3_col7\" class=\"data row3 col7\" >inf</td>\n",
+       "      <td id=\"T_2d608_row3_col8\" class=\"data row3 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2d608_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_2d608_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
+       "      <td id=\"T_2d608_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
+       "      <td id=\"T_2d608_row4_col2\" class=\"data row4 col2\" ></td>\n",
+       "      <td id=\"T_2d608_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_2d608_row4_col4\" class=\"data row4 col4\" >0.63057</td>\n",
+       "      <td id=\"T_2d608_row4_col5\" class=\"data row4 col5\" >0.00191</td>\n",
+       "      <td id=\"T_2d608_row4_col6\" class=\"data row4 col6\" >-inf</td>\n",
+       "      <td id=\"T_2d608_row4_col7\" class=\"data row4 col7\" >inf</td>\n",
+       "      <td id=\"T_2d608_row4_col8\" class=\"data row4 col8\" >deg</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x16b8b8910>"
+       "<pandas.io.formats.style.Styler at 0x36a332ad0>"
       ]
      },
      "metadata": {},
@@ -1596,20 +1641,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "39",
+   "execution_count": 27,
+   "id": "41",
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=4)\n",
-    "experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=4)\n",
-    "experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)\n",
-    "experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)"
+    "for param in project.free_parameters:\n",
+    "    param.set_fit_bounds_from_uncertainty(multiplier=3)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "40",
+   "id": "42",
    "metadata": {},
    "source": [
     "Displaying the free parameters again is a convenient way to confirm\n",
@@ -1619,8 +1662,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "41",
+   "execution_count": 28,
+   "id": "43",
    "metadata": {},
    "outputs": [
     {
@@ -1634,140 +1677,152 @@
      "data": {
       "text/html": [
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        "</style>\n",
-       "<table id=\"T_a35b7\" class=\"dataframe\">\n",
+       "<table id=\"T_d733a\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_a35b7_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_a35b7_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
-       "      <th id=\"T_a35b7_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
-       "      <th id=\"T_a35b7_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
-       "      <th id=\"T_a35b7_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
-       "      <th id=\"T_a35b7_level0_col5\" class=\"col_heading level0 col5\" >uncertainty</th>\n",
-       "      <th id=\"T_a35b7_level0_col6\" class=\"col_heading level0 col6\" >min</th>\n",
-       "      <th id=\"T_a35b7_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
-       "      <th id=\"T_a35b7_level0_col8\" class=\"col_heading level0 col8\" >units</th>\n",
+       "      <th id=\"T_d733a_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
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+       "      <th id=\"T_d733a_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_d733a_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_d733a_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
+       "      <th id=\"T_d733a_level0_col5\" class=\"col_heading level0 col5\" >uncertainty</th>\n",
+       "      <th id=\"T_d733a_level0_col6\" class=\"col_heading level0 col6\" >min</th>\n",
+       "      <th id=\"T_d733a_level0_col7\" class=\"col_heading level0 col7\" >max</th>\n",
+       "      <th id=\"T_d733a_level0_col8\" class=\"col_heading level0 col8\" >units</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_a35b7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_a35b7_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_a35b7_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_a35b7_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_a35b7_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_a35b7_row0_col4\" class=\"data row0 col4\" >3.89066</td>\n",
-       "      <td id=\"T_a35b7_row0_col5\" class=\"data row0 col5\" >0.00021</td>\n",
-       "      <td id=\"T_a35b7_row0_col6\" class=\"data row0 col6\" >3.88983</td>\n",
-       "      <td id=\"T_a35b7_row0_col7\" class=\"data row0 col7\" >3.89148</td>\n",
-       "      <td id=\"T_a35b7_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a35b7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_a35b7_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_a35b7_row1_col1\" class=\"data row1 col1\" >peak</td>\n",
-       "      <td id=\"T_a35b7_row1_col2\" class=\"data row1 col2\" ></td>\n",
-       "      <td id=\"T_a35b7_row1_col3\" class=\"data row1 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_a35b7_row1_col4\" class=\"data row1 col4\" >0.03329</td>\n",
-       "      <td id=\"T_a35b7_row1_col5\" class=\"data row1 col5\" >0.01633</td>\n",
-       "      <td id=\"T_a35b7_row1_col6\" class=\"data row1 col6\" >-0.03205</td>\n",
-       "      <td id=\"T_a35b7_row1_col7\" class=\"data row1 col7\" >0.09863</td>\n",
-       "      <td id=\"T_a35b7_row1_col8\" class=\"data row1 col8\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a35b7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_a35b7_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_a35b7_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_a35b7_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_a35b7_row2_col3\" class=\"data row2 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_a35b7_row2_col4\" class=\"data row2 col4\" >-0.09343</td>\n",
-       "      <td id=\"T_a35b7_row2_col5\" class=\"data row2 col5\" >0.00911</td>\n",
-       "      <td id=\"T_a35b7_row2_col6\" class=\"data row2 col6\" >-0.12988</td>\n",
-       "      <td id=\"T_a35b7_row2_col7\" class=\"data row2 col7\" >-0.05699</td>\n",
-       "      <td id=\"T_a35b7_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a35b7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_a35b7_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_a35b7_row3_col1\" class=\"data row3 col1\" >instrument</td>\n",
-       "      <td id=\"T_a35b7_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_a35b7_row3_col3\" class=\"data row3 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_a35b7_row3_col4\" class=\"data row3 col4\" >0.62207</td>\n",
-       "      <td id=\"T_a35b7_row3_col5\" class=\"data row3 col5\" >0.00294</td>\n",
-       "      <td id=\"T_a35b7_row3_col6\" class=\"data row3 col6\" >0.61031</td>\n",
-       "      <td id=\"T_a35b7_row3_col7\" class=\"data row3 col7\" >0.63383</td>\n",
-       "      <td id=\"T_a35b7_row3_col8\" class=\"data row3 col8\" >deg</td>\n",
+       "      <th id=\"T_d733a_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_d733a_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_d733a_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_d733a_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_d733a_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_d733a_row0_col4\" class=\"data row0 col4\" >3.89134</td>\n",
+       "      <td id=\"T_d733a_row0_col5\" class=\"data row0 col5\" >0.00011</td>\n",
+       "      <td id=\"T_d733a_row0_col6\" class=\"data row0 col6\" >3.89101</td>\n",
+       "      <td id=\"T_d733a_row0_col7\" class=\"data row0 col7\" >3.89166</td>\n",
+       "      <td id=\"T_d733a_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d733a_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_d733a_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_d733a_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
+       "      <td id=\"T_d733a_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
+       "      <td id=\"T_d733a_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
+       "      <td id=\"T_d733a_row1_col4\" class=\"data row1 col4\" >9.13288</td>\n",
+       "      <td id=\"T_d733a_row1_col5\" class=\"data row1 col5\" >0.03329</td>\n",
+       "      <td id=\"T_d733a_row1_col6\" class=\"data row1 col6\" >9.03302</td>\n",
+       "      <td id=\"T_d733a_row1_col7\" class=\"data row1 col7\" >9.23274</td>\n",
+       "      <td id=\"T_d733a_row1_col8\" class=\"data row1 col8\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d733a_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_d733a_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_d733a_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_d733a_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_d733a_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_d733a_row2_col4\" class=\"data row2 col4\" >0.08167</td>\n",
+       "      <td id=\"T_d733a_row2_col5\" class=\"data row2 col5\" >0.00783</td>\n",
+       "      <td id=\"T_d733a_row2_col6\" class=\"data row2 col6\" >0.05817</td>\n",
+       "      <td id=\"T_d733a_row2_col7\" class=\"data row2 col7\" >0.10517</td>\n",
+       "      <td id=\"T_d733a_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d733a_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_d733a_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_d733a_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
+       "      <td id=\"T_d733a_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_d733a_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_d733a_row3_col4\" class=\"data row3 col4\" >-0.11691</td>\n",
+       "      <td id=\"T_d733a_row3_col5\" class=\"data row3 col5\" >0.00566</td>\n",
+       "      <td id=\"T_d733a_row3_col6\" class=\"data row3 col6\" >-0.13389</td>\n",
+       "      <td id=\"T_d733a_row3_col7\" class=\"data row3 col7\" >-0.09993</td>\n",
+       "      <td id=\"T_d733a_row3_col8\" class=\"data row3 col8\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d733a_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_d733a_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
+       "      <td id=\"T_d733a_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
+       "      <td id=\"T_d733a_row4_col2\" class=\"data row4 col2\" ></td>\n",
+       "      <td id=\"T_d733a_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_d733a_row4_col4\" class=\"data row4 col4\" >0.63057</td>\n",
+       "      <td id=\"T_d733a_row4_col5\" class=\"data row4 col5\" >0.00191</td>\n",
+       "      <td id=\"T_d733a_row4_col6\" class=\"data row4 col6\" >0.62483</td>\n",
+       "      <td id=\"T_d733a_row4_col7\" class=\"data row4 col7\" >0.63631</td>\n",
+       "      <td id=\"T_d733a_row4_col8\" class=\"data row4 col8\" >deg</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x16b8b8910>"
+       "<pandas.io.formats.style.Styler at 0x36a332ad0>"
       ]
      },
      "metadata": {},
@@ -1780,7 +1835,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42",
+   "id": "44",
    "metadata": {},
    "source": [
     "## Step 6: Configure and Run DREAM\n",
@@ -1789,15 +1844,17 @@
     "\n",
     "The settings below are intentionally small so the tutorial runs\n",
     "quickly. For production analysis you would usually increase the number\n",
-    "of steps and often the burn-in as well. When needed, the DREAM API\n",
-    "also lets you tune how chains are initialized through the `init`\n",
-    "setting."
+    "of steps (`steps`) and often the burn-in (`burn`) as well. When\n",
+    "needed, the DREAM API also lets you tune how chains are initialized\n",
+    "through the `init` setting. Other sampler settings such as `thin` and\n",
+    "`pop` can be adjusted  as well, but here we keep them at their\n",
+    "defaults."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "43",
+   "execution_count": 29,
+   "id": "45",
    "metadata": {},
    "outputs": [
     {
@@ -1811,119 +1868,119 @@
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-       "<table id=\"T_90fe9\" class=\"dataframe\">\n",
+       "<table id=\"T_7ea7d\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_90fe9_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
-       "      <th id=\"T_90fe9_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_90fe9_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
+       "      <th id=\"T_7ea7d_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
+       "      <th id=\"T_7ea7d_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
+       "      <th id=\"T_7ea7d_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_90fe9_row0_col0\" class=\"data row0 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
-       "      <td id=\"T_90fe9_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
+       "      <th id=\"T_7ea7d_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_7ea7d_row0_col0\" class=\"data row0 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
+       "      <td id=\"T_7ea7d_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_90fe9_row1_col0\" class=\"data row1 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
-       "      <td id=\"T_90fe9_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
+       "      <th id=\"T_7ea7d_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_7ea7d_row1_col0\" class=\"data row1 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
+       "      <td id=\"T_7ea7d_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_90fe9_row2_col0\" class=\"data row2 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
-       "      <td id=\"T_90fe9_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
+       "      <th id=\"T_7ea7d_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_7ea7d_row2_col0\" class=\"data row2 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
+       "      <td id=\"T_7ea7d_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_90fe9_row3_col0\" class=\"data row3 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
-       "      <td id=\"T_90fe9_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
+       "      <th id=\"T_7ea7d_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_7ea7d_row3_col0\" class=\"data row3 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
+       "      <td id=\"T_7ea7d_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_90fe9_row4_col0\" class=\"data row4 col0\" >*</td>\n",
-       "      <td id=\"T_90fe9_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
-       "      <td id=\"T_90fe9_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
+       "      <th id=\"T_7ea7d_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_7ea7d_row4_col0\" class=\"data row4 col0\" >*</td>\n",
+       "      <td id=\"T_7ea7d_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
+       "      <td id=\"T_7ea7d_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_90fe9_row5_col0\" class=\"data row5 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
-       "      <td id=\"T_90fe9_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
+       "      <th id=\"T_7ea7d_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_7ea7d_row5_col0\" class=\"data row5 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
+       "      <td id=\"T_7ea7d_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_90fe9_row6_col0\" class=\"data row6 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
-       "      <td id=\"T_90fe9_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
+       "      <th id=\"T_7ea7d_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_7ea7d_row6_col0\" class=\"data row6 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
+       "      <td id=\"T_7ea7d_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_90fe9_row7_col0\" class=\"data row7 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
-       "      <td id=\"T_90fe9_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
+       "      <th id=\"T_7ea7d_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_7ea7d_row7_col0\" class=\"data row7 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
+       "      <td id=\"T_7ea7d_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_90fe9_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_90fe9_row8_col0\" class=\"data row8 col0\" ></td>\n",
-       "      <td id=\"T_90fe9_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
-       "      <td id=\"T_90fe9_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
+       "      <th id=\"T_7ea7d_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_7ea7d_row8_col0\" class=\"data row8 col0\" ></td>\n",
+       "      <td id=\"T_7ea7d_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
+       "      <td id=\"T_7ea7d_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x16b8b8910>"
+       "<pandas.io.formats.style.Styler at 0x36a332ad0>"
       ]
      },
      "metadata": {},
@@ -1936,8 +1993,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "44",
+   "execution_count": 30,
+   "id": "46",
    "metadata": {},
    "outputs": [
     {
@@ -1955,21 +2012,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "45",
+   "execution_count": 31,
+   "id": "47",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 2000  # 1000\n",
-    "project.analysis.fit.minimizer.burn = 400  # 200\n",
-    "project.analysis.fit.minimizer.thin = 1\n",
-    "project.analysis.fit.minimizer.pop = 4"
+    "project.analysis.fit.minimizer.steps = 100  # 1000"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "46",
+   "execution_count": 32,
+   "id": "48",
    "metadata": {},
    "outputs": [
     {
@@ -1986,139 +2040,267 @@
      "data": {
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        "  text-align: center;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_f1d86\" class=\"dataframe\">\n",
+       "<table id=\"T_d9212\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_f1d86_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
-       "      <th id=\"T_f1d86_level0_col1\" class=\"col_heading level0 col1\" >progress</th>\n",
-       "      <th id=\"T_f1d86_level0_col2\" class=\"col_heading level0 col2\" >time (s)</th>\n",
-       "      <th id=\"T_f1d86_level0_col3\" class=\"col_heading level0 col3\" >log posterior</th>\n",
-       "      <th id=\"T_f1d86_level0_col4\" class=\"col_heading level0 col4\" >phase</th>\n",
+       "      <th id=\"T_d9212_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
+       "      <th id=\"T_d9212_level0_col1\" class=\"col_heading level0 col1\" >progress</th>\n",
+       "      <th id=\"T_d9212_level0_col2\" class=\"col_heading level0 col2\" >time (s)</th>\n",
+       "      <th id=\"T_d9212_level0_col3\" class=\"col_heading level0 col3\" >log posterior</th>\n",
+       "      <th id=\"T_d9212_level0_col4\" class=\"col_heading level0 col4\" >phase</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_f1d86_row0_col0\" class=\"data row0 col0\" >1/2401</td>\n",
-       "      <td id=\"T_f1d86_row0_col1\" class=\"data row0 col1\" >0.0%</td>\n",
-       "      <td id=\"T_f1d86_row0_col2\" class=\"data row0 col2\" >0.30</td>\n",
-       "      <td id=\"T_f1d86_row0_col3\" class=\"data row0 col3\" >-543.37</td>\n",
-       "      <td id=\"T_f1d86_row0_col4\" class=\"data row0 col4\" >burn-in</td>\n",
+       "      <th id=\"T_d9212_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_d9212_row0_col0\" class=\"data row0 col0\" >1/151</td>\n",
+       "      <td id=\"T_d9212_row0_col1\" class=\"data row0 col1\" >0.7%</td>\n",
+       "      <td id=\"T_d9212_row0_col2\" class=\"data row0 col2\" >0.43</td>\n",
+       "      <td id=\"T_d9212_row0_col3\" class=\"data row0 col3\" >-1157.01</td>\n",
+       "      <td id=\"T_d9212_row0_col4\" class=\"data row0 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_d9212_row1_col0\" class=\"data row1 col0\" >13/151</td>\n",
+       "      <td id=\"T_d9212_row1_col1\" class=\"data row1 col1\" >8.6%</td>\n",
+       "      <td id=\"T_d9212_row1_col2\" class=\"data row1 col2\" >5.49</td>\n",
+       "      <td id=\"T_d9212_row1_col3\" class=\"data row1 col3\" >-1157.54</td>\n",
+       "      <td id=\"T_d9212_row1_col4\" class=\"data row1 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_d9212_row2_col0\" class=\"data row2 col0\" >26/151</td>\n",
+       "      <td id=\"T_d9212_row2_col1\" class=\"data row2 col1\" >17.2%</td>\n",
+       "      <td id=\"T_d9212_row2_col2\" class=\"data row2 col2\" >10.82</td>\n",
+       "      <td id=\"T_d9212_row2_col3\" class=\"data row2 col3\" >-1158.21</td>\n",
+       "      <td id=\"T_d9212_row2_col4\" class=\"data row2 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_d9212_row3_col0\" class=\"data row3 col0\" >38/151</td>\n",
+       "      <td id=\"T_d9212_row3_col1\" class=\"data row3 col1\" >25.2%</td>\n",
+       "      <td id=\"T_d9212_row3_col2\" class=\"data row3 col2\" >15.85</td>\n",
+       "      <td id=\"T_d9212_row3_col3\" class=\"data row3 col3\" >-1159.61</td>\n",
+       "      <td id=\"T_d9212_row3_col4\" class=\"data row3 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_d9212_row4_col0\" class=\"data row4 col0\" >50/151</td>\n",
+       "      <td id=\"T_d9212_row4_col1\" class=\"data row4 col1\" >33.1%</td>\n",
+       "      <td id=\"T_d9212_row4_col2\" class=\"data row4 col2\" >20.89</td>\n",
+       "      <td id=\"T_d9212_row4_col3\" class=\"data row4 col3\" >-1159.52</td>\n",
+       "      <td id=\"T_d9212_row4_col4\" class=\"data row4 col4\" >burn-in</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
+       "      <td id=\"T_d9212_row5_col0\" class=\"data row5 col0\" >51/151</td>\n",
+       "      <td id=\"T_d9212_row5_col1\" class=\"data row5 col1\" >33.8%</td>\n",
+       "      <td id=\"T_d9212_row5_col2\" class=\"data row5 col2\" >21.36</td>\n",
+       "      <td id=\"T_d9212_row5_col3\" class=\"data row5 col3\" >-1159.49</td>\n",
+       "      <td id=\"T_d9212_row5_col4\" class=\"data row5 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
+       "      <td id=\"T_d9212_row6_col0\" class=\"data row6 col0\" >56/151</td>\n",
+       "      <td id=\"T_d9212_row6_col1\" class=\"data row6 col1\" >37.1%</td>\n",
+       "      <td id=\"T_d9212_row6_col2\" class=\"data row6 col2\" >23.41</td>\n",
+       "      <td id=\"T_d9212_row6_col3\" class=\"data row6 col3\" >-1159.26</td>\n",
+       "      <td id=\"T_d9212_row6_col4\" class=\"data row6 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
+       "      <td id=\"T_d9212_row7_col0\" class=\"data row7 col0\" >62/151</td>\n",
+       "      <td id=\"T_d9212_row7_col1\" class=\"data row7 col1\" >41.1%</td>\n",
+       "      <td id=\"T_d9212_row7_col2\" class=\"data row7 col2\" >25.92</td>\n",
+       "      <td id=\"T_d9212_row7_col3\" class=\"data row7 col3\" >-1159.52</td>\n",
+       "      <td id=\"T_d9212_row7_col4\" class=\"data row7 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
+       "      <td id=\"T_d9212_row8_col0\" class=\"data row8 col0\" >67/151</td>\n",
+       "      <td id=\"T_d9212_row8_col1\" class=\"data row8 col1\" >44.4%</td>\n",
+       "      <td id=\"T_d9212_row8_col2\" class=\"data row8 col2\" >27.96</td>\n",
+       "      <td id=\"T_d9212_row8_col3\" class=\"data row8 col3\" >-1159.24</td>\n",
+       "      <td id=\"T_d9212_row8_col4\" class=\"data row8 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
+       "      <td id=\"T_d9212_row9_col0\" class=\"data row9 col0\" >72/151</td>\n",
+       "      <td id=\"T_d9212_row9_col1\" class=\"data row9 col1\" >47.7%</td>\n",
+       "      <td id=\"T_d9212_row9_col2\" class=\"data row9 col2\" >30.23</td>\n",
+       "      <td id=\"T_d9212_row9_col3\" class=\"data row9 col3\" >-1159.00</td>\n",
+       "      <td id=\"T_d9212_row9_col4\" class=\"data row9 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
+       "      <td id=\"T_d9212_row10_col0\" class=\"data row10 col0\" >77/151</td>\n",
+       "      <td id=\"T_d9212_row10_col1\" class=\"data row10 col1\" >51.0%</td>\n",
+       "      <td id=\"T_d9212_row10_col2\" class=\"data row10 col2\" >32.42</td>\n",
+       "      <td id=\"T_d9212_row10_col3\" class=\"data row10 col3\" >-1158.87</td>\n",
+       "      <td id=\"T_d9212_row10_col4\" class=\"data row10 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
+       "      <td id=\"T_d9212_row11_col0\" class=\"data row11 col0\" >83/151</td>\n",
+       "      <td id=\"T_d9212_row11_col1\" class=\"data row11 col1\" >55.0%</td>\n",
+       "      <td id=\"T_d9212_row11_col2\" class=\"data row11 col2\" >34.90</td>\n",
+       "      <td id=\"T_d9212_row11_col3\" class=\"data row11 col3\" >-1158.90</td>\n",
+       "      <td id=\"T_d9212_row11_col4\" class=\"data row11 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
+       "      <td id=\"T_d9212_row12_col0\" class=\"data row12 col0\" >88/151</td>\n",
+       "      <td id=\"T_d9212_row12_col1\" class=\"data row12 col1\" >58.3%</td>\n",
+       "      <td id=\"T_d9212_row12_col2\" class=\"data row12 col2\" >36.91</td>\n",
+       "      <td id=\"T_d9212_row12_col3\" class=\"data row12 col3\" >-1159.39</td>\n",
+       "      <td id=\"T_d9212_row12_col4\" class=\"data row12 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
+       "      <td id=\"T_d9212_row13_col0\" class=\"data row13 col0\" >93/151</td>\n",
+       "      <td id=\"T_d9212_row13_col1\" class=\"data row13 col1\" >61.6%</td>\n",
+       "      <td id=\"T_d9212_row13_col2\" class=\"data row13 col2\" >39.10</td>\n",
+       "      <td id=\"T_d9212_row13_col3\" class=\"data row13 col3\" >-1159.59</td>\n",
+       "      <td id=\"T_d9212_row13_col4\" class=\"data row13 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
+       "      <td id=\"T_d9212_row14_col0\" class=\"data row14 col0\" >98/151</td>\n",
+       "      <td id=\"T_d9212_row14_col1\" class=\"data row14 col1\" >64.9%</td>\n",
+       "      <td id=\"T_d9212_row14_col2\" class=\"data row14 col2\" >41.21</td>\n",
+       "      <td id=\"T_d9212_row14_col3\" class=\"data row14 col3\" >-1159.22</td>\n",
+       "      <td id=\"T_d9212_row14_col4\" class=\"data row14 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
+       "      <td id=\"T_d9212_row15_col0\" class=\"data row15 col0\" >104/151</td>\n",
+       "      <td id=\"T_d9212_row15_col1\" class=\"data row15 col1\" >68.9%</td>\n",
+       "      <td id=\"T_d9212_row15_col2\" class=\"data row15 col2\" >43.71</td>\n",
+       "      <td id=\"T_d9212_row15_col3\" class=\"data row15 col3\" >-1159.44</td>\n",
+       "      <td id=\"T_d9212_row15_col4\" class=\"data row15 col4\" >sampling</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d9212_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
+       "      <td id=\"T_d9212_row16_col0\" class=\"data row16 col0\" >109/151</td>\n",
+       "      <td id=\"T_d9212_row16_col1\" class=\"data row16 col1\" >72.2%</td>\n",
+       "      <td id=\"T_d9212_row16_col2\" class=\"data row16 col2\" >45.72</td>\n",
+       "      <td id=\"T_d9212_row16_col3\" class=\"data row16 col3\" >-1159.53</td>\n",
+       "      <td id=\"T_d9212_row16_col4\" class=\"data row16 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_f1d86_row1_col0\" class=\"data row1 col0\" >101/2401</td>\n",
-       "      <td id=\"T_f1d86_row1_col1\" class=\"data row1 col1\" >4.2%</td>\n",
-       "      <td id=\"T_f1d86_row1_col2\" class=\"data row1 col2\" >27.35</td>\n",
-       "      <td id=\"T_f1d86_row1_col3\" class=\"data row1 col3\" >-545.75</td>\n",
-       "      <td id=\"T_f1d86_row1_col4\" class=\"data row1 col4\" >burn-in</td>\n",
+       "      <th id=\"T_d9212_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
+       "      <td id=\"T_d9212_row17_col0\" class=\"data row17 col0\" >114/151</td>\n",
+       "      <td id=\"T_d9212_row17_col1\" class=\"data row17 col1\" >75.5%</td>\n",
+       "      <td id=\"T_d9212_row17_col2\" class=\"data row17 col2\" >47.82</td>\n",
+       "      <td id=\"T_d9212_row17_col3\" class=\"data row17 col3\" >-1159.41</td>\n",
+       "      <td id=\"T_d9212_row17_col4\" class=\"data row17 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_f1d86_row2_col0\" class=\"data row2 col0\" >200/2401</td>\n",
-       "      <td id=\"T_f1d86_row2_col1\" class=\"data row2 col1\" >8.3%</td>\n",
-       "      <td id=\"T_f1d86_row2_col2\" class=\"data row2 col2\" >53.82</td>\n",
-       "      <td id=\"T_f1d86_row2_col3\" class=\"data row2 col3\" >-545.34</td>\n",
-       "      <td id=\"T_f1d86_row2_col4\" class=\"data row2 col4\" >burn-in</td>\n",
+       "      <th id=\"T_d9212_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
+       "      <td id=\"T_d9212_row18_col0\" class=\"data row18 col0\" >119/151</td>\n",
+       "      <td id=\"T_d9212_row18_col1\" class=\"data row18 col1\" >78.8%</td>\n",
+       "      <td id=\"T_d9212_row18_col2\" class=\"data row18 col2\" >49.95</td>\n",
+       "      <td id=\"T_d9212_row18_col3\" class=\"data row18 col3\" >-1159.63</td>\n",
+       "      <td id=\"T_d9212_row18_col4\" class=\"data row18 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_f1d86_row3_col0\" class=\"data row3 col0\" >300/2401</td>\n",
-       "      <td id=\"T_f1d86_row3_col1\" class=\"data row3 col1\" >12.5%</td>\n",
-       "      <td id=\"T_f1d86_row3_col2\" class=\"data row3 col2\" >80.18</td>\n",
-       "      <td id=\"T_f1d86_row3_col3\" class=\"data row3 col3\" >-545.22</td>\n",
-       "      <td id=\"T_f1d86_row3_col4\" class=\"data row3 col4\" >burn-in</td>\n",
+       "      <th id=\"T_d9212_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
+       "      <td id=\"T_d9212_row19_col0\" class=\"data row19 col0\" >125/151</td>\n",
+       "      <td id=\"T_d9212_row19_col1\" class=\"data row19 col1\" >82.8%</td>\n",
+       "      <td id=\"T_d9212_row19_col2\" class=\"data row19 col2\" >52.50</td>\n",
+       "      <td id=\"T_d9212_row19_col3\" class=\"data row19 col3\" >-1159.64</td>\n",
+       "      <td id=\"T_d9212_row19_col4\" class=\"data row19 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_f1d86_row4_col0\" class=\"data row4 col0\" >400/2401</td>\n",
-       "      <td id=\"T_f1d86_row4_col1\" class=\"data row4 col1\" >16.7%</td>\n",
-       "      <td id=\"T_f1d86_row4_col2\" class=\"data row4 col2\" >106.78</td>\n",
-       "      <td id=\"T_f1d86_row4_col3\" class=\"data row4 col3\" >-545.39</td>\n",
-       "      <td id=\"T_f1d86_row4_col4\" class=\"data row4 col4\" >burn-in</td>\n",
+       "      <th id=\"T_d9212_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
+       "      <td id=\"T_d9212_row20_col0\" class=\"data row20 col0\" >130/151</td>\n",
+       "      <td id=\"T_d9212_row20_col1\" class=\"data row20 col1\" >86.1%</td>\n",
+       "      <td id=\"T_d9212_row20_col2\" class=\"data row20 col2\" >54.61</td>\n",
+       "      <td id=\"T_d9212_row20_col3\" class=\"data row20 col3\" >-1159.33</td>\n",
+       "      <td id=\"T_d9212_row20_col4\" class=\"data row20 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_f1d86_row5_col0\" class=\"data row5 col0\" >401/2401</td>\n",
-       "      <td id=\"T_f1d86_row5_col1\" class=\"data row5 col1\" >16.7%</td>\n",
-       "      <td id=\"T_f1d86_row5_col2\" class=\"data row5 col2\" >107.05</td>\n",
-       "      <td id=\"T_f1d86_row5_col3\" class=\"data row5 col3\" >-545.35</td>\n",
-       "      <td id=\"T_f1d86_row5_col4\" class=\"data row5 col4\" >sampling</td>\n",
+       "      <th id=\"T_d9212_level0_row21\" class=\"row_heading level0 row21\" >22</th>\n",
+       "      <td id=\"T_d9212_row21_col0\" class=\"data row21 col0\" >135/151</td>\n",
+       "      <td id=\"T_d9212_row21_col1\" class=\"data row21 col1\" >89.4%</td>\n",
+       "      <td id=\"T_d9212_row21_col2\" class=\"data row21 col2\" >56.70</td>\n",
+       "      <td id=\"T_d9212_row21_col3\" class=\"data row21 col3\" >-1159.39</td>\n",
+       "      <td id=\"T_d9212_row21_col4\" class=\"data row21 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_f1d86_row6_col0\" class=\"data row6 col0\" >506/2401</td>\n",
-       "      <td id=\"T_f1d86_row6_col1\" class=\"data row6 col1\" >21.1%</td>\n",
-       "      <td id=\"T_f1d86_row6_col2\" class=\"data row6 col2\" >134.74</td>\n",
-       "      <td id=\"T_f1d86_row6_col3\" class=\"data row6 col3\" >-545.28</td>\n",
-       "      <td id=\"T_f1d86_row6_col4\" class=\"data row6 col4\" >sampling</td>\n",
+       "      <th id=\"T_d9212_level0_row22\" class=\"row_heading level0 row22\" >23</th>\n",
+       "      <td id=\"T_d9212_row22_col0\" class=\"data row22 col0\" >140/151</td>\n",
+       "      <td id=\"T_d9212_row22_col1\" class=\"data row22 col1\" >92.7%</td>\n",
+       "      <td id=\"T_d9212_row22_col2\" class=\"data row22 col2\" >58.78</td>\n",
+       "      <td id=\"T_d9212_row22_col3\" class=\"data row22 col3\" >-1159.63</td>\n",
+       "      <td id=\"T_d9212_row22_col4\" class=\"data row22 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_f1d86_row7_col0\" class=\"data row7 col0\" >612/2401</td>\n",
-       "      <td id=\"T_f1d86_row7_col1\" class=\"data row7 col1\" >25.5%</td>\n",
-       "      <td id=\"T_f1d86_row7_col2\" class=\"data row7 col2\" >164.08</td>\n",
-       "      <td id=\"T_f1d86_row7_col3\" class=\"data row7 col3\" >-545.50</td>\n",
-       "      <td id=\"T_f1d86_row7_col4\" class=\"data row7 col4\" >sampling</td>\n",
+       "      <th id=\"T_d9212_level0_row23\" class=\"row_heading level0 row23\" >24</th>\n",
+       "      <td id=\"T_d9212_row23_col0\" class=\"data row23 col0\" >146/151</td>\n",
+       "      <td id=\"T_d9212_row23_col1\" class=\"data row23 col1\" >96.7%</td>\n",
+       "      <td id=\"T_d9212_row23_col2\" class=\"data row23 col2\" >61.23</td>\n",
+       "      <td id=\"T_d9212_row23_col3\" class=\"data row23 col3\" >-1159.52</td>\n",
+       "      <td id=\"T_d9212_row23_col4\" class=\"data row23 col4\" >sampling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_f1d86_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_f1d86_row8_col0\" class=\"data row8 col0\" >717/2401</td>\n",
-       "      <td id=\"T_f1d86_row8_col1\" class=\"data row8 col1\" >29.9%</td>\n",
-       "      <td id=\"T_f1d86_row8_col2\" class=\"data row8 col2\" >192.06</td>\n",
-       "      <td id=\"T_f1d86_row8_col3\" class=\"data row8 col3\" >-546.77</td>\n",
-       "      <td id=\"T_f1d86_row8_col4\" class=\"data row8 col4\" >sampling</td>\n",
+       "      <th id=\"T_d9212_level0_row24\" class=\"row_heading level0 row24\" >25</th>\n",
+       "      <td id=\"T_d9212_row24_col0\" class=\"data row24 col0\" >151/151</td>\n",
+       "      <td id=\"T_d9212_row24_col1\" class=\"data row24 col1\" >100.0%</td>\n",
+       "      <td id=\"T_d9212_row24_col2\" class=\"data row24 col2\" >63.47</td>\n",
+       "      <td id=\"T_d9212_row24_col3\" class=\"data row24 col3\" >-1159.74</td>\n",
+       "      <td id=\"T_d9212_row24_col4\" class=\"data row24 col4\" >sampling</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
@@ -2129,6 +2311,14 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⚠️ DREAM sampling completed, but convergence diagnostics indicate the posterior may be poorly mixed.                              \n",
+      "✅ Bayesian sampling complete.\n"
+     ]
     }
    ],
    "source": [
@@ -2137,7 +2327,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47",
+   "id": "49",
    "metadata": {},
    "source": [
     "## Step 7: Inspect Bayesian Results\n",
@@ -2149,110 +2339,2287 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "48",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.analysis.display.fit_results()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49",
-   "metadata": {},
-   "source": [
-    "The correlation and posterior-pair plots are complementary:\n",
-    "\n",
-    "- `plot_param_correlations` summarizes pairwise structure in a compact\n",
-    "  matrix.\n",
-    "- `plot_posterior_pairs` shows marginal densities on the diagonal and\n",
-    "  posterior contours off-diagonal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "50",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.display.plotter.plot_param_correlations(show_diagonal=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.display.plotter.plot_posterior_pairs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52",
-   "metadata": {},
-   "source": [
-    "The one-dimensional posterior distributions below make it easier to\n",
-    "inspect individual parameters in isolation, including asymmetry or\n",
-    "multimodality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.display.plotter.plot_param_distribution(structure.cell.length_a)\n",
-    "project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)\n",
-    "project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)\n",
-    "project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54",
-   "metadata": {},
-   "source": [
-    "Finally, the posterior predictive plot propagates the sampled parameter\n",
-    "uncertainty into the calculated diffraction pattern. Comparing this to\n",
-    "the zoomed measured-vs-calculated view helps assess whether the sampled\n",
-    "model family explains the data in the region of interest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56",
-   "metadata": {},
-   "source": [
-    "A final zoomed measured-vs-calculated plot is useful for checking how\n",
-    "the posterior-supported model behaves in a narrow region of the pattern\n",
-    "after the Bayesian run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)"
-   ]
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;34mBayesian fit results\u001b[0m\n",
+      "✅ Success: \u001b[3;92mTrue\u001b[0m\n",
+      "i Status: DREAM sampling completed\n",
+      "🧪 Sampler: dream\n",
+      "🎯 Committed point estimate: Max posterior\n",
+      "🔁 Sampler completed: \u001b[3;92mTrue\u001b[0m\n",
+      "⏱️ Fitting time: \u001b[1;36m63.47\u001b[0m seconds\n",
+      "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.29\u001b[0m\n",
+      "📉 Best log-posterior: \u001b[1;36m-1157.00\u001b[0m\n",
+      "⚙️ Sampler settings: random_seed=1708713996, steps=100, burn=50, thin=1, pop=4, samples=2000\n",
+      "📊 Convergence: converged=\u001b[31mfailed\u001b[0m, max_r_hat=\u001b[31m1.291\u001b[0m, min_ess_bulk=\u001b[31m56.4\u001b[0m, draws=100, chains=20\n",
+      "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.65\u001b[0m%\n",
+      "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.92\u001b[0m%\n",
+      "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m4.09\u001b[0m%\n",
+      "📈 Committed parameters:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_6e9f7   {\n",
+       "  border: 1px solid #dadada;\n",
+       "  border-collapse: collapse;\n",
+       "  margin-top: 0.5em;\n",
+       "  margin-left: 0.5em;\n",
+       "}\n",
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+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_6e9f7_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
+       "      <th id=\"T_6e9f7_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
+       "      <th id=\"T_6e9f7_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_6e9f7_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_6e9f7_level0_col4\" class=\"col_heading level0 col4\" >start</th>\n",
+       "      <th id=\"T_6e9f7_level0_col5\" class=\"col_heading level0 col5\" >max posterior</th>\n",
+       "      <th id=\"T_6e9f7_level0_col6\" class=\"col_heading level0 col6\" >uncertainty</th>\n",
+       "      <th id=\"T_6e9f7_level0_col7\" class=\"col_heading level0 col7\" >units</th>\n",
+       "      <th id=\"T_6e9f7_level0_col8\" class=\"col_heading level0 col8\" >change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6e9f7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_6e9f7_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_6e9f7_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_6e9f7_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_6e9f7_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_6e9f7_row0_col4\" class=\"data row0 col4\" >3.8913</td>\n",
+       "      <td id=\"T_6e9f7_row0_col5\" class=\"data row0 col5\" >3.8913</td>\n",
+       "      <td id=\"T_6e9f7_row0_col6\" class=\"data row0 col6\" >0.0001</td>\n",
+       "      <td id=\"T_6e9f7_row0_col7\" class=\"data row0 col7\" >Å</td>\n",
+       "      <td id=\"T_6e9f7_row0_col8\" class=\"data row0 col8\" >0.00 % ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6e9f7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_6e9f7_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_6e9f7_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
+       "      <td id=\"T_6e9f7_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
+       "      <td id=\"T_6e9f7_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
+       "      <td id=\"T_6e9f7_row1_col4\" class=\"data row1 col4\" >9.1329</td>\n",
+       "      <td id=\"T_6e9f7_row1_col5\" class=\"data row1 col5\" >9.1336</td>\n",
+       "      <td id=\"T_6e9f7_row1_col6\" class=\"data row1 col6\" >0.0305</td>\n",
+       "      <td id=\"T_6e9f7_row1_col7\" class=\"data row1 col7\" ></td>\n",
+       "      <td id=\"T_6e9f7_row1_col8\" class=\"data row1 col8\" >0.01 % ↑</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6e9f7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_6e9f7_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_6e9f7_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_6e9f7_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_6e9f7_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_6e9f7_row2_col4\" class=\"data row2 col4\" >0.0817</td>\n",
+       "      <td id=\"T_6e9f7_row2_col5\" class=\"data row2 col5\" >0.0817</td>\n",
+       "      <td id=\"T_6e9f7_row2_col6\" class=\"data row2 col6\" >0.0060</td>\n",
+       "      <td id=\"T_6e9f7_row2_col7\" class=\"data row2 col7\" >deg²</td>\n",
+       "      <td id=\"T_6e9f7_row2_col8\" class=\"data row2 col8\" >0.01 % ↓</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6e9f7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_6e9f7_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_6e9f7_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
+       "      <td id=\"T_6e9f7_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_6e9f7_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_6e9f7_row3_col4\" class=\"data row3 col4\" >-0.1169</td>\n",
+       "      <td id=\"T_6e9f7_row3_col5\" class=\"data row3 col5\" >-0.1169</td>\n",
+       "      <td id=\"T_6e9f7_row3_col6\" class=\"data row3 col6\" >0.0044</td>\n",
+       "      <td id=\"T_6e9f7_row3_col7\" class=\"data row3 col7\" >deg²</td>\n",
+       "      <td id=\"T_6e9f7_row3_col8\" class=\"data row3 col8\" >0.01 % ↑</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6e9f7_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_6e9f7_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
+       "      <td id=\"T_6e9f7_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
+       "      <td id=\"T_6e9f7_row4_col2\" class=\"data row4 col2\" ></td>\n",
+       "      <td id=\"T_6e9f7_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_6e9f7_row4_col4\" class=\"data row4 col4\" >0.6306</td>\n",
+       "      <td id=\"T_6e9f7_row4_col5\" class=\"data row4 col5\" >0.6303</td>\n",
+       "      <td id=\"T_6e9f7_row4_col6\" class=\"data row4 col6\" >0.0017</td>\n",
+       "      <td id=\"T_6e9f7_row4_col7\" class=\"data row4 col7\" >deg</td>\n",
+       "      <td id=\"T_6e9f7_row4_col8\" class=\"data row4 col8\" >0.05 % ↓</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x36a332d50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📊 Posterior parameter summaries:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "  text-align: right;\n",
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+       "#T_077c2_row0_col8, #T_077c2_row0_col9, #T_077c2_row1_col8, #T_077c2_row1_col9, #T_077c2_row2_col8, #T_077c2_row2_col9, #T_077c2_row3_col8, #T_077c2_row3_col9, #T_077c2_row4_col8, #T_077c2_row4_col9 {\n",
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+       "</style>\n",
+       "<table id=\"T_077c2\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_077c2_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
+       "      <th id=\"T_077c2_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
+       "      <th id=\"T_077c2_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
+       "      <th id=\"T_077c2_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
+       "      <th id=\"T_077c2_level0_col4\" class=\"col_heading level0 col4\" >median</th>\n",
+       "      <th id=\"T_077c2_level0_col5\" class=\"col_heading level0 col5\" >std</th>\n",
+       "      <th id=\"T_077c2_level0_col6\" class=\"col_heading level0 col6\" >68% interval</th>\n",
+       "      <th id=\"T_077c2_level0_col7\" class=\"col_heading level0 col7\" >95% interval</th>\n",
+       "      <th id=\"T_077c2_level0_col8\" class=\"col_heading level0 col8\" >r_hat</th>\n",
+       "      <th id=\"T_077c2_level0_col9\" class=\"col_heading level0 col9\" >ess_bulk</th>\n",
+       "      <th id=\"T_077c2_level0_col10\" class=\"col_heading level0 col10\" >units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_077c2_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_077c2_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
+       "      <td id=\"T_077c2_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
+       "      <td id=\"T_077c2_row0_col2\" class=\"data row0 col2\" ></td>\n",
+       "      <td id=\"T_077c2_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
+       "      <td id=\"T_077c2_row0_col4\" class=\"data row0 col4\" >3.8913</td>\n",
+       "      <td id=\"T_077c2_row0_col5\" class=\"data row0 col5\" >0.0001</td>\n",
+       "      <td id=\"T_077c2_row0_col6\" class=\"data row0 col6\" >[3.8912, 3.8914]</td>\n",
+       "      <td id=\"T_077c2_row0_col7\" class=\"data row0 col7\" >[3.8912, 3.8915]</td>\n",
+       "      <td id=\"T_077c2_row0_col8\" class=\"data row0 col8\" >1.291</td>\n",
+       "      <td id=\"T_077c2_row0_col9\" class=\"data row0 col9\" >56.4</td>\n",
+       "      <td id=\"T_077c2_row0_col10\" class=\"data row0 col10\" >Å</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_077c2_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
+       "      <td id=\"T_077c2_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
+       "      <td id=\"T_077c2_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
+       "      <td id=\"T_077c2_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
+       "      <td id=\"T_077c2_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
+       "      <td id=\"T_077c2_row1_col4\" class=\"data row1 col4\" >9.1341</td>\n",
+       "      <td id=\"T_077c2_row1_col5\" class=\"data row1 col5\" >0.0305</td>\n",
+       "      <td id=\"T_077c2_row1_col6\" class=\"data row1 col6\" >[9.1029, 9.1650]</td>\n",
+       "      <td id=\"T_077c2_row1_col7\" class=\"data row1 col7\" >[9.0758, 9.1901]</td>\n",
+       "      <td id=\"T_077c2_row1_col8\" class=\"data row1 col8\" >1.208</td>\n",
+       "      <td id=\"T_077c2_row1_col9\" class=\"data row1 col9\" >78.6</td>\n",
+       "      <td id=\"T_077c2_row1_col10\" class=\"data row1 col10\" ></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_077c2_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
+       "      <td id=\"T_077c2_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
+       "      <td id=\"T_077c2_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
+       "      <td id=\"T_077c2_row2_col2\" class=\"data row2 col2\" ></td>\n",
+       "      <td id=\"T_077c2_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
+       "      <td id=\"T_077c2_row2_col4\" class=\"data row2 col4\" >0.0816</td>\n",
+       "      <td id=\"T_077c2_row2_col5\" class=\"data row2 col5\" >0.0060</td>\n",
+       "      <td id=\"T_077c2_row2_col6\" class=\"data row2 col6\" >[0.0750, 0.0872]</td>\n",
+       "      <td id=\"T_077c2_row2_col7\" class=\"data row2 col7\" >[0.0684, 0.0928]</td>\n",
+       "      <td id=\"T_077c2_row2_col8\" class=\"data row2 col8\" >1.230</td>\n",
+       "      <td id=\"T_077c2_row2_col9\" class=\"data row2 col9\" >72.5</td>\n",
+       "      <td id=\"T_077c2_row2_col10\" class=\"data row2 col10\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_077c2_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
+       "      <td id=\"T_077c2_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
+       "      <td id=\"T_077c2_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
+       "      <td id=\"T_077c2_row3_col2\" class=\"data row3 col2\" ></td>\n",
+       "      <td id=\"T_077c2_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
+       "      <td id=\"T_077c2_row3_col4\" class=\"data row3 col4\" >-0.1167</td>\n",
+       "      <td id=\"T_077c2_row3_col5\" class=\"data row3 col5\" >0.0044</td>\n",
+       "      <td id=\"T_077c2_row3_col6\" class=\"data row3 col6\" >[-0.1211, -0.1126]</td>\n",
+       "      <td id=\"T_077c2_row3_col7\" class=\"data row3 col7\" >[-0.1251, -0.1076]</td>\n",
+       "      <td id=\"T_077c2_row3_col8\" class=\"data row3 col8\" >1.226</td>\n",
+       "      <td id=\"T_077c2_row3_col9\" class=\"data row3 col9\" >72.6</td>\n",
+       "      <td id=\"T_077c2_row3_col10\" class=\"data row3 col10\" >deg²</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_077c2_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
+       "      <td id=\"T_077c2_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
+       "      <td id=\"T_077c2_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
+       "      <td id=\"T_077c2_row4_col2\" class=\"data row4 col2\" ></td>\n",
+       "      <td id=\"T_077c2_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
+       "      <td id=\"T_077c2_row4_col4\" class=\"data row4 col4\" >0.6304</td>\n",
+       "      <td id=\"T_077c2_row4_col5\" class=\"data row4 col5\" >0.0017</td>\n",
+       "      <td id=\"T_077c2_row4_col6\" class=\"data row4 col6\" >[0.6286, 0.6320]</td>\n",
+       "      <td id=\"T_077c2_row4_col7\" class=\"data row4 col7\" >[0.6271, 0.6337]</td>\n",
+       "      <td id=\"T_077c2_row4_col8\" class=\"data row4 col8\" >1.281</td>\n",
+       "      <td id=\"T_077c2_row4_col9\" class=\"data row4 col9\" >58.6</td>\n",
+       "      <td id=\"T_077c2_row4_col10\" class=\"data row4 col10\" >deg</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x36a332d50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⚠️ \u001b[31mr_hat\u001b[0m: exceeds 1.01 (consider longer sampling, tighter bounds, or reparameterization).                                         \n",
+      "⚠️ \u001b[31mess_bulk\u001b[0m: less than 400 (consider longer sampling, tighter bounds, or reparameterization).                                     \n"
+     ]
+    }
+   ],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51",
+   "metadata": {},
+   "source": [
+    "The correlation and posterior-pair plots are complementary:\n",
+    "\n",
+    "- `plot_param_correlations` summarizes pairwise structure in a compact\n",
+    "  matrix.\n",
+    "- `plot_posterior_pairs` shows marginal densities on the diagonal and\n",
+    "  posterior contours off-diagonal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".ed-fixed-aspect-plotly-wrapper .plotly-graph-div,\n",
+       ".ed-fixed-aspect-plotly-wrapper .js-plotly-plot,\n",
+       ".ed-fixed-aspect-plotly-wrapper .plotly,\n",
+       ".ed-fixed-aspect-plotly-wrapper .plot-container,\n",
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+       "    display: block;\n",
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D0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A8QVywB8hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQGfXrTe3IQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A8QVywB8hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQEHhGT2xIQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BxopkEZSEPQAhC3HqHIQ9AQgwwIh0hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BxopkEZSEPQBRhacuHIQ9ACo0FgVkhD0CGsSshXSEPQEGzRqw0IQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0AasKbUZSEPQBRhacuHIQ9ATL3N1S4hD0CGsSshXSEPQEGzRqw0IQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"evSFwwFAIkBKvYxDCEEiQENO3pGQNiJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJApZPD6ERCIkCVUBKRKzMiQMNWn5lAMCJAyZ2bOpNUIkDZ1EiqsDQiQEa4fpOARyJA32HWDm5AIkBfB6lznkoiQJDBsNflQCJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJAe2nuRn1FIkCVUBKRKzMiQMNWn5lAMCJA2bu+UplfIkDZ1EiqsDQiQHe+Nd+PNiJA32HWDm5AIkBfB6lznkoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJA2bu+UplfIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkByZF\\u002fiTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkAFUKpjVTAiQAlcmXLQQyJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQADxZjOxPCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFeNB+DQRSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkBFxE8IDCEiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQMEejU0aJCJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQL2+RkeoLSJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkDdabLXn0oiQNtqESF6OyJAltJCqQU3IkA7uHYX2UkiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkByOZsZoUoiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQMjtbCrMRCJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQOksL4G7USJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJAltJCqQU3IkAY71HF2UkiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQEFDsf9hVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDbwrNk+TAiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQMCN30mCSiJA5Krx\\u002f4pcIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkDLosEn8ToiQMCN30mCSiJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJADwK6GT8+IkAo8V8fRksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQHdnZVcwSiJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkDNzDIUsTQiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkC4tYSgZzMiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAvZLk0ElNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAKCSRd5s0IkDHWtt6REsiQPCEbx5CSyJAjazWjqI5IkA4trUkS0ciQCDLFA+sQiJAvCGU7qtLIkDU73tBaT0iQJsC5N1uOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAVdIqpZ00IkDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDPxpYQhUMiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkC+QPFbfTwiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ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w9GUCuT8ja+OEaQDIEyoXqGhpAht9guG0eF0CgPuVy7a4SQNxKuxu7mgtAkDGGqwW+AkAFZlhj3Mz3P6pske\\u002fr6e0\\u002fIijASHLw5T+9HmwuLMzoP30zM9UTyvY\\u002ffCgppFrCCkBr977hgqIgQN21JejxJDRA9Bnm47vsRkDGpoQogCFYQC+sLRyxU2dAaQhi3oqkdEDExDBTMrOAQF+QoFzRsYhAaK2CSH2ykEB\\u002f76a\\u002fLrGUQE1ublb5nJdAp4aOCUsSmUBb8Emx0EeZQLhl0hUbF5lAP58vVL64mUDcBjrRzU2cQKcRgBxhtqBAUZaAI+5ypEDCzgIszeqoQC4ZsZKzeq1AmSYnqD\\u002fAsEBb+960fEGyQNjtDQ19IbNAU5u53jFbs0AEg49xuvqyQM6LO3bEErJAEz08+qu1sEADctsFuuqtQN2PJK5N0qlAySLgM7BopUAuTAGyuQ2hQHgFm27jUZpA3kDkQDQXlEDmNzM4jlmPQOg0FZ+qxolA8KhgdgNxhkCrpNC5TCGEQBlfTIBx34FAt29doB5lfkD9o2a5hT94QO9usELg8nFAQXylKdqEaEBy0Ggg3tNeQJorYrWV0VFAZ9J3hdrvQkAvf9HDtYIyQIfYhyHvqCBALr4H9x2pC0DwoQV0fTz1Pw8A4BEyRd4\\u002fC\\u002frshd8gxD\\u002f0rf1VLyKpP3vlJ1verI0\\u002fPRYHSsGtcD9AbGR0sfBRP18NuIEogTI\\u002fZqRp\\u002fDdJEj+xp2CueUfxPmjJLAaPTs8+tl396ba8qz6ukaXiIbmJPl\\u002fWVdw\\u002fLG4+WDc\\u002fFDuTYT4iGBD92GC6P9EDR4RxzMM\\u002fJ4YtTyKd1T8XP\\u002fRC94vnPwDPqJ0Qrvc\\u002fVPf9T9O+BUD9981nbDUSQL0\\u002faBYjzRtAmMQig1xZI0CKzrKA4Y0oQHA\\u002f2QqCaCxAee5q5Hf3LUDMZ7tlK9MsQKAQ5iS0SilAPTlTmQBDJEADL6EI7bcdQOCLgZNrGxRAvib3AyyzCUCWl9UMgpsAQLPLtzA2bPk\\u002fEahVIaAI\\u002fD9gc4TVppwGQP34fQYsBBdAimIpQrHbKUBsH8FjcWs9QFIfUcqcG1BAnn3MBuaFYEAoPNEzcVVvQPBXFEt4SntAmggx\\u002fJDFhUD4ZnLGh8iPQHndu\\u002f7zOpVAYxOuN\\u002fX\\u002fmUCWkBsDl1GdQHZy4J8Ivp5AVe+gjyuTnkBBUQTVhuGdQFkaAAJPJZ5A586\\u002fP3NcoECSaIGAnyijQAi6BHysXqdABaZM5fV+rECruqCtCOiwQIix7oZNS7NAqwJoCIwgtUBwzJ3XEj62QP84nRO5l7ZAehF4hvU1tkBJgFqsNyu1QLLojbjijLNAAxaqzzBzsUCfXzq65v2tQNV1m7G0vqhAORUJMQSho0BR+idMFT+eQLXZJQfuKJdAFWmqsts6kkA1s8GRVlmOQPy0HEO8topAhlErqq8diECSGSldE3WFQGNKoAr4PYJAHVPulS8WfUD1kecxj4h1QJFJK+MzeG1ATlbZqX+aYkA++NTtKalVQEK7hFiZRUdADymZqhoaN0APv8rJKjwlQF5fdCrYIBJA0iO0F+Lc\\u002fD9NEvdPeYjlP9vuLQwQSM4\\u002fQQ+ZWjoutD+i3L0iMZyZP9fLqUIGBX8\\u002fDhVSxirtYT8VEu35E7pDP7Ok2elvniQ\\u002fi0rl7YiBBD+gdOEptLTjPs\\u002f1BwsPOsM+OyopaEZRpT4ZJVGn2C+QPiu+lTd1AoY+E82qu4tQzj836Pg8lpXWP6gP0w0GYeg\\u002fg\\u002fM30T5K+j8hJVUOeS4KQLVmPEGTzxdABDnHVg6\\u002fI0C7Zmkv69stQK9alBzglDRATrjcNyXeOUC4dwmwVqQ9QB0INgi5+D5A73dA44iDPUDHGm6bGqo5QGGTeb74ZDRADfERwxe9LUBYPKB76B4kQAFQmPEzGRpA4OSfEVShEUCIWgxJq9wMQD7QBlRlGhBABDWl5QWbF0A8zmMb5LokQOX3eNfrZTRAa8qDHF0lRUAPBhyeMtJVQOLgOBMnimVAmkf77MLhc0DrFpCDjvaAQMeiFbdAm4pAKRR6Pushk0DSq2mVaDeZQH2euZdnf55ACEVeIcH+oEBM\\u002f3YXH6ChQF3KX5s+WqFAkMXbv2\\u002fPoEDzPUaTRtmgQPqWTgnROKJAds5hl45PpUBwtHq55P6pQJk0L247t69ApEFCyf3UskBQr0tG14K1QJj2CIyhkrdAgUNxY0zTuEDFzxbgZTS5QJF0FthdvbhAYB+B8riCt0BXvyr1z561QBqWzB1rMbNAlj6CdOFksEBbdrNhC+WqQK0tUjJTPqVA87MsSr1aoEBsQoV7NSuZQMSxs8Q0CpRAbS4jaOrwkEDL+1r0\\u002fkKOQMoAQIP+nYtAJ\\u002fycHIzDiEDaf6iTwTGFQDssCykHBoFAtfxCWnByeUAtZt43rKFxQM\\u002fySmN6n2ZA4JVpJUbiWkBUvlWYsp9NQOu4LWBgVD5ALGKWgoLuLED10d\\u002f+2c0ZQEzftlZkmwVADpcTGDoP8T9pNjQk3n\\u002fZP\\u002fZ6p8fqFsI\\u002fY3LyL95iqD8nfHp6STWPPyV+ivCG6nI\\u002f3micAViuVT9rfImmb4g3PxycZJkyfRg\\u002fKLgQLeZm+T41yr7UvqfcPvTIgOpA3MM+Swcgp1m9sj5vK0lQnemrPnAVwTADJuA\\u002fRvQaHzbi5z\\u002fpflFfIX75P2ZvlOk\\u002fNgtAhtA8OvvVGkAy5R8hrysoQCcJTSMU2jNA9q7x0++6PUC4Cydm0EtEQIMvOJffQ0lA6bnmUSatTEDVWr5llK5NQDR9q0EfB0xAHRlqCYQqSEBmEOOvwRFDQB6tnuMStjtAfrOHRQfaMkB22eHP5BkpQDGcxhENDCJAVTcS9n4JIEDk83bwWnEiQE\\u002fwBf\\u002fCcylA5fvRKa+4M0AWhtP\\u002foc9AQL3Atlz62U5AkEg8PKUxXUDwPr3NfjRrQNBvprXdJXhAxWR0em0GhEAikssHuLeOQKROmDNUr5VAUipsUnQhnEAEK2HpCMegQDmKwEQ3eqJAC6JN+Y36okAUWL+Jh5CiQF1ImFpZ86FAaivIFD0NokBxRS1Iw6ajQJ2yoweNHqdAoRlIUIBKrEBlU5oTA0WxQOYqJTuofbRAhthpa0Bdt0CpyLoT2Im5QFMQmULgzbpAMaQts\\u002f0Xu0Dda3IdVnK6QGnge1pC97hAAVPgejDJtkC\\u002fNmgXGBG0QPWzNRIjArFAQ7oqm\\u002fC4q0BzX\\u002fTO0NClQLUM2glu0KBA2nd9aWIYmkAqE3oO5B+VQP1OZJBUQJJA\\u002fCiujJKpkEBQHVKKCgKPQFTpKsoYTIxAKHIfL7amiEBc1rWRpDCEQEQDYeeE3H5AE\\u002fz0K4nwdUA7T59WRf1sQHzwttAkzmFAi7AdwuZbVEDgJioK8rVFQF7vO022pTVA7LJpk8Q9JEA6+t+V9M0RQCEOedpPjP0\\u002fkr3t9gct5z94Iu+GAjLRP\\u002f2R\\u002fUXJH7g\\u002fPuihX1f0nz+5K5Gdk\\u002fWDP+h7yGK8jWc\\u002fAd8qXceESj+5GcM26mAtP5wpogNlMhE\\u002fEcliglKi9z4WqQ\\u002fY6EjkPh54nA0tBNY+BgsBUo8m0T6RxTXeHebvP2ZZpOyHavc\\u002fehp+ap62CEA+Y7K3mxwaQDcurJQLgClAjPgJWAq\\u002fNkDHpCHJnYBCQExKdmmCcUtAPldOn0aOUkBkxxrKBeFWQFbK6e5auVlACV7gkdFhWkAus2rTI7JYQFBdUZSqIVVAnUZFrcGWUEDwBMUTyB5IQNNVCGpBo0BAw\\u002fvOWBYXN0DKMHhar\\u002fgxQDaker86ZjFAQuuxUY2rNEAdzOxliIQ7QCiHUW3mdkNAijXXQ5Q2TUDcADdRjXRXQGu6e1R52WNA\\u002fEevvugNcUC0gvs9JZ18QIzvuKQdy4ZAUr4PXzL2kEBO\\u002fsFC4GGXQKxeUKYSv51A6tBsrLd1oUAHKwe6g\\u002f2iQCLitkabWKNAQpkA2NfhokAxtGoFYF2iQHkDMmROuaJAvnzIiTe5pEC\\u002fGH5Gg7CoQK77BzxWZq5AAHTjkaqUskASUaoS5gK2QKxujOtBA7lAQJSnmf00u0Amk460i1+8QAzthBK8c7xAilMi6hWDu0CVZ6p6K7O5QH1RO78cM7dAvZWgXEw3tEBe9Ivbl\\u002fqwQGezbalvf6tAhAPoVCuZpUA4TI\\u002fXmLugQOWQX5ZzXJpArh4626PklUD0tnUIZIqTQMjW\\u002fcLhcpJAb1O3cLq3kUD\\u002flkIQyKmQQMAD3eu6641AkzIh6r5IiUBEzyQI\\u002fPeDQDbcy3RZYn1AWjNdzDMedEDHPgSr76JpQOeGL6Rebl5AZolN5BXaUEChY90y2nNBQLj5fx978TBAPXq+pfjpHkASdlTj140KQA6xwOFkgPU\\u002funJoGZZq4D+uwe4gVaDHP8d7EBXwBLA\\u002fBCYAGuSAlD9wDq83SPh4P5mB48bLpV0\\u002fsmwToa4vQj8pA8OXolkpPxbZHLLWdhU\\u002fCbd0xm6BBT\\u002fByWuBtx\\u002f5Pge6bIWn8\\u002fM+qmLvoBw1\\u002fT9AW3X4r0gFQJa+hYn4NRZA4kl4cIU6J0DgnAVcsHY2QI\\u002fzjII62ENAc+shy3T5T0BZDt12Y3xXQPoyKSkmdV9AWHb4W\\u002fo1Y0A04R8toGZlQIMHQEdkwWVAhbQmUSI0ZEDMqBcQ1S5hQEL1kH\\u002fN7FpA0T8xr6+2U0DkDrsnmOtLQN9nZykpkURADUp6rLSIQUAWFQyN7llCQFh417\\u002ffWkZALcUoFQwRTUDq4lpQvFNTQBOII2WcU1pAXPO4A\\u002fu5YkBFsuTovRFsQIdkV4GHynVA3EHvOYLxgEADDdheCouJQMNNGSi2PpJAH32TuaBfmEDiRIvHP0GeQAzv9gP\\u002fbKFAxQVeBGK0okCK\\u002fmtpr++iQNlB1yIZiqJAKw+pX\\u002fxLokCZZGoTNhyjQKRpf4CssKVAqMbR4cBNqkAKxSPd9FWwQICTR\\u002fzSBLRA9v7Ob7yvt0DLgZvM+NC6QMM+illW\\u002frxA0l6bPnn7vUAOgt3T2L29QLzyOuERZLxA4GyWGiImukCLUlb590W3QEFm4OwxBrRAEXof6hSosECj0bIB7NiqQL3\\u002fa93wIqVATI27YF2UoEBLnrW1bMuaQCr34n3wIJdADxrslbWdlUB659RibV6VQACo6ECzaJVAuSB0EWfulEA3zgCzXH2TQGdG+J2bDZFAJ2fRqzTai0BqsyPiBimFQPyrls1p4H1A+P5YEPiZc0C6vyQMuu1nQMdvibSqOVtAeFInwcLvTEBbyFezSs08QAtxGPPC6CpAhTbXFimkF0DYLgGPhI0DQP03Rr10du4\\u002foC4olptc1j9P8EyKEf++P03LHP4Da6Q\\u002fL96HHqgPij9bsDQTC+FwPwR5B2cfHlg\\u002f+7r4en2GRD8qjP+PM4s0Pw6TqBuahCY\\u002f1j\\u002fCH7wvGz9aJ\\u002fZSALsVPzQfjyPVyghAaAhqQ3fvEUArceGEk4EiQOa6ZWMiKDNAc1r9mnJYQkCWbU6n\\u002fQxQQGwQUfUvnVlAsU5M14uiYkA6UVr0l7loQOSOwRN3621AqQMsHYeEcECwt4zyvadwQDBKoQxEu25Apiy8N6MMakDXrjH4vHpkQD8d95LWg15AMQ\\u002fCdNGQVkCQAoDOXflRQL9H7qaM0VBAAETzwvK3UkAwFiVzXh5XQLdi7i6jfV1AvYKftA3KYkAIqqqAKehnQOZdKwaZ7W5ANkYhslK8dEDSVcDxceR8QLfIINP6hIRAXTM91w7ZjECrY0CdmYyTQFvUirdHGJlAMwd9cDQ5nkDUem\\u002fT5AehQATpOiWwCaJAX78ed\\u002fQzokDbUFkhY\\u002f6hQEg0pzp0LqJAMfWESaCdo0C9MPgbK\\u002fKmQJP2ZkWvY6xAojzDan7PsUAqKAuFwui1QPFkY14z7LlApOZQb5NCvUAUZqZh+nG\\u002fQHVF54qwG8BAQJTqtVSPv0DjoBKMmqy9QLhWSAN34rpAsMgdJcGMt0AYFSGyPv+zQAWfgMQagLBALX9CoF2UqkCtkP5i5B+lQF8z7QDd8qBAHy4QIwZznEA\\u002f4jmp\\u002f9WZQD2paEM0g5lATYg3wkKImkAAt3p58MGbQJNRxxYIJ5xAvzG6mr4Om0D6Xc0xwVeYQOrLncUkYJRAmP+TsXimj0DHt3QjXsuGQC9LCdkbd35AZw8uXrPrckBbINzUDeRlQH3Zj3NBp1dAz6yBatPuR0BV3bOgMbs2QN+H0EMkTiRAyHEj8kAVEUA62cNoZRv7P+eGCKGjU+Q\\u002fbSFT7Sb6zD+mmn5rZ+6zP6wpdiqNcJs\\u002fn3gNVu1GhD\\u002f11wwGh3BxPxG+ErgYsGE\\u002ffKNX+L2yUz8kuCVtapNGP38P0VNxkjs\\u002fYgQwUesLNj\\u002f8giGybYITQBHgKmkPBhxANGh4gx+XLEC9aBKzFUs9QHTKRwyhx0tAaQbuaWASWEDv8vbgLgZjQC+fgd2ca2tAh3GtH8gFckCfBbp0ep11QFYML6F\\u002fq3dAYWZPkBK0d0Ck+8saMMd1QLgvuEtnfHJAeC\\u002fLGKNtbUC1V0FXSKdmQPaEKVy62mFAepcb7hAnX0BUOwMjH65fQDAai34qXmJAN\\u002fnSLYy9ZkBA\\u002f7X4EXpsQKghIOH9knFA2BHPTh1RdUC3vcFxdbx5QO2MVPT5in9ACBf6HVLkg0DB3pWuZbmJQGN+3X5yuJBAFSe3lfVQlUBt937M4x6aQHppf1FYZp5Ao6oFoL+8oEAqXOF0FYWhQDrBMQGjtKFAXYnlFtzKoUDy9boNroaiQAANMrLCtKRAaJqWnz3xqEBBBXiOc26vQJSN6BbE6LNAElgC3hyZuEBMzrMy4iG9QM1UDHcnaMBAYk9A8xCJwUDU+7n6Lc3BQGIsMcQYOMFA3jkYJj7jv0DHvUtp12u8QHdplMCihrhAjNmpHZCatEAVlrklj\\u002fCwQN6fNrnZc6tAK4mBZuA2pkDMBg0Ub2aiQHSOjaCuKaBADGA4Clf5nkBudL0eCxugQJxA4lMOfKFAdCyv5JnpokCMeEjcIK+jQNK10FyRT6NAfu8goXmqoUDk8iVaOv2dQOi+bm7dkpdAxBJkHp0pkUDSkLbCzSyHQGmiMttaFX1A6lBnJpEBcUDBAgKgv5ZiQLBIP\\u002fQkDFNADZIrb0BYQkDsza\\u002fa9qQwQH0OdQuGgxxAGR3CPageB0Bn8EBYN9bxP+D\\u002fNByZgNo\\u002fRQ\\u002fU+fp\\u002fwz+ByYgHexSuP7cMa8VdR5o\\u002fHB+aRYT5ij8jrl2C+e1+P0js+titQXI\\u002fnqeGlhBTZT\\u002fyzbwWtBBaP0ICx+tOyVQ\\u002fODGR2wt3HEA1bBftEkwkQL7bDMaGeTRALjWHeI3DREACzgzi+H9TQAmiaoBdvGBAh2VjcBk0akApdolzoLVyQOqCrBIFYHhAwpbKtfT9fECRA4wAYYZ\\u002fQELE293BbH9AZ2hsB+TgfEDo0xY58b54QD57M42yOHRA4vhouahpcECOmZkExBpsQFd4CVWG4mpAy72w+5kkbUDzS8Z3CEJxQNqWlmu0N3VAsNsudGEQekAizXfrgFh\\u002fQCP\\u002fPeQhW4JATnr\\u002f7GYWhUAzwS8N4CWIQAbyCknkCoxA0+\\u002fnXgGjkEAYDJQnWAKUQA3r+zmg8ZdARa+tG3j2m0A\\u002fgQa88XOfQHWR9YiV9aBAzo1uLVefoUAiFdydve+hQH0H83NTaaJA12OhTu7Eo0AvQHGWRMmmQEdz03xkE6xAiXGg4wztsUDL4cUzZ962QNq3LfDeWbxA+GeDqwrNwEA1mMi6leLCQGGU+HQpFMRAsgIL36c1xEA8v2Bh8lLDQOX0LaI0p8FAIQs8bHULv0BbVLS6W3+6QNHpRILwI7ZAsivmyntBskArIJ\\u002fiDfmtQCeBM2HM16hAdGf+pFZOpUDsCaBZT4mjQHwYgeOdkaNAMOOc\\u002f7kjpUBuOFkCGpynQClKF3hPDapASRTLOYZ\\u002fq0Cz9BiyyUCrQKTbQOwaIKlAEA0S0cp1pUDC5il5j\\u002fagQG0kx2Dc1ZhAFj4TVFfekEDNdq86PVGFQEtunP\\u002fuI3lAvVwlSAXDa0AOwU1+68xcQOvo08ciKExAVVAWOPgEOkCIdxaLgc0mQG4TxG20CxNAzJydUP6d\\u002fj\\u002fogQkK3jHoP1EU8kTSpdM\\u002fDlPkcd2PwT8QZtKamTSyP7R3z3VEZaU\\u002fTrv6x5hlmj9uCHNCuAeQP+AHOHkK1II\\u002fVZ2FOsXodj88C0PgTTFyP1aYSCFMQCNA5mK54EBCK0DoZqmQLzA7QB5+9Id2SktAPwNYpsRhWUDOb5Q5BpRlQPZ+Sk6GvHBA3Crt+8ivd0DVX8hEcZp+QO67vbkEEoJA2n\\u002fBbMWNg0DvOgHJZXuDQCSASYC5CoJAyzq\\u002fWruef0C31\\u002fAozwx7QE9GBvt5qHdAOhcy9Ys5dkDtvLb\\u002fMwV3QLrpTcb2\\u002fXlAT7gjrZ3vfkBDx668GMKCQCax7qKVloZASRrCOwiQikB00eYkhUeOQJCrMs3RxJBA6E6PTl5BkkC249DRD92TQIHm37Ks55VAnvZU\\u002flCMmEAddfiWa66bQH9RbrTf655AagRII0PjoEBh2CBeRPGhQIjzUmMMoKJAk6ucTjYvo0A+huc7jCCkQAL45+OpKqZAxN6XBukYqkCR1TbT5UqwQPCbDF0f8rRAYMXR24nNukDQaBBYGaHAQORJkbx4sMNAakIA24YPxkDJj05ldFLHQPYYUhgPScdAxzTMscwLxkDm4j26Ee7DQANO1JJ2XcFA\\u002fCKpKrt8vUBpi4PBbqq4QMJ4QOJohrRAkeuUvlQlsUAeAhj0VSKtQFO1w2umv6lAhQ+u0vNgqEDaYhgz6yOpQNaD9tp0xKtAhFis+wB5r0CCvIAzAIaxQApjaFJ5mbJACJWyLY9\\u002fskCh9i3mbxixQAIZGHedQ61AP06Yb7Mvp0DhdoW09wahQBMYUVcBPJdA0hzJdEeMjUDOvl4VAZOBQIZgKe1BoXNAHxwiuFyrZEDs1a9u1JhUQLkBor4RgUNAcJjXPb+jMUDGeTj+abgeQHAU3qt1LApAUx+UwW2O9j8rdOXBedDkP97GcFsJz9U\\u002fqMuvtXMSyj8QyHKVI7zAPxdyQYeVerU\\u002fExk\\u002f1K1lqj9cAsFW6\\u002fWeP7Lt7tNispI\\u002fIAKJ2VaLjT9xX7\\u002f9+iMoQKbQDOxg+DBAu9+uuom8QEAeA326\\u002faBQQGCjZOKrol5AOrA1dJrNaUBblvC3v9VzQLtEHqTG1ntAUIEY9vzagUDToZkzKfuEQES1WnPSroZAN7p+4x3AhkDxJWr3ZH6FQGj4RE5xoINATsrYK\\u002fD6gUB8jk5pgDWBQHuRHLYupoFA6NMCaeNdg0BIlz8u8U2GQKWFaIB5YIpAfjpH621tj0BahGSSpoySQKPk2hT\\u002fYpVA4cnPCy3cl0BipzRaYraZQO1AmjTZ85pAoltAV\\u002fDhm0BE0WtClvOcQOo0kSz5gp5AcOCfM5JQoEC1SYDKn4ihQFDuWHZfuKJAxMQwqJu6o0B2GLArQpKkQDNwDlJ9fKVA76hkF9T3pkC14y2nTb6pQCJYm393qq5AZ7k\\u002fH7s+s0Cd+UcT68O4QHXTXsdip79AbjfsdlqXw0BU97t0OBzHQMvYYtRrwslAaGXbGA8Ny0BsmQYpF8zKQBVl7t6sKclAEZvFswGVxkCRkBSXfJbDQHeTGGg9o8BAhIvyHXUGvEBwy2MTWKK3QOJMuRb6G7RAwaTKg39ysUBaU3MBZn+vQJS1JuZpY65AggWgBtfKr0AL6aC9QrixQBbBV3HgLrRAwMz3G86FtkBQQfTlSOu3QPdOd0MxybdAjx3lmG76tUAktMHfK9GyQDLj0wdV261AwkMWg8D8pUBC8w2IFSaeQDbBEiGyUJNAS3GOjyE6h0CXALKrC1Z6QP9bGUALS2xACkLMPo\\u002fxXEAJfzjN81hMQGJyG9R8xDpAEwOdSXGpKEAzXz1QVLUWQGAU8QoA2AVAqOScAxc99z8bS5YOVBfsP+nT9dYml+I\\u002fYN4lJekk2T8d\\u002fCmvvXPQP8niQ1GfRsQ\\u002fjls9YYGotz8rPGZMrVGsPz3wSsrrP6Y\\u002faaQHkJgQLECFOSP0RJczQLnlz2H\\u002fGkNAfPRGIJXKUkDvuLzrsyVhQEwwSJijoGxAuWEGgOvSdUAH07yndWl+QIMO2upiaINAGCeAP7nHhkCjv5NNC8eIQC6Pojy6UIlAG1K83y7ZiEC+0Mse3TCIQEIJKyd7LIhAMVtKpBVXiUA2uAWfSdqLQGFi5ZMmoo9AiVO6gWFLkkAtEs6rqFqVQKrKbydT9phACWLLTEnrnEDkuis5FWSgQCJLg\\u002fK++qFAIa8mGaP7okB\\u002f8cLh\\u002fFijQD6pJMxpQaNACzKQejYLo0BV+hn2ugqjQEdEgEyHbaNAX5txcSEvpECafBWTCiqlQL4FlMkwOKZAzGc\\u002ftGtTp0D8crj\\u002fMK6oQMXcyKTCwqpAcZy0aPZTrkD3m+VJLCmyQMpw8BLFy7ZAu4ppX2I9vUDqeyP0VZjCQOQEzRo14sZApAJp0+7QykAAr5unM6zNQD1rbD1Z685A6z1Kr+hkzkDmfTd1clnMQHoq76rqVMlAP6tyC373xUBhj3huesHCQLcKbiIk8b9AJRm5oBBau0CNYdjtXKq3QLrW04cR2bRAxpLRik4Js0CSS4iIdH2yQE1XD\\u002fSyY7NARqOMC\\u002fqZtUB29dZZtY64QKkFYyAwV7tA5hQUxlr4vEB7jaMQPMG8QJsmnuf1ibpAJzg7sp+4tkBYB2m46w2yQBqMiQBLsapAeYsLmGhsokBL3mlb79qXQC9W\\u002f6OxII1AqbjAtdLdgEASF3w\\u002fRqNyQIILZeTTxGNAjr3mNHxBVEAcuFh01jlEQKc5jJ\\u002f+ATRAc3Jed4dAJEBGKvgaegsWQA1iaz7h4ApAogOdkV0pAkC\\u002fLSG5Y6z5P2EUVFVN3vE\\u002fFzA\\u002fQFyR5z9bSPQsEPvcP8\\u002f+RU\\u002fwyNA\\u002fBSEjYvHkwz8xQ2JXkhO\\u002fP9ub7lX3Py5AMImAN3L4NEDWmejUnjlEQFiOJ+IlslNAYaEXQoPPYUDtN7OhTX1tQDNd1d5oUnZASSMG\\u002fpLzfkDpprqq5bqDQDtaSvoaS4dAFJKVXLXJiUAOmQ+Ow1KLQIPabhmvgYxAC7dmh3kvjkCaBMWWXIeQQJCe49ZdsZJAYzXRJqKAlUB6A67Ix82YQG87aIIcipxAnTCb0HploEATl4lz99GiQDQsfz8dc6VA4Oo7YI78p0DkBuuHGfypQFpSTibVCqtAA4as7pQEq0Bsh1QxkSGqQOnlN6884KhAvCozgt\\u002fMp0DkTzYTWUmnQDpSsJoZc6dAAy0VWvQuqECH96xunU2pQKLjh14jtqpAwxdiBpWKrEAYUFECHkGvQNhsWzNS1LFAEXi19SBktUB59SGZisW6QAdKNcwcEMFAfukN9naHxUDrnoy890LKQDOCZkTYfs5AMz1iS2i40ECboI1oekbRQNh2ZzK929BALLVAhhFKz0Clwuqs0urLQDifQxUNS8hATryyt0TuxEB5ncvBSRHCQI8k\\u002fLiIbL9AGZqZGNWQu0BnXh3btYG4QDvZXPZYcLZAQJJGaD23tUBOh4gcdZi2QMZ\\u002f5ntF9bhAHJxWbQwrvEBbMzeSPzC\\u002fQH\\u002fzo81AdcBAvpU7JPJKwECQjR726Am+QHnijp+lu7lAQ+Te1A6CtEC2JchsVYGuQADW0EN7RaVA1MS9hb\\u002f5m0BWYg4333aRQILzi3du2IRAtyoFni3yd0CX7kzgd6JqQH87\\u002frQR3lxAIlkng3bOTkDrNHRTB4tAQPadxwISmjJAFOGoXVbrJkCb7cawll0fQJre0LXq7hZA9KhZroHeEECiwF8BadIHQLcYT6I8df8\\u002f1\\u002flQHRo+8z8CmkJAyBnmP4bc2gBX7Nk\\u002fp+IpGnof1D8T384oiDsuQDpqFqgj0TRAjG2G2y\\u002fcQ0CNT1l4IihTQPG+7brVLWFAnQO7Oa8+bEDLPoOQ0EZ1QKU3jcg1eX1AjSsIefHlgkBEnOWD+bOGQNLdFZIACopAqMYA7XxEjUCmsi8Fd5GQQGadw0SCN5NAn4okmqTHlkC91cCNrCabQA7g1Uw5AKBAys\\u002fOkIaDokAKvQJPHhilQF6AFhhJ3qdACldwUqL5qkCZRqtydVuuQL7isy5d0LBALOJqNnISskDoSRs6faSyQDZxoQPlY7JAqQx3qltxsUDiv9nqViWwQCqYQb1W1K1AvwScYkMlrEBxdsw6QoWrQC9zsaxK46tA\\u002fL6l4+UArUB\\u002fXtcb26uuQHblsC1kebBAzJXe5B4kskA7O7RMlsO0QC3pV9O147hA1xDn\\u002f977vkBcGj6TaZPDQMWQJPCacMhAP+OpMUd7zUAKN9uqmu7QQJBY1LrbYdJAvX9yC5nU0kCqDJwzkULSQBbQsrzh5NBAGMJ8gRMxzkCTjYs3UnTKQEMZl6ZpEcdA8S\\u002flOEs2xEB3uHd5qdTBQH2hzxpemr9Ad6WGTPswvEBAaDqf56+5QFDg1ui2i7hAJyjGQA0cuUABz\\u002fi240m7QI8ZKogUa75ACxzRlNOywECUmp4jqYfBQGrF4sLbTcFAjkCOwsviv0BU56JRm1+7QFRosHi877VAb5RVUM56sECM1JcwRVqnQCWnZTbUcJ9AnvL0\\u002fwJFlEB+rlUSqTeJQO8xHlXoc35AQ+oaXMTtcUBrtvZMjbNkQKK5wt8WwFdAJ\\u002fgPuA7dS0ALoiDd2nJBQMFSWX6YEDhAeWDlXr8BMkCDgnqTTL8rQCHJRGEh4SRA7vrp7PimHUBX6\\u002f7vJIYTQD9grIvKuAdAXvsYwIH++j\\u002fYK1C64lLvP4rO0X6LKeg\\u002fyUxwie8FLEA2lXKLkCszQJt\\u002fjTepGUJAYlCBu5lOUUANatCUI9NeQDQ4r0s\\u002fOWlANntpngj9ckDPO4NclXd6QK7MIMKTRYFAt9\\u002fCC2l9hUAih0s9Ph6KQF9lA62azY9ATjIYhzSzk0BqeudSV8OYQCQlTi+ADp9At2bSjGgSo0CV\\u002fIxMA7GmQCzaane0J6pA5TzCZEV7rUAGPGhsr3ewQDADKfhtZ7JAllykDt+MtECvIzWhcqi2QD5I2SsQR7hA+iXzA\\u002ffzuEBZNNe8aHW4QJGK\\u002fSSJ77ZAmMc6C5DYtEDzcfVRwcayQGICDe4kNrFAoFn0xx9ksEBudn5A0U2wQJvIv0+iyrBA1bMzK+CysUDIi5UAUAOzQOGLAJI287RABwUrcID0t0CpnA47vZa8QD0vjxTEpcFAA73lUMIMxkCBfgJAuy7LQK+L8n5sMNBAEbfFtlhh0kBPmKcymMHTQHIui1BYFdRAksrP6\\u002fxi00DrM4QKuu3RQEFh6ygiGNBACtkBIux7zEBy43MWOi\\u002fJQN6Dx1V\\u002fZcZACbBC68H\\u002fw0CFUbIj+dXBQMgav5CkxL9A3DRNYMWfvEB22QPSsMu6QJ9XN07ztbpA\\u002f1qsQjRUvEBBYsiN2wC\\u002fQEdbPeub0cBACJowenqJwUDVJIgmMEPBQHyFoDCR1b9Aez7kFut1u0DRdsTmQjm2QKnYHfl1+LBAPz2gFE2lqEBJUt5NpSqhQIMy5HX\\u002fIJdASA4\\u002fzDtSjkAqPItDPGaDQNFoPAIkR3hAQVLh4ETnbUCWoLeCAXhiQHlfoe\\u002fOt1dAY165Bhd6UED9rK65pP5IQC3OIv\\u002fz9ENACaHi+UjIP0DxT8aR5TE4QIqVRII4LzFAbATgU9eCJkCsld2adCIbQAj6yGE2lA5ArePYNDmMAUA1QXPH7+X6Px9nQqLfGihAz6x8VutjMEBLXwX7iqk+QPWgn6b2G01Auv\\u002fQEK3PWUBMnXfmiB5lQHCdVdZBAnBACkxRIPG5dkAoRlwwi7t+QBr2DXmqQIRAq4k64w+iikCe3WnyDbiRQJYLj6o8yZdAF1bdnEmvn0AT+h6AgImkQKIejqHTj6lAXj6vtHBtrkAN0U3dvWuxQHcoYA2FdLNAiSdTJ1OMtUA2RlQWF\\u002fK3QIhpDrgcrrpA3nR2dbNtvUDzqLbdd5O\\u002fQEzpCcCYPcBAVk2eVAbNv0DzgWhVOK+9QMJ\\u002fcd3YubpAULl6KR+2t0CmMI3X+E+1QM\\u002f59rk04rNAz1cvfVJws0BvnFFdHMezQIqMivStrrRATmpMbisYtkAJV9O8zTe4QCeDgtrGgbtAMC9hCa5CwEB7taGd+dTDQKtofHomcMhAi7n55dS0zUCjN44LtHbRQJ8C2Z6hm9NAYTAKrfbk1ECuOlGgPx\\u002fVQC04mAwRWtRA5OpubPbe0kBzKGS0hhHRQBNcvOe3kc5ANaLPd5tpy0Aqleu+MbPIQETXeuU2QcZAZpXXmfrlw0A3spNXGp\\u002fBQH0HHI+SQr9Adm\\u002fQzFp+vECVsHp5dXO7QDbLeSNrKLxABiZFbCwIvkAZXKgYYwbAQBmfJ5BhkMBAR61B5ltDwEDDCuB4SBW+QLRZW4s7MLpATI4syPOLtUDbFgF26+CwQP0sbWHyZ6lAJSrwYxCIokCN9tlBdlyaQCDPLK8qUpJAqeR+bIvdiEBHxyBfKH6AQN+nPbLem3VAZhht6LS7bEAEPoMGLDFkQKHV3NdU2F5AFkUxCKM7WUC5l604SRFVQNsKbnWGGlFA\\u002fM33ELonSkBt2XZkc4RCQGgS9hizFzhAAmIlFobKLECMZRNFrg8gQL18hqaxOhJAqvc1+dXCC0AlyiAo6EUjQDs4xHfNEypAA37IEnw3OEDEtTfDo+ZGQCKQC90sU1RAwLa2p6XEYED5xcqc\\u002f+1pQHZqo5\\u002fyF3NAl\\u002fHceF9ce0BWT8OYin2DQNwry1p\\u002f9ItAZhLbKBgflEAxjfrxn52cQBZtGqAiuKNAHWaJSdPzqUAFSiIJLDKwQFHxLE+fM7NAK79poUvXtUBRJbqB1D24QN3vLYq\\u002fwrpA1CtU3DfBvUCjf95hc6PAQBDFd1wBc8JASZxRRTnmw0ArmQ\\u002ffj47EQLHM365LLsRAoNNMCWrZwkD7fIegye7AQMPzsAux3r1ACkHHm\\u002faWukB2v1XtKoG4QH4jx5Idp7dArIXrPTPJt0D34qF4C524QGa7HEEiBrpAP5LvYxM0vEAoD4gjRZy\\u002fQN618JDUZsJATgdx3t0TxkDaF47kvMXKQF1M7+M2CtBApxSjkM6i0kDcByvEmbzUQGC1pkkh+NVAN6r4BlUo1kAqPhf5EWLVQJzMTq\\u002fH8NNA\\u002f19Mg5g10kCaSi7fboHQQBus0LYt+M1A4u21W7VEy0BAMnVK0LHIQKzvgaAAEMZA6iwswa9hw0B0pK2g0+PAQLi8uSJ6471ArD8T1Dapu0DOinmAYDK7QFH5GbjgA7xAqptwVYg4vUD4oPDjxNq9QOkwrIECQL1APX9ISws7u0A0Sglk9xO4QHobY\\u002fHfU7RAGy5k7pSFsECQNZzYJxSqQMn36QqfGqRAe6SuYqxQnkAm+VuMEUuWQD+kjU1C5I9A\\u002fxGB2kNGhkDeCYh+ePJ+QKCYkfsXKnZA+n3jYH4CcUAb9HbNHTZsQKSRR3xibWhAMeAz2Oz+ZEAhkvPPgDdhQAW7+EueUVpAvnijJE+IUkAXAkV\\u002fpuxHQEttpbPnVDxACyjLcw1KL0B3JSbi144hQHgRLnMJkBpADwG69v7JHECdU63kI2wjQIaojZie\\u002fjFAXqGv+YcRQUBRypuV6aFOQPGreifj2llAkH5sW8HKZEAWjptWBz5wQK9kVvQQIXlAHNVgqNd1g0DJcqzuPBWOQNCSQOfU35ZA1qkx0BDOoEB5EfcitYGnQHa9m0YhCa9AX7cy5mhOs0A8I9qCf8K2QFoexR3XwblAt2eHCi6IvEBTHBsxx5S\\u002fQLt57jFHrsFAMljRy3v1w0DKkuYzmVbGQK7fXXGwSchA18hyRttAyUAQJLVJpevIQFvF1iycX8dAns4Qrv8PxUBYjUKXk5vCQEdfF5gAjsBAFC8+RRdpvkBE4XfWoCy9QKKfaE2gF71AfHCd0\\u002fDJvUCJbk+xtha\\u002fQCDlVVz4k8BAgC4VBxo7wkAUJjH\\u002fIMvEQCQ6h+srdchA3fsX1MUozUBR7fEbBT7RQIHodE\\u002f92dNA574\\u002fYQf51UCgdASEHj3XQEA3aGBCetdAfaNUOgLF1kBTMrefrmbVQH+UYt6dvNNA\\u002ffECfeQS0kApqDlze4zQQERTzAJBRc5Adr9linRxy0Dhz6TcT3DIQMO2XzdtScVAabJKWzs\\u002fwkACZu2aCmO\\u002fQLO3rZwM3btAz3UkxUQYukAY8dQRBrm5QDc4fPmbA7pAQX5oWd8pukAlbYHc8Za5QGuITK8SF7hAdX+kjf7PtUA7So1xihezQP5XKH7\\u002fQ7BA7G9RvY4cq0C0sWHhshymQNUKVl7ok6FA6PeOmBcYm0Dt+J3BKzqUQDBHTUbLko1A0aZDUD\\u002fHhUCYVSnMStWAQOjI+ybKEnxA4Fgnc9nneEByR0\\u002f9onN2QHUWN90No3NAUx0hgBspcEATzp+sLZ5oQNC4++I\\u002fN2FAj2KP53kJVkB1es4FZdpJQL16aRxWQjxA7myMxthbL0CqcW1GC5AnQBwA6sf7ZhRAzGd\\u002fDGqbG0DvYs5Khc8pQB2JxciJ6ThAQLQvKfADR0C6cPySZ05UQD7o+RVWW2FAd6nuYEQzbUCL2AEaCmR4QB6YdzCvL4RAIL80JNFbkEBOyKKM4I+ZQIN8J0MV\\u002fqJAsSk5biKYqkBCGprCUXqxQHa8QujCmrVA9EVeqRBTuUDh3dPNLJe8QABWMO2Sw79AbuODJa68wUCNtWbEYh\\u002fEQInj\\u002fz71CMdAEsSOEN4YykD09XFYIqbMQPO8qDTUA85AvBp7DXLQzUCNC7WnqyTMQGQJpWiaiMlAZsKD\\u002fQO4xkCnv+2WGFjEQBf7T9Tlw8JANP6WpaUCwkAWNcbpOOPBQDhJ7zgBKcJAXAgB0mm0wkA3exsnLZjDQAyWfsY+FcVA0sGUhxR\\u002fx0ChPq5s4hDLQEKx+u5cws9AMmlfYvyW0kBTCinJGEzVQElrDjxdjtdASz34fZj62ECaruzcXF7ZQMb1DncLyNhAxrQ97U9810ABstv95NXVQPRm0+sNINRAQOcsCkt+0kBGqh9mf+vQQBnk\\u002f9RLm85AWLh8\\u002fwwiy0Chb5sKNXfHQIXK2QF43sNAuCCvZbi2wEAvLxQNCZ68QOPJaa9niblAAzI3A5f0t0AtZLWXilC3QE7GHUeF+LZABv7OiMVstkCTcmdHeHC1QGwhmcJABLRADU\\u002foMSxJskDwdFXePGGwQFAblxc8vqxAsdY5AX6TqECSN4UoZlakQMFy1LrnOKBAoiALqm8QmUCpq7osXSKTQMDDMVFl941A04e+vm0GiUAfeXvVuXqGQNP7GlNxAoVAP9XKZ+9zg0CYw3HPBiuBQFSxtNhNPHxA8EdYLs5idUDS3sW9YK1tQFUcUF1L02JAvnLCTALgVUDtTqSB9qlHQK4sd6OX9jlAmMdQDZxgM0Ae3idQp8kMQGFGOxzByhNAx1AfQwkhI0DaRdEutUMzQJuWlT2WxEJACyOzWrChUUACWn3fJh5gQJrmBN5F3mxAWASKqoJTeUDQtKsiS56FQIlfp9OUw5FAEOqyoDfMm0A\\u002fUjWJzYekQBMPLcpjeqxA8n2BWjeHskDMUpC1HLW2QJyyVWxBfLpAQuAxoB7tvUDUYm7P9sLAQA4ZmfjMAMNANoLqj0v1xUCEVUE9+ZLJQOrUMZbHX81ASD+CYL9J0EAgsDVVUTPRQOS7MQ99NdFAb9gZ7w9f0ECdl1OUcPvNQIca0Wea98pAZDnhX29syEDV0YrBlL7GQC6borGP8sVAVTi0bg3MxUBgkCrCOwHGQPZ+5ua4acZAf0YaURAYx0CXvExGplbIQJuBilTKicpAE\\u002fGCiEgAzkA6YsuCJGHRQM18ILb1ONRAK\\u002fm3e2Qn10Cx5p3cbrHZQIHxT0KkZttADlLbAKcG3EDJU8czHpXbQCVGtxOZUdpA2jsFuW+X2EDNXt4nnrbWQOy5en5n2dRA1pdhz9YB00BZELudNxzRQNjzcewtMs5A2HYkMjr\\u002fyUCMXmJaVN\\u002fFQJWQvd1eKsJApgzko99WvkBP6HxrcQm6QN3xZrA3ULdAs2\\u002fXGXDHtUCgV0aLUfG0QBHBvgMMYLRAN4ckQL\\u002fLs0Dr5lxT8hCzQDGvwZlMILJAjg1BzUzusEDSjpjkpN2uQK0y\\u002fVKaPKtA5VM7cDwfp0Bsd\\u002fbzGOOiQFYZWMeZEJ5Aghkc3jcGmEDEwblcghmUQKbUrQcEGpJAQ42Og31PkUCbBekFqcyQQGz6f2\\u002fWnY9ApDwRi9T8i0B1Ccp+p+2GQJ\\u002fQ5SiqPoFAc85lW0q6d0CNLjx4h9NtQJfAuB5sKWFARRGdI2tfUkAWLv1ZDO5DQOqYzKIOjD1AacPdvLTFBkAy+0ICVEEQQFLUIC2+wyBAVuYcYgH\\u002fMUAk36+WPJ9CQEj7laYRcFJA0x2qVwmGYUCQbzh5AQhwQK+BXpGbO3xA\\u002fsX0uB7bh0A4z+oeMT6TQO5tGrb5dZ1AAQOdYcVKpUDKVaGBn\\u002fesQE\\u002f4jM0+jbJAsv6uS6l9tkBsZxEUAx+6QKtUxzHYo71AagIM0rnTwEC13cd71XjDQF4ZH6tEAcdAX6PCQtFOy0AzyjrPRM\\u002fPQH++XELS0NFAZKiMayL20kDLHtfhJx3TQLBsAeiiV9JAh07WsLL60EBb3GseqPLOQDwOcoy1bcxA9esxDS7PykDaiZUWTxbKQHQPj32J+8lAkpgiAlgpykBlHx+\\u002f93DKQBg5PTo96MpASyz15P7ny0Bvg3SR4+vNQBjSMDF1rtBAYqLbYOIr00AjzbmPhULWQOb7p6rni9lA6qo9g+d93EA3fJGmFJTeQBZg65gZe99AtTjYuwAq30DwblIsqdzdQDGPFh3z8ttADT8GBtLG2UBDAFxtxo7XQLbe3iLDWNVADlKcrooa00DLch7y4snQQP3rCWzI28xAdb0Agn9DyEB2iJ+TxBXEQJTjXkLtlMBAQgzxTfbJu0B2ziYrJwq4QOnrTHZ2rbVAVK4Jy71XtECEK7DtqaizQAAvOUVXTLNAoUp5WmT\\u002fskCo6kpjEYuyQFE9wn3\\u002fwbFA7Kmc7rOEsEBMsL6unputQM5AbPqmeqlA5cmIYasupUC8ibbmSGGhQHUiXT\\u002fnPZ1AfKbfQzA8mkBIe3ytYlKZQFOoCu7fbplAAGS7lqBPmUC144LlmAOYQPQHx396O5VACIxppgdLkUAYgcTTT86JQDuipM9amIFAn4hGRejodUDCgzGjwvdoQAgqjoqbc1pA+ONbqT5eTEAkzypdgeJEQFK62WUrrAdAaI54gA2sEUDEYBiGW3EjQGv3PzOM5DVAAAB9V8RdR0Cijgf8fnJXQMswxY0BLWZAX7db7b3Yc0AwOCuefN6AQNFXQT1ATItAyZ3L7PcIlUDd5Yc019WeQDKELvuZc6VA6Rjzs4tJrEDxShwSbrKxQIo2iRZxJLVAQdBRpxtyuEDZXYErD\\u002fC7QCwJ8K2IJsBAnIj9VX0nw0ANXyhVkS\\u002fHQNb1pQfLDsxAo\\u002fZgVTOQ0EAmgwJRhLrSQNpgA6jJFdRAM3mnXHhn1EBHy9rF78PTQMLcBuE3hNJA8oXIdNMe0UBxdJKLkvHPQM56XYkXi85Ak0rwmg0DzkCuflRfLwnOQBIB1oudQM5AW\\u002fiUHr94zkBUwvBBkc7OQHRjXQlVrM9ADZD+TnPT0EDewsutyaDSQDq0xdB7VdVAvLkK7S3G2EA5w7hFI4LcQJSdtM0l7N9AXxKOb5U04UCG1fOXLsnhQACwwknAqeFAAqOZI5nz4EBJURsts7TfQKDe+Wn2It1ApK5OiO932kAnv02Dl9DXQEoltZJ9LtVAeYvrR2yN0kBn4FhW4ObPQO+uq8OX6cpA4d4AWapbxkAzYw5bXXLCQILES0uOoL5ADsb+m979uUBA4+q1G+C2QA6\\u002fy0M9BrVAp2KXui0ZtEASXhKZv7mzQP3pGYaMi7NAIb3ZUWY8s0DeIbqkQIuyQEPd\\u002f5ktUrFAwhiEuzklr0Dt9TjlrPiqQJjg+EXRyqZAawG1MAlfo0CWkz5km0WhQPkpfWaInaBAmuHnyKgCoUDHP0A9CbShQI9aHNho4qFAuDDe4woCoUAl3aqCT\\u002fOdQCAg5nmTN5hAPn4TZcjpkUBNSjzlkDOIQN\\u002fi7Akv2X1ArV79\\u002fh\\u002fXcEA0WZFQvqdhQI6VtBAruFJAvw3vT15fS0BoV2\\u002fCOD0QQAA6jjoazBhAnQJ0eZcBLEAWOZCB4OM\\u002fQAALJ8Gq+lBARAJT8TDHYEBpZsoPFdhuQBEKxNH+fXpAibWpxL5ghUDfOL8SZUyQQLiUvrOSmJdAHM2aaqJGoECD4L1nB3GlQNU6BKmDBKtARFzmNrZVsED3sXNA+hqzQOyoC3LM8bVA6CE9WplLuUAj6r5JZt69QC\\u002fsFa5PMMJAGCvMwPWOxkCdxtDN+cvLQD2K+5ltnNBA8Or27gXy0kAI1RWd53fUQOhmZur69dRAehaTog2C1ECP\\u002f8E1ynTTQO\\u002fDGm+6QtJAthjtQAFO0UBikMr1AsfQQMqWxx+Qp9BAHrgc9dzF0EC\\u002fvtcTTfPQQGlQWCubGdFAWViG7j1M0UAATk3sZsjRQLSn+UR34tJAxukmOQPl1ED3iT5pYevXQNowmnw0xttA9ydKOx\\u002f430CGezkGt+jhQJOA2BbtUeNAhDqNJOT840B0KRRA6tvjQDW4GrDCDONAQc1YGd7G4UC9LkCQ9kPgQF70zkuPXd1A+a\\u002flzrg72kAsbbQEUTHXQPiQH\\u002f0kQdRARIyTDwFw0UCq+PPL05PNQFIoLJaMwMhA2cV7r6WKxEAvT8A+ww\\u002fBQPYIYdtvwLxA\\u002fIsvEYL0uEBPra8FtY+2QK4pa8TAQbVAewpk3mSjtEAw6g7PTEi0QLSLFZsB0LNAYZ\\u002f4jc7zskDxeEPmvJKxQDKoLg9Ddq9AwgqB9T1Yq0AX1Ocl+o6nQJ5XNPXU46RAmQbpRV\\u002fVo0CH1cgAxlikQEjZyOcqz6VAc8U7bSA7p0C1d6KHVqKnQGlqt3qvbqZAUqf78ISgo0CPJdxlLXqfQNPzztAiEpdAdMmUfJzfjkD1er2kvNqCQDRMcRiuEHVAFobZaaHaZUCCg3plWOhWQPR1RUnBoVBACtahna4rGUA48d\\u002fJVTwjQM0WSzTQqjVAaRYGWqx0SEAV3FB9FqZZQLOPfChSyWhAkIpk6DsadkCVfpM3lECCQMz\\u002fTO8sDoxAe8lmC6IwlEB3XdcqwWabQBqsSbCIqaFAsqwnIUjIpUBwZXcLDOGpQNi9VCvq3K1A11faBe7vsEAKymn2kzqzQANteLyoU7ZAA37x1VzsukDP7ZzrMNHAQHqQT6d5UcVA0QbZJvOtykBTHjlgIRzQQFv6VS79g9JAHeglfMIl1ED0NKr8YM3UQC2Qp75skNRAwyk6TSPD00BtlBLXVNPSQLE63Q6wHNJAiBLK+n7L0UCxqJnOvdjRQESasYfIHNJAX6T7Nh9t0kDmn5eudbjSQGhDYY9aF9NAk+njVqvL00AQwvE2LC3VQA9ZeoAah9dAg8yMiTbx2kAX6TSWyDPfQG5M+2zS4uFAF\\u002fF7Z1v140BXr6bAanXlQJW3cKJcJeZAoqnQgrn25UDPzSDlHQjlQMOu221Pk+NAxAoFtTTW4UCOrMmJzwHgQOvbep6taNxAeVCBTsH42ECxhQlbDL\\u002fVQMUS88\\u002fkv9JAMu9bfK79z0AaSTLvdQLLQOeeNeBYnsZABqoY+ejlwkB7j2aK+te\\u002fQMD0zOpgb7tAIzP7avB2uECJ2\\u002fsmH6a2QFd2TisClLVAbmmedAjPtEDmhNw+7fSzQMqTzjKPxLJAGbquBTApsUBsCjjhOX+uQFkcGj0ipKpA9cuoau+Kp0DCZXZeeO6lQFX4ifWLKaZAxB0x51z8p0BtlpcXIIuqQPyKNiMSoqxAc4cg4mwnrUBtG9FTY4erQLp+LR1j5adAXGAFIvX8okBXZQhzkJGbQOSl99VYRJJAnxpWz8IXhkA\\u002flXSKIHB4QLl+YWeEFmlAC0zYrVz\\u002fWUAbOcdcJ75SQESK5yGjkCNAzl0\\u002f5CqqLUCMOCudlHxAQNEvDIHTV1JA\\u002f3M+KGfrYkCo2L2uTetxQLDJFHbCLX9A64gXLGH5iECj1QmyCHySQKF5BqA+aZlANd6M7nlUoECmojbgYMqjQLuFpuRB2qZAzqcwj3p1qUBT9e2\\u002fvsirQMfrDEhJSK5Aglm+vInesECXXO1gHaKzQP3FNzp0EbhA+jUcfE2tvkBXBQBBacTDQJMUCLdOBclAxoFvGsZzzkCscEjrapzRQIdSvcVNStNAmhZZtCgU1ECA18XI2A3UQH1uk9g\\u002fhNNAphcXx0rb0kACWftFL2bSQMK\\u002f\\u002fxNJTdJAl9UFLVyL0kD5AXHoL\\u002f\\u002fSQP0ijWqhhtNAY5MlbJEX1EDl1wLOF87UQLpOJCO+6tVAZIvljhG\\u002f10B811srNIzaQP5PHxDfXd5AUhfctZx44UCZLwJZ7NrjQM99g7EM9+VAILFavqhy50AIjxtT3hLoQFWVT54oy+dAA9FpQHa65kCroSCbIBrlQDdoqLjsKONAl4bVwq8b4UD5iqaAlC7eQCMrr9pVY9pAbLW+ktDq1kCnGb9LWcrTQH7aKguD\\u002fdBAo970YKb5zEB2Jl1914jIQHGWIgE2r8RAWXe8Nn59wUCOXYxfkgG+QNA1LmDXZ7pAhZ3joZ3yt0D6bCMdN0G2QPiqy6lR6LRAG78\\u002fKf6Ns0B3tJycHP2xQPFxh1p\\u002fLrBASntrkQuTrEAbWO5fCjepQKzZgwV0BadArR+kyfSYpkASGaYBHx6oQCsIhFIGIKtArN1AaEyQrkDRS6ccQYuwQCnUmutGxrBAylw7Y\\u002fpur0CXBGz1oQirQDPJLxiCRaVA+3WlEc6TnkC0AFwWLQ+UQI82WhcOBYhAwtNlqK5NekAYUZ0vM7hqQHjVEUYUX1tATQLETu2XU0AngaRX0CQtQGKUNrkg2DVAGXbESrTeR0AhV74cACVaQD24GR+rimpAKDvVu6u1eEAZkYR7TxWFQMa7Tx1DgpBAeV3\\u002fA5bIl0C5Xe7dE6OfQPPbX4syiaNAN77h6HqUpkBW1mPGX7SoQC\\u002fWL9W6\\u002fqlA1ebbh0nhqkBWF8wNKBSsQC7jm8b3ia5AWo1+XsOusUA5Lvo+vtS1QAKyh8HVGrxAe+K+E5FAwkD74JfpJTnHQPQlmog+YMxAxiq3c2h60EDJFEprgSXSQPAmhwbKBtNAdw+FKJsv00DCi5cWJuTSQNj5T5MtfdJAas1Z1AFF0kDUm64oN2HSQC6Pkb8\\u002f0dJAjg2Hizp+00BmEVVgv1HUQAX26hK2SdVAQCZjeYSC1kAV7aUmrjPYQMsdoJe8ndpAqw2GL93s3UBngWzz3AzhQAaf0jsha+NAOP7GDTnJ5UA5p7dWGs7nQPfRZq07KOlARj3TzKqk6UD5Az+8wzrpQHOvZCyMCOhA4OKpas1D5kBy1\\u002fQutyjkQNPE9eJd7eFAE8JvmBd430BH5oRz4mPbQExm4llkvtdAI33EHTOP1EAwzfMqU8rRQBXUUsIMtc5Aj+G8VotbykAO2C\\u002fZdHvGQFKKw6VMH8NACeMc7xpZwEBqt93naV28QEWsdNRfHblAA6FUIr2mtkCT3o0Esp+0QA67MP01vbJAqEsTmcjWsEC2MjFEitutQHK92DDzVapAK6om8f2np0Cddk4dWG2mQIkmAjemFKdAPn3RUMCbqUB\\u002fs9hHtWStQNeMqH+HorBA3P9BuVrtsUBdoc9kQgWyQFPoV4ahtrBAPOwijx1yrECXDaLWzyWmQJQTzQ3hgZ9A9x7HG2V1lECHHjPAKkCIQMzDY7GdSXpAFFHvLt9rakCp48u5MsFaQE60f1zHA1NAGsqvDByYNECqfj5sUXI+QCcUQfeLTVBA0ahKMpiNYUCbGWJ8AopxQLWZlAuGEoBAnMIuv5P6ikDc8xoqW8CUQBU\\u002fZa90TZ1AN61aNhsLo0BN0PYeJuOmQGNlTNGvnqlAEgkX02wEq0C+rRopWFCrQDQOqp4hKKtAD44+bap0q0BzWSW82DqtQAFz8Fr\\u002fv7BA67YotzCWtEDnAnRuw2m6QAmUQf1\\u002fF8FAzcXUVVSuxUCwR+2K6XPKQOwVwVhBvs5AKfwer8T60EB7UZVT5+bRQEWxeCtiMtJA2y3a3N8Y0kBj0FSOWejRQEwTuAkg49FAPmdOaMst0kB\\u002f4kb6Lc\\u002fSQG5m4lSmvdNAdPpG1U\\u002fw1ECqQUb3PGzWQBGUIi6aSdhAMmVJdeCu2kCrGjSh18HdQAGFbRiqyOBAHDL6KZr\\u002f4kD7rwzqjFrlQGJN1hE8ludAy9NPs4Vl6UAwdMDXUoXqQDThAOIjzupAuhyQ3gE86kAbjMuEquroQJp9d2rpCedAwq+VReLQ5EAd3Awh\\u002fnTiQH5r\\u002foO9JOBAxFXZQgcI3ECqFk7XxVDYQGVUvwmILdVAwrtQwzOK0kCxTHD4LELQQOHLY9+RZcxADR8SiTOTyEAlIy0hJQ\\u002fFQDSOQbIj8sFAPN3s8q+lvkCqFKg0GWi6QMbIH\\u002f2\\u002fBLdAbRm\\u002fdlI9tEBh8uWbF9ixQHTl9OE6aK9A0yhxS1mhq0BBSTkWw5WoQGTejP2rqKZAM9kztU5EpkDFTJuE36anQE7TypssqapAqCvFhNKarkDW85RRBiqxQIyI2SMgRLJAJNMrX8oeskByIFhixJewQG+UV43X5qtAy9p+CBJ6pUBQVR8wSDieQNfDp79aapNABXRWbsPGhkDbd4UZhm94QAwO+JZJTGhAEXpQELRRWEBVNVXbgChRQPOwhDOtvztAJHpNlH0pREACVC5PDBBVQHqtwWa9OWZA3hvnSWzSdUCsqbxKdayDQOJDDUHjQJBAqt873cKcmEANgGLa9BihQLQcwoBS2KVAy5LJMVjGqUAq4mVuF0msQJCZrJcuN61AlV4P4aTxrEDPe8wx6UesQGGuyZAcPKxAjs9iUbHMrUA5btSnI+ywQFlm0m0NiLRA2\\u002fTJ1x7tuUCpJANR5oTAQMO4aNTMssRALLa0+8kMyUBmWxxu6v7MQLzJZQgQAtBAj7e8Qr7s0ED7uf5bbEzRQDu9oa5jVdFAIqqYo1tM0UBkyus2hG3RQEBa6QOm3tFAgQogiB6w0kDyRFOa\\u002febTQJLQdKCoh9VAfgO2WrOb10BMrQQ81jHaQJ0aEBGTWN1A9HyHHR6K4EA7G9MOIqniQOE03yMk7+RAhoA0q4Ev50BMY0CG8i\\u002fpQFhklaSNs+pAz6gXq7OI60BzivEOh5TrQPsNaPF41+pAaGX2bxlq6UClH+KoUnXnQO9AARh8KuVA08uqBIK94kCx\\u002fL4jDmDgQJePHxgCd9xAHTvfeGHT2ECgSUVnLt\\u002fVQFD55LQVfNNAeLNR3rpz0UCbAP1CwSDPQBswV1\\u002fMY8tAZdm94mOqx0DrZ4LAKRnEQJRSaf5s4MBAAuNpLgVCvEDMJaDJZ8O3QLoF39rUKbRAlRxZ\\u002fCpLsUCo9Y09rwiuQLl25StAiKpAQuw3jIYZqEDPRJ1eut+mQC+G0XfmB6dAjP1\\u002fOiyjqEAeCp7XNXerQJbY+grM3a5ArpcYWUvpsEAeVP0RdZ2xQCmkxzwwJ7FA7YcxhIvqrkAAvJ\\u002frkKGpQAV0fvf\\u002feqNAUjTx8CMXm0CghAKqRjaRQAUTY8DD+YNAlpsZnng0dUCWnK+oTdtkQHzzmQoWoVRAov+BUNzkTEB7VbI7bA1CQHYYGfCrq0lAa4WxF+r6WUAAzXeJwLpqQBH8qt+nsXlAiFS4mLm7hkBKDCmzL3aSQBc+vCFog5tABmCXCMjTokCXDXl4RbenQBxwVyYQnqtAijASjKf3rUDbat7FJ7WuQE\\u002fZhpE6Wa5AfaokwcvLrUAQsm8JDA+uQOak6TxkAbBAuKyd9LwnskBa3CsO3bW1QFDQeJfnyrpAlFGd7tapwEDDUJPsVHXEQJO6RSrPZchAOLs1FYL7y0BdgbHQXsLOQJic528CQNBAk95YNOql0EDlWIz2XcHQQGdsl24L0NBATzet4pEK0UAOrbG6TZrRQIRx1l2emtJAQlL+lwof1EArUuKE6TbWQMRKl1YN7NhAVXnihnc+3EAY7QIb+Q\\u002fgQBmIWORsOOJAD85aYiZ\\u002f5EAgpqPDLsHmQGSMk4rd0+hApoHwbh2J6kBAUOqKDbbrQHMkqTeTOuxAJ9aBfmIH7EAgKm5WUSDrQP0T1++pmulAfxfwVUiZ50D+2pk\\u002fs0jlQKK8rV2U2+JA2B+wg8mF4ED+ErWmkejcQM+fQQjThdlAQ8KnjjPp1kB0db9qmebUQLYiLSYWM9NAszLy0AmE0UC5rvxr6VbPQJ66xxl8SMtAUL6thbwVx0ArsxuscRPDQCWQELp2Fb9Awok38fpGuUAk+5e7nce0QJ4bGXVid7FAQwJ5ddxIrkDTJBpSIjqrQLbwRE+mc6lADz69DtbEqEDs5EM2aRipQOJATZezXKpA4Rrdy7darEAY0Uv8FpOuQHWdnCE0ILBAj+TDEKVGsEBbAYMRPOmuQBnLOo2gTKtAQpVbVLxBpkBpTuwecKygQHCPl6i645ZABjPnLba9jEACdDmBhn2AQF826GNCUHFAj0ZCsMPWYECNqIfgYXRQQGALjc4F30ZAe3TATOcVR0B2Zm7HwPZPQKDANXQNEl9AliZLDxvxbkDRuK\\u002fzU\\u002fN8QKuF8Qd8BYlAKTM2RWDnk0CgRb4dpB+dQEMoMiRlnKNA6zmmmXJeqEA2qsD1JBasQM6zh8Y\\u002fUK5AXdJojf0hr0BYeqXP6CqvQI92s8\\u002f0Wa9AIe9z0uZMsEBwnulAA8uxQL7HVxuuWbRAou8kKPEUuECrkKI5mAm9QF9oELWdksFARM\\u002fBpgYLxUApf0NOQpzIQFU3eDy92MtAjdISZ2xbzkD0HRXjn\\u002fHPQFcLWgOmWNBAR\\u002fUHZoR30EAbJtNaso7QQKTsubRJ1tBAbgd1QqZ90UCx3NFEyqvSQFldxYXOgNRAj0ssLsIS10AcXTt5WWbaQN92GYI3aN5AZI9Wzq914UBHhQanANfjQGSQNW1BMeZA4w5eyJ1c6EB2z\\u002fL0\\u002fjPqQMA8bdNdl+tA2fOU1Vlt7ECyRL1YAaXsQA4A7ZLsN+xA1iQlHesr60ArkO7KjZPpQGlFogefjedARQ\\u002f4JuZD5UAkKZfLoOjiQGZwzLnusOBAZKND4fCV3UDTa\\u002fPVMqPaQJmmGU\\u002fShdhAUBw6DKv\\u002f1kBcNLpUsK7VQERwouDNMtRAolG3mMxR0kDqXKG0ZgjQQNwiyBkWBctArRC+4sYExkDhfGBwE4rBQNTw8uANxbtAYI9PcktPtkAPYvnzXJSyQGR8bpTrRbBAchZxmhQGrkA67P7SS92sQAezRW1HgaxA8ejnSBmdrEChSUTTowutQIa1Rr\\u002fHr61AIIJFRY5HrkDUHdM2xVyuQIGxiGuiZa1AVkPkAZ4Fq0DQyRTAr0SnQO6m+6Bzl6JARi6rStBkm0CnXmYAcIiSQB7rGXgu+IZARMb8vUcKekDqYOyKHAZrQKktZPrw+VlAPhQ0DMwQSUBnIv2MDUhBQFi4zC74iE1AEEj\\u002ftxzWU0A\\u002fGRxhQ19iQIQQeQf\\u002fkHFAO0mEX7LCf0DTt4mpZaOKQGXn\\u002f5N7o5RAmpzmtKyDnUC8VgtqPX6jQHXSDcPX26dALGohq6U9q0Ab3+9tFl+tQLAsHAVKga5ANG8GTS5cr0Ah7r6BqmuwQHBDhROj2bFAZeRutkkrtECSBV4Ln2u3QO8cyYY4lbtA6v8H+1ZRwEAKjNUeh0HDQB6t7lnie8ZAxs8tYra9yUB6srRLg6nMQJeJgmcQ585Asz4V\\u002fhIl0ECU5BOfDnTQQCxkVpJdiNBA76b4etOZ0EBjabQ5SuPQQFjH7zdFnNFAZk9\\u002frRP30kDTUq4YCx3VQF9ZDaxzJdhAjSvYHzUK3EBjieSei1DgQPdFW9lzz+JAxIu9emtS5UAODG\\u002fxl6nnQHIep3\\u002ftrOlAVDOzJTRA60DUtm0kJlLsQLAUhuXY2OxAK4rXydvO7EDK181YOzPsQDG\\u002fQCkaDOtAOxwWc+5p6UCn\\u002fXsik2nnQLc3Lkc\\u002fNOVA3P\\u002fnm6784kC0+sYyW\\u002fjgQCc9bP9xqN5A1UnvjDJO3ECkAAo9087aQOm3KAc71tlA8\\u002flprsDp2ECuSMHOApXXQFeC8XckldVAzMspDuft0kA6VUb7hsDPQOFaYpN6lclAEgZuph0LxECirHN6fyq\\u002fQMUXO88ou7hA\\u002fVhRWOqftEAFWWGvTGGyQLwbFiFjZLFAzRncUkoQsUCYQcex5+ywQHKnmdxxtbBAmJe0P45WsEBhi\\u002fWJFa+vQK5Bg126d65AM\\u002fbzvcfhrEANN94vy6eqQPzBBxwqladABD85Ffq1o0C6kNw30MSeQNg1V\\u002fqXPZZA+\\u002fZ0noacjUCTIQNlCxeCQPwwX9DnPXRAAufpvES\\u002fZEBLFgMKT7JTQMIBVDJNwUJA8vPohLSkOUC3o8xT8BpTQNIlnLBK5FhAhMqbhGXjZUC9dhUEDfpzQI0reOPrVIFA\\u002fIDgxI8DjECxIDDvFP6UQD3cGu8MJ51AdA9k3+7IokAUCG5UAZGmQKz45FMOgalA\\u002f19dPuuPq0DHuyIqSTCtQHIMohriL69ASqjszREzsUACCY3EI6+zQN6Zez7fF7dAXD4WTVZNu0C\\u002fh6rCWRPAQOl\\u002fPcarxcJA8s+n0Eu1xUCeRcblUcvIQJh1PqEK0stA1eAVUWd5zkBslwopyznQQMVzsp\\u002fbzNBARGtyyuEA0UAnOXDUZf3QQPQG3T7u+9BAvcVjVjE90UAbbXkuVwLSQGwBSzueh9NAVEmPBlD71UD62i5BGnDZQALN\\u002f6EP0N1AK6Xghhxs4UAmTiVH7BDkQHK32+8VnOZA4kCHY3nZ6EAqBhTJV6XqQG\\u002fF+68M7+tAWKDZwhS07EB91X55fPfsQMGJDuaLu+xAOO+yIlEB7ECrSuShYc3qQFaxy70dLulAMLRL5L0\\u002f50BfbX7bYizlQF2zHAXrJ+NANHVavH1n4UDU0omnABXgQLKRCtqcgd5AuNTehOau3UBOe2PCNkXdQBNkTv5vsdxAoeOwJ\\u002fdt20DqZyrZiDTZQC74xILWFdZAtmlf8bZs0kBaag3U\\u002f2zNQFjbAY6rzMZAQNKRzuqNwUA5pDYVD8i7QIr+i1sXX7dA5AYSNQ43tUBWXVc1FXe0QHwqTg9VTLRA3W4rnHUVtEA6frllr3yzQHxDwq1wdrJA28NPioEksUC818iNSluvQMc8NiGjQqxAHnRHuP\\u002frqECvqZhTtDqlQNMqC22tNqFAZi\\u002fDUR86mkBlwQ950ZWSQJIgF86fVIhAt27vfGpJfUCyuHH0cSlwQDq4SyjJWWBAla50MZWkTkAfXPVA98U8QJHbOKGqfjNAD8bYLC\\u002f0WECpO6NhuqdfQFSvQRHBhGpA+ZUiYnYZd0BvCJKw1S6DQBPv2qwNvo1ArBd8NZVulUDHcmd7crecQG\\u002f+c72f8KFAQ+tCw9cLpUDSIuL4mH2nQA6dLdwnf6lAFaeMTFK4q0DH+FZklgyvQAlnHKZ+ILJARd\\u002fjT1PUtUCW6QWL0YS6QAfHkgZf5r9AyqwOdwDWwkDn79aALdTFQL5e1Bji48hA2EA01dnzy0CPOiU1ztfOQBjfSeTHpNBA2yKDSJ2B0UCVRLqQdPHRQB7wF7fJANJAiCyTs0DZ0UCTw+CsIrnRQBySzI1t6dFAynuhU1q10kDIaKfKzmDUQD4bA5GNGtdALxa0cqXr2kAGC\\u002feS7arfQDnuH6BTfuJAWcipaUEx5UDcTlDmgqznQDhYjdHyu+lA2hE52rxD60BneCg8tkDsQKeFsS7XvuxAkbCaMQLM7ECGePKDJG\\u002fsQE68ckJUqOtAM8s0gn546kAe+kEi9uroQLMGgtVKG+dA5fF\\u002fePY05UC0LBotUW3jQK4OkVoA+eFAhZKRlf\\u002f94EAgAQTG44TgQDqK0Bonb+BAtIZ6tNV54EAbaQjqL1DgQLm+yPBzUt9Ahuany5nG3EDAHIn1IiDZQENO9J7B1tRAF8qYmP+F0ED7pUMUfXDJQBq+tIbYh8NAj3k\\u002fr60Kv0DCkQFC3G+6QPrEgtRrX7hAb\\u002f9sx5rHt0CdbltZ2qa3QMtLCm9OQLdAoG1EXjE5tkAX69v0S5S0QOOfbn\\u002f\\u002fjLJAbsQteYNlsEBT2Zr48IasQFxzijKPVahA6CeCB74ipEDAyU8BJ96fQCiM7EumyZdAqvfeB82QkEAwpJFc7lqFQEI+AslyWHlA8LHycMSaa0B8Gx0435FbQBEoUKrAf0lAH6vOOGCbN0DsbccBJLIvQGYkiq8eP2BA5Ba\\u002fjY8wZEAgVDsQX0JwQC+w3sfPM3tA0geST9CyhUB5ASOD8SaQQFyQy+3pWpZASN3zeF7OnEDqa4CcDl6hQDkS2Cx0zaNA3wDcaCTCpUCzuevhDrmnQNfzshsUkKpA1czMlPxBr0AB7EaUNUKzQPXJVJucO7hAy0bEy6hKvkAURbVLl33CQBzEKXLv7sVAWIk8dMBXyUCaxJp5EqrMQMi+Rtux1c9ADUzAbTFZ0UCu8IxAq3\\u002fSQCbfWOM8PtNAWd92weqG00COFOAUiWnTQFbdYpxzFNNAVhVBDB\\u002fN0kCcMZMM9OXSQDHNLt1cs9NAhYhlzBV+1UDxSpcnm3HYQHCZ86tBitxAMaVCX1HF4EBCAAkXB4DjQOoUCkKMLOZAXrghVsaE6ECSea3qgVfqQKMcAgUAk+tAfkMornVC7ECKr6bA63\\u002fsQDPIa5ypYuxA9o09ijb060D6R5M+bTHrQDPaxlxlFepAqpoez5am6EAy8yZjv\\u002f7mQMAbpedPSuVANa7EeVvA40DpiXiMgZXiQEfa9FkZ7OFAhOLsfeDE4UDCUUDwNfbhQHOnm2I4MeJAg1px1u0X4kD3ovGbQl7hQEeKK12jzt9ALeLeFnmg20BKx\\u002fz3scbWQBKdPXW79tFAbZCAWwyWy0CSxbTJSDzFQB\\u002fAr\\u002fQvCMFAipd2gdRkvUB4hKYGa2S7QGYhdU5V07pAbB0EuVaVukBIXn5mHuW5QET4ipmZb7hAL8WtJxtJtkA30AWdTMCzQGNH3+YGJrFAn3JX9QFPrUBzdjGo4JSoQOzKaxi+BqRA4\\u002fuVvEI\\u002fn0BXE\\u002fO64gSXQJ7XhdTKqY9AhKymfBYqhEAvMinnm6Z3QGhQ4+BhdWlAA3cDwAAhWUCpiFggefVGQPtMwzhw+DRA0u6YzvnnK0CX9cOYWLxkQPq++YljZWlAXkGisT3ec0DSCfXuhhyAQNLiWK1q4ohAXm6xmzrmkUDBns4SU+WXQAqhZ7EnrZ1AlZWa+gdEoUCLbf+55hijQMCcL+dPmqRA9LwQs3eEpkBpGaB9eeipQOhL2nIM2a9Af5S\\u002fJvWAtEDm3hmKJ626QO3R0g8sCMFA2QDQA8YKxUBwPwh5TBjJQG4x2d5MBM1Avc9NPKpc0EBOA8pgFRHSQKHIT9D3idNAGQeLtjyn1EAdBoVaLUvVQBBwvyLva9VA9AAIwtYe1UCyqbyYCZnUQPLWYBwmKNRA\\u002fTw+sF0n1EClBIjnLfLUQPEz3A9r09ZAjSxgMSvx2UAMchgamjreQCb8Iefgr+FAWgcmHTht5EBZV\\u002fJJLwXnQJCyOGVEL+lAWmIfboe+6kDRqOrXxavrQDFeHcJLEOxAw2Wr73UT7ECoMEMU\\u002f9TrQJRXzi\\u002foX+tAmM9lTVys6kBzSblCxq3pQC1ai4fKY+hAdRlQScfk5kBdXAYIhl3lQHtISZBEB+RAFojz0LcX40D3iOr4wq7iQF7ob35ExuJAlabB8oQq40B5axcoZoLjQJdn2RZuaeNA8CQ5SsqT4kA0Ab+vHuzgQH5k2pfBN91Apl33jD7010D7Oe4lBNXSQHIYtu1I9cxARK8dMiJ2xkCAKro6zj7CQE1\\u002ftOgT4L9ADsulEVHdvUD6HTpDuCa9QGO+rtFPn7xAAj7BEm2Ou0DLdaPOobO5QFdH49TxM7dAKZBZY\\u002fpmtEC\\u002fj5EyYZ2xQLfi2iIn\\u002fK1A++EivBoPqUCG0Ic+Q1SkQL00zQLXi59AP+ziysYUl0BG\\u002fwIY0H6PQPOR3+Ai4YNA0N2FOSgYd0AwZZtOZZxoQCoY98AhClhA\\u002fH+0TYm4RUD8SC1aGJgzQGcEwqee2ylAdkMunouMaUCO8hbm0fpuQHa22KTCv3dApVJbC3vVgkA4tPuozGeMQOG0DT9l6JNAKdmgNh\\u002fVmUDqUe8ToB6fQCayyEo4jaFASfqSiyzfokCsDJfMC\\u002fqjQMIJ0MBjz6VAQ+Qx0mSbqUBrpKr+4EKwQCQdta+umrVAWnf6z5nDvECKYChx36TCQKlrGL7cQcdANrPrzvDoy0CYEr5H7THQQG\\u002fYkFAhSdJAkBrBrbgp1EBqa8yjrLnVQNYDwAhk19ZAF4lOaqFn10A2EP+9O2bXQHRL7iS879ZAyMwHEKpA1kCWnM\\u002f3q63VQBiOU9QkmNVAHNQUbKZf1kBtx0NxGU\\u002fYQOma+vw6h9tA\\u002fP7K52\\u002fs30DKw\\u002fiGIZDiQHQAnkjBRuVA+4mEXFbE50B5L1t5177pQJr7+QBZDetA7CubiAiy60DoFwPEsdLrQL\\u002fEoHWWoutAJNh9l\\u002fFI60CRqjQ3rNHqQNKLhhurL+pA7E10cr1N6UBu2ztkgCPoQMasoBz1wuZAie9+ogNZ5UBvxsLI0CHkQA3uxGCRVeNAPGHY4FET40CLJU9kq0\\u002fjQABMO82pzuNAbykYqnsv5EAIoc+XYQrkQMH88lboFuNA5kw6euNI4UDxRkTyV6rdQHTObTyzN9hANTcqKCUI00AlY4eMfm7NQD02uiwjHMdAFxUTSYoOw0Bdt5JFL9HAQMcYyA5fgb9AcTRKGpd0vkBEGiCZR3m9QLKc\\u002fABQ9rtAoIrl19rEuUALSrjwFBa3QDOAkGG3PrRA3fB3RM6BsUDDVqDRjuutQFASKQjyGalAdyAJXZ9mpEAJWc6lj6CfQBQ2AaSHDpdAMxuAIyhIj0AS+bAt9pmDQOnxfpgNlXZAJ49LKh\\u002fZZ0BBe1s+YhJXQCpSFuDpoURAviAJbpZlMkBiWkyMVBYoQKHsYYmiD25Aap5ZMKMYckCMXLOPHVh7QNOmrvIoV4VA2WRlBqqjj0DBQL3Xb8KVQPw37EcApptAMYxknuJFoEBtqUFKSOmhQMILa7\\u002fWzqJASRoKeGWPo0CWYIMiw0KlQCkva+ELQqlALVn4XfJgsEBNZXa2jTO2QPXihe4zAr5A\\u002fsBzvMapw0CmP\\u002f8fvbzIQGvxF95+5s1AgeX4lXd00UDzI4YoGsvTQMoms+Z33tVAu7O\\u002fIMGN10Aw4xXdPLXYQCPc5wwhPdlAy\\u002fDGCGIo2UCzxfrDG5vYQOBOkYmU19dAbxobJ\\u002fM110A\\u002fZTchiRnXQMzwSNmy4tdA+5p\\u002f0Jfb2UDyG2HWySHdQFukh3+\\u002fyeBAawo+oaxk40APRnJaDBLmQEFOKCf7d+hAc1Fg3v5K6kAOOy1R0mTrQN7OZfuFzutA1v6oX3q360CjyTH6UlzrQNXUjV1K6upAm1mVqs1t6kDM\\u002fWdt09TpQCwSMzMaAulAFe+FG6Xl50CNlw0mdY3mQMJ8FOAdJ+VAcwmaBZLy40BDXO2LuyvjQFE\\u002f4Tk98uJAir5hBi0340BSBfVZwLjjQAET1CubEeRA9K7qBh3a40Bwxg\\u002fdmc\\u002fiQJL2FIAo7+BAbR+JKEbs3ECxsofyyZDXQE1+Cl7ulNJA\\u002fAGUnOIJzUAUeLl1JC7HQAHsekF3bcNAv9M63i1FwUAkqG1wBxbAQLa57BjLnr5AOH3KX2YQvUD5ntxU8hO7QN4dW\\u002fPSnbhAq6FYfnDktUDGe3+XHS+zQEeToxY+qrBA0z0iCvmtrEBAByfd7jWoQDy+KpOVvqNAH4J8YjmcnkCir9mXkT+WQBP2fYRYBo5A8ubtf\\u002f+ugkCuvEpNUlp1QAKxJEuCWmZA1EYmeiRtVUBVZBjCrfhCQM7nCL0ZujBAcOH4Puy7JUCb+CJwwsZwQOeZZiwrFnRA6WQUHpkCfkCpGPP9byKHQAxoW+257ZBAiTKePDf1lkC7WqPEobecQJg4K0J8nKBAE430J5PxoUB1RVBhYH2iQKi4FYXf7qJAjVnIxgZxpEDcS1YDwGaoQPlbVEORArBA0bUezX73tUCsTUQaIv69QH2jaSfq0sNAYz\\u002fqZfwlyUBzBL+Mc6fOQLY7jsvzCdJA7n717IOX1EAUTXhwWNrWQH7TajmUqthAyZRypmnj2UAxNEmjHnLaQNmr+h9zYdpAFeuVnZDb2UCXGM8ogyTZQFd0fRF9kthAAiMwAJuF2ECVPxgBEFzZQO5RAEycX9tA4tSPZTWu3kD9k9ZGvZLhQA2WTdOQLeRAuqKYlV7V5kBCTrftcS3pQD866Xiu6OpAH0lMZJPh60AUyqnctCTsQEU7jr3j5utAHQtGTVdq60CHLYY45d\\u002fqQF+labkRVOpA937wN0Cx6UD\\u002fjJ1RgtToQOd6xjzLqOdAWSGjuSk65kDO\\u002fTqZMbjkQPlF3m9ZZ+NAeqhIGq+H4kBsGyxxFzriQGyGZpmgbeJA4B6MaUnd4kDByhYXoSLjQNGMyTFV2eJAnLKm5t\\u002fF4UBqPQRqFNrfQNzNeu5vINtA4nq+1+8h1kAEtWrb85bRQBu+xWCi6ctATm8oyj+5xkC\\u002fCJ1GtlnDQCigUzePRMFAZiIcUHTVv0DRbvWYL7C9QOXA0hrIhLtA5r\\u002fPvW4WuUAWwc7UDXG2QE\\u002fcc2\\u002fByLNAOqLzIJFQsUCqDyfmETauQEp\\u002fNkXnKapAWb832VY0pkAnngSKjiaiQL\\u002fcHAB9HJxArS2mXbRalEBXyK5ZyU6LQK7mtw+V3oBAp7aC5DQfc0C2KcPCsNdjQDZR4Ywp2FJA1+ZYH5OFQECiP4znQdAsQCTD1Y7\\u002fkyJAxK6\\u002fkV+zcUC4FqJaThd1QHyRz1HdM39AKZCKZV7Nh0D1Esab2zqRQF5uP8BLGZdAjrV9jqeInEBlxb+W1UegQOEIksgEVKFAWlY5RBuWoUCT00CznsShQCwm+C7tCKNAXjkzEKa6pkBRNaWo9ACuQLwK5aByvbRAz2lxakaJvEAmg8wOBQTDQDbfTj8PWMhAt9z6Sc73zUAy26v6os7RQEWJfA1zf9RAFnurXNHj1kChmFScxM7YQNPDDbr2HNpA5bxO6obC2kCdgb0xktHaQPUDg4oKd9pA8vDOmkHz2UCRLiiOTpTZQDZFOYMxstlAZwh1QcOm2kAJhOYqY7zcQBXpGIGOCuBAVlavZ5RJ4kCWdBfpXufkQKSwHppykedAHKcol7zp6UC2zeohhaHrQDkgrqIrkuxAOmcKGujH7ECru+lF1XfsQLRXDbK+5OtASR571Ng\\u002f60DN29M4VJXqQHtiUxdrzulAKIo5h8HG6ECTkd2jzGjnQN4tHfYtwuVA7vDbdzcG5EDrOu719X3iQDGznuPUbOFACVIsR4n04ECT0LAl\\u002fQLhQC5uFa96UuFACkZ7Z9t+4UAF7tOowCnhQK71h78SH+BAvs5gWPjQ3EC1B40zbIzYQNz29YZ5I9RAgnyGABI10EBoBGGlHDjKQFMD4v3Wy8VAdXedehfSwkDwH\\u002fhSCszAQLp8jaIxiL5AzTUpyonPu0BLsoyuvhi5QA+gi\\u002f8jUrZAkXM4O22Xs0DwBzo2+hOxQMKHewSPyK1Aaq4\\u002f6wsHqkALLEJQ5p6mQOvtTmVUPqNABqlIf7Bzn0BAZRHLF0iYQOoalKX2fJFAP65qVPNMh0DuramoJo98QEJCPUL4C3BAxxgS2kCAYEDA8TQfcAxPQOhi5nQR9DpAeY97GH8+J0B9smKLU78dQCK0fKOGoHFAull+6urndEBNvv5qj6N+QF6jliZOJIdAC9NmeRaWkEDPRQHQFgKWQA1DfYdV5JpAhWOLnVlVnkCQjsAk9+OfQFx5ujnV9Z9ALJwElobrn0A0pXKfF\\u002fegQCSlhx0vOKRAr2hI5XvFqkDWxlgH95uyQLEr4kEYyrlA7d9468pWwUCkLHLL3m7GQEP9SlA978tAhsEK8dzI0EBQNtzRY4HTQKlHqLT28NVAqkTJqSfp10DrQ3nJbEvZQEq7zXcmFNpAfSDGgVNb2kCKyYTWjUzaQLuGqVjrHtpAgrrJHSkT2kBpveeFhnXaQNHFqQ4jmttA3YzglNbM3UAE8AgtKpvgQCDKOk+S4OJARWuGgTWG5UAw9btinTvoQE9QBMYso+pAH5SbyhJt7EBxtw3a42\\u002ftQFx44Edfs+1A017lvCNn7UC9CK953sjsQKjgALuGBuxAxUeWptEr60C+CK4AMCTqQH47vOt+z+hArib6Rp4d50Aw6ugrAyLlQMJKFapNFeNAxPy\\u002f\\u002fDZE4UBFHxMl2ubfQDcPqRu3h95Ajazv7OZE3kAKKIa7wJXeQJSHdvVXvN5AfG+8z6QL3kAtNfVzKyncQAo\\u002fxQVkK9lAKpVygrSG1UBSzRoQEtXRQEGrnZqhLs1AbCoQ69gcyEAUzshT33HEQEdkkUZv1sFAW\\u002fMavY+6v0DtqEnsMV68QE8UCs21NLlAunKkLNYetkBHqlMQOiqzQCxPLIOteLBAtYFx+SBRrEAYN3LhqIOoQIYrx8yvXaVA7S6qXwKMokCCrfzU9nyfQJ7AliSfoJlAfse8yqyqk0DUxTzmWR6MQItxQ789k4JACTUbTi6QdkBXkI6xnR5pQMmSTU7TlVlAkuYNiZ3WR0AHWXOReXw0QHp6GaJkeCFAHgaF7ckvFkAv7397qI9wQK9K9GuHjHNAZRBAMpFgfEC+9jjryDiFQP0g+zlhII5AGM6e1AnLk0BrvVrN\\u002fPGXQF5b2aYpvJpANQB3VjLRm0DhDaBtJZibQGIXoRGVTptAZADBBOXWnEAsCoYsDyahQMZBsNR1wKZAl3tzYZTLr0BB+\\u002f6VzjS2QDPgOdUpLL5AIudth6bCw0A5AQ04cO3IQDHA3RXKUc5AaWozQljL0UCyfzS+4yvUQI6iq\\u002f7sHtZAlDWkPNSN10BXsK+K2X3YQHiw5prSCtlAsxQuVE9b2UDLlslARJnZQEQHyIgM9dlArmrpeFCt2kCoyIF7nA\\u002fcQMRflJohad5AA1Xft\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9A2z9JhBUg8EAB\\u002fVH8aALwQKfnfpBM0O5AbwqWOx607EDU5byJBNbpQCm4RZUnceZAGIciccLS4kCJYelXUJ\\u002feQFy4ei5IadhAeJXDiYlu00AmF2830bLPQCYwtX2gJctALn5VGMWjyEBQMC1rCGvHQAxcBjn4vsZA0loVc5gSxkB20O9NGxvFQOSZrlPXycNADRE39vE0wkDoqiVdB3zAQMoPcp4da71AvkAj2irWuUA4enJ+m0K2QBPaAb47xbJAFwsdx+8Lr0ABbsOUMWipQHVxsuzp36RAK9YshT10oUB166Bfz8ydQCerTZlsrplAmAhEZUHjlUClUKAeKBGSQBiAohByb4xAxbaeGr4ghUCp2wi9EYB9QAKGjkD4UHNApGgigTO4Z0A3xUMPB1FbQH846yqsf01AFH9RtjrXPUBXG+NgTzksQLgA\\u002fenP5BhAOaDTm9NsBEDhpNxiYUrvP9yHk2t32dc\\u002f8gPMMyUjzD8UsKGexmdMQH7axIUXg1BA+fSSBklrV0CO+WL6QGhhQF4VseBYNGlAUyD2NflrcUCtEYga7P12QJoMnJbbJH1AoXKTh3zkgUCOAD1vG4KFQLsyAqn7polAw+FCXmnVjkCt019CjvKSQPudBPYF\\u002fpdAQ+C1fbwzn0Do1z0GvJOkQL6OEa9SEKtAzRnH8U12sUAUrfQYdtW1QNLdF4HpQbpALO8hY+hUvkAIACSVVevAQBzlO8KmcsJAo5gazHv5w0DdId5hq8nFQJ8Tq58vGchAHHLxUCfxykDsEChRnDHOQPmNWYCL2NBAcneFgNKy0kAK5z4N0b\\u002fUQJj+6zxdM9dAAmm5BCtM2kCY4E6L5DneQNKZ\\u002fdr0geFAZPzmvzs95EDBrs9sLxznQIyNNJeF2ulAN3aJsN4v7EDRrmhVoNztQGDX6l7kse5A+X6gjBKU7kACLajQUXvtQKSriEladetA45Rhyseo6EDavHYS8VXlQKWAFJGT0OFADmdha97i3EAfkTuEVgjXQK9QL3jJcNJA3aKAyIhszkDDxZ6QuWfKQH9YQ\\u002fIWNchApbPxELsYx0CI2UubqmrGQO9pIseytsVAQ9jHs4HExEAuUIsJ+YrDQLVE7PfnGMJAlBRh07R\\u002fwECO\\u002f4E9I5K9QInhEasD87lAdUcJCQw0tkDt+jHNVX+yQJo6j4wpKK5Aphyy9y5iqEDc579RYfOjQOoxYMwIzKBALsVfl80gnUDgF7BA8oqZQASZslpdC5ZAHpH1pHVEkkAPqpuVVpOMQEvLHYj74oRAXO\\u002fBmkNlfEC2LM2\\u002ftutxQFsPC9J0AWVApR38wVzkVkBf7SqzSztHQCgFZKZp\\u002fDVA5qoo5OhrI0A5J0eAPwQQQILVPoYsqPg\\u002fevFvKYjK4T+7OIo77pXJPy3oX\\u002fHYCL0\\u002fky+MiigGQ0C\\u002fGSvzrW9GQOiS5GyOelBA7esHb9uxWUD66HN9hbhjQMDy6JCwJ21AkVfjms+qdEDrYZbDQht8QPFjZz\\u002f4X4JA+v3ilR44h0AE01mFDZ+MQKB9YKEmcJFALqJjJKRQlUBGb9oAQ2yaQDfVQv+Mp6BArowncHA2pUB8gD420fWqQI2BgwTt27BAsgME0nyBtEBGqEVCrBS4QIiqggiSRbtAajmFNpT3vUBX665o9y\\u002fAQHULxiimfsFA8v6r8+Qxw0A+mdGxSXvFQPkaFmOMXMhAfMKRw5ury0BWfq++0zHPQJqqgPX9atFAm3+jqTFd00Da+zb0E5\\u002fVQDfPaHENb9hAjlrChBcA3EBv9BiNly\\u002fgQP3Jw6feseJAO7Aj\\u002fetY5UAPP0z8iubnQHN3\\u002fXdjGepAnKoXFuC360BCTfiNhJXsQCDXrzEelOxAp3MqYF+l60C7Qz7J\\u002fs\\u002fpQPFaG9BSNudA1ZPFHTwY5EAL9AvRMsvgQPywj7AHUttAOwppZET31UBpZXDhGNzRQAzztKsNHc5AXWB0\\u002fD6+ykAw06IVCPPIQGq0i4uVAshAdo2sDExUx0BQbWkr2YnGQELOOm5Kf8VAvYY0Bns3xEDT01JLD8LCQL7U+z+ZKMFArVzFf9TUvkAIC3gU9Aq7QPST\\u002fMcGB7dAct2f7o8Bs0AWosPVf5quQO7rPrKYcKhAG83yMy\\u002fXo0CU58LsNrWgQFovUJcWMp1AHO9pV3ngmUDrlMhXFIqWQFjMC4l7xpJA5S0rJyFhjUBiB14Mo16FQGoZcRKLwHxAhQwmtsPZcUC3ZCs9F3JkQPHB8E6mnFVAcw6LfcsaRUCAaE2VjBAzQNaZQEUa6B9AP31f9grECECjymZmF9vxP0fay+hLC9g\\u002fLQXxvgIGwD97kDdrZzCxPzdjRy4xvTlApi6AzD47P0BLhIgOmmdIQLxExItFa1RA5\\u002fyPVwvWYEA70cItpKdqQKO55rGrFXRAMeR3ty26fECXqbKtmoODQPJiDEj0TolA\\u002fCVSLOijj0BkM7f7V1STQB7pzQg2b5dA4nTO9aiJnEC6FU1TKYuhQMtGRctgtaVAd4DhKu3IqkDTW95pckmwQGygTUQoUrNAuHkE9EQ2tkAclKu5WLu4QMd\\u002fTM8l27pAUKtjP1XbvEAUo08r+EC\\u002fQPCg6u5eTsFA4DZy\\u002fKejw0BnJKHIWZrGQPDqHeBr\\u002fslAmuJ5NkONzUDxA1FFmpDQQK5YDBEMadJANCDiCw981EDbS1Ij9gXXQFPgtzEMONpAft3lxyIe3kCvgzEStEjhQCYxv8pEn+NAKAudCmzd5UA0aIakEM7nQN1lDh2JQ+lA9w0Zk3oY6kBngUcjfS7qQDEZPlWvcOlAZROicYzc50BvB0MSeYzlQL1p1hGSu+JAYTZ9pTZ630B9PvL7t9LZQLpXuT3PD9VAXSJhfz+A0UCEL76gFFPOQPaSYtJJqctA4n3iLX1RykCm2cFWx5PJQEFAGyZL48hAdGM7yp\\u002f1x0CjrJ\\u002fPz7zGQGgz3duGTMVA421u0xu7w0Do7DNVoA7CQDoTpJNbPcBA2CZTJI16vEAGTKWo5Su4QAa3XtsR0LNApjZVsI+Tr0DDZR+rzuuoQCU1wtXgCKRAldXKbCHOoEDyd6+D4nOdQHpQpaxyTJpAxp14jYIal0CY8q6NfmSTQHA7rnjdhY5AhYyIoOlIhkBTYFLltwZ+QLgcmbg7nnJAtrCBi3s6ZUCMutEHtj9WQDBuvfUFcEVAe0g90Fz\\u002fMkC4XcdK5fseQPOIOSAURAdAk7xbbDIa8D+9h3fUrqLUP0qrc45+1bk\\u002fJvBT5I5Kqj80VuGJhUcyQJYxt9cFHDdA7od41OWHQ0AgLDuo6ZhRQEWZf1tp4V5Aicm7iyixaUA0QmRi9Bl0QJOwF5+Fhn1AG52Xm1xnhECT8ntssraKQBbv5Magv5BAElP6PehnlEAOfMds0IeYQPiEYbDvdZ1APpOhf+3HoUBjAMVOgoqlQL28givpAKpAbSWT8WP6rkD10xyf1guyQHqxenp3crRAFpEJ5X6ItkBr\\u002f+\\u002fy8Ve4QPT\\u002fEEGOMLpApcBcDE2ZvEC37O6Dsw7AQEN7krpBhcJAHwkjkZuixUBPjvXcISnJQIVamvG+y8xAkn66KpUt0EAoRiD80PPRQHV+HgVG4dNA26zumK0t1kAAuPNhlwPZQLh8hNTjZ9xAZTLGKe4Y4ECsnQD2tgviQMiIBzZm4uNASdLvb\\u002f135UDWbdOCgq7mQC1ot21zaudA3E2lJaiO50D224KgDwDnQCMNfVBXsuVAFhvZVVS240Dnk349zz\\u002fhQDPG9M\\u002fVOd1AJRGsIxlE2EBn7LsqribUQA001da9KdFAXSzRhSGZzkA+2X5\\u002fqaXMQHUyTNEAwctADrvBkf0zy0C5P8Z8+XvKQEsdAgd5YclA\\u002fPQGlVTtx0Ag+I6QnUbGQAdlMaqbjcRAJcBFkGjHwkBHfxFdOOLAQFI\\u002fHPuylr1AmidJyM8MuUBL3rPre2y0QMVWBvWMJLBAu7gkoEs8qUBm6Z9y2R+kQFU4uCDo1aBARyCP7W6hnUB39opML7OaQG3RaWpTt5dA+dhmBnEglEBzDLFAUwCQQDuf4H5VlIdA0ltbGCoDgEAeVgiBQf1zQJVyh6X252ZAdn9SxDEUWEAy+J4yTjZHQARrY2xzhDRALfP7+bGgIEBajHSW7rUIQM8Kpbv41vA\\u002fOKoTpiMf1T+mgWSJq6O5P4WkK13mWak\\u002fU0PTstPuK0BCfRecXG4yQI5SQ9V6ukBAmNKMFpThT0D7kQOG6hhdQFvvShon3WhAitV8PaTJc0BsoiP+e1l9QD7vPSdEX4RAFn+ppeuvikCHI9giL7GQQMEEGF6xPJRAmKRP05IlmEBlCjpLSLacQB+ysFgvH6FAbJbJnkZ5pECllm80kWeoQGjM+rXHwaxAEcQKEEWdsEBIVpPGZruyQIGzmDiyn7RAFfYp8WNetkCHPoBvN0y4QAKUUjY077pAzi3uuDfMvkA9oheB4xLCQGS9kLiGaMVAjUW96kggyUD5ySXAKOTMQHm0q2HfPtBABXVIhc370UCWj8NlwM3TQBppIpBq5dVAbU65J8hi2ECwzqVwO0DbQPGc3hfBUd5AYgi6jP6r4EAL5vjUtwziQFB26n\\u002fsN+NApvphIYIg5EDIax6Dg7XkQLdF27rO2+RAplGJ3Exz5EB21yOvCmfjQHPvVaKwvuFA0VnJAIxL30D\\u002frK\\u002fGncbaQPjoTOU4i9ZAkTl9eb0X00CnCPQf7azQQLP\\u002f0kLhic5A2qb8aV1AzUAjGg0NjcLMQKnp\\u002f9tOXMxA+nFcgdmVy0DAWjzRM0rKQFNpw5tdmMhAFczRspK6xkBtBIqhetvEQPKVcJIlAMNAzl8EOakPwUARdoS8G969QOu7qLi6OrlAq\\u002fvISPx7tEAGeBJ6ZRiwQHpJCoCXAqlAzI5lCiLjo0AMAlv0iLKgQIuUoRvSsZ1AgtNDH38jm0BEvT2kR3aYQEDKIyNcC5VAKHMABqHvkEBbWOUJezqJQN+Iv9vfS4FAN\\u002f5Z0oDFdUDo8tETMR9pQE2TupN6jVpAiomjSeuxSUCkw4ZP88E2QG7plThicSJAWFTb3KZYC0DYhnRKH4zyP9o+3kbPFdc\\u002fGvjSE8evuz8OmYtbywyrP1gDW+1luiZA0bl\\u002fXPD7LkCMlZEGr349QPnRL4M1Ak1ADGs3Dmr4WkC1nP\\u002foAUdnQP3x+NqkmnJABqGyLxGbe0ACQfa2SB+DQMMtGAKs9YhAIgl5\\u002f\\u002fMUj0AcgamhoL6SQO\\u002fBEBuxPZZAkHRhCr1KmkD8bQBWBy+fQLiy8KzYj6JAine2iXMSpkBL38B7dgOqQKC8rV4JJ65AorffXcsasUDRfkebnwSzQBp5SMr46rRAArDrzTYkt0C1KLtncDO6QOtlIWDBlb5Aig+rjYVAwkDoTeNH69zFQCC2w69u0MlAShGSS6K9zUCXa45xoLTQQPIM6XTYbNJACFwewTQo1EAMYvNb7A7WQBGL0iKuNNhAEN+3CZWI2kDAosWLDNzcQJCn3tT\\u002f+95Ax1BEBAhl4EBGqro9ViHhQBve1+tvteFATFAYb\\u002fAZ4kAKeeUDCzbiQEjEzzp15uFAK3LrsxoQ4UCOFFuKpGbfQIL8EfRi59tA+it3CM8f2ECwWwj1e5zUQJEx7jh1ztFAfUfBm+Paz0C2XbOi7t3NQGMx9Lp8JM1A7yeaOZL2zEC7cH5htafMQEVUiSU6zctATcbgahJUykDhWqNcrW7IQAU7gh0hZ8ZAEklYKyRwxEDCqp04Q47CQFAHs0eFosBA\\u002fVgvxC4WvUCyXHs+24W4QGKwLqXW2bNAwD2zK6YXr0CAuwf\\u002fxCCoQBZeP0osRqNAQjYDHVFjoEAEwSj5u66dQPAfr12VpptApUKEgh5YmUBNq29moxqWQF2AUhK4\\u002fJFAvtPkKn8Ji0DtWxRaRqyCQJ+\\u002fUQ6CpHdA4fy7R25na0CNTb8xmg5dQElFNGLMK0xA3irAB\\u002fL2OECunGitXDckQLZLW4Dh6Q1AsfBnKyI49D8\\u002fUS1QHAvZP1XhhAAu070\\u002fLbpgVPHxrD8isZluxvciQKgCOe6oVCpABOq2JQirOUDXIuJSRZJJQI6ihEo55VdAWXusA76hZEAm+YAIAXNwQHwzg\\u002fufTnhA8cFpncnAgECB3+kFz8CFQKR2Bjaj9IpAD6YfSEwwkEC0xP4nFSeTQPShD51mnpZARhPyQHTfmkCE2yjg1xOgQCxPoWwgSaNAmxuFYr0Bp0CPiWdgLBKrQAQldlC7Qq9AzV4hgii9sUA2AjGiBfqzQAmB2J3orbZAy0Y85EhWukCuuzhdqmS\\u002fQKVv12jw\\u002fsJAqFUlBlbpxkAnmbjbAxnLQFGpRkPvKs9AhvpEcshw0UCxxa6\\u002fiCDTQJ\\u002fCaGqPwdRAQ8R+Y5Bz1kChJhPfiD7YQGF4okMWCNpAMSQVk2ah20C9NvpRxuTcQG\\u002flnaQZzd1ACZXZoHB03kAl5iJ1yPjeQMN0UK8WWt9AkB7xfFRr30CyteUqvePeQGl5SBwfh91AP\\u002fVlUa9M20DS79xWyW3YQFWBkVn5VdVAghCT97l+0kAkFuhitUjQQKGUm\\u002fYNws1A8X69NVFyzEDUay5hUiPMQBTf02fIJsxA75QXRvXdy0CTpHDcFe7KQIvhvvl7VMlAPSrYsQJSx0Cqs7LNfzrFQCAYny4pRMNA11EbYYJxwUDLWGBCbD2\\u002fQF7BbDedSrtA32MgFw37tkAhzZGUjpKyQJSjEsQFFK1AmtUEb6aopkD3WekONVSiQC\\u002f85pSu1Z9AudiVrB6JnUBRU09PBBmcQKgnkke8KJpAJvGhaKUQl0AapvRbJ+mSQMwdb45DkoxA2yV8c3LMg0DKcTKTox15QLAt6xGcIW1ALxeaBQ\\u002fgXkBcygee+uNNQBRHpjnNbDpATNFhxCdUJUCh7ABjFW4PQLjWXsTeI\\u002fU\\u002fEexq+6cI2j9up8de8Mi+PzK6mQfxsK0\\u002ftfK85dAyH0AiGmszwM0lQH4x2D7xaTVA2WJBgExeRUCPQX2YbexTQAZhiWVbIGFABjYRxdcna0AnkLFeDPFzQI3ShTnOUntAdT6eogKlgUB5nM5WvsaFQGQ+K670GopAxF5kAT3rjkB0QxwAeFqSQGMIEOBECJZAGDXua\\u002f3HmkCeAObO+mSgQNAun9mgB6RAOeUKsMUwqECzqmCo8LisQMjOI4A7yrBAO95GUpKCs0A9IKAt39i2QMg520D0P7tAcmdKTCqLwEC9ECPLMDTEQFHAbXp+Z8hAeKd8tVrEzEAzxXTrinLQQCz0m63HRtJAbDbbrDzh00BkCZaxwFvVQDlkdsS2ztZAiC2m79Y42EAFNCFl2XnZQJe02vXdZdpA\\u002f6B5Ou7k2kAU0cmPIQfbQI9ul8Fa\\u002fNpATCT2WvXy2kBQfI0T8vPaQE2i\\u002fQU31tpAap+c8MJS2kBqvYYRYS3ZQLk7GqP8WNdAfsJakLQD1UBxyII8HIfSQINz69\\u002fPR9BAVVtlUHUozUB\\u002f6VbbZhzLQH6g+nDBSMpAzBL88jg3ykCOBg\\u002fKsUrKQCa+jZRQ+MlANbvdi2D3yEAsJhYqnFLHQO\\u002fJja5iUsVAZLtDSGFMw0A29B9HiXTBQFNfqEfJkr9Axb6R4lBHvEA4Ql8c5rW4QJc+8uBQzbRAQdo292rPsEAs77UdIGKqQDZeIhCovqRAZG0mutIZoUDhtKZ2on6eQFBYbawKB51AtFNM6KQknEAY59Lz4IKaQMR5tysAh5dAG0xa2VJZk0A0rUt8C0KNQCXvKiqiQoRAwpvgtU2neUAceQHC2qttQESKC4ljVV9Ar04sktozTkAI7fXsXpE6QOCxpbIrUyVACKujab87D0CY\\u002frL9wN70P0pRIp\\u002f7hNk\\u002f4R2EuF7uvT9uwkmQfqusPxigY45jlBhApXa3EA8uIUBz7tzO2tcwQOhr\\u002fNGhvEBA9+9bHdcHT0CMVAgDxH9aQOzV7Vq53WRAscY9UKdybkDT3PHWUb50QHnkgw1br3pAQXjziIZxgEBC6hLE48GDQKpX+svNkodAhBK8ZoFfjEBqDinq0mKRQDoPK6L8tZVANtIxk6Btm0DXeKllK1ShQLy6NyuSqqVACLsq5w+nqkBX0SpF0CewQGe3TTR\\u002fdrNA\\u002fseQIpmMt0CeBCHXH8q8QOGcNBEUuMFAJDTIRhO1xUCfxOWsWxvKQEDSN1SAhM5AvBN2Oo1G0UBDEf08sv\\u002fSQGXBxdn2cdRAsB5uyU211UCe\\u002fXpAf9zWQPAaMftA4NdAS6jEG7ue2ECSX504QPLYQDxaCdUf0NhAzCivwDpY2EC7hwZNSMfXQN4Pka7ZUtdAAkR4CSQH10DNN4twDr7WQKAyWb93NdZA0271rOo11UAedlRgJbLTQAjNW6Jaz9FAJojoV4Grz0DIxvauminMQL4eUQb5kMlAGWAZ8SQQyEAqd1y0tILHQJwLUWsQgcdAM+Nu8POFx0DGxMtBIh\\u002fHQFIN2CKGFsZAPmBBgvJ\\u002fxEDrphIQjKPCQMecLvCv0MBAwhOPDvlmvkDKokrs6Im7QP\\u002fpNwkYuLhALVd2QaCitUAgXZxbgDqyQD1XAgq2f61AmlisytpBp0CYjexNY4OiQBWP6MNKNJ9AbhIIju+UnEAh\\u002fezTgdWbQOiJvRaTX5tAhOa0jk32mUDR04lAsxWXQCvXAUJ095JAhTwa2QCajEDDAghti7qDQKA5rsPB3HhAufpiVvWZbECDU1VOUwZeQLZttieHwUxAkcco7ZYfOUBemHr2awUkQH+NZv7SGg1A1tH8gv1L8z\\u002fJcR7mhWfXP3pivNbBM7s\\u002fxj1oq1XcqT8IFnOS6UASQKeJR3EbbhlAVI4j9AHGKEAhCfK0enI4QHSL4bVagEZAC0qWibcUU0BFlmCitttdQFeIrj9trGVAEmaSDHxxbUDwOkdOS\\u002fFyQKDRCO2hdHdAJ2yKJJB7fEAnaYQzB0uBQDS9cCj4VoVAIxnMp8P9ikAvNFR0i3mRQDE5KiuQ8ZZAfCOlV08lnkCbyaJv2JijQEJ3S44cE6lAeRkir3+dr0CbjoMrpcKzQKhMYEmopbhAPeqcRHu6vkBzFZFm3wzDQLKl0OfWQ8dAtVmiYmq1y0CAX5yjp\\u002frPQPVIJ2uu3NFAnAdLLuRj00BlJyY3oprUQCrGlyOul9VAVHttVqFp1kBd5slRQQbXQLDwWJYhTtdAJ0UUq1ck10Dmyyv9LIrWQB0CKAZwqdVAMoZMRAzD1EB+YZF6rQvUQEYWDAPtjdNA2QO7SvEl00C1c+0vjpjSQEL6ghcDttFAlnLChFJz0ECG3P4guNzNQAIDtQs2vspAwH4FKRgCyEDSzI5xhv7FQNy8QRPe0MRASV8XZM9XxEDL3Bteoz\\u002fEQHtLni1fH8RADjZh+o+fw0A\\u002fvHb6XJvCQGErla+oKsFAJoudTm0cv0CKhgGcxBC8QD\\u002fNE0YBc7lAaBlGAispt0AQ6M4kuuG0QAwHGIvXVLJAUlyqNoTxrkDB7Y9PXxepQPfyfYK\\u002f5aNA3oVwyXoNoEDUFGEfiaabQKzzZUeZ7plAznvYJUiymUBG1Js0LH6ZQEYZbBEjO5hAWn3IsseBlUAkY0EidJmRQNwzqTJBZopAu7KLPkUZgkBso02EDqh2QMn\\u002fpNcd4WlAEz+5yzX3WkCn824ZjaBJQPrVLgU0NjZAA1aQ1WSOIUAS4Wf8404JQEQmw\\u002fDvovA\\u002fDy2rTS4A1D9MUr0fpAW3Pwjz3nR7tqU\\u002fJUdzbd5YCUCrT2JMsJERQFlqcSiV+SBAN5rRftGfMECUJRc7T2U+QJHLL6wVoklAMZBuhXX8U0CZm5LkJgJdQAtnUmP\\u002fymNAnNAyvUK\\u002faUAb+t1iXT1wQBlHWwLjP3RAnycLtStqeUB5dbMXmUWAQNg02xs0YoVAl9+0mm7BjEADSykvKZeTQBfdVybbtZpAL2yCGUQIokBVjpcWGvunQPBpQp3WZq9AQK2okEtLtEByz+1mDvG5QLOeia7hX8BAMe4gC8BPxECLvNd4KZbIQIeVWMOF3sxA5J1I2XFk0EDg6SQUCAbSQB6yQsqsR9NAgi4MKC811EB5O+uBEuTUQGK8t+5oX9VAQp3I3zCc1UATw2Z2LoDVQMsDimt5+NRAA\\u002fAcWTkQ1ECaiechp\\u002fXSQN+12Zzp5tFAHQtUSwQS0UDyWDuPEn3QQL7sVTVOBtBAT2St8EL1zkA6WtXtMGbNQMFgzV5ST8tAVnqUgUrfyEBudagQlmjGQCcA6AX8PsRAdxEMHJWcwkCixetYKpPBQPYYXxcSCcFAfMpFloLBwEC6wpY3DHHAQO4PpxhYtL9A2dCn+l3MvUADeNbab1i7QISwmpvpwLhACMljP6VotkDgWxJLv3W0QFHH3h4ZxLJA8jImLx0HsUBwrAUx2wOuQL+DZxn5YqlApYuRmeGhpEANxwSgbHSgQJaUcTO04ZpAL1r2Z\\u002fGVl0CrLUWGioaWQLBQ08l6k5ZApsogRz57lkATcY0RGFaVQB8B\\u002f15G2pJAZDO8ormqjkDbz0bsDdeGQDjstYH7FH9AMNIVsjpNc0B+m6SnQt5lQDU1jDyjmFZAyJ2ykrxKRUDkNrlSaksyQAujGmyRqhxASJQ59Gx6BECVGjxISK7qP8GjBTYpyM8\\u002fvr3JL8Qbsj\\u002flro62J\\u002fCgP1e5tlreZQBA\\u002frZayXCdBkCxgRvbVa0VQHGpVM\\u002fTGSVAwaSdLcQ4M0ASAjug0DVAQBVFPaNhaklAM7yvWnuwUkDHUzowuRtaQKatVlsalmFAaG2sNi03Z0ADVKNqTXZuQEn9A63pFHRARNfDZjTMekBpW\\u002f2G7SqCQMR9Sq5GCIlAwdFaE+VxkUDqHyYycWWYQAdLH0Ys96BAfN5bctFOp0BgvmUUBIGvQDMBqpxY5rRA0W68qNQou0AbWJngTjrBQFvQOrupP8VARxfgamNhyUBJeUymsUnNQA3Vjq7qU9BA42x2fxyl0UAjNSvhE5bSQEg2uDp9NtNA5gEi88aZ00DqTHOvaMfTQF\\u002fXRNmctNNAgpkVdXNN00BcS6qBoIjSQIduc0dceNFAOduL2LhK0EB9LlCSK23OQCP2BZ4awcxAmFAsEEaTy0BIEsNXZaXKQCYN978hoslADNW3Cy9MyEAjiYHK5JfGQGK92K8bp8RA17ADm2yywkDTdblc1PDAQJQhbRY7D79AFmiOkLkFvUDQUJ5zXKe7QNWwTZ\\u002fHqLpAGvVM\\u002f+ipuUAvO0reeVy4QCvRtKc6p7ZAUXsIbpSvtECIJGK90L+yQB21yKOVF7FAHFY+zyOJr0CQMbUZSDmtQNT8Yl5MuKpAs0YN\\u002fY6mp0BsF6MyqgqkQGhMtKTkUKBAxtbhfYgkmkDY5Mm1OJGVQN2\\u002fTw\\u002foMJNAa\\u002fEQkz6OkkBJdZvKh7WSQNMCtz2XoJJA9+0R0eGakUBRk7kxPuqOQIq5zBIa9YhA6TpiLEtwgkAvEuXR3uF4QHp0detPo25A\\u002fPkI1Dk0YUCgMSOZc55RQLC8j5Hvc0BAk1DfOmUELED4mFIQ9L8VQBGsgSLnyf4\\u002f97Oilgff4z8dLkgMmXLHPzqK1FNlcqo\\u002fWOWXlpOHmD\\u002fIHIJZcsbzP70Nrn\\u002fbK\\u002fs\\u002frTAS4q3uCUDF\\u002f77FpD4ZQAF7O5\\u002fBJCdA5hUx2GzSM0B8up4wUfc\\u002fQMbZ+b4ekUhAMtZrCSg9UkB\\u002faQlroXVaQGiU25gP4GJAUQIeF\\u002fiIakBHdZVxEmNyQC10\\u002fyPPMnlAkIJwl80zgUDBJgoKsZ+HQAtX62vka5BAEY1NmOYYl0Du1SznElCgQJC61sXE5qZAol72QYOkr0AW3FaODFm1QITuRliK97tAi+My2NG1wUBj0\\u002fbcKJ\\u002fFQCGoOkWIaclAK2LzwWXFzEC5qjqmpXfPQOB8Ip\\u002fTtNBAXD3hFj9V0UADTodbd6\\u002fRQOZGdxe\\u002f09FAmObvP5fF0UBo0c5Uh3rRQJa1UfJn5NBAW1hux5sB0EAVujCHdtDNQOtQy5TmhctAZf1ZZwJ\\u002fyUAq0k5rZvLHQLoNsBGS2sZAYyokP4kAxkD9gOou\\u002fh7FQFnClY33BcRAbuucCROrwkBSYNs+UiPBQNwZUI9gI79Aa6fgk3AvvECRn69l8Zq5QBfsl52ofLdAjAvxZ2fOtUBj7tnlpm20QB3Julc6J7NAQ9Wa7N\\u002fOsUAHuWmOIVewQBSTsev2sK1AzNNov5L\\u002fqkAoTCkSj9uoQOPNNw8mO6dA1CgmClTIpUCZWshyQxOkQIRJcCqd1qFAqsHORlpEnkDbRSxUu6eYQKKzpZ18zpNAbl063rpzkECtHKKgFI6NQGZ2tMu+x4xAn\\u002fWJdZERjUCcZimYiNqMQBJNIvaLG4tAxAok9Wafh0BNVoKMr+mCQPIwThgotHtArMsLvcuGckAv8yAQLZtmQDkZIkYGKFlAZQvkq7WGSUBRiRwOwJ03QMNpPEyu6yNA9MlDfsqjDkBi2nBDrHv1PwgnnGwmeNs\\u002f9IVNYU4NwD8mi8HcE+uhPy9KmIxLepA\\u002fIhe7E45b5j9MsphahMLuP+xJfDiekf0\\u002f686Pf6BJDUD0GBuyR7cbQIjCoeHq+ChAOMfPOEClNUAnZ8I4gDtCQMWEvUqr+01A3dgLgVQBWEBdoSA7CZViQBCsPDHTn2tAjDwFwOSrc0ClRi7N9\\u002fp6QDb7F3TLD4JA8M5\\u002fv0ATiEDWgZ9wn0eQQEybDngBi5ZA0GFHg7XMn0AFzHhwF4KmQH6GBux\\u002fcK9AxzmFZypftUDjZIvSnga8QIZM8ud6osFAlrwj6JtBxUDY7fA6t47IQLiFYoSKRstAAELs0jZGzUD6T9q35JDOQJEb+mEPRs9AjAqdirOMz0ATOafQgH3PQOtOjgZbF89Aemj35hlFzkB6qrNhu\\u002fLMQI8G02adJstAaLQULocOyUDKiD55dPXGQIgLkO5eJcVA2alhg4bGw0BW3CG\\u002fgtDCQD9ZEZVoFcJAXefioqxdwUCEMC4KC4LAQN+1763K675AlR5wXNODvEAk5WJMJvG5QM+UuYlpW7dA2\\u002fjKFVbmtEBbhudrZq6yQDIBmPHtwrBA0G4vMxRBrkAn86PqfWerQAsKPUOZzKhAgvgvbLlepkD0tdQ6dDOkQBS0sto9c6JAp\\u002fm3vCkxoUBCa+1eGU2gQGTv7kgQ8Z5AC9ZABYC6nEAy5BRZL5qZQA8BpDyEuJVAbV\\u002f0VSyukUDYW2tG9W2MQEk6UWcmtIdAAsxNhiZnhUAkqwEsLOeEQK0vRvchFYVAHNv1yFLShEAy1c3V9WuDQJvDvGsSyYBAjzJM4EykekCckFhsD1ZzQKOIievCn2lAn+fj1wD6XkDagBNFGxNRQPBhHULHKUFARUI2b4B1L0BDlOgZHEkaQKo91hQEBgRA9wU94gLQ6z8bd2FxsZzRP64NSXa2YrQ\\u002fVpx4D\\u002fqElj+32mcy9YeEP31EALIZJdg\\u002fVujCOsLc4D\\u002fh\\u002fp1oTNDwP4hPlwgOngFA2QDZCuUNEkA6tYuTgwMiQKO4\\u002fG8rhzFAC20A9ZSPQECp86WGLhZOQIlw6exf+VlAgPnIj9UcZUA+CpkC3hNwQEOYctl\\u002f9XZAO7sGuh38fkDqQZosMReEQFBdxkdArYlAm+elu3SkkECeojo\\u002fGVKWQANZLXHc955AIq6ApozSpUA4ILbH\\u002foCuQI6FIrlTvLRAjqwsuX4Vu0AN8+94HeTAQNvq6JEoF8RAXUEGdDTVxkAO20M2OurIQIQNpZ+GScpA984PE5MKy0BApuYnoFfLQPCaKprbVstAhUGy3SgZy0Dt3R3AYZbKQAtk7BRpuMlAUS5BKY9vyEA9vJR5zsXGQFepUjml5cRAVHNHyMIOw0ARh5xNJH3BQIM7HV5fUMBAPVe5W8wCv0AtNyPIKte9QGmcuI7BwrxAGzrPHFV+u0C4PoHLnOa5QOE\\u002fIXvR97dAlqGtkuzBtUDlOpdCjGCzQBsO2MA197BAfYfviIRYrUCsb+8XFD+pQAfvwFs3wqVAygz92aLeokCx9MIuZ32gQGjyGvw2F51AQ5dteXANmkCX9AbSFOeXQMEz3xfCj5ZAxNAUss+1lUC4QxcOU9eUQJ+DshOOeJNAHqGDrWlhkUAFiR1NcHSNQAmJcwjl7IdANeqDZ0I3g0AiiHxYDAqAQBsFUY4XBX1Ar0Y\\u002fJw5WfEAzv4TlrXl8QJ1fO\\u002fCi8XtAtvja1M7beUDZuL3+ayd2QBFI4+Hxa3FAcD99zeYNaUBntEs7A3JgQNkqOzh6sVNALaGDmkaBRUAeUBvjWWk1QF0la7u7byNA3eF8ruUVEEAFkOMu8UX4Pz4GGvtdsuA\\u002fnOHuRKXxxD8TVDmWCAKoP5XsYjCrPoo\\u002fMIk7iIi4dz9L\\u002f6m7WSXKP0h3iNDAHdM\\u002fCb7vJvfl5D+5xQQWUHj4P6kn55uJWwxA6BI4JJvzH0BKqnT1+kcxQHFucdrWtkFAyinMEYkDUUDB1KoK0l9eQJ2MmX\\u002f1FGlAYOz5GZYjc0BzbV+pHhJ7QA7MVLrv5IFAMDPmgkN6hkDd\\u002f8Em9YyLQDEq2MMoDZFApsJxngH1lUBjdEWWc6KdQE4mvXOUlKRATIVjT4yQrEA7aXRgKVGzQCqtfxWHELlAt7+Hqwb5vkDa9X+l\\u002fjLCQBW4rVZrZcRA0OPQoWLuxUCsOR9vZ9HGQG7q0q4KMsdAiyWmjuk+x0ABmNK7TxvHQC8RHmMG0sZAJm4p4VBWxkCux2vfNJHFQIu3ZXMmdMRAxWZb6yQIw0DU98HcjHHBQMuB5MJzy79ADoZSLtUtvUBxzlbwfEG7QIjYkScG\\u002f7lARPYOYYAruUAD48+n33W4QM9Mp5jJlLdAjFLMmTFYtkBBYqqpr620QGcggIaonbJAPrmAtltGsEC2FrfIQaqrQBOhWs82+KZAfgNBz\\u002fPKokBKPWyKXqWeQCGWOMtiLplAFMkuQtsJlUDxoTgOVAKSQGe19gwb249AK2xSoKNRjUDUXVgDSgWMQO7lq\\u002fsWV4tAjJCLBT6CikABVLUZZOGIQA42NBPHNoZAqG\\u002f5I9nGgkCiW5KT3GZ+QBXhRt+sWnhAXRNkNI1OdEBnPd552ltyQKqxs1jG4XFADlxWG2TgcUBr1t97YmpxQDq6avTW9W9APxT4npEia0CXWttBDCRlQPvOrNDxHV5Aycpq8syUU0D33\\u002fguwTlHQJXhRXXhHjlA6QYiatPFKEBAIPYU00UWQEPXrJqrQQJAdFE7MxdJ6z\\u002fqKbzpx5bSP8Luq\\u002fEDGLc\\u002fjMrtAU03mj8y7ZvJIVx8P6ROhhcEaWk\\u002fKvvuuXY4vz\\u002fUylrx1e3IPyYzsuxzaN8\\u002fJ+hvnrnq9D8pkz5nzAALQDMbxCfDgyBAGp8lfdrlMkCBs6Rpkw1EQD3iPR0tnlNAiWC8iYiiYUBBGcoDKRhtQMMYpl5zCnZA4ITGiR3GfkCPo1FPcPODQEBjm9PfYYhAtSpUOWjPjEDoE97eVRGRQJC77ygwC5VA86mAISFzm0B33eKfa6iiQFpONOTwlKlAJQCFc6MpsUDyDEXFSRy2QG1fK8FFG7tAfz8PB+6Qv0Chg9G3JIPBQJP7HSSAo8JAiTvKPlw1w0DyjHvWn1\\u002fDQENhSayPT8NANhSRf8Akw0DnHoZXtubCQEzch6x8h8JADkQjsG\\u002fwwUAc\\u002fwMyuBLBQEv3DDcj6L9AGDATkPJjvUAgnFv5lvK6QMSv0nCF67hAiLX\\u002f6ESCt0CJv+2GBbS2QMAkIkaWS7ZASOEQPRT3tUDFfoZ81WO1QM5Gg5rfVbRAJ8ULuFK1skCMGsXDz5CwQGrYwyBtLKxABbikAp8Ip0BMRqM+wjKiQKPA4VrgD5xAMMt+AzNrlUDK8BbEiHeQQOCFk\\u002f7t9YlA55qbxhVThUBA77rm4X2CQEHe\\u002fFv6AoFAL8Vc8hVugEAXXHb0jTmAQHUrroApt39A1W8R6yfafUDUXx\\u002ff+556QKktJTrSa3ZAzVIMdUoSckC\\u002fiSO6gNdsQDTphcPb+2dABCPN\\u002fO+gZUDtb7BTLvtkQLeAposN2GRAlsL2haAlZECPMtNPHlNiQMrb+RyP1F5AjPbzgVXLV0B4h+N6rclQQKkl7R83n0VA8vurA6xmOUDnCNia8DUrQAz5i4ytkxpAN5MG6W6qB0DcgF60WDbzPzvfKg09cNw\\u002fJYiaSj4wwz978t3XBZynP9W9wGakioo\\u002fMAGDI6xpbD86kQL0czxZP0\\u002fKApS5qbY\\u002fEEFCaQv\\u002fwz9523eWQIrcP+5Sb84isfQ\\u002fTdVJ12w8DEAwsDiAA9whQHe2SJrjzjRA4lDhs7Q+RkATTXUE7MVVQC6lxkUafmNAGMX9jpjsb0Dq13tWIPF3QMluKb8wf4BALYkg6dcGhUCKKx\\u002fIoByJQPPJBn\\u002fQw4xA5Qg5o1tikEBKFvheQ1uTQFq8U2OOVJhAP\\u002fPPEjofoEBHLf3EOcelQG\\u002fg86rX+KxA9JYghgGOskB5XGTbOaO2QH9AabyNPbpAje74lpb7vEB0SmtXJLa+QBuiP2Rehr9AdsBOEkeyv0Dgr6EhxIm\\u002fQDcgIwwTRb9AmMBr9vnyvkCW5+wZNny+QATSM2xVuL1AKpYv1vKJvEBIxq3T9fW6QPpKUWsmK7lAnqUzp391t0BjikMN0SG2QDW8iocWXrVAb5iCDbIktUAdVHzTYDu1QATzXtXpRbVAXgiFn9rktECqvcLK0dSzQIA3dd07A7JA\\u002fu3R4z8jr0ByatlN\\u002fo6pQKGxAFda86NAzmt14xvGnUA70w9ugnOVQKWU5LmUUo5ADVvVeEd\\u002fhUDOMBjy1Wt\\u002fQCUdvDNRWnhAfJ55iLtzdEDnJXHDRa1yQAUczLu6KHJA0EuwW0oZckBL9+TOX8pxQJ9qKHVdw3BAw6LOmaDSbUCyxNB6xwBpQGvvVA\\u002fYC2RAKsOhpszWX0BqAD+VUl9aQCSYkS7wsFdAf0t\\u002f3uncVkA2bXUAM45WQLNfn2cUn1VAd7mSI1J9U0BhJ8aOFz5QQBwRQMCR1UhARtYZIsRaQUANNazrWSM2QHeSmvYawilAu5OrkCFUG0BDuCzDlG8KQD5t74JsUPc\\u002fSJqls7u+4j9bWmAu73rLPwCse0wbXbI\\u002feenDM5pglj9NeDHLlel4P8VlgKMVZFo\\u002fRwD5bWU8Rz9RgKU9IcuzP8u+ZViwnsI\\u002fvLUuWY5Y3D+Au+kfvkT1Pxi8jntohw1AtQVLfY7OIkAVtzUztOs1QOtNfosTWUdAbuzuuqq1VkA9TVPxMCtkQFICoQOtXHBALGqoReFHeECo5qo++IWAQFxrMygjvoRAtLIPXiRKiEDTKvp0ZRiLQOLCvFrEz41AHBUI2h3qkEDK8y\\u002f6W3qUQJrtmVC3appALLNbLz2SoUAQ+5ONXjWnQIGTl2umpq1ArCwRvSEWskBbw278SP60QAdgMZy2PLdAlutlpb6uuEBlr+m5ImS5QOT8uoo6krlAtTGuvpJ6uUBCZmS8WVC5QBv1azVrJ7lACju+yBrzuEB3BXS\\u002fgJS4QNylfYng8bdAHsVm720Mt0CBxdS3Xgm2QC9uSIw7KLVAfF69SDGptEBnTvzsPa60QGgHllIiJbVAtZr2J8rEtUDKDaHWLyG2QKKo6YCGz7VAyPkYFM6MtEBQniheh1ayQGnjhWVY1a5A+C\\u002f124ZfqEACWL\\u002fgrCKiQPe9xyBvhJlAZCdcigggkUD+DNasAkSGQMzu0RqTvHxA\\u002fNoqn\\u002fETc0CSVDzFvDxrQAscyWlstGVAw0dlAfRtY0As0obmluRiQI+kebvj7mJA8+ifJomrYkAOpss5j5RhQP9oBm9zKV9ABBfspBT8WUDqHovmfrVUQJFGFgujWlBA7rbbeyb2SkA\\u002fv\\u002fRAsBpIQC8Lpk6NH0dAwF+kDn+kRkDBFqWLJoVFQGq5WwTGOENAN+hzxwe9P0DsZZakaQg4QCeUBcBMojBAwgUcKPgDJUDjibkqpTcYQBBbxBdxcglAdhanwxJh+D\\u002ftvLQZL0vlP7DT3p659NA\\u002fBciFbk6euD9ybmb9BUugP5DMYwm\\u002fqYM\\u002fV+uUVqWtZT9VHV4hz7lGP2fCW\\u002f+D1TM\\u002fEWJpAIG8sj9Nr+JSdRHCP5HwbnCKE9w\\u002f6Ue6EO829T\\u002f8Ma6983QNQOxuczFdsCJAcNhTvO6lNUBtOhyOl+FGQKGt1o6LEFZADiplhiFqY0BBtG\\u002fcnzBvQEYV0iKz5XZAC3IPSzPMfkAT4rnv2BCDQA\\u002fTMcJI8YVA\\u002fMJWPGTrh0AhSkOlbYWJQFib\\u002flwy6ItAcy3iCnpLkEBEqesaQHqUQLFzrIGC4JpAsNJFXauvoUAAQLyHmKemQNb51rE+0KtAm\\u002fM7DOFHsECqlReo3TGyQEAai18igrNAsmV8HvM5tED7k\\u002fs0Qnu0QDROh0diebRA\\u002f17JT79ktEBK8krbqVm0QL87TSC7WbRAAmGSc2JTtEDvIz3JazS0QHL7kufN\\u002fbNAkhNAQwrNs0C6ANz5ANSzQOzsxU0eQbRA9YJu0SoitUDZAbL4p0+2QJb6ZPJPbLdA8WgHLOb8t0A4LtBr5JG3QDVdKJSD8rVAhZJQfSc1s0DZy\\u002faiaHGvQKqOl4IzBKhAosFaVjgfoUDCjfUQb9qWQB7xMlWqvYxABoatCH49gUDq+i5JNi10QMnXyCff8mdAdBqSuWqYXkBcRsQLSWpWQALalbWuPVNApjL\\u002fRemAUkCqr9xVr4tSQOxr1C9rS1JAWE4vvtUvUUBX05X5RFJOQMAzlH4uIUlAb1rX7ZTlQ0A1SlG9cDs\\u002fQHccfU2amjlA6o7cwevDNkAikgqHgLI1QGxziMvFFDVAMKGjkavcM0ADx8qf55MxQMh7kpcXwCxAK1k6fRSQJUCl95hmGpAdQNJe\\u002fxzqfhJAoLjTPeMbBUCF64F0sff1Px56Gonl1+Q\\u002fvWi+\\u002f74H0j8OS6SwPXC8P1+No5dBcqQ\\u002fIlFak6rNij8RdqsVmQRwP4LmIWwBfVE\\u002fkOhWf4ckMj+iG\\u002f3IDGQfPyVfDgbPmLE\\u002fsQHS+CIKwT\\u002fmEjBYsH7aP4TspsEL8fM\\u002fAjhV5M6DC0DSfTzJsFIhQAviVjVM5TNAD2Ke5fbWREAliEMcfehTQOLbJ\\u002fMtWWFAYVDtqtOXa0DU6KJUoQt0QCT5ksWLqHpA4dK4Y+xKgEDPBU7GzXWCQGPAG47Dt4NAmlhe7995hECl6ZKh4KiFQIqp9q33eohAwUj7noQSjkDf1vnd04+TQDdo6wJyz5lAUnRfcYaxoEA8JELjDMmkQMBWlElvu6hA+F0VhtUZrEAJTPrWWZWuQLbeh80dCrBAPe94ZBZcsEBg2zWKcGiwQPu4xxTMXrBAxKwuL\\u002fJksEDN8Cjnq4uwQC28mFd60LBAmX0bqTwrsUB2fT\\u002fS2p6xQF8EQv09QbJA+vi6DYIzs0Dr6t9tWoy0QDixqbumPrZAyfUFqHQJuEAteSlvZHy5QEhNM4AMFLpALkeKF9VmuUAnmxGsBVC3QMnbDCCuA7RAMVbxUVf\\u002fr0CtRBF2ocCnQJ3RokVtXKBAoBWofXLwlECOV4CilP2IQM9ioHx6CnxAw3TB5U4TbkDVVgIFAuVfQEYDSyv91VFA2fN0MzgVR0A3pWaZ41NCQKU3UKjOGEFAjUHoPfH\\u002fQEAwCDiQy7dAQBwOwRkrST9AhUCqEy5xO0B6pb3eYJc2QBKvCekrwzFARbG+hMyyK0BRp\\u002fQxYpImQEIFmQc18yNAC3tUhD7iIkCpxiInZzMiQBhMolBF\\u002fyBAOE15uXzOHUBIQkj8QiUYQBUNWzCz7xFAX+aTse1aCEBltEgqty7+P2y2qyLODvE\\u002fRRXjo7iU4T8Id9GJUIXQP9v\\u002fhSOhTrw\\u002fGuoASu4bpj+5Rb1BTXyPPwKNwt5YcHQ\\u002fpoOA5ZYxWD8VmcIciCg6PxwKsw\\u002fw2ho\\u002fMR4E+l8HBz\\u002fYW4gaTWyvP0r7SUkyTr4\\u002fZOZUoq1m1z+vV28a1nnxPzhaHvxS6AdA7WHoLmbVHUABF2xYOfkwQLraqOxqnEFADa1aVZupUEDPkXS67sFcQEJvLKWMpGZAn5i23cFHcEDiCz6pU2p1QLD7pLVd3HlATN5rW1zifEBkw3LqX0x+QFd1p+5eu35ApsrtGAOaf0CkGFfILmOBQMJj5iSDB4VA\\u002fQbHxE5Zi0BCT0Hiv0CSQAl+XiQXDphAA5sLPqidnkAwFta+bqGiQPXvn2zKnaVAxLZhHZ3vp0AL2BYFEGmpQF9i5pQyFKpAL3s9SpUxqkBWARWnGyGqQKa6U0zvPqpA30IFHYbHqkBTcSS1W9KrQNg9Cxy6Y61Aqrt4SW+Fr0CQA2XQjSixQIB+bPo88LJAsZiFUYEUtUBjEsas1Gq3QOr6Rcq\\u002fmrlAb2Nj4Vsou0DTiPI12pO7QLiMhSWHhbpAxoz1TfHzt0DQACPuGDC0QKyC24z4nK9AMGC7Bx\\u002ftpkAPGlrB\\u002fr+eQKjKIKHJEJNANMERuzzkhUAXIb2Bh2B3QL5kHoKgc2dALjpAmI6gVkCKMYS1CixGQKxtgEyEhDhAy0yGU8YgMUBuFE7Zd\\u002f4tQFO8PJA7MC1APQIIMQp6LEBU7NFn9XwqQEtaykIJFSdAFMsCW4ndIkCjv0q8xnIdQOSd37xdzRZAdMO3L\\u002ft2EkA8sTVFqjcQQHvpOyihew5AxYzp7wwkDUCaCK5ow\\u002fgKQKI1PIfzbQdAWOc4vMHMAkBARp185qn7PzD5\\u002fkgFmvI\\u002fwnrKjK\\u002fU5j8FAsa+y47ZP9GH2R2SFso\\u002fi1WdJnNHuD+\\u002fVhd\\u002foJmkPwTJQpCW3o8\\u002fpoKZy2l5dj+Z+EukxeVcP2YONypB8EA\\u002f99Nzw+giIj\\u002fAUzs1JG0CP4yrJxntUu8+23t1l\\u002fo8qj+VT0P3eyG5PyQR\\u002fHIxOdM\\u002f89J2b0Rz7D\\u002fk78Qxr0cDQKtA+W4j1RdAgoKLWlLcKkD2WQQBL5o7QA4620cU3UlASkPemfMZVkAekZEd4TphQKo4rWGjhmhAXShm+mfrb0BYo\\u002fEzFg1zQI7E4JHM\\u002f3RAEzbtX+mtdUDT6uZC25B1QM7VvNIOtXVA9KQ+Ppp\\u002fd0DCddXgOVl8QIxdC3NfrYJADw71BkmEiUCmzln3VUeRQKbQ6ZW9mZZA1y\\u002fvh9YynEBR7EXf3bKgQLBP6fqyxKJAtRxFvVoYpEC8ga8OcqykQCpKyKXXuaRA3vsiczqhpEAyOXQ1wcmkQKdCoRdDg6VAazt1Wnr7pkCK4DKL\\u002fUapQNcJ0rELcKxAYpjLoP48sEBGO6EjDqmyQKrPdMuHWrVAMgi38IcMuEBHV0C601a6QIaoO13cvbtAfcgn+nnSu0DoGvTElVi6QNSziH1YZLdASCz0Jjdds0AdAUDgj8KtQM5r88PKKKVAAmBZy1fHm0BTTh2epNCQQHPXCP7RxYJAFT7+R3pcc0Blqvzfs4piQKleZKbktlBAGraUaytsPUBzMgD17a8rQOp\\u002fXn7vMiBA9nkpTlZQGUC5KmajvosXQG756wbsmxZAm1yGKQHaFEBr5ZWbrAgSQIStYYOxQA1ANXcSeUGoBkC735wgFmsBQAoKTHVtBvw\\u002f0JkIjhV0+D8l2i8sZdD2P0ZEMRUYofU\\u002fBIS\\u002fRhTX8z9ekZby8BLxP1IHYwTxI+s\\u002f\\u002fHLtvQHH4z9cU6QhmVfaP+0NadacAtA\\u002fIN5PrAjAwT9LUJ8GtPGxPzH5uVIEiqA\\u002fo7LuELzLiz+YLn4ZPkt1P2RWjElovl0\\u002fK6SrhDXwQj\\u002fgRf2V5PwlP3tzF1GHUAc\\u002f2F6xLBpx5z6+G7uG37\\u002fTPoqZdecCX6Q\\u002f\\u002fTi86Lpbsz9F+ffbxk\\u002fNP1NL55qOe+U\\u002fIfMWLNvW\\u002fD8yqAVQdKcRQITxGOTKtCNAZNqbRyoONEDqXyDdQJxCQGX4UWNEf09AHb3i00RQWEDKpnhgmCFhQMvuvZqYEGZAvhMJmdcMakDNAUBbFV9sQOJ1Y5qJ52xARwsI1U5cbEAQ79tl0EFsQHsstMuqo25AKwqwk7nWckD10pv\\u002fIZ95QJgosxuqHoJAlpMaqGZViUD2UCcaGwSRQO7RXiRnrJVADjx3LogJmkASWWbzDn6dQHNs2R3doJ9AAOqQoIs2oECRQg9f3SegQAN\\u002fRw4nBKBAQc2\\u002fIUE2oEAG2ZTOyhehQJUvaLmu46JA0s\\u002f2Epi3pUCjW9PDIJmpQESw9Pj9cq5Al6LWfQIEskAsSNo+PvK0QGUEyo0DrbdAU6eOFLPHuUD+vLTvotO6QNmozZahfLpAr4OglqumuECiW1UZ\\u002f3+1QEUpE+Gye7FA\\u002fJPnFadlqkB4HI5s7m+iQDhh6MxfxJdAyGsMzvo4jEDLOaX63Nd+QLZGfk7xBm9AQDJ4cH\\u002fKXEDf3kDqBdBIQHVH0n0hRzRA2ccoU++2IEAl38l2og0QQBitZFOxDQVAvwRPCsn8AUBByCZvssIAQP6iR\\u002fY8hv4\\u002fT7xDlrAq+j8+IzoVlgz1P7x0f0y5LPA\\u002fG2zk+MSv6D+\\u002fbTT+zLnjP5Gzdn+rGOE\\u002fTCtqscap3z8EUHGVrcTdP6BGnwZ5D9s\\u002fhNVPYvsR1z9zTYpgbSnSPw51jx9LN8o\\u002fDApE15hKwT94gY20DNG0PyuKTtNa26Y\\u002fFRlKE2Xilj92IYshuOOEP1b4UC54YnE\\u002fxVf4yjphWj8Nn\\u002fNXIT9CP1cXkVobAyc\\u002f5uKLsNx1Cj\\u002f8XQBj9sjrPraIxQe9pcs+CRKxSVEWtz6\\u002fuzldm1qdP6HTAyHKqqs\\u002fieFWbYW5xD\\u002f1Yuw\\u002fNBXeP3u9iJ6R\\u002f\\u002fM\\u002fGmP1YLc\\u002fCEAUQkzjnc4aQKRGpwDeBCtA0xhIg3zUOECtwPYJ1M5EQCDXaB2Cz09A2pPbKikxVkBzmHnppktcQDL1CWYdiGBAfZCP3P7RYUAuBJWkiP1hQGbsIwaAlGFA9yLH7vquYUDIzRW\\u002f7MxjQFsaFBySmWlALgqSgNxPckBbpX42BfR6QFunU9x9X4NAXuiYIv19ikBw4XO0TQmRQL8bNmIYh5RAX8wkvDcwl0AwxEE5466YQOSHj2aoCZlAFQUTK9atmEAwCaQmQlOYQIdMKo08yJhA9+SAmFDAmkBTDSafaLieQOaJM6Ayd6JAs1Q\\u002fJO2tpkCX4sU27tOrQF03im8wxbBAHnsp68qes0AF4d6DVxi2QEK1DQyPzLdAe6P+Hz5juEB7burCyKe3QIMZDPw5nLVAVeq9xnqAskA0LG+3qY2tQDAcujYh7aVA0Zd1xPUdnkC+zePmfBeTQDelsR4pS4ZAC+um4s\\u002fwd0AUwYSqWKBnQFtsn8WIb1VAhN8MV6TuQUBbLy1+4\\u002fQrQFbU5zIMChVA0yLcBn\\u002f7AECovx050NrxP2j3qrWFbuo\\u002fhwq60FBM5z\\u002f4BnjQ\\u002fcnkP36K9WN5oOE\\u002fYB3Kt9wb3D\\u002fl+66TYmzVPx80x5DQOdA\\u002fJ05DOXvAyT8GNqf8oCrGPyCvoAs7X8Q\\u002fZJxqNeb9wj9y3MhexxvBP1T\\u002feWQb5rw\\u002fvaxtUL6Itj8t9od\\u002fpBuwPytImYF3C6U\\u002f3DJS4nMXmT+5rgQYK0mLP1v5cjxGDns\\u002fU9UGFKp1aD+m\\u002fuXm6ihUP6oK0m3nSz4\\u002fVGw80AjBJD9\\u002fsdIxKuwJP7iZCV0Whe0+0m8STX+yzj4dQw9ZtzquPgaHiZgoBZk+fsLnudyckz9WA3JpXFWiP+awongkLLs\\u002fknpuuieH0z9PrY9ZlrbpP00qRODg4P4\\u002fd3AYD1bnEEDZ73SngN8gQNB37Ll2ti5AeXM7Xh59OUAfrMZ6VUtDQEB3tdPBqEpAVbbDTBHVUEBv+juRVH1TQK79bLpj21RAllBo5RsHVUCdYJ\\u002fBLNdUQMHWavfn6FVAWhHVTyR8WkBaEyoTJpRiQLl8D4\\u002f3MGxAHm0onixwdUA5aAgeiUR\\u002fQC1ZhoQdcYVAzcVLOa1yi0BuysikkF6QQJ9Y8lS3OpJADk\\u002fJCd0Sk0Ch+NnrcP6SQP+j60Uka5JAIwtcGo8CkkBnp4\\u002fkTn+SQPkGF1VKhZRAl\\u002fxJJhGImECajqluhLueQI5O1Xk\\u002fgqNAp11jBpxzqEAUniKNU7+tQAVGHzYWY7FAIfI2+oVps0AHau7JE560QMxW6GdHw7RAfQPxUUHCs0DLBXkgqLOxQHiZepyHvK1AU1vlyz5Rp0A8G2gHLwChQLTi\\u002fh2295ZAWqcREdaojECZPyRWm3mAQEOLI4Jha3FAickKJaXqYEDrC\\u002f15gylOQOiZLNK2tDhAxbD\\u002fHcuuIkBolwpJjpEKQMFgTtFY7fI\\u002fkINFOwrl3z\\u002f\\u002fB5TyKy3TP00LdSU0oc4\\u002fyobxlRVnyj933IggyA7GP4qxoTgDasE\\u002flukvD8RRuj94Adx9+8ezP2+RQTq9LK8\\u002f2itWWO2lqj\\u002fVtEaQ702oP8e\\u002fQ15gd6Y\\u002fqt3\\u002fSDQOpD9gV6d3pMegPz8SY1Vx65k\\u002fJBebBsVZkj\\u002fab3Z0nb6HPycMWE3RCXw\\u002fHLeNvWcybj\\u002fy0WHWdqddPxovCJj5jEo\\u002fK7fE7jSsNT+onead1iAgPwfLpW5R4gU\\u002fVncaQCMS6z4wKsxX3YfOPo57O2facK8+mUaCz0ukjj5jRMvDESN5PtJMyOuoTIg\\u002fghueOeuGlj9eXtXhuoOwP6OTTx0Igcc\\u002fOB5Gz3Gm3j\\u002fJlIqt8TnyPycyE7FSwwNAH3MIcoOJE0C\\u002fBkkfFJwhQLnoydaL8ixAgKiPVt2zNUBp2ksKnrQ9QKDqHm6Gl0JAOcbfMMNhRUCjQbNQW9xGQHY5fHF9YkdA8H3RvfRSSEB3FkHJdS1MQD3rOjdcOlNAALL5QLmfXUCNo50165RnQN6md9+CN3JAtxzh0nGDekCiosjmtPeBQFWn9UDpmIZA1Ig85p1gikBzlXdq36qMQDrnOTekOI1ACaoDmYNYjEAwQWCMOeCKQN06Qewx+4lANlAB6cDmikBpVqjQR7aOQEq6PJMhFJNAg+Hx7jvBmEDGBv1wuxqgQAKkgki+caRA6Wl0SSzvqEBCY9x\\u002fo\\u002fusQB1NWOAC+69APE9ZDqu2sEDtMCEeR4WwQC3ts2G30q5AcdsMkQ0Qq0BHVD2xl0mmQHJDrVKsJqFAKsm0fwuSmEDTgAeqoVKQQBI+YXGuDIRA20ppwuG0dkDq4kSO0ahnQOl+oZ32pFZAYSPcVvriQ0DBnfuGlwUwQEZRVhASuhdAVIcTjutJAEAuUjoqyXPlPxvtH7\\u002fqR84\\u002fbXaWuyd3vD+U1iMpmFyzP7tIHpwkaK8\\u002fLnquHJupqT8Kas0tzwWkP7Ivx8Jt\\u002fZ0\\u002fAvWq+\\u002fhclj8mt0uENH+RP+bAjLTls40\\u002fkSyEk\\u002fPhij8jZ3kGfaOIP9Up1MSgy4U\\u002fbUO0A5cQgj\\u002f6AMsDn6N7P0TekmpwYXM\\u002ff21z4SHWaD\\u002fzHV9vrwtdP41nr\\u002fcT+04\\u002fRm4+FpQhPj+Dp6Y617cqP5h0Oc51mRU\\u002fovZRZ5DW\\u002fz6TyH7+bGTlPsqCFKEzNco+uf71D9VFrT7rVnHKeNqNPgm2LDlky2w+tHtB7YBpVz7nu\\u002fgpiOl7PxXT\\u002f1e7qYk\\u002fUm61IGGcoj85ATm2Jzq6P3b9klqB79A\\u002f1tFc5Z3y4z9YLmvo1Wv1P3HHAacC+QRAGaR6+tq4EkD0404GlHseQP+RLW0JpCZAiRRxfJy4LkCWBZGZ5hgzQI9DEpaL7jVAXzwVItzAN0BsJz3NWXQ5QLRP+oqZZD1Aw4c+zvzUQ0DnXMSMhP5OQMzf9SFhullAIOgnAXIAZUDcsOD33zJwQMX1gTofPHdAzYAdAnLSfkBMPB+vmOOCQAKjEIUdbIVAuhftwg6RhkBUgbl26EGGQOsTgk0644RAFl5N3lM8g0D0jTNKtkmCQM2ct5KHCYNA+R2xcn1RhkBp0zgRBa6MQPOnYUA6IJNA3vW\\u002f47BMmUBqlNpd6CegQLaSuz\\u002f7p6NAbaa6oSSlpkBi55MYbqWoQHmAdTWXUqlAH9zuSd+MqEA69ORGHHCmQHDQoL4VTKNAGob2GBAmn0BcRFCeJ4OXQBtFCe3wiZBARfG8YqSahUCmNlzvCR56QJLVmYwiI21AlUnrA3LtXUBPhPMSaT9MQNr3fe6RdzhAwYOtk5RuI0D\\u002flIvZo08MQMrwtfY1\\u002ffI\\u002f4K3bXY3a1z8F9pIQo+W9P5y6CLfsRqY\\u002ffH77zzoymD83cPjWPqiRP\\u002fy+K6bm0Ys\\u002f3p4AKX9fhT\\u002fYAREbJbJ\\u002fPwWVUVexcXc\\u002f\\u002f5kD9uw1cj9R\\u002f9YxCLJuP\\u002fgUJwkXkWs\\u002fa38QZe8MaT+FIJ5bbvVlP1AXWg6RB2I\\u002futiK9aBSWz+U+lfcrPlSPzo\\u002fCXdeFUg\\u002fRH8n9urkOz+H8JDNY3ctP+AOzlzrYRw\\u002f9OUjVeHsCD\\u002f4jNlUx\\u002fTzPnaTM\\u002fT8Id0+hCzMvNhiwz4MPhOmfYWnPoQEolDvBIo+5l31aUVHaj4tpUwCxxVJPp\\u002fjV2UGNzQ+zLbiCZ67bT+TOlT2mRx7P7TVOXvkcpM\\u002flCKXWEsjqz+LsKgYq1rBPz276yNgPtQ\\u002fPsDg7ZWH5T\\u002fESeq3V+D0P+3KsDxcdQJAGXCl0gDGDUAueciL6esVQFo27zXMiR1AzBfoCdpSIkC0e0J3s0YlQCrFoBYJDShA8kjJLv6VLEALjpCmQ40zQLUiaU4CUj9APJGIei01S0Bs27C1jodXQAOhrF27S2NAugkkeulmbUA9gRlM66h0QDHGnsHWuXpAGeo7vTfWf0AaIvqQw36BQN2xBEq4z4FA8MDdGHHvgEAP2IyZ6Zh+QOvoW8yMMHtAPM+NSGA9eUCG\\u002fjBh5D96QDjcw+K8an9AWkoRMLW6hEDcZcGh3zKMQBCHDrbG1JJAUzWBT\\u002fgRmEBHPUiCGxydQFlXgFHnlKBAm5FkDs7GoUC5farPbPChQCWnFDaaDKFANFp7pq+EnkAFbapKLrKZQJgH6hPrT5RAw9QOk5cPjkDj0pMBnMCEQI3ayMIXo3pAIzn8Zeytb0AXzRzbD2dhQCcsIbo0nlFACMlx365mQEBPQ8XftwgsQCjqkttP+BVAs1XEkdCP\\u002fz+S47WZk87kP2vC1xndYMk\\u002faIvsuDyYrT\\u002ffWlEufXeSPwTOn7AT\\u002fH4\\u002ftABzjsMXcz++TKbk1TtsP8OOO+\\u002fUM2U\\u002fttqahakTXz\\u002fx2qAcYcpWPyJ6C+7MklE\\u002fsQvoxOpoTT\\u002fofbOcFzVKP2py6jimnEc\\u002fgIYXdHWCRD+D8nwsi65APw\\u002fHXmotCjk\\u002fBILIXiQ5MT+\\u002fRpQNV6YlPzbiAl6T1Rg\\u002fQpCT5Uf7CT\\u002f2vsFv+Mj4PiTbnAR7juU+4eO2VMgX0T5PG7PScra4PoEuR5daSaA+KyW5AAuSgz6PriAlt3BlPpPSn021cUU+cewrVXZCJD488jZQXS4QPnO6Q3fobF0\\u002f5k4lV8ibaj+OhhxJ++CCP+PJcBeBFJo\\u002fw1rNoNODsD+zAH8m0BPDPwiSePy6F9Q\\u00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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0AgHbzHhiEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQCFYNERwIQ9AmSuYop0hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0BML1yqCyEPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQGrsb7GnIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0DlhDI4qyEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9AdIR0j1EhD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQDwZnmN9IQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AlFvdRUwhD0DSw5hLLCEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQB6D3s6HIQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Axcnyt0chD0BiA5bK9iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQM4MYRlwIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"evSFwwFAIkBKvYxDCEEiQENO3pGQNiJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJApZPD6ERCIkCVUBKRKzMiQMNWn5lAMCJAyZ2bOpNUIkDZ1EiqsDQiQEa4fpOARyJA32HWDm5AIkBfB6lznkoiQJDBsNflQCJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJAe2nuRn1FIkCVUBKRKzMiQMNWn5lAMCJA2bu+UplfIkDZ1EiqsDQiQHe+Nd+PNiJA32HWDm5AIkBfB6lznkoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJA2bu+UplfIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkByZF\\u002fiTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkAFUKpjVTAiQAlcmXLQQyJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQADxZjOxPCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFeNB+DQRSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkBFxE8IDCEiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQMEejU0aJCJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQL2+RkeoLSJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkDdabLXn0oiQNtqESF6OyJAltJCqQU3IkA7uHYX2UkiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkByOZsZoUoiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQMjtbCrMRCJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQOksL4G7USJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJAltJCqQU3IkAY71HF2UkiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQEFDsf9hVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDbwrNk+TAiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQMCN30mCSiJA5Krx\\u002f4pcIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkDLosEn8ToiQMCN30mCSiJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJADwK6GT8+IkAo8V8fRksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQHdnZVcwSiJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkDNzDIUsTQiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkC4tYSgZzMiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAvZLk0ElNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAKCSRd5s0IkDHWtt6REsiQPCEbx5CSyJAjazWjqI5IkA4trUkS0ciQCDLFA+sQiJAvCGU7qtLIkDU73tBaT0iQJsC5N1uOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAVdIqpZ00IkDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDPxpYQhUMiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkC+QPFbfTwiQDBLfR7kL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hD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0AgHbzHhiEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQCFYNERwIQ9AmSuYop0hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0BML1yqCyEPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQGrsb7GnIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0DlhDI4qyEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9AdIR0j1EhD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQDwZnmN9IQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AlFvdRUwhD0DSw5hLLCEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQB6D3s6HIQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Axcnyt0chD0BiA5bK9iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQM4MYRlwIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A8QVywB8hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQGfXrTe3IQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A8QVywB8hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQEHhGT2xIQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BxopkEZSEPQAhC3HqHIQ9AQgwwIh0hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BxopkEZSEPQBRhacuHIQ9ACo0FgVkhD0CGsSshXSEPQEGzRqw0IQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0AasKbUZSEPQBRhacuHIQ9ATL3N1S4hD0CGsSshXSEPQEGzRqw0IQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u0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0ONKrjPQ0\\u002fny8607A98XGFbwlNej2lLTEPU8tCPakai+WOlwg93Or8TZ+IzTx07AAvjFiQPE8jjWSLzFA81m0zlR9HEDxQJ2Y+UgfeO6iXuqz8CrE\\u002fV3V\\u002fKVUywT+mYgArYh\\u002fcP0pnVDSWJPY\\u002f57imvEfvD0BiVISBgAUlQFghfyuJQjlANFrE2LG0S0D7\\u002faARBb5bQPbZTxpmXWlA+Ymz7aUudUAP4gPl+imAQJs3qiVjjoZAo61xJmnOjEB4VWgyDtuQQIc33b8HHJJA9WfRmcDokUAGAa5i\\u002f1yQQORVD4gSxotA3x6M0e0QhkCVECj+LJ2AQLVSCLNhLnhA6W3CRH6NcUCGhNGMSItqQNSXvwi5xmVAHbq39JKXY0B4Uhgo88tiQHUOS2iWaGJAkPIXgSrCYUCKk7jeN4NgQBgwkd\\u002ffSl1A3+nrMp7LWEAi6G9KzXRUQO7rGALXnlFA9xAS8zMbUkB5APLgl0NYQDLwjes4bWNAP+VJ09IdcEBJ5NO2e5Z5QAabdFzk3IJA6CZFPZqSiUDH2iGL4sePQAqsZ4FBFpJAzF5oyfvakkC9qjR+xQOSQCkmJVzLmY9ASYnWUXiKiUDCCo8KYi6DQLZD+6BVO3tAA33R5FTbckCfjBR4MK1qQKRpJiqRGWRAytOTxnI3YEBhaOViQwNbQPeSZDOHIVZAN5t+Aw81UUBBkuJx1+FIQKLTmPtmj0BAHsoPvsgxNED8bgq5G4MmQKxCfuou6xZAE1nXDoZMBUDYZqxZlhDyP6Z5BoLm9ts\\u002fWGEChWHAwz+6UzmJ3nWpP7H6m\\u002fm+8o0\\u002f8M5Vj5UScD+\\u002fLy0MInxPP2izFKYBJCw\\u002f5V9cBbLzBj+e6XJiURXhPjHRGrRPNbc+6IF2kNzGjD6MfFfTK0lgPizAPFba1DA+d9r1OzXI\\u002fz2UwXQjA2\\u002fLPYyGlDUnrpU9BOhzK+ZyXz1bdH1cDAUlPbf+n3CFEuo8Gogv8xNYrjy0+xRNhdpwPBdjgZdnYjI81QZbI6b4AzyR5JZv56u6P96w4AMvCcs\\u002fv4WUVgY95j9Ktvn+7psBQM1YfU99ihlAQkm09u3oMECjrWTbIHBEQLmgNrimjFZAQar\\u002fzs62ZkBRfEuxy+R0QAJBqTd\\u002fjoFAQKietDL3ikCSjAref\\u002fCSQEHpIlcuW5hAjf8nK2m3nEDX6RvPuxqfQKuQqFyzCp9A5u8IfXSpnEDUf2A796GYQCg7NXVO4pNAwz+WioOWjkC9p50CqfKGQH9v8OdRaIFAgZ5pDNTre0DhCN8wVHR4QBkmFUx+SndAxqa0fzJBd0BJPAK0S1d3QOci48Vh2nZA\\u002fu82OhJ0dUAajG\\u002fV3iZzQBO9QQY+QnBAmemJcfiqakCuFM8eoFBmQB\\u002fDMAepemVAWprw\\u002f2WxakA5YCUxQXN0QFfsE7BXuoBArn552TmOikDRFf6pX62TQDhQq1gK3ppAJ285DhrUoEAtFEBnN1GjQCLPvsP5UKRA3HR4gwiXo0AnT0YJm1mhQCA5S5crWJxAidpfhbWLlUCa6LgJyQqPQOFY\\u002fDE05oVA4bkEUHKwf0DDbakdfHJ4QMNKVc9VH3RAD1tjZ+IAcUBhhp1oISVsQPUpzcODDWZAklvMo68MYEAW7f66U35VQJS1E1hhXUpAtVa7y7OPPUCRGJou6UQuQBOKfZTXShxAgbEdYYciCEC0BPeY18nyPya4PdTBsdo\\u002fgesUuAJOwT8zOA9OI3mkP72jJx9rGoY\\u002fsE0qyq\\u002fGZT+dAJ72C5RDP6RAzOV2ECA\\u002f9+WOuZUP+D6\\u002fvyAT3nLQPsyXpLcIiaQ+Dfdbb5trdz6IbR0DvWtIPn2q2ORIUBc+l273k\\u002fZs5D3FsIBYVH2wPWkyzig7sng9biKWeCZWQT2Y0eEZQy0HPSnOtrIvH848xsF\\u002fMxN6kzxRCFYLc8JZPAY7J47oQTE8xCAqyDnZwj88A4S0CizTP3RduBZKsu8\\u002f0V0b2WM6CUCBWiCoQ2UiQFHZRiT1fjhAp+oxfFPHTUAvSSSuXYZgQBgYXdUHv3BAt525kK7\\u002ffkBxtETajzaKQM9VRxlSQ5RA2cpitMKonEDlCLgw6I+iQE2Bhi6aDqZArIc1FA0ZqEALOpSxwkioQKEE9rl8raZAEDtMq13Co0AwSkWcxTugQLrw2fLslJlABAfSdL7ek0B1+L4KjKuPQPLe13yMCItAUEYK6zA8iUCmcLwe\\u002fkuJQHqe1j7mIYpA\\u002fxKtb3vHikD5vMW+8o6KQFVTS4CFJolAOlJtdxiZhkBCM23WMUODQKWID6G8h39Ahe13x\\u002fnheUBaObhg5ql3QIC5Y7hUZntAtnuXWqL+g0Awcim57w6QQOWmyu0PcJlAWtGGjf3tokArhFGqngWqQMEJMB1zbbBAQNHmv2cEs0AJVdt2BS60QHPhTe8bpLNA4FV3pWiRsUB9VqtlEgKtQJWZY9xzUKZAzyqcSJhMoECtm1PpQGGXQIGAzew4PJFAyH1J6vMVi0DUZw5s0KGGQPH7MJH3VYNAU4SDAWkigEBpBsNha3R5QJAigaGKo3JAjGvsHpMbaUAERwOvBPpeQGh1535qd1FAU8zGCO78QUB150WNFukwQNkfLA+yBB1ASonwAKy4BkC\\u002fcAPzNTzwP5Qia7ccLNU\\u002fdSlxBVIyuT\\u002fwvMz7VV2bP300B21vH3s\\u002f\\u002fnQimWuJWD9MwYYqY0M0P7M7g6Vojw4\\u002fJLEfhnQN5T6C81aKB4W6PmgJ3WXzlY4+USufussvYD7vD+M8eZIvPl9qLjJPjvw9+7yvG84yyD22xB0FiH+TPXb3O1K1cl4931CbdXeAJz0R2sIzVzfyPMXvsJ2Dkrw8zqhNyQvphjxgxyw+YDNiPBS71YU4Q8g\\u002fCYkMEZKr2D+brGcANm30P+pO2DJ6UBBAOlNqMSnmJ0CcvfgwAv0\\u002fQNDKVng7jVNAUcCy8qvTZUC8DS0AK0F2QHDDWMN6uoRAlkemieSkkUDrgPmBbHebQGmvkxTDkKNAb\\u002fyIStGKqUDptRSL4ZuuQEgf\\u002f5i94LBA2abRocIwsUB2LJxHP0GwQNZnT+0bw6xAfldtn2caqEApvvGEH4WjQK3UQ06Nip9AYj2cnTaImkBt4EcpViOYQAQMNF6J65dAwsvrXLwSmUA8t7iBLKeaQKpTBCY1y5tAumWU4ATgm0CsPMR8bJyaQPYYTympEJhAGaFDUg2dlEDJnsC4aeKQQAd2qILqZotAVotGp3IciEDAVq49njuKQGAaGhksNJJANG1j9PianEAuXwkZSI+mQAMnE8ux17BArG9sfH1Mt0DH6CYSfqS9QDFWGodvTcFA9QeKeXCGwkD4bKySkDPCQMCaauh0ccBA+bgQQ7twu0BKpFuPKlq1QNr4lwb5lq9ASGTPPbT4pkA0KHN\\u002f6SyhQGpH3QhTVptAQDMT+mISl0B5fUPErNyTQAlMpEGRrZBAphSDXPp2ikAtkUXH93yDQG0m6qeWZnpAXfwy6AdhcEC9TX0FyZNiQFV4hKYqPlNAitYfsw8yQkD9CHhBCGgvQD\\u002f1TO10vBhAhVoQLKbHAUCXdD5WgFPnP8x\\u002fSCAF7ss\\u002fcVKjth2Grj+HJs2u\\u002fXOOPwgAmZVZvms\\u002fVdW5Oj0XRz8VbTXKXJIhP\\u002fay8haCfPg+t1Z5MNZQzz7riavrZXOiPnAXtM9fKXQ+RFXizVCiRD4F3HSFvQ8UPvqAfVCd3OI9+qkyRsl1sT13EWHu1B2APU5yw4fFx009XMMNJv1zGz3Ylup1RifpPJOu8DInL7c8zT7p7knFlDw1Y7KzUDPNP4M2Y25DVN0\\u002fFWqizxIR+D9godE1\\u002fi0TQI16RkrpISxAl\\u002fq0RMfjQkB8DeYDmTJXQMQPZhjXCWpAzeo3mr21ekCWwYlMqQqJQLEvARUZd5VAtaXC7S3VoEAjJTERpSyoQHQrOIGs1a9AWNUri\\u002ftBs0CUzcwRGHe1QGeKPlxMIbZApJGbXuo5tUAdS6KtNByzQIaEANa6YbBAUp1DUgtgq0A4q94HbBunQK92PN37k6RAOwsmNnvro0Cjifz4ANOkQAiRyuzvqaZAVElDxV2qqEB9JiqeWRmqQPeG98\\u002fAbqpA7QrHc1BsqUAHS9FMWiOnQGf40j6W7aNAySCKr7JeoEAm83pD82maQI5F2tAvoJZAaAT\\u002fv85gl0BVlhIaeOaeQI7M\\u002fTL+sKdAI7mQ3yqRskAobiZkcMi7QK\\u002fi7hiKU8NAF4ANMwrFyEAMFcsTAyjNQDIRleLwfs9A9kjKoTA9z0CAwxPC4IDMQA3pJbBqCMhAFezrctnnwkDfO5v1kEi8QEKtqOAfyrRACBvD8YJgr0AWf+smlySpQAHxezEMU6VA2jInKVBvokAW+Cc\\u002fPRifQEOiR7MbyphAZwBZTIBZkkC7rYj8o\\u002f6IQBfdMDxpMH9AnZaYUTHKcUBIUF1ICYliQIyH51pyoVFAQCLFdEudPkCsnaOQfkIoQBAhIjTXixFALnYA4fQq9z\\u002fiUdz8Vu3bP6GUUfDlvb4\\u002fIHJ49djqnj98h5cJgG58P4ZC1QHB8Fc\\u002fAgyXvHmAMj+V+\\u002ff4nFkKP2rTwmgcZuE+JaUSQa2DtT4meS22k0GJPt2pqvJUoVw+TWOXfzDeLz41FePpBZsBPtMpo4soVtM9QU1Q22z7pD0YoFPa31N2PXKUifETO0c9Oowes8G3Fz12Ob\\u002fVt0voPNdEKwK\\u002fAMg8WGYljKun0T9V1nz0JPrgPyaaeyHpyPo\\u002fHUO1vunuFEDotTPdN20uQB+PC+8xX0RAqRBUoIALWUAyvCHL6TZsQHgKuU8AGX1AyOtzDvl1i0C913S\\u002f17eXQGM3tJv7waJAS0nXRlkxq0AR193kYBeyQCbgJ7s\\u002fJLZAD28kWCQAuUAkrRxwAyi6QBFBZdsfhblAC0nbMyZ0t0CD8\\u002ftATqO0QPhNwMlg27FAD9eWgQWWr0Ce\\u002fLvgbMOtQASSe9Gueq5A0c6leFupsEC5BFKNoq+yQHqIK2I9uLRAva+m5NYvtkCJy9qVR6m2QBuwIck08rVAIn2XVFcZtEA3dm6NgWmxQPHFjdA4u6xAwDeQVEglp0DojjTavHujQPBtgSY9VaNApfZRiodwqEBCsd3j1UOyQK0deYjhZrxABRGVV59FxUBJagzpi77NQA3bd7K+MdNACNiF07zG1kAaDtthSdLYQOvfPZzk2dhAG9\\u002fygk3m1kAcwpTmd4HTQFEiQtXoAM9ASxPKLtRpx0DBUFp7e1fBQNxy2t7BSbpATEvvfpgWtUBrDHIx2OGxQLCZ\\u002fqem8K5AtJEBXvInqkCrp+xiiO6kQNb23dfAIp9AJKot86BRlUD5ADPG8cCKQHbd06NNs35AUDKIAD4XcEDZ89MIUs1eQJ+lrGol6kpA2AOLm1Z4NUDGYKbXakYfQE+pXu26zQRA3d96sOBJ6T\\u002fxxN6sgBzMP8pOCuzPnaw\\u002fYRGkQS69ij9GMI4KxAVnP3UzS9pRYUI\\u002fTC8pkSp5Gz8NAEWJG3vzPgMfHl1Apco+EJgU\\u002fBXboT6tf17LTK53PrVIqqBxFE8+yT3kpd4FJD4+KX0s\\u002fBL5PcOXa8IMU849LyclMhu3oT1zCIDnIyJ0Pd1WNleHfkY9dndTR2dcGT0kWkwzGWL7PLvFjQ9Hltg\\u002foW6M9wSE5T8aWicw5qX+P2dJ4tlLfxZAYnJmWgB6L0BRKKuC4J9EQIl6OIARGFlAjPFA10IwbEBzQr1u0yF9QCYkSsovpYtAkIxiOYwRmEB9t8OrfDmjQKIPtyrxMaxAiDYVRR8Cs0A3tB+yHZ23QFCspQT4HLtAJgcRx9nnvEDbYaa3ws+8QO5zdT7bJLtATq8sYEScuEDVCIy6bRq2QM6JTAxpdrRAt2FUbLhFtEBn3N0b3bi1QNHJDrT5jrhAtIOZKcUlvECVFwabDKS\\u002fQHHcMxX7GMFAzz5u4j6WwUAhQ85CSSPBQIl9NgmhlL9A0VilXnCGu0CHF0tQJdW2QCjdTLe+bLJAqWfv7ay1rkDQ46\\u002fZgJKtQFdARAwW\\u002fLFAv4gO4HQ2ukCuHmx99zHEQIeoGWqjQM5AVs74YHo+1UCbmnUJJpnbQGKlVNycgOBABrnhe+Aj4kCaiaOwt1TiQDk4MRxtDuFAKMwCCIlY3UB+X99jg4vXQCA1AAGe7NFAU9jSo\\u002fytykC82hNJUTjEQCA2butwJsBA8dhncHM\\u002fu0BN3t0PVYG3QBd9B7t\\u002f4LNAs1jWPQznr0B1r1nb0NSnQMn2ycuMZ6BAK6PxbVO1lEBFBoU20emHQMU\\u002f0BjiO3lAiwFVLlBSaECqNHIQGWlVQOMZn1q2N0FArnLBxsBOKUB1cmlJEAMRQH89dh648vQ\\u002foZm+hFyu1z9\\u002fZmoPu6u4P7A\\u002fRWO71Jc\\u002fRWyYxw+LdT+22uLlz3ZSP8e9EtYPfC4\\u002f3BaABvmbCD9scVrXl5vjPmIaB2ha074+VU0q9gSylz4E7L1t2ppxPj1CtaG4Dkk+zvnrjiYHIT6DUo2Cly32PTJXPhaw8Ms9F1XSMgY7oT15GrZ2Pwd1PbL4BJMyAUo9E1REaDlwLj1j7Z57yLzlP0exKN5SA\\u002fE\\u002fFnvwnGa+BEC7GztXMecaQLyYTE3PIjFAN9xYFkYJRUC6A5Ugq5BYQFrphroD+2pAmaRhBaege0DN3CKBbjeKQF8f9GVn+JZASoKZA8eMokB5Pj5tiZqrQJ4gzIzq77JAGhfHMFkAuECZt15Y1S68QCg+b8g7zL5A2u+k1d+Jv0ADUTlmE5u+QKlejm6\\u002fq7xAMMDLIlCyukDuPbs0d625QG3HKuXaXrpAKTlYkT4SvUAnFqtFeMDAQJDQkrn\\u002fbsNActM5QoEFxkC891dh7vHHQDXF0ENyw8hAB\\u002fjhMZpCyEB9SGKWrHrGQDsM5GBltsNAd38+e9VzwEAnmN1moai6QPOUd\\u002fezHrZA1VjbMc3btEDrvc9BIJO4QMXOHXM+fcFA7gBQX8OvykCLPRqc+PjTQCY0Adz1J9xADIh7ZaFm4kAoOH\\u002fXyivmQMnUZ+LVk+hA2iyQtx0Q6UChlv9rM4vnQITdnnr8cuRAbfu3KdaN4ECNiaE8oWDZQE\\u002fQFihT79JApGtxLECYzEDXfXalMqLGQBmEZV\\u002fh58JAfJVWtZM0wEBd6vFPGFy7QFqKXJVc\\u002f7VA7e9tpHmAsECEwFJxAdmmQCvsH0mSCJ1Ah3lNSx3jkECqqv9DuvaBQNVWX0n9d3FAs4CvLakQX0Cwk5cEP0ZJQG78Beow1zJAYU\\u002fP22XLGUBvnIw8g0YAQKtCLnrwCuM\\u002fl5v3orPXxD+2vpB0I5ulP5u+JM+siIU\\u002fKG6\\u002f8rXvZD8N3DIAZQlEP2MPlTTj3iI\\u002fd2Qwso5WAT9OCBQzZLPePuJxfpu76bk+qUNdUHa6lD6w4x7mmW1vPpgalkOEtkY+QDSZbImiHz596RxCjoL1PRpox8614cw9pMGIWt8+oz13iURGJ\\u002fN5PTZOFsovTGA9d7tPY8eM9j8GgPiCo3cAQCQkPxsEvRFAsSnhnOEjJEDhLhuS\\u002fHk2QFo4EhdShUhAvu2Z+VwYWkDAYTnQNeFqQA1M31isfnpA9tZkV5q0iEA0GevOLZiVQHHHiAZ1laFA1aEagWSVqkCGw0a2KaGyQPWMIPx+N7hAXZcvRfFAvUBBtMbqE3vAQCUVSDfBbcFA1mNdBaR6wUCgn3A0B+rAQG8KGz31OcBAlA1vQr\\u002f3v0BudBnxcKnAQCN5jqxVfMJAiBaSLSBWxUCLsaAYm8LIQJ+z7qyqFcxAKy2d3fuZzkCxg60fXb\\u002fPQDjCaS4cO89As7FZBMoVzUCaGHUxjajJQFybUYh2jsVAfsJm0fuPwUC9Y751DSO9QBTGCJolGbtAZddwUV0ev0D\\u002fUcpn+qvFQPFIRAl4XtBAI1J2rf122EC8BKqpgUzhQOLXxksbveZAAbUAHxSY60CRWkIg+9buQHOA8nbYuu9ASaIAsCQW7kBGjx3BvmDqQBBRdrJsieVA37qdUQ6c4EApajVu9NDYQHT\\u002fa\\u002f5VoNJAbzQcGVkfzUBUCTHD5\\u002f7HQF99dmH8X8RAZ2aHkNQjwUA7Gq7P3Zm7QEMwM3rezbRAatCNFkUBrUDqqaxpK5aiQKFdmPJQ1pVAhhq13zd\\u002fh0DgfHaceCh3QIWl7Wl57WRA9DzngF1gUUDtE\\u002flf85o6QBMtnm0t4iJAIcxNOiMPCUCkiq20UHLvP8uhjIS569I\\u002fjyvrn68itj8Z\\u002fD1zc2eZP0GoeH4hnnw\\u002fKpMTBYNmXz8PIqIgUJRAP7ZZ1dc0qCA\\u002f4AwOMzmY\\u002fz4UXQTEazPcPv0hdjEdvrc+JDOOALH9kj7hLlDZgjJtPpPYhm\\u002f+3UU+avXL+rIlID5hi3R\\u002fRaX3PU9jZqg6IdE9+RRTKBnyqD0mE4EDhaGQPcOx2PPe2QdAZGgeEsH1EEAYWmflJUMhQKsozM6LJDJAdrz4\\u002fCtVQkAg6tQxe9BRQPXgLqXI1mBAnOeLllQ6b0DOZOWtC2p8QN\\u002fzm9IKLYlApRd7h2t2lUDq2WqkImWhQJcKXrG7jqpAPggKU9v6skCmFngGo1K5QEHbHKMKhr9AItqnSxFYwkCx\\u002fkSuvQ3EQOOS\\u002fyY7xcRAJrgAS5OuxEDLfNNEVEHEQMXDvIiBG8RAwi5V0unPxEBAxb34D7PGQNLtiMdruMlAgMMtQkxuzUDx5LG8vI3QQOtXdqKe+dFAAxGbd\\u002f2l0kDnMM70t2PSQM4Eh+\\u002fiMtFAN1Uo5OaEzkBj+EGk1tDJQIFDTWcoLMVArEwvUCGbwUAsUBKu4zrAQGToIl4pQcJAHs+ZBKPsyEBVqjzo2qHSQIiT5aYXydtAB43Ypiiy40Cg7fgKzgXqQMI1c2r8yu9A281IEQbo8UAVWbF0XJXyQOBsdiOXyPFASBGu3SV570DMJpMFlOrpQJNGwDokHORAldKWiuYU3kCyqROhgW\\u002fWQNWgdL2iTdFAAT1zjG8TzEAeNE\\u002fB2JPHQGBgQXaLwcNAdFMNkYXjv0An\\u002fvALAzS4QDkJ0birC7FAn72R95UhpkDoG4os5WmaQHSmQ8Ro94xAiHOYnmg5fUAbXH1KADRrQJrXHB7TeVdAhobpm6rrQkAH+hiOGsYsQBFjzIm65hRAWQnXUb9g\\u002fT\\u002feJRAgXyjkP6eh+W4dEMs\\u002fzKkX+u+rsT+RTABPJDaWP\\u002f64WuxekHo\\u002fLZ4TLAj1XT9FOwWYNbE\\u002fP8++iB5sbB8\\u002fkaAo5IRN\\u002fT7c1dkgpebZPsoKvS379LU+3IAbqfIZkj4bvuDVO2NtPhNRo6G5pEc+SefXSD3RIj5BrFCcAmP9PdH2kP5429Y9LsnJkGkDwD33K6GRecIXQKTw6faexiBAAJZoYrTGMECdN\\u002fNvESBBQEuX09AViVBAPCwCsqwYXkACcRl0mx9qQKOL8Hv6+HVAXuP3nzAqgkCcFF+BkrmNQMSYLVEf\\u002fpdARdbbQD7kokCDdUfXUp+sQIMFaxSZnLRAG7n81zf9u0CavPS2ZNfBQF1iB\\u002fznVsVA2jzlLhwCyECg8FfvjpHJQI\\u002fQRPfxEMpASscw\\u002fdniyUATEzC3JKXJQPIOm27u\\u002fclA+HSmEXRhy0BpIg6jN+XNQCG5v4n9mNBA+8Yr3HlN0kCi24RgaKjTQMOP5MssTtRADUHuJLAG1EAxDLhaL8rSQEAKwX4iw9BA000NC5COzEDPTPSyCpfHQPBQuw3RsMNARbxUNHwJwkA6lCfEUurDQAIWlVShqcpAl1N\\u002flgG100Dx\\u002ffoZX0rdQFEvz4y+y+RALj3EgReZ60AHWrgYuvbwQEsHmMZrQPNA+DipM\\u002fIl9EBvafYW\\u002fnTzQAoRILg6YfFAyAM4fTTh7EBV+V+wopHmQAVOxWTP7eBAi9daaXIn2UAWYGY3hDDTQDtYsss+uc5A59ImkDmRyUAsODyhZGXFQOicjiO8YMFAvei97421ukAEroTK1SOzQHKmdgbHZqlAgXuogNgkn0AKBlxwxaaRQBd6EML3joJAmUfAMXszckDLhwzT\\u002fslgQNZx2nbfb01A52uRe4LNOEBhsN0cBkUkQDF3EupWIRBA+hgrmuXt+D+Q+fov+I3iPxaHU3o4VMo\\u002fbonoDsqjsT\\u002fNBBLKYiuWPytZBXimC3o\\u002fsqy9dnmcXD+Qew5vlIE9P\\u002fhKK3KFyxw\\u002ffNEQNlnn+j5yAi25FmfYPtJeVToMxLU+roWcYIg7kz67ehPW1NFwPm6p9p5K30w+6ug5zJILKD4CzDQ6+sMDPt4J5Cw72ew90trETJyqJUCSSr+bC5MuQOof8SmNeT5AzQC7KNDcTkCUdkMgp1ddQJkcHeLP\\u002f2lA6vN7kKakdUCRglF1MSqBQE7ZIDgKZIpAawmkutH9k0BnkGsslyGeQOEkqs0fjqZA2eGqTYuWsEA4ptXOwZ+3QMdNpeJ\\u002fFMBAKUVCshW\\u002fxEDGADfedkTJQL8Kk+pUDc1Adno0Mn2nz0BSBWX2infQQH5JaGI+j9BAXqLw3vBe0ECSY6vNqTvQQBDJ0mB3btBAQH7ZTDoa0UA5WhDF5i3SQJIcq\\u002fLcaNNAiSjlR2xv1EBV7hZk+ObUQFCBr5bMj9RAbMmefiBW00Bf4ybZMVjRQGloQZBpxM1AUloCD4vGyEA70bWAmMLEQJYxeJsI5MJAPTtU\\u002fZpyxED83RHrP8TKQEeUiMCWgNNAgRysaKrS3EBUgLrEZ3bkQEpE8+pCPutA0gY9073V8EA1tSzKhzzzQIJjMNaVS\\u002fRAdQxpOSLH80AxhoeNatfxQJfQi5MM8+1AHqgGtKSg50AWYbwtSNnhQI2MygLVltpAgUrAYa071EBccygNOCDQQLd8VYbQ0spAuO2XgXybxkB4LBtSFaXCQKqOlyJgT71AvxFRVlCftUD\\u002fOXwmabutQESZChOZBaNA2mj1oSWzlkAyd6OJhGWJQBeYorDx13pAq1fW\\u002ffEMa0B0yeC1aDdaQNWTQaULlEhA00tbXNtONkAo6R5Kw4MjQDMFToV6UhBAHQBmB73f+T9Ynuev4EjjP68P8Xjf48o\\u002fjZyF47N+sT8A91Oub0KVPxdua6y9OXg\\u002fQPfDcawWWj\\u002fz9BvM3No6P3JJZsxsyho\\u002f2Qvvs+I++j409TKQQ3XZPsDXRw\\u002fuc7g+UA3uRPwTlz4HR1NzZyR1Pi6AsHYclVI+DGIO+rj6Lz6bUIjJoyEYPplCSpff+DFArIEHuTteOUBNSTK54ElJQCbcCUCElFlARgLWctQ4aEDavbxLGUR1QJj+LSbTZYFABRalVU\\u002fOikBwvdH6L72TQK0YGGz1SJxAiWRjrK4KpEBxih+4gFWsQIIGNwLo7rNAS24Az3qau0CXhSIXM43CQB2KRtY77cdAAZ2szKlmzUAUAE1HKSrRQCMoww12ENNAMlGdiO451EAQJyiYGabUQJEGZe+QgNRAPF4NE78T1EDIcs+dQLHTQAjkShacltNAHU9m3NjX00ALjTcomFjUQJFkSLl01tRAmSTGqvH\\u002f1EAsRkWHJ4\\u002fUQHW\\u002fAqYbYNNAEt7XM7B90UAVFcajTUbOQJsmHxBmZ8lATS7pi3ZbxUDaq68P8EHDQM50BlldU8RAqf+ATPLRyUDaa5S\\u002fgGrSQJurBU4O7dpAa81MUd0L40Dgu2CR8F\\u002fpQNhh805WeO9AF\\u002fYMHZwU8kBm0ndqrDfzQPOejeom5vJAn7zjp9g78UCNzivDckrtQMiGDEGmbedAvDvYBqX04UAP86xxKiDbQPV2nThj6NRA4qjlQnXg0EATRFPsX4PMQBQ0Sp5li8hAduGVL4HPxED21ma78+zAQHDhC7byALpABlL6C0G9skDOlAE01FCpQHBN41xqFKBAyRHnuHJSk0AdRfDMbB+GQLQX83ItTHhAJLCG0C20aUC4mippMjBaQKDwWhZ2lklAnr+yuPfON0BW+cexZe8kQKkmDBwsSBFAVKj9EQ2q+j85AG2cNC\\u002fjP0FKS10dxMk\\u002f4XoNfpU2sD9S7SzP6EKTP5fu4cLQ1nU\\u002foaqK07T0Vz\\u002fpDNbvVMQ5P+oXbY6abBs\\u002fE\\u002f9\\u002f7Ifq\\u002fD6ow1LKxv7dPgz1v7DaPr4+aQQJFTpDnT65JtMZ1+J6Po8YBAoCBVg+6jZEAH2gQj4xVsXzlTo7QIIagEYsL0NAQyqsCBEXU0Bki7k51UtjQJlTdKPtQHJAULa6mWj1f0CyVQfvyvaJQDCC1vSUupNACsEP6kFgnEDs8CmYQZ6jQH90baoGiapAA6pmWhbPsUDBEvIhfd63QAev43\\u002fm0b9AqwFfG9HfxEAbhb\\u002fu\\u002fqPKQAbZyyc7Y9BATPA1pstV00CwMmGSItfVQHUdPpbco9dA2Dx+7qKY2EDLswXtLrzYQHNnN2skP9hA8b+rvkhv10BruxnzaJ\\u002fWQCslLTZwC9ZAbHOXlvTD1UAZOlSm8KnVQANaSNMJfNVAqTa+ZM7v1EALksUkC87TQA6RQp5WCdJAe7eNkmSQz0AeMvMR2sHKQGA2T3ral8ZAcsIi9vopxEDGT1m0AaLEQOQbo4yKJ8lAhxz+HEBZ0UACm0Sx8NfYQPHM4DpeYeFApObINrYL50BqemVqipHsQKjgqWPfdfBAekqYw\\u002fSY8UBJmZpnUnXxQOhar12pHPBAF2PdCwTR60BiwzBhNrLmQCP2FvmJ0+FAHh9P2gi420AU6I3RuQ3WQAqqVIJ+Y9JACRnt14wK0EDveD+sW4nMQKdY5LVyDslAPObHSlMtxUDLbdQjDPrAQN693unmqLlApoaCAeRGskBQR1Prm6CoQJ2XKl88kJ9AtyZzi7pYk0Cr\\u002fdddgMSGQKcApEDiuXlA6hOMeHfQa0DyOEn2yplcQKpYfmRvx0tAKBbd8k5UOUD5wGM4R5YlQLsEIvfyKBFAmhtWz8B1+T+TC5DYOrDhP\\u002flixtZ9Lcc\\u002fwwxiqA\\u002fxrD+jMKFUMXORP5Vyxm7NmXQ\\u002f2aqo3yMMWD+YWC0g3NE7P3kcoeTKsx8\\u002fjsQ7OPeVAT+CgzXxZL7iPg5ExIIN98I+JAa3hvEQoj7DCqlLfqSAPu7Rl\\u002f26ZGo++yK4f2wUQ0ARVeh9KbRKQBBKhTZzWVpAQCk6txSEakCIldeg0QZ5QDSIEeu13YVAKPFqAGy0kUDZSniuZ7iaQHUawwgt96JAB2lVJvGmqUBtx7k58cqwQBFutwLRn7VA\\u002fvN+UdS0u0DFJL71E7nBQO9NpT0KisZAv0j9euI+zECdPnVbC0nRQA50uRrshdRACc\\u002f8ZKyJ10AL6VbAWATaQLokoovXsttA0tOc5Bdx3EDEK08LkUjcQD20Ne+OcttAxpU+15xI2kCMdQkbPybZQNV+r4cJSNhA20MZY26210Bm8CbHVEXXQC+HI3CjqNZA7g6KrN2V1UDS0HJNFObTQOdHuRBcq9FAh36XB55lzkAfH71huOvJQNQSQPRNCsdAM4NQQqPsxkDoY5xgzqnKQImFlXwFjdFAuiYo9EtG2EA6qhdiV5ngQIMN\\u002fMwZtOVA+XjPa6qz6kDwQm0cLbLuQLVdaq4TcvBA8vcLFCpu8ED8A+EqC7PuQKFStKf2\\u002fOpA7T77VSiV5kC3TMG1HlTiQNP5NY3kpd1AMLL6huCd2EA9GwgppmDVQPEz9pvFUtNAbGQ4hGq80UA8Q5azMg3QQO9lbe4HAsxAlCNMnSxAx0BL1mSo70XCQMe5\\u002fMhONLtAb8FH1hdAs0D77oUgTAWqQOGD+LRM26BAaFMDQjbzlEDWgbSfF+mIQIQIvqLvL3xAt947GxwobkCH3kT8FFJeQDpbUjLdhExAoIy7XIoIOUDZJ7eFQYIkQJ8yMpZgdQ9A+Q0PHP269j+DNtHtSUHfP5nkfRuWtsQ\\u002fuWuX\\u002fBrVqj9T+lX7WCuRP9eTqlU7yHU\\u002fsFpO9zdBWz8rxILifaBAP\\u002fzmhoBXhCM\\u002f9tt+7oXEBT+xDbhhqdbmPlhv+8oUZ8Y+T1LiIN8opT43nG\\u002fbHRaRPjli4WfeV0lALXV2AzF0UUCWMqYv\\u002fdpgQJ64391NvXBA\\u002f19x0+xbf0DQUeu+5USLQLMJcqm8\\u002fpVAd5GwU42EoEDKj0kPvkKnQO61pJQ3CK9AweiPpHDgs0CDFDGostK4QEtYGv6do75A94Lqt6XawkB9j3KRXS\\u002fHQB1kVP8zY8xAcME13vMx0UB9wDxTMXnUQE9TT3sA0NdAVa1Sxfzp2kCFkmjaunDdQFpeFFpMGd9ABM2BOse+30AaJta393TfQATSB4SCht5AAZsm5SFb3UC\\u002fgI+Kg0\\u002fcQC7yugx1kNtAVKj470QK20DhoY31lXTaQFteA7dgdtlAt3Oqz8HS10Bep7a8EIvVQHmA2fzG6NJAYZOgSQBv0EBN\\u002f06d\\u002fX3NQDPrlHix+cxAInpdjtc40EDUU54ofFfUQMpya3cg8NpAfCF\\u002fWXLS4UBdeXnTq8PmQD\\u002fueHYEk+tAHwZ0DxRq70AKn3ChpcfwQNJP+GGb0PBAZ7LJqWi370CVUGeg0VjsQJ8WDooYTOhADyHBWdpX5EASWVSuoAzhQIdi5lOGUt1AO5zF\\u002f6c52kCxVotHXD7YQNw9nSx0qdZAxqBTGfzh1ECzpfm59ZnSQJOevCy3rM9Ai6d3xAmsyUA6mPa51dLDQArZjmltQ71AvLEQlmW2tEALLcgZCyasQObG32tiU6JAdJo\\u002fWCzAlkAU\\u002fo0tMcKKQBz+q5RUoX1AsBiGmVe8bkD6hkuXdcddQIpcgt3T70pA3oZL+6nNNkCvygu1iCsiQAXZ+3vAggtAf9gG4pkJ9D9UplnSFnrcP7oNpcRK+MM\\u002fsBSYAz\\u002fFqz8fzh6AGBGTP\\u002ffNQo2Bknk\\u002fcdlrhcOFYD8xdAQ7z05EP\\u002fCRYPQ7fic\\u002fo6imLwdhCT\\u002fNJITYwH3pPgG5N2PLksg+dKtD2jQktD7jnTpfUoxQQGyYdLpCJVZAOYAcnGCBZEDKuN0sBclzQHVfy8PhMIJAms5854RAj0Dp6gvqJ\\u002f+YQAHq0s\\u002fSpaJAReO2vvYTqkAOdb6BtzqxQGE\\u002fL1h6wLVA4egkZOmWukAtACzfTOe\\u002fQGgUjnPLAMNAhqhZbrKjxkA6VUrHPArLQK7mdzUJKtBATwMS\\u002fhNC00A\\u002f48Oie7bWQFFQWmCiT9pAyPmNUlW23UDihhrv6kLgQFKIA4UNNuFAThqk5Uql4UDHii6jwaLhQDCRRixNW+FAqnWxnhkD4UDe6EMh+L3gQM4wzKr4juBAoP2bKQRY4ECB4ab0\\u002f9TfQIQXQXxuQt5ADqYYjCTu20ASA5m+TSHZQHvbCcMqbdZAkS1hVV6M1EBLl0wgzj\\u002fUQHWFTytrLtZAkSTyRdfE2kDPGM5o2wfhQDWLyfevyuVA\\u002fD4stzwh60Blop+NySDwQN6b\\u002f8RBJfJAtc0dsCdA80D1VZPwF0PzQOenfaaJPfJAmJ2\\u002fYhp28EAP30U1SaTsQERI\\u002f80ebehA5QAnXYTb5EDumWakCjTiQEqwvC8qa+BAZ4+IZ2F23kCj03IBLI3cQJTC6bfjdtpAPdHtrPHY10DP3oQhL6\\u002fUQGzDsh0dNdFADwzWI\\u002f55y0CehZB9nBjFQD1NJTK6Lr9Ab\\u002fY0z9oktkDgjKvAJhSuQIEPQ1EQaaNA\\u002ff7hirmkl0B7QVAHQAeLQGvLUh2G5HxAvLdj0VfZbEAo3PmyD\\u002fRaQGDTOrPIrkdAYYR\\u002fWLq9M0AZan8qnpofQJHKlomAoghAIw3+D5\\u002ft8j+UukM3IdfcP9ADx2tZuMU\\u002fcZnCccgAsD+hRXA+ZsGWPzLP9e6Rz34\\u002fQmlBQkGlYz9aGnZMcGZHPyj++jy94Ck\\u002fEaQ00PV+Cj8V+oTUaPnpPgcgaJcWjtU+T5Jlgo0SVkDLVmqjWGBcQLq8dlCtn2hAtDFPtr6WdkB0bbcXmQaEQE8RIIzgwZBAWftZM4JPmkAFmY4D012jQPQn5LfqzapAfMeQj2+KsUBxgoACXua1QHgHoeP8X7pAFPFLEVAFv0DSNG2rpwfCQKUFqv3R7cRAXegeP+9yyEBjgf5uTd7MQO0IVKy2ONFAbDSvftOk1EBAoqdCdp3YQLMeDAzt3NxAx5ziOQl74EDUi\\u002f8BQDniQPx33oESfuNAysLqxhw\\u002f5EBpMGLgW5XkQDSudkUPr+RALMqwnr+25EB3+yCIU7zkQBKhV1gbreRAZB7zittf5EClhvyLmK7jQHBf1d+qkOJAyAlBWQsp4UD8rp8Ul4nfQM0nPQdCm91AoZNRz8Nt3UAslt3BvNDfQF+tpjzIpuJA5N2GBLD55kD7h3zviZTsQOqerUNZZ\\u002fFApqIXiKxb9ECu3YCtjKT2QBy4X71G2PdAQ072fJnF90D79HwNsIH2QBgOlfeTXvRAZKPRGwHP8UDgsGEPJojuQAc1DlACI+pAgMyKUUS\\u002f5kASeFXF1VfkQKE2NCQEqeJAmWucFMxT4UCTAznB9AHgQI6g6OUz\\u002f9xAoIWm4+OE2UA8p0dovL7VQK+iXcfG7tFAvq51H9aezEDzPGQlZQ3GQLBDuodDU8BA0nPCzp8Pt0D\\u002fXi6p9diuQATvimj7aKNAAfsplpzglkAOCJkneTSJQGxeb9be+3lAgN21U3AsaUCM6TkP5BpXQOy6q0aOU0RACWvqbkxhMUAGHTBpTkIdQIhJ4eRfaghAiOO6gQ0p9D8PqIwIyU\\u002fgP+\\u002fcPgCigsk\\u002fqeijZFwEsz9ghaL2D7maP4TYmYMgi4E\\u002fZCUNKT1jZT\\u002f04HMnOBpIP2DT9ABTESk\\u002fpCCeRmbrCD\\u002fBJYCrlOX0PjTIYLZei15AiGUrRfDUYkCs5i2rT0huQEWCjpfp63lA9Dnzt6OzhUCWw2IrQleRQL\\u002f1LfwTRJpAieJXapjRokDQ+uk5r4ipQApqyc9VdrBAkDW5GWNNtEDyY5knpSi4QPiz+AYhB7xAQpVO6IUHwEAHnGGHpUnCQFRlVAxRDsVAJSwANhq3yEDiA6zKabfNQNcMJrpBOdJA5kKzvhWE1kCYPSz5dpPbQF6B6faFgOBAjClEtlof40BqW8gZUmHlQDirO594G+dARJMTICBK6ECs3+zRmAvpQOz5q608iulArFFcgf3h6UDjDGT07xDqQGg\\u002fNRuf++lAQGZKlOeB6UAfkVZ2dJjoQAWaW3gIW+dA5\\u002fNosiUS5kCDkra\\u002fuizlQGMuNgwMMOVAsFKO24Se5kBez9bV2dXpQA46LILB6O5Akalr7Ku\\u002f8kCFXw8BLmf2QFVIH2KJ2flAB9RZKW16\\u002fEBL7lC5Qc39QN83xRGlmf1ARPboIV36+0B27R23uVD5QFzqQ\\u002ff4IvZAOam3+4T08kBOP7extCbwQJWFMeQQ1OtAykzRVvh\\u002f6ECh1M9jKRHmQID6+918KuRAy3PRDIN14kCkbG+dHrrgQHLETe9WyN1ApUqViAvz2UDyUUsVGRTWQLKrcKigSNJAwbq+3tdNzUDTWatHJYvGQFDXQe7ggMBAVxODRobQtkB5lVBCs5utQBXOeLhi+6FAXZapteBxlEBd0nRAu9WFQH+H4V7bEHZA0esSwjFWZUBzRpIuzQNUQGu2ln\\u002fjdEJATYpuGczdMEAXj6Ip\\u002fIYeQPop1mgbHAtAQQp3MYpN9z\\u002fTpI63QR7jP\\u002frgAaa4ls0\\u002f0Oly+jdntT+VgssTj8GcP6iAc4lN24E\\u002f7K1B5N9uZD\\u002fnPOXuNYdFP9FTcoakpyU\\u002frfxg35pSEj\\u002fc36nN\\u002fH5lQEz1ea3cqWlA7rhZpZdNc0BbCEkEssV+QNwPr4MKC4hAPOoSbDAEkkCdQ9USG8mZQLTeQ1y\\u002fn6FAK+fAYEcLp0AP6GyeoeesQP3V4Vw6ebFAZ9eqPByAtEDAW\\u002folyoi3QMX8+xahsrpApmdPr5xJvkDnf36k923BQKZ\\u002f0tAWpsRAPRM0mbViyUBz4d+zPx7QQGnYshI4ytRAvyaBeieq2kBURMB\\u002f+7LgQEPQepbWMeRAhE9G62R450AicWStbj3qQKemqGhOXuxAQTwRPsbj7UCpsKAEn\\u002fLuQH9Z27uNse9A0+ISn+kZ8EA80Pp01DnwQHGe6mOHLvBAIeAXi5jh70ARLYEo4AzvQBZRY6I0FO5AUh5w9HBd7UByjITWo3ftQJPJMphpAO9ATM7sDig78UDukTB6MAH0QNsz2UoEpvdAW3t2lK+2+0CNLLpCSIv\\u002fQPWUVUR1NwFB8IlhOQfpAUHxX\\u002fwFKLoBQZF2X2dVugBBispzXyZC\\u002fkBaohi1CXX6QKi49bOyofZAhmcdJtA180AwJga\\u002f+GjwQA6prRJ9gOxAdvYsH3Y86UDPcqugpK7mQFO0+3gdhuRA8MzaeduL4kAR0kXzo6TgQNusIN\\u002fKjN1Ag+9HUEDX2UAnhuy5ehzWQK6u9nZQV9JAU\\u002fetTKUvzUC+oFqQTQ7GQIFR8qida79ARRw5Onn\\u002ftECTsJyqzUyqQF9fu5Dw8Z5AEGBQ64E2kUDe1YyjuEuCQOhXNacb1XJAy5kRD1IHY0CHtZug\\u002fApTQLyP8oTQ4UJAhxEFgzdlMkD5XHPI6V4hQPdbnsEWWQ9AfnvpImGz+j8A+MlyyETlP7g1Qhyuec8\\u002fHW1FeRqItT\\u002fwT5LXeCSbPwqRAmhbc38\\u002fBXEfTr+\\u002fYD8rKF9CCQVBP43eNZ5qBy0\\u002fSYLAhlmYbUCwGeFQzVNxQJa0XbuD8HhAlHWV5fDRgkAa8CtT8qOLQCPhheGMXJNA5fnRM9\\u002fbmUDWupHx3YqgQIk9R37aZaRAiJ3Vif5jqEBXGwwfoXasQG0OFSu8UbBAKR6ZtL6GskCmz3C29AK1QJ2N6SebGLhAxMcWwaZgvEDtWOszPmrBQPgeA6YdZMZA2cNIHqTVzUDyQfDrcSPUQNlwRjoO5NpArpA7Rf1v4UCnjPW5L7zlQGd0LjE08OlA\\u002f9sEvxis7UCxQp49T1nwQDTzQTxte\\u002fFAXN9TrLJK8kDeQnW5Q9\\u002fyQFzcHjbkTvNA+FSkqN2k80CzEg++M+DzQJ1XNEAD+PNAqq8rLFrl80BT8CrR1bDzQGKKy\\u002fo9f\\u002fNANOKF9z6V80Cl0FECXkv0QPJ8YvO\\u002f8fVAEHwTNDes+EC5sK\\u002fHHlX8QJHwJDGrOgBBoK4KPXYsAkHl7k2pbp4DQWI6xVt7QwRB\\u002fr94+G35A0F0dkHvQ9ECQbc8gKZwBgFB42P0ma7T\\u002fUAJXuG3XpP5QJe7z4aFvvVA2yE7DpuO8kDeB3THUwnwQAzjkLXAJexAO1COe6UG6UDPspQzWm7mQIGKmiGpLeRA\\u002fdS7I\\u002f8p4kDkYr4MZFDgQEuZ32wnFd1ASdTnovR92UBl3vS+ULbVQPNX03d5v9FAbUbNR1COy0DO8wFSwivEQMaRfdrgwLtADc5OxLb1sUDWrkyGkvqlQB2PWUo1rZlAC9C8gTkCjUA\\u002ftkW9SBCAQEyklQ2Em3FAl88K9UEgY0AR+0g8WW5UQC7gxwnwLUVA\\u002fKhFi0IANUAVkK20C6wjQIyzf9DvPRFAqqD439MV\\u002fD8YIFbw2SblP9tsla4OXM0\\u002fZJeYmBO7sj9eNmV1k+6VP\\u002fHFg+v5k3c\\u002f0u1cdLwsWD+4+mM3TMNEP1pqcAMmPXNA8bTI1DBSdkD0CbxTcmZ\\u002fQDZYHzOo8YZAUs86ee4nkEDQJthTr3iVQONbxUA97ZpAw4Bx8L8NoEDF1JZRuGeiQDxWeRZbjqRAAutLdoOtpkCTVa0rdAupQBrr9ljZBaxAjgdgyF4QsECkcxcPgRizQFwqXxzXzbdAe4TOnwxEv0DLIWyoXHPFQK+PDq0+G85A8oAXzW8T1UB3xyseX9TcQBZdHGGGAeNAAQJns9j950ADWzdS\\u002fvHsQL2EVsCluPBAUwOpCXCW8kBTImPYYQP0QPrN73LpCfVA8HwSs7bG9UDWdoASvlz2QHbClC5A6PZAvaK4N3V090B0R98+Efj3QCJ9C3bTXPhADC6kqoGS+ECfHgBY86X4QCQmIM92zvhAC21sAnJl+UBmKxCursb6QGb+7KYRJf1A7apfcZszAEHNq8IlrQ4CQYgXe1vdywNBkDOGkZcLBUFNW+5BOIMFQZZhzucrEwVBJIFMPjvOA0EPpe6dE\\u002fABQXzG1CU6kP9A2D+Zo4xD+0ANOGk212X3QIdUhzzgKfRARkGaQ1KQ8UDVjcdsrfbuQEuq7olpietAkHSyGN6b6ECi9JJqBAzmQDiDS2XUyuNAp2tZCurK4UB49jl0NOffQJC70VhIRNxAMNkYEUBw2EDQHXL+t1jUQL0eAe80I9BAQ\\u002fL0gzQ5yED001a12yvBQDX8u+ieELdAsL8+Jc2WrUClcdy8HViiQDY1V7CHRpZAgUsyKBLAikCFwOvbb9R\\u002fQERS6vnDoXJAeXIJekIvZUC1i+Q5OQ9XQAUh3YVxukdAUwfHdsHbNkBvs\\u002fZGgXgkQKw2qsQ39RBAF1qmV+\\u002fl+T++DCyO+C7iP2v\\u002f4EpYb8c\\u002fNRryPoGvqz8INHzE+QGOP6wRzdLKBG8\\u002fPQuW9CzRWj8avrTvpxl3QO4DKUIcuHpAGMqIcnKhgkC6Y8eZItWKQLRGSTKTe5JAEQIlGuXMl0CHnhEvWpacQBsCstpsHqBAEFuc0+BGoUD0h7bS+uyhQH4dRa6mc6JAhfkw24Vlo0CO9hLyRGmlQEFCn1UUT6lAuDgLqr4esEBd1zh5y\\u002fa1QJuFjdt1Zr9ALfDhS8\\u002fzxkDA8vCj4bnQQCcFdlpm0tdATz5B0+Rc4ED\\u002fyzh\\u002flIjlQNkT2gDcFOtAQyJqWexI8EAAhBOrucfyQDQjdJK02\\u002fRA2U0t3d1w9kCXLfEqCI\\u002f3QPpnTHh5V\\u002fhAqA90xfD6+EAXDKQn\\u002fqj5QJdWS0\\u002fvfPpAgKjb\\u002fPNw+0BAK8bRQ2H8QAwmZVqbIv1ALPfvYd6i\\u002fUCSg6iexQD+QADM2etMif5A02gjMMmX\\u002f0Bk1YrYd7MAQUISMfQb9QFBMq8t0H1hA0G9SWMep6sEQRxxNcm\\u002fgAVBsDnJZ6GiBUHYwY0N9PoEQUlKZeIKoANBkbtAudjKAUHBpy63dIb\\u002fQASKO1SelftABrGYmHwb+EB+32I8djr1QAij32fo5vJAd65OKwr78EASocDM55ruQI88msuFg+tAm5MSXICc6ECLsEQO4uzlQI2CILUVf+NAJG8ssDVN4UCmSvCTs3reQHZBwliUXtpAqK5ndLch1kB9UuDnVtTRQGHzMZxKZ8tAoWLi2MITxEDUoZu0SjG8QAn32PleKLNAHhqWZhiAqUA\\u002fazpfqcagQNTMcNTG3JVAoGM8wC4IjEASfbjEynWBQHpfT6mK1XRAXFcYBOSDZ0DAPLMri9xYQIH35g+ZckhAIReGoqo\\u002fNkDxuNVCDawiQE5K9tA01AxAQGM3mgdv9D+MJDCiJI\\u002faP9HjNkNYn78\\u002fANLiUPJDoT9oXL8jYvyBP\\u002fJWiLIhTW8\\u002fjk2dno9YeUBOSo0pZVN9QEUKi6tEb4RA3yATjvxRjUCuiM26jgeUQPlw99tAbJlAX7lqaTnanUAR55t4QEigQGsW2\\u002fYYraBAds\\u002fRhu1aoEA8vwW1tsWfQOHbgPzYD6BAGKInAjfWoUA6dW+Uj1+mQCTlUe9JNq9AnQdiYDc8t0AJ1LjCHLjBQLPzS3XN28pAM4sdkrrU00B4xOrfLDDcQFgb\\u002flRNKuNAO2+yz7\\u002fh6EBDefC73t3uQDtM2UfjVvJAFQQdAELy9EDdYRdnZxb3QASJ1OGJrfhA2wfR\\u002fhu++UAnEzvxo236QI5l0cFR+vpA3enq4Gyo+0BaO9lzhqf8QEuQzPra+\\u002f1AeJJZABZ7\\u002f0DhOAOlmnEAQfEeJgjUAAFBAh3+c9ZpAUFvz+EHTsUBQUgjol3KNwJBbF0jnB\\u002fbAkE2vEeFB6wDQX5bEV24hQRBlsJ+azotBUGqxaNBn2YFQbbFF5qJCgVBeJ8CMHQTBEEtc+r7eZ4CQWBsBVW44ABBHDdxh9Ms\\u002fkBKzjGzVeT6QA4vyVwpI\\u002fhAcbT752fz9UDvv7uLWzb0QP\\u002fIYAKat\\u002fJAQKK1I85D8UCu\\u002f\\u002fyienbvQJnkNq0jM+xAw2CH9EHh6EDGbXn0rrPlQN1QaKmRzeJA0CUxnRwz4EA3E0xoGaHbQC3LEaU9HtdAw8+0BkPP0kCp47a6tJrNQJxII3GTisZA4g9yy5q0wEDGqGgEAlG4QASUdencgbFAmaswZD\\u002f3qEAlDa+UXYKhQNcZlFMi3JdAV+raDuImj0ALzezGsz6DQJC90jtFSXZADdaTOfcEaEDo8QHPJflXQAIjolhYFEZASt6s84q3MkDYdBgpEicdQE4FtTnt0wRAr\\u002fRrHgFG6z+wE9mMgVrQP7ED6Aym\\u002fLE\\u002f+IbseE\\u002fjkj82D9udLY2AP3C8J9j7ZHlAJLwXQaRyfUAsbrndx5mEQE2+dn7goY1AsoYlWOI9lECvkIimLpuZQKqSgdqx1Z1AdHRsdI4NoEA0ZynrMhygQIFfSjaqzp5AS03yNBBLnUBxdDRgCLedQOpRnID7SKFAedzbHfBvp0BGVY1IQpyxQDsKup+4irtAcVt6FvZ8xUAjf35JRljQQClknY4F8ddAIfIQ7pfF4EBmy0u\\u002fkWrmQFORk7mMluxA0rlbkYJv8UA26rFuqmn0QB5evZ6fBvdAo8XPY5se+UA9oV6W25v6QDrv8cmrg\\u002ftAQt9z9En++0Clg6TgJlX8QH5oAcJ74PxAxaFYTuTm\\u002fUAf0GAp8n3\\u002fQM8wFoyovwBBUy+gFULOAUHADtt668MCQaGQ3jHhjANBoE+QSrMtBEHyHsK7abgEQVR1GcabOQVBv0B9ohOqBUEuXXjlhO0FQUI58Owl3gVBY7n4BPBdBUGDizhPl2QEQc6jR5RyBANBAymqCZZlAUEMXi4ax3T\\u002fQN8oV6ugZPxAQYq9U7Pd+UAXrQqX0Pn3QMeKKjnxpvZAxrlYLS6v9UAVtwssGcr0QMnQbkG3s\\u002fNAQdEYOElC8kCjOLCDkHLwQI7gWTuryexA7cohsKKX6EDzGSWXbazkQBaYeVkUPeFABM3qSbWh3EBO+OKkZKbXQOT+OnVLWNNAwGoQwhg7z0ClF7s7kOLIQI9jCan5o8NACDHmGB7NvkAkLEyTjfS3QKxBq16hVLJAuw+Y0BJBq0Ar9GwAeGqjQCAKLhL3MJpAdL5Aim+RkEDX1TbyKIaDQJMZosITU3VAvR\\u002fS6ueCZUBlTLh7f\\u002f1TQJXNpF+TFEFAXoLMS2bMKkDvLi5l3EcTQOo1oCzAbPk\\u002fUNKDdSu23j9OgbUwxgXBP3I9mnbtC6I\\u002fe2hQO8rpjz8s13JIaYJ3QGSkjeHJXXtAft01HOhJg0AWc93Ad\\u002fCLQOm1wfkHMZNAHm0iP09gmEAFOouzpHicQEgLxqN9q55AalFpcVvQnkAyeLKb9aedQFB3E9hH4pxAn0ZA+f30nkDYl7TmYnajQOlbEpS+MaxA51cB4ADptUAqBPB4rTnBQMI1NXBqkspAbUrFSFnT00CoRx1KTmrcQBSCkxyue+NAudwUxDKG6UBi8ck4zPfvQFsJy35BMPNAMmnrfUwp9kCbSQy7Arj4QIEdqxUVuPpAq6ekHhsU\\u002fEDwXnhlb8\\u002f8QAj\\u002f2AxUEv1AmQ4S4yEt\\u002fUC3kdhz34f9QA5jt\\u002fAdf\\u002f5AGcoWsQofAEF0UVn9K1YBQd1t5KfcvQJBw3ewaCsnBEE+GvNMJm4FQSLeSHvugAZB6WJ5EJdYB0FzwiWr2OsHQSsYT79FJwhBNtVQYVjxB0Ft31Y3czcHQZK8QQD9+gVBs+ut6JVWBEFQ7tLlDXkCQQ5sm+yLmQBBZ+jufW3V\\u002fUDH7oWAdCL7QCz\\u002fOiKmPflASHHBT5Eh+EBr5H62cqH3QMVJTLAdcPdAj5tve8Mu90CWCrrG\\u002f4P2QN7fgr3kNfVAVAplYaE880B156cqw8PwQPqkHbpwL+xApZTOYhsQ50BM6\\u002fFXgpriQF8mTIGC991AMjtiEWFR2EDvPwzeG\\u002fXTQNrLfwvsj9BA6IERi0m+y0CjiHQAUF3HQHo4QW2gpMNAy4kjpCRMwEAep+0Yn0+6QB8iXMxBX7RAiyiB4DrlrUBwocvG25ekQB8vgpVEdJpA1fyNeZOIj0DsWsrY1mCBQNtvZzWCqnFABi\\u002faQu+HYEC2ujBsum9MQGe17TypdDZA1use061EIEDv7XF3U50FQKfEPqVJVeo\\u002f4b5uMNp9zT8QENmVjrOvP95AwKP0a5w\\u002f7vxJCcvSdECGC+JmzGJ4QODBb22aZYFA6vpXXYOBiUANPV19tLeRQGsgq4COv5ZAMoYvCrDdmkBwJ\\u002fs25lCdQE\\u002fKkCpqAJ5AQHmQ+efPnUA1dQRrf7eeQOQf3qawz6FAx1F1fGAOqECo1XXW5Q2yQAnoImsvFLxAWc6aI\\u002f6ixUDpFeYl0jTQQEhW5bnpbtdAdi63t4NN4EC3v3rBscnlQKIdHSPi8OtA5fxNBvMy8UAaJZsNKV70QH1g7M6fRvdAjtLe0FzB+UAFJ7vs6qz7QCQ8eBQC9PxA0pxtKKGX\\u002fUDtO01pzL39QOAMx3uPuP1AB\\u002fK\\u002fzXD4\\u002fUBPzwymcOn+QLV9iMkRZQBBIes+WAXKAUGfMUKAj4EDQcB2mp\\u002f9WgVBX3y7U+cnB0GL8eIZUcMIQTkeytxKDQpB8tR4pzfjCkEnvQhcWh8LQcYdBBCRowpBP9MlnFJpCUGV\\u002fv\\u002fmGYwHQbX6rXnKRgVBMEcUYOjkAkHDJKbpS68AQcSwHb\\u002f7uf1AnWeg6CMZ+0BlDkN1L4n5QMMVlOd27fhABvf0sdcE+UCCW3sFCm\\u002f5QC\\u002fvMNZduPlAxG+jnZ9w+UBeYvpitkn4QNL6mqyiMfZAJwUhyGtZ80A6nEYosCLwQM9j5Foe9+lAzFJU0Up15EASZl\\u002fbyBbgQPCdjHqSvtlAwgCDdus71UDwPiJZzR7SQP06g5VJzM9ARTrU5NBMzECoDPnEmxrJQKpmhwgsy8VAiOsyvuE2wkDlr9F9x+y8QPUNc1rCmrVAyY7ZZC4lrkC0yVHJ3YujQGGJtRWDeZdA3fH13X0KikAUtjkNkaF6QD4qcXzWEWlAS1G1miOzVUAgMjFf+EFBQO6mXmgxNilAK1r5smXqEEDSuK\\u002fRm930P4bpQ3UtwNc\\u002fIj0YbJgkuj9Ty3d02QSoP\\u002f87Etb\\u002f9HJA++5GsvFndkBrLNNXPUiAQGt8ARJKUohACOpld5wykUBIDtsL0nmWQFr0eYtiDJtAhU1kpvYpnkCw7It3ZcGfQE3qJK8VbqBArjT9AB\\u002floUDRGK7Hh\\u002f2lQBBKvvdBya5ACdfRgKA4t0CYuMH7X7\\u002fBQMeeohEWjspA7TEwa14000DZ0+WYHcraQFXB3kDyB+JAC9aF3RVv50Czih9cZGftQGk3qNRM0PFAgEeeB3Dd9EB4tYbGKqz3QCPs+KUkFPpAr1f1WVf1+0Bnw7RSGDv9QDfEMpAG5v1AsYBww8Aa\\u002fkARG\\u002fAI2Cn+QCLjyRpBhP5A6\\u002fYfmnWa\\u002f0BfzaLkNNoAQeC7ZtUqbAJBf4BW4AtnBEFTq\\u002frnz5wGQU0lhym72whBh82j5Q70CkHCWUWBZbQMQeSpl1RS5Q1BauYF4mpODkHJiSGNCscNQaSrd513SgxBevuyxmwCCkHRArZ7E0AHQWjkHC0WZgRBMq+YKrjOAUFykqTY7XL\\u002fQOWum96YivxAAGtmIzLp+kAfkyUbUmf6QBEHkuFyufpADbeS1bNz+0AAaHaysBL8QKfS6BoDEvxAnZwC4uQO+0A6jo65K+n4QA58DAVQ0PVAZI+vhnsy8kCO33AraCjtQGP8Ro3lyOZA3ouRN4TC4UB8VqFaoVPcQHzJmKZWlNdALZP21zqb1ECHKJUj2rTSQMD79CPPRNFAwOnjPRioz0AU6gFZ4jTMQGHow702CMhAb2qI7MRYw0Asu8+2izC9QATxHQ1zhLRAsiLsKfPEqkCeILPsiCmgQIOOAxaJBpJAqgX1jUWKgkCDfXfdSZFxQMdhkl1ppl5A3GcAJoucSEAW3CYQ2TIyQEJKdp7szxhAnZXZiFJC\\u002fz9\\u002fv8abQk\\u002fiPyUZNFFOCMU\\u002fSCeAKawrtD\\u002fboYTINWdzQExofUJ3M3dAFjTCOIw\\u002fgUDAtxqWkFqKQJjE4KfoCJNAl8+lCFFmmUCGtI\\u002fnmDifQKXDClAgzaFAXn4+o\\u002fYyo0CFtlRpXGKkQHTOlFSst6ZAXtUoDiQ8rEBKTHvjdaizQGo87oHKHL1APE0qX5CsxUBTBEPt6G\\u002fPQB\\u002fdlKpJ+dVASWThVtWb3UDcHr8e\\u002fEvjQCO9mgYoaehA\\u002fjZT9GAE7kC9YLHHnfHxQG+vwJjt2vRA49EgDNiR90Ak+9TA6+z5QNVezeI\\u002fy\\u002ftArImplnga\\u002fUBT83wxQ+D9QBXghUKoRf5AfzfYqICb\\u002fkAGsNeiH07\\u002fQFgXBbOAYwBB+ayxRWakAUGgez26eGwDQX8mPBGIoQVB5naT8WkYCEEauNouYaEKQQOYH0MMDA1BcFCZ+4ciD0HUjvZWYlIQQfRByb5apxBBfROS7\\u002fN1EEF2E\\u002ffogG8PQWxzXu\\u002f7AQ1BH3fAGPH6CUEiuF7f88gGQZj4+I6j0wNB64T58aBlAUEBTxpKWUr\\u002fQDJJIe4aMf1A69iXsthb\\u002fEAzkXvkHYD8QNkWP5KGL\\u002f1AX8j4KK\\u002fe\\u002fUDk9l7oUPv9QK\\u002fCbM6UEf1AyiEovTjy+kDj3Sbpv8T3QIWX\\u002fLf1+PNAkgJY5CEd8ECK34waZVvpQKPWukiY6eNAwlrN5OIH4EAzZPKnegjbQJOAPQR7AthA5uYUfAMv1kCAZkdFktLUQLAP3bI7W9NAUv9Dvatw0UB2iksrlPnNQDt5p1N3S8hAlxYl21FuwkBkJvzW2Qq6QFMulIEwE7FAp\\u002fL37B67pEBLTJ\\u002fJJkSXQGgI8vmbHIhAr60xox4Qd0CTzMo35FxkQGqll39GnFBAXf18xxIVOUBg+eRKspQhQGMoaw\\u002f++wZAX0jWAmpK7D9+8Q7usWvRP529kvjy0ME\\u002fgzOYfxvvdkD0aJwudbR7QKqPg05aAYVAzjMMoU5bkEC19TlITAyYQLbaJhvaUKBAuAFEnmlhpED6etBBiJanQDdq+Na\\u002fvKlAE\\u002fvCSEpuq0Avh8qLbD+uQAFfr6bZSrJAfGj9\\u002fJ6UuECDwCOfapXBQB7dtdz0X8lA5JWv1bXb0UCepla+IjnYQG\\u002fzRuZRrt9AIL3\\u002fiTsX5EChn1qQltvoQL\\u002fBitizGu5AflSL4bDY8UCi0cE+Rq30QJRBwW\\u002fbW\\u002fdA0bCJSIyz+UAMMYQYCo\\u002f7QJYTlANg4PxAfVFwvfG5\\u002fUCzzWbVIlP+QECax+5vBP9A\\u002fOFb5zIbAEFb4NntTSIBQf1xSqigrwJB9\\u002fyD82+9BEFfxdpv\\u002fCsHQbDXebx9zglB5+9xWe53DEF2I4r0mP4OQYdS9kGumhBBBxVO7atxEUGPeiUq6eIRQQ0x0Hqw0BFBxMJXJ6YtEUEChPDIMwYQQThl\\u002fNQMAQ1BDPrGPgelCUHeF4QuxGUGQXtfgm81mQNBSjXE4WhyAUELYVm79wIAQRHREhw7g\\u002f5AWeBe9Zcd\\u002fkBaW1vbYmz+QIjQy2pa5P5AHhC9\\u002f9vt\\u002fkBUNXYdPgv+QJV1YH0iAfxAQ2ht0XHr+EDo2mq+UDH1QOp9TJqZXPFAQBK7D8HS60BqiC2oo0jmQOI2lWSKReJAVxGvOJc930CNGvWzwPfbQFVw9YgR7dlAWro8b6pc2EDvMY13cqrWQIZyue0sdNRAIviszT2f0UDOvRb3eKrMQFfsa\\u002f4A2MVAdvIUpx8Iv0B95WIEqXq0QK3PNEuvD6lAuD4z\\u002fNFmnEAuOlXvCcyNQO+YIDz283xAOMmLR9kXakBWqimMAt5VQD3Q0dVWG0FA+O7iJdAcKUCHNeDmCmcRQOhm\\u002fRMkBvc\\u002fIomWEP0N3z8AsA5iBhjRPzLly\\u002f2kQn1AaxgrUufJgUA2NPoDXk+LQOG7DtcZhpVALbgk5Of\\u002fn0AgOpgPu\\u002fClQN7JfF5NrKtAeK+v9qwjsECwsfLYdKqxQBKFC821srJASuuKAhcgtEDpFC4JClS3QOgiemth1r1AnA9O4EZwxECJGwGPbX7MQC4FBbmVe9NAgIRLaAu\\u002f2UAOX6TxYXDgQMErLsRXbuRA0ZPYdPHp6EA8gpjVLvLtQLjLvO0Eu\\u002fFAs36hcfSX9EDD+LM8+FX3QKh3sOA1tflA\\u002fzAfP5CH+0Dk4xyMbsT8QIYXRvuLkf1AWUJx3N0+\\u002fkB9+PlBsjb\\u002fQNrzatUzcgBBtBHn0j7LAUG33b+6j7IDQZ6B4HFsFAZB8YemVD7ECEH23UAMcIwLQY6Iw3RAPg5BmstZE9ZbEEHod1seG24RQYeNnz7CQxJB8XBxvU7DEkHJIXiwb9ASQZ8dOvNHVxJBg\\u002fCDLy5YEUH\\u002fqNGbL9kPQfdHuwDhgwxBt\\u002f5S6VwaCUFklemXSPsFQTanPjk5aQNBxSVIqiaGAUFLZ0kBEFYAQWUkIXq2hP9Albya2Ro4\\u002f0C5Gp0h8ED\\u002fQHZFnXIiCv9As4YrqBEV\\u002fkDLr\\u002ftkyCD8QLQBiLtiPvlAxfIdZiXG9UDLZHZx7zHyQK6gwMv24O1AWAIdmGSN6EBsXsGIm5HkQHj3CZDvy+FA2yBt1xjm30BvkUiLWmDdQG+gsjrTW9tA7Mkk23FF2UC2MvWv2bjWQMZmboIElNNATq6+2FP0z0CeUL9aCX\\u002fIQGCL2lKSi8FAyj9+D1xnt0BTsNIEqQStQErkMlqFtqBAv9zUT6LlkUDpvfwJqtmBQBHo9dm8onBARfPwC4UaXUB6DeLjDwZIQE0791\\u002f20DJAS3gxhqkaHEBUwtMRbi8EQN6il2iHyu0\\u002fvhdYG7t44T9z6C3Y\\u002fIuCQFL1LVnBn4ZA5X8g6St6kUDIzekYhLqbQKU8i0V7vqRAyJhtJBqgrEB9+wFEwCiyQKFjWvphR7VAAjHpLdNQt0D6QCEHKYG4QGHqMPM\\u002fz7lA2i71sBzFvEBcxCs9IIjBQFT\\u002fuDgj+cZACSBcvGrpzkATzxfIwozUQOh9OHV2h9pAxk8w9k+d4EAboPaWqFvkQPjlSuUYpOhA02mRTkqc7UByHku4PZ\\u002fxQMbNR1cAnfRAq+885NF890DsznfAXOr5QNttFAPTqvtAvSRWssG5\\u002fEBkHYJpWlL9QHwnJRyL4v1AU\\u002fAeTjvu\\u002fkDLUVhrGXYAQZh6ELurEgJBjbp40RdPBEHdSG4gUQkHQQKdUel6BApBrTGbp\\u002fj7DEHWJuPH\\u002fbcPQagLgOxiDBFB+I6d+\\u002fMIEkHdziMEn8wSQRhz2gT3ShNBQPnHUoxuE0H1m5WubyATQUxfs8PdVBJB75ImU1gVEUE7jZoxGwQPQYiamn\\u002f4kwtBeb99RAY9CEF5f5IJ0VAFQb+pVDtqAwNBiIgDHetnAUFTRM5zcHEAQccLWjak7P9AjTqPiY9u\\u002f0AyxmhIIdr+QBMzt7Lztv1AwSPfYYTG+0AFRk\\u002f5mBT5QIJkALf46vVANJw8Ja6u8kBLjllMXXLvQF9+VTyqf+pA3v0DRHuh5kAhyceAGr3jQCZKYrmhneFAjrOUbXYF4EAlAfnzY2ndQBtC\\u002fThw3NpA36DH8wcE2EDG+NbsSrPUQA0i83C+\\u002fNBAn8bAeE1NykDRG7e\\u002f7hfDQMzlmhNM57lAyIJoKVBlsEDrw51ZwF+jQO\\u002fnwh2EaZVAkh2JDB02hkAGbAwnNLp1QHgZEsW1JWRAxK2f42zKUUBlybZdEQA+QKsoR+uqKShAQtK1lz2tEkAddfL4M5f9P9pJNMHdJfI\\u002fCfEgqhRRhkBOKeH1iT6LQCjyqXxXFpVAcVDxvtLFoECOZW5noi2pQLo2Lg0kcLFAdbEtmtw2tkBtUE6hziC6QEgg0nMHsLxAZEkZc+YRvkBbSVrGuki\\u002fQOu3eqas\\u002fcBAcyR3STP4w0ChbLsslTTJQKoi2BbiatBAw8oBsRlB1UD14IAv2tbaQBER0pm5g+BAfRrTAaX+40DLA5\\u002fTyRXoQCX\\u002fjtp6Au1A+N61XTNj8UCV3SUfmYH0QJ6XnazKgPdAV1YYYtH3+UBQ2fRG9Z37QGHQvmTvb\\u002fxAl87yVHW6\\u002fED4EbBwlAb9QIcvAlBV8v1A9MqQx4kCAEFKjVoTucUBQRtT0yHTQARB9lLiVGhIB0FiIUFOn5EKQXpVnNAFxw1BJVKHm9xQEEElruyU0n0RQdfDuNlTaBJB\\u002fvqmNJ8WE0H23b0FnYoTQS2QPyKduRNBfyfBjmOOE0FiSpudsvQSQf3XUJo+5xFBDH6vRKd3EEF7MEonm5gNQdO9TJ0cLApBhXDieyUHB0Gv88ADjG4EQf8KGArLgwJByx8fHWBCAUFtFx4YwYMAQUL3cHzOCQBBwyoDLC4f\\u002f0BKZsG\\u002fWrr9QDqxOK5Zr\\u002ftA0r2vA50M+UAi6VdtRhT2QIIxcCQrGvNArcwhOoli8EBic4a1piHsQGhc114bVOhAFn9blHxE5UCIUvChXtHiQAL1hF192OBAY2jJ16lm3kAhSnZPc2nbQB\\u002f3O6M+YNhAUzAkwfMP1UAOKl8nRXLRQN4\\u002fQrKWa8tAoB5yN5FNxECcOPmAvzm8QMQDzt\\u002fkZrJApa+4bFqJpkC1mEZo6gGaQKK3kfRyaIxA1iozsTSCfUAnJQCrrEBtQKDL0fHbtFtA5mwbf6UGSUBL\\u002f5LNJHo1QAynuXPQjiFAHiBSGLs+DUB88hyc1XkCQOSy1GHSJYlApmaJ\\u002fX+njkAccgU4Cq2XQAOCNkMt0aJAAZGtTwhErEACq+vzAp2zQGNVCqU0D7lA6KjGyHaWvUAxTPtVr0\\u002fAQGlg2havJsFAULEmnBzUwUDLiFamzjLDQFvB3HXOKsZAjgZdaNhWy0CqQJHwN2PRQAVSOYx+AtZA09bqstg420DdhYboTXHgQO8RczajneNABiBNFkNq50DbWPFGuSDsQFBJ55vK5fBAlvNPDo4D9EAuBOy+0QP3QCjiX7vBb\\u002flAZonAy8rz+kBH87yZxor7QPv50jBJivtAWFpAp6aL+0BtX7f5oD78QPojts7oOP5ACHrhRhvoAEF2dhbKJ4IDQcUThXByvQZBitM\\u002fckdICkHpnicfaMANQU7n8m5eZxBBgWfe8BKgEUEFd7xOLocSQQxnQW0vKRNBFqKcBJOTE0Ec8SQtN8cTQWBPHY6KtBNBdoGhXRhEE0HRjQgLDWYSQeolBytFHxFB4g3jY48ZD0EDIFblhLkLQSYgBtf0gghBZCTJ+DzIBUFLIa7CLbcDQdFzDKmxUQJB3\\u002fROPpBxAUFMBjZdMdYAQTSGEc5\\u002fOQBB7kObRezL\\u002fkBiOlVnfov8QChrDchQyflAIK+at3XJ9kCLqkCnWdjzQBPmCzmSLPFAdRACRJi37UDr\\u002fsBomMXpQKxj6c0Ka+ZAAduvbwWV40DUNOD5wDfhQDbLLNRHhd5ATjvIbvgs20ByfR2oDhLYQAQSmuyT6NRAWmXzlnyN0UB6wwYBuCXMQE0dWxBeY8VA8lPlJImwvkCzXMg7R8i0QEI4PrqRoKpAWO9jWrgyoEAi7SXFo8mSQFMqKlCe04RA8B0qLCoQdkDHpOi+hkRmQP9d6mbDS1VAwpsLL\\u002fgwQ0CMPIk2VVgwQGjglJ0SNhxAMDAuIyEyEkDtSH59FqeKQKCtvAMoLpBA8heaI8jUmEARzwAUrqWjQCROI9bDc61AzRYhAExwtEAmHaWrGSy6QJCFAXAmC79AbK5aSQE9wUDjAjwepE7CQCHN+bXpP8NAKxZUuh3txEDPTp0eSDzIQIIlpc68vc1A0Goslze10kDVApyOBlbXQFaXvb9OXtxAFAaovEnO4EAEaK935qTjQGnUzRTWAedAHc1TEq8660BkMsxwBjTwQIrgNWmGGPNA\\u002f6nNVe\\u002fm9UATKBdKvCb4QHxr3hTBgPlAN91RqMPs+UClqe7tZr\\u002f5QA1PyIFLk\\u002flAAKgYGhUb+kCWV4v45vD7QIuOek1\\u002fcf9Alr0sp1RTAkE0nkimOaAFQWYeKhLnTwlBUOEK8uP7DEH0iS\\u002fL5iAQQSuuvowTcBFBr2Js2VJjEkHqqsYWkQcTQQt0jJmqcBNBwwRlnNaoE0E43fZN6qcTQZ+bDazfVxNBwuwzRY+jEkGvsa2ihoYRQXdDL1gcFRBBGYRDEWbyDEEyaP9ja8wJQZOVISMtFAdBGXOk57oBBUG6XQyRjJwDQaqG44SbvQJBA4AdBGcfAkEYsBv+BncBQf83W5yajQBBjm7QADig\\u002fkCgDH+nqJ77QFXtUlHbYvhA1+cUD8k79UCm3NONDF3yQBoo5JB8ru9Ah+ZvKfFH60AKJhaafnDnQEpxPeKnH+RAuIg\\u002fx4dY4UABm6Hc0zDeQAiYfsf+j9pAIGEh3Ch410C4MTqmv4rUQLAXQxhZiNFAhuoOJffMzECEe\\u002fp2eZLGQPnFA4LR0cBAk+xypBHWt0CzhGTGmhmwQGliLvBcyqRA049OBSyzmUDu+pY0dV+OQJ0m26fDFoFAYfdx32UyckBuZl3b6TViQJUt+dL4CFFASTnu3I\\u002ftPUCeHEdvOIoqQKiAwczGZCFALG6Ep\\u002fnmikBoKZcGGzuQQAtweLIVpZhAtH0CFllYo0CJNlsry96sQCdH7BoKA7RATZUyheWvuUApyTxOmKu+QJVs6zAyPMFANAf1gBGkwkDJqSq6UhTEQLxKx1DTZsZAKjNcyyd8ykBdsHl25mzQQFxxKxk+sdRA4zIMRh2s2UBi9rzh2d3eQAWMnCEn+uFAQuz5l5+G5EAEVdinuV3nQGorDG2R2+pA5Z5wDKkr70Ble3ehXwjyQP5IFLJLcPRAdEsEU\\u002fNg9kAOUsudBon3QGXD48nO3vdAZoqyFM2u90BNoH2uP4f3QGvbk660DfhAndUFCs7S+UA6At02oTH9QN8QMmhbHgFBSn21jdlaBEGqnXHWhwgIQckW9k+dxgtBGy+\\u002ffHkwD0Gf1bgAXf0QQcbGWixOBBJBDxrlaxu2EkGGiWinlicTQe7zVHJoaBNBBN3qTzV3E0HhUAt8HEITQXWChxXsshJBKpeGZWm\\u002fEUHX2pS2aXQQQTUdpvmK7A1Bae6ahxzuCkHoc29JjU4IQYZHzeVjTgZB7ArXrZH7BEHSxmToXzAEQQAXfgaxowNBQVL1euYEA0GKZpF1qRgCQY7J4kKvygBBHxLRofVa\\u002fkCsC31BpdT6QPttJSY5V\\u002fdAnxy6m54a9EA5PvJFJTPxQM9BUDW7Pe1AUqMLcd+w6ECStmQza8PkQPNzlYwHg+FAYtDvRTPn3UAXlUn09f3ZQCPjJIGE6tZA8Hwqqcw61ECRq9px55XRQCQg3zqbrs1Ax4qG\\u002ffAayEDDuqRpk8XCQPqAc6yoCLxA1yMkIYEXtEBjMt4pT6OrQL9EkhuMMaJARwjqOu3NlkCyBidNSwaLQOaNMHvSDn5Ap\\u002faTMlcsb0Br6OGObgVeQO2v8wElBktA+KtaDy6BOEBz5RrciUYwQISVUQi\\u002fN4pACPCrmyloj0DkjgGIE4uXQIZF\\u002fzn9TaJAmj7sugEuq0AFUSCQCdWyQEWbKzE7SrhAjohBUjlUvUBlYLWhM8zAQHZ1A\\u002fDArcJAzhyGncjVxEAvjx720hjIQKX9j2WsUc1AoO8MfsB\\u002f0kC9+ldP63rXQBqwT7nBKN1AYjZUJt5z4UBHwgjaohvkQESigeCuguZAt04DqzXi6EB3Uec8xZXrQDhRG+gY3O5A0pC8tIxO8UDxG4UHxy7zQL8f7V1RuPRAGo6012Go9UB1GDV9qvf1QFhzc5Yp6PVAZ5WVc6Hz9UBP\\u002fV7Pz6b2QCKxt4EafPhAs1ahNsjB+0C9wiGEEEYAQcTMF5YOVwNBx0B46\\u002fjbBkH2n9g9qIEKQULAWubW6g1BJxOzgPlkEEEhXZkZKXwRQW99E9hQPhJBQctb40u9EkGwnatZQgoTQfH3nmisKBNBbN02t40LE0EWaNveUp4SQUCpH1e60xFBEZW5LEGyEEFMSTtgUq8OQYSoG9E45AtBYZxWOD1kCUEmufQgFXYHQe0dVVz2LwZBhyb3mX1yBUHljHMOxvYEQS7HPPBlagRBb\\u002fOs6ciNA0EN8+tb1kcCQSzOhVERpwBBrLjmeIek\\u002fUCTbC83c+b5QAADb43+TvZAahMvIC768kDkKe9jneLvQA7hSXjYdupAfdrKjhHK5UC56FbknffhQFjhbSm5EN5AadY5CfrH2UCx89SQgqfWQHNs+5g0KtRAltl8COng0UAC\\u002fJlXBR7PQL3gNygMVspAXkS0ZYuVxUAziBj26RfBQM6Ym8KXF7pATYT1B9IZs0AQ30rvsqOqQDwktjENjqFAoVz6M5OxlUB4isyub\\u002feIQMdhRXHBnnpA466Sx5Q6akAhJCyEMhVYQEhV9fOTQEZAkprByovmPUD\\u002fPMlVwuGIQAAwqp1CoI1At5iwxJP6lUDzD42UnvigQKrv6M6xKKlA2ykhDb+BsUCqKL62RNW2QFgIR1naGbxA928v9xaMwEAduZJdrRDDQB5jhVJCHMZAvhrbLEh5ykAy9KPlZH7QQDqzk4yLE9VAw+FvIlDl2kA9Jw9VwrvgQOkNORoHBuRABTwfqZT55kCI1hERcHHpQJrFw4d6j+tAthK\\u002fY4Cm7UCl8Sue8ADwQONtmBYhVvFA7PnB9Eau8kDf02IRRs3zQKMi79olhPRAHoc9baHS9EDXmT9KbPL0QJ\\u002fuDPinR\\u002fVAMmK4mbtB9kBX9JLInj34QBv3ZTIvdPtA0DyB1F7z\\u002f0DSBm85Rc0CQSIXP4tICwZBPSCi+UVyCUHr16CtO7MMQQvL09HVhQ9B\\u002fpc6hj7fEEHo1qJDeawRQZRJMEeQNxJBWvYqbDuQEkG5uBzRebwSQS+l646zsxJB27BqCEdkEkGm3MAJ+r8RQRlYD1QxyBBBnMdF\\u002f\\u002f0mD0GqDEA\\u002fj5MMQeoRDwcyMQpBSF3z3utICEFv0Cuf3PkGQU\\u002fYtq+CMAZBgUa46r+wBUFlhKGOki4FQWNnGDTBagRBpCwus7RGA0H+gA6zf8cBQW3zSgyICQBBTmzRkOZc\\u002fEAJd0yVraD4QIeuqcaw\\u002fvRAf\\u002flBWdqN8UDWYIZyt87sQIGGaRZ4UedAyI42SN7W4kBKZZpV9eTeQOIrvNLgGNpAjXGwnG7R1kAE9giW1XnUQKLF7amcj9JAebjUfsm90EDlaun61LrNQNTCjsCry8lAhoA88IC5xUAn95XdY57BQM1XnYObRLtAq\\u002f1kAefxs0DYh7NP7VerQC+mfVgzcaFA1W73\\u002f+SYlEAznmi\\u002fNm6GQFSoTcgYgHZAgM9wdOv+ZEBerlBAG7ZTQFBymCi8v0pAPT+RfI3+hkBSNk37r1CLQH+NwY1SNZRAdn22i8c\\u002fn0BIU60cLVOnQLg1b1awc7BAhVBnqq7otUCvLI8NYLy7QIsNvso058BAYutWNJM0xEBbrxlmfTfIQLLLsc1Pp81AkLUu5h2o0kAQ42qHIN3XQIwV+WWPZN5AwseG8Cjj4kBTYBRpo5zmQOPZahoX9+lASq3VV6a17EBBYqc7A+HuQBRtMbhzXfBA\\u002flGIGiVH8UA03hOFJT3yQEP7S01MLvNApyNAQRr280D3xcFRLXr0QNx9hSvIxPRAqvihOwIM9UAe1q6DkKL1QJMbtqz33PZAlr99SMH6+EBIZI4QyRz8QEOZWFf+IgBBy8F+m2OuAkFWUCXDgZMFQfRav7LLoghBCyESQUmeC0EMoLzvhEcOQR4\\u002f+7FHORBBLR0exE8JEUEtiv\\u002fabZsRQXhUZfYT\\u002fBFBTHXBWbIxEkHkvje7ADcSQT3qSPTg\\u002fRFBhIxaYfJ4EUHheri1VKYQQcOOdGLJKw9Bd+B\\u002fEpHPDEGbSzF1eocKQQcRyYFVmQhB+1ppSAwrB0GyO6pdBzgGQSVkDlR8lgVB0iCMrx0KBUFLPkFIg1sEQfyAw2Z+aQNBBzSu+0stAkG\\u002fKMcZh7IAQUDITiVqFf5AQZv0WpyK+kDo7OditN\\u002f2QNyQ2Ex\\u002fNfNAMekyGzJx70BpYJ0H+DfpQPozJtfdFORABFofGGIw4EC+3THGZPbaQOxBDy\\u002frc9dAtpO8jC491UDZbG+3e8DTQIIlTnHxjtJAr6Z8XGFc0UC\\u002f\\u002fsngzOrPQK831zYxdsxASZqC1MRPyEBVzq0QhKnDQJktAEnC0b1AZJCFq1gKtUCiVuRkp3mrQA7ULNv7iKBA8FuQ6alMkkBEC4S5WJ6CQInQxQwWm3FAA423ZffAYEDvyGmrsO1WQC3wbHY7jYRAQdYwYsJ+iEBZvXutVEiSQMWIUtk\\u002fqJxA8+s+kCvMpUDxFXhrU4SvQLUy\\u002fFW0oLVAoIE03SZUvEDicOwG+N\\u002fBQP5VKLffBsZAX5qJPGTxykD3Z1lXkJvQQKTwH+z6ydRAxTb01hBN2kATQlqBKpTgQA8K9AEVfeRA7HJVBnuE6ECHhd5Ui0HsQMk7vBLHau9AHrg7OIL58EA474TT5wHyQDY9hlCb6\\u002fJAyuMqdJnF80AlfKAcn4f0QLZnbukrHfVAUneVVxx99UB1yHL1OL31QHNiHMtdFPZAqlCUxzDK9kCAt3spSB\\u002f4QJnsPY79O\\u002fpAdgVUtmkt\\u002fUCwc5QRSnYAQUcDBsxIsgJBPhFaCrA3BUE\\u002fFRPfcuYHQZrglVGpkApBqa1bddYCDUFoX8rFNBMPQYOYZQBQVxBBXx5qCM\\u002fsEEEkIoRaDVIRQX\\u002fFjA0IjBFBK+soxl6XEUF4ZYSskWkRQbyTihXn9xBBuDQvgN4\\u002fEEHMnlhHU5kOQbKSsqvjbQxBYdInU889CkFr0uwhDUYIQewlr76DrgZBFt0PsyeABUHpApqFeaYEQbWanvIK+wNBH3xPZy1VA0EHc5OINZYCQdI2Jl1NrQFBDdVecYiTAEGMJMRx8ov+QNVFjF\\u002fdh\\u002ftARNHEVb8l+EB8i2SasIf0QFQcWjLE6PBAqU6Fbegg60BzoCWvgHrlQAOi0nWYJOFAMTS3ofhE3ED7KrsmU4XYQP2lbYeCdNZAZAd7ljp51UCnsPlmNAvVQK1jsvmZstRAaQfdXAgG1EAiPLdDybXSQPjs7kcHodBAMUnKNKHHy0C5Sq29pJ3FQNT5YAmrJL9ApyzIXOautECDnyz3yUGpQBiYu6\\u002fzTpxASGDzoRUjjUAC2UCEz9x7QNIYS3tW02pA2+XDiMR9YkBWBXytzaCBQH7sMlsXOIVAVzTTafAvkEAQ1fadqQ2aQM8HkBF5a6RAeFMXT3+ErkBmEl1jaK61QBHy9nM0X71AzUvIqJMUw0Anuk4DCv\\u002fHQAtiTEpwls1A\\u002f3pkUT4j0kD\\u002fvolstljWQDdLfO4DudtAqwMRaO0v4UAcCa0+rQnlQOby83LOIulAy1dJ7UIi7UAUk1maUWDwQE7kf9Db8PFAxu9+3x5J80BbehEW4XX0QNECKDpcevVAsojMIolL9kCNPcYhUNv2QAEQ+3TNK\\u002fdA7wy+5GZc90CTPbLNyab3QHxrfNMlTfhAP6NLXJiE+UBCvuPMXmj7QB1ewfN1+\\u002f1Al9APzdWZAEEYqUTzL4ECQWmdKHr+qARBIKbWHzD9BkGntVzWgFwJQXcDi7mXnAtBZUJVTuqVDUFcz+tH3C8PQVZHDAAqMhBBB+36eiWcEEG9TQ+8pNcQQRrBvfeO4RBB2AM9AqmyEEEttsZ9akQQQctDpg2LLQ9BTPCoG0tnDUHYSMjz+1wLQajXQ6xoQAlBjNT05z9CB0FL1XZTzIYFQZqXfHNCHwRB1tAOPHAJA0G48qik8zQCQWctTRrUigFBz2t4jIjzAEHJgKRv9FgAQZV8Cg6USv9AOS5VUGKF\\u002fUAmHi72hj77QFoZe9fxafhAlT6gJjoh9UA6id+F+6PxQNdGVAi1jexAlPqPhMCx5kBESo1M5BniQJpNJBBpxt1AgUaTkWHe2UAH8T0kPP3XQMMqfyc+jtdARKDDdtv110CP9T05nZDYQLpLJJokuthAyRy1yDbq10Bq9mcNet3VQOCGEeozr9JAsctnNiiezUAptflw66nFQGL85eXlJr1AwiTIa0ABskDhb+00g2SkQA4JE5OfMpVAC1bKH1x4hEDlhXGM8elzQF4+jA82omtAGwCtdVzxfEDaVijxjrWBQINxeF4T9ItAutD3\\u002fY9Wl0CELFtravqiQO6D4GO3Zq1AjOeR2FSYtUAvrJpzRhi+QMxkaCUf9sNAX5GTmSdhyUAP6cio7EPPQMAS\\u002fr4d7NJAVlNcfizQ1kBjN5Da+53bQJZWVicnxuBApDKPjCpK5EDmqElcnDToQClidsBHSuxAQtQUh6Qp8EDj0vviDxbyQABhnmiw4fNA4AGm8rWD9UCPCT76D+v2QNVvhqlfAPhAHBr6FS+z+EDa0yMVCAr5QEQ7HFHJKflAiW1H5DBO+UCv\\u002fecIWLf5QDyn6NanlvpAHuLfGIIF\\u002fEDfv9BsGgn+QDRb17H\\u002fTgBB1kr5zzPgAUEg0TtuhLQDQZPevd8owAVBqKRlz97pB0ExSqGVrAwKQbeQRAJJAQxBIKNsPxSpDUHqthBJ9PIOQbWI13OC1w9BXdh3nowoEEEpmaU2iysQQX75fBMj4A9BKbQa8VfpDkFP63WVy3wNQTMqDT9KswtBrOj0bpCwCUGIlN0DVJwHQYN1NQ49mwVBmdzD3gHKA0Fa7ZhWSjsCQYmxzfPH9wBB6THjRN7+\\u002f0Dsqyufc5X+QG8\\u002fOnwzlv1Aq75LVgTV\\u002fECEsP1bwBb8QF5l2dUSF\\u002ftApVayIbiX+UDlm07x+3X3QA68kSESvPRAepZ0HuGi8UDmkB69TgHtQGppfZ7uWOdAF8xRXQ3K4kAvlZNMoBffQAaUdiwuMNtAK1U5HkyF2UAS51CXnZXZQBMmr71sv9pA9VvDeQA+3EAod7k3dTjdQOLcowSq79xAXuZZI8H62kAJ\\u002fMQqbm7XQKa8zp6P09JA2G5ttFLey0AQZiQGJfPCQNw5NRDIordAkXre8Q4Eq0B8JvwlsFScQGTM3jJOmotAK6Q+KOobe0AjctFX9etyQMtVaDKq33ZAEusMhzChfEARdppdc5iHQNDn0uJgjZRAy71IwrtcoUAVZtNkcc+rQFSzZ2h4\\u002f7RAiCgs04vfvUDbiDYwthDEQI91loolmclA6k4B5dVSz0ACpEPki6TSQG2z129K79VAb+B6eJva2UDIu990W7feQMOe4001WuJA0ssXCFnn5UC7iGBtB+3pQKRqE\\u002f5CS+5AghjPJeZu8UDTq4KnkLvzQBg0yoiA6\\u002fVAvbcWu0DV90DDe8QehU\\u002f5QHR2+g9PQfpAYjHqwJmv+kBOsGkA07\\u002f6QJpKCYQ7rfpA0yDINhq3+kBw9XySGRH7QFLJmePQ3PtAMCT\\u002fO30t\\u002fUCHJLoHiBD\\u002fQIM0tGYTyQBBc0RYfwJcAkFkiTZwDjoEQXkGLfn5TAZBY1lYx2FwCEFVNEgX53kKQb6f6kZXQwxBibELhz2wDUERDdBFpawOQd4mj2boKA9B3Sk99XAYD0HHKf09tHUOQZRYEvE0SA1B36VPbSunC0EtAr7zCLYJQRwfdlePnAdBa2G1PN1+BUEIb8XHE3kDQQLsJJZYnwFBcf\\u002fydUMAAEEUp7KT1E39QG3Z35+bNftAwKgEsVy7+UB\\u002fH4od79D4QHpy32WHUfhAptKwMTEB+EDKA03pM5T3QCyLH8cMwPZA\\u002fD6qTHBS9UDiX5cSsETzQPqdTZiRwPBAXtcWxHQk7EC5WQFnchvnQHvLWq6V6+JAGeZSsei830DyxFfxiwrcQK\\u002fHxuKLlNpAJOpG3g362kCkSSrvFaXcQLs2uh9oxN5APQgeBz4v4EByc4szp0XgQPVDdxy4wN5AoWfBr74G20DFqM3FT\\u002fLVQA7SZP\\u002fxZtBALP2hXWCCxkBZB\\u002fa+h1G8QILEbBegUbBARyBRn7tAoUAuIf\\u002fUHvOQQMAsDxW6y4BA9GU+j\\u002feTd0CY9hW0JKxxQAnQmCMruXZAltbT14Klg0Bc+WZd49mRQFXvkshaNp9AKQcJ\\u002fZ+uqUAwCKwE\\u002f8ezQPK2SyvPgLxAR33GumY\\u002fw0C+pGoPfX7IQEMcsEv4pc1AgH2c\\u002fyFP0UCniMaDtdjTQF1yXXZqwdZAGrHlXxhw2kDvlEeGQ0PfQFdRW6ZDvuJAL6mB3GKc5kA5CXIg5zXrQMYgjloINfBAiS5txSv68kApFUDk6LT1QM8MxtkCJPhAfl80OxUL+kDQpMaAvUX7QIjkKkpw0\\u002ftAgqFQgx\\u002fW+0B+jbiU2IX7QAEyIn8eIftASIotvIXh+kC4yFesuvf6QDzZbz9TjftABfdX8rHH\\u002fEABPZjEg8b+QCsMIFN2zQBBBRL+4iKdAkEtjmO1eboEQaEF\\u002fRza\\u002fAZBna4hHQMzCUERMoi2SC0LQT6ROwoAxAxBv+8QYC3YDUEDVYwt51EOQckQrfaHIQ5BJhratAxFDUFMkC3Kpc0LQZnAEF3R3wlBFZpvmtirB0Ea1V0F+WEFQSuNoetWKANBs5m84zsXAUE7qYLfs3f+QOaSOlwsPPtA+AmIcPqJ+EByMl6+1272QLzcu\\u002fqL8vRAv31nkJMO9ECTBmcJUKfzQH00nZ4DivNAlSeNjC9y80CBYW8qqRfzQAnChc72QvJAwcZA0Ufg8EAsCSIDUg7uQOnP8UlE4+lAAt5VL57N5UDbgEaovUjiQGRD4sziRt9ASoA3lsj920D6PTuf87PaQD4xwHkuMNtASkAToOn53EDHee\\u002fEx03fQF8GgXqwl+BA14DdhmfT4EAkYdTsgArgQNtb9Rp4cdxAF7TurMJL10C3dJDIaI7RQLrGyAULSshAmzIzAeXMvkC97TekHOOxQIaJSwaNDqNAVLVbMejekkCBNo1KGdyCQHafpkzxnXpA0S2LwapQa0B8MyL1RRByQAxbCauOW4BALpGbTkzXjkDfwLn0S7ObQBAI3fBbO6dA5COj7uocskA3qg9Xrj+6QBN5ty3etsFAlV\\u002fqDo5hxkBD2W1L3LTKQBQle\\u002fL5hM5A0JU0pJ8G0UCktPA8YfnSQKLTuJVXkdVAcP5EyHBQ2UCZRc\\u002fqc63eQJ4fqp8pAONA6hNfJO+050Bs4oWS2lntQARoKwVVzfFA3njeZ+r19EAUFLbPPtL3QGwGGi6IFfpAXur6IFqQ+0C5TYl\\u002fNTv8QIu6aZN7MvxAfGMolmep+0D9en3wTd36QNpyUYSyDvpATKAAhoB\\u002f+UDhAlnWMnL5QB\\u002feEc0NJvpAZt0M6X7O+0A9ceLtH4b+QMMNjG+XIAFBaqGxZTtjA0Fdw9DwgdoFQXkNdgdDSwhBxXQWjPR6CkGb6IKBjzcMQe3cfUrsWQ1BJar2AJPFDUGzgs2IyGoNQVy+iKsfTAxBRCOAf3qDCkGhr2+ipEAIQRnD8mpvvwVBInCRrjQ5A0EDg6sq09gAQf9KXIm+bP1AHyR7O5K1+UCKf6g\\u002f0472QCGoPY\\u002fD+PNAy9+jdsD18UAM9vlaDYfwQFO\\u002fNecNTO9AF1tvQMJ77kC\\u002fyYD7JFHuQAAA3YnQYe5A5agle8U27kAxdhDGXWrtQBYHPmxUyOtA4VQEebJf6UD4nN6vHHvmQMEwS0+wguNAE77+toLY4EBMrVEJgYTdQHK1xLUOzNpAdiEXP2Ki2UDiZ8jnLvXZQPXNnXKPddtA26WfoU+G3UCgZyX1gUffQAd\\u002fmjXizt9AyT3yQ2h63kCjWrT+YjHbQF1fpgObbdZA9yBfI6YG0UBNzRhd9rrHQFC8gX+hUL5AsJidDDu8sUCavcK75QijQLb1VaJN\\u002fpJANQJJlcsjg0DeoZsUyiZ7QLP5owgLqWVAkGg1ryBkbUAP7ZYxG6x7QKYVvI9c1YpA4QiBb8eOmED8lZ4sutakQLDkLsBvV7BASyvVCtywt0DrR6cvctG\\u002fQDWoe3QX48NAa4OXxnBUx0BhhqSCnQjKQBZQEl10NMxABMK4chB+zkD7tBWZ9fHQQH2pv3iZydNAf\\u002fr1z25d2EA+y+e2xivfQF8F6MeoOuRANZdoaPkH6kDouRnjL1HwQNSzJvjUtfNA\\u002fTrHaafR9kD5EN89Ek35QHe4IBSf8PpAhr9v48qu+0C+5EAxYZ77QM0hQS967fpA\\u002fXXarXXX+UCQGN75+aH4QOFQonQYnfdAnYuxeDMg90BESSdta4D3QC+J9DmlAflAc0xlYZvF+0CrOGJKqr3\\u002fQHPGwBqYUgJBOSeUgboEBUHMn41TWa8HQbeLuzYaDQpBynk01ynjC0GXuH8RWQUNQVopFdmaVg1BBYMF5o3KDEFal\\u002fiT5GkLQUrUh1I4VglB5NT6qKfHBkGtxrBGAQIEQSEs9A1BRQFBkklhPCOB\\u002fUD7wpVdnRr5QP3RODb1ZPVAY+fR3VhZ8kBgiBm+itXvQDizwZTwGuxAcJSgJmxu6UD\\u002fu4nzqLfnQDtPlXpu0uZA4ElDl8GI5kDpK9+WvJHmQOxhpnbGmOZAoDJUX\\u002fZO5kBMMd68rYHlQCNvwrfIKuRAHeZCK8pv4kBse2ZPpo\\u002fgQCvV1cAXld1AydCF8Nmj2kAVSOC8gozYQPfCXKL3dtdALNecLsZw10A7gUwEjljYQExQqr5sxtlAN\\u002fD3vnAO20DFSd\\u002fZD23bQFF8p4xdT9pAuKdWe+WQ10CpT6JRLYvTQOYqdX113M1ADyFWL3zzxEBXpNQQlvO6QCKqjPz8wa9AjGxQtv0poUAvo3W7G0GRQAb32Tooh4FAvGzAjk\\u002f9eEATvVzKgP9hQIgGXeYP62hAtYiO5coleEDw8Mg5dOCHQCl6IUq7HZZARy54APjjokCiUrGlhq+tQInl+WaGd7VAdhGmxfGhvEDTE4rmb67BQGLVihxCXsRA+S4dOn8pxkBf7QiCEULHQAcEnyy9TMhA4zFACxNMykBFR4VMHXvOQK8qw6glEdNA3NkahREw2UC5CPWa+fXgQMSlNdYpk+ZA8ZYE4W0V7UAdEuqvoO3xQJiOXqnyDvVAWVsKlKiT90A27Yp3DEH5QB7WYHkmBvpAQQKsv3T1+UClqbYhPzj5QJ\\u002fhDaKEBvhAXXmjD7ym9kBK5bDJoG\\u002f1QAexs5wcxPRAZPYEm0IG9UCJSqEBPYT2QCAjezGOZPlAFMNqk++W\\u002fUAJXybne2cBQTx99\\u002f1qRgRBazlFgQYaB0GKo928aZQJQeh3jVI\\u002fdAtBznhYWsCKDEFebJNZu7wMQZJ9gPENAwxBDXt35hltCkFgkpLj1CIIQXqG8F98YQVBLfH3lKtwAkFGGEP1SSb\\u002fQClth8EK9PlAmwiLFjN\\u002f9UA5APoeq9TxQMWqSEke0e1A\\u002fJrFOIlJ6UA9oEFXquPlQGX3j+IEdONAlvji2o\\u002fS4UAF+7TextjgQGL3TKm1XuBAl8cjW5434EA66l6TUzHgQGkvopD8GeBAn212xWyX30DETdSM227eQLLkRQfLzdxAKb71Psvn2kDxOJZ8TvbYQPL7WB78KNdAQwJH3qGn1UBlBxYHS5rUQEX5PkbVJNRA1b45LWpP1EDSP25\\u002fbOrUQMke8cmMh9VA5TgJEI+X1UCoANhlL6PUQDpoCSKegNJAyPuFLHDNzkBl2s6i66fHQNUfAIDossBAb24TCjWftUBJE6fsVqapQKe7mXbT6ptAc7RPkaJEjED4nK5ZjfF8QHiTatGuu3RAh0CUqPeeX0C7mx8DJjRmQJTpsIy\\u002f8nVASNcGBY\\u002f9hUDQGEB8EYmUQBEtziQ4nqFAuvnyX8q3q0BLz\\u002f0NRgC0QDEqeFmbhbpApy0pglg1wEAMqfDBf17CQKiJud7eg8NARymN\\u002fGLTw0A1N4793ezDQNqPOOjRzMRAMyImX5Cnx0BmUwsRvb\\u002fNQND77QsdGdRABnLaWa3U20BtacJCAv\\u002fiQN1vqHXC+OhAXmyYPcRA70Ai4Esok43yQJI9P+g87fRAIXeuiOaG9kAZcxM2VUn3QKmbD8NMRPdA\\u002f4n5H2Sc9kB5CaJ7\\u002f4P1QLS18XKPPPRA4ly+6MUZ80CxYHohF37yQCUFc6QxzfJAQ4zh5MdX9EBvNwcMykb3QH5C5SCTi\\u002ftAozkvquZsAEFKc5vPlFcDQYeMkefHNAZBirGR9cuyCEGT5yNg+owKQTIjsIJ2kwtBbl4yEROtC0GqUwk0KtcKQc2WVGFNJglBiQg8AHnGBkELU1fzzfYDQdfLSH7f\\u002fgBBxe4LeQJB\\u002fEAHD4EHExj3QF72bqY0t\\u002fJAfwTdzepW7kC5UwOBoc\\u002foQJTyQNVhpORAJhRhL\\u002fOX4UCEpsCZ8tneQFCjsGr01ttAHsDyAuDK2UC5FD0uv3HYQPdUKCb5mtdAcBu3yaIe10BCVOkystXWQMi25uccm9ZAmiU1ipZS1kBoOq4W\\u002fuvVQHesaXImX9VAkyhvO6ml1EAIslo2R7zTQFecjHN\\u002frdJA5SrnmcCa0UBQT1HvA7bQQKVfEGJZKNBA9+hEfd7lz0D8Bcr7hcHPQE8wT2z0Lc9AzoE5oe97zUBieKKlaVjKQOrKGI2e9MVAoc\\u002fQJfbswEA6fTsMYQW4QLPF6ewcSq9ANeknC5StokBvbNT0AHeUQANDS9Zx3oRA5Mu8UoGIdUAb7NycQ\\u002f9uQEZ6IHScGl1A4gq50vOjZEAsw+sfrrB0QAMgzNyh74RA52\\u002fwb4avk0D47STzqPegQC4VqyxEw6pAsbp5UmhRs0AODcbBA465QN2gck4fDL9APHWizQNmwUAwedxEHyXCQNkgAded6cFAt3kAH2ZKwUD1XG16WzfBQG3GfxHM0cJA6hUtWhdDx0DosNeEx47PQIymBEpDKdZAZa9dKyW63kCxWEixJnjkQPJ3WQYQ5OlAv70AuGz97kAjhWeDVpbxQKfef32wB\\u002fNAfEFLKT3E80CjUBseOtrzQDMf4ZodafNAAu\\u002ftKQCb8kBM8r8ebKbxQAIBkOTN0vBAazlGhNt28EBXvd4tCe3wQKrYbtTXgPJA\\u002fhwjmfFa9UDzYkKEf3H5QP58p78bgf5A+HJGJRkJAkHu85QvCMYEQfN11xuGKAdBZD2UOPDsCEH1I\\u002fDioOMJQZhlVkAV9AlBFtSdgS8dCUFFOFUEJ3UHQXfsjTZuKAVBNBNYOpR0AkET1DporDv\\u002fQJSUi2FTxPlAuTm3Cerd9EA9RyG7Y7rwQJuoFbBez+pAOQj1pIy15UA5yruyMfThQAVmZkx0kd5AkgDcICHU2kBU8ZGiBiTYQLVQttGpENZA65JrSbNW1EA1\\u002fn062eHSQA6AffKEvdFAVUPnDxf90EDiE2G0\\u002fKfQQN3giYMBsNBAr48+asHw0EDYbR6BHzXRQGZvWk59QtFAVyjGeT7q0EBC+bcAGR3QQPTh3vi+6s1AdS1ZxmJcy0B\\u002fs4iI2w\\u002fJQGi7cSEOT8dAkKAf4IoHxkCtXwITFtjEQEl3qqokScNACuEF\\u002fmoOwUDyMrax7Vi8QJ9bYTTR4rVAj3Ds2ukur0DKLGZljWqkQGLQjrwQhZhAVKlDLHgJi0AbZiWIycJ7QCl2DhGv3GxAnTSeXdreZEBwc+SC81dbQLeRj2ifiWNAsfzUqKXMc0B+s1dmzjSEQIvHAGJMIZNA+zSHp\\u002fKUoEC+ym4inkWqQEZISPP9BbNAvdliJVkzuUCuVr22rpW+QP9GA3RwDsFAV406skaYwUCvBkHzHAPBQPW3jwYdsr9ARpT5ljXyvUA\\u002f9SgaQte+QDuvlSbhN8JAFb6VAoY8yED8Cq0wvP\\u002fQQIJbwiJiuNdAx3j7suHb30BK6aRcs03kQFXXGO2weOhABFdWsPz160BL2v1y4njuQJYcbJUn7u9A6MSXDlQ48EDMeZ9OyRnwQAxsy6erde9ABZrIJUeF7kDvMrHKicvtQMtrxXJwzu1AQtwEEmsg70CBPKSskiDxQIbcUAA4vfNA9luNzu1h90CXcGDIV9r7QNCLHhx\\u002fYgBB9\\u002fk6pL3QAkHy\\u002fb6EafMEQZqiwNVjjQZBqJw6lXhxB0H5z\\u002fg8tIYHQbteb+8MygZBXL4T1OlOBUFFSEVbxj0DQS0rro7CzgBBCFdfoUN\\u002f\\u002fEAzuqKbPpD3QCxrV61fIvNAFG5H\\u002flPI7kBj\\u002fyuKdsroQKeKy\\u002fT\\u002fPeRA\\u002fQ04c7\\u002f34EDwbkR7eW3dQA7ju8auVtpAenR4tNAG2EAFH9rKfffVQIX9wFoy4NNAxzPYgVbB0UBe3mkAiqXPQClUmBeivMxApcc74sYwy0DhpQACzgzLQCKqLxXX8stAKmwcULc2zUB6OCCgtBLOQCnP0\\u002f07581AoAbqTX9wzEAtNGK8F9vJQJa3NPg9q8ZAoo3q+e9\\u002fw0A0pGQwdM\\u002fAQMfMWaIJfb1APJ0z+ipHukBwVVglDV23QLv5Lt58NbRAEJQFOKegsEAssOROtJypQMKmQA5ER6JA1bzGXBEImEAJoFCmogONQKcxl66cF4BA3FS5NQiicEA9i1ATvWthQGITqmM0T1lAzlreh8NXWUAIs6I0gDhiQPgSzCJgo3JAbNJC3e8rg0COVPqq30aSQLmH1L6g4p9AJsL+uHFnqUAQiTPktXyyQKWNeM2llrhAPT0eBpfqvUBUjkRiD6\\u002fAQL1LOB\\u002fAIsFAF+HHcsVdwEAO5lYwd7O9QJ3BkrR7wbpAIETE8kC+uUBdkyp6s2+8QAxhwgZ+IsJAeLptg\\u002fYJyUBHo9oRRXLRQHfe1QcciddAh1VeW7sw3kAwJwJknFTiQLhLRodtIuVAKUxEk3BO50C8guaP2NHoQMXf3AvDxulA\\u002fZNmWpxT6kDyeoNC+Z7qQK3TTgAc1OpA4gjH9bow60D75gwcWQzsQPhBZNqozu1AWWRcoGxr8EDy2fltPK\\u002fyQMzqxmVrsfVAGWWeLYBN+UA+RgLzHD79QKEPmkkQkwBBrdF0KWNPAkHzNQPDkKMDQYjXvcGiaARBgr1BD+OFBEG84c8nRPQDQTXE+zuLwAJBgUCe8tgKAUHJh6T+kwP+QNfWrLLLsvlA63KUDuWB9UAQTR8dg7fxQMd5ootW\\u002fOxAKiXbiybQ50AvY1rND+rjQC6hSc60K+FAdQ5VlUi13kDmN5u8jEncQFn2qjxPXdpAqQSdzU5W2EAV7sA7peXVQC8+8cxtHNNAD8KMLyxZ0ECaqVcgpSzMQJLAfuBeY8lAjHNzL+OJyEDW734vUTjJQEfPq5ROk8pAPHFMQ3yay0BsePVK93\\u002fLQE17lS1L6clAjIEAxtYBx0AYM6MlSlnDQIstOKk4QL9A7Z0\\u002f4lrFuEBf5dVq78GzQDgKEVpNHbBAmho5pGjIqkANJ5Cn3jGmQL\\u002fWcBAD2aFAMoDyS904m0B9Mc+YT2KTQGCo4vLHiIlAoXRLjs70fkDePmkF80JxQLanmZwn9GFA1KVwzWvwUkBnUywoHqNLQMsPY+ywc1ZACVBJYC07YEBSpYaNTr5wQNIlH7asWYFAdb6fqkGnkECml3eF2j2dQFtoHcgqcadAhatPPpQosUAxVFRb\\u002f\\u002fC2QH0+70TOCLxAB0mmBk1bv0AmRvwlDhrAQL4oolIVlb5AjWigdQZKu0Cu7Pg357O3QFz5QoIybbVAzPOAZwz0tUC6wIJNIm+6QDWMmp4owcFAZhfzRAaRyEClcbkwBJzQQLIarQfdctVAItpLF9pN2kCN\\u002f+O77cDeQAwP1owqTOFAWwST4JTw4kA1KkUH5mfkQMJUPcsizOVAU9SK4fUr50Cw0LEPapDoQFQz8D5VDepAStFUbwLN60Cb8\\u002fNUaQvuQAGwhFcRgfBAVKeO031o8kCa4DGmvbf0QLbZpArQVfdA7Am2rO4Y+kB5\\u002fycVA8z8QDXOb+rQM\\u002f9AIhM\\u002fufWJAEGO5uA0RBoBQYGA0PcbNQFBKANbvqLPAEGCuzP\\u002fDdz\\u002fQKHn5\\u002fKYSf1AFLFjpOwq+kBoLkRFYM72QI+83esvffNAu6hPVWlw8EBmcPR+HJzrQMaxd240V+dAPEQeJBAh5EDwULP+QOvhQBQPemQ0heBAvXa7qcw730D64T6UPajdQGNyG1tKp9tABgbwrP\\u002fl2ECHyHg+pIPVQLRYiXA1\\u002ftFAyrwBXKfuzUCilbsak9zJQBKgYnMrHMhAIYyg819CyECTG0TFtVXJQCbxz79EMMpAdZGFYljoyUAWD7vNVxjIQA8rmNae7cRAszK3DOj+wED1nzPmZwa6QLRvH3GNGbNAcY28Vk+Oq0DzaIVwphikQF6SJi6ZKZ5ArnTUS\\u002fQsl0AU8W1CCcqRQGsf0\\u002foDfIpAi+OkTSGrgkCjjwRad4l4QK6fZs9qzm1ARyuB3dqwYEBeC3Q+enVRQFCdtaDxikJAOnXv9ZIsO0CbwTr3NpJSQHG6utDJ\\u002fFpASsJHmpcObEA4jYGo+kZ9QNcCL68hSYxAOq0aL+D9mEBqBUrz2iikQI5nS+pzsK1Aixb0zfr1s0Bowz5Pt4S4QA\\u002f1g9kmirtApkcxwrZZvEDUtoXJ9eK6QI0tQMOMxrdAimBzxR0ptED+L+lfYGCxQMNTuVIxpLBAXz+hsoPbskAclUvNiYC4QIiTC\\u002fBuxsBAa99jGKK3xkB3DrG3BZLNQLqQztiYYdJAL4gEW0fy1UDuOkQvTXXZQADJomCCEd1A\\u002fvrE77F+4EB8a7B\\u002fM6ziQExDXIqWC+VAUZvb9TKJ50AZfACILhbqQA4AtUm\\u002ftuxAg4cuVWN\\u002f70Cf9EQ6ZEHxQLBi\\u002fzp73\\u002fJANSwh+jOM9EA85qEksDL2QG\\u002fudg05v\\u002fdA6HefJycj+UBsSnox41H6QLYI\\u002f2FpOvtAuPtwc4LD+0CwcNzEEM\\u002f7QGC7c94QRPtAWLBy9E0a+kBnEA4CImH4QCkX\\u002fSE8PfZAMcs9m+He80D+v3bJhXbxQB\\u002ftl6AJW+5AGFRuTQpJ6kAkL4f4jejmQIfnr12yWORA4K0GjkGc4kDE0KM2qY7hQNvGenJ24+BArXeNk1844EABI7k4Y23eQPAhyr\\u002fVattAvtnB6wuU10DdwS4Sa3zTQJOx7L1lvc9AXTra0IieykDq2hgIKQnIQNidyscAlMdARo3qjFc5yEBtSajO\\u002fMPIQLmVFMBKQMhArPRUvfFFxkCYMPHvEAPDQEr\\u002fy6cyG75Am5\\u002f9nmswtkB42vfcwr2uQPjxa+UMYKRABibPCIeOmkDwAuKqJI+RQBVgf24ZC4hAt5RtIXL1gEAWmOOxF\\u002f13QD7s5OP\\u002feHBAQbxwrtFqZUCGmz9FCvRZQBVJhzbpH01A19NRjGWaPkBDdvUkJVowQJyS9t1GDyhA\\u002f0sABjQ\\u002fTEDxJs7QXJ9UQLL6CgM4mWVAASZ29CWwdkBLT23Q3w6GQBUTjYYpnJNAYgg9cHbUn0CMlva7GZSnQMveQASX469AIZa\\u002f91Cxs0ALrv73Wjq2QKc26+wf+LZAnyLy2yDRtUB7dm5wqTuzQKdxPdx+G7BAeubX3\\u002f\\u002f7qkACwZZNm66oQHsl1jl5rqpAz1ixrkrWsEBpr+M6+s+2QNbhWMc16b5ATLAitEBIxEBF4HKSeaHJQMn3afPPcc9ASoXYOyj80kB2MVbJyNfWQP9sH2iVhttAVIlXj3SN4EAq9KNyyLbjQBmV1zjpF+dAWmeJw9iJ6kBiseXCyPTtQDwvS7xppvBAjuimSOc\\u002f8kA9dNCCDbPzQJH3qtDN4\\u002fRAqYFcJJG69UB5L4zKFzL2QFaH2FWdW\\u002fZAqf9Ni29V9kDrL8f79zn2QNN9\\u002fCcMEvZAR+u\\u002f7FrS9UBBo9Z6LGT1QE3JlggAs\\u002fRASCj7iNm280BWP97PpnbyQAmyqgECBPFAxNgb3tHq7kCMoQ4ZbsbrQEA6vgABz+hA9FLOV3w65kDTQHjYiTbkQG7kPbGG1uJAMLomWwcD4kCEvoBQwnfhQN5cpjX\\u002f1uBA9oOxobaa30B8f4VeAGvcQALzWJ+EUthAsOcuYZrt00CGem\\u002fGYP3PQOjcWU12P8pAw9msTa8Vx0C18fZqTBzGQNUKY8GwU8ZA0d2fCkuOxkBr8Df199\\u002fFQBjZGuYz5MNAyzVd6uLFwEDNtuCKVyC6QE+VNokEzbJA\\u002fPd6T2kcqUCwod\\u002fPfG6fQObZQQSWxZJAF0M3Y30DhkC5RUFNFTt6QE8IHVSdRHBAICiUVhTvZEDijuTKeShbQDt8WBrkJlFAnohIxmCJREDmhN83K\\u002fs2QKpSyeHpMihAhyrQNPX8GUDcbJ3Aoi8TQL3NQFp\\u002fkUNAFw3WcoW0TEDVY1ACJ0deQN2p1GxKAXBAQtEN2eBPf0DDxR8BQgGMQNYM40Uw3ZZA3Oosj38JoUC+H5IvDy2nQLFe4rkbyaxAQSYe3SBVsEBygVWvVfWwQGc0ImZxK7BAgGJJbS6SrEDTYnJ6oOinQIrqu67B6qNAdX8agHEFokDgY3eUMTujQDsYw4KmBahAQhJuEN8qsEBsa1QMNNm1QCfENl+awrxAbNPX0EdmwkBNjSIcPSjHQFVoKoCNGs1AU+UazB9t0kBQHZFDsXTXQGOsRTmqst1ApH6QNzR64kD3dbmUnGzmQJKME7T0fOpAkCGvdtGB7kB5WvacbivxQAJLmLcX5fJAxBtsqU1I9EC60F1a0yn1QCEdNLy8a\\u002fVAOKMxU3UQ9UC5Imzj1j\\u002f0QITm2zliOfNAug7G6WA78kDnKlJDKG3xQNURJckJ1\\u002fBAmXYdg95o8EArNBew+QfwQL+19cn6Nu9AI8GfSWgk7kB4xEMsH8bsQCO6y8s8GetACaOQG6Is6UB1VSeVgyTnQDSsA4LvNuVAU1uKLemb40D6bZ5R4HTiQM1fBEjut+FAv3yPLYos4UAFQpV3a3\\u002fgQE7MHJu2zN5AzI7QzWKF20A1\\u002fsyApmHXQKwsdTHl+dJAZvIHvUwNzkCSva7vxy\\u002fIQDfHWZGoxsRAkV4NHRN2w0CxoCvPs1TDQA+Z9OsIUcNAr12Ax8mVwkArp6tHecfAQMbuSIPtErxA8GsksJiltUDewl554LmuQG35G\\u002fQyGaRA3kHpTFZXmED5z0j1\\u002fouLQEUUoiYmpX1AWJvgjDA4b0C\\u002ffmvQTbBgQLDwTlk1pFJAee186djBRUAlKk9cPsc5QPoM24Gm4C1Aq7SCO719IECDQM3\\u002f0U4RQOLlxgrpoAJAzssPMCqT+z\\u002fOYMwLjpA4QIIpWXlJGUJA15xuPSI5U0Bo6qmqYnNkQJlTvlX4H3RAheCDgSYbgkCGu3elKr2NQCtQJTI1SpZAwV4kRHt\\u002fnkBjoyCjkwyjQAFDX7WIvaVAe72Yh3a0pkAMQ5y7lsmlQONb1BDtaKNAr4tfRdx3oEBGCUU9aSScQN1PbUKGfZpAtcQj33ZrnUCUjhG++8KiQKqJm7QIRqlAvoQMjXnrsEDRc0cNdg22QPaCYHVdILxA83e7zvLcwUDqYkejoPzGQDO+TWuDIc5AEsxWRgTu00Dr70YyUiraQAe4ZB8DzOBArV0n\\u002foXs5EBqIoFJ0UDpQP1i1VnElu1AQnVPiJTc8ED2G4Na7K7yQHRJXRwYD\\u002fRAQ7jeHhXG9EDPDcmAPbD0QNKZx9BP0\\u002fNAPazDiPxj8kD+Vr6ZqrXwQBTQOs95Ou5AnsAMMZCt60DKky73IfTpQL6\\u002f4vUh\\u002fuhAoO5sUgiW6EBCwOA\\u002fsnnoQFMGZSHRaOhAAkklBZAr6EAG7iN0M5nnQLXfI+8douZA4YFdNxRY5UCnG6vrXevjQESSNXgamOJAahpJfdKJ4UAjoYbY1MPgQCnzkw+OHOBAjA+fJpqg3kB2T8E3ckXcQF6DVAqM+9hAFAi8dMD81EDYAxWudNLQQBt\\u002fqidwOspAIxQQoPatxEAqUnCenFXBQAwQo3Amvb9ARIQbGuQBv0AWkmz87Kq+QJHvsC21UL1AA8DyB7pVukDHKc\\u002fpSuy1QJRiebN2zrBAiO6hD32sp0AJqEj+kqGeQA5t4vpsO5JAqWn2qRINhEDmhcxp\\u002fIt0QHqlnuMs8WNA82DyZyfaUkD\\u002fndZoIAlCQPVDYzJ1DjJA+2aGLm4EI0Bd6qZM+oAUQH51YGyEzwVALWbZTsuI9j9dKTvigyfoP7n+AO684eE\\u002fyZHEn5TaK0DPKW5qMZ00QCjPnrNuC0ZADMpzBAWZV0C5pxk2A1xnQCZFp0WtI3VACU8yrUh2gUCoxWtsRlSKQFVpNC81HpJAyRMZ\\u002fp7ElkBjWxtH+iWaQPYPCx4wgptApkqX+vuqmkA7IIN7NzCYQG9DzuKeR5VARr+bJgV8k0AmlDpXEkWUQHhY2Lo\\u002fp5hAuyfPOPF4oEAuQ8mpz1imQDIm8uVFe61A\\u002f+fzkQHBskBO\\u002fTPrSlO3QITKmMZaFL1ACxqvOXiXwkDyfnHZAovIQE\\u002fI7T8pgdBArzVeVwwf1kBwOqtlHQPdQAIdoXu2cOJAphCm6kas5kCORtouzP\\u002fqQPL3706oK+9Aq5nkmiBr8UAUb9P+x8TyQPpbfSVtZvNApEn4fpIq80BM\\u002fPpRkhjyQL4MyoxmafBAgP2e7h7t7EB2RQ2JcTjpQJ0grb76POZA7TawWJY65EBq9i4Xly\\u002fjQE9BmJOh7+JAHw48mT0540BACJCrgcLjQIKR51a5QORAjN1DVNBy5ECRQuHZqDDkQFtZyH5XeONAsuWEEGNu4kCf9y8L\\u002fkvhQD38wHbvQeBAwJ0w+jm\\u002f3kAlfAnBERndQEVZcN4jMNtAJjznzMGs2ECWOLmOzXTVQAKANgP8vtFApYGwDHb8y0CQ+OAhImbFQI4iqWhndsBAfhg3Q13QukBmavdPduS3QN9LKDqR5bZAsZw\\u002fGDtltkCzlMSFe0e1QGcrG36lC7NA4qRUun+dr0AXWYyc4yioQMNDQDmQ8qBAZQ1ZCiLOlUCOZNolvbyJQOKuTyOx6XtAhLEElOPqa0A5lpVKY\\u002fJZQJlpz\\u002f4Mv0ZAgaWLS49QM0C9JSeTWYMgQOOsYf50hw1A9pt+U0fy+z9hR0yz2mLrP2oHmR4cG9s\\u002fX5+04y6DzD+F2639PQDFP0GQSZTDehxAPKjbe8IrJUAD0rGJOcs2QMHXRZSwjEhAuOhE6NNxWECOynU3GkBmQBQPvoapfHJAY5o4ir8JfED3dFuez2iDQPEOaVyji4hArQWu3XFljEB\\u002fWiXimiyOQOgxdJG5wo1AcYCWkMf0i0AY87P9726KQJveiN0zZItAP4R0wPV\\u002fkECDvMXUdV+WQBrHyArVbJ9AQasDgjeBpUAIRqTz2\\u002firQD+pq\\u002ff6S7FAAFWNZDu1tEAxbpnvJb+4QKAwZG1Ofb5AK1E856irw0Ct\\u002fGwKX0zKQB\\u002fPRBMjv9FA\\u002fDUCU1ec10Dw1X+m2YveQEzCw3sPH+NAn\\u002fvDq3Um50B8l07EKxfrQJE1cGvDkO5ANaXh61CN8EBLB\\u002fTp1h\\u002fxQFSXoU223fBA8LQtZ+mf70AhTNjYFlvsQOvrh7dMlehALs1jlqj65EAS1abf5AviQDTLle8hCuBAF3dggRv73UCBUYIio5DdQPDRf5febN5ARnk\\u002fVk0F4EA0DxiHhejgQB9teelilOFAQ7oYs+HP4UD0Mz7lO4bhQJF1+FLty+BALb6IAvuh30BfPtHhxIzdQA9wXWCuittAhhngZUKN2UB29AjIVVvXQKRp3t+2vtRADOMtiTOt0UD3xinLtq3MQAA9rR4YKsZA088NQnuNwEDf62k5kLW4QGnDOBueZbNAyqklG5+ssEAFiQpMTyKvQOHoG8ZUAa5A7y7mmBlOrEAjcG8xKT+pQNiHC9Db6aRAD+D+t07pn0Dngn4UrleWQAxIIiusq4xA7ot7WlXagEAyIHhZ8ChyQFliba5F9mFARouwshlcUECAEVQJ6587QDZyc2oK6SVAJJZvxnG7EECy8rz3DZD5P33Z8AjdXeQ\\u002fbDF7oUdK0T9Xbm98lSe\\u002fP56aMzmXIa8\\u002f2oK79OyGpj+H0KOoiTsKQH8bVrtUlhNA1e5hSiY7JUCoZnbpjgE3QDcbl+jBCkdAQOZpFYMYVUAXdlD5TqFhQH0JlCnV5WpA7YIyeJO8ckA\\u002fNdej7Nx3QBGzOOvG3XtA6HOZDDYSfkCHfuVriZV+QDfBjJPdp35AJuVkNKJegEB9LKXtShOEQPJ\\u002fAbAyJYxAp+9aeOrelED1TxVpVW2eQNOWEhWG+6RAUnR8oLkLq0Aef8We51qwQEzW6mmb4rJASHKVXph6tUAwpehhuQq5QPiLZJs7275A05lCyXIcxEDEnazcxwfLQPnseAttKtJAsn5a4Abq10CsP2iXA4beQNv8ZnrmzuJA1hFG\\u002fSBS5kAjvB8WlGzpQFGugqaJretA+0fulgav7EBlPKTxdjvsQLGUybmGaepAZl5L26Kb50CM5cph1GDkQBkISktnR+FA4gDtyGdx3UDvRBAXYNjZQLgDJ7B73tdAFyIHv\\u002fxn10CzQYf4DDXYQK+mZUC849lAb2tFAf3v20A8dy9NxsPdQMQJxbtx295AtPsF5Gzu3kCQKlEIIwPeQBTGi6wxX9xAAW89JrZb2kAGnlYLqjfYQLnc+HNdAtZA7ngm2fWm00AflcqhxgzRQIbUPtqWa8xA9DsPPQyTxkBwWZDu3RHBQAPTCeI61bhABw8W8fThsUAMQMdjl8eqQMEbTGoa4qVAy1bcFvqgo0DtYwrTY3miQK1DjyquOqFAkBXHsgqWnkCQpZgzgEmZQIOTFbiCRpNANMh3bPT4ikBoDwIDFkqBQOZlN7kkS3RAgIFtRuPOZUD87TcIundVQM1nP+vZYUNA53WmmvsXMEAaKX\\u002fQdbgYQCiCyYWvxAFA3KrRdRZy6D+Uv29LirLQP0t9NFE8trc\\u002fkeNZzrM5oj+MYpxzaEuQP2EBPtyriIY\\u002fhRLBQYTB9T9V440IG1EAQKIcPV4dzhFAjZ3sq0dpI0BcfpBeL44zQPqyNdQUAkJA7o4RjBBHTkDYkMLyQz1XQIRdneCUTGBAjCtxyYLwZECrGgvec8loQHGGIRc0dWtApRJlCcGFbUDY\\u002f1nXm4RwQAeKmXQQ5XRALJAmF\\u002fyBfkBSxJrdUxGIQHEeTMS57JJAv1K\\u002f4xJAnEDI2I+MDZujQE4dZzSELKlAvZzhb6oJrkAfBuEW1uSwQHfBJMWnarJAjxP8EaxLtEA0PlFk7qO3QLrOLojAr71AawXiuzu7w0COgVmnosPKQGIMuO6u9dFAr5Pq6lps10BcF3MJqWvdQAMjFDhrtOFA1WB8dDdX5EAn7kPyzjvmQH9e62VxEedAL8jU+2q15kCvKWUEg0PlQNEOrh9gD+NAFrh518KI4ED25JbiUjLcQJGC2so8GNhAb7hyaOIU1UCVEo3I00bTQOUE09w4r9JAjDciA6A300A3\\u002fyhQ16fUQBXWlxxanNZAIw72osiP2EAFsvqTBvzZQGV3fy6DhNpAFOpPeX4R2kCVCxaI3MvYQK8bHYf5\\u002fNZAGOzLwjHn1EDzB2b0Eq7SQL0sMMYsWNBAm2ueUS3Gy0B2B7FDj7HGQDk7636isMFAYItrsPw6ukDqH5BnOJuyQBBpzsh5zalASIlDlGFAokC\\u002f10\\u002fkFcybQITpTQmbY5dAHvSch7kalUDiuzQ170GTQKzlkUxS7pBABY0lQ\\u002fvki0AKJ9j7aD2FQM+E6sBdt31AYm26GUwMc0CH0VdvQFpmQOoKAGurAFhABojNI3aWR0AtqwbBMzk1QO5vDKbMgCFAfAv\\u002fXcaFCkAsyjqRMI3yP8LQz55RNNg\\u002foVdZ2fkMvj\\u002fnh0jimmSiPyavBdGteIc\\u002fvr0M7PN7cT8Q07Ak5\\u002f5lP8nr6rq3PeA\\u002fj4yVCUp46D\\u002fTQzsYkuH6P6MgXHv3ew1AIg4AjabgHUAU9mac+KwrQP4NHoFUaDdAdhcFh94VQkDuMlsOIJNJQLs7EnlfoFBAaDm+qZcfVEAUBYD251tXQB02ZWFVpVtAoR4gkjIoYkA2utNkxKFrQP7iAJkOMXdAK34v23q7g0B8yX3zNQ+QQDGX5OmEVJhA+MclvxD7oEAQOebbLc2lQJZE7ZaB2alAy\\u002fPwKZafrECoH9GVWUquQLU6hY4K5q9A8zDq0DejsUCzfSXoWFG1QL4pfZwrBrxAZ7rVJuc6w0Agg\\u002fLCuWXKQIaRK6OpoNFAxqmFFNOT1kAnMCs35YjbQOP\\u002f0Nig299AD5EjdTp04UDCltO3txziQBzhEcF\\u002f1uFAw3cc4yDB4EBeC5xq5kHeQMNYL2lmjtpAAoQL4Xj01kBF+TfRotLTQIyC8AywXdFAyYxDHUZpz0Cu\\u002ffii9d3NQOUCVBbaMM5AF3iyQh0e0EA7dsy\\u002fZL7RQMcSKq5rjNNAb2ejCBwM1UD6sZJREtrVQFnuaaL\\u002fydVAAg31T0Xr1EBG\\u002f68+vXPTQEARPHvln9FAcGvqFew2z0DOlj5DIvnKQF2yltOFnMZA\\u002f\\u002fC8Pls9wkAFVBOavRi8QB4r8s8wkLRAhGeVDfGjrED9ELuHeTijQF2eAxGepZlAg2feKNXkkUB9clLdq1iLQH1oCoIP44ZA11TWocz7g0AheHEUcTOBQOicCg1lF3xAQpPd0lRTdUCUY+0QrtFtQLb+hn3EHmNA20foBuVyVkC7C5UmQR1IQCDOqrj5sTdA8PEYxSJNJUCI45SGwYYRQNn3SJO3a\\u002fo\\u002fejFwdl9H4j+q6XognE\\u002fHP1AYHw9vpKs\\u002fG1YPkIcJjz8+CgjoriNxP7F+\\u002f6di0FQ\\u002ftx7K7IueRj8weDLkqNLFPwmJdA1lg9A\\u002fiRHgCx9D4j\\u002foSBcDFif0P8PHk\\u002fDQigRAzWcaLZskE0CR7yRxk0sgQJfwEm1ZXylAd\\u002fwZ9RAiMkCfqH6g6gY4QMMJbi1kPT5Ahd+2+XMNQ0CQydtpvRxKQCRLWRlnr1RAG\\u002fDwfOdoYkA7025X1+1wQAmTG0e3F35AnuJ2ETP9iEBxgUX2xh6TQKOu4z2C1ZpA1O2tPf5DoUBS2gqR+3CkQGNE0jS4dqZA+PBcd7Zcp0BpQm27H+GnQL4NTHyvcqlAgnxsoqPvrUBDOO4Z6p6zQI+3fg2OabtAS4qIU1RPw0CKS7bNNm7KQBOo16usPtFAjUOgWTtH1UAAQM1dtcHYQPQIqBG+KttAjyplEBox3EAy5tAc1s3bQGcuUD6TQdpAE0Mnxsb310ACRsvWFV\\u002fVQEKMNAN6y9JA5OMnMWVv0ED+ddqSN9PMQLdL9o4lsclAb\\u002fONqZDEx0Cf4esuklLHQPb8Zep4a8hAeI3mzQ\\u002fFykBhFDVuabrNQOHjibxBPNBAtQ3O68Qh0UCCDNlqhljRQEDr59ZB3dBAPnsGQSGkz0Bizhf3AM3MQJu2gK1YhslAgpF0b0sExkDY2TEJE2fCQKj3QLVsmr1AELQ8LXO9tkCueG4m+5SwQCcf7zMi56ZAI99nJCQankBC732AqSiTQBs7lA3AgYhAz2++hieXgED3wdEgi6R4QJnym99ExXNA\\u002fSraUCA+cEBOITraP\\u002fVpQMOgWg\\u002fUiGNAToJzlxI9W0CRp0XdBHZRQG72RE7fg0RATyUv6M0ONkDaFfkUObElQBVsCK10ghNAfpOuyn4MAECrWREtpCjoP3F3Jb1tptA\\u002fCvE\\u002fpZ8MtT\\u002fm89ckEH6YP7KnnKzvbno\\u002fhW0Y8EH3Wj9IBnznOic8PzmwC6whUSo\\u002f0l0fCjtjqj+eAVlPiQ60Pwt9TFx6VMY\\u002fFNTz593K2D86Iw6uemzpP4iKuCdv1\\u002fc\\u002fUAH9tw1xBEBqDpDmtREQQIFBXLIPWBdAVa028k71H0A1LueuRI4lQCdgEoLw9y5AppHSXAWDOUAEL6IGGPhHQHGow1KDv1dAnRgeKN4HZ0DyqIhn0vt0QAE4BmOlpYFASTdVbc02i0Bi887GRzOTQJmmcvz7zJhA0cVyQ+9onUDZdGrUAiGgQL20oqzQpaBAxRcwCXXFoEBNXuNrP5GhQBdjlW\\u002fPg6RArQHvr983q0CEFti+PYKzQKGme8f5Q7xA6njlE8zNw0AKeM47qEfKQApCZMaxYdBAW+0Tyf8o00AMTNNnvxLVQJgm1HAv6NVAx9O5CdCw1UARPOeWXafUQJKJGvM4HNNA0ijuzfdV0UChK4pxWgHPQJsNaQ5LZMtABNE9C8j3x0BxclWsuPfEQOp86cMmvsJAEi8LsJymwUD0QW4BEeDBQLPa4DoZScNAq2aFf+9sxUDKZ4pLy6XHQPLlVBBmVMlAVw2R\\u002fyQRykBiStCeNsDJQGYeNKDSg8hAv6e6nLmaxkArDX2D00DEQA0mGp4EocFAvqVW7ymyvUDDDsBjBgu4QHqDpHXGkrJAIAij1Boyq0C+L1Sgu8iiQBxX8j8keJhA16P9ptBEjkA4Tq+33iOCQC98W1jW53VAlnox+D8LbEAxSBgOOJ1jQP9eFW08X11A3J2ZgyFUVkB3t4KDMWxQQLV51lLosEZA85Fz46cCPUBbprHNgAoxQE23fhQyVyJAlrT33FoPEkCuPcgZQEMAQK0q3szMx+o\\u002fS1YvSPkp1D\\u002fCC9ANcsa7P9L79pCDgqE\\u002fXcEIhS88hD8ReHgl2X9lP0hgYzgnLkU\\u002fTNG4jVdqJD8QCMPDWjwRP+fefo4vtow\\u002fxUDX8ePrlT9mSuzODpKoPyV0CHEAc7s\\u002fZaRy0elSzD8tRX6Syb7aPwlqFKujIuc\\u002fbIRcX7Jx8j9xrFazYYn7PxKY2BB8+gNAhc8gIaM8DkDugcuZ6+YZQO\\u002fs\\u002fl8RkilAWa0SBGUvO0Amd7mHJrZMQJ7zhjiusFxAVG\\u002ffINyIakDLGioecYR2QH0adTlQeoFAoshO8FvMiEBnlihjLheQQEIzZ3RHJpNATwlcxJsLlUDXLDUzLLiVQJ3Zs8x+2pVATPHULO70lkDBPiWFCjqbQL8l\\u002feI8l6JAeaEJqG6Iq0DMMzJnCYS0QKm398y+Xr1AmT1Y8KnIw0AalDE\\u002fU+rIQGoRCrNFVs1AOG3tkBw30EA9XilPFPPQQOXvezMh6tBASLK3Fv9K0EATFcIXWpvOQFe\\u002f5ozUN8xASdVNkY2SyUBW7wuINLnGQB3jXP9zw8NAftdYYkzswEDOOSNjNR+9QHycv6ysGLpAl0H06jM4uUAZtpi68Wu6QHGUNv40GL1AmUX1xZMlwECHPAHFyofBQOUdYLqJW8JAJ9CA5KR9wkD3RQu9hffBQIGHPhI06cBAleELBGjovkCRIp8eLGS7QO5QvfH\\u002fbrdA2vwjZ744s0D1rwU\\u002fVgKuQLK\\u002fHwQFJKZA46q3RRa3nkBOiEJBwviTQIyLzBlWX4hAOWIEnUIkfEALfyfhp2VvQF\\u002fgbUo2mWFAys88YeTXVEBoScf5J91KQGlSw0cNeUJA9JW8sIDAOUCfwhcyUVsxQHb2e6wg+SVA29Juc\\u002fy8GUCDu5C4qbMLQNpMzTevT\\u002fs\\u002fzIN+pvOh6D928\\u002f57W1DUP+nXuLWEob4\\u002fqs+azlIdpT9rTYv7BZ+KP1mCc\\u002fkxtm4\\u002fl9Leycg6UD9zA7fHYI4vP\\u002fi2EwjlZQ0\\u002fap77zfOa9z6ZLPRCcBxsPyVD0fpoj3U\\u002fz73oh39UiD\\u002flr5Dnl1qbP18W7fhXaqw\\u002fyGVTyooNuz8tlU4zFrHHP8bnPbbFU9M\\u002f+vQ9ELdK3j9NhYkmMkboPzk9I2eSpvU\\u002fIQV\\u002fOwhdBkB\\u002ff82GXWQZQD8qTO0qIS1AnuUoLTPkP0AsMWQ1QzdQQB3HrM3zTF5AVNEraqDmaUAUcZawVzp0QFpYPO+f3HxAqBdzx2XTgkAisU3n2IOGQJvfmTOs3IhA34CsZ93NiUDdalzLTTqKQL6+jMZJJ4xAKP310xtTkUAEu2jcYbSYQJ+OwIiC+6JAye9eNXsSrUB2KANYTTm1QINvoxlRA71ATQe6phl6wkAY8oAQ2fbFQG8oEuHQgMhACbolQlrfyUAPP47M\\u002fyHKQFrb93+SiMlA63SL2qZZyEBVbKKG48HGQGH1nsmjzsRArKU9pdyCwkCFDdJexOi\\u002fQLnxDeOkuLpATGxM10AftkCy9q9F8seyQMF7iqSrH7FASZiZ8JErsUC\\u002fzayLhoiyQBtCLg2bkbRAvpgqHdWZtkC1h\\u002fYzch64QLeTQnNF3bhAmhEfDIbLuECazcIjo\\u002fq3QF5QjgzAf7ZAMXyF5LRrtEDG6SszFNOxQO4DGuEsta1A0d1h75J2p0BSJfHrVHOhQN0QkOK5UZhABQnRxHCkj0C\\u002fg2C0hDGDQNmbiVzvwXVAqlnw4PA\\u002fZ0C9grXiOOlXQPs7BrjrlkhAMHCm7DqUOkC3xZGCOCsvQGQ3KbHVgiNAWJVBPxnRGEDcal4yopUOQKy7aX+PuAFA3RNI1+IC8z8ZSgy+k77iP6Vlc+QK7tA\\u002fVY1FF7D5uz8KoYaGwSGlP3nzpb\\u002fQLo0\\u002f1gsac61rcj8qvaiSPENVP7wxQ9vCczY\\u002f3771IBe9FT9HISBQIQD0PgEn3E+3ed8+QN\\u002fZwSHESD\\u002feKexv+RRTP8Cb+VbYrmU\\u002f8nC3B+OKeD+jVZYsY7GJP5YSQvkGu5g\\u002fl79y\\u002fJkVpj+BUoFutMKyPxck5dPVAMA\\u002fWge\\u002fpATNzT+xDanIzwzgP+YEns89XfM\\u002fHs2XXnMNCECHKLDWeM8cQM2Os8yMGTBAJs0TRViPQEB6ZA51qy9PQL9C+JKn1FpAvxdLrSwUZUBoS1gmoD9uQMueZo2x13NA3IrHfCPfd0C3tFGBtYt6QAEe6jaa2XtAvvcWc7XmfECG5YwH5CGAQCSz\\u002fjSP\\u002fYRA8SkyMIGTj0CYT26EPzqZQI8QjCMY1qNA1npP452FrUBtclFFU320QHMXSy6Oc7pAZYlFYdDZv0Dfl3qTjwDCQLGn\\u002fpV8R8NAV5maaq\\u002fFw0Bc3\\u002fopCp\\u002fDQGScQd81+sJA+V8xhb7rwUDndj7mIXbAQAj\\u002f\\u002faW6N71A3qr+0+nouEA+t+jE7Wm0QMVtvK42TLBAJX+P5Dc3qkBDaqVZ0WCmQK0hLufCK6VAucW6iYIcpkAkV0WNLV6oQAh4eQOaDqtA26dDVNp7rUCOtsojfzyvQKpE9WPaELBAXLR4ao4LsEBELFyhKwuvQFn9Z+Sl76xAV415yGrNqUBe8+8AQ9ilQDvlg6eFcKFAMVV1+locmkC2Sm82EUCSQCf\\u002f0\\u002fYNv4dAcoi3uJK0fEDScVd2yR1wQFRi45uP22BA2cIoOveRUEDQmEqyPjo\\u002fQIU75SjNQy1AIewdwdKpHED\\u002fqHLV6mEOQE7Zo9QgOwFAj5vXlFHx8z8Oa9vc9XDmP4yFsZKvytc\\u002fGpJ1T5pfxz\\u002fnfh0tmRy1P8EvYmhSeKE\\u002fGv\\u002fbAcRyij8Akgz5a01yP7EC+74LJ1c\\u002fzXADDAfFOj9zXAOJ9UscP5iZsBX4Y\\u002fs++C\\u002fsV9kc2T540ZsvOKDDPgAYtylWoiM\\u002fKrgu49JlLj97klNYWmVBP8ajqxHX1lM\\u002fVPJ22N\\u002f5ZD+HkkKutId0P7JC5e+K9oI\\u002fMQmhAFxOkT\\u002ffJHrKXeKgP1cKRnsM7bI\\u002f\\u002fSSx1S8jyD\\u002fd8FBZnCvgP60Oh05HJ\\u002fU\\u002f\\u002fkxgeef2CUA\\u002fNgBUMmUdQDdKkUCbfS5AQr8OyGnnPEBuBxyw3ARJQLeoHStmxVNAUOuai7qJXEBWdSovddViQIvO6Bgj0GZAYkr8k9maaUDbhlvCL01rQFUPPXUuQ21AOK0lqFVLcUAuODtLWip4QEhS5AiYS4NAYhKmGKIJkEACVaSJ2OKZQG\\u002fLgPllpKNASxi9d5e5q0BqoyTyJC6yQNwEMCPnPLZA1iYKOuWLuUCQfIOa3NC7QDsVf8ENAL1AmyFypww3vUBeXjGmHpi8QI2SawXHMbtAlAz7aSUDuUCfsDZg0xa2QERgzJOYnbJA3PQEHirorUA5ewci7xynQHYcIqX5q6FA4kqGlrgpnEByQ5fmcNOYQDMX7xa2uZhACTOrIhTUmkBP9asFhQyeQMG083fYwaBAxl7LVDlSokBd7\\u002f6uRIajQGIOqFHBM6RAy3SfdrsupEDfMO3egVKjQGX1AVuzlaFAVaUwQbYznkDQ4kNQqFOYQEh2J\\u002fKHS5JAQl2v1YubiUAbZyA0YKOAQCDJ4A99CnRAQ66Ped5bZkBjz0eG7x1XQE7jWc29N0ZAn0IlU3cENEAP0R8NBzohQMHDlNHGVA1AymymPg7y+T9KHTKHzMboP9Zb\\u002fSvfZNk\\u002fxsjzZhOzyj8LdGlPQWm7PzG7RmkmlKo\\u002f\\u002fM0T2tvolz+aOwGl68iDPx85\\u002fDPJAG4\\u002fw2YFBXXPVD\\u002faNxZzwmM6PzEWpBeBlR4\\u002fQNXXKYQyAD+ZSmt4QmffPoiJxsp7zLw+FkrbXi96pj5H6UQE9AP8Pj+SzlEjywU\\u002fznVu1ikkGT8Y3O6fhvAsP57pYEg8Cz8\\u002f1i8GXhtBTz8DF9griqheP4eN319NfG8\\u002fNHSPGg1agj9Z7o3jHp6YP6qxM5PFtrE\\u002fSl1geYVAyT+HG+cnLwHhP51PB2O\\u002fLvU\\u002fByI1FZUzCEC6MlsFkEcZQK26h\\u002fT3HChAKojWYf\\u002f+NEBv4T6agrBAQIzhncvyO0hA5zWsar8YUEBQEDi82qZTQGuulvYsU1ZAV6kEpOZcWED8K7Gsq1xbQLIv4DDbdGFAEMhy2T+HakDj0MIYEIx2QCLuURDoe4NARkD+Y3oikEBDEzmQmfuYQDp7nfOG86FANWMPWib0p0D6\\u002fRzCIs+tQN1RwIVfa7FAqE\\u002f5zuZJs0Cx7emR0Gy0QLTP48dR2LRAb7aDXU+WtEBa6KnWIqmzQKfdyuF4ELJApUE3VFu2r0AsA9437myqQAH9FQVX2qRAhifvT+5Xn0CwpJTJseqWQFA7BGS\\u002fFJFAtwDdY0bRi0BWX\\u002flUuwOKQHIbUMAVf4tAc4wfEU\\u002f8jkDSCu+KLrqRQJEUAYacE5RAMVTxCN46lkCbwqCMjNuXQLCuCYNOmZhANqn6HnglmEACc25F\\u002fGCWQNnNz+WXc5NAXUjmE\\u002fuNj0BXtbT\\u002f9cyHQPPaZzqWp4BA5ESInfaWdUCUZ7vpbeVpQKGBjroUuVxA2FeW5dF0TUDIEiOMpPQ7QHqNYpn0oChA2tOQxxFNFEAN4gmaad3\\u002fP67aP\\u002fLenOg\\u002fpwwc5Caj0z+iJCZgGt7AP7YM1ix3Ma8\\u002flI2xL8nCnT\\u002fezLVCFdyLPyHIOUpqs3g\\u002fIKJ93IdYZD8khg70edlOP7HSPEVqbzU\\u002ftP8Rcfs\\u002fGz9xscjbgqr\\u002fPu6GQKt60OA+XEHrC6ZWwD4KZ66oPAaePmkVykFCdYc+wuYaWFv90T5nrlFljyPcPsDcwWvRYfA+2guHbB4aAz\\u002fd2Zn3+PsUP7wlyrAUNyY\\u002fQgVpHAwjOD9hU25+mElNP5PuteWukWQ\\u002fLyNBQvOkfz9QbcCroYOYPxBPT+ifF7I\\u002fT+dEEIXJyD8FeE6TSTDfP7Dw2d+t8vE\\u002f34DxW9bdAkDoDYnSkRoSQFYyq7Jath9AXpREN1VbKUBDHJpv\\u002foUyQKCI66J4xzhA59NTo66JPkAcx9YrXqFBQBHiUxA15UNAf37YWsXeR0BJ79jC78dQQIWjvX989ltAdeVZuFVFaUBmbvBldqh2QJe0GTqQPINAMagIG+ljjkDLy5pgMT2WQOATQ+z7Mp5A6PbZnvcco0BvIfes0LSmQCdtt3arhqlAAGzn3Hliq0B\\u002fL4+WMTqsQLtzdN0TDKxAXh\\u002fLKAnVqkCn5CdUBZioQEzOWAKcc6VAXgasPp+xoUBTHby7wIGbQOx7mcKnM5RAtmoa5\\u002f10jEBGehJFTfiDQGSUZ66Z9H1ADtsQh2rteUDhBDnbrld6QALqWekuvX1AMJZoxBl7gUDF7B0PBY+EQCVyE6ABrYdA2OYCWENUikAabjFjrvOLQFo18gV4DYxAloIoN05oikBvnhE24y6HQJXG2O3854JAf3C4AaSSfECpuxdUmflzQEjoETib0GlArHIGBiPSXkCOVLGxCv5QQP7RK2N\\u002fTUFAQ6IAsTVGMEB3IDqcmE4cQD6lXtCj1AZArrvbM5gz8T+cNLFVzZvYP+i1c+2xPcE\\u002foUpQr5vOqD+k9AIMFCeTP1yJmEek638\\u002fMq8TiIWcaz+zRCQ8eI5XP00CPfL3F0M\\u002f6PTxLszMLD8nxJe4SwEUP532TF9fevk+gnx2Roiv3T6baZdiEJ6\\u002fPpm9cz62z54+WJOSurxjfD6TRPN1bDpmPkR3BIEuzaQ+SHOptgZfsD7oLv2pCEzDPhDmMj\\u002f49tY++GIK0dhO6j57qA1cF2X+Pnan+vksPRM\\u002fYz+KPRoyLD+9rCFvcBFHP3A7Ci+MXGM\\u002fnE1C\\u002fBRHfz\\u002fTUjpcIo2XP\\u002f30UWp3ULA\\u002fIIArkgawxD9etXHiDffXP2mEvgsEWOk\\u002fn6wVrVV2+D8wpI\\u002ffC40FQF2wwYnXVRFAXx5EfLl9GUBVXy3sLC8hQCJRRhWMbiVAYd1iqz5TKUAo\\u002f+a26AYuQF87Gt0JvDNA23FTujIlP0AFqEzzpntMQDY+yJx7OltAwqZ\\u002fNKI8aUAkpfBOzOx1QJufXoaEpoFABkO9XCBIikAWnXQP7CKSQKHC\\u002fX99T5dA\\u002f2uHEW0VnEAkiEL0u\\u002fWfQDSuAcXzT6FAUlcpgVL3oUDbnwb8dOmhQOh6OIvtIqFA+oPBc4ZRn0BSvbHKuCibQPFLhmTeNZZAJ4suy2gOkUCFYOo7yJqIQMuSfCs+04BASx3x1nZzdkBcuvLdziRvQMY7DBkAsGhAgH0wjSm9Z0BBRLi7GKhqQG\\u002fuS8aHF3BAU1DK6pupc0Bn5SNRLYZ3QEkOp59vCHtA8owHL\\u002fVyfUCMvzU35h5+QAybPzPyuXxAs\\u002f8Q+jtteUDpRhpxdtN0QCszn1+Sg29A+ukSOkEBZkDPEmcqallcQKsfwFZI2FBAk6FKe1p3QkAVxyRINq0yQHTvRhs9biFANyHL2hgJDkBPqeqETur3P7x2y1yVpeE\\u002fEtSMfOpLyD\\u002fDOgMoQK2vP1lZgkYqIpQ\\u002fh\\u002fXgBWEgej+vKKVvSyBiP\\u002fOet+l4NEs\\u002foVHbRqtRNT9rkTS+eZEgP+jdiZudjAg\\u002fDAD\\u002fsI3z8D53zrPz4pPVPp+xQEX5L7k+hJjeKMLmmj7l7QVWJkx6PiMMc8+DT1g+YJXl3jcWQz5tXSEK7qt1PiQkn\\u002fSSMoE+K1SUA8CllD6w+EI0ZnSpPjVZ32LPZr8+Yb262ADP1D4lsiXGRL7vPr5UuqWCegs\\u002f7KONSm7lKD9NpspfUPhFP2rtAO\\u002fHK2I\\u002f5bYrLiq2ez8QDcQUNluTP0tpkXC3tag\\u002f+aV6Y67MvD8f37D52qLOP6sbuf9Rvt0\\u002fd2\\u002fzDMRb6j\\u002fnlzu8TlX1P8ZXQZnLlv8\\u002fD\\u002f7bHd9+BUCud+en+j8LQAQ0yH0jqxBAVTSH9bFEFUAPaTFBwYQfQI91snz+MCxAF8dm3Z4TPEBA64gyCSVMQLI06o1L1FpArZbyvBDIZ0CO1SzMNHhzQDALGyf4bn1AZ7JG58WYhECUAojqdc+KQIWx6ZeEVJBAuU1IA+rAkkB6y8g883OUQJC6GytdT5VABLjJrxVGlUDBz7tdqlSUQDVRbmNDhZJAy7+vB0n3j0CP8AstzfeJQMn1lOvuwoNA3EOyDxQhfEA9NaRebdFyQO9WEjxxJGhAALe1ubtJX0DN8Es\\u002fUaNWQGHlNu44RlRA\\u002fNih43FlVkADf96NeaxbQG8bol3bh2FAac73Tay4ZUDwLMT9ebFpQMJAEvC\\u002fnGxAm27pYJ+7bUA7uNVahKpsQERZCAhvjGlAf\\u002fm7cfQAZUB3wLnlv9FfQIqIDtdMMlZAt0XJ94SETECKhBTooN9AQC8IleXMZTJASCJtaiF9IkBrTYrcriERQJAzXTF4SP0\\u002f89TyloQY5z9hOUDVDNTQP9OKM7u3trY\\u002fTmH1pM6RnD\\u002fd7aJcZ\\u002fmAP\\u002f\\u002fk9cwSlWM\\u002fP124jZntRj9KVpHHrpYsP3EkzePwThM\\u002fJ3eC5jpl+z75SxljkWLjPvTYR3ZcP8o+g+sXPcmXsD5lDBKbelqTPtJHXKJLtXQ+Kn9SWW1NVD73RkyumNUyPn43CrSFqh0+9IDzVqpfRD6NdIH7gV5QPsd3CRWsRGQ+QygXJXqaej6k3Qcsx4WSPuj4SqrIYa0+Y3n3EmvGyj5\\u002fPnrLxBPqPmdgM8GDFgk\\u002fNC8Nf5nFJj8XhmKUehtDP3JsGxgRZ10\\u002fYNr4qT2udD\\u002fP5FxgeJCKP3AhTOUDJZ8\\u002f+RgkQ4SpsD9lIlfmekXAPwNgXbYrA80\\u002fVMhKtiWi1z\\u002fTOaMC5KLhP3i9Tfm8S+g\\u002f4OX1NLON7z990VgDal70P9WB9Z7Y4\\u002fw\\u002fKx+LPrC2CEDasHgz1e0YQNkvLAUVoSpAVRvtjxm0O0CZ9CpiggJLQEwotvwPWFhAKliNVrc2ZECjcdzAmPJuQKr3FFaB53VAFoUHjHHMfEBGEcOUCLCBQFHX9TEKcYRAeX0ngdZihkDaJ4QgWV6HQN1jaBqeU4dAqIzc4FtBhkCGIBrLXzeEQMwAkM97YYFAmvO0PuAafED4QwAPDkF1QK5Io06t+m1ArczeWubAY0CbezzuF6JYQJeqjwovPk5AMRaLGbATREDEp+sR6IZAQOQTnu4fsUFAOro57+Q1RkA90tmzuwtNQK187oJhjVJAuWH5KmN9VkB9zXdcPYJZQLS+fMAY3FpA3QYuQUEiWkAZcrjKbW5XQJbJwn5pVFNAspn6Rq5STUC9tIvYI3JEQO17RYgKNzpAJHIpN2HqLkDXjPf0f8YgQDz30jghwxBAGlUqsuvb\\u002fj\\u002fBOWPzLS7qPxXllXYKe9Q\\u002fRuBp0GWQvT+uX\\u002f2YIrWjP0eD\\u002fGkKU4g\\u002fpSSlwEXzaz\\u002fQGezS0kZOP0HUWEe8ti8\\u002fPFexe+PCED\\u002f9yOjldsjyPgmuLHyo0tY++BceNThLvT4hKVEPEt2iPvFN0f+kVYc+ramKAjECaz7JVNiN8t1MPmqO+0YvWiw+UeM\\u002fJRhjCj41BlsE7tj0PcbojkSeVhE+LZguVYByHD79wFqs9ZgyPtomlysjI0s+E4TYqgxhZj4txvZbYF6FPpKAS952RKY+JY5I25VLxz5c0Yp9+C3nPn87Nn+SZAU\\u002fTm4RZcEgIj9yQkrNWhk8P63mCdWI41M\\u002fVJMK0yWzaT80Z3Fipk5+P8E3cvoJT5A\\u002fNkIFwTcFoD+r3P3V4L2sPykqifwTlLc\\u002fwuLJDCLEwT+8EBZg+ObIP8igbj37y9A\\u002fesmmqA2R1z+iUVljxz3jPxif8LorIvM\\u002fHlpl86xYBUBuVwwf6QUYQIJYyQa6rylAPzEvjI57OUDanQ20JEdHQL2WaEGRjVNAI7hrGd0+XkDxgU\\u002fHU5plQHmK0cJpn2xAh5bd0A6wcUA0VlJ0n4d0QBkUa3wtiXZAFn3RXZuJd0BnVI9fN3l3QLNW8GVcWnZAlk9DUYRBdECTxphAIV5xQJIR7lhy\\u002f2tAsv8p640XZUBBlVHaoJVdQNHLoluFTlNAh+eBoJ2fR0C1hZvJtOA7QPM261y\\u002fMDFAhU+wuuvVKUB9pZ1raVQqQEW0SpWGmjBAxLkfWptDNkCH9vcBfys9QENfT\\u002frsCkJAMStXRPrGREAvVfnR0B5GQLCYW3RNsUVAiWnrTBqOQ0B6SehKBDBAQOu7skJzmThAMvoMvxEoMUAuomQY7PglQNUHm5dI2RlAiKmD1wzzC0B\\u002fHlciVMv7P7vqHvjvb+k\\u002fy8HSVy1w1T\\u002fQhe2J0KXAPyE1m8D41ac\\u002f8bhAoyt7jz8UgrfpVzJzP1D5JZxdqlU\\u002f\\u002fErD1GW+Nj+MDJuLzXYWP6ksXBkBYPU+CIQfhP1o1D4QuHHurI+0PusNYivSdpY+kUqzPaQQej45z5PkSYhePkFhTsiIP0E+bOpkBXpGIj43KfJbQOwBPrpYFIZ0tuA9dvqELHh7yj2hwzwXaOPaPavGRNWH5eY9gtl7EfxyAD4AtyrLUBQcPmxW9Wkb+Ts+pOMPh+4HXz4BXDE0vJiBPsr6WfkDK6M+RIOWtZhywz4qe0yIYiXiPk5QTjOH\\u002ff4+uNwnmXssGD+J8KLhqzYxP2aGLnmTX0Y\\u002fUGZc2bmJWj8KAfHLarpsP61PXt\\u002fnY3w\\u002fF+mkdliiiT9S7zxykDOVP3ztSX4ZL6A\\u002fxwbgjqtMpz8fmjEPkLSwPwtnRYStd7o\\u002fRp+Lz0d7yT9W\\u002f\\u002fPrJf\\u002fcP6b7gSJibfE\\u002ffFgE2NBVBED\\u002frf6J1SwWQP2jfdNwSyZAqqIH3xaWNECqs+MZsXRBQPvv9h+lOktAWVmonGKXU0CFw3oUux1aQN7lhFayNmBA9iYvbwXgYkCWy45WA79kQFYWOHbLqWVAqt26yFmTZUB\\u002f54YRxIFkQDGQEB3li2JA0kHw5CK\\u002fX0DB1Ykiv4tZQBLbWNFjNVNAYhk6tN3dSkAMCP9wmHBBQDcwg5zjFzVA9K3r9co7KEBOUrMsjjMcQKEGIWxvXhNAScSiEXt2EkAsnPPKgw0XQBAYb2oecB9AO+sLwXsCJUAhdBsHHmwqQNjA4K2hzy5A1SzT0leOMECZm\\u002frlfVkwQBc5cZHUoC1AO4arazadKEDhNpxilb4iQJzQ8Q0qLBpAtf8676fBEEAHLZ1pmK8DQEgG6JZ2O\\u002fU\\u002fRuUn9KkJ5T9hsdp02CnTP4HO1Tc3D8A\\u002fZFoxI4\\u002fHqD8fkac8S5yRP80wEqRcEnc\\u002fkeui7czgWz+7WvbNZxk\\u002fPxvIfin\\u002fCyA\\u002fInb6sCDI\\u002fj4iUNxKhLrbPjtAN0Gj+rc+0Sz1qkyylD5YIdmb2b9yPiKl4w6RaVI+WCNM13hMMz4ar9nlv4wUPuZRpL1WM\\u002fU9N3\\u002fQvZOW1D3iUlj39imzPY1gXjW+aJ494jrDQt49oz1HhsyrgYOxPUwwtdMDK809b2Pbk1AU7j1m8uCEiakRPnLmxQJQozU+acoMVYPCWT5dwMGOQLZ8PsN9YwbcfZ0+I1HAlYi+uz4z9UQJb9nXPuy1y1KHt\\u002fI+EvemzAnQCj+h3uoYu4YhPwHfor3p6DQ\\u002fV2OO2QbFRj\\u002fE2+rEfaRWPxmCPU\\u002fUl2Q\\u002fL56XBFE1cT8x3ItDKsh6P3oMD3BCIoQ\\u002fBaqKFy\\u002fEjz8aEV9bTYCdP9kDqyHQuLA\\u002fBpQ6nzkcxT8iYTfwN6\\u002faP4uK2Xpo5+8\\u002fi0PEmhKlAUArZQsOI+0RQNYgxE4OsyBAuWaZBjmLLEDRrlpgdms2QFij0rrkOkBABKibTIm+RUA\\u002fEf3+cxlLQCDCLVpTnk9AkqHJbaFjUUCA12FwxSVSQA4Zlj9NDFJAGNwo5MUfUUCnISLoEO1OQDEIBSSgcEpARbNDnzpDRUD5BOiEaPY\\u002fQPEpBuX3VTZAELY4dB\\u002fvLECgBjHQz2MhQFQ9gudPpRNAnYUcf0DpBUAVzojZebz7P0XiMNkGYPg\\u002f5qn9HD2g\\u002fT+tEcF6blsEQJjuj3aXnQtAMiUmdMaYEUAw+GHudbwUQJbDT9W1dxZAyh1XeF1UFkAcQ258Y1UUQMFarXj59BBAHxJwAOPlCUDKYt4EvRwCQM\\u002fQ3yqqNfc\\u002f\\u002fERZiIlC6z8DSBa\\u002fwFzdP4Xgni8KBs0\\u002f74UffqBYuj9OwINGt\\u002fqlP8YlQFg+3ZA\\u002fV5lkkYXRdz8267Rpd\\u002fteP7PDlvPbkUI\\u002feisQRROHJD8sTD3KH\\u002fIEP3yzUlvbw+M+SIQ4JVBSwT44ScLO1HecPt26dk95YnY+ANKqepp5UT4fjlvY0XssPuWbmwB2Igk+gvkbOPLH5z0VoxeNigHHPb2FSNKbsaU9lDSMSCf4gz33lyL8UJxvPTnGl4mtAmo9aotqfWdsej2Rgqn0h7CaPVNqAxhPcsA92IhkBdur5T2yOo4AWSMMPmmPpihlNjE+WbP3Bs10Uz5j51IijCl0Ph\\u002f4\\u002fE9TGZM+ekmZPO+EsD4fmtun\\u002fxTKPgsWx81hyuI+33szdzq1+D6OZI9dmqYNP725lAH1PSA\\u002fQ\\u002fAdUHlCMD8pV8Sm4tQ9P23Ye8bzSUk\\u002fAGZyP+FKVD+S8hd9yF9gP4iQQPOrpm0\\u002fgMlmllVxgD9OiLQojVyVPzOXQffc8qw\\u002fFMHVuRHmwj9T1jzU+\\u002fjWPycagIFbr+k\\u002f5gmwWY9R+j\\u002fxkwQi0rMIQEfb2F6XQRVAua\\u002fzhn3MIEBHt+3253MoQEQbDIPcczBArThfxCSSNEBeMDBJBAw4QFoMRCrsdjpA\\u002f5sM0fCZO0DEmYxWjWk7QLQCQfn79zlAtb3Ci+VrN0AhHvygXAM0QIMsudXGGDBA0yJNRH42KECWh2AbuO4gQOzTI9Bi7xVA\\u002fsR2ZZpNCkAx+y0+h279P\\u002f6nxW4e4O8\\u002fkCOqK\\u002f7S4j\\u002fcsZeOUzzeP2g\\u002fSDMlkuE\\u002fhs+9oB8Y6D84jZHn54HwP6rwvX7zQvU\\u002fnEi5CqVH+T8PgdopdJf7P4dkbHdUlvs\\u002fUlzonAk\\u002f+T+VafSpySP1P6nSGPclMvA\\u002f5JJ0daG15j9o7bgH5yXdP5iCSqBVIdE\\u002fvmtrMLZywj8TDT9wzjayP2vOciNpf6A\\u002ftM80\\u002fQNwiz8mYGIpafZ0Pxm8L8Skc10\\u002fzoWvTZQJQz+59Jf0dqkmP3U5DzMs2wg\\u002fv8Kawg8j6T6XWeyR8HXHPmr73kJIPqQ+M9JL\\u002fBg2gD5VGqMDb09YPnY6731qYzE+4ezqaD6SCD51bsfTzQXiPd7+pHYFk7w9v+wOMVtjmD3VqZEPp3J1Pc0AEjybGlM9XYPHVrbhPT3SU8IT4zIxPeVnG97GY0Q9ktAYp2AkaT0Mb4RFPLKRParr3EzG\\u002frg9WvXYWNzD4D0PiMrRk9gEPoTFegLwyic+Dp3oZqPVSD7llZLFUKxnPqeCmGsGmYQ+skOhGhFboD7NBVwuHbS3PlfMVp6XWc8+JTermETs4j6XtV6X7Nz0Przc\\u002fV9YCwU\\u002f7XD8nBqGEz9pVKkAu+sgP1sOdCZlkyw\\u002fW7FvCP+\\u002fOT8tiSgp0r5LPxMvQqm0KGI\\u002fCoIqMnoGej+Ru0OXHXOSPykFSOyUmag\\u002fTqc1ywlDvj8fDEuZqhDRP3zSvE7BneE\\u002f94r814al8D9go1N68NP8P0d+x2ag6QZAs+LSGn3CEEBZ94n\\u002ff6UWQDka9TcJZxxAHF8ZIryhIEC96VgQilAiQNSjwThnFyNAa9TsawnwIkCYjmTOjOohQO9BPI7WJCBAxfP0PMOVG0A\\u002fF66p3DMWQCJJlMvhuBBAostEJhBuB0A2NZhjamb+P3MNyDbXOvI\\u002fXi+GbvRN5D+Je5BojJHVPzLcfQLwFcg\\u002f28GWocOewT9km\\u002ffKZzzDPww0qIYzCco\\u002ff2x2EGDv0T9IPW3Ex0vXP4gvnraD6ts\\u002f1MuGb8+s3j+L+QTbVNneP\\u002flL4wG6X9w\\u002fOQcWYeTc1z\\u002fNaZ80wVnSP7VzyJeHz8k\\u002fC8NPJLyawD+Sk2qTvoyzP\\u002fy7z7uTEqU\\u002ferI1eDzOlD8FX0zn2dOCPxmmlvmHQG8\\u002fqWaG6WfOVz+70UHBJahAPwoYXt8ibSU\\u002fRE6sh8ZaCT8MNQFL4J7rPmA72IgDuMs+sNOnWN6kqT5tpcsVH+SFPtC0oQeIRWE+pBZnBVNHOT60AVx3vEsRPvF6Wd9Yg+Y9WyArssnNvD1Y\\u002fPwSkASTPcxxU40KE2s9tNo+\\u002f\\u002fLYRD3GJoNWozAhPRaO+fCc\\u002fQk9h3g5AoRK9zxa+A4v6nIQPRVVghZj4Tc9Dx\\u002fAqgNJYj30sZWw4d6KPZQMZ6swX7I9SBRtJjoY1z3C9ycdj5D6PWOKUWQ+6Rs+3\\u002f2t7x7FOj6EJqC8sW5XPgAH7KCyt3I+8uoGRdtJiz6PhOOtqyeiPqTU8GE1DrY+IWeq0719yD5fba\\u002fh3PHYPjrsyTPSi+c+LKdgXqI19T7kRtou45EDP461sqZTxRQ\\u002fXVxczEUiKz8wOa9RtVNEPzIT7uQsIF8\\u002fDQMwcUm5dj9UMh1EccGOPyR85J6uGqM\\u002fAB5rciC4tT9ynbQHT5XGP+is0XwrfNU\\u002fHwAXkMq44j\\u002fC1FodXO\\u002ftP39BsJO7AvY\\u002f7ACghlTf\\u002fT8YkJI6k8sCQDtypYkNDwZAtYdcNqBPCEDG02AXk1UJQDu+AiiIGglAV7MI7JG4B0BxcVHaxVsFQE643GkIQAJAxlbzpKdp\\u002fT\\u002f0jVOq8zL2P1ngxL1BMO8\\u002fhdfXKLFK5D8kt95IamPYP2lZxm0KIss\\u002fhvLY7Mh2vD95xnZYtFyuP5hEInR6K6Q\\u002fZTkt78Y5pD9oed2haaOqPx\\u002ff9X\\u002fXW7I\\u002fQiqM7IYCuD\\u002fvAmVMq\\u002fu8P76vld4HCMA\\u002fkyoYrXM4wD8o\\u002fD9wrgC+P7ItU7m7W7k\\u002fYqzoz3mWsz\\u002fR14eqzqirP4QYMO6N2qE\\u002fLH\\u002fVvAUUlT\\u002fMhB+JIMSGP36J16plgXY\\u002f4E5mFXxfZD+Mgqbf++VQP4M7SyvMszk\\u002fWNrlvzPvIT\\u002fTCedqM\\u002fsGP1MyP0iRD+s+crUZ+sBNzT7hejSEQDStPi01OyARzoo+x3iGC96sZj5fVypt57FBPp\\u002fqTTnSgxk+D85WO5gM8T3u\\u002fDbZdUTFPdg71USaJZk9JtmXX8cLbT2HovWzty1BPfUhGgxw1BU9E0SEy2X97jy5GcnIaZjVPKgtavXaFNA8D0zpqiZZ6jxHCuK74D8VPTosUwxlDEE9EmTL4zabaT2VTAgn87aRPY339djpcbY9aoJlz\\u002fP72T1M96xjdHb7PTctvsqQfRo+thT66JFRNz4tGcJmSrtSPqAovtO8dms+h6CTe79ggj4kdaOHfHeWPio5Yzb\\u002fI6k+Ux7esmXquT7DPmSlzhHJPtvI5wIc59c++gkBL9PT6D5sbJ6bUkb\\u002fPtflI9hHqRc\\u002fTKpcN380Mz\\u002ff7bLQWHZOP3WfcYtQmWY\\u002fEanPa7zdfj80CGHGL02TPy2Vn3g0EqY\\u002fcpENIbgStz+o+hBqQhDGPzVhWFSfUdM\\u002fRaF4cyQH3z\\u002fQ9rgC6efmP6P3yxqJMe8\\u002f5dS2ALSt8z\\u002f+yGoi0CL3P+sFtPWzhPk\\u002flPYAOhqX+j+JM5NColT6P5+VJNs\\u002f3Pg\\u002fgehBEr5f9j8ALZWwSB\\u002fzP0L1owHU2e4\\u002fa2KqO8ZT5z+QzwJxJm3gP6bu7UWTbtU\\u002fvH\\u002fLwwvSyT\\u002fRR5ZVlL68PywXWpWJ+K0\\u002fYfdHX3sUnz9\\u002futXbLDiTP6L6ka\\u002fIz5E\\u002fyXPGyhW4lj+li4fI3jafP5OCw6evgKQ\\u002f\\u002fwAnfcLjqD+KjT9NZa+rP7540Xp5J6w\\u002f7hTuGB0pqj8YYDleEjWmP3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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0AgHbzHhiEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQCFYNERwIQ9AmSuYop0hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0BML1yqCyEPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQGrsb7GnIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0DlhDI4qyEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9AdIR0j1EhD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQDwZnmN9IQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AlFvdRUwhD0DSw5hLLCEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQB6D3s6HIQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Axcnyt0chD0BiA5bK9iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQM4MYRlwIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002f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qI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u0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fG8CQC8xn3+hQwNAZeppDmElA0Bo+X3WUf0BQF5JjLgb0\\u002f8\\u002fXhXhwpNs+j+At3rrs470P0g0EAbs8O0\\u002f3L6GBkxn5D+TPlx9bwfaPxDT\\u002fULZGs8\\u002fWJJtDPltwT\\u002fVVr3bTViyP4TVAw6AKaI\\u002ff7z+2XjwkD\\u002fLhloFJc59P2ciZbJ8wWg\\u002fTc2Jj7JrUz\\u002fBunAIwsg8P+wPwPeQKSQ\\u002f\\u002foW+koLGCj9t9aPCRQTxPoLW8UQmP9U+NIG\\u002fVc2Luz6lwxdB2ROkPqhaIILTMZE+Tq2qluKRgD5X4aW6oZZwPsEgf\\u002fyqhWA+h5TGH7RSUT42Hlnv7HFJPh4gK\\u002fAg1+49B+cpf6uu9z1GN9qL3KAMPj50G81a1CM+bzqD4OfZPj6MD7qSH\\u002fdaPpdHkDnYMnk+qInI6GGnlz58zpOmxWC1PiukFzMeNNI+SB7fh\\u002fbs7D7h5jxKB1sFP5+pWAwRQR0\\u002f3HL1tNiTMj\\u002fyysHPet9FP24ZyV5A4Fc\\u002fPcIdyZ4raD9et0mso7R2P3W1sDJv0IM\\u002fKnNm7e8XkD\\u002fi2Z9Ps2qYP5d1A2XLZ6E\\u002f1wET39GPpz\\u002fXL\\u002fPdaMiuP6hUaBWb17M\\u002fBiFeKEnWuT+qhJYs4DzBPzSrSEtygcc\\u002fDeUaZUsX0D8FPy34eaLVP0+F1pnrDtw\\u002fwtmXXyBd4T\\u002fRDDt1b23kP7Ufj4pi3uY\\u002f1DvVqp176D+21mykIEHpP0PUkPGNU+k\\u002fzQxeL5fv6D9ifPNxWl7oP4aJ0+dC9ec\\u002fdOpD5gIb6D8Dv3yOakPpP8xeW9GQ2Os\\u002fX8NzeVkL8D+hmzEeZffyPy2+U9hWg\\u002fY\\u002fyT0LXCdw+j\\u002f6xqAmRo3+P33SFVzAagFAPgnPQXS7A0B+nfsob14GQAv7Ws21cAlACPZCwZ\\u002frDEDhbeGutkgQQDvAC4yj9RFAuf+7zL8uE0DGMD3Lm6cTQBNcnDneLBNAx+kQ5vi2EUC7CaX5Y98OQI8q133KTglA67NlY1Z8A0ANEAIHnCn8P+WgSenTGfM\\u002fxwe2PMxU6D9ZPnhIaiLdP9j3ZwjUatA\\u002fnJsSDRtwwT8OIzWGQXqxP4Jz4oA\\u002fi6A\\u002fkwfni26XjT\\u002fljgFkWAB5P9m1ZYHP8mM\\u002fAsRJM4sRTj80zMkhrHU1P1Ap1smnRx0\\u002fMoOAKRaWAz+Htlr6XiLrPoXndBh8DtU+hV6lUB8mwz64MozIUJWzPqznys5QxKQ+SahlsiGRlT5POzh3jKSFPucVsKZplnY+n2n0276CcD7uxwaybqIfPuZF7gRoiyc+4iGGjo54Oj7rANlq4VJQPoXg4\\u002fKWpmU+PBippSkWgD4d5C2XkaeaPoHocUYlR7c+uJ+X7mcx1D5MxMnxR8PwPmJn24f+JQo\\u002fEmqRbQoCIz9\\u002ftY90jas5P2JryNSTFFA\\u002fCKBYaoauYj8OUYe1SyB0P6sE1R6XHIQ\\u002fuEnDTgSnkj8N73eEjBKgP6SMx26TyKk\\u002fTVqZbdZQsz9yRMzZnzC7P\\u002fH6ztuvKMI\\u002fVQXmpLJgxz8UeA8jqqLNP1r9ptUw7dI\\u002fQMAA6Oy62D9zQTBCVoLgP+ob8LxPKeY\\u002f9BS7akRG7T8hHFOcm7DyP6McADGw0fY\\u002fYj2KKemH+j\\u002f7YKM5vGj9P\\u002fGA0zmdPP8\\u002fNe9AA9EDAECtxJ8qaPv\\u002fP48kb1UuYP8\\u002fhzCJK0+G\\u002fj+OQqRd0sb9P2JQsvA+iv0\\u002fO2lL80lF\\u002fj9ohfvOCTEAQN1\\u002f3\\u002fyqDgJAyPL01UK2BEArtTY2zgAIQDLXPQnYuAtAg5Qz4c+2D0BjHeuocvsRQMiG5WEpTRRA5QsoHdHnFkCAm9n68tQZQAvOrBjT+xxAJJhft\\u002fwKIEBm0LAKwVkhQG\\u002fQ4IqDKCJAZhBwPUs9IkDiLl2cZ3ghQB4\\u002f6NPpwR9ApGHdHk1LG0C2OFidnCEWQDHpa3SN6BBAay\\u002fCQUNVCEBRlk5Le34AQL6Yul5fFPU\\u002f6qknhrJr6T+nW+Q0mPTcP2ZopPkNLc8\\u002fFNAsX2G9vz\\u002f4nczFjYyuP9m2rbdbx5s\\u002fCmjrATHYhz+8Bln9G1FzP41+fuwdm10\\u002fj2kLgEyoRT+4xOXH5QIvPwhn4dUM7RY\\u002fDzjBPWfrAj994iYRPkHyPs+URIy9z+M+42HtFEZQ1j7UQaRfbpHIPhZ60L9hu7k+zNvrcTS9qT7au0eyHq2aPg9BVmt7apM+XEgtBopmTT6JaEUpEm1VPigpv\\u002fww+2Y+xVTX13E4ej5pN2bfZqqOPodtikAiVKM+00RCPR5nuz7Ot5qZkXXVPlTqQ9iWd\\u002fE+PAZujoP2Cz+Tsz+3Ak8lP9vuFF6ocT4\\u002fQaaNapBAVD+MzLwmKQZpPw\\u002fKCdEgsHw\\u002fBirVsuaBjj9SznZeiRmeP7+tqTifkas\\u002faatHPCl3tz9imrCqY5jCP5Sv5CRNh8s\\u002fnYJDbhck0z8OYK\\u002fE\\u002fj3ZPwDRsfkrBeA\\u002fq3DjbFr64z+WZaWTTgjpP1pmSPoNAvA\\u002fwMURTu7o9D\\u002ft4eEApH77P7Mjr5+30wFANx5TtOFlBkDL33cltPcKQAhjlesu\\u002fA5A+5H8euUBEUAEoT9gDu4RQD5BuckVShJAofj3aww0EkB2hHz10tIRQMCzE9szThFA51XMz6vPEECzDG1ViIYQQPi72\\u002fX7phBAqxobgz5gEUDWEhEy+80SQIF56\\u002fej7hRAjtHlHpOmF0AbBCVeKtAaQKy0UZBzUB5AgK5wvOwRIUDDgjzA\\u002fiwjQOnhfvI6gCVAtBfuAKACKEBJfbJWJI8qQBBhdc0Y4SxAgGDRsHmcLkD1hZRsnWEvQA8aLpNp5y5ASNGU3ZMTLUDE4CuIZgcqQO6+IrvLGiZAXZcMAoHGIUCd0c1qbwsbQPkvGnlodRNA0t7WzX59CkDqz7ZuIREBQI9m01kO0\\u002fQ\\u002f\\u002fHe2hHQQ6D\\u002fpFjOH2FPaP+gAVJAfP8s\\u002fGi09Lr6juj9wrH8RHpOoP5u+JjCYYJU\\u002fiZ8C3FeSgT+jTVmXvolrP6wnEjxwFFU\\u002fbBw3wTqdQD\\u002femquQUSEtPxrHzDOWyx0\\u002fyEja30ImET8hUD0D7YQEP9wgjwuUBPg+WDg9d86v6j4k5PFRrOHbPlHSLIu\\u002fu8s+s7RwrlKcvD6KoWtn68m0PugYfl55pXg+iOkrCbyugT7shO7u0VySPo\\u002f9MyDT7KM+A3LxzDhgtT4Hq3qWfYPHPkhbZObsM9w+5vk5aOAO8z5fLF7BmiIMP4\\u002fmi6JYSyU\\u002f+1He8TJrPz+CH59OhvhVP3CR7BwPwWw\\u002fcmoLzASDgT9vkV5vP8+TP9jwBugwzKQ\\u002fyzM\\u002fQg1EtD+IN5JDglbCP0jgEtWD2c4\\u002fyFeKZmUq2D8n+yUmPa\\u002fhPxZZMl7NT+g\\u002f8Jw4B0Ot7z8P8SqQ1dXzPyI0WLdZWPg\\u002feuUVPHzt\\u002fT+QNUTNmLkCQMWwLpQM6AdArR6VK7jBDkCN6aI1Yo4TQNOFdWF5KBhAUlpGtMurHEA9BZTjdEYgQFOr\\u002fjOIsiFAUADfhS+HIkAs0e1\\u002fbdEiQFOqjWnqsSJAGhKNZXdOIkCUqpn4cMohQGyx5bruRyFAJoGATR\\u002fsIEBekb89TN8gQJb13unARSFAaN3OnLI1IkBGcb6wkbEjQE7RHZF7qyVAIsKu8SMQKECQimhXJdIqQKZK5Rre7S1A7Gkg2hexMEDMv7aWb5AyQODHm4cRfzRAFqaoHeRTNkASdnplydQ3QOVeGpD9vzhASJlv1tbaOEDmooRe+gE4QLMfsBO1NTZApFuHaHqdM0BD4PGcTIAwQPhFmAV6aCpASIskZ+0VJEASrkn1fwgdQBj\\u002fiUGt7xNAUT3YNXsBCkANN+\\u002fmrBkAQBDB39rM5vI\\u002fmxxhRFYB5T9LE+2h4A3WP2YE+gR51sU\\u002ffwh1twFgtD88IuS2\\u002ffGhP3wtZcy1GI4\\u002f5xtFRAGceD8mNrPxpqhkP586xLFgOFM\\u002faOtZTz\\u002fSRD\\u002f36MIeZWg5P3zD4amyJDA\\u002fpJvW7S4YJD8qWpFBGMAXPw4o0UmXVQo\\u002ftnXezIlf+z6XG460+SLrPpTlGXFbCdw+CI5t0WNv1D7OPRGx85eiPvD0xcXZVqo+AVXHlDmxuj5rUYwZMgLMPhAvtzInbNw+Bjrx2vhj7D5V0p\\u002faeWz9PsiKiWEs0hA\\u002f7PAAHBylJT92UH2JUQk+P5H7++itGFU\\u002fyeVecy2qbD8iMnbr5GSCP0KbJsGfD5Y\\u002fIMnw1kGeqD84sBbMHoW5PyEm+cZOkcg\\u002fZcruSt341T\\u002fuzWLpM0biPwmkdZ+DUew\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+OdUBOgXOiP2o\\u002fRh44+JU\\u002f\\u002fzLpCqohiT9WHNbbWyN8P7S3KDbi\\u002fG8\\u002fuQrVqgAnZD+2Y4QwT6NgPxO35jbLLE0\\u002fqNpkw7ueUz\\u002fzco1nrjBiP5UZ53QKZXE\\u002fhfc0JE+Afz8pBCB275WKP5+d+uag6ZQ\\u002fPjA9pZffnj9e0S7fpb6lPxt0HpEbQ64\\u002fQSYMR7gNtj97CIvylePBP4MQ4fGvW9A\\u002fFB1Q\\u002fsIP4D\\u002f0oIqnrM3vP5Gx5UC9bv4\\u002f6LTmsBePC0CYF\\u002f9fkmkXQKGXU51EmyJAA1qkQeuqK0DquR6tPUczQI12TOFyQjlA2aFeSrFGP0DQnawQL29CQAaVbezS5kRAfOWWQr4WR0ABduIAii9JQChh0+mBa0tALDkNpdzrTUBJlQjcaFBQQMlGWawZplFAto2KWOjPUkCOEtTU+bNTQF7BcQ8gVVRAaxDQdwLUVEA+5pUn42FVQEVuBPMuLFZAvLB8nM1JV0CHmjLCG7BYQBuN8ZbMMVpArR6sTmGIW0ADJLLx+mdcQNMTz3kAmlxAXKTJ0ZcVXED+H\\u002frx1glbQKD1+HqJ0VlA4tpnQ\\u002fTSWECXeU7F5FhYQCgftZBldlhAFPLr+RwBWUCWptQxSKNZQGzxiG76+1lAS++OQqa\\u002fWUCN7qZH7cxYQO3VMN0BMFdAMUtwrUYWVUArUT4BgblSQIhK7LIfTFBAo+V3\\u002f17cS0CGbK60C1tHQHTLePzsG0NA9TO5jkNMPkCyZXcOQBY3QPTuhbDHyTBAhlziD4EuJ0BZKvjE91ceQG+E+ERl6RJAcpbombjRBkCpuHUCNKr7P2ZNpVvq6vE\\u002fZ4t\\u002fT31C6j8ngcObHMPlP5SAw4O2O+M\\u002fVQim6xcD4T9xS0qfdPXcPxGO2XY2Stc\\u002fyhTGY+yW0T\\u002fr3ByzifTIPxYb9gDjssA\\u002f9vFcjLg9tT92pcmGMfSpP2ggnSZu354\\u002frxc1ed0ykj\\u002fJNBIuofiFP+4UrWLbUn0\\u002fj39fYkbeeD\\u002fNgXyPESBnP0IUV0rGzm4\\u002fqtuN0j8afD+6Cc2BVniKP6WjJPPynpc\\u002fJJQ5ogaioz\\u002fKPdZJ+liuP3I96Cd54bU\\u002fID28hMGyvT\\u002fHDIGkuFvDP83Nq5utPsk\\u002fPjpwK2aI0T9aaMsvmGXbP+I8zzmi\\u002fuc\\u002fkiG55\\u002fU+9j\\u002f57VXrgpEEQBdfGTweSRJABhJbbNS6HkBhbp68Yj4oQI\\u002f1gw6d7TFAdDMjLDzgOEA\\u002fOrj2bjtAQPQW\\u002fYshAkRAvRFFZV1wR0B+2JPwvVFKQLShIfx\\u002fo0xAf6Lv4fqQTkAQQEMnzCxQQGStAqphFlFAzT2evw0IUkBe0erqEfBSQFVkFgVAtlNARYoKQdxPVED9KuQqgc1UQGcHfFDeWlVAsNW+YzowVkAUWCp0YHxXQLjC+4R1T1lAy915eVKNW0ATzdny2utdQDA7pF48AGBA4VW5Q3euYEBOXhsLg9pgQENLNgtxf2BARcyczoR9X0BAezK51KxdQJ37wT4oFVxAmlFDLUMZW0DP4KBhkNRaQAUxerHhFltA4pAsxMx8W0AkH\\u002fZD0ZdbQDdWm9H1FFtA30VzttXTWUCdQx76N+hXQKWaCTVNiVVAhbYJtUT4UkCN+\\u002fa+32lQQJpS5gwA90tA+z+rBhdrR0CG8zqjsixDQEtS2RnJdj5AcCKS01ZKN0Du\\u002fZStvAYxQKs\\u002feprsuidAEXXCf9ycH0B3522A2WQUQJnrS+UgSwpA950B2IPdAUDvB5obhAv7Pz2OWbWiDPc\\u002fAib5Cikd9T\\u002fJVkLMKY\\u002fzP18qbAZqkfE\\u002f4aQ9ApL17T9Yv+i8sQ7oP3hr7Ph\\u002fLuI\\u002fRp7lpuH32T9I76MvmqfRP\\u002fexTj\\u002foFcc\\u002fLxsOAHpdvT\\u002fyvtr++F+yP6m16MiW6aY\\u002fXLvP\\u002fGo0nT\\u002f0MJJX2FqUP0e8m5B3k5E\\u002fUg1\\u002fZCt2gD+deccCsLmFP2Bbk5JSgJM\\u002f1kVtzZkYoj\\u002f3Xef\\u002fy9evP5DVTjqpF7o\\u002fNKzZx0Hcwz\\u002fIwIhdwh\\u002fMPwxYTk52nNI\\u002foHjYdLpH1z\\u002fdesyuCjXcP9Q8Ucw+WeE\\u002f\\u002fku173wi5z+oTQ0dkHvxP8xO9daMWP0\\u002fsD0Xq+OxCUCu5Bh11jgWQIGH24xoaCJA4IsvpjbQLED1+nKupzM1QKpiwRjKUD1AqOhhj14SQ0BYm8WhRG1HQOeuipRJTUtAaLJFOBZmTkBZNtYH71BQQMvcL+M1EVFAggfmbiiVUUDZt8Vq9\\u002fxRQPIIcRUVWlJAkN8wMXKtUkAK2zF4ffJSQB7rHmO\\u002fL1NA7w+tu26CU0DChEV+cx1UQG2vtfg3O1VAHodmtIkHV0CFSkXSE4lZQF8c59jXkVxAW3mQnYK9X0AVGhB2d0FhQDVBb3tmLGJAHZ+v0ORxYkB5gPg12g1iQFU6wfhNKmFA2NQNsfIRYEB2ynls0CpeQNmBbqk03VxAzSlC7Z5cXEBUPfjfR3VcQEKOHkCTulxAuC2bY0C0XEC4J0cFGQlcQNjp04fVl1pA6lUvtAJ4WECw8IKIzOZVQB\\u002f1OCbWKlNAxB6HWxp8UEDJl8DkiPFLQAmg8QWsUEdAHrcV5NoOQ0BOBdW75U4+QPdHRbKdRzdAhqhrPVkxMUCdiox7ZWwoQBo9V2Cw1SBAFSAeDXMPF0BtkP3BmmAQQJi3BLXdXQlAmw3CCgHnBUB8VJm2rnsEQI5ceCAPpwNANhiJkfR\\u002fAkBm1hFjeKgAQDvMsctqYvw\\u002foBW5c77U9j+tC\\u002f21EmXxPylCVpKHSek\\u002feg1l7vi04T\\u002fFQpAdyyPYP4AY7XXKJ9A\\u002fe1Cj+yJXxT+Sexba8fe7P0PTqoaHjLI\\u002fNthDGSmMqj9vOQF7xCSnPwItO5FxDJU\\u002fLTH1jx+Gmz\\u002fk5KDQp1CoP12QslRUO7Y\\u002fqNTu\\u002fJRKwz+1ysNI9i\\u002fPP+FP\\u002fTjYaNc\\u002fBWfj+zdT4D9fPqJZcjXlP9Mhwbv90uk\\u002f+AdjNyLj7T+Xv3t7wO\\u002fwP7AiWTOL1PM\\u002fGnKALvmb+T8RXNlOA8sCQAuutZ5WPQ5AVZWKdKkEGUBwrXiIn0ckQJNY2\\u002fJFaC9AiTGAbgf8NkACuJJXvq4\\u002fQDTzU7bYkERA6+\\u002fXC98ySUC2h6Xc+j9NQMBs5DT2K1BABxBf22spUUCtii\\u002f3vaNRQEWbE5afvFFAzeeJifybUUCAD0Qg12FRQDmOeyrmIVFAIJ50gXnqUECDqo8VCNFQQByWL3Ju+1BAsFxDwAGfUUD4lZc4+PNSQNdVBKunIFVAfGbX\\u002fwgjWEBdtuUefsBbQPY2V\\u002fV\\u002fhF9AqMXPH2JqYUCSYrCOFo1iQMrHrf1U+WJAuOCKTr6rYkDmMUsHPNJhQE7DldrGvGBACr0daeCBX0CRJblHNDheQHOgTZf6wF1Add9tBzTnXUA1MjPiwTleQBf+jNJEOl5Aw679nPSJXUCausfMYgRcQGMZZY5BwVlAy7EWwu8AV0C2zpfvXg9UQMCVvbvRKlFAQT6uoirvTEA7MFYBAwRIQBNyrpJjkkNAsLDIXMwpP0DcaqWBih44QGrAmW7jHDJAPr81rLeCKkCi00woAzIjQN68UqThWRxAC403E21DFkAszq4J+ykTQEx9TKHY8BFA0lt3cACIEUCWVOwiSxkRQNLRqNB\\u002fJRBA2fK28P0SDUDDVzYW9tUIQOz7XszDKgRA7e9WAJNV\\u002fz9ZLMR7noD3P\\u002f\\u002ft7ghtLvE\\u002fMXxWjbCm6D9sGsSvEmPhP6ZQhx9SENg\\u002fl4C8eC5X0D+XKZRpeyzGP7CTTrOfC8A\\u002fzYVITtcOvD+J6MEbjiyoP88\\u002fzuJwU68\\u002fFkh11m09uz9NEfAfRIvIPzS9DLi2AtU\\u002fH+9hrfLC4D8wQPYE+dXoP9fPPiGyFvE\\u002fHuTq9\\u002fbf9T8SQgYRkyT6P29x6jaeaP0\\u002fpeig5mmz\\u002fz\\u002fXBFGYTPkAQED2PsqiJwNAIaWZUtswCEBaBBV2cEoRQLmu+9\\u002feqBpAHWxqfzPZJEDEKO6Nfr4vQI9sCDv6DDdApHGU+J6pP0DzVvUVZoREQPNIds4GGklAhbWod4QQTUD6NJQW+\\u002ftPQAfNwoul01BAb9PPX1gPUUBJ8FKAe9FQQJgUAfN+RlBAgZBkNpAxT0Ayd4WUK9RNQEx57TUirUxAc+CfrDT7S0AZ8kqAMRJMQHuJJO8\\u002fWk1ACMkLKJodUECUyOiN33tSQPFlwJCRwVVApaKf6cGwWUBImTwnstJdQJPXJsf+xWBAQ05I5ooiYkBnmFktcs1iQLX64G3KwWJAoyZe\\u002fq8qYkA1nXyQtVRhQOvmCYmikmBArPdZWI4gYEB3IqNMzRBgQCwfUrBgSWBAE82hDXKRYEBEhlghMahgQNChNUaWW2BAmalnGYQtX0BT3u5GE8hcQMOml6VKyFlAPxY\\u002fcgqAVkCv6ucdvTdTQGaEZdP0H1BAX5a2BrigSkAcPr1DJZ9FQGwF6wN3OkFA1Yp0jjTkOkA3SyH9UpQ0QIbGNfJWIS9A92SZQR62J0D47d3eh8AiQEkhQi+Ryh9A9F6sf0ozHUA2kZ\\u002fSi3AcQJ+oumtMQBxAfAkemlyoG0CQZmDQ7iAaQGPKkKTumhdAEPpPUZphFECQa36z6OYQQIRowdAjJAtAU9nKA3dFBUC5f179m1sAQPUwdx6Tr\\u002fg\\u002fAyi21KMv8j9K\\u002f2OweP3pP+2Qqip6\\u002fuE\\u002fo44hbSic2D\\u002fRqYmDVM3RPzcyS\\u002fwQE88\\u002f+16DcrfwuD9+ck8bUwTAP\\u002fDshpuUbMs\\u002f1XZ1GfBa2D8Ri0WK3ZLkP2hcXzKoNPA\\u002fWykMCZ239z8UC24lGx8AQPZiVrwRYgRA+ZvLGYMHCEBFRSCRmY0KQDt8+WClzwtA9PBL1x5ADEDydiRbyAsNQHqFQqXYDxBAUTd63f4HFEAtwq364OsbQNqfIDtomCRAZUOvaVRzLkAEWqw+jtA1QGfKJthjzD1AcLjZ+w5GQ0DXxBnF2pFHQA07yrhjRktAkEXe0K\\u002f0TUBWr5if119PQGcqEpiVik9Aefk7AhivTkCLBskDtyZNQEQ+2AM3T0tA56QJNAl7SUAaQhYH4\\u002fFHQOWeqEac\\u002fEZAhs3qde3wRkAP6NW+DzJIQJ4AOtObIEtAiIJSjh\\u002f7T0CybK7A\\u002fFtTQCGF5Xync1dArkd7UXbXW0Barac1mPZfQEGmXMNgoGFAIhMSxUSpYkArth1xIAxjQNod\\u002f5Bb62JAmWMNSbmJYkApQjZ1FTJiQJYuJkJEHGJAVQCUYNBZYkARgmRKv9FiQKuNgr8ETGNADLiSMf+GY0ABP2HT9E1jQKDUKBxEiGJATdWbnwo9YUCiILWjYxhfQPdwJyJNQ1tAEvnAiRhRV0BhhzSXS4VTQKoKjH6tClBARMxsUpDsSUB\\u002fxWdKr51EQLYW11+cKEBAE2Lvx94SOUCQYKFCoXYzQFQSKg\\u002ff1i5A81MFFaScKUCfhkHjqcQmQHRSX6HzoSVAwZ06yuxqJUAuH6ulvF8lQJzv1F4K8iRATINuB5zdI0CnjF2KmSciQMM+ezpeBiBAoBiYPqp9G0DmcbTUlQ8XQMyuJByZ+xJA\\u002f80bzjeeDkD0yAyGGwoIQIzFbx+8NwJAjh8m3ft1+j+WzTHzr2vyP4a6E\\u002fD8HOk\\u002f6tv4\\u002f2EE4j\\u002fHreQDckvfPzqjf\\u002fEwHsc\\u002fsOcQ7AZwzT8mRDQVTtHYPzNfSMOvuuU\\u002fHVx4YYse8j+wpuELxzL8P9OOjRRIZQRA2x59TTFsC0DQMpqDqycRQMgZzvi2BBRAGFCQauTgFUDBXSEQI5MWQHiKKzQcVhZABtqTN2XSFUBDGXKkDxMWQH15rhyPaxhAW7xnnwhTHkAjGAoYGpgkQK61zx1DBi1AZFjri89LNEDFzZunQG47QCK3h88VrkFAGKyl3IueRUBHLFi6KQxJQIZog2RaiUtAHFsqjLDUTEC47PldeOhMQPNOHYZK9ktALWyb2I9SSkCNCg7ztltIQNvFs\\u002fqDaUZAj7K14WTKRECZP0hArstDQOHwimaFw0NABoOE7I8SRUDtlN0pVBdIQI7EqoVIE01Asq8txyoEUkDDDVoj6E1WQDEoJxyUB1tA6uCuTn2tX0CjaKByRtthQAsizmI0XmNAAHbJqr1OZECyknEnl8RkQMgNe\\u002f0i92RA3Ejh0xEoZUDIh3j\\u002fvIllQOP8mz2wK2ZAZ1UTHhT1ZkBhIXAYXq5nQCHJkOQuFWhAsz2\\u002fJNPxZ0BwjQz+pyZnQClV8Hx\\u002ftWVAvnqz+oq7Y0CAqB7XPmdhQKiPnJvd2F1AQmdQxjf0WEA3x4Vg+2dUQAZm1G2xYFBA0PigQdblSUCU0sAXukREQN0sGlfIpT9AWQMYWWz5OEBlJ4mBzFE0QEDN4QiwazFAEowxbc3YL0Bvm75dvsUuQPpE71mvqS5Afkci+F2pLkA45qQz6iwuQOpU\\u002f41m9CxAvcZut0sNK0CxLFcqu68oQKJB0kLGFiZAgaL7DVZnI0DL9nv9Iq4gQDJRzQcf3RtAi3zx0uJnFkBlktIMQzIRQL5ez6fdDQlAzRzUCHNcAUB4Akx8QWv3P0qqi0vVk\\u002fA\\u002fCT4qCU+X7D8UN2V2tUHTP5sj603iUNg\\u002fjznJVa8x5D\\u002fCJCb88G\\u002fxPxhkntgvuPw\\u002fgWOTEwAXBkBmwFmOs5wPQJ8f1kM6CxVA+YUg5DUaGkAVNhLNVD0eQK2ZR0CbbSBA5BQqOjPcIEAiheyBdYwgQNHPPvSEvx9AmOsZhkvRHkB5LuffR64fQCJYx89k5SFAgpzw0ddCJkBQ4YwPFHYtQK7EYBRn2DNAn774Sh1POkDPZLOs19BAQE52c8Rkg0RARA\\u002fgakrHR0CBnPRVIS9KQNqINzbydUtAOmg1a5+QS0C4LecLyKtKQKleopgYGUlAoeFeSxg3R0B3DJOQxV9FQFfkbHuD5ENAmztHdi0VQ0AMR+Dfz0lDQMFARuY75URA6e+Cb\\u002fVJSEDOCAPm18BNQDD85iXEq1JA4ADCTXVhV0CWB55kiapcQJOKAijcBGFAnxccgtd7Y0D1EUe5wIRlQKp4gSSCCWdAJt10rpEaaED8pwRudedoQOfe\\u002fmR2qmlA\\u002fDqbMbCPakC6O8TZJKJrQJRCy7lvxmxAiP\\u002fXpPnDbUCHBNylRVduQGQWivVzRW5ATVa0utBqbUDW4UVU3cFrQGzWTqMYY2lAwKJB2PN+ZkB+P94eslNjQLf9cPduIWBAcHaRsVI+WkBcC0fHPOdUQIA2q+7VZlBAB7+ZZFGPSUDS8KJUov9DQLRXtQ9c6j9AgWmPczx4OkCVXCGttDI3QPxlIXWWlDVADbjqNOUNNUAgBo7pKBI1QDEUQIUkLTVA4V+9T7ITNUC2bSdOE6c0QMDqrNFs6DNAqxTaHbbjMkDJLBt0bp4xQAE925BrEzBAKaN1OgF4LECKYmJLMTwoQGBs0pYdqSNAcnLVJCwwHkCql8OCztoVQL5Pa8\\u002fU8g1Al9mqkibkA0CveRrLr7D7P0wnVNrFrPc\\u002fdVpkTzPW3D+xJM1gSQ\\u002fiPx\\u002fcsofmj+0\\u002fF6Xcv4sv+T\\u002ftwhoPu34EQN2CE1pqMA9Az6Yw2+oZFkBDqSJRIC4dQBoYEvSW+iFAhNqzpeG9JEAkLh3n2YImQCXmts02KCdAz7eYINjYJkCK3BN61wQmQI18WGmVSyVAMsIaqLtjJUC14AE6aQknQBlfQwgI7ypAqN2PoTjSMEAiu3kunrU1QBoQBhe4\\u002fztAa+DmS92bQUAgrlosV0lFQAv4qpnujEhA1wdM8nv1SkAgXATbtzxMQF8n9rsvWkxAGWnU5oqAS0Cb\\u002fnJAdghKQDq37ri7VUhAP9I73H3ERkAsr8mQ86VFQI5hcVcDSkVAH2YS1K4LRkAoT30RvlRIQGtTOR07kUxAf+rzSFGJUUBPc8wBTPVVQLZIdS2iZVtAz\\u002fgyGn+\\u002fYECDWKwRM99jQCTIYM\\u002fpy2ZAOnVNcjRPaUBzKjWXXFNrQAoW4T5Q6WxAyuLpxyg\\u002fbkDsz8LxYopvQIVGUbChd3BAxEGfgyA4cUBjdLhjz\\u002fVxQI+zIeDuknJA5Z5i+TrtckBq1BsuDOZyQDp\\u002fWO6QaHJAXFs2WPttcUBM2Iv3V\\u002f9vQIfKS5lwbWxAgRRTSBlwaEArAlSDAFtkQGJ+8LeNe2BAEU0QebwXWkARlr3BmFlUQANmg+QXnk9AnenGpy3QSECKMhf75QJEQPgso8DW30BAoF0aGDkZPkAqlCG5ZF88QFXNL7d56DtAtw9xSikgPEDF\\u002fNQcKpU8QAI7\\u002fqJo\\u002fzxAY4eyvRg4PUCcww9lHSY9QB5FjmZxqjxADAXVzvyZO0AXWsYHLso5QLGdr2vvKDdAmR\\u002fqHU\\u002fQM0DcqpfWdAgwQMz0w7EbayhAgE89+XV2IUBMS5LrGJAXQLZR3zLnug5AADBN6sv7BEA1t585qsQBQHRB3Kc3auM\\u002fFMW\\u002fa20h6D\\u002fETB9Od3nzP9E7YelCYQBA8Bsz2f9cCkBqj4MeVd0TQMWXS7NP6xtAIf9t+LFPIkAaFdjCR3kmQDGcMRAV6SlAZysM75I6LED0UgG4a1AtQOP2ziYsYC1Ay0PzVCLlLEDIaNXTy4UsQBUJ\\u002feft+yxApztVmi0HL0Dw8SUaPrExQH9CJtdWVTVAJmFkjGqVOkDLdZ8sUKZAQJHh7Z4UekRAJ3p6l0tVSECdrbXv7rFLQP2Dwf\\u002f0Gk5AGNCUBDVRT0CZbfHMvF1PQNXQfmlNiU5A9tVCvPw+TUClegFVzOpLQA4PgPu35UpAZlOP21R3SkBz6N6pwudKQDXQAtsok0xAzjJ7UtXuT0ArSn\\u002f\\u002ffrtSQBy6fTGzw1ZAXMfjQhEWXEDufnYjoT9hQHv4nQyGyGRAWsBS0S1baEDS6qMY\\u002f6xrQDMgBxW2iW5A2H8p3bVxcEBWnB48CWpxQHONj+YxR3JAm2jDskgic0COEvbdPwh0QImOWZb883RACLAb6PDPdUCOW9MDq3x2QF\\u002f7Jt9F2HZA7Xqe8EbEdkBgL3QKKCp2QKJ1XtXY\\u002f3RAk6Dl2SNMc0Cqqnyq1yhxQLucn7V2gG1A13UVtK+KaEBY1K1I89JjQFsWOvNbS19AIfZq8RleWEBklhZ\\u002fk\\u002fZSQKf2e4wK+k1AvJE\\u002fO7h8SEBMxBMqzPtEQO2GuFFaAENA3vDuj+cfQkBVDX42hgBCQPmpdioZW0JA7rmc\\u002fGj7QkB7D8b7YrtDQNweBAPueERAJMfdq7AKRUDDF+jIgztFQKxkh3RE0kRAKLKLyHqkQ0DhM1gYhKpBQN6D+e7oEj5A6epTyvwXOEDyeDoSRhoyQF39vUTHeylA9XmsVrriIECP3te0bJQVQBSB5cdj3QxAu71qoYMvCEDoZsk4toTnP5yhsf95BO0\\u002fVainh80b9z\\u002fRWvAURzQDQDCblKkDmQ5A5po+J4vcFkDyNA2VwesfQMtkTJNj2SRAm775992QKUBUYqKLYZQtQDaBODSLQzBAnyAs10ErMUBScHtvf6AxQAkY9q044jFAhoSRSP1BMkCdeet1ehozQKGlhDLVyzRAwt6r9xG3N0A\\u002f8jD3oi08QOCMHfMwKEFARkvaWe7zREA\\u002fG\\u002fpEsSZJQCLVZUp5Pk1AE1DZIcJVUEBmBmoQbH9RQOHYE\\u002fraCFJA1J5x5AQJUkBYrVvFj7ZRQBFWdW8bUlFAsGdUP10RUUAbjcTlHxZRQAtUY3wGdVFAkDPFdqpGUkD+Syseh7dTQB4sv1U+C1ZArgu9cs2NWUD5B7foRnZeQMccNztOY2JA7\\u002fbA3skbZkBD+OkGox5qQPVHw4bWE25A6XdsSZLVcEB\\u002fl\\u002fFRvlpyQBVvKkg6mXNAY11yzESldECtjf8EAJx1QCx6wcCtlHZAHoXyMNqVd0AmBRyK9JJ4QEXHbqCYcXlA3WWwfL0RekCsXkBi\\u002fVN6QI1gZB63HHpAc\\u002ffOkWJWeUDiaUCj1vV3QJAUxJhTAHZAlDdKoeCPc0CQThOgC9JwQF3ANmLD\\u002f2tAegYzKZyjZkDotZzCYOhhQNYK48QMA1xAE+XlZPr4VUA6ph4NUpNRQLNGo+tBJU1AxPG6+6BcSUCRKot5yUZHQHU4v0+8dUZAyxRQuDeVRkCQNS1JhWZHQGW9Z9DMt0hA0noCdP9WSkApmIgtVANMQH\\u002fQD1HoZE1AXL4bwRITTkB3A0GBBKxNQB22irXD9UtAy+1kFof4SEAEvrmKiANFQOzQQwL6l0BAo1xc+qaGOEDuEupiK\\u002fQwQKF8NMNHCCZAxkW4DBCPG0AQRF+84wcSQHfTG4Sf4A1ARMpJAOmk6T9MicEN5WzvPx\\u002fnHkzHt\\u002fg\\u002fLbHt30ZRBECrk5V8QQsQQOIYLxl50BdAipGly9KNIEB\\u002fmHj2NJklQLbGPUshkCpADXSq+UT+LkDDvy7yz00xQE\\u002f9ri2PsjJAnvRqg8vOM0DWXyu22d00QMAQLikxKjZA2VNCrdAHOECZGflfCdM6QAkoYKwg6j5A2iKuR05LQkDEHz0MifJFQKUcnYDOPUpAIbbXFcrFTkCYRkfylH1RQPvxLdMmKVNA9Z1xm\\u002f47VEDn0camtrpUQFjrLLBp11RAZfBF9zPdVEDZQxzW6BFVQEaI070NnlVAT+JEiUSIVkCMUMDWvsZXQHiOoOfkXFlA6Ma0B9RzW0DqvNvTxl1eQL+ovT4mP2FAv6NxZ2oPZEBssES5GqFnQPhVAOiKxmtA5dOSppcUcEDaRuyk7jByQJxmyxwxDXRAggcF5f2UdUAm4mPJic92QLpU2a5x2HdAOwIRZujPeEAprvWiQsp5QJEXpZbzxnpAZItcczyye0AF1WtDSG58QO5CZkHI3HxAyCeekNrjfEDaIz\\u002fARW58QF3rSn9Da3tAV1I1L8vReUByj4C1MKd3QMzjXInVBHVAhT3LRBAYckDHj3mtNTVuQItCpExYjGhAbrVE5ryQY0BVCwYgwO1eQPleTjOrllhAiqwGJsT4U0CYOx50idBQQAmn8RNxqE1AaBWFLsOHS0CaMdBEMN5KQFQtBDBoZktAv7dx4oTqTEDffvvs1jJPQDMY3zxc+VBAO6f8uvdcUkDLMGV0UnpTQPhwfzfOAFRASmGTggauU0BdKfVVr2RSQFyEyIpJOlBAblIX8ZHnSkDb2nwmR+NEQE2OjvVLUD5A3bLqeeWNNECmFN9kvisqQFq0KdcTAiBAvCHH\\u002f3p5FECRx5nCW8MQQCQnkbpXMOk\\u002ftxYy1TCt7j9NqPhRGNz3P2B0zG\\u002fBbANAcZLkRgl2DkA7FQI1I4EWQO2luutcPR9ARqT94MhtJEA9M83HclApQNwtJpO+8y1AU04TQ6UWMUBOatAkzgUzQI8riSJ56TRASewcQ6P2NkA0x6jw0m05QGXszDDEmDxAPLWqW6dkQEAoy98XZCdDQOLBs\\u002fuRrEZANQpAfC\\u002fnSkBmQiTPIJRPQKMfj3YkHlJAOqSRXXApVEDCeYoT8LRVQMycq2oMs1ZACXDUb0BLV0AUwoysVM5XQAkfO90AllhAw9L6htreWUBgUIUlg7BbQMzlzTlI4l1AOdM5vg8eYEBJ89aTK1JhQEOKvhzloGJA35wDKfU+ZEC7CMDbQXBmQLuMLfoXaWlAS8Am\\u002fGkwbUBLuOsj8shwQFpKizdGFHNAFyxigsA\\u002fdUAe1KaDoR13QBHbSxNCnHhAkRVXz7zIeUBXncdvksN6QLnmXmRTrXtAkLVdkX6VfECNxEWW\\u002fXN9QK6DWKTmLn5Awb4RTuSmfkCllvXaxsF+QJhfOJUBbn5AA8ATAz2gfUDbtvnFhFB8QKHVjpHIe3pA+g5BO7UpeEBez87wiHJ1QF4f8yTUf3JAvA26h1gMb0DziD0gIXRpQFS6GN+RiWRAB3rwUFV7YEA7eaO8paZaQCBQJcN5AFZARWXm3SvHUkCqw2LTArxQQN+XrccjWU9AwAGIuvzqTkAA4H9JdvpPQNfSOR9dKlFAUkBNHCnYUkBfvNmBMsxUQDck5+KrsVZAbFx1iOYfWEBIKZ1nb65YQEji410qElhAisNoLsY3VkBjBkqhZk5TQC9FyGGZd09ATBCyk5L8R0ABMuXtGhNBQETV5qDNsjZARIw0vapNLEDnZdsy6+wgQM+p7TaTJRVAvrnGV68ZEUCnN1IHWk\\u002fmP2B3hfjPBes\\u002f51FCCYbQ9D+dokpOQNAAQGtBa7JqPApAUWNlQQBZE0DvLvUG8ugaQJYkn+N6tyFAedspo6M8JkBe25SdcNgqQBl3xqp7gS9AWzCeErMsMkDXEgGEuNM0QEyLcX\\u002fd5TdAFYI2UciaO0DG9LjQ8BdAQASxGtIB8kJAOS0WG7tzRkD3ZCwUTJ5KQD\\u002fKUOv+SU9An2cy5TYPUkDyUHUE3FBUQJ3hadX\\u002fMFZAjvNcwBqWV0DsL3mj1JpYQGBNftWHjllAk4k0GtfbWkAVKJf0CN1cQIMG57agsV9ApPHGudmVYUCKrPz\\u002fRnFjQFsLSPtEM2VA2two+eq\\u002fZkC2ruRElCxoQI5IYknUvmlAcu4OWZnSa0BMIihBVrFuQI750bJHNnFAT0oPzhlnc0CUYeyi8rN1QM2pYvfR3XdAAQatOqO1eUDPsBxV\\u002fit7QNb4jDpYUXxAHWZIlyBHfUDVKpyVPyp+QJtQuQKTAn9AC8JJa26\\u002ff0CnD\\u002fypgiCAQFIM3gqpM4BArBIvxZgOgEBzb8Xnw1p\\u002fQL6PVF73IH5AkblEgEx0fEBXHdbG11t6QJ\\u002fO2Yjq5HdAm3a15KgndUA16eusC0hyQHdEUZWr4m5AFroEyC+baUC+u7QDMPpkQPAPc5qXImFApLG1zBw0XECUjo1+sqNXQMoiYtLgZVRA6lGdK55PUkDcgjIC5kFRQLsWVtEiKFFAkZHA3pbvUUB1MZCTlXtTQND8eQSQmVVAo6qxmob4V0CiV8ssIClaQPO9b\\u002f53q1tAjXSxbPcKXEDB3uS5WgBbQLiMk3+Qi1hAhMuk5hf4VEBV1Fyx2MdQQHe6SsPZGUlAbxgL\\u002fFGFQUCJ051eh9M2QLaKEIG03itAAlXLc59KIEBvyyi4WuATQGMeq\\u002f2zvA9A2t5pzfnX4T+9IuJ48oPlP9z1fJ2UcfA\\u002fY2ipRx9r+j+zdK\\u002fn+5AEQBYvFSR\\u002fYQ5AIHQQODpCFUBTycLID1QcQBU42AIsHiJABXjQ3fh3JkC0Ax8qWE4rQJt1dt5GbDBADRXEBga0M0CphOeura43QC5m1mrjjjxAdkGFZi5CQUCOhmrXx9hEQI5Y7v\\u002fSC0lAVqx\\u002fwtm9TUB6gFQAF1VRQBMtNLaStVNAGCqT9KDLVUCENzwJjXhXQGlHrEyry1hAThJCCOQMWkDB1xFm\\u002fK5bQGkFNVHGKF5AWgQ69eLgYEASflF1eDRjQFUIuhdC1mVAhSwRJgdvaEDV\\u002fyHTvaxqQLq9f9g2aWxA0zxJ1ZXBbUC9HrVCKxFvQEKtynhrZ3BAIXYAQ1GtcUDSUe3pJGlzQDSLwCDNgHVAy0DHfHa7d0AHtXYAKtl5QCgrVYE5rHtABvwRHAMofUCqpMu0OF5+QHNsev05bX9AI6j2\\u002fvA0gECqZpeKdKiAQHNk6PpjA4FA2iGO9CUygUBotqrDqCOBQLtTNWgoz4BA5boFoiQ1gEDcfp8rMLl+QFaeITe4nnxA6cGo6k8wekBEZh3eUIV3QML6aPnnuHRAYn1\\u002fgXDqcUAXL6reUHVuQNgZgi85jWlAFkSx74FFZUCtdD4sOrFhQHYswxohoF1A9vFd4U4sWUCf4JHgHOpVQLUJoE+Qw1NAEp740vKrUkARi6EIzplSQE1Oq3FQfFNAKEBhspwvVUDXzRYuxHJXQF7PAov74llAWlfp5w8BXEBcoOpgCkVdQA8BFJcmPl1AU7iDVfS0W0CeBZcnjMBYQJdBR34pw1RAI1kni6FNUEBUeXQR7+pHQIbwRZYOXkBAvUsVaYPlNEA6LHjDuvsoQAOp5lWsjRxAxqbtW6QBEUCYhsclAc0KQDA2oUKf2Nk\\u002flZ0NBBMQ3z8jsI1zJ57nP0C4U\\u002f\\u002f17PI\\u002fzyLyTDCC\\u002fT8p7WD\\u002f9OwFQPk5SSs9Cg9ArhOwXUYQFUCwh64v+qgbQBch5eH6wSFA9EinbWWFJkC1WazCZ28sQEZTXJHb8jFA0rppzd6qNkBWo0nYM5E8QAPUe1zg4kFAXT45CO0iRkDzNy5dLexKQAMQtDT1\\u002f09Ahy91O8iAUkBpp4XmKMZUQAkpMTbRrVZABDsxRNI8WEBVd0cme69ZQEel7rU9dltAKLB902QZXkB6yYWFCARhQJIhoclzsWNAxHipUSHtZkBDeP55j1pqQCRAVZDZg21AHzkYArMCcEDVckK7ZdxwQJ9zyLlwZXFAfxz3Ev\\u002fXcUBOpbG7JnxyQAqr1XwCi3NAmwWg4b8XdUDeqTvSZAl3QNMAShEbKHlA7HLVLRU3e0DTOsR\\u002f0g19QNhiQ1U1pH5A3On7fyEGgEBrbAqzLq+AQOSbz9fTUYFA5G4nGSflgUCLAOZ5f1SCQLI+\\u002fDAeh4JAKVqxvkVpgkDRL4r8VPKBQAh7WTvwJYFAh3be8CoRgEBqXFKGZYx9QLQ3Lk9YsXpACb267SO3d0C9LgGuaMB0QP+vDAQk63FAQ5XIi2aebkDPDDpipPlpQLAFItvY92VADRf7PRaaYkD6dqpQxa9fQDYJR78ZTFtAGUGVcn34V0BoyR6k\\u002fK1VQAH7GLxea1RAnUxZ0t4rVEDUElhfY9xUQI7FogHrUVZAnknQvg1DWEALnCJZY0daQHXX8Ojt4FtARtXufBSSXEAFyhOfy\\u002fxbQEANTn8lAFpA+FblYXPGVkAsZ7JDQbtSQBTqaLKG10xAv+wdpa69REBJU9pzddM7QEUlvUxLaDFA1u3U0sBhJEAOU3FN4cYWQE8KItK6fQpAP\\u002frAqgGZBEDriQeCfBnRP6esTzbqhtQ\\u002fiqRCbPEw3z92fbBYNA\\u002fpP8U6NZBwrPM\\u002faifMWEiZ\\u002fT+xmC+ogVkFQHZoQlSjwQ1Aw8B8vFg9FECpIzGAQigbQIlBQV2CICJArEqxuyUxKEAfbIiKEiQwQKd+8z0GdDVAgkIJYt05PECcVJiiKUFCQFQM89DiD0dABN37\\u002fTZOTEBPennBStBQQMxAzukRT1NAeCnY82x6VUB6CZ2vMktXQFL2WSeP7lhAwQ6ghT3KWkC8BfMMpGpdQLWcRkMGrWBAmq3jyyZ1Y0AGUPv74wFnQFLIzfdmCGtAhknr74ELb0Aqce72\\u002fD9xQKOWQ43xfnJAM\\u002fUuh9Q1c0CXJX8+OodzQNowN\\u002fWVunNAYIzifwgidEDLSZ1GZ\\u002ft0QKwi32VoWnZAMpbzqNsleEDKGG5OfSh6QIYriYgaLHxAIn19atEQfkBpeoyO7tR\\u002fQFcni\\u002fpXxYBAv9TaPXuhgUDOOpzIm3yCQPfyNdydRYNA4yXPogjgg0ARwZV10yyEQHMobfcUFIRAToHx2paLg0AoAYJGkZiCQHv7SokyTYFAPJhL96+Hf0DJ3CXioDN8QAOQowGk1XhAil6l5l6bdUBmXtw0BqVyQKbkDdeZBHBAYBdpMSd\\u002fa0BEU7K\\u002fhKRnQOGgZRnrZ2RAwAqv6ji2YUCsHBzJ0QFfQLx4XQXkgVtAuZm09SHrWEBFzMnJ3D9XQMa6dxRPe1ZACLnthB2IVkAJJRzL7TlXQJ0cibWaSlhAxPXAVY5cWUC65T9VLgVaQJPImvLe4FlAPvb+LOmsWED23dy5n11WQErKyqycJVNAnhfgDgvRTkC5ELeJZDtHQCh\\u002fmWgGXkBAMWJpzHCENUDs6QZSgGEqQB6C4gYVQR5AJIMVsJSIEEANekusW8cCQHXGY\\u002fjoz\\u002fw\\u002fSmh6kyF5xT9qHx4AZeHJP7Nla9GD2tM\\u002fCMXBRisq4D84HgszHdPpP0NV1ugU3fM\\u002fTLI6fLR5\\u002fT\\u002f\\u002f+l\\u002flmEYFQIWyKDu\\u002fMw5AezdnKGpKFUDU8t0u8gEeQHbQHoMSKiVAogVtO\\u002ffALUDSGmqj3a40QChCL8KlLzxAZqgGsZ2sQkA3ez4tW+lHQG4BhsgLeU1A8Y\\u002fWgO52UUCS+RjyjvFTQKKnq1CAFVZA0BJ2fbT8V0A2E4e3R\\u002fxZQL\\u002fv2KRZm1xAmk33VoY5YEC0b\\u002f+JGf5iQEHpX7oNrGZA6fAmZW0Ua0DJfQZZLcdvQFPrgoUvFXJAMlZS94\\u002fTc0DTJ04HJu90QDPk3PfhZ3VAURsoiIxwdUCMeu5Td151QAvbuBQxi3VA9h2RkxQ1dkBXEFZsrGx3QEFS6OrYFnlADGt6HFABe0CI+il6+f18QAxZ\\u002f0PM9n5AaeF+qFh4gECouhbMwH6BQN98bCoEk4JA7FQcjB6sg0CnXwYGR7GEQMXS5ePefoVAHhtW2v\\u002fvhUAt4oSDf+iFQN6y21fwW4VA18Kc5hBQhEAX92VV6NqCQK32ZRjJHYFAlk5P12p9fkAMmKWGRsN6QGdQ+fUcRndAvUSV7OkpdEC4jCo4OH1xQE7pibB2em5AgcwlVbW4akArzp4IEZJnQNmZPdKv5mRAuw5N4MGgYkByTJBQFrZgQNJ\\u002f8sUCSl5AI57T7CzdW0A\\u002fZIUyxCFaQNPEg\\u002fEEBllAisdqZdVmWECc5DTAiQ9YQOLExYnhvFdA78qiBh8lV0BtLX5dhgZWQPOdYoqNOVRAdIjxlW2\\u002fUUDth8wl9YlNQC60aXDuLEdA1KGNIY4RQUC\\u002fdiF3DYc3QPE8gR\\u002fpSC5AglS0NnovIkDfzT3iTG0UQEi1+JGZ1wVALyiCuzc8+D8mj60+LljyP8s+bRYs770\\u002fMfs52IVWwj+vCv9mPhDNP1lCkwpoc9g\\u002f3hFQvGok5D8QU6+ogejvP08\\u002fe04RWvg\\u002fAT\\u002fiaN0RAkDYq4YonGAKQHmTndf+JBNAk94UebrRG0BsqPPtXTskQAKt5adKQy1A7ivipRXUNEClDREfud88QOf3watpUkNA1NQl4VnTSEBgsvqNzoxOQP3iP\\u002fuvBlJAmuL7S+GCVEB4zRm+ir1WQKuMM4Ik9VhAUT+PEo+kW0ATiYEYcWxfQMqlX0Qqc2JAyDMGwF01ZkBeGemsru1qQPLlTuDcIXBAXmlIY2LPckACNMWmaCR1QEAJvjFO0nZA6NO1IDuyd0DbsF3titV3QG5cTPy9gndAZDjJC2oed0DHApLB9wh3QAulULu\\u002ff3dAwUIpB8qOeEDsJqjiVRh6QNVIBhOP7HtAJk8qhzrkfUCnFE3ct\\u002fB\\u002fQDmM6cIxDYFAjw9p7A04gkA80b9B+XiDQGwV8CwvwYRAP3SrBGLyhUC04u1JmuSGQLORqZ3ib4dA+cMZNhF2h0Dyw1aEzumGQCVY+p530YVAeb8oObtFhEAKvlH3j2yCQN9AtoWwcYBA7gmWFL\\u002f8fEDFGGj2GmV5QN8uQ+YtQnZAT4gK1FGfc0D\\u002fDhbvE3NxQDkk53dPT29APrkc6PVGbEDTWQbFIaBpQJI83v0nPGdACWTgslwMZUBezr5mKg5jQJljGn1SRGFASlv2J51hX0C60UmH66JcQDzWuMNtPlpAZIbjTeAcWEBlE0XXKh9WQOhNuBmfIVRAh7PO5jsDUkAaI3bMYGBPQBd5HxvtVUpAY1SquJocRUAWhJ4ZTwtAQDCsLjEs\\u002fDZAzINmj3vnLkCifpVDwXAjQPTFBHmc2RZAahkYY\\u002f0iCUCs1CHTblD6P+Z+gQjmj+w\\u002flNwg9olZ5T8nf7md8zvAPyjLd5h3W8Q\\u002fW95EFjjL0D89phxDeCrdPwBmFrgvhOg\\u002ft0kBqMiY8z9lbmRBZdn9Pw8a5PzY2wVAKvkt9N8xD0ArDmNHFQMWQBV5laesFx9A2vVL\\u002f7EUJkBw0rYqrWMvQDoRv4uVFDZA2jgqav9YPkDB3ClTHChEQBTn7FIKtUlAyS4EbnVpT0AwO5\\u002f0DnBSQDH2cqn99lRA+RY1dpNqV0AV657zQS9aQF72xMuV411Adcsk73KeYUCumGthHGdlQNx\\u002flzljY2pA70qy0F0vcECsjMw3e2hzQKjbNltbeXZAfSSl6gn3eEDFxRWOeo96QJ+DmooOJntA3p0TiSTgekCITkXNyBp6QFRj2M9cTHlAYBP1dmLeeEBwdgM3Zg95QBVxPuz56HlADKLAZMxLe0BalQggZQl9QEWG+XVr\\u002fX5Awn1\\u002fKSWNgEDxbxLBbLKBQLxD5A3j8YJAHxsEonBGhEDfhRLlBp2FQMh87AtD1YZAi3P3x4zHh0B7XH\\u002fXAE6IQG4n0jwfTYhAK+\\u002fxWgi6h0ClQY0FKJ2GQKiJ5aTxEIVAvcUC6eE8g0BhhJMbvU2BQDtTcu7g2X5Ah9ya2\\u002fhye0D1Hk9oiIh4QIzs0c5sIHZAnLM12cIqdEC5ZGkF6opyQJXwFfTRIHFA4G9LJJKhb0BVJGK62BBtQCv0qoiqf2pAsws4YqLuZ0B1PJBuxmZlQDCwTECv8mJAdojOiWibYECkilNykdBcQDGy+n5fvlhASDsgejYHVUCvZJdTl6pRQMNJ46FdQU1AqBQ+1e+9R0DZTU1F0r1CQAuJRTKeiDxALHKeFYjFNEDuOFZnrLUsQDjsR6DVuSJAPpzG5TX1FkDdiS+nPmMKQG1t2HyxePw\\u002fU0eT4RRQ7T\\u002fsB6vm42HfPyNm4SOKTdc\\u002fiS4T0lxuzD+mQaZHQenRP64KjS56t90\\u002f8AOE+ZzH6T84U5aNsnv1P8XWibf54ABAaHjzNPECCUCbvvGTlpkRQLTtzI37zRdAyvwkTAdwH0A1icBxuZ8kQGHZlNk9PytACJvtPF4xMkBbKstDOWI4QMAHEsTfLUBAEtYCEeb3RECbYxUiX0lKQLK1QswDw09ARj2IfNOMUkASFoWHNShVQL29rlIl9VdAPowFzEJ9W0BSF1x1OEBgQDlYVtL44mNAY5kWRYvraEBzwYtagVpvQP5JwPMGaXNAh+0ygHJKd0CNW1DDl9N6QIZ9BiQah31ALhnflhoOf0DDZSdt+FV\\u002fQEgIGfPRmH5AAz+iqclLfUCPEiHtiPl7QCGS3BlXGHtAEANxQ5bsekBaElHz0YF7QJD8QR2AuXxABqBV8f9kfkBh2EQveC6AQHH54hbZRYFAJ8N0nX90gkBvciVuG7aDQFj2oFtB\\u002f4RAMILDASk6hkD\\u002fN30iaEiHQCUZUfeRCIhA\\u002fFK2MWJdiEAKWuL0NjSIQGZ4fkZqiYdAYfjBiNFphkAaDkILCfGEQC93ufSWRINAWJmdg76MgUAE3j\\u002fksNl\\u002fQG6F1cPR+nxAsrCFGemSekBT4zmvYZ54QOIcFcIpBndA6vMOZzmodUDrGAd2t2B0QNSJlx0vEnNAuGrhd\\u002fiqcUAPfB51SyZwQJv92TBaEm1ABs+eugi7aUD9\\u002flZ3kFxmQFCYU5FtDGNAghuiXtnAX0BOqNmNpd1ZQOGGxShdl1RA9AGYfAYJUEDyTgPZXnVIQOlrdAXHREJAXvPZeOKrOkCwv\\u002fbv0fAyQFdVedUOASpAtj6HdrIiIUAPlh79hIoVQKrALqHitAlA0CofxlMO\\u002fT9Rw\\u002fjQVzLvP910slmjPOA\\u002fTpf8Uevq0T+z0Hqod0DLP2+PLOOnptw\\u002fWeV\\u002fZCjn4T9DlwT3tTvtP0ZEHkF87fg\\u002fJOBXaj5iBEDHtqW2TlIPQCpiwxI4kRZAHSCn1\\u002fWgHkDdEinBar4jQH4NEhv8fihAL6eJ0f7BLUB738InXwgyQJQrKjJ6HTZAGwW0JheKO0A2NKzhjUVBQBAWYwJlhUVACetnrRhPSkBJmPsdbVtPQD7ldiBaQVJAJ9EYG278VEAv7u47kD5YQMNDcBK3sVxAMIlzo8eYYUB1MTjLQ0lmQBNx4vdmqGxAajmlXj9PckAzta6QytF2QMvY555mYntANHBzWA1pf0CTE\\u002fEWNiuBQF5d8a2X54FAlajgezvlgUCmFpyKEEuBQFiCZvAgX4BA\\u002fWzixbjhfkD5I8x+mIV9QL2pymwZ+HxACXF3iiJLfUCAnljMOmN+QPQfIzrfB4BAR+3qHuoPgUDxTpwJKTaCQJ8dU8EKa4NA0QOqFBSfhEARblwwZL+FQEpbVqgTtYZAcOPeZMRnh0C5esjlDMKHQAy0LwXotYdAowoNltY\\u002fh0D2XxtqK2iGQF9Mt4dnQoVAO5d5yZfqg0B75TvizoCCQArU8l52I4FAB8O3Wd3Tf0Aj0zrmI8J9QE\\u002fqm9O9FHxAotp70ui6ekBpRER3\\u002fpV5QJFzXzzmgHhAmdS5SlZYd0DKR9vWhAJ2QDfntN7pc3RA6GmskhuvckAPlCnGOsBwQNSiQf0Qbm1AGA3fllFGaUDW7\\u002f7emSdlQOxSFNAYMmFAGk5w+I4QW0DEecdJEJVUQLtk6z7uP05ASrLM2TWERUC+OMmG+bY9QCbXXgI8+zNAqvyZxEE6KkCnwrK1iM8gQJcBIi1gBxVAnNtuw+SiCUBn\\u002fgW14HL+P20X5exbqfE\\u002fTVIvzUAz5D+yIWoHKXHXP\\u002fiOsx3\\u002f9s0\\u002fxJ9w383dyD+X7ODyQ7DrP9r4tzjhG\\u002fE\\u002fmE1n5qxb+z8jowoJf9sGQCxEPa7zUhJADwS3SIiTG0BSmfnHZmgjQGId1XWQmylAkHoRUvDZL0AVYbfDntkyQCpDAYa5iDVADKH8NpcwOECm1qoY+kE7QJ4pfHpgQz9AR\\u002fOsyBlOQkALwgabJrVFQFpJC9bhvklAMRUJ74lFTkA639gZlqNRQOuZJmOjkFRA6gxxmWJfWEC5PzxJmc5dQLiXYner5GJAW0v4DFWTaECesbMF5RNwQK4Wc7tEuXRAWGaOUSfjeUAv1OqOAgJ\\u002fQIqf6aL3tIFArdFtFew\\u002fg0DX2QkwC\\u002fSDQIK\\u002fbp8D0oNAK4+TTv4Ig0B7cHCCwOeBQJOsg3BLxYBAAAVplZXUf0DpGKBzAgZ\\u002fQGq290UXOX9ABO5cTTUrgEDslywOsBSBQH9eLxorOYJA0vNne554g0BpbdalfLSEQG7EkcXPz4VAyeidaOqvhkCcUD2ynD6HQFcba89TbYdA3I7Up603h0BbnnsUSKSGQFqM8quFw4VAF6xZSdqshEDzcBg+YnuDQBD2c4U2SoJAFNxXTbgwgUCTyup+Rj+AQOysJRlI+n5AIOi3DlrQfUCrAnu3hOx8QLVyh2htL3xAMcMf9DZ0e0A7QhoGpJZ6QIY7mX+\\u002feXlAozNprroNeEC5JPTcZlJ2QCls7UGKVHRAoQp4OVwnckDZRzB8SrxvQPGjQO5bE2tAQqukNs9zZkAVFc+qsQFiQARfoYbEzFtApjbgjs2UVEA6In0Jni1NQCOEiQTV0UNAxB63S73lOUBk018pIGMwQEAGzODvSyRAw4DHQ2zvGECsmoXQC9UOQL2u3yuGagNA4hl9ZwkJ+T+TCS5K7XrwP1snS0h4EuY\\u002fPZHQ4vdt3j8dV7GUIr3WP7A8OR8WP9Q\\u002fQ6DycvCI+D\\u002fx4yfZu\\u002fb9P\\u002f3qAV5ZbgdAZYjHNEsqE0BDkAj2hyAeQJ9Dmp2BOiZAzdJMTCylLkDjDy+qcMAzQPifTAGn5DdApkX2oqRNO0BCbn+5xMo9QL29zVz0ij9Ah7SQ3sWLQECCNx++2JRBQAZ46AIhNkNARYjDHEykRUD9xkNXsO1IQAQgqjtLEE1A7oLqiSUUUUAzN\\u002fkZj1RUQNMPkn8HxVhA3ekIz2YlX0DIXttP2i1kQJA+t86nmGpABg\\u002fpkl99cUDNJdk0j4h2QJo8gFdVC3xAd68Dgny1gEDu3lPYaPqCQErF+WkBh4RAS4maQeswhUD6t1YhG\\u002f6EQNTP\\u002ffHAIoRAVSWj9ijxgkBtEZ7LlcKBQJSKoETr4YBAaw55O4l+gEAOxFbsA6mAQIgqJ4RTV4FAc0TJOGFsgkDaDwrZi7+DQJEZ6kytIoVAXZ\\u002flXfNmhkB8MHPOmWGHQM\\u002fyO8EZ8YdAi\\u002fwQ5jcCiEBrGPJHZJOHQO5e3O3ctIZAivftehSFhUBcEczhWCqEQIcftGKny4JAnktrrH6KgUAV6PeWpn6AQDmM5t0qaH9AUzZ0NJZVfkDow4pM47B9QLb+91K4Vn1AKhiu5TMcfUDAA+s0iNV8QDNCr2BgW3xAYrr6RpOPe0BHFezDb2F6QKlLZG9W0HhAkMFL617qdkBapOLVTsZ0QLPoX311e3JAmSBgIgwccEC3Int5bWprQG4d2Ys0p2ZALwdpPkgQYkC5w5fGKaNbQGUA1JVmMVRAa8YdOaQfTEBo55p4OrFCQNmZ3vQw3zdAR3F3U6C0LUCxLjOK13AiQAtPS\\u002fNxjxdAh0b85ZTKD0BBpVscaMAGQCLg2E5k6wBAOHy9eQ1k+T\\u002ff8f\\u002ftcN\\u002fyP2xWE7JdC+w\\u002fWBFAtVP25T\\u002fhWPGe6OHjP3VwCRDRxANACpJABADaB0BYAt2i3zkSQHkC1SKQKh1AYqD2ou93JkBGmu6hp0IwQJHz\\u002fgZ2+zVAnxZFuqbAO0Ds9d3PEmZAQLAPs642OkJAtLTpBLs2Q0DJC8qGE3hDQEkSdO7hWUNAG9vwgQldQ0DA8FJzdwJEQGA4jKFsq0VA4efeGPKNSEB2RnZifMRMQBWOVDgQO1FAlLCg8KMFVUAvHq3ZFS1aQA5Zr\\u002fMDrGBAXnSsbkGnZUBiWIEtqV1sQAFWFE2xdHJA\\u002fCLc1yJ\\u002fd0CaY6cE3Op8QEMBM0DsEIFAQM8wFAc+g0D2JrZooraEQN4epfaRV4VAQXfP9FErhUDcQ5PzbmaEQGhfDx3iV4NAq7c2cDhUgkAllnvK\\u002fKKBQEHOLigFc4FAzIP\\u002fTVTWgUDLjVPYj8OCQDvwqbd2GoRA4FutdrKphUDmsySuoDWHQBx4o8ZegIhAARPQjh1TiUDGj6DruoeJQDAnPih4EIlAynhZWDj7h0D0KR2AnW6GQMbsdsVpoYRAHPkhGq\\u002fOgkCZiP0JWCqBQMV8dPnisX9AbZan3eLXfUAqeCVvRMR8QKiqnU4fVnxA1dqXo2VYfEAAsrvZUY18QIJOqbe4uHxALcY6t8unfEDBeM0RJDZ8QKkg4BDLUHtASj55WG33eUC92wjrmjp4QC6EBXbXNXZAXbYxGGYGdEBVpZIisMJxQJKNLJbV7W5AOCvEZn1RakAVvaXIFr5lQHoFsrpCUGFAYJGmioZoWkDevceMjzVTQF\\u002f7f9xDpEpAzlm18K+1QUAHt1jhEug2QOy67hr2oi1A1VQuM+DtI0D9ym2wQsYcQAEe2vlgVxZAIYBrtiMXEkBFIzOJoloNQA9EmQZiMAdAwJ+9i1atAUDKBmzBX3z6P2T+WJEgufQ\\u002fsJEVJNe58j\\u002f0BNF8yfEMQGr11NjJPhFAbuVzLRC7GUCAQ4TXdR4kQOVgHaJYXC5AxLI4xbmLNUBZsIXTQpI8QP+1gQEor0FA18aAua93REBXORmYFTtGQAXje3Bk0UZA87TC9BJoRkDPljed\\u002fXtFQGAVQsu\\u002fuURA6ucx1LrRRED4RdcRMlFGQCfQzT15jklAr80kGfiuTkAJZQ02IeNSQMwt6fcLg1dAkDwQZjB3XUDALnA30ZhiQIz8cw0zo2dA0eFoHRAcbkC4m5XMbApzQEnBqzlHpHdAD22S5BiFfEDYxcPYM5aAQBbSsyychoJApORfqu7ag0AtTZB5VnmEQCfNDCLfboRAv7N8Kkfrg0CMh052hDODQI8WGLPYkIJAQ7bok\\u002ftBgkBQuodjLXGCQIGaqBE7L4NAloLEpzJyhEDO3TXUzhaGQHY7kiiM5IdABVVVlfGUiUDLweJ4Vd6KQIz9OyBHgYtAIwjlHyNWi0Cv9cLyC1eKQACrRmmRoohA5v3qNkt1hkAd3o3j7BuEQPvN0C864oFAlXk0ZyQEgEC44qy5tEl9QCv16OOimXtAZgJMv9zaekBfiTLv3s96QOzNSlDHKntAMDhYyjCce0ApJAGUCd97QDmGVwaPwHtA3aq7tJAke0D0b3HVbQZ6QNO3jE+2dnhAWchvv9WUdkANyLW2EIV0QIwetSGBZXJAsJtLp7pGcECFZyEi1VdsQJWT1\\u002fGcJGhAioHMJLL2Y0DWof+\\u002f+MxfQGqPAx6nQFhAQqNWGH6sUUCcvC+nEbhIQHlj5\\u002fcC1EBAOpn+hdzyNkAdqRip9VAwQPXYgG8JBSlA6nCJAUizJEC98nRUOd4hQOGEWpXg1R5A9z7dlwvFGUDavYx1OIcUQP4Wl+dULQ9AssNlCK4bB0BAlaAn7doBQJt3U6xUCQBAzsPYDw1EE0AifqGQ5aoWQPY6rDZUfiBAT8BWuQ0tKUBd+8Oer5YyQFYdoyZa2zlAnKk1DG\\u002fQQED2pr+rCGtEQLVtqxhpLUdARfIC70ytSEBElRaxQNBIQEbz3MtD4EdAwoM7AKaBRkACdwLn841FQIe5aFDf4UVApYRy\\u002fowuSEAQmDAHEttMQBelcBTn\\u002flFAfnaPqYi4VkA\\u002fBBjfcoBcQJpAq0FjqWFAUCT3jTOsZUDguUfcM3NqQPdpJUK5FnBAJk1DQgt3c0AoqsV3+UR3QG4J\\u002fX0HQ3tAfxn4fwMTf0CRiF2GtiaBQG2YvbRkUIJA1X0dMPv1gkDBPjWodySDQC+VRRSjAoNA8eLTqM\\u002fGgkBeSyyBiKqCQJqy7hi434JAhtpfMpqIg0Ah2XS8MbKEQDjDv3vJUIZAgCtUD9I+iECQLKXOSD+KQEwLmyf+BIxAzW6KITc\\u002fjUA9Se4DRaqNQDVMthVRII1Af50xsfqki0Cbrnmt7maJQHW2w8fLtYZA+0nYpTjvg0AjUSqQkWmBQGpz6UAUxn5AQLGErPPye0CCcnedOVZ6QD8L6603u3lAYWCZjpnPeUDiGyFMvjZ6QJIpFRPamXpA6BmdgbqzekD8202VAlh6QLHl1JgGdnlAa0Ft91UXeECd3hr2z1l2QGbdYxuRZXRA2qIQ65xgckB2HJfML2RwQN8Mwc998GxAu7qkW80uaUCfvVIlTnJlQNeVf5tPuWFAxOCKAzA4XEDFrFxFUo9VQO+bcAjbs09Aahq7Hb65RkA+0sQLEVNAQKXnRnVcczhAQJRyiNK9M0AkwSt\\u002fqDQxQLGzm5q7UC9AC\\u002fapJEuCLEAfh0YjOyIpQEIQ3UUMECVAx0uhkIKoIEAMB3K1ifsYQEDhJMlVOhJAwK4qey+8C0DGH9sjG7sIQFGoDOv0VxdAGjdfohYdG0CcnSVYmDkjQFBulQMRnCxABTGXHGKmNEAk7vd+qyE8QABknr+N70FAz8q63dBeRUCN3MQkttFHQAIlzqsQ7khAtgDJAP20SEBkXr9C5pNHQDar7dltVkZAK5srzrT9RUC5kIJCdolHQL3G1R7MwEtAmn+aiUKDUUD+MO+P8aFWQDdPrDAV+lxAHBsbLZAVYkAV\\u002fiTOzO5lQAWPxdsa9WlAvfDX0vo1bkAcg9AyV2txQJ95nE2Y\\u002fHNAcUs4U7TNdkCZGVNNUMF5QL29A1GZoHxA5tJaWosrf0BYXrEW1JeAQPoWHWJlTYFAS8DG0DfBgUCUZEPcHQ+CQEQZWmAZXYJA9Ek6DFTTgkBvt08O4ZSDQJw+LkuduYRAy6iINsRIhkBfwdd\\u002fOjSIQG4x3DObVYpAHcGxqv1ujEAYyvM8jzGOQNMTqVP4So9AcjB83PJ3j0CHQm0NQJeOQHybolUjtoxAOn6L\\u002fJMQikBlGVpGwgOHQO+mNYFL94NAa1kDW0lFgUCXzD4RSlJ+QD4GWMt1cntAG4+2QQbVeUD5WhMvojR5QB\\u002fhD66dM3lAhY8BmY9weUAjenrKHZZ5QLClZxCoZXlA0UBWOAO9eEDYmuO5Spd3QH\\u002fnO9GzCHZAbu6CDrU1dEDE9YAz+kZyQLVnEec7XXBAdIaECEoSbUDqD\\u002fWrO5VpQDXOl72AL2ZAQhEGNaPPYkA+qEcRp\\u002fFeQIraIXF1kFhA4k+w\\u002fCjXUkBzAsP7AT1MQFXCYuV2NUVAQ56mWP2UQEAF5Zwhp+k7QBRMn3dCXzlACet18nskOEDgN0TUZQ03QMJWAlQLXjVAHs8vA6DYMkDOSKNArFEvQEKBe10bcyhA6fcN78YMIkAgADwK0uIZQLLSv9HMXhNA2ySLRM0jEUAxwWlR2cYZQASRQBxLjh1AIWbLFdVoJEDLnfTU1pQtQKbfma612jRAxxBkuBLPO0C6k5p3r2FBQOfv2LzfVERAZGabseNJRkD8yOnNhgVHQJbw8Y0yrEZAnAJcp4bORUC3mqGg\\u002flhFQMtZqZwDakZAn4MCEkIXSkAwWqsXaZZQQEyvvbZV+VVAPqthQZsEXUDoUFk5IZxiQBtcB+yY8WZAgT8PZ38sa0DcTGft8xVvQI4i\\u002fqkUVHFAWFhAxFgFc0CujQk6sbx0QK7xgVH1kHZA8KDFUx2FeEBDxW2SroZ6QH+peFDNd3xAHXv\\u002fVwM\\u002ffkDnZM56c9R\\u002fQLLogcF0o4BAgnt\\u002fBjdcgUCuK+o79CqCQAQf8JvnJoNASWF5J+FjhEAhV59CSO6FQBk\\u002fWoi0xodA+ty0xxXdiUAMvHoimgyMQMm5T57CGo5AWFdPAJO8j0CpjTepyFGQQHyulquESJBAAQW6nvFpj0C\\u002flmg+c0ONQAe3AtezYYpAkyrKpgMoh0AD7vC9EQCEQF2YiHRzQYFAiInsU0lDfkBR+t8UuF97QPle4vO2tXlAHthH0iH4eEBDwfK6Dsd4QFy3DnUCxHhAC62geregeEDmjUvMYCh4QJqlFYV6Q3dAjEtfgRv2dUBzDHKRI1l0QApHiv2jj3JAvps2vR67cECsnV8ILuRtQMurZ5lVeGpAJ1BQTSAqZ0COPaNZo+ljQPw\\u002fJ0cYsmBA2x+jQrkpW0Dry20jVG9VQBDTRoRMj1BAzV5zqEWhSUCjl3JHbplEQCHUBWYrzUFA6UzhiwupQEBGXF4IUGtAQHnwNJgvVkBAckEZY060P0CJbcA481w9QK08X3DbpzlATE5ryD0INUB+8om9wSowQKIF0j7ieCdATSZQTbuHIEDVqs8mrVEYQLfy7lniWBVAV7VN94n8GUDab6iVWWwdQF4iGLMFyiNA+IMvRE7tK0DNJOYUCDozQE88lHpaGjlApAoffdTKPkDQcROT6rZBQLSLEzsgLENA9VZuZ+iuQ0Cwn5233YVDQNtD8KuTXUNAQpUws104RECA5MyYBkVHQBvv9OqPoE1AHrlGsHAFVEAdb3s1105bQFt56fmnJ2JA5Byo8mgiZ0D6VNTV8RhsQP4phq0NSXBArbi+JPMfckDThSQ7eYtzQBDooUknpnRAjC4N\\u002fDiddUBfq9Emi512QFqOjV9zxHdAfmgnJRUbeUDXqhpXXJx6QMrbKsMfQHxAJ8gt2nIEfkAPe3VFBfF\\u002fQLdEpfttCoFAyz9cXL9AgkDN2XnhSqODQOKuOlJsN4VA4gVA\\u002fn7+hkB3X8RPQPOIQBb8prmSBYtA87A2GJAVjUBeCBmJt\\u002fCOQPcUmLCDKpBAfUvbikd+kEDLVwkSDliQQOcwUETZWo9Ab9rwcT4WjUBJT1JM4yeKQG9KQUL58IZAFLYU+dLVg0BSV9SA8SaBQMuC3bA3Jn5ApG\\u002fq5slJe0A5q9qSV5J5QPAGc6n5s3hA1vxxNrlTeECMh0XKmRp4QEtCbjp6w3dAhYXyISYjd0BIhxH3JCp2QPmu+11z4HRAGM7Q3Zxcc0BXP9la\\u002fbhxQHKVfOSJCnBA3vK81vm3bEAS3xUPWF1pQIodS18nAGZAyDvcEUGiYkCLrTsj1a1eQMnahUbPh1hATme2IeIwU0Chn0zsZvlNQDZWnXATMEhA7qSPYcTyREAc1pxkGMVDQHlMU7i34UNAUSoPsRVrREBqTiGK651EQE6X+lCx+ENApTcGYBRPQkAAVRsKmos\\u002fQKabzNhJdDlAkLilvTc+M0BZ6K5dl3QrQIrdP3ib+yJA4KT00mZxG0BTmPlBIOQXQL9iJq2a\\u002fxdAJFR0BLvbGkAZANJQjJ8hQL4dRiYPQShA9pfcNrlWMEBuU9yp0vE0QMzZxPKQTzlASxoOd4nQPEDzIOfVvRA\\u002fQGEB1fnPEkBAok49YwNnQEAkF5ajJkFBQGgUZbAGrENAWU3yJhDZSEBfioUkEu9QQMa49TIssFdAS73md\\u002fBPYEBikp4jgZllQCLgTrLgNmtAtaey3mdGcEB0r74JxYZyQMs1bgLvMXRARmBqH\\u002fpFdUAcVzvWQOd1QN76bAPAUHZApqF\\u002fRD6\\u002fdkDZx4i8AmF3QAQbjYgFUHhA\\u002fzatAfWVeUDca5vPhDR7QLo51qnpK31ATyOMNol8f0BzEz96chKBQAXuKzN5j4JA69lLEcgvhEAjBqsKPOyFQHkOUBM+vYdAFUvRWTyaiUDP6gLAM3aLQHxxwoTjOY1A2l3RNNm+jkDvG10uBNCPQKgdGtKNGZBAmcFQ5ay5j0Cjyx3geVOOQFcwrqOAGYxAvMPX5axLiUDTfDNniEGGQGvc+G1TVINARBa3XiTLgECKCfwKjp59QDoKp\\u002f211HpA98TMOaUXeUByV2fQQyR4QNz9y4lOqXdAiZs\\u002fnxZZd0ChIAKP8fZ2QJGhUVx4XnZAbTV8wYWDdUBoI0s7xmt0QL8fTz3cJHNA7RulpDq7cUCyNG6tKzVwQBbnRq66Jm1AeBFWGpGraUAISiP26wNmQMkYdepuTmJA7H7kHRh6XUA8kf5RKhdXQNMOj4Sd4VFAebh1FUI\\u002fTEB4a8GsUrtHQDprdd\\u002fQ10VADiv50GLVRUD7I6ze2MFGQJJe0r7FrEdA6OYAUcHXR0AMILj6+9dGQF5N2J63oURAuNZjy317QUBODYwscLw7QKPgUQTGmzRAvIS\\u002fWeDiLEAuFcmSuZojQMndeOXX1xtAxAzml\\u002foIGEArKRG9WnUUQHoQiO30rxZA7At3tpAzHUDZlLabe7gjQOvRv9fUMCpA6r9oZMGhMEBhuK5bIQc0QIvEf0OK4zZAPXHyW3QKOUDEE7M24rk6QJ74AdSLxDxAYdfoxhtSQEAftjgMvDFEQD7JDWPPJ0tAAfFhlzobU0B1V2Ipeu9aQKqHNFc1cmJAZOqCNDAlaEDPEI5HXgBuQIqmuE7ArXFADTYb7t\\u002fSc0DOt+St4kh1QMsWW6DEFXZAwacT1kJndkDa67hVOIF2QE3qNvAxqHZAtBDWJK0Rd0DeYMZ1ut53QMXcnhVQH3lAXw7ApKfYekDOvYe+YQl9QNJhL6uYqX9AvsxUtFlUgUAaToIiOvaCQGelgSHuqYRApCs0juZehkDWCXV0GAiIQOvZQK5GnYlA+Oq3OAUYi0B2uI+Q22yMQJQ7cdUchI1A3ksyftY3jkDj5S2goVqOQNYzONxcxo1AWlXVJatsjECTW9bfIGGKQNbHwjHi14dAI7vEfEwZhUDMxuDQSnCCQHOTfA5MGoBA2gqk1\\u002fd6fEArZhgjbMx5QPlqJspAFHhAtGTUbnAdd0CtRfv\\u002fCKN2QNja+KC+YHZAL1wQ3jkgdkCI7ZO6zb91QJKWG5YhMXVALowWTppxdEC+HP6XDIFzQI8gUCCzW3JAgFKSADj6cEAPx4gBV65uQKqGe3nd7GpA8dvB0YbUZkDvT4IDFqZiQEN54hCRZV1AIAOTHnuTVkDQ5txT60pRQOixkGzqfUtAeU2x22C0R0DrGH5gcZJGQC4vemGfIUdA6AiLrTRSSECoe2vdfDBJQFbqR8VxD0lAHzVE8kShR0CNFQu95\\u002fhEQHj07IHddEFAgMum2AI1O0AqQopEmtwzQCyjfWfYWCtA\\u002f2FFTzo4IkD+iu0vWmwZQIWWsbCkwhVA3QnE5MJlEEDJsYiHfB4SQDDtgMiWLRdASoSA22Y7H0BVd95uddMkQFzxWGwQwipA8RSR7MZuMEATLUkqQ1YzQF6pGsfSBTZAQjenPQHCOEDsrEUpuk88QJsy40YqBkFA0r4Az7vuRUBlzVqHHPJNQCOo+IEy+1RAztulJ28sXUDo\\u002frwG\\u002f6VjQKgeF68rTGlApzs4w6r0bkAO3zFe2f1xQCxJU26e7nNAPgULpM0udUCu46CO08x1QPq7018d\\u002fHVAhhtd28sCdkAMBDFOaSV2QEgUFqH9mHZAkEYjkMB+d0CbUbWu6eZ4QGBOgrBK1XpAOoGbrn5DfUBcQp638g+AQHVYvVfmpYFAcDQSUyxOg0DVSWktR\\u002fCEQKo2MmbydYZA8x8m+5jQh0Ba2Eow0fuIQCjeGB9r+olAI6GHQ2rOikDM3NE31W+LQLhZ+WkjyItAiKDEBVS2i0Bjl2gg8hqLQLtsfQ175olAv0H9rQ4jiEDNRTg4\\u002f\\u002fSFQEVflAuxkoNAUuKE8Pk3gUDb7piKyjN+QBkBQAUEvHpAhbVExE8seEDbQKi81YB2QIgb0bRllXVAbjZCGlkydUD9XtZx\\u002fRt1QNGX9Xm7H3VAmSBTwnsadUB\\u002f4TGry\\u002fZ0QBiCh0OFpXRAMLCaa4kWdEA2908FoTZzQJA1Wnou9HFAactxwCFIcEAGJ\\u002fttHnxsQCgSJd1K7WdAcgSemy5FY0DYZzjtwt5dQCCesaYUkVZAcslLh48VUUA9VCtKDRRLQEjADi2ndkdAiT27DGp2RkCuGxwRnftGQBtWsHqs7kdAgEcIRvJpSEDKlxcajttHQHopjh4DE0ZAzfwfAwA5Q0DGXDRtsGg\\u002fQDKRFodhCThAvEjF3Yg9MUDlyNsf2lMnQIMJfqp6ih5AjsaIwXv1FEB2Nu2qLswRQCU91cXduQlALSJU19mjDEAFBa6oqK4SQOK1V\\u002fzy4xlAj74WExXgIUB2cSSZZOwnQEUZ2rzPwy5ANImb10AFM0AQxMG0ncg2QJm0s1K81DpApnLTsg\\u002fCP0B\\u002fWiWoXVxDQAx7wccis0hADs2L9ZJsUED44L3+uktWQOuwD829GF5ARh\\u002fNCffFY0B5BzECjfRoQK6nJ4QhD25ASxN2d29CcUBQj5CSL\\u002fRyQFZhGeTZCHRA\\u002fgZWtV6UdECzksaoQcp0QItI8FeI7HRAesGQXgs6dUAjt4TB2eJ1QM1scQWmBHdAhD7S55mseEDYyc\\u002f6JNp6QFE3USnBf31AFZSNtdFAgECElnYkbNqBQOit9kAPcYNA8sa4l0bnhEBls\\u002fT3fSSGQDaclTOtG4dAB9gbAnHOh0C+dw5I30mIQH3GkvpxnYhAtcu\\u002f\\u002fh7QiEBFyXBChdmIQErTrbFSo4hAteFSc4USiEDyNMJ44hOHQPnjq8eypYVAnHRjeAHbg0CF7WLAMdiBQNvyoKSlln9AqPPO8B3Ge0A78UGDh4x4QMPnH6UtG3ZA99ADKWCAdEDV6IlCEKlzQBQkv51panNAkfPeHPGPc0AdOuUJV+hzQMgmaoLdS3RAEw+z3AObdEAtxcM8yrh0QH96vhVYhnRAdCoxGpLkc0DHGMUdGrxyQBWMrbHfB3FAaB1eVcK4bUAzj2txJc5oQFDZiohcymNAShTQyfRUXkCY03hgP6NWQPddBUup7FBAuvSOIfN3SkDevr7duJJGQBJTaMb4MEVANf6+TyU0RUDX14VHk5JFQJeg\\u002fvUPgkVApNDY6h2PREBaNJkQEaBCQHw8\\u002fPVQyz9AsS5BOIt8OUDgGOritygzQB9Un\\u002fOzBytApEPNjhACIkCEzAnosz8XQEbQn7HViQ9ANF5xVVikCkAQQadq\\u002figFQKJxQkdNGQhAHSqC+fyNEEDsCWb+rFMYQJvZ0A8jyCFAjrDvoEsaKUCQJxq06fAwQDfuJV6t1jVAKF94Ee4DO0Bs56I4PzVAQMssWeV6MENAtGJf7vzlRkCq7oQn1QxMQIA9OztUvlFAowUpBG30VkB6lEmtfMZdQKA6InC7+mJAgWXsEJxvZ0CJuB1ols5rQJMIC0VWoW9AYP4odFtJcUDM\\u002fDSOvkRyQPNYXKFc13JAhMKSukEvc0AQnRI0yYVzQF3d+QRCEXRAUoMMn1b7dEA9wMdUcl12QIUR2hAUQXhAs+EsdOagekD2xxDHoWh9QJQF0+H+OYBACZ8RbnjHgUAdP0+LFz2DQKZezljaeoRAJ5A4xQlohUDLZylWo\\u002fqFQDIeOz69OoZAqQQQdA8\\u002fhkDCRTwgACOGQOsNJRFV+oVAuGbAdWzIhUCZNC4WMH+FQDg4W1nfBYVAC9IinAhEhEAxsotU0yuDQCzRn6MqwIFAb\\u002fG0\\u002f70VgEAC0B8E9Z58QFom69tqL3lAn4wTq\\u002foudkBLK06sEdxzQO3c2lpgVXJAA2gdBt+WcUCT2hUChoBxQMXxl4sG43FAD94ZLj+MckBtUQPbK05zQJGIFyUc\\u002f3NAh3PcFW12dECUDrmqD4t0QPLt9hqdF3RA+ae6+bMDc0DPA1pnW05xQBfIaVk6J25ALAEZMLUSaUB\\u002frMjZCudjQKW+027rT15A0yPEwP9vVkBSJa7vQpBQQCI\\u002f6GCyW0lASFStvqjvREAY+T6Q9uJCQMSAGoqVKEJA4aRiKx\\u002fVQUDJc7EM9z9BQDEPlRdjEEBAQjvD8axxPEC+Q9xsysg3QIvImu62uDJAQf10FsC1K0D48v0gY04jQIDeXm7ZhRlAd1hIga9yEEAxEzHZm2oGQIPVDdA4AwNAx2vEoveJA0ChZuYO+\\u002fUGQLDLvSt2zhBAO3a7Y186GkCJZFCdDR4kQOiqJszYeC1A1C6FSTBxNECQbe9LR9Y6QHQ8gSHFvUBAJvLKFt4GRECEzJ91LU1HQMGihfhW4UpATWvkGGBeT0D5PoOKvsNSQHTAtNOxA1dAU4KGeHOSXED1oik1uqJhQImTF0sTUGVAMarkjz73aEC0M7O33ztsQGEozdTY225AMJlHW\\u002f5hcECTWZUSlQpxQKxZlHGWjHFAQQ7i\\u002f8UWckCrg7alotZyQFC43Wk28HNAwwour+d5dUCZGNMSKnt3QJos3uEW7HlA9r2gi5+0fECly2qQpKt\\u002fQIf5nQn0S4FAdHhTwM+agkASdhtmL6GDQBp3rTXwRoRARlYG7f2EhEC\\u002ftWSS4miEQI69huQBEYRAebaxnO+hg0DIgOqmDDmDQHiwpoA94oJAroGhRfWUgkBEDGpdIjmCQNJbbBIysYFA5dsgHxrlgEBcbQFHGZZ\\u002fQLuFSATQ13xASmAgfjjFeUCZ9fxuT612QBAHL6He5XNAAuPSOBC3cUC34reXfUtwQFS0GIrOU29AW3jqTj5zb0DNVWOl\\u002fE1wQNSOUSiwMnFAz2Sm89UzckCkjCCVXCBzQMQTKn1FyXNAkKfopBkCdEDjqfQ8raVzQMUey3bcnnJAFsT3Y2vxcEDifsBur3ttQJTAAapkeWhAACbXRixkY0B3A5y3v3ZdQA4s+ZQFtVVADLynY1yvT0DP38snIqlHQOnbWyBpuUJAZ6YrKx\\u002f6P0Dw5aM1LBQ9QB7hrwWnNztAWivObgxVOUBx2mt7ZuE2QMLpORAAxTNA2EPrU+03MEAAnUqNNi8pQCrx6F5QgSJA02QLioLWGUABCcqSX1gRQN6fIZhYHwdAQpfAV36FAEBPut28qMf8Pw7mc0XUUgRAa\\u002fqykL55CECki17xHLsSQDsMXezzSx5A8JBtVMLLJ0Dutk6el68xQGBbEzqouDhAgNTVSgxBQEAf6\\u002f6YqzBEQOhB\\u002fvLz30dAdbnfYV01S0DCPVYH+WZOQAFjJTKM\\u002flBAobHODdBYU0AsLkpfbZlWQGfJg7U671pAHETo0+sjYEBK7DWqzSVjQLAlFQceOWZABHftaScVaUA1BGzq54JrQHcepzTdbW1A1DVheMXpbkBGR9AZNxZwQLk+Ov2Wv3BAuBZaMfGWcUDwIj\\u002fqR7xyQET1B\\u002fD7RHRArm40nDQ5dkDVU2dyLJJ4QH1FuxJvOHtAhH77OGQCfkDJnCcwmlqAQEkhTz16hYFAcHDOeudfgkAtw+H3HtKCQFM45mX81oJATYHP7I9\\u002fgkC3VcSB++6BQNJE\\u002fOZBT4FAKDQ+AU\\u002fDgEAly76T61uAQLBMrwO4E4BAHAIujuWlf0ALdQ8gr\\u002fF+QLcs0aQtzn1Aq4PhBCYYfEBSot7yrtJ5QHT7+EZGKXdAV\\u002f1FDC9mdECyeUxIod9xQLgpfhd5w29AlQiKA0A5bUBfE8qCfzhsQHvMBGpxlmxA2AA136v6bUDZi2gMKvhvQPaqHk0wEHFA6\\u002fCCkDkGckAkYJMXt69yQIZEUxsf5HJA6rxw84OEckC7kjcZJ4JxQHOssTkxym9A5yaIUiOda0B5A1AZIOhmQIA8omF\\u002fJ2JATjBJXw2ZW0BAA6A9xUxUQLWbcJGlX01Ax+QswTdsRUBOuR5BxjVAQNa\\u002f32Mp\\u002fTlAmg2TeRQRNkBbPfZpFW4zQGt+qAD3PDFAVIJTvLsULkAMP9g6hWkpQMilsbrGnyRANgPk4EMNIEDuWsbyYAsYQNmkVPw3fhFAF3VAAf4uCUAVQisw1IsCQNiAQucBg\\u002f0\\u002fw2Hz\\u002fOAO+z8ukrv3K1cGQLx3hXxQNgtAdyUBt+w3FUBISg4f118hQH1VrpnLcitAD\\u002fzTBIdrNECRXi5\\u002ftng8QPbSyeQFnkJAICIBIhrtRkAfey4XBsVKQJASG01\\u002f9k1AMCpMPxpUUEC0bPP1H61RQKTmCurdYlNAlGHl2KzQVUCoHCQzFDBZQErtusWXgV1AN5ABC\\u002f5DYUC9Qox\\u002fIexjQOiJKZ+KfGZA5aKnu3XDaECIj3xWiahqQOoc1hRyM2xABjT391aJbUCND7RVGeNuQLKHtsYsQHBAHdgqLaNNcUDCHfQruK9yQKDoJuumcXRAvzylVTmRdkBIP1Jsi\\u002ft4QCS9L649intAnLbIKrMDfkAfFo6i0hCAQEqSWdXDz4BA0LdO1f8mgUDYSpIBHxKBQBpE\\u002fRtRo4BAP6F33qX9f0DQ7UjWPZ9+QMk9mg9vc31Ay4QOb8uefEB5X\\u002fOsLBp8QGHUsHwEuXtA3d7ziZE6e0Dwfcee9V96QNHH8L\\u002f7AHlAR0TL6iIad0AYqSki+M90QNyNTn9zZnJAFxa7YdUucEAImRErA+RsQFWc4wo9vWpAD5lMSNEAakCe0Vk1RoRqQCWwBeTe7mtAHehvIgbTbUAX6DULy8NvQIWzhgKbsHBAgJ5eJhQucUCMEhxecDtxQIQJMdrsw3BAiCjETyCBb0C5S1XzynhsQD1qfzLDq2hAd+WJNI91ZEAlS2QCiz1gQJBn\\u002fzxCwFhAPHjCP5w5UkBu8J\\u002fYYjtKQKtLg+5VxEJAyDe844tEO0D3SahbB4Y0QGuUi4a2KjBAQrv+etyEKkBduBn1CEYmQNn3xT4HziJA\\u002fDY0D3h9H0AHExyZSv4ZQFl9JEInKxVARL67zmAaEUDSbecMwagLQCyLJbUBpwZAfFZ8lDYcA0APyKRvAv0AQFbJ4s+pRwBAignLgIJJCEA7ViTEmqQNQEDf6HGvKhdA+ztpH+T1IkBwzGL4Yt0tQOM99vWnHDZAIutUV2OkPkDiEj49l+NDQP24X7C6RkhAwDMFVHwKTECw7xRIovZOQIM0L8Z7llBAVda90+aVUUDF8n8\\u002fS9ZSQLbkHIp\\u002ftFRArqx4pRhuV0DyWxdhxAtbQFmj6CAOXV9AzEwVNk8DYkA55KjiyU5kQLvXE\\u002fBNY2ZADtL9KYQnaEB0tjs1f5xpQE\\u002fAbNht3GpAJA3vc4oTbED67yCPh3ZtQGJO0mKSOW9Ad1sjjJPEcEBvxB2FyEFyQPK7aW5yGHRAj0xIdmw8dkBFF34N2Yx4QH6oX5SQ03pA7W\\u002fFNQzMfEA\\u002fTHhDqjJ+QOea+4yI2H5AhZkbbjW0fkCDExaCIud9QAP1IsAetHxAAgYZj\\u002f9pe0ArnpUd9Up6QGcZUc3QeXlAJb0FOPvyeEBIIao+xJF4QCb9KHTpHnhAWYW0XYdjd0CBjWQEXzt2QMeJw9ydoXRAFqKTaly0ckCu4hBua61wQMP+PXgTpW1APUYdoZHEakCgV0BREAhpQJFDeQnNf2hAKJUAp2EAaUBpZRslrTRqQOIK3xZZtmtArgSNJ98jbUClqQuf4y1uQGDKt5Timm5AtlfwXRpFbkA4asuqmhhtQARaTY4kFWtAYoe22Z1SaECv5E5e+wJlQE8PQrvUbGFAp+OlqGO8W0BZYaCTYjhVQKuA4q+ZWE9A0gH2wHyCRkBOqNxhocM\\u002fQMmYUF9XVTZAxVECR\\u002fXSL0CVyBTwMVsnQCgTR2fP3CFALsL30diTHEAb+dPvutYXQG60ubURnBRAb4n1DmBVEkDBOU9TYKMQQJ2FusnIgQ5AbcN7Ds4BDEAhRYIei6oJQI5rEjTJoAdAAq1tMWEzBkBSJGMDEq8FQFlubcu2OAlAcRMmfteyDkAwgDQ1BdsXQGSzJQA9ZyNAgHAVTWBeLkCTnZNu1FY2QEMC8SolwD5As9oCJTjSQ0ABwB7eJQRIQP06t5YahUtAOQrdxXceTkCmAm2e0fJPQIWFJngfv1BAen9PAee5UUBu8Uatx0FTQOiZeWmcj1VAQqc8s\\u002fapWEAD0hnoTGNcQESKjaWcNGBAPoEo3jAvYkAL7UhQx\\u002fpjQCkfjJaigmVAIeMOn5DHZkAqam2sst1nQD5TmwXF5mhA4sKNnvsLakALVnz3UXlrQBS0eXayWW1A50+Ot0zRb0BadRia03lxQJXYjjwHW3NAcHF6YchxdUCGKT1DWY13QLp4\\u002fzTCbXlArf8v0ATTekBGgLo\\u002f4497QKQ\\u002fxQyjmHtA9w4yewEHe0AJfJs6ORF6QMhQqLPi93hAmh6T+yTyd0CICUIIUB93QHUIDlGVgXZAAe5DmcQBdkCpmQS8n3l1QHIdFEyTwXRA1OW+5W2+c0D+0OuHiGtyQCgTv5Q53nBAmjSigjGCbkD64XMAtpBrQL+L2zRKRmlAh8tvzordZ0ALNqYRq19nQCiXxJzTpWdAq9YMHIFnaEDo6o\\u002fynE9pQG1SopZnEGpAlSUYFtZvakAlxga0R0pqQNQcoy12jmlAUTT9PlI4aEBTJ34qSE9mQB6T+bco6GNANVUBUQUnYUA98HrMg3lcQGbPC8tXvlZAAS7XKL18UUCTfOi1JvBJQML5aalUn0JAc7zOLgwJOkC7mq9IduAxQC9ZQ0TOcihA5uhd7w4BIUBNgzBW9cUYQPNZ9HlKgxNAh9KAv7fwEEA6kookwRMQQLH1GyehJhBAqW1ipdaIEEDFK5c4ScEQQMDuL82DjBBAXB1YApTQD0DfF+LTnBsOQITRQyctqwxAfRMfl7YbDEDkjCYq4+UIQA8HYXIyIg5AU9UyZhgsF0CpLZBkmasiQGSY40HE\\u002fSxAWSmAvxouNUBbXdol6vk8QN5kIyGNkkJAfpwnLJdiRkClp2e+8oVJQNWy\\u002foDyzEtAv6buNKFaTUA1iJqmwqJOQBeRaq26I1BA57YvtidxUUBHEXOQZmdTQGjRczrWBVZAmxXVTk4fWUCzqhBSY2tcQGdjifcxn19AkcPeytJBYUC9VUu61IBiQMuz2eUjkWNA3XFyAkWDZEAa1DuRB29lQOkb7msDcWZAsaUVSo6qZ0DZrNGZcUFpQEkI5g1ZW2tAMR\\u002f2ULMSbkAiTEozjrNwQLibky0CmHJA\\u002fdsJngGNdEAxdKMMHFp2QEu+OJLxxXdAHmPCCFmmeECTxWM1Tex4QMiVt2yrpnhAKWgeCxT7d0Ba5c9YoRh3QE56veAhKnZAJs3VdF9MdUAX3v7RL4p0QJubJaW23XNA\\u002fo76sGY1c0ATy0xeY3tyQFyZjqzRnXFANz9vhh6WcEC9L7W92dluQKrB7xwVcmxABwBa2fIyakAwlZQs8VhoQPJK3pMuDWdAxfv1rpVZZkBKBqPe0CZmQNYiuSpTRGZANGilshR3ZkDS2QzbFYlmQDXt0+jEU2ZAZNDp82jCZUCsWLvkNM5kQDqZEjy\\u002feGNAlp\\u002fuCTjJYUBPfT4qtJpfQN79DPIVN1tAyjDZy0ClVkA9+a8tdStSQL4QmB6HFkxAmS4\\u002fq8jqRECLRTe7Ew4+QNe8rSVY5DRA6u0PwkhHLEAyOfAMFOMiQOgll16RnBlA9pFP0VKIEkCSpj4+2WMOQOpk9W7X7AxAs1W2ZT+0DkDrpjo6RfwQQGYpf4BHmRJABmUTf4ueE0DYxST\\u002fMMUTQAvTFz0PIRNAQa66GFoUEkBAmNVW0CURQBLaF35\\u002fxxBAcarimsvgB0BjH5GU854MQOdsQ7n4oBVAGzh23PksIUARhfvXv2IqQDdhpXA3HzNA8lZvDef\\u002fOUA8++Ts4pVAQLybVfnK6kNAv9EdmU2lRkDEBRWN\\u002f5xIQIhw4ihN8UlAlkkCLCgGS0DWrxteHGRMQH4vVMZKh05A8\\u002fU6xALZUEBQmhTzdetSQBTNBeO+UVVAtKP\\u002fkmPSV0DXGqcwBzpaQCj2Hoaua1xAZDu8vAFlXkCffcHVwxtgQPMcGpTY\\u002fmBA0+n3ZgHqYUBpEZ8FY+1iQLoYWavFHmRAgvSFRuybZUCkm7kl64ZnQEMrgJzw+mlAy5dnKsr8bEC0w8jrITdwQOzGWfjlBXJASfQuJvC7c0B3GSZ0tSh1QHnG4PFaJnZAWWJazZmidkCdAj9p+6B2QKbn+A+cNnZAfAW1LFSCdUBud5XKyaR0QF2Mk2iTunNAugGYaLPYckBBNcHCwgpyQA9hDl9yUnFAGcvQGL6ocEAk3WEmOwFwQLEesNw4nm5AKASrMPwUbUCcbh15JWhrQPMhD9lwq2lAecnn9SL\\u002fZ0CPG14HGYVmQK2Ms168VGVAx2oTnKxyZEBDUYt3oM9jQGp8TiexTmNAMzFEDZjPYkD5M37eqTdiQLXXJ9bTdWFA2ul+xYaBYECIMzSJOa9eQALUAnlZ8VtA68Rhhv7TWEAIEiADKG9VQNNOd\\u002fI06VFAYjp1y9PjTECftJMgE3JGQN9RDhR2yUBA8xr90WouOEAMOqhVhs8wQEXfZobGtCZAAYz1uzpKHkAC8OEV+7gUQOpqNK3UHA9A7wlYFftzC0A3HSfHxoEMQDRluy8bMxBAGxjjp+6nEkB+tsZJXM0UQBQOaYwOCRZAuuwKHJsbFkBKzZmLkjAVQChLRuc\\u002fzBNAFNhZ9wqYEkAlnWZlaR8SQGmzp0WiagdA3fD8SR6VC0BYo82xgDAUQMhdL\\u002fOMOh9APREDp4mKJ0DbCFIHctYwQISiP\\u002fQyrzZAYVmQrTjCPECcvNkArjFBQDVdBSIBfkNAyXIUTPIiRUDqGyqqVDhGQFLKZ4uvDUdAGPpGpPMOSEDQ1f0jhJtJQGQVuYgf4ktA4BLoXOTSTkAHcKbn6RZRQJIcIMPG0lJAPjZyo+qBVEBMeBd2zx5WQL7tw+hgt1dAzB7sIP9hWUCQK20xCzJbQBhLedwXNF1Av6g+IUF0X0AsQK33zARhQAoP\\u002fKd3jmJAsOTTDVRxZECAyHj5XMNmQFp5veUliWlAJXO3UcCrbECMMUY5FvdvQPmhdjc1kXFA7mQkjBHwckCa8RfdefdzQG2JVng4k3RAkMjEtvS9dEAk+FyTHIB0QIIwe8\\u002fh7HNApmrD38Uec0AEFJn3ITRyQMOy0DwvS3FAHaBH0il9cEB6JVKLWbJvQGR\\u002fJw7swG5AS\\u002fP\\u002fjfILbkBYlJUifmRtQDjlR3DUlWxADiNpzDd3a0BdyXHcDvppQBOcRNnCLmhAWVoJ0Rs+ZkCf7kdi3llkQAognsZ2q2JA0miJ5BlIYUBaHrQ3ai5gQAZLUefpmF5AgN3Q\\u002fD0SXUBQBiwo6JdbQMn8F1o2AlpAVz+Bjck0WEA2jkkTWR9WQP+1+WaSv1NAuGtEtPYiUUAPC32xwMtMQN2YJJVSXEdATd7D9XZFQkDYO8ddC407QGJ5mYsVDDRAwoYs+ZBALEAhjYvWem8jQL4EMok6nRpA4p49YeznEkAkZGHjitENQKkNi5C1oQtA7dXYHAagDUAPtAZsbwoRQM339v6LnBNA6blbhc60FUAouQEcRb0WQAqpDVpbghZApV3nZBtBFUByuiNFNpATQGchNj\\u002f6JxJAZsZMlgCdEUCcSspY9yUJQLhruRcCzwxAwBFGImz6E0Dxv5hdPXYdQLGVs0jybyVAEzHrsSHlLUCXt\\u002fzPX8YzQO06cDG4vzhAGynycJRSPUCH+hVOW4FAQGLNeprsyEFAyrpo9A6PQkA6VX3q3Q5DQJyISiMhmkNA1viUiUh5RECf1J4VQNBFQDj2mbIclkdAkSLHAMajSUAQ1i5J\\u002fdBLQL0WsZsqD05ALHvZFJY5UEDFRUW6BJVRQMDFS5i3MFNAKDArJBMaVUBjL\\u002fKvyVNXQHmLQdq83VlAXYV07SPAXECvwGhqrwhgQMBky5Zh+GFAiZuZOqU7ZEAAys88gtJmQCPBmfcCqmlAJkmp\\u002fVybbEDQidVOJXFvQIHt+mRO+HBAtFfpMwrycUA8MBE\\u002f4pFyQBOb1z3ezXJA6MzJcHWnckC\\u002fQL\\u002f05CtyQEPC0LNYc3FAgtk+62WecEDZ7C5OZaJvQMj196aQWG5AssxdtqSIbUAQ8MelijZtQEkUQVK1O21AcPz+DedObUDNq4gPshhtQBKY3NsoT2xAa2Wuj+\\u002fOakDknzzhgadoQGzrFD0+FWZAfOwwJs9rY0BNDt6PkfpgQJHS4CsE7V1AsHy3X03iWkBChVA5kblYQMgBloQ+MVdATfxjHCT8VUC5TWVJCNVUQOZnqxwwiVNAKdB\\u002fayr8UUA9DEiivCdQQLaQxLhgMkxAINZg2QjYR0CkJZEys4RDQAXEd\\u002fLa8j5AKUVauhbON0AhuJlMZ9AxQNwvxqTcCypAWUgVfCi9IkC5a1wUUPMaQFWitZoxABRAmEqHHZkfEEB+xt23LpENQJ5DSesdtA5AoijluaYSEUCSoDTLDx0TQAqgqBVMshRAMsc+BElHFUAkOsrVV7IUQN5SD4RrNRNAjh4gXYtkEUCTSrML0d4PQCzmHnrcwg5AeAq32xSXDkCtuxNdOPQQQIK+SVyO7hVATBsbesowHkAkA9VKfcQkQArXzI2PuStA\\u002fubcrlK9MUDKPhctGqc1QBis0cdsLDlAmpBed7DqO0AcAlYuaqw9QEI2XjtJgT5ANpZzoga9PkCu9vWnLNk+QExyYJNbRj9AKD6M1nYiQEBNw3gPNe1AQPALX1mM9EFAfeAVW2UtQ0CUAoO9PaJEQNRq0iatdkZANcrsJBbbSEAx21hfAflLQBgMVcb85E9AyFytW1BPUkDqzpdysg5VQEF610oLMFhAJjngYJe5W0AGj9cDYbRfQMmg0tmIEWJA5ipXQTN8ZECYiDp4uAhnQOEMVDaZmmlAVsEx33wMbEADarxzLzRuQFWUe7wM6G9ArW64I7+CcEDVVajL77twQDzDhVRVn3BAheg3Qr04cEABZ0kiR0BvQAo66wRT7m1A1M1treXGbEB0phbrdgxsQJ\\u002fOnshV5WtALTrdVmZLbEDDqLa18AVtQCHNvgsZs21AHdyXm6fhbUCurUEveDRtQEQv5Qm8gmtAFj\\u002fhU2vnaEBDDdWMpLhlQKIy1ESOamJAuzFXpwjVXkAl4v7CRwNaQHvSGe7TkFZA24grdSBYVEBNY2SoNf5SQPqQl9iTGFJA8oia\\u002frRKUUAhBTNqAFZQQL5foFjaOk5AvUqDBbZBS0B8y5jyQehHQLpvZXuOaURAMoKAQSsBQUBXcKJhz7o7QIXf24IUMjZAodx+E015MUCMosajiRQrQFJffdyVrSRADIUSA4pMH0CknMYg\\u002feAXQH\\u002f4wUJ8\\u002fBJAy88l75VlEEDLiPetTl4PQG6qT85KNhBAlcFxN7o4EUCbQ96CHf4RQF1aW6wwBhJA7rpIRsspEUDsLTVqD0EPQFv14ce0zgtAOQ2pO7UhCUDKfq\\u002fsciAIQMd60kBMPhRAi0rQfnzRFUC6iOzYjnIaQNsV2YbM7CBAlpby32rIJUBLS6D9SHgrQLOgXH13xTBAqxYT5x64M0BohA95EUM2QMYmMyaFHDhAgu49454YOUD4pInrNTw5QIBkTWmsvzhAbT0Ke938N0D4fCLDfk83QDOBIRHB+DZAyNj1lB8WN0Ax1RB0w683QPr4AtcP1DhA\\u002fHMVFdWtOkBqa3c5yYY9QJJ1Gvel2kBAL5BLwfPAQ0CN9HxInopHQCsL9T21O0xAoFxD1SXnUEAIqDT6VxxUQJX\\u002fHwQVt1dADoit9BOtW0BfYfZXIO1fQEU4UqLOLmJAasoSkVBuZEB\\u002fBxOlH6BmQAdzGDMmq2hAFjj2p5FyakB8BlLyIdhrQOmSC6Q7wWxA8pAvWxAebUAIaZBS0PBsQKqjqcleUmxACg5IKm5ya0AdV30Xv5FqQEnKtfA09mlAJtotCr3ZaUBWtpe3BFdqQGPB3TOGWWtABBbNZUWYbECgvrWfhKFtQN1BRI1L+G1AkeYH3wo8bUA2T3c6cUtrQMTvdO\\u002f1UmhAjTFZI6G\\u002fZEBH2WY7YhthQJt85KLUxFtAsamOaHbFVkC+HuyHjmBTQMUH1kctW1FAAqQHhd5BUEDteclRZy5PQBNdJiFs7U1AaDS2oilLTECScPGu6iRKQPrDi\\u002fKXlEdARM4VTRrSREAgjgRcZBdCQJNHB62KGz9AIqhdCdWNOkD3fjmgvIA2QJ4sqMch2zJAoH9xXhwML0BcmiaaSPcoQAseZBJjkyNADlSPAbEkHkAtpszQkkEXQPP\\u002fStS7mhJA0p\\u002fa22\\u002fyD0AAVxebzrENQOnn0zAOFA1AHtE6OBHBDEAVBeDdOMILQGhyyRYsuglA8vCLLxPpBkB9TnFi7P4DQGCenpHzzgFAWV+FsnH\\u002fAECyOr5J\\u002fRQbQPw1cx6bohxAUX9t4SeNIEB1UA3kd\\u002fsjQD\\u002fOPbyRQChAOG6+xw34LEDU7R+k3d0wQKTB8RKIEDNAFzM0zHbbNEBcn5I0IAg2QInlrwqdbzZAE0r\\u002f2OUKNkBw0zg+Xvs0QKhwWGs7hDNAo7wHG5f2MUD3lWXsBZswQJ3IQZS6Si9AsMkhScpwLkCC4S4vIukuQHAZA2k5hjBATmzpYYmpMkA3pGbWeiI2QNFtXYs6LztAnNQcyqP+QECpKBl7Y1JFQJuerT9wk0pAO8vDBoRaUEANSrN6Bs1TQJMp5TXriVdA2iedUwZzW0Bflbu8bWdfQG2GZLCNo2FAVLnMZqV5Y0DP6EpYtiVlQGEQ2NP\\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\\u002fO9FugFPn+T98N7JambH2PxrP6\\u002f7JhfU\\u002fTdAIle1PIUD5n1yycRciQNIoXjSRTCRAr0RjYZ+XJ0BzefQz8ogrQE12x+fPti9A945c+QvlMUAxrxKXwLozQHXJ13TtMTVAknyZAQIZNkD2zTY04UA2QBP01tbCjzVARIV2b0gSNEDIG6HVJ\\u002f8xQDmh4zf7Uy9AQn8z4AnZKkB6PWQHkyUnQAiQWowopyRAjVTB0U6rI0AipSKMEXYkQLnU6wxbVCdAfoBBoXyhLECLWCeRgWAyQA2IBnzGCDhAp6BVezdtP0Dht1zg0UxEQGe\\u002fa0MsuklANK+KsZbaT0CluWiFUzpTQDH\\u002fB4CioVZAICt9oZgBWkD2jphhZz5dQI3akwIUIWBA2PqP+k98YUDzYrtcUqViQHOpIzbrjmNANK6yBGotZEDxMdCHAHxkQCk4cJcTgmRAwRmpExFWZEDZUxnWWRxkQD8uz5XlAWRAhwpzzuIyZEA65+dIBM5kQAFQij1j1mVAMJBcPj4oZ0ATFBf9E3doQDpUBOfgWmlAvS3+Jr9saUACKQtvfmpoQPj5qmNMUmZAG8Ip7phoY0AQGqFI0SNgQAf5u0tdDFpAB1+s2hzsVEAkbAhVL0lRQI6oQdHwKE5ASXAtt6PZS0AC5bQENKNKQHMOuW74pElASD8MrLBZSECN2vhBJ6NGQPEi3FxKr0RAys4THnbKQkChMmLN+jNBQNCjVKKSA0BA6EE8RatJPkAb15sEB8o8QK4TCqlpFDtA3W6mVZbTOEBcJJrubOw1QLrEG3s5gTJAldziOYO\\u002fLUAkQrIWSsgmQNyRYKc9uSBAJZN8r9ncF0BugmW6VPMQQECfOHA5vwhAnFy50nQMA0By+AVhoP3+P+d5vyciC\\u002fo\\u002fETnkoo\\u002f19T+TTwY7BkjyPySC64z8Ue4\\u002fikkXEKcK6j+59yv7JoPoP+F+tQbQiCRAqy4eF5hRJUDmOnKkkIgnQGDosqY80ypApL2WH7nELkAyre0Hvn8xQN1cTJkJnzNAz2ypilmfNUASP7P2\\u002fVE3QKg4gq04dDhAgUFOp++7OEAYR06e2fM3QGvH0VLHFzZA5RjLk+VfM0AG8s4DqTMwQNfthrLbFipAVcdN\\u002fIyhJECp4xc3tpcgQOOijox3ixxARQ7AqA6bG0DBovoXoo8eQMPJphhf3SJAiV3zmFXQKEDsK0\\u002fE97kwQN3wbI0RijZAUIwvKSTpPUDmubXEkGBDQI\\u002fYfX5VZkhAiSYz4TbPTUDwEHbpAq5RQAdLHRSRaVRA55UFetYEV0A9g2V\\u002fh3FZQFQZdKk7pFtAwqJmX8WPXUD9acaXEyRfQF8cuY8gKWBAAs9ndRuKYEASNapRHbpgQBPL8HtzyGBAK4dDzdbNYEBM2fufw+hgQMTBg8nzNmFAg4hrHSHMYUBCw2OjO6diQFSaa\\u002faNqWNAnQjYyXuVZEBeZn1c4xllQLmH+3cV6mRA9t3r+a\\u002faY0CNWu6lbvZhQJQo4eeI\\u002f15AovqBzaK5WUCOs4mbCexUQNYQgzTcK1FAg9HyAntcTUDL0wwEbZVKQINWSXwxOklA8aQ8NSBpSED8x6NaTXhHQE0m49oUH0ZAGgZYtwJyREA6J9dhIb1CQIDkvjh6UkFAR\\u002fvG0\\u002fZhQECHPMGixs4\\u002fQI92izKAYD9AZ6Y\\u002f1HjkPkCWmCUVO849QJJCWd\\u002f3xjtAAt+SLETDOEDE6lEKFP40QCaeCwhD3zBA1sft+u+yKUDWY4IkSZciQOQNmWw8uhlAmM40RwVEEUB3T1o7ev8GQCcerVe1Tv8\\u002f1EOXMQpP9j8RH5nmarTwP3rCbqVu2ek\\u002f6qI21mFG5D9XYaBpHSPgPxJ2v6hr\\u002fto\\u002fXXGc4XIp2T+mCenu108mQBUq7K08GidAjjiGxIVcKUA0ieg3\\u002fs4sQGM6RzM6kDBAMbIGEZ0HM0DOnDvB0LE1QM9+qTdHZjhAietYwnncOkDvHMkgcak8QMqYyDrlVz1Aykihx0qTPEDeev4Wuk86QEkozDj21zZAUNrcNfe4MkBqUyFpFistQHHRN2Fz8CVAwedLqKluIEBGgVs9FssZQKEiFKJwlxZA14Rs71wCF0D+CytrgfIaQJ1js\\u002flmTiFAJULlPJE8J0A3K6TsLoYvQIjj6EMBJzVAaEbR9XO6O0AwMXWNA6NBQJS5rWtfuUVAYFCY6ofvSUDCQEmdXBtOQLmTLv5OEFFAbI0pYQj4UkBvN7v4D8BUQNOk7vg9YlZABjl5BzvVV0DW5XGkRw5ZQNzoLY2sBVpAd7itP5q7WkAptDWVaTtbQHtgBvEsnFtAVG9UnhX+W0BCR5sl1oNcQMQVSnz7Rl1Ao86nVC1IXkCBVsyt0WBfQC9oInrfH2BARFle28U8YEC6SJs2QaxfQMG7WiehqV1AwzXdjXWTWkBC2eD1YNlWQMzVsJdWFlNAZHc72PS4T0CKz2IDIw1LQDYrRvlDREhAL\\u002f7FRQTxRkCOwn6+S15GQJBc86A23kVAH2x07boGRUC3qAld+8dDQGoTx5\\u002fOWkJAuF7BX\\u002fgVQUBEx\\u002fhP2z9AQKrRFzLt3j9AQY7WrYoFQEBYGs8lkjtAQMu3pqvCPkBA43QTi9mWP0A8Fi2cgH89QFf8e1mbQzpAcMDrG70yNkAV2BbwScMxQBUpatsZ4SpAEKNdHus7I0AyGch1tRkaQGVZKCL\\u002f5hBA9zNxVAUrBUDaZQLoiTD6PzJEYhgHdfA\\u002fc6eV7XSH5T9+XJOPJpHdP+0bwVT3LdU\\u002fUnUcDr6lzz\\u002fncboHu3DJP61Gwh4ZYcc\\u002fEjmewDEgJkDXrSUOw+0mQNZ8mrLdRilAAkNBPTcHLUAPXqy9BwUxQO7Cf3e5FzRAmqm6gtmgN0A47bNIL2I7QP0\\u002f2RUU6D5AfweSqdnIQEC67bvMIF1BQAfgVBA8+kBA2cIw\\u002fcQ6P0C\\u002fbSwwr\\u002fk6QHWAUT\\u002fT7TVA0b6FvH\\u002fmMECqV8n6iBQpQJT+DrFUayJAEaWlQ8HuG0C1JyCP8\\u002fwWQJUkndiHUxVAOkoZKU94FkDs3T0QalcaQGZyzOZAnCBAaUTU4GDDJUBynwJwe8MsQFvHSpbNxjJARAuy6qLrN0BifTnsx5Q9QCwOZCy2wEFAT84AlbW+REAdEaY+zLVHQMqavxjkoUpAmRNl+V+DTUDDUGDW0itQQDTCwaIJilFARg4fabXSUkBoWVENr\\u002flTQKB3Tb9C81RAoGAXbpO3VUBv2Snk1kVWQMVY46V9plZASViCZVrqVkCYhblzjiRXQGisVQEZX1dAbBldmVyOV0C1c48Ax4tXQFuzehbXHldARnrp0IkUVkCBnLoteFtUQIPCDymNFVJAeOgE698nT0BKhMG+8XlKQA2oYi+oz0ZAOzpJEVl9RECQohFrsGhDQNTPkYcfIUNA7BWB6ugWQ0BBCewisNZCQA0zjgCQMEJAExDVRWU9QUDTRemI+ERAQMdDVx8LKT9AbwOxQ8CyPkChoGFIrxw\\u002fQB1XqwPL\\u002fz9AH6WmDSVhQEArS6PzhWZAQKmUBkXstz9APBbZtU9hPUBTNRul+O45QAu2GerwvDVAOFkJZlNCMUAWVBfHc+gpQPI7C3ojXCJAPgaBwl6RGEDUBpJHkhoPQLvUmtAvvQJAXKA1PLy\\u002f9T9zHnqqDc\\u002foP4YwQwPflNw\\u002fclM\\u002fCAMW0T+OOsr5V5fFP1zpE6jwRb0\\u002fDgXY99oVtj8eMXbfDtazP+tTe\\u002fagDCRALjKZW7beJEAWTohmLlUnQOnPd5MtdCtAVgJiGYOiMEDfkETKXmE0QLkGSmxF1ThATTGuqtOhPUD0pjL67RFBQE1pCGY1xEJAKnfxebqDQ0DjAb491hVDQIE6TZMcgkFAkGVMGG4lPkAtvkgI5XY4QNWeul9l5zJA76FXgihTLEDE55N9RiYlQFEAyNBsRyBAXrpQVj6aGkBa2cQ3QmsXQH\\u002f8\\u002fZQjRxZArXiGCr\\u002fuFkD1uV1Gi44ZQFR\\u002fBb\\u002f+gx5AWgn1shwMI0DpIOYFkCYoQGzndEzRZy5ACWwLT3fEMkCJxiQEsZ82QOTJu5+HqzpAmlIvJBjgPkB51wTLw6JBQEwKKZf19ENAivsTHMZpRkDMtHpnU\\u002fpIQJK1cb86kktADK1ZZrEPTkDycwUlvyNQQCvpb\\u002flvBlFAunMjQVGeUUBmLbf0ReRRQPqxJv3N3lFAO6o6gpufUUBzrx6h5zpRQCU4EhgFvFBAg3\\u002fqwQgeUEDXJccYK51OQNkJPNtzdkxA6qn6wTLFSUAKGA8p9L1GQNUQjDB\\u002fxUNATj+IN4JOQUCOsUFL81g\\u002fQMv\\u002fZdO85D1A0HdQm5zaPUCFxOoEOno+QJOF3AD5\\u002fD5ArzSjTenlPkDgTQurLio+QHqCfyMrIz1Ai5lfXapVPEDyjfQg8ys8QI3o\\u002fVVBwzxA\\u002foozsOrfPUCYe+FLBgc\\u002fQE0IrfVjrD9AUaHzli5fP0CA9IL3E+Y9QJob7USyRjtAby8ufmi8N0BdzyCmk6UzQD3Swlyq2i5A9SsgHLHsJkBezVqCexQgQB8aGHmuQhVAgtzCDxR7CkAxnD1Bph\\u002f\\u002fPwvzZQv3WfE\\u002ffDe4YTWP4j+dWQL48GnTP9MQlWdPbMQ\\u002fO3Fc76hatj\\u002fDPAmvBH+qP+ac+pvHFqI\\u002f\\u002fuPO\\u002fXFDnz9ePq8E3qwgQPm4CZBAgSFAnGGXavQLJEDshrtjwHEoQEUoBxYN3S5A9MPGIlKqM0AAE8m8IL84QPE72\\u002fw6ND5AO88cQYehQUB9mtYXIH5DQCZAoSQXR0RAp9a4gi\\u002fIQ0CU+KnyZxdCQJTC1nO4HT9AKyfig7daOUC0NdD1l9kzQDLmlQVtgy5AB12ZuXunJ0BBqOvZr\\u002fEiQJTmIGC3qh9AuQH2AfB4G0DNdSrNvoQYQHIsRNktjxZAjXR7T9vIFUD0YJZumJMWQCFl8r30QRlAFR3jb\\u002fbtHUAo8EbDLDkiQJsLNp16QSZAOkUcr8XqKkBvos9u3REwQFoV0Qva+zJADpKBg8JENkBjWMFAqwA6QGkiFth5OT5ADU9qtf5wQUDoxk2CVeZDQKvlHFqTVEZAE78275OFSEDU5DhhEz5KQIQyuV4jS0tAkdiKmsyQS0DhHtueyxNLQC7wFatX+ElAv9n0tkx0SEAI8PeUabpGQLIrLhK+6URAq0UNpBgLQ0BnR6775B1BQN52qrlxVz5Am6DgwuOpOkBNBz5xu5k3QMGLLvp5jDVAnRgaFoS1NEDsopezDfc0QGkj5IaV5zVArEUek0f7NkBBc0LO6703QDzuVMJYAThAGCqrs+7pN0Am7rS7ndM3QEfNc7ixHThAd6lxB5\\u002f3OEAnjkC390Y6QIK7B91AsDtADMvcwWm6PEAmCFCedfk8QIBrhdimLTxAU3dmAGdOOkAHYNkMeIM3QAtR\\u002fCfvFDRAurfbVKxZMEB6xS2M8E8pQDan6ncGkiJA4Xqa7+G+GUDh\\u002fIDEz9AQQLyAVpHqpwRAEWKatdnX9z\\u002f8srldUubpP1jqgmn6mto\\u002fUpm7qHAeyj9FRPKT9fm4P9NmATEpAqg\\u002fIkfaWTJwmD\\u002fvlY+g7AKNP4e8o0p1kIc\\u002f75XQTpqhGUAebpVZ9z4bQEOrKseBICBAhsbPlmaJJEB\\u002fa2fsbBErQIfyblrt3TFAw+Q8ds8LN0Cxqeow0Ik8QGOg9Ctmw0BAdiuBem6LQkAaIUzrAj5DQEVfl2iDtUJACaqyOxQVQUB1t5he\\u002fXg9QLb+lYwXTDhAyF4ZHhF9M0D5PB0eBSMvQG3WaEksVylAoLXlHpdBJUA\\u002fbV4scEgiQBIv+W6Lux9A4UhNFXtgG0AOpNtMVGkXQPeSPNsZJBRAszcwlyECEkCIMpeJOF4RQG8h1ZUBXhJAGCMvYkvzFEABVy9KZPUYQOlKoD59Qx5Aljy\\u002fCzhwIkBR9TH1JX4mQPJSesNdcytAudAjwSm7MECc\\u002fLc\\u002faEo0QILxoQ9dVjhA903HjcmtPED3WWODu35AQDV+scS9a0JAxGOMSGfiQ0BSXlQZs69EQGvV\\u002fe8gt0RAofdFHEj8Q0Dgy33GzqJCQAvjvsNe40BAd7eRLPjzPUB57AJmaSw6QEJcWQ9oqTZA08Lb396HM0DKX4WLZd0wQG322\\u002f0wkC1Ae16f6kjaKkBQ8hCcTc0pQILRZoqjWipArHywDt4bLEAjjAWeImkuQEXm8RLjSjBAioVLQW0bMUAFqpR2WacxQF1+\\u002fFrrITJAYFwIeD3SMkBSa2qHtukzQKm\\u002fbEMzZDVAZ+9P1+MBN0D4xBsYDV04QFdQjWVzDzlAVh69j6\\u002fUOEDq3MORKps3QBCuJ1rTgDVARDXcqtXCMkAKvBpQu1UvQJCM2dbBASlAbB33I0cFI0Dd1Q6vjXsbQJ8fqA2ZyRJAfOP9wRY4CEC5Kig0Qln9PwZURSBprvA\\u002fG+4wWqfG4T\\u002fp5L0U7MfRPxMtaAjlxcA\\u002ffhDMLXgmrj+RS1bziFuaP1JGgKxoboc\\u002fTJv6hV65dz\\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\\u002f6TkshKdKEB21G5EYVAqQGwo4fKHmixAMBFJgUGQL0DDbNLkYHMxQEvXvO2FADNABWMF4G8SNEAqOmhjhF40QMGf4KyoxDNAGft2oY9UMkBq2RG7rUEwQLRsaLtnnitA7wYLvcp8JkDIl\\u002fUITIkhQMWTzcpVIhpA7M2sNHiKEkCsVZEQNPEIQHRYjd59rf8\\u002fUjc0WZDr8j9Zz+kxwDHlPxmp9mOjONY\\u002fZTHbw\\u002f7KxT9VvpH8\\u002fAS0P4fHMEwbTqE\\u002fRVj5BLl3jD\\u002fGuDx22PR2P9W3l+XuLGQ\\u002fO9spd6peWz+WKJKy9aUIQMFij31CHgtAXJ5ytwloEUCEbCS68DMYQEoeMcJ7HCFAmAylRPenJ0DzoQ4dRUcvQP63UuWcijNA5q1DQVfvNkBkdeVrMTo5QFk6loDfBzpA85P0aPdNOUCQJOZYc143QCrVg1oOxzRAb8B0\\u002fQEcMkDSy\\u002fHma44vQO\\u002fCoSxe4CtAHH+S3L8EKUBzyjoNW5EmQI67rBu8GyRAxpdhY6BnIUAhcki6O+wcQLj5qfiR8RZAyLNBV+1rEUC7Y+HJ\\u002fLMJQMAlt7o1FANAaNai0XZQ\\u002fj8ZLiEi5Kj7P7lLOi\\u002ff0f0\\u002fZqwIZrlTAkAhc2Ll7joIQEPz7mEDexBAsZoz\\u002fhd0FkBYkYA3viMeQGLYVuCguCNA85FA3439KECQAP+j9YcuQGmCFaXv7TFAPzBq3n4zNEBRTF478Mo1QEiFlAUDeDZA63zX0dsaNkCCqNga+7o0QCjT1cvViDJAQLLDUCCnL0B3RXlOnespQMl0gphUgiRAJJjV3xHBH0DbDA2KIY0YQHqgVUYPkxNA6zF6r17EEEBOEupiCtsPQE7jahxbuhBAFToYw1S2EkDljZrW\\u002flgVQIfdgmOXJhhACj01jnLbGkAE6CkvcoodQKthJnn0SiBAbSeO+zc9IkDdnNWjxb4kQOHaa5ygsydAyFpAFoK3KkDIlFwFjjYtQBUkl5fCni5A6XAT7tySLkC1cS0Y7wYtQPKdifP4OypA4kQYyqyhJkB4xG\\u002fgq7AiQJ6xEUqcnB1ApTFXbeCBFkCPqoUGsGIQQJqwk6iaxQZAus2y8JMS\\u002fj+gr5IXq8fyP4Xh9ndDFuY\\u002fquPP0CRf2D9Ooe1erCnJP0QwTESLRLg\\u002fh\\u002fCr2I3apT\\u002f75FKUNGeSP0SnDVFDI30\\u002fweNn1qQVZj9U1+mVEYdRPwifaxWT7EU\\u002fbQMfYwQ5\\u002fz\\u002fI\\u002f\\u002fUq7H0BQDw9o8XNVAdAF\\u002frBgfXJEEDahK5t6kMYQGKLYNLG8yBA8UxSWM56JkCfaAe4QQwsQDU8tqTTaDBASIWiDUgDMkCFxUiEIp4yQMR\\u002fs1wsQTJAPyrKCa4uMUAqmGbBPo4vQEBSdmfgxSxAkmJ3eERpKkCtyKtaGH8oQPbaPlzUxyZAstGFMaPtJEDxtCFShrIiQPrhM+ILCiBAXVTDLJsyGkC27cr\\u002flUcUQJ+bPNa7xQ1AyMS3DETlBEAnDrtRvb78P9AFKNsHh\\u002fQ\\u002fjw6u\\u002f17Q8D9x+GkAuDrxP5n8gZKipvU\\u002f6wWsC0NR\\u002fj82ckEj7dsFQGNeHLtsJw9AHmU5atWJFUB20SPP3KgcQJRWGjxTSiJA73YgqHlfJkCh45ozfTkqQNkh2Kh4cy1AEIVCRsypL0DyE0g180QwQNGUtsYm5S9ANnuo4J7BLUBp1RYGbWAqQAVkvDyaMyZA6TLb6cXEIUAqoWqyyisbQKa+an+JEBRAkD4iuRpHDUCjCUa7IgEGQBSXdUnYEgJA3ymc91f5AEAi2SrOzQoCQCGHr16pjQRA3wDb92HVB0DYR7sP72oLQFvRi0f\\u002fNA9AnI5kXdPCEUBNQB5v3noUQF7NFdRvBhhAjdxjrN5qHEB\\u002fV1aeLKUgQKoY1Mkp9CJAG0GGnCuuJEA92WQ0um8lQN0syfVTCCVAzD3enVaII0Db2KhR7jUhQDQd\\u002fryD4hxA1PzR7XMtF0CdYYwQKNcRQD06z5\\u002fxYQpAk1ZLxdG4AkAmTycVo2z5PxmcYoSucfA\\u002f096t4wgp5D+fEHBAwU\\u002fXP4Get9YTUck\\u002fo7Ixi768uT+ogkDMRG+oPz0CXnFaopU\\u002f4HpZtGTcgT964FNK\\u002fo5rP+xfV6\\u002fWF1Q\\u002fga2ePAXiPT\\u002fvNmTzqqQxP0Y8Q4pFqPI\\u002ftqhsaoc99T+gpajSNDn9P4TCDsAFjgVAmSCtV4iRD0CA9anuLycWQOl09Za9Wx1A2\\u002f3StrREIkDBpgXViFYlQKZQ7G37didAn3IrAJp1KEDKmwHspW4oQC8tymoRuydAsFvFsV7GJkA8X5DHid8lQFWOvRU6HCVAFvTM6zxaJECEZ5BFmVojQLQfLTO\\u002f5SFAPx++0BrOH0CbA7xRCOsaQIyRkmzjkxVAHC+6kvZUEEB3s6gch1YHQL85U2Lto\\u002f8\\u002fjQI16z669D9mVJrpsqzrP+6q1mfZKeU\\u002fETARxYY25T9ZlQIwBJnrP9CxfVT1UvQ\\u002fqEL0w1N+\\u002fj\\u002f\\u002fDjNoJEMGQHddhpVCJQ9AWIhViXnKFEACIrN3eH8aQPryAtpyJiBADFXScdjdIkBk3sXZHCUlQIS64cOJuiZAURUi4FRjJ0C2JhYWafgmQDwaYy8NdSVA6wcvRhYAI0Db2Em3V9EfQCFlXUiwKhlAY\\u002fgmVhHXEkC\\u002frJWGLOYKQOz7feDUqwJA4919\\u002fulJ+j+mLzwrViT0P4jF\\u002fS9d9vE\\u002fWXQAFByo8j+dzaD0HjH1P+Ll8YDHxfg\\u002fOtc9JkgB\\u002fT9GsE4cRwABQKC6T3V+KgRA6nsJKvVdCEAIZZxsIdINQAuW0tgnLBJAg4IWVSieFUBsk4T3Aa8YQH1buohPwRpAt7QwYc1eG0A1MvAhpGAaQCD+gIgT+RdA+\\u002fXnHiacFECccInDhtUQQKapdPqIQApAkcNoZVmfA0ACVE3P9DD8Pyc\\u002fmCj8dvM\\u002fC8CPTJzG6T+REKQ7EkzgP7SDGSbUk9M\\u002fIsghtYQ5xj8umuQgCru3P8SAt4\\u002fTvac\\u002fIZ96lswwlj++m1gIsVaDP+Xz8uoGZW8\\u002fdBNhQW2\\u002fVz\\u002fP31rf9N1AP9xwVegIEyg\\u002flWErDpVhGz+figBFb\\u002fTkP6h7axYrJeg\\u002fxuXysCP68D8ZohcTB275Pzd36UmhvQJAWSCvDw9RCkDujShZt2cRQM\\u002f54Sy5oRVAHf05Jr1RGUB\\u002fhQy9yhUcQOyGKagH0x1AL1p23pG7HkBaKHB\\u002fGC8fQAGLM1XJhB9A52ViEzjbH0BxMkNs7AMgQCQZi7twrR9AisHGL49rHkDXfGUhwg0cQFTRV1\\u002fYpBhAODqMRmKCFED9cc7XPh0QQIGNKB4f3AdAwpHYnzGkAEBvd8+3RvT1P1KSWgpB1+s\\u002fwmJWFIHm4T8PB4Fw7KfaP8IVS53kDts\\u002fNBW37CxS4j+OJF+Gn+rrP5XA0M1vV\\u002fU\\u002f\\u002fxW9N0Rl\\u002fz9fvwpA9PkFQGQKibQ9Qw1AQkW0eqGVEkDKLy3eT5oWQLB0gkGIZxpAtDMmGsGrHUCIDWWoOAYgQGjbPFs3lyBACgba+TdlIEAr4FrWZsgeQMVjhCvlUxtAeI8iMs7bFkDrYeX8lvkRQNZ+cw5ukwpAVICBcNKMAkCfB0VY2Mv4P7rb3wZ2bvA\\u002fA4HEdUki5z\\u002fGuAJUJuHiPzAuXtMwdOI\\u002fvO7bzZV05D\\u002fdj+Hr5vTnPzAel8Rlo+w\\u002fpuv4Uqhm8T8NG0TU\\u002fZf1P5bf7BlsRvs\\u002fxg70VUtFAUARwx45+3kFQGtoDfHeuglAjIyVXmNMDUDa207IZHQPQPsOLd5zuA9AUOf+bEMHDkAQoeRUWLoKQM3OtfaKcQZAUiLWz1rcAUBCG2yn9hT7P9aDaHQPqPM\\u002fwAWUuU9q6z9hHGJOfGTiPzWdmMSQtdc\\u002f4+KN2S5BzT+KHglNKDDBP9\\u002fxOQ\\u002f6ILM\\u002fC5KW1y0PpD+7qAbbGbyTP2RoMBdfJ4I\\u002fFV4y0MUmbz\\u002fhfqbDmuNYP7cMcMjOgkI\\u002fdTX6kiTGKT\\u002fjNoHjZuIRP5ARZkPP1gM\\u002fwD\\u002fzPor21T\\u002ftMtFXwoDZP9EiGdmSKuI\\u002fL24dPHhr6z8sm82RfD70P4IrrGrwZfw\\u002fMaoeuHvEAkBP+lWFcGIHQGvPKW\\u002fIngtA9vrWPrU4D0DLLudwkBoRQNXLEJ1OaRJAQYB0i9asE0ATSDU9svAUQE\\u002fefl5ZFRZAH7NH5N3UFkArcZf1Wt0WQCCT3aTH8xVAh8OjegUOFEDevkYb71gRQOH8Y08KVAxAUHdF57rKBUA3TZ4FKY7\\u002fP34hMnLVgfU\\u002fLlyO0Nu06z\\u002f4srIiPifhPwW+eNTip9U\\u002fBte8A2000D\\u002fmT9kYxRPRPyHB90i4Bdg\\u002fF6FFcrKn4j\\u002fQybTV\\u002fa7sP2RDms3\\u002fFPU\\u002fB86vX0tt\\u002fT9wQUnJXYoDQBKPgrUAzghAIbrYkPE8DkD8W9BbAcIRQEDeuxStHBRAe4UvF+brFUAMGsGo3+UWQKEu4uPAyxZAUz87L\\u002fWBFUCc4x\\u002fKTSITQAQpP6EJ+Q9ARrNKiCEFCUDXPBWGWVICQIaAhrF3Kvk\\u002fZN\\u002fBzLpb8D\\u002fOnmEndKTkPx\\u002fyPS14x9o\\u002f931Wepue0z+lwwR1gnnRPymxV6zhf9I\\u002f88MQBiaX1T9d82Th5HLaP0jmKMCMvOA\\u002fNR19cZa45T+i6\\u002fcvbH7sP0trcnRBfvI\\u002fkuuArM1B9z+\\u002fx+1kAeL7P0qHf\\u002fJ7iv8\\u002fUNk57OK8AEBBS+SBM6EAQCDi4ZSw7\\u002f4\\u002fWQMxSYP9+j9QETXfMin2P3ZzlAvUM\\u002fE\\u002fuaGi\\u002fGpi6T+25hLI9uXhP8523QtnOtg\\u002fbHv7rRqPzz\\u002fFaSYkBMbDP4y4GNXWxLc\\u002fA6WsrPZGqz\\u002fplFTP77edP9Jd4zkxko4\\u002fX+QfXxmNfT+tegz8Q71qP1MZGos0lVY\\u002faLZm3vnDQT8atGHcMwMqP1v1v1ESzRE\\u002f18fTN9ks+D4IooUij1TqPlKTKOE1T8U\\u002fDYutWQzXyD8ffr5nYMzRP6ubAVhG7to\\u002fUsq2mwXh4z9pthnTSOLrP06V8xlefPI\\u002fQV8kkMRB9z+5gAFSRgL8P3MHQFMnVwBAH7SDGBW1AkAabVZCMTYFQFuBm0qu4AdAgKm1ZZ+KCkDDbIwvM9QMQCV66GQJPw5Azl5UOQdaDkCT3wTJEu0MQHZ\\u002fKqAtEApASN1mosAkBkAG2PfmvrYBQJlBZEHFqfo\\u002fDHdtCqTf8j+H6i5PISbpP3Xl5Dzkr98\\u002f\\u002flpneUQ\\u002f0z8PhIMRqCXIPyhJ6TccisI\\u002fsoM82feIxD\\u002fwSmu9ztjNPzMw0O6Nadc\\u002fwyunaiD\\u002f4T8Jlu5eiWHqP5IDis0oYPI\\u002fIfDgbIlr+D+9voCVwiD\\u002fP\\u002fTF98KWHgNAYoEo0YuwBkBlVnocNP4JQFRUlMWTowxA0Ujdl08tDkBm+wAFxDcOQDzwWZKmlQxAL4KoYo9qCUABmBP1UikFQBEGHPyUdABAkJSeVarf9z8w6dyQ9ivwPxJHBbE5k+Q\\u002fT3bkcDwB2T8GDpdR+UzOP2nPecrp9MM\\u002fjFX5lyzBvz+VkCseYXW\\u002fP19BoYpJL8I\\u002fapIJLh\\u002fixj\\u002fK8ASn9jHOP3kcuDIlZtQ\\u002fQ0KMObmM2z9a7y42CSniPyV9avy+7uY\\u002fG59dWr1i6z96mXJ5y7HuP7LjvJxhFfA\\u002fqXEg9pB67z+YjkLo18fsP5soibPEoeg\\u002fhq8JwxPM4z905pHQ8QPeP1WqBetMk9U\\u002f85HHVPSTzT+MuhIF527DP4FS0+r0j7g\\u002fcDY5MtnjrT91\\u002fLd5KXqhP+xm8c0cj5M\\u002fY2Xz\\u002fiPVhD\\u002fNBxBpeP90P73PHp287GM\\u002flBv7Z\\u002fu4UT+0pIrGm3Q9P\\u002fVSOW1r0CY\\u002fEGhplIRyED\\u002fJIF46hSf2PqPnldEjiN0+Cx9P3K2uzz5ptAt45P+yP2TCFr3jK7Y\\u002fW7wfYZ\\u002fPvz9MU6FDWRHIPz5A4GtYxtE\\u002fVLFcYBUH2T8ek6W\\u002f3cPgP4B62hW0heU\\u002fmKbV\\u002ffXF6j+15ZFQQVTwP20jcOVJsvM\\u002fZ\\u002fYR3KCH9z+OLrVkNK37P4Tsg1+ut\\u002f8\\u002fW724L7CBAUBoc2Q5JnACQKhuf2ifZgJAThyJA4NTAUDGg1PhSrb+P71wtNqZl\\u002fk\\u002fT7C\\u002fTuoK9D+harCT0oDtP3AAFa\\u002fUaeQ\\u002fzjYY2CaX2j8s9ml5hGXQP3liD2mBmcM\\u002ffTFboUyjuD8QAiopFqqzP5K8fNWq+rY\\u002fqWTFy6oowT+r9K0VBRLLP6kB8Puox9Q\\u002fRAUIMKNq3j\\u002f3wP1ifjblP9fbxDKwVew\\u002fqbynkh058j8O3Ha3Wqj2P4mvelmlP\\u002fs\\u002fy\\u002ftanMib\\u002fz+I4bITkJYBQLi6UF9qpwJA\\u002fDfYq0e7AkCSWzBlEbUBQISPdrHcX\\u002f8\\u002f7s0E2lf2+T\\u002fYPq0BqgX0P2K6dgSHv+w\\u002foIk03Cs44z94OipUbAPYPwBcI0f5WMw\\u002fKB2MNwpLwD+kXAzlApSzP+\\u002fgjp1Ks6s\\u002fnyehuKpAqT++5DejhrSsP+eyl\\u002fw7i7I\\u002fyngr3yxwuT\\u002fAuqwg6MHBP5v3wJBCd8g\\u002fprkqMMZA0D\\u002fhw\\u002fEmen\\u002fUPzaMPjl7Tdg\\u002f1fu7k0fx2j+h8uIO\\u002fNvbP5kPMZYQ2to\\u002fUXbk\\u002fSgk2D+0asmfiEjUPxMoQ0h\\u002f8s8\\u002fOckCdbCtxz+l4HEL35nAP3aynAxOJbY\\u002f\\u002fS0u6kVDrD\\u002fK+vpUcFShPxA8ML0jd5Q\\u002f3bmJx8VBhz9TYb5x2Vh5P9f3sOufW2o\\u002fU1gqKR8AWj9S6RrPCjJIPyidGLDxIzU\\u002fusL4bB9HIT\\u002fCHyqiA1gKPz7uZai7s\\u002fI+8zUtJMrN2D5W+I\\u002flhj\\u002fAPtMpT5QCMrE+zXUMkJ\\u002f2nj+1a68a\\u002fw6iP9Z3jTdT5Kk\\u002fWKUHngaasz9VPElLWxS9P3i8Lgups8Q\\u002ftZtRIT9TzD+toONswcvSPzz97Smiddg\\u002fn4L2kM1x3z8IG0gdSPvjP\\u002feZ1\\u002fTm8eg\\u002fhNGS9JRD7j+fcA6k+KPxPwNYLMcEk\\u002fM\\u002fovtRjueI9D8lo97C60f0P27Zx8dp0PI\\u002fumTY2rJg8D\\u002fK9IlmisDqP4oYHclEgeQ\\u002fShnxCKSD3T\\u002fhhf51lfXTP7ImZ6p0ask\\u002f5FHQUJ2xvj\\u002fRnOdwzxayP+c4AVNJ7qY\\u002fCjJqjJ4voz9vgSAU2KGnP\\u002f8QxAX2C7I\\u002f8ic5NoyVvD\\u002f0cb1Uq+\\u002fFP+Cr5e\\u002fSE9A\\u002fbAFdfgGL1j+SuG2MFGbeP6S93bM9zuM\\u002frJZebuz86D9DuS\\u002fcmXnuP4BxbCu54PE\\u002fpEVA288L9D8MoPMtEFX1P6RaT+XqaPU\\u002f8ac5QLAp9D9NoG1t3r\\u002fxPyxJ2T6FIe0\\u002fsg0I+lc+5j\\u002fmLUighZPfP60lCPCp1dQ\\u002fUIMDmoGcyT8UHlU7KYm9PxLokiNQT7A\\u002fpalCPqM3oj9+RAs8yB2XP94osuYbMZM\\u002fDqBSe61BlT\\u002fKFkgHHRWcP0kb7R7J5aM\\u002fBEew3Rx0rD\\u002f7UcUSANWzP7zdyxR+ZLo\\u002fzU62spKOwD9aLacLH3LDP\\u002frRqr2gTMU\\u002fFzn2LMq4xT8VU6QJDJ\\u002fEP3iTLr\\u002fgPMI\\u002fgm6VNhwbvj+Iup6gkUC3P56va8Uw3rA\\u002fYa12QKsYpz+0heYSPwOePzwDxmdBnZI\\u002fYnj6nVknhj9R9HWGNWF5PzZZlI2dAGw\\u002fMxRNJJmvXT8souLIixlOP+MZ\\u002fFgRBj0\\u002fc9cbEQZ2Kj+l9rs+ZLEWP6I8893aOgI\\u002fud38AjFZ6z6Xon26kB3TPlUFOI7P9rg+r2RCascUoD5ufT8rZsyQPgrPYK7b+4Y\\u002fFvZ6q5rLij\\u002fz0A2OjzeTP00hSnNJO50\\u002fNAgiZvPwpT81U2KWdu+vP5Z9oJGxm7Y\\u002fSsQO4CNpvz\\u002f38Qb054vFP2P23oTnLs0\\u002fNIVsCi9g0z\\u002fBct4kLPPYPxwF\\u002f2YVzt4\\u002fWj\\u002fyBVMQ4j8gbNhcBf3jPwy5KvzUx+Q\\u002f1Qs2bydA5D863y9Qs3riPy5jlZzhk98\\u002f5uFqYatF2T+E7XSgaPXSP8t1OBx9r8o\\u002fj7D\\u002f\\u002frajwT\\u002f14obHrfW1PxITg6nE+qk\\u002fxVw8pXhAnj+Y6p2kKHKTP3sybkNOJpE\\u002fzCik32kylj+QlRgzM0ahP9lggCWFeKs\\u002f5ZhmTRogtT8toD6z7B6\\u002fP+xEdxZQBcY\\u002flKSqHUMWzj+vVV414+fTP3M9mw\\u002fRf9k\\u002f1H1DCJZ\\u002f3z9Fm7MgSKTiP+UxJx04\\u002fuQ\\u002fpYty8WNX5j9bA1IdB1jmPwlD3Fbo6OQ\\u002fcoPWqqNA4j+uobYQr6jdP6iWwXw+ZdY\\u002fagyBQHtozz9Z\\u002fqLXQ3PEP5xPFcAmwrg\\u002fz6cgV1X+qz91Jraln\\u002fmdP3A+k8PoqI8\\u002fm+qU4yVOgj8gtj6Vtpt7P3U+Kf4PgX0\\u002f1IR9PxHIgz+t4GGsPreMP75GFwOh2ZQ\\u002fpBAqTuw0nT8lVRTjjGCjP1qGeMG2Hqg\\u002f1sDBWCQFrD9\\u002fbqp160yuP4VOiXddeK4\\u002fU\\u002f7WGW5+rD8TYSrrgsyoP+xSJMtmHqQ\\u002f55pSKqeAnj95vsE9zq2VPxUyzUK1BI0\\u002fP+mvqu1igj\\u002f7KeJvITB2P\\u002fTJ9REXpGk\\u002fEgF2UcR+XD\\u002f\\u002fwKMwqYFOPzKzYcjxbD8\\u002f7w91Ev4HLz+XD1zkuzYdP96FiikdEQo\\u002fQ7wa923s9T5MxKbuykzhPm2uB7c7hsk+kT7yGWKPsT4biaUNXJSWPh+hgepknHw+IyLFeI+BbT52XUSOtQ9vP+oCAc6OH3I\\u002fdEB4EuYfej97ba9awh6EP\\u002fb+CReB6I4\\u002fLjWuzSFNlz9Gi7cfcUihP\\u002fV385CzUqk\\u002fwkEmejxMsj9jKrQjFeK5PxQd1+RWuME\\u002f98WrYsA4xz9TICkr+9rMP4DlbdHz4tA\\u002fkfK+oH2J0j8Rqz4fCwrTPzHD9022RtI\\u002fPGH8iqFk0D8ZrY6oTX3LP78jwZOQj8U\\u002fSCm0Kyurvz8EkLKgCs21P0qVI+dwLaw\\u002fcn77x3cmoT9DScCVtOKTPxod7qvO64Y\\u002fbPedN\\u002fYLfj\\u002fTNUgITwd8PxZjt65j\\u002fII\\u002fHdCXz3sLjj8iBUROlQOYPwCd9G2Dj6I\\u002fnFoeh46Uqz8iUt2rD8SzP\\u002fdeAynxbbs\\u002fL4q+ZP5wwj\\u002fV0o24TfXHP4P8fQ445s0\\u002fuHjMaFnL0T8Qo9FeKQ\\u002fUP3fdFZ8wSdU\\u002fmvdBHE8p1T\\u002fGFwI9zqPTPwnMW6Vt+dA\\u002fCBsRgttGyz\\u002f7gcsP71nEPxLff7GALrw\\u002fHVacTs4asj9O7++CeZmlP+B8H3Nm\\u002f5c\\u002f1kgVexkRiT81RYoxAV95P8wtGlxhNms\\u002fqxWlzYy3Yj\\u002f4WbN9bR9jP0ovwSM5yGk\\u002flHSvisz\\u002fcj+QDQE\\u002fyc57P+hX4F\\u002fGd4M\\u002f0Ag5bLiriT\\u002fypxo3PKSPP6m3ErUVKZI\\u002fRtrOXyxhkz8R0SwSuDaTP6ug3Oxxs5E\\u002fVIXoZV9Ujj9O4lymlDKIP5\\u002f9nqA0BII\\u002fSofwGsUceT9Hl7KWk3JwPyk\\u002f4N1DWWQ\\u002ffOlvfAzqVz\\u002fewuXJntpKPwtr0vng9Dw\\u002fVZNaDvURLj9SBHc4LBMeP\\u002f+Ol2yx4ww\\u002f\\u002f12vZcKF+j7d+ej31SLnPqn102LfENM+lESPiE+JvT6yR0qq72qlPv7l1R0A\\u002f4w+AjlaCvFZcj6vAq7ll95WPqAdsZXGSEc+G7MoMtAlUz\\u002f2TWwCf3BWP7SNsSaUZGA\\u002fnyW\\u002fwgXgaT+VKTK+t510P\\u002f3kylOxTIA\\u002fV\\u002fZayyeCiT+SPCNWG66TP0og3mKfr50\\u002f66F3qXCjpT8wlKDJ4CSuP1QCPU5G4bM\\u002fz7iyOfCpuD+SxNiId6i8P2pRLKP8G78\\u002fRRe3Qq+Cvz\\u002fvJjB7McW9P0qpgj+dPbo\\u002fNVK3Wb6XtT\\u002f8fv43QZqwP0LBRsnf4ac\\u002fBzZ\\u002fj3gWoD+oLMHm11WUP3EKuYFiNog\\u002f1OBsOQmIez9GXUjMWHtvPwITxJRhJ2U\\u002fu3+Hf4LqZD+btKWYXottP2KC9IdkvXc\\u002fwqM07kYcgz\\u002f1k0sp0cmNPwr\\u002fdqynZpY\\u002f2YnjpkxNoD\\u002fl7krz9\\u002f+mPydB8b1mZq8\\u002frdCDSvOitD+aVxt0f++5P6OPgBWj8L4\\u002fn5nXLIxnwT\\u002fY7\\u002fPCsl7CP\\u002fNi1spXHsI\\u002fQloBsxynwD918IpjOXi8P8Omi9h8mrY\\u002fpE6NhHWnsD9TjbpWWsOmP9a4QQqc2pw\\u002fiz11JoT3kD9cE9O2gYyCP3eY3SSx+XI\\u002fms+8TF+UYj\\u002fNL9uwuMZSP16rSYVXwUc\\u002f5bdZmKgHRz9MYDfvOuhOP3aUqe5k7lY\\u002fSDEsvnPPYD+DordKFW9nP0rYr9S\\u002fom4\\u002ftujA3Ourcj\\u002f+\\u002f7R48il1P2SDGMnBR3Y\\u002fhepKwgnHdT\\u002fFnrTzgMRzP1cPdeHZrHA\\u002f9dwqDHEtaj8OrsJynCdjP4M2DLKIM1o\\u002fP5JcVlDPUD+XcWDEdFNEPyPmjA3rSTc\\u002f\\u002fyl66T1vKT94GwO5yZ8aPwGyrXUX0Qo\\u002ffXfViGkG+j4ZaUj4hkvoPnoW9XveudU+ZJCsD+iBwj6IbkJs0dutPnXQ8ytgr5Y+6IufhvIogD6OwcvUc4RlPtfaGm7XzUo+LC48YIFsMD5dnLZItYEgPkhOpmljqDU\\u002ffn13eBChOT\\u002f4yOa\\u002f7TdDP+7SXf5Xik8\\u002f3JijgkJlWj\\u002feLZfhawdmP9AX8cIhJnI\\u002fd2QM0QY2fT96KEOkZbCGP8FPCOtf0ZA\\u002f9yBERauTlz8wcZVXhQ2fPwsV+hqMIaM\\u002fYTRWsg\\u002f\\u002fpT9S+0zpYJGnP59eHALxhKc\\u002f2K\\u002fOwJ3cpT870v83yu+iP+950\\u002fgRmJ4\\u002fr05oOOIRlz+4mdDX6EGQP1\\u002fr9klCcYU\\u002fh98eW4mEej9QaFabJOVuPyE\\u002faDgXPWE\\u002fmIErgPCaUz9\\u002fBU3W8ChLPyhIeo14f0w\\u002flgtfKVjqVD+Uec7BsxBhP34cXVuxvWs\\u002fjZP\\u002fbGnfdT\\u002f6gUgmzLGAP+\\u002f1+fAqtYg\\u002fvOVtnVW2kT9XK1UU3H+YP5YZVwpmPaA\\u002fCWLV2Ph7pD84FV4qYWqoP3eG1fPuV6s\\u002fdLOs8uqlrD8oEpGjyv2rP4\\u002f8V0sYc6k\\u002fxzJ2lKp\\u002fpT+q++80ZdugP7GgM29FhZg\\u002fVF6piDqJkD8CIlaTO62EPx1NI26h+Xc\\u002fP7I8KwbQaT8df4hdBu1ZP5xvTxgSt0g\\u002fklpR02DTNz\\u002ftwKw95\\u002f4rP2sQMlgdnik\\u002fVop\\u002fAv3yMD\\u002fFXHOuxiM5P\\u002f4APUmWX0I\\u002fXT2qpjFtST97PXqdZ3NQP5gR18wP0FM\\u002fx8L4s1wqVj\\u002f9n+9WIQRXP\\u002fn9XaezLVY\\u002f5w3wZm\\u002fWUz9pUjEsKntQP9oB8Za6dkk\\u002fJXljRSBSQj8LEreyPpw4P0SnsJWc9S4\\u002fbHNbrBBQIj87IrI4hXoUP0m3oVsuxgU\\u002fqSywOUIk9j4DyfGHs6LlPpnA53THXdQ+V3bI7jB2wj5oncHzXRCwPp+Wjowzspo+H+CvxO4OhT661MDF0VtvPkpRM3xY7lU+hzM1ePG0PD7c4A76KJchPmFX+rWjMQU+Rf2fbPYF9T1rz9gyF70WPzhY8b+UYhs\\u002fzqv6VjdqJT+dLP2F24YyP9aPfBvjZUA\\u002f6g53zVPUTD9v39ImfsNYP7PdbOshgmQ\\u002fMbwCNwowcD8EoxeiXyN4P5feg0Av5IA\\u002f8dC4tnoYhj+p31b2HPKKP5qoHlRql44\\u002ftfoN394nkD\\u002ffPOSab7+PP\\u002fZTIxqwBo0\\u002fkPliNha1iD9LRAMikpiDP7bg4N6r\\u002fXw\\u002fDpXiKBYGdD9hFwNVAt1pP03WENawTV8\\u002ft1WkpJfZUT9DVL9j4pBDP+q0ss68MjY\\u002fpcuzpU7lLz+0wdDJM8cxP8sXn6g9Dzs\\u002fyj0T4vlwRj\\u002fNoBlhu3hSPxexsuY8j10\\u002fbVZsBfLxZj8PbCLB1kRxPwFaEzepHnk\\u002fasmoItWMgT96sKsV5WKHPyZWsSUIgo0\\u002fH+pSsyiFkT\\u002fz8NQtwHyTP56Q4CVJO5Q\\u002fhyBAKIqOkz+CXFpnYpKRP0QeUfggUY0\\u002fyV3QolOwhj8KMs6RNUiAP8XZ1nmmqXU\\u002fHsSV77+2aj\\u002fOvePQ44lePw98JnUoMlA\\u002fVgzT7Oz8Pz9xvKvLUM8tP1dft6QMqBs\\u002fViaqMXR0Dj+3TTQbpjUKP2UGEc8D9BA\\u002fvNvh3gn8GD8VDqrQWiMiP71rZiuR3Sg\\u002fG0hGISLPLz80hlJjzOkyP8TwkHB84DQ\\u002fM7SVtu5gNT8\\u002fbNpy+U40PzuCweyc5jE\\u002fCZzFn3NMLT9zhhvjHkgmPy2Dgh75iR8\\u002fzlagrG3QFD\\u002fLrjiGmbAJP0hF4D7owv0+C2BzmxhC8D4aos8T09jgPrHvEIrWp9A+nOf5mgKXvz5DBBoG4dasPjMY8Rw1Xpk+5MVm2KV0hT58oxXW015xPgtv9HnAxFo+byT\\u002fKrmDQz6xKSXPDccqPkfzi3wBOBE++AQqQJXA9D0YE8ZDhJPYPSldnyM5Dsg9POrPECBx9z6lC084OQ\\u002f9Pl2adW8dKAg\\u002f63A24aZJFj9Vm+lRS98kP5L3ZzAQKjM\\u002fdBRpQ\\u002f\\u002fxQD9RS\\u002fXwIYJMP2XEguqAnVY\\u002f\\u002fVrStinRYD+1FSqnT1pnP\\u002feXXM2XNG4\\u002f2dGq3Ygrcj\\u002f1w4oWx1F0PxIg96PnHXU\\u002fDLxBgoxldD\\u002fxxkWwTl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kDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u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hD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"BgAD0MqRvr+m5x+msCu+v8pkuZhLQ76\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fv0unV87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fQw9Bb7uIvb8wAVFTefm9v52rhV\\u002fHCb6\\u002fhYY8lDW0vb8ChPTWRHO9v2zB8r3efr2\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fkDHQk7qIvb8wAVFTefm9v9qxEqjHCb6\\u002fhYY8lDW0vb8ChPTWRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002fkDHQk7qIvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002f8NfH+Nrm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr\\u002fRhCsMs0W+v2LsgnYpDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v\\u002fIGgj0mDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v7sBnnCWsLy\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb+HFjgXXOu9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v\\u002fCT6KGYsLy\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v7Sa1mbW4by\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr96flRLR3O9vw7qaQZQR76\\u002fXtCiUADbvr\\u002fNxLe0FBu9v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr9sP678RHO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8vwfAQNZPDb6\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8v+NJ43ynXr2\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fXtCiUADbvr8D+Q6PjbK8v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR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CIkGCDlD7+GUBoPJ6kPudXDTfK9bc++1zw1k35zz7Wc4x+CJPmPgDHGWOmUv8+AtQ5lY+dFD+l1VMJW6IpP59F+Lgzvz4\\u002f1YgNE3WoUj+ti6qwg3RoP4XLfIfEAoI\\u002f31MvBgIdnT982Kcf7ku4P7WBGEJY4NM\\u002fDuUW1mvs7j+5JWEvH4UGQAdgTUj4dB5AdvshNpUIM0BwQVIN6+pFQOOjsQ03NFdAJve\\u002fxHCNZkDvU60SxRp0QG4twDByboBAWrMXMC6giEDx9oLbxe2QQH+Py9bPYJVA9Ye8T3rYmEBXPRUxDKeaQBofgLRsf5pATOTarrGLmEA3QRBHe1KVQF9EYZvCfZFASFqgWjlHi0BtSq0YA1GEQBkwwFag93xAkU5tC1LDc0Am8oUbedNpQNYtqDY7YWBAOrl3UBuBVUAd80clS4FRQDkWkj0CQFVARz+g1UoZYUA57TzazyRtQL16cvDurHdAXQDXrpvEgUAKyy97f26IQAcm+o1ytI5AfqPQnWGfkUDDe00\\u002fsHiSQL1PUtiyrZFAabXODSjmjkD+WYmer6iIQCCMqAIT+YFA5Enc3rfud0B9VqBUCx5tQB2IaVZRMGBAMiZcNoN1UEDAjhtXBZ4+QGTAUIj8EipAEu98hrxcFECNrTnZLkH9P44vaPCkd+M\\u002fJngdMw9lyD9eupGI\\u002ftKtP\\u002fvUdCvT85I\\u002fI4KyFRbiej8IEDXStqJlP8T\\u002f5sbEqFI\\u002f4jt+L6YHQD+gjVwoaykqP43sDJ780BM\\u002fHzlvTkyX+z4flq57OZXhPsxlNCQ5e8Q+RTbb4rPKpT75msnXVDOFPphKYChOfGM+ggqKjJRuTj4DfLjkycVVPoLeT5myOWE+hczMxo9YdD5c8Dpfi9qHPj9QELsm2Zk+bOIxenHJqT4kidYsxPe3PoHIWon7a8U+NW2VQmun0z4GaP26DDvkPhHvBVZaT\\u002fg+MEgICBUcED9R2bMKbpolP+7C1W9AxTs\\u002fZB3vU3fIUD\\u002fRRmXDuiljP7P9lqeUN3U\\u002fEr4sv8Xrhz8KbYPOFhmdP+JynTlfnLM\\u002fuMam5YF4zD\\u002fukSXTqRXlPzsY8rMAff4\\u002f5Pn2\\u002f7L4FECmRrdejBMrQIbM7fMJSEBAPvetvGQnUkAsCvds8rRiQJigIVYwxnFA3j6FoF0Yf0CaYJ6r8QSJQAlcLRGRg5JAKb5ACyA3mUALQ\\u002fhKEaefQOR6rOXHW6JALAKUJG\\u002fBo0Bk0mFdJdOjQEUx2MwXrKJAiWHU2eWfoEDGkgdtTSycQPuJ454d1ZZAim5gO3a8kUCg77iD72KKQEl0Y2BzvoJA8+MZI1tmeUBmb12HT5JwQE1dj19a2WVAZaDnYILLYECOSmjJwEBiQOUvC9ykEGtA7LQPZ7MudkAeVIA9cK6BQIPHbUbQPIpAmn3KR8XjkUDXn4QtQVWWQE55watofZlAqYibOiSWmkDFHrUbYlaZQPIdh9RUEJZA0qy6W82OkUAN\\u002fGER3YqJQLCYmi7Z\\u002fIBASS+ZpmardEDIGmxFjQRnQNELt55keldAy06OKaf1RUCktifyBd8yQNkPhG+r6h1Aap2hOJkPBkDm\\u002fYDxr9ruP+btH40AStU\\u002fbneTMo\\u002ftvj\\u002fXBB75zCSpP0slNuNy45Y\\u002fVOFn2lvvhT8xKv9aRLR0P1EZooVTeGI\\u002fEznFIjyITj+QguavOi43PysK6dGqGyA\\u002fziwrgOF1BD8uH1SXPL3nPtG06wTPJ8k+x5Nv3\\u002fdfqD5vWE4YBVCGPvlLDCKYXXE+RBBybeHshj4EFQcNlw2SPry3Gvp0LaU+gQqyzTmquD7ku9tcPofKPoE9A2CtMdo+9P3XmLbq5z6WuHM5tp\\u002f0Polj3D+joAE\\u002foWglk1E6ED8DnNX\\u002f1FMhP7Z7FEOlJjU\\u002fPw1AyP4gSz831XhtTAFhP78+ogMWIHQ\\u002f8TUvgxVRhj9o9f447XWXPzthIup9H6g\\u002fLicXzECAuT8kSm8gFhzNP+bs4DZBJ+I\\u002fStE6zl3m9z\\u002fO0yGMCqIPQK7R+6cOSyRAqnVSNIy4OEAx+891tUdMQDAFVriNMV5AypCW51r3bUAln2bRUZF7QIJK61XvdYdASb88yWlykkA2dhonOcuaQA3bhHM\\u002f\\u002fKFAN4ljaypcpkCwu\\u002fFhatSpQAbe\\u002fEdT3KtAajmmoiY9rEDzt5iQzBerQNX0bdbsx6hAtryCqj26pUAUBD4cgE6iQE7Lq2HPnZ1ADH7vvvvqlkA2EVPIf+SQQBYOyCymrIdA122WKkHGf0AtLrHNly11QEnQpCjrQm9AoKuxKDp4bkDZn0GO\\u002fVN0QPmCJfYtYn9AYs\\u002f\\u002fO8tBiEA+a682hK6RQLdVLSF81ZdAKa6ViU2CnUBf\\u002f8l08bygQF5c7lx+YaFAzbzRZ5eDoEBQtbw\\u002fPLacQKSNv7JG15ZABpoPLFGikEAJgvmL4zCGQEd8xJCXIntAPHUo7XJxbkAJnZwDomFfQEWGGqIVy01A8VuKcBgsOkBFNFxgEXwlQEMzmuQX1hBAs1CCqFFE+j\\u002fvG4VuErjlP9Sm0PkQB9Q\\u002fc80VSVRjxD\\u002fORYpXjY21P7fsXg4FRqY\\u002fuiAnGeiwlT\\u002fww\\u002f0tqo2DP\\u002fs9JZoHMHA\\u002fxKecckOIWD9fkbJTwf1AP84qLF5dgCU\\u002fDuUkbxHaCD9hQOZRqjzqPsR+CRJxVck+uhWn8cUcpz4HKBXe4fCRPitN3CQgtbU+3ksZBe4DwT6A3lN5O9PTPviuhLNS8eY+vfpZveSB+D5CFNj8pv0HPzmrkm3XnBU\\u002f2+7xvU4qIj\\u002fLmNUefHctP3aSffKAsDg\\u002ftAOrjiBZRz\\u002fM7gSoF8BZPzGGtYK3HW8\\u002fvqUK6WXogj9flTLP8O+VP0vyqNxp1Kc\\u002fIMY14bZDuD\\u002f5R815iJTHP86Q\\u002fgKLrdY\\u002fOLJvrHCv5j8krcWWTYX4P1ItZ5zLiAxA6hKh\\u002fCYtIUCebOkMYHo0QPoyRAGLeEdAXqJPzU1yWUBT6FfGTOFpQD\\u002f0oq7kk3hAAa9ouUO7hUCPCmQ+zdqRQCfuq4P3OptAwzxzDaVEo0Dbvl\\u002fPD1SpQOKK5THsAK9Am2Vf\\u002f6y+sUC3UZFKUByzQIN5wOFJgrNA0asOor8Gs0DCQj+9+dexQMLKtCQhJrBA7OO2dTEyrEBoKKjtZ6enQJLtC66p9qJAXTpbvj3pnEAPQ6ovHeqUQNb8geCn54xABv7tq6Wkg0ARrQYhBX58QL4PjTJLcXlA79luRU4lfkAJkV\\u002f07VOFQE1s4x0tUY9AAWMTCqoklkC\\u002fUCQLg0mdQAxCuam\\u002f6aFABb+2BNgrpEDIqI1\\u002fFd2kQGs+nEtRzaNAjSlFYKY9oUDJIpUcOIubQF4WelcMMpRApad9gmE0i0BPbpniotmAQEGYSqXtOXNAZkJ8vZNCZECvMMKKKtFTQEQsqlUcL0JAOM0K2iYBMECrboTKDiwcQNfdkLZzSgpADlnZpkAk+z9\\u002fhMWomozuPw+PrSmCseE\\u002fXpUdTxr10z9+slSZljDFP4Jvbz7L0rQ\\u002fi+GmEbfLoj9md+IgBxCPP4C7UB7\\u002fdnc\\u002fJf3ou\\u002fkxYD9C74bLCGxEPy18I5BRhic\\u002fw3L55DPCCD9SxcEaHNfnPrBNR7bGssU+1HxF9BrRsD4usJbL3nviPs7dygsm2Ow+wB5GltewAD+2sF6a7TETP3iT5ixBXyQ\\u002fO3bpWt7KMz8q7HTVWKRBP\\u002fjU+1K4G00\\u002f+YvoCzC8Vj8Yrd0rXrFhPzuon8LI320\\u002foTIh7E1XfT8dpw14kGSQPx2kqyrDHaM\\u002f\\u002flI5wxKwtT\\u002fNNZUgZSfHP1yOMkxuEdc\\u002f1BCPgyyf5T9Ewotva4fzP3oYKWsptQFAETRQsJjmEEAYS4wR3GYhQGmQgtGQ+zJA4dWcZfkERUAvkwRtDbxWQIaC1OQVdGdA9p9cNwXLdkDnxCTPjr2EQL+XE2x0nZFAoWSPZwnhm0Dyo426QYqkQOFYV+pdL6xABxPdAFIJskCf1DW8HZq1QGDYLt6NV7hAjIdaF97\\u002fuUCZ\\u002fJIy6Y26QPad39TJJ7pAZAcaNU\\u002f+uEDPSgqEmDS3QBW1cPND3rRAPpNxf98PskCIa+cLQeGtQFvZ\\u002fJMqfadAYMlnOseFoUCgDTwBo\\u002fuYQIEYV2gwd5FAAAcygTeIiUAUioRR47aFQHAyC4iCI4dAGP0me9pfjUAPDz91wOmTQMVs29qUtppAb8DzbLEUoUBxXS5pb3ikQG+lTzMizKZAg8fBmK18p0COLUWrvFSmQI6FEFhckqNAWZVzp0+dn0DKYtM3XImXQLK8JSQnKpBArh6MrMKChEA3iM+bxRl4QLqmzdRlWmpAaemVBUsYW0Ap46+tKcNKQOfA0G53aTpASVZNpnJ6K0CNmrfpxksfQIiqDkiGQBNAXF448PpGCEAn2\\u002fkeAsz9P49r6j1UOfE\\u002ff\\u002fWaF25x4j8O5I7g+SXSP3a9Xc6mWsA\\u002fs9o6iJHzqj+wuSK2a0uUP7dP0ofp7Hs\\u002fkUx0b+aNYT8L7ywYzSpEP6uTlutbLSU\\u002fksdNYXVbBD9XyYPiGIPiPqeAoVR+ssw+7tmy149ODD9qgLP1avwVP\\u002fuu7J4tRik\\u002fuiQWrS7jPD+mXoFgAXhOP5MtC5UFZl0\\u002foprH51f5aT8jYQ3zRSF1P7DRnQx6EIA\\u002ff1emwjeqhz+Q2ZYh9CGSP3GXB8D5jp8\\u002fUB\\u002f8+jD8rz8RwnnBSKPBP1m\\u002fW9efc9M\\u002fv63UvSJp5D+Vg4gwpP\\u002fzP9BcY55MSwJAstQS8HaxD0B5ySMhRskaQOL9VJNpFydA3ljpk9opNUBXqTua885EQMW1kPPPTVVAhSIPRirDZUCxvoLPK3d1QArBYAUYEIRAr32fo4SakUDtu7YBod+cQCROkr\\u002f0FqZA3sjcxjiDr0Bt\\u002fKuFT\\u002fi0QFF972KCF7pAB0Wk2KN5vkBYVV0u2dDAQHhaHCkjscFAcnImuNHuwUDuq8x0Sa\\u002fBQCJ7duYnEsFAAfkI6\\u002fUhwEBAEbZtd7C9QC\\u002f1WCUTX7pAChUfXNlqtkA3nBom6SCyQEoW6Kg836tANcCzyVOLpEDrccjSOc+dQAvZYFyfeZZATcAPDZAEk0C8hvY6xviSQKqwQ1ffuJVAMUyw0d+gmkDtfULlWX2gQGgfBjkV7qNAxJRZp4ILp0CabteFcy6pQInThg1dy6lAznASqoyjqEDmfbGW1t+lQKzYUFkHBKJA77UgvqyBm0BzlfuXNXeTQBoyjwENk4lAT4ur4jJPf0Dw9Q55IgVyQEmUYlDb4WNAtVaBIbHIVUDxKtJ0AuRIQNIVny5PyD5AbuhpZBpzNEBKJpTxvd8rQPVdJVMkjCJAIQGWLqBPF0DUZ6cDYy4LQKoZkP1CJ\\u002f0\\u002ffDS3rIGm7D9cYdjtMsHZP2e8w9eOKcU\\u002f+5WEwUnIrz9kfesTiM+VP5IPYyl4XHs\\u002ft4DawohhXz+EwNcHpnVAPzi7+dVqpB8\\u002f+Y7dZarR\\u002fD5ZcY9wRGfmPsfYvI7afTM\\u002f9ulGlBUkPj9yeIO9ajVRPzedj+WEjGM\\u002fZcxYKIx+dD+7A7fnl6WDP2ZgD5GsOZE\\u002fJkYMJDC4mz83x023gaqkP2s\\u002f3tUAQq0\\u002f2po5aCXCtD9loio3xhrAP7S9ArA1KM0\\u002fa60tfRjf3T9qLfeU+7fvPzJENj47UABA4zq+3lWBD0ARXcB10VIcQPj4Pqj02idAcpwrcAM0M0DgD3UhtJ4+QCHjyxMnTElAkuheSdpYVkABLhppLvZkQK5jJCeqHHRAggRJ+tH3gkBpRlM5yx2RQBg2S\\u002fBHIZ1AP7vPmdk2p0BBU7ZvXEaxQAIZSm3r\\u002fLdApIzjX5IZv0CBQ+jUwN3CQJrXp6g+hMVAlSPsUBo8x0CPu71eLgDIQJVCWKVPAshAOIukZXSIx0AFMkXO7sPGQIPhar+Au8VABirHfPJTxEDnN1ixym7CQOZUl4FACsBA70y410qfukAYo+GHjxS1QPSpAPGtHLBA1YDa1ah3qEDfTwcxRGGjQPqIja5z4qBAhc6AMnWCoEDzejRTFKOhQKsLkmlus6NABIBMSv9BpkAU1gum+O6oQL9h8YDkUqtAG+lpuMLzrEAjb9Rxf1etQFqcz46uLKxA4\\u002fhKPhlxqUA3d0vAIH2lQOK04TQJ66BA2ZTEAAHMmEBMPkQE3vCQQAbpBPBwuIVA9LZtNlSMekBaqSV7QORvQHf7plwtsGNAsNBOPQH+WUDjXCEjDmRSQP5gp8s\\u002f20pA9bYXFNRIQ0AxAB43QEo6QOIzoTG1rDBAnJkc1LR8I0AXsYYqeOIUQF+kIJm8ewRAVvlBxJ1e8j\\u002feoa12SB7eP3KqByr0kcY\\u002f+bKTsW3trj+2uwjlB2GTP6fRtGd6OHY\\u002fgpVECV9UVz+rFLO4THs2P8zXUCPImBQ\\u002fgv6fCKMlAD+TYr+0niNYP6tfM6ZWlGI\\u002fdtoD1+4SdT\\u002f431NNZcuHP0tJStP1y5g\\u002fEf4Yycifpz9HVI+Gh5G0P4Tu1HOEZsA\\u002fLXRcJY8ZyD+9k2mazpTQPyjvbdRmNdY\\u002fs8t\\u002fHIQx3z\\u002fx9sCxGw3pPxMcp20\\u002fZPc\\u002fI1UWkoGYB0Aw69ZBYqoXQPS+pK+PhCZAXVwgMA75M0CPW\\u002fc684BAQH0G2TpitklAAjiGlW5gU0BEnexXcmhdQJZly6CPcGdA1nSV5LT3c0AmbdxqlM2BQOSpENlf6o9AIzAvK3bGm0Bnahgr2vimQMhFpU9v3LFAM\\u002fy2sy\\u002f8uUAmKXpDHKjBQBPsMFxwbMZAT0M81vKrykDq8A7gjNjNQLTbYm5ap89AMWrHeN4U0EDEYCkjPbzPQGLdTtLm0c5A\\u002ffGpFyS5zUDdPyLAl3vMQAIu1PFf6cpAjCgTS6zFyEDMvAXEe\\u002fjFQEIyvvOvqcJAavaImrFyvkCsTHl0Zjm4QL\\u002foEtlOZ7NAR3zoy85VsEC0KL1+kNatQCLerMBDdq1AZKOnCGd6rkA7Yx3\\u002f3ACwQKU4lWcuxrBAHplaorF2sUBJ9H\\u002fwsAiyQGkegryhYrJAm8Bt2mBSskA01ZskDZ6xQOL4EyM3I7BAvrNZHNTfq0Ay3rOz64qmQKwrNLpFBqFAEfMfUgQUmEBVOdyoACCQQL8j996w9oRAnkqK9T+Ie0BIVZg5Fg9zQOsc1U4nQGxAx+uISunUZUB437KX0ctgQAMOWClAv1hACQkPWy4MUUBL8\\u002fPJtrFFQBbeGmS4WTlAAbehfA8hK0AQgURFA48aQKWaq6x2xgdAHk4W3jV28z+6ogs00CPdP14gb8p59cM\\u002fw3z4IyEHqT8u7nvWucCMP04O2zkLTm4\\u002f+PT3w\\u002fhpTT\\u002fyjXZ3T1crP+uRQfKh3BU\\u002fqe\\u002ftTjTiej8j+ptqOpmEPyXChAtVNZc\\u002ffgt2PLYLqj8YFd+mkvu6Pzvvs7pDjck\\u002fFPRV5yka1j9NRs0YXnzhP12dINZgZ+k\\u002fGY5HCGUf8T\\u002f831hx\\u002fv31P3iTq1Tkg\\u002fw\\u002frT\\u002f6YytnBEDJvy\\u002fLtRYRQIqfodddGiBA4rMPK2Y6L0A\\u002f80OZtzY9QINUrCtdnElAONnxt2rhVEC9LbyVqdVfQBfisDlUFmdAFNXIMNRvcECZC6q9a\\u002fZ3QCdqSB77gIJASOPt6INSjkAEWGZSIZCZQB2\\u002f\\u002fsyBVKVAyfT7G+wdsUCPMpByvv65QAlmsLkiicJA26f0hoPAyEAmSRh75\\u002fLOQAByb81FJ9JAvgI7aeIQ1EDgTqkjNAzVQMExI4zSK9VAj5QzU7Sy1EAaBJiiIPLTQFEenohZI9NAQWTe\\u002f4RR0kDifAmt8l7RQOTFUJ4NIdBA6BnOOA8EzUA6a9nRAivJQI+mW64nLMVAba2AeFidwUDU7xm9TvK9QMLyi5kh5LpAHh2Uo13PuUCCPH6nkv25QAYmE0HrmrpAUhCJLpgKu0CUD2lYrBG7QKykkEOSybpA2hMyyZFoukCMU3xxPQS6QA8+KdT8crlAaTx+oplduECCRW\\u002femXO2QH5dg4JKoLNA1KM5iOsfsED4VetxddSoQCOYEItrA6JAPgetcyIQmUAr7q+y3UiRQGex42D+rohAKtFX8ZPLgkCkxjuMYDp+QHINF\\u002fZ8o3hAbTtI6FN\\u002fc0BZY6lwFRdtQCeS4x08IWRA5IM3NOOiWUCEAsaeDO9NQEFzdW9y\\u002fj9APUo1MZZIL0Dqyfp+L\\u002fsbQLUlNX\\u002fx5QZAoSgtp78m8T8W9mWrTYjXP8E5Os\\u002fpmb0\\u002fwz5Jr6EYoT9BBswpvC+CPyHsRagD8WE\\u002f69ZRjlk6QT\\u002fBOjtiDsQsP\\u002fDnB+HA7Jo\\u002fgZl4G5KJpD9jd5x5K\\u002fy2P0\\u002fg+\\u002fSdo8k\\u002f4CdpW95n2j\\u002fdBxemdNvoP9x6Q28hXvU\\u002fzb3F6SPJAEDprZPYFikIQKJwXs6bCRBAsDYY4BH9E0AkUv+PgGQYQPXcyuGghx9ACpsF7UJ+J0DwJW4c4U40QG6uiRjJz0JANy2EwoI5UUB01ydS8dldQGEqTiO0GGhALOCJfMQcckCnAP+jZqB5QN3emwDFdIFAuYGJmoK7h0B\\u002fg2ZX9sCQQOH8zhhDGJlAlLqBRieuo0BXBt5f9TSvQAwag8ljLbhAcKRze7LmwUCimBWQmQvJQAzt5vURetBAU7BDOaZf1EDWIFx3YrfXQIA4paDNF9pAhECeAL5O20A3HcegV3DbQDGQzT0EytpAYcuoII692UCx754e3JXYQNMG+Dpca9dAAYvJsCYm1kAYcF\\u002fKbJvUQEjlOO2PtNJAHd7\\u002fjVaJ0EA7GNArkr3MQPN6VXwTGMlAybV8B9uoxkDUX3aLWpXFQP\\u002fxVyHalcVAgFkK\\u002fUIaxkDWlUzJUovGQM5qblbah8ZA4adkiE4BxkBfR12+OCzFQPnuJQ0IUMRAm26OMdaSw0AVVNJMFd\\u002fCQLXdrioo8cFAn2iZYa+CwEBW5bD65u+8QK05dRS16LdAKNdOfTGYskCL9rHCo3qrQBWxhFJow6NAQUTqXt3InEDIFmDvpxiWQK6i48QxF5JADD6X9Ji2jkCgGEOY+9aJQPBbV54TxIRASZtThiAuf0CMcakHRJ51QGPliEipiGtAfmLdYeIQYECEoRuOpClRQE2kUr55x0BA1fMAkuwHLkC2JBTSNpwYQLpHQiqYewJAPn9cw\\u002ft46T9I8zS3CSPQP+SaMz4c3LI\\u002fBViP+Sl2lD+cBv23KOd0P\\u002f807yvnf1U\\u002foWjqYwCIQz+hNxcsSkC4P1fXMSA+asI\\u002fq+l\\u002fTNl41D+hEjyhpbLmPyO8VJlqPfc\\u002flFZsZbC\\u002fBUB43k\\u002fNJ5USQPSfKLsXAR1ApqecehC1JED5DTuvzSorQFHDi\\u002foIkTBAkcWhUCRXM0DyPf4wqAI3QI\\u002fw13vzkj5AKBk3zqLcR0BDPuBfbs1UQA7qdiMWg2JAFENMp2Spb0BqYH7Qi1l5QLo06FYD4oJAYF8WNxVQikA\\u002fYvDxBGyRQGUZA9ppi5ZAPuqN4JuYnUCDIL\\u002fSyFqkQGm9\\u002ftuAg61A95BexDYItkCHNPdW\\u002fl3AQHNOiwgYjcdANoVMGHon0EA6fe5uOfvUQDv+Ed3fwdlA\\u002fC1nPrjt3UC5vwFif4PgQGbpQNQIZuFAWInQBNCg4UDyyNVIVFXhQLwzT4TDtOBAx9Sdz87W30DKcs4F2CHeQP01xO6ZS9xAedimtlM+2kAbFjkGO\\u002fPXQG7JDq47jtVAZNSxa3ld00Dqmil4a7vRQKnRd2cL5NBAkcTy3PzU0EAWlD5HT0rRQAwIdAEe2tFA7Q2PMXIh0kAcaiKCKezRQOMLpd87RNFABSKrrmRh0EBioB+5tAXPQBWUqKE8kc1Al5hR3AFEzEAWTMMu97TKQOnY3vEGeMhAK2g6vzFjxUCH02+o\\u002fqvBQAfJlfvRn7tAapSIPQe1tEBwsE4x\\u002frCuQK2\\u002fs9xcd6dA55tCgUgeo0CXlpZ4p4GgQMtBiJADC51AGu3tPLnpmEDnj1G9sjOUQCYQGNRQcY5A0zNsQ7YhhUD8ts0AMOx6QB0QTMg3bW9A8HYFdP7LYEDn1pZ1pnFQQH02hktbgD1AUMqOx+hEKEANNLoW0VUSQEB\\u002fPUKdgPk\\u002f69KkrHxk4D\\u002f42EICL5\\u002fDP3rUHbp7K6Y\\u002fHP78sBJPiD+bStWB3WVsP8vTSUhWbF0\\u002fm6FcNyOl0z+lSe4zfrPdP1QD4kwiZvA\\u002ftXpFul8SAkB1bQueOGUSQHQit0pjHSFAqeNYtK4SLUCl2EySCow2QNZwakjo9j9Aa9mDA4XEREAMgMNnuOxIQLDpt8B1L0xAZCeSjKV3T0C4n7XirNxSQDu8EyJTVlpA2M0DsAlCZUBZSuhHxjRyQHPt4fLnoH5AXGTp9ntViECKurVwMQWSQJEjc0\\u002fq5JhABh5Qh1k1oEBsmjO9MVGkQGFJ5tFFTalAE6hIoi80sEAXUGm7YMa1QMCpxucrdL5AVNwKnFaOxUC3Y2jCHgPOQOHR2caXJ9RABHcPVLXd2UDB1iFIZ6DfQOg6n9rBbuJAdIKbb1iI5EA3Zb92se7lQHxgO4GtkOZAmDnngLp85kDBEcc2zdnlQCKSWsMa2ORAtYTX1V6g40DllbbIFUviQNlphNfj5OBAU0mS\\u002fJj43kDF3qvbSlvcQKKQfwGIRdpANYsdF14I2UAKVm\\u002flhsfYQBLYunG+W9lAKcY6m3NW2kCofKs8HCnbQPUKHA2WXttA+kemfQXK2kCNk14Fv5LZQEciYa2AFthAXLWpQqex1kAIOzkOw4vVQC55qD\\u002fthNRA2g0d2JtN00DyizAb5JnRQFjYOC7ios5AmwcUS\\u002flByUCGQeZO+MvDQAnzrRzlDb5AJQapYI\\u002fltkCMEBVqGkeyQKjsHOZ5Qa9ACE3gpa8QrEAjl7xTlE2pQKlkYuoC\\u002fKVAz7B2vh\\u002fvoUCWDZydExmbQIXcgy8e1pJAo72BNs0GiEBeggKF4hZ8QLkNyXKMGG5AHh8EX+OQXUAL7VxTYKhKQMihnz2eGDZAJPhncVfiIED\\u002f1AjlCOcHQJPSmARxnu8\\u002flZEPYkLY0z8yK4IkiUG4P5UfqnjnHp4\\u002fPYhut+YchT+U84jIy0N5P5hriF\\u002fzoOw\\u002fASZ+\\u002fGyL9T+Mndjij6EHQD6TFCor4hlALxBfJ3UxKkAOtAgDbDo4QHc9laYhdURAIDiBFOCJT0B\\u002f2OdRNjVWQBwi08ROn1xAmyVQghr3YEBdr2smrr9iQBTxgglB+WNAcTmz20\\u002f9ZUBCYqKsq3lrQC5PUyCMO3RAnkiRoW15gECpFxasfh6LQP9xApTYXJVAFuy6jCSDn0CWeN76MqylQBA+mw3h+atACT1AU1U2sUAJVwm41LW0QGr\\u002f6\\u002fzqKLlAQbCdWYqvv0AYOoX05cvEQDTyNlFo98tAYGIBgkfF0kDzagP92aHYQKAUnCm7O99AHmCo+1MM40D2M23++FXmQBkXqXjCMulA4KuI3vRg60DDQf9fc7DsQEgdvGJND+1AAEFhTw2Q7ECILMTQZGPrQBpUkJY6x+lAwITpKnj150AFKYmu9xzmQE2hxbQDZuRAJjGi+dL54kAmqQi38gHiQPS\\u002ftjuKm+FAYCSJLA\\u002fF4UA1kJ6hY1PiQOTjNPPy+OJAlHyPB4hf40AJCt6HD0rjQD9R8j0ureJAYkLE2syw4UApvL07apjgQDLM4PbvO99A1OML0FKm3UA9pBLZXTTcQPxiVK2ec9pAeqWPnQMC2EAvGOikqsrUQMcYBFygFtFAdjl8wg\\u002fXykAnR4WLi53EQNWeA7znHsBAFUpDvdG\\u002fukDR6XMpx823QNGs\\u002fXaOB7ZAw4w92XU5tEBjXFx+b72xQF1lzF7BFa1A6lw3kFIJpkDQxTZQ97SeQDj+z2lpopNA1e7KejkIh0B67TZb28p4QNtO8a7mg2hAWEi\\u002fb4pPVkDQvaMfmr9CQDdgBd3zQi1A0iQ7tL1rFUDqWjz1u+\\u002f9P6BlevouhOQ\\u002f9AU3n7iOzD8e3hcfUve0P9zJkr1YpaE\\u002fk9uA9sCGlz8vteSDp8QCQNfabiLnHgxAsyM8TZqiHkDjRzcm0qwwQJYL54WexkBA3IblrsPaTkCroy1rleZZQOkU+pCq2GNAMkyV4iDGa0Dtt2JOu8RxQIpuy7cu23RASzoUrp2pdkBvQygXPVd3QKGg7fPeDHhASEeZxpcne0CHC5063BOCQOoS6JMon4tARdtOG1wWlkA3FWCR9zahQGQBRsHLTqlAxj1jr51gsUAuLFwrvVq2QIjobR37QLtA3st3H\\u002fwRwEBfsmaeMtnCQOeMuTY5mcZAmXYQEVIWzEDCB\\u002fbxiPnRQIkcv+HROtdA0v3T2AS53UBymPG3FZfiQIyvKnUxmuZA1\\u002fLsAbCm6kD6ccZ7\\u002f2\\u002fuQMsn6v0Fz\\u002fBAAqlXFZTt8UBdUzyFXHbyQOkuv1ytZPJAIXljR8PO8UBMTkK8oN3wQPrRZx3cf+9A0ZMjzn0\\u002f7UAyIBLgDD7rQNYbpLKasOlAQ5oOwLa86EAI4T4lu2\\u002foQESrvW1SsuhA6NPUYrBD6UBVMLoKfcbpQO81nCZ\\u002f3+lAYQ\\u002ff5aZZ6UCTqGfvhDzoQDBopGnjxuZAtoWmbpdN5UBxIw9Tqg7kQIa1qUnkEONAVUy7nw8i4kCLbeAjZ\\u002fXgQKWfQExBot5AmTO+nq5i2kAvwFjJ7prVQAuS7bo1\\u002ftBASTWaWYJoykAyi9RF2y3FQOZ5eMiINMJAbsCSi3PEwEDRi6a3+M+\\u002fQB4PH6JPo71ANYRyKOU1ukALrVelx5e1QBVaYAAObLBAxLosEpL4pkBkkqcpw4KdQPgSWPGHaZFA5ZKOI\\u002f\\u002fkgkCi4C1a7uRyQOR4OX8yemFASA3SV4EbTkCWOC4Uqm04QD7rCcEUCSNAQ5lKZ6FPDUCbrOUHcQX3P6zCgqZ52eI\\u002fSJIHUaI40D+eTfZwjw+\\u002fP2+DvzRL\\u002f7U\\u002fgi8Ee3gmFkBP4ZZCpoQgQNDVEm0V3zFALDyspshVQ0DZrg1vXFZTQE7Ve\\u002fTorWFABGVQ302CbUCgpiy9dXp2QApIrBwtRH9Ak4597aTeg0DYWdrqGiGHQLK10DMT0IhAJOtAmGbxiEB9iWGrXHiIQMcyALwAYIlAUuMQT3uGjkB0xyLF+6CVQOFVKY7EqKBAdDqmgcCaqUCviyUqrr+yQHr\\u002flCycvblAHRQyJoWNwEA\\u002f1ppGjx3EQNVfkjpFfMdA7fXNHMX5ykAUWHPmUUfPQPMbi5k4otJALic9T07Y1kB9erYpAXbcQGKdJDcluuFAAGt\\u002fQq\\u002fR5UA0jd7w+VzqQAg+bdOHKO9APOFHtsHz8UB6KMvFhxf0QNcHcdAFw\\u002fVANFj6LWTE9kDfaQBXCgf3QG8CEi55m\\u002fZAZ5HeHySw9UA3M3kxwoD0QBW9qasIRvNAq2C3TnEs8kB44rt22lHxQDVNe8dzxfBAcUvtZDSG8EDg7vzoXIDwQKudWglZjvBA9uFxJFCB8EAhbrlQLjHwQOFklYtsG+9A6xru7ulN7UCS6Mhn3kzrQCvneUMidelAIEs8k8b850CXn1GymtXmQDX9wkxntOVAKfyY+k065EAuUNX4ASriQPu8924hF99Ay9HE58xX2UBoG2zdn\\u002fTTQNzTuIHIYM9Aa9Sy54LOyUCcegYs2\\u002fDGQLOUH4ipysVAtVp\\u002fbXsfxUDA2MRdMfDDQBak8s9cxsFALcHnhzt1vUCcGW9sQYa2QHtOa9hDsq9ANdy3GBCDpECFdK1wK3GYQIuCeXfA4IpA25p1bLNke0B0MxfTTBJqQGNqZlq9eVdA3tN3s21sREBvv0ypaawxQPRlehtMUx9Af+2G70LSDEAMmbv6lUH7P+tiRcHRIOo\\u002fOAe2qBXT2j\\u002fJhvswh4rTPxeMHj9\\u002fjSdAl9Ea0yN7MUDTEreVcsdCQKeLvt5XMFRAplVBPxASZEA4vYP58j1yQLWWpn5DRX5ADnbulX\\u002fshkB2xttfXLKPQKaGpl1gBJRA9zBBatQhl0C++ZvIRZGYQGkvWQqERJhATz1ZwBX8lkByFO3gfE2WQEDQJ37UcphAjb95l1zsn0CumTSHHHunQHGxgpK7tbFAus4BTYnLuUAbRACAYrTBQBOsCVzazMZApwGZ2+G2y0D2TxOH4B7QQGF2CNxYUdJAV8TcxKvO1ECzBkFjbxDYQFCwZWtygdxALUOPbkEu4UDBJJfdkNTkQDof8YjgJ+lAMmiz04kU7kCaUoEw4LvxQLEe4zNBhvRAhD4DrgUw90ACqI3tkHD5QECBc4ZQBftA9pisi6PJ+0AZLmQxbsP7QD8eF53yHftAQ8iDIcAX+kBA5jJEme74QNn0eS0Q0vdAou6Zu\\u002fHe9kAtVgdSFiD2QJPVJcMdkPVASIbXFpsa9UD6E+DGKp\\u002f0QHb\\u002fMnsG+vNAoJdQ2iAS80BpZiPq8ebxQMMVwlv6lPBAGtQ0pq2W7kC3iJmun2rsQFVmCDE1xupAW3VC7UZ+6UAF5O4XaC7oQGhKNJ53a+ZARjVueAr840A42Q\\u002fmZvjgQOFTyvGMg9tAeQSwM8Ou1UBQWhThyUHRQFHMXXdTIM1ArDkb66PAykDO2rXeEifKQGtdLf9s28lAcpx3F2K3yEAlT3qtXjrGQOe60H1rjsJAbpNJI92YvEA53OjhHE60QN8nL\\u002flAlqpA+pxdJqYWoEApA0kbTxWSQNoQPBBrCYNAbvRQ160Kc0AZ3VFdwIFiQME\\u002fcU66\\u002fVFAGI\\u002fGJdPzQUAwj2qOhoYyQHwtQ5EXciNAPhJKy0gzFEDyupd+bYMEQOR7spaXvfU\\u002fJqJRbwgF8D8LFAiMm5o2QE3OliYusUBAgn7QsfPLUUAuc6D1rQFjQDvCbiJux3JAP+Z1uFL3gECutSzq0\\u002fyLQI7ejIo4EpVAyInhR\\u002fj2nEA0ByAapi6iQE9wcrvy36RAy2Fc6GH9pUBiIygbK3KlQDLZ9OEi0qNAN2IkAMdSokCeCfH+XpiiQKNkItkHaKZAzgOesHhIr0D\\u002fLt9oXAq3QI\\u002fiAAfJpMBA3B2+lirRxkC2b\\u002fOYNGzNQKhWVIti6dFASpA3iMPV1EBsUrAdtJHXQN\\u002fL54ugd9pAIDiXbKAE3kBoO83PvVPhQF5jBLmiT+RAGXuzeb\\u002f+50D1xqikmWDsQLog23v1ufBA8eL4fs6V80DtVOb6Wqr2QLdgaHP0wflAhGNKnaGQ\\u002fEArfhEXycn+QOkci1I2HQBBZ14dgn9sAEEWxGRYjWEAQceKwHBYFgBBEhA9iENL\\u002f0Dxef4DE0f+QAYn22JJNv1AayEKCssf\\u002fECdqSz2RwD7QD20yn3QzflAMEGQbZR6+EDXqRFcMPr2QIA8O6vNSvVAA5TysNx980CzXSIexrjxQNR0FMPrKPBA+YOQXBnd7UC8uiF4ZBPsQLkDwJKSqupA9AHSggMr6UDg1d8CAyfnQK5\\u002f2tKPcORAbGK9\\u002f0Ix4UAgm7CaWbHbQBhl3bfK19VAakJ8V8ae0UBPKKbQqo3OQEjK0kSK\\u002fMxATFp6cDEezUAAlbpqoVXNQLu0oTPHZsxAD4BksTfNyUARh7AOSsDFQOSNk9Qn8sBA41ZilO1kuEDtNVbnMEKwQCzG0+i9K6RALO48zbiBl0Bak9jWRyKKQNzzwWnBXnxAQ6HdkAfvbkAwufMiF1JhQGtVSrTn91NAjO83zOo7R0A12tV82H46QApgSpv46ixAXxShXDwlHkAgG8yJHysQQEaLz4Hb6AdAJ4OqycmlQ0Ac3zfXFtpMQCMiLOiZgF5ACECq890rcEACeU0\\u002fYb9\\u002fQIoLvjk0gYxAcFPJcpBel0AG5bCpmX2hQOFWiRtI56dAaJ94vi7WrUAZr1Q3gQaxQD1qIAhW0LFAz8mpGG82sUA5vFgIE02vQGXMiHRmAqxAoo8Ul3DYqkBfpf+Q1S2uQNtGDhvo+7NAwXV4jGeivEDftYPq0XLEQOqTLncO7stAE0p9dBwB0kAkPzyDHvLVQDkRwygIjNlAMcFMxjHa3EAXV5ii3RjgQO1eoEFKBuJAcTtx6cNq5ECL8amNRGTnQK\\u002frzoO3\\u002fepAcANcMylA70DIUPRaRB7yQD+iDoQ9\\u002f\\u002fRA9S9AQ7A2+EDndCQl35n7QI3q4WRn4v5Aap1jIMjgAEENxTwd0\\u002fwBQb7scaiFtwJBnY\\u002fbK2wUA0FGJDsoqCEDQXdQ887U7gJBiLZKrqyGAkEz+vSQAO4BQXA8LRfzJgFBozXpJgI1AEGlxCHYpj3+QEbNjv0A2\\u002ftAh5lpgDdX+UAvrbusvcv2QNDfua5sWfRAFbgQejon8kDx8wlUJljwQMkxZhNL9u1AmvKLkj\\u002f960DqdDAx7GLqQAiZG1UNqehANs9K6Rhr5kAbljG1r4vjQMJ35TEgQuBACl1AXn8D2kBCOZAsk5HUQDtCI8yF3dBA1m+HacQbzkAB6wKRjX3NQNyJFC1TYs5AQTRAoS0nz0Dxj8GQV4\\u002fOQHjkWWz5F8xA1TP9g0r9x0CMs\\u002fVsaPvCQIJuASgb4btA8ABXxB8Zs0DMymxzZqWoQIZwIni7cp5AsLmVV29vkkBktqtpBXuGQF4Y5fSAIXxAMuR5M1UIckA\\u002fbhI4Ey5nQCIkgWLdBl1AG0B1fqBHUUDhinFAjklDQMAnlZwpTjRAKstD3TTWJUDZTTJN9CMgQIhgS\\u002f2LI09AT7TSKZuzVkDwCgjHcMFnQEdnKMPQ+nhAUsRW6L5XiECxTPWJ3rWVQDaShErbr6FA3XhZZddQqkAC7z\\u002fHwuCxQE5fUO7NMLZAFkB1d7EvuUDcIf33mTS6QAu0A7wRKLlAa2frQT2mtkATxfw2deOzQIf\\u002fuEG2ZLJAppz3ZSars0D\\u002fdEeypOa4QD4qm0G\\u002fWMFADw\\u002fvktNryEAyR\\u002fAvTpDQQNz2l6kFStVAtIiWtq3q2UBOt3+4JCTeQOL0oJtd++BA7CRlGzHY4kAkoeErMuLkQBWAYFDrSudAs2zqKF4t6kBPGmMpf5XtQKzXDXkoyvBAe706WWol80BDaO6AE+71QFNku\\u002fknJvlAicb3NtKx\\u002fEB5suLicSsAQXHu69z75QFBtLFLIB1nA0Feu9Q7nJgEQUZ\\u002fFyOacAVB7Ins+8DsBUESw4JAtQsGQfgUH5+pyQVBh8mp54UiBUEHhH9S8hcEQcRbRouStgJBb2Muq0sWAUGxdghkpar+QBjRju09I\\u002ftAbHGWUpDJ90B1hBqqc8b0QIM+upeMOPJA5hkNfo8w8EB4ynOySlPtQHDGpabCCOtAwcN1BY4X6UDzq\\u002fglagznQEKlNplsl+RAJMwONfqs4UACB3Hw+BLdQIXa0hhBLNdAkS2UbqZ50kA5\\u002f1YlZv\\u002fOQE40pxMukcxA2h9+9dbQzEDoMvEtHFDOQP0J6syIh89A2q1H7gpPz0AUGVUP8ynNQP6gW3GTT8lAGP+9DSF1xEAglYfTW+6+QIUnGJAQFbZAuWlsdjRErkBu00zpoGGkQM4gn3t9q5tAJVRSrv08k0DGx1QvFEmLQJA89ZjqTINAigpBYLp8ekCXeX8Z\\u002fDlxQNt8\\u002fBI56mRACYs6T7SKV0CsspzTntRIQPsrWCkDpjpAXkfr64qnM0BeLMzJccRWQDKDuW9ZcGBADfeLhej3cECmpKVCsqeBQLPiyF1WDpFAAVzx1DUxnkD9Lv3uf22oQCr6OAvEDrJAEV77CklkuEApbx2Ymhy+QOz\\u002fMGz3AMFAMyamSgadwUBs7uQbSNfAQLTon660Mr5Aw40g0WhWukCZjn1cHwK4QEKDS6F1CLlA9vf4d9TJvkC9feAYtfXEQKNiZ22JD81AUiIQrIWE00AhXByzQOvYQHDAyVf3Ld5APjmPUDZ24UCLxFs4g43jQIujZG2kgOVAq7LxfYKH50Dlfq+5wdLpQOOa6CIWfuxA8G15eSmZ70Ac3G3KT57xQKLF7O6wyvNAzy4Pv71r9kBRpzXPDo\\u002f5QOmBKLxPJ\\u002f1AtDUTtbWEAEHYZWWngXsCQQTTENhiVwRBQ1mdejL8BUFnJxRgLlMHQYEScQy9RwhBTFoIlx\\u002fECEH\\u002flKeBgrIIQZi62mj9AwhBNuOZklK6BkHO7z8dA+4EQVf0UxihywJBHt8GgsWIAEGlX2ap+6z8QKvOhPECsPhAXmlRuitB9UB8nqMY12zyQDa11aU8LPBAdmYee5PR7EDCPHB8s\\u002fvpQK7Nm2ZAfOdAvnbYXJX+5EDSMFAwe1DiQLB0f3iV6d5AKqXv7EVB2UBoFtJ3REfUQPrF0TWnjdBAM1SwP\\u002fzDzEDKHChoMGfLQJfEvrhgNcxAsuWFmz7yzUCU3Ic6oVHPQDv57tWvVM9AdN\\u002fEA6iKzUDhG1RhUh\\u002fKQACvXTGps8VAH8s1dTIUwUB17Axbndy5QMbJRuDIQ7NArZ+PB4zsrEAIUIAI\\u002fTCmQMOlqwkjTqFArvWqAXbUmkBqNqxXLyCUQKiWJtyPkoxAWT6JviLogkBjNsG1QB93QNRToVVUEGpAfdzK7ytvW0DsynQB7VBNQJuWfmc0jkVAmL7lrA9YX0CeZK1U\\u002fFBmQBm\\u002fYSrUlXZA54F8wIEoh0Baq7U+4x6WQJmKGHSCZaNAJD5Msv4ir0AE5yfDotu2QJmzzL1qsL5AXV15VAvYwkB4UgVAojDFQEblYuSL4cVASP3TdZXkxEAScl8lRcHCQHxxCMKNbcBAvckO15wcvkAzmxVihGi\\u002fQOiOeEV\\u002fJsNAKV4su1O6yUDKan8vrprRQMQftl2VYtdADPXvYymS3UDt0kgo\\u002fr3hQKyy97yVUuRArCZKLRB55kB7BsdSkVPoQMNMmZASHupALaADZHoR7EChGR8YK1TuQK05xxdegPBAdNeHv2Ic8kDWteu9whf0QHFxyrutj\\u002fZAkfDEs4OX+UD39SJZZiz9QEtKCQzbmABBq2E51qe7AkGQEuBbuuEEQUZnYGpi7AZBdmFyLYK6CEGLrAA6JSYKQRJRET6NBAtB\\u002f8rwH5gsC0GtywCimoMKQQKbO2xeCwlBLW9mp9foBkGsPWiDr10EQdfwMQUgtgFB1eT7T2Vn\\u002fkDWb3P44P35QERhjIgwSvZAI5i5KS8\\u002f80BCdQKwOL3wQMsEIZqJQ+1Ac5inQxac6UAWPVzYNlHmQBYN14tgPuNAGu6mqUtU4EBEIP1dADvbQMtKfKmjc9ZAPT49DMSg0kDmvYm+9fnPQOt9gYDYK81AkzCiI+uGzEAIC2BqP1zNQEphdh9dwc5AlgRPOVLDz0AR9bAf0qHPQFDmgeJLAs5AAQ1DeXMFy0C5PJdLkjHHQEda7TgMOMNA98OnZylgv0Bdeq8DBcy5QBP+ggPspLVAN2\\u002fr2IptskCXHXI9lSivQBkFEUHfealAWHzwVRm1o0ArkcmgGGucQMV+YN7O7JJAEZqddS8rh0CmTxGkQRB6QN6pjvUBU2tAgL0nDiEKXUAb2bSdukZVQC1TvIyB6GRA9\\u002fUVRYwtbUCETnzqAbF8QMaj3ri8zoxAzZcLjS0Xm0Db1kTZGnenQLjk3C1EprJA1Q23Begpu0Domk8CFh7CQGofDGsNI8ZAqPwo8wHOyEB+pDer3pLJQBq8q6fHdMhAPZwLf5gZxkDUGwyrRqPDQC1yXOiSZcJA+Vtz0+iYw0DHjtsFCB3IQLpP6U0CJdBAQjz3Rdvl1UCubGKt1MrcQGGO01gxA+JAw\\u002f+9wEFd5UD6eOOcHyHoQKwFMOgnNepA7+42XVO860A2LJt9\\u002fv\\u002fsQIUUfth2T+5AsJ2uHXXq70BUutb\\u002fWgDxQDKbsMWPX\\u002fJALAN6z6ss9ECmciUTMYL2QBMWQVKccvlARlcikRQA\\u002fUDhwyzc3YwAQWqMwO4yzwJB0438RukvBUFmH4dLv5AHQXQISF5kyQlB0iy4ZyqlC0HFGrkxDOYMQbZyZUpbUA1BSmjYq4u9DEFnyBnkgy4LQd9KLUMB0whBMxOze2r\\u002fBUGcZTvqpRMDQRY2NupNXgBBneNMdG4T\\u002fEDNZdJ5ZzT4QLUCk41R9vRAkmaanQMj8kAENunAgybvQOgMxEN0cOpASCpinKMr5kDoP5vd3m7iQFyKsAzInN5A3PMTWrCn2UByN+jeIPnVQHsswFTXdNNA6Bz9Mnvs0UC1Jt+4XCfRQCDt23zo6dBAAoYEE3340ECGuY\\u002fPARbRQGcIyeXRBNFAkSz47Z6Q0ECzyDR7uj\\u002fPQHAtgayJg8xAK0TsFlRYyUDLIRMY1krGQJS\\u002fE1+HyMNAXFx\\u002f\\u002fqDuwUCArwkxgoTAQC0e\\u002fqV9Rr5A6wFHln\\u002fiukCMSqpYZp62QKVXfxB8wLFAwfRkaEW\\u002fqUDWQ3eAvyihQNLtsl5t9pRA+1H7hw9+h0C7F\\u002f8qb4F4QOKNN5D44mlA\\u002feYcX9TkYkCi\\u002fnx8vt9rQJdLToq\\u002f8XJAlXU3pV7qgUD3kwn\\u002fRHmRQJw3lc7aFaBAYG8414hqq0CFmq5ZuoS1QHb6c7lGC79AhcBBVgCPxECsbF+x8P7IQAqsgoX87stAxiIISxvOzEAxbiQ+1KvLQDbPMFURTMlAfOEa+Xz7xkDfoLikJj3GQMODE7uTdMhALtBusgifzkAYRGk5sozUQA36ogKrt9tAd7Udxs4R4kB9bc8ABGTmQIIk1X8\\u002fQepAWqknSBU57UAjGeprrCHvQEMwsnTQEPBAM+SlCfVM8EBnWOHYhX\\u002fwQOIFa99E3PBA7xYp\\u002fkaK8UAJGAzIeqbyQOaeS9dLSPRAklSRZPqC9kDhdjmoMWP5QP3sJ4X56\\u002fxAik+HlbuKAEEJYZQLO+YCQTlgUbOYdwVBTOLyHIsiCEHT0KqjBboKQcPlIkKp\\u002fQxBMeHSXiOfDkFy7LIKUVIPQf3IKo055Q5BDDEFOFpWDUHZlaYNCd0KQeH5rgxs3AdBXR+yjU\\u002fEBEEdkztvl+0BQaEiSLT2Bf9AwQUNhs7\\u002f+kCezu07L4b3QMShY\\u002fpgUfRAZK5Ul6k78UDzRwqkPZjsQIQux9AEUOdAg8LCNMTy4kCq20JE3mffQLpaZlkNJ9tAhPgTfSnE2EAOQA5wTabXQCrYe69xKtdAm6Es4RDO1kCZm3D+20bWQPHnA8ZbgNVApdfNCeKE1EA7bxcf4mLTQObR53qxINJApYQWeyTE0EBdIh1wAMXOQKmN8\\u002fZQS8xA4sJb4\\u002fN3ykD3jsYb3nTJQB62Lq8RDMlAYqz1fiyuyEBMsGu3LLDHQL7uAEj0ncVAWBKrmfRwwkDMRufhVxu9QP\\u002fFxbwEH7VAHSm39FoXrEBB\\u002f1uQLxihQHQiQy4qEZNARLt5rX3Hg0DtPlHFyMJ0QFWQ14htL25AURIB1ynhckC+h12fLe14QJj4D64ThYZAYrUJj0oylUDl291ZnP+iQEzb\\u002f3izuq9A4wvqS\\u002f+FuEC4XrHadn3BQM5y2t91+8ZAZFMqaHfOy0CITiDsowTPQANi9EfgBdBAryVvVE4Bz0D3mvfWhcfMQJZACyFv1spAp\\u002fw+wOLiykDfgRCkwn\\u002fOQFs9a9UFZtNAK2T\\u002fvTIV2kC3b52fMIDhQJGY5CmApuZAOVfnIibW60AgoHp\\u002fhyrwQH17842Vx\\u002fFAWtJ0N\\u002f+j8kDJ5Wx1F9byQNBZBcyZnfJAr99YYdZL8kC8slP1JCzyQElN0FpxdvJAQSKpfJpN80CzqLQ16MT0QGPlJ0Gk5vZA+sj5OY+4+UAhiGDYxD39QNoiZ\\u002fwduwBBueoHDW4tA0G+2jMNPOoFQa+vj7ju1ghBd40Cm8jCC0Hn1HRjQGQOQasKJkAKMBBBIqTpwUyuEEGrTbrtw44QQbrQHV6MnQ9BHAcLkgEYDUEnBnyOUvoJQeoTe1xzwAZB0lJ0RdjLA0HXR3VCW0gBQdW+uW99Vv5AAZVFHluU+kAbannuXPf2QC2J3nPhXvNAM3J4IjfQ70Ck+kwjF67pQNckDt5t4ORAxDXS1vqw4UAK4x3MbA7gQJvSm62dLd9AhySBE41330CEkOkOG+DfQLZJxmLNnN9AUL9SwHFb3kCmZyofCDrcQFRHuTKamNlAUmVJ2gTh1kAogfycl2LUQOkY4hQFTtJAepQZUWTF0ECKzVArws3PQF9TJoLRgs9AKV3pksU50EAduTxV9PjQQAFR2cttetFAP60W4j070UCOX8U9juPPQFNWDrIhW8tAFblAHkKaxUC7y8Izjki\\u002fQFWyKRu2ubRAoHdAbFobqUAbT+7de9ubQE2M\\u002fCixuoxAlD8YCBT1fUBhbb\\u002fojrB1QNNcjKg9wXlAEi0PhbWMgEBDuiP6w5WMQCntrEY+65lAbSBWeZWQpkBqvieOmmuyQMhuD7yN\\u002fLtAUBp\\u002f5821w0BYgJr5Gq7JQO1kIkck7M5AoFTw\\u002faI60UBUkayL9dvRQBXT\\u002fAEycNFA1iYSFaN30EApcMbujYfPQAttc\\u002f18R9BAjmD1Jg7j0kAA5ia2iDXYQIkCeVHXOOBARgpzsleb5UBQ75UEUr7rQEp3+8Ap7PBAmIFLlhaE80DczlLcF0z1QDhkvxp3HfZA+R1iLOYR9kAQxfUc1nf1QBn2PlTjtfRAf\\u002fqcYhMs9EC\\u002fAJfu3R\\u002f0QF0O+JW4t\\u002fRAq58dgpoC9kCSJeNpHwT4QBC9HLjJvvpAjh7GctY4\\u002fkBjPFXLSz0BQbgWFT85wwNBXb5cn56lBkGqJqbqnckJQZOPGDaL+gxBLxmh6dPnD0Gqd3TunBYRQW+xNPvasxFBBcHSy5ypEUEsqsk5JPMQQd96yj0OXQ9BcW7kVX0qDEEEj0PeetIIQTMyIS1KvQVB2vH2Un4ZA0F6kDDDetoAQfm1sVfXo\\u002f1AI8d1VaGo+UBvzGzBAKn1QCMZvJ9k0\\u002fFAbNvFRQQE7UCtBXiRHxboQFr3u415MOVABB4VBaoq5ECIS0HJbHDkQFj1lURONeVA+H8ZZY215UDIsKe\\u002fcm3lQD1B9FA2NeRA8KR14Qk64kAw+S16CbffQDmzlLmFAdtAEi7D0+fz1kBiZ+YV6+\\u002fTQPC67sASJ9JAgu\\u002fGCF+p0UCpef0ph17SQIUxU0Oq79NAZdLxe8y91UBAl\\u002fTVu\\u002f7WQLd05oTA\\u002ftZArVNLTwVn1UBT8QW82F\\u002fSQC3PwB1B9cxALd0goX\\u002fjxEBA4Zn7Z4y7QPlyYTV8mbBAY4yd1ddQokCF0JcVcsaSQCAZhQVgcoNAvmEQgv8KfEANibOsfjSBQObDP5jroYVAld6OzrT4kUDd0JQpTn+fQIzv0VBRq6pACiQnXSBKtUDImNLW8Mm\\u002fQKRGBL7MGsZAJj8JjNWOzEDWhyyiqh7RQOH86JmcE9NAtC7lVXLf00CXaBuc\\u002fp\\u002fTQDlqAX3b4tJAhs6SZKWK0kD1UImGFJzTQF3x2ACABtdAbufPs5Jx3UA2BQheJofjQH5Zv3LvtelAnGz\\u002fLYFY8EDtkYP118vzQBrle11ft\\u002fZAt8bFpTu1+ECJzklMNJn5QOGhjs7tfvlA6x+\\u002fwm69+ECwS+S5C8f3QE00nqsKBvdA2sjQB1LF9kCoB9fSqCv3QL9RMVETRvhA5hKirx0Y+kCKdW8O2Kj8QK40pyOJAwBBfCGEawgiAkFDhB8olLMEQVlyPYPfrgdBbxPmqp71CkFH9SLYnU4OQRz4TmmYshBB09Cg\\u002fvnoEUFS8cTcm5cSQS0PtNkPmxJBlvNlpCLtEUF1n9tf0aoQQTfwa5qzGg5BtUxFevuuCkGYOBMKz30HQTtg9e8QuQRBYwgoMshWAkG+PRImnyoAQTz8UQWnFvxA6Ecl6Wve90BldJEVoeTzQJPl8qVajfBAZoeU7fRo7EBSU7OLpAHqQK5jVee4oelAKPqxMTyJ6kCI51\\u002fFIrrrQMewMbz2R+xAyHvGjguc60A6D\\u002fN9TpfpQM8ZwOkpiOZAZEwlqnz94kCdhrreKh3fQPEVAaK8Y9lAtzg8jj9d1UDFaR7uaEbTQEXlyvg5HNNAVQk8AGya1EBhejuFuivXQOLdfOeh6tlAR4WfvkrN20ADTLUkt\\u002ffbQIT71m4JDdpA8nf\\u002fr91Q1kC4CNqiS4TRQNzg\\u002ffNzJslAizfbkCN\\u002fwEDVauo\\u002fAMWzQLPMz7rar6VAtTngGtAXlkBKrEdAILqGQG5+gQQ3UYBAvHGBJmzYhUAbWYlyWgKLQLYSBnEPwJVAdc8Be8mEokC89hAoaZquQPY6Wlqh8LdA5QeN5JeWwUBzaDmERSzIQOUXbrTV\\u002fs5AcNntYwOG0kD1SAb8vK3UQCYtsaKbtdVAve66w8TC1UCdvH4p7GnVQC04Ko2vktVAIM3bQZBD10Dubin\\u002fnWrbQP1\\u002fjkwlVuFAhLtzOMua5kBQkAuur0jtQIXUYpCbZPJA9q+R0lgc9kBRUqxBM0z5QMPBBDd1jPtAE9AA3ASv\\u002fEALRZ3dX838QJzR+cCQO\\u002fxAos8MxqRn+0ANM6NpoLb6QH2hJhO5bvpA6tfgnha1+kD8epmbkpr7QJLKJ4q9LP1AJbzPc7qA\\u002f0DZ9A8vSlkBQfy8tnoYbQNBhoGz9b39BUFuju0a8\\u002fsIQbCKRXFbQgxBj2YNkJyTD0F7ZHn+Ik4RQUueSq1\\u002ffRJB3\\u002fyZmNMoE0H2tEeZJi8TQf4hCoUGihJBlLBhO3BTEUFpJpgKP4APQXcKA7R8HQxB1\\u002fl1nf7lCEGqoKJBeQ0GQWNq1GCRkANB+KT2FZlLAUFdyTEH1Dn+QENFi9u5\\u002fvlAuoHPaPIf9kDZ9Pc5tv3yQLpeKFfX6\\u002fBASYDFhM0F8EB+AhAt7hvwQMgLRCmOvvBAZ7nNQDtg8UCaH9RPeoHxQKontk1E1vBAM\\u002flVuBaw7kAmRolniH3qQDPUz0FXzOVA8pJeP99Z4UCy9+yX73DbQENi7vnaetZAt9Ip4\\u002fIN1EAm\\u002fzxOsQ7UQJWitLNHDtZA0aBmVdRC2UAAO7\\u002f6F5LcQGCJUwK3yN5AYGNAzg\\u002f23kAvtXzbPMHcQO0n91bahdhA4CpzPfgl00DvFp9FWlbLQEPNvwZ508FAw11BCCc8tUDDPZIq+yanQDPcSFUQb5dA3ZEAv7rvh0DUjq37PxyBQNxGPNFRxolAWmTH03p0j0Buw2fT1bSYQISpQq65iaRAuBSu\\u002f2qesECtfbphO4u5QPAsbD6VgMJANmfcU70ryUAaxEEQYwvQQKQoOaizKdNAL+mi\\u002fHuB1UB\\u002fRQLHX93WQLbR7KftZ9dA5MQvfzSx10DO4VmBcJHYQENtz9hy+NpAd3rlYRa930DXHzFFDLrjQBha\\u002fKWFJOlAiS14k5jr70B21mpfpcTzQDXoSqPyl\\u002fdAf0XBzzH7+kCfbDXAyI39QGawAiiKIf9A7tXtmMDG\\u002f0CYoA5B6L\\u002f\\u002fQKDbeZsOZf9AjOI1\\u002fv0G\\u002f0BqkKTn6N\\u002f+QK5bHSNYFf9ApauV6QXF\\u002f0CCS\\u002f5Bx4gAQSQDMEzPkgFBte4DoUsVA0GM\\u002fi4zjRsFQVxPHzhLnwdB1SwUdd6ECkGMw62UwJwNQb8fLrP6UxBBmdFoAGiuEUFR2QxkT7YSQZYwMrnjRhNBHSoiYzZGE0GrTfHssq4SQbTdCSiXlBFBHXnQIIUiEEG9gW1lchkNQfDNAKJyAApBLStWgZMsB0ElJuLKnaQEQdjWqKGiVAJBN+YBv+EoAEEt4mhee0X8QEv4R2QCuvhAv8DBvj\\u002f+9UChlfyb5E\\u002f0QBjY1V1gtvNAf0fkZb7z80DgJJGXrJD0QM1Qtmt2\\u002fvRAT2Xs7GfC9EAxncJasprzQH21mOj8jvFAUWhsyOXN7UCAjFIiQBnoQM8t+PNe0uJAXkaVsLIs3UDQXU5Hn3XXQCc8mF0Jq9RAN47cYX6U1ECqMozJNKHWQIAC4FbB59lAOExTR5w33UDyod9FpVTfQKrZAr\\u002fuUt9AxVUXwRfn3ECTSV4BwnvYQLstM+gB\\u002fdJAilRwcjTtykD5959K9XDBQCoEfT6lo7RApUAzh4hapkDnDHMTKniWQF8uA3KHx4ZAhgHdeCE1gEC\\u002fRAPQ2uCLQFLpgndK1ZBAo8UBejznmUD+ZUmRlRmlQCuWpvGRxLBAmJHdRN1guUBTxuv0GCnCQOU22bQXgshAh6oaf6Mmz0ABNvPCxKnSQJ5kSWMPLdVAC28DjUr51kBYmwxccTrYQCqNoqNwbNlAHYKNosw\\u002f20AmC3oZ\\u002fXLeQFPhnL1v2OFA1yW7GGC75UAxNVhAGPzqQPeJ5QTivvBAFq8CMHRv9EDN4pv\\u002f\\u002fD74QLSc4NI0zftA0gQpFD\\u002fF\\u002fkCvnZOkVHsAQQQzXD\\u002fCLwFB+hQb\\u002fsCRAUHYHXKR67sBQSCdg9mwyAFBnbz6LDrPAUEES4OzFeYBQXWLwh24JwJBBLn9sf6zAkGZ\\u002fGcpt6sDQdqRuaqzJgVBDGk66KUoB0EhGHa23ZoJQVINGPkgTwxB4J3e8GwKD0HuRktKnMgQQeHV6iw21xFBRJR1TNyZEkERHO4WUPwSQftoIvZ27xJB0PN4o71uEkGRO2GSKoURQfVFoZ10TRBBPbPTgaDWDUF5j8OAmgELQTepdMfOSghBHL3CirzGBUEHjh7i6ncDQZnrIYddXgFBJoMR6LkH\\u002f0BYgo+55\\u002fv7QNM1eNlKyvlAZXvkcGqN+EDpOTAg9zD4QOCEWltxafhALeuEycTA+EDEngse9bT4QB\\u002fhDsbK3vdAX18XaP0R9kCxb8IrlWnzQFc489JJO\\u002fBAw4sScn3w6UBxAO5F3RPkQJg53C0M6N5ASQqOOqmt2EDeE8RIIIHVQH10T4UEC9VAxIAbV\\u002fWl1kCcsIlqNGPZQIl2ya2xHdxAIrO+ha2x3UBXVsOmKU\\u002fdQB1+SfG3vtpAF0o\\u002fj6Bu1kCvTzgDbj3RQNzMr8xtPchArcxsQP0lv0BTJ5MFQ0myQIbn3BxIp6NAyp1DPqiZk0BWYfZDDbSDQOlScbIw5HtAOHVQdK51i0AolgemWm+QQIdXK6F12ZhAOJnkcVrjo0BH5zS7ex2vQPZelEBQPLdABTuaNOF2wEBUuk21UhvGQAYzvN9IIsxAUYiBK8gD0UAkxp69HK\\u002fTQD44uLtUBNZAJXl6UdUr2EB+3ktCIn\\u002faQJyAnlu1ct1AXpFu5ci\\u002f4EAVd4I+3ovjQAztycFYUOdAhvzsddox7ECIf6k6zRrxQK1cqgCylvRAzS7NhiFY+ECbpwmlzxX8QFqDt1+NhP9AJ+ZRxI41AUHYLSvuVlYCQbeLcXh6IwNBhr2ZMQSlA0GaghJYhekDQa2yx9bsBgRBWA8OggwdBEH1BeNy31UEQfrGfPta4ARBPxanbizlBUFxN1QjM3gHQUD9OfnRjAlB6hFm3FL0C0FYxU6DDWoOQUpE1emSVBBBuo5jQUtAEUGRc5zmKe0RQR3KYuuCWRJBmHHtUGOFEkFVVOFgtG0SQVRK99tuDRJBfMggvgNjEUHzg2AiNnUQQY8uW2Wspw5BoEdUwaEoDEFnXIZJbJkJQc6H2IMfIAdBrTnn9XzaBEEZ9XrBXN8CQZsZVeGIQAFBYdxKomUKAEH88ym8hoH+QEa9UNditP1ANeiP7Kp4\\u002fUC0aDgiLHj9QG5AQQ56R\\u002f1AyXTlBlZ+\\u002fEDCh+Z56NX6QDBh3\\u002fraQPhAtCX3Wmvx9ECcsN9NH0nxQH3iIWMIcutARHpg2DFC5UDSDmcO4XjgQAIfzaoCe9pAV\\u002faRDA\\u002f31kDqn204ePDVQIApp9qQu9ZAEaAc+X972ED7bDHkLTHaQMUJbOW76NpAJgTs9Nn82UCKRhMGr0zXQOY\\u002fBwxJQdNAx2YrDXY3zUBRanhpYE3EQN8wa5BJz7lATQBRR77\\u002frUBiUVhl9e2fQIEm1B9ViI9ANqPZRwBff0BC1w2Y3Q92QHL+UB30nYhARME8qKdAjUA39QahD8qVQEg7czW8LaFAIMye2YeEqkCP+98fcZazQKErAV1ZkbtAuA+nM4F9wkCchei7kbTHQN8nFo\\u002fvMc1AMhw+vcVo0UA8DhUpv1DUQOqQvRV7ctdAPfm7zzf+2kCUrq6e6CPfQNJleyYNCOJAlPjOO8f85ED2AYhZDZnoQIeymrOgEe1Apb4r90tK8UCdDYbEGpb0QMoV3z\\u002fmU\\u002fhAwxDf9yhN\\u002fECoC1LHShwAQSI+blC95QFBWCepPmxkA0FV4+KQJIUEQePfapV0QgVBQgM0uPymBUE+vaDHhc8FQWHDJTJ86gVBttazdh4yBkEp+rrcY+AGQQ\\u002fyN7w1HghBaPM2+YfyCUGAWsRsszgMQR7CgjLPpQ5B52XInL1vEEE08F84IU4RQXOIgDed3hFBTNIibwQpEkGZV1Kxk0ISQT+ArrOpPhJBDrponHcjEkEdJc18aegRQRwUcN9OfRFBTt\\u002f3EsLUEEGVsrhAgtgPQRSP7qltng1B8NFL9PMpC0FzAcFffrYIQY2rj0cAfgZBOm7TBgSsBEE7e4CsslUDQb0x+GOPeAJBdOt0lbj+AUGqaS+FZMUBQX7Oo0iKogFBO\\u002fuV\\u002fSBpAUHAE6Kl+O0AQZiOXAJSDwBBCIPQ1Nd8\\u002fUBoiSMLQw36QELdl3fcGfZA1bUXxTEM8kA\\u002fGH53x5zsQJ17f6jVXeZAEws2Rxem4UAciOoj2uncQAYXEKQ7M9lAc\\u002f1s6W2Z10Cam91MOnbXQIuhhyXPDNhAUDRiIJ6Q2ECV3iUYX0bYQOeiuY+Rt9ZAoHAEaPnZ00CfPQ3WqA7QQBMP4VbH6sdAp+lWPQJZwEBt4JRlx3i0QPAn6lqfdqdAM7deyW2kmEAmZz\\u002fd6gOIQNhpCczmj3dA\\u002fCbuhp9tcED\\u002fFxssL0SEQAlkKvOP9YdAbIFrS0iokUCO\\u002fHPq1YibQO1CEM9SCqVAITKxCPXYrkCWVbVvoaO1QN97j+RHJr1AiYh4sH32wkCjQS6+9gPIQF4AiI9R3M1A4SV8BQhc0kDCcFEeU2jWQHoqhbPsHttAualAkRQ54ECI+zSzdCTjQM08ULSgUeZAsJ++y2zg6UBDE3z1ABXuQP5vPlP5ofFArh22n0vV9EA2sMaTAaP4QIvcK84o3\\u002fxAmNCph2acAEGeOrn7bKYCQZm9JJVLXwRBfzHinBenBUEmJwCnkXQGQUvJoQ5u2QZBhDXnircBB0FgecuXRC0HQXq5HvEZowdB6ZQagwigCEG7pXd0aUMKQav\\u002fISEafwxBI1mGw30SD0F7wIVeCswQQepjAhcg0xFB2RUg7rZ7EkF56SQQf8ISQTK096kewhJB7iyTZuOiEkFpgXPh74QSQWSZtiIGcRJBMBAtNfVWEkH8aqPtQBgSQSCyGUIBmBFBHAu44pLIEEFRNj5MCGUPQX+RkR2L5gxB\\u002fwLfZBZpCkGkorWszT4IQaQYx82cnwZB1ekFoaqaBUEB21slJBcFQQzqV+nY4QRBFgscg1+\\u002fBEFyzIyoN3oEQayqHiFn6QNBqNuM+B7yAkERJkOYNokBQYk+9sHTbP9AyDj+jhst+0Cps1bKQrD2QNacv+RAYfJAz25bd+o47UAvPdDsaTLnQLNXTAMGx+JAHbyOWzKY30BOZHZIo+3bQPO2dS1J7tlAA5tRFPoA2UA\\u002fvvnRTozYQAUuztun9NdACWR87rOy1kDWg9ZzhXrUQBR+oD6VV9FACcUr1OZMy0D3r86Jn9jDQATtAU7eirpAYJlgwU5JsEChT87lsFCiQCE9lxSp45JAT10KUSQUgkBnshNII2dxQECTuXQJ+2dAlB67w4Gff0Cs0ud3wK6CQCZM8GFGfYtAbPlAbYhelUBG8xdntkSgQIy0g\\u002f5nx6dAZ3nKp56vsEDDMg9jkKW2QMlsSn3OBr5A1mDNDNemw0B2DfjEDJjJQC22IUqPndBAvJQVO+No1UDjgLdVQybbQKyGuXIOz+BALZk764VA5EBNrVPYlM7nQGp2bPnqi+tAkjS7Ff\\u002fC70DJm7FYjHHyQPHR8pi\\u002fp\\u002fVANhW20UeS+UBCOlpzFAj+QJ3u5TcQVQFBCe7WEqh9A0GiKCIN9kIFQcFIR5lXgAZBy1V5KZIzB0Hx65OTbH8HQRdA7k+XpAdBb710oCHzB0GaLkhNyrcIQQHumuRCKApBAtpDxT5RDEHOWx0kugoPQUKDSurX\\u002fBBB31VUle9SEkGxAEqJ61ATQbY9fGfy2hNBuZQ8lUr7E0GR9gREhtsTQXbaO7ssrhNBuiLGdSGVE0GtfBIxlZITQfboF00EixNBN170ztdUE0FWD3mPj8wSQaCu9kLP5BFBnge+H0+tEEHVWIZfg54OQfITOuUbAQxBvR\\u002fmQhfkCUEvAxNkqX4IQRaStMubzQdBjEZtFRSaB0HGfVtXKZIHQYXM6D3+ZQdBgkVE1xXcBkGH6ZfNetcFQQXu6Wu\\u002fUwRBDDUgLOleAkFWTBiDJBYAQaKRi\\u002fvQR\\u002ftAxHCqtip29kBg6MfJDRryQFFxqSfP9uxAEu+vgVpv50CpiVKu5oTjQIng0gTo6OBAzI5sRWp73kBo9TSUHmDcQKOLAa0j99pAAmlUZ7PF2UARz\\u002fJFmlbYQKrXyLK\\u002fStZA0RkAuK5400C0vWf0bgDQQB8+HuKRgMhAmv52m\\u002fNbwUClqocAIKm2QIcedMjhLKtAPG+ofwfknUAEMq0mDiuOQOjxS6txP3xATrSY74yPakA4n6OwIgdiQOrbVdd5U3lAoujLSSIvfkBJgQD8go2GQMh\\u002fFoKpvJFAOAItGS4qm0CtnJYn9d+jQKEh8kvO3KtAhKYwXGjtskD6Kk+4J1C5QHgVsFAA6sBAXN6pV8PGxkAMVfrQIdXOQHZGZrlzvtRA9PIy45Nb20DLH1M86nzhQFY8CyIMkuVA2TqgvRO76UCk8sSJKfTtQKwVq+EhP\\u002fFAX+67AtDo80AhIMWPvzP3QNLXuQySN\\u002ftAH7Np8ULK\\u002f0Dmb3YZNT8CQay6M9\\u002fvYARB6Y5XG6IIBkEQeFtHDhUHQd4OdNZEkwdB0Wx4K4+8B0EZxZwgqeYHQTp\\u002fjIL6bAhBFAWJtbCZCUEdXDVdw5ILQRLqy1eeTA5ByjkR2qPCEEFD3\\u002fpM8WYSQUJLiFOz0xNBLh1u3\\u002fDWFEGTi6w\\u002fCWAVQY2hcUXvhRVBTFAJPPl5FUHPl1yuJ20VQZkzU0WsdhVBb0o1bIKJFUFfN1xWJH0VQSVvSESpIhVBYhIbowlbFEHbdP\\u002f+pCUTQVdfz9cvoxFBmx7nzRwNEEHHZYsDUEgNQd3\\u002fz7uNNgtBaqy64x8NCkF6aegbvKoJQXz57C3ztAlB\\u002fb3zWHi8CUGGslLFgGMJQaQKFnhVdQhB52bzA\\u002fjoBkFltlV5LdUEQbewRo+3YQJBGUCk3Fl7\\u002f0AzzIru0zX6QK\\u002fxZLanU\\u002fVAKy7vqHsk8UBviD3cBK3rQIIWagTE2+ZAWA9hodyU40BdVbGtBHLhQNkrQ6DGDOBAQAGhbawn3kAk4X6QvZXcQKo\\u002fEWNy\\u002f9pAsx6C1Q8K2UAoTh9DH3PWQCnDBzfQK9NABl46\\u002fHbNzkByAC4\\u002f8A\\u002fHQDqBqrkD+b9AXtjJF3tttEDKjcYpH\\u002f2nQPj2I9gd2JlAGlddGYuMiUAjTT\\u002fpI2p3QK34Ox7dgGVAhMRnpli+XEAf0EBr8ql3QMcajx+juXxAXr\\u002flPj8phkBKlMH8GdiRQAUq2DZppJtAyRwAmAA4pEAm3MReWwqsQA1ShW9Qs7JAM9Kpw06CuEDauKKo\\u002fybAQINfineavcVAngwm1ibazUCjUhvkx5XUQDLLqwOD8dtA5+Gdmlhf4kCMovsulzfnQHrsK2TsM+xA+3jBjmGX8EAUGdzdPifzQNVRL8cUAfZApZFXIyVl+UCcMCyofXD9QH6Ab7tB\\u002fABBpH3MDmZDA0GDCfyiID8FQbORVZxkrQZBOYMNYlt2B0H8ABySx7kHQWVnd63gxgdBiF6DgwgECEF8oJwiadEIQeopewMecApBTWPCwRTyDEGM9OzXdhkQQfonWJGF7RFBVpWVsHK4E0GoqwM2Yz0VQRtRVsB5VBZBuDg4JTf6FkFTNqbytE4XQQwdukpFghdBK24h8P+4F0F0\\u002fREMevUXQU5Qjn+fFhhBB3RhUq\\u002foF0FO0NyR5T4XQTJ2dfoGCRZBnW+jUudcFEHNlyTldXISQdF\\u002fgMnIlBBBiXpsMs0YDkEoLyRAnhEMQRt8T0vwIQtBQP0ZLjUDC0FBzfzUGTYLQUrvHSDdMQtBXZDU5beRCkHlFWYXBS0JQYuiulksFAdBmCjq+kp7BEFx2VPqNaIBQSSudrTkif1AU3W6odsr+EBdN\\u002foAA3nzQGghpu2XSu9ArhVSazWG6UCfuCVhTILlQO60cvcy5eJAW6HqIN4+4UBfsu11MCngQKP\\u002f8uHLr95AETzRb6Yq3UAsr4woSm7bQKXFWQMzM9lAHFS0HipN1kDDnXQzssLSQKpe+eVFqs1AxYxQFOLZxUCjXMuEN829QOukKwlsu7JAM4qaUWekpUCr44ZzXfCWQBUEccJCToZAOvqErXMXdEBjd3+6UxliQMuRq2\\u002f14VdAEDmkf1tme0A6IWJYsuiAQNTMpWnMzopAsu3F7CH4lUAggJSr8ByhQFuD5QXh4KhAHUBQiBztsECLMw0Pb9u1QOVKP3GAbbtA\\u002f+KA4iFBwUDRY3dGgmPGQBfpe2LeO85AkrmJR4Ls1ED2yRJ01OLcQN7Lga\\u002fLauNAE0SDJUIT6UAEKY0DuQDvQHy3UOFscvJAzSI7pt1f9UAgkpJauHT4QPaBOifP7vtA7ykCqp7t\\u002f0AkOGyVDyQCQaF8z892QwRB8gicSQoHBkH18xu9JTIHQUIR2CwquQdB3Tm1I8nMB0EnoV8xX8wHQU2y842oJwhBFvTtY8o9CUFKYm8RhEQLQfv+97lLOw5B3pJzNeX0EEFIdzBKBPUSQf7eGNxC4BRBqgJR1nCAFkEVlpdwx7kXQdUzZsI0lBhBGeImaiczGUF7QVSgkb0ZQS462tDeQhpB7PEXxgGtGkGlrdc5FcgaQRT4hEHdWhpBxu1oDIlCGUFuTPh3qoQXQVd4GyuiURVBkZzZbgL5EkGxCJvoxNQQQRlJ1a2ZYQ5BNNdAMMBpDEEXEf4Ig7ALQbi5V+CryQtBlWHszLUUDEGh8uuGK\\u002fULQe\\u002f7nh\\u002fYBAtBG7zsufIqCUFg5S+7nZIGQVZ9bxsWjANBe37cRMJrAEEItk9q8ur6QKjt0qd1rfVA2X5HlwpT8UCMcM43luDrQDnrNm7M++ZAAsaLpcm240A16cflSq3hQAkhXAy0cOBAdvZRWpJA30B+noyAl+ndQAOsaiiYetxAdhuePh+s2kAjje6P50nYQEiBfOQYPtVAjpBU8+6h0UCwWJXUSX\\u002fLQJJcC4QD+sNARZ4XAWnhukB4v25VG62wQHKK1pW2BaNAVqWOhgzpk0CBsxZBmh6DQBhE6ffO\\u002f3BAUmDz2gYwXkAn5vPmcLlTQFRPkcFG84FADGqfT0VNhkANGVnDZtmRQK8\\u002fMLqUX51AFvb5g4TSpkAVbe2aum2wQHkTpgzM8LVA7wFq3Wl8u0DJxAqmiYHAQMQ2nJ3rqsNAo1P4lUcxyECxWi3I8HzPQDqQuaKxgdVA1OmdVhDa3UAJQYVwC13kQOQ\\u002fksoevupAlJ2ZV\\u002fbG8ECmo+RQ0Sv0QHiuAl+AefdAyk8b3M\\u002fM+kCe4ZsZvlv+QH8Gvy8YIgFBaHjfl8svA0GDWR0pbx4FQV6OlUYWpgZB1ePPwbuTB0HtkLrJw+cHQUMxjB853gdBBq8KkM\\u002fdB0Fgz21svFcIQXljypDYpglBCnCFRh34C0EWSXlDrD8PQcp8dMcpnRFBTUuRqC2+E0EMxcb+KccVQbF3nJtbiRdByKP43bryGEEfZfWLIxEaQcTYyncbBBtB9hPxvQDjG0EI\\u002f8diV6YcQRYzRSSbIh1BYUM4Bp8YHUF0EVVjjFIcQcmhRttEvhpBJWM91355GEEs1yvKzMwVQY2BRzvJGRNBBwMOFMjCEEF87dhRniMOQQNydFjwSAxBC5NbXQLGC0HfcZf0DA8MQfh\\u002fPWU8agxBf+ffeBAuDEEf6lZ\\u002fbfYKQcqonxR0ughBAQ1j92O9BUH\\u002fzSyKfmcCQTc0igdROv5AtK7JbapL+EBZNEUaGE7zQN6CiyMbse5Au+t2Ga3E6EBWWHht65\\u002fkQOHH7JQ18eFAbcjNKtdU4ECuHJnsLcbeQFEwVVokgd1AcXp+DhtR3EAv7YsrudraQAvKTcE35thAX27Rc4pY1kC0mJV1sTbTQBpxTdMoV89Aw9sSyMUGyECeB4f96y7BQCqUGJwey7ZArJOAVVLqq0AsozrmuXafQLs5Uk6RSJBA52juCjbxfkCqlWLLZjhrQEuqR3zy5FdAs4HThbD8TkAjtJkhA6SHQBJh4akjWo1AMAVnwvJvl0But3IISjOjQHS8VerToa1AYTYUZBYdtUDAxyiZe8W7QKdkFfmPAMFAnJ5u6JW+w0AiiKYCPnrGQPO0M1QtQ8pArGoL5epf0ED3ITpXtt7VQHk7I0iYQt5APmTYS6nO5ECiMudBR6frQNnkqIzzjfFA87kqw+FO9UDE3Qxw4\\u002fH4QMCZyodpg\\u002fxA1RNRvw8WAEFzTdnhHgECQfLQYmDy7wNBS+oOU4avBUEMmiDYJQIHQZOm3k\\u002fuwAdBdSPSswv2B0HKeNaBjuEHQROQVfkZ6AdBTyKjOop1CEG\\u002fdi0Xut8JQVrkX2XiUAxBMGdWfU+8D0GXqeKWp+8RQX27j1qFKBRBLO+rTpNQFkHRjb84Qz4YQeCN1Oxh4xlBqnUAHplLG0FNwQI9J4scQeQWs6qlph1B8kzxlNiCHkHMQXmayOceQapkc8uzlh5BYBGbrBtnHUHHcJGVKlwbQdKX4HauqRhBb4Gl7AmpFUHJQwOwJ8QSQRpJoIv2XBBBGeTfmZpsDUG3JuwyYcULQSqPSvOXfAtBlP5ovCXxC0FCmT06VVoMQVg7+wPHCAxBMbLwS8ScCkFa0jtUHhsIQRMysREh2gRBNhuwtDBUAUGNBofkWvH7QDYGLTQoH\\u002fZAsNz7\\u002fIFr8UBwIUvUu6brQPQJX42UbeZAhVjw3wva4kA7cH7H2JTgQIZo4A2JgN5AYk09IoT23ECllZVa5tjbQFLGnbZpndpAfyvSBSXv2EBba8CI3KvWQIKkTkiU2dNAMgIbzN+c0EBhLJT5HWLKQGh0\\u002fhl4u8NAyB0+MQ2du0CfckhipfixQH3rbXi8p6VAjo5\\u002fFpkRmEBbEmkpDZyIQIgKQ258IHdAgkhapvMiZECsg5q1nX1RQNmB0oLihkZATKhaEYtxjUCkefdLMTSSQJxr0jm715xAW8cWmipvp0CdZf8D7OyxQLIXoeGdSblAJ7YAZ7xrwECjWaSBM8PDQLb4a09ZZMZAeio+kqCgyEAsizOiDJTLQBdQyJLbhNBAaE074rKC1UAomu8RTYfdQPdBzXnFYORAPStscxBT60D4T98cUIfxQMNiYzHVfvVAkU9aQ6ph+UA0EOtPUiz9QCSLhZZpegBBNC8Vs2NhAkH\\u002fckye0DYEQRdLvlJrzgVB3L+uXGb3BkGav5yRCZgHQTiqVEsuwQdBRncaL9qvB0Hsp2C+kL4HQYanGX7ETghBQcoT6\\u002fqxCUFIImLyRRYMQZ0ttXCVeA9B7a\\u002fzNtjQEUFKfSfirBkUQfJ1C8+zYhZB9pBCGOyDGEEM1sTwrGsaQdJvwQ7wGhxBRSljdKeUHUFHo6zqZcoeQWsozqLIkx9BaAFoxei3H0G7fwexqwQfQSJWioddZx1BeBrplkP6GkF\\u002fJd2CSQIYQRyVgBwN4BRBtYxd\\u002ffn6EUEgKqvuk1cPQb\\u002fJFJ\\u002feUgxBUL5b3aP2CkHVOMrKreoKQaGO1kwyhAtBU5DZGxH3C0HQV5yC6ZULQTT4R1XyBwpBLonJDxhdB0E08G0zwfgDQamKsDSjYABBJEB6e3UT+kAvsbi\\u002fK3T0QF\\u002f9WNolDfBA7673DoeO6UAjD65jm+XkQBj2RELDv+FADFw6hZiM30De85eeZkPdQPJWTQW+6NtAoxGO9nvH2kBKDznc51LZQGHlQ0xhQNdAfApYYaeJ1EA40ywjxFrRQLL3d8138ctAVvEGpTFYxUCEZkoj9sq+QIE9Eyi73bRAVyESuQ5tqkARxW2QCh+fQPihBBH895BA7XITlaQTgUBegvy+tqpvQN6HsYoFP1tA\\u002fUhX6HNlR0Bwm15PNOo9QFERwPOoBZFALUvyfAHtlEDUHDRn\\u002fWWgQCx0ec4mXqpAQy1LBn73s0AMJUv1xd+7QBPJjbOf48FAyC1M3sc4xUBeAXWXjZbHQF4RoArLQ8lAVshrg3pZy0Db2HTTbqbPQDnJnbNdHdRAuhHD84Zc20BYxY9vLeziQKzo6WvJlOlASCKIk8uX8EBkrGCqS5n0QPT7cFxanfhAzPo2WfSR\\u002fEBDPQFGgDsAQY5d0AhMIAJB+rVLrobgA0HWrLkNQlcFQQHMdKi\\u002fYQZBVntY+wzzBkHqD8KnfR8HQTdFHkarGwdBadGujosyB0E7TawEAbgHQY898i7D+QhBbiGkDRIuC0Gy0AbfpmIOQW+uCEHeORFBoPZ0Y8yJE0GuoqZQfPEVQY5EBG9YRxhB9jVuoFNwGkHEMA4NdFwcQXXV1SES+h1B0IH29MIpH0FqLJp+Wr0fQci6ku4mhR9BVJL\\u002fLMRkHkGbAnBG9GMcQd8LeQ36shlBQq\\u002flW76hFkG5ldBDNI8TQYaaSFxH1xBB1igQoTuFDUHvg3eTxPEKQbd\\u002fBzqa7QlBMQX+mj8XCkHlNUcbS8UKQfsq6UKxMwtBWSYAvb2\\u002fCkEeILSiNBsJQR9Mc38sXwZBzAGe\\u002fQn2AkHzJjVQmtL+QACSdYgjW\\u002fhAXvUO49EI80CVWleD4+7tQPJAd4xqBOhA6hvzw9Dg40Cqw8hBXx7hQOaFgIAN0d5A9p4dM5rW3EADFhO1jpPbQFETzCtXUNpAs27pvImD2EAwrv3aRvTVQGiUSiqRvNJAsLalNH5azkAMHNtYu0zHQOrYnx8278BA8Ik62xNGt0AQ5ITJ4imuQNyw1qNwXaJAm4Vs03LrlEB4S5b7MTGGQAzGxvO11XVAZHhhkpffY0AlDCKGYtVQQDEzBxzefzxARd137IkPMkCgDWoQCUeSQBP38Hk3T5ZArHujkJ1AoUAUkWRqyWSrQH6CMw7fgLRAxesbMQJMvED\\u002fSp5ffvHBQJr4rEyt\\u002f8RAtOfkFejzxkDBDk01\\u002fgzIQF8S6dLaUclARPpEAe90zECBwRJaBLXRQIetK6ZV5tdAqjjDNDeU4EBXyFD8SqDmQG5TyqUGve1AAvTHV7G88kBlzJvcZLv2QAcWY5rRvPpA3ZnKkUqr\\u002fkDlWwTdqTMBQf4zQkI\\u002f3wJBcFApJe86BEHpRKhBSjEFQfmEZk0CwAVB8cjEmMj7BUFad8GJug0GQSOA+tsSLwZBQngvPgOlBkHUvUEcLrkHQcwnm+6lqglBzhq6XeCZDEG9F6zZ3zwQQRFzpZcgiBJB7lskzBEEFUHz2h1WWIQXQS2GN5wM4hlB5WR\\u002fwDb6G0G3Ki1N7aYdQdV040Njux5BVsYxD44JH0GalBO4zHAeQTQFzCnI7RxByY+OZkCiGkFlHppeg9AXQZwjmKd9zRRBOsGtZCrwEUHhXwZpTQcPQQtmCeH\\u002feQtBnOTOA4hmCUHaULC29rIIQfGQk9Dm+ghBh2ccE+WfCUHGqKTOlPAJQVzbT8uiXQlBtiaOkYSnB0HYf2\\u002fv3O4EQXKG5q26ngFBJx+\\u002fJpd4\\u002fEAbvQuLiGr2QInb8H7Uh\\u002fFAE9ZarkXA60AgDNx0VI3mQFeYgnLk\\u002feJAJuVpQ5Cq4EDVkEeBzn7eQJE3MVP30dxApjykSA+W20B1lDDdrRnaQA2DzFOX49dAZbBEKhnW1EBVucihEyzRQP\\u002fX\\u002fbt0pspA1MqAT352w0CPS13P78i6QE5v0mKLY7FAdbAsfBBQpUA9ZGomD6OYQH2XeUjcy4pA4Cwg4hZTe0DMSGfCAwRqQCGFGNjfDFdA3qcWxiwYQ0DPr706v7IvQBWhJzwt2iNAaE5yueZckkCz3KQOejqWQIeTcRyR7KBAqMYIJDh6qkD+Ux50H42zQLDtqzg2orpAwKAAu9+qwEB8MSyLejvDQOI1hgb0rsRAVljDUog+xUA2Q65IldPFQNkdXN7668dAllpF+cdAzUAVtoe9U6XTQL03wk\\u002fFaNtAXn\\u002fgqsvt4kCI74S0pkXpQAwYaKi9NPBAapehQ2ID9EDug6N99On3QPcrXxLbwvtAtlbla3dg\\u002f0B1c8EiUUcBQQM2sNrnjwJBlo2jA7F+A0Eljw8ywxcEQdFiSG\\u002f1bARB5ggYRhGbBEF33rnFqMoEQYayZWtWMwVBdB8z+gwaBkHNGv8XM8QHQcKWQ7T3YQpBhJaiAAX7DUGZIP2cfzQRQV5jyT38sBNBp3UoYPBEFkELH85kUb8YQaPGS\\u002fUB7RpBCLnS2PiXHEFv1Tf5oYodQetlxQh1mh1B3VNgTla2HEHsTp+h5fAaQZw4\\u002fGMqgBhBt07LohWyFUELoDGtX9sSQYu\\u002fQx37RxBBi4kyCW5iDEF9BxBg53EJQdH1qHbSzQdBbQ3P+dxMB0FHvQTEd40HQVPaZxa2AwhBj2t1q0UZCEElwbjII1kHQcKGGTd6lQVBRdkdlT\\u002f0AkHH2vkRRbX\\u002fQC6C1jj4gvlA\\u002fBuPK8MS9EAJbbxTFovvQDroohUvQelApwhpeufn5ECQlyXCRAbiQJvuqAK5NOBAh1AZiLY63kA2CbRsDdfcQFC0moTxjNtAdXJcAum\\u002f2UClmYGSPBfXQGnZUeREmdNAULcu3V89z0AoM2aMQ0vHQC+ycUu6PcBAMqDKQsM0tUCmhVH4kP2pQKUfckym+p1AFWHCMM5NkEBVpRLVdruAQIVoXiVIL3BAF9UPlfxuXUAmJhn5BRZJQBihIFjVHTRAi9nOVpU6IEBWse4ZJvwTQIHxSMTUa5FARkuIrgLllED5lu0jGkOfQBjnh7JmC6hAYk1eqXt7sUCTDVsqLne3QCzdgnKa8LxAi4vm5EpxwEBOUGzTJ2PBQOKEuOTfgsFArRvBdMaVwUBkp\\u002fi2N9jCQGquIpXYt8ZAg1U6oF54zkA5NbJEh3HVQH+QRJ9TD95AU3uDRCtv5EB58LYKFbvqQPkHIO9o1fBAusF2qkF69EARiC1JRSD4QBPzXdW1lPtAoOK0kFWl\\u002fkDlIEyqq5YAQanGHsB7jwFBg0Kd1mJBAkFMXuiZ+LcCQajQrqCXBANB\\u002fdONhvJCA0GGz\\u002fEjdqADQaBqic+SXQRBij80\\u002fkTCBUHzxu4hYwgIQTa9CySXRgtBrZS01fNmD0Fy4R7lzxUSQZx\\u002fNxJRnRRB75lIq+QTF0HQvGjvID0ZQQvd59\\u002f22RpBM67vIiGxG0E4073OfZwbQfgWdFE8lhpB+1\\u002fHZ6S+GEFGXr9BTVYWQZXCFj2+rhNBesfbDi4YEUFU1L+bpqYNQRCxZTbnFApBQbQ1EoygB0E2cJz9GkIGQdx+unc4xwVBH\\u002fX\\u002fum7WBUFfbsAaQfsFQQlVTgYFvwVBUNCIRkvKBEHwK5+iTwEDQcD7N1kMjABB5365k2OH+0C5crvEShz2QGCNTTJldPFAdathLe6z60DyFlISWZTmQGJDjfcOLONAjTKfT5kH4UDuJo0Fvn\\u002ffQHdaKwIq9t1Ajqfsl4XG3EC2FaZP7E7bQH+FJ9QfF9lAeeKK5TP11UA+zYyjFhnSQPXz42r85stAV+mHkVMLxEBlwMTvgc66QHN5aP1jsrBAsi4Ns9dpo0DD6xmJEiGVQPY2cA68mIVAdK4jZnjKdEBZ7jvz5uJiQKSgBs9\\u002fMVBA7ocM5TIwOkAdsmEGdg4kQAag9\\u002fihDA9AvV1J8jikAkD1Pf461W2PQNsQYfSWqpJAvROu1vBom0BoVL6QFrOkQJ6gkc\\u002fomK1Au220F3uLs0B5W82He7i3QBSDCvF+g7pAQnrmTJWLu0CLH52aszG7QIvXRaattLpALrl1pkkFvEBEhA+eQrDAQPIiaFbQacZASHQdC4rpz0Adt4dlFMHWQAF8r2a0kN9A2MTA6AgZ5UCLTFvBVynrQG7wNl5P3\\u002fBA93KW1jVF9EDdplVtgJf3QM4Phnqap\\u002fpAc2Zsq4pR\\u002fUAz\\u002frTAyYH\\u002fQEa+RKKgmQBBEMo57zs1AUHjht32zZsBQbcVejfD4QFBjUU+vqgvAkHEHhxUGcMCQUWmN9174wNB20ccTnjNBUFTtTdP754IQeulnL\\u002fRTAxBTVH3MmtSEEEoGCBYvKoSQdV6tnfX+hRB7jtQZdQFF0GDDQBUC4wYQe4ztAZ9VRlB\\u002f4oWcKM\\u002fGUH8uDl4H0kYQdGhBAoBlRZBrO0d8hNjFEE\\u002fnKo1GwASQbvs2b3saA9B9SyCmJ9uC0F+jlSwAFIIQXgolgLOJgZBmBpsS+jZBEHRR9310zcEQZ+Qq5qY8wNBrt61r\\u002fKxA0FQje+2AhwDQTiuGXLG9gFBPpuxv\\u002fk1AEGLOdk+Rv\\u002f7QPkLzECZOPdA58YHAbG18kDuEJ68\\u002f9ftQGjWx47HHehADPvrMmos5EBNmyqh5K7hQLE41N0LPOBAerl7gbfd3kA\\u002fEF6l59PdQLAmhUsnr9xALjrcxvnk2kBdxZLgTyvYQLVgdvjkjdRA1BTke+Vl0ECiJhlNz2jIQMeqlxgj5cBAttMq4P63tUD2efQWsuypQFjJY1aEw5xAx+IBI4+5jUDZz8xZc7F8QPuMpomf9WlACYazipgWVkCN7K7\\u002f4LlBQPu3Shyr4ypAsoLsLAZlE0BXM2kIKFv8PzQFgG+XafA\\u002flvaMm83\\u002fikB+1JMjtcWPQIKOvoyT5pZAt0funPj6oEC+HUBEuN6nQCeSol7bA69At9u7xfSEskAoUqDQCV20QN\\u002fgFYQfy7RAmIR3sQIktECgMnS6Ql2zQEDy8vZQ6bNAKtbQ5hVyt0ArXROZ1ou\\u002fQBDOzKVAvMZAEQ0CRHGC0EBphkixT2jXQLOM4sGKB+BAd51TZgwu5UAvyrKDRQnrQFMNqA5osvBAXfxU2hj780CoNLaGeTL3QL3MzFvpK\\u002fpAvn1EVB\\u002fA\\u002fEDQ01LXo9D+QMr0Be7pJQBBMe6\\u002fkNGaAEGri9PnaNoAQTya\\u002fnhZDgFBhj+02R10AUGC6TWwiFACQTo60hb13ANBc92VaXs2BkHgnHkaSFYJQbREGA+6Ew1B7lj79HiVEEEy5ZgbPKESQSDSVKIfeBRBTZT94bLhFUGn2IFVc6sWQSGMaesWtRZB10\\u002f\\u002fP836FUEvWKyIOpgUQekm\\u002fAoywhJBfFnJQJy5EEFmpyheWHkNQRaknzGr9glB7eNx0yAjB0FgAuoIJg4FQdWYW07CpANBMsvWtBW6AkFD7UnllhACQVlo+EIkZQFBO4MXBQ59AEFtF2a3U2r+QM8BJVuOGPtAeFO9xr5F90ASiuTHyFzzQD0FHKkTlu9AkrnkAoC76UBtGHTYI2blQNsiVP1IgOJAXUNM1oHD4ECKz1n08avfQA+RQzkSst5AgAkMRwPf3UCbymUeS47cQIAxWnVuUdpAL5xTlXAN10AJA9bI0f\\u002fSQJE43LUqRs1AddQu\\u002fJ7+xEAhuxzzE\\u002fe7QBTQyoFARrFA51SKfzHJo0DvIOMgpwSVQFZScSOzuoRA3pdguw8Ec0Aozr5fYURgQMAiAUWoCUpAyvVja\\u002fWQM0B1b6ruwbcbQDWtiSoJpwJAXCYMab5i6T8msOuZDPjbPxl7FMKz\\u002f4VAFRCnsaOsiUC37VWOnjOSQPi2+cXijJpAmCpD1F9hokAKwVViXounQHniOanwvKtAR8JzgNAarkCx+nVuDlmuQB9PJJP1\\u002f6xABpS+4nBzq0BRcRwCXr2rQHW8cH04G7BA23cxJ6GYtUCnq61MMWG\\u002fQGcUNxgiLMdA2E6bXYnQ0EDaNnuN7rLXQKEwLxhGL+BAu56JAj9u5UCSTsBPp4PrQLyVMmJtHfFALd8Wq5qc9ECune6sUQL4QO86URS7CvtAc0AJ2J14\\u002fUCpA8eiSCX\\u002fQEvNpoH6BwBBa\\u002fQSagg0AEHwoW\\u002fpVEYAQR7MFRhXfgBB4Mm4Z7ocAUEHkyt2iFICQbOSBe4oNQRBUbCoBsu7BkGKULoXzsQJQWT96ib0HA1By4sCpYtBEEEI12G3iNQRQWGAs8rcGxNBezZOsIDtE0GcJgJUbyoUQZorEv2xxxNBTaMkUKvSEkHjU1pagG4RQZiBHDVIlw9BUa9L2GU5DEG9\\u002fMrYbxgJQZYQIp\\u002frawZBA\\u002fNfykFJBEGl8xNO46YCQVBsafJiZAFB2RfSyfRUAEEL9V8nfZX+QOtLlLSeQ\\u002fxAiZF+C8uR+UCPy9rOj4r2QIbCQuASX\\u002fNAySAxd51T8ECCJcBimk7rQLeImYzXA+dAPyqBJTjd40AraYMdAMPhQKJF\\u002fUY8geBAIrKiyWCm30CRerFEOdDeQO65o0MJ1d1AVl+08kQh3EATO0hHVGPZQGhsb\\u002f64ptVA39TzSg5M0UBdOPUPzb\\u002fJQOLIXqiPzMFAeDPJhNrPtkBn\\u002f9MnnBSrQJSaEjT5xJ1AiwbPXlRPjkD0biSQbpl8QHaFLCDhCGlAVfDPwORcVEDAvf6cm9k+QEYxJMv90yVAzqeMr1L2DEDqDd1t1yjyP6nFreHa1dY\\u002f7v06LJuzxz\\u002f7mmbON+CAQE9FuZqbkYNASr\\u002fMENFki0Bshf89pryTQMBmEOfEDJtAs3\\u002fcbKIvoUBA4x5b2iKkQLXmAEeuzaVAYEWIXu4BpkDsp9kVPR2lQN8UYm8WCaRAjBOr2g4ZpEA1bPWkeNymQE1JCe8\\u002f+q1AA7bqUS2TtUB0tpbCfQ3AQANy5EA70sdAZgmm7u1c0UB+mtbCU7vYQLXMyKJiKOFAfIqws4Mb50AXnkL\\u002f5RPuQLVEyec21\\u002fJAOgjyZGel9kAqyoYfyhP6QHFaIdqMyfxANjfcWg+R\\u002fkDyS+MFAmz\\u002fQD5DOnyDmP9A5t\\u002fTsXuE\\u002f0A2Vl1DJrL\\u002fQBztTZHySgBBg59VrZA8AUG65JaqxrYCQe54+SRvqgRB03MQCKj6BkGIOPkyu4UJQXjxwivZJgxBtcWGg5WxDkHZk7hyAXcQQbq45yckThFBawUA1HXAEUEj0b2Aa7oRQTwfXXbnOBFBCTmW5BBLEEGqkgmQUB4OQSzunaz4VgtBraDTIHaLCEGaOhFWYfYFQVsBDZYeuANBTNFG5HbVAUGJ6+I3hj0AQXBE\\u002fNJ2qP1AZ2yVIpD7+kAQKQKZW0\\u002f4QLtIYTBlmvVA36lLhwzt8kD3oMqVCmfwQFKfNHsbUuxA88Vlu6eR6EDCOWsNUJnlQOelPsz+XONABeE2xu3D4UCRu+T4nK7gQIE++pBW5t9AaBgCQymw3kA8ER5sfTLdQNs6oGES9tpAJQ10SObD10Axz1LJrb7TQFL6ApGoqM5AB3VbJHAfxkCeLiqewpS9QPZ\\u002fZ2dkS7JAnd2AzDbrpEBWrK50ZRyWQOwncOhJnIVAVXMDK6GKc0A\\u002fMQuqsVxgQD7ydNwSZklACRXnu+9MMkDyNCnuJIgYQLqfgtdMrf4\\u002fpNRDLcYC4j8sxtkiVfXEP8oZZ333abQ\\u002f1vBJzho7eEB8hExxwAB8QPnZl761fINAQYznH1z9i0DKCXmivDOTQDKc3Y\\u002f4kJhARkF3wm0xnUCe1oGj3y2gQOPpTmAo5KBAZuN1pfjloEAAD9YBqK2gQGElxgV\\u002f+aBArbZLhz2+okA5h9gXSCSnQJx4V2BUma9Ay9EiGT\\u002f+tkA3ohf\\u002fYDfBQCk++C+r6slAGKzrmKpQ00DXwXeRby3cQH+9eBen6eNAUOA6SKoI60ArfrHDhIPxQOvdBe8Fk\\u002fVAudfZxaM9+UBQ6QRIphn8QPgmQmTT6\\u002f1APIeftRi9\\u002fkBpfWmtbdn+QOyCxntluP5A1SmBNHPY\\u002fkBZ15J9Jpr\\u002fQK0YimFXlABBcOGI2kq8AUF1yj0JGS4DQbjR65bK0ARB2bJuG7WTBkE0e8ePCG4IQdASO7HLVApBXQKHn0gvDEGHNK0oktINQQm6t5unBw9BwE95+GyZD0FrLUPUUGQPQfbmp7SrYQ5BgmP4r\\u002f2qDEGbyC715HMKQVKmViWi\\u002fAdBub1tYLKBBUGoQNBaQS4DQQFJoWQQFgFBhxEgl3tw\\u002fkArkJ2bKRH7QC68UzAQ9fdAt7B9ZcAR9UCSaCAIx27yQF+oiSztHfBAmU96fNRc7EDkTrmiGUXpQLLmI+t72uZAchVMhzPw5EBnkjAbZlvjQHRXMva\\u002fAeJAT2WNNwbY4EB4BLDYy6XfQAB1BQanr91AxZrANxB520C2Smr1HbDYQAsgbBO7M9VAv3WYEJMs0UAg25k1ov7JQG3Zq4c5RMJACOVVgZm\\u002ft0ATZTDlWYGsQML7hPSUj59AuvycekYdkEAL2anK6Fx+QATsxBQhaGpA5Ae4f+E3VUAOQCSF44g\\u002fQHiFndyMsiVAd2dlwc+vC0DsxSc6w2nwP82uwJ7AK9I\\u002fXjs6C8y0sz8L9Xs6Vw6iPwN32dw1UnBAM\\u002fZx4xzqckC6B+Nxm456QMaVc1GtYYNAKW5t4NBHi0DFs4rFqh2SQOMw4DQQqpZAkvqJfdbXmkB8n\\u002fHq0lKeQABqVKVZfaBAXiHfb657oUCedF\\u002f7xmeiQPDD9wtfwqNAREf\\u002fc25cpkDMCG4XSXarQOpYLpCAerJAXwUWr37VukCmbr45PGPEQN5TJSonYM9Ax5uuR8nM10ARtoUhznPhQKpXbNctbehAQHlyONAw8EBKa6ylPkP0QLXldhVB8vdAk0OhypDR+kAjZL7Bl6n8QAlGkAE7i\\u002f1ASxeNFdzI\\u002fUD7OjRf3df9QOS05l+TJ\\u002f5AcUXJ4q39\\u002fkCEnQouyjIAQceaYwSiHAFB1qPu9cIfAkEftZMp\\u002fCcDQREwOexUNARBzSbuwkhUBUGjCPIdV5kGQYh1a2W+BQhBSnqtrBGCCUFuaTvse94KQW3HijBe3gtBIyZrnkBJDEHzUWeyMfsLQTWswBdX7wpBX\\u002fRt\\u002fRZBCUGtn\\u002fRCPSMHQR9MNShq0ARBb3yFlRh7AkF+UHjLqkMAQZX84uaOb\\u002fxA2i2kKAqz+EBlO0f3P1L1QGpeYie8VvJAYtjm7ASm70B3niNJWa\\u002frQEvSE+Ljy+hAQzAF7xfR5kDmxWBkhG\\u002flQAvv6ebNTeRAqwt55g0o40BpF5p8\\u002feHhQPjz8x4YguBA\\u002fF9MFJo13kASm7THmmXbQKy5Gv9AethAVl6r\\u002fK5J1UD5c46Wn8LRQCw6aickC8xAVDj3jYe6xEDjYPmDwXm8QJSAflWjFrJA0E+ETWsxpUAf+wJtqd2WQMQ1L2rCtIZAvawZR7HAdEBzpGmKGndhQGh7aQObFktAsUVMAOZfM0DyFHDK35YZQKgSksHAPv8\\u002f8hf2pLSp4T\\u002fG8PhFzo\\u002fCPzlg\\u002fPb\\u002f66I\\u002fUp6fx+5mkD\\u002fFB2gnnB1lQPJfvfIp3WhA05+eHsQkckAZOXNZ0Ol7QISerM4v+4RAJ7sb\\u002fLgojkBWVfZqZ6SUQN5LykU355pAmFaK6E6xoEC6oMKp0LujQMgEwChbSKZA3Jz+5g80qED7pY+AyqipQF\\u002f9nHaFPKtAN9dd4LYRrkBnPdQxNgSyQLACEKhRAbhAOsiUTmKFwUAMXJNpjtTKQCmPJoXVmNRACGvEeKKz3kCUqdrIZs3lQNIiTVK7O+1AVPay2i5y8kBFQQR1NfD1QCoN+PkOtvhAfliWyNOV+kAkS24nB6X7QBoL33lsMfxAtMj5hH6g\\u002fECSw\\u002fc21Ef9QGLlGD2hTv5AzC4hPnuk\\u002f0AZyKkEyYgAQbWLGdXjLAFBdbRkoO6wAUFsVxNnaCECQe8DzAUPoAJBhtP\\u002fDe5SA0EGEBtlUFAEQcmz6coekQVBL2swB7DuBkEt98OtnysIQXE\\u002fWXD3AwlBmb0I07M\\u002fCUG\\u002f3aDqJMIIQWkI7WL\\u002fkAdBN4VEsVrQBUEYcrO7VbUDQQV4giJxdQFBegrPegl0\\u002fkDfHDMi8jj6QDI79UF5VfZAL\\u002f+u6ADe8kB2c3GxEtPvQPjbdNbgH+tAvvOg1DK950Af9aEsDZjlQLKsPRrbZuRAJmrB2B2540BCFfCmOBrjQIjgHJfANuJAj1ydDmrx4EDAkRGuGLveQHB71TOnSdtApYLsF9TT10D5gUNFbnPUQBUfbmcyIdFAm05ER2uqy0C1n7MaTUDFQJFwmPOLsL5An8743g2ltEAXTgIjnLepQIR0nTn9i51A27xBbM88j0DBGYuMe1p+QClJ01K0GGtAi3yLhlE5VkAHDQyx8sBAQP2L\\u002fia2OydA11E3f\\u002f2oDUArVjBNlXLxP8WBzmc78dI\\u002fDIeGPkoJsz8\\u002flRoI2meSP7eckjlKZn4\\u002fM5\\u002fRuULjW0B5Fg+9mRFhQHUsI2i0EmtAyALE1wjhdkDRaMKf6++CQK\\u002fjAGyh7I1AAyAKrXVelkARLAGHeH+fQJhOKb3N1aRAW7RpMc7jqUAm6qE3XUuuQNf8Rh5oy7BA0zGUtdLXsUC1L230V4GyQCdD+QZ+TLNASrTAItwdtUBX6F9ZrFW5QMhXn3ON8cBA75QRtV2TyEC3644cfV7SQEFrgZNAFdtArbJmGDcq40DogT91V7HpQCSGqHbZPfBAZL0XNeJj80Cu1j0lEfv1QCcS2lGq4PdAjqYEsiop+UClGkDZgxP6QNx\\u002ful5R6\\u002fpA\\u002fRzzXm\\u002fn+0BEngO6IhP9QIioSmOPS\\u002f5AR1moGoRT\\u002f0AqzPjKC\\u002fb\\u002fQLxLwNnuEwBBBUx\\u002fWx8LAEHwANMEPA0AQbpHrwG+SwBBvkzUaP3oAEHAu\\u002fw\\u002fwOkBQQVc0tvEMANBFYH55ZGEBEHDYJU+bp0FQb2OM3P3NwZBTK228SwnBkHB+mhoyl8FQSbGaIF++ANBdP00gkEfAkEQ7jd1PgkAQV8OYs3myftAsOP8Ctin90ALn9FT1NjzQPmPZGuoffBAqecr\\u002fSRm60BD1iDMOyDnQD13FG21OeRAvHyxDXCR4kBWDutFp9DhQG1Ks+fpfOFArvIe3Vwb4UBXfkoG8FXgQMSKXd1qId5ArExDi9zK2kAF7kdCqhPXQBHGbb\\u002fMYdNAtlX4r2ngz0CeK+n7lpzJQP9AW+K278NA1j2IN620vUCi81kdm+u0QH3m3RLakKtAzSE\\u002ffRncoEDfKL7k2QuTQI2lmmk4zoNAow0\\u002fG63rckBzJQWfqpZgQJCnOA05r0pAqvmXTIOwM0AWLUrwOKoaQKg5AdlFlABAtK58evnx4j+J147O++nDPz2ECwCsTaM\\u002fpd1MhsDggT\\u002fhhlT+MVxsP8XEwVBjmVRABS44QEvAWkDL6dsiQaNnQHloOfpzCnZA2vY4+6rLg0DPmDxOMraQQCWHQUcCU5pA7Qk7p9lKo0CUQZ96OlCqQJeicPbMvLBAq4M7Udv0s0Br71S9ani2QNeQ68q\\u002fJrhA3WcwDj8nuUBqsjq6yu+5QNe3qlLPTrtAQKVQ0i5\\u002fvkC2EWEbuJrCQHFMs5HpwMhAQfCFVIhZ0UDARw6NO4jYQH+zutLv7OBAw63sk5Ve5kDD3+irxxnsQCyVhoN2xfBAytJY+jYd80BCbtg1cfr0QLtTb\\u002f5Wa\\u002fZACvEo6k2a90BpE\\u002fXGRLb4QLuNriX+2flAueetYlL9+kAL6SNuV\\u002ff7QGfQSDnWkfxAX+vgWNim\\u002fECxZzx+DDv8QMviwuZzh\\u002ftAN\\u002fM2MDrs+kArSuEm7NH6QAo3ucn+hftAGgP0nXcf\\u002fUD8YFLXH3P\\u002fQL0HyYC7DAFBv1fkO8FCAkHEQdVpExQDQT4xl7czSwNBPTkVon\\u002fSAkFhsoNBL7cBQSgTyyblIABB+zqIGmGF\\u002fEBRAdpUv5n4QG6cKeEoyvRA+na+c\\u002f5K8UDJB21TkoDsQB+3G2GMh+dALoCPaGDK40A3k\\u002fded0rhQMNcWSAPwN9AZCMRfPNx3kCjuBTFGNDdQLxrsLEUA91AW95R67F020CgDk29ufXYQA3dTQ34uNVAqBvIIl8o0kBEPoEwJVzNQA2PIjEjHcdA8DhIoWbDwUCHjncp2Iu6QNzpAjthGrNAm9uh41cpqkDFl06nINqgQBLZgM5zPJRACmruk7F\\u002fhkALtQ66lA53QK41KpLRt2VAhXInFULFUkBTZWIxbb49QNqXNYc6mCVA2FsKYF+7DEA1sTZMq4TxPxtkl11TltM\\u002fsDetDxYYtD8\\u002fpBDHjPOSP2pGFemx+3A\\u002fJiPpxoUaWj8w6+CXksdRQO4bpF5YW1hAxJK3w4piZ0Dw8si+yzJ3QHzuoKtpwoVAJmpUv7\\u002fxkkBNLaxJOYKeQMkFBLi2uqZAjxzFd2Jqr0DW9m1tZDu0QNV7DMUdc7hASod5NgX5u0DnoOBP6KO+QInNbu2kSMBAxpC3kG0SwUBFjI4q3gXCQJ9w7HcrpcNAecLz0VaixkDcgvE71tDLQA\\u002f8+aK3\\u002fNFAxN8gyZ3K10A0bUt7QUTfQEflArZWAORAtne8pbul6ECMahh3xirtQIelb5ZznfBAMYsrTwhX8kDxJs7WTsfzQHyiywUPBPVAtfq015oj9kDN+kBt9yz3QJvfGoDXD\\u002fhAp85NYNeo+EBB2BAmgtH4QCgU28hIdvhAfn+s5MGo90AGtpCl6KT2QNUxOlGDxfVAzaZZGnVr9UAtDMMet+D1QE5PEhXfP\\u002fdAwPhT6ppm+UCk5mzRVvf7QBRnpDEZbP5AJ282AvobAEEYRCTdq3YAQYCl6\\u002fxxLQBBednxITWT\\u002fkCaOFO\\u002fOd77QJgCCS5JnfhANZLV0Yov9UA0EqLIj+DxQCm546Wixu1A85jEsBmr6EBxriIKEIvkQPOymUP1a+FAFp1y\\u002fJl73kCp69Kl7rLbQOWHi5I2AtpA5N5ol+7P2ECqzSkO34XXQB4QODKludVApK0qpUFJ00AFGfVMVlvQQMnM2ZnshMpAJi+HwlKoxEBq\\u002fawg6ya\\u002fQHPWfWge3bZAdZu0AEdXsEBLqrvBCZ2mQFb\\u002fMHf3+51AGmnNZ0HTkkAp16UklyaGQOqQov8uOXhACddiAtJ5aEAExBZs9sJWQCnNYqTybkNAb+rakWhrLkBeUyzVes4VQJqFfKXSn\\u002fw\\u002fYOjX\\u002fdcy4T8hiJ7UJOzCP3GrX9dmEaM\\u002fJXfypCmggT8hJ6rmLtZeP7l4b0tAJkc\\u002fk6wZPajcUEA9j8dmU8tXQKqDl0fwy2dAm\\u002fAsdhk9eEBcuhdBphyHQF2mub4bV5RAwIHB4rCBoEDlcHGdP8OoQNuLKkkpPrFAXpdLVjxvtkAZEYbN64C7QCStUPKEDcBA6XR9EkoOwkBZ5rvaP9DDQJwfAazpesVAPN+JM9FOx0CsUeX9j6rJQEOTSfO7Dc1Ax51j0jMG0UAvztvegJbUQCtWXVGGXNlA9hi6\\u002fFBK30Blf9BlWAzjQKkavVjWqeZANxSnirM76kBemodXq4ntQP2vH2NaOfBA6VfmPxx48UBJdgzNeobyQEpBLDPeafNA5YmG11wf9EAjGbBWP5j0QJZLhwMIvvRA3shPvGV99EBJNM7PJNTzQAr3QFEf3PJA8ba7TJ\\u002fN8UDXi9inu\\u002fbwQCVbSS9UqfBAAxqtIkgk8UD2MQPZuX3yQCXhl+SzlPRAansENZQQ90CDDUFhTXL5QAHYEsEWNftA6mYeAMrz+0C4siAB8YP7QGMcRFTI+\\u002flApK0AvRCj90AYGKwoLtj0QHOQ5IHV9fFAxJXTqgCD7kC+aiiZT8npQK5wloJE3eVAFU6tQme34kB17FgSxzvgQGWSn1KOkdxAYidJS+B82UBquOVvSvzWQHaXD7iZz9RAAURTceS10kBtmCIcF3zQQME8BEWpHcxA\\u002fIhtGU8Cx0BbkUaLGgLCQFwW6bC+AbtAZTv7eY2Cs0D\\u002fSTPBSVWrQO\\u002f6nO2ZoqJArfMWS\\u002fyvmEBAAcgA74mPQILmGduTN4NAea27EnoWdkDGeNQ7oLZnQF9xSr72mldAUvKs88usRUAWc+51t0syQJkdUt\\u002fMVBxAOb3PlAIYBED352Bk2BbqP8+rohwn\\u002fs4\\u002fAPF\\u002f7EzXsD8BAsQ5qb6QP+FCAqY7gG4\\u002fauWNmIA3Sj9J+\\u002fmfPlkzPw5BLppP9U9A4WLi\\u002fRzMVkDKhwRaIh5nQObArThIu3dABgexDmm5hkAqHz2fIA6UQEtCi5iIUqBAggv6PRyYqEDoRWgX20CxQClHIollubZAxPdVjxhjvEBivET\\u002fhgTBQDBvJaue0sNAbDuo9yCtxkBvp2ruWbHJQMCsDGnqBM1A+nMVs8ls0EAgYe1aObjSQK1CVU7PiNVA2ifvWff62EDbMFEKuhbdQF2sSUpU5OBA+vAFi+px40C9sdphOhXmQCzwlP5OrOhAlOQDnC4X60A4JkUw\\u002fzvtQKlUFKY5Ce9Apa42XzY68ED\\u002fOdXTwLrwQI4ZIN+lAPFA2XSJQOkD8UA9OuyQurzwQL3yR3JFKfBAgwnrqaGp7kDpNoL4i7rsQNrkVx5\\u002f6OpAUNKACDiz6UC86OZk7p3pQJROMnC\\u002fCutAuoKulK0S7kDNPEAYQjTxQNBfg9rGq\\u002fNAYPlD9Rb09UCMgM\\u002f0qpH3QK+bnPi5LfhAROuQoNet90AVvavLgDX2QNPzPeXJE\\u002fRAdUDGJc2n8UDw0i4vcY7uQMXIbDj6WupAfvRvAAfq5kCwk\\u002fxCADPkQOgrXIulA+JAgNZWTtIc4EAQ29\\u002fedZvcQNZPijOQBNlAM0Ywm6J\\u002f1UBS2Xi4GizSQFOQErZyQc5A6eSSfj3CyEC7Gy538szDQPKoDAUwtr5AUp9QnEfytkDejEgEuniwQFyZ23PKwaZAqB2\\u002fSdtmnkCMImGNoLuTQMThmwn77YhAFlZbgaGHfkA21FrtJvZxQA7V+46iFGRAsrk+V6gZVUANDne5U6dEQCo+ECJFtTJAAa7Cejk4H0CRc5AFUOwHQDJu1fyWzfA\\u002fR0XyuSKc1T9K\\u002fsX28Gy5P4aXJZB5W5s\\u002fNBl2017qej\\u002fEWY8CdzxYP2sz1p9QjzQ\\u002fY9gj9XX3HT+XiEmsz3hMQOHEtY4FWlRAVoOac7atZEDqS\\u002fvmbDx1QCENOOw0VYRAiVEdKlD0kUCZH\\u002fGV+UqdQFfvMoudMKZAiaDjwft0r0DR2v3mJxO1QI+45EMcBLtAXSppoJLHwED+Aylj1mzEQOUJ6BtfjMhAeecQP4k6zUARd2TQFD\\u002fRQPPrakOKKdRAbCKL1MRX10Bs1Xd6rsHaQIGdpL9cWN5Abos\\u002fWi0C4UD\\u002frVGSJtXiQHeWiiLEmeRAzwTx8j5J5kBDiwO8zd7nQO\\u002f9Rj0sUulArnMXSxKU6kCiBJ5UnY\\u002frQLjSvLdCL+xA6hUfoqJh7EBi\\u002fe2rNxzsQLYaij\\u002fAXOtAEJW9lUQr6kCruT8ZnJ3oQI8pUt0E3OZAmpgqBHYl5UCCF8EAENHjQDZr4cEzSONA1yBWwC3040B5X9rW5h3mQGxld8SzxulAu8Y9Y2yL7kCOpBkULNPxQGRUOl0wDfRAnoo2U0p+9UDzLjRLvt71QOvjtFKVKPVAe77UiU6T80BZG9gMNnzxQJ2qSG\\u002f0j+5Ax3DK5N+R6kBD5u3JsV7nQApi1ktkCOVA\\u002f8tmO9pb40AfsKO3iPzhQBb0vdnhi+BAPUcEQ6mc3UCi85Q7JXvZQDWwgJM\\u002fAdVAsctjvlGi0ECoWfLZb33JQJWrcDKnC8NA+QO3KE3ju0DTKxXYyf+zQHpAFWLx96tAIwMp8Jn3okCP7yogqeCYQJpTv\\u002f2GlI9AojCTEhR3g0A3kEn\\u002fdl53QNhBDR99TGtAvX5udXvXXkDK48zLo65QQFkX2yzwFUFA\\u002fH7JUKBlMECFIYh5xz8dQJ5c1UzaGQhAbbrRp69F8j8WpOGSoGzZPxs\\u002fwmESNMA\\u002f8gEuTVrmoj+EGMKP\\u002fCiEP6ClsCW8qGM\\u002frOyOBs6JQT+xiJGHf3IdP85J060lQAU\\u002fjnULuUI0R0DWGbPAJpBQQEWq\\u002fQw2ymBA1+mc3n01cUCISdxfE3aAQNqEIY3lGI1AtEK+hcvUl0AAa6K4MzaiQJGcGm6mOapA7QeNYpMCskD4vmb3buq3QMbTZSRqFb9AJjfLDF3uw0C6RxcwUkfJQO1HYHNgos9ATyicGchy00AWg60Zn2jXQCqiqFnmhNtAse8qWg2R30BdcfCVi6nhQO1DAHpSS+NAfRafVo2d5EAqXWGBI6DlQOVTABkHY+ZAknmosPD85kCm5x5wb33nQDqjYTEN4+dAY4nGsXMb6EAkJNWSUAzoQOmBXXvTnudAEf7TM0nI5kAH23utBY7lQNltu4CgBeRAFIELoHtT4kCLh6FMsqngQCr\\u002fTzvTkd5Aa++J16MD3UAq1jozsWPdQHUEXdkvM+BAvax33Hw840CsXzhu97fnQG99ax17Ju1ATQh83SFT8UBl9A53xZPzQFMSmVrW3\\u002fRA4yszDTv79EAb6+a8pfTzQMboq7g6G\\u002fJAKeI4m\\u002fK\\u002f70BsHRDAhmTrQARoqPyVyOdAR\\u002fTXiN445UD5pRSkDqbjQI4ToBtCtuJAQz8nUa3p4UAjIa7UgcrgQBLabLsBL95ArFMS8k6t2UD\\u002f2VofBJDUQL1mVNG8CM9AC8+5SGU1xkAJHdZk03O+QCCBexSNPLRAUWZH0jJRqkB6qJrT9tCgQB389EqoF5VAFszFP77ciUANegXGS+d+QC9DfS3e\\u002fHFAWHHFDE1uZED0zDYum6ZWQOLv6UQobUhAtlD6abhqOUDU+Zf\\u002f5z0pQDnrxM4DqBdANyY7\\u002fdy3BEAJRvzmUNTwP7wQUN8SOdk\\u002fol31Z8ZfwT8r5vYk\\u002fvKlP9vVZ22zY4k\\u002fh2ieCIzdaj+KZ0evSf1JP8IvqOuH\\u002fyY\\u002fmIzeqzMjAz8EYkW5rGXrPsUuMpFwHkFAJ3rxVf1gSEBjKWR6g6BYQANAhn5SMGlAvpIdNFsXeED4NgbuglqFQJ+\\u002f+HINn5FAoWwrOrNSm0BEknw7ZyWkQKUsW3yGqKxAQ5FNO0zus0CVFMmgiFu7QG0Rwc97ksJAFo+PBUjYyEDFw\\u002fCjWULQQCUgZhz9rtRAQCwnU4112UCgVs7FykHeQOKAudLjWeFAaFKQ8z0340DowNwI85TkQDcwy9CiY+VA2vEGLUuv5UBG+Bn3cpzlQBbJWjC7WeVAeqw4Ze8L5UCHECQqiL\\u002fkQCDtcIRCZ+RAkQRCpuDm40BBW9JWviLjQLGLuAKoDOJAPuxrAD2q4EBrV8\\u002f9FyreQEpyiJPx7dpA2rn7xT0N2EBXgp9lZg3WQM\\u002fhu7Y2j9VATiUBHExJ10Cw4x2AnebbQAxfMtwX4OFAaHptPS5C50Aj5+vSjX3tQKQbtOMnx\\u002fFA6h5nn7cj9ECMFMeA8Fj1QHHsY57NMPVADaVaH8HL80AvgUiGGJHxQF9kWjRrEe5ApdV0e5hh6UCb2vQkC77lQF+AUOeHZeNA5DzDCs8t4kB8LfusPp\\u002fhQOlhrkcyIuFAYnh9zfIz4EDDVfeyISTdQEvZIfS4lNhAM8TXQmRS00C3un+B0EXMQGfbtkv3UMNAO2D9GfrUuEDqm+0r3l6uQB04djW36KFA9FBn2kWhlECk744ueGmHQE8JNJAnPnpAgTxQPLr\\u002fbEAcTylUc39fQCfwI46IyVBAGjByGQ2MQUDILp3uRfExQDlFGpFh1iFAqWFS5hoSEUCUqtgAkRb\\u002fP\\u002fRJF0+Aouo\\u002fUO71sf5F1T9ZFlx7cHa\\u002fPwiRoBvwb6U\\u002fcwqSKGnUij9uGs7oTMZuP2VfvG50J1A\\u002fnu3BF7oELz+VQhThMj4LP3wDzb18feY+EYdhbVf3zz6oUPz7hMg2QHucYYSnLkBACizMMc5LUEDC+25D86ZgQIYQAx7V629AjQOpRzN8fEDnmoMiS9SHQAJ40RQJ6ZJAyBigTCLknEBIhF\\u002fl9Y6lQCoXSvKLwq9AUVfWfekot0CnFctLaaPAQB2NvVbJVsdA3aQnvKqrz0C8eg9zkqbUQJzICJZPytlAcf0PdjPQ3kBQQSoSNZ7hQK0fg7MmT+NAdKixoSFU5EAmzM6Y36LkQNeFWQklU+RAvwbpXEOZ40AYWG2jf7TiQMYBxkYr2OFAPbbkPNga4UDViP\\u002fXu3PgQALBhe11i99Apg4SBYvh3UAqusgfbMLbQKEBXKSzMNlAJbdoLwdb1kC66CiqmJTTQNY3ddTiTNFAGpfov\\u002fAN0EAVYS+Vd3\\u002fQQJIi2LD8X9NAx0\\u002f6awVf2UBod8l8jmfhQPUAjx5UoudAfEpJoRWr7kCUiZ0CMa7yQN7OFznRM\\u002fVAZ6klATNg9kDIy1Au0P31QBS9HW8MOvRAItESMc2R8UA5BRd5nUftQMhEXjK5\\u002fedAN8SyPor940AhhcUmi37hQMCbEUBnROBAHOLC\\u002fKCK30CZi\\u002fWvHL3eQF9ROsRIFd1AlYGxZU4S2kAU8aGg0dPVQFf2pFbG8NBAMd40ryVPyEB63zaUtSLAQOHTzbr95LNALI6MXx\\u002fzpkBJgQO+LguZQHfCLptPOIpAUYtgTzy9ekABNCBQleVqQPL+vxP73lpAWEB8zK+wSkCXu8EUtEw6QLpD6Ll9oilAieFYUBmiGEAtFBI13zQHQPqiscgTPvU\\u002fBXawTqis4j9\\u002fUmvjCSbPPx1lHMI7YLg\\u002foZUg\\u002fMe+oT8KKWA0HOKHP12Hu63ulW0\\u002fP217s1PRUD+Z2fDmsoMxP7S7FmBrsBA\\u002fZ18uvmca7T7eknQij9jHPo6b6Ilq1bA+1\\u002fJDaONUK0CJY8XjkmAzQNEJ+IA6fENAD0dV8MDyU0B0FUUU0D9jQLdRzC1Ra3FAQRamIEHcfUAi49yfV5mIQL3ntGt+x5NAkZof8DBnn0CMjh1RN7GoQBE8wo4zILNAjKGjjGzgvECdOef\\u002fRQPFQLoqzD6DO81AG3oXqDBY00C5V+sjiVDYQKcoRU2gCN1AxHUqtc194EAYUgZdEdzhQJoqY3XDfOJADA9Rg6hd4kBqy+L9w5\\u002fhQKeNHgu+f+BAONz0x+eG3kDp6h6lmkbcQL9Ydbx0c9pA4RqW8nAA2UCICmTPS7PXQDWam729RtZAiH104omK1EAS3Tdg\\u002fnbSQPhrgHV5MdBAL5XJMFgNzED2RuxuP8fIQGWzv86\\u002fqcdAV39HVx8TykC80CPVzcrQQDwRlFrB0ddAekBNGpU54UCciayqZBvoQBFFUpE1ye9A5qILNvKB80AAFSbMvC\\u002f2QPQRx1YuXPdAlG0bXWrO9kDoID9qPrv0QGPtVby7r\\u002fFA6kUZuzq77EATzyCJP8XmQKgwJ0WKQuJABPfXqtXQ3kAvIEMzQeLbQBcELu2tl9pAZZ8BRzGs2UBb28bTuB\\u002fYQL6KjrrFedVAf\\u002fW\\u002fcGjU0UDuR8qnCWLLQL8f94wNYMNA2Rq1Z709uUC7lqwTkVCuQKFk8Muw2KBAVPOjqNlxkUDeP6WYKAGBQNCDmWaApW9AAvMeL5eUXECEnwgeWXRJQLotZ0mfnTZAH4WyUKgkJEC7Fsyc3\\u002fgRQEIpd04s\\u002f\\u002f8\\u002fI\\u002fEtcwk97D\\u002fruGrUNnzYP6wwI6MkncQ\\u002fDvnTTs2gsD8h9UnU9GCZP7CHAMS8III\\u002fezRNRVgLaD8Go8HBnnBNP+QZg1ujkjA\\u002fEuyp\\u002fAcdET+dDh6HdC7wPkWw1KhZA8w+DWhg3czIpj5ALE+0IvaPPs7XJ5pemh1AyxPR0y39JEAgiNTzkyo1QLKot+Cu2kVAatEUmohyVUAPndWgJfljQLJOnk3D4XFAR8mRw+RAf0BPg4MQHPWKQGsdQ+gFApdADPCUg7JNo0D\\u002ffiBmpnevQNmRHa40nrhAwNB5Xx1RwkDJtqJHJcjJQIkJqxfwH9FAfAIahDl51UB4tX5hyHTZQBonsoW\\u002fl9xAZp6zek+B3kBqJT88ogDfQPaLvhPXIt5AoxCmqcwz3EB5ATPQ0qzZQJVFRy6dEtdAT+W\\u002f487N1EAVNl944Q7TQIKV+wc2ydFAJLpRvh7F0ECgPu9L2nzPQDuRAedqBM1ArCO0olwEykBbTVR5grXGQHGCZ71en8NATP1SLgiPwUAPLqSoz5jBQJlhi5cGH8VAQ4UcHZ6+zUCM\\u002fS3niHvWQNnPF4fr3uBAedkXtKEW6ECTylzNNg3wQMW4aRfAz\\u002fNAQ38JY7CU9kBkGZh5JcL3QOVNJXD+GvdAGg9OMEzW9ECnVSXas4nxQDZ3HlK75OtASrXXZgNv5UC18NTnEIDgQKWGnJhPmdpADnT2E7kX10AJcBsrvVzVQEKTj6qKNNRAuXnbT5S10kBZ6q\\u002fhvnPQQBv9CB0sAMtATZND6kR8xEDJRxAPHpy8QPoRGh5pWrJA5KeJE1KhpUCJuD9O+HOXQJBM\\u002flC3eYdAox+b6lrSdUDhHIj4kQNjQLJwUsVyek9A0KDIRdsuOUCFn+zn\\u002fNYjQDeAQWk\\u002fSQ9AdqWal7vs+D\\u002frwpfAQxjkPwvZ1kRQUNA\\u002fFaEXvThruj\\u002f7\\u002f8l9DxClP+qLaTaxSpA\\u002f38EXSLYXeD9cIc6OJ89gP1JcaP2E50U\\u002f8keN06R0Kj+SCCUBuXUNPxUItTXjI+4+OjCIKF9EzD4Nj7rQ9kioPmHDNVk2m4M+3i4MxNtTaz6Nshc8BScNQK9NETL3yBRAB\\u002fBy4zVGJUDH9HjgnYk2QAjUC9i4CUdAv9zUTFjBVkAbL2ms7fplQKBOnO\\u002fF7XRA7JDjQlqgg0BzKWCNbPeRQHLJjcxVtp9A9WPQeFmrqkBfRxZU\\u002fzC1QPZaqty8qb9APV5jsmIyxkAIowVENTbNQJ153YrtEtJARpGZZWMV1UDvJITdBEDXQEMGBEpTTthAQoMrZego2EBVy5MGPuzWQM0lX3215NRA89vHCMB90kABguW6FSbQQONYnCf6YsxAaEld\\u002fzSIyUDQQ95kN5\\u002fHQG+o5qYgRMZAwMybl5j9xEDiEtSTam7DQNoohfhjd8FA0Tk\\u002fYCCIvkBqht\\u002f96pC6QBNaPSMBe7hAYmHUM\\u002fxMukBLtYuG41jBQOEevvqcaMpAsMS\\u002fz4ro1ED10tlsexLgQLgaUWcxPOdAM3zenfss70Da9GiX70\\u002fzQC1\\u002fzryLDfZAz\\u002feowaA090B5mkfo9IX2QJtwC9v3NvRAReV8wVTd8EDq2XKC7m7qQMszkuz92uNACSgDNcmW3UB7POdMouPWQLW4TaP1CdNAFA1KCmP40EBnUrd1nCzPQE5c6toIMcxAD3gg661JyECVr0Qz4o7DQCXsqmUFJr1AmzGKpJr\\u002fs0Cj\\u002fgugPTSpQBHfNR8HJp1A\\u002fEC35+DwjkCGr\\u002f122i9+QGaaYe2zJWtAsw5vXDOeVkB90xDTI5pBQDbAmkox6SlADuY1LVJZEkCCEDV0w4f5P+Fmu6w90OE\\u002fPOH40jVOyT+T8n131WKyPyKxa8VbLZs\\u002fKOrxnisqhD8q7FDAKIhtP2Fbalrw9lQ\\u002fBAfrEUNkPD8Qm0RZlxgiP7Dc69x3fwU\\u002fPbOClUai5z65LmCT0+3HPsyci5ZjPqY+\\u002fvJP0az1gj6XQk4wm2FePugm48ArCUU+vG3L+h3E+j+7jyJNv4MDQLh6l8T+9xRAyth\\u002fwCvGJ0AGg8kuOY86QMc90EhVIE1AlSQQFbZUX0Cs1Z6VuWhwQFCF767rioBApoF6AaG4j0AE433HkaacQKR3h3bsOahAe5zSNbgds0ChZlW7jiC8QHSrrR2ZT8NAljtjkrDMyEBiepYAl+DNQLwoUIYt8dBAD8PJv3om0kC6Ue\\u002fhRG3SQHvUBd1kyNFAG9Ll4MVf0EAWh\\u002fA7UPLMQPiR2XoB1MhAdPJWt7cHxUCRiCT36wDCQJo\\u002fUgq0079AseNySsREvUBgW3cFDq67QFYKfRvcS7pA7nOHACuIuEBVmT1IRC+2QKQk8H+JhrNARV+Xn+VOsUCc59kWrMewQHsZ\\u002fysEwLNAb7SxUranvEAk+EeBwzTHQMi2Mx038tJAoahvWRiJ3UCc3pjC5nzlQEBB55KE7exAXq+6KUH08UDOGiNs94X0QDIMtKuQmfVAWAxgjxby9ECqPTsaF8HyQGwGjXt4KO9A3JIROt9A6ECWg2XwogniQFhS1ITAgdpA7Y1hjSsf1EACediZ0VfQQLieM5TQV8xA6jM8BXtGyUCWIqlmBy3GQPXMZupTicJAJ81\\u002fa736vECsi2Jey\\u002fq0QOgGiQas\\u002fqtAXs1cDbgroUBBcb3vP1eTQG27u0DL\\u002foNAL2plgpf6ckCXtj8dlY5gQL6YqPm\\u002fnEpAZIEFZ6zIM0DfpA+8kmYbQEaJoMO53wFAdhyDqX9V5j81sIDvIFTLPzGIJlG2ybA\\u002fFXLkolMmlT\\u002fArLvLoJN7P0lQGmVOgGI\\u002f7iwO090cST9COXMRLN4wPxfUnQmx9BU\\u002fOrzkJmI0+z6Hc4asgajfPrdfLbh0IsE+I8uFnNgjoT6XkkKlJY1\\u002fPsj14vINqlo+FgD7nHUxNT4gb3YQ6SYdPlKD4tVF1eg\\u002frPlyIQ5U8z+JqedxW2YHQMf6upmXPx5AdraavOlLM0BZQQ6hdtlHQM0g84bpLVxAateMvEJ0b0Bem\\u002fRfPm6AQJhCS0Cg7o9AeHkFr8nBnECURwsg\\u002ffanQFgz0dh6e7JAqNo\\u002fQSprukCrVzSCHYrBQC7pSQiitMVAMxSpnokiyUC20m4LAFvLQErH42LzGsxAlO7Y3bRey0CmD4g8XlvJQD4ExsjPb8ZArwB\\u002f1NcSw0APkpfca36\\u002fQK7etTLKurlAIY+x4fddtUBJaKuIlIWyQJ0KUXuJ7rBAxv2bSDwWsEAMUETlDdmuQBJRmRTLC61ANz5T6h2AqkAciddfkqinQApwMREmvaVAxMIbhsHPpkB3XRAHL\\u002fatQNyX0gjqtLdABsUwqEwQxEANMwjf46nQQKQVrH7fHtpAft5vzzoH40BmpWYxBZ7pQFXdCWSDyu9AYmKQEkcq8kB1oxdclxzzQET1x\\u002fNRhvJAx8867UuU8ED6XSxr8ovrQIqLOU0\\u002fe+VABlh50gwR4EAQQ66eDuHXQO1il+CiZ9JAY1MisaY7zkCi5RDxzyzKQJ0Nthm298ZAeUeTSA2dw0AK5mKHhbW\\u002fQO2BPT\\u002fS47dARclMMAClsEBYfD4ku12lQGAQn+BiOJlA5qCBN7BZi0AUIGYDcz17QHSM0cJ96mhAjrezJgnwVEB9C833vi1AQP01Pp4XCCdAWbJkefpMDkDUa8Wdo4byP6FXbuKCQtU\\u002fXLvcq91Atz9nVTDrH82YP87okinMjXo\\u002fHv1nuzpcXT8dzrPGnwdBP0lQPZ3IoCQ\\u002fgTR0qpeHCT+OGu1ND2bvPkaNZhPFtdI+Kym2TtwwtT4ag47jJX+WPiP91mC8LXY+GSXqSVcuVD70Urcw3uQwPrKjBCIYoAo+cGHyLP0u8j2cTz7kLM3bP\\u002fxkKRCPZOg\\u002fpBATigh6AUBRlTEPJN8ZQKp6f7JKSTJAY+EznThASEBH\\u002f4lxw+ZdQAunPgTGCnFAmj6X79HlgUCLJUWocUmRQLz8Ds+osZ5AVY72kPINqUCKG7jeANOyQCp+2oeCFbpA7JVkuEu1wEAWXLNeldvDQFPU\\u002f6EA\\u002fcVAiXQT3NTLxkBQPQ0oOj\\u002fGQPedEFXyi8RAB+\\u002f1aqYMwkBEn2liT1C+QGEWJaS\\u002ffbhAfn22CNg\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5WGFsUBcHUtgbJivQMP0Gpx2JapAuOmsYsLno0CtVwppzPibQBrcIq3iN5JAkJeKEBAihkACVB7FBlB5QFmycb1WtWtArWjk\\u002fA8IXkA7dykU7DdRQH0XxdAW6EZAMSJfGpGaQkD8P5BA7+9BQKHXqtzAKERAf4coO4TBS0Cj6aCJGOdXQHVVC0qgkWdA214oQEbad0CMUuddq02HQLdyuhZgdZVAOo9GxSx7okCJca1x8rStQK6YnExoQrZAUHvnbO0Rv0BpN0hc8yvEQNCa4ynKVchAN+T2onhCy0DiDojS5GLMQFgiNHZlnctAg9TdY1BmyUBaENK8NJvGQO5dzs0mK8RAjlra+0u0wkBepaRtG0fCQPqEvaK5bMJAAQUliqRqwkC5hK3G0J7BQJ1YRokgi79ARt8TQ1sVukCOclgghsyzQBAzDwd1gatAnfcn+Fl1oUD2YpwSpTyUQKZ1tyeeaYVAWQIzEKStdEDQPCw\\u002fcTliQB5DFpDfUE1AjOK+HqSENUD3B1SrDNQcQKNRkQ6inwFAhD9wAUSq4z9F5glyzQbEP26w9ZspnaI\\u002f7gRBy+iUfz+HhlBHW3RYP8HkSZPvSDE\\u002fNJmspXBOBj\\u002fwcd4slEjaPjC2RKvZSKw+ZL90J5TQez6r7\\u002fDVBgdJPhWTbRBmqxQ+HNiH3uyW3z0RFiqR886mPUPS6nFNXnA9euuDsOKtOT0e5ZJTgDIIPXPWPVQ6nds8hifLkbEqwDz8erdxOe\\u002fEP\\u002ft0kJmBRNU\\u002fIvofQ5iJ8T9xxr48a9QLQNHj5X8KOCRAEWO3UMHQOkCCmndfOjpQQMBDKfFb7GFAeWrIv\\u002foQckDHGBcAsJ6AQEXX1x\\u002fM6ItAFv\\u002f4o+5jlUBScHUx3+6dQKtoZLe1H6NAVNC7u6JRpkBuw9x08cynQGDc1TNQNKdAAIqMkkGzpECOPJhPl+qgQLLPgIl2XplAM1aL13d+kUBAF4tbPkKGQOROBHQOP3pA2aqpQGvhbEAi7KKUgQZeQEYRGWw7RU5ASg39z7pUP0AtO2LXIHEyQHgBRMUMcStAm66CFhCuKkBvJ96VLP0wQIPAH+v2RzxASM8cVWpCTEAdziIEjexdQGzR8szjAW9At0cq5ERpfkCZGMY\\u002fcuyLQAr5\\u002fQ2U75dAG4Mc1dEko0CCmcXDzo+sQGxxNlRW27NAIteUdTGzuUA+Quz4guy+QKtBL0bkScFAah2KxhP+wUAcTGgtJ4jBQJcBvFJtN8BAPjeHS1M3vUAcu+NPL5a6QMf+S2cuRblAuxUUltI\\u002fuUB4j+nUGdK5QIlacd4x+LlAArJe\\u002fgPcuEBq\\u002fIOT6zC2QAspdYgfRbJAucrhQsSXq0DpizBuXg+jQNUve79UDphAhVlNxm+4i0C7XhgD\\u002fid9QOj6mUmx\\u002fGtA9P6+ASWEWECxY9UdBZlDQL\\u002fanZ6ClyxAowzitGAIE0ALupZufB\\u002f3P1vRM+23otk\\u002fAEKS36DvuT+umzCKHPKXP2WQHGjpLHQ\\u002f9dyCgJ8GTz\\u002fGauBfZcUlP3Pw7AFr4vs+Zf68czZM0D7IKKB\\u002fbWOhPv14+a1I73A+dIFRNtwdPj5OohntbnYIPucKXYBnLNI9n0+Jhk\\u002fHmD007rI0J0lfPXRSYvf5vyI9Y3XWgKes5jw5bQe7uTWvPBiRB3Egq4k8Sqzde0O2uz\\u002fkDZuK3ALMP9QwUG\\u002fH8+Y\\u002fYOE+X8YaAkCvrnLBbicaQBipjwQUPjFAF+3ZyiK\\u002fREDVhd7ppsdWQDutHW9Y02ZArszyclDfdEAilZvlNmuBQBarkzgCiYpAQvcjY9NykkDUSdOSE2uXQHZsvIyGJJtATQPt74W7nEDeEmGjNMqbQGWy4zxSkphANIie0pXgk0CWIELpy3SNQIl3mprPBYRAOpDP1TAHeUDeQbkRrdtsQDy7Fjaw2F5AeTT1X+vVTkBjgoL4bF49QL0ZaV+52ytATIn5YmESHUBHo29kNqITQKNEfezHtBNAb9NHYjWcHUBXuAXQzkotQPV+xZxHIUBACol2UyC8UUCiG+7uapJiQMmaOTnTOnJAmnWg3t6zgEBub1q8RI6MQK509b19x5ZAMbZOmED0oEC+DuhU\\u002fYanQGhkiDriZa5A02M1WNZCskDUpPkjR2W0QGVT6BEeOLVArGOHTG60tEAgXQZCsj2zQCPFl\\u002fIAgbFA2v28vuEtsEA94KiBYVOvQBTmi07TyK9AbZ8QYF5lsECLAWET5YiwQBffZJm9nK9AsH80NPAdrEDx4diF6wmnQMg\\u002f6L8fTqFAbn7kdjLGl0AepdJMq9WNQLa8zKcoF4FAaB9tegXfcUBJ+tpx7A1hQGq3LoobtE1Ath4E68yaN0BZrqcf\\u002fR0hQI+rM3lMpwZAcrx05ZZb6z\\u002fQDWkqDCbOP\\u002fE8HN9XUa4\\u002fNegLyiPSiz9r+aKI5UtnP0xQ5OMyzUE\\u002f9lpUNHXTGD9jBCIMK5jvPvwq\\u002f7GWWMI+dSEeTn1xkz6+kI7ZFc5iPnhlolg8mTA+O\\u002fxLKn2++j0W74t5GqvDPfhlkbL7a4o9REBi9OQ6UD2943Idf00SPdktUmuEJtM8eblwiEs+kzwLwb+t+axjPB\\u002fvPW2hf7A\\u002fj08hWJ+XwD8oZb1TyQTbPyd9Ukf+L\\u002fU\\u002f0gcuKTRuDkBHCxXsFPIjQKZTMXEa3DdAnt8jmqcLSkAYSJ5S7PFZQAkgRtDIlWdAaMXPjNeQc0D\\u002fq4FRaKB9QOq\\u002fqTTMeIRA42GyMfDSiUBeXTFCzLyNQFXZqwNBRI9ATm0\\u002fmCAGjkCwEXyIP1eKQI8TN6AOIIVAL+nsjsr\\u002ffkAqkjcAjNR0QFQCayzQrWlAXA59j7saXUC8quftE29OQO\\u002fXJ\\u002f8eij1A7IfcNzf0KkBoJw8C0+AXQFiP5I6rcAZAUF3za7G2+z9X5GtPkaH9P7TQ8oT0eQpAX7bb8I2uHUBfWym9RUIxQOaVj2qDUkNA6WKHh69SVECA8\\u002ftB1\\u002fJjQNtPK44VQnJAohf5sZwuf0AYJM8Xk9qIQOjkJEt5fJJAon8Ai1OjmUAZJjOHJI2gQNdI5zvY3qNA+k0yFkstpkAcsX0u7xCnQLAvZqarh6ZAYSIHtJ0DpUC8I9vmZkSjQHoskIrkBqJA1FfdA+KwoUDKVr2QCSSiQKP4eAQi0KJAnCnvySH7okAQenxQJRmiQAiA13aYB6BAgh12iS8jmkDP9TcNyIaTQG6twPH3rIpAWayFPICkgEAZKmKYKPVyQLDLuG6dtWNA433z5TGzUkC5vPqcyjBAQPyOeXVZlSlAWvlZHr9xEkA9yYgdnET4P7dAz2EEI90\\u002f9FW7dPDrvz\\u002f9cmFnremfPxnq4kDVHH0\\u002fdbkCiAw8WD8hjCrgq2gyP6SkwK47hQk\\u002fuRXCH18k4D7Cjxr+QqKyPmQsUqrpoIM+neR+NCfeUj5c0uwT2owgPq\\u002fmawLbfuo9Uk15s8Jasz1yW0PgaM55Pe3ZLRCsaD89fBCOBtt0AT03iNOubb\\u002fBPDUAQHlxn4A8tIkD\\u002f+nPTDxNCtbo7qqhP39av1ecrbE\\u002fzt9RBqSazD\\u002f\\u002fWz5psEzmP96Z1D3B1\\u002f8\\u002fAb9jgjbAFEBVte0Z8q0oQD7DhErDyDpABwloH52GSkCT+WPT7\\u002fhXQE6ii\\u002fA5xWNAlTBnPWfCbUBVyS23zXB0QAaOZoC\\u002foHlAFyEjqxJUfUCaAry5k6N+QMccoQqAOX1AR4hP3UB1eUCQOOsA2UJ0QJ\\u002ftFZfeem1Amodgl5GeY0BG5NMEUepXQBW8rFxjvUpAY4sqd6B9O0C9Rfp7NRgqQCkmLtanEBdADLHfHyhsA0CeLaD6VtPwP+3FCe6Mc+M\\u002fhC0smhS\\u002f5j+UJFY2zoH3P0IEwpR6bwxAHyxRd7X+IEBvuTYT0C4zQLlnUNKFNkRAJy3rkvfXU0CWOVQwjyliQNW\\u002fFxbGBm9A4tLFCbS9eEDVbJxFL2mCQI5WGZlXiolAVLtL+cB8kEB7K8ZRE8mTQMNDnECLEZZAqPPwV7nxlkDB8R6SlGyWQIPZS5lH+ZRALQb07pBZk0Ds+NFtKEeSQBQIKztMH5JAk0GYSEC4kkCU8c9o2neTQLoxbQyWn5NAfz7wJDCmkkA1kbrNR3GQQMMTtkL5q4pAlXyHqQLRg0Cf1PW7Yet6QDzmHNjzsnBAD2IMM7npYkCGQkwmzoxTQI2L3YYmcUJA3RS2TSHAL0BFpyg8xvAYQOZOtNyL4AFAUFEuJs9i5z\\u002fkNTQxZurLP0\\u002fDMgovaK4\\u002fGyV21QA5jj8NQ9UGPmlrP6YNm5GRr0Y\\u002fLBbyG+EhIT+xsgFf05z3PhrJGoNRss0+WCXk1EgKoT5BMERlO9hxPvQp3TpaDUE+APGH9Qu9DT6V\\u002ffFfsanXPUBi5YRvLqE9PMJESKLEZj31PtReeYgrPdQZPSvWYu48jjJXY2Ccrjzun3scdzxsPNwV+8GctTc8yo7vx94DkT+4HjuAbPCgP9oGou7qO7s\\u002fv1BbxYAb1T8U3MV1c\\u002fftP\\u002fKDpVllagNAudobuE71FkDjjMnuscUoQNq8H80pZDhAeBb8HlfqRUD0\\u002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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"BgAD0MqRvr+m5x+msCu+v8pkuZhLQ76\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fv0unV87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fQw9Bb7uIvb8wAVFTefm9v52rhV\\u002fHCb6\\u002fhYY8lDW0vb8ChPTWRHO9v2zB8r3efr2\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fkDHQk7qIvb8wAVFTefm9v9qxEqjHCb6\\u002fhYY8lDW0vb8ChPTWRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002fkDHQk7qIvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002f8NfH+Nrm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr\\u002fRhCsMs0W+v2LsgnYpDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v\\u002fIGgj0mDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v7sBnnCWsLy\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb+HFjgXXOu9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v\\u002fCT6KGYsLy\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v7Sa1mbW4by\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr96flRLR3O9vw7qaQZQR76\\u002fXtCiUADbvr\\u002fNxLe0FBu9v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr9sP678RHO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8vwfAQNZPDb6\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8v+NJ43ynXr2\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fXtCiUADbvr8D+Q6PjbK8v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fj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qI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51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6TxAdAbC1gRa4KB0Cjmijw58UEQA5Sikv8YwFA0hK+qk0D+z+YH580hHPzP5iXzWsw9uk\\u002fs4W1CqoN4D9N7MxQy2TSPwsm3f++h8M\\u002fde0jg6U+sz9j5\\u002fjbF7qhP3aW1m2Ll48\\u002fb7HYzRI2fz9JkFoYjAZ3P9sdKltHSXs\\u002fKkBGVV9fgz+XMdesTEeLP6qY0saCDpI\\u002f+ekro5k2lj+VyyDEgFSZPyUE\\u002fgFNwpo\\u002f9eOLLlovmj9KAf3c4ruXPw59d\\u002fIc7ZM\\u002fz39XZi3+jj84VeKl61OGP2Zs1XrjzX0\\u002f3MMrNHBucj+my5RVeCBlPz\\u002fUUpsIc1Y\\u002f\\u002f\\u002fd+Go0fRj\\u002fMImIXTzs0P+QvdC8qLyE\\u002fALseeK0lCz\\u002fdGgc8ufrzPuq4dQvhe9s+fIWc2o3CwT5PR\\u002fBk3cilPqHl1edn5ok+9KuX2Mwjbz4glEX56ldUPvDDYWP0gj8+vRQAbdatLz6g7gutIiooPgQrkhilMrY\\u002fCEAyWIGgtz9wWw4JiPG7P\\u002feoOo1ElcE\\u002fFNoBdemTxj8uWv+smMTMP2JB+z\\u002fp9tE\\u002fWdwBv9cF1j+npU\\u002f8sdbaP\\u002ftkeljwkuA\\u002fuOYZT9MY5T8zgB1x+sfrP8RWhFYZsPI\\u002fYyxlOQQh+T88QYhjd4QAQPEGxeCY3QRA5RPjbXMHCUC5Honvp0kMQDmnNrAl+A1AQeA+gsmpDUCGef7mWF4LQPVLuuJ3gQdAT1V\\u002fiCLIAkA44S54k\\u002fD7P+ULB0q2ZvM\\u002fgTDAK2Vf6T\\u002f3qWlQowDgPzKOhUxsttQ\\u002fUL66tqcczz\\u002fl2fjqBI7OP4UJUnnjCdM\\u002fKHsSm4N\\u002f2j8qFfYJ+d\\u002fiPzuls8cEoOo\\u002fSUZDUSVv8j9CZRS2jQD5P8tYhABQjQBAWwdTsBJCBUDky3u\\u002fm0EKQI\\u002fNQR4\\u002f6A5AD4jth9wyEUBAA5eaY\\u002fsRQAhHIQmRlBFAyquEGvMEEEAFNhPHQSYLQK4+QFAzXQVAkvrCX4Ux\\u002fz8YeIrASh31PyTTekRRfuo\\u002fRJVwzcbO3j8\\u002ftyyjSpvQPxQtX6Eno8A\\u002funw26pBVrz\\u002fA8bRopjSdPx5Yk5HwL5A\\u002feFQYW4uUjD+8V5Ry5++SP1fzS4elBZw\\u002f23LQ3ZsepD9NfK9ABQurPzg2VRqJ4LA\\u002flbYCccyGsz\\u002f6ApozlO60P\\u002fzSy21rybQ\\u002fSb49pk8fsz96y2mwrEuwP+m7W7Owuqk\\u002fNOabVpLRoj\\u002fIyAdy\\u002f4GZP\\u002fZmZDOVBZA\\u002fmzsQWsWogj\\u002f9w7Mapyd0P12TfMVANGQ\\u002f6A8IxCDQUj+Rddyi4UpAP07q6ZJXSyo\\u002fgSBBiVXTEz9jN\\u002f5PBQ\\u002f8PoMh7MQEyOI+khgls\\u002f0kyD5jU57zVbeuPq8mGBz8aZQ+6JkLc6+Afj5hkLNFEi5rPnWjPWvRZV4+8AUse2EuWD4RmjzrLJ7NP9QBNCoPbM8\\u002fdio+LNVw0j84MojHagTXP5yBl5fjVd0\\u002fQEbrhjKI4j840UH+odXmP2GKYLUHUes\\u002flIwB2w\\u002fr7z9hSS8vmXzyPyZGxxAvpvU\\u002fBx9QQkEK+j+Vc01uaSIAQCsKc41IWgRAz8JG5JeaCUC62\\u002fT6c3cPQDlDCuWjlRJAh1xrhSrcFED+Gf9WehQWQPYC5HrK7RVAt6gMfRheFEDAoi9+J6gRQF373wH6jQxA464FwnOPBUCPRolTOor+P7ZKtOBhiPQ\\u002f7FvuzeP76j+ewEsOEXriP28Yr+olUN0\\u002fDSM8iR5E3T8V97eIgcrhPyQNQ5gH0+c\\u002flkM8sX5S8D\\u002f2jsfOsiX2P0+tqP3Adf0\\u002fdyp9iK8uA0CrRCAkk3MIQCpj1+03bA5AjOiynL1bEkA36pY0+E4VQOOrrhagkhdAc3jwfQeoGECikRZNyToYQE2HK1T7QxZAIftWxSgSE0AcdOajMGIOQHXiXcVDegZArZFph77b\\u002fj+unPtB0aTzP5XppevlMOc\\u002fgREWShJo2T\\u002ftwT+vS+7JPwGVRXljG7k\\u002fjtUGrgLmqD\\u002fUvwMZadqfP59qP+fJqaA\\u002fEk+Xei7\\u002fpz9OAxUSD0qyP2cr9xtDsbo\\u002fxCVdYCExwj+QWzS+qgbHP1N8h5TRBcs\\u002ffOgzxMpjzT\\u002fzwGAr5p3NP2cNfWrRpss\\u002fPLM0OEnrxz8Q9fjEqyvDP4n0q6fUebw\\u002foqkseReasz8+PScnLwWpP3Xe8uFVn50\\u002f\\u002f8GXYYNGkD8VM1JwSp2AP8kZNBiGjW8\\u002foWHoEr3rWz+Y8sIhzhVHP4WaKaOb5zE\\u002ff8B1Wxo4Gj\\u002fKBShGzVMCPzO+yR\\u002fj\\u002fOg+9QOykuRI0T6H+VfyS8q5Pq5GfAk7CKY+v5sKECcblj7Wd3VU7MmKPvAM7jZY9oU+gzADcs7g4T+p5v6SkOjiP4sjPuBjCOY\\u002fqhGqchRK6z8ngs7+k0jxP9WAvZ9LufU\\u002f6P7KHYeV+j8zXBjj\\u002f2X\\u002fP+R42xti5wFAiCMQh+jjA0AjVh0aq98FQHwPPj8NRghAqN9ELPWUC0A1PZPBeBQQQNHzcec5BBNA3nYRF61gFkDOU11tYbQZQCS+JKiiYhxAKg2T6ULPHUAwTU9ZHI4dQP0wEYPTiBtAk4uzMiYKGEB7n2cyQagTQNevQIl2LQ5AHrMuBgroBUBaJd91VJr+Pyy2pD4XUfU\\u002ffzPSnQqI7z+JTt8LJ8XqP8eIUpQzNOs\\u002fVKq2KeEH8D8Mpn+venr0P6VkVEx11vo\\u002fIqoGWPF9AUB7SliHLWAGQI+d8xLT\\u002fAtA048bJNEfEUAoBtMS+IEUQHPssWnP+BdAmnKqJZUuG0Av9FidEqUdQOQGRkVp0R5AEByJH6JLHkBF7jed8\\u002fsbQESrCYonLRhAG6V3LuB4E0A3leuQIS4NQHxezx05UARApyG7NhZC+j+ZY9Hx7YPvPy\\u002f9p1VnluE\\u002fJ6rYna5h0j\\u002fJRSVJoITCPyIqLCwoD7Q\\u002fO6NAU9lGrj\\u002f62aU\\u002fKkGyP8EAvySZxbs\\u002fgvvLVEqUxT+SKx3KzuTPPx+q5EU+AtY\\u002fG1Ws8bw53D+VRtRZq8rgP8u+L\\u002fUIhuI\\u002fz3\\u002fmE4Pw4j+W\\u002f8IhJ\\u002fLhP82aJXoyhN8\\u002fiFdamOml2T+4yyfD\\u002f1jTP5Nj8yW\\u002fEMs\\u002falHt9Z6PwT9eQQjlPSe1P3yUr0eZrKc\\u002frnt6s2GmmD8zgMtlEOyHP20HqQjJsXU\\u002f3kwjI9lzYj+BOhpV8plNP7AMlYAbljY\\u002fnWb6CeGlID9ytjEvkWIIP5oMDzaqmvI+DUCOdn1b3z7MF2SND0TOPoMqz+A4ssA+8Icqir1+tT6jV9F2IQayPuAufiH+hPM\\u002fviyFybuV9D+WXQxOOdL3P8+2hMD4SP0\\u002fG1f0\\u002fidxAkDvnGHs9BcHQIiJ9UOZKgxAlqWzEnGJEEBY89ZV\\u002f58SQATfoZUxMRRAbvn8QEpMFUAyLufQezgWQJ\\u002frAl8LXBdAYpJYqTsSGUA6t0KO0YAbQFT2VWLQfR5AdsBdMq\\u002fHIEAFy0QTjQMiQAoq7MwYmSJAv0bPU05IIkA\\u002ffl\\u002fpjgAhQO\\u002fGQBmj0B1ANtQ9vhqlGEBgUfrDNUsTQH43p9t99wxAKLrxbylgBUCj5OD3tygAQF7l9pAOXPo\\u002fuMWJ6vci+D8CkOrLze74P12qJIr2Uvw\\u002fuMQV7DEVAUDISL5CMzEFQJfdHXBuYApAU+VSd74zEEAGku1vxHwTQPxcG9ED5xZAn7u\\u002fq2pTGkCdS4D8dJwdQID+KmYaQSBA6TdTctVPIUBWGPA24bwhQJkXJcpQVCFAAyUBnSwEIEAUp0iFTtUbQC9byIBzohZAW56rNwYtEUD408Pl\\u002fEcIQG8Z57jq8\\u002f8\\u002fOsiRKYGV8z986DZ33HDmP\\u002f3O17+TV9g\\u002fNE+iVxAmyj9YdgHjGwTAP\\u002fnJGDSvfrw\\u002ffqkrNzLkwj9kodnA4n3NP908WmxQGtc\\u002fDyx0Yf004T\\u002fxvREmVfznP4L1xcSXH+8\\u002fPENTiI7B8j+DHaa50fn0P0aoXlExwfU\\u002fBLsdnhTr9D\\u002fZcpT9qKXyP7ZPGLHA0u4\\u002fNowHKwug5z8teFc64czgP2\\u002ffZ\\u002fbYLtY\\u002fZRA0fvs3yz8o3+KTxhO\\u002fPyqo35ktirA\\u002fdKYzFIV0oD87c1YP4bWOP6fGrwbZAns\\u002fe0y3EYyNZj947E4CwhRSP6djE5lcZTw\\u002fCIid5baaJj+hmZka\\u002fS4TP\\u002fCrp239MAI\\u002fXN+cNcN68z64\\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\\u002f9uwyj7nL6z8A3q68oCHgP9IrQpjLPdM\\u002fmxekej1Lyz\\u002f5QbfWrFzLP3lKZUPCuNI\\u002ffagAwHkJ3T870QeAL5vmPwCwpLbQ2PA\\u002fKdjy9Sif9z+fjy\\u002fVqe\\u002f+PxuSdfBq2wJAw5rElE5cBUB8ByWj9XUGQIxIxp+k6QVAkFnJFdHUA0CdLQupmKYAQNTHMIb08vk\\u002fG0qLkdTG8j\\u002fkACGH\\u002f0HpP3HBno6nn98\\u002f28ZtNhN20j\\u002ftAr8e7ibEP+QAUqoeo7Q\\u002fXgUDEMzqoz\\u002fnK7AIIzqSP+tGuFkn6n8\\u002fqKB8hM4baz8Hec5Vh+JWPzvr8jx\\u002f9UM\\u002fd2aVUXncMj\\u002fTmbAen+UjP6WDkx7mMhc\\u002fQbE3q9I9DT8GwDP2V3wEP\\u002fTGjvK+vQE\\u002f3v\\u002fJboMtEUDA6Vd8pgcSQDJk0cZHnhRAqtT9XKX\\u002fGEBhW5SeKSIfQGB0MJN1XSNA0RwanPONJ0DXuQovfZorQDC5C4fJ5C5AV0gMs010MEBlEgLYZ7YwQJFTGc6STTBASTAMV6rvLkAl55kaSwYtQLf45ZIPXytAed5O4mQ0KkB4sAovk3IpQDGG6jLtzShAq04vQ3TpJ0CybFyFin0mQJ15QuClciRAsLbRLtPpIUAJ\\u002fuqaK2QeQNxHIGhnXRlAz3rHc4xuFUAuwoNHPPoSQOSMNugPABJAJJnBaggtEkBuFTYyrwoTQKFM0DgSNxRABHJAK3mLFUCtKYB6sx8XQEW+OOOvKRlAy6q61EvMG0Dh7cS+8PEeQFiHTaVIJCFArP4EVeivIkBwcvFIQuojQB3NO2yqtiRAR7wQqVsMJUABSLsO8ekkQH7XfZlnSSRAL1oaNkgfI0C7mdue+mUhQBms52K3WB5Afcl+F1E4GUDwze1EwuUTQM+LtdX2ww1AXp5VfvgiBUCr\\u002ftfsgKT8P56plwycu\\u002fI\\u002fyUNFqRtI6D9yDdYNJ4zgPwZbq+7Wcdo\\u002f63T5rkZ82z+R9zgUDyXiP2MvOsgk+Oo\\u002f8qY0hvZ29D8948kadi3+Pww4NWXLIgVAR1j43v7RC0AxSj81ihsRQK\\u002fr2Ir7mBNA+t2P2tXfFECePlhQmqcUQLf5IMV6+hJAhvDnCcMxEEAQ9ILqe60JQESLYH8F7gJAhblRW9z8+T8q4UHXKKTwP62IGwjk7uM\\u002fqgmsIClo1j\\u002fOlDwVfr3HP2DwNuCL1rc\\u002fzUzVvCrZpj+tjDTujh6VP7XYh9xTI4M\\u002fKBA35ux5cT+WMAUKjr5gP9ckchmQhVE\\u002fVyrgCQhERD8au9gPvVE5P0\\u002fKf\\u002fcovDA\\u002fB6+hS+EzKD+gBIkhVzklP66Y7lx1qhtAfCq8Xrn9HEA9SgP28IAgQE8yN9m+5CNAg5JtBUukKEB6pnCMEowuQORpI23LjjJAyr\\u002fqgePDNUD04sYA0GM4QNslSCrn9zlANbxVEMI9OkAFcdJSQUE5QIkAX1kEWDdAejVVPWn\\u002fNEA6dQptIa4yQHqC\\u002f32OrzBAwqr88VQuLkAglQJyKpgrQDE0qJwxRClALzaLqhbmJkDsSlzgQF8kQA0uaDIbySFAz7T22sfTHkDU3fAu\\u002fTMbQJAHciLRRBlAMwntN3A+GUCvgFvnuOIaQFBeJfXMjR1Ac4vtKKk4IEDonmj653EhQGieM+xGUCJAkV4LVgLsIkA58pwkbIMjQGQnP4nTUyRAShCgC5V0JUCw6Dxl\\u002f8cmQFutpMHiCChA5BCGVtvqKECSi2qkizkpQNuPvaUw5ihA8woFKTQAKEDjXE6muqImQASW1IKN5SRAwwOeR6bZIkD5g4bKU48gQBaOqgBoOxxAZKXlgBtJF0CZtpZkBI8SQG\\u002fmMPb0mQxATOg5UCxgBUBb0rHb2T3\\u002fP5l3Ebjzv\\u002fY\\u002fbh3H8UYv8T\\u002fFBXfpL9vsP6CpseZD9ew\\u002fforIC3iR8T9EPGgRRy74P0eWzm9PdAFAi6+us1gMCUAVrlA2SFYRQGJpwBBPxhZA+a6wrp8hHEDDSh7HD0AgQNfRNGOPgSFAjRAl3UWOIUDR9HUmbGAgQE0bfp1baxxARCeZ0wDzFkActxadWEQRQJ3LqOfUQAhAI\\u002fNyHdng\\u002fz9VPsSzjqvzP0mZ8cL04uY\\u002f0\\u002feTMr872T9k+Zlv24XKP2kPWwtQyLo\\u002fMs47tPxFqj9wZnowUYGZP8tx1Uz8P4k\\u002fZHgW3ayEej9mUOFGUHBuP\\u002fOH3HAiB2M\\u002fR9cV15MoWT+iIEpPzk1RP7qw7oZNuUk\\u002fqK4DFhnPRj9yBc5YpxwkQAW+frUDDiVA+OPM4pDmJ0BSsr377q0sQM5xe8g4rTFAIYMkv7vVNUDvZU0T1346QPiaQaXiGj9ABdlHliN4QUD4eCgKAKRCQAc\\u002flq1T1UJAxvtv4S8HQkAhviCc+m1AQICgnNXGxjxAHm7l57mLOEB0J2FQGLc0QOIVviFtiTFAUxSOBYn\\u002fLUD2L62we+QpQGdE\\u002fbL\\u002fayZAGpDuPXZpI0AJ8V+a3OkgQC7yPBWuUh5A5\\u002fOROW7sHEDsjXlyHhYeQNOZ6k0N6yBAlh0vA+bNI0CKHJKeDCcnQBHTBVxVSypAaRnwN9ypLEB99TNyTQAuQL3W0F2HcC5AQhvm7EFqLkC7VJqGxXEuQASJOCvu3y5AzkOJrHu7L0DjStP3jl4wQIEeuDvmuzBAkdWYeFbGMEBHh9A1xGcwQN7bwIDTTS9Ai+z7b088LUC45ZEDsdoqQBaDfsypXChAnQRDCczfJUDswkyOXm0jQLvacYQWAyFAncdZMUs7HUDzuFLS1YIYQIbRuhBq\\u002fRNAHdLLQTbBD0DDe4Vg99QIQDoeRdjipANAl7P8MzeAAECdkUYDsjb\\u002fP4rM7UU4JwFAMBt1alBoBUChzGAvFLQMQCrt3NDcoRNAeliZK81wGkAEaSlSwyQhQOYm3CwDHSVAEzU8d5t8KEA6Lhn8a5sqQKs4KPPNAStA3CAmNRiSKUAV+VK+iZMmQG9NZmkomSJAC7VPmgeiHECuAK60qKQUQEjlhd959wtAuBnN3rzeAUDSADNzq6T1P+Q9ozcw+eg\\u002fF9E9mOSd2z97Xn1oAn7NP+f5\\u002f7jExr4\\u002fhkbMSP8DsD+660IojCqhP1CzSghPp5M\\u002fS7MNGxl8iD+rWd7aXliAP+MFKkIgqXY\\u002fHJeZ32ohcD94\\u002fM0eqpNoP2Z\\u002fKMvmAmY\\u002fBE7TMMZjKkB78dW9+Z4rQMjQ6iJuUS9AtiWb6z69MkC9fKgW2QM3QGQDZK2UVjxAPWsSYGQrQUCFEO9j1ixEQBeaH9npuUZAiHqK5PNWSED3wBGq06lIQOqAOj0tnEdAElM7f79lRUCO5tZNPHhCQP5aPJVprT5AYMtTJ5HZOEAc3fkpne4zQLi9B4yrCDBAmV76jBEQKkDbWPMR\\u002fHglQN6V6aN9DCJAPZvFglGQH0DxGGPlz78dQLfEJ0plPB9AiSyVtJA2IkA1H15QWZcmQARCZaOQSyxAZ2UNZWA\\u002fMUDdhgmz6RU0QMQDMENMNjZAnrmNemNlN0AZh783jbU3QEJL5ER6fDdAthY4ifwrN0A5vxogBCA3QIE4vibEeTdAvtOWwWcYOEDIl57mt684QArRABls7jhAluLpvpagOEAaq\\u002fri9783QBfdbXv1bjZATjGpOqvkNEDu9yAr3FQzQMjbk7CY3TFA45A98nmAMEByWESIEVEuQCSxmoC2citADaorvh9BKEAbwSWPzcMkQCjUXQ+eMSFARhzUpQu+G0DtTYp1CkQWQOjycvJcdRJAurCOB6qVEEBijVIDorsQQF77kCvG7xJAYJCV1P9AF0DUdjvs1LodQATjVSr6HCNALluVDPQYKECBQtLuyUMtQKOLBHcB6TBAW6SaLAhzMkDtr9YJ9ugyQE\\u002fwQhJAKTJATco28RVUMEAYCJz4HYArQD6CwNcGuiVAMBsoxtclIEArXT\\u002fCaakWQJMQmR1SJw5AtCn9PHAbA0AmdQ7U+Sv3P9y7FeulB+s\\u002fwTAoJK2M3j\\u002fJZEcDIPHQP40h+rPX3cI\\u002fvrmNSYzQtT8in5lpaQqrP3WP5TWHGaI\\u002fT8AG7CuOmT\\u002f22AEC33KSP6JsqTi0E4s\\u002fvhlOSKcYhT\\u002fTspCktBGDP+pcc4plPy9AUDxdNzRdMECLfHVX7JIyQIWruT0COTZAO2E+GOw+O0BEQVs7DL1AQDIFJpdWQ0RAsfwHntLVR0DppEFTCO9KQMMqb6Or+kxAEDx+\\u002fMiCTUBJHN7d6FtMQDXAR1mfuElAhhbqI8sYRkBQ32kXmhxCQPJnXELYozxA2X\\u002fJG7glNkB9guW0f\\u002f8wQCGsR4GKPipA+\\u002fS29VimJEB8ea4uIeggQNE2BT287B1AxCcHBO4HHkAhojW0S2IhQGiFSvRTSiZAGFgSrTmgLUCrLTDGmWMzQGFJTaSRTzhAK3xT0jrePECHi88mcDRAQH0n6IwESUFA\\u002fAXyw6+0QUBom6NoSbJBQOMnOTtflkFAo1zH10mrQUCkw19DYxNCQAd+xi6hvkJABZ0yD5V2Q0CuNZinFflDQDc6+7JPFERA+WctkXa4Q0C7DvCohPlCQMNo6K+TAkJAz0gHfnMBQUAhFJf5PBNAQOlAlg9jcz5A3D7DbDO7PEDGa46GwLk6QG0kDqKZOzhAPeGBe2s8NUDtpr3fFu8xQCuiyYXYWS1Agg+3ylWsJ0Akh9kFXmgjQIDJPWkk1SBA9oVVIz\\u002frH0DLDSLJ\\u002f6cgQEjqi540xiJAlfX9ngwzJkAaHFYW+8sqQF2RIIbuIzBALjO7oxwMM0CxGq01ybI1QPIuugo2mjdARP4mDGRVOEAhACgibKw3QJt8nDHMsDVAD+pLdGq3MkBAqNcMRXcuQOr3oQlmcydA77b2vpAhIUDFl737p9cXQA4hq6Y5uQ9AARwqPwU\\u002fBECADWOKFOX4PzYZUbSctO0\\u002f2PfNiKVz4T8Owrn7crjUP4dPnbekusk\\u002fviMLL1IlwT\\u002fafGo+OnO4P8s32FsgHbI\\u002fmMs3KIQZqz\\u002f7QMhsmHGkP50swrmMQ6A\\u002f9l3foeGnnT9NZxN0oLIwQJ9yoskVhTFAE6knd\\u002fr1M0CbZ5DyrvQ3QDus9jM1aD1Au\\u002ftQxmIRQkBJgbm6fuFFQP4MmGoiyUlAJvLMZGM\\u002fTUBy39O7h6RPQCmGivsXOFBAXq6h8l5jT0CCKaeNJKRMQNVM6Ba7skhA+hjxVOU8RECyRGI8XMw\\u002fQOJ\\u002f247YPThABPywOJMwMkCbZSZyhkorQAJ0rOCW4SRANokIYFHbIEDEyqsfm18eQOOsVPdwICBA6kH8MawUJECmHp\\u002f2IForQMMkqvPA7DJAYUDNcWJtOUC+l2YEkT5AQAkmDoDymUNAesb5+4laRkAriK8WTj5IQG8cOUalSUlA6sLOU03GSUDMTsTJeSRKQBVxBLX8ykpAiv1rs2DsS0BoFd43OXRNQBrhbgaCE09AxD2pPhwyUEDJymcSkIlQQDg4enoYf1BAjKCuGGMeUEAuZfAkLhBPQD5lBIsZxE1ADUgBQFOMTEAVz5ksdW5LQCZqDNjTQUpAfc2cvzrGSEA+TK6bC8dGQJBuVQ22OURAAdZFaahIQUDDrcbVR4w8QDFfuUOzHTdA3bsoTxDXMkAV+1Gi+vwvQOxo0TcCHi1A61DFHwuoLEBMzUP6HRkuQNR59flfgzBAwtxL6VySMkBjcEjOeBM1QAdI884iyzdAL4CYqdxbOkAcKDArWE48QPPyJw9ZLj1A1tg+lQ+wPEA1f3i5XMs6QNtfCoAXvzdA\\u002fkGoglf7M0D0JZZD6vwvQKzfSt\\u002fjaShAcvnS7tHOIUB2FtdXveAYQDXqHea3rBBABwX3kACHBUCGG\\u002frzyv36P6DQKXIvufA\\u002f6xrdcGAW5T+tIj69s\\u002f7bP9xOHFw97tM\\u002fruLjAEYNzj9XvJr4JjnHP9zD0Y+w7sE\\u002f4VFpRBXBuz\\u002fNf2ncMoS2PynfNJLbsbQ\\u002fu+x7aWAeMECyLUPP9PYwQIPJZbSldzNAH7ubv56JN0B5+HxSFQ49QNUEc\\u002fdz60FAYsAQEYLFRUCn5obFnsFJQNUiP7KVXk1AlXfAqI3\\u002fT0CwfyyydYtQQKkITnZ7LFBAV3qjFRPcTUDmCDFw4RNKQNEK2SrZqEVAFnmLhRVBQUDEuJU6pak6QFG9myELPjRAO59HqqylLkCVEMiBo5snQF8OHODbMyNAucv6bw+KIUBBKtqTiwcjQK+mpy3VPShAz0ZPOQvQMEAWgc6ZRpY3QJ0MqmDJCEBAbN0SR1S8REB9Q+NhG11JQBHLv0KJZE1ArC2uS4w+UEDCU+koWlJRQH2rIGh+F1JAqZYr8gfRUkDlDsSixr1TQM2vkc86\\u002fFRAHqmD\\u002fLt8VkCAuJhCmwdYQPM4dp2yU1lAIppr6g0jWkA+tJMnv1laQFoekTkeBlpAzmDUrKlXWUBpH02uXohYQOo3+hnawVdAJMlQfAIKV0DH6jR\\u002foEBWQBPeHN4IMFVAPHotg5ipU0B3jeiqP6BRQMO47C7YZ05ABhSuBP1QSUD3fQSeOptEQJ723lCpx0BADRarsu0sPEDHc618UAU5QES1SQDlrjdA8PejQtufN0DQ4KjJWmI4QCA33aSvqjlAHgRz2U1OO0Ct2j4viyg9QJlJAUorAD9AfBPmnxg\\u002fQEBoSfcNGJ5AQNN7Y6aFcUBA\\u002f\\u002fBOks1JP0B7o2zzloQ8QJJFT1BF3ThAnNsURau7NEANKt6\\u002f64kwQI08s\\u002fGoPylAXnVMv2lyIkAsxOGnP9QZQNnbp6fxZRFAVU6fWFfKBkDsuX0XS6b9P+tGigGdyPM\\u002fsQ4tCzj36z\\u002f4euOzuSXlP0BIkuy7wOA\\u002fkz5rlfrg2j8CCbA8w1zVP1pEq38j7tA\\u002f54GnrR38yz9pBPnt++rJPxQgXgvbJyxApZPhrbjGLUBW2XjlhkgxQCjGDzp2LDVAEcMR5S5xOkB\\u002fsm9GkHZAQFMo\\u002f8LBKURAMNIt8bIJSEAvDP7yz6NLQCpbeX38aE5AtmMdcM\\u002fST0DUBj2UP5JPQB85TPLHr01AjHFhm2CKSkCUoEvPzbRGQNO+Ga2Nw0JAlhS3MdpIPkCfh4rGTyA4QAHI34sfLDNAbCV01E\\u002fULkDtyBnGc8wpQEwd5sYTpidAmkSBWF8TKUC0IapYAeguQOsRGnRk5zRAcGRneEr3PEATV8cpX6ZDQDEgm1YKjElAGVNTgkKYT0C+0ossM5lSQF+iYvEP+VRAf5TUDlLkVkB8MirKwYNYQFGbRlMxHlpAfU9JQ1j3W0BgY\\u002fQheC5eQOOjztohVmBApPZgqSmUYUB404CwyaFiQI3d04AWVmNA01o7jwSeY0DD0vZz0YJjQA\\u002fQo9vbJGNAFQBb4aCsYkBXuk\\u002foyjdiQCV8b8+kymFAs46gzkFNYUA0U23hx5ZgQLk2I3ahBF9At1+TdOcGXEAceYVUamBYQGITRizldlRAgvkjpubFUEBwCKad\\u002fGhLQN8y2DvL70ZAN+HSixEbREDrMOs4VpRCQJ0pY7ol5kFAWcIYJ3KpQUAjrX8z+p1BQIHKYwWuqkFAfE6pxTHNQUDpMugbNwRCQJdYVh+0P0JAcP+MpWZdQkCDX8eAjTFCQHKfruEHlkFAciLH69R3QEAttO\\u002ftPLo9QPPcBH5kxTlA2s97k5BnNUDJeowxRAAxQIWw0WhpzSlA8H2fMSq6IkCPzW9nmCQaQFej4VvbyRFAXrigWMUzCEDAVE8YjgoBQKJ7azrWjfk\\u002fxwLrkqJq9D+e3qVssefwP3AGyOFLDOw\\u002fu\\u002fL0hU3k5j\\u002fDXz0PXo7iP9jRoD1fON8\\u002fPhIR\\u002ffkf3T9tTcOiZFUmQNmw5yv3ySdAvX1QVd0aLEAX00WgCpQxQNHijcTKYzZAUsCmlK9dPECRcUWcAKJBQJWeCd7RTkVA67AYAKLTSEAppbMEgrRLQDIsDGWbek1Ae2H6GFDcTUAY+7bWVdlMQJb8W80gukpAxynogufxR0C8olI3svNEQMe4YGRNEUJAGQbw0YvlPkA3HMH1Z0o6QK+\\u002fHfDkZjZA3nKdb5ZzM0DSLw\\u002fcY9kxQCKyxVgGJjJAwlT\\u002fWg3wNEDEg+jdMbE6QC9+Zra5zUFABFOP3fi4R0COJwVLvrtOQCrw2TfmIVNAFgBkFmDYVkCzAYrAWERaQG0QDYKoUl1AjgvTJDAQYEDB0IAqi3VhQOfZdX8R+GJAFyOTyqqkZEA4tm6p9WtmQH3YGvAjJGhADHbvE9CWaUBL8KyVHJVqQKra29bCCWtAD712\\u002fWEBa0CB8chR6KRqQJx\\u002ftTdPJ2pA54YUG4ytaUD1WdjFUztpQE\\u002f597CsrmhAmsWDr1LOZ0AAKDVMqGVmQBnRp9U4YWRAGxZNs4vdYUDj6z1CW0FeQKlXqNacA1lAFJRVYlKWVEDpc+gAPEhRQCjlfmijLk5A99TkXtCJS0D1x6B8E\\u002fRJQAoexF6t00hAsYgYz0PLR0ABXulmfr9GQAhANAiWwkVAV0l1T9L0RECaFukkTWhEQHr0RhuZEURASZ6d+nLIQ0ANTLM+V1VDQMLvLMifhEJAYCYUzSY3QUBF8eT0H9Y+QLcpd06jdjpA9npUn4CvNUCLP\\u002fVqtPEwQFEn6xFDSylAGV6d1+YrIkCAbpCsWpAZQCKzWQFFIRJAaUTuFwTgCkDq2tPpZ0wFQGaHcz4S5AFAicDpKWbT\\u002fj+xmtR+qVz6P5YUSv49DvY\\u002fhoHgFmtE8j8EmwGZpELvP4fkNgFpXe0\\u002fQ80+54Y6IEAa6kpgzXghQA0I1b8eMCVAiAAoOHZYK0Dhez\\u002fK9PIxQOydllpzXDdAKE+eoYu7PUDn\\u002fmgkzlZCQNbK4lHFx0VA1MneZsjISEBUlCu2RvZKQI\\u002fDC7odEkxApQgsknYZTECiveEdlkFLQHxwFkQi3ElA+SfcvrUySECEnkDk2W5GQOsKSTDBmkRAnUBL8fa2QkATf80DStVAQAXDPA\\u002fsUT5AotJ6wRALPED+4jWXQaE7QLWlxfzL3T1AG5JML5CxQUAzCSfZPkVGQD3B6UhgokxABJQJ\\u002f\\u002fpBUkAo0AvY1LZWQITXcIS4Y1tAraRYYYMCYEAGFsUjrjpiQPXuKlTBW2RAJtQ1Bpd2ZkCFhepsXJ5oQI0LVxPO2GpAnsfpEq0TbUBSTqxGQCVvQIlf8AVabHBAZgs79tUAcUAVRPL5Y0dxQBHun25KSXFA7jP5fcMecUBydOO\\u002fvuRwQITGXmkSr3BAUNyBVkN9cEDT7a97+TdwQMwrCd+lc29A+UztkFrBbUBqu4NEJT9rQF4dc9G2EGhAYiSF7tSPZECJdCHBdC1hQKutRj8klVxA9iPnaLs1WED0FoB\\u002fQjlVQKc6+VNSS1NAg2B1L0r3UUA3oPdBb9dQQDtzVB4tYk9AqZRr0kjyTEDE1KOy74xKQC1lu2Z6eEhACadzV\\u002fHqRkBmHOw5\\u002fe9FQNgYwBPpYkVAAkKlyT37REDRHBIfJ2VEQHOw6xECXENAS7\\u002fSwxO+QUC3ed2+8yc\\u002fQG+Vp+FHFjpAzqhetlvNNEColpPO37AvQCX2D2SgSidAQPpFfnvdIEC0IbllVN8YQG+HD+iiWxNA8GE6R8snEEBr2qSIqGcMQFnwf8syXAlADrOtTPhMBkCT\\u002fcs1VhkDQBb21esoJgBAulMB6jMX\\u002fD\\u002fUnUSLB5D6P+dEuUl+CRZAZqknTfAcGEDPVBjmxmQeQCiYOlEbgyRAEYw21TQWLED7UsimUfYyQE4CDEG95ThA2KqdseCJP0AMsL4OfC1DQLHHQlS9VkZA6SxSg4vwSECwHd32YctKQORQpYCo50tAKelaJTduTEDKfcJsYpRMQEmB7urGe0xACYsnRiYiTECMdlGm+2tLQCZbRrRAQ0pAUo9UUEy3SEAoxx3lWQtHQOZU876qrUVACVS9DmgfRUC2pdxUU9tFQC83hcZoREhAB7VP9RacTEDzViXnRX1RQIBARbhdo1VAajiQ3oyZWkD892VR2xFgQGJvduS3\\u002fmJAyhUl5JP1ZUDp02MbkeNoQJfUG+VBwWtAHM14k0uMbkB0BM0Mmp5wQEY36GqN33FAlcfeRmv3ckAoiOm309FzQCw59sNrX3RA1cSOmr2ddEAQp3R9\\u002fpp0QFbcYU+qcnRAJl4mU8hCdEDXXepjdR10QPRA8IJF\\u002fXNAQQBBEDfDc0Baqu8gT0FzQPrzVGWwTnJAK+WeP1XccEBsyNF1RgBuQKMnucyP4GlAJ6EFL1fhZUBN6sYXjnZiQJGU1v8BuV9AHXri49AbXEDaqAvHWKhZQGNkyLuPzFdASg4wiHYNVkCyhl1raSpUQP+MTN5\\u002fIVJApHazYIUcUECv3Nj6CqhMQIyEVyki6UlAcHIrCW8bSEBCC6T77hpHQDY4cMSFjkZA8+D1wN0FRkCCey4UOBxFQMAmc5a5lENA7PsUiO9nQUCmbIb3IIA9QHHFuYXgzjdAh2sCKuhgMkA6CH3RVXwrQKsepxNcaCRAC+Pe3201H0ChfBFuLG8ZQDDUltbEQxZASDWlEjBlFEBcZ42Qi84SQP1e+2dO+BBApWhYkJW4DUBI7gfDUaMJQPo3\\u002fl8BpwZAD8fpoXCOBUAk8olWWi0NQF67qz0bWxBAidUKsbXHFUCMnNiZ5i8fQGpoQGE8fCZAagA7FoWsL0DoQLPqvYI1QIQycrl6ATxA88me60NvQUCWycksd8VEQOPqYs4txEdAomz8RSlLSkDmFSVt1WJMQF7OHplbME5ABlwAxN7XT0BtsFdG1K5QQMK86qxLTVFA4rPE4fumUUAZ77KgWKJRQKrVznHTP1FA44ZqtL+hUEBuKiAKUAVQQOYAK411ZE9AOY0vWS3WT0BaEh6XouVQQEr+McDYylJAlJPuL+e4VUDlt\\u002fTDHMFZQE1\\u002fB9jh4F5ACkHfPDR9YkAJ6nC6s+llQNPSJKVrjmlA6p8bT3hAbUBq1GpxKmxwQPiOZ5KTGnJA250EXdadc0DKWmSRcel0QDO8XOIz8XVAJB27tveqdkA3sDVhAhN3QD59LWn7MHdAMCwIkfsZd0C2BIScjOx2QCdWfBj8xXZAZlkQWEq0dkCiyLXfR6t2QG2FrmrNg3ZAWmYl\\u002fzcIdkAaHkZOqgp1QN99ssaFe3NADPZ80vt0cUDsxcs5TGpuQNvZkURWEWpAUa+77INfZkAKmFt61ZNjQJZbHohIoWFAztcZ1vdCYEBCaEEP\\u002f0JeQH6Uasfp7VtAoD1RYOhGWUDARUj5kVxWQCc36dhNcVNAc\\u002fyaI7LVUEBQXLjLCY9NQHOiCXVRvEpAw9cE+i0NSUAa39i\\u002fZRlIQNQyNPmFWkdAkxp2puhURkDxNL19N7lEQMAZJzkIdUJAqOZJxv9eP0CamTnUuGs5QNgRTOTCwDNAcWBmf8XjLUDOZH7wnagmQLAm0upv5yFApdwEoHdyHkD1tl1dp9UbQPkAfANLdRpA97AodsEfGUBIGVnwAzkXQA6bgktSxhRArWtaVpRFEkAzsZIAU2QQQM6Nz5JOYw9AeSo08BZJBECStWt1RX4HQJnNwuHWuxBAUE5cZfqQGUBEOfqTkmYjQND3RwJIZSxA\\u002fRQmHHDWM0Dysvql9WE6QHMBb0FptUBAUdHbybg5REBh+zibM4tHQO7f+VFml0pA5w6YFzlyTUDUQsYHSyNQQDg5rmkjm1FAKQ1cFfQcU0Acn9sFuIpUQGfiDyUYtlVAh\\u002fojtV92VkAVZrO+uL5WQFGDLw88qFZAoy\\u002fpWc9qVkAMiMMQm0pWQHjVyYl5hlZAv0nniXhRV0AmwNC4P9hYQBkCLNPkSVtAArPCBB\\u002fZXkAeda\\u002frNdhhQNmwRQCf7mRAOlgGPpaeaEBb5kvyEbxsQJZATeIOg3BAc5jZ0b+ZckC61tkJbYB0QKyKOhhvHXZAXERyw\\u002flgd0CguPM1a0R4QMshWOgsyXhAWyIU3N34eEAq8IzTi+Z4QLXRQr+SrnhAOGFwkBNyeEAH475O2kx4QIHWGypiSHhAQGj\\u002f1jdSeEDhm5Bylzx4QGjUyc1mzHdA7Do55NbQdkBRsIzbtTp1QNlJ6M3hJ3NAv3dhVRbccECN7WR6sVJtQF0y\\u002fpMopWlAQzWGhYTrZkAQab+qeA1lQNgqNZx2tmNAqkZncA+BYkCPmPAdVCBhQCuqOnSG7V5ASLRlbeArW0C8ookw\\u002fFlXQOE+IVo44lNAOMwS78YTUUDUUU2L8hpOQILvU+5zd0tAnmkQlbLKSUAV4bWxYYFIQIGquGhpFEdAVVhcxzcsRUBgD7ZGZLFCQOJRl5qMkD9AzDb1Uk14OUBx26ImdcYzQD0S1OLgEy5AqK6DqlolJ0DPR4FQjM0iQIibJB+2mCBAPFnMEWuIH0CXdJ+SPvoeQM6L8ptrNh5An7jnVfaQHEC2RIyjOxYaQEiIqDqmYRdAN\\u002fXZp5RJFUCszgRAE4AUQHIT+SYpBwBAVwUWa5QtA0DFt31+LQ8NQHEFc2QJZhdAQ\\u002f7YJbJuIkAkwZwk46wrQHA38wIKqzNAWR1qqpx1OkDZyhzlIOVAQPp+aF34l0RADXLTUFAnSEBiroH8lYtLQM9PtmZP5k5A+p\\u002faQ2o4UUCPdAY57CxTQCc4iT8DUVVA2VZDFw6GV0ABlrKZhZZZQCEizcinS1tA\\u002fWbIW9OFXECYeDoIQkpdQC8vzi8Kv11Axgt9B7IZXkDfYr7bW41eQCr2C9sbRV9A32GM4KA2YEDsCYOjFCFhQK7d0GasimJAhhraI1SfZEAQ\\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\\u002fnxbIEDxrFxmqlggQBB3V\\u002fywlB9AyjP9kdtrHUCADrjTMtwaQCFFjJVXzhhAj0gcQ9gFGEDMcyr\\u002fJE39P5OB5w5s6QFAcC8\\u002fG7EvDEAoPwCiz18XQAXqaTPcxSJAqBjZHsWHLEDQytnz4ms0QLwiLr+NkztAb5dqgvaiQUDnGY6MjoJFQNCQWaPNPklA+uAiiLLYTEArx\\u002f0EhkBQQMvhRpA3Q1JAjjMwKSyYVECCIO5QWktXQIQn4mz6RFpA7H2RCi9NXUCV73iBkQ9gQABuCwthQWFADUBHX+MvYkCHmEeDRuBiQHq6EJ7KYmNASAEPgIrKY0Ac1W1jPSpkQKdopbfNmGRAZLNMVtY5ZUAsyFcGkUFmQHRlC7ca7WdAQISN57RuakC8xKjWqtVtQC\\u002fIEr7e\\u002fnBA5K6hS95Hc0Bzb8Semop1QBrCaA\\u002fxi3dAi+88f+gfeUBTyxALKTJ6QJDqtT+fxXpAX9ExqLbtekBrgz5NNMZ6QKcfsiq2bXpAFYqJ4zYDekB7UWZjnaN5QJnRAewVZHlAUeMHnDBJeUCFui0vQD95QNi1c\\u002f6dGnlAizmz5u+jeEDFZYTmDa53QNbClCxtLHZAwekOc4c+dEDd1O0Q0yhyQNPd+YnNPHBAIWjEXd90bUBv+6d+CnJrQCL199BjQ2pAKHn7nhp6aUD2BenoZJhoQK0EIYpgQmdAFa26LpJZZUAm5qHw3PtiQCULJDidbGBAHNFaqC7sW0BOsVjpE6FXQISak7zBK1RAMo2+mDeEUUCMyMY\\u002fPvFOQArdDPyOjEtAGYmDbwpiSEBAYYmYUiZFQOQGETqWxkFAcjJ3YV\\u002fBPECHFD1Yv1w2QIu9y5332TBAanAJJdQmKUAZNznapVojQIzOTfq0IyBAjIJTHyPsHUCV6r+BqBceQINfNAXjKh9Afhb1RXPVH0BicRmMB2EfQBJv8DrTzR1AAZWBjpmzG0BaoIREJfAZQKRIZdYJQRlAVDNz3p6i\\u002fT+n+QtjlD8CQL3KMvDsEA1AmIRwh1xQGEADdKtVEaAjQFgFcteP5S1ANLIToAxpNUAJm441K+M8QB2J\\u002fvNxcUJA86v1BaNyRkCJSZBV\\u002fU1KQD\\u002fEh3yWDk5A4zNRPvH7UEC\\u002fxxbybTlTQPlJuhjm8lVAkIq2JolCWUCydhcyiRhdQKqdXnaknGBA4lNVRaKmYkAmw\\u002f9ZUH5kQKAwmt3iBGZAs1lHO1wvZ0C9LhLkjARoQBW2GV4OlmhAT3uXOQv7aEB8s09+fFFpQA1JHQEzxGlAWR\\u002fepDmOakAdz18eoPNrQFzzP\\u002f2iLW5AXojeUnOocEBHpWBxNJ1yQIBr6O1nx3RArRbHgOLndkDWwZzCCsB4QClcnMexI3pAnHuJfg0De0DbjPjyUml7QED7SshtcntAdLu9h3o+e0BKnKkZZOl6QJg\\u002fy3LBiHpA5YUU\\u002fQwtekBNMPOHROF5QEfoI+9ppnlAct7ylltteUCZk\\u002fJvIhV5QGYXVJDucnhA3RMesHRjd0BD7GN3id51QL\\u002fq6dRdAnRALgWJe34OckAe8V4vrE5wQGQUTFq0\\u002fG1A4VjI8vNjbEAjrGPOM6FrQOljv\\u002fJ0QGtAuSCjkHS8akAEH09TY7BpQMm0h+yu9WdAN0PE2kOlZUD826gnxQFjQKSnN9WtWGBArIlgpH\\u002fQW0BAztrF\\u002f6NXQAThqMRuNFRAKS\\u002fD7p1gUUCaZ\\u002foJiepNQEbRWsCRhUlAIFHzIkhXRUC7K7oeTVJBQNho4e0IHjtAgFOLLPxxNECzfXUMNeotQAR0\\u002ffKstiVAyT5Us3NUIEBOd4VipOcaQEQBphbP\\u002fhhABODwgUp3GUCY1oGUNtcaQB3aUW\\u002fF7BtAo3cyD48KHEBHronVGCEbQBNV3iiGpxlA6tMnjxxXGEBoAvzRwdEXQM2qky7o\\u002fP8\\u002f4wFD1aScA0DOEJrPOfwOQI6oiIK+uBlAx1aefseeJEBdHSGQIDcvQNA26B8jOTZAkKSTJAfSPUDQxNcuh\\u002fJCQHnvUKlr+kZA8pZWgBfjSkAEpyYWoclOQLxqjofchVFAel+sAQkZVECSugNINWFXQNJXS9HIg1tATCrkJSc5YEBhy1XG6vFiQCAsH7ufsWVAamNJH303aEAAka8qEU9qQIITbszS3WtA0CSuVqDmbECBZu3Q5oNtQIjPnutf3m1ARlSgz\\u002fcmbkB\\u002fSxlFyZVuQDaKR+XKaW9ATV9PWQdxcEC8hPfyjJZxQJovv8xkKXNAt++qpv0RdUCWjnCv3B13QEgTlzpQC3lAEskGceqcekCXvTiouqx7QNtRHW18NnxAr1E0WBxUfEDh12xp9S58QMpnfnng7XtAnGen4gGpe0Aka+R3vGd7QL0YU4p9JntAvjiYGsrdekAxImYh04R6QPzrWL7iDnpAGeuGtTxoeUD2FrzJgXh4QDnXH3z7LHdAehHbUNiGdUBi143V0aRzQPftQZh1wHFAl8GLe2cdcEDoRGsi1OFtQPrW9NDemWxAGOtOrqIxbEDq+nEWTTZsQDNbbtaXH2xAl9+o9qJ\\u002fa0Ap7ms7bSJqQM2UEScKE2hAhaYFcvyJZUAKFt4r\\u002ftBiQGyGowN9KWBAiYNFabd7W0CChMB0Yj1XQJvj4yYtkVNAdXM9WGFbUECc+VyQrPtKQE6iZ+xGyUVAtefyrMAVQUBF15KnI+A5QLuxnBqn4zJAI7NO2pC\\u002fKkBobTTZ\\u002f78iQPBTbbNCLBtAayhmimKgFUDTciT2o6UTQPHJNr7O4xNANJZAIHwTFUDci+okJy8WQFZfAg\\u002fSmxZA9YhodE47FkBz4ULfl1sVQCrUEGgZgxRATEmxfOMqFEBkwBz7JmcCQGOBcSutSwZAEkWdLv83EUAIl7BPAf4bQMDyijepCCZAeOt8NGxoMEDvg2KvmwU3QMBjmPYxfj5AyszqYGwqQ0Ba0h\\u002fU0hBHQEbpHuIo6kpAn7ii2nDzTkAa0yIRuthRQKsrcWM76lRAIrZL1z4AWUDjsht09kReQP37CIaZT2JAawn2+4jUZUCMPgZRH2BpQN1s1RnvlmxA9vI\\u002fudAsb0AKX30jPn1wQPfMsKp4A3FA812Q7RhBcUChlm369lpxQHKL550\\u002fenFAngDLrjzGcUCNfGvdiGByQDf+znNhYXNAPyGx7\\u002f\\u002fRdEBGTKujsaV2QJaT\\u002fxGdtXhAptbLnyTEekAqsApCSIp8QADLkT41zH1Aofm1Jy5tfkDKq\\u002fplanl+QMOs8WA6IH5Ac\\u002fsGDkegfUBQ1xe9+y59QAgs1lOO5nxA7eb5nQPCfECXeehmiqZ8QLsSate1cnxAwAlN7J4KfEC2GhqiJVx7QNV0YyABXXpAKtAetGMIeUDYmtIYB2F3QN5xvs2Ld3VArtuUzQVwc0BGDovuMoBxQHhYl7bMxm9AQyDpLQyPbUAZJwQd8nZsQJ\\u002f4u\\u002feTT2xAnk75LUGrbEBsjmfbYwJtQEBzVQ\\u002fl3mxAhNEdUhD8a0BIBTHvc1BqQPq6PgqfAmhAdE17MTZSZUBMcymE1oBiQHHx3n8AhV9AANaP4idvWkBjvrulptxVQHtTnsHg0FFAvANvRmKGTEDPypOERFNGQHpqn4KI\\u002fUBAeNx0oXIIOUDtfyYH+tIxQG3U\\u002fscCpyhAZFB3\\u002fAjYIEAkDPcnZZoXQLW\\u002fHNfB8RFA578lJFv9DkC\\u002f3v8+2yEOQCywpWHnNw9AzLRA8k9PEEBEAU3dBqsQQJNIxxmXiBBA0SZxvlQPEECmoJVkXSMPQJb\\u002fYqCtug5AssyzqiylBkDU77gwuw0LQHEv\\u002fiW+UhRA8Kk\\u002fHXQRIEBFlcxauqUoQE7vaBtT5zFAQAXcyPOGOEACK+MWHMM\\u002fQICXCj+HkENAqRorNXcsR0AP\\u002fzYNIs5KQIc6dfkp4k5AMZdZBUcSUkBTVoZxSLxVQHPqyW0Q0VpAuxLoX++8YEBsK\\u002fC4V8NkQLnYWDxeMmlAIqK44YWabUAwZVtmScBwQAceiy3QP3JA\\u002fC3bP5ozc0AZIwR1BqVzQBEXIpsvvHNAsYlmqoC0c0B1vhxgvc1zQOefQxnJPnRAkrqnl+osdUBeLtwqDqd2QCPRvj8Mo3hACiaPiyL8ekBE3nHhVnR9QGzPPA4DvH9AVnTLxL\\u002fAgEDmFbBQYkOBQG4qkdD7WoFASUUsBbsVgUC3IuyKSpeAQGa3LXDYDIBAN5dNc506f0A\\u002fe3uC8bh+QKDwUE3mjH5A3RHrrRCJfkA3bChGG3B+QNqtQ3Q2C35AhfFTgCo4fUDQu0sCjOx7QC+g8SHVMXpAn6aRqFcgeEAaXm+XkNx1QMBhCdvxlXNARHAo1lODcUCrl742G7RvQIfkpzMagW1A3zjd2dSCbEANNJ3sw4psQGnTDq5TMG1Arq7WEhbubUAsfW\\u002fjAEduQG9ALB4j5W1AiEF9ILOnbEDTssdVC59qQMoH1N2z\\u002fGdA8pTk3D8AZUDbB8jMoudhQGUOfiQwy11AqCn8B1w7WECDnM\\u002f23EhTQO6L+D6XCE5Anc0qi4jbRkCPArY3B\\u002flAQNaBtSm+jzhAqcEqAuVVMUA1EkLTHwQoQMAsIUwxkSBAZT4hdI9eF0DM+QvezHYRQGa3aBmXgwxAqgqP7R2ICUABL4i3PWwIQOpfuufFCwhAyyDuJBuwB0C3Y\\u002ffWChAHQAION+R5QAZAyZB+rSaOBUBV1P250EcFQBg9wS\\u002f+KQ1ADKAdyCo9EUBwH23241YZQNkJ+Mq\\u002fjyNAGRjglExHLUA1crOjOLo0QDnt1gLRojtAGYlrdahlQUBpoxaKgdZEQNS1xN\\u002fRDUhAtBLVCt1JS0CEYXN6yjFPQLFKITePY1JAQXImQqyWVkB05W843adcQATH6oZAX2JAHDJtYfVGZ0CrdGoWs6ZsQIU8mmAW9nBAvowMN9I8c0AIT4lSxuh0QBi53Zhr3nVA9icQWgAvdkCtzKYu1RR2QPxUbI7j43VAkB39Z3v0dUD7ilg9I492QImnXVa43ndASOFy9SHqeUCqxP8QBpR8QJnaWaFvnn9AzqfVX\\u002fNYgUALD3GHo7SCQByHXCTRsYNAIe\\u002fpImUthEArDVEAYByEQN4YLQmKkYNAOOGu3TO6gkCa\\u002fTx\\u002fXdGBQJSHzx8vDYFAUhysNvKNgEDmCeEjdlWAQDivpPHrSYBArV+3N\\u002f1BgEBQi4GCMBSAQEAr\\u002f6K5Rn9As8Z0\\u002fTLJfUC3Bs07e717QJ8UROH8S3lAhwnxlimtdkBKlhc0ByB0QFNgwi\\u002f64XFA0pM7nFUmcEBwfF52expuQLq2Nq56N21Axb2vSANxbUAh9g+7BGBuQCPn308CgG9AOV1bKu0mcED8B4mV8zFwQFnLperbim9Af6Kcl3W8bUAH9+3r7BprQAnAQe3S4mdAfY0wxEBcZECbwVDe\\u002fs1gQDvGVMFZ51pAsHv0PyDvVEAM2+yyTcJPQK5RvwElkUdAB6nPpl8uQUD6uSFKV8A4QBvQD2cuwTFAIkC8G3SiKUCkiY0zY9kiQMXunEgNhhxA6Nzb9LdQFkBlGqSRTwYSQBvbyB5p5w1AVtHaudpUCUAMmMDvqdEFQKRKrTWqEANAxYqHLT3pAECNj\\u002fS4XaD+P1q4Dpaynvw\\u002fsnnHU1ju+z+H9TvYG+YSQOtSbadMORZA6EqdScIkIEAOhN8d6pQoQJCXu5QBHDJA+chzzaAtOUACVOV9h25AQNm3UVS2LkRApZEWRPSAR0D58gkLwFFKQEWPkOai\\u002fkxAIPSFncM0UECgu8Rq4O9SQDhMia7BZ1dAglJ6dskrXkD8AGaFo7NjQOI2fwLXWmlAMnALp\\u002fuNb0AfnQXnssxyQJz0XO8aX3VAw+mW\\u002frczd0DMehoW4St4QNwG+8vQX3hAHqSQtcUZeEAWRNSd9cJ3QEFryQ4EyXdACa07CniEeEC+1tKz0yZ6QLPeXzTlsnxAgsJSoBX+f0AHejzW4duBQKPbbQU4uoNAIkSdoTVehUCAQjVKaI+GQN8RE8wnJIdAk69U6MsMh0DgfHBNq1qGQKu8MIWOPoVASmflDLz8g0DCuhUtVtmCQIoR7FnxA4JAuEsLHFyKgUCdqwskmleBQAB32GauPoFAwAH4mP4LgUA0V2IcP5aAQHGpeC+zkn9ABPYAQWpSfUAr0giaQpt6QOsStQjxtHdA5968hibudEClEfx\\u002fB45yQKVwoOaAyXBAANjzLYZ4b0CrZweVRspuQMeOVPrGTG9A2kUpV7JKcEA9zjY71w5xQBThi2JGrnFApTRUk3jvcUBkujmJ66xxQBPHT98l2XBAEPzxTd\\u002f7bkCq6GV1CXBrQJmJl+spYWdA\\u002fPsHmC4rY0DRe4G87kdeQMNYxHyEHFdAzkQb8QQkUUD42\\u002fdCZO5IQIVv977wA0JAVo2QxLhPOkDlUsEwJ8MzQFjsKgH55i5Ah5iKyAomKUA39aJi2QclQGeUEAOZuCFAxISdlAqJHUDI0WdwoQYYQJhYJQdk9xJAB+f1yKQaDUAj3xi6uegFQNZLndtYfQBA1jUI8ESW+T9WgKlDUVf1Pz4omFZf9vM\\u002fh0mVCr3PF0BUmb70pPQbQM7Yyb85OyRAesE9EH2nLkD4D2wmdXA2QBZghhtD7z5AOsTearn1Q0ALxMUq7iRIQFAVrGpKjUtAMJtSMewMTkDxhqhYJAhQQJqKAoa+U1FA2JNhGLeyU0ByH4mfhgJYQJtAmE438l5A7UklYQpeZEBa4uazlHlqQCY62577nnBAZS4wzVfvc0C5VP2mEsF2QIw35EdSv3hAw1DrKTLHeUD366hVBvR5QB8M3jd7mXlAHhkMD9wteUCZqvTZKyx5QFonFt6P93lAkgfMqHfHe0AEtAZ1gJ9+QO3JcE7bKIFA2cOvpcVDg0CNErZvG2eFQKiZ7BK1UodAGlK\\u002fn5fIiECeCJ6Y1JeJQA1jheEcqIlAUV\\u002fl1oUCiUCbqt6g1dKHQOckJIE+XoZAPoVabbbwhEBCkKuU5saDQHsI9f0H\\u002fYJA8pTmWkCJgkAWEDcuHUOCQDi4fuZm9IFANArUImJsgUBhHLz8wY6AQPV+S7yLtH5AGRZrlBzNe0BtX3A+JLd4QJPvI2jszHVAH+Bo8Zhec0B4XU943KNxQDEZC+IStXBAuqxYiKGKcEAeKKjOwgBxQNq7I5NV3nFAXwUxTt7dckDWfOkc47dzQHwNbMlOLXRAknDPFT4QdEABgPhF7ElzQP7T57EY3XFA1EdhWmPJb0BD\\u002fWHggR1rQEp4jAv2J2ZAbYxnZYdaYUBt\\u002fqCwNyZaQDGxvmU8G1NAPOIQNhl+S0APgIsMPfNDQMnOp+E9Fz5AJ7jHE1UoOEBzm9wmNLQ0QPJTZYMijDJAY8IjR77TMEAx2zap0A8uQF7Yiyrs7ClANGjYa05VJUBsLGfQ96UgQDm8zeD3mhhArMCej4FHEUDa\\u002fHJqd1sHQEfN0yGraf8\\u002fS8Uh7LGs9j9mAjM6d9rzPwBR8aWUlBxAlEWyXK7GIEAm1+9Mq0coQP8Goy+BZDJAAe37t9TpOkAYcZwZ84VCQE3VNG0y00dAkjQCMYakTECpIcA4PStQQNoMRlcbVFFAeQpygNj\\u002fUUD35\\u002fsMO8JSQDsllcjPfVRAtFtYakIxWECn7FpBga1eQKM6G4\\u002fOI2RA5goTf9ZPakDZE6uYd6VwQJE6nVokH3RAuVyUUnUgd0C9fMYItEx5QEHCfukqe3pAnK5lCIvEekCLCNQYxXx6QBXq91wZHHpAxnuDXfcfekAiAq4q9O16QK7eYMh5wHxAgcT2xsOff0BMGue2fLKBQFB8G6Rt4YNAM\\u002fSkyBgohkA6lVpkgEmIQP2m62sxB4pA\\u002fGxpubAqi0AI58kzD5GLQEMtL7CeNYtANN5rBcs2ikBMis8409CIQPOt5vb4TYdAbf4wFeLwhUALW25XQuGEQFHmLB1MIoRAQ8WlDiyWg0Dpb+PoYAyDQBbur6bWVIJA2wZXr8BQgUCqP99uAvh\\u002fQAnDFhln2nxA0+pfDn2beUDz\\u002fJymNpt2QHoBSZ9ULnRAYKQnqAONckBeNG0FXctxQPWty3G72nFA+EjeRIeQckCnsmZjEa9zQFGFGld87nRAtHffci4FdkCq4JCI9q92QBLRw1PsuXZAHIBkzZEDdkDSqjdhIoh0QM4AjRSBX3JA6JtM\\u002f2t2b0AkitvwwLtpQNa2DxDbHGRA\\u002fctSf30pXkAxFNGP0OxVQIG+WL+gjk9Ap\\u002f61cMhDR0AVKiyhTElCQIi59tqTXT9Awr6bdCP7PEDAvnNeRsQ7QPZE+sXphjpAYlH87aqVOEDBaLWmEL81QHshZrmeMTJAneoCKRGnLEAtnSYxry8lQGxDFY17cB1A8hIiQERbE0CuUS3Gdb4IQL1GXuhisQBAh5iOMMYp\\u002fD9UjLTsazYgQFn\\u002f91vnBiNA3cV\\u002fSGqJK0AdEbwaZOE0QN8DL0p5mz5ArteEhRwbRUDd6x0M+C5LQNUhzPkyVlBA\\u002ffaUjwRiUkCd6R6QOYZTQGLZA2nN6FNAWcET\\u002fMYbVEDKV25gXwxVQM+5Yw8Az1dAbl6IrnRSXUBZymJn6AZjQNUGK\\u002fwj4WhAHasfuSu1b0B46yw1nVlzQJDjDaXpeHZAtQPsBvLXeEB5PflIi0d6QAtClLNV2HpAjeEGET\\u002fVekBCHYgj\\u002fKx6QCLUtLK203pAD7kyQdOne0CKTkoXaGF9QJtS1wYJB4BAQKMF1MHKgUBB7RQ3e+CDQMRjAMadIIZAcIQqyp9ZiECWE5QxY1OKQGIM3AYM1otAoxab9vC0jEBiRVNvBtuMQMWnfnxnU4xA9VwChqNIi0C+azUf+viJQAZ4VH4Eo4hA1EooJVJyh0DJ35aYwXOGQNBWiP\\u002fNlYVAIxJKItGzhEBRPgngEKeDQIGlgnz1VoJADzEerfnCgECdChQdeQd+QGmyeF20h3pAdA3IuZJnd0AxkI3Qb\\u002fp0QEJOAcHEc3NAqsNzD\\u002fPfckDthMM1eSdzQNnWDDJdGHRABYsA4dtwdUDU9jPaLeh2QEKaX0HgNHhAk09jZywSeUCiA+7BEkZ5QGvJEl7qqHhANfvkwkYtd0C9xiZQdOV0QLXc7iFPA3JAgRssnxahbUDAxplcxkBnQOg+1M5FfWFARJ\\u002fZ8pmFWUC3jP6A04ZSQIY0Vx864UtAAG5sliK+RkBpua\\u002fASH1EQMq8Z5Hf4kNAD9rmLLTXQ0BanW4oFI1DQKgcv7C3jEJASf3iyzm3QEBs7vwrX2c8QMSh5VnpqDZAdl0q6Q\\u002fwMEB\\u002fAYkd3LknQLn5oPckUh9Ac36dEvb4E0C4mSjE1rYKQDld8F14ZQZAjWUvGvZjIUD0YZeG\\u002f14kQLUIEJH9Zi1ATDLjm2xHNkB80lq3rVlAQHZtpJfgm0ZAUelPGDs2TUDVNO6J95pRQLHChDtZ1VNAY5tg8PYBVUCIS9oa+DNVQFjXLBl771RACgcVGkwcVUBAnPwJqtRWQCXiwKSIGltArWVCwRlDYUDh6hBIAoNmQHNI1AqX42xA43p4\\u002fBHWcUBE6FmqOgF1QNf2biZ7lHdAETwM\\u002fNBbeUAXMEqX9Vx6QNnYXQMg03pAxhmHVy0ae0CgV7HCppJ7QCwT0Sb3iXxAuT4dxF4ufkBfizQvp0eAQMNLS65Z0oFA00uEX8Wqg0BitDEvWbyFQKNQOsFc6IdAMks5V3QEikBEb\\u002fB5n92LQG9fb3SEQY1AhI7YXtkLjkBZsyRrKDKOQFrPpZOXx41AK+AFwKn1jEC10tPZc+yLQEvR1usw0YpA0qjkI9myiUAEdSiBKomIQCKmxrOQPYdAhslMIVK5hUAiTA66\\u002ffKDQILkwrc59oFArY04XV3Hf0AHGXsuWtN7QD7KEq\\u002f8cHhAbkCK4gntdUA5KR6fC3B0QKfUUSd3+XNAoGBJxLtldEDn0uvldnp1QMGggPLF8nZAeKlyQpOHeEDKwzHsVfN5QD4Wo9nS83pAoixdumZNe0AOasi\\u002f9NF6QK2BX9ytanlAWMWidg0hd0Ct\\u002fd2LOyJ0QIx7GYduuXBAsp5y2PyCakCrZKN1oCNkQIsG6Vdps11A0uo0dmfWVUDs7GmtZ7hQQF8nkUEK7UtAF7zZuSvYSUDrIz9ecbxJQCAgP6huQEpAJdyLsLxZSkAVEPpQcGhJQK3CWj0jPEdArornCB4EREDm4TALzy5AQG7Mb64rgjhAnknwww5iMUBNmyVQhDonQMXT3GXm+B1APU3yU3VAFED9yTpMQg8RQDMQCOPwuCFA98tf0aurJEAOeFsfN54tQFZ6IzCUVzZAeYr\\u002fVP9gQEAqC++RSa5GQDTv+cHoZE1AspFq12\\u002fIUUC+z+v9nhpUQGv+gLZtU1VAccJLGq9yVUAdyg6SUuZUQBJDDvQnglRAqT5Xa+1UVUACoqHZNWRYQBjOY3NpYF5Ac42wyACzY0DVpun8vnBpQIfNDcgc029AMKHV3oYOc0CPFYlsstB1QKcFeIGE+3dArUCT9OqKeUDokxvDHah6QIYM3OrolntAe3c\\u002fAMacfECYhR8kWe19QOx4bYLuo39AdZXzy+XkgECZGP0P\\u002fjCCQLR+rT1ZuINALfBc4+Z7hUCkOzdS3HSHQBvUGyydjYlAY5b326yfi0BA+CiL8nmNQJK+OErP7Y5AnUDn5Qrdj0DiBoNztCCQQJCk1qHuFJBAbFyb5Ayvj0Bb3mRLfOWOQDUhxA2s1I1A4nnJYy13jEBKPGDxwMKKQGz06mbUs4hARs5cAtlWhkDkgKvEocuDQDlT2NfBQYFAxECJV8refUBZeHLaawt6QLMPW3zoTHdAIGSapNC7dUAlgS29kEV1QA1dA23WtnVAFaLKuzTLdkAcnETXNDt4QDQ8GnwCxHlAbb6h3DQoe0BM43zDTi18QLrXRl4sm3xA3nEie\\u002ftAfEBe1KULlf96QJpPQWMu1XhALKEax3HkdUBLyU5KQHJyQBsGluhnsm1AG1OAmvjsZkDFNCdJ9CxhQMruTjXLnFlAg5Of9yvPU0AyC2M7RKJQQJm2PCix5E5A8HuyQrPpTkAPzkUxJMZPQArvAMw1G1BApGXyM3BqT0Dg1awlwBRNQPN8gP4OX0lA9EeIj7vHREBWDpUC5eM\\u002fQHfiE3Zq7TZAWojIhM8SL0BxCtCMFVokQHqyLLZv6htACLgKv7SwF0Az+vh1eEUhQK8Y7VInASRAcOci+X9NLEAGmmzu5yU1QGKFzUXj2j5AHxa5+jVWRUDpA\\u002f3oV7FLQNGvb1xZz1BAPgicX2IWU0DvY2MT6FRUQCRXumLRe1RABB7hH6\\u002ffU0DZ8Q\\u002fm0zVTQCDG9Qj4cVNAOmqIRjmMVUBSstitRD5aQDNuWnjc5GBAq6lr0h7tZUBOtxfnCclrQB0e0Pc28XBARuj8HYDUc0DKJs5biF92QIBi1zpRiXhA+1TRp5NrekCF\\u002fd6EojF8QKfg\\u002foNhAn5AoFSTPAnxf0BWuPZfQ\\u002f6AQDlyYHFjDoJAbFfpdnUqg0A3ZiSSBl+EQKUSf\\u002foIwYVAIcls6Z5jh0D5s58kaEuJQAjptt57ZotAdH7QggmOjUCPJvLZO5GPQI7pJ4jOoZBApFrf0qdDkUCea2byJaeRQNmv6IrMyZFArNFKyiupkUDBmPB5o0CRQGNcGKcBi5BATijpoRcOj0CRpvc5lHiMQDyPBtDZeolATgXcwQtOhkAC2nuTADaDQAmCIwjGdIBAUgEIS9R8fEBvCMBnbmB5QMrcCO7+mndAd9m\\u002fQTAFd0Brdql9hVl3QD2wyW8FSHhAq3SyUFuGeUBOQFCPJNd6QEuxP2L3B3xACsCNc5XqfEAMdt8Y6k99QDF+uw3NCX1AuUDWtDH0e0DZWyKV5gF6QFmwvv6fRndAR82kTs74c0AdNeR8L2lwQBjhwFjB5GlAn6iRmqrLY0CN5p+luvBdQBj9AQYvNFdAQ6J40lE9U0CexMJuO4FRQKlSphXMOVFAg46Ksd6TUUC4\\u002fJrvb9dRQBbaS4c3hFFAvpvgs59fUECDYt\\u002fY8uVMQKJSsFvY9kdAvg7Rpz2hQkBOGrMhEyg7QGoczccqrDJAoicZ\\u002fcrZKEDIuc1g6VAhQDEmmmpknx1AvY6EMoouIECo8\\u002f\\u002ftapMiQD7jyAFx1SlAtFw9kqEGM0CRQDXg7n87QJ0AqWV37EJARLGKluKHSEDwS9x0m9VNQFu2sR5LAlFAHnrJmNU4UkAroMmk1ndSQGSM8cc2\\u002fFFAqJmnjM5XUUDCZ7EQWVhRQIaPx4Ry2VJAXemmJ3SNVkCXPuXOAsxcQGgG4yLjumJAL4W95BX9Z0D9q\\u002fImvsFtQH3KceyNz3FAYAHXQBSodEBNoBi1pWN3QHJtqZirEnpAAuXD1f3LfECsymCrsZh\\u002fQGtnO7csNIFAh2anx+yKgkDaZ+16Ab6DQLoj5kvax4RA0arKqmm4hUC0DPIWfrOGQAfrA\\u002fqL5IdAvWhvVj1tiUDeLDo8JFeLQMsfxGhDj41A\\u002fnLZ85nsj0D3jhMxax6RQGPHE8rOJ5JA7faQOY39kkD8qZ3nDI2TQF0xIcZmxJNAVhK7UAiTk0C\\u002f5JGEqe2SQKU\\u002fveEC1JFA+XXGnRxVkECcMnx6Fx+NQH1BgqTwWYlAC0bYoNi0hUCug3Yl9H+CQCaG9P8z6H9AFExTVB9XfEDZQWWgiEN6QAOVzPHlcnlAFwEOKm6OeUCb5+udojp6QLr5u2icKXtAZFIlv48ifEBLn9S1\\u002f\\u002f18QEwvXqWLm31A1vjL5d3YfUBCyp4MqY99QL+ncQ0bnHxAQntXQIroekA0E0HMrHh4QKYXWfASb3VADQFrfB0KckB58Cd11TFtQH\\u002fyuL\\u002fs1mZA+0W3cSuFYUBEdmR+8RBbQI53Em8y21VA9k+L03QPU0CHDOegDgNSQPxSAEsa6FFAZeaaIRX7UUDiK5BlD6dRQKsRNMimmlBA9NxpfuqWTUBr4UcuVdJIQF16gCemikNAnD4Aco3fPECKgEYbDyU0QHsbaoM\\u002fPitANCaKUPpJI0AX\\u002f5\\u002ffDaEgQMTCDcyOMh1A4kB9ZZiaIECwmdL8BakmQGyEpAMwZjBAFOY3JVJlN0BwJcFfuOw\\u002fQLtVhcXsmURANIDH0x4LSUAaGXpD1qVMQL7QYqes5U5A01Mi8EWeT0BmJulLvCRPQOQ6KbJRV05AczZXWgh8TkBjqJMaZH5QQJ5gs15DiVNAnfiwyGq7WECgAkZhRQ1gQECQncwzrGRATT8KWy36aUBppPmNArlvQDgGLlZ443JAU+1nbaAUdkDaP0VG9X55QFomgW\\u002fkK31AoNE6XMKFgEDJgjuSeXWCQJLeBa+3PoRAxsdTL9W7hUDPFcu8MdmGQCNUn53eoYdAEUazHbpAiEAooS17UvSIQPMZjuVg+olAgbENMcJ7i0ASDtzfF4GNQPx\\u002fHj4e849AGANGd7tRkUCk5aNMlKuSQFGbGCq85pNAHsB41VXhlEACGrumaHmVQFSg1r2pkJVAmDi7viITlUAU+u9fe\\u002f6TQAiut0g6ZpJAmTU74D1ykEBlmLpCrq6MQJkZ0AsimYhAvrcNwXwBhUBXTLoTTCaCQCxJ2DcCIYBAwmyLymTQfUC6UZOUarB8QCHuTS1Kf3xAabEudtvYfEB\\u002fRIKelWt9QN\\u002fL3jLWAH5Acf5pyK94fkC7p0D9Gb5+QIkzEwpLu35AGsMd\\u002f4NUfkCMO9P0G2t9QHVQGWRG5ntAmrMnoCS9eUCHMKpVIv52QKtEQ0cO0XNAsvR6oNZycEAwPxpdmVZqQCDTAF4tfmRAwUzIFVSGX0AaOawZ66tYQMpNF2k\\u002fVlRA0yrn1QAKUkDl+DYZQxBRQKZOntztplBA0ZGJSV4rUEAWEOHDUmxOQG+QVZCASEtAmpV5SgMdR0DzvOYEfGlCQAuhO7cKkDtA1Cgh1W2CM0CZ9dxG684qQFzG8KG\\u002fSCNAVx3xpfLAIEDlS3fABEoZQBiuZZCRixxAfqiir88pI0C\\u002f7eG9BEcrQHd8Q\\u002fiSLzNA7\\u002fI8tfTmOUCcYUWl3ZlAQNTFUlYpIURAE\\u002fF\\u002fxysUR0A8RRGbqRRJQG8HA01FA0pAox7FQj8eSkADaRM5XQlKQHfD92iKuEpAwuspN7tATUDrB6f7pk1RQFckJ8ndtlVAe6rE08v0W0BjRSXYcfVhQOi+RPFtsWZAX5niAFUbbED1ocG5ER1xQMvWDWh+mHRAMAFbsPaUeEA\\u002fgE+I0hZ9QMyL9zuB\\u002foBA4Ddvzvx7g0DlWtq4q8qFQJ6b9GsrsIdAbmVVYQ8GiUCXXwOlG8uJQJa+RTh4KIpAdqfutL1nikDeRrj5g92KQAX1XA8X0YtAqlHomWtqjUD8O7d4QquPQJjZunAtOZFAQFvJZrvBkkAx1axXnEeUQMvP6H\\u002ffnJVAs0LYGnORlkBxdg7c2PmWQKp1LHNVuJZAYvOTzNvFlUCNbQkxnzaUQGjUFuxxN5JAelfIjD4EkEBNwtxU87WLQKDN0ywP34dAFRn7eMnHhEB\\u002fR4Y4M4uCQPhcgH1iH4FAvkUlbG5fgEA8VUHyhxiAQGMdNMc9F4BAEi\\u002fOmb8xgEBupliQgEyAQE1yZcijWIBAbi6BfFVOgEBecrnexCaAQEqf9ACNsX9Au6je2+2xfkD33t9vmTh9QMqbZeoYN3tA5I5M6jateEAzl1ppK651QM+0oJuaYnJA4AyuTFAKbkCOrLi5KbFnQFTFECXMMmJA6fTpCca\\u002fW0Bq+mh5X6BVQKl9nYUTxlFA2aQOx6o8T0DSzh2fF+pMQI1aIJidRktAPv347l1hSUBHrAxIRMNGQLQ9qEPfZ0NA52Vju7c4P0DZlByxo6Y3QPHrFnkk+TBAnA+Efm6wJ0Cc\\u002fuW3ZE4hQB58eUrISx5A0zsqQwjmFECZgtvNw30XQKBQ5My5PB9AKK8WtKL7JUB4uJ20o5YuQGsrj51WdDRAR1ft6nEKOkDgXacwoHs\\u002fQIIcZfEqF0JAGRNznsvXQ0AQo50edvNEQGhVsli7nkVALAMqqjdTRkDRDZUxN8BHQKdZHWU4qkpACerVIUrCT0AAMm8jdcFTQHElgJTeEFlAv3LQog7RX0BYMB5gUQJkQN83VM1w6GhANVfgOQ\\u002fKbkCScw0er\\u002fZyQBP6f8+uSXdANo7aQhZlfEDYK2A9\\u002fg+BQAtxoqzOC4RAJJkAgdXdhkDE2xR2zDiJQB3dbp1J4opA0R\\u002fp+hHIi0DJIT6G4AmMQG3cHR4f84tAehbhNrDmi0AIVfyndUSMQEUopFbtUY1AfOsyC2gsj0BJJRF6AuOQQGaDr3QSdZJAtm+5ZIkilECsExESlLaVQHd4rCeD95ZAotDH1ZWvl0DtTze++LeXQAIpoj0BA5dAbi9xo2ChlUDBbG8LVr+TQJMWfTFImpFALSn\\u002fmR3ljkBsCrQu5fyKQOQxAqASxIdACqtHA7xZhUAO1TSSXbiDQAPAJIW+v4JAK77v3TFBgkDeZeYW2guCQFsKuo1\\u002f9oFA\\u002fUxfP6TkgUBCN6C\\u002f+8WBQML3LOjqkYFAf\\u002fPMhltCgUBzT6zco9CAQEkSRyoaNYBAEpHxcVjQfkB+RPLhosd8QOB1HHiYSnpA81PikmVfd0ABdalxkh10QC5lxhKlsHBAXGmp92OnakBru2eEjopkQBvccFSM315Aky0JoG0WV0BhcbHGBLBRQDu1GwIriExAg7MAXYhtSED\\u002fxJNHpc9FQJPPjo3UrUNA+sHlT8drQUDoMFQSG6Y9QG1553Ud9zdAYRspre1QMkDnUW51KJoqQOJuv8ma1CJA7CHqzQrqG0Ab1nOMrpoYQM9WJMvmXBBAXReFXdplEkBzmVs0G3UYQA1LLBBuLSFAsA4suYjRJ0CCp0PJj7ovQHF33Dj5IjRATxvMAONWOEBa\\u002fsUONBs8QDPoz3jFNz9AZ5o32mzdQEA+j7qftAZCQCR+iQsaeENAkUtQr\\u002f6uRUA9pc2vwixJQFQS2uzTW05AS9De0yG\\u002fUkB6UxouyFhXQN8NPHlWB11AwjvRHYf4YUDGIEid0zlmQIXr+MS8mGtAfQPV56s+cUCHCfOOT6J1QMgU8uTtBntA88TnUmOigECZYrd7lPiDQO77UUzdNIdATPBopBD8iUA5wYbhYwKMQPeCNYOsI41AW43l70txjUAYOb1tNjCNQNOzVhGbx4xAkvfvFWymjEDcX1v\\u002fuiiNQECFVpB8hI5AKcJRt3pgkEBuOLkbv9uRQIAACd8UjZNAw5m+cPo+lUDVJFrxYbSWQG+AXWw8spdAdu3uTwkLmECf1CIMrKmXQFWAF6trl5ZAYUnCawf6lEC6TFggBgqTQOpXdYLdBJFA4qc9Qg1AjkAVgFYhlwGLQH5Zxm+Oc4hA2Ly+bHeZhkB9ZXwYdF2FQIDv9CBamoRAewfWpPUlhEBRLQ5N39mDQE3jV0znmINASocs0OZPg0C5WMLe8vKCQMF6H1O1eYJAspPWmWHdgUD9n3j4nRiBQGXBq0zwJ4BA8v\\u002fgXvcSfkCVdKikLXl7QCtDCWaYhXhAzVgB3ItFdUC+wMRvz9ZxQKP9cJbO0GxAW\\u002f7sNnllZkCWWgp8C9NgQC7F7838t1hAvSTTCSofUkAegt0UXTZLQHpXsSdVXkVA8N31qxqaQUAkzxflXuQ9QDqS3kvMdTlAgyk0ZXI3NUBBZI6ecwAxQPfSjV8t+ilAqB6NTUf3IkDbJF9tIRwbQCycvtrYURRAgL5TWM8CEkA358vZbiQIQBvgTXvDRwtAGWjPDcBQEkCeQE11s\\u002fUZQIXL\\u002frWJHSJAF4196+0+KEBf7j6WjeguQKfn9pbmyTJAZp5LdrDpNUD7bafd18M4QJeliM3GhTtAruPee++hPkAqgn+Q0GBBQKJy51yVUERAUSd02D5wSEC91Y52kO1NQPYd87qJZ1JACpgiYcSDVkCADQLGOFJbQFEJviGYg2BASTZmFcEQZECJCmtGV7loQBfURblcAG9AWRof7Leuc0Cjy7MIiQN5QA06F1c4X39AdHY15Rstg0DcxSVU56aGQPyXzhTpuolAe\\u002f3qkJQRjEDWiobEf3eNQBcJdNZV7o1A1C8PV+6ujUARYCIAqxuNQLCZ1tPKp4xACtQ\\u002f8M27jEAcmdgs\\u002fJ6NQMb9GBHsao9AwsFNFLYEkUBHouoGAZ2SQJuGlxP4TZRAgNvQBNrdlUCkSrnUSxKXQEVPDRHwupdA3MJsQ8u7l0D6ckeW8BOXQIWBgXZf3ZVAeLEccJ1FlEBZUUg734GSQCAuksJOw5BA50zVRz1ejkDl5JcuGLeLQOX2OotJoYlAzV6ijesViED\\u002fdyn86\\u002f2GQPD7u0+MOYZABb\\u002f91cinhUBQa3B7\\u002fyuFQBzXX05DsIRApRrna4gkhEDmWe2WxHyDQCgSv5g7sIJAjhPu1z+6gUB0ZyQGvZqAQBUQywUNqn5Agmf4Eu7ae0C4a9CwT9B4QNxKSPvIlHVAD7+uX+k7ckAJlMffJs1tQAI5xM0hgGdAVZZcI97nYUBSee7xG4daQDv8z9IbVVNAbiOUwpAuTEAJ23F87OREQN76SoTy1j9AaoRfiVviOECf+NUwibAzQIVHz7XI+i5AxXQuwa3UJ0CvOMv5zbohQGP4Z2IHgRlAFms7Dx4eEkC9t9+CHigLQN3np9HMGAhA8seur0+6AED29MbnvxQDQBuLOr4SIQpAGB+alj\\u002fhEkC29VQmj8kaQNrPlvFXLiJAqKeLcLN6J0A5oq9Opu4sQKmRA1CTJjFAtA6a4mvTM0AlLk+rWb82QGAQR\\u002fHRZjpAApIDo2hrP0B0CyDjITRDQDCBekwn30dAAOWm+oK3TUCselqcz0dSQGNi\\u002fijiFFZAfCtYg085WkDqrWEYOOVeQADi7qRyUGJAUdOUV68pZkBvWzeK1pBrQDBcTXBwiHFA05itqId7dkDxDpBTz458QA9j6jFls4FAXAFARaoyhUAoCNBkxWOIQCLcYfKd6opAOxFkf8uJjEC3hlB1HTWNQGgSbkzqFo1AKAxKWzuGjEDzLZZND\\u002fGLQFJDhdS+wYtASx8sxJRHjED7slPkfqiNQNFydRN43Y9Aw4n52KRbkUDcDiej5POSQEp0RaqahpRAo6mAcuPflUBG\\u002frOCDdGWQP42XJ5dOZdARYxRsOQMl0AR3lxvh1aWQJEmKsYBNJVAB9DeaXXNk0CkFj4pAEySQAVxp2yd0pBA2Cyb90D1jkDcYzw7GKaMQLW3GMWuwopA2jox\\u002fqFFiUDy\\u002foTWTB2IQBvsmhczMYdABwl9W5JnhkBeiOnKMKmFQF+kWC6p4oRAr2Xj2G4EhECGEGfVaQODQHWd\\u002f9+R2oFAvmFfUvCLgECnzZCwdj5+QAZGy8d4PHtA8gijktEieEB9E1e7Uf50QPERApYO3HFAbJjgZvadbUCMp+vhSOJnQOiapgZKvWJAWAShoke1XEDHMx+9I5lVQPF3PdIgE1BArP3GfnTQR0B\\u002fD\\u002faAHZtBQJ9T3OsT8zlA2g1MHjH6MkA1IU\\u002ftRGArQBNMmmdTWyNAPd0Iety5GkB6EqZrLhASQNy1mwwEcAhA8I9bU1WyAUCbg52xe\\u002f3+P0\\u002fyRpRqwfU\\u002f3Ar7NpIl+T\\u002f\\u002fn8EQVLIBQAYNsXFoSQpA0COQqoobE0Btoym3cIIaQINkPtGIeCFA7rg7hQ35JUDWbuhcQ6QqQDOdJoMIpC9Aj\\u002fM4JofGMkCBcyap8K82QGAfHeAALDxAf+j\\u002fsfriQUCEvKtRzd5GQAL+fM+S70xAhsB1nRfiUUBnt0ojcXtVQOMsW4oFJ1lAc0SKXXgFXUCFxl4abcpgQKlgLDEI3GNAb39US95LaEAjjg2ifKxuQAlAjieVrXNAwfM8BCcmeUCKm5VQ0nV\\u002fQLIkw+V4CINA4G\\u002fmwZshhkCIjjx146yIQLB807E8aYpAJsg2IpVAi0C+uO3sRE+LQCYR7mCg3YpAfEqngbFNikA7\\u002fJimBgOKQF+NNBBDTIpAtVG6OM5Ui0BTyWa5CyGNQJbclr74ko9AL2rwZ0s5kUBYqf3eRbuSQDSefEJCJpRAP2HF+o5RlUARUF+PFxuWQLXGWcGPbZZAIfHkjktElkC3pHtJ7qqVQBftBEoYuZRAVndO+iyMk0CKbDBxvEGSQI9WbqDq85BAFWzfih1vj0CMFL5yKjaNQEsq1ahsS4tAY43tzd2uiUAMB7NKglSIQMPPiYMHKYdA1ranNpcWhkBIvL26QwiFQFX28OUg7INA\\u002fsVyBjy1gkBU02Nh512BQAfJSMg70n9AGX3vW4PCfECI4IeXF6h5QIYmoNuLm3ZAyIYyKC2tc0CC6q1KC+ZwQPTFeD+bmWxApL97xdDSZ0DSdFKyf4ljQIXfr+jllF9A2cleEXIuWUAnW9ZDbstTQCbKPbDGlU5AqqAxe0cZR0A835P3XvJAQO3pCMjRBDhAyP+6AANgMEA8TuF1\\u002fXElQO025SzkBBtAfEZsa2yCEEBnoY2AxhwEQOCa0Djee\\u002fo\\u002fUtsdP\\u002fMy9j+DV16e7JzqP6gphJJFQ+8\\u002fMZVIj1K89j9C+WDQb3IBQEQw68ijHgpADdowCm+dEkBaTkmXxi0ZQKKCG2TtPyBAdmL3vbVAJECamX9NK84oQI9o+SK+ey5ApZIbKC8fM0CRKBp22584QHDxtyhIIEBAK6VbkSUaRUCHY\\u002feTrBpLQLKcY3Gf4VBAWaeW+hhNVEDqu9S+H6VXQJBidERC+FpA9yA9PlyuXkDVt+XC0MhhQMCamIYWWmVAI80ll6KXakB8vlrK1fFwQDAu\\u002fbp4o3VA\\u002fqQw99Eoe0Bbo3D77oaAQCrdex7wWoNA3ody4WG\\u002fhUBXRVIyZnWHQOG\\u002fsYzOYYhAdGZCGn6ViEBcjFrctEmIQM7RXQ5M0YdAqWWOebmDh0BqmHKw16iHQKYAaevLa4hA\\u002fQIp\\u002ft3XiUCdcC1bWt2LQMOJrltvWY5AR3bciTaOkEB4ZCROp\\u002faRQGkuOgOARpNAlPflazlelEAtiGiv8SKVQFS2HJYdg5VAv3DR0i95lUC7+FKkMguVQLO8JVEOSJRAze353ChEk0CbLu25khaSQH\\u002f3qFDD1pBAK7wehXw0j0DymF3o0+OMQKvsXXyez4pAlDDPd6X8iEAuUee5m2KHQLbKmorO8IVAiZB6Q0KThED3iGAXDTeDQJ893Br+zYFApLVNH69RgEBfjcMGTYp9QNG8+TluZ3pA3i+X585Zd0Ax8XSdKn50QD4Z\\u002fM7s5nFAWgO8REc0b0C+\\u002f6D67yhrQFrdZpUim2dA\\u002ftUacOR1ZEBlwNzsG6JhQDgkPC0dEV5ALdmi6KMsWUCr2yWQDIVUQEDZGI0DI1BAcrp7SfxJSECaiKzTsGFBQHr+KHlpjDdA6pTnjlsgLkBkZctMcDMiQO6tOxia4xRAYDQPFulSB0BURxnWtK\\u002f7P9rWQ+wi6vU\\u002fPPBiOdT13j8QsWsArIPiPx2UmdXy5+s\\u002ftue8vwUu9j+ZwROxnyQBQLrXJdSsMAlA915rFhCPEUA0II7qd2EXQDW7a783Hh5Arn8kqbcaI0D8Xp+YGFgoQPg8J95unS9AJF6xG973NEDzFi1ULxo8QNHHDm2xskJAojg+VntOSEDgag1E+IlOQPdc5FdueFJAhpe0UfqTVUAbCmcsaJlYQMnNLouM11tAYo+BImL0X0DmupYvVOxiQEPTW5RGPWdA4pkbUzVIbUCEpLFmBZByQMFABeqXOXdAtk050TZEfEDpjUsx65aAQHCqdqixtYJAE0eFvutFhEDluRDMpSuFQBrjZ8Tfb4VATDAR0cU+hUCQ1cAQE9yEQItQ9AB2kYRA8C1iTRmdhEDbonR0SSaFQJmxlSYxO4ZAx8wt7hTWh0D7gVzC8+SJQLL\\u002fnYhVToxA5JUtRnHxjkB1UrlVEtKQQInVtKaTGZJADWzgVI0zk0DPF6HdogaUQFV0oFIRgJRA8KHSWCyWlECNapjfu0iUQIAQ\\u002fM0WoJNAflxRreWrkkDwXJ2jm4GRQC0DadUCOpBA0dnJOhTbjUCibiL8KWCLQIn55\\u002f+nG4lA6AfRyLYUh0Bo01StGESFQCo8SR7UmYNAGaNQqo4DgkDd32C0PHKAQGKpXpbPvH1AExiKd3iUekB1VcUEGIF3QJugAr2qoXRAW8XBdZ4TckA529jrK9NvQHu8pitSTmxAl0Ymy4uDaUDIW8Mm2khnQH67RZwFaGVA+XIpBS+nY0AMMxnahNJhQKRan1lyjl9A8S12ry75WkDytpm7yAxWQHG+W3K9G1FA58Cc\\u002fwwOSUAxgajsmD5BQGCJ5liCQTZAt4vVsvToKkDWwNK2Z5YeQNHkudnhqxBAdgQ1o+0FA0CRr4SK41X9PyTTNknb8dE\\u002fn42GoBDW1T8KbbnDywXhP5Q3tSu+\\u002fOs\\u002f2WtEKBxX9j9MvPY52fQAQLureTO\\u002fdghAZ5D3lhXmEEDWCe8QV6MWQBAYUfm94R1AjuY4nue+I0DZd2e292UqQIoq+aNO2jFAr5qjPJoyOED4E7+gNzRAQE9KvkNRK0VAKjxMcsS\\u002fSkCYUvXUb0xQQAaxfp3ENlNAzksZP5sdVkDTiiBhdztZQEZ1pHprEF1AIvixK+QpYUBKtOa4geVkQBOl+WcyBmpAyk6CilNPcEBqfim0fDh0QAzi9x\\u002f3dHhAnXbr2+qafECbk88aTBuAQA5IAB2Nc4FA61iain48gkA0tiTZ43qCQJSxM7xVUYJAKfIO7BP3gUAIsAhpXKmBQIMvknWRnIFApv3XM5nygUDmdRW3aLmCQNoEOoqD8INAsvaC9KaQhUBklmbxNJCHQEW\\u002fF4nT4YlA84aH8ytvjEDSbVPKkxSPQCXedwPb0JBAljFY4ejvkUD21ftDWM2SQOrwvfNHVJNAgdDb5YZ3k0CLXZK3zjKTQKNt7\\u002fxoi5JAFnDMPD2QkUBzfQh3dFiQQA0Mpx\\u002ft\\u002f41Afqj+LFlEi0C2X1EecamIQMFfHqw3SIZAYUXvOqsmhECCPs3sRDyCQFsJGl1NeYBABy4qnuGcfUD643FYTGV6QONwXFhSTHdAvBpAQeJjdEDcKVCNEMhxQB22LUwiKW9AqQjKrzu2a0Cd6aVutTtpQJxqZ7urnGdA8otwkLCfZkCRt4aEPvdlQIK5fwRUTWVArDvy4rBSZECj1zJWNdBiQLqE1tA8tWBADeP5ZH44XEDYmc7ZCIRWQP2BD3QD5FBAUadNu4O\\u002fR0DYUT\\u002fi0zg\\u002fQCzQLLpoLTNAeKo7PKgUJkBvQjZ2jUoYQCme\\u002fJi92AtARIp2I699BUCrVgfMtxzIP+yXcxpmms0\\u002fBCFzupqB1z8c3HYod7vjP\\u002fQpzXRDIPA\\u002fJse2pSYp+T9HgTNIAb4CQBl2RWcS2gpA6WtsUwSxEkDq8ov8M5UZQGotRqKGYyFAWNCwzladJ0ARotUPVQMwQAsIxVaYkDVA4NbkgT2aPEBXRkR8r4lCQF3ObPUPXUdA8FqchwGXTEAWrjPtIwlRQGjVrsei7FNA6WA0JlUkV0BMXwOOuQ9bQKH865A6G2BAiK3yAHSVY0CL81iF9C1oQFG0gRaA8G1ANb3BXo5UckAcunCLXu91QPefhshHcXlAMjSgTMJ5fEBNQn\\u002fx87Z+QJ5SEQxw\\u002fH9A1WBI2pAogECbftwF6O5\\u002fQEPiog8+M39AVfk\\u002fTfuFfkCN+HKleT9+QKDSbCD7l35Afk1S7AOmf0CBXLqm9bSAQNgpX2b87oFAElBZfiSAg0DJxRqAFmiFQP\\u002fikHaSoIdAj3EOaDMWikCvwwv6RqWMQML0Ip2\\u002fHI9A102gzM6ikECEBVLsp3WRQKgN\\u002fLc\\u002f8ZFAev5iowYHkkDVTxohprGRQOOZirLD9pBAsyQjGeTOj0C+Ki+PGTyNQFJzVnV3copAH180pwKrh0ArxoLQag+FQHhdYH3GtIJAAfpqgiSdgEDXjQjSU3x9QGxgk9IvE3pAZwQkrH\\u002fpdkDNs5yxw\\u002flzQFwzbZ9XT3FAlSSw908BbkCxgG7XaEtqQLWtD7dwnWdA1HicHJr7ZUAtpVWyp0hlQNOujccBR2VAgR8xVr6eZUD9xnC4CullQAX0f2vQwWVAPPeWrabdZECBtGO2qB1jQIXmlnKQmGBAQdEEKvUmW0D6baSyENhUQGqzQwGo8k1Ayhxtf5QYRECuMGPRHi45QHYAvj5cji1AE2vXphqTIEBOat26Vl4TQJhiPW3SNA5Aen4\\u002fW0Gmxz+7bitMa9LMP52nBelGm9Y\\u002fE1+QVo3T4j+ZOccB0LXuP1n4dEGHDvg\\u002f\\u002fYT67WoZAkCovRPzaE8KQE8IXk3bnBJAbHxZjGDQGUAl428bYaMhQNGEy0vLzidAtyLRAHC7L0ARyVxe3tY0QJiLVfCk5zpADsAJetMJQUAFbMtpEChFQGeBXoxNyElA5uMjltrvTkAm8pwupGBSQAauk3t6x1VAqV2dG7\\u002fyWUAFlCu+WUFfQHsPPW0MDGNA+iOUY3JgZ0DOdHGFVKFsQOg1GNTOT3FAfmu7ANJ5dEDprf5TJoR3QKDmI6wiHHpA9+STSYv7e0DnE8jZ0vx8QBzdLA3TJ31Ap\\u002fsoiZmxfEDV07lgWe57QM7lrGAKOntA3DRvpG3gekCXdGN54w17QIS3JwzIzntADzXamR8cfUBoPTWHjO1+QNijyZfkooBAdcvNlhoZgkBu3o6V5d6DQPb+Pwqo74VA2P52NH82iEC\\u002fCbwO\\u002fY2KQP1ai6xOxYxAMTDyi8CnjkA\\u002fICiy2AGQQDQrXVsXWJBAgVEtXyRJkEAItrhevaKPQK4XuZZa8o1AwZIFVNepi0DsT2jk8QKJQMg+7A0LPIZAqyaRta6Kg0Bu6nC9IxKBQEQ\\u002f2f\\u002fewX1Az2IQKt3peUAwn5MVdIZ2QIkfvda\\u002fgHNAXUphaV3McEC3Dh84\\u002fdZsQGktg5fB12hAxVrj87u+ZUCMoMktOadjQLtyZIkHl2JAUoy+QNF2YkBRednGgA9jQMXDmb+EDGRA4REA9HMEZUAnXs23y4llQNxla3ZqQmVAHw05M0L\\u002fY0AAjumKgcphQHhzTBQ+yV1AVujetiVgV0AhR3\\u002fNoydRQL9GkCZFgkdALZw5KFwSPkDkRsqTgAUyQIctU7l7qSRAfQhFGlS0GEDFs3XqEYITQACz\\u002ffV6cNA\\u002frtd2ltjQ0z9fPZBnhWnePz4alqBLxeg\\u002f1qVwB23J8z+8UepJcWb+P+fHLdVodQZABAlVM+MLEEDbR2jnLlMWQKH+SDrjaB5AXwJQ7UdYJEDh3+tMp8MqQJvINYHhSzFAhBLF2WryNUAdDC6Fr1k7QNckG05bxkBA5oAEcLxURECxWBimlXRIQKvw4UqOUU1AWqWakIqTUUBfeiu3iSFVQDl9RLD0hFlAOadXPIj6XkATSmkgdeBiQORf0UMnAmdAe8TwcSnga0AXnEIScKhwQAtnoqzXfXNAH1JyJgQwdkAHXckm2nZ4QOLFt8reFHpAXY4HbTzqekCEllvqFwF7QJLkWfx9jXpA6Vojx4\\u002ffeUDXljJ1OEx5QBGCvlIqFXlAlvKI1dlZeUCXInjMpBd6QKsbvdppOHtA+VXd5rSnfEBYImD9LGN+QC7sg7y3PoBA2cuKkPyIgUCZ5ZzlfBiDQBp6e2L+5oRAG+LKbODchkAvO\\u002fopNNSIQG+TvTtDn4pA17dznQsPjEDmT0I\\u002fN\\u002fiMQNRk47CmN41ArPovAy24jEAy18mh\\u002fniLQFwNsU+lkYlAom7l5dkvh0Dw9SkCUo6EQPHNrXV454FA95MzBo3SfkAJq6I2Q116QJhWfUJVf3ZAFRujaMIuc0Ax01DCwFZwQEzT1gU6ymtAVR\\u002feej2jZ0DNhfLxZj1kQDmo4SJdqmFAS9hi+v73X0CTxbScA3FeQMgynmS6ol5Aj6oW0XAdYECrhZ9WCldhQOZf+w0poGJABv3NfZuPY0DRr4aUrcZjQB6f2PzSCWNAIsFRUqFSYUBkLP0bi6RdQGcc7J4UxFdA0c47QgLQUUDi5K+SxuxIQHJvnlUeR0BAGPGyW0buM0DdiaydWGAnQE60xmbpnhxAGjS0UuzkFkAjnDFPQFXZP2HEo3xIZ94\\u002faU4DuhwX5z8gGk5RyovyP8CCSxW3HP0\\u002fvQPOeS\\u002fiBUC388Rey4APQNZxJapM1xVAggRgZz1jHUCZApCc5FAjQHNZbu0v7ChAqAs5B6yeL0An456v37czQL\\u002fi\\u002fh5GKThACqlhg6QdPUBN9Kg181FBQAKsQeASekRAlHKhCJw7SEAHnc\\u002fFR91MQGW8pLumVVFAzU+nKQ\\u002f2VECz9geS8W5ZQL2l3Sji3V5AdaE4dPqvYkBVfBEY2oRmQOFw3ctd6WpA8uUqZ0O9b0C26z0QLl1yQE5lYI9hu3RA2j+aOfK7dkBqF3aczyl4QNcnixNv6nhAASIu3FsKeUAP+52Qqr14QFYh3\\u002fNBUnhA57HSqOcXeEAhaxmcDEd4QHZks23c8HhAMBW7A8X\\u002feUCkI\\u002ftBdEh7QOnQ8yJao3xAkQIkijoCfkCoIq57GHd\\u002fQO1FzdKmlIBAUfZyNH6fgUCpKsTSZ+OCQJE+vS5YVoRA4GS3yVvehUCzK4NTpVeHQOnIJhPqmohAK6IEjnyBiUBrNRac9+eJQH9po\\u002fMpsolAmgYvvYTRiEAdknCr5EuHQGNiJS1ePoVAlBX0Ee\\u002fYgkC3F0iDzFOAQHPauLkaxXtA5eBoF5RUd0Dhm6NLoHtzQN40Ui\\u002fXP3BAuGfOVbEla0AWQ425Vr9mQJ0aVUxYKWNApzJf8RZQYECvc57snF1cQGqk25a3kVlARsGflrBBWECCUTm2UF9YQF1BgxHauFlAhECCX4TtW0DLH65EWmxeQA\\u002fcqiMFQmBAtfOtOa3CYECuHrNIFXhgQMcZk4MTml5A\\u002fJw6v8G8WkBlB4FWt+NVQBG8xk1nwFBAKdPgLc\\u002fuR0CrjPKWk+s\\u002fQARex5g49zNAMXy8et71J0C0qW2smAoeQHCh\\u002fg6bVhhAqKr73X7D4j\\u002fVwSpMPYjmP7h3U9kzGvE\\u002fpDGQMCJb+z9wV+ZxdEcFQP+x+z1jiA9ASs17+4Y7FkBOvDIzjP4dQLlE9vIUgiNAPnL+lFqrKEDb7Hwfe4suQEOkD1VumjJASvr\\u002fuvVWNkD2ul+os3Y6QL47BIDM9z5Aa42hpRj5QUBJg4Z98ddEQF0aGslSWkhAnwkHWYHUTECUFzLrektRQIdyKp0f7FRAD+OUZ1tWWUDAoCk8kIheQBvwS\\u002fSbP2JA4tHKH5icZUCaUCIy\\u002f1ZpQIi5gahgW21AgvZbpoi9cEByFFdGdbRyQD5nyB7qYnRA4Ii8rWaedUAQ3OIHoVJ2QCzTws5bjnZAoKmxB9uEdkAumdgg84B2QPgUQxrhzHZAhNDHidaXd0ChRolCieR4QLZwwSpaiXpAxkaTv0NDfECBJmYRz9N9QEvF9DFhHH9ALzBPDPYUgEBlJYjz1pWAQEB1ck8uLYFAWNadcmzugUApwNLx0tuCQMHDiDhx5oNAxWydfiz0hECJbvaasOaFQEb0XhxOn4ZAlDidHs3\\u002fhkAGRvEsleuGQKwZk3pPTIZAHEURP5kZhUDWhdtPp1+DQEAmI5ciQIFAXAQ\\u002fCxTXfUAMywLDQit5QMuDmL6+03RAOF0VGlcIcUBLfiMXQr5rQExxyS4Rp2ZAGw\\u002fa7MKjYkASGlyHsAlfQBWp6zszQ1pAqvZPajW9VkA00zE1FVtUQE0R1VNlEVNAbd0eCp7XUkC4e4h81ZZTQIlAKHUtGVVAFSY\\u002frl8AV0BlKhCStcpYQPUu3FDl6VlAOOF9TwznWUCPzGEOkIZYQBpN8vfs21VA6ZcDyHpDUkBq3H2FlIhMQHmZGgrSzkRAjuiuxS1WPECPE920zRsyQN6ZtgdjPCZA3eAUCR6LHEDeZefWuWkXQNa9E5thiuk\\u002fmR2NHsrE7j+LflukHXj3PwD7W8FFzgJA0kJdFCcwDUD3OkxwL3gVQCFMCimo4B1AROWM84XAI0ANeP0Ek\\u002f0oQN\\u002fH5hGxhC5AK4bAwVomMkAsCvjMhTI1QBY3jhdPdDhAsu3U88j5O0Bk1lECENc\\u002fQIkXJURzHEJA7OsEHFa9REC3hA5WPBZIQIOnQSLMfExAJ7aTBIccUUAjoG89UbRUQLu0ddIp+VhAfVPMsjzKXUAygbC+0IFhQKlrejP4RmRAeh7RVN0vZ0BqTSq0TzZqQCWNrRKFRW1AUwM+IdsYcEAuMB2nqF5xQM3nUwMMV3JAt04rQZ71ckDzLEa9P0tzQMFAaxXKiHNAGxiAb+Xzc0BZpjyjAdB0QANXjnufQ3ZAZ2tkh3NFeEBppiUlNJt6QDr9lDnk7HxAutHOvM3lfkAGIKYpeCqAQCvHc8rznoBAhZjl8zzpgEDDhroyXCyBQJOimOuqhYFAwSMMh+cBgkCMvoSkdZuCQDDAJ05IQINAbTaV2s3Zg0AWlJJmc1GEQNeIqT2BkIRAB1aH3ap+hEB6FXuE7AOEQJ9XELpJD4NA6Il1OGGfgUB62WZW5o5\\u002fQD1cS6JXWntA2TzhUXT\\u002fdkBALv9ph9hyQJzRfwMLUm5ACVAkFOcpaEBwy4qAd0VjQMnP4hDnDl9Atj7BJu96WUDv+KZO0WNVQCqYC1Vcb1JAuc5cC11fUEAjReD6NyROQCAB3k1X9ExAxEniwNIeTUDex8Ahz3lOQHoXDh8pUlBAmdE43R5\\u002fUUAzqwj1h19SQE5MfA\\u002fKk1JAvjBGlw\\u002feUUAh880h6TZQQOcRI+Jon0tAOirXBxwDRkAZeFQijmFAQOwfeKLMxTZATfPNUaO7LUAEeAk3BK0iQG0xd8QmiRhAdomEJU9eFEB5IJdsT4nvP75UBmpaFvM\\u002f2ee7sTlc\\u002fT+knWM1NqoHQNjg33xCaBJAYtYCpjMIG0DniAGWQK8iQA+Jd0dKZChA\\u002fROEDqw\\u002fLkBB+ZIN0fUxQIre+pzPnjRAv241W1MhN0DntieEfZs5QP1pZGONOjxAGLZHmRU4P0D549uxPXFBQEKtRYI11UNAJ0jWAfwRR0CVp5osa3VLQI0A3fdtmlBAvv84nUUnVEC5LQYd9T1YQFLo\\u002fKxMoVxAKhOtJBmIYECnnlNn3q5iQFCRwddOvmRARlOKFMS6ZkBSKFWw3aZoQNGUohJvd2pAMhbTMMkRbEA\\u002fiyNA+VhtQOuUfMzLRW5A9DMbhbD9bkAI+QvtS9pvQF6hGw4trHBAzgNhCov3cUA\\u002fWDRKxu5zQMw0uZjAgXZA3119Il1teUCHcaCy4Ux8QEVWcYsfu35AJQJnGfM6gEAD5\\u002fBJ3rmAQOaPbETc8IBAp01aWYkFgUCrDh7jqRuBQG7qXUgBSIFADgsHS3+MgUDzwAanZt2BQKcm7gfuKYJA+eHrkvJhgkBaJKnmBnaCQDZkVbVuVIJAziwdlT3ogUCefrPD3h2BQDtbdQ0j139Ab7FQxpyxfED2Vk7fjf94QBqFbsN8D3VADYGUh\\u002fI1cUACLbiMVHNrQA3xV5yLjmVAMDZx25vdYEASJTFQX6NaQPX8nI0KfVVAN\\u002f4aShjYUUClYh81KpFOQHyBMWmf8UpAoLv1xGRjSEBTh1n+KrNGQG34HV2I1EVArQrov\\u002f\\u002fBRUA+le5rN1xGQKh5JDRWVkdA2EF4SO45SEDJW2vlEYNIQKr0g3huyUdA6JNI\\u002fQHkRUCBzGNh4PZCQBJk6PA5yj5AQjv2ItVcN0ClLO1deZEwQF23Ct4kFyZAI1o\\u002f9dxhHEBcJETaShITQKr6MBcTBBBAeFbqZbuV8T+iFcyWrGn1P9SQ7XjmpABAP4WPtmoQC0B5JRTIwCsVQO+fXV\\u002fYKh9AV6RP41yDJUCF1tnFbugrQDoWsuPSFjFA1QLWaDHkM0BXn4brbzQ2QNyssq1dCzhAUsHDipOYOUDHtn4J4yw7QI2T3aHiLj1AkSDAbD4LQEBxidE3tTZCQLM8kJ\\u002fTY0VAt1x91sjSSUDWYnG7XZ9PQPMVAl2ZUlNATL5IjVc6V0AYqi6TXDNbQACnnYvQ5l5ACJWPPqMLYUBA5VLwWVliQAIZHMSiZ2NAUtvc9W9KZECXvYPXyxBlQJSDqIP8vWVAM2C2a3lPZkDpNM7dEs5mQDx8AJsxYWdA\\u002fFdDvbVXaEA20etGwxxqQI07pZ8dF21AY45JOk2+cEAMk1V\\u002fBJZzQIdhAjPS0HZAcjGHXuEIekAGajAsW9J8QPH1pcef3n5AtRkCrqYKgEBo+Xz4rkuAQB7OS8WVVYBA\\u002fR8PfO1NgED9\\u002fRNHUk6AQHMGW093XoBAovtobKp3gEAYk1iORo2AQJxrb1Z6k4BAYZnto3CAgEChwYy+EkmAQPFAp3N5vH9AmbgN3khdfkBVHEhJS1t8QEw3ixAYtnlAP2dNB9iNdkClYOj3JR5zQNlDsY6EXW9A+AxsujMDaUBSduJx2IpjQDIRCdwxQl5AjSdRtOKLV0B9fFUMrbJSQPUK3C0OqE5AC6Mx3n0KSkDX08I1I89GQD1z\\u002fHJWakRAdISx61eMQkA900GNCxpBQEwbPPmzFkBAYW\\u002fhOEcNP0DwdTQbaq0+QK9PiD0\\u002fpj5AQf9kLipjPkBOOF8pBFM9QOhc8WcDHTtA2JSwBqzAN0Dq3p9s7ZIzQLWeLeLmOC5ARGG\\u002f26bWJUCXzdJse7UdQOWgA6zrgRNA+g\\u002fEPk\\u002fMCkBoFSCJ\\u002fcAGQCHrSZ0SrfE\\u002fvJIsAoqr9T96SAh0fA0BQEiwfUSsBgxArw87q1saFkDY2R5sqFwgQFn07fcqpiZA9a9vEtBZLUBQP2Gqj90xQG1lhkmNijRAOlGp5UR7NkAx+eUTAa83QMzfcVF9YjhAmoe1RIUFOUAZet7h8Sg6QCyEsOnibTxAq5MLUtw7QECeifaFi2xDQH+Mgzwi+UdArumFs7ngTUB+iaqqzG1SQIdCbWtJK1ZA1dnHMZrFWUA1vAk3e9lcQBlFTodgIV9Ayw39j9REYECE\\u002fK\\u002fff5hgQJ9xtil+qmBAXALkZjWbYEBkWs0Y7IJgQE70x3xOcmBAGfPuyl99YEDLpPP0icpgQEeLlhTOm2FAbgQQ4CJJY0CnQw0O1yhmQN+A96qHa2pATUJkbYv5b0C7z4ucpzFzQHcTHNK+eHZA2x2RVWtqeUCj13+bvLJ7QCRbR05zK31AT0IhSFjkfUD7jtYHAxZ+QDDjQHn\\u002fBX5ANGYsSYLqfUBR1C0fBNt9QNf5ztif0n1AxojIflu+fUDYG2iok4t9QD2wemNILX1A+YNWSaeXfEAUG1XxHbl7QCUsXkWeeXpAK177xBjDeECBVvbkhY52QIhkorwl7XNAynXX0xsIcUDjjAQHMi1sQKf8VoqFn2ZANrLnAjW9YUCAU03q0W5bQBm0M4MONFVAu7tZDZOmUECQzpKoX\\u002flKQEFZRx24tkZAn612Ij\\u002fQQ0AzVsoonq5BQEFPvgdW1T9AbBkaF\\u002fCkPEB7qzhj0L45QMJ68f3hQTdAw\\u002fDTnk5JNUDYwCBz\\u002fMozQECuPR4WkTJAZXpYYOdNMUBwVLNu7nkvQP2Vz1vAfitAp9t9lyHMJkASRxlI8s4hQECpt0WjIhpAthyjK8YZEkDXt\\u002fvfNkMIQB01hHrUAwFAwFVcac0x\\u002fT9mvwmZKf3vP1bwJQlMwfM\\u002fGxMR4EuE\\u002fz8TlNp3SzcKQDbMLYQp4hRAZ7BVdAcrH0AiQY2H6bIlQF1a6KmVNCxAbv1qjiooMUA+Dc+VDZ4zQHFaVeaqODVAiSIIdRDyNUAHZScLwAw2QKGcohsBDDZAcloLNECfNkB8hcYPpYg4QNrrFeJQgjxAyel+QIWPQUDucU6SGVFGQJrvPeG5aUxAX8dTM\\u002f+3UUC6IMbzj1hVQBN8jGYAqFhAWhCbx3k9W0CCNCb\\u002f\\u002f9JcQGFyD6ifWF1AEAio7vb0XECYyFlMRvNbQLLL\\u002fkiZqFpAPM+q5Q9eWUBzJjNJQktYQAtRCOXKoldAoeoKctGoV0DbbWbuyMRYQGrgtVFpf1tARniQpWwyYECh0vXcsOdjQESx0HN41mhAcmewhGytbkBMFHieMGxyQHOmZcawUHVAN0Z5rQuyd0Dh5NTUll95QG1lZ1G2VnpAU8FDDEu9ekB\\u002frQNi7sx6QK6wWoeTuXpAoB2fHOefekAZOJAjcIJ6QOhamc9qU3pA3uKBVnICekAwadBIsIR5QB1EYGUh1HhAXiWjNtLpd0D9XD8kQ7p2QLDAzkJ0OHVA6QZtUThec0BsyZOzMzRxQIKZbt5oqG1Atum3nNTIaEDyGaldWxxkQLfJIRYN019Ai0Ntf9i9WEC6NRlwwh9TQBdVy2ge3k1AW+p5v5f7R0AacNBV2A5EQDFfyfx+fEFA6fm9jrVhP0BqZ3diaG08QO+U2YYmjDlAHYudWa2MNkDatB7exIUzQBe\\u002fYwV7rjBA\\u002f2Q1wltsLEDXupmHLlgoQLox2Mc7+iRASl41Id0GIkCLMsdFInAeQDLsTVQl2RhAjK21y6ZdE0CqPAvpmJsMQD60BoVhEARAWFWlAR9b+z\\u002f+HXZRR4rzP5dbYbJW6\\u002fA\\u002fO30+nJMI6j+aSDR7yTTwPzsHt9bvO\\u002fo\\u002flEC41\\u002f8cBkDVMfIpItERQPQTyYai2hpAi8gSX3LZIkBoRQQHJacoQDNcj2eWGC5AXNQ\\u002fdFA0MUASJ74SaoYyQNxrnZKE+DJA48QNr0rLMkAcx7WaG4cyQBesy+tS6TJA7J+kFdPHNECI+7EVF+44QGCnztsc8z9APTtKMlwERUBcg1NL1GZLQG6on5SpTVFAPCVgMEnpVEAoeIaFUw1YQDcbjFXRTFpAuI6fVfVjW0At+L6rmklbQF4q03sCLlpANIYvhCZoWEABLCN+yVpWQFOezbiJXVRAzgICT1azUkA3CqD8qJBRQPkN1YpGLFFAIbIsKg3QUUCUx4cEJd5TQFCWYsM9wldAbGi\\u002fnCjOXUBRlt+WygVjQLpTOoIfC2hAW8yfrVuKbUCv5ZGbW3dxQBVWKwic0HNA7A2yJjiadUAhSWBkmsF2QCwtUmzbWXdAnZGOQrOOd0Ck\\u002fEPrX493QC8u2Abze3dA8lrJC2pdd0CkxKL\\u002fQCp3QB5fBr0l0nZALtkp4YdIdkC3F\\u002fMdcIh1QDgU\\u002ftrOkXRA7wryjN9kc0ChxcMQcwByQECCT6OXZHBAsJhJFJ4ubUBqpABQQ09pQBrdkpukWWVAYkrjUTOEYUAhHXo0dARcQKUPjyup9VVAWsY1Y3kIUUAGj3GVrYxKQDsnQxk5M0VA+LV8MVuoQUBwujK0IeA+QJewucB8BzxA7RElF3HTOUCXpvbmMJQ3QNTJngGD9DRAJE5g98b1MUBWjdWlwqYtQCK13K4XtSdA6GVATMGOIkCsug59wswcQO1z7VkeWRZAdxPkHwtZEUCoW4guvLQKQLQaXMziGgRApicLNIU+\\u002fT\\u002fDzcPJGHr0PxaDPXdwJOw\\u002f7DI3MKVb5D97w+u4PMDhP4e6ewbqCuM\\u002fLpC1Kcjo5z+5AAPdZabzP8Y6IvhOzABA4Odm\\u002fA1oC0AesxxBreIUQIJ4b3ninB1AYjuB462HI0B0PsCu1P8nQFlbBTgljStAX0Fg3cayLUCahfHgdlkuQK\\u002f4xnCc8y1Ananc0d6BLUC8PZKEz3guQOH4Day0RjFAKHlv3IK1NUBFvh615yg9QCRymo\\u002f46ENAFeForvueSkAB8iBDtQ9RQGLja5wawlRA7SoFF8HiV0CrTn+Sav1ZQGr\\u002fQL7GzFpANOHObTVMWkAWqODOXLVYQIO71Q3Ma1ZAOoHr8UfgU0A+VmkXmHdRQIRvO9vZ+k5Ax7ijkpdLTED2lV8\\u002f+DJLQI1lOVghDExAHTu\\u002f5mNRT0CZzgEvvMtSQFRIqEYMs1dAhV0XvOt5XkD1kRPpQ3djQEfvrdG8PmhAjf6meDofbUBQwxPluclwQKDuunUFlXJAnOxCv5rTc0Bl9fjKj4l0QKh0Wp621nRAkq\\u002fd+8\\u002fldEA6P4UgfNl0QJC9XxAHwHRAMbbH\\u002feKSdEA\\u002fF8tk9z90QEh+BuQMtnNAikZBNVHtckBsc2KbvehxQB8Vnav\\u002fsXBACE3TVE2obkC7DvfMVrFrQFyM9L2Dj2hANsGEjwNVZUBPREQrbxliQPL+yDL6811AQAgNomcsWEDrYX2drxVTQOHp+MpvtE1AU7UjNlUdR0AxkvabMF5CQPT6VZzbfj5ABXO6SALFOkCt8oOIJqQ4QEQFHKIrMDdAIB1VKwKqNUDhn8XRL6IzQBFjZhT\\u002fAjFAvqHQoVb+K0DbI3ULVNslQM0BqMczRSBAWqFb8xNRF0BjIMs2\\u002fUsQQJb+9DsdiAZAtBDiv4Ea\\u002fz+tqodwNnT1P9DAt7BdYu0\\u002f25mIl27f4z+8jeM5denaP3qLrG0Qb9M\\u002fXV7a07\\u002f40D\\u002faVnBEvQbZP55bI44ms98\\u002fAfpYd\\u002fh36j\\u002fwiR7WCPb2Pyrc63d\\u002f+gJAkULLO1tHDUCwx1puvP0UQBM5BzOu+BtAVC3ncPZWIUC7f2NQPQ0kQL+i5U8NvCVAmE6dN8ROJkBOMh4CYCMmQMB6UOY1GCZAl2J3ohWAJ0AUwdkOo\\u002fwrQBFlTmj2nTJAOJFS9nVKOkBrk91fOKJCQA1UAhjskUlAtBSDKcStUEAdnvOI6YZUQE3Y7TTexldAm+\\u002fKpWruWUDcwnePoa9aQB74zhX6AlpAEqUki5YmWEAIB1cYbolVQKR8oKAwrFJACYdHRj4EUED8jJ68jNNLQCcQbXxCKElAHDhtHlZESECKfkO3kFJJQDZgCv2TiExAs05DAtkaUUBKsaggCFtVQGgc5m\\u002fXIltAyHZXzbovYUBuc2aAgVhlQIg2zkXFsmlAQYaWRq7LbUBx12duj5pwQHcB0+b\\u002f03FA+m0sxQeNckBBX1MUtN1yQOphF9Lk7XJAWJwD6TrickC+SEovscxyQPOZ3ib\\u002fp3JAhwB+KMxeckAuaewejNlxQHSIVREvC3FAZrxICnPtb0BlMTRuNFltQJcEaKzrhWpAl4kk06ibZ0AMsOx8s7VkQAs2M5HZ4mFAr7+f3xBXXkAw61h7ZDFZQOXCOIIxb1RAturEcHUyUEDnmDOuGDpJQJKObHgRjkNAd51CX5vbPkCRcz2DTHc5QIFY1XkmYzZABeWoq9XZNEB9I7wHfwM0QD5huusGIzNANCR45Jm8MUA7qwqiYVAvQDYeOmxTEypAUiROFDpmJEBIxa4MPfwdQI82TgMpxxRAF+QHcL1ZC0AMdffbXksBQPF\\u002flhgzU\\u002fU\\u002f\\u002f1VWrIYI6j8sKRRsvtffP08dBZuertM\\u002fF\\u002fQFRP8NyT9LGdJOtnbBPxZxPbj2Jr4\\u002fyR258zOJzT8byfbbgeDSP4aybVUpBeA\\u002fw6aPUmY37D+4ZvSTxKT3P7lz2bdOeQJAG1MLrGrQCkCi+D1SOxISQKnUMD7VpBZAuQytlcpwGkC\\u002fQg1xmuscQD\\u002fENVvw+B1A9nBeqdIqHkB0T0rpDugeQLapV0+5NyFACylxrHrXJUAAlr+VXu8uQEwy\\u002fSNh8zZAmxKBFjDcQECOwwWyj8BHQEGAsT6DnU9A9aib8g\\u002fEU0AHm7govS5XQLxTGTLhhFlAPek5XaFqWkAHt7+twcxZQKyMdGr55FdAHj8qapMnVUA5+KhvQSNSQOeCX2Hvv05AB0W9j2eOSkAMgMKodjRIQN7JxCUR60dAAL49IDK7SUAZsjntsp9NQKhFTf0L0VFAq5TvK5TxVUBR9Ace7EFbQLzXr8Af3GBAn1gbRyGJZEBdao9T8WJoQBnGzD+kCGxAq7og1jkWb0Cz+A8yqqJwQIcRKvMUQ3FAv94mCleBcUCoC2o6L4RxQNEXpvhUcXFAIrNAs0NccUDe7\\u002f+ADz9xQLBjP7Y2AHFA4uogaYuBcECnMZ0SG2BvQGz6AakFGm1Ad57jDfdZakDBLC+IuV1nQB+MXNTwYGRAqyMWwYeMYUD4mk2iN+RdQILURzthJFlAAbHhM4LOVEBOGuslPt5QQLVXM8EBu0pABHx2BWPBREC0nRJIy\\u002fE\\u002fQCOdzbYg7DhAx7pI9FRWNEC\\u002fdBlrRssxQPu4pknbqzBAagi9UsY+MED3VV6RhrAvQJCaMdgT\\u002fS1A+1y7Iin+KkBg81zW1tomQPEIbGA5GiJAcNcWtc\\u002fEGkBhXaRnEXsSQFpV2yE74wdA9CsgByQO\\u002fT\\u002fdGeBw0sXwP6ugYqJinuI\\u002feExT2ds31D+KPVO5C\\u002fjFP96Kfge9xbg\\u002fdpxr1Hhsrz9YvIuv2xmqP8RWN8NfS78\\u002fGhmVD14wxD\\u002fchpaHdGvRPw9Up28OKN8\\u002fVkME0zR66j92tfHWhPf0Pyd2dpvZ0\\u002f4\\u002fyyidCQcJBUB484nwZ6wKQNGevc38gQ9AaWcDORRwEUBsnm8zs1USQBCDlhNg3hJA+fqO3usZFEDFnKKXZNEXQGZbmm\\u002fAPiBAu71jds6DKEDo7Cceqw4zQBXA7xPJ9jxAJ\\u002fQ5oLDxREDFZcW+QH5MQPWj2WLgJ1JANQOdk12nVUBR+kpUXC9YQLPYqla\\u002fVllA6xbPGET5WEBGxPp50kJXQCWWIUi9olRAa2nJ44atUUDSCuXAFfNNQJ9qIK01BEpAN2hTOMguSEDjn1En7rdIQHUsIdwOm0tAxUb3KPJUUEBnQ88a7NdTQD5Qcva0RFhAac+FRD6QXUCX4NzoMdJhQOkBfcvjImVA0hLi4g+BaEDAWomNPJxrQAmgrnOaIG5AeumxMxHUb0CsZQbL+lZwQMT2D9WgbXBAY\\u002fzT2ihVcEDCcqfcNjNwQLW7LnNtGnBAUwM8lHoCcEC3vvKM55pvQJTrZ7LKrm5AsnIUbAwQbUCojFefJ7xqQLVjtuaJ32dAMVRwPgLDZEC3Dpyk1LBhQLz8hAwMvV1Aczu8+4rHWEBWSi7+J35UQPo+IEjexFBAWAw+pqcAS0DFheiQLklFQC8OLqYhbEBAc7qJBqoDOUB2Kma7ezczQDOVcU9dzy5ArH3zRF+aKkB8Kqvg8NwoQCW0FU\\u002fSbihAdvwM4tAxKECzB+9Jl04nQMaBUM9FXCVA7SDMqrVlIkA6ptnrM5gdQJWCFQLGKRZAti52hGfgDkB2FeU2LQMEQPpPiMQKLvg\\u002f4pUqsp9T6z+WconOoxDdP\\u002fxVA6emZc0\\u002feInomPrKvD+Q25Va3WCsP6ECM98uK58\\u002fYaBPnCkOmD9xz0fJ78KtP97BMNSGYrM\\u002f8UOJXV8CwT8xMrqDRubOP5vdoC6Ro9o\\u002fmF3iVTxj5T\\u002f4Hh8UAN\\u002fvP+kBjmvPCPY\\u002frTe1\\u002fqVN\\u002fD+Qtvl1u+8AQPB8aNPsBANArpubGoBhBEAZDV2uJ6AFQH6pKJ30RAhA0Pz8cB76DkDTrTuIEtQWQCtDp0QVTyJAvS7uR2+4LUATK222ylA3QA3Z849iTUFA9Zjv+OwUSEA745F1l1ZPQJeqTJIFDlNAKMTz4xWrVUDwQVMxlBNXQM8W5axSE1dA9SFG\\u002fH\\u002fCVUCLEM5vR4BTQEQvAXff2VBAB3LciV3RTEAHKigFbGZJQLsQHoGdMEhA9i0QMPaNSUAE8nhcI4NNQHX37rGW6VFA3DSOAxsQVkBchMwmLgRbQM00BC7xS2BAaAP3XPNLY0Aap6peK11mQBx11k0tSWlAcoApAP\\u002fLa0BNUOjt\\u002fKRtQBXXTBgBr25ANqrmijPzbkAmej+ODqpuQDEGobZiJm5A9TyctTCybUCJ8P1Q0G1tQAD3PkyzQW1AEIfPSPzpbEDfZotINRZsQMg8NpDNjWpAG9BJ3ZtIaEAvgvFKmHFlQLfYsUULVWJAg00QWxyJXkCiWZmxTPxYQEVdUC\\u002fzP1RAkQNJ9jdVUEBKpunCgzRKQHziedN\\u002fzERA87PgsCc5QEDFWFfUJck4QOZEkXelojJATMAEbR0bLEDQAf\\u002fEQAEmQG9vfiPrlCJAbLUWEoQnIUBVvwmKFd4gQAgHRraO3iBALOFsawR9IEBA\\u002f22LybYeQLk8nWYh4xpASQPw2QD4FUBiGqrjabIQQBlD83l\\u002fkgdA1eS3Q7vk\\u002fj+tpEXSoczyP8IizcNjReU\\u002f1nQs4KBt1j\\u002fCHV3m3SLGP3FO6Y7tpLQ\\u002fCqFGXPisoj\\u002fl0b8oOxaSP\\u002f\\u002fdnAS7t4k\\u002fu2VNBpFmmT\\u002fAsGlMBLWgP878PXUs0q0\\u002fftbxc\\u002fSDuz8pnMNN1hHIP7P8OqVRmdM\\u002fg3LKILOc3T8DZZI5JMHkPwYHKKoaB+s\\u002f8acQgXxo8D+DisQ6iL3yPyGAbhB1kfQ\\u002fdIK0R1+29j\\u002f0E\\u002fZ+9jX7PzpS4Nog5QJA\\u002ffnezb8bDkC6oBaKyY4ZQOUin8H6kyVAFdcAtmRxMUAPBE1UUo46QJv6u4Nl6kJAjjZGXQEqSUDRtg8XvUFPQJBVotu8I1JARwN03oO0U0CTCZKISxZUQPFB8qQ8TVNAXB70rP+fUUDAj9m\\u002fhwxPQDtkedXfGEtAtvkvishtSEAUYKwro9hHQBpZW5q600lAUuUYZhyBTkCMOvJoZdpSQLjWDaHIhFdAioO8Jxf8XEDuR\\u002fABanlhQEUMZ4VqiGRAyh62Bat2Z0DVhjna6gZqQEmGsSlX+mtALLFAZvEgbUDu3T3tOW1tQLC4yvM6AG1AfhBCAlQkbECDARmuEjVrQAH8GB28fGpAvYBEzMQWakAbCHL3M+VpQE0GejlInmlAWUIvbkLsaEDlbD\\u002fgApFnQPV0JdMofmVALcRwwazYYkAQByR7ndFfQOTRHtsU\\u002f1lA8Wbizoy7VEAH0Ega\\u002fUxQQJjdLySBeklA6VF\\u002fRNzbQ0DGQCQzkco+QGKwUVrvlTdAeWme8TS\\u002fMUAYpq0\\u002fcUMqQHaorHBBZCNAZrgQD0GFHUAMJCb6Ei0YQJ5Hc9GR0RVA4Zfgg1RHFUAvwISlo14VQKXrclgqHxVAFPIullT0E0ADd9FzmLwRQKVRX4T\\u002fbg1AboqgqAO2BkCj92AKRUQAQJUqq4EAnfU\\u002fPwD9f4Oh6j+NTHbT62\\u002feP7x9KhyXJdA\\u002fFioZh87cvz9+mhl8e12tP4yb5F1Qtpk\\u002f94WsJAdYhz8y7zxyxYF\\u002fP+a0\\u002fiv6c4M\\u002fnMvY36PYiT8iw9jP6HaXP8IPZuGw\\u002fqU\\u002fVBthzZmGsz93I2IxySDAP6kYuoAHt8g\\u002fLiP0O5SR0T\\u002fE0kK9YzbXP31qEz3Wodw\\u002fNr+iaSCt4D8loDHhPdviPwd9\\u002fI7y5+U\\u002ffS1u79Rk7D8dzHBMaJf1P+qRSEwPiwJAhajbZbCSEEAk2Ge1oQIdQNCjnGpSIChAU3NWCOHUMkAB6USPHXg7QElxMz\\u002fUskJAIglhEUXER0Bqn83QsDxMQElrHQ+AbE9AyoiZkn1uUEDPbqGJAjxQQIf8pbU6k05A4F3KpTLcS0ADLoPl0zBJQMXjvlLnc0dAz5WLmMJpR0AxcDz7uqBJQO911hDEY05A5mv8U5vaUkBpsfyyYKhXQBiqi+UjWV1A96NNF03HYUArDITmZehkQIY4ybgayWdANCEzeRseakD8ZPCfuKRrQCZ7dgGjNWxAieDwgijWa0B1aQ+wD75qQERQ7whhS2lAZ1x5s6vlZ0CqTflUGttmQGJHFVbRRWZAyPgCdRAFZkDXxdbb78xlQHB0iMG1RWVAzoyLf3UtZEBkvF\\u002fDp25iQIobLdM5JGBA+IKDLP4XW0CHAgPsw95VQD85FDZ5GVFAPnx0KdMlSkB1qTuL2L5DQLFSntQRqz1AZP6+Pvk2NkB1QOZtaoQwQNSEIk2DPyhAvTa\\u002f4sCJIUBed+aFvT0ZQCAbxrf1lhJAouxclftLDUC5ccXliZEJQN6so\\u002fzhfAhAVHoES3qICEBX4gy\\u002fZG4IQN0R+07VXQdAatAzFD8RBUAvY8\\u002fcFL4BQCVxme87y\\u002fs\\u002fn3gAwYo09D\\u002f7\\u002fnQdcj3rP10IN1f+BOE\\u002flI98PAu20z8jXJ28hSnFP9mOkDWPE7U\\u002f8e\\u002fYejeFoz8RdQr+8weRP+JopD8zXX4\\u002fRzqyT3YcdD\\u002f5rw3bD7xqP8WVhSXn8HE\\u002fj55l9+qRgD8+\\u002f+2db4+PP4M3\\u002f\\u002faZb5w\\u002fUtcZ\\u002fIzUpz\\u002fqm05BE4ayP1OZVw2TuLo\\u002fRy\\u002fquEzswT93FCbxeX7GP8VDXMbu0Mo\\u002fxMdEUO14zz9\\u002f9QQPvXrTP3m1HhrDp9s\\u002fRYbQEeoT5z+y\\u002fLktvT71P+YFkyp34gNAio94YSD9EUBYSX1Rr78eQOdxpYKJnChAq7IqndBlMkBA84qc3645QPBQm03zwUBAE6Dztjl8RED6VBwA5YhHQBmQK7qnh0lAzFumfSFTSkBI88BDvQxKQP6YQYZaFElAzgE3ElnzR0BxBeiw+UVHQMThzJxzqUdAPCWdi6mtSUCKlh7zxcVNQNfe1zmyGlJAHzmWlU1+VkD5g85ksONbQKnm15sE+WBAuwH1n0kRZEBwMqjxX+VmQAYVHl4wGmlAZhJDtmRiakB6b0V5V5VqQOg5qhKIwGlAbt8NKeMpaECDXkr8Sj5mQIz5xsjAcGRAcMqU11EWY0A\\u002fgqhs\\u002fExiQI0lScYQ92FAxQhqMcPLYUD4hNAMH3VhQDm1ipEMr2BAXFIhHDm4XkCVm6d2ahRbQDQ5m7Rk0FZA94iYkOFlUkBD1hZAZYhMQCzzEOzdbEVAvjKtoQNuP0ADYijy5LU2QKx6jw1OQzBAC97zFL0eJ0B7DvYqwUcgQKV7mIxfrRZAKLTJy2JvD0ARhbOQDy8GQNpG5De9owBAS\\u002fPSoce3+z8zzWealL\\u002f5P183PPsthfk\\u002f\\u002f7ZF77x9+T9LW2wGp6D4Pxd3d1ybf\\u002fY\\u002fESclccI48z+\\u002fkJLBDY\\u002fuP0V7yGoejOY\\u002fuAH5UY7Z3j+e6U4u747TPzmn\\u002fGhM+sY\\u002f+YlS3RYDuT9NX1WSGDypP8DKIr2No5c\\u002f8PutYgnNhD99Qw2AbpxyPyWrPK++pGg\\u002fuT35y3N7UD\\u002fSVgbplFlWPx8DTkdBAGU\\u002fUQmL79lRdD8qmKUMR5WCP47nVvrtmo8\\u002fELJjmOPtmD+nfNl4XkCiP2+B8SNy4ag\\u002fHtMG\\u002f2TVrz+1Px\\u002f\\u002fK4KzP\\u002fkL7CGS\\u002frc\\u002ftA+xX8cNwD\\u002fpy4sF5zTJP9JNFo+aBtc\\u002f9z\\u002f8o6KP5j\\u002fv8mIYtgH2Pyy1wsJTiQRAlHvE7hQJEkBV9oVj6aQdQNjADa8sxiZARK1idbdfMEC5OvN1KBc2QDnCxibBETxAshtLXgDiQECk7h4Z81lDQNW1MjOQS0VAuzBQTwqvRkD0dddTwpZHQCcz2JN+KkhAM5N9EwaoSED\\u002flXr0LGlJQMTPNx8n50pAfzutTqWwTUBmO5NvDCdRQGKrUH53jFRA5MI9i4QIWUBvoHLMcl5eQED8h9HxB2JAHJKACBa0ZEClqY85u81mQHbzHBq6+2dAOP3dcrwMaECtVSqhswpnQHgtKy3DPGVAIlXsfAIUY0AgiOsy+whhQAaN1ygX7l5AWOYu0WQHXUB0EuQ4NTRcQCX5GqrZ9FtASzwu2N+pW0D31V70q8taQKutFQl6EFlA4QPXPKd3VkBEkEdnVjxTQPkb4KLvck9AcSVe30aXSEAKlGWmH3VCQJk6zBaWujpASFpJwtvHMkCA4hevesQpQB6oV0v5WCFACQtlrB0BF0Bk7AZ78yIOQKU\\u002ft97QowNAUn4d+nH5+T+xwVLGmyXyP54U90oZNew\\u002fVxluNtXq6D9bF9AQWx7oP3fxMBoOAeg\\u002ffoh5gwRb5z+55z75HpflP9\\u002fGdHqIs+I\\u002fTNXaeUoq3j\\u002fPIg95VJbWP7GU8zVdXc8\\u002fZyOldyEuxD9ezKsQ5A64P2gNJczmkao\\u002f0dMZH20wmz\\u002fCGeaBLdKJP6pRuRAfBXc\\u002fV+76eSTaZD+RJ2Yfus5bPwoZkbiWOzI\\u002feAkVvmj7OD\\u002fkl+9XMuJHPz1M+7V+elc\\u002fgqWukbPLZT8xq1qNS89yP2oDzPv9HX4\\u002fFzRx1mxkhj+6dG4YoQ2PPxuk8VhATpQ\\u002fUnwLGYy\\u002fmT8qYugDvMygP3icmSDAqqg\\u002ff41OaTCHtT9oT90HXWTFPxTiJO0ZJtY\\u002fefpC3Jdv5j\\u002fpGJLgjpb1P+x7jStjhANAwtGWZKqIEECxWUjAtkYaQNSir1p+pSNAVuYZBz7GK0C9XYJl56syQE6C6QojCThAWjSN7tfQPUCtN12Eu+hBQCVq0JNm40RAA8FAz3OoR0BM2BPbyv9JQCmosC3DvktAuZXnYDDoTEDcSUp30cZNQLFJs+Xd705AzpaC51eSUEAFPfy7x4xSQPAt3SqcmVVAT58BCP6nWUBNl6wXQ1deQC0BsvTNf2FApjNvl2RpY0B548qgWohkQC\\u002f\\u002fYFwIoWRARUamv7qxY0BlnIWoiPZhQJn0avLfr19AakackkSUW0AL2G9lqlNYQPSe2aXmR1ZA4D\\u002fUGqxfVUDxNcDrvTZVQLW6McVeQ1VAnW\\u002fMqMIFVUDVO1MR+SlUQHXNbg06lVJAhuCkqilgUEC2KN7TTopLQMZ6rLocFkZAD90KEOTlQEAS8H8pV7E4QPrLdi55QjFAo8XLJe4kJ0CPUNmh9uAdQI3EonTlpxJA51Kr6nitBkDD3w9c5Br7PwMl1SfuPvA\\u002f2Ndgm8hR5D\\u002fQlPucRy\\u002fcP+EDX8McutY\\u002f7x+bmxvq1D8UTQz67nfUPyG+z4d08tM\\u002fS4UuZWGe0j9z\\u002fVF0LVbQP+K5MkbWuco\\u002fyuxf609OxD9HPooijZ28P\\u002fyrGRpur7I\\u002ffdEE\\u002fmSbpj9l+nSCPlaZP2APId4UT4o\\u002fQqKR9MpZeT\\u002fAqKFMqO9mP8U1krjYGVU\\u002fhZtUUaFqTD\\u002fZWyvEGBgSPwQ2eX\\u002fvDRk\\u002fQnvWmP9eKD8W8ZIAR1c4P\\u002fut0CIw8EY\\u002fggVBpJcXVD8JyOswglRgP44+IVPQrGg\\u002fZUayDV5ucT9j\\u002f6jElGN3P\\u002fizaJs8834\\u002fzRJPLC3AhT9hTw6hr8KRPyn3dsEROqE\\u002febMCyk53sj\\u002f8vX89ZhbEP0LonhxiG9U\\u002fzC3u1Sb+5D\\u002fjqMb8g6XzP29rvog7TwFAnsWhqOjRDEC2BVwB88sWQFgNJTkVPyFAUhaUzz4eKUCzgsOQ8rAxQNYMYBnBJThAB3EY1xfjP0BlaUMF0UdEQMu+AnfGtkhAY3rDcre2TEBAmD\\u002fteLtPQKwN\\u002fEattVBA79olXeHoUEAuNjlZa7lQQOC6KqSQmFBAu628kk0FUUBPOoG612FSQJmhBTRZzlRAmHuHmMkUWEBdJ1eQFa1bQIuX9XhG215AWoP7Sy9xYEC1u\\u002fwQEp9gQNoQ+E68z19Asu8gTmnqXEBce+myRDlZQPvZxnqkh1VA\\u002fldawJKCUkCfEWUhK4tQQGiH8wnDT09Ar+Xsv3YoT0CniBn9dcZPQMx43fH9JFBA0t8wyWADUECCZWnXkaBOQH7AayEQDUxAb7rP+nWDSEBhszENxmNEQGTH3LRvHEBAxdfeFb4lOEAOj8b3gCQxQBClh4KxCydA6qaKT5lZHUCuqmILRL0RQIqcfo4fbwRAwyCPAJGZ9j9GWIGHQl7oP9V0q\\u002fJ\\u002fd9o\\u002fin8\\u002fm1bazj+zWMPyHPvEP1nDEg4LNcE\\u002fJc29XloAwD+\\u002fUl4NXM6+P634nk6q47w\\u002f0S4GtPWhuT+NFZ5ofkC1Pw5qYwVVYbA\\u002fnnX7S4Jspz\\u002fE24YZiAufP9lgtEpXD5M\\u002fwZg0TOKthT+wqJ5\\u002fd9h2P4ALBxGrV2Y\\u002ffimth+qGVD+UpvC0rTNDPx4NUT0fIzo\\u002fvWY\\u002fS9Eb8D4SLe1fQYv2Poxn5FX7TwY\\u002fSX8MqhukFj82ph3EBKklPy0yrxJqQjM\\u002f\\u002fbmsziTLPz\\u002fcST5BlW1IP0ynXXdrnlE\\u002fy+C5\\u002fyxjWD\\u002fyD0yz1QlhPy5ynyFyPmo\\u002f1qcnKvwReD9VAswrd9GJP3cIj99wn50\\u002fcRBrRu\\u002fisD9uXvwlJHbCP3wPSTWjH9M\\u002fqOkzw6+\\u002f4j9CE+VHrXbxPxvYgWswGP8\\u002fSkJo5bugCkBiKAtjlwsWQLnEFpfzriFA2wCjxFByK0AnI3TFCIM0QC5VbaJMVT1AxNQisY7uQ0AB9azLSpdJQFE+UmV36U5AuxWcpHqHUUAdRkcKgq5SQDwqPd53zVJAzpeG2uwTUkBDbYwn+\\u002fRQQOZGxt\\u002fyBFBAtZLiEEuTT0A5zWwoYI5QQFi1H5XLSVJA9OZ79ESdVEBCWUelM+5WQBxlwlIzk1hAP\\u002fhZDWQIWUBLiheerBlYQA8EN5YF81VAkQz6KPoPU0DqIgcmKhFQQEnVs7rYEktAK9UR5zqsR0Aq6lWOjR1GQKoXWdiCGUZAFFmfR60ER0CueyLNsStIQAM1clyi70hAzoXHbADhSEB2Q8RkgspHQPj2JLAqsEVAYjgtokvGQkC7QGVUNsQ+QB9FLtrhyjdAghK\\u002ffbxSMUBEEFnHm7QnQI6BLy67ch5AHFlkBFJZEkCZIa9cYsUEQMuszgu8JfY\\u002fvYNdmJxl5j8sItY8TtfVP\\u002fuLib6AYsU\\u002f4wf2B7Xctj9HnIp5O7KtP0RN6No89qc\\u002f0+TCy9LVpT\\u002fBjMZs\\u002f0GkP27BOK55FaI\\u002fpiY8IkNYnj+QI7G8yLaXP8YO6xnlM5E\\u002fD5SqBBkjhz904BCdCNV8P05ccmMxpHA\\u002f8JnET1vMYT9uSD\\u002fjGqtRPxECFgnifEA\\u002fWk0nCt9gLz82uFdjSpclPx4lRMLfusk+F8rH+AQz0j7ZYqjC0FPiPh3i+If55PI+MzcwlPVZAj\\u002fjT3p8GpEQPwVYP29TyRs\\u002fltrEReG8JT+IcYjVDA8wPy2gLXSmHDc\\u002fDXp\\u002f4qhRQT9h5PFG3MNNP1ApHiGcz14\\u002f8N5G1OElcj8AYP92OjKGP4KAf3kXnJo\\u002f3RuYApCQrj9JahmYNL3APzwmbo7liNE\\u002fNNqhxrup4T\\u002fS9rZlCjLxP1MUXQhTOABALK6g4FKeDUDikAMoeA8aQEB9c8se9CVAlKIdRbWUMUCIsQomCJg6QLqufkFO5UJAk8cu4rIgSUCm06TY4DBPQO9r+ta2DFJAXqV0mcp8U0CKSX91Ja9TQKxxTJhou1JAfXZYfWUIUUB0dNJvElpOQDG4RC6fgEtATah4wQ9VSkDcdWG9NQ9LQOPSgFNmRE1AOaqwCm4HUECSbhfBXixRQA3mO0AdmlFAtC6AtZgSUUBi+N\\u002fFkUNPQE5qi4n4KktAcHxWoQbARkBCWDnes+RCQHc9OhMMOUBA\\u002fGOaL7\\u002f3PUBJznH5Ohw+QKZuy5HEDUBAJxl+ZKZ4QUBiod4PF8tCQGZLUVINlkNAay0F3bKNQ0AGlMChW5JCQDcm5xWMs0BA2p64zGlVPEDRg1mR4pg2QD4ksWhI5jBA0mcXSsGmJ0Cc+fBCDuweQAMsnOEd3RJA4fX\\u002fXx14BUDupKuqms32P\\u002f5S73WjpeY\\u002fWSk5Cb4l1T+WWuj1JtrCP4mErtv7uLA\\u002fwMkhq1U4oD9v+hQo6YuTP4ZbaRreAo4\\u002fw48ar2gnij+jncPmJBOHPyb7mE5le4M\\u002frV0xCyDSfj8i97BBDKx2PzCMChxy8G4\\u002fj8pnMCCQYz8\\u002f4nxeOOtWP2OsJETM4Eg\\u002fPmkI1NIROT9h45\\u002f60cMnPw+FIEDdARc\\u002fqXaRrnEBED+izY\\u002fuAm+iPtmUpqavXKo+T4yHcxQDuz4vesod4UrMPiyIuLF+5ts+eY1J5xWU6T4op5RG+c71PtXQcwtFZwE\\u002fQangVTJ3Cj9PMeLqDAgUP2EjcGKSbCA\\u002fVCiY+sUEMD+qPI1y37pCP5GsFWjfIFg\\u002fkE\\u002fNL\\u002f6Nbz+FCVs\\u002f\\u002fh6EPxjv6aMRuZg\\u002fbkUnhpVErT+b\\u002fXr6o7\\u002fAP1OleM16k9I\\u002fjOUR8a3z4z8Be2m8F6v0P4OHbTHMhARA1O0b08lhE0CBX14Nh00hQPU4UgZ9CC1ARiLhBt\\u002fKNkAVHBeC57BAQAs1G\\u002f1TwUZA1hgvjATXTEDlDoV9KPtQQKZvNd9GllJAGd47lT3xUkBOa4H\\u002fTAxSQLSm\\u002f2HzN1BAzMSLKJP4S0D89v198NpHQP\\u002fWLmqF\\u002fURAsWQf9g7IQ0D6JfbUmxNEQFZ1dvyeRkVABmFRj6ePRkBcErhLLCtHQJiJlCUvn0ZAbgTpu8jaRECMoUb7CjFCQNpkfGlzaD5AHkQGITcEOUCsZHOrnys1QM+WM5QZWzNAfgRaTK6OM0CGAlkmLlw1QIOWHEMPGjhAPT8Yg3wEO0BVvfTvdFw9QIPqdzCdgz5Ao2WFv24WPkCBnrxaRwA8QMmQmf5AgDhA9fhzZj4YNEApD\\u002fx7wtEuQJYsSiruDSZA1KwQwgJpHUA9DRX1SEASQFCtXDRMEQVA1jJKjTOc9j8RzRIo9pDmP9T8RWMl+dQ\\u002fvu8EWcY6wj\\u002fX7g0sOQauP0jo9A+kVpg\\u002fivgaZRZZhT88NbtbHVt3P4zCRamonXA\\u002fqOiJw1Q5az+FR0FdyaVmP\\u002fv\\u002fYlYRBGI\\u002f1CGCJ2TTWj9KXuLClY9SPy4OVBrf0Ec\\u002fgxILcKlQPD8PVuNlxTEvP50XcIoB6R8\\u002fKwHWDga7Dj+rgNwoTEX+PmZBFncwSvU+cBKB2ouxdz7pGRGjFyGBPhT3MQ7T25E+5n\\u002fJjg4Boz5PziJMtQezPoafkkxPucE+I0ubO1LCzj6vvTf4PBzZPrT6yZMAzeM+rxb+fCUI8D7B0F9dg3z9PiplgME+kxA\\u002fjFv0TpnpJT95ZfxrOPY+P1RukNc561U\\u002fNfoOa21Rbj9SjgfQ71uEPyKn5zuwiJo\\u002fdWT\\u002fvuvBsD9XmR00um7EP2ajXg5p5tc\\u002f60kfTl2g6j9vAEwyIQ\\u002f8P5oCIniFzgtAyDsAEArMGUBm0wSfaVQmQGTOCKPV\\u002fTFAimNq3JLxOkDUFXTU1LpCQID4SRLtKEhABWNlkW3oTEDsFojx7QtQQPpIdzHMi1BAV20xLEHFT0B+PnAGl41MQOxSf2QDS0hApe7\\u002fM+X+Q0BUWLBNT4NAQLxepwqmpDxALrAV\\u002fmblOkDPFPnYTxM7QMb1b73vCzxARdQ42k2oPEBzW6KzcBc8QO+PH2hlEjpA2cGgMlXeNkDur9w6gSIzQG+X0OUqTi9A1Uj1fWEuKkA\\u002fpQOYU7MnQLmpOA7MCyhAjRcmxNfaKkDXzLbLd1wvQJcJ5zrERTJAM6zyRGyjNEDPGvEj8D02QLvhce3HrDZAigWeRYvANUDSVSu1UZAzQGGybFmSczBAx3atv37QKUDk9JYgU90iQCN3rZEBphlA5KY5CU00EEBYXW3wAwQDQKfZEwX0tvQ\\u002fRaobZWLx5D\\u002fY14TLgKfTP\\u002fo7VqVeI8E\\u002fB9pTZRjdqz\\u002fNEskOb1iVPwrqR5rQ3X8\\u002f7tdPdeVRaT\\u002fpNq6Rt9hYP4T5tIlLFlA\\u002f9NrziQl8SD\\u002fvaKonhRlDPy6VDFExkDw\\u002fu0w5yNP+Mz8OTSkM2wMqP9R7yzrjYR8\\u002f6k9rHz+LET\\u002fpLb902DcCP3lWGm4c0\\u002fE+VYBn\\u002fond4T7lWE2lU2fZPiqHm7GiUUs+tysMXHT4Uz6F8XbXcy9lPtsDzX+y53Y++wVgu35Lhz59pMy9uQ2WPuoF2MUegqM+jZlN4F5esD55efL4bRq7PuKgr8snE8g+fRdt0Mqa2T4IAvKLjNnwPgOK0XgFVAk\\u002f9zifK3PaIz+DRiXh3fk+P61LC2kOpVc\\u002fiyQvUfKKcT+kalNIXyqJP6ReLqMdVqE\\u002fb568OzfGtj89MPn+XVPMPxKvE9rdk+A\\u002f6irB89Es8j+b2NQ6XJkCQEMVR7SCuBFAsndJpatiH0CbVaZCa80pQFib9q90rTNAE3ukW9\\u002fTO0Byd8GgHz1CQLZSeHDOKEZAyEVnM1j1SEBX9LT1hxNKQHAVvFyRT0lAyjbONcPmRkCweZi\\u002f1XRDQPMs2z2kgT9ATkSTYYf\\u002fOEDGGvSHNko0QBH2m2MonjFAR7LJmA+gMEBjz9CXd5cwQBttoC4CuDBAVeTCme1hMEDqZkNaypIuQGatTO8H+ypAOp+2qBalJkDLhVaJj3ciQJLmPxZ3lB5AKoa9CTRiG0A44V30gc4bQOo\\u002fWtDXox9A1wprLhwOI0Chuj2NEwAnQNuF\\u002fgmH5SpAIyAfUfnwLUABSEOKEnEvQO70SEE6\\u002fC5AZcfc7YKPLEBHKBoeiZAoQLDRpRLorSNAOwV36HZUHUCu6MRuTU8UQAk+6cnPHQpA3uW40Q8q\\u002fz+x2biQVz\\u002fxP+S9ouwPtOE\\u002fZ\\u002fCAFUvZ0D8TDYXPlb29P+mOEvLvW6g\\u002ftXupoZiQkj9t\\u002fig+1JB6PygRi1TAW2I\\u002fgqfEjCeHSj8W7Q1Cbjw3PxlD4+kNECs\\u002fEa9iWLT3Ij\\u002fpUAkvzqMbP928u2aFZRM\\u002fdY7fa++ECT9uPjk12DX\\u002fPkkAwnUOtPE+uAjSX0Gp4j5J5cdD04zSPjIGLMIg68I+6QlmiPkwuz4v2jIogEEcPqSID8pd4yQ+kuQz4AiMNj61oDL468RIPg9W+Pqsl1k+zG91TtymaD64Q6\\u002fhNEt2PsqPPCTGYoM+VPsrpwk2kT5ttqQbCXChPhnE\\u002fZcARLY+ODKjtVxt0T4iYHOkawzuPp7gUyN6bQo\\u002fS2NvrSLrJj92UOTSXE9DP5YPDtH4UV8\\u002ffSTRRzpAeD\\u002fFis6jz8uRPzpUx\\u002f+cmag\\u002fyN0ttNTevz+bq4ijGkfTP6tnZqZRuOU\\u002fRMbgOea\\u002f9j85nUi8ph8GQJuaMQqM9RNARUOz0PCxIEAc5UAf1eIpQGvVeq+JmDJAS1VlkAvBOEDJpYIYn4c+QOL+y2D6cUFA3BLOiVh7QkCfuyiKJitCQCspqFb5m0BA64b3CA9gPECxvQ9GC+E2QDHpBbdGxTFAGtK0QouOK0A62zm9mF4mQIE23s5jtCNA+CpAfE67IkBmwTPl+W8iQLy\\u002f4dQ+9iFAJFqBLwjQIEDnEegvHtkdQCMMjzRaKBlAP56IYQN+FECQWePOYNEQQE0S1Mfkvg1AvO8Qa84WDkClrvRMV1kRQNpi3CFgdBVAwM8bbAO3GkCmiQYwZRwgQPuxmjfcdSJABkjdUcHnI0CRTYDfwhgkQKq7NhHh8CJAiuLbg4OhIEAj3TMDHisbQJ8GUfMHnxRAgCkneqMQDUCHbqqhswEDQEO+XhVKDvc\\u002fxr7bD1Hu6T87q1nBuwjbP57ZttkIH8o\\u002faqLGxOZjtz\\u002ff\\u002fA3sommjP1qYuK0w4o0\\u002fOx2mVIZgdT8XLvzM3KFcP4A6PWBlYkI\\u002fGYOxVDZCKD\\u002fAnDTDT\\u002fUSP1VW7dXktQM\\u002f2NjLHmJG+T55EF\\u002flfyXxPpXzR58Vj+Y+PLApJk\\u002fh2z6yGxFP6QfQPkVcLf4iJsE+LkiezLFRsT56YeDKAPmhPkvwj6KZG5o+DBKgtEo36j17Gg0Gx5nzPat6zwddhwU+H88Eq8wIGD4Xm8+6Mz4pPl\\u002f3VupWyDg+4FVU6nEJRz5wAb9DPxhVPnxQmCHt4GQ+KSw4mruSeT6mI5pyI1mUPrHwqCOJErM+YAnNW2rW0j7nonWaTIDyPjyvIiq9nxE\\u002fhT2tdioTMD\\u002fHsZnz+dRLP8EOa2HKtmY\\u002fYXQXnIFigT\\u002fPgGOMd9yYP12byBOmj7A\\u002fEckl8R2ExD8pebeA35nXP1dH7a1sMOk\\u002fwmxsEXjt+D+Z\\u002fwXjSd0GQMdep2PSbhNARDqDJnmaHkBdOHhzrlImQHFbc1iYKi5Akp4kVZPhMkCJG6pKeeU1QCjRf2wBiDdADjtbRARzN0B9NgoRM7M1QDyu3\\u002fdqtTJAONSsUU1ELkDcP7y4XE8nQD6uFl1RkyFAvCCd\\u002fBgbG0ANZSsko2MWQCLnF2nsJBRAaH\\u002fd4ywzE0DJAcLtR3wSQO4nTSBjThFASGeHZ6ThDkA+zUwYJSgKQALLusuYUAVAZr4eHSZcAUDLCWe6ukX+P4vB1ZyqVv4\\u002fb8H9iBmdAUCTkSvgZj4GQJIPbQSbcwxAOXz1\\u002fHCiEUByoFm5R7wUQNYXedci5hZA3POodHKiF0Cw6RuIi7wWQGTQ4OfzWRRAsswXkeTtEED46aJq+yYKQJ\\u002f2\\u002fFCGvgJAxtDUBMLr+D8sjdFQK7juPxaiRPWzjOE\\u002fkW+9nByV0j8AsWbOVDvCP7+ar4a3krA\\u002fCRKQ02bqmz9p8EfDxsiFP7B\\u002fb8GphG8\\u002fH\\u002f1Hy3wsVT+gvhqBx5M6P0LmSPInyh8\\u002f1sZYcdc6Az+NBbNsY9\\u002fqPtfCYxO80tg+HRb+xS\\u002fzzD7Fm5AL4jvCPhnqagJQd7Y+iBIUYegVqj7yjwbTeUmcPs+BkYYJBI0+V3osmtOifj57UMfaf3x2PveoIpKx0bU9x1mX0D6AwD0HRo8AnHHSPcClIjg67uQ9EnnpjJNe9j2OHO7Za3YGPqbBBY78tRU+8AYW0xSnJT4NZHv4HZ45PhD8ksEdS1Q++y0OX5lZdD6Kym8uvGeWPp0uOwfTybg+WAxgoy2N2j5E+a99hBf7Puo8BM1uGRo\\u002f2cLuN26ZNz+X5dv3BvJTP1NZbeypa28\\u002fyW1FS5IEhz8jgqKlaVKfP+mCKe0hxrM\\u002fVzXlRdsmxz8e6XRkBCDZPzp1z+gkROk\\u002fh\\u002femqiqK9z9\\u002fnHSs5lAEQIcnIKG\\u002fPRBAlD3cmUwNGEDB2bbl3X4gQMlO9yUY9SRAGyjzevupKEDdy1AWdeQqQGk4i3DLLStABYs+Lol8KUAQ230QrTkmQPt6FpooHSJAYmf4whvkG0CZrpccqLgUQIhvuzP31Q5AMSNLGc8eCEDIBTrzToUEQHp7oZQc0AJAmyedFDTLAUD070EjPJsAQG7VLFjuv\\u002f0\\u002fQKrXZAVV+T\\u002fsj097Tq70PzXQsFlwvfA\\u002fC+8eM3nA7D9JHmcAsmXsP4TiFXLDe\\u002fA\\u002fGUxlA4Ei9T+rniRI5ab7P0YgQSrfjgFAKf4W0dEhBUDv8+99AtoHQIlDfefXHwlA3lGj3xGjCEDdSVDw8nQGQBF5K5Q2AgNAsranaMTc\\u002fT8lKwxJpsH1P1RbuVqGZO0\\u002fo3xoskFn4j+OcaLqRVvVP\\u002fxZQ1699sY\\u002fKuPT5lzgtj8SevNjpBylP4\\u002fT0n1lDJI\\u002fBLFOF\\u002fyVfD9Bii1v4PhkP1hkKyQVhUw\\u002fPYlXV3H\\u002fMT8g+PaQpi8VPymocBdbpfc+s\\u002fL8aRRT2j55fODuWIDAPixOHW+fA6s+P2lgJKSAnD4lrq\\u002f82Z6QPkTw8voIK4M+NpNOFQ74dD4oAMdVKNFlPpTNpkaKbVc+xeji9KFfUT6PG6mPSEqAPZqQNDkd7Yg9hokHyH5bnD3DKKgW4F2wPXmW6s0u18E9I1N2JF550j1sQqGdrRPjPb46pdcyPfY9N\\u002fFEucpUET4Ab\\u002fPwLEAyPnUuoXLRKlY+jTfkheNwez64sLHlbW6gPjXs\\u002fsUiqsI+iIM7fnbu4z5hNcoKt+gDP8nI+OWLiiI\\u002fXe89J1gQQD8wt5MzQttZP\\u002fPHl1eTT3M\\u002f5KO7\\u002fdG\\u002fij8nPK+NTSyhPxUK5XDobrQ\\u002f82XR7WWGxj+M2zXDnQDXP0yqrX0hwuU\\u002fCSWYKDAQ8z8A5fCP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kDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\u002fd2llIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQAgX+vttSyJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAIJXuHs1gIkC0XXi8dT4iQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQEPQ6TgoRiJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAUrjU19lZIkCNmJQBUzsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQF07b+ttKyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJAVTQWtLA+IkBpWmsIGVsiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNOYfiA6TyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJABM2Z8Dk9IkBpWmsIGVsiQO\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"BgAD0MqRvr+m5x+msCu+v8pkuZhLQ76\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fv0unV87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fQw9Bb7uIvb8wAVFTefm9v52rhV\\u002fHCb6\\u002fhYY8lDW0vb8ChPTWRHO9v2zB8r3efr2\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fkDHQk7qIvb8wAVFTefm9v9qxEqjHCb6\\u002fhYY8lDW0vb8ChPTWRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002fkDHQk7qIvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002f8NfH+Nrm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr\\u002fRhCsMs0W+v2LsgnYpDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v\\u002fIGgj0mDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v7sBnnCWsLy\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb+HFjgXXOu9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v\\u002fCT6KGYsLy\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v7Sa1mbW4by\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr96flRLR3O9vw7qaQZQR76\\u002fXtCiUADbvr\\u002fNxLe0FBu9v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr9sP678RHO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8vwfAQNZPDb6\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8v+NJ43ynXr2\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fXtCiUADbvr8D+Q6PjbK8v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fOdRfhvbnvr9Pdou7TCy9v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv7J3A0IbHr6\\u002fZxNLL87Ivb\\u002fTEOkjUTm9v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqv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zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P9UwnWfMg7I\\u002fog+G5\\u002fV\\u002ftD+CW0L4UXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD+IPruMV422P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9SzDykVY22P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj9zqHjQD8y1PwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762Pwsgb9WiW7I\\u002fqqpyhYhzsz9d2oQaF1GyPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002feUpi1BAXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fhzk0nO8etD9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fUdacTvEetD8TMHXFtYa1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz\\u002f6jNnAmXyxPxCfr3bfM7E\\u002fUdacTvEetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\u002fSeHsoLRItT9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT+Rswun7tW1Pwsgb9WiW7I\\u002f3kSRwYpzsz\\u002fMHvG7a1qzPxCfr3bfM7E\\u002fHTYIv\\u002fIetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\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DKwrrvL87VA7AF2G\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v1efHtiFOb6\\u002fo5ZDgWlZvr\\u002f6X\\u002fhZIvm+v+mEmUnr176\\u002fBp5YUB\\u002fjvL+1O2AFGVC+v9fdl\\u002f3Qqb+\\u002futG9XDqsvb8GwqpG6eO8vxsdHkBjfr6\\u002fDgPDKwrrvL+jVJOEFmG\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002f2\\u002fPfkDGwv795aUHyJ+S+v+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9fdl\\u002f3Qqb+\\u002fDfUXY7ffu7+pVBoT8pm+vxsdHkBjfr6\\u002fDgPDKwrrvL+jVJOEFmG\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002faGcifi+wv795aUHyJ+S+v+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v0aZKQ7Oqb+\\u002fQchJ0dO2u7+pVBoT8pm+vxsdHkBjfr6\\u002fhF95bf\\u002ffvL+jVJOEFmG\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002faGcifi+wv78MY0tDNi3Av+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v0aZKQ7Oqb+\\u002fQchJ0dO2u78\\u002fONbIe\\u002fO+vxsdHkBjfr6\\u002fhF95bf\\u002ffvL+XMPBhF2G\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002faGcifi+wv78MY0tDNi3Av+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fQchJ0dO2u78\\u002fONbIe\\u002fO+vxsdHkBjfr6\\u002feZIzIphlv7\\u002fDyuPfGWG\\u002fv+HpO\\u002fCdwr2\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fb9hjWC4QvL9OpDvtuq+\\u002fv+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fdwlVfrtqvr8\\u002fONbIe\\u002fO+v9uL6bZifr6\\u002feZIzIphlv7\\u002fDyuPfGWG\\u002fv+HpO\\u002fCdwr2\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fb9hjWC4QvL8\\u002fjnXLua+\\u002fv+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fgXE8r7pDvr8\\u002fONbIe\\u002fO+v9uL6bZifr6\\u002feZIzIphlv78P4veGSh+\\u002fv\\u002fNoQHwNYb6\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fb9hjWC4QvL8\\u002fjnXLua+\\u002fv+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fgXE8r7pDvr9Wl2ipz3m9v9uL6bZifr6\\u002feZIzIphlv78BCXMfLJm+v\\u002fNoQHwNYb6\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fcbRc0OvUur9h6Xg+vK+\\u002fv\\u002fbSTZUfJ72\\u002fH5GMvhYku7+VPaYjzt++v9kro0Z4HL+\\u002fgXE8r7pDvr9Wl2ipz3m9v9uL6bZifr6\\u002feZIzIphlv78BCXMfLJm+v\\u002fNoQHwNYb6\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fcbRc0OvUur9h6Xg+vK+\\u002fv\\u002fbSTZUfJ72\\u002fH5GMvhYku78cXtyoPxi+v9kro0Z4HL+\\u002fgXE8r7pDvr9Wl2ipz3m9v8WdtfTqs76\\u002feZIzIphlv78BCXMfLJm+v0s3Qd0NYb6\\u002fpIWMabvevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fcbRc0OvUur9gJgXW1AHAv\\u002fbSTZUfJ72\\u002fH5GMvhYku78cXtyoPxi+vw91DJ+qxL6\\u002ffnjP9\\u002fatvr9Wl2ipz3m9v8WdtfTqs76\\u002feZIzIphlv78BCXMfLJm+v0s3Qd0NYb6\\u002fpIWMabvevb90loINRsG7v5UvxXiX4r2\\u002fQIWhTskcvb9gJgXW1AHAv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fktd0LKhuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v0s3Qd0NYb6\\u002fIIInD7zevb+hkl5ewyG8v5UvxXiX4r2\\u002fQIWhTskcvb9gJgXW1AHAv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fktd0LKhuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v2YbMBj4QL2\\u002fIIInD7zevb+hkl5ewyG8v5UvxXiX4r2\\u002fQIWhTskcvb9kHrR8RpXAv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fESNXladuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v2YbMBj4QL2\\u002fIIInD7zevb9QKDgvxP+7v5UvxXiX4r2\\u002fQIWhTskcvb\\u002fm7UmmuWm\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fESNXladuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v2YbMBj4QL2\\u002fIIInD7zevb9QKDgvxP+7v5UvxXiX4r2\\u002fljdLOUpUvL\\u002fm7UmmuWm\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v6x58HGmxL6\\u002fESNXladuvL9Wl2ipz3m9v5LOcqnHU7+\\u002fYWYfaxMIwL\\u002f8EgCHxdm+v2YbMBj4QL2\\u002fNkdcSbnevb\\u002f7k\\u002fqQGN28v5UvxXiX4r2\\u002fljdLOUpUvL\\u002fm7UmmuWm\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v6x58HGmxL6\\u002fESNXladuvL9Wl2ipz3m9v4Sev6qMGL6\\u002fYWYfaxMIwL\\u002f8EgCHxdm+v2YbMBj4QL2\\u002fS+n60bfevb+C65p7KKK8v5UvxXiX4r2\\u002fljdLOUpUvL+MDh\\u002fyu2m\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vwTYoverxL6\\u002fESNXladuvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fYWYfaxMIwL93STKd3q69v2YbMBj4QL2\\u002fS+n60bfevb+C65p7KKK8v5UvxXiX4r2\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vzMN6YPo27y\\u002fESNXladuvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fjqediUlvwL93STKd3q69v37XYSb2QL2\\u002fS+n60bfevb+C65p7KKK8v\\u002fJn6RjK0b+\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v\\u002fIF5JT0kb2\\u002fluJSjwxPvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fqCks6Cmdv793STKd3q69v1AGMJf3QL2\\u002fS+n60bfevb+C65p7KKK8v5XgxHYCAMC\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v\\u002fIF5JT0kb2\\u002fluJSjwxPvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fqCks6Cmdv793STKd3q69v1AGMJf3QL2\\u002fS+n60bfevb+C65p7KKK8v5XgxHYCAMC\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002f19OmGkR8vL8\\u002fecALPBi+v\\u002fIF5JT0kb2\\u002fuhUo8myOvr9Wl2ipz3m9v9Mw9x+MGL6\\u002fqCks6Cmdv793STKd3q69v1caeH0wBcC\\u002fS+n60bfevb+C65p7KKK8v5XgxHYCAMC\\u002fLX\\u002fIUj2hvL+7wdN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02fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP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hD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"u7uf++cs5D\\u002fe0PSAMSLkPxAE\\u002f5S+PuQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002fQEgwcpEQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002flnzOUnpD5D8MQsHvNCvkPxrIavggKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP6jhY2yUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002f5OXkhpMQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002fu71xi7ov5D8MQsHvNCvkP5DOktoiKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002fu71xi7ov5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D+Uop4+FBfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002f2LWk47hTkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkP71runVCUOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP9lKWq7rM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D85zSncJS3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP5JdXovsM+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP2qI3WoiL+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D+hZttwQBfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+T9ri1kwzkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D86e\\u002ftxQhfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkP409VM4dTeQ\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPxNhQyesS+Q\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se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13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4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GeE1OX2GLkJ5nzT6oYF4G0xLMyewhmcvT+YxuyyUX08ucTAyUy8eoO\\u002foLnqscwdDDVEtJ24GHKv5pSv1QMWWsq\\u002fqKSH9M4iaiR4oMMwi\\u002fNF2QCZd93q4krpQJP1tEkdZBFAiBIHBT7rmPCCC\\u002fSy0CpIYHnvXn77zruMb3wtdNJe3nRlyFgUadSNFF32hiZ5mVtwUD9mptu\\u002flYRKCweIyBU7VD5aeVmWC5TcN\\u002fwwoi85BiQqqNlmboSfJB1\\u002fdN12DmwcFOTykttR1fDGaFFr9ezPUMsCugrESBis0XnAmrn8IcTU1cj\\u002faHTykNg4dOVdlp8Y3vvvjEi7n1zgAB0n6wMXXOVOdl2\\u002fLScY6c+UfmXK0ozsnDDAHSW9wPJklG8QK4ik9dpFofVBF0z0DpNs36zRrPgtRH7gDT\\u002fI+MLD0Ma+\\u002fZz\\u002fN167vqlPOP+QVdx6pySNAPhr1v2PLa0A8qMVrc\\u002fylQCvNHtrl3NNA1mHb\\u002fruY9EAaCDDIHToKQRslkfBlyBdBwHU5k+RuIEGObMIacBMgQeOr5IG8EBdBG3xZtMuCEkFxSij\\u002fG+AaQcbOfQgSCShBiakp3Sx\\u002fMUF4Anpwm0EzQYVeCGHFtjJBU7r2gLg\\u002fNkF0NUkbBwk+QQQQW5DJ30BB1L8aijwSPkFTkyG37qU3QezVIJQQ2TBBSCSTKzSfJEGjwJXAR5sXQSG2s5OiVw9BN9pU0fMnDEGvairZirUJQc645wP7IgBBBwDPDS4k6EAweI+q3orEQHZF9OYL6ZJAgQZBn97aUUA\\u002f4322ZqEAQNzqjVlL4Z0\\u002fnhW+dliXKT8ReXhf08akPtIMaX+b4w8+QbgpU5wZZz0bWd3NBJGvPFaNfkjOVeQ7EqnH1V6yCDt0FiQZ70QcOir\\u002fc\\u002fbgfh45G48WWncADzjwOyHkGLPtNtWMyq62z7o1N210uNrOdjSEcNLpgEgiM\\u002fIL6s0Dn7sxnHT6LxSpQzBnrbAsnV+6Lnk+KZ0yqyAtoPfMPcDacytpd8QJ3ki2KSQNKNCvkecnfzZv5EN9BybEoHHoIg8WJD3JROBPhRMiBQSFARxHACCJncNGrZTZHaJLgJDH8KIbSj8sHv5tWhnSTTo\\u002fRGABF40XW4yBh5UUcMI6b98iGRIrh4OKdKeLDwGAtlE4q+wMQMFxe4UBPAp3+AZMpceJB33D8BLdaDQw4DluRghrnDFeepXas6QCM4FoJPsIDVc00YcyxX7bmjVuTRcuPn3NNgHHYl2ug+434X08DEHC\\u002fTgbmrIO8Vr7OVhN8quftOc66VLDnw5fwztE1sGX492NPC8LG3y+vEU9kf+r6LLp7T3IFTTKtYGDPnXehR3+Ogg\\u002ft3c5yH3xfD\\u002fF\\u002fcRKAPDgPypzrG72FTRAPhr3kytaeUC+\\u002fxea1+CxQFzmkmYA09xAgwfsNVjg+kCGYFPRECoPQc32NLAhLhpBiRh7d9XsIEG2V3TmwUQfQVSqnx90PxZBtb71Y70bFEGElZyjL3YgQVgSsgwtHy1BjookGVndM0FohR73Qss0QQIjgPMRPDVBg1JPVcSVO0HHv0DrtidCQeDNI1NrBENBlXPGrTvZP0HDOiCv0ac3QUu5Y36EDTBBg66oAcZnI0Fsrf0KMroWQccsCx6CRA5BWudZX4TvCUF1NpfEbmgFQZ9fzfmhHfhASTmEiNxt4EDP1t5WWMS5QExI\\u002fN4t9IVAAv8E3yAxQ0Cw3ReGa4rwPyvbcCE\\u002ffIs\\u002fSykdFMLDFT+4sG9csVWQPkxBn\\u002f7GLPc9NxaaB7wITz1iXOP6VJmTPAOk+lXeVsc7vgXuNt8y6jrlxR3MpLf7OY9UrK27ovs4M\\u002foydG\\u002f36TftbZ4r4P3GNoOxVUYIL5M1RgfS8ZsqTjTxAz9Qp1n2MiEao40yNY8xzZu4lueHFDCak\\u002fEDfXSJLiwz+i1Eve0sOXTR9bleQCsf3RbiYvuAKWnD8paxmbAnrk4DJwWVziUXyh1YcIvaIxPot9L9tdUhyn24i5i7wB8YaTbW2E2YHYwovqrZoWAbdApevmFzFRkqXa8dpRG6Fmz4SuR82k0UQnHXgIEb0BGK+5qu8GBAD3cOyskXY58Mos5M1MNW\\u002fAn2NYpak3fdLk7cjMokH1QwJ6h8jSHlyTFITusEI2cvM3k5JnVu8YE09zVs9ZxSwzVwGA\\u002fK\\u002f5vzNvU4OxcgwRI4FYQqHa7nIDkS7xj127kcOjR6mv5EAgc7P4RH3Phg4Tuw23LHQ8SoPK4mWQ0sqWA96t\\u002fe640wBT7vK0iR8YiZPit0lMMOSR0\\u002fnssdr9oekD+BLafGj1LxPyuaeVnzuUJAnaVL0wRWhUA\\u002f+ffqBfe6QL3zRAp\\u002fguNAaoPlYgd\\u002fAEEIQqC4qZoRQQnLuUwXkBtBoQAo7e+nIEEWTNutwiQdQc7yBy7X9hRBuUxwXUHnFUHT1NWjreojQdwDznsYWjFBI6X+hVxsNkGkINV88Nw2QTodlTCAFzlBLIcsWiorQUFo5WhrZYNFQfFuOjrFKUVBnqgdkHXzQEGGhZV3y\\u002fk3QUxZRI+eFC9B5GdgXq3zIkFVdW+vvk4XQYNfhEMsYQ9BG\\u002fEhg4HQCEFl9KHucKoBQeCUJGvZOfFAclvMJH\\u002fm1ECME9rll9mtQHGJMgf9ZndA4vm0nybjMkC5SgrbnhTeP2Y9Qil7GHc\\u002fW9Qn5t3mAD9od4exfXJ3PoQi9DDBvt49QBYfZ7kGMz22CzyI\\u002fzV2PJAuV7xDcqg76wRfC+RcyTrc6zj39czYOX+mqj7H2tY4121eYAPZwzcfmx12JT6gNlA3YHZVDWk11TdWcI00IjRvtH\\u002f94u7IMoTDqBC1FmAx5fWYRLqQ4y\\u002frV4xxp2tWLs1MHHTENbgs9fruz1CiCCu0L9mziZ5HKezXPcj2VnUnVyjnY+8qkiWvMRYPTCadIz+ymdQdCZYhTyInobNkfx8cX\\u002fLrfhJVHZ9FXFoUqBob2CW952\\u002fGzxjgMP5NedhxFjfyXrlt4wIUqUj2qa\\u002fWghGwaqy\\u002fc7TxDgUjGiPSW18MZGoe0dBpdS2EM\\u002f3eF6\\u002f8LilBuSZzGoIwEsR9dXGI9TE30bitmiJYM6NAvZbtfak0vi+Xom5f6TVwAEIIkcwXN1rfUSqnCTU4ncAkCvaGQTlgIy8FPIc7OqBEZB5cYSQ7n2NBaet0\\u002fDvsjBMaGL7CPB6KeS78Tnc9BWO12l1mGz4aHHTVSoOuPnOBQk4bKTA\\u002ff2KV1AFpoD83oINxNTcAQOnJ81VMB1BAoIcslmmQkEAiZEN+kt\\u002fCQGfdCLiBr+hA19LfmPcXA0GX97xor+0SQQQBJddSwBtBITYjzEFnH0GF601aTkMaQaDuAMe\\u002fpBNBGAIyN1MjGEEKmVAb07onQUmkwspZQjRBehavwAQkOUEI4vGDf6Y5QYKpGe21Wj5BlQnDihoTRUGCkTz+EfJIQcUqqpdGX0dBjYev3cEyQkHVfZ\\u002fOd6w4QfT5kc91rS5BLWHjrVUfI0Hv\\u002fCUluucYQUdeY2fMDxFBoRw6Leq+CEEEZY3Akrn9QGxbq5twSOhAS3VApzhxyUCcVMqWGjSgQMNFbG8xGmdAA15I7TwaIUBfFxht2RPJP8CaMjYVwmE\\u002f37E+3GX+5z7Hdm9iM75ePk21OO0kn8I9RZQyOAJMFT1oMY6Js\\u002flWPMUv0tHXXoc7v7BKibZopjqAx66JgEC0OdjgKO65P7E4a1P5yIWwmzfMNzqtl\\u002fF0NkJsvNEQ2z01qzJ59XkN9DPUW4mHCWKZMjjz0OjPRi4xh5Y2Yd8DsS8ZbQ9GcQUiLtCn6XFU\\u002fIEsqVhPpBvq0CovG9Zth\\u002foNKXY6HJyhCDknusLhbMWyUyU8RTbTIzZdIyBPDx\\u002fnaFQhOn8sY+rfOh9IDaH3O6wQHaFwNEBhftMaWZwtaw56hRhz2MS+4UsmFjgvCXPQz7UT6AV\\u002fv0UbNBHNDBzYF3exDpNEoRYFVP8rUILkvKaUlC2Sz8gW\\u002fXopL1gchEIRuq0wjKX1wBdXIDLeXEoHfO2AMwZEmiVrhtA0H34dNH1nDjbsl\\u002fU\\u002f0Vs6NwvvlaAoiVU4Gh6\\u002fJGKVYDmrtwO2+hJYOtXdd\\u002fRSeUA7vaKxJfJCFTy9LNuzXuPZPNhVGVl6wo09gM6mwRorMD7g55MDKqTAPlvveuY2SUA\\u002fHga4uj+Hrj89vaJXXcgLQMMqMD4GMFlA+xDJv9+8l0CH0FCUbpTIQNvtY4WzWe1AJHm2m+T+BEGMPkffmYATQYkx9fRk1hpBcUSuRjSEHEH4ksPFgC4XQe9Sk7fRuBJBzIP\\u002fyLMFG0E3L6rViLkrQfPfJHRjFDdBvN57vLHtO0F\\u002fA2+5Jjc9QWOQAJXgZ0JBvls+tno5SUGvjvBp1kpMQbSG\\u002fjFvoklB+iLQQ42bQ0F28joGCpk5QT8hW1CErS5BGbexPRyeI0HfyXCinO8aQdIDmEUNzxJBk063y4hPCUHKIzt4\\u002f\\u002fX5QLQhaMJvmuFA9SAXQ3fzvkBXIe+gWQqRQCODpUmBlVVA1gW\\u002fDj3iDECybf6FW1OzPzwNvH73HEk\\u002fGmfN5JI6zz67mBoZCHJCPscsZEZun6Q9P6l6OanH9TwuN7FBhbQ1PMeuH8w5ZmQ71GbpjiETgjpsWAlU6TCOOcohd0Ksw4c4kWDJF+OgcTex204Ig6VINuXUDqNoPBA1NvU05UsoxDN0KduLIJVnMmJonhiG\\u002f\\u002fkw4X77MfwBey97pxn+HHDqLQ0cbXkUY0gsHE8eDoYylSpCNTdPoVzRKPin6CC7zPomwy5R25l9EyXqz8FR1LYaI98B\\u002fLpLQBEh25acV7X+9B46TXbytBPIHK0ORA6xBIoawP8JkXR+OhiCpYd49WvZFQeMqipG\\u002fGYTxH9nvWWV8xAzVcTxrBF3KmR2iJ6vuh0sw\\u002fI5qgsNwi1VGStF3KdUL6gYNr7VRdYwywoqfZOhRjKRO918R6ulM5bOQCgwjfM0\\u002fsloqMGfMDbmLenUIaRaN8dCNXhDHnQ40mqdSUSjfDnHgoYZfDZzOt5jfgXpTVg7k3ox1XP+LDxpgWc\\u002fAFHwPIffyACZVqE9+0xvgV9qQT5T8QgUuJHQPhb6+gjf+E0\\u002f+W8L4l3yuT+mYlFsG8gVQIVbN21nLGJA7+a3ij9yn0C6gWB98OnNQHk5iQhCivBAlJa5VZIyBkEXKBCyJ3YTQQmpzCokGRlBFSc7PDAgGUHxLYBMcWIUQWNvuSX5jBJB2IbIhcqgHkF76vA0dZovQYDlj7qqjzlBTlJiXGikPkEcDM2qo7dAQULtBLI+\\u002fEVB1zoUV2xETUGAkDnVoFxPQcBKpi67zEtBJYVGP4L1REGVsObyKWs6QdhjWNMMtC5BIma9RFocJEEj8dSOedUcQaqTuR2FbBRBUvmz2L\\u002fmCUGUbB3Q+of3QASFf5gxR9tAWL9Yp4oXtEC7fWkRSY+CQGY9oNdjHURAmH9eWTSX9z\\u002fy1gcnQTScP2oCfwJnljA\\u002fm83qjRfTsj7DrXm3R2UkPmqaeEgB+oQ9o6sfhVBq1Dxn+HdR18QSPE6JwaigSEA7Na5zBRimWjpnbLgNJY9kOWm0EGYJ5104R0P0Vfp+RDdUU3BS3noaNpgAnnPDHuA0zn20mOp+kjPhD33Le\\u002f8zMghk02kWYMQwC7ygtf+PQy\\u002f1f7DN3bKxLQWA28lzLQ4syC84YEU+WCoUjJzeMFqSKAQvqZ2\\u002fLromxiCWCXCZ0SQyiDo6dEvWIjFw0jQCncogg8dmLHzvrR7B40M1xrp\\u002fHJX6h5HXsD8aFkS07mfT7RdWOH5Cc3OKFSXQm53UGiYToAzsU5oZ4SiMPI4PZJ2VKsAPL\\u002frLvkksyx0rZVzl7C1JbYsUpY9+L7XX6Jwpdf4w8lWibYqabDK3rpJzNFDJM\\u002fhN6QAaHBU1ZIr82u6WUDZqVggvjZJ4NzL0W06dJpE4M8F\\u002fha+Qljn3+ykRQPyLOhhOjn5uXHA7+IQYrYwKQjwrWuXXEsUCPTAhPpoWcLI9oDOXLwYfUT5RsyyjfB7ePmScyuFJL1k\\u002f40Rxo2MnxD+aNOkfdEUfQBGzGArxHGhAgnkiK0lRo0CzlRfccSLRQM3+DHQf5PFA4TszLbDlBkFKGZaPHQgTQXJQ0y1l3BZBE\\u002fy455CqFUFUkdg\\u002fUjkSQaiFqx0QVhNBlPnvB0JpIUFNnqrZb34xQRtMyQf0djtBEyGpvnWOQEGzUyJNcwBDQdmLydQEkklBKPztrdFrUEGUyQxeafVQQUl5gm67kk1BBjmQdYruRUFuOgh8Y8I6QWUTnpcyZy5BXAeRlyVfJEHYjPEK\\u002fC0eQTUqf58LexVByxQxJBUICkHJ4u5stun1QGkwpsapC9dAG\\u002foGBqC8rUBR5Wb33SF3QG\\u002fbGUhLwzRA7Efogkth5D8QePGkn96EP0bxW4qVgxU\\u002fBEJjjwTIlT469yOo9U8FPuCGb7hS9GM9jfOBoTDFsTxpa+G0Cf\\u002ftO6NBKI8r8Rc7uzNLE3EMMjoikZQPFq45OUbqauNwOzE4nn6w06XOFTeHzF++TgTqNZWU648CQq00YyYDhvUCXzPQc98Vqfr+MSppxcIzKo0wMKUnUgDgCS\\u002foo4YxGqJ1LY+h0LtJC9ErRezrDu9OGSpLnxThyrRRKPowtuGlWHcmOnfvLNwBjSQWkxwLHvuQIseCMtkCvIIgzTqO4\\u002f95Yx58j+VtdhQzHGV5JYTtnPEZU\\u002f3yTECknheBJ3QCHh5JFV1dDCbkhDknDKJ1CpCj\\u002fyiNBb9ESHvCKtcHs72PWHQs+nROIy0bFS7FqTxAtKGkL6x6ygUhASMxmCa2P+t+kDI+BRkFLfzqM2i8QWSrzDQ1ALYwcy03bjZXX4Td0a6UNx6JFm+Ir6o4DNw1hc45sDkV\\u002fdbjj5miOgGL6goiGoQ78\\u002f4Jm019VDyzzcCAi7QTPVRp1iQB5cE9P0DU7eu5Xj4GEJpj1P3oPn4N9x41VGM\\u002frHf5EP+gzD+lmZI4kJQkQIpVh7jNgW1A8PWmsP4npkC5IgXiarLSQLsRWNg23vJAV7inI3ZhB0GWG70cBmoSQY2tKy7vaBRBAxUUpzB8EkEIvwNjsOkQQdsNb1AxHBVBTuJcrG+kI0ECr4dko8QyQV6yNGTCpjxBxR9hG7SZQUFDVJeUWj9FQRKCOF56yExBos4cOwnQUUGKf1+RvNdRQXeeSDuFl05BupvZY+k4RkHK8enEXlo6QYxRGlfOnC1BYwblevhVJEEXy8YhQcseQQkXLFL0yxVBgR\\u002f2IMqdCUEETKoe5OX0QJWe78moP9VATMXNsb+vqUDnnls9ablxQHJvzlZT2ipABnknl9qt1T8LQD1HrD5yP41ZESU8Yv8+Ek\\u002fknKAQez6pK3H4hALnPSR30syFBkM9OvZLV7xLjjyYZ+5ANxHHO4Efuz3Ct\\u002fA6lqpvIGj+BjquV\\u002f0ZZvINOXgpapWbcAI4ZzLnOrRz5TatRrVAEI+3NS4RAsqNaXg0c64fFlPcJzNri3Dg8P3FMaIC\\u002f1FPHFMwyCEjV1xPzy7\\u002f46nBFy04LTq3qnNAmJErjtg8PPUi2CnHG2rKVDQPKJCy0P9SAjMmsgMdokTTRSR1cTwCPZ1HIgThb7yhEzggRBJb9\\u002fIhFx7akebpl\\u002fHkG0qoZslH3qEZFo22sYi9XBeMbwt5ASuDJQnAxp8\\u002fT1cnhyY\\u002fh+W1KikwKH5Of9fsKsV\\u002fGJSnWJ0sedyePhAjPC79vlMWz2vJL5fCQLV8pEUxD4NfCOBcsTLfUUuGiUAKNJpc3e21s1I1JmXF\\u002f2AciTY4w9ttHcavNzig9NcS8sI4ZTvv6nNLxTmmaPILBZC2OhFBg6cfipY7b0ER4ns8ZTw7ETssSuEiPVuA87ncs889wHDybDkraT78QzG87e\\u002fyPmxaj1ohHms\\u002fgZN2DRKf0j9V\\u002f3dyy+ooQI3Td1saxHBAzEXdHb0HqEA6DwEWOMzTQH+HqbfHwfNAZ0z1QC\\u002fnB0FFW8VhmroRQYZ8\\u002f34W9xFBEUQqCAGuD0H9JOpyX4gQQe8A1RTuuxdBuJWofZ\\u002fPJUGvCPltJYwzQeb3nJJ7KD1B2oYfmL5rQkFiRyb+EjlHQYHTX4XvRk9BLrbi8BKtUkEz0oM\\u002fzzJSQV+cseGkjE5BCKovMTmpRUGIz9WJ+SU5QYkmKKLOaixB48mDQtIbJEEsnDsNEMseQe8MTz3riRVB9Uv9M1wPCUEAnOoSR6X0QO9LHPUBONVAG3EEWoMqqUBTpRO5HUtwQMIGVD7lLyZAV5ozqGAUzz8VzeLvUBtmPzWoKoOUpe8+ZHCALVObZj7tUlSTzPjPPRRr4LqFNCY93WMV4rgTbjzFyNdJ\\u002f7yjOynaGLD798g68ltsgPBN3jmZ7mxnPpThOHzQPH2gb9M3s669b9pttDa9aF68sl+ENfeZc6mzP0M0unOsvhw18TIZkyhBDBWNMXYjrDDcNhcwW+QFf2d+kS6Ys26oRuH4LKOrDFGSsFAr79XJPekdlSmV+TQzTjHJJ2QBg8+jVewlfnf7mWIK\\u002fiO+uVs1IQX+IfhBK+BLRuwfJP6e+h8ZyR1nVxIn+f6UG4nOLIaxlWAZ5fKcd3D8vCO1YHSwdkmhJQ03+yoIboMnCUO+wwqUVCljj4y8hYkUK4JmGs5MUMMsydE\\u002fnnsdYS5OysboeZXsL0YCb3bxfWYxQJrfE3St0DKWAZMmYE4nNGC1bccLsW41aQ+WX0YLozbZ9M0lukXGN5FFsVKVjNg4IS2kvAWB2Tmk9p\\u002f\\u002fIfrIOi1OwC1VEKc77VOJOV8WdDwB0\\u002fDApYIwPXvRKle9otk9gxatXMHTcj56O1vPzDr6Pmvuzf3IanE\\u002fNP6GRbVE1j9gdEdRaPgrQOgSCRcg93FACwiiIdw\\u002fqUDG3aKGn+fUQKfcv7Wb7fRAreev1nqjCEHA\\u002fi3EZAgRQRjMuCdpaA9Br9i8PWDMC0FsOiIzdgkRQRKl7Na78RpBgeukSXvEJ0HJ\\u002fzwam+czQfZ+iLV2Nj1BwsG9kyYHQ0Fkq5S\\u002fl7dIQQA61EvfY1BB6YkDSG3qUkGeBzHdNu1RQepdMpCdT01BnibGQcxFREHsmc5OaVE3QcpE6WjBGytBoZv+1q7rI0F2hdgIDJQeQRIQbC4IMRVBQV+neFEWCUHg1ElU73v1QLPsmo7RodZAkfJWC6WeqkBH34ZSgZNwQKlHMyL2OCVAyiPzxr5vyz\\u002f5A4tVoK5hP8LzffGwfuY+NaE\\u002fH04EXD4c26ay7vnAPearw83h7hM96d\\u002f51AueVjwfTseIUsOIO4MwULX2I6o6BIsYukGXujmxj1OLKgy6OJG2EY69i6g3pY\\u002fwEl84hjaRXau9yElTNXJGNHWhBRA09Cz8\\u002f5ZquTKhOkrYfjVTMWVtzAOlnNsv4BJ\\u002f5vXWUi4ZEF\\u002f4nV64LPUy+2jt1w0rHgvOuk1IUSlEQ6wsZ+qCJ7m6laZBjaMl4FHtxmETsyOwyOMUcY+xIdCcTAvnfp4fofLg1xn5eB1dGY08+nxTG7ASegTeD+YhF4Jp25\\u002fP2SP+Mk\\u002f+cnTMJT8tS\\u002fZCj60nckGqFIDvfCmYTrp3wbA6K8E9lj\\u002f7Mucsro2BA04Agy5moIkRglQNMEImPy7BVIUxjkPLA9A87TKVxJBkveFCNAm+Ncth+4Y1pxkFr0lcujZ7WJ3klH7cN+3kayZ3B+04K9CsDA3g6znWNxfvxDvZOhx1GtjfibU7VdBEn6NXgTwf3mwMZ1w6Paq3rRma7uI9DTIz0Ee\\u002feT4F8ApRKaMAPxZSc64PlHQ\\u002fmF\\u002fdyla22D+pyX0NQactQPw22eLHunJAnBHzqs3CqkCYXsEwesLWQF6hOHQEyPZABX2n8r2zCUFfIS1+nV4QQTdh0Jj5iwtB\\u002fzltuqh0CUHfVtKv3UISQTjrUcMsbR5BIvXoXJd5KUEYfLY2Zg80QbJ34ks3Kj1B9rUH069zQ0GoVxilqpBJQQqMwBpVj1BBv+dtvlZ7UkEIUxU1tf9QQSi49nYx90pB5iWL9FRBQkGRExvKTi01QYhaGlxaECpB3H6ViE0MJEEsDrhsxrYeQW1EznBAXhVBWq8VPvZiCkHy281ozpn3QPfzKgLoKNlAO6PNtPkKrUC5idYSl2pxQM8Q0WRvPyVAS5ei9dj4yT+8YYKJy2NfPxE7VQ0CiuI+\\u002fr\\u002fl\\u002f6czVT6W3JI0yU+3PV0kKEephgg9ARELBnOeSDxhaNKD6Yp3O0bWUJhocJU6ez0jc3CZojnfcI7mTMaeOO5FQ5gXUIg32QncXDVfYjZnLGH7Y5kqNfceY37AeOIzhWEfISOiiDJz7qIbNI0fMYnv2iZ+ZqMv2EMSHRDiFi6rsfgsYtl5LAP9qIK06csqbUl8O4DBDCnAzC3V8TQ8J+MvTcPKS1ol9I4tjM9CZyOkk9\\u002fLJH9jIauDpqXx8U4fI\\u002fDwgTWMOB0owACtPecAIBn5jH6fZQMirmVEwWn5BCRbG3TlC1\\u002f1JVwGHYv3hNQnYIbXtpWQoinSKCQJkqdfK1jbwfYObgktm5A8Fi1Aoy6sbB8d6nYrMPlexjccdqIxYbjivDljBzMoZvHod+tbNHp+G4moaJ81pX\\u002fDbR+m0DZNoj0fSKLwN0qk3uR+U\\u002f84ngGtR+fN+znN\\u002fINPCkTnOuSLn1DzW8I7wvKbZZRWizxp5kGxzThDPRdEyphzkek9DgKwVnYigD4CnHUICHADP1IvbODdnnY\\u002fdqMwoXwH2j8S3VhVN+EuQNJp1QNdDnRApn6DvgAXrkBGJNOQ5UHaQFq7wqVDr\\u002flAjhhtFoksC0HPs108JZYPQfa2LF1xiwhBwR0i\\u002fQWLCEHMwHG\\u002fZ\\u002fQTQRi0RBSg8iBBHn+MbK8FK0HmWxXjJU80QXkWVWfqWD1Ba2NWVDO1Q0FpOTDPWqpJQSInMV4eH1BB2ce7iUNjUUGEhG20p\\u002fVOQSvMvGSBy0dBFDYdnsHLP0GudmDBWw8zQWqM3a3dmilBXN7MLju1JEEnvY7SeLEfQfwxkOx7hhZBwkVG\\u002ffE6DUGbdPObRcn6QAQVRadvOdxA\\u002fADvEYCBr0DyaEu\\u002fcxRyQKrGs5+y\\u002fSRAaGFNkOhNyD\\u002fXrn\\u002fzO65bP7ct1o5ro94+MlApCrhSUD7kBLf+LaGwPQS+03S2HwA9FN4m6HGrPTz26i02attpO\\u002fG1lowTU4U6bMh6G8aikDmoJeBB6Y2IOHMQP0w7JnE3Ogz0y96vRjboNrbBBHQMNf+HQGz+78AzqTW3tRosYzK4fEVWtav0MLYgAaexRHUvz2n4YJTs5C1fqNRG27dDLBLhWa\\u002fu0pEqPTh5J6fvziim01Zorsf5JiZW\\u002fROGnhQlQsEYtyyfHyN\\u002f2TiLnkAXIXR3WdjIohQf3wypF9ESCh7NlBvh1VgdIK0nejssIC8iNkmpKoAbLyS+tro3oUsdJk9727BH\\u002f\\u002fknRCX7Lj29xSmrGLmzOyGBK18AmUwjcCkt2ITpsFrMwS4pikoMFXhHMOxQEXRdKb0xM0gaHo4SITN8CMvEV9ZyNEvfhzkdlrM1+QC+AyMx4zYsSxm7DrkBOMJsqi7R2Q45IHhzRsFPCToWk4NonpTzOhW4GZp+k8w7jWhxGm+vkzwJRib6ZqRJPTzszLVWrO89\\u002f4m3t02ogj4cNXGcLTUFP1kMMMEbxnc\\u002fdI4rC4BJ2z+zFm39td8wQBZ31OvTfHdA7GEW3ZWHskDJwdYhlhzgQCvWzifB3P1A4f50f3APDUGcnzEZVK4OQY\\u002fSAuWLbwZBngRGfFS7CEHkHbCIm9UVQTv1s1ZkkyJBWGFo0JSTLEHijWYIz+c0QWppViti7T1B0GDdi3LFQ0HlIfeb7\\u002f9IQb3ydaayPU5BDJw5j5F0T0HD4JnG3AtLQaGZDhrZLkRBUcTStC\\u002f4OkEmdGKFKDcxQVLd27f33ilBPwrnRzvxJUEdEvLqGNUgQaDvDvT3tRhBOdL7lXSfEEGSg3cMqlv+QCV0aLUt+d5A3L+4O7qGsEC3+91wCwhyQETREa9v0yNAjKl4OPmmxT9I9wkE6ShXP+6ZLouj+dc+QeJ9uVvORz5EkxE+VIimPUY48dEOP\\u002fQ8ixylVqM5MTxWDJEL+rRbOyXVA\\u002fxpC3U6E06pcJ8sfjlEETQuoml0OETxKEr8Dlo368NoDdVjLzZ6Q+2p7dfxNJZqRDz4JqMzj2aqsMhqQzIto+LfFpvSMCWi9Xkx3VAv7+LH1kHyvC2keyv+Po0XLDTPNYSbMWIq8ehZ4OW7mihGdAypM7fCJjwaOa33BNkkruhJhpgR4CL\\u002fKPnS6ejwID3rnj4kPgQcmLP6qAxZJh44mnX8lD9HIKcyceYsylYiEzJn5BsNVST8F9h6x1JCJtaMJIS8Dh4odtMjkRM75ynHr25JKuugK5ZTZb+sOEctz0c9oo0I3i70dVEqLU1iMPKPqe2lBNUxoDqoYPi+NjMsWx5NTTKHNDK03j3fSsY15ymZA48w9DYmLbKKsjsROAmY85rcuhs5buX1x5UIFTrzDNT9RRj+OvNyrAZqUtQ7XQJoswDymTwNJGyTPmlPPfnT8ySSJPI9LoEzYcA+hD4UBudgulMGP99Q7SzeQXk\\u002f0zTY9Yyv3j9t1F7Rrqw0QGUrcG8JzX5Aae0DcQShuEBr0RmGSonkQIak1Z\\u002fLmwFBPQzZkYUxD0HFXG80qfYNQXer8LgOEAVBU8iIjpuOCUGTCpDpHqUXQegCShvoDCRBQ7n8B3tNLkGj+ExKl\\u002fM1QdTPpqsN0z5BtSbpVkCWQ0GILrL376ZHQRe5yCEGXEtB9apTfg9bS0HD+l9b4K5GQVcbl\\u002fALg0BB\\u002fduPaZVzNkEJThBXNIAvQUQ9+M61vSpBKP\\u002fOQAOJJ0GgiJxnsCEiQSrYF1BxeBtBK0W6Zu+5EkFOBmaJracAQeq7a\\u002fB2POBAdeT26BZxsECfpFipQfdwQChebJBLlSFAMKKIB\\u002fIHwj\\u002f+igBDWgpSP8xpzZlFadE+ye+LB20TQD5Sum\\u002fW5T2cPeEcnAoUhOc8w\\u002fCiLhiFIjxYkApiRY1LO9m1\\u002f3k4V2M6yA3XFK6caTnt7AECa\\u002ftfOOri+xB700I3IlNnePvkFDY\\u002fPMvvmdzVNAzOJlN5kIUzlRXUVxcOJDI+6IfcY5axMBXoUiHCFy0vlTfPTB6zli3R1\\u002fZSxbbwK2HlKvyrPDcqIJZ1XS+Ebij8EB1tI\\u002fGSJkHozkwGXqYkg+zBdqkKyCKmw+\\u002fy66PvGcOYsCiAISEcFwPLxdd6UR4DDrjBjs5wIGKaY9hudH4i9ZDVYTAAeiR4+pAm1epkJuocwDO1tj8oX\\u002fC2qnqnBio3D\\u002f3Vgn++KwmOKSk0WGMt0j8mlc8f9y4Vi684Qgx6MHM3Cc8TpusxclaqRM6nSzO5OtCbRRGaNOox2tGkJ9c1ViWAyTpiAzd3Pg7vspYeOEQp7XfVwCY5xyJ+x6fsHzrGDGg+uCMFO9kKrvIxeNo7rAl0JadxnzwG58wy+NdRPZK75ZUynvM9xWk0X15thT79P8akKSsIPxk8sHGhYH0\\u002f9sSKPvOo4z\\u002floUFmFow8QHKeZzDht4VADSD3\\u002f8XSwEAnL8FyhTXqQBoRVTzmlARBEUS9rWyUEEFYqfPpCDQNQWQ41c1rIARBuNN3QC+KCkHZQlFgUjQZQZPeZe9+XyVB1EUAA6okMEF3t6Sf0Vg3QaB1G5l8vj9B\\u002f8+Myb4bQ0FNdAIxatBFQbOU1PIy+kdBKFDmmrzxRkGNVHOjEFJCQSVXuQ\\u002fDKzpBHKmE5bd4MkG+onQSBkMtQSZbh+rf1CtBxUBcL08FKUG8sfZvw1QjQVUJ2ykD+R1B0WXTXGxIFEHuCQFqXFEBQbtop8JOAuBA3xgvXmGgrkD\\u002faeMtuMVtQINUHf5i+RxAMvLforDMuz\\u002fZ5w2+2u9JPxHDu5fDR8c+VfyW\\u002fnDzMz4lbwGFSj6QPRpR9lhtDNk852ImSkBBEjzg2jpLjx45O7oThFLfTVA6MGW5EE\\u002f1UznThfbZPAhHOCjIkze7DSk3ehXjVoiv+TUyC9sBh9G4NCG9ggCtmWYzn62RDFZlAzJ2wfnvOWCPMM+IsDEV6wcvucMuf34vcS1eX6zjR0fHK+d+r1F3uQ0qfqkO85LkQSh4MbRp42lkJiQumyzpg5QkNfmGZUTkyBdlg8+nD3AKGqRyx5yudUocQoXySij0eB7Vf5at+CyWID9raiThkaIi7tsiwGVOnSTjXQj4b8qFJiSsM3MaiV4oIRP7jBMpJCpd+LJRyxXZK9M\\u002fQP56aX0t6hj8QHc\\u002fEC+lt0C3e+qQMPD9L4B\\u002fmAAy3iGalnavXjOnF1Ux0buqNFPERrAi8+U1GRlEkW38EDfhtwfsscgoOH1ybECGDTE5MlQpuBMnJjo1CCkXyzcLO01ACRVBwd87Kq5bYti7oTxaxYeydURTPeoJMo6g8vQ9uUZqdTW9hz6dlmLjPSgNP8SNI+ohn4M\\u002f1dn5Yiw67D\\u002fd2\\u002f3A0+FEQDx3MjX93o5AL8jooI2CxkCRZ7gidTTwQDBt4qGgTgdBgIlN1lstEUGQTi5NoBIMQbJv6kFUSwNBk2p2waNLC0HZ2fwtnWsaQf1s4yJ+kyZBtt7rpnc+MUE0xU2479E4Qe0BtiU9KEBBALZpS0dYQkHhxG4kM8JDQfvtQN7siURBTEJhxIa1QkEsmlcP+q88QYSATrC7MTRBp38KMCAzLkEh\\u002fA0rxXIrQRk9rO8llyxBuP88I1jQKUEkzURabO4jQe4yfPKoTh9BhFoDS+K5FEE5D1AI8tgAQSbKm3KXaN1AXOYosfh8qkA26Z4xni9oQB6HQGLBCRZACCPvMVO7sz+tr3MxSiFBP+crJM4Hjbw+qjhdcKiuJj6gQIUqCxqBPdIPozLlZ8g8aeqo\\u002fPBzADz5W9xno+8kO8SoDeewIDk61oDqOVZvPDmITYj\\u002f5VQuOFH7\\u002friufg43RNyL\\u002fuTk3DX1Hy0v3syZNLfPBjbAtUUz0cRohQ034THaBEj4RLppMJZvvaPQHeIu6AL7HRILSC3\\u002f+LBc7RGeK6UROMqIuOEpoiV06+fEEyh6Pnh2ulJSJrClg6hdtpMVdIB+UCiJ5BfegVT0wSg0GtzIPVLSpXIcFFc1FhxBoB46\\u002f1G+iLO6IIBgjiN+qsQiiacleNokviQ2FKQTSbekJjnJ40+j1Hoo9hufdmJfQCpXUYfQNtTyK7Ukbk2UZ5QtZzfU3yXWJC\\u002ffFTJD3wykMF1Uo7leLhIyzOGI3G0SbzMVXQGovgW5NCduZg4X\\u002fvI1PZAXXEsuGzdRu57XaFgyOO3QDOnpYTc5LfXHwfgzLDrPU+di4CsQO5\\u002fGJOltzeE7+JmVQoUqozywc6tfmNRUPWFNxzsP2Pc9ucgLc4K9jT4P6UKEYTcUPze1MEEz\\u002fow\\u002fM7fUNTkT9T8qksksFTVOQCg+4+nvEZVAt9zM9KeSzEDtrzwYvPryQGmpDaq2HQlB5REZv9YXEUHeosO3Nk0KQe0yZ+StTgJBpzuT05eXC0HWtNrtYEcbQSjFBDRusidBHKSZBtNdMkE9ktaN3wY6QZK2GUXkIEBBOPzJFc1hQUG80VDowcdBQRBBUty0dEFBPuzjRTsaPkFJrWK1sAQ2QcAd06WRii5BsI8uOYufKEHcO0CBy7kpQYYOBy42eixBvzTX+EZtKUEjm5DLsYsjQTMJCMLs1B5BDpR6qqrGE0HElt+c\\u002fnT+QKFRE0\\u002fZBtlADgZHerospUCZbitaAR5iQJ6b8TPu1w5AS4EaUmy4qT9sAdZFzL40P0f3Z0F3CLA+g3li7LCYFz55ncuISndwPaRoNrrFvLU8P4h0gEka6zvw3h9O3+IPOyIe0d92sCE6+f0nJuWAIjn7yi5MbT4SOGa2IYf98\\u002fA2iCzZo1axvTXLAgiEGoF4NDjlB8x8DiMzujFCNUPuuzEGbPedwElDMFFd32tdGrku8M+HZVXJHi0NYRePMspxK6WmEANlcrMp5CGcM7yoAChk7moRuW1PE5wFdnujDrAVMlMdYvbrDhhOjrn1Ng5cGutXn15+\\u002fJcckCYE8vVSwx5qT9Ah71bdIFTZ48\\u002fp\\u002fOQiD0AHZnZL3CS3LKbA5\\u002fjBJkAP9kiWg5Uonucu05BEWCoDllrue8sJLHsAufYU1qkt4u4fyZJiOC+hhW3+KLC1MAY1fZp6LSIyjy9Zi123fDOf8vADDGHFNGxBcNkqA\\u002f414cgLQ2PfIze1vwGqeds4OKsvNacfcj05wDUMeBOcMDoSh4h0ygwSO57pPmqeTuM7YukOa44OpTy03XQVpnBYPdzB\\u002f2JgBf89PhtBQlpPlT7RGjBG0oAeP8OrkFBq35U\\u002foVOBb\\u002fGh\\u002fj8f0+rborFUQMwtgiB755pAFh0A3aHn0EB+tHcE\\u002fcP0QPJa3RPJcQlB8JDk54UqEEGTqjXKjM8HQaaxQY2jCgFBXXoBtcpZC0GrDzhMotAbQZCJk42YvihB10j9TlZeM0FdlgnBIqw6QSXG0ZXCgD9BZ4941aVcQEFOCjyxoB9AQSqwq01sCD5BKe4eZBRpOEFBExjG89gwQS9WLi\\u002f51SZB0\\u002fmvekIYJEF7virXK9MnQWBDBEl+KitBsCeMZQysJ0GobbV2WQ8iQaXZPBJHahxBbVj9KpOKEUHdVzhnh4H5QHTjCCVCsNNAdfqwT5M7n0CXnCEikf1YQHyk1WcR1gNA2kHabi3Enj\\u002fYNvXpVAcnPzvo6+zdfaA+KsPoDc13Bj6TSI7qRgJdPbmCKZ8FtaE8e8m\\u002ftVlp1Duqw\\u002f9M1jL2OrZ35MQMxAY6JTIr+5YCBjl\\u002foHawmQ70Nz1Q5eq4OdE2H8mLUwvimzUO7HbVNERVNOMX2MTHkf4yrUiwlTS0lDFbP+qcSW0aMIWZy6s5yY8ueTKuUXkD8iynvHmNV01DK2hYzTq1sZ4pJo2HMHA4+RDNbXeT90ZpE7Q2X\\u002fNT39cV0BRn5ak+NRi\\u002fNYh2x9CBGuePdkLbJ7wclINVCgb35B6EkIrMfmv9IDP9w5WKcwMj6scUMKk8+CStXpKKInXcJr76vdI\\u002ffK8opnzKpKppcCo9l4nezB8gLJ2uIwKj2r0t5ww5hy4LSi8+pUxsAmnFMIcTPjJ6ljAy+bi5kCc6iDNo28PJiK7QNP+Z5ODurAU2EjzLTBSdKjdugT4M9\\u002fk+OAE6JPhINEE5sgHbpG18MjoUvqbCErMTOzgCVKndouU7NyRsup+IqTyM5Fkoln5gPWOFwG4j3gY+vkyVr9ZRoD5BN0mmFhwnP8AZWuNGu58\\u002fmu9DdrHkBEAofdUoPU5aQFS9K8kMvZ9AShGfqVx70kAPvLOj6A\\u002f1QPqo6Do7EAhBSIJXWY7lDEF7BjJiub4EQS\\u002ftxmiLAP9AoLZrFOicCkFPYIQjmRUcQXxILTuaqylBibWXVW4UNEFUG9Z2KZo6QbFNB\\u002fz0Mj5Bct7HcebcPkG71lpartk9QbQfPIsPqTpBudilHtdsNEHYD3pHYT8qQY\\u002fodLz4GyFBfcgtql2AIEHL0TCGQqMlQR5FDGIVrChB3fb3RJu+JEF9jUmQ71ofQRVx0EVcehhBe9rm\\u002fmjwDEHEGfivWM\\u002fzQHgTKY+krMxA9xDlhjtIlUBJxVOC8MpPQKfJ7lKNfvc\\u002feusW2xnskD8NZcyrOHwXP6Xc2yIlJY8+ZGmTdseg8z2wSRWHGW9HPXm53AdAcoo8yryilDItvDuj2PCJ2VHcOl+VT3bm1uo5lJ7GSY365zhdlwHQHzHUN9a6SK1IBrA2qSe8ZCz4dzVOMt5buOQwNDiRAHyTcNYybJO6B40WbDFI1Hd8spDwL\\u002fnx4b6MaWIuP\\u002f7R0dxWwyzkva9ziqcsKxFLBW51X5QOwlmvPhYHFBEpTlY7Z42SE58vCZeyMQAW9ZBx\\u002fd+jWhgvdA1+26WkGgBYLVcFKd4cSDV4sarBBB9a70hW0+saIYXJReV6cyAjkNOBryjyEiUEI9q1bI\\u002f0JlNKNvFcBsUoji7VXXdChCoXEk\\u002ftbGUyLMNNuudZe88tB18bMa1iWS9PKe1jp0rTMC8Tii6HozsyfrxvloOrkjPSsffDDs7XNNDA7xqCsgw2K3vZpetqMDdwJSYlN\\u002flBOOWO0t4+IUM5lRggv\\u002fRdNDo6Dbtu0pkWO6Tw8K4ZK+s70PC8++vYsTzm2lcivuhoPQ\\u002fYDU3qsRE+oFwQSkW8qD7snXSIGawwP2Z+r42Ed6U\\u002feipttx5RCkAugveXor9eQMjCQ5b3MqFAcFrluuKW0kBflm\\u002fTubrzQMvXBKdILgVBUYn9SIVgCEGEDzXPXmUBQdlw0KjHjPtAnvFgZH2CCUHcTWaj7iQcQQrrfAwCXCpBWk0dmR5XNEEfGNsBUtQ5QXezzh8knTxBdwE5wtNgPUGLWKWK\\u002fWI8QZsCpaReqDhB6xtSCSTnMUGD85QMFD0lQbwxdlJrMxpBpKJm87SEG0HAUozHozwjQe57dVbkViVBDH5hUeYkIUHkMkgqPa0ZQbtYm1sX0xNBgZ5ehy5aBkGbLzmnMb\\u002fsQBVEOUwHecNAlatWQ4AAi0ASipN8ostCQD+mcIJC0uk\\u002fqjCRBes\\u002fgT+U\\u002fFH3BSsGPy5Ud5CoMXs+DvZXfWyv3z1DYu1qbXoxPRzC3jckOXI8SIfiN27voTuBbTkSt6bAOq5lJu9HJ805DDZbwgIPyDjEM\\u002f9UqbayN4O9\\u002foDXb4s2K3gqjX\\u002f0UjWb4uJjfa4INEww3kktSa4ymjCmaZeCQTEE8vReixTDLzyCJPDJozMu\\u002fxqrF5Uuqywd+WRR9JAgDC3F32OA8q8OTA\\u002fLEvIGPRE7oXseKdq4EziepfXPDCQWSYUG4yl8fhi\\u002f13ffltbFGiwtlZOae\\u002f0cGDb8jeHAIh+9nh3yGns2IQA6azfxZDkjwHnolsAHKyWRp\\u002f16mRwLJ9wv9zjon9kowx8cJ3rSlioFJ9GHeidDLKJEhL+QTN4tyF5U3\\u002feVZi9VelaW\\u002fr3fMOu9+qlcCUUy8QDfK+5SmjOnPpaRjyrfNJT8aZeeiRE24DLvUX3xMjcfHhNE0QJEOL+Zx9VQWUU5gJOYzKUBODr53ZUl1msdOwUf59psnvM7oBWpDpF9uzxerc4652ZzPagasg9VwRo+RJbM84u2sT57WUbzwVU2P8BAlyx7vao\\u002f8LVfGZxnDkB+6IMywHZgQAaoOtVvFaFAB0DAG2Yr0UADA3Ep7QvxQC6yYgZDWwFBUiqU+x52A0FspNRecSf8QOxCRP1p\\u002ffdAZRvBJa0+CEHAENbIdwscQSPVRgy2pipBHIGMeJwKNEFEyxrGjn84QQ\\u002fqPf2iATtBHn9c5UxPPEEMxn0r5qU7QTU7\\u002f8lvoDdBNw032FlpMEGIob3k2hEiQZkn3p6lCBVBukDZtmqWF0HByt3HQM8gQXNW8tEksCFBzp9j0Cb4GkFBoa1qrR4UQRC4AFZatg5B7ZOk4QN4AEFQonQKJd7jQA5Xe1wUKrlAO728C4BGgEDTLS5iHBQ1QPjxCU\\u002fB3do\\u002fBLCfg3qdcD9tUtlx3rzzPnTHYXFsXGY+mxAE6JgMyD0n+Lbp5HsYPVSyP9fnjVc8FuAXrb1jhTultTPns1KiOn35Lw8Gmq05bfAVfMeKpji20\\u002f\\u002fISy6QN8RJHm\\u002fm5GU2oJCpbJ3rKzWxR4fVIMfgM7j2nQ7IAIMyLI6xka1IFDFJ4adPCXKUL0LGCyFRaxouk+c5XtMemwmBswDBQ6Y5DC7uDgvR29YOxHNxWzQyYxGZJCdYbWHeE7y96ShYp0YW9mhxYbXVnxgfiqmY6xPlGlnJhb4CTRodgdOHOLDsPh\\u002f6E0kpsiFRIYJ2ARbV4lEjrqmOhKyYQSUSmBtrD1AgJxHF24dBgOwohiuGyGZ2pyqdMD66wjNSLE0oYXVvnuotxrSPEX9Zci87Qr\\u002fOIt3nMApbWJCwT00yoaJfrOYLoTOcDi+JxeHiNIjGS2gSJhQ2hgWwOO4rNTf5rE762rNGOGSXAuE\\u002f70k5oDySUeozQDpTPtG1+OElOwtPXcYRs\\u002f47gFawC5B8xTyM1W3W\\u002fjR9PehyRjd6\\u002fiI+kvpy5lF5tz4pSYzk6n07Pwz3At8Ogq4\\u002fhywUCFcREECTQgS8ySBgQI6wjzI0E59AXhUG5ZMczUA39LdibjDrQKVy984WkPpAcE4caXKQ\\u002fUDeXjxBUg\\u002f2QIbruvLSofRAUfo0aywUB0GBdG2UItIbQe\\u002f0kyIKYypBD5r+ZJEoM0H+ob8FQtA2QZV5\\u002fe5dhzlBboOHp9SLO0FamXpky0g7QbGa3kOSDjdBUh\\u002fch338LkFgQsO7Ki4gQapD5dRpIhJBCj48EIcAFUG8bqEOBRcdQWpyhRoabhxBpd1bAruVFEHM5+LoURwPQTW3CKzzlAdBxzlJXMUQ+ECUxlFgE0PbQLwXb6LjJrBAd1SDU\\u002fV6c0AQo7F80W4nQJ9ZdY\\u002fwpMs\\u002fqQegoFaRXz97TfHjeEjhPqOE0pAuCFI+1VeqeDDasT1tZXw7cLoAPaFbGZvGnz08FCCD2hbDaDv8TBaW+IaDOnWbCAiQC4058liZ9e9dhDhVJg+WZ+1qN\\u002f31DjiLx0A2feTdrxK2AzUmVFvNL9O1MwknbzXSxlYyxdSkxadw5jAT7i\\u002fehOZ6L5X\\u002f7ay3WAYHyVjastK6tAm4ifm11R5iDImVC6bv2f0OoYBnhmkrhxEqnqF3IvIAFDMN06KLW2cWIdUR63dWvhgZ3WwwsJACG4Rq59syaTUdJiOi86dEVx+odNXU39NnIRkut5Uo\\u002fmYjAy1H2kboVCUa61ubIuoxJ548aMNH7vwo4jytRvQDtioQEZul2ZRfLPQf\\u002fciIW\\u002fUtRPxW1kdBey9AHmlkwW7wMNm7SCnJyFIyO3elW4x+pDMUi3Ch6aLlNFG\\u002fDwxwqxY27ID0EhSCODdrZqZhrX9MOK5Ybj5+I1I5srvPFFi9SDqM3dlQCFQxO8m8KGWgAwg8UO00k5cT0Dy\\u002fu1Osx4mEPThm5Ydu4yg+r7bSkcmKvD7m6vhqS\\u002fc+P9Hjc8ku0a8\\u002fEHZxr1wKD0ACvmJ74eNcQFgMUbjd35lAd7Ohnr6qxkDUnmEf\\u002fA\\u002fkQKvASHwjFvNAaRVMl72A9UAlBekEQM3wQLoqGYQW1PFAK+Vh7ZRKBkH9yB4Ej3obQf4B4PXJdilB247WHxfDMUGqKLMcTPk0QYzhbk6dMzhBVf133TrhOkFi3+I9yOc6QRSFHXN5ezZBCJyGSqWXLUFA8\\u002fjbuUseQcmRuNhV7xBBdeORhQdvE0E0Qdq0ORUZQb6B5JSifhZBToOm6SWFD0EH6lD+qvUIQeWeHjxc+wJBtzxdOA2I8kAebZYMY9LTQPI7m6i9DqZADjEjw4jWaEDtTgaXvbobQPC+YeF+Nr4\\u002fWuSsBMLBTz9wi7YMS\\u002fHPPvFHfXiLlD4+uiJAa0zEmz0yIarU1tvnPPmTd8dlXyM8nxYB5smzTTtY8oe6SXxlOqA1dULaUW05REShrqXdYjjbFv\\u002fUauRGN9WT4LqIMBo2k8gi5Cs\\u002f3DTS+r2leLiMM1WslyDAjysy9q4LB8w4zjD5JBBGAIliBCOZG7Jm3SAHhUPAWFvr3AkpmyZPS12HDA+lHnWRySEPTe937D2FqRGBtsXKX0AhFKsC7wJO+oUW3LhQgidi2hiHOWNmR9gdG1U4L4AC0E8dAFcqIBv0bx9uOh83WT5+IRQvOdtO+Xoj29xfPpmrZiWeGoRSjPRBJ+Yr5sgDzgop8C+70RLcwiqgzL9elQZpLFpmlhApV\\u002f8tzRj+eQGOgi+oOngZq9r0MFcLtDNWblYyBwergd53pzNu1J9KgpXoNMOLJWv\\u002f3xo2hptD8FraPzdWcoRwaJ9UOLueEPS0Ulw5AU59SYjCUzq7PUaYKxI7OzypQ0DR1hE8b8BtbbNh1jytP6Cj1pmKPbh2foDA4y0+iN54Ffu8vz7rhQsKTN4\\u002fPwi2NVlWT64\\u002fLt5j42ZkC0CI57K4GKhXQEYaWXhzu5NAuShZ9Es8wEANOp319YLbQK\\u002fO4H8D8+lASnPUmwkh7kD0WhqJzRTpQE4Cs0b66+9A7pBCdYIdBkFUHi2jzPsaQVULobqZ4CdBO372oGABMEHOrPqmyCIzQY5XMJVB9DZB1MszikwUOkF7YgstAi46QVQaDwotmjVBxtmXIFdGLEHJQo4ymmUdQf28hKXG4BBBWYSu9M9yEkGrvQvxu5IVQT1DyEkPzxFBjMaj2RRKCUHThah+oekFQdUbwhdH3ABBiEZ4rUG\\u002f70CkLJ7LXynQQL0mCnQe+6BA61j9S6\\u002fmYUAOn4umcZASQPXcE74+rbI\\u002f7EEWk7MNQj+qPSkdc6jAPsn5OpJuNy0+bp4GsyVIiD1KSU6SCRfTPNPGaKA5XAw8bWH8bGfjMzsKdBCQTVJKOhR9m+kvblA5A\\u002fNH5XVYQziLIThosHolNzT4Gw+WfPY1\\u002fPak6S8ytjQy72I6xatkMxi\\u002fIqbEeBQySe\\u002fL\\u002f57zrgGLc5nwgJ97BKdJ40vkOkcHyuU70pNoAgqxj9NF932rDKdMAfUsWEMPzLCzvvimyREqZIIW7wZAFNnIHZIU36IWdsUubj7w9BgDBvhVOuQ1G04rhatEkWUdiceelroFhB\\u002fL53tQDISRIQIyVZAE4YwjrxE22mhvdiVzSV7xNG1QJ\\u002fs3VvTyrBYpvzqUhn+FzSocvneViiRyLIUqIr2OGQUuTQtWgkJUhy\\u002fLLeOQwsP4MJVeRHgCulkybKapu2oFqzPm66Pak\\u002fPtNP5PCV0jGiI2PeS4MCzLRzeVupGjZmdgOFRjtdVAv2Y5h89tppLEXjrOvtRKOPJDOyMraM6ukhg8B5k4o8il3DwyHbjjEY2PPTynLHwOaDA+pme\\u002fmDQewD7r2bHI8PI9P2+TgKBDXqo\\u002fQ97V0VsVBkDfMnL39LZRQMlDt2Fll4tAI5X3HWx0tUBXeZCBTaPRQByEkBUb1OBAgaN6OAJv5EBmjLACIcbiQOT4PKz3yu5AOI5mSfGpBkGV8nBx1EIaQb4WqB0buyVByRz5X3AuLEFuTYjoj2gxQYKAMChlsDVB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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0AgHbzHhiEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQCFYNERwIQ9AmSuYop0hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0BML1yqCyEPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQGrsb7GnIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0DlhDI4qyEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9AdIR0j1EhD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQDwZnmN9IQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AlFvdRUwhD0DSw5hLLCEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQB6D3s6HIQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Axcnyt0chD0BiA5bK9iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQM4MYRlwIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"u7uf++cs5D\\u002fe0PSAMSLkPxAE\\u002f5S+PuQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002fQEgwcpEQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002flnzOUnpD5D8MQsHvNCvkPxrIavggKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP6jhY2yUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002f5OXkhpMQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002fu71xi7ov5D8MQsHvNCvkP5DOktoiKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002fu71xi7ov5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D+Uop4+FBfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002f2LWk47hTkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkP71runVCUOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP9lKWq7rM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D85zSncJS3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP5JdXovsM+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP2qI3WoiL+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D+hZttwQBfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+T9ri1kwzkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D86e\\u002ftxQhfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkP409VM4dTeQ\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPxNhQyesS+Q\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPxv3JX0eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D\\u002fX5YqTtjfkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q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qI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51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D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPxv3JX0eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D\\u002fX5YqTtjfkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5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mgWya3qT86oEpq\\u002fcCyPyeqAjD7XLk\\u002fSqSetkXOvz\\u002fXJUNyyn3CP\\u002fGcz6zp78M\\u002f0jdCMvDuwz85utZmv3vCPzjDZGXWzb8\\u002fBPXCq0FsuT9Yr7Cyh\\u002fiyPy4Y3JYf3ao\\u002flYgNrqIIoz+eN4RhSfWeP8aTnh81QqE\\u002fTVKNS2D\\u002fqT8lvHIPCDW2P6ulcnA1NsM\\u002fyKaEH3k90D805wvUdLDaP8PyAR2QXuU\\u002f4BjYlyaz8D9mAxYvD275P9kvrECYwwJAmB9\\u002fFB+jCkDPJcUimAsSQPzwPK0OLBdApwA+GwYLHECs9kofV9ofQMFZWm9L7iBArAhl1c\\u002fQIECDrTW4Ji4fQA9VYCR9+xpAylv7y4XPFUDCYgW+un8QQHcue1AwcwdAnuO13EaP\\u002fz95R\\u002f4KtnL0P5wSt3WVZeo\\u002flJp4VU3\\u002f4T+9XkWg\\u002f6\\u002fbP9sX1ecjgtg\\u002f6OUvFtLJ1z9UiDpKjaTXP42KmmmK8dY\\u002f4ade+yE71T\\u002fZrNgkNZHSPzq4ycMhrM4\\u002fNx4RrIMOyD9Wzm0ARCDCPybr55kfwbo\\u002f\\u002fwBZh4Xdsz\\u002fHP3yIGoOuP8yb\\u002fXGXmKg\\u002fSi7x27mjpD+MoH0PDJGhPxkMlvGsc50\\u002fhRtpYKzClz915M1uPiuSP8Swkbv1GIo\\u002fwu4XHeCCgT8WFWBDzON1P5B7dksQcmk\\u002f43NDLeR6Wz9W\\u002fRhVy45LP65iJ1wYqDk\\u002fvna44+gsJj9tuqqX1csRP4p+ELojivo+ZOm2Sux94j65nAEq95rJPn3z+VenSrw+yI5K1uH6oT7V40ovI7mzPk19JyjgINI+SJctQ3gv8D63XBoHyt0KP5SdHHmCrSQ\\u002fJNalb1CDPT8jPcQdVodTP4lKfB2O9mc\\u002fph7M0qpDez\\u002furziJNsOMPxGDUtJ2Ipw\\u002fMat2KHOEqT+04TILi3W1P7bENJyeu8A\\u002fhVpwKicyyD9qhRz3eTjQP5WSYYdgKtQ\\u002f0stVnfI+1z+29p54FdnYP3FydAmCodg\\u002fByNcdpCl1j+QUBhwV1PTP0kkg\\u002f5cq84\\u002fAu9I5a\\u002fExj8\\u002fIJVKxyPAP1xQClzSRrc\\u002f85X7UzDKsz+am\\u002f6OfyW3P8uzQLtdpcE\\u002f5dhsJ2eWzT8KGciP3bnYP8TeIU87+eM\\u002f3YZpErcP7z89aKiwjFb3Pxot\\u002faa0BwFAqQf5yb02CEB0S8Gsu8MQQAiaNLMShRZA1H8zVeAoHUBrdjPr\\u002fxAiQPHhlRNrRyVAL\\u002fKDfNqwJ0DGYo9VqtQoQMLhTfiGbyhARfd0GQGMJkA8n4OQuoAjQDqiltvuqR9AfdCxtdkwGEDVEEmm034RQJTU4ySWPQhAut6KyIJ4AEAnGMFsxur2PwSZdjUPU\\u002fE\\u002fPn+Lgwq17T92xDX8nXjsPx9NlwMXyew\\u002fPDr4rHcL7T9q97vXr0fsP+i96iBKKOo\\u002fZsREBHbe5j+qiz4tO+7iP29G8jr9490\\u002fO3F1NqfQ1j\\u002fL0tvrozHRP0jtRqzjRco\\u002fbl9G++HUxD\\u002f4k8DEJUXBP9FfMK\\u002fDh70\\u002fx72wniZauT8KWsqMOUe1PwGCANUqHLE\\u002fF8oB59AEqj\\u002fLX6u7Bo+iP19vlmvguJg\\u002fOgqpbrerjj\\u002flUy70MrGBP7WomS8r93I\\u002f04x84RTiYj\\u002fuMD5L8HVRPwo5L+3M\\u002fT0\\u002fRlB7diDuJz9q3i1CKsERPy4H\\u002fRtUpPg+wlIxhQoD4T5Dqa+M\\u002fMXSPj9QZaMwYbg+5u8iasqKyj4Yg7wIWizoPiRDh5ZRZgU\\u002f6FccFaOcIT+EfxHsBOI6P7gjmjAgBlM\\u002frlfEIRf3aD8yEpAam2B+P31uw+TgIpE\\u002fgSjWiiTtoT97y+QGUGOxP471l+G7Rr8\\u002f8NJYbeIUyj8+3oYPqyrUP9UX2SWp6tw\\u002fE6fxEQw54z\\u002fpCyAMprLnP5cHstvzFus\\u002fA9KeE1m37D\\u002fG7LAUVTvsPx+KPH4SwOk\\u002fRE696kbP5T\\u002fsBjQsrjLhPyWeN6tBddk\\u002fjE6E+Ggl0j8jYzpVn9bKPwtYp+9YBMg\\u002fQ9hc1pBrzT+6C+N14afWP9bWvCh5tuI\\u002f\\u002fO27piF57j9nWdGyOM\\u002f3P1CkZrJexQFA+sekqldsCUAI9IIrO4cRQJrdMWhebhdA\\u002fnyHaad7HkDU4X7e3k8jQAm3Jm+UxCdAZDFkvDxDLECAq4iYaiEwQICZnFN2kDFAjT5BuHAmMkD\\u002fVIie1LsxQKPJJv1AWTBAi\\u002fqyWYBtLECWEkC8jFYnQFFhY6xBKCJAqgmALcP6GkCVGyZk+G4TQOL3Ikss7gtAOv7hXMb2BEA9HZLfe0kBQGeP3Qmz7\\u002f8\\u002fJtMTi3IDAEBDEDVeGYMAQPdMq6kTwQBAfHF4uXRQAECJI20UZSf+PxXBnQeQYfo\\u002fTpG7SYns9T9J0I1bN3bxP0uw9mowEes\\u002fCir19\\u002fPc5D\\u002fs6mQMGmXgP0\\u002fs0m3Ewdo\\u002fmsGEVAGz1j\\u002fIHZxwba3TP+vwCPiq+9A\\u002fMezd3WR9zD83mgZwvNDGP7AJUHDbPsE\\u002f86x4EshvuD8VW15ZWimwP4ooGxOH6KM\\u002fXr8fa\\u002fXOlj\\u002fShTkRdEmIP9qoYYuoB3g\\u002f71XKe7IXZj9DLNzP3eBSPxECNrt\\u002fAD4\\u002fElJNG2gxJj+h872vfcEOP1I4yOOcPvU+aBq1es975z5iHwIXQWbOPlgqYAaKbOA+Jg13ubSk\\u002fT7fyT3APgUaPwEsvlBTPDU\\u002fxpJ5Y1kSUD\\u002fcjUlwc45mP4zO+b2CWn0\\u002f6n5Q1ZO1kT+2mXNiJdCjP3DxIYCpjbQ\\u002fsy7hpPvEwz+pTdZ2pqHRP7th9+8MKd0\\u002fUSdDMuJb5j+uPmc94crvP1OqIQFe9fQ\\u002fcnXlCWyf+T\\u002fPoTGEQQz9PyLObPhGiv4\\u002fjmUqcPHI\\u002fT9he2vD5fX6P+K8ik5qrvY\\u002f7luEezXN8T\\u002fkRShDsVvqP2UBQs3vA+M\\u002fSIVeMV4j3T9JqWYd6p3bP1xwLVaMoOE\\u002f7N4O2TVF6z\\u002f2R8QGLi72P1Vg36j1qAFAGcb26ADgCkDQ4+fcj3ATQNSDn9wTyBpAoInQjFCnIUCjocNMCmkmQJcv2FObkCtASOl8rNOAMEA1JROjAEYzQEqaVAvF6zVAVxBa6rotOEA8j22IY7c5QDaOtqqeOTpARiYulKKFOUBmxR1LAKE3QDaNRgPFyDRARvdCVC5iMUCpc6Eyb8ArQDzn1stZUiVAaiDFQ\\u002fQJIED8anadvlsYQNMmOHIAghNAY1SvD4QiEUAZP0SlipIQQNzLR\\u002fKIBRFAx4VrxtayEUAoWppCivkRQFCzrShLexFAcainC9UlEECSYqinak8MQCoHMhsZqgdADCjX4rAOA0CtzPVd3Q7+Pzcw0H2kuPc\\u002fS4IGRlwn8z9mzVavZwjwP7loPOSBt+s\\u002fD2YgeW5F6D+DfAShAgLlPyo1Q55IluE\\u002fhCX+ReoJ3D9wJKosVBHVP9NRIkhgqM0\\u002fvhuz1c57wz+dGr6v09e3P7F6YV81Jas\\u002fxotQIce8nD8YqdEvWUmMP2CX5ESq5Hk\\u002fJxTiC60NZj9NPk19TH1RPw0gr0fB3Tk\\u002f8JJbsKf0IT+vyNdswe0IP7mG0tv9s\\u002fs+2yOFa1Rs4T7Y3qzVjq\\u002fyPtBNvLl4tRA\\u002fIrRq9fEVLT9PpI5T6YlHP9iffFx1qmE\\u002fh8WXRDOWeD+vavNRc7qPP0mVfU1q+6I\\u002fbbTvb0YPtT+hM+yv\\u002f6nFP4BwmfnrqdQ\\u002ff7+CnW9G4j+ZtkpDAfntP4AsLQxEyvY\\u002f7gPT714RAEDTfmFVPQIFQKriJZfSeAlAvtCcYgakDEBiM3F\\u002f7d4NQMNp7rmj6AxApWVUaFD8CUAZsVDKOb4FQFzDyRUYCAFAWuVTugBd+T8UzmHu3rTyP9sv6xN2Fe4\\u002fT7QiDAtQ7j9vjK7Od\\u002frzP0kdWUn3yP4\\u002fthLiRBGTCEBjp\\u002fdoQiQTQHPtYAYCdxxABgh0CDsTJEDLNQF+XNwqQK+JwymhGjFAqlYE3fjYNECxDBPXs3w4QAb6\\u002fu0i6jtAhM\\u002fsqM4QP0DPZRwKoO1AQCrM9K5BD0JAPV9po\\u002frKQkDvN4\\u002fZ7PhCQOMFKTIPekJAGQto2NlGQUDLd0q08O0+QP1Lal\\u002fTfDpAk\\u002fHtUGPDNUBY5cdnl0wxQFoVUidlEytAtJER9FGBJUAQKNB3kwsiQMWVe2KVfSBAg1B6FcZZIED3S28tcfggQE4c2kBLrSFASvEuOs3sIUDeogbsymchQEzxodzWFCBAA4zvbixKHEBLKXoEdNQXQIi5YUwscRNA84oYbXw\\u002fD0CM06HPSEAJQPsNQlW\\u002f6ARAxtQO2HLjAUCjlSNHWWL\\u002fP6ocS5c3rfs\\u002fjeEx9M\\u002f69z+0arN\\u002f6gT0P58Ah1MBw+8\\u002f6tvwGvK55z+IibZP+5jgP9\\u002fnutC4rNU\\u002f36mCAwJfyj89dy63qt29Py\\u002fnPL6Lea8\\u002fVsvMbffenj\\u002ft2QymeTGMP66iSMs3\\u002fnc\\u002fyi9DPjQMYz+Kpb++hENMP2yji2ymvTM\\u002feMDlGlWxGz+ejeZhTxIPP7N5Yu8IXPI+P+5vwgWLAz8FSXO101AhP+ZqfOko4z0\\u002fLmdoGDX8Vz\\u002f+P5WrvNlxP6TyJKbeoog\\u002fJ6kf2s+Gnz9P34xNMLSyP8eYs0\\u002fik8Q\\u002f+gzKJND91D8Kn8IX5drjP8zS6jXUafE\\u002fGxFy+jtS\\u002fD88fvxb6VoFQA+dYzy+3A1Akp+Xx3lcE0CaqK6fTkgXQN9gAEwI+BlAnieSQCTgGkAxpM2ZgtMZQLBJ3yXUFBdAT5YDMGBBE0CDPVr1nkAOQI4V4VhQ3QZArugW24h\\u002fAUD4NqVx2fb9P9Ngm3u5CABAuI5LJKSJBUByL8mUxFkQQOiCuDNieRlAJybMjFJfI0BZeKAY7SssQA2k4GgjbzNAnC4MLH1lOUDd3l\\u002fg1n0\\u002fQElVz0Hvm0JA3zl\\u002fMfwZRUD\\u002f1fD\\u002f\\u002fSVHQOdQ0uo5yEhAk8H85u4USkBt0jmjwxZLQOA5XA0Ev0tApRKFihTlS0CEEgxHXFZLQBEhB28M70lAET6aZr+vR0A\\u002fk2HSEMZEQCffm3YPhUFAUNYWOAqjPEAqEsv99BM3QGsWUjhO5DJArMmsnURNMEBt+DsErmkuQC0iQ8Fhdi5ABcwU+YesL0BL2LjKUXUwQMkPrAHIozBAV9vy5OYhMEClaC\\u002frT9gtQFEnEBzcYCpALZveqNZtJkBl8YvPBpEiQPOlX+x5bR5ApXwgfm4nGUCOzLhQYU0VQAuUFgeajhJAgMJjjlB2EEBP5ont6CoNQPR6TaVzQwlAnVO7CJsFBUCqEZOop5gAQDn1e+81qfg\\u002fxFQ+qCoo8T9y6wR3B0rmP1U4as2M\\u002fto\\u002fGX0WMBF1zj+T6F\\u002fEwgHAP7BkX16IXK8\\u002fimdvTgConD+qYNpgb3OIP8Td\\u002f019gnM\\u002fN1lkeLMtXT9QsMAnj5xEP8yHAa3bYy0\\u002f5ooVRRS4ID90oqV8ycgBP1imlnQwyhI\\u002fUVrXVvh+MD9W08dzUDtMP2\\u002fnt8qOd2Y\\u002faV9MFbuUgD+2dXxVYbGWPzM\\u002fjn8WzKw\\u002fo05bFiHxwD+cELGGy3vSP3+7srKnsuI\\u002fBWpKPsGJ8T8rO3yAoIH+P8UY0Fz3mQhAbKXaOnllEkBS27AxOYMZQHQ7EpDtZyBAhYt9UA+SI0DfTYU2C6klQPm+\\u002fNTNQSZA4gHndehCJUAk\\u002fuOaZO8iQGb4aLlTox9AydZJQ50iGUDhq81cDZETQAqXm9zcsQ9AP8NGpzFDDUBjSZkxyYEQQBO08UkWPBZAo\\u002ftpYR1uIEDfOEYg7MkoQIrHYE2DUzJARJRPY7UFOkCflTmLDZNBQIpw8YPsf0ZAqWhMukRPS0CfnP2vVYhPQEBPy5ABbVFAjc+C1xSaUkD4vV9r8mJTQP\\u002fCnAl871NAhkhm78xgVECYuaRYir1UQCljbrGo61RAeSr99xq7VED2zFUqd\\u002fxTQG\\u002fZCa7rmFJAjq8Be8WgUEDkGcmzmJVMQJbnDLl8y0dAuKbC8QyFQ0Cr79xwrT5AQJ1YifKQZzxAnz5qmRGuOkAOf4vZ5ro6QHqKJxmurjtAd5w5YHGfPEBHjLVEl9M8QDeOpe4M6TtA\\u002fuYCVtPeOUCm7+c6egE3QIFcOkxgxTNA9kxaf1ieMEAiRivkPcMrQPSXromjbydAACe8N947JECJC5U1++EhQHKITIpd\\u002fR9ALbw9AWRqHEC8\\u002fbd1SpUYQJVHplDvYhRAY\\u002fP2HQkGEEAQKX6U4bIHQCjmLCwqagBAD8MbPo0+9T8j1KoYBKjpPzZ\\u002fjXwi59w\\u002fGRD+psRhzj\\u002fc5b5iydK9P3v+rbWrXqs\\u002fxP\\u002f7woaElz\\u002fgnK+X6POCPxCjAOIeuGw\\u002fdQYBHsCgVD8Nk519Sg8+P2gMDOkEZjE\\u002fwrVjuUCsDz8HsIcCfpsgP6zKV7CO5Dw\\u002fAZAsRs6DWD8D\\u002fKtIklhzPyAKW6ERUYw\\u002fGibCsSQ3oz\\u002foAPcbQS64P4IJtlDeNsw\\u002fH3Xs61+G3j\\u002f4jjdUD5\\u002fuP0YyoeWWe\\u002fw\\u002fMT4ECuaQCECdv2U\\u002fk6UTQGnRFHAJJB1A6U7H67gKJEAAvjZ165EpQCmj1tB\\u002fRC5AEIPeem2hMEDA+FItf\\u002fwwQLlv+l5PKjBALHySx9jPLEBQhz96XksoQHE97QLzyiNArbbLwsY0IEC0+xSZW2EcQGnPo5dJZRxAqlTA2EamIEB9Nvae3h8mQJjeJwcocS9A1mbNEFjDNkCuu3VNFUJAQFP0DlgMdkZAiyfToKeqTUBQBXQSgZ9SQDcRbGW7MVZAvpFEKbwqWUB+Z7Dk1lBbQJTrgdypplxA9YIwPXpnXUDxKR1VDOpdQAqIhnWwdV5A7lbaJkcdX0B+wVc8UrJfQAFMHogU119AcVUD5dsnX0DI7ZHepmhdQJJKcd33olpAPr4+zP0nV0CPlajqOHhTQJYvGYd5HFBAGXrRlbP\\u002fSkD0yQKi7K1HQDVGKcQnM0ZAUkvEtlAaRkBBbswmO7VGQBrsykMwUEdAzqsxVFdfR0AAhsRGp5pGQOusAsPHAkVAqoaNZv7QQkBerek59FpAQL35A7T\\u002f6jtAo\\u002fztQw++N0BLJbmREGo0QHZ7SwBd6DFABPTXDyIBMEAZXjUIzsssQLcNoCYVnSlAHqSN7uUfJkCIaXXweEoiQHe\\u002f6QAdphxA8maz28YcFUCJacKOIioNQHMTQH4I1wJAWMnPpPG+9j+Mc6lIw6npP7BEuxudE9s\\u002fnd347ta+yj+jy5gg5cS4P3cAHfzRiqU\\u002f0d7OfTagkT+wxaEE\\u002fzF7P8\\u002fFB\\u002fjc8mM\\u002f0x58ZhLQTT9AXarAh5VBPzDSt2Fk7Rk\\u002f5LJyH9b8Kj9MsaeDr0JHP1DCpDamkWM\\u002fRA1sOaGgfj8NWbJ9OjqWPxHQr5gu6q0\\u002fiyMaPlGqwj+W02pT5pjVP76imAbWK+c\\u002fSfk5nw8N9z9TaUmSX0MFQN\\u002fFDQ0EMBJAP5H9cCraHEB9c6A7OzklQAEBOycd9ixANQ0PRG1VMkDFnZvWbIw1QHh+FQbXiTdAPOs9swfzN0Af8mxjLcw2QL+0SipkezRAfo0qb7apMUBA\\u002fA4rhyEuQC\\u002f5Xv9umSpAREn4gwKOKUATZBQG6oIrQFpXPDuzeTBACH8Uw+tPNUBJs+Arq9Y8QGUghLtL20NAcz1T60kxS0CSUwVr4SxSQMasTDXrZldApBHWhuHJXEA6lPUQMd1gQNUwqKxk12JAkpD5IQQxZEAXqCNyOvhkQBqRitZXZmVAYe+m\\u002fjXGZUA2vQvDnlBmQBM\\u002fcbDUDmdAOLWyeabRZ0D0sx+mTkJoQKHP4wwzBmhA1oosP6jlZkC315gpzORkQOVTTculRGJA+XYHErXbXkAdwM1G\\u002fZ5ZQHzvd\\u002fc9f1VAVfMTJrjPUkB1TKSfsoNRQF1u7gtqQlFA+5aiMdOKUUDRLBRYttpRQIX6T1uszlFAZ1L\\u002fihk0UUDspnGM3QpQQLWXDjrF8kxADnE2frxySUBJi1lsuAhGQAILcOpNCkNA7jQ9+UOgQECDFKNgPYo9QG1vBPY5nzpABv9z0a0LOEBIAu2ttGY1QCjI7lGidTJAVHG3QJRzLkAwlBE7DcsnQE01x3FWgSFA\\u002fXQjiYIrGEA5AkL4FUMPQFC1qDxR7gJAFdz3uzN69T+X0M0xGNnmP1S72PSg0tY\\u002fWI+3rGBwxT9\\u002fuEkVrPeyP03kzGT7pp8\\u002fFh44RjzziD+CLQ0Hc7tyP\\u002f3Czssou1w\\u002futSxKLlCUT\\u002fJ2AzHG4MjP5iKIiS9KDQ\\u002fccD8Jww3UT8Reo+BZbhsPwQGBhZ9SYY\\u002f47D\\u002fAhgKoD8JfOuCD2i1P4EYW+yLfco\\u002fwcBPVHdl3j903b+5mCvwP9FUhH8C6P8\\u002f8EWUXWIwDUB6GBhNRcMYQBLtXF+meyNAiWlVSfpvLEB75IqFmkEzQOOClyfvNThAxIV6\\u002fUFKPEDTc6dsX8k+QLRaKRB5VD9AgFqC8MQMPkBO+ia3BZE7QAvITCxjzjhAxlYStI65NkBFAUsrQhA2QHg9yJijPjdAu9BKuzhzOkCg5qyfZdY\\u002fQEYnXl7F40NAt68fXkV8SUDjn7aU2pFQQONdKDQolFVABL2OKIK3W0CMmhu+aU9hQJljdmXlz2RAtISITgf2Z0CTsiwF0GtqQCxjn8XFD2xAuVqoxHgGbUC60y05E65tQH4Me5VadW5AjP6ZZUumb0CqXaStTp5wQLFAFJx8bnFA6NOtN6r5cUAI2cg0QPZxQHtwfsL1OHFAvXE94LuNb0CorCJ3GqlrQIavQEImaGdAV+1mFy91Y0AiGb7Z9FBgQNen8PYDb1xA6ENathQ8WkDIC3D59YhZQPZ0VMGSoVlAmlAkL63UWUALeX1OIJ1ZQJOZqrHEtlhAUruvdoMeV0AilPrxdAJVQJu1JjcDqlJA4cWlr\\u002fVdUECLYOykeKtMQOEvJ3jfWElAgYZfFbW8RkCMtypsFZ5EQNT+lQe8rEJAmPa66+CgQEDwbiF79qk8QJ3wlBjQnjdABlg1kCJzMkA0E0\\u002fAOiwrQGhSSzUszyJAC9nZcHJyGEAN57nqO9QNQBJD3ItxGAFAsGz+Ntpt8j9ZXnLLY7XiPxvdls8c6NE\\u002foTGy3rUswD86qBCan5erP4GEimykPpY\\u002fGZ4fLBsWgT+mRZyBldpqP5aX2BgcaGA\\u002fgIUYlLgBKz9ubquAQrE7P4Eb5tiPbVc\\u002fNWE6DElgcz83a9wAydGNP1vNvwLmR6U\\u002fV7R\\u002fkzIqvD9KQhWeKUjRPzjeUoRfquM\\u002fhMebDCPA9D8NPCnGyE0EQCRhw8l8bBJAiIrvtKACH0CEXf5JgDUoQMnsTnSIiTFA747AYzyXN0AyDGW+5X49QEb0onN8LEFAcRGFjcSyQkB1IDwDayxDQIFepG6tw0JAGCP5WbPsQUCca12ktUNBQCTni3wrXUFAin56lICcQkAS1phPhSNFQPpVFQaj4khAeJfE7ojDTUA7zB5Y\\u002fuxRQDpyVREiv1VAuRfANEGcWkB5mh3w+11gQBcr+A\\u002fZC2RAgdM4488iaEAxes\\u002fC\\u002fz1sQKwh\\u002fsmO5G9AEj5lGjFccUDCbjpchlFyQGAkUcpL9HJA2x55hIKIc0DIv43WSlZ0QKT4b3xehXVAqGH+wtECd0Ctv5M82354QNGqIcvphXlAZOquLXCreUDTqFDlF7R4QAKQT\\u002f42rXZAU1gH5GToc0A3XTM79N5wQF9IxC0KFmxAfpa+rkqMZ0BgYom+UHBkQH7GK01bsGJAseGvom71YUCvVjoy3MlhQCD12jidvGFAJYW1Za15YUD8\\u002fti1BdVgQOHY4U1Bk19AOBhPHjrgXEDjRvVZoOJZQPcnOuYL8VZA15\\u002fgC+hPVEBms9Cg7SFSQArvnlvDYlBAtl\\u002fNYuTaTUDPZrKnvxpLQCs3b+T2J0hAhd+lRbTUREDSJQtiFi9BQH\\u002fTOFRy6DpAjZcUzTXmM0Cj71OEJ7srQDZyH4+JMCJA4Q2OkkR3FkCrUGSIZyQKQAldM\\u002fmbr\\u002fw\\u002fTvXayui07T+OlXJyHgzdP6ZN0QHS0co\\u002fIu\\u002fvQWlgtz+4kR1pST6jP3ySP+IELY4\\u002fSIqEPDxBeD8iHgc2nR5uP7U9sOWXNDE\\u002fMmPMoJ6AQT8mu7jot1NdPyhyQt4DC3g\\u002fjw6R1upXkj8CA9KhFfapP18UvK01CcE\\u002feVnYXZm71D\\u002fQXg9g3GXnP8enrdS8fPg\\u002frWPBgWvEB0A6DT6JjmUVQO9aJaXP3iFArGaAf+izK0AdAfGjsPAzQG9fInzkrzpAix4G1gWjQEAN9ppEqmVDQKL\\u002fNf6JS0VAFFmyiFZJRkBA6kGjJ7FGQDFdiSKsJEdAx400CYNmSEAxvRjPOBtLQLPaadgzk09AA2K25tbaUkD3EvdX1YxWQGzbaqaVo1pAGeIW6+gCX0Dg6YDU+ONhQA\\u002fyPTAFoWRASanYyfviZ0BNEAAYD7ZrQJ27tZ6I7m9AQUyDwmUUckBu2VxhefVzQKlUUv25bnVAQUn9Qud\\u002fdkATSsrLO113QJpenWAvXnhAWEchstDVeUBPhffl\\u002fOR7QOkQiSpTXH5AbjFvyVlfgED3lyStczOBQEe5+F4oYYFAi6jXDObDgEA455xdN9N+QPbrMeP0GntAK23e2UYGd0AJGFRGLThzQCXefa5hJHBAvcTloXb3a0DdObMXwGFpQF2JTXyEGWhA0pQmVEaPZ0AYJX+eIDxnQDlF9ycYvGZAFG\\u002fYWaLYZUA1KoZ16YZkQN+ydIAo3mJAYVoJdO8JYUDZHIPOYXZeQErNf6utNFtAADE6eVp5WEDGJxxZhj9WQALxKhthWVRALyN+YVKFUkAtAcQ0volQQCcAhb4Jl0xA5TIl15+rR0B\\u002fzynp8qJCQKKWE5qVyTtApk2lJZCSM0AoVdYwrQcqQI2cV0EwVyBAEk3p4bpfE0BHwEuRgLIFQPI4qmye8\\u002fY\\u002fYllJwRzs5j8xPIW4CZnVP0iZPAMfL8M\\u002fpqTVDh0TsD9S6VYbqq2ZP\\u002fPYHhq8I4U\\u002fzr0wNlPBej9PkDIIiUU0PwuAS4AJbEQ\\u002fjtf9oHnqYD8FYDy3Knh7P00fcnmwxJQ\\u002fzEXfGwUjrT+68Nr2\\u002f\\u002fTCP42A2nnX4NY\\u002fdxIasqib6T+GL6CKF5b6P7CyNVzrmglAy\\u002fuSq73hFkDJCHxpNvsiQCcqJtTKQC1A0OLiVBr3NEBqXZXWbQI8QHgXYEOEgUFArfvlLsWcREDvpwO40yBHQGa\\u002f530bMklAz25mUdZaS0CakEymknJOQLK+yJ\\u002fKr1FAGvKI\\u002fWdiVUBcrbZKjl1aQOOUFdqJLWBA1SEMJ4NqY0AXvvG+iZtmQNsZGjyLjmlAmtc928BAbED9MpkytN9uQMAksrOi13BAb4IhdIFvckCf\\u002fIqj7DJ0QJNulDAp\\u002fnVAC2xOnNOfd0B7HpvzQ\\u002fh4QFIHWltNFnpA8FBaRQQ\\u002fe0BT3fGNxtZ8QCOXBKI4MX9AKQ4xm0sugUDtdZeDSwGDQAl4fvz0uIRAKQrq09PohUDdillQxzWGQOiDnj\\u002fxdoVAmh7pFk3Eg0CcEUlnTm2BQELJS5\\u002feuX1AXy4CEWPweEDWOP\\u002fhGwl1QCq8GIG8O3JAkiNMHF52cEB\\u002ffgWEAfFuQK2F5Oii4W1AgCRcNBQobUCK8KQh+lNsQHhBgDilJGtAUs0RjHqHaUA+cPR9iI9nQFRPgealaGVA6bYN07NHY0D1wQnxLVphQBzugYgPcV9AcQZOqiq\\u002fXEAK8o1k32daQBf\\u002f\\u002fPceHVhAeQCFR+eaVUAkF7Wp\\u002fMFSQNLLdsbsQU9A\\u002fnwdnPTTSED6fR7KmblCQFjkfWpswjpArYtSwYsYMkD8nrJlWCYnQM4upjrS\\u002fxtAyZg2ihz\\u002fD0DB6CFf+0EBQAnBqBqMjfE\\u002fsRERoGLQ4D+TABjocVLOP1Sq+AYdy7k\\u002fAzKOsrMCpT9CFjGzI+CRP8zLsM16SIc\\u002fIYCoc4x9Nj8tHpNHU0dGP\\u002fjYQQ6iH2I\\u002fnaAXEBgKfT9Y3NMkYbWVPxJReVQ3Jq4\\u002fW\\u002ftnYQtvwz+TM6PC5z\\u002fXP1KwX65hz+k\\u002fYrgC9eGW+j8cJ07ZCW0JQJgVwPwhlBZAVxsw+oGiIkBTO9wPtqAsQHGkWW2GgzRACT7q6QuJO0AnGxRg+mxBQAVZwCVwB0VANBkjZvqcSECjd66RCJhMQFviiQBk5VBAg2GKn1GkVEAQGWhf+QlaQDW1UaoWrWBAuY4YqL82ZUCd1ETCElBqQC1XOhH9dm9Aa6knMe4OckCXtXk4su1zQOtC5f6lSHVA9+QZP6A6dkCDT7e4\\u002ffl2QNiyd0ZNvXdAqlw4SJ6heECuv3yl16F5QA+IotvppXpAuvMZY5eie0BWzeSz8rZ8QIU\\u002fJ6jnMn5AYY7ABy8+gECsaCZLHu2BQGV7Jb1mHoRAAAVx3GOQhkAtvSzumNCIQOkJ0KW9W4pA3nLfdCHJikAomrT9L++JQBHZ3fxS8YdAZzXPrt8xhUBrREL6ny+CQJOLQA2rvX5AqsZlvW4aekArAFZQWLF2QEtX9d+NbXRAqT2losMJc0D1wO8WoDByQFBS+m+Vk3FAyGzDjN32cECybUD1yTVwQFNSf15whW5AH\\u002foH4UNIbEBb5Zal1N1pQCTATzFGfGdAMkVRbUVTZUD6Vuuv4ntjQDxQiyXB8GFAZCnnu\\u002fuRYECSX+X502ZeQIrn1XM9XltA51gZOAroV0CkWWdLkxVUQPklefO1IFBAB8vMdk+oSEAx8sfT7uVBQJAMR3lOoThAm7\\u002f5p8AKMEAGjMuxDcEjQOHQsWAp9hZAcvAGRLMnCUDhXs6cxu\\u002f5P\\u002frYode1Jek\\u002fKZ\\u002fZhDzy1j\\u002fhlnAA99HDPyKkCNI5mbA\\u002fb\\u002fEgYzzVnT\\u002f8yORpKl+UP7mI5DILIjk\\u002fk8OUvWXeRz+2hugBu5hiPyGoQz54+Xw\\u002f6y7gveU4lT9\\u002fOoPauAKtP+aUlTY5dsI\\u002fjGo17Cfa1T\\u002fGy9G0PgroP6kHHmTjk\\u002fg\\u002fylXdfNhbB0D9tnWIoacUQLiqCry5BCFA0xtQ4UYzKkCAkMzeIOwyQAmMaik20jlAYTKLGoTSQEDdbPoBzUFFQK9TAGIvkkpAdtp3G2fDUECuFo3zUqJVQKXEIDVpjVxAtcslrEsGY0DEarvM1iJpQGzfceChMHBAxHt8ITAXdEB0OYVHndl3QI94IUWaBHtA4aFLoJI\\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\\u002fvjVACKsdts8hK0Cf0fLbZOkfQKMPRuSSqBFALgCzLFFeAkAvrYiCyPbxPxjkODp6lOA\\u002fvZKEPfxCzT8V4182GrG5PyEsWDvlTqk\\u002frJf9XN2Ooj9Q\\u002fDUUMPVAP5upeXmkJ00\\u002fdzhuusxLZD\\u002fE7ciEqVV9P\\u002fu4c7DsYZQ\\u002fzPHG4E7Vqj8gWDGaJp\\u002fAP9flJpD3S9M\\u002fE5ABqjPw5D+xtNctQDX1P0FgmdgFDQRA6Q2ZKgi3EUABu6I8v1MdQAlav9hG2CZAGMuLaP\\u002fcMEDKbQj2LtU3QHKkw\\u002fwNWEBAcvxPQpUbRkClvHUC7fFNQKiorlgGgFRAA3EcftheXECTjAssnKpjQJfT95AL82pAJ41ZWNABckB+OvlmCTd3QALZw2gFqHxAfUqhJODbgEDP6CuUHuCCQMFGD150IIRA7FnQbGqDhECa499uTB6EQDIEXnu2LINA9PxrUoP7gUClo+h2Z9GAQEccF9MDwX9AoyQpBmCNfkBJD2VtEzN+QN8BUeCP535AcWtoX7F4gED\\u002fAdGk2keCQMAqyj4g5YRAiriHASchiEANEfOzJZGLQLJKoIw8n45ALlLMc5ZYkECHUIkosquQQAOZGr9HOJBA4Iiu8yEyjkCSChi1uxKLQOFOeWFMoYdAJw25IGtbhEAc\\u002fYKDzJKBQE5DCaNkz35AvP7qVAmne0CQ9JnME3N5QMsqbzmk6ndA2fanL1XHdkC\\u002fFh0Gi851QI5Z6gm21nRAVML2pWLKc0B8neUr96dyQF\\u002fRrUK7fHFABPtkdeJbcEA04azKLaluQHu0OYtV1WxAGm0v2OIoa0CBEth5SXxpQPI4nO5aomdA1U7nnb94ZUBAoZwNRfRiQHKJpt78JGBA47t+TxFjWkCuKb++P5hUQI+YV9lwlk5AIEtUIfmKRUBbJUSE17I8QIiBFY6RCDJAp3gKhhVWJUBcs3O0ZbsXQOtNWOEd0AhAfp8SkiJ0+D9HQV+14u7mPyyFbWyBANU\\u002f6dDtu4UAxD+Yy1dVAIy2P9CHuUtvB7I\\u002f1KyDUKjIUD9Qtwh1WFxZP9pF07lJV20\\u002fVc1J+2Uvgj+T\\u002fAUV1EyWP1jX8Ov5n6o\\u002fDddPhmWuvj\\u002ft3k9NrOzQP0dx9IUrweE\\u002fQzlOAIyg8T9RbwKlsIYAQPDS1lsjRg1AJ27e6MiRGEBUJJUyB6gjQOMBmxe0PS5Anv8Bo7ycNkAY6lige6BAQB+uHOcISEhAWGorg\\u002fWyUUAEHkp4br1ZQJow5fFGkGJAtKTR1MRTakBUlyupIjByQCYhywdeSHhAL5W6iLEef0CyJ5V5YwyDQPzbHH0jNIZACnt1Qh2biEBTMaiPOu6JQGpjKgH+C4pARhfB51EPiUC5VbGGtUaHQNOLwTmuG4VADOADr9\\u002f1gkDde8EWiyaBQAVWiIkmx39AZC+KIrKffkBAYyV2eQ5\\u002fQEa6IE8Qo4BA4SWhRA+xgkCE6xtzDaCFQIRQW06FLYlAfTy3Y3DhjEDH+a51RRCQQKdOA0soKZFAodvWmoOGkUBTN7rjpRqRQLnnLKWhAZBA8T8B4cnrjEB11Hyp83iJQCrg206BI4ZAvAHCG3o7g0CzGoH4IOSAQF2aHreVO35AWA9VaF2oe0DiK2WXYdR5QNmOhWOHgnhAhy4+7tl7d0BKjEVpQpZ2QKwDEfjsuHVAobn+SivcdEBOYqF76gN0QGrSCJDiNnNAWh6JH4B2ckAFgNA+yrpxQJsrQxUt83BAAVacSfcKcECEcutu9t5tQKHAdxzIK2tACBgWZHn+Z0DcXvzvLnNkQKcC3MvBu2BAidv+PDYrWkA1d5NgK3lTQCsiVdtce0tAOVG22M1UQkAwof4q5Q43QN3tv\\u002f4pTCtAQ3VzqL9lHkBIm1M1seYPQNnqFUTHyf8\\u002f4ctbB2St7j+55Yqhpv\\u002fdPyQqSUBTK9A\\u002fIRUUhcRexT+EImzn+obCP8oCedM1D2Y\\u002ftWqr8lfmbj\\u002ffVF5b9iF\\u002fP0UWo8PXlJA\\u002fOL2KkHhRoT+AElggcKGxP9IM7Q0Tk8E\\u002f9z5cSYUy0T\\u002fbZk9p7X7gPxmGvy9O5O4\\u002fXhwjRp0c\\u002fD+IKLjPVswIQF5ueIlKNhVA0XIu4VWmIUD+WQit1bUsQCD\\u002fLy2P8DZA1+zByVYRQkBtXyD2\\u002fBNMQAokSw\\u002fje1VAtLbjEUUgYECcgzWZuKJnQEmK23l903BArwMS8qAod0B369rxL6x+QDnJ\\u002fmGLd4NAcfIUDd6Zh0BkZDZb\\u002fkGLQEJzR+5j8Y1AeJq7S7dGj0AX\\u002fm5awRqPQCyemaOVjY1Alp1pK+f9ikBO85u9M+6HQHNrJbmK44RAyqTy7otLgkAOUCMG7HGAQHkVYXBECX9AIXiDOmU8f0CrNoGWfcuAQN5rKr3qBoNA55ibcvcqhkAr1Vp0geWJQLgkBM1Wt41AWm2ltHSDkEBZf79Fr6GRQBTlvWDHBZJAc76MYU6kkUA4zhjBQJiQQJ6qtgscMI5A5NXtp5THikAxeV7nq2iHQJX6hV6wYoRA4q1GHczegUCVoA+az85\\u002fQAeG\\u002fIS\\u002f5XxAGELUF\\u002fHYekCNiC8MZ3V5QLKDOqKGiHhAP79BzV7od0AZ9ZYHUXh3QIupcgK\\u002fJ3dA\\u002f+bxnXHrdkDnkcSw3rV2QK1gQHLzcnZApoKFG0IJdkA\\u002fP5cX9F51QKkqcD5mX3RAd59HAlD\\u002fckBx\\u002fdOPHD9xQPNuMmDHV25AMFAhH+i9aUA2bqTldfZkQKkorFY3UGBA1ewUO\\u002fopWECH1VU\\u002fU\\u002fhQQJEdbtlniEZAQzY2khU4PEAUgMcA2aUwQJudvRAUhyJACUG8USyQE0A8ARjQmekDQAYYU3UdQPQ\\u002fju8Mvj\\u002f55T9vc5QrEKPbPyYHnM3zIdU\\u002fFuU2q6Nu0z8FqliP0eR+P88B\\u002f4t+B4U\\u002fMr6Dkef\\u002fkz+arkME2cqjP04\\u002f3PugzbI\\u002fkyrBc7L+wD8F98iazXLNP82nzcrxxdg\\u002fy8wk8UOK5D8DuWLEfQXxP8ZFm3+Bd\\u002fw\\u002f1ewRO0kXCEBLy16Ty5EUQFGnjvK8mSFAXEgx49vyLUBVh1GQiSU5QPOgp7eftERA6HymBS2iUECBxkxNXPlZQAhIdX\\u002f5p2NAs8PEU2TGbEAKD+JReVV0QHhQIflLsHtAHHDrGxAfgkDgVrSU672GQEY8LJEGS4tAo3WsW1lAj0BW4NDztAqRQD5yKgG0sJFAphOo1+98kUCYoe1v0ICQQFFmsqUu4Y1AFg9uuJ4rikApvDjBIXuGQBo02AvqUoNARdzUzbQPgUCivzKuaM9\\u002fQJGGMe7V5X9AlIAgoEMygUCmsyrA946DQB+VcE3h0oZA5iKEYAShikANklmd6XeOQNlrbzHL4ZBAYVNzvVT+kUAygVH\\u002f8WSSQL0iJSshC5JA\\u002f+IZlxcJkUA\\u002f3A4+EiCPQBG4CtzEtYtAEP2M6uE\\u002fiEBh\\u002fecUJxCFQBIkTNqlWIJAUp7FzJIvgEChelBOTix9QFpzzPu+\\u002fnpAOWucUkqseUBQA1yhSQZ5QFleICbm4nhAS\\u002fIW2I4geUC9xJFXOKJ5QJ\\u002f+t2umR3pAFHbEo\\u002frnekDrBHkuIlN7QG6zsvW4WXtAhJijhcDWekDI3GpgRbZ5QIS\\u002fz1Mt93dARWn6QK6pdUAHHuFctuxyQAtnL88K1m9A2jLHDxCuaUDoLAHpBcljQC3\\u002fJp+zAl1AXhngIt4rVECeGyjAiIhKQN6Avlgze0BAbLXrBmhYM0DH+9HrAo0lQAhT8KwJFRdAfxsSQ5h6CEA+N8TWuSP7PysRWbCs4vA\\u002fOb7Tx4ew6D+g+AuJsfvkPw7e2Jwn\\u002feM\\u002f5bhT3Ky7lD9GeYeQ5uSbP8SqyKf966k\\u002f893AJofvuD9PIAJAW9PGPy2BduoQnNM\\u002fhclVJ9qz3z\\u002f2bNAa6VPoPw\\u002f7UPsH\\u002fvE\\u002fYQlTvPc6+j9JpvmcjmADQEPszWWVwA1AyCSwHeH7F0CQKJ1\\u002f3RskQDLS9dLuKDFAb3NtYwQoPUANKkT1W0NIQGT92AiTllNAApzUhlGJXkA+UygADfBmQNl4\\u002fpbumHBA5Pywy\\u002foid0DDqNPQWQ5\\u002fQH3anRzDDIRAxGRDMaDciEBeCz\\u002f8ZIuNQM1mysaRyZBAJky6QPE1kkD9wDsYNtaSQH+4TDthlJJAM3uo+CeBkUABGO4Hp6KPQEPjn+5YootALUGcLqalh0CoU0zYIzyEQA5beKJWzYFAJNcnlL+UgEBIQ+XUOKiAQKkMHNvi\\u002f4FASKkxDd94hECGo8KAFtKHQJF4GiGKqItAC9z7MEF7j0BPGih+\\u002fl2RQH6pl3lfdZJABJV3HoLZkkBq2\\u002fd+eX+SQMzTK8JMfJFAWGf7ZaL5j0DyO7lMcnGMQJTS1hYmzIhAIavrd7phhUCPsDNJ8G6CQEcfMy5WFYBATqB1WT2+fEB64aJ4uYl6QEaNjhnPaHlAfvepGnUveUCIfT9GOLN5QH79LCMuzHpADFCFKyZOfEDOdX6sOQB+QB1TeiyYmn9A45wsiS5ngEBeWeIYKqqAQP5xRDKhfYBAWhX4sI6vf0A2sWV2KXt9QBunon67gHpApi+QuJj1dkApo70WlxtzQP\\u002f57Aivc25AlI\\u002fU348nZ0A2PqQsJr9gQLw9Ys8O+1ZAGVYMeAzdTUBuajKSm19CQDI5shnLejVAHagDzf0bKECWep87PaAaQNzXujchTQ5ATDXj1frsAkCIRy1fDiv7P1Jsp6\\u002fOTPY\\u002fEwiSxxsw9D8ZcnnZo5bzP+lSYbQv26k\\u002fMjpOPcpDsT\\u002fIkp3p76i\\u002fPypxIc57Bc4\\u002fopPvWk4H2z9uyJ8AoLzmPytykVp43fE\\u002fr1DacJVZ+j+O3DMlNmsCQB72vuMD1ghAA4T2fpOaEECLvl\\u002frBdEWQD\\u002fEt9DEnSBAgLuzDqDWKUCqf5rs\\u002fQU1QJb2KzBxWkFAJMgEym9RTEBDbBeqQ3tWQAw7w4\\u002f2O2FAZsjuhZ5yaUBTdAJjTBdyQC6E7azSyHhAIRV1ecRdgEBRRx+cFdWEQMTpVny3h4lAe\\u002f\\u002fhcwISjkDcApNHMvyQQOe2vqgsXpJANUKUL9v8kkDTMymatcGSQLZvfy5mupFA812gvdoXkEA45tIfNEeMQEmNgoFrXohAMwBNIsUHhUB4hgxO\\u002fq6CQMSUPdEzkoFAHqTMe3HGgUAzOXOEyD+DQH6EvL\\u002fR1YVAVt4jFc5CiUCc9kFRviGNQFpGcBRheZBAaE\\u002f0clwVkkDx3j+QVSaTQPrzeAlzgpNA06pmdJwdk0Ar8P51mwqSQLLsSZFNdJBAlb4aFmMkjUD+I+iLWTmJQBsowa1giIVAefiyBZBYgkChjBCKcKh\\u002fQLOB1+AzFnxAR1uAFNXweUAyf0UeExZ5QGitwuaDWnlACeuNGe6RekBU4PQbIY18QM23YCVREH9APvmrDEjlgEAdIwZkhiuCQL1dDv1LJYNA1Un0LPyjg0Dyt9TN5YiDQByQHwqxyYJAEIY\\u002fmAdwgUB8OZpdnyx\\u002fQG3gIr+JxnpACDx2CuMJdkAGexvkjFVxQGtMQsI5\\u002fGlAqr8K8yaEYkCKTMLMUwtZQJ2HJojMEVBAEJ1ca4udQ0DYVxrFL\\u002fg2QKwOtYRHVSpAxWE1nYnAHkB2+G1jj18TQH47Rn2wnAtASB5+ADdIBkBAkPdQdKgDQHgITAU0YAJAYfwnynf6AUAgr328mbu9P1Ez1ZUrusM\\u002fnIc9DmDj0T\\u002fSVSj6ycfgP2JfGUkg4u0\\u002f8v6g3WHU+D8ZcczETzgDQK8N3ag6zAtAcTYHSh\\u002fmEkDm8BrnVXMYQBRhC53iux5Ajzex8bBgI0B9W9vQEXUpQJckfy5n4TFAcQvgrIPHOkArxb8tpc5EQI9B2lBUQVBAmW0AUFj3WECLxhscRJ5iQOkyTx3U1GpAhYSiXkCpckB+3k5AiBF5QPPU3AchRoBASkwowgZthEAzkGKnHsSIQB\\u002fYaKOT9oxAdyT2CgFNkEB4lTxyV5+RQNU+LaghRJJAxazY2BwkkkA4Du0aGkiRQNJTn673s49AqPp5AcY4jEBYIjrLoreIQKzLNSeBvYVAqzlSYE60g0DwzSBrP9qCQBgKz8oLRYNASlbyzH3phEDYXuVd85+HQBmG2iWII4tAN\\u002fioowIQj0CI6drC73KRQBQxSAHQDJNAaeOvEYUWlEAvy1d9NmWUQJYlGyvE65NAT1ca74K8kkAr2FIhIwORQDn4ZwRW841Apdd4YAa5iUCfFrkfY8GFQHVsKQlqW4JA54ycbrRsf0Dgq3aefcd7QPKCjTk4vXlAjinmbxgqeUAmHcK2DeF5QAOwyR+wsntAr\\u002fMbDm5ofkDk7gb7kNyAQHl8xWSNoIJAA+0VSw5DhEBghGlIP4SFQHaqtcBILYZA5RtA3UwahkCXQJBkkz+FQHsHsP3CqYNA7qsvjbl6gUCDZ+WbkMh9QGJxlIQ6QnhALbt7iirYckC4GjrE\\u002ftxrQAtZD+9ck2NAOdkKeq0hWkA1UQaeg5pQQEFLuoZdOURAQanGkjoCOEC0u\\u002frjgrksQC0USGahPiJAbZqLdQ7eGUAw4cbfPasUQEyWVwg3AhJAq7TZkIV8EEBOwiSBtjkPQBqS7wT8oQ5A\\u002fj342q16zz9shbFzxMHUP5f1cJszn+I\\u002fgHzpWWlL8T+IlQuVoYD+P9VGItlUFwlADmmq9GA1E0BZwJ9n02obQFaB3+Q4UiJAMr7d5KYeJ0C6ee00hvcrQDgowjKhnzBA6MrOflAaNECsMCjNTpY5QCrVqhAoZkFABOmtUVX0SEDqwtAq21VSQDjcBrho4FpAZcpFsiBRY0AYoaFwlgFrQDlvNoZ5T3JA2DjUKpIUeECJHmzo1Lt+QI+NYQUWCoNAgTHtMrnihkBUWv4J96SKQK5cyXKz+Y1Ap52aXpBAkECfHdTzkvOQQAOU+ybI\\u002fZBA5bkZaZxikED3UovFV4eOQPXR4yEstYtA79JkyNTXiEA0RiV+gnCGQKdbvloc4oRA9SQR\\u002f1VohECZt9erRxqFQArOgB\\u002fu8IZAB6hpPhTKiUDO4nEcmWWNQKb02S\\u002flsJBA62CTdGqfkkC5sCeCVTeUQPXQWebbN5VAgixb9+V0lUBqFHiSKuGUQFDOowREkJNAX3QoH5SwkUDcT5K4uP+OQGwbWYQifYpAra88p2dKhkBCzB5D8bqCQDsBmUfi\\u002fn9A+NL9OzdRfEBh7OleLF96QNQc6uUVAXpAA64JWBsGe0DsRisL9Dl9QIrbbQJoL4BALqElQ+kQgkA2n2HzJwqEQHUxryS02IVAz6x\\u002fbA44h0D9089Hdu2HQGUSk54U0odAapN7Ld\\u002fYhkAOCKPg+Q+FQE3m4oZinoJA1QTzbo58f0CpGXxIImh5QE0VCSYmhnNADpkoJYCJbEAuwXI6CtRjQHBnsM3OPVpAgTM9oSKhUEAqAnVufXREQIpPjLb9FzlAOAWoQhIQMEBah++iK5UmQBbFZppy0SFA4wunRRvJHkAsB13ny+obQIcxT34g6hlArvwYMNqXGEBkLAH6Lh4YQJ5R9Xwbsd4\\u002f0ECLBkMc5D9yExhzzNjxP5ZC2YnGaABAttB\\u002fUE+rDEB9zI7ChFwXQDZj4pIxtSFA+a1Ls3j\\u002fKEB8hYw3znswQFFz+QYRbzRAaijCJ94TOEALPUcxi3s7QFJjEYjhQj9Ap4yy26FTQkC7Z5b2brhGQIXyPYHi201AWskaeA1mVEBb3l0UmDtcQORZnh9yZWNAqdc+AhgpakAlLozGmzxxQJnQO1tKK3ZAJQbQZKfVe0D3E64w0A6BQKUt8nZ3Y4RA7IHZnce2h0DbxFD4v8KKQD63OamGNY1AOgZ+mJ\\u002fCjkBYzG87PzmPQD7yvx+Cl45A4KYHwZoRjUBsC1\\u002fxWQiLQPscsB0f8ohAtC1uNNM\\u002fh0CGNOl9xkiGQF\\u002fySikrQ4ZAG9pS89VGh0DwsJUDYlKJQBgns\\u002fh1S4xAn7W0XwL5j0Cvrsq6s\\u002f6RQAGHdQ2w7JNAEjX65Ih+lUDa6XYNZHKWQNK398vYm5ZApHyJ6HrulUBtRnuW2n+UQG9MWsr8gJJA1gamsWgykEAgQToa8q2LQH+TBmKtUYdAm1aD5fKkg0AVuQPxZ9iAQA6SayRb931ASF7C3JsMfECc01jE08R7QI2P2eaz6XxADahHyK8\\u002ff0CMSA5SSkCBQE6114H3KINAyG1N9zYghUAFCfG0M+OGQOtyJN92LohApZAqPafIiEAWp7nd84uIQIqypjAmbIdA5FRcdPN4hUC5M4LXetyCQOaftSPoqn9Ag\\u002fC1hOdZeUCBTvJnz1JzQDC1KHZCCWxAoeb0FiViY0CYJgu+v6hZQDj86n4RblBAtS1i6\\u002fvJREDk59jWhfQ6QBBlORN+xTJAvvyzIHEDLUBbyHiM8aooQJAYbf1qMSZAxV9RdoJnJEAX7RSQBvAiQDKcIs+Y5SFA9lASkoyDIUD+rPcZC5brPwLD9kvD9PE\\u002f2KkzNTaC\\u002fz+oBDdvp6sMQMo9FEhdzhhASai1OTgGJEBq2aWoCxEuQIOtMNCeATVA7gnLSxNiO0B6r94OzrpAQM4bfvZ\\u002fU0NAQS7M+ARrRUBSL5cs\\u002fktHQFXCouTXoElA0agGht5YTUAH3enM7btRQNzJal9yclZAb5pumhwmXUA3I8JETwdjQKYJCSK+p2hAf3WOCm6Cb0A8Nx+O7dFzQBL5+R7wh3hAiMuyU9vXfUBDw6jUHM6BQIQNUggHxYRAWnIpbR6Xh0Dr7CErav2JQOFprSL0sotApQRLWVeJjEA1zx26rHqMQIKTrPJgsItAfPWG6LV6ikCOrWLhGjyJQHpZYRStUYhAGzsdMOECiECu7QVe33yIQN+uFMEv1olAYrcWgn8SjEB8OvrS4x+PQPPphWPpZpFAY6hXY5Bik0BJGcIKQkWVQFc2IANNyJZAaL8c4fKql0BFXDTsE8KXQLTRj5eRApdANHTRjeyClUCpUQhffnSTQJ5CKJaeF5FAy\\u002foGV59djUCMJd5SOuiIQJzyKJekJIVA5Idx8O5EgkD4z31bUFqAQHd5US9Hun5A3pfBxittfkBfqm6LU5B\\u002fQKJj30Kg7oBARsq1\\u002f5mAgkCEOFqd2kuEQOTyvS\\u002fjFIZAXE8nLwabh0BO67WxTKCIQIHNVgqK8ohAutxRi\\u002f9yiEA6Rl8OSRuHQNK7r\\u002fJE\\u002f4RAgTU3FXxLgkDRyeFtSX5+QFr0rFdMQ3hAWpE1AjtrckDvjCNF87RqQCuWHdwRj2JAnMOUDevsWEAGGPKUEW5QQDKl4RPZ1kVAmrZNOilkPkCR8bVzWO42QPSYZV\\u002fk7zJAP0CffLC2MED6H2mtImwuQKbSohm94CtAGtimggqlKUBfNIxAeAkoQGZKJO32cSdAMoMlEiXn9j8PWM4rDJr9PyAgENv\\u002fpglAmeRlVEkVF0CahwRzlMUjQOgja2w7nS9AoCekncaBN0BW9Ihys0JAQBgsxkvp90RAiGcw65tMSUDq1JXvfsFMQFOKtIH9Jk9ALPITVFZiUEBsswLOrCtRQGkxo1+lcVJAvVY4fJG3VEC6WPAPJWRYQM5gmh77tV1ATVVif9xmYkAptL+JuuJmQGXUioj+YmxAIrUoELqCcUBu9ZNqQHN1QPzl3y4wBXpAJMMShnAgf0A8u\\u002fyTxUOCQLNFqAw364RAsJnssjdIh0CsxWoXyh6JQNbFSPOWR4pA0xEI6La+ikB6EBDS1aeKQDzKo6vuRIpAlTUky6DjiUBVtk9kDcqJQNr10nqzK4pAYbEuhN0oi0CgAbZg29OMQHr1JUqrM49AYcV6vUEfkUAxmqsU\\u002f+aSQLi7AdGpypRAtjMRyKGRlkCb19L+9PmXQBOVDlYHx5hAvLGWboLQmEAg5Wa\\u002f6QuYQGPPAI3ojZZAF60ZJB2ElEBh+UCyliqSQGxwzUYkgY9A5EhPnuH9ikDl4XXnSiOHQGX0xUcRKYRAIzRSRfUmgkAQlynzIBqBQCP1Hzu67IBAjiqn4Wl8gUDOGF1\\u002f0p2CQFYCuZFVHYRASWG8Ho2\\u002fhUAc3cAGI0SHQNU2teP5a4hAzczMvzwBiUDJprWss96IQD6iQURr9IdAnx1DJVhJhkDLMt54e\\u002fqDQKqOuHuaN4FAyvH+JuJ5fEAq2SCFopN2QGatjlXLK3FAPfs2OishaUAaaYXsps1hQGL6bDhzs1hA7xPbrlYWUUDFVWHv5C9IQMZq3ssjBEJAY8KmYWDDPEDyU0l2ZYE4QO2HLXsgxTVAW1TevrikM0DD68wD2rwxQCTG6kS8EjBAzyOUEpzKLUBgwTKQFu8sQIsIMWAIoAFA\\u002f0J7mkKYBkB52VbAn04TQJYfAufIKCFAGvZrapsTLUDjq+wLmQI3QHiY+Fax8EBA5AXOXPczR0C\\u002fDJpT0JtNQCTjfSOip1FArUGueWnJU0BF4rEk5ApVQCNftjaEkFVA1JsJi2HGVUD295nKSD9WQOVIg0fdh1dAWmqffVsEWkDaZshvUeldQP7qtDMSqGFAqKe7n08sZUCC0BR8zqBpQDEJHBydM29AK\\u002f6uaGUIc0DQ7yEbbiN3QKzRTlxT1XtAwo70s8xxgEBVnd24JvmCQGNJOFumSoVAr8CPPjQ2h0Bt3AYutaCIQHCkUirHjYlASiBv9UkfikAetxOXQ4qKQKwDhMZFBotApR\\u002f\\u002fzYm\\u002fi0B0qtDuB9KMQDVNf5GxTY5AEc1zMdQekEDXjzeeIlWRQAZ5ihLVyJJA95rJbf1slEC\\u002fpgViGCOWQMzbAv5au5dAr6D12ZL8mECKQj6OMbGZQOzl6lNstJlAZAJ6NJz6mECPoGnDvZKXQBcAzWgJopVA447pFkBck0BRheWgFvuQQBPV+yxYbI1ASZfpNap4iUDCxtEt6V2GQEeFaLD4P4RABOS6HRQlg0DmJyf0W\\u002fqCQDNOtIf\\u002fmINAsOo2BlDKhEDsTmBaL0uGQIyYi4La0IdAkV5iEqkQiUANTfp4LcqJQFOuo7rX0IlAwI7GFrsRiUAbAU0l2ZOHQIdJKtjsc4VAsBGRwRHegkBiov+bEAeAQAFrdAJoS3pAJIArAvjXdEDe9Wq8jgFwQKbH4JYG7mdALDYzeCyNYUAHN6ot1I1ZQD+14RRovlJAa3yfg2svTECEsQe2aQ1GQKNFkahnE0JALg\\u002f+J1XcPkB83A2sEgI7QNS8BRq61DdAYwrZo54GNUAQMOtdjKsyQIhANvYwDjFAg0jQbg56MEBluZI7a0sJQDryekkvDhBAyS1uJTn7GkCo3TQbUKInQLy5APMzxzNAcxi1oIr0PkBdTW09vYxGQKt14z2zkU5APodjqyZNU0DE4DMlX8NWQA6FMj\\u002fJMFlAtE18RTJfWkAZghLvKHdaQCeoLP4w9llAfq\\u002fwkk2EWUBHtmZFRb5ZQOjJfMA+EFtAm00wXmWyXUCwoTq7ieJgQIvzImnVu2NAf4sc1Z2PZ0BulNUti5VsQLwhgaTvfXFAo+7niKtkdUDDH6LMrOJ5QC9ypLaOuX5A3COLX4PJgUBxVRvpEwuEQKxWxxamAIZAjiF2zXufh0Cj\\u002fzr3LPaIQEhqIT+eJopAaOGk0+RYi0Ah+R+1OK2MQAZqp8L2M45AL2ueDV3vj0DHerTiWu6QQH7\\u002fwvrf\\u002fpFAfjNstFktk0D41s8H8HyUQBVv6kJv6JVAoxiStQ1cl0AKSRs9g7SYQH5xsmPMxJlAbJVAQmtgmkCXiDap8WWaQP4F+4AuxplAcKGRZKuGmECsAbmoUsCWQEizt6+6m5RAE98j0OxLkkDqjiqQ2weQQLsqHP0KBoxAts1nti3NiEA\\u002fhfVhSJmGQJqE3wKTfIVARJWyDF5ohUBUucezkTCGQPQXKmE8kIdAJyY3TuoviUCRk34Woa+KQC+87HAQtYtA9VxZ0XP7i0BYvxsVE2CLQEwtZnKP5olAX2LKfX6zh0BeDX0rIACFQMKiu6dgDIJA0kZW0iYnfkAkWVj+Qot4QEfWgGIeiHNAa9iaRD+GbkAZlyiMi41nQP7C5a2\\u002fEGJArraT0TW\\u002fW0CRjwefdX1VQHBs9\\u002fzM5lBAhFayhq4lS0B6f\\u002fJyIFFGQFx2UgcLu0JA+piJ0T\\u002fcP0BV5f0gAjk7QJondqAkTDdA9QmPKG8pNEAEYOBQCxcyQM+NgcVoXDFAp1\\u002fWY8UXEUC4L3hl+24VQGa25Qe7oSFApN+cWXFWLkC0DVdzFQQ5QIX9PZWZU0NAdnHK9nTUS0DwhNQ\\u002fPqhSQNE9TQheT1dAZM4Z1OQxW0BceNTzH79dQOQnB9MiuV5A4ZR4GBBQXkBSWS9QpxNdQLKpRMtewVtAs0u4OdQIW0CmIZL5M2VbQOJrGl3aHF1AfV\\u002fbUYQwYEAa9DqpKbpiQIcA24pUXWZApJTKr5xRa0BW3s5SytxwQLruH2PbwnRAi\\u002fHrhokyeUBQ0R1dZed9QHtVgxIoSYFAsGahC9R4g0CJemaNfHOFQG487OplQIdAOQIUy6L5iECoVOlctcCKQHVmoxgfsIxAauQncXXQjkDTTK9hLYuQQEOFl9E1tZFAIVBXGGrbkkDnSghkcfmTQMoaJ+beEpVApCYnnxgvlkA6BLjz1lGXQHf0cf84dJhANZn0Qw2CmUDRFgcekFyaQFDug7oY4ZpA6eLgwMPwmkBeQ4PQxHaaQO3gA7OsbJlAgNttnPrcl0C7Hj9Z9uOVQPicj1rnrZNAaMQA5LdxkUBTl91f6tCOQHQycquxh4tA6UZMZCRNiUAFMt7azD+IQIpB\\u002fgf3VIhAWojYNXtaiUBg0N9wq\\u002fuKQFSPhf+ayoxAqf7gZatPjkCRf29p6B6PQKx7gnB27o5AgzY6z12mjUAdA3GCHmOLQBmw7Qi+aYhA7Zf9HF4ShUCimLZ067GBQAoSqvGDFH1AOMmBsnOHd0DkELFGT91yQM0ObMMJJW5A4573+dEaaECF5he8vlVjQH2AhjSKH19AGuuExPYcWUAhMDHni01UQJggk2eEdFBASOusUCfHSkAZ5z2m8+NFQI\\u002f5KbMv9UFAhr8zR+iBPUBxDS6GUVM4QCKS1KMJYzRAjBMku0bhMUARQF2\\u002fvgMxQNvayOhyGBZACsWIRjBFG0ASdeQiwtElQD7CAcBkVjJAT0g8NTGvPUD9Z10AX5RGQCI3\\u002fpPXCVBAVc340a0+VUCUj8itej9aQPJP3Rl0TF5Az6eo\\u002fVllYEA2PuRrSb5gQPDlpMvfTWBAZ6ceiOvGXkBRvYEiAcxcQCTddrYOa1tADpHzfMgwW0AEuT09uHdcQHyLCGHAhl9AnGDDoM9YYkACWUharC9mQKd8RUKUdWtAhcZIE0wbcUDUVGbkmyJ1QJsZN908m3lA6lbZA\\u002fI8fkCUm8mozWGBQGfseKfwg4NA4whgGFGHhUDMlhpen4OHQBmmCXv2m4lAT4a6zo\\u002fvi0DCr26qNYuOQBc9J\\u002fjhsJBAPndNraInkkD\\u002fTq0k0pKTQN\\u002fUkoPA3pRAjbJQONoBlkAUUtQ3vP6WQPGxVJzF4JdAkd3qvuC0mEAQ1YAB2oGZQHzty8htQ5pAcps4RYHpmkA2s2w9+FqbQFHCRPWieptA0YT\\u002f9yQtm0C0sIT6fV+aQM2qW+3ADZlA\\u002fObyzldIl0AjUfQJPzWVQDS7lU1jC5NAUxdJGcQIkUCNPOnK88yOQISOFnrAnIxAtbnBBzipi0CRyaFMA+qLQIWTLPSmJo1AUtDpze\\u002f7jkCG1kJTA3SQQMCGYdaSL5FAyvxju7dzkUAbvJBjghqRQH3snPYaHpBAq0mPtM0xjUCoJLcjp3iJQNJPCzH3g4VAR8kdLvi5gUDyzlJvecF8QBQsEhqTMXdA2\\u002f1HEcPFckCiLQvEca1uQBNF43RGV2lAGr\\u002fstl4YZUD9KktTwZZhQIlLOnPbLl1AAyB1MQH4V0BiPaqqA3NTQPzkpLmBNE9AjBpHBijOSEDCtHqBipZDQMVnKOg30j5AoM6LsclROEAN\\u002f+livpYzQGG7RiF2rjBA7EInaK5kL0AZrk5xcOwbQAcxKlZ34yBA1mbkrroQKkA6e64kUj01QEBexu\\u002fWx0BAvLbuBeMISUBgsIJKrn5RQKoQUQXt2lZApVtS4jDnW0BMW9qwe+BfQFdl4E+CF2FAmMX26mhOYUD4jEIIV7ZgQJP5wdmaRV9A+UUFyo\\u002f9XEBysORofF9bQPxQ85HdCFtABTTxpn1qXEBW5Mwx9+BfQE92bXBj5WJArNfd4xRCZ0CI+++SpCFtQG2Q+MKrOXJA\\u002f0xd6qt1dkAVzTVGoAV7QP5w5zDVon9AMsAOiUAMgkDb35yo5iyEQPdi1OjORIZARDNH+293iEA07DbfSeyKQAclvPr2vY1AZIR3iG92kEB\\u002fI05vay2SQBFmCADN6JNAyVrN3PuIlUD5iyVdJPOWQKzrtqnDGJhArpsCt9T6mEC\\u002fDBinm6eZQHAKSQgcNJpAHfRdvMqzmkBMpLg5UDGbQIISCIM7qptAcGlaczMOnEDZpKWLfUGcQDNxOje8IpxAm6iY26+Tm0BjdVX4poOaQKLbfGF9+JhASJEBwjUSl0DBcr4RvgaVQD4jGAjUFpNAgNfbvwaAkUAPYKg1bHCQQEXx58Vt\\u002fY9AN1G0ysgmkEAuxepBu8mQQCt\\u002f8hsNsZFA2jtBDeqVkkDMnr4Cay2TQMhv8gCTOJNAIZ5TaNCTkkApAsdYOUCRQBF1WOSIxI5AjEI9sQ1sikDX68lBPPuFQBuXs7Q24YFAhn9hAZXHfEC57TCpUTd3QLKV0YbT+nJA2GLd01Chb0DWD04tw9JqQCqsz4Mi+WZAWkTQTx6pY0DvZcC1OqRgQPRIA0BWp1tAGcXcpYd2VkB+ws1YxNNRQIQctB7LsktAORdrCXEjRUBHqGlOCd4\\u002fQJA3T9bF+DdAKJzBXJJpMkBF0Z9LuS8uQMhYiOiF+StAqCNDQNuwIUAOte6BUuYkQE9zK5Xx2i5AtfYKH4osOEAvUAr1joRCQLregvLA9EpASEgHJMl3UkCN0ZAQaL5XQEEnQ+VAn1xAmsZU17cwYEAiz0rtdT1hQCUZ+Xr3YGFArO2SbPi\\u002fYEBICOQaMF1fQCp53\\u002fkpMF1AaZDGp0HKW0DigjcBENlbQNOf3Yrk411AieDMitssYUBks5EK18xkQBlmIWjc8mlAs0im9RFQcECjG+Y0U1J0QHNeVizLzXhARPpa3Mh\\u002ffUC06fPMExWBQMxT8mObVYNAnYIelOiGhUCYsCw1i8SHQNLiNyUGN4pAxbCy05MEjUC0cwbJpyCQQIAThFMq8pFAwcyBasTik0DdhNi+eNKVQAjpYtTMnJdAcJzLAushmUBwVZmew06aQIFt5NiiIZtAy2N0KbOpm0DqlrcKjgGcQGHVnl6iRpxAIk4\\u002fzQyQnEB3NSEPPuecQEDnp2j\\u002fQ51AsRyxvtKMnUArh1scYJydQPLOyADSS51AyUMWehSAnEDewk4FqTWbQFOv\\u002fsARhplARV3N2fyjl0BzrmD2bM+VQF2Kuul+RpRAPn7bG\\u002fo3k0B4UsQgrrqSQKgmLDvXyZJANFtXK31Fk0BX99t1YvaTQCHq6SU8lpRA1Ewmk7fclEB6MwPhsY+UQAdGbRQxkpNAoLL7m2vskUAPijoPC5KPQDSguXEs0IpASJxrjA4ahkDi46dac9yBQOkes\\u002fmqrnxAJ9JeR9g3d0B39TrULi5zQM5nRTvlQXBAGZTznPotbECE1BbObbVoQMJCwVT\\u002fn2VA2YadR8qnYkA2sEBj8W1fQBmmKz4ztFlADwrY1j5cVECo0xbIG0BPQJc2SAw2S0dA+PvqaR70QEBspJQxVGA4QP\\u002fXO7+ByDFArczUuqvWK0BXAmggKkspQLAyvNHC7SZAlFGX\\u002ft9vKkDmclAXm5gyQI4AALn02jtAi8pf9lOTREDpMTBOnhhNQDLQfZBWe1NAAfPp9kObWEDJiTQWTEddQOma517ea2BAjButtGxqYUB6LxDA35BhQGiLm8sHBmFALrxAMfIcYEA3rmmnq4BeQH1lgciauV1ADURXPTKlXkBudkcP0vBgQNoVyVgK9WNALRt2eBSIaECmtGmWc7RuQOuOroARLXNA6r7Al46Ud0DdtMzfC1Z8QGsDGvqImYBArezndNb+gkCkMf8JH1WFQHsxKtdEq4dAWH3Br\\u002fMgikByhBDvV9yMQIwjsjrl\\u002fI9AmhcMfqHHkUCoiXw2zMKTQEQTPR8K25VABjiifi7wl0Cxq0AEvNyZQNzl2+EUfptALTKRTfS8nEB336aimZOdQFgfYnRdD55AFON2CNdMnkAR08yYnm+eQCF639gPmJ5AnmNYtbXZnkA8OMJLozSfQLDyIIibk59ArPPj4XvQn0A\\u002f74tVFL+fQDucGLMIPJ9AosKBaag6nkB8Cip+\\u002fsucQGVHj9TgG5tA3U2k1EVlmUD5BJZZyOKXQJTtpH3cwJZAeBrD+AUVlkCBZU9\\u002fg9qVQGG5nyho85VAs6620GktlkDM\\u002fZyUg0qWQO4esJZbDZZA2dXbGqRHlUBrGszNgeaTQOVNpNM8+JFAH\\u002f+VwL1Qj0BSJmnHimaKQPOpIgPNq4VA3CeDMp5\\u002fgUAtYAyoXi58QAzX+PJn+nZA+AOKwhw3c0Dt52J+8ZFwQJS1j6h1V21An\\u002fMge85YakATgKviDaBnQGdWJGrC2mRABX66LHDsYUDn3PCtb8ddQAwM2rcy0FdATOlTQTNNUkBSwGRCHA1LQMhLWhKkSkNAMX+GiLbTOkCeGPWCzLQyQFBAqqlh8ytAbOoL+5zZKEC94C5S84ouQCiK9ALkPDFA1VdEtyYyN0AloHCd059AQIsYHPOZq0dAUnhFLro+UEABjGinqj9VQItYaBTxXVpAi6Qaz4L+XkD9v5OdnURhQB2+25kNS2JAGWF5eX6JYkDzq7uC6ChiQNcRYfNDfWFAfRFxM670YEDktnA7gwRhQKVQqic2GmJAmlah+ZWQZEAmJBm2baZoQDSUP1cbdG5ATL7PhwnxckBODUjpSFN3QGCImC2OKHxABJdi71qZgECtdYah0R+DQIwEYF2RnIVA6BfZZRAWiEAxngZ6EKKKQHPrEvOOXo1AqRfhvGM0kEDU7CNtg+qRQNVLM8I\\u002f1JNAmx1T74DqlUA09MSnVxuYQIv3Yd6MSppA0bkiNQBVnEASpb55OxeeQO9JUAX9dZ9A5Cxr3PcyoEDujuvlwXegQGIK4xSZlqBA8XwqpkaioEAH2HzeDa6gQCSGU2dCyKBA2BA6zsn1oEB78ETAzy+hQBiuj7XbZKFAMLN64Ll9oUA4xyoHH2WhQPYmsYZTD6FAJVt3Wbt+oECBkOKpr4afQEi5qeho651A338aijNfnEB4vR7OngqbQJNWM3+AAppAMmAKfeNEmUA06W0JvbqYQHg4rnhGPZhAWh9+jECel0DNBmtRWbKWQATUAx4CXJVAV0rBBdmTk0BniFtPkWuRQM94CT+PE45AMBgQLOk\\u002fiUDKMevOV7yEQGctNAjz0YBAph7O06JQe0BTfSBh14p2QB4IOHzvKHNA6ILOWO\\u002fecEBwvh8knqJuQDvJzLYXS2xA\\u002fE1FH+sfakA9lpZHYLtnQESGNGQ08mRAZRdnieXOYUBMatmvsQJdQIACs5PTkVZA+YFP47nAUEARg7h0zcRHQCSi+\\u002fc1QEBAOYIhtCUDNkDeeqzJtcIvQBvDii5WvStA7JN\\u002f31OmNEA0VnrdzPA2QMWIWWrn0D1AuHPisNqcRECGIMZOanNMQBm0YlPaCFNA9OvbDmpmWECozQm8\\u002f9JdQJNvX1V+WmFAEdwroBw9Y0CkEiunWWFkQHpMr0dFwmRAUnbmoJONZEAKRyABfh5kQJsl0VYl72NAzCC6ye+FZECs5TiTdWJmQLnOhpBk6mlAkIj51EdWb0BHjkFSwFBzQOqzA2d4wndAZAdDBAfCfEDxSR2XdgOBQLeiOFM6qoNA8xhyUFlGhkBR1uToC9yIQP7w+OAbf4tAbbk0EQZKjkAewrs4T6qQQKMXR69AV5JAs0zW0xwvlEC\\u002fIdJ3HzGWQEKOi1HuV5hAd5Ebv1yXmkDX5k\\u002fpBdqcQH+tMhx1AZ9Agoh7\\u002fFd1oECbCmUzPjuhQISpABEzyaFARnR1snMfokAIEoueMUiiQHQwOepWVaJAHP7YuStcokAORkDbw2+iQEcY3MJXm6JAEhC+yhreokBoZ99iXSqjQHtHzEQYaaNAsbU65DKBo0AQRNiD3F+jQAo6DxiJ\\u002fqJA7zER6DtkokC+TScgE6KhQI6jMsMMzaBAgbkRBl3vn0CY63rLKVueQCk5t1Fq45xA+KVw3jB+m0BGbW1TsRaaQMbnbzUolJhAfmLlnDzhlkDs2L39d\\u002fKUQFwgzT9mypJAgFrmAix6kEBz6pqkcDyMQE7crkMUsIdAaCX8hTyOg0CTyv5JfQaAQG\\u002fnkKznZXpAAf8HbccudkAipe\\u002f5YUdzQBjidUQCcHFA4pxP1NlTcEAV58\\u002fLzStvQKtv+GAKwG1AkAckzjTja0Dokie1eFNpQMsBsQS8EmZALEHu2cBZYkB3of2lyP5cQPArpQJ2ulVA0djn74fpTkAWffSdNwZFQD9019NCFDxAwBDchTncM0BFpCXkqigxQJBcBpR6vTtA5KEt1\\u002fJ8PkC8W\\u002f6eoFdDQAo9t9bNEUpArobXd0mYUUB6r8ZKdh1XQCEN56RRM11AjPP\\u002fBPelYUA8gAJl3GBkQI46pgJ6fWZA06VbY1TLZ0ByjvFLokloQCoc1dowMGhAMbApHSLrZ0CkI+D3vwtoQLOaHORvMmlAArXkyFv0a0AoVwF\\u002fEV9wQBX7pyas23NAoI3asf9XeEA7rw00K5J9QCQGEVuWl4FAXaYVRcVrhEBXkBptryqHQM2bC+390olA+0Vxku56jEAqY+j2SUSPQMBBr7loJpFAqyRNJdDRkkC\\u002f\\u002fFcXSKSUQED15WHmmZZA2aFxtRKvmECw\\u002fbarQuGaQLh4W4VDK51A0Xi3aKJ\\u002fn0CohAJjwuKgQDGuJ+R97aFAHfUxSFLOokDtGd4HyXejQCqmpvjc5KNATMP2wCIbpEC\\u002fAwIWYiqkQA40oreUKaRAx324IYExpEDtscynwFWkQEY87x4SnqRA8nXxFKICpUDjCWoZZm2lQBxwpUOXwKVAaNUnUdvfpUC4MchPYbilQGQyZTjuRKVAzWUF5PeMpEAFmHUwFqCjQA6B23ctkKJABa4cA5BsoUDlJtPsZD+gQEISbb4GG55A0O0shCCwm0B+NLZxjj2ZQIW6yLnww5ZAf0BKDnRHlEAnX1evi9CRQGb3MevZ1o5AXG9BiYhNikCIda8gCCSGQMWMjuQ9d4JAiqrE+ZK9fkAVjhRcJNR5QMiYSXzaOnZAV+r7Ztbec0BuDHQ9VY5yQEvmj35\\u002f+nFAE7Wiif\\u002fAcUB5jZmYIX1xQJC096Rb3HBA5oDD2Q5fb0C8jSywNuVrQHJTP+CCkGdAkut7\\u002fvTWYkAZA3ahXXRcQJ1Nv5WaTlRAOQfyJBKUS0CSM9OVBkpCQA\\u002fnXCMymTlAAXMvO6\\u002f7NUBkg\\u002fI94h9CQAxHSXa8x0NApb\\u002fWGPCzSEBwv3+cSF5QQLhw3bQQxlVAQ6kRrKJDXECVGEqoFa5hQLkkZ7ZFNGVANhIVMeZUaECpFW8fPLlqQAEUBZpnLWxANCTFU9i2bEAvp21RpZ5sQCBdGxs2bGxA6TWgs\\u002fvRbEDPnH53EZBuQKBSNyTvJ3FAit4j+Rs+dEDH6x+JEI94QFT0XVNI5n1ANpcTUaXvgUDmZrlKKgGFQI4zFQGK9odAzpDB6WS7ikAIUvuM3l6NQP2kdFNYBZBASnhsWUR3kUAo6D+\\u002fQxWTQCngYY094pRAx3nkoWvWlkCTBReZNueYQLJouvWTDptAJ2X2zqpMnUCDwR1mqaKfQNg1GegvBaFA6S6Pixc4okCVqBURllmjQBvJvknXVaRALP6KQcwapUAxsZLncp2lQAInrrrs3aVArvUJQ2vppUAzAhx5\\u002fdilQIbp8gZ+zKVAxilnF\\u002f\\u002fhpUB9wFlA9yumQO2RIYPsqaZA53QZ+wNHp0A8L2lKet+nQEAdrgHoSqhAxBsTuzZnqEAkReYmVyCoQP88gWyycqdAivAvLK9opkBWi5ceTRWlQDrilFlajqNA4Hp+9TnooUAOZsvsBzSgQJ3QwwhR\\u002f5xAvzocgyutmUBxP8LuqoSWQK8APQd8lJNAa9fNvezlkEBymCjbkPmMQIvPH0iisYhAd\\u002fz0zt\\u002fwhEARpNBnx7aBQD4kYXt8EH5AIVkYGi\\u002fceUBQayGsA992QAoNF7BZFHVAVX\\u002fPFX1WdEDyHYRA8Fh0QEtF5muwr3RApUM8I1vjdEAYCneUh4x0QPSewBfcbHNAZMzDMJx8cUCc6ihfvc9tQDajX7l2+mdA04HvM5QqYkDz+9WL5PBZQG17aAxRklFA+6mhdS4tR0ABRjDmqRhAQMEsiw2DhztAxgpzlXOrRkA3iNpmcZ9IQPJT\\u002fr\\u002f5a05AYux9lDDuU0BhAc\\u002frA0JaQJCMgnrL6GBAuPvISBcEZUBI2Q+lIA9pQPdpM7mWmWxASREkHfA\\u002fb0CY+8vddWVwQPr6EWYHpXBAK3aTC12PcEBNxedpp3lwQCO4rxUo0HBA8KYpMD8CckAKwzD3kWp0QDdmCPG2NnhAP6xQOZRUfUAvQ9aDNreBQDQaKoev\\u002foRApzcPQBI5iEAXFj4t1zOLQKmhS6Tq5I1ASM00Je02kEAFQsqt3YWRQGfYwLx7\\u002fZJAR9cFh7qulEDUCjfAUZiWQA2BxdLNq5hA8PldlrHYmkA9MsYWNhadQBt9IaCZZZ9AupAQHwfmoED61Jy+QiSiQER\\u002f9GjAZaNA1tHq45GapEAzu\\u002fx7GK2lQGKPLP0Ch6ZAPRaZ\\u002fn0Xp0CWs7eWU1mnQNmtKJZsV6dAelHFzJ8tp0BAignP8QOnQG2e4Y0oBKdAMo7F6G9Mp0CmYAXlBuSnQCoOW6PNtqhAqFdN1syZqUC2ppoW8VaqQOa605phu6pAMbebxGOjqkAFBfI5cgCqQAiOBN7Y2KhAMXMXLnxCp0CfHjF9p1ulQEGw5O2bRKNAb3RKsGkboUCZhCw\\u002fHvSdQFjH\\u002f8aS65lAqV1WrfE8lkBAKrwklPuSQJLDLoFwLZBAgZwn4vOYi0ADI3M66JWHQD0qM1g9M4RABaDVWu9agUChV7Hjcwt+QM92gQ98c3pAdbQtpzIEeEAkgleVOMV2QEehAxS7mHZAnFGNlfkvd0A9xSRigRF4QBmvxcEtsXhAukbonrSSeEAc0jhycmp3QOb+5QUcMHVAW5kjDIYcckBskm3fqihtQC2+H3LNEWZAdSSAniRuX0B0LnLJ5DFVQMcENKLoxktAkpsAobElQ0AtBWJ3DU9AQFwaP1Gb2EpAd8MQEBgPTUBwNue3ONBRQK5ZYHJqJ1dAqNE0FFBNXkCdBufln2hjQFWF3llyAmhA4KWJmvd\\u002fbED7hoHkFzBwQEKgAPUImnFAtqdlbvxhckDr3DEl85ZyQMcRXSn9d3JAvlowC91sckD7nu05dvNyQCcCn+S6hXRAuTYqM4Z8d0ALH2zB6PR7QCm3MCvo34BAZNtSYrkyhEBqwmanRKGHQMQAHtm53YpAfqDVFDO6jUBr2+4Qex2QQPuhhARsSpFAX2ruK0yLkkD3KlXPlgOUQCxRma7SxJVAKinuOkjKl0AAin2e7v+ZQL3+xUOWT5xACKBbrSesnkBtq+6XX4qgQEJxH9lrx6FAaC4cfeANo0D9YyZHplekQOt9Y8\\u002fulaVA5T4AQJGypkCoOJX+bZSnQK3LXtwxJqhAgBF1g1ZeqEBMjFywKUaoQK2cwJWS\\u002fKdAbVMBE5exp0D41zBsF5qnQAhecAgE36dAJTksVEKOqEBmGjmog5OpQLd\\u002fGYMtvKpACck3x83Eq0CIhwXg6GqsQCxUnaHgfKxAtu0B6Uzjq0BcYcIAMqKqQJqDvjGI06hAeCn4\\u002fl+epkAzXpHPHS6kQM9BNSsLrKFAFi7EUlV3nkDiS6bAyfGZQDwJbcvd7ZVA1kjlMBF\\u002fkkAuYDK3PkyPQPX\\u002f32B0qIpA0Bbb0TbjhkCZvfBtv8uDQAwP7gXmPoFAs5x0pt5dfkC+dazpWUN7QKWmhdOOS3lAp+jvU1uGeEC9FVyGD9l4QAStE9oy73lACFau0\\u002fBAe0BSTAxzRi98QJPy3g4nLXxAqLtkd57mekDpwHUK8VZ4QO+HBHSLxXRApT94oySscEDVTIY+AiNpQGaQz3UAz2FAbeWCG2\\u002feV0B\\u002fAnGDXQtPQMzoEAiVOUVAgcZ1nacAQkDCzrxrXeRNQDaTMPkMI1BAWNn7lyysU0DHafjaoWxZQHAqWX18j2BAanJMulcjZUAqNeAW+hBqQJAiNZSl125AFbv0GCF1cUDQWYzqrelyQLt7Snnbr3NAbfTsXo\\u002ffc0Aq04gOU8ZzQEBCNcsm3HNAg14JvyCrdEBfFVojTK92QCq0khChNXpAHQ5ySeFBf0AEGzQXHsKCQNVhtzlsM4ZA4ahbuOKaiUCYqXq0e6yMQFGbA0slRo9A2M1t9DG\\u002fkEAmB\\u002fBwk86RQAWY6JUAAJNAx3EsU8d6lEC2tmYwgVCWQEuhUPZteZhADbP7ay7cmkDIK1cbqVudQCNyElU4459AGA9xA6M1oUB0jTuFrHqiQBNOlzB0waNAgtnR3eMFpUDvwxfV\\u002fjumQJHtj3qbT6dAyLi0fIsnqEDhI0kBYayoQBHbWEbb0ahAT23L9RqgqEDSRkqiLTioQJ4uBlrtz6dACdAgqlGkp0CXfa8s8OWnQJnpyUXzpqhAJmWvp\\u002fnRqUDxiWkfzS2rQOncQruEbKxA5XFx5TpArUBGQcIg\\u002f22tQNY43IRT2axANLOQExaGq0DQnleUXJKpQAEx1FcQLKdAV+M66GCGpEAV7y2iw9GhQHLU655dbp5Avmx2O3mrmUB3MARFHYOVQLkuYSriBpJA+l00vTFmjkB1vNnPsuaJQAzAYCqAVIZA5\\u002frMrGR1g0Cy7a8j0B+BQPno02lIhn5AIkEQitLKe0Ahhv25fC56QJriM+quxHlAemjy1DlyekAFeOIDUNx7QICHrDEBcH1ArRWwNxOCfkDAKgVR6nx+QDH7+b5kC31Al5TxGvovekCDOWmajD92QGjEhglfw3FArniHmgCfakBp6OleDbxiQMgW72gJ7FhAK7pXJowRUECRflVz38ZFQPENJF0bY0JAF1Lv80QqT0BwCQsqiMtQQOYrfvj5ZVRAIYx3h\\u002fxEWkBYg1DVnRFhQMijtQpTwGVAgsL5foXJakAWLLkSxaZvQJAuUeA\\u002f43FAgORUFvdZc0DYPM4mkCJ0QCikj4lNXnRA7rJczfhmdEAF22jCdL90QEOflfme9XVAyhptAxp9eEAQI4J+oYx8QJN61cfoA4FAt9oOwBU\\u002fhEBu7dh2NaKHQOqraibT0opA4lziUbKPjUDfsaydtMmPQLs0ZVla1pBAJAm5eFPJkUCifmn5QPKSQIdE6c2ZepRAc6O4vRtzlkBbTa6HbtCYQDCBSwSTcptATKk51I4ynkAy77Fzy3egQOmwmLI2y6FAa+HjTpgQo0Dpw591iEikQEIw4nt7cqVAhV9jIT6IpkB+M+VcYHunQGvYRdFaNqhA8oQz2JWiqEArIi4HlrKoQC2u0UjabKhAld17vP\\u002fxp0A6ZVuAU3mnQAF0y4ejQqdAOPZQ2FqBp0ClaqAo5kioQBAw9vaagqlA5+uM9IzxqkAUN1b120KsQD55zyUVJK1AbWHLyIlXrUCw9V3wW8CsQJu\\u002fkpoOZKtAaOPUE31jqUBbyLH0he+mQNAisknUPaRAyB3KhY2AoUD\\u002fkqU1+cKdQClEUgCd\\u002f5hAt2HxdFXelEDZY2kgMW+RQKmSaIKoWY1AGhcECIsCiUC2fiuKbJuFQNerdDiJ54JAWe9jcC67gEA\\u002ftYGtoQh+QMLgk2fBkHtAyP6d6NQvekCMGrCy0fh5QO62uQOTznpAV+Z3UrtSfECbamcMku59QCsSty0c9X5AzziA6t3SfkBBlbnjAjl9QKE9\\u002fFHpMnpA328yQDAedkBACXkXB4pxQBNS+u5oGGpAAftzzSs5YkCq+FUIRgpYQNRVjLKwtk5Ai7eq4JCdRECYAsLpp1JBQBGHoh6UXU5ARdJoxAxaUEDumPl7HNVTQMfXRw\\u002fbhVlAlOP2lOqWYEDwSvtJrSllQAvxCLSTGGpA8lrDo3vfbkC+aav6U3hxQNdeJ5Rv73JATa98hXPGc0BOHcp4Fid0QPwSIBtXc3RAyYgH99ExdUBGsdi6o+p2QHfDQ2vQ\\u002fnlALOZ\\u002fYqCGfkCwlxnSOCGCQMKjTeO7UoVAr7O5kIB9iEBRJebd+k6LQF\\u002fOX7j+mI1AhaQmOP5kj0AXFJ2+4nqQQKgxPgSvWpFAcSqe\\u002fjaKkkAsc\\u002f6lQTOUQHp+k8wbZJZAWV0T\\u002fQMMmUCyQRvrhwGcQHwYU3SvD59AlMpJgWsCoUDB8m3UKF+iQEFWY3jIlqNA9B14p9CqpECCrUgQ8KClQINUPhmHfKZAWpRTKgk5p0C0yYbyWMinQD8yikxNF6hAZF6usOYXqECN6\\u002fRQU82nQAA1crucU6dAxv+NqordpkCFlHceQKemQHxT51mi4aZAN4lkP72ep0BItWswo8eoQAHahxRcIKpAHOmXPFpYq0CAO385yCCsQEepXQFNQKxASy40KAmeq0Df0GfN+kGqQCnM9YY2TahAUEB\\u002f15vupUB4EMd7YFijQDbnfgbTuKBArU7b++9snEDag0DXotyXQHUE12Ir5pNArmQ6VseZkEAYN7DVPOeLQB7J+mJiv4dAT023KLiBhEAuJQKRQ\\u002fOBQBcvuYDS0H9AUL\\u002fT5PKbfED0gE63WE96QCysVvWVCHlAlKQDSM7XeEBos+93rZ55QGvz5NSMAHtAbK887ntsfEA\\u002f+KsZgD99QHIjIBDA8nxAQxGWW35De0BtRhTZAkV4QHGA+R8nVnRAKDRHy2QAcEBkICS2zp1nQB4sV+u6WmBAB9J0Vh9iVUALw3BC6A1LQF1juIXf+kFAT+yLMIAQPkDXihfzkKhLQPAyVStyzk1A6Wcgl4McUkAQHQcJ9WJXQL9vgtMeiV5ARwY541aSY0A1I6ZmikBoQFWUViYr1GxA+9JDS\\u002fxkcEC7NcwWiOBxQGmiEnP+1HJA\\u002fuQoc3pyc0A8dwDGRB50QLCIsaJXW3VAq9zSHISjd0D3XB1Nkj17QELDh7KgD4BA7agvzLDzgkBU0SZqifqFQLNz7HS7y4hAi9G7FPsji0Ba5lpsEO6MQFGciqDZTo5AFt2Al5afj0CgTsVEu6uQQH4+QzRm9ZFAQzLKWsfUk0B5nJ5tdlOWQJe2CTOBWJlAABiXrIytnEBsKMkf+AWgQP1n4zcml6FA6WuU0NHwokDUg1HJSgikQC0bI39F4qRAVBBqQdaNpUBVNmdxdxumQMAlo0LTk6ZAXAdfqtHypkAm8mYhDyqnQNDM7EeKKqdAraQctmfxpkAVp7UtO5GmQEbknav\\u002fMaZAck6wnewFpkDbfen\\u002f3DamQNcOXUIh1KZAraIb4vbIp0B2a2bbHeCoQKvW2YgM06lAerbMgz9eqkDyRNu\\u002fl1KqQHWRMLYTnqlAuQTkxLlKqEDqPOiypHamQPWOw0\\u002fpSaRArNSIHjfuoUDADd1vlBOfQGVEQm84eppA9Elff6JDlkBEVmn3fJOSQPnIkP6z945AySZeDjP5iUCMT37roguGQDZ4\\u002fw10+4JADiwHVtGRgEC\\u002f3q6VD0l9QKlZHqIhP3pAXgoJXMsIeEBul+tUY752QKUBvLmJbHZAPAao5NP1dkBYqUHiMAV4QNbQyeSEGHlAtm29\\u002f8qgeUAMWOTVgCt5QOveMXG6hHdAEbxygFnEdEA2lv9mI0JxQEo5JBxf7mpA\\u002fCRjXBy0Y0DWeuQYsAxbQIzA2dw0hVFAajUleO\\u002fxRUCvgH\\u002fJN988QM6eBdM8AzhAw4RSVA6hR0Dwi7xrZYhJQEWYZYA\\u002fP09AzWi5O9FcVEARCgSHS9taQIfhF9LfYmFAe0QWIsO\\u002fZUA8ETpmYxZqQEhP5CRk8W1AEev7zOx+cED42\\u002fwDTJxxQOvP192LhHJAHl7GqHibc0Bgd8V2Xlp1QDbcJXwDJ3hAZF6Qj6YqfECSYbDH0ZyAQIWfKEXZa4NAGFAET0wthkA9ke+lr5CIQEk66erXZopASd0XsMy1i0C\\u002fHQcHK72MQEji3P+p6Y1AdPMULnK7j0BZ9ek9\\u002fVKRQKbTvMtweJNAHvxJc1pQlkBluWoty7aZQBslVpUDZp1AlsTL2DGCoECjr1MjyxyiQEUonxWyYqNA1aiNyTpIpEBnDfGgBdikQGRurQoILaVAaVeQPbZmpUDUA2pLHZylQPGAli+f06VAe0rAZTcDpkD0Uv2iURmmQBEpXUYGCqZALuIrahTapUAluKzHnKGlQDoXmSlnhKVARWg3j6yipUCnJ5QOWQmmQL603ExIqaZAk4mNK7VZp0Bw1dqIqOSnQAx28a6ZF6hAtCj+qxzRp0BDYHmQ1QanQJMBqbE5w6VAg7rKoFEepEBFdyP\\u002f3jWiQEDz06fzJ6BAjLgTByIhnEADT+4QARKYQC8wVAVZTpRAX0J7Ojz4kEA3DPD1EUiMQHq4XgWnq4dAPQeKWY0EhEBAEPefKieBQABq6NuwxH1A7YDCP7UdekB0lUmk7DF3QAUVgpS2AHVAh4J+Giedc0Dvm9ESpBBzQN47GIiuQHNAbsjgnSjkc0BtonvAzo10QLtENJtbyHRAk6qP4gI5dECfyaKQLbpyQI5OSar\\u002fY3BAwyCG1cMAa0DFAL8Q4+FkQKmui\\u002fSVSF5A83bA6xqZVEB8WTKbNW5KQAhxBUA3YkBA2hXSkbhUNUADNdmWUKUxQBprzsFZFkNAkCd+Tgy\\u002fREARrTk4OcJJQOdGJWizFVFAFejk1qLuVkDPD91yxCxeQLbGjHhBJmNAn0Nwp2tGZ0BurtiICA9rQJqznztrOW5Am7ZKKn9lcEAlb+nDQ5ZxQDPkjcGHDXNAf\\u002fjP80o3dUDQwHZOfGN4QJmjELabn3xAuoYdinvSgEDrmLtkJXODQIZNN+aa3YVAbSRpPKHNh0DOwcRypimJQJM0EvMhEYpAL2TQXhjaikD1RBWsPf+LQPxjSrU+BI5AJk9k0VSrkED5rpfHfBiTQOnZvLdVQpZArrS+E3T4mUC\\u002fZhWv4OWdQB\\u002fBvKNb0KBAb0rOeHthokCLMKum\\u002f4KjQO7qFjt7K6RAgKHIUpttpEBKeHlSw3CkQGWoZP9+YqRA1q5rE4FmpEB+SAmvqoukQIx2s59ryqRAon1+p6wMpUBv+PcYQzulQCmZKAqISqVAUFDH7i5ApUCLLEVYuS+lQDwHi4l3L6VAbGnfRbNLpUCTJ9rRfH6lQDeDgGgOsKVAfY7Ani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K1GpPvdOfEA4mJ7mMZd2QDnfn086WnFAqCfrRFuraUCJoU6pI1ZiQMkj4W5Ve1lATYdvd\\u002fR2UUBbGpB9WCtIQNWYBO8EaEFAp3TUJAPDOkA1W3M2IgM2QL4qYVvP6TJAl3MHX6xkMEAC6BaFFtIrQOGRj9qJpyZAShAFK4N\\u002fIUBaZiiSr3kZQG\\u002fKRP5TaxFAHsLyfNdaBkBJpM9XiQb7P158noU\\u002fce8\\u002fDAVVufAs4z\\u002fRvCDxW6feP7QBnH7iwihA\\u002f+8OkKK0LUCUo06w+Hw2QN8g8WwQ4kFAkDNhUEF6S0Cmzi1W6eFTQC0rHNHo8FpAdKr4o6MZYUBgoSPXW2tkQDFqPAC6HGdAzdaO\\u002fsAbaUBxvlviSqlqQMZ+h8dxTGxA0F532HulbkCrm5f58RdxQPxn+zI+hnNAKwBCuK51dkCR19dE7pB5QBIZXkLXc3xAAD9j9QbTfkDHTzZytk6AQL4lZEInBYFAKCbxhMTGgUCuI12j99qCQKgcPkncgIRAbo\\u002fzNb3bhkBXmFh2eOeJQD7uiUhnd41AsvfUlcqfkEDhL+QJCHOSQJybM+9HDJRApBaW+rFQlUAVOPQnETuWQGTdKU3u3ZZALoAG6MZfl0DLms9nS\\u002fKXQDhuqbV2xphAw9oSi3v\\u002fmUCJi8\\u002fxoqabQCB1igfaop1AEr1ztF63n0AA4Z\\u002fQMsagQFviI+HhYKFA320yoqiDoUBEQ2uGnxmhQBbt7RszJ6BAsxh\\u002fC0qSnUCzgFZkL1qaQLSSJwE0DZdAQ6r85JkIlEDnykh\\u002fs4+RQMnue+Oxh49AiI18VKVIjUBz51UZcSmMQPHbclzczotATt5waYTSi0CyRMODQNaLQFxTXYyfj4tA3EefOH3LikAQIoA3S22JQPUV9mlCbodAPpFfO+zdhEDcqxdI2uGBQDosprtTYn1A7utstkYVd0Dj\\u002fMGR509xQKIv+w6fymhANCnG59v6YEDMul5aU1xWQILoMZTYmUxA1F6d8RUbQkA\\u002ffurLNmI3QOGpcr8+7y9A3M+sVdWZJ0A6QXl4erkiQEga2pOI0h5AwTTvbPlTGUCaxiwW6jIUQAU\\u002f+epIvA5ApGVcKtcZBkBSu2qlR+X9P7F9iupq\\u002fPI\\u002fP8r+Geu35j+BSh9F4CXaP+MGuYsxis8\\u002fy+R2IPEOyT8ZXBwd9EYnQGwkWV3jBSxAP3OAlS9TNUC073GpGf9AQKmbv+5\\u002fGUpAvxlAckLWUkBbLfC4QGxZQM\\u002fc6EZyDWBAGgMQdS8LY0C1nryrFmJlQHTSAYXkAmdAgP0jjXYwaEBo0lzlonVpQKQ4AP\\u002fXe2tA7o6gb2zPbkAlWV6x09NxQAL9TW2V5HRAy2CPBJtIeECOIkwPzJR7QA8h9Exfan5Aw1g6kmJNgECGd595ex2BQB6ZPTkCz4FAJg\\u002fgqpecgkA\\u002f8STakb6DQNiVF2yRWYVAA3qD0a50h0Bajl6TNfmJQJlko0OKuoxA++R74fyCj0C2HjgJ6RCRQC\\u002fqDfiUO5JAp6\\u002fA5q09k0BUG9wTZCCUQGiz24M8+JRApdO\\u002fQCrhlUDQ4rJBgviWQBoQlMGmVJhAXhHVMAz7mUDiOxtbFtebQFF5YV4Qtp1A4OG9fF1Mn0AfEp5egyKgQOc3E0gELKBAluhW0eJfn0CbzC6IaWadQGhHVCsxpJpAzJCOpU5zl0D85tNslzqUQLDtMDIiWJFAn92xGBEhjkCDRQsVpQuLQHIWsl4NbYlAKmsAY0cHiUA0zTuRPHaJQAE8ft58R4pAbBxzf4MOi0DKydrLuXGLQDDKGDlnMItA287PodokikDhBcSbF0aIQPOtbhlbqYVAWEx9R6GAgkBJcrUMBSZ+QP05YEeWXXdA5puco3MycUC2iNGYogZoQGjhs9I24l9AxusexbImVEAgt97q8WlIQAaihkGVtDxAZ9jb2xnCMECujijVPSQkQEzQ3ykh8hlAZsVz235HEkBkrXp9fK4LQKNuHnt0mwVAtY9DgSq1AEAkK14TQuz4P4K8RKLtqfE\\u002fME6+bjmc5z8nyHubrqrdP8gCJyiDkNE\\u002f1vp2ilX\\u002fwz96PfmmB9q3PzbXGLbB1bI\\u002fVTko8ABQJUBSDx93YacpQIrA2eN2gDNAyPTVZwgEP0BY3+opNbxHQA6R\\u002f1vCD1FAePyyftTrVkCHp55+bcxcQKzKWb23\\u002fGBA+ux8ES32YkD877hOxkpkQClR9xhmQWVAMPvbbVZnZkCUdpeLcmpoQDoSGyvt32tAOC49FXmFcED0BGCnhdxzQLv\\u002fpsB5ondAmUForOhhe0By50S89Kd+QB\\u002fBzlxklYBALI1hYO1wgUBcUPyFJwOCQMC9TGpDfoJAjaUKgN8Yg0CdWl6nFv6DQGOnzsMZRIVACXbpz83qhkBgl037puCIQMIL27edCotAr7GstmdMjUBjk\\u002fVMLY+PQB3Q34oM45BAYfPucHz4kUDIagMgoA6TQDUUUmxlMpRA5TPvXS90lUAE6GJu6OGWQF3Jf4cifphA2+YkuuA2mkC5cw3rm+CbQEIfOnvXOZ1ABF5Fo434nUB\\u002fX\\u002fyjJt6dQLEbmL0cy5xAPK5bSQHLmkDzxtTykRSYQLSgQfI4\\u002fpRADD\\u002fIUD3rkUBK+SWx9m6OQHuQUIR8TYpAl+TT5ry1h0As5h3Ya6OGQNQZvAKA1IZAbItqm5jdh0Ang+kxV0KJQAzj1yJAi4pAaYmPffBUi0B33XYDS1iLQLyo7Io+bopAPjWDCNuRiEDs6EsyzuCFQAbq\\u002fWu9l4JAMRUn6hoQfkBk1UnZpg93QNFHw1nowHBAObKWMW4HZ0COHAdYXPFdQM5AF4OxblJA3bA4nE+NRUDSwLSfuRY4QLnlY0mMFSpAjJ7UgAkQHEBnlWZftzcPQPzSLatZwwJASZP+Vb\\u002fZ+D8qF1\\u002feUbfxP1reVugG9uk\\u002fOlQqXKnD4j9SWw5FUhPaP5lZv\\u002fnBLdE\\u002fD3vG87JUxT+i4Z8Xwvu4PzuUoyl0Iaw\\u002fuBB5kT6WoD+XqlTP5AiaPworv6ABnyJAHtHGoxtgJkAbUqAaufIwQNnbcm7+2DpAtwSKhH52REB+yGe9iExNQNV081+tmVNAAbiQdfaGWED5CQtCCtRcQDL7L\\u002figC2BA87\\u002fyA2QqYUCeYqtUvxBiQLiJntuQSmNAfpxOxeKCZUBOkWZb7E1pQIcNy838725A64IC3TAbc0A7R1ouRDp3QOA9UlYyVntAyBJtIfzrfkA4cPyZeM6AQILFVMZkpYFA3KHwRX0OgkCpsQ7\\u002fmzeCQDcRIpRuWYJAxi4TXUGogkB40jcxtkmDQE7vrxrJUIRA\\u002f691e9m+hUCudcnQnYeHQKU\\u002fYUhmlolAmS28Q83Ti0DwZtdnWyuOQMIUdG5USJBAWf0zQpCBkUAAEPgbYMeSQDrujKfLI5RAJjJDlkWflUDOyGz6aTiXQE+zBk3h25hA\\u002f0Ziy4lgmkDPAXRPX4ubQCIOsG28G5xAs09a68bcm0A7FU6SLbaaQCJotWhGtphAubBBXRYSlkArpufichuTQM4FeTJ+MJBAEtoIPExSi0DoI28fII6HQK6qeVVRVoVAq7wg9walhEC1iDTWJjWFQLVpqEwyloZAWXAQX4tFiEDOIzEYP8aJQOWEdnBJsopABwCtHgjEikDRGtWQ3dmJQFEdynCw9odALxgXIgBAhUCWpoyPBPiBQJrznT+T5HxA04g0DaIHdkDUq2l7mstvQBfnvb5nr2VAFNcQm73uW0AestSbZPxQQJ2PP058hUNAJre+vN1ANUCSSzaoLwwmQH+M2zsgERZAZ\\u002fBDlObWBUA8JgnKdEL2P1l+l8BsgOg\\u002fqz506s7U3T9E8CjBkqfTP9Gc8Y0Stco\\u002fAD1A5SPrwT8z+ySxpRy3P5Iv77urRaw\\u002f8h8+1cBaoD81p9A6kTOSP+GUJlBUN4U\\u002fAAIHhZeLgD\\u002fbB\\u002f7Ei70eQFVsFk9GbiJAL5rB+L\\u002fOK0DCsE2fK\\u002fA1QNtzvNNTqUBA+oBP82THR0AC5wT9MrlPQNvIzQmLzVNA\\u002fnoipk4\\u002fV0BaiW5I9edZQHtQ9QHy3ltAVeHN8T67XUDdNS8y00NgQB+5E370wmJAoOQzsbXlZkD18yno6ulsQDYbydxSTHJA9+D\\u002fefWfdkCgWnbtfet6QCCq3lmuon5Aa21BPxKtgEDeV2\\u002fN9XKBQN7tnxp0sYFAIYbKme2UgUCi1iYV+FmBQASpoUwwPoFAfsw4Swl0gUAUCVCkbhuCQBGWZDkwP4NAReiwoKnWhEDwhxBJGsuGQBLmS4O+\\u002f4hASu3D3uJai0A2sk2Ohs2NQD9KYjiTK5BAV8fVH\\u002feBkUDNUqaUVvKSQD\\u002fhOH\\u002fRgJRAGl2vVsMllkBbBlVtHsiXQN3+fZmPPJlA9v8a7y5LmkCkzq4YLbuaQL5FdnZwYZpAN1DsjaotmUA6IlfIwjGXQF6YxzdJoZRA11nfYRHJkUAnUJOdRAKOQBxsARejOIlA5ZRyVESyhUD6M5O4L66DQGZHKCB0JYNA2zxgDgjSg0Du5qKoMEKFQEza40eU8oZAxL9Eg4NniEBDac3xcT+JQH0GrqlmPIlAyfiUYwJGiEDn2Dj5OWeGQA6XkgGnyYNAy9X2BDOugED5m0baiMR6QEiatZpVZHRA5YTvGlxvbUBImN59NRdkQBn0KqCV5llAUkONEQmBT0DkTzpmXRFCQLi7XKfaizNA\\u002fJqDUR75I0BJ65S4qFsTQBVhBCbC+AFAzU8RFPdQ8D8R0iDARhHeP8P7Nbz9is0\\u002fbbfPz9oHwD\\u002fM+xmBkwazP5oTXQ1Slac\\u002f5mpXzkcenT+liJpbg12RPxEucItFwYM\\u002fwEvXYjmwdT+Shj+bRvdoPxjcz90ZV2M\\u002fddGvM6vlF0CykKP3J5scQC7vm\\u002fh7giVAlyO5AXXrMEDyENPZq6M5QGyK4T0nQ0JAYT\\u002fDU4tVSED\\u002frulBqV9OQJ52geJ021FANpfxM8kAVEDOp7nH9sVVQGwWkANEvVdARelNF2rbWkAyV\\u002f7fhyFgQOvH9w0tdmRA3pvRWxOgakAn405AuDJxQI+IhCfiinVAilBc7r7UeUDRuMqaJIJ9QPvUyzMjEYBAXKmIc9TAgEBZ5U2Qk9uAQJ0neeObjYBAdn\\u002fYLYAXgEBCkud5y3x\\u002fQIxOwL2Ven9AZL4yaTE6gECtWuDZ3kCBQO+ZS+E2xIJArYoWwCamhEBFyObSK8SGQNNqsoVwBIlAH2FJoKtei0BPyJzJJt2NQFtzIoKNSpBAD5QqD1bMkUBiv5Dlh3OTQFZfNuYfMJVADfTkqWjglkAlKJyoK1SYQGwRYmDPVJlAgpHbLYqwmUDADeJz5EWZQOy2Zoq0DJhATCEXSHIalkCVy3BT2KCTQA8dMLXq5pBA8gu0F\\u002fh6jEA\\u002fArliW+KHQFOmswNDeYRAZSb3UNp7gkDqqJLW\\u002fOOBQEt3tQGaboJAweMbCkWug0AA9S8qTSWFQChNeHB8X4ZA0l44D60Eh0C+ggKXReGGQFNayMgL5oVAX4UEow0jhECTh0rcxMCBQF2XRC7p8H1A6ygVU6gWeECDU1i1DHFyQIlmprCNzWpAIv+LAVlyYkCRrDPSzf9XQLk\\u002frZXidU1AiV52MF4JQUDsSOoVJosyQM68Bkil+yJAbfS2HVZIEkBygHFUqJoAQIA0rq5soOw\\u002fCvRPnQLH1z8UPbQUcprDP7l+FKzxz7A\\u002fyQN8xut3nz\\u002fKJPKdlkeQP19kmcGq\\u002foE\\u002flm8Cih4zdD\\u002fBYGah7ThmP2ma9dR941c\\u002f4YzRCSYRSz8+C4XFVs5EP9OEqcY5hRFAB6hbGb3zFEBMzbjKM3QfQF7o6oh1tShAeKzhFgK1MkBrGtdPKKU6QCCxe6F3w0FA9oWxWWQ3RkAMo+ifkD1KQDk0BVRyqk1Aw0OD061tUEA3QrgzxG9SQCyLK8CbtFVA58QFIzM7W0A26cLrMO5hQPjES7OF+GdAp0QJetCEb0CGIb09OvZzQJ1i4wPgGXhA2A9jh7Kie0BcK\\u002fguCx9+QAHHSYgkV39AlCta4aldf0A42a+bE4t+QK\\u002fK+XyNZn1A\\u002fxfVxzqEfEBsrk3mMGJ8QI3Hu9zOTH1AigotSYxRf0ABpk6AUyKBQLFuVZGN7IJAwvjA073hhECBSefPSO2GQInl4Nz3E4lAW51MDTlwi0DFldbAUiOOQCoGGN+doJBAUducr3xfkkDctFZ7HzWUQNNYLxSp9pVAaZ93IN5ul0C7RmQwfWiYQBBk5UWsuJhAdX0Oqi5HmEC2FxuLPROXQDO00UzSNJVA+QT9VYnakkCzGVU\\u002fK0SQQDKX9Bbhc4tAxJ7uC1wCh0C6amYpaaCDQKvruIxpiIFAypb6\\u002fES4gEAQUf8E9vSAQBNopPtS24FAZMu2D+X4gkDtTgjA4eSDQDZ6XJgNUoRAt+byNxkWhEC8o8VHuSeDQGJi7Vepl4FAJU2xWG8Rf0B9SpzcllF6QBCEK0ZTVXVAJepdyjh\\u002fcECOoO44EkRoQJ3JE+dO7GBA85WSUh1SVkCPZ\\u002fM1B8VLQHVRZRokQkBA1pJeSVPiMUA786uCqHMiQNNHG5M91hFAo3IXbTMmAECIFlMhkWrrP1LuxusM59U\\u002f4yV+8rmgwD9QSAhhmH+oP06ECZqSMpI\\u002fBqqGKJG8fD9tM3Gl6BVpP1HxecxIBVg\\u002fEPUDmWwkSD+2TLRm87Q4P0AQhBIOMys\\u002fTVS26MuoJD811KPEl1QIQPbqJ6cmGA1A9ZdCyirXFUBCkMdaRyohQHIpJycTBCpA7NU6y0COMkC0G4zW2sw4QH9EJ2MdJD9AXKL0JZyFQkCbH96TNTZFQHhP2SlCB0hA+OoS4QLsS0B9aaXs3S5RQPeXtl84i1ZAd3HZgCbJXkATt012tBxlQE9pm725PWxAxhHUkKgYckAE8QTy5wZ2QGatyFTgZHlA1VhiapzBe0Dtu+BV3uF8QK9MWxNY1HxAtBMrPcTwe0C72+70wcF6QNMgD0Me4HlAwdjkPXDKeUBANaDMJcV6QNtYrotkzXxA07g\\u002fIjOkf0AvNuegrneBQBWrXfI8MoNAwq8TZUf2hEA0QqLDB9iGQDK1xEoeAolARQwrOy+gi0Cz8r\\u002fqMMaOQN+0Fs9XL5FAGFqVFZETk0Au3BArp92UQFAKzZBhVZZAI9FdwC5Il0DbHe8UWJKXQCd1Nk\\u002f2I5dAWhz+sQkClkA9+JhTLUWUQMWUXzMKF5JAboT9LBZdj0C53mFv+5OKQP8KaOs3TYZAwT+DirLsgkCpckqXyayAQMcskfMEJn9AmIT9YBbifkCOtvRuFeV\\u002fQAm9ac9ns4BA4BOqUg1XgUD\\u002fXwUTaZmBQDH7fbrLVYFAUA24iXyEgEA30qH4WGZ+QFtgH1PR+XpAe0pyg0QId0AZjO5oct1yQBOf0vsiim1A69sCO28FZkBOliRm2iRfQKBSgw0F0VRAchwWZXY4SkAPro0gcwk\\u002fQDszEAsQOTFAC3KnpofjIUAP5RfXDV0RQENFYwpwd\\u002f8\\u002fF7wognKa6j+pWv3IZ\\u002f7UP7Ljk1oe\\u002fr4\\u002fz++4ynaFpT82a49HG36MP2JyenjUeXI\\u002f7LfzB7GWWD\\u002f\\u002fo72egb5BP9gQatYbmCw\\u002fznPl7ZZ7GT\\u002fzBPzXAPgJP+54pBKDKgM\\u002fEA5gcIMWAEAu4Np3GUEDQF2+sV7H9QxAYurjC+rOFkB\\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\\u002fXtAC4ubCmFVfEDh6fgqR0V9QP8OKSf0J35ArWoY9FeAfkD4\\u002fKeh\\u002fAd+QMmXIX7Vq3xAgkZjdUZ+ekAOmYbHy6d3QBh2RUJgXHRAXcbiTx3WcECPUStdQKNqQIIVwzZqEWRAteFvCv6qXEBQAXvGz1VTQGHEhyOXikhAmUnXSYg5PUD8SuqLikkwQPwrODDc9SBA+djPqcl5EEDJd4a73dP9P+YvTuugI+k\\u002fe63InhK40z9xMElgN8u8PzpENL\\u002fPlqM\\u002fKuDoz7HqiD8Jan+CHNxtP\\u002fqcAqOGJlE\\u002fDfnfsx6LMz92yUC9qVkXP9xZX4t3RP8+7OcAjJPP6T5CKnE7fHHhPs+J8o70XfQ\\u002fx6jd+29p+D\\u002fkMgdC0mkCQJyai9SjFw1AFeTYwTkqFkC1XOL95s8fQPZM6ID+bSVA4vn7gS89K0CzYpILWIswQEs\\u002fJ05bqzNAch\\u002fi+Le6N0ANjdrlMDQ+QKfb3EhZqURAXtneaePiTUAWpGyPlAJWQDrW2\\u002fQ33l9AMw66HfMoZkAzogOg2TxtQJY+mOtzMnJAr9uAY4tWdUCTJxZxeJp3QCL0fLKUvHhAXKSW\\u002fUTEeEBwh2JHwQV4QB27PAX+CndAuV5vnYFodkDytXsovo12QPGyov48o3dAKRQNjUyEeUB7lnCYhtd7QAKsXJUVPX5A0yGMNu8\\u002fgEBjFvzODFqBQEzSIv5LmoJA8mxc4\\u002fc+hECfoqgICnuGQAtaCF4AXIlAjGz0cb+8jEBSt6S5VCWQQHbxHQ4QzZFAeC8Yhb4gk0D+4kXs+\\u002feTQGeEwO6qPJRASSaYt77qk0AUx+kYUwyTQDA7UFJ3tZFAICQQquwBkEASxYiieCmMQIO0C7orL4hAwibFKU9vhECXz8zwVD6BQBCVbbZ2tH1Azc1R7\\u002fC4ekBgmnwawGh5QAnhJsLGWXlALrJdzt\\u002f3eUDaHTKCvKt6QPt9K7k9\\u002fHpAnmmDB0+gekCeSnYDH395QNYSzy9JpHdAtvz+QwgxdUCbfF2PclFyQLvZ196jbm5AwO3hVAcvaEBGF5wt2ktiQBJ95c0DO1pAALHFvg+9UUCsaNgh84xGQGSTYkGZ3TpAf7cIt0fsLUD+MLDD\\u002fhkfQNddKKXOHw5AybXaAi0p+z8UQMSCMcfmP6BwPLgNwtE\\u002fsyt02Dq7uT\\u002fczgbqDFOhP5MJksLxsIU\\u002fqj+m0exLaT8xyVwjSZlLP14bWdUadSw\\u002fTvH2TYtdDD+wCcy337DsPheQLST\\u002fvdA+09S0BGk9wj548rK5WcPoP01uXTn2uu0\\u002ffscpzB9\\u002f9j\\u002fmI6z8h9MBQC4bJ53RPQtA2IgcL6qcE0AkJzTZ74oaQJ1cEaG0ACFAM\\u002fVuotv3JEAInEhEWaQpQMRqYlhmNDBAIMdV0iz0NUB+ePCUqgZAQO+UEmiUeEhA6gIiNEW\\u002fUkCurhIlLehbQDRl2GtyzGNAMoig0oGFakA8hcrQ8rNwQMFvmzW8xHNA+DqB8qQGdkCNukF3Ujd3QKT358RDXHdA+tGvr+zGdkAWHxDFU\\u002fx1QGLBZ9Txh3VA2ID0coLLdUADn7gitt92QPUU+8Qik3hASYFfTUqIekBrCjU0X2Z8QJ7cE9IICH5AvbhSYIaTf0CX5VXqT7iAQLsMxBmxD4JAFtJHWy0AhECMVolFvpKGQGQyquJ7n4lAv\\u002fuN13fVjEBrrToHFtCPQOgI+nBfGJFA0LTAYITZkUB3dF4SmRqSQJr\\u002fjAh02pFArAUaftQikUCbzP2b8gOQQOuei\\u002feJJY1AXoEBv6rTiUB8i49xulSGQMShbPfg9YJAYhWIq9cEgEA0wVrWwoB7QChwg7IvknhAj\\u002f1S5uoxd0B4MSDUEwt3QNpCVoLnmHdAlka3MSpLeEC4iQbpxah4QE4oU\\u002fQ7ZXhAHrddSnZkd0AOU65Qv7B1QJ69nPiXa3NA80\\u002fjEGHBcEB1aVjTi8VrQAm5a0NhA2ZAyppkRrubYEC1ltc6FL1XQFA6y0Ju\\u002f09AwYnyMLpCREAxLitNJwg4QI9vqW6AoipAgKcOITuIG0DOLd68voEKQBQktEdovvc\\u002fF5Tkxv3F4z9ixpv925nOP+4S3FMg\\u002frU\\u002fMp9rXIFanT8BiwPVPjCCP+hE80Yf72Q\\u002f40MniKllRj9bJtvftFMmP\\u002fUWqizH2wQ\\u002fb+A\\u002fIRWM4j6sbffIK9nAPokebDVhWqw+lJmQy3zq3D8GL\\u002fJ6c2HhP9bzMuHVXeo\\u002fFJ6WNCXw9D8FCPoS+wcAQDQkd8cpJQdACOs7qs56D0A5TjITLGAUQDNR9AUurRlAb5RSf5lZIEA375x78gMmQLBsvPOxGTBAGH0qTQk8OUCbF7PM11JEQOIKA\\u002fE\\u002fJVBATFd9j\\u002fCgWEAPliodlMZhQKSYk7QgHmhAx7JM5VOubkAiFduSS01yQLpTPVcGiHRAiKj43JXHdUAk0PghDhF2QP80OI6isXVA1d0deFUldUAzUGJeyel0QNSVtvwPUHVAy8NEKqlhdkBLkwkNpeR3QDQz7ZkYfXlAz0nOhDHdekC7LUjx2O57QCmQe6KT5HxAtRUWeOUpfkA0WuLsrByAQHBUuoO2tIFAXxVnaIrig0DCccSlmn+GQC7tbYBUQolAGnIzYAvSi0DBO\\u002f5SgN2NQMLgPo+kK49AQ7pW17Shj0BgHyWnHT+PQOhIKnAnFI5AywPMlfI5jECZywrWENCJQH1t+Elz\\u002f4ZAErqXaUj+g0BykDozSQ+BQO4nV\\u002fHW8nxA4vYwlbvyeEDX7JVDBGR2QFY5rmKuRHVA9SSR\\u002fdFKdUAHt\\u002f6Wyfl1QPj2FqMlxXZAV8g7hHM0d0Da5fL7Uft2QHanQgaU\\u002f3VAVQOuOeNPdEAb0F+pbRNyQLToOerm9m5AKu82u7N1aUCmKnsMzARkQL5kTIZ+911Ai+jpOKQ\\u002fVUDw+9d2q2tMQLQjgP1820FATpYsoXcENUCd9mGqbBwnQDTW2GTFshdAxDJsyvegBkBtdo3ulBn0Py3jF5BdmOA\\u002f7rFfsvt0yT\\u002fNgfMNnCGyP1UpP7Pk+Zc\\u002fQNsMREptfT+X\\u002fEVPasJgP3xVM6rjt0E\\u002fwOY508hkIT8IU6WT3b\\u002f\\u002fPiEmjfR6Dds++eO+3uxbtj5MLx+YLnWgPgMcF67RKNA\\u002flb9jw6Rv0z\\u002fMYsnii4LdP9dPgM5tc+c\\u002fuT2shhD58T8ueoDHZgP6P7FBlvmezgFA54eI4ztrB0Bsz4nEzYMOQF0L410NnxRAFwN7fMQqHkAAeJ4IiA4oQIBbcy+GNDRA3BwNanEMQUBy6f1bduRLQI9HjA9JrVVAO4Pyms+xX0BK6xIIrLRlQNgoL1lW0mtASyUQ85W0cEDPk3ojR9xyQLrjicBbJnRAFpu1M1GYdEDmDqKN6Xh0QM8Z5+0BN3RAoId6sZ4+dECPo93S+c50QLnx56Ov5HVA4AAlxe5Bd0A1ybJYp5B4QCz3r1sJj3lALm3rWX4yekAbZujdC7J6QCbz2y8FdHtAVyGkCAvnfEADE+HWZlZ\\u002fQHdQjc5iZYFA+VH7LjCBg0A42r+05sCFQMVT4Fiu2odAcxK\\u002f3dGKiUAmTCSMLaGKQEm1DmAfBotAh9n9B1+3ikBMGa8HqsCJQNTj2aV6NYhAee+IblgvhkC2oUHon9CDQI\\u002fQb9QaSIFA4n7EZzGhfUALpdQmQFN5QGBKbTVOFHZAqrn1\\u002frgjdEANGKnzF35zQDmUjmWv23NAb3oTG8vDdECJnjw3i651QMWAqzeGKHZA+euTEkzrdUDDOAHNw+N0QGP6wc5KKHNAZ543TSfncEAbIE\\u002fVPK1sQIMYnPnlVmdAbiuRisIpYkBiJf3ay+laQN6k8ijl5FJAOwH9YbcGSUBNL++NOSY\\u002fQDHUChZfKDJAFYrpSUzHI0Ch85UgABcUQAq8CaFPAANAfiGRsGe38D\\u002fwGPncS1bbPxa+y75xw8Q\\u002f0RttKkhJrT8MADFB3yuTP1sdGnIcS3c\\u002fZik6X8RCWj8DlqemE3g7P1lvTiG6qBo\\u002fLPvPBsMC+D4gYRVxJhnUPgma28nHGLA+87cpiOyolj4FIVS4vy\\u002fBP+0v1DF4qcQ\\u002fQg8KVWlXzz+npEKiHePYP9rIy\\u002fCJFOM\\u002fFK\\u002fApEi26z\\u002fm31hyhi3zP3MNeK7I5Pk\\u002fm14Z4jfHAUDy0qoV6hsKQBmpVk6jERVAPk7QJSdOIkBgIOdApk4wQA1ETBbEgzxAzPMGC5HRR0DhtYymiMBSQIdlbm4tqVtAhQPhWjkSY0A6ciowXpZoQNdSBSR1sm1AQ9hwUYHfcEA\\u002fhjHKPyxyQON6JmNTxXJAl1HsC9vockA4JNUmrPVyQJ\\u002ftsVGxRHNADxLhEa8EdEDjEkD4aCl1QATcNkfXdXZAwti2mCecd0CmvRt3b2R4QFOHS0PRyHhAXKN2TI\\u002f7eEDTnEf2oVV5QHSa0Vb0NXpAdbpJK5Xee0Ci+SZii1x+QNxuwgXcwIBA\\u002ft0Ue314gkAn67C8SxuEQBaM50PMcYVArHef5adRhkBNs6S\\u002fv6KGQFp+Bvt3XoZApclrnYuLhUAv7HjG5DmEQANtVSAegYJAR88vvNqBgECGRmw7\\u002f858QLeCBWOGzXhAowAc1I9tdUA1mnwr3ApzQDY\\u002fnMQu1XFAbFgNki2\\u002fcUCaBXjIK35yQFjBgpYZnXNAdVFcLaecdEDKKzPwpBR1QAIPVxlCynRA4Yfx\\u002f5W0c0C9N7OznfFxQNNrGOmDaG9AmfCRUgRpakCI8RbslExlQN1YKCHba2BAFBNkdKQaWEBuBCHWLcNQQLUVX6oN\\u002fkVA3cr2I8UcO0DtupaAL04vQJxtIIZ44iBAwBd1MIX7EEDYM8i0f87\\u002fP1Jt5nvMs+s\\u002fVqG7tDps1j8mJB9CftvAP9czExVfiKc\\u002f3hhVKrx9jj9dHMc\\u002fl1RyP04WeuXNclQ\\u002fYHPIWYEpNT+TXrn76lAUP7XTSAm\\u002fF\\u002fI+7Jt9DnTrzT4rDdEzVpenPtkF2rfUT5A+J\\u002fhFqUVGsT9z8WBEOr20P\\u002fizkdvwYL8\\u002fbVnkjbPfyD8z1qFKAhbTP3JavKoS6ds\\u002fjeiWmrGz4z9x+m1lC83rP2pHdG6Po\\u002fQ\\u002flJoA5mPSAEAo+W6nNAgOQOsrEsDeEhxAYt5\\u002fJMEhKkBMjr7hVmk3QIodthgV1ENA4J6\\u002fRuh9T0Dpp+F78F5XQLwUisShMmBAKenWEIL9ZEDW6JBAh39pQClEy7XnLW1AaZOMffO2b0A68GvvDZNwQAwyqlft8XBAiNvi8UpGcUCs7iJAZtdxQISw9Kpyx3JAsgate4sGdECKZt39dVx1QNMitWDYg3ZAochP4+NJd0BlzNOtt6R3QMKIq\\u002fFBuHdALk2VGnPJd0BOXrp6NSZ4QPTL38ODC3lAiXYUEA2SekDSXMBbaKZ8QN8jTbKmDX9AVY584sC5gEDHc5XFm76BQIaK0znKbYJAOc8nfdqrgkBFhmoZNmyCQJa\\u002fLS+ksIFAHkgHgUGHgEC1EIInXBF+QPZzYigirnpAz+bYtOQ5d0C+Mxkn7BN0QBIlBGwvmHFAxrYY6OQLcEDmWSqarxNvQDFgTXpd7W9AiSsmuOQDcUA1\\u002fGqW1EJyQCNw4BobQnNAP3Nj0Xaqc0BDDcIUaVBzQA6S1XiNNXJAoShcYpt8cECGVifuxrBsQNGN14PA+2dAIKjIYJU5Y0DBaVx6f3RdQFggzTlleFVA0mtXkpekTUCV5SNAL0tDQCKui+CWljdAY\\u002fIrf9T\\u002fKkDdp8NWXNwcQOgIZ2K\\u002fwQxAbfbm+NOr+j\\u002f3dWA3iADnP5ZzsDbKbtI\\u002fVypSYG9vuz9NJ0OKKfSiP2+aCGfyTYg\\u002fKSKYXvLqbD9oTUX2lutPP7Uw+qenVzA\\u002fV5LpWh4LDz94gXOPFVnrPp3i1cjpXMY+ipWUYDRroT69Rzi3rM6HPoaVx7AOTaA\\u002friQMjrqHoz9Wp5OtIHKtP2hf3N8ATrc\\u002fJ\\u002f2GeXfxwT+m6l78153KP\\u002fPx+WJCedM\\u002f\\u002f+Pl9uJ53T+vErOoXU3oP3Qi237JLvY\\u002fj0wcgkKjBUBra7BF4lwVQMnusG86fCRAXSWBiaylMkAo7ubR9eA\\u002fQO9PEKX9eElAg9ehfMH\\u002fUkDBCbTtW3ZaQBcF1L44PGFA59XRRLcQZUACDs+afkpoQAeiPUgLqWpAkEHkGcY2bEDlLTp09UhtQIb+DLBWXm5AotHlz6rpb0AD0lFkYRBxQF7Byp1Vc3JA4Srvetfsc0CBFEUFTD51QMJunPfTM3ZArwClktm2dkD0H5whCNV2QLw6hnktu3ZAzCSfnjindkBqcog8HdZ2QJjw0NV0cndATFwKrPuHeEDinIcuxv55QNHcca1Jn3tAAAz\\u002fjDMefUCUe1W3+Sx+QB7ozsu6in5A1CkD7RsRfkCIAcwGh7p8QOYsKrTDoXpA0TFdTu38d0ByJ92OFhZ1QC+9nkOWQnJA2MWMQcqwb0CTeiZTjEFsQJXdfWaBkmpAPNClsXitakDcx8JJkEBsQNyke+rYq25AMja8NJOTcEAh+XsvcXpxQIA4EWCyx3FARcuRPx5gcUDapMCX3E1wQLB\\u002f8JJzaG1Ai++AThGCaUA6XptEBERlQFT16pHq\\u002f2BABQ\\u002fJMw72WUD+CnGSy9hSQMiwQf8b5ElAMncgVnvAQEDtvfmUj1c0QC7Vsjf9GydA8r1XmsOAGEBD8\\u002fAtsjQIQGqksCPuP\\u002fY\\u002ftjI5kf0C4z8tRJk7di7OP5nVW7FzPrY\\u002fNz6lM8punj\\u002f4Hi65YVGDPw4QkpDlwGY\\u002fUHtt52\\u002fcSD+dQ36PNjIpP+TJMW8vrwc\\u002fwODbMgGm5D4KXzDjILXAPuGWlxjOvJk+ybKo1NZogT6Z8ca6ZLmMPweg+9WuKpE\\u002fGLJ5aerKmT9a1jgp7mukPxSuBJXiv68\\u002f4yPUtmk5uD\\u002fi+tSCpNbCP6hoJmL7i88\\u002fpoK7Fntg3T8jX2uexMPtPyG6dbZcEP8\\u002fch5e\\u002f47TD0A+l2WIAB0fQOnkXVignyxAxqS2efqhOEBgtmwiDslDQHDx95q2p01AjBHD\\u002f\\u002fLAVEDWpZf6ky5bQOhIIluqt2BAUDX6MmxtY0DAxwYsh4dlQAAQ0outD2dADmRaiEFLaEAo4PwwC6JpQGAS8cVrdGtAIhy6lYDzbUDYK2YhLIZwQFaZi4+sNnJA\\u002fdM8UILQc0BMnOjNHBl1QBzEe+P\\u002f5nVA9DZkn2MtdkBnmrg9aAB2QDwxjZfqkHVAFg5AdkcgdUAX3gmhYu50QPdWQXWQJnVASCfkPDHRdUB3\\u002fxlB5M52QOkdzbRW33dAb0XmZOqxeEDtunvJKvt4QBShzuOEiHhA+cKqoqBMd0CmY1dntGF1QEk6HrfDAnNAnBIxxiZ\\u002fcEBYypQkCFhsQD0FRAU2rGhAb5THFtlpZkBxxPYxc8BlQERD6SzFk2ZAYEIqzxJ+aECbkdwzquRqQPxEy\\u002fn3G21AcaTJd5aQbkCMKx2PzeZuQA+DS3GSBW5AtZztWfsLbEBaE1MiPjppQHQyJisp22VAQ4M6sa42YkDWxMHweRtdQGESEk6LM1ZADl937Q8VUEDsc\\u002fRaigRGQFiqf1cjXDxAXJhj\\u002fPMeMUCGjtIYaVIjQML2BaBlVhRAZFK4ucXuA0DQsuA5ayvyP6zuZXwvyN4\\u002f2lsNDjI3yD81oXjyI6+xP6loW\\u002fEu+Jc\\u002f8QZmVLkkfj\\u002f1uiz2NpVhP7Fn1mbLBkM\\u002f7pJoNYcYIz9nfJRoS8YBP+iud+AKsN4+x2682piVuD4N7L9o3bySPt1MQkHzG3k+6DGUgUCIdz8CGhSTmhF8P2BQei40DIU\\u002fMFbhSN3BkD+cb6sQcKCaPwf1sjlOZqU\\u002fV1aXmXtKsj\\u002fHFGPXd2DBP4CieLbAOtI\\u002fmTVkfu4N5D\\u002foSB74kuv1P0AcLRRaAAdAfPjSRBnGFkCFr40\\u002fBh0lQM17UxqhRTJApza4cuR+PUB\\u002fBB9\\u002fjzVGQASQ37MLPU9Awd8vTw+RVEBeoifdjHNZQPNOopAdyl1AbFXNiwWnYECdlb7Q3AliQNM6WzaYR2NAi2ulxRy4ZECytMwwRrFmQAw\\u002f0bTFZWlAJ87mlT7PbEAnxhPeE1VwQCbDgGeQQXJA5SwG67Toc0B\\u002fHT9qdQx1QEphXfmEhnVAvkclg5xVdUDHPduje6F0QEgsKMpGsnNAi7v0E0ncckAMpM8kI2ZyQDpbT7NAcnJAnEm9aIbzckAPIa3TH7FzQJd5KVgNWHRAHGVIGYSUdED13lLazyp0QKcYfJf2B3NANNvLfLJFcUBdq0J8HkRuQBsgHU3X3WlAEyIUYDr+ZUD2XRB5uytjQIkWXiTptWFAK+ARouGoYUCggq5t3MxiQHxFZYzSsmRAEDiMgJnNZkBsQIUpfJJoQN3hMhbcl2lAvHLXwYmnaUAQFJdzUL9oQMEsvsCLAmdAb5ljnESmZEBEjyT54OFhQPH71U3a0V1Ax3vpDeHWV0AWk0RUky1SQITWxebQS0pAaKKz5AHzQUDPmD3gEgY3QF2A184NpytAHhXVJbEDH0AEBtkrmjUQQAr1hnEnif8\\u002fTN8mUsyE7D8b55leTPXXP\\u002f4CEsvtr8I\\u002fxmZlV24Oqz\\u002fic9wokyySP8FuKt18pnY\\u002fVqrEhUEvWj+\\u002f+qshSxM8P9r11jYB6xs\\u002fnrc\\u002f1de++T7lXWc4fQTWPlw1j00UebE+4PvbeERfij6UwhZs\\u002f4JxPgku9SNS4WE\\u002fXrWOvIlOZT9sa2rljgRwP3QbBsle7Hk\\u002fFurmiUOEhT\\u002fzKAXRwNuSP\\u002f4b9OpkT6I\\u002fXuWXLs3asz\\u002fHqbYAjfnGP8KTah9mwto\\u002fbTg3Do8g7j\\u002fc+wpWJQ0AQOSjWxK9CBBAf61dtEzpHUCgqtQ1fgMqQDRzFtAoFzVAp+SJ6xjmP0CGnuCXMohGQOAk+xUXzk1AYg25huaIUkAYAs5LjNJVQK9HjuQ2lVhAEI\\u002flhszoWkBJudbfbjJdQLf5U480BmBAX4L2wfMKYkBgGP6IPd1kQMu2NFd7gWhAmA\\u002f5vV++bEAgBwEK3I9wQAjaPv8uhXJAD7f7mB\\u002fwc0D2Dd5ozpZ0QOZlUXjMaHRA+r72qZCIc0AWGWl\\u002fYkRyQOQFHEuC\\u002f3BA3cMRfjwTcEChrla1LGZvQBTq+8Wlu29AigOaWfNfcEBC2k1fIuZwQCmTHTnhGHFA5eLY5Ci4cEBvbzSRU1xvQN6d3+C3J2xAVAtSWctOaECpLtssZXJkQKd52XaPLWFAyEWaK57lXUAZWIIXFe9bQOrMiTh8XVxA7QT2IsaiXkCf4Srt7fFgQHYC6cr7lmJAKVrp5\\u002frWY0D4e0+79WxkQJKjIq0mQGRANuL+yjhdY0A21KLzjeZhQPW47klqBGBAjAcQOhq4W0DD5e+C5x1XQOHL8hUVfFJAStXv2YYsTEBUPm9YlldEQMqc5VBkrztAOA2zDw2uMUCgDwev\\u002fh4lQMigfy3ZihdAHlyZNphwCEBLxHBNYpn3P1ovrqAULeU\\u002fbkHc9I+l0T\\u002fknRX\\u002fH027P309ocv6mKM\\u002fkZjk0Ecaij+UMv30AiBwP6Hw\\u002fKpyelI\\u002foUNpJWmjMz\\u002fDyMBo\\u002fVoTP7jOLRDFsPE+1OGAhNP8zT4tSfymhZWnPiDXLbVDoYE+rMavCpI2Zz5k6tZKvS1JP7AsBT\\u002fADk4\\u002fvNMaq2reVj\\u002fgxP5QQjJjP4zEo\\u002fE3PXE\\u002fAE35\\u002fR8kgT8lq2UDajSTP5FVhe+6V6c\\u002fBsTw0P3xvD9R3rm1\\u002fHnRP+HuyMNUCuQ\\u002fPlIkXQqS9T\\u002fRWNwTmK8FQJ2Z1PQTUxRACXK9yVq\\u002fIUDpbtFeseEsQEHrK0cm6jVA4xevGd8OP0CVeKflZZtEQCs2G2cNuElA3n\\u002fzDipqTkC4x+a7Tz5RQPoEolkFFFNAEfhEuskQVUCvBU4O8r1XQEdZO2AFr1tAwZtuUHioYEBw2BbImFxkQNh2AR04wWhAzyNaIWJibUCW\\u002fVchg81wQIsR7gFVXXJAxyqK6nkcc0CvLkJH0\\u002fJyQMn16xIpAHJA\\u002f+cHuz+VcEBVJorLyjZuQJQpSA2v5mtAEgv2sHqvakDVHrNlr5pqQJJMws2nSmtACHNbb50fbEAfygb7QW9sQEAUV+YivGtA6hFmtwfbaUBLj\\u002fgwd\\u002ftmQDmqeeFkk2NAylfMhDA4YEDrBbp0CuhaQI7X+bZ\\u002fSVdAzRmoD4jWVUCUrqqwfldWQDXuaJrtNlhAVxLmwmOuWkBSQTCWJvlcQKZwy3UFgF5AjerGWW\\u002fyXkD6r9pJnUZeQCUT30t9pFxAA7DwZQpIWkCqHshbB2pXQCPrraPRN1RA4AbXZ3HYUEDxpzRC0epKQEgUO057e0RA8UibSWuAPUCGj0XWSgA0QNXVA1mGbClAD6s79DszHkAKl9B0MLkQQFwNfg1sPQFAxHkhZxOG8D8IXfSX2mzdP+S\\u002f3NBZU8g\\u002fG99ip+ipsj88jVavk5KaP5UXikRHjIE\\u002f0U94dj9\\u002fZT+vQDNslmxIP3w5HOsYvCk\\u002fYHdzczwlCT\\u002fcAaZhn8jmPoXcmRecJMM+FdHdhAjZnT6wHybtuht2Pm9Zh\\u002f9O3lw+vTcqABZ0MD\\u002f+1yYf4MozP6vpcGisDz8\\u002ftTRK83IDTD+geVMr+ndcP\\u002f+hHfgeenA\\u002ffWMo4moFhT\\u002fjaFgW8bubPx6AXIdP9LE\\u002fSBoboa4mxj8tJlY0aa\\u002fZP\\u002fbLLO+u0+s\\u002fNhzXXmIZ\\u002fD9vycF\\u002fUGwKQFf\\u002fu0eHIhdA8fbOJ7bdIkCR66K3q64sQCt6lZkaWzRAV332nzQNO0CSDNi6iulAQPLutXN8EERA2l5Zvs7lRkDGRvd0TKhJQIx6FwRY+UxAIICwt6HkUEA\\u002fTN674Y9UQKeim9s05FlAvZZ9vg99YEB+oGzj1LpkQD5pV39YPGlAaptrYY9fbUDU666+aDpwQK\\u002fbBp+G+XBAWFMTrArUcEBZY6yWwc9vQFu58j5sBW1Axc0owJYTakD4LlzmGbRnQLS+B2WXWGZADjBBxrANZkBsWB2JRYBmQMH2hwtMIGdALhX5MzZUZ0CSh9VrmqtmQHnEPsLVAGVAoIKof8p\\u002fYkC1shN28iNfQAqP0FeSc1lAQQ0Nqu3WVECjdPQ6aeBRQJB5VAqZs1BAJpsGQRENUUDTYoIBFWJSQMOdMAFbDFRAXqCzebR1VUC\\u002fxLcWTzhWQMn9KquBK1ZAjjzLZZRbVUA4MiFhGfNTQLnOyw\\u002f5IVJANKOIrJ4NUEAwB\\u002frpup1LQFOkJYM580ZAwOVvsSdLQkB6Itgli8M7QFAhR+Zb7DNAUwl2WqbkKkDU\\u002fdBPJgAhQDw6I\\u002fczEhRAkn1MRXYUBkCMBJSOUJn2P\\u002fguG3BPf+U\\u002fyfKJ8f390j82evu6oia\\u002fP\\u002fKkngnStKc\\u002fHZkKQ7W8kD8UCtNACOx1PxeAnO5woVo\\u002fQugH9oMAPj9pzE+pOFgfP1+gio30Xf4+AJX8K95H2z4SNWuK27m2PnSxQ4DCkJE+QZUV5tDJaT7meRW4JbNQPt3T8XZjEhQ\\u002fhAt6axmgGD+nSN95daIkP0xMYZ2LBDU\\u002fsmvXSW4OST+cmL9Mq8NgP\\u002fsEX+SjhXc\\u002fIqvJnSZSkD+qxGCW+aelP6hsoV9mCrs\\u002fIvOWb9eNzz9CnIbjCSjhPy9rQlimXfE\\u002fdcSyoxJbAEBWwLdUGKsMQEOodPCTYxdAo4g3IZPHIUDBPMCR0zopQICSylrFwjBAZBFG6xr4NEBDovD6vfI4QKCY0W09sjxAE+Gu839aQEBp0oy6DQhDQFp6EZUaK0dAEf8ZXruhTUBtTC4Qy4xTQFZ1fs3n3VlAAa1Wosu4YED3PVlhA8BkQKEYB96+cmhAJxkyaoEza0ClAUzXoohsQO4VG9oeR2xAYPf5sqilakAz1A0s9C1oQIO\\u002f\\u002fAEAkWVAxsgkB3JvY0CDaxsUlCpiQGduWyoTzWFAalh0F7oQYkCbxbfPw3xiQIrXGD2HkWJAqYwGvu3yYUD9dDSECIRgQHbGfCww11xA5a51VRwEWEDDUu2XqGNTQPjylbQLWk9ACXYPWDeXSkCTPQyT8JxIQNB4C7lp50hAxSojEpeNSkAXPrrmyoxMQGOe9HFIDU5AYfaPQSWOTkCMwL4M3+5NQBGKf2\\u002fkWExALzWU0EkXSkAgr1iUv3BHQF5KCdqhk0RAh+VozuSXQUBf1SQldBw9QEjYu4oWHTdANUFjBM93MUDlhLr2PfQoQN8q8ZiuwCBAFw4Ikt8NFUBoSbpEwbEIQOsV60zB+fo\\u002f9j1Xufdn6z8C23TD697ZP8wyxTklrcY\\u002f0s4ovKNysj\\u002fNmfSZj9mbP1IHKY5BgIM\\u002fIFuO3qBUaT+ut5kQ3oNOP++NSBueCzE\\u002fXsSpPaSoET+11dA1z\\u002fbwPjTEBPIcOc4+hh0Wt+32qD6OC5bS1yGDPm+guWr31ls+wYLzyQ3iQT5BQSWqfSz3PiRdzNTZrf0+coCRl+wLDD9kUU9eauggP016GjgJqDc\\u002fVfYJ8DujUT+JyzvuoDhqP7O0RD0qs4I\\u002fWf+HHa8gmT9Jx0N6rI+vPxjTVfeJd8I\\u002fS1bWVHsb1D+b\\u002f1k1KFzkP4MV7ZZkK\\u002fM\\u002fxt5UpADJAEDOCPptw1kLQMx8K4pFwRRAXR2d9ZtlHUDQEHvGDoAjQOvnSKOUZShAaX2Xv5UiLUAdXbhPePIwQESZSiXxyDNAvlpTngD1N0AWsKtBGLQ+QLpfTIU2sURAMF3WBAKHTEABaeujU3tTQEPBYqMruVlA05l6ujIrYEDx+GYuYDFjQC8Gldocb2VAbjBFyXyDZkD6rkl+lU5mQPtr8LYm\\u002f2RACDyjVfAEY0A8TmdAwuxgQAEny0b6aF5AJ7mMZRtOXEBhFw1PB5dbQGxyq8A31VtATMMF\\u002fONNXEDGUy6fHj5cQEU6vaoqHFtAwKoQHeLAWEAFeKrNumxVQJtl9MLZqVFAXZHA\\u002fWozTEAGPHX3OIVGQFQeDBbX20JA8wm0cYY8QUCo+ctN3zZBQGk0aYfTF0JAYpHZgnwkQ0BXyhkvYMtDQHiAUivbv0NAnJhZ8uP6QkDFfhLCmKVBQBjniorV8z9AvoI7tklQPEBsfxbglpY4QLX2h7wr2jRA+4fqrz0kMUCWnGwCxBErQBxXLbT9WCRA6ONsP4LnHEAOl4uqhkkTQMBz2LznFAhAH\\u002fTCs6YM\\u002fD99SKAk2WruP8pWPlNFq94\\u002fVbxX18G5zD\\u002fAIqzvh\\u002fq4P7l9pywoKKQ\\u002fah57oIQujj8u6CePw\\u002fV0P7dwa+8gAFs\\u002fgGlswdUgQD+Rvd8cLt4hP2a+ln0sWwI\\u002fZwQME4R84T705ZPFcOS+PnthzpSYTZk+DvJx+HE6cz5fDVqRwbtLPkj7NgYOrDE+0qzwprsP2j4YwUqNNhviPoS7g33CfPQ+9T2Mlu2JDT\\u002ffIDXl2FknP7UrD4DqlEI\\u002fTSEDTVN4XD9fQYxxyZF0P9rwn8l0yYs\\u002fyZLqhoJ7oT\\u002fe0mmr6HW0P6Q0ZSEXQsY\\u002fxZ2+2IKA1j+QoBmOBSTlP+lrE39rdvI\\u002fblkgw1L+\\u002fT++42kI8K8GQHkvQEGFBBBAZ6\\u002fVl4w0FUB4oGww64oaQNQbdh2p5R9A+bXIs\\u002fHfIkBoSRMPZMgmQEmj8wRm\\u002fixABVD62zyqM0DtDX2wAuI7QIXFOtoW80NAgGZeqkHpS0Dxs\\u002fn1ALFSQBqULbzDrldAhK0D7Pg4XEDynnTLMJJfQPFfqAw7l2BAyRtXPd9xYEDBDGHTDvteQBB9lkq1GVxAsQ0lgq4LWUBYxDyZf4RWQLNp9ZMQ7VRAVOVLrk9NVEBp7BdrdlVUQD9T34Y5gVRA22R5g3FIVECJ1UMyP01TQNU19TiOeFFAUXRpBWP4TUAEn5w+LXhIQIQtH0UiU0NA70ygV9d+PkDZg3G+4zI5QCHNuFsDtDZA\\u002fJVuZBxWNkAVcrquPxw3QI\\u002fAgHmxCzhAvOQeYbtsOEA6J2NU6ek3QBlMD\\u002fodijZA\\u002freGpp6ONECLjv2keUcyQM\\u002frWE594y9AkYKOGPRVK0CwQDHNt\\u002fEmQMK1WzeHtCJA+m4BS0hUHUDV14DWquYVQDd2C\\u002flU6A5AiHJhETV8BEDmwGBk+mX5P\\u002fbsKiYcXu0\\u002fiUm5FmSb3z\\u002f9t22qNZ\\u002fPP3XsDJlXYr0\\u002fHNFeoqNYqT\\u002fpWbqMl0mUPx0YtevnIH4\\u002fYFeLjyzAZD\\u002fe0lmMcYJKP69ncEQraC8\\u002fRbQov5tAET8ffqbpb5PxPnry44dtmtA+tbn7azUWrT7o4qy07Z+HPvldMFVXzWE+47MjSZFzOT7rC3AkpRYgPpNRT6qOAL4+v3JBZoynxz7bw+jkXm3gPngaVcD7Q\\u002fs+gkfwiw4wFz90wsPozxAzP1WdIhf3mU0\\u002f1PsWhFd8ZT\\u002frVcm2Awx9Pzlqh9ByQZI\\u002f9Xj0myhSpT9moIaoSSG3P3Iuwg16Tsc\\u002fVqdj76XQ1T\\u002fFkon\\u002fRfniP+XVRWDhsO4\\u002f7Hi9yoEd9z+yiGz6U0IAQNssnCR7ewVAV04PRMjyCkBHCmfK\\u002fl0QQIdG5M9d3hNA2sOTfqwQGUC30Qibi+YgQL47S6+tRyhAQYh0nkL\\u002fMUAbud2FEoI6QIxa7xy52kJABdA7Xip5SUDdVtZlLTRQQHT6cQ6JV1NAwibtRqumVUCTH7C0XsVWQFENs7x\\u002fmlZAapcKaG9ZVUCYTrJ7K3FTQMSGpsMkaFFAgRp0XUhmT0BCCWSDAytNQCI33akaJ0xAhAkPU2zyS0AtzCRRdOJLQND1noLUSUtAmUmJtC20SUBhilehQgpHQC6RVDPqkkNANnYGKbKmP0DRuq+RK7w4QInXjjmjSTNAXPP4+89yL0ACgyV66+krQNbA+9kgCStAN\\u002f8m\\u002flGHK0At+FdeliwsQMuu0OVKICxAA2ziDzkJK0DHJgOG3\\u002f4oQHngM056WiZAnWbTNhiAI0DtiIIpBbcgQHCEjF\\u002fMOhxAaKK2sjpnF0B\\u002fIf9GIOMSQIOHOMV\\u002fXA1AazeJ\\u002f3\\u002fBBUAHdjBZ63n+P1u35n8fDfQ\\u002fvWQ5j\\u002fSs6D928nc8w1DcP2slxgoWPc4\\u002f9xOmEqsDvj8rbe9uyqqrPzHj4hwKrJc\\u002ffkQKVu7Kgj+SlMK5561rP\\u002fKmn6oH6FI\\u002f2a5yigT0Nz+bLQSKeCQcPzSy\\u002foouqf4+fVQMHrT53j5L86lTUgS9Pra34bhfNJk+j3gQVedMdD5iGn33d1ZOPrrqE1dIfyU+SbezuQH2Cj5RvlJ5HL+iPquc2NccHLE+wc4Vh9EpzD5rEoQ\\u002fJ2\\u002fpPmapDB6EagY\\u002f3rSRhXCuIj8AXd\\u002f9UR49PynLHJJMIVU\\u002fOTFLMyqBbD9n0nw7qduBP5XsN1L4xpQ\\u002fLXMFL5xxpj\\u002fhw+Pwd4K2PxWrWdHa9sQ\\u002ff5mAZZcj0j+1jkoP4S\\u002fdP97JaErm3+U\\u002ffye7OSao7j+PHN\\u002fRCTz0PxXP+p8\\u002fhfk\\u002fIsH7qy+L\\u002fz8zPvnjrtcDQKBZPiYhcApAscKy4rz3EkB7W11AlbscQKpp7a94EyZAk5lUHsiYMEDEqiUOmNs3QMxAbpABMUBAT6H6PyyiRECMzpdmU6RIQKiKx0cwmUtAM\\u002fotOzgRTUBMN9TERfBMQO2btEDCdktAIIjcXGMrSUA2BrqsMq9GQAFlockZjURAnn766x8VQ0CXLq92\\u002fUxCQCjGXWku+EFAkFwRF3CzQUCWGPVukhpBQBqlKUA92D9A45K2oQI9PEAdOA9XXb83QCQYIdkkAjNAq6QVY0hmLUBQbfAat6gmQEAdYUkXOiJAHRQicTDgH0DEnmPF2WQeQFU8WFZrdx5AAnRgwpCwHkBrZ0QhWyIeQCy1NBG7dRxA6\\u002fE+00DSGUBjIwkrEKQWQIaSY5DiXBNAYMjFx2VJEEBQg2QvFQwLQFzMwWn2GgZAsdyotkOhAUA33SLMfyL7P0HkPnMP7vM\\u002f+yk+B\\u002few6z9oHGSiVBPiP5EDocQGEtY\\u002fu+MAV40gyT96copk+Z66P+EFNeM4Nqo\\u002fYMaoFG73lz+E0n+pq1aEP\\u002fGDs4iKA3A\\u002fqsm8cVNkVz+L7xIz87A\\u002fP6A3Rmqm6CM\\u002fEgttyjkyBz\\u002fIppYZvQ\\u002fpPiF566GCG8k+OxSPl9xSpz5HHA6\\u002fFheEPrOlI0HDC2A+W4nSR2XHNz6yz2mY27MQPjq3VC7Yx\\u002fQ97N8Dyxgdij7o6QxHgd6aPqCcsWUWjbg+rQ\\u002fgy8cX1z6iXc40kKP0PmxQ62nSQRE\\u002fa6T+QR7eKj\\u002fzou7MnW9DP\\u002fcHAh6QHFo\\u002fGLughTNIcD\\u002f9GArqw9iCPyq6uXlnP5Q\\u002fYnLNXu8wpD8GbgWLpbGyP0YNBMj2E8A\\u002f8Y8GsoG4yT+yn9bT2y3TP0dUF9CCy9o\\u002fDd4YrGK14T8Baa8WZJHmP+QcT5ETo+w\\u002f+mUjUBvi8j8rrYVEb8j6P4uiOgbqawRAeJNCYYAtEEBYi+YjfYgZQGDpD4VMdSNAhei3jdsiLECV45fjESIzQLX4HlNwZDhAP119MKojPUCkJBrVG1ZAQLoW8VO0QEFAxDePjjVCQUCUHgfoGH5AQPiF0Ouhez5AwLw7Lq2xO0CPLtOWwzU5QHPI9a7vYDdAavhGFiA9NkA5hq7oFo01QGl4mkV16TRAyVRdHqvpM0CkxHzsBksyQAeJdgTDBzBAemeztNmrKkAlCkklxiAlQCqn57ujKiBALljgDuuhGECj3xgrwI0TQOWrAmkJ2RBAJ+dfS1KkD0Al8G6LxT0PQJLkD5iE\\u002fw5AWfHVSLb2DUCX\\u002fE6fDtQLQPEXmKIyywhAdqIDX+dRBUByZ4V6Ct4BQC7G9jHCdv0\\u002fjoWkje4E+D\\u002fU9HMXv1HzP9GH6fe6Z+4\\u002f45iU2+ki5z+1USCZ6NPgP6FyjsahLNc\\u002fvQKT\\u002fkMAzj9xbBFQnSrCPwO0WDzog7Q\\u002ffjE+CiCPpT9g7VNCJQ6VPxFax3cvGIM\\u002fB\\u002fiN5S8ScD8\\u002fCdwvUxlZP1DSzV4GLkI\\u002fcbWh4PVsKD9VjFcKFW8OP6gF4dYClfE+nvhh0G7W0j5Abgx4\\u002fbayPsJC0SR4PZE+BHumm7tzbT6E7SAPp1NHPsPC7yO6IyE+wbTSNEDe9z0KD1amd3XdPd+w+621o3M+YZTkBEmzhT67cADyMNukPjIZ696Z7MM+eG4Axtfb4T7vWCCC3s79PuoLk2\\u002fmHBc\\u002fSQ9MUVCjMD+AxhhrHjtGP2ftN2hMkFs\\u002fC0jPfCe2bz+12riFVe2AP8Tm6IdNxZA\\u002f8\\u002fM4ad3Znj\\u002fspFJI2FyqPxEji5Tj9bQ\\u002f0T+tKJscvz8B3AfrNbLFP+rXAPkg0cw\\u002fgMwQxE6v0j\\u002fOGxz2y5rYP7iXqgZzMOE\\u002foSi3fYYJ6j9CoTiaqvj0P86FfltFNQFAPlEHOsSqC0B860l8\\u002f0EVQJizZ3Ff0x5AoqXbiUb5JEDX1y7a7r0qQE8EHjP++C9AD7qOjpn3MUBtAPJSXQ8zQCy6742CMjNAxMcSq4+CMkCX1fns0kYxQE5iVtp6qi9ArVT3jnzzLEBB8+0fs8YqQIYZv3CNNilAllkJT2UPKECurannZvImQHYcvlW8fSVAoX5u7QB0I0AwUnwUzNMgQGvQnPzFrBtAC3k3FdCvFUCNAzh0Y2oQQAHdYm4WuQhAh+wgYNJeA0DmSsVDW3MAQAch1Y31cP4\\u002f83tseoaf\\u002fT9hLvm7Xfn8P+dkk0mPmPs\\u002fd8RD38Q5+T\\u002f\\u002f6Ap8Rhb2P3\\u002fl7sguovI\\u002fyWRBghWa7j\\u002fY1RCu+rboP\\u002fybdRBKweM\\u002fB3MQVyg63z\\u002fS2460tzXYP\\u002f2yOIl3MdI\\u002fQG8WqNYuyj+c6gK3jtvBP2+\\u002f3gsF6rY\\u002flZNILe2Dqz+HOXuppdCeP4EHQCjiDpA\\u002fXchXy50bfz++os+CjPprP+gXQrGuWlc\\u002f7TGchfoVQj\\u002fglIpdnPspP5VMmvxMTxE\\u002fIeLljmRj9T5XwULruIHYPpq0QrezCbo+8vfTGACnmT7glC6FC293PnZTdsVw2VM+W7OMJAYuLz5vvawxx7cGPlnOmKZiXN89VjN4aeUywz18x\\u002f8eBBJePg0XBR68KXE+IEsYFdfQkD70LqlJDh2wPlRhDxKI0Mw+EhdFX8fv5z519bv+E3UCP1PVYrSmaRo\\u002ffRuDRJyIMT\\u002fO78Mmr5hFPy74Fd+erVg\\u002fA6ffO+opaj\\u002fj8uEjuL15P1a5sNoVg4c\\u002fUaC3pi31kz80FNwzIY2fP155jAesU6c\\u002fU7PI6XlHsD\\u002fSWO38Jte1P39neOWpFb0\\u002fpQY+pxMbxD+T57CtNP7NP7E63f1rNNg\\u002f+F\\u002fQXWZn5D+CHwsisC3xP2c8m4Cm8\\u002fs\\u002fm3zg\\u002fZOSBUBKdkAh4lAPQM36HMdETxVAT3eXL5kvG0DgCpImv0ggQNP8yfBhYCJAp5Vi4CifI0DqUUhC2fAjQLs7YDQ6byNANHgR3\\u002fdWIkCPmurXJPQgQFs9HgdsGh9A2n+1uuqjHEBIwfDtGKQaQFieBQnP+hhA3joYi5ddF0BhgwsxSXsVQAlnDlXmIBNAT\\u002f8IJmRQEEByCaGE9H8KQHe8j\\u002fwFiARA1CI\\u002fgCG9\\u002fj9ilkEAz+D2P6q7L9ylsfE\\u002fGvKfKrGh7T8EUgH2WQbrP4yyb+9n7+k\\u002fHbPRKwoG6T90FRPDnIDnP\\u002fRhJ2DYKeU\\u002fIQuHipc54j++\\u002fUXxzC\\u002feP2y\\u002fuFZ+Stg\\u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kDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"u7uf++cs5D\\u002fe0PSAMSLkPxAE\\u002f5S+PuQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002fQEgwcpEQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002flnzOUnpD5D8MQsHvNCvkPxrIavggKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP6jhY2yUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002f5OXkhpMQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002fu71xi7ov5D8MQsHvNCvkP5DOktoiKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002fu71xi7ov5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D+Uop4+FBfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002f2LWk47hTkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkP71runVCUOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP9lKWq7rM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D85zSncJS3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP5JdXovsM+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP2qI3WoiL+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D+hZttwQBfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+T9ri1kwzkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D86e\\u002ftxQhfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkP409VM4dTeQ\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPxNhQyesS+Q\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd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Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3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zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P9UwnWfMg7I\\u002fog+G5\\u002fV\\u002ftD+CW0L4UXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD+IPruMV422P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9SzDykVY22P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj9zqHjQD8y1PwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762Pwsgb9WiW7I\\u002fqqpyhYhzsz9d2oQaF1GyPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002feUpi1BAXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fhzk0nO8etD9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fUdacTvEetD8TMHXFtYa1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz\\u002f6jNnAmXyxPxCfr3bfM7E\\u002fUdacTvEetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\u002fSeHsoLRItT9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT+Rswun7tW1Pwsgb9WiW7I\\u002f3kSRwYpzsz\\u002fMHvG7a1qzPxCfr3bfM7E\\u002fHTYIv\\u002fIetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\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fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+I+eZ6XzDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkP42KEhOGPuQ\\u002f\\u002f8cQg9Mz5D8TCFcwrCvkPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002faIG5Rawq5D9IAZ4svTjkP59Qjz3qKeQ\\u002f2VHc4DUk5D+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02fJ\\u002fKT7FkKWBafAFPqNIIcGv\\u002fuA9hB8Bd2aztz1f8v3aYcGNPYmxT+dIDGE9G5Hp9KwVQT3ScQKC0FhUPxBHexosxGI\\u002fFqSvPtEIez993Ux7LKiSPzUgGoGRSqc\\u002faZyZ3LYtuj8KyPaxSHvKP4lzg5nZG9g\\u002fNjXesdjA4z8TkO9kviHtPwtBDptBVfM\\u002fGqEQChoY9z9cUMM\\u002fNNT4P615DkUaB\\u002fg\\u002fp2WyVc7v9D\\u002flFE4uhnHwP1h5jllGW+c\\u002f16jWtStP3j\\u002fDQmtawIjSP7woQcelNMc\\u002fHMHPCklIwT+XoZzW8\\u002fDAP1rF\\u002fXJKWMQ\\u002faTMgOWG2yj9T7RrJWCrSP43MKEncCtk\\u002fmzuCa8oj4T8ctzjnjL7mP+dKoBfboew\\u002fCrqmRHTW8D+aJzHNp1ryP5J8SiMAjPI\\u002f22hhRACa8T+wU9Kc5EnwP5rCpO0lwe8\\u002fEF0eywQD8j+gBviNvsP4P2XydAgGgwNAxqu4NjpGEED8+ahorDobQDUXtvn2GCZA8x+0zSb+MEDsyBHgy1Y4QAQxgeJ7CUBAec08B5xKQ0BUmGjMoxVFQFOCwEk+40RAdBqfmYq+QkCVtiWyF4E+QKqN0MbkkjZAwXl8bsCSLkBgQFT6+isjQOUi02y+xBZAcietl\\u002fWmCkBmrZCOy1gAQJnAezPbXvY\\u002fC\\u002f7n6zVc8T9FiavK7y3tP74KBs8i1Og\\u002ftGqXtPVQ5D\\u002fNikbzOQPfP0bWmO8euNU\\u002fqsBk5QKtyz\\u002f0tS9buvK\\u002fP95S4odHrrA\\u002f2n0wycKLnz9WL+fUxyGLPzikeJmyc3U\\u002f7gG9McHbXz9wd5BcJgtHP\\u002ftCZeKhADE\\u002fcMCNhwJjGj93vOIuHk0FP\\u002ftStx2\\u002fJfE+VTPgxyNc2j7h8B8KkMrCPkaM14aXdag+Bsf2lpTXjD5oHwEpNLNuPn1SQcHdck0+ZNJbBjJwKT400TzWDMgDPsImRqH8sNs9P8CyNLFysT2H7xgP8hGEPcquZs2qL2Q9RnIKTHaQZz+bYXveZc11P0tTqTSRiY8\\u002fDms7opvYpT9fYeEFhF+7P+Ll72sg4c4\\u002fau1\\u002fs\\u002flZ3z9AXwIcpKXsP0JSsEXIjvc\\u002f2C9sOFhvAUCOjNd30jkHQFHz7CeZ2AtAzlFWhEkMDkAW2BCewS8NQO2aVjiMhwlAw17wxHciBEBbgkJRE8D8P+k1wJjNz\\u002fI\\u002fShq3sDZm5z8NbLz9KFPePyYlVySmvdc\\u002fYO2cy4Ea2D\\u002fL6G71qQPdP\\u002fE4ydcgqeI\\u002fQcye+FWY6D+YDkmPvGXwP61VMyGl0PU\\u002fno8ge\\u002fpd\\u002fD9vI0rSqqUBQOIXHYtQqgRAT\\u002f7SNziPBkAYg4Oc4usGQIPnYH1A5AVASNKfHdpBBEDoQwgfQlQDQHlxS\\u002fFxsgRA1SwsHwsRCkCUxv7T+68SQCYyFrFnmRxA29OirBxFJkCAhzUo7B8xQGYCQLnzaDlANOqNgjjUQUCLpn0poElHQDWs4niV\\u002fktAE1ozxjvBTkBQY17eyMNOQLenACRz\\u002fktAEkCaZpM0R0CkzV0y9plBQFSxMsmypThAWL8A3M4uMEDo0cBkbnokQD\\u002fbTLF3ERpASNzZARqhEUC1e56X3mkKQI7Y\\u002f9KV1QVAq159qMn\\u002fAkBO2QthpXMAQOqa1quULvs\\u002frkrvBJTg9D+5CspaaWPtP3fIz8RXz+I\\u002f+QlHCRbR1T+XWbTN+ejGP+91paED1bU\\u002fNr5m5FsDoz9JsTdDVryOP6WW6TAUvHc\\u002fLq3lbmxJYj8jrb4yJVNNPzSk0Hh8uTg\\u002fZYcwZEFBJT\\u002fJA1PsAsoRP++jn95E8\\u002fs+neLxugAs5D6Ntz2C2XTKPrRpSiF4Xa8+3yWCwgPEkD7BEjmDKSVwPvPFpLoC\\u002f0s+bL\\u002fz2FvZJT55mulK37H+PfMa5G69aNM9ZPzP5z5opj3sHvY8QJ2GPd56kdnPYnk\\u002fq81G+zKPhz\\u002fgtBE5aRuhP4EyGL8+ybc\\u002f2O4pOuzozT8DoN5uLe7gP6TsJdcJQPE\\u002fAVQlgtGi\\u002fz++CxwVohsKQKUA+8cNZBNAD56xT0zsGUA2P8I1szAfQKxYOpJt4yBAbucvYLN2IED1ZwG7FekcQNJf3NXl5RZA+90By6ZwEECWogXtZrgFQBeluSOJjvs\\u002fJLHFEYaR8j8+44TX4qHuP9yCgzTMFfA\\u002fLP3ln8Zq8z\\u002faQMFSuIr4P\\u002fRTt3sxdP8\\u002fFqPlh7VTBEBQ\\u002f\\u002fS1G0wKQIjbn+CrvxBADAss99SWFECaCNUFFAMYQFXdtKNKRxpAAXeooH\\u002flGkCuw5CyZe0ZQC5U\\u002feU7HRhAy34kcwnCFkD64gJekmcXQD2wge2khxtAPffB5Cc5IkCgElGa9cUpQJhyJZ8YrDJAVBsw4rIKO0BOcpeRjjNDQFoX2mutOUpANT5lXvToUEDYraQfOUpUQKb61uvLbVZACG1nA8S3VkCL0LeipQxVQKrXFuuQ31FAlQe54t74S0Czh8j2a2NEQJHXnwWRMDxADgBtGOwEM0AZZ+NtFxQqQEEv\\u002fFlGACNALzhngsk+HkDFNNrSGwAaQFPeh9lpHxdAzCvPmqlDFECv8XCIq90QQPd7r3jcDApAi5nhcsxrAkBMdX3LGrD3P0Zu94GqnOs\\u002fcIjlbH0u3T9ZWMv8uhPMP0lNgjM027g\\u002fqFQxMQ2xpD8hYiCnb86QP4GrQXn35Hs\\u002foelJW+paaD+shjBGEBlWP1lgB6vL9UM\\u002fYBtflz4xMT9SYw8uqnIbP6r+MuHj\\u002fgM\\u002fcIHUq1pj6j4Krr6voW7PPlhVeFGr3rA+iT0F5npOkD4xfNQz62BsPqEJWkj\\u002fOUY+9y5QR\\u002fFVHz53B1OJaOLzPeYTvJUGCsc9DHe5gexUpz2nDffK1zGJP8USMLHic5c\\u002fp\\u002fj9+r4YsT\\u002f2aggBadvHP+SrL7biGt4\\u002f4I3pWdUZ8T9DfCuLPHwBQAPTGuISFxBAXYJXzZOmGkA6m6uyHd0jQIR0IDsipipAxflyMKsWMED2h+c\\u002fHnwxQAUDx+9sGzFAjDJhhvknLkAYbRmk3P0nQGF4\\u002fPZxVSFAnvULjaQpF0AEsprW+BgOQJta+LxnNwVA46HFzmN8AkDz31A\\u002fowwEQO6ZAIkJQwhAHunuD20vDkBkTm+mYdsSQNpN+vWeqxdAyOBKhyLNHUD4ebdzDpgiQGJuHaA9liZAiJABreBAKkC42t6KjdgsQPEHaowvzi1A4Dhs3WsULUCJtVRcTUorQGPM9QuDpClAqdpsTheaKUDH3Cjd0XwsQLfWvaUtnTFA1zLhFuY6N0Az+mbr\\u002f3U\\u002fQPiM8nbDbkVA55HjeCH+TEB90gVOdCtTQHUHc\\u002fSlU1hA7NBnNCUdXUCmqjt2iDdgQPL\\u002fzTbkr2BAAMY9BgShX0BTAxiLMqZbQAYZUUGqbVZAFgq228oSUUCq8jiFMddIQJg3BY6TvUFAQmSMBVzIOUCezy2CmckzQIQojRzPXzBA8\\u002f\\u002fbSkXcLEB+Vz9XoAsqQAebj\\u002fhmCCdAtWEKdHxKI0CtUWTcIfIdQOJB9t+7RRVA7nSHwYR7C0CtiHy5NxsAQHjvcu5CJ\\u002fE\\u002f2PEwDAe24D+wwaU95j\\u002fOP2h0S+7SMLo\\u002fDUENpriopj\\u002fn1EMJu2uUP5SMW\\u002fyYWIM\\u002feKQLKPqpcj+Z9fhjCYJhP0NjSGiPzk4\\u002ff3T7\\u002fXHjOD\\u002fwbC4PK0QiP+a\\u002fl7nTOwg\\u002fF5lAPp797D5sb4JRVz3PPgjSxzvGTq4+AE7CtJp3ij4Mvoerms1kPq5D1UrAbj0+ZD2tWya+Ej6PegZsm8vlPVjfszLnJcY9MUzTyT8Clz90q\\u002fylmHulP4KdLanJcb8\\u002fNcMJ5pcE1j\\u002f+6C1U3uHrPyK6bqjHyf8\\u002fAyk+lgBPEEC\\u002f7iGc0R4eQLOfjO5cCClAMC4hijq5MkDwAjGYPzU5QO8IgoqMiz5AsFJsNFCoQECWq55dk1tAQCmX7mo08zxAbQ6Fu8gkN0DcB58A3dcwQD4IPKaH0iZAzdRNRr+JHkBuqHMj8qwWQO4gnRhX6BRAp2PGmD5cF0DqfcmZmE4cQNY7WaavWSFASD5wEMosJUC7hsQKYt0pQMNk2Twyuy9AJwG6Uu5oM0C\\u002flWaSblE3QLSQUMlEDDtAb82WIqDkPUC\\u002fjIszaEA\\u002fQBYB7dyo7z5ACPZbf3VhPUCg2\\u002fn+OZ07QD7CK4ij+DpAoqBRACmoPEBjq4nBirNAQI6vN6yHuURA6eo8p+lySkA8YtPUFglRQLTb7h43+FVA6ykI5mEWXEDL5upItINhQLZavvL86GRApZ6nmM2DZ0Bn1FnAEqVoQPkKtWeG9GdAPAF4B4maZUBuIbCv5CtiQF2mBxV3z1xA\\u002fNTIzRfjVUDmWPfE3VNQQGZAZu+1s0hAM+XplSaYQ0Au47r9NZtAQCUFOtqtuz1A6vbJwZcbO0AmuvjdSiQ4QFNHe29NVDRAwjTXz1K0L0DkF4oyYZ8mQCiBofECXx1A0VYj6KJSEUDfOQKfqqACQBp2dXZVc\\u002fI\\u002fwK05boI04T8fU8xEzVrPP\\u002fiyyqY2O70\\u002fXyHSecqurD8sXbdHvTidP\\u002fNj7c5EmY0\\u002fn5cyy2WPfD9GK35fYIZpP2e7wB73z1Q\\u002fWcmD0rO8Pj+EEF7fNnwkP23jTy8Mmwg\\u002faXiQJjCd6j6ijDiZ6OnJPo\\u002f5DbnxtaY+B\\u002fo+qO7pgT5CKe2YMG9ZPo3Td4sHQTA+Hbz2Cmz4Aj5spgfSMVjjPVuQI7QnVaM\\u002fdi+esfAasj9OIjxS3prKPwkYNU02suI\\u002f5vw5H2bC9z+iI9fr5i4LQIHob+KO\\u002fRtAfAL07WDwKUBgkEJkNaI1QBbDlTMEPUBAcpp1cIPwRUBSy4KL461KQCZHzBUmNE1Avxc8lEDJTEBaiTSXZZRJQICNSyE\\u002fj0RAsorz4uQvPkDJV6wg3c80QBry1Jen4CxAs1svg8\\u002fAJkBA5wmqbTYmQHG\\u002fS9K0eylAhhvtBdjeLkAJouLhLKYyQLysuKNtQzZAiaNJgk+COkDI\\u002fOGgXr0\\u002fQM\\u002f7GR1tD0NAE4MQI+KtRkDfsRo3I01KQBwtUmfLTE1AJNWlfMUOT0CwGJ9YjkBPQKLVphyyGE5Anr5X6EpjTECUvh\\u002fUc0lLQNbTFS8l5ktAnNjzG8XhTkAtexIJXCtSQPR\\u002f7liTDFZAX2LnXL0XW0APAW+s18JgQCiPBQhPyWRAQq\\u002ffdSmEaUB2tbyxQmtuQB6niupVR3FAGtbhn8dyckBnexcIR2ByQPPvLbFMDXFA3bEbzxGYbUDBLQ21HjtoQE6FoFsyAGNAJZE9xvcuXUCh+V84Tp9WQHYs0ehFSVJAxD02NRlrT0CFA6YvlmVMQAqzXeABEkpATYPmhQFYR0ClILQxP79DQOYfnEo77T5AgEIys0cqNkAI6qgcT+4sQOlc0K5iMSFAPj9WnE+4EkAih6ns6fUCQJpawm\\u002fuZfI\\u002f5DVRmADe4T8ziwTj7hfSP0CO0IqFQcM\\u002fV8wZOiLYtD\\u002f9zh075+mlP8C8GjAdl5U\\u002fmtvaRmGGgz8FSjaukwlwP6BAkQdPz1c\\u002faRbQyZXfPz87CiorYTcjP0XlsuJ43AQ\\u002fVh3f551i5D6p3cp61+3BPvWTt3hqYpw+MN95gMk4dD4lpsiMJvBJPs483xavYh4+zD1jK48Y\\u002fz08KeVf9uOtP19V+KryE7w\\u002fe4uqWv+11D\\u002f6jyt2kDbtPyhnjzDBoAJAPe83iTpjFUA2F5ArvxkmQIFel\\u002fOdjTRA\\u002fihvMdMzQUB\\u002fRruhhOpJQP1zt2bekVFARYx5iTNxVUA2Vq8wpY5XQDmHx++HT1dA1tuyjKzOVEA4\\u002fjJo3NNQQOixNADi+UhA3q3BkQicQUB0y9cgFI85QC4W\\u002f\\u002fcLhjVA84OItPw4NkDHnDTZqBI6QEQ9yMP3gj9A5tXtzt7BQkAd6arxu+tFQJ+c6I4xeklAWxCkBh\\u002fRTUBMinBDmJtRQLYZTYR3ylRAW8sBjpYoWED46N7sYy9bQLwXRIksRF1AuiXkWEr4XUB8Th9grktdQLUDQIQRy1tAmbb7YhBsWkAkLIZiei5aQEkXqkTjtVtAsmKkGbAeX0BLYwHpmhhiQM1qwJUyaGVAm\\u002fc+iw+jaUB0ReoG\\u002fQlvQHgtq+pU0XJAn5AsyMZsdkDoA26NeLZ5QNEEq7XY5HtADXgdzvBYfEANGK+s4eV6QL4p94mG4ndAq4mWZJ7\\u002fc0CRkTTGxwBwQAT7zD\\u002fX\\u002f2hAfT9Ni5ShY0C1Jdg4ugNgQETSJ8jAsVtAO0\\u002fZsPknWUCCaXS9OjFXQO\\u002f0GKYz2FRASyqihA2yUUCYwPO74tBLQCkGSDu0BURADYLZUgBKOkCLsZQVSoovQFxJZ5PtdCFAfGKUSIU4EkBdt6jIm50CQCMkKwkfffM\\u002f6B5yUyJ85T+Ok4xZtZLYP1KY6zSc6Ms\\u002fGfe4w4cqvj+DekUGCS+uP\\u002fNLtQ+8i5s\\u002fq7exm2bDhj8bGd87B\\u002fpwP1+TkwNh0VY\\u002f9YM2c+qdOz\\u002f4hSqh6hYeP07MzKIJgv0+aMlhf3oL2j4NcBQWlbC0PuRKDoPflY0+aNZXrBwLYz4\\u002fEY4cLmU2PpdTxir3ARc+Cii40LVCtT9GDCfDRQjEPywdJoZQq90\\u002fp7fk0Pz\\u002f9D\\u002f9t3r8Z+AKQE6xVo669x5Abbrar4kOMEBOeS\\u002fdo\\u002fg9QKYKxfujLElAnbno\\u002fN0HU0Cn5SsKeeVZQNVaOdoJuF9AgY0ZBkJ9YUC+xuuMgGBhQFJ3S1QbLV9A6vE2ipVmWUDTox3EFh1TQD2g+iQBwEtA3IyYUQtJRUCDYlXRNUZDQNq9fwFJ+0RAWtao+gQQSUCe\\u002ffGJiSdOQGyIERjJrFFA\\u002fsiN\\u002fJU5VEBqYOQ\\u002fUfZWQGb6en+sTFpAD2K3bnWYXkCdMP6IbPZhQF4dF\\u002fWb9WRAsy8hwDveZ0CkojwPJCBqQDY9CVozPGtACT+FmhcHa0CeVwwK7dNpQOnfOSRcY2hAp4fEMGeVZ0Avp0rdDgloQFMALNpY6WlArix\\u002fmiYNbUBtXXusbKlwQEj0CW\\u002fEdXNAAytf+ikkd0DDpu6FQ857QEM4KU6kkIBAoshujfYfg0BX9I1w5\\u002fyEQMrzXnkTooVA49P4RpXXhEDlIBbgCMqCQA8lhgqq539A3RJDdCDVeUBxk+dPPlt0QD5syyt+E3BAUYnP4+ZOakAO5kWhPMhmQEubT2O7t2RAUENDyDQgY0BerMU8CDhhQJ3g3Jt4TF1AlLNYsDMYV0B6p\\u002fasorFQQNZGWktlEUZA6JN882TLOkA8Lqnf5VIuQMiNUmNJeCBAr39fNXjwEUBR55cytWYEQP79uESjYfg\\u002fN1TV032b7T9sGp27rnDhP9oNSYfCPdM\\u002fTUUJMEJ6wz\\u002fcPwC9nOexP\\u002fLs5sYpv50\\u002fl+xLAatHhj+f9vRgxA9uPw7m3M1GQlI\\u002fZBjE+hj3Mz\\u002ffUvwtMaYTP98hMzivZ\\u002fE+1UjYapjAyz6\\u002fDfcYS+qjPgn2qmF3vHk+eyoyfnllTj7qVTkFO18vPg4ihzdz1Ls\\u002fjbTnknhNyj\\u002f7OR0fIY7jPxbKQ7JryPs\\u002foFB1E6bXEUANp5+Hu6EkQEaCBgzGeDVAL1wPFnscREAPY4baB\\u002fRQQDFkTPJ4uVlAyxntzf6QYUB4nXLAnJhlQJLWdzKh6GdAKhUzjB7cZ0D5qxiawYZlQFrKUtWps2FAmpjBehwiW0DKgpYVp3ZUQBDCMzT6x1BANdlx6SpmUEBhf0C05LlSQDGRcFPqqlZA6S1upP4XW0BG2P+7eEBfQJOC6kEAgWFApV5VgclqY0DMdqN7mMdlQGy2gjKv\\u002fWhA9HDiJYw8bUA0ZO5OjilxQChRnJ1RznNA+jXaxC0NdkDauaMElmR3QK6mtLPljndAk5V3iECydkADzjdS7111QLGVrQ5uTXRA+23fxsMTdEBnKlBgb+d0QGOXCoUDs3ZAfjnQ3bVbeUCeZp12EAJ9QFNwynTZ\\u002foBAOPrf9GtChEC5gDk52BqIQDPvcmbe7otAfcsCqLrejkCCsEh58AyQQMW2C5wYOY9AvwHpWV5pjEConDpmX1OIQJsz4M571INA62AgK55lf0D0WjUvuNh4QO9GbmKcUnRAQmpZye6PcUCREZa0ydtvQMz1uBxFWW1AL51Fq8xkakAvQAPobHVmQAxXHnKru2FAbyd\\u002fMyK9WUChe22mACZRQK\\u002fiViP1JUVAfmrUafumOEDeinJbVzEsQE8pv\\u002fwwjSBAf7u1c8V7FEC+tlJZ0kkKQJUVDhsTvABAj9yQ\\u002fJVC9D\\u002fYCBEdzbHmPxRs7L8oLtc\\u002fNaDbP9JvxT+EpU8Y5eOxP6kJGAI16Jo\\u002fXPjTTHE4gj8btrrtxzZmPxDkZkeIYEg\\u002fjjNbocgTKD+wAyLSm2cFP\\u002fyX8knjIOE+nk+T2fCtuD5lvN2vCgSQPtmIqYGwBGM+YkdGmfXBQz78w8LeRMPAPzFYtja4yM8\\u002f83xQc5+55z8nyFhlXOoAQPPWUuwdzhVAStxsQjJOKUBhCluveG46QMGzxfff2EhAU9wPkb0FVUBV7y3nMQJgQODAxoEW8mVANKHldLYVa0C0rsQiSBxuQP3U1wSIMm5AvfvFBupua0Bw85jEeNVmQBB9VFQa8GFALDaM4J9vXEAy9apO5DVZQDL4LW3MlVpA1F6YCsKgX0BDaH+3pk9jQKjyJ7+\\u002f42ZAEpK7GDr2aUDIFdl+6nVsQDqUo12I125ARcThmknxcEBfnNxkzDBzQO+tSPGqZnZA79+lK0OAekAvWmNGRwR\\u002fQKPaGacqj4FAPToXIDzwgkBUU2zr9FaDQGDGVW7Ay4JA7DNtnKazgUATwhJWLaOAQGZG\\u002fSuXGIBAr55S90dMgEA3ODciMzeBQOJ45j9qxoJAE1nHJpIOhUBoit3te06IQDzYRNEbs4xAwNEc6\\u002fQAkUDs4yvquLWTQKucniFh2pVANa5RGJrWlkDbr+ZoJFiWQCHmRgz4dJRAV0UyuRiekUD103ZqsN6MQMpqJx7p8IZAlU59rjAvgkCboc5idrR9QNvGN2gxjXlA+ZnnexQGd0CO46MbVhJ1QAirgTrK2nJAsKVqOen+b0CFuCkDTEJpQJQpoDQ+ZWJAvwgyPFy\\u002fWEANWL1HeyZPQM7F0tEL4EJAg778u9L\\u002fNkClaUjnn1EtQOnNICWlpiNA+1jmJubAGkD4QHSxj6cRQI1CBqDkzgVAIijo+p61+D9WJEyacGvpPyZ7Nr8lodc\\u002ftNYQ6uTNwz8Ufpl7meatPwfDrMSBUpQ\\u002fEIQ2H7jdeD8t3LmD0mJbPxDoiSr2JTs\\u002fGl16q+s5GD\\u002fXQV8t\\u002fnbzPrP8IUqfLMw+7555xDVjoj51a3FGEAR2Pp4hye7YIlc+B1V+pd2Wwj8giUz\\u002f8a3RPwM5+EJUgOo\\u002fbD5nhTn3AkCy+LD07IkYQFyt8+CAlSxAxeChyMj3PUCUaMCdLEdMQN1ifwg5BFhAXWwpYgJcYkCws20Ds0RpQE1E6DPhUG9Ak96XEvl8cUAUFHPvIqRxQFXWV2Q4K3BA8UQ7xLZca0CbHlhCfTtmQIy0Hs01xmJAx75s6oUmYkBBZeFu+o5kQPv3k1tkR2lAfXY8amoDb0B\\u002fjTOqJTRyQB8CoUbsS3RAD\\u002fLmwn\\u002fFdUDIYW89nAp3QMAtzsUzynhAXWxMRrG5e0B7iq6n2CiAQNiCoCp7R4NAtQOfI9jihkC04gzculSKQJwssuJ41oxARmPAmdvVjUCDhYmuOESNQLWuqMu0qItAKw6HXIngiUADLSECI7OIQL8YIhP\\u002fgohA1TTfKmtPiUD0ISJgIf2KQABxqxEGpI1Ac6Jc4vjMkED6qw88J5KTQFtkdoITAZdAijCOUHyUmkA3k8HN83qdQMYTgyw75J5ATgYcQ1xZnkBDhAW9yO2bQO8tkPp8MZhAZzVgmVXyk0BwM0DiruOPQMGYsnOJYYlACKvpsCq6hECxCcQVjriBQJ3eDlVDl39AYHOcSnOPfEAy5hbBJkl5QDlA3qxBTHVA7luJbfLCcEB2G78\\u002fNHpoQLY4zaTvp2BAjIV+gO6HVUD\\u002fpqjn8GlLQNjxVAJx+kFAZUN1QMvoOEDJVJSSkugxQLxRADoaiilAnWAQ1UlPIUCB12m+9LMVQLbQqA3bzghAFgMWTUCs+T+JIa7xlPnnP+RIhBKsLNQ\\u002fpmFjVP6Svj+uPCo9I9ukP\\u002fOhVGS7nIk\\u002f9bPtoPxPbD+vM+2u4yxMPweqMoGqPyk\\u002f5x+0KsRiBD8LQQgRdLHdPnDS6x8xjrM+e\\u002ffd69HHhz5PSZbjtJZpPlPgb+Hl\\u002fMI\\u002fky14kSQe0j\\u002fAUDC180XrP9nFhhwwmANA7N9JJ2dzGUAJrTNCN8MtQKK3lu3IUz9AdK7Iwf2tTUB3OY89ZE9ZQAaUqtz5bWNAbLmDv7vbakBmfnFK0LhwQKVYQ6+Lx3JA1PyABn8Vc0D6ywegybNxQLDUAViKp25AWdQhQO4SakCSFqvoHstnQLt\\u002fcFIBK2lAc+TIGXZXbkBe9JxhIh5zQD\\u002fnorz7endAqPjMdnRHe0DpOi3AEOh9QKMDJv99Zn9A11iVJEk6gEDsaG8jiReBQNW27QdF34JAA48p0xf5hUDZcTSNPXCKQKbaQHPpz49AlwAiV2qSkkD+e5Zk7J+UQBHNdfVwmZVAr5Lw62dllUDMfY56p1mUQK2ZDYhgEJNA16Wb+NsZkkDtYYUXN8CRQNdyhF++BJJAax5Z1MDRkkCxBAziCDOUQOTugKPeYZZAP2CObsSTmUA1OsFFKaydQH3ksPVsBqFAf4zsXpXYokDTLqdqMsejQE5IMCFPgqNABEewpyIQokBVD+H3RIyfQGAnVpj1RJpAa5dYXXE6lUC8L09ujw2RQKo5Q9L98YtAxGcDuW6+h0Bldu1s+tiEQEPiDLDJfYJAWsJZ61cUgEByu7VnEbh6QIGMnlHi3nRA\\u002fV1rCZ2BbkCLMAId0QtlQF1q4YTWI1xAWcjmBPsDU0DF9aucbwFLQK3sPEGnOURA\\u002fse+g6nOPkB4kfP2a8A2QFfoJ9yMcy9AKvDIwynwI0Bg7F42g\\u002fMWQBvW0Otk3wdA6n0QsmNj9j9jPbMZfOniP3sD75V3xMw\\u002fzzmqDDKysz8vDzi8c0eYP68F4xge8no\\u002fRSO71SDvWj\\u002fJRUqx1kM4P0tR0VcyuxM\\u002fePOZgS0M7T6WXaFXnXjDPpObPUcgeZg+jEm8\\u002fzi0ez5VxlILKuLBP94EOcCjH9E\\u002fp\\u002fqhbLrk6T91L9\\u002fWeq4CQAkw2RgOXhhAn24way6eLEC6RlF2H0E+QG74pFK1ykxAGSosyqWqWEDeopd94gZjQP3+PC7db2pA93NHMOqNcEDeTWn3B7lyQEgTrIf4N3NA7kdEG3gickByMBXeCTlwQC2Mj0AYUm1AEM0XbM4\\u002fbUAiGOAO1dpwQCVCJAVCW3VA\\u002ftlHmD9ae0BS\\u002fFZ70sOAQDWcOXEmRYNA3EhVyRvKhEAN56ajcGOFQOBM87tRm4VAmUCggGpIhkA8LIi7EEmIQM62s6aXO4xArag5YgIekUCicYEzJ9mUQKfk9NUZqZhAcs9ohbK5m0Cn3Dbq1V2dQNzMXgO\\u002fZJ1AhUjfujM2nECJiRtDzpqaQE2246CAUZlAn0UXQxS7mEDgrvEmyNOYQEJ+uHVVdplAer4gCiermkCepXSkLr6cQDRM5v+JAaBA8j\\u002fuzxc4okDl13qZaq6kQJtsQpjdzaZAqeA70kX2p0C\\u002fttOiYsKnQO246Nj\\u002fMKZArPiZR62eo0ClrllZKJmgQF70tVG0UJtAiwCn8oROlkAn7FE7H2+SQANTAqU+LI9A6VBl2IPfikB3tmPq\\u002fDiHQEOOHn2+qYNAMsV\\u002f4Ez3f0Aeg5wDE6F4QKwj5+1c93FAccb8VvIuaUDw2PBoZ4lhQE8bUziwWVlAmQ7kQkJ7U0AhyxymSDdPQKTVQdTZ3EhA4mT5JjPXQkAqyhsZ3Ws6QBbs5n6N5DBA4mgXL+SOI0ACEp9MhW8UQDX+CjPTPQNAnA6pSsNQ8D\\u002fOHYP1OunYP7qyPEMkH8E\\u002f1qDLnWwxpT\\u002fLlUu6waGHP1WMAU\\u002fEwmc\\u002fzcEhoguVRT+zuMNZXMYhP4VD3nGjvPo+7\\u002fJEpGSd0j46CfAn9RipPuEb5oSyWI8+7+DNQAYgvz+cOnw9SOvNPzgKI3h7vOY\\u002fLigVDnd7AEBUgM\\u002fyYpoVQJ\\u002fgQcJ+fylAXktq6GEYO0Crlk0keuxJQGzqnJ+XVVZAIlFLg8FUYUCgVyicNj1oQNCMgTe4lm5AsrjOsf93cUBdLUc2KzJyQEpIJ35dm3FAM6hTqHR9cEDeVE\\u002fVzxNwQDzp1s\\u002f8m3FAHAfa+k\\u002fXdUAuOHRYu6p8QIDqWpzvgoJABLvvZZSWhkA2uqL44K+JQECgrKtZTotAAZtjTGmUi0DIAb79V0iLQA4BD3PDlYtAQ5NvxIGvjUBAsC5BFziRQC491PunApVA9RA\\u002fht\\u002fYmUCetH5rKueeQO7TiZopjqFArvMOqGrKokAD9jS17QOjQE1dVSSWdqJAWHuVCwGcoUC2MMvAkOegQLBlh5iyj6BAi0z1OIKJoECjeXzA8LSgQF2J\\u002fvBXEqFAbkazDwnUoUD3dmJMDjujQAjMQWKTV6VAcAwgVmLcp0D2jTRcDCmqQG7eh3L+iKtAFsmI3maCq0BdNDuALweqQKXQghkrcKdAuuMib6VMpEAJ3nE3fSahQFlQLj0espxAXXM3WisNmEC1qMDO0UmUQI6fy0WkJpFAVPlO0PW6jEDznqNyFn+HQLOO95WPhIJATvdpAZbue0AmNK8DrUB0QL+puJsC3mxAYKABFFALZUAtFURLFE5gQBtbgEga7FpAry3ecRHEVkDrQlysmr1SQLTwdk4+7kxAfgncXcF\\u002fREAIY7kPA2M6QPswN\\u002fcWsy5AMGDW2NkaIEDFhyfv5HEOQLVy2R0H6vk\\u002fPiPumLPc4z9TzQ1K\\u002fGvLP3A9EkjmDrE\\u002fmMBntswlkz\\u002fqnNI6NHBzP6UXlLFg7lE\\u002fPucY8HhULj+ji2Sq2OQHP8A4jDRE\\u002f+E+GTInTQ6Zuz7TJSkedvejPjEEphOXH7k\\u002fPHff40hDyD9SU1GsFo3iPykB94vnDfs\\u002fENemkSjWEUDWB457RS8lQFBu8Kn5qDZA5mhKfs\\u002fURUAYOBgdzvJSQOQ4SIxTpV1Az\\u002ft8JNjrZEBkyBqYn7FqQIyrzYt78G5A0bEvm3l5cECinSIjVIpwQDMM+HM1eXBADWcyVLOEcUBbLsWgsOx0QHTi6pHmaHtAb3kMmmtbgkDfN\\u002fXLPbuHQBwdyBo5wIxAR2UP2FgskEAwhb2Pp\\u002feQQGM83wbS3pBAcGkKmoBlkEAZAvTXW02QQKfRbOmrWZFAuqO+k9oTlECE3UrjV5mYQO5bmfSneZ5AZ9ygYqJcokAqf5GSxwmlQG4j8EMwvKZAhc+DVNBFp0ACBRuV5OimQPH7aYHBLqZAMzJjl2mWpUDQ3r\\u002fJ5lKlQCkXzcJeR6VAnCO278A\\u002fpUAGi7dEWDalQCivZxVwbaVADA7Gb9ZGpkCN6QI+uPWnQH6bCSvOQ6pAG4oUwDqVrEDmgkc7riquQDcvHLIreK5A129xGptcrUDS497mFB+rQHe4KSNvPahAffNbkiAwpUDVGKGnfESiQJqm2Z\\u002fYL59AaL+9HhlSmkCOD4LaUNmVQDDtcl\\u002fDtpFA4qFfbOfai0DR2gURMyKFQBBP7GoI+X5A9E4Zjrc1dkCCcP+LcxRwQKFdPlg1jmhA46+lO3xJZEBlaim9tsFhQLwqrXB5Pl9AtUXMyeNPWkAtlNSUEZFUQNlTDAMyXU1AlCHL\\u002fC3\\u002fQkCR\\u002f+Pw5TE2QGJwydadYCdAfPipk7kuFkD9SZ2nkvUCQJf68s2vMu0\\u002fB40CuRZF1D\\u002fzKb\\u002fZgWm5Py4F4TAQ25w\\u002fDSYz9fTafT8ec1B0uHVcPyWyo3yweTk\\u002fTpg555cFFj+QmdQpQ\\u002f\\u002fyPjZjqdGjWNE+tS1OAWsovT7HDRu6t\\u002fGyPwUu1Sv3Z8I\\u002fmOlhvzFf3D+1Yke4S9n0P+8UehhztgtAi1BtyASZIECx0Fx0J+oxQGH385BUbUFAM4h+AgeSTkAWlvhsvjFYQMz2ZpwbTmFAqbhTmdxyZkDnrDlQVZtqQJTQVHPMSW1ABlSVie30bkD2mXn1U51wQOek0ZQnRXNABY5xmyKleEAvU7kH7K2AQBcIoqQVe4ZATa8kkfLjjEB9ZhVHzlORQGflvaTkR5NA6vKLKLT+k0CVCuMXAaOTQEnmBEE915JAzvF0OJWDkkBGg8uVno2TQL\\u002f4wSsik5ZAse6sG32zm0D8pz4gQjehQMOWrGI81qRAoKMVT0EEqEADsQ7ykSyqQAFW86uZHKtA7t\\u002fvAcMaq0AR0dM+XLeqQMkbsefycKpAjGMA9EprqkDd3paDOG6qQKH0Wez2K6pA\\u002fW7ljGmXqUA8HQrfcwapQLy+lEx3BKlA9vhJQe70qUDDcNDVWMirQH3wysTI9q1AbT1fIIbBr0BsrDp6OkewQBioXZMdErBAVu0xJb6prkBLHvLQcXasQKQze+FY26lAcMDGIA0Hp0AYffcU6QukQPtLIfbO9qBAhKwI4y\\u002fAm0AVkrhpatmVQGlOEFvCiJBASqqRc6sbiEA6d3LsFBmBQPyjKLjBJ3hAL1RN34+1cUAa5nDMmydsQLpf\\u002fRvzeGhAeKcy6SVRZkAyIm5qmSJkQH5ccT7FMGFAT+3CuxcUW0ATtd2i52xTQDNFWKFtO0lAytOwUZuWPUDGKV2mt0gvQCyXwICwzx1Acy8wJ5CbCUDXOy49cdjzP8vLl\\u002fIqzts\\u002fI+l97FmtwT8hXDLdJY2kP66y32NfJYY\\u002fvMxzqeKdZj8yzdiooY5GP5Q6HiLFmiY\\u002flE7bXkENBz98yVpNVa7oPlGvi3uV\\u002f9Y+Ks1yFtAFqz+OWUkdrnm6PyvPM2bUodQ\\u002fS94fSgCn7j86mT3lmpoEQG3FgZmi+hhAdwnLgVRPK0BbSZNt5u86QCS+Mdia\\u002fEdAJ7emdvJOU0AEycl9MCxcQA+un1HyuWJA6W5BuDjuZkCPHMJ5cW1qQAoD16aDyG1AacU5EVtLcUB+mPVMcJJ1QACSfLZFz3xAluTMk\\u002fm6g0DgpXyB5W2KQDyzEk+LxZBA2Ht8My7bk0CEYxxj5dWVQC7sg0I+apZABuxoMgzTlUCzpIRvjMuUQGOL+iJmVpRAS+FSxBVtlUB6Rt56B7WYQHYM4Hu+SJ5AyAG8pfDNokBzPwjTPMWmQJGBzDZlVKpAKgDFEJXmrEAumXFrXkuuQNkeCPDEyq5A7cX8voDxrkBQYfeb\\u002fC+vQE4Xh2YLj69AV5cIXCa0r0AbKh8HOjWvQLBcKtU6\\u002fq1AqaziuXJ7rEDxf8OGVWerQIl1CzV6XqtAJXjSSfeDrECI8L4rvnCuQOlyXTbPOLBAV2PiCOnxsEB6QoHmgjmxQOl+VbEeDbFAH9t7BfZ7sEDq4eEREyGvQKeXLYutlaxA+4totiFXqUBRatVtAoalQK8Q7CmMa6FAOfo9aTjQmkBDPVOq96mTQBJT\\u002fRups4tAFbEMq4cTg0AbSTJeUJB6QE6gdlt9pHNALiOa7dgNcEDAQYesp8VsQBKCuLMXyWpAPsvVROpraEBdGXulIvNkQAVxmgD8imBA+xQftqrJV0CngFsAnfpOQEs8ZkQrOUJALRh3JTFZM0Dek8s9yYkiQPLNS6YwCxBAYhzWBbkk+T9W4605PO3hP90iZOP7ecc\\u002fFdaM3CazrD\\u002fZc7ck3sqQP39i+IywZHM\\u002fUYKN4XmfVj\\u002fgu5TMW686P\\u002f0eMGv0Oh8\\u002fi1k1usJ8Aj92Duu4iS7yPm1nFBmml6I\\u002fAxDtIoNusj+MRYDHZi3NPyXTI681AeY\\u002fsiZG5OgN\\u002fj\\u002fJ\\u002fgOBp4YSQIL31IGQniRARnVqkUu7NEBsnVy859lCQG0GU892EU9ARWx69slKV0C0QuZG+QFgQGMb+cmqdWRABlNRM+nuaEDaC8+yLhVuQDGJHxTYt3JAET45OAmJeEBBuCQfI7KAQLH+DaaMwIZAu6IdsG78jUAv9nIOF66SQD6KSuBqwJVAKBsObH+bl0Ak4mJ\\u002fLgOYQK4yf0gYRJdAh0UBWC8plkCXB54o376VQE6EYfe6\\u002f5ZAhwU55mmJmkBYdPSpITWgQAsD09EZB6RAE+8fXj4rqECHTr4qfvWrQP8lxlvP3a5A1FKYDkResEAV6dukKeywQLYJjDzHV7FAkDr439nLsUDX63a\\u002f4TqyQCVk+cgrZbJA4G9y14kIskDJnIgd7RixQJBEuaSvsK9A119IZ1Z3rUATFL1w71ysQJcojhu1u6xA4FkM7R5VrkCC7cldEEWwQGCPXPLLWrFA2xVMF5I0skB7vWB377ayQBGoQiSXzrJApQmlIN1gskB4nzLZw1CxQH8PA3cFKa9A04owULWQqkDaal9UPlylQHure7hKMaBAZJ6jItxBl0C9tGH7YgSQQNOQvoihq4VADOS+fMn8fUC5acT6fVV2QAUFn0vyenJANWfvp3WqcECkuQ8O0\\u002fluQIQ4lM1MFmxAt\\u002f6SLpT3Z0B7n\\u002fbJztxiQHqyri2EGFtAw86nmSeqUUC4Y5mJAtpEQKOG47rHQzZA0RQK5XSFJUC3uiI4ReISQK4N0OkbQ\\u002f4\\u002f6LbNvFhk5j8eXJ3l4i7PP7t2ZWjp\\u002fbQ\\u002fv8fvjMMfnD87Ii+0EA6DP3xCaV1h62k\\u002fzFrLC9U+UT8SR8bw\\u002fOA1Py8i3lhDPhs\\u002fzw3Tltx4Cz\\u002fGUt7iDFeZP+8Oc4ndhak\\u002fA\\u002f1FQEKfxD8LHJreyrrfP6F7mqcuHvY\\u002fD6M2Z0HcC0BnbLCxRLgfQPSKBJ8jVjBAwLTQV8l\\u002fPkAw7R4ctuBJQDMTr93FEFRATenCxHi2XEAmPoT4JEVjQH5LgRJr4mhADPtWgKvqb0CZudbGHuR0QElIPrY2HnxAA6wmAw0kg0DEwadMgqaJQGWhgAZ7gpBA17HVm1IXlEDngGkzGO6WQKSA8L7shJhAsFSnhBq6mEA\\u002fvyF0Fe2XQD4aWmcx7pZA9nm5fJrBlkBsQMwSik+YQO5d\\u002fr4CH5xABGdYGRUWoUDo+j4YL\\u002fCkQPIPYMohGalAaH6\\u002fCCz6rEC9WFGVYhGwQONeupyJPLFA723rxpofskB\\u002fVzFE2u2yQG2JTHuQv7NAciA57QhztEBMCPZidre0QMf70\\u002ffaQLRAIgb\\u002fq5oDs0BFsiFYTk2xQKPk2UFlT69AlbMbymowrUBbMCpPK8usQKFrbnygCq5A\\u002f1IrT2s0sEC2A43\\u002fR6KxQGZ+\\u002fkwFDbNADO0c+dlAtEDwvmcG1Aa1QDQRUakYHbVA4HEWfw9ItEDPwR4cJHOyQEBfR4QPj69Ae+G1ES5LqUCK0sKVsAajQDmvNjS7EZtAE2JEFtaCkkC7ewp9lQyJQJBQmTYufoFA\\u002fZmbTz1aekCjTWAUOc51QBTHOxF2WnNATnYmI1+OcUDpvHg7UBxvQInpFydWFmpAd66eV5NOZEBsa5tYmgVdQJ8HoVt561JAyr3wBnxwRkCE0SLY5TM4QHaWot59zSdAvX3bYgx9FUCMhRKp+wgCQNmLOvKBuuw\\u002fs4sqAA9P1j8iwJuGy1DBP+KIArtVGqs\\u002fa01hzOIYlT\\u002fLVd2Dw8x\\u002fP7AlO9xbmWY\\u002f59R23jTQTT+MnCLprwAzP3+3sKogZCM\\u002fx7YGaZmNkT+w86tmfAWiP6\\u002fSg2kB170\\u002fJF\\u002fVokx+1z+NPLgNiMTwPwdWJPIApQVAuPGBQJlIGUAcBl4EpMMqQDeMmZQevDlAG+uM+hWRRkBWf6uI7ypSQIfjtjKhJltAY0ZDe4klY0AEF37Jng9qQArQSx5rh3FA8ivkm0+vd0DYt5hs0hSAQCKPs6tZpYVAjMlKdm1UjECH77UI3rqRQHg1vXS9\\u002f5RA3MRA95Rsl0APMJPw5aOYQAmlyGyLpphAh3HvGkPml0BbQWNL3y6XQFJ0Fm1pbJdAON8Q1XVkmUDOLpEl9nudQIQ7kkfHzKFAM4zcqNOUpUBw\\u002f1jMo6KpQCzANIUKfa1A5k8gLgVpsECI7E7lIsyxQBbr5HAZBbNAMbEQgMY3tEBNzi0MCme1QFw8asn8XLZA+b8XujC7tkC3dKQHtTC2QBH7SqBHtbRAe35J5sCgskAB4vYJaIywQNpEFjRJIa5AytTWyWUQrUDvV7pWOfWtQC9biJjTMbBA+4t3NcPhsUBHbwtIpsGzQIK5tXCxibVAvywC887jtkDNG1smS2u3QOXVg7d6ybZAJ6ozsNPgtEBDoFh73eaxQHBtRqiurqxAGN8keQyNpUAr8X8pDbeeQGyQCY42M5VAWpjGyltHjUCGZHXTWQaFQBkS1r\\u002fRKIBATGYOweahekAkzj1sHPR2QLYhSCV3+nNAFr2\\u002fWO0CcUCcgRi4dKlrQP9wmTmDG2VAUKNWFQDiXUCJZAkhY3xTQDw1ZqXVVkdA4IhZoSG0OUDuTze0DzEqQKq+ERNIAxlAMMkM9sPXBkD99BEmSHH0P5kQGrg6RuI\\u002f4EHKEMtb0D9E4c2+Zdq8P\\u002fSSjLd5cKg\\u002fgsFKyIJrkz+3k0bBanl8P6dZNceMIGM\\u002fXsBt2UKrSD+hUI7lRFY5P1DiDKRGC4k\\u002f+jQ77TI8mj+r3K8\\u002fS0a2PwtYmf\\u002fS9dE\\u002ftdvc1+FE6j\\u002fiwxRrGGQBQPFJ4FWZ3hRAlOeenC++JkCeIOlNx5M2QGv6YPnjg0RAq4D8WW0wUUC5q+0EkdlaQN7W5HY71mNAU\\u002fhPj582bEDE+9R0\\u002f59zQJuThFff8npAEy9nJhY9gkA6AWjo7xiIQBR7h+iGr45A2529rmqhkkBc6TJ7lW+VQIAwEEAVVpdA9CN0ysYimEDLvWllyfuXQD5BGKEHYpdAjBQUr0cUl0BWomY47tqXQBj7LDMuTppAzqW203aqnkB0DJF+TmCiQGy1YVO6AqZAz7OK\\u002fJLeqUAMiSy1tZytQJhZyX5ThrBAdhNHhecaskAwRYNaQKSzQIzEdzwZNbVAkX5\\u002f\\u002fWK6tkC1V\\u002f\\u002fpNey3QNcpGFtZY7hAuGXdpynPt0AB1TFzLC22QEwVN0Tz3LNAGNUmZzaAsUC6hfVEYHKvQNLOY+vT3K1ALsKn4oRorkCIhg8T7V+wQEhiw\\u002fCEK7JAg6YcUwNMtEAPhMFtEG22QNU0HKo5I7hAw5+OU37xuEA5HcChxG64QFyKf1h0eLZATlUAhxpOs0BzGfLZT\\u002f6uQBHlmYCxZ6dAV\\u002f8K81HjoECl0DWFquaXQCKPqbqJKJFAfiDIUz+\\u002fiUDL\\u002fOVgQWKEQELNqdCZy4BAi+FjYfQbfEBI39Lyk1t3QJgfUoPF8XJANMhF8+KVbUCsVtUDZftlQHUIBiY9yF5Ard8pthctVEBAV+YI07NIQMvLL2L0VjxA5+ErntDELkDhQCEEQxEgQPaG2RpUcxBA9Z9+a+C6AEBrkVfhqOLwP6Mtin27p+A\\u002f6uLXHYhozz9ak\\u002fEC87u7P9dT9HmWlqY\\u002fshkjbw7OkD9t2cmNz8l2P7Bwj80vmF0\\u002fXKzdGW+HTj9gBXpa4UiCP+VZzQxGh5M\\u002fBdIOiO78sD+LyRn09wvMP1rExQXYBeU\\u002fLYYG3TeV\\u002fD9BsKYzJacRQBn25kBw3CNAg5Arb1NtNEAVpDKRcE1DQF3HpbPd31BA\\u002ffZlQYmLW0BpgjYcPDllQIPAX2l1Pm9AWirTPpwxdkAhxPHkAJl+QN31W2NedYRA7Ynl1jJlikBYsapqdE+QQGCQ239qMZNA+JepiiZylUBX19+iD8uWQCf+dgkpNpdAW2pmXUv7lkBzSAvXWaSWQNtdhxrz25ZAdMRcWzpAmEB8WSKFLzebQG8A6hv10p9A0j8ElCHookCdZVAmNFimQPqTMn9T86lAjK+ggDSFrUA6uyvdEn+wQJ09YEZaOrJAtBOtkuQGtEA2a4xlpOa1QCCsd1jGsrdAXpUI9rQVuUCvXNpWD6W5QJlf84A\\u002fFblAeMXJtdFqt0Ct7UZ4Twm1QAV3fJK+krJAPziRifyosEBIsKUtummvQAcGn9JXoa9An0n7aHjZsECubxvtDpCyQCmF4vECrLRAqldp+gLWtkDpd2876Jq4QG95noaTdLlAibodsd7zuECgbwqLZva2QEvCV0f2wbNA362dVzDdr0Bqg1DADlWoQLaaX4d99qFA33JbrX1umkDI77ijRQGUQEwm64tXqo9ANGuReL76iUBztDHSTZGFQNKjPV4EtYFAxoEfL2JdfEAseB4Vtgl2QDj7tmHPkXBANdHCDkgLaECRedu3VcNgQG7mt01nXVZANRYAMjmBTECWkoz3mWhBQDpU9o\\u002fwjDRAIyRhJse0J0BQx4HR8u4aQFIS0ChcGQ5AuxU1Ya1ZAEAgrckoIffwP98YTQ31g+A\\u002fKCwzite8zT\\u002fF42MK2oK4P19cQB8uYaI\\u002fJz70cHsRiT+0t+D5KlxwPxjJ\\u002fWfR72A\\u002f\\u002f\\u002fnbMQbfej\\u002fYORIGmUCNP8tD3W\\u002fxFKo\\u002f6SCnPVATxj\\u002fcgp9SWQHhP7cUzLL31Pc\\u002fOisA5T9yDkAWfdRCNcchQJcm7tJRCzNAlwfDAqLKQkAVh5nC+CpRQBYb9aj0OF1ARtgopqdUZ0AJD7\\u002f0o5lxQACaDwMIQXlAP+YgDB9PgUDm5pXL+LSGQKEtJN9Qe4xACPWzDFwKkUBAMgzT+WqTQBRRAYEdFpVADEL0ef\\u002ftlUDUnKMDshSWQCU3AQgp65VA73KvV3z7lUCjEJJTNNaWQIxHS7mM7JhA8NMo4\\u002fZxnECTJ6V+qaegQMfEkxuNl6NA2s3Hqk\\u002fQpkAwu8Y8uiKqQD\\u002fpoC4We61ALb0I6XRzsEDuGNxyfkSyQCG5dK97PbRAaPSabQdStkCwn9yjXUy4QA7lntqUz7lAk0gqu1l0ukAlNT0nHPi5QPAN4ktgZrhAOJ1iPfMhtkB6nVPutMSzQFTg+U3t5bFAWr0x0ODmsEDXrnPE89+wQCZ2bcZesbFAYuin0QUis0Dgx9ysofC0QJcI\\u002fk5BzbZAGMM\\u002fr2BMuEAGCFjf3e+4QFNV2npAULhAgrTdRNRQtkAzd00VATmzQH1d3gMYOq9ASti9CJ1EqEBNM++ODoSiQGkijwYAqJxAiXvhV+sNl0DB3208DUyTQHdv7w3FbpBAIKVxMeOti0BxsIMf35qGQAbnPcvexIFALpEKynXyekCLE75ArNhzQLF+9OVZk2xAn3X5Zz4nZECx3ysNlMlbQFu\\u002fnqrIqFJA5rfYk49fSECho6l4FgU\\u002fQF\\u002fNy\\u002fb9SDNAgNqH1appJ0Ax8o2\\u002fHI8bQIsrrZ35DQ9A8y2XnfR\\u002fAECkgGd4dE7wPzj25bVIpt0\\u002fZL5LUsyayD\\u002fiL09krouyP8IfSWYTaJk\\u002fur89AxqngD+r15JCuEtxP1JEiKaYqnM\\u002fsF7hBqPfhT8aKPHr\\u002fRCkP4GZxERSgME\\u002fWUwsOrbg2z9dHDmXR0X0P4fTxWny9gpAPZno3aFwIEDsqxUQpmoyQEeOY+Og\\u002f0JAMUMxxJ8YUkA5q2qo1e1fQLtWKPVxL2pAauE1OZkMdEDGFRveaMx8QHiCrvuXfYNABXq2whf2iECFuZFKqFaOQNP+sN3yiZFAjq80hcJXk0B5zQn\\u002f1HGUQO6j1OQA5ZRAdG0xWsT0lEAZQT9tIxCVQJ0PicG3t5VASqVeaiJdl0BluIW3fkSaQOqQSiF8cJ5AAd5\\u002f4iHRoUAvPGgILrmkQCRxVAk+vqdA6nNSU97FqkB9ED11lditQE1aa7PrjbBAbv6XnY5dskBEcXCHG2O0QBciVsXYh7ZASzfvrNiNuEABR31Zoxi6QIcTmJ51yrpAzP1ycB1tukBezvwlSxK5QCYcv+lAF7dAoFw0U+4FtUD\\u002fudDmuGGzQIdf9IQEe7JACq\\u002fzQEpgskAyxTvXdO+yQCCGzBLv87NACqAoP0M3tUCLWxJLdHq2QBbkf4cPaLdA\\u002fqHpA9eZt0AVJwBeYry2QKneXaItvbRAf9VvN5XgsUA0Of1h0FetQCQyAFrDXadAFBAjDoOZokBoBGG6r4WeQOnb3y21KJpAkjIrNzsXl0C6hZiMOl+UQNLDydmpepFAXnjyxXS6jEC6Zz1I+46GQDEfZZ2jDoFATK2YgwYseUCwwOOKdVpyQI7akTVNl2pALMPnXk4TY0AVXy+Dm+JaQLcol62Ve1JARUrXzlu1SEAMHerLMglAQJozicu5JTRAu2v538JRKEDPwcagFeIbQHhvpkck+g1AYuZ2rWLd\\u002fT98v+LD6U\\u002frP+9uiP7cxdY\\u002fSnn0PHU8wT+FkuqvVrSnP1Qn4V5PM48\\u002f8rq6iM9AgD\\u002fHNHQ10f5sP7ABqqBAjYA\\u002fKMX7lolxnz9XXphFTXK8P7apGWXXk9c\\u002fRPL85cbj8T\\u002f1Q3aNtN8IQJ+\\u002fpR9OtR9AwP+Ww9OJMkB4+S1vq+RDQOBY51aEmlNAFoYjhLvCYUBSrlsQoqRtQPqwP4as2XZAodg7WkBVgED2X2\\u002fcb8GFQO+YEs3iJotAlEXmAfDxj0C\\u002fVt+YVNWRQFVKTmGaCpNAMmu6yw+kk0DyLdclr9mTQPmfrhtlCZRA9lQoic+llEBIAdr1lx2WQPGERignwJhADTbIkGelnEBbhqzRntCgQJBjgg24pqNAFH9\\u002fWz+XpkCv0x74D3SpQISCr4rqM6xA912ZtX\\u002f2rkBn3tnl0PmwQNCvfHIVrrJAtozoJx6ctECQwIwvaae2QLL7TQQIkbhAmUqw06AEukCVH+P887O6QFThI1kCeLpAf7uvyRRouUA7dzfPXNi3QPnShpQdPbZAkXoGbK3+tEA6UG4HyVS0QH9m1rOlPLRAWyX5pEKMtEBe9iPkLRG1QDIOxt3wobVAPylP\\u002f70XtkDPm+dKVz62QFfw4N1o07VA698N6YWgtEA\\u002fONA2F6CyQKijytaJD7BA8zwSMlS6qkC3GBoPgPWlQNctutQLYKJACViAkFUKoEC7r0uaWDGdQH0YJXimAJtAnDONEGOWmEC3UdWpV4aVQErLyo7N7ZFAWdZolBN5jEDZb1yd5s6FQBVerOZcY4BA+BRFtW2FeEB9DFYCSVlyQFJTKj9nSGtAMrsP5XrqY0DOt9OvU0ZcQIPIJLSJbFNAeGbx7ubGSUCRHvlJXHhAQFFwuHVdITRA7gaAMyJKJ0DCX0lB3DQZQDQYevhuPglAuJYbyTAw9z8C3vo1QGnjP4HM8rhPfs0\\u002fXHBx2PZatD8sCZDj5eWaP1pfexMXHIw\\u002fdpmSLeGJZj8oKpMLVXN6P6iLD01xDZo\\u002fadTcRzZ4uD\\u002fCAvzKVxjVP7r+ZHTgqPA\\u002fFxiUDhsXCEBiWWh+4d0fQDajiijOQzNAfO0yOsBGRUDhqYgGDnVVQCRot7QbxGNA3gxm+kimcECq9SANeLN5QM7mBqpWPIJAmVC9kmXmh0BbqTy9aByNQEO3iM47nJBAD\\u002fKD3XjxkUADJSut2ZeSQKCyAjyfzJJACxyJfY3xkkDkF5fd63iTQAxBo\\u002fJU0ZRA3riYGTtSl0BZj2ULpiebQLompqlwH6BA3viv2SAfo0BVLe8gbkumQGCNWzGTWqlAQVQRa34frEA3fYKtfp6uQPGxjW4MhbBA7o66lCPUsUCQUGO4n1azQJPD3qn4DbVACAsvFrjbtkAx+mYKXoW4QML9Ds8BxLlAVZE8J8FcukCHZc6j0Tm6QJkuFlqReLlAKqC+QmFjuEBV93Hrvla3QB\\u002ftXlJEnLZAOiphvO1NtkC3KIjN7VG2QJPi5q04cbZADODvDHB4tkCQwiymRku2QLhPylbV3rVA6jhQWFsmtUAOtc2qFAq0QO+1IwoKdbJAiHkEwlRvsEB2lMmuqlmsQHA2I5eUAahAdBiIK8Z3pEBa56kE4xKiQNUn5VFMvqBAYxjo\\u002fCIMoECCLOW7m9ieQDGcY8it15xA2VhIn3a+mUDXkjzU58yVQDsXqa\\u002fBm5FAgGqfqleGi0Cbk6Jj+TOFQJuLdgKGUoBAd1HXqaMoeUBLw+yf1zhzQCgC3SLBuWxAmc2ub1XIZECwEAghgv1cQKfk84Nad1NA12GAAEMbSUB0TdcSFeg+QDu4+TYE+zFAWVTXy3qNI0AQrVAIvakTQBo4jD6KIQJAoBE\\u002fxV537j8EgdimXTrXP2muG0etFsA\\u002fHYNHvLJXpT8\\u002f4mPJHF+WPwHT+dDTDmQ\\u002fPv+nj+Dedz8TB4SnAf6XP8dYZ5G9C7c\\u002fwItNkkha1D\\u002fQEFIM7njwPwjhEPMHZAhAUU5pmhd9IED8u9sawVI0QKR1016fzkZApvL4ZvtIV0C0aecI059lQJNgeir\\u002fRnJAhOwmk3gpfEA4OJh99tODQLJCp4VSn4lArlemIQCTjkAtF7bXzv+QQMpgl7MV2pFA6w92RA4LkkCadCUiPACSQKtxbOIwQZJAo\\u002fZeIK9Pk0Ao4jG9ipKVQCVXT6bmSJlAkdCbMvN8nkAgLO2M23miQOSUUGlcE6ZAL40VfBiuqUACxSUd8\\u002fGsQMHxCz8TsK9AR\\u002fmcFsL7sEDYkZi\\u002flgWyQHZS4nHXHrNAiSW4RnhjtEAa24tTstO1QEUyhDw4UbdA08IGttenuEAqBz5h9J65QPoYjxMkD7pAwdyjrlH0uUCsH45dyXO5QL0vsNwK0bhAZh0MgG9TuECPUGGrqSa4QGi7O+hlRbhAkQ8\\u002fkml7uEAVA9AEi4C4QMx8UIQLHbhAF8DLId4\\u002ft0CaUmdr5fm1QAXWBHD6ZrRAvX\\u002fuMhebskAMAJs48aKwQJXL887HJq1ADz5j3P8qqUBG6sjcL8KlQI92j2EmTqNAOUcdam3yoUDpQ\\u002fxOXnyhQFoX1Ni\\u002fcaFAuZzUifw\\u002foUBw\\u002fs\\u002fbiXagQKIRoj6v4Z1AfGQVvFq2mUCV9gereiOVQMdfJ00o3pBATjZ1W36cikCboapr+P+EQBacl8a\\u002fj4BA+WgS+xTNeUDsQheOOZNzQIR4bZlOqWxAadhGKRItZEBjbVpB2EtbQP8dt\\u002fCKtVFAp4XlOzXpRUBSUOSJUZs5QFcMi17d9CtAdzSYEBg4HEB63W0THR0KQB3XvvRYBPY\\u002fWO+JNDDY4D86uZpxkmrHP1m2x3+1Lq8\\u002fz8ykFEhkoD9oYAdkav9lPz75yJdkw3k\\u002f4eCALAeEmT+CcnmiT0u4P2MCn\\u002fFVW9U\\u002fxwHYeXlD8T9smCeCxpcJQItq\\u002f34sWSFAGEhJsol0NUDvxb+\\u002fFShIQDBsaFaNuVhAyq9DCpf8ZkAgr5PfDWlzQORfQuAlzH1AB\\u002f1kmVLUhEDE7Taa0ZyKQBbDuuE4QY9Al+GC4NAIkUAsUqY\\u002ft4GRQPlmi5kOYpFAKoNVfk5AkUA1v1bQDMORQCPuhQyyd5NALi3mcUm9lkDl6x1Pub2bQHHWgTfYMqFAn6sqA7gqpUB0JNl8Y22pQNdrzeikh61A1BH56IqIsEDqX+4kb+uxQPny4nyZ+LJAft6ew57Zs0Bz6kCLAr+0QI8xfG4xyLVATGUB66n1tkBt+VRKWCm4QAlSVHTvMrlArZa4ZJ\\u002fjuUAJ6iSj\\u002fiK6QJoSy0QM\\u002fblAWJ0qd82huUA463+YQlS5QNjeL57kTblA0ih6ytmhuUDL4fa2ly26QI2o+kebobpAeKrVzPahukAD6ki5OO+5QCGuKLA\\u002fgLhAPQrq+o1+tkCVBT\\u002fVbyu0QJWr3+pRw7FAmNldkNLirkDDeHW+MqqqQHQ8+oC+GKdAEE1OkKJrpEB6cz4nfs2iQCEZgzbSOKJAF21bqCdmokBxMX3YftWiQMz4JHLh9KJAbC5AHMZVokBlzYqqO9egQKz7xlC1WZ1A8oei4DZ7mEBS8v1ZlN2TQFOgLzUg7I9A8PA8S2yniUBOZ1v0ZY+EQIVLjRmnOYBAADoA+ffeeECiFyB7iVpyQNhFxyRBAmpAIeHnCO2wYUBTJIWa7RBXQCfAmhySqUxAdz3txJDQQED0FdIJ\\u002fWwyQFogk2cwqiJATLe6LfhUEUBywSvoQlP9P2+7Q3oDg+Y\\u002fcGF1XOxmzz8GZU+On\\u002fy0P9XRY4n1IKY\\u002f1wtUEa5dbT\\u002fYGftAvI6APxfPtgpqYJ8\\u002frRrTV7W+vD+HrFGaTm3YP9csBNd5MPM\\u002frB7cOF\\u002fNC0BqG8JJ+IMiQAXMP8I\\u002fmTZAZfCaUvUySUAEKrkgZZtZQMfivAfEq2dAtMG3rlDic0DhyHDoT1p+QERrOLgTEoVA92DSe5WtikAxG2zorfeOQEWZ2ehApJBAMFa8lzbbkEA+7BS\\u002fa5KQQJBgzPDcfZBAF\\u002fQ2ZWNakUCFkHYPOL6TQHwVzX20A5hAu+LeJfxFnkCNfMY7dy2jQH7cRsNg4adAwRZHwMPQrEClMJd55riwQOj6RKVKpbJADoNof20RtED89RTmxA21QAwWOsbuzLVAciI35+KItkBiZacfYGe3QI2zIUPTarhAnUA088NzuUBqL\\u002fby5U+6QMdqMLdT0bpAtF5agkHmukDIsbjg3aa6QJrI4yakULpAfyz96GUuukCgcCacHna6QMh5qkYiK7tAboaOZ7wSvECk0HCLk8O8QGgZ17r0zLxAqWsujUzlu0AFWZN45Qe6QCFk8nE7crdACmBXR0GFtEDmG37vc5+xQO6Qgd+zCa5AjcOgdQu4qUC1nH4b\\u002fG+mQCyZDtgwQ6RALxrYJCowo0A6In1e\\u002fxOjQDC6lLnzm6NA533BJLRIpECbhK7zVoykQDcguv4E+6NA9FEXdp91okB6CLbiWjOgQM3SxloZQ5tAcDfmtDBZlkCpWZ8bqieSQHKR0Mv9fo1AnMEg0vndh0DvFAiS1vyCQK7AH+a+TX1AZ4yY6L3AdUB\\u002f\\u002f4QDpv1uQI1oxo0YL2VA01r0FxS\\u002fW0BWegG8y09RQHoUWg8GZERAlfy2fexsNkDXZVXPdMsmQPECsevIPBVAR2Aw2XsGAkDcVO3OpMPrPxZ+WYqMbdM\\u002fZYbxNMcPuj8mCZTDc46rP8L5HI1O3XU\\u002fhNYV4cnEhz9uAw0IKnSlP8feucyOuMI\\u002fwMsNfwBN3j9abcqp6Lf2P90+94fNjQ9APecDladDJEAEg58qTwE4QEcahB8kJEpApYXFmVAVWkDw3eu0zsZnQN95AgfUwXNA5kNVOFvkfUBVtekW0pWEQLoBUFq62olABpICbAvAjUDGP9AOKq2PQORKL6AB0Y9ATlPbadIuj0DVFAb5n0+PQFkQ7UTK45BAopq3xNfok0BsRcXA\\u002fhGZQOCG27AoOqBAD1nQd4LrpEB7JQXy40+qQOu+OPbU5a9AWbSCvZSGskA1O36F+Zy0QAt0heHkFrZAPCOHUVEJt0BvN0xwV7G3QO9HMadJVbhAdgGGFYUkuUAEly9K5iS6QKdiRd4FM7tAZPjIey4TvECrweVVsY28QAkXbBAZjbxALuao6BkwvEDmiUXVEcO7QBbgko6DoLtACcTelPMFvEAm5NMhHvG8QOHxWPwXFr5A+ynFchb1vkBXLu1vggm\\u002fQPIK9r\\u002fs\\u002fr1AQTeJF7fSu0DqLkmW0dG4QPSdQM\\u002fmdbVAs8us3B03skD+1a8GJtWuQOkycJDGcqpAxNuNBfVWp0CqpKLvuG+lQNaELBybm6RAI04nbkKmpEDFvensvDqlQBH\\u002fZ9gr4KVAau1l5REQpkD\\u002fAIad7mKlQHY4jLVZu6NALQCP3y9SoUAE++f67i+dQPkd4fws+pdAdE1T\\u002fXOHk0DeEfTcA9OPQBDzPEri1olAPAluIRWjhEBo6SOdQf1\\u002fQPKfa9F23HdAxtUbDIcVcUBWeo1x7XlnQIinCruW5l5A40iqUANeU0DdyEk\\u002fmudGQKZM5n9ISDlA2TrMhNTIKUAyVB10JBkYQBk1fWPcgwRAcyIQg1ix7z+\\u002fFFy2Qj7WP11Ulbia770\\u002fAiv2T9S7rz9axO37H7CAPy0Xd7h+tJE\\u002fnYozpzDXrj\\u002fcWQHwitnJPys2Ozt\\u002f\\u002feM\\u002f53b5PSGM\\u002fD9WPhch1t0SQCRFSYsPHCdAzsmdDXI5OkBS+tU1nYVLQJIqyX+io1pAE3n94Z+0Z0BuyG7exFVzQIEY43AU13xA9wNrDbSkg0Coj19OJXSIQHYKMsN27YtARsv2OAqMjUCsAWElQY6NQCzLDglgAo1ADkOC0ix7jUCahUnRWUyQQB5mWlxv0pNAzrSlGD+ymUBHQlyIGAKhQIKEqm6HRKZAj2zx06tIrECWwRXSdz2xQIN09gOuErRAwXsOYv5UtkBHP\\u002fnGFuK3QODb0vrn07hA8Xmd341zuUA4xcoZUha6QORIFRDI97pAFeda2PQhvECEwPbLu2q9QDfoyidMhr5ALuVumMApv0C2nIeJaDK\\u002fQMZt95b2vr5AxlWQSqspvkD6n8KDv+G9QNvSzjxhNb5AQH+pmq4mv0Cr+dgyUjDAQIv\\u002fc2YyqcBAV9FeoVGzwEAgPCpW2B\\u002fAQAGdj+mm3L1AFfsWY2SWukBvuQfz\\u002fPe2QBtYql9GibNAO\\u002fp6n6qmsEAUHc\\u002fNGuusQPSVmyLa46lAecWO4TcKqEAOJQrfEiqnQI0ogNlwCqdAD3RXny9dp0DsjYxO57SnQOvey1kSladABPhcTPmbpkBW08DOgq6kQL4c6ZCRBqJA7W84lR0snkB8ZvzMkJ6YQE7v63G565NAyH\\u002fQIB0lkECtXnBbwCOKQB511Se534RACeZJz9E3gECX8sb\\u002fYU14QI1Kw53Sf3FAqFmsoi8yaECC5HJXDAVgQAGFmSQuMFRAh0nO1K\\u002f7R0BbzgCBU5I6QAv\\u002f5shCLytAvZckxBV6GUD\\u002frWmLZ74FQGwvV08U1vA\\u002fZCTygayw1z+xXwGJ7fe\\u002fP2fmGRLB+rA\\u002fkRL26IHTiD+RxvvvGwGaP9vNMN1pMLY\\u002f9y6YreQg0j+OxiaUTCjrP8vz9DybqgJA5O\\u002fVVaqhF0COl942dKQrQD+zFAID8j1At1gSB6QSTkCT1bt2XvtbQFmkWQtYFGhAks+ruS8dc0AXqV7cb+p7QEzY9oY8t4JAF7lb1FwIh0Bz1cjklBeKQNPn3TqddYtABoijX5loi0DjvuyH2fqKQC5+5vOYuYtAw99rlTpJj0DoV\\u002ff3a4OTQNWX3UjW6ZlA\\u002f3H56OF9oUCVa4bUAj2nQEAMq1zZ0a1A6h3UWNlLskDVTrGZxGG1QK254QET07dA1g8uDNV6uUDsG1wWy3i6QJQkF21pI7tALVM8xWfgu0BW23rGSfi8QNfTIuNheL5AqpAAeZIWwEAjP2PgIdvAQFd27MfvV8FAdNnGH61xwUBO0oYQfTTBQP3ergJ20sBAP\\u002fhEg9iNwECl1AB10JjAQLwUJXHz+sBAlkJwxceJwUCpm2tJO\\u002fjBQFfIH5lL9sFA13MfVJNSwUC9UPloOQ7AQD8tZ8uStLxAzbKq3T8CuUBCFBRdc5G1QGXyuzIVurJAmw+QL92VsEDb8F0JkiauQDk5Bcm+JKxAUvh7S53uqkDhee8HVVWqQJd+rj+PG6pAjpeH5uDjqUBMV2EdtDypQJ753q7TyadAGPkfEh5ypUD9DZ66EnCiQJE3nux+bJ5AIvspiPtvmECoAm8\\u002fwXOTQJkSfMn7EY9ACFd4c5fkiEBm411V+8iDQBECngmlw35AVveFC+8od0BuMznyr8xwQJpVEyxObGdApEunZcpDX0D72zJKc9VTQK08TNl7r0dAFB4sqpJXOkCKZhPzJQYrQCoh8UvaYBlAtdS0y\\u002fyxBUC\\u002faidXsNLwPwwQ4BxqtNc\\u002fcR347t0EwD8FZ176nAmxP4JMUsu\\u002fjpE\\u002f8U8bOI9Moj+477Bg4PG+Pw\\u002fEs2BU9dg\\u002fdrjdcJpd8j9ErAvwf6oIQPocIkVxVR5A369QUqQkMUCwQlLd0d9BQBq3YdhOPlFAJZ9BdHjXXkCe6zyHSpVpQD0awbENq3NAe6p6ekT7e0COCA2pVmKCQG4btUTRS4ZA\\u002f2P9LhYDiUD3sIQygCuKQANEtGQSDopAFLqRCluriUC1z6Arl4eKQJiG1BSdTY5ApUH3ou86k0BD5\\u002f3PhwSaQLYtbkPE26FAdCCXfnILqEBQX+9mRyivQKclBmRWPrNA8a1jC+KUtkDH0TkBvji5QGmPRr73A7tAeyKMfr4dvEAn+ZOA7eq8QBQVBz5s4b1A\\u002fX6mUL5Uv0Dga7cmOqnAQDnwiS1lzcFAeJgK+K3bwkDUMi1nFJXDQHBKoH\\u002fz0sNAB0\\u002fLMX+bw0CPVmXIeSLDQLoTYyW6ssJA\\u002fRwKmCyKwkCEDBgdabrCQHcmwWwaHsNA7fnVTdFow0CBvUXCp0nDQJVQaHfOj8JARLKi5kI\\u002fwUCan+xllhi\\u002fQLzne1KWg7tAX6jVM9RCuEC4D4Hmkpy1QAbLUbLllbNAOLfLSMMNskDp6grTh9+wQEHzS34C7K9A76N\\u002fPnKOrkCYg1+9PYWtQBxeWQJyh6xAzxfxm68tq0A\\u002fKQRtYB6pQALsg3u4P6ZAUaKwYmfLokA+5YxLjmieQHBt1OYi3pdAaubj7AmEkkBXLTRhLNmMQJKKA1htrIZARTkPhM3VgUAv3bb\\u002f+rF7QMmw47m4+HRAekW9qSC9bkBgqhPraq9lQBQ2LCLFRF1AieHKTKW7UkAME8BYfoJGQKk4OgUbITlAgZzs2b7UKUCLJRJgDkcYQANxk2zAwQRAjyCMBIoX8D8Vk8Xs0KrWP9TW6XM+oL4\\u002fU5Z6B5RIsD+rQ80a012XP8AAfUgyWag\\u002fX8liweKMxD9mJn3QyYHgP5bgNDbVH\\u002fg\\u002fYSpdbMQHEEDJ6U1sLW0jQDEwZKBtijVAyMZPFbfvRUDZF0B2+pdUQKjxUfi04WFAIgAVluPKbEB44SDMEIV1QEOgZq\\u002fV3X1Akv89OyA7g0B+cORTPvmGQLWzAorFgYlANALUK0qDikDEXB5R20eKQOtJdVoBy4lAvAk2jnWSikC65xDZC12OQH95MSRuZZNAyThzqieJmkDG+CF4JG6iQA98IVBWD6lAJBstD85ZsECcq336qUm0QBap+73x3rdAPtwd3tm1ukByNguB4aq8QMNW7RWQ771ABaZOLRH4vkBToSbYNCTAQA4V\\u002f6wvHcFAIp7pMDNrwkB+8n5duebDQJLb2ljnScVAW+XO5ilKxkCnYyPNH7bGQOQ6U\\u002f8xjMZAG\\u002fdUn9n9xUDCx1xUsVrFQJ4c4aQ168RA4MEUt37NxEAnNdTRrebEQLv4JbNB8cRAd2ne3PygxEBecYYfV8nDQJ2QSIpMcsJAZdbz8pPRwEB5oVTr9lu+QHvmNJLefLtADO8N6uMtuUC6VspDEFe3QE9xBfH5xbVA+eFdr7BVtEBF\\u002fmFdpv+yQKXnUGamz7FAG1s0Wx\\u002fJsEBIO7EXVqivQL7NiPvegq1A0fi88f7FqkAVTyMnLVKnQKyJjQoVXaNAPn38Dw2ynkByz1KNGHqXQDbgGqGqpZFAs1cENlyXikDPMVy73FCEQLkvqxPBa39ARZ3MkrtNeEDeqmS58YlyQF9wrpZ1j2tA5LIrp0rDY0CxFm69FhJbQI8h4+cMhVFAyVXbROUyRUCY9CZGw743QDGuenbIbChAJRPtG43uFkDL7Nu+2ZADQEdjL51YQu4\\u002fOIKPT4lA1T8fkghrjqC8P+roN6hnX64\\u002fmtu9DTAtnT+MTI1RGX2uP0EfUFJrzsk\\u002fdvra7SDD5D\\u002fImBOSAVr+PwLbbnK0IxRA2ilYy7JRKECHbOFbGM06QODdSkDGDEtApAr4T5sYWUCGzJzbintlQBqWO8DXBXFAnUVFjuwIeUAFICImpRuBQPZcP2EivoVAfpelwi+1iUCofbQ\\u002fDlWMQGQqIX15So1A0rnU01PgjEBcVlofShuMQPImp7j0moxAShcgEx8mkED96CkuXYSUQDSgkZ0SG5xACw96iXmZo0AHVqTl1ryqQPsZQscDeLFArn2QnOOqtUAeCe1jVne5QFng+EBXebxA7+9KXLiVvkAmUPU7fgbAQLqak07WscBAfnkVTDSUwUBn09jdK9rCQNFu\\u002fDHtgcRA7+7RD7FaxkCg5x0cHRXIQF\\u002f2O+T4XclAr3pa62L7yUCMa1ynVuPJQDf6zlVbQclAbYyKSDtlyEAMIQPfap\\u002fHQJnA38WpG8dAXFzp6CnOxkChgbW\\u002fin7GQLEKf+7U6sVA1DKbcD3uxEC+q5udWZXDQNOqbqAdFcJAYDExPvGrwEB4iZ56pwG\\u002fQGqQ0LYlKL1AHWjis9uQu0C8Jx6ZKve5QLW2eBkOObhAecrzIDJktkBs4ULFHqC0QNZjU17uCLNAuEEOxJSVsUBTbyM5rxqwQFs7bC+szaxApqMFkL7IqEAmJFbqZVOkQKSeNOpuup9AJE3k2\\u002fG\\u002fl0B9n5W5nFWRQElgSjOrN4lAWijBwpKhgkBvHtPzWit8QDxBWJ+XpHVAW7CHBsKjcEAACOKXvidpQEM9Q+wrY2JAltm4k2acWUDgpL0YrMVQQAlpWgeobURAFghjiYnuNkC8pA+vOJAnQDm\\u002feXiTDRZAxYIfjrC7AkDlpha1LtPsP+NvCQKvItQ\\u002f\\u002fCCxC3r2uj9qHsheRHqsP8BmtMjxHaE\\u002fangKx6L5sT+J7CNb15zOP8s5hctbw+g\\u002fpBg+FHkxAkDGwVjmJ0IYQFtcFD8lai1A7ZgrszFDQECT+I05eXNQQLiy6Vbpjl5AvXhgGAQnakBKGDodvrF0QBvHG+POXX5AoJKxELiyhED+fi+bTT6KQIch\\u002fHde+Y5AMCrHafELkUAnHSZPXJiRQAqgdPzYRpFABtg6xCqokECcA9UC5qqQQF06GGXib5JA7X8dP58Nl0Cx2zOj3k6fQAOY4PHquaVAOODy11V\\u002frUASz2\\u002f7\\u002fCezQBUE3hnTmLdA9bwtPGiMu0DESVNFmaS+QLV8NLtca8BAuxFPRFM8wUDjvtgdYhDCQFAQguoZMsNAwb2zavHJxEB4qfO\\u002fe8zGQLp5yV9N\\u002fshAnvhtWysIy0AoKESv5JHMQP+VXq2AXM1AAae5ymxWzUDI5XM736LMQNS+ZWKVjctAn7v0uHNrykD8WEy8K3XJQCYtJc+JsMhARaW3w8X2x0DtbpZqdRTHQHzcmuvl7sVAeD4HtBSXxEBWTpJ5+j7DQAsI4kOOGcJAmOagEEs7wUAfNTB8aY\\u002fAQOghpN15zr9AnUjkBscvvkA8eTdO5CS8QJC3tJLF0LlAH4UoaeB3t0AUstGknVC1QNFq\\u002fzp8YrNA3Ol5rN2GsUAtJZugIxCvQHcvooSYkKpAfXVUBLKtpUDsjTXaRtCgQGTw2hvC5phAOcdvsBDZkUBpkvYsQUyJQAYLgU1RJ4JAGqyY+e\\u002fRekCm7ivuu2l0QGKIB4Tqi29AdnNRJaUvaEA2RdtcP\\u002flhQA1P6Z3vYllA0USqQ17HUEDNgY4MK4ZEQMsYL61QCzdAZ\\u002f6y0TidJ0BUq0jNTv8VQDw+xs+dkwJAY58LuQVm7D9pesZvdLPTP6IfE\\u002f46Lro\\u002fiyR3MB99qz\\u002fOp4xyIPSiP2IBql0CCbQ\\u002f0rYiB0Q00T+i8wFJ6w\\u002fsP9XYA+ApywRA4exAUpf5G0CviEBiux4xQKouSixlHUNA6vNa63aHU0Dx+tkIvVFiQGKDFN3jqW9ATnz4Q1xKeUCkHQgm1LeCQA8WJb\\u002fKuIlAoGy5+alskECwCRVoJoKTQFfL\\u002fLCJlZVAAmDfbllZlkBhbfmU\\u002f+2VQNQrSHMf\\u002fJRAGQIyhryllEBmIxeXdU6WQClNNhGDSJtAlFtBn5I8okC9ZhpMnAOpQJaqA54+yrBAqMmh2MGKtUCzGouEBzK6QB4QGpaaNL5A3vHO4FmgwEADw7tbtrLBQLge+wenisJAaj0BHkR9w0DxLzT9itbEQL1eAI6Rt8ZA3sjXipAIyUCFTQvQ0ILLQOfWmwgpyc1AM8KdtUaCz0D2T77UFzfQQIj9Ud4YO9BAuNdHx8a0z0CKuOKUlm7OQMIxTWbv+MxAuUiA4X+Wy0ATFNu2AV7KQDmXlP6GO8lAThXx9N8LyEBG\\u002fc5mBL\\u002fGQI6iHHZaacVAtFJTkyc4xECieWzhA1LDQDoIqLHXucJAJrh9\\u002fHVIwkCE2fnV3MDBQP2xBYti8cBA2hbdb4mZv0AQ4Nor3Nm8QNjns8YeALpA5tVcDERat0CvzmvMtQC1QGt56SPL07JAtfG4IEubsEB3js1HImKsQBxPZ2NfNqdAJB4yVMsPokAhS3uV28yaQPOFkjRlKZNAaJBrRTfpikAiHaoK4QODQFpU34wtoXtAW1YzGPTMdEDRZmInhQpwQNy+Vp6Lt2hAxCVbbq2AYkBod6+axEtaQIVwBt6QcFFAuQBmmZ5XRUC2JWkt7uk3QIJFMEgMaShA3PT\\u002f2JifFkDMSrl4iv4CQHjS2kk22+w\\u002fQsVz\\u002fGbj0z+505wwfj26P\\u002foKf03LZas\\u002fT5Z1lv\\u002froz+rgjGGKDq1P6zAd+mRatI\\u002ffQJreZxZ7j9VZg8v3bwGQAMkbn3Z9B5AJw9Ki3wwM0BzeeaU\\u002fbhFQHUONgJThVZA9WdSSpJzZUCBAR0v29RyQOkcNAR2jn5Al7fpLCv2hkBs1AATDgGQQBO6ZKRUtZRADFI6CiXimEC5xn9eZ8ybQJ4UokgX\\u002fJxAXRAfFEmEnECL8lFj0CqbQEbezAQzWJpAjnjDo+7Mm0ClbVYhnJagQFFi5CkzwKVAN85rgl5jrUAbge2OsnKzQAV2\\u002f5yok7hA5CRtTvVnvUCZPhBup67AQHZE0SKvGcJA23rTfY0Nw0BwfG+9E9XDQIpdQikj0MRAcfla5eZKxkB91nPpKV3IQPBel1O\\u002f4cpAjapZ7z6HzUAZEzXP1ezPQKl+p5PQ3dBAzvPgSGtb0UAA1W4uJWPRQEANsV2r\\u002ftBA0bPZsRFM0EAIjURtVOjOQDIZnciENc1AN72HYyujy0AaCE\\u002fosC3KQJ6UETG2wMhAu18Sr4FVx0A\\u002fMG5ESgLGQL6jj7nB7sRAO9CXJno2xEDV6fGKG8\\u002fDQCreDGZxhcNA5eTSnScUw0A+EZDj3kXCQKY3I6zkDcFA4uOAb2MUv0AiIgULWt+7QIq4ZuLx2rhATs1RvPEutkAaKWdnpMWzQKbG9SODaLFAWblUJsnWrUBpmXeSpJSoQDOB9GeQVKNALu3Jai0JnUCP7E+C6v6UQP24LizRsY1Agtc5OPn6hEBuol7NC0N+QKM16YBxh3ZApg6jVYwxcUDT55ubGExqQIS3cVKjl2NA944kumu\\u002fW0Agf+GUWVdSQPrNKdi2XEZAh8iG+I3zOEArim9FR1opQDlJwXcoYhdAQgnBbCmJA0CDPrP0YojtP5rXXwMMQdQ\\u002fnPfSRfGUuj9fVFfyOKSrP0yK0deP\\u002f6M\\u002fvwHVlQiBtT9p7IN1xuDSP+AYgXabe+8\\u002flKQqHsTjB0B3PFhWTn0gQChCPzKuwDRAMur+8h3gR0Ad3dj1yC5ZQKBcogFobGhA5GN+oSbXdUCVo8g4xwyCQG37o+6LnotAu1KEPL6Uk0DcZpjpoLyZQPNPOnN\\u002fXJ9AO78uOkO6oUDdAZRoZqeiQBtroFiIcaJAcH5ffDiJoUDE8xYYF8ygQJZD8cHSU6FAqIeJq88mpED5dWD5MuCpQI3l0\\u002fZyNbFA72+koAN2tkCnwb5NV\\u002fi7QJcwIDwmd8BAIB28yjtewkCwKZSLApvDQP7iB2r4WMRAFB3pOvX1xECw7cZAt9zFQJ7ZFurkWMdATge4hNd4yUDpkBXEJQvMQBQ2amnrtc5AiYZKVqiL0ECj\\u002feVe8W7RQGPUTZTc6dFAUrw7PKfx0UDnL+Ak943RQFBGVYip2NBAfBkQu7rtz0DmVKCLPhfOQJcVRnYyV8xAKE\\u002fQsMi0ykDdeb+tmCbJQKIncAiIrMdAtVoodeFcxkDhHm5JvlnFQJuKlOeYtsRATL6KwyNixEAP68ZidyXEQPK7xYuCusNAeXsUOCfswkD7CaT5Pa3BQASs4DS0GsBA7rkkvjvTvEDc2216eZi5QHJRHuOruLZAbC6E2p8qtECNV13H6L6xQCrn2QtBj65Ag5x5nR5vqUC3rm1Qnk+kQLdiF0jzGJ9AXXfQAl7zlkDhuXCuCIaQQMielk+2mYdAhGYIDUgEgUA7yPDpOg95QE4FN+esy3JAGnBrJKM6bEB+0NHAIrRkQDqv0jqB9VxAiWEy3w31UkDAPjDRgu9GQK6VJcnKbTlAELKxpr+yKUD1eBvIM5cXQJ1ctTx9oANAnaxOUGmP7T8E8wp6RzTUPyXyGTCSbbo\\u002fxg26dSRqqz9coQxHZESjP1ssmGcT7bQ\\u002f0\\u002fSL2N2b0j9Xo57IWnDvP\\u002fUmvzvIMAhAeAjVzjzzIEBVoKhfU641QHYUx50VYUlAvCiD0TVDW0ArwrCbQPJqQOkagjp9kHhAFIG5SCGxhEAsCkZSJyCQQGphf8ojRJdA1wzqNCUTn0BRbXnLHTWjQIsZHO2T\\u002fKVADJ4lRXtdp0CuQQCWlT2nQC\\u002f28z0IFqZAU07J2RvupEA0guKWPB6lQMjU1a4H6adA8sfP4IMIrkCjDMvwWauzQNzXCANhXLlAoED4nVMyv0DleZ+koh3CQGqr4t1S6MNA6vqCTsHrxEBq5QxgaGXFQBWVekuYxMVAl+fqQz99xkAN+L4D4NnHQLCHslvW4MlAcSowXNRXzEDxOEEnVuDOQMDSvWFLjdBAMXXsvHZe0UDIxrEohc3RQBjTlYHb0tFAHAolf9F10UCjuJcugczQQEKNRx917c9AIaIaSNMmzkDQkLjxWmzMQKn2\\u002fGo7ycpAl0T5DmE6yUBmlSTxnMPHQLieJakiesZADWw0sBx7xUB0V85xL9XEQLX3nzEqdsRAxGFlUjQrxEDofCp+obTDQELwakqu4cJA8BMeeoukwUD2F9dZHhXAQIrN172+xbxAMTrFJjV8uUAbSyHw2Ie2QMTKIwzG7LNA622fazaHsUDkUEVphliuQB0W61T+jqlAq960Y2LJpEALj73Qq0mgQFAGb72ZqphAvJ2d66UvkkB2weuj6maKQLuP0Y0RG4NArq+YgtPUe0Dnp8GyEGx0QEAt2Eyw4G1AuaxA2JhgZUAAI3GRzUtdQPHjPc6K4FJAeNjkaWaRRkBj9YKdJMw4QGN8VNed4yhApTumvT26FkD2YW4Qw9QCQEDS7z0pRew\\u002fvmse0exF0z8U5yUTCyi5P+t2eexWD6o\\u002fk981OOPboT+zpLf8LJqzP\\u002f7A6g08q9E\\u002fdT4Jr0hC7j+byW15dZ8HQF77V6X0zyBAi1\\u002f\\u002fxJfdNUCM+MIAvQxKQKKBCLIpgFxAnHtecBO0bEAB\\u002fqprlal6QBPzL\\u002ftJ4IZAX8F\\u002fe\\u002fwkkkCQrgOXUp2aQPpLgqp9C6JAjyoKRAKdpkDggormqjGqQEAahSK3GqxAkTysE1QgrECGv\\u002f2tA7yqQL+M\\u002fJfuF6lAYYnc4JXHqEAfkyqCSkqrQISGn9kQv7BAU\\u002fheSwWbtUA8uz2ZK5K7QMy3ro9EzcBAMizBjWdRw0BQhl2hwfzEQGWUHyRlyMVAXLMY+E7\\u002fxUDIhfS9BR3GQF7l78SEmsZACHHgYvW\\u002fx0AijgdoHo7JQJN7Gi7DxstAuiUpjvoLzkCh4k9Z1wHQQNzqJMXxttBAz6WuK7kU0UCJAKtM7RfRQPM6AJ7lyNBATjGvDFM60EC2Rs6EggrPQFht1KK8gs1AwFoV8Cb7y0BXa\\u002fsS+HzKQIBwit0PCMlA5GlJCA6jx0DQXgHXVGLGQMtNyktZX8VARI0tzkumxEBCcVd34SfEQLPlua4eusNALNYkDHUow0CAiNboyknCQHG\\u002f6XuhEMFAM6+GYKocv0Dqsx6f\\u002fdK7QEjJdAB9mbhAPsV6vZurtUDfDn88ERmzQLOKhDaYy7BAZYsxRms9rUA7XSqPmPKoQE32WZ\\u002fds6RAGIekcCOloEBSEF7uM+6ZQHKfdxA\\u002foJNAsG2krgsGjUAVGJVrax2FQCmo+wR5b35AhXhDo8\\u002fLdUDY8RwMYONuQF13t9dyZWVAsSIv3CGBXEAA0b+OFvVRQJjw60ysGkVArvptc5blNkAcJ34\\u002frMQmQBy2exYyphRA\\u002fnZVxhwGAUB9mukxBHfpP\\u002f0TU57uUNE\\u002fBeNnDJmPtj+EOZelM1mnP7Ttquik1J8\\u002fd8Vf3LqosT9Qt2CjlCPQPzGBlMNiBuw\\u002fK4xVGEk0BkA+BRjKtA0gQB3MRP\\u002fHOjVADR1goH28SUCwBIcwe6pcQBavQK8ZZW1AbDPYw1HLe0D6HEcjXUKIQKoEQCskjpNAqfqag+sdnUCIq8H1QwOkQFZfzBI1Y6lAzayGb7q4rUC4jb9YPRWwQMFpoA3IL7BAebGSpLPIrkA2k9dtm6WsQMb48xjLtqtA+RNy9FqurUABYDlV8saxQCvNQLLMlrZAzdrhyKKSvECQx04OJk3BQFZCtu+BxMNAjAF4HiJTxUCRQtjzdvXFQPOCgR3H\\u002fMVAhAHSL1HoxUDlkUCHly\\u002fGQMVdK\\u002f4wFsdATmCEIdGZyECcFZ8O6X3KQNlY8+Qga8xAz6otCigRzkAeWd\\u002f5dTvPQH+6z9Wz1M9AFvbX3eLfz0AVE0pFi3DPQPQpb8C5pM5AHsxcUNydzUAfcT4XcnfMQEjrdwtCQMtA5sDkd+37yUDbUAn+qqzIQDpxMkqZXcdAwghXN7MkxkCoCHMVlRnFQM8ltFLwRsRABiOUCtagw0Ajp23iwQbDQBxH2SduUMJAZWGP+ylewUAoSsAKrCTAQCdnYG4kYL1AP50XByE8ukDse5RWPiS3QKoArO2OULRAqFVHm2bYsUDtZznHvGKvQPavAhuZfKtAU49gRgLHp0BTzskD7CekQDa7xAVXpaBARip+oAOxmkDeLOtk0r2UQEZedy0vQI9AyrAgBbffhkBhp1QqZFSAQF0\\u002fUpJ90nZAA7EZa1U7b0CnVvwy49BkQLAVIfjHvlpAzcnRq7hXUEDud\\u002fRFPMBCQMLP3fxy+jNAEqpuozGaI0A8xfoHFJsRQHUUt+iy0\\u002fw\\u002fqNiKexN05T+oQk80mxPNPyJJxDjA5bI\\u002fex535KaIoz\\u002foZeKKrS6bPyuw3cmVfq4\\u002fiUOzSlRDzD+JftqcB+PoP5Vt+0y5AwRApcBHWQJnHUDUjzJ1JMQzQKv+0msTYEhAg5NCvJ2fW0BJDAyFm9BsQCkJuRNZs3tAn0fa+SOPiEC9oECYIBaUQGcsT2NDTp5APYNM5g8UpUAT4UQFawWrQDgywU70669AkKhz+ZZmsUCX2NJX8ZixQKnZZYlQvbBAv\\u002fE2ci3rrkA0V8KA8FmtQN+RmkVPl65Ak7hAdQvgsUDvjT+LPV62QLZgwR\\u002fYE7xAUb8G0lLvwECfsBej\\u002fUvDQCparPDfw8RAE5eht89TxUBCWQKuD0vFQJxn29SPIsVA23bg+IxIxUAVRdeN9\\u002fjFQOuvoutQMMdAg3DC59S4yEBbEiJve0fKQCc\\u002f6TrzmctAyLip5ReJzEC0rN8umAvNQJ7sgtVdLc1A6N1YCKUDzUCT5xk57qLMQCyhtaFIGMxAve8r88lmy0BSGkqIjYrKQNnaOvu6gMlAvjt+HkpQyEBSf9y6cQ3HQLOi8LRj1MVA27O92oy9xEBjsydo+9HDQL5e1P6wBsNAbWlTj2pBwkDkSXM75GPBQHuZUXawV8BAMTQBQqMrvkCOIGfbxVC7QMUM9d62TbhACEJVaH5ctUB31JSK4a6yQB5UyLGUXrBAbblD7AnRrEA2Q+IvK2qpQNScwqtVTqZAuW0+uZNRo0CwJW01mGCgQMLmX9+q\\u002fZpAMVyrMQh\\u002flUAhYLeE6XqQQAHZoS18SIhAaVx6Njg1gUCXPcmwuYV3QDGt3sPYF29AdIp7AAzfY0AGRon3J3RYQO\\u002fWwEy5u0xApWNEt9fmP0Dywsnea48wQP7KmZc83R9ARv\\u002fRx3U0DEDvGudy7Nj2P4wIXfn33+A\\u002fQKcTPuS\\u002fxj\\u002fluM81x3WtP6xPrncIZJ4\\u002fyM035oQjlj+4hc5RPR6pP05iXT\\u002fQncc\\u002fkJTBIuMY5T+uS3eAKzoBQL5u\\u002fEBQtxlABhW1q6+TMUBoEMskGQtGQO2ub+bZZllAvTovjxfuakDIv9\\u002fh0Ul6QKoxCZWqo4dAxImQPFCVk0ATee2Le+KdQNWK0dd0\\u002fqRAG4CtCIMlq0A7PrMvJiewQO62pinFtrFA0a3vd4v7sUAufxzs2xuxQOhEvpMRZa9Ayoic+mlPrUCiIaATrMmtQPyy3Pwr\\u002frBAGmbswa3ytEB8WdGGrB+6QGARXMOQdb9A6cUytXTvwUAs4oL\\u002fwFbDQAsqtN\\u002f268NA0z5WJgf1w0A9pdswedvDQLQk6+MS\\u002fcNAMmpSeO+KxEBq8zTvO4HFQCx\\u002fLDiItMZAvmyvIXrqx0ApbavNYfLIQCpMFVN9tMlAxlM6VaszykA7P8VkW4PKQBDJrJHrt8pAW92RE9bYykCwy+jM0drKQCBzNxX0pMpAzWKEg9AeykCmBQKdJkDJQIxNZFLlGMhAtC5DQITMxkBkHb2TUYPFQMvIAJjyWcRAQzIDVIpXw0Cu\\u002fOykcG7CQOllTcR1hcFABtbHcuuDwEC6LcxPu7W+QPIiPoEHErxA0Hz8NCc0uUAa2GvxJ0a2QNInGXM7ebNAUS5WbLb3sEAaZKI9NLGtQC8jCyikNKpABY90PJ5Op0DoHcSscsKkQDoOaRJsWKJAtqvb5HzXn0Bu6Z7w\\u002fd6aQHIhBESU25VAOYxZ\\u002foIEkUAQet3iHD+JQCE2yvvXz4FASHG1\\u002f4Ptd0BJ5gBDjq5uQO1oaKDC0mJAn+PAlC8cVkACvqWpospIQE3hpjlkYDpAl3nOGllqKkCWygQzI7EYQLazIouiYAVAkQLpgz8I8T+5+iH6NtvYP9V0VJffm8A\\u002f1Pd\\u002fPc9epT\\u002fsl\\u002f6dH\\u002feVP\\u002fQ0ONxrHZE\\u002f9ujKbRemoz\\u002f2o9xDsMDCP6Moa4d0AeE\\u002fX8gWh602\\u002fD+qYrUn4mcVQEuXKwQmwS1AKNRcQ4j4QkAvICBWsTdWQE5FcoDc6mdAVqb+zMutd0B8gyBFXZCFQJyknmI4EJJACcSWBHXUm0DWwDORpbWjQHtPhfLQqKlAy39giiW1rkDEB\\u002fFSVumwQDOA6f0jN7FAvnzZO8lgsEDDXc3Kq+StQMFLpZhqhqtApzvwmvFeq0BoGu32A4+uQFkR6DbKlLJA5idHUigOt0C8CTVx9sa7QP2gTpsHy79AFU6ufok\\u002fwUBTnjrpwerBQE67BPo+H8JA37GcwjwxwkCI0PeZX2nCQPehQLWg68JAuNdz3a6zw0CPa2\\u002fhoqHEQEM1eCe5jcVAy5GALu5axkA\\u002fz+aOXALHQJCZXm8\\u002fk8dAtCFKahAnyEA1+KKuec7IQOyMfWuOgMlAVlJypaYWykDJxIwg4lnKQE0SYybzHMpAm9GzWdpTyUCVDzW2OxvIQLjhnyYIq8ZAljEFNfo8xUDSOXYJkvXDQE+08\\u002fCh2sJAKYj28DrZwUB6F8SLm9TAQGjtGTwTar9Aa7e7vLLgvEBNMOQYNBa6QMzuDcEeKrdABaRsBP9HtEDHZ4BkeZyxQBa7acgNla5AdXAGTFTFqkCUB5PFE7+nQDpewtLQU6VAQDkMecRDo0CBtMiNglGhQKCB1IvKn55AohJeJK5SmkB\\u002fGRdJAcSVQI7inKogKZFA3lgfqRqiiUClQD9ulBCCQBaT2eRf\\u002f3dAeKuROvwTbkDW9sbUHdRhQDG59tCFClRAveAk9A1jRUAB17z\\u002fOaQ1QCfxDTj9ryRArAT6bwSTEkBEo3swdR7\\u002fP4zYqdEOKeg\\u002fxoh\\u002fizxJ0T8E8rOOycO2P\\u002f84Y1sb95w\\u002fcUX5Il+TjT\\u002fWHuVaIQaJP0i4xyA1GJ0\\u002fORwHHr02vD8LAyzHyP7ZP2HbExct7PU\\u002frbQWkHjqEEAoe6oC8OknQMwA70X1\\u002fz5AFk6OZh1wUkDYtjNpvCJkQGC4AKxAMXRAFAJnIFOYgkBTEbMRqnKPQODTRRo1aphArs4LqXJmoUC8+Rj7TsSmQDXgd8RoWqtAuU3kpnY3rkCEajUXTtCuQA7SDpUlT61AdJH3VF+kqkD8PJOmGEioQMlbeJkLv6dADG4uWX0PqkDpZazDRluvQEYNoauRZbNAp2C\\u002fkLRut0AZL2IVwf+6QCs\\u002fsqLGlr1AEX9C3J8jv0DjXesuufi\\u002fQInO2NeLSMBA9+Np7eCowECUVO5HgzPBQHo\\u002fPUyv4sFA2WZfia6gwkDBQXh2KVfDQERvd6lg\\u002fMNA21n+SXeaxEAf9hbS5EzFQPI4rtYOMsZAunMkYBFVx0DT+15pC5vIQLn3HruWwclAUHEH96V0ykDsTKB\\u002fPHPKQMArkaaerslAS\\u002fPkiB9RyECP5ttHw6jGQKjdksM9A8VAbr+cedOPw0BROQObCVXCQG88rO64OsFAC39Z1ZIewEADmXJyjs29QH5rncSQFrtAlB7gxYMquEA4hu69zTG1QMxlvwYjXLJAEB6ECQ6or0AvnlqRB2urQPVnT543EahAIo6soMKBpUCF5uZpoIWjQDV5M+ie2aFASs8xMec\\u002foEAH11NpPRedQHgcknQxTZlACamByGUmlUBIRnOzhNSQQH5hJ1\\u002fpTIlALQfp37TcgUD5BSzJ15x3QGjlS72HMW1AVSxahXXkYEAD+e8jW1ZSQBVEHcgKuEJAVZ6qwQQBMkBR1tRutFIgQGeShVmQ2wtA4t\\u002f+vbhL9j\\u002fCz687qKfgP9vPim3AGcc\\u002fP7rD4R2wrT9ff0gcoIWSP9SLwOzUr4I\\u002ffaOuIPRCgT8eVrDuP1mUPyz7z7Q3E7Q\\u002f5WMfcQfQ0j+emEjtYiTwP7JtofXsWAlAWA7roig5IkAc+M7o2AE4QOpwpkJj\\u002fExAjDu8bRUKYECwxvtEV0VwQCZUI5L\\u002fQX5A3TwqjlTJiUBqg0KHWSSUQHIFw\\u002fqk15xAGBrygr\\u002fuokDmrrgPC82mQAJfE7maOqlAAxXFgFXAqUAYAeBzFX6oQJ1uS8ZBOKZAXS7oQTQkpEBIhyEwuoOjQFVcWqGcNKVADb3+CKheqUATYgj5vmKvQH9V\\u002fMlhDLNAe+dKKDYktkCHEfJeD5O4QGDH4ZmTSbpAkXCmE\\u002fSAu0CHNMl5W4y8QJphNpK6qb1AQbAaU9vrvkATDJuOxCDAQJ50rIxdxsBAyyKn2bddwUA7ZTpJOe3BQJ+nX2JejsJAbbi55\\u002f9nw0A24nJ\\u002fy5zEQHJwiTA7MsZA\\u002fYoBRQL9x0BMZkTmj6LJQICg3BcYtspAA1XrxOflykD7xeKIUiDKQH0GDDFPmshAaxIZnU20xkAAvfmRN87EQNM7ixdCI8NAEqlLa9a9wUCp7IdJz4PAQFEH8xrdnb5AQSmvlLQFvEDXWjtThC+5QGciaDhoMrZAJfkYYjg8s0DiSciSGH+wQCQKAUQeRqxAs1C9wpl6qEC6Gmw9EJqlQOH8oZXae6NAFz4IuQ\\u002ffoUDrSFp\\u002fCH6gQJn0b\\u002fSfPZ5AvIBXSzY3m0B+KgLrzciXQMUz7IaZ+5NArLK1U5T6j0B2Z4vYOSqIQDjRsQYJIIFASKsNWMWldkBwLg1bydtrQFxfj0qx1V9AAb5rNVXnUEBuxIWambVAQGfNZu6S0i5A3MUMt+6WGkAoaI1\\u002fPn4FQIx1k3t3SfA\\u002fD6cmF6Qc1z9+JZPJMqC+PxpyyIqG7qI\\u002fEKaUl+XZhj\\u002fBBmsmDpB2PwhOZ0IHdXY\\u002fo6DLLPPgij\\u002f5Sa\\u002fR4wOrPwTw3u2oxsk\\u002fu1JxmL6E5j\\u002fOWhwjjP4BQDvF8h7hTxpAHMDBjGmaMUD9KQZHXo1FQB3aWkhvJFhAqcc2uHu9aED2l4DHLTF3QNXB0r6X44NAL\\u002fksDmk2j0D60cHokGqWQIUa6CglfJ1AgaMt2RXGoUCsUezjuK2jQEOIfRbmGKRAEjyEfrAho0BNuGJ+pWGhQLMVgFgdkp9ALr2C23qjnkAtQIyNyqmgQKIzr3js9aNA8Ymd3MnEqEBaoLUCiD2uQMnj7iFfvbFApWoSxwL0s0AUP612Hri1QPC8puc3MrdAEAaCNMuYuEDGhK7isgy6QDTP42nei7tADUyh8Hb8vEDVwwJPYES+QDLEstwyYL9AqVJFpj05wEBECJC09uLAQLLoqzex3cFAlgdToRBQw0BXH3BvqjvFQBvx412GacdAxeArNHxuyUBIoAL+Cc3KQJjoIPKJJ8tA\\u002fwN14i5oykAPaRFc4sfIQBfu2eLXsMZARw0zSfmOxEDxa4r4RqjCQOv9ulmVDsFAy9oS3LFWv0D8iEaqMLK8QPy4VV339blA\\u002fi1FrFYRt0AJi5gT9Bq0QG5C3cnUP7FAf8PVI+herUBR5MjQMhmpQNZGeppZyqVAnK96EUBho0Be4SpnZqihQGm+Bve5V6BA4oFtiHtTnkBBS8CGONebQF+ZMz1pBZlAmWA8wsLSlUAvxJrKoVKSQPFjsJ+WXo1AY7fq8d5GhkArictIiLp\\u002fQKmAqUmkEnVAFo7rjaP5aUAaRXKIwJ1dQKtmnqmKL09AtDHJO9ZSPkBZFPHunUErQLvukQ8UsRZAyP3P1HWKAUBXHzkC1j3pP0ODmKuv79A\\u002fzEvx3Zo2tT+EK6y2YtqYP4DuKuI3fnw\\u002fK1PiO6gfaz+v1MJf5ZJrP\\u002fOcaPUcx4A\\u002fpKef0aIzoT8WpxtWZ7nAP3nhdVIfw90\\u002fl4acK+Ez+D++MfdBJPwRQOtMJjdZayhA4rEH1TJIPkA2DMNL8yRRQNzqE+YLuWFA5FkKjtm5cEBK3kl3yNN8QMI8PU4nsYZAcU1ntX9TkEAobPwejX2VQEieV4Dn6ZlAbQ7VD7eynECSLSkiz1idQBbCY6dnC5xAT4L0SwCymUCMqThIMq6XQOQbIGIsa5dAgWXahw\\u002fkmUAXZCGqVE2fQGS+k2E9gqNAyNdP2XLqp0C8VTYsrD6sQLLUIz0wDrBAEsk9k6e\\u002fsUAhxhU7QVWzQIAM\\u002fsyi9bRAzaJTmAawtkALf4TXHHO4QBA7zjvhGbpA7ZAV0X6Eu0A3F8\\u002fBGLG8QHVh8Emgy71AWcs9SV8tv0Bdkz3PmqLAQOlZxfLtM8JAE4EJHEdLxEBLcer\\u002f5azGQFKnPKkx58hAwyQqC0V2ykAmj6jUkPbKQOK9H221TMpAOtrCEwmtyEAsfcBQ93\\u002fGQGIC5rAzNMRAlo++ukEXwkDAJOl1NkXAQN+qCILUYr1AG3u5irl6ukBq00\\u002f2lZ63QJKwErgNvbRAzKEmxm\\u002fnsUALZyDGf4WuQB8KU76J6qlAn\\u002fFCVjoxpkARtBr+gGWjQMqpgnGbaqFADaBk2XYEoECy7G9sgNOdQJzKbMNlsptAP7eqcJpSmUCEkVQRpZeWQBgETOcBjZNAc00FZflRkECGtxp7dBuKQPV59l932INADvmSD9NsfEA9M6ujDgZzQE2VZPg+o2dAqjKUtpIhW0Dz3X47a6xMQOZtIXBz2jtAbgz6yPjaKEBxTxmdgmIUQGuIhAXLxP4\\u002fmMQZjVFo5T8fKoGdkYXLPz7fHtPLYbA\\u002fL5\\u002ftzx0ikj8L8MTQJ4JzPw0iwXxTo2E\\u002fiHJDtugAYD9+4MENXNNzP+G9PZjowJQ\\u002fBkLp2EiRtD\\u002fWYjP+9aLSP\\u002fdmLx2+0+4\\u002fTid19adCB0DQd8ORKgEgQLPsFSM8EzRAcV88mwzyRkAMIb8VF+VXQIae5aVurGZAbuAvhUacc0CnBUv4PPB+QAlxA29JR4ZAvjD3qCJUjUBpLbtcMK+RQPP6Uv9knJNA7Hza82UmlEB4k+kZwnuTQDzzqLnyRpJAibzd0kR\\u002fkUAEfmFQxxmSQNUnZ6lCt5RAxq1ljpNsmUAgRAO6R7yfQFscK560ZaNAPz3KRL3hpkD+xppo1hqqQH3I8DIWI61AVW5Sww4gsECBTyJz3dqxQJFSUhF2ybNA24TWPE\\u002fQtUDaqIvbhL23QOI6l7bOY7lA\\u002f1FOZsO3ukB5UEA1a+O7QEDxgIZ2Rb1AeQpaSidVv0D2yCeEATfBQIFELELTSsNAvPl\\u002fOL6sxUAaPZBSGu\\u002fHQIndJpsOkslAG9DOGGYyykDVMqy8Pq7JQLl8UDprLchAHsXy6zMLxkATYlwsTK\\u002fDQFEkWAssasFAFWY77AXHvkCTxeEidTu7QCmy6jIlDrhA3fMZc0MZtUD+3KFONkyyQI+TZZNqXq9Adkw7ytixqkDAcAACvcCmQPxiD52upKNASn4qrPtYoUDmdCNie3SfQNsB3s2vG51A3TNJItwhm0Du\\u002fSAcpBaZQMDIykQyvJZAdYN8HyYMlEA8Xr8fiySRQFZ3QOxoW4xAOmD5gKSVhkAimfZ6CDKBQPgxhbuqzXhAR4kKuD7JcEAU6zaP\\u002fSRlQMKDtI6QoFhAPzjKkFdmSkALzu3wwPc5QBXKMdtPZSdAuk1rEitKE0DH2fWooRr9P8ic5giXGOQ\\u002f8iQJWMhryT+j0KlIhn2tPwd0np6HeY8\\u002faQ8gbBQZcD9\\u002fhfMIG7tbP82IPrfQmFE\\u002f1JDEaV41Zj9MHEyAFr6HP6ToT70++qc\\u002fNgaWj8Mbxj\\u002fn97xtgJPiP5T1v+tObfw\\u002fVudB8qLLE0Dc3iYNVxQpQIlyj2dY5TxAYuzspZ5GTkC4FO5TZdpcQCxnB2UEBmlAK\\u002fHeW17Ec0AokQ1VyHt8QNCcLQbcwYJAf0+ic0uphkC03gFI30aJQCgWk23dWYpAj6ZMxRVEikAFHf2VjwCKQFM\\u002fdtAi2YpAyjoJnpj3jUD2HKiQaP6RQJiR6yBDYpZAet1QK\\u002fW3m0CUaanH8a+gQJkTo4cdaqNAq78M0Tv4pUDrQI0M8oWoQHFwTVJ9ZqtAa0rNHtLirkAlKIv+v4OxQHHvHGwIyLNARoo54vH+tUCkSH8tV+m3QEGfjyE6a7lAu9pMSr6kukDaaW4EqfK7QHZDWJCNz71Avbuxh2JOwEA7zh6epTTCQN2jMlqYacRA\\u002fk7M\\u002f9aLxkBtz0Ut9ifIQB8kfCNw4MhAccP\\u002fweiMyEC+5HoeXEPHQO0Cj+mMScVAYcd0hDT4wkA\\u002fHjSwC57AQO57zffR27xAJTSYkxb4uEDGw2LLiY61QOCvDmS5jLJAhODkOdPIr0C0jjXM+iSrQDevXQPwNadAVi\\u002fNM8MCpECJydhDnYahQBBniplUWZ9AXmeAq5KgnED+PeDgGoGaQAzUrl8RkphAZTSRrAx\\u002flkDnAoRBXyCUQF9XMPJ5f5FATbQakPuHjUCM8vb\\u002fqzCIQFVXhpYkMoNAcxCRK8BVfUDmf7kT7GB1QLNsws+eZm1AuRaF2sfiYkAW8w5rEnxWQD39c7hmqUhABK\\u002fhSZ\\u002fROEDR9W+i29omQFD4hW1UORNA8n230UaB\\u002fT\\u002fcc8lkZaTkP2jhlET6Uco\\u002f3ZjGTfKTrj+PkpU1ETaQP\\u002fWuQj+ySnA\\u002fZzxFjCtwWz\\u002fjyiR0mltCPy4G6YD1mlc\\u002fyzaRtVzCeT9S5vJX03uaPwG7N04D0bg\\u002fYRPSaOAm1T+dCTOkzmLwP57c\\u002fs6dDwdAf86Mn9Z3HUCxAZe\\u002fYxcxQIrOXhah\\u002f0FA\\u002fJLo+582UUB1qWPil+xdQMfOb+22q2dAt5gI5K4TcUBWfxX6Uo52QMFsAzivdHtAcT6DKZgsf0AQ7X8zid2AQDqAblDK+4FAuoVaD5Swg0Annvl78c2GQK8jjcFQ5otATQ2HKqqGkUAieFhwUuOVQPlsSg17lppAlgLWKMYln0DSX44M2qShQNx41nCsj6NA0+3kaY2ZpUD8qPVAGSioQCBQPPyLj6tA3ZnzQ5Dfr0BmgsLfEmWyQJj2zkW027RAbg0Z6hICt0Dr1YSDbam4QOL0WxtU5LlATohk8CIJu0B244yP25G8QJVKSco75L5AoeKfT5UNwUDdCQ9vG\\u002fbCQHroJPx028RAPwcS1KZbxkBROfrndSLHQDyAKhAiAsdAhmpZchb9xUBXJI3crD\\u002fEQARG8AP3DsJA1Y4XRANov0A12NGUYtW6QGLCGkz8sbZADrKio+0es0Av01Avzh+wQGzWWcUMVKtA338jOhpdp0CDy+Cq5zmkQLeJj2T6xqFA\\u002fGEU8B+\\u002fn0Bwh98L6sCcQPYbLlOUUZpAPaWh0J4wmEASpUjRnR+WQIvUCe5e7pNAOAvHr0iLkUDE20Pm0w+OQK73s4fNFIlATTEb8TFshECLa6zx+DmAQFAr7rSJBnlAY68otMGJckBB6sXlLw5qQNXzzsCKKmFA8aZUSMwAVUAvcMvqmbFHQFSK7JsJhjhAkJm5BZA1J0DyLcD0ewkUQOtYwklfgf8\\u002fgbzMZKWG5j8FgvnutEPNPxExgVHIQbE\\u002fBrCAqad+kj\\u002fGqGS2ELByPy4fZY11el8\\u002fpprRS3ouMj+W9H++jM1HP0LBlTmpemo\\u002fztjlTzCsiz9ynsFSx06qP+5ksnPqtMY\\u002fsInozMTH4T9j41LxqD\\u002f5P83qWf7sPxBAQJrkOCn1IkA4a1Td2gw0QF08YY5iPENArfdCl5vCUEBo+jF58JRaQIihrimwQmNA54e\\u002fKguwaUDBmTsiRvNvQAfPgdSx9XJA7i3UpA44dkA2JQZQ\\u002frR6QM72WpCh04BA2jhf7dQGhkA3M4n4GxGNQDYU2W4DxZJAtVrBMxdMl0CdMPPxuZybQGvNGRezU59AIoaD1jQuoUCBd\\u002fVkooeiQMKX9U+0FaRA+AEUpLRPpkAdqHVvN5SpQBYe0yid9a1Ain8CtgePsUAF8h5cbzC0QPJmYXPnfbZAFNsA7SA4uEAO7x1IVWS5QApVTwCZULpAODa6C4hzu0CT8DhkLjW9QBzKRPQjvL9Av0QJHYVrwUChQqibKQXDQCHrWH\\u002f2W8RAQiwPcLQmxUAxuPFrLTTFQBKruavsdMRA\\u002ft8Gpn78wkCQEWR0LvvAQFECad9bY71AEsMKQF3DuEBpOW5lf4C0QK3ENn+n3LBA+OyKeETiq0Cnu\\u002fwUt2+nQC5pcUK3LaRAOtnhqNvToUDC6gkcHBWgQLISYKhTXJ1A9bLZdKnfmkB8Q0rBXIGYQMfJSX2lK5ZAhC90prXTk0AeMsFMPnORQAoR7RVeHI5ATnW7m7NuiUDSE4TFlRKFQJG26MIAL4FA+r6lGxyVe0BjyeJJTaZ1QFcQSF1eanBAX2X9GwmyZ0DnVVW5uQ5gQF7awwHDNlRAxf+YjWRzR0BIrX3n\\u002fO84QJEJniuQNyhAq3dGSI9rFUDNXGoMJTkBQPbgxd78I+k\\u002fq8v5K5Ci0D8dEp7ak\\u002fCzPyz8dRYNq5U\\u002fZItCUkwndj9siSXFPs5iPxNYB3gQFSE\\u002fKh\\u002fNmQ29Nj+HhRFJt7xZP6j6TgfgSXs\\u002faqT5lkZFmj+C99GO0uu2P3m4LX4MHdI\\u002fYGIj+Cbr6T9kiCV67sgAQGj8aJ\\u002f\\u002frRNAxa0ae47lJEAe2vyr0R00QFDiXhLal0FA3ttF2ssRTEBJCVTCoJVUQCFnFBZ6KVxA8BdOXYBuYkBjasSdqPNnQIEA\\u002fGz6CnBAcpQcQIKAdkD+sEaF1DqAQC5SsfxQPodAk+YUxSIHkECeWfCY9+uUQFF0IdjLs5lARZnuiBbKnUBvP6MRWG2gQCpbpSMPfqFA0uy0OMldokDGyGpAA4KjQEC979Ufb6VAM+CJndCIqEBwBMAyt+CsQK2MOSM9DbFAwcmyePa9s0CENeMdfBe2QJwUcpYxz7dAKscZ6cXfuEBPoHRSt465QLuEM2bTTLpA5EB1eIeBu0Ckhc6EfVy9QCkfKvPNwr9ALElHQHAtwUB0Y9AFFlXCQGbWkUZZGsNA4zXddPJKw0AcjcF2osnCQJD8plwYlcFArSrv\\u002fCKVv0B2ZJWW\\u002fEG7QL096y9AuLZAzPQ4Wbd3skAvniNZs7mtQGt4h3pQKKhAODopfm45pEAc4ghZ26ehQG6KLvzXCKBAGBxbO0HUnUCQI21syNmbQHdfo9sPsJlA2KCcbZA2l0B6jRDPBIaUQBuQ6T1Ey5FAhYCJFlFZjkAaJSt\\u002fgYaJQKB0b82xPYVADmTgq3ePgUA1GaVK4O18QApFRdb8rndANc\\u002f3\\u002fzYTc0ANV4yuEsBtQDvxTDdTFGZAXFfHHd26XkBgLHaSK9RTQFijrRTsikdAKhAtxXyUOUAeKohcqlcpQACMnT9o1RZACki0i6isAkAbDUmKg6\\u002frP2TQE5A1lNI\\u002fuVAE4NSNtj9u5erQ7smYP9hyt1jdnHk\\u002f73sdYzDqZT8z7BsLHmMOP8g0n9qAhiQ\\u002fCw3oB\\u002fiVRz\\u002fup+KW9FFpP0JKoStSo4g\\u002f9Ajo5d6xpT\\u002fWim40OEfBPz+SZpGl4tg\\u002fTR8RSDU08D959LPmOhYDQEM5b7mkWhRARfljKYawI0Aqo8KPLlkxQA7JU+cCEzxAQ8\\u002fTBnQxRUDyBB7Uq7FOQK5XXPX3MFZAxdD5HcWkYECvBCpMnS5qQCNA6k+0D3VAFLVyAnqmgEDx\\u002fil+Qg+JQLdYuFTpopFA5ixn5O4Rl0DoDwC9dxScQMYD5neUAaBAvyf9iBRKoUBG+2azeQCiQICXzevAgKJABno1IXpQo0Cx5DThcPqkQAfDye0N4adAHJIEdtIRrEAVj8t5SpawQJeQlTG0ObNAIIeoAzmFtUAQHfuPeim3QLUWYC86GrhA3tm74k6UuEByMkVfCAC5QJNPLI19wLlA6WCy\\u002fVEKu0AhuR62KNS8QLRltjdz4L5A8DV0onJpwEBPBAfIOiLBQGv\\u002fa0OaacFA54QltJwZwUCklN0rqyHAQPsthXW2Hr1AQlR4GRgfuUAQ14MvD8m0QOfQKbCEqLBAD1sYNH5ZqkBPy+o4xSOlQN0L\\u002foSOuaFAkBKjpHOVn0B23YLog6OdQFUnEJhdepxAmM+kRRksm0Ce08jFkTSZQAU+8l6lhJZABQarWhVik0C2T0UoxjCQQOokjgwRiIpA5zZsa2eUhUCL+zFpJJ6BQB1DT7lNGn1AQBBJ3NZYeEC\\u002fM3jRyHR0QAEr9be893BAaIWI87g3a0ArDAeCCLVkQEhjdWW1cV1A+eX061tbU0CEUNuBp11HQGnZ+h0exTlAFCqnuaDhKUCqhhfWBp0XQODy615SiQNAPU6P4N9F7T8+a+vvR9bTP5B7lvJcTLg\\u002foPEz4HLtmj\\u002fP5fxU9gt8P0rjU9wIJmg\\u002fHtCsNZeB+T6JkpwT\\u002f3IRP\\u002fIYY0lhTzQ\\u002fyOGO65YIVj\\u002faHi\\u002fOaKJ1P3yvNVXXMpM\\u002f+sEdFu\\u002fIrj+jehDNFkzGP6\\u002fhrCydL90\\u002fdUViSjpG8T+lrgDAAYcCQJH8QRL\\u002fExJA+eIeJDAvIEBzExGqDgkrQBWtEsAhtDVAoPtlHNt5QUANCzAliFtNQBRR\\u002f8HO6llALzAMcjJUZ0CTUcTcEIJ0QOhLWtgiCYFASM33R2dDikAYfWM2waaSQBke6WoEZphA0EL8goSFnUBTppPHPKigQCpACq+8waFAFAqNha43okASsvowpHOiQEoVttstA6NALz4TtV1wpEC13tg5YRenQFEIAri9AKtADDQrAaDNr0CrJE4x0GOyQMxHJRbBjbRA+SHQxacWtkCWziWtPe62QDDtLtniR7dAwF1TMymAt0D4ySlFv\\u002fG3QEn2IeXk0bhAWBWGroIjukCflOUSor67QPPhTKIqX71AVxuOE5ywvkCD0EsScVe\\u002fQDftqEvg\\u002f75Aj9g48oN1vUDm9ZVKE7y6QO58A9IrGrdA0r1WcuwLs0DPn7rvbkGuQKHxZCMppqdAYyCWvUPWokBvuHmjjtCfQPld70RVEJ1AjOl+yiM8nEDXDtRhc\\u002f+bQBUMNs3HQZtAGUz1XSBsmUDk+NaW4XuWQJIKTDkn3pJADrJEEppTjkAtGUO+OruHQNq9qbD+e4JAcc81JM5SfUB+R4aelw14QPUZKkjZZHRAkXfbGb+QcUCEv1yUJe9tQC2Nwc2DlWhARTdn8MUQY0Af5RryLodbQBaAIERxU1JAWFzikkJcRkCJ31Qd1OU4QPkQPUB3OSlAnkZyp4QzF0AMKKfOclcDQHx9KT9BMO0\\u002f1HxWSsXp0z+t5NL+8Yu4PzvStO73XZs\\u002fsgilA4mtfD+9qLplHtBoP7JCX94zIeQ+sNVqouHW+z7u3HSZi2AgP+IdIBGN7EE\\u002fUAOrne+7YT+48a46bK9\\u002fP4judaS3jZk\\u002fK8omA7Sbsj9nfRpWGX3IP8S3uv53LN0\\u002fPx1vGduW7z8nT0zwlVv\\u002fP\\u002fXr8Y\\u002f9CA1AExyvTeTeGUALEMGgFTInQAiVXXEEyTVAH7jUFeSMRUD+O6qxerRVQK\\u002f+bEqmRWVAv\\u002f2QD1qdc0BxDl0ZgrSAQOuB7Z5aE4pAWb5w4+GekkDRGQrL5GOYQFzY7HEGdp1AumSh9sWNoECHkTQJj4mhQGP8VOeX2qFAxWLYfIPtoUCi6spqRU6iQD9Radrhf6NAK77\\u002ftUTWpUCFJTYtrFepQAK4To8nrq1AmTj9ehsbsUDot8MtwBizQP8AqN3lh7RAw8oDCYtVtUAgaoj7xqm1QMl778Lz0bVAWvIYHLgctkCmDfszeLy2QD641mFAvLdAW0FpfWAEuUDp3ucBlWO6QFaa+vselbtA615DBA5GvECcuoAI3SG8QKTPC4I26rpAJNYgPZSSuEBlkRqielC1QETJrx8alLFAvuCjRGfTq0CaknXApKGlQFmqHZ3CNKFA9t0Qhu1inUD3ScWKU42bQOkd\\u002f0gTpZtAG0TO6Z4wnEAbg7HfmPGbQEjOsP9CO5pA9wZCg+wPl0BLP3zuVvuSQHa4UswfgY1ABeOLDYcNhkCCRX\\u002fygFKAQKEVphb0zXhAo5LkETTnc0BZyqfFf+VwQG3N2I8akG1AoQMNR2ataUAuYCFFPXJlQHF5Q0Tx2WBAGghR1CGTWECPFlB\\u002fMn1QQDZzZRmjQERAABElRxuvNkBmZu75uBsnQEIbxK7kXRVAlmCu2DbnAUCuaaig6CbrPwOviE0QndI\\u002fh0dW35MNtz+orPpYL9KZP0yr+GVGMHs\\u002f13U8VUWeZz8401XDzMLNPhrWOXfYw+Q+efffYcqmCD+EjQq\\u002fZy4rP9Gq6p\\u002ftEUs\\u002fKkloXGZTaD\\u002ffTVkxGLqDP9dnXzyf5Jw\\u002fsYmsZyMksz8zu0JgywjHP90CrVLtZNk\\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\\u002fSTNAq9hlF2S3I0CU35iFmksSQO0TQPwlxf4\\u002fNED79zlr5z+FjhPIQB3QP7ZRg8htCLQ\\u002fUFLGIyOGlj\\u002fnXdI0adB3P+zviCUewmQ\\u002fMnsFosWItD5BkygW0t7MPpInY2NwRPE+S2yoOVcpEz8QmqBwxjEzP39BWQkwWFE\\u002fq0AVHilLbD8b63HLmN2EP6wMB5Rp75s\\u002fc8Lx2tsesT+ciTbeMY7DP3\\u002fGHYpdgNU\\u002fxJSoQNra5z8r7NpDnNr7PxPFOTxSMBFAwdrKZ0eaJUBjA7oV9Vo6QEoilJT7JU5ARQXZduW6X0D8\\u002fadcp3ZuQAX1D5\\u002fPmnpA56WhAjYkhUCWnAtGvKSOQBKiBwMOVpRARUnSHNLdmEDr32TW\\u002f0GcQI0nKp7jN55AhY53+\\u002f3ynkB91NqG+ROfQM+d+Jf3cp9AhHuOtblvoED+\\u002fv08Y\\u002fShQF1PJnbnWqRADn\\u002fP8zl4p0AK9Q1GxuiqQIAC\\u002fDFzLK5AoKDyY85psEC7oH\\u002fFn1SxQE8FOI624bFAl7oBq\\u002fY4skCv6p5+AYyyQJ+RkdTRA7NAiylMkS+2s0A4hY00lKK0QBe0CWEDsrVAGRMZbBe3tkBP\\u002fCVgV3C3QN9mnjkDkrdAGJosqDratkBV+Ga8DSu1QIfKxRW\\u002fnrJAzT+NgqwTr0BZIsIDac2oQNKF6WspZKNA5RaeB\\u002fIbn0AeeqkVcxybQBCBYaNdVppAb4Lje1+Om0CK779KKyqdQF917N3Ns51AEyvYEehNnEDZwRT\\u002fWeuYQP0\\u002fRnC1MpRAIoUmaqw\\u002fjkB\\u002fasEosi6FQAlGL1zjfHxAEaUrk7FEc0AjZcbpBshrQGA3FVETD2ZA7Do49ezaYkBO8yrc0GhgQJRdiAhusFtA31hDiob7VUBAYTlEpipQQOQzfSL42EVAbmK6Ner\\u002fOkAKbaaWOWsuQJcCef7HKx9AOKxTToj\\u002fDEAEju1Qz3P4P\\u002fGnvLA+quI\\u002fJHb0fAXEyT+ISvA1NRGwP13NPMcjIJI\\u002fNg6mivA7cz88fKF\\u002fjtFgPxb9VsH3XZo+Dwlt4WSnsj4\\u002fUmuLwnXWPscg0UoAEfk+Ig9UV3E\\u002fGT+kGIPPZfI2P406gQ1q2VI\\u002fSgoG6GYYbD9os1WrkSiDPyhoAdv0VJg\\u002fJkpcd7S6rT9Zjm5YhlPCP4kpk8mCzdc\\u002fSAyMGEJd8D\\u002fSbr1P5+8GQCGSCB7sNx9AgzumR4rqM0BUz4kDpF5HQCTNsSPs\\u002f1hAU3dFq1dOaECemAaVwHl1QPbUQSO8RYFA003QmSteiUDXm6ZMnBWRQMlPTtvOO5VAmRlO7YCOmECCa1\\u002fI7rqaQLKbq6mDz5tAFIk04Zc2nEAqpiZO25KcQCKVGyh1kJ1A+uKdKne5n0CI7cZqJqmhQB8JnD+AIKRARmmuis0Ep0BXUUgeUPapQFhaVu8vk6xAtEF5YvqarkCIa3M+DgKwQAh759+qfLBAWnzrJs3hsECeJY370laxQO6N2QhF9rFAm9tPtHrIskBBNfd6wL+zQIw+uJkZt7RAM7XZOqp0tUCZtjdfvbK1QO8ox8V8MbVABLrkhYDOs0BB3fPv+JixQNdDQIgerq1AIfBa2Wntp0B1ZoKd+tyiQEpGW9usa55A6l3BupuTmkDN8ncLdeKZQDYNqylwOJtA1PFVpnkEnUAEQ\\u002furdsedQJ2lAB74kJxAQejqc9JCmUBjj0rna32UQAqyNivyhY5AAQzZV+cLhUABOy3nm2l7QLz7YB05jXFAtNJIjNqAZ0CEAZf8AGBhQA6a8e8cU1xA76gHzSIZWEDsVzShOCpUQCAAATs1809ACOEaP2J\\u002fR0APSMhHSsc\\u002fQKNuA\\u002f9VqDNA3t4DEVMtJkAzhfO9SsMWQJaDnhafNwVA0RJa9Vnu8T+zEob\\u002fN3HbP\\u002f0Q+P8B\\u002fsI\\u002fJwhRtU3Bpz8G\\u002f\\u002fBMX+GKPyAtOiU9n2w\\u002fJaMy1+0ZWT8eAkE3omx\\u002fPjcSgKv0WpY+1mTpChYTuz58kRbfZ2HePtiH8EvPxv4+YcOvG44rHD938fkoSWI3Px0jXOs2wFE\\u002fFt4UfaQTaT+Fxy83awaBP7em47zZT5c\\u002fhZ8+XCHSsD\\u002fWZg5aILzJP2HP7qZUGeQ\\u002f3JWtlHuD\\u002fj9+xplbALkVQGwt5\\u002fZ1cixAzYVbqBb7QEDPkgTRH2xSQFN7vjo\\u002fKGJA4Jv3INNHcEDg+8k4yqB6QMiGO3a97oNASjFyCepxi0Ahl5oe1XuRQIbqU8kaxpRA8aHwVRQ\\u002fl0AksLkt1c2YQE6Sm4uDoJlARBZerBIemkBRIaSTGMyaQKbnVCxIMJxAmKQ4IWavnkC4JkGLKzahQGqmGt4ymaNALVUJp589pkCDDPfF\\u002fMuoQEhdVtfS96pA8h7sJDibrEA2yrWH3cGtQHuCdEcsoK5AWIWmqXR8r0CJnnB\\u002fHEqwQNmCL1wcA7FALreKKtLisUCorNeNbMuyQEeUuQ1\\u002firNAtSQdpx3hs0CIcgJdypKzQIDRRgMBerJAruBvoKCbsEBHFXnN\\u002fF2sQAnJxVx6J6dAcgG8tvduokBKwk7pSNadQDVQ0bt+85lAILoIK6gOmUBEcc1EQzGaQNCiGYPz5ptAqzr71eO5nEBPDQVr5rCbQNtmXw8Ym5hAceZTJFUGlEDgGsObiNONQGxCRSBfbIRALvjG870SekBHTCDAC\\u002f9vQCKUx6Sl8mNA\\u002fexOGzYIW0CFauSzXn1UQDZ2s1hMtFBAIxETENhpS0BveX4gsYxFQNtuKNFEnD9AoWKM\\u002fSRcNUC3wrhwVW4qQJQpyKc+1x1A5AJ95zaqDkD\\u002fIwWbh6D8P5PJT0YvPeg\\u002ftIpC47yW0j+IcvguC8u5P0e0eR6MLKA\\u002fUx94pc5agj9rzDermJtjP5qgONSdPlE\\u002fzZK3AZVYYT4gmkPr3814Pm+pbrA7NZ4+TqKVi28LwT71\\u002fXl1WGPhPgQNAwPgFAA\\u002f7nEEAggwGz\\u002f3ANka6l01P984vfsYH1A\\u002fKI882qWBaD8XEGwhVqGDP1MnE5OWsaA\\u002fBoW6jtgKvT+G39BLqJbYPwZqzUc6ivM\\u002fa1+WFxaUDEBlh4UgNA8jQGGZEoLtGzdAtRdLEER2SUAeswDQ14ZZQJTzPigoV2dA4Usbh9iGc0AhSK+\\u002fAQR+QGHLbYVWTIVALOpLTVARjEASKvCrXUqRQEcGRI4PEpRAFA\\u002fOY2AllkDDfUorHHqXQP5qhHYpPJhAWjQ2IarImEAQIVrYWZ2ZQDLTshnMOZtAOgp+EkP1nUCs+DIZjOugQNAEBcKwQ6NAJSOFaH+zpUBfJCde9eSnQEPGQ37\\u002fnKlAgY39hCzRqkCbPVrD+6erQP3Yr\\u002fIFZaxATC6v0FVLrUDl+V5S6oKuQF5VWpftBLBA7\\u002fARyenYsEBwYsZqD5OxQKQW0jxi\\u002fLFAmGvhxNjcsUCKtdZKlwyxQJjREf7NDK9ACrJJ2YDkqkCrAMXp+T+mQMdw46Tt46FASF\\u002f3\\u002fDL\\u002fnECGH1HtK\\u002fKYQJ+c8c5lopdASfph305MmEAgOrfNsamZQLg\\u002fDnhTYZpA5Iw9wNJ+mUBpSQ0Uz8CWQExE3UspmZJArPGlo17Hi0DLpTRSrv6CQIJ11QGT\\u002fndAUGa984GabEDl8Neu\\u002fM5gQNztsDlv1FRA3VFnVNDHTEDiSkV8HvpFQG1kWXI1bEFAqyD91+X5OkC7s7h3iKkzQMgl4HmEgipA5AlMVhpkIED+4LlIg4ISQN5aB\\u002fZNCQNA\\u002ftUUgjTL8T9H\\u002fbXqti7eP+WVtS9uMcc\\u002fnre787QgsD8TKMdJFUeUP0REKDFFE3c\\u002fN4PAWX+6WD8CnXDf2s5FP0j1JoRwtkE+vjdEUIxzWT4BPL\\u002flTC1\\u002fPqvMSaiStqE+d0Ub7mVBwj6PEXDx7y\\u002fhPuEgfY35D\\u002f4+7IdOczU1GT+bF\\u002fpsfEM1P+UsZpqq5VI\\u002fe0W6BJfacT\\u002fF6qD0hkqRP5kjNtD0U7A\\u002fspDeJDv6zD9NemyMMa7nP006rW3ZqAFAcNhLEyD1F0DcPF2hzY0tQJBuO7XUmEBAkxD79t8CUUB8nIdoSetfQGGvAAjpgmtARrTFhajgdUDbBwCGnyCAQDCQkfU0J4ZAC3H1Xvl\\u002fjEAi3+ufvUKRQMZgxsfAy5NArgS5HyyklUAVwB14BsSWQAowWY0+YJdAjXg36efnl0DsubYXF+mYQHQcsPCU35pAOFoBArn+nUCY4dab1gihQOWoaYLbQ6NAV6ciGD5WpUCNib0cffmmQMxncOj0FqhAm1g4Aw\\u002fNqEDInI46g1ypQKmLcAFACapARDQEbPT+qkC3ZV5tSkOsQOVCGh0fta1AftQAxuQRr0DTXfc\\u002fz\\u002f2vQJYVQ0KQCbBA7Jhmwer9rkAVMfxDlp+sQI0\\u002frVQeKalAhKTV8MQZpUB+LD68mB2hQNWEM+T4sZtA7kIb\\u002f3ttl0CNq3ZqEpSVQFUzVRJqmJVA\\u002fi2IMZ5tlkAa7g8iheaWQFrdmjTSIJZAyzXsBtPPk0DdP0CYiUSQQMQevjjHZohAsQHhZf23gECXEp6VHwl1QFS5n\\u002fQmpWhAhW\\u002fFCLO9W0C1GdSebMJPQJizS3G6z0NAumVX5T60O0Aca0j+ttA0QLlXFtdPZi9A0ctB\\u002fRShJkCtxop\\u002fjl4eQAGqKlrTvhJAzlvSxmwpBUAQTlC7KMb1PzBWE+mmX+Q\\u002fjaUqCPxN0T\\u002fK\\u002f6WCyKS6P77YNNYxkaI\\u002fKY3KGcJmhz+G8i4rfLNqP85UPcDDs0w\\u002fc8fMwW9hOT9OC700ubYgPoZ6ojLDIzg+9eeWSE3HXT5wdtpwyRaBPsyT8mbz66E+00mPsoJxwT6H2x5aXUHgPpDV+6AMbv4+KvQ+Kdn9HT\\u002fkQ6AFxnc\\u002fP4O6Oz5d9mA\\u002fY7f9gdjdgT9JI3\\u002fPebWhP\\u002f7pIkxELsA\\u002fhXtfxXgB2z+YjMqR+oP0P27RxckvXgxAlhVtgJPfIUBr13g3Q5I0QPkitF9UsEVA6lWiadAEVUClvloJU8tiQP+vEnKUIm9AG0PzKHf8d0BSPh5ucECBQPdCfdtFP4dANkXWQl9vjUCM8cHIZpCRQOwJPPCR1pNAIVknMKFalUAD8iqIwSyWQMnrocBxppZAkK1ztsxUl0CJvOCSYMSYQGin1HpRQptADCA5IHaynkAE\\u002f9GIKkqhQFGuZMatHKNAPedjTC6JpEDrJgeN8HWlQLglW7tl\\u002faVA\\u002f3kTibRbpkAafweYe9CmQFBCLu\\u002fxhadAJ9nreq+GqED9awlF1b6pQD40D387\\u002f6pAf2MSAfP+q0BXv7FuFmKsQBlVjdiWz6tAy+UZ8qYUqkBzE40Yz0SnQDuOddZVwaNAuqFlNHgfoEBxW8xxBfGZQCsesUgDc5VAS22g7WEJk0Bi76HiM1mSQFeTEPvukJJA0Jvuhxu3kkBuBIMe8QSSQGxPAP29KJBA3h9OfpylikBofg\\u002fdNxWEQKdBH9D0oHtA6M9iaktjcUBHg1fyWDJkQFPeK4XEGlZAOIW\\u002fTZrKR0AXD6GOfuo6QPPvdSBt8TBAWJk3sGuWJ0CoCzIoZBEhQL\\u002fG6AHnIhhAQ0aLybIQEEA7SbZct8QDQHDwposwS\\u002fY\\u002fiAjLlNnw5j8NG3fAcXvVP8OgqU4PRcI\\u002f6liQInItrD9STp0XGK2TPx0NeyJe23g\\u002fBH5LInRvXD+iLpNwj6g+P02hxaWeLis\\u002fcWh\\u002fbFMj\\u002fT0b3HTSqiwVPjdkCwipYjo+3E9sTJ7NXj4GfFpEM6+APq6gzjuiR6E+Yxp6TPDxwT5L+hTVPpzjPhkL4CNC2AY\\u002fVLQC7QBqKz+NxVXxehtQP\\u002f8lECf41nE\\u002ffLenXCk8kj8Ek00IPgqxP9fIUfncBM0\\u002fiu7y\\u002f8aC5j8IPda\\u002fO9z\\u002fP4elRM9JnRRAsX8gNGV1KEDx7VleCbE6QFdMSWnE4UpA0ULwRhASWUA5Wlrdq7hlQCnUCOKWiHFAK6nEPe1uekAngvdOt6OCQKvObX0JoIhAykk\\u002fD\\u002fOFjkDygNSkU8yRQJ+EsSompJNA6v0PbeS8lEAQFAXJJFaVQPk+BjeJ7ZVASKMVRMMMl0Cu3vEXGwmZQBt9Yc8X1ptADl1sh74Gn0DvT86GEQChQOxUHbp5IaJAOkbMuMXRokAIx505ySmjQEpaeY72XqNA8npKsGSoo0AvLoNhQCukQO4nwNJk9aRA+SzfB9H\\u002fpUDIO25ZEC6nQAzAMaEBSKhA\\u002fHtjj4j4qEBhul2ACd+oQLalPKidtKdAyatVmtRypUC86+JG3mKiQIXkUzIiEp5Ac0wQEAbul0C6nSFx\\u002fzaTQEF\\u002fM+soSJBANgA1uo7ejUBFbODpQR6NQGwNExu9wIxA3PSHw2V2i0DLzvEA\\u002f5yIQOmRRwZdXIRAhK9qfOLXfkAgICWXv1J1QPjzQms87GpAPq+ijAAxX0AAx1AzPMxQQCA006cXUkFASg25m\\u002fAMMkCeHTwAu1skQDOODxdvrhlA+Zfjz8x2EUDfBOO1T\\u002fEHQGOyA1NBcP8\\u002f1eA2T7A78z8Z+M96+6TlP1xW1H5tQ9Y\\u002fHZhm67nbxD9xW4O4P8KxPxO33GGmbps\\u002fvyeZMVYxgz\\u002fAPUWf7UxoP\\u002fGsfONs3ks\\u002fHk1QY9wiLj\\u002fUA0xJ1skaP12t8OM7etc9nbNCg5Q38T3EvnCGM9IVPoX2Fxv4Sjo++64OYetDXj6e3Gafnm+BPpe90x7nH6U+v2CGQSdSyz4A+KqDnT\\u002fyPrHyjwoz7hc\\u002f8Eg9sQuhPT\\u002fqQMVRIfNgPz5G3ViTv4E\\u002fHwZ93NvzoD8rFhClHYm9P\\u002f68e25hf9c\\u002fS+3pJh8c8T+SlodMsd0GQOhxD3WlHxxAFqc4OnbtL0CFYdOSlMZAQJe6F65VXFBAGo2QGU6uXUC6Dic4CRVpQB2zXW2jxHNAHM3lCgoUfUDysUitdfeDQFFNZitXn4lA9US3ChzMjkCHtP4NDW6RQGiOkyQpxpJAIu78d+GTk0DsV0jSLzuUQF7TTDsOM5VA9bEZrifKlkD9lxsdMfyYQPGkVtAUcptAOwgiI8aunUCSwSo4NE+fQF69XWocHKBAuTDrejVNoEB5Ik\\u002f+MGigQHpKmsiBmaBAQE3LGuj\\u002foEBXjMCbzquhQJ9HnK3doaJAmUHYR3LVo0ARcetelxqlQLVtNsvwHaZAiRMYbBx0pkB03+wco8KlQBp18rv\\u002f7aNAL1N5m0cvoUA226wdlgScQARPccto7ZVA\\u002fgSyo6j8kEAlxn54yjqLQIQ8fp0dc4dA8I05WTW2hUA+gA6jvceEQCv3l6pVkYNAhIKMyhJ5gUD9LeTZyfZ8QP9NYTczCnZAEDi8CaqkbkDk9NDZC3BjQBAZ58ADjVZAd5gWZHMcSEARebn7YjU4QCn9Spw9vCdAuyO80Fo1GEAIBO9CdEULQInDmpHKAwFAM00NvVw59j8aiSlIm4bsP75y9KE\\u002fSOE\\u002fCgoss8df0z+eQPUAgefDP5twfYx7prI\\u002fYlj3EDHJnz\\u002fCLiQ5sJWIP2XQXRlVO3E\\u002fY2QMl1TdVT8rtGAQ8iI5P5v\\u002f20KLQhs\\u002fGMDyNSJLCD8rcqyX9oaxPXsGHIlbG8o948UDykUS8T3hPH\\u002fWyNMVPhaOjzOY5zs+iWUbAdW9Yj79uBsdf+mKPsCFps+0A7Q+xHyxJNxM3T7kSf80mEkEPy9OM8A4\\u002fyk\\u002fZnviOEuGTj9oonR+f11wP5jbfsehBZA\\u002f61bhsqKurD\\u002fj8L8sP4XHP0KM51mYtuE\\u002fOya1LGCQ+D9TDZzg+HAPQJvRTjW1nSJAI+BCxNFvNEDOUskWi9VEQHLVQ\\u002f9Jv1NABPNxbSZqYUA446ov25VsQL7te9Vg1XVA4RHFZaAKf0BrbExdqI2EQETM3\\u002fT1aYlAnJQslMGAjUC1qltiKT6QQFqE\\u002fFKDPZFArqQkZYAEkkAl+fJVwO2SQGD9B3xGN5RAAyNwPhbflUBlSpAeGaSXQHM\\u002fDKCgK5lA50IDLTo0mkD7OH7giraaQCd16tvN4ppAjQ0legMDm0BGthoiQFubQHIuK5pXHZxAaor5Cg9vnUBnVQLmAW2fQDF3v0FsCqFA8GGlJ8GNokDk7oi8u+mjQHxM29TApaRAjfUEI1NVpEAqLIC6Qs2iQFxKPspcPqBAN\\u002fRN7bdHmkAI\\u002fqMCZSKUQP+mjK8F9I1Alk\\u002fZEOmIhkA1iDrvv\\u002feBQBpqyacZC39Ahh4YeWdffEDad5lO0xl6QBeYMzDiHHdAt0d\\u002fmConc0CHgz928UJtQHeYH+jDdGRA6uDVn5kZWkBXHHM822VOQGIghqO7OUBA0nZn9O8OMEAhjyfAHT0eQAcJ\\u002f2EHdAxAigKQgeac\\u002fD+x6nJ+ywbwPykY9zezfeM\\u002fwteLwF0d2D8e3pFq\\u002fb3MP4cEHQViAMA\\u002fmBd+xh5msD+hVkjGqreeP+jpVTsAMYo\\u002fe6mgNVxIdD+OpDTIHntcP01HrPctG0I\\u002fG04kUm7dJD\\u002fAXcWV\\u002fbAGP8gpSkXPRfQ+b9qI7Q1kiD0P9mMh6LGiPYck5E+S78k9lXXGz9Zi8j0LoxkvsGIbPpe5jtHO1UU+ldhmAjMWcj4Yd\\u002feqk5KdPnwWoukk6sY+cQCCb0508D687lzA7q0VPyX\\u002fvUiGHTo\\u002fsiili2TBXD\\u002fdoJvqNPp8P6xFhtJ1xpo\\u002fi7oc9NG7tj97cUKOWMbRP5Zc1W31pek\\u002fnH2deXEbAUAuSEjOnCEVQBIufQeBMyhA8B6gDxG5OUDM02SqEGNJQFhdLyC7RFdAjjIg9WPNY0A78g95oklvQJPMy3gu8nZAwh6ouMNGf0BG1MzWON2DQLGF3UekpodAL9IsGYypikBGwKFqIeSMQIgfyWCYqY5Aw1AzBIs4kEBWJ0lyMkaRQP82suoxepJAvWmHrhCnk0BA6DmUEJOUQNVAYo79HJVAax6NV\\u002fdPlUBp5Pqms1uVQJg1Scmre5VAcy5ebgXjlUAsHrgiUruWQDwkeHg5L5hArEabs21pmkAyb3+AO3WdQGO6\\u002fq9PhaBAezCgR5s3okBU8omF3UujQEWL4xfoRqNAqdufQB3zoUD2M0GOHv2eQNPOr2v30JhAKiS7k+qVkkBz5zFQA4iKQPF7i6rTr4JAiEUuxy9Me0BPDBOZLJJ1QNOSRgCdcnJArBAeyu5ZcEAj1Qv1IIJsQOOkmY5WimdAAD2f4vYGYkD8Meecb1ZZQGYquWpmQ1BA8heHzwYLQ0AKHm+Ng180QHDtGx3xCiRASRE5C1tsEkAS4E7MMl8AQK1T5hna0u0\\u002f2t7TfBOq3T9QvdiAiWDQP+RT87bmI8M\\u002f3hfOikAytj+gfgNyxGyoPwaJDn6W6pg\\u002fE8ArZbNNhz82V27edt9zP0K6TW5Zz14\\u002fZJD520aqRT9AmsWgmpkrP7S\\u002feW3K4A8\\u002fzEs9gMlh8T4918YWeyLfPt\\u002fsjw+JBWA9aN3BscjveT2sBMAU8\\u002fSjPa8FXMvyedA9KO\\u002flCt0v\\u002fT0Mo\\u002fiKhPAqPq8TqGV1nFg+ivKkLeBahT5kCZWsmzKxPiIUlmjCcdk+yEt\\u002fAKw6AT\\u002fyPCzAqlklP63us+g6PEg\\u002fGdPqZdQ9aT853hfzRiqIP6ActeZ3TaU\\u002fNMmnvO5RwT\\u002fzHbcSaQPaPz2\\u002fNAvaEPI\\u002f7hw6Xdw6B0DY\\u002fpQWpKwbQEqchxVsjy5A3DZWsCBJP0Bah4ZR669NQOh9IHMvGlpAV5d3sZ5CZUAOps5FuQtwQHpzjFSfeXZAwBOFah1PfUAxkcZnDOeBQEuB4BUysYRAJBy\\u002fWIDxhkCBiIddLs2IQG6zi3Wzg4pAHZtvsBg+jEBBEa3Hve6NQFme6HbsWY9AggBmlCQhkECQ4f6\\u002flkyQQCt8Nv5FSJBARevryXxCkEBDNU3mUGuQQJr4USga6pBAXNcAOsDjkUBnFKUE64aTQIKMtS2nBpZACa2D3B90mUAwW7\\u002f2OIWdQFfVMTn5uaBAyDfY63oRokDrpHX86kSiQG0g15kKGaFACgUnd9p2nUAfAc9B9V2XQBzaJvJSJZFAwBEygZSXh0DNt1EaFEN\\u002fQPB5WNEx33RACy28nZmubUAUCy\\u002fh8ipnQHjzmZ8zYmNAsVAFOY1wYECUKsRBQOdaQD42o3sLm1RAJglvo\\u002fwWTUAbivlHKMlCQMEDTEbTITZA7R6vGjDIJ0A8ZjEK7mEXQMKBEl5WOwVAfOJVDvsx8j\\u002fG\\u002fO\\u002fpF7zeP0yuEKliSss\\u002fvuQkNvjRuj8t4A7Q5N2sPwtOcCejA6A\\u002fHsiYz1NFkT8JNbpIEHmBP9I\\u002fU+coS3A\\u002f5SGtgWDGWz\\u002f6LD4bcotFP2P5tOZOVy4\\u002fAOtk2o9cEz8ol3oQa2n2Pr+nItyWgdg+FXjBJSQAxj53gmdYXGE0PYrG6m\\u002f5LFI9Kkdsv\\u002fdUgD3dQGDmJBKwPSvKoQ+yieA9jrd0EADlED4TsZ1CVm1APgDo6NnesW0+T1s6yHCvmD6XA\\u002fpO6c3CPp5EfbhSPeo+xQ3\\u002f6UPIED9smtRYqLQzP4cSD\\u002fwzRVU\\u002fzVAz2WEidT\\u002fZdCo+j1qTP2MxNyg4WrA\\u002fPcyFdWGFyT9hSZKLvWjiPyBqA8xPkfg\\u002firmv5RxYDkCwwR8B8lghQGqkeAAXXDJAb\\u002fKvHgP7QUD2F1Hn9UlQQECz3CJcTFtADDozfecrZUAZjTRK+nNuQCdDavRiY3RA+Af45vmTeUBJLn\\u002fgv1p+QJ8AnpcOQIFAlZF5J8kHg0BVEfGeG5qEQLS+dbYkAIZASK4mW2Amh0AJYo2sjueHQC6wTVmELIhAXI\\u002fMiMIIiEDoalA4YL2HQKolHqD+oIdARofj2agCiEBG0PgyNiKJQGnZVwajRItAfR6AhQTLjkCm4p4DdhCSQLCmtHcFtZVArR9leDYHmkCT+Pqr8D2eQJy4fnJunKBABzGu9Xr2oEDY0JEiROGfQG\\u002fRvFdsbptAD7Clk8CXlUAyZASpMjGPQDBv3fcX2oRAWRGYukBJekBCsuxDUTBwQK7Swqdqm2RAuJCY7A2rXEDTY3r8RQVWQAiQ4oYj1VFA2o9Adq+lTEAnw6NxDttFQDmLWegb8T5Ajtt1UUEYNECvnTK5ltMnQH3kWCddvxlAgWxUfTpiCUDBh\\u002fxz5fH2Pyqom7ymQ+M\\u002fAoMm5Kfrzj\\u002fE2xZk2P+4P427Swpnv6U\\u002fcg6F72wElT+9XK8+9suFPzxAMtEzu3Y\\u002fqkGsZSqqZj9jrrxDAQRVPxoQQDHD4UE\\u002fjZxoe+C+Kz9nc4rU6o4TP55RqOPUAPk+v30eALsA3T4LX4ETs8u\\u002fPlBMx+KZnKw+cwn07rEwCj3J9LbbmxArPZfby9XtK109Z36Z\\u002fvy9kD030tvcmyPDPU3HRqyY3vQ9wEZ++Y4vJT5VokUaDc5TPnEim4uE+4A+En4mU12yqj7VjnZSBT\\u002fTPrN0TDVcevk+c3Ur+tX\\u002fHj\\u002fnu2uOo1lBP8PrHcs\\u002f4mE\\u002fQ\\u002fck+P\\u002f8gD8QRvvvrMSdP3owlW0ME7g\\u002foovQ\\u002fBT80T802jh1+tXoP6uyhQPgtf8\\u002fGNg+n9y3EkCE4tJDom0kQCd0n0pKmjRAMTyqVIoyQ0DY93aOyYdQQMy++x\\u002f5VVpAKerFqmN0Y0ATwbZRwcRqQB3qJl2NQ3FABtWxWvoPdUCUXh0N\\u002fpB4QFy0QvY4r3tAWSpxfcNhfkBPQ9X7fEmAQC46POoXC4FAX6Xhe1BfgUDtd9RhkUWBQMOqzH0c4oBAz3cW\\u002fcB4gEDW4sSTbFaAQL+OK5+IvIBAHWqbkUjhgUDtch60rAWEQL0f+\\u002fN0iIdAl5utfdjQjEBIgo2VSP+RQPSLf\\u002foUP5ZAKPvA23NomkDbJif5rWqdQO0\\u002faeZZSZ5ABr5xYf6QnEC873Wj4ZKYQP\\u002fs17MMQZNALKbPSLqFi0C\\u002fhbpASQmCQA6xmgCM7nVAZYEd3tBbaUAs2rm+njJdQKTfob314FFALMwq+jt0SEAnjRbuGmRCQNo9rWb6izxAyuqVhDOKNUAqyBowRH4uQCs6BKMI5SNAYDQbr7a8F0C6Rfrbtc8JQAyV8c51kPk\\u002f\\u002fj+DLyYd5z8CrX0JdDfTP3Jnvupl570\\u002fogn6lfCXpj\\u002fSD1VHs5yRP0cO0eiwFn4\\u002fGjMsfgZibD\\u002f9U6Fj+w1cP2q3eC0fSEs\\u002fWODqdGUJOT+YxoxKpTklP+SPyPF0dBA\\u002fYU3pWgU39z4XppdHsbjdPhvv3XuWRcE+oXDNX4n7oj7lgIKO6B6RPoKoMNmR5+E83boNFK0OBj3vErIxT\\u002fY7Pdn3H0XZ5XE9lKEsx1DnpT2LsSSqH\\u002f7YPZPTtDlNSgo+rkV0acRjOT6omQN9Z31mPjWwq8vzRpI+jYRf7hRGuz4sAobPFbLiPkEw5EApkAc\\u002ffbx1PX5RKz+q3rGjDidNP5BA6eLCp2w\\u002ftJDmuNj2iT\\u002fsIM2bHLSlP4+fxU6cv8A\\u002fG1kCaCbg1z8ufvCme3HvP85f52xYIANAkBpRNg99FUAs4FgzHkomQGz2iBGHWDVANg5C81TiQkACYJ0s1OlOQIToHV8EeldA+axzdtOdYEA0uo+ntA9mQAQbRp8DsWtAC1RQV1aTcEBgZQoQ6xNzQK2F63krOXVAoVQOpAXcdkBupWEfcdF3QGpmduc7\\u002f3dAE2yZjtZ4d0BelHtHFo12QKvI1gDhtXVAhvD5PjtzdUBRNJP6lC92QAYWFRSWR3hAWGhm5IAxfEDgrVTkr0yBQFU9MVQFG4ZA+13IAMadjED2L17oWiiSQC4LGRz88JVAlJJPvuK4mEAuunCcmaGZQGMBRN3cQ5hA1x+nq4TklEC6zCWnC1aQQDyw7dACNodAsGO9mMkMfkB6lMe2DdlxQHvMRXPWwGNAWK6Y\\u002fD0JVUDSQotxBNpGQJ2kJvF+OTtA5qDc6GRcMkA\\u002fi6dyk9kqQNJ0yWGFySNAEpvwLoDdG0BK82m0RDoSQNUjj0XV3gVAwuAhj8zu9z8uBDp\\u002fTdbnP7XAzk83ntU\\u002fceDVP+zqwT\\u002fOO7LKBGyrP1mDqxNl1ZM\\u002fI06JuEpkfD9uM\\u002fspbXplP8PTFe53AFI\\u002fr+HwZCltQD8OxslEb6YuP0mCwe1Knhs\\u002fvgSc6WI\\u002fBz879s6r4fvxPi8O2+O3X9k+rTf8HgBDwD4zuVN2Ke+iPnPF3D0Q3IQ+OVSPl\\u002f3acj5XgOz8XuO6PN7Eu9t7auM8qrkgMzilGz3WlFbKEwBTPRWFi\\u002fXCYog90ddWnsXhvD0OW6dqQG3vPQM8DQqGYB8+AbBys26+TD4z+Dbn9yp4PivslkNEqKI+KESPFVB1yj6o\\u002fCW+6jzxPtKIFCcqphQ\\u002fJhsxlxbBNj9A+1I7qBVXPz2ZiaGllHU\\u002fH3ryDSmbkj8PMQKjuZutPwzPrHjXv8U\\u002fhP7\\u002fGHWA3T\\u002fXmTqqgnjyP1TjLbxsWAVAfh3wMVLDFkCx8MC\\u002fLGgmQEZ\\u002fzOX8YDRAGnEYJywoQUCB5oJSytJKQPC624nqjVNAIwg9to29WkBDp2f\\u002fnUNhQP1ST3\\u002fPMWVAZ13tFu3haEDnQzkUOAhsQEJJ0K\\u002f8U25AKAFQMVd9b0Avy743ZmZvQO+9kF6VP25ALZkSfv+RbECo1sZaaCBrQOPhBuywsGpA2XWjLuDoa0Av7fQZBWJvQKjNLKsY83JAYt3RN19LeEBZ9K4tFCiAQO59ueMolYVAJwNTGkcAjEC7CNhG9SqRQK7kbBBqh5NAZJS5TjdflED0dla8AVyTQEiel8M2s5BAFgHLsvocikD2R14Kf4CCQMMNJdxey3dAvPFf5Cjha0AYBgv9xgZeQIMDR0cOU05As0yHrWTzPUDs82R05hMvQG2GPA2aNCJAF6vzWlAzGEC2I+kmIQ0RQCr6JG1eqQdA5MjLihbs\\u002fj+EP65FXaHyP8IXEIKQguQ\\u002flqmc3hWN1D8pf9swRrnCP0vlEdcFEa8\\u002fflGe0eiYlz96NFa7NKSAP6Wkp7peeGY\\u002fRq8llo2wTj+x7eDqkqU2PxQZR\\u002fdOjiI\\u002fblVo6rs4ED+JYBnaFFX8PiCWsqPgiec+n8Wj7bEf0j5WEfvKvou5Plq6oWIhYqA+ONEh0xUbgz6YUn1WqhhlPvQLqaQ2HFM+1bFP0nHnlTydMz1xX77BPN2Dba3WQfs8UKPnq+KvMz32nP9IBURqPYYCX4njGqA9dluB\\u002f\\u002fAf0j0ydpXjVrYCPvkxrCThuDE+2ESwYwXMXj4Ft6aCbI2IPnm1SeHq9rE+yrkbXjMj2D4K5MM9Rcz9PvG+5TsE6iA\\u002fUQj7dQOtQT9igdXOVgVhP\\u002fbCa+kcO34\\u002fEbW0Qt\\u002fFmD\\u002f7k8JupLyyP7M0+uLDKMo\\u002fXtrurMrZ4D874sIbnwf0P5qN1jIc+AVA33Bbh2Q\\u002fFkCFTI5hm9MkQAtM2agjDzJA8otPUngYPUC0YoHMFtxFQJTibaALyE5AFxbCsgxrVEAdnOYDX6dZQKPaaSkWp15A5aoCVZ50YUDW6qjE6fRiQLIourNsn2NA9OCyOsFjY0BON0nYzWtiQHVxuMB+HmFA4rCOLSAIYEDQ\\u002fxuaBWxfQLZm5q72oWBAy31uttZCY0A0qCGpmThoQKnktfsgMnBAKH4+mdlVdkBkUIc8oaZ+QA+pKD6VQIRAZVVLDW4jiUCiRYJCdtSMQE29f55WPo5A2aAPNVbajEDo8SGCMfSIQE+dNyzkiINA57g2LQ2se0DE+0xEW75xQGe4fiRcpGRA8jFYehrkVUBdmC0a02dFQHsuqS68zzNA1MN0LttTIkDkptLj5m0SQO10w5DZZwVApv6JFQXm+z9D9pcoQsHyP7RcGkDNUOg\\u002fL6kK3INZ3T8f9YzQEz3QP6VT\\u002fqxAXsA\\u002ffG21mTb8rT+ooJKSrvSYP07zX3sC7II\\u002fwdmOCqJYaj8vqoo9ESlRP20mdlk9vzU\\u002fbsV\\u002fO8+NHD9YDr0KG50EP1NT0TDJaPA+N6xQjzw62z64VVflVBrGPk4kGeMx3bA+HnuVM+azlz5MRmdbXGZ+PttnFihRv2E+LBUJEBmjQz7gBIs5HNMxPtttKVPImXI8OzqKf1dAoDyHTXWnaU\\u002faPD2GJxcHxxM9gzl1zV5WSz0yh8b0YFaBPeC5d9pVKrQ95FNlR0OA5T2PrrshJgMVPrX2UJ0c0kI+EKxlyxrobj6agkMzz0WXPvuthxM5FcA+q\\u002fE4zpVr5D69t51PaNgHP7qI1fuIoyk\\u002fZFNgssJoST\\u002fdq4tFaTpnPxoQumZsmIM\\u002fsP821GWDnj9RPa5Viuy1PyOnmi8DEs0\\u002faxCiWLHI4T8OmBSjjRX0P3eqTLy+8wRAK60kF2w3FEC\\u002fdazNYxMiQG+h54LiBi5AIOW\\u002flaM9N0ACSW9wRdNAQHBU\\u002fYAX4UZA+5udfnpVTUC3Pqn4MslRQC40GjB2b1RAt2Lm5xRBVkAeSCHOTPhWQOpGGdI2hFZAyh67pyYhVUApj2tToFhTQOihUd9b4lFAgokYi114UUD7seah58BSQN8RU2QcZ1ZA19VIAnVUXUAT9T4aQGZkQOnuiUY5C21AP9Qut5tjdEBTQV5uxlx7QOlIwPSbKIFAOCWX6gPSg0Bj5BKS4OaEQJn6KAFfBYRA8PXf9XRegUACGkEHGkF7QDcSZPGFU3NAs31IFJHGaEBrg32OIb1cQEv8OFW2PU5AGNt2bcwLPUCofxgNrtgpQHWbxt7bDxZAXDS7HktdA0D8kys9ISbzP4AOuivsHOY\\u002fNRvZjWMA3D93UK\\u002fT2MfRP63e\\u002fB3gZMU\\u002fFEOnowrBtz\\u002fBs+KE8RKoP2cBaOx3LJY\\u002ffCYUFUCJgj\\u002fPmAckbSZsP4rl8JLhflM\\u002fRM6GpublOD9HLmjXWQ4eP6D8820s\\u002fwE\\u002f+dgn0uTh5j7RoiipaSnQPmEt1u52zLg+CqYVqdRaoz72drBWdwuNPtoXoFyhSXQ+oQNIh4P7WT4ToGyYO1s+Pi2\\u002fBR4e0yA+PCwJlhmaDj7pLGNvdsFPPCovfT8LPH08trYyAdupuDwHARUTTjbzPBSIrewodSs9XTx2siz7YT2wSgPPB5KVPX\\u002fAwSNNscc9PDmpOxDT9z1pv\\u002fmWje8lPsHIhidWgFI+yCQn2bucfD7u9KMuP06kPqI84ChwfMo+l3OfM3\\u002fK7z4WxoAqBZQRP4KCQBhF7TE\\u002fp6FwiNreUD+uXmwVFU9tP3jj02cGgIc\\u002fdNXUOjJkoT\\u002fqdXLgEMK3P1o3KFym9c0\\u002f9HE\\u002fpsdx4T\\u002ffdXEiC8byPzv478TVsAJAg1lq+AM9EUDsPcArVoIdQN9EE4IEfidAX4hisF5xMUAz+hgrvDs4QIMg9xTfmD9AkfQHBFZiQ0D6jWMH92pGQDNJ3lnic0hA3Gf\\u002f2okqSUAam6X1FH9IQNTaJzjfvUZAlhj6weeMREA4Zi4h78dCQLClJKxnUEJATv+vqTT4Q0B0jkw\\u002fhqJIQJU01jWbxFBAH2\\u002faRV4tWEA+2jZCJ7JhQMr0VflYVWlABYDSKUU5cUAk5ffmL8x1QH0ERZokWHlAMUK5rdLcekBxMK9lJNZ5QAs4vrTDfXZAU+mVPi6ycUCSbVFnoiZpQJvVn3oQJGBAiQr4iYe3UkAK1Jo4S6JDQFkJmA0GsTJACrEuQiRHIEBf8GqfmnQKQApcbpLxAvU\\u002fvxWeDnbF4T+hEbla9H3RP2O3BD3PAsQ\\u002fAc1\\u002fVTFWuD9gLdwAIuysP6Fny2mHDqA\\u002fxmacUblXkD9ZIvJRMkR+P+COihExbWk\\u002fS7MOosReUz\\u002fDPtGGy886PxKV7ddP9SA\\u002fJyCraY7lAz8lqTCf81zmPgHszIeGgsk+lTWCg86Yrz4\\u002floZBY8SVPhJK7t7\\u002f5n8+LAb1eFE+Zz5QIwHrrQtQPj0C6tLDeDQ+UK7cCOXoFz5EudQDIor6PWjV\\u002f813Leg9Ktzk8jiqKjzwKnXtipFZPCQujYh\\u002fW5Y8Qu7MgHv+0Tzf6ld48oUKPWyry4v24kE9+V2uxI4Qdj3q50EF+uOoPQZriGc\\u002frtk9ocy4jQU\\u002fCD6gUNXfkfc0PpV1I8GUoGA+RbtITPI3iD5zfQK46DiwPqJOVecrBNQ+qZPyIAvG9j7Es9woWukXP0Y63Hn6LTc\\u002fK6r\\u002fHMC\\u002fVD8tiXFqjiZxP0E2pyn6LYo\\u002fRdzfVThzoj8BNSvQMQS4P0vGGWr34Mw\\u002f0yGaWZwL4D+vzX5xRnzwP0HDhyG0V\\u002f8\\u002f1LmbBz6YC0BEUs4lpIgWQM95IDv0GSFABfQzGV4vKECfR8ii5fQvQFLOOT75xjNAjZPSX1j5NkB9C1xsXxI5QOR\\u002fnBfDuTlAo4qKcJHiOEA0bFmN0eM2QBGrdwMWdDRASyCdjESEMkCjFDzh9g8yQKs3ak3ACzRAjjMfAmmNOUBOoKif9gxCQMdi6sLh4UpAUd70pmooVEDNcFe\\u002fsFhdQGwSoWvALmRAkqJfX0HAaUDEr9d4EiBuQBCxeptOC3BAmnOiNyH+bkD9QHa0WxRrQFmjKTAaYGVA6NOowhR3XkBMSQtkHJhTQJua07IPwEZA46s\\u002fd1TbN0C3eqAakaEmQPSOgDcmgBNAP7CKg3TZ\\u002fj+t3KbpjQLnP7MdTTaRNNE\\u002fr5cKMN2DvD\\u002fYyRYHdB2sPxhDh3f1j58\\u002fs8zGQI41kj\\u002f\\u002flCzncRaEP3wyqxVhfHQ\\u002faMTVFLcOYz+5ocU8FhdQP6IF9Nsbnjg\\u002fhMfHxc8RIT\\u002ffyb\\u002fyUIUFP4R43gC\\u002f3+g+CXXw30ziyj4FNnyBrk+sPj0SIkfJBo8+W\\u002fpWxQXJcj42nXGSxxhZPtvvXQVyaEE+N\\u002f65m0GGJz4iryfVdMYNPloW31UbWvE9Emlzis1F0z0jfAcCH5XBPevG7LVyxgU8ngjO2SmdNTwQ4yiKBoNzPN8VxBdKLLA8Vez\\u002fGueB6Dzo0spT2fYgPRazPXb9c1U9kyxvbHrJiD3gNL1apC66PYinC6MaT+k98EfJXE9rFj4CwHrORjpCPm29uYf3P2s+g3oiHqrBkj62sL3rEc+3PqvENGEz5ds+DvwWiJIv\\u002fj4OeM6AfS0eP5qBLzH53zs\\u002fLf1BO9nJVz\\u002f0NK0aOMFyP+NqaFtJUYs\\u002fC+EFsUFgoj8ZMvx+5dS2P6OA8wlwMco\\u002f6ojCTNS92z+zHXzE6x\\u002frP5x17+A\\u002ffvg\\u002fTvWLvLFxBEC4z1tFOJkPQE9BN9YxqRZA16gYtrM+HkAf++ctSdUiQEtbdMoV7yVA6benravsJ0C7TrpgZHgoQPun2n25iSdAyOFwABN7JUBxIQLrWQQjQFvBs3opGCFA8Nqb4EK5IEDvjYkfuO4iQLfDS6uu8ShAHsX\\u002fcLo\\u002fMkCOBYSGKvs7QFplt6Elb0VA0CX2kp6rT0D36i+g5gFWQKk02jPiSlxA1c66tbelYEBUb+1t0NFhQDyZEsUOSWFAnVSwQcFSXkD0PSW5TgVYQJFudf0xLFFAqpQbJtgmRkDK8JzN9sY5QOMh1crcECtAU0dWZ6ipGUA7qb9FbwQGQNuWGqg+L\\u002fE\\u002f666f+mHB2D8oQlC8zhHBP+z2dnYMY6g\\u002f9JhHETcZlD95Xf7pyNKDP4Wg8SqqjHU\\u002fsypkQEVTZz9pw3R8Q7pXP1ZmCJYtJEY\\u002f7NB3973HMj9hIGpS59wcP0eSkX8TFAQ\\u002fOxE4yeNR6T4QYwB6ARLNPhP\\u002f7FEBva4+fE6qZEu6jj50WQo5Zo5uPlTcb63BN1A+5FCiMLE0Mz4h\\u002fzFVu64YPt\\u002fU+ixYEQA+gWtYa5wH5D2syRf\\u002f8znHPd\\u002fpqErdx6k9Im5vhMCKlz1AODNeDSrhO\\u002ffNa795jhE8ARc\\u002f6b1MUDzZx64+xbqLPFVqAdi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02fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP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D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+I+eZ6XzDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkP42KEhOGPuQ\\u002f\\u002f8cQg9Mz5D8TCFcwrCvkPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002faIG5Rawq5D9IAZ4svTjkP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D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPxv3JX0eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D\\u002fX5YqTtjfkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzK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rIwjO0BAaRfcG7U1QGCJSW4L9DFAwvzAR87FLkAODwvfMhQrQD8J\\u002f03eGihA14BovFhfJUAAVmEPvKAiQMcm0+zMrx9A0zxHZbN0GkBraBCeRXcWQAP7sCChIRVAKXsU6gdnGECyjf32L0ohQF8lqhTrzCpAsSWpFlFuNEAd7zt6ggc9QAvkESGazUJAKOZZh0sGRkDWypKefEBHQA8\\u002fBGT6GEZAB6HqMC7lQkD3VHE6lRE9QNKO2QjTHDRAF5HmQqMIKUCYM5onrAYcQCByiMKgOAxAhGU5Up6P+T+fKb6sDtXkP1wE2C8htM4\\u002fNlTxe7SytT8cM\\u002fJoEdenP+8X6IHoRZk97ZA8OTjKqD1dWh4+BFHHPfEZcOMSTuw9mi9i2H9\\u002fFD5vXGcolrs\\u002fPvvgmyqBFWg+xNNgaVkpkT7VjRVuloi2PlRpC+rUDNs+Suz1Vfqf\\u002fT46QxsEXKYdP4dzx+7mQTs\\u002fGe63YuE2Vz\\u002fN60HTHIdyP5qTQkEMEIw\\u002fu88nWz1dpD9Wb1A3B2m8P6zmkMuz9NI\\u002f63eSCCLv5z+tpypbvTv8P33delRlxQ5AWqe9wyG9HkDVqzVVQAUsQAVyGx4vSzdA2cwdr526QUBnsUnkgvZIQPXHkvQzkVBA8vWXqNZSVUAnKml4\\u002fnJbQIQmw83A\\u002fWFAJoEtFkbYZ0C1viIx7RxvQOWkgPqNZnNAVhG5hk+edkAnVFfk4lR4QCA8BxVaAnhAh2URTtm7dUDeIkg0szByQJW5rj18xmxAI5n40LyLZkAeoQ5tbtliQItm6amV32FAwG\\u002fTYuD5YkBdajB1fQ5lQK6V0axH+2ZAQKH8SFfsZ0AZIpJBWYVnQAArDBw12mVAmbg9hQlHY0B\\u002f3RHQLUFgQGMGF6KvaVpA5uBQ3\\u002fjnVECxHtdSv2BQQKS8Ndrn60lA\\u002fbzp9O4gRUCSJURA\\u002f+hBQHfAikzNcD9AK7zI\\u002f\\u002foxPEDw0z43GnM5QL1ZfgYA0zZA018vwLoiNECapM5\\u002fsmExQKT\\u002fkDpUgS1A5yeqDi1KKUAvfva+yl0nQE2epZgReylA1j7WlObaMEC4LHnSSOc4QOxRq9vbf0JAuDxIxfnuSUCb5w7M+qpQQCXdRTJva1NAhQfamuBpVEBocMSdLlNTQHxeM3eadlBA1gpq5VI8SUC7h94p22VBQOHw8OzRkzVAj3hKngoSKEDgBarS1yYYQPBYA1UXzAVAfkbJUo2z8T++U+QejP7ZPxNMRKP3S8I\\u002ffOAoVboKtD8UjdrLnD3HPTkDIrXdX9U9VV\\u002focjFH8T1+FhMB5fgQPqMTMmCZNDQ+7zT3rBB\\u002fWz6zgffVbWGDPjtQD0iRcao+DiBLtBLh0D5KghzjW9nzPjcms5ECWxU\\u002fq96Hger7ND8TOR+hYd5SP485jZucOW8\\u002fxqCmsZ79hz++d1Y\\u002fo1GhPwJ+GEXGyLc\\u002fcnb\\u002fwsxXzz\\u002f8pIAdX9njPxa3ozmeAvg\\u002f92grWMRrC0AWsFsjLTMdQIRI\\u002fWSqsSxAajAs4c7XOUC1vGy8xEVFQJqAw4BmCFBAFxMBcCpNVkAatHRa7xVdQHh0rp17PWJAQmNVmKOvZkA+3jQvup9sQKtbCZI1U3JAAqQnRCNMd0DqNCssgZB8QMneKyCffYBAcjS44ZKrgUAaLF94BXWBQD3kLF841H9AaIyUmkEOe0BvHykV1Qd2QC+CchpwHHJAw54sxqsfcECORpftLitwQOt8DuActHFAvZbtUp3Sc0AhVZD\\u002fA5p1QNgk56u4YHZARSQzSzjidUA\\u002fNY\\u002flQzh0QFnhtfbouXFAWUm88MCqbUCtIOv4vuVnQG5t1dDzxWJAjlaIeHhVXUAjXOZdZ1lXQCka6xeZUVNA15gZgLC0UEAoRsosDOVNQL0hs3a6OUtA03hsOg7hSEDI1XbbpJRGQJZNGH3eNURA8d\\u002fyVbjFQUBoQLJbFs8+QLCn77sfzjpA6bt0P9aDOECZ8nvO2V85QIC0M5BtJD9AnebhjSmqRUCfeNfV5xVPQGEZTxeoZVVAmU\\u002foAa87W0Ce71Ny1IdfQJMmWr55fmBAZY6rwCAaX0D3jMGPc2VaQF7ulCYZKFRAbdWmykCxS0A6C4uSaRxBQCCNw7cSBTNAvvOVBxcEI0A8gsDJ2hkRQHI1HugQrfs\\u002fExpXbkQ\\u002f5D+jMacJpWPMPwbdp3KcAL8\\u002fuzAJmoEL9D1\\u002fSpe7M8MBPuOpBfjTHxo+ijjtOHLKNT5cTr1M6ixVPogahsjvSng+8NXUbrWinj6G8ZousojDPv2dceeA5uc+uwNOET1PCz8FVWxDHcEsPx9zP2X5uUs\\u002fuxpTPnNyaD8W37ShjsGDP76ZTTXlcJ0\\u002fkoGY3LhrtD9iVItrA7HKP3kz6QcEouA\\u002f9USo08vq8z83YZyqLuoGQMZDy+LyIhlAmsQde3b6KUAIyogGD\\u002f44QKznQhfOLEZA0VrufVYNUkC\\u002fw5eKPvVaQKvtvms\\u002fkWJAo4cg5VbqZ0C1XnYEJnltQM6vAqKk5nFAPFr\\u002f2Hf6dUBxbEnZQGx7QADX+FhDFYFAkS6rAI6qhEDr5b6e6q+HQFu\\u002fX542U4lAUaGWvUsWiUBC9gIkNhGHQP0Nya8o8oNAL3B4O+O7gEBOAHt41sh8QIgIyUudAHtAQP\\u002fPZ0M0fEC0hRCAWH5\\u002fQJOEjXFAtYFADI5J5V5Ag0BN+gr1I9yDQDsnud5WVoNAl4c4NwfJgUCUiYrqBAJ\\u002fQPEdTB4EwXlAZMr9UzOOdEAlanHeZABwQNSj4uUX3mhA8D56narVY0Bju3TOsJRgQKw6yge5G11AShcS\\u002fN1uWkAiI\\u002fqk\\u002fWBYQO2yeuKUiVZA157Pb126VEDFhPCE7uBSQDLkQHTu+lBAHOE2TcwwTkDXYXoBzclKQPUEI5Rra0hAF7Kw86IrSEB1OkfZlHNLQA9hgx0Yw1FAf5KQ6jJVWECTIse1oFZgQMUDmgvphWRAnLnAshuUZ0DofIJ+SIloQGlYpQ1nCGdAHQcyw614Y0Bced8+ZqBdQN9zxvn7RlRAncIDyNz3SEAtUapndqc7QC2UEtu\\u002fjCtA\\u002f22HOuKvGEBAKkkFvucDQK1we4y8BO0\\u002f32kNKUlD1D9WuJ6Bfw\\u002fGP37lCPHmESA+ycNsBlPXKz70glVw6lJDPugajGvX3Fw+jcFHwoGhdz7SMxzeYH6WPvBKZAgIerg+sF9xqBBJ3D4bUif\\u002fXjwAPydd6R4syCE\\u002fzZe9K64sQj88NjT2YiFhPz5E4z7Um30\\u002fc\\u002fZqf2Zulz9mE6JeSAaxP4UQ6gix3MY\\u002fpgewUtSo3D+MCn5cB\\u002frwP8bfg4jOOQNA9bKAjJ3xFECVXzIKreglQASKeW9i0DVAcZc9v4NsREAXeBwQ38dRQA5yyepCkVxAa7HOO5weZUAdHyYtINdsQBk1svq8ZXJA2fzuz9pkdkCWfUSjjMJ6QH6ioB28HIBAJdi+m5m5g0BinTEH7DOIQKYzAlTo+4xANQ12OGuGkECieVHnRaiRQMVCX2EzkJFA7e8Gg3hPkED4TY\\u002fx\\u002fbmMQB3wuQ5u1IhAoZEd93xBhkDMhOostsuFQCCGhpsvdodAXmeYtf+KikACcbHKQuqNQPV+33f\\u002fOZBAu2\\u002fEJ+WvkEB3rX3MTDKQQNoKdZLhr41AHUmLjLnAiUBzfNZeFj2FQPws\\u002fEg8y4BAMGx+M2bheUCHJJxGePNzQOU4WLAzxG9AXgviYEGzakDMnqzIiaxnQAS\\u002fn\\u002f5JvGVA9qIMU3BCZECgXi88tPJiQOcZ+jXosWFAWCdiQS11YEA+0+cnmWdeQNMXNY1r0ltATXLzkkJCWUB6pKigOxRXQDBH6\\u002fq8CVZA\\u002f83JtaI4V0Bk0rXSn6lbQB3VbvGY2GFAy9E0k9MpZ0AjGsTdJ5RsQG6U4mvxQHBAFC3+BFnOcECsNB2kimZvQMzFpgF7b2pAfyr7XMEIZECYhJV4klJbQEU5DWmywlBAdQzWqSZ\\u002fQkB3BHRSYFwyQOx3XE7kZCBA2Mh+UrlXCkCoz\\u002fl7uCHzP6YqBvqenNo\\u002fwrZIxWTizD+hNK0gAdhHPh4nv00yWVQ+8ghrv3k7az78C0eby++CPsIyaa0LV5s+LmJvNiz2tT4Fw33PAT\\u002fUPmrQaA7TlfQ+hHJeRaGbFT8s1K1cMUA2P7rti8EQyVU\\u002fOlXtvkzocz+dORhIesuQP9JE+rP3BKo\\u002f8wsoGJV8wj\\u002fjKPzeDSvYPwTAN8mJRu0\\u002fUoiqwuiaAEBzjTjQrdwRQOjE6xV2aiJA83aLbppIMkDNZLKxU2tBQHpBsQLeiU9ADVNbj9zOWkB19UgMpi9lQAF63wdE+W5AVkzKxR34dEBXEOiWTYt6QO8MQ9pOAoBA71cLyS3ogkBpajJLfHKGQBIdwekpFotANYJRkJZrkEAivV4mL4KTQPEPNhozKpZAOe6Do+6yl0BG5vm+trKXQHK0kiBAQZZATIzjHZX6k0C8YplDxsiRQJcJjeTYjJBA4Vde4pvLkEDUJc6+Tn+SQMQJtSIcH5VAmYYSRZPWl0C3CO3vUdKZQB5zqgLNf5pAVwumokGqmUDDjoD+LnWXQMO88SqkQ5RAEUVWjXqakEBsO65\\u002fsASKQGRlpefu1INADLKzQE1GfkAmOyF5xPh3QKHA02X2KnRAuV8hTRT8cUAvL5sZRaRwQHb7C3ygT29ADNTGHaulbUCqkcz0bi9sQF8jTZPK3mpA\\u002faFhiRKVaUCePK7+ziZoQEcwRsFLeGZAmXak04uoZEBksTI+rTNjQO9mBA9k6WJA6j6khgagZEBtYjOVzrVoQOKgEh6FoG5A3oEM98BtckA7uTSdtq50QM8s3BDzNnVA+it6nVS1c0DD4nXIa4RwQP7GZlFA72hAkDIg4C3wYEDLeP\\u002fKQrRUQEPD+XcExEZACw\\u002fu71mENkC2wdn3awgkQA\\u002fg1vd9CRBAHRoEn5o19z8CJtBi1RPgP0gaNvylZdE\\u002feYJYlN1UcD6SZs6M7JR7PlhktZZtApI+1Nyfkdjnpz7khTp\\u002frqO\\u002fPolOSmWGM9Y+NqM5WOh08T6wN0jm77kOPxD1iWA00Cw\\u002f9\\u002fZRMepKSz9LmuyX\\u002fSRpP92pmb+N\\u002fIU\\u002fgAuG\\u002fcX0oT8yqIDu3x27P37nLoHt1NI\\u002frwmGjL0J6D\\u002fmjUc6vUz8Pzakub4Z+A5AvEvJhxzcH0AsOHnFXTMvQN6OWs7qXj1AkoRVzdumSkD85tMRrDBXQJrz7KAcJWNAEt2NV2CpbUBtOxKrQWZ1QKvXUNTNuHxAuH3qKsgOgkDq09isq6KFQPlZzsPsVYlAfLem8cfBjUCTd125hr6RQC3jVAIdS5VASIb0V9YemUCeWaBzKHScQAUzS50Kdp5AeLKvCHqlnkDDjJg9qiGdQE\\u002f2cNHVrZpAuXH6CXxwmEDXQ2n3pYaXQLnoTlHvlphAiOns9nKQm0AGxCEC7bCfQL5WToAo56FAJlQng\\u002f1fo0B9oXC3qNujQHi\\u002fNfx\\u002fNKNA7mxnO\\u002fSDoUBRbuZSUSaeQHbQRBHbjZhA415\\u002f5TYSk0Ddlz1P+cGMQEAyhKyPtYVA4XhNtKMNgUABpoGH4p58QKk8\\u002faqmlHlA4uEe0JrMd0ByhoIagZR2QDVf4kOgq3VASZjqFC4IdUB6ltOtVJ90QMeoiEJpSXRAz8fbS8bFc0B0U6d76thyQFPYM4TSeHFAyGXO\\u002frneb0DCOj9YaqltQESTNxBKp21AGCN5Ta5LcEDegqlhdwJzQGhOEY64GHZAPmmpnCxXeECMIzHlObR4QAq3a70iy3ZA1fzGVEUBc0AHy9JNr5BsQIp76zrDU2NA44F+A9eIV0DJW3k6NshJQO3hMf1JaDlA71RuXsuFJkBNwR1+F\\u002fcRQM979w2f5\\u002fk\\u002fzSCFenXf4T+pC2yb0EfTPyhPBQryoJQ+EU8StAxIoT4OX+TXrC22PpkKLR\\u002fThcw+yd6pVhbI4T6FSBPW1JD2PmnyNOhz+g4\\u002fRS4AnlukJz8UrAa535BDP147xoo\\u002fv2A\\u002fDE7W7sB+fD8S1AQW43GXP2U4vm\\u002fGS7I\\u002fA2GY9tqzyj8QADFpLQziP0rZPMSte\\u002fY\\u002fI3ssT8nNCUA5KEz5imYbQEVVzTXOJytAp4hpprFrOUC2tYZH+cFGQIZLMj6spVNA1+8WRuxdYEBmGDRWsSBqQHNb6cbwxHNAkG\\u002fNejkZfEBKwNlPqa+CQAt0ZkN6XYdAyviczwvei0B9YmknOjmQQL6Q7WYI5pJAbIfv7YNPlkB\\u002fy6KTNIKaQK1UScZ5Cp9Aa3TQYjSEoUD3gd4oCcWiQJT0btU9AKNAD3UAZOJJokD+piMIPhahQH1cqeF7FaBAUbtmue3mn0C6pWuTABShQJAerLN5Z6NAMfjhadRrpkDA7XMTY1qpQIJO8TIxbKtAknENdgYVrEAr0HwGVCCrQCQ2TpFisKhApnlvpisspUAAhplw6CShQCETmSXfaZpAtaT3J5a1k0B2DNzOdG6NQF33Bq4J6oZA8NX0k4wkg0DX\\u002fH8ujRyBQMhRJpx89n9ASeIsJRKTfkA\\u002f6e2gD8d9QMf\\u002fhgkUk31AQH7n7uTkfUBgLQIsNWh+QCVsjMq\\u002fjn5AwIe4eaDJfUDeRZiHr9t7QGzRBY1FF3lAA+XoRjRXdkAkJP\\u002fVKqR0QP+ELg8CrXRAlZtvJKNbdkD+TyYwgMd4QAP0clV5lXpAKkshXKaWekAGlm\\u002f9llB4QCH9cqXfJXRABGRvXEkibkAP88GI+0xkQPdCe3IEn1hAytKCBYjeSkCMw+lGSWE6QG2hg\\u002fWLTCdA5hhcVE6EEkC+AH2gF5r6P0YOI2u+R+I\\u002fPQKnn8Oo0z9nirg99wG4PnDPuwlO\\u002fMM+f95TZzlR2T7KOilh29bvPilBTeadCQM\\u002fOvdYdfVyFj\\u002fWAk8dYqMrP5t\\u002fkZLDjkI\\u002fAzhE6xwkWz9zDFdFF9Z0P0iBx0tGJ5A\\u002fCagFz3GbqD+U5wNdXQ\\u002fCP7mWgj6XJdk\\u002f251UIZVl8D9Xc1K3utgDQPncghH+MRZAyPxTcf7wJkAmqluWOQg2QENYoMie2kNANLTggZ4AUUAAHFnIKAFcQN3CdSqcUWZAcxej4d4tcUDq0us1i1N5QBCZlqwWtIFAJwPgTbVVh0Bdd\\u002fpOsw6NQN8ivPQzS5FA8sUmyO8UlEAsHBPy8kKXQCz99X+WN5tAQuQ4SPEAoEA+KLt3CZOiQJsj48AC2qRAJ5ByCfdUpkBEbcnZhLmmQP6QXVOgIaZAr6YmZtQRpUA\\u002f+Wzs6VKkQLpcD0VcqqRAJrAxRZ2KpkBHmQuNDdypQCnKS2ML+q1A7pukJy70sECRoBSNw1OyQP9mpsw\\u002fwLJAoRU610sWskAePzZyNGywQAPNFhwdD6xAT\\u002f2DIZyXpkDBjqfasUWhQOx\\u002fktHXiplAuhfW31PkkkCtvYpMuDGNQBquN7ahSohAWB71aBy1hUBBxP8ecFaEQDdv6gPbm4NASXFUsnVkg0Aq9n3k8rqDQDyKYCRpkIRAFPxYO6yXhUBsFUI7b02GQAUXxGLWKYZAGQiH47LlhEAX100SrqyCQC40v1cxGIBArtH5k6\\u002fZe0Ai2gMj93Z5QEiw6uiUNHlA6XIByDg5ekCetxJf6Bh7QK5qGQXFiXpAq7Gjpbr4d0BLE5TVTbRzQI8A27qMS21A\\u002fqany4KjY0BN0lAx37ZXQHL1RlhLxklAlEKqxwI1OUBACjyU2C0mQON\\u002fTm+3jxFA\\u002frCyKKUi+T\\u002fN3Jao3TPhPyVqFKVhcdI\\u002f1KG8IhW72T7rocxo5U7lPjZ9PUNAuPo+jUQI+iCHED\\u002fWwSgClS4jPzV38HB7bzU\\u002fGl7wx448SD9dsILXjitdP8wM5pJTB3M\\u002foe1078lIij9TSmeYgIOiP2asyFH\\u002f3Lk\\u002fQZgZNYea0T8kh0qdzgnnP2B\\u002fOvZ+nvw\\u002f+Sn0xkGuEECidyOCZBgiQHzCysWjMjJA1mctOkL9QEBpwiMOraVNQIWqIBUjc1hAf7vcUbZPY0DUutHFsYZtQK4MymiS63VA06Hk0Ot0f0DPAevdqJ6FQKh0oksJQ4xAWoEAEVCPkUDp7g4NAO2UQNboKXy3UphAaAqDulUbnEAuKvFuVFOgQFjq\\u002fuGI\\u002f6JAEk2WKO3QpUCPsoavqkyoQPzIkOED8qlAGa5xTsh6qkB+uYHyeAmqQDBxxcQoMKlABR\\u002f5+STKqEAUSLWJpbCpQEdv5MbIYaxAqyaSqxNfsED9c82p7f+yQJwsDbZ3erVA6xzv42Axt0CTU4MK77G3QMXN4t\\u002fwzrZAnYHR6xqltEBTHvtQJ4+xQDtJiyOsHaxAYyKBORNapUC\\u002f+Y4KOFufQMQrC9A+DpdA4YZswxfBkUATXkAny4eNQFoAsyw7bYpA2zXlL\\u002fDWiECshUNubB+IQF55ZK2fM4hAwv7HVQwwiUD7t55bof6KQP0hbRSEIo1AomkknBrHjkCsWE3aQg6PQAF74atQeo1AbnF+yjk4ikBGIWsOcBeGQH\\u002frOkiCLIJAvW\\u002fkGHKjfkAJ49xe4ph7QKHNv2YDh3pASGIvPvcDekB6uQvcK7V4QEVp50nL63VARS\\u002fFLkbRcUDNezLSqkhqQGyHW++gg2FArxo9AMoKVUCp5aSCcMRGQJFQ5YpWLDZAHQ\\u002fuNKtuI0DMCidgo6cOQCQnoX2t2vU\\u002f2zgds4rK3T\\u002fN+ifHttbPP5fV3tGPY\\u002fk+ZI0Z6jfuBD\\u002fZCTJosQcaP3zBXGGqzS8\\u002frboAa+QNQj9zRyoB4WNTPy1KHUUghmQ\\u002fJLWQMCqJdj9bHzV0fomKP14yk5jTnqA\\u002fzMS3DgpltT\\u002fq8YWB5W7LP3bVxvgcNuE\\u002fRc2BBJTs9D8glkiRP2wIQD918SQsFhtALno+nddBLEAMgemTs4Y7QLU0L9cg+UhArHVhZ3EqVUDL7WpsfOdgQCK6kJ4OxWlAg6H7DGr9ckCZtV8PP0V7QO8cyX\\u002fpEYNAxH1DCX\\u002fLiUDy\\u002fDwDo76QQIRobFKk0JRABbjBty7rmECg6J+5LhydQG6oMwsq2KBAwTyjezh7o0CJJNtRB3SmQAj3bsDre6lAmqcGNA8ZrEDpEnahp9KtQChZsESSbq5ASm9dILEbrkCew1cVoXetQEqZuiT2aa1AtsfB0QXZrkDR793JpSWxQJw4SGhtzbNAhAq9yVLytkAJjHtUauK5QFkLreel47tAu1kt0D1svEDrFrn91kW7QM2qxf1\\u002flbhAL0PVnJrNtEDHKdXXm4+wQIUNeQ3hBKlA5c80fBROokAstzoUyOqaQGLLOCdM0JRAsbCERgBrkUDfs4C9DVePQDlcoK9Ck41AmX6Gx+3hjEB6+rmYbUyNQPqgMqtrCo9Av4wfvaX\\u002fkECZ+B7K17qSQIB0BnxqHJRA8gha2dyClECaO5ttz4+TQCMSdXYwWpFAg77n83fPjEAKXyYVQ96GQHVbHXGuDYJAvLlTf3O2fUDyd76IERR6QMM5I2\\u002fw23dAPPaV6K6rdUCM8kbb7LZyQLOc0Ywb621AiGPwa93SZUANBgRj1dlcQP7IbfW6OFFAVSfbMMSIQkC2Xp\\u002ftIfcxQFaUec1MWh9AHeIr+u6gCEDr\\u002f7ep83zxP1dNDN0Tvdc\\u002f4kKLctxJyT8WCrIZ+w4XP1JVIfft7iI\\u002f4QqE6+thNz+ff+u\\u002fYEpMP9IFyWd1mF8\\u002feczze\\u002f54cD\\u002f+nluyO5KAPzSBM5nO3ZA\\u002fiXeGJOEgoj+TFgTF\\u002fsC0P62DWxh2nsg\\u002f53vnPbM33T8GLK23S\\u002ffwP6KzdP4tGgNANaQq4p7BFECLVlVHyqMlQOFjGyTgdzVAOi7HQSsYREA9LtDD56RRQHbfpv9XDF1AjFFnUd2JZkDgZb1lCqpwQHmP4oXMzHdADtfi6yGYgEA\\u002fBpghOKyGQLKJqiVKOY5AmV36xLSDk0CeOEmuF1GYQL7NCNsGVJ1AdAdoi0NHoUBFYmb8nRqkQMSz5MsJO6dAYz84AOWUqkCDH3+SvNetQPoSY0jgRbBAYZ4FadgisUArFQTsFW+xQHMQ\\u002f9UpSrFAJt03ixsIsUCM6waxPiCxQB04g49KCbJA6iljhycJtECkAWjCpAq3QGTLicvdkLpAZMQI9PrUvUByuFM74QHAQDGkig6cP8BAjh3MfmwKv0D\\u002f47HpK9W7QFMHEx+wardAp+9wh66KskByANiYG+urQA0Xmqtyc6RAK1F3hWhRnkA4\\u002fuP8kMiXQHH\\u002fPzWRNZRAT3sHTCdfkkDUpfKaMWyRQKnTxyisEJFATBaCHnVnkUDeyi65zJ6SQFvCyl6zppRARZzZYn4Hl0C8K5G1VPWYQJ\\u002fIdfiJm5lAGcuqqUV8mEDULLe\\u002f866VQCdTYeSc1pFASPdVPi2qi0BhpGuVv9KEQJQphIatu39ACZjPYSxeeUDUZlEp01B1QO6xsGfnMHJAc49c9xsrbkAp4jQRHIBnQMWBndrQ2mBAsfOGm4EFVkAY76AGrQ9KQL6XQkKv2jtAMVCSfvfXKkBHdG+6LU4XQNJqVzs5OQJAKXErwbLE6T\\u002fAZA\\u002f3O2nRPydSxZVQfMI\\u002fh2EKsOxFMz8wgMWD1Yg\\u002fP7\\u002fpyoWuWlM\\u002fmSFPflk7Zz8xIJ\\u002fK1J55P40VU6b4IIo\\u002f1koo7utHmT9mCMaDDjGoP6F1r9i6\\u002fbc\\u002fBMIYpAJCyT923uPWS8rbP5wkcVAk0u4\\u002fDrii58C8AEBgRDHXqpERQKLJyIb+xCFAkj+1WIlNMUBOB03VqCpAQMJcnN5YzkxAh3FZP9hPWEA6OC\\u002fxUF9jQMDYJbmWNW1Ajy6CFq0BdUC+5ieZvS99QIjG5ofh04NA2CLcjJ6EikCAL3oPyW6RQDbFTQsyZpZAjlkHhyP\\u002fm0Bmuyzi5QehQKfzQgoOSKRA1I7RBRPLp0AreytoiJCrQM5SjxZhbK9AcMd7Ckt\\u002fsUDGCqZz4OeyQP40MnIdwrNARAAkrVYFtEDAfyvCG9qzQCXE5e\\u002f6mLNAvYt20ba6s0AdEG3bG7e0QOoIytBz1bZAiY85i4\\u002f\\u002fuUCc5Yu7U7G9QMxN45WeisBAVDdJ7t+iwUDJRkIdYcvBQPNBfJWa5MBA90\\u002fy2rYZvkCkjZrbLCu5QL9knteg1rNACWiOYhPcrUCqYvQrKwmmQMbvs4DMoKBAAO73LhLDmkAWpk3tzkqXQAfQzgFyfpVA0FkioYV\\u002flEDr23kuTxSUQHVLF25gdJRA26Q\\u002fxOHjlUDEIUsejlOYQMxiIlh7MptA3FgPOoWJnUBM1ciiFFieQDIZIvjnBZ1A4HQd1O+qmUBmHP0LUwKVQNFh231ZEZBA9ov8ctNxh0COcknRlNuAQMQKRAhlx3hAjozjLyD6ckCoRun0Y+VtQBzcKko+YGdAGZoQ\\u002fYd\\u002fYUCaD5sjYnBYQEEcKh3lWU9AWtx3fjZPQkCzLzg6kmAzQJTLyaGehyJAjooc4DL4D0BWkt91f9z4P+t+g6N\\u002ffeE\\u002fxO6z3ueExz\\u002fHmnPLU+G4PxulQ2rfpU0\\u002f\\u002fhyydv4rWD9B0MG6QYJtPyvJIJrgl4E\\u002fJRscGPA1kz+eP3pF0EOjP9XSDvc9GLI\\u002fPP+EY6p7wD\\u002fVd7PKa3\\u002fOP+13iQ\\u002fOsd0\\u002ffk5JsXBp7j+CObPedrr\\u002fPw6SRX32SBBARXrlW5UfIEDkTuKnIJ4uQFN5tzWX6jtAGU0Js797SEAw4n1rq59UQO8obP9znWBA5LyG1HmAaUAJYrIU96JyQAHKQ5qLE3pAv3GqWuylgUCW6TXiVGSHQD9DmGh8n45AUfQbNh\\u002fUk0B8oun270yZQIThGYWeqZ9AB+wTbFVoo0CwivZq\\u002flqnQLCK\\u002fYIXqKtAyvl8ePwZsEAzCHPGalqyQL\\u002fjpxNAW7RA9jnLMpLftUDTelIGesC2QM+MceBb\\u002f7ZAsjKAHVnMtkD+zfMzmIK2QFSCd3\\u002f8mbZAMf1hKh2Jt0B8v00YWJe5QLTXPwm2rrxAsuZPXW8kwEAtGXomIcXBQCJdJ8W2wsJAPZ4wh8jEwkDx8dZlqq3BQKxubyn0RL9AlDx7QpP8uUBHawD6B2y0QNjI9u\\u002fw2K5A83cdB9UYp0D\\u002fGsEln+mhQHuUA6jc1Z1Ao8tOYpK9mkBWLpjkoBqZQBdZX5bOFJhAqQZAlfmLl0AX7WGY3taXQA0eSRiyT5lAYZz+t\\u002ffpm0CsgWRVBgOfQKSNBrTCwqBAZxfsUrQpoUCRknxzuWGgQDzkvGUb7pxAvziLvNWdl0AGIU1Mm+6RQEbdiohQsIlA3uCgjG3EgUDR49A91XJ4QJkOeJgeKHFATC7h266uaEBq8iTFBNxhQChZYj\\u002f1N1lA0FelepnkUEADBdhA6QtFQG4JDju3EzhA9934q\\u002fwYKUBf1+4lLroXQAPO1eJeSQRAtYi\\u002flhFT7z\\u002f53AIz2ebVPxAPvN\\u002feR70\\u002fpG+CKuXYrj\\u002fFNEtDpvtkPxlsWFk7DXE\\u002fJ43FWjO2hD\\u002fzuYt+MY6YP31btatXmao\\u002fhEOsLqJUuj9QOprS4i3IP9iVfD02MdU\\u002fBMmJr4l+4j+cRNA3lMPwP\\u002fTjFhsEBABAgo0uYg6HD0CfxLTkNc4eQBaW1hn7FC1Ap6bIaeE7OkCHHJrMz5xGQKyEcqImtFJARnX9uGTJXUBXnuqv\\u002f9RmQEAVAtlTz3BAuGIX8gS\\u002fd0AimRWTwyKAQLJwl90AQ4VAt1VxK6F1i0DvV4M+5ouRQHrwh5QvSZZAIbwEbbAYnEDbNwC32IShQM2WZKTDjqVAabSdTf4mqkBWtPbZJzyvQKFTMxkzTLJAIyCxGhLqtEBfBhF7OjO3QBm1F4eL6LhAgJsgoq\\u002fnuUA8VJPLUDi6QO8Z4AqqDbpAFq+CPUbBuUAIIxOsnMa5QD8UZgchkLpAcIt8mHxjvEDB1P1p9iu\\u002fQNyL33leM8FA6ttd1IeewkBgEzVllmPDQBKnDiLKLcNAvlxUiX7kwUB3+evgA2a\\u002fQPCnauEl9LlAwHed\\u002fHRitECAUPj7fAuvQJz1lD7cvqdA1K7GvdEOo0DSJRf+3HygQAs\\u002fniYrcp5AJy0TgxIQnUCMXFFhGQucQJ8wAC3mXptADKs4nYx9m0AVk\\u002fHIaNKcQBKKa\\u002f8BVJ9AjP5uUqkroUDdzTXVHl2iQF7DnbgjqqJAshhCKb67oUAfn+JoUD2fQBExWBpfdZlA4WMpd5RDk0AYxuHJLVOLQHXdVINwdIJAyrRB9RpBeEA9PSlpyLRvQLQnLbyB42RAl4+BDJ2oW0DyVg+IMBRSQGNGAiiqy0ZANMwv7okkO0BqzGiYywkuQPrwcdyqjx5A19OXRv1gDEByR9sbEfD3P+j\\u002f7S7qSeI\\u002f5oSdFSdcyT+MU3b4bNOwP5tuZyTDoqE\\u002fBiDY1QZUez8zyCQIbiOGP+qQnXNBw5o\\u002f+V97AUeRrz84mlq5zvvAP8SsnoRYpNA\\u002fWX99htkK3j9FG7Nc8o7pP6Zgo3b7RPU\\u002fxA9d2HcYAkBSb0Vy0SsQQGx1ml3KDR5AKMeaIf0JLEDiztW7n2s5QKuEhpz+AkZAgEbvtesjUkBfJIcEMpNcQPUd5jR8o2VA0Vdy502ib0DmT\\u002fM\\u002fzlZ2QG5tRJsnd35AzbpTgWQVhECGnQzVaLqJQCl5GditJpBArL\\u002fu\\u002fHMQlEBnQrAbU9WYQJ60VcZZsp5A4MBNIrrtokB0DQrrXzunQO5cV8csSKxAPTSfRUT+sEB9ofuKfQe0QLuJdLZXALdAOgUWSnicuUB2M+Ey1Zq7QMazYma22rxA2aZoaCBkvUAcU2ZSI2a9QCUTiADWMb1A0LJ2AKgwvUADIExQkMy9QIGX+mzpRr9AU0ve2o\\u002fGwEBZCDT0NRPCQNepAlyjJsNArZRjPH2Xw0Cvn056JhvDQAC0xOjVoMFAnccKVJqwvkBXRwJFWUW5QGz51RoJ57NAwNPk1AW2rkC0Naud\\u002fB6oQDxc4050FaRA4zsdcIwDokA4XRXPGRGhQEH+MqXnhqBARpNC57oLoEBgij6mF1KfQLDJzHkpQJ9AyQN23vQpoEC2D4rtQkKhQLCMtXTkk6JAZhdL9J2Qo0BWgZNzEqmjQKzRNgadiqJArY7iwlNDoEAgZvAGi3KaQCl5dg1d95NAH67KvNIojEAcWuFu7b6CQPK421I+6HdAdsLBl1GvbUBjreMUGy9iQM4D96FPGFZASIpk2e57SkAYzV5cveU+QFrWCSPWQDFAiGhtxM8oIkCkV9Xmm8gRQO5r2e0WDABAZhr27CN\\u002f6j8wKN6F9OrTP2cFGH4gRrs\\u002f4ETL0NvnoT+Arc7JXKOSP+MyVce2X5A\\u002fEHeEx5lymj+7i+xitNOvPxIujmzNrsI\\u002f4QD4bPv80z\\u002f+7BnUG23jP0swtSCITfE\\u002f5PmC8dbG\\u002fD+qmEb14w8HQDAaU6zJlhJAIPR6D1U5H0D4BguIt2grQLPUmch0bjhABtoKlplXRUCEx5cFCttRQOMvYDXMY1xAoD6js0l\\u002fZUD31Q1klzlvQILTMZyj5XVAXGy\\u002fPqDEfUD5awm5tqGDQD5JWofuIYlAIWxB0JROj0BkypVT4xWTQEZzCIqa+JZAISMZsp+Im0BxfC9SyoqgQPlCOT6p\\u002fKNA3ajUahJDqEA4qZrqonOtQNES82e1vLFAkjO25T4BtUD9tUQzfUK4QGcIjVo9MbtABp1JWnmMvUCYzWQslDK\\u002fQMX2\\u002ffeuEsBAbvb2on1DwEAMvSs2mEvAQGZcznHXVsBAfoFAFQGTwEDgREpJ\\u002fxzBQGU3n9k17cFAjGMq39rPwkA8R1FoGXHDQFoTR\\u002f9yecNAdtDPQRyuwkBR8V7o7AfBQPa8gXnEcL1A+dYcvF02uEALWyYWITOzQDrJRznnJ65A82Q46fJhqEDOEefPLwOlQDsrtztPaqNAGPQcivXCokDtiu1feGGiQHA8yX\\u002fz96FAxf8qJuyVoUAmoqAldHyhQPZOIw8v4aFAdsT8p+m9okBanJvmzMGjQMEuBb6yaaRA1TqFL080pEBTiOOt0tmiQOD7yJJ\\u002fa6BASDsrmC+WmkArf\\u002fzoVQCUQFLdu7I9EIxAujivz5t8gkCej18YPxh3QNPxbYsrqmtAeQB6JE4NYEBo79HlIipSQAhY5MMTEERAi4fCpYGGNUAg7y88FTwmQIDu+8It3xVA0DthvPlCBED+V9fqUX\\u002fxP+xI+XaM7ds\\u002fsXzcnFB1xD\\u002fNzr00IHerPzcAh\\u002fxCvpE\\u002f1pOqbNFHgj9qk5Bucw2iP60tIlFaEq0\\u002fS3S4AFlqwT+49NnIWFvUP7IjpppCq+U\\u002f8ov2NMjq9D+t1s8lD3ACQPBVU+0zIg5A4AuskNVzF0C0tQYuYxUiQHqWExuSvSxAKHb\\u002fFzziN0C5bgT\\u002fMVdEQPYlFIvxIlFAgzg0BTnKW0Cbm\\u002fWzdWxlQNXTHLtvZ29AQAFNoa0HdkCYTgX4x9R9QI46iB5ynYNA8X5jYY4diUAOw0ZMT1CPQPuhspaBA5NAZ3S\\u002feCOOlkCcYpoYDE6aQLNU6B5kcZ5ASyAM+1WpoUD3NB6VKbekQDkVP5vooqhAm1djURWXrUDSwsONfMWxQFkyHZvGGLVAk8NaJ3WCuECW3gzY6La7QOLuOdBXeL5Au1CJloNRwECELKsmkRbBQLm2ntbMkcFAFkR5SL3VwUD0hg+\\u002f7ADCQBfdnIqKNsJAUZNVMEOOwkAErRU4QwPDQI3GN5ggbsNAhUFCyIKPw0AFw8dKUyfDQOwGkXITDsJAXH2mYENGwEBXPEVkbP+7QPiBsCq8FrdAYJUhO1mGskBbFq0brbqtQNU+1m75vKhA72SeQ8vvpUCgJQdtI7WkQKYKNYdnSKRAiqIwMXUOpEDfqm2PEcKjQDEzQoDRcKNAbdn0yFFSo0DR5HBf\\u002fZGjQHPjHUD2JaRA\\u002fyAGw8fEpEC3B4Zm1fykQMHY2aYlYqRA024ym269okBvgiYLyCWgQFisA+sZ8JlA6Kn7li9ik0BDaeX7S\\u002f+KQOtHLbe2mIFALmvY7nGcdUCSQmYkKDNpQAvkM7eRGlxA0U9GOXMiTkB+peO7jCI\\u002fQF5eVKeg9y5Auis54FWMHUAOrqyoI+gKQNJehWdaO\\u002fc\\u002fS8AZ\\u002f23h4j9bB5dpOqrMP\\u002fyuy+uCLbQ\\u002fPNFJCppBmj+7vPzAJ4mAP2JW2hGjw3A\\u002fd4pCjtxPsj\\u002fGp+iB\\u002fWa9P+P0Ql6oidE\\u002fYe12nB9q5D9XnXxpZKH1Pym5BxtSwgRADjsY\\u002fmglEkCMIh3WyEIdQCXIdVNRRCZAcGzFMOWSMEBAyr1h+yA5QAP13R+p2kNABybjN\\u002fYrUEDui3O31UhaQEsXP3IprWRALj6vR5H8bkAJwJ0neA92QNWgJN4ABn5APlcQvDG2g0C4\\u002fBn0jy2JQAD+\\u002fM9Wao9AvveZM\\u002fElk0DCI7BtOsWWQE3ZqF6+aJpA8Oy2s8b5nUCUVrXWsMigQJ01cd\\u002frxKJAhkywHLM8pUDmwGqY+IaoQO4f+FpX5qxAy4lssVkzsUDUAa+NRGa0QA1ig10V0rdAIhfZEO80u0Bgp9KD+Fe+QPzdLlriisBAaETsbvSqwUDFSo4VHIXCQA8GOmaHGsNASaNF10t4w0C615zh9rPDQAU3rEEx38NAj4NRbcj5w0BEYoPpWuzDQK3ntjPwj8NAp5aPFWS+wkBt5vrrxmPBQDfkQGVaFL9AhrB\\u002f+im4ukBhtquGgze2QCEhh2pLIrJAna0BcuzRrUA9ZEMg+nmpQAOfWdB1FKdARiHRbcIUpkAg8rjyss6lQGMpJwWZuKVAfcs1Qk2QpUCGv\\u002fBO+FilQHUhtiZHOaVAmYv9F2lOpUC9geLVuIqlQPwMUjLrrqVA6aBgHzNfpUDOCtf+kUmkQNyRO2JyS6JArP\\u002fKoy4Jn0CQ+Em1UpuYQArFh41QLpJAifizmdQIiUCKgvF4uRmAQC\\u002fVF2u+bnNA56XsMbwhZkB0n7nr5elXQPuPnqtInEhAsx7dUckpOEBfACoEWJ8mQGn7BBLPKBRAvZGx2fQOAUB83lnzQFfrP5Mo6keYr9Q\\u002f94UQW65uvT+\\u002fHvJhKJejP+E4lHFeVYg\\u002fao79YQN3bT9mx\\u002f2JLR5dPw\\u002fw\\u002fof7FsE\\u002fcx25+BNcyz9AsYK8EUDgPyZBzvgK1\\u002fI\\u002fG6deuG3gA0CoV6ovkPoSQH2mQiTdeSBAzXgbDD9KKkDNeIecOqozQGDAgxFIezxAD\\u002fqgnXDGRED1c1Ai22dPQC57qR6niFhA4DPIQ5k\\u002fY0CYnlE7e2RtQABlInzLbHVAoR3zbxisfUCCGbrh5J6DQCl7I6JLCIlA03PGZ9gfj0DmB6vLH\\u002fmSQLwQR3zHspZAH5\\u002f7Z5aRmkBQJJTy\\u002fFSeQJpoCX+k4qBAssPMZhJuokB\\u002f+rlNkvKjQI9kK5HvwKVAboxk7NZBqEAaa8lNadCrQBc1fmN4SbBAqUgG34E0s0B6dxnk5H22QDjRsssa8rlAn4FQVIBkvUCuaI105lfAQJS+NV2O1sFAYRQo73MYw0AyTi3bDgvEQArrB9Udp8RAxskZ+pzyxEAds+pXc\\u002fnEQOU3NDRwwsRAEVdrfitJxED4LN3uj4DDQOlRIy9xW8JAHO9s8VrWwEBDC8DdLP69QLOVfhPt7rlAFdqjE1HitUDnDD\\u002fHLESyQDTpORIF1K5AgsIehqH0qkAaG6NGFsuoQC1g6Qby5adAxESbMTm0p0A3GmLLvrmnQBpDVPIesKdAgtLbhKeJp0DduQPDRVmnQH8D4MHYLqdAg7onA8j7pkB6RV77ro2mQMLJZJ9xoKVASetiUZL9o0At9MGNA5mhQNYrYHg2Op1ATKOoaHm+lkCMpBTPL4WQQLlk0gAjX4ZA5NM43nVFfECmq+I+ibhwQOQuiabSl2JAzUqs1EmFU0D93ItGqGlDQMYc25PSTjJA\\u002fp2p+uJbIEBDAoivSKMLQP6fC0CuBPY\\u002fa7Y\\u002f25mD4D+QewSNFkzHP2XFkebc4q4\\u002fSxg9gFU3kz\\u002f+XQ9UEnJ2P6hCFzuWsVk\\u002fAYEvRshtSD+4M7X28FjNP8cGuEBubNc\\u002fnM3O3Va16z\\u002fdw6MQoP\\u002f\\u002fP+zz2sKozxBAM\\u002fZxdaH0H0C58voDdJQrQJIbNtD60TVAoqSAIXsbQECUKaKv4NdGQLZLjWlkKVBAt7Q0MoeKV0BKrs10nLdhQCvu4Qje5WpAq8tV0O7zc0BpjCLLeVB8QBn18V2rFINAnpkOTyqIiEDSMC\\u002fVEGyOQHhcUqMLbJJAjJJJl5L6lUA+hsYPb+KZQE7P8yCc+p1A+ZvkyPn4oEDQaEa6L7eiQJfSzZMMIKRAAow39WlJpUDnoj1NHoCmQIKyzhJIN6hAwvUi\\u002fCPgqkCND5JBCr6uQEe12pGk6LFAPhg5j9bytEA5iLFAv1q4QL1+RX4SALxAevhk\\u002f0XAv0BE3uTbb7TBQIyOjZhwWcNAG+9MXMqoxEDj8asADIbFQN6Q6qA758VAmdmNG4zTxUDhzQtF5lrFQGRfRhYvjcRAIDUXIPR1w0CSY2XE5hzCQIRwsqGjicBAmIh3ykWSvUBGeMS3zeS5QE2nmGP5TbZA5BiktmMas0AU59ilcYuwQEbbDJvahq1AVLQ1j0R4q0BRLas5hZeqQFLcoZKSaqpA0SbK1b96qkC8OK2ok3aqQLcUZc2sPapA76ca\\u002fAjTqUCepuElB0CpQN+pcEMNe6hAXaeieSlhp0CErvBNMMWlQH06Yf8XiqNAUamUZf64oEA10EGnSg2bQK8zst6hipRAAY+ERbcojUBUpZDU402DQJERqhb513dAoIE1F0KHa0BXjevDMc9dQIA2QXxLZE5AAyNWwmBDPUA0l+hgkqQqQEkomcjN6RZAqUN+nfSPAkDB0YkvtjvsP56jAiJJGdQ\\u002fEoP+mLa+uj\\u002flnlQy4aCgP6J+EJWMVoM\\u002fODtPklUYZT+0pdqCOohGP1Bu3WUlVjQ\\u002fDcwECuku1z8i7lE0RnPiP6TNBmsXvPU\\u002f8H789fIACUA\\u002f7HhhlCsaQBoCP7RXxChAc0+\\u002f5ExENUDfF14Q6rVAQPnxjXdSa0hAOTTXklsKUUDFZD\\u002fW+oxXQCoquzOhpWBA1S7qotFGaEBoWeNvluRxQLJXt0KK2HlAncELrPDfgUA8u3s2WXeHQEar70dsWI1AV8K6DlawkUBY7ZtNo96UQBVBU0aEY5hAe1z2PgNanEADM9gJ4FOgQDs9L47ceaJAGkmD6lphpEDACrhRZdmlQNlWfCEG36ZAuLibH0Owp0CCayvNdMGoQA9+ZhEblapAWz7j\\u002fk+LrUCdIARiieOwQET8jJsvnLNAIlUuWaDdtkBSkfJclZS6QN7Z3q3Xo75AbQzYdxNpwUC4lEr3YWHDQKU48wcFA8VABKihc9kdxkAKsaD1\\u002fZjGQA6sHHQCeMZAhJx+KIDUxUD2RyhnbdHEQI0q\\u002fE76jsNABGs+ZqAjwkDoX2NQGpzAQCcJIjItAr5AaGwcbZy9ukDcpQ8YJZO3QNQSj6WJuLRAmACEJd9iskBQCQrPMrWwQM69CeVEZ69AROahFsqArkDNCllfcUmuQNirOkZZRq5A9\\u002fDdGi8XrkDXf5QMWY2tQAd\\u002fNEWYpqxARhBwc7Nxq0BtWaFKGPKpQEu8nJ4HFqhA10F9gL\\u002fDpUBcSPhd8\\u002fGiQPwndVH1c59A8ohWRUqwmEDN9txp3DSSQCXELiwBHolAjt0ZXNYqgEDfngRAxWlzQDsH1uq\\u002fxWVArwxGmBnfVkAewqN4fpVGQHjLBDRwBzVAERhxqhp\\u002fIkCUszdtorcOQDefdhXxAvg\\u002fNNfYPauY4T\\u002fFYwoZiBPIP6+QVYPZp64\\u002fShxQ1owikj\\u002f25N1\\u002f9\\u002fFzP0F2R8iUdlQ\\u002fBbJ43+JpND+sLhXdgmkhP3MB3Ixj2eA\\u002fP\\u002fWVT+e96j\\u002f7AFUJUV\\u002f\\u002fP5ZT6tEp+hFAqJiEjrG+IkBzKjsCM6sxQHtloHGOND5AF3uqKiaZR0BhoSAQcRpRQGoV76balFdAqz3VxqYBYEDsrz10lRtmQFe\\u002f3Znfa29AVNEF5RWWdkCf2ARdwd1\\u002fQK3NCam1iIVA6eUfVMeXi0BlawjqI8+QQB1DWsdeuZNAn81MlmuxlkBmvCyrBASaQOuKaAD98J1AlCwwq\\u002fQ7oUByubrfpaCjQJRw3yQ82qVAs+3rkwycp0BC77w9+MKoQLCDpUX2c6lAvMhTibsaqkBGxYAMbUSrQETGHbcta61ASdHsuP1psEDYArx+3siyQLzZJlvcz7VAo4ft8Ql4uUDePg4ncau9QGedtEbcF8FA\\u002fKI12HlNw0D97\\u002fx30THFQAZsZqSlhMZAFPybHC4gx0CRL1bXggTHQPwFV8r6UsZAM32I2kc9xUBMbZQm8fHDQJDidVGaj8JARiomYoMjwUBtDTz3aWK\\u002fQBu+i0ILerxAHXU\\u002fXDymuUCTGd+j\\u002fg+3QLQdmVSZ5rRAYmPMCiZPs0BoXZUiaFSyQA9c+qvM3bFAd1RPEcuzsUCbA9z4qJKxQHCMG8R4Q7FAOXJeuzOtsEAKanS9B6ivQB7AVMWNkq1Arvct1q8yq0CvEUNjJoioQD9gQGqXhqVAD981bbMvokDRqW3A5UmdQMtzTFaCSZZA+fjLoI\\u002fZj0A8+plskEeFQMSkgDfMhnpAmxRQJenUbkBXFi6vdrlgQB0tzGJj+lBA65+71N8uQEAHZtqLehEtQPYx4Ud8qBhACPCa\\u002fqvDA0BujQyFbuDtP4ixljvRNdU\\u002fOdEivN8nvD+tlZzG5GahP+SGTPL8+4M\\u002f+qGAY3JSZT+j4Z3+rzhFP2Z7bxNhjSQ\\u002fCSUwmYQpET8HkAupZYjmP6mOL\\u002fSL1PE\\u002fWowTP\\u002frUBEDyByhUFckXQHDF0P\\u002fJtShA6rGXqvczN0DUFlM4R8BDQESVC\\u002fHxtU5ADHDnKYEdVkCRXnjcDDZeQJQ9KQzvO2RAu8P7HjZ1a0DhGrMeox9zQER6XCJ\\u002f8HpAkj3VPDOigkBO5ygYQbGIQKW\\u002flgKz\\u002fI5A4SxjWoB0kkCdgMx4lCaVQHRnPX1Kz5dAFS4P2DHfmkCoybSWP7+eQASgodCOx6FAxI1fWhmApEA5n+yOJzGnQJtFxI+fcqlAFtIWCGX9qkDeaU4JytWrQEduc23PVaxAMFrVVacNrUDdoZuY9IyuQChsLCIqnLBAvEOtC4GhskDmH47rvmG1QBjf7Fab4LhA\\u002fa3D\\u002fkkQvUC3uy\\u002fCbN3AQDdiV\\u002fmaOcNADcaJC\\u002f9QxUB6DO7rRdjGQFr7RkoWn8dAG+0ietSgx0B8ZPPf3wHHQJZbM\\u002fd\\u002f+8VAjyegRt\\u002fCxEBRkRa76HjDQNA8UXxPKcJALQoWJdfUwEDNxw2dQ\\u002fu+QKFm76OOXLxAEW+FENrzuUBPMtrRYu63QL3VZvVRcLZA8oumaDeDtUB+ZvluXwq1QIpBRvJSxrRAj2LdM8tqtEAwagc0c72zQHRdC\\u002f0Jq7JAfB5aYLZFsUCZreTUbl+vQGXBieeOBqxABEz3kICQqECWy3wa0\\u002fakQP3WG1njO6FAkyERMcP+mkBKKO0SePWTQNp+lmBHrYtAknOXw8jrgUB\\u002fRnKS+aB1QJO++WCmUWhAbjAukcqAWUAeMsVJUQFJQATNc6cgADdAX28f4LbsI0Dofb4qyk8QQEGLqA27S\\u002fk\\u002f5\\u002f3O0fKV4j9PQUraldrJP4Mb9tQ3ALE\\u002fclSwV1MjlT8HfdtwjOh4P\\u002fCz2wYQ91s\\u002f\\u002fU6JhUIxPj8CdFh6AYUgP8j0cZXAHQ8\\u002fTJhNzmW56z\\u002fg74lLTOD1PzZIGsB+dAlAbaxjERP1HEDJ8A11x\\u002fktQCtQG1PJCzxAdCzzx6bIR0Db0RTs0mlSQMdeJtdlYlpAtY4GncDlYUDwyxfjyL1nQGsAqYopzW9ADjSV30PQdUAM0SVPtD5+QJ45g\\u002flrmIRAlLX9PIveikDe\\u002fTtnp5aQQGpxycbEbJNAlLAHPTTilUDzVanG+UWYQEjLpUHPKJtA+etZQvQXn0Bk5lGYiiiiQENfSRHUSKVAjeY8Or2MqECktyikfXSrQIXNg0H4la1AeOjh53\\u002fRrkCiAtTsi2ivQIEyNCXZ5q9A7X9lv\\u002ft1sEDst0WLaXyxQOxxhm\\u002f+LrNAB0kM2VuktUCvh9yXgOi4QDRjz8WS9bxAdUagEkHPwECjuzUpdj7DQLN8x3M1fMVAUJh4T6g3x0Cf1iUMJTjIQADD\\u002fbPzcchAPWH\\u002fYtsFyEBvWEsO+ivHQE3Eo84zGMZAiBtNNlLpxED0YpIE8qnDQOXKyVr1XMJAc2cYG5UJwUAGqKS0oH+\\u002fQOMgqbJ3K71A3rNKxs1Cu0AcUSXcyuK5QC7F4KzfCblAE7EcUKyNuECx5uS9RSG4QAn76L4HcLdA8h7lf11BtkAh4tk2c4+0QEt4zKi2gbJA6FVxfiRQsEBSlATa6UisQDCp5wgoGqhAsIeJeBcMpEDXDAX26BegQNP+A+9lo5hA8j909xzNkUDnYUTM0xqIQHoqi51jdX5ArGPCgOzscUCNVhTyBahjQLmNWNDDHFRA\\u002fLSwDHRBQ0C3MI+o81AxQI54pO0BZR1A9KAkpxqvB0DpH8B4XD3yPxUu4i28Eds\\u002f99e0K2uIwz967LUNm6urP0UWnybwXpM\\u002fbw4++CbTej+I7urTdDxiPyCCS77jG0g\\u002fhG1kCN8oMD8Ik3XC2YQhP8u2yemTYu8\\u002fen147hCy+D\\u002fLAdC0254MQOUzjAuJOCBANE80QeK7MEAhe09DYjU\\u002fQLQd74GrYEpA3ZneqAdaVEBYCyQ4CA1dQLQTYkjAnGNAjTGkIUvYaUCKc\\u002feTaSdxQE3nh7gCRndAzMSamezif0CN4N63mHWFQBb2Gzdqr4tA6t5k6AvqkEBZ7FV\\u002fM5+TQIGgJzCj8pVAe+mvVPZEmEAFeB5CSTybQOZ37Z7be59Ah9pCmBWqokA4swHOCUamQKCec6pELapAAd2jjdDOrUCOSxy1oFGwQAhtd5NANLFADnQRwx+isUCraZLy7tuxQIg2DAquNrJAcrxNnRb\\u002fskAP9ULQcGq0QLTic8NhmLZACaquyWKauUCId1YrTXG9QJf4hRbv\\u002fcBAN52IYFZxw0DRCvLRDszFQHOVztwUvcdAffIZdoEFyUAviGk\\u002fwY\\u002fJQGMWsOMKcslAA2yTiLjayEDjPaJdevXHQMY95lYr3MZAbI5mkkaaxUApmtc7WjrEQO6wUXGhz8JA1bgE02d1wUAmYFg3x0fAQAbDM5S5t75AKXGS0hJzvUBiyhTxTqy8QG5asFOGJLxAYgTn+fB+u0DARnfN3WC6QFxF+7SkmLhAS7nX9EsztkBui4BtVXGzQPvq6NwZorBAC1ZJBCj9q0DkhQm6QzSnQO7K6Q2M2qJA+hHtzUaxnUApAtbiWl6WQBQUAmUB449AffmONjJXhUC+VCPkR7d6QJs\\u002fxTQpQG9A7jcZSpIZYUByWWwBw45RQF37Eeew+0BAoblvD\\u002fkeL0BC6a9LHzcbQLv9fsXc9gZAc15Y32758j92EVX42DffP0YAodB55Mk\\u002fk+96diKstT9MiO857hCiP8jgyHsjWI0\\u002f06OTqrW0dj+2WE4w8oVgP5IG\\u002f\\u002f1yvkc\\u002fmFBnTGW3Oj81s\\u002fDvgVjwP8aWKZw1p\\u002fk\\u002faAX\\u002fzk+dDUCvPsT+ErogQMREW1yXMzFA\\u002fMENDZb8P0AbfZ8qpfZKQMQBg28ywVRABz44LQOQXUADBQ+9RuljQP6c5wPHJmpA8OJXjxlEcUB7E\\u002fi+mEB3QFDjeEv1lX9ARH5bLV0VhUB3Qa3WQAaLQOB9oqfYc5BA4dsAEn4ak0D1eDQx\\u002fIOVQE1Ey7wDHphAloiMpqmUm0BXQ9vk8kWgQJfQv7XrqaNAX2c+rpvZp0CeLzXB8m2sQLCnxH4RZrBANZ3LPTksskBqakUxZ1qzQHcfp2Vd9rNARK6T+mY6tEAePMNksH60QKYtZDgnGrVA1hHbS99MtkDHlpMPrzy4QOR\\u002fsy1e\\u002fbpADxhWdHyUvkCkohHa+XbBQFyNXEas48NAa9RdBLZTxkDldi4Fj3rIQNNH0AQkFcpAIDVoRO8By0C3fhxh2EbLQDpZq\\u002fyDActAogx5\\u002fUJQykCqBKotbUbJQBL3D05R8sdAAZJy\\u002fEJqxkBCpM3U\\u002fdHEQJ+Wy4CSU8NA9\\u002fo88rsSwkAh6ka4QSPBQFo4ZruWhMBAV0xFRWUgwEAchW0Eg5i\\u002fQHE1zyBipb5A\\u002fXTt7UEKvUCXQm3Bnp+6QJezGH16hrdA4gkBLlkXtEB4K9j50rawQEqp55h3VqtAYkxH7BgXpkBshqkc9pahQDARDFwPVZtA6AvURtJ0lEDARUkIsyqNQNzYqP2SqYNAOgGA6lX+eEAnAp\\u002fM\\u002fvZtQAMpHsuP\\u002fGBA3FyE3oNIUkBTypehysVCQIb8h86lgTJAI11k66iuIUDNVqBiQZcQQFmSnjjpBv8\\u002fH1Nu\\u002fCk17T\\u002fItclCUqPbP3SH9tQF6Mk\\u002fJu1bg4qItz+PnTS6hkakP5Bi0ItdS5A\\u002fgHBS6t1FeD\\u002futLCOk7BhP6oNdt9VCVQ\\u002fFEfCai9W7z+Km38GmYb4P+gEfUWVNQxAoQTBu7PEH0D\\u002foX1wvkowQJwGKqghOz5AuSGZ+HpySUCISnE455NTQGJksba35ltAWjQqgw3RYkB11+GO9rxoQBh9xLopUXBAYx\\u002feiODjdUCok9HMW5F9QPPkl1+vo4NAGIjdMc4jiUCFasHnc8SOQEqKTDCfHpJAqBhAUJ\\u002fslEApuQ8QuUKYQN5xLJKdwpxAAArxZ098oUCtR6mc342lQP1Frdv\\u002fcKpA5M6mK9m6r0DLDrB0ZmayQIy1z86tgLRAPDw7yZj0tUBaN8Nqfr62QP9UDkWCE7dAMgbZChBOt0DjwP6d1cy3QJ5\\u002f3WFO17hAv54gD7KVukDjVDUJixq9QIAnaMfxNsBAPvki+5lEwkDVt67vhaHEQDCgukvgHsdA1vG2X5N4yUAjKLuEtmnLQLnMToEJxMxAw0GMOHF5zUCSCbNCK5LNQBDZXgjFG81APP4qOA8izEDR+8Ss97bKQA67Uuji\\u002fchAspkAC+wrx0Ax5LBENXrFQIzhwHEnFMRA47CsXnQMw0C3HDaqClvCQJi7UZHT38FAYVowvktowUAe9aBO7rrAQDiCU\\u002f\\u002fSU79A1+uPogZNvEAqoJfvUJK4QG5g2kgBjrRAD0yzyFe0sEC+USpSmKuqQP37jC9OGKVAAmY832uNoEAePnqYapeZQL9C3z2lRZNAhpxxrG37i0CQTcBCXXSDQPi9b2WE13lAuL0YYHJmcED+UNcSgutjQJA35ANPMldA5ExfYk74SUBL6BCtlhk8QDTrVstIpS1ABdqMI2TYHkCqlxaFGfQPQO35th6NgABATCAUXtPU8D\\u002fCfejmqKfgP7CBl0axW88\\u002fSEdv+z+euz8QRtyd13umP\\u002fcFxdMf3pA\\u002f8Mg2XaiteD9vZoQmev1rP+FjnRacpus\\u002fLwMUXwaX9T8RnHr9\\u002fMAIQHjvxPWozxtAJtYUSB17LEAIrcB0Umk6QBkPh+0+QUZA7JJBGp8sUUDYwCN7MpxYQH7IKvffuGBA\\u002fYdJltEnZkDf4n74bl9tQEpF9YoNtXNAN0Hg4QmKekBvy7xTSZiBQPXONVYAoIZAwPj3MqghjEB9w5xhAhWRQIA3UejhoZRAFTxJ9S80mUBAvMMAFVWfQGQQpofIsKNAuenUmS2uqEDTAA0QRmquQJKbbH7nOLJAltCz\\u002fAUTtUCfJbrjTnG3QG\\u002fS7zujGrlASj\\u002fTbWgIukDUkTcaVW66QN+je0kRqbpA0gJ+b6gbu0BZAv1pvQ+8QIvpOT+wqr1A5bTQrPz5v0CTwhPfJIPBQGfMYbB2a8NAW511Ay6vxUBKPEcLGDDIQHvCOGUotspAzyehD3T9zEDj+Ib3k8vOQB+\\u002f5pKM\\u002fM9ARzMREyFA0EBQCP+rmSjQQNGZGxyecs9AlT4uNGr5zUBNEX2CmhTMQMI+c4UgCMpASF13+XoZyEBp7QXjUXrGQKDK3+E4PcVAhpJCzElWxECaThj4caHDQF7uDaVv6sJAUTHoIqb4wUDLXBB55aDAQGMfpmuEr71AZrcfPWByuUA8mQksJvy0QPAnh62ZyLBAHGHsPJFcqkB8Mdfv\\u002fZKkQKp3FWmxDaBAAJSe4xT7mECyh+q98TaTQPiQrCxK8YxAT55YZv8vhUC2dlVflgJ+QMEauSVDfnRAp70ZKMPqakBWq7iS2\\u002flgQOdxj0KhllRAJPIZpeMhSEAraTmZj5Q7QIa5VnHdBi9AF7OsyFxDIUAkccSQgO0SQOziwO36MARAR8Cy5W2a9D92mFTakcfjP72q6tqQoNE\\u002f\\u002fQoa4d3kvD\\u002fdRZlUwcClP3gQ2Sgw348\\u002fR6qd7hUUgj9qTEonZnnmP9iVpcwYhPE\\u002fFQo4oWAKBECiv3pTKX8WQBlFWB8ODidAaV9DZgRxNUCx\\u002fLrExCxCQBD+l483VUxAH9p8IxCZVEAfXD359IFcQFUGTaLgPGNAv6UP2qrZaUA\\u002fi74cOW9xQBekWtsbd3dAl1PMSiwkf0A3JQBoKj2EQHkBm1Je3YlAork7e1dzkEBU\\u002foazxxeVQJP2TBdEXptAM4re7orWoUBhijEG7A6nQFqLZWyJMq1AVjqkdrz0sUCuzetZuVi1QBzXpunRdrhA1rb3keUCu0Ag2bAxzMq8QFFvrT9szb1AYI6oh1RCvkCIFYY9coi+QFSde2E8Ar9AUWNksBX0v0CjjsGs9LzAQCW1q+opzMFAYhsuxs8rw0DV4MHVBOfEQELXviBmBsdA7UmE2\\u002f9+yUBmzXu0YSjMQOUqXynwws5ABi8sH86E0EAlzm4\\u002f02DRQIR5XRkr4NFAE74v8IT10UCmvOR+j53RQDOABv+C49BA+c07QoHGz0C\\u002f7v8x2ofNQAZuhZo2WMtAbbZbztJsyUCLjbbYUtnHQFBs+gNhk8ZAE50dvi97xUATZSkBRGPEQH6aTpzoGcNAs04B0Dl4wUC+DYfKuOa+QDZZlTJrSrpAx7Rp5aKItUDZqCV+gRuxQFeoYwSutqpAaj8E0dvNpEB4DI25OVWgQL5ve6r75plAKk\\u002fZNPGdlEAUJTewPU6QQID+vFTrW4lAqCirlLk0g0Cpp\\u002f+GZRt8QOvUkbT3wHNAT2TRaN2ZakCJUIRl9jBhQNnHOIcXdlVAmOldsZ8aSkBhXYr7dSg\\u002fQI+rTX0ORDJAmA3n+zHiJEDpOMgZ+PcWQMCEvLen7AdASibZTN5E9z9SRMhlMubkP3jTGWEHNNE\\u002fjh7c0gf1uT9X48HEuAajP0fp3TJMlJU\\u002fR9eE8qvZ4D+8gHq\\u002fdz\\u002fqP0Fw8FEmCf4\\u002f5YdQr\\u002fblEEC1SszBRWkhQIkeRAeeWTBAMETG+3EmPEA1qxIthHFGQCv3gizfz1BAABnwpZQWWEBes07FMs1gQL\\u002fUUFy1HWdA+uSGQNmBb0AFbasQnDt1QGG4yDFlQXxA\\u002f0YrWxWmgkBW1VTakLCIQBEXcJXFkZBAFkXyXuyTlkDWMnpRheaeQI9XIs5X46RAANsZYz5+q0Bsa5Vgi3axQAHzDhs9V7VAlosUkKYVuUBxhBKjVmW8QGdlnSK+Bb9AFs+lMjFqwEADxHT5P+\\u002fAQINlcpxVMsFA+b310IxjwUCIkdF3erDBQNzU7sUfNMJA7o+WGAL0wkBQZTbiRezDQOyz8pPpIMVAAdSTDnulxkDkwrV\\u002fm5PIQFV23F8x9spALChC5Qa4zUCAibMGslDQQKs9Y4c6stFAZSRZN43X0kA19f5G3pzTQCDirTsw6dNAd4T6UZWz00BuUttaawfTQPfCLg2iA9JAsQuBtj7S0EC7wMK1DDbPQEibWdVb8cxA4HKmoMzrykDWDqNw3CHJQPd9D1dFgMdALSIvELHoxUAIOSav+TXEQEP+IzbuRsJAVQSSvZ0OwECaxjQTjT+7QPXX2SBgUrZAmCNIVyHGsUBvi2ZuQOKrQOfCiJWb6aVAH7bF12h\\u002foUCYZ0awM4ycQPH1R00Zq5dA5ApP2bayk0CsU2m2djaQQItBv2KYBopAffTQ1nolhECPFx6VluB9QBOMNDPqLnVAymW2TFPQbED9TYwNvu1iQKvPMuTBNVhA83fCllBHTkDJ8zYXGnlCQD54wW+3yTVA8FQNqd+AKECY7hAv8ecZQF0mdAgUbwlAdVXvJJH49j\\u002fteCQWFvfiPwUMvVnOpsw\\u002f4zH+qlQAtT+VrJEfn86nPz3lOtjAYdc\\u002fY\\u002fUaNWE+4j9cbVTv4Pr0P+eFq02n0QdA0yctPYbnGEDWK6GLjuonQJpAAkr\\u002fQTVAsvZJ1DWuQUAtwUD03dxLQFjumS5RC1VA7pljbXi8XkCwaie60s1lQA+GJCg7H25AHQp6s4hYdEBahhKUECd7QDLqgdBIM4JA5BYiKjXsiEDw2RcQqoaRQAZ6YH7cB5lAOjLpTCHDoUC3luzPwomoQInmZaoTSLBA72VFacOktEAobPis+QK5QAhR0k+IC71APgYfPcg6wEDJlUjHlYjBQP2OgCiUacJAGEkhlDnvwkCFjlItGUDDQIdycoaui8NAIp4hzvn3w0AiN36v\\u002fZPEQFbbBP8wWcVA4+ECFCE8xkDjB8q1\\u002fUDHQBUzB4XnhMhAUNBLAww0ykAd1\\u002f0IdXHMQFCIaCxpPs9A6ZdVZUE30UBSXvqlodbSQKttTlaCSNRAz6o3f79Z1UAFfcXSuOTVQGqJa7Nk2tVAG0AfTwFG1UBRKZdDykjUQMguxG\\u002f4DdNAsRdK31O80UA2SqH9Bm3QQI+NFtAQV85AMXUDtUz2y0B2Byfxe7bJQGkTxuPkjcdAiqHdgZBnxUD\\u002fVuzq+CjDQOpRLeqowcBAkZGnQap0vEDNjhTMt223QL6ezVJ00LJAsjfq0izerUB4M298WdynQFq0CfXpgKNAbHaA71duoEDVyMmf5GCcQFGW\\u002fjE\\u002fvZhAfu\\u002fkfbpdlUAPSXMfbvmRQEpqy7PtGY1ADxxA7YuGhkDj3sr5H6mAQEM5QHK7pXdApJQEb402cEBXdRwD9JxlQJ7fw5M8EFxAl64L4heqUUBryH\\u002flSlZFQHwv8jq2ZzhAwhnBnuYXKkDVaLcobcwZQOr1dNdOZQdA1chlyx5b8z\\u002fTd8dT\\u002f0LdP+eWznGycMU\\u002fBYAfTohJuD\\u002fHFFaZLjvOP2Z1KERuwNc\\u002fmg4\\u002f8qO36z8IGz5MzhMAQG\\u002fSFmO7VxFAlDywriJiIUDDKxCLUFIwQHfrpJpQ8zxA21ip6gdnSEBRDcCZO5tTQJx4uIqlCl5A1QygGDHzZUC+vaDH4rNuQKq1bR9fx3RANPcsyvDFe0C+e3cm0NCCQPnoJgbAXYpA2D9ehiwak0AaU7fW1wicQEU5bL4URaRApZYU2DpDrEAwO5OB38ayQOLdxtQVsrdAdPUQFdt0vEC+\\u002fJAwqFbAQOP4pGQNDMJAalspHdVMw0DWU1lVjyHEQOnXXijHqMRArrgtJ\\u002f0PxUADCzo6S4TFQOw5yUpfIMZAGtNXbAvjxkATSC6ZtbbHQA791Esyh8hAyKc6vyJZyUCkJulp7lPKQM4mV7gRt8tAc8zYc+y+zUCFas4rskLQQAwA8uYj9tFANVPxLIvN00Aa5c87DInVQC9YdQY25tZAE+aNk3iy10CJ+VpSatjXQFGCxSH1YtdAalH2hC511kBozGUomzrVQJUDRpXa19NAT734FJ1j0kDOR36cxunQQE+0hj695c5AtTMEWh0PzEDBaa0Y5FrJQC9AfH1lxMZA5MJapQE4xECU2GGINaLBQEXbaAZSBL5A7DjnXTziuEDKz+FBBTK0QKXD4lpQQrBAUh76yYh3qkA3NJcp9CymQCr3nO\\u002fESKNAz3p4TAhKoUBwWEpYzmGfQBM7yoItNpxAdqan6WWpmEBl\\u002fnwDkbWUQB40FJnlnJBAqpPLqT94iUA4uCrn7r2CQN1RvJbTpHpA\\u002fu0uRfhgckBMJeNMr5loQOy\\u002fkmlhv19Abp8jCl+JU0BvtrHXX6RGQJyALpiRazhA6vlMlmhGKEBoA7M71hQWQKyQ3AVcSwJARGaa6u6p6z9ImCyodUHUPzF9HhPm7MY\\u002fiodIqI55wj\\u002fuHotcIofNPwoU\\u002fXlHzOE\\u002fJM8mKjaL9T9vgkq2V4wIQJXmJkWgSxpAGFAWsmaVKkCoXBstb2Y5QN\\u002fZQo4n5kZAPdtrovdpU0DE8GunM+BeQA9YS6HCC2dAa7Re71VCcEDbuwxtmQl2QMPff4YAd31AS48K0YUOhEBiIQEH7meMQABYjUzN2ZRAaKB2i\\u002fzunkBMczwEYYGmQE09p0+mba9A1VOX3rvUtEDxh1CE\\u002fSS6QNKbpc+xJr9APH2DSNu3wUChxzFivWHDQA\\u002fj9yvwjcRA0XnsOOpTxUDjagBi6d\\u002fFQOOdlZAjZsZA7fiZ6cYPx0AXhuwSkunHQKysKiJd38hAxvS\\u002f4HfKyUB0PHfbEo3KQLD9N8AELMtA+LwV3LLYy0D2x0QzNOXMQLY6SO6Fps5AWoqj8bSn0EB5R1fi9WjSQB7CeSuratRAnyc1uC1k1kD8Sy1MiwXYQGC9iz0QENlAtubQ+bVn2UD+n4ETORbZQJ\\u002fDNL+sP9hApK4FkHkP10ArsDILQqjVQBN\\u002fKNejHtRAGslknN1+0kBfm0S5edXQQGweZqEOZM5AJ3GMDJZDy0AnUVWcH1HIQL7alvQhf8VAYfcrB2C5wkAzSoUb3vK\\u002fQMCalYS9o7pA9PH3lNDQtUA0yZ6yc8ixQP754qWeca1AkvQUsc0+qUChbMqSu5umQHm73c3k9qRA2aDlhQm0o0BodovKxFKiQI9Vx9dciqBAePyVqdqlnEAZL0IzabCXQDkSeQQeuZJASNBf2YBrjECvMboZnNOEQNDRrzqTkH1AGukrXzVJdEBpqdjKP7ZqQGYY1Sj5r2BAeAxqqI+LU0CLFfpXDzpFQNmx1iKwMDVAgS3tvdFRI0B1Ux+3YAUQQOa1tL9yOfg\\u002fuVwPmKy44T\\u002f04vSOgQnUP5KfiM1987U\\u002fozmBf6smwj+d2BlGJzbXP595rgkHEO4\\u002fAaTVasB4AkAlwIdnMmYVQHklLBtYTCdAhOBle+65N0DHH\\u002fKv53tGQHFokW8LwFNAyqTdPaUQYECmz8t45khoQB2GNNNHOnFAn77R1O9id0AthMzORUh\\u002fQG6oAzFIUoVAe+rKutpEjkAq7WsDs0SWQCQbv\\u002fPGiKBAzzf8ghALqEAc3vXsn8CwQPdg72NzIbZAcJafe4qku0Do1w4mGl7AQECi8\\u002fFPfMJAWvDITsMRxEDhXfd6DyXFQOcEiXuD28VA01F2SF5uxkBDebapEhjHQBxcgJpW\\u002fcdAasbujjUcyUA90k1DqkzKQGfsOvAaVstAVADDNGAQzECADfXcs3\\u002fMQEI02qUD3sxAdWUmXISNzUAA2w3NtvjOQAtZn0WMtNBAX03\\u002fNPVw0kDJo3BWtYfUQH2UfVVmq9ZANgiZBG+C2EClMiwg7cPZQEI4fsK0TNpAen2dNTcj2kAWO8uClmrZQHhyzILSTdhAJ0Vg3BTv1kCaedLE\\u002f2LVQP+Gy\\u002fjrttNA42\\u002fcZnb50UDo0q0KpzzQQIf20C14IM1Afpt2ycL0yUA54\\u002f8y0uzGQL4iKs4h98NAH8QS3xAOwUC2gjaV24e8QAuvpfzkfbdASgOxV01Rs0BWmsG6BDewQFt7MBetYaxABpR7R4gbqkAEthFnM\\u002fSoQK8hSjrWLahAig2X+fskp0Du\\u002fk5KZnelQEomIwdxFKNAxMz8JIMwoEAA9lIWo0SaQMJYQtMHeJRAJ8dv8zbJjkB\\u002fU3TwE2KGQIPbqzfYXH9AUEJMVxL\\u002fdEDFrMnj8I9qQKVntmHhYV9Aef\\u002fg50ojUUAAEsg0jipBQBXa8\\u002fgdWy9A1hARKTsEGkBj3+nnp6kDQKQbrFPYvOw\\u002fHUHQRpU64D+sAfMH44aqPzmSBMrGL7c\\u002flcJKNwcg0D9y3kP5O6jmP7yuD4lqEf4\\u002fxJYzWDiiEkCSG3V\\u002fvW0lQEMQS9gmvTZAZrV4nPUtRkA4G1VFZNtTQDdYqwwwWGBAdNclb0DiaECbzuDU\\u002fLpxQBBT5WycJHhA8xcZ1B0vgEBbaeWe4hWGQDO6VdEcVI9AUyZJ7fb8lkAc304okAKhQJ7yiiujpqhA5aPnaJ0fsUCfYYbBLY62QNwGNmpuFbxAPPxY+FeQwEBuKAa4AZ7CQCJhYSFhGsRAxI3l7TgTxUBQigRRPLnFQD1RXyksUcZA9skyonwbx0Bem73ROjnIQMCc84sIm8lAvkh2KiEGy0D1uCLAgjDMQD5rn\\u002fpw58xAkqLVZXUtzUBWMpUYtULNQOwqGbV2lc1A0xcs9GCgzkBHl0Eek1\\u002fQQCL+0MzqAtJATOF0nbsV1EB6KUhpPUvWQCeP\\u002fheaRdhA1bpCxYq02UAsKiGV0GzaQBvf5aw0btpAB4D1mNvX2UDFLiZU7dPYQE8P7XCUhtdA8vJvF\\u002foH1kCqM7GVVmjUQKL8wk2QttJAtI+nJyAC0UCIhXgQTq7OQBibIGZfc8tAVQbKLpBLyEDy9NEfbCvFQGoiPyHOFMJAAleu7j1BvkBPDXcx+\\u002fS4QHGg7EYnobRAUnp90ryBsUDgaSZMTjWvQBLY\\u002ftIna61A0w2JT7TjrEC9W04YU72sQCT0Hh8NMKxA8eFt\\u002fxa+qkDYHxn0I0uoQJ5K\\u002fapXEaVAqzpNq+R5oUDzEgtptdubQGs+RvWka5VAM6LtOvbLj0BwNd90Mq2GQHUEGyQ5zH5AXwMYeRuwc0BtEJ3QdW5nQDgZqsxwtllAfSjGZmjTSUA3lIvKCp43QEhwzP3ImSNAIg\\u002f14bucDUDKhsCaBZ31P1F5DoL9Yeg\\u002fyAVvEmgmoT+npzln1AuwP2SKnZ1tXcg\\u002fSCAtKztg4j9vzzrNjND5P+OE0NHZtBBAgnl9JPLRI0C+gh8bBXw1QNTfyIGsQEVAc0kkmlc0U0CLEpmj1tJfQE4VxJJJXGhALtI52Bl1cUB5Eo0bn+t3QEeANecmIYBAduGY9toWhkBh6H38B1OPQELPEneC55ZA+f57\\u002f\\u002f7eoEAxwsnWXluoQCU6HIey4LBANFRTZEMztkDYD+6ZtJy7QFbdvm3+QsBA2WYONqk7wkAUlMVjwp7DQKM0DzXXfcRAaXQwobkRxUCnV\\u002fvctqjFQJxG7bRHicZAlVxlTrXSx0BmM1w8M2vJQBnwkzwvCMtAVcB330RQzEDXXgyK4gbNQNB0sjE4LM1AQIcO6BsEzUBB+D4h6APNQLwdumpbr81ABFk+Hr5tz0D6gwNc2jDRQNOYs9wCKdNAgIPt+6lY1UD2TdeplmPXQI5rShBB9dhAK8MxjWXZ2UB0Dc8McAXaQOz79sAYkdlA1ZMuFh2l2EBuO7z2VWrXQLPptKlvANZAclUjk\\u002fZ81ECw4PgnNe7SQN4rI4QXXdFAI8xnO\\u002f2Zz0Awke+2r3fMQL7iWJriSclAbWkzUCENxkBNOvE\\u002feM\\u002fCQMlE5oKWar9AQ4Kz0avluUCDOdVuyHe1QFmJrWmRYrJAu1zq5HOpsEDQDOeO\\u002fA6wQGJUh6qkJbBAqMz2jW5ssEAp8cEcMW+wQL\\u002f1a7WtxK9AKBMzTMZirUClPjJM9\\u002fSpQHURk4Bh8qVAvViaJvXWoUCXQcr\\u002faPmbQN3jnMydJJVAtqVtgiGijkDioOs5LRCFQAPE3k\\u002faLXtASlneHK9GcEBIWScqpu5hQCnzuosvDVJA741mBQiGQEBlqT7LYW0rQNyyD5o9tBRA20CqbMcu\\u002fj9kK\\u002feNEQHxP2gDHbdGUJg\\u002f34Pvx7kiqD9MgEpifo\\u002fDP7+kMOPJ3N4\\u002faXA33yRg9j+XiiCfnpgNQDzE+jtQzyFA4ZYBw\\u002f58M0C6R0Q3vWdDQC8z+jiooVFAPDRSQ41jXUDE60q3JKpmQP2r\\u002fDBVZnBA+A\\u002fhYRW7dkD\\u002fNIe80QB\\u002fQEug5hCZZYVA12t+om5ojkDT77zvGS2WQNyjKSXcQ6BAXRgbyXhnp0AbYQV73zGwQHGMwPWeTrVAewRrXlCHukDZa45HzEW\\u002fQGOx5HYPh8FAqKzRSDDUwkBP79LmNpzDQBkFlbmBHMRAzUYbYoCqxEC0BTGTb5LFQNWS+db+88ZA5sK1h+GuyEBOGqjmKW3KQJGOcsnTyctAG\\u002f2gNs6AzEBI+VcZX5DMQGuFXc7yPcxAIn0UfbIAzEAVq2pceF3MQL4cC0Dvvs1AvaULge0o0EBXUV1UgPfRQNwh67jLDtRAWoW5VT8Z1kCOzYdDN8HXQGZq63lBydhANSYOVNMZ2UCF\\u002fj2xksDYQIbXE8A\\u002f5NdAvpGEdlS01kA5mC51JVvVQDWgIWrw9dNA1e7QxLGS0kCjUP5iFTLRQCC5CR8pmM9Afi96UT2szEDw+zp815LJQNkjltIbUMZA0ny8ME3\\u002fwkAbD+Rv7qK\\u002fQNd1xB+GB7pA7L0LkZedtUCiyPlGjKmyQPaCEeIZLLFAGKzZGM3gsEDD3T0cQlCxQGFUqwNJ7rFATK0ZQ6w7skBxfjLLl+OxQF50fQz1y7BAhBJz\\u002fzQkrkCOzVKs9umpQMQ0Zk8GcKVAEfLHjGgboUDdW3mpA0qaQFkk3FIfUZNAMIhm0kbdikBdMqJks3mBQLwnyywEDHVA09k+U7ZEZ0BnCeUmXHdXQGvMMVyHfkVAUXw\\u002fgvPVMUA6SDZKt+caQNS36sZKlQNAJeW2Wm4J9j8U8XfxvKCSPyUTxo1tI6M\\u002fCQRpFzsJwD9o4lH9YM\\u002fZP\\u002fru+u0q8vI\\u002fJS4R6epACUCDcq+6kIseQOH1RX7\\u002fxjBAvp\\u002fJVvDGQECMP8jm\\u002fapOQKQg6Wq3yFlAkb4R2m8iZEAoKC+LhJ5tQGbx\\u002f6lp63RAYdAFa4AOfUDoF7CRd1CEQJIkxxbzAo1Ale6ZYxYflUAJKRgS9tmeQNwDZcKBH6ZAAi6tAWqWrkAKq4RJYyW0QBJFn9fOJLlA+E21bqe2vUCvEChAoK3AQPUk2JC96cFA2GBnEUWfwkC26WiZnwzDQPXElpVNi8NAahszGA5sxEDo1lJJY9DFQMEFADd9lcdAJpApk5RfyUBB\\u002fmHIlMPKQBhEGz0uestAocDBvZqBy0AQV2miex\\u002fLQEErw6C0x8pARglMwA34ykBfsVV2exXMQAUL7pEoUM5AcasKb53I0EBII5\\u002fKKLzSQPrq\\u002f0A3t9RAwhbUiRhm1kCIs\\u002fxanoPXQEPteC8Y69dAm7lGIj+f10Cm+5ZMY8TWQFV2yFIQkdVApGHDIWk71EB6JCtoxOnSQLnSVWkeq9FAdsfu4N140EBkZtRmDIDOQCHqxJJ\\u002f2stAEOc2WVTtyEA62zmXXMHFQM962bz5e8JAOQJVav+vvkBjIH5MwDG5QK9+uSNA87RAW0DwTyQ4skDjdzZiZv2wQJWgYfr5+bBAUinJ0USysUD1CbH7dJayQK7DbRO8I7NAiZEGM18Bs0BRgJcHmRGyQIInROQ1b7BAvqXYtymwrEANAnivIx+oQGm53wrKkaNAfs3\\u002f27CKnkAGqzPshb2WQOrmHNVc9I9APd+aMkPxhEDbIHaaHFl5QKZHplRwG2xA0VV8onVjXEDiSpI4eANKQL\\u002fXTIXskzVA+oa3AWRCIEA3C7\\u002fn56EHQK2x01Szjvo\\u002fDoqmP0WKjj9qbJlgYIKfPxQyE3gVcro\\u002fR4EkSAJA1T+EYF3utR3vPyuw+4ctsARAIS4DBeP+GEBqcitu6X0rQBZNeBhLnjtAR3WiPh15SUAfPmy04LpVQLIdpu19UWFAYTMq\\u002fwYiakBisxXtxPlyQEm5r0BsBntAzUBa8bQ3g0AGK\\u002fUJTJ6LQKIcJsABEJRAz30D5bUjnUBiXa8CyMukQFSlWaDtsaxAQqEEw6jpskCWdNQjc6m3QH0ouTGBC7xA5nyHOdqOv0Bxy09R0fbAQAcgH\\u002fa7n8FACoDK3Xf+wUC03CbnvmzCQOToqSYEPcNAlUy5l3aSxEAqOq8a+ErGQBRcs1XZCshA7SAtivBnyUChSRwMmx3KQMLvQ4EcLcpAwbPxHY\\u002fbyUALujnc8pTJQE5hnDBSyMlASQhM+9bLykC7y87Nx8rMQCDxBbxSuc9AR7E+3zin0UDsw\\u002fo\\u002fQIXTQMXr89oRKNVAf3HhYDNG1kBqy60njLDWQLvQY0g0YNZAIU9Mjrl21UDASi8EUDDUQEJj01SEzdJAYjgw5J590UD5B+60UlLQQPBtELW8gc5AHLjekdRczECngn3N3wDKQGTitcC5UsdA6EVgFCdbxEAsEBxFeEPBQIjvQrzylLxAXwCUu6lqt0DR+iGrxXyzQL3\\u002f\\u002f78nC7FAFJeetZcPsEB+nuSTkUCwQOp8GHROJbFAMJRFYugyskAxifwUQeuyQOnIMRbo97JAh2DyfhM6skDjzwp3psiwQIYiadact61AJC4TzaphqUBqGZp\\u002fi+ykQMhunPqxkqBABHZJP\\u002fH9mEBSxBVd+7qRQMNEIgeHZodAEouRliBzfEDXSbu1rqBvQHgTPe5a+19Au9fp765PTUBQgEgMvkw4QF2RZKdHSiJAfeex\\u002fzyKCkBoWH1FQcj9PzamJVJAZIw\\u002fnBAai49cnD\\u002f0bb24yvC2P\\u002fQ\\u002fh5O46NE\\u002f6SsbqF2g6T8PBBOWc78AQBULiGUQBBRAuayHwKDtJUD4EuHZ2Bw2QDpSHH57pURA\\u002fb4voWj8UUD1v9aQK4JdQPYes640CWdAo66OjrlQcUBmo10WhGJ5QIS0QCMTYYJAQMVFQcqNikDULEiRJDSTQOl0371Np5tAMbpqGYGUo0AyBnvhieKqQIzt8wB9sLFAu3w9+VoltkCFN8uAfEy6QFpe19OTqL1AF7kySQLxv0C8IkTa+5rAQAWS1cJk88BAFcAGGEZXwUAzc2gpYhbCQAcvX66BU8NAV+Ni3v7uxEAzvf34x5LGQJNgTnsR3cdAJk\\u002fB2JKSyEAnCkuYN7vIQOzZQiA2m8hAa5KrF8SSyEAR3GquyfvIQMZYJhC9FcpAulBdo4L+y0BBLRwcrK3OQC0fqkhy9tBA89JenCeq0kARkh6xZivUQGBMiJiHMdVAQgSrKzGI1UDrVmvD3SHVQE0iEecGHdRAm5aftXO40kBaDyKYYjvRQF42ToVpuM9AEyH6j3lhzUCimJE+SVzLQMMghFihbMlA+vKmRqhUx0CV8x39j\\u002fHEQKBnNWLmRsJAV+gNp0v1vkAYs+zzm5G5QF0MXTKx5rRAVcSt20pisUAO1S4rqn2uQMYDudFQ6axAcWNjVN18rUAAFlw7zFmvQMyQNPPAwbBAsGoZNvqKsUDOJsZRK7qxQOcoBd9GMrFA4gaBJmcFsEBhGY\\u002fTUcSsQPnC65449ahA+RhLxvHnpEBZuEwmqc2gQOF3BUkipplA66I4oStdkkAPKnuB+2OIQIEHvt6NxX1AOtzEW7aVcEAjdM3j+8hgQPx5MpDFw05AHMN1v1x8OUCng7T0pCgjQPfnSnMmwAtANzZ4hfgX\\u002fz\\u002fNw7uEB+GQP5Kt+D0jnp8\\u002fi6\\u002fy8Rehtz\\u002f05B3itETRP8xJjg3dWec\\u002fMqOw6J8k\\u002fT\\u002fk57fDsdMQQPMPZCffDCJA8nuu4lcUMkADLnwZNgNBQEMw1f0SRk5AaRZ+Yp2fWUBVmKMT0L1kQCf96rptJHBAKSINL8lQeECRtKFsDeCBQOxkP54e5IlAgaeq+s+WkkBNL50OnHOaQFHHggsWgqJARU2cMlsuqUCbvgKo9HiwQOhvaGRVkLRAdFHzVp9nuEDPDmIWTIy7QFvZWVL+ub1Anj2H06D8vkCBGINXJ7e\\u002fQJXFOa+3QcBAgqqix2T4wEASk5riPB7CQGsB1vQVlsNAeM+gfp8TxUCZnWGn+EPGQLWjchFI+8ZAHN6I2WJLx0BJBhtCZ3fHQIKPpY730MdAX2c9VJGYyEAC9VnPb\\u002fHJQF3\\u002frowS5ctAB1FqhptmzkBq8cB3HqbQQOOr\\u002fkJnItJAVgmNBr5s00D\\u002fkslbLkPUQI\\u002fqB6NpctRArEIDyg\\u002fq00BUaRQYB8XSQJa2uwd9QNFAUrEJFkVKz0D1Yvm6cFrMQBUhveJ65slAwdprcSDgx0CqrHPtFg7GQHKTZ8QVMMRA1e8\\u002fK2wcwkBHXt6GyZq\\u002fQBBEA7LkvrpAXcC3BRgMtkDQSJBpJ\\u002fixQGMFfddIzK1Ac8KkAUcVqkC4TGwQRr2oQFkvGwGDSalAfDAtICv4qkDnm72SePCsQA2NeeoAca5AkLD+qID1rkAXLBUO0kuuQKsQD9UGjqxAtOQSLY0BqkBgKyQA3e2mQNObbmJMhaNAjK0QRyPXn0CRNf3Er5aYQNoi3MTxwZFAhzPQDO24h0DxVnIFyg59QByWZ+tON3BAoVAAhL5rYEAL\\u002f0PgXBZOQBQzWOqs5jhAcYQ1vU6xIkDR\\u002fGUv3QULQOe+JjbEOv4\\u002fwlE1C7cnmj\\u002finpbighenP5JAZ2YZxb8\\u002fkszEC+pt1T\\u002fixdZQr8LqP6nrU6cy8\\u002f4\\u002fT0GNIi+yEEA3poXJIvUgQApYh69EXzBAwWMgysRPPkBBioW\\u002fHg1LQGmOM1TYTVdAT8AT2Q5YY0CBADoDheBuQPN6QY61tXdA\\u002fTAkweiagUCadxh1RH6JQJiG9txEJJJAvgE8+8V0mUB\\u002flV5XqYqhQHopVVAuiqdAgeS8mqZ2rkDEZNhak96yQAPaL0wyS7ZA3lwH4WUhuUC0B8Rysym7QDRB4TYUcbxAEFZGbSxNvUCGqozQRz++QCeC3jrCu79A7BfamTD3wECdHn4RRU\\u002fCQJ6XemGOpcNAp8S0f126xEBc2DWDZ3XFQHCo0qyE9MVAasdOcaJ5xkDqnVKMnUbHQJrfx4gTgchA4iYGFNItykAIjQdB2T3MQA5FSIvQmc5AVZBRbWOP0EAZzMDrYcjRQONV5ME4ytJAt2He1xlf00AnNUnxrlrTQNlCuK4yrdJAaCxv\\u002fjBt0UB\\u002f4\\u002fUUf6PPQGIIsv6VPsxAakSQnPUfyUCYJ0jL8ITGQEIR89joa8RA83ciUC+mwkDmUfrL9\\u002fbAQC\\u002fyw5AjX75Aix8yAGp+ukDblmnAaGq2QGgk3NwkcrJAQBEBQKHxrUAXVR97PqqoQJ94JMsgbKVAUkD000QtpEAD5nQlioakQN\\u002foIFcP16VAsrYnnphup0Dv8XMLU7OoQH6FNwD\\u002fPalAeoWn00vnqEDF6FTI4b+nQP0otdag9KVAOXbbP2evo0An31sw3gmhQB1SK4dbMpxA1Gc7nvIFlkBTIpP0qgiQQKCFQ60qiIVA4xO9FTZ0ekDep1cA\\u002fpBtQEIWZmM68l1Af1Z8dBdrS0AyCllibao2QNyzpVzC\\u002fCBAuyWBTJCBCEA466CD8V37P2POldzlC6c\\u002fmMC6hrO3sz9IvPjUYdXJP1jdd2N0g+A\\u002fJn6fMxJj8z+pHd9Hf+gEQLs7VhB+6BRAqk63pzuoI0BMhmFnGK0xQMtBq3Lg7T5AsBZwYoWgSkCoi9UcQJZWQMztdDbhvWJA3\\u002f7at0chbkBV9VxIllJ3QF\\u002fyPDHBZIFAPWPhdn4kiUDu5ZNwsr2RQDhFG5Y5jJhA+RYugtGgoECBsW+VLuylQD6I6AxB6qtArgyfhS4QsUBBauhRGfmzQN+wBcrCabZA+V1H6a0\\u002fuEDdVTAa1465QBqsAoKroLpA63RcD3DZu0DHJfnqWIq9QDIfhVZCxb9A2JIcfwYnwUBDSgohK13CQDDpS+CyWcNAHrLmWBgZxEBOg\\u002fA+psXEQOk5jCwjoMVAPH8PN1XbxkAjw382oYPIQATJE+fjgMpAY41aPiSrzECrMS0wft3OQHTaHS72e9BAuS3H8xBp0UDTrwUU2BfSQBdVQLuSYtJAJ1N0Ijso0kA+lIj9C1zRQGaQGi85D9BAOGjqhHndzEANeXN\\u002fPGvJQHAN8anwM8ZA1phdjn16w0AgReFrKUzBQOVKFluJE79AbnKAgWz9u0AcP6CQQfS4QEIiyMbOwbVAkt2xt3NpskCsFfxGfUGuQAkOMZjFbqhAzH5RhAbvo0CzY\\u002fH7NBWhQBu9fycBtp9AmW00kOzXn0Dxzs5g\\u002fMagQC+PRQWz46FANg\\u002f81xLQokAvvA8I3kKjQIVQZksVIaNA5x465\\u002fRzokCNMaNoqlKhQAQFZ3d4mp9AplmIlofQm0Bsia4Q4leXQP+9zTICbpJA2l83h9wIi0C1oIxRaTyCQDkXlme1dXZAgIuQlSggaUAUvLCb4HFZQOb32sHHRkdA285P6gs3M0CVmZ6TJsAcQGYCtT7UsARAI+7fqE0Q9z\\u002fEi2IW4Vy0P+E4v0ewL8E\\u002fGauO1TUH1j85miK8eXPrP7JVqJEuNv8\\u002fFYnnQ74nEEDjQo7ubqseQKnIm1V+BytAYE2\\u002foPWINkANiHqs9TNCQH\\u002fuerN6Jk1AEOiCBBFrV0BLhY4Blc5iQJGFiMQ5yW1AdesjBeHzdkCcmMNDdhqBQFuPwGo3q4hAMm7WlFlKkUC3NWEHRKGXQPfApQRtdp9A\\u002fwMaMMVSpEAVxSLis1ipQAp3y+u6aK5AXc8jR1qKsUAeql8K7IWzQGbhO9PnHrVAi5yzOBB1tkDdiq9bFMi3QFBIzVeDXrlAINHYKORiu0Ck67IkEMi9QAfjOOOQJMBAsc181+RGwUCU3NCL4jHCQMB7pw3Y9MJAVKomcB7Fw0CUYLnFceLEQBy3XRzpcsZA0hqAXhluyEC96CfutKPKQIUOn0LN1cxA+A2XChPSzkDGiMPJvzzQQLV2PsPl2tBAE5v94Rg10UBFTQ5ZbTfRQNrQqORGztBAZIj5XNzhz0BlA\\u002fwWnlTNQHprYIjsNMpAT3UCMbzexkBSkh0szq\\u002fDQBO5inMG7cBAZMttpTBkvUClUSA\\u002fceK5QLJZUckuArdAORw8CNdjtEDkBEs8IcKxQNov+GnFEq5AROCQsTWxqEAtD6XPcNujQLM511G1DaBAGHl1mO8cm0DlfnDSjK6YQMp78TctQphASUpSQIMKmUAisrkpDz6aQHAcniUhQ5tAXE6C72\\u002fBm0AhtXdybJ2bQJID9NS\\u002f4JpAQtxIacCYmUBNykhAvL6XQGLVLiJlPZVAMViBCy0RkkA4AfR3F9CMQBYk5nS\\u002fRYVAmj1kcljPfECOZT8hW8ZxQNyJznTU5GNAX6uXt+0iVECVfpoNIGZCQLJYVO7bUy5AwYGyOcekFkA77JFKd0EAQMX4LVGcFfI\\u002fyA6Qam0JwT+7vTmPEpLMP2M7nqzvHuI\\u002fjZmf14VQ9j9OFQAKVv0IQOXSCxHZWBlAkMJDwuxiJ0AEtRvWDM0zQFEyB3EoPD9APMoBS2l7R0CS7\\u002ffLxVJRQL4tqdhYv1lAzwdeHBJ7Y0CTtRqD6cFtQMOTvfDIinZAWZW9taGxgEBwzuZJYAKIQEkzKKNGvpBARlcwgWmllkBMW45aTqmdQEXLnFJ1waJAoCgOST7XpkBFBOXTncWqQL+hFPjuSq5A971RktyosEA\\u002fWKVCHwCyQGmf1yroWLNAuRbPtyzstECl09Lw9OW2QMmZTatSTLlAQXIemxnzu0DZ\\u002fVvhYYm+QDlXq\\u002fYeYcBAhM28aFxDwUDJYQ+W7AjCQDV\\u002f9uUs78JAyEpVj8Q0xEBTfAyYkvbFQC5Cv8uIHchAIQb5KS9oykAB+8EFQYjMQMvLhfa9QM5AVFEE81Bzz0DJwd4wygzQQG49VvAOGtBALS0sBlC9z0BfyHD1VqjOQJYYXNCX68xANERT8VePykBBlPwVsbfHQFHwiZdZo8RAZznAgfudwUCJVuxiAda9QC2mAgsIZLlAdtHu7ojrtUA+S\\u002fUj1zKzQFwoqsB24rBA4JOoXK1brUDqirFbqOOoQMLM7qtpeaRAG6J12vx7oEAjllaUGaKaQJLwei1iXpZAq5E1++ARlEB2Fb87mDiTQLlojQbdKpNAKBAgeWtbk0AxWlwDwHSTQJFZdQXlVpNAGLzBDSIDk0BV64NudoGSQHPbkI2yyZFA8evHLMm7kEAf1YIMgl6OQGdHA6kfJYpAfngedbMHhUAVWaLKFDp\\u002fQJ7iDg0SNnVAeaQVfdk0akDaYDmBmVRdQPk03NGDqU1A841RlicQO0D30wxAS0MmQI8PSF72lRBAC8tBVCvA9z\\u002fiskeK\\u002fl3qP8Ctm\\u002f54d8o\\u002fEhuZJaAZ1j83io+trdvrP8FeKkeCCQFASczUEVzsEkB1R81sh\\u002fwiQIcdTnUtQDFAE1bON2mNPEDX+JiWL8FFQOgesTR1Fk9AQ39GFI5rVUCUihqxkHFdQB1Alw+8u2RARE9NRkQVbkAgfLqoEyZ2QFtH5TXkM4BALIBza+svh0CHY5b+eheQQIlnlcxrkZVAztHI48DTm0CLx6gJQjyhQGXuSJXfeqRAhTzF4DNmp0AbiLiieOKpQKMwWqrTDKxA\\u002f9meYtA6rkDF4l20ynCwQAZ8urWwNbJACAzcXvKDtEDa9c8vvUS3QPqtzr9HMrpArcicUL\\u002fvvEDu3pipEDW\\u002fQHqSl6ODfcBAnPT9z1NGwUACT7KKKDbCQOx6Lx8cisNA0lqdtmVZxUAsdfLb5YTHQKGQFw+6wclAijtt+Cm4y0BjOwBtyCTNQPl6P5mc6s1AUfIpIVQQzkDHyrdWzK7NQKyZkKoa3cxARra4bZKky0CP4wD2igHKQFvB+LE578dA7Oap7pN3xUBmZ9YCkr3CQAuliBzP9L9AP0WaIrbaukCZvAPeD4y2QMLVtIELKbNAJlCiQVOPsEANJRIcI+esQEvzggDOE6lAnGuXmjRRpUDpahyqyaihQJs0usjFyZxAdNRPy4+rl0BVGknirDqUQGRCRKmFPJJAVtggUKojkUDxHNolE16QQF5OJdoZIo9AvdPZnCNPjUB0SI4POXWLQIUf4mJa2IlACSn2nTmTiEAcxV9bcn2HQEPhTIQYOIZAAWbgDktbhEDp8\\u002fvmxq+BQKbrUNg5pnxAe0i4ktBcdUATUL9b4hVtQEJ7OymN+mFAaj1hdrccVEACX\\u002fcFgFBEQDkiHJOIgDJAbOc9SHVgHkBKyiLnmpMGQFkQAFh5HvA\\u002flfSCmuba4T8VCXEQkffSPw0Ohbu0kd8\\u002f2CXnjtDM8z+gbiflkBkIQBnkbfm2nhpAp7+TL7mGKkBJmLwUIeE3QJtRUkI8f0NACoeZTCcZTUD01DupcCBUQCdvBykcblpAAmCdLBsNYUB9c3H78m5mQH\\u002fXyLYGwG5A88+OwrXUdUBaeHFz6Ft\\u002fQAetJdNuP4ZA8uiiwP2vjkBW0usBHmOUQEI4y6Ja9JlARDrxhd2Qn0C+anGsVFaiQObEuB5pcaRAShBT8QsZpkBUMYmWxI2nQAaLKeMoRqlA3SxhhGTKq0Dux5wa94GvQN\\u002fgt4JKQrJALsz3mJFAtUBUG9bl+2W4QMBKm6lQTbtAjnFQ9reuvUAncbxIn4e\\u002fQDPTyl0Vk8BALhwgTJWFwUAoAXun09XCQDxJpiQfmMRAsMJYlt2qxkAKf99otsDIQCVESnYmf8pApuxhFPOgy0CDCSx5jQvMQAP7w55+z8tAZAfAXiIXy0BrzIqREg7KQNzihtEpzchAnyMa9R1Ux0CkWtL1UJPFQKEXVAgZgMNAC\\u002fhBMFInwUDzOFACs2S9QF\\u002fhmHpQtbhA22AMgkCmtECv18DkdGixQI500guI161A28i\\u002fBjflqUCgx0WLPnGmQPCdGTwuLKNAxQxg7b8aoEBtVTwUBfWaQK6RfyD7DZdA9yDH5LGPlEA8uBV86g2TQMYHH6xI35FAEFy\\u002fnvh5kED7d7ZETV+NQC45rv8sV4lA2D8iNrWFhUCHL1dqunmCQOSQOybOY4BA8OcBCJ4efkD2l3Q3FhF8QI6UI4DurXlAuK7JzOhjdkA0thcEZzRyQDjcv9FlOGtABN2hKkeNYkBT\\u002fkjC+u5WQDltehpSoUlATqefxcPYOUBFSIvXRn4nQJH5NeawPRNALL2xe+OG\\u002fD99VIiMcE7kP6+F\\u002fLfZcNY\\u002fo3QuxmwI2T8OD622ssTkP7f9oqb67\\u002fk\\u002f8wX54RVwD0B8ljMXDkkhQDDQndD1IjFAGAAFo0aoPkAEvzHWfNBIQC0Z8xGcR1JABAqzGXrIWECMHpDc0YpfQKaCBaLXdGNApp77M\\u002f08aECWZtNslodvQEB9hnkpinVAfAbF295AfkDZcD5rbjWFQBsbbHtLBo1AzJuuayAgk0DgYjS1HxeYQPvE9XGa5JxAyGd+K15\\u002foEAokWdlMgiiQD56QeQmGqNADk3eFQYHpEA20BhTj1WlQJoy+Z1HmKdAy9SN0CE4q0CT1gw\\u002f7yKwQNJHoaSxMrNAb66wwNJwtkBYyydclXW5QAcZwABy97tA6xphhgvwvUAr4HhI7aW\\u002fQK+\\u002f29QHyMBAlY8cra0NwkAQEA9UrbbDQPxDvgaHosVAI38pT1GGx0DHVEHR4gnJQHzCM50r6slA7ZZTEyUQykC+60FtN5PJQBIfQkpJqMhAJj3zzpiGx0CZ\\u002fse3Lk\\u002fGQKRTvuOZAsVARzQG2fyJw0CpaY4L\\u002fc3BQOQZQkNCm79AeGfVEg5Ou0DvHN4y\\u002fRm3QOcN9qxcYbNA\\u002f1csGA5asEC3lgVSKf2rQIfSbJwgPahASue2T2IEpUCiHSFX6xWiQLf2N0r\\u002f+55AlnPwH8LnmkBJCAmG2jyYQHmIhpqJzJZAey7OoqHqlUBhH9ard8SUQE5OoofZz5JAaVkDdgMIkEDoTNfAp7OJQARsJLgVs4NAH1y2SGTdfUCBcp8jJ2Z3QNJzq5oWiHNAUe8Z4I42cUCaVfVoW71uQMWRztrhl2pAeHLptd+WZUBZT719+iNgQMTy6SUD\\u002f1VACeq1HPwqS0DDARu+YlE+QKOIW0vNgy5ASWUCa1WsG0Bxjw7XcZsGQNL\\u002fFLuAtvA\\u002fBSdNJ\\u002fi31z9Ie4WewyXKP+R205OkZ94\\u002fonpGX14m6T8mHBt8E0f\\u002fPyyGxm6c4RJAirjGHiSuJEAp7xnk+Wk0QN1SpOSEK0JAvY0EMIs7TUA1Ke+fMFpVQI88\\u002feWolVxAf2sDOU\\u002fWYUAqCbt8fF5lQLUJZybPm2lAXiKLu131b0CJn8Iezhh1QBQT8Vfx+XxARsLl9ssOhEBUMdANLz+LQA+BTUjQ2pFAT0uetgZdlkAGxvLHeKOaQKhdifydJp5AwyclN7RJoEDPWrTOvwKhQKR8\\u002fNaakKFAVTTplVZ7okB6hSOQmFikQMdZ0DO1lqdATvXYTGpOrEAwWuQNIRexQNErsSJrRrRAKX3d+1BRt0C235gtJO25QN8G85odC7xAZyZXA1fjvUCRZ07RDN2\\u002fQEL2yj\\u002fzLMFAovY6Sxi+wkBrhtLetoPEQM+bXKx+N8ZAVlwugXyGx0DCkEtKhTLIQCAMqCI6KchAwLl3s+CGx0BTGBCKUIXGQKPlndS4X8VAKJrbgcI4xEAP0YjI2Q\\u002fDQE3EHTbzysFAJ0RKx3hOwEBb726p1Si9QM31\\u002fnV+ablAOwldJ2q2tUDfpLvMI2WyQKi+2P\\u002fQSa9AeK5YxMLlqkAF9teI5FOnQLtndTjYQ6RAXckchzWVoUAcj8k5wsieQE7nRyAWzZtAWBJR4NFbmkAQEyUGLQOaQMqlI5zr0JlAjvwKjFHEmEBsvKnBC1CWQFoA5phnl5JAJEX4B1KPjEBAagt5GWaEQBMibwwZx3tAErek4zbgckCz65ONAiFrQDIaK8y0SGVADf\\u002faKiLHYUBA3ElNs9ddQGx5xWbZ8FdAbrvZxNvQUUCYS0FN7zhIQPhg\\u002f4lN3D1Ato5hgZigMEBqAVHeaLIgQEcox5IkNQ5Aj\\u002fbM\\u002fF6c+D8It21XNSTiP2Kwu9hHqck\\u002frNqoDow3vD+e8gU2U\\u002f3gP5KoTXgTBuw\\u002fK7POzaRaAUBIqiYEgt8UQCzyJjifxiZAESpaGhNlNkANpXz6WdlDQGODnLKVx09AMx8Y0r0SV0Ai1mCPbaJeQPy9pj6U4mJArjpD9a43ZkAlHqnvHvhpQLDXJSIPf29ADHBw2eBFdECEcPBJXWB7QMGAqu38yYJAqTJyGhp1iUApfwjKurKQQKNbD1yH+JRA1dfScygNmUBh2zlrQmWcQGhukSuGn55AKw5hQQjBn0BOkHncGiugQNdKMu6Kr6BAynnpgB8DokAwDC9biJ2kQHZIRLN+qqhAYfdEaX\\u002fyrUA82YK3fvOxQCoOknuN6bRAUWNZJ4mRt0Dv9TDrOtO5QJdRYkTA1btAM1QP1NntvUANWws2tjfAQFmiWyuUu8FA3kYfmoJlw0D8g3yoWPPEQNrkbrzsF8ZAkM3P+mmbxkA\\u002frn+BU3HGQHyOG4w5usVASRYLR5GxxEAcslW\\u002fLpHDQHGcciOheMJA\\u002fZAVdvZkwUB5mAcpjDzAQBgCW7dDzb1A8x2Zut++ukDYZp744nm3QGAfOt42R7RABnf+pW9osUCHkKsV6vmtQMq5Mtmc+6lAY9BNIFydpkBRTdAyPrCjQPMA\\u002fdd+NaFAXP\\u002fy4pG7nkDoYiE0gLycQPXcK5jKapxAFf8N1boUnUCmfF\\u002fxgIedQHlIY04RmpxArKk94F2+mUCG2FAkiTyVQJ9UUQOH9I9Av7qEUAL9hUAOGgJl5Al8QDaZPUlLFHFAOVloyKwDZUBa32u3CulbQKIXMjAFi1RAutn0gOQRUEClp+lVc\\u002fhIQJoSU\\u002fMdVUJAHPm+2LDFOEDsu1S1620uQN5p6g5m5SBAC70O\\u002fLTrEEC0McW+AYf+Px7a8TGvzOg\\u002f86jUDJ450j+sbh8xBrG5P4utf4D+LKw\\u002fvUSnpf134T\\u002frcOJD4brsP8pEgesBuAFAfPLFTYo7FUB+NHSXbBUnQGASWY0XnTZAwQVHXGL3Q0A9P0QTO9ZPQJGTcIQXAldAc2HXD0lgXkA3rZEi35ViQG\\u002f9MofbomVAyStVCLPvaECV4meRmMRtQPcNMSQX6XJAdQUXPvxfeUC+PhUPJHCBQKAQGmgAzodAsj7jk1CXj0B4m\\u002fJ1JRuUQFcASETIVphAD37zWvTjm0B1uFgq3kKeQD2fLvPCUZ9Auj+sizR5n0Adi98fvqOfQK6lrMBdfqBAxA3s8+lGokBqaIDwZHClQL3HmBvA5KlAuzOcxqc3r0B1FAasamayQB9otGsaC7VAXUCBTaJpt0C23YfLG5m5QH4H9XUC3LtAYYmmUyN3vkBZBV2xG7\\u002fAQLJ1f1J3WsJAblCcA3vNw0CIjZzq0s\\u002fEQCJNxWCZMMVAfjj4YJzqxECX5vOXdSPEQDPsELQoF8NApx8Wmoz7wUDtN3rIi+rAQJPKknC4vL9ASjtS6F6AvUBgpro78\\u002fy6QN5gs9\\u002fiN7hAKi8KSOldtUAf4f8+B6myQAQGAdfBQrBASVf5DKRqrECfKGW3beKoQOqSjdHuwKVApI57qgP3okASvg1OU6egQP6uaqujLZ5AkyZaw572nECT\\u002fK2pdYWdQJhXevzL9Z5ASSv7Bxjmn0DZ4kFRaRSfQEUxtFxm95tAggEpX3j0lkD4KJV5yRiRQFywGUrHGodA\\u002f1eZtVp8fECjVoG6WkdwQBM5ngfm4GFASy8j6tAbVEDKg68e4dZIQOBrWXl6G0FAb36hBcfTOEAUfbdJFqwxQP\\u002fHMLfwjidAil+k+h2\\u002fHECLHQSzm8oPQJ6ZuWp8u\\u002f8\\u002fkeHW+k6J7D+l87yQYBzXP40Wt2kl7sA\\u002fwhU1yDXIpz8EW+wZygKaP+fE+DNkhuA\\u002fLLOUbFAZ6z9ZlmYWFKUAQHcyweFJ3xNAqLmDAP6GJUAvtejWdgM1QMjx0lmffEJAfYF9+LleTUAuwiRGMyVVQCUMyQfhzVtApPp86HTvYEBMXRldQ51jQAovIaxQfGZANO+fBqO+akDE8LDhOgNxQOVzGdttB3dA5HNO3PoUgEAPpkZk52yGQGvyw8A+eY5AS9c9eBbak0Ciylsbr4eYQG+cIvC\\u002fjpxA6Z4X+OdEn0BOj\\u002fn6cy2gQJYe8Mj\\u002fCqBAVuJJV\\u002f1Qn0DsK1YMJD+fQGYLmTzihKBAk51+C7m3okBXfPM3lkOmQDXjr68Y3qpANgA2mv8BsEB4+EAXmpeyQIGmtbjoCbVARxXnNwJjt0ClYh5gZNW5QNXTt128lrxAnMlf+cGuv0CDv\\u002f653GzBQHcfeW0ly8JA+xmeOnOtw0Bsw9y0MerDQFoViZXyhMNAL4U+lLSpwkCAIL7IYJXBQMAr\\u002fro3ecBAEr7WBrbRvkDAXD1Rp7e8QGqpoRR0frpAYWn\\u002fdysUuEA6L2KvM421QKhzZuTKF7NAXfvMSdfesEDwfw8xIeatQOzRunuMkKpA2Pzy4z+Jp0COsh3\\u002fUqykQHH1TlphBaJAL0WIE+inn0AgX+fKA92cQJ07it2hGJxAlguU0pMhnUCT5PqtefeeQA0Y8R+mEqBApcvu5nJln0Bpydp23zicQPJaN5qfFZdAPyWlmLkXkUCOC1cWT+GGQH1GNh\\u002fGvHtAotZxTLWtbkD9LhD9LoxfQHGpushmcE9AyyN4Y1RKQECqYXeRscsyQGooMT2zJihA55f86I8kIEDtMbqIMusUQBVD\\u002fq+TMwlAbX+hr9Gv+z\\u002f0xSWu84PrPyNrmOIFqNg\\u002fHWn\\u002fvwrnwz\\u002fpEqlJPhCtP8ptY0G2VpQ\\u002fLbfpUdIuhj9ifCUJjcPcP7Zw+zMphOc\\u002f53T1JnbF\\u002fD9JxYdysBwRQJhP1bi1eCJA77SUkKv3MUBMno5DMIE\\u002fQOn4mCLw8EhAp\\u002fRWjg7mUUDjtP2NSHdXQHL8CmYhhVxATpdt5ZCAYEBh5a9TZPdiQMtC+UhRw2ZAHbF98TJ9bUAHINwibIF0QHNyBHCFmH1A07N\\u002f13xchUBOXHefvv2NQO\\u002fXL37SF5RAZeOMNZJgmUBqvwOBnP2dQK9KGJb7iqBArtslei8YoUCDyXKiOLygQLS4Gh6GxJ9Afw+3fVhXnkBz+R30enqeQEKy\\u002fOxejaBAqlxHX25Do0ARwcZF6TCnQCSO0S5856tAase4o3t0sEDB3Ku1P\\u002fGyQF\\u002fSo3a5arVArdSLcpwGuEDLa6f+gey6QMkBRLRvFr5A9x3bauyawED29UrvpuLBQEuENJ6joMJA6s+aZzmzwkAguChJ4ifCQM2WnP7xMcFAcGstVbEPwEADWPIySty9QK4FNH9\\u002ftrtA31qZowqZuUDB324B\\u002f2W3QNvL4pKYGrVA4K721QnXskDtdvR7wsiwQPSQ7aYmF65AKzuDUcAyq0DZqRP5NKOoQKdmCLmtIKZAxLP6BiiLo0Crks324wGhQKFxx1KutJ1A5moi+IjxmkArXdubmS+aQO3OW5azMZtATHG\\u002f70D3nEDaV\\u002fR\\u002fGxaeQAiFB+5fWp1ApU+hFn9SmkDz2J+jFHWVQLZkBc\\u002fOoo9AzECZjikMhUAkEgv72Ud5QAZgd0LHfmtA1zi2XExSW0D+QetZLlxJQKYUWbqxDjdABJUBJAwnJkBufS6wyO0XQC\\u002fj2vaOlgxAb4y71hyDAUD2APS3qJL0PxKOqQvgVuY\\u002f9CrrXG4P1j\\u002frqyEug67DP+SpBp9WqK8\\u002f3k4nsLoIlz97A1P8HQ+AP9gvVqwZd3E\\u002frT0Y1AcI1z+KkDTqcMbiP3PZnt+s4PY\\u002f4SVorjAdC0BBOhAeOiodQJGpJHZdRSxACBji4r6zOEBF5eS2V35DQKR+veI+5ktAWh7OMz5AUkCItaDBcSxWQPiYSJRQv1lAzTwWBaPrXUCrTsm3V2FiQJ9lPMc8tmhAjukOhqn9cUDW3vAKLDh7QFFgijwsgIRAquwg+YbOjUDoI3DxrYCUQNdxhcDqXZpALcV7LmN+n0CXORk3ZHChQJ9F1pbi9qFALcC0sX9ioUA+hnPvPiGgQDhm1b4Eu51ANm196\\u002f6RnEDOBe15W9SdQBqzlnYC+6BAaA6pa7JfpEDSJI2mcrioQBvjvGpija1AhYKbdRpEsUCtFTcUDM+zQNl3pp+fg7ZAmEclORp9uUA6\\u002fKznAqi8QGfNVd6wqr9A3b59bQf+wEAUieHAC5DBQHSRH85zcsFAJJsgQuG8wEDpp0PSLVO\\u002fQLN3Fvuj77xAm8azXGWhukB4Y87\\u002fQ3u4QMmHVQAyZrZAHNb0JEFLtEAhgSt0cjKyQKQWk1n6QbBAEEbtHXBFrUDx5VsnbMOqQLievFkey6hAZM9co376pkAdLdWsr\\u002fikQIAYCVpNpqJAGE4Na9suoEBfOfB1Du2bQMQr4Qt22JhAGHc\\u002ffBSil0AtZKSVbRuYQIkSugFfXplAKzpJatklmkCP0JA2xV+ZQJLad8Cwp5ZAWVuqX6BkkkAlV6gNcQGLQJrZSCoO4oFAfcFgL6dZdUCEq1ArkQBnQPX4FWwZdFZAOE64FqoSREB02qvn2t0wQAipfqGUHRxAypKqeMEpCUCo2aKdB3f5P9o9HdEuMew\\u002f+FkjbluI3z8xQ99UNb\\u002fQP7XI+RdZXcA\\u002fb7+9qXQGrT9md8VSCz6XP+kh6+nz2IA\\u002fvXWjgYBmZz9+z5Ig\\u002f19ZP1YCmMpf99A\\u002fbbIAEwOV2z+TcgzNgrzwPyFplQ5iwwNAKNgJK\\u002f8uFUBRzK6RxXYkQIQ2+hlj0jFAraSlu\\u002fsKPEBYrHp6tgREQICebQzwKEpAbHlWE67WT0DIivLHIKRSQA3xb7EUIlZA0aAIKp5RXEC6Zp2siydkQLX0QBwYNG9ANFKyo\\u002fbseEB6tgPR3JeDQIlHqJoUYY1AfosrqoiglEAMfgsrVdqaQMup40QtIaBARNh0PmrfoUDIAik3q1WiQBhSNYn8k6FAZQ+DziINoECyTOWtQuOcQKV94j9W8ppAPI4tumRhm0BnAqHUe7SeQDP3X3tNY6JAbY4EqnZ0pkCqgmB3thqrQFpmUCVN\\u002f69AEmV\\u002fd0yIskAtMzR6cjy1QFir8PglLbhABJkT5pE7u0DrggZwAAa+QBeamDXcAcBAaRWiluFhwEBE80U4YRPAQGt5gGIGbr5Ac04qGrIXvEBJx05bzaC5QFHky+2LU7dAh3LfRxY8tUBNSO9egEOzQE636iLmWLFAxjQ1AswUr0BegM\\u002fyp\\u002fyrQH\\u002fE3RzfqqlAwXdPlWQdqED4lsoJ8AKnQESCOi2J4aVAxv2VMsNTpEC+5f5F+ziiQER4gZL0hZ9ApwnkQYW9mkBorFyvrAeXQOWwRxwg+pRAnnVZr8+ElEBHKhVok++UQOWfVZL8KpVAL36U1n5OlEB4zDu7Uv+RQFAUo7fOEI1AsSkjfJA9hUD9blRr7gF8QEZefseIo3BAUebWY6DQYUCPF4zbXTdRQLJnVVjKLj5AYJD63+JOKEDTPkfrtYoSQM8pF3ledfw\\u002fm+s9BkXQ5z\\u002fwl8ntzJDWP0CekBJrEsc\\u002fzgk2Fwt9tz8pNo28XoSmPyCkhIwByJM\\u002fd7N6q1SAfz9YLGpRY7xmP9y4FBD8ck8\\u002fJLEwC\\u002fr\\u002fQD\\u002fmm2KpWP\\u002fGPzBQWPe\\u002fo9I\\u002feHwBZmSH5j+HAaXep4H6P\\u002f7irYHwTwxAtee1D\\u002fpBG0ACrfloe6knQBbqrnYpkDJApJGUTNZyOkBGnRH7mkdBQKSa0aOLIEVAH8leAFQeSUAWAGYPdeROQK6rPWoS\\u002flRASZ71wwwZYECFX7LGl7VqQMC7ZGDIf3ZAhBK76DVYgkDr1WO+VyiMQETV9YjRCpRAKEnqQFFFmkA9tpqRnp+fQHrLk6uwfqFA1yVYB2bfoUAwTtfE9wWhQNsGcSFyz55AgifvS7tom0DtYkbNA0CZQH6m3W5\\u002fZ5lA+md\\u002fhCpgnEDVZTeUUAShQJHpDgla3aRA969hOvJMqUB88at\\u002fzv6tQABrSYY+b7FA4MIHNCAGtEDtxQbEK822QNEcQa3vnblATTCrV1kUvEDteF39Wq69QCtWIPwlCr5AEkEAe04avUAEOFG17iy7QNIPNoKpwrhA0b+nQvNRtkDA7V0Z7xu0QDWw1m8vKLJA+RFFFPJhsED7YQF\\u002fWHutQJta9qf5l6pA+kl2OBNeqEAIGGtUjPqmQL9dW0HfUaZAv0\\u002fvWz78pUC4XjQKenClQAfasL+5QaRAS3sCQwFOokChnIYyHZOfQMoJzAseTJpAxp8JXyPElUByVRofjKiSQGQPFg6SEJFA6y7iz\\u002fx6kEBfQMORBBOQQJa8rjJxMo5A1u15J4FvikBsn6xDTS6FQPPjID1xyH5AsxF+dTYzdEB5X\\u002fYQ3+RnQEff7KwldVlA0kn5zP9wSEApHtbjvjA1QID+gz2tsCBAPPjH57dACEA+DX9ygdfwP0tUP1535tc\\u002fJOrNbH6\\u002fwj\\u002fJL\\u002f7GBZywP9+\\u002fl69HQ58\\u002fbOUglM\\u002fljD9pPEddDvh4P8apHI+ptmM\\u002fGjxkH5FNTD89oHaMVHwzP6bfKPG1\\u002fyQ\\u002fodMWiN6tvD\\u002fePwlWkC3HP5hglVeW5ts\\u002fwpiy2oxa8D\\u002fZNQ2wN2gBQNmJ1dPVsxBAnxwGkEvoHEChH5V2Pp4mQKFHmq\\u002fWFjBAPnwRWaQNNUBhUCtl8vY5QAzIe6UHpj9ADI84TBCFREAOAmwrcjpOQBTAKE71PllA\\u002f0Tahi5oZkBG9tC6nr5zQEVNECZbjIBAyQMKMYPNiUATvz\\u002fmd4OSQA4aOAbqVphAc4FdebZKnUC5oZL0PCugQEtOKIrGdqBArBv+53tBn0DwPe4lNjecQD8ailVEGplA1W1YkKs3l0C9cSh4+oeXQFR+zhMqe5pAzBrK063qn0DAYEdFCpujQL+cGUdSxqdA7AQDEsAnrEA7aNBANlWwQGV63ACLs7JAzDXt\\u002f4YytUDMJhFyAam3QCwffoSct7lATzvu37jpukD9rAovoe+6QFavBwvzyrlAXdFj+wjPt0Cp9CZ8tXa1QJtkJA6VLLNAd4j5HcQnsUCNSC7cxtmuQO8ONCBy1atANJiHI9U0qUBnP8jukBmnQJTbBCVMu6VA1ilvI98xpUB7IaZ9x0ylQFmAa0mSlaVAnPbp6AJ8pUCmIaH4U5GkQG1ZNsics6JA0TGVrKIXoEDb81umS2GaQHB\\u002fO9M\\u002fBJVA7bNaoSHckEAfGq2OVE+MQDtuQTrxNYlA5Z5oFpouh0CNddfG1QCFQBLACQcJA4JATVgHbqiEfECzegTulZB0QPxQGCfC1mpARj5\\u002fqSGZX0AkyNQD08FQQC60lRW+AUBA6bVhrqSPK0BfwltMPHEVQPX+rcShXv4\\u002fxfU2qufv4z9reHaj\\u002f0jJP4t6P5SJp7A\\u002f2ERFLqOImD84GHkaIEaEP6JkewKUgXE\\u002fcBsslxdTXT8LpLP6RtJGPxX03NuzQTA\\u002f3vP7OgxDFj+kATvEFegHP9Ccjqe1c7A\\u002fK0eV1SOEuj+qVjS8OMrPPxzmN5zRkOI\\u002fA2aZnHSx8z+hRdXeV9UCQBRo1U6QPxBASXQzg9FdGUBbyvWGQggiQFarUzszrydA9JHeMkilLUDCWj1I28cyQPicRFgbOjpAiLmRIzZIRUDuEHL0jl5TQIAQayLRN2JATwvMzU6ccED\\u002fd0+SqV58QKxx44IjVIZAvcAe84wVkECIsw1woiiVQCsTRHp3bplAqp8ECowEnEC9lTR\\u002fQHqcQL1sTUZjB5tAkJzV8jh+mEAqGvj1YQWWQDaCIsnyvpRApLyVVEt+lUAsXtpmnJuYQIg0MuOf6J1AXokgGpZlokCShA7jFUCmQBeavR7dOapAIshz2cU9rkA7ck3hPy+xQIExLiL9TrNAKqWNvs1XtUD5oh0xL\\u002fa2QCc9\\u002f7d6yLdATjTr9b6Rt0B5LZKBkV22QCxC5PEyfLRAV5bpeSFbskBhJACmV1WwQEzGg6yTL61A\\u002fsse\\u002f81KqkDdbrVrvOGnQL1CCZby7aVAN0uica6OpECi2AjEcu6jQM4battUE6RAksdY\\u002fHS+pEBF7apD93KlQKynDbtYoaVALbXq82XepEBUuXmEcgqjQMdgN0C\\u002fWaBAUASQcih8mkAjeNM\\u002fdHiUQJc46n\\u002fi945Adc3lu1HKh0DRwl26WyiDQLBfHvEhLIBAaW0MhjSoe0ApiO3VTet2QI06Cwdpy3FAOTbYGjRfaUA+g9IzG29gQAc4AogGPlNABuOizmFQREAss3JX31AzQDcL5pepiiBAhbMOTtWKCUDCqPj4TtbxP5X8qcxsu9Y\\u002fmBjqP8oDuz+sfvVJaGOfP9091NVIQ4M\\u002fSmhmy0Kpaj9R3cD15H1UPzjNfReSL0A\\u002fkEfWzBeIKD84n7kDgEERP1a\\u002fmSqUdPc+x9GNHVQV6T7Kox19pV2hP2sn3i2w6Ks\\u002fRjsvSISpwD\\u002faOhoxLmTTPzxk3fqKf+Q\\u002fpATekBGK8z87jnD3Dc8AQNpMvYrmMApAPf\\u002fzXDKhEkAWZqbMX6YYQOXATlvWmx9ATMxgmc40JUDBVv2yiEswQCPsT\\u002fFuVz1AnZtD8\\u002fXUTECf\\u002fZlp61tcQDYuVqc8eWpAfUuVfWTfdkDl3Wea+BeCQABDQ5hxGopAQhRhDrEokUB3UfJ2+ZeUQGpvf59jqJZA03UXMQgIl0BYvTikoO+VQFbxHXNcFJRANYb8rRxtkkBIb2oGaumRQHlS3TNWNJNA84zJibOOlkD6GVyzT8SbQGqyfXAHIaFAT8WDPzqopEA+ap2r6DCoQGFurLOBo6tAiDlBtIkNr0DWNl9Q+zixQGxR2zW7yrJAK4zG4Ij6s0CR14Q4\\u002fny0QLJZt94aJrRAoc5m\\u002fx4Fs0AO1AeFqF+xQDQPbDpnIa9Ax1qWsGjEq0AI1u1TAvCoQBAVuQCUqKZAW5iscRvapEAU0upFO4KjQE5aSkPfu6JA5bC0xU6mokDrT2iSzDujQDOpPlxvN6RA\\u002fC0vhpUepUBs\\u002fC7Tx2mlQL4UK6BStqRA43sHY7HookBbSXZ+ezOgQNeaf6rFBppAb7RVMrGyk0B\\u002flPLK5YOMQMcyC44ETIRA9H+5n6V2fUCkAGr1r1h2QFTtldtgjnFAqEtw\\u002fGWKa0BYXIyvY7VkQKFbAPx5\\u002flxAruJOne+TUkBIiHCnC5lFQPP7ik60rTZAN\\u002fZfMdV2JUAI0esDyksSQIULBxsbGPw\\u002fvdcWSz904z\\u002fGrRkl0WXIPw3nOcqiAKw\\u002ffr5wE1Uujj+eQYp3ix5wP+v9RMijflI\\u002fN8+7e94KOD\\u002fgk+hxxSEhPxqQcNbesQg\\u002fmdfq9pz68D7tFex5SNDWPtjKqRjcQsg+HY6Uiz3dkD9vaVluhQabP8KzjS7LEbA\\u002fR0da+0+iwj\\u002f6Sbb3aKHTPzvVHUQWp+I\\u002fuV6q8eYA8D8+tCp9uef4P1voZR73wgFA5azbmGzPB0BqI\\u002fk6VLYPQEuTJwpIDRdABWemc7TRI0ApCij6ErQzQGnadlLRlURAoDaET63lVEBsyvhxuc1jQJjmZxcSOXFA8HbTXhNPe0AVLE6hpLKDQE8pMNt03olAwIqAHAgDj0B19eXWlQ6RQHXZRbzfYJFAPc1l0SKtkEBVZYjUQg2PQP\\u002f4Q2hzZI1AR3+JDuzcjUBFaOdJ4cKQQBV3rIiyXpRA5yCPvFGLmUAy97jFB7ufQDD+OYYdHaNAE\\u002fAcuT07pkCYfMlq8RmpQCjUHnuDwqtAawUv0Q9ArkBiCoU8qTmwQBd6JWQYA7FARr8RKOFHsUDznW3CL+mwQEqIABBE569ASoIuAHM5rUBDSTsmelWqQIo2WuM8r6dAYBppsk2ApUCWPHGQJM2jQIxOTJkLhqJAOSaN1dyooUAklZw2kkqhQJZEULQGgqFARPfOi35EokAsUr8cuE+jQHK1\\u002fTmJM6RAFkuMmNB2pEB3Suh1E8OjQEfUq5H4AaJAlVtqkZLFnkAbeMYwMZKYQAUV5XxBWJJApUAzlnDJiUAXnx2k3VqBQMcjDg\\u002faCXdA1erJIoY5b0BjIkWKWAJmQHhYpaGB0F9AoWUpEh63VkAfIhyerN1OQE1md9E9cUNASUwXVyNfNkBaPOGQdlQnQMd2N9fx9hVAya6PksWiAkB2DWNI1XrsP5YuYM2xmtM\\u002fWnjl1uBauD8UHELgJ3CbP28xv0A8ZHw\\u002f0EWWSNbQWz\\u002fDo+RedWo7P3adzfdBfB0\\u002fmbgizUr6AT+BJZ0Nq63nPt5a+xYdMc8+BIQa4QaJtD5R4FWwv6alPuogldRnIn4\\u002f1oStrbETiD\\u002fRblqVVoScP0m5Gal3ebA\\u002fe1IxOghMwT\\u002f0ae84lmLQP8iKNw40Ddw\\u002fVeUrW\\u002fDS5T\\u002fmInzaw0\\u002fvP8s1oxHYc\\u002fU\\u002fYjZAvs8\\u002f\\u002fj+UU3MJGWUIQGkSvey0mRdA1v42rGWHKUDPZ0ZW8OM7QKUB+FOR50xAtSfEZc+iW0DauvBTsxxoQLCNKEgaIHNAgTKe6MORe0ADSh+lqxWCQNsbsoCSq4VAoWJJ+kveh0ATyt5hjXCIQPsu5fEYvodASHgVr\\u002fSrhkAAlaxfx2qGQL0gj9cKJ4hAJ9Lywny8jEBfXegmRDuSQI31pFGOdJdANl8yCBx9nUBGokOiucyhQJQSlTg6kqRAolgSi4jmpkDSYEUyQdCoQJnCx3efaapA6jIMdsC3q0Bs1DFZgpWsQORx\\u002fuV4xKxAm6sn+tIXrEAmJz3LLpmqQPJrQZyrjahAMPSOMYVZpkBHjErPGVakQJWkZdwntKJATfdvEt15oUB7otV6n5mgQIai9PsoDqBAd64GNnvKn0BbLJCk0i+gQDH2sMsE5KBAt3m5+W7JoUBZdlo81ICiQJGW1F\\u002fTo6JAkvx\\u002fRj7qoUALNs0S5EKgQEu\\u002ftw\\u002fCr5tAVga1ev7\\u002flUDr+hi990aQQBepKHN0dYZAwJAmB1YifUADEuZQDxtyQLUvzn2hO2ZAmXyHl0bgW0A3B2SVVRhSQNDVUdeG5EdAxPoqjNrrPkDWgqvTK\\u002fAyQNkJfMWJcSVA8hHWdNokFkCGNy9trbUEQKTv6XHtevE\\u002fzgyUf+uW2j+R7UhFmjbCP+Ka0FqKe6Y\\u002fSvLttFsPiT\\u002fx6aSb3F9pP8\\u002fe4nFUs0c\\u002f1t+IQ2ApJT+Hl\\u002fiiVVEDP1bN9GKQg+M+p75dS9dHxj4oBAiF4yGrPivpRHKYM5E+pIKZyvvdgT44QiehFsVoP1jSrkMLvHM\\u002f9xKIeJBHhz+RpIGAJc2aP4stWPriC6w\\u002fpVSPzdV9uj\\u002fm3viTX6PGP0bBL2QWpNE\\u002fgA+ocpGX2T\\u002fOT+uF1CniP0I3omolxus\\u002fAcOvQA5P+T+S4WGEYlYLQGVhe5mbjR9AqfU2zojDMUDHsuvZxKRCQAFfyy7H6FFAXOERYEVJX0AbyUHr581oQHl2JZdS3HFAnJh6c5Vqd0D83SPNsxJ8QK9jjngiBH9AY0CnWSwDgEAHd9KHkbJ\\u002fQNNYHaO5ZX9AzVllC3R7gEChKKGLoCaDQEaX6q6jcohAZIEoikNTkECEUC+Z8ayVQGHAp9gstJtASYURZjLNoEAYmukVRk6jQKBXVljGJ6VALHIFc+pfpkBF6z00USWnQHnHhiJlpadAZe87n57qp0DiGCP24dmnQOQ2pD3JTadAnmKMS9M5pkAGmZmA1rykQDeNvLbPF6NA+6R9zKCQoUAepV\\u002fZe1SgQBxIA7Ef2p5A9YVQ3rabnUArQDSCYNmcQFnV4ikSn5xAh4oCghoGnUAYg8ZHVAOeQKfcoTH7QZ9AgNAsnCoWoEDZJZXpeQ+gQB2\\u002fnqXUpp5A5BZIEn6qm0AXRAB50nCXQCWy3LMOiZJAvy\\u002fJbJQ9i0CFQKyqDJGCQD5EY4kfiHdAf4N+kMkAbEDOcNu9lPFfQEddxNx5BlJAmXF\\u002fz7TEREBD4XAfPa44QDl+IW\\u002f7oi1A1EJytcZVIUAVCuvnwR0TQFo790AtcQNAVGDu2VgD8j+JzLN1VDbePwpD0x1p3MY\\u002fd4LqAQQsrz83QKryKiSTPwkWm0wDMHU\\u002fNFzutrcxVT92mGWdt0wzP\\u002fr+plCxSxA\\u002fPP9uiwOZ6j71M4vRT5LGPhELeLPjcaU+s\\u002fI8gmjxhj7KuP4WzCBrPge1S9qAZls+K9f0KFq7Uj9f9oJ9bMNdP61BvDb+e3E\\u002fCcJkKN0OhD\\u002fbGBCBLeyUP+hz+59AtqM\\u002fPMBPq\\u002fTUsD\\u002fqDbxoAlq6P7mL7wK7fMM\\u002fpqzgVwM0zT\\u002fCoQITddfYPx9GxLbLvOk\\u002f0PyozQF6\\u002fj+C4VZMTWkSQK\\u002f3UW8LISVAn\\u002feRwmRVNkAOYzFLUH9FQBJIswNexlJAmgboNWC+XUD0EQ++LWZlQHDKAjywDWxA84Ik7FjYcECcfhANRrZyQIo\\u002fG26dknNAKRjnmI7rc0CLEb1gJM10QATY8Ku3rndA8WDI9fIwfkBKtpMhctuEQKEjo6qba41AJ8kd+uUxlED0DF6Sr0aaQFnN71OiBqBA1Ww199BQokBr\\u002fLHCJcCjQKcL1yELXaRAI9DRW1BspEDmlgZ2nkGkQBisnMoOEKRAUmizYE3Wo0CTcHsc126jQMafD48vtaJA7lddxgamoUC5CqGHX2agQE66AdTBXp5A\\u002fVjKQL9dnEA7PJmAg+eaQP\\u002fTRfEc5plA8skmNcM7mUD6dD66meWYQNk+Y16A9ZhAZ8wk3VJqmUBP7rObYQyaQC2TajXAbppAtfW4nRQWmkCcAi1hsKqYQHO8jjYoGpZAmIulv8mckkC4d4T6okSNQKFOLA7gXoVA+HnO1CbffEA8wtvvqwVyQDh7EQyk3WRAkqB7KxypVkDIII2GdZtHQGHbJi8zXjhAk85jbheuKUA+1l67x9IbQEklmX6pTA5A6c+Cfvz8\\u002fz+CCzpgALvvP+rgnJq9+dw\\u002f1KcRjOQUyD+FmYfikxuyP0tlL6zekJg\\u002ftfruhc4Gfj+57pMdiYdgPzDWDnmPaEA\\u002faDSpHgJ4HT9Aj\\u002fk6oib4Phy1sq0xd9I+bwBQzeSiqz5EBsqQyuGFPm2zSjg8nWM+5LRP2g+VRD6mNbjdprgzPu2d\\u002fpm4EDo\\u002fX1ayfz6mRD9XxkLx4ClYP\\u002fxHnzSln2s\\u002fbEkxfva5fD8gLeGFIgKLP1cOtRmrFJc\\u002f06hujyg8oj96KzqkaNirP\\u002fDJjHnLkrY\\u002fUvSZbobPxT8YSulTk3jZP3G48vuJKfA\\u002fm3cjsngfBEBzWao+uVoXQCPSiFSxxShAvVv29JnaN0ANa7RBGtFEQCksVisceVBAsV4dfu2wV0DHh1pywhRfQPp4wUCYuWJAOp0woyP7ZEDgHsnLKWNmQCZkkeKmwmdAHpks7kbCakBOypfVs+lwQHptT+5A8XdAbQkWR1rogUB\\u002f4StRD5WKQPFbl4PGzZJA2SBlvCPamEDkKOAoj3SeQH7KBuvvT6FAtJ7WpmlkokBRcVUMY4SiQKBiiIMSCKJAb\\u002fTxG\\u002f9goUCw710UO9+gQBibUPz2kKBA9aoTCiVMoEBYJMgRLa6fQOT2IWa0J55ATJmmEsQmnEAwHrnZmQ6aQCw\\u002fCwyeQJhAs4pP6gnnlkDvjQ8BpPGVQECtpwvNPJVAFqSB9Xe5lEB7kXCRCHKUQN2nzmNnbJRAeJ9V0BmJlEB6uUw\\u002fdX6UQHPUQJ0P9JNApa3CyMipkkC2RMPvo5KQQJ4\\u002fyz0XsotAjkt4rROfhUBKRVvOFFp\\u002fQM4\\u002fSXXmAXVAtVFunLXvaUArUoVRB39dQEdDLEOJDE9AGET6zUOlPkBMGU3vdQwtQHrEe2ulVBtA3CxYCZpCCkC3AiE8F9\\u002f5P6oQKK73gOk\\u002fVYlliwBG2D+E9BBXmKXFP7OmwOd+vrE\\u002fxnluhFJ0mj9zwZhytNWBP+crq9CYsGU\\u002fDkU06dbDRz9Tu5J+KnUnP2rlGFPH4gQ\\u002fEBw11pzX4D5z6YzR\\u002fN24PoDiEb1zQJE+27vjz2HCZz41ehdCxKRBPmrceFmPIh8+\\u002fUX7QcgsCz5DiQYXOK8gP6YHYc5zXCo\\u002fDaFbwoG5Pj+ThkzojIBRPzdcjCF0JmI\\u002f1Tp\\u002f2ygLcT\\u002fl\\u002fjpfmTp9P9NTKKuue4c\\u002fl7JwW53akj8PrzH7pv2gP0BAWuYrzbI\\u002fMtNk86RAyD9ZTGeRSSXgP8zNT4VKevQ\\u002fKdoU6CfpB0B45wOj2WQZQIwUDWpgcChAqyYxigVONUD7i96jUthAQEAFNtzyOUhA0DE3H1rXT0D+K+xuKEpTQOWw1jAG6VVALKroXXQdWECPZlurtERbQEBhKRPSCmFAIS6x+bBhaEC28gOQLylzQMazCI2Run5AZeGpqujEh0CCcMcpKDWRQHDI4xWTAJdAtuhtdbtCnEDJ5Q+nWvafQF10KUrawqBAOunOrRGKoEAzW19jm2qfQGjgAXRrjZ1A22vN8ic6nEAUx5pxkZmbQOp3yhqhTJtAoaztnq\\u002fCmkB+IkxZh52ZQO4AJaG05ZdASbJGrX71lUCmluaq6jOUQHuh4vHy1pJA6TozdZLWkUDg8BHKRw6RQH13fRDGZpBAkWnPY6vFj0Cq4CtdQRWPQN17ka4Kno5AOzWAFisCjkDo\\u002fHPMYciMQKS0Bd6QlopADsetdzRbh0Dmu4WziFODQKJKrQQZ531ATyJOnyx+dUC22YPRGY1sQCweTdF7c2FAl8G3lCmWU0AiGL3CYzNEQFZKTQzcQjNA4Iv16GU5IUAQiFym1aENQC6j4eMUWvk\\u002fFmH7wUop5j\\u002f8Z0eIV9HTP6t7chWvqcE\\u002fAO9GyNhJrj\\u002favYqbYEaYPwF24ADI24E\\u002fiRHO4PngZz+PFIbWwN1MPysLeUxGeC8\\u002f7KaFwdDoDj8eWrRjw1rrPir8Ws9G2cU+edJL6EOonz6ckYYSERN1PrcNNgw1mEo+H86VK4nvID5HepCrW334PVoQ0HzzheI9Y0QvdbKmAz+OAq3ZN\\u002fYOP2gMMEfu+CE\\u002fBCe4mKBoND+eIqg3Wx1FPw9esqeE1FM\\u002fcEHosEIiYT++0dZl11NsP64+mZ0IgXg\\u002fgQIS5MQfiT8jCT\\u002fUjpefP48C+Rzs87U\\u002fKuqUQG8mzj9r6MDEJFPjP54jhCmWoPY\\u002f2nkc2TQHCEBFCHnKrRkXQNlI+7WnHSRACu+GEQfLL0AJ8dzjHOE2QAh9d3n5Kz5Af2dByY5xQkCxuj8ZNGlFQAzZ0N3Tv0hAJMdC9eu6TkCEhULjaOxVQL94e\\u002fJ31GFAuqUR0EysbkBkU\\u002fYGUfB5QCObmbNYqYRA1hdXG+9hjkB6gZb\\u002fJXKUQPEYrVmpIZlA48qnKjJHnEDX9R4Mg1idQLJwEyIfgZxA5vx+9fqOmkAzrBOXc4mYQCXwfOeAL5dAQcCHjiaolkBFxMP8m42WQPJl\\u002f8s5Q5ZAMmEEtv9blUDoPttOU9OTQC3qxp2A\\u002fJFAhdRcNQNAkEBWu4lhy7eNQLlbNmEqootA0P+1nkD9iUB542iZtJKIQCNgHkRJWodAwo1dhnxghkDFz\\u002f7FRpiFQAiGe7rUx4RAyTfD6NSeg0AjkfnPb+CBQHOZiTSOA39AuEoIu31beUB8vHhFu2RzQL7Cfqoek2tAHges8osfYkA12iTQE+xVQCQ888rGUUhANhk1mxO1OECi5UHqfQcnQNo7Cay01RNAZq8Br+QIAECP9MpMBv3oPwchhYjzaNM\\u002fbrFi+xHUvj8Uj0zOI\\u002f6oP7JJrSYpH5Q\\u002f7SppeIMRfz8y3ll4Kl9mP6pj+ZPdiU0\\u002f3seopdG1MT8L9GqPsDETP3vAfb0NwvI+hL3UuyKE0D5cYH8mSDaqPmbMDbEAyYI+ZI8nZj53WD5iaWcXXmktPrhdcw5r7wA+6DffRxmM1D20WmYftA+6PY3DgTtYS+U+LixPAZq68D7vNAC6oFgDP0OwDx7a5hU\\u002fK+qMzWOfJj8oRwz0UEw1P00loeqIq0I\\u002f20l0plcoUD9dTHXvnPheP8KVuu1aRHI\\u002fREMzKh2QiT8glHO65bKiP52L+uxqK7o\\u002fYkLKbQbe0D8W5fz3wMPjPz997vZB+PQ\\u002fBBGUYIwiBEBGSDaFYoQRQGj9mc1ArxtA3dks7Fz1I0BjIlTceHgqQDpJivD1azBApZUPRprBM0DwKD\\u002fOF6k4QFOTASmMaUFAMSKfpUvHTEAyEKTfkzhaQJJ1KIc5IWhANI57OA0ndUD39jilMiqBQAbZDZ4oeIlAq45NcIcxkUCUFZaj8BuVQE4NnyXDopdAA\\u002f78N\\u002ftRmEAKsBaA3liXQM1vTw7NdpVAbozOu1ibk0BYF3SSpnCSQBFL3LnME5JAgCgxBuofkkCdMM8gov2RQGQ8RIMPQpFAy9og6V\\u002fIj0Bn2sQHy16MQEKExbV3DolAGp+oR\\u002fxXhkCAZPRQNEyEQNtjg\\u002fjbtYJAeUt4qrtegUBzIHOpUzSAQN5dG5ERdn5AqxIDkuPXfED3EymYdUZ7QMGIr4D3WXlA4Ay6IJvDdkCcrwvruHdzQMlOcyl1Ym9AEF+ZFIiqZ0B8MpBBW5hgQK2HRtHDh1VAIVV\\u002fT8S4SUB0ksr\\u002f\\u002fi88QAJ0TqqPQixAZq9r\\u002fEjnGUAKyqa4ssMFQA1l8TlD5vA\\u002fWL8qevOp2D+m2eYzTmPBPxtoTjbxf6g\\u002fgwSMwoKokT99NfrxD+95P4mVaWtR2WI\\u002fCbrJYsMxSj+vr1G2f\\u002fEwP6StvwW6EhQ\\u002frgu7QNaV9T6mfJ7LFPnUPkZkBiGnX7I+eLV6LX8BjT4yl1h\\u002f2qNkPsIUa5UJjTo+Kxvw6lEaDz4WOYtrmungPaEgO4DGKbI9kTGBUl6Ckz0tzefnxDrFPjIBQlHrodA+c3WNmKQp4z46fOPx1aL1PiYyAonMVgY\\u002fyazTvYUpFT8l4LS8QAYjP8BKkPrhpTE\\u002fi9KVyR85Qz8G2+GMD\\u002fFZP5CJGjOSrHM\\u002f3EuEEzi\\u002fjT\\u002fSmZkaOAqlP3b\\u002ff9LsL7s\\u002fMZgH4crXzz9\\u002ffSSL5d7gP9o\\u002fPyTWLfA\\u002fXU20Xhoi\\u002fD+OZglYAD8GQDM3w\\u002f+aFxBAINehY8qMFUDM9kwH\\u002fGgbQFVyZ9UofCFAhOa43d13KEDd1BWQxTE0QOoLWptyKENADhFTiXMFU0Dz1YShdU1iQNwCXjldZnBAbclKZITlekBx46XVnQqEQOBIN1bVEotAIJM0VC+VkEBOwKNvf3uSQI8ziITI45JAUyQapMP7kUD9UvTrdWWQQCX8wVlFzY1AWAPa6hIPjEAO8kvEnLGLQAh+P7oi\\u002fYtA2Plfcpvoi0DftnsIM7qKQKwUeJ5yYYhAqIpSe\\u002fZghUAcRchoOGmCQG7KwALc5X9AXrEZwxcwfEBFrwH\\u002fD2N5QPvt6SgqHndAQSMV9J8xdUBrFcDTH5ZzQMf4sRy1PnJAD10JZA\\u002f9cECexEKtkh5vQHtMrgQriWtARG\\u002f01ocwZ0AbKlkaP2RiQFYQILFkRltAklajiK3OUkBEcrIVRgJIQFUetAMlQDxATGRkFbCFLkC7nRk7tC4eQLpO+okuQwtAKL3EVeJ99j8BIDjzHAHhP6acjlAyxsc\\u002fgarl5\\u002fJJrz+0EYz+e\\u002fGTPwElI6BdeHk\\u002f\\u002fTaw8m+mYD98KuAyoCJGP2rLPbNcCC0\\u002fG9gooMsmEj9riVPL\\u002fBb1Pl3B7ovFatY+pEjQr8edtT6pv5z3UtSSPofeLZUgk20+9sYqFaztRD5u6Aw4PLYaPj2oolAm2e495Ad2ZptEwD3JYNS\\u002f70KQPTBoKopR7G49TZ9SbQB5oz7D\\u002fNnkNW6uPlofIGq7d8E+JR2sDKmu0z6LpmCpzlrkPoyI286zhfM+VH7vAJFKAj\\u002fJSMSlJ7USP\\u002fv0zgkwfyc\\u002fqahS3lPGQT+ioTe4VYBcP5y2Q2ng+nU\\u002fF\\u002fjj9qxDjz+MvO\\u002f8fjWkP48ytfOgpLc\\u002f9g5fb2EEyT+E2X6kOPfXP013EHZU1eQ\\u002fc53Gn6+A8D8yydkyLgX4P6qvhnl\\u002fWgBAnTOGQIezBUBbDLK412YOQFQ\\u002fvhaBxhhA+FMq1Fj8J0DiMsy7CV85QLzfnWQGqUpAPE0hZiFYWkDRWY5JYORnQERW5p3ZsHNAK9QdRJdhfUAi+S08wtKDQAMWR6MaN4hA9MSNySfgikDpfOcG5lKLQAIDXrXC3olAAvP4Aol+h0AiQBMBB1yFQGYPdQbEPIRAuoZFnFUshEDXXIUkBpGEQKRktMPCloRAgrzyqaCrg0B5qTQtCsWBQNSuJrp8m35Avnq+o3SteUAjsJ5BSo51QL+rsEwAd3JAj6X6xzozcEB7UCerLulsQLjcL5BFFGpAxv4uZPDDZ0ANBx0VL9plQHjg6T0WGGRAl7QyM3ArYkAE4RWWVrVfQFcbEXRHSVpA1HGEDMh7VEBxCZ9S6M1NQJCbLgGlJkRAej0odaU6OUDdXuLnvSQtQNd8kbsb9B5AjFN4zTEhDkDZMjGMYM76PzQZp7PYw+U\\u002f\\u002fEdmV2Ui0D\\u002f0BQKoiOy1P\\u002fGWONdZkps\\u002fzMn4HtpZgD+ZUEh5U9ZiP5ZHqCMfzEU\\u002fGbNUs+PVKT9bCR4WPxMPP4S+YWR8YfI+WXIYnMis1D7QcSyiSZK1PjtXN\\u002fsMkpQ+LR1mv2HJcT6loEyTlsdLPgTwGuqfjiM+kDTrHtXQ+D1eIvS07mjMPeRkIwZeeJ09lt7lqbdWbD01uoo9mgRJPQVNn4rrboA+LCuUklKdiT7uJkVp7U+dPjUspxaVf7A+HiNzWbUlwT5XquZ0btDQPnBvr+ARy+A+ibaQlc518z7fs5LkshwMP2xM4IqCMyc\\u002fbGoV6QlHQz9GbnIA1RFeP1ikdcs+b3U\\u002fb8X5xRexiz\\u002few2BZ0CygP5yQWPJgF7E\\u002fccD9skdcwD8JqIua9HXMP\\u002fOQT+zpn9Y\\u002fEAJ44\\u002fGk4D9mMEW6WVbnP8putOtiofA\\u002f4ZTbtWy2+j8V6srH6d8JQHdRteVxrBxAnJIAPcVSMEBP8SzCLL1BQNLiaAGHyFFAVmXtPZY4YEBVGu4LosdqQGH4cDFF93NAKongapLiekCNG8c9rF6AQE7ex0RzGYJAc5cQJgZUgkBfZjoayEyBQHcE5no3a39AWYqeiUO3fECuutm8p4F7QO8fQxuUxHtAhEmfYG6TfEBwKA7lW7Z8QNi+3lpRXHtAu\\u002fayvVt8eEBGMGN10bl0QKzvwiJS8XBAJpowgCuIa0ALmyMQ2ctmQPT0f2nlbGNAq3lf9ED2YED7ZCGetyNeQCcM\\u002fgYhKVtA87mZrFK+WEAakcoi+4tWQCBnTkbsLVRAYw0E0LpjUUC6f6z6O2VMQC0GdYALu0VAUlAiH7b6PkBw1XL2t300QJ+It3RnFilA59IK1\\u002fhaHEDMl+1puIENQJ9oqdkPMPw\\u002ftx0bU7Wm6D9rltM\\u002fzrDTP\\u002fjBu3Visrw\\u002fTPXWdHcXoz+7aB1KLUuHP1mP1l+gVWo\\u002fxbCx0oMjTD+rKLk0NUotP\\u002fCDjjqUsw4\\u002f3+QaxCN98D4J83qIwO\\u002fRPric+VqqIbM+mu1c\\u002fXJakz7B7mFzcCFyPrMqmTQaBU8+e0l11esMKD4uWsMuV9YAPlcWG9uoQNU9DwMCotItqD2Fy+yZBNZ4PdEdY1ptZEc914SXRMHTIz0Fjdj8IYVZPsJDjwI\\u002f12M+JAG\\u002fYimldj6vlXKSVoOJPl3G65Pdy5o++zgqn25Eqz5Nn\\u002fYrztm9Pvx9lzDR8tM+w34MKXVE8D49Wz7uMYcMPy8S0mtwOCg\\u002feTkq7MH\\u002fQj\\u002f0FUkPghhbP2\\u002ff6DbqenE\\u002fdiVOoHVjhD\\u002f5mI30bISVPwrjyr91l6Q\\u002fi63Dg1PxsT8n6Xc+D7i8P2InKpddi8U\\u002f\\u002f1h7xtPNzz8csYWw+WDZPyTLvjoLO+g\\u002fUCm3IdOR+z9OYKVAuNAQQBX0SxgaAiRAkxhzPjkkNkClCEfHnFlGQB8iJ7jvbFRAw3j6OCHaYED7AXmRDBVpQLmkTZeb13BAf7RxetVxdECTBL+mjYd2QPrCf5kewHZAm+B0vKZ0dUAj376P2YxzQAveTceyC3JACuHnw3mMcUDn3Qa9bvtxQNVToQmnsHJADZMifKjYckAMEC4YFOpxQDC+52NByW9AFJ25krB5akDxzb5dfRxlQHYo0FT6mGBANi9H+0aCWkB6BW+kZd9VQC3MM+x0p1JAHM55sGZUUECYkDB\\u002fRi1NQEXek55obUpAjXP8+WTyR0CN6eKYM0ZFQFZlwmGtJEJAasHc7lk3PUB3chvCkfg1QA9KM0Znri5A85vKd8TUI0DE\\u002fpP2mLEXQJ0hWtX4IApAf9YLdh6N+j9MTFBjCc\\u002foP3\\u002fbvuohQ9U\\u002f3G3W7WeswD9f5qx85+CnP253jxVoMo8\\u002fBa+OkYaccj9pmfp4l2BUP6LmpIW\\u002fszQ\\u002f\\u002fJ3G7pvsEz+TVvlyrLzyPsFVE5HnydE+xRNnALxPsT6kxEGPVQaRPvoGH3kmXnA+pvhB9ajGTT49JarXLgspPvXQCNTCNwM+n9Kp7l+42j2qHfllHMWwPSlQlZf2+YI9Z8eNSF1cUz2FyBEUXgUiPU+8Rmex4\\u002f08dkpGxao7Mj7kzBojGko8PpSyVsjyHVA+yTcp9Vc6Yj5ObPtauoRzPr9KUpSlDYU+qTtMUsT+mT4h65T2TQ+0Pq0TYpFU+NE+e46\\u002fpNVi8D6t9JL0YCoMPw6Ve89+JSY\\u002fWvov9FWPPz+J+mrq0FNUP6GCmv9crGc\\u002fAgGCUDD2eD\\u002fZNKtvm+aHP352C7FH6JQ\\u002fkDnrvqvxoD9YKyBqT1eqP4WQpn1mNrU\\u002fPhY8I4nYwz95oqorGbLWP0y1FWS2Nu0\\u002fMJSk4c\\u002fnAkCLsy0ubwoXQHVfJfWluClADLyHf9AGOkDFAB2D3cZHQKj+f8MylVNARQbwYskSXUBE+NPh7XZjQKs4+jHqjWdAvHO6wv3haUAcyR3X8xdqQCO3HPwOpGhAOPwxGNubZkC7lQ5RWitlQDop77iU+mRAQpPdcajcZUBxqCeMVfVmQCKtniJVO2dARhZq3WsIZkDnPqX2tmRjQBmiZLwR1l9A3bjk6WDNWEA6nzZZDuFSQOoLHfOFCU1AP76hPHYgR0A0RriT9TZDQDEZONnlkEBAz1UCJfdkPUDyh\\u002fPJuow6QOaDHk2T\\u002fzdAUD0NeYM4NUAupi6RgvMxQCVNYAKRjyxArqAs\\u002fx0jJUDsvckH7vAcQN+BbSOlSBJAmchY+25NBUBxBhWKduH2P2OuuqgnpeY\\u002f9lyqrAyh1D9Yo2p4okTBPw6bnDLkgao\\u002fSNHLKPmckj\\u002fG\\u002fTs+AN93PyYQ0TmQ71s\\u002fbMjDxkHfPT9etncEmlYdP0X7dS1Px\\u002fo+GQX9njs21z6wKX23qrizPhxcN\\u002f4Q9ZA+Q1HepkHlbT4QBUDzPpJKPnf6PpMMBSc+zLCsqOrSAj7XG3iO0WzcPRbhPLDKkLM9+jJS7khkiD33K6MWrXJbPXORdCL+2Cs9Erl2QrC4+Tzgcxd52xHVPOo+eziY+Qc+LR94gBiREj7tpI7cjSclPu8++tDUIjg+gwE3uhu2Sj7jYwiVtmFfPgqq04ZLUHY+czoypv+Qkz4JO8oNRrqyPpcTlJvWfNE+oRC7WKw\\u002f7j752MnpDssHPxwuPfXz7SA\\u002ffZsxYmTFNT8zXlsTEVJJP7aK1WR3sFo\\u002fY32tG8qaaT9zY5wmnZJ2P7iGOCsMu4I\\u002fOZYjoOkEjz91fvOXepScP2FrWYpgILA\\u002fOiOQI9Z8xT+hBhgCRffdP5QxfPv\\u002fCfQ\\u002fE9TbNwy2CED9W0qeh60bQN7wKomnACxA683Hg+iJOUDd+w0SYvtEQHYVvCuHD09AW3RydTO7VEAYs+O3XANZQJs8p+RUbFtABqPLCGyiW0A80\\u002fXFnzBaQMhrDB4bSVhAzXQ4bvMqV0B2gBQdDXpXQOMadc6j6lhAs+I5u0JxWkCbDkXHeNlaQCzwQPaYaVlARQtY4A02VkASd19qMf5RQD4su9pxcUtAWS3PulFARECKrg2c3\\u002fg9QElIZBQa+TZA2M94M3SIMkAvgjWydW0vQAroKhYstitAr1MVpVwDKUAx7BemCJwmQI+J5AE08iNAv5y\\u002fJs\\u002fFIEChjArj\\u002fGsaQGrE8diiSBNAMjY1rBzwCUDR7aU2TAoAQPQvNcT4PPI\\u002fevptqD0T4z+aBh8Xu1zSPyfWW9gpRcA\\u002fuA55nM+Fqj9LY2AWw9uTP4INUiv5Q3s\\u002fICtDl30gYT8FEM0yrqdDP7gus7+OlyQ\\u002fgnVfXcO5Az+RWMK8mF\\u002fhPpN8TY6Efbw+dgemPQI+lj4IRJFsBBNxPpfVg5Gll0o+LFMHZXg6JT6agjcyFQwBPjvMf5oNnNo9vj8tLP+Osz31smTMtoKKPfaoZ80FXmA9eyuYUYVLMj2YhLyfQXUCPeenzwYx8tA876eVNK2LqzwkWx2+qQPdPSI7NDtJcuY9Dcj1epmk+T36xsffU8ANPvthWUf\\u002fWiE+FMe\\u002f+YHeNj59sYIuZtNSPgneCVRYQ3I+eVbLFW9Akj6zxVjJjEaxPiu+F3sq9s0+NXTUMlyM5z6tpNeoALoAP0S85XODeRU\\u002f2YnEcn\\u002fzKD\\u002fT3SiqslA6P\\u002fOhIOsHXE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Acp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8Bziv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+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.display.plotter.plot_posterior_pairs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54",
+   "metadata": {},
+   "source": [
+    "The one-dimensional posterior distributions below make it easier to\n",
+    "inspect individual parameters in isolation, including asymmetry or\n",
+    "multimodality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-3.5.0.min.js\" integrity=\"sha256-fHbNLP+GlIXN+efbQec78UkemUz3NJp7UmfGxC1tNxs=\" crossorigin=\"anonymous\"></script>                <div id=\"c4c18a69-88ac-4717-9bb8-850a16878c95\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"c4c18a69-88ac-4717-9bb8-850a16878c95\")) {                    Plotly.newPlot(                        \"c4c18a69-88ac-4717-9bb8-850a16878c95\",                        [{\"fill\":\"toself\",\"fillcolor\":\"rgba(140, 140, 140, 0.08)\",\"hoverinfo\":\"skip\",\"line\":{\"color\":\"rgba(140, 140, 140, 0.08)\",\"width\":0},\"mode\":\"lines\",\"name\":\"95% credible 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LlKl\\u002fGoIQ9AdSEvuTshD0C2VT4iViEPQNO0cm9\\u002fIQ9AOSaFg6shD0DAijCVeyEPQPhM891jIQ9A82tk9Z0hD0Co4s5HeSEPQLvmmP5VIQ9Aj4fhwnkhD0BVbyRPSyEPQLRL7JCgIQ9ACJirzVchD0B469HgcCEPQAx\\u002f\\u002fhlTIQ9AHQb4em0hD0BauK4hiCEPQFmkqjKoIQ9AQAVqgXchD0C5SpfxqCEPQHUhL7k7IQ9AtlU+IlYhD0DTtHJvfyEPQDkmhYOrIQ9A1QpPOHEhD0D4TPPdYyEPQIXmek2eIQ9AqOLOR3khD0C2vx+WZSEPQOVeFgOQIQ9AVW8kT0shD0AY\\u002fswjoSEPQAiYq81XIQ9AeOvR4HAhD0AUxkfDZiEPQB0G+HptIQ9AWriuIYghD0BZpKoyqCEPQEAFaoF3IQ9AuUqX8aghD0B1IS+5OyEPQEAneWRcIQ9A07Ryb38hD0A8F3+PwCEPQNUKTzhxIQ9A+Ezz3WMhD0AlJnBFdyEPQKjizkd5IQ9Atr8flmUhD0DlXhYDkCEPQFVvJE9LIQ9AGP7MI6EhD0AImKvNVyEPQHjr0eBwIQ9AFMZHw2YhD0D2ADERRSEPQFq4riGIIQ9AWaSqMqghD0BABWqBdyEPQNVObNzGIQ9AdSEvuTshD0BAJ3lkXCEPQNO0cm9\\u002fIQ9APBd\\u002fj8AhD0DvXtt7RCEPQPhM891jIQ9AJSZwRXchD0Co4s5HeSEPQLa\\u002fH5ZlIQ9A3Ni9P5EhD0DQSiANTSEPQBj+zCOhIQ9ACJirzVchD0Ao\\u002fHFYXCEPQNXO8SBlIQ9A9gAxEUUhD0BauK4hiCEPQFmkqjKoIQ9AjnxxT3QhD0DVTmzcxiEPQHUhL7k7IQ9AQCd5ZFwhD0DTtHJvfyEPQDwXf4\\u002fAIQ9A717be0QhD0D4TPPdYyEPQCUmcEV3IQ9AqOLOR3khD0C2vx+WZSEPQPT5iZigIQ9A0EogDU0hD0AY\\u002fswjoSEPQAiYq81XIQ9AKPxxWFwhD0DVzvEgZSEPQKCUoIgiIQ9AWriuIYghD0BZpKoyqCEPQI58cU90IQ9A1U5s3MYhD0B1IS+5OyEPQEAneWRcIQ9A07Ryb38hD0A8F3+PwCEPQAFmtQNgIQ9A+Ezz3WMhD0AlJnBFdyEPQKjizkd5IQ9Atr8flmUhD0AZAQdTfSEPQNBKIA1NIQ9AGP7MI6EhD0B35P1bViEPQCj8cVhcIQ9A1c7xIGUhD0CglKCIIiEPQPFlk4p3IQ9AWaSqMqghD0COfHFPdCEPQKXXRf\\u002fWIQ9AoNa3ilkhD0BAJ3lkXCEPQNO0cm9\\u002fIQ9ABgewmcUhD0AkREn1NSEPQPhM891jIQ9AJSZwRXchD0Co4s5HeSEPQLa\\u002fH5ZlIQ9AGQEHU30hD0DQSiANTSEPQBj+zCOhIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9AoJSgiCIhD0DxZZOKdyEPQFmkqjKoIQ9AVEPX1XUhD0Cl10X\\u002f1iEPQHvNVDJwIQ9AQCd5ZFwhD0CR7m3fgCEPQAYHsJnFIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AqOLOR3khD0C4uVKwdCEPQBkBB1N9IQ9A8iaJfUohD0AY\\u002fswjoSEPQFEnmdhVIQ9A\\u002f5p\\u002f3FohD0CiRiWBZyEPQKCUoIgiIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9A4U7EC9chD0B7zVQycCEPQEAneWRcIQ9Ake5t34AhD0AGB7CZxSEPQCRESfU1IQ9A+Ezz3WMhD0Cd9OjzeSEPQKjizkd5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AGP7MI6EhD0BRJ5nYVSEPQP+af9xaIQ9AokYlgWchD0CglKCIIiEPQEc+WEZ5IQ9AuRBBx5ghD0BUQ9fVdSEPQOFOxAvXIQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9ABgewmcUhD0AkREn1NSEPQPhM891jIQ9AnfTo83khD0Co4s5HeSEPQHeM3G1LIQ9AGQEHU30hD0DyJol9SiEPQBj+zCOhIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9AxqJTiTQhD0BHPlhGeSEPQHLSlRaaIQ9AVEPX1XUhD0DhTsQL1yEPQHvNVDJwIQ9A\\u002f6YBHVYhD0DqLSWMfyEPQAYHsJnFIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9AqOLOR3khD0B3jNxtSyEPQC9v4b99IQ9A8iaJfUohD0CjkZbafyEPQFEnmdhVIQ9A\\u002f5p\\u002f3FohD0CiRiWBZyEPQMaiU4k0IQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9A4U7EC9chD0B7zVQycCEPQN2R15FWIQ9AYarGq30hD0AGB7CZxSEPQMRQlOtEIQ9Av9wVV2QhD0BaETo23iEPQKjizkd5IQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AAykSjoIhD0BRJ5nYVSEPQEB6SWSCIQ9AokYlgWchD0DGolOJNCEPQEc+WEZ5IQ9AiqD2b6YhD0AddkjLtCEPQOFOxAvXIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9ABgewmcUhD0DEUJTrRCEPQL\\u002fcFVdkIQ9AWhE6Nt4hD0Co4s5HeSEPQAqgCJAkIQ9A2sNrBWUhD0A5sSOXbCEPQFPSJn+BIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9AxqJTiTQhD0BHPlhGeSEPQIqg9m+mIQ9A\\u002fErdrbIhD0AVna4+iyEPQIplC+xvIQ9A3ZHXkVYhD0BhqsarfSEPQAYHsJnFIQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9AqOLOR3khD0AKoAiQJCEPQB0ESIt3IQ9AGvrObn8hD0BT0iZ\\u002fgSEPQFEnmdhVIQ9AQHpJZIIhD0CiRiWBZyEPQCmVkh9lIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AFZ2uPoshD0CKZQvsbyEPQCD7tWxVIQ9AYarGq30hD0AGB7CZxSEPQMRQlOtEIQ9AKeqLGGIhD0BaETo23iEPQKjizkd5IQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AOxXZ4YEhD0BRJ5nYVSEPQGbGaEG+IQ9AokYlgWchD0AplZIfZSEPQEc+WEZ5IQ9AiqD2b6YhD0D8St2tsiEPQBWdrj6LIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9ABgewmcUhD0Bd1aD4diEPQCnqixhiIQ9AHyF3o7QhD0Co4s5HeSEPQJDZjVE7IQ9AHQRIi3chD0BRZrR5XCEPQDsV2eGBIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9AKZWSH2UhD0BHPlhGeSEPQIqg9m+mIQ9A\\u002fErdrbIhD0CAcWIEnyEPQIplC+xvIQ9AxIaz6FchD0BhqsarfSEPQAYHsJnFIQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AqOLOR3khD0CQ2Y1ROyEPQDivtJl3IQ9Ak0GP824hD0BodoZelyEPQFEnmdhVIQ9AZsZoQb4hD0C200jIZSEPQCmVkh9lIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AgHFiBJ8hD0AWgTF5jyEPQH0df+NKIQ9AYarGq30hD0AaKx0akCEPQBkDOVtEIQ9AnAJAy1IhD0AfIXejtCEPQKjizkd5IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9AaHaGXpchD0CL+397eiEPQGbGaEG+IQ9AttNIyGUhD0Bs1ikDZSEPQEc+WEZ5IQ9AXMRjGq8hD0CRy8lXiCEPQIBxYgSfIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGisdGpAhD0AZAzlbRCEPQJwCQMtSIQ9AHyF3o7QhD0Co4s5HeSEPQJDZjVE7IQ9A5tX74HchD0CTQY\\u002fzbiEPQPL932iOIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9AbNYpA2UhD0Cmwv0akiEPQFzEYxqvIQ9AkcvJV4ghD0CAcWIEnyEPQBaBMXmPIQ9ANwsBjHMhD0BhqsarfSEPQOaIQ5h3IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AqOLOR3khD0D4f+fMLyEPQFc5XqqYIQ9Ak0GP824hD0Dy\\u002fd9ojiEPQDGovRZzIQ9AZsZoQb4hD0C200jIZSEPQILwrntGIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AgHFiBJ8hD0AWgTF5jyEPQDcLAYxzIQ9AYarGq30hD0DmiEOYdyEPQBkDOVtEIQ9AnAJAy1IhD0D9dMbOsSEPQKjizkd5IQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9A8v3faI4hD0AxqL0WcyEPQGbGaEG+IQ9AcJYs72YhD0C5PQvUSSEPQMGnjFqGIQ9A6z0QRJ4hD0CRy8lXiCEPQBSSEl1+IQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9A5ohDmHchD0AZAzlbRCEPQJwCQMtSIQ9A\\u002fXTGzrEhD0BCtILpUiEPQFOgAA4oIQ9AEPO16pYhD0CTQY\\u002fzbiEPQPL932iOIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9A9UO1J0chD0DBp4xahiEPQMTFWZaDIQ9AkcvJV4ghD0AUkhJdfiEPQPbfxNyMIQ9AzvfI+XUhD0BhqsarfSEPQOaIQ5h3IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AVe1dT28hD0BToAAOKCEPQBDzteqWIQ9Ak0GP824hD0Dy\\u002fd9ojiEPQDGovRZzIQ9AZsZoQb4hD0CVgpncbSEPQPVDtSdHIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AFJISXX4hD0CzOJKWbCEPQGbY7QJWIQ9AYarGq30hD0DmiEOYdyEPQFaHp0VFIQ9AnAJAy1IhD0D9dMbOsSEPQFXtXU9vIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9A8v3faI4hD0AxqL0WcyEPQGbGaEG+IQ9AlYKZ3G0hD0B13q1TUSEPQITg1Q\\u002fIIQ9AxMVZloMhD0C8JnMNhSEPQBSSEl1+IQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9A5ohDmHchD0BWh6dFRSEPQJwCQMtSIQ9A\\u002fXTGzrEhD0BV7V1PbyEPQN8BU0cyIQ9AfhdyQoohD0CTQY\\u002fzbiEPQIml8PmMIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9ApIZS9WYhD0BcHbdTvSEPQMTFWZaDIQ9AvCZzDYUhD0AUkhJdfiEPQLM4kpZsIQ9AF\\u002f0Z+BEhD0CPmu8\\u002fqCEPQH5KWcF4IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9AVe1dT28hD0DfAVNHMiEPQH4XckKKIQ9Ak0GP824hD0C4Jq4fiCEPQDGovRZzIQ9AZsZoQb4hD0CVgpncbSEPQKSGUvVmIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9ABbjOHnkhD0CzOJKWbCEPQGT3EygVIQ9Aj5rvP6ghD0B+SlnBeCEPQFaHp0VFIQ9AnAJAy1IhD0D9dMbOsSEPQFXtXU9vIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AuCauH4ghD0C\\u002fJHlDeyEPQD32zfiSIQ9AlYKZ3G0hD0CkhlL1ZiEPQFwdt1O9IQ9AR4juKqshD0C8JnMNhSEPQAW4zh55IQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AfkpZwXghD0BWh6dFRSEPQJwCQMtSIQ9A\\u002fXTGzrEhD0BV7V1PbyEPQKcDBHQJIQ9AOP3fbIkhD0BO\\u002fPSyayEPQLgmrh+IIQ9AvyR5Q3shD0CjxLauXiEPQJWCmdxtIQ9ApIZS9WYhD0BcHbdTvSEPQEeI7iqrIQ9ALClO\\u002fZshD0AFuM4eeSEPQLM4kpZsIQ9AZPcTKBUhD0Bq7G+xpyEPQH5KWcF4IQ9Av92a4UkhD0CcAkDLUiEPQP10xs6xIQ9AVe1dT28hD0CnAwR0CSEPQCAdvMeGIQ9ATvz0smshD0ASglnBcCEPQL8keUN7IQ9Ao8S2rl4hD0AhWDREcCEPQKSGUvVmIQ9AmSuYop0hD0BHiO4qqyEPQCwpTv2bIQ9ABbjOHnkhD0CzOJKWbCEPQGT3EygVIQ9AauxvsachD0BnHy4YeCEPQL\\u002fdmuFJIQ9ATC9cqgshD0BzMZXpqiEPQFXtXU9vIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AEoJZwXAhD0C5M7BAISEPQKPEtq5eIQ9AIVg0RHAhD0CkhlL1ZiEPQPITYKOKIQ9AR4juKqshD0AsKU79myEPQAW4zh55IQ9APFn0hJAhD0Bj3AbsLSEPQGrsb7GnIQ9AZx8uGHghD0BtfQhPSyEPQEBxBBz2IA9AczGV6aohD0BV7V1PbyEPQHSEdI9RIQ9AIB28x4YhD0BO\\u002fPSyayEPQM\\u002fxPtJnIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9ApIZS9WYhD0DyE2CjiiEPQEeI7iqrIQ9ALClO\\u002fZshD0C5fhoSfyEPQDxZ9ISQIQ9AY9wG7C0hD0DYSqfVoyEPQGcfLhh4IQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9A89q4y00hD0B0hHSPUSEPQOWEMjirIQ9ATvz0smshD0AobgVVaCEPQLkzsEAhIQ9Ao8S2rl4hD0AhWDREcCEPQKSGUvVmIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9AuX4aEn8hD0A8WfSEkCEPQGPcBuwtIQ9A2Eqn1aMhD0BnHy4YeCEPQG19CE9LIQ9Awwyy5QYhD0BzMZXpqiEPQPPauMtNIQ9AdIR0j1EhD0DOl9o49iAPQE789LJrIQ9AKG4FVWghD0C5M7BAISEPQKPEtq5eIQ9AIVg0RHAhD0CkhlL1ZiEPQKqizESHIQ9AR4juKqshD0AsKU79myEPQLl+GhJ\\u002fIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AibFqrXghD0BtfQhPSyEPQMMMsuUGIQ9AczGV6aohD0AhQrNQUCEPQMXJ8rdHIQ9AzpfaOPYgD0BO\\u002fPSyayEPQChuBVVoIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9A\\u002fwiX0iYhD0CqosxEhyEPQEeI7iqrIQ9APBmeY30hD0C5fhoSfyEPQDxZ9ISQIQ9Al\\u002fiKZTAhD0DYSqfVoyEPQImxaq14IQ9AlFvdRUwhD0DSw5hLLCEPQHMxlemqIQ9A+Z2DHGQhD0DFyfK3RyEPQM6X2jj2IA9ATvz0smshD0BqtmsBaCEPQLkzsEAhIQ9Ao8S2rl4hD0B5s8HHbiEPQP8Il9ImIQ9AqqLMRIchD0BHiO4qqyEPQB6D3s6HIQ9AuX4aEn8hD0A8WfSEkCEPQJf4imUwIQ9A2Eqn1aMhD0CJsWqteCEPQBnk6PlLIQ9AdO7zRC0hD0BzMZXpqiEPQPmdgxxkIQ9Axcnyt0chD0BiA5bK9iAPQE789LJrIQ9AS980H1EhD0C5M7BAISEPQKPEtq5eIQ9AzgxhGXAhD0D\\u002fCJfSJiEPQKqizESHIQ9AR4juKqshD0DMvzjPMyEPQLl+GhJ\\u002fIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AibFqrXghD0AZ5Oj5SyEPQHTu80QtIQ9AczGV6aohD0D5nYMcZCEPQHqsXI+5IQ9AgKsI0vcgD0BO\\u002fPSyayEPQEvfNB9RIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9A\\u002fwiX0iYhD0CqosxEhyEPQEeI7iqrIQ9AzL84zzMhD0C5fhoSfyEPQImLmHRlIQ9AbVgDpFghD0B2wLCeoyEPQImxaq14IQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9A+Z2DHGQhD0B6rFyPuSEPQICrCNL3IA9ATvz0smshD0CfRHjGPSEPQLkzsEAhIQ9Ao8S2rl4hD0AIQtx6hyEPQP8Il9ImIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9AuX4aEn8hD0DUZgDwQSEPQG1YA6RYIQ9A\\u002fCvAJpkhD0Bm7uAhyiEPQBnk6PlLIQ9AdO7zRC0hD0BTNhCZWSEPQHP92tyFIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9An0R4xj0hD0C5M7BAISEPQKPEtq5eIQ9ACELceochD0D\\u002fCJfSJiEPQJ3UyJFkIQ9AR4juKqshD0DMvzjPMyEPQLl+GhJ\\u002fIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AZu7gIcohD0AZ5Oj5SyEPQHTu80QtIQ9AUzYQmVkhD0BSMmPBXyEPQHqsXI+5IQ9AgKsI0vcgD0BO\\u002fPSyayEPQJ9EeMY9IQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A\\u002fwiX0iYhD0DxBXLAHyEPQEeI7iqrIQ9AzL84zzMhD0C5fhoSfyEPQNRmAPBBIQ9AbVgDpFghD0AD4mzomyEPQGbu4CHKIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9AUjJjwV8hD0B6rFyPuSEPQICrCNL3IA9ATvz0smshD0CfRHjGPSEPQKApVC9zIQ9A8UO3kGAhD0AIQtx6hyEPQP8Il9ImIQ9A8QVywB8hD0CaVbgwbyEPQMy\\u002fOM8zIQ9AuX4aEn8hD0DUZgDwQSEPQG1YA6RYIQ9AA+Js6JshD0Bm7uAhyiEPQBnk6PlLIQ9AdO7zRC0hD0BTNhCZWSEPQFIyY8FfIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AYOpoEpwhD0CgKVQvcyEPQHGimQRlIQ9ACELceochD0D\\u002fCJfSJiEPQPEFcsAfIQ9Addmj9W8hD0BBs0asNCEPQLl+GhJ\\u002fIQ9A1GYA8EEhD0BtWAOkWCEPQGfXrTe3IQ9AmDNGp\\u002fchD0A3gXyLcyEPQDzpLy8xIQ9AUzYQmVkhD0BSMmPBXyEPQL++MlPEIQ9AcGWPlyQhD0BO\\u002fPSyayEPQGDqaBKcIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A\\u002fwiX0iYhD0DxBXLAHyEPQHXZo\\u002fVvIQ9AQbNGrDQhD0C5fhoSfyEPQNRmAPBBIQ9AbVgDpFghD0BB4Rk9sSEPQJgzRqf3IQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9AUjJjwV8hD0CFyt+PwCEPQIagfQMkIQ9ATvz0smshD0BOSzuCVCEPQBPrvcKAIQ9AcaKZBGUhD0AIQtx6hyEPQP8Il9ImIQ9AQgwwIh0hD0B12aP1byEPQEGzRqw0IQ9AuX4aEn8hD0DUZgDwQSEPQG1YA6RYIQ9AQeEZPbEhD0CYM0an9yEPQGOXqO+EIQ9APOkvLzEhD0BTNhCZWSEPQFIyY8FfIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AMNeLQF8hD0AT673CgCEPQHGimQRlIQ9AFGFpy4chD0D\\u002fCJfSJiEPQAqNBYFZIQ9AhrErIV0hD0BBs0asNCEPQLl+GhJ\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AmDNGp\\u002fchD0Bjl6jvhCEPQDzpLy8xIQ9AUzYQmVkhD0BSMmPBXyEPQIXK34\\u002fAIQ9AhqB9AyQhD0BO\\u002fPSyayEPQGOQR7ZaIQ9AE+u9woAhD0AasKbUZSEPQBRhacuHIQ9A\\u002fwiX0iYhD0BMvc3VLiEPQIaxKyFdIQ9AQbNGrDQhD0C5fhoSfyEPQALJ8DBfIQ9AbVgDpFghD0BB4Rk9sSEPQJgzRqf3IQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9AUjJjwV8hD0CFyt+PwCEPQIagfQMkIQ9ATvz0smshD0BjkEe2WiEPQBPrvcKAIQ9ARHZ351YhD0AUYWnLhyEPQJ\\u002fthkhMIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AwAL9gHAhD0ACyfAwXyEPQG1YA6RYIQ9AQeEZPbEhD0CYM0an9yEPQI1zYsFuIQ9APOkvLzEhD0BTNhCZWSEPQFIyY8FfIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AY5BHtlohD0AT673CgCEPQER2d+dWIQ9AFGFpy4chD0Cf7YZITCEPQEy9zdUuIQ9A7BSj+VkhD0BIJv4BfyEPQMAC\\u002fYBwIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AmDNGp\\u002fchD0CNc2LBbiEPQDzpLy8xIQ9AUzYQmVkhD0BSMmPBXyEPQIXK34\\u002fAIQ9AhqB9AyQhD0BO\\u002fPSyayEPQGOQR7ZaIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9A8otr7nUhD0BMvc3VLiEPQOwUo\\u002flZIQ9ASCb+AX8hD0DAAv2AcCEPQI8pBlFgIQ9AbVgDpFghD0BB4Rk9sSEPQJgzRqf3IQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9AUjJjwV8hD0CFyt+PwCEPQIagfQMkIQ9ATvz0smshD0BjkEe2WiEPQA7mXWmzIQ9ARHZ351YhD0C2LPHkhSEPQPKLa+51IQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AwAL9gHAhD0CPKQZRYCEPQG1YA6RYIQ9AQeEZPbEhD0CYM0an9yEPQAFf8R91IQ9APOkvLzEhD0BTNhCZWSEPQEjvBJ51IQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9A948Ugy0hD0B4FOzLUSEPQEld\\u002f9pVIQ9Atizx5IUhD0Dyi2vudSEPQEy9zdUuIQ9AoGS5CmMhD0BIJv4BfyEPQMAC\\u002fYBwIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AmDNGp\\u002fchD0ABX\\u002fEfdSEPQKc6sHUuIQ9AUzYQmVkhD0BI7wSedSEPQOiBbKGrIQ9AhqB9AyQhD0BO\\u002fPSyayEPQPePFIMtIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9A8otr7nUhD0BMvc3VLiEPQKBkuQpjIQ9ASCb+AX8hD0Drt0uBdyEPQI8pBlFgIQ9AbVgDpFghD0C70yXQcSEPQJgzRqf3IQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9ACmLNVoAhD0DogWyhqyEPQIagfQMkIQ9ATvz0smshD0D3jxSDLSEPQHgU7MtRIQ9ASV3\\u002f2lUhD0C2LPHkhSEPQPKLa+51IQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9A67dLgXchD0BB8ayMriEPQG1YA6RYIQ9Au9Ml0HEhD0CYM0an9yEPQOrwYfN8IQ9ApzqwdS4hD0BTNhCZWSEPQApizVaAIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9A948Ugy0hD0B4FOzLUSEPQO\\u002fZUfNWIQ9AiMM61ZohD0Dyi2vudSEPQEy9zdUuIQ9AfPe3ppUhD0BIJv4BfyEPQOu3S4F3IQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9AY5F3feIhD0Dq8GHzfCEPQKc6sHUuIQ9AUzYQmVkhD0AKYs1WgCEPQDyd382aIQ9AhqB9AyQhD0BO\\u002fPSyayEPQPePFIMtIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9A8otr7nUhD0BMvc3VLiEPQHz3t6aVIQ9ASCb+AX8hD0BpGFyLGiEPQMHIu7hhIQ9AbVgDpFghD0C70yXQcSEPQPlapEzUIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9Ag8POBlQhD0A8nd\\u002fNmiEPQIagfQMkIQ9ATvz0smshD0D3jxSDLSEPQA0NUtdQIQ9AOhRfwIAhD0CIwzrVmiEPQPKLa+51IQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ACwOdpHwhD0CulZnfbCEPQG1YA6RYIQ9Au9Ml0HEhD0D5WqRM1CEPQGJ+PbN8IQ9A5z4dkj8hD0Al70TceyEPQIPDzgZUIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A948Ugy0hD0D3e2cncyEPQDoUX8CAIQ9AiMM61ZohD0Dyi2vudSEPQFNkWcAvIQ9ASacHFmkhD0BIJv4BfyEPQJRB0pSEIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9A+VqkTNQhD0Bifj2zfCEPQJ1p9TKcIQ9AJe9E3HshD0A\\u002flIvaVCEPQDyd382aIQ9AhqB9AyQhD0BO\\u002fPSyayEPQPePFIMtIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AgulIjmQhD0BTZFnALyEPQMPOQx5VIQ9ASCb+AX8hD0CUQdKUhCEPQGjvGOBlIQ9AbVgDpFghD0A9gYprNSEPQKomChrYIQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9ATgPxZVghD0A8nd\\u002fNmiEPQIagfQMkIQ9ATvz0smshD0D3jxSDLSEPQGmO1+V9IQ9AOhRfwIAhD0CIwzrVmiEPQAU8Z\\u002flnIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AlEHSlIQhD0Bo7xjgZSEPQG1YA6RYIQ9APYGKazUhD0CqJgoa2CEPQGSR71R7IQ9A12Am45ohD0Al70TceyEPQA4lYnxXIQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9A948Ugy0hD0BpjtflfSEPQDSeGDiCIQ9AiMM61ZohD0AFPGf5ZyEPQAsbSBROIQ9Aw85DHlUhD0BIJv4BfyEPQJRB0pSEIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9AqiYKGtghD0DkYF93fiEPQNdgJuOaIQ9A5GfaCD8hD0CTshkaViEPQJDcWqB5IQ9AhqB9AyQhD0DErMdIwiEPQPePFIMtIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9ABTxn+WchD0C0N7R+jSEPQMPOQx5VIQ9ASCb+AX8hD0CUQdKUhCEPQOmlqyJHIQ9AbVgDpFghD0DZCFY\\u002fbyEPQC4dM2ugIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9Ak7IZGlYhD0CQ3FqgeSEPQIagfQMkIQ9AAVMtDb0hD0BhhBeeKyEPQK5mcmx9IQ9ANJ4YOIIhD0AfetrKeCEPQAU8Z\\u002flnIQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9AlEHSlIQhD0DppasiRyEPQG1YA6RYIQ9A2QhWP28hD0AuHTNroCEPQORgX3d+IQ9A12Am45ohD0B3GNZqcCEPQDYKfKM9IQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9AYYQXnishD0CuZnJsfSEPQDSeGDiCIQ9AheZ6MnshD0AFPGf5ZyEPQPvXDFfIIQ9AwArhw1YhD0BIJv4BfyEPQJRB0pSEIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AByx6c50hD0CAFB6efyEPQMA0in6WIQ9AdxjWanAhD0A2CnyjPSEPQHKDaWZmIQ9AhqB9AyQhD0ABUy0NvSEPQGGEF54rIQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9ABTxn+WchD0DVcDbqpCEPQPS\\u002fnrdXIQ9ASCb+AX8hD0CUQdKUhCEPQOmlqyJHIQ9AbVgDpFghD0DZCFY\\u002fbyEPQDTp4X+pIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9ANgp8oz0hD0DdnlufaSEPQCKoSUd4IQ9AAVMtDb0hD0BhhBeeKyEPQK5mcmx9IQ9AXINj7YIhD0D+kQXgkyEPQAU8Z\\u002flnIQ9A1XA26qQhD0D0v563VyEPQPtq3Dt6IQ9AlEHSlIQhD0DppasiRyEPQNbQjzxoIQ9A2QhWP28hD0A06eF\\u002fqSEPQIAUHp5\\u002fIQ9AwDSKfpYhD0B3GNZqcCEPQDYKfKM9IQ9A3Z5bn2khD0AiqElHeCEPQAFTLQ29IQ9AYYQXnishD0CuZnJsfSEPQAcuEVeGIQ9A\\u002fpEF4JMhD0AFPGf5ZyEPQNVwNuqkIQ9A9L+et1chD0D7atw7eiEPQJRB0pSEIQ9A6aWrIkchD0DW0I88aCEPQHD5QEF5IQ9ANOnhf6khD0CAFB6efyEPQMA0in6WIQ9AdxjWanAhD0A2CnyjPSEPQN2eW59pIQ9A7LjFcnkhD0ABUy0NvSEPQGGEF54rIQ9ArmZybH0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+       "                            {\n",
+       "const graphDiv = document.getElementById('c4c18a69-88ac-4717-9bb8-850a16878c95');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/html": [
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TsTQiQFeuK5pISSJAn8tklANWIkAPAroZPz4iQCjxXx9GSyJAGK+eCaQyIkBW5rcW+EsiQIT42gB0OyJAOLa1JEtHIkB04rYdlEAiQHjeGHymRCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJASXXeNyVHIkBnt+cPOiwiQM3tptXISCJA5ZhVNcFDIkCWjoWQH1EiQPxd\\u002fKkFaiJANXMaE7E0IkBXriuaSEkiQCvZATcDViJAbqcQpRc5IkDNhoy6RUsiQBivngmkMiJA0fq8v\\u002fZLIkCE+NoAdDsiQDi2tSRLRyJAdOK2HZRAIkB43hh8pkQiQDvVfJbJVSJAwy6ntRM6IkCbAuTdbjgiQEl13jclRyJAs\\u002fGTOWtDIkDN7abVyEgiQHdnZVcwSiJAlo6FkB9RIkCFgQ10bm0iQGlWW2KxNCJAV64rmkhJIkCLr4enqlgiQG6nEKUXOSJAzYaMukVLIkBDIG1zQ0siQNH6vL\\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\\u002fYXtBRiJAi6+Hp6pYIkAoJJF3mzQiQMda23pESyJA8IRvHkJLIkC8TJU5jzQiQI2s1o6iOSJAOLa1JEtHIkAgyxQPrEIiQKweQAGoMiJAvCGU7qtLIkDU73tBaT0iQJsC5N1uOCJAKRb58+RIIkC9DKf+IFEiQM3tptXISCJAVG8aajBXIkCWjoWQH1EiQIWBDXRubSJA+jN3ILo9IkDrv2F7QUYiQIuvh6eqWCJAVdIqpZ00IkDHWtt6REsiQMFubWDLSCJAvEyVOY80IkCNrNaOojkiQDi2tSRLRyJAYstgm6pPIkCsHkABqDIiQJIIrwpJPiJA1O97QWk9IkAD8N0wjjgiQCkW+fPkSCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAlo6FkB9RIkBPf+xWK1ciQPozdyC6PSJARuhQshtFIkCLr4enqlgiQFXSKqWdNCJAIRpvgD1LIkDBbm1gy0giQLxMlTmPNCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJA0\\u002fXAtqoyIkCSCK8KST4iQNTve0FpPSJAA\\u002fDdMI44IkApFvnz5EgiQL0Mp\\u002f4gUSJAze2m1chIIkDExs5rLlciQJaOhZAfUSJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAi6+Hp6pYIkBV0iqlnTQiQCEab4A9SyJAwW5tYMtIIkC8TJU5jzQiQHSR++yFQCJAuVUODU1HIkBiy2Cbqk8iQNP1wLaqMiJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAKRb58+RIIkC9DKf+IFEiQM3tptXISCJAxMbOay5XIkCWjoWQH1EiQBpQZiBhXSJA+jN3ILo9IkBG6FCyG0UiQIuvh6eqWCJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJA1GhIdA9JIkB0kfvshUAiQB2t7OFORyJAYstgm6pPIkDT9cC2qjIiQJIIrwpJPiJA4r9h1Sg+IkBLEp7ir0UiQCkW+fPkSCJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAlo6FkB9RIkAaUGYgYV0iQOmBlWq5PSJARuhQshtFIkCQGz1A4FgiQFXSKqWdNCJAIRpvgD1LIkDBbm1gy0giQNRoSHQPSSJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJA0\\u002fXAtqoyIkCSCK8KST4iQGKOh5cpPiJAKofg4LBFIkApFvnz5EgiQKf68Pr2VyJAE+x3VchIIkAK731euUEiQJaOhZAfUSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAwAyySuFYIkBV0iqlnTQiQJOoaeH0RCJAwW5tYMtIIkDUaEh0D0kiQHSR++yFQCJARf1UPmg+IkAB56wN+ksiQNP1wLaqMiJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAKRb58+RIIkCn+vD69lciQBPsd1XISCJACu99XrlBIkCWjoWQH1EiQEMI1HWzYSJAToWf3qpBIkDm4d5jeUciQFyMDiR2VyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJA1GhIdA9JIkB0kfvshUAiQEX9VD5oPiJAcL1M9PlLIkCUCLiPgCsiQJLnUmVJPiJAYo6Hlyk+IkAqh+DgsEUiQCkW+fPkSCJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAlo6FkB9RIkBDCNR1s2EiQAs+Msx3WSJACbQwzg1PIkBcjA4kdlciQFXSKqWdNCJAk6hp4fREIkDBbm1gy0giQCkxqwi4MSJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAlAi4j4ArIkCS51JlST4iQAkJjDkRNyJAKofg4LBFIkApFvnz5EgiQKf68Pr2VyJADfRcVLxKIkAK731euUEiQJaOhZAfUSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAAAsukzFVIkBV0iqlnTQiQJeE13oEKiJAwW5tYMtIIkApMasIuDEiQHSR++yFQCJARf1UPmg+IkBwvUz0+UsiQJQIuI+AKyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAKRb58+RIIkDP\\u002fDnFcUgiQA30XFS8SiJA8ZWw17lBIkCWjoWQH1EiQFmGRM+PaSJACz4yzHdZIkC2oJClijsiQAALLpMxVSJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAKTGrCLgxIkB0kfvshUAiQEX9VD5oPiJAcL1M9PlLIkDjiX\\u002fe0TYiQJLnUmVJPiJAGguTNBA3IkAqh+DgsEUiQCkW+fPkSCJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAlo6FkB9RIkBZhkTPj2kiQKwgyIt1WSJAR42pNoo7IkCCLtHV3UQiQFXSKqWdNCJAl4TXegQqIkBfTJAM71QiQCkxqwi4MSJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJA44l\\u002f3tE2IkBt5vjJjjAiQHiwAG7pLCJAKofg4LBFIkDaGSFNXUQiQK6kfmKrWyJAoxapdrVCIkDxlbDXuUEiQJaOhZAfUSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAgi7R1d1EIkDGZlJWDUMiQJeE13oEKiJAX0yQDO9UIkCeYSdkYi0iQHSR++yFQCJAroY4QjA1IkDTm5fKD0giQOOJf97RNiJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJA2hkhTV1EIkCupH5iq1siQKMWqXa1QiJA8ZWw17lBIkCWjoWQH1EiQFmGRM+PaSJArv6K4VRNIkBHjak2ijsiQHJOMR4pQSJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAnmEnZGItIkAggnKBOjUiQK6GOEIwNSJA05uXyg9IIkDjiX\\u002fe0TYiQG3m+MmOMCJAa4SWuBEdIkAqh+DgsEUiQGvMutllOSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAlo6FkB9RIkC+MZovT3EiQKoO1ORBMCJAR42pNoo7IkByTjEeKUEiQIssxzC\\u002fPiJAl4TXegQqIkBfTJAM71QiQOksocVWOyJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJA44l\\u002f3tE2IkBt5vjJjjAiQGuElrgRHSJAKofg4LBFIkBrzLrZZTkiQK6kfmKrWyJAoxapdrVCIkCANw\\u002fSuUEiQJaOhZAfUSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAck4xHilBIkCLLMcwvz4iQJeE13oEKiJA9K1L0u9UIkAKABT92TsiQLVZf5q+TSJAoS5lBYg\\u002fIkDTm5fKD0giQLdFYE7TNiJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJAa8y62WU5IkCupH5iq1siQKMWqXa1QiJAgDcP0rlBIkDmo9+FW1ciQBhZyAWfPCJAzYc\\u002f4yo2IkBHjak2ijsiQHJOMR4pQSJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAGrKQndg7IkC1WX+avk0iQIi4xJQBMyJA05uXyg9IIkC3RWBO0zYiQL95H\\u002ftENCJAny\\u002f9MhAdIkAqh+DgsEUiQGvMutllOSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAPmKbzZNMIkAYWcgFnzwiQM2HP+MqNiJAR42pNoo7IkByTjEeKUEiQIssxzC\\u002fPiJAl4TXegQqIkDP6z4TlloiQBqykJ3YOyJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAt0VgTtM2IkC++uCR2C8iQLYxy2ATKiJAKofg4LBFIkBrzLrZZTkiQKHuNLHPYCJAoxapdrVCIkCANw\\u002fSuUEiQD5im82TTCJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAck4xHilBIkCLLMcwvz4iQJeE13oEKiJAz+s+E5ZaIkAwol31RUAiQKrbUDcANiJAiLjElAEzIkAyDVL\\u002fDUgiQLdFYE7TNiJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAa8y62WU5IkCh7jSxz2AiQKMWqXa1QiJAgDcP0rlBIkA+YpvNk0wiQA0lNPjvRiJAsTQtPyAnIkBHjak2ijsiQLTIMejHLCJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAFFH6PhU7IkBinRxaSDciQIi4xJQBMyJAMg1S\\u002fw1IIkC3RWBO0zYiQL764JHYLyJA2hXd5qU+IkBBX8N540ciQLUlSBQ+KyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAPmKbzZNMIkANJTT470YiQLE0LT8gJyJAR42pNoo7IkDbjrGSxiwiQIssxzC\\u002fPiJAl4TXegQqIkDP6z4TlloiQBRR+j4VOyJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAf65nzfVLIkC++uCR2C8iQBeIjsGmPiJAQV\\u002fDeeNHIkC1JUgUPisiQKHuNLHPYCJAoxapdrVCIkCANw\\u002fSuUEiQD5im82TTCJAaxanuo9UIkCiylRyICciQMeOFE4LTyJA246xksYsIkCMXGSqJk8iQI+Hu4M+PiJAz+s+E5ZaIkAUUfo+FTsiQGKdHFpINyJAfWJCd\\u002fE+IkAyDVL\\u002fDUgiQH+uZ831SyJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAtSVIFD4rIkCh7jSxz2AiQKMWqXa1QiJAgDcP0rlBIkA+YpvNk0wiQGsWp7qPVCJAospUciAnIkDHjhROC08iQNuOsZLGLCJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAFFH6PhU7IkBinRxaSDciQH1iQnfxPiJAVrHUH45TIkB\\u002frmfN9UsiQL764JHYLyJAF4iOwaY+IkC3g6dm5EciQLUlSBQ+KyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAPmKbzZNMIkBrFqe6j1QiQB2Qln2jHSJAx44UTgtPIkDAKnzl90EiQIxcZKomTyJAwRLX46JSIkCTlx1cxV0iQBRR+j4VOyJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAf65nzfVLIkC++uCR2C8iQBeIjsGmPiJAt4OnZuRHIkDAf7Z5PisiQFpwi9rPYCJA9zTpI5ZEIkBItS2ktkEiQD5im82TTCJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAwCp85fdBIkBzg593Jk8iQMES1+OiUiJAk5cdXMVdIkAUUfo+FTsiQDuvZVCfUiJAfWJCd\\u002fE+IkBWsdQfjlMiQH+uZ831SyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAwH+2eT4rIkCxF4Rqz2AiQNO3YGvHVCJASLUtpLZBIkA+YpvNk0wiQMSD\\u002f3dpZSJAHZCWfaMdIkDHjhROC08iQKTHQsRURyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAFFH6PhU7IkA7r2VQn1IiQH1iQnfxPiJAVrHUH45TIkCBqozg0UIiQFU0FrSwPiJAa3x4\\u002fMo1IkAZ5wwKO1oiQMB\\u002ftnk+KyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAA8uYonJWIkDEg\\u002f93aWUiQALO4p1BKCJAx44UTgtPIkBaa1K1zUUiQHODn3cmTyJAwRLX46JSIkCTlx1cxV0iQBRR+j4VOyJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAgaqM4NFCIkBVNBa0sD4iQGt8ePzKNSJAGecMCjtaIkDAf7Z5PisiQLEXhGrPYCJAJS+J0XdlIkBItS2ktkEiQAPLmKJyViJAxIP\\u002fd2llIkAKsF08UjsiQMeOFE4LTyJAWmtStc1FIkBzg593Jk8iQMES1+OiUiJAk5cdXMVdIkAUUfo+FTsiQNlN4\\u002fqgUiJAfWJCd\\u002fE+IkBWsdQfjlMiQIGqjODRQiJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJADD1hET8rIkCxF4Rqz2AiQCUvidF3ZSJASLUtpLZBIkAhTxqakSgiQFK41NfZWSJACrBdPFI7IkDHjhROC08iQFprUrXNRSJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJAU\\u002frhkSdGIkDZTeP6oFIiQH1iQnfxPiJACBf6+21LIkCBqozg0UIiQFU0FrSwPiJA7MVAXuU2IkAZ5wwKO1oiQAw9YRE\\u002fKyJAIJXuHs1gIkC0XXi8dT4iQEi1LaS2QSJA0Tc51go6IkBSuNTX2VkiQAqwXTxSOyJAx44UTgtPIkC4IVKczUUiQHODn3cmTyJAwRLX46JSIkAZoZH6xl0iQFP64ZEnRiJA2U3j+qBSIkB9YkJ38T4iQEPQ6TgoRiJAgaqM4NFCIkBVNBa0sD4iQOzFQF7lNiJAGecMCjtaIkAMPWERPysiQL3AlOTjNCJA\\u002fmfpPnU+IkBItS2ktkEiQNE3OdYKOiJAUrjU19lZIkCNmJQBUzsiQMeOFE4LTyJAO3Ety1lYIkBzg593Jk8iQMES1+OiUiJAXTtv620rIkBT+uGRJ0YiQNlN4\\u002fqgUiJAfWJCd\\u002fE+IkAR6MhsJ0YiQIGqjODRQiJAVTQWtLA+IkBpWmsIGVsiQBnnDAo7WiJADD1hET8rIkC9wJTk4zQiQP5n6T51PiJASLUtpLZBIkDRNznWCjoiQAtcjvl2VSJANXJu7VA7IkDHjhROC08iQDtxLctZWCJAc4OfdyZPIkDBEtfjolIiQNOYfiA6TyJAU\\u002frhkSdGIkDZTeP6oFIiQH1iQnfxPiJAEejIbCdGIkCBqozg0UIiQATNmfA5PSJAaVprCBlbIkDvyy0EO1oiQAw9YRE\\u002fKyJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJA0Tc51go6IkALXI75dlUiQDVybu1QOyJAx44UTgtPIkA7\\u002fNk4DkoiQHODn3cmTyJAwRLX46JSIkDW4nFdDzciQFP64ZEnRiJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJAgaqM4NFCIkDbTpqaFDwiQGlaawgZWyJAJEauhZZWIkBug9TLPSsiQL3AlOTjNCJA\\u002fmfpPnU+IkCshvX4xksiQNSb9igYOSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAO\\u002fzZOA5KIkBzg593Jk8iQMES1+OiUiJA1uJxXQ83IkBT+uGRJ0YiQEd2qiafUiJAfWJCd\\u002fE+IkAR6MhsJ0YiQIGqjODRQiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAboPUyz0rIkC9wJTk4zQiQP5n6T51PiJArIb1+MZLIkD3Vj3DnkUiQAtcjvl2VSJANXJu7VA7IkDHjhROC08iQDv82TgOSiJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAU\\u002frhkSdGIkBBYrFU3FkiQH1iQnfxPiJAEejIbCdGIkCBqozg0UIiQNtOmpoUPCJAaVprCBlbIkDDof\\u002f0bVkiQG6D1Ms9KyJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJA91Y9w55FIkALXI75dlUiQDVybu1QOyJAx44UTgtPIkA7\\u002fNk4DkoiQK6CGi8YIiJAAEgQ80tJIkDW4nFdDzciQFP64ZEnRiJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJAgaqM4NFCIkDbTpqaFDwiQGlaawgZWyJAw6H\\u002f9G1ZIkBug9TLPSsiQL3AlOTjNCJA\\u002fmfpPnU+IkCshvX4xksiQPdWPcOeRSJAUNcrgjE8IkCktVa1bkciQMeOFE4LTyJAnyRFSNE\\u002fIkCughovGCIiQOdEGeJ+PCJA1uJxXQ83IkBT+uGRJ0YiQEFisVTcWSJAcKUPHGFeIkA3tEymKEYiQIGqjODRQiJA206amhQ8IkBpWmsIGVsiQPWHIkduWSJARwmQbNg3IkAwo\\u002fJ5cC0iQGSHrwYjOSJArIb1+MZLIkD3Vj3DnkUiQFDXK4IxPCJApLVWtW5HIkDHjhROC08iQJ8kRUjRPyJAroIaLxgiIkDnRBnifjwiQNbicV0PNyJAU\\u002frhkSdGIkBBYrFU3FkiQHClDxxhXiJAN7RMpihGIkCBqozg0UIiQNtOmpoUPCJAaVprCBlbIkBMleMYcEIiQEcJkGzYNyJAMKPyeXAtIkBkh68GIzkiQKyG9fjGSyJA91Y9w55FIkB95wvlJFUiQLP7QSTtRyJAx44UTgtPIkAmuqv6rEsiQO7chduVGyJA50QZ4n48IkDW4nFdDzciQFP64ZEnRiJA5+iKwNtZIkBwpQ8cYV4iQDe0TKYoRiJAgaqM4NFCIkDbTpqaFDwiQGlaawgZWyJATJXjGHBCIkBHCZBs2DciQM3LwC1tOSJAZIevBiM5IkCshvX4xksiQPdWPcOeRSJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAPmsO7gtEIkDu3IXblRsiQOdEGeJ+PCJAuqYwsQ03IkBT+uGRJ0YiQCk\\u002fwiJTYSJAPgXpKiZlIkA3tEymKEYiQIGqjODRQiJAMvd8Q5JMIkBpWmsIGVsiQEyV4xhwQiJARwmQbNg3IkDNy8AtbTkiQGSHrwYjOSJArIb1+MZLIkD3Vj3DnkUiQH3nC+UkVSJAs\\u002ftBJO1HIkDHjhROC08iQH+9usYLRCJA7tyF25UbIkAEryNHfjwiQLqmMLENNyJAU\\u002frhkSdGIkAKsp1NUmEiQD4F6SomZSJAN7RMpihGIkCBqozg0UIiQDL3fEOSTCJAaVprCBlbIkBMleMYcEIiQEcJkGzYNyJAzcvALW05IkBkh68GIzkiQKyG9fjGSyJA91Y9w55FIkB95wvlJFUiQLP7QSTtRyJAx44UTgtPIkB\\u002fvbrGC0QiQO7chduVGyJA+T5TjfQ4IkC6pjCxDTciQBPSiNcFPSJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJAUPmwFVlAIkAy93xDkkwiQGlaawgZWyJATJXjGHBCIkBHCZBs2DciQL9FZqXUMCJAZIevBiM5IkCshvX4xksiQPdWPcOeRSJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAf726xgtEIkDu3IXblRsiQPk+U430OCJAuqYwsQ03IkAT0ojXBT0iQAqynU1SYSJA9rMkak9iIkDnEzUPiEsiQFD5sBVZQCJAMvd8Q5JMIkBpWmsIGVsiQEyV4xhwQiJARwmQbNg3IkC\\u002fRWal1DAiQGSHrwYjOSJArIb1+MZLIkD3Vj3DnkUiQH3nC+UkVSJAs\\u002ftBJO1HIkDHjhROC08iQH+9usYLRCJAU60okpYbIkD5PlON9DgiQO\\u002fAss0ONyJAZCcR0z9UIkAKsp1NUmEiQPazJGpPYiJA5xM1D4hLIkBQ+bAVWUAiQOkg5f+RTCJAaVprCBlbIkBMleMYcEIiQEcJkGzYNyJAycIymiokIkBkh68GIzkiQKyG9fjGSyJA91Y9w55FIkB95wvlJFUiQLP7QSTtRyJAx44UTgtPIkB\\u002fvbrGC0QiQJ4P7ZQ9HSJA+T5TjfQ4IkDIH0oeDzciQGQnEdM\\u002fVCJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJAUPmwFVlAIkDpIOX\\u002fkUwiQGlaawgZWyJATJXjGHBCIkBHCZBs2DciQFu3xThGFiJAZIevBiM5IkCshvX4xksiQLC4qbOeRSJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJAbqF3j4syIkBKe7L7Oh0iQMcJqCJUVyJAyB9KHg83IkBkJxHTP1QiQAqynU1SYSJAK+b8V4tVIkDnEzUPiEsiQFD5sBVZQCJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJARwmQbNg3IkBbt8U4RhYiQEvCGcHURyJArIb1+MZLIkCwuKmznkUiQPPPzpQ+NiJAs\\u002ftBJO1HIkDHjhROC08iQG6hd4+LMiJASnuy+zodIkDHCagiVFciQMgfSh4PNyJAZCcR0z9UIkAKsp1NUmEiQCvm\\u002fFeLVSJA5xM1D4hLIkBETu1BHkQiQOkg5f+RTCJAaVprCBlbIkD87k7g1FMiQEcJkGzYNyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAMFRs\\u002fRFAIkDzz86UPjYiQLP7QSTtRyJAx44UTgtPIkBuoXePizIiQEp7svs6HSJAxwmoIlRXIkDIH0oeDzciQGQnEdM\\u002fVCJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJARE7tQR5EIkC4F2VZG0YiQGlaawgZWyJA\\u002fO5O4NRTIkBHCZBs2DciQNsea1CtJSJAS8IZwdRHIkCshvX4xksiQDBUbP0RQCJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJAbqF3j4syIkBKe7L7Oh0iQM\\u002fGlhCFQyJAMEt9HuQtIkBkJxHTP1QiQAqynU1SYSJAR1pkdaBRIkDnEzUPiEsiQERO7UEeRCJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJAZAanlehDIkDbHmtQrSUiQEvCGcHURyJArIb1+MZLIkAwVGz9EUAiQM0Ka70+NiJAs\\u002ftBJO1HIkDHjhROC08iQG6hd4+LMiJASnuy+zodIkC+QPFbfTwiQDBLfR7kLSJAZCcR0z9UIkAKsp1NUmEiQEdaZHWgUSJA5xM1D4hLIkAS+ILegVIiQBux5JVFTiJAaVprCBlbIkD87k7g1FMiQIqSs0gPSSJAtuXgzFUvIkAGWp945DQiQDioWXjRSSJAsMmJ1\\u002f5KIkDNCmu9PjYiQLP7QSTtRyJAx44UTgtPIkBuoXePizIiQJC4OHE6HSJAvkDxW308IkAwS30e5C0iQGQnEdM\\u002fVCJACrKdTVJhIkCy8ZhSVEMiQOcTNQ+ISyJAw7mnmBxRIkCCpSe6xykiQGlaawgZWyJA\\u002fO5O4NRTIkCKkrNID0kiQLbl4MxVLyJABlqfeOQ0IkA4qFl40UkiQLDJidf+SiJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAbqF3j4syIkCjHx19JxgiQL5A8Vt9PCJAMEt9HuQtIkBkJxHTP1QiQH+ggBNRYSJAsvGYUlRDIkDnEzUPiEsiQOJjfDzDSiJARWy0Wv5SIkBpWmsIGVsiQPzuTuDUUyJAipKzSA9JIkC25eDMVS8iQHHHo0uzKSJAOKhZeNFJIkDGZjBt\\u002fEoiQM0Ka70+NiJAs\\u002ftBJO1HIkDHjhROC08iQG6hd4+LMiJAhZlH07FPIkC+QPFbfTwiQDBLfR7kLSJAz39up+JQIkB\\u002foIATUWEiQLdtr184TiJA5xM1D4hLIkDiY3w8w0oiQEVstFr+UiJAaVprCBlbIkAk3YJZ2mQiQH2F\\u002fRz2QCJAz4fPPFUvIkBxx6NLsykiQDioWXjRSSJA3pRdAvtKIkDNCmu9PjYiQLP7QSTtRyJAx44UTgtPIkBuoXePizIiQBljpR\\u002fSViJAvkDxW308IkAwS30e5C0iQO\\u002fjc96uTyJAf6CAE1FhIkC3ba9fOE4iQOcTNQ+ISyJA4mN8PMNKIkBFbLRa\\u002flIiQGlaawgZWyJAJN2CWdpkIkB9hf0c9kAiQM+HzzxVLyJAfcWd4rEpIkA4qFl40UkiQCVGvyb6SiJAdMcbPSxfIkCz+0Ek7UciQHqFvW0xSyJAbqF3j4syIkAZY6Uf0lYiQA6EaEx6PCJAMEt9HuQtIkDv43Perk8iQGA7d3GGSSJAt22vXzhOIkDnEzUPiEsiQOJjfDzDSiJA4MG0A41cIkBpWmsIGVsiQCTdglnaZCJAfYX9HPZAIkCbd7xu6D4iQH3FneKxKSJAsTqYLjRMIkDUeuNx+EoiQHTHGz0sXyJAs\\u002ftBJO1HIkD\\u002fUnC3L0siQG6hd4+LMiJA4Xx8fN1VIkAOhGhMejwiQI7hqPniLSJA7+Nz3q5PIkCEAlQ0ThwiQLdtr184TiJA5xM1D4hLIkDiY3w8w0oiQPS2JjedUCJAaVprCBlbIkCBLHyBWGwiQLiypnAWRCJAm3e8bug+IkB9xZ3isSkiQLE6mC40TCJA1HrjcfhKIkB0xxs9LF8iQLP7QSTtRyJAmkHhnfQnIkBNJqDGjDIiQOF8fHzdVSJADoRoTHo8IkA1sFJkqEAiQO\\u002fjc96uTyJAhAJUNE4cIkBrTabGzFYiQOcTNQ+ISyJA4mN8PMNKIkD0tiY3nVAiQGlaawgZWyJAgSx8gVhsIkC4sqZwFkQiQJt3vG7oPiJAfcWd4rEpIkAZ3VM9QzkiQA2hJytDQSJAa6ralSRQIkCz+0Ek7UciQJpB4Z30JyJATSagxowyIkDhfHx83VUiQA6EaEx6PCJARe3tB\\u002fY6IkDv43Perk8iQFUunL6\\u002fNyJA4mFpfcxWIkDnEzUPiEsiQOJjfDzDSiJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJAklxsB5FUIkDvVsPIwSgiQODUXYAnRCJAGd1TPUM5IkANoScrQ0EiQGuq2pUkUCJAs\\u002ftBJO1HIkCaQeGd9CciQE0moMaMMiJA4Xx8fN1VIkDR5AtBeDwiQOIi4cTjRCJA7+Nz3q5PIkA8SU6IvzciQK+j57\\u002fMViJA5xM1D4hLIkDiY3w8w0oiQPS2JjedUCJAaVprCBlbIkCBLHyBWGwiQIA2ophsSyJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJADaEnK0NBIkDPgqEQIlAiQE\\u002f99XbhJyJAmkHhnfQnIkBNJqDGjDIiQOF8fHzdVSJAFO\\u002fnUDFXIkDiIuHE40QiQO\\u002fjc96uTyJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA4mN8PMNKIkD0tiY3nVAiQAONK5mrSyJAgSx8gVhsIkCANqKYbEsiQO9Ww8jBKCJA4NRdgCdEIkAZ3VM9QzkiQA2hJytDQSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJATSagxowyIkDhfHx83VUiQKAKhVYyVyJA4iLhxONEIkDv43Perk8iQDxJToi\\u002fNyJAr6Pnv8xWIkALjaKKfT0iQOJjfDzDSiJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJAgDaimGxLIkDvVsPIwSgiQODUXYAnRCJAGd1TPUM5IkANoScrQ0EiQM+CoRAiUCJAz4x7P+AnIkCaQeGd9CciQE0moMaMMiJA4Xx8fN1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+       "                            {\n",
+       "const graphDiv = document.getElementById('f2774784-b9c6-4aad-b94d-c64b92301221');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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J8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002fH4BT11a4sj\\u002f5fIEBD4q2P9F59sJ5uLU\\u002fSKDhE\\u002fZVtj9nxU4e6n61P+Lv+EvaPLQ\\u002fDpQRJk+\\u002ftz84gv9K5ZK2P86Ua3UamrU\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002flgUzRPAosz8OmFc+9a2zP8mX4+M9eLU\\u002fCj1Iv4IotT8I9JsH6AC2PzSfLQKeOLE\\u002fB03hv6QEtD+SggDmn021Px+AU9dWuLI\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002fdaWaPuFztT\\u002fi7\\u002fhL2jy0PwqC0VwOl7U\\u002fOIL\\u002fSuWStj\\u002fOlGt1Gpq1P63f36SX+bY\\u002fJUqMrxQftT8iLN+YVVy0P5YFM0TwKLM\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fCPSbB+gAtj\\u002f6zw6b3MywPwdN4b+kBLQ\\u002f6cci6aJNtT8fgFPXVriyP6idyKRdsbU\\u002f0Xn2wnm4tT+mIA1IvlC2P3Wlmj7hc7U\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002fzpRrdRqatT+t39+kl\\u002fm2PyVKjK8UH7U\\u002fIizfmFVctD9o5IalKDOxPw6YVz71rbM\\u002fnkOZVTx4tT8KPUi\\u002fgii1Pwj0mwfoALY\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fH4BT11a4sj+oncikXbG1P\\u002fYYz415uLU\\u002fJbkIBcFQtj9K9\\u002fza9Y60P+Lv+EvaPLQ\\u002fCoLRXA6XtT8wvL1t5JK2P86Ua3UamrU\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002faOSGpSgzsT\\u002fFhR\\u002fDr\\u002f2zPwLYith4oLY\\u002fCj1Iv4IotT8rf1won8i2P3yy42PWI7M\\u002fEzM0dNEzsz\\u002fpxyLpok21Px+AU9dWuLI\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fSvf82vWOtD90yDBrD32zPwqC0VwOl7U\\u002fMLy9beSStj+qpt+liHu1P63f36SX+bY\\u002f4Virletzsz+vaBnvaIu1P2jkhqUoM7E\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002fK39cKJ\\u002fItj98suNj1iOzPxMzNHTRM7M\\u002f6cci6aJNtT8fgFPXVriyP6idyKRdsbU\\u002fA3G5OCwLtz8luQgFwVC2P\\u002fVDblSeBbM\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fqqbfpYh7tT+QX2\\u002fJfn+0P+FYq5Xrc7M\\u002fr2gZ72iLtT9o5IalKDOxP8WFH8Ov\\u002fbM\\u002fJepcAXegtj8KPUi\\u002fgii1Pxm8VrbvBrc\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fH4BT11a4sj\\u002fKvl\\u002fBLxW3PzBDBS8sRbc\\u002fJbkIBcFQtj\\u002f1Q25UngWzP8z\\u002fpcUqGbQ\\u002fCoLRXA6XtT8wvL1t5JK2P+EONuJW9bU\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002faOSGpSgzsT\\u002fFhR\\u002fDr\\u002f2zPyXqXAF3oLY\\u002fCj1Iv4IotT8ZvFa27wa3P3yy42PWI7M\\u002fEzM0dNEzsz\\u002f1\\u002fiQ4ok21Px+AU9dWuLI\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002f9UNuVJ4Fsz\\u002fM\\u002f6XFKhm0PwqC0VwOl7U\\u002fy8Y9VuGStj+gGNv0V\\u002fW1PwdV63qKiLE\\u002fhgLt6GBGtT+vaBnvaIu1P8guSxUqM7E\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002fGbxWtu8Gtz98suNj1iOzPxMzNHTRM7M\\u002f9f4kOKJNtT+216RKdGa0P6e+slcwFbc\\u002fQufAySxFtz8luQgFwVC2P\\u002fVDblSeBbM\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fc3Qa5VX1tT8HVet6ioixP2RYBTi3C7Y\\u002fr2gZ72iLtT\\u002fILksVKjOxP4dT4ISw\\u002fbM\\u002f097BrXegtj8KPUi\\u002fgii1Pxm8VrbvBrc\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002f0Gi8qZSOsz+nvrJXMBW3P0LnwMksRbc\\u002fJbkIBcFQtj\\u002f1Q25UngWzP8z\\u002fpcUqGbQ\\u002fCoLRXA6XtT9nw04TBvG1P3N0GuVV9bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fyC5LFSozsT9Y3IPluQW3P30vkuJ2oLY\\u002fCj1Iv4IotT8ZvFa27wa3Pxi\\u002fu2+EU7Q\\u002fEzM0dNEzsz\\u002f1\\u002fiQ4ok21P9BovKmUjrM\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002f9UNuVJ4Fsz\\u002fM\\u002f6XFKhm0PwqC0VwOl7U\\u002fZ8NOEwbxtT9RNzMAcC20PwfRmJvXmrQ\\u002fZFgFOLcLtj9HyjbdaYu1P8guSxUqM7E\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGbxWtu8Gtz8Yv7tvhFO0PxMzNHTRM7M\\u002f9f4kOKJNtT\\u002fQaLyplI6zPyIn\\u002fWyAzrI\\u002fpuhdKuXctj8luQgFwVC2P6vMBhruxrI\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002foOGATHOLtj8Enz4L8oe0P2RYBTi3C7Y\\u002fR8o23WmLtT\\u002fILksVKjOxP1jcg+W5Bbc\\u002fYmVOGgumtj+I9yywSwi2Py81bihXbLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002f0Gi8qZSOsz8iJ\\u002f1sgM6yP6boXSrl3LY\\u002fJbkIBcFQtj870XPCoza1P8z\\u002fpcUqGbQ\\u002fCoLRXA6XtT9nw04TBvG1P6DhgExzi7Y\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fubt68CkzsT9Y3IPluQW3P5J87W4Vg7U\\u002fiPcssEsItj8vNW4oV2y2Pxi\\u002fu2+EU7Q\\u002fEzM0dNEzsz\\u002f1\\u002fiQ4ok21P9BovKmUjrM\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002fO9FzwqM2tT9oAt4iz+CwP2NkdDb\\u002fW7Y\\u002fZ8NOEwbxtT+g4YBMc4u2PwSfPgvyh7Q\\u002fePJJiKvhsz9HyjbdaYu1P7m7evApM7E\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fLzVuKFdstj8Yv7tvhFO0PxMzNHTRM7M\\u002f9f4kOKJNtT\\u002fQaLyplI6zPyE7qKO2q7A\\u002fWHOqmuTctj\\u002fJq94+kv2zPzvRc8KjNrU\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002foOGATHOLtj8Enz4L8oe0P3jySYir4bM\\u002fAq9UTq2XtT+5u3rwKTOxP1jcg+W5Bbc\\u002fknztbhWDtT\\u002f3ydaHSgi2Py81bihXbLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002f0Gi8qZSOsz8hO6ijtquwP3kO3AM8R7c\\u002fyavePpL9sz\\u002fOhLJC7ua0P2gC3iLP4LA\\u002f1N3WZcWZtT96Iba7qp61P6DhgExzi7Y\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fubt68CkzsT9Y3IPluQW3P5J87W4Vg7U\\u002f98nWh0oItj9D7mfgVWy2P96d90+BU7Q\\u002fIv3DsDeOsj\\u002f8tso1Yg60P9BovKmUjrM\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002fzoSyQu7mtD+KCLyTpQ6yP9Td1mXFmbU\\u002feiG2u6qetT+g4YBMc4u2P0UmkrLzxrI\\u002fePJJiKvhsz8Cr1ROrZe1P7m7evApM7E\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fQ+5n4FVstj+ddvQMg1O0Py4poj0ZQrI\\u002f\\u002fLbKNWIOtD\\u002fQaLyplI6zP2qF\\u002f4STD7M\\u002feQ7cAzxHtz\\u002fJq94+kv2zP0T\\u002fZuNl3rM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002foOGATHOLtj9FJpKy88ayP3jySYir4bM\\u002fAq9UTq2XtT+ipcEvW4SxPxQkR0Gw5Lc\\u002fO0SUIbuQtT+DCY32CxS0P0PuZ+BVbLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002f\\u002fIXPv5eDtT9qhf+Ekw+zP5Q2rZYw3bg\\u002fyavePpL9sz8IrXv1fsazP4oIvJOlDrI\\u002f1N3WZcWZtT96Iba7qp61P6DhgExzi7Y\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002foqXBL1uEsT8UJEdBsOS3PztElCG7kLU\\u002fgwmN9gsUtD9D7mfgVWy2P5129AyDU7Q\\u002fTfv8VUc7sj\\u002f8tso1Yg60P\\u002fyFz7+Xg7U\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002fCK179X7Gsz+KCLyTpQ6yP9Td1mXFmbU\\u002feiG2u6qetT+g4YBMc4u2PxbjupnzxrI\\u002fePJJiKvhsz8Cr1ROrZe1P6KlwS9bhLE\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fPsahIksttT+ddvQMg1O0P037\\u002fFVHO7I\\u002f\\u002fLbKNWIOtD+KAeP0mIO1P2vfQRjoPLI\\u002f9pnMxeQQtz\\u002fJq94+kv2zPwite\\u002fV+xrM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fqJ9pRUZxtj8W47qZ88ayP3jySYir4bM\\u002fMQtn8AuItj+ipcEvW4SxPxQkR0Gw5Lc\\u002fnmuI27mQtT+DCY32CxS0Pz7GoSJLLbU\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002f+5QQjvCYtj9r30EY6DyyP\\u002faZzMXkELc\\u002fyavePpL9sz\\u002feEmbXfcazP4oIvJOlDrI\\u002f1N3WZcWZtT+GTqwarZ61P6ifaUVGcbY\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002foqXBL1uEsT8UJEdBsOS3P55riNu5kLU\\u002fgwmN9gsUtD8+xqEiSy21P7MZOz2EU7Q\\u002fLiQ5XKPasj\\u002f8tso1Yg60P\\u002fuUEI7wmLY\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002f48+nZ91ztD+KCLyTpQ6yP9Td1mXFmbU\\u002f5atsvayetT+on2lFRnG2PxbjupnzxrI\\u002fePJJiKvhsz9Qr3kkpGm0P6KlwS9bhLE\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fPsahIksttT+zGTs9hFO0Py4kOVyj2rI\\u002f\\u002fLbKNWIOtD\\u002f7lBCO8Ji2PxKT\\u002fu39trI\\u002fcG9ppO3MtT\\u002fJq94+kv2zP+PPp2fdc7Q\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fqJ9pRUZxtj8W47qZ88ayP3jySYir4bM\\u002fUK95JKRptD+ipcEvW4SxP4tIPxWi4rg\\u002fVWM3J75atD+WTHm5CRS0Pz7GoSJLLbU\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002f+5QQjvCYtj8Sk\\u002f7t\\u002fbayP3BvaaTtzLU\\u002fyavePpL9sz+Xv7guoqO0P4oIvJOlDrI\\u002f1N3WZcWZtT8QghTUD3m0P6ifaUVGcbY\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002foqXBL1uEsT+GU5+qbJe3P1VjNye+WrQ\\u002fHvGhj+Hbsz\\u002fUgYhsSi21P7MZOz2EU7Q\\u002fLiQ5XKPasj\\u002f79igkUaK3P6jqZ4\\u002fwmLY\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fl7+4LqKjtD+KCLyTpQ6yP9Td1mXFmbU\\u002fEIIU1A95tD+on2lFRnG2P69SPD3Op7I\\u002fePJJiKvhsz9Qr3kkpGm0P6KlwS9bhLE\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002f1IGIbEottT+zGTs9hFO0Py4kOVyj2rI\\u002f+\\u002fYoJFGitz+lRblFqO+0PxKT\\u002fu39trI\\u002fcG9ppO3MtT\\u002fJq94+kv2zP5e\\u002fuC6io7Q\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002fqJ9pRUZxtj\\u002fVGOw6FMe1P3jySYir4bM\\u002fUK95JKRptD+ipcEvW4SxP4ZTn6psl7c\\u002fVWM3J75atD\\u002f+Lj9Uz0q3P9SBiGxKLbU\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fpUW5RajvtD8Sk\\u002f7t\\u002fbayP3BvaaTtzLU\\u002fyavePpL9sz+Xv7guoqO0P9zx\\u002fFOBTLY\\u002fu3pOcsSZtT8QghTUD3m0P6ifaUVGcbY\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002foqXBL1uEsT+GU5+qbJe3P1VjNye+WrQ\\u002f\\u002fi4\\u002fVM9Ktz\\u002fUgYhsSi21P7MZOz2EU7Q\\u002fLiQ5XKPasj\\u002f79igkUaK3P6VFuUWo77Q\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002fwX\\u002f2wp03sz\\u002fc8fxTgUy2P1swnTohf7Y\\u002fEIIU1A95tD+on2lFRnG2P9UY7DoUx7U\\u002fOuNLPrebuD+wsDSCpGm0P6KlwS9bhLE\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002f1wfVILwBtj9tgN+kDoG1P+IBN7TFgbM\\u002f+\\u002fYoJFGitz+lRblFqO+0P7P+daIuMLQ\\u002flr4JtcVgtD\\u002fJq94+kv2zP8F\\u002f9sKdN7M\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002fqJ9pRUZxtj\\u002fVGOw6FMe1PzrjSz63m7g\\u002fsLA0gqRptD+ipcEvW4SxP4ZTn6psl7c\\u002fVWM3J75atD8Fa4rJz0q3P9cH1SC8AbY\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fpUW5RajvtD\\u002fS\\u002fF45YhezP+gMnIL7lrM\\u002fyavePpL9sz9FhVR5WSWzPwKyUaMf7rQ\\u002fWzCdOiF\\u002ftj8QghTUD3m0P6ifaUVGcbY\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002foqXBL1uEsT+GU5+qbJe3P1VjNye+WrQ\\u002fBWuKyc9Ktz\\u002fXB9UgvAG2PxDu1cmdibU\\u002f4gE3tMWBsz\\u002f79igkUaK3P6VFuUWo77Q\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002frMZ7lYoasz8CslGjH+60P1swnTohf7Y\\u002f0qH3axB5tD+on2lFRnG2Pzhgp0Kxy7Q\\u002fkLlUXLBIuD+wsDSCpGm0P6KlwS9bhLE\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002f1wfVILwBtj8Q7tXJnYm1P+IBN7TFgbM\\u002f+\\u002fYoJFGitz+lRblFqO+0P9L8XjliF7M\\u002f6AycgvuWsz\\u002fJq94+kv2zP5M\\u002fdtGKGrM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fqJ9pRUZxtj8TFyYRscu0P5C5VFywSLg\\u002fsLA0gqRptD+ipcEvW4SxP0\\u002fPrA6ui7c\\u002fVWM3J75atD8Fa4rJz0q3P9cH1SC8AbY\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fpUW5RajvtD\\u002fS\\u002fF45YhezP+gMnIL7lrM\\u002fyavePpL9sz+TP3bRihqzPwKyUaMf7rQ\\u002fSQAVScDTtz\\u002fSofdrEHm0P0bo2KUqpbU\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fi\\u002fy0MnYStT9Pz6wOrou3P1VjNye+WrQ\\u002fBWuKyc9Ktz\\u002fXB9UgvAG2P6fP99EfP7Y\\u002f4gE3tMWBsz\\u002f79igkUaK3P6VFuUWo77Q\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fkz920Yoasz8CslGjH+60P0kAFUnA07c\\u002f0qH3axB5tD9G6NilKqW1PxMXJhGxy7Q\\u002foRpDvQL6tz\\u002fEd\\u002fSDic2yP4v8tDJ2ErU\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002f1wfVILwBtj+nz\\u002ffRHz+2P+IBN7TFgbM\\u002f+\\u002fYoJFGitz+lRblFqO+0P9L8XjliF7M\\u002f6AycgvuWsz\\u002fJq94+kv2zP5M\\u002fdtGKGrM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fOtNjKxOStD8TFyYRscu0P6EaQ70C+rc\\u002fxHf0g4nNsj+L\\u002fLQydhK1P+T9jWGvi7c\\u002fVWM3J75atD8Fa4rJz0q3P9cH1SC8AbY\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fpUW5RajvtD\\u002fS\\u002fF45YhezP+gMnIL7lrM\\u002fyavePpL9sz+TP3bRihqzPxB5NJUDh7g\\u002fSQAVScDTtz9+yb2zD3m0PzrTYysTkrQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002fi\\u002fy0MnYStT\\u002fk\\u002fY1hr4u3P1VjNye+WrQ\\u002fBWuKyc9Ktz\\u002fXB9UgvAG2P\\u002f0QnMuVC7g\\u002f4gE3tMWBsz\\u002f79igkUaK3P72pP1eo77Q\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fJ2S33siLtD\\u002fF7JiUA4e4P7Hew9+\\u002f07c\\u002ffsm9sw95tD8602MrE5K0PxMXJhGxy7Q\\u002fyM\\u002fcBJzItD\\u002fEd\\u002fSDic2yP4v8tDJ2ErU\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f1wfVILwBtj\\u002f9EJzLlQu4P3EzC3LgbLI\\u002f+\\u002fYoJFGitz+9qT9XqO+0P2oRNM1+XLM\\u002f6AycgvuWsz\\u002fJq94+kv2zPydkt97Ii7Q\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fOtNjKxOStD8TFyYRscu0P8jP3AScyLQ\\u002fxHf0g4nNsj9Q0\\u002f\\u002fRdZmxP+T9jWGvi7c\\u002fVWM3J75atD+SasPRXZ62P9cH1SC8AbY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002feCOFAIldtT9qETTNflyzP+gMnIL7lrM\\u002fyavePpL9sz8nZLfeyIu0P8XsmJQDh7g\\u002fsd7D37\\u002fTtz9+yb2zD3m0PzrTYysTkrQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fUNP\\u002f0XWZsT\\u002fNbXE6r4u3P1VjNye+WrQ\\u002fkmrD0V2etj\\u002fXB9UgvAG2P6iai\\u002fEN3bY\\u002fcTMLcuBssj\\u002f79igkUaK3P3gjhQCJXbU\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fJ2S33siLtD\\u002fF7JiUA4e4P+79VuTVK7k\\u002fJRkQJP3Ssj8602MrE5K0PxMXJhGxy7Q\\u002fbgmswzvVtD\\u002fEd\\u002fSDic2yP1DT\\u002f9F1mbE\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fcpfvSudetj+omovxDd22P3EzC3LgbLI\\u002f+\\u002fYoJFGitz94I4UAiV21P7cxbvB\\u002fXLM\\u002f6AycgvuWsz\\u002fJq94+kv2zPydkt97Ii7Q\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fOtNjKxOStD8TFyYRscu0P24JrMM71bQ\\u002fxHf0g4nNsj8VwSeMdtmwP8k1tqC9yrU\\u002fVWM3J75atD+SasPRXZ62Py7uhQptabc\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ft6B7FCsFsz+3MW7wf1yzP+gMnIL7lrM\\u002fyavePpL9sz8nZLfeyIu0PxaNEHMDh7g\\u002fTvS6LiYYtj8lGRAk\\u002fdKyPzrTYysTkrQ\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002fbUHKM6INtD\\u002faSXipD7a1P1VjNye+WrQ\\u002fkmrD0V2etj8u7oUKbWm3P3cQfi60ibY\\u002f0spAqBzdsj+BbLAF6Jq2P7egexQrBbM\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fJ2S33siLtD8Tv6Ikl0i1P070ui4mGLY\\u002fJRkQJP3Ssj8602MrE5K0P+MQ4Vuj2LI\\u002fheH6BzzVtD\\u002fEd\\u002fSDic2yPwEzPa56qLI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fLu6FCm1ptz93EH4utIm2P1da5EF2tLU\\u002fgWywBeiatj9Nxrh\\u002fKgWzP7cxbvB\\u002fXLM\\u002f6AycgvuWsz\\u002fJq94+kv2zPydkt97Ii7Q\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f1Sxay8jFsz\\u002fjEOFbo9iyP8PAseZ9xbQ\\u002fxHf0g4nNsj8BMz2ueqiyP8\\u002fLddg3vbQ\\u002fVWM3J75atD\\u002fLHK1sSqazP4sZHFuxHrc\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002fmqvdRy0Fsz+3MW7wf1yzP+gMnIL7lrM\\u002fyavePpL9sz8nZLfeyIu0PxFMd5\\u002fMxLQ\\u002fTvS6LiYYtj8lGRAk\\u002fdKyP2k7JG4LwbM\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fATM9rnqosj\\u002fPy3XYN720P1VjNye+WrQ\\u002fyxytbEqmsz+LGRxbsR63P\\u002fKhjF2yibY\\u002fJe5ec3W0tT+BbLAF6Jq2P3GnvlwuBbM\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fJ2S33siLtD8RTHefzMS0PwHBzUcmGLY\\u002fJRkQJP3Ssj9pOyRuC8GzP2F1ZoKgGbQ\\u002fw8Cx5n3FtD\\u002fEd\\u002fSDic2yPwEzPa56qLI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002fixkcW7Eetz\\u002fnuDH0oHm0PyXuXnN1tLU\\u002fUk4qly0ctT9cZoeXLAWzP5UCDaRQTrU\\u002f6AycgvuWsz+nPZ1Cvcm1Pydkt97Ii7Q\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002faTskbgvBsz8PWMZ84JGyP8PAseZ9xbQ\\u002fxHf0g4nNsj8BMz2ueqiyP\\u002fJeoJ60k7I\\u002fVWM3J75atD9rv9pcj9uyP8wxtj0fUrg\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002fXGaHlywFsz+VAg2kUE61P+gMnIL7lrM\\u002fCmQG\\u002fNpotT\\u002fxFdlJx4u0P0jridXfobQ\\u002fAcHNRyYYtj+bv4inTteyP2k7JG4LwbM\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002fATM9rnqosj\\u002fyXqCetJOyP1VjNye+WrQ\\u002fa7\\u002faXI\\u002fbsj\\u002fMMbY9H1K4P+e4MfSgebQ\\u002fJe5ec3W0tT\\u002fvVTvt0+W0P\\u002f6VdE7AM7M\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002f8RXZSceLtD9I64nV36G0PwHBzUcmGLY\\u002f1TCdZ8yDsj9pOyRuC8GzP6IPhuf1f7Q\\u002fgltC+FF0sz\\u002fEd\\u002fSDic2yPwEzPa56qLI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002fGbq1AB5SuD\\u002f9PMzvzFK0P5qJhW2ewrU\\u002f71U77dPltD\\u002f+lXROwDOzPyusNNS65bY\\u002f6AycgvuWsz8KZAb82mi1P\\u002fEV2UnHi7Q\\u002fSOuJ1d+htD+IPruMV422P0L1k3qkW7I\\u002faTskbgvBsz+qqnKFiHOzP\\u002fqdu9ZSdLM\\u002fxHf0g4nNsj8BMz2ueqiyP\\u002fJeoJ60k7I\\u002fVWM3J75atD9rv9pcj9uyP8lWlUNV0Lc\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002f\\u002fpV0TsAzsz+xjUpmu+W2P4AgUs9ei7M\\u002fCmQG\\u002fNpotT\\u002fxFdlJx4u0P0jridXfobQ\\u002fUsw8pFWNtj9C9ZN6pFuyP2k7JG4LwbM\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002fATM9rnqosj\\u002fyXqCetJOyP272PtqknLU\\u002fa7\\u002faXI\\u002fbsj\\u002fJVpVDVdC3P\\u002f08zO\\u002fMUrQ\\u002fmomFbZ7CtT\\u002fvVTvt0+W0P\\u002f6VdE7AM7M\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002f8RXZSceLtD9I64nV36G0P2kiN0PjvrY\\u002fQvWTeqRbsj9pOyRuC8GzP6qqcoWIc7M\\u002f+p271lJ0sz8Qn6923zOxPwEzPa56qLI\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDM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+       "                            {\n",
+       "const graphDiv = document.getElementById('71be23dc-2930-4806-bd3d-748f6cdc7708');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+       "                            {\n",
+       "const graphDiv = document.getElementById('113c35f6-02d1-4b56-89b7-050fdc83ed11');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
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+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
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iD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fIf\\u002fp1Agc5D80FCyW\\u002fCvkP6ZvNWo2ReQ\\u002fu\\u002fsXTytE5D\\u002fkdeGYnSrkP5E6yksfKuQ\\u002fye2\\u002fiEUf5D\\u002fpWiyfYTTkPxlRzEXUQ+Q\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fxOOs3MIu5D8d1BjwSxnkP2IeGkVTLuQ\\u002fuj2nTjYr5D9UacNI5TDkPxmXYg9RKOQ\\u002f+DabNV4s5D98mm+W7CjkPwguT40iK+Q\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002f5HXhmJ0q5D+ROspLHyrkP2oFoXBFH+Q\\u002f6Vosn2E05D8ZUcxF1EPkP1hp7NgIIuQ\\u002fCyHZexoo5D9HdaBjEULkP8TjrNzCLuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fSPC4qFcx5D8Zl2IPUSjkPyzkRybuQ+Q\\u002ffJpvluwo5D8ILk+NIivkPzQULJb8K+Q\\u002fpm81ajZF5D+7+xdPK0TkP+R14ZidKuQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fGVHMRdRD5D\\u002fv\\u002fbCgBynkPwsh2XsaKOQ\\u002fUjkFli415D\\u002fE46zcwi7kP4t1NMQLJOQ\\u002fYh4aRVMu5D\\u002fm4IVlbx7kP0jwuKhXMeQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fCC5PjSIr5D80FCyW\\u002fCvkP6ZvNWo2ReQ\\u002fu\\u002fsXTytE5D\\u002fh6TNZuzPkP5E6yksfKuQ\\u002fUGu9B0Qf5D\\u002fpWiyfYTTkPxlRzEXUQ+Q\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fxOOs3MIu5D+LdTTECyTkPzsgku1SLuQ\\u002fKS3loWos5D+w0NzYZjPkPxmXYg9RKOQ\\u002fLORHJu5D5D8QtolV6yjkPwguT40iK+Q\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002f4ekzWbsz5D8GTaQryjHkP3YwUBJNF+Q\\u002f6Vosn2E05D+zM+VrEj3kP0R08osuIeQ\\u002fNUjYcvUn5D9SOQWWLjXkP8TjrNzCLuQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002fsNDc2GYz5D\\u002fVRAhWwC7kPyzkRybuQ+Q\\u002fELaJVeso5D+4NGqbaivkPzQULJb8K+Q\\u002fGCcYiHhI5D++fi3Z3zbkP+HpM1m7M+Q\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fszPlaxI95D9EdPKLLiHkPzVI2HL1J+Q\\u002fUjkFli415D\\u002fE46zcwi7kP4t1NMQLJOQ\\u002f\\u002f3g6fMov5D8pLeWhaizkP\\u002fYdsLsvLeQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fuDRqm2or5D\\u002fAfhvCXjTkPxgnGIh4SOQ\\u002fvn4t2d825D\\u002fh6TNZuzPkPwZNpCvKMeQ\\u002fG9oM79Eq5D\\u002fpWiyfYTTkP6o\\u002fuiPhLeQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fxOOs3MIu5D8T71+HJB\\u002fkP2L\\u002f7YpsO+Q\\u002fKS3loWos5D\\u002f2HbC7Ly3kP2GipUswKuQ\\u002fLORHJu5D5D8QtolV6yjkPzpGpUY+JOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002f4ekzWbsz5D8GTaQryjHkPxvaDO\\u002fRKuQ\\u002f6Vosn2E05D+qP7oj4S3kP0R08osuIeQ\\u002fNUjYcvUn5D9J1lOmKzXkP8TjrNzCLuQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002f9h2wuy8t5D9hoqVLMCrkPyzkRybuQ+Q\\u002fx5RD9+oo5D+oC9rjPSTkP9\\u002fzMMJhMeQ\\u002fJpmTsexB5D++fi3Z3zbkP7H+S3LuMOQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fqj+6I+Et5D9EdPKLLiHkPzVI2HL1J+Q\\u002fSdZTpis15D\\u002frQSv5QCbkP8ANSJR+G+Q\\u002ftCPXSmo75D8pLeWhaizkP\\u002fYdsLsvLeQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002fgxnmnz4k5D\\u002ff8zDCYTHkP0jXlpR4N+Q\\u002fvn4t2d825D+x\\u002fkty7jDkPwer1FgNMOQ\\u002fcO\\u002fOA88q5D\\u002fpWiyfYTTkP6o\\u002fuiPhLeQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fd3QNbXIy5D\\u002fADUiUfhvkP7Qj10pqO+Q\\u002fKS3loWos5D\\u002f2HbC7Ly3kP2GipUswKuQ\\u002fLORHJu5D5D+PgMRBOC3kP4MZ5p8+JOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fsf5Lcu4w5D\\u002fMQ4fARCbkP6QM9tfPKuQ\\u002f6Vosn2E05D+qP7oj4S3kPyzlazUcJeQ\\u002fNUjYcvUn5D9J1lOmKzXkP3d0DW1yMuQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002f9h2wuy8t5D9hoqVLMCrkPyzkRybuQ+Q\\u002fj4DEQTgt5D9WuiiypCbkP0m4GoHtReQ\\u002fSNeWlHg35D841B9H3zbkP7H+S3LuMOQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fqj+6I+Et5D8s5Ws1HCXkPzVI2HL1J+Q\\u002fSdZTpis15D93dA1tcjLkPzmOtjaCGeQ\\u002fZcwXOQ805D8pLeWhaizkP0z6xWQ3MeQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fjHWHKSsq5D\\u002fmT+Hp40HkP0jXlpR4N+Q\\u002fONQfR9825D+x\\u002fkty7jDkP8xDh8BEJuQ\\u002f5ESgwtMY5D+nEzr\\u002foTzkP0RCVACzK+Q\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fd3QNbXIy5D85jrY2ghnkP2XMFzkPNOQ\\u002fKS3loWos5D\\u002fX3isBUCrkP2GipUswKuQ\\u002fLORHJu5D5D+PgMRBOC3kP4x1hykrKuQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002f5dd1R+4w5D\\u002fMQ4fARCbkPyBebBMxDeQ\\u002fpxM6\\u002f6E85D9EQlQAsyvkPyzlazUcJeQ\\u002fNUjYcvUn5D9J1lOmKzXkP3d0DW1yMuQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002f194rAVAq5D8obvPDgi7kPzgbhpaXNuQ\\u002fj4DEQTgt5D+MdYcpKyrkP+ZP4enjQeQ\\u002fiOEoGAg\\u002f5D841B9H3zbkP+XXdUfuMOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fREJUALMr5D8s5Ws1HCXkPzVI2HL1J+Q\\u002fSdZTpis15D93dA1tcjLkP3DhCGdGDeQ\\u002fjvJrdQ005D\\u002fIhAdy6SrkP9feKwFQKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002fjHWHKSsq5D\\u002fmT+Hp40HkP4jhKBgIP+Q\\u002f\\u002fSxEP+c85D\\u002fl13VH7jDkP8xDh8BEJuQ\\u002fIF5sEzEN5D9owkM9oTzkP0RCVACzK+Q\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fd3QNbXIy5D9w4QhnRg3kP2aenkO1MOQ\\u002fyIQHcukq5D9+qmGkHSfkPyhu88OCLuQ\\u002fHGTdg4cm5D8ZdeZzzirkP4x1hykrKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002f5dd1R+4w5D\\u002fMQ4fARCbkPyBebBMxDeQ\\u002faMJDPaE85D\\u002fgBJiPsivkPzKHkx7jGeQ\\u002f7eHIE+AW5D8vMntrOz7kP3d0DW1yMuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002ffqphpB0n5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002fGXXmc84q5D+MdYcpKyrkP3hvqigRNeQ\\u002fiOEoGAg\\u002f5D\\u002f9LEQ\\u002f5zzkP+XXdUfuMOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002f4ASYj7Ir5D8G4nZK3hnkP3wIzcnID+Q\\u002fLzJ7azs+5D93dA1tcjLkP8xb3302IuQ\\u002fZp6eQ7Uw5D\\u002fIhAdy6SrkP3p7FXUnJ+Q\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fjHWHKSsq5D94b6ooETXkP4jhKBgIP+Q\\u002f\\u002fSxEP+c85D\\u002ffcUIlujDkP6BswtNCOOQ\\u002famwKVjEX5D9DA1rFqDrkP+AEmI+yK+Q\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fapAUEl8m5D\\u002fMW999NiLkP7g8M638POQ\\u002fyIQHcukq5D87Tdq2lCfkP\\u002f2IqMpdE+Q\\u002fHGTdg4cm5D8ZdeZzzirkP4x1hykrKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002f33FCJbow5D+gbMLTQjjkP2psClYxF+Q\\u002fQwNaxag65D\\u002fgBJiPsivkPwbidkreGeQ\\u002f+UEwpxEY5D8vMntrOz7kP2qQFBJfJuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002fO03atpQn5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002fGXXmc84q5D+MdYcpKyrkP4QEQJ0QNeQ\\u002fiOEoGAg\\u002f5D\\u002f9LEQ\\u002f5zzkP99xQiW6MOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fAD3g+rEr5D8G4nZK3hnkP\\u002flBMKcRGOQ\\u002fLzJ7azs+5D\\u002f92LAFXibkP4Vw1nf7IeQ\\u002f0tzJxIoO5D\\u002fIhAdy6SrkPztN2raUJ+Q\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fXp\\u002fHJUUX5D+EBECdEDXkP4jhKBgIP+Q\\u002f\\u002f61fC9o15D\\u002ffcUIlujDkP6BswtNCOOQ\\u002fuaF+OC4X5D9DA1rFqDrkPwA94PqxK+Q\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fVBlikZsx5D+FcNZ3+yHkP9LcycSKDuQ\\u002fyIQHcukq5D\\u002fYI63QlSfkP\\u002f2IqMpdE+Q\\u002fHGTdg4cm5D98KETMzirkP16fxyVFF+Q\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002f33FCJbow5D+gbMLTQjjkP7mhfjguF+Q\\u002fQwNaxag65D8APeD6sSvkP0j3Ra\\u002feGeQ\\u002fgA5WznQl5D8vMntrOz7kP1QZYpGbMeQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002fV+dk+h4i5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002frz\\u002fPj80q5D9en8clRRfkP4QEQJ0QNeQ\\u002fiOEoGAg\\u002f5D\\u002fMyTUPMRvkP99xQiW6MOQ\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fAD3g+rEr5D9I90Wv3hnkP4AOVs50JeQ\\u002fLzJ7azs+5D9UGWKRmzHkP1TfG6MuROQ\\u002fin64GY0O5D\\u002fIhAdy6SrkP1fnZPoeIuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fXp\\u002fHJUUX5D+EBECdEDXkP4jhKBgIP+Q\\u002fzMk1DzEb5D\\u002ffcUIlujDkPzTmilMgLeQ\\u002fTfnYcp4j5D+Ymx4bqDrkPwA94PqxK+Q\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVBlikZsx5D9U3xujLkTkP4p+uBmNDuQ\\u002fyIQHcukq5D\\u002fJpRJPKBnkP\\u002f2IqMpdE+Q\\u002fHGTdg4cm5D8tQs1PjjHkP16fxyVFF+Q\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002f33FCJbow5D+spRWUOCHkP0352HKeI+Q\\u002fXeoVDKk65D\\u002f\\u002fw5TrCUnkP0j3Ra\\u002feGeQ\\u002fgA5WznQl5D+5rFDEsyrkP\\u002f9R4PqcMeQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fyaUSTygZ5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002fLULNT44x5D9en8clRRfkPwXmN+7DJ+Q\\u002fiOEoGAg\\u002f5D\\u002fMyTUPMRvkP99xQiW6MOQ\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002f\\u002f8OU6wlJ5D9I90Wv3hnkP4AOVs50JeQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP1TfG6MuROQ\\u002fin64GY0O5D\\u002fIhAdy6SrkP8mlEk8oGeQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fXp\\u002fHJUUX5D8ij6tIMRDkP4jhKBgIP+Q\\u002fzMk1DzEb5D\\u002ffcUIlujDkP6ylFZQ4IeQ\\u002fTfnYcp4j5D9MUSfeqTrkP\\u002f\\u002fDlOsJSeQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D9U3xujLkTkP4p+uBmNDuQ\\u002fyIQHcukq5D\\u002fJpRJPKBnkP99Aw2nOKeQ\\u002fc1vxSIYm5D8tQs1PjjHkP16fxyVFF+Q\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002f33FCJbow5D+spRWUOCHkP0352HKeI+Q\\u002fTFEn3qk65D\\u002f\\u002fw5TrCUnkP0j3Ra\\u002feGeQ\\u002fgA5WznQl5D+5rFDEsyrkP\\u002fy\\u002fZxDBIeQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002fD4WWlrEy5D\\u002ffQMNpzinkP7lmB4qbKOQ\\u002fLULNT44x5D9en8clRRfkPyKPq0gxEOQ\\u002fR+cG\\u002fLo05D\\u002femrQ+MRvkP99xQiW6MOQ\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fE1VG3wZN5D9ERZitKirkP\\u002fNsz6DTJOQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP\\u002fNXV8EUR+Q\\u002fJ6+GzRgT5D\\u002fIhAdy6SrkPw+FlpaxMuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fXp\\u002fHJUUX5D8ij6tIMRDkP0fnBvy6NOQ\\u002f3pq0PjEb5D\\u002ffcUIlujDkP6ylFZQ4IeQ\\u002fTfnYcp4j5D8nQAJeg0PkPxNVRt8GTeQ\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D\\u002fu6t7woUfkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D8khVx14BzkPymV1LDKLOQ\\u002fuWYHipso5D8tQs1PjjHkP16fxyVFF+Q\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002f33FCJbow5D+spRWUOCHkP0352HKeI+Q\\u002fJ0ACXoND5D8TVUbfBk3kPxdKQbhcMeQ\\u002f82zPoNMk5D+5rFDEsyrkP\\u002fy\\u002fZxDBIeQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fVWG6cnIn5D8pldSwyizkP7lmB4qbKOQ\\u002fbEOJLI0x5D9en8clRRfkP63yTqQAHuQ\\u002fqkPDh1Ys5D\\u002femrQ+MRvkP99xQiW6MOQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fE1VG3wZN5D8XSkG4XDHkP\\u002fNsz6DTJOQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP+7q3vChR+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkPx24pM1xJ+Q\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fXp\\u002fHJUUX5D8tKRalXBvkP6pDw4dWLOQ\\u002f3pq0PjEb5D\\u002ffcUIlujDkPyJ4xldUJ+Q\\u002fTfnYcp4j5D8nQAJeg0PkPxNVRt8GTeQ\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D\\u002fu6t7woUfkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D8duKTNcSfkPymV1LDKLOQ\\u002fyxeUKoQh5D9sQ4ksjTHkPztW6fs4G+Q\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fhmni0yUt5D8ieMZXVCfkP0352HKeI+Q\\u002fJ0ACXoND5D8TVUbfBk3kP+Nn53PUKuQ\\u002f82zPoNMk5D+5rFDEsyrkP\\u002fy\\u002fZxDBIeQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fHbikzXEn5D8pldSwyizkP8sXlCqEIeQ\\u002fbEOJLI0x5D87Vun7OBvkPy0pFqVcG+Q\\u002fNR9Xldgr5D\\u002fFx4fI3i7kP4Zp4tMlLeQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fE1VG3wZN5D\\u002fjZ+dz1CrkP\\u002fNsz6DTJOQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP+7q3vChR+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkPx24pM1xJ+Q\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fY4Z7abAr5D8tKRalXBvkPzUfV5XYK+Q\\u002fxceHyN4u5D+GaeLTJS3kPxrCKnZWJ+Q\\u002fTfnYcp4j5D8nQAJeg0PkPxNVRt8GTeQ\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D\\u002fu6t7woUfkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D8duKTNcSfkP5EBOyPRPeQ\\u002fyxeUKoQh5D9KhJSpjjHkP2OGe2mwK+Q\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fhmni0yUt5D8awip2VifkP0352HKeI+Q\\u002fJ0ACXoND5D8TVUbfBk3kP5UtlTEBLeQ\\u002f82zPoNMk5D+5rFDEsyrkP9C+1yMuKeQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fzxWO1hYV5D9q2vxyUSHkPy\\u002fbQ5OEIeQ\\u002fSoSUqY4x5D9jhntpsCvkPy0pFqVcG+Q\\u002fPQ0TeH0w5D\\u002fFx4fI3i7kP4Zp4tMlLeQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fE1VG3wZN5D+VLZUxAS3kP7OM3LWVIuQ\\u002fuaxQxLMq5D\\u002fQvtcjLinkP1KBtUUfP+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkP88VjtYWFeQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fY4Z7abAr5D8tKRalXBvkPz0NE3h9MOQ\\u002fxceHyN4u5D\\u002f0RmBPPy\\u002fkPxrCKnZWJ+Q\\u002fTfnYcp4j5D+B0bhwSzPkPxNVRt8GTeQ\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fVn1mSA425D9SgbVFHz\\u002fkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D\\u002fPFY7WFhXkP2ra\\u002fHJRIeQ\\u002fL9tDk4Qh5D9KhJSpjjHkP2OGe2mwK+Q\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002f9EZgTz8v5D9j+Sj47ETkP0352HKeI+Q\\u002fgdG4cEsz5D8TVUbfBk3kP0xEQj5yM+Q\\u002fs4zctZUi5D+5rFDEsyrkP1Z9ZkgONuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fzxWO1hYV5D9q2vxyUSHkP3hIG0qEIeQ\\u002fC74mMmc85D9jhntpsCvkPy0pFqVcG+Q\\u002fph4Vlfc55D\\u002fFx4fI3i7kP\\u002fRGYE8\\u002fL+Q\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fXZS\\u002fgbpF5D9MREI+cjPkP7OM3LWVIuQ\\u002fuaxQxLMq5D9WfWZIDjbkP9wz6BAgP+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkP88VjtYWFeQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fY4Z7abAr5D8tKRalXBvkP6YeFZX3OeQ\\u002fxceHyN4u5D9MgC8yJhbkPxE\\u002fRv5QNeQ\\u002fTfnYcp4j5D+B0bhwSzPkP1ZGUojSQOQ\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002fZSv8RXwi5D\\u002fcM+gQID\\u002fkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D\\u002fPFY7WFhXkP7RsZBdRIeQ\\u002fk+tkqzIx5D8LviYyZzzkP2OGe2mwK+Q\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fc1KKC7Us5D9OltluhTTkP0352HKeI+Q\\u002fgdG4cEsz5D9WRlKI0kDkP5D2azeAMuQ\\u002fstN\\u002fBWcn5D+5dMGeyDfkP2Ur\\u002fEV8IuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fzxWO1hYV5D+efJcYpijkP5PrZKsyMeQ\\u002fC74mMmc85D9jhntpsCvkPyaoiSPZD+Q\\u002fSovJn5st5D\\u002fFx4fI3i7kP9xFRCkYL+Q\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fVkZSiNJA5D+Q9ms3gDLkP8xalUyGPOQ\\u002fuXTBnsg35D+87w4wfCLkP9wz6BAgP+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkP88VjtYWFeQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fZLEJU7Er5D8mqIkj2Q\\u002fkPyAttWeCHeQ\\u002fxceHyN4u5D\\u002fcRUQpGC\\u002fkP3earOE6NuQ\\u002fTfnYcp4j5D\\u002fUyPSNbRXkP6QgDejRQOQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f2utkHH0i5D\\u002fcM+gQID\\u002fkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D\\u002fPFY7WFhXkP9ssbwDoKOQ\\u002fk+tkqzIx5D8LviYyZzzkP3sUeBmRLOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002f3EVEKRgv5D93mqzhOjbkP0352HKeI+Q\\u002f1Mj0jW0V5D+kIA3o0UDkP69JyEaCMuQ\\u002fSqaFL4Y85D+5dMGeyDfkP2aIdy59IuQ\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002fzxWO1hYV5D\\u002fbLG8A6CjkP8\\u002fmYDQvMeQ\\u002fC74mMmc85D97FHgZkSzkP9l\\u002flnpVGuQ\\u002fIC21Z4Id5D\\u002fFx4fI3i7kP9xFRCkYL+Q\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002fpCAN6NFA5D\\u002fzQ2QVfzDkP0qmhS+GPOQ\\u002fqRnJXUIi5D\\u002fK1S2KfiLkP52PCdahK+Q\\u002fkaJRZn8R5D8rombmw0PkP88VjtYWFeQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002fexR4GZEs5D\\u002f88jRAHjPkPyAttWeCHeQ\\u002fxceHyN4u5D\\u002fcRUQpGC\\u002fkP7DurtQOLeQ\\u002fTfnYcp4j5D8EUJ465yzkP3fUgK6lMuQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fytUtin4i5D+djwnWoSvkP5GiUWZ\\u002fEeQ\\u002faz34m4885D8fr4nGFhXkPzKw\\u002ftVmKeQ\\u002fz+ZgNC8x5D\\u002fXdtqqdS7kP3sUeBmRLOQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002f3EVEKRgv5D+w7q7UDi3kP0352HKeI+Q\\u002fBFCeOucs5D931ICupTLkP\\u002fNDZBV\\u002fMOQ\\u002fSqaFL4Y85D+fUI896inkPwM\\u002f4JBwF+Q\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fH6+JxhYV5D8ysP7VZinkP8\\u002fmYDQvMeQ\\u002flIILXsww5D97FHgZkSzkP4Tv5AFhP+Q\\u002f6Ng5+Gct5D\\u002fFx4fI3i7kP9xFRCkYL+Q\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002fJ0Nsw6gy5D\\u002fvGA2wVjfkP0gBniy9OOQ\\u002fn1CPPeop5D8DP+CQcBfkP74GMnNSK+Q\\u002fkaJRZn8R5D9rPfibjzzkPx+vicYWFeQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002fexR4GZEs5D\\u002f\\u002fxxCD0zPkPwYcjuxnLeQ\\u002fxceHyN4u5D\\u002fcRUQpGC\\u002fkP7DurtQOLeQ\\u002fTfnYcp4j5D8EUJ465yzkP1kavgWqMuQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fAz\\u002fgkHAX5D+Ixd4HICzkP59k3L5gMOQ\\u002faz34m4885D8fr4nGFhXkPzKw\\u002ftVmKeQ\\u002f\\u002fHouCjEx5D86CwsVhj7kP3sUeBmRLOQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002f3EVEKRgv5D+w7q7UDi3kP8UUjwHOK+Q\\u002fBFCeOucs5D9ZGr4FqjLkP+8YDbBWN+Q\\u002fSAGeLL045D+fUI896inkPwM\\u002f4JBwF+Q\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fH6+JxhYV5D8ysP7VZinkP7BChW0RL+Q\\u002fOgsLFYY+5D97FHgZkSzkP\\u002f\\u002fHEIPTM+Q\\u002fBhyO7Gct5D8qdvHDvizkP9xFRCkYL+Q\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\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+       "                            {\n",
+       "const graphDiv = document.getElementById('5fdcca1a-1f79-4b23-9f90-b899fcdd4ef1');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
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+       "\n",
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+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
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+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for param in project.free_parameters:\n",
+    "    project.display.plotter.plot_param_distribution(param)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56",
+   "metadata": {},
+   "source": [
+    "Finally, the posterior predictive plot propagates the sampled parameter\n",
+    "uncertainty into the calculated diffraction pattern. Comparing this to\n",
+    "the zoomed measured-vs-calculated view helps assess whether the sampled\n",
+    "model family explains the data in the region of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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qWh0hZUBiuFUtmiBlQH6\\u002f0JUXIGVAp23QkZUfZUD1shUfFB9lQFeSbTuTHmVAqNiw5BIeZUDe1cMYkx1lQFgYltUTHWVAKSoiGZUcZUBXUG3hFhxlQP1LhyyZG2VASB2K+BsbZUAuyJlDnxplQPAa5AsjGmVANnagT6cZZUDclg8NLBllQEZhe0KxGGVARa427jYYZUByGZ0OvRdlQADREqJDF2VA+WYEp8oWZUDSo+YbUhZlQFdaNv\\u002fZFWVA6jx4T2IVZUDxszgL6xRlQJ61CzF0FGVAy56Mv\\u002f0TZUAUDV61hxNlQA+6KRESE2VAnFeg0ZwSZUBZbXn1JxJlQBY3c3uzEWVAWYRSYj8RZUDxmOKoyxBlQGkO9U1YEGVAnrZhUOUPZUAmfwavcg9lQLpVx2gAD2VAhg2OfI4OZUBWRUrpHA5lQMNO8a2rDWVAHhZ+yToNZUBNC\\u002fE6ygxlQIILUAFaDGVAtUumG+oLZUAPRASJegtlQBScf0gLC2VAoRczWZwKZUC9hD66LQplQDaqxmq\\u002fCWVACjf1aVEJZUCcsvi24whlQLFtBFF2CGVASHRQNwkIZUAegBlpnAdlQCXsoOUvB2VAqqgsrMMGZUBVMAe8VwZlQP99fxTsBWVARAPptIAFZUAIoJucFQVlQLqa88qqBGVAeZlRP0AEZUAhnBr51QNlQCP3t\\u002fdrA2VAX0+XOgIDZUDZlirBmAJlQF8K6IovAmVAMzBKl8YBZUCq18\\u002flXQFlQNUZ\\u002fHX1AGVAO1tWR40AZUCqTmpZJQBlQC75x6u9\\u002f2RAKrcDPlb\\u002fZEC9QrYP7\\u002f5kQF27fCCI\\u002fmRAz674byH+ZEB\\u002fI9D9uv1kQEikrclU\\u002fWRAyk1A0+78ZEBR3TsaifxkQGjBWJ4j\\u002fGRAOCxUX777ZECwJ\\u002fBcWftkQLmr85b0+mRAabYqDZD6ZEBsZma\\u002fK\\u002fpkQL0Xfa3H+WRAz4JK12P5ZEBW3q88APlkQNEDlN2c+GRA\\u002fJbjuTn4ZEBwMJHR1vdkQIeLlSR092RA47fvshH3ZECuTqV8r\\u002fZkQPOrwoFN9mRARCxbwuv1ZEATb4k+ivVkQPmdb\\u002fYo9WRAYrk36sf0ZEDn6hMaZ\\u002fRkQOTdPoYG9GRAox78LqbzZEC0gJgURvNkQPyMajfm8mRAEPjSl4byZECIIT02J\\u002fJkQAidHxPI8WRA0sX8LmnxZECjXWOKCvFkQPc37yWs8GRAn\\u002fJJAk7wZEDavCsg8O9kQEUuXICS72RA7C6zIzXvZEAP8hkL2O5kQFgFjDd77mRARnYYqh7uZED2D+Njwu1kQISzJWZm7WRAh80xsgrtZECF63FJr+xkQGJ0ay1U7GRAY4fAX\\u002fnrZEBxBTLinutkQATJobZE62RAfxEV3+rqZEA2J7ddkepkQEA+3DQ46mRAp54EZ9\\u002fpZECJGeD2hulkQJjTUecu6WRAeW50O9foZECzm572f+hkQFMlaBwp6GRA33mvsNLnZEAgy5+3fOdkQFjRtzUn52RA3kbRL9LmZEDeMimrfeZkQEEdaa0p5mRAXEmxPNblZECHGaRfg+VkQJTCch0x5WRAI3zrfd\\u002fkZECkYYmJjuRkQOM\\u002fhkk+5GRAkpTux+7jZEBLELgPoONkQOj42ixS42RAp9luLAXjZEA8AcscueJkQCdkqw1u4mRAaJRaECTiZEBfn+E32+FkQF3JPpmT4WRAvkykS03hZEDpesBoCOFkQDfiEA3F4GRAm29CWIPgZEBLZ0FxQ+BkQJTaxoAF4GRAy9X5rsnfZECDPnYtkN9kQEIUITRZ32RA3YMXAiXfZEAEE9De895kQA96dhvG3mRAAEeLFJzeZECNClYxdt5kQFGh2tpU3mRAXtL\\u002frDjeZECVOCFJIt5kQLHAtWoS3mRAEjCI7AneZEC9xDDPCd5kQBGtKkET3mRAusX9qCfeZEDb1h2ySN5kQE8fWV143mRA8Ev\\u002fFbneZEBrWlrNDd9kQHpDtB5632RAdPAQfwLgZEAtfjF9rOBkQMZQ\\u002fhh\\u002f4WRApYdZP4PiZECHJWqJxONkQMfVK5xS5WRABN\\u002fzTkTnZEBDDW0FwOlkQGgPrCIS7WRAqedWw+TxZEAI7buJjflkQPDD5JMQB2VAMQunf2QgZUANIzDJV09lQAaEvVdjoWVA9ofVTX4kZkA0rqfnQOBmQKs3p034zGdAmINzOO3KaECN1uCcQKFpQNXk7mfVD2pA41UAxcHqaUCnQgnGKUJpQL0qPrH6UGhAcF83gBhVZ0AViIpRs3tmQBswDtdr2WVAdoDyHuRtZUArzEioci1lQCZ3ENS6CWVAnnMERID2ZEBl5cCC1utkQPYUbaJD5WRA4WaHSsHgZECcYSFmV91kQOdOxTiV2mRA5KIv30XYZECl9klnTNZkQKGjECmW1GRAK87awhXTZECub68WwdFkQF\\u002fYz0yQ0GRAyJkSOn3PZEAYN8j2gs5kQKgN+JKdzWRAUJB73snMZED\\u002f6C0\\u002fBcxkQNycSpFNy2RA9LRCD6HKZEAOdBk\\u002f\\u002fslkQEl75ONjyWRAa1xs8tDIZEAHUi+IRMhkQDT9OOS9x2RAOotlYTzHZEAwjMBxv8ZkQDvTwppGxmRATN9ActHFZEAc4uWbX8VkQIx3H8fwxGRA8jBkrYTEZEAgvMMQG8RkQJ4Bs7qzw2RAAFsJe07DZEC6KyYn68JkQA3ZN5mJwmRA9XOerynCZEDTd2VMy8FkQNLY0FRuwWRAkEr6sBLBZEAeM3xLuMBkQB0vKBFfwGRAhmfH8AbAZEAmROPar79kQAVDlcFZv2RATO9cmAS\\u002fZEBuG\\u002ftTsL5kQMWkUepcvmRAUCNHUgq+ZEDw\\u002f62DuL1kQFx\\u002fLndnvWRApmAzJhe9ZEA7vNiKx7xkQAHc3J94vGRAVM+SYCq8ZEAChtbI3LtkQLBEAtWPu2RAT0vlgUO7ZEBPi7vM97pkQC5QJrOsumRAdsAlM2K6ZEDxIBNLGLpkQGFxV\\u002fnOuWRA6KTaOIa5ZECuEkgNPrlkQCi4Lnb2uGRA4CNjc6+4ZECH+PwEabhkQNXVVCsjuGRA+J8C5923ZECTH9w4mbdkQCD18yFVt2RAfduYoxG3ZEAlNlW\\u002fzrZkQEro7naMtmRA0XJnzEq2ZECdV\\u002fzBCbZkQGTBJ1rJtWRAuG+hl4m1ZECO5199SrVkQBjpmQ4MtWRASSzITs60ZED1ZadBkbRkQAaYOutUtGRA3a\\u002fNTxm0ZECNdvhz3rNkQDzXoVyks2RAsIADD2uzZEDV562QMrNkQK1Dguj6smRAe1JQIsSyZEBJE4k+jrJkQIq+TERZsmRAI04tOyWyZECldTUr8rFkQBU\\u002f8BzAsWRA7VpxGY+xZEDnM14qX7FkQAvZ91kwsWRApNIlswKxZEA9+YFB1rBkQJVoZRGrsGRAeqv2L4GwZEA7QTmrWLBkQDGhHpIxsGRAmOWY9AuwZEABTK\\u002fj569kQK+\\u002flHHFr2RA26jAsaSvZECjQwq5ha9kQFDKxp1or2RAsMjqd02vZEAh+i5hNK9kQFohOXUdr2RApVjJ0QivZEBoa+yW9q5kQNHeM+fmrmRABmj059muZEC2qovBz65kQFY8rp\\u002fIrmRAdw+\\u002fscSuZECDmDErxK5kQHiZ8UPHrmRAFc6zOM6uZECRhidM2a5kQAGy6MbormRAtJZu+PyuZEC7ANk3Fq9kQG4e2uQ0r2RANrfBaFmvZEBkSK83hK9kQPui8dG1r2RAtO6bxe6vZEAwiVmwL7BkQFsljEF5sGRAXe7BPMywZEAf4iM+tLFkQD4wI3mnsmRAU5IZAaezZEAGH2AMtLRkQG3kz\\u002fnPtWRAQ6RLV\\u002fy2ZEAnraTEOrhkQPCWNFWNuWRAuNYfZ\\u002fa6ZEBGwlyieLxkQE3IPxgXvmRApMWzV9W\\u002fZEAyHBuGt8FkQImpHH7Cw2RA8AYF9vvFZECsE+6vashkQM9im7YWy2RAYiX\\u002fqgnOZEALCMgnT9FkQDELXEL11GRAJ0uFMw3ZZEBwYEE2rN1kQNcnULHs4mRA6m28ye\\u002foZEDS5NaN3+9kQDQZ+hDy92RAsIkgUW4BZUDNzMWBtQxlQNywqyxZGmVAYVzmW1UrZUDfoJ7kuEFlQON0KGqqYWVAJD+H0\\u002f6UZUCqUAakR\\u002fFlQPq3\\u002f6mQnmZAMtBO8lzcZ0BPejsm+vppQLKxi6ckQG1AzlPKsI7bcEACYXLgbX1zQD58R7mj\\u002fnVAim2KbAuxd0BxiIvOK\\u002ft3QDNSUZv2tHZA4D6Ts1RodEDyUaO+M71xQAKrkYgBqW5AZJRtF0H2akBDkCa4MXhoQI7iGBlI9mZAhC5dOv0fZkCgwFDoYa5lQPyYPT9YcWVAi1aCPH1NZUDWtKta7TVlQGvxVRXOJGVAmlj0ZH8XZUCFgZeNywxlQH0GGD4HBGVA\\u002fA2Hc8X8ZEDcHNh6uPZkQIgmn5Gl8WRA06GFYl\\u002ftZEAGf+hxwulkQC7+u6qy5mRAVUEHnBnkZEB3uygu5eFkQJlBrawG4GRA6oMBDXLeZEDKAIBhHd1kQNiYM20A3GRA9p\\u002f3TxTbZEAee+xEU9pkQOgz12642WRA9\\u002fEprz\\u002fZZEBWwkeF5dhkQB1YMPSm2GRA234xbYHYZEBSjY++cthkQLl5VAV52GRAlAekoZLYZEANABctvthkQMd0unL62GRAQO2KCkbZZEDNnphvoNlkQOF+bOAI2mRAKc98tH7aZEA0uJ5cAdtkQI3N8Y6Q22RABPDv7CvcZEDTaTfs0txkQGCvi0uF3WRAyTmp2ELeZEDW+RFvC99kQCLFC\\u002ffe32RAKjPKZL3gZECqgr63puFkQDoRCfqa4mRAR7kIQJrjZEBjFAWopORkQFIw8Vm65WRA78BEh9vmZEDtQ+pqCOhkQETpQElB6WRAVmYxcIbqZEAIJVU32OtkQOCELwA37WRA3CR5NqPuZEBLan1QHfBkQBRkOM+l8WRAbDB6Pz3zZEDMOIY65PRkQJiKGmab9mRAXMiEdWP4ZEDEu34qPfpkQPZpJVYp\\u002fGRADPwM2ij+ZEAvKnSpPABlQMc\\u002fmsplAmVApUM7WKUEZUDHUjaD\\u002fAZlQNzRYpRsCWVAF8SZ7vYLZUBxWfkQnQ5lQGCmaZlgEWVA+XlqR0MUZUA+cjP\\u002fRhdlQA3JMM1tGmVALufo6bkdZUABoVi+LSFlQLQl1ujLJGVA\\u002fi2NQpcoZUCb7KnlkixlQEe\\u002fSzTCMGVAJr5c4Cg1ZUC2KnD0yjllQAGPz92sPmVAb0zld9NDZUBwpTsZREllQGdCUaIET2VAvTGQjhtVZUCu48IHkFtlQNY6dPxpYmVARTi9ObJpZUDS\\u002fByJcnFlQLQCGNO1eWVATxiDR4iCZUDivI+M94tlQDzQVNwSlmVAto3HoOqgZUAZfqMdk6xlQCVkAl4iuWVA+eoEbbHGZUDman3WXNVlQPqtaEFF5WVADO5FKpD2ZUBn1gTFaAlmQC1daBABHmZAcGKVJ5M0ZkBhm0viYk1mQFB0V9m\\u002faGZAM2Si6weHZkDiIplpqqhmQM8XcRkszmZADh6lWCz4ZkAfMAC4aydnQF4LBJPUXGdAjARmVoaZZ0Bt6XZz5N5nQAZIS2yqLmhAz99kDweLaEDFjV0nwvZoQE7SrKVgdWlAr1f957YLakA+1JzFS8BqQFe7jPW7nGtAygu5692xbEBI3c3bnSJuQOYyjvf0G3BALwD0W0vQcUBQeCTwhut0QIhwtnbYzHpAZLb10kjMgkDD0e4k7fWLQDi\\u002fkfF89ZRA41BX0BJLnkDMf\\u002fbCW2+kQODBjTt0JqlA7PMnZHLiq0C8vYwYl42rQAwW1yNuSKhA\\u002fURiSD9So0A\\u002fbN52uSScQF0dfP9sRZNAwR3iVrGgiUDgsWYGj1aBQMYChPoEFXlA+cggv\\u002fHpc0APUF7hLy9xQCFp9OawU29AxkaXbuFvbUDU8I+7jR5sQItEFz6DHWtAEP371EtQakCXwV9uIqhpQLt9Y+w+HGlAa6WQ6p+maECuSVkw10JoQI5Hj8OC7WdArn55TQykZ0AP+glDZWRnQKbP7pnrLGdAPVebO1D8ZkBbwSwuhNFmQHNEAYSqq2ZABzZavg2KZkCjA3a1F2xmQNdWs11LUWZAxfHo8D85ZkAVKk8knSNmQM0DwioYEGZAJpDVU3H+ZUD1MT4kcu5lQAZoz8zr32VAem\\u002fB67XSZUBbPbGIrcZlQNUPXT+0u2VA9pbNj6+xZUBb8KNMiKhlQF8w0iEqoGVAvv41L4OYZUBJlXmzg5FlQH\\u002fsV8Qdi2VA3XvxEUWFZUBu8FCz7n9lQLyUmfoQe2VAFSOhT6N2ZUBWY\\u002fAPnnJlQFxeVHP6bmVAqXtPdLJrZUCIYNi7wGhlQBxU7Y8gZmVANGCXxM1jZUDTRgmvxGFlQDlklBoCYGVA6EI5P4NeZUDPdqO5RV1lQB2haIRHXGVA85Np8oZbZUC6qDqqAltlQH2wfaK5WmVARJ4cH6taZUANTFiv1lplQKGjoiw8W2VAZ3dW2NpbZUAELmLNs1xlQJ\\u002fSeffHXWVA3F0UbRhfZUDBg4Xvp2BlQHP59LB2YmVAPMXOtIZkZUAmwrxc2mZlQEF9gW90aWVAP+8cIVhsZUBT5mQciW9lQMGoQ44Lc2VARAvoMOR2ZUB2XOReGHtlQNr69SSuf2VAlvEkU6yEZUDPq72WGoplQCr0h5YBkGVAHcLBE2uWZUBjlsEQYp1lQKBmVv7ypGVASsQ58SutZUBufjfhHLZlQJMIEfTXv2VA\\u002f3eg1nHKZUBs6V4nAtZlQFFNP\\u002faj4mVAJ1HXXnbwZUCG5idDnf9lQLxtGC9CEGZAr4QHb5UiZkDS8+VmzzZmQPuoajsyTWZAY0d35AtmZkD5DkcUuIFmQNymPEChoGZAMKHYhU7DZkCZ46fkXupmQDqGQpKTFmdA8+ueHNlIZ0C4uPF+U4JnQJtiZ0luxGdA+jZ1fvIQaEAxWXecJGpoQHfJj+ru0mhA8N4t5vtOaUDnHR4nu+NpQLVMxKVImGpAh+ZZhEV4a0B+96McKZpsQMywmeNMLm5AvO1RzmhOcEDCzxrDVXByQFKaDdYidXZAglQsX2cCfkCbFTwNBayFQNU\\u002fvvuDKZBAMKhaKmCsl0ACDU8tlWCgQMbvrt7oyaRAR6oNWGHap0DTY7XAO2KoQD4QWxccKaZA83lWa7cYokBIf0y\\u002fAfCaQD0WuZnxuZJAGADv4d4kiUA7QkClxRGBQL08Jya6p3hAnPy0bWCDc0B9R14Cos5wQH7h2vYJom5AKVWLFivSbEBckY61DZNrQG91lIvqoWpAcH77Al7iaUBz1Z\\u002fA8UVpQKpKPcEhxGhAwajHqy1XaED0r2Du2PpnQJpEWJb6q2dATDn0Vh5oZ0BF2YI1VS1nQMIF5GcX+mZA5bmzvivNZkA0\\u002fw6XlaVmQMNo62yHgmZAKqBywVhjZkBkYcptfkdmQO7FxL6ELmZAQoue5AoYZkCZR1hivwNmQIR1\\u002fD5d8WVA\\u002fBJSy6ngZUAoxBnactFlQGCPYFGNw2VA4aueAdS2ZUCLouezJqtlQOBJ1mNpoGVANwtsnIOWZUBg\\u002fwjxX41lQOgiG43rhGVAuw1E1hV9ZUDoCpQd0HVlQL0sJ10Nb2VA6OP4\\u002f8FoZUAlji2y42JlQM4IZjhpXWVA8Jf0TEpYZUApNgCCf1NlQLuOzicCT2VACLmPNsxKZUDpByM72EZlQEw5Y0YhQ2VAphmb3qI\\u002fZUA7F9PyWDxlQI3ytM8\\u002fOWVAmTfNFVQ2ZUB84fqwkjNlQGzL5ND4MGVAyqJS4oMuZUCEJUyJMSxlQNzB5Zv\\u002fKWVAoU2lHewnZUB5oGw79SVlQAt\\u002f2kcZJGVAiIgUuFYiZUCVze4grCBlQNltZzQYH2VAIhxuv5kdZUD9w\\u002fCnLxxlQIHHJ+vYGmVACHMdnJQZZUA1X23iYRhlQNKQOfk\\u002fF2VAmURULi4WZUAKhZ7hKxVlQDT4nIQ4FGVAatJFmlMTZUBEngy3fBJlQKysMoGzEWVA4KNlsfcQZUDT+bgTSRBlQO1sC4mnD2VAfB3vCBMPZUBmPDKkiw5lQDrB7l4XDmVAm0ScsLANZUABGN4NWA1lQIFi+RgODWVAFkIuq9MMZUDvWubgqQxlQNb+rSmSDGVAqeFOXY4MZUCXwfHXoAxlQDQh\\u002f5\\u002fMDGVAYyG3mRUNZUBuZIDOgA1lQPo93+QUDmVAo62Iv9oOZUD\\u002fL1gI3g9lQBhn0dovEWVAfV1HLOwSZUBJl2ClSRVlQLqtl8jDGGVAEUZVg3weZUDh0BuN8ihlQBloQP3CPGVAE6GkurphZUCCDKcV\\u002fKFlQBVzTQIlBmZAPxMOM+KOZkAqfblowStnQOPdacJXumdA3Ucpy4kMaEBjrWCcqgBoQKnYTKSDnGdALWpbIFAGZ0CZoXHHu2tmQH336hIl62VAeBYdOmOPZUBm4YTuuVVlQIKgZ28BNWVAHncLfpcjZUDxnzHxXRplQK3FuiQhFWVAuUsHeccRZUB\\u002fVZWbYQ9lQPuAmJSJDWVAXZjqGREMZUDgJ\\u002f5s3wplQPR1B9PkCWVA30GGJRYJZUDSsfcRawhlQEx0wDrdB2VARZ2brmcHZUDGDY2LBgdlQJl3o7y2BmVAkYHTyXUGZUCUqHu0QQZlQFNr7NwYBmVAffFy7vkFZUA\\u002fXiXQ4wVlQLLjMpnVBWVA0LvTh84FZUC16TP6zQVlQPbf32jTBWVAUK5ZYt4FZUBwDZSH7gVlQDkkIYkDBmVAwQPvJB0GZUDw1XQkOwZlQLZ0OltdBmVAUjOppYMGZUBHgBjorQZlQFj+Cw7cBmVA1P+aCQ4HZUAlHfvSQwdlQCINKWh9B2VAuyvdzLoHZUBS7+oN\\u002fAdlQFXviDVBCGVA3iRaV4oIZUBMH3eM1whlQDfdmvMoCWVAvoNjsX4JZUAlwKfw2AllQHcg4uI3CmVACDi0wJsKZUBF+YPKBAtlQPZUNklzC2VAC+4Kj+cLZUAXkJ34YQxlQAweE+7iDGVAaep45GoNZUCo5l5f+g1lQFrntvKRDmVA+HEFRTIPZUCLAuwR3A9lQHxniSyQEGVA+hoRhU8RZUCK4vcqGxJlQAitnVL0EmVAAvHfW9wTZUBJLZfX1BRlQCxrW5HfFWVA1iXFmf4WZUAyM8VSNBhlQFAo5H6DGWVAmJnzU+8aZUAdARuSexxlQDfecaAsHmVABJy5sAcgZUD9\\u002fE3sEiJlQH+qGK1VJGVAUkVRxtgmZUCzPTbipillQH6q4vvMLGVAMFg5\\u002f1owZUAwmfqcZDRlQKDMLGcCOWVAyCclU1M+ZUCje+\\u002fMfkRlQAnz5aG3S2VAqX3sRkBUZUBELxTLcV5lQKpYHg\\u002fFamVAoGlt\\u002fQV6ZUDcdRmMqY1lQHDn57fEqGVAtD6ABb7SZUDm1rzaShtmQMxtr+Bdo2ZAtz79zd6iZ0BsYksYkmVpQI3iw2RKM2xAw4GROSoQcEB5Ya3x82NyQGM9r8OqmHRA6DQWrkAGdkBeTY5Ibw92QHJqgfbztHRAYDA1nMeGckDceamemy9wQNXdmc0YaWxAhmwfPLCLaUDlWxi2HrpnQJkpnhUbsGZA9vSFph0iZkBvFQRhB9dlQAnNnIukrGVAETXbFbyRZUDqP4+3i35lQKP22JnKb2VA6HIEnP5jZUC7xd0kXlplQCARfGRpUmVAj453A8ZLZUCwQTPSMEZlQPZn1Il2QWVA3pC1bG89ZUDEKZFR\\u002fDllQNrsV4YEN2VAIWyuR3Q0ZUCbyyigOzJlQGXqPpFNMGVAvZVacZ8uZUC3sZxwKC1lQNqyLTrhK2VAxbXSqsMqZUBgkoOXyillQMSUKKDxKGVAtjioCzUoZUBAgSarkSdlQC8C3sIEJ2VAxEBd94smZUD48js+JSZlQEH+kdHOJWVAovKiJYclZUDcsU\\u002fgTCVlQBaQ99EeJWVAxF+E7\\u002fskZUCj4mpN4yRlQIYEdBvUJGVADdEroc0kZUA60Nk6zyRlQInJ61bYJGVAsirAc+gkZUAus8Ad\\u002fyRlQA2\\u002fwO0bJWVA676Uhz4lZUBZM9qYZiVlQOb16NeTJWVAGsrnAsYlZUAcKv\\u002fe\\u002fCVlQJkQpjc4JmVAXisF3ncmZUB8b22ouyZlQDh933EDJ2VATaShGU8nZUB+q+KCnidlQJHDZ5TxJ2VAZ0hEOEgoZUAwJJpboihlQNvSYe7\\u002fKGVAfyc542ApZUAJFDgvxSllQKrNysksKmVA076QrJcqZUAZyz\\u002fTBStlQNp5izt3K2VAPqsP5esrZUCohz7RYyxlQMdkUQPfLGVAPGU8gF0tZUBFoKRO3y1lQHem2HZkLmVAbj\\u002fLAu0uZUBJQhD+eC9lQGpw23UIMGVA5T4BeZswZUCRf\\u002fkXMjFlQBzf42TMMWVAWzGOc2oyZUDpiHxZDDNlQNUa8y2yM2VAlfIBClw0ZUDofZIICjVlQOX7dka8NWVArN174nI2ZUD6Knv9LTdlQEUAcrrtN2VA8z+YPrI4ZUATlHqxezllQDLiFj1KOmVAZlf7DR47ZUBEN2hT9ztlQL+ddD\\u002fWPGVAV2k2B7s9ZUAZie3ipT5lQIfwMg6XP2VAv3oryI5AZUAEDr9TjUFlQMpZ1PeSQmVA5pKR\\u002f59DZUBEnKK6tERlQIYWhX3RRWVA\\u002fN3aofZGZUBxjcOGJEhlQGWsPZFbSWVAE0KQLJxKZUAEm73K5ktlQDQmAOU7TWVA22hS\\u002fJtOZUASJQOaB1BlQLHwVlB\\u002fUWVA\\u002fJ44uwNTZUDOCfqAlVRlQMT2JlM1VmVADwts7+NXZUAR\\u002fpMgolllQCSBnb9wW2VApqLstFBdZUDczZr5Ql9lQKvx6JhIYWVA58\\u002fXsWJjZUDJAOt4kmVlQEvUGzrZZ2VAPPIBWzhqZUCmbDhdsWxlQCftBuFFb2VAb8FXqPdxZUCb1QSayHRlQCwsh8W6d2VAfyUWZ9B6ZUAX+EXsC35lQJEmN\\u002flvgWVAuJlrbv+EZUDMYllvvYhlQFMm12mtjGVAheuCHtOQZUDFr0mqMpVlQOvZPJHQmWVAUbzqyrGeZUBTCnnQ26NlQHLdy6xUqWVAKhcSDyOvZUB6KyFgTrVlQNZrH9veu2VAPrEVqt3CZUB0+iAHVcplQHS\\u002fI2NQ0mVAQlgHk9zaZUBncNoECORlQCxuZf7i7WVAf1Et53\\u002f4ZUAjl1ag8wNmQKnAc+1VEGZAqWgQ8sEdZkDF7cfIVixmQMw6ADo4PGZAaDsPmo9NZkB6zMbYjGBmQDJ0T89ndWZAvhEh3WGMZkChvyjqx6VmQNhSS+v0wWZAHnY2EFXhZkBx1vHOaQRnQGuAtRnRK2dAkwtWE0hYZ0Cxat7ytIpnQIWiBrE0xGdAR5nLYykGaEAsspIjT1JoQApNzinWqmhAsVK+tIcSaUCHCvqQEI1pQGpdMW1DH2pAbESdkJrPakAepCTghKdrQOyfJxTGt2xAzeAvGkshbkBorowQXRZwQJe8ssSSwXFAgNIdHEbFdEDc7rXVDW16QB5c1gxoXoJAn4hw7v0Ei0DTufVXoO+TQMpH0Ta5N5xA73vs\\u002fKSUokC+NUJa+EumQFYD1a7GxqdAsXhnJu5VpkDmm5ENPLeiQAhwIHKPcZxAPH7dGxkXlECEpOuGvzSLQILRL0GOfYJAWfzvrA6MekBHNd2fM850QH7ex9M8wHFApP44pIEOcEBRsWN5qw1uQBB1zAnFo2xAZHFFAxOUa0BL9wq27bxqQBk81sHUDWpAI\\u002fSvtwd9aUCix8Ij5gNpQGoXEA6XnWhA3cpMWnFGaEAzWm20o\\u002ftnQNe5A4f7umdAUwfVF72CZ0DUK5bXhlFnQAVz12g8JmdAU6fiF\\u002ff\\u002fZkCIgNA3+t1mQFfEnFKqv2ZAfxmKxoakZkDwjMe6IoxmQM3q3oUkdmZAM2AarD9iZkDn25U0M1BmQM2gh6bHP2ZAV1DBbM0wZkDNz+2HGyNmQIgaP3+OFmZA07iWggcLZkCMViS0awBmQKUlrpGj9mVA2c1md5rtZUC8kX83PuVlQJBypMJ+3WVAYRdW3k3WZUCwzq\\u002fmns9lQG1poZlmyWVAQpIE6prDZUBfbEHZMr5lQIkUc1YmuWVAxG4tIm60ZUBZDi22A7BlQBX7WTDhq2VA3ymgQAGoZUAO6TUZX6RlQIk3+mD2oGVAycajJ8OdZUAt5IXbwZplQI05u0Dvl2VADLOPaUiVZUB0RQuwypJlQEwuh7BzkGVAyPY+RUGOZUAncdaCMYxlQGKV1rVCimVApDAtYXOIZUDJpMc9woZlQNuncDsuhWVAwL8tg7aDZUAblHV7WoJlQCTPv84ZgWVA3rwndfR\\u002fZUBkySzC6n5lQLc4FXj9fWVAZz4w4y19ZUBIbmQAfnxlQKWL17Twe2VAyVYHI4p7ZUAlB1NAUHtlQKlQ9R1Me2VAS4QiU417ZUAEmVl5M3xlQCv9CH2GfWVAWiEXRyOAZUAWYucIUYVlQJf1WD7Bj2VAvzsRkUSjZUC7KL2MQcRlQOHfLCBO9mVARhpkvmk3ZkBGvdTmcX9mQJw2kT5FvWZA1ijyY7zXZkCU6AdMxMRmQH63P\\u002ffai2ZAvuZCwDVDZkDA1Dwo4P5lQLs93fryyGVA4ixSvw2kZUBPh5Rsf41lQCzyKKvqgGVAZbRaoBx6ZUCGFVbyRHZlQCJmNj\\u002fsc2VAXiKpHlFyZUD+atUOHnFlQPYqqGotcGVAn5sDyGxvZUD\\u002fTqxx0W5lQIYScDZUbmVAhqIP2+9tZUDge8FsoG1lQDpPBOJibWVAG5U23TRtZUD0B+eBFG1lQA1SN1YAbWVAQoGHLPdsZUDgG\\u002fgS+GxlQAfFGkcCbWVA4R6sLBVtZUDKKYlGMG1lQDQSTjFTbWVAqaI1n31tZUAVdO1Ur21lQDByJyfobWVA7\\u002f2\\u002f+CduZUCQqFq5bm5lQKViX2S8bmVA2t9HABFvZUA6bjGebG9lQE7\\u002fqlnPb2VAyO65WDlwZUAiZRLMqnBlQFY3gu8jcWVAYfmOCqVxZUBOu0lxLnJlQLSxXIXAcmVAYvJYt1tzZUDqjUyIAHRlQFbCq4uvdGVAcOuaaWl1ZUDpWKrhLnZlQEOFGc4Ad2VAn4i8J+B3ZUD4ZKUKznhlQEpbu7vLeWVAcm11r9p6ZUDoS\\u002fuR\\u002fHtlQOU6AlEzfWVArmTTJ4F+ZUACiwiu6H9lQNrQtelsgWVAbzfvZRGDZUDuyuNO2oRlQI4QL5XMhmVAIzGOGu6IZUAk0unqRYtlQO+Et4XcjWVAHug5PbyQZUA5FFOz8ZNlQPycxH6Ml2VASHVdCKCbZUDDZZG2RKBlQBGp+YiZpWVAmrsgWcarZUDMzTTKAbNlQEbVWAWRu2VAHf1EpNPFZUBTSMdgTdJlQE47YhET4mVAjibKq1L3ZUBU87WNAhdmQFiEL1wdTGZAOZY4ifmrZkCfg27ax1tnQE\\u002fweuAmkGhA8ABpUmR8akDx0pXDYi9tQJJdHM3cPHBAZVIzZMHkcUAJX8FejQxzQEjEobWaMXNAm6DhQf1RckDqeO5BPcpwQHahHpUXNG5AxjVHIHNJa0CG0Srq2SFpQNYg87nDuWdARt6Nh0LnZkACFS9tCXVmQKvyD9MDOGZAeCoLzpkVZkBhpFfzr\\u002f9lQDMBXn2p8GVA7bXh3ablZUAJqbRgQN1lQPsWRIi+1mVAVoSn7LLRZUDnlfplz81lQFW4+7ndymVAsk1UwbbIZUAc3SZeOsdlQGuOQ79PxmVAkM0zqOPFZUBW9GsQ58VlQIH3BT5OxmVALA4pFBDHZUBpL3iOJchlQHvgcl2JyWVA0u5EmzfLZUCu4vSSLc1lQFHBlZVpz2VAOP1U2urRZUDLAhpnsdRlQDTrDAC+12VAL\\u002f3PHBLbZUBPc5Dir95lQP9aVyKa4mVAzo85W9TmZUBjbTLAYutlQEc7m0FK8GVALj1UmpD1ZUAg7NVgPPtlQNkcgxxVAWZAZ5u5X+MHZkBp+0fn8A5mQKgtH8CIFmZAHl5PdLceZkCoyaRAiydmQNyEjlQUMWZAPppjHmU7ZkB1\\u002fqCmkkZmQL1AZf20UmZALBpJvudfZkAZjs2vSm5mQAs8BoUCfmZABpYFyjmPZkDQnQgGIqJmQJDipyH1tmZAHFm5I\\u002ffNZkCocYBdeOdmQE4W5SbYA2dACQ+TVogjZ0AlCWexEUdnQPWz8PEXb2dAQ3nCF1ecZ0B4sOWr0c9nQCJCGNq4CmhA+xFuv4lOaEDal833JJ1oQHgTfBjv+GhAYys5i\\u002f5kaUCZXW67X+VpQGwIFWiHf2pADzBGwz47a0AB7uCr+CRsQP5DDn8tVG1AEuth7737bkBeYio3UsdwQPzR5LpgAXNAe89BtSUTd0BSzsmAC3F+QIJl7jmpeoVACjz35N0Oj0DTH9JczgGWQC7KzSg5iJ1AwJNgmDw8okCUEBfdQD6kQEgGJvhS8qNArwVEEZd5oUCf2jyPusKbQAqZG8oXbJRAPGOOCnypjEBUjSl8vNqDQE3QJnA3cHxAmZqNdGHxdUDfLtMiomVyQFsBtmpEc3BAzCTfemKUbkDwMtk+0QptQGrIooch7GtAmRAGusoPa0ARnxWVkl5qQIJKYFEOzWlABPH39f9TaUCNS\\u002fTnKu5oQMeNhofFl2hAY2uOUOtNaEDQIf8ZXQ5oQCC6+61W12dAmTefzHCnZ0Cp9WNhi31nQGOLXG29WGdAGtRsAkk4Z0D12kYvkhtnQL77WBIYAmdA2f1vhG\\u002frZkAf2TH0PtdmQHCAxyc7xWZAOPb1rSS1ZkC9Mh\\u002fWxaZmQL\\u002f85RDxmWZAoAWqon+OZkB5FJKWUIRmQFE18ORHe2ZA3mH9t0pzZkBkTnFgR2xmQLc28MwtZmZAMLje6u9gZkDJ4xY8glxmQKQ6\\u002fJzbWGZASVzeGPVVZkDUA2zEyVNmQEHFt6lWUmZA76i865pRZkBGLGqRl1FmQAeK3qlPUmZAjjnRZ8hTZkB5L5tKCVZmQHxiwVYcWWZABcqfYA5dZkAhDLts72FmQEZtZyrTZ2ZAVeX0jtFuZkC5f4yZB3dmQMwMkkqYgGZA\\u002fEny3K2LZkCcGapUe5hmQH0WlHw+p2ZAfav1dkK4ZkBKMTuG1ctmQLLo2zRz4mZAYlSke6f8ZkC7GkngHxtnQPUV46C2PmdAJgjG34BoZ0DXaJFo4plnQAvHh1Wq1GdAJzhlc0AbaEBDNV0W+3BoQBsyG7Ho2mhABapDtehhaUDSk8fOLxhqQBuX\\u002fXBFJmtAEupzjp\\u002fkbEBOpKBZVwFwQPTpppIr1nJAsEKJ\\u002fkrCd0D6oYk2K55\\u002fQNoufh7TaoVANzRfheJojEBJiWzyg9aRQNa94\\u002fGDgpRAKbEdKNFUlUDgPxQ04uWTQDwt5pDC1JBAzSXAOMZ0ikD2seGBMtGDQF7L4d11V31AJMbuaYA\\u002fdkAP0Szl6+1xQF4R9UJy+25AQK9nSxpObEBGuX+l48dqQKqeASit0GlA3dXXcjwlaUBBZYjoSqRoQDgZNtXkPmhADhdGbETsZ0C8kiUb0KdnQHHs92VYbmdA5dVGqKE9Z0CjOMVn\\u002fRNnQHu54TUg8GZAZzL8uQbRZkD8qa6u47VmQMRutPoSnmZAvCK5OxCJZkAEogm+b3ZmQOqVWTLZZWZADUljqANXZkCxeNF4sklmQFKaSOGyPWZAHj9XJtoyZkDPSf8aBClmQKnqAfYRIGZAuOMtY+kXZkBCkFnDcxBmQO+l7ZCdCWZA66v8g1QDZkDUcyLhiv1lQPm5mXA0+GVAQwdi5kXzZUAtCYJUte5lQMvIo\\u002fl56mVA+oanF4zmZUA2SsDQ5OJlQOA09Ql+32VA+GkbUlLcZUCjJInMXNllQPVf5h6Z1mVAHgqcYQPUZUDYsqYNmNFlQAaP3\\u002f9Tz2VA1O8yYDTNZUCaOPmdNstlQO+Q2mhYyWVAcfaVqpfHZUB3XbmB8sVlQMzJND1nxGVAi0O5WPTCZUAfoNt5mMFlQE\\u002fC+m1SwGVAf9fxKCG\\u002fZUDVDKrEA75lQFdyrYH5vGVAmgnwyAG8ZUBR\\u002fx4vHLtlQIkK7XlIumVApHcNqIa5ZUDnZ+\\u002f81rhlQKT5CxI6uGVARZyl8bC3ZUDK2JVFPbdlQHX\\u002fNrvhtmVARDcAAKO2ZUBHnLFdirZlQBRqjyOptmVA1uAlhSm3ZUAwjl3xdbhlQApkX+4cu2VAMb9mJi\\u002fAZUB07TtdBsllQCbFQK7Q1mVAPw+ePM\\u002fpZUD5XRwadgBmQEYR83i\\u002fFWZANiRqg3gjZkC8Q\\u002fPfoiRmQMIV6GGKGGZA\\u002fjVeMMwDZkCGo+Co++xlQPWQYcut2GVAeqoQZBvJZUCsEp+0mr5lQGt9YHg2uGVA6CGazY+0ZUB0TqQngrJlQO+DdlFLsWVA\\u002ff9PrYSwZUBbYGYN+69lQDKt++2Vr2VAF2iG60mvZUCuDG0sEa9lQE0uZzLormVAxIO+oMyuZUCtBYrBvK5lQBRlQE63rmVAa+kBUbuuZUAiGcgQyK5lQCS\\u002fogPdrmVA3TPfxPmuZUB3sOYNHq9lQKknDrFJr2VADqzRlXyvZUCbgKaPtq9lQMFQgM73r2VAj3jCbECwZUCgC0uSkLBlQMV9GXXosGVA6CpKWUixZUCcvGORsLFlQPUF8X4hsmVAMXhlk5uyZUAzZE1RH7NlQN0Qzk2ts2VAf3V8Mka0ZUCSTJS\\u002f6rRlQPtdnM6btWVAh3yIVVq2ZUA8721qJ7dlQLoU5EcEuGVAnkIxUvK4ZUC0i2sd87llQOSkvnQIu2VAFyUUYzS8ZUDjlWo9eb1lQEY5O6\\u002fZvmVAt19nyVjAZUCsdUYU+sFlQEgBmKXBw2VAeBjAaLPFZUDB2QYC1cdlQCB6BWQtymVAJIxUIMTMZUAPYKwpos9lQKBXEiPS0mVA8npnxGDWZUDYzSRbXdplQDTTk3Da3mVA1qdiou7jZUC2RZW\\u002ftellQGv+VENS8GVA67RZUu\\u002f3ZUCHno1xwwBmQHNXAE4UC2ZA9aOkQDwXZkBvZG05syVmQFxnHxAgN2ZAl1nUWoFMZkBwxjEpl2dmQJLIEsLsi2ZAPWyDcyzBZkAmyKShgBdnQCxM0Tj5rWdAoQGPvmi4aEDRi1xORn5qQHxJInj4SW1A8WdYikKicEAM5Hl60SJzQLjnI8DZx3VA+uHbMIDJd0AyKHxej6l4QDQTgEBUAnhAASGkHYYYdkDKZPb4iZRzQJccEEGKFnFAoy1d7o4HbkDh97waTP5qQA9hItCYCGlAMFjuQ1DeZ0DJElv8BjVnQI25mYin02ZAoPflr7mYZkASX2+ozXFmQOtTgf0kVWZAA3xrvxQ\\u002fZkBuPHLjWi1mQDeL3inNHmZAE4KAuqgSZkBB6kA+awhmQDLTq\\u002fu1\\u002f2VAILtVa0D4ZUA1Kbw80vFlQDIam\\u002fE+7GVAkajUAGPnZUAG38NbIeNlQCN0Ippg32VAXqDdzRDcZUDeI4EWItllQAH8\\u002f0SH1mVAovbESDXUZUA324vuItJlQPu6BmJI0GVAF3j6+57OZUBISN0NIc1lQEonwbfJy2VAPI9MxpTKZUCqAgyXfsllQD1JzAGEyGVAEj\\u002f5RaLHZUC\\u002fhjb71sZlQKEnkwQgxmVAtafahXvFZUCNDqDa58RlQP1Vs45jxGVAeH\\u002fBV+3DZUCm2usPhMNlQP7ILLEmw2VAGQhoUdTCZUDZugofjMJlQAtfJF5NwmVAYdzlZRfCZUDqFXee6cFlQPgIFn\\u002fDwWVAHqp0jKTBZUD9c0xXjMFlQEwOIHt6wWVANqYknW7BZUD2jk1raMFlQPaUdZtnwWVAGx6i6mvBZUB4xVwcdcFlQOacIPqCwWVAZqfYUpXBZUDGd236q8FlQPApYMnGwWVAJC1xnOXBZUDVi1FUCMJlQJeMXdUuwmVAi69fB1nCZUBhLlvVhsJlQPRCXS24wmVAeu1k9+zCZUB7EesgJcNlQCz5m69gw2VAW+46nJ\\u002fDZUAKjP7h4cNlQEIIgH4nxGVAJ2KucXDEZUDdQ8W9vMRlQIRyR2cMxWVARLD8dF\\u002fFZUC9\\u002ffLvtcVlQOIwg+MPxmVAC+BYXW3GZUAzqH1tzsZlQOfdZyYzx2VA38AMnZvHZUDjUvboB8hlQNj7XCR4yGVAQy5FbOzIZUD2SaHgZMllQHYFeKThyWVA37IP3mLKZUAOwh636MplQHrwAV1zy2VARKf4AAPMZUArGmjYl8xlQDTOJR0yzWVAy0XKDdLNZUDiqQzud85lQANlKAckz2VAgcZNqNbPZUA56h4nkNBlQO1MOuBQ0WVAQKfUNxnSZUAj42Oa6dJlQOFBXX3C02VA9BC3QaTUZUAjLIJSj9VlQApd03yE1mVAoCzOYoTXZUAjJb20j9hlQG6YgTKn2WVAI7IwrcvaZUBNPOQI\\u002fttlQJ+Hxj4\\u002f3WVAbyNiX5DeZUBfhz+V8t9lQIyN3Cdn4WVA+q4Lf+\\u002fiZUCtb8smjeRlQKJjqdNB5mVAIs7HZw\\u002foZUBiNqH49+llQMiTqtX962VA2CL7jyPuZUBi0igDbPBlQEfikV\\u002fa8mVA5zhYNnL1ZUBeuGGHN\\u002fhlQAc9w9Eu+2VACOkSJ13+ZUBbQDtCyAFmQGkRjaJ2BWZAUHf8q28JZkAcarDNuw1mQOJPWLBkEmZALz0dbnUXZkAHYoTW+hxmQImKP8EDI2ZA0nXFc6EpZkACU7Ee6DBmQNXvcXnvOGZApUbkhNNBZkAWuUiAtUtmQGor5R+9VmZAakILGhpjZkDZq8omBnFmQPlrUZnHgGZAGUxoybSSZkBoOzCaOKdmQA==\"},\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"},{\"fill\":\"tonexty\",\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PoB9lQAfzeOweH2VARZ792J0eZUC2KcRSHR5lQBXUsledHWVAXwm75R0dZUDjINn6nhxlQFcdFJUgHGVAsW59sqIbZUDitTBRJRtlQEmKU2+oGmVA2EAVCywaZUDutK4isBllQLkSYrQ0GWVAOKN6vrkYZUC0mUw\\u002fPxhlQLviNDXFF2VAhPSYnksXZUC\\u002foOZ50hZlQLPnk8VZFmVAvswegOEVZUAKLA2oaRVlQJuR7DvyFGVAfxFSOnsUZUA5IdqhBBRlQFRyKHGOE2VAFs7nphgTZUBR8slBoxJlQERvh0AuEmVAj4bfobkRZUAtC5hkRRFlQG5CfYfREGVA+MVhCV4QZUC5Zh7p6g9lQMoQkiV4D2VATbChvQUPZUApFziwkw5lQLrjRfwhDmVAXmjBoLANZUDfk6acPw1lQMba9u7ODGVAfSG5ll4MZUBSp\\u002fmS7gtlQEzyyeJ+C2VA07tAhQ8LZUAx3nl5oAplQNhClr4xCmVAitG7U8MJZUBAYBU4VQllQOmj0mrnCGVA9CEo63kIZUCsIk+4DAhlQGSkhdGfB2VAbU8ONjMHZUDrajDlxgZlQG3SN95aBmVAZux0IO8FZUB+oTyrgwVlQLdU6H0YBWVActzVl60EZUBffGf4QgRlQD\\u002fgA5\\u002fYA2VArRcWi24DZUC5kg28BANlQJEfXjGbAmVAHOl\\u002f6jECZUCRdu\\u002fmyAFlQDGsLSZgAWVA+8y\\u002fp\\u002fcAZUCPfS9rjwBlQDHICnAnAGVA\\u002fiHktb\\u002f\\u002fZEBncVI8WP9kQPEV8QLx\\u002fmRAUvFfCYr+ZED1cUNPI\\u002f5kQPieRNS8\\u002fWRAvSURmFb9ZEAQaVua8PxkQAOS2tqK\\u002fGRAnqJKWSX8ZEBkimwVwPtkQOI8Bg9b+2RAT8riRfb6ZEBuetK5kfpkQL7pqmot+mRAMylHWMn5ZECT4IeCZflkQJxzU+kB+WRAKSqWjJ74ZECPW0JsO\\u002fhkQEmdUIjY92RAW\\u002fW\\u002f4HX3ZEB8EJZ1E\\u002fdkQGt830ax9mRAruavVE\\u002f2ZEABYCKf7fVkQOekWSaM9WRAnWuA6ir1ZEDmt8nryfRkQCY1cSpp9GRAL5e7pgj0ZEBmAvdgqPNkQKl7e1lI82RAvmCrkOjyZEDY6fMGifJkQPi0zbwp8mRACly9ssrxZECEFlTpa\\u002fFkQJVnMGEN8WRA79n+Gq\\u002fwZEBIynoXUfBkQABCb1fz72RAL+O325XvZEC750GlOO9kQDI1DbXb7mRAR4ctDH\\u002fuZEAPs8urIu5kQEcFJ5XG7WRAQL6WyWrtZEBDrotKD+1kQJX1kRm07GRAp+tSOFnsZEBRMZeo\\u002futkQHrySGyk62RABlx2hUrrZEB4S1T28OpkQGc+QcGX6mRAf4jI6D7qZEDH16Vv5ulkQLgPyViO6WRAyoRapzbpZEBto79e3+hkQJwOoIKI6GRAEUTrFjLoZEDG1d4f3OdkQKRKDaKG52RAwbplojHnZEBBPjwm3eZkQEdJUzOJ5mRAMhPmzzXmZECsK7QC4+VkQFhlDtOQ5WRAvkPlSD\\u002flZEAyIdls7uRkQI9JTEie5GRAWFB35U7kZEAx9H9PAORkQPXukpKy42RAyiEBvGXjZEB1n2DaGeNkQL4tsv3O4mRA6\\u002fGLN4XiZECdHEqbPOJkQKCQRj714WRAd68YOK\\u002fhZECir96iauFkQKomkpsn4WRA+sVpQubgZEBzskq7puBkQJ1fTC5p4GRA5XZSyC3gZED3GcG79N9kQD\\u002fHUEG+32RA3V4ImYrfZEBwT2ULWt9kQHLku+os32RAQyzclAPfZEArHAt13t5kQGy3Ywa+3mRA7li51qLeZED5QBuKjd5kQPnEIt9+3mRA6MJBtHfeZEC4ZFcOed5kQLJn5yCE3mRA20RuWJreZECEB3Znvd5kQOIaSVfv3mRA6+ZwnTLfZEC0O6A3it9kQMZsTdD532RA2kcw74XgZEDiyko6NOFkQGaBs84L4mRAgcRHvhXjZECsqJHRXeRkQJBQN+vz5WRAe9d7Ne\\u002fnZEBrojWNdupkQCFXvibX7WRAUuOs2bzyZEDelJwovPpkQMxX58JJCWVAZm8174wkZUBoL8Dhp1ZlQEaKrK8WrWVAEDtSSyo1ZkBwgtEh1fRmQOMLZzZl4mdA4SseKR7faEB6NkkEnrhpQLvKpCi3KmpA7H3dCHYHakBymnNpnl1pQFwcNCO2aWhA6n6le9hqZ0DG1yy3bI9mQHe76qbR6GVAvzUjBXd4ZUDxhIBy9zNlQINxL9BRDWVAGuS0b1\\u002f4ZEAzg1mY2+xkQCyRtwYb5mRATDLZLoLhZEC+k266CN5kQB24G70622RA6f396+HYZEBJs6am4NZkQA+Y7uAj1WRA+WX49Z3TZECywl2WRNJkQFiNb8QP0WRA\\u002fsdKN\\u002fnPZEBmb6rv+85kQLDvr+oTzmRAY9ft6D3NZEDQMMxDd8xkQKCkTc29y2RAF4Zvtw\\u002fLZEC0WSyBa8pkQNDqtefPyWRANv7e2jvJZEDjufJzrshkQBPyae4myGRAL7wSoqTHZEAuDVn+JsdkQLKVcoatxmRAYHk+zjfGZEBBSbR3xcVkQEfMxTBWxWRA6E+dsenEZECD9Sa7f8RkQFAW1xUYxGRAZaqikLLDZEBY0yAAT8NkQF1lzj3twmRAn6ZtJ43CZEBSkX2eLsJkQInBxIfRwWRAfOjsynXBZEA1LSxSG8FkQEZV+gnCwGRAzO3P4GnAZEDM+O3GEsBkQGnhLK68v2RAiqzRiWe\\u002fZECrhWhOE79kQKXqo\\u002fG\\u002fvmRAGtY\\u002fam2+ZEBnYOivG75kQE9jI7vKvWRAWL08hXq9ZEA24DQIK71kQLdhsT7cvGRAlVDvI468ZECZF7ezQLxkQGfAUerzu2RA9m1\\u002fxKe7ZEDb624\\u002fXLtkQDY0tlgRu2RALNJLDse6ZEAJCoFefbpkQF6z\\u002fEc0umRAzLO2yeu5ZECZC\\u002fTio7lkQAJnQ5NcuWRACCl62hW5ZED25bG4z7hkQC9FRi6KuGRAFkPTO0W4ZEDnzDPiALhkQEaxgCK9t2RAQuEP\\u002fnm3ZEA6\\u002fnN2N7dkQOIxfI31tmRASk40RbS2ZEBoNOWfc7ZkQFSAFaAztmRA5XqKSPS1ZED6UEmctbVkQFKRmJ53tWRASfIBUzq1ZECBYVS9\\u002fbRkQPdfpuHBtGRAsq1YxIa0ZEDFSBlqTLRkQAzE5tcStGRAz\\u002foTE9qzZEATJ0whorNkQE1hlwhrs2RADJFfzzSzZED61nV8\\u002f7JkQOt5GBfLsmRAhmH5ppeyZECmKkU0ZbJkQMriqsczsmRAnnlkagOyZEBT+T8m1LFkQKCYqQWmsWRAQLu2E3mxZECR+DFcTbFkQHFCqOsisWRA8Ul3z\\u002fmwZEDhQt0V0rBkQEwrCs6rsGRAy8AyCIewZECKUqXVY7BkQDOl4EhCsGRAiSStdSKwZEBxpjhxBLBkQEIMNVLor2RARRn6MM6vZEB74aontq9kQExBX1Kgr2RAo95Rz4yvZEB8RhO\\u002fe69kQPzOwkRtr2RAP\\u002f9NhmGvZEDRWresWK9kQJ+QZeRSr2RA+jR8XVCvZECgXD9MUa9kQOOlg+lVr2RAuH0sc16vZEBcu7osa69kQIEI7l98r2RArvh7XZKvZECJNOB9ra9kQKK5SSLOr2RA6+qptfSvZEB5EOutIbBkQIj3VI1VsGRAZLEn5JCwZEBBB3ZS1LBkQC8sS4ogsWRADp0pUnaxZEChl4NJYbJkQOI1dKdXs2RA5m9tg1q0ZECl414Ya7VkQG8hScqKtmRALmPdLLu3ZEBZzmQL\\u002frhkQBhkOXJVumRApR4uusO7ZEBj2FyWS71kQIpn8STwvmRAzYC0A7XAZEBNJFJpnsJkQMa5o0SxxGRADNysY\\u002fPGZECLh4Wka8lkQC\\u002fbKzMizGRAOZdA2CDPZEDN8iNec9JkQMzG8xMo1mRA\\u002fiPYeFDaZEBNIEceAt9kQGKQK9ZX5GRAJPSVS3PqZEDizPY1f\\u002fFkQDyvmn+y+WRAzPGlN1UDZUA6YmDmyQ5lQFMSyaijHGVACBWWTOItZUBqVlW5lURlQGsyepSZZWVAFebX7H2cZUBG2pzS+P9lQMaBaC3ruWZAn+KYnYwKaED62yLv+UBqQHK9pgeonm1A5lttLIEQcUCZ\\u002fZqxhq1zQBVYlb34NnZABXrWs9z6d0BIv3DCEkl4QGJfUpGG\\u002fXZA93KYY9WidEB3Fjr6SvJxQMZ4LEQnFG9AYbYc+FNOa0B4sYKfb7VoQJxL14yuG2dABaP72H80ZkDYgkXG1LhlQJ8sIDqWdmVAQAA6wQxRZUAJgakm8DhlQBRUS0t0J2VAbxTr2eEZZUBlUMFk9w5lQDiz79oGBmVA965KtqD+ZED7qKhVdfhkQKj+g85I82RAQMoj6uzuZEDsSVp9PetkQO8ejegd6GRAj4ZGTHflZEDS5f43N+NkQK86Ja9O4WRA9\\u002fQzbLHfZEBm1wBRVd5kQA5PZfgx3WRA0Se+YEDcZEA5WhupettkQC42oNzb2mRA1IXByF\\u002faZEAt6efbAtpkQLb1mQrC2WRANerEuZrZZEARABCtitlkQFfuZfiP2WRAr\\u002fMP9KjZZEC15uEy1NlkQOthEnoQ2mRAf1BvulzaZECFgq8KuNpkQNetrqIh22RAkjpr15jbZEC1FKUXHdxkQBj0Aumt3GRALX+o5UrdZEDfoiu6891kQHKp2iOo3mRA+i1H72ffZECYIQz3MuBkQJzNxiIJ4WRAISU8ZurhZECJ4aTA1uJkQMXbGzzO42RArOIq7dDkZEDh+3Ly3uVkQGaQbXT45mRA14NEpR3oZECaor\\u002fATulkQNMyRgyM6mRA5sjz1tXrZEDDzL95LO1kQIpjt1eQ7mRA2LJJ3gHwZEC0saaFgfFkQDr4MNEP82RAFzwDUK30ZECFZYqdWvZkQHxoNWIY+GRAH1E8VOf5ZEBMOoA4yPtkQCgzhuO7\\u002fWRA5m2POsP\\u002fZEBocNE03wFlQM9o0dwQBGVALD7mUVkGZUBce+XJuQhlQKnJ\\u002f5IzC2VA413SFcgNZUDMgLPXeBBlQB4\\u002fQX1HE2VA6VM6zTUWZUAwj6qzRRllQEZadUV5HGVAJ5RKxNIfZUC93BOjVCNlQPiS6ooBJ2VAxlapYNwqZUCX3y5L6C5lQAOBa7ooM2VAg+RWb6E3ZUCUc\\u002fCEVjxlQP7QcnpMQWVAk8bpPohGZUBsdGI+D0xlQK2l93DnUWVAIVYIbBdYZUC6JPV1pl5lQOxA05ycZWVAQBSZ0AJtZUAecGMB43RlQEeXkkJIfWVANpym8z6GZUA4PfTv1I9lQOYxicYZmmVAxxXk+x6lZUA3FYVX+LBlQMDt1j+8vWVATzSLJ4TLZUBHK0sQbdplQCUgnSeY6mVAq1knhCv8ZUDDkyULUw9mQPBoFohBJGZAGB+JAzI7ZkDUdcppaVRmQKWvUpc4cGZA2xrI5\\u002f6OZkCwAt9tLbFmQGzPYgdL12ZAg87GkvkBZ0C7wPqk\\u002fDFnQO+yPUJCaGdAEBdXUO6lZ0DgMM\\u002fDaexnQBjESPl2PWhAO8riWE2baEBzH7uPvwhpQPi\\u002fg1xyiWlAW+IEQDIiakCEvkHrk9lqQDP3XLlvuWtAgFO92fHSbECYEYylx0luQJWviyHhOnBAzsFrbNUNckC9daiE72t1QIeKMiKbvntAMmMtEYeWg0BbHyiaXhKNQLI2i2gZppVAjYq0o94In0D6IMYuSsekQE0fDOJRoalABY5Y+0uArEA0HCF9HSysQD9UiWaryahAQo3rRZS1o0C\\u002fgjc+auWcQBn3RSqd+5NA1nbTpI+8ikBsyzK1gBOCQDjwihj\\u002f8HlAO09Ui9lcdECSzC8YjGdxQGRLCB1Ljm9Ajvwsc1qcbUBECzlQP0NsQKGTWmYaPWtAX5GR0sZrakArXpGeQMBpQACVQgiVMWlAVjPUHaO5aED+3w685lNoQJv6Mdfu\\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\\u002fHu4nHJlQA55n6ssdmVA2D3xQRR6ZUDXcxTrWH5lQP28vMkAg2VAdVW7yBKIZUC1qIazlo1lQMfh2FKVk2VAkTEjjhiaZUAEhLySK6FlQAo65QHbqGVADO34JjWxZUC7xHY3SrplQAkd7Z4sxGVAwzRUWfHOZUBDbwVfsNplQE4oTCaF52VAVbOYP4\\u002f1ZUDDkbkS8wRmQKwYRcXaFWZA\\u002fK6zVHcoZkCH7sPxAT1mQMNK5q69U2ZA4kUDmflsZkCsC4dZE4lmQH4WCYt6qGZAwMdX+LTLZkB5v+gRZPNmQEOJTgJMIGdAJLVQ81xTZ0An\\u002fa9Qv41nQI5jFi3k0GdA9qbAcZseaEDjQXBWMnloQITf5Dme42hAz+nnLbxhaUBGpPvsu\\u002fhpQKg+cVsAsGpABgVIqWaTa0CLAtdxrLlsQNjycyd4VW5AXP83J7t0cEBuD\\u002frYR8NyQM80EhWDHndAblrXO5wsf0AKfwPEqYmGQDlJGQ\\u002fDtJBA8lX3oFtBmEBP4HHoW6agQBiRrtzFKKVA1T0OF\\u002ftcqEBXq+p9w\\u002fOoQFDGZFEso6ZA7u39mvxwokAm5l7ia4GbQAwv\\u002faaeS5NA6U+y0fMWikCso7iJhbqBQJGy9TfEcnlAplq\\u002fJaLvc0AYjoylBwRxQMIR4yZu2m5A9RAt1jr8bECEMwzBPbVrQNSGwh4kv2pAqFDfy6z7aUDSHTcGDFxpQGFTAT2X12hAYP+gsG9oaECAG0NKSQpoQDrRQpDhuWdASNk6nbN0Z0DCEqGOxjhnQHSaesiKBGdAvCgJ38DWZkDEScMqZ65mQI1JDRWsimZAkshLyuNqZkCGuf5kgE5mQH6fC+sLNWZAl0T7pSMeZkAWm\\u002f9\\u002fdAlmQIti0ia49mVAkoL\\u002fxbLlZUCc7Ag2MdZlQJ30aoYHyGVAJaDTzg+7ZUCC7oU4Ka9lQEgRWTQ3pGVAu7Fd1CCaZUDPgytC0JBlQI65WUwyiGVAIu3LBjaAZUCaC2F6zHhlQNoJQ2HocWVAE9Sg7X1rZUDM+wiZgmVlQLPy8frsX2VA1T1BpbRaZUCt49kF0lVlQGNhZkw+UWVAS\\u002fu1U\\u002fNMZUDDByKO60hlQKFCh\\u002fQhRWVA0V1y95FBZUCyui5yNz5lQAwpc58OO2VASjxzDxQ4ZUBlqSSfRDVlQOqFj3CdMmVABnAG5BswZUBu2CiSvS1lQHQGlkaAK2VA3SE7+2EpZUC0sinUYCdlQBi35ht7JWVAg70kQK8jZUCKcd3O+yFlQIDHwHNfIGVAoH3x9dgeZUCRCgg2Zx1lQCtTViwJHGVAU61n570aZUCq1bmKhBllQLyZrE1cGGVAiRunekQXZUBiyHJuPBZlQNhxzZdDFWVAanw1d1kUZUCV4fSefRNlQM\\u002fscbOvEmVAdUjPa+8RZUCnUOiSPBFlQLf2ugiXEGVAgiFWxP4PZUCL4GrWcw9lQPJjZ0P8DmVARX6Xg5IOZUD1pgUNNw5lQHZqCYTqDWVACc6\\u002fxK0NZUCRG2LvgQ1lQE4Ac3hoDWVAc7AcPmMNZUAelaqkdA1lQBvG3byfDWVAMOgeeOgNZUDiN6DwUw5lQBPRl8\\u002foDmVAMM3c5q8PZUAxX885tRBlQABhkjsKEmVAsyqIw8sTZUAmYoTTMhZlQE1pJa\\u002fAGWVA0f8M2qIfZUDc6jdwvyplQNEQ6bjpP2VAg6DN\\u002fCNnZUBQOFiptqllQK4re7YSD2ZAxOUmhM+XZkCC5hgfGzVnQFrA1QXMx2dApXD3FBAfaEC\\u002fNXco9xNoQNbLCm64q2dACjtVwEkSZ0DF9TZ2cnVmQEAgKD4E9GVAPmiH2lCWZUCBf2UVaFplQPry41bYN2VAsdcTvjolZUC6JOiPfRtlQI6KQrgQFmVAfJo+HZ0SZUC+15RaJxBlQHjvZBVEDmVAlKkm08IMZUCS23\\u002fviQtlQGk7fEKJCmVAr3MoZbUJZUAoFljYBQllQB+vDR10CGVAGKpGKPsHZUDUvt0DlwdlQEht6opEB2VADwGeOAEHZUD8tAwEywZlQEm\\u002fKkWgBmVAOS9uoH8GZUDHiUv3ZwZlQGyrSFxYBmVAf1+9CVAGZUBHJ5laTgZlQOLOssRSBmVAlZpG1FwGZUCZA18obAZlQGjX9G+ABmVA2O6fZ5kGZUAV4LrXtgZlQFjs4pLYBmVAXZjCdP4GZUDvTxlhKAdlQFV89UJWB2VAOc4YDIgHZUC3UoC0vQdlQC5gCzr3B2VAMJM9oDQIZUDHCBnwdQhlQPjODji7CGVAVTIEjAQJZUBnKGwFUgllQCOhdMOjCWVAzQxI6\\u002fkJZUCy42OoVAplQB2ABS20CmVAKiWvshgLZUDlo8d6ggtlQHq5V8\\u002fxC2VA5gLpA2cMZUDMRIt24gxlQFrRBZFkDWVA+BU8yu0NZUCG3M2nfg5lQJuh\\u002fb8XD2VAmKDpu7kPZUDD+yZaZRBlQATW0XEbEWVAqnwp9twRZUDSHtX6qhJlQB9W9LiGE2VA4FsnlXEUZUARu8UmbRVlQLZgiEB7FmVAcRb++p0XZUC9ETnB1xhlQFYtQ2ArGmVAOScPGpwbZUB2aNO8LR1lQEdnA8DkHmVAQq5+aMYgZUBFFBL22CJlQElQH90jJWVAbCQ+ELAnZUAuYBBfiCplQL8VfvG5LWVAP\\u002fto6VQxZUAt2wo5bTVlQCcjUMIbOmVAMvq33H8\\u002fZUDJD+ttwUVlQBwZPdwTTWVAbCRGZbpVZUDS96lAD2BlQNDFYxqSbGVApa9C6wx8ZUA152O5+49lQJ8Su8ikq2VAv2kpEn3XZUD7jUUIsyVmQIoGVDftuGZAmUpL6s\\u002fIZ0Avac8TTp1pQEHjIMwLdWxAAYlmF6IucEA9A9t2V4NyQLq7EIGLyHRAynr+10lHdkD2r38q61d2QLQNvjR38XRA5AWzoX6zckBjdWIun1VwQLj4oTsSqmxA2CyvOCm\\u002faUCwdHuew9xnQNvqn+5+xGZA38888AMtZkA0F46JrNxlQMcdn1aJsGVA2oCQgMGUZUDNH2G9EIFlQP1f01P7cWVACDJeYfBlZUAT5JKSHVxlQFeINlv\\u002fU2VAM842WDlNZUBzN+nqhkdlQEbpVr+zQmVAHNx\\u002fTJc+ZUC9WNPJETtlQMzKDQcKOGVANL\\u002fh22s1ZUAA83ABJzNlQGPI8zYuMWVAq\\u002faInHYvZUB56YE19y1lQMSozIeoLGVA4Y0PUYQrZUC0PBhMhSplQM3QsAKnKWVAOQH1qOUoZUCK7f3\\u002fPShlQEB0RD6tJ2VABn1+\\u002fDAnZUBZ+gUmxyZlQB7YD+xtJmVAKLsiuyMmZUC+CF0y5yVlQIDEMRy3JWVAS1VWaJIlZUA3kqkmeCVlQBqc54JnJWVAZsAGwV8lZUDifyA6YCVlQN1Cz1loJWVA0Zftm3clZUDJTqeKjSVlQIiLz7ypJWVAtiZx1MslZUA5iJB98yVlQDCgF20gJmVAF9rlX1ImZUAq5P4ZiSZlQAT302XEJmVAw\\u002fajEwQnZUAbV+\\u002f4RydlQJsj\\u002fe+PJ2VAnPFu19snZUBe1eGRKyhlQGO6mgV\\u002fKGVA2Lo8HNYoZUAWRIjCMCllQFMBIuiOKWVA0algf\\u002fApZUAX7yB9VSplQGPinti9KmVAZD9UiykrZUANHtuQmCtlQEqe1OYKLGVAYi\\u002fTjIAsZUB1IUiE+SxlQAE7dNB1LWVAGRZbdvUtZUDWEbl8eC5lQB+s++v+LmVA7h48zogvZUBkIjwvFjBlQIi6ZBynMGVAr\\u002fzGpDsxZUBKvR7Z0zFlQBUb18tvMmVAzOAQkQ8zZUBRuqo+szNlQAM+S+xaNGVAcs5sswY1ZUBCXGuvtjVlQI8TlP1qNmVAiAM3vSM3ZUC+0roP4TdlQCmWshijOGVAzeT1\\u002fWk5ZUDnRbvnNTplQMEbtQAHO2VA3jExdt07ZUDkGTt4uTxlQASIwTmbPWVAIOW+8II+ZUA3U2XWcD9lQBtnTidlQGVAFeKuI2BBZUDlvY4PYkJlQJzmBTNrQ2VAqgd+2ntEZUA13PlWlEVlQBd\\u002fYv60RmVAM0TbK95HZUDpsxxAEEllQBNS16FLSmVAv+wevpBLZUDTQ98I4ExlQEXyWv05TmVA9pu1Hp9PZUAagYr4D1FlQH65kB+NUmVA\\u002fH9OMhdUZUASId3ZrlVlQDVQv8pUV2VAet7LxQlZZUCJCy+ZzlplQFDxhCGkXGVArNsPS4teZUBaug0ThWBlQGhFMImSYmVADuY70bRkZUC\\u002fAdIk7WZlQO7la9U8aWVA0UyMTqVrZUCWSS4YKG5lQCRjednGcGVAdsTDW4NzZUA5ueyNX3ZlQCQ0G4hdeWVAjuXtj398ZUAogCwdyH9lQIQ+DN85g2VA7aAcwteGZUAxxfT2pIplQO7FvfmkjmVAV1y7mtuSZUBvuPkHTZdlQEVZTtj9m2VAguDhF\\u002fOgZUCjtoJWMqZlQFo4DLjBq2VAYYs8B6ixZUBvt2TL7LdlQEj5c2GYvmVAC2v5GLTFZUBwRtdVSs1lQFW\\u002firdm1WVAi8AcRxbeZUARSA2tZ+dlQJga2HBr8WVA\\u002fXISRDT8ZUDKAJxb1wdmQFKx+dlsFGZAJam4ThAiZkBSVLlP4TBmQN9AkDIEQWZAIhLe7aJSZkDsVrws7mVmQBf9UqEee2ZAVm+dp3aSZkBf7bZORKxmQK7VO+bjyGZAXbUtN8PoZkDLZ2+dZQxnQKT9AEppNGdAUqcQEY5hZ0D1W2pNvpRnQJTgZZoaz2dA0tPcdgkSaEADLOhcTF9oQBkzA5QcuWhAanXkN1MiaUAm4+5kop5pQJpgp27sMmpA9e12aN7lakDu5dSJScFrQOIvvwrE1WxANGJrqcZEbkAQdi4NjC9wQHoSuG9h8XFAgirKGkwpdUCbM9KFFit7QOUFPYm89oJAkj1SA13Ni0CdeEGF\\u002f2OUQGEO19dIuJxA6JhmqubnokAmg9Ogvq6mQIVZrq0ZP6hAlVCSaSnQpkBehw2u1BejQAsbd+FiEp1ATe7Voa2RlEAkETpezu6LQBNpKTqQAoNAJubP3yIwe0BTiJwceCV1QKx7vU9J7XFAElNT6TgtcEBy3fby+jtuQJbFnmORyWxA5BU7o\\u002fKza0B+B1vMYdhqQCl6sbzHJWpAWLCxbBySaUA9cmhhlxZpQLKxsSpGrmhAa6SWQWxVaEB1Xl2JKQloQFIpdPU\\u002fx2dAqw7ZyuqNZ0Du8mRVwVtnQP37kH6hL2dAYe2Z8p8IZ0BOs9E\\u002f\\u002fOVmQBaeFtgXx2ZA8lSrL2+rZkBge3JtlJJmQBtmzkcrfGZANEvYxOVnZkCsiQ6ogVVmQDUffmXGRGZAAlt1fIM1ZkAYCCEjjydmQMsS2jHFGmZAsknrQAYPZkC68pbuNgRmQB+rZ0U\\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\\u002fdWvP9ZtZUA2CE+v8G1lQF2lnBMTbmVAOKU6HT1uZUBpL5iPbm5lQIhZdj6nbmVAuX8EDOduZUDfkXXnLW9lQGS698t7b2VA0cf9v9BvZUAMWM7ULHBlQLZVUCaQcGVAfDMP2\\u002fpwZUCcr3MkbXFlQCP2Lz\\u002fncWVAntXfc2lyZUDdg98X9HJlQG1BXY6Hc2VAQxqsSSR0ZUDbKuDMynRlQHdOv617dWVA7g4Ulzd2ZUA3QHRL\\u002f3ZlQCsVkajTd2VAPPArq7V4ZUAxD9JzpnllQO3jiUynemVAdAqor7l7ZUB5HRBQ33xlQNg8OCMafmVAMF5ebWx\\u002fZUADc37Q2IBlQMLUwl5igmVA0xphsQyEZUAm+yEF3IVlQPzqOl7Vh2VALC2vtf6JZUArSjQzX4xlQNreq3f\\u002fjmVASITO\\u002femRZUA+H9iYK5VlQI1DORzUmGVAE2kHPPecZUD1oPm8raFlQEnK4hUXp2VA7oqNuFutZUDak9NmsLRlQAK5R41bvWVAGJoLqr\\u002fHZUBRI2OUdNRlQGQdX6eM5GVAxLIHZk36ZUAZ6FHjYRtmQBScoYcHVGZADI1SjiS7ZkCqEsDaCndnQBCqQgVsumhAYKnXjla0akDs6RntwXFtQIFTgfmtYnBA9s6g8fUHckACITYjpjVzQPY0wH1CanNAbhL0bp9\\u002fckCx9qTyne5wQPylXjaCdG5AvOdPrnx\\u002fa0CF67nlH0ppQHSkI1mU1WdAB++Bzpb4ZkC5uygCn35mQPdeWi0xPWZA9o2g9yQZZkA+f9xD5QJmQB6DR7ak82VAntYzzmToZUBBG1\\u002fgyN9lQFEWe5sb2WVA6PItk+3TZUArKVOp8c9lQAvdszLvzGVAqdhpZbvKZUD087V9NcllQOuMpitEyGVAE1Ezw9PHZUAOfGXt1MdlQLBUN7M7yGVAQP80yP7IZUDKjR4DF8plQOOTMfh+y2VAg45mrDLNZUCO2W9bL89lQKB\\u002fBkxz0WVAjEtMr\\u002f3TZUB45+uIztZlQC1nQ57m2WVAbD9ea0fdZUCnWt4c8+BlQJXBOY7s5GVA3pfpSzfpZUDrpVSZ1+1lQJ3OZnrS8mVAd13qwC34ZUAfTtod8P1lQL2GBzchBGZADGKQwckKZkAuzNOh8xFmQJDOuRGqGWZALUVlzvkhZkBqv6hO8SpmQPo17wOhNGZAIl61pxs\\u002fZkB1nTiYdkpmQFe\\u002frUfKVmZA5kMvwjJkZkBdLbFP0HJmQNL3uzjIgmZA7SWXtkWUZkBi4g0ae6dmQLPVTTejvGZAbmXYKQPUZkDBEomJ7O1mQAza8DHACmdAUzWeyPEqZ0Ai3a4+DE9nQIM+MaC3d2dA+eBWo8ClZ0A3k7mWItpnQB4Y148UFmhAChRXHBtbaEBBdthMIKtoQMEwUuK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ATwRxHk4AZUCCzM6i9\\u002f9kQLSULCeh\\u002f2RA5lyKq0r\\u002fZEAYJegv9P5kQErtRbSd\\u002fmRAfbWjOEf+ZECvfQG98P1kQOFFX0Ga\\u002fWRAEw69xUP9ZEBF1hpK7fxkQHieeM6W\\u002fGRAqmbWUkD8ZEDcLjTX6ftkQA73kVuT+2RAQL\\u002fv3zz7ZEBzh01k5vpkQKVPq+iP+mRA1xcJbTn6ZEAJ4Gbx4vlkQDuoxHWM+WRAbnAi+jX5ZECgOIB+3\\u002fhkQNIA3gKJ+GRABMk7hzL4ZEA2kZkL3PdkQGlZ94+F92RAmyFVFC\\u002f3ZEDN6bKY2PZkQP+xEB2C9mRAMXpuoSv2ZEBkQswl1fVkQJYKKqp+9WRAyNKHLij1ZED6muWy0fRkQCxjQzd79GRAXyuhuyT0ZECR8\\u002f4\\u002fzvNkQMO7XMR382RA9YO6SCHzZEAnTBjNyvJkQFoUdlF08mRAjNzT1R3yZEC+pDFax\\u002fFkQPBsj95w8WRAIjXtYhrxZEBV\\u002fUrnw\\u002fBkQIfFqGtt8GRAuY0G8BbwZEDrVWR0wO9kQB0ewvhp72RAUOYffRPvZECCrn0Bve5kQLR224Vm7mRA5j45ChDuZEAYB5eOue1kQEvP9BJj7WRAfZdSlwztZECvX7AbtuxkQOEnDqBf7GRAE\\u002fBrJAnsZEBGuMmosutkQHiAJy1c62RAqkiFsQXrZEDcEOM1r+pkQA7ZQLpY6mRAQaGePgLqZEBzafzCq+lkQKUxWkdV6WRA1\\u002fm3y\\u002f7oZEAJwhVQqOhkQDyKc9RR6GRAblLRWPvnZECgGi\\u002fdpOdkQNLijGFO52RABKvq5ffmZEA3c0hqoeZkQGk7pu5K5mRAmwMEc\\u002fTlZEDNy2H3neVkQP+Tv3tH5WRAMlwdAPHkZEBkJHuEmuRkQJbs2AhE5GRAyLQ2je3jZED6fJQRl+NkQC1F8pVA42RAXw1QGuriZECR1a2ek+JkQMOdCyM94mRA9WVpp+bhZEAoLscrkOFkQFr2JLA54WRAjL6CNOPgZEC+huC4jOBkQPBOPj024GRAIxecwd\\u002ffZEBV3\\u002flFid9kQIenV8oy32RAuW+1TtzeZEDrNxPThd5kQB4AcVcv3mRAUMjO29jdZECCkCxggt1kQLRYiuQr3WRA5iDoaNXcZEAZ6UXtftxkQEuxo3Eo3GRAfXkB9tHbZECvQV96e9tkQOEJvf4k22RAFNIag87aZEBGmngHeNpkQHhi1osh2mRAqio0EMvZZEDc8pGUdNlkQA+77xge2WRAQYNNncfYZEBzS6shcdhkQKUTCaYa2GRA19tmKsTXZEAKpMSubddkQDxsIjMX12RAbjSAt8DWZECg\\u002fN07atZkQNLEO8AT1mRABY2ZRL3VZEA3VffIZtVkQGkdVU0Q1WRAm+Wy0bnUZEDNrRBWY9RkQAB2btoM1GRAMj7MXrbTZEBkBirjX9NkQJbOh2cJ02RAyJbl67LSZED7XkNwXNJkQC0nofQF0mRAX+\\u002f+eK\\u002fRZECRt1z9WNFkQMN\\u002fuoEC0WRA9kcYBqzQZEAoEHaKVdBkQFrY0w7\\u002fz2RAjKAxk6jPZEC+aI8XUs9kQPEw7Zv7zmRAI\\u002flKIKXOZEBVwaikTs5kQIeJBin4zWRAuVFkraHNZEDsGcIxS81kQB7iH7b0zGRAUKp9Op7MZECCctu+R8xkQLQ6OUPxy2RA5wKXx5rLZEAZy\\u002fRLRMtkQEuTUtDtymRAfVuwVJfKZECvIw7ZQMpkQOLra13qyWRAFLTJ4ZPJZEBGfCdmPclkQHhEhermyGRAqgzjbpDIZEDd1EDzOchkQA+dnnfjx2RAQWX8+4zHZEBzLVqANsdkQKX1twTgxmRA2L0ViYnGZEAKhnMNM8ZkQDxO0ZHcxWRAbhYvFobFZECg3oyaL8VkQNOm6h7ZxGRABW9Io4LEZEA3N6YnLMRkQGn\\u002fA6zVw2RAm8dhMH\\u002fDZEDOj7+0KMNkQABYHTnSwmRAMiB7vXvCZEBk6NhBJcJkQJawNsbOwWRAyXiUSnjBZED7QPLOIcFkQC0JUFPLwGRAX9Gt13TAZECRmQtcHsBkQMRhaeDHv2RA9inHZHG\\u002fZEAo8iTpGr9kQFq6gm3EvmRAjILg8W2+ZEC\\u002fSj52F75kQPESnPrAvWRAI9v5fmq9ZEBVo1cDFL1kQIdrtYe9vGRAujMTDGe8ZEDs+3CQELxkQB7EzhS6u2RAUIwsmWO7ZECCVIodDbtkQLUc6KG2umRA5+RFJmC6ZEAZraOqCbpkQEt1AS+zuWRAfT1fs1y5ZECwBb03BrlkQOLNGryvuGRAFJZ4QFm4ZEBGXtbEArhkQHgmNEmst2RAq+6RzVW3ZEDdtu9R\\u002f7ZkQA9\\u002fTdaotmRAQUerWlK2ZEBzDwnf+7VkQKbXZmOltWRA2J\\u002fE5061ZEAKaCJs+LRkQDwwgPChtGRAbvjddEu0ZEChwDv59LNkQNOImX2es2RABVH3AUizZEA3GVWG8bJkQGnhsgqbsmRAnKkQj0SyZEDOcW4T7rFkQAA6zJeXsWRAMgIqHEGxZEBkyoeg6rBkQJeS5SSUsGRAyVpDqT2wZED7IqEt569kQC3r\\u002frGQr2RAX7NcNjqvZECSe7q6465kQMRDGD+NrmRA9gt2wzauZEAo1NNH4K1kQFqcMcyJrWRAjWSPUDOtZEC\\u002fLO3U3KxkQPH0SlmGrGRAI72o3S+sZEBVhQZi2atkQIhNZOaCq2RAuhXCaiyrZEDs3R\\u002fv1apkQB6mfXN\\u002fqmRAUG7b9yiqZECDNjl80qlkQLX+lgB8qWRA58b0hCWpZEAZj1IJz6hkQEtXsI14qGRAfh8OEiKoZECw52uWy6dkQOKvyRp1p2RAFHgnnx6nZEBGQIUjyKZkQHkI46dxpmRAq9BALBumZEDdmJ6wxKVkQA9h\\u002fDRupWRAQSlauRelZEB08bc9waRkQKa5FcJqpGRA2IFzRhSkZEAKStHKvaNkQDwSL09no2RAb9qM0xCjZEChoupXuqJkQNNqSNxjomRABTOmYA2iZEA3+wPltqFkQGrDYWlgoWRAnIu\\u002f7QmhZEDOUx1ys6BkQAAce\\u002fZcoGRAMuTYegagZEBlrDb\\u002fr59kQJd0lINZn2RAyTzyBwOfZED7BFCMrJ5kQC3NrRBWnmRAYJULlf+dZECSXWkZqZ1kQMQlx51SnWRA9u0kIvycZEAotoKmpZxkQFt+4CpPnGRAjUY+r\\u002fibZEC\\u002fDpwzoptkQPHW+bdLm2RAI59XPPWaZEBWZ7XAnppkQIgvE0VImmRAuvdwyfGZZEDsv85Nm5lkQB6ILNJEmWRAUVCKVu6YZECDGOjal5hkQLXgRV9BmGRA56ij4+qXZEAZcQFolJdkQEw5X+w9l2RAfgG9cOeWZECwyRr1kJZkQOKReHk6lmRAFFrW\\u002feOVZEBHIjSCjZVkQHnqkQY3lWRAq7LviuCUZEDdek0PipRkQA9Dq5MzlGRAQgsJGN2TZEB002achpNkQKabxCAwk2RA2GMipdmSZEAKLIApg5JkQD303a0skmRAb7w7MtaRZEChhJm2f5FkQNNM9zopkWRABRVVv9KQZEA43bJDfJBkQGqlEMglkGRAnG1uTM+PZEDONczQeI9kQAD+KVUij2RAM8aH2cuOZEBljuVddY5kQJdWQ+IejmRAyR6hZsiNZED75v7qcY1kQC6vXG8bjWRAYHe688SMZECSPxh4boxkQMQHdvwXjGRA9s\\u002fTgMGLZEApmDEFa4tkQFtgj4kUi2RAjSjtDb6KZECoctpT8opkQMO8x5kmi2RA3ga131qLZED4UKIlj4tkQBObj2vDi2RALuV8sfeLZEBJL2r3K4xkQGR5Vz1gjGRAf8NEg5SMZECZDTLJyIxkQLRXHw\\u002f9jGRAz6EMVTGNZEDq6\\u002fmaZY1kQAU25+CZjWRAIIDUJs6NZEA6ysFsAo5kQFUUr7I2jmRAcF6c+GqOZECLqIk+n45kQKbydoTTjmRAwTxkygePZEDbhlEQPI9kQPbQPlZwj2RAERssnKSPZEAsZRni2I9kQEevBigNkGRAYvnzbUGQZEB8Q+GzdZBkQJeNzvmpkGRAste7P96QZEDNIamFEpFkQOhrlstGkWRAA7aDEXuRZEAdAHFXr5FkQDhKXp3jkWRAU5RL4xeSZEBu3jgpTJJkQIkoJm+AkmRApHITtbSSZEC+vAD76JJkQNkG7kAdk2RA9FDbhlGTZEAPm8jMhZNkQCrltRK6k2RARS+jWO6TZEBgeZCeIpRkQHrDfeRWlGRAlQ1rKouUZECwV1hwv5RkQMuhRbbzlGRA5usy\\u002fCeVZEABNiBCXJVkQBuADYiQlWRANsr6zcSVZEBRFOgT+ZVkQGxe1VktlmRAh6jCn2GWZECi8q\\u002fllZZkQLw8nSvKlmRA14aKcf6WZEDy0He3MpdkQA0bZf1ml2RAKGVSQ5uXZEBDrz+Jz5dkQF35LM8DmGRAeEMaFTiYZECTjQdbbJhkQK7X9KCgmGRAySHi5tSYZEDka88sCZlkQP61vHI9mWRAGQCquHGZZEA0Spf+pZlkQE+UhETamWRAat5xig6aZECFKF\\u002fQQppkQJ9yTBZ3mmRAurw5XKuaZEDVBiei35pkQPBQFOgTm2RAC5sBLkibZEAm5e5zfJtkQEEv3Lmwm2RAW3nJ\\u002f+SbZEB2w7ZFGZxkQJENpItNnGRArFeR0YGcZEDHoX4XtpxkQOLra13qnGRA\\u002fDVZox6dZEAXgEbpUp1kQDLKMy+HnWRATRQhdbudZEBoXg67751kQIOo+wAknmRAnfLoRlieZEC4PNaMjJ5kQNOGw9LAnmRA7tCwGPWeZEAJG55eKZ9kQCRli6Rdn2RAPq946pGfZEBZ+WUwxp9kQHRDU3b6n2RAj41AvC6gZECq1y0CY6BkQMUhG0iXoGRA32sIjsugZED6tfXT\\u002f6BkQBUA4xk0oWRAMErQX2ihZEBLlL2lnKFkQGbequvQoWRAgCiYMQWiZECbcoV3OaJkQLa8cr1tomRA0QZgA6KiZEDsUE1J1qJkQAebOo8Ko2RAIuUn1T6jZEA8LxUbc6NkQFd5AmGno2RAcsPvptujZECNDd3sD6RkQKhXyjJEpGRAw6G3eHikZEDd66S+rKRkQPg1kgThpGRAE4B\\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\\u002fJKr2RAbEPFOH+vZECHjbJ+s69kQKHXn8Tnr2RAvCGNChywZEDXa3pQULBkQPK1Z5aEsGRADQBV3LiwZEAoSkIi7bBkQEKUL2ghsWRAXd4crlWxZEB4KAr0ibFkQJNy9zm+sWRArrzkf\\u002fKxZEDJBtLFJrJkQONQvwtbsmRA\\u002fpqsUY+yZEAZ5ZmXw7JkQDQvh933smRAT3l0IyyzZEBqw2FpYLNkQIQNT6+Us2RAn1c89cizZEC6oSk7\\u002fbNkQNXrFoExtGRA8DUEx2W0ZEALgPEMmrRkQCbK3lLOtGRAQBTMmAK1ZEBbXrneNrVkQHaopiRrtWRAkfKTap+1ZECsPIGw07VkQMeGbvYHtmRA4dBbPDy2ZED8GkmCcLZkQBdlNsiktmRAMq8jDtm2ZEBN+RBUDbdkQGhD\\u002fplBt2RAgo3r33W3ZECd19glqrdkQLghxmvet2RA02uzsRK4ZEDutaD3RrhkQAkAjj17uGRAI0p7g6+4ZEA+lGjJ47hkQFneVQ8YuWRAdChDVUy5ZECPcjCbgLlkQKq8HeG0uWRAxAYLJ+m5ZEDfUPhsHbpkQPqa5bJRumRAFeXS+IW6ZEAwL8A+urpkQEt5rYTuumRAZcOayiK7ZECADYgQV7tkQJtXdVaLu2RAtqFinL+7ZEDR60\\u002fi87tkQOw1PSgovGRAB4Aqbly8ZEAhyhe0kLxkQDwUBfrEvGRAV17yP\\u002fm8ZEByqN+FLb1kQI3yzMthvWRAqDy6EZa9ZEDChqdXyr1kQN3QlJ3+vWRA+BqC4zK+ZEATZW8pZ75kQC6vXG+bvmRASflJtc++ZEBjQzf7A79kQH6NJEE4v2RAmdcRh2y\\u002fZEC0If\\u002fMoL9kQM9r7BLVv2RA6rXZWAnAZEAEAMeePcBkQB9KtORxwGRAOpShKqbAZEBV3o5w2sBkQHAofLYOwWRAi3Jp\\u002fELBZEClvFZCd8FkQMAGRIirwWRA21Axzt\\u002fBZED2mh4UFMJkQBHlC1pIwmRALC\\u002f5n3zCZEBGeeblsMJkQGHD0yvlwmRAfA3BcRnDZECXV663TcNkQLKhm\\u002f2Bw2RAzeuIQ7bDZEDoNXaJ6sNkQAKAY88exGRAHcpQFVPEZEA4FD5bh8RkQFNeK6G7xGRAbqgY5+\\u002fEZECJ8gUtJMVkQKM883JYxWRAvobguIzFZEDZ0M3+wMVkQPQau0T1xWRAD2WoiinGZEAqr5XQXcZkQET5ghaSxmRAX0NwXMbGZEB6jV2i+sZkQJXXSugux2RAsCE4LmPHZEDLayV0l8dkQOW1ErrLx2RAAAAAAADIZEAbSu1FNMhkQDaU2otoyGRAUd7H0ZzIZEBsKLUX0chkQIZyol0FyWRAobyPoznJZEC8Bn3pbclkQNdQai+iyWRA8ppXddbJZEAN5US7CspkQCcvMgE\\u002fymRAQnkfR3PKZEBdwwyNp8pkQHgN+tLbymRAk1fnGBDLZECuodReRMtkQMjrwaR4y2RA4zWv6qzLZED+f5ww4ctkQBnKiXYVzGRANBR3vEnMZEBPXmQCfsxkQGqoUUiyzGRAhPI+jubMZECfPCzUGs1kQLqGGRpPzWRA1dAGYIPNZEDwGvSlt81kQAtl4evrzWRAJa\\u002fOMSDOZEBA+bt3VM5kQFtDqb2IzmRAdo2WA73OZECR14NJ8c5kQKwhcY8lz2RAxmte1VnPZEDhtUsbjs9kQPz\\u002fOGHCz2RAF0omp\\u002fbPZEAylBPtKtBkQE3eADNf0GRAZyjueJPQZECCctu+x9BkQJ28yAT80GRAuAa2SjDRZEDTUKOQZNFkQO6akNaY0WRACOV9HM3RZEAjL2tiAdJkQD55WKg10mRAWcNF7mnSZEB0DTM0ntJkQI9XIHrS0mRAqaENwAbTZEDE6\\u002foFO9NkQN816Etv02RA+n\\u002fVkaPTZEAVysLX19NkQDAUsB0M1GRASl6dY0DUZEBlqIqpdNRkQIDyd++o1GRAmzxlNd3UZEC2hlJ7EdVkQNHQP8FF1WRA7BotB3rVZEAGZRpNrtVkQCGvB5Pi1WRAPPn02BbWZEBXQ+IeS9ZkQHKNz2R\\u002f1mRAjde8qrPWZECnIarw59ZkQMJrlzYc12RA3bWEfFDXZED4\\u002f3HChNdkQBNKXwi512RALpRMTu3XZEBI3jmUIdhkQGMoJ9pV2GRAfnIUIIrYZECZvAFmvthkQLQG76vy2GRAz1Dc8SbZZEDpmsk3W9lkQATltn2P2WRAHy+kw8PZZEA6eZEJ+NlkQFXDfk8s2mRAcA1slWDaZECKV1nblNpkQKWhRiHJ2mRAwOszZ\\u002f3aZEDbNSGtMdtkQPZ\\u002fDvNl22RAEcr7OJrbZEArFOl+zttkQEZe1sQC3GRAYajDCjfcZEB88rBQa9xkQK36XG2l3GRA3gIJit\\u002fcZEAPC7WmGd1kQEETYcNT3WRAchsN4I3dZECjI7n8x91kQNQrZRkC3mRABTQRNjzeZEA2PL1Sdt5kQGhEaW+w3mRAmUwVjOreZEDKVMGoJN9kQPtcbcVe32RALGUZ4pjfZEBdbcX+0t9kQI51cRsN4GRAwH0dOEfgZEDxhclUgeBkQCKOdXG74GRAU5YhjvXgZECEns2qL+FkQLWmecdp4WRA5q4l5KPhZEAYt9EA3uFkQEm\\u002ffR0Y4mRAescpOlLiZECrz9VWjOJkQNzXgXPG4mRADeAtkADjZEA\\u002f6NmsOuNkQHDwhcl042RAofgx5q7jZEDSAN4C6eNkQAMJih8j5GRANBE2PF3kZEBlGeJYl+RkQJchjnXR5GRAyCk6kgvlZED5MeauReVkQCo6kst\\u002f5WRAW0I+6LnlZECMSuoE9OVkQL5SliEu5mRA71pCPmjmZEAgY+5aouZkQFFrmnfc5mRAgnNGlBbnZECze\\u002fKwUOdkQOSDns2K52RAFoxK6sTnZEBHlPYG\\u002f+dkQHicoiM56GRAqaROQHPoZEDarPpcrehkQAu1pnnn6GRAPL1SliHpZEBuxf6yW+lkQJ\\u002fNqs+V6WRA0NVW7M\\u002fpZEAB3gIJCupkQDLmriVE6mRAY+5aQn7qZECV9gZfuOpkQMb+snvy6mRA9wZfmCzrZEAoDwu1ZutkQFkXt9Gg62RAih9j7trrZEC7Jw8LFexkQO0vuydP7GRAHjhnRInsZEBPQBNhw+xkQIBIv3397GRAsVBrmjftZEDiWBe3ce1kQBRhw9Or7WRARWlv8OXtZEB2cRsNIO5kQKd5xyla7mRA2IFzRpTuZEAJih9jzu5kQDqSy38I72RAbJp3nELvZECdoiO5fO9kQM6qz9W272RA\\u002f7J78vDvZEAwuycPK\\u002fBkQGHD0ytl8GRAk8t\\u002fSJ\\u002fwZEDE0ytl2fBkQPXb14ET8WRAJuSDnk3xZEBX7C+7h\\u002fFkQIj029fB8WRAufyH9PvxZEDrBDQRNvJkQBwN4C1w8mRATRWMSqryZEB+HThn5PJkQK8l5IMe82RA4C2QoFjzZEARNjy9kvNkQEM+6NnM82RAdEaU9gb0ZEClTkATQfRkQNZW7C979GRAB1+YTLX0ZEA4Z0Rp7\\u002fRkQGpv8IUp9WRAm3ecomP1ZEDMf0i\\u002fnfVkQP2H9NvX9WRALpCg+BH2ZEBfmEwVTPZkQJCg+DGG9mRAwqikTsD2ZEDzsFBr+vZkQCS5\\u002fIc092RAVcGopG73ZECGyVTBqPdkQLfRAN7i92RA6dms+hz4ZEAa4lgXV\\u002fhkQEvqBDSR+GRAfPKwUMv4ZECt+lxtBflkQN4CCYo\\u002f+WRADwu1pnn5ZEBBE2HDs\\u002flkQHIbDeDt+WRAoyO5\\u002fCf6ZEDUK2UZYvpkQAU0ETac+mRANjy9Utb6ZEBnRGlvEPtkQJlMFYxK+2RAylTBqIT7ZED7XG3FvvtkQCxlGeL4+2RAXW3F\\u002fjL8ZECOdXEbbfxkQMB9HTin\\u002fGRA8YXJVOH8ZEAijnVxG\\u002f1kQFOWIY5V\\u002fWRAhJ7Nqo\\u002f9ZEC1pnnHyf1kQOauJeQD\\u002fmRAGLfRAD7+ZEBJv30deP5kQHrHKTqy\\u002fmRAq8\\u002fVVuz+ZEDc14FzJv9kQA3gLZBg\\u002f2RAP+jZrJr\\u002fZEBw8IXJ1P9kQKH4MeYOAGVA0gDeAkkAZUADCYofgwBlQDQRNjy9AGVAZRniWPcAZUCXIY51MQFlQMgpOpJrAWVA+THmrqUBZUAqOpLL3wFlQFtCPugZAmVAjErqBFQCZUC9UpYhjgJlQO9aQj7IAmVAIGPuWgIDZUBRa5p3PANlQIJzRpR2A2VAs3vysLADZUDkg57N6gNlQBaMSuokBGVAR5T2Bl8EZUB4nKIjmQRlQKmkTkDTBGVA2qz6XA0FZUALtaZ5RwVlQDy9UpaBBWVAbsX+srsFZUCfzarP9QVlQNDVVuwvBmVAAd4CCWoGZUAy5q4lpAZlQGPuWkLeBmVAlfYGXxgHZUDG\\u002frJ7UgdlQPcGX5iMB2VAKA8LtcYHZUBZF7fRAAhlQIofY+46CGVAuycPC3UIZUDtL7snrwhlQB44Z0TpCGVAT0ATYSMJZUCASL99XQllQLFQa5qXCWVA4lgXt9EJZUATYcPTCwplQEVpb\\u002fBFCmVAdnEbDYAKZUCneccpugplQNiBc0b0CmVACYofYy4LZUA6kst\\u002faAtlQGyad5yiC2VAnaIjudwLZUDOqs\\u002fVFgxlQP+ye\\u002fJQDGVAMLsnD4sMZUBhw9MrxQxlQJLLf0j\\u002fDGVAxNMrZTkNZUD129eBcw1lQCbkg56tDWVAV+wvu+cNZUCI9NvXIQ5lQLn8h\\u002fRbDmVA6wQ0EZYOZUAcDeAt0A5lQE0VjEoKD2VAfh04Z0QPZUCvJeSDfg9lQOAtkKC4D2VAETY8vfIPZUBDPujZLBBlQHRGlPZmEGVApU5AE6EQZUDWVuwv2xBlQAdfmEwVEWVAOGdEaU8RZUBqb\\u002fCFiRFlQJt3nKLDEWVAzH9Iv\\u002f0RZUD9h\\u002fTbNxJlQC6QoPhxEmVAX5hMFawSZUCQoPgx5hJlQMKopE4gE2VA87BQa1oTZUAkufyHlBNlQFXBqKTOE2VAhslUwQgUZUC30QDeQhRlQOjZrPp8FGVAGuJYF7cUZUBL6gQ08RRlQHzysFArFWVArfpcbWUVZUDeAgmKnxVlQA8LtabZFWVAQRNhwxMWZUByGw3gTRZlQKMjufyHFmVA1CtlGcIWZUAFNBE2\\u002fBZlQDY8vVI2F2VAZ0Rpb3AXZUCZTBWMqhdlQMpUwajkF2VA+1xtxR4YZUAsZRniWBhlQF1txf6SGGVAjnVxG80YZUDAfR04BxllQPGFyVRBGWVAIo51cXsZZUBTliGOtRllQISezarvGWVAtaZ5xykaZUDmriXkYxplQBi30QCeGmVASb99HdgaZUB6xyk6EhtlQKvP1VZMG2VA3NeBc4YbZUAN4C2QwBtlQD7o2az6G2VAcPCFyTQcZUCh+DHmbhxlQNIA3gKpHGVAAwmKH+McZUA0ETY8HR1lQGUZ4lhXHWVAliGOdZEdZUDIKTqSyx1lQPkx5q4FHmVAKjqSyz8eZUBbQj7oeR5lQIxK6gS0HmVAvVKWIe4eZUDvWkI+KB9lQCBj7lpiH2VAUWuad5wfZUCCc0aU1h9lQLN78rAQIGVA5IOezUogZUAVjErqhCBlQEeU9ga\\u002fIGVAeJyiI\\u002fkgZUCppE5AMyFlQNqs+lxtIWVAC7WmeachZUA8vVKW4SFlQG7F\\u002frIbImVAn82qz1UiZUDQ1VbsjyJlQAHeAgnKImVAMuauJQQjZUBj7lpCPiNlQJT2Bl94I2VAxv6ye7IjZUD3Bl+Y7CNlQCgPC7UmJGVAWRe30WAkZUCKH2PumiRlQLsnDwvVJGVA7S+7Jw8lZUAeOGdESSVlQE9AE2GDJWVAgEi\\u002ffb0lZUCxUGua9yVlQOJYF7cxJmVAE2HD02smZUBFaW\\u002fwpSZlQHZxGw3gJmVAp3nHKRonZUDYgXNGVCdlQAmKH2OOJ2VAOpLLf8gnZUBsmnecAihlQJ2iI7k8KGVAzqrP1XYoZUD\\u002fsnvysChlQDC7Jw\\u002frKGVAYcPTKyUpZUCSy39IXyllQMTTK2WZKWVA9dvXgdMpZUAm5IOeDSplQFfsL7tHKmVAiPTb14EqZUC5\\u002fIf0uyplQOoENBH2KmVAHA3gLTArZUBNFYxKaitlQH4dOGekK2VAryXkg94rZUDgLZCgGCxlQBE2PL1SLGVAQz7o2YwsZUB0RpT2xixlQKVOQBMBLWVA1lbsLzstZUAHX5hMdS1lQDhnRGmvLWVAaW\\u002fwhektZUCbd5yiIy5lQMx\\u002fSL9dLmVA\\u002fYf025cuZUAukKD40S5lQF+YTBUML2VAkKD4MUYvZUDBqKROgC9lQPOwUGu6L2VAJLn8h\\u002fQvZUBVwaikLjBlQIbJVMFoMGVAt9EA3qIwZUDo2az63DBlQBriWBcXMWVAS+oENFExZUB88rBQizFlQK36XG3FMWVA3gIJiv8xZUAPC7WmOTJlQEATYcNzMmVAchsN4K0yZUCjI7n85zJlQNQrZRkiM2VABTQRNlwzZUA2PL1SljNlQGdEaW\\u002fQM2VAmUwVjAo0ZUDKVMGoRDRlQPtcbcV+NGVALGUZ4rg0ZUBdbcX+8jRlQI51cRstNWVAv30dOGc1ZUDxhclUoTVlQCKOdXHbNWVAU5YhjhU2ZUCEns2qTzZlQLWmeceJNmVA5q4l5MM2ZUAYt9EA\\u002fjZlQEm\\u002ffR04N2VAescpOnI3ZUCrz9VWrDdlQNzXgXPmN2VADeAtkCA4ZUA+6NmsWjhlQHDwhcmUOGVAofgx5s44ZUDSAN4CCTllQAMJih9DOWVANBE2PH05ZUBlGeJYtzllQJYhjnXxOWVAyCk6kis6ZUD5MeauZTplQCo6ksufOmVAW0I+6Nk6ZUCMSuoEFDtlQL1SliFOO2VA71pCPog7ZUAgY+5awjtlQFFrmnf8O2VAgnNGlDY8ZUCze\\u002fKwcDxlQOSDns2qPGVAFYxK6uQ8ZUBHlPYGHz1lQHicoiNZPWVAqaROQJM9ZUDarPpczT1lQAu1pnkHPmVAPL1SlkE+ZUBtxf6yez5lQJ\\u002fNqs+1PmVA0NVW7O8+ZUAB3gIJKj9lQDLmriVkP2VAY+5aQp4\\u002fZUCU9gZf2D9lQMb+snsSQGVA9wZfmExAZUAoDwu1hkBlQFkXt9HAQGVAih9j7vpAZUC7Jw8LNUFlQOwvuydvQWVAHjhnRKlBZUBPQBNh40FlQIBIv30dQmVAsVBrmldCZUDiWBe3kUJlQBNhw9PLQmVARWlv8AVDZUB2cRsNQENlQKd5xyl6Q2VA2IFzRrRDZUAJih9j7kNlQDqSy38oRGVAa5p3nGJEZUCdoiO5nERlQM6qz9XWRGVA\\u002f7J78hBFZUAwuycPS0VlQGHD0yuFRWVAkst\\u002fSL9FZUDE0ytl+UVlQPXb14EzRmVAJuSDnm1GZUBX7C+7p0ZlQIj029fhRmVAufyH9BtHZUDqBDQRVkdlQBwN4C2QR2VATRWMSspHZUB+HThnBEhlQK8l5IM+SGVA4C2QoHhIZUARNjy9skhlQEM+6NnsSGVAdEaU9iZJZUClTkATYUllQNZW7C+bSWVAB1+YTNVJZUA4Z0RpD0plQGlv8IVJSmVAm3ecooNKZUDMf0i\\u002fvUplQP2H9Nv3SmVALpCg+DFLZUBfmEwVbEtlQJCg+DGmS2VAwaikTuBLZUDzsFBrGkxlQCS5\\u002fIdUTGVAVcGopI5MZUCGyVTByExlQLfRAN4CTWVA6Nms+jxNZUAa4lgXd01lQEvqBDSxTWVAfPKwUOtNZUCt+lxtJU5lQN4CCYpfTmVADwu1pplOZUBAE2HD005lQHIbDeANT2VAoyO5\\u002fEdPZUDUK2UZgk9lQAU0ETa8T2VANjy9UvZPZUBnRGlvMFBlQJhMFYxqUGVAylTBqKRQZUD7XG3F3lBlQCxlGeIYUWVAXW3F\\u002flJRZUCOdXEbjVFlQL99HTjHUWVA8YXJVAFSZUAijnVxO1JlQFOWIY51UmVAhJ7Nqq9SZUC1pnnH6VJlQOauJeQjU2VAF7fRAF5TZUBJv30dmFNlQHrHKTrSU2VAq8\\u002fVVgxUZUDc14FzRlRlQA3gLZCAVGVAPujZrLpUZUBw8IXJ9FRlQKH4MeYuVWVA0gDeAmlVZUADCYofo1VlQDQRNjzdVWVAZRniWBdWZUCWIY51UVZlQMgpOpKLVmVA+THmrsVWZUAqOpLL\\u002f1ZlQFtCPug5V2VAjErqBHRXZUC9UpYhrldlQO9aQj7oV2VAIGPuWiJYZUBRa5p3XFhlQIJzRpSWWGVAs3vysNBYZUDkg57NClllQBWMSupEWWVAR5T2Bn9ZZUB4nKIjuVllQKmkTkDzWWVA2qz6XC1aZUALtaZ5Z1plQDy9UpahWmVAbcX+sttaZUCfzarPFVtlQNDVVuxPW2VAAd4CCYpbZUAy5q4lxFtlQGPuWkL+W2VAlPYGXzhcZUDG\\u002frJ7clxlQPcGX5isXGVAKA8LteZcZUBZF7fRIF1lQIofY+5aXWVAuycPC5VdZUDsL7snz11lQB44Z0QJXmVAT0ATYUNeZUCASL99fV5lQLFQa5q3XmV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fZUCXIvJhWh9lQDqnYx\\u002fZHmVAXyz8algeZUBwTZtC2B1lQFL9K6RYHWVAHkOkjdkcZUDN+AT9WhxlQNaLWfDcG2VAl7+3ZV8bZUCVcT9b4hplQHNfGs9lGmVAmu57v+kZZUCH9aAqbhllQLGGzw7zGGVA87xWangYZUCDiY47\\u002fhdlQE+D14CEF2VA2beaOAsXZUBgfUlhkhZlQHJGXfkZFmVAw3ZX\\u002f6EVZUBHOcFxKhVlQJdXK0+zFGVAfhIuljwUZUC8+2hFxhNlQOvQgltQE2VAglcp19oSZUD4ORG3ZRJlQPHl9fnwEWVAfWuZnnwRZUBZXcSjCBFlQC+yRQiVEGVA26byyiEQZUCdoabqrg9lQD4WQ2Y8D2VAK2uvPMoOZUBo39hsWA5lQH9xsvXmDWVAPsc01nUNZUBeFl4NBQ1lQAIOMpqUDGVADcG5eyQMZUBXkQOxtAtlQKsbIzlFC2VAnyQxE9YKZUA9hks+ZwplQHEelbn4CWVAWL41hIoJZUBMGlqdHAllQMW6MwSvCGVABO74t0EIZUCQuuS31AdlQHrSNgNoB2VAeoczmfsGZUDNvyN5jwZlQPjrVKIjBmVAR\\u002f0YFLgFZUA9XcbNTAVlQMTlt87hBGVAS9pMFncEZUC74eijDARlQE8B9HaiA2VAYJjajjgDZUANXQ3rzgJlQOtZAYtlAmVAn+wvbvwBZUCBxRaUkwFlQEvoN\\u002fwqAWVA0a0ZpsIAZUDfxkaRWgBlQDRATr3y\\u002f2RAq4fDKYv\\u002fZECscj7WI\\u002f9kQN1FW8K8\\u002fmRAKb667VX+ZEAzGwJY7\\u002f1kQDsr2wCJ\\u002fWRAj1j05yL9ZECWuAANvfxkQIgcuG9X\\u002fGRA9yPXD\\u002fL7ZEApUR\\u002ftjPtkQHcfVwco+2RAvhtKXsP6ZED\\u002f\\u002fsjxXvpkQF\\u002fLqcH6+WRAkuvHzZb5ZED0VAQWM\\u002flkQGCsRZrP+GRABm54Wmz4ZEBhGI9WCfhkQItago6m92RAMUZRAkT3ZEBMhQGy4fZkQPeTn51\\u002f9mRAr\\u002f4+xR32ZEAvpfoovPVkQGwC9cha9WRA3XlYpfn0ZECdqle+mPRkQL\\u002fILRQ49GRAWP0ep9fzZEC8zXh3d\\u002fNkQIaKkoUX82RA+cbN0bfyZEB+2ZZcWPJkQNplZSb58WRAD\\u002fK8L5rxZECkhi15O\\u002fFkQGVbVAPd8GRAnpHczn7wZED4\\u002fH\\u002fcIPBkQEf8By3D72RArWNOwWXvZECReT6aCO9kQDwI1rir7mRA04UmHk\\u002fuZED4VFbL8u1kQCYgosGW7WRAgFJeAjvtZEDRsPiO3+xkQOEV+miE7GRApVUIkinsZEAfS+gLz+tkQEkVgNh062RA2IfZ+RrrZEA81SRywepkQOp3u0No6mRAqWEjcQ\\u002fqZEBweBL9tulkQHRpcupe6WRA09tkPAfpZEDWDUj2r+hkQNLouxtZ6GRAkpmnsALoZEDcvD+5rOdkQMgxDTpX52RAJab0NwLnZEDG8z64reZkQP9oosBZ5mRAPxpNVwbmZEARX\\u002fCCs+VkQPuhzUph5WRAf7DEtg\\u002flZECNv2PPvuRkQMRg+Z1u5GRAia6oLB\\u002fkZEAzAICG0ONkQN2EkreC42RAWTMVzTXjZEBykH\\u002fV6eJkQCvjsOCe4mRAjIkaAFXiZECWQPBGDOJkQABZX8rE4WRAHgHOoX7hZEA2BSTnOeFkQOKsHrf24GRAmbCxMbXgZEBxqnd6deBkQCrlNLk34GRA5gpwGvzfZEAc+iTQwt9kQCH9lhKM32RA6tVIIVjfZEBOlSJEJ99kQOcjz8z53mRABNpdGNDeZEAhrjeRqt5kQBKYe7GJ3mRAHhfbBW7eZEB+vRYxWN5kQFPOQ\\u002fBI3mRA7SYQIEHeZEBS90nDQd5kQHnKBgtM3mRAtE3jYGHeZEDQZv5zg95kQDo1i0m03mRAo44lUvbeZEAH04WFTN9kQKWf04a632RAs4jF1ETgZECG2SwK8eBkQHBTHDbG4WRAL2bTWM3iZEC+kPMkEuRkQFNmQ2Ok5WRAycshJJvnZECdUgI3HepkQFRz++d37WRAaAWwX1fyZEDqptOaQPpkQOSVp+NsCGVAz8zHm\\u002fkiZUDemIxb5FNlQHMYt9SbqGVAdyyPJZ0uZkBZ34YuhuxmQP+6HzBo2WdAalkOMUrWaEBYggYMk61pQLsiu\\u002fqmHWpAv3L4kv75aUD65MzSp1BpQD5tdoPKXWhAcnLDZ+tgZ0DkEjaeCYZmQELQyPdb4WVAyalE91NzZUCOFFPi1jBlQMh\\u002fUcGZC2VAHPZRw333ZEAn5UElYuxkQB8yyImv5WRA53sEiyDhZEA7NImwrt1kQOAGN9Dm2mRAgNmpAJPYZEA+qS7gldZkQBIAmJDc1GRAlKcJkFnTZED\\u002fKwiqAtJkQGJvvvXP0GRApwuWOrvPZEBL5yeGv85kQJ8N19\\u002fYzWRA4DV2EATNZECgDh94PsxkQEybTe6Fy2RAl52BqdjKZEDGJXMsNcpkQJFRczeayWRAhiD0vAbJZED2iHfYechkQDOEV8fyx2RAApX84nDHZEBRZzKc88ZkQPZfXXd6xmRASDdiCQXGZECyZhv1ksVkQLI+QOkjxWRASJmnnrfEZED7ytTWTcRkQAEQwVrmw2RAmH7W+YDDZEC6tROJHcNkQFI1UeK7wmRATKai41vCZEDyas9u\\u002fcFkQEuq3migwWRAFLizuUTBZEDCR7hL6sBkQJ1KkguRwGRAmrXj5zjAZEC3thLR4b9kQGQfGbmLv2RANvxZkza\\u002fZECue3xU4r5kQMxoS\\u002fKOvmRAjpqYYzy+ZECh0SOg6r1kQHqRhKCZvWRA2ZIWXkm9ZEDYe+jS+bxkQJiVrPmqvGRAtkGrzVy8ZEA5+rZKD7xkQPetIW3Cu2RAvVGzMXa7ZECpg6GVKrtkQPUiiJbfumRALsJiMpW6ZEBO3YZnS7pkQCjAnjQCumRADwylmLm5ZEDkzeCScblkQLcX4iIquWRAxRJ\\u002fSOO4ZEAkgNEDnbhkQNifNFVXuGRADndDPRK4ZECQb9e8zbdkQCFLB9WJt2RAlGYmh0a3ZEAmScTUA7dkQDx9rL\\u002fBtmRAkrDmSYC2ZEBNGrd1P7ZkQB4mn0X\\u002ftWRANmRevL+1ZEBGvvPcgLVkQHnynqpCtWRAtlXiKAW1ZEAv34RbyLRkQLh\\u002flEaMtGRAB8ho7lC0ZECj4aVXFrRkQMveP4fcs2RAe2Z+gqOzZEBZwgBPa7NkQCBVwvIzs2RAEIEfdP2yZEDIBtvZx7JkQATmIyuTsmRA58qbb1+yZECkE16vLLJkQPR7B\\u002fP6sWRAEH2+Q8qxZED7cjyrmrFkQJSZ1zNssWRAUvaN6D6xZEDzRRHVErFkQBwI1AXosGRAQcYXiL6wZECOtvxplrBkQKrfkrpvsGRA0+bsiUqwZEDotjTpJrBkQAo1wuoEsGRAPT40ouSvZEA4MYwkxq9kQJZQTIipr2RAslOZ5Y6vZEBqiF9Wdq9kQJj1e\\u002fZfr2RAuv3p40uvZECfE\\u002fY+Oq9kQO4ndiorr2RAHo8IzB6vZEAjPFpMFa9kQJdNddcOr2RAmBIZnQuvZEDH2hzRC69kQDsb36sPr2RAvrLCahevZECuY7xQI69kQKX08qYzr2RA0NR0vUivZEDjoQbsYq9kQGaIDpOCr2RADjGhHKivZEDCzrX9069kQOzwh7cGsGRAHQkv2UCwZEAWJ3UBg7BkQNxd+ODNsGRAQ6akPCKxZEA3hyayC7JkQIJonXYAs2RAbaFBnwG0ZEBk5oFkELVkQDeKiycutmRAbx3deFy3ZEBDGR4gnbhkQOAqhSXyuWRAPuop3V27ZEBQtbf04rxkQJ\\u002f4F4SEvmRA+APVIUbAZECmTi\\u002f8K8JkQB3gKvg6xGRALqtA2HjGZECiVups7MhkQN8B\\u002fdGdy2RAlp7OvJbOZECZLpHg4tFkQITEVnCQ1WRAncQXybDZZEA9ykhSWd5kQArZxamk42RASxaJObTpZED3egNnsvBkQLAGHrXV+GRA46WZs2UCZUBEm05cxA1lQAmstl6EG2VAlMdoyaUsZUBhX56POkNlQMRRKND7Y2VA4XmA2KGZZUBGPqB4nfplQFaX0rg1sGZA4OpetW\\u002f6Z0BUgZg3DihqQO3xy\\u002fJCem1ArvzP87v6cEDk8NIoUphzQJ+8qnXgHXZADpPjaXjad0BygPsOHCV4QIbk7+xk23ZAClYwClCEdEDA0pwKfdtxQHrieAe94m5A0BwEo88ja0AuW8rRqpZoQF8ssLlsCGdAEURt6NgpZkC7jJvJebNlQEhHaHoEdGVA+BFH\\u002fk5PZUAr5viFajdlQIubDy4aJmVAKGMhqqoYZUAvymGe3A1lQPMTNWMDBWVA8MpkhLD9ZED700gzlfdkQBGW2iZ28mRAMwO9pCXuZECIP1Xif+pkQHhJy4to52RAt\\u002fWx\\u002fcjkZEBMOYj3juJkQL0gf6Or4GRAG3KZ2xLfZEDCaYWbut1kQB3fcJOa3GRA8WRv06vbZED+ylyJ6NpkQA4NycxL2mRAq6SkddHZZEB\\u002fZjr7ddlkQGWdn1k22WRAbhM1\\u002fA\\u002fZZEDgAiisANlkQMXCIIIG2WRAlT982h\\u002fZZEBTF5FLS9lkQOV6nJ2H2WRA7uwHxNPZZEC6K8nXLtpkQAEwqhKY2mRAtAlRyw7bZEBdKedxkttkQEHMRY0i3GRAWyeSuL7cZED22DehZt1kQOhPMwUa3mRAXmSgsdjeZEB5cIOBot9kQGnoxFx34GRArNVYN1fhZEDJvowQQuJkQDR5d\\u002fI342RADi+H8TjkZEA1jiosReVkQC+okspc5mRAFYeL\\u002fn\\u002fnZEAW6GkDr+hkQH7qDB7q6WRAotbynDHrZECZa2DYhexkQIBomjLn7WRABkcxGFbvZEBMW18A0\\u002fBkQIjJeW1e8mRAdv507fjzZECflH0ao\\u002fVkQAvQpptd92RAqhywJSn5ZEASQ+N7BvtkQHNUDXH2\\u002fGRAdqOU6Pn+ZEA\\u002ffa7XEQFlQHO+t0Y\\u002fA2VAWdWzUoMFZUDxRvYu3wdlQD5j+yZUCmVAy4B1oOMMZUDz25Qdjw9lQEkYkT9YEmVAyWZ8yUAVZUBYgGmjShhlQP0A7913G2VA60UUtsoeZUBQybWZRSJlQLkfcSzrJWVA9UIrTb4pZUAk0EYcwi1lQJ1gowL6MWVApkqDuWk2ZUCbAXpTFTtlQD8li0YBQGVAxUipdzJFZUDS0ctHrkplQPJS3aJ6UGVALNXPEJ5WZUCoGDPJH11lQE+EuskHZGVA8QU28F5rZUC2eJsYL3NlQPl43j+De2VAE2J8rGeEZUAqhNMd6o1lQPb1mQMamGVAzAwVvwijZUAqyBLwya5lQBgEINBzu2VACoQQnx\\u002fJZUDpzLIk6tdlQF6Yhkz052VACFmT4WP5ZUCjIiVyZAxmQPmfYmUoIWZA9IOMT+o3ZkCl\\u002fYWV7lBmQATlX3WFbGZA4K2EkA2LZkASjIcd96xmQHRbhPTH0mZAwazvuiD9ZkC64eWMwyxnQFhJxaWcYmdA2\\u002fIuvc2fZ0B2fzQYveVnQNlbSL0pNmhA5qOF5UaTaEA+C6D04f9oQMRGIvqYf2lAvOTNOzAXakAbn1ZhM81qQJwnqcB0q2tAnFinBSnDbECe8PaRLDhuQHuwVpmCLnBA5aRskhL3cUAGLOJMKzt1QJoiJLpvY3tANvyS8qtKg0ARZvFVr62MQDelxFgrZpVA\\u002fXmAaiG6nkCyX1GmaaKkQL7imGLdbKlA4GGaoNA9rEAqJfYEu+SrQOIioZ6iiKhAu6Vzo1aCo0BCkhK0l42cQDsLuJknoZNA2oxu7YcoikDEUa397a2BQLHLnN\\u002fOeHlAYxG+CxQedEB4TIIToElxQD\\u002fAbreccm9ARPlXt02GbUCi3fURfTBsQGw9Lsf0LGtA\\u002fnrY2L5dakBoFAAR9LNpQDZQXei2JmlAecgArvavaECzXRSpO0toQHvRLkse9WdArlSoZvqqZ0CShWzDvGpnQITJ6Jq\\u002fMmdAL7Sq07ABZ0Bf3loBf9ZmQPwbejVLsGZAwi99Sl6OZkC\\u002faM25IHBmQI2Q4VIUVWZAaL2bWc88ZkDsZY2z+CZmQD6gXeNEE2ZAIS9jonMBZkDMnKX0TfFlQMx+TJyk4mVAta7\\u002f107VZUBK5ohcKcllQG\\u002fPnX0VvmVATFBtfPizZUDTppf0uqplQGbmyWFIomVAHutpuY6aZUDlua4UfpNlQKJnO2gIjWVAW4fkRiGHZUBZIrutvYFlQHFm09fTfGVAE9uFGFt4ZUCpGyS7S3RlQMnqSOeecGVAZFMRiU5tZUBPV6s8VWplQJvQvzyuZ2VAnc9RU1VlZUAZx77MRmNlQJ34mGx\\u002fYWVAQFwhZPxfZUCAJTBKu15lQNRmYxS6XWVA1ntyEfdcZUB7C4vkcFxlQCDVoYEmXGVAXD6mKhdcZUAy4ottQlxlQJpTISOoXGVAYPqubkhdZUANhFy+I15lQOfrXsw6X2VAhLX0oI5gZUCJtjmVIGJlQBjQ31byY2VAhF3e7AVmZUCJBC+9XWhlQP0itJP8amVApntrquVtZUC5NhezHHFlQNMMkOKldGVATdb9\\u002fYV4ZUB5IUBqwnxlQCZe3z1hgWVAfDjxVWmGZUCer3Bu4otlQA9IpT7VkWVAga9VmkuYZUBs2aqYUJ9lQHYa28HwpmVAf8LzRDqvZUBkpmY3PbhlQL6HZOALwmVAE1uLErvMZUCEFxCXYthlQIC2Wa4d5WVAN78MqwvzZUBUuN+tUAJmQP3VU4oWE2ZATPq+3o0lZkBytidt7zlmQHXykMZ9UGZAgl7eX4dpZkCUMAUvaYVmQKYdlvmRpGZAD10FjIbHZkAX7yol5+5mQGAR4312G2dAprdr\\u002fSJOZ0BdNCHmEohnQGHiHp20ymdAJRaWstQXaEAMkzclvHFoQNzxa\\u002fha22hAAeFwgodYaUBzmbmHae5pQABWEpRcpGpAbDSdBj6Ga0AkYALf5KpsQHOhWGRIRW5A3i6F3hZmcEB4zkT9zKJyQMYqV+Jn2XZAk0IKoeGzfkAd50rvGDSGQHCtdyaKgZBA6X5utVAImEAH7sWu9YigQB6Ij8i8AKVA+E8jUtcmqEDtISIH7LWoQNyjAZbmZ6ZAIETAI15AokAp+CSpUjubQK33z28uAZNAgJ583hCViUAkkLwyml2BQF5XOQ7IAXlAjLNZKcyzc0BGd1Qyl+dwQFUcy6rUwG5AQZ+KebHnbECFedS54qNrQAp2AyNHsGpASe+vMdHuaUAq1Qjf0lBpQJ8rULq2zWhAEo9R661faEDkfklneAJoQIP9adzcsmdAXuM5aV1uZ0B6ZC+aBjNnQNYExRRN\\u002f2ZARBuhy\\u002fTRZkBMKKbI\\u002fqlmQJDjaqGbhmZAkDTaRiFnZkA55IZGA0tmQAC52NjMMWZAa6I\\u002fRhwbZkAPBkNOnwZmQMerN1IQ9GVAq\\u002fC2FTTjZUBuJqny19NlQOOQNGfQxWVAdnkb6ve4ZUCnwaz2La1lQC5N10RWomVAOkqDJFiYZUCt50b1HY9lQPccCLWUhmVA9+5Aoat+ZUBQFX3nU3dlQDk4V2KAcGVAcCfFYCVqZUA8VO51OGRlQFM3HVCwXmVA9C6glYRZZUBypKTGrVRlQC7cQiMlUGVAjvsTleRLZUAzT8ib5kdlQGwMSzwmRGVA87wT8p5AZUCDEVWiTD1lQDunxZArOmVAv+PJVTg3ZUCM1s7VbzRlQNVgrTnPMWVAxgTz51MvZUCk4PJ++yxlQGmuhc\\u002fDKmVAWUpj2KooZUDWXwPCriZlQF+E9drNJGVAvFGjlAYjZUCDCnGAVyFlQG0OM02\\u002fH2VAV+rvxDweZUCgLejKzhxlQJ1t3ll0G2VALwmbgiwaZUCzW6hq9hhlQCcoRUvRF2VA9iWMcLwWZUCWy9A4txVlQCnFMhTBFGVATQdrhNkTZUDYNNccABNlQA40yoI0EmVAnHgrbnYRZUBF4nGqxRBlQHhdCxgiEGVAgApIrosPZUDNDOd9Ag9lQPWuK4uMDmVADET2TSQOZUAHQps7yg1lQOzcZPd+DWVAQMn6W0MNZUBFaaOHGA1lQPerVOz\\u002fDGVAIgftZPsMZUDL23tRDQ1lQEz8U704DWVAl7HkkoENZUA1AGXj7A1lQJkIekyBDmVAgABWkUcPZUDnjTakSxBlQI\\u002fIr+meEWVAPwcyP14TZUB2HIPtwhVlQMl1sQZPGWVA6kwcfTEfZUCjbsjR\\u002fCllQNAQ2oyZPmVA9t2Pnd1kZUD8IaE7k6ZlQIgv0B+4C2ZAtgZjUiaUZkDg0p0xGTFnQDO2hoDKwWdAzuAegU0XaEAEynsffwtoQBBe\\u002frzso2dAXNWhEhIMZ0A7SsTfBXFmQBwY2LeP72VAMO4NGamSZUCTjfBq7VdlQPMJ1btnNmVAsFmHYH0kZUDZ+dMS\\u002fhplQEezEM6eFWVAOrELYTQSZUDCLAeQxQ9lQE300NXnDWVA1ZTgG2sMZUAauvf6NQtlQDbUiXY4CmVA2bzkR2cJZUBv2CAIughlQLeSr0oqCGVAJzq2ErMHZUA\\u002f0g51UAdlQNhOdFX\\u002fBmVADvXyNb0GZUBSxRwTiAZlQDmUVUleBmVAztevgD4GZUDMsJWdJwZlQAKg\\u002fbQYBmVAox1FAxEGZUCfiArlDwZlQBJ8jNEUBmVAjGI0Vh8GZUA9BQkTLwZlQF9+1rdDBmVAxTPlAV0GZUAikSK6egZlQHsDpbOcBmVAQ9Z5ysIGZUDjeK\\u002fi7AZlQPSxkecaB2VABZgPy0wHZUB9+USFggdlQCROIhS8B2VAf3Uve\\u002fkHZUBwd2bDOghlQBVGJPt\\u002fCGVAQCwuNskIZUAQK8uNFgllQKYS8SBoCWVAqqOFFL4JZUBlhrSTGAplQBNmW9B3CmVAEgiOA9wKZUBdzDRuRQtlQK2syFm0C2VA1Y8wGSkMZUCFp8QJpAxlQO+XfZQlDWVASmZWL64NZUB2qulePg5lQHhSVLjWDmVAtoFq43cPZUBo1k2dIhBlQO7Id7vXEGVACBpPL5gRZUCDmmQKZRJlQG9ReoM\\u002fE2VAIY6A\\u002fCgUZUDbVb8JIxVlQKKVcHovFmVAL4whY1AXZUAlVkkqiBhlQJQ9oZfZGWVAC+7z5kcbZUDPfk7f1hxlQH9Xxu+KHmVAGExoU2kgZUBk82k+eCJlQGSvbBe\\u002fJGVAHVOfwEYnZUAWF+\\u002f1GSplQG5\\u002fccdFLWVAE\\u002fAFOtowZUAvFUwc6zRlQNsUICSROWVAMS35ces+ZUCvHCSmIUVlQCuB8M5mTGVAfsvQuv1UZUB0iI0bQF9lQN9W2hita2VAgR70jA57ZUDPvBbU4Y5lQBa30+lxqmVA\\u002fL2Ubm\\u002fVZUAudiPv8SBmQOL66uyxrmZAeK6oOda2Z0BKZQgOpYNpQKq4MkzLWGxA7lh8ZFwicEA6aKTVEXZyQLxIGuRktHRA7dKaGgcsdkCgDMBd7Dh2QAWoN8QB1HRAeJyJRX+bckAJJyF8JkRwQICAM+MfjGxA1HxIs6elaUA7fdekHstnQMvxLu4xumZAaKbARs4nZkAI6BiSUNplQKAN9xHArmVAGQogXUKTZUCZKHkyx39lQAqJCj7bcGVAIVA8ZfFkZUBzU2F4OVtlQKU3s1sxU2VAkZMxt31MZUA7EiWz2kZlQFSzA5IUQmVAtob\\u002fPQM+ZUCMQtNHhzplQA1KPsWHN2VA1xFtxfA0ZUDvGOUtsjJlQKHfteG+MGVA7iQsHgwvZUCIU4r+kC1lQHVghxxGLGVA50U\\u002fRiUrZUBe8UZEKSplQIJsAqxNKWVAl6tfu44oZUAIGNM76SdlQId6+mpaJ2VAF0Su598mZUDKsJKidyZlQPwMcdEfJmVAdJrK5NYlZUCeyTV\\u002fmyVlQDdNLm5sJWVAu9sSpEglZUCtmRkzLyVlQJArD0kfJWVAfw69KxglZUBnoNo1GSVlQE+hcdQhJWVAMD6jhDElZUDgIb7RRyVlQIjImFNkJWVAUoolrYYlZUDznjeLriVlQOTScaPbJWVAFtpYsw0mZUC6J4R\\u002fRCZlQF8C6dJ\\u002fJmVASjc8fr8mZUBrXmdXAydlQA4XDzlLJ2VAdwgoApcnZUDaxJiV5idlQO7y5tk5KGVAgl3uuJAoZUAnuqAf6yhlQLAizf1IKWVACWHuRaopZUADTP\\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\\u002fUCBPZUAFs1GajlBlQLxrqRcJUmVAVnHIZZBTZUAYwm0sJVVlQOa3qh7IVmVAKe3L+3lYZUDoj1iQO1plQJyeKrcNXGVAsdqiWvFdZUDUmPt1519lQHoAvhbxYWVAycRdXg9kZUDf6\\u002f+DQ2ZlQBXZcdaOaGVAM4dWvvJqZUAwtJDAcG1lQPW28YAKcGVAgNU1xcFyZUDhPlh4mHVlQLJQSq6QeGVAQJUbqKx7ZUBa+6HY7n5lQP84tOlZgmVAmykLwvCFZUDAXOKLtollQJkOdLyujWVAc4hxHN2RZUBGlp7RRZZlQMWHvWntmmVAlFYB59ifZUAZVEXODaVlQIaPVTeSqmVAaXGh32ywZUAzasM\\u002fpbZlQDzFXaRDvWVAY6rmSlHEZUCXSh2D2MtlQPapCdbk02VAW3KZM4PcZUAwRSkowuVlQLt9lhuy72VAD6HVm2X6ZUCsnYO18QVmQBsmg11uEmZASxh+7\\u002fYfZkCFxiLGqi5mQDiwQPOtPmZAxiSZHypQZkCGR3GcT2NmQGxI5LNWeGZASuzhSIGPZkDs+A3dHKlmQAp64BmFxWZAcV84BCflZkAFiiUPhQhnQHtsplU8MGdAUdfcXQtdZ0A0gXzu2o9nQLe+f7bJyWdA9u8r2DsMaEBhVQrh71hoQLsfknEbsmhAXJqAC5MaaUAy1GL3ApZpQGpm7DhGKWpAmRDyOwHbakAQJVAI\\u002fbRrQJx88JHOx2xAQsU1HQM1bkA\\u002fMt6ZhyRwQMDF1\\u002fSz2XFAZtWmgznzdEBXZqVa6cN6QAsysaHTpIJADGlyQyVki0D\\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\\u002fNzQipZUBrUIbXXaVlQGETwtPsoWVAXSknybGeZUAznNEdqZtlQC5aaY7PmGVALH16JiKWZUAJAfE5npNlQHFMoV9BkWVAtq\\u002fObAmPZUDp\\u002fKhx9IxlQNwbw7YAi2VAGqCOuyyJZUB8yfQ1d4dlQDAqNRPfhWVA5yFHemOEZUD08hfQA4NlQLogJ76\\u002fgWVAf6g7PJeAZUAlMT+ein9lQEpuxqeafmVATj6Ip8h9ZUAjIjieFn1lQFryonaHfGVAEmCdWx98ZUDIi4NP5HtlQKL6pXrfe2VA8l6ZpCB8ZUAlm3DWx3xlQARfbfodfmVAnmZFicqAZUDotfScMIZlQO+ZYyffkGVA7sg\\u002fn7ykZUANho\\u002fcUsZlQEH3TTvA+GVAITXQ5Z86ZkDvvgA8KINmQESbdEfVwGZATURqr6TcZkDY9KP7w8hmQHKQV2H4jmZA7uamqBdGZkA2NoXUPgFmQP2C05bUymVAVzUr7G2lZUC9W9B4hI5lQPzYn0mggWVAhfnmUaV6ZUA0CKtrwnZlQGno6BhjdGVAGCIWjsNyZUCt3coxjXFlQIs9feaZcGVAE5aLC9dvZUCa3crQOW9lQNWsUfS6bmVAnMoXL1VuZUCJ5YCFBG5lQE1kQ+fFbWVA7uZz8ZZtZUBBt3bDdW1lQL6NHuBgbWVAmG4mF1dtZUDgDo90V21lQLgLNDRhbWVA0RtxuHNtZUARtAeDjm1lQJ7NsS+xbWVAYhr4b9ttZUBLGf8HDW5lQNgkE8xFbmVAHmvKnoVuZUCzhp5vzG5lQIti5zkab2VADAUnBG9vZUC9Zprfym9lQG0AB+gtcGVAJJC\\u002fQ5hwZUAg7NwjCnFlQGTCqcSDcWVA9ftBbgVyZUDcQGh1j3JlQB\\u002fnlTwic2VAIYNLNb5zZUBfdKrhY3RlQLpCYtYTdWVAHo\\u002f\\u002fvM51ZUA75q1WlXZlQEAggn9od2VALWVoMkl4ZUAMv9eNOHllQAPJddk3emVArxfgjEh7ZUAsM99XbHxlQCRyWSylfWVASSd0SvV+ZUDBWHFPX4BlQCRjAkjmgWVAVVAAx42DZUDhwMYBWoVlQMvT1fNPh2VAELrti3WJZUC1+5vl0YtlQBEVR5NtjmVAcBpN\\u002flKRZUBhHfzkjpRlQMTtWgExmGVAGvlp6EycZUCQE5E4+6BlQEn1CDlbpmVARFP4H5WsZUCeVvJj3bNlQJXsOhJ6vGVAIVtWMc3GZUCgEScHbtNlQJb60UZu42VAQKOuqBL5ZUBVKem7hhlmQHj7HvgvUGZAJ3mUSpqzZkDmmyiEwWlnQBzYCasSpmhAPvUaygSaakB\\u002f5ZJvTlZtQPLBWIQBU3BAIL\\u002fE5I35cUDePZVZ\\u002fiJzQCiBNwUkUHNAlqi4ZA5qckAlk+NcydtwQDXExNfgVW5AeqVc2xpla0A+ll7+ZjVpQMH02ldnx2dAymFteefvZkB6LRAi2nlmQI03Y\\u002fOOOmZAALqvlRQXZkALPbP0JQFmQNsRgqYX8mVAYYAHj\\u002frmZUBnw52aeN5lQGhrPG3f12VAwec5JsHSZUBp\\u002fOOd0c5lQHsgbtXYy2VAb99sgqzJZUDgYLpBLMhlQEL0jA0\\u002fx2VAA5BGc9HGZUBDVqhI1MZlQMEtoLk7x2VAhUUQlf7HZUCXoAnHFcllQHRCR\\u002fR7ymVAk8NRLi3MZUAZcSy6Js5lQPsgJuVm0GVARx+f5OzSZUDBP3S+uNVlQHG6YDjL2GVAabAfzSXcZUBv12+myt9lQNTGYJu842VAnQ6IMv\\u002fnZUBYQOmnluxlQFHTgvaH8WVApwaV5dj2ZUBYzNkZkPxlQI5ZByu1AmZABBQcvlAJZkB5LxylbBBmQEyUGQUUGGZAiyeYg1MgZkBsZaZ8OSlmQIdgWEPWMmZACrS+bjw9ZkC0aPo1gUhmQHLvut68VGZASM9PQgtiZkDLIZRujHBmQNLCa2llgGZASUhuH8GRZkAeoNqI0aRmQEOIPxPRuWZAwY+3YgTRZkCbcJqDvOpmQDf6uK1ZB2dAlICDxU4nZ0DZyS7XJUtnQPC+\\u002fN2Fc2dAU2cpSTqhZ0BpmBHdPNVnQKJuztDCEGhA1W6caU5VaEBYrxHpxqRoQN7ylZSZAWlAKwKyeOhuaUDSqsOF0PBpQF317l\\u002fhjGpAYGUwAxtLa0CXrXz+dzhsQJGAOcfTbW1AYzYUunIhb0C8kDVpvuZwQJiqbW8iOnNAVLyqk9l7d0CsEdnqNiV\\u002fQADAG83jAoZAATR4efHIj0Agk07xhW+WQPDnu7Hg851AJRaBVIJzokAkOQlgkoGkQLIs30YpNKRAZ7d7zECyoUCgpRo1SR6cQBZPR4nyu5RAwB3eIZEgjUAK9vJ3NzOEQN64NEAI8HxAB2HCSA0+dkCjhysrOI1yQFr9cLmrhXBAiLRKjnKqbkCdyv4AJx1tQGKYy6vK\\u002fWtACCbQIx8fa0B0VnZH6mtqQNxvT3Ss2GlAGqJ44BdeaUCynB2UA\\u002fdoQOI83FSXn2hAyBGTmeJUaEBwuDUEnRRoQEVoA2H73GdARzXoFZGsZ0C7N3PpOYJnQBEz6J0JXWdAonWtsz88Z0BCSBMsPh9nQKoG+32CBWdAS6D1KaDuZkAkddCEPNpmQE1WYmwLyGZAzrfGrsy3ZkCWwbv6SalmQJjoUDpVnGZAWB60QMeQZkDDRYe5foZmQIxHUUxffWZAHJ686VB1ZkAWQLE6P25mQAtuJSsZaGZAraPli9BiZkDcPKvHWV5mQMCWt6irWmZAkubdLL9XZkCTc3xmj1VmQLezZ2kZVGZAUrs+Q1xTZkCeSw\\u002f\\u002fWFNmQFr6nrQSVGZAZvYkpI5VZkAiCMRf1FdmQPUarwTuWmZAjXuehuheZkBswSYR1GNmQPxjroPEaWZAmTVFDtJwZkDl6qP3GXlmQHLcSZe\\u002fgmZAlAxRku2NZkA63oZu15pmQOYyMpa7qWZA+HmA8OW6ZkBI0hNAs85mQDpLdIyV5WZAlHEG9xkAZ0BKRA2F8B5nQCuKn6X2QmdAnE7wm0VtZ0AQRDOqRp9nQFQofpHQ2mdAEoU6cVciaEDqwSfHSnloQOTZMoz45GhAX1p4NutuaUBpKewUBytqQIQLaAaIRWtAZX4iaJwdbUBNdfU4BjZwQHyiX21hMnNAiGyHXYZUeED8YtjVSDWAQNISCdzL5YVA3c+olKzjjEBpOAPjbwySQN\\u002fYRCii0JRAKHSwqaiolUDYnfXKvyqUQAnDKxO+CpFABOXA2XzIikAG3+jbpBmEQNB4zqN\\u002fxH1ARGObuQCZdkDsgssLcylyQIFW7OJbPm9ArBnxD+5ubEDsRcaQ5NZqQPiy2qtJ22lAx+ZtVIsuaUAV8OPYA61oQLix+SV6RmhA9lRT\\u002ft7yZ0A20ANbhq1nQC9jYIVSc2dA0ll3\\u002fwBCZ0BVPr3K3BdnQCW1S8aU82ZAtPMnPyHUZkDKsM6fsbhmQK0AuUOfoGZAwO\\u002fBzmOLZkBRuvP\\u002fkXhmQCtYvUzQZ2ZAwQE6yNRYZkDY2Nf+YUtmQPGRvYdEP2ZAzLu0HlE0ZkC105UlYypmQGQ64HRbIWZAHeRhaR8ZZkAu0lUhmBFmQJWnrd6xCmZAB7mqhlsEZkBKo7s4hv5lQLo\\u002f8Pck+WVAz6VdYyz0ZUDt8pR6ku9lQP7U6WtO62VAUv28aljnZUAtRFuMqeNlQEe8Sqo74GVAcvsXSQndZUCTAeOCDdplQGEYD\\u002fVD12VAr9GVsKjUZUAagZQsONJlQBhovjrvz2VA7jtt\\u002fcrNZUApdBbfyMtlQF6K9ormyWVAn4DM5iHIZUAHFIgNecZlQHFY1ErqxGVA0n5vF3TDZUDHo0cWFcJlQK82XBLMwGVAhnNs\\u002fZe\\u002fZUCRZIbvd75lQL8gmCdrvWVAcEU4DXG8ZUBrq\\u002fUzibtlQItapWCzumVA6r9gke+5ZUBEUUkJPrllQFtP+GGfuGVAamLSphS4ZUBFfyeGn7dlQDOlFb1Ct2VAXq4LKwO3ZUCS0Eqj6rZlQP\\u002f9Ib4Ot2VALTTtEp+3ZUA9TTNG\\u002fbhlQBC7eF\\u002fau2VA9R09wEXBZUAxeWXqicplQDB4IUO22GVA760HD93rZUDtpHj4RAJmQKOSsm7YF2ZAt\\u002fOzh0AmZkBnPUgEZSdmQKTJ7GWZGmZAHYbOjlgFZkBGCkkiPO5lQFCBxk3M2WVAcdqspyXKZUCFwZuee79lQJuQ+zDguGVADs7qgAu1ZUD\\u002fV8w+4LJlQF5sJBmcsWVAFevK08+wZUBBFWlpQ7BlQLRy8rzcr2VAB5Fsr4+vZUALMkUeVq9lQPOS8XEsr2VAzlA9RRCvZUDi9+\\u002fe\\u002f65lQLO9u\\u002fb5rmVAkwGJlf2uZUCZ4GQBCq9lQGjbqq8er2VASwkjOzuvZUDzKNpcX69lQDmf7OaKr2VAZBi+wL2vZUBs20Dk969lQF3JC1w5sGVAA1YRQoKwZUASaNe+0rBlQHvcGQkrsWVAWp\\u002fIZYuxZUBgw1co9LFlQBZLXLNlsmVAVN5zeeCyZUDAtnj+ZLNlQOriBdnzs2VAschStI20ZUDKt29SM7VlQLCM8I7ltWVAXwYWYqW2ZUAdtYrkc7dlQCWHzVNSuGVAhTZqF0K5ZUBskSfHRLplQOQzXTJcu2VA2mGuaIq8ZUC5AnfE0b1lQBNMS\\u002fc0v2VA+sQDGbfAZUBnxO+5W8JlQMLV8vgmxGVA2OGInh3GZUAJPPY9RchlQNM4Sl6kymVASNJdrULNZUA1\\u002fag\\u002fKdBlQGonu+Bi02VAOqNwefzWZUDao86SBdtlQEnE+\\u002f6Q32VAJ59qtbXkZUAmYYP0j+plQLY7vcJC8WVAJr2x9Pn4ZUD+XA7x7AFmQF6DM5NiDGZAv25D+rYYZkBLZjxxZSdmQDGGdwAdOWZAMLX4xfJOZkDKOWYm42pmQAXakQgKkWZA0DnxgznKZkBUFdwkiChnQFSn7bRlzWdAA3ky5CnuaEBA287ap9BqQF\\u002fTPUZbuG1ABKER\\u002fyzhcEDJ190OqlxzQHpeFOR4+HVA7LoQwT0beECdATqdXAd5QNbmj0wLU3hAjjhXnSFRdkDu9GUjC71zQHrn+vY1OHFAwDVoeHlCbkDIAjI8QDNrQFt\\u002fJbXALmlAQEag41n1Z0CejETi9kBnQKS3vqX02WZA4qX49lKcZkBVHWtqCXRmQO4BxfQYV2ZA1szqVctAZkAEdV\\u002fX3S5mQE+4DzIdIGZAU7JHEM0TZkDlzB15awlmQNhBIFCYAGZA9BIuAgr5ZUDlSqEsh\\u002fJlQB9Qynvi7GVAgYYlw\\u002ffnZUDHj7DfqeNlQOOyQSbh32VAmg3sNYrcZUDg1hQTldllQEGk\\u002fnf01mVAHgQSTZ3UZUDYg\\u002fs+htJlQO\\u002foZGun0GVAJzn2HvrOZUB9daigeM1lQB4CcQcezGVA1dEFGObKZUCnBgMpzcllQMFjIAzQyGVA5TJx++vHZUBO6eKJHsdlQCPBWpZlxmVAwSj0QL\\u002fFZUCb5vvhKcVlQB0BWAKkxGVA3EAcVSzEZUDsiReywcNlQPoVMBFjw2VAL2Jshg\\u002fDZUA024w+xsJlQOpOH3yGwmVA0TD5lE\\u002fCZUB4\\u002fwjwIMJlQFi\\u002fcQP6wWVAzqjlUtrBZUCx7DZuwcFlQHzoFvCuwWVAVVf9fKLBZUAcDTLCm8FlQEad9HSawWVA3gC+UZ7BZUDc45gbp8FlQMu+jZu0wWVAK0sgoMbBZUAAOtz83MFlQJBf74n3wWVAmsfPIxbCZUAnXOyqOMJlQC34ZgNfwmVAjOfWFInCZUDH+BLKtsJlQAtjAhHowmVAQt1z2hzDZUDlWfoZVcNlQEjvzsWQw2VAzoW31s\\u002fDZUBV9fFHEsRlQBRJIxdYxGVA+O1KRKHEZUAIm7nR7cRlQAPOC8Q9xWVARsAnIpHFZUBhwT7158VlQJXt0UhCxmVAHz+6KqDGZUCoATSrAcdlQJ637dxmx2VA1oka1c\\u002fHZUCWYoirPMhlQFveuXqtyGVAWkYEYCLJZUAJ07F7m8llQDWAKPEYymVAGMgV55rKZUB8qJ+HIctlQE9jmwCty2VAlnzKgz3MZUDoiR5H08xlQNt7BIVuzWVAJyG4fA\\u002fOZUAHvaByts5lQEuot7Bjz2VA9hX6hhfQZUDsOudL0tBlQDhEDF2U0WVA5bmfH17SZUCzJi4BMNNlQPYfWngK1GVAexayBe7UZUBGs54029VlQHTrbJzS1mVAJXB34dTXZUB9rXO24thlQPQx59382WVAgxrLKyTbZUCTBWSHWdxlQM4dVu2d3WVAMA7+cfLeZUA8LRlEWOBlQJ\\u002fxyK\\u002fQ4WVA6d7\\u002fIV3jZUDKn2gs\\u002f+RlQGYM24m45mVA\\u002fYN2I4voZUA6bn0WeeplQPcXE7uE7GVAKasDrLDuZUBOBMbP\\u002f\\u002fBlQJX98GJ182VAfeppBBX2ZUBTBqHD4vhlQMRMQzHj+2VAepfgchv\\u002fZUC6PyJakQJmQIeiU4BLBmZATgotZ1EKZkDwtA2gqw5mQFADH\\u002ftjE2ZAttM+wIUYZkCmDxH0HR5mQHe\\u002fQqw7JGZAoI7pdvAqZkCMGhXaUDJmQPIGN\\u002fJ0OmZAyFwiOHlDZkBUakF6f01mQN\\u002fYkhiwWGZAvf92mDtlZkD8BAGtXHNmQInZX9tag2ZALJ+v8o2VZkCh61CmYqpmQA==\"},\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"},{\"hoverlabel\":{\"bordercolor\":\"white\",\"font\":{\"color\":\"white\"}},\"hovertemplate\":\"%{text}\",\"marker\":{\"color\":\"rgb(255, 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2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (2 2 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (3 0 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 75.38\\u003cbr\\u003eMiller indices: (3 1 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 79.72\\u003cbr\\u003eMiller indices: (3 1 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 83.99\\u003cbr\\u003eMiller indices: (2 2 2)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 88.23\\u003cbr\\u003eMiller indices: (3 2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 92.45\\u003cbr\\u003eMiller indices: (3 2 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02f2RK8DQiu5GS2suQLBROCaGZypAAEhM4O+wyD\\u002fAiNoGJboYQBAAoyqBqSvAgDYhatlhG8Awao3h2EYqQIDfGV257vE\\u002fACjFbjsKuj+gcILslSkiQAD0p5kCDf+\\u002fQHTzy+kkHEAgD24wUPQXwHB5sTOK+CVAAJXKKTKqB0CAZF9GCCHxv4DZELEBmRdA4PByGI9DIsCAGkcVlqLyv0D0VVvNaAZAoOJw0f\\u002fvFMCgfmHJD3UvQCBYMR9eYSFAgFdKw5gzDUDgjPLzRG8aQAB4WdaMISdAoI\\u002frfJ4KIUCgJQX9Z3k5QIBnYygZ2ihAUAxYlDhgNEAQtiZYzqUiQFD\\u002fkkUmiiBAgNX4k7a1CUBw5+UgzCcxQNC4Ku+YMCRAYDdKiZ7vL8Dw0+d6HREgwGgMQsoF5jJAgKzqWanB8r\\u002fwkJsQDYIpQIDP2L4JlQLAwDJgHeg4C8CABpI1cTj4P7g9dwM+bTxAcJM5aPOrJEAA6p8lV+0JQDDwDyGCSChAoEJ3PakmFEAAKrk2s0gcwEDuRxzLegnAUC+00uSbJsDgfskd75E9QBj75OovcDJAQKuWz8gyHUDAZoDeVMUGwPBTsmgUACzAwM6tFWKzDkDAxYUGN6oWQJCX6hbVgzLAoFaBeQ9LNEBAxZhJ\\u002fugzwIjdK7LB3z5AeNBPfgilM0Bgi7eJ8TIlwDg4DAHmIztAQHvn4BTmBkBQIZzuUG86wKCgYMZffiBA0MyrA93zR0Cwvml\\u002fsIk0QIBBAAI9ty\\u002fAgAIhxOdHCsBAOrMsifoFwMAL05CJ\\u002fhzAwDdl8+A0MEDQ1a3duDkhQHAA1ql57S9AABj45mAr0D\\u002fYEtBTi3gwQICg\\u002fKuukBHAAHgrIWTDAsAgwXjYcc8gwMBIureA0yhA8DMFUfQtJEDAyENjjmcWQICVVFzKKSLAYFvepbf5E8DA7o8Go7cUwHBCeitknSBAgAaXnkYuC0AgTP+14AEkQIDYSEQf8xfAINqQIX5GMEBg49MqbKYVwIAM2+pnkR5AcGKdFqSMQMDwhLePZME1wNiDtcoU9EXAKCdlk5S4RsAA6E93zFLTPwCz7u0iSAZAoDzQ1kBVQkDwnumgCiVGQJCstXNK3VpAYMDwr9K7RkAAZL+iTNMcwOArjinWbWHAAB6mCQp9YsCASok9wS5DwODR7RXWlWhAoEiLkSt1WEDg29a+hOZcQAA7C7iZJxXAgDajW\\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\\u002fcvjeijAgM7nqKmq8r9wjhM0k8slQDB2cDYJ6C1AAB713hwANUAAUPu5ligcQMCSybleJC5AoELY6I4wIkCgkJ5u43EcQACu4uWXPSZAQBqcVY0+IEA4d9BJ9g5AQDBj9ea+GjRAgBZERd4KQkAwvzcaxB0oQJBBETpTDCJA4FNa+Ir7M0Bwl2Ss9t0vQID+ApLawClAwCcaMqGfJUDgbh9aSPQaQEC\\u002fCO0kvwLAYLq3plreKsBwlIzONO4gQEDL\\u002fFuH1gJAMJsiIKB3KEDg0Pu3smcYQEA7FETY0x9A4MOJr0ozPMCA5yucm\\u002fgKwCD+VitYxRFACHyt0lzCMMBgy1bFhPo4wGCnsR0MbyzACOxh2C2GNUDAmZWJLHwoQEAIb\\u002fODvhdAaCelayeaMUDAh\\u002f6O1nkmQAAwyTRKXBNAgJtfhtWF+b+A9QhE6rUTQIABFda6dPY\\u002fQFsQFCKnJ0BwTMKWhDA8QGCx4S1H8BFAEPTy\\u002fjpMMsBACVPwklYkwNASTzNw2zVA8NBVwGh4LMCQDlGtjREvQADUnUMwkRxAQAuJUqA7FkAwuuyd0X4nQPDZES62VC9AALbT05pW0D+AYfKmhDcewAASB8qDUvI\\u002fEBAOJ3iII8BgLjP3MM0RwAAAKwlKLvK\\u002fYKPpNPAxN8BAAibwTa4kwJgLxSJDKjfA8HYp9LYwQsCA50zUzywwwMBY5Ur8LCnAMCmkEBo+NsBgHKMW4nw+QAAphZhd511A4CIwUul1Q0AA8iN0ohQWQID4HdzN0EJAAP9pROpaWcDQHiJywH5lwIB7NMDS\\u002fFHAAPjuD3foQUCA64Ntq1YiQACt989vLh3AAMCwwZB3FUBAAskro9lFwPC6ynFAjkfAYJzNSmEeRcDA6I5K5vImwFDFsaxKDSrAEPSpmBd7JsBgz3DFqIMbQLBP5OfGfTFAcAsB5ewSJUDAWuUepOUdQBBG\\u002fVqUJCtAQOIxar31H8DAEWiLedgtQOgTsBwZaTZAAKIcxpai4T9Aj+xF02AewIDKvs66LABAwEvuRbPgIkBAe511E2AlQACOo9KLLBdAgFsuyfXGPEA43sjL5ac0QACQi43NHCnAVHYB546TQUAQn88bC5YtQJBDhdz6viJAYBXetoJmEMAAyWyrBhT2P6DjbRnzRRNAoGhIXoFwKkAgy2cqQVoeQABwabomsrK\\u002fQEdpkAQLF8AwhZGrIA4jQIB7DoRaSvO\\u002fkBDx60UVKkAAVl0QgxUVQMjBuRIDhDHAgMTW+dSuPECAeDVC8XgXQNCKLP76FCpAgN4FtJIwE8DguLWVI7UkQEiP9HJrv0JAIEfArrZBL0Bg5gl50\\u002fwYwKCcpx3O3jlAgHneCXlP8L9An7tq16wIQCjG0nmjLj5A4Hc8uQrnGsAAXUqMCuQKQFj5lDKGcTFAQGefAYMVHkDg+mgIJJg+QIDLKD0Ynvo\\u002fOK3lPKm6NkCgAsrfE5UhQAB8EdZrN8O\\u002fwBs1SmkwIsDQV+\\u002f4h+opQGAynlkWCBBAgNQKiJ3h8D8AJn1REzMoQIAxyRDTRfI\\u002f4CVhU5BFE8DA7Baqmj0GwCD7WpfWBRVAQEF77nEoEUCwXIbGua0\\u002fwEBKsQxFMAXAmMWn3DNhOUCATXPNATAhwHCgURakkTDAQHZIHZnoJkBQJB6nWgwhwMDrWsAQgTPAAGD9bRQdDECAmXkMwX74P2AhippuLhpAgIHIvonEGcAADoQ2d5Tbv6AY2UQIlDFAAGxTQMorJUBAUdroIUYLwHC\\u002fSsX+zyrAAG98iU2C+T9geXsvqqEZwMDEP9WL0ybAIM1po81PEkCoASgbZ28zwIBMBD2mQPe\\u002fACCAVeTRC8Bw3mhDugQnwBB3A2UR5iBACMZvBg5lMkBgxzHYLlwrwLhRdMqHNTlAUGfi6Iv5NcAARt2Qo\\u002fnTvwA0hDZjpg\\u002fAUBCBExPZNUCAPyR0Z9IUwIAH\\u002fQJ+RCdAAKV8ztbs9T8AXLozJiMWwDBjawioHCrAwBncI7DpEsCAQHnv428VwADh5c\\u002fLPibAgKs6VEKCHcCguHYHRN8RQAAHBvbt4wvAAB0hb27VLkBok3uolEA3QNBgr0KEmSJAAMoU71NwG0BwYsDSHlAmQABymd5jwtQ\\u002fYOxE77ncIkCAMMsBZPELwGiih64+bzPAAKopwTfW2L+goXuvX7MmwLChTrTDUTHAcDK0gYtpKUBA5Mn3fdENQJDUBlVbfSNAkF2c1M6EJUBAcEu84isGQGA70VX9m0PAIK\\u002fQoCyUJ0AgVcwxgWgswHC8pmobmitAgPP1K6CPAcDADFvZeWIowEAAtJ8WwxDAwGhH0UCEAcAg7K5eAz4fQOA+OOeynitAgK5zORU8E0DQU5Aw8WIgwAChgSlIvOk\\u002foHimPKDLGMBAJFJEV88QwMDeQOkspwnA0JtiWNOTIUCg4xCqYyITQEAfG3mucSTAoADzYZnpEMBg0GBXrw8bQACJkwgF7wHA4K\\u002fuZS\\u002f\\u002fHMAAuDuzYR30v1QYkez\\u002fIUDAACAWF5tk9L+AWFlutEgwwGAqEQ+CliLAgBWX3nqQDUCwmbfEdl4lQADtmaQviOU\\u002fgASC4wC3CsBAFzONW9ECwKCVFTmLdhnAYAXu2dx6GkCg8JQ4gZQZwHi\\u002f7KMsaTfAUDKzenFtNMBgp5ru5eMmwACxdfUoJR5AwDZQbfzuGcCAZt3UKQIvwCBxg0iE8iJAQNB4esGMNcAY1CRTKJM2wIBI10I+YPY\\u002fEOIIyI9CK8CAElUge271PxCthNN7sTrA8MIYMgZ1LcAosUpSQcQywKDsCb3MzjfAWHCfNz\\u002faMcDg2c\\u002fp25obwPB3Zfyu6C\\u002fAAOizl6yW7z8gm0\\u002fzwRMwwED2ynbxSyrAoGPqE9joGMCwuHivz1A0wLj9xR0XNUHAMAFfoKMNJ8B4qWDiWKc5wNimACGJzDHAIKYlP27dE8DglYPENKQYwKDa7yEmcxJA8FuG1u2nNMAAHWLjBtAHwPBtpY3RuijA8Be+MBdPLkDAM68FdbgDwGBxO50epyzA4O+ldHurOsBwsRt5jwcywEBcX5091h3AADHqyjglFUBgq3kfr8UtQKCKK8O0jCXAQOJ0cHO0KUAwvtxScc9EQBDdlm9sblVAmGgpK8efQUAAm\\u002f8RnThNQNi\\u002fQt7xX0tAgDhmpwtIM0C4x\\u002fYezF5HQAD8Y+YAnzVAAFDzIc2exr\\u002fY6bsm9Vg3wMByvIt7jAbAQDMFNnI+MsAAAR1DEkoTwAC04T4C2BXA4Lz+W7SXHUDIRMmTOf47wMDerj6Y5BlA+H4d1nTYP0AwNxWGl7MnwKD0ZnYrZhbAIHIy5raPGcDsSJTMaptBwEA1OyBJISrAwNbwv2fAH8CArpcFF68UQNCg5FN8eCHAIMVdUudhEUCIONeQbmoxQCD0uzbcFxbAkNieDp+HMkAAcUrRH7IZwGDxE288dx5AkKELnKtNJ0DgFW9ElKIqwECQTYC1KRnASKMCJYq7NEAAAWN6J\\u002f0cwMgrLKgsxTBAAFwQuZ5\\u002fy7+gDCspemciwACCeUcX8Pw\\u002fQKepTG5dIsBAzEYZkEwTQOCmycWNrRjAUETSvnWrJUBgysycGVobQACOGt4WXBNAIDBeiPpcH0DI\\u002fCpRNtcwQPAUGkcdUijAgA7m2nPWMkAAPMQ3+qgcwICIjDlFViLAcK3TlsrTOkDQEIZMF6UhQOBoceQi0TJAYKGNNdtgIMCge0L4R2QowBDab9ECmCtAACjd8Pj9yr9Y8zq7GjgzwJBHhzemxTlAIPH+uh8NF0CgTYxZaX4iwAB3efPsHPS\\u002f8BONOHq7NUCQXfQFTnEnQABYkeZ+pPS\\u002fcO8ZoVtlJ0AwwPTP7qAmwEDdSKVupyLAiB7auxFXMsAAugLyl1f6P8CHZnis8ALA4FU+xn3DIsAQtFMRBcsowEC7KjP7tARAqC3WPVltPcBAQb6omnQEQLhNxyObdTPAAMCrDjYMI0Do+e2GSn4wwACWpbzxTN8\\u002fMPuy8irxLECAfCDpSxgnwKBVBawDvB1AkE5rD+0rJcAwQ5T0GTYpwAAHHYcyBPq\\u002fICfDuDNLL8AABlMxhDvVPyhfh0mssDbAMKeNx9NsIcCgxdjj0A4VQABACTB51s4\\u002fuM3W44DIMMBAai8jWjsTwOBZRz1VVRfAwK5ShAWQEEBg8Lbpp8UjwNB8sRopFjJAkE3Ixu0dKECgn1yfkwc7QIA9IUkKABzAgBfRQ8ngF0Dg15Klnx8qwMCUZ+YsYBDAAPga8e37BkAA6XBzu0kJwMADyltKNxNAWBv2BGc7O8BgnLFB50Q2wKDaX8c6xR5AcPgY6G5OI0AQOP6SMGM9wBhIjsoSkj9AgHkDwNdv+D+AgtkoTUf4v+BdS71IkDvAsIpDLoOcOMAADOE2lpL6vzC\\u002fVfVAtjjAkNK+vJ+HLcBAf8SC3NE3wMDpqXLbwCnA4Kmkq9RBHECAGnO\\u002f7vwfwIAegK9FfAjAIJm4y+uBHMAQQgHAx5shQFg3cUyJuzFAYObQyldeHcAAvfT28ygjQGAhyeGv\\u002fh1AAKyuKQ6WOMDgI\\u002fj08EwdQJCFUnKFxDXAAFDlBirrDsCgtR+K7e4vwBBwvGRh2iFAwGbKgjB8AcAANNpBl2QhQHAWvzgUJSdAmEOM4+iOM8DAyNIzusgawKD4sDetXRvAAKVVwDn2D8CA81XdGHn9PzB8rLqRVDDAkBYX+s0GIcBgRm34p9QSwMBT3EU61CPAoLMqo9Z1G0CQVivOd0ItQEDByj5lgxFA2JLLnBnkMcAArWt93aMrQLDbZ7kh+yDAQLyXCplmAUCwdg7WxhdAwEB5Np9oZAtAoPY8ECIBFEDYy+nOqYo1QAAHFmi+1RfAoLNtBl79KEBgeBTH+TQqwGBDIp+1wjbAsJ3wuPWDMECgOJAXGbc2QAD2hR\\u002fmeuo\\u002f4BH0eB9bF0Bgv04bXu8SQLDHZlrFAy\\u002fAgFeUEVF68T9AE8jnrv0owLj1\\u002fbNNTjLAYP++gr3DJMDwqVrvAXEkQGD3QzJuQxrAwKUJuDCpIcAgQy12L2AtwKBmxo5jlC7AQCaE\\u002fE7ABsAAECVQCP3kvwAnH3yk8wnAIFRc0zFQLcDwI+OdeUgzwABcfE0\\u002fmzjAMKs2HcwZQcBwZVaqlT4zwLAgExs6zULAwJAmN1RCTMCA\\u002fzndW3MuwCDUNl0A3kjAgLimatCWVMAAiPucBwsBwAApQR3NpznAID2JphQjVkAALvr3Xz1jQJCDfC+5b15AgCeSWlbfKUBwrPZOQYxBQIAgbM7Fb0rAIFtJPkBAOcCwZHAt9GlCwMCPLJkCfTdAAOcY4dUiNUA8PM3jN\\u002fFDQOAjvWdgTxVA+M\\u002f57jnoMUAAyUyxT3ICwJhE2FlCNzNAgFiO9yLIOkCA0wXYSjADwID0P+Xa0zlAQFhKk3BNGkAAM0wscd4vQEhLCTQt+TBAKB8puTe\\u002fPECQDyhD\\u002fJgiQIBXh7BAsArAAOeuwKQIAcBAtdGiXYoHQEC6a9MAaBjAcBFM5zGCJUD4Jt9h\\u002fkJBQABPSmOih\\u002fy\\u002f8G3CITuwJ0AALDba9MXiv2B7lcJ3uRtAAE03XeZiGsAARkKhWlEJwCgjcwsnOzBAgJbI+NhgFkAA4ygBst3+P0BL2HLTPDRAgMhHgi4uBECAr49X2LUBwAAglE8MSp8\\u002fgOz1nwoPCkCAd3R7u74DQICFfk\\u002f7oPo\\u002fWF+xRtvYOUCACXf2FSQIQPCssnGOWiZAoIbjKXOyGsBw\\u002fAdC6REvwECjqpF5wwRAMIOUkbm3MkAAla95KKLqPwibEJ5mDzPA6Eo5SY71M8BghjO6I3UXwMBFK83xGRPAgKVPz0TEFsCQEBCf4zkjwBDHFPoVFC\\u002fAoAQVM2kPKUBI50eNo2c1wAAJOU\\u002fSPw1AYP4VRzRtLUAAXzJ9zd0BwACjUjPxXSDAyPDGLeTcP0AAvfwF9MAIwDD6xg4C8jJAIPCKeBjtE0BQEvPjqfgtwADbyByssvA\\u002fkA2+w7oRO0DAiAiOp0UPwDBt6ErEGzNAQAKsc+uDG8CAvDj4RL4vwCCqX6r3vSHAkPfKJAXhNsDYJntMwRk7QEDw1abf4SvA4GlWlKgMLMBAr6XnhDE2wIgw48QGeThAwB3555OXGMBgPg9upDUXQPCLIEv8cyZA8K38ogFWKEDw7gvAgzImwOAlW8RV+T9AYHa3EmprFEBAWeAi4LkxQMDxDdXzIBZA4G5uinphIsBgY1NI7RMkwAADfSxpK+U\\u002fwFXNCntbCcAYIn053IsyQEBwAmb75SlA2DPIcNUqM0DAfE9F2ZMiQLi8uDjnXDBAAEPEwnFYHsAwJVLjLOcqQMBiT595JgzAwITl4sL1C8BAs1sZOucRwFDNGkWv6yrAALIadGjV+D\\u002fgTuNPbz8WQJjd5MXQkTVAkMhgdEiTMkDAL1Iij6IEQODb6AEO1iTAwHQzubhqOMAA3OGR7qoZwECiX172lDBAAC\\u002fkjkeM4j\\u002fgfQmfL04SQMCzOfYlthHA4Fl+AEkiLUCg1hwAXz4aQIBNMsFc5CrAgMeasmehC8DQlBcGORMhQFgUO8\\u002fReDHAwFBwQvD2IsBAr5nmt\\u002fILwJj8x7+QfjZAgATydwgTHsAACDv3iEIEwEAmnEo8GCfAoH4\\u002fyK1AHsAA3EBodY\\u002fiv8DHeSWPyATAIGRwqcZ3EsD4olMNH6M1wACj3s4UdvU\\u002fIA4HMBSjJkDAjnmrVaUFwAD83Qd4iSBAgGnNy2bb8z+gB0KROzY3QKDfRtEEuRhAiEL\\u002fmLslNUCAmhkEdXIcQCCRy2Ip7TLAQNIJnVK9B8AgGCvu6QscwABAMiaAZK6\\u002fgCoogOPG8b\\u002fg4yfHv1QXQKCGRYUBFh9AAEK3fbEuEcBA7f5mefQKwHhXx2WTjDFAAJxGk79UBMDgU3xgI24VQEDcrhUQgy3AYG5geTE7KEAAjt3tmOAfQAAk1SPkbMm\\u002fQDSF\\u002f1HJJMAgltrAKcIkQGBtori9cBxA+CZNdZbJMUAAGnFy4DYhwMBSP9rIbRzAAOaKumo3OECwam1BlGcmQIBIEH4A\\u002fSZAyDZcqy1jPUBgvolCGJwuwADkJ\\u002fZU7ek\\u002fEFYor9kvMUAI1GiDjE09QJAPxiIMGEHAAOBAOxty9j\\u002fg3VOZ5S8wwIASeFNAAjnAsETFJXNQRsAomRznSl5CwEANMfE1eA3AMNQaJSnXM0CAcFpoQKYCQBBMr311dijAwMZT0hADFkAwlH7vLjE2QMAcMidDXAlAAGAEpbaOKkAA9cJMC9ruPyhx78tCbzdA+PzDhyvIMUDgbLhTE88TwABtjaft+xLAEHyeYxIsI8C4HOAQc4kxQMAeiFs19grA4O2bTZA1FcAAHEw3iAUZwOC7MCcPjCFAgAFIo7lo879Ab+rV7UoOQAA\\u002f0l9GxOg\\u002f2NN9VwvAMEBwCZpwXJEgwMDYi7tAWClAaONxjZaeMEBw7jhdlB0jQGAfxKTcDBrAQC442MZEA0D4AV4MOlI+QPBY9HlMcypAkAb70VzCIcAgEgUyqwYUQABLzifZEA9AwFj8XTMADkCABZR+askowPDK0peA5yhA4NSgshzbGsAA6oyJ+dsIQMAx62ClVhzA4F8fD3q1OEDgEHpca\\u002fIdQEC7Zq6\\u002ffiRAqMM+42P9PUCArcyULycdwJD3ecqb0i5AYL8UFDksJkBguixQ9rs6QMARoihkaBFAAEB6fhJasL9Q470WOfcgQODSQ2mp+StA3N5Ggvu4Q0Ag\\u002flVy57IrQGAI8WciOhfA+IFD6tx3OUCAsscyvqH6PyQXHEmZYkBAYPvj04nFMUBwYhONok0rQAARNCCixyBAoBXT2rbYH0C4PHMXGVY6QCDq5gwtDCvAUO3GmeZUJcCA9RqRbkwkwBBpUFsz8zJAsCIgi4fuJMDApYpH7+UZQKCr3v0rnBXAACuDGdhYM8CAy1Y+\\u002fzv8v\\u002fD2Z4zDIiFAjNlQ6MoFQMAAL2RNmq8RwICp2vYmojrAYB2qYjOhSEBA5W5Skaw8QAAAiFyGoz5A8L98aOjwQ0AAnC3WQY9HQAACg8jpA0NAQIlFIJVgR8CAJCcBTDJSwECW5dsoBVTAAM5u95iBEMAATKtc2TZFQACow+LaNfA\\u002fALjDOyQSNcCgYC9\\u002fdzNLwPDGpQFCgEDAOAgTRmrwRcBg9HBlBacpwGAq8GhEpTRAALyl2rjG+j8Yqwn4xxYxQMDzjIaqRhxA4Ge\\u002fcIODS0BgTE3EraA3QOB+e6JjxTLAMO86\\u002fEAPNEBwGhNf40cyQPAYHllFAzhAgOPOZtaxLkAA8kh5X2wZQNi95PckGDBAyFW+UHebMkCwezOXniMkwOCd+ULMXhxA8FKUYgIeMED43rJPB4NEQLDKJxDs0zhAUPulYP0VJUAASR00IY8OwACzqZ2T9Oc\\u002fQAYpJ2oGFUBQ8yEqsLUwQEC7eC1WHTFAQA1f+sV5PECgRxfPKDAbQKDDdZ0FFT9AIA8bsnhVMkBgARSr8+MuwKiP1KY2vzRATHFp0L10QEAgGabCMQ0yQACUhnRFVShAcMsQmQZCMEC0MQ5mwilDQJDEhGlpviRAgCtRMO8oC0DAc+nhH2sSwIBpQdhS+wpA0EzY3opTNEAAvO93QFfQP1goh9qPKDJAzBGG5V2ESEDAFmwSQT0iwCDgjOLbsTdAnCnrxrc8SkCAcvXR+4z8vziO2KIB9UFAYJt3bZOQOEDAOCQvEqUQQNBobk4jsj5AQDD8e9AoMUCAJD0BNOsqwNBZomOsLDLAYHPMRzD\\u002fMkCw3ZXXewg+QFBdB6aV0ClAQBb7QKYrKUAAQTSjavQtwADW60C3l\\u002fI\\u002f4K5X7IjaKUBAPfjkWCkTwACHSfPcwQ5AILT0MJkiGkCQlsJOcbAuwOCjv8y\\u002f0zpAsBnYfTkmIkBAU109joERwADCLlBJzwZAgDeJJ5q3MkCAXwy7Gqk0wMCl3X6ERjxAwAjM1doUI0AgDWdg\\u002fA1BwIBvbCIUUSjAgL34ZHV1VkAA7M565V8twOCeh5o9KENAgF\\u002fjx2TwPUAAYPCNvk3KvwDmMIYLcCtAQDS2mQuQMsAAu+DywlwawPCXOtFBGipAACpzB\\u002fiI+D+g0M1520g0QCA0lVDZkkNAQE6G5CpdDEBYf+A44ZczQEByMtAuzDZAQGGVNSCkFUBA+YWfNE8WQERzfuq1MkZAQDrr7h9AEMBgNVioZgQdQFBSu2RMOSfAwMSADey9IECweYoBczoyQGDq34gX7BNAAAThE8NJI0B4LWIAcDs\\u002fQKD6VGj2BRNAgB\\u002f\\u002f4puV8z8AyglKgCcNQODBTQhvFxhAAPMurUcUBcCA2unKkjH1v0CmA063FSbAwPiGpyW4CUCQblH1PnMyQGANSyrEqRpAIN+oKo90F0BAaXQXx9AfwAAj4AeEbfM\\u002fEKMlyjm9LEDARMNaYRsEQEBgxYIylhJAwFVgqPIUF0CA6V3SOSv+P+CIV0l1BxDAcCRAt7UXMkCgzZ+jT74YQODzHF6BFxVAiHJRe7paN0CAud\\u002fa9HEDQICB5qvz\\u002firA4L7T1q\\u002fcIMCA68V0kBv6PyBal1CXYS9AEPY3k+F9JUCAAwrEhjzzv4DH05Xaiv0\\u002fIO33dkE3LsCQw4Wbrt4rQIhKHm2f+ThAQI9xUgANGEAQSWb4fucjwCDo+wybUhhAgNQ9lnccMECgkkqB2Y4UQEBdqdYnUwlAYAH06QZhIkAA8Lra2gfLP7D1hB\\u002f2iSPAoCYmbUqBIcCwC4idB4YmQGjWUhfqRTFAQDSKnpofHUCQhFYaVbc+wMB\\u002ff4ivww3AaKFpl\\u002fi8N8AwLMucK3AqQACe6BC0hBxAQH1HD3BRCMDg2VCzwq4SQIDBCxmyxTzAAN5bDx66179gYuvwYLcXQACjkrJu2Oe\\u002fcDs\\u002fewhkKsA460EiKDs5wMBlM6FpViZA6DB0dsQqOMCAkUKSCI8LwEAFGjcxZ0HAgMeSKSzt+j+gMHjTc+8XwCh7I3j+ODdAkI+p+KNXMEBAAOpM8EcDQMCLjSSDMxrAsE5RwwLzIECAX3VLS973P6CVk+flfjPA4CCS7fXxFcBQ36wbngorQDAvGR\\u002fH8iTAAJyheoog3r8gfv\\u002fu\\u002fe8owMBs7659vgvAoM3fTk0gEkAAZx+b\\u002fvXgPwC0bdVXj\\u002fe\\u002fYClhZGfnGcBgHkWb6+sVwJDzyW6u+CLAwHke9CMEI0CwbQOR3z9AwABdyQtcOeC\\u002f6E917+17MEDA\\u002fRIlqOUZQLDHnZGwEifAQOsTuWwxHsAANnyFQh8jwMBiiWu2TB7AAJX3XB64DMAAUNJhwCYDQMCiQ9+EgR1AgKeb1iW59T+gfPsmNVMpwAAsI7xjeQ3A4MvAQqzUHsCAcaW6DUb0P0DJYfOUFAbAgEIbtQuh\\u002fL9A6YaNs1siQEA+0yXDtSHAwMQz6rJuHEAgV+COOLoXwKB9lhZhGRRA0CceyLgFMcDAGfF77pYAwOCj0nCQvSlAAMXjLkw8G0CQwGPfU4QmwDCNo+RFqibAgCTd1SrUIMBQ04\\u002f6kwIrwEDtZBdcbB7A0BmFy+O3OsAAmHDFtOnDP7jdIQh4AzRAIBusUklpF0CASdgg\\u002faMCwGC70yssFC\\u002fAQFvIacHgEEBAYDTeYcIbQPAanJkUYzTA8Os2pG+LK8DAkhlPXNkBwPCch\\u002fgvbiZAALTSgU5DBEDAxjgzm6MaQMB+idt9vQtAAMbYgc+4EkCAVX1k++4WQMCKJbGkKSlAYCBPhjzFFcAAJokpwXo2QCCURTiX9BBA4PdyB5h2QkAg+brbQZUlwEDPi4IcDyfAwK4LEdyzPMDoDNDp5DpHwGBt\\u002fsi0MDPAIHeMKubtMUDAnb5sZKEpwEDvXN++BifAAEDKl4eG7T+ALCDDAzQlwMDWX0ktsAPAAGQEOp5VJ8AgNrfbkfAvQIALKUhrwRRAgIfWQULrAEBQ1bGmlkAjwAAJf52Fc\\u002fQ\\u002fgMpMhSrND0DArwiKIhIhQIg9gm8W\\u002fzJAAFqbcN9l2D\\u002fYmBE3pLQ1QADF9\\u002fv17BdAgKE9ur\\u002feHECwUes1jdcgQCD8tYawYxZA8JenzYOALUAAOXBPIFbsP+ho8s32ADZAAJoN7DWK7L8Abk0xUZkvwEAQqf8dvQ3A4EEg0dRJL8AAe3Dfx1ASwMA7OtnaKQTAcJJj76HvLsDQV4cKitcswAAPgbgDD\\u002fa\\u002fqI4uwDBXO8A4NRhIaU4\\u002fwAgeA2GARjjAKJeJ218\\u002fM8DglC6e6HEswBgJ1rIsMzPAgJ\\u002frhV8g\\u002fT8oyyDwsNY3QGDc\\u002f7R\\u002faxNAwA3EUcVCKsAAFHboTT7sP9CvgIkYGzHAgBcxNsOH8b+AZpvRx1gYwIC60wefoQjAgGiYfMon8b+ACHD\\u002f8N0hQMD6jRvQDzrA4IxaLqUdLMBALLuNW3AIwIAP3QLeNRzAWEUVGOzyNEAA5PLNPWTuP9AVW1gs8zlAAJH\\u002fINcw\\u002fz8AJBxn5FjuP6gJkiNb8jNAUE37\\u002fZXjL0AA0OHm5w40wAD59Z54HyDAoHn8PGIhJMBwwsWuiiMgwNCCbzbwJSjAQIeRsm7XIUA4xpdQthU5wMDCmEAEugjAINQ9p80xIsBgw7TAXJUbQIALEaPzxitAIKOHlALDI0BQVR9\\u002fJEEowECRNHKBRSbAgN+uRBRKIsCAT2bkIrEhQKA\\u002fhn5HWB9AMAI+EYksMsAQFuxTf14uwFS2RyMJmUHA8PGjqwJqIsCAGkCzGnAkwCCGJDGSiSNAYJ2oUf18LsBgKYa4yoMuwNjyztVrRTPAoGVEACaSKMBImI7d20wxwFADiBKPoSzAAAO54lxTGcCAD\\u002fXzMGQZwHga2wRoXTfAYMmnPNjDKMCAnuhxNM0mwLC9R1Do1iDAcBKCy\\u002ffgKsA46AWVs3UwwFhCvYUdezfAYF+hb3gBJ8CAohiDtuURQIBDxNBFGSHAKM\\u002f9\\u002fPCSM8Awa+ISADMtwLD\\u002f0MJToDbAUJjepB+xIkAAdJnClknUP0C3zsYpbSvAUAJ3F059IcDoa52zFcc4wIDBGUNkwBBA0FdOQ71NIkCYLCA7zOI3wADnDqv2zv6\\u002fAOPgHyfvJcAAsbT57ukHQABhDjdQL+4\\u002fkO79H9I1KsCA8icayz8BwDBj2E7ENTXAMMCXyE13IUDouPVZ5KlAwJCAzk60NyFASFkdYIV1M8AA2f0cGID3P1DZDy9WNynAQJ96GkGFBcCgNd+Lp+MQQIBnlteZgxRAoHcJLrfxJcAARsDdpW7tPzgUnQJcMjXAcFJoOYtSPcAAT9sAu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+       "                            {\n",
+       "const graphDiv = document.getElementById('4be9ca91-b770-4ff7-905a-dd93590dfa34');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
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+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
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+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
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+       "    );\n",
+       "};\n",
+       "\n",
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+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
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+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
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+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
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+       "\n",
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+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
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+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
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+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
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+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58",
+   "metadata": {},
+   "source": [
+    "A final zoomed measured-vs-calculated plot is useful for checking how\n",
+    "the posterior-supported model behaves in a narrow region of the pattern\n",
+    "after the Bayesian run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"tickmode\":\"array\",\"tickvals\":[1.0],\"ticktext\":[\"lbco\"],\"range\":[1.5,0.5],\"showgrid\":false},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0.0,1.0],\"matches\":\"x\",\"showline\":true,\"linecolor\":\"rgba(120, 140, 160, 0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"range\":[45.4,46.3],\"title\":{\"text\":\"2\\u03b8 (degree)\"}},\"yaxis3\":{\"anchor\":\"x3\",\"domain\":[0.0,0.18108737864077668],\"showline\":true,\"linecolor\":\"rgba(120, 140, 160, 0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"range\":[-416.2923561559134,416.2923561559134],\"tickmode\":\"array\",\"tickvals\":[-400.0,0.0,400.0],\"scaleanchor\":\"y\",\"scaleratio\":1},\"margin\":{\"autoexpand\":true,\"r\":30,\"t\":40,\"b\":45},\"title\":{\"text\":\"Posterior predictive for experiment \\ud83d\\udd2c 'hrpt'\"},\"legend\":{\"bgcolor\":\"rgba(255, 255, 255, 0.5)\",\"xanchor\":\"right\",\"x\":1.0,\"yanchor\":\"top\",\"y\":1.0},\"height\":600.0},                        {\"displayModeBar\": true, \"displaylogo\": false, \"modeBarButtonsToRemove\": [\"select2d\", \"lasso2d\", \"zoomIn2d\", \"zoomOut2d\", \"autoScale2d\"], \"responsive\": true}                    ).then(function(){\n",
+       "                            {\n",
+       "const graphDiv = document.getElementById('eafdb772-2ad2-46ec-955e-1b143a986a54');\n",
+       "if (!graphDiv) {\n",
+       "    return;\n",
+       "}\n",
+       "\n",
+       "const parseColor = function (colorValue) {\n",
+       "    if (!colorValue) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
+       "    if (rgbMatch) {\n",
+       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
+       "        if (channels.every((value) => Number.isFinite(value))) {\n",
+       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
+       "    if (!hexMatch) {\n",
+       "        return null;\n",
+       "    }\n",
+       "\n",
+       "    const normalizedHex = hexMatch[1].length === 3\n",
+       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
+       "        : hexMatch[1];\n",
+       "    return {\n",
+       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
+       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
+       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "const resolveLegendButtonFill = function (opacity) {\n",
+       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
+       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
+       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
+       "        ? graphDiv._fullLayout.font.color\n",
+       "        : null;\n",
+       "    const parsedColor = (\n",
+       "        parseColor(referenceFill)\n",
+       "        || parseColor(fontColor)\n",
+       "        || {red: 68, green: 68, blue: 68}\n",
+       "    );\n",
+       "    return (\n",
+       "        'rgba('\n",
+       "        + parsedColor.red\n",
+       "        + ', '\n",
+       "        + parsedColor.green\n",
+       "        + ', '\n",
+       "        + parsedColor.blue\n",
+       "        + ', '\n",
+       "        + opacity\n",
+       "        + ')'\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const updateLegendButtonAppearance = function (legendVisible) {\n",
+       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const legendIconPath = legendButton.querySelector('path');\n",
+       "    if (!legendIconPath) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    legendButton.classList.toggle('active', legendVisible);\n",
+       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
+       "    legendIconPath.setAttribute(\n",
+       "        'style',\n",
+       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "const applyLegendVisibility = function (legendVisible) {\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
+       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
+       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout) {\n",
+       "        graphDiv.layout.showlegend = legendVisible;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout) {\n",
+       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "const readLegendVisibility = function () {\n",
+       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
+       "        return true;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
+       "        return false;\n",
+       "    }\n",
+       "\n",
+       "    const legend = graphDiv.querySelector('.legend');\n",
+       "    if (legend) {\n",
+       "        return (\n",
+       "            window.getComputedStyle(legend).display !== 'none'\n",
+       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
+       "        );\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
+       "        return graphDiv.layout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
+       "        return graphDiv._fullLayout.showlegend;\n",
+       "    }\n",
+       "\n",
+       "    return true;\n",
+       "};\n",
+       "\n",
+       "const syncLegendVisibility = function (legendVisible) {\n",
+       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
+       "        ? legendVisible\n",
+       "        : readLegendVisibility();\n",
+       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
+       "    applyLegendVisibility(resolvedLegendVisible);\n",
+       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
+       "    return resolvedLegendVisible;\n",
+       "};\n",
+       "\n",
+       "const toggleLegend = function (event) {\n",
+       "    if (event) {\n",
+       "        event.preventDefault();\n",
+       "        event.stopPropagation();\n",
+       "    }\n",
+       "\n",
+       "    const currentValue = readLegendVisibility();\n",
+       "    const nextValue = !currentValue;\n",
+       "    syncLegendVisibility(nextValue);\n",
+       "};\n",
+       "\n",
+       "const installLegendToggleButton = function () {\n",
+       "    const modebar = graphDiv.querySelector('.modebar');\n",
+       "    if (!modebar) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    if (!modebar.querySelector('.modebar-group')) {\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
+       "    if (!legendButton) {\n",
+       "        const legendButtonGroup = document.createElement('div');\n",
+       "        legendButtonGroup.className = 'modebar-group';\n",
+       "\n",
+       "        legendButton = document.createElement('a');\n",
+       "        legendButton.className = 'modebar-btn';\n",
+       "        legendButton.href = 'javascript:void(0)';\n",
+       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
+       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
+       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
+       "        legendButton.setAttribute('role', 'button');\n",
+       "        legendButton.setAttribute('tabindex', '0');\n",
+       "        legendButton.innerHTML = [\n",
+       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
+       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
+       "            + ' aria-hidden=\"true\">',\n",
+       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
+       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
+       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
+       "            '</svg>',\n",
+       "        ].join('');\n",
+       "\n",
+       "        legendButtonGroup.appendChild(legendButton);\n",
+       "        modebar.appendChild(legendButtonGroup);\n",
+       "    }\n",
+       "\n",
+       "    legendButton.onclick = toggleLegend;\n",
+       "    legendButton.onkeydown = function (event) {\n",
+       "        if (event.key === 'Enter' || event.key === ' ') {\n",
+       "            toggleLegend(event);\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    syncLegendVisibility();\n",
+       "};\n",
+       "\n",
+       "if (graphDiv.on) {\n",
+       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
+       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
+       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
+       "            syncLegendVisibility(eventData.showlegend);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        syncLegendVisibility();\n",
+       "    });\n",
+       "}\n",
+       "syncLegendVisibility();\n",
+       "window.requestAnimationFrame(installLegendToggleButton);\n",
+       "}\n",
+       "                        })                };            </script>        </div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=45.4, x_max=46.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "395b00f6-635c-43b9-9d86-aa00608a4b9b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 364abeaf..1fadc54f 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -224,9 +224,6 @@
 # %%
 project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
 
-# %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
-
 # %% [markdown]
 # ## Step 5: Prepare for Bayesian Sampling
 #
@@ -238,7 +235,7 @@
 # on the current parameter value and expands them by a chosen multiple of
 # the reported uncertainty.
 #
-# Default multiplier is 8 to give a wide range for the sampler to
+# Default `multiplier` is 8 to give a wide range for the sampler to
 # explore, but here we use 3 to speed up the tutorial.
 
 # %%
@@ -268,6 +265,11 @@
 # through the `init` setting. Other sampler settings such as `thin` and
 # `pop` can be adjusted  as well, but here we keep them at their
 # defaults.
+#
+# Default `steps` is 1000, which is often need to be increased for a
+# real analysis to ensure good convergence and sampling of the posterior
+# distribution. Here we use much smaller value to speed up the tutorial,
+# but this is not recommended for a real analysis.
 
 # %%
 project.analysis.fit.show_minimizer_types()
@@ -276,7 +278,7 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 100  # 1000
+project.analysis.fit.minimizer.steps = 50  # 1000
 
 # %%
 project.analysis.fit()
@@ -329,4 +331,4 @@
 # after the Bayesian run.
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=65, x_max=68)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=45.4, x_max=46.3)

From 469ed50cd1e6d092f774e8d4c0c79ed52f948c57 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 23:57:59 +0200
Subject: [PATCH 067/106] Update posterior pair plot marker and background

---
 src/easydiffraction/display/plotting.py        | 18 ++++++++++--------
 .../easydiffraction/display/test_plotting.py   | 16 ++++++++++++++++
 2 files changed, 26 insertions(+), 8 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 105976c6..733e54e6 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -158,6 +158,8 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 26
 POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 42
 POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
+POSTERIOR_PAIR_SAMPLE_MARKER_SIZE = 6
+POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE = 6
 
 
 @dataclass(frozen=True)
@@ -1408,7 +1410,10 @@ def _add_posterior_pair_off_diagonal(
                 x=x_scatter_values,
                 y=y_scatter_values,
                 mode='markers',
-                marker={'color': POSTERIOR_SCATTER_MARKER_COLOR, 'size': 3},
+                marker={
+                    'color': POSTERIOR_SCATTER_MARKER_COLOR,
+                    'size': POSTERIOR_PAIR_SAMPLE_MARKER_SIZE,
+                },
                 name='Posterior samples',
                 legendgroup='posterior-samples',
                 showlegend=legend_state.show_scatter,
@@ -1433,7 +1438,10 @@ def _add_posterior_pair_off_diagonal(
                 x=x_scatter_values,
                 y=y_scatter_values,
                 mode='markers',
-                marker={'color': 'rgba(0, 0, 0, 0)', 'size': 6},
+                marker={
+                    'color': 'rgba(0, 0, 0, 0)',
+                    'size': POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE,
+                },
                 showlegend=False,
                 hovertemplate=sample_hovertemplate,
                 zorder=3,
@@ -1656,8 +1664,6 @@ def _finalize_posterior_pairs_figure(
                 'y': 0.995,
                 'groupclick': 'togglegroup',
             },
-            paper_bgcolor='white',
-            plot_bgcolor='white',
         )
 
     @staticmethod
@@ -3626,8 +3632,6 @@ def _build_correlation_heatmap_plot(
             shapes=subplot_border_shapes,
             meta=self._posterior_pair_layout_meta(),
             showlegend=False,
-            paper_bgcolor='white',
-            plot_bgcolor='white',
         )
         return fig
 
@@ -3918,8 +3922,6 @@ def _plot_meas_data(
             ``time_of_flight``, ``d_spacing``) and ``meas`` array.
         expt_name : str
             Experiment name for the title. *,
-        include_draws : bool,
-            Experiment type with scattering/beam enums.
         x_min : object, default=None
             Optional minimum x-axis limit.
         x_max : object, default=None
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index a26fb9ec..7ee026fa 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -307,6 +307,9 @@ def test_correlation_from_posterior_samples_returns_labeled_dataframe():
 
 
 def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_MARKER_SIZE
+
     plotter, _, _ = _make_bayesian_plotter_fixture()
 
     figure = plotter._build_posterior_pairs_plot(parameters=None)
@@ -341,8 +344,19 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
     assert subplot.yaxis.title.text is None
     assert bottom_subplot.xaxis.showticklabels is False
     assert bottom_subplot.xaxis.title.text is None
+    assert figure.layout.paper_bgcolor is None
+    assert figure.layout.plot_bgcolor is None
     assert len(figure.layout.shapes) == 30
     assert any(trace.name == 'Posterior contours' for trace in figure.data)
+    sample_trace = next(trace for trace in figure.data if trace.name == 'Posterior samples')
+    hover_trace = next(
+        trace
+        for trace in figure.data
+        if getattr(trace, 'mode', None) == 'markers'
+        and getattr(trace.marker, 'color', None) == 'rgba(0, 0, 0, 0)'
+    )
+    assert sample_trace.marker.size == POSTERIOR_PAIR_SAMPLE_MARKER_SIZE
+    assert hover_trace.marker.size == POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE
 
 
 def test_build_posterior_pairs_plot_fast_mode_skips_contours():
@@ -1359,6 +1373,8 @@ class Project:
     assert fig.layout.yaxis.layer == 'above traces'
     assert fig.layout.yaxis.showticklabels is False
     assert fig.layout.yaxis.title.text is None
+    assert fig.layout.paper_bgcolor is None
+    assert fig.layout.plot_bgcolor is None
     assert len(fig.layout.shapes) == 3
     assert all(shape.type == 'rect' for shape in fig.layout.shapes)
     assert all(shape.xref == 'paper' for shape in fig.layout.shapes)

From fcd9722904d93be48eaeb701f4379006b62ea314 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Sun, 10 May 2026 23:59:37 +0200
Subject: [PATCH 068/106] Update notebooks

---
 docs/docs/tutorials/ed-13.ipynb |    2 +-
 docs/docs/tutorials/ed-21.ipynb | 4092 +------------------------------
 2 files changed, 86 insertions(+), 4008 deletions(-)

diff --git a/docs/docs/tutorials/ed-13.ipynb b/docs/docs/tutorials/ed-13.ipynb
index 4d605962..69eae9aa 100644
--- a/docs/docs/tutorials/ed-13.ipynb
+++ b/docs/docs/tutorials/ed-13.ipynb
@@ -2665,7 +2665,7 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "tags,title,-all",
+   "cell_metadata_filter": "title,tags,-all",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   }
diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index 2a938413..0a198508 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "0",
    "metadata": {
     "tags": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3",
    "metadata": {},
    "outputs": [],
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "5",
    "metadata": {},
    "outputs": [],
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "7",
    "metadata": {},
    "outputs": [],
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "8",
    "metadata": {},
    "outputs": [],
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "9",
    "metadata": {},
    "outputs": [],
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "10",
    "metadata": {},
    "outputs": [],
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "12",
    "metadata": {},
    "outputs": [],
@@ -223,20 +223,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "15",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mGetting data\u001b[0m\u001b[1;34m...\u001b[0m\n",
-      "Data #\u001b[1;36m3\u001b[0m: La0.5Ba0.5CoO3, HRPT \u001b[1m(\u001b[0mPSI\u001b[1m)\u001b[0m, \u001b[1;36m300\u001b[0m K\n",
-      "✅ Data #\u001b[1;36m3\u001b[0m already present at \u001b[32m'data/ed-3.xye'\u001b[0m. Keeping existing file.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data_path = ed.download_data(id=3, destination='data')"
    ]
@@ -251,19 +241,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "17",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mData loaded successfully\u001b[0m\n",
-      "Experiment 🔬 \u001b[32m'hrpt'\u001b[0m. Number of data points: \u001b[1;36m3098\u001b[0m.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.experiments.add_from_data_path(\n",
     "    name='hrpt',\n",
@@ -276,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "18",
    "metadata": {},
    "outputs": [],
@@ -297,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "20",
    "metadata": {},
    "outputs": [],
@@ -308,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "21",
    "metadata": {},
    "outputs": [],
@@ -332,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "23",
    "metadata": {},
    "outputs": [],
@@ -345,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "24",
    "metadata": {},
    "outputs": [],
@@ -364,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "26",
    "metadata": {},
    "outputs": [],
@@ -393,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "28",
    "metadata": {},
    "outputs": [],
@@ -403,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "29",
    "metadata": {},
    "outputs": [],
@@ -426,510 +407,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "31",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mMinimizer types\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
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-       "<table id=\"T_c23e7\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
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-       "      <th id=\"T_c23e7_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
-       "      <th id=\"T_c23e7_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_c23e7_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_c23e7_row0_col0\" class=\"data row0 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
-       "      <td id=\"T_c23e7_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_c23e7_row1_col0\" class=\"data row1 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
-       "      <td id=\"T_c23e7_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_c23e7_row2_col0\" class=\"data row2 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
-       "      <td id=\"T_c23e7_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_c23e7_row3_col0\" class=\"data row3 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
-       "      <td id=\"T_c23e7_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_c23e7_row4_col0\" class=\"data row4 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
-       "      <td id=\"T_c23e7_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_c23e7_row5_col0\" class=\"data row5 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
-       "      <td id=\"T_c23e7_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_c23e7_row6_col0\" class=\"data row6 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
-       "      <td id=\"T_c23e7_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_c23e7_row7_col0\" class=\"data row7 col0\" ></td>\n",
-       "      <td id=\"T_c23e7_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
-       "      <td id=\"T_c23e7_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c23e7_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_c23e7_row8_col0\" class=\"data row8 col0\" >*</td>\n",
-       "      <td id=\"T_c23e7_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
-       "      <td id=\"T_c23e7_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.fit.show_minimizer_types()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "32",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mCurrent minimizer changed to\u001b[0m\n",
-      "bumps \u001b[1m(\u001b[0mlm\u001b[1m)\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.fit.minimizer_type = 'bumps (lm)'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "33",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mStandard fitting\u001b[0m\n",
-      "📋 Using experiment 🔬 \u001b[32m'hrpt'\u001b[0m for \u001b[32m'single'\u001b[0m fitting\n",
-      "🚀 Starting fit process with \u001b[32m'bumps \u001b[0m\u001b[32m(\u001b[0m\u001b[32mlm\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[33m...\u001b[0m\n",
-      "📈 Goodness-of-fit progress:\n"
-     ]
-    },
-    {
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-       "  line-height: 1.15em;\n",
-       "}\n",
-       "#T_c22af  td {\n",
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-       "  padding-bottom: 0.25em;\n",
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-       "}\n",
-       "#T_c22af th.row_heading {\n",
-       "  color: #dadada;\n",
-       "  font-weight: normal;\n",
-       "}\n",
-       "#T_c22af tbody tr:nth-child(odd) {\n",
-       "  background-color: transparent;\n",
-       "}\n",
-       "#T_c22af  tbody tr:nth-child(even) {\n",
-       "  background-color: transparent;\n",
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-       "#T_c22af th.col0 {\n",
-       "  text-align: center;\n",
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-       "#T_c22af th.col1 {\n",
-       "  text-align: center;\n",
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-       "#T_c22af th.col2 {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "#T_c22af th.col3 {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "#T_c22af_row0_col0, #T_c22af_row0_col1, #T_c22af_row0_col2, #T_c22af_row0_col3, #T_c22af_row1_col0, #T_c22af_row1_col1, #T_c22af_row1_col2, #T_c22af_row1_col3, #T_c22af_row2_col0, #T_c22af_row2_col1, #T_c22af_row2_col2, #T_c22af_row2_col3, #T_c22af_row3_col0, #T_c22af_row3_col1, #T_c22af_row3_col2, #T_c22af_row3_col3, #T_c22af_row4_col0, #T_c22af_row4_col1, #T_c22af_row4_col2, #T_c22af_row4_col3, #T_c22af_row5_col0, #T_c22af_row5_col1, #T_c22af_row5_col2, #T_c22af_row5_col3, #T_c22af_row6_col0, #T_c22af_row6_col1, #T_c22af_row6_col2, #T_c22af_row6_col3, #T_c22af_row7_col0, #T_c22af_row7_col1, #T_c22af_row7_col2, #T_c22af_row7_col3, #T_c22af_row8_col0, #T_c22af_row8_col1, #T_c22af_row8_col2, #T_c22af_row8_col3, #T_c22af_row9_col0, #T_c22af_row9_col1, #T_c22af_row9_col2, #T_c22af_row9_col3 {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_c22af\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_c22af_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
-       "      <th id=\"T_c22af_level0_col1\" class=\"col_heading level0 col1\" >time (s)</th>\n",
-       "      <th id=\"T_c22af_level0_col2\" class=\"col_heading level0 col2\" >χ²</th>\n",
-       "      <th id=\"T_c22af_level0_col3\" class=\"col_heading level0 col3\" >change / status</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_c22af_row0_col0\" class=\"data row0 col0\" >1</td>\n",
-       "      <td id=\"T_c22af_row0_col1\" class=\"data row0 col1\" >0.19</td>\n",
-       "      <td id=\"T_c22af_row0_col2\" class=\"data row0 col2\" >377.51</td>\n",
-       "      <td id=\"T_c22af_row0_col3\" class=\"data row0 col3\" ></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_c22af_row1_col0\" class=\"data row1 col0\" >8</td>\n",
-       "      <td id=\"T_c22af_row1_col1\" class=\"data row1 col1\" >0.37</td>\n",
-       "      <td id=\"T_c22af_row1_col2\" class=\"data row1 col2\" >56.31</td>\n",
-       "      <td id=\"T_c22af_row1_col3\" class=\"data row1 col3\" >85.1% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_c22af_row2_col0\" class=\"data row2 col0\" >14</td>\n",
-       "      <td id=\"T_c22af_row2_col1\" class=\"data row2 col1\" >0.50</td>\n",
-       "      <td id=\"T_c22af_row2_col2\" class=\"data row2 col2\" >39.54</td>\n",
-       "      <td id=\"T_c22af_row2_col3\" class=\"data row2 col3\" >29.8% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_c22af_row3_col0\" class=\"data row3 col0\" >21</td>\n",
-       "      <td id=\"T_c22af_row3_col1\" class=\"data row3 col1\" >0.66</td>\n",
-       "      <td id=\"T_c22af_row3_col2\" class=\"data row3 col2\" >37.66</td>\n",
-       "      <td id=\"T_c22af_row3_col3\" class=\"data row3 col3\" >4.7% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_c22af_row4_col0\" class=\"data row4 col0\" >26</td>\n",
-       "      <td id=\"T_c22af_row4_col1\" class=\"data row4 col1\" >0.77</td>\n",
-       "      <td id=\"T_c22af_row4_col2\" class=\"data row4 col2\" >34.14</td>\n",
-       "      <td id=\"T_c22af_row4_col3\" class=\"data row4 col3\" >9.4% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_c22af_row5_col0\" class=\"data row5 col0\" >27</td>\n",
-       "      <td id=\"T_c22af_row5_col1\" class=\"data row5 col1\" >0.80</td>\n",
-       "      <td id=\"T_c22af_row5_col2\" class=\"data row5 col2\" >23.47</td>\n",
-       "      <td id=\"T_c22af_row5_col3\" class=\"data row5 col3\" >31.3% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_c22af_row6_col0\" class=\"data row6 col0\" >33</td>\n",
-       "      <td id=\"T_c22af_row6_col1\" class=\"data row6 col1\" >0.93</td>\n",
-       "      <td id=\"T_c22af_row6_col2\" class=\"data row6 col2\" >8.74</td>\n",
-       "      <td id=\"T_c22af_row6_col3\" class=\"data row6 col3\" >62.7% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_c22af_row7_col0\" class=\"data row7 col0\" >39</td>\n",
-       "      <td id=\"T_c22af_row7_col1\" class=\"data row7 col1\" >1.06</td>\n",
-       "      <td id=\"T_c22af_row7_col2\" class=\"data row7 col2\" >1.85</td>\n",
-       "      <td id=\"T_c22af_row7_col3\" class=\"data row7 col3\" >78.9% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_c22af_row8_col0\" class=\"data row8 col0\" >45</td>\n",
-       "      <td id=\"T_c22af_row8_col1\" class=\"data row8 col1\" >1.19</td>\n",
-       "      <td id=\"T_c22af_row8_col2\" class=\"data row8 col2\" >1.30</td>\n",
-       "      <td id=\"T_c22af_row8_col3\" class=\"data row8 col3\" >29.9% ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c22af_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
-       "      <td id=\"T_c22af_row9_col0\" class=\"data row9 col0\" >77</td>\n",
-       "      <td id=\"T_c22af_row9_col1\" class=\"data row9 col1\" >1.87</td>\n",
-       "      <td id=\"T_c22af_row9_col2\" class=\"data row9 col2\" >1.29</td>\n",
-       "      <td id=\"T_c22af_row9_col3\" class=\"data row9 col3\" ></td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🏆 Best goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m is \u001b[1;36m1.29\u001b[0m at iteration \u001b[1;36m76\u001b[0m\n",
-      "✅ Fitting complete.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.fit()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "34",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mFit results\u001b[0m\n",
-      "✅ Success: \u001b[3;92mTrue\u001b[0m\n",
-      "⏱️ Fitting time: \u001b[1;36m1.87\u001b[0m seconds\n",
-      "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.29\u001b[0m\n",
-      "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.65\u001b[0m%\n",
-      "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.92\u001b[0m%\n",
-      "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m4.08\u001b[0m%\n",
-      "📈 Fitted parameters:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "#T_9c9a1   {\n",
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-       "#T_9c9a1  tbody tr:nth-child(even) {\n",
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-       "#T_9c9a1 th.col1 {\n",
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-       "#T_9c9a1 th.col2 {\n",
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-       "#T_9c9a1_row0_col0, #T_9c9a1_row0_col1, #T_9c9a1_row0_col2, #T_9c9a1_row0_col3, #T_9c9a1_row0_col7, #T_9c9a1_row1_col0, #T_9c9a1_row1_col1, #T_9c9a1_row1_col2, #T_9c9a1_row1_col3, #T_9c9a1_row1_col7, #T_9c9a1_row2_col0, #T_9c9a1_row2_col1, #T_9c9a1_row2_col2, #T_9c9a1_row2_col3, #T_9c9a1_row2_col7, #T_9c9a1_row3_col0, #T_9c9a1_row3_col1, #T_9c9a1_row3_col2, #T_9c9a1_row3_col3, #T_9c9a1_row3_col7, #T_9c9a1_row4_col0, #T_9c9a1_row4_col1, #T_9c9a1_row4_col2, #T_9c9a1_row4_col3, #T_9c9a1_row4_col7 {\n",
-       "  text-align: left;\n",
-       "}\n",
-       "#T_9c9a1_row0_col4, #T_9c9a1_row0_col5, #T_9c9a1_row0_col6, #T_9c9a1_row0_col8, #T_9c9a1_row1_col4, #T_9c9a1_row1_col5, #T_9c9a1_row1_col6, #T_9c9a1_row1_col8, #T_9c9a1_row2_col4, #T_9c9a1_row2_col5, #T_9c9a1_row2_col6, #T_9c9a1_row2_col8, #T_9c9a1_row3_col4, #T_9c9a1_row3_col5, #T_9c9a1_row3_col6, #T_9c9a1_row3_col8, #T_9c9a1_row4_col4, #T_9c9a1_row4_col5, #T_9c9a1_row4_col6, #T_9c9a1_row4_col8 {\n",
-       "  text-align: right;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_9c9a1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_9c9a1_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_9c9a1_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
-       "      <th id=\"T_9c9a1_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
-       "      <th id=\"T_9c9a1_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
-       "      <th id=\"T_9c9a1_level0_col4\" class=\"col_heading level0 col4\" >start</th>\n",
-       "      <th id=\"T_9c9a1_level0_col5\" class=\"col_heading level0 col5\" >fitted</th>\n",
-       "      <th id=\"T_9c9a1_level0_col6\" class=\"col_heading level0 col6\" >uncertainty</th>\n",
-       "      <th id=\"T_9c9a1_level0_col7\" class=\"col_heading level0 col7\" >units</th>\n",
-       "      <th id=\"T_9c9a1_level0_col8\" class=\"col_heading level0 col8\" >change</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9c9a1_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_9c9a1_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_9c9a1_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_9c9a1_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_9c9a1_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_9c9a1_row0_col4\" class=\"data row0 col4\" >3.8800</td>\n",
-       "      <td id=\"T_9c9a1_row0_col5\" class=\"data row0 col5\" >3.8913</td>\n",
-       "      <td id=\"T_9c9a1_row0_col6\" class=\"data row0 col6\" >0.0001</td>\n",
-       "      <td id=\"T_9c9a1_row0_col7\" class=\"data row0 col7\" >Å</td>\n",
-       "      <td id=\"T_9c9a1_row0_col8\" class=\"data row0 col8\" >0.29 % ↑</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9c9a1_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_9c9a1_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_9c9a1_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
-       "      <td id=\"T_9c9a1_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
-       "      <td id=\"T_9c9a1_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
-       "      <td id=\"T_9c9a1_row1_col4\" class=\"data row1 col4\" >9.1351</td>\n",
-       "      <td id=\"T_9c9a1_row1_col5\" class=\"data row1 col5\" >9.1329</td>\n",
-       "      <td id=\"T_9c9a1_row1_col6\" class=\"data row1 col6\" >0.0333</td>\n",
-       "      <td id=\"T_9c9a1_row1_col7\" class=\"data row1 col7\" ></td>\n",
-       "      <td id=\"T_9c9a1_row1_col8\" class=\"data row1 col8\" >0.02 % ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9c9a1_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_9c9a1_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_9c9a1_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_9c9a1_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_9c9a1_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_9c9a1_row2_col4\" class=\"data row2 col4\" >0.1000</td>\n",
-       "      <td id=\"T_9c9a1_row2_col5\" class=\"data row2 col5\" >0.0817</td>\n",
-       "      <td id=\"T_9c9a1_row2_col6\" class=\"data row2 col6\" >0.0078</td>\n",
-       "      <td id=\"T_9c9a1_row2_col7\" class=\"data row2 col7\" >deg²</td>\n",
-       "      <td id=\"T_9c9a1_row2_col8\" class=\"data row2 col8\" >18.33 % ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9c9a1_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_9c9a1_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_9c9a1_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
-       "      <td id=\"T_9c9a1_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_9c9a1_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_9c9a1_row3_col4\" class=\"data row3 col4\" >-0.1000</td>\n",
-       "      <td id=\"T_9c9a1_row3_col5\" class=\"data row3 col5\" >-0.1169</td>\n",
-       "      <td id=\"T_9c9a1_row3_col6\" class=\"data row3 col6\" >0.0057</td>\n",
-       "      <td id=\"T_9c9a1_row3_col7\" class=\"data row3 col7\" >deg²</td>\n",
-       "      <td id=\"T_9c9a1_row3_col8\" class=\"data row3 col8\" >16.91 % ↑</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9c9a1_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_9c9a1_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
-       "      <td id=\"T_9c9a1_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
-       "      <td id=\"T_9c9a1_row4_col2\" class=\"data row4 col2\" ></td>\n",
-       "      <td id=\"T_9c9a1_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_9c9a1_row4_col4\" class=\"data row4 col4\" >0.0000</td>\n",
-       "      <td id=\"T_9c9a1_row4_col5\" class=\"data row4 col5\" >0.6306</td>\n",
-       "      <td id=\"T_9c9a1_row4_col6\" class=\"data row4 col6\" >0.0019</td>\n",
-       "      <td id=\"T_9c9a1_row4_col7\" class=\"data row4 col7\" >deg</td>\n",
-       "      <td id=\"T_9c9a1_row4_col8\" class=\"data row4 col8\" >N/A</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x369840cd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.display.fit_results()"
    ]
@@ -947,509 +458,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "36",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       ".ed-fixed-aspect-plotly-wrapper {\n",
-       "    width: 100%;\n",
-       "    aspect-ratio: 1 / 1;\n",
-       "}\n",
-       "\n",
-       ".ed-fixed-aspect-plotly-wrapper > div,\n",
-       ".ed-fixed-aspect-plotly-wrapper .plotly-graph-div,\n",
-       ".ed-fixed-aspect-plotly-wrapper .js-plotly-plot,\n",
-       ".ed-fixed-aspect-plotly-wrapper .plotly,\n",
-       ".ed-fixed-aspect-plotly-wrapper .plot-container,\n",
-       ".ed-fixed-aspect-plotly-wrapper .svg-container {\n",
-       "    display: block;\n",
-       "    width: 100% !important;\n",
-       "    height: 100% !important;\n",
-       "}\n",
-       "</style>\n",
-       "\n",
-       "<div class=\"ed-fixed-aspect-plotly-wrapper\">\n",
-       "<div>                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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+   "outputs": [],
    "source": [
     "project.display.plotter.plot_param_correlations()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "37",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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ATwRxHk4AZUCCzM6i9\\u002f9kQLSULCeh\\u002f2RA5lyKq0r\\u002fZEAYJegv9P5kQErtRbSd\\u002fmRAfbWjOEf+ZECvfQG98P1kQOFFX0Ga\\u002fWRAEw69xUP9ZEBF1hpK7fxkQHieeM6W\\u002fGRAqmbWUkD8ZEDcLjTX6ftkQA73kVuT+2RAQL\\u002fv3zz7ZEBzh01k5vpkQKVPq+iP+mRA1xcJbTn6ZEAJ4Gbx4vlkQDuoxHWM+WRAbnAi+jX5ZECgOIB+3\\u002fhkQNIA3gKJ+GRABMk7hzL4ZEA2kZkL3PdkQGlZ94+F92RAmyFVFC\\u002f3ZEDN6bKY2PZkQP+xEB2C9mRAMXpuoSv2ZEBkQswl1fVkQJYKKqp+9WRAyNKHLij1ZED6muWy0fRkQCxjQzd79GRAXyuhuyT0ZECR8\\u002f4\\u002fzvNkQMO7XMR382RA9YO6SCHzZEAnTBjNyvJkQFoUdlF08mRAjNzT1R3yZEC+pDFax\\u002fFkQPBsj95w8WRAIjXtYhrxZEBV\\u002fUrnw\\u002fBkQIfFqGtt8GRAuY0G8BbwZEDrVWR0wO9kQB0ewvhp72RAUOYffRPvZECCrn0Bve5kQLR224Vm7mRA5j45ChDuZEAYB5eOue1kQEvP9BJj7WRAfZdSlwztZECvX7AbtuxkQOEnDqBf7GRAE\\u002fBrJAnsZEBGuMmosutkQHiAJy1c62RAqkiFsQXrZEDcEOM1r+pkQA7ZQLpY6mRAQaGePgLqZEBzafzCq+lkQKUxWkdV6WRA1\\u002fm3y\\u002f7oZEAJwhVQqOhkQDyKc9RR6GRAblLRWPvnZECgGi\\u002fdpOdkQNLijGFO52RABKvq5ffmZEA3c0hqoeZkQGk7pu5K5mRAmwMEc\\u002fTlZEDNy2H3neVkQP+Tv3tH5WRAMlwdAPHkZEBkJHuEmuRkQJbs2AhE5GRAyLQ2je3jZED6fJQRl+NkQC1F8pVA42RAXw1QGuriZECR1a2ek+JkQMOdCyM94mRA9WVpp+bhZEAoLscrkOFkQFr2JLA54WRAjL6CNOPgZEC+huC4jOBkQPBOPj024GRAIxecwd\\u002ffZEBV3\\u002flFid9kQIenV8oy32RAuW+1TtzeZEDrNxPThd5kQB4AcVcv3mRAUMjO29jdZECCkCxggt1kQLRYiuQr3WRA5iDoaNXcZEAZ6UXtftxkQEuxo3Eo3GRAfXkB9tHbZECvQV96e9tkQOEJvf4k22RAFNIag87aZEBGmngHeNpkQHhi1osh2mRAqio0EMvZZEDc8pGUdNlkQA+77xge2WRAQYNNncfYZEBzS6shcdhkQKUTCaYa2GRA19tmKsTXZEAKpMSubddkQDxsIjMX12RAbjSAt8DWZECg\\u002fN07atZkQNLEO8AT1mRABY2ZRL3VZEA3VffIZtVkQGkdVU0Q1WRAm+Wy0bnUZEDNrRBWY9RkQAB2btoM1GRAMj7MXrbTZEBkBirjX9NkQJbOh2cJ02RAyJbl67LSZED7XkNwXNJkQC0nofQF0mRAX+\\u002f+eK\\u002fRZECRt1z9WNFkQMN\\u002fuoEC0WRA9kcYBqzQZEAoEHaKVdBkQFrY0w7\\u002fz2RAjKAxk6jPZEC+aI8XUs9kQPEw7Zv7zmRAI\\u002flKIKXOZEBVwaikTs5kQIeJBin4zWRAuVFkraHNZEDsGcIxS81kQB7iH7b0zGRAUKp9Op7MZECCctu+R8xkQLQ6OUPxy2RA5wKXx5rLZEAZy\\u002fRLRMtkQEuTUtDtymRAfVuwVJfKZECvIw7ZQMpkQOLra13qyWRAFLTJ4ZPJZEBGfCdmPclkQHhEhermyGRAqgzjbpDIZEDd1EDzOchkQA+dnnfjx2RAQWX8+4zHZEBzLVqANsdkQKX1twTgxmRA2L0ViYnGZEAKhnMNM8ZkQDxO0ZHcxWRAbhYvFobFZECg3oyaL8VkQNOm6h7ZxGRABW9Io4LEZEA3N6YnLMRkQGn\\u002fA6zVw2RAm8dhMH\\u002fDZEDOj7+0KMNkQABYHTnSwmRAMiB7vXvCZEBk6NhBJcJkQJawNsbOwWRAyXiUSnjBZED7QPLOIcFkQC0JUFPLwGRAX9Gt13TAZECRmQtcHsBkQMRhaeDHv2RA9inHZHG\\u002fZEAo8iTpGr9kQFq6gm3EvmRAjILg8W2+ZEC\\u002fSj52F75kQPESnPrAvWRAI9v5fmq9ZEBVo1cDFL1kQIdrtYe9vGRAujMTDGe8ZEDs+3CQELxkQB7EzhS6u2RAUIwsmWO7ZECCVIodDbtkQLUc6KG2umRA5+RFJmC6ZEAZraOqCbpkQEt1AS+zuWRAfT1fs1y5ZECwBb03BrlkQOLNGryvuGRAFJZ4QFm4ZEBGXtbEArhkQHgmNEmst2RAq+6RzVW3ZEDdtu9R\\u002f7ZkQA9\\u002fTdaotmRAQUerWlK2ZEBzDwnf+7VkQKbXZmOltWRA2J\\u002fE5061ZEAKaCJs+LRkQDwwgPChtGRAbvjddEu0ZEChwDv59LNkQNOImX2es2RABVH3AUizZEA3GVWG8bJkQGnhsgqbsmRAnKkQj0SyZEDOcW4T7rFkQAA6zJeXsWRAMgIqHEGxZEBkyoeg6rBkQJeS5SSUsGRAyVpDqT2wZED7IqEt569kQC3r\\u002frGQr2RAX7NcNjqvZECSe7q6465kQMRDGD+NrmRA9gt2wzauZEAo1NNH4K1kQFqcMcyJrWRAjWSPUDOtZEC\\u002fLO3U3KxkQPH0SlmGrGRAI72o3S+sZEBVhQZi2atkQIhNZOaCq2RAuhXCaiyrZEDs3R\\u002fv1apkQB6mfXN\\u002fqmRAUG7b9yiqZECDNjl80qlkQLX+lgB8qWRA58b0hCWpZEAZj1IJz6hkQEtXsI14qGRAfh8OEiKoZECw52uWy6dkQOKvyRp1p2RAFHgnnx6nZEBGQIUjyKZkQHkI46dxpmRAq9BALBumZEDdmJ6wxKVkQA9h\\u002fDRupWRAQSlauRelZEB08bc9waRkQKa5FcJqpGRA2IFzRhSkZEAKStHKvaNkQDwSL09no2RAb9qM0xCjZEChoupXuqJkQNNqSNxjomRABTOmYA2iZEA3+wPltqFkQGrDYWlgoWRAnIu\\u002f7QmhZEDOUx1ys6BkQAAce\\u002fZcoGRAMuTYegagZEBlrDb\\u002fr59kQJd0lINZn2RAyTzyBwOfZED7BFCMrJ5kQC3NrRBWnmRAYJULlf+dZECSXWkZqZ1kQMQlx51SnWRA9u0kIvycZEAotoKmpZxkQFt+4CpPnGRAjUY+r\\u002fibZEC\\u002fDpwzoptkQPHW+bdLm2RAI59XPPWaZEBWZ7XAnppkQIgvE0VImmRAuvdwyfGZZEDsv85Nm5lkQB6ILNJEmWRAUVCKVu6YZECDGOjal5hkQLXgRV9BmGRA56ij4+qXZEAZcQFolJdkQEw5X+w9l2RAfgG9cOeWZECwyRr1kJZkQOKReHk6lmRAFFrW\\u002feOVZEBHIjSCjZVkQHnqkQY3lWRAq7LviuCUZEDdek0PipRkQA9Dq5MzlGRAQgsJGN2TZEB002achpNkQKabxCAwk2RA2GMipdmSZEAKLIApg5JkQD303a0skmRAb7w7MtaRZEChhJm2f5FkQNNM9zopkWRABRVVv9KQZEA43bJDfJBkQGqlEMglkGRAnG1uTM+PZEDONczQeI9kQAD+KVUij2RAM8aH2cuOZEBljuVddY5kQJdWQ+IejmRAyR6hZsiNZED75v7qcY1kQC6vXG8bjWRAYHe688SMZECSPxh4boxkQMQHdvwXjGRA9s\\u002fTgMGLZEApmDEFa4tkQFtgj4kUi2RAjSjtDb6KZECoctpT8opkQMO8x5kmi2RA3ga131qLZED4UKIlj4tkQBObj2vDi2RALuV8sfeLZEBJL2r3K4xkQGR5Vz1gjGRAf8NEg5SMZECZDTLJyIxkQLRXHw\\u002f9jGRAz6EMVTGNZEDq6\\u002fmaZY1kQAU25+CZjWRAIIDUJs6NZEA6ysFsAo5kQFUUr7I2jmRAcF6c+GqOZECLqIk+n45kQKbydoTTjmRAwTxkygePZEDbhlEQPI9kQPbQPlZwj2RAERssnKSPZEAsZRni2I9kQEevBigNkGRAYvnzbUGQZEB8Q+GzdZBkQJeNzvmpkGRAste7P96QZEDNIamFEpFkQOhrlstGkWRAA7aDEXuRZEAdAHFXr5FkQDhKXp3jkWRAU5RL4xeSZEBu3jgpTJJkQIkoJm+AkmRApHITtbSSZEC+vAD76JJkQNkG7kAdk2RA9FDbhlGTZEAPm8jMhZNkQCrltRK6k2RARS+jWO6TZEBgeZCeIpRkQHrDfeRWlGRAlQ1rKouUZECwV1hwv5RkQMuhRbbzlGRA5usy\\u002fCeVZEABNiBCXJVkQBuADYiQlWRANsr6zcSVZEBRFOgT+ZVkQGxe1VktlmRAh6jCn2GWZECi8q\\u002fllZZkQLw8nSvKlmRA14aKcf6WZEDy0He3MpdkQA0bZf1ml2RAKGVSQ5uXZEBDrz+Jz5dkQF35LM8DmGRAeEMaFTiYZECTjQdbbJhkQK7X9KCgmGRAySHi5tSYZEDka88sCZlkQP61vHI9mWRAGQCquHGZZEA0Spf+pZlkQE+UhETamWRAat5xig6aZECFKF\\u002fQQppkQJ9yTBZ3mmRAurw5XKuaZEDVBiei35pkQPBQFOgTm2RAC5sBLkibZEAm5e5zfJtkQEEv3Lmwm2RAW3nJ\\u002f+SbZEB2w7ZFGZxkQJENpItNnGRArFeR0YGcZEDHoX4XtpxkQOLra13qnGRA\\u002fDVZox6dZEAXgEbpUp1kQDLKMy+HnWRATRQhdbudZEBoXg67751kQIOo+wAknmRAnfLoRlieZEC4PNaMjJ5kQNOGw9LAnmRA7tCwGPWeZEAJG55eKZ9kQCRli6Rdn2RAPq946pGfZEBZ+WUwxp9kQHRDU3b6n2RAj41AvC6gZECq1y0CY6BkQMUhG0iXoGRA32sIjsugZED6tfXT\\u002f6BkQBUA4xk0oWRAMErQX2ihZEBLlL2lnKFkQGbequvQoWRAgCiYMQWiZECbcoV3OaJkQLa8cr1tomRA0QZgA6KiZEDsUE1J1qJkQAebOo8Ko2RAIuUn1T6jZEA8LxUbc6NkQFd5AmGno2RAcsPvptujZECNDd3sD6RkQKhXyjJEpGRAw6G3eHikZEDd66S+rKRkQPg1kgThpGRAE4B\\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\\u002fJKr2RAbEPFOH+vZECHjbJ+s69kQKHXn8Tnr2RAvCGNChywZEDXa3pQULBkQPK1Z5aEsGRADQBV3LiwZEAoSkIi7bBkQEKUL2ghsWRAXd4crlWxZEB4KAr0ibFkQJNy9zm+sWRArrzkf\\u002fKxZEDJBtLFJrJkQONQvwtbsmRA\\u002fpqsUY+yZEAZ5ZmXw7JkQDQvh933smRAT3l0IyyzZEBqw2FpYLNkQIQNT6+Us2RAn1c89cizZEC6oSk7\\u002fbNkQNXrFoExtGRA8DUEx2W0ZEALgPEMmrRkQCbK3lLOtGRAQBTMmAK1ZEBbXrneNrVkQHaopiRrtWRAkfKTap+1ZECsPIGw07VkQMeGbvYHtmRA4dBbPDy2ZED8GkmCcLZkQBdlNsiktmRAMq8jDtm2ZEBN+RBUDbdkQGhD\\u002fplBt2RAgo3r33W3ZECd19glqrdkQLghxmvet2RA02uzsRK4ZEDutaD3RrhkQAkAjj17uGRAI0p7g6+4ZEA+lGjJ47hkQFneVQ8YuWRAdChDVUy5ZECPcjCbgLlkQKq8HeG0uWRAxAYLJ+m5ZEDfUPhsHbpkQPqa5bJRumRAFeXS+IW6ZEAwL8A+urpkQEt5rYTuumRAZcOayiK7ZECADYgQV7tkQJtXdVaLu2RAtqFinL+7ZEDR60\\u002fi87tkQOw1PSgovGRAB4Aqbly8ZEAhyhe0kLxkQDwUBfrEvGRAV17yP\\u002fm8ZEByqN+FLb1kQI3yzMthvWRAqDy6EZa9ZEDChqdXyr1kQN3QlJ3+vWRA+BqC4zK+ZEATZW8pZ75kQC6vXG+bvmRASflJtc++ZEBjQzf7A79kQH6NJEE4v2RAmdcRh2y\\u002fZEC0If\\u002fMoL9kQM9r7BLVv2RA6rXZWAnAZEAEAMeePcBkQB9KtORxwGRAOpShKqbAZEBV3o5w2sBkQHAofLYOwWRAi3Jp\\u002fELBZEClvFZCd8FkQMAGRIirwWRA21Axzt\\u002fBZED2mh4UFMJkQBHlC1pIwmRALC\\u002f5n3zCZEBGeeblsMJkQGHD0yvlwmRAfA3BcRnDZECXV663TcNkQLKhm\\u002f2Bw2RAzeuIQ7bDZEDoNXaJ6sNkQAKAY88exGRAHcpQFVPEZEA4FD5bh8RkQFNeK6G7xGRAbqgY5+\\u002fEZECJ8gUtJMVkQKM883JYxWRAvobguIzFZEDZ0M3+wMVkQPQau0T1xWRAD2WoiinGZEAqr5XQXcZkQET5ghaSxmRAX0NwXMbGZEB6jV2i+sZkQJXXSugux2RAsCE4LmPHZEDLayV0l8dkQOW1ErrLx2RAAAAAAADIZEAbSu1FNMhkQDaU2otoyGRAUd7H0ZzIZEBsKLUX0chkQIZyol0FyWRAobyPoznJZEC8Bn3pbclkQNdQai+iyWRA8ppXddbJZEAN5US7CspkQCcvMgE\\u002fymRAQnkfR3PKZEBdwwyNp8pkQHgN+tLbymRAk1fnGBDLZECuodReRMtkQMjrwaR4y2RA4zWv6qzLZED+f5ww4ctkQBnKiXYVzGRANBR3vEnMZEBPXmQCfsxkQGqoUUiyzGRAhPI+jubMZECfPCzUGs1kQLqGGRpPzWRA1dAGYIPNZEDwGvSlt81kQAtl4evrzWRAJa\\u002fOMSDOZEBA+bt3VM5kQFtDqb2IzmRAdo2WA73OZECR14NJ8c5kQKwhcY8lz2RAxmte1VnPZEDhtUsbjs9kQPz\\u002fOGHCz2RAF0omp\\u002fbPZEAylBPtKtBkQE3eADNf0GRAZyjueJPQZECCctu+x9BkQJ28yAT80GRAuAa2SjDRZEDTUKOQZNFkQO6akNaY0WRACOV9HM3RZEAjL2tiAdJkQD55WKg10mRAWcNF7mnSZEB0DTM0ntJkQI9XIHrS0mRAqaENwAbTZEDE6\\u002foFO9NkQN816Etv02RA+n\\u002fVkaPTZEAVysLX19NkQDAUsB0M1GRASl6dY0DUZEBlqIqpdNRkQIDyd++o1GRAmzxlNd3UZEC2hlJ7EdVkQNHQP8FF1WRA7BotB3rVZEAGZRpNrtVkQCGvB5Pi1WRAPPn02BbWZEBXQ+IeS9ZkQHKNz2R\\u002f1mRAjde8qrPWZECnIarw59ZkQMJrlzYc12RA3bWEfFDXZED4\\u002f3HChNdkQBNKXwi512RALpRMTu3XZEBI3jmUIdhkQGMoJ9pV2GRAfnIUIIrYZECZvAFmvthkQLQG76vy2GRAz1Dc8SbZZEDpmsk3W9lkQATltn2P2WRAHy+kw8PZZEA6eZEJ+NlkQFXDfk8s2mRAcA1slWDaZECKV1nblNpkQKWhRiHJ2mRAwOszZ\\u002f3aZEDbNSGtMdtkQPZ\\u002fDvNl22RAEcr7OJrbZEArFOl+zttkQEZe1sQC3GRAYajDCjfcZEB88rBQa9xkQK36XG2l3GRA3gIJit\\u002fcZEAPC7WmGd1kQEETYcNT3WRAchsN4I3dZECjI7n8x91kQNQrZRkC3mRABTQRNjzeZEA2PL1Sdt5kQGhEaW+w3mRAmUwVjOreZEDKVMGoJN9kQPtcbcVe32RALGUZ4pjfZEBdbcX+0t9kQI51cRsN4GRAwH0dOEfgZEDxhclUgeBkQCKOdXG74GRAU5YhjvXgZECEns2qL+FkQLWmecdp4WRA5q4l5KPhZEAYt9EA3uFkQEm\\u002ffR0Y4mRAescpOlLiZECrz9VWjOJkQNzXgXPG4mRADeAtkADjZEA\\u002f6NmsOuNkQHDwhcl042RAofgx5q7jZEDSAN4C6eNkQAMJih8j5GRANBE2PF3kZEBlGeJYl+RkQJchjnXR5GRAyCk6kgvlZED5MeauReVkQCo6kst\\u002f5WRAW0I+6LnlZECMSuoE9OVkQL5SliEu5mRA71pCPmjmZEAgY+5aouZkQFFrmnfc5mRAgnNGlBbnZECze\\u002fKwUOdkQOSDns2K52RAFoxK6sTnZEBHlPYG\\u002f+dkQHicoiM56GRAqaROQHPoZEDarPpcrehkQAu1pnnn6GRAPL1SliHpZEBuxf6yW+lkQJ\\u002fNqs+V6WRA0NVW7M\\u002fpZEAB3gIJCupkQDLmriVE6mRAY+5aQn7qZECV9gZfuOpkQMb+snvy6mRA9wZfmCzrZEAoDwu1ZutkQFkXt9Gg62RAih9j7trrZEC7Jw8LFexkQO0vuydP7GRAHjhnRInsZEBPQBNhw+xkQIBIv3397GRAsVBrmjftZEDiWBe3ce1kQBRhw9Or7WRARWlv8OXtZEB2cRsNIO5kQKd5xyla7mRA2IFzRpTuZEAJih9jzu5kQDqSy38I72RAbJp3nELvZECdoiO5fO9kQM6qz9W272RA\\u002f7J78vDvZEAwuycPK\\u002fBkQGHD0ytl8GRAk8t\\u002fSJ\\u002fwZEDE0ytl2fBkQPXb14ET8WRAJuSDnk3xZEBX7C+7h\\u002fFkQIj029fB8WRAufyH9PvxZEDrBDQRNvJkQBwN4C1w8mRATRWMSqryZEB+HThn5PJkQK8l5IMe82RA4C2QoFjzZEARNjy9kvNkQEM+6NnM82RAdEaU9gb0ZEClTkATQfRkQNZW7C979GRAB1+YTLX0ZEA4Z0Rp7\\u002fRkQGpv8IUp9WRAm3ecomP1ZEDMf0i\\u002fnfVkQP2H9NvX9WRALpCg+BH2ZEBfmEwVTPZkQJCg+DGG9mRAwqikTsD2ZEDzsFBr+vZkQCS5\\u002fIc092RAVcGopG73ZECGyVTBqPdkQLfRAN7i92RA6dms+hz4ZEAa4lgXV\\u002fhkQEvqBDSR+GRAfPKwUMv4ZECt+lxtBflkQN4CCYo\\u002f+WRADwu1pnn5ZEBBE2HDs\\u002flkQHIbDeDt+WRAoyO5\\u002fCf6ZEDUK2UZYvpkQAU0ETac+mRANjy9Utb6ZEBnRGlvEPtkQJlMFYxK+2RAylTBqIT7ZED7XG3FvvtkQCxlGeL4+2RAXW3F\\u002fjL8ZECOdXEbbfxkQMB9HTin\\u002fGRA8YXJVOH8ZEAijnVxG\\u002f1kQFOWIY5V\\u002fWRAhJ7Nqo\\u002f9ZEC1pnnHyf1kQOauJeQD\\u002fmRAGLfRAD7+ZEBJv30deP5kQHrHKTqy\\u002fmRAq8\\u002fVVuz+ZEDc14FzJv9kQA3gLZBg\\u002f2RAP+jZrJr\\u002fZEBw8IXJ1P9kQKH4MeYOAGVA0gDeAkkAZUADCYofgwBlQDQRNjy9AGVAZRniWPcAZUCXIY51MQFlQMgpOpJrAWVA+THmrqUBZUAqOpLL3wFlQFtCPugZAmVAjErqBFQCZUC9UpYhjgJlQO9aQj7IAmVAIGPuWgIDZUBRa5p3PANlQIJzRpR2A2VAs3vysLADZUDkg57N6gNlQBaMSuokBGVAR5T2Bl8EZUB4nKIjmQRlQKmkTkDTBGVA2qz6XA0FZUALtaZ5RwVlQDy9UpaBBWVAbsX+srsFZUCfzarP9QVlQNDVVuwvBmVAAd4CCWoGZUAy5q4lpAZlQGPuWkLeBmVAlfYGXxgHZUDG\\u002frJ7UgdlQPcGX5iMB2VAKA8LtcYHZUBZF7fRAAhlQIofY+46CGVAuycPC3UIZUDtL7snrwhlQB44Z0TpCGVAT0ATYSMJZUCASL99XQllQLFQa5qXCWVA4lgXt9EJZUATYcPTCwplQEVpb\\u002fBFCmVAdnEbDYAKZUCneccpugplQNiBc0b0CmVACYofYy4LZUA6kst\\u002faAtlQGyad5yiC2VAnaIjudwLZUDOqs\\u002fVFgxlQP+ye\\u002fJQDGVAMLsnD4sMZUBhw9MrxQxlQJLLf0j\\u002fDGVAxNMrZTkNZUD129eBcw1lQCbkg56tDWVAV+wvu+cNZUCI9NvXIQ5lQLn8h\\u002fRbDmVA6wQ0EZYOZUAcDeAt0A5lQE0VjEoKD2VAfh04Z0QPZUCvJeSDfg9lQOAtkKC4D2VAETY8vfIPZUBDPujZLBBlQHRGlPZmEGVApU5AE6EQZUDWVuwv2xBlQAdfmEwVEWVAOGdEaU8RZUBqb\\u002fCFiRFlQJt3nKLDEWVAzH9Iv\\u002f0RZUD9h\\u002fTbNxJlQC6QoPhxEmVAX5hMFawSZUCQoPgx5hJlQMKopE4gE2VA87BQa1oTZUAkufyHlBNlQFXBqKTOE2VAhslUwQgUZUC30QDeQhRlQOjZrPp8FGVAGuJYF7cUZUBL6gQ08RRlQHzysFArFWVArfpcbWUVZUDeAgmKnxVlQA8LtabZFWVAQRNhwxMWZUByGw3gTRZlQKMjufyHFmVA1CtlGcIWZUAFNBE2\\u002fBZlQDY8vVI2F2VAZ0Rpb3AXZUCZTBWMqhdlQMpUwajkF2VA+1xtxR4YZUAsZRniWBhlQF1txf6SGGVAjnVxG80YZUDAfR04BxllQPGFyVRBGWVAIo51cXsZZUBTliGOtRllQISezarvGWVAtaZ5xykaZUDmriXkYxplQBi30QCeGmVASb99HdgaZUB6xyk6EhtlQKvP1VZMG2VA3NeBc4YbZUAN4C2QwBtlQD7o2az6G2VAcPCFyTQcZUCh+DHmbhxlQNIA3gKpHGVAAwmKH+McZUA0ETY8HR1lQGUZ4lhXHWVAliGOdZEdZUDIKTqSyx1lQPkx5q4FHmVAKjqSyz8eZUBbQj7oeR5lQIxK6gS0HmVAvVKWIe4eZUDvWkI+KB9lQCBj7lpiH2VAUWuad5wfZUCCc0aU1h9lQLN78rAQIGVA5IOezUogZUAVjErqhCBlQEeU9ga\\u002fIGVAeJyiI\\u002fkgZUCppE5AMyFlQNqs+lxtIWVAC7WmeachZUA8vVKW4SFlQG7F\\u002frIbImVAn82qz1UiZUDQ1VbsjyJlQAHeAgnKImVAMuauJQQjZUBj7lpCPiNlQJT2Bl94I2VAxv6ye7IjZUD3Bl+Y7CNlQCgPC7UmJGVAWRe30WAkZUCKH2PumiRlQLsnDwvVJGVA7S+7Jw8lZUAeOGdESSVlQE9AE2GDJWVAgEi\\u002ffb0lZUCxUGua9yVlQOJYF7cxJmVAE2HD02smZUBFaW\\u002fwpSZlQHZxGw3gJmVAp3nHKRonZUDYgXNGVCdlQAmKH2OOJ2VAOpLLf8gnZUBsmnecAihlQJ2iI7k8KGVAzqrP1XYoZUD\\u002fsnvysChlQDC7Jw\\u002frKGVAYcPTKyUpZUCSy39IXyllQMTTK2WZKWVA9dvXgdMpZUAm5IOeDSplQFfsL7tHKmVAiPTb14EqZUC5\\u002fIf0uyplQOoENBH2KmVAHA3gLTArZUBNFYxKaitlQH4dOGekK2VAryXkg94rZUDgLZCgGCxlQBE2PL1SLGVAQz7o2YwsZUB0RpT2xixlQKVOQBMBLWVA1lbsLzstZUAHX5hMdS1lQDhnRGmvLWVAaW\\u002fwhektZUCbd5yiIy5lQMx\\u002fSL9dLmVA\\u002fYf025cuZUAukKD40S5lQF+YTBUML2VAkKD4MUYvZUDBqKROgC9lQPOwUGu6L2VAJLn8h\\u002fQvZUBVwaikLjBlQIbJVMFoMGVAt9EA3qIwZUDo2az63DBlQBriWBcXMWVAS+oENFExZUB88rBQizFlQK36XG3FMWVA3gIJiv8xZUAPC7WmOTJlQEATYcNzMmVAchsN4K0yZUCjI7n85zJlQNQrZRkiM2VABTQRNlwzZUA2PL1SljNlQGdEaW\\u002fQM2VAmUwVjAo0ZUDKVMGoRDRlQPtcbcV+NGVALGUZ4rg0ZUBdbcX+8jRlQI51cRstNWVAv30dOGc1ZUDxhclUoTVlQCKOdXHbNWVAU5YhjhU2ZUCEns2qTzZlQLWmeceJNmVA5q4l5MM2ZUAYt9EA\\u002fjZlQEm\\u002ffR04N2VAescpOnI3ZUCrz9VWrDdlQNzXgXPmN2VADeAtkCA4ZUA+6NmsWjhlQHDwhcmUOGVAofgx5s44ZUDSAN4CCTllQAMJih9DOWVANBE2PH05ZUBlGeJYtzllQJYhjnXxOWVAyCk6kis6ZUD5MeauZTplQCo6ksufOmVAW0I+6Nk6ZUCMSuoEFDtlQL1SliFOO2VA71pCPog7ZUAgY+5awjtlQFFrmnf8O2VAgnNGlDY8ZUCze\\u002fKwcDxlQOSDns2qPGVAFYxK6uQ8ZUBHlPYGHz1lQHicoiNZPWVAqaROQJM9ZUDarPpczT1lQAu1pnkHPmVAPL1SlkE+ZUBtxf6yez5lQJ\\u002fNqs+1PmVA0NVW7O8+ZUAB3gIJKj9lQDLmriVkP2VAY+5aQp4\\u002fZUCU9gZf2D9lQMb+snsSQGVA9wZfmExAZUAoDwu1hkBlQFkXt9HAQGVAih9j7vpAZUC7Jw8LNUFlQOwvuydvQWVAHjhnRKlBZUBPQBNh40FlQIBIv30dQmVAsVBrmldCZUDiWBe3kUJlQBNhw9PLQmVARWlv8AVDZUB2cRsNQENlQKd5xyl6Q2VA2IFzRrRDZUAJih9j7kNlQDqSy38oRGVAa5p3nGJEZUCdoiO5nERlQM6qz9XWRGVA\\u002f7J78hBFZUAwuycPS0VlQGHD0yuFRWVAkst\\u002fSL9FZUDE0ytl+UVlQPXb14EzRmVAJuSDnm1GZUBX7C+7p0ZlQIj029fhRmVAufyH9BtHZUDqBDQRVkdlQBwN4C2QR2VATRWMSspHZUB+HThnBEhlQK8l5IM+SGVA4C2QoHhIZUARNjy9skhlQEM+6NnsSGVAdEaU9iZJZUClTkATYUllQNZW7C+bSWVAB1+YTNVJZUA4Z0RpD0plQGlv8IVJSmVAm3ecooNKZUDMf0i\\u002fvUplQP2H9Nv3SmVALpCg+DFLZUBfmEwVbEtlQJCg+DGmS2VAwaikTuBLZUDzsFBrGkxlQCS5\\u002fIdUTGVAVcGopI5MZUCGyVTByExlQLfRAN4CTWVA6Nms+jxNZUAa4lgXd01lQEvqBDSxTWVAfPKwUOtNZUCt+lxtJU5lQN4CCYpfTmVADwu1pplOZUBAE2HD005lQHIbDeANT2VAoyO5\\u002fEdPZUDUK2UZgk9lQAU0ETa8T2VANjy9UvZPZUBnRGlvMFBlQJhMFYxqUGVAylTBqKRQZUD7XG3F3lBlQCxlGeIYUWVAXW3F\\u002flJRZUCOdXEbjVFlQL99HTjHUWVA8YXJVAFSZUAijnVxO1JlQFOWIY51UmVAhJ7Nqq9SZUC1pnnH6VJlQOauJeQjU2VAF7fRAF5TZUBJv30dmFNlQHrHKTrSU2VAq8\\u002fVVgxUZUDc14FzRlRlQA3gLZCAVGVAPujZrLpUZUBw8IXJ9FRlQKH4MeYuVWVA0gDeAmlVZUADCYofo1VlQDQRNjzdVWVAZRniWBdWZUCWIY51UVZlQMgpOpKLVmVA+THmrsVWZUAqOpLL\\u002f1ZlQFtCPug5V2VAjErqBHRXZUC9UpYhrldlQO9aQj7oV2VAIGPuWiJYZUBRa5p3XFhlQIJzRpSWWGVAs3vysNBYZUDkg57NClllQBWMSupEWWVAR5T2Bn9ZZUB4nKIjuVllQKmkTkDzWWVA2qz6XC1aZUALtaZ5Z1plQDy9UpahWmVAbcX+sttaZUCfzarPFVtlQNDVVuxPW2VAAd4CCYpbZUAy5q4lxFtlQGPuWkL+W2VAlPYGXzhcZUDG\\u002frJ7clxlQPcGX5isXGVAKA8LteZcZUBZF7fRIF1lQIofY+5aXWVAuycPC5VdZUDsL7snz11lQB44Z0QJXmVAT0ATYUNeZUCASL99fV5lQLFQa5q3XmV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BlQIt28lZeIGVAekNs8dsfZUDI1HEeWh9lQM48y9vYHmVAV3JMJ1geZUApCdX+1x1lQL3sT2BYHWVA8hyzSdkcZUC3bP+4WhxlQKdCQKzcG2VAc1uLIV8bZUAdjgAX4hplQPKRyYplGmVAKsYZe+kZZUA8+y3mbRllQLs9TMryGGVAxqLDJXgYZUD4Fez2\\u002fRdlQMgoJjyEF2VAX+Pa8woXZUDHlnsckhZlQHKwgbQZFmVAGI9uuqEVZUDNWMssKhVlQGXSKAqzFGVAADgfUTwUZUDLFk4AxhNlQOcnXBZQE2VAZSz3kdoSZUBxytNxZRJlQHZrrbTwEWVAXxtGWXwRZUDYaGZeCBFlQIxG3cKUEGVAau1\\u002fhSEQZUDTvymlrg9lQMYtvCA8D2VA75ke98kOZUCjPz4nWA5lQMcZDrDmDWVAlMqGkHUNZUA3hKbHBA1lQFLycFSUDGVAUiTvNSQMZUCgeC9rtAtlQKSIRfNEC2VAlRVKzdUKZUAn9lr4ZgplQPUEm3P4CWVA0A8yPooJZUDLx0xXHAllQBqyHL6uCGVAvxnYcUEIZUD+Abpx1AdlQK0ZAr1nB2VARa\\u002f0UvsGZUDGpdoyjwZlQHNqAVwjBmVAVuu6zbcFZUCrjl2HTAVlQBQrRIjhBGVArwDOz3YEZUAPs15dDARlQBNEXjCiA2VArA85SDgDZUCLyF+kzgJlQMt1R0RlAmVAi3FpJ\\u002fwBZUCQaENNkwFlQPNaV7UqAWVA2J0rX8IAZUBJ3kpKWgBlQC8lRHby\\u002f2RAfdyq4or\\u002fZECf1RaPI\\u002f9kQCRRJHu8\\u002fmRAyQd0plX+ZEDqNKsQ7\\u002f1kQF+ic7mI\\u002fWRA87V7oCL9ZEBmgHbFvPxkQCvOGyhX\\u002fGRA5DkoyPH7ZEDBQF2ljPtkQNxYgb8n+2RAowlgFsP6ZEB7BsqpXvpkQLVLlXn6+WRA\\u002fj2dhZb5ZEBtzMLNMvlkQF6V7FHP+GRAPA4HEmz4ZEB4rgQOCfhkQNcd3kWm92RAYGaSuUP3ZEAPKidp4fZkQK3cqFR\\u002f9mRA\\u002fQErfB32ZECdcMjfu\\u002fVkQPWZo39a9WRAedfmW\\u002fn0ZEDFvcR0mPRkQOZ1eMo39GRAXB1GXdfzZEBLLXstd\\u002fNkQH3pbjsX82RAs9eCh7fyZEAZQCMSWPJkQG+3x9v48WRA1rPz5JnxZEAULTcuO\\u002fFkQDdJL7jc8GRAxBaHg37wZEB+VfiQIPBkQCJPTOHC72RAa8FcdWXvZED72hROCO9kQMxMcmyr7mRAHnKG0U7uZEDokHd+8u1kQBg1gnSW7WRAOan6tDrtZEBFj05B3+xkQMycBhuE7GRA8n3IQynsZEAV41i9zutkQHS9nYl062RAta+gqhrrZECTt5EiwepkQNYWyvNn6mRAPoLPIA\\u002fqZEAFnlestulkQG7QS5le6WRA+HPN6gbpZEARdDqkr+hkQFtgMslY6GRAZwScXQLoZEB0k6tlrOdkQO956eVW52RA4Og54wHnZEAmMuVireZkQL8PoWpZ5mRABPSaAAbmZEAhhYMrs+VkQEBrm\\u002fJg5WRAh57BXQ\\u002flZEBYaYN1vuRkQAhbLkNu5GRAEXHk0B7kZEDwx7Ip0ONkQDMyq1mC42RAFCQBbjXjZEC7dCp16eJkQMOMBH+e4mRAV7T+nFTiZEByU0riC+JkQJ4dEmTE4WRAZlG5OX7hZEBkbCR9OeFkQET6DEv24GRAV3liw7TgZEBOt7oJdeBkQISI1EU34GRAcFowpPvfZEC\\u002f5sJWwt9kQFFCx5WL32RADLi2oFffZEDmZG6\\u002fJt9kQLx4jEP53mRAMXYRis\\u002feZEDr9FT9qd5kQBJ\\u002fYReJ3mRASnHQZG3eZECUtkWIV95kQBZhtD5I3mRAuEihZEDeZED+Han8QN5kQJdUpTdL3mRAfi3pfmDeZECROTmBgt5kQPGkWEOz3mRAy3BXNfXeZEBY3z1OS99kQPa9UzC532RAl24wWUPgZECPpDBi7+BkQDvUfFjE4WRAcmu\\u002fOcviZECsee60D+RkQD8\\u002f+Iyh5WRAomIJx5fnZEDIFe8YGepkQL6JKY9y7WRA5mwnuU\\u002fyZEBy9jFFNPpkQMfrMgZXCGVAuCYTh9EiZUBt+hbgnFNlQK7U0\\u002fIkqGVAFn5MluktZkAWZF9rlOtmQKKFwK9L2GdAfGOGSjDVaED6idrIv6xpQPoXCd5fHWpA5KBxClv6aUBLigCMe1FpQAVGAu\\u002fNXmhAb8gX2uJhZ0DI0DjG0YZmQEef5qjp4WVAHNMGZK1zZUA8zNApCjFlQFlnZzm1C2VA4SIMIoz3ZECoPiT1aexkQH\\u002f3cDe05WRAm2AUryPhZEBfE039sN1kQKNWiJTo2mRAfJShY5TYZECSZNj4ltZkQFZ8oG7d1GRAZdH7PlrTZEDetbUyA9JkQMPEBF\\u002fQ0GRAHgDeibvPZECcpL+\\u002fv85kQIajMwfZzWRA3TpkKATNZECOIeeCPsxkQLEU0O2Fy2RAF8VLn9jKZEC7lc8ZNcpkQJW8dh2ayWRAhlmHnAbJZEBZc1+yechkQE1nO5zyx2RAJC9rs3DHZEBsBqZo88ZkQBo1P0B6xmRA8Q0NzwTGZECI3923ksVkQHumXqkjxWRARm9dXLfEZEAJBlaSTcRkQH0uOxTmw2RAvmpxsYDDZEAshvI+HcNkQAfQkpa7wmRA70pillvCZEDLKCUg\\u002fcFkQA7F3xigwWRAUP5yaETBZEA3XUb56cBkQFfo\\u002fbeQwGRAXuE5kzjAZEAL81574b9kQFCVZWKLv2RAlZ+vOza\\u002fZEDjKuP74b5kQHYIypiOvmRAyS01CTy+ZEBIj+NE6r1kQNT2a0SZvWRA\\u002f3MpAUm9ZEAkEip1+bxkQFqMH5uqvGRAXsFRbly8ZEAvspLqDrxkQEbdMwzCu2RAqc78z3W7ZEBiwiIzKrtkQGk7QTPfumRAJXVTzpS6ZEDWmK4CS7pkQGmj\\u002fM4BumRAjuo3Mrm5ZEBKMqcrcblkQCdG2ropuWRAuwqn3+K4ZED5\\u002fSaanLhkQOQdtepWuGRAeC7s0RG4ZEDGWKVQzbdkQP8d92eJt2RAS5o0GUa3ZEDJE+1lA7dkQBHT60\\u002fBtmRAGkM42X+2ZEADVxYEP7ZkQPE0B9P+tWRAuyXKSL+1ZECiyV1ogLVkQAOTATVCtWRASYg3sgS1ZEAsT8bjx7RkQLqDu82LtGRAO15udFC0ZEDFq4LcFbRkQMId7Arcs2RAjPXxBKOzZEDsETPQarNkQA5lqnIzs2RAcNqz8vyyZEAytRFXx7JkQEJw8qaSsmRA\\u002fyr36V6yZEA4rjooLLJkQN0WWWr6sWRAMzV4ucmxZEAtsVAfmrFkQJEGOKZrsWRAnm0rWT6xZECSx9tDErFkQACpunLnsGRATJ8I872wZEAI0uTSlbBkQP4kXiFvsGRAYASG7kmwZEC4CYVLJrBkQDKusUoEsGRAm0Wp\\u002f+OvZEBVhWt\\u002fxa9kQEDjeOCor2RAASP0Oo6vZEC0c8eoda9kQN+NzUVfr2RAAVH\\u002fL0uvZECIcqaHOa9kQNzklW8qr2RAGLZoDR6vZEBtQsiJFK9kQPO3uhAOr2RA7xD70QqvZECx11sBC69kQL09NtcOr2RAPVDnkBavZEDLX11xIq9kQEsMuMEyr2RASNH90UevZEA7cOr5Ya9kQMQw2pmBr2RAwajWG6evZEDonsr00q9kQGKq46UFsGRA7nwpvj+wZED\\u002fWFTcgbBkQDUk7rDMsGRAKujLACGxZEBrBoBpCrJkQIYADCD\\u002fsmRAZOCFOQC0ZEDN3TTuDrVkQNmtGJ8stmRAifx63Fq3ZEBgscVtm7hkQCGD51rwuWRArJCj91u7ZEAynEHx4LxkQJUNNV+CvmRAH6t710PAZECxrK2HKcJkQL+JBFQ4xGRACt4D\\u002fnXGZEBtRPpU6chkQLfgTXOay2RA+wiPDJPOZECGvbrR3tFkQFOAHfOL1WRA77osyqvZZECTRd+5U95kQAN\\u002fQ1qe42RAIY69Da3pZECbkq0vqvBkQLM+SDXM+GRA3DCPmloCZUBV9i45tw1lQAHCA4F0G2VA1q+L6pEsZUCVQQPvH0NlQMDB3h7UY2VAvIqgJF+ZZUBvexflI\\u002fplQB6oJh1Vr2ZAvDuhP+P4Z0A5DkMQiyVqQDDs9DWbdm1ApHT7L2X4cECahM33wZVzQNL7daifG3ZACtzjdTbZd0C1R114UCV4QB9glAnj3HZASjcekniGdEBz7iYBtN1xQO4pD1x75m5AirB2Iooma0Ba\\u002fpHua5hoQGFtz6VwCWdABQzgLGQqZkA0iCgTwrNlQN7TdkkrdGVA3Yq++2VPZUB0pV3keTdlQHzeLnUlJmVAgoZtYrMYZUDcQSuH4w1lQN0CCewIBWVAHgml+LT9ZEBu6CfKmPdkQDBYRQp58mRAz8Yj9SfuZECN1iC5gepkQOyrFv1p52RABZOKGcrkZED+NdrKj+JkQES2xTis4GRAswZkOxPfZEBZ\\u002ftzMut1kQOUSJpya3GRAj\\u002ftWuKvbZECzAn9O6NpkQIzshnVL2mRAkpbVBNHZZEBE4UNzddlkQBooiLw12WRAHKezSw\\u002fZZEDxi7Dp\\u002f9hkQEtZ7q4F2WRAZYuZ9x7ZZEBUO99ZStlkQFvP2J2G2WRAjsHQttLZZECMvqG9LdpkQCv9\\u002feuW2mRAB5F2mA3bZEABQSIzkdtkQGCTyUIh3GRAFKiCYr3cZEAtZqs\\u002fZd1kQCOpM5gY3mRAF6grOdfeZEDA44z9oN9kQJOZNc114GRAlB4QnFXhZECcqmBpQOJkQMgQNT8242RAWK7yMTfkZED3hP9fQ+VkQF8HhPFa5mRAZZxDGH7nZEBsSIoPrehkQB5MLhzo6WRAaNukjC\\u002frZEBwWym5g+xkQA\\u002fc9gPl7WRAUsKT2VPvZEDe1y+x0PBkQG8vFQ1c8mRATIsse\\u002fbzZECCL5aVoPVkQENJWANb92RAc1ckeSb5ZEDIRzW6A\\u002ftkQBRJSJnz\\u002fGRAF6iy+fb+ZEDMaZfQDgFlQJe+PyY8A2VAEd+ZF4AFZUDEZ+LX2wdlQJHffLJQCmVADcAADeAMZUDCG4Fpiw9lQMffFWlUEmVAXbKuzjwVZUAKnTiCRhhlQEsNIZRzG2VAZExCQcYeZUCgZ0f3QCJlQKaql1nmJWVAhlXcRrkpZUA8MTbfvC1lQB8nPIv0MWVAOSTgA2Q2ZUCMe1xbDztlQHPRUQf7P2VALZJE7CtFZUDUS7Fqp0plQPhF+W1zUGVAC790fZZWZUDwyQbQF11lQOFyoGL\\u002fY2VAC1k2ElZrZUDUOca5JXNlQIk2KlV5e2VAoWufKV2EZUAluRb13o1lQK7moyUOmGVAFOWrGvyiZUCYINNxvK5lQGtEJmJlu2VADPCRKBDJZUCgm4KJ2ddlQOSGgmzi52VAFvbxl1D5ZUBNfp6UTwxmQK78KcMRIWZAf+4OsNE3ZkBJFN6201BmQJ07agpobGZAgat5Pu2KZkC3pfZ406xmQA4wgH2g0mZATkop2PT8ZkCQNl2FkixnQM41kphlYmdAfZ60lo+fZ0DktMeDduVnQDRu5BHZNWhAXQt9C+qSaECWtOxAdv9oQGSGr\\u002fgaf2lA7hgpV5sWakD77IsvgcxqQNlGdz+bqmtAesXttRXCbECKa3BstzZuQP5BGfVoLXBAPEOkXTP1cUBcSvm7uTd1QHhHCnsMXXtAOHm0SAtFg0BIidnBn6SMQCasrYuqX5VAZDUFo\\u002fqxnkC6zFL5F56kQP4rNtpRaalA0jeluD08rEBCztmBs+WrQJx3tTaLi6hAfurprgqGo0BOHMfOrJScQOb++DnPppNA5jZWIFowikCvGX3Xt7KBQG8Tq0YgfnlATsmsqtsgdEAb5t19E0txQKGaGYg9dG9AlOO4MFaHbUCMxxuQOTFsQGFSvJCFLWtA9zicfTJeakAaUopPUrRpQFXmeKkEJ2lALKo8bDewaEBhUpb9cUtoQDUlGTNM9WdA8jlobSGrZ0BMXJgf3mpnQJ+n4ELcMmdAGJeljMkBZ0Cp\\u002f4hqlNZmQBmxWdBdsGZABAIngW6OZkC9EsTjLnBmQMMtxrggVWZARSsPONo8ZkC+03E9AidmQB1ynkNNE2ZAGmZf\\u002fXoBZkCY3VFpVPFlQAqjHEWq4mVAzfyjy1PVZUDyN4iuLcllQKHQ0z4ZvmVAwmdyu\\u002fuzZUCU4ha+vaplQGxZyMBKomVAKTKCt5CaZUDhz0K6f5NlQICDnrwJjWVAm2Z+UCKHZUAx3yVyvoFlQE7C9FvUfGVAErukYFt4ZUAD0vrKS3RlQHDMFMKecGVAcQKgMU5tZUCrGGa1VGplQE40tYetZ2VA4uY9clRlZUBEGRLBRWNlQF5ffjd+YWVA2uiCBvtfZUCBKrvEuV5lQOm8i2e4XWVA8BN1PfVcZUDE3G\\u002fpblxlQNwxPV8kXGVA3peZ4BRcZUBdAUb7P1xlQMII3oelXGVA8kR2qUVdZUD4LwHOIF5lQE6gfK83X2VAnG3tVYtgZUDlmjIaHWJlQJBjvanuY2VAoO0\\u002fCwJmZUCPSWmkWWhlQH33ykD4amVATo4KGuFtZUB7k4nhF3FlQGJTtsugdGVAzetCnYB4ZUBiIYy6vHxlQH+CiDlbgWVAMWOq9mKGZUA8LTWt24tlQOpOoRPOkWVA1wPL\\u002fEOYZUDg3tB+SJ9lQNtyuSDopmVA0Gc2EDGvZUDVLiphM7hlQH\\u002fz+FgBwmVA2WYryK\\u002fMZUDCjop1VthlQNibqZ4Q5WVAY03ckv3yZUApKvFuQQJmQD17yQEGE2ZAiUM65HslZkCRarTR2zlmQKJyTlNoUGZARmdU1G9pZkBccgs\\u002fT4VmQCyqrUp1pGZAXUj3sWbHZkDEMa+dw+5mQMyN+6pOG2dA9ff9HfZNZ0DD+igM4IdnQBvVi6B6ymdAZNJ4H5IXaEBhrCkjb3FoQDUTmScB22hAI1A9xh1YaUCDV4Gn6+1pQFideGTEo2pA+iAtKIGFa0ADxdh97qlsQIAcb0\\u002fpQ25AQhkf4\\u002fxkcED3MJrA1aByQPaTD0++1XZA8WXe8zutfkAsSZOmgS6GQAzM8HJQfZBAyN9Do7MCmECiKId2xIWgQO6vgCXQ\\u002faRArF8YZxclqECXGzq5A7aoQDDtB6uiaaZArMBCIu9CokBJyAvDj0CbQEnrFz6KBZNALCjhBUebiUBBRBb2fGGBQJUeXgUnBnlA8AgtHha2c0D0mBBzxehwQOMMZuAfwm5AcKHrFX7obECOxrKgcaRrQJGAwoqzsGpATrxkGifvaUBCoqF7GFFpQLdoxuLvzWhA3hYpUN1faEDs++0GoAJoQNJOZjv+smdAij\\u002ffs3luZ0B+TlS7HjNnQP277MVh\\u002f2ZARjK2oAbSZkD3h504DqpmQBIeswyphmZASBI0\\u002fCxnZkBj97SGDUtmQALJc9nVMWZAoLfnMyQbZkBrglVOpgZmQJQHKIQW9GVAFvoflDnjZUA\\u002fEyfU3NNlQDUnFL\\u002fUxWVA\\u002fJznyPu4ZUCHgaFqMa1lQCnrP1pZomVA+XgH5lqYZUAajypsII9lQJw9YOmWhmVAcLEfmq1+ZUDWpharVXdlQDDBIPaBcGVAN+eOySZqZUC8ufm3OWRlQO+kL2+xXmVACfgSlYVZZUCGuHKprlRlQDx\\u002fFOwlUGVA\\u002ftlIRuVLZUCHLoA350dlQJNObcQmRGVAM+NVaJ9AZUCQZEAITT1lQPUjvOcrOmVADYcKnzg3ZUCkXnkScDRlQM2WxWrPMWVAaJ5jDlQvZUAGCJCb+yxlQNA6D+PDKmVAOrSG46ooZUAqeF3FriZlQHn2E9fNJGVAiPUFigYjZUBADItvVyFlQB7waza\\u002fH2VAX2KkqDweZUBj5WqpzhxlQFCieDN0G2VAehCNVywaZUApDSo79hhlQPwrhhfRF2VAyii0OLwWZUBvlv\\u002f8thVlQPE3gNTAFGVAQvPmQNkTZUA8E4nV\\u002fxJlQBO0sDc0EmVAxt07H3YRZUBwKpdXxRBlQLgiJcEhEGVAlhEpU4sPZUD5a1MeAg9lQINy1iaMDmVASm1+5CMOZUBg8YfMyQ1lQItnIIJ+DWVA1WPM30INZUBW8aYDGA1lQGnrb1\\u002f\\u002fDGVAi4vBzfoMZUCLGlSuDA1lQJXsCQw4DWVACCi\\u002f0IANZUBooOcM7A1lQM5\\u002fIF2ADmVAEAgqg0YPZUDurQ9vShBlQIHReoGdEWVAiDlHj1wTZUAtLMXLwBVlQGl6NRNMGWVA8klj3SwfZUChL0a99CllQIFc28iKPmVAOaOJWMNkZUCLlOEAaKZlQL8d9mN4C2ZAfebnGNSTZkD9W+VCvzBnQHh6GqB9wWdANFHFwCcXaECQtyX4jQtoQFQDVNEjpGdAhuZQgVkMZ0C39yz9SXFmQEyyH3LF72VA4AtXis2SZUCTaV4nA1hlQLD0NmhzNmVAVHSJOYMkZUCtbGD1ABtlQK6Za02gFWVA9qhxOzUSZUDN86AWxg9lQAllnSroDWVArw+UTmsMZUCfMkoUNgtlQPBnMHw4CmVAdg\\u002f4PWcJZUAJELHxuQhlQGbCEioqCGVAZFC66bIHZUCfLBxFUAdlQJx9ph\\u002f\\u002fBmVAwcYq+7wGZUAMmA3UhwZlQMtFjwZeBmVAt0OmOj4GZUDBY6ZUJwZlQC84dGkYBmVAR61etRAGZUAuOfiUDwZlQHCfdX8UBmVAHBQ4Ah8GZUDlcz+9LgZlQIXyUWBDBmVAfd+yqFwGZUCPNUtfegZlQLhzLVecBmVAS15jbMIGZUBqLfiC7AZlQAitNIYaB2VAbiUFaEwHZUAdtYIgggdlQP41m627B2VAwfDTEvkHZUA\\u002fUyRZOghlQMio5o5\\u002fCGVAQn\\u002fdx8gIZUB1+0wdFgllQEjoJ65nCWVAUs5Qn70JZUBn4O8bGAplQGML31V3CmVAtAAuhtsKZUCxqMDtRAtlQMERC9azC2VAmLHukSgMZUAbrr1+owxlQAHtaQUlDWVAXebmm60NZUCntcbGPQ5lQOm2HBvWDmVA2DiyQHcPZUDCjJz0IRBlQEMmRwzXEGVAVb0IeZcRZUCnul9MZBJlQHjv+Lw+E2VA2SGsLCgUZUBV16QvIhVlQAC\\u002f+5QuFmVATSUXcU8XZUBZT0AqhxhlQDJO+ofYGWVA9VXPxUYbZUB7bX+q1RxlQIhUw6SJHmVAd8k372cgZUB26om9diJlQGXIs3W9JGVA244V+UQnZUCiM5sCGCplQMtAF6FDLWVAge7P19cwZUC+d1lz6DRlQLBQ6yaOOWVAGtyHD+g+ZUBS3efIHUVlQHY9MVtiTGVAeChQjPhUZUCcZIoBOl9lQNWCtc2la2VAvDeioAV7ZUCrgF9w1o5lQECfoTZiqmVA48WvM1fVZUBhamwYyCBmQCJ2TUplrmZAviYrlku2Z0D8PrXIuoJpQOpP311mV2xA9J4mEG0hcECyZwL2\\u002fnRyQDo72glns3RA\\u002fvLLDG4rdkAI86+M7jh2QPxOxuqH1HRAWAWMgUScckAgJALN7ERwQLsTUmpjjWxAxncZsoimaUBpWh2Hp8tnQC5V9al8umZAdOHUTvQnZkBAkNm8Y9plQHaqJWnKrmVADatarEiTZUAtxDmGy39lQBHB9W7ecGVAavt01PNkZUAFvRRaO1tlQO\\u002fySdEyU2VAzq1e2H5MZUDCa4OR20ZlQK\\u002fA8DoVQmVARkzEuwM+ZUBHRHCihzplQLTZ\\u002fAKIN2VAxr1I6\\u002fA0ZUBJLdk\\u002fsjJlQIAG9+K+MGVAjbJTEQwvZUBCILflkC1lQHzwdvlFLGVAujZhGiUrZUDZGcwQKSplQE666HFNKWVAz4J7e44oZUC0x9b26CdlQA40fCFaJ2VA+Ogsmt8mZUAxtnlRdyZlQBWmG30fJmVAN0+GjdYlZUCJl0QlmyVlQPVoyBFsJWVA6yVoRUglZUAa1VLSLiVlQGoAT+YeJWVAnuMgxxclZUAXUnvPGCVlQIUbZGwhJWVADP\\u002f4GjElZUBrqIVmRyVlQOLy3eZjJWVA\\u002fuXxPoYlZUBqrZIbriVlQEhEYTLbJWVAfb\\u002fgQA0mZUBMHqcLRCZlQGhYqF1\\u002fJmVABAyYB78mZUBVvV7fAidlQFoPoL9KJ2VAJ8FPh5YnZUDujVMZ5idlQA5UMFw5KGVAbCLBOZAoZUAx\\u002ffae6ihlQI1VoHtIKWVADVQ3wqkpZUAMNLZnDiplQPkIcWN2KmVAAl30ruEqZUAxLOhFUCtlQM\\u002fP9iXCK2VAx3y3TjcsZUBIBJzBryxlQEOR4YErLWVA\\u002fSaElKotZUDMrTQALS5lQIZiUc2yLmVAL4TgBTwvZUB2IY21yC9lQDDtpelYMGVABQYesewwZUApoo8chDFlQEuUQD4fMmVA76IoKr4yZUA4r\\u002fn1YDNlQMurKbkHNGVAHGj+jLI0ZUDUN5uMYTVlQI+BENUUNmVAo0Nthcw2ZUAyodK+iDdlQHaNiaRJOGVA8q4aXA85ZUA9mGgN2jllQEh4zOKpOmVAb2g1CX87ZUCKg0qwWTxlQDf2jwo6PWVAUD6PTSA+ZUCH1QKyDD9lQLqJBXT\\u002fP2VA\\u002fsxF0\\u002fhAZUAvTz0T+UFlQNo7bXsAQ2VA+YCfVw9EZUDOjS34JUVlQDgFTbJERmVAGuxi4GtHZUCB613im0hlQINOGB7VSWVAvnbC\\u002fxdLZUDZllb6ZExlQAqaFoi8TWVAljkVKx9PZUAHX8ttjVBlQPESu+MHUmVAtl0hKo9TZUBNqrjoI1VlQAxrjdLGVmVAPvfmpnhYZUAf1kYyOlplQMDygE8MXGVApYTv6O9dZUAl1cX55V9lQMhyhY\\u002fvYWVAvNyZyw1kZUAiOx\\u002flQWZlQJpY2SqNaGVA3clgBfFqZUCuBI35bm1lQEgeI6sIcGVAawfS379yZUD6aIaClnVlQGDIIaeOeGVAe2Cijqp7ZUBFL8ur7H5lQM0oXqhXgmVAUF\\u002f9au6FZUBUVMsdtIllQOOq5jWsjWVAzzfie9qRZUBKG2AVQ5ZlQFJY\\u002fY\\u002fqmmVARYHD7dWfZUA32V+zCqVlQEcXa\\u002fiOqmVANkUbemmwZUCqkcqwobZlQNIh0+g\\u002fvWVAB+VZX03EZUD0XcJj1MtlQOLIrH7g02VAZ\\u002fyQn37cZUCEakVSveVlQFEeDv6s72VAFdkvMGD6ZUCST3306wVmQKWo7z5oEmZAXfkhavAfZkBCTofPoy5mQAR3fn+mPmZAy5oXISJQZkBddJgDR2NmQBJBvW5NeGZAWnKhQnePZkDGT4P9EalmQNiYwER5xWZAAw87GBrlZkAgaObkdghnQInNNr4sMGdAAW\\u002fsIPpcZ0BKovTHx49nQOJlXlO0yWdAHF5W0iMMaEDPEq+51FhoQPHOgYn8sWhArH6YmG8aaUCbf5z12ZVpQGKPbVQWKWpAmxSfrcjaakCn9LFQubRrQPRvCNF7x2xAPhUXSpo0bkAXUn27QCRwQKYiIQ5K2XFAVsqlT4nydEA8tvuGsMJ6QChwzsa+o4JAc3jlC1hii0CR1eNwiiuUQAN\\u002fZjAfhZxA5CfVw6zHokC7CSldUIOmQFt8bG69CKhAtOHeSdaWpkAYPB2Ye+SiQFoZD6ECvpxAvKqLBEFZlEAz+Y1O1KCLQGj+YVkOyIJAvfEqZUHlekAhrOzd4P10QLjGk2x72HFApDlrRPQdcEASJBbA0CNuQPh2YGKUtWxAAjrsTQGja0BtcfwzzslqQMs0NHIaGWpACNZN2vyGaUCoqctPwQxpQPNjxf+DpWhAwRg6R5NNaECJMR5RFwJoQKMQjXPYwGdAQq6F7xaIZ0AcYOL\\u002fbVZnQE9037C+KmdAFlWmNCAEZ0Ay4sEl1OFmQD1F7aI9w2ZAqP7hgdqnZkC1pdoOPo9mQMIZQPUMeWZArWYTCfpkZkAoONa7w1JmQOFJbRUyQmZAAYRjExUzZkAaaTFYQyVmQHIMgRmZGGZA9HBcQPcMZkAeaCuxQgJmQIkeo7Nj+GVAvlZ9dEXvZUCAGhuc1eZlQEkGOPYD32VAuwmbJ8LXZUA9Zk1vA9FlQG8uW3G8ymVABUaBCePEZUB2tnkkbr9lQJOG1J5VumVAgIF8KJK1ZUD6Fi8sHbFlQFO8TrrwrGVA04SRdgepZUDoQiOIXKVlQOFi5IvroWVAm5p9iLCeZUBHIg3kp5tlQEP2PFvOmGVAGhWb+SCWZUDkLBUTnZNlQK4agT5AkWVA0l4jUQiPZUADqSxb84xlQMtdMKX\\u002fimVAmhqgriuJZUDMn2QtdodlQKBZvA7ehWVApLWceWKEZUDqBfDSAoNlQNycMcS+gWVAA6wiRZaAZUCG+KSpiX9lQJOTQrWZfmVAYgmktsd9ZUDyimmuFX1lQDrlRYeGfGVAJx\\u002frax58ZUAsuYle43tlQKwURIfee2VApQyVrR98ZUApx7HaxnxlQGG4M\\u002fkcfmVAkQ8\\u002fgcmAZUAEwmOHL4ZlQAD8YuvdkGVAidGc\\u002fLqkZUBKOthYUMZlQNE4Tim8+GVA9kmdn5k6ZkBPzYmSH4NmQHTzNA\\u002fLwGZAmC4q95rcZkC1\\u002fxTsvMhmQILf0ar0jmZAh8HmnRZGZkDINLhGPwFmQPC2yHHVymVAS6Okhm6lZUCipwybhI5lQOSMeQOggWVA8nihyqR6ZUDRkhbFwXZlQJ\\u002flQmdidGVA9rm82sJyZUAckB6AjHFlQGU5UTeZcGVA8\\u002f\\u002fWXtZvZUAJXU4mOW9lQIybyUu6bmVAwHVHiFRuZUBmXzPgA25lQN5MSkPFbWVAKCGmTpZtZUAKXK8hdW1lQD4ZPD9gbWVAjxoKd1ZtZUC7ThzVVm1lQB0iUJVgbWVAlcMCGnNtZUAV2PbkjW1lQDxM55GwbWVAypFd0tptZUDnun5qDG5lQCKOly5FbmVAq4I+AYVuZUB\\u002fXO3Ry25lQK8S\\u002fJsZb2VAN5zsZW5vZUDix\\u002ftAym9lQBzL7kgtcGVAKQcYpJdwZUD424+DCXFlQDJnoCODcWVAjeZkzARyZUCLOp\\u002fSjnJlQNvTx5ghc2VAsUFekL1zZUAWvII7Y3RlQGF+4y4TdWVAf7QLFM51ZUDZSSWslHZlQJ9EQ9Nnd2VADcVPhEh4ZUDdkb\\u002fdN3llQNLANSc3emVArRdN2Ed7ZUDj\\u002fcqga3xlQGROknKkfWVAunrEjfR+ZUAaN5+PXoBlQAwRz4TlgWVALbknAI2DZUAC3\\u002f42WYVlQIYLziRPh2VA0ChOuHSJZUCavgQN0YtlQDeNT7VsjmVAIDqDGlKRZUDJ0OP6jZRlQBG+bRAwmGVAxRwX8EucZUDrRj44+qBlQBryFDBapmVAZuDBDZSsZUAC\\u002fvBH3LNlQGJ7Yex4vGVAc0qTA8zGZUCKGaLabNNlQKwdkDtt42VAtjUwHBL5ZUDJsmqUhxlmQPpaQww0UGZAF+7qm6SzZkAD2hio1WlnQMv8ALIypmhA2tMVyy2aakDMxE16dVZtQGrv1I4KU3BA\\u002fVbrHoH5cUDWnlqp0yJzQIQ3fE3fT3NA5vTGOcFpckDp9nA5hdtwQOlT+fR6VW5AJtymz9dka0ASzY\\u002f1PjVpQIaVnJlQx2dArr06ONrvZkDk\\u002fLuP0XlmQLr9iKaIOmZAJc0ygw8XZkD7LXWtIQFmQKYL4vQT8mVAf8Z5UffmZUBeUnm4dd5lQGwxk9Pc12VAJml1xr7SZUAxl71sz85lQBBsG8rWy2VA7\\u002ff+larJZUDCKahuKshlQNeCXU89x2VAHI1Sxs\\u002fGZUB+p+up0sZlQNVAlyY6x2VAAcadC\\u002f3HZUClWGJFFMllQIGP4nh6ymVAfHvdtyvMZUDOZINHJc5lQG6iSHVl0GVA4hitduvSZUAUiahRt9VlQD1rDczJ2GVATEOrYCTcZUCO+VI5yd9lQEu7JC2742VA95PFwv3nZUBu7Ec2lexlQCjfuIKG8WVAnohnb9f2ZUBiYx2hjvxlQNBQoa+zAmZA3AoEQE8JZkBkV14kaxBmQGCc2YESGGZArfQU\\u002flEgZkCOvz71NylmQMDIkLrUMmZAtqVI5To9ZkA7Wr2sf0hmQDh\\u002f31a7VGZA+adNvQliZkDJSUPuinBmQK8\\u002fGvBjgGZA\\u002fEX7r7+RZkA4qtgm0KRmQNviH8PPuWZAFGsBKgPRZkCTnzFpu+pmQOAbObpYB2dAxlW2A04nZ0Dk86ZUJUtnQGS34auFc2dAyaRGfTqhZ0DpTEKTPdVnQGVS6izEEGhAX8AamlBVaEAACLMqyqRoQNftnTeeAWlA\\u002fQ3f6O5uaUAY8JlY2fBpQPgj7nXtjGpAWAp65itLa0AkY3P7kDhsQC6Tzef8bW1A3Omwsr8hb0B0d8H4DOdwQLaIVEHEOnNAejknRxR9d0ASBfnDYCd\\u002fQECOPxeRBIZA7fFOLS7Lj0DGsnlww3CWQJzWKF3r9J1APUjnn7VzokAOT6wcQYGkQOb1BMpWM6RAIhroqjCxoUBeWO4jPhycQKPmTaRNupRAMHQ2Xz8ejUBCaJbevjGEQCohI+FN7nxASZC\\u002fmhE9dkDxTPS1pIxyQInwJ6pNhXBA8NOKpu2pbkAmkglRwRxtQP96MQx5\\u002fWtAZ1Yb2Nsea0CkYsDdsWtqQB5nxJN82GlA5Ro60u5daUBRspEM4PZoQDWjn1R4n2hA2u0sWcdUaEBESvXlhBRoQNIP4ebl3GdA5Zgm2n2sZ0DuQi6ZKIJnQA3ZA\\u002fX5XGdAsmXCeTE8Z0DeQBUyMR9nQONsaJx2BWdAJLdtP5XuZkB6Gfd0MtpmQHPa\\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\\u002fzJJnCM3NAwmeqycdWeEA1N7LS6TaAQGujEpzT54VAqxhGhsXljEAIyurbOg2SQE8576fP0JRAy\\u002f3d6gColUDW4ouBcimUQMFKnSI6CZFAF3FrRbnFikDcqIC5fBeEQHIWJMCEwX1Ajiv9LyOXdkCYFU8CXChyQJaNDGAdPW9A5dMZtTFubEBojcAma9ZqQCYMNzzz2mlA5Qqi5UguaUBxgspdzqxoQLcNFvRNRmhA+XB237nyZ0CVmNbMZq1nQN55C203c2dAxsqJhulBZ0D+YW9LyBdnQFbwq76C82ZA44AqSBHUZkDc+99mo7hmQJbyCIaSoGZAnAbGVViLZkCpyMeeh3hmQCXhJd7GZ2ZASUsdLcxYZkAiKQ8dWktmQF6YKEk9P2ZAFB9\\u002fcEo0ZkBKGqP3XCpmQPtIVblVIWZABn9FFBoZZkB4J0MokxFmQNUVlTitCmZAKOedK1cEZkAhpsIhgv5lQGMJ5h4h+WVA3lvSwij0ZUDNErUNj+9lQHJHaS5L62VAm2bFWFXnZUBxAX2ipuNlQEYacOU44GVAfUh6pgbdZUABLgEAC9plQMampY9B12VAPOOXZqbUZUC50yX8NdJlQEgjLiLtz2VAgG8x+8jNZUA3McjxxstlQBGDTrHkyWVAOR+gHyDIZUDu\\u002fMZXd8ZlQBtOhqXoxGVAfaGygXLDZUAJCk+PE8JlQGnfbpnKwGVA6pXkkZa\\u002fZUARKNGQdr5lQKjNNdVpvWVAQgi9xm+8ZUBlEgv5h7tlQJPIDDGyumVAzi\\u002f3bO65ZUDbAw3wPLllQFOtEVSeuGVAPqujpBO4ZUD13G6QnrdlQC7xRdVBt2VAReo\\u002fVAK3ZUBAnrfk6bZlQOHpjCgOt2VADmC5yp63ZUCd4T+R\\u002fbhlQL1Ppa\\u002fbu2VA7beZqkjBZUDy9n34jsplQDHiNZu92GVAE6apD+brZUDIDgv2TQJmQL8GErLeF2ZAT7L78UAmZkDn6VQ+XidmQMD3iJmNGmZAZ+rVWUsFZkBoft8tMO5lQLILet\\u002fC2WVAj78n8R7KZUCfj7grd79lQOPhZVPduGVAtX0Lmwm1ZUBemFni3rJlQJjYigWbsWVA3lXV586wZUC8gEaVQrBlQLf9Hfnbr2VAcE\\u002f\\u002f946vZUCtbelwVa9lQMfPEc0rr2VA0hixpw+vZUA2ctZH\\u002f65lQLpIZ2X5rmVAPjVyCf2uZUBZliF6Ca9lQAPF5ywer2VABQmfvDqvZUC36GLiXq9lQPcOXHCKr2VABZf4Tb2vZUAG4DN1969lQNHurPA4sGVA87Nd2oGwZUBoKtNa0rBlQDIP0KgqsWVAoCdLCYuxZUDKgb\\u002fP87FlQKBkyV5lsmVAvisOKeCyZUBKW3CyZLNlQAENlJHzs2VAi6C6cY20ZUCRgf8UM7VlQJUFA1fltWVA\\u002fgUUMKW2ZUAQH+24c7dlQJCbHy9SuGVAAlRM+kG5ZUBOf1KyRLplQGYRpiZcu2VAB14LZ4q8ZUDrCQXO0b1lQMLjVA01v2VAmV0JPbfAZUDj3LHtW8JlQP9lfj4nxGVAoaNG+B3GZUAul72uRchlQOLTeOmkymVAPhv1VkPNZUD1bHMMKtBlQDGce9Zj02VAsQIfn\\u002f3WZUAmH+bwBttlQNu94J+S32VAI43wpbfkZUCGc55EkuplQLKpcIZF8WVAZdRTRf34ZUD8Vgvv8AFmQDCjoWhnDGZA5\\u002fUh37wYZkBSJv6zbCdmQHM77xomOWZAL8TzlP5OZkCyCqZ382pmQAVP8NwikWZApzjGdWPKZkB8R8r80yhnQDr67N7vzWdAARMoQhrvaECMe736KNJqQKSEqDOGum1A0t3FeY3icECkFF2fKV5zQChBaKbE+XVAdBb66eMbeECj2Yn\\u002f\\u002fgZ5QFAGfaS5UXhAgxj+KVNPdkC8sLhYObtzQGyjGBurNnFAGXngVytAbkB9xPetsjFrQHwL0hrKLWlAg1eQwsj0Z0C0pIc3okBnQKhfdwfB2WZALhgvuTCcZkAtai2M8HNmQPdiNJ0FV2ZAlbWnpbtAZkAbcwLO0C5mQO+RTjISIGZAVQbQrcMTZkAx3wRkYwlmQDpkUkuRAGZAaacT3gP5ZUAANabDgfJlQGbD76\\u002fd7GVAR6Q+fPPnZUAlwxQKpuNlQHDS0rHd32VAdh9ZFYfcZUATR0k7ktllQBNtst\\u002fx1mVAwIJy7JrUZUBz9WYPhNJlQKIkNGel0GVAgF9RQfjOZUDnIGTlds1lQHEd8moczGVAh6sql+TKZUAVixDBy8llQPunsrrOyGVA66huvurHZUC\\u002funFfHcdlQBTp13xkxmVAtfrrNr7FZUAnwSPmKMVlQLP3iBOjxGVAXJJOcivEZUCTwV7awMNlQLy6t0Niw2VAgR11wg7DZUCCBGqDxcJlQBnMNMmFwmVA9Z656U7CZUAAGfRLIMJlQL72Emb5wWVAQfDSu9nBZUB7pA\\u002fdwMFlQLzugmSuwWVAnDSs9qHBZUASOtpAm8FlQBveUviZwWVAudOU2Z3BZUD1\\u002f6+npsFlQBaisSu0wWVA4dQhNMbBZUCAUJCU3MFlQEygLiX3wWVA7z92whXCZUAlStlMOMJlQIeSfKhewmVA3Sv6vIjCZUCffyt1tsJlQDI5+r7nwmVAuGE3ixzDZUA0IHjNVMNlQNCl93uQw2VAyN99j8\\u002fDZUA9mEoDEsRlQFG9BNVXxGVAiZKtBKHEZUAfnJeU7cRlQGMeYYk9xWVAYxXy6ZDFZUAgkn2\\u002f58VlQLBzhhVCxmVAMXvn+Z\\u002fGZUDhwd58AcdlQCqhHLFmx2VAGSXWq8\\u002fHZUBJKtuEPMhlQLhRsFatyGVAff6sPiLJZUC9mx1dm8llQGN0atUYymVALXJDzprKZUAHJ9FxIctlQEOS6+2sy2VAmiNXdD3MZUArkAg708xlQAIicHxuzWVA7kHNdw\\u002fOZUBXFYtxts5lQJQnp7Njz2VAsjcjjhfQZUBwaoNX0tBlQPNLWm2U0WVAiD\\u002fkNF7SZUDFNbQbMNNlQLfEc5gK1GVAJAu5K+7UZUAUIvVg29VlQDpIfs\\u002fS1mVApGy5G9XXZUAqTGf44thlQHv7GSj92WVA+3rYfiTbZUA21\\u002fbjWdxlQOdnKlSe3WVANgDj4\\u002fLeZUBSX\\u002fPBWOBlQGT1lDrR4WVAay3Vul3jZUDT83zU\\u002f+RlQB07hkK55mVAUOk27ovoZUCqBv30eeplQFtmLa+F7GVASI\\u002fMt7HuZUCzrpH1APFlQGI2XaV282VAmOxoZhb2ZUC5PoZI5PhlQL5f0tzk+2VAICRfSR3\\u002fZUCb9mxgkwJmQI9o97tNBmZAHA+F3lMKZkDhnmdarg5mQO6f5gBnE2ZAOB4zG4kYZkCr4ISvIR5mQF4jbNU\\u002fJGZApaFDHfUqZkD6dtkPVjJmQB5n9cx6OmZAgzh\\u002f0X9DZkAcSOfwhk1mQGgaZpG4WGZA2ZohQEVlZkAvlfO5Z3NmQM7kcJBng2ZASdGSopyVZkDXRju4c6pmQA==\"},\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"},{\"hoverlabel\":{\"bordercolor\":\"white\",\"font\":{\"color\":\"white\"}},\"hovertemplate\":\"%{text}\",\"marker\":{\"color\":\"rgb(255, 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2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (2 2 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (3 0 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 75.38\\u003cbr\\u003eMiller indices: (3 1 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 79.72\\u003cbr\\u003eMiller indices: (3 1 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 83.99\\u003cbr\\u003eMiller indices: (2 2 2)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 88.23\\u003cbr\\u003eMiller indices: (3 2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 92.45\\u003cbr\\u003eMiller indices: (3 2 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xxAULaYee+RK8DASgxiWmsuQFAKcyKWZypAAMj5vCS1yD+ALshLSLoYQLDS379uqSvA4CDSDrNhG8Dw79vN7EYqQABQNpte7\\u002fE\\u002fAGC\\u002f6usUuj8wnUkFrCkiQICR1BlMDP+\\u002fIP2K2hglHEAAeJyxH\\u002fQXwNBmpiyj+CVAAFv4+5iqB0AATlWwNCDxvwDnXRk4mRdAgOUqH3NDIsCAe8L\\u002fr6Hyv0Ao\\u002fp5DaQZAoIxzCMPvFMBAeVsHL3UvQCA+Gz1+YSFAACbJ1hw0DUAAktTaiG8aQBA\\u002fksCvISdA4NrDZsIKIUAQQYF2enk5QBAJrS4\\u002f2ihAoKWbJkxgNEDgB52m9qUiQNBre8pPiiBAQCPqtmG2CUDAwVUu4icxQMBue2vGMCRAcIEqm2\\u002fvL8DAnHYJ7RAgwEgLAs4e5jJAgIjvzAvA8r\\u002fAg9mBQoIpQEDkN58slALAQAMwQAM4C8AAH3I\\u002fSzr4P8gBUbdcbTxAMNoUFTOsJECAvdhxX+4JQFAr773GSChAgHO21zcnFEAA9OzoHkgcwICp6mWWeQnAYFjFbZSbJsAgdk4GGZI9QAjHHqZbcDJA4Hj7g38zHUAA4x7X1sMGwDAXHXWw\\u002fyvAwHTb7gS1DkCAhdapEqsWQMAvym+bgzLAqAdaFExLNECATzaAvugzwPho\\u002fOoE4D5AqDdNbk+lM0BAnWOcWzIlwLhLrlY1JDtAwBclmLXoBkAoybWo9246wCCVwaUdfyBAsGtQlQ\\u002f0R0BA+2ZxHIo0QLBGZCJWti\\u002fAAAN8JApECsAA6KZgYvYFwIDcUJBN\\u002fBzAUE9wQHs1MEAwGxi2BjshQCA1YM3j7i9AAAIj4p9c0D+4XQ9JYnkwQKBzR00BjRHAQOBqnk+7AsBwW2qPN80gwCD\\u002f\\u002fCf41ShAIFtrfbIwJEAALllUr20WQOBcI4lZJiLAoM+T1vLxE8CAnPZ40K4UwMAcueFuoiBAwCi9IH1FC0CgtjTxmwgkQIDM8BVf4xfAkDi3RiVLMEBgn33xJZAVwOAkFxGYrB5A6BW311aIQMBQXINju7U1wPAPyqhH60XA4Bki7ZqpRsAASGsNiIzgPwBE3HrCeQlAgGy4dEuvQkCAa2fiA7ZGQKA9JUVXRVtAgFNZn6vAR0AAdJml8i8UwOC\\u002fYqMdNWHAIH1TittjYsCAkHN24GxDwECGqJRMZ2hAQLDCIqr+V0AgO44TM3VcQADm\\u002fvg5zxrAgLmNFYgWJMDwmtF3fatEwPA2sWoE4j7A4JTMqroNPMDYMO\\u002fum9hFwAjVzEDsoTLAYOM4ek7FMECgwyF\\u002fM3Y7QCBMiheysBHAcI\\u002fD2SfjLcAwb62DbV0wQKgyx0slOD3AoK4ankR+NEDw2Zom4EgnQMCytjnzrApAcDC+lPSmM0CAdvQMPKQSQBCG9dE70iZAADqa1pyND0C4ArirXEszQDh3Mn0RfTpAAH8\\u002ftl9kDEBArwTxuAsEwOiRzjn6VjdA2KWGPy4ZNEAQYnEU7sc5QBhvDOOVZTBAYJ4JKlDoJ0CgibharrpBQGDPNa5b1StAmBngomFVOUBwQL6LkrY3QPh6YQk2Dz1A8MFsJCJgN0Bg60gPEqowQFCa3vzUdkNAcN3ch\\u002fRWKEB8wPQWAbJLQABw0JM3oRXAlGUGvnZjQUDwDKLdGOQtQJDtWSBdGTJAcCfa+iQ9NEDQ31JQQ7sqQADHTCHsCSXA8Nj\\u002f5RwrL0CoOs9UWq0zQOBEU3vYeijAAHHzHjmq8r\\u002fAa97uo8slQCAKGogc6C1AMLnoyycANUDgr5poxygcQHAxRId5JC5AAMGuKKwwIkCAZwTSInIcQEDiLAy6PSZAIIJm9rE+IECM+ucSAA9AQPC5D8HTGjRAOOwmWukKQkCAAO0f8x0oQCD7NQiFDCJAIJOUUKX7M0CwUdZcLt4vQADHKWQVwSlAACYBTN+fJUAgztZyy\\u002fQaQEDfvTIQvgLA4OSooBHeKsBQyGbnge4gQIAnaxLN1wJAMEPRK\\u002fZ3KEDA03uuaGgYQCCw786Y1B9AiBlTtRczPMAAT0tN6\\u002fYKwMAi1os9xhFAuB5Y5h\\u002fCMMAA94b2Q\\u002fo4wLAtlwuCbizAgMFMfneGNUCwEl3tyXwoQCCQ4eDUvxdAOMmkvoGaMUDgE1enmHomQACFzCrsXRNAgLEmbsl++b\\u002fgutoh0rcTQIBhQhv\\u002ffPY\\u002fcMdbvEGoJ0B4q1xyITE8QMCrMZb18hFAMDqjon5LMsDAJbfw81QkwKCukqpV3DVA0IV0H2t2LMDA4wwlxhMvQIBGjqAqlhxAYAFBQDxBFkDQU3A9\\u002f4EnQFCuQvdVWC9AADhbDsHb0D8gjDVl5C0ewIBldjNsf\\u002fI\\u002fMALVY9yBI8Bg8CrwdL0RwACsTjwy4vG\\u002f0AdpQQksN8AwUIzd554kwADkeHtKHzfAEMr4GOcnQsBwD6MJXA0wwMB+8uHJtyjAEF\\u002fmPb\\u002fTNcCA2pYtyy8\\u002fQEA\\u002f89D4Kl5AAAeEl4spREAAvK7xEnccQIAE1J\\u002f2i0NAgPUL4+wiWcBwuaGTO4BlwACm\\u002fWBVNFLAANVPbzdEQUAAtzf0PHAfQICk9QsfxSDAAOprD31cEkAQRGRhzxdGwKj08Co4sUfAgEdo8bAwRcCAHhNirhgnwDDOYAb+ISrAABe6XuGHJsBALqfpy3EbQHj766ljejFAIDu0WY4NJUDAt8uL8NwdQJB0mdMBIStAwNsiBar7H8BAQSCR\\u002f9UtQHCJzSQOaDZAAHbAIEyG4T\\u002fAz4lq12MewMAA0YSOJwBAoNuc9JXfIkCQgCd2HF8lQMA9nGneKhdAwG1fHpjGPEDoRFjKk6c0QCCQPJddHSnAgCFhMG+TQUBQ2acam5UtQMCGf72XviJAwEL\\u002fgzJnEMCAYHbslRH2P2AZex1oRRNAQDCGcUNwKkAgz8+r0lkeQABIWf\\u002fRyrK\\u002fIB\\u002fvwFwLF8BgDlc9+Q0jQADOHrB0S\\u002fO\\u002fAOkEXiYVKkBAJSudShUVQIAJBrEPhDHASMaIs8muPECAyMgAyXgXQBCxBQ3pFCpAIAFforIwE8Cgd9RoFbUkQBADrk9ov0JAIGBym6tBL0Dg0AXw5vwYwGiLldzJ3jlAgJnxKrRP8L8A3ObvvawIQFjgHsKgLj5AoOFQ4RPnGsAAV6hh++MKQJhJ06mEcTFAADOMM34VHkDQv38jI5g+QACYYngOnvo\\u002fMF7K46i6NkBgfSiqE5UhQADk2U9cN8O\\u002fgFhfoGgwIsAAPE8HieopQED8gTIZCBBAgNDOravh8D\\u002fQqVFpFTMoQADYrkPmRfI\\u002fQA+i8YpFE8BASoPKjj0GwICAOg\\u002fdBRVAwOZ66XgoEUB4s\\u002fznt60\\u002fwED8DSA1MAXA8GXI+DVhOUDAM5FY\\u002fS8hwJighb2hkTDAoCNCDJ7oJkAAp3J5VQwhwMAVKQkOgTPAgJq7NSsdDECAA0rW8H74P6CvMSV7LhpAQKnNj3zEGcAAwOIPmZPbv6jD\\u002fO4LlDFAsMI5A9IrJUCAVbzpAEYLwJC2\\u002fvb1zyrAgDo6H5mC+T9gUYPKlaEZwFDJnsCA0ybAAP8a6OVPEkBAAz1nYG8zwADnP5AuQPe\\u002fAASCyqDRC8Dg3vrwpgQnwPDnUugn5iBAwDPGhRtlMkDQwlK8DFwrwLgsVGafNTlAkE8a62b5NcAAQl+MeunTv0Ag1zayog\\u002fAOOayO+XZNUBgkTIcAM0UwBAknsB5SCdAgMEMjPMV9j+gf6tc6BcWwICnpwHaFyrAgCaqGPjkEsAA8rYEv3EVwEA1QBU9QibAwNAcKjCLHcAgCWFawNYRQACT7Idc8QvAAEKPWifTLkBoswzF5j83QAC1kHzJmCJAgHXRzphvG0AwNfmp8E8mQACkzChlv9Q\\u002foHDlSKzcIkBA8zyohfELwEgo61RBbzPAAF4fKJ3W2L\\u002fwKaNEYbMmwIA\\u002fg+HDUTHAoAh\\u002fIIxpKUDA\\u002fbuTg9ENQKDZ015dfSNAwPlaZNGEJUBA2PS47isGQHD2mX78m0PA8JNTTTCUJ0DAgNlAfWgswFCjC5cfmitAwO2QqY6PAcAQPGZKdWIowOAFhy4NwxDAwFGrVy2EAcBA2vhgDT4fQAAJpgi4nitAgHz9uB88E0BQPvfT62IgwAB7Da6fvOk\\u002foO9bFpXLGMDgsWbpS88QwADuXMsVpwnAUBvKOdmTIUDAUvqgbyITQIDQSmOocSTAwK2kAI3pEMBgXKnvuw8bQIB\\u002fzWbr7gHAIBh+WiL\\u002fHMCAnymSLB30vyCjmjv+IUDAAKG\\u002f7mNk9L+o22fqsEgwwICEfuJ6liLAgGvMK5iQDUCQ+QFBfl4lQACd9CCqiOU\\u002fAC2Ai+G2CsBALCpwO9ECwCA4YsF6dhnAAM0pwu16GkBgw7XXb5QZwAhoTysoaTfA6DI332xtNMBwWmts3OMmwOAiaZw8JR5AABtHFujuGcAgzMhJHwIvwNCbjTuP8iJAqOpFyLuMNcA41f1iIpM2wABEiIOhYPY\\u002fkB3CyoJCK8CAVZQt6G71PwD43ad0sTrA0FRyEfd0LcDIegJSOcQywJBx0j\\u002fEzjfAqK96LjbaMcBgr+1PtZobwIBINUya6C\\u002fAAIk2yBCY7z+wU0\\u002fstRMwwFCGPFvXSyrAYNuxIp\\u002foGMAQndkUwFA0wCwDXYQONUHAEOj+fH0NJ8DwvcuaQ6c5wICFWjdxzDHAQIP78AHdE8BAqvscuaMYwEBR2Jm0cxJAwEOBYsSnNMAAJ5ligM4HwFAtWNtcuijAQITc9aVPLkDAKuAXnLUDwAD0GWojpizAGC9+nbmqOsAIU2PDQAYywEDErkmpzB3AQCibOo02FUBAEKx0U9QtQKD+9N1ldiXAgCEs+13SKUBwwuxPCNhEQBgTl9hjclVAEGigmY+kQUDAZ4CaizhNQCCIzanAW0tAgKo\\u002f57c7M0AA3+6XmVhHQChib63klDVAABjfZcgiyr9I0+o4PF03wIBLVX0qnwbAoAundqI\\u002fMsAACDKbd0wTwMBOtSRN2RXAYJ6qdOqWHUBoIc4xXP47wODdRyEy5BlAsCRYXGHYP0BQ0EuhtbMnwOBdPilaZhbAwLnVC9uPGcAIrw1GbptBwPAKDK9TISrAwIiJeHfAH8Agd\\u002fexC68UQECbzS+AeCHAQEfoluJhEUC4lTYBbmoxQADQ4F7cFxbAmGtidZ+HMkBACOS2HLIZwIDwIdFAdx5AYJTsWa5NJ0CQncEMkaIqwMBJFz2uKRnAiOkjJIy7NECA9tjaHv0cwJBfHvQuxTBAAOCjs2h+y78QY5sXdWciwID1LHJB8Pw\\u002fcPNk2GhdIsDgDm1Xm0wTQKAeDTmCrRjAUKF9qXurJUDAXKW1JVobQMDyHzYjXBNAQIzjGwddH0BIbyWEOdcwQFC4QcYWUijAoAc4KHfWMkBgDbXQ7KgcwCAu320+ViLAENBwCM7TOkBgKdVGHqUhQMDd9Wwm0TJA0PcLDtRgIMDA5HG6QGQowIB5eiUKmCtAABAwYB78yr+o6vX6FjgzwDCF\\u002fwKqxTlAINsHFi8NF0Dg3jiVYX4iwAAHKhiuHPS\\u002foOz2MX67NUDwLJAQVnEnQIDGKtA9pPS\\u002fML+K3GNlJ0DAQGN75qAmwJCPEDdmpyLAEOiidw1XMsCA5+kL3Vf6P8Dzs32J8ALAcMx363TDIsCARMAZ\\u002fMoowECvm4cftQRA6DchpFRtPcAAjdTyv3QEQDAUi2qWdTPAEL33oT8MI0CwC2msRX4wwACgJbQsTt8\\u002fsJ8f7jTxLECQIvrIQRgnwKB27TcYvB1A8C6KouIrJcCA85pfDzYpwIDl1ZTcA\\u002fq\\u002fwIHmzyhLL8AAWJDJ5jzVP3gMhKimsDbAMDrUVshsIcDA2asl6A4VQAAoytlt2c4\\u002fkHfV4HrIMMCgBxOtQTsTwAAJj1k8VRfAIPJS2R6QEECgOKgEm8UjwEhOgKsvFjJAABsMJ\\u002fsdKEDIU+lvmgc7QEA3sYDu\\u002fxvAQGBGl+XgF0Dw8tQzkR8qwEB7p20PYBDAwMEfGCr8BkCAc2MLfkkJwABZX7ZpNxNA0GAXA187O8AIXO8S30Q2wKAv9jxcxR5AIJTYA4BOI0DItrTSJ2M9wLAvS78bkj9AADVjdWpw+D+Ag6\\u002fltkb4v4iX2B0\\u002fkDvAsO0KUXmcOMCAJlVc9JH6v2BYa5Q2tjjA4HNvboqHLcD4sDaS0dE3wAAsD\\u002fjEwCnAYGsP4gJCHECgpLo4v\\u002fwfwACyXOHjewjAgJc7c7mBHMDgTQyu4ZshQDA7NamWuzFAoDsZrCBeHcAgtS9nECkjQAA3nJvq\\u002fh1AWDuQ\\u002fv6VOMDA4DKvL00dQABDDjl1xDXAwB6YqKPqDsBQ9LK8yu4vwDBzHXqF2iFAANRXv5p7AcDAukojvmQhQNBRlaE8JSdAeL4R39OOM8BAaQOsYsgawEAKq\\u002f9RXRvAQFHgcHv1D8CAZBNQpnr9Pzi6WMN3VDDAYFO0oZcGIcBANVIZNtQSwCAdMo3+0yPAIF\\u002fDFFR2G0DAINrDuUItQMDjZirwgxFAOOOH\\u002fPTjMcDAV6nbKqQrQBDl4eDP+iDAwLoJ9PNnAUBIPvXRrxdAwMDWFUTwZQtAYNTAu\\u002fIBFEDwjcWD4Yo1QIDgzu\\u002fP1BfAUFOG7t39KEDQRYc5cDQqwJAI6nVrwjbAMG306kWEMEDQgeUTcLc2QAAoZz+7huo\\u002foB+e+LxcF0AA\\u002fDJjI\\u002fESQJDYbOPLAi\\u002fAgH\\u002fIie+C8T+gJEp\\u002ffPwowBAv85qiTTLAwOFlJT3CJMAQ0w5ltHIkQCDeOTCRPxrAwOqHifmmIcCw+cdZn10twCD22EZlkS7AwCbFZyuyBsAAp\\u002fSxULnkvwD9G0L03gnA4FNxoaRJLcBwIdW3C0QzwGAqEuKglDjAsFIufUoUQcDAY7tvCCszwIAC52zsu0LAMIdXvoAlTMCAyOpxOMUtwGDgzwzmo0jAgPykeph1VMAA2E1I6YL6v4AtPja3XjnAgMkjxDYlVkCAPix+RjhjQGBqDu\\u002fVX15AAKIqun1fKUDQbCAXu3JBQIDmH5blgErA0BuvUhZUOcAIYWXvBm9CwICUwzZJeDdAwGVMubsgNUC4b6f\\u002fvPBDQAAh8bNzTRVA8C+ekPXnMUBAWxz\\u002fjHMCwKhZXm4sNzNAwE+RLRnIOkAAauryUzADwGjg1AHg0zlA4Oe8GJdNGkBw5xzuit4vQOh6l2M8+TBA8I3Sg0i\\u002fPEBA\\u002ftkBIJkiQMAT3TesrwrAgEWVKQ0IAcCAc4eP9ooHQKCnqF20ZxjAgBXg4VeCJUAsaZXEB0NBQADhDKB6hvy\\u002fMJXJbl+wJ0AAKDjWu8Piv+DDVlK9uRtAIIBwbKJiGsCARloM1lAJwHCc9zM3OzBAgOFx9BdhFkAA8Utqp97+P7gL52LiPDRAgFCq4KIuBEAAoMYGZ7UBwADgNv84gZ8\\u002fQJE9GXYPCkDAcKYsJL8DQIDIaFLHofo\\u002f2M\\u002f1s+fYOUCAYpLhdiQIQNCWtxKmWiZAADCQD0WyGsCgb\\u002fHC0hEvwEDrUGzRwwRAaNlzS8S3MkAAGL3cd6PqPwgXI19cDzPA2NTsQ4T1M8DgSKSB\\u002fHQXwGDInmfLGRPAQKNiMx\\u002fEFsBAzlIx0TkjwOCqEegDFC\\u002fA4BLK7XoPKUAYSGXZmmc1wECN6LMWQA1AYFb+FUVtLUAA8ydZi90BwACaxevgXSDA4FIaM+zcP0CAegG8tMAIwCAZc94J8jJAoH+qWzftE0BgiE+amvgtwIA2tl4ls\\u002fA\\u002f8LTfSsIRO0CAvGKaa0UPwDDW0MXLGzNA4ORjfc2DG8DAkpvoNb4vwMBKQXTovSHAKGWobP3gNsC4xnEqyRk7QBCGO5PP4SvAEPnwE5gMLMAgEB47fDE2wAAg6KQQeThAIDGak1+XGMDAtvjk9DUXQPByHGs9dCZAoGArBmZWKEDw1Jwo+TEmwGBkWIan+T9AAC26GqFsFEBYAlifGLoxQMAPxKVqIRZAcBhs3mlhIsCATINr9BMkwAAQSTeOKuU\\u002fwNIoqaFbCcDwwpon24syQMAxZ8j\\u002f5SlAcDj0qtkqM0Dw0pau45MiQAjT6MXsXDBAwD6XV1tYHsBA\\u002fhb+N+cqQEBZTtRNJgzAwPy\\u002ftZf1C8AgocvJJOcRwMC4mbyk6yrAACBF3LvV+D9AE5T5gz8WQBCZreXVkTVAwPbOik2TMkCA\\u002fSiUt6IEQOCTwfMD1iTAeNRQuLNqOMBg14mj2qoZwBjvflX7lDBAAGs8\\u002feWM4j9g\\u002fSRhQ04SQICH6TwSthHAYOMm2lIiLUAgoyiwcj4aQCDieOlS5CrAwKqgT0ChC8AQOCrhQhMhQHiV4N\\u002fMeDHAcMPJXub2IsCA+PE+kPILwCCnibiVfjZAIOUAg\\u002fQSHsAA\\u002fvbjYEIEwCBzBjoyGCfAoNGcjJlAHsAAizqf0o7iv8D29DFmyATAIDbIC7J3EsCw4BXcGaM1wIDPQI5odvU\\u002fELhEvx6jJkBAdlIJK6UFwBC2y8uCiSBAgHkd2L3b8z8YcQMSQTY3QMDlRxkbuRhAmEKXPcElNUCgQ6Dmi3IcQCBzkpQj7TLAgK4ecSO9B8BA4+bz0QscwADAEPFMWK6\\u002fgJbcE4DG8b\\u002fAHyQg2VQXQECPPmYbFh9AABrFCZcuEcCApi9BQ\\u002fQKwEiWg1WajDFAAIjOoIZUBMDg5oWjQG4VQBDh2wYBgy3AsDOO\\u002fkA7KECgIjf4uOAfQABoyFPAaMm\\u002fYAYe3EDJJMDgH\\u002fCAO8IkQMCT0HPicBxAaKxl45\\u002fJMUCgmCGqzTYhwIC1A3KnbRzAUFJ+Hm83OEBw01S5hmcmQGBQyju\\u002f\\u002fCZASI+oINtiPUAwoI2BWp0uwAA1A\\u002f9Nzek\\u002fMGFRp5EuMUCg2ZEtVEw9QFB7p3ZUGEHAAAOpFOF+9j9g7amVOi0wwEB4w9f0\\u002fTjAMKc3zglORsBIt4fLKVxCwED6VD69Xg3A0B7JgkHZM0CAuwycQrACQGBYyZkJdSjAQEqo+LgEFkDgGCCCczE2QICRwF3WXQlAsC3TzAePKkAABdKKUt7uP9Ci71hgbzdACMwxdEXIMUDASyoPt84TwIAtZnKa+xLAYJJWZ+wrI8B4RhOahIkxQAAE24ay9QrA4P3ev1I1FcBAOAXVTQUZwJDSJworjCFAAI5GKeNn87+AIBaFVUsOQAArv2jZxeg\\u002f+M8RoxfAMEBQiiVWRJEgwPAH13FYWClAICQUQaKeMEAgs8mHqx0jQMBNFKmuDBrAgMe5VCJFA0Bgt7tyRVI+QDBMKT9jcypAwDS0CkbCIcBAzqDV2AYUQEAt0bY0EQ9AQAKbTo8ADkDgxn5kU8kowIANctSX5yhAwBPx7O3aGsCAJ6e4V9wIQAAaKvR1VhzAIKnf\\u002f4W1OECAEzV0m\\u002fIdQAA6ZuLXfiRAmFpYD3D9PUDA8den\\u002fiYdwAB081a00i5AoKR1q1EsJkAoLhWaArw6QAAZECSVaBFAAMg\\u002fbepNsL9wY8sbUfcgQBAFXP7A+StAEOgSQAG5Q0CAXHWS\\u002fbIrQGBb\\u002fGP4ORfAYKf0r+Z3OUCANjBnS6L6P4CQGxedYkBA0FFN4o\\u002fFMUDAwZC1qk0rQMCJ5EGlxyBA4GYrV7DYH0C4mO1lE1Y6QFAmpc5CDCvA8AWsoQlVJcAAgDCrokwkwEiREEMO8zJA0N\\u002fwje7uJMAA\\u002fcHs1OQZQAB\\u002fxL2unRXAwFLQM19ZM8AAkrG5fUj8vyDNJoMxICFAcKfDyv4GQMAA3V0wPsMRwGCLSBVErDrAMDTGxl2XSEDgrm\\u002fA84k8QAA4DhjdbT5AMOEQKx3NQ0BAp8nwkWdHQIAs5VqU4kJAQA\\u002fS+WdtR8DA4YmVIyhSwMC8nkDZ6lPAAIhooKvCDMBA9DSCO3hFQAB0ZchuyfY\\u002fAIbO5uvHNMAghGbp7RtLwFAJGQlvckDASIL81YzoRcAgnom+lpQpwHD3gF0lqzRAAAiWuiwJ+z\\u002fQbrN39RkxQCCg0HneUBxAZKaSn5CES0Dg6vwRcaI3QPA4I57kwzLA2CgvbokQNEB4bXKb\\u002f0gyQFjmAls9BDhAYCIxbYqzLkCAt1ZBY28ZQHCB98jQGDBA2DjLLhGcMkDgLuSSiSIkwGDehF\\u002fBYBxAcNLsMXQeMECI\\u002fKo3O4NEQOiYvBxL1DhAwI0kCawWJUCAXsY9nYwOwACNJQHh\\u002fec\\u002fQMP3HX4HFUCI+LdJ8LUwQPBC5OqRHTFA8BAywf15PEAggnCG+TAbQOBq8IM2FT9AGHSBmKZVMkBAhuVhneMuwLgM+j9fvzRAaB7y7tB0QEAgQr7MVQ0yQFDSpWmJVShASHxWoiZCMECcFcB+0SlDQDBELUaiviRAAIiaDcUpC0DgsduNu2oSwECGC4gO\\u002fApAaEvTuKBTNEAAgDiFT1zQP7BR9XGiKDJAxEV1WGaESECQNmzQIj0iwFB64CPpsTdAsPr2Zb08SkAAjkUDa4z8v3QEhPME9UFA0HCtWZeQOECAWtGaFaUQQAhuyqQgsj5ACEeKvckoMUCQbZ8RS+sqwMCHdJm9LDLAKOrQQxj\\u002fMkBAUraVWwg+QMAK2vVA0ClAYHqlmzgrKUDQE4uF9\\u002fQtwADRJ98SkvI\\u002fgEQJ3aDZKUBAXNf3tCsTwAAM7pGEuw5AQN0+CAgeGkBAzON4G7QuwDBMrZdy0DpAgMRAVX4ZIkCAUUOrdbIRwIBQhm2zHgZA4INZZYOTMkCg5kZWOt00wKCSq32MBTxAQNV5bp6OIkAAQVl9WydBwICnnPfTZyjAUCMgUvF\\u002fVkAAa\\u002fHFQLkswOCnVqy7WENAIN2RUtdIPkAAgOTqz2iwP8AxfftnzytA4LjS\\u002fzJyMsAAZsWTABcawKAmN\\u002f8pLipAgA0WcyXn+D\\u002fAlPvJpkw0QGjPIw8zlENAwEZ9l8ZtDEB47KsRjZkzQEiST1+QzTZA4OAxEcSoFUBg7SxlJlMWQIgY0ksiM0ZAwFg50TA9EMBAwBOS9gYdQGAFv+orOCfA0PFXfeu+IEAgIQDJ5DoyQECt4T6v7RNAQJafo3pKI0C4usEJwzs\\u002fQGDbQzskBxNAgFtacemZ8z+AtzW8eCkNQED07NpWGBhAAMXHH5wSBcAAJY3Rey71v7CPVJVbFSbAgD6g7nq5CUBAxOa9ZnMyQGBFXe1YqhpAABtDjBp1F0AgxFQ4RNAfwIBO+4xwb\\u002fM\\u002fIELa0nO9LEDATLuSPBwEQMAR1zKalhJAoCxT51QVF0CAR3\\u002fBriz+P8BIA64cBxDAGLwtzMoXMkDgP9r\\u002fn74YQEAnSwvOFxVAIOZAy8xaN0DAEYv2gHIDQIA04iLS\\u002firAAPgWs4\\u002fcIMCAZOcbhxz6P\\u002fDOF+u0YS9AcAz+Bf59JUAAd37jqzvzv4Dy2Dyti\\u002f0\\u002f0BcqGyg3LsBwXw8Lx94rQLgEiTSr+ThAwEJtwy0NGEAQgRINaecjwABLRlnFUhhA8L0XyoEcMEBgs53eAI8UQEDbzbxzUwlAIAONMBlhIkAAlPDLPwzLPzDVGkHliSPA4LM6SjqBIcCwMBL5FoYmQJB20FXxRTFAYLcCeLUfHUAA8rwlT7c+wEB4OiOKww3AcADLVfa8N8Aw5gHsJnAqQGAIVguKhBxAQPttpipSCMDAIUHwIK4SQIgRr9nsxTzAACZMUx\\u002fM178AJ54+QbYXQAC\\u002fBhKy3ue\\u002f8CS7Hw9kKsA4T6fy8To5wACEcGcmVyZAOFOvzloqOMAAmt93C4wLwMgu6H0LZ0HAgDggbIfw+j\\u002fg8xF35e4XwOjw0GQVOTdAWBKkJ7NXMECA6JlpR0gDQAATW7FgMxrAIKKqghHzIEAAor9ctd73P7jt78jffjPAAO7p\\u002f97xFcAwJWnxqAorQHD8HNG88iTAAKQxYk8f3r9gI2d99O8owIAu0llZvgvAQFi50V4gEkAAp2nehfbgP4CB4nMWj\\u002fe\\u002foCDhk1fnGcDgFl1M3OsVwHDvwAWn+CLAsI92ICsEI0AYgM\\u002fU3T9AwADR7qzwOOC\\u002faGASLfF7MEAAs5qltOUZQCDzAI2qEifAAPRkKWExHsCgHPj7PB8jwACULNmrTB7AgO+KQwq4DMCALelj0yYDQOBffs2NgR1AgLqvIke59T8QGfhPMVMpwEBlwcBVeQ3AwL+AAqbUHsAAeHCJI0b0PwDk5seLFAbAAAEqJv2g\\u002fL8gC9jatFsiQGAWYWrCtSHAID+UHrNuHEBgPaHAOboXwMCHY1VeGRRAyOxK6LkFMcDAOHds+5YAwBCgGRiMvSlA4Isr90A8G0DgctnrWoQmwCA+jZdOqibA4LNRbzXUIMBQzzbHoAIrwCCGc896bB7AiBX4+Oy3OsAAaINnPOTDPygR+gBrAzRAoFvuQQtpF0CA4ZwnkaQCwCCbCmdYFC\\u002fAYHOFVVfgEECAIJUe4sEbQIAZDUU7YzTAcF4f8s2LK8CAlIn\\u002fLNsBwNBIDFGebSZAQPQOw1pABEDAqT4LkaEaQMA+7MNItwtAIOs4R5GzEkCAELdmgOUWQGBcMBECISlAIGACRUjjFcCgIxQquG42QIBr74o5rxBAcBHRMZRrQkCAlKLrM8UlwAAlCM2UOCfAQGehnz6+PMAYzU789zdHwABl0EeaGzPA0HceYM0KMkCAFxYXK2cpwIBtFGNj1SbAAOeGH6jU7z\\u002fQR3zfKhslwADfgrSGcgPAMHgFKYxMJ8DAtIWH3PUvQAALFBHfxxRAgPQ5tNHzAEDQotbCCD8jwICEzmUxffQ\\u002fwJoSlhbRD0BQztgf8xIhQIhwi21u\\u002fzJAAFbzX6R42D94Btnf5LQ1QMB4s5XW7RdA4BKLPYTfHEAAsJzF49cgQECTBwJKZBZAkLsVPMiALUAA2zzr9VnsP4BsaXESATZAAHYfWRWH7L8wcZS0I5kvwMBEm+x3vA3AACwox65JL8Bgrt7sgVASwIAoCc1ZKQTAAPgVFYTvLsBwDkJWbtcswIC4Dnk1Dva\\u002fOFxVuSRXO8CoWIQIXk4\\u002fwNg\\u002fldV1RjjAWEd181U\\u002fM8Dwqxv31XEswKBIv+YjMzPAgKUCiuQg\\u002fT\\u002fI9uHOuNY3QKAJ4Y6daxNAwCXpJLdCKsAAbT6hJT\\u002fsP+DVvR0SGzHAgMCOOmGH8b9AkEBtsFgYwEAGM01yoQjAgHrP3HQn8b8AcL5A+90hQPC1lzDLDzrAEAQvvZsdLMDAHulDN3AIwIDXXZDMNRzAIFueSvDyNEAA7sUlv2TuPygPaT0w8zlAgCOWNRMx\\u002fz8ACwBQWFnuP1DvcqJe8jNA8LHivZzjL0AAhIKk5A40wMAE6lJyHyDA8P5jJ1whJMBQopTNhCMgwHAoyYfqJSjAMEJdMHTXIUD4\\u002fFupsxU5wIBMjr7vuQjAgBt2s8gxIsCA+ftQZpUbQACjhUD4xitAgAMiCAfDI0DQg6k0IEEowBDVS1B9RSbAkCjZShBKIsAQPoa2JrEhQKAz3NNOWB9AGKuQT4csMsAAItn3e14uwMDOGVYImUHAELN3nv9pIsAQHuzNF3AkwGDtNe6UiSNAkFFivfp8LsCQpLJNyIMuwMCNgrVqRTPA0OfP6iOSKMDo3ezo2kwxwDBGp1aNoSzAoEVuyFlTGcDg4CQ6LmQZwBiSXG9nXTfAoDlyRdfDKMCwAomwM80mwCAgAsfn1iDA4B7UfPfgKsC4qliMs3UwwKA8OZ0dezfAIHsz4ngBJ8AAspIPteURQDC\\u002fpNVGGSHAQPwhp\\u002fGSM8BQXEO7ATMtwLglnsNUoDbAwE1vRB2xIkAA2LsVPknUP6CD5PcsbSvAQMqWu1F9IcBQYTrDF8c4wKCQwPxawBBAUFB4ErhNIkCwubYfz+I3wIDzMxUqz\\u002f6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-       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
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-       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
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-       "        legendButton.innerHTML = [\n",
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-       "            '</svg>',\n",
-       "        ].join('');\n",
-       "\n",
-       "        legendButtonGroup.appendChild(legendButton);\n",
-       "        modebar.appendChild(legendButtonGroup);\n",
-       "    }\n",
-       "\n",
-       "    legendButton.onclick = toggleLegend;\n",
-       "    legendButton.onkeydown = function (event) {\n",
-       "        if (event.key === 'Enter' || event.key === ' ') {\n",
-       "            toggleLegend(event);\n",
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-       "    syncLegendVisibility();\n",
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-       "\n",
-       "if (graphDiv.on) {\n",
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-       "            syncLegendVisibility(eventData.showlegend);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        syncLegendVisibility();\n",
-       "    });\n",
-       "}\n",
-       "syncLegendVisibility();\n",
-       "window.requestAnimationFrame(installLegendToggleButton);\n",
-       "}\n",
-       "                        })                };            </script>        </div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "38",
-   "metadata": {},
-   "outputs": [
-    {
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0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"tickmode\":\"array\",\"tickvals\":[1.0],\"ticktext\":[\"lbco\"],\"range\":[1.5,0.5],\"showgrid\":false},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0.0,1.0],\"matches\":\"x\",\"showline\":true,\"linecolor\":\"rgba(120, 140, 160, 0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"range\":[65.0,68.0],\"title\":{\"text\":\"2\\u03b8 (degree)\"}},\"yaxis3\":{\"anchor\":\"x3\",\"domain\":[0.0,0.18108737864077668],\"showline\":true,\"linecolor\":\"rgba(120, 140, 160, 0.28)\",\"mirror\":true,\"zeroline\":false,\"tickformat\":\",.6~g\",\"separatethousands\":true,\"range\":[-399.76114173758486,399.76114173758486],\"tickmode\":\"array\",\"tickvals\":[-250.0,0.0,250.0],\"scaleanchor\":\"y\",\"scaleratio\":1},\"margin\":{\"autoexpand\":true,\"r\":30,\"t\":40,\"b\":45},\"title\":{\"text\":\"Measured vs Calculated data for experiment \\ud83d\\udd2c 'hrpt'\"},\"legend\":{\"bgcolor\":\"rgba(255, 255, 255, 0.5)\",\"xanchor\":\"right\",\"x\":1.0,\"yanchor\":\"top\",\"y\":1.0},\"height\":600.0},                        {\"displayModeBar\": true, \"displaylogo\": false, \"modeBarButtonsToRemove\": [\"select2d\", \"lasso2d\", \"zoomIn2d\", \"zoomOut2d\", \"autoScale2d\"], \"responsive\": true}                    ).then(function(){\n",
-       "                            {\n",
-       "const graphDiv = document.getElementById('816a43a0-2d36-4983-9ccf-f9eaa4a3acff');\n",
-       "if (!graphDiv) {\n",
-       "    return;\n",
-       "}\n",
-       "\n",
-       "const parseColor = function (colorValue) {\n",
-       "    if (!colorValue) {\n",
-       "        return null;\n",
-       "    }\n",
-       "\n",
-       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
-       "    if (rgbMatch) {\n",
-       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
-       "        if (channels.every((value) => Number.isFinite(value))) {\n",
-       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
-       "    if (!hexMatch) {\n",
-       "        return null;\n",
-       "    }\n",
-       "\n",
-       "    const normalizedHex = hexMatch[1].length === 3\n",
-       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
-       "        : hexMatch[1];\n",
-       "    return {\n",
-       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
-       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
-       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
-       "    };\n",
-       "};\n",
-       "\n",
-       "const resolveLegendButtonFill = function (opacity) {\n",
-       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
-       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
-       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
-       "        ? graphDiv._fullLayout.font.color\n",
-       "        : null;\n",
-       "    const parsedColor = (\n",
-       "        parseColor(referenceFill)\n",
-       "        || parseColor(fontColor)\n",
-       "        || {red: 68, green: 68, blue: 68}\n",
-       "    );\n",
-       "    return (\n",
-       "        'rgba('\n",
-       "        + parsedColor.red\n",
-       "        + ', '\n",
-       "        + parsedColor.green\n",
-       "        + ', '\n",
-       "        + parsedColor.blue\n",
-       "        + ', '\n",
-       "        + opacity\n",
-       "        + ')'\n",
-       "    );\n",
-       "};\n",
-       "\n",
-       "const updateLegendButtonAppearance = function (legendVisible) {\n",
-       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
-       "    if (!legendButton) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const legendIconPath = legendButton.querySelector('path');\n",
-       "    if (!legendIconPath) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    legendButton.classList.toggle('active', legendVisible);\n",
-       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
-       "    legendIconPath.setAttribute(\n",
-       "        'style',\n",
-       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
-       "    );\n",
-       "};\n",
-       "\n",
-       "const applyLegendVisibility = function (legendVisible) {\n",
-       "    const legend = graphDiv.querySelector('.legend');\n",
-       "    if (legend) {\n",
-       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
-       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
-       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.layout) {\n",
-       "        graphDiv.layout.showlegend = legendVisible;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv._fullLayout) {\n",
-       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "const readLegendVisibility = function () {\n",
-       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
-       "        return true;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
-       "        return false;\n",
-       "    }\n",
-       "\n",
-       "    const legend = graphDiv.querySelector('.legend');\n",
-       "    if (legend) {\n",
-       "        return (\n",
-       "            window.getComputedStyle(legend).display !== 'none'\n",
-       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
-       "        );\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
-       "        return graphDiv.layout.showlegend;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
-       "        return graphDiv._fullLayout.showlegend;\n",
-       "    }\n",
-       "\n",
-       "    return true;\n",
-       "};\n",
-       "\n",
-       "const syncLegendVisibility = function (legendVisible) {\n",
-       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
-       "        ? legendVisible\n",
-       "        : readLegendVisibility();\n",
-       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
-       "    applyLegendVisibility(resolvedLegendVisible);\n",
-       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
-       "    return resolvedLegendVisible;\n",
-       "};\n",
-       "\n",
-       "const toggleLegend = function (event) {\n",
-       "    if (event) {\n",
-       "        event.preventDefault();\n",
-       "        event.stopPropagation();\n",
-       "    }\n",
-       "\n",
-       "    const currentValue = readLegendVisibility();\n",
-       "    const nextValue = !currentValue;\n",
-       "    syncLegendVisibility(nextValue);\n",
-       "};\n",
-       "\n",
-       "const installLegendToggleButton = function () {\n",
-       "    const modebar = graphDiv.querySelector('.modebar');\n",
-       "    if (!modebar) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    if (!modebar.querySelector('.modebar-group')) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
-       "    if (!legendButton) {\n",
-       "        const legendButtonGroup = document.createElement('div');\n",
-       "        legendButtonGroup.className = 'modebar-group';\n",
-       "\n",
-       "        legendButton = document.createElement('a');\n",
-       "        legendButton.className = 'modebar-btn';\n",
-       "        legendButton.href = 'javascript:void(0)';\n",
-       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
-       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
-       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
-       "        legendButton.setAttribute('role', 'button');\n",
-       "        legendButton.setAttribute('tabindex', '0');\n",
-       "        legendButton.innerHTML = [\n",
-       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
-       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
-       "            + ' aria-hidden=\"true\">',\n",
-       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
-       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
-       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
-       "            '</svg>',\n",
-       "        ].join('');\n",
-       "\n",
-       "        legendButtonGroup.appendChild(legendButton);\n",
-       "        modebar.appendChild(legendButtonGroup);\n",
-       "    }\n",
-       "\n",
-       "    legendButton.onclick = toggleLegend;\n",
-       "    legendButton.onkeydown = function (event) {\n",
-       "        if (event.key === 'Enter' || event.key === ' ') {\n",
-       "            toggleLegend(event);\n",
-       "        }\n",
-       "    };\n",
-       "\n",
-       "    syncLegendVisibility();\n",
-       "};\n",
-       "\n",
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-       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
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-       "\n",
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-       "syncLegendVisibility();\n",
-       "window.requestAnimationFrame(installLegendToggleButton);\n",
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-       "                        })                };            </script>        </div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "39",
+   "id": "38",
    "metadata": {},
    "source": [
     "## Step 5: Prepare for Bayesian Sampling\n",
@@ -1462,187 +491,24 @@
     "on the current parameter value and expands them by a chosen multiple of\n",
     "the reported uncertainty.\n",
     "\n",
-    "Default multiplier is 8 to give a wide range for the sampler to\n",
+    "Default `multiplier` is 8 to give a wide range for the sampler to\n",
     "explore, but here we use 3 to speed up the tutorial."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "40",
+   "execution_count": null,
+   "id": "39",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\n"
-     ]
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-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_2d608_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_2d608_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
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-       "      <th id=\"T_2d608_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
-       "      <th id=\"T_2d608_level0_col4\" class=\"col_heading level0 col4\" >value</th>\n",
-       "      <th id=\"T_2d608_level0_col5\" class=\"col_heading level0 col5\" >uncertainty</th>\n",
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-       "      <th id=\"T_2d608_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_2d608_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_2d608_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_2d608_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_2d608_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_2d608_row0_col4\" class=\"data row0 col4\" >3.89134</td>\n",
-       "      <td id=\"T_2d608_row0_col5\" class=\"data row0 col5\" >0.00011</td>\n",
-       "      <td id=\"T_2d608_row0_col6\" class=\"data row0 col6\" >-inf</td>\n",
-       "      <td id=\"T_2d608_row0_col7\" class=\"data row0 col7\" >inf</td>\n",
-       "      <td id=\"T_2d608_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2d608_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_2d608_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_2d608_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
-       "      <td id=\"T_2d608_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
-       "      <td id=\"T_2d608_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
-       "      <td id=\"T_2d608_row1_col4\" class=\"data row1 col4\" >9.13288</td>\n",
-       "      <td id=\"T_2d608_row1_col5\" class=\"data row1 col5\" >0.03329</td>\n",
-       "      <td id=\"T_2d608_row1_col6\" class=\"data row1 col6\" >-inf</td>\n",
-       "      <td id=\"T_2d608_row1_col7\" class=\"data row1 col7\" >inf</td>\n",
-       "      <td id=\"T_2d608_row1_col8\" class=\"data row1 col8\" ></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2d608_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_2d608_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_2d608_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_2d608_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_2d608_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_2d608_row2_col4\" class=\"data row2 col4\" >0.08167</td>\n",
-       "      <td id=\"T_2d608_row2_col5\" class=\"data row2 col5\" >0.00783</td>\n",
-       "      <td id=\"T_2d608_row2_col6\" class=\"data row2 col6\" >-inf</td>\n",
-       "      <td id=\"T_2d608_row2_col7\" class=\"data row2 col7\" >inf</td>\n",
-       "      <td id=\"T_2d608_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2d608_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_2d608_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_2d608_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
-       "      <td id=\"T_2d608_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_2d608_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_2d608_row3_col4\" class=\"data row3 col4\" >-0.11691</td>\n",
-       "      <td id=\"T_2d608_row3_col5\" class=\"data row3 col5\" >0.00566</td>\n",
-       "      <td id=\"T_2d608_row3_col6\" class=\"data row3 col6\" >-inf</td>\n",
-       "      <td id=\"T_2d608_row3_col7\" class=\"data row3 col7\" >inf</td>\n",
-       "      <td id=\"T_2d608_row3_col8\" class=\"data row3 col8\" >deg²</td>\n",
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-       "      <th id=\"T_2d608_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_2d608_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
-       "      <td id=\"T_2d608_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
-       "      <td id=\"T_2d608_row4_col2\" class=\"data row4 col2\" ></td>\n",
-       "      <td id=\"T_2d608_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_2d608_row4_col4\" class=\"data row4 col4\" >0.63057</td>\n",
-       "      <td id=\"T_2d608_row4_col5\" class=\"data row4 col5\" >0.00191</td>\n",
-       "      <td id=\"T_2d608_row4_col6\" class=\"data row4 col6\" >-inf</td>\n",
-       "      <td id=\"T_2d608_row4_col7\" class=\"data row4 col7\" >inf</td>\n",
-       "      <td id=\"T_2d608_row4_col8\" class=\"data row4 col8\" >deg</td>\n",
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-       "<pandas.io.formats.style.Styler at 0x36a332ad0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.display.free_params()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "41",
+   "execution_count": null,
+   "id": "40",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1652,7 +518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42",
+   "id": "41",
    "metadata": {},
    "source": [
     "Displaying the free parameters again is a convenient way to confirm\n",
@@ -1662,180 +528,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "43",
+   "execution_count": null,
+   "id": "42",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mFree parameters for both structures \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🧩 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\u001b[1;34m and experiments \u001b[0m\u001b[1;34m(\u001b[0m\u001b[1;34m🔬 data blocks\u001b[0m\u001b[1;34m)\u001b[0m\n"
-     ]
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-       "  text-align: right;\n",
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-       "<table id=\"T_d733a\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_d733a_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_d733a_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
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-       "      <th id=\"T_d733a_level0_col8\" class=\"col_heading level0 col8\" >units</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d733a_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_d733a_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_d733a_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_d733a_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_d733a_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_d733a_row0_col4\" class=\"data row0 col4\" >3.89134</td>\n",
-       "      <td id=\"T_d733a_row0_col5\" class=\"data row0 col5\" >0.00011</td>\n",
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-       "      <td id=\"T_d733a_row0_col8\" class=\"data row0 col8\" >Å</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d733a_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_d733a_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_d733a_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
-       "      <td id=\"T_d733a_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
-       "      <td id=\"T_d733a_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
-       "      <td id=\"T_d733a_row1_col4\" class=\"data row1 col4\" >9.13288</td>\n",
-       "      <td id=\"T_d733a_row1_col5\" class=\"data row1 col5\" >0.03329</td>\n",
-       "      <td id=\"T_d733a_row1_col6\" class=\"data row1 col6\" >9.03302</td>\n",
-       "      <td id=\"T_d733a_row1_col7\" class=\"data row1 col7\" >9.23274</td>\n",
-       "      <td id=\"T_d733a_row1_col8\" class=\"data row1 col8\" ></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d733a_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_d733a_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_d733a_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_d733a_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_d733a_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_d733a_row2_col4\" class=\"data row2 col4\" >0.08167</td>\n",
-       "      <td id=\"T_d733a_row2_col5\" class=\"data row2 col5\" >0.00783</td>\n",
-       "      <td id=\"T_d733a_row2_col6\" class=\"data row2 col6\" >0.05817</td>\n",
-       "      <td id=\"T_d733a_row2_col7\" class=\"data row2 col7\" >0.10517</td>\n",
-       "      <td id=\"T_d733a_row2_col8\" class=\"data row2 col8\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d733a_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_d733a_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_d733a_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
-       "      <td id=\"T_d733a_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_d733a_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_d733a_row3_col4\" class=\"data row3 col4\" >-0.11691</td>\n",
-       "      <td id=\"T_d733a_row3_col5\" class=\"data row3 col5\" >0.00566</td>\n",
-       "      <td id=\"T_d733a_row3_col6\" class=\"data row3 col6\" >-0.13389</td>\n",
-       "      <td id=\"T_d733a_row3_col7\" class=\"data row3 col7\" >-0.09993</td>\n",
-       "      <td id=\"T_d733a_row3_col8\" class=\"data row3 col8\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d733a_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_d733a_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
-       "      <td id=\"T_d733a_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
-       "      <td id=\"T_d733a_row4_col2\" class=\"data row4 col2\" ></td>\n",
-       "      <td id=\"T_d733a_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_d733a_row4_col4\" class=\"data row4 col4\" >0.63057</td>\n",
-       "      <td id=\"T_d733a_row4_col5\" class=\"data row4 col5\" >0.00191</td>\n",
-       "      <td id=\"T_d733a_row4_col6\" class=\"data row4 col6\" >0.62483</td>\n",
-       "      <td id=\"T_d733a_row4_col7\" class=\"data row4 col7\" >0.63631</td>\n",
-       "      <td id=\"T_d733a_row4_col8\" class=\"data row4 col8\" >deg</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x36a332ad0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.display.free_params()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "44",
+   "id": "43",
    "metadata": {},
    "source": [
     "## Step 6: Configure and Run DREAM\n",
@@ -1848,486 +551,57 @@
     "needed, the DREAM API also lets you tune how chains are initialized\n",
     "through the `init` setting. Other sampler settings such as `thin` and\n",
     "`pop` can be adjusted  as well, but here we keep them at their\n",
-    "defaults."
+    "defaults.\n",
+    "\n",
+    "Default `steps` is 1000, which is often need to be increased for a\n",
+    "real analysis to ensure good convergence and sampling of the posterior\n",
+    "distribution. Here we use much smaller value to speed up the tutorial,\n",
+    "but this is not recommended for a real analysis."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "45",
+   "execution_count": null,
+   "id": "44",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mMinimizer types\u001b[0m\n"
-     ]
-    },
-    {
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-       "  <thead>\n",
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-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_7ea7d_level0_col0\" class=\"col_heading level0 col0\" ></th>\n",
-       "      <th id=\"T_7ea7d_level0_col1\" class=\"col_heading level0 col1\" >Type</th>\n",
-       "      <th id=\"T_7ea7d_level0_col2\" class=\"col_heading level0 col2\" >Description</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_7ea7d_row0_col0\" class=\"data row0 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row0_col1\" class=\"data row0 col1\" >bumps</td>\n",
-       "      <td id=\"T_7ea7d_row0_col2\" class=\"data row0 col2\" >Bumps library using the default Levenberg-Marquardt method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_7ea7d_row1_col0\" class=\"data row1 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row1_col1\" class=\"data row1 col1\" >bumps (amoeba)</td>\n",
-       "      <td id=\"T_7ea7d_row1_col2\" class=\"data row1 col2\" >Bumps library with Nelder-Mead simplex method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_7ea7d_row2_col0\" class=\"data row2 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row2_col1\" class=\"data row2 col1\" >bumps (de)</td>\n",
-       "      <td id=\"T_7ea7d_row2_col2\" class=\"data row2 col2\" >Bumps library with differential evolution method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_7ea7d_row3_col0\" class=\"data row3 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row3_col1\" class=\"data row3 col1\" >bumps (dream)</td>\n",
-       "      <td id=\"T_7ea7d_row3_col2\" class=\"data row3 col2\" >Bumps library with DREAM Bayesian sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_7ea7d_row4_col0\" class=\"data row4 col0\" >*</td>\n",
-       "      <td id=\"T_7ea7d_row4_col1\" class=\"data row4 col1\" >bumps (lm)</td>\n",
-       "      <td id=\"T_7ea7d_row4_col2\" class=\"data row4 col2\" >Bumps library with Levenberg-Marquardt method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_7ea7d_row5_col0\" class=\"data row5 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row5_col1\" class=\"data row5 col1\" >dfols</td>\n",
-       "      <td id=\"T_7ea7d_row5_col2\" class=\"data row5 col2\" >DFO-LS library for derivative-free least-squares optimization</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_7ea7d_row6_col0\" class=\"data row6 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row6_col1\" class=\"data row6 col1\" >lmfit</td>\n",
-       "      <td id=\"T_7ea7d_row6_col2\" class=\"data row6 col2\" >LMFIT library using the default Levenberg-Marquardt least squares method</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_7ea7d_row7_col0\" class=\"data row7 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row7_col1\" class=\"data row7 col1\" >lmfit (least_squares)</td>\n",
-       "      <td id=\"T_7ea7d_row7_col2\" class=\"data row7 col2\" >LMFIT library with SciPy's trust region reflective algorithm</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7ea7d_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_7ea7d_row8_col0\" class=\"data row8 col0\" ></td>\n",
-       "      <td id=\"T_7ea7d_row8_col1\" class=\"data row8 col1\" >lmfit (leastsq)</td>\n",
-       "      <td id=\"T_7ea7d_row8_col2\" class=\"data row8 col2\" >LMFIT library with Levenberg-Marquardt least squares method</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x36a332ad0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.fit.show_minimizer_types()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "46",
+   "execution_count": null,
+   "id": "45",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mCurrent minimizer changed to\u001b[0m\n",
-      "bumps \u001b[1m(\u001b[0mdream\u001b[1m)\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.fit.minimizer_type = 'bumps (dream)'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "47",
+   "execution_count": null,
+   "id": "46",
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 100  # 1000"
+    "project.analysis.fit.minimizer.steps = 50  # 1000"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "48",
+   "execution_count": null,
+   "id": "47",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mStandard fitting\u001b[0m\n",
-      "📋 Using experiment 🔬 \u001b[32m'hrpt'\u001b[0m for \u001b[32m'single'\u001b[0m fitting\n",
-      "🚀 Starting fit process with \u001b[32m'bumps \u001b[0m\u001b[32m(\u001b[0m\u001b[32mdream\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[33m...\u001b[0m\n",
-      "📈 Bayesian sampling progress:\n"
-     ]
-    },
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_d9212_level0_col0\" class=\"col_heading level0 col0\" >iteration</th>\n",
-       "      <th id=\"T_d9212_level0_col1\" class=\"col_heading level0 col1\" >progress</th>\n",
-       "      <th id=\"T_d9212_level0_col2\" class=\"col_heading level0 col2\" >time (s)</th>\n",
-       "      <th id=\"T_d9212_level0_col3\" class=\"col_heading level0 col3\" >log posterior</th>\n",
-       "      <th id=\"T_d9212_level0_col4\" class=\"col_heading level0 col4\" >phase</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_d9212_row0_col0\" class=\"data row0 col0\" >1/151</td>\n",
-       "      <td id=\"T_d9212_row0_col1\" class=\"data row0 col1\" >0.7%</td>\n",
-       "      <td id=\"T_d9212_row0_col2\" class=\"data row0 col2\" >0.43</td>\n",
-       "      <td id=\"T_d9212_row0_col3\" class=\"data row0 col3\" >-1157.01</td>\n",
-       "      <td id=\"T_d9212_row0_col4\" class=\"data row0 col4\" >burn-in</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_d9212_row1_col0\" class=\"data row1 col0\" >13/151</td>\n",
-       "      <td id=\"T_d9212_row1_col1\" class=\"data row1 col1\" >8.6%</td>\n",
-       "      <td id=\"T_d9212_row1_col2\" class=\"data row1 col2\" >5.49</td>\n",
-       "      <td id=\"T_d9212_row1_col3\" class=\"data row1 col3\" >-1157.54</td>\n",
-       "      <td id=\"T_d9212_row1_col4\" class=\"data row1 col4\" >burn-in</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_d9212_row2_col0\" class=\"data row2 col0\" >26/151</td>\n",
-       "      <td id=\"T_d9212_row2_col1\" class=\"data row2 col1\" >17.2%</td>\n",
-       "      <td id=\"T_d9212_row2_col2\" class=\"data row2 col2\" >10.82</td>\n",
-       "      <td id=\"T_d9212_row2_col3\" class=\"data row2 col3\" >-1158.21</td>\n",
-       "      <td id=\"T_d9212_row2_col4\" class=\"data row2 col4\" >burn-in</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_d9212_row3_col0\" class=\"data row3 col0\" >38/151</td>\n",
-       "      <td id=\"T_d9212_row3_col1\" class=\"data row3 col1\" >25.2%</td>\n",
-       "      <td id=\"T_d9212_row3_col2\" class=\"data row3 col2\" >15.85</td>\n",
-       "      <td id=\"T_d9212_row3_col3\" class=\"data row3 col3\" >-1159.61</td>\n",
-       "      <td id=\"T_d9212_row3_col4\" class=\"data row3 col4\" >burn-in</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_d9212_row4_col0\" class=\"data row4 col0\" >50/151</td>\n",
-       "      <td id=\"T_d9212_row4_col1\" class=\"data row4 col1\" >33.1%</td>\n",
-       "      <td id=\"T_d9212_row4_col2\" class=\"data row4 col2\" >20.89</td>\n",
-       "      <td id=\"T_d9212_row4_col3\" class=\"data row4 col3\" >-1159.52</td>\n",
-       "      <td id=\"T_d9212_row4_col4\" class=\"data row4 col4\" >burn-in</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
-       "      <td id=\"T_d9212_row5_col0\" class=\"data row5 col0\" >51/151</td>\n",
-       "      <td id=\"T_d9212_row5_col1\" class=\"data row5 col1\" >33.8%</td>\n",
-       "      <td id=\"T_d9212_row5_col2\" class=\"data row5 col2\" >21.36</td>\n",
-       "      <td id=\"T_d9212_row5_col3\" class=\"data row5 col3\" >-1159.49</td>\n",
-       "      <td id=\"T_d9212_row5_col4\" class=\"data row5 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
-       "      <td id=\"T_d9212_row6_col0\" class=\"data row6 col0\" >56/151</td>\n",
-       "      <td id=\"T_d9212_row6_col1\" class=\"data row6 col1\" >37.1%</td>\n",
-       "      <td id=\"T_d9212_row6_col2\" class=\"data row6 col2\" >23.41</td>\n",
-       "      <td id=\"T_d9212_row6_col3\" class=\"data row6 col3\" >-1159.26</td>\n",
-       "      <td id=\"T_d9212_row6_col4\" class=\"data row6 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
-       "      <td id=\"T_d9212_row7_col0\" class=\"data row7 col0\" >62/151</td>\n",
-       "      <td id=\"T_d9212_row7_col1\" class=\"data row7 col1\" >41.1%</td>\n",
-       "      <td id=\"T_d9212_row7_col2\" class=\"data row7 col2\" >25.92</td>\n",
-       "      <td id=\"T_d9212_row7_col3\" class=\"data row7 col3\" >-1159.52</td>\n",
-       "      <td id=\"T_d9212_row7_col4\" class=\"data row7 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
-       "      <td id=\"T_d9212_row8_col0\" class=\"data row8 col0\" >67/151</td>\n",
-       "      <td id=\"T_d9212_row8_col1\" class=\"data row8 col1\" >44.4%</td>\n",
-       "      <td id=\"T_d9212_row8_col2\" class=\"data row8 col2\" >27.96</td>\n",
-       "      <td id=\"T_d9212_row8_col3\" class=\"data row8 col3\" >-1159.24</td>\n",
-       "      <td id=\"T_d9212_row8_col4\" class=\"data row8 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
-       "      <td id=\"T_d9212_row9_col0\" class=\"data row9 col0\" >72/151</td>\n",
-       "      <td id=\"T_d9212_row9_col1\" class=\"data row9 col1\" >47.7%</td>\n",
-       "      <td id=\"T_d9212_row9_col2\" class=\"data row9 col2\" >30.23</td>\n",
-       "      <td id=\"T_d9212_row9_col3\" class=\"data row9 col3\" >-1159.00</td>\n",
-       "      <td id=\"T_d9212_row9_col4\" class=\"data row9 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
-       "      <td id=\"T_d9212_row10_col0\" class=\"data row10 col0\" >77/151</td>\n",
-       "      <td id=\"T_d9212_row10_col1\" class=\"data row10 col1\" >51.0%</td>\n",
-       "      <td id=\"T_d9212_row10_col2\" class=\"data row10 col2\" >32.42</td>\n",
-       "      <td id=\"T_d9212_row10_col3\" class=\"data row10 col3\" >-1158.87</td>\n",
-       "      <td id=\"T_d9212_row10_col4\" class=\"data row10 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
-       "      <td id=\"T_d9212_row11_col0\" class=\"data row11 col0\" >83/151</td>\n",
-       "      <td id=\"T_d9212_row11_col1\" class=\"data row11 col1\" >55.0%</td>\n",
-       "      <td id=\"T_d9212_row11_col2\" class=\"data row11 col2\" >34.90</td>\n",
-       "      <td id=\"T_d9212_row11_col3\" class=\"data row11 col3\" >-1158.90</td>\n",
-       "      <td id=\"T_d9212_row11_col4\" class=\"data row11 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
-       "      <td id=\"T_d9212_row12_col0\" class=\"data row12 col0\" >88/151</td>\n",
-       "      <td id=\"T_d9212_row12_col1\" class=\"data row12 col1\" >58.3%</td>\n",
-       "      <td id=\"T_d9212_row12_col2\" class=\"data row12 col2\" >36.91</td>\n",
-       "      <td id=\"T_d9212_row12_col3\" class=\"data row12 col3\" >-1159.39</td>\n",
-       "      <td id=\"T_d9212_row12_col4\" class=\"data row12 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
-       "      <td id=\"T_d9212_row13_col0\" class=\"data row13 col0\" >93/151</td>\n",
-       "      <td id=\"T_d9212_row13_col1\" class=\"data row13 col1\" >61.6%</td>\n",
-       "      <td id=\"T_d9212_row13_col2\" class=\"data row13 col2\" >39.10</td>\n",
-       "      <td id=\"T_d9212_row13_col3\" class=\"data row13 col3\" >-1159.59</td>\n",
-       "      <td id=\"T_d9212_row13_col4\" class=\"data row13 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
-       "      <td id=\"T_d9212_row14_col0\" class=\"data row14 col0\" >98/151</td>\n",
-       "      <td id=\"T_d9212_row14_col1\" class=\"data row14 col1\" >64.9%</td>\n",
-       "      <td id=\"T_d9212_row14_col2\" class=\"data row14 col2\" >41.21</td>\n",
-       "      <td id=\"T_d9212_row14_col3\" class=\"data row14 col3\" >-1159.22</td>\n",
-       "      <td id=\"T_d9212_row14_col4\" class=\"data row14 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
-       "      <td id=\"T_d9212_row15_col0\" class=\"data row15 col0\" >104/151</td>\n",
-       "      <td id=\"T_d9212_row15_col1\" class=\"data row15 col1\" >68.9%</td>\n",
-       "      <td id=\"T_d9212_row15_col2\" class=\"data row15 col2\" >43.71</td>\n",
-       "      <td id=\"T_d9212_row15_col3\" class=\"data row15 col3\" >-1159.44</td>\n",
-       "      <td id=\"T_d9212_row15_col4\" class=\"data row15 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
-       "      <td id=\"T_d9212_row16_col0\" class=\"data row16 col0\" >109/151</td>\n",
-       "      <td id=\"T_d9212_row16_col1\" class=\"data row16 col1\" >72.2%</td>\n",
-       "      <td id=\"T_d9212_row16_col2\" class=\"data row16 col2\" >45.72</td>\n",
-       "      <td id=\"T_d9212_row16_col3\" class=\"data row16 col3\" >-1159.53</td>\n",
-       "      <td id=\"T_d9212_row16_col4\" class=\"data row16 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
-       "      <td id=\"T_d9212_row17_col0\" class=\"data row17 col0\" >114/151</td>\n",
-       "      <td id=\"T_d9212_row17_col1\" class=\"data row17 col1\" >75.5%</td>\n",
-       "      <td id=\"T_d9212_row17_col2\" class=\"data row17 col2\" >47.82</td>\n",
-       "      <td id=\"T_d9212_row17_col3\" class=\"data row17 col3\" >-1159.41</td>\n",
-       "      <td id=\"T_d9212_row17_col4\" class=\"data row17 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
-       "      <td id=\"T_d9212_row18_col0\" class=\"data row18 col0\" >119/151</td>\n",
-       "      <td id=\"T_d9212_row18_col1\" class=\"data row18 col1\" >78.8%</td>\n",
-       "      <td id=\"T_d9212_row18_col2\" class=\"data row18 col2\" >49.95</td>\n",
-       "      <td id=\"T_d9212_row18_col3\" class=\"data row18 col3\" >-1159.63</td>\n",
-       "      <td id=\"T_d9212_row18_col4\" class=\"data row18 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
-       "      <td id=\"T_d9212_row19_col0\" class=\"data row19 col0\" >125/151</td>\n",
-       "      <td id=\"T_d9212_row19_col1\" class=\"data row19 col1\" >82.8%</td>\n",
-       "      <td id=\"T_d9212_row19_col2\" class=\"data row19 col2\" >52.50</td>\n",
-       "      <td id=\"T_d9212_row19_col3\" class=\"data row19 col3\" >-1159.64</td>\n",
-       "      <td id=\"T_d9212_row19_col4\" class=\"data row19 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
-       "      <td id=\"T_d9212_row20_col0\" class=\"data row20 col0\" >130/151</td>\n",
-       "      <td id=\"T_d9212_row20_col1\" class=\"data row20 col1\" >86.1%</td>\n",
-       "      <td id=\"T_d9212_row20_col2\" class=\"data row20 col2\" >54.61</td>\n",
-       "      <td id=\"T_d9212_row20_col3\" class=\"data row20 col3\" >-1159.33</td>\n",
-       "      <td id=\"T_d9212_row20_col4\" class=\"data row20 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row21\" class=\"row_heading level0 row21\" >22</th>\n",
-       "      <td id=\"T_d9212_row21_col0\" class=\"data row21 col0\" >135/151</td>\n",
-       "      <td id=\"T_d9212_row21_col1\" class=\"data row21 col1\" >89.4%</td>\n",
-       "      <td id=\"T_d9212_row21_col2\" class=\"data row21 col2\" >56.70</td>\n",
-       "      <td id=\"T_d9212_row21_col3\" class=\"data row21 col3\" >-1159.39</td>\n",
-       "      <td id=\"T_d9212_row21_col4\" class=\"data row21 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row22\" class=\"row_heading level0 row22\" >23</th>\n",
-       "      <td id=\"T_d9212_row22_col0\" class=\"data row22 col0\" >140/151</td>\n",
-       "      <td id=\"T_d9212_row22_col1\" class=\"data row22 col1\" >92.7%</td>\n",
-       "      <td id=\"T_d9212_row22_col2\" class=\"data row22 col2\" >58.78</td>\n",
-       "      <td id=\"T_d9212_row22_col3\" class=\"data row22 col3\" >-1159.63</td>\n",
-       "      <td id=\"T_d9212_row22_col4\" class=\"data row22 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row23\" class=\"row_heading level0 row23\" >24</th>\n",
-       "      <td id=\"T_d9212_row23_col0\" class=\"data row23 col0\" >146/151</td>\n",
-       "      <td id=\"T_d9212_row23_col1\" class=\"data row23 col1\" >96.7%</td>\n",
-       "      <td id=\"T_d9212_row23_col2\" class=\"data row23 col2\" >61.23</td>\n",
-       "      <td id=\"T_d9212_row23_col3\" class=\"data row23 col3\" >-1159.52</td>\n",
-       "      <td id=\"T_d9212_row23_col4\" class=\"data row23 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d9212_level0_row24\" class=\"row_heading level0 row24\" >25</th>\n",
-       "      <td id=\"T_d9212_row24_col0\" class=\"data row24 col0\" >151/151</td>\n",
-       "      <td id=\"T_d9212_row24_col1\" class=\"data row24 col1\" >100.0%</td>\n",
-       "      <td id=\"T_d9212_row24_col2\" class=\"data row24 col2\" >63.47</td>\n",
-       "      <td id=\"T_d9212_row24_col3\" class=\"data row24 col3\" >-1159.74</td>\n",
-       "      <td id=\"T_d9212_row24_col4\" class=\"data row24 col4\" >sampling</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "⚠️ DREAM sampling completed, but convergence diagnostics indicate the posterior may be poorly mixed.                              \n",
-      "✅ Bayesian sampling complete.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.fit()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "49",
+   "id": "48",
    "metadata": {},
    "source": [
     "## Step 7: Inspect Bayesian Results\n",
@@ -2339,386 +613,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "50",
+   "execution_count": null,
+   "id": "49",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1;34mBayesian fit results\u001b[0m\n",
-      "✅ Success: \u001b[3;92mTrue\u001b[0m\n",
-      "i Status: DREAM sampling completed\n",
-      "🧪 Sampler: dream\n",
-      "🎯 Committed point estimate: Max posterior\n",
-      "🔁 Sampler completed: \u001b[3;92mTrue\u001b[0m\n",
-      "⏱️ Fitting time: \u001b[1;36m63.47\u001b[0m seconds\n",
-      "📏 Goodness-of-fit \u001b[1m(\u001b[0mreduced χ²\u001b[1m)\u001b[0m: \u001b[1;36m1.29\u001b[0m\n",
-      "📉 Best log-posterior: \u001b[1;36m-1157.00\u001b[0m\n",
-      "⚙️ Sampler settings: random_seed=1708713996, steps=100, burn=50, thin=1, pop=4, samples=2000\n",
-      "📊 Convergence: converged=\u001b[31mfailed\u001b[0m, max_r_hat=\u001b[31m1.291\u001b[0m, min_ess_bulk=\u001b[31m56.4\u001b[0m, draws=100, chains=20\n",
-      "📏 R-factor \u001b[1m(\u001b[0mRf\u001b[1m)\u001b[0m: \u001b[1;36m5.65\u001b[0m%\n",
-      "📏 R-factor squared \u001b[1m(\u001b[0mRf²\u001b[1m)\u001b[0m: \u001b[1;36m4.92\u001b[0m%\n",
-      "📏 Weighted R-factor \u001b[1m(\u001b[0mwR\u001b[1m)\u001b[0m: \u001b[1;36m4.09\u001b[0m%\n",
-      "📈 Committed parameters:\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_6e9f7_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_6e9f7_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
-       "      <th id=\"T_6e9f7_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
-       "      <th id=\"T_6e9f7_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
-       "      <th id=\"T_6e9f7_level0_col4\" class=\"col_heading level0 col4\" >start</th>\n",
-       "      <th id=\"T_6e9f7_level0_col5\" class=\"col_heading level0 col5\" >max posterior</th>\n",
-       "      <th id=\"T_6e9f7_level0_col6\" class=\"col_heading level0 col6\" >uncertainty</th>\n",
-       "      <th id=\"T_6e9f7_level0_col7\" class=\"col_heading level0 col7\" >units</th>\n",
-       "      <th id=\"T_6e9f7_level0_col8\" class=\"col_heading level0 col8\" >change</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6e9f7_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_6e9f7_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_6e9f7_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_6e9f7_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_6e9f7_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_6e9f7_row0_col4\" class=\"data row0 col4\" >3.8913</td>\n",
-       "      <td id=\"T_6e9f7_row0_col5\" class=\"data row0 col5\" >3.8913</td>\n",
-       "      <td id=\"T_6e9f7_row0_col6\" class=\"data row0 col6\" >0.0001</td>\n",
-       "      <td id=\"T_6e9f7_row0_col7\" class=\"data row0 col7\" >Å</td>\n",
-       "      <td id=\"T_6e9f7_row0_col8\" class=\"data row0 col8\" >0.00 % ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6e9f7_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_6e9f7_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_6e9f7_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
-       "      <td id=\"T_6e9f7_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
-       "      <td id=\"T_6e9f7_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
-       "      <td id=\"T_6e9f7_row1_col4\" class=\"data row1 col4\" >9.1329</td>\n",
-       "      <td id=\"T_6e9f7_row1_col5\" class=\"data row1 col5\" >9.1336</td>\n",
-       "      <td id=\"T_6e9f7_row1_col6\" class=\"data row1 col6\" >0.0305</td>\n",
-       "      <td id=\"T_6e9f7_row1_col7\" class=\"data row1 col7\" ></td>\n",
-       "      <td id=\"T_6e9f7_row1_col8\" class=\"data row1 col8\" >0.01 % ↑</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6e9f7_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_6e9f7_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_6e9f7_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_6e9f7_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_6e9f7_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_6e9f7_row2_col4\" class=\"data row2 col4\" >0.0817</td>\n",
-       "      <td id=\"T_6e9f7_row2_col5\" class=\"data row2 col5\" >0.0817</td>\n",
-       "      <td id=\"T_6e9f7_row2_col6\" class=\"data row2 col6\" >0.0060</td>\n",
-       "      <td id=\"T_6e9f7_row2_col7\" class=\"data row2 col7\" >deg²</td>\n",
-       "      <td id=\"T_6e9f7_row2_col8\" class=\"data row2 col8\" >0.01 % ↓</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6e9f7_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_6e9f7_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_6e9f7_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
-       "      <td id=\"T_6e9f7_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_6e9f7_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_6e9f7_row3_col4\" class=\"data row3 col4\" >-0.1169</td>\n",
-       "      <td id=\"T_6e9f7_row3_col5\" class=\"data row3 col5\" >-0.1169</td>\n",
-       "      <td id=\"T_6e9f7_row3_col6\" class=\"data row3 col6\" >0.0044</td>\n",
-       "      <td id=\"T_6e9f7_row3_col7\" class=\"data row3 col7\" >deg²</td>\n",
-       "      <td id=\"T_6e9f7_row3_col8\" class=\"data row3 col8\" >0.01 % ↑</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6e9f7_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_6e9f7_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
-       "      <td id=\"T_6e9f7_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
-       "      <td id=\"T_6e9f7_row4_col2\" class=\"data row4 col2\" ></td>\n",
-       "      <td id=\"T_6e9f7_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_6e9f7_row4_col4\" class=\"data row4 col4\" >0.6306</td>\n",
-       "      <td id=\"T_6e9f7_row4_col5\" class=\"data row4 col5\" >0.6303</td>\n",
-       "      <td id=\"T_6e9f7_row4_col6\" class=\"data row4 col6\" >0.0017</td>\n",
-       "      <td id=\"T_6e9f7_row4_col7\" class=\"data row4 col7\" >deg</td>\n",
-       "      <td id=\"T_6e9f7_row4_col8\" class=\"data row4 col8\" >0.05 % ↓</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x36a332d50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📊 Posterior parameter summaries:\n"
-     ]
-    },
-    {
-     "data": {
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-       "<table id=\"T_077c2\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_077c2_level0_col0\" class=\"col_heading level0 col0\" >datablock</th>\n",
-       "      <th id=\"T_077c2_level0_col1\" class=\"col_heading level0 col1\" >category</th>\n",
-       "      <th id=\"T_077c2_level0_col2\" class=\"col_heading level0 col2\" >entry</th>\n",
-       "      <th id=\"T_077c2_level0_col3\" class=\"col_heading level0 col3\" >parameter</th>\n",
-       "      <th id=\"T_077c2_level0_col4\" class=\"col_heading level0 col4\" >median</th>\n",
-       "      <th id=\"T_077c2_level0_col5\" class=\"col_heading level0 col5\" >std</th>\n",
-       "      <th id=\"T_077c2_level0_col6\" class=\"col_heading level0 col6\" >68% interval</th>\n",
-       "      <th id=\"T_077c2_level0_col7\" class=\"col_heading level0 col7\" >95% interval</th>\n",
-       "      <th id=\"T_077c2_level0_col8\" class=\"col_heading level0 col8\" >r_hat</th>\n",
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-       "      <th id=\"T_077c2_level0_col10\" class=\"col_heading level0 col10\" >units</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_077c2_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_077c2_row0_col0\" class=\"data row0 col0\" >lbco</td>\n",
-       "      <td id=\"T_077c2_row0_col1\" class=\"data row0 col1\" >cell</td>\n",
-       "      <td id=\"T_077c2_row0_col2\" class=\"data row0 col2\" ></td>\n",
-       "      <td id=\"T_077c2_row0_col3\" class=\"data row0 col3\" >length_a</td>\n",
-       "      <td id=\"T_077c2_row0_col4\" class=\"data row0 col4\" >3.8913</td>\n",
-       "      <td id=\"T_077c2_row0_col5\" class=\"data row0 col5\" >0.0001</td>\n",
-       "      <td id=\"T_077c2_row0_col6\" class=\"data row0 col6\" >[3.8912, 3.8914]</td>\n",
-       "      <td id=\"T_077c2_row0_col7\" class=\"data row0 col7\" >[3.8912, 3.8915]</td>\n",
-       "      <td id=\"T_077c2_row0_col8\" class=\"data row0 col8\" >1.291</td>\n",
-       "      <td id=\"T_077c2_row0_col9\" class=\"data row0 col9\" >56.4</td>\n",
-       "      <td id=\"T_077c2_row0_col10\" class=\"data row0 col10\" >Å</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_077c2_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
-       "      <td id=\"T_077c2_row1_col0\" class=\"data row1 col0\" >hrpt</td>\n",
-       "      <td id=\"T_077c2_row1_col1\" class=\"data row1 col1\" >linked_phases</td>\n",
-       "      <td id=\"T_077c2_row1_col2\" class=\"data row1 col2\" >lbco</td>\n",
-       "      <td id=\"T_077c2_row1_col3\" class=\"data row1 col3\" >scale</td>\n",
-       "      <td id=\"T_077c2_row1_col4\" class=\"data row1 col4\" >9.1341</td>\n",
-       "      <td id=\"T_077c2_row1_col5\" class=\"data row1 col5\" >0.0305</td>\n",
-       "      <td id=\"T_077c2_row1_col6\" class=\"data row1 col6\" >[9.1029, 9.1650]</td>\n",
-       "      <td id=\"T_077c2_row1_col7\" class=\"data row1 col7\" >[9.0758, 9.1901]</td>\n",
-       "      <td id=\"T_077c2_row1_col8\" class=\"data row1 col8\" >1.208</td>\n",
-       "      <td id=\"T_077c2_row1_col9\" class=\"data row1 col9\" >78.6</td>\n",
-       "      <td id=\"T_077c2_row1_col10\" class=\"data row1 col10\" ></td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_077c2_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
-       "      <td id=\"T_077c2_row2_col0\" class=\"data row2 col0\" >hrpt</td>\n",
-       "      <td id=\"T_077c2_row2_col1\" class=\"data row2 col1\" >peak</td>\n",
-       "      <td id=\"T_077c2_row2_col2\" class=\"data row2 col2\" ></td>\n",
-       "      <td id=\"T_077c2_row2_col3\" class=\"data row2 col3\" >broad_gauss_u</td>\n",
-       "      <td id=\"T_077c2_row2_col4\" class=\"data row2 col4\" >0.0816</td>\n",
-       "      <td id=\"T_077c2_row2_col5\" class=\"data row2 col5\" >0.0060</td>\n",
-       "      <td id=\"T_077c2_row2_col6\" class=\"data row2 col6\" >[0.0750, 0.0872]</td>\n",
-       "      <td id=\"T_077c2_row2_col7\" class=\"data row2 col7\" >[0.0684, 0.0928]</td>\n",
-       "      <td id=\"T_077c2_row2_col8\" class=\"data row2 col8\" >1.230</td>\n",
-       "      <td id=\"T_077c2_row2_col9\" class=\"data row2 col9\" >72.5</td>\n",
-       "      <td id=\"T_077c2_row2_col10\" class=\"data row2 col10\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_077c2_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
-       "      <td id=\"T_077c2_row3_col0\" class=\"data row3 col0\" >hrpt</td>\n",
-       "      <td id=\"T_077c2_row3_col1\" class=\"data row3 col1\" >peak</td>\n",
-       "      <td id=\"T_077c2_row3_col2\" class=\"data row3 col2\" ></td>\n",
-       "      <td id=\"T_077c2_row3_col3\" class=\"data row3 col3\" >broad_gauss_v</td>\n",
-       "      <td id=\"T_077c2_row3_col4\" class=\"data row3 col4\" >-0.1167</td>\n",
-       "      <td id=\"T_077c2_row3_col5\" class=\"data row3 col5\" >0.0044</td>\n",
-       "      <td id=\"T_077c2_row3_col6\" class=\"data row3 col6\" >[-0.1211, -0.1126]</td>\n",
-       "      <td id=\"T_077c2_row3_col7\" class=\"data row3 col7\" >[-0.1251, -0.1076]</td>\n",
-       "      <td id=\"T_077c2_row3_col8\" class=\"data row3 col8\" >1.226</td>\n",
-       "      <td id=\"T_077c2_row3_col9\" class=\"data row3 col9\" >72.6</td>\n",
-       "      <td id=\"T_077c2_row3_col10\" class=\"data row3 col10\" >deg²</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_077c2_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
-       "      <td id=\"T_077c2_row4_col0\" class=\"data row4 col0\" >hrpt</td>\n",
-       "      <td id=\"T_077c2_row4_col1\" class=\"data row4 col1\" >instrument</td>\n",
-       "      <td id=\"T_077c2_row4_col2\" class=\"data row4 col2\" ></td>\n",
-       "      <td id=\"T_077c2_row4_col3\" class=\"data row4 col3\" >twotheta_offset</td>\n",
-       "      <td id=\"T_077c2_row4_col4\" class=\"data row4 col4\" >0.6304</td>\n",
-       "      <td id=\"T_077c2_row4_col5\" class=\"data row4 col5\" >0.0017</td>\n",
-       "      <td id=\"T_077c2_row4_col6\" class=\"data row4 col6\" >[0.6286, 0.6320]</td>\n",
-       "      <td id=\"T_077c2_row4_col7\" class=\"data row4 col7\" >[0.6271, 0.6337]</td>\n",
-       "      <td id=\"T_077c2_row4_col8\" class=\"data row4 col8\" >1.281</td>\n",
-       "      <td id=\"T_077c2_row4_col9\" class=\"data row4 col9\" >58.6</td>\n",
-       "      <td id=\"T_077c2_row4_col10\" class=\"data row4 col10\" >deg</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x36a332d50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "⚠️ \u001b[31mr_hat\u001b[0m: exceeds 1.01 (consider longer sampling, tighter bounds, or reparameterization).                                         \n",
-      "⚠️ \u001b[31mess_bulk\u001b[0m: less than 400 (consider longer sampling, tighter bounds, or reparameterization).                                     \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.analysis.display.fit_results()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "51",
+   "id": "50",
    "metadata": {},
    "source": [
     "The correlation and posterior-pair plots are complementary:\n",
@@ -2731,300 +636,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "52",
+   "execution_count": null,
+   "id": "51",
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D0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"evSFwwFAIkBKvYxDCEEiQENO3pGQNiJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJApZPD6ERCIkCVUBKRKzMiQMNWn5lAMCJAyZ2bOpNUIkDZ1EiqsDQiQEa4fpOARyJA32HWDm5AIkBfB6lznkoiQJDBsNflQCJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJAe2nuRn1FIkCVUBKRKzMiQMNWn5lAMCJA2bu+UplfIkDZ1EiqsDQiQHe+Nd+PNiJA32HWDm5AIkBfB6lznkoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJA2bu+UplfIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkByZF\\u002fiTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkAFUKpjVTAiQAlcmXLQQyJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQADxZjOxPCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFeNB+DQRSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkBFxE8IDCEiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQMEejU0aJCJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQL2+RkeoLSJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkDdabLXn0oiQNtqESF6OyJAltJCqQU3IkA7uHYX2UkiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkByOZsZoUoiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQMjtbCrMRCJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQOksL4G7USJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJAltJCqQU3IkAY71HF2UkiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQEFDsf9hVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDbwrNk+TAiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQMCN30mCSiJA5Krx\\u002f4pcIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkDLosEn8ToiQMCN30mCSiJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJADwK6GT8+IkAo8V8fRksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQHdnZVcwSiJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkDNzDIUsTQiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkC4tYSgZzMiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAvZLk0ElNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAKCSRd5s0IkDHWtt6REsiQPCEbx5CSyJAjazWjqI5IkA4trUkS0ciQCDLFA+sQiJAvCGU7qtLIkDU73tBaT0iQJsC5N1uOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAVdIqpZ00IkDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\u002fd2llIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQAgX+vttSyJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAIJXuHs1gIkC0XXi8dT4iQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQEPQ6TgoRiJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAUrjU19lZIkCNmJQBUzsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQF07b+ttKyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJAVTQWtLA+IkBpWmsIGVsiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNOYfiA6TyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJABM2Z8Dk9IkBpWmsIGVsiQO\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDPxpYQhUMiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkC+QPFbfTwiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ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w9GUCuT8ja+OEaQDIEyoXqGhpAht9guG0eF0CgPuVy7a4SQNxKuxu7mgtAkDGGqwW+AkAFZlhj3Mz3P6pske\\u002fr6e0\\u002fIijASHLw5T+9HmwuLMzoP30zM9UTyvY\\u002ffCgppFrCCkBr977hgqIgQN21JejxJDRA9Bnm47vsRkDGpoQogCFYQC+sLRyxU2dAaQhi3oqkdEDExDBTMrOAQF+QoFzRsYhAaK2CSH2ykEB\\u002f76a\\u002fLrGUQE1ublb5nJdAp4aOCUsSmUBb8Emx0EeZQLhl0hUbF5lAP58vVL64mUDcBjrRzU2cQKcRgBxhtqBAUZaAI+5ypEDCzgIszeqoQC4ZsZKzeq1AmSYnqD\\u002fAsEBb+960fEGyQNjtDQ19IbNAU5u53jFbs0AEg49xuvqyQM6LO3bEErJAEz08+qu1sEADctsFuuqtQN2PJK5N0qlAySLgM7BopUAuTAGyuQ2hQHgFm27jUZpA3kDkQDQXlEDmNzM4jlmPQOg0FZ+qxolA8KhgdgNxhkCrpNC5TCGEQBlfTIBx34FAt29doB5lfkD9o2a5hT94QO9usELg8nFAQXylKdqEaEBy0Ggg3tNeQJorYrWV0VFAZ9J3hdrvQkAvf9HDtYIyQIfYhyHvqCBALr4H9x2pC0DwoQV0fTz1Pw8A4BEyRd4\\u002fC\\u002frshd8gxD\\u002f0rf1VLyKpP3vlJ1verI0\\u002fPRYHSsGtcD9AbGR0sfBRP18NuIEogTI\\u002fZqRp\\u002fDdJEj+xp2CueUfxPmjJLAaPTs8+tl396ba8qz6ukaXiIbmJPl\\u002fWVdw\\u002fLG4+WDc\\u002fFDuTYT4iGBD92GC6P9EDR4RxzMM\\u002fJ4YtTyKd1T8XP\\u002fRC94vnPwDPqJ0Qrvc\\u002fVPf9T9O+BUD9981nbDUSQL0\\u002faBYjzRtAmMQig1xZI0CKzrKA4Y0oQHA\\u002f2QqCaCxAee5q5Hf3LUDMZ7tlK9MsQKAQ5iS0SilAPTlTmQBDJEADL6EI7bcdQOCLgZNrGxRAvib3AyyzCUCWl9UMgpsAQLPLtzA2bPk\\u002fEahVIaAI\\u002fD9gc4TVppwGQP34fQYsBBdAimIpQrHbKUBsH8FjcWs9QFIfUcqcG1BAnn3MBuaFYEAoPNEzcVVvQPBXFEt4SntAmggx\\u002fJDFhUD4ZnLGh8iPQHndu\\u002f7zOpVAYxOuN\\u002fX\\u002fmUCWkBsDl1GdQHZy4J8Ivp5AVe+gjyuTnkBBUQTVhuGdQFkaAAJPJZ5A586\\u002fP3NcoECSaIGAnyijQAi6BHysXqdABaZM5fV+rECruqCtCOiwQIix7oZNS7NAqwJoCIwgtUBwzJ3XEj62QP84nRO5l7ZAehF4hvU1tkBJgFqsNyu1QLLojbjijLNAAxaqzzBzsUCfXzq65v2tQNV1m7G0vqhAORUJMQSho0BR+idMFT+eQLXZJQfuKJdAFWmqsts6kkA1s8GRVlmOQPy0HEO8topAhlErqq8diECSGSldE3WFQGNKoAr4PYJAHVPulS8WfUD1kecxj4h1QJFJK+MzeG1ATlbZqX+aYkA++NTtKalVQEK7hFiZRUdADymZqhoaN0APv8rJKjwlQF5fdCrYIBJA0iO0F+Lc\\u002fD9NEvdPeYjlP9vuLQwQSM4\\u002fQQ+ZWjoutD+i3L0iMZyZP9fLqUIGBX8\\u002fDhVSxirtYT8VEu35E7pDP7Ok2elvniQ\\u002fi0rl7YiBBD+gdOEptLTjPs\\u002f1BwsPOsM+OyopaEZRpT4ZJVGn2C+QPiu+lTd1AoY+E82qu4tQzj836Pg8lpXWP6gP0w0GYeg\\u002fg\\u002fM30T5K+j8hJVUOeS4KQLVmPEGTzxdABDnHVg6\\u002fI0C7Zmkv69stQK9alBzglDRATrjcNyXeOUC4dwmwVqQ9QB0INgi5+D5A73dA44iDPUDHGm6bGqo5QGGTeb74ZDRADfERwxe9LUBYPKB76B4kQAFQmPEzGRpA4OSfEVShEUCIWgxJq9wMQD7QBlRlGhBABDWl5QWbF0A8zmMb5LokQOX3eNfrZTRAa8qDHF0lRUAPBhyeMtJVQOLgOBMnimVAmkf77MLhc0DrFpCDjvaAQMeiFbdAm4pAKRR6Pushk0DSq2mVaDeZQH2euZdnf55ACEVeIcH+oEBM\\u002f3YXH6ChQF3KX5s+WqFAkMXbv2\\u002fPoEDzPUaTRtmgQPqWTgnROKJAds5hl45PpUBwtHq55P6pQJk0L247t69ApEFCyf3UskBQr0tG14K1QJj2CIyhkrdAgUNxY0zTuEDFzxbgZTS5QJF0FthdvbhAYB+B8riCt0BXvyr1z561QBqWzB1rMbNAlj6CdOFksEBbdrNhC+WqQK0tUjJTPqVA87MsSr1aoEBsQoV7NSuZQMSxs8Q0CpRAbS4jaOrwkEDL+1r0\\u002fkKOQMoAQIP+nYtAJ\\u002fycHIzDiEDaf6iTwTGFQDssCykHBoFAtfxCWnByeUAtZt43rKFxQM\\u002fySmN6n2ZA4JVpJUbiWkBUvlWYsp9NQOu4LWBgVD5ALGKWgoLuLED10d\\u002f+2c0ZQEzftlZkmwVADpcTGDoP8T9pNjQk3n\\u002fZP\\u002fZ6p8fqFsI\\u002fY3LyL95iqD8nfHp6STWPPyV+ivCG6nI\\u002f3micAViuVT9rfImmb4g3PxycZJkyfRg\\u002fKLgQLeZm+T41yr7UvqfcPvTIgOpA3MM+Swcgp1m9sj5vK0lQnemrPnAVwTADJuA\\u002fRvQaHzbi5z\\u002fpflFfIX75P2ZvlOk\\u002fNgtAhtA8OvvVGkAy5R8hrysoQCcJTSMU2jNA9q7x0++6PUC4Cydm0EtEQIMvOJffQ0lA6bnmUSatTEDVWr5llK5NQDR9q0EfB0xAHRlqCYQqSEBmEOOvwRFDQB6tnuMStjtAfrOHRQfaMkB22eHP5BkpQDGcxhENDCJAVTcS9n4JIEDk83bwWnEiQE\\u002fwBf\\u002fCcylA5fvRKa+4M0AWhtP\\u002foc9AQL3Atlz62U5AkEg8PKUxXUDwPr3NfjRrQNBvprXdJXhAxWR0em0GhEAikssHuLeOQKROmDNUr5VAUipsUnQhnEAEK2HpCMegQDmKwEQ3eqJAC6JN+Y36okAUWL+Jh5CiQF1ImFpZ86FAaivIFD0NokBxRS1Iw6ajQJ2yoweNHqdAoRlIUIBKrEBlU5oTA0WxQOYqJTuofbRAhthpa0Bdt0CpyLoT2Im5QFMQmULgzbpAMaQts\\u002f0Xu0Dda3IdVnK6QGnge1pC97hAAVPgejDJtkC\\u002fNmgXGBG0QPWzNRIjArFAQ7oqm\\u002fC4q0BzX\\u002fTO0NClQLUM2glu0KBA2nd9aWIYmkAqE3oO5B+VQP1OZJBUQJJA\\u002fCiujJKpkEBQHVKKCgKPQFTpKsoYTIxAKHIfL7amiEBc1rWRpDCEQEQDYeeE3H5AE\\u002fz0K4nwdUA7T59WRf1sQHzwttAkzmFAi7AdwuZbVEDgJioK8rVFQF7vO022pTVA7LJpk8Q9JEA6+t+V9M0RQCEOedpPjP0\\u002fkr3t9gct5z94Iu+GAjLRP\\u002f2R\\u002fUXJH7g\\u002fPuihX1f0nz+5K5Gdk\\u002fWDP+h7yGK8jWc\\u002fAd8qXceESj+5GcM26mAtP5wpogNlMhE\\u002fEcliglKi9z4WqQ\\u002fY6EjkPh54nA0tBNY+BgsBUo8m0T6RxTXeHebvP2ZZpOyHavc\\u002fehp+ap62CEA+Y7K3mxwaQDcurJQLgClAjPgJWAq\\u002fNkDHpCHJnYBCQExKdmmCcUtAPldOn0aOUkBkxxrKBeFWQFbK6e5auVlACV7gkdFhWkAus2rTI7JYQFBdUZSqIVVAnUZFrcGWUEDwBMUTyB5IQNNVCGpBo0BAw\\u002fvOWBYXN0DKMHhar\\u002fgxQDaker86ZjFAQuuxUY2rNEAdzOxliIQ7QCiHUW3mdkNAijXXQ5Q2TUDcADdRjXRXQGu6e1R52WNA\\u002fEevvugNcUC0gvs9JZ18QIzvuKQdy4ZAUr4PXzL2kEBO\\u002fsFC4GGXQKxeUKYSv51A6tBsrLd1oUAHKwe6g\\u002f2iQCLitkabWKNAQpkA2NfhokAxtGoFYF2iQHkDMmROuaJAvnzIiTe5pEC\\u002fGH5Gg7CoQK77BzxWZq5AAHTjkaqUskASUaoS5gK2QKxujOtBA7lAQJSnmf00u0Amk460i1+8QAzthBK8c7xAilMi6hWDu0CVZ6p6K7O5QH1RO78cM7dAvZWgXEw3tEBe9Ivbl\\u002fqwQGezbalvf6tAhAPoVCuZpUA4TI\\u002fXmLugQOWQX5ZzXJpArh4626PklUD0tnUIZIqTQMjW\\u002fcLhcpJAb1O3cLq3kUD\\u002flkIQyKmQQMAD3eu6641AkzIh6r5IiUBEzyQI\\u002fPeDQDbcy3RZYn1AWjNdzDMedEDHPgSr76JpQOeGL6Rebl5AZolN5BXaUEChY90y2nNBQLj5fx978TBAPXq+pfjpHkASdlTj140KQA6xwOFkgPU\\u002funJoGZZq4D+uwe4gVaDHP8d7EBXwBLA\\u002fBCYAGuSAlD9wDq83SPh4P5mB48bLpV0\\u002fsmwToa4vQj8pA8OXolkpPxbZHLLWdhU\\u002fCbd0xm6BBT\\u002fByWuBtx\\u002f5Pge6bIWn8\\u002fM+qmLvoBw1\\u002fT9AW3X4r0gFQJa+hYn4NRZA4kl4cIU6J0DgnAVcsHY2QI\\u002fzjII62ENAc+shy3T5T0BZDt12Y3xXQPoyKSkmdV9AWHb4W\\u002fo1Y0A04R8toGZlQIMHQEdkwWVAhbQmUSI0ZEDMqBcQ1S5hQEL1kH\\u002fN7FpA0T8xr6+2U0DkDrsnmOtLQN9nZykpkURADUp6rLSIQUAWFQyN7llCQFh417\\u002ffWkZALcUoFQwRTUDq4lpQvFNTQBOII2WcU1pAXPO4A\\u002fu5YkBFsuTovRFsQIdkV4GHynVA3EHvOYLxgEADDdheCouJQMNNGSi2PpJAH32TuaBfmEDiRIvHP0GeQAzv9gP\\u002fbKFAxQVeBGK0okCK\\u002fmtpr++iQNlB1yIZiqJAKw+pX\\u002fxLokCZZGoTNhyjQKRpf4CssKVAqMbR4cBNqkAKxSPd9FWwQICTR\\u002fzSBLRA9v7Ob7yvt0DLgZvM+NC6QMM+illW\\u002frxA0l6bPnn7vUAOgt3T2L29QLzyOuERZLxA4GyWGiImukCLUlb590W3QEFm4OwxBrRAEXof6hSosECj0bIB7NiqQL3\\u002fa93wIqVATI27YF2UoEBLnrW1bMuaQCr34n3wIJdADxrslbWdlUB659RibV6VQACo6ECzaJVAuSB0EWfulEA3zgCzXH2TQGdG+J2bDZFAJ2fRqzTai0BqsyPiBimFQPyrls1p4H1A+P5YEPiZc0C6vyQMuu1nQMdvibSqOVtAeFInwcLvTEBbyFezSs08QAtxGPPC6CpAhTbXFimkF0DYLgGPhI0DQP03Rr10du4\\u002foC4olptc1j9P8EyKEf++P03LHP4Da6Q\\u002fL96HHqgPij9bsDQTC+FwPwR5B2cfHlg\\u002f+7r4en2GRD8qjP+PM4s0Pw6TqBuahCY\\u002f1j\\u002fCH7wvGz9aJ\\u002fZSALsVPzQfjyPVyghAaAhqQ3fvEUArceGEk4EiQOa6ZWMiKDNAc1r9mnJYQkCWbU6n\\u002fQxQQGwQUfUvnVlAsU5M14uiYkA6UVr0l7loQOSOwRN3621AqQMsHYeEcECwt4zyvadwQDBKoQxEu25Apiy8N6MMakDXrjH4vHpkQD8d95LWg15AMQ\\u002fCdNGQVkCQAoDOXflRQL9H7qaM0VBAAETzwvK3UkAwFiVzXh5XQLdi7i6jfV1AvYKftA3KYkAIqqqAKehnQOZdKwaZ7W5ANkYhslK8dEDSVcDxceR8QLfIINP6hIRAXTM91w7ZjECrY0CdmYyTQFvUirdHGJlAMwd9cDQ5nkDUem\\u002fT5AehQATpOiWwCaJAX78ed\\u002fQzokDbUFkhY\\u002f6hQEg0pzp0LqJAMfWESaCdo0C9MPgbK\\u002fKmQJP2ZkWvY6xAojzDan7PsUAqKAuFwui1QPFkY14z7LlApOZQb5NCvUAUZqZh+nG\\u002fQHVF54qwG8BAQJTqtVSPv0DjoBKMmqy9QLhWSAN34rpAsMgdJcGMt0AYFSGyPv+zQAWfgMQagLBALX9CoF2UqkCtkP5i5B+lQF8z7QDd8qBAHy4QIwZznEA\\u002f4jmp\\u002f9WZQD2paEM0g5lATYg3wkKImkAAt3p58MGbQJNRxxYIJ5xAvzG6mr4Om0D6Xc0xwVeYQOrLncUkYJRAmP+TsXimj0DHt3QjXsuGQC9LCdkbd35AZw8uXrPrckBbINzUDeRlQH3Zj3NBp1dAz6yBatPuR0BV3bOgMbs2QN+H0EMkTiRAyHEj8kAVEUA62cNoZRv7P+eGCKGjU+Q\\u002fbSFT7Sb6zD+mmn5rZ+6zP6wpdiqNcJs\\u002fn3gNVu1GhD\\u002f11wwGh3BxPxG+ErgYsGE\\u002ffKNX+L2yUz8kuCVtapNGP38P0VNxkjs\\u002fYgQwUesLNj\\u002f8giGybYITQBHgKmkPBhxANGh4gx+XLEC9aBKzFUs9QHTKRwyhx0tAaQbuaWASWEDv8vbgLgZjQC+fgd2ca2tAh3GtH8gFckCfBbp0ep11QFYML6F\\u002fq3dAYWZPkBK0d0Ck+8saMMd1QLgvuEtnfHJAeC\\u002fLGKNtbUC1V0FXSKdmQPaEKVy62mFAepcb7hAnX0BUOwMjH65fQDAai34qXmJAN\\u002fnSLYy9ZkBA\\u002f7X4EXpsQKghIOH9knFA2BHPTh1RdUC3vcFxdbx5QO2MVPT5in9ACBf6HVLkg0DB3pWuZbmJQGN+3X5yuJBAFSe3lfVQlUBt937M4x6aQHppf1FYZp5Ao6oFoL+8oEAqXOF0FYWhQDrBMQGjtKFAXYnlFtzKoUDy9boNroaiQAANMrLCtKRAaJqWnz3xqEBBBXiOc26vQJSN6BbE6LNAElgC3hyZuEBMzrMy4iG9QM1UDHcnaMBAYk9A8xCJwUDU+7n6Lc3BQGIsMcQYOMFA3jkYJj7jv0DHvUtp12u8QHdplMCihrhAjNmpHZCatEAVlrklj\\u002fCwQN6fNrnZc6tAK4mBZuA2pkDMBg0Ub2aiQHSOjaCuKaBADGA4Clf5nkBudL0eCxugQJxA4lMOfKFAdCyv5JnpokCMeEjcIK+jQNK10FyRT6NAfu8goXmqoUDk8iVaOv2dQOi+bm7dkpdAxBJkHp0pkUDSkLbCzSyHQGmiMttaFX1A6lBnJpEBcUDBAgKgv5ZiQLBIP\\u002fQkDFNADZIrb0BYQkDsza\\u002fa9qQwQH0OdQuGgxxAGR3CPageB0Bn8EBYN9bxP+D\\u002fNByZgNo\\u002fRQ\\u002fU+fp\\u002fwz+ByYgHexSuP7cMa8VdR5o\\u002fHB+aRYT5ij8jrl2C+e1+P0js+titQXI\\u002fnqeGlhBTZT\\u002fyzbwWtBBaP0ICx+tOyVQ\\u002fODGR2wt3HEA1bBftEkwkQL7bDMaGeTRALjWHeI3DREACzgzi+H9TQAmiaoBdvGBAh2VjcBk0akApdolzoLVyQOqCrBIFYHhAwpbKtfT9fECRA4wAYYZ\\u002fQELE293BbH9AZ2hsB+TgfEDo0xY58b54QD57M42yOHRA4vhouahpcECOmZkExBpsQFd4CVWG4mpAy72w+5kkbUDzS8Z3CEJxQNqWlmu0N3VAsNsudGEQekAizXfrgFh\\u002fQCP\\u002fPeQhW4JATnr\\u002f7GYWhUAzwS8N4CWIQAbyCknkCoxA0+\\u002fnXgGjkEAYDJQnWAKUQA3r+zmg8ZdARa+tG3j2m0A\\u002fgQa88XOfQHWR9YiV9aBAzo1uLVefoUAiFdydve+hQH0H83NTaaJA12OhTu7Eo0AvQHGWRMmmQEdz03xkE6xAiXGg4wztsUDL4cUzZ962QNq3LfDeWbxA+GeDqwrNwEA1mMi6leLCQGGU+HQpFMRAsgIL36c1xEA8v2Bh8lLDQOX0LaI0p8FAIQs8bHULv0BbVLS6W3+6QNHpRILwI7ZAsivmyntBskArIJ\\u002fiDfmtQCeBM2HM16hAdGf+pFZOpUDsCaBZT4mjQHwYgeOdkaNAMOOc\\u002f7kjpUBuOFkCGpynQClKF3hPDapASRTLOYZ\\u002fq0Cz9BiyyUCrQKTbQOwaIKlAEA0S0cp1pUDC5il5j\\u002fagQG0kx2Dc1ZhAFj4TVFfekEDNdq86PVGFQEtunP\\u002fuI3lAvVwlSAXDa0AOwU1+68xcQOvo08ciKExAVVAWOPgEOkCIdxaLgc0mQG4TxG20CxNAzJydUP6d\\u002fj\\u002fogQkK3jHoP1EU8kTSpdM\\u002fDlPkcd2PwT8QZtKamTSyP7R3z3VEZaU\\u002fTrv6x5hlmj9uCHNCuAeQP+AHOHkK1II\\u002fVZ2FOsXodj88C0PgTTFyP1aYSCFMQCNA5mK54EBCK0DoZqmQLzA7QB5+9Id2SktAPwNYpsRhWUDOb5Q5BpRlQPZ+Sk6GvHBA3Crt+8ivd0DVX8hEcZp+QO67vbkEEoJA2n\\u002fBbMWNg0DvOgHJZXuDQCSASYC5CoJAyzq\\u002fWruef0C31\\u002fAozwx7QE9GBvt5qHdAOhcy9Ys5dkDtvLb\\u002fMwV3QLrpTcb2\\u002fXlAT7gjrZ3vfkBDx668GMKCQCax7qKVloZASRrCOwiQikB00eYkhUeOQJCrMs3RxJBA6E6PTl5BkkC249DRD92TQIHm37Ks55VAnvZU\\u002flCMmEAddfiWa66bQH9RbrTf655AagRII0PjoEBh2CBeRPGhQIjzUmMMoKJAk6ucTjYvo0A+huc7jCCkQAL45+OpKqZAxN6XBukYqkCR1TbT5UqwQPCbDF0f8rRAYMXR24nNukDQaBBYGaHAQORJkbx4sMNAakIA24YPxkDJj05ldFLHQPYYUhgPScdAxzTMscwLxkDm4j26Ee7DQANO1JJ2XcFA\\u002fCKpKrt8vUBpi4PBbqq4QMJ4QOJohrRAkeuUvlQlsUAeAhj0VSKtQFO1w2umv6lAhQ+u0vNgqEDaYhgz6yOpQNaD9tp0xKtAhFis+wB5r0CCvIAzAIaxQApjaFJ5mbJACJWyLY9\\u002fskCh9i3mbxixQAIZGHedQ61AP06Yb7Mvp0DhdoW09wahQBMYUVcBPJdA0hzJdEeMjUDOvl4VAZOBQIZgKe1BoXNAHxwiuFyrZEDs1a9u1JhUQLkBor4RgUNAcJjXPb+jMUDGeTj+abgeQHAU3qt1LApAUx+UwW2O9j8rdOXBedDkP97GcFsJz9U\\u002fqMuvtXMSyj8QyHKVI7zAPxdyQYeVerU\\u002fExk\\u002f1K1lqj9cAsFW6\\u002fWeP7Lt7tNispI\\u002fIAKJ2VaLjT9xX7\\u002f9+iMoQKbQDOxg+DBAu9+uuom8QEAeA326\\u002faBQQGCjZOKrol5AOrA1dJrNaUBblvC3v9VzQLtEHqTG1ntAUIEY9vzagUDToZkzKfuEQES1WnPSroZAN7p+4x3AhkDxJWr3ZH6FQGj4RE5xoINATsrYK\\u002fD6gUB8jk5pgDWBQHuRHLYupoFA6NMCaeNdg0BIlz8u8U2GQKWFaIB5YIpAfjpH621tj0BahGSSpoySQKPk2hT\\u002fYpVA4cnPCy3cl0BipzRaYraZQO1AmjTZ85pAoltAV\\u002fDhm0BE0WtClvOcQOo0kSz5gp5AcOCfM5JQoEC1SYDKn4ihQFDuWHZfuKJAxMQwqJu6o0B2GLArQpKkQDNwDlJ9fKVA76hkF9T3pkC14y2nTb6pQCJYm393qq5AZ7k\\u002fH7s+s0Cd+UcT68O4QHXTXsdip79AbjfsdlqXw0BU97t0OBzHQMvYYtRrwslAaGXbGA8Ny0BsmQYpF8zKQBVl7t6sKclAEZvFswGVxkCRkBSXfJbDQHeTGGg9o8BAhIvyHXUGvEBwy2MTWKK3QOJMuRb6G7RAwaTKg39ysUBaU3MBZn+vQJS1JuZpY65AggWgBtfKr0AL6aC9QrixQBbBV3HgLrRAwMz3G86FtkBQQfTlSOu3QPdOd0MxybdAjx3lmG76tUAktMHfK9GyQDLj0wdV261AwkMWg8D8pUBC8w2IFSaeQDbBEiGyUJNAS3GOjyE6h0CXALKrC1Z6QP9bGUALS2xACkLMPo\\u002fxXEAJfzjN81hMQGJyG9R8xDpAEwOdSXGpKEAzXz1QVLUWQGAU8QoA2AVAqOScAxc99z8bS5YOVBfsP+nT9dYml+I\\u002fYN4lJekk2T8d\\u002fCmvvXPQP8niQ1GfRsQ\\u002fjls9YYGotz8rPGZMrVGsPz3wSsrrP6Y\\u002faaQHkJgQLECFOSP0RJczQLnlz2H\\u002fGkNAfPRGIJXKUkDvuLzrsyVhQEwwSJijoGxAuWEGgOvSdUAH07yndWl+QIMO2upiaINAGCeAP7nHhkCjv5NNC8eIQC6Pojy6UIlAG1K83y7ZiEC+0Mse3TCIQEIJKyd7LIhAMVtKpBVXiUA2uAWfSdqLQGFi5ZMmoo9AiVO6gWFLkkAtEs6rqFqVQKrKbydT9phACWLLTEnrnEDkuis5FWSgQCJLg\\u002fK++qFAIa8mGaP7okB\\u002f8cLh\\u002fFijQD6pJMxpQaNACzKQejYLo0BV+hn2ugqjQEdEgEyHbaNAX5txcSEvpECafBWTCiqlQL4FlMkwOKZAzGc\\u002ftGtTp0D8crj\\u002fMK6oQMXcyKTCwqpAcZy0aPZTrkD3m+VJLCmyQMpw8BLFy7ZAu4ppX2I9vUDqeyP0VZjCQOQEzRo14sZApAJp0+7QykAAr5unM6zNQD1rbD1Z685A6z1Kr+hkzkDmfTd1clnMQHoq76rqVMlAP6tyC373xUBhj3huesHCQLcKbiIk8b9AJRm5oBBau0CNYdjtXKq3QLrW04cR2bRAxpLRik4Js0CSS4iIdH2yQE1XD\\u002fSyY7NARqOMC\\u002fqZtUB29dZZtY64QKkFYyAwV7tA5hQUxlr4vEB7jaMQPMG8QJsmnuf1ibpAJzg7sp+4tkBYB2m46w2yQBqMiQBLsapAeYsLmGhsokBL3mlb79qXQC9W\\u002f6OxII1AqbjAtdLdgEASF3w\\u002fRqNyQIILZeTTxGNAjr3mNHxBVEAcuFh01jlEQKc5jJ\\u002f+ATRAc3Jed4dAJEBGKvgaegsWQA1iaz7h4ApAogOdkV0pAkC\\u002fLSG5Y6z5P2EUVFVN3vE\\u002fFzA\\u002fQFyR5z9bSPQsEPvcP8\\u002f+RU\\u002fwyNA\\u002fBSEjYvHkwz8xQ2JXkhO\\u002fP9ub7lX3Py5AMImAN3L4NEDWmejUnjlEQFiOJ+IlslNAYaEXQoPPYUDtN7OhTX1tQDNd1d5oUnZASSMG\\u002fpLzfkDpprqq5bqDQDtaSvoaS4dAFJKVXLXJiUAOmQ+Ow1KLQIPabhmvgYxAC7dmh3kvjkCaBMWWXIeQQJCe49ZdsZJAYzXRJqKAlUB6A67Ix82YQG87aIIcipxAnTCb0HploEATl4lz99GiQDQsfz8dc6VA4Oo7YI78p0DkBuuHGfypQFpSTibVCqtAA4as7pQEq0Bsh1QxkSGqQOnlN6884KhAvCozgt\\u002fMp0DkTzYTWUmnQDpSsJoZc6dAAy0VWvQuqECH96xunU2pQKLjh14jtqpAwxdiBpWKrEAYUFECHkGvQNhsWzNS1LFAEXi19SBktUB59SGZisW6QAdKNcwcEMFAfukN9naHxUDrnoy890LKQDOCZkTYfs5AMz1iS2i40ECboI1oekbRQNh2ZzK929BALLVAhhFKz0Clwuqs0urLQDifQxUNS8hATryyt0TuxEB5ncvBSRHCQI8k\\u002fLiIbL9AGZqZGNWQu0BnXh3btYG4QDvZXPZYcLZAQJJGaD23tUBOh4gcdZi2QMZ\\u002f5ntF9bhAHJxWbQwrvEBbMzeSPzC\\u002fQH\\u002fzo81AdcBAvpU7JPJKwECQjR726Am+QHnijp+lu7lAQ+Te1A6CtEC2JchsVYGuQADW0EN7RaVA1MS9hb\\u002f5m0BWYg4333aRQILzi3du2IRAtyoFni3yd0CX7kzgd6JqQH87\\u002frQR3lxAIlkng3bOTkDrNHRTB4tAQPadxwISmjJAFOGoXVbrJkCb7cawll0fQJre0LXq7hZA9KhZroHeEECiwF8BadIHQLcYT6I8df8\\u002f1\\u002flQHRo+8z8CmkJAyBnmP4bc2gBX7Nk\\u002fp+IpGnof1D8T384oiDsuQDpqFqgj0TRAjG2G2y\\u002fcQ0CNT1l4IihTQPG+7brVLWFAnQO7Oa8+bEDLPoOQ0EZ1QKU3jcg1eX1AjSsIefHlgkBEnOWD+bOGQNLdFZIACopAqMYA7XxEjUCmsi8Fd5GQQGadw0SCN5NAn4okmqTHlkC91cCNrCabQA7g1Uw5AKBAys\\u002fOkIaDokAKvQJPHhilQF6AFhhJ3qdACldwUqL5qkCZRqtydVuuQL7isy5d0LBALOJqNnISskDoSRs6faSyQDZxoQPlY7JAqQx3qltxsUDiv9nqViWwQCqYQb1W1K1AvwScYkMlrEBxdsw6QoWrQC9zsaxK46tA\\u002fL6l4+UArUB\\u002fXtcb26uuQHblsC1kebBAzJXe5B4kskA7O7RMlsO0QC3pV9O147hA1xDn\\u002f977vkBcGj6TaZPDQMWQJPCacMhAP+OpMUd7zUAKN9uqmu7QQJBY1LrbYdJAvX9yC5nU0kCqDJwzkULSQBbQsrzh5NBAGMJ8gRMxzkCTjYs3UnTKQEMZl6ZpEcdA8S\\u002flOEs2xEB3uHd5qdTBQH2hzxpemr9Ad6WGTPswvEBAaDqf56+5QFDg1ui2i7hAJyjGQA0cuUABz\\u002fi240m7QI8ZKogUa75ACxzRlNOywECUmp4jqYfBQGrF4sLbTcFAjkCOwsviv0BU56JRm1+7QFRosHi877VAb5RVUM56sECM1JcwRVqnQCWnZTbUcJ9AnvL0\\u002fwJFlEB+rlUSqTeJQO8xHlXoc35AQ+oaXMTtcUBrtvZMjbNkQKK5wt8WwFdAJ\\u002fgPuA7dS0ALoiDd2nJBQMFSWX6YEDhAeWDlXr8BMkCDgnqTTL8rQCHJRGEh4SRA7vrp7PimHUBX6\\u002f7vJIYTQD9grIvKuAdAXvsYwIH++j\\u002fYK1C64lLvP4rO0X6LKeg\\u002fyUxwie8FLEA2lXKLkCszQJt\\u002fjTepGUJAYlCBu5lOUUANatCUI9NeQDQ4r0s\\u002fOWlANntpngj9ckDPO4NclXd6QK7MIMKTRYFAt9\\u002fCC2l9hUAih0s9Ph6KQF9lA62azY9ATjIYhzSzk0BqeudSV8OYQCQlTi+ADp9At2bSjGgSo0CV\\u002fIxMA7GmQCzaane0J6pA5TzCZEV7rUAGPGhsr3ewQDADKfhtZ7JAllykDt+MtECvIzWhcqi2QD5I2SsQR7hA+iXzA\\u002ffzuEBZNNe8aHW4QJGK\\u002fSSJ77ZAmMc6C5DYtEDzcfVRwcayQGICDe4kNrFAoFn0xx9ksEBudn5A0U2wQJvIv0+iyrBA1bMzK+CysUDIi5UAUAOzQOGLAJI287RABwUrcID0t0CpnA47vZa8QD0vjxTEpcFAA73lUMIMxkCBfgJAuy7LQK+L8n5sMNBAEbfFtlhh0kBPmKcymMHTQHIui1BYFdRAksrP6\\u002fxi00DrM4QKuu3RQEFh6ygiGNBACtkBIux7zEBy43MWOi\\u002fJQN6Dx1V\\u002fZcZACbBC68H\\u002fw0CFUbIj+dXBQMgav5CkxL9A3DRNYMWfvEB22QPSsMu6QJ9XN07ztbpA\\u002f1qsQjRUvEBBYsiN2wC\\u002fQEdbPeub0cBACJowenqJwUDVJIgmMEPBQHyFoDCR1b9Aez7kFut1u0DRdsTmQjm2QKnYHfl1+LBAPz2gFE2lqEBJUt5NpSqhQIMy5HX\\u002fIJdASA4\\u002fzDtSjkAqPItDPGaDQNFoPAIkR3hAQVLh4ETnbUCWoLeCAXhiQHlfoe\\u002fOt1dAY165Bhd6UED9rK65pP5IQC3OIv\\u002fz9ENACaHi+UjIP0DxT8aR5TE4QIqVRII4LzFAbATgU9eCJkCsld2adCIbQAj6yGE2lA5ArePYNDmMAUA1QXPH7+X6Px9nQqLfGihAz6x8VutjMEBLXwX7iqk+QPWgn6b2G01Auv\\u002fQEK3PWUBMnXfmiB5lQHCdVdZBAnBACkxRIPG5dkAoRlwwi7t+QBr2DXmqQIRAq4k64w+iikCe3WnyDbiRQJYLj6o8yZdAF1bdnEmvn0AT+h6AgImkQKIejqHTj6lAXj6vtHBtrkAN0U3dvWuxQHcoYA2FdLNAiSdTJ1OMtUA2RlQWF\\u002fK3QIhpDrgcrrpA3nR2dbNtvUDzqLbdd5O\\u002fQEzpCcCYPcBAVk2eVAbNv0DzgWhVOK+9QMJ\\u002fcd3YubpAULl6KR+2t0CmMI3X+E+1QM\\u002f59rk04rNAz1cvfVJws0BvnFFdHMezQIqMivStrrRATmpMbisYtkAJV9O8zTe4QCeDgtrGgbtAMC9hCa5CwEB7taGd+dTDQKtofHomcMhAi7n55dS0zUCjN44LtHbRQJ8C2Z6hm9NAYTAKrfbk1ECuOlGgPx\\u002fVQC04mAwRWtRA5OpubPbe0kBzKGS0hhHRQBNcvOe3kc5ANaLPd5tpy0Aqleu+MbPIQETXeuU2QcZAZpXXmfrlw0A3spNXGp\\u002fBQH0HHI+SQr9Adm\\u002fQzFp+vECVsHp5dXO7QDbLeSNrKLxABiZFbCwIvkAZXKgYYwbAQBmfJ5BhkMBAR61B5ltDwEDDCuB4SBW+QLRZW4s7MLpATI4syPOLtUDbFgF26+CwQP0sbWHyZ6lAJSrwYxCIokCN9tlBdlyaQCDPLK8qUpJAqeR+bIvdiEBHxyBfKH6AQN+nPbLem3VAZhht6LS7bEAEPoMGLDFkQKHV3NdU2F5AFkUxCKM7WUC5l604SRFVQNsKbnWGGlFA\\u002fM33ELonSkBt2XZkc4RCQGgS9hizFzhAAmIlFobKLECMZRNFrg8gQL18hqaxOhJAqvc1+dXCC0AlyiAo6EUjQDs4xHfNEypAA37IEnw3OEDEtTfDo+ZGQCKQC90sU1RAwLa2p6XEYED5xcqc\\u002f+1pQHZqo5\\u002fyF3NAl\\u002fHceF9ce0BWT8OYin2DQNwry1p\\u002f9ItAZhLbKBgflEAxjfrxn52cQBZtGqAiuKNAHWaJSdPzqUAFSiIJLDKwQFHxLE+fM7NAK79poUvXtUBRJbqB1D24QN3vLYq\\u002fwrpA1CtU3DfBvUCjf95hc6PAQBDFd1wBc8JASZxRRTnmw0ArmQ\\u002ffj47EQLHM365LLsRAoNNMCWrZwkD7fIegye7AQMPzsAux3r1ACkHHm\\u002faWukB2v1XtKoG4QH4jx5Idp7dArIXrPTPJt0D34qF4C524QGa7HEEiBrpAP5LvYxM0vEAoD4gjRZy\\u002fQN618JDUZsJATgdx3t0TxkDaF47kvMXKQF1M7+M2CtBApxSjkM6i0kDcByvEmbzUQGC1pkkh+NVAN6r4BlUo1kAqPhf5EWLVQJzMTq\\u002fH8NNA\\u002f19Mg5g10kCaSi7fboHQQBus0LYt+M1A4u21W7VEy0BAMnVK0LHIQKzvgaAAEMZA6iwswa9hw0B0pK2g0+PAQLi8uSJ6471ArD8T1Dapu0DOinmAYDK7QFH5GbjgA7xAqptwVYg4vUD4oPDjxNq9QOkwrIECQL1APX9ISws7u0A0Sglk9xO4QHobY\\u002fHfU7RAGy5k7pSFsECQNZzYJxSqQMn36QqfGqRAe6SuYqxQnkAm+VuMEUuWQD+kjU1C5I9A\\u002fxGB2kNGhkDeCYh+ePJ+QKCYkfsXKnZA+n3jYH4CcUAb9HbNHTZsQKSRR3xibWhAMeAz2Oz+ZEAhkvPPgDdhQAW7+EueUVpAvnijJE+IUkAXAkV\\u002fpuxHQEttpbPnVDxACyjLcw1KL0B3JSbi144hQHgRLnMJkBpADwG69v7JHECdU63kI2wjQIaojZie\\u002fjFAXqGv+YcRQUBRypuV6aFOQPGreifj2llAkH5sW8HKZEAWjptWBz5wQK9kVvQQIXlAHNVgqNd1g0DJcqzuPBWOQNCSQOfU35ZA1qkx0BDOoEB5EfcitYGnQHa9m0YhCa9AX7cy5mhOs0A8I9qCf8K2QFoexR3XwblAt2eHCi6IvEBTHBsxx5S\\u002fQLt57jFHrsFAMljRy3v1w0DKkuYzmVbGQK7fXXGwSchA18hyRttAyUAQJLVJpevIQFvF1iycX8dAns4Qrv8PxUBYjUKXk5vCQEdfF5gAjsBAFC8+RRdpvkBE4XfWoCy9QKKfaE2gF71AfHCd0\\u002fDJvUCJbk+xtha\\u002fQCDlVVz4k8BAgC4VBxo7wkAUJjH\\u002fIMvEQCQ6h+srdchA3fsX1MUozUBR7fEbBT7RQIHodE\\u002f92dNA574\\u002fYQf51UCgdASEHj3XQEA3aGBCetdAfaNUOgLF1kBTMrefrmbVQH+UYt6dvNNA\\u002ffECfeQS0kApqDlze4zQQERTzAJBRc5Adr9linRxy0Dhz6TcT3DIQMO2XzdtScVAabJKWzs\\u002fwkACZu2aCmO\\u002fQLO3rZwM3btAz3UkxUQYukAY8dQRBrm5QDc4fPmbA7pAQX5oWd8pukAlbYHc8Za5QGuITK8SF7hAdX+kjf7PtUA7So1xihezQP5XKH7\\u002fQ7BA7G9RvY4cq0C0sWHhshymQNUKVl7ok6FA6PeOmBcYm0Dt+J3BKzqUQDBHTUbLko1A0aZDUD\\u002fHhUCYVSnMStWAQOjI+ybKEnxA4Fgnc9nneEByR0\\u002f9onN2QHUWN90No3NAUx0hgBspcEATzp+sLZ5oQNC4++I\\u002fN2FAj2KP53kJVkB1es4FZdpJQL16aRxWQjxA7myMxthbL0CqcW1GC5AnQBwA6sf7ZhRAzGd\\u002fDGqbG0DvYs5Khc8pQB2JxciJ6ThAQLQvKfADR0C6cPySZ05UQD7o+RVWW2FAd6nuYEQzbUCL2AEaCmR4QB6YdzCvL4RAIL80JNFbkEBOyKKM4I+ZQIN8J0MV\\u002fqJAsSk5biKYqkBCGprCUXqxQHa8QujCmrVA9EVeqRBTuUDh3dPNLJe8QABWMO2Sw79AbuODJa68wUCNtWbEYh\\u002fEQInj\\u002fz71CMdAEsSOEN4YykD09XFYIqbMQPO8qDTUA85AvBp7DXLQzUCNC7WnqyTMQGQJpWiaiMlAZsKD\\u002fQO4xkCnv+2WGFjEQBf7T9Tlw8JANP6WpaUCwkAWNcbpOOPBQDhJ7zgBKcJAXAgB0mm0wkA3exsnLZjDQAyWfsY+FcVA0sGUhxR\\u002fx0ChPq5s4hDLQEKx+u5cws9AMmlfYvyW0kBTCinJGEzVQElrDjxdjtdASz34fZj62ECaruzcXF7ZQMb1DncLyNhAxrQ97U9810ABstv95NXVQPRm0+sNINRAQOcsCkt+0kBGqh9mf+vQQBnk\\u002f9RLm85AWLh8\\u002fwwiy0Chb5sKNXfHQIXK2QF43sNAuCCvZbi2wEAvLxQNCZ68QOPJaa9niblAAzI3A5f0t0AtZLWXilC3QE7GHUeF+LZABv7OiMVstkCTcmdHeHC1QGwhmcJABLRADU\\u002foMSxJskDwdFXePGGwQFAblxc8vqxAsdY5AX6TqECSN4UoZlakQMFy1LrnOKBAoiALqm8QmUCpq7osXSKTQMDDMVFl941A04e+vm0GiUAfeXvVuXqGQNP7GlNxAoVAP9XKZ+9zg0CYw3HPBiuBQFSxtNhNPHxA8EdYLs5idUDS3sW9YK1tQFUcUF1L02JAvnLCTALgVUDtTqSB9qlHQK4sd6OX9jlAmMdQDZxgM0Ae3idQp8kMQGFGOxzByhNAx1AfQwkhI0DaRdEutUMzQJuWlT2WxEJACyOzWrChUUACWn3fJh5gQJrmBN5F3mxAWASKqoJTeUDQtKsiS56FQIlfp9OUw5FAEOqyoDfMm0A\\u002fUjWJzYekQBMPLcpjeqxA8n2BWjeHskDMUpC1HLW2QJyyVWxBfLpAQuAxoB7tvUDUYm7P9sLAQA4ZmfjMAMNANoLqj0v1xUCEVUE9+ZLJQOrUMZbHX81ASD+CYL9J0EAgsDVVUTPRQOS7MQ99NdFAb9gZ7w9f0ECdl1OUcPvNQIca0Wea98pAZDnhX29syEDV0YrBlL7GQC6borGP8sVAVTi0bg3MxUBgkCrCOwHGQPZ+5ua4acZAf0YaURAYx0CXvExGplbIQJuBilTKicpAE\\u002fGCiEgAzkA6YsuCJGHRQM18ILb1ONRAK\\u002fm3e2Qn10Cx5p3cbrHZQIHxT0KkZttADlLbAKcG3EDJU8czHpXbQCVGtxOZUdpA2jsFuW+X2EDNXt4nnrbWQOy5en5n2dRA1pdhz9YB00BZELudNxzRQNjzcewtMs5A2HYkMjr\\u002fyUCMXmJaVN\\u002fFQJWQvd1eKsJApgzko99WvkBP6HxrcQm6QN3xZrA3ULdAs2\\u002fXGXDHtUCgV0aLUfG0QBHBvgMMYLRAN4ckQL\\u002fLs0Dr5lxT8hCzQDGvwZlMILJAjg1BzUzusEDSjpjkpN2uQK0y\\u002fVKaPKtA5VM7cDwfp0Bsd\\u002fbzGOOiQFYZWMeZEJ5Aghkc3jcGmEDEwblcghmUQKbUrQcEGpJAQ42Og31PkUCbBekFqcyQQGz6f2\\u002fWnY9ApDwRi9T8i0B1Ccp+p+2GQJ\\u002fQ5SiqPoFAc85lW0q6d0CNLjx4h9NtQJfAuB5sKWFARRGdI2tfUkAWLv1ZDO5DQOqYzKIOjD1AacPdvLTFBkAy+0ICVEEQQFLUIC2+wyBAVuYcYgH\\u002fMUAk36+WPJ9CQEj7laYRcFJA0x2qVwmGYUCQbzh5AQhwQK+BXpGbO3xA\\u002fsX0uB7bh0A4z+oeMT6TQO5tGrb5dZ1AAQOdYcVKpUDKVaGBn\\u002fesQE\\u002f4jM0+jbJAsv6uS6l9tkBsZxEUAx+6QKtUxzHYo71AagIM0rnTwEC13cd71XjDQF4ZH6tEAcdAX6PCQtFOy0AzyjrPRM\\u002fPQH++XELS0NFAZKiMayL20kDLHtfhJx3TQLBsAeiiV9JAh07WsLL60EBb3GseqPLOQDwOcoy1bcxA9esxDS7PykDaiZUWTxbKQHQPj32J+8lAkpgiAlgpykBlHx+\\u002f93DKQBg5PTo96MpASyz15P7ny0Bvg3SR4+vNQBjSMDF1rtBAYqLbYOIr00AjzbmPhULWQOb7p6rni9lA6qo9g+d93EA3fJGmFJTeQBZg65gZe99AtTjYuwAq30DwblIsqdzdQDGPFh3z8ttADT8GBtLG2UBDAFxtxo7XQLbe3iLDWNVADlKcrooa00DLch7y4snQQP3rCWzI28xAdb0Agn9DyEB2iJ+TxBXEQJTjXkLtlMBAQgzxTfbJu0B2ziYrJwq4QOnrTHZ2rbVAVK4Jy71XtECEK7DtqaizQAAvOUVXTLNAoUp5WmT\\u002fskCo6kpjEYuyQFE9wn3\\u002fwbFA7Kmc7rOEsEBMsL6unputQM5AbPqmeqlA5cmIYasupUC8ibbmSGGhQHUiXT\\u002fnPZ1AfKbfQzA8mkBIe3ytYlKZQFOoCu7fbplAAGS7lqBPmUC144LlmAOYQPQHx396O5VACIxppgdLkUAYgcTTT86JQDuipM9amIFAn4hGRejodUDCgzGjwvdoQAgqjoqbc1pA+ONbqT5eTEAkzypdgeJEQFK62WUrrAdAaI54gA2sEUDEYBiGW3EjQGv3PzOM5DVAAAB9V8RdR0Cijgf8fnJXQMswxY0BLWZAX7db7b3Yc0AwOCuefN6AQNFXQT1ATItAyZ3L7PcIlUDd5Yc019WeQDKELvuZc6VA6Rjzs4tJrEDxShwSbrKxQIo2iRZxJLVAQdBRpxtyuEDZXYErD\\u002fC7QCwJ8K2IJsBAnIj9VX0nw0ANXyhVkS\\u002fHQNb1pQfLDsxAo\\u002fZgVTOQ0EAmgwJRhLrSQNpgA6jJFdRAM3mnXHhn1EBHy9rF78PTQMLcBuE3hNJA8oXIdNMe0UBxdJKLkvHPQM56XYkXi85Ak0rwmg0DzkCuflRfLwnOQBIB1oudQM5AW\\u002fiUHr94zkBUwvBBkc7OQHRjXQlVrM9ADZD+TnPT0EDewsutyaDSQDq0xdB7VdVAvLkK7S3G2EA5w7hFI4LcQJSdtM0l7N9AXxKOb5U04UCG1fOXLsnhQACwwknAqeFAAqOZI5nz4EBJURsts7TfQKDe+Wn2It1ApK5OiO932kAnv02Dl9DXQEoltZJ9LtVAeYvrR2yN0kBn4FhW4ObPQO+uq8OX6cpA4d4AWapbxkAzYw5bXXLCQILES0uOoL5ADsb+m979uUBA4+q1G+C2QA6\\u002fy0M9BrVAp2KXui0ZtEASXhKZv7mzQP3pGYaMi7NAIb3ZUWY8s0DeIbqkQIuyQEPd\\u002f5ktUrFAwhiEuzklr0Dt9TjlrPiqQJjg+EXRyqZAawG1MAlfo0CWkz5km0WhQPkpfWaInaBAmuHnyKgCoUDHP0A9CbShQI9aHNho4qFAuDDe4woCoUAl3aqCT\\u002fOdQCAg5nmTN5hAPn4TZcjpkUBNSjzlkDOIQN\\u002fi7Akv2X1ArV79\\u002fh\\u002fXcEA0WZFQvqdhQI6VtBAruFJAvw3vT15fS0BoV2\\u002fCOD0QQAA6jjoazBhAnQJ0eZcBLEAWOZCB4OM\\u002fQAALJ8Gq+lBARAJT8TDHYEBpZsoPFdhuQBEKxNH+fXpAibWpxL5ghUDfOL8SZUyQQLiUvrOSmJdAHM2aaqJGoECD4L1nB3GlQNU6BKmDBKtARFzmNrZVsED3sXNA+hqzQOyoC3LM8bVA6CE9WplLuUAj6r5JZt69QC\\u002fsFa5PMMJAGCvMwPWOxkCdxtDN+cvLQD2K+5ltnNBA8Or27gXy0kAI1RWd53fUQOhmZur69dRAehaTog2C1ECP\\u002f8E1ynTTQO\\u002fDGm+6QtJAthjtQAFO0UBikMr1AsfQQMqWxx+Qp9BAHrgc9dzF0EC\\u002fvtcTTfPQQGlQWCubGdFAWViG7j1M0UAATk3sZsjRQLSn+UR34tJAxukmOQPl1ED3iT5pYevXQNowmnw0xttA9ydKOx\\u002f430CGezkGt+jhQJOA2BbtUeNAhDqNJOT840B0KRRA6tvjQDW4GrDCDONAQc1YGd7G4UC9LkCQ9kPgQF70zkuPXd1A+a\\u002flzrg72kAsbbQEUTHXQPiQH\\u002f0kQdRARIyTDwFw0UCq+PPL05PNQFIoLJaMwMhA2cV7r6WKxEAvT8A+ww\\u002fBQPYIYdtvwLxA\\u002fIsvEYL0uEBPra8FtY+2QK4pa8TAQbVAewpk3mSjtEAw6g7PTEi0QLSLFZsB0LNAYZ\\u002f4jc7zskDxeEPmvJKxQDKoLg9Ddq9AwgqB9T1Yq0AX1Ocl+o6nQJ5XNPXU46RAmQbpRV\\u002fVo0CH1cgAxlikQEjZyOcqz6VAc8U7bSA7p0C1d6KHVqKnQGlqt3qvbqZAUqf78ISgo0CPJdxlLXqfQNPzztAiEpdAdMmUfJzfjkD1er2kvNqCQDRMcRiuEHVAFobZaaHaZUCCg3plWOhWQPR1RUnBoVBACtahna4rGUA48d\\u002fJVTwjQM0WSzTQqjVAaRYGWqx0SEAV3FB9FqZZQLOPfChSyWhAkIpk6DsadkCVfpM3lECCQMz\\u002fTO8sDoxAe8lmC6IwlEB3XdcqwWabQBqsSbCIqaFAsqwnIUjIpUBwZXcLDOGpQNi9VCvq3K1A11faBe7vsEAKymn2kzqzQANteLyoU7ZAA37x1VzsukDP7ZzrMNHAQHqQT6d5UcVA0QbZJvOtykBTHjlgIRzQQFv6VS79g9JAHeglfMIl1ED0NKr8YM3UQC2Qp75skNRAwyk6TSPD00BtlBLXVNPSQLE63Q6wHNJAiBLK+n7L0UCxqJnOvdjRQESasYfIHNJAX6T7Nh9t0kDmn5eudbjSQGhDYY9aF9NAk+njVqvL00AQwvE2LC3VQA9ZeoAah9dAg8yMiTbx2kAX6TSWyDPfQG5M+2zS4uFAF\\u002fF7Z1v140BXr6bAanXlQJW3cKJcJeZAoqnQgrn25UDPzSDlHQjlQMOu221Pk+NAxAoFtTTW4UCOrMmJzwHgQOvbep6taNxAeVCBTsH42ECxhQlbDL\\u002fVQMUS88\\u002fkv9JAMu9bfK79z0AaSTLvdQLLQOeeNeBYnsZABqoY+ejlwkB7j2aK+te\\u002fQMD0zOpgb7tAIzP7avB2uECJ2\\u002fsmH6a2QFd2TisClLVAbmmedAjPtEDmhNw+7fSzQMqTzjKPxLJAGbquBTApsUBsCjjhOX+uQFkcGj0ipKpA9cuoau+Kp0DCZXZeeO6lQFX4ifWLKaZAxB0x51z8p0BtlpcXIIuqQPyKNiMSoqxAc4cg4mwnrUBtG9FTY4erQLp+LR1j5adAXGAFIvX8okBXZQhzkJGbQOSl99VYRJJAnxpWz8IXhkA\\u002flXSKIHB4QLl+YWeEFmlAC0zYrVz\\u002fWUAbOcdcJ75SQESK5yGjkCNAzl0\\u002f5CqqLUCMOCudlHxAQNEvDIHTV1JA\\u002f3M+KGfrYkCo2L2uTetxQLDJFHbCLX9A64gXLGH5iECj1QmyCHySQKF5BqA+aZlANd6M7nlUoECmojbgYMqjQLuFpuRB2qZAzqcwj3p1qUBT9e2\\u002fvsirQMfrDEhJSK5Aglm+vInesECXXO1gHaKzQP3FNzp0EbhA+jUcfE2tvkBXBQBBacTDQJMUCLdOBclAxoFvGsZzzkCscEjrapzRQIdSvcVNStNAmhZZtCgU1ECA18XI2A3UQH1uk9g\\u002fhNNAphcXx0rb0kACWftFL2bSQMK\\u002f\\u002fxNJTdJAl9UFLVyL0kD5AXHoL\\u002f\\u002fSQP0ijWqhhtNAY5MlbJEX1EDl1wLOF87UQLpOJCO+6tVAZIvljhG\\u002f10B811srNIzaQP5PHxDfXd5AUhfctZx44UCZLwJZ7NrjQM99g7EM9+VAILFavqhy50AIjxtT3hLoQFWVT54oy+dAA9FpQHa65kCroSCbIBrlQDdoqLjsKONAl4bVwq8b4UD5iqaAlC7eQCMrr9pVY9pAbLW+ktDq1kCnGb9LWcrTQH7aKguD\\u002fdBAo970YKb5zEB2Jl1914jIQHGWIgE2r8RAWXe8Nn59wUCOXYxfkgG+QNA1LmDXZ7pAhZ3joZ3yt0D6bCMdN0G2QPiqy6lR6LRAG78\\u002fKf6Ns0B3tJycHP2xQPFxh1p\\u002fLrBASntrkQuTrEAbWO5fCjepQKzZgwV0BadArR+kyfSYpkASGaYBHx6oQCsIhFIGIKtArN1AaEyQrkDRS6ccQYuwQCnUmutGxrBAylw7Y\\u002fpur0CXBGz1oQirQDPJLxiCRaVA+3WlEc6TnkC0AFwWLQ+UQI82WhcOBYhAwtNlqK5NekAYUZ0vM7hqQHjVEUYUX1tATQLETu2XU0AngaRX0CQtQGKUNrkg2DVAGXbESrTeR0AhV74cACVaQD24GR+rimpAKDvVu6u1eEAZkYR7TxWFQMa7Tx1DgpBAeV3\\u002fA5bIl0C5Xe7dE6OfQPPbX4syiaNAN77h6HqUpkBW1mPGX7SoQC\\u002fWL9W6\\u002fqlA1ebbh0nhqkBWF8wNKBSsQC7jm8b3ia5AWo1+XsOusUA5Lvo+vtS1QAKyh8HVGrxAe+K+E5FAwkD74JfpJTnHQPQlmog+YMxAxiq3c2h60EDJFEprgSXSQPAmhwbKBtNAdw+FKJsv00DCi5cWJuTSQNj5T5MtfdJAas1Z1AFF0kDUm64oN2HSQC6Pkb8\\u002f0dJAjg2Hizp+00BmEVVgv1HUQAX26hK2SdVAQCZjeYSC1kAV7aUmrjPYQMsdoJe8ndpAqw2GL93s3UBngWzz3AzhQAaf0jsha+NAOP7GDTnJ5UA5p7dWGs7nQPfRZq07KOlARj3TzKqk6UD5Az+8wzrpQHOvZCyMCOhA4OKpas1D5kBy1\\u002fQutyjkQNPE9eJd7eFAE8JvmBd430BH5oRz4mPbQExm4llkvtdAI33EHTOP1EAwzfMqU8rRQBXUUsIMtc5Aj+G8VotbykAO2C\\u002fZdHvGQFKKw6VMH8NACeMc7xpZwEBqt93naV28QEWsdNRfHblAA6FUIr2mtkCT3o0Esp+0QA67MP01vbJAqEsTmcjWsEC2MjFEitutQHK92DDzVapAK6om8f2np0Cddk4dWG2mQIkmAjemFKdAPn3RUMCbqUB\\u002fs9hHtWStQNeMqH+HorBA3P9BuVrtsUBdoc9kQgWyQFPoV4ahtrBAPOwijx1yrECXDaLWzyWmQJQTzQ3hgZ9A9x7HG2V1lECHHjPAKkCIQMzDY7GdSXpAFFHvLt9rakCp48u5MsFaQE60f1zHA1NAGsqvDByYNECqfj5sUXI+QCcUQfeLTVBA0ahKMpiNYUCbGWJ8AopxQLWZlAuGEoBAnMIuv5P6ikDc8xoqW8CUQBU\\u002fZa90TZ1AN61aNhsLo0BN0PYeJuOmQGNlTNGvnqlAEgkX02wEq0C+rRopWFCrQDQOqp4hKKtAD44+bap0q0BzWSW82DqtQAFz8Fr\\u002fv7BA67YotzCWtEDnAnRuw2m6QAmUQf1\\u002fF8FAzcXUVVSuxUCwR+2K6XPKQOwVwVhBvs5AKfwer8T60EB7UZVT5+bRQEWxeCtiMtJA2y3a3N8Y0kBj0FSOWejRQEwTuAkg49FAPmdOaMst0kB\\u002f4kb6Lc\\u002fSQG5m4lSmvdNAdPpG1U\\u002fw1ECqQUb3PGzWQBGUIi6aSdhAMmVJdeCu2kCrGjSh18HdQAGFbRiqyOBAHDL6KZr\\u002f4kD7rwzqjFrlQGJN1hE8ludAy9NPs4Vl6UAwdMDXUoXqQDThAOIjzupAuhyQ3gE86kAbjMuEquroQJp9d2rpCedAwq+VReLQ5EAd3Awh\\u002fnTiQH5r\\u002foO9JOBAxFXZQgcI3ECqFk7XxVDYQGVUvwmILdVAwrtQwzOK0kCxTHD4LELQQOHLY9+RZcxADR8SiTOTyEAlIy0hJQ\\u002fFQDSOQbIj8sFAPN3s8q+lvkCqFKg0GWi6QMbIH\\u002f2\\u002fBLdAbRm\\u002fdlI9tEBh8uWbF9ixQHTl9OE6aK9A0yhxS1mhq0BBSTkWw5WoQGTejP2rqKZAM9kztU5EpkDFTJuE36anQE7TypssqapAqCvFhNKarkDW85RRBiqxQIyI2SMgRLJAJNMrX8oeskByIFhixJewQG+UV43X5qtAy9p+CBJ6pUBQVR8wSDieQNfDp79aapNABXRWbsPGhkDbd4UZhm94QAwO+JZJTGhAEXpQELRRWEBVNVXbgChRQPOwhDOtvztAJHpNlH0pREACVC5PDBBVQHqtwWa9OWZA3hvnSWzSdUCsqbxKdayDQOJDDUHjQJBAqt873cKcmEANgGLa9BihQLQcwoBS2KVAy5LJMVjGqUAq4mVuF0msQJCZrJcuN61AlV4P4aTxrEDPe8wx6UesQGGuyZAcPKxAjs9iUbHMrUA5btSnI+ywQFlm0m0NiLRA2\\u002fTJ1x7tuUCpJANR5oTAQMO4aNTMssRALLa0+8kMyUBmWxxu6v7MQLzJZQgQAtBAj7e8Qr7s0ED7uf5bbEzRQDu9oa5jVdFAIqqYo1tM0UBkyus2hG3RQEBa6QOm3tFAgQogiB6w0kDyRFOa\\u002febTQJLQdKCoh9VAfgO2WrOb10BMrQQ81jHaQJ0aEBGTWN1A9HyHHR6K4EA7G9MOIqniQOE03yMk7+RAhoA0q4Ev50BMY0CG8i\\u002fpQFhklaSNs+pAz6gXq7OI60BzivEOh5TrQPsNaPF41+pAaGX2bxlq6UClH+KoUnXnQO9AARh8KuVA08uqBIK94kCx\\u002fL4jDmDgQJePHxgCd9xAHTvfeGHT2ECgSUVnLt\\u002fVQFD55LQVfNNAeLNR3rpz0UCbAP1CwSDPQBswV1\\u002fMY8tAZdm94mOqx0DrZ4LAKRnEQJRSaf5s4MBAAuNpLgVCvEDMJaDJZ8O3QLoF39rUKbRAlRxZ\\u002fCpLsUCo9Y09rwiuQLl25StAiKpAQuw3jIYZqEDPRJ1eut+mQC+G0XfmB6dAjP1\\u002fOiyjqEAeCp7XNXerQJbY+grM3a5ArpcYWUvpsEAeVP0RdZ2xQCmkxzwwJ7FA7YcxhIvqrkAAvJ\\u002frkKGpQAV0fvf\\u002feqNAUjTx8CMXm0CghAKqRjaRQAUTY8DD+YNAlpsZnng0dUCWnK+oTdtkQHzzmQoWoVRAov+BUNzkTEB7VbI7bA1CQHYYGfCrq0lAa4WxF+r6WUAAzXeJwLpqQBH8qt+nsXlAiFS4mLm7hkBKDCmzL3aSQBc+vCFog5tABmCXCMjTokCXDXl4RbenQBxwVyYQnqtAijASjKf3rUDbat7FJ7WuQE\\u002fZhpE6Wa5AfaokwcvLrUAQsm8JDA+uQOak6TxkAbBAuKyd9LwnskBa3CsO3bW1QFDQeJfnyrpAlFGd7tapwEDDUJPsVHXEQJO6RSrPZchAOLs1FYL7y0BdgbHQXsLOQJic528CQNBAk95YNOql0EDlWIz2XcHQQGdsl24L0NBATzet4pEK0UAOrbG6TZrRQIRx1l2emtJAQlL+lwof1EArUuKE6TbWQMRKl1YN7NhAVXnihnc+3EAY7QIb+Q\\u002fgQBmIWORsOOJAD85aYiZ\\u002f5EAgpqPDLsHmQGSMk4rd0+hApoHwbh2J6kBAUOqKDbbrQHMkqTeTOuxAJ9aBfmIH7EAgKm5WUSDrQP0T1++pmulAfxfwVUiZ50D+2pk\\u002fs0jlQKK8rV2U2+JA2B+wg8mF4ED+ErWmkejcQM+fQQjThdlAQ8KnjjPp1kB0db9qmebUQLYiLSYWM9NAszLy0AmE0UC5rvxr6VbPQJ66xxl8SMtAUL6thbwVx0ArsxuscRPDQCWQELp2Fb9Awok38fpGuUAk+5e7nce0QJ4bGXVid7FAQwJ5ddxIrkDTJBpSIjqrQLbwRE+mc6lADz69DtbEqEDs5EM2aRipQOJATZezXKpA4Rrdy7darEAY0Uv8FpOuQHWdnCE0ILBAj+TDEKVGsEBbAYMRPOmuQBnLOo2gTKtAQpVbVLxBpkBpTuwecKygQHCPl6i645ZABjPnLba9jEACdDmBhn2AQF826GNCUHFAj0ZCsMPWYECNqIfgYXRQQGALjc4F30ZAe3TATOcVR0B2Zm7HwPZPQKDANXQNEl9AliZLDxvxbkDRuK\\u002fzU\\u002fN8QKuF8Qd8BYlAKTM2RWDnk0CgRb4dpB+dQEMoMiRlnKNA6zmmmXJeqEA2qsD1JBasQM6zh8Y\\u002fUK5AXdJojf0hr0BYeqXP6CqvQI92s8\\u002f0Wa9AIe9z0uZMsEBwnulAA8uxQL7HVxuuWbRAou8kKPEUuECrkKI5mAm9QF9oELWdksFARM\\u002fBpgYLxUApf0NOQpzIQFU3eDy92MtAjdISZ2xbzkD0HRXjn\\u002fHPQFcLWgOmWNBAR\\u002fUHZoR30EAbJtNaso7QQKTsubRJ1tBAbgd1QqZ90UCx3NFEyqvSQFldxYXOgNRAj0ssLsIS10AcXTt5WWbaQN92GYI3aN5AZI9Wzq914UBHhQanANfjQGSQNW1BMeZA4w5eyJ1c6EB2z\\u002fL0\\u002fjPqQMA8bdNdl+tA2fOU1Vlt7ECyRL1YAaXsQA4A7ZLsN+xA1iQlHesr60ArkO7KjZPpQGlFogefjedARQ\\u002f4JuZD5UAkKZfLoOjiQGZwzLnusOBAZKND4fCV3UDTa\\u002fPVMqPaQJmmGU\\u002fShdhAUBw6DKv\\u002f1kBcNLpUsK7VQERwouDNMtRAolG3mMxR0kDqXKG0ZgjQQNwiyBkWBctArRC+4sYExkDhfGBwE4rBQNTw8uANxbtAYI9PcktPtkAPYvnzXJSyQGR8bpTrRbBAchZxmhQGrkA67P7SS92sQAezRW1HgaxA8ejnSBmdrEChSUTTowutQIa1Rr\\u002fHr61AIIJFRY5HrkDUHdM2xVyuQIGxiGuiZa1AVkPkAZ4Fq0DQyRTAr0SnQO6m+6Bzl6JARi6rStBkm0CnXmYAcIiSQB7rGXgu+IZARMb8vUcKekDqYOyKHAZrQKktZPrw+VlAPhQ0DMwQSUBnIv2MDUhBQFi4zC74iE1AEEj\\u002ftxzWU0A\\u002fGRxhQ19iQIQQeQf\\u002fkHFAO0mEX7LCf0DTt4mpZaOKQGXn\\u002f5N7o5RAmpzmtKyDnUC8VgtqPX6jQHXSDcPX26dALGohq6U9q0Ab3+9tFl+tQLAsHAVKga5ANG8GTS5cr0Ah7r6BqmuwQHBDhROj2bFAZeRutkkrtECSBV4Ln2u3QO8cyYY4lbtA6v8H+1ZRwEAKjNUeh0HDQB6t7lnie8ZAxs8tYra9yUB6srRLg6nMQJeJgmcQ585Asz4V\\u002fhIl0ECU5BOfDnTQQCxkVpJdiNBA76b4etOZ0EBjabQ5SuPQQFjH7zdFnNFAZk9\\u002frRP30kDTUq4YCx3VQF9ZDaxzJdhAjSvYHzUK3EBjieSei1DgQPdFW9lzz+JAxIu9emtS5UAODG\\u002fxl6nnQHIep3\\u002ftrOlAVDOzJTRA60DUtm0kJlLsQLAUhuXY2OxAK4rXydvO7EDK181YOzPsQDG\\u002fQCkaDOtAOxwWc+5p6UCn\\u002fXsik2nnQLc3Lkc\\u002fNOVA3P\\u002fnm6784kC0+sYyW\\u002fjgQCc9bP9xqN5A1UnvjDJO3ECkAAo9087aQOm3KAc71tlA8\\u002flprsDp2ECuSMHOApXXQFeC8XckldVAzMspDuft0kA6VUb7hsDPQOFaYpN6lclAEgZuph0LxECirHN6fyq\\u002fQMUXO88ou7hA\\u002fVhRWOqftEAFWWGvTGGyQLwbFiFjZLFAzRncUkoQsUCYQcex5+ywQHKnmdxxtbBAmJe0P45WsEBhi\\u002fWJFa+vQK5Bg126d65AM\\u002fbzvcfhrEANN94vy6eqQPzBBxwqladABD85Ffq1o0C6kNw30MSeQNg1V\\u002fqXPZZA+\\u002fZ0noacjUCTIQNlCxeCQPwwX9DnPXRAAufpvES\\u002fZEBLFgMKT7JTQMIBVDJNwUJA8vPohLSkOUC3o8xT8BpTQNIlnLBK5FhAhMqbhGXjZUC9dhUEDfpzQI0reOPrVIFA\\u002fIDgxI8DjECxIDDvFP6UQD3cGu8MJ51AdA9k3+7IokAUCG5UAZGmQKz45FMOgalA\\u002f19dPuuPq0DHuyIqSTCtQHIMohriL69ASqjszREzsUACCY3EI6+zQN6Zez7fF7dAXD4WTVZNu0C\\u002fh6rCWRPAQOl\\u002fPcarxcJA8s+n0Eu1xUCeRcblUcvIQJh1PqEK0stA1eAVUWd5zkBslwopyznQQMVzsp\\u002fbzNBARGtyyuEA0UAnOXDUZf3QQPQG3T7u+9BAvcVjVjE90UAbbXkuVwLSQGwBSzueh9NAVEmPBlD71UD62i5BGnDZQALN\\u002f6EP0N1AK6Xghhxs4UAmTiVH7BDkQHK32+8VnOZA4kCHY3nZ6EAqBhTJV6XqQG\\u002fF+68M7+tAWKDZwhS07EB91X55fPfsQMGJDuaLu+xAOO+yIlEB7ECrSuShYc3qQFaxy70dLulAMLRL5L0\\u002f50BfbX7bYizlQF2zHAXrJ+NANHVavH1n4UDU0omnABXgQLKRCtqcgd5AuNTehOau3UBOe2PCNkXdQBNkTv5vsdxAoeOwJ\\u002fdt20DqZyrZiDTZQC74xILWFdZAtmlf8bZs0kBaag3U\\u002f2zNQFjbAY6rzMZAQNKRzuqNwUA5pDYVD8i7QIr+i1sXX7dA5AYSNQ43tUBWXVc1FXe0QHwqTg9VTLRA3W4rnHUVtEA6frllr3yzQHxDwq1wdrJA28NPioEksUC818iNSluvQMc8NiGjQqxAHnRHuP\\u002frqECvqZhTtDqlQNMqC22tNqFAZi\\u002fDUR86mkBlwQ950ZWSQJIgF86fVIhAt27vfGpJfUCyuHH0cSlwQDq4SyjJWWBAla50MZWkTkAfXPVA98U8QJHbOKGqfjNAD8bYLC\\u002f0WECpO6NhuqdfQFSvQRHBhGpA+ZUiYnYZd0BvCJKw1S6DQBPv2qwNvo1ArBd8NZVulUDHcmd7crecQG\\u002f+c72f8KFAQ+tCw9cLpUDSIuL4mH2nQA6dLdwnf6lAFaeMTFK4q0DH+FZklgyvQAlnHKZ+ILJARd\\u002fjT1PUtUCW6QWL0YS6QAfHkgZf5r9AyqwOdwDWwkDn79aALdTFQL5e1Bji48hA2EA01dnzy0CPOiU1ztfOQBjfSeTHpNBA2yKDSJ2B0UCVRLqQdPHRQB7wF7fJANJAiCyTs0DZ0UCTw+CsIrnRQBySzI1t6dFAynuhU1q10kDIaKfKzmDUQD4bA5GNGtdALxa0cqXr2kAGC\\u002feS7arfQDnuH6BTfuJAWcipaUEx5UDcTlDmgqznQDhYjdHyu+lA2hE52rxD60BneCg8tkDsQKeFsS7XvuxAkbCaMQLM7ECGePKDJG\\u002fsQE68ckJUqOtAM8s0gn546kAe+kEi9uroQLMGgtVKG+dA5fF\\u002fePY05UC0LBotUW3jQK4OkVoA+eFAhZKRlf\\u002f94EAgAQTG44TgQDqK0Bonb+BAtIZ6tNV54EAbaQjqL1DgQLm+yPBzUt9Ahuany5nG3EDAHIn1IiDZQENO9J7B1tRAF8qYmP+F0ED7pUMUfXDJQBq+tIbYh8NAj3k\\u002fr60Kv0DCkQFC3G+6QPrEgtRrX7hAb\\u002f9sx5rHt0CdbltZ2qa3QMtLCm9OQLdAoG1EXjE5tkAX69v0S5S0QOOfbn\\u002f\\u002fjLJAbsQteYNlsEBT2Zr48IasQFxzijKPVahA6CeCB74ipEDAyU8BJ96fQCiM7EumyZdAqvfeB82QkEAwpJFc7lqFQEI+AslyWHlA8LHycMSaa0B8Gx0435FbQBEoUKrAf0lAH6vOOGCbN0DsbccBJLIvQGYkiq8eP2BA5Ba\\u002fjY8wZEAgVDsQX0JwQC+w3sfPM3tA0geST9CyhUB5ASOD8SaQQFyQy+3pWpZASN3zeF7OnEDqa4CcDl6hQDkS2Cx0zaNA3wDcaCTCpUCzuevhDrmnQNfzshsUkKpA1czMlPxBr0AB7EaUNUKzQPXJVJucO7hAy0bEy6hKvkAURbVLl33CQBzEKXLv7sVAWIk8dMBXyUCaxJp5EqrMQMi+Rtux1c9ADUzAbTFZ0UCu8IxAq3\\u002fSQCbfWOM8PtNAWd92weqG00COFOAUiWnTQFbdYpxzFNNAVhVBDB\\u002fN0kCcMZMM9OXSQDHNLt1cs9NAhYhlzBV+1UDxSpcnm3HYQHCZ86tBitxAMaVCX1HF4EBCAAkXB4DjQOoUCkKMLOZAXrghVsaE6ECSea3qgVfqQKMcAgUAk+tAfkMornVC7ECKr6bA63\\u002fsQDPIa5ypYuxA9o09ijb060D6R5M+bTHrQDPaxlxlFepAqpoez5am6EAy8yZjv\\u002f7mQMAbpedPSuVANa7EeVvA40DpiXiMgZXiQEfa9FkZ7OFAhOLsfeDE4UDCUUDwNfbhQHOnm2I4MeJAg1px1u0X4kD3ovGbQl7hQEeKK12jzt9ALeLeFnmg20BKx\\u002fz3scbWQBKdPXW79tFAbZCAWwyWy0CSxbTJSDzFQB\\u002fAr\\u002fQvCMFAipd2gdRkvUB4hKYGa2S7QGYhdU5V07pAbB0EuVaVukBIXn5mHuW5QET4ipmZb7hAL8WtJxtJtkA30AWdTMCzQGNH3+YGJrFAn3JX9QFPrUBzdjGo4JSoQOzKaxi+BqRA4\\u002fuVvEI\\u002fn0BXE\\u002fO64gSXQJ7XhdTKqY9AhKymfBYqhEAvMinnm6Z3QGhQ4+BhdWlAA3cDwAAhWUCpiFggefVGQPtMwzhw+DRA0u6YzvnnK0CX9cOYWLxkQPq++YljZWlAXkGisT3ec0DSCfXuhhyAQNLiWK1q4ohAXm6xmzrmkUDBns4SU+WXQAqhZ7EnrZ1AlZWa+gdEoUCLbf+55hijQMCcL+dPmqRA9LwQs3eEpkBpGaB9eeipQOhL2nIM2a9Af5S\\u002fJvWAtEDm3hmKJ626QO3R0g8sCMFA2QDQA8YKxUBwPwh5TBjJQG4x2d5MBM1Avc9NPKpc0EBOA8pgFRHSQKHIT9D3idNAGQeLtjyn1EAdBoVaLUvVQBBwvyLva9VA9AAIwtYe1UCyqbyYCZnUQPLWYBwmKNRA\\u002fTw+sF0n1EClBIjnLfLUQPEz3A9r09ZAjSxgMSvx2UAMchgamjreQCb8Iefgr+FAWgcmHTht5EBZV\\u002fJJLwXnQJCyOGVEL+lAWmIfboe+6kDRqOrXxavrQDFeHcJLEOxAw2Wr73UT7ECoMEMU\\u002f9TrQJRXzi\\u002foX+tAmM9lTVys6kBzSblCxq3pQC1ai4fKY+hAdRlQScfk5kBdXAYIhl3lQHtISZBEB+RAFojz0LcX40D3iOr4wq7iQF7ob35ExuJAlabB8oQq40B5axcoZoLjQJdn2RZuaeNA8CQ5SsqT4kA0Ab+vHuzgQH5k2pfBN91Apl33jD7010D7Oe4lBNXSQHIYtu1I9cxARK8dMiJ2xkCAKro6zj7CQE1\\u002ftOgT4L9ADsulEVHdvUD6HTpDuCa9QGO+rtFPn7xAAj7BEm2Ou0DLdaPOobO5QFdH49TxM7dAKZBZY\\u002fpmtEC\\u002fj5EyYZ2xQLfi2iIn\\u002fK1A++EivBoPqUCG0Ic+Q1SkQL00zQLXi59AP+ziysYUl0BG\\u002fwIY0H6PQPOR3+Ai4YNA0N2FOSgYd0AwZZtOZZxoQCoY98AhClhA\\u002fH+0TYm4RUD8SC1aGJgzQGcEwqee2ylAdkMunouMaUCO8hbm0fpuQHa22KTCv3dApVJbC3vVgkA4tPuozGeMQOG0DT9l6JNAKdmgNh\\u002fVmUDqUe8ToB6fQCayyEo4jaFASfqSiyzfokCsDJfMC\\u002fqjQMIJ0MBjz6VAQ+Qx0mSbqUBrpKr+4EKwQCQdta+umrVAWnf6z5nDvECKYChx36TCQKlrGL7cQcdANrPrzvDoy0CYEr5H7THQQG\\u002fYkFAhSdJAkBrBrbgp1EBqa8yjrLnVQNYDwAhk19ZAF4lOaqFn10A2EP+9O2bXQHRL7iS879ZAyMwHEKpA1kCWnM\\u002f3q63VQBiOU9QkmNVAHNQUbKZf1kBtx0NxGU\\u002fYQOma+vw6h9tA\\u002fP7K52\\u002fs30DKw\\u002fiGIZDiQHQAnkjBRuVA+4mEXFbE50B5L1t5177pQJr7+QBZDetA7CubiAiy60DoFwPEsdLrQL\\u002fEoHWWoutAJNh9l\\u002fFI60CRqjQ3rNHqQNKLhhurL+pA7E10cr1N6UBu2ztkgCPoQMasoBz1wuZAie9+ogNZ5UBvxsLI0CHkQA3uxGCRVeNAPGHY4FET40CLJU9kq0\\u002fjQABMO82pzuNAbykYqnsv5EAIoc+XYQrkQMH88lboFuNA5kw6euNI4UDxRkTyV6rdQHTObTyzN9hANTcqKCUI00AlY4eMfm7NQD02uiwjHMdAFxUTSYoOw0Bdt5JFL9HAQMcYyA5fgb9AcTRKGpd0vkBEGiCZR3m9QLKc\\u002fABQ9rtAoIrl19rEuUALSrjwFBa3QDOAkGG3PrRA3fB3RM6BsUDDVqDRjuutQFASKQjyGalAdyAJXZ9mpEAJWc6lj6CfQBQ2AaSHDpdAMxuAIyhIj0AS+bAt9pmDQOnxfpgNlXZAJ49LKh\\u002fZZ0BBe1s+YhJXQCpSFuDpoURAviAJbpZlMkBiWkyMVBYoQKHsYYmiD25Aap5ZMKMYckCMXLOPHVh7QNOmrvIoV4VA2WRlBqqjj0DBQL3Xb8KVQPw37EcApptAMYxknuJFoEBtqUFKSOmhQMILa7\\u002fWzqJASRoKeGWPo0CWYIMiw0KlQCkva+ELQqlALVn4XfJgsEBNZXa2jTO2QPXihe4zAr5A\\u002fsBzvMapw0CmP\\u002f8fvbzIQGvxF95+5s1AgeX4lXd00UDzI4YoGsvTQMoms+Z33tVAu7O\\u002fIMGN10Aw4xXdPLXYQCPc5wwhPdlAy\\u002fDGCGIo2UCzxfrDG5vYQOBOkYmU19dAbxobJ\\u002fM110A\\u002fZTchiRnXQMzwSNmy4tdA+5p\\u002f0Jfb2UDyG2HWySHdQFukh3+\\u002fyeBAawo+oaxk40APRnJaDBLmQEFOKCf7d+hAc1Fg3v5K6kAOOy1R0mTrQN7OZfuFzutA1v6oX3q360CjyTH6UlzrQNXUjV1K6upAm1mVqs1t6kDM\\u002fWdt09TpQCwSMzMaAulAFe+FG6Xl50CNlw0mdY3mQMJ8FOAdJ+VAcwmaBZLy40BDXO2LuyvjQFE\\u002f4Tk98uJAir5hBi0340BSBfVZwLjjQAET1CubEeRA9K7qBh3a40Bwxg\\u002fdmc\\u002fiQJL2FIAo7+BAbR+JKEbs3ECxsofyyZDXQE1+Cl7ulNJA\\u002fAGUnOIJzUAUeLl1JC7HQAHsekF3bcNAv9M63i1FwUAkqG1wBxbAQLa57BjLnr5AOH3KX2YQvUD5ntxU8hO7QN4dW\\u002fPSnbhAq6FYfnDktUDGe3+XHS+zQEeToxY+qrBA0z0iCvmtrEBAByfd7jWoQDy+KpOVvqNAH4J8YjmcnkCir9mXkT+WQBP2fYRYBo5A8ubtf\\u002f+ugkCuvEpNUlp1QAKxJEuCWmZA1EYmeiRtVUBVZBjCrfhCQM7nCL0ZujBAcOH4Puy7JUCb+CJwwsZwQOeZZiwrFnRA6WQUHpkCfkCpGPP9byKHQAxoW+257ZBAiTKePDf1lkC7WqPEobecQJg4K0J8nKBAE430J5PxoUB1RVBhYH2iQKi4FYXf7qJAjVnIxgZxpEDcS1YDwGaoQPlbVEORArBA0bUezX73tUCsTUQaIv69QH2jaSfq0sNAYz\\u002fqZfwlyUBzBL+Mc6fOQLY7jsvzCdJA7n717IOX1EAUTXhwWNrWQH7TajmUqthAyZRypmnj2UAxNEmjHnLaQNmr+h9zYdpAFeuVnZDb2UCXGM8ogyTZQFd0fRF9kthAAiMwAJuF2ECVPxgBEFzZQO5RAEycX9tA4tSPZTWu3kD9k9ZGvZLhQA2WTdOQLeRAuqKYlV7V5kBCTrftcS3pQD866Xiu6OpAH0lMZJPh60AUyqnctCTsQEU7jr3j5utAHQtGTVdq60CHLYY45d\\u002fqQF+labkRVOpA937wN0Cx6UD\\u002fjJ1RgtToQOd6xjzLqOdAWSGjuSk65kDO\\u002fTqZMbjkQPlF3m9ZZ+NAeqhIGq+H4kBsGyxxFzriQGyGZpmgbeJA4B6MaUnd4kDByhYXoSLjQNGMyTFV2eJAnLKm5t\\u002fF4UBqPQRqFNrfQNzNeu5vINtA4nq+1+8h1kAEtWrb85bRQBu+xWCi6ctATm8oyj+5xkC\\u002fCJ1GtlnDQCigUzePRMFAZiIcUHTVv0DRbvWYL7C9QOXA0hrIhLtA5r\\u002fPvW4WuUAWwc7UDXG2QE\\u002fcc2\\u002fByLNAOqLzIJFQsUCqDyfmETauQEp\\u002fNkXnKapAWb832VY0pkAnngSKjiaiQL\\u002fcHAB9HJxArS2mXbRalEBXyK5ZyU6LQK7mtw+V3oBAp7aC5DQfc0C2KcPCsNdjQDZR4Ywp2FJA1+ZYH5OFQECiP4znQdAsQCTD1Y7\\u002fkyJAxK6\\u002fkV+zcUC4FqJaThd1QHyRz1HdM39AKZCKZV7Nh0D1Esab2zqRQF5uP8BLGZdAjrV9jqeInEBlxb+W1UegQOEIksgEVKFAWlY5RBuWoUCT00CznsShQCwm+C7tCKNAXjkzEKa6pkBRNaWo9ACuQLwK5aByvbRAz2lxakaJvEAmg8wOBQTDQDbfTj8PWMhAt9z6Sc73zUAy26v6os7RQEWJfA1zf9RAFnurXNHj1kChmFScxM7YQNPDDbr2HNpA5bxO6obC2kCdgb0xktHaQPUDg4oKd9pA8vDOmkHz2UCRLiiOTpTZQDZFOYMxstlAZwh1QcOm2kAJhOYqY7zcQBXpGIGOCuBAVlavZ5RJ4kCWdBfpXufkQKSwHppykedAHKcol7zp6UC2zeohhaHrQDkgrqIrkuxAOmcKGujH7ECru+lF1XfsQLRXDbK+5OtASR571Ng\\u002f60DN29M4VJXqQHtiUxdrzulAKIo5h8HG6ECTkd2jzGjnQN4tHfYtwuVA7vDbdzcG5EDrOu719X3iQDGznuPUbOFACVIsR4n04ECT0LAl\\u002fQLhQC5uFa96UuFACkZ7Z9t+4UAF7tOowCnhQK71h78SH+BAvs5gWPjQ3EC1B40zbIzYQNz29YZ5I9RAgnyGABI10EBoBGGlHDjKQFMD4v3Wy8VAdXedehfSwkDwH\\u002fhSCszAQLp8jaIxiL5AzTUpyonPu0BLsoyuvhi5QA+gi\\u002f8jUrZAkXM4O22Xs0DwBzo2+hOxQMKHewSPyK1Aaq4\\u002f6wsHqkALLEJQ5p6mQOvtTmVUPqNABqlIf7Bzn0BAZRHLF0iYQOoalKX2fJFAP65qVPNMh0DuramoJo98QEJCPUL4C3BAxxgS2kCAYEDA8TQfcAxPQOhi5nQR9DpAeY97GH8+J0B9smKLU78dQCK0fKOGoHFAull+6urndEBNvv5qj6N+QF6jliZOJIdAC9NmeRaWkEDPRQHQFgKWQA1DfYdV5JpAhWOLnVlVnkCQjsAk9+OfQFx5ujnV9Z9ALJwElobrn0A0pXKfF\\u002fegQCSlhx0vOKRAr2hI5XvFqkDWxlgH95uyQLEr4kEYyrlA7d9468pWwUCkLHLL3m7GQEP9SlA978tAhsEK8dzI0EBQNtzRY4HTQKlHqLT28NVAqkTJqSfp10DrQ3nJbEvZQEq7zXcmFNpAfSDGgVNb2kCKyYTWjUzaQLuGqVjrHtpAgrrJHSkT2kBpveeFhnXaQNHFqQ4jmttA3YzglNbM3UAE8AgtKpvgQCDKOk+S4OJARWuGgTWG5UAw9btinTvoQE9QBMYso+pAH5SbyhJt7EBxtw3a42\\u002ftQFx44Edfs+1A017lvCNn7UC9CK953sjsQKjgALuGBuxAxUeWptEr60C+CK4AMCTqQH47vOt+z+hArib6Rp4d50Aw6ugrAyLlQMJKFapNFeNAxPy\\u002f\\u002fDZE4UBFHxMl2ubfQDcPqRu3h95Ajazv7OZE3kAKKIa7wJXeQJSHdvVXvN5AfG+8z6QL3kAtNfVzKyncQAo\\u002fxQVkK9lAKpVygrSG1UBSzRoQEtXRQEGrnZqhLs1AbCoQ69gcyEAUzshT33HEQEdkkUZv1sFAW\\u002fMavY+6v0DtqEnsMV68QE8UCs21NLlAunKkLNYetkBHqlMQOiqzQCxPLIOteLBAtYFx+SBRrEAYN3LhqIOoQIYrx8yvXaVA7S6qXwKMokCCrfzU9nyfQJ7AliSfoJlAfse8yqyqk0DUxTzmWR6MQItxQ789k4JACTUbTi6QdkBXkI6xnR5pQMmSTU7TlVlAkuYNiZ3WR0AHWXOReXw0QHp6GaJkeCFAHgaF7ckvFkAv7397qI9wQK9K9GuHjHNAZRBAMpFgfEC+9jjryDiFQP0g+zlhII5AGM6e1AnLk0BrvVrN\\u002fPGXQF5b2aYpvJpANQB3VjLRm0DhDaBtJZibQGIXoRGVTptAZADBBOXWnEAsCoYsDyahQMZBsNR1wKZAl3tzYZTLr0BB+\\u002f6VzjS2QDPgOdUpLL5AIudth6bCw0A5AQ04cO3IQDHA3RXKUc5AaWozQljL0UCyfzS+4yvUQI6iq\\u002f7sHtZAlDWkPNSN10BXsK+K2X3YQHiw5prSCtlAsxQuVE9b2UDLlslARJnZQEQHyIgM9dlArmrpeFCt2kCoyIF7nA\\u002fcQMRflJohad5AA1Xft\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9A2z9JhBUg8EAB\\u002fVH8aALwQKfnfpBM0O5AbwqWOx607EDU5byJBNbpQCm4RZUnceZAGIciccLS4kCJYelXUJ\\u002feQFy4ei5IadhAeJXDiYlu00AmF2830bLPQCYwtX2gJctALn5VGMWjyEBQMC1rCGvHQAxcBjn4vsZA0loVc5gSxkB20O9NGxvFQOSZrlPXycNADRE39vE0wkDoqiVdB3zAQMoPcp4da71AvkAj2irWuUA4enJ+m0K2QBPaAb47xbJAFwsdx+8Lr0ABbsOUMWipQHVxsuzp36RAK9YshT10oUB166Bfz8ydQCerTZlsrplAmAhEZUHjlUClUKAeKBGSQBiAohByb4xAxbaeGr4ghUCp2wi9EYB9QAKGjkD4UHNApGgigTO4Z0A3xUMPB1FbQH846yqsf01AFH9RtjrXPUBXG+NgTzksQLgA\\u002fenP5BhAOaDTm9NsBEDhpNxiYUrvP9yHk2t32dc\\u002f8gPMMyUjzD8UsKGexmdMQH7axIUXg1BA+fSSBklrV0CO+WL6QGhhQF4VseBYNGlAUyD2NflrcUCtEYga7P12QJoMnJbbJH1AoXKTh3zkgUCOAD1vG4KFQLsyAqn7polAw+FCXmnVjkCt019CjvKSQPudBPYF\\u002fpdAQ+C1fbwzn0Do1z0GvJOkQL6OEa9SEKtAzRnH8U12sUAUrfQYdtW1QNLdF4HpQbpALO8hY+hUvkAIACSVVevAQBzlO8KmcsJAo5gazHv5w0DdId5hq8nFQJ8Tq58vGchAHHLxUCfxykDsEChRnDHOQPmNWYCL2NBAcneFgNKy0kAK5z4N0b\\u002fUQJj+6zxdM9dAAmm5BCtM2kCY4E6L5DneQNKZ\\u002fdr0geFAZPzmvzs95EDBrs9sLxznQIyNNJeF2ulAN3aJsN4v7EDRrmhVoNztQGDX6l7kse5A+X6gjBKU7kACLajQUXvtQKSriEladetA45Rhyseo6EDavHYS8VXlQKWAFJGT0OFADmdha97i3EAfkTuEVgjXQK9QL3jJcNJA3aKAyIhszkDDxZ6QuWfKQH9YQ\\u002fIWNchApbPxELsYx0CI2UubqmrGQO9pIseytsVAQ9jHs4HExEAuUIsJ+YrDQLVE7PfnGMJAlBRh07R\\u002fwECO\\u002f4E9I5K9QInhEasD87lAdUcJCQw0tkDt+jHNVX+yQJo6j4wpKK5Aphyy9y5iqEDc579RYfOjQOoxYMwIzKBALsVfl80gnUDgF7BA8oqZQASZslpdC5ZAHpH1pHVEkkAPqpuVVpOMQEvLHYj74oRAXO\\u002fBmkNlfEC2LM2\\u002ftutxQFsPC9J0AWVApR38wVzkVkBf7SqzSztHQCgFZKZp\\u002fDVA5qoo5OhrI0A5J0eAPwQQQILVPoYsqPg\\u002fevFvKYjK4T+7OIo77pXJPy3oX\\u002fHYCL0\\u002fky+MiigGQ0C\\u002fGSvzrW9GQOiS5GyOelBA7esHb9uxWUD66HN9hbhjQMDy6JCwJ21AkVfjms+qdEDrYZbDQht8QPFjZz\\u002f4X4JA+v3ilR44h0AE01mFDZ+MQKB9YKEmcJFALqJjJKRQlUBGb9oAQ2yaQDfVQv+Mp6BArowncHA2pUB8gD420fWqQI2BgwTt27BAsgME0nyBtEBGqEVCrBS4QIiqggiSRbtAajmFNpT3vUBX665o9y\\u002fAQHULxiimfsFA8v6r8+Qxw0A+mdGxSXvFQPkaFmOMXMhAfMKRw5ury0BWfq++0zHPQJqqgPX9atFAm3+jqTFd00Da+zb0E5\\u002fVQDfPaHENb9hAjlrChBcA3EBv9BiNly\\u002fgQP3Jw6feseJAO7Aj\\u002fetY5UAPP0z8iubnQHN3\\u002fXdjGepAnKoXFuC360BCTfiNhJXsQCDXrzEelOxAp3MqYF+l60C7Qz7J\\u002fs\\u002fpQPFaG9BSNudA1ZPFHTwY5EAL9AvRMsvgQPywj7AHUttAOwppZET31UBpZXDhGNzRQAzztKsNHc5AXWB0\\u002fD6+ykAw06IVCPPIQGq0i4uVAshAdo2sDExUx0BQbWkr2YnGQELOOm5Kf8VAvYY0Bns3xEDT01JLD8LCQL7U+z+ZKMFArVzFf9TUvkAIC3gU9Aq7QPST\\u002fMcGB7dAct2f7o8Bs0AWosPVf5quQO7rPrKYcKhAG83yMy\\u002fXo0CU58LsNrWgQFovUJcWMp1AHO9pV3ngmUDrlMhXFIqWQFjMC4l7xpJA5S0rJyFhjUBiB14Mo16FQGoZcRKLwHxAhQwmtsPZcUC3ZCs9F3JkQPHB8E6mnFVAcw6LfcsaRUCAaE2VjBAzQNaZQEUa6B9AP31f9grECECjymZmF9vxP0fay+hLC9g\\u002fLQXxvgIGwD97kDdrZzCxPzdjRy4xvTlApi6AzD47P0BLhIgOmmdIQLxExItFa1RA5\\u002fyPVwvWYEA70cItpKdqQKO55rGrFXRAMeR3ty26fECXqbKtmoODQPJiDEj0TolA\\u002fCVSLOijj0BkM7f7V1STQB7pzQg2b5dA4nTO9aiJnEC6FU1TKYuhQMtGRctgtaVAd4DhKu3IqkDTW95pckmwQGygTUQoUrNAuHkE9EQ2tkAclKu5WLu4QMd\\u002fTM8l27pAUKtjP1XbvEAUo08r+EC\\u002fQPCg6u5eTsFA4DZy\\u002fKejw0BnJKHIWZrGQPDqHeBr\\u002fslAmuJ5NkONzUDxA1FFmpDQQK5YDBEMadJANCDiCw981EDbS1Ij9gXXQFPgtzEMONpAft3lxyIe3kCvgzEStEjhQCYxv8pEn+NAKAudCmzd5UA0aIakEM7nQN1lDh2JQ+lA9w0Zk3oY6kBngUcjfS7qQDEZPlWvcOlAZROicYzc50BvB0MSeYzlQL1p1hGSu+JAYTZ9pTZ630B9PvL7t9LZQLpXuT3PD9VAXSJhfz+A0UCEL76gFFPOQPaSYtJJqctA4n3iLX1RykCm2cFWx5PJQEFAGyZL48hAdGM7yp\\u002f1x0CjrJ\\u002fPz7zGQGgz3duGTMVA421u0xu7w0Do7DNVoA7CQDoTpJNbPcBA2CZTJI16vEAGTKWo5Su4QAa3XtsR0LNApjZVsI+Tr0DDZR+rzuuoQCU1wtXgCKRAldXKbCHOoEDyd6+D4nOdQHpQpaxyTJpAxp14jYIal0CY8q6NfmSTQHA7rnjdhY5AhYyIoOlIhkBTYFLltwZ+QLgcmbg7nnJAtrCBi3s6ZUCMutEHtj9WQDBuvfUFcEVAe0g90Fz\\u002fMkC4XcdK5fseQPOIOSAURAdAk7xbbDIa8D+9h3fUrqLUP0qrc45+1bk\\u002fJvBT5I5Kqj80VuGJhUcyQJYxt9cFHDdA7od41OWHQ0AgLDuo6ZhRQEWZf1tp4V5Aicm7iyixaUA0QmRi9Bl0QJOwF5+Fhn1AG52Xm1xnhECT8ntssraKQBbv5Magv5BAElP6PehnlEAOfMds0IeYQPiEYbDvdZ1APpOhf+3HoUBjAMVOgoqlQL28givpAKpAbSWT8WP6rkD10xyf1guyQHqxenp3crRAFpEJ5X6ItkBr\\u002f+\\u002fy8Ve4QPT\\u002fEEGOMLpApcBcDE2ZvEC37O6Dsw7AQEN7krpBhcJAHwkjkZuixUBPjvXcISnJQIVamvG+y8xAkn66KpUt0EAoRiD80PPRQHV+HgVG4dNA26zumK0t1kAAuPNhlwPZQLh8hNTjZ9xAZTLGKe4Y4ECsnQD2tgviQMiIBzZm4uNASdLvb\\u002f135UDWbdOCgq7mQC1ot21zaudA3E2lJaiO50D224KgDwDnQCMNfVBXsuVAFhvZVVS240Dnk349zz\\u002fhQDPG9M\\u002fVOd1AJRGsIxlE2EBn7LsqribUQA001da9KdFAXSzRhSGZzkA+2X5\\u002fqaXMQHUyTNEAwctADrvBkf0zy0C5P8Z8+XvKQEsdAgd5YclA\\u002fPQGlVTtx0Ag+I6QnUbGQAdlMaqbjcRAJcBFkGjHwkBHfxFdOOLAQFI\\u002fHPuylr1AmidJyM8MuUBL3rPre2y0QMVWBvWMJLBAu7gkoEs8qUBm6Z9y2R+kQFU4uCDo1aBARyCP7W6hnUB39opML7OaQG3RaWpTt5dA+dhmBnEglEBzDLFAUwCQQDuf4H5VlIdA0ltbGCoDgEAeVgiBQf1zQJVyh6X252ZAdn9SxDEUWEAy+J4yTjZHQARrY2xzhDRALfP7+bGgIEBajHSW7rUIQM8Kpbv41vA\\u002fOKoTpiMf1T+mgWSJq6O5P4WkK13mWak\\u002fU0PTstPuK0BCfRecXG4yQI5SQ9V6ukBAmNKMFpThT0D7kQOG6hhdQFvvShon3WhAitV8PaTJc0BsoiP+e1l9QD7vPSdEX4RAFn+ppeuvikCHI9giL7GQQMEEGF6xPJRAmKRP05IlmEBlCjpLSLacQB+ysFgvH6FAbJbJnkZ5pECllm80kWeoQGjM+rXHwaxAEcQKEEWdsEBIVpPGZruyQIGzmDiyn7RAFfYp8WNetkCHPoBvN0y4QAKUUjY077pAzi3uuDfMvkA9oheB4xLCQGS9kLiGaMVAjUW96kggyUD5ySXAKOTMQHm0q2HfPtBABXVIhc370UCWj8NlwM3TQBppIpBq5dVAbU65J8hi2ECwzqVwO0DbQPGc3hfBUd5AYgi6jP6r4EAL5vjUtwziQFB26n\\u002fsN+NApvphIYIg5EDIax6Dg7XkQLdF27rO2+RAplGJ3Exz5EB21yOvCmfjQHPvVaKwvuFA0VnJAIxL30D\\u002frK\\u002fGncbaQPjoTOU4i9ZAkTl9eb0X00CnCPQf7azQQLP\\u002f0kLhic5A2qb8aV1AzUAjGg0NjcLMQKnp\\u002f9tOXMxA+nFcgdmVy0DAWjzRM0rKQFNpw5tdmMhAFczRspK6xkBtBIqhetvEQPKVcJIlAMNAzl8EOakPwUARdoS8G969QOu7qLi6OrlAq\\u002fvISPx7tEAGeBJ6ZRiwQHpJCoCXAqlAzI5lCiLjo0AMAlv0iLKgQIuUoRvSsZ1AgtNDH38jm0BEvT2kR3aYQEDKIyNcC5VAKHMABqHvkEBbWOUJezqJQN+Iv9vfS4FAN\\u002f5Z0oDFdUDo8tETMR9pQE2TupN6jVpAiomjSeuxSUCkw4ZP88E2QG7plThicSJAWFTb3KZYC0DYhnRKH4zyP9o+3kbPFdc\\u002fGvjSE8evuz8OmYtbywyrP1gDW+1luiZA0bl\\u002fXPD7LkCMlZEGr349QPnRL4M1Ak1ADGs3Dmr4WkC1nP\\u002foAUdnQP3x+NqkmnJABqGyLxGbe0ACQfa2SB+DQMMtGAKs9YhAIgl5\\u002f\\u002fMUj0AcgamhoL6SQO\\u002fBEBuxPZZAkHRhCr1KmkD8bQBWBy+fQLiy8KzYj6JAine2iXMSpkBL38B7dgOqQKC8rV4JJ65AorffXcsasUDRfkebnwSzQBp5SMr46rRAArDrzTYkt0C1KLtncDO6QOtlIWDBlb5Aig+rjYVAwkDoTeNH69zFQCC2w69u0MlAShGSS6K9zUCXa45xoLTQQPIM6XTYbNJACFwewTQo1EAMYvNb7A7WQBGL0iKuNNhAEN+3CZWI2kDAosWLDNzcQJCn3tT\\u002f+95Ax1BEBAhl4EBGqro9ViHhQBve1+tvteFATFAYb\\u002fAZ4kAKeeUDCzbiQEjEzzp15uFAK3LrsxoQ4UCOFFuKpGbfQIL8EfRi59tA+it3CM8f2ECwWwj1e5zUQJEx7jh1ztFAfUfBm+Paz0C2XbOi7t3NQGMx9Lp8JM1A7yeaOZL2zEC7cH5htafMQEVUiSU6zctATcbgahJUykDhWqNcrW7IQAU7gh0hZ8ZAEklYKyRwxEDCqp04Q47CQFAHs0eFosBA\\u002fVgvxC4WvUCyXHs+24W4QGKwLqXW2bNAwD2zK6YXr0CAuwf\\u002fxCCoQBZeP0osRqNAQjYDHVFjoEAEwSj5u66dQPAfr12VpptApUKEgh5YmUBNq29moxqWQF2AUhK4\\u002fJFAvtPkKn8Ji0DtWxRaRqyCQJ+\\u002fUQ6CpHdA4fy7R25na0CNTb8xmg5dQElFNGLMK0xA3irAB\\u002fL2OECunGitXDckQLZLW4Dh6Q1AsfBnKyI49D8\\u002fUS1QHAvZP1XhhAAu070\\u002fLbpgVPHxrD8isZluxvciQKgCOe6oVCpABOq2JQirOUDXIuJSRZJJQI6ihEo55VdAWXusA76hZEAm+YAIAXNwQHwzg\\u002fufTnhA8cFpncnAgECB3+kFz8CFQKR2Bjaj9IpAD6YfSEwwkEC0xP4nFSeTQPShD51mnpZARhPyQHTfmkCE2yjg1xOgQCxPoWwgSaNAmxuFYr0Bp0CPiWdgLBKrQAQldlC7Qq9AzV4hgii9sUA2AjGiBfqzQAmB2J3orbZAy0Y85EhWukCuuzhdqmS\\u002fQKVv12jw\\u002fsJAqFUlBlbpxkAnmbjbAxnLQFGpRkPvKs9AhvpEcshw0UCxxa6\\u002fiCDTQJ\\u002fCaGqPwdRAQ8R+Y5Bz1kChJhPfiD7YQGF4okMWCNpAMSQVk2ah20C9NvpRxuTcQG\\u002flnaQZzd1ACZXZoHB03kAl5iJ1yPjeQMN0UK8WWt9AkB7xfFRr30CyteUqvePeQGl5SBwfh91AP\\u002fVlUa9M20DS79xWyW3YQFWBkVn5VdVAghCT97l+0kAkFuhitUjQQKGUm\\u002fYNws1A8X69NVFyzEDUay5hUiPMQBTf02fIJsxA75QXRvXdy0CTpHDcFe7KQIvhvvl7VMlAPSrYsQJSx0Cqs7LNfzrFQCAYny4pRMNA11EbYYJxwUDLWGBCbD2\\u002fQF7BbDedSrtA32MgFw37tkAhzZGUjpKyQJSjEsQFFK1AmtUEb6aopkD3WekONVSiQC\\u002f85pSu1Z9AudiVrB6JnUBRU09PBBmcQKgnkke8KJpAJvGhaKUQl0AapvRbJ+mSQMwdb45DkoxA2yV8c3LMg0DKcTKTox15QLAt6xGcIW1ALxeaBQ\\u002fgXkBcygee+uNNQBRHpjnNbDpATNFhxCdUJUCh7ABjFW4PQLjWXsTeI\\u002fU\\u002fEexq+6cI2j9up8de8Mi+PzK6mQfxsK0\\u002ftfK85dAyH0AiGmszwM0lQH4x2D7xaTVA2WJBgExeRUCPQX2YbexTQAZhiWVbIGFABjYRxdcna0AnkLFeDPFzQI3ShTnOUntAdT6eogKlgUB5nM5WvsaFQGQ+K670GopAxF5kAT3rjkB0QxwAeFqSQGMIEOBECJZAGDXua\\u002f3HmkCeAObO+mSgQNAun9mgB6RAOeUKsMUwqECzqmCo8LisQMjOI4A7yrBAO95GUpKCs0A9IKAt39i2QMg520D0P7tAcmdKTCqLwEC9ECPLMDTEQFHAbXp+Z8hAeKd8tVrEzEAzxXTrinLQQCz0m63HRtJAbDbbrDzh00BkCZaxwFvVQDlkdsS2ztZAiC2m79Y42EAFNCFl2XnZQJe02vXdZdpA\\u002f6B5Ou7k2kAU0cmPIQfbQI9ul8Fa\\u002fNpATCT2WvXy2kBQfI0T8vPaQE2i\\u002fQU31tpAap+c8MJS2kBqvYYRYS3ZQLk7GqP8WNdAfsJakLQD1UBxyII8HIfSQINz69\\u002fPR9BAVVtlUHUozUB\\u002f6VbbZhzLQH6g+nDBSMpAzBL88jg3ykCOBg\\u002fKsUrKQCa+jZRQ+MlANbvdi2D3yEAsJhYqnFLHQO\\u002fJja5iUsVAZLtDSGFMw0A29B9HiXTBQFNfqEfJkr9Axb6R4lBHvEA4Ql8c5rW4QJc+8uBQzbRAQdo292rPsEAs77UdIGKqQDZeIhCovqRAZG0mutIZoUDhtKZ2on6eQFBYbawKB51AtFNM6KQknEAY59Lz4IKaQMR5tysAh5dAG0xa2VJZk0A0rUt8C0KNQCXvKiqiQoRAwpvgtU2neUAceQHC2qttQESKC4ljVV9Ar04sktozTkAI7fXsXpE6QOCxpbIrUyVACKujab87D0CY\\u002frL9wN70P0pRIp\\u002f7hNk\\u002f4R2EuF7uvT9uwkmQfqusPxigY45jlBhApXa3EA8uIUBz7tzO2tcwQOhr\\u002fNGhvEBA9+9bHdcHT0CMVAgDxH9aQOzV7Vq53WRAscY9UKdybkDT3PHWUb50QHnkgw1br3pAQXjziIZxgEBC6hLE48GDQKpX+svNkodAhBK8ZoFfjEBqDinq0mKRQDoPK6L8tZVANtIxk6Btm0DXeKllK1ShQLy6NyuSqqVACLsq5w+nqkBX0SpF0CewQGe3TTR\\u002fdrNA\\u002fseQIpmMt0CeBCHXH8q8QOGcNBEUuMFAJDTIRhO1xUCfxOWsWxvKQEDSN1SAhM5AvBN2Oo1G0UBDEf08sv\\u002fSQGXBxdn2cdRAsB5uyU211UCe\\u002fXpAf9zWQPAaMftA4NdAS6jEG7ue2ECSX504QPLYQDxaCdUf0NhAzCivwDpY2EC7hwZNSMfXQN4Pka7ZUtdAAkR4CSQH10DNN4twDr7WQKAyWb93NdZA0271rOo11UAedlRgJbLTQAjNW6Jaz9FAJojoV4Grz0DIxvauminMQL4eUQb5kMlAGWAZ8SQQyEAqd1y0tILHQJwLUWsQgcdAM+Nu8POFx0DGxMtBIh\\u002fHQFIN2CKGFsZAPmBBgvJ\\u002fxEDrphIQjKPCQMecLvCv0MBAwhOPDvlmvkDKokrs6Im7QP\\u002fpNwkYuLhALVd2QaCitUAgXZxbgDqyQD1XAgq2f61AmlisytpBp0CYjexNY4OiQBWP6MNKNJ9AbhIIju+UnEAh\\u002fezTgdWbQOiJvRaTX5tAhOa0jk32mUDR04lAsxWXQCvXAUJ095JAhTwa2QCajEDDAghti7qDQKA5rsPB3HhAufpiVvWZbECDU1VOUwZeQLZttieHwUxAkcco7ZYfOUBemHr2awUkQH+NZv7SGg1A1tH8gv1L8z\\u002fJcR7mhWfXP3pivNbBM7s\\u002fxj1oq1XcqT8IFnOS6UASQKeJR3EbbhlAVI4j9AHGKEAhCfK0enI4QHSL4bVagEZAC0qWibcUU0BFlmCitttdQFeIrj9trGVAEmaSDHxxbUDwOkdOS\\u002fFyQKDRCO2hdHdAJ2yKJJB7fEAnaYQzB0uBQDS9cCj4VoVAIxnMp8P9ikAvNFR0i3mRQDE5KiuQ8ZZAfCOlV08lnkCbyaJv2JijQEJ3S44cE6lAeRkir3+dr0CbjoMrpcKzQKhMYEmopbhAPeqcRHu6vkBzFZFm3wzDQLKl0OfWQ8dAtVmiYmq1y0CAX5yjp\\u002frPQPVIJ2uu3NFAnAdLLuRj00BlJyY3oprUQCrGlyOul9VAVHttVqFp1kBd5slRQQbXQLDwWJYhTtdAJ0UUq1ck10Dmyyv9LIrWQB0CKAZwqdVAMoZMRAzD1EB+YZF6rQvUQEYWDAPtjdNA2QO7SvEl00C1c+0vjpjSQEL6ghcDttFAlnLChFJz0ECG3P4guNzNQAIDtQs2vspAwH4FKRgCyEDSzI5xhv7FQNy8QRPe0MRASV8XZM9XxEDL3Bteoz\\u002fEQHtLni1fH8RADjZh+o+fw0A\\u002fvHb6XJvCQGErla+oKsFAJoudTm0cv0CKhgGcxBC8QD\\u002fNE0YBc7lAaBlGAispt0AQ6M4kuuG0QAwHGIvXVLJAUlyqNoTxrkDB7Y9PXxepQPfyfYK\\u002f5aNA3oVwyXoNoEDUFGEfiaabQKzzZUeZ7plAznvYJUiymUBG1Js0LH6ZQEYZbBEjO5hAWn3IsseBlUAkY0EidJmRQNwzqTJBZopAu7KLPkUZgkBso02EDqh2QMn\\u002fpNcd4WlAEz+5yzX3WkCn824ZjaBJQPrVLgU0NjZAA1aQ1WSOIUAS4Wf8404JQEQmw\\u002fDvovA\\u002fDy2rTS4A1D9MUr0fpAW3Pwjz3nR7tqU\\u002fJUdzbd5YCUCrT2JMsJERQFlqcSiV+SBAN5rRftGfMECUJRc7T2U+QJHLL6wVoklAMZBuhXX8U0CZm5LkJgJdQAtnUmP\\u002fymNAnNAyvUK\\u002faUAb+t1iXT1wQBlHWwLjP3RAnycLtStqeUB5dbMXmUWAQNg02xs0YoVAl9+0mm7BjEADSykvKZeTQBfdVybbtZpAL2yCGUQIokBVjpcWGvunQPBpQp3WZq9AQK2okEtLtEByz+1mDvG5QLOeia7hX8BAMe4gC8BPxECLvNd4KZbIQIeVWMOF3sxA5J1I2XFk0EDg6SQUCAbSQB6yQsqsR9NAgi4MKC811EB5O+uBEuTUQGK8t+5oX9VAQp3I3zCc1UATw2Z2LoDVQMsDimt5+NRAA\\u002fAcWTkQ1ECaiechp\\u002fXSQN+12Zzp5tFAHQtUSwQS0UDyWDuPEn3QQL7sVTVOBtBAT2St8EL1zkA6WtXtMGbNQMFgzV5ST8tAVnqUgUrfyEBudagQlmjGQCcA6AX8PsRAdxEMHJWcwkCixetYKpPBQPYYXxcSCcFAfMpFloLBwEC6wpY3DHHAQO4PpxhYtL9A2dCn+l3MvUADeNbab1i7QISwmpvpwLhACMljP6VotkDgWxJLv3W0QFHH3h4ZxLJA8jImLx0HsUBwrAUx2wOuQL+DZxn5YqlApYuRmeGhpEANxwSgbHSgQJaUcTO04ZpAL1r2Z\\u002fGVl0CrLUWGioaWQLBQ08l6k5ZApsogRz57lkATcY0RGFaVQB8B\\u002f15G2pJAZDO8ormqjkDbz0bsDdeGQDjstYH7FH9AMNIVsjpNc0B+m6SnQt5lQDU1jDyjmFZAyJ2ykrxKRUDkNrlSaksyQAujGmyRqhxASJQ59Gx6BECVGjxISK7qP8GjBTYpyM8\\u002fvr3JL8Qbsj\\u002flro62J\\u002fCgP1e5tlreZQBA\\u002frZayXCdBkCxgRvbVa0VQHGpVM\\u002fTGSVAwaSdLcQ4M0ASAjug0DVAQBVFPaNhaklAM7yvWnuwUkDHUzowuRtaQKatVlsalmFAaG2sNi03Z0ADVKNqTXZuQEn9A63pFHRARNfDZjTMekBpW\\u002f2G7SqCQMR9Sq5GCIlAwdFaE+VxkUDqHyYycWWYQAdLH0Ys96BAfN5bctFOp0BgvmUUBIGvQDMBqpxY5rRA0W68qNQou0AbWJngTjrBQFvQOrupP8VARxfgamNhyUBJeUymsUnNQA3Vjq7qU9BA42x2fxyl0UAjNSvhE5bSQEg2uDp9NtNA5gEi88aZ00DqTHOvaMfTQF\\u002fXRNmctNNAgpkVdXNN00BcS6qBoIjSQIduc0dceNFAOduL2LhK0EB9LlCSK23OQCP2BZ4awcxAmFAsEEaTy0BIEsNXZaXKQCYN978hoslADNW3Cy9MyEAjiYHK5JfGQGK92K8bp8RA17ADm2yywkDTdblc1PDAQJQhbRY7D79AFmiOkLkFvUDQUJ5zXKe7QNWwTZ\\u002fHqLpAGvVM\\u002f+ipuUAvO0reeVy4QCvRtKc6p7ZAUXsIbpSvtECIJGK90L+yQB21yKOVF7FAHFY+zyOJr0CQMbUZSDmtQNT8Yl5MuKpAs0YN\\u002fY6mp0BsF6MyqgqkQGhMtKTkUKBAxtbhfYgkmkDY5Mm1OJGVQN2\\u002fTw\\u002foMJNAa\\u002fEQkz6OkkBJdZvKh7WSQNMCtz2XoJJA9+0R0eGakUBRk7kxPuqOQIq5zBIa9YhA6TpiLEtwgkAvEuXR3uF4QHp0detPo25A\\u002fPkI1Dk0YUCgMSOZc55RQLC8j5Hvc0BAk1DfOmUELED4mFIQ9L8VQBGsgSLnyf4\\u002f97Oilgff4z8dLkgMmXLHPzqK1FNlcqo\\u002fWOWXlpOHmD\\u002fIHIJZcsbzP70Nrn\\u002fbK\\u002fs\\u002frTAS4q3uCUDF\\u002f77FpD4ZQAF7O5\\u002fBJCdA5hUx2GzSM0B8up4wUfc\\u002fQMbZ+b4ekUhAMtZrCSg9UkB\\u002faQlroXVaQGiU25gP4GJAUQIeF\\u002fiIakBHdZVxEmNyQC10\\u002fyPPMnlAkIJwl80zgUDBJgoKsZ+HQAtX62vka5BAEY1NmOYYl0Du1SznElCgQJC61sXE5qZAol72QYOkr0AW3FaODFm1QITuRliK97tAi+My2NG1wUBj0\\u002fbcKJ\\u002fFQCGoOkWIaclAK2LzwWXFzEC5qjqmpXfPQOB8Ip\\u002fTtNBAXD3hFj9V0UADTodbd6\\u002fRQOZGdxe\\u002f09FAmObvP5fF0UBo0c5Uh3rRQJa1UfJn5NBAW1hux5sB0EAVujCHdtDNQOtQy5TmhctAZf1ZZwJ\\u002fyUAq0k5rZvLHQLoNsBGS2sZAYyokP4kAxkD9gOou\\u002fh7FQFnClY33BcRAbuucCROrwkBSYNs+UiPBQNwZUI9gI79Aa6fgk3AvvECRn69l8Zq5QBfsl52ofLdAjAvxZ2fOtUBj7tnlpm20QB3Julc6J7NAQ9Wa7N\\u002fOsUAHuWmOIVewQBSTsev2sK1AzNNov5L\\u002fqkAoTCkSj9uoQOPNNw8mO6dA1CgmClTIpUCZWshyQxOkQIRJcCqd1qFAqsHORlpEnkDbRSxUu6eYQKKzpZ18zpNAbl063rpzkECtHKKgFI6NQGZ2tMu+x4xAn\\u002fWJdZERjUCcZimYiNqMQBJNIvaLG4tAxAok9Wafh0BNVoKMr+mCQPIwThgotHtArMsLvcuGckAv8yAQLZtmQDkZIkYGKFlAZQvkq7WGSUBRiRwOwJ03QMNpPEyu6yNA9MlDfsqjDkBi2nBDrHv1PwgnnGwmeNs\\u002f9IVNYU4NwD8mi8HcE+uhPy9KmIxLepA\\u002fIhe7E45b5j9MsphahMLuP+xJfDiekf0\\u002f686Pf6BJDUD0GBuyR7cbQIjCoeHq+ChAOMfPOEClNUAnZ8I4gDtCQMWEvUqr+01A3dgLgVQBWEBdoSA7CZViQBCsPDHTn2tAjDwFwOSrc0ClRi7N9\\u002fp6QDb7F3TLD4JA8M5\\u002fv0ATiEDWgZ9wn0eQQEybDngBi5ZA0GFHg7XMn0AFzHhwF4KmQH6GBux\\u002fcK9AxzmFZypftUDjZIvSnga8QIZM8ud6osFAlrwj6JtBxUDY7fA6t47IQLiFYoSKRstAAELs0jZGzUD6T9q35JDOQJEb+mEPRs9AjAqdirOMz0ATOafQgH3PQOtOjgZbF89Aemj35hlFzkB6qrNhu\\u002fLMQI8G02adJstAaLQULocOyUDKiD55dPXGQIgLkO5eJcVA2alhg4bGw0BW3CG\\u002fgtDCQD9ZEZVoFcJAXefioqxdwUCEMC4KC4LAQN+1763K675AlR5wXNODvEAk5WJMJvG5QM+UuYlpW7dA2\\u002fjKFVbmtEBbhudrZq6yQDIBmPHtwrBA0G4vMxRBrkAn86PqfWerQAsKPUOZzKhAgvgvbLlepkD0tdQ6dDOkQBS0sto9c6JAp\\u002fm3vCkxoUBCa+1eGU2gQGTv7kgQ8Z5AC9ZABYC6nEAy5BRZL5qZQA8BpDyEuJVAbV\\u002f0VSyukUDYW2tG9W2MQEk6UWcmtIdAAsxNhiZnhUAkqwEsLOeEQK0vRvchFYVAHNv1yFLShEAy1c3V9WuDQJvDvGsSyYBAjzJM4EykekCckFhsD1ZzQKOIievCn2lAn+fj1wD6XkDagBNFGxNRQPBhHULHKUFARUI2b4B1L0BDlOgZHEkaQKo91hQEBgRA9wU94gLQ6z8bd2FxsZzRP64NSXa2YrQ\\u002fVpx4D\\u002fqElj+32mcy9YeEP31EALIZJdg\\u002fVujCOsLc4D\\u002fh\\u002fp1oTNDwP4hPlwgOngFA2QDZCuUNEkA6tYuTgwMiQKO4\\u002fG8rhzFAC20A9ZSPQECp86WGLhZOQIlw6exf+VlAgPnIj9UcZUA+CpkC3hNwQEOYctl\\u002f9XZAO7sGuh38fkDqQZosMReEQFBdxkdArYlAm+elu3SkkECeojo\\u002fGVKWQANZLXHc955AIq6ApozSpUA4ILbH\\u002foCuQI6FIrlTvLRAjqwsuX4Vu0AN8+94HeTAQNvq6JEoF8RAXUEGdDTVxkAO20M2OurIQIQNpZ+GScpA984PE5MKy0BApuYnoFfLQPCaKprbVstAhUGy3SgZy0Dt3R3AYZbKQAtk7BRpuMlAUS5BKY9vyEA9vJR5zsXGQFepUjml5cRAVHNHyMIOw0ARh5xNJH3BQIM7HV5fUMBAPVe5W8wCv0AtNyPIKte9QGmcuI7BwrxAGzrPHFV+u0C4PoHLnOa5QOE\\u002fIXvR97dAlqGtkuzBtUDlOpdCjGCzQBsO2MA197BAfYfviIRYrUCsb+8XFD+pQAfvwFs3wqVAygz92aLeokCx9MIuZ32gQGjyGvw2F51AQ5dteXANmkCX9AbSFOeXQMEz3xfCj5ZAxNAUss+1lUC4QxcOU9eUQJ+DshOOeJNAHqGDrWlhkUAFiR1NcHSNQAmJcwjl7IdANeqDZ0I3g0AiiHxYDAqAQBsFUY4XBX1Ar0Y\\u002fJw5WfEAzv4TlrXl8QJ1fO\\u002fCi8XtAtvja1M7beUDZuL3+ayd2QBFI4+Hxa3FAcD99zeYNaUBntEs7A3JgQNkqOzh6sVNALaGDmkaBRUAeUBvjWWk1QF0la7u7byNA3eF8ruUVEEAFkOMu8UX4Pz4GGvtdsuA\\u002fnOHuRKXxxD8TVDmWCAKoP5XsYjCrPoo\\u002fMIk7iIi4dz9L\\u002f6m7WSXKP0h3iNDAHdM\\u002fCb7vJvfl5D+5xQQWUHj4P6kn55uJWwxA6BI4JJvzH0BKqnT1+kcxQHFucdrWtkFAyinMEYkDUUDB1KoK0l9eQJ2MmX\\u002f1FGlAYOz5GZYjc0BzbV+pHhJ7QA7MVLrv5IFAMDPmgkN6hkDd\\u002f8Em9YyLQDEq2MMoDZFApsJxngH1lUBjdEWWc6KdQE4mvXOUlKRATIVjT4yQrEA7aXRgKVGzQCqtfxWHELlAt7+Hqwb5vkDa9X+l\\u002fjLCQBW4rVZrZcRA0OPQoWLuxUCsOR9vZ9HGQG7q0q4KMsdAiyWmjuk+x0ABmNK7TxvHQC8RHmMG0sZAJm4p4VBWxkCux2vfNJHFQIu3ZXMmdMRAxWZb6yQIw0DU98HcjHHBQMuB5MJzy79ADoZSLtUtvUBxzlbwfEG7QIjYkScG\\u002f7lARPYOYYAruUAD48+n33W4QM9Mp5jJlLdAjFLMmTFYtkBBYqqpr620QGcggIaonbJAPrmAtltGsEC2FrfIQaqrQBOhWs82+KZAfgNBz\\u002fPKokBKPWyKXqWeQCGWOMtiLplAFMkuQtsJlUDxoTgOVAKSQGe19gwb249AK2xSoKNRjUDUXVgDSgWMQO7lq\\u002fsWV4tAjJCLBT6CikABVLUZZOGIQA42NBPHNoZAqG\\u002f5I9nGgkCiW5KT3GZ+QBXhRt+sWnhAXRNkNI1OdEBnPd552ltyQKqxs1jG4XFADlxWG2TgcUBr1t97YmpxQDq6avTW9W9APxT4npEia0CXWttBDCRlQPvOrNDxHV5Aycpq8syUU0D33\\u002fguwTlHQJXhRXXhHjlA6QYiatPFKEBAIPYU00UWQEPXrJqrQQJAdFE7MxdJ6z\\u002fqKbzpx5bSP8Luq\\u002fEDGLc\\u002fjMrtAU03mj8y7ZvJIVx8P6ROhhcEaWk\\u002fKvvuuXY4vz\\u002fUylrx1e3IPyYzsuxzaN8\\u002fJ+hvnrnq9D8pkz5nzAALQDMbxCfDgyBAGp8lfdrlMkCBs6Rpkw1EQD3iPR0tnlNAiWC8iYiiYUBBGcoDKRhtQMMYpl5zCnZA4ITGiR3GfkCPo1FPcPODQEBjm9PfYYhAtSpUOWjPjEDoE97eVRGRQJC77ygwC5VA86mAISFzm0B33eKfa6iiQFpONOTwlKlAJQCFc6MpsUDyDEXFSRy2QG1fK8FFG7tAfz8PB+6Qv0Chg9G3JIPBQJP7HSSAo8JAiTvKPlw1w0DyjHvWn1\\u002fDQENhSayPT8NANhSRf8Akw0DnHoZXtubCQEzch6x8h8JADkQjsG\\u002fwwUAc\\u002fwMyuBLBQEv3DDcj6L9AGDATkPJjvUAgnFv5lvK6QMSv0nCF67hAiLX\\u002f6ESCt0CJv+2GBbS2QMAkIkaWS7ZASOEQPRT3tUDFfoZ81WO1QM5Gg5rfVbRAJ8ULuFK1skCMGsXDz5CwQGrYwyBtLKxABbikAp8Ip0BMRqM+wjKiQKPA4VrgD5xAMMt+AzNrlUDK8BbEiHeQQOCFk\\u002f7t9YlA55qbxhVThUBA77rm4X2CQEHe\\u002fFv6AoFAL8Vc8hVugEAXXHb0jTmAQHUrroApt39A1W8R6yfafUDUXx\\u002ff+556QKktJTrSa3ZAzVIMdUoSckC\\u002fiSO6gNdsQDTphcPb+2dABCPN\\u002fO+gZUDtb7BTLvtkQLeAposN2GRAlsL2haAlZECPMtNPHlNiQMrb+RyP1F5AjPbzgVXLV0B4h+N6rclQQKkl7R83n0VA8vurA6xmOUDnCNia8DUrQAz5i4ytkxpAN5MG6W6qB0DcgF60WDbzPzvfKg09cNw\\u002fJYiaSj4wwz978t3XBZynP9W9wGakioo\\u002fMAGDI6xpbD86kQL0czxZP0\\u002fKApS5qbY\\u002fEEFCaQv\\u002fwz9523eWQIrcP+5Sb84isfQ\\u002fTdVJ12w8DEAwsDiAA9whQHe2SJrjzjRA4lDhs7Q+RkATTXUE7MVVQC6lxkUafmNAGMX9jpjsb0Dq13tWIPF3QMluKb8wf4BALYkg6dcGhUCKKx\\u002fIoByJQPPJBn\\u002fQw4xA5Qg5o1tikEBKFvheQ1uTQFq8U2OOVJhAP\\u002fPPEjofoEBHLf3EOcelQG\\u002fg86rX+KxA9JYghgGOskB5XGTbOaO2QH9AabyNPbpAje74lpb7vEB0SmtXJLa+QBuiP2Rehr9AdsBOEkeyv0Dgr6EhxIm\\u002fQDcgIwwTRb9AmMBr9vnyvkCW5+wZNny+QATSM2xVuL1AKpYv1vKJvEBIxq3T9fW6QPpKUWsmK7lAnqUzp391t0BjikMN0SG2QDW8iocWXrVAb5iCDbIktUAdVHzTYDu1QATzXtXpRbVAXgiFn9rktECqvcLK0dSzQIA3dd07A7JA\\u002fu3R4z8jr0ByatlN\\u002fo6pQKGxAFda86NAzmt14xvGnUA70w9ugnOVQKWU5LmUUo5ADVvVeEd\\u002fhUDOMBjy1Wt\\u002fQCUdvDNRWnhAfJ55iLtzdEDnJXHDRa1yQAUczLu6KHJA0EuwW0oZckBL9+TOX8pxQJ9qKHVdw3BAw6LOmaDSbUCyxNB6xwBpQGvvVA\\u002fYC2RAKsOhpszWX0BqAD+VUl9aQCSYkS7wsFdAf0t\\u002f3uncVkA2bXUAM45WQLNfn2cUn1VAd7mSI1J9U0BhJ8aOFz5QQBwRQMCR1UhARtYZIsRaQUANNazrWSM2QHeSmvYawilAu5OrkCFUG0BDuCzDlG8KQD5t74JsUPc\\u002fSJqls7u+4j9bWmAu73rLPwCse0wbXbI\\u002feenDM5pglj9NeDHLlel4P8VlgKMVZFo\\u002fRwD5bWU8Rz9RgKU9IcuzP8u+ZViwnsI\\u002fvLUuWY5Y3D+Au+kfvkT1Pxi8jntohw1AtQVLfY7OIkAVtzUztOs1QOtNfosTWUdAbuzuuqq1VkA9TVPxMCtkQFICoQOtXHBALGqoReFHeECo5qo++IWAQFxrMygjvoRAtLIPXiRKiEDTKvp0ZRiLQOLCvFrEz41AHBUI2h3qkEDK8y\\u002f6W3qUQJrtmVC3appALLNbLz2SoUAQ+5ONXjWnQIGTl2umpq1ArCwRvSEWskBbw278SP60QAdgMZy2PLdAlutlpb6uuEBlr+m5ImS5QOT8uoo6krlAtTGuvpJ6uUBCZmS8WVC5QBv1azVrJ7lACju+yBrzuEB3BXS\\u002fgJS4QNylfYng8bdAHsVm720Mt0CBxdS3Xgm2QC9uSIw7KLVAfF69SDGptEBnTvzsPa60QGgHllIiJbVAtZr2J8rEtUDKDaHWLyG2QKKo6YCGz7VAyPkYFM6MtEBQniheh1ayQGnjhWVY1a5A+C\\u002f124ZfqEACWL\\u002fgrCKiQPe9xyBvhJlAZCdcigggkUD+DNasAkSGQMzu0RqTvHxA\\u002fNoqn\\u002fETc0CSVDzFvDxrQAscyWlstGVAw0dlAfRtY0As0obmluRiQI+kebvj7mJA8+ifJomrYkAOpss5j5RhQP9oBm9zKV9ABBfspBT8WUDqHovmfrVUQJFGFgujWlBA7rbbeyb2SkA\\u002fv\\u002fRAsBpIQC8Lpk6NH0dAwF+kDn+kRkDBFqWLJoVFQGq5WwTGOENAN+hzxwe9P0DsZZakaQg4QCeUBcBMojBAwgUcKPgDJUDjibkqpTcYQBBbxBdxcglAdhanwxJh+D\\u002ftvLQZL0vlP7DT3p659NA\\u002fBciFbk6euD9ybmb9BUugP5DMYwm\\u002fqYM\\u002fV+uUVqWtZT9VHV4hz7lGP2fCW\\u002f+D1TM\\u002fEWJpAIG8sj9Nr+JSdRHCP5HwbnCKE9w\\u002f6Ue6EO829T\\u002f8Ma6983QNQOxuczFdsCJAcNhTvO6lNUBtOhyOl+FGQKGt1o6LEFZADiplhiFqY0BBtG\\u002fcnzBvQEYV0iKz5XZAC3IPSzPMfkAT4rnv2BCDQA\\u002fTMcJI8YVA\\u002fMJWPGTrh0AhSkOlbYWJQFib\\u002flwy6ItAcy3iCnpLkEBEqesaQHqUQLFzrIGC4JpAsNJFXauvoUAAQLyHmKemQNb51rE+0KtAm\\u002fM7DOFHsECqlReo3TGyQEAai18igrNAsmV8HvM5tED7k\\u002fs0Qnu0QDROh0diebRA\\u002f17JT79ktEBK8krbqVm0QL87TSC7WbRAAmGSc2JTtEDvIz3JazS0QHL7kufN\\u002fbNAkhNAQwrNs0C6ANz5ANSzQOzsxU0eQbRA9YJu0SoitUDZAbL4p0+2QJb6ZPJPbLdA8WgHLOb8t0A4LtBr5JG3QDVdKJSD8rVAhZJQfSc1s0DZy\\u002faiaHGvQKqOl4IzBKhAosFaVjgfoUDCjfUQb9qWQB7xMlWqvYxABoatCH49gUDq+i5JNi10QMnXyCff8mdAdBqSuWqYXkBcRsQLSWpWQALalbWuPVNApjL\\u002fRemAUkCqr9xVr4tSQOxr1C9rS1JAWE4vvtUvUUBX05X5RFJOQMAzlH4uIUlAb1rX7ZTlQ0A1SlG9cDs\\u002fQHccfU2amjlA6o7cwevDNkAikgqHgLI1QGxziMvFFDVAMKGjkavcM0ADx8qf55MxQMh7kpcXwCxAK1k6fRSQJUCl95hmGpAdQNJe\\u002fxzqfhJAoLjTPeMbBUCF64F0sff1Px56Gonl1+Q\\u002fvWi+\\u002f74H0j8OS6SwPXC8P1+No5dBcqQ\\u002fIlFak6rNij8RdqsVmQRwP4LmIWwBfVE\\u002fkOhWf4ckMj+iG\\u002f3IDGQfPyVfDgbPmLE\\u002fsQHS+CIKwT\\u002fmEjBYsH7aP4TspsEL8fM\\u002fAjhV5M6DC0DSfTzJsFIhQAviVjVM5TNAD2Ke5fbWREAliEMcfehTQOLbJ\\u002fMtWWFAYVDtqtOXa0DU6KJUoQt0QCT5ksWLqHpA4dK4Y+xKgEDPBU7GzXWCQGPAG47Dt4NAmlhe7995hECl6ZKh4KiFQIqp9q33eohAwUj7noQSjkDf1vnd04+TQDdo6wJyz5lAUnRfcYaxoEA8JELjDMmkQMBWlElvu6hA+F0VhtUZrEAJTPrWWZWuQLbeh80dCrBAPe94ZBZcsEBg2zWKcGiwQPu4xxTMXrBAxKwuL\\u002fJksEDN8Cjnq4uwQC28mFd60LBAmX0bqTwrsUB2fT\\u002fS2p6xQF8EQv09QbJA+vi6DYIzs0Dr6t9tWoy0QDixqbumPrZAyfUFqHQJuEAteSlvZHy5QEhNM4AMFLpALkeKF9VmuUAnmxGsBVC3QMnbDCCuA7RAMVbxUVf\\u002fr0CtRBF2ocCnQJ3RokVtXKBAoBWofXLwlECOV4CilP2IQM9ioHx6CnxAw3TB5U4TbkDVVgIFAuVfQEYDSyv91VFA2fN0MzgVR0A3pWaZ41NCQKU3UKjOGEFAjUHoPfH\\u002fQEAwCDiQy7dAQBwOwRkrST9AhUCqEy5xO0B6pb3eYJc2QBKvCekrwzFARbG+hMyyK0BRp\\u002fQxYpImQEIFmQc18yNAC3tUhD7iIkCpxiInZzMiQBhMolBF\\u002fyBAOE15uXzOHUBIQkj8QiUYQBUNWzCz7xFAX+aTse1aCEBltEgqty7+P2y2qyLODvE\\u002fRRXjo7iU4T8Id9GJUIXQP9v\\u002fhSOhTrw\\u002fGuoASu4bpj+5Rb1BTXyPPwKNwt5YcHQ\\u002fpoOA5ZYxWD8VmcIciCg6PxwKsw\\u002fw2ho\\u002fMR4E+l8HBz\\u002fYW4gaTWyvP0r7SUkyTr4\\u002fZOZUoq1m1z+vV28a1nnxPzhaHvxS6AdA7WHoLmbVHUABF2xYOfkwQLraqOxqnEFADa1aVZupUEDPkXS67sFcQEJvLKWMpGZAn5i23cFHcEDiCz6pU2p1QLD7pLVd3HlATN5rW1zifEBkw3LqX0x+QFd1p+5eu35ApsrtGAOaf0CkGFfILmOBQMJj5iSDB4VA\\u002fQbHxE5Zi0BCT0Hiv0CSQAl+XiQXDphAA5sLPqidnkAwFta+bqGiQPXvn2zKnaVAxLZhHZ3vp0AL2BYFEGmpQF9i5pQyFKpAL3s9SpUxqkBWARWnGyGqQKa6U0zvPqpA30IFHYbHqkBTcSS1W9KrQNg9Cxy6Y61Aqrt4SW+Fr0CQA2XQjSixQIB+bPo88LJAsZiFUYEUtUBjEsas1Gq3QOr6Rcq\\u002fmrlAb2Nj4Vsou0DTiPI12pO7QLiMhSWHhbpAxoz1TfHzt0DQACPuGDC0QKyC24z4nK9AMGC7Bx\\u002ftpkAPGlrB\\u002fr+eQKjKIKHJEJNANMERuzzkhUAXIb2Bh2B3QL5kHoKgc2dALjpAmI6gVkCKMYS1CixGQKxtgEyEhDhAy0yGU8YgMUBuFE7Zd\\u002f4tQFO8PJA7MC1APQIIMQp6LEBU7NFn9XwqQEtaykIJFSdAFMsCW4ndIkCjv0q8xnIdQOSd37xdzRZAdMO3L\\u002ft2EkA8sTVFqjcQQHvpOyihew5AxYzp7wwkDUCaCK5ow\\u002fgKQKI1PIfzbQdAWOc4vMHMAkBARp185qn7PzD5\\u002fkgFmvI\\u002fwnrKjK\\u002fU5j8FAsa+y47ZP9GH2R2SFso\\u002fi1WdJnNHuD+\\u002fVhd\\u002foJmkPwTJQpCW3o8\\u002fpoKZy2l5dj+Z+EukxeVcP2YONypB8EA\\u002f99Nzw+giIj\\u002fAUzs1JG0CP4yrJxntUu8+23t1l\\u002fo8qj+VT0P3eyG5PyQR\\u002fHIxOdM\\u002f89J2b0Rz7D\\u002fk78Qxr0cDQKtA+W4j1RdAgoKLWlLcKkD2WQQBL5o7QA4620cU3UlASkPemfMZVkAekZEd4TphQKo4rWGjhmhAXShm+mfrb0BYo\\u002fEzFg1zQI7E4JHM\\u002f3RAEzbtX+mtdUDT6uZC25B1QM7VvNIOtXVA9KQ+Ppp\\u002fd0DCddXgOVl8QIxdC3NfrYJADw71BkmEiUCmzln3VUeRQKbQ6ZW9mZZA1y\\u002fvh9YynEBR7EXf3bKgQLBP6fqyxKJAtRxFvVoYpEC8ga8OcqykQCpKyKXXuaRA3vsiczqhpEAyOXQ1wcmkQKdCoRdDg6VAazt1Wnr7pkCK4DKL\\u002fUapQNcJ0rELcKxAYpjLoP48sEBGO6EjDqmyQKrPdMuHWrVAMgi38IcMuEBHV0C601a6QIaoO13cvbtAfcgn+nnSu0DoGvTElVi6QNSziH1YZLdASCz0Jjdds0AdAUDgj8KtQM5r88PKKKVAAmBZy1fHm0BTTh2epNCQQHPXCP7RxYJAFT7+R3pcc0Blqvzfs4piQKleZKbktlBAGraUaytsPUBzMgD17a8rQOp\\u002fXn7vMiBA9nkpTlZQGUC5KmajvosXQG756wbsmxZAm1yGKQHaFEBr5ZWbrAgSQIStYYOxQA1ANXcSeUGoBkC735wgFmsBQAoKTHVtBvw\\u002f0JkIjhV0+D8l2i8sZdD2P0ZEMRUYofU\\u002fBIS\\u002fRhTX8z9ekZby8BLxP1IHYwTxI+s\\u002f\\u002fHLtvQHH4z9cU6QhmVfaP+0NadacAtA\\u002fIN5PrAjAwT9LUJ8GtPGxPzH5uVIEiqA\\u002fo7LuELzLiz+YLn4ZPkt1P2RWjElovl0\\u002fK6SrhDXwQj\\u002fgRf2V5PwlP3tzF1GHUAc\\u002f2F6xLBpx5z6+G7uG37\\u002fTPoqZdecCX6Q\\u002f\\u002fTi86Lpbsz9F+ffbxk\\u002fNP1NL55qOe+U\\u002fIfMWLNvW\\u002fD8yqAVQdKcRQITxGOTKtCNAZNqbRyoONEDqXyDdQJxCQGX4UWNEf09AHb3i00RQWEDKpnhgmCFhQMvuvZqYEGZAvhMJmdcMakDNAUBbFV9sQOJ1Y5qJ52xARwsI1U5cbEAQ79tl0EFsQHsstMuqo25AKwqwk7nWckD10pv\\u002fIZ95QJgosxuqHoJAlpMaqGZViUD2UCcaGwSRQO7RXiRnrJVADjx3LogJmkASWWbzDn6dQHNs2R3doJ9AAOqQoIs2oECRQg9f3SegQAN\\u002fRw4nBKBAQc2\\u002fIUE2oEAG2ZTOyhehQJUvaLmu46JA0s\\u002f2Epi3pUCjW9PDIJmpQESw9Pj9cq5Al6LWfQIEskAsSNo+PvK0QGUEyo0DrbdAU6eOFLPHuUD+vLTvotO6QNmozZahfLpAr4OglqumuECiW1UZ\\u002f3+1QEUpE+Gye7FA\\u002fJPnFadlqkB4HI5s7m+iQDhh6MxfxJdAyGsMzvo4jEDLOaX63Nd+QLZGfk7xBm9AQDJ4cH\\u002fKXEDf3kDqBdBIQHVH0n0hRzRA2ccoU++2IEAl38l2og0QQBitZFOxDQVAvwRPCsn8AUBByCZvssIAQP6iR\\u002fY8hv4\\u002fT7xDlrAq+j8+IzoVlgz1P7x0f0y5LPA\\u002fG2zk+MSv6D+\\u002fbTT+zLnjP5Gzdn+rGOE\\u002fTCtqscap3z8EUHGVrcTdP6BGnwZ5D9s\\u002fhNVPYvsR1z9zTYpgbSnSPw51jx9LN8o\\u002fDApE15hKwT94gY20DNG0PyuKTtNa26Y\\u002fFRlKE2Xilj92IYshuOOEP1b4UC54YnE\\u002fxVf4yjphWj8Nn\\u002fNXIT9CP1cXkVobAyc\\u002f5uKLsNx1Cj\\u002f8XQBj9sjrPraIxQe9pcs+CRKxSVEWtz6\\u002fuzldm1qdP6HTAyHKqqs\\u002fieFWbYW5xD\\u002f1Yuw\\u002fNBXeP3u9iJ6R\\u002f\\u002fM\\u002fGmP1YLc\\u002fCEAUQkzjnc4aQKRGpwDeBCtA0xhIg3zUOECtwPYJ1M5EQCDXaB2Cz09A2pPbKikxVkBzmHnppktcQDL1CWYdiGBAfZCP3P7RYUAuBJWkiP1hQGbsIwaAlGFA9yLH7vquYUDIzRW\\u002f7MxjQFsaFBySmWlALgqSgNxPckBbpX42BfR6QFunU9x9X4NAXuiYIv19ikBw4XO0TQmRQL8bNmIYh5RAX8wkvDcwl0AwxEE5466YQOSHj2aoCZlAFQUTK9atmEAwCaQmQlOYQIdMKo08yJhA9+SAmFDAmkBTDSafaLieQOaJM6Ayd6JAs1Q\\u002fJO2tpkCX4sU27tOrQF03im8wxbBAHnsp68qes0AF4d6DVxi2QEK1DQyPzLdAe6P+Hz5juEB7burCyKe3QIMZDPw5nLVAVeq9xnqAskA0LG+3qY2tQDAcujYh7aVA0Zd1xPUdnkC+zePmfBeTQDelsR4pS4ZAC+um4s\\u002fwd0AUwYSqWKBnQFtsn8WIb1VAhN8MV6TuQUBbLy1+4\\u002fQrQFbU5zIMChVA0yLcBn\\u002f7AECovx050NrxP2j3qrWFbuo\\u002fhwq60FBM5z\\u002f4BnjQ\\u002fcnkP36K9WN5oOE\\u002fYB3Kt9wb3D\\u002fl+66TYmzVPx80x5DQOdA\\u002fJ05DOXvAyT8GNqf8oCrGPyCvoAs7X8Q\\u002fZJxqNeb9wj9y3MhexxvBP1T\\u002feWQb5rw\\u002fvaxtUL6Itj8t9od\\u002fpBuwPytImYF3C6U\\u002f3DJS4nMXmT+5rgQYK0mLP1v5cjxGDns\\u002fU9UGFKp1aD+m\\u002fuXm6ihUP6oK0m3nSz4\\u002fVGw80AjBJD9\\u002fsdIxKuwJP7iZCV0Whe0+0m8STX+yzj4dQw9ZtzquPgaHiZgoBZk+fsLnudyckz9WA3JpXFWiP+awongkLLs\\u002fknpuuieH0z9PrY9ZlrbpP00qRODg4P4\\u002fd3AYD1bnEEDZ73SngN8gQNB37Ll2ti5AeXM7Xh59OUAfrMZ6VUtDQEB3tdPBqEpAVbbDTBHVUEBv+juRVH1TQK79bLpj21RAllBo5RsHVUCdYJ\\u002fBLNdUQMHWavfn6FVAWhHVTyR8WkBaEyoTJpRiQLl8D4\\u002f3MGxAHm0onixwdUA5aAgeiUR\\u002fQC1ZhoQdcYVAzcVLOa1yi0BuysikkF6QQJ9Y8lS3OpJADk\\u002fJCd0Sk0Ch+NnrcP6SQP+j60Uka5JAIwtcGo8CkkBnp4\\u002fkTn+SQPkGF1VKhZRAl\\u002fxJJhGImECajqluhLueQI5O1Xk\\u002fgqNAp11jBpxzqEAUniKNU7+tQAVGHzYWY7FAIfI2+oVps0AHau7JE560QMxW6GdHw7RAfQPxUUHCs0DLBXkgqLOxQHiZepyHvK1AU1vlyz5Rp0A8G2gHLwChQLTi\\u002fh2295ZAWqcREdaojECZPyRWm3mAQEOLI4Jha3FAickKJaXqYEDrC\\u002f15gylOQOiZLNK2tDhAxbD\\u002fHcuuIkBolwpJjpEKQMFgTtFY7fI\\u002fkINFOwrl3z\\u002f\\u002fB5TyKy3TP00LdSU0oc4\\u002fyobxlRVnyj933IggyA7GP4qxoTgDasE\\u002flukvD8RRuj94Adx9+8ezP2+RQTq9LK8\\u002f2itWWO2lqj\\u002fVtEaQ702oP8e\\u002fQ15gd6Y\\u002fqt3\\u002fSDQOpD9gV6d3pMegPz8SY1Vx65k\\u002fJBebBsVZkj\\u002fab3Z0nb6HPycMWE3RCXw\\u002fHLeNvWcybj\\u002fy0WHWdqddPxovCJj5jEo\\u002fK7fE7jSsNT+onead1iAgPwfLpW5R4gU\\u002fVncaQCMS6z4wKsxX3YfOPo57O2facK8+mUaCz0ukjj5jRMvDESN5PtJMyOuoTIg\\u002fghueOeuGlj9eXtXhuoOwP6OTTx0Igcc\\u002fOB5Gz3Gm3j\\u002fJlIqt8TnyPycyE7FSwwNAH3MIcoOJE0C\\u002fBkkfFJwhQLnoydaL8ixAgKiPVt2zNUBp2ksKnrQ9QKDqHm6Gl0JAOcbfMMNhRUCjQbNQW9xGQHY5fHF9YkdA8H3RvfRSSEB3FkHJdS1MQD3rOjdcOlNAALL5QLmfXUCNo50165RnQN6md9+CN3JAtxzh0nGDekCiosjmtPeBQFWn9UDpmIZA1Ig85p1gikBzlXdq36qMQDrnOTekOI1ACaoDmYNYjEAwQWCMOeCKQN06Qewx+4lANlAB6cDmikBpVqjQR7aOQEq6PJMhFJNAg+Hx7jvBmEDGBv1wuxqgQAKkgki+caRA6Wl0SSzvqEBCY9x\\u002fo\\u002fusQB1NWOAC+69APE9ZDqu2sEDtMCEeR4WwQC3ts2G30q5AcdsMkQ0Qq0BHVD2xl0mmQHJDrVKsJqFAKsm0fwuSmEDTgAeqoVKQQBI+YXGuDIRA20ppwuG0dkDq4kSO0ahnQOl+oZ32pFZAYSPcVvriQ0DBnfuGlwUwQEZRVhASuhdAVIcTjutJAEAuUjoqyXPlPxvtH7\\u002fqR84\\u002fbXaWuyd3vD+U1iMpmFyzP7tIHpwkaK8\\u002fLnquHJupqT8Kas0tzwWkP7Ivx8Jt\\u002fZ0\\u002fAvWq+\\u002fhclj8mt0uENH+RP+bAjLTls40\\u002fkSyEk\\u002fPhij8jZ3kGfaOIP9Up1MSgy4U\\u002fbUO0A5cQgj\\u002f6AMsDn6N7P0TekmpwYXM\\u002ff21z4SHWaD\\u002fzHV9vrwtdP41nr\\u002fcT+04\\u002fRm4+FpQhPj+Dp6Y617cqP5h0Oc51mRU\\u002fovZRZ5DW\\u002fz6TyH7+bGTlPsqCFKEzNco+uf71D9VFrT7rVnHKeNqNPgm2LDlky2w+tHtB7YBpVz7nu\\u002fgpiOl7PxXT\\u002f1e7qYk\\u002fUm61IGGcoj85ATm2Jzq6P3b9klqB79A\\u002f1tFc5Z3y4z9YLmvo1Wv1P3HHAacC+QRAGaR6+tq4EkD0404GlHseQP+RLW0JpCZAiRRxfJy4LkCWBZGZ5hgzQI9DEpaL7jVAXzwVItzAN0BsJz3NWXQ5QLRP+oqZZD1Aw4c+zvzUQ0DnXMSMhP5OQMzf9SFhullAIOgnAXIAZUDcsOD33zJwQMX1gTofPHdAzYAdAnLSfkBMPB+vmOOCQAKjEIUdbIVAuhftwg6RhkBUgbl26EGGQOsTgk0644RAFl5N3lM8g0D0jTNKtkmCQM2ct5KHCYNA+R2xcn1RhkBp0zgRBa6MQPOnYUA6IJNA3vW\\u002f47BMmUBqlNpd6CegQLaSuz\\u002f7p6NAbaa6oSSlpkBi55MYbqWoQHmAdTWXUqlAH9zuSd+MqEA69ORGHHCmQHDQoL4VTKNAGob2GBAmn0BcRFCeJ4OXQBtFCe3wiZBARfG8YqSahUCmNlzvCR56QJLVmYwiI21AlUnrA3LtXUBPhPMSaT9MQNr3fe6RdzhAwYOtk5RuI0D\\u002flIvZo08MQMrwtfY1\\u002ffI\\u002f4K3bXY3a1z8F9pIQo+W9P5y6CLfsRqY\\u002ffH77zzoymD83cPjWPqiRP\\u002fy+K6bm0Ys\\u002f3p4AKX9fhT\\u002fYAREbJbJ\\u002fPwWVUVexcXc\\u002f\\u002f5kD9uw1cj9R\\u002f9YxCLJuP\\u002fgUJwkXkWs\\u002fa38QZe8MaT+FIJ5bbvVlP1AXWg6RB2I\\u002futiK9aBSWz+U+lfcrPlSPzo\\u002fCXdeFUg\\u002fRH8n9urkOz+H8JDNY3ctP+AOzlzrYRw\\u002f9OUjVeHsCD\\u002f4jNlUx\\u002fTzPnaTM\\u002fT8Id0+hCzMvNhiwz4MPhOmfYWnPoQEolDvBIo+5l31aUVHaj4tpUwCxxVJPp\\u002fjV2UGNzQ+zLbiCZ67bT+TOlT2mRx7P7TVOXvkcpM\\u002flCKXWEsjqz+LsKgYq1rBPz276yNgPtQ\\u002fPsDg7ZWH5T\\u002fESeq3V+D0P+3KsDxcdQJAGXCl0gDGDUAueciL6esVQFo27zXMiR1AzBfoCdpSIkC0e0J3s0YlQCrFoBYJDShA8kjJLv6VLEALjpCmQ40zQLUiaU4CUj9APJGIei01S0Bs27C1jodXQAOhrF27S2NAugkkeulmbUA9gRlM66h0QDHGnsHWuXpAGeo7vTfWf0AaIvqQw36BQN2xBEq4z4FA8MDdGHHvgEAP2IyZ6Zh+QOvoW8yMMHtAPM+NSGA9eUCG\\u002fjBh5D96QDjcw+K8an9AWkoRMLW6hEDcZcGh3zKMQBCHDrbG1JJAUzWBT\\u002fgRmEBHPUiCGxydQFlXgFHnlKBAm5FkDs7GoUC5farPbPChQCWnFDaaDKFANFp7pq+EnkAFbapKLrKZQJgH6hPrT5RAw9QOk5cPjkDj0pMBnMCEQI3ayMIXo3pAIzn8Zeytb0AXzRzbD2dhQCcsIbo0nlFACMlx365mQEBPQ8XftwgsQCjqkttP+BVAs1XEkdCP\\u002fz+S47WZk87kP2vC1xndYMk\\u002faIvsuDyYrT\\u002ffWlEufXeSPwTOn7AT\\u002fH4\\u002ftABzjsMXcz++TKbk1TtsP8OOO+\\u002fUM2U\\u002fttqahakTXz\\u002fx2qAcYcpWPyJ6C+7MklE\\u002fsQvoxOpoTT\\u002fofbOcFzVKP2py6jimnEc\\u002fgIYXdHWCRD+D8nwsi65APw\\u002fHXmotCjk\\u002fBILIXiQ5MT+\\u002fRpQNV6YlPzbiAl6T1Rg\\u002fQpCT5Uf7CT\\u002f2vsFv+Mj4PiTbnAR7juU+4eO2VMgX0T5PG7PScra4PoEuR5daSaA+KyW5AAuSgz6PriAlt3BlPpPSn021cUU+cewrVXZCJD488jZQXS4QPnO6Q3fobF0\\u002f5k4lV8ibaj+OhhxJ++CCP+PJcBeBFJo\\u002fw1rNoNODsD+zAH8m0BPDPwiSePy6F9Q\\u00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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"evSFwwFAIkBKvYxDCEEiQENO3pGQNiJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJApZPD6ERCIkCVUBKRKzMiQMNWn5lAMCJAyZ2bOpNUIkDZ1EiqsDQiQEa4fpOARyJA32HWDm5AIkBfB6lznkoiQJDBsNflQCJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQBRvhHb4PSJAe2nuRn1FIkCVUBKRKzMiQMNWn5lAMCJA2bu+UplfIkDZ1EiqsDQiQHe+Nd+PNiJA32HWDm5AIkBfB6lznkoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkAVjbfuTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJA2bu+UplfIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkBKvYxDCEEiQAlcmXLQQyJA5Krx\\u002f4pcIkByZF\\u002fiTjMiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkDZ1EiqsDQiQE5qYiZTNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJABWQEKwJAIkAFUKpjVTAiQAlcmXLQQyJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFXNLWUXMSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJA32HWDm5AIkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQADxZjOxPCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQFeNB+DQRSJAz8jrDd5fIkA1cxoTsTQiQL5RBlpSNCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAfe8beAY3IkAFUKpjVTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkCVUBKRKzMiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQAdzx0GdViJAe2nuRn1FIkBFxE8IDCEiQMEejU0aJCJAz8jrDd5fIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkAv41sIsisiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQMEejU0aJCJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkAjjcuqnUoiQNtqESF6OyJAltJCqQU3IkDFjDAaWTAiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQMlzeIYvPCJAe2nuRn1FIkB4T6q3+SUiQL2+RkeoLSJAE7CHIYdKIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkDdabLXn0oiQNtqESF6OyJAltJCqQU3IkA7uHYX2UkiQIfHmlTMRCJA5Krx\\u002f4pcIkBJEfypzyYiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJAQiQiCD8+IkByOZsZoUoiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQMjtbCrMRCJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQL2+RkeoLSJARKfeNbBAIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQNtqESF6OyJAltJCqQU3IkAY71HF2UkiQOksL4G7USJA5Krx\\u002f4pcIkA87QJJpEIiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJAltJCqQU3IkAY71HF2UkiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQCRpcqUvPCJAe2nuRn1FIkBrp3Z5oTYiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQN5pCw+6USJA5Krx\\u002f4pcIkCf1ZFC7DQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQEFDsf9hVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAe2nuRn1FIkDbwrNk+TAiQC1y6e+AMiJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkBOszuqREsiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDbwrNk+TAiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQIlk6+N5OyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJA+TocdllSIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQOBA3\\u002fJjVCJA5Krx\\u002f4pcIkAqONLx6jQiQMecPoViLyJAyqlb5JImIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQGcjcSe+QyJA4sPRpoE2IkDLosEn8ToiQMCN30mCSiJA5Krx\\u002f4pcIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQIGvgJWLWSJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkDLosEn8ToiQMCN30mCSiJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJAS70ZZQtZIkA1cxoTsTQiQDDVIux\\u002fVCJADwK6GT8+IkDSZggERksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAAIcOH8lVIkDOUhUA7TQiQMecPoViLyJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJADwK6GT8+IkAo8V8fRksiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAZ7fnDzosIkDN7abVyEgiQOWYVTXBQyJA\\u002fF38qQVqIkA1cxoTsTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQBivngmkMiJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQHdnZVcwSiJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAs\\u002fGTOWtDIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkBpVltisTQiQFeuK5pISSJAbqcQpRc5IkDNhoy6RUsiQEMgbXNDSyJAhPjaAHQ7IkA4trUkS0ciQHTith2UQCJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkDNzDIUsTQiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAV5uBlUhNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkC4tYSgZzMiQOu\\u002fYXtBRiJAbqcQpRc5IkDHWtt6REsiQPCEbx5CSyJAhPjaAHQ7IkA4trUkS0ciQCDLFA+sQiJAO9V8lslVIkDU73tBaT0iQJsC5N1uOCJAvZLk0ElNIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAKCSRd5s0IkDHWtt6REsiQPCEbx5CSyJAjazWjqI5IkA4trUkS0ciQCDLFA+sQiJAvCGU7qtLIkDU73tBaT0iQJsC5N1uOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAhYENdG5tIkD6M3cguj0iQOu\\u002fYXtBRiJAVdIqpZ00IkDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\u002fd2llIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQAgX+vttSyJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAIJXuHs1gIkC0XXi8dT4iQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQEPQ6TgoRiJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAUrjU19lZIkCNmJQBUzsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQF07b+ttKyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJAVTQWtLA+IkBpWmsIGVsiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNOYfiA6TyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJABM2Z8Dk9IkBpWmsIGVsiQO\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDPxpYQhUMiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkC+QPFbfTwiQDBLfR7kL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hD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9A3Z5bn2khD0AiqElHeCEPQAFTLQ29IQ9ArmZybH0hD0Bcg2PtgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQPtq3Dt6IQ9A6aWrIkchD0DW0I88aCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9A3Z5bn2khD0AiqElHeCEPQAFTLQ29IQ9ArmZybH0hD0AHLhFXhiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQPtq3Dt6IQ9A6aWrIkchD0DW0I88aCEPQHD5QEF5IQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9A3Z5bn2khD0DsuMVyeSEPQAFTLQ29IQ9ArmZybH0hD0AHLhFXhiEPQFfjQt6nIQ9A1XA26qQhD0CjM9T9TSEPQPtq3Dt6IQ9A6aWrIkchD0DW0I88aCEPQHD5QEF5IQ9ACNbOOlwhD0DANIp+liEPQHcY1mpwIQ9AArRTAWYhD0DsuMVyeSEPQDiVehi6IQ9AOsoBPVkhD0AU1CLyjCEPQFfjQt6nIQ9A1XA26qQhD0D4sCjbfCEPQPtq3Dt6IQ9ATj7nKhkhD0DW0I88aCEPQHD5QEF5IQ9ACNbOOlwhD0DANIp+liEPQHcY1mpwIQ9AArRTAWYhD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0AU1CLyjCEPQFfjQt6nIQ9A1XA26qQhD0D4sCjbfCEPQPtq3Dt6IQ9AquvsTxUhD0CfDOMwTyEPQHD5QEF5IQ9ACNbOOlwhD0DANIp+liEPQHcY1mpwIQ9AArRTAWYhD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0AU1CLyjCEPQFfjQt6nIQ9A1XA26qQhD0DszcntVyEPQPtq3Dt6IQ9AquvsTxUhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u0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0ONKrjPQ0\\u002fny8607A98XGFbwlNej2lLTEPU8tCPakai+WOlwg93Or8TZ+IzTx07AAvjFiQPE8jjWSLzFA81m0zlR9HEDxQJ2Y+UgfeO6iXuqz8CrE\\u002fV3V\\u002fKVUywT+mYgArYh\\u002fcP0pnVDSWJPY\\u002f57imvEfvD0BiVISBgAUlQFghfyuJQjlANFrE2LG0S0D7\\u002faARBb5bQPbZTxpmXWlA+Ymz7aUudUAP4gPl+imAQJs3qiVjjoZAo61xJmnOjEB4VWgyDtuQQIc33b8HHJJA9WfRmcDokUAGAa5i\\u002f1yQQORVD4gSxotA3x6M0e0QhkCVECj+LJ2AQLVSCLNhLnhA6W3CRH6NcUCGhNGMSItqQNSXvwi5xmVAHbq39JKXY0B4Uhgo88tiQHUOS2iWaGJAkPIXgSrCYUCKk7jeN4NgQBgwkd\\u002ffSl1A3+nrMp7LWEAi6G9KzXRUQO7rGALXnlFA9xAS8zMbUkB5APLgl0NYQDLwjes4bWNAP+VJ09IdcEBJ5NO2e5Z5QAabdFzk3IJA6CZFPZqSiUDH2iGL4sePQAqsZ4FBFpJAzF5oyfvakkC9qjR+xQOSQCkmJVzLmY9ASYnWUXiKiUDCCo8KYi6DQLZD+6BVO3tAA33R5FTbckCfjBR4MK1qQKRpJiqRGWRAytOTxnI3YEBhaOViQwNbQPeSZDOHIVZAN5t+Aw81UUBBkuJx1+FIQKLTmPtmj0BAHsoPvsgxNED8bgq5G4MmQKxCfuou6xZAE1nXDoZMBUDYZqxZlhDyP6Z5BoLm9ts\\u002fWGEChWHAwz+6UzmJ3nWpP7H6m\\u002fm+8o0\\u002f8M5Vj5UScD+\\u002fLy0MInxPP2izFKYBJCw\\u002f5V9cBbLzBj+e6XJiURXhPjHRGrRPNbc+6IF2kNzGjD6MfFfTK0lgPizAPFba1DA+d9r1OzXI\\u002fz2UwXQjA2\\u002fLPYyGlDUnrpU9BOhzK+ZyXz1bdH1cDAUlPbf+n3CFEuo8Gogv8xNYrjy0+xRNhdpwPBdjgZdnYjI81QZbI6b4AzyR5JZv56u6P96w4AMvCcs\\u002fv4WUVgY95j9Ktvn+7psBQM1YfU99ihlAQkm09u3oMECjrWTbIHBEQLmgNrimjFZAQar\\u002fzs62ZkBRfEuxy+R0QAJBqTd\\u002fjoFAQKietDL3ikCSjAref\\u002fCSQEHpIlcuW5hAjf8nK2m3nEDX6RvPuxqfQKuQqFyzCp9A5u8IfXSpnEDUf2A796GYQCg7NXVO4pNAwz+WioOWjkC9p50CqfKGQH9v8OdRaIFAgZ5pDNTre0DhCN8wVHR4QBkmFUx+SndAxqa0fzJBd0BJPAK0S1d3QOci48Vh2nZA\\u002fu82OhJ0dUAajG\\u002fV3iZzQBO9QQY+QnBAmemJcfiqakCuFM8eoFBmQB\\u002fDMAepemVAWprw\\u002f2WxakA5YCUxQXN0QFfsE7BXuoBArn552TmOikDRFf6pX62TQDhQq1gK3ppAJ285DhrUoEAtFEBnN1GjQCLPvsP5UKRA3HR4gwiXo0AnT0YJm1mhQCA5S5crWJxAidpfhbWLlUCa6LgJyQqPQOFY\\u002fDE05oVA4bkEUHKwf0DDbakdfHJ4QMNKVc9VH3RAD1tjZ+IAcUBhhp1oISVsQPUpzcODDWZAklvMo68MYEAW7f66U35VQJS1E1hhXUpAtVa7y7OPPUCRGJou6UQuQBOKfZTXShxAgbEdYYciCEC0BPeY18nyPya4PdTBsdo\\u002fgesUuAJOwT8zOA9OI3mkP72jJx9rGoY\\u002fsE0qyq\\u002fGZT+dAJ72C5RDP6RAzOV2ECA\\u002f9+WOuZUP+D6\\u002fvyAT3nLQPsyXpLcIiaQ+Dfdbb5trdz6IbR0DvWtIPn2q2ORIUBc+l273k\\u002fZs5D3FsIBYVH2wPWkyzig7sng9biKWeCZWQT2Y0eEZQy0HPSnOtrIvH848xsF\\u002fMxN6kzxRCFYLc8JZPAY7J47oQTE8xCAqyDnZwj88A4S0CizTP3RduBZKsu8\\u002f0V0b2WM6CUCBWiCoQ2UiQFHZRiT1fjhAp+oxfFPHTUAvSSSuXYZgQBgYXdUHv3BAt525kK7\\u002ffkBxtETajzaKQM9VRxlSQ5RA2cpitMKonEDlCLgw6I+iQE2Bhi6aDqZArIc1FA0ZqEALOpSxwkioQKEE9rl8raZAEDtMq13Co0AwSkWcxTugQLrw2fLslJlABAfSdL7ek0B1+L4KjKuPQPLe13yMCItAUEYK6zA8iUCmcLwe\\u002fkuJQHqe1j7mIYpA\\u002fxKtb3vHikD5vMW+8o6KQFVTS4CFJolAOlJtdxiZhkBCM23WMUODQKWID6G8h39Ahe13x\\u002fnheUBaObhg5ql3QIC5Y7hUZntAtnuXWqL+g0Awcim57w6QQOWmyu0PcJlAWtGGjf3tokArhFGqngWqQMEJMB1zbbBAQNHmv2cEs0AJVdt2BS60QHPhTe8bpLNA4FV3pWiRsUB9VqtlEgKtQJWZY9xzUKZAzyqcSJhMoECtm1PpQGGXQIGAzew4PJFAyH1J6vMVi0DUZw5s0KGGQPH7MJH3VYNAU4SDAWkigEBpBsNha3R5QJAigaGKo3JAjGvsHpMbaUAERwOvBPpeQGh1535qd1FAU8zGCO78QUB150WNFukwQNkfLA+yBB1ASonwAKy4BkC\\u002fcAPzNTzwP5Qia7ccLNU\\u002fdSlxBVIyuT\\u002fwvMz7VV2bP300B21vH3s\\u002f\\u002fnQimWuJWD9MwYYqY0M0P7M7g6Vojw4\\u002fJLEfhnQN5T6C81aKB4W6PmgJ3WXzlY4+USufussvYD7vD+M8eZIvPl9qLjJPjvw9+7yvG84yyD22xB0FiH+TPXb3O1K1cl4931CbdXeAJz0R2sIzVzfyPMXvsJ2Dkrw8zqhNyQvphjxgxyw+YDNiPBS71YU4Q8g\\u002fCYkMEZKr2D+brGcANm30P+pO2DJ6UBBAOlNqMSnmJ0CcvfgwAv0\\u002fQNDKVng7jVNAUcCy8qvTZUC8DS0AK0F2QHDDWMN6uoRAlkemieSkkUDrgPmBbHebQGmvkxTDkKNAb\\u002fyIStGKqUDptRSL4ZuuQEgf\\u002f5i94LBA2abRocIwsUB2LJxHP0GwQNZnT+0bw6xAfldtn2caqEApvvGEH4WjQK3UQ06Nip9AYj2cnTaImkBt4EcpViOYQAQMNF6J65dAwsvrXLwSmUA8t7iBLKeaQKpTBCY1y5tAumWU4ATgm0CsPMR8bJyaQPYYTympEJhAGaFDUg2dlEDJnsC4aeKQQAd2qILqZotAVotGp3IciEDAVq49njuKQGAaGhksNJJANG1j9PianEAuXwkZSI+mQAMnE8ux17BArG9sfH1Mt0DH6CYSfqS9QDFWGodvTcFA9QeKeXCGwkD4bKySkDPCQMCaauh0ccBA+bgQQ7twu0BKpFuPKlq1QNr4lwb5lq9ASGTPPbT4pkA0KHN\\u002f6SyhQGpH3QhTVptAQDMT+mISl0B5fUPErNyTQAlMpEGRrZBAphSDXPp2ikAtkUXH93yDQG0m6qeWZnpAXfwy6AdhcEC9TX0FyZNiQFV4hKYqPlNAitYfsw8yQkD9CHhBCGgvQD\\u002f1TO10vBhAhVoQLKbHAUCXdD5WgFPnP8x\\u002fSCAF7ss\\u002fcVKjth2Grj+HJs2u\\u002fXOOPwgAmZVZvms\\u002fVdW5Oj0XRz8VbTXKXJIhP\\u002fay8haCfPg+t1Z5MNZQzz7riavrZXOiPnAXtM9fKXQ+RFXizVCiRD4F3HSFvQ8UPvqAfVCd3OI9+qkyRsl1sT13EWHu1B2APU5yw4fFx009XMMNJv1zGz3Ylup1RifpPJOu8DInL7c8zT7p7knFlDw1Y7KzUDPNP4M2Y25DVN0\\u002fFWqizxIR+D9godE1\\u002fi0TQI16RkrpISxAl\\u002fq0RMfjQkB8DeYDmTJXQMQPZhjXCWpAzeo3mr21ekCWwYlMqQqJQLEvARUZd5VAtaXC7S3VoEAjJTERpSyoQHQrOIGs1a9AWNUri\\u002ftBs0CUzcwRGHe1QGeKPlxMIbZApJGbXuo5tUAdS6KtNByzQIaEANa6YbBAUp1DUgtgq0A4q94HbBunQK92PN37k6RAOwsmNnvro0Cjifz4ANOkQAiRyuzvqaZAVElDxV2qqEB9JiqeWRmqQPeG98\\u002fAbqpA7QrHc1BsqUAHS9FMWiOnQGf40j6W7aNAySCKr7JeoEAm83pD82maQI5F2tAvoJZAaAT\\u002fv85gl0BVlhIaeOaeQI7M\\u002fTL+sKdAI7mQ3yqRskAobiZkcMi7QK\\u002fi7hiKU8NAF4ANMwrFyEAMFcsTAyjNQDIRleLwfs9A9kjKoTA9z0CAwxPC4IDMQA3pJbBqCMhAFezrctnnwkDfO5v1kEi8QEKtqOAfyrRACBvD8YJgr0AWf+smlySpQAHxezEMU6VA2jInKVBvokAW+Cc\\u002fPRifQEOiR7MbyphAZwBZTIBZkkC7rYj8o\\u002f6IQBfdMDxpMH9AnZaYUTHKcUBIUF1ICYliQIyH51pyoVFAQCLFdEudPkCsnaOQfkIoQBAhIjTXixFALnYA4fQq9z\\u002fiUdz8Vu3bP6GUUfDlvb4\\u002fIHJ49djqnj98h5cJgG58P4ZC1QHB8Fc\\u002fAgyXvHmAMj+V+\\u002ff4nFkKP2rTwmgcZuE+JaUSQa2DtT4meS22k0GJPt2pqvJUoVw+TWOXfzDeLz41FePpBZsBPtMpo4soVtM9QU1Q22z7pD0YoFPa31N2PXKUifETO0c9Oowes8G3Fz12Ob\\u002fVt0voPNdEKwK\\u002fAMg8WGYljKun0T9V1nz0JPrgPyaaeyHpyPo\\u002fHUO1vunuFEDotTPdN20uQB+PC+8xX0RAqRBUoIALWUAyvCHL6TZsQHgKuU8AGX1AyOtzDvl1i0C913S\\u002f17eXQGM3tJv7waJAS0nXRlkxq0AR193kYBeyQCbgJ7s\\u002fJLZAD28kWCQAuUAkrRxwAyi6QBFBZdsfhblAC0nbMyZ0t0CD8\\u002ftATqO0QPhNwMlg27FAD9eWgQWWr0Ce\\u002fLvgbMOtQASSe9Gueq5A0c6leFupsEC5BFKNoq+yQHqIK2I9uLRAva+m5NYvtkCJy9qVR6m2QBuwIck08rVAIn2XVFcZtEA3dm6NgWmxQPHFjdA4u6xAwDeQVEglp0DojjTavHujQPBtgSY9VaNApfZRiodwqEBCsd3j1UOyQK0deYjhZrxABRGVV59FxUBJagzpi77NQA3bd7K+MdNACNiF07zG1kAaDtthSdLYQOvfPZzk2dhAG9\\u002fygk3m1kAcwpTmd4HTQFEiQtXoAM9ASxPKLtRpx0DBUFp7e1fBQNxy2t7BSbpATEvvfpgWtUBrDHIx2OGxQLCZ\\u002fqem8K5AtJEBXvInqkCrp+xiiO6kQNb23dfAIp9AJKot86BRlUD5ADPG8cCKQHbd06NNs35AUDKIAD4XcEDZ89MIUs1eQJ+lrGol6kpA2AOLm1Z4NUDGYKbXakYfQE+pXu26zQRA3d96sOBJ6T\\u002fxxN6sgBzMP8pOCuzPnaw\\u002fYRGkQS69ij9GMI4KxAVnP3UzS9pRYUI\\u002fTC8pkSp5Gz8NAEWJG3vzPgMfHl1Apco+EJgU\\u002fBXboT6tf17LTK53PrVIqqBxFE8+yT3kpd4FJD4+KX0s\\u002fBL5PcOXa8IMU849LyclMhu3oT1zCIDnIyJ0Pd1WNleHfkY9dndTR2dcGT0kWkwzGWL7PLvFjQ9Hltg\\u002foW6M9wSE5T8aWicw5qX+P2dJ4tlLfxZAYnJmWgB6L0BRKKuC4J9EQIl6OIARGFlAjPFA10IwbEBzQr1u0yF9QCYkSsovpYtAkIxiOYwRmEB9t8OrfDmjQKIPtyrxMaxAiDYVRR8Cs0A3tB+yHZ23QFCspQT4HLtAJgcRx9nnvEDbYaa3ws+8QO5zdT7bJLtATq8sYEScuEDVCIy6bRq2QM6JTAxpdrRAt2FUbLhFtEBn3N0b3bi1QNHJDrT5jrhAtIOZKcUlvECVFwabDKS\\u002fQHHcMxX7GMFAzz5u4j6WwUAhQ85CSSPBQIl9NgmhlL9A0VilXnCGu0CHF0tQJdW2QCjdTLe+bLJAqWfv7ay1rkDQ46\\u002fZgJKtQFdARAwW\\u002fLFAv4gO4HQ2ukCuHmx99zHEQIeoGWqjQM5AVs74YHo+1UCbmnUJJpnbQGKlVNycgOBABrnhe+Aj4kCaiaOwt1TiQDk4MRxtDuFAKMwCCIlY3UB+X99jg4vXQCA1AAGe7NFAU9jSo\\u002fytykC82hNJUTjEQCA2butwJsBA8dhncHM\\u002fu0BN3t0PVYG3QBd9B7t\\u002f4LNAs1jWPQznr0B1r1nb0NSnQMn2ycuMZ6BAK6PxbVO1lEBFBoU20emHQMU\\u002f0BjiO3lAiwFVLlBSaECqNHIQGWlVQOMZn1q2N0FArnLBxsBOKUB1cmlJEAMRQH89dh648vQ\\u002foZm+hFyu1z9\\u002fZmoPu6u4P7A\\u002fRWO71Jc\\u002fRWyYxw+LdT+22uLlz3ZSP8e9EtYPfC4\\u002f3BaABvmbCD9scVrXl5vjPmIaB2ha074+VU0q9gSylz4E7L1t2ppxPj1CtaG4Dkk+zvnrjiYHIT6DUo2Cly32PTJXPhaw8Ms9F1XSMgY7oT15GrZ2Pwd1PbL4BJMyAUo9E1REaDlwLj1j7Z57yLzlP0exKN5SA\\u002fE\\u002fFnvwnGa+BEC7GztXMecaQLyYTE3PIjFAN9xYFkYJRUC6A5Ugq5BYQFrphroD+2pAmaRhBaege0DN3CKBbjeKQF8f9GVn+JZASoKZA8eMokB5Pj5tiZqrQJ4gzIzq77JAGhfHMFkAuECZt15Y1S68QCg+b8g7zL5A2u+k1d+Jv0ADUTlmE5u+QKlejm6\\u002fq7xAMMDLIlCyukDuPbs0d625QG3HKuXaXrpAKTlYkT4SvUAnFqtFeMDAQJDQkrn\\u002fbsNActM5QoEFxkC891dh7vHHQDXF0ENyw8hAB\\u002fjhMZpCyEB9SGKWrHrGQDsM5GBltsNAd38+e9VzwEAnmN1moai6QPOUd\\u002fezHrZA1VjbMc3btEDrvc9BIJO4QMXOHXM+fcFA7gBQX8OvykCLPRqc+PjTQCY0Adz1J9xADIh7ZaFm4kAoOH\\u002fXyivmQMnUZ+LVk+hA2iyQtx0Q6UChlv9rM4vnQITdnnr8cuRAbfu3KdaN4ECNiaE8oWDZQE\\u002fQFihT79JApGtxLECYzEDXfXalMqLGQBmEZV\\u002fh58JAfJVWtZM0wEBd6vFPGFy7QFqKXJVc\\u002f7VA7e9tpHmAsECEwFJxAdmmQCvsH0mSCJ1Ah3lNSx3jkECqqv9DuvaBQNVWX0n9d3FAs4CvLakQX0Cwk5cEP0ZJQG78Beow1zJAYU\\u002fP22XLGUBvnIw8g0YAQKtCLnrwCuM\\u002fl5v3orPXxD+2vpB0I5ulP5u+JM+siIU\\u002fKG6\\u002f8rXvZD8N3DIAZQlEP2MPlTTj3iI\\u002fd2Qwso5WAT9OCBQzZLPePuJxfpu76bk+qUNdUHa6lD6w4x7mmW1vPpgalkOEtkY+QDSZbImiHz596RxCjoL1PRpox8614cw9pMGIWt8+oz13iURGJ\\u002fN5PTZOFsovTGA9d7tPY8eM9j8GgPiCo3cAQCQkPxsEvRFAsSnhnOEjJEDhLhuS\\u002fHk2QFo4EhdShUhAvu2Z+VwYWkDAYTnQNeFqQA1M31isfnpA9tZkV5q0iEA0GevOLZiVQHHHiAZ1laFA1aEagWSVqkCGw0a2KaGyQPWMIPx+N7hAXZcvRfFAvUBBtMbqE3vAQCUVSDfBbcFA1mNdBaR6wUCgn3A0B+rAQG8KGz31OcBAlA1vQr\\u002f3v0BudBnxcKnAQCN5jqxVfMJAiBaSLSBWxUCLsaAYm8LIQJ+z7qyqFcxAKy2d3fuZzkCxg60fXb\\u002fPQDjCaS4cO89As7FZBMoVzUCaGHUxjajJQFybUYh2jsVAfsJm0fuPwUC9Y751DSO9QBTGCJolGbtAZddwUV0ev0D\\u002fUcpn+qvFQPFIRAl4XtBAI1J2rf122EC8BKqpgUzhQOLXxksbveZAAbUAHxSY60CRWkIg+9buQHOA8nbYuu9ASaIAsCQW7kBGjx3BvmDqQBBRdrJsieVA37qdUQ6c4EApajVu9NDYQHT\\u002fa\\u002f5VoNJAbzQcGVkfzUBUCTHD5\\u002f7HQF99dmH8X8RAZ2aHkNQjwUA7Gq7P3Zm7QEMwM3rezbRAatCNFkUBrUDqqaxpK5aiQKFdmPJQ1pVAhhq13zd\\u002fh0DgfHaceCh3QIWl7Wl57WRA9DzngF1gUUDtE\\u002flf85o6QBMtnm0t4iJAIcxNOiMPCUCkiq20UHLvP8uhjIS569I\\u002fjyvrn68itj8Z\\u002fD1zc2eZP0GoeH4hnnw\\u002fKpMTBYNmXz8PIqIgUJRAP7ZZ1dc0qCA\\u002f4AwOMzmY\\u002fz4UXQTEazPcPv0hdjEdvrc+JDOOALH9kj7hLlDZgjJtPpPYhm\\u002f+3UU+avXL+rIlID5hi3R\\u002fRaX3PU9jZqg6IdE9+RRTKBnyqD0mE4EDhaGQPcOx2PPe2QdAZGgeEsH1EEAYWmflJUMhQKsozM6LJDJAdrz4\\u002fCtVQkAg6tQxe9BRQPXgLqXI1mBAnOeLllQ6b0DOZOWtC2p8QN\\u002fzm9IKLYlApRd7h2t2lUDq2WqkImWhQJcKXrG7jqpAPggKU9v6skCmFngGo1K5QEHbHKMKhr9AItqnSxFYwkCx\\u002fkSuvQ3EQOOS\\u002fyY7xcRAJrgAS5OuxEDLfNNEVEHEQMXDvIiBG8RAwi5V0unPxEBAxb34D7PGQNLtiMdruMlAgMMtQkxuzUDx5LG8vI3QQOtXdqKe+dFAAxGbd\\u002f2l0kDnMM70t2PSQM4Eh+\\u002fiMtFAN1Uo5OaEzkBj+EGk1tDJQIFDTWcoLMVArEwvUCGbwUAsUBKu4zrAQGToIl4pQcJAHs+ZBKPsyEBVqjzo2qHSQIiT5aYXydtAB43Ypiiy40Cg7fgKzgXqQMI1c2r8yu9A281IEQbo8UAVWbF0XJXyQOBsdiOXyPFASBGu3SV570DMJpMFlOrpQJNGwDokHORAldKWiuYU3kCyqROhgW\\u002fWQNWgdL2iTdFAAT1zjG8TzEAeNE\\u002fB2JPHQGBgQXaLwcNAdFMNkYXjv0An\\u002fvALAzS4QDkJ0birC7FAn72R95UhpkDoG4os5WmaQHSmQ8Ro94xAiHOYnmg5fUAbXH1KADRrQJrXHB7TeVdAhobpm6rrQkAH+hiOGsYsQBFjzIm65hRAWQnXUb9g\\u002fT\\u002feJRAgXyjkP6eh+W4dEMs\\u002fzKkX+u+rsT+RTABPJDaWP\\u002f64WuxekHo\\u002fLZ4TLAj1XT9FOwWYNbE\\u002fP8++iB5sbB8\\u002fkaAo5IRN\\u002fT7c1dkgpebZPsoKvS379LU+3IAbqfIZkj4bvuDVO2NtPhNRo6G5pEc+SefXSD3RIj5BrFCcAmP9PdH2kP5429Y9LsnJkGkDwD33K6GRecIXQKTw6faexiBAAJZoYrTGMECdN\\u002fNvESBBQEuX09AViVBAPCwCsqwYXkACcRl0mx9qQKOL8Hv6+HVAXuP3nzAqgkCcFF+BkrmNQMSYLVEf\\u002fpdARdbbQD7kokCDdUfXUp+sQIMFaxSZnLRAG7n81zf9u0CavPS2ZNfBQF1iB\\u002fznVsVA2jzlLhwCyECg8FfvjpHJQI\\u002fQRPfxEMpASscw\\u002fdniyUATEzC3JKXJQPIOm27u\\u002fclA+HSmEXRhy0BpIg6jN+XNQCG5v4n9mNBA+8Yr3HlN0kCi24RgaKjTQMOP5MssTtRADUHuJLAG1EAxDLhaL8rSQEAKwX4iw9BA000NC5COzEDPTPSyCpfHQPBQuw3RsMNARbxUNHwJwkA6lCfEUurDQAIWlVShqcpAl1N\\u002flgG100Dx\\u002ffoZX0rdQFEvz4y+y+RALj3EgReZ60AHWrgYuvbwQEsHmMZrQPNA+DipM\\u002fIl9EBvafYW\\u002fnTzQAoRILg6YfFAyAM4fTTh7EBV+V+wopHmQAVOxWTP7eBAi9daaXIn2UAWYGY3hDDTQDtYsss+uc5A59ImkDmRyUAsODyhZGXFQOicjiO8YMFAvei97421ukAEroTK1SOzQHKmdgbHZqlAgXuogNgkn0AKBlxwxaaRQBd6EML3joJAmUfAMXszckDLhwzT\\u002fslgQNZx2nbfb01A52uRe4LNOEBhsN0cBkUkQDF3EupWIRBA+hgrmuXt+D+Q+fov+I3iPxaHU3o4VMo\\u002fbonoDsqjsT\\u002fNBBLKYiuWPytZBXimC3o\\u002fsqy9dnmcXD+Qew5vlIE9P\\u002fhKK3KFyxw\\u002ffNEQNlnn+j5yAi25FmfYPtJeVToMxLU+roWcYIg7kz67ehPW1NFwPm6p9p5K30w+6ug5zJILKD4CzDQ6+sMDPt4J5Cw72ew90trETJyqJUCSSr+bC5MuQOof8SmNeT5AzQC7KNDcTkCUdkMgp1ddQJkcHeLP\\u002f2lA6vN7kKakdUCRglF1MSqBQE7ZIDgKZIpAawmkutH9k0BnkGsslyGeQOEkqs0fjqZA2eGqTYuWsEA4ptXOwZ+3QMdNpeJ\\u002fFMBAKUVCshW\\u002fxEDGADfedkTJQL8Kk+pUDc1Adno0Mn2nz0BSBWX2infQQH5JaGI+j9BAXqLw3vBe0ECSY6vNqTvQQBDJ0mB3btBAQH7ZTDoa0UA5WhDF5i3SQJIcq\\u002fLcaNNAiSjlR2xv1EBV7hZk+ObUQFCBr5bMj9RAbMmefiBW00Bf4ybZMVjRQGloQZBpxM1AUloCD4vGyEA70bWAmMLEQJYxeJsI5MJAPTtU\\u002fZpyxED83RHrP8TKQEeUiMCWgNNAgRysaKrS3EBUgLrEZ3bkQEpE8+pCPutA0gY9073V8EA1tSzKhzzzQIJjMNaVS\\u002fRAdQxpOSLH80AxhoeNatfxQJfQi5MM8+1AHqgGtKSg50AWYbwtSNnhQI2MygLVltpAgUrAYa071EBccygNOCDQQLd8VYbQ0spAuO2XgXybxkB4LBtSFaXCQKqOlyJgT71AvxFRVlCftUD\\u002fOXwmabutQESZChOZBaNA2mj1oSWzlkAyd6OJhGWJQBeYorDx13pAq1fW\\u002ffEMa0B0yeC1aDdaQNWTQaULlEhA00tbXNtONkAo6R5Kw4MjQDMFToV6UhBAHQBmB73f+T9Ynuev4EjjP68P8Xjf48o\\u002fjZyF47N+sT8A91Oub0KVPxdua6y9OXg\\u002fQPfDcawWWj\\u002fz9BvM3No6P3JJZsxsyho\\u002f2Qvvs+I++j409TKQQ3XZPsDXRw\\u002fuc7g+UA3uRPwTlz4HR1NzZyR1Pi6AsHYclVI+DGIO+rj6Lz6bUIjJoyEYPplCSpff+DFArIEHuTteOUBNSTK54ElJQCbcCUCElFlARgLWctQ4aEDavbxLGUR1QJj+LSbTZYFABRalVU\\u002fOikBwvdH6L72TQK0YGGz1SJxAiWRjrK4KpEBxih+4gFWsQIIGNwLo7rNAS24Az3qau0CXhSIXM43CQB2KRtY77cdAAZ2szKlmzUAUAE1HKSrRQCMoww12ENNAMlGdiO451EAQJyiYGabUQJEGZe+QgNRAPF4NE78T1EDIcs+dQLHTQAjkShacltNAHU9m3NjX00ALjTcomFjUQJFkSLl01tRAmSTGqvH\\u002f1EAsRkWHJ4\\u002fUQHW\\u002fAqYbYNNAEt7XM7B90UAVFcajTUbOQJsmHxBmZ8lATS7pi3ZbxUDaq68P8EHDQM50BlldU8RAqf+ATPLRyUDaa5S\\u002fgGrSQJurBU4O7dpAa81MUd0L40Dgu2CR8F\\u002fpQNhh805WeO9AF\\u002fYMHZwU8kBm0ndqrDfzQPOejeom5vJAn7zjp9g78UCNzivDckrtQMiGDEGmbedAvDvYBqX04UAP86xxKiDbQPV2nThj6NRA4qjlQnXg0EATRFPsX4PMQBQ0Sp5li8hAduGVL4HPxED21ma78+zAQHDhC7byALpABlL6C0G9skDOlAE01FCpQHBN41xqFKBAyRHnuHJSk0AdRfDMbB+GQLQX83ItTHhAJLCG0C20aUC4mippMjBaQKDwWhZ2lklAnr+yuPfON0BW+cexZe8kQKkmDBwsSBFAVKj9EQ2q+j85AG2cNC\\u002fjP0FKS10dxMk\\u002f4XoNfpU2sD9S7SzP6EKTP5fu4cLQ1nU\\u002foaqK07T0Vz\\u002fpDNbvVMQ5P+oXbY6abBs\\u002fE\\u002f9\\u002f7Ifq\\u002fD6ow1LKxv7dPgz1v7DaPr4+aQQJFTpDnT65JtMZ1+J6Po8YBAoCBVg+6jZEAH2gQj4xVsXzlTo7QIIagEYsL0NAQyqsCBEXU0Bki7k51UtjQJlTdKPtQHJAULa6mWj1f0CyVQfvyvaJQDCC1vSUupNACsEP6kFgnEDs8CmYQZ6jQH90baoGiapAA6pmWhbPsUDBEvIhfd63QAev43\\u002fm0b9AqwFfG9HfxEAbhb\\u002fu\\u002fqPKQAbZyyc7Y9BATPA1pstV00CwMmGSItfVQHUdPpbco9dA2Dx+7qKY2EDLswXtLrzYQHNnN2skP9hA8b+rvkhv10BruxnzaJ\\u002fWQCslLTZwC9ZAbHOXlvTD1UAZOlSm8KnVQANaSNMJfNVAqTa+ZM7v1EALksUkC87TQA6RQp5WCdJAe7eNkmSQz0AeMvMR2sHKQGA2T3ral8ZAcsIi9vopxEDGT1m0AaLEQOQbo4yKJ8lAhxz+HEBZ0UACm0Sx8NfYQPHM4DpeYeFApObINrYL50BqemVqipHsQKjgqWPfdfBAekqYw\\u002fSY8UBJmZpnUnXxQOhar12pHPBAF2PdCwTR60BiwzBhNrLmQCP2FvmJ0+FAHh9P2gi420AU6I3RuQ3WQAqqVIJ+Y9JACRnt14wK0EDveD+sW4nMQKdY5LVyDslAPObHSlMtxUDLbdQjDPrAQN693unmqLlApoaCAeRGskBQR1Prm6CoQJ2XKl88kJ9AtyZzi7pYk0Cr\\u002fdddgMSGQKcApEDiuXlA6hOMeHfQa0DyOEn2yplcQKpYfmRvx0tAKBbd8k5UOUD5wGM4R5YlQLsEIvfyKBFAmhtWz8B1+T+TC5DYOrDhP\\u002flixtZ9Lcc\\u002fwwxiqA\\u002fxrD+jMKFUMXORP5Vyxm7NmXQ\\u002f2aqo3yMMWD+YWC0g3NE7P3kcoeTKsx8\\u002fjsQ7OPeVAT+CgzXxZL7iPg5ExIIN98I+JAa3hvEQoj7DCqlLfqSAPu7Rl\\u002f26ZGo++yK4f2wUQ0ARVeh9KbRKQBBKhTZzWVpAQCk6txSEakCIldeg0QZ5QDSIEeu13YVAKPFqAGy0kUDZSniuZ7iaQHUawwgt96JAB2lVJvGmqUBtx7k58cqwQBFutwLRn7VA\\u002fvN+UdS0u0DFJL71E7nBQO9NpT0KisZAv0j9euI+zECdPnVbC0nRQA50uRrshdRACc\\u002f8ZKyJ10AL6VbAWATaQLokoovXsttA0tOc5Bdx3EDEK08LkUjcQD20Ne+OcttAxpU+15xI2kCMdQkbPybZQNV+r4cJSNhA20MZY26210Bm8CbHVEXXQC+HI3CjqNZA7g6KrN2V1UDS0HJNFObTQOdHuRBcq9FAh36XB55lzkAfH71huOvJQNQSQPRNCsdAM4NQQqPsxkDoY5xgzqnKQImFlXwFjdFAuiYo9EtG2EA6qhdiV5ngQIMN\\u002fMwZtOVA+XjPa6qz6kDwQm0cLbLuQLVdaq4TcvBA8vcLFCpu8ED8A+EqC7PuQKFStKf2\\u002fOpA7T77VSiV5kC3TMG1HlTiQNP5NY3kpd1AMLL6huCd2EA9GwgppmDVQPEz9pvFUtNAbGQ4hGq80UA8Q5azMg3QQO9lbe4HAsxAlCNMnSxAx0BL1mSo70XCQMe5\\u002fMhONLtAb8FH1hdAs0D77oUgTAWqQOGD+LRM26BAaFMDQjbzlEDWgbSfF+mIQIQIvqLvL3xAt947GxwobkCH3kT8FFJeQDpbUjLdhExAoIy7XIoIOUDZJ7eFQYIkQJ8yMpZgdQ9A+Q0PHP269j+DNtHtSUHfP5nkfRuWtsQ\\u002fuWuX\\u002fBrVqj9T+lX7WCuRP9eTqlU7yHU\\u002fsFpO9zdBWz8rxILifaBAP\\u002fzmhoBXhCM\\u002f9tt+7oXEBT+xDbhhqdbmPlhv+8oUZ8Y+T1LiIN8opT43nG\\u002fbHRaRPjli4WfeV0lALXV2AzF0UUCWMqYv\\u002fdpgQJ64391NvXBA\\u002f19x0+xbf0DQUeu+5USLQLMJcqm8\\u002fpVAd5GwU42EoEDKj0kPvkKnQO61pJQ3CK9AweiPpHDgs0CDFDGostK4QEtYGv6do75A94Lqt6XawkB9j3KRXS\\u002fHQB1kVP8zY8xAcME13vMx0UB9wDxTMXnUQE9TT3sA0NdAVa1Sxfzp2kCFkmjaunDdQFpeFFpMGd9ABM2BOse+30AaJta393TfQATSB4SCht5AAZsm5SFb3UC\\u002fgI+Kg0\\u002fcQC7yugx1kNtAVKj470QK20DhoY31lXTaQFteA7dgdtlAt3Oqz8HS10Bep7a8EIvVQHmA2fzG6NJAYZOgSQBv0EBN\\u002f06d\\u002fX3NQDPrlHix+cxAInpdjtc40EDUU54ofFfUQMpya3cg8NpAfCF\\u002fWXLS4UBdeXnTq8PmQD\\u002fueHYEk+tAHwZ0DxRq70AKn3ChpcfwQNJP+GGb0PBAZ7LJqWi370CVUGeg0VjsQJ8WDooYTOhADyHBWdpX5EASWVSuoAzhQIdi5lOGUt1AO5zF\\u002f6c52kCxVotHXD7YQNw9nSx0qdZAxqBTGfzh1ECzpfm59ZnSQJOevCy3rM9Ai6d3xAmsyUA6mPa51dLDQArZjmltQ71AvLEQlmW2tEALLcgZCyasQObG32tiU6JAdJo\\u002fWCzAlkAU\\u002fo0tMcKKQBz+q5RUoX1AsBiGmVe8bkD6hkuXdcddQIpcgt3T70pA3oZL+6nNNkCvygu1iCsiQAXZ+3vAggtAf9gG4pkJ9D9UplnSFnrcP7oNpcRK+MM\\u002fsBSYAz\\u002fFqz8fzh6AGBGTP\\u002ffNQo2Bknk\\u002fcdlrhcOFYD8xdAQ7z05EP\\u002fCRYPQ7fic\\u002fo6imLwdhCT\\u002fNJITYwH3pPgG5N2PLksg+dKtD2jQktD7jnTpfUoxQQGyYdLpCJVZAOYAcnGCBZEDKuN0sBclzQHVfy8PhMIJAms5854RAj0Dp6gvqJ\\u002f+YQAHq0s\\u002fSpaJAReO2vvYTqkAOdb6BtzqxQGE\\u002fL1h6wLVA4egkZOmWukAtACzfTOe\\u002fQGgUjnPLAMNAhqhZbrKjxkA6VUrHPArLQK7mdzUJKtBATwMS\\u002fhNC00A\\u002f48Oie7bWQFFQWmCiT9pAyPmNUlW23UDihhrv6kLgQFKIA4UNNuFAThqk5Uql4UDHii6jwaLhQDCRRixNW+FAqnWxnhkD4UDe6EMh+L3gQM4wzKr4juBAoP2bKQRY4ECB4ab0\\u002f9TfQIQXQXxuQt5ADqYYjCTu20ASA5m+TSHZQHvbCcMqbdZAkS1hVV6M1EBLl0wgzj\\u002fUQHWFTytrLtZAkSTyRdfE2kDPGM5o2wfhQDWLyfevyuVA\\u002fD4stzwh60Blop+NySDwQN6b\\u002f8RBJfJAtc0dsCdA80D1VZPwF0PzQOenfaaJPfJAmJ2\\u002fYhp28EAP30U1SaTsQERI\\u002f80ebehA5QAnXYTb5EDumWakCjTiQEqwvC8qa+BAZ4+IZ2F23kCj03IBLI3cQJTC6bfjdtpAPdHtrPHY10DP3oQhL6\\u002fUQGzDsh0dNdFADwzWI\\u002f55y0CehZB9nBjFQD1NJTK6Lr9Ab\\u002fY0z9oktkDgjKvAJhSuQIEPQ1EQaaNA\\u002ff7hirmkl0B7QVAHQAeLQGvLUh2G5HxAvLdj0VfZbEAo3PmyD\\u002fRaQGDTOrPIrkdAYYR\\u002fWLq9M0AZan8qnpofQJHKlomAoghAIw3+D5\\u002ft8j+UukM3IdfcP9ADx2tZuMU\\u002fcZnCccgAsD+hRXA+ZsGWPzLP9e6Rz34\\u002fQmlBQkGlYz9aGnZMcGZHPyj++jy94Ck\\u002fEaQ00PV+Cj8V+oTUaPnpPgcgaJcWjtU+T5Jlgo0SVkDLVmqjWGBcQLq8dlCtn2hAtDFPtr6WdkB0bbcXmQaEQE8RIIzgwZBAWftZM4JPmkAFmY4D012jQPQn5LfqzapAfMeQj2+KsUBxgoACXua1QHgHoeP8X7pAFPFLEVAFv0DSNG2rpwfCQKUFqv3R7cRAXegeP+9yyEBjgf5uTd7MQO0IVKy2ONFAbDSvftOk1EBAoqdCdp3YQLMeDAzt3NxAx5ziOQl74EDUi\\u002f8BQDniQPx33oESfuNAysLqxhw\\u002f5EBpMGLgW5XkQDSudkUPr+RALMqwnr+25EB3+yCIU7zkQBKhV1gbreRAZB7zittf5EClhvyLmK7jQHBf1d+qkOJAyAlBWQsp4UD8rp8Ul4nfQM0nPQdCm91AoZNRz8Nt3UAslt3BvNDfQF+tpjzIpuJA5N2GBLD55kD7h3zviZTsQOqerUNZZ\\u002fFApqIXiKxb9ECu3YCtjKT2QBy4X71G2PdAQ072fJnF90D79HwNsIH2QBgOlfeTXvRAZKPRGwHP8UDgsGEPJojuQAc1DlACI+pAgMyKUUS\\u002f5kASeFXF1VfkQKE2NCQEqeJAmWucFMxT4UCTAznB9AHgQI6g6OUz\\u002f9xAoIWm4+OE2UA8p0dovL7VQK+iXcfG7tFAvq51H9aezEDzPGQlZQ3GQLBDuodDU8BA0nPCzp8Pt0D\\u002fXi6p9diuQATvimj7aKNAAfsplpzglkAOCJkneTSJQGxeb9be+3lAgN21U3AsaUCM6TkP5BpXQOy6q0aOU0RACWvqbkxhMUAGHTBpTkIdQIhJ4eRfaghAiOO6gQ0p9D8PqIwIyU\\u002fgP+\\u002fcPgCigsk\\u002fqeijZFwEsz9ghaL2D7maP4TYmYMgi4E\\u002fZCUNKT1jZT\\u002f04HMnOBpIP2DT9ABTESk\\u002fpCCeRmbrCD\\u002fBJYCrlOX0PjTIYLZei15AiGUrRfDUYkCs5i2rT0huQEWCjpfp63lA9Dnzt6OzhUCWw2IrQleRQL\\u002f1LfwTRJpAieJXapjRokDQ+uk5r4ipQApqyc9VdrBAkDW5GWNNtEDyY5knpSi4QPiz+AYhB7xAQpVO6IUHwEAHnGGHpUnCQFRlVAxRDsVAJSwANhq3yEDiA6zKabfNQNcMJrpBOdJA5kKzvhWE1kCYPSz5dpPbQF6B6faFgOBAjClEtlof40BqW8gZUmHlQDirO594G+dARJMTICBK6ECs3+zRmAvpQOz5q608iulArFFcgf3h6UDjDGT07xDqQGg\\u002fNRuf++lAQGZKlOeB6UAfkVZ2dJjoQAWaW3gIW+dA5\\u002fNosiUS5kCDkra\\u002fuizlQGMuNgwMMOVAsFKO24Se5kBez9bV2dXpQA46LILB6O5Akalr7Ku\\u002f8kCFXw8BLmf2QFVIH2KJ2flAB9RZKW16\\u002fEBL7lC5Qc39QN83xRGlmf1ARPboIV36+0B27R23uVD5QFzqQ\\u002ff4IvZAOam3+4T08kBOP7extCbwQJWFMeQQ1OtAykzRVvh\\u002f6ECh1M9jKRHmQID6+918KuRAy3PRDIN14kCkbG+dHrrgQHLETe9WyN1ApUqViAvz2UDyUUsVGRTWQLKrcKigSNJAwbq+3tdNzUDTWatHJYvGQFDXQe7ggMBAVxODRobQtkB5lVBCs5utQBXOeLhi+6FAXZapteBxlEBd0nRAu9WFQH+H4V7bEHZA0esSwjFWZUBzRpIuzQNUQGu2ln\\u002fjdEJATYpuGczdMEAXj6Ip\\u002fIYeQPop1mgbHAtAQQp3MYpN9z\\u002fTpI63QR7jP\\u002frgAaa4ls0\\u002f0Oly+jdntT+VgssTj8GcP6iAc4lN24E\\u002f7K1B5N9uZD\\u002fnPOXuNYdFP9FTcoakpyU\\u002frfxg35pSEj\\u002fc36nN\\u002fH5lQEz1ea3cqWlA7rhZpZdNc0BbCEkEssV+QNwPr4MKC4hAPOoSbDAEkkCdQ9USG8mZQLTeQ1y\\u002fn6FAK+fAYEcLp0AP6GyeoeesQP3V4Vw6ebFAZ9eqPByAtEDAW\\u002folyoi3QMX8+xahsrpApmdPr5xJvkDnf36k923BQKZ\\u002f0tAWpsRAPRM0mbViyUBz4d+zPx7QQGnYshI4ytRAvyaBeieq2kBURMB\\u002f+7LgQEPQepbWMeRAhE9G62R450AicWStbj3qQKemqGhOXuxAQTwRPsbj7UCpsKAEn\\u002fLuQH9Z27uNse9A0+ISn+kZ8EA80Pp01DnwQHGe6mOHLvBAIeAXi5jh70ARLYEo4AzvQBZRY6I0FO5AUh5w9HBd7UByjITWo3ftQJPJMphpAO9ATM7sDig78UDukTB6MAH0QNsz2UoEpvdAW3t2lK+2+0CNLLpCSIv\\u002fQPWUVUR1NwFB8IlhOQfpAUHxX\\u002fwFKLoBQZF2X2dVugBBispzXyZC\\u002fkBaohi1CXX6QKi49bOyofZAhmcdJtA180AwJga\\u002f+GjwQA6prRJ9gOxAdvYsH3Y86UDPcqugpK7mQFO0+3gdhuRA8MzaeduL4kAR0kXzo6TgQNusIN\\u002fKjN1Ag+9HUEDX2UAnhuy5ehzWQK6u9nZQV9JAU\\u002fetTKUvzUC+oFqQTQ7GQIFR8qida79ARRw5Onn\\u002ftECTsJyqzUyqQF9fu5Dw8Z5AEGBQ64E2kUDe1YyjuEuCQOhXNacb1XJAy5kRD1IHY0CHtZug\\u002fApTQLyP8oTQ4UJAhxEFgzdlMkD5XHPI6V4hQPdbnsEWWQ9AfnvpImGz+j8A+MlyyETlP7g1Qhyuec8\\u002fHW1FeRqItT\\u002fwT5LXeCSbPwqRAmhbc38\\u002fBXEfTr+\\u002fYD8rKF9CCQVBP43eNZ5qBy0\\u002fSYLAhlmYbUCwGeFQzVNxQJa0XbuD8HhAlHWV5fDRgkAa8CtT8qOLQCPhheGMXJNA5fnRM9\\u002fbmUDWupHx3YqgQIk9R37aZaRAiJ3Vif5jqEBXGwwfoXasQG0OFSu8UbBAKR6ZtL6GskCmz3C29AK1QJ2N6SebGLhAxMcWwaZgvEDtWOszPmrBQPgeA6YdZMZA2cNIHqTVzUDyQfDrcSPUQNlwRjoO5NpArpA7Rf1v4UCnjPW5L7zlQGd0LjE08OlA\\u002f9sEvxis7UCxQp49T1nwQDTzQTxte\\u002fFAXN9TrLJK8kDeQnW5Q9\\u002fyQFzcHjbkTvNA+FSkqN2k80CzEg++M+DzQJ1XNEAD+PNAqq8rLFrl80BT8CrR1bDzQGKKy\\u002fo9f\\u002fNANOKF9z6V80Cl0FECXkv0QPJ8YvO\\u002f8fVAEHwTNDes+EC5sK\\u002fHHlX8QJHwJDGrOgBBoK4KPXYsAkHl7k2pbp4DQWI6xVt7QwRB\\u002fr94+G35A0F0dkHvQ9ECQbc8gKZwBgFB42P0ma7T\\u002fUAJXuG3XpP5QJe7z4aFvvVA2yE7DpuO8kDeB3THUwnwQAzjkLXAJexAO1COe6UG6UDPspQzWm7mQIGKmiGpLeRA\\u002fdS7I\\u002f8p4kDkYr4MZFDgQEuZ32wnFd1ASdTnovR92UBl3vS+ULbVQPNX03d5v9FAbUbNR1COy0DO8wFSwivEQMaRfdrgwLtADc5OxLb1sUDWrkyGkvqlQB2PWUo1rZlAC9C8gTkCjUA\\u002ftkW9SBCAQEyklQ2Em3FAl88K9UEgY0AR+0g8WW5UQC7gxwnwLUVA\\u002fKhFi0IANUAVkK20C6wjQIyzf9DvPRFAqqD439MV\\u002fD8YIFbw2SblP9tsla4OXM0\\u002fZJeYmBO7sj9eNmV1k+6VP\\u002fHFg+v5k3c\\u002f0u1cdLwsWD+4+mM3TMNEP1pqcAMmPXNA8bTI1DBSdkD0CbxTcmZ\\u002fQDZYHzOo8YZAUs86ee4nkEDQJthTr3iVQONbxUA97ZpAw4Bx8L8NoEDF1JZRuGeiQDxWeRZbjqRAAutLdoOtpkCTVa0rdAupQBrr9ljZBaxAjgdgyF4QsECkcxcPgRizQFwqXxzXzbdAe4TOnwxEv0DLIWyoXHPFQK+PDq0+G85A8oAXzW8T1UB3xyseX9TcQBZdHGGGAeNAAQJns9j950ADWzdS\\u002fvHsQL2EVsCluPBAUwOpCXCW8kBTImPYYQP0QPrN73LpCfVA8HwSs7bG9UDWdoASvlz2QHbClC5A6PZAvaK4N3V090B0R98+Efj3QCJ9C3bTXPhADC6kqoGS+ECfHgBY86X4QCQmIM92zvhAC21sAnJl+UBmKxCursb6QGb+7KYRJf1A7apfcZszAEHNq8IlrQ4CQYgXe1vdywNBkDOGkZcLBUFNW+5BOIMFQZZhzucrEwVBJIFMPjvOA0EPpe6dE\\u002fABQXzG1CU6kP9A2D+Zo4xD+0ANOGk212X3QIdUhzzgKfRARkGaQ1KQ8UDVjcdsrfbuQEuq7olpietAkHSyGN6b6ECi9JJqBAzmQDiDS2XUyuNAp2tZCurK4UB49jl0NOffQJC70VhIRNxAMNkYEUBw2EDQHXL+t1jUQL0eAe80I9BAQ\\u002fL0gzQ5yED001a12yvBQDX8u+ieELdAsL8+Jc2WrUClcdy8HViiQDY1V7CHRpZAgUsyKBLAikCFwOvbb9R\\u002fQERS6vnDoXJAeXIJekIvZUC1i+Q5OQ9XQAUh3YVxukdAUwfHdsHbNkBvs\\u002fZGgXgkQKw2qsQ39RBAF1qmV+\\u002fl+T++DCyO+C7iP2v\\u002f4EpYb8c\\u002fNRryPoGvqz8INHzE+QGOP6wRzdLKBG8\\u002fPQuW9CzRWj8avrTvpxl3QO4DKUIcuHpAGMqIcnKhgkC6Y8eZItWKQLRGSTKTe5JAEQIlGuXMl0CHnhEvWpacQBsCstpsHqBAEFuc0+BGoUD0h7bS+uyhQH4dRa6mc6JAhfkw24Vlo0CO9hLyRGmlQEFCn1UUT6lAuDgLqr4esEBd1zh5y\\u002fa1QJuFjdt1Zr9ALfDhS8\\u002fzxkDA8vCj4bnQQCcFdlpm0tdATz5B0+Rc4ED\\u002fyzh\\u002flIjlQNkT2gDcFOtAQyJqWexI8EAAhBOrucfyQDQjdJK02\\u002fRA2U0t3d1w9kCXLfEqCI\\u002f3QPpnTHh5V\\u002fhAqA90xfD6+EAXDKQn\\u002fqj5QJdWS0\\u002fvfPpAgKjb\\u002fPNw+0BAK8bRQ2H8QAwmZVqbIv1ALPfvYd6i\\u002fUCSg6iexQD+QADM2etMif5A02gjMMmX\\u002f0Bk1YrYd7MAQUISMfQb9QFBMq8t0H1hA0G9SWMep6sEQRxxNcm\\u002fgAVBsDnJZ6GiBUHYwY0N9PoEQUlKZeIKoANBkbtAudjKAUHBpy63dIb\\u002fQASKO1SelftABrGYmHwb+EB+32I8djr1QAij32fo5vJAd65OKwr78EASocDM55ruQI88msuFg+tAm5MSXICc6ECLsEQO4uzlQI2CILUVf+NAJG8ssDVN4UCmSvCTs3reQHZBwliUXtpAqK5ndLch1kB9UuDnVtTRQGHzMZxKZ8tAoWLi2MITxEDUoZu0SjG8QAn32PleKLNAHhqWZhiAqUA\\u002fazpfqcagQNTMcNTG3JVAoGM8wC4IjEASfbjEynWBQHpfT6mK1XRAXFcYBOSDZ0DAPLMri9xYQIH35g+ZckhAIReGoqo\\u002fNkDxuNVCDawiQE5K9tA01AxAQGM3mgdv9D+MJDCiJI\\u002faP9HjNkNYn78\\u002fANLiUPJDoT9oXL8jYvyBP\\u002fJWiLIhTW8\\u002fjk2dno9YeUBOSo0pZVN9QEUKi6tEb4RA3yATjvxRjUCuiM26jgeUQPlw99tAbJlAX7lqaTnanUAR55t4QEigQGsW2\\u002fYYraBAds\\u002fRhu1aoEA8vwW1tsWfQOHbgPzYD6BAGKInAjfWoUA6dW+Uj1+mQCTlUe9JNq9AnQdiYDc8t0AJ1LjCHLjBQLPzS3XN28pAM4sdkrrU00B4xOrfLDDcQFgb\\u002flRNKuNAO2+yz7\\u002fh6EBDefC73t3uQDtM2UfjVvJAFQQdAELy9EDdYRdnZxb3QASJ1OGJrfhA2wfR\\u002fhu++UAnEzvxo236QI5l0cFR+vpA3enq4Gyo+0BaO9lzhqf8QEuQzPra+\\u002f1AeJJZABZ7\\u002f0DhOAOlmnEAQfEeJgjUAAFBAh3+c9ZpAUFvz+EHTsUBQUgjol3KNwJBbF0jnB\\u002fbAkE2vEeFB6wDQX5bEV24hQRBlsJ+azotBUGqxaNBn2YFQbbFF5qJCgVBeJ8CMHQTBEEtc+r7eZ4CQWBsBVW44ABBHDdxh9Ms\\u002fkBKzjGzVeT6QA4vyVwpI\\u002fhAcbT752fz9UDvv7uLWzb0QP\\u002fIYAKat\\u002fJAQKK1I85D8UCu\\u002f\\u002fyienbvQJnkNq0jM+xAw2CH9EHh6EDGbXn0rrPlQN1QaKmRzeJA0CUxnRwz4EA3E0xoGaHbQC3LEaU9HtdAw8+0BkPP0kCp47a6tJrNQJxII3GTisZA4g9yy5q0wEDGqGgEAlG4QASUdencgbFAmaswZD\\u002f3qEAlDa+UXYKhQNcZlFMi3JdAV+raDuImj0ALzezGsz6DQJC90jtFSXZADdaTOfcEaEDo8QHPJflXQAIjolhYFEZASt6s84q3MkDYdBgpEicdQE4FtTnt0wRAr\\u002fRrHgFG6z+wE9mMgVrQP7ED6Aym\\u002fLE\\u002f+IbseE\\u002fjkj82D9udLY2AP3C8J9j7ZHlAJLwXQaRyfUAsbrndx5mEQE2+dn7goY1AsoYlWOI9lECvkIimLpuZQKqSgdqx1Z1AdHRsdI4NoEA0ZynrMhygQIFfSjaqzp5AS03yNBBLnUBxdDRgCLedQOpRnID7SKFAedzbHfBvp0BGVY1IQpyxQDsKup+4irtAcVt6FvZ8xUAjf35JRljQQClknY4F8ddAIfIQ7pfF4EBmy0u\\u002fkWrmQFORk7mMluxA0rlbkYJv8UA26rFuqmn0QB5evZ6fBvdAo8XPY5se+UA9oV6W25v6QDrv8cmrg\\u002ftAQt9z9En++0Clg6TgJlX8QH5oAcJ74PxAxaFYTuTm\\u002fUAf0GAp8n3\\u002fQM8wFoyovwBBUy+gFULOAUHADtt668MCQaGQ3jHhjANBoE+QSrMtBEHyHsK7abgEQVR1GcabOQVBv0B9ohOqBUEuXXjlhO0FQUI58Owl3gVBY7n4BPBdBUGDizhPl2QEQc6jR5RyBANBAymqCZZlAUEMXi4ax3T\\u002fQN8oV6ugZPxAQYq9U7Pd+UAXrQqX0Pn3QMeKKjnxpvZAxrlYLS6v9UAVtwssGcr0QMnQbkG3s\\u002fNAQdEYOElC8kCjOLCDkHLwQI7gWTuryexA7cohsKKX6EDzGSWXbazkQBaYeVkUPeFABM3qSbWh3EBO+OKkZKbXQOT+OnVLWNNAwGoQwhg7z0ClF7s7kOLIQI9jCan5o8NACDHmGB7NvkAkLEyTjfS3QKxBq16hVLJAuw+Y0BJBq0Ar9GwAeGqjQCAKLhL3MJpAdL5Aim+RkEDX1TbyKIaDQJMZosITU3VAvR\\u002fS6ueCZUBlTLh7f\\u002f1TQJXNpF+TFEFAXoLMS2bMKkDvLi5l3EcTQOo1oCzAbPk\\u002fUNKDdSu23j9OgbUwxgXBP3I9mnbtC6I\\u002fe2hQO8rpjz8s13JIaYJ3QGSkjeHJXXtAft01HOhJg0AWc93Ad\\u002fCLQOm1wfkHMZNAHm0iP09gmEAFOouzpHicQEgLxqN9q55AalFpcVvQnkAyeLKb9aedQFB3E9hH4pxAn0ZA+f30nkDYl7TmYnajQOlbEpS+MaxA51cB4ADptUAqBPB4rTnBQMI1NXBqkspAbUrFSFnT00CoRx1KTmrcQBSCkxyue+NAudwUxDKG6UBi8ck4zPfvQFsJy35BMPNAMmnrfUwp9kCbSQy7Arj4QIEdqxUVuPpAq6ekHhsU\\u002fEDwXnhlb8\\u002f8QAj\\u002f2AxUEv1AmQ4S4yEt\\u002fUC3kdhz34f9QA5jt\\u002fAdf\\u002f5AGcoWsQofAEF0UVn9K1YBQd1t5KfcvQJBw3ewaCsnBEE+GvNMJm4FQSLeSHvugAZB6WJ5EJdYB0FzwiWr2OsHQSsYT79FJwhBNtVQYVjxB0Ft31Y3czcHQZK8QQD9+gVBs+ut6JVWBEFQ7tLlDXkCQQ5sm+yLmQBBZ+jufW3V\\u002fUDH7oWAdCL7QCz\\u002fOiKmPflASHHBT5Eh+EBr5H62cqH3QMVJTLAdcPdAj5tve8Mu90CWCrrG\\u002f4P2QN7fgr3kNfVAVAplYaE880B156cqw8PwQPqkHbpwL+xApZTOYhsQ50BM6\\u002fFXgpriQF8mTIGC991AMjtiEWFR2EDvPwzeG\\u002fXTQNrLfwvsj9BA6IERi0m+y0CjiHQAUF3HQHo4QW2gpMNAy4kjpCRMwEAep+0Yn0+6QB8iXMxBX7RAiyiB4DrlrUBwocvG25ekQB8vgpVEdJpA1fyNeZOIj0DsWsrY1mCBQNtvZzWCqnFABi\\u002faQu+HYEC2ujBsum9MQGe17TypdDZA1use061EIEDv7XF3U50FQKfEPqVJVeo\\u002f4b5uMNp9zT8QENmVjrOvP95AwKP0a5w\\u002f7vxJCcvSdECGC+JmzGJ4QODBb22aZYFA6vpXXYOBiUANPV19tLeRQGsgq4COv5ZAMoYvCrDdmkBwJ\\u002fs25lCdQE\\u002fKkCpqAJ5AQHmQ+efPnUA1dQRrf7eeQOQf3qawz6FAx1F1fGAOqECo1XXW5Q2yQAnoImsvFLxAWc6aI\\u002f6ixUDpFeYl0jTQQEhW5bnpbtdAdi63t4NN4EC3v3rBscnlQKIdHSPi8OtA5fxNBvMy8UAaJZsNKV70QH1g7M6fRvdAjtLe0FzB+UAFJ7vs6qz7QCQ8eBQC9PxA0pxtKKGX\\u002fUDtO01pzL39QOAMx3uPuP1AB\\u002fK\\u002fzXD4\\u002fUBPzwymcOn+QLV9iMkRZQBBIes+WAXKAUGfMUKAj4EDQcB2mp\\u002f9WgVBX3y7U+cnB0GL8eIZUcMIQTkeytxKDQpB8tR4pzfjCkEnvQhcWh8LQcYdBBCRowpBP9MlnFJpCUGV\\u002fv\\u002fmGYwHQbX6rXnKRgVBMEcUYOjkAkHDJKbpS68AQcSwHb\\u002f7uf1AnWeg6CMZ+0BlDkN1L4n5QMMVlOd27fhABvf0sdcE+UCCW3sFCm\\u002f5QC\\u002fvMNZduPlAxG+jnZ9w+UBeYvpitkn4QNL6mqyiMfZAJwUhyGtZ80A6nEYosCLwQM9j5Foe9+lAzFJU0Up15EASZl\\u002fbyBbgQPCdjHqSvtlAwgCDdus71UDwPiJZzR7SQP06g5VJzM9ARTrU5NBMzECoDPnEmxrJQKpmhwgsy8VAiOsyvuE2wkDlr9F9x+y8QPUNc1rCmrVAyY7ZZC4lrkC0yVHJ3YujQGGJtRWDeZdA3fH13X0KikAUtjkNkaF6QD4qcXzWEWlAS1G1miOzVUAgMjFf+EFBQO6mXmgxNilAK1r5smXqEEDSuK\\u002fRm930P4bpQ3UtwNc\\u002fIj0YbJgkuj9Ty3d02QSoP\\u002f87Etb\\u002f9HJA++5GsvFndkBrLNNXPUiAQGt8ARJKUohACOpld5wykUBIDtsL0nmWQFr0eYtiDJtAhU1kpvYpnkCw7It3ZcGfQE3qJK8VbqBArjT9AB\\u002floUDRGK7Hh\\u002f2lQBBKvvdBya5ACdfRgKA4t0CYuMH7X7\\u002fBQMeeohEWjspA7TEwa14000DZ0+WYHcraQFXB3kDyB+JAC9aF3RVv50Czih9cZGftQGk3qNRM0PFAgEeeB3Dd9EB4tYbGKqz3QCPs+KUkFPpAr1f1WVf1+0Bnw7RSGDv9QDfEMpAG5v1AsYBww8Aa\\u002fkARG\\u002fAI2Cn+QCLjyRpBhP5A6\\u002fYfmnWa\\u002f0BfzaLkNNoAQeC7ZtUqbAJBf4BW4AtnBEFTq\\u002frnz5wGQU0lhym72whBh82j5Q70CkHCWUWBZbQMQeSpl1RS5Q1BauYF4mpODkHJiSGNCscNQaSrd513SgxBevuyxmwCCkHRArZ7E0AHQWjkHC0WZgRBMq+YKrjOAUFykqTY7XL\\u002fQOWum96YivxAAGtmIzLp+kAfkyUbUmf6QBEHkuFyufpADbeS1bNz+0AAaHaysBL8QKfS6BoDEvxAnZwC4uQO+0A6jo65K+n4QA58DAVQ0PVAZI+vhnsy8kCO33AraCjtQGP8Ro3lyOZA3ouRN4TC4UB8VqFaoVPcQHzJmKZWlNdALZP21zqb1ECHKJUj2rTSQMD79CPPRNFAwOnjPRioz0AU6gFZ4jTMQGHow702CMhAb2qI7MRYw0Asu8+2izC9QATxHQ1zhLRAsiLsKfPEqkCeILPsiCmgQIOOAxaJBpJAqgX1jUWKgkCDfXfdSZFxQMdhkl1ppl5A3GcAJoucSEAW3CYQ2TIyQEJKdp7szxhAnZXZiFJC\\u002fz9\\u002fv8abQk\\u002fiPyUZNFFOCMU\\u002fSCeAKawrtD\\u002fboYTINWdzQExofUJ3M3dAFjTCOIw\\u002fgUDAtxqWkFqKQJjE4KfoCJNAl8+lCFFmmUCGtI\\u002fnmDifQKXDClAgzaFAXn4+o\\u002fYyo0CFtlRpXGKkQHTOlFSst6ZAXtUoDiQ8rEBKTHvjdaizQGo87oHKHL1APE0qX5CsxUBTBEPt6G\\u002fPQB\\u002fdlKpJ+dVASWThVtWb3UDcHr8e\\u002fEvjQCO9mgYoaehA\\u002fjZT9GAE7kC9YLHHnfHxQG+vwJjt2vRA49EgDNiR90Ak+9TA6+z5QNVezeI\\u002fy\\u002ftArImplnga\\u002fUBT83wxQ+D9QBXghUKoRf5AfzfYqICb\\u002fkAGsNeiH07\\u002fQFgXBbOAYwBB+ayxRWakAUGgez26eGwDQX8mPBGIoQVB5naT8WkYCEEauNouYaEKQQOYH0MMDA1BcFCZ+4ciD0HUjvZWYlIQQfRByb5apxBBfROS7\\u002fN1EEF2E\\u002ffogG8PQWxzXu\\u002f7AQ1BH3fAGPH6CUEiuF7f88gGQZj4+I6j0wNB64T58aBlAUEBTxpKWUr\\u002fQDJJIe4aMf1A69iXsthb\\u002fEAzkXvkHYD8QNkWP5KGL\\u002f1AX8j4KK\\u002fe\\u002fUDk9l7oUPv9QK\\u002fCbM6UEf1AyiEovTjy+kDj3Sbpv8T3QIWX\\u002fLf1+PNAkgJY5CEd8ECK34waZVvpQKPWukiY6eNAwlrN5OIH4EAzZPKnegjbQJOAPQR7AthA5uYUfAMv1kCAZkdFktLUQLAP3bI7W9NAUv9Dvatw0UB2iksrlPnNQDt5p1N3S8hAlxYl21FuwkBkJvzW2Qq6QFMulIEwE7FAp\\u002fL37B67pEBLTJ\\u002fJJkSXQGgI8vmbHIhAr60xox4Qd0CTzMo35FxkQGqll39GnFBAXf18xxIVOUBg+eRKspQhQGMoaw\\u002f++wZAX0jWAmpK7D9+8Q7usWvRP529kvjy0ME\\u002fgzOYfxvvdkD0aJwudbR7QKqPg05aAYVAzjMMoU5bkEC19TlITAyYQLbaJhvaUKBAuAFEnmlhpED6etBBiJanQDdq+Na\\u002fvKlAE\\u002fvCSEpuq0Avh8qLbD+uQAFfr6bZSrJAfGj9\\u002fJ6UuECDwCOfapXBQB7dtdz0X8lA5JWv1bXb0UCepla+IjnYQG\\u002fzRuZRrt9AIL3\\u002fiTsX5EChn1qQltvoQL\\u002fBitizGu5AflSL4bDY8UCi0cE+Rq30QJRBwW\\u002fbW\\u002fdA0bCJSIyz+UAMMYQYCo\\u002f7QJYTlANg4PxAfVFwvfG5\\u002fUCzzWbVIlP+QECax+5vBP9A\\u002fOFb5zIbAEFb4NntTSIBQf1xSqigrwJB9\\u002fyD82+9BEFfxdpv\\u002fCsHQbDXebx9zglB5+9xWe53DEF2I4r0mP4OQYdS9kGumhBBBxVO7atxEUGPeiUq6eIRQQ0x0Hqw0BFBxMJXJ6YtEUEChPDIMwYQQThl\\u002fNQMAQ1BDPrGPgelCUHeF4QuxGUGQXtfgm81mQNBSjXE4WhyAUELYVm79wIAQRHREhw7g\\u002f5AWeBe9Zcd\\u002fkBaW1vbYmz+QIjQy2pa5P5AHhC9\\u002f9vt\\u002fkBUNXYdPgv+QJV1YH0iAfxAQ2ht0XHr+EDo2mq+UDH1QOp9TJqZXPFAQBK7D8HS60BqiC2oo0jmQOI2lWSKReJAVxGvOJc930CNGvWzwPfbQFVw9YgR7dlAWro8b6pc2EDvMY13cqrWQIZyue0sdNRAIviszT2f0UDOvRb3eKrMQFfsa\\u002f4A2MVAdvIUpx8Iv0B95WIEqXq0QK3PNEuvD6lAuD4z\\u002fNFmnEAuOlXvCcyNQO+YIDz283xAOMmLR9kXakBWqimMAt5VQD3Q0dVWG0FA+O7iJdAcKUCHNeDmCmcRQOhm\\u002fRMkBvc\\u002fIomWEP0N3z8AsA5iBhjRPzLly\\u002f2kQn1AaxgrUufJgUA2NPoDXk+LQOG7DtcZhpVALbgk5Of\\u002fn0AgOpgPu\\u002fClQN7JfF5NrKtAeK+v9qwjsECwsfLYdKqxQBKFC821srJASuuKAhcgtEDpFC4JClS3QOgiemth1r1AnA9O4EZwxECJGwGPbX7MQC4FBbmVe9NAgIRLaAu\\u002f2UAOX6TxYXDgQMErLsRXbuRA0ZPYdPHp6EA8gpjVLvLtQLjLvO0Eu\\u002fFAs36hcfSX9EDD+LM8+FX3QKh3sOA1tflA\\u002fzAfP5CH+0Dk4xyMbsT8QIYXRvuLkf1AWUJx3N0+\\u002fkB9+PlBsjb\\u002fQNrzatUzcgBBtBHn0j7LAUG33b+6j7IDQZ6B4HFsFAZB8YemVD7ECEH23UAMcIwLQY6Iw3RAPg5BmstZE9ZbEEHod1seG24RQYeNnz7CQxJB8XBxvU7DEkHJIXiwb9ASQZ8dOvNHVxJBg\\u002fCDLy5YEUH\\u002fqNGbL9kPQfdHuwDhgwxBt\\u002f5S6VwaCUFklemXSPsFQTanPjk5aQNBxSVIqiaGAUFLZ0kBEFYAQWUkIXq2hP9Albya2Ro4\\u002f0C5Gp0h8ED\\u002fQHZFnXIiCv9As4YrqBEV\\u002fkDLr\\u002ftkyCD8QLQBiLtiPvlAxfIdZiXG9UDLZHZx7zHyQK6gwMv24O1AWAIdmGSN6EBsXsGIm5HkQHj3CZDvy+FA2yBt1xjm30BvkUiLWmDdQG+gsjrTW9tA7Mkk23FF2UC2MvWv2bjWQMZmboIElNNATq6+2FP0z0CeUL9aCX\\u002fIQGCL2lKSi8FAyj9+D1xnt0BTsNIEqQStQErkMlqFtqBAv9zUT6LlkUDpvfwJqtmBQBHo9dm8onBARfPwC4UaXUB6DeLjDwZIQE0791\\u002f20DJAS3gxhqkaHEBUwtMRbi8EQN6il2iHyu0\\u002fvhdYG7t44T9z6C3Y\\u002fIuCQFL1LVnBn4ZA5X8g6St6kUDIzekYhLqbQKU8i0V7vqRAyJhtJBqgrEB9+wFEwCiyQKFjWvphR7VAAjHpLdNQt0D6QCEHKYG4QGHqMPM\\u002fz7lA2i71sBzFvEBcxCs9IIjBQFT\\u002fuDgj+cZACSBcvGrpzkATzxfIwozUQOh9OHV2h9pAxk8w9k+d4EAboPaWqFvkQPjlSuUYpOhA02mRTkqc7UByHku4PZ\\u002fxQMbNR1cAnfRAq+885NF890DsznfAXOr5QNttFAPTqvtAvSRWssG5\\u002fEBkHYJpWlL9QHwnJRyL4v1AU\\u002fAeTjvu\\u002fkDLUVhrGXYAQZh6ELurEgJBjbp40RdPBEHdSG4gUQkHQQKdUel6BApBrTGbp\\u002fj7DEHWJuPH\\u002fbcPQagLgOxiDBFB+I6d+\\u002fMIEkHdziMEn8wSQRhz2gT3ShNBQPnHUoxuE0H1m5WubyATQUxfs8PdVBJB75ImU1gVEUE7jZoxGwQPQYiamn\\u002f4kwtBeb99RAY9CEF5f5IJ0VAFQb+pVDtqAwNBiIgDHetnAUFTRM5zcHEAQccLWjak7P9AjTqPiY9u\\u002f0AyxmhIIdr+QBMzt7Lztv1AwSPfYYTG+0AFRk\\u002f5mBT5QIJkALf46vVANJw8Ja6u8kBLjllMXXLvQF9+VTyqf+pA3v0DRHuh5kAhyceAGr3jQCZKYrmhneFAjrOUbXYF4EAlAfnzY2ndQBtC\\u002fThw3NpA36DH8wcE2EDG+NbsSrPUQA0i83C+\\u002fNBAn8bAeE1NykDRG7e\\u002f7hfDQMzlmhNM57lAyIJoKVBlsEDrw51ZwF+jQO\\u002fnwh2EaZVAkh2JDB02hkAGbAwnNLp1QHgZEsW1JWRAxK2f42zKUUBlybZdEQA+QKsoR+uqKShAQtK1lz2tEkAddfL4M5f9P9pJNMHdJfI\\u002fCfEgqhRRhkBOKeH1iT6LQCjyqXxXFpVAcVDxvtLFoECOZW5noi2pQLo2Lg0kcLFAdbEtmtw2tkBtUE6hziC6QEgg0nMHsLxAZEkZc+YRvkBbSVrGuki\\u002fQOu3eqas\\u002fcBAcyR3STP4w0ChbLsslTTJQKoi2BbiatBAw8oBsRlB1UD14IAv2tbaQBER0pm5g+BAfRrTAaX+40DLA5\\u002fTyRXoQCX\\u002fjtp6Au1A+N61XTNj8UCV3SUfmYH0QJ6XnazKgPdAV1YYYtH3+UBQ2fRG9Z37QGHQvmTvb\\u002fxAl87yVHW6\\u002fED4EbBwlAb9QIcvAlBV8v1A9MqQx4kCAEFKjVoTucUBQRtT0yHTQARB9lLiVGhIB0FiIUFOn5EKQXpVnNAFxw1BJVKHm9xQEEElruyU0n0RQdfDuNlTaBJB\\u002fvqmNJ8WE0H23b0FnYoTQS2QPyKduRNBfyfBjmOOE0FiSpudsvQSQf3XUJo+5xFBDH6vRKd3EEF7MEonm5gNQdO9TJ0cLApBhXDieyUHB0Gv88ADjG4EQf8KGArLgwJByx8fHWBCAUFtFx4YwYMAQUL3cHzOCQBBwyoDLC4f\\u002f0BKZsG\\u002fWrr9QDqxOK5Zr\\u002ftA0r2vA50M+UAi6VdtRhT2QIIxcCQrGvNArcwhOoli8EBic4a1piHsQGhc114bVOhAFn9blHxE5UCIUvChXtHiQAL1hF192OBAY2jJ16lm3kAhSnZPc2nbQB\\u002f3O6M+YNhAUzAkwfMP1UAOKl8nRXLRQN4\\u002fQrKWa8tAoB5yN5FNxECcOPmAvzm8QMQDzt\\u002fkZrJApa+4bFqJpkC1mEZo6gGaQKK3kfRyaIxA1iozsTSCfUAnJQCrrEBtQKDL0fHbtFtA5mwbf6UGSUBL\\u002f5LNJHo1QAynuXPQjiFAHiBSGLs+DUB88hyc1XkCQOSy1GHSJYlApmaJ\\u002fX+njkAccgU4Cq2XQAOCNkMt0aJAAZGtTwhErEACq+vzAp2zQGNVCqU0D7lA6KjGyHaWvUAxTPtVr0\\u002fAQGlg2havJsFAULEmnBzUwUDLiFamzjLDQFvB3HXOKsZAjgZdaNhWy0CqQJHwN2PRQAVSOYx+AtZA09bqstg420DdhYboTXHgQO8RczajneNABiBNFkNq50DbWPFGuSDsQFBJ55vK5fBAlvNPDo4D9EAuBOy+0QP3QCjiX7vBb\\u002flAZonAy8rz+kBH87yZxor7QPv50jBJivtAWFpAp6aL+0BtX7f5oD78QPojts7oOP5ACHrhRhvoAEF2dhbKJ4IDQcUThXByvQZBitM\\u002fckdICkHpnicfaMANQU7n8m5eZxBBgWfe8BKgEUEFd7xOLocSQQxnQW0vKRNBFqKcBJOTE0Ec8SQtN8cTQWBPHY6KtBNBdoGhXRhEE0HRjQgLDWYSQeolBytFHxFB4g3jY48ZD0EDIFblhLkLQSYgBtf0gghBZCTJ+DzIBUFLIa7CLbcDQdFzDKmxUQJB3\\u002fROPpBxAUFMBjZdMdYAQTSGEc5\\u002fOQBB7kObRezL\\u002fkBiOlVnfov8QChrDchQyflAIK+at3XJ9kCLqkCnWdjzQBPmCzmSLPFAdRACRJi37UDr\\u002fsBomMXpQKxj6c0Ka+ZAAduvbwWV40DUNOD5wDfhQDbLLNRHhd5ATjvIbvgs20ByfR2oDhLYQAQSmuyT6NRAWmXzlnyN0UB6wwYBuCXMQE0dWxBeY8VA8lPlJImwvkCzXMg7R8i0QEI4PrqRoKpAWO9jWrgyoEAi7SXFo8mSQFMqKlCe04RA8B0qLCoQdkDHpOi+hkRmQP9d6mbDS1VAwpsLL\\u002fgwQ0CMPIk2VVgwQGjglJ0SNhxAMDAuIyEyEkDtSH59FqeKQKCtvAMoLpBA8heaI8jUmEARzwAUrqWjQCROI9bDc61AzRYhAExwtEAmHaWrGSy6QJCFAXAmC79AbK5aSQE9wUDjAjwepE7CQCHN+bXpP8NAKxZUuh3txEDPTp0eSDzIQIIlpc68vc1A0Goslze10kDVApyOBlbXQFaXvb9OXtxAFAaovEnO4EAEaK935qTjQGnUzRTWAedAHc1TEq8660BkMsxwBjTwQIrgNWmGGPNA\\u002f6nNVe\\u002fm9UATKBdKvCb4QHxr3hTBgPlAN91RqMPs+UClqe7tZr\\u002f5QA1PyIFLk\\u002flAAKgYGhUb+kCWV4v45vD7QIuOek1\\u002fcf9Alr0sp1RTAkE0nkimOaAFQWYeKhLnTwlBUOEK8uP7DEH0iS\\u002fL5iAQQSuuvowTcBFBr2Js2VJjEkHqqsYWkQcTQQt0jJmqcBNBwwRlnNaoE0E43fZN6qcTQZ+bDazfVxNBwuwzRY+jEkGvsa2ihoYRQXdDL1gcFRBBGYRDEWbyDEEyaP9ja8wJQZOVISMtFAdBGXOk57oBBUG6XQyRjJwDQaqG44SbvQJBA4AdBGcfAkEYsBv+BncBQf83W5yajQBBjm7QADig\\u002fkCgDH+nqJ77QFXtUlHbYvhA1+cUD8k79UCm3NONDF3yQBoo5JB8ru9Ah+ZvKfFH60AKJhaafnDnQEpxPeKnH+RAuIg\\u002fx4dY4UABm6Hc0zDeQAiYfsf+j9pAIGEh3Ch410C4MTqmv4rUQLAXQxhZiNFAhuoOJffMzECEe\\u002fp2eZLGQPnFA4LR0cBAk+xypBHWt0CzhGTGmhmwQGliLvBcyqRA049OBSyzmUDu+pY0dV+OQJ0m26fDFoFAYfdx32UyckBuZl3b6TViQJUt+dL4CFFASTnu3I\\u002ftPUCeHEdvOIoqQKiAwczGZCFALG6Ep\\u002fnmikBoKZcGGzuQQAtweLIVpZhAtH0CFllYo0CJNlsry96sQCdH7BoKA7RATZUyheWvuUApyTxOmKu+QJVs6zAyPMFANAf1gBGkwkDJqSq6UhTEQLxKx1DTZsZAKjNcyyd8ykBdsHl25mzQQFxxKxk+sdRA4zIMRh2s2UBi9rzh2d3eQAWMnCEn+uFAQuz5l5+G5EAEVdinuV3nQGorDG2R2+pA5Z5wDKkr70Ble3ehXwjyQP5IFLJLcPRAdEsEU\\u002fNg9kAOUsudBon3QGXD48nO3vdAZoqyFM2u90BNoH2uP4f3QGvbk660DfhAndUFCs7S+UA6At02oTH9QN8QMmhbHgFBSn21jdlaBEGqnXHWhwgIQckW9k+dxgtBGy+\\u002ffHkwD0Gf1bgAXf0QQcbGWixOBBJBDxrlaxu2EkGGiWinlicTQe7zVHJoaBNBBN3qTzV3E0HhUAt8HEITQXWChxXsshJBKpeGZWm\\u002fEUHX2pS2aXQQQTUdpvmK7A1Bae6ahxzuCkHoc29JjU4IQYZHzeVjTgZB7ArXrZH7BEHSxmToXzAEQQAXfgaxowNBQVL1euYEA0GKZpF1qRgCQY7J4kKvygBBHxLRofVa\\u002fkCsC31BpdT6QPttJSY5V\\u002fdAnxy6m54a9EA5PvJFJTPxQM9BUDW7Pe1AUqMLcd+w6ECStmQza8PkQPNzlYwHg+FAYtDvRTPn3UAXlUn09f3ZQCPjJIGE6tZA8Hwqqcw61ECRq9px55XRQCQg3zqbrs1Ax4qG\\u002ffAayEDDuqRpk8XCQPqAc6yoCLxA1yMkIYEXtEBjMt4pT6OrQL9EkhuMMaJARwjqOu3NlkCyBidNSwaLQOaNMHvSDn5Ap\\u002faTMlcsb0Br6OGObgVeQO2v8wElBktA+KtaDy6BOEBz5RrciUYwQISVUQi\\u002fN4pACPCrmyloj0DkjgGIE4uXQIZF\\u002fzn9TaJAmj7sugEuq0AFUSCQCdWyQEWbKzE7SrhAjohBUjlUvUBlYLWhM8zAQHZ1A\\u002fDArcJAzhyGncjVxEAvjx720hjIQKX9j2WsUc1AoO8MfsB\\u002f0kC9+ldP63rXQBqwT7nBKN1AYjZUJt5z4UBHwgjaohvkQESigeCuguZAt04DqzXi6EB3Uec8xZXrQDhRG+gY3O5A0pC8tIxO8UDxG4UHxy7zQL8f7V1RuPRAGo6012Go9UB1GDV9qvf1QFhzc5Yp6PVAZ5WVc6Hz9UBP\\u002fV7Pz6b2QCKxt4EafPhAs1ahNsjB+0C9wiGEEEYAQcTMF5YOVwNBx0B46\\u002fjbBkH2n9g9qIEKQULAWubW6g1BJxOzgPlkEEEhXZkZKXwRQW99E9hQPhJBQctb40u9EkGwnatZQgoTQfH3nmisKBNBbN02t40LE0EWaNveUp4SQUCpH1e60xFBEZW5LEGyEEFMSTtgUq8OQYSoG9E45AtBYZxWOD1kCUEmufQgFXYHQe0dVVz2LwZBhyb3mX1yBUHljHMOxvYEQS7HPPBlagRBb\\u002fOs6ciNA0EN8+tb1kcCQSzOhVERpwBBrLjmeIek\\u002fUCTbC83c+b5QAADb43+TvZAahMvIC768kDkKe9jneLvQA7hSXjYdupAfdrKjhHK5UC56FbknffhQFjhbSm5EN5AadY5CfrH2UCx89SQgqfWQHNs+5g0KtRAltl8COng0UAC\\u002fJlXBR7PQL3gNygMVspAXkS0ZYuVxUAziBj26RfBQM6Ym8KXF7pATYT1B9IZs0AQ30rvsqOqQDwktjENjqFAoVz6M5OxlUB4isyub\\u002feIQMdhRXHBnnpA466Sx5Q6akAhJCyEMhVYQEhV9fOTQEZAkprByovmPUD\\u002fPMlVwuGIQAAwqp1CoI1At5iwxJP6lUDzD42UnvigQKrv6M6xKKlA2ykhDb+BsUCqKL62RNW2QFgIR1naGbxA928v9xaMwEAduZJdrRDDQB5jhVJCHMZAvhrbLEh5ykAy9KPlZH7QQDqzk4yLE9VAw+FvIlDl2kA9Jw9VwrvgQOkNORoHBuRABTwfqZT55kCI1hERcHHpQJrFw4d6j+tAthK\\u002fY4Cm7UCl8Sue8ADwQONtmBYhVvFA7PnB9Eau8kDf02IRRs3zQKMi79olhPRAHoc9baHS9EDXmT9KbPL0QJ\\u002fuDPinR\\u002fVAMmK4mbtB9kBX9JLInj34QBv3ZTIvdPtA0DyB1F7z\\u002f0DSBm85Rc0CQSIXP4tICwZBPSCi+UVyCUHr16CtO7MMQQvL09HVhQ9B\\u002fpc6hj7fEEHo1qJDeawRQZRJMEeQNxJBWvYqbDuQEkG5uBzRebwSQS+l646zsxJB27BqCEdkEkGm3MAJ+r8RQRlYD1QxyBBBnMdF\\u002f\\u002f0mD0GqDEA\\u002fj5MMQeoRDwcyMQpBSF3z3utICEFv0Cuf3PkGQU\\u002fYtq+CMAZBgUa46r+wBUFlhKGOki4FQWNnGDTBagRBpCwus7RGA0H+gA6zf8cBQW3zSgyICQBBTmzRkOZc\\u002fEAJd0yVraD4QIeuqcaw\\u002fvRAf\\u002flBWdqN8UDWYIZyt87sQIGGaRZ4UedAyI42SN7W4kBKZZpV9eTeQOIrvNLgGNpAjXGwnG7R1kAE9giW1XnUQKLF7amcj9JAebjUfsm90EDlaun61LrNQNTCjsCry8lAhoA88IC5xUAn95XdY57BQM1XnYObRLtAq\\u002f1kAefxs0DYh7NP7VerQC+mfVgzcaFA1W73\\u002f+SYlEAznmi\\u002fNm6GQFSoTcgYgHZAgM9wdOv+ZEBerlBAG7ZTQFBymCi8v0pAPT+RfI3+hkBSNk37r1CLQH+NwY1SNZRAdn22i8c\\u002fn0BIU60cLVOnQLg1b1awc7BAhVBnqq7otUCvLI8NYLy7QIsNvso058BAYutWNJM0xEBbrxlmfTfIQLLLsc1Pp81AkLUu5h2o0kAQ42qHIN3XQIwV+WWPZN5AwseG8Cjj4kBTYBRpo5zmQOPZahoX9+lASq3VV6a17EBBYqc7A+HuQBRtMbhzXfBA\\u002flGIGiVH8UA03hOFJT3yQEP7S01MLvNApyNAQRr280D3xcFRLXr0QNx9hSvIxPRAqvihOwIM9UAe1q6DkKL1QJMbtqz33PZAlr99SMH6+EBIZI4QyRz8QEOZWFf+IgBBy8F+m2OuAkFWUCXDgZMFQfRav7LLoghBCyESQUmeC0EMoLzvhEcOQR4\\u002f+7FHORBBLR0exE8JEUEtiv\\u002fabZsRQXhUZfYT\\u002fBFBTHXBWbIxEkHkvje7ADcSQT3qSPTg\\u002fRFBhIxaYfJ4EUHheri1VKYQQcOOdGLJKw9Bd+B\\u002fEpHPDEGbSzF1eocKQQcRyYFVmQhB+1ppSAwrB0GyO6pdBzgGQSVkDlR8lgVB0iCMrx0KBUFLPkFIg1sEQfyAw2Z+aQNBBzSu+0stAkG\\u002fKMcZh7IAQUDITiVqFf5AQZv0WpyK+kDo7OditN\\u002f2QNyQ2Ex\\u002fNfNAMekyGzJx70BpYJ0H+DfpQPozJtfdFORABFofGGIw4EC+3THGZPbaQOxBDy\\u002frc9dAtpO8jC491UDZbG+3e8DTQIIlTnHxjtJAr6Z8XGFc0UC\\u002f\\u002fsngzOrPQK831zYxdsxASZqC1MRPyEBVzq0QhKnDQJktAEnC0b1AZJCFq1gKtUCiVuRkp3mrQA7ULNv7iKBA8FuQ6alMkkBEC4S5WJ6CQInQxQwWm3FAA423ZffAYEDvyGmrsO1WQC3wbHY7jYRAQdYwYsJ+iEBZvXutVEiSQMWIUtk\\u002fqJxA8+s+kCvMpUDxFXhrU4SvQLUy\\u002fFW0oLVAoIE03SZUvEDicOwG+N\\u002fBQP5VKLffBsZAX5qJPGTxykD3Z1lXkJvQQKTwH+z6ydRAxTb01hBN2kATQlqBKpTgQA8K9AEVfeRA7HJVBnuE6ECHhd5Ui0HsQMk7vBLHau9AHrg7OIL58EA474TT5wHyQDY9hlCb6\\u002fJAyuMqdJnF80AlfKAcn4f0QLZnbukrHfVAUneVVxx99UB1yHL1OL31QHNiHMtdFPZAqlCUxzDK9kCAt3spSB\\u002f4QJnsPY79O\\u002fpAdgVUtmkt\\u002fUCwc5QRSnYAQUcDBsxIsgJBPhFaCrA3BUE\\u002fFRPfcuYHQZrglVGpkApBqa1bddYCDUFoX8rFNBMPQYOYZQBQVxBBXx5qCM\\u002fsEEEkIoRaDVIRQX\\u002fFjA0IjBFBK+soxl6XEUF4ZYSskWkRQbyTihXn9xBBuDQvgN4\\u002fEEHMnlhHU5kOQbKSsqvjbQxBYdInU889CkFr0uwhDUYIQewlr76DrgZBFt0PsyeABUHpApqFeaYEQbWanvIK+wNBH3xPZy1VA0EHc5OINZYCQdI2Jl1NrQFBDdVecYiTAEGMJMRx8ov+QNVFjF\\u002fdh\\u002ftARNHEVb8l+EB8i2SasIf0QFQcWjLE6PBAqU6Fbegg60BzoCWvgHrlQAOi0nWYJOFAMTS3ofhE3ED7KrsmU4XYQP2lbYeCdNZAZAd7ljp51UCnsPlmNAvVQK1jsvmZstRAaQfdXAgG1EAiPLdDybXSQPjs7kcHodBAMUnKNKHHy0C5Sq29pJ3FQNT5YAmrJL9ApyzIXOautECDnyz3yUGpQBiYu6\\u002fzTpxASGDzoRUjjUAC2UCEz9x7QNIYS3tW02pA2+XDiMR9YkBWBXytzaCBQH7sMlsXOIVAVzTTafAvkEAQ1fadqQ2aQM8HkBF5a6RAeFMXT3+ErkBmEl1jaK61QBHy9nM0X71AzUvIqJMUw0Anuk4DCv\\u002fHQAtiTEpwls1A\\u002f3pkUT4j0kD\\u002fvolstljWQDdLfO4DudtAqwMRaO0v4UAcCa0+rQnlQOby83LOIulAy1dJ7UIi7UAUk1maUWDwQE7kf9Db8PFAxu9+3x5J80BbehEW4XX0QNECKDpcevVAsojMIolL9kCNPcYhUNv2QAEQ+3TNK\\u002fdA7wy+5GZc90CTPbLNyab3QHxrfNMlTfhAP6NLXJiE+UBCvuPMXmj7QB1ewfN1+\\u002f1Al9APzdWZAEEYqUTzL4ECQWmdKHr+qARBIKbWHzD9BkGntVzWgFwJQXcDi7mXnAtBZUJVTuqVDUFcz+tH3C8PQVZHDAAqMhBBB+36eiWcEEG9TQ+8pNcQQRrBvfeO4RBB2AM9AqmyEEEttsZ9akQQQctDpg2LLQ9BTPCoG0tnDUHYSMjz+1wLQajXQ6xoQAlBjNT05z9CB0FL1XZTzIYFQZqXfHNCHwRB1tAOPHAJA0G48qik8zQCQWctTRrUigFBz2t4jIjzAEHJgKRv9FgAQZV8Cg6USv9AOS5VUGKF\\u002fUAmHi72hj77QFoZe9fxafhAlT6gJjoh9UA6id+F+6PxQNdGVAi1jexAlPqPhMCx5kBESo1M5BniQJpNJBBpxt1AgUaTkWHe2UAH8T0kPP3XQMMqfyc+jtdARKDDdtv110CP9T05nZDYQLpLJJokuthAyRy1yDbq10Bq9mcNet3VQOCGEeozr9JAsctnNiiezUAptflw66nFQGL85eXlJr1AwiTIa0ABskDhb+00g2SkQA4JE5OfMpVAC1bKH1x4hEDlhXGM8elzQF4+jA82omtAGwCtdVzxfEDaVijxjrWBQINxeF4T9ItAutD3\\u002fY9Wl0CELFtravqiQO6D4GO3Zq1AjOeR2FSYtUAvrJpzRhi+QMxkaCUf9sNAX5GTmSdhyUAP6cio7EPPQMAS\\u002fr4d7NJAVlNcfizQ1kBjN5Da+53bQJZWVicnxuBApDKPjCpK5EDmqElcnDToQClidsBHSuxAQtQUh6Qp8EDj0vviDxbyQABhnmiw4fNA4AGm8rWD9UCPCT76D+v2QNVvhqlfAPhAHBr6FS+z+EDa0yMVCAr5QEQ7HFHJKflAiW1H5DBO+UCv\\u002fecIWLf5QDyn6NanlvpAHuLfGIIF\\u002fEDfv9BsGgn+QDRb17H\\u002fTgBB1kr5zzPgAUEg0TtuhLQDQZPevd8owAVBqKRlz97pB0ExSqGVrAwKQbeQRAJJAQxBIKNsPxSpDUHqthBJ9PIOQbWI13OC1w9BXdh3nowoEEEpmaU2iysQQX75fBMj4A9BKbQa8VfpDkFP63WVy3wNQTMqDT9KswtBrOj0bpCwCUGIlN0DVJwHQYN1NQ49mwVBmdzD3gHKA0Fa7ZhWSjsCQYmxzfPH9wBB6THjRN7+\\u002f0Dsqyufc5X+QG8\\u002fOnwzlv1Aq75LVgTV\\u002fECEsP1bwBb8QF5l2dUSF\\u002ftApVayIbiX+UDlm07x+3X3QA68kSESvPRAepZ0HuGi8UDmkB69TgHtQGppfZ7uWOdAF8xRXQ3K4kAvlZNMoBffQAaUdiwuMNtAK1U5HkyF2UAS51CXnZXZQBMmr71sv9pA9VvDeQA+3EAod7k3dTjdQOLcowSq79xAXuZZI8H62kAJ\\u002fMQqbm7XQKa8zp6P09JA2G5ttFLey0AQZiQGJfPCQNw5NRDIordAkXre8Q4Eq0B8JvwlsFScQGTM3jJOmotAK6Q+KOobe0AjctFX9etyQMtVaDKq33ZAEusMhzChfEARdppdc5iHQNDn0uJgjZRAy71IwrtcoUAVZtNkcc+rQFSzZ2h4\\u002f7RAiCgs04vfvUDbiDYwthDEQI91loolmclA6k4B5dVSz0ACpEPki6TSQG2z129K79VAb+B6eJva2UDIu990W7feQMOe4001WuJA0ssXCFnn5UC7iGBtB+3pQKRqE\\u002f5CS+5AghjPJeZu8UDTq4KnkLvzQBg0yoiA6\\u002fVAvbcWu0DV90DDe8QehU\\u002f5QHR2+g9PQfpAYjHqwJmv+kBOsGkA07\\u002f6QJpKCYQ7rfpA0yDINhq3+kBw9XySGRH7QFLJmePQ3PtAMCT\\u002fO30t\\u002fUCHJLoHiBD\\u002fQIM0tGYTyQBBc0RYfwJcAkFkiTZwDjoEQXkGLfn5TAZBY1lYx2FwCEFVNEgX53kKQb6f6kZXQwxBibELhz2wDUERDdBFpawOQd4mj2boKA9B3Sk99XAYD0HHKf09tHUOQZRYEvE0SA1B36VPbSunC0EtAr7zCLYJQRwfdlePnAdBa2G1PN1+BUEIb8XHE3kDQQLsJJZYnwFBcf\\u002fydUMAAEEUp7KT1E39QG3Z35+bNftAwKgEsVy7+UB\\u002fH4od79D4QHpy32WHUfhAptKwMTEB+EDKA03pM5T3QCyLH8cMwPZA\\u002fD6qTHBS9UDiX5cSsETzQPqdTZiRwPBAXtcWxHQk7EC5WQFnchvnQHvLWq6V6+JAGeZSsei830DyxFfxiwrcQK\\u002fHxuKLlNpAJOpG3g362kCkSSrvFaXcQLs2uh9oxN5APQgeBz4v4EByc4szp0XgQPVDdxy4wN5AoWfBr74G20DFqM3FT\\u002fLVQA7SZP\\u002fxZtBALP2hXWCCxkBZB\\u002fa+h1G8QILEbBegUbBARyBRn7tAoUAuIf\\u002fUHvOQQMAsDxW6y4BA9GU+j\\u002feTd0CY9hW0JKxxQAnQmCMruXZAltbT14Klg0Bc+WZd49mRQFXvkshaNp9AKQcJ\\u002fZ+uqUAwCKwE\\u002f8ezQPK2SyvPgLxAR33GumY\\u002fw0C+pGoPfX7IQEMcsEv4pc1AgH2c\\u002fyFP0UCniMaDtdjTQF1yXXZqwdZAGrHlXxhw2kDvlEeGQ0PfQFdRW6ZDvuJAL6mB3GKc5kA5CXIg5zXrQMYgjloINfBAiS5txSv68kApFUDk6LT1QM8MxtkCJPhAfl80OxUL+kDQpMaAvUX7QIjkKkpw0\\u002ftAgqFQgx\\u002fW+0B+jbiU2IX7QAEyIn8eIftASIotvIXh+kC4yFesuvf6QDzZbz9TjftABfdX8rHH\\u002fEABPZjEg8b+QCsMIFN2zQBBBRL+4iKdAkEtjmO1eboEQaEF\\u002fRza\\u002fAZBna4hHQMzCUERMoi2SC0LQT6ROwoAxAxBv+8QYC3YDUEDVYwt51EOQckQrfaHIQ5BJhratAxFDUFMkC3Kpc0LQZnAEF3R3wlBFZpvmtirB0Ea1V0F+WEFQSuNoetWKANBs5m84zsXAUE7qYLfs3f+QOaSOlwsPPtA+AmIcPqJ+EByMl6+1272QLzcu\\u002fqL8vRAv31nkJMO9ECTBmcJUKfzQH00nZ4DivNAlSeNjC9y80CBYW8qqRfzQAnChc72QvJAwcZA0Ufg8EAsCSIDUg7uQOnP8UlE4+lAAt5VL57N5UDbgEaovUjiQGRD4sziRt9ASoA3lsj920D6PTuf87PaQD4xwHkuMNtASkAToOn53EDHee\\u002fEx03fQF8GgXqwl+BA14DdhmfT4EAkYdTsgArgQNtb9Rp4cdxAF7TurMJL10C3dJDIaI7RQLrGyAULSshAmzIzAeXMvkC97TekHOOxQIaJSwaNDqNAVLVbMejekkCBNo1KGdyCQHafpkzxnXpA0S2LwapQa0B8MyL1RRByQAxbCauOW4BALpGbTkzXjkDfwLn0S7ObQBAI3fBbO6dA5COj7uocskA3qg9Xrj+6QBN5ty3etsFAlV\\u002fqDo5hxkBD2W1L3LTKQBQle\\u002fL5hM5A0JU0pJ8G0UCktPA8YfnSQKLTuJVXkdVAcP5EyHBQ2UCZRc\\u002fqc63eQJ4fqp8pAONA6hNfJO+050Bs4oWS2lntQARoKwVVzfFA3njeZ+r19EAUFLbPPtL3QGwGGi6IFfpAXur6IFqQ+0C5TYl\\u002fNTv8QIu6aZN7MvxAfGMolmep+0D9en3wTd36QNpyUYSyDvpATKAAhoB\\u002f+UDhAlnWMnL5QB\\u002feEc0NJvpAZt0M6X7O+0A9ceLtH4b+QMMNjG+XIAFBaqGxZTtjA0Fdw9DwgdoFQXkNdgdDSwhBxXQWjPR6CkGb6IKBjzcMQe3cfUrsWQ1BJar2AJPFDUGzgs2IyGoNQVy+iKsfTAxBRCOAf3qDCkGhr2+ipEAIQRnD8mpvvwVBInCRrjQ5A0EDg6sq09gAQf9KXIm+bP1AHyR7O5K1+UCKf6g\\u002f0472QCGoPY\\u002fD+PNAy9+jdsD18UAM9vlaDYfwQFO\\u002fNecNTO9AF1tvQMJ77kC\\u002fyYD7JFHuQAAA3YnQYe5A5agle8U27kAxdhDGXWrtQBYHPmxUyOtA4VQEebJf6UD4nN6vHHvmQMEwS0+wguNAE77+toLY4EBMrVEJgYTdQHK1xLUOzNpAdiEXP2Ki2UDiZ8jnLvXZQPXNnXKPddtA26WfoU+G3UCgZyX1gUffQAd\\u002fmjXizt9AyT3yQ2h63kCjWrT+YjHbQF1fpgObbdZA9yBfI6YG0UBNzRhd9rrHQFC8gX+hUL5AsJidDDu8sUCavcK75QijQLb1VaJN\\u002fpJANQJJlcsjg0DeoZsUyiZ7QLP5owgLqWVAkGg1ryBkbUAP7ZYxG6x7QKYVvI9c1YpA4QiBb8eOmED8lZ4sutakQLDkLsBvV7BASyvVCtywt0DrR6cvctG\\u002fQDWoe3QX48NAa4OXxnBUx0BhhqSCnQjKQBZQEl10NMxABMK4chB+zkD7tBWZ9fHQQH2pv3iZydNAf\\u002fr1z25d2EA+y+e2xivfQF8F6MeoOuRANZdoaPkH6kDouRnjL1HwQNSzJvjUtfNA\\u002fTrHaafR9kD5EN89Ek35QHe4IBSf8PpAhr9v48qu+0C+5EAxYZ77QM0hQS967fpA\\u002fXXarXXX+UCQGN75+aH4QOFQonQYnfdAnYuxeDMg90BESSdta4D3QC+J9DmlAflAc0xlYZvF+0CrOGJKqr3\\u002fQHPGwBqYUgJBOSeUgboEBUHMn41TWa8HQbeLuzYaDQpBynk01ynjC0GXuH8RWQUNQVopFdmaVg1BBYMF5o3KDEFal\\u002fiT5GkLQUrUh1I4VglB5NT6qKfHBkGtxrBGAQIEQSEs9A1BRQFBkklhPCOB\\u002fUD7wpVdnRr5QP3RODb1ZPVAY+fR3VhZ8kBgiBm+itXvQDizwZTwGuxAcJSgJmxu6UD\\u002fu4nzqLfnQDtPlXpu0uZA4ElDl8GI5kDpK9+WvJHmQOxhpnbGmOZAoDJUX\\u002fZO5kBMMd68rYHlQCNvwrfIKuRAHeZCK8pv4kBse2ZPpo\\u002fgQCvV1cAXld1AydCF8Nmj2kAVSOC8gozYQPfCXKL3dtdALNecLsZw10A7gUwEjljYQExQqr5sxtlAN\\u002fD3vnAO20DFSd\\u002fZD23bQFF8p4xdT9pAuKdWe+WQ10CpT6JRLYvTQOYqdX113M1ADyFWL3zzxEBXpNQQlvO6QCKqjPz8wa9AjGxQtv0poUAvo3W7G0GRQAb32Tooh4FAvGzAjk\\u002f9eEATvVzKgP9hQIgGXeYP62hAtYiO5coleEDw8Mg5dOCHQCl6IUq7HZZARy54APjjokCiUrGlhq+tQInl+WaGd7VAdhGmxfGhvEDTE4rmb67BQGLVihxCXsRA+S4dOn8pxkBf7QiCEULHQAcEnyy9TMhA4zFACxNMykBFR4VMHXvOQK8qw6glEdNA3NkahREw2UC5CPWa+fXgQMSlNdYpk+ZA8ZYE4W0V7UAdEuqvoO3xQJiOXqnyDvVAWVsKlKiT90A27Yp3DEH5QB7WYHkmBvpAQQKsv3T1+UClqbYhPzj5QJ\\u002fhDaKEBvhAXXmjD7ym9kBK5bDJoG\\u002f1QAexs5wcxPRAZPYEm0IG9UCJSqEBPYT2QCAjezGOZPlAFMNqk++W\\u002fUAJXybne2cBQTx99\\u002f1qRgRBazlFgQYaB0GKo928aZQJQeh3jVI\\u002fdAtBznhYWsCKDEFebJNZu7wMQZJ9gPENAwxBDXt35hltCkFgkpLj1CIIQXqG8F98YQVBLfH3lKtwAkFGGEP1SSb\\u002fQClth8EK9PlAmwiLFjN\\u002f9UA5APoeq9TxQMWqSEke0e1A\\u002fJrFOIlJ6UA9oEFXquPlQGX3j+IEdONAlvji2o\\u002fS4UAF+7TextjgQGL3TKm1XuBAl8cjW5434EA66l6TUzHgQGkvopD8GeBAn212xWyX30DETdSM227eQLLkRQfLzdxAKb71Psvn2kDxOJZ8TvbYQPL7WB78KNdAQwJH3qGn1UBlBxYHS5rUQEX5PkbVJNRA1b45LWpP1EDSP25\\u002fbOrUQMke8cmMh9VA5TgJEI+X1UCoANhlL6PUQDpoCSKegNJAyPuFLHDNzkBl2s6i66fHQNUfAIDossBAb24TCjWftUBJE6fsVqapQKe7mXbT6ptAc7RPkaJEjED4nK5ZjfF8QHiTatGuu3RAh0CUqPeeX0C7mx8DJjRmQJTpsIy\\u002f8nVASNcGBY\\u002f9hUDQGEB8EYmUQBEtziQ4nqFAuvnyX8q3q0BLz\\u002f0NRgC0QDEqeFmbhbpApy0pglg1wEAMqfDBf17CQKiJud7eg8NARymN\\u002fGLTw0A1N4793ezDQNqPOOjRzMRAMyImX5Cnx0BmUwsRvb\\u002fNQND77QsdGdRABnLaWa3U20BtacJCAv\\u002fiQN1vqHXC+OhAXmyYPcRA70Ai4Esok43yQJI9P+g87fRAIXeuiOaG9kAZcxM2VUn3QKmbD8NMRPdA\\u002f4n5H2Sc9kB5CaJ7\\u002f4P1QLS18XKPPPRA4ly+6MUZ80CxYHohF37yQCUFc6QxzfJAQ4zh5MdX9EBvNwcMykb3QH5C5SCTi\\u002ftAozkvquZsAEFKc5vPlFcDQYeMkefHNAZBirGR9cuyCEGT5yNg+owKQTIjsIJ2kwtBbl4yEROtC0GqUwk0KtcKQc2WVGFNJglBiQg8AHnGBkELU1fzzfYDQdfLSH7f\\u002fgBBxe4LeQJB\\u002fEAHD4EHExj3QF72bqY0t\\u002fJAfwTdzepW7kC5UwOBoc\\u002foQJTyQNVhpORAJhRhL\\u002fOX4UCEpsCZ8tneQFCjsGr01ttAHsDyAuDK2UC5FD0uv3HYQPdUKCb5mtdAcBu3yaIe10BCVOkystXWQMi25uccm9ZAmiU1ipZS1kBoOq4W\\u002fuvVQHesaXImX9VAkyhvO6ml1EAIslo2R7zTQFecjHN\\u002frdJA5SrnmcCa0UBQT1HvA7bQQKVfEGJZKNBA9+hEfd7lz0D8Bcr7hcHPQE8wT2z0Lc9AzoE5oe97zUBieKKlaVjKQOrKGI2e9MVAoc\\u002fQJfbswEA6fTsMYQW4QLPF6ewcSq9ANeknC5StokBvbNT0AHeUQANDS9Zx3oRA5Mu8UoGIdUAb7NycQ\\u002f9uQEZ6IHScGl1A4gq50vOjZEAsw+sfrrB0QAMgzNyh74RA52\\u002fwb4avk0D47STzqPegQC4VqyxEw6pAsbp5UmhRs0AODcbBA465QN2gck4fDL9APHWizQNmwUAwedxEHyXCQNkgAded6cFAt3kAH2ZKwUD1XG16WzfBQG3GfxHM0cJA6hUtWhdDx0DosNeEx47PQIymBEpDKdZAZa9dKyW63kCxWEixJnjkQPJ3WQYQ5OlAv70AuGz97kAjhWeDVpbxQKfef32wB\\u002fNAfEFLKT3E80CjUBseOtrzQDMf4ZodafNAAu\\u002ftKQCb8kBM8r8ebKbxQAIBkOTN0vBAazlGhNt28EBXvd4tCe3wQKrYbtTXgPJA\\u002fhwjmfFa9UDzYkKEf3H5QP58p78bgf5A+HJGJRkJAkHu85QvCMYEQfN11xuGKAdBZD2UOPDsCEH1I\\u002fDioOMJQZhlVkAV9AlBFtSdgS8dCUFFOFUEJ3UHQXfsjTZuKAVBNBNYOpR0AkET1DporDv\\u002fQJSUi2FTxPlAuTm3Cerd9EA9RyG7Y7rwQJuoFbBez+pAOQj1pIy15UA5yruyMfThQAVmZkx0kd5AkgDcICHU2kBU8ZGiBiTYQLVQttGpENZA65JrSbNW1EA1\\u002fn062eHSQA6AffKEvdFAVUPnDxf90EDiE2G0\\u002fKfQQN3giYMBsNBAr48+asHw0EDYbR6BHzXRQGZvWk59QtFAVyjGeT7q0EBC+bcAGR3QQPTh3vi+6s1AdS1ZxmJcy0B\\u002fs4iI2w\\u002fJQGi7cSEOT8dAkKAf4IoHxkCtXwITFtjEQEl3qqokScNACuEF\\u002fmoOwUDyMrax7Vi8QJ9bYTTR4rVAj3Ds2ukur0DKLGZljWqkQGLQjrwQhZhAVKlDLHgJi0AbZiWIycJ7QCl2DhGv3GxAnTSeXdreZEBwc+SC81dbQLeRj2ifiWNAsfzUqKXMc0B+s1dmzjSEQIvHAGJMIZNA+zSHp\\u002fKUoEC+ym4inkWqQEZISPP9BbNAvdliJVkzuUCuVr22rpW+QP9GA3RwDsFAV406skaYwUCvBkHzHAPBQPW3jwYdsr9ARpT5ljXyvUA\\u002f9SgaQte+QDuvlSbhN8JAFb6VAoY8yED8Cq0wvP\\u002fQQIJbwiJiuNdAx3j7suHb30BK6aRcs03kQFXXGO2weOhABFdWsPz160BL2v1y4njuQJYcbJUn7u9A6MSXDlQ48EDMeZ9OyRnwQAxsy6erde9ABZrIJUeF7kDvMrHKicvtQMtrxXJwzu1AQtwEEmsg70CBPKSskiDxQIbcUAA4vfNA9luNzu1h90CXcGDIV9r7QNCLHhx\\u002fYgBB9\\u002fk6pL3QAkHy\\u002fb6EafMEQZqiwNVjjQZBqJw6lXhxB0H5z\\u002fg8tIYHQbteb+8MygZBXL4T1OlOBUFFSEVbxj0DQS0rro7CzgBBCFdfoUN\\u002f\\u002fEAzuqKbPpD3QCxrV61fIvNAFG5H\\u002flPI7kBj\\u002fyuKdsroQKeKy\\u002fT\\u002fPeRA\\u002fQ04c7\\u002f34EDwbkR7eW3dQA7ju8auVtpAenR4tNAG2EAFH9rKfffVQIX9wFoy4NNAxzPYgVbB0UBe3mkAiqXPQClUmBeivMxApcc74sYwy0DhpQACzgzLQCKqLxXX8stAKmwcULc2zUB6OCCgtBLOQCnP0\\u002f07581AoAbqTX9wzEAtNGK8F9vJQJa3NPg9q8ZAoo3q+e9\\u002fw0A0pGQwdM\\u002fAQMfMWaIJfb1APJ0z+ipHukBwVVglDV23QLv5Lt58NbRAEJQFOKegsEAssOROtJypQMKmQA5ER6JA1bzGXBEImEAJoFCmogONQKcxl66cF4BA3FS5NQiicEA9i1ATvWthQGITqmM0T1lAzlreh8NXWUAIs6I0gDhiQPgSzCJgo3JAbNJC3e8rg0COVPqq30aSQLmH1L6g4p9AJsL+uHFnqUAQiTPktXyyQKWNeM2llrhAPT0eBpfqvUBUjkRiD6\\u002fAQL1LOB\\u002fAIsFAF+HHcsVdwEAO5lYwd7O9QJ3BkrR7wbpAIETE8kC+uUBdkyp6s2+8QAxhwgZ+IsJAeLptg\\u002fYJyUBHo9oRRXLRQHfe1QcciddAh1VeW7sw3kAwJwJknFTiQLhLRodtIuVAKUxEk3BO50C8guaP2NHoQMXf3AvDxulA\\u002fZNmWpxT6kDyeoNC+Z7qQK3TTgAc1OpA4gjH9bow60D75gwcWQzsQPhBZNqozu1AWWRcoGxr8EDy2fltPK\\u002fyQMzqxmVrsfVAGWWeLYBN+UA+RgLzHD79QKEPmkkQkwBBrdF0KWNPAkHzNQPDkKMDQYjXvcGiaARBgr1BD+OFBEG84c8nRPQDQTXE+zuLwAJBgUCe8tgKAUHJh6T+kwP+QNfWrLLLsvlA63KUDuWB9UAQTR8dg7fxQMd5ootW\\u002fOxAKiXbiybQ50AvY1rND+rjQC6hSc60K+FAdQ5VlUi13kDmN5u8jEncQFn2qjxPXdpAqQSdzU5W2EAV7sA7peXVQC8+8cxtHNNAD8KMLyxZ0ECaqVcgpSzMQJLAfuBeY8lAjHNzL+OJyEDW734vUTjJQEfPq5ROk8pAPHFMQ3yay0BsePVK93\\u002fLQE17lS1L6clAjIEAxtYBx0AYM6MlSlnDQIstOKk4QL9A7Z0\\u002f4lrFuEBf5dVq78GzQDgKEVpNHbBAmho5pGjIqkANJ5Cn3jGmQL\\u002fWcBAD2aFAMoDyS904m0B9Mc+YT2KTQGCo4vLHiIlAoXRLjs70fkDePmkF80JxQLanmZwn9GFA1KVwzWvwUkBnUywoHqNLQMsPY+ywc1ZACVBJYC07YEBSpYaNTr5wQNIlH7asWYFAdb6fqkGnkECml3eF2j2dQFtoHcgqcadAhatPPpQosUAxVFRb\\u002f\\u002fC2QH0+70TOCLxAB0mmBk1bv0AmRvwlDhrAQL4oolIVlb5AjWigdQZKu0Cu7Pg357O3QFz5QoIybbVAzPOAZwz0tUC6wIJNIm+6QDWMmp4owcFAZhfzRAaRyEClcbkwBJzQQLIarQfdctVAItpLF9pN2kCN\\u002f+O77cDeQAwP1owqTOFAWwST4JTw4kA1KkUH5mfkQMJUPcsizOVAU9SK4fUr50Cw0LEPapDoQFQz8D5VDepAStFUbwLN60Cb8\\u002fNUaQvuQAGwhFcRgfBAVKeO031o8kCa4DGmvbf0QLbZpArQVfdA7Am2rO4Y+kB5\\u002fycVA8z8QDXOb+rQM\\u002f9AIhM\\u002fufWJAEGO5uA0RBoBQYGA0PcbNQFBKANbvqLPAEGCuzP\\u002fDdz\\u002fQKHn5\\u002fKYSf1AFLFjpOwq+kBoLkRFYM72QI+83esvffNAu6hPVWlw8EBmcPR+HJzrQMaxd240V+dAPEQeJBAh5EDwULP+QOvhQBQPemQ0heBAvXa7qcw730D64T6UPajdQGNyG1tKp9tABgbwrP\\u002fl2ECHyHg+pIPVQLRYiXA1\\u002ftFAyrwBXKfuzUCilbsak9zJQBKgYnMrHMhAIYyg819CyECTG0TFtVXJQCbxz79EMMpAdZGFYljoyUAWD7vNVxjIQA8rmNae7cRAszK3DOj+wED1nzPmZwa6QLRvH3GNGbNAcY28Vk+Oq0DzaIVwphikQF6SJi6ZKZ5ArnTUS\\u002fQsl0AU8W1CCcqRQGsf0\\u002foDfIpAi+OkTSGrgkCjjwRad4l4QK6fZs9qzm1ARyuB3dqwYEBeC3Q+enVRQFCdtaDxikJAOnXv9ZIsO0CbwTr3NpJSQHG6utDJ\\u002fFpASsJHmpcObEA4jYGo+kZ9QNcCL68hSYxAOq0aL+D9mEBqBUrz2iikQI5nS+pzsK1Aixb0zfr1s0Bowz5Pt4S4QA\\u002f1g9kmirtApkcxwrZZvEDUtoXJ9eK6QI0tQMOMxrdAimBzxR0ptED+L+lfYGCxQMNTuVIxpLBAXz+hsoPbskAclUvNiYC4QIiTC\\u002fBuxsBAa99jGKK3xkB3DrG3BZLNQLqQztiYYdJAL4gEW0fy1UDuOkQvTXXZQADJomCCEd1A\\u002fvrE77F+4EB8a7B\\u002fM6ziQExDXIqWC+VAUZvb9TKJ50AZfACILhbqQA4AtUm\\u002ftuxAg4cuVWN\\u002f70Cf9EQ6ZEHxQLBi\\u002fzp73\\u002fJANSwh+jOM9EA85qEksDL2QG\\u002fudg05v\\u002fdA6HefJycj+UBsSnox41H6QLYI\\u002f2FpOvtAuPtwc4LD+0CwcNzEEM\\u002f7QGC7c94QRPtAWLBy9E0a+kBnEA4CImH4QCkX\\u002fSE8PfZAMcs9m+He80D+v3bJhXbxQB\\u002ftl6AJW+5AGFRuTQpJ6kAkL4f4jejmQIfnr12yWORA4K0GjkGc4kDE0KM2qY7hQNvGenJ24+BArXeNk1844EABI7k4Y23eQPAhyr\\u002fVattAvtnB6wuU10DdwS4Sa3zTQJOx7L1lvc9AXTra0IieykDq2hgIKQnIQNidyscAlMdARo3qjFc5yEBtSajO\\u002fMPIQLmVFMBKQMhArPRUvfFFxkCYMPHvEAPDQEr\\u002fy6cyG75Am5\\u002f9nmswtkB42vfcwr2uQPjxa+UMYKRABibPCIeOmkDwAuKqJI+RQBVgf24ZC4hAt5RtIXL1gEAWmOOxF\\u002f13QD7s5OP\\u002feHBAQbxwrtFqZUCGmz9FCvRZQBVJhzbpH01A19NRjGWaPkBDdvUkJVowQJyS9t1GDyhA\\u002f0sABjQ\\u002fTEDxJs7QXJ9UQLL6CgM4mWVAASZ29CWwdkBLT23Q3w6GQBUTjYYpnJNAYgg9cHbUn0CMlva7GZSnQMveQASX469AIZa\\u002f91Cxs0ALrv73Wjq2QKc26+wf+LZAnyLy2yDRtUB7dm5wqTuzQKdxPdx+G7BAeubX3\\u002f\\u002f7qkACwZZNm66oQHsl1jl5rqpAz1ixrkrWsEBpr+M6+s+2QNbhWMc16b5ATLAitEBIxEBF4HKSeaHJQMn3afPPcc9ASoXYOyj80kB2MVbJyNfWQP9sH2iVhttAVIlXj3SN4EAq9KNyyLbjQBmV1zjpF+dAWmeJw9iJ6kBiseXCyPTtQDwvS7xppvBAjuimSOc\\u002f8kA9dNCCDbPzQJH3qtDN4\\u002fRAqYFcJJG69UB5L4zKFzL2QFaH2FWdW\\u002fZAqf9Ni29V9kDrL8f79zn2QNN9\\u002fCcMEvZAR+u\\u002f7FrS9UBBo9Z6LGT1QE3JlggAs\\u002fRASCj7iNm280BWP97PpnbyQAmyqgECBPFAxNgb3tHq7kCMoQ4ZbsbrQEA6vgABz+hA9FLOV3w65kDTQHjYiTbkQG7kPbGG1uJAMLomWwcD4kCEvoBQwnfhQN5cpjX\\u002f1uBA9oOxobaa30B8f4VeAGvcQALzWJ+EUthAsOcuYZrt00CGem\\u002fGYP3PQOjcWU12P8pAw9msTa8Vx0C18fZqTBzGQNUKY8GwU8ZA0d2fCkuOxkBr8Df199\\u002fFQBjZGuYz5MNAyzVd6uLFwEDNtuCKVyC6QE+VNokEzbJA\\u002fPd6T2kcqUCwod\\u002fPfG6fQObZQQSWxZJAF0M3Y30DhkC5RUFNFTt6QE8IHVSdRHBAICiUVhTvZEDijuTKeShbQDt8WBrkJlFAnohIxmCJREDmhN83K\\u002fs2QKpSyeHpMihAhyrQNPX8GUDcbJ3Aoi8TQL3NQFp\\u002fkUNAFw3WcoW0TEDVY1ACJ0deQN2p1GxKAXBAQtEN2eBPf0DDxR8BQgGMQNYM40Uw3ZZA3Oosj38JoUC+H5IvDy2nQLFe4rkbyaxAQSYe3SBVsEBygVWvVfWwQGc0ImZxK7BAgGJJbS6SrEDTYnJ6oOinQIrqu67B6qNAdX8agHEFokDgY3eUMTujQDsYw4KmBahAQhJuEN8qsEBsa1QMNNm1QCfENl+awrxAbNPX0EdmwkBNjSIcPSjHQFVoKoCNGs1AU+UazB9t0kBQHZFDsXTXQGOsRTmqst1ApH6QNzR64kD3dbmUnGzmQJKME7T0fOpAkCGvdtGB7kB5WvacbivxQAJLmLcX5fJAxBtsqU1I9EC60F1a0yn1QCEdNLy8a\\u002fVAOKMxU3UQ9UC5Imzj1j\\u002f0QITm2zliOfNAug7G6WA78kDnKlJDKG3xQNURJckJ1\\u002fBAmXYdg95o8EArNBew+QfwQL+19cn6Nu9AI8GfSWgk7kB4xEMsH8bsQCO6y8s8GetACaOQG6Is6UB1VSeVgyTnQDSsA4LvNuVAU1uKLemb40D6bZ5R4HTiQM1fBEjut+FAv3yPLYos4UAFQpV3a3\\u002fgQE7MHJu2zN5AzI7QzWKF20A1\\u002fsyApmHXQKwsdTHl+dJAZvIHvUwNzkCSva7vxy\\u002fIQDfHWZGoxsRAkV4NHRN2w0CxoCvPs1TDQA+Z9OsIUcNAr12Ax8mVwkArp6tHecfAQMbuSIPtErxA8GsksJiltUDewl554LmuQG35G\\u002fQyGaRA3kHpTFZXmED5z0j1\\u002fouLQEUUoiYmpX1AWJvgjDA4b0C\\u002ffmvQTbBgQLDwTlk1pFJAee186djBRUAlKk9cPsc5QPoM24Gm4C1Aq7SCO719IECDQM3\\u002f0U4RQOLlxgrpoAJAzssPMCqT+z\\u002fOYMwLjpA4QIIpWXlJGUJA15xuPSI5U0Bo6qmqYnNkQJlTvlX4H3RAheCDgSYbgkCGu3elKr2NQCtQJTI1SpZAwV4kRHt\\u002fnkBjoyCjkwyjQAFDX7WIvaVAe72Yh3a0pkAMQ5y7lsmlQONb1BDtaKNAr4tfRdx3oEBGCUU9aSScQN1PbUKGfZpAtcQj33ZrnUCUjhG++8KiQKqJm7QIRqlAvoQMjXnrsEDRc0cNdg22QPaCYHVdILxA83e7zvLcwUDqYkejoPzGQDO+TWuDIc5AEsxWRgTu00Dr70YyUiraQAe4ZB8DzOBArV0n\\u002foXs5EBqIoFJ0UDpQP1i1VnElu1AQnVPiJTc8ED2G4Na7K7yQHRJXRwYD\\u002fRAQ7jeHhXG9EDPDcmAPbD0QNKZx9BP0\\u002fNAPazDiPxj8kD+Vr6ZqrXwQBTQOs95Ou5AnsAMMZCt60DKky73IfTpQL6\\u002f4vUh\\u002fuhAoO5sUgiW6EBCwOA\\u002fsnnoQFMGZSHRaOhAAkklBZAr6EAG7iN0M5nnQLXfI+8douZA4YFdNxRY5UCnG6vrXevjQESSNXgamOJAahpJfdKJ4UAjoYbY1MPgQCnzkw+OHOBAjA+fJpqg3kB2T8E3ckXcQF6DVAqM+9hAFAi8dMD81EDYAxWudNLQQBt\\u002fqidwOspAIxQQoPatxEAqUnCenFXBQAwQo3Amvb9ARIQbGuQBv0AWkmz87Kq+QJHvsC21UL1AA8DyB7pVukDHKc\\u002fpSuy1QJRiebN2zrBAiO6hD32sp0AJqEj+kqGeQA5t4vpsO5JAqWn2qRINhEDmhcxp\\u002fIt0QHqlnuMs8WNA82DyZyfaUkD\\u002fndZoIAlCQPVDYzJ1DjJA+2aGLm4EI0Bd6qZM+oAUQH51YGyEzwVALWbZTsuI9j9dKTvigyfoP7n+AO684eE\\u002fyZHEn5TaK0DPKW5qMZ00QCjPnrNuC0ZADMpzBAWZV0C5pxk2A1xnQCZFp0WtI3VACU8yrUh2gUCoxWtsRlSKQFVpNC81HpJAyRMZ\\u002fp7ElkBjWxtH+iWaQPYPCx4wgptApkqX+vuqmkA7IIN7NzCYQG9DzuKeR5VARr+bJgV8k0AmlDpXEkWUQHhY2Lo\\u002fp5hAuyfPOPF4oEAuQ8mpz1imQDIm8uVFe61A\\u002f+fzkQHBskBO\\u002fTPrSlO3QITKmMZaFL1ACxqvOXiXwkDyfnHZAovIQE\\u002fI7T8pgdBArzVeVwwf1kBwOqtlHQPdQAIdoXu2cOJAphCm6kas5kCORtouzP\\u002fqQPL3706oK+9Aq5nkmiBr8UAUb9P+x8TyQPpbfSVtZvNApEn4fpIq80BM\\u002fPpRkhjyQL4MyoxmafBAgP2e7h7t7EB2RQ2JcTjpQJ0grb76POZA7TawWJY65EBq9i4Xly\\u002fjQE9BmJOh7+JAHw48mT0540BACJCrgcLjQIKR51a5QORAjN1DVNBy5ECRQuHZqDDkQFtZyH5XeONAsuWEEGNu4kCf9y8L\\u002fkvhQD38wHbvQeBAwJ0w+jm\\u002f3kAlfAnBERndQEVZcN4jMNtAJjznzMGs2ECWOLmOzXTVQAKANgP8vtFApYGwDHb8y0CQ+OAhImbFQI4iqWhndsBAfhg3Q13QukBmavdPduS3QN9LKDqR5bZAsZw\\u002fGDtltkCzlMSFe0e1QGcrG36lC7NA4qRUun+dr0AXWYyc4yioQMNDQDmQ8qBAZQ1ZCiLOlUCOZNolvbyJQOKuTyOx6XtAhLEElOPqa0A5lpVKY\\u002fJZQJlpz\\u002f4Mv0ZAgaWLS49QM0C9JSeTWYMgQOOsYf50hw1A9pt+U0fy+z9hR0yz2mLrP2oHmR4cG9s\\u002fX5+04y6DzD+F2639PQDFP0GQSZTDehxAPKjbe8IrJUAD0rGJOcs2QMHXRZSwjEhAuOhE6NNxWECOynU3GkBmQBQPvoapfHJAY5o4ir8JfED3dFuez2iDQPEOaVyji4hArQWu3XFljEB\\u002fWiXimiyOQOgxdJG5wo1AcYCWkMf0i0AY87P9726KQJveiN0zZItAP4R0wPV\\u002fkECDvMXUdV+WQBrHyArVbJ9AQasDgjeBpUAIRqTz2\\u002firQD+pq\\u002ff6S7FAAFWNZDu1tEAxbpnvJb+4QKAwZG1Ofb5AK1E856irw0Ct\\u002fGwKX0zKQB\\u002fPRBMjv9FA\\u002fDUCU1ec10Dw1X+m2YveQEzCw3sPH+NAn\\u002fvDq3Um50B8l07EKxfrQJE1cGvDkO5ANaXh61CN8EBLB\\u002fTp1h\\u002fxQFSXoU223fBA8LQtZ+mf70AhTNjYFlvsQOvrh7dMlehALs1jlqj65EAS1abf5AviQDTLle8hCuBAF3dggRv73UCBUYIio5DdQPDRf5febN5ARnk\\u002fVk0F4EA0DxiHhejgQB9teelilOFAQ7oYs+HP4UD0Mz7lO4bhQJF1+FLty+BALb6IAvuh30BfPtHhxIzdQA9wXWCuittAhhngZUKN2UB29AjIVVvXQKRp3t+2vtRADOMtiTOt0UD3xinLtq3MQAA9rR4YKsZA088NQnuNwEDf62k5kLW4QGnDOBueZbNAyqklG5+ssEAFiQpMTyKvQOHoG8ZUAa5A7y7mmBlOrEAjcG8xKT+pQNiHC9Db6aRAD+D+t07pn0Dngn4UrleWQAxIIiusq4xA7ot7WlXagEAyIHhZ8ChyQFliba5F9mFARouwshlcUECAEVQJ6587QDZyc2oK6SVAJJZvxnG7EECy8rz3DZD5P33Z8AjdXeQ\\u002fbDF7oUdK0T9Xbm98lSe\\u002fP56aMzmXIa8\\u002f2oK79OyGpj+H0KOoiTsKQH8bVrtUlhNA1e5hSiY7JUCoZnbpjgE3QDcbl+jBCkdAQOZpFYMYVUAXdlD5TqFhQH0JlCnV5WpA7YIyeJO8ckA\\u002fNdej7Nx3QBGzOOvG3XtA6HOZDDYSfkCHfuVriZV+QDfBjJPdp35AJuVkNKJegEB9LKXtShOEQPJ\\u002fAbAyJYxAp+9aeOrelED1TxVpVW2eQNOWEhWG+6RAUnR8oLkLq0Aef8We51qwQEzW6mmb4rJASHKVXph6tUAwpehhuQq5QPiLZJs7275A05lCyXIcxEDEnazcxwfLQPnseAttKtJAsn5a4Abq10CsP2iXA4beQNv8ZnrmzuJA1hFG\\u002fSBS5kAjvB8WlGzpQFGugqaJretA+0fulgav7EBlPKTxdjvsQLGUybmGaepAZl5L26Kb50CM5cph1GDkQBkISktnR+FA4gDtyGdx3UDvRBAXYNjZQLgDJ7B73tdAFyIHv\\u002fxn10CzQYf4DDXYQK+mZUC849lAb2tFAf3v20A8dy9NxsPdQMQJxbtx295AtPsF5Gzu3kCQKlEIIwPeQBTGi6wxX9xAAW89JrZb2kAGnlYLqjfYQLnc+HNdAtZA7ngm2fWm00AflcqhxgzRQIbUPtqWa8xA9DsPPQyTxkBwWZDu3RHBQAPTCeI61bhABw8W8fThsUAMQMdjl8eqQMEbTGoa4qVAy1bcFvqgo0DtYwrTY3miQK1DjyquOqFAkBXHsgqWnkCQpZgzgEmZQIOTFbiCRpNANMh3bPT4ikBoDwIDFkqBQOZlN7kkS3RAgIFtRuPOZUD87TcIundVQM1nP+vZYUNA53WmmvsXMEAaKX\\u002fQdbgYQCiCyYWvxAFA3KrRdRZy6D+Uv29LirLQP0t9NFE8trc\\u002fkeNZzrM5oj+MYpxzaEuQP2EBPtyriIY\\u002fhRLBQYTB9T9V440IG1EAQKIcPV4dzhFAjZ3sq0dpI0BcfpBeL44zQPqyNdQUAkJA7o4RjBBHTkDYkMLyQz1XQIRdneCUTGBAjCtxyYLwZECrGgvec8loQHGGIRc0dWtApRJlCcGFbUDY\\u002f1nXm4RwQAeKmXQQ5XRALJAmF\\u002fyBfkBSxJrdUxGIQHEeTMS57JJAv1K\\u002f4xJAnEDI2I+MDZujQE4dZzSELKlAvZzhb6oJrkAfBuEW1uSwQHfBJMWnarJAjxP8EaxLtEA0PlFk7qO3QLrOLojAr71AawXiuzu7w0COgVmnosPKQGIMuO6u9dFAr5Pq6lps10BcF3MJqWvdQAMjFDhrtOFA1WB8dDdX5EAn7kPyzjvmQH9e62VxEedAL8jU+2q15kCvKWUEg0PlQNEOrh9gD+NAFrh518KI4ED25JbiUjLcQJGC2so8GNhAb7hyaOIU1UCVEo3I00bTQOUE09w4r9JAjDciA6A300A3\\u002fyhQ16fUQBXWlxxanNZAIw72osiP2EAFsvqTBvzZQGV3fy6DhNpAFOpPeX4R2kCVCxaI3MvYQK8bHYf5\\u002fNZAGOzLwjHn1EDzB2b0Eq7SQL0sMMYsWNBAm2ueUS3Gy0B2B7FDj7HGQDk7636isMFAYItrsPw6ukDqH5BnOJuyQBBpzsh5zalASIlDlGFAokC\\u002f10\\u002fkFcybQITpTQmbY5dAHvSch7kalUDiuzQ170GTQKzlkUxS7pBABY0lQ\\u002fvki0AKJ9j7aD2FQM+E6sBdt31AYm26GUwMc0CH0VdvQFpmQOoKAGurAFhABojNI3aWR0AtqwbBMzk1QO5vDKbMgCFAfAv\\u002fXcaFCkAsyjqRMI3yP8LQz55RNNg\\u002foVdZ2fkMvj\\u002fnh0jimmSiPyavBdGteIc\\u002fvr0M7PN7cT8Q07Ak5\\u002f5lP8nr6rq3PeA\\u002fj4yVCUp46D\\u002fTQzsYkuH6P6MgXHv3ew1AIg4AjabgHUAU9mac+KwrQP4NHoFUaDdAdhcFh94VQkDuMlsOIJNJQLs7EnlfoFBAaDm+qZcfVEAUBYD251tXQB02ZWFVpVtAoR4gkjIoYkA2utNkxKFrQP7iAJkOMXdAK34v23q7g0B8yX3zNQ+QQDGX5OmEVJhA+MclvxD7oEAQOebbLc2lQJZE7ZaB2alAy\\u002fPwKZafrECoH9GVWUquQLU6hY4K5q9A8zDq0DejsUCzfSXoWFG1QL4pfZwrBrxAZ7rVJuc6w0Agg\\u002fLCuWXKQIaRK6OpoNFAxqmFFNOT1kAnMCs35YjbQOP\\u002f0Nig299AD5EjdTp04UDCltO3txziQBzhEcF\\u002f1uFAw3cc4yDB4EBeC5xq5kHeQMNYL2lmjtpAAoQL4Xj01kBF+TfRotLTQIyC8AywXdFAyYxDHUZpz0Cu\\u002ffii9d3NQOUCVBbaMM5AF3iyQh0e0EA7dsy\\u002fZL7RQMcSKq5rjNNAb2ejCBwM1UD6sZJREtrVQFnuaaL\\u002fydVAAg31T0Xr1EBG\\u002f68+vXPTQEARPHvln9FAcGvqFew2z0DOlj5DIvnKQF2yltOFnMZA\\u002f\\u002fC8Pls9wkAFVBOavRi8QB4r8s8wkLRAhGeVDfGjrED9ELuHeTijQF2eAxGepZlAg2feKNXkkUB9clLdq1iLQH1oCoIP44ZA11TWocz7g0AheHEUcTOBQOicCg1lF3xAQpPd0lRTdUCUY+0QrtFtQLb+hn3EHmNA20foBuVyVkC7C5UmQR1IQCDOqrj5sTdA8PEYxSJNJUCI45SGwYYRQNn3SJO3a\\u002fo\\u002fejFwdl9H4j+q6XognE\\u002fHP1AYHw9vpKs\\u002fG1YPkIcJjz8+CgjoriNxP7F+\\u002f6di0FQ\\u002ftx7K7IueRj8weDLkqNLFPwmJdA1lg9A\\u002fiRHgCx9D4j\\u002foSBcDFif0P8PHk\\u002fDQigRAzWcaLZskE0CR7yRxk0sgQJfwEm1ZXylAd\\u002fwZ9RAiMkCfqH6g6gY4QMMJbi1kPT5Ahd+2+XMNQ0CQydtpvRxKQCRLWRlnr1RAG\\u002fDwfOdoYkA7025X1+1wQAmTG0e3F35AnuJ2ETP9iEBxgUX2xh6TQKOu4z2C1ZpA1O2tPf5DoUBS2gqR+3CkQGNE0jS4dqZA+PBcd7Zcp0BpQm27H+GnQL4NTHyvcqlAgnxsoqPvrUBDOO4Z6p6zQI+3fg2OabtAS4qIU1RPw0CKS7bNNm7KQBOo16usPtFAjUOgWTtH1UAAQM1dtcHYQPQIqBG+KttAjyplEBox3EAy5tAc1s3bQGcuUD6TQdpAE0Mnxsb310ACRsvWFV\\u002fVQEKMNAN6y9JA5OMnMWVv0ED+ddqSN9PMQLdL9o4lsclAb\\u002fONqZDEx0Cf4esuklLHQPb8Zep4a8hAeI3mzQ\\u002fFykBhFDVuabrNQOHjibxBPNBAtQ3O68Qh0UCCDNlqhljRQEDr59ZB3dBAPnsGQSGkz0Bizhf3AM3MQJu2gK1YhslAgpF0b0sExkDY2TEJE2fCQKj3QLVsmr1AELQ8LXO9tkCueG4m+5SwQCcf7zMi56ZAI99nJCQankBC732AqSiTQBs7lA3AgYhAz2++hieXgED3wdEgi6R4QJnym99ExXNA\\u002fSraUCA+cEBOITraP\\u002fVpQMOgWg\\u002fUiGNAToJzlxI9W0CRp0XdBHZRQG72RE7fg0RATyUv6M0ONkDaFfkUObElQBVsCK10ghNAfpOuyn4MAECrWREtpCjoP3F3Jb1tptA\\u002fCvE\\u002fpZ8MtT\\u002fm89ckEH6YP7KnnKzvbno\\u002fhW0Y8EH3Wj9IBnznOic8PzmwC6whUSo\\u002f0l0fCjtjqj+eAVlPiQ60Pwt9TFx6VMY\\u002fFNTz593K2D86Iw6uemzpP4iKuCdv1\\u002fc\\u002fUAH9tw1xBEBqDpDmtREQQIFBXLIPWBdAVa028k71H0A1LueuRI4lQCdgEoLw9y5AppHSXAWDOUAEL6IGGPhHQHGow1KDv1dAnRgeKN4HZ0DyqIhn0vt0QAE4BmOlpYFASTdVbc02i0Bi887GRzOTQJmmcvz7zJhA0cVyQ+9onUDZdGrUAiGgQL20oqzQpaBAxRcwCXXFoEBNXuNrP5GhQBdjlW\\u002fPg6RArQHvr983q0CEFti+PYKzQKGme8f5Q7xA6njlE8zNw0AKeM47qEfKQApCZMaxYdBAW+0Tyf8o00AMTNNnvxLVQJgm1HAv6NVAx9O5CdCw1UARPOeWXafUQJKJGvM4HNNA0ijuzfdV0UChK4pxWgHPQJsNaQ5LZMtABNE9C8j3x0BxclWsuPfEQOp86cMmvsJAEi8LsJymwUD0QW4BEeDBQLPa4DoZScNAq2aFf+9sxUDKZ4pLy6XHQPLlVBBmVMlAVw2R\\u002fyQRykBiStCeNsDJQGYeNKDSg8hAv6e6nLmaxkArDX2D00DEQA0mGp4EocFAvqVW7ymyvUDDDsBjBgu4QHqDpHXGkrJAIAij1Boyq0C+L1Sgu8iiQBxX8j8keJhA16P9ptBEjkA4Tq+33iOCQC98W1jW53VAlnox+D8LbEAxSBgOOJ1jQP9eFW08X11A3J2ZgyFUVkB3t4KDMWxQQLV51lLosEZA85Fz46cCPUBbprHNgAoxQE23fhQyVyJAlrT33FoPEkCuPcgZQEMAQK0q3szMx+o\\u002fS1YvSPkp1D\\u002fCC9ANcsa7P9L79pCDgqE\\u002fXcEIhS88hD8ReHgl2X9lP0hgYzgnLkU\\u002fTNG4jVdqJD8QCMPDWjwRP+fefo4vtow\\u002fxUDX8ePrlT9mSuzODpKoPyV0CHEAc7s\\u002fZaRy0elSzD8tRX6Syb7aPwlqFKujIuc\\u002fbIRcX7Jx8j9xrFazYYn7PxKY2BB8+gNAhc8gIaM8DkDugcuZ6+YZQO\\u002fs\\u002fl8RkilAWa0SBGUvO0Amd7mHJrZMQJ7zhjiusFxAVG\\u002ffINyIakDLGioecYR2QH0adTlQeoFAoshO8FvMiEBnlihjLheQQEIzZ3RHJpNATwlcxJsLlUDXLDUzLLiVQJ3Zs8x+2pVATPHULO70lkDBPiWFCjqbQL8l\\u002feI8l6JAeaEJqG6Iq0DMMzJnCYS0QKm398y+Xr1AmT1Y8KnIw0AalDE\\u002fU+rIQGoRCrNFVs1AOG3tkBw30EA9XilPFPPQQOXvezMh6tBASLK3Fv9K0EATFcIXWpvOQFe\\u002f5ozUN8xASdVNkY2SyUBW7wuINLnGQB3jXP9zw8NAftdYYkzswEDOOSNjNR+9QHycv6ysGLpAl0H06jM4uUAZtpi68Wu6QHGUNv40GL1AmUX1xZMlwECHPAHFyofBQOUdYLqJW8JAJ9CA5KR9wkD3RQu9hffBQIGHPhI06cBAleELBGjovkCRIp8eLGS7QO5QvfH\\u002fbrdA2vwjZ744s0D1rwU\\u002fVgKuQLK\\u002fHwQFJKZA46q3RRa3nkBOiEJBwviTQIyLzBlWX4hAOWIEnUIkfEALfyfhp2VvQF\\u002fgbUo2mWFAys88YeTXVEBoScf5J91KQGlSw0cNeUJA9JW8sIDAOUCfwhcyUVsxQHb2e6wg+SVA29Juc\\u002fy8GUCDu5C4qbMLQNpMzTevT\\u002fs\\u002fzIN+pvOh6D928\\u002f57W1DUP+nXuLWEob4\\u002fqs+azlIdpT9rTYv7BZ+KP1mCc\\u002fkxtm4\\u002fl9Leycg6UD9zA7fHYI4vP\\u002fi2EwjlZQ0\\u002fap77zfOa9z6ZLPRCcBxsPyVD0fpoj3U\\u002fz73oh39UiD\\u002flr5Dnl1qbP18W7fhXaqw\\u002fyGVTyooNuz8tlU4zFrHHP8bnPbbFU9M\\u002f+vQ9ELdK3j9NhYkmMkboPzk9I2eSpvU\\u002fIQV\\u002fOwhdBkB\\u002ff82GXWQZQD8qTO0qIS1AnuUoLTPkP0AsMWQ1QzdQQB3HrM3zTF5AVNEraqDmaUAUcZawVzp0QFpYPO+f3HxAqBdzx2XTgkAisU3n2IOGQJvfmTOs3IhA34CsZ93NiUDdalzLTTqKQL6+jMZJJ4xAKP310xtTkUAEu2jcYbSYQJ+OwIiC+6JAye9eNXsSrUB2KANYTTm1QINvoxlRA71ATQe6phl6wkAY8oAQ2fbFQG8oEuHQgMhACbolQlrfyUAPP47M\\u002fyHKQFrb93+SiMlA63SL2qZZyEBVbKKG48HGQGH1nsmjzsRArKU9pdyCwkCFDdJexOi\\u002fQLnxDeOkuLpATGxM10AftkCy9q9F8seyQMF7iqSrH7FASZiZ8JErsUC\\u002fzayLhoiyQBtCLg2bkbRAvpgqHdWZtkC1h\\u002fYzch64QLeTQnNF3bhAmhEfDIbLuECazcIjo\\u002fq3QF5QjgzAf7ZAMXyF5LRrtEDG6SszFNOxQO4DGuEsta1A0d1h75J2p0BSJfHrVHOhQN0QkOK5UZhABQnRxHCkj0C\\u002fg2C0hDGDQNmbiVzvwXVAqlnw4PA\\u002fZ0C9grXiOOlXQPs7BrjrlkhAMHCm7DqUOkC3xZGCOCsvQGQ3KbHVgiNAWJVBPxnRGEDcal4yopUOQKy7aX+PuAFA3RNI1+IC8z8ZSgy+k77iP6Vlc+QK7tA\\u002fVY1FF7D5uz8KoYaGwSGlP3nzpb\\u002fQLo0\\u002f1gsac61rcj8qvaiSPENVP7wxQ9vCczY\\u002f3771IBe9FT9HISBQIQD0PgEn3E+3ed8+QN\\u002fZwSHESD\\u002feKexv+RRTP8Cb+VbYrmU\\u002f8nC3B+OKeD+jVZYsY7GJP5YSQvkGu5g\\u002fl79y\\u002fJkVpj+BUoFutMKyPxck5dPVAMA\\u002fWge\\u002fpATNzT+xDanIzwzgP+YEns89XfM\\u002fHs2XXnMNCECHKLDWeM8cQM2Os8yMGTBAJs0TRViPQEB6ZA51qy9PQL9C+JKn1FpAvxdLrSwUZUBoS1gmoD9uQMueZo2x13NA3IrHfCPfd0C3tFGBtYt6QAEe6jaa2XtAvvcWc7XmfECG5YwH5CGAQCSz\\u002fjSP\\u002fYRA8SkyMIGTj0CYT26EPzqZQI8QjCMY1qNA1npP452FrUBtclFFU320QHMXSy6Oc7pAZYlFYdDZv0Dfl3qTjwDCQLGn\\u002fpV8R8NAV5maaq\\u002fFw0Bc3\\u002fopCp\\u002fDQGScQd81+sJA+V8xhb7rwUDndj7mIXbAQAj\\u002f\\u002faW6N71A3qr+0+nouEA+t+jE7Wm0QMVtvK42TLBAJX+P5Dc3qkBDaqVZ0WCmQK0hLufCK6VAucW6iYIcpkAkV0WNLV6oQAh4eQOaDqtA26dDVNp7rUCOtsojfzyvQKpE9WPaELBAXLR4ao4LsEBELFyhKwuvQFn9Z+Sl76xAV415yGrNqUBe8+8AQ9ilQDvlg6eFcKFAMVV1+locmkC2Sm82EUCSQCf\\u002f0\\u002fYNv4dAcoi3uJK0fEDScVd2yR1wQFRi45uP22BA2cIoOveRUEDQmEqyPjo\\u002fQIU75SjNQy1AIewdwdKpHED\\u002fqHLV6mEOQE7Zo9QgOwFAj5vXlFHx8z8Oa9vc9XDmP4yFsZKvytc\\u002fGpJ1T5pfxz\\u002fnfh0tmRy1P8EvYmhSeKE\\u002fGv\\u002fbAcRyij8Akgz5a01yP7EC+74LJ1c\\u002fzXADDAfFOj9zXAOJ9UscP5iZsBX4Y\\u002fs++C\\u002fsV9kc2T540ZsvOKDDPgAYtylWoiM\\u002fKrgu49JlLj97klNYWmVBP8ajqxHX1lM\\u002fVPJ22N\\u002f5ZD+HkkKutId0P7JC5e+K9oI\\u002fMQmhAFxOkT\\u002ffJHrKXeKgP1cKRnsM7bI\\u002f\\u002fSSx1S8jyD\\u002fd8FBZnCvgP60Oh05HJ\\u002fU\\u002f\\u002fkxgeef2CUA\\u002fNgBUMmUdQDdKkUCbfS5AQr8OyGnnPEBuBxyw3ARJQLeoHStmxVNAUOuai7qJXEBWdSovddViQIvO6Bgj0GZAYkr8k9maaUDbhlvCL01rQFUPPXUuQ21AOK0lqFVLcUAuODtLWip4QEhS5AiYS4NAYhKmGKIJkEACVaSJ2OKZQG\\u002fLgPllpKNASxi9d5e5q0BqoyTyJC6yQNwEMCPnPLZA1iYKOuWLuUCQfIOa3NC7QDsVf8ENAL1AmyFypww3vUBeXjGmHpi8QI2SawXHMbtAlAz7aSUDuUCfsDZg0xa2QERgzJOYnbJA3PQEHirorUA5ewci7xynQHYcIqX5q6FA4kqGlrgpnEByQ5fmcNOYQDMX7xa2uZhACTOrIhTUmkBP9asFhQyeQMG083fYwaBAxl7LVDlSokBd7\\u002f6uRIajQGIOqFHBM6RAy3SfdrsupEDfMO3egVKjQGX1AVuzlaFAVaUwQbYznkDQ4kNQqFOYQEh2J\\u002fKHS5JAQl2v1YubiUAbZyA0YKOAQCDJ4A99CnRAQ66Ped5bZkBjz0eG7x1XQE7jWc29N0ZAn0IlU3cENEAP0R8NBzohQMHDlNHGVA1AymymPg7y+T9KHTKHzMboP9Zb\\u002fSvfZNk\\u002fxsjzZhOzyj8LdGlPQWm7PzG7RmkmlKo\\u002f\\u002fM0T2tvolz+aOwGl68iDPx85\\u002fDPJAG4\\u002fw2YFBXXPVD\\u002faNxZzwmM6PzEWpBeBlR4\\u002fQNXXKYQyAD+ZSmt4QmffPoiJxsp7zLw+FkrbXi96pj5H6UQE9AP8Pj+SzlEjywU\\u002fznVu1ikkGT8Y3O6fhvAsP57pYEg8Cz8\\u002f1i8GXhtBTz8DF9griqheP4eN319NfG8\\u002fNHSPGg1agj9Z7o3jHp6YP6qxM5PFtrE\\u002fSl1geYVAyT+HG+cnLwHhP51PB2O\\u002fLvU\\u002fByI1FZUzCEC6MlsFkEcZQK26h\\u002fT3HChAKojWYf\\u002f+NEBv4T6agrBAQIzhncvyO0hA5zWsar8YUEBQEDi82qZTQGuulvYsU1ZAV6kEpOZcWED8K7Gsq1xbQLIv4DDbdGFAEMhy2T+HakDj0MIYEIx2QCLuURDoe4NARkD+Y3oikEBDEzmQmfuYQDp7nfOG86FANWMPWib0p0D6\\u002fRzCIs+tQN1RwIVfa7FAqE\\u002f5zuZJs0Cx7emR0Gy0QLTP48dR2LRAb7aDXU+WtEBa6KnWIqmzQKfdyuF4ELJApUE3VFu2r0AsA9437myqQAH9FQVX2qRAhifvT+5Xn0CwpJTJseqWQFA7BGS\\u002fFJFAtwDdY0bRi0BWX\\u002flUuwOKQHIbUMAVf4tAc4wfEU\\u002f8jkDSCu+KLrqRQJEUAYacE5RAMVTxCN46lkCbwqCMjNuXQLCuCYNOmZhANqn6HnglmEACc25F\\u002fGCWQNnNz+WXc5NAXUjmE\\u002fuNj0BXtbT\\u002f9cyHQPPaZzqWp4BA5ESInfaWdUCUZ7vpbeVpQKGBjroUuVxA2FeW5dF0TUDIEiOMpPQ7QHqNYpn0oChA2tOQxxFNFEAN4gmaad3\\u002fP67aP\\u002fLenOg\\u002fpwwc5Caj0z+iJCZgGt7AP7YM1ix3Ma8\\u002flI2xL8nCnT\\u002fezLVCFdyLPyHIOUpqs3g\\u002fIKJ93IdYZD8khg70edlOP7HSPEVqbzU\\u002ftP8Rcfs\\u002fGz9xscjbgqr\\u002fPu6GQKt60OA+XEHrC6ZWwD4KZ66oPAaePmkVykFCdYc+wuYaWFv90T5nrlFljyPcPsDcwWvRYfA+2guHbB4aAz\\u002fd2Zn3+PsUP7wlyrAUNyY\\u002fQgVpHAwjOD9hU25+mElNP5PuteWukWQ\\u002fLyNBQvOkfz9QbcCroYOYPxBPT+ifF7I\\u002fT+dEEIXJyD8FeE6TSTDfP7Dw2d+t8vE\\u002f34DxW9bdAkDoDYnSkRoSQFYyq7Jath9AXpREN1VbKUBDHJpv\\u002foUyQKCI66J4xzhA59NTo66JPkAcx9YrXqFBQBHiUxA15UNAf37YWsXeR0BJ79jC78dQQIWjvX989ltAdeVZuFVFaUBmbvBldqh2QJe0GTqQPINAMagIG+ljjkDLy5pgMT2WQOATQ+z7Mp5A6PbZnvcco0BvIfes0LSmQCdtt3arhqlAAGzn3Hliq0B\\u002fL4+WMTqsQLtzdN0TDKxAXh\\u002fLKAnVqkCn5CdUBZioQEzOWAKcc6VAXgasPp+xoUBTHby7wIGbQOx7mcKnM5RAtmoa5\\u002f10jEBGehJFTfiDQGSUZ66Z9H1ADtsQh2rteUDhBDnbrld6QALqWekuvX1AMJZoxBl7gUDF7B0PBY+EQCVyE6ABrYdA2OYCWENUikAabjFjrvOLQFo18gV4DYxAloIoN05oikBvnhE24y6HQJXG2O3854JAf3C4AaSSfECpuxdUmflzQEjoETib0GlArHIGBiPSXkCOVLGxCv5QQP7RK2N\\u002fTUFAQ6IAsTVGMEB3IDqcmE4cQD6lXtCj1AZArrvbM5gz8T+cNLFVzZvYP+i1c+2xPcE\\u002foUpQr5vOqD+k9AIMFCeTP1yJmEek638\\u002fMq8TiIWcaz+zRCQ8eI5XP00CPfL3F0M\\u002f6PTxLszMLD8nxJe4SwEUP532TF9fevk+gnx2Roiv3T6baZdiEJ6\\u002fPpm9cz62z54+WJOSurxjfD6TRPN1bDpmPkR3BIEuzaQ+SHOptgZfsD7oLv2pCEzDPhDmMj\\u002f49tY++GIK0dhO6j57qA1cF2X+Pnan+vksPRM\\u002fYz+KPRoyLD+9rCFvcBFHP3A7Ci+MXGM\\u002fnE1C\\u002fBRHfz\\u002fTUjpcIo2XP\\u002f30UWp3ULA\\u002fIIArkgawxD9etXHiDffXP2mEvgsEWOk\\u002fn6wVrVV2+D8wpI\\u002ffC40FQF2wwYnXVRFAXx5EfLl9GUBVXy3sLC8hQCJRRhWMbiVAYd1iqz5TKUAo\\u002f+a26AYuQF87Gt0JvDNA23FTujIlP0AFqEzzpntMQDY+yJx7OltAwqZ\\u002fNKI8aUAkpfBOzOx1QJufXoaEpoFABkO9XCBIikAWnXQP7CKSQKHC\\u002fX99T5dA\\u002f2uHEW0VnEAkiEL0u\\u002fWfQDSuAcXzT6FAUlcpgVL3oUDbnwb8dOmhQOh6OIvtIqFA+oPBc4ZRn0BSvbHKuCibQPFLhmTeNZZAJ4suy2gOkUCFYOo7yJqIQMuSfCs+04BASx3x1nZzdkBcuvLdziRvQMY7DBkAsGhAgH0wjSm9Z0BBRLi7GKhqQG\\u002fuS8aHF3BAU1DK6pupc0Bn5SNRLYZ3QEkOp59vCHtA8owHL\\u002fVyfUCMvzU35h5+QAybPzPyuXxAs\\u002f8Q+jtteUDpRhpxdtN0QCszn1+Sg29A+ukSOkEBZkDPEmcqallcQKsfwFZI2FBAk6FKe1p3QkAVxyRINq0yQHTvRhs9biFANyHL2hgJDkBPqeqETur3P7x2y1yVpeE\\u002fEtSMfOpLyD\\u002fDOgMoQK2vP1lZgkYqIpQ\\u002fh\\u002fXgBWEgej+vKKVvSyBiP\\u002fOet+l4NEs\\u002foVHbRqtRNT9rkTS+eZEgP+jdiZudjAg\\u002fDAD\\u002fsI3z8D53zrPz4pPVPp+xQEX5L7k+hJjeKMLmmj7l7QVWJkx6PiMMc8+DT1g+YJXl3jcWQz5tXSEK7qt1PiQkn\\u002fSSMoE+K1SUA8CllD6w+EI0ZnSpPjVZ32LPZr8+Yb262ADP1D4lsiXGRL7vPr5UuqWCegs\\u002f7KONSm7lKD9NpspfUPhFP2rtAO\\u002fHK2I\\u002f5bYrLiq2ez8QDcQUNluTP0tpkXC3tag\\u002f+aV6Y67MvD8f37D52qLOP6sbuf9Rvt0\\u002fd2\\u002fzDMRb6j\\u002fnlzu8TlX1P8ZXQZnLlv8\\u002fD\\u002f7bHd9+BUCud+en+j8LQAQ0yH0jqxBAVTSH9bFEFUAPaTFBwYQfQI91snz+MCxAF8dm3Z4TPEBA64gyCSVMQLI06o1L1FpArZbyvBDIZ0CO1SzMNHhzQDALGyf4bn1AZ7JG58WYhECUAojqdc+KQIWx6ZeEVJBAuU1IA+rAkkB6y8g883OUQJC6GytdT5VABLjJrxVGlUDBz7tdqlSUQDVRbmNDhZJAy7+vB0n3j0CP8AstzfeJQMn1lOvuwoNA3EOyDxQhfEA9NaRebdFyQO9WEjxxJGhAALe1ubtJX0DN8Es\\u002fUaNWQGHlNu44RlRA\\u002fNih43FlVkADf96NeaxbQG8bol3bh2FAac73Tay4ZUDwLMT9ebFpQMJAEvC\\u002fnGxAm27pYJ+7bUA7uNVahKpsQERZCAhvjGlAf\\u002fm7cfQAZUB3wLnlv9FfQIqIDtdMMlZAt0XJ94SETECKhBTooN9AQC8IleXMZTJASCJtaiF9IkBrTYrcriERQJAzXTF4SP0\\u002f89TyloQY5z9hOUDVDNTQP9OKM7u3trY\\u002fTmH1pM6RnD\\u002fd7aJcZ\\u002fmAP\\u002f\\u002fk9cwSlWM\\u002fP124jZntRj9KVpHHrpYsP3EkzePwThM\\u002fJ3eC5jpl+z75SxljkWLjPvTYR3ZcP8o+g+sXPcmXsD5lDBKbelqTPtJHXKJLtXQ+Kn9SWW1NVD73RkyumNUyPn43CrSFqh0+9IDzVqpfRD6NdIH7gV5QPsd3CRWsRGQ+QygXJXqaej6k3Qcsx4WSPuj4SqrIYa0+Y3n3EmvGyj5\\u002fPnrLxBPqPmdgM8GDFgk\\u002fNC8Nf5nFJj8XhmKUehtDP3JsGxgRZ10\\u002fYNr4qT2udD\\u002fP5FxgeJCKP3AhTOUDJZ8\\u002f+RgkQ4SpsD9lIlfmekXAPwNgXbYrA80\\u002fVMhKtiWi1z\\u002fTOaMC5KLhP3i9Tfm8S+g\\u002f4OX1NLON7z990VgDal70P9WB9Z7Y4\\u002fw\\u002fKx+LPrC2CEDasHgz1e0YQNkvLAUVoSpAVRvtjxm0O0CZ9CpiggJLQEwotvwPWFhAKliNVrc2ZECjcdzAmPJuQKr3FFaB53VAFoUHjHHMfEBGEcOUCLCBQFHX9TEKcYRAeX0ngdZihkDaJ4QgWV6HQN1jaBqeU4dAqIzc4FtBhkCGIBrLXzeEQMwAkM97YYFAmvO0PuAafED4QwAPDkF1QK5Io06t+m1ArczeWubAY0CbezzuF6JYQJeqjwovPk5AMRaLGbATREDEp+sR6IZAQOQTnu4fsUFAOro57+Q1RkA90tmzuwtNQK187oJhjVJAuWH5KmN9VkB9zXdcPYJZQLS+fMAY3FpA3QYuQUEiWkAZcrjKbW5XQJbJwn5pVFNAspn6Rq5STUC9tIvYI3JEQO17RYgKNzpAJHIpN2HqLkDXjPf0f8YgQDz30jghwxBAGlUqsuvb\\u002fj\\u002fBOWPzLS7qPxXllXYKe9Q\\u002fRuBp0GWQvT+uX\\u002f2YIrWjP0eD\\u002fGkKU4g\\u002fpSSlwEXzaz\\u002fQGezS0kZOP0HUWEe8ti8\\u002fPFexe+PCED\\u002f9yOjldsjyPgmuLHyo0tY++BceNThLvT4hKVEPEt2iPvFN0f+kVYc+ramKAjECaz7JVNiN8t1MPmqO+0YvWiw+UeM\\u002fJRhjCj41BlsE7tj0PcbojkSeVhE+LZguVYByHD79wFqs9ZgyPtomlysjI0s+E4TYqgxhZj4txvZbYF6FPpKAS952RKY+JY5I25VLxz5c0Yp9+C3nPn87Nn+SZAU\\u002fTm4RZcEgIj9yQkrNWhk8P63mCdWI41M\\u002fVJMK0yWzaT80Z3Fipk5+P8E3cvoJT5A\\u002fNkIFwTcFoD+r3P3V4L2sPykqifwTlLc\\u002fwuLJDCLEwT+8EBZg+ObIP8igbj37y9A\\u002fesmmqA2R1z+iUVljxz3jPxif8LorIvM\\u002fHlpl86xYBUBuVwwf6QUYQIJYyQa6rylAPzEvjI57OUDanQ20JEdHQL2WaEGRjVNAI7hrGd0+XkDxgU\\u002fHU5plQHmK0cJpn2xAh5bd0A6wcUA0VlJ0n4d0QBkUa3wtiXZAFn3RXZuJd0BnVI9fN3l3QLNW8GVcWnZAlk9DUYRBdECTxphAIV5xQJIR7lhy\\u002f2tAsv8p640XZUBBlVHaoJVdQNHLoluFTlNAh+eBoJ2fR0C1hZvJtOA7QPM261y\\u002fMDFAhU+wuuvVKUB9pZ1raVQqQEW0SpWGmjBAxLkfWptDNkCH9vcBfys9QENfT\\u002frsCkJAMStXRPrGREAvVfnR0B5GQLCYW3RNsUVAiWnrTBqOQ0B6SehKBDBAQOu7skJzmThAMvoMvxEoMUAuomQY7PglQNUHm5dI2RlAiKmD1wzzC0B\\u002fHlciVMv7P7vqHvjvb+k\\u002fy8HSVy1w1T\\u002fQhe2J0KXAPyE1m8D41ac\\u002f8bhAoyt7jz8UgrfpVzJzP1D5JZxdqlU\\u002f\\u002fErD1GW+Nj+MDJuLzXYWP6ksXBkBYPU+CIQfhP1o1D4QuHHurI+0PusNYivSdpY+kUqzPaQQej45z5PkSYhePkFhTsiIP0E+bOpkBXpGIj43KfJbQOwBPrpYFIZ0tuA9dvqELHh7yj2hwzwXaOPaPavGRNWH5eY9gtl7EfxyAD4AtyrLUBQcPmxW9Wkb+Ts+pOMPh+4HXz4BXDE0vJiBPsr6WfkDK6M+RIOWtZhywz4qe0yIYiXiPk5QTjOH\\u002ff4+uNwnmXssGD+J8KLhqzYxP2aGLnmTX0Y\\u002fUGZc2bmJWj8KAfHLarpsP61PXt\\u002fnY3w\\u002fF+mkdliiiT9S7zxykDOVP3ztSX4ZL6A\\u002fxwbgjqtMpz8fmjEPkLSwPwtnRYStd7o\\u002fRp+Lz0d7yT9W\\u002f\\u002fPrJf\\u002fcP6b7gSJibfE\\u002ffFgE2NBVBED\\u002frf6J1SwWQP2jfdNwSyZAqqIH3xaWNECqs+MZsXRBQPvv9h+lOktAWVmonGKXU0CFw3oUux1aQN7lhFayNmBA9iYvbwXgYkCWy45WA79kQFYWOHbLqWVAqt26yFmTZUB\\u002f54YRxIFkQDGQEB3li2JA0kHw5CK\\u002fX0DB1Ykiv4tZQBLbWNFjNVNAYhk6tN3dSkAMCP9wmHBBQDcwg5zjFzVA9K3r9co7KEBOUrMsjjMcQKEGIWxvXhNAScSiEXt2EkAsnPPKgw0XQBAYb2oecB9AO+sLwXsCJUAhdBsHHmwqQNjA4K2hzy5A1SzT0leOMECZm\\u002frlfVkwQBc5cZHUoC1AO4arazadKEDhNpxilb4iQJzQ8Q0qLBpAtf8676fBEEAHLZ1pmK8DQEgG6JZ2O\\u002fU\\u002fRuUn9KkJ5T9hsdp02CnTP4HO1Tc3D8A\\u002fZFoxI4\\u002fHqD8fkac8S5yRP80wEqRcEnc\\u002fkeui7czgWz+7WvbNZxk\\u002fPxvIfin\\u002fCyA\\u002fInb6sCDI\\u002fj4iUNxKhLrbPjtAN0Gj+rc+0Sz1qkyylD5YIdmb2b9yPiKl4w6RaVI+WCNM13hMMz4ar9nlv4wUPuZRpL1WM\\u002fU9N3\\u002fQvZOW1D3iUlj39imzPY1gXjW+aJ494jrDQt49oz1HhsyrgYOxPUwwtdMDK809b2Pbk1AU7j1m8uCEiakRPnLmxQJQozU+acoMVYPCWT5dwMGOQLZ8PsN9YwbcfZ0+I1HAlYi+uz4z9UQJb9nXPuy1y1KHt\\u002fI+EvemzAnQCj+h3uoYu4YhPwHfor3p6DQ\\u002fV2OO2QbFRj\\u002fE2+rEfaRWPxmCPU\\u002fUl2Q\\u002fL56XBFE1cT8x3ItDKsh6P3oMD3BCIoQ\\u002fBaqKFy\\u002fEjz8aEV9bTYCdP9kDqyHQuLA\\u002fBpQ6nzkcxT8iYTfwN6\\u002faP4uK2Xpo5+8\\u002fi0PEmhKlAUArZQsOI+0RQNYgxE4OsyBAuWaZBjmLLEDRrlpgdms2QFij0rrkOkBABKibTIm+RUA\\u002fEf3+cxlLQCDCLVpTnk9AkqHJbaFjUUCA12FwxSVSQA4Zlj9NDFJAGNwo5MUfUUCnISLoEO1OQDEIBSSgcEpARbNDnzpDRUD5BOiEaPY\\u002fQPEpBuX3VTZAELY4dB\\u002fvLECgBjHQz2MhQFQ9gudPpRNAnYUcf0DpBUAVzojZebz7P0XiMNkGYPg\\u002f5qn9HD2g\\u002fT+tEcF6blsEQJjuj3aXnQtAMiUmdMaYEUAw+GHudbwUQJbDT9W1dxZAyh1XeF1UFkAcQ258Y1UUQMFarXj59BBAHxJwAOPlCUDKYt4EvRwCQM\\u002fQ3yqqNfc\\u002f\\u002fERZiIlC6z8DSBa\\u002fwFzdP4Xgni8KBs0\\u002f74UffqBYuj9OwINGt\\u002fqlP8YlQFg+3ZA\\u002fV5lkkYXRdz8267Rpd\\u002fteP7PDlvPbkUI\\u002feisQRROHJD8sTD3KH\\u002fIEP3yzUlvbw+M+SIQ4JVBSwT44ScLO1HecPt26dk95YnY+ANKqepp5UT4fjlvY0XssPuWbmwB2Igk+gvkbOPLH5z0VoxeNigHHPb2FSNKbsaU9lDSMSCf4gz33lyL8UJxvPTnGl4mtAmo9aotqfWdsej2Rgqn0h7CaPVNqAxhPcsA92IhkBdur5T2yOo4AWSMMPmmPpihlNjE+WbP3Bs10Uz5j51IijCl0Ph\\u002f4\\u002fE9TGZM+ekmZPO+EsD4fmtun\\u002fxTKPgsWx81hyuI+33szdzq1+D6OZI9dmqYNP725lAH1PSA\\u002fQ\\u002fAdUHlCMD8pV8Sm4tQ9P23Ye8bzSUk\\u002fAGZyP+FKVD+S8hd9yF9gP4iQQPOrpm0\\u002fgMlmllVxgD9OiLQojVyVPzOXQffc8qw\\u002fFMHVuRHmwj9T1jzU+\\u002fjWPycagIFbr+k\\u002f5gmwWY9R+j\\u002fxkwQi0rMIQEfb2F6XQRVAua\\u002fzhn3MIEBHt+3253MoQEQbDIPcczBArThfxCSSNEBeMDBJBAw4QFoMRCrsdjpA\\u002f5sM0fCZO0DEmYxWjWk7QLQCQfn79zlAtb3Ci+VrN0AhHvygXAM0QIMsudXGGDBA0yJNRH42KECWh2AbuO4gQOzTI9Bi7xVA\\u002fsR2ZZpNCkAx+y0+h279P\\u002f6nxW4e4O8\\u002fkCOqK\\u002f7S4j\\u002fcsZeOUzzeP2g\\u002fSDMlkuE\\u002fhs+9oB8Y6D84jZHn54HwP6rwvX7zQvU\\u002fnEi5CqVH+T8PgdopdJf7P4dkbHdUlvs\\u002fUlzonAk\\u002f+T+VafSpySP1P6nSGPclMvA\\u002f5JJ0daG15j9o7bgH5yXdP5iCSqBVIdE\\u002fvmtrMLZywj8TDT9wzjayP2vOciNpf6A\\u002ftM80\\u002fQNwiz8mYGIpafZ0Pxm8L8Skc10\\u002fzoWvTZQJQz+59Jf0dqkmP3U5DzMs2wg\\u002fv8Kawg8j6T6XWeyR8HXHPmr73kJIPqQ+M9JL\\u002fBg2gD5VGqMDb09YPnY6731qYzE+4ezqaD6SCD51bsfTzQXiPd7+pHYFk7w9v+wOMVtjmD3VqZEPp3J1Pc0AEjybGlM9XYPHVrbhPT3SU8IT4zIxPeVnG97GY0Q9ktAYp2AkaT0Mb4RFPLKRParr3EzG\\u002frg9WvXYWNzD4D0PiMrRk9gEPoTFegLwyic+Dp3oZqPVSD7llZLFUKxnPqeCmGsGmYQ+skOhGhFboD7NBVwuHbS3PlfMVp6XWc8+JTermETs4j6XtV6X7Nz0Przc\\u002fV9YCwU\\u002f7XD8nBqGEz9pVKkAu+sgP1sOdCZlkyw\\u002fW7FvCP+\\u002fOT8tiSgp0r5LPxMvQqm0KGI\\u002fCoIqMnoGej+Ru0OXHXOSPykFSOyUmag\\u002fTqc1ywlDvj8fDEuZqhDRP3zSvE7BneE\\u002f94r814al8D9go1N68NP8P0d+x2ag6QZAs+LSGn3CEEBZ94n\\u002ff6UWQDka9TcJZxxAHF8ZIryhIEC96VgQilAiQNSjwThnFyNAa9TsawnwIkCYjmTOjOohQO9BPI7WJCBAxfP0PMOVG0A\\u002fF66p3DMWQCJJlMvhuBBAostEJhBuB0A2NZhjamb+P3MNyDbXOvI\\u002fXi+GbvRN5D+Je5BojJHVPzLcfQLwFcg\\u002f28GWocOewT9km\\u002ffKZzzDPww0qIYzCco\\u002ff2x2EGDv0T9IPW3Ex0vXP4gvnraD6ts\\u002f1MuGb8+s3j+L+QTbVNneP\\u002flL4wG6X9w\\u002fOQcWYeTc1z\\u002fNaZ80wVnSP7VzyJeHz8k\\u002fC8NPJLyawD+Sk2qTvoyzP\\u002fy7z7uTEqU\\u002ferI1eDzOlD8FX0zn2dOCPxmmlvmHQG8\\u002fqWaG6WfOVz+70UHBJahAPwoYXt8ibSU\\u002fRE6sh8ZaCT8MNQFL4J7rPmA72IgDuMs+sNOnWN6kqT5tpcsVH+SFPtC0oQeIRWE+pBZnBVNHOT60AVx3vEsRPvF6Wd9Yg+Y9WyArssnNvD1Y\\u002fPwSkASTPcxxU40KE2s9tNo+\\u002f\\u002fLYRD3GJoNWozAhPRaO+fCc\\u002fQk9h3g5AoRK9zxa+A4v6nIQPRVVghZj4Tc9Dx\\u002fAqgNJYj30sZWw4d6KPZQMZ6swX7I9SBRtJjoY1z3C9ycdj5D6PWOKUWQ+6Rs+3\\u002f2t7x7FOj6EJqC8sW5XPgAH7KCyt3I+8uoGRdtJiz6PhOOtqyeiPqTU8GE1DrY+IWeq0719yD5fba\\u002fh3PHYPjrsyTPSi+c+LKdgXqI19T7kRtou45EDP461sqZTxRQ\\u002fXVxczEUiKz8wOa9RtVNEPzIT7uQsIF8\\u002fDQMwcUm5dj9UMh1EccGOPyR85J6uGqM\\u002fAB5rciC4tT9ynbQHT5XGP+is0XwrfNU\\u002fHwAXkMq44j\\u002fC1FodXO\\u002ftP39BsJO7AvY\\u002f7ACghlTf\\u002fT8YkJI6k8sCQDtypYkNDwZAtYdcNqBPCEDG02AXk1UJQDu+AiiIGglAV7MI7JG4B0BxcVHaxVsFQE643GkIQAJAxlbzpKdp\\u002fT\\u002f0jVOq8zL2P1ngxL1BMO8\\u002fhdfXKLFK5D8kt95IamPYP2lZxm0KIss\\u002fhvLY7Mh2vD95xnZYtFyuP5hEInR6K6Q\\u002fZTkt78Y5pD9oed2haaOqPx\\u002ff9X\\u002fXW7I\\u002fQiqM7IYCuD\\u002fvAmVMq\\u002fu8P76vld4HCMA\\u002fkyoYrXM4wD8o\\u002fD9wrgC+P7ItU7m7W7k\\u002fYqzoz3mWsz\\u002fR14eqzqirP4QYMO6N2qE\\u002fLH\\u002fVvAUUlT\\u002fMhB+JIMSGP36J16plgXY\\u002f4E5mFXxfZD+Mgqbf++VQP4M7SyvMszk\\u002fWNrlvzPvIT\\u002fTCedqM\\u002fsGP1MyP0iRD+s+crUZ+sBNzT7hejSEQDStPi01OyARzoo+x3iGC96sZj5fVypt57FBPp\\u002fqTTnSgxk+D85WO5gM8T3u\\u002fDbZdUTFPdg71USaJZk9JtmXX8cLbT2HovWzty1BPfUhGgxw1BU9E0SEy2X97jy5GcnIaZjVPKgtavXaFNA8D0zpqiZZ6jxHCuK74D8VPTosUwxlDEE9EmTL4zabaT2VTAgn87aRPY339djpcbY9aoJlz\\u002fP72T1M96xjdHb7PTctvsqQfRo+thT66JFRNz4tGcJmSrtSPqAovtO8dms+h6CTe79ggj4kdaOHfHeWPio5Yzb\\u002fI6k+Ux7esmXquT7DPmSlzhHJPtvI5wIc59c++gkBL9PT6D5sbJ6bUkb\\u002fPtflI9hHqRc\\u002fTKpcN380Mz\\u002ff7bLQWHZOP3WfcYtQmWY\\u002fEanPa7zdfj80CGHGL02TPy2Vn3g0EqY\\u002fcpENIbgStz+o+hBqQhDGPzVhWFSfUdM\\u002fRaF4cyQH3z\\u002fQ9rgC6efmP6P3yxqJMe8\\u002f5dS2ALSt8z\\u002f+yGoi0CL3P+sFtPWzhPk\\u002flPYAOhqX+j+JM5NColT6P5+VJNs\\u002f3Pg\\u002fgehBEr5f9j8ALZWwSB\\u002fzP0L1owHU2e4\\u002fa2KqO8ZT5z+QzwJxJm3gP6bu7UWTbtU\\u002fvH\\u002fLwwvSyT\\u002fRR5ZVlL68PywXWpWJ+K0\\u002fYfdHX3sUnz9\\u002futXbLDiTP6L6ka\\u002fIz5E\\u002fyXPGyhW4lj+li4fI3jafP5OCw6evgKQ\\u002f\\u002fwAnfcLjqD+KjT9NZa+rP7540Xp5J6w\\u002f7hTuGB0pqj8YYDleEjWmP3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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002f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qI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\u002fd2llIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQAgX+vttSyJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAIJXuHs1gIkC0XXi8dT4iQEi1LaS2QSJAUrjU19lZIkAKsF08UjsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJA2U3j+qBSIkB9YkJ38T4iQEPQ6TgoRiJAVTQWtLA+IkDsxUBe5TYiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAUrjU19lZIkCNmJQBUzsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQF07b+ttKyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJAVTQWtLA+IkBpWmsIGVsiQBnnDAo7WiJAvcCU5OM0IkD+Z+k+dT4iQEi1LaS2QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNOYfiA6TyJA2U3j+qBSIkB9YkJ38T4iQBHoyGwnRiJABM2Z8Dk9IkBpWmsIGVsiQO\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDPxpYQhUMiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkC+QPFbfTwiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAG7HklUVOIkBpWmsIGVsiQPzuTuDUUyJAtuXgzFUvIkAGWp945DQiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAkLg4cTodIkC+QPFbfTwiQDBLfR7kLSJACrKdTVJhIkCy8ZhSVEMiQOcTNQ+ISyJAgqUnuscpIkBpWmsIGVsiQPzuTuDUUyJAtuXgzFUvIkAGWp945DQiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAox8dfScYIkC+QPFbfTwiQDBLfR7kLSJAf6CAE1FhIkCy8ZhSVEMiQOcTNQ+ISyJARWy0Wv5SIkBpWmsIGVsiQPzuTuDUUyJAtuXgzFUvIkBxx6NLsykiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAhZlH07FPIkC+QPFbfTwiQDBLfR7kLSJAf6CAE1FhIkC3ba9fOE4iQOcTNQ+ISyJARWy0Wv5SIkBpWmsIGVsiQCTdglnaZCJAz4fPPFUvIkBxx6NLsykiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAGWOlH9JWIkC+QPFbfTwiQDBLfR7kLSJAf6CAE1FhIkC3ba9fOE4iQOcTNQ+ISyJARWy0Wv5SIkBpWmsIGVsiQCTdglnaZCJAz4fPPFUvIkB9xZ3isSkiQDioWXjRSSJAdMcbPSxfIkCz+0Ek7UciQHqFvW0xSyJAGWOlH9JWIkAOhGhMejwiQDBLfR7kLSJAYDt3cYZJIkC3ba9fOE4iQOcTNQ+ISyJA4MG0A41cIkBpWmsIGVsiQCTdglnaZCJAm3e8bug+IkB9xZ3isSkiQLE6mC40TCJAdMcbPSxfIkCz+0Ek7UciQP9ScLcvSyJA4Xx8fN1VIkAOhGhMejwiQI7hqPniLSJAhAJUNE4cIkC3ba9fOE4iQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJAm3e8bug+IkB9xZ3isSkiQLE6mC40TCJAdMcbPSxfIkCz+0Ek7UciQJpB4Z30JyJA4Xx8fN1VIkAOhGhMejwiQDWwUmSoQCJAhAJUNE4cIkBrTabGzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJAm3e8bug+IkB9xZ3isSkiQBndUz1DOSJAa6ralSRQIkCz+0Ek7UciQJpB4Z30JyJA4Xx8fN1VIkAOhGhMejwiQEXt7Qf2OiJAVS6cvr83IkDiYWl9zFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAa6ralSRQIkCz+0Ek7UciQJpB4Z30JyJA4Xx8fN1VIkDR5AtBeDwiQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u0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fG8CQC8xn3+hQwNAZeppDmElA0Bo+X3WUf0BQF5JjLgb0\\u002f8\\u002fXhXhwpNs+j+At3rrs470P0g0EAbs8O0\\u002f3L6GBkxn5D+TPlx9bwfaPxDT\\u002fULZGs8\\u002fWJJtDPltwT\\u002fVVr3bTViyP4TVAw6AKaI\\u002ff7z+2XjwkD\\u002fLhloFJc59P2ciZbJ8wWg\\u002fTc2Jj7JrUz\\u002fBunAIwsg8P+wPwPeQKSQ\\u002f\\u002foW+koLGCj9t9aPCRQTxPoLW8UQmP9U+NIG\\u002fVc2Luz6lwxdB2ROkPqhaIILTMZE+Tq2qluKRgD5X4aW6oZZwPsEgf\\u002fyqhWA+h5TGH7RSUT42Hlnv7HFJPh4gK\\u002fAg1+49B+cpf6uu9z1GN9qL3KAMPj50G81a1CM+bzqD4OfZPj6MD7qSH\\u002fdaPpdHkDnYMnk+qInI6GGnlz58zpOmxWC1PiukFzMeNNI+SB7fh\\u002fbs7D7h5jxKB1sFP5+pWAwRQR0\\u002f3HL1tNiTMj\\u002fyysHPet9FP24ZyV5A4Fc\\u002fPcIdyZ4raD9et0mso7R2P3W1sDJv0IM\\u002fKnNm7e8XkD\\u002fi2Z9Ps2qYP5d1A2XLZ6E\\u002f1wET39GPpz\\u002fXL\\u002fPdaMiuP6hUaBWb17M\\u002fBiFeKEnWuT+qhJYs4DzBPzSrSEtygcc\\u002fDeUaZUsX0D8FPy34eaLVP0+F1pnrDtw\\u002fwtmXXyBd4T\\u002fRDDt1b23kP7Ufj4pi3uY\\u002f1DvVqp176D+21mykIEHpP0PUkPGNU+k\\u002fzQxeL5fv6D9ifPNxWl7oP4aJ0+dC9ec\\u002fdOpD5gIb6D8Dv3yOakPpP8xeW9GQ2Os\\u002fX8NzeVkL8D+hmzEeZffyPy2+U9hWg\\u002fY\\u002fyT0LXCdw+j\\u002f6xqAmRo3+P33SFVzAagFAPgnPQXS7A0B+nfsob14GQAv7Ws21cAlACPZCwZ\\u002frDEDhbeGutkgQQDvAC4yj9RFAuf+7zL8uE0DGMD3Lm6cTQBNcnDneLBNAx+kQ5vi2EUC7CaX5Y98OQI8q133KTglA67NlY1Z8A0ANEAIHnCn8P+WgSenTGfM\\u002fxwe2PMxU6D9ZPnhIaiLdP9j3ZwjUatA\\u002fnJsSDRtwwT8OIzWGQXqxP4Jz4oA\\u002fi6A\\u002fkwfni26XjT\\u002fljgFkWAB5P9m1ZYHP8mM\\u002fAsRJM4sRTj80zMkhrHU1P1Ap1smnRx0\\u002fMoOAKRaWAz+Htlr6XiLrPoXndBh8DtU+hV6lUB8mwz64MozIUJWzPqznys5QxKQ+SahlsiGRlT5POzh3jKSFPucVsKZplnY+n2n0276CcD7uxwaybqIfPuZF7gRoiyc+4iGGjo54Oj7rANlq4VJQPoXg4\\u002fKWpmU+PBippSkWgD4d5C2XkaeaPoHocUYlR7c+uJ+X7mcx1D5MxMnxR8PwPmJn24f+JQo\\u002fEmqRbQoCIz9\\u002ftY90jas5P2JryNSTFFA\\u002fCKBYaoauYj8OUYe1SyB0P6sE1R6XHIQ\\u002fuEnDTgSnkj8N73eEjBKgP6SMx26TyKk\\u002fTVqZbdZQsz9yRMzZnzC7P\\u002fH6ztuvKMI\\u002fVQXmpLJgxz8UeA8jqqLNP1r9ptUw7dI\\u002fQMAA6Oy62D9zQTBCVoLgP+ob8LxPKeY\\u002f9BS7akRG7T8hHFOcm7DyP6McADGw0fY\\u002fYj2KKemH+j\\u002f7YKM5vGj9P\\u002fGA0zmdPP8\\u002fNe9AA9EDAECtxJ8qaPv\\u002fP48kb1UuYP8\\u002fhzCJK0+G\\u002fj+OQqRd0sb9P2JQsvA+iv0\\u002fO2lL80lF\\u002fj9ohfvOCTEAQN1\\u002f3\\u002fyqDgJAyPL01UK2BEArtTY2zgAIQDLXPQnYuAtAg5Qz4c+2D0BjHeuocvsRQMiG5WEpTRRA5QsoHdHnFkCAm9n68tQZQAvOrBjT+xxAJJhft\\u002fwKIEBm0LAKwVkhQG\\u002fQ4IqDKCJAZhBwPUs9IkDiLl2cZ3ghQB4\\u002f6NPpwR9ApGHdHk1LG0C2OFidnCEWQDHpa3SN6BBAay\\u002fCQUNVCEBRlk5Le34AQL6Yul5fFPU\\u002f6qknhrJr6T+nW+Q0mPTcP2ZopPkNLc8\\u002fFNAsX2G9vz\\u002f4nczFjYyuP9m2rbdbx5s\\u002fCmjrATHYhz+8Bln9G1FzP41+fuwdm10\\u002fj2kLgEyoRT+4xOXH5QIvPwhn4dUM7RY\\u002fDzjBPWfrAj994iYRPkHyPs+URIy9z+M+42HtFEZQ1j7UQaRfbpHIPhZ60L9hu7k+zNvrcTS9qT7au0eyHq2aPg9BVmt7apM+XEgtBopmTT6JaEUpEm1VPigpv\\u002fww+2Y+xVTX13E4ej5pN2bfZqqOPodtikAiVKM+00RCPR5nuz7Ot5qZkXXVPlTqQ9iWd\\u002fE+PAZujoP2Cz+Tsz+3Ak8lP9vuFF6ocT4\\u002fQaaNapBAVD+MzLwmKQZpPw\\u002fKCdEgsHw\\u002fBirVsuaBjj9SznZeiRmeP7+tqTifkas\\u002faatHPCl3tz9imrCqY5jCP5Sv5CRNh8s\\u002fnYJDbhck0z8OYK\\u002fE\\u002fj3ZPwDRsfkrBeA\\u002fq3DjbFr64z+WZaWTTgjpP1pmSPoNAvA\\u002fwMURTu7o9D\\u002ft4eEApH77P7Mjr5+30wFANx5TtOFlBkDL33cltPcKQAhjlesu\\u002fA5A+5H8euUBEUAEoT9gDu4RQD5BuckVShJAofj3aww0EkB2hHz10tIRQMCzE9szThFA51XMz6vPEECzDG1ViIYQQPi72\\u002fX7phBAqxobgz5gEUDWEhEy+80SQIF56\\u002fej7hRAjtHlHpOmF0AbBCVeKtAaQKy0UZBzUB5AgK5wvOwRIUDDgjzA\\u002fiwjQOnhfvI6gCVAtBfuAKACKEBJfbJWJI8qQBBhdc0Y4SxAgGDRsHmcLkD1hZRsnWEvQA8aLpNp5y5ASNGU3ZMTLUDE4CuIZgcqQO6+IrvLGiZAXZcMAoHGIUCd0c1qbwsbQPkvGnlodRNA0t7WzX59CkDqz7ZuIREBQI9m01kO0\\u002fQ\\u002f\\u002fHe2hHQQ6D\\u002fpFjOH2FPaP+gAVJAfP8s\\u002fGi09Lr6juj9wrH8RHpOoP5u+JjCYYJU\\u002fiZ8C3FeSgT+jTVmXvolrP6wnEjxwFFU\\u002fbBw3wTqdQD\\u002femquQUSEtPxrHzDOWyx0\\u002fyEja30ImET8hUD0D7YQEP9wgjwuUBPg+WDg9d86v6j4k5PFRrOHbPlHSLIu\\u002fu8s+s7RwrlKcvD6KoWtn68m0PugYfl55pXg+iOkrCbyugT7shO7u0VySPo\\u002f9MyDT7KM+A3LxzDhgtT4Hq3qWfYPHPkhbZObsM9w+5vk5aOAO8z5fLF7BmiIMP4\\u002fmi6JYSyU\\u002f+1He8TJrPz+CH59OhvhVP3CR7BwPwWw\\u002fcmoLzASDgT9vkV5vP8+TP9jwBugwzKQ\\u002fyzM\\u002fQg1EtD+IN5JDglbCP0jgEtWD2c4\\u002fyFeKZmUq2D8n+yUmPa\\u002fhPxZZMl7NT+g\\u002f8Jw4B0Ot7z8P8SqQ1dXzPyI0WLdZWPg\\u002feuUVPHzt\\u002fT+QNUTNmLkCQMWwLpQM6AdArR6VK7jBDkCN6aI1Yo4TQNOFdWF5KBhAUlpGtMurHEA9BZTjdEYgQFOr\\u002fjOIsiFAUADfhS+HIkAs0e1\\u002fbdEiQFOqjWnqsSJAGhKNZXdOIkCUqpn4cMohQGyx5bruRyFAJoGATR\\u002fsIEBekb89TN8gQJb13unARSFAaN3OnLI1IkBGcb6wkbEjQE7RHZF7qyVAIsKu8SMQKECQimhXJdIqQKZK5Rre7S1A7Gkg2hexMEDMv7aWb5AyQODHm4cRfzRAFqaoHeRTNkASdnplydQ3QOVeGpD9vzhASJlv1tbaOEDmooRe+gE4QLMfsBO1NTZApFuHaHqdM0BD4PGcTIAwQPhFmAV6aCpASIskZ+0VJEASrkn1fwgdQBj\\u002fiUGt7xNAUT3YNXsBCkANN+\\u002fmrBkAQBDB39rM5vI\\u002fmxxhRFYB5T9LE+2h4A3WP2YE+gR51sU\\u002ffwh1twFgtD88IuS2\\u002ffGhP3wtZcy1GI4\\u002f5xtFRAGceD8mNrPxpqhkP586xLFgOFM\\u002faOtZTz\\u002fSRD\\u002f36MIeZWg5P3zD4amyJDA\\u002fpJvW7S4YJD8qWpFBGMAXPw4o0UmXVQo\\u002ftnXezIlf+z6XG460+SLrPpTlGXFbCdw+CI5t0WNv1D7OPRGx85eiPvD0xcXZVqo+AVXHlDmxuj5rUYwZMgLMPhAvtzInbNw+Bjrx2vhj7D5V0p\\u002faeWz9PsiKiWEs0hA\\u002f7PAAHBylJT92UH2JUQk+P5H7++itGFU\\u002fyeVecy2qbD8iMnbr5GSCP0KbJsGfD5Y\\u002fIMnw1kGeqD84sBbMHoW5PyEm+cZOkcg\\u002fZcruSt341T\\u002fuzWLpM0biPwmkdZ+DUew\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+OdUBOgXOiP2o\\u002fRh44+JU\\u002f\\u002fzLpCqohiT9WHNbbWyN8P7S3KDbi\\u002fG8\\u002fuQrVqgAnZD+2Y4QwT6NgPxO35jbLLE0\\u002fqNpkw7ueUz\\u002fzco1nrjBiP5UZ53QKZXE\\u002fhfc0JE+Afz8pBCB275WKP5+d+uag6ZQ\\u002fPjA9pZffnj9e0S7fpb6lPxt0HpEbQ64\\u002fQSYMR7gNtj97CIvylePBP4MQ4fGvW9A\\u002fFB1Q\\u002fsIP4D\\u002f0oIqnrM3vP5Gx5UC9bv4\\u002f6LTmsBePC0CYF\\u002f9fkmkXQKGXU51EmyJAA1qkQeuqK0DquR6tPUczQI12TOFyQjlA2aFeSrFGP0DQnawQL29CQAaVbezS5kRAfOWWQr4WR0ABduIAii9JQChh0+mBa0tALDkNpdzrTUBJlQjcaFBQQMlGWawZplFAto2KWOjPUkCOEtTU+bNTQF7BcQ8gVVRAaxDQdwLUVEA+5pUn42FVQEVuBPMuLFZAvLB8nM1JV0CHmjLCG7BYQBuN8ZbMMVpArR6sTmGIW0ADJLLx+mdcQNMTz3kAmlxAXKTJ0ZcVXED+H\\u002frx1glbQKD1+HqJ0VlA4tpnQ\\u002fTSWECXeU7F5FhYQCgftZBldlhAFPLr+RwBWUCWptQxSKNZQGzxiG76+1lAS++OQqa\\u002fWUCN7qZH7cxYQO3VMN0BMFdAMUtwrUYWVUArUT4BgblSQIhK7LIfTFBAo+V3\\u002f17cS0CGbK60C1tHQHTLePzsG0NA9TO5jkNMPkCyZXcOQBY3QPTuhbDHyTBAhlziD4EuJ0BZKvjE91ceQG+E+ERl6RJAcpbombjRBkCpuHUCNKr7P2ZNpVvq6vE\\u002fZ4t\\u002fT31C6j8ngcObHMPlP5SAw4O2O+M\\u002fVQim6xcD4T9xS0qfdPXcPxGO2XY2Stc\\u002fyhTGY+yW0T\\u002fr3ByzifTIPxYb9gDjssA\\u002f9vFcjLg9tT92pcmGMfSpP2ggnSZu354\\u002frxc1ed0ykj\\u002fJNBIuofiFP+4UrWLbUn0\\u002fj39fYkbeeD\\u002fNgXyPESBnP0IUV0rGzm4\\u002fqtuN0j8afD+6Cc2BVniKP6WjJPPynpc\\u002fJJQ5ogaioz\\u002fKPdZJ+liuP3I96Cd54bU\\u002fID28hMGyvT\\u002fHDIGkuFvDP83Nq5utPsk\\u002fPjpwK2aI0T9aaMsvmGXbP+I8zzmi\\u002fuc\\u002fkiG55\\u002fU+9j\\u002f57VXrgpEEQBdfGTweSRJABhJbbNS6HkBhbp68Yj4oQI\\u002f1gw6d7TFAdDMjLDzgOEA\\u002fOrj2bjtAQPQW\\u002fYshAkRAvRFFZV1wR0B+2JPwvVFKQLShIfx\\u002fo0xAf6Lv4fqQTkAQQEMnzCxQQGStAqphFlFAzT2evw0IUkBe0erqEfBSQFVkFgVAtlNARYoKQdxPVED9KuQqgc1UQGcHfFDeWlVAsNW+YzowVkAUWCp0YHxXQLjC+4R1T1lAy915eVKNW0ATzdny2utdQDA7pF48AGBA4VW5Q3euYEBOXhsLg9pgQENLNgtxf2BARcyczoR9X0BAezK51KxdQJ37wT4oFVxAmlFDLUMZW0DP4KBhkNRaQAUxerHhFltA4pAsxMx8W0AkH\\u002fZD0ZdbQDdWm9H1FFtA30VzttXTWUCdQx76N+hXQKWaCTVNiVVAhbYJtUT4UkCN+\\u002fa+32lQQJpS5gwA90tA+z+rBhdrR0CG8zqjsixDQEtS2RnJdj5AcCKS01ZKN0Du\\u002fZStvAYxQKs\\u002feprsuidAEXXCf9ycH0B3522A2WQUQJnrS+UgSwpA950B2IPdAUDvB5obhAv7Pz2OWbWiDPc\\u002fAib5Cikd9T\\u002fJVkLMKY\\u002fzP18qbAZqkfE\\u002f4aQ9ApL17T9Yv+i8sQ7oP3hr7Ph\\u002fLuI\\u002fRp7lpuH32T9I76MvmqfRP\\u002fexTj\\u002foFcc\\u002fLxsOAHpdvT\\u002fyvtr++F+yP6m16MiW6aY\\u002fXLvP\\u002fGo0nT\\u002f0MJJX2FqUP0e8m5B3k5E\\u002fUg1\\u002fZCt2gD+deccCsLmFP2Bbk5JSgJM\\u002f1kVtzZkYoj\\u002f3Xef\\u002fy9evP5DVTjqpF7o\\u002fNKzZx0Hcwz\\u002fIwIhdwh\\u002fMPwxYTk52nNI\\u002foHjYdLpH1z\\u002fdesyuCjXcP9Q8Ucw+WeE\\u002f\\u002fku173wi5z+oTQ0dkHvxP8xO9daMWP0\\u002fsD0Xq+OxCUCu5Bh11jgWQIGH24xoaCJA4IsvpjbQLED1+nKupzM1QKpiwRjKUD1AqOhhj14SQ0BYm8WhRG1HQOeuipRJTUtAaLJFOBZmTkBZNtYH71BQQMvcL+M1EVFAggfmbiiVUUDZt8Vq9\\u002fxRQPIIcRUVWlJAkN8wMXKtUkAK2zF4ffJSQB7rHmO\\u002fL1NA7w+tu26CU0DChEV+cx1UQG2vtfg3O1VAHodmtIkHV0CFSkXSE4lZQF8c59jXkVxAW3mQnYK9X0AVGhB2d0FhQDVBb3tmLGJAHZ+v0ORxYkB5gPg12g1iQFU6wfhNKmFA2NQNsfIRYEB2ynls0CpeQNmBbqk03VxAzSlC7Z5cXEBUPfjfR3VcQEKOHkCTulxAuC2bY0C0XEC4J0cFGQlcQNjp04fVl1pA6lUvtAJ4WECw8IKIzOZVQB\\u002f1OCbWKlNAxB6HWxp8UEDJl8DkiPFLQAmg8QWsUEdAHrcV5NoOQ0BOBdW75U4+QPdHRbKdRzdAhqhrPVkxMUCdiox7ZWwoQBo9V2Cw1SBAFSAeDXMPF0BtkP3BmmAQQJi3BLXdXQlAmw3CCgHnBUB8VJm2rnsEQI5ceCAPpwNANhiJkfR\\u002fAkBm1hFjeKgAQDvMsctqYvw\\u002foBW5c77U9j+tC\\u002f21EmXxPylCVpKHSek\\u002feg1l7vi04T\\u002fFQpAdyyPYP4AY7XXKJ9A\\u002fe1Cj+yJXxT+Sexba8fe7P0PTqoaHjLI\\u002fNthDGSmMqj9vOQF7xCSnPwItO5FxDJU\\u002fLTH1jx+Gmz\\u002fk5KDQp1CoP12QslRUO7Y\\u002fqNTu\\u002fJRKwz+1ysNI9i\\u002fPP+FP\\u002fTjYaNc\\u002fBWfj+zdT4D9fPqJZcjXlP9Mhwbv90uk\\u002f+AdjNyLj7T+Xv3t7wO\\u002fwP7AiWTOL1PM\\u002fGnKALvmb+T8RXNlOA8sCQAuutZ5WPQ5AVZWKdKkEGUBwrXiIn0ckQJNY2\\u002fJFaC9AiTGAbgf8NkACuJJXvq4\\u002fQDTzU7bYkERA6+\\u002fXC98ySUC2h6Xc+j9NQMBs5DT2K1BABxBf22spUUCtii\\u002f3vaNRQEWbE5afvFFAzeeJifybUUCAD0Qg12FRQDmOeyrmIVFAIJ50gXnqUECDqo8VCNFQQByWL3Ju+1BAsFxDwAGfUUD4lZc4+PNSQNdVBKunIFVAfGbX\\u002fwgjWEBdtuUefsBbQPY2V\\u002fV\\u002fhF9AqMXPH2JqYUCSYrCOFo1iQMrHrf1U+WJAuOCKTr6rYkDmMUsHPNJhQE7DldrGvGBACr0daeCBX0CRJblHNDheQHOgTZf6wF1Add9tBzTnXUA1MjPiwTleQBf+jNJEOl5Aw679nPSJXUCausfMYgRcQGMZZY5BwVlAy7EWwu8AV0C2zpfvXg9UQMCVvbvRKlFAQT6uoirvTEA7MFYBAwRIQBNyrpJjkkNAsLDIXMwpP0DcaqWBih44QGrAmW7jHDJAPr81rLeCKkCi00woAzIjQN68UqThWRxAC403E21DFkAszq4J+ykTQEx9TKHY8BFA0lt3cACIEUCWVOwiSxkRQNLRqNB\\u002fJRBA2fK28P0SDUDDVzYW9tUIQOz7XszDKgRA7e9WAJNV\\u002fz9ZLMR7noD3P\\u002f\\u002ft7ghtLvE\\u002fMXxWjbCm6D9sGsSvEmPhP6ZQhx9SENg\\u002fl4C8eC5X0D+XKZRpeyzGP7CTTrOfC8A\\u002fzYVITtcOvD+J6MEbjiyoP88\\u002fzuJwU68\\u002fFkh11m09uz9NEfAfRIvIPzS9DLi2AtU\\u002fH+9hrfLC4D8wQPYE+dXoP9fPPiGyFvE\\u002fHuTq9\\u002fbf9T8SQgYRkyT6P29x6jaeaP0\\u002fpeig5mmz\\u002fz\\u002fXBFGYTPkAQED2PsqiJwNAIaWZUtswCEBaBBV2cEoRQLmu+9\\u002feqBpAHWxqfzPZJEDEKO6Nfr4vQI9sCDv6DDdApHGU+J6pP0DzVvUVZoREQPNIds4GGklAhbWod4QQTUD6NJQW+\\u002ftPQAfNwoul01BAb9PPX1gPUUBJ8FKAe9FQQJgUAfN+RlBAgZBkNpAxT0Ayd4WUK9RNQEx57TUirUxAc+CfrDT7S0AZ8kqAMRJMQHuJJO8\\u002fWk1ACMkLKJodUECUyOiN33tSQPFlwJCRwVVApaKf6cGwWUBImTwnstJdQJPXJsf+xWBAQ05I5ooiYkBnmFktcs1iQLX64G3KwWJAoyZe\\u002fq8qYkA1nXyQtVRhQOvmCYmikmBArPdZWI4gYEB3IqNMzRBgQCwfUrBgSWBAE82hDXKRYEBEhlghMahgQNChNUaWW2BAmalnGYQtX0BT3u5GE8hcQMOml6VKyFlAPxY\\u002fcgqAVkCv6ucdvTdTQGaEZdP0H1BAX5a2BrigSkAcPr1DJZ9FQGwF6wN3OkFA1Yp0jjTkOkA3SyH9UpQ0QIbGNfJWIS9A92SZQR62J0D47d3eh8AiQEkhQi+Ryh9A9F6sf0ozHUA2kZ\\u002fSi3AcQJ+oumtMQBxAfAkemlyoG0CQZmDQ7iAaQGPKkKTumhdAEPpPUZphFECQa36z6OYQQIRowdAjJAtAU9nKA3dFBUC5f179m1sAQPUwdx6Tr\\u002fg\\u002fAyi21KMv8j9K\\u002f2OweP3pP+2Qqip6\\u002fuE\\u002fo44hbSic2D\\u002fRqYmDVM3RPzcyS\\u002fwQE88\\u002f+16DcrfwuD9+ck8bUwTAP\\u002fDshpuUbMs\\u002f1XZ1GfBa2D8Ri0WK3ZLkP2hcXzKoNPA\\u002fWykMCZ239z8UC24lGx8AQPZiVrwRYgRA+ZvLGYMHCEBFRSCRmY0KQDt8+WClzwtA9PBL1x5ADEDydiRbyAsNQHqFQqXYDxBAUTd63f4HFEAtwq364OsbQNqfIDtomCRAZUOvaVRzLkAEWqw+jtA1QGfKJthjzD1AcLjZ+w5GQ0DXxBnF2pFHQA07yrhjRktAkEXe0K\\u002f0TUBWr5if119PQGcqEpiVik9Aefk7AhivTkCLBskDtyZNQEQ+2AM3T0tA56QJNAl7SUAaQhYH4\\u002fFHQOWeqEac\\u002fEZAhs3qde3wRkAP6NW+DzJIQJ4AOtObIEtAiIJSjh\\u002f7T0CybK7A\\u002fFtTQCGF5Xync1dArkd7UXbXW0Barac1mPZfQEGmXMNgoGFAIhMSxUSpYkArth1xIAxjQNod\\u002f5Bb62JAmWMNSbmJYkApQjZ1FTJiQJYuJkJEHGJAVQCUYNBZYkARgmRKv9FiQKuNgr8ETGNADLiSMf+GY0ABP2HT9E1jQKDUKBxEiGJATdWbnwo9YUCiILWjYxhfQPdwJyJNQ1tAEvnAiRhRV0BhhzSXS4VTQKoKjH6tClBARMxsUpDsSUB\\u002fxWdKr51EQLYW11+cKEBAE2Lvx94SOUCQYKFCoXYzQFQSKg\\u002ff1i5A81MFFaScKUCfhkHjqcQmQHRSX6HzoSVAwZ06yuxqJUAuH6ulvF8lQJzv1F4K8iRATINuB5zdI0CnjF2KmSciQMM+ezpeBiBAoBiYPqp9G0DmcbTUlQ8XQMyuJByZ+xJA\\u002f80bzjeeDkD0yAyGGwoIQIzFbx+8NwJAjh8m3ft1+j+WzTHzr2vyP4a6E\\u002fD8HOk\\u002f6tv4\\u002f2EE4j\\u002fHreQDckvfPzqjf\\u002fEwHsc\\u002fsOcQ7AZwzT8mRDQVTtHYPzNfSMOvuuU\\u002fHVx4YYse8j+wpuELxzL8P9OOjRRIZQRA2x59TTFsC0DQMpqDqycRQMgZzvi2BBRAGFCQauTgFUDBXSEQI5MWQHiKKzQcVhZABtqTN2XSFUBDGXKkDxMWQH15rhyPaxhAW7xnnwhTHkAjGAoYGpgkQK61zx1DBi1AZFjri89LNEDFzZunQG47QCK3h88VrkFAGKyl3IueRUBHLFi6KQxJQIZog2RaiUtAHFsqjLDUTEC47PldeOhMQPNOHYZK9ktALWyb2I9SSkCNCg7ztltIQNvFs\\u002fqDaUZAj7K14WTKRECZP0hArstDQOHwimaFw0NABoOE7I8SRUDtlN0pVBdIQI7EqoVIE01Asq8txyoEUkDDDVoj6E1WQDEoJxyUB1tA6uCuTn2tX0CjaKByRtthQAsizmI0XmNAAHbJqr1OZECyknEnl8RkQMgNe\\u002f0i92RA3Ejh0xEoZUDIh3j\\u002fvIllQOP8mz2wK2ZAZ1UTHhT1ZkBhIXAYXq5nQCHJkOQuFWhAsz2\\u002fJNPxZ0BwjQz+pyZnQClV8Hx\\u002ftWVAvnqz+oq7Y0CAqB7XPmdhQKiPnJvd2F1AQmdQxjf0WEA3x4Vg+2dUQAZm1G2xYFBA0PigQdblSUCU0sAXukREQN0sGlfIpT9AWQMYWWz5OEBlJ4mBzFE0QEDN4QiwazFAEowxbc3YL0Bvm75dvsUuQPpE71mvqS5Afkci+F2pLkA45qQz6iwuQOpU\\u002f41m9CxAvcZut0sNK0CxLFcqu68oQKJB0kLGFiZAgaL7DVZnI0DL9nv9Iq4gQDJRzQcf3RtAi3zx0uJnFkBlktIMQzIRQL5ez6fdDQlAzRzUCHNcAUB4Akx8QWv3P0qqi0vVk\\u002fA\\u002fCT4qCU+X7D8UN2V2tUHTP5sj603iUNg\\u002fjznJVa8x5D\\u002fCJCb88G\\u002fxPxhkntgvuPw\\u002fgWOTEwAXBkBmwFmOs5wPQJ8f1kM6CxVA+YUg5DUaGkAVNhLNVD0eQK2ZR0CbbSBA5BQqOjPcIEAiheyBdYwgQNHPPvSEvx9AmOsZhkvRHkB5LuffR64fQCJYx89k5SFAgpzw0ddCJkBQ4YwPFHYtQK7EYBRn2DNAn774Sh1POkDPZLOs19BAQE52c8Rkg0RARA\\u002fgakrHR0CBnPRVIS9KQNqINzbydUtAOmg1a5+QS0C4LecLyKtKQKleopgYGUlAoeFeSxg3R0B3DJOQxV9FQFfkbHuD5ENAmztHdi0VQ0AMR+Dfz0lDQMFARuY75URA6e+Cb\\u002fVJSEDOCAPm18BNQDD85iXEq1JA4ADCTXVhV0CWB55kiapcQJOKAijcBGFAnxccgtd7Y0D1EUe5wIRlQKp4gSSCCWdAJt10rpEaaED8pwRudedoQOfe\\u002fmR2qmlA\\u002fDqbMbCPakC6O8TZJKJrQJRCy7lvxmxAiP\\u002fXpPnDbUCHBNylRVduQGQWivVzRW5ATVa0utBqbUDW4UVU3cFrQGzWTqMYY2lAwKJB2PN+ZkB+P94eslNjQLf9cPduIWBAcHaRsVI+WkBcC0fHPOdUQIA2q+7VZlBAB7+ZZFGPSUDS8KJUov9DQLRXtQ9c6j9AgWmPczx4OkCVXCGttDI3QPxlIXWWlDVADbjqNOUNNUAgBo7pKBI1QDEUQIUkLTVA4V+9T7ITNUC2bSdOE6c0QMDqrNFs6DNAqxTaHbbjMkDJLBt0bp4xQAE925BrEzBAKaN1OgF4LECKYmJLMTwoQGBs0pYdqSNAcnLVJCwwHkCql8OCztoVQL5Pa8\\u002fU8g1Al9mqkibkA0CveRrLr7D7P0wnVNrFrPc\\u002fdVpkTzPW3D+xJM1gSQ\\u002fiPx\\u002fcsofmj+0\\u002fF6Xcv4sv+T\\u002ftwhoPu34EQN2CE1pqMA9Az6Yw2+oZFkBDqSJRIC4dQBoYEvSW+iFAhNqzpeG9JEAkLh3n2YImQCXmts02KCdAz7eYINjYJkCK3BN61wQmQI18WGmVSyVAMsIaqLtjJUC14AE6aQknQBlfQwgI7ypAqN2PoTjSMEAiu3kunrU1QBoQBhe4\\u002fztAa+DmS92bQUAgrlosV0lFQAv4qpnujEhA1wdM8nv1SkAgXATbtzxMQF8n9rsvWkxAGWnU5oqAS0Cb\\u002fnJAdghKQDq37ri7VUhAP9I73H3ERkAsr8mQ86VFQI5hcVcDSkVAH2YS1K4LRkAoT30RvlRIQGtTOR07kUxAf+rzSFGJUUBPc8wBTPVVQLZIdS2iZVtAz\\u002fgyGn+\\u002fYECDWKwRM99jQCTIYM\\u002fpy2ZAOnVNcjRPaUBzKjWXXFNrQAoW4T5Q6WxAyuLpxyg\\u002fbkDsz8LxYopvQIVGUbChd3BAxEGfgyA4cUBjdLhjz\\u002fVxQI+zIeDuknJA5Z5i+TrtckBq1BsuDOZyQDp\\u002fWO6QaHJAXFs2WPttcUBM2Iv3V\\u002f9vQIfKS5lwbWxAgRRTSBlwaEArAlSDAFtkQGJ+8LeNe2BAEU0QebwXWkARlr3BmFlUQANmg+QXnk9AnenGpy3QSECKMhf75QJEQPgso8DW30BAoF0aGDkZPkAqlCG5ZF88QFXNL7d56DtAtw9xSikgPEDF\\u002fNQcKpU8QAI7\\u002fqJo\\u002fzxAY4eyvRg4PUCcww9lHSY9QB5FjmZxqjxADAXVzvyZO0AXWsYHLso5QLGdr2vvKDdAmR\\u002fqHU\\u002fQM0DcqpfWdAgwQMz0w7EbayhAgE89+XV2IUBMS5LrGJAXQLZR3zLnug5AADBN6sv7BEA1t585qsQBQHRB3Kc3auM\\u002fFMW\\u002fa20h6D\\u002fETB9Od3nzP9E7YelCYQBA8Bsz2f9cCkBqj4MeVd0TQMWXS7NP6xtAIf9t+LFPIkAaFdjCR3kmQDGcMRAV6SlAZysM75I6LED0UgG4a1AtQOP2ziYsYC1Ay0PzVCLlLEDIaNXTy4UsQBUJ\\u002feft+yxApztVmi0HL0Dw8SUaPrExQH9CJtdWVTVAJmFkjGqVOkDLdZ8sUKZAQJHh7Z4UekRAJ3p6l0tVSECdrbXv7rFLQP2Dwf\\u002f0Gk5AGNCUBDVRT0CZbfHMvF1PQNXQfmlNiU5A9tVCvPw+TUClegFVzOpLQA4PgPu35UpAZlOP21R3SkBz6N6pwudKQDXQAtsok0xAzjJ7UtXuT0ArSn\\u002f\\u002ffrtSQBy6fTGzw1ZAXMfjQhEWXEDufnYjoT9hQHv4nQyGyGRAWsBS0S1baEDS6qMY\\u002f6xrQDMgBxW2iW5A2H8p3bVxcEBWnB48CWpxQHONj+YxR3JAm2jDskgic0COEvbdPwh0QImOWZb883RACLAb6PDPdUCOW9MDq3x2QF\\u002f7Jt9F2HZA7Xqe8EbEdkBgL3QKKCp2QKJ1XtXY\\u002f3RAk6Dl2SNMc0Cqqnyq1yhxQLucn7V2gG1A13UVtK+KaEBY1K1I89JjQFsWOvNbS19AIfZq8RleWEBklhZ\\u002fk\\u002fZSQKf2e4wK+k1AvJE\\u002fO7h8SEBMxBMqzPtEQO2GuFFaAENA3vDuj+cfQkBVDX42hgBCQPmpdioZW0JA7rmc\\u002fGj7QkB7D8b7YrtDQNweBAPueERAJMfdq7AKRUDDF+jIgztFQKxkh3RE0kRAKLKLyHqkQ0DhM1gYhKpBQN6D+e7oEj5A6epTyvwXOEDyeDoSRhoyQF39vUTHeylA9XmsVrriIECP3te0bJQVQBSB5cdj3QxAu71qoYMvCEDoZsk4toTnP5yhsf95BO0\\u002fVainh80b9z\\u002fRWvAURzQDQDCblKkDmQ5A5po+J4vcFkDyNA2VwesfQMtkTJNj2SRAm775992QKUBUYqKLYZQtQDaBODSLQzBAnyAs10ErMUBScHtvf6AxQAkY9q044jFAhoSRSP1BMkCdeet1ehozQKGlhDLVyzRAwt6r9xG3N0A\\u002f8jD3oi08QOCMHfMwKEFARkvaWe7zREA\\u002fG\\u002fpEsSZJQCLVZUp5Pk1AE1DZIcJVUEBmBmoQbH9RQOHYE\\u002fraCFJA1J5x5AQJUkBYrVvFj7ZRQBFWdW8bUlFAsGdUP10RUUAbjcTlHxZRQAtUY3wGdVFAkDPFdqpGUkD+Syseh7dTQB4sv1U+C1ZArgu9cs2NWUD5B7foRnZeQMccNztOY2JA7\\u002fbA3skbZkBD+OkGox5qQPVHw4bWE25A6XdsSZLVcEB\\u002fl\\u002fFRvlpyQBVvKkg6mXNAY11yzESldECtjf8EAJx1QCx6wcCtlHZAHoXyMNqVd0AmBRyK9JJ4QEXHbqCYcXlA3WWwfL0RekCsXkBi\\u002fVN6QI1gZB63HHpAc\\u002ffOkWJWeUDiaUCj1vV3QJAUxJhTAHZAlDdKoeCPc0CQThOgC9JwQF3ANmLD\\u002f2tAegYzKZyjZkDotZzCYOhhQNYK48QMA1xAE+XlZPr4VUA6ph4NUpNRQLNGo+tBJU1AxPG6+6BcSUCRKot5yUZHQHU4v0+8dUZAyxRQuDeVRkCQNS1JhWZHQGW9Z9DMt0hA0noCdP9WSkApmIgtVANMQH\\u002fQD1HoZE1AXL4bwRITTkB3A0GBBKxNQB22irXD9UtAy+1kFof4SEAEvrmKiANFQOzQQwL6l0BAo1xc+qaGOEDuEupiK\\u002fQwQKF8NMNHCCZAxkW4DBCPG0AQRF+84wcSQHfTG4Sf4A1ARMpJAOmk6T9MicEN5WzvPx\\u002fnHkzHt\\u002fg\\u002fLbHt30ZRBECrk5V8QQsQQOIYLxl50BdAipGly9KNIEB\\u002fmHj2NJklQLbGPUshkCpADXSq+UT+LkDDvy7yz00xQE\\u002f9ri2PsjJAnvRqg8vOM0DWXyu22d00QMAQLikxKjZA2VNCrdAHOECZGflfCdM6QAkoYKwg6j5A2iKuR05LQkDEHz0MifJFQKUcnYDOPUpAIbbXFcrFTkCYRkfylH1RQPvxLdMmKVNA9Z1xm\\u002f47VEDn0camtrpUQFjrLLBp11RAZfBF9zPdVEDZQxzW6BFVQEaI070NnlVAT+JEiUSIVkCMUMDWvsZXQHiOoOfkXFlA6Ma0B9RzW0DqvNvTxl1eQL+ovT4mP2FAv6NxZ2oPZEBssES5GqFnQPhVAOiKxmtA5dOSppcUcEDaRuyk7jByQJxmyxwxDXRAggcF5f2UdUAm4mPJic92QLpU2a5x2HdAOwIRZujPeEAprvWiQsp5QJEXpZbzxnpAZItcczyye0AF1WtDSG58QO5CZkHI3HxAyCeekNrjfEDaIz\\u002fARW58QF3rSn9Da3tAV1I1L8vReUByj4C1MKd3QMzjXInVBHVAhT3LRBAYckDHj3mtNTVuQItCpExYjGhAbrVE5ryQY0BVCwYgwO1eQPleTjOrllhAiqwGJsT4U0CYOx50idBQQAmn8RNxqE1AaBWFLsOHS0CaMdBEMN5KQFQtBDBoZktAv7dx4oTqTEDffvvs1jJPQDMY3zxc+VBAO6f8uvdcUkDLMGV0UnpTQPhwfzfOAFRASmGTggauU0BdKfVVr2RSQFyEyIpJOlBAblIX8ZHnSkDb2nwmR+NEQE2OjvVLUD5A3bLqeeWNNECmFN9kvisqQFq0KdcTAiBAvCHH\\u002f3p5FECRx5nCW8MQQCQnkbpXMOk\\u002ftxYy1TCt7j9NqPhRGNz3P2B0zG\\u002fBbANAcZLkRgl2DkA7FQI1I4EWQO2luutcPR9ARqT94MhtJEA9M83HclApQNwtJpO+8y1AU04TQ6UWMUBOatAkzgUzQI8riSJ56TRASewcQ6P2NkA0x6jw0m05QGXszDDEmDxAPLWqW6dkQEAoy98XZCdDQOLBs\\u002fuRrEZANQpAfC\\u002fnSkBmQiTPIJRPQKMfj3YkHlJAOqSRXXApVEDCeYoT8LRVQMycq2oMs1ZACXDUb0BLV0AUwoysVM5XQAkfO90AllhAw9L6htreWUBgUIUlg7BbQMzlzTlI4l1AOdM5vg8eYEBJ89aTK1JhQEOKvhzloGJA35wDKfU+ZEC7CMDbQXBmQLuMLfoXaWlAS8Am\\u002fGkwbUBLuOsj8shwQFpKizdGFHNAFyxigsA\\u002fdUAe1KaDoR13QBHbSxNCnHhAkRVXz7zIeUBXncdvksN6QLnmXmRTrXtAkLVdkX6VfECNxEWW\\u002fXN9QK6DWKTmLn5Awb4RTuSmfkCllvXaxsF+QJhfOJUBbn5AA8ATAz2gfUDbtvnFhFB8QKHVjpHIe3pA+g5BO7UpeEBez87wiHJ1QF4f8yTUf3JAvA26h1gMb0DziD0gIXRpQFS6GN+RiWRAB3rwUFV7YEA7eaO8paZaQCBQJcN5AFZARWXm3SvHUkCqw2LTArxQQN+XrccjWU9AwAGIuvzqTkAA4H9JdvpPQNfSOR9dKlFAUkBNHCnYUkBfvNmBMsxUQDck5+KrsVZAbFx1iOYfWEBIKZ1nb65YQEji410qElhAisNoLsY3VkBjBkqhZk5TQC9FyGGZd09ATBCyk5L8R0ABMuXtGhNBQETV5qDNsjZARIw0vapNLEDnZdsy6+wgQM+p7TaTJRVAvrnGV68ZEUCnN1IHWk\\u002fmP2B3hfjPBes\\u002f51FCCYbQ9D+dokpOQNAAQGtBa7JqPApAUWNlQQBZE0DvLvUG8ugaQJYkn+N6tyFAedspo6M8JkBe25SdcNgqQBl3xqp7gS9AWzCeErMsMkDXEgGEuNM0QEyLcX\\u002fd5TdAFYI2UciaO0DG9LjQ8BdAQASxGtIB8kJAOS0WG7tzRkD3ZCwUTJ5KQD\\u002fKUOv+SU9An2cy5TYPUkDyUHUE3FBUQJ3hadX\\u002fMFZAjvNcwBqWV0DsL3mj1JpYQGBNftWHjllAk4k0GtfbWkAVKJf0CN1cQIMG57agsV9ApPHGudmVYUCKrPz\\u002fRnFjQFsLSPtEM2VA2two+eq\\u002fZkC2ruRElCxoQI5IYknUvmlAcu4OWZnSa0BMIihBVrFuQI750bJHNnFAT0oPzhlnc0CUYeyi8rN1QM2pYvfR3XdAAQatOqO1eUDPsBxV\\u002fit7QNb4jDpYUXxAHWZIlyBHfUDVKpyVPyp+QJtQuQKTAn9AC8JJa26\\u002ff0CnD\\u002fypgiCAQFIM3gqpM4BArBIvxZgOgEBzb8Xnw1p\\u002fQL6PVF73IH5AkblEgEx0fEBXHdbG11t6QJ\\u002fO2Yjq5HdAm3a15KgndUA16eusC0hyQHdEUZWr4m5AFroEyC+baUC+u7QDMPpkQPAPc5qXImFApLG1zBw0XECUjo1+sqNXQMoiYtLgZVRA6lGdK55PUkDcgjIC5kFRQLsWVtEiKFFAkZHA3pbvUUB1MZCTlXtTQND8eQSQmVVAo6qxmob4V0CiV8ssIClaQPO9b\\u002f53q1tAjXSxbPcKXEDB3uS5WgBbQLiMk3+Qi1hAhMuk5hf4VEBV1Fyx2MdQQHe6SsPZGUlAbxgL\\u002fFGFQUCJ051eh9M2QLaKEIG03itAAlXLc59KIEBvyyi4WuATQGMeq\\u002f2zvA9A2t5pzfnX4T+9IuJ48oPlP9z1fJ2UcfA\\u002fY2ipRx9r+j+zdK\\u002fn+5AEQBYvFSR\\u002fYQ5AIHQQODpCFUBTycLID1QcQBU42AIsHiJABXjQ3fh3JkC0Ax8qWE4rQJt1dt5GbDBADRXEBga0M0CphOeura43QC5m1mrjjjxAdkGFZi5CQUCOhmrXx9hEQI5Y7v\\u002fSC0lAVqx\\u002fwtm9TUB6gFQAF1VRQBMtNLaStVNAGCqT9KDLVUCENzwJjXhXQGlHrEyry1hAThJCCOQMWkDB1xFm\\u002fK5bQGkFNVHGKF5AWgQ69eLgYEASflF1eDRjQFUIuhdC1mVAhSwRJgdvaEDV\\u002fyHTvaxqQLq9f9g2aWxA0zxJ1ZXBbUC9HrVCKxFvQEKtynhrZ3BAIXYAQ1GtcUDSUe3pJGlzQDSLwCDNgHVAy0DHfHa7d0AHtXYAKtl5QCgrVYE5rHtABvwRHAMofUCqpMu0OF5+QHNsev05bX9AI6j2\\u002fvA0gECqZpeKdKiAQHNk6PpjA4FA2iGO9CUygUBotqrDqCOBQLtTNWgoz4BA5boFoiQ1gEDcfp8rMLl+QFaeITe4nnxA6cGo6k8wekBEZh3eUIV3QML6aPnnuHRAYn1\\u002fgXDqcUAXL6reUHVuQNgZgi85jWlAFkSx74FFZUCtdD4sOrFhQHYswxohoF1A9vFd4U4sWUCf4JHgHOpVQLUJoE+Qw1NAEp740vKrUkARi6EIzplSQE1Oq3FQfFNAKEBhspwvVUDXzRYuxHJXQF7PAov74llAWlfp5w8BXEBcoOpgCkVdQA8BFJcmPl1AU7iDVfS0W0CeBZcnjMBYQJdBR34pw1RAI1kni6FNUEBUeXQR7+pHQIbwRZYOXkBAvUsVaYPlNEA6LHjDuvsoQAOp5lWsjRxAxqbtW6QBEUCYhsclAc0KQDA2oUKf2Nk\\u002flZ0NBBMQ3z8jsI1zJ57nP0C4U\\u002f\\u002f17PI\\u002fzyLyTDCC\\u002fT8p7WD\\u002f9OwFQPk5SSs9Cg9ArhOwXUYQFUCwh64v+qgbQBch5eH6wSFA9EinbWWFJkC1WazCZ28sQEZTXJHb8jFA0rppzd6qNkBWo0nYM5E8QAPUe1zg4kFAXT45CO0iRkDzNy5dLexKQAMQtDT1\\u002f09Ahy91O8iAUkBpp4XmKMZUQAkpMTbRrVZABDsxRNI8WEBVd0cme69ZQEel7rU9dltAKLB902QZXkB6yYWFCARhQJIhoclzsWNAxHipUSHtZkBDeP55j1pqQCRAVZDZg21AHzkYArMCcEDVckK7ZdxwQJ9zyLlwZXFAfxz3Ev\\u002fXcUBOpbG7JnxyQAqr1XwCi3NAmwWg4b8XdUDeqTvSZAl3QNMAShEbKHlA7HLVLRU3e0DTOsR\\u002f0g19QNhiQ1U1pH5A3On7fyEGgEBrbAqzLq+AQOSbz9fTUYFA5G4nGSflgUCLAOZ5f1SCQLI+\\u002fDAeh4JAKVqxvkVpgkDRL4r8VPKBQAh7WTvwJYFAh3be8CoRgEBqXFKGZYx9QLQ3Lk9YsXpACb267SO3d0C9LgGuaMB0QP+vDAQk63FAQ5XIi2aebkDPDDpipPlpQLAFItvY92VADRf7PRaaYkD6dqpQxa9fQDYJR78ZTFtAGUGVcn34V0BoyR6k\\u002fK1VQAH7GLxea1RAnUxZ0t4rVEDUElhfY9xUQI7FogHrUVZAnknQvg1DWEALnCJZY0daQHXX8Ojt4FtARtXufBSSXEAFyhOfy\\u002fxbQEANTn8lAFpA+FblYXPGVkAsZ7JDQbtSQBTqaLKG10xAv+wdpa69REBJU9pzddM7QEUlvUxLaDFA1u3U0sBhJEAOU3FN4cYWQE8KItK6fQpAP\\u002frAqgGZBEDriQeCfBnRP6esTzbqhtQ\\u002fiqRCbPEw3z92fbBYNA\\u002fpP8U6NZBwrPM\\u002faifMWEiZ\\u002fT+xmC+ogVkFQHZoQlSjwQ1Aw8B8vFg9FECpIzGAQigbQIlBQV2CICJArEqxuyUxKEAfbIiKEiQwQKd+8z0GdDVAgkIJYt05PECcVJiiKUFCQFQM89DiD0dABN37\\u002fTZOTEBPennBStBQQMxAzukRT1NAeCnY82x6VUB6CZ2vMktXQFL2WSeP7lhAwQ6ghT3KWkC8BfMMpGpdQLWcRkMGrWBAmq3jyyZ1Y0AGUPv74wFnQFLIzfdmCGtAhknr74ELb0Aqce72\\u002fD9xQKOWQ43xfnJAM\\u002fUuh9Q1c0CXJX8+OodzQNowN\\u002fWVunNAYIzifwgidEDLSZ1GZ\\u002ft0QKwi32VoWnZAMpbzqNsleEDKGG5OfSh6QIYriYgaLHxAIn19atEQfkBpeoyO7tR\\u002fQFcni\\u002fpXxYBAv9TaPXuhgUDOOpzIm3yCQPfyNdydRYNA4yXPogjgg0ARwZV10yyEQHMobfcUFIRAToHx2paLg0AoAYJGkZiCQHv7SokyTYFAPJhL96+Hf0DJ3CXioDN8QAOQowGk1XhAil6l5l6bdUBmXtw0BqVyQKbkDdeZBHBAYBdpMSd\\u002fa0BEU7K\\u002fhKRnQOGgZRnrZ2RAwAqv6ji2YUCsHBzJ0QFfQLx4XQXkgVtAuZm09SHrWEBFzMnJ3D9XQMa6dxRPe1ZACLnthB2IVkAJJRzL7TlXQJ0cibWaSlhAxPXAVY5cWUC65T9VLgVaQJPImvLe4FlAPvb+LOmsWED23dy5n11WQErKyqycJVNAnhfgDgvRTkC5ELeJZDtHQCh\\u002fmWgGXkBAMWJpzHCENUDs6QZSgGEqQB6C4gYVQR5AJIMVsJSIEEANekusW8cCQHXGY\\u002fjoz\\u002fw\\u002fSmh6kyF5xT9qHx4AZeHJP7Nla9GD2tM\\u002fCMXBRisq4D84HgszHdPpP0NV1ugU3fM\\u002fTLI6fLR5\\u002fT\\u002f\\u002f+l\\u002flmEYFQIWyKDu\\u002fMw5AezdnKGpKFUDU8t0u8gEeQHbQHoMSKiVAogVtO\\u002ffALUDSGmqj3a40QChCL8KlLzxAZqgGsZ2sQkA3ez4tW+lHQG4BhsgLeU1A8Y\\u002fWgO52UUCS+RjyjvFTQKKnq1CAFVZA0BJ2fbT8V0A2E4e3R\\u002fxZQL\\u002fv2KRZm1xAmk33VoY5YEC0b\\u002f+JGf5iQEHpX7oNrGZA6fAmZW0Ua0DJfQZZLcdvQFPrgoUvFXJAMlZS94\\u002fTc0DTJ04HJu90QDPk3PfhZ3VAURsoiIxwdUCMeu5Td151QAvbuBQxi3VA9h2RkxQ1dkBXEFZsrGx3QEFS6OrYFnlADGt6HFABe0CI+il6+f18QAxZ\\u002f0PM9n5AaeF+qFh4gECouhbMwH6BQN98bCoEk4JA7FQcjB6sg0CnXwYGR7GEQMXS5ePefoVAHhtW2v\\u002fvhUAt4oSDf+iFQN6y21fwW4VA18Kc5hBQhEAX92VV6NqCQK32ZRjJHYFAlk5P12p9fkAMmKWGRsN6QGdQ+fUcRndAvUSV7OkpdEC4jCo4OH1xQE7pibB2em5AgcwlVbW4akArzp4IEZJnQNmZPdKv5mRAuw5N4MGgYkByTJBQFrZgQNJ\\u002f8sUCSl5AI57T7CzdW0A\\u002fZIUyxCFaQNPEg\\u002fEEBllAisdqZdVmWECc5DTAiQ9YQOLExYnhvFdA78qiBh8lV0BtLX5dhgZWQPOdYoqNOVRAdIjxlW2\\u002fUUDth8wl9YlNQC60aXDuLEdA1KGNIY4RQUC\\u002fdiF3DYc3QPE8gR\\u002fpSC5AglS0NnovIkDfzT3iTG0UQEi1+JGZ1wVALyiCuzc8+D8mj60+LljyP8s+bRYs770\\u002fMfs52IVWwj+vCv9mPhDNP1lCkwpoc9g\\u002f3hFQvGok5D8QU6+ogejvP08\\u002fe04RWvg\\u002fAT\\u002fiaN0RAkDYq4YonGAKQHmTndf+JBNAk94UebrRG0BsqPPtXTskQAKt5adKQy1A7ivipRXUNEClDREfud88QOf3watpUkNA1NQl4VnTSEBgsvqNzoxOQP3iP\\u002fuvBlJAmuL7S+GCVEB4zRm+ir1WQKuMM4Ik9VhAUT+PEo+kW0ATiYEYcWxfQMqlX0Qqc2JAyDMGwF01ZkBeGemsru1qQPLlTuDcIXBAXmlIY2LPckACNMWmaCR1QEAJvjFO0nZA6NO1IDuyd0DbsF3titV3QG5cTPy9gndAZDjJC2oed0DHApLB9wh3QAulULu\\u002ff3dAwUIpB8qOeEDsJqjiVRh6QNVIBhOP7HtAJk8qhzrkfUCnFE3ct\\u002fB\\u002fQDmM6cIxDYFAjw9p7A04gkA80b9B+XiDQGwV8CwvwYRAP3SrBGLyhUC04u1JmuSGQLORqZ3ib4dA+cMZNhF2h0Dyw1aEzumGQCVY+p530YVAeb8oObtFhEAKvlH3j2yCQN9AtoWwcYBA7gmWFL\\u002f8fEDFGGj2GmV5QN8uQ+YtQnZAT4gK1FGfc0D\\u002fDhbvE3NxQDkk53dPT29APrkc6PVGbEDTWQbFIaBpQJI83v0nPGdACWTgslwMZUBezr5mKg5jQJljGn1SRGFASlv2J51hX0C60UmH66JcQDzWuMNtPlpAZIbjTeAcWEBlE0XXKh9WQOhNuBmfIVRAh7PO5jsDUkAaI3bMYGBPQBd5HxvtVUpAY1SquJocRUAWhJ4ZTwtAQDCsLjEs\\u002fDZAzINmj3vnLkCifpVDwXAjQPTFBHmc2RZAahkYY\\u002f0iCUCs1CHTblD6P+Z+gQjmj+w\\u002flNwg9olZ5T8nf7md8zvAPyjLd5h3W8Q\\u002fW95EFjjL0D89phxDeCrdPwBmFrgvhOg\\u002ft0kBqMiY8z9lbmRBZdn9Pw8a5PzY2wVAKvkt9N8xD0ArDmNHFQMWQBV5laesFx9A2vVL\\u002f7EUJkBw0rYqrWMvQDoRv4uVFDZA2jgqav9YPkDB3ClTHChEQBTn7FIKtUlAyS4EbnVpT0AwO5\\u002f0DnBSQDH2cqn99lRA+RY1dpNqV0AV657zQS9aQF72xMuV411Adcsk73KeYUCumGthHGdlQNx\\u002flzljY2pA70qy0F0vcECsjMw3e2hzQKjbNltbeXZAfSSl6gn3eEDFxRWOeo96QJ+DmooOJntA3p0TiSTgekCITkXNyBp6QFRj2M9cTHlAYBP1dmLeeEBwdgM3Zg95QBVxPuz56HlADKLAZMxLe0BalQggZQl9QEWG+XVr\\u002fX5Awn1\\u002fKSWNgEDxbxLBbLKBQLxD5A3j8YJAHxsEonBGhEDfhRLlBp2FQMh87AtD1YZAi3P3x4zHh0B7XH\\u002fXAE6IQG4n0jwfTYhAK+\\u002fxWgi6h0ClQY0FKJ2GQKiJ5aTxEIVAvcUC6eE8g0BhhJMbvU2BQDtTcu7g2X5Ah9ya2\\u002fhye0D1Hk9oiIh4QIzs0c5sIHZAnLM12cIqdEC5ZGkF6opyQJXwFfTRIHFA4G9LJJKhb0BVJGK62BBtQCv0qoiqf2pAsws4YqLuZ0B1PJBuxmZlQDCwTECv8mJAdojOiWibYECkilNykdBcQDGy+n5fvlhASDsgejYHVUCvZJdTl6pRQMNJ46FdQU1AqBQ+1e+9R0DZTU1F0r1CQAuJRTKeiDxALHKeFYjFNEDuOFZnrLUsQDjsR6DVuSJAPpzG5TX1FkDdiS+nPmMKQG1t2HyxePw\\u002fU0eT4RRQ7T\\u002fsB6vm42HfPyNm4SOKTdc\\u002fiS4T0lxuzD+mQaZHQenRP64KjS56t90\\u002f8AOE+ZzH6T84U5aNsnv1P8XWibf54ABAaHjzNPECCUCbvvGTlpkRQLTtzI37zRdAyvwkTAdwH0A1icBxuZ8kQGHZlNk9PytACJvtPF4xMkBbKstDOWI4QMAHEsTfLUBAEtYCEeb3RECbYxUiX0lKQLK1QswDw09ARj2IfNOMUkASFoWHNShVQL29rlIl9VdAPowFzEJ9W0BSF1x1OEBgQDlYVtL44mNAY5kWRYvraEBzwYtagVpvQP5JwPMGaXNAh+0ygHJKd0CNW1DDl9N6QIZ9BiQah31ALhnflhoOf0DDZSdt+FV\\u002fQEgIGfPRmH5AAz+iqclLfUCPEiHtiPl7QCGS3BlXGHtAEANxQ5bsekBaElHz0YF7QJD8QR2AuXxABqBV8f9kfkBh2EQveC6AQHH54hbZRYFAJ8N0nX90gkBvciVuG7aDQFj2oFtB\\u002f4RAMILDASk6hkD\\u002fN30iaEiHQCUZUfeRCIhA\\u002fFK2MWJdiEAKWuL0NjSIQGZ4fkZqiYdAYfjBiNFphkAaDkILCfGEQC93ufSWRINAWJmdg76MgUAE3j\\u002fksNl\\u002fQG6F1cPR+nxAsrCFGemSekBT4zmvYZ54QOIcFcIpBndA6vMOZzmodUDrGAd2t2B0QNSJlx0vEnNAuGrhd\\u002fiqcUAPfB51SyZwQJv92TBaEm1ABs+eugi7aUD9\\u002flZ3kFxmQFCYU5FtDGNAghuiXtnAX0BOqNmNpd1ZQOGGxShdl1RA9AGYfAYJUEDyTgPZXnVIQOlrdAXHREJAXvPZeOKrOkCwv\\u002fbv0fAyQFdVedUOASpAtj6HdrIiIUAPlh79hIoVQKrALqHitAlA0CofxlMO\\u002fT9Rw\\u002fjQVzLvP910slmjPOA\\u002fTpf8Uevq0T+z0Hqod0DLP2+PLOOnptw\\u002fWeV\\u002fZCjn4T9DlwT3tTvtP0ZEHkF87fg\\u002fJOBXaj5iBEDHtqW2TlIPQCpiwxI4kRZAHSCn1\\u002fWgHkDdEinBar4jQH4NEhv8fihAL6eJ0f7BLUB738InXwgyQJQrKjJ6HTZAGwW0JheKO0A2NKzhjUVBQBAWYwJlhUVACetnrRhPSkBJmPsdbVtPQD7ldiBaQVJAJ9EYG278VEAv7u47kD5YQMNDcBK3sVxAMIlzo8eYYUB1MTjLQ0lmQBNx4vdmqGxAajmlXj9PckAzta6QytF2QMvY555mYntANHBzWA1pf0CTE\\u002fEWNiuBQF5d8a2X54FAlajgezvlgUCmFpyKEEuBQFiCZvAgX4BA\\u002fWzixbjhfkD5I8x+mIV9QL2pymwZ+HxACXF3iiJLfUCAnljMOmN+QPQfIzrfB4BAR+3qHuoPgUDxTpwJKTaCQJ8dU8EKa4NA0QOqFBSfhEARblwwZL+FQEpbVqgTtYZAcOPeZMRnh0C5esjlDMKHQAy0LwXotYdAowoNltY\\u002fh0D2XxtqK2iGQF9Mt4dnQoVAO5d5yZfqg0B75TvizoCCQArU8l52I4FAB8O3Wd3Tf0Aj0zrmI8J9QE\\u002fqm9O9FHxAotp70ui6ekBpRER3\\u002fpV5QJFzXzzmgHhAmdS5SlZYd0DKR9vWhAJ2QDfntN7pc3RA6GmskhuvckAPlCnGOsBwQNSiQf0Qbm1AGA3fllFGaUDW7\\u002f7emSdlQOxSFNAYMmFAGk5w+I4QW0DEecdJEJVUQLtk6z7uP05ASrLM2TWERUC+OMmG+bY9QCbXXgI8+zNAqvyZxEE6KkCnwrK1iM8gQJcBIi1gBxVAnNtuw+SiCUBn\\u002fgW14HL+P20X5exbqfE\\u002fTVIvzUAz5D+yIWoHKXHXP\\u002fiOsx3\\u002f9s0\\u002fxJ9w383dyD+X7ODyQ7DrP9r4tzjhG\\u002fE\\u002fmE1n5qxb+z8jowoJf9sGQCxEPa7zUhJADwS3SIiTG0BSmfnHZmgjQGId1XWQmylAkHoRUvDZL0AVYbfDntkyQCpDAYa5iDVADKH8NpcwOECm1qoY+kE7QJ4pfHpgQz9AR\\u002fOsyBlOQkALwgabJrVFQFpJC9bhvklAMRUJ74lFTkA639gZlqNRQOuZJmOjkFRA6gxxmWJfWEC5PzxJmc5dQLiXYner5GJAW0v4DFWTaECesbMF5RNwQK4Wc7tEuXRAWGaOUSfjeUAv1OqOAgJ\\u002fQIqf6aL3tIFArdFtFew\\u002fg0DX2QkwC\\u002fSDQIK\\u002fbp8D0oNAK4+TTv4Ig0B7cHCCwOeBQJOsg3BLxYBAAAVplZXUf0DpGKBzAgZ\\u002fQGq290UXOX9ABO5cTTUrgEDslywOsBSBQH9eLxorOYJA0vNne554g0BpbdalfLSEQG7EkcXPz4VAyeidaOqvhkCcUD2ynD6HQFcba89TbYdA3I7Up603h0BbnnsUSKSGQFqM8quFw4VAF6xZSdqshEDzcBg+YnuDQBD2c4U2SoJAFNxXTbgwgUCTyup+Rj+AQOysJRlI+n5AIOi3DlrQfUCrAnu3hOx8QLVyh2htL3xAMcMf9DZ0e0A7QhoGpJZ6QIY7mX+\\u002feXlAozNprroNeEC5JPTcZlJ2QCls7UGKVHRAoQp4OVwnckDZRzB8SrxvQPGjQO5bE2tAQqukNs9zZkAVFc+qsQFiQARfoYbEzFtApjbgjs2UVEA6In0Jni1NQCOEiQTV0UNAxB63S73lOUBk018pIGMwQEAGzODvSyRAw4DHQ2zvGECsmoXQC9UOQL2u3yuGagNA4hl9ZwkJ+T+TCS5K7XrwP1snS0h4EuY\\u002fPZHQ4vdt3j8dV7GUIr3WP7A8OR8WP9Q\\u002fQ6DycvCI+D\\u002fx4yfZu\\u002fb9P\\u002f3qAV5ZbgdAZYjHNEsqE0BDkAj2hyAeQJ9Dmp2BOiZAzdJMTCylLkDjDy+qcMAzQPifTAGn5DdApkX2oqRNO0BCbn+5xMo9QL29zVz0ij9Ah7SQ3sWLQECCNx++2JRBQAZ46AIhNkNARYjDHEykRUD9xkNXsO1IQAQgqjtLEE1A7oLqiSUUUUAzN\\u002fkZj1RUQNMPkn8HxVhA3ekIz2YlX0DIXttP2i1kQJA+t86nmGpABg\\u002fpkl99cUDNJdk0j4h2QJo8gFdVC3xAd68Dgny1gEDu3lPYaPqCQErF+WkBh4RAS4maQeswhUD6t1YhG\\u002f6EQNTP\\u002ffHAIoRAVSWj9ijxgkBtEZ7LlcKBQJSKoETr4YBAaw55O4l+gEAOxFbsA6mAQIgqJ4RTV4FAc0TJOGFsgkDaDwrZi7+DQJEZ6kytIoVAXZ\\u002flXfNmhkB8MHPOmWGHQM\\u002fyO8EZ8YdAi\\u002fwQ5jcCiEBrGPJHZJOHQO5e3O3ctIZAivftehSFhUBcEczhWCqEQIcftGKny4JAnktrrH6KgUAV6PeWpn6AQDmM5t0qaH9AUzZ0NJZVfkDow4pM47B9QLb+91K4Vn1AKhiu5TMcfUDAA+s0iNV8QDNCr2BgW3xAYrr6RpOPe0BHFezDb2F6QKlLZG9W0HhAkMFL617qdkBapOLVTsZ0QLPoX311e3JAmSBgIgwccEC3Int5bWprQG4d2Ys0p2ZALwdpPkgQYkC5w5fGKaNbQGUA1JVmMVRAa8YdOaQfTEBo55p4OrFCQNmZ3vQw3zdAR3F3U6C0LUCxLjOK13AiQAtPS\\u002fNxjxdAh0b85ZTKD0BBpVscaMAGQCLg2E5k6wBAOHy9eQ1k+T\\u002ff8f\\u002ftcN\\u002fyP2xWE7JdC+w\\u002fWBFAtVP25T\\u002fhWPGe6OHjP3VwCRDRxANACpJABADaB0BYAt2i3zkSQHkC1SKQKh1AYqD2ou93JkBGmu6hp0IwQJHz\\u002fgZ2+zVAnxZFuqbAO0Ds9d3PEmZAQLAPs642OkJAtLTpBLs2Q0DJC8qGE3hDQEkSdO7hWUNAG9vwgQldQ0DA8FJzdwJEQGA4jKFsq0VA4efeGPKNSEB2RnZifMRMQBWOVDgQO1FAlLCg8KMFVUAvHq3ZFS1aQA5Zr\\u002fMDrGBAXnSsbkGnZUBiWIEtqV1sQAFWFE2xdHJA\\u002fCLc1yJ\\u002fd0CaY6cE3Op8QEMBM0DsEIFAQM8wFAc+g0D2JrZooraEQN4epfaRV4VAQXfP9FErhUDcQ5PzbmaEQGhfDx3iV4NAq7c2cDhUgkAllnvK\\u002fKKBQEHOLigFc4FAzIP\\u002fTVTWgUDLjVPYj8OCQDvwqbd2GoRA4FutdrKphUDmsySuoDWHQBx4o8ZegIhAARPQjh1TiUDGj6DruoeJQDAnPih4EIlAynhZWDj7h0D0KR2AnW6GQMbsdsVpoYRAHPkhGq\\u002fOgkCZiP0JWCqBQMV8dPnisX9AbZan3eLXfUAqeCVvRMR8QKiqnU4fVnxA1dqXo2VYfEAAsrvZUY18QIJOqbe4uHxALcY6t8unfEDBeM0RJDZ8QKkg4BDLUHtASj55WG33eUC92wjrmjp4QC6EBXbXNXZAXbYxGGYGdEBVpZIisMJxQJKNLJbV7W5AOCvEZn1RakAVvaXIFr5lQHoFsrpCUGFAYJGmioZoWkDevceMjzVTQF\\u002f7f9xDpEpAzlm18K+1QUAHt1jhEug2QOy67hr2oi1A1VQuM+DtI0D9ym2wQsYcQAEe2vlgVxZAIYBrtiMXEkBFIzOJoloNQA9EmQZiMAdAwJ+9i1atAUDKBmzBX3z6P2T+WJEgufQ\\u002fsJEVJNe58j\\u002f0BNF8yfEMQGr11NjJPhFAbuVzLRC7GUCAQ4TXdR4kQOVgHaJYXC5AxLI4xbmLNUBZsIXTQpI8QP+1gQEor0FA18aAua93REBXORmYFTtGQAXje3Bk0UZA87TC9BJoRkDPljed\\u002fXtFQGAVQsu\\u002fuURA6ucx1LrRRED4RdcRMlFGQCfQzT15jklAr80kGfiuTkAJZQ02IeNSQMwt6fcLg1dAkDwQZjB3XUDALnA30ZhiQIz8cw0zo2dA0eFoHRAcbkC4m5XMbApzQEnBqzlHpHdAD22S5BiFfEDYxcPYM5aAQBbSsyychoJApORfqu7ag0AtTZB5VnmEQCfNDCLfboRAv7N8Kkfrg0CMh052hDODQI8WGLPYkIJAQ7bok\\u002ftBgkBQuodjLXGCQIGaqBE7L4NAloLEpzJyhEDO3TXUzhaGQHY7kiiM5IdABVVVlfGUiUDLweJ4Vd6KQIz9OyBHgYtAIwjlHyNWi0Cv9cLyC1eKQACrRmmRoohA5v3qNkt1hkAd3o3j7BuEQPvN0C864oFAlXk0ZyQEgEC44qy5tEl9QCv16OOimXtAZgJMv9zaekBfiTLv3s96QOzNSlDHKntAMDhYyjCce0ApJAGUCd97QDmGVwaPwHtA3aq7tJAke0D0b3HVbQZ6QNO3jE+2dnhAWchvv9WUdkANyLW2EIV0QIwetSGBZXJAsJtLp7pGcECFZyEi1VdsQJWT1\\u002fGcJGhAioHMJLL2Y0DWof+\\u002f+MxfQGqPAx6nQFhAQqNWGH6sUUCcvC+nEbhIQHlj5\\u002fcC1EBAOpn+hdzyNkAdqRip9VAwQPXYgG8JBSlA6nCJAUizJEC98nRUOd4hQOGEWpXg1R5A9z7dlwvFGUDavYx1OIcUQP4Wl+dULQ9AssNlCK4bB0BAlaAn7doBQJt3U6xUCQBAzsPYDw1EE0AifqGQ5aoWQPY6rDZUfiBAT8BWuQ0tKUBd+8Oer5YyQFYdoyZa2zlAnKk1DG\\u002fQQED2pr+rCGtEQLVtqxhpLUdARfIC70ytSEBElRaxQNBIQEbz3MtD4EdAwoM7AKaBRkACdwLn841FQIe5aFDf4UVApYRy\\u002fowuSEAQmDAHEttMQBelcBTn\\u002flFAfnaPqYi4VkA\\u002fBBjfcoBcQJpAq0FjqWFAUCT3jTOsZUDguUfcM3NqQPdpJUK5FnBAJk1DQgt3c0AoqsV3+UR3QG4J\\u002fX0HQ3tAfxn4fwMTf0CRiF2GtiaBQG2YvbRkUIJA1X0dMPv1gkDBPjWodySDQC+VRRSjAoNA8eLTqM\\u002fGgkBeSyyBiKqCQJqy7hi434JAhtpfMpqIg0Ah2XS8MbKEQDjDv3vJUIZAgCtUD9I+iECQLKXOSD+KQEwLmyf+BIxAzW6KITc\\u002fjUA9Se4DRaqNQDVMthVRII1Af50xsfqki0Cbrnmt7maJQHW2w8fLtYZA+0nYpTjvg0AjUSqQkWmBQGpz6UAUxn5AQLGErPPye0CCcnedOVZ6QD8L6603u3lAYWCZjpnPeUDiGyFMvjZ6QJIpFRPamXpA6BmdgbqzekD8202VAlh6QLHl1JgGdnlAa0Ft91UXeECd3hr2z1l2QGbdYxuRZXRA2qIQ65xgckB2HJfML2RwQN8Mwc998GxAu7qkW80uaUCfvVIlTnJlQNeVf5tPuWFAxOCKAzA4XEDFrFxFUo9VQO+bcAjbs09Aahq7Hb65RkA+0sQLEVNAQKXnRnVcczhAQJRyiNK9M0AkwSt\\u002fqDQxQLGzm5q7UC9AC\\u002fapJEuCLEAfh0YjOyIpQEIQ3UUMECVAx0uhkIKoIEAMB3K1ifsYQEDhJMlVOhJAwK4qey+8C0DGH9sjG7sIQFGoDOv0VxdAGjdfohYdG0CcnSVYmDkjQFBulQMRnCxABTGXHGKmNEAk7vd+qyE8QABknr+N70FAz8q63dBeRUCN3MQkttFHQAIlzqsQ7khAtgDJAP20SEBkXr9C5pNHQDar7dltVkZAK5srzrT9RUC5kIJCdolHQL3G1R7MwEtAmn+aiUKDUUD+MO+P8aFWQDdPrDAV+lxAHBsbLZAVYkAV\\u002fiTOzO5lQAWPxdsa9WlAvfDX0vo1bkAcg9AyV2txQJ95nE2Y\\u002fHNAcUs4U7TNdkCZGVNNUMF5QL29A1GZoHxA5tJaWosrf0BYXrEW1JeAQPoWHWJlTYFAS8DG0DfBgUCUZEPcHQ+CQEQZWmAZXYJA9Ek6DFTTgkBvt08O4ZSDQJw+LkuduYRAy6iINsRIhkBfwdd\\u002fOjSIQG4x3DObVYpAHcGxqv1ujEAYyvM8jzGOQNMTqVP4So9AcjB83PJ3j0CHQm0NQJeOQHybolUjtoxAOn6L\\u002fJMQikBlGVpGwgOHQO+mNYFL94NAa1kDW0lFgUCXzD4RSlJ+QD4GWMt1cntAG4+2QQbVeUD5WhMvojR5QB\\u002fhD66dM3lAhY8BmY9weUAjenrKHZZ5QLClZxCoZXlA0UBWOAO9eEDYmuO5Spd3QH\\u002fnO9GzCHZAbu6CDrU1dEDE9YAz+kZyQLVnEec7XXBAdIaECEoSbUDqD\\u002fWrO5VpQDXOl72AL2ZAQhEGNaPPYkA+qEcRp\\u002fFeQIraIXF1kFhA4k+w\\u002fCjXUkBzAsP7AT1MQFXCYuV2NUVAQ56mWP2UQEAF5Zwhp+k7QBRMn3dCXzlACet18nskOEDgN0TUZQ03QMJWAlQLXjVAHs8vA6DYMkDOSKNArFEvQEKBe10bcyhA6fcN78YMIkAgADwK0uIZQLLSv9HMXhNA2ySLRM0jEUAxwWlR2cYZQASRQBxLjh1AIWbLFdVoJEDLnfTU1pQtQKbfma612jRAxxBkuBLPO0C6k5p3r2FBQOfv2LzfVERAZGabseNJRkD8yOnNhgVHQJbw8Y0yrEZAnAJcp4bORUC3mqGg\\u002flhFQMtZqZwDakZAn4MCEkIXSkAwWqsXaZZQQEyvvbZV+VVAPqthQZsEXUDoUFk5IZxiQBtcB+yY8WZAgT8PZ38sa0DcTGft8xVvQI4i\\u002fqkUVHFAWFhAxFgFc0CujQk6sbx0QK7xgVH1kHZA8KDFUx2FeEBDxW2SroZ6QH+peFDNd3xAHXv\\u002fVwM\\u002ffkDnZM56c9R\\u002fQLLogcF0o4BAgnt\\u002fBjdcgUCuK+o79CqCQAQf8JvnJoNASWF5J+FjhEAhV59CSO6FQBk\\u002fWoi0xodA+ty0xxXdiUAMvHoimgyMQMm5T57CGo5AWFdPAJO8j0CpjTepyFGQQHyulquESJBAAQW6nvFpj0C\\u002flmg+c0ONQAe3AtezYYpAkyrKpgMoh0AD7vC9EQCEQF2YiHRzQYFAiInsU0lDfkBR+t8UuF97QPle4vO2tXlAHthH0iH4eEBDwfK6Dsd4QFy3DnUCxHhAC62geregeEDmjUvMYCh4QJqlFYV6Q3dAjEtfgRv2dUBzDHKRI1l0QApHiv2jj3JAvps2vR67cECsnV8ILuRtQMurZ5lVeGpAJ1BQTSAqZ0COPaNZo+ljQPw\\u002fJ0cYsmBA2x+jQrkpW0Dry20jVG9VQBDTRoRMj1BAzV5zqEWhSUCjl3JHbplEQCHUBWYrzUFA6UzhiwupQEBGXF4IUGtAQHnwNJgvVkBAckEZY060P0CJbcA481w9QK08X3DbpzlATE5ryD0INUB+8om9wSowQKIF0j7ieCdATSZQTbuHIEDVqs8mrVEYQLfy7lniWBVAV7VN94n8GUDab6iVWWwdQF4iGLMFyiNA+IMvRE7tK0DNJOYUCDozQE88lHpaGjlApAoffdTKPkDQcROT6rZBQLSLEzsgLENA9VZuZ+iuQ0Cwn5233YVDQNtD8KuTXUNAQpUws104RECA5MyYBkVHQBvv9OqPoE1AHrlGsHAFVEAdb3s1105bQFt56fmnJ2JA5Byo8mgiZ0D6VNTV8RhsQP4phq0NSXBArbi+JPMfckDThSQ7eYtzQBDooUknpnRAjC4N\\u002fDiddUBfq9Emi512QFqOjV9zxHdAfmgnJRUbeUDXqhpXXJx6QMrbKsMfQHxAJ8gt2nIEfkAPe3VFBfF\\u002fQLdEpfttCoFAyz9cXL9AgkDN2XnhSqODQOKuOlJsN4VA4gVA\\u002fn7+hkB3X8RPQPOIQBb8prmSBYtA87A2GJAVjUBeCBmJt\\u002fCOQPcUmLCDKpBAfUvbikd+kEDLVwkSDliQQOcwUETZWo9Ab9rwcT4WjUBJT1JM4yeKQG9KQUL58IZAFLYU+dLVg0BSV9SA8SaBQMuC3bA3Jn5ApG\\u002fq5slJe0A5q9qSV5J5QPAGc6n5s3hA1vxxNrlTeECMh0XKmRp4QEtCbjp6w3dAhYXyISYjd0BIhxH3JCp2QPmu+11z4HRAGM7Q3Zxcc0BXP9la\\u002fbhxQHKVfOSJCnBA3vK81vm3bEAS3xUPWF1pQIodS18nAGZAyDvcEUGiYkCLrTsj1a1eQMnahUbPh1hATme2IeIwU0Chn0zsZvlNQDZWnXATMEhA7qSPYcTyREAc1pxkGMVDQHlMU7i34UNAUSoPsRVrREBqTiGK651EQE6X+lCx+ENApTcGYBRPQkAAVRsKmos\\u002fQKabzNhJdDlAkLilvTc+M0BZ6K5dl3QrQIrdP3ib+yJA4KT00mZxG0BTmPlBIOQXQL9iJq2a\\u002fxdAJFR0BLvbGkAZANJQjJ8hQL4dRiYPQShA9pfcNrlWMEBuU9yp0vE0QMzZxPKQTzlASxoOd4nQPEDzIOfVvRA\\u002fQGEB1fnPEkBAok49YwNnQEAkF5ajJkFBQGgUZbAGrENAWU3yJhDZSEBfioUkEu9QQMa49TIssFdAS73md\\u002fBPYEBikp4jgZllQCLgTrLgNmtAtaey3mdGcEB0r74JxYZyQMs1bgLvMXRARmBqH\\u002fpFdUAcVzvWQOd1QN76bAPAUHZApqF\\u002fRD6\\u002fdkDZx4i8AmF3QAQbjYgFUHhA\\u002fzatAfWVeUDca5vPhDR7QLo51qnpK31ATyOMNol8f0BzEz96chKBQAXuKzN5j4JA69lLEcgvhEAjBqsKPOyFQHkOUBM+vYdAFUvRWTyaiUDP6gLAM3aLQHxxwoTjOY1A2l3RNNm+jkDvG10uBNCPQKgdGtKNGZBAmcFQ5ay5j0Cjyx3geVOOQFcwrqOAGYxAvMPX5axLiUDTfDNniEGGQGvc+G1TVINARBa3XiTLgECKCfwKjp59QDoKp\\u002f211HpA98TMOaUXeUByV2fQQyR4QNz9y4lOqXdAiZs\\u002fnxZZd0ChIAKP8fZ2QJGhUVx4XnZAbTV8wYWDdUBoI0s7xmt0QL8fTz3cJHNA7RulpDq7cUCyNG6tKzVwQBbnRq66Jm1AeBFWGpGraUAISiP26wNmQMkYdepuTmJA7H7kHRh6XUA8kf5RKhdXQNMOj4Sd4VFAebh1FUI\\u002fTEB4a8GsUrtHQDprdd\\u002fQ10VADiv50GLVRUD7I6ze2MFGQJJe0r7FrEdA6OYAUcHXR0AMILj6+9dGQF5N2J63oURAuNZjy317QUBODYwscLw7QKPgUQTGmzRAvIS\\u002fWeDiLEAuFcmSuZojQMndeOXX1xtAxAzml\\u002foIGEArKRG9WnUUQHoQiO30rxZA7At3tpAzHUDZlLabe7gjQOvRv9fUMCpA6r9oZMGhMEBhuK5bIQc0QIvEf0OK4zZAPXHyW3QKOUDEE7M24rk6QJ74AdSLxDxAYdfoxhtSQEAftjgMvDFEQD7JDWPPJ0tAAfFhlzobU0B1V2Ipeu9aQKqHNFc1cmJAZOqCNDAlaEDPEI5HXgBuQIqmuE7ArXFADTYb7t\\u002fSc0DOt+St4kh1QMsWW6DEFXZAwacT1kJndkDa67hVOIF2QE3qNvAxqHZAtBDWJK0Rd0DeYMZ1ut53QMXcnhVQH3lAXw7ApKfYekDOvYe+YQl9QNJhL6uYqX9AvsxUtFlUgUAaToIiOvaCQGelgSHuqYRApCs0juZehkDWCXV0GAiIQOvZQK5GnYlA+Oq3OAUYi0B2uI+Q22yMQJQ7cdUchI1A3ksyftY3jkDj5S2goVqOQNYzONxcxo1AWlXVJatsjECTW9bfIGGKQNbHwjHi14dAI7vEfEwZhUDMxuDQSnCCQHOTfA5MGoBA2gqk1\\u002fd6fEArZhgjbMx5QPlqJspAFHhAtGTUbnAdd0CtRfv\\u002fCKN2QNja+KC+YHZAL1wQ3jkgdkCI7ZO6zb91QJKWG5YhMXVALowWTppxdEC+HP6XDIFzQI8gUCCzW3JAgFKSADj6cEAPx4gBV65uQKqGe3nd7GpA8dvB0YbUZkDvT4IDFqZiQEN54hCRZV1AIAOTHnuTVkDQ5txT60pRQOixkGzqfUtAeU2x22C0R0DrGH5gcZJGQC4vemGfIUdA6AiLrTRSSECoe2vdfDBJQFbqR8VxD0lAHzVE8kShR0CNFQu95\\u002fhEQHj07IHddEFAgMum2AI1O0AqQopEmtwzQCyjfWfYWCtA\\u002f2FFTzo4IkD+iu0vWmwZQIWWsbCkwhVA3QnE5MJlEEDJsYiHfB4SQDDtgMiWLRdASoSA22Y7H0BVd95uddMkQFzxWGwQwipA8RSR7MZuMEATLUkqQ1YzQF6pGsfSBTZAQjenPQHCOEDsrEUpuk88QJsy40YqBkFA0r4Az7vuRUBlzVqHHPJNQCOo+IEy+1RAztulJ28sXUDo\\u002frwG\\u002f6VjQKgeF68rTGlApzs4w6r0bkAO3zFe2f1xQCxJU26e7nNAPgULpM0udUCu46CO08x1QPq7018d\\u002fHVAhhtd28sCdkAMBDFOaSV2QEgUFqH9mHZAkEYjkMB+d0CbUbWu6eZ4QGBOgrBK1XpAOoGbrn5DfUBcQp638g+AQHVYvVfmpYFAcDQSUyxOg0DVSWktR\\u002fCEQKo2MmbydYZA8x8m+5jQh0Ba2Eow0fuIQCjeGB9r+olAI6GHQ2rOikDM3NE31W+LQLhZ+WkjyItAiKDEBVS2i0Bjl2gg8hqLQLtsfQ175olAv0H9rQ4jiEDNRTg4\\u002f\\u002fSFQEVflAuxkoNAUuKE8Pk3gUDb7piKyjN+QBkBQAUEvHpAhbVExE8seEDbQKi81YB2QIgb0bRllXVAbjZCGlkydUD9XtZx\\u002fRt1QNGX9Xm7H3VAmSBTwnsadUB\\u002f4TGry\\u002fZ0QBiCh0OFpXRAMLCaa4kWdEA2908FoTZzQJA1Wnou9HFAactxwCFIcEAGJ\\u002fttHnxsQCgSJd1K7WdAcgSemy5FY0DYZzjtwt5dQCCesaYUkVZAcslLh48VUUA9VCtKDRRLQEjADi2ndkdAiT27DGp2RkCuGxwRnftGQBtWsHqs7kdAgEcIRvJpSEDKlxcajttHQHopjh4DE0ZAzfwfAwA5Q0DGXDRtsGg\\u002fQDKRFodhCThAvEjF3Yg9MUDlyNsf2lMnQIMJfqp6ih5AjsaIwXv1FEB2Nu2qLswRQCU91cXduQlALSJU19mjDEAFBa6oqK4SQOK1V\\u002fzy4xlAj74WExXgIUB2cSSZZOwnQEUZ2rzPwy5ANImb10AFM0AQxMG0ncg2QJm0s1K81DpApnLTsg\\u002fCP0B\\u002fWiWoXVxDQAx7wccis0hADs2L9ZJsUED44L3+uktWQOuwD829GF5ARh\\u002fNCffFY0B5BzECjfRoQK6nJ4QhD25ASxN2d29CcUBQj5CSL\\u002fRyQFZhGeTZCHRA\\u002fgZWtV6UdECzksaoQcp0QItI8FeI7HRAesGQXgs6dUAjt4TB2eJ1QM1scQWmBHdAhD7S55mseEDYyc\\u002f6JNp6QFE3USnBf31AFZSNtdFAgECElnYkbNqBQOit9kAPcYNA8sa4l0bnhEBls\\u002fT3fSSGQDaclTOtG4dAB9gbAnHOh0C+dw5I30mIQH3GkvpxnYhAtcu\\u002f\\u002fh7QiEBFyXBChdmIQErTrbFSo4hAteFSc4USiEDyNMJ44hOHQPnjq8eypYVAnHRjeAHbg0CF7WLAMdiBQNvyoKSlln9AqPPO8B3Ge0A78UGDh4x4QMPnH6UtG3ZA99ADKWCAdEDV6IlCEKlzQBQkv51panNAkfPeHPGPc0AdOuUJV+hzQMgmaoLdS3RAEw+z3AObdEAtxcM8yrh0QH96vhVYhnRAdCoxGpLkc0DHGMUdGrxyQBWMrbHfB3FAaB1eVcK4bUAzj2txJc5oQFDZiohcymNAShTQyfRUXkCY03hgP6NWQPddBUup7FBAuvSOIfN3SkDevr7duJJGQBJTaMb4MEVANf6+TyU0RUDX14VHk5JFQJeg\\u002fvUPgkVApNDY6h2PREBaNJkQEaBCQHw8\\u002fPVQyz9AsS5BOIt8OUDgGOritygzQB9Un\\u002fOzBytApEPNjhACIkCEzAnosz8XQEbQn7HViQ9ANF5xVVikCkAQQadq\\u002figFQKJxQkdNGQhAHSqC+fyNEEDsCWb+rFMYQJvZ0A8jyCFAjrDvoEsaKUCQJxq06fAwQDfuJV6t1jVAKF94Ee4DO0Bs56I4PzVAQMssWeV6MENAtGJf7vzlRkCq7oQn1QxMQIA9OztUvlFAowUpBG30VkB6lEmtfMZdQKA6InC7+mJAgWXsEJxvZ0CJuB1ols5rQJMIC0VWoW9AYP4odFtJcUDM\\u002fDSOvkRyQPNYXKFc13JAhMKSukEvc0AQnRI0yYVzQF3d+QRCEXRAUoMMn1b7dEA9wMdUcl12QIUR2hAUQXhAs+EsdOagekD2xxDHoWh9QJQF0+H+OYBACZ8RbnjHgUAdP0+LFz2DQKZezljaeoRAJ5A4xQlohUDLZylWo\\u002fqFQDIeOz69OoZAqQQQdA8\\u002fhkDCRTwgACOGQOsNJRFV+oVAuGbAdWzIhUCZNC4WMH+FQDg4W1nfBYVAC9IinAhEhEAxsotU0yuDQCzRn6MqwIFAb\\u002fG0\\u002f70VgEAC0B8E9Z58QFom69tqL3lAn4wTq\\u002foudkBLK06sEdxzQO3c2lpgVXJAA2gdBt+WcUCT2hUChoBxQMXxl4sG43FAD94ZLj+MckBtUQPbK05zQJGIFyUc\\u002f3NAh3PcFW12dECUDrmqD4t0QPLt9hqdF3RA+ae6+bMDc0DPA1pnW05xQBfIaVk6J25ALAEZMLUSaUB\\u002frMjZCudjQKW+027rT15A0yPEwP9vVkBSJa7vQpBQQCI\\u002f6GCyW0lASFStvqjvREAY+T6Q9uJCQMSAGoqVKEJA4aRiKx\\u002fVQUDJc7EM9z9BQDEPlRdjEEBAQjvD8axxPEC+Q9xsysg3QIvImu62uDJAQf10FsC1K0D48v0gY04jQIDeXm7ZhRlAd1hIga9yEEAxEzHZm2oGQIPVDdA4AwNAx2vEoveJA0ChZuYO+\\u002fUGQLDLvSt2zhBAO3a7Y186GkCJZFCdDR4kQOiqJszYeC1A1C6FSTBxNECQbe9LR9Y6QHQ8gSHFvUBAJvLKFt4GRECEzJ91LU1HQMGihfhW4UpATWvkGGBeT0D5PoOKvsNSQHTAtNOxA1dAU4KGeHOSXED1oik1uqJhQImTF0sTUGVAMarkjz73aEC0M7O33ztsQGEozdTY225AMJlHW\\u002f5hcECTWZUSlQpxQKxZlHGWjHFAQQ7i\\u002f8UWckCrg7alotZyQFC43Wk28HNAwwour+d5dUCZGNMSKnt3QJos3uEW7HlA9r2gi5+0fECly2qQpKt\\u002fQIf5nQn0S4FAdHhTwM+agkASdhtmL6GDQBp3rTXwRoRARlYG7f2EhEC\\u002ftWSS4miEQI69huQBEYRAebaxnO+hg0DIgOqmDDmDQHiwpoA94oJAroGhRfWUgkBEDGpdIjmCQNJbbBIysYFA5dsgHxrlgEBcbQFHGZZ\\u002fQLuFSATQ13xASmAgfjjFeUCZ9fxuT612QBAHL6He5XNAAuPSOBC3cUC34reXfUtwQFS0GIrOU29AW3jqTj5zb0DNVWOl\\u002fE1wQNSOUSiwMnFAz2Sm89UzckCkjCCVXCBzQMQTKn1FyXNAkKfopBkCdEDjqfQ8raVzQMUey3bcnnJAFsT3Y2vxcEDifsBur3ttQJTAAapkeWhAACbXRixkY0B3A5y3v3ZdQA4s+ZQFtVVADLynY1yvT0DP38snIqlHQOnbWyBpuUJAZ6YrKx\\u002f6P0Dw5aM1LBQ9QB7hrwWnNztAWivObgxVOUBx2mt7ZuE2QMLpORAAxTNA2EPrU+03MEAAnUqNNi8pQCrx6F5QgSJA02QLioLWGUABCcqSX1gRQN6fIZhYHwdAQpfAV36FAEBPut28qMf8Pw7mc0XUUgRAa\\u002fqykL55CECki17xHLsSQDsMXezzSx5A8JBtVMLLJ0Dutk6el68xQGBbEzqouDhAgNTVSgxBQEAf6\\u002f6YqzBEQOhB\\u002fvLz30dAdbnfYV01S0DCPVYH+WZOQAFjJTKM\\u002flBAobHODdBYU0AsLkpfbZlWQGfJg7U671pAHETo0+sjYEBK7DWqzSVjQLAlFQceOWZABHftaScVaUA1BGzq54JrQHcepzTdbW1A1DVheMXpbkBGR9AZNxZwQLk+Ov2Wv3BAuBZaMfGWcUDwIj\\u002fqR7xyQET1B\\u002fD7RHRArm40nDQ5dkDVU2dyLJJ4QH1FuxJvOHtAhH77OGQCfkDJnCcwmlqAQEkhTz16hYFAcHDOeudfgkAtw+H3HtKCQFM45mX81oJATYHP7I9\\u002fgkC3VcSB++6BQNJE\\u002fOZBT4FAKDQ+AU\\u002fDgEAly76T61uAQLBMrwO4E4BAHAIujuWlf0ALdQ8gr\\u002fF+QLcs0aQtzn1Aq4PhBCYYfEBSot7yrtJ5QHT7+EZGKXdAV\\u002f1FDC9mdECyeUxIod9xQLgpfhd5w29AlQiKA0A5bUBfE8qCfzhsQHvMBGpxlmxA2AA136v6bUDZi2gMKvhvQPaqHk0wEHFA6\\u002fCCkDkGckAkYJMXt69yQIZEUxsf5HJA6rxw84OEckC7kjcZJ4JxQHOssTkxym9A5yaIUiOda0B5A1AZIOhmQIA8omF\\u002fJ2JATjBJXw2ZW0BAA6A9xUxUQLWbcJGlX01Ax+QswTdsRUBOuR5BxjVAQNa\\u002f32Mp\\u002fTlAmg2TeRQRNkBbPfZpFW4zQGt+qAD3PDFAVIJTvLsULkAMP9g6hWkpQMilsbrGnyRANgPk4EMNIEDuWsbyYAsYQNmkVPw3fhFAF3VAAf4uCUAVQisw1IsCQNiAQucBg\\u002f0\\u002fw2Hz\\u002fOAO+z8ukrv3K1cGQLx3hXxQNgtAdyUBt+w3FUBISg4f118hQH1VrpnLcitAD\\u002fzTBIdrNECRXi5\\u002ftng8QPbSyeQFnkJAICIBIhrtRkAfey4XBsVKQJASG01\\u002f9k1AMCpMPxpUUEC0bPP1H61RQKTmCurdYlNAlGHl2KzQVUCoHCQzFDBZQErtusWXgV1AN5ABC\\u002f5DYUC9Qox\\u002fIexjQOiJKZ+KfGZA5aKnu3XDaECIj3xWiahqQOoc1hRyM2xABjT391aJbUCND7RVGeNuQLKHtsYsQHBAHdgqLaNNcUDCHfQruK9yQKDoJuumcXRAvzylVTmRdkBIP1Jsi\\u002ft4QCS9L649intAnLbIKrMDfkAfFo6i0hCAQEqSWdXDz4BA0LdO1f8mgUDYSpIBHxKBQBpE\\u002fRtRo4BAP6F33qX9f0DQ7UjWPZ9+QMk9mg9vc31Ay4QOb8uefEB5X\\u002fOsLBp8QGHUsHwEuXtA3d7ziZE6e0Dwfcee9V96QNHH8L\\u002f7AHlAR0TL6iIad0AYqSki+M90QNyNTn9zZnJAFxa7YdUucEAImRErA+RsQFWc4wo9vWpAD5lMSNEAakCe0Vk1RoRqQCWwBeTe7mtAHehvIgbTbUAX6DULy8NvQIWzhgKbsHBAgJ5eJhQucUCMEhxecDtxQIQJMdrsw3BAiCjETyCBb0C5S1XzynhsQD1qfzLDq2hAd+WJNI91ZEAlS2QCiz1gQJBn\\u002fzxCwFhAPHjCP5w5UkBu8J\\u002fYYjtKQKtLg+5VxEJAyDe844tEO0D3SahbB4Y0QGuUi4a2KjBAQrv+etyEKkBduBn1CEYmQNn3xT4HziJA\\u002fDY0D3h9H0AHExyZSv4ZQFl9JEInKxVARL67zmAaEUDSbecMwagLQCyLJbUBpwZAfFZ8lDYcA0APyKRvAv0AQFbJ4s+pRwBAignLgIJJCEA7ViTEmqQNQEDf6HGvKhdA+ztpH+T1IkBwzGL4Yt0tQOM99vWnHDZAIutUV2OkPkDiEj49l+NDQP24X7C6RkhAwDMFVHwKTECw7xRIovZOQIM0L8Z7llBAVda90+aVUUDF8n8\\u002fS9ZSQLbkHIp\\u002ftFRArqx4pRhuV0DyWxdhxAtbQFmj6CAOXV9AzEwVNk8DYkA55KjiyU5kQLvXE\\u002fBNY2ZADtL9KYQnaEB0tjs1f5xpQE\\u002fAbNht3GpAJA3vc4oTbED67yCPh3ZtQGJO0mKSOW9Ad1sjjJPEcEBvxB2FyEFyQPK7aW5yGHRAj0xIdmw8dkBFF34N2Yx4QH6oX5SQ03pA7W\\u002fFNQzMfEA\\u002fTHhDqjJ+QOea+4yI2H5AhZkbbjW0fkCDExaCIud9QAP1IsAetHxAAgYZj\\u002f9pe0ArnpUd9Up6QGcZUc3QeXlAJb0FOPvyeEBIIao+xJF4QCb9KHTpHnhAWYW0XYdjd0CBjWQEXzt2QMeJw9ydoXRAFqKTaly0ckCu4hBua61wQMP+PXgTpW1APUYdoZHEakCgV0BREAhpQJFDeQnNf2hAKJUAp2EAaUBpZRslrTRqQOIK3xZZtmtArgSNJ98jbUClqQuf4y1uQGDKt5Timm5AtlfwXRpFbkA4asuqmhhtQARaTY4kFWtAYoe22Z1SaECv5E5e+wJlQE8PQrvUbGFAp+OlqGO8W0BZYaCTYjhVQKuA4q+ZWE9A0gH2wHyCRkBOqNxhocM\\u002fQMmYUF9XVTZAxVECR\\u002fXSL0CVyBTwMVsnQCgTR2fP3CFALsL30diTHEAb+dPvutYXQG60ubURnBRAb4n1DmBVEkDBOU9TYKMQQJ2FusnIgQ5AbcN7Ds4BDEAhRYIei6oJQI5rEjTJoAdAAq1tMWEzBkBSJGMDEq8FQFlubcu2OAlAcRMmfteyDkAwgDQ1BdsXQGSzJQA9ZyNAgHAVTWBeLkCTnZNu1FY2QEMC8SolwD5As9oCJTjSQ0ABwB7eJQRIQP06t5YahUtAOQrdxXceTkCmAm2e0fJPQIWFJngfv1BAen9PAee5UUBu8Uatx0FTQOiZeWmcj1VAQqc8s\\u002fapWEAD0hnoTGNcQESKjaWcNGBAPoEo3jAvYkAL7UhQx\\u002fpjQCkfjJaigmVAIeMOn5DHZkAqam2sst1nQD5TmwXF5mhA4sKNnvsLakALVnz3UXlrQBS0eXayWW1A50+Ot0zRb0BadRia03lxQJXYjjwHW3NAcHF6YchxdUCGKT1DWY13QLp4\\u002fzTCbXlArf8v0ATTekBGgLo\\u002f4497QKQ\\u002fxQyjmHtA9w4yewEHe0AJfJs6ORF6QMhQqLPi93hAmh6T+yTyd0CICUIIUB93QHUIDlGVgXZAAe5DmcQBdkCpmQS8n3l1QHIdFEyTwXRA1OW+5W2+c0D+0OuHiGtyQCgTv5Q53nBAmjSigjGCbkD64XMAtpBrQL+L2zRKRmlAh8tvzordZ0ALNqYRq19nQCiXxJzTpWdAq9YMHIFnaEDo6o\\u002fynE9pQG1SopZnEGpAlSUYFtZvakAlxga0R0pqQNQcoy12jmlAUTT9PlI4aEBTJ34qSE9mQB6T+bco6GNANVUBUQUnYUA98HrMg3lcQGbPC8tXvlZAAS7XKL18UUCTfOi1JvBJQML5aalUn0JAc7zOLgwJOkC7mq9IduAxQC9ZQ0TOcihA5uhd7w4BIUBNgzBW9cUYQPNZ9HlKgxNAh9KAv7fwEEA6kookwRMQQLH1GyehJhBAqW1ipdaIEEDFK5c4ScEQQMDuL82DjBBAXB1YApTQD0DfF+LTnBsOQITRQyctqwxAfRMfl7YbDEDkjCYq4+UIQA8HYXIyIg5AU9UyZhgsF0CpLZBkmasiQGSY40HE\\u002fSxAWSmAvxouNUBbXdol6vk8QN5kIyGNkkJAfpwnLJdiRkClp2e+8oVJQNWy\\u002foDyzEtAv6buNKFaTUA1iJqmwqJOQBeRaq26I1BA57YvtidxUUBHEXOQZmdTQGjRczrWBVZAmxXVTk4fWUCzqhBSY2tcQGdjifcxn19AkcPeytJBYUC9VUu61IBiQMuz2eUjkWNA3XFyAkWDZEAa1DuRB29lQOkb7msDcWZAsaUVSo6qZ0DZrNGZcUFpQEkI5g1ZW2tAMR\\u002f2ULMSbkAiTEozjrNwQLibky0CmHJA\\u002fdsJngGNdEAxdKMMHFp2QEu+OJLxxXdAHmPCCFmmeECTxWM1Tex4QMiVt2yrpnhAKWgeCxT7d0Ba5c9YoRh3QE56veAhKnZAJs3VdF9MdUAX3v7RL4p0QJubJaW23XNA\\u002fo76sGY1c0ATy0xeY3tyQFyZjqzRnXFANz9vhh6WcEC9L7W92dluQKrB7xwVcmxABwBa2fIyakAwlZQs8VhoQPJK3pMuDWdAxfv1rpVZZkBKBqPe0CZmQNYiuSpTRGZANGilshR3ZkDS2QzbFYlmQDXt0+jEU2ZAZNDp82jCZUCsWLvkNM5kQDqZEjy\\u002feGNAlp\\u002fuCTjJYUBPfT4qtJpfQN79DPIVN1tAyjDZy0ClVkA9+a8tdStSQL4QmB6HFkxAmS4\\u002fq8jqRECLRTe7Ew4+QNe8rSVY5DRA6u0PwkhHLEAyOfAMFOMiQOgll16RnBlA9pFP0VKIEkCSpj4+2WMOQOpk9W7X7AxAs1W2ZT+0DkDrpjo6RfwQQGYpf4BHmRJABmUTf4ueE0DYxST\\u002fMMUTQAvTFz0PIRNAQa66GFoUEkBAmNVW0CURQBLaF35\\u002fxxBAcarimsvgB0BjH5GU854MQOdsQ7n4oBVAGzh23PksIUARhfvXv2IqQDdhpXA3HzNA8lZvDef\\u002fOUA8++Ts4pVAQLybVfnK6kNAv9EdmU2lRkDEBRWN\\u002f5xIQIhw4ihN8UlAlkkCLCgGS0DWrxteHGRMQH4vVMZKh05A8\\u002fU6xALZUEBQmhTzdetSQBTNBeO+UVVAtKP\\u002fkmPSV0DXGqcwBzpaQCj2Hoaua1xAZDu8vAFlXkCffcHVwxtgQPMcGpTY\\u002fmBA0+n3ZgHqYUBpEZ8FY+1iQLoYWavFHmRAgvSFRuybZUCkm7kl64ZnQEMrgJzw+mlAy5dnKsr8bEC0w8jrITdwQOzGWfjlBXJASfQuJvC7c0B3GSZ0tSh1QHnG4PFaJnZAWWJazZmidkCdAj9p+6B2QKbn+A+cNnZAfAW1LFSCdUBud5XKyaR0QF2Mk2iTunNAugGYaLPYckBBNcHCwgpyQA9hDl9yUnFAGcvQGL6ocEAk3WEmOwFwQLEesNw4nm5AKASrMPwUbUCcbh15JWhrQPMhD9lwq2lAecnn9SL\\u002fZ0CPG14HGYVmQK2Ms168VGVAx2oTnKxyZEBDUYt3oM9jQGp8TiexTmNAMzFEDZjPYkD5M37eqTdiQLXXJ9bTdWFA2ul+xYaBYECIMzSJOa9eQALUAnlZ8VtA68Rhhv7TWEAIEiADKG9VQNNOd\\u002fI06VFAYjp1y9PjTECftJMgE3JGQN9RDhR2yUBA8xr90WouOEAMOqhVhs8wQEXfZobGtCZAAYz1uzpKHkAC8OEV+7gUQOpqNK3UHA9A7wlYFftzC0A3HSfHxoEMQDRluy8bMxBAGxjjp+6nEkB+tsZJXM0UQBQOaYwOCRZAuuwKHJsbFkBKzZmLkjAVQChLRuc\\u002fzBNAFNhZ9wqYEkAlnWZlaR8SQGmzp0WiagdA3fD8SR6VC0BYo82xgDAUQMhdL\\u002fOMOh9APREDp4mKJ0DbCFIHctYwQISiP\\u002fQyrzZAYVmQrTjCPECcvNkArjFBQDVdBSIBfkNAyXIUTPIiRUDqGyqqVDhGQFLKZ4uvDUdAGPpGpPMOSEDQ1f0jhJtJQGQVuYgf4ktA4BLoXOTSTkAHcKbn6RZRQJIcIMPG0lJAPjZyo+qBVEBMeBd2zx5WQL7tw+hgt1dAzB7sIP9hWUCQK20xCzJbQBhLedwXNF1Av6g+IUF0X0AsQK33zARhQAoP\\u002fKd3jmJAsOTTDVRxZECAyHj5XMNmQFp5veUliWlAJXO3UcCrbECMMUY5FvdvQPmhdjc1kXFA7mQkjBHwckCa8RfdefdzQG2JVng4k3RAkMjEtvS9dEAk+FyTHIB0QIIwe8\\u002fh7HNApmrD38Uec0AEFJn3ITRyQMOy0DwvS3FAHaBH0il9cEB6JVKLWbJvQGR\\u002fJw7swG5AS\\u002fP\\u002fjfILbkBYlJUifmRtQDjlR3DUlWxADiNpzDd3a0BdyXHcDvppQBOcRNnCLmhAWVoJ0Rs+ZkCf7kdi3llkQAognsZ2q2JA0miJ5BlIYUBaHrQ3ai5gQAZLUefpmF5AgN3Q\\u002fD0SXUBQBiwo6JdbQMn8F1o2AlpAVz+Bjck0WEA2jkkTWR9WQP+1+WaSv1NAuGtEtPYiUUAPC32xwMtMQN2YJJVSXEdATd7D9XZFQkDYO8ddC407QGJ5mYsVDDRAwoYs+ZBALEAhjYvWem8jQL4EMok6nRpA4p49YeznEkAkZGHjitENQKkNi5C1oQtA7dXYHAagDUAPtAZsbwoRQM339v6LnBNA6blbhc60FUAouQEcRb0WQAqpDVpbghZApV3nZBtBFUByuiNFNpATQGchNj\\u002f6JxJAZsZMlgCdEUCcSspY9yUJQLhruRcCzwxAwBFGImz6E0Dxv5hdPXYdQLGVs0jybyVAEzHrsSHlLUCXt\\u002fzPX8YzQO06cDG4vzhAGynycJRSPUCH+hVOW4FAQGLNeprsyEFAyrpo9A6PQkA6VX3q3Q5DQJyISiMhmkNA1viUiUh5RECf1J4VQNBFQDj2mbIclkdAkSLHAMajSUAQ1i5J\\u002fdBLQL0WsZsqD05ALHvZFJY5UEDFRUW6BJVRQMDFS5i3MFNAKDArJBMaVUBjL\\u002fKvyVNXQHmLQdq83VlAXYV07SPAXECvwGhqrwhgQMBky5Zh+GFAiZuZOqU7ZEAAys88gtJmQCPBmfcCqmlAJkmp\\u002fVybbEDQidVOJXFvQIHt+mRO+HBAtFfpMwrycUA8MBE\\u002f4pFyQBOb1z3ezXJA6MzJcHWnckC\\u002fQL\\u002f05CtyQEPC0LNYc3FAgtk+62WecEDZ7C5OZaJvQMj196aQWG5AssxdtqSIbUAQ8MelijZtQEkUQVK1O21AcPz+DedObUDNq4gPshhtQBKY3NsoT2xAa2Wuj+\\u002fOakDknzzhgadoQGzrFD0+FWZAfOwwJs9rY0BNDt6PkfpgQJHS4CsE7V1AsHy3X03iWkBChVA5kblYQMgBloQ+MVdATfxjHCT8VUC5TWVJCNVUQOZnqxwwiVNAKdB\\u002fayr8UUA9DEiivCdQQLaQxLhgMkxAINZg2QjYR0CkJZEys4RDQAXEd\\u002fLa8j5AKUVauhbON0AhuJlMZ9AxQNwvxqTcCypAWUgVfCi9IkC5a1wUUPMaQFWitZoxABRAmEqHHZkfEEB+xt23LpENQJ5DSesdtA5AoijluaYSEUCSoDTLDx0TQAqgqBVMshRAMsc+BElHFUAkOsrVV7IUQN5SD4RrNRNAjh4gXYtkEUCTSrML0d4PQCzmHnrcwg5AeAq32xSXDkCtuxNdOPQQQIK+SVyO7hVATBsbesowHkAkA9VKfcQkQArXzI2PuStA\\u002fubcrlK9MUDKPhctGqc1QBis0cdsLDlAmpBed7DqO0AcAlYuaqw9QEI2XjtJgT5ANpZzoga9PkCu9vWnLNk+QExyYJNbRj9AKD6M1nYiQEBNw3gPNe1AQPALX1mM9EFAfeAVW2UtQ0CUAoO9PaJEQNRq0iatdkZANcrsJBbbSEAx21hfAflLQBgMVcb85E9AyFytW1BPUkDqzpdysg5VQEF610oLMFhAJjngYJe5W0AGj9cDYbRfQMmg0tmIEWJA5ipXQTN8ZECYiDp4uAhnQOEMVDaZmmlAVsEx33wMbEADarxzLzRuQFWUe7wM6G9ArW64I7+CcEDVVajL77twQDzDhVRVn3BAheg3Qr04cEABZ0kiR0BvQAo66wRT7m1A1M1treXGbEB0phbrdgxsQJ\\u002fOnshV5WtALTrdVmZLbEDDqLa18AVtQCHNvgsZs21AHdyXm6fhbUCurUEveDRtQEQv5Qm8gmtAFj\\u002fhU2vnaEBDDdWMpLhlQKIy1ESOamJAuzFXpwjVXkAl4v7CRwNaQHvSGe7TkFZA24grdSBYVEBNY2SoNf5SQPqQl9iTGFJA8oia\\u002frRKUUAhBTNqAFZQQL5foFjaOk5AvUqDBbZBS0B8y5jyQehHQLpvZXuOaURAMoKAQSsBQUBXcKJhz7o7QIXf24IUMjZAodx+E015MUCMosajiRQrQFJffdyVrSRADIUSA4pMH0CknMYg\\u002feAXQH\\u002f4wUJ8\\u002fBJAy88l75VlEEDLiPetTl4PQG6qT85KNhBAlcFxN7o4EUCbQ96CHf4RQF1aW6wwBhJA7rpIRsspEUDsLTVqD0EPQFv14ce0zgtAOQ2pO7UhCUDKfq\\u002fsciAIQMd60kBMPhRAi0rQfnzRFUC6iOzYjnIaQNsV2YbM7CBAlpby32rIJUBLS6D9SHgrQLOgXH13xTBAqxYT5x64M0BohA95EUM2QMYmMyaFHDhAgu49454YOUD4pInrNTw5QIBkTWmsvzhAbT0Ke938N0D4fCLDfk83QDOBIRHB+DZAyNj1lB8WN0Ax1RB0w683QPr4AtcP1DhA\\u002fHMVFdWtOkBqa3c5yYY9QJJ1Gvel2kBAL5BLwfPAQ0CN9HxInopHQCsL9T21O0xAoFxD1SXnUEAIqDT6VxxUQJX\\u002fHwQVt1dADoit9BOtW0BfYfZXIO1fQEU4UqLOLmJAasoSkVBuZEB\\u002fBxOlH6BmQAdzGDMmq2hAFjj2p5FyakB8BlLyIdhrQOmSC6Q7wWxA8pAvWxAebUAIaZBS0PBsQKqjqcleUmxACg5IKm5ya0AdV30Xv5FqQEnKtfA09mlAJtotCr3ZaUBWtpe3BFdqQGPB3TOGWWtABBbNZUWYbECgvrWfhKFtQN1BRI1L+G1AkeYH3wo8bUA2T3c6cUtrQMTvdO\\u002f1UmhAjTFZI6G\\u002fZEBH2WY7YhthQJt85KLUxFtAsamOaHbFVkC+HuyHjmBTQMUH1kctW1FAAqQHhd5BUEDteclRZy5PQBNdJiFs7U1AaDS2oilLTECScPGu6iRKQPrDi\\u002fKXlEdARM4VTRrSREAgjgRcZBdCQJNHB62KGz9AIqhdCdWNOkD3fjmgvIA2QJ4sqMch2zJAoH9xXhwML0BcmiaaSPcoQAseZBJjkyNADlSPAbEkHkAtpszQkkEXQPP\\u002fStS7mhJA0p\\u002fa22\\u002fyD0AAVxebzrENQOnn0zAOFA1AHtE6OBHBDEAVBeDdOMILQGhyyRYsuglA8vCLLxPpBkB9TnFi7P4DQGCenpHzzgFAWV+FsnH\\u002fAECyOr5J\\u002fRQbQPw1cx6bohxAUX9t4SeNIEB1UA3kd\\u002fsjQD\\u002fOPbyRQChAOG6+xw34LEDU7R+k3d0wQKTB8RKIEDNAFzM0zHbbNEBcn5I0IAg2QInlrwqdbzZAE0r\\u002f2OUKNkBw0zg+Xvs0QKhwWGs7hDNAo7wHG5f2MUD3lWXsBZswQJ3IQZS6Si9AsMkhScpwLkCC4S4vIukuQHAZA2k5hjBATmzpYYmpMkA3pGbWeiI2QNFtXYs6LztAnNQcyqP+QECpKBl7Y1JFQJuerT9wk0pAO8vDBoRaUEANSrN6Bs1TQJMp5TXriVdA2iedUwZzW0Bflbu8bWdfQG2GZLCNo2FAVLnMZqV5Y0DP6EpYtiVlQGEQ2NP\\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\\u002fO9FugFPn+T98N7JambH2PxrP6\\u002f7JhfU\\u002fTdAIle1PIUD5n1yycRciQNIoXjSRTCRAr0RjYZ+XJ0BzefQz8ogrQE12x+fPti9A945c+QvlMUAxrxKXwLozQHXJ13TtMTVAknyZAQIZNkD2zTY04UA2QBP01tbCjzVARIV2b0gSNEDIG6HVJ\\u002f8xQDmh4zf7Uy9AQn8z4AnZKkB6PWQHkyUnQAiQWowopyRAjVTB0U6rI0AipSKMEXYkQLnU6wxbVCdAfoBBoXyhLECLWCeRgWAyQA2IBnzGCDhAp6BVezdtP0Dht1zg0UxEQGe\\u002fa0MsuklANK+KsZbaT0CluWiFUzpTQDH\\u002fB4CioVZAICt9oZgBWkD2jphhZz5dQI3akwIUIWBA2PqP+k98YUDzYrtcUqViQHOpIzbrjmNANK6yBGotZEDxMdCHAHxkQCk4cJcTgmRAwRmpExFWZEDZUxnWWRxkQD8uz5XlAWRAhwpzzuIyZEA65+dIBM5kQAFQij1j1mVAMJBcPj4oZ0ATFBf9E3doQDpUBOfgWmlAvS3+Jr9saUACKQtvfmpoQPj5qmNMUmZAG8Ip7phoY0AQGqFI0SNgQAf5u0tdDFpAB1+s2hzsVEAkbAhVL0lRQI6oQdHwKE5ASXAtt6PZS0AC5bQENKNKQHMOuW74pElASD8MrLBZSECN2vhBJ6NGQPEi3FxKr0RAys4THnbKQkChMmLN+jNBQNCjVKKSA0BA6EE8RatJPkAb15sEB8o8QK4TCqlpFDtA3W6mVZbTOEBcJJrubOw1QLrEG3s5gTJAldziOYO\\u002fLUAkQrIWSsgmQNyRYKc9uSBAJZN8r9ncF0BugmW6VPMQQECfOHA5vwhAnFy50nQMA0By+AVhoP3+P+d5vyciC\\u002fo\\u002fETnkoo\\u002f19T+TTwY7BkjyPySC64z8Ue4\\u002fikkXEKcK6j+59yv7JoPoP+F+tQbQiCRAqy4eF5hRJUDmOnKkkIgnQGDosqY80ypApL2WH7nELkAyre0Hvn8xQN1cTJkJnzNAz2ypilmfNUASP7P2\\u002fVE3QKg4gq04dDhAgUFOp++7OEAYR06e2fM3QGvH0VLHFzZA5RjLk+VfM0AG8s4DqTMwQNfthrLbFipAVcdN\\u002fIyhJECp4xc3tpcgQOOijox3ixxARQ7AqA6bG0DBovoXoo8eQMPJphhf3SJAiV3zmFXQKEDsK0\\u002fE97kwQN3wbI0RijZAUIwvKSTpPUDmubXEkGBDQI\\u002fYfX5VZkhAiSYz4TbPTUDwEHbpAq5RQAdLHRSRaVRA55UFetYEV0A9g2V\\u002fh3FZQFQZdKk7pFtAwqJmX8WPXUD9acaXEyRfQF8cuY8gKWBAAs9ndRuKYEASNapRHbpgQBPL8HtzyGBAK4dDzdbNYEBM2fufw+hgQMTBg8nzNmFAg4hrHSHMYUBCw2OjO6diQFSaa\\u002faNqWNAnQjYyXuVZEBeZn1c4xllQLmH+3cV6mRA9t3r+a\\u002faY0CNWu6lbvZhQJQo4eeI\\u002f15AovqBzaK5WUCOs4mbCexUQNYQgzTcK1FAg9HyAntcTUDL0wwEbZVKQINWSXwxOklA8aQ8NSBpSED8x6NaTXhHQE0m49oUH0ZAGgZYtwJyREA6J9dhIb1CQIDkvjh6UkFAR\\u002fvG0\\u002fZhQECHPMGixs4\\u002fQI92izKAYD9AZ6Y\\u002f1HjkPkCWmCUVO849QJJCWd\\u002f3xjtAAt+SLETDOEDE6lEKFP40QCaeCwhD3zBA1sft+u+yKUDWY4IkSZciQOQNmWw8uhlAmM40RwVEEUB3T1o7ev8GQCcerVe1Tv8\\u002f1EOXMQpP9j8RH5nmarTwP3rCbqVu2ek\\u002f6qI21mFG5D9XYaBpHSPgPxJ2v6hr\\u002fto\\u002fXXGc4XIp2T+mCenu108mQBUq7K08GidAjjiGxIVcKUA0ieg3\\u002fs4sQGM6RzM6kDBAMbIGEZ0HM0DOnDvB0LE1QM9+qTdHZjhAietYwnncOkDvHMkgcak8QMqYyDrlVz1Aykihx0qTPEDeev4Wuk86QEkozDj21zZAUNrcNfe4MkBqUyFpFistQHHRN2Fz8CVAwedLqKluIEBGgVs9FssZQKEiFKJwlxZA14Rs71wCF0D+CytrgfIaQJ1js\\u002flmTiFAJULlPJE8J0A3K6TsLoYvQIjj6EMBJzVAaEbR9XO6O0AwMXWNA6NBQJS5rWtfuUVAYFCY6ofvSUDCQEmdXBtOQLmTLv5OEFFAbI0pYQj4UkBvN7v4D8BUQNOk7vg9YlZABjl5BzvVV0DW5XGkRw5ZQNzoLY2sBVpAd7itP5q7WkAptDWVaTtbQHtgBvEsnFtAVG9UnhX+W0BCR5sl1oNcQMQVSnz7Rl1Ao86nVC1IXkCBVsyt0WBfQC9oInrfH2BARFle28U8YEC6SJs2QaxfQMG7WiehqV1AwzXdjXWTWkBC2eD1YNlWQMzVsJdWFlNAZHc72PS4T0CKz2IDIw1LQDYrRvlDREhAL\\u002f7FRQTxRkCOwn6+S15GQJBc86A23kVAH2x07boGRUC3qAld+8dDQGoTx5\\u002fOWkJAuF7BX\\u002fgVQUBEx\\u002fhP2z9AQKrRFzLt3j9AQY7WrYoFQEBYGs8lkjtAQMu3pqvCPkBA43QTi9mWP0A8Fi2cgH89QFf8e1mbQzpAcMDrG70yNkAV2BbwScMxQBUpatsZ4SpAEKNdHus7I0AyGch1tRkaQGVZKCL\\u002f5hBA9zNxVAUrBUDaZQLoiTD6PzJEYhgHdfA\\u002fc6eV7XSH5T9+XJOPJpHdP+0bwVT3LdU\\u002fUnUcDr6lzz\\u002fncboHu3DJP61Gwh4ZYcc\\u002fEjmewDEgJkDXrSUOw+0mQNZ8mrLdRilAAkNBPTcHLUAPXqy9BwUxQO7Cf3e5FzRAmqm6gtmgN0A47bNIL2I7QP0\\u002f2RUU6D5AfweSqdnIQEC67bvMIF1BQAfgVBA8+kBA2cIw\\u002fcQ6P0C\\u002fbSwwr\\u002fk6QHWAUT\\u002fT7TVA0b6FvH\\u002fmMECqV8n6iBQpQJT+DrFUayJAEaWlQ8HuG0C1JyCP8\\u002fwWQJUkndiHUxVAOkoZKU94FkDs3T0QalcaQGZyzOZAnCBAaUTU4GDDJUBynwJwe8MsQFvHSpbNxjJARAuy6qLrN0BifTnsx5Q9QCwOZCy2wEFAT84AlbW+REAdEaY+zLVHQMqavxjkoUpAmRNl+V+DTUDDUGDW0itQQDTCwaIJilFARg4fabXSUkBoWVENr\\u002flTQKB3Tb9C81RAoGAXbpO3VUBv2Snk1kVWQMVY46V9plZASViCZVrqVkCYhblzjiRXQGisVQEZX1dAbBldmVyOV0C1c48Ax4tXQFuzehbXHldARnrp0IkUVkCBnLoteFtUQIPCDymNFVJAeOgE698nT0BKhMG+8XlKQA2oYi+oz0ZAOzpJEVl9RECQohFrsGhDQNTPkYcfIUNA7BWB6ugWQ0BBCewisNZCQA0zjgCQMEJAExDVRWU9QUDTRemI+ERAQMdDVx8LKT9AbwOxQ8CyPkChoGFIrxw\\u002fQB1XqwPL\\u002fz9AH6WmDSVhQEArS6PzhWZAQKmUBkXstz9APBbZtU9hPUBTNRul+O45QAu2GerwvDVAOFkJZlNCMUAWVBfHc+gpQPI7C3ojXCJAPgaBwl6RGEDUBpJHkhoPQLvUmtAvvQJAXKA1PLy\\u002f9T9zHnqqDc\\u002foP4YwQwPflNw\\u002fclM\\u002fCAMW0T+OOsr5V5fFP1zpE6jwRb0\\u002fDgXY99oVtj8eMXbfDtazP+tTe\\u002fagDCRALjKZW7beJEAWTohmLlUnQOnPd5MtdCtAVgJiGYOiMEDfkETKXmE0QLkGSmxF1ThATTGuqtOhPUD0pjL67RFBQE1pCGY1xEJAKnfxebqDQ0DjAb491hVDQIE6TZMcgkFAkGVMGG4lPkAtvkgI5XY4QNWeul9l5zJA76FXgihTLEDE55N9RiYlQFEAyNBsRyBAXrpQVj6aGkBa2cQ3QmsXQH\\u002f8\\u002fZQjRxZArXiGCr\\u002fuFkD1uV1Gi44ZQFR\\u002fBb\\u002f+gx5AWgn1shwMI0DpIOYFkCYoQGzndEzRZy5ACWwLT3fEMkCJxiQEsZ82QOTJu5+HqzpAmlIvJBjgPkB51wTLw6JBQEwKKZf19ENAivsTHMZpRkDMtHpnU\\u002fpIQJK1cb86kktADK1ZZrEPTkDycwUlvyNQQCvpb\\u002flvBlFAunMjQVGeUUBmLbf0ReRRQPqxJv3N3lFAO6o6gpufUUBzrx6h5zpRQCU4EhgFvFBAg3\\u002fqwQgeUEDXJccYK51OQNkJPNtzdkxA6qn6wTLFSUAKGA8p9L1GQNUQjDB\\u002fxUNATj+IN4JOQUCOsUFL81g\\u002fQMv\\u002fZdO85D1A0HdQm5zaPUCFxOoEOno+QJOF3AD5\\u002fD5ArzSjTenlPkDgTQurLio+QHqCfyMrIz1Ai5lfXapVPEDyjfQg8ys8QI3o\\u002fVVBwzxA\\u002foozsOrfPUCYe+FLBgc\\u002fQE0IrfVjrD9AUaHzli5fP0CA9IL3E+Y9QJob7USyRjtAby8ufmi8N0BdzyCmk6UzQD3Swlyq2i5A9SsgHLHsJkBezVqCexQgQB8aGHmuQhVAgtzCDxR7CkAxnD1Bph\\u002f\\u002fPwvzZQv3WfE\\u002ffDe4YTWP4j+dWQL48GnTP9MQlWdPbMQ\\u002fO3Fc76hatj\\u002fDPAmvBH+qP+ac+pvHFqI\\u002f\\u002fuPO\\u002fXFDnz9ePq8E3qwgQPm4CZBAgSFAnGGXavQLJEDshrtjwHEoQEUoBxYN3S5A9MPGIlKqM0AAE8m8IL84QPE72\\u002fw6ND5AO88cQYehQUB9mtYXIH5DQCZAoSQXR0RAp9a4gi\\u002fIQ0CU+KnyZxdCQJTC1nO4HT9AKyfig7daOUC0NdD1l9kzQDLmlQVtgy5AB12ZuXunJ0BBqOvZr\\u002fEiQJTmIGC3qh9AuQH2AfB4G0DNdSrNvoQYQHIsRNktjxZAjXR7T9vIFUD0YJZumJMWQCFl8r30QRlAFR3jb\\u002fbtHUAo8EbDLDkiQJsLNp16QSZAOkUcr8XqKkBvos9u3REwQFoV0Qva+zJADpKBg8JENkBjWMFAqwA6QGkiFth5OT5ADU9qtf5wQUDoxk2CVeZDQKvlHFqTVEZAE78275OFSEDU5DhhEz5KQIQyuV4jS0tAkdiKmsyQS0DhHtueyxNLQC7wFatX+ElAv9n0tkx0SEAI8PeUabpGQLIrLhK+6URAq0UNpBgLQ0BnR6775B1BQN52qrlxVz5Am6DgwuOpOkBNBz5xu5k3QMGLLvp5jDVAnRgaFoS1NEDsopezDfc0QGkj5IaV5zVArEUek0f7NkBBc0LO6703QDzuVMJYAThAGCqrs+7pN0Am7rS7ndM3QEfNc7ixHThAd6lxB5\\u002f3OEAnjkC390Y6QIK7B91AsDtADMvcwWm6PEAmCFCedfk8QIBrhdimLTxAU3dmAGdOOkAHYNkMeIM3QAtR\\u002fCfvFDRAurfbVKxZMEB6xS2M8E8pQDan6ncGkiJA4Xqa7+G+GUDh\\u002fIDEz9AQQLyAVpHqpwRAEWKatdnX9z\\u002f8srldUubpP1jqgmn6mto\\u002fUpm7qHAeyj9FRPKT9fm4P9NmATEpAqg\\u002fIkfaWTJwmD\\u002fvlY+g7AKNP4e8o0p1kIc\\u002f75XQTpqhGUAebpVZ9z4bQEOrKseBICBAhsbPlmaJJEB\\u002fa2fsbBErQIfyblrt3TFAw+Q8ds8LN0Cxqeow0Ik8QGOg9Ctmw0BAdiuBem6LQkAaIUzrAj5DQEVfl2iDtUJACaqyOxQVQUB1t5he\\u002fXg9QLb+lYwXTDhAyF4ZHhF9M0D5PB0eBSMvQG3WaEksVylAoLXlHpdBJUA\\u002fbV4scEgiQBIv+W6Lux9A4UhNFXtgG0AOpNtMVGkXQPeSPNsZJBRAszcwlyECEkCIMpeJOF4RQG8h1ZUBXhJAGCMvYkvzFEABVy9KZPUYQOlKoD59Qx5Aljy\\u002fCzhwIkBR9TH1JX4mQPJSesNdcytAudAjwSm7MECc\\u002fLc\\u002faEo0QILxoQ9dVjhA903HjcmtPED3WWODu35AQDV+scS9a0JAxGOMSGfiQ0BSXlQZs69EQGvV\\u002fe8gt0RAofdFHEj8Q0Dgy33GzqJCQAvjvsNe40BAd7eRLPjzPUB57AJmaSw6QEJcWQ9oqTZA08Lb396HM0DKX4WLZd0wQG322\\u002f0wkC1Ae16f6kjaKkBQ8hCcTc0pQILRZoqjWipArHywDt4bLEAjjAWeImkuQEXm8RLjSjBAioVLQW0bMUAFqpR2WacxQF1+\\u002fFrrITJAYFwIeD3SMkBSa2qHtukzQKm\\u002fbEMzZDVAZ+9P1+MBN0D4xBsYDV04QFdQjWVzDzlAVh69j6\\u002fUOEDq3MORKps3QBCuJ1rTgDVARDXcqtXCMkAKvBpQu1UvQJCM2dbBASlAbB33I0cFI0Dd1Q6vjXsbQJ8fqA2ZyRJAfOP9wRY4CEC5Kig0Qln9PwZURSBprvA\\u002fG+4wWqfG4T\\u002fp5L0U7MfRPxMtaAjlxcA\\u002ffhDMLXgmrj+RS1bziFuaP1JGgKxoboc\\u002fTJv6hV65dz\\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\\u002f6TkshKdKEB21G5EYVAqQGwo4fKHmixAMBFJgUGQL0DDbNLkYHMxQEvXvO2FADNABWMF4G8SNEAqOmhjhF40QMGf4KyoxDNAGft2oY9UMkBq2RG7rUEwQLRsaLtnnitA7wYLvcp8JkDIl\\u002fUITIkhQMWTzcpVIhpA7M2sNHiKEkCsVZEQNPEIQHRYjd59rf8\\u002fUjc0WZDr8j9Zz+kxwDHlPxmp9mOjONY\\u002fZTHbw\\u002f7KxT9VvpH8\\u002fAS0P4fHMEwbTqE\\u002fRVj5BLl3jD\\u002fGuDx22PR2P9W3l+XuLGQ\\u002fO9spd6peWz+WKJKy9aUIQMFij31CHgtAXJ5ytwloEUCEbCS68DMYQEoeMcJ7HCFAmAylRPenJ0DzoQ4dRUcvQP63UuWcijNA5q1DQVfvNkBkdeVrMTo5QFk6loDfBzpA85P0aPdNOUCQJOZYc143QCrVg1oOxzRAb8B0\\u002fQEcMkDSy\\u002fHma44vQO\\u002fCoSxe4CtAHH+S3L8EKUBzyjoNW5EmQI67rBu8GyRAxpdhY6BnIUAhcki6O+wcQLj5qfiR8RZAyLNBV+1rEUC7Y+HJ\\u002fLMJQMAlt7o1FANAaNai0XZQ\\u002fj8ZLiEi5Kj7P7lLOi\\u002ff0f0\\u002fZqwIZrlTAkAhc2Ll7joIQEPz7mEDexBAsZoz\\u002fhd0FkBYkYA3viMeQGLYVuCguCNA85FA3439KECQAP+j9YcuQGmCFaXv7TFAPzBq3n4zNEBRTF478Mo1QEiFlAUDeDZA63zX0dsaNkCCqNga+7o0QCjT1cvViDJAQLLDUCCnL0B3RXlOnespQMl0gphUgiRAJJjV3xHBH0DbDA2KIY0YQHqgVUYPkxNA6zF6r17EEEBOEupiCtsPQE7jahxbuhBAFToYw1S2EkDljZrW\\u002flgVQIfdgmOXJhhACj01jnLbGkAE6CkvcoodQKthJnn0SiBAbSeO+zc9IkDdnNWjxb4kQOHaa5ygsydAyFpAFoK3KkDIlFwFjjYtQBUkl5fCni5A6XAT7tySLkC1cS0Y7wYtQPKdifP4OypA4kQYyqyhJkB4xG\\u002fgq7AiQJ6xEUqcnB1ApTFXbeCBFkCPqoUGsGIQQJqwk6iaxQZAus2y8JMS\\u002fj+gr5IXq8fyP4Xh9ndDFuY\\u002fquPP0CRf2D9Ooe1erCnJP0QwTESLRLg\\u002fh\\u002fCr2I3apT\\u002f75FKUNGeSP0SnDVFDI30\\u002fweNn1qQVZj9U1+mVEYdRPwifaxWT7EU\\u002fbQMfYwQ5\\u002fz\\u002fI\\u002f\\u002fUq7H0BQDw9o8XNVAdAF\\u002frBgfXJEEDahK5t6kMYQGKLYNLG8yBA8UxSWM56JkCfaAe4QQwsQDU8tqTTaDBASIWiDUgDMkCFxUiEIp4yQMR\\u002fs1wsQTJAPyrKCa4uMUAqmGbBPo4vQEBSdmfgxSxAkmJ3eERpKkCtyKtaGH8oQPbaPlzUxyZAstGFMaPtJEDxtCFShrIiQPrhM+ILCiBAXVTDLJsyGkC27cr\\u002flUcUQJ+bPNa7xQ1AyMS3DETlBEAnDrtRvb78P9AFKNsHh\\u002fQ\\u002fjw6u\\u002f17Q8D9x+GkAuDrxP5n8gZKipvU\\u002f6wWsC0NR\\u002fj82ckEj7dsFQGNeHLtsJw9AHmU5atWJFUB20SPP3KgcQJRWGjxTSiJA73YgqHlfJkCh45ozfTkqQNkh2Kh4cy1AEIVCRsypL0DyE0g180QwQNGUtsYm5S9ANnuo4J7BLUBp1RYGbWAqQAVkvDyaMyZA6TLb6cXEIUAqoWqyyisbQKa+an+JEBRAkD4iuRpHDUCjCUa7IgEGQBSXdUnYEgJA3ymc91f5AEAi2SrOzQoCQCGHr16pjQRA3wDb92HVB0DYR7sP72oLQFvRi0f\\u002fNA9AnI5kXdPCEUBNQB5v3noUQF7NFdRvBhhAjdxjrN5qHEB\\u002fV1aeLKUgQKoY1Mkp9CJAG0GGnCuuJEA92WQ0um8lQN0syfVTCCVAzD3enVaII0Db2KhR7jUhQDQd\\u002fryD4hxA1PzR7XMtF0CdYYwQKNcRQD06z5\\u002fxYQpAk1ZLxdG4AkAmTycVo2z5PxmcYoSucfA\\u002f096t4wgp5D+fEHBAwU\\u002fXP4Get9YTUck\\u002fo7Ixi768uT+ogkDMRG+oPz0CXnFaopU\\u002f4HpZtGTcgT964FNK\\u002fo5rP+xfV6\\u002fWF1Q\\u002fga2ePAXiPT\\u002fvNmTzqqQxP0Y8Q4pFqPI\\u002ftqhsaoc99T+gpajSNDn9P4TCDsAFjgVAmSCtV4iRD0CA9anuLycWQOl09Za9Wx1A2\\u002f3StrREIkDBpgXViFYlQKZQ7G37didAn3IrAJp1KEDKmwHspW4oQC8tymoRuydAsFvFsV7GJkA8X5DHid8lQFWOvRU6HCVAFvTM6zxaJECEZ5BFmVojQLQfLTO\\u002f5SFAPx++0BrOH0CbA7xRCOsaQIyRkmzjkxVAHC+6kvZUEEB3s6gch1YHQL85U2Lto\\u002f8\\u002fjQI16z669D9mVJrpsqzrP+6q1mfZKeU\\u002fETARxYY25T9ZlQIwBJnrP9CxfVT1UvQ\\u002fqEL0w1N+\\u002fj\\u002f\\u002fDjNoJEMGQHddhpVCJQ9AWIhViXnKFEACIrN3eH8aQPryAtpyJiBADFXScdjdIkBk3sXZHCUlQIS64cOJuiZAURUi4FRjJ0C2JhYWafgmQDwaYy8NdSVA6wcvRhYAI0Db2Em3V9EfQCFlXUiwKhlAY\\u002fgmVhHXEkC\\u002frJWGLOYKQOz7feDUqwJA4919\\u002fulJ+j+mLzwrViT0P4jF\\u002fS9d9vE\\u002fWXQAFByo8j+dzaD0HjH1P+Ll8YDHxfg\\u002fOtc9JkgB\\u002fT9GsE4cRwABQKC6T3V+KgRA6nsJKvVdCEAIZZxsIdINQAuW0tgnLBJAg4IWVSieFUBsk4T3Aa8YQH1buohPwRpAt7QwYc1eG0A1MvAhpGAaQCD+gIgT+RdA+\\u002fXnHiacFECccInDhtUQQKapdPqIQApAkcNoZVmfA0ACVE3P9DD8Pyc\\u002fmCj8dvM\\u002fC8CPTJzG6T+REKQ7EkzgP7SDGSbUk9M\\u002fIsghtYQ5xj8umuQgCru3P8SAt4\\u002fTvac\\u002fIZ96lswwlj++m1gIsVaDP+Xz8uoGZW8\\u002fdBNhQW2\\u002fVz\\u002fP31rf9N1AP9xwVegIEyg\\u002flWErDpVhGz+figBFb\\u002fTkP6h7axYrJeg\\u002fxuXysCP68D8ZohcTB275Pzd36UmhvQJAWSCvDw9RCkDujShZt2cRQM\\u002f54Sy5oRVAHf05Jr1RGUB\\u002fhQy9yhUcQOyGKagH0x1AL1p23pG7HkBaKHB\\u002fGC8fQAGLM1XJhB9A52ViEzjbH0BxMkNs7AMgQCQZi7twrR9AisHGL49rHkDXfGUhwg0cQFTRV1\\u002fYpBhAODqMRmKCFED9cc7XPh0QQIGNKB4f3AdAwpHYnzGkAEBvd8+3RvT1P1KSWgpB1+s\\u002fwmJWFIHm4T8PB4Fw7KfaP8IVS53kDts\\u002fNBW37CxS4j+OJF+Gn+rrP5XA0M1vV\\u002fU\\u002f\\u002fxW9N0Rl\\u002fz9fvwpA9PkFQGQKibQ9Qw1AQkW0eqGVEkDKLy3eT5oWQLB0gkGIZxpAtDMmGsGrHUCIDWWoOAYgQGjbPFs3lyBACgba+TdlIEAr4FrWZsgeQMVjhCvlUxtAeI8iMs7bFkDrYeX8lvkRQNZ+cw5ukwpAVICBcNKMAkCfB0VY2Mv4P7rb3wZ2bvA\\u002fA4HEdUki5z\\u002fGuAJUJuHiPzAuXtMwdOI\\u002fvO7bzZV05D\\u002fdj+Hr5vTnPzAel8Rlo+w\\u002fpuv4Uqhm8T8NG0TU\\u002fZf1P5bf7BlsRvs\\u002fxg70VUtFAUARwx45+3kFQGtoDfHeuglAjIyVXmNMDUDa207IZHQPQPsOLd5zuA9AUOf+bEMHDkAQoeRUWLoKQM3OtfaKcQZAUiLWz1rcAUBCG2yn9hT7P9aDaHQPqPM\\u002fwAWUuU9q6z9hHGJOfGTiPzWdmMSQtdc\\u002f4+KN2S5BzT+KHglNKDDBP9\\u002fxOQ\\u002f6ILM\\u002fC5KW1y0PpD+7qAbbGbyTP2RoMBdfJ4I\\u002fFV4y0MUmbz\\u002fhfqbDmuNYP7cMcMjOgkI\\u002fdTX6kiTGKT\\u002fjNoHjZuIRP5ARZkPP1gM\\u002fwD\\u002fzPor21T\\u002ftMtFXwoDZP9EiGdmSKuI\\u002fL24dPHhr6z8sm82RfD70P4IrrGrwZfw\\u002fMaoeuHvEAkBP+lWFcGIHQGvPKW\\u002fIngtA9vrWPrU4D0DLLudwkBoRQNXLEJ1OaRJAQYB0i9asE0ATSDU9svAUQE\\u002fefl5ZFRZAH7NH5N3UFkArcZf1Wt0WQCCT3aTH8xVAh8OjegUOFEDevkYb71gRQOH8Y08KVAxAUHdF57rKBUA3TZ4FKY7\\u002fP34hMnLVgfU\\u002fLlyO0Nu06z\\u002f4srIiPifhPwW+eNTip9U\\u002fBte8A2000D\\u002fmT9kYxRPRPyHB90i4Bdg\\u002fF6FFcrKn4j\\u002fQybTV\\u002fa7sP2RDms3\\u002fFPU\\u002fB86vX0tt\\u002fT9wQUnJXYoDQBKPgrUAzghAIbrYkPE8DkD8W9BbAcIRQEDeuxStHBRAe4UvF+brFUAMGsGo3+UWQKEu4uPAyxZAUz87L\\u002fWBFUCc4x\\u002fKTSITQAQpP6EJ+Q9ARrNKiCEFCUDXPBWGWVICQIaAhrF3Kvk\\u002fZN\\u002fBzLpb8D\\u002fOnmEndKTkPx\\u002fyPS14x9o\\u002f931Wepue0z+lwwR1gnnRPymxV6zhf9I\\u002f88MQBiaX1T9d82Th5HLaP0jmKMCMvOA\\u002fNR19cZa45T+i6\\u002fcvbH7sP0trcnRBfvI\\u002fkuuArM1B9z+\\u002fx+1kAeL7P0qHf\\u002fJ7iv8\\u002fUNk57OK8AEBBS+SBM6EAQCDi4ZSw7\\u002f4\\u002fWQMxSYP9+j9QETXfMin2P3ZzlAvUM\\u002fE\\u002fuaGi\\u002fGpi6T+25hLI9uXhP8523QtnOtg\\u002fbHv7rRqPzz\\u002fFaSYkBMbDP4y4GNXWxLc\\u002fA6WsrPZGqz\\u002fplFTP77edP9Jd4zkxko4\\u002fX+QfXxmNfT+tegz8Q71qP1MZGos0lVY\\u002faLZm3vnDQT8atGHcMwMqP1v1v1ESzRE\\u002f18fTN9ks+D4IooUij1TqPlKTKOE1T8U\\u002fDYutWQzXyD8ffr5nYMzRP6ubAVhG7to\\u002fUsq2mwXh4z9pthnTSOLrP06V8xlefPI\\u002fQV8kkMRB9z+5gAFSRgL8P3MHQFMnVwBAH7SDGBW1AkAabVZCMTYFQFuBm0qu4AdAgKm1ZZ+KCkDDbIwvM9QMQCV66GQJPw5Azl5UOQdaDkCT3wTJEu0MQHZ\\u002fKqAtEApASN1mosAkBkAG2PfmvrYBQJlBZEHFqfo\\u002fDHdtCqTf8j+H6i5PISbpP3Xl5Dzkr98\\u002f\\u002flpneUQ\\u002f0z8PhIMRqCXIPyhJ6TccisI\\u002fsoM82feIxD\\u002fwSmu9ztjNPzMw0O6Nadc\\u002fwyunaiD\\u002f4T8Jlu5eiWHqP5IDis0oYPI\\u002fIfDgbIlr+D+9voCVwiD\\u002fP\\u002fTF98KWHgNAYoEo0YuwBkBlVnocNP4JQFRUlMWTowxA0Ujdl08tDkBm+wAFxDcOQDzwWZKmlQxAL4KoYo9qCUABmBP1UikFQBEGHPyUdABAkJSeVarf9z8w6dyQ9ivwPxJHBbE5k+Q\\u002fT3bkcDwB2T8GDpdR+UzOP2nPecrp9MM\\u002fjFX5lyzBvz+VkCseYXW\\u002fP19BoYpJL8I\\u002fapIJLh\\u002fixj\\u002fK8ASn9jHOP3kcuDIlZtQ\\u002fQ0KMObmM2z9a7y42CSniPyV9avy+7uY\\u002fG59dWr1i6z96mXJ5y7HuP7LjvJxhFfA\\u002fqXEg9pB67z+YjkLo18fsP5soibPEoeg\\u002fhq8JwxPM4z905pHQ8QPeP1WqBetMk9U\\u002f85HHVPSTzT+MuhIF527DP4FS0+r0j7g\\u002fcDY5MtnjrT91\\u002fLd5KXqhP+xm8c0cj5M\\u002fY2Xz\\u002fiPVhD\\u002fNBxBpeP90P73PHp287GM\\u002flBv7Z\\u002fu4UT+0pIrGm3Q9P\\u002fVSOW1r0CY\\u002fEGhplIRyED\\u002fJIF46hSf2PqPnldEjiN0+Cx9P3K2uzz5ptAt45P+yP2TCFr3jK7Y\\u002fW7wfYZ\\u002fPvz9MU6FDWRHIPz5A4GtYxtE\\u002fVLFcYBUH2T8ek6W\\u002f3cPgP4B62hW0heU\\u002fmKbV\\u002ffXF6j+15ZFQQVTwP20jcOVJsvM\\u002fZ\\u002fYR3KCH9z+OLrVkNK37P4Tsg1+ut\\u002f8\\u002fW724L7CBAUBoc2Q5JnACQKhuf2ifZgJAThyJA4NTAUDGg1PhSrb+P71wtNqZl\\u002fk\\u002fT7C\\u002fTuoK9D+harCT0oDtP3AAFa\\u002fUaeQ\\u002fzjYY2CaX2j8s9ml5hGXQP3liD2mBmcM\\u002ffTFboUyjuD8QAiopFqqzP5K8fNWq+rY\\u002fqWTFy6oowT+r9K0VBRLLP6kB8Puox9Q\\u002fRAUIMKNq3j\\u002f3wP1ifjblP9fbxDKwVew\\u002fqbynkh058j8O3Ha3Wqj2P4mvelmlP\\u002fs\\u002fy\\u002ftanMib\\u002fz+I4bITkJYBQLi6UF9qpwJA\\u002fDfYq0e7AkCSWzBlEbUBQISPdrHcX\\u002f8\\u002f7s0E2lf2+T\\u002fYPq0BqgX0P2K6dgSHv+w\\u002foIk03Cs44z94OipUbAPYPwBcI0f5WMw\\u002fKB2MNwpLwD+kXAzlApSzP+\\u002fgjp1Ks6s\\u002fnyehuKpAqT++5DejhrSsP+eyl\\u002fw7i7I\\u002fyngr3yxwuT\\u002fAuqwg6MHBP5v3wJBCd8g\\u002fprkqMMZA0D\\u002fhw\\u002fEmen\\u002fUPzaMPjl7Tdg\\u002f1fu7k0fx2j+h8uIO\\u002fNvbP5kPMZYQ2to\\u002fUXbk\\u002fSgk2D+0asmfiEjUPxMoQ0h\\u002f8s8\\u002fOckCdbCtxz+l4HEL35nAP3aynAxOJbY\\u002f\\u002fS0u6kVDrD\\u002fK+vpUcFShPxA8ML0jd5Q\\u002f3bmJx8VBhz9TYb5x2Vh5P9f3sOufW2o\\u002fU1gqKR8AWj9S6RrPCjJIPyidGLDxIzU\\u002fusL4bB9HIT\\u002fCHyqiA1gKPz7uZai7s\\u002fI+8zUtJMrN2D5W+I\\u002flhj\\u002fAPtMpT5QCMrE+zXUMkJ\\u002f2nj+1a68a\\u002fw6iP9Z3jTdT5Kk\\u002fWKUHngaasz9VPElLWxS9P3i8Lgups8Q\\u002ftZtRIT9TzD+toONswcvSPzz97Smiddg\\u002fn4L2kM1x3z8IG0gdSPvjP\\u002feZ1\\u002fTm8eg\\u002fhNGS9JRD7j+fcA6k+KPxPwNYLMcEk\\u002fM\\u002fovtRjueI9D8lo97C60f0P27Zx8dp0PI\\u002fumTY2rJg8D\\u002fK9IlmisDqP4oYHclEgeQ\\u002fShnxCKSD3T\\u002fhhf51lfXTP7ImZ6p0ask\\u002f5FHQUJ2xvj\\u002fRnOdwzxayP+c4AVNJ7qY\\u002fCjJqjJ4voz9vgSAU2KGnP\\u002f8QxAX2C7I\\u002f8ic5NoyVvD\\u002f0cb1Uq+\\u002fFP+Cr5e\\u002fSE9A\\u002fbAFdfgGL1j+SuG2MFGbeP6S93bM9zuM\\u002frJZebuz86D9DuS\\u002fcmXnuP4BxbCu54PE\\u002fpEVA288L9D8MoPMtEFX1P6RaT+XqaPU\\u002f8ac5QLAp9D9NoG1t3r\\u002fxPyxJ2T6FIe0\\u002fsg0I+lc+5j\\u002fmLUighZPfP60lCPCp1dQ\\u002fUIMDmoGcyT8UHlU7KYm9PxLokiNQT7A\\u002fpalCPqM3oj9+RAs8yB2XP94osuYbMZM\\u002fDqBSe61BlT\\u002fKFkgHHRWcP0kb7R7J5aM\\u002fBEew3Rx0rD\\u002f7UcUSANWzP7zdyxR+ZLo\\u002fzU62spKOwD9aLacLH3LDP\\u002frRqr2gTMU\\u002fFzn2LMq4xT8VU6QJDJ\\u002fEP3iTLr\\u002fgPMI\\u002fgm6VNhwbvj+Iup6gkUC3P56va8Uw3rA\\u002fYa12QKsYpz+0heYSPwOePzwDxmdBnZI\\u002fYnj6nVknhj9R9HWGNWF5PzZZlI2dAGw\\u002fMxRNJJmvXT8souLIixlOP+MZ\\u002fFgRBj0\\u002fc9cbEQZ2Kj+l9rs+ZLEWP6I8893aOgI\\u002fud38AjFZ6z6Xon26kB3TPlUFOI7P9rg+r2RCascUoD5ufT8rZsyQPgrPYK7b+4Y\\u002fFvZ6q5rLij\\u002fz0A2OjzeTP00hSnNJO50\\u002fNAgiZvPwpT81U2KWdu+vP5Z9oJGxm7Y\\u002fSsQO4CNpvz\\u002f38Qb054vFP2P23oTnLs0\\u002fNIVsCi9g0z\\u002fBct4kLPPYPxwF\\u002f2YVzt4\\u002fWj\\u002fyBVMQ4j8gbNhcBf3jPwy5KvzUx+Q\\u002f1Qs2bydA5D863y9Qs3riPy5jlZzhk98\\u002f5uFqYatF2T+E7XSgaPXSP8t1OBx9r8o\\u002fj7D\\u002f\\u002frajwT\\u002f14obHrfW1PxITg6nE+qk\\u002fxVw8pXhAnj+Y6p2kKHKTP3sybkNOJpE\\u002fzCik32kylj+QlRgzM0ahP9lggCWFeKs\\u002f5ZhmTRogtT8toD6z7B6\\u002fP+xEdxZQBcY\\u002flKSqHUMWzj+vVV414+fTP3M9mw\\u002fRf9k\\u002f1H1DCJZ\\u002f3z9Fm7MgSKTiP+UxJx04\\u002fuQ\\u002fpYty8WNX5j9bA1IdB1jmPwlD3Fbo6OQ\\u002fcoPWqqNA4j+uobYQr6jdP6iWwXw+ZdY\\u002fagyBQHtozz9Z\\u002fqLXQ3PEP5xPFcAmwrg\\u002fz6cgV1X+qz91Jraln\\u002fmdP3A+k8PoqI8\\u002fm+qU4yVOgj8gtj6Vtpt7P3U+Kf4PgX0\\u002f1IR9PxHIgz+t4GGsPreMP75GFwOh2ZQ\\u002fpBAqTuw0nT8lVRTjjGCjP1qGeMG2Hqg\\u002f1sDBWCQFrD9\\u002fbqp160yuP4VOiXddeK4\\u002fU\\u002f7WGW5+rD8TYSrrgsyoP+xSJMtmHqQ\\u002f55pSKqeAnj95vsE9zq2VPxUyzUK1BI0\\u002fP+mvqu1igj\\u002f7KeJvITB2P\\u002fTJ9REXpGk\\u002fEgF2UcR+XD\\u002f\\u002fwKMwqYFOPzKzYcjxbD8\\u002f7w91Ev4HLz+XD1zkuzYdP96FiikdEQo\\u002fQ7wa923s9T5MxKbuykzhPm2uB7c7hsk+kT7yGWKPsT4biaUNXJSWPh+hgepknHw+IyLFeI+BbT52XUSOtQ9vP+oCAc6OH3I\\u002fdEB4EuYfej97ba9awh6EP\\u002fb+CReB6I4\\u002fLjWuzSFNlz9Gi7cfcUihP\\u002fV385CzUqk\\u002fwkEmejxMsj9jKrQjFeK5PxQd1+RWuME\\u002f98WrYsA4xz9TICkr+9rMP4DlbdHz4tA\\u002fkfK+oH2J0j8Rqz4fCwrTPzHD9022RtI\\u002fPGH8iqFk0D8ZrY6oTX3LP78jwZOQj8U\\u002fSCm0Kyurvz8EkLKgCs21P0qVI+dwLaw\\u002fcn77x3cmoT9DScCVtOKTPxod7qvO64Y\\u002fbPedN\\u002fYLfj\\u002fTNUgITwd8PxZjt65j\\u002fII\\u002fHdCXz3sLjj8iBUROlQOYPwCd9G2Dj6I\\u002fnFoeh46Uqz8iUt2rD8SzP\\u002fdeAynxbbs\\u002fL4q+ZP5wwj\\u002fV0o24TfXHP4P8fQ445s0\\u002fuHjMaFnL0T8Qo9FeKQ\\u002fUP3fdFZ8wSdU\\u002fmvdBHE8p1T\\u002fGFwI9zqPTPwnMW6Vt+dA\\u002fCBsRgttGyz\\u002f7gcsP71nEPxLff7GALrw\\u002fHVacTs4asj9O7++CeZmlP+B8H3Nm\\u002f5c\\u002f1kgVexkRiT81RYoxAV95P8wtGlxhNms\\u002fqxWlzYy3Yj\\u002f4WbN9bR9jP0ovwSM5yGk\\u002flHSvisz\\u002fcj+QDQE\\u002fyc57P+hX4F\\u002fGd4M\\u002f0Ag5bLiriT\\u002fypxo3PKSPP6m3ErUVKZI\\u002fRtrOXyxhkz8R0SwSuDaTP6ug3Oxxs5E\\u002fVIXoZV9Ujj9O4lymlDKIP5\\u002f9nqA0BII\\u002fSofwGsUceT9Hl7KWk3JwPyk\\u002f4N1DWWQ\\u002ffOlvfAzqVz\\u002fewuXJntpKPwtr0vng9Dw\\u002fVZNaDvURLj9SBHc4LBMeP\\u002f+Ol2yx4ww\\u002f\\u002f12vZcKF+j7d+ej31SLnPqn102LfENM+lESPiE+JvT6yR0qq72qlPv7l1R0A\\u002f4w+AjlaCvFZcj6vAq7ll95WPqAdsZXGSEc+G7MoMtAlUz\\u002f2TWwCf3BWP7SNsSaUZGA\\u002fnyW\\u002fwgXgaT+VKTK+t510P\\u002f3kylOxTIA\\u002fV\\u002fZayyeCiT+SPCNWG66TP0og3mKfr50\\u002f66F3qXCjpT8wlKDJ4CSuP1QCPU5G4bM\\u002fz7iyOfCpuD+SxNiId6i8P2pRLKP8G78\\u002fRRe3Qq+Cvz\\u002fvJjB7McW9P0qpgj+dPbo\\u002fNVK3Wb6XtT\\u002f8fv43QZqwP0LBRsnf4ac\\u002fBzZ\\u002fj3gWoD+oLMHm11WUP3EKuYFiNog\\u002f1OBsOQmIez9GXUjMWHtvPwITxJRhJ2U\\u002fu3+Hf4LqZD+btKWYXottP2KC9IdkvXc\\u002fwqM07kYcgz\\u002f1k0sp0cmNPwr\\u002fdqynZpY\\u002f2YnjpkxNoD\\u002fl7krz9\\u002f+mPydB8b1mZq8\\u002frdCDSvOitD+aVxt0f++5P6OPgBWj8L4\\u002fn5nXLIxnwT\\u002fY7\\u002fPCsl7CP\\u002fNi1spXHsI\\u002fQloBsxynwD918IpjOXi8P8Omi9h8mrY\\u002fpE6NhHWnsD9TjbpWWsOmP9a4QQqc2pw\\u002fiz11JoT3kD9cE9O2gYyCP3eY3SSx+XI\\u002fms+8TF+UYj\\u002fNL9uwuMZSP16rSYVXwUc\\u002f5bdZmKgHRz9MYDfvOuhOP3aUqe5k7lY\\u002fSDEsvnPPYD+DordKFW9nP0rYr9S\\u002fom4\\u002ftujA3Ourcj\\u002f+\\u002f7R48il1P2SDGMnBR3Y\\u002fhepKwgnHdT\\u002fFnrTzgMRzP1cPdeHZrHA\\u002f9dwqDHEtaj8OrsJynCdjP4M2DLKIM1o\\u002fP5JcVlDPUD+XcWDEdFNEPyPmjA3rSTc\\u002f\\u002fyl66T1vKT94GwO5yZ8aPwGyrXUX0Qo\\u002ffXfViGkG+j4ZaUj4hkvoPnoW9XveudU+ZJCsD+iBwj6IbkJs0dutPnXQ8ytgr5Y+6IufhvIogD6OwcvUc4RlPtfaGm7XzUo+LC48YIFsMD5dnLZItYEgPkhOpmljqDU\\u002ffn13eBChOT\\u002f4yOa\\u002f7TdDP+7SXf5Xik8\\u002f3JijgkJlWj\\u002feLZfhawdmP9AX8cIhJnI\\u002fd2QM0QY2fT96KEOkZbCGP8FPCOtf0ZA\\u002f9yBERauTlz8wcZVXhQ2fPwsV+hqMIaM\\u002fYTRWsg\\u002f\\u002fpT9S+0zpYJGnP59eHALxhKc\\u002f2K\\u002fOwJ3cpT870v83yu+iP+950\\u002fgRmJ4\\u002fr05oOOIRlz+4mdDX6EGQP1\\u002fr9klCcYU\\u002fh98eW4mEej9QaFabJOVuPyE\\u002faDgXPWE\\u002fmIErgPCaUz9\\u002fBU3W8ChLPyhIeo14f0w\\u002flgtfKVjqVD+Uec7BsxBhP34cXVuxvWs\\u002fjZP\\u002fbGnfdT\\u002f6gUgmzLGAP+\\u002f1+fAqtYg\\u002fvOVtnVW2kT9XK1UU3H+YP5YZVwpmPaA\\u002fCWLV2Ph7pD84FV4qYWqoP3eG1fPuV6s\\u002fdLOs8uqlrD8oEpGjyv2rP4\\u002f8V0sYc6k\\u002fxzJ2lKp\\u002fpT+q++80ZdugP7GgM29FhZg\\u002fVF6piDqJkD8CIlaTO62EPx1NI26h+Xc\\u002fP7I8KwbQaT8df4hdBu1ZP5xvTxgSt0g\\u002fklpR02DTNz\\u002ftwKw95\\u002f4rP2sQMlgdnik\\u002fVop\\u002fAv3yMD\\u002fFXHOuxiM5P\\u002f4APUmWX0I\\u002fXT2qpjFtST97PXqdZ3NQP5gR18wP0FM\\u002fx8L4s1wqVj\\u002f9n+9WIQRXP\\u002fn9XaezLVY\\u002f5w3wZm\\u002fWUz9pUjEsKntQP9oB8Za6dkk\\u002fJXljRSBSQj8LEreyPpw4P0SnsJWc9S4\\u002fbHNbrBBQIj87IrI4hXoUP0m3oVsuxgU\\u002fqSywOUIk9j4DyfGHs6LlPpnA53THXdQ+V3bI7jB2wj5oncHzXRCwPp+Wjowzspo+H+CvxO4OhT661MDF0VtvPkpRM3xY7lU+hzM1ePG0PD7c4A76KJchPmFX+rWjMQU+Rf2fbPYF9T1rz9gyF70WPzhY8b+UYhs\\u002fzqv6VjdqJT+dLP2F24YyP9aPfBvjZUA\\u002f6g53zVPUTD9v39ImfsNYP7PdbOshgmQ\\u002fMbwCNwowcD8EoxeiXyN4P5feg0Av5IA\\u002f8dC4tnoYhj+p31b2HPKKP5qoHlRql44\\u002ftfoN394nkD\\u002ffPOSab7+PP\\u002fZTIxqwBo0\\u002fkPliNha1iD9LRAMikpiDP7bg4N6r\\u002fXw\\u002fDpXiKBYGdD9hFwNVAt1pP03WENawTV8\\u002ft1WkpJfZUT9DVL9j4pBDP+q0ss68MjY\\u002fpcuzpU7lLz+0wdDJM8cxP8sXn6g9Dzs\\u002fyj0T4vlwRj\\u002fNoBlhu3hSPxexsuY8j10\\u002fbVZsBfLxZj8PbCLB1kRxPwFaEzepHnk\\u002fasmoItWMgT96sKsV5WKHPyZWsSUIgo0\\u002fH+pSsyiFkT\\u002fz8NQtwHyTP56Q4CVJO5Q\\u002fhyBAKIqOkz+CXFpnYpKRP0QeUfggUY0\\u002fyV3QolOwhj8KMs6RNUiAP8XZ1nmmqXU\\u002fHsSV77+2aj\\u002fOvePQ44lePw98JnUoMlA\\u002fVgzT7Oz8Pz9xvKvLUM8tP1dft6QMqBs\\u002fViaqMXR0Dj+3TTQbpjUKP2UGEc8D9BA\\u002fvNvh3gn8GD8VDqrQWiMiP71rZiuR3Sg\\u002fG0hGISLPLz80hlJjzOkyP8TwkHB84DQ\\u002fM7SVtu5gNT8\\u002fbNpy+U40PzuCweyc5jE\\u002fCZzFn3NMLT9zhhvjHkgmPy2Dgh75iR8\\u002fzlagrG3QFD\\u002fLrjiGmbAJP0hF4D7owv0+C2BzmxhC8D4aos8T09jgPrHvEIrWp9A+nOf5mgKXvz5DBBoG4dasPjMY8Rw1Xpk+5MVm2KV0hT58oxXW015xPgtv9HnAxFo+byT\\u002fKrmDQz6xKSXPDccqPkfzi3wBOBE++AQqQJXA9D0YE8ZDhJPYPSldnyM5Dsg9POrPECBx9z6lC084OQ\\u002f9Pl2adW8dKAg\\u002f63A24aZJFj9Vm+lRS98kP5L3ZzAQKjM\\u002fdBRpQ\\u002f\\u002fxQD9RS\\u002fXwIYJMP2XEguqAnVY\\u002f\\u002fVrStinRYD+1FSqnT1pnP\\u002feXXM2XNG4\\u002f2dGq3Ygrcj\\u002f1w4oWx1F0PxIg96PnHXU\\u002fDLxBgoxldD\\u002fxxkWwTl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kDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"dVGBbj8btj9BeNsgz9u1P+\\u002f1JUF7\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fsP5cXHdxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fJiJZT4xWtD+GgcysjXK0P7z7aaDLT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zP+R4zk2DrrM\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P0miaicx\\u002frQ\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P8Ro8swVdrQ\\u002fyx8Y1ItWtD+GgcysjXK0PzwbiuXKT7U\\u002fRTBwnFC3tD\\u002fPGUGiXc2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj\\u002ftA2MSt3C2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002fyx8Y1ItWtD+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj9BeNsgz9u1P0DmLGJm\\u002f7U\\u002fCfhR0adPtj9p0O05tnC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz+GgcysjXK0P\\u002fCK0duEH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fNhMc4D4btj+wxG9TAr21P0DmLGJm\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P3BqzeGhwbQ\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fRTBwnFC3tD9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21P\\u002fQkhnFk\\u002f7U\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0P5H3kH0\\u002fCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P7uxu+CFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fP2x7fc04tj+wxG9TAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT93m\\u002fE3Zbe0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P5xMcqkUELU\\u002fyLGrY3pxtT+nSPelZre0PyOlWntBCLM\\u002f3dCDZ9L\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj91QZZGtXC2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60PyOlWntBCLM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT9gq5PoXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9CUv9EAr21Pxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P9fY62Lyf7Q\\u002fyLGrY3pxtT+NN061tQ60P0bwBwRI+LM\\u002fIlFbBdL\\u002fsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT8mPniUW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj8oYdKR7kCzPxfd8ISl3LU\\u002fCfhR0adPtj97SdI0GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002fM8jYSJRitT\\u002fytxGdW82zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP5aK\\u002fd5ENbU\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP0bwBwRI+LM\\u002fKtWpvjmTsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zPx8\\u002f\\u002fOlOjrU\\u002fU+xgk8w4tj9zVlLzQJyzP4IPso++7rM\\u002fCfhR0adPtj+gfTF+GEe2P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fU+xgk8w4tj9zVlLzQJyzP8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P3JP6NU517U\\u002fyLGrY3pxtT\\u002fODB5g3U2zP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P8tdZHO87rM\\u002fCfhR0adPtj8FCQA2fIW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P0YBrImlE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fyLGrY3pxtT\\u002fg++zO1PWzP\\u002f+q1w+4OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+nygodXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz\\u002fg++zO1PWzP3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP11VejJNjrU\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fpPQu8BEMtT\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P83MFeelE7Q\\u002fCfhR0adPtj9QzaFLeYW3P1RQLuzBm7U\\u002fXoKVhtD8sz+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zPzXuaKWlh7U\\u002fOi0txDpDtj+IVUsMeGy0P6H1pzVrrbQ\\u002fCfhR0adPtj\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P3crj7K2OLY\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+IVUsMeGy0P6H1pzVrrbQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fb9Pb23LRsz\\u002fCsOlrjHK0P801ZsSFH7Q\\u002f9rF8CZZitT+mYEtHXM2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002fUrRSg3gruD\\u002fHydGHt7i3P1RQLuzBm7U\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002f9rF8CZZitT\\u002fcVlnGWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002frnNCmlFnsT+J9XBLsZW0P9bz7SUdxrM\\u002fAlIZV78usz\\u002fCsOlrjHK0P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u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hD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0AgHbzHhiEPQE789LJrIQ9AvyR5Q3shD0CjxLauXiEPQCFYNERwIQ9AmSuYop0hD0BHiO4qqyEPQCwpTv2bIQ9AsziSlmwhD0Bk9xMoFSEPQGrsb7GnIQ9Av92a4UkhD0BML1yqCyEPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQGrsb7GnIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9A8hNgo4ohD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9AdIR0j1EhD0DlhDI4qyEPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9AdIR0j1EhD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AbX0IT0shD0DDDLLlBiEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQDwZnmN9IQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AlFvdRUwhD0DSw5hLLCEPQHMxlemqIQ9Axcnyt0chD0DOl9o49iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9AqqLMRIchD0BHiO4qqyEPQB6D3s6HIQ9APFn0hJAhD0CX+IplMCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Axcnyt0chD0BiA5bK9iAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQM4MYRlwIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"BgAD0MqRvr+m5x+msCu+v8pkuZhLQ76\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fv0unV87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fQw9Bb7uIvb8wAVFTefm9v52rhV\\u002fHCb6\\u002fhYY8lDW0vb8ChPTWRHO9v2zB8r3efr2\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fkDHQk7qIvb8wAVFTefm9v9qxEqjHCb6\\u002fhYY8lDW0vb8ChPTWRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002fkDHQk7qIvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002f8NfH+Nrm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr\\u002fRhCsMs0W+v2LsgnYpDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v\\u002fIGgj0mDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v7sBnnCWsLy\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb+HFjgXXOu9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v\\u002fCT6KGYsLy\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v7Sa1mbW4by\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr96flRLR3O9vw7qaQZQR76\\u002fXtCiUADbvr\\u002fNxLe0FBu9v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr9sP678RHO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8vwfAQNZPDb6\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8v+NJ43ynXr2\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fXtCiUADbvr8D+Q6PjbK8v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR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CIkGCDlD7+GUBoPJ6kPudXDTfK9bc++1zw1k35zz7Wc4x+CJPmPgDHGWOmUv8+AtQ5lY+dFD+l1VMJW6IpP59F+Lgzvz4\\u002f1YgNE3WoUj+ti6qwg3RoP4XLfIfEAoI\\u002f31MvBgIdnT982Kcf7ku4P7WBGEJY4NM\\u002fDuUW1mvs7j+5JWEvH4UGQAdgTUj4dB5AdvshNpUIM0BwQVIN6+pFQOOjsQ03NFdAJve\\u002fxHCNZkDvU60SxRp0QG4twDByboBAWrMXMC6giEDx9oLbxe2QQH+Py9bPYJVA9Ye8T3rYmEBXPRUxDKeaQBofgLRsf5pATOTarrGLmEA3QRBHe1KVQF9EYZvCfZFASFqgWjlHi0BtSq0YA1GEQBkwwFag93xAkU5tC1LDc0Am8oUbedNpQNYtqDY7YWBAOrl3UBuBVUAd80clS4FRQDkWkj0CQFVARz+g1UoZYUA57TzazyRtQL16cvDurHdAXQDXrpvEgUAKyy97f26IQAcm+o1ytI5AfqPQnWGfkUDDe00\\u002fsHiSQL1PUtiyrZFAabXODSjmjkD+WYmer6iIQCCMqAIT+YFA5Enc3rfud0B9VqBUCx5tQB2IaVZRMGBAMiZcNoN1UEDAjhtXBZ4+QGTAUIj8EipAEu98hrxcFECNrTnZLkH9P44vaPCkd+M\\u002fJngdMw9lyD9eupGI\\u002ftKtP\\u002fvUdCvT85I\\u002fI4KyFRbiej8IEDXStqJlP8T\\u002f5sbEqFI\\u002f4jt+L6YHQD+gjVwoaykqP43sDJ780BM\\u002fHzlvTkyX+z4flq57OZXhPsxlNCQ5e8Q+RTbb4rPKpT75msnXVDOFPphKYChOfGM+ggqKjJRuTj4DfLjkycVVPoLeT5myOWE+hczMxo9YdD5c8Dpfi9qHPj9QELsm2Zk+bOIxenHJqT4kidYsxPe3PoHIWon7a8U+NW2VQmun0z4GaP26DDvkPhHvBVZaT\\u002fg+MEgICBUcED9R2bMKbpolP+7C1W9AxTs\\u002fZB3vU3fIUD\\u002fRRmXDuiljP7P9lqeUN3U\\u002fEr4sv8Xrhz8KbYPOFhmdP+JynTlfnLM\\u002fuMam5YF4zD\\u002fukSXTqRXlPzsY8rMAff4\\u002f5Pn2\\u002f7L4FECmRrdejBMrQIbM7fMJSEBAPvetvGQnUkAsCvds8rRiQJigIVYwxnFA3j6FoF0Yf0CaYJ6r8QSJQAlcLRGRg5JAKb5ACyA3mUALQ\\u002fhKEaefQOR6rOXHW6JALAKUJG\\u002fBo0Bk0mFdJdOjQEUx2MwXrKJAiWHU2eWfoEDGkgdtTSycQPuJ454d1ZZAim5gO3a8kUCg77iD72KKQEl0Y2BzvoJA8+MZI1tmeUBmb12HT5JwQE1dj19a2WVAZaDnYILLYECOSmjJwEBiQOUvC9ykEGtA7LQPZ7MudkAeVIA9cK6BQIPHbUbQPIpAmn3KR8XjkUDXn4QtQVWWQE55watofZlAqYibOiSWmkDFHrUbYlaZQPIdh9RUEJZA0qy6W82OkUAN\\u002fGER3YqJQLCYmi7Z\\u002fIBASS+ZpmardEDIGmxFjQRnQNELt55keldAy06OKaf1RUCktifyBd8yQNkPhG+r6h1Aap2hOJkPBkDm\\u002fYDxr9ruP+btH40AStU\\u002fbneTMo\\u002ftvj\\u002fXBB75zCSpP0slNuNy45Y\\u002fVOFn2lvvhT8xKv9aRLR0P1EZooVTeGI\\u002fEznFIjyITj+QguavOi43PysK6dGqGyA\\u002fziwrgOF1BD8uH1SXPL3nPtG06wTPJ8k+x5Nv3\\u002fdfqD5vWE4YBVCGPvlLDCKYXXE+RBBybeHshj4EFQcNlw2SPry3Gvp0LaU+gQqyzTmquD7ku9tcPofKPoE9A2CtMdo+9P3XmLbq5z6WuHM5tp\\u002f0Polj3D+joAE\\u002foWglk1E6ED8DnNX\\u002f1FMhP7Z7FEOlJjU\\u002fPw1AyP4gSz831XhtTAFhP78+ogMWIHQ\\u002f8TUvgxVRhj9o9f447XWXPzthIup9H6g\\u002fLicXzECAuT8kSm8gFhzNP+bs4DZBJ+I\\u002fStE6zl3m9z\\u002fO0yGMCqIPQK7R+6cOSyRAqnVSNIy4OEAx+891tUdMQDAFVriNMV5AypCW51r3bUAln2bRUZF7QIJK61XvdYdASb88yWlykkA2dhonOcuaQA3bhHM\\u002f\\u002fKFAN4ljaypcpkCwu\\u002fFhatSpQAbe\\u002fEdT3KtAajmmoiY9rEDzt5iQzBerQNX0bdbsx6hAtryCqj26pUAUBD4cgE6iQE7Lq2HPnZ1ADH7vvvvqlkA2EVPIf+SQQBYOyCymrIdA122WKkHGf0AtLrHNly11QEnQpCjrQm9AoKuxKDp4bkDZn0GO\\u002fVN0QPmCJfYtYn9AYs\\u002f\\u002fO8tBiEA+a682hK6RQLdVLSF81ZdAKa6ViU2CnUBf\\u002f8l08bygQF5c7lx+YaFAzbzRZ5eDoEBQtbw\\u002fPLacQKSNv7JG15ZABpoPLFGikEAJgvmL4zCGQEd8xJCXIntAPHUo7XJxbkAJnZwDomFfQEWGGqIVy01A8VuKcBgsOkBFNFxgEXwlQEMzmuQX1hBAs1CCqFFE+j\\u002fvG4VuErjlP9Sm0PkQB9Q\\u002fc80VSVRjxD\\u002fORYpXjY21P7fsXg4FRqY\\u002fuiAnGeiwlT\\u002fww\\u002f0tqo2DP\\u002fs9JZoHMHA\\u002fxKecckOIWD9fkbJTwf1AP84qLF5dgCU\\u002fDuUkbxHaCD9hQOZRqjzqPsR+CRJxVck+uhWn8cUcpz4HKBXe4fCRPitN3CQgtbU+3ksZBe4DwT6A3lN5O9PTPviuhLNS8eY+vfpZveSB+D5CFNj8pv0HPzmrkm3XnBU\\u002f2+7xvU4qIj\\u002fLmNUefHctP3aSffKAsDg\\u002ftAOrjiBZRz\\u002fM7gSoF8BZPzGGtYK3HW8\\u002fvqUK6WXogj9flTLP8O+VP0vyqNxp1Kc\\u002fIMY14bZDuD\\u002f5R815iJTHP86Q\\u002fgKLrdY\\u002fOLJvrHCv5j8krcWWTYX4P1ItZ5zLiAxA6hKh\\u002fCYtIUCebOkMYHo0QPoyRAGLeEdAXqJPzU1yWUBT6FfGTOFpQD\\u002f0oq7kk3hAAa9ouUO7hUCPCmQ+zdqRQCfuq4P3OptAwzxzDaVEo0Dbvl\\u002fPD1SpQOKK5THsAK9Am2Vf\\u002f6y+sUC3UZFKUByzQIN5wOFJgrNA0asOor8Gs0DCQj+9+dexQMLKtCQhJrBA7OO2dTEyrEBoKKjtZ6enQJLtC66p9qJAXTpbvj3pnEAPQ6ovHeqUQNb8geCn54xABv7tq6Wkg0ARrQYhBX58QL4PjTJLcXlA79luRU4lfkAJkV\\u002f07VOFQE1s4x0tUY9AAWMTCqoklkC\\u002fUCQLg0mdQAxCuam\\u002f6aFABb+2BNgrpEDIqI1\\u002fFd2kQGs+nEtRzaNAjSlFYKY9oUDJIpUcOIubQF4WelcMMpRApad9gmE0i0BPbpniotmAQEGYSqXtOXNAZkJ8vZNCZECvMMKKKtFTQEQsqlUcL0JAOM0K2iYBMECrboTKDiwcQNfdkLZzSgpADlnZpkAk+z9\\u002fhMWomozuPw+PrSmCseE\\u002fXpUdTxr10z9+slSZljDFP4Jvbz7L0rQ\\u002fi+GmEbfLoj9md+IgBxCPP4C7UB7\\u002fdnc\\u002fJf3ou\\u002fkxYD9C74bLCGxEPy18I5BRhic\\u002fw3L55DPCCD9SxcEaHNfnPrBNR7bGssU+1HxF9BrRsD4usJbL3nviPs7dygsm2Ow+wB5GltewAD+2sF6a7TETP3iT5ixBXyQ\\u002fO3bpWt7KMz8q7HTVWKRBP\\u002fjU+1K4G00\\u002f+YvoCzC8Vj8Yrd0rXrFhPzuon8LI320\\u002foTIh7E1XfT8dpw14kGSQPx2kqyrDHaM\\u002f\\u002flI5wxKwtT\\u002fNNZUgZSfHP1yOMkxuEdc\\u002f1BCPgyyf5T9Ewotva4fzP3oYKWsptQFAETRQsJjmEEAYS4wR3GYhQGmQgtGQ+zJA4dWcZfkERUAvkwRtDbxWQIaC1OQVdGdA9p9cNwXLdkDnxCTPjr2EQL+XE2x0nZFAoWSPZwnhm0Dyo426QYqkQOFYV+pdL6xABxPdAFIJskCf1DW8HZq1QGDYLt6NV7hAjIdaF97\\u002fuUCZ\\u002fJIy6Y26QPad39TJJ7pAZAcaNU\\u002f+uEDPSgqEmDS3QBW1cPND3rRAPpNxf98PskCIa+cLQeGtQFvZ\\u002fJMqfadAYMlnOseFoUCgDTwBo\\u002fuYQIEYV2gwd5FAAAcygTeIiUAUioRR47aFQHAyC4iCI4dAGP0me9pfjUAPDz91wOmTQMVs29qUtppAb8DzbLEUoUBxXS5pb3ikQG+lTzMizKZAg8fBmK18p0COLUWrvFSmQI6FEFhckqNAWZVzp0+dn0DKYtM3XImXQLK8JSQnKpBArh6MrMKChEA3iM+bxRl4QLqmzdRlWmpAaemVBUsYW0Ap46+tKcNKQOfA0G53aTpASVZNpnJ6K0CNmrfpxksfQIiqDkiGQBNAXF448PpGCEAn2\\u002fkeAsz9P49r6j1UOfE\\u002ff\\u002fWaF25x4j8O5I7g+SXSP3a9Xc6mWsA\\u002fs9o6iJHzqj+wuSK2a0uUP7dP0ofp7Hs\\u002fkUx0b+aNYT8L7ywYzSpEP6uTlutbLSU\\u002fksdNYXVbBD9XyYPiGIPiPqeAoVR+ssw+7tmy149ODD9qgLP1avwVP\\u002fuu7J4tRik\\u002fuiQWrS7jPD+mXoFgAXhOP5MtC5UFZl0\\u002foprH51f5aT8jYQ3zRSF1P7DRnQx6EIA\\u002ff1emwjeqhz+Q2ZYh9CGSP3GXB8D5jp8\\u002fUB\\u002f8+jD8rz8RwnnBSKPBP1m\\u002fW9efc9M\\u002fv63UvSJp5D+Vg4gwpP\\u002fzP9BcY55MSwJAstQS8HaxD0B5ySMhRskaQOL9VJNpFydA3ljpk9opNUBXqTua885EQMW1kPPPTVVAhSIPRirDZUCxvoLPK3d1QArBYAUYEIRAr32fo4SakUDtu7YBod+cQCROkr\\u002f0FqZA3sjcxjiDr0Bt\\u002fKuFT\\u002fi0QFF972KCF7pAB0Wk2KN5vkBYVV0u2dDAQHhaHCkjscFAcnImuNHuwUDuq8x0Sa\\u002fBQCJ7duYnEsFAAfkI6\\u002fUhwEBAEbZtd7C9QC\\u002f1WCUTX7pAChUfXNlqtkA3nBom6SCyQEoW6Kg836tANcCzyVOLpEDrccjSOc+dQAvZYFyfeZZATcAPDZAEk0C8hvY6xviSQKqwQ1ffuJVAMUyw0d+gmkDtfULlWX2gQGgfBjkV7qNAxJRZp4ILp0CabteFcy6pQInThg1dy6lAznASqoyjqEDmfbGW1t+lQKzYUFkHBKJA77UgvqyBm0BzlfuXNXeTQBoyjwENk4lAT4ur4jJPf0Dw9Q55IgVyQEmUYlDb4WNAtVaBIbHIVUDxKtJ0AuRIQNIVny5PyD5AbuhpZBpzNEBKJpTxvd8rQPVdJVMkjCJAIQGWLqBPF0DUZ6cDYy4LQKoZkP1CJ\\u002f0\\u002ffDS3rIGm7D9cYdjtMsHZP2e8w9eOKcU\\u002f+5WEwUnIrz9kfesTiM+VP5IPYyl4XHs\\u002ft4DawohhXz+EwNcHpnVAPzi7+dVqpB8\\u002f+Y7dZarR\\u002fD5ZcY9wRGfmPsfYvI7afTM\\u002f9ulGlBUkPj9yeIO9ajVRPzedj+WEjGM\\u002fZcxYKIx+dD+7A7fnl6WDP2ZgD5GsOZE\\u002fJkYMJDC4mz83x023gaqkP2s\\u002f3tUAQq0\\u002f2po5aCXCtD9loio3xhrAP7S9ArA1KM0\\u002fa60tfRjf3T9qLfeU+7fvPzJENj47UABA4zq+3lWBD0ARXcB10VIcQPj4Pqj02idAcpwrcAM0M0DgD3UhtJ4+QCHjyxMnTElAkuheSdpYVkABLhppLvZkQK5jJCeqHHRAggRJ+tH3gkBpRlM5yx2RQBg2S\\u002fBHIZ1AP7vPmdk2p0BBU7ZvXEaxQAIZSm3r\\u002fLdApIzjX5IZv0CBQ+jUwN3CQJrXp6g+hMVAlSPsUBo8x0CPu71eLgDIQJVCWKVPAshAOIukZXSIx0AFMkXO7sPGQIPhar+Au8VABirHfPJTxEDnN1ixym7CQOZUl4FACsBA70y410qfukAYo+GHjxS1QPSpAPGtHLBA1YDa1ah3qEDfTwcxRGGjQPqIja5z4qBAhc6AMnWCoEDzejRTFKOhQKsLkmlus6NABIBMSv9BpkAU1gum+O6oQL9h8YDkUqtAG+lpuMLzrEAjb9Rxf1etQFqcz46uLKxA4\\u002fhKPhlxqUA3d0vAIH2lQOK04TQJ66BA2ZTEAAHMmEBMPkQE3vCQQAbpBPBwuIVA9LZtNlSMekBaqSV7QORvQHf7plwtsGNAsNBOPQH+WUDjXCEjDmRSQP5gp8s\\u002f20pA9bYXFNRIQ0AxAB43QEo6QOIzoTG1rDBAnJkc1LR8I0AXsYYqeOIUQF+kIJm8ewRAVvlBxJ1e8j\\u002feoa12SB7eP3KqByr0kcY\\u002f+bKTsW3trj+2uwjlB2GTP6fRtGd6OHY\\u002fgpVECV9UVz+rFLO4THs2P8zXUCPImBQ\\u002fgv6fCKMlAD+TYr+0niNYP6tfM6ZWlGI\\u002fdtoD1+4SdT\\u002f431NNZcuHP0tJStP1y5g\\u002fEf4Yycifpz9HVI+Gh5G0P4Tu1HOEZsA\\u002fLXRcJY8ZyD+9k2mazpTQPyjvbdRmNdY\\u002fs8t\\u002fHIQx3z\\u002fx9sCxGw3pPxMcp20\\u002fZPc\\u002fI1UWkoGYB0Aw69ZBYqoXQPS+pK+PhCZAXVwgMA75M0CPW\\u002fc684BAQH0G2TpitklAAjiGlW5gU0BEnexXcmhdQJZly6CPcGdA1nSV5LT3c0AmbdxqlM2BQOSpENlf6o9AIzAvK3bGm0Bnahgr2vimQMhFpU9v3LFAM\\u002fy2sy\\u002f8uUAmKXpDHKjBQBPsMFxwbMZAT0M81vKrykDq8A7gjNjNQLTbYm5ap89AMWrHeN4U0EDEYCkjPbzPQGLdTtLm0c5A\\u002ffGpFyS5zUDdPyLAl3vMQAIu1PFf6cpAjCgTS6zFyEDMvAXEe\\u002fjFQEIyvvOvqcJAavaImrFyvkCsTHl0Zjm4QL\\u002foEtlOZ7NAR3zoy85VsEC0KL1+kNatQCLerMBDdq1AZKOnCGd6rkA7Yx3\\u002f3ACwQKU4lWcuxrBAHplaorF2sUBJ9H\\u002fwsAiyQGkegryhYrJAm8Bt2mBSskA01ZskDZ6xQOL4EyM3I7BAvrNZHNTfq0Ay3rOz64qmQKwrNLpFBqFAEfMfUgQUmEBVOdyoACCQQL8j996w9oRAnkqK9T+Ie0BIVZg5Fg9zQOsc1U4nQGxAx+uISunUZUB437KX0ctgQAMOWClAv1hACQkPWy4MUUBL8\\u002fPJtrFFQBbeGmS4WTlAAbehfA8hK0AQgURFA48aQKWaq6x2xgdAHk4W3jV28z+6ogs00CPdP14gb8p59cM\\u002fw3z4IyEHqT8u7nvWucCMP04O2zkLTm4\\u002f+PT3w\\u002fhpTT\\u002fyjXZ3T1crP+uRQfKh3BU\\u002fqe\\u002ftTjTiej8j+ptqOpmEPyXChAtVNZc\\u002ffgt2PLYLqj8YFd+mkvu6Pzvvs7pDjck\\u002fFPRV5yka1j9NRs0YXnzhP12dINZgZ+k\\u002fGY5HCGUf8T\\u002f831hx\\u002fv31P3iTq1Tkg\\u002fw\\u002frT\\u002f6YytnBEDJvy\\u002fLtRYRQIqfodddGiBA4rMPK2Y6L0A\\u002f80OZtzY9QINUrCtdnElAONnxt2rhVEC9LbyVqdVfQBfisDlUFmdAFNXIMNRvcECZC6q9a\\u002fZ3QCdqSB77gIJASOPt6INSjkAEWGZSIZCZQB2\\u002f\\u002fsyBVKVAyfT7G+wdsUCPMpByvv65QAlmsLkiicJA26f0hoPAyEAmSRh75\\u002fLOQAByb81FJ9JAvgI7aeIQ1EDgTqkjNAzVQMExI4zSK9VAj5QzU7Sy1EAaBJiiIPLTQFEenohZI9NAQWTe\\u002f4RR0kDifAmt8l7RQOTFUJ4NIdBA6BnOOA8EzUA6a9nRAivJQI+mW64nLMVAba2AeFidwUDU7xm9TvK9QMLyi5kh5LpAHh2Uo13PuUCCPH6nkv25QAYmE0HrmrpAUhCJLpgKu0CUD2lYrBG7QKykkEOSybpA2hMyyZFoukCMU3xxPQS6QA8+KdT8crlAaTx+oplduECCRW\\u002femXO2QH5dg4JKoLNA1KM5iOsfsED4VetxddSoQCOYEItrA6JAPgetcyIQmUAr7q+y3UiRQGex42D+rohAKtFX8ZPLgkCkxjuMYDp+QHINF\\u002fZ8o3hAbTtI6FN\\u002fc0BZY6lwFRdtQCeS4x08IWRA5IM3NOOiWUCEAsaeDO9NQEFzdW9y\\u002fj9APUo1MZZIL0Dqyfp+L\\u002fsbQLUlNX\\u002fx5QZAoSgtp78m8T8W9mWrTYjXP8E5Os\\u002fpmb0\\u002fwz5Jr6EYoT9BBswpvC+CPyHsRagD8WE\\u002f69ZRjlk6QT\\u002fBOjtiDsQsP\\u002fDnB+HA7Jo\\u002fgZl4G5KJpD9jd5x5K\\u002fy2P0\\u002fg+\\u002fSdo8k\\u002f4CdpW95n2j\\u002fdBxemdNvoP9x6Q28hXvU\\u002fzb3F6SPJAEDprZPYFikIQKJwXs6bCRBAsDYY4BH9E0AkUv+PgGQYQPXcyuGghx9ACpsF7UJ+J0DwJW4c4U40QG6uiRjJz0JANy2EwoI5UUB01ydS8dldQGEqTiO0GGhALOCJfMQcckCnAP+jZqB5QN3emwDFdIFAuYGJmoK7h0B\\u002fg2ZX9sCQQOH8zhhDGJlAlLqBRieuo0BXBt5f9TSvQAwag8ljLbhAcKRze7LmwUCimBWQmQvJQAzt5vURetBAU7BDOaZf1EDWIFx3YrfXQIA4paDNF9pAhECeAL5O20A3HcegV3DbQDGQzT0EytpAYcuoII692UCx754e3JXYQNMG+Dpca9dAAYvJsCYm1kAYcF\\u002fKbJvUQEjlOO2PtNJAHd7\\u002fjVaJ0EA7GNArkr3MQPN6VXwTGMlAybV8B9uoxkDUX3aLWpXFQP\\u002fxVyHalcVAgFkK\\u002fUIaxkDWlUzJUovGQM5qblbah8ZA4adkiE4BxkBfR12+OCzFQPnuJQ0IUMRAm26OMdaSw0AVVNJMFd\\u002fCQLXdrioo8cFAn2iZYa+CwEBW5bD65u+8QK05dRS16LdAKNdOfTGYskCL9rHCo3qrQBWxhFJow6NAQUTqXt3InEDIFmDvpxiWQK6i48QxF5JADD6X9Ji2jkCgGEOY+9aJQPBbV54TxIRASZtThiAuf0CMcakHRJ51QGPliEipiGtAfmLdYeIQYECEoRuOpClRQE2kUr55x0BA1fMAkuwHLkC2JBTSNpwYQLpHQiqYewJAPn9cw\\u002ft46T9I8zS3CSPQP+SaMz4c3LI\\u002fBViP+Sl2lD+cBv23KOd0P\\u002f807yvnf1U\\u002foWjqYwCIQz+hNxcsSkC4P1fXMSA+asI\\u002fq+l\\u002fTNl41D+hEjyhpbLmPyO8VJlqPfc\\u002flFZsZbC\\u002fBUB43k\\u002fNJ5USQPSfKLsXAR1ApqecehC1JED5DTuvzSorQFHDi\\u002foIkTBAkcWhUCRXM0DyPf4wqAI3QI\\u002fw13vzkj5AKBk3zqLcR0BDPuBfbs1UQA7qdiMWg2JAFENMp2Spb0BqYH7Qi1l5QLo06FYD4oJAYF8WNxVQikA\\u002fYvDxBGyRQGUZA9ppi5ZAPuqN4JuYnUCDIL\\u002fSyFqkQGm9\\u002ftuAg61A95BexDYItkCHNPdW\\u002fl3AQHNOiwgYjcdANoVMGHon0EA6fe5uOfvUQDv+Ed3fwdlA\\u002fC1nPrjt3UC5vwFif4PgQGbpQNQIZuFAWInQBNCg4UDyyNVIVFXhQLwzT4TDtOBAx9Sdz87W30DKcs4F2CHeQP01xO6ZS9xAedimtlM+2kAbFjkGO\\u002fPXQG7JDq47jtVAZNSxa3ld00Dqmil4a7vRQKnRd2cL5NBAkcTy3PzU0EAWlD5HT0rRQAwIdAEe2tFA7Q2PMXIh0kAcaiKCKezRQOMLpd87RNFABSKrrmRh0EBioB+5tAXPQBWUqKE8kc1Al5hR3AFEzEAWTMMu97TKQOnY3vEGeMhAK2g6vzFjxUCH02+o\\u002fqvBQAfJlfvRn7tAapSIPQe1tEBwsE4x\\u002frCuQK2\\u002fs9xcd6dA55tCgUgeo0CXlpZ4p4GgQMtBiJADC51AGu3tPLnpmEDnj1G9sjOUQCYQGNRQcY5A0zNsQ7YhhUD8ts0AMOx6QB0QTMg3bW9A8HYFdP7LYEDn1pZ1pnFQQH02hktbgD1AUMqOx+hEKEANNLoW0VUSQEB\\u002fPUKdgPk\\u002f69KkrHxk4D\\u002f42EICL5\\u002fDP3rUHbp7K6Y\\u002fHP78sBJPiD+bStWB3WVsP8vTSUhWbF0\\u002fm6FcNyOl0z+lSe4zfrPdP1QD4kwiZvA\\u002ftXpFul8SAkB1bQueOGUSQHQit0pjHSFAqeNYtK4SLUCl2EySCow2QNZwakjo9j9Aa9mDA4XEREAMgMNnuOxIQLDpt8B1L0xAZCeSjKV3T0C4n7XirNxSQDu8EyJTVlpA2M0DsAlCZUBZSuhHxjRyQHPt4fLnoH5AXGTp9ntViECKurVwMQWSQJEjc0\\u002fq5JhABh5Qh1k1oEBsmjO9MVGkQGFJ5tFFTalAE6hIoi80sEAXUGm7YMa1QMCpxucrdL5AVNwKnFaOxUC3Y2jCHgPOQOHR2caXJ9RABHcPVLXd2UDB1iFIZ6DfQOg6n9rBbuJAdIKbb1iI5EA3Zb92se7lQHxgO4GtkOZAmDnngLp85kDBEcc2zdnlQCKSWsMa2ORAtYTX1V6g40DllbbIFUviQNlphNfj5OBAU0mS\\u002fJj43kDF3qvbSlvcQKKQfwGIRdpANYsdF14I2UAKVm\\u002flhsfYQBLYunG+W9lAKcY6m3NW2kCofKs8HCnbQPUKHA2WXttA+kemfQXK2kCNk14Fv5LZQEciYa2AFthAXLWpQqex1kAIOzkOw4vVQC55qD\\u002fthNRA2g0d2JtN00DyizAb5JnRQFjYOC7ios5AmwcUS\\u002flByUCGQeZO+MvDQAnzrRzlDb5AJQapYI\\u002fltkCMEBVqGkeyQKjsHOZ5Qa9ACE3gpa8QrEAjl7xTlE2pQKlkYuoC\\u002fKVAz7B2vh\\u002fvoUCWDZydExmbQIXcgy8e1pJAo72BNs0GiEBeggKF4hZ8QLkNyXKMGG5AHh8EX+OQXUAL7VxTYKhKQMihnz2eGDZAJPhncVfiIED\\u002f1AjlCOcHQJPSmARxnu8\\u002flZEPYkLY0z8yK4IkiUG4P5UfqnjnHp4\\u002fPYhut+YchT+U84jIy0N5P5hriF\\u002fzoOw\\u002fASZ+\\u002fGyL9T+Mndjij6EHQD6TFCor4hlALxBfJ3UxKkAOtAgDbDo4QHc9laYhdURAIDiBFOCJT0B\\u002f2OdRNjVWQBwi08ROn1xAmyVQghr3YEBdr2smrr9iQBTxgglB+WNAcTmz20\\u002f9ZUBCYqKsq3lrQC5PUyCMO3RAnkiRoW15gECpFxasfh6LQP9xApTYXJVAFuy6jCSDn0CWeN76MqylQBA+mw3h+atACT1AU1U2sUAJVwm41LW0QGr\\u002f6\\u002fzqKLlAQbCdWYqvv0AYOoX05cvEQDTyNlFo98tAYGIBgkfF0kDzagP92aHYQKAUnCm7O99AHmCo+1MM40D2M23++FXmQBkXqXjCMulA4KuI3vRg60DDQf9fc7DsQEgdvGJND+1AAEFhTw2Q7ECILMTQZGPrQBpUkJY6x+lAwITpKnj150AFKYmu9xzmQE2hxbQDZuRAJjGi+dL54kAmqQi38gHiQPS\\u002ftjuKm+FAYCSJLA\\u002fF4UA1kJ6hY1PiQOTjNPPy+OJAlHyPB4hf40AJCt6HD0rjQD9R8j0ureJAYkLE2syw4UApvL07apjgQDLM4PbvO99A1OML0FKm3UA9pBLZXTTcQPxiVK2ec9pAeqWPnQMC2EAvGOikqsrUQMcYBFygFtFAdjl8wg\\u002fXykAnR4WLi53EQNWeA7znHsBAFUpDvdG\\u002fukDR6XMpx823QNGs\\u002fXaOB7ZAw4w92XU5tEBjXFx+b72xQF1lzF7BFa1A6lw3kFIJpkDQxTZQ97SeQDj+z2lpopNA1e7KejkIh0B67TZb28p4QNtO8a7mg2hAWEi\\u002fb4pPVkDQvaMfmr9CQDdgBd3zQi1A0iQ7tL1rFUDqWjz1u+\\u002f9P6BlevouhOQ\\u002f9AU3n7iOzD8e3hcfUve0P9zJkr1YpaE\\u002fk9uA9sCGlz8vteSDp8QCQNfabiLnHgxAsyM8TZqiHkDjRzcm0qwwQJYL54WexkBA3IblrsPaTkCroy1rleZZQOkU+pCq2GNAMkyV4iDGa0Dtt2JOu8RxQIpuy7cu23RASzoUrp2pdkBvQygXPVd3QKGg7fPeDHhASEeZxpcne0CHC5063BOCQOoS6JMon4tARdtOG1wWlkA3FWCR9zahQGQBRsHLTqlAxj1jr51gsUAuLFwrvVq2QIjobR37QLtA3st3H\\u002fwRwEBfsmaeMtnCQOeMuTY5mcZAmXYQEVIWzEDCB\\u002fbxiPnRQIkcv+HROtdA0v3T2AS53UBymPG3FZfiQIyvKnUxmuZA1\\u002fLsAbCm6kD6ccZ7\\u002f2\\u002fuQMsn6v0Fz\\u002fBAAqlXFZTt8UBdUzyFXHbyQOkuv1ytZPJAIXljR8PO8UBMTkK8oN3wQPrRZx3cf+9A0ZMjzn0\\u002f7UAyIBLgDD7rQNYbpLKasOlAQ5oOwLa86EAI4T4lu2\\u002foQESrvW1SsuhA6NPUYrBD6UBVMLoKfcbpQO81nCZ\\u002f3+lAYQ\\u002ff5aZZ6UCTqGfvhDzoQDBopGnjxuZAtoWmbpdN5UBxIw9Tqg7kQIa1qUnkEONAVUy7nw8i4kCLbeAjZ\\u002fXgQKWfQExBot5AmTO+nq5i2kAvwFjJ7prVQAuS7bo1\\u002ftBASTWaWYJoykAyi9RF2y3FQOZ5eMiINMJAbsCSi3PEwEDRi6a3+M+\\u002fQB4PH6JPo71ANYRyKOU1ukALrVelx5e1QBVaYAAObLBAxLosEpL4pkBkkqcpw4KdQPgSWPGHaZFA5ZKOI\\u002f\\u002fkgkCi4C1a7uRyQOR4OX8yemFASA3SV4EbTkCWOC4Uqm04QD7rCcEUCSNAQ5lKZ6FPDUCbrOUHcQX3P6zCgqZ52eI\\u002fSJIHUaI40D+eTfZwjw+\\u002fP2+DvzRL\\u002f7U\\u002fgi8Ee3gmFkBP4ZZCpoQgQNDVEm0V3zFALDyspshVQ0DZrg1vXFZTQE7Ve\\u002fTorWFABGVQ302CbUCgpiy9dXp2QApIrBwtRH9Ak4597aTeg0DYWdrqGiGHQLK10DMT0IhAJOtAmGbxiEB9iWGrXHiIQMcyALwAYIlAUuMQT3uGjkB0xyLF+6CVQOFVKY7EqKBAdDqmgcCaqUCviyUqrr+yQHr\\u002flCycvblAHRQyJoWNwEA\\u002f1ppGjx3EQNVfkjpFfMdA7fXNHMX5ykAUWHPmUUfPQPMbi5k4otJALic9T07Y1kB9erYpAXbcQGKdJDcluuFAAGt\\u002fQq\\u002fR5UA0jd7w+VzqQAg+bdOHKO9APOFHtsHz8UB6KMvFhxf0QNcHcdAFw\\u002fVANFj6LWTE9kDfaQBXCgf3QG8CEi55m\\u002fZAZ5HeHySw9UA3M3kxwoD0QBW9qasIRvNAq2C3TnEs8kB44rt22lHxQDVNe8dzxfBAcUvtZDSG8EDg7vzoXIDwQKudWglZjvBA9uFxJFCB8EAhbrlQLjHwQOFklYtsG+9A6xru7ulN7UCS6Mhn3kzrQCvneUMidelAIEs8k8b850CXn1GymtXmQDX9wkxntOVAKfyY+k065EAuUNX4ASriQPu8924hF99Ay9HE58xX2UBoG2zdn\\u002fTTQNzTuIHIYM9Aa9Sy54LOyUCcegYs2\\u002fDGQLOUH4ipysVAtVp\\u002fbXsfxUDA2MRdMfDDQBak8s9cxsFALcHnhzt1vUCcGW9sQYa2QHtOa9hDsq9ANdy3GBCDpECFdK1wK3GYQIuCeXfA4IpA25p1bLNke0B0MxfTTBJqQGNqZlq9eVdA3tN3s21sREBvv0ypaawxQPRlehtMUx9Af+2G70LSDEAMmbv6lUH7P+tiRcHRIOo\\u002fOAe2qBXT2j\\u002fJhvswh4rTPxeMHj9\\u002fjSdAl9Ea0yN7MUDTEreVcsdCQKeLvt5XMFRAplVBPxASZEA4vYP58j1yQLWWpn5DRX5ADnbulX\\u002fshkB2xttfXLKPQKaGpl1gBJRA9zBBatQhl0C++ZvIRZGYQGkvWQqERJhATz1ZwBX8lkByFO3gfE2WQEDQJ37UcphAjb95l1zsn0CumTSHHHunQHGxgpK7tbFAus4BTYnLuUAbRACAYrTBQBOsCVzazMZApwGZ2+G2y0D2TxOH4B7QQGF2CNxYUdJAV8TcxKvO1ECzBkFjbxDYQFCwZWtygdxALUOPbkEu4UDBJJfdkNTkQDof8YjgJ+lAMmiz04kU7kCaUoEw4LvxQLEe4zNBhvRAhD4DrgUw90ACqI3tkHD5QECBc4ZQBftA9pisi6PJ+0AZLmQxbsP7QD8eF53yHftAQ8iDIcAX+kBA5jJEme74QNn0eS0Q0vdAou6Zu\\u002fHe9kAtVgdSFiD2QJPVJcMdkPVASIbXFpsa9UD6E+DGKp\\u002f0QHb\\u002fMnsG+vNAoJdQ2iAS80BpZiPq8ebxQMMVwlv6lPBAGtQ0pq2W7kC3iJmun2rsQFVmCDE1xupAW3VC7UZ+6UAF5O4XaC7oQGhKNJ53a+ZARjVueAr840A42Q\\u002fmZvjgQOFTyvGMg9tAeQSwM8Ou1UBQWhThyUHRQFHMXXdTIM1ArDkb66PAykDO2rXeEifKQGtdLf9s28lAcpx3F2K3yEAlT3qtXjrGQOe60H1rjsJAbpNJI92YvEA53OjhHE60QN8nL\\u002flAlqpA+pxdJqYWoEApA0kbTxWSQNoQPBBrCYNAbvRQ160Kc0AZ3VFdwIFiQME\\u002fcU66\\u002fVFAGI\\u002fGJdPzQUAwj2qOhoYyQHwtQ5EXciNAPhJKy0gzFEDyupd+bYMEQOR7spaXvfU\\u002fJqJRbwgF8D8LFAiMm5o2QE3OliYusUBAgn7QsfPLUUAuc6D1rQFjQDvCbiJux3JAP+Z1uFL3gECutSzq0\\u002fyLQI7ejIo4EpVAyInhR\\u002fj2nEA0ByAapi6iQE9wcrvy36RAy2Fc6GH9pUBiIygbK3KlQDLZ9OEi0qNAN2IkAMdSokCeCfH+XpiiQKNkItkHaKZAzgOesHhIr0D\\u002fLt9oXAq3QI\\u002fiAAfJpMBA3B2+lirRxkC2b\\u002fOYNGzNQKhWVIti6dFASpA3iMPV1EBsUrAdtJHXQN\\u002fL54ugd9pAIDiXbKAE3kBoO83PvVPhQF5jBLmiT+RAGXuzeb\\u002f+50D1xqikmWDsQLog23v1ufBA8eL4fs6V80DtVOb6Wqr2QLdgaHP0wflAhGNKnaGQ\\u002fEArfhEXycn+QOkci1I2HQBBZ14dgn9sAEEWxGRYjWEAQceKwHBYFgBBEhA9iENL\\u002f0Dxef4DE0f+QAYn22JJNv1AayEKCssf\\u002fECdqSz2RwD7QD20yn3QzflAMEGQbZR6+EDXqRFcMPr2QIA8O6vNSvVAA5TysNx980CzXSIexrjxQNR0FMPrKPBA+YOQXBnd7UC8uiF4ZBPsQLkDwJKSqupA9AHSggMr6UDg1d8CAyfnQK5\\u002f2tKPcORAbGK9\\u002f0Ix4UAgm7CaWbHbQBhl3bfK19VAakJ8V8ae0UBPKKbQqo3OQEjK0kSK\\u002fMxATFp6cDEezUAAlbpqoVXNQLu0oTPHZsxAD4BksTfNyUARh7AOSsDFQOSNk9Qn8sBA41ZilO1kuEDtNVbnMEKwQCzG0+i9K6RALO48zbiBl0Bak9jWRyKKQNzzwWnBXnxAQ6HdkAfvbkAwufMiF1JhQGtVSrTn91NAjO83zOo7R0A12tV82H46QApgSpv46ixAXxShXDwlHkAgG8yJHysQQEaLz4Hb6AdAJ4OqycmlQ0Ac3zfXFtpMQCMiLOiZgF5ACECq890rcEACeU0\\u002fYb9\\u002fQIoLvjk0gYxAcFPJcpBel0AG5bCpmX2hQOFWiRtI56dAaJ94vi7WrUAZr1Q3gQaxQD1qIAhW0LFAz8mpGG82sUA5vFgIE02vQGXMiHRmAqxAoo8Ul3DYqkBfpf+Q1S2uQNtGDhvo+7NAwXV4jGeivEDftYPq0XLEQOqTLncO7stAE0p9dBwB0kAkPzyDHvLVQDkRwygIjNlAMcFMxjHa3EAXV5ii3RjgQO1eoEFKBuJAcTtx6cNq5ECL8amNRGTnQK\\u002frzoO3\\u002fepAcANcMylA70DIUPRaRB7yQD+iDoQ9\\u002f\\u002fRA9S9AQ7A2+EDndCQl35n7QI3q4WRn4v5Aap1jIMjgAEENxTwd0\\u002fwBQb7scaiFtwJBnY\\u002fbK2wUA0FGJDsoqCEDQXdQ887U7gJBiLZKrqyGAkEz+vSQAO4BQXA8LRfzJgFBozXpJgI1AEGlxCHYpj3+QEbNjv0A2\\u002ftAh5lpgDdX+UAvrbusvcv2QNDfua5sWfRAFbgQejon8kDx8wlUJljwQMkxZhNL9u1AmvKLkj\\u002f960DqdDAx7GLqQAiZG1UNqehANs9K6Rhr5kAbljG1r4vjQMJ35TEgQuBACl1AXn8D2kBCOZAsk5HUQDtCI8yF3dBA1m+HacQbzkAB6wKRjX3NQNyJFC1TYs5AQTRAoS0nz0Dxj8GQV4\\u002fOQHjkWWz5F8xA1TP9g0r9x0CMs\\u002fVsaPvCQIJuASgb4btA8ABXxB8Zs0DMymxzZqWoQIZwIni7cp5AsLmVV29vkkBktqtpBXuGQF4Y5fSAIXxAMuR5M1UIckA\\u002fbhI4Ey5nQCIkgWLdBl1AG0B1fqBHUUDhinFAjklDQMAnlZwpTjRAKstD3TTWJUDZTTJN9CMgQIhgS\\u002f2LI09AT7TSKZuzVkDwCgjHcMFnQEdnKMPQ+nhAUsRW6L5XiECxTPWJ3rWVQDaShErbr6FA3XhZZddQqkAC7z\\u002fHwuCxQE5fUO7NMLZAFkB1d7EvuUDcIf33mTS6QAu0A7wRKLlAa2frQT2mtkATxfw2deOzQIf\\u002fuEG2ZLJAppz3ZSars0D\\u002fdEeypOa4QD4qm0G\\u002fWMFADw\\u002fvktNryEAyR\\u002fAvTpDQQNz2l6kFStVAtIiWtq3q2UBOt3+4JCTeQOL0oJtd++BA7CRlGzHY4kAkoeErMuLkQBWAYFDrSudAs2zqKF4t6kBPGmMpf5XtQKzXDXkoyvBAe706WWol80BDaO6AE+71QFNku\\u002fknJvlAicb3NtKx\\u002fEB5suLicSsAQXHu69z75QFBtLFLIB1nA0Feu9Q7nJgEQUZ\\u002fFyOacAVB7Ins+8DsBUESw4JAtQsGQfgUH5+pyQVBh8mp54UiBUEHhH9S8hcEQcRbRouStgJBb2Muq0sWAUGxdghkpar+QBjRju09I\\u002ftAbHGWUpDJ90B1hBqqc8b0QIM+upeMOPJA5hkNfo8w8EB4ynOySlPtQHDGpabCCOtAwcN1BY4X6UDzq\\u002fglagznQEKlNplsl+RAJMwONfqs4UACB3Hw+BLdQIXa0hhBLNdAkS2UbqZ50kA5\\u002f1YlZv\\u002fOQE40pxMukcxA2h9+9dbQzEDoMvEtHFDOQP0J6syIh89A2q1H7gpPz0AUGVUP8ynNQP6gW3GTT8lAGP+9DSF1xEAglYfTW+6+QIUnGJAQFbZAuWlsdjRErkBu00zpoGGkQM4gn3t9q5tAJVRSrv08k0DGx1QvFEmLQJA89ZjqTINAigpBYLp8ekCXeX8Z\\u002fDlxQNt8\\u002fBI56mRACYs6T7SKV0CsspzTntRIQPsrWCkDpjpAXkfr64qnM0BeLMzJccRWQDKDuW9ZcGBADfeLhej3cECmpKVCsqeBQLPiyF1WDpFAAVzx1DUxnkD9Lv3uf22oQCr6OAvEDrJAEV77CklkuEApbx2Ymhy+QOz\\u002fMGz3AMFAMyamSgadwUBs7uQbSNfAQLTon660Mr5Aw40g0WhWukCZjn1cHwK4QEKDS6F1CLlA9vf4d9TJvkC9feAYtfXEQKNiZ22JD81AUiIQrIWE00AhXByzQOvYQHDAyVf3Ld5APjmPUDZ24UCLxFs4g43jQIujZG2kgOVAq7LxfYKH50Dlfq+5wdLpQOOa6CIWfuxA8G15eSmZ70Ac3G3KT57xQKLF7O6wyvNAzy4Pv71r9kBRpzXPDo\\u002f5QOmBKLxPJ\\u002f1AtDUTtbWEAEHYZWWngXsCQQTTENhiVwRBQ1mdejL8BUFnJxRgLlMHQYEScQy9RwhBTFoIlx\\u002fECEH\\u002flKeBgrIIQZi62mj9AwhBNuOZklK6BkHO7z8dA+4EQVf0UxihywJBHt8GgsWIAEGlX2ap+6z8QKvOhPECsPhAXmlRuitB9UB8nqMY12zyQDa11aU8LPBAdmYee5PR7EDCPHB8s\\u002fvpQK7Nm2ZAfOdAvnbYXJX+5EDSMFAwe1DiQLB0f3iV6d5AKqXv7EVB2UBoFtJ3REfUQPrF0TWnjdBAM1SwP\\u002fzDzEDKHChoMGfLQJfEvrhgNcxAsuWFmz7yzUCU3Ic6oVHPQDv57tWvVM9AdN\\u002fEA6iKzUDhG1RhUh\\u002fKQACvXTGps8VAH8s1dTIUwUB17Axbndy5QMbJRuDIQ7NArZ+PB4zsrEAIUIAI\\u002fTCmQMOlqwkjTqFArvWqAXbUmkBqNqxXLyCUQKiWJtyPkoxAWT6JviLogkBjNsG1QB93QNRToVVUEGpAfdzK7ytvW0DsynQB7VBNQJuWfmc0jkVAmL7lrA9YX0CeZK1U\\u002fFBmQBm\\u002fYSrUlXZA54F8wIEoh0Baq7U+4x6WQJmKGHSCZaNAJD5Msv4ir0AE5yfDotu2QJmzzL1qsL5AXV15VAvYwkB4UgVAojDFQEblYuSL4cVASP3TdZXkxEAScl8lRcHCQHxxCMKNbcBAvckO15wcvkAzmxVihGi\\u002fQOiOeEV\\u002fJsNAKV4su1O6yUDKan8vrprRQMQftl2VYtdADPXvYymS3UDt0kgo\\u002fr3hQKyy97yVUuRArCZKLRB55kB7BsdSkVPoQMNMmZASHupALaADZHoR7EChGR8YK1TuQK05xxdegPBAdNeHv2Ic8kDWteu9whf0QHFxyrutj\\u002fZAkfDEs4OX+UD39SJZZiz9QEtKCQzbmABBq2E51qe7AkGQEuBbuuEEQUZnYGpi7AZBdmFyLYK6CEGLrAA6JSYKQRJRET6NBAtB\\u002f8rwH5gsC0GtywCimoMKQQKbO2xeCwlBLW9mp9foBkGsPWiDr10EQdfwMQUgtgFB1eT7T2Vn\\u002fkDWb3P44P35QERhjIgwSvZAI5i5KS8\\u002f80BCdQKwOL3wQMsEIZqJQ+1Ac5inQxac6UAWPVzYNlHmQBYN14tgPuNAGu6mqUtU4EBEIP1dADvbQMtKfKmjc9ZAPT49DMSg0kDmvYm+9fnPQOt9gYDYK81AkzCiI+uGzEAIC2BqP1zNQEphdh9dwc5AlgRPOVLDz0AR9bAf0qHPQFDmgeJLAs5AAQ1DeXMFy0C5PJdLkjHHQEda7TgMOMNA98OnZylgv0Bdeq8DBcy5QBP+ggPspLVAN2\\u002fr2IptskCXHXI9lSivQBkFEUHfealAWHzwVRm1o0ArkcmgGGucQMV+YN7O7JJAEZqddS8rh0CmTxGkQRB6QN6pjvUBU2tAgL0nDiEKXUAb2bSdukZVQC1TvIyB6GRA9\\u002fUVRYwtbUCETnzqAbF8QMaj3ri8zoxAzZcLjS0Xm0Db1kTZGnenQLjk3C1EprJA1Q23Begpu0Domk8CFh7CQGofDGsNI8ZAqPwo8wHOyEB+pDer3pLJQBq8q6fHdMhAPZwLf5gZxkDUGwyrRqPDQC1yXOiSZcJA+Vtz0+iYw0DHjtsFCB3IQLpP6U0CJdBAQjz3Rdvl1UCubGKt1MrcQGGO01gxA+JAw\\u002f+9wEFd5UD6eOOcHyHoQKwFMOgnNepA7+42XVO860A2LJt9\\u002fv\\u002fsQIUUfth2T+5AsJ2uHXXq70BUutb\\u002fWgDxQDKbsMWPX\\u002fJALAN6z6ss9ECmciUTMYL2QBMWQVKccvlARlcikRQA\\u002fUDhwyzc3YwAQWqMwO4yzwJB0438RukvBUFmH4dLv5AHQXQISF5kyQlB0iy4ZyqlC0HFGrkxDOYMQbZyZUpbUA1BSmjYq4u9DEFnyBnkgy4LQd9KLUMB0whBMxOze2r\\u002fBUGcZTvqpRMDQRY2NupNXgBBneNMdG4T\\u002fEDNZdJ5ZzT4QLUCk41R9vRAkmaanQMj8kAENunAgybvQOgMxEN0cOpASCpinKMr5kDoP5vd3m7iQFyKsAzInN5A3PMTWrCn2UByN+jeIPnVQHsswFTXdNNA6Bz9Mnvs0UC1Jt+4XCfRQCDt23zo6dBAAoYEE3340ECGuY\\u002fPARbRQGcIyeXRBNFAkSz47Z6Q0ECzyDR7uj\\u002fPQHAtgayJg8xAK0TsFlRYyUDLIRMY1krGQJS\\u002fE1+HyMNAXFx\\u002f\\u002fqDuwUCArwkxgoTAQC0e\\u002fqV9Rr5A6wFHln\\u002fiukCMSqpYZp62QKVXfxB8wLFAwfRkaEW\\u002fqUDWQ3eAvyihQNLtsl5t9pRA+1H7hw9+h0C7F\\u002f8qb4F4QOKNN5D44mlA\\u002feYcX9TkYkCi\\u002fnx8vt9rQJdLToq\\u002f8XJAlXU3pV7qgUD3kwn\\u002fRHmRQJw3lc7aFaBAYG8414hqq0CFmq5ZuoS1QHb6c7lGC79AhcBBVgCPxECsbF+x8P7IQAqsgoX87stAxiIISxvOzEAxbiQ+1KvLQDbPMFURTMlAfOEa+Xz7xkDfoLikJj3GQMODE7uTdMhALtBusgifzkAYRGk5sozUQA36ogKrt9tAd7Udxs4R4kB9bc8ABGTmQIIk1X8\\u002fQepAWqknSBU57UAjGeprrCHvQEMwsnTQEPBAM+SlCfVM8EBnWOHYhX\\u002fwQOIFa99E3PBA7xYp\\u002fkaK8UAJGAzIeqbyQOaeS9dLSPRAklSRZPqC9kDhdjmoMWP5QP3sJ4X56\\u002fxAik+HlbuKAEEJYZQLO+YCQTlgUbOYdwVBTOLyHIsiCEHT0KqjBboKQcPlIkKp\\u002fQxBMeHSXiOfDkFy7LIKUVIPQf3IKo055Q5BDDEFOFpWDUHZlaYNCd0KQeH5rgxs3AdBXR+yjU\\u002fEBEEdkztvl+0BQaEiSLT2Bf9AwQUNhs7\\u002f+kCezu07L4b3QMShY\\u002fpgUfRAZK5Ul6k78UDzRwqkPZjsQIQux9AEUOdAg8LCNMTy4kCq20JE3mffQLpaZlkNJ9tAhPgTfSnE2EAOQA5wTabXQCrYe69xKtdAm6Es4RDO1kCZm3D+20bWQPHnA8ZbgNVApdfNCeKE1EA7bxcf4mLTQObR53qxINJApYQWeyTE0EBdIh1wAMXOQKmN8\\u002fZQS8xA4sJb4\\u002fN3ykD3jsYb3nTJQB62Lq8RDMlAYqz1fiyuyEBMsGu3LLDHQL7uAEj0ncVAWBKrmfRwwkDMRufhVxu9QP\\u002fFxbwEH7VAHSm39FoXrEBB\\u002f1uQLxihQHQiQy4qEZNARLt5rX3Hg0DtPlHFyMJ0QFWQ14htL25AURIB1ynhckC+h12fLe14QJj4D64ThYZAYrUJj0oylUDl291ZnP+iQEzb\\u002f3izuq9A4wvqS\\u002f+FuEC4XrHadn3BQM5y2t91+8ZAZFMqaHfOy0CITiDsowTPQANi9EfgBdBAryVvVE4Bz0D3mvfWhcfMQJZACyFv1spAp\\u002fw+wOLiykDfgRCkwn\\u002fOQFs9a9UFZtNAK2T\\u002fvTIV2kC3b52fMIDhQJGY5CmApuZAOVfnIibW60AgoHp\\u002fhyrwQH17842Vx\\u002fFAWtJ0N\\u002f+j8kDJ5Wx1F9byQNBZBcyZnfJAr99YYdZL8kC8slP1JCzyQElN0FpxdvJAQSKpfJpN80CzqLQ16MT0QGPlJ0Gk5vZA+sj5OY+4+UAhiGDYxD39QNoiZ\\u002fwduwBBueoHDW4tA0G+2jMNPOoFQa+vj7ju1ghBd40Cm8jCC0Hn1HRjQGQOQasKJkAKMBBBIqTpwUyuEEGrTbrtw44QQbrQHV6MnQ9BHAcLkgEYDUEnBnyOUvoJQeoTe1xzwAZB0lJ0RdjLA0HXR3VCW0gBQdW+uW99Vv5AAZVFHluU+kAbannuXPf2QC2J3nPhXvNAM3J4IjfQ70Ck+kwjF67pQNckDt5t4ORAxDXS1vqw4UAK4x3MbA7gQJvSm62dLd9AhySBE41330CEkOkOG+DfQLZJxmLNnN9AUL9SwHFb3kCmZyofCDrcQFRHuTKamNlAUmVJ2gTh1kAogfycl2LUQOkY4hQFTtJAepQZUWTF0ECKzVArws3PQF9TJoLRgs9AKV3pksU50EAduTxV9PjQQAFR2cttetFAP60W4j070UCOX8U9juPPQFNWDrIhW8tAFblAHkKaxUC7y8Izjki\\u002fQFWyKRu2ubRAoHdAbFobqUAbT+7de9ubQE2M\\u002fCixuoxAlD8YCBT1fUBhbb\\u002fojrB1QNNcjKg9wXlAEi0PhbWMgEBDuiP6w5WMQCntrEY+65lAbSBWeZWQpkBqvieOmmuyQMhuD7yN\\u002fLtAUBp\\u002f5821w0BYgJr5Gq7JQO1kIkck7M5AoFTw\\u002faI60UBUkayL9dvRQBXT\\u002fAEycNFA1iYSFaN30EApcMbujYfPQAttc\\u002f18R9BAjmD1Jg7j0kAA5ia2iDXYQIkCeVHXOOBARgpzsleb5UBQ75UEUr7rQEp3+8Ap7PBAmIFLlhaE80DczlLcF0z1QDhkvxp3HfZA+R1iLOYR9kAQxfUc1nf1QBn2PlTjtfRAf\\u002fqcYhMs9EC\\u002fAJfu3R\\u002f0QF0O+JW4t\\u002fRAq58dgpoC9kCSJeNpHwT4QBC9HLjJvvpAjh7GctY4\\u002fkBjPFXLSz0BQbgWFT85wwNBXb5cn56lBkGqJqbqnckJQZOPGDaL+gxBLxmh6dPnD0Gqd3TunBYRQW+xNPvasxFBBcHSy5ypEUEsqsk5JPMQQd96yj0OXQ9BcW7kVX0qDEEEj0PeetIIQTMyIS1KvQVB2vH2Un4ZA0F6kDDDetoAQfm1sVfXo\\u002f1AI8d1VaGo+UBvzGzBAKn1QCMZvJ9k0\\u002fFAbNvFRQQE7UCtBXiRHxboQFr3u415MOVABB4VBaoq5ECIS0HJbHDkQFj1lURONeVA+H8ZZY215UDIsKe\\u002fcm3lQD1B9FA2NeRA8KR14Qk64kAw+S16CbffQDmzlLmFAdtAEi7D0+fz1kBiZ+YV6+\\u002fTQPC67sASJ9JAgu\\u002fGCF+p0UCpef0ph17SQIUxU0Oq79NAZdLxe8y91UBAl\\u002fTVu\\u002f7WQLd05oTA\\u002ftZArVNLTwVn1UBT8QW82F\\u002fSQC3PwB1B9cxALd0goX\\u002fjxEBA4Zn7Z4y7QPlyYTV8mbBAY4yd1ddQokCF0JcVcsaSQCAZhQVgcoNAvmEQgv8KfEANibOsfjSBQObDP5jroYVAld6OzrT4kUDd0JQpTn+fQIzv0VBRq6pACiQnXSBKtUDImNLW8Mm\\u002fQKRGBL7MGsZAJj8JjNWOzEDWhyyiqh7RQOH86JmcE9NAtC7lVXLf00CXaBuc\\u002fp\\u002fTQDlqAX3b4tJAhs6SZKWK0kD1UImGFJzTQF3x2ACABtdAbufPs5Jx3UA2BQheJofjQH5Zv3LvtelAnGz\\u002fLYFY8EDtkYP118vzQBrle11ft\\u002fZAt8bFpTu1+ECJzklMNJn5QOGhjs7tfvlA6x+\\u002fwm69+ECwS+S5C8f3QE00nqsKBvdA2sjQB1LF9kCoB9fSqCv3QL9RMVETRvhA5hKirx0Y+kCKdW8O2Kj8QK40pyOJAwBBfCGEawgiAkFDhB8olLMEQVlyPYPfrgdBbxPmqp71CkFH9SLYnU4OQRz4TmmYshBB09Cg\\u002fvnoEUFS8cTcm5cSQS0PtNkPmxJBlvNlpCLtEUF1n9tf0aoQQTfwa5qzGg5BtUxFevuuCkGYOBMKz30HQTtg9e8QuQRBYwgoMshWAkG+PRImnyoAQTz8UQWnFvxA6Ecl6Wve90BldJEVoeTzQJPl8qVajfBAZoeU7fRo7EBSU7OLpAHqQK5jVee4oelAKPqxMTyJ6kCI51\\u002fFIrrrQMewMbz2R+xAyHvGjguc60A6D\\u002fN9TpfpQM8ZwOkpiOZAZEwlqnz94kCdhrreKh3fQPEVAaK8Y9lAtzg8jj9d1UDFaR7uaEbTQEXlyvg5HNNAVQk8AGya1EBhejuFuivXQOLdfOeh6tlAR4WfvkrN20ADTLUkt\\u002ffbQIT71m4JDdpA8nf\\u002fr91Q1kC4CNqiS4TRQNzg\\u002ffNzJslAizfbkCN\\u002fwEDVauo\\u002fAMWzQLPMz7rar6VAtTngGtAXlkBKrEdAILqGQG5+gQQ3UYBAvHGBJmzYhUAbWYlyWgKLQLYSBnEPwJVAdc8Be8mEokC89hAoaZquQPY6Wlqh8LdA5QeN5JeWwUBzaDmERSzIQOUXbrTV\\u002fs5AcNntYwOG0kD1SAb8vK3UQCYtsaKbtdVAve66w8TC1UCdvH4p7GnVQC04Ko2vktVAIM3bQZBD10Dubin\\u002fnWrbQP1\\u002fjkwlVuFAhLtzOMua5kBQkAuur0jtQIXUYpCbZPJA9q+R0lgc9kBRUqxBM0z5QMPBBDd1jPtAE9AA3ASv\\u002fEALRZ3dX838QJzR+cCQO\\u002fxAos8MxqRn+0ANM6NpoLb6QH2hJhO5bvpA6tfgnha1+kD8epmbkpr7QJLKJ4q9LP1AJbzPc7qA\\u002f0DZ9A8vSlkBQfy8tnoYbQNBhoGz9b39BUFuju0a8\\u002fsIQbCKRXFbQgxBj2YNkJyTD0F7ZHn+Ik4RQUueSq1\\u002ffRJB3\\u002fyZmNMoE0H2tEeZJi8TQf4hCoUGihJBlLBhO3BTEUFpJpgKP4APQXcKA7R8HQxB1\\u002fl1nf7lCEGqoKJBeQ0GQWNq1GCRkANB+KT2FZlLAUFdyTEH1Dn+QENFi9u5\\u002fvlAuoHPaPIf9kDZ9Pc5tv3yQLpeKFfX6\\u002fBASYDFhM0F8EB+AhAt7hvwQMgLRCmOvvBAZ7nNQDtg8UCaH9RPeoHxQKontk1E1vBAM\\u002flVuBaw7kAmRolniH3qQDPUz0FXzOVA8pJeP99Z4UCy9+yX73DbQENi7vnaetZAt9Ip4\\u002fIN1EAm\\u002fzxOsQ7UQJWitLNHDtZA0aBmVdRC2UAAO7\\u002f6F5LcQGCJUwK3yN5AYGNAzg\\u002f23kAvtXzbPMHcQO0n91bahdhA4CpzPfgl00DvFp9FWlbLQEPNvwZ508FAw11BCCc8tUDDPZIq+yanQDPcSFUQb5dA3ZEAv7rvh0DUjq37PxyBQNxGPNFRxolAWmTH03p0j0Buw2fT1bSYQISpQq65iaRAuBSu\\u002f2qesECtfbphO4u5QPAsbD6VgMJANmfcU70ryUAaxEEQYwvQQKQoOaizKdNAL+mi\\u002fHuB1UB\\u002fRQLHX93WQLbR7KftZ9dA5MQvfzSx10DO4VmBcJHYQENtz9hy+NpAd3rlYRa930DXHzFFDLrjQBha\\u002fKWFJOlAiS14k5jr70B21mpfpcTzQDXoSqPyl\\u002fdAf0XBzzH7+kCfbDXAyI39QGawAiiKIf9A7tXtmMDG\\u002f0CYoA5B6L\\u002f\\u002fQKDbeZsOZf9AjOI1\\u002fv0G\\u002f0BqkKTn6N\\u002f+QK5bHSNYFf9ApauV6QXF\\u002f0CCS\\u002f5Bx4gAQSQDMEzPkgFBte4DoUsVA0GM\\u002fi4zjRsFQVxPHzhLnwdB1SwUdd6ECkGMw62UwJwNQb8fLrP6UxBBmdFoAGiuEUFR2QxkT7YSQZYwMrnjRhNBHSoiYzZGE0GrTfHssq4SQbTdCSiXlBFBHXnQIIUiEEG9gW1lchkNQfDNAKJyAApBLStWgZMsB0ElJuLKnaQEQdjWqKGiVAJBN+YBv+EoAEEt4mhee0X8QEv4R2QCuvhAv8DBvj\\u002f+9UChlfyb5E\\u002f0QBjY1V1gtvNAf0fkZb7z80DgJJGXrJD0QM1Qtmt2\\u002fvRAT2Xs7GfC9EAxncJasprzQH21mOj8jvFAUWhsyOXN7UCAjFIiQBnoQM8t+PNe0uJAXkaVsLIs3UDQXU5Hn3XXQCc8mF0Jq9RAN47cYX6U1ECqMozJNKHWQIAC4FbB59lAOExTR5w33UDyod9FpVTfQKrZAr\\u002fuUt9AxVUXwRfn3ECTSV4BwnvYQLstM+gB\\u002fdJAilRwcjTtykD5959K9XDBQCoEfT6lo7RApUAzh4hapkDnDHMTKniWQF8uA3KHx4ZAhgHdeCE1gEC\\u002fRAPQ2uCLQFLpgndK1ZBAo8UBejznmUD+ZUmRlRmlQCuWpvGRxLBAmJHdRN1guUBTxuv0GCnCQOU22bQXgshAh6oaf6Mmz0ABNvPCxKnSQJ5kSWMPLdVAC28DjUr51kBYmwxccTrYQCqNoqNwbNlAHYKNosw\\u002f20AmC3oZ\\u002fXLeQFPhnL1v2OFA1yW7GGC75UAxNVhAGPzqQPeJ5QTivvBAFq8CMHRv9EDN4pv\\u002f\\u002fD74QLSc4NI0zftA0gQpFD\\u002fF\\u002fkCvnZOkVHsAQQQzXD\\u002fCLwFB+hQb\\u002fsCRAUHYHXKR67sBQSCdg9mwyAFBnbz6LDrPAUEES4OzFeYBQXWLwh24JwJBBLn9sf6zAkGZ\\u002fGcpt6sDQdqRuaqzJgVBDGk66KUoB0EhGHa23ZoJQVINGPkgTwxB4J3e8GwKD0HuRktKnMgQQeHV6iw21xFBRJR1TNyZEkERHO4WUPwSQftoIvZ27xJB0PN4o71uEkGRO2GSKoURQfVFoZ10TRBBPbPTgaDWDUF5j8OAmgELQTepdMfOSghBHL3CirzGBUEHjh7i6ncDQZnrIYddXgFBJoMR6LkH\\u002f0BYgo+55\\u002fv7QNM1eNlKyvlAZXvkcGqN+EDpOTAg9zD4QOCEWltxafhALeuEycTA+EDEngse9bT4QB\\u002fhDsbK3vdAX18XaP0R9kCxb8IrlWnzQFc489JJO\\u002fBAw4sScn3w6UBxAO5F3RPkQJg53C0M6N5ASQqOOqmt2EDeE8RIIIHVQH10T4UEC9VAxIAbV\\u002fWl1kCcsIlqNGPZQIl2ya2xHdxAIrO+ha2x3UBXVsOmKU\\u002fdQB1+SfG3vtpAF0o\\u002fj6Bu1kCvTzgDbj3RQNzMr8xtPchArcxsQP0lv0BTJ5MFQ0myQIbn3BxIp6NAyp1DPqiZk0BWYfZDDbSDQOlScbIw5HtAOHVQdK51i0AolgemWm+QQIdXK6F12ZhAOJnkcVrjo0BH5zS7ex2vQPZelEBQPLdABTuaNOF2wEBUuk21UhvGQAYzvN9IIsxAUYiBK8gD0UAkxp69HK\\u002fTQD44uLtUBNZAJXl6UdUr2EB+3ktCIn\\u002faQJyAnlu1ct1AXpFu5ci\\u002f4EAVd4I+3ovjQAztycFYUOdAhvzsddox7ECIf6k6zRrxQK1cqgCylvRAzS7NhiFY+ECbpwmlzxX8QFqDt1+NhP9AJ+ZRxI41AUHYLSvuVlYCQbeLcXh6IwNBhr2ZMQSlA0GaghJYhekDQa2yx9bsBgRBWA8OggwdBEH1BeNy31UEQfrGfPta4ARBPxanbizlBUFxN1QjM3gHQUD9OfnRjAlB6hFm3FL0C0FYxU6DDWoOQUpE1emSVBBBuo5jQUtAEUGRc5zmKe0RQR3KYuuCWRJBmHHtUGOFEkFVVOFgtG0SQVRK99tuDRJBfMggvgNjEUHzg2AiNnUQQY8uW2Wspw5BoEdUwaEoDEFnXIZJbJkJQc6H2IMfIAdBrTnn9XzaBEEZ9XrBXN8CQZsZVeGIQAFBYdxKomUKAEH88ym8hoH+QEa9UNditP1ANeiP7Kp4\\u002fUC0aDgiLHj9QG5AQQ56R\\u002f1AyXTlBlZ+\\u002fEDCh+Z56NX6QDBh3\\u002fraQPhAtCX3Wmvx9ECcsN9NH0nxQH3iIWMIcutARHpg2DFC5UDSDmcO4XjgQAIfzaoCe9pAV\\u002faRDA\\u002f31kDqn204ePDVQIApp9qQu9ZAEaAc+X972ED7bDHkLTHaQMUJbOW76NpAJgTs9Nn82UCKRhMGr0zXQOY\\u002fBwxJQdNAx2YrDXY3zUBRanhpYE3EQN8wa5BJz7lATQBRR77\\u002frUBiUVhl9e2fQIEm1B9ViI9ANqPZRwBff0BC1w2Y3Q92QHL+UB30nYhARME8qKdAjUA39QahD8qVQEg7czW8LaFAIMye2YeEqkCP+98fcZazQKErAV1ZkbtAuA+nM4F9wkCchei7kbTHQN8nFo\\u002fvMc1AMhw+vcVo0UA8DhUpv1DUQOqQvRV7ctdAPfm7zzf+2kCUrq6e6CPfQNJleyYNCOJAlPjOO8f85ED2AYhZDZnoQIeymrOgEe1Apb4r90tK8UCdDYbEGpb0QMoV3z\\u002fmU\\u002fhAwxDf9yhN\\u002fECoC1LHShwAQSI+blC95QFBWCepPmxkA0FV4+KQJIUEQePfapV0QgVBQgM0uPymBUE+vaDHhc8FQWHDJTJ86gVBttazdh4yBkEp+rrcY+AGQQ\\u002fyN7w1HghBaPM2+YfyCUGAWsRsszgMQR7CgjLPpQ5B52XInL1vEEE08F84IU4RQXOIgDed3hFBTNIibwQpEkGZV1Kxk0ISQT+ArrOpPhJBDrponHcjEkEdJc18aegRQRwUcN9OfRFBTt\\u002f3EsLUEEGVsrhAgtgPQRSP7qltng1B8NFL9PMpC0FzAcFffrYIQY2rj0cAfgZBOm7TBgSsBEE7e4CsslUDQb0x+GOPeAJBdOt0lbj+AUGqaS+FZMUBQX7Oo0iKogFBO\\u002fuV\\u002fSBpAUHAE6Kl+O0AQZiOXAJSDwBBCIPQ1Nd8\\u002fUBoiSMLQw36QELdl3fcGfZA1bUXxTEM8kA\\u002fGH53x5zsQJ17f6jVXeZAEws2Rxem4UAciOoj2uncQAYXEKQ7M9lAc\\u002f1s6W2Z10Cam91MOnbXQIuhhyXPDNhAUDRiIJ6Q2ECV3iUYX0bYQOeiuY+Rt9ZAoHAEaPnZ00CfPQ3WqA7QQBMP4VbH6sdAp+lWPQJZwEBt4JRlx3i0QPAn6lqfdqdAM7deyW2kmEAmZz\\u002fd6gOIQNhpCczmj3dA\\u002fCbuhp9tcED\\u002fFxssL0SEQAlkKvOP9YdAbIFrS0iokUCO\\u002fHPq1YibQO1CEM9SCqVAITKxCPXYrkCWVbVvoaO1QN97j+RHJr1AiYh4sH32wkCjQS6+9gPIQF4AiI9R3M1A4SV8BQhc0kDCcFEeU2jWQHoqhbPsHttAualAkRQ54ECI+zSzdCTjQM08ULSgUeZAsJ++y2zg6UBDE3z1ABXuQP5vPlP5ofFArh22n0vV9EA2sMaTAaP4QIvcK84o3\\u002fxAmNCph2acAEGeOrn7bKYCQZm9JJVLXwRBfzHinBenBUEmJwCnkXQGQUvJoQ5u2QZBhDXnircBB0FgecuXRC0HQXq5HvEZowdB6ZQagwigCEG7pXd0aUMKQav\\u002fISEafwxBI1mGw30SD0F7wIVeCswQQepjAhcg0xFB2RUg7rZ7EkF56SQQf8ISQTK096kewhJB7iyTZuOiEkFpgXPh74QSQWSZtiIGcRJBMBAtNfVWEkH8aqPtQBgSQSCyGUIBmBFBHAu44pLIEEFRNj5MCGUPQX+RkR2L5gxB\\u002fwLfZBZpCkGkorWszT4IQaQYx82cnwZB1ekFoaqaBUEB21slJBcFQQzqV+nY4QRBFgscg1+\\u002fBEFyzIyoN3oEQayqHiFn6QNBqNuM+B7yAkERJkOYNokBQYk+9sHTbP9AyDj+jhst+0Cps1bKQrD2QNacv+RAYfJAz25bd+o47UAvPdDsaTLnQLNXTAMGx+JAHbyOWzKY30BOZHZIo+3bQPO2dS1J7tlAA5tRFPoA2UA\\u002fvvnRTozYQAUuztun9NdACWR87rOy1kDWg9ZzhXrUQBR+oD6VV9FACcUr1OZMy0D3r86Jn9jDQATtAU7eirpAYJlgwU5JsEChT87lsFCiQCE9lxSp45JAT10KUSQUgkBnshNII2dxQECTuXQJ+2dAlB67w4Gff0Cs0ud3wK6CQCZM8GFGfYtAbPlAbYhelUBG8xdntkSgQIy0g\\u002f5nx6dAZ3nKp56vsEDDMg9jkKW2QMlsSn3OBr5A1mDNDNemw0B2DfjEDJjJQC22IUqPndBAvJQVO+No1UDjgLdVQybbQKyGuXIOz+BALZk764VA5EBNrVPYlM7nQGp2bPnqi+tAkjS7Ff\\u002fC70DJm7FYjHHyQPHR8pi\\u002fp\\u002fVANhW20UeS+UBCOlpzFAj+QJ3u5TcQVQFBCe7WEqh9A0GiKCIN9kIFQcFIR5lXgAZBy1V5KZIzB0Hx65OTbH8HQRdA7k+XpAdBb710oCHzB0GaLkhNyrcIQQHumuRCKApBAtpDxT5RDEHOWx0kugoPQUKDSurX\\u002fBBB31VUle9SEkGxAEqJ61ATQbY9fGfy2hNBuZQ8lUr7E0GR9gREhtsTQXbaO7ssrhNBuiLGdSGVE0GtfBIxlZITQfboF00EixNBN170ztdUE0FWD3mPj8wSQaCu9kLP5BFBnge+H0+tEEHVWIZfg54OQfITOuUbAQxBvR\\u002fmQhfkCUEvAxNkqX4IQRaStMubzQdBjEZtFRSaB0HGfVtXKZIHQYXM6D3+ZQdBgkVE1xXcBkGH6ZfNetcFQQXu6Wu\\u002fUwRBDDUgLOleAkFWTBiDJBYAQaKRi\\u002fvQR\\u002ftAxHCqtip29kBg6MfJDRryQFFxqSfP9uxAEu+vgVpv50CpiVKu5oTjQIng0gTo6OBAzI5sRWp73kBo9TSUHmDcQKOLAa0j99pAAmlUZ7PF2UARz\\u002fJFmlbYQKrXyLK\\u002fStZA0RkAuK5400C0vWf0bgDQQB8+HuKRgMhAmv52m\\u002fNbwUClqocAIKm2QIcedMjhLKtAPG+ofwfknUAEMq0mDiuOQOjxS6txP3xATrSY74yPakA4n6OwIgdiQOrbVdd5U3lAoujLSSIvfkBJgQD8go2GQMh\\u002fFoKpvJFAOAItGS4qm0CtnJYn9d+jQKEh8kvO3KtAhKYwXGjtskD6Kk+4J1C5QHgVsFAA6sBAXN6pV8PGxkAMVfrQIdXOQHZGZrlzvtRA9PIy45Nb20DLH1M86nzhQFY8CyIMkuVA2TqgvRO76UCk8sSJKfTtQKwVq+EhP\\u002fFAX+67AtDo80AhIMWPvzP3QNLXuQySN\\u002ftAH7Np8ULK\\u002f0Dmb3YZNT8CQay6M9\\u002fvYARB6Y5XG6IIBkEQeFtHDhUHQd4OdNZEkwdB0Wx4K4+8B0EZxZwgqeYHQTp\\u002fjIL6bAhBFAWJtbCZCUEdXDVdw5ILQRLqy1eeTA5ByjkR2qPCEEFD3\\u002fpM8WYSQUJLiFOz0xNBLh1u3\\u002fDWFEGTi6w\\u002fCWAVQY2hcUXvhRVBTFAJPPl5FUHPl1yuJ20VQZkzU0WsdhVBb0o1bIKJFUFfN1xWJH0VQSVvSESpIhVBYhIbowlbFEHbdP\\u002f+pCUTQVdfz9cvoxFBmx7nzRwNEEHHZYsDUEgNQd3\\u002fz7uNNgtBaqy64x8NCkF6aegbvKoJQXz57C3ztAlB\\u002fb3zWHi8CUGGslLFgGMJQaQKFnhVdQhB52bzA\\u002fjoBkFltlV5LdUEQbewRo+3YQJBGUCk3Fl7\\u002f0AzzIru0zX6QK\\u002fxZLanU\\u002fVAKy7vqHsk8UBviD3cBK3rQIIWagTE2+ZAWA9hodyU40BdVbGtBHLhQNkrQ6DGDOBAQAGhbawn3kAk4X6QvZXcQKo\\u002fEWNy\\u002f9pAsx6C1Q8K2UAoTh9DH3PWQCnDBzfQK9NABl46\\u002fHbNzkByAC4\\u002f8A\\u002fHQDqBqrkD+b9AXtjJF3tttEDKjcYpH\\u002f2nQPj2I9gd2JlAGlddGYuMiUAjTT\\u002fpI2p3QK34Ox7dgGVAhMRnpli+XEAf0EBr8ql3QMcajx+juXxAXr\\u002flPj8phkBKlMH8GdiRQAUq2DZppJtAyRwAmAA4pEAm3MReWwqsQA1ShW9Qs7JAM9Kpw06CuEDauKKo\\u002fybAQINfineavcVAngwm1ibazUCjUhvkx5XUQDLLqwOD8dtA5+Gdmlhf4kCMovsulzfnQHrsK2TsM+xA+3jBjmGX8EAUGdzdPifzQNVRL8cUAfZApZFXIyVl+UCcMCyofXD9QH6Ab7tB\\u002fABBpH3MDmZDA0GDCfyiID8FQbORVZxkrQZBOYMNYlt2B0H8ABySx7kHQWVnd63gxgdBiF6DgwgECEF8oJwiadEIQeopewMecApBTWPCwRTyDEGM9OzXdhkQQfonWJGF7RFBVpWVsHK4E0GoqwM2Yz0VQRtRVsB5VBZBuDg4JTf6FkFTNqbytE4XQQwdukpFghdBK24h8P+4F0F0\\u002fREMevUXQU5Qjn+fFhhBB3RhUq\\u002foF0FO0NyR5T4XQTJ2dfoGCRZBnW+jUudcFEHNlyTldXISQdF\\u002fgMnIlBBBiXpsMs0YDkEoLyRAnhEMQRt8T0vwIQtBQP0ZLjUDC0FBzfzUGTYLQUrvHSDdMQtBXZDU5beRCkHlFWYXBS0JQYuiulksFAdBmCjq+kp7BEFx2VPqNaIBQSSudrTkif1AU3W6odsr+EBdN\\u002foAA3nzQGghpu2XSu9ArhVSazWG6UCfuCVhTILlQO60cvcy5eJAW6HqIN4+4UBfsu11MCngQKP\\u002f8uHLr95AETzRb6Yq3UAsr4woSm7bQKXFWQMzM9lAHFS0HipN1kDDnXQzssLSQKpe+eVFqs1AxYxQFOLZxUCjXMuEN829QOukKwlsu7JAM4qaUWekpUCr44ZzXfCWQBUEccJCToZAOvqErXMXdEBjd3+6UxliQMuRq2\\u002f14VdAEDmkf1tme0A6IWJYsuiAQNTMpWnMzopAsu3F7CH4lUAggJSr8ByhQFuD5QXh4KhAHUBQiBztsECLMw0Pb9u1QOVKP3GAbbtA\\u002f+KA4iFBwUDRY3dGgmPGQBfpe2LeO85AkrmJR4Ls1ED2yRJ01OLcQN7Lga\\u002fLauNAE0SDJUIT6UAEKY0DuQDvQHy3UOFscvJAzSI7pt1f9UAgkpJauHT4QPaBOifP7vtA7ykCqp7t\\u002f0AkOGyVDyQCQaF8z892QwRB8gicSQoHBkH18xu9JTIHQUIR2CwquQdB3Tm1I8nMB0EnoV8xX8wHQU2y842oJwhBFvTtY8o9CUFKYm8RhEQLQfv+97lLOw5B3pJzNeX0EEFIdzBKBPUSQf7eGNxC4BRBqgJR1nCAFkEVlpdwx7kXQdUzZsI0lBhBGeImaiczGUF7QVSgkb0ZQS462tDeQhpB7PEXxgGtGkGlrdc5FcgaQRT4hEHdWhpBxu1oDIlCGUFuTPh3qoQXQVd4GyuiURVBkZzZbgL5EkGxCJvoxNQQQRlJ1a2ZYQ5BNNdAMMBpDEEXEf4Ig7ALQbi5V+CryQtBlWHszLUUDEGh8uuGK\\u002fULQe\\u002f7nh\\u002fYBAtBG7zsufIqCUFg5S+7nZIGQVZ9bxsWjANBe37cRMJrAEEItk9q8ur6QKjt0qd1rfVA2X5HlwpT8UCMcM43luDrQDnrNm7M++ZAAsaLpcm240A16cflSq3hQAkhXAy0cOBAdvZRWpJA30B+noyAl+ndQAOsaiiYetxAdhuePh+s2kAjje6P50nYQEiBfOQYPtVAjpBU8+6h0UCwWJXUSX\\u002fLQJJcC4QD+sNARZ4XAWnhukB4v25VG62wQHKK1pW2BaNAVqWOhgzpk0CBsxZBmh6DQBhE6ffO\\u002f3BAUmDz2gYwXkAn5vPmcLlTQFRPkcFG84FADGqfT0VNhkANGVnDZtmRQK8\\u002fMLqUX51AFvb5g4TSpkAVbe2aum2wQHkTpgzM8LVA7wFq3Wl8u0DJxAqmiYHAQMQ2nJ3rqsNAo1P4lUcxyECxWi3I8HzPQDqQuaKxgdVA1OmdVhDa3UAJQYVwC13kQOQ\\u002fksoevupAlJ2ZV\\u002fbG8ECmo+RQ0Sv0QHiuAl+AefdAyk8b3M\\u002fM+kCe4ZsZvlv+QH8Gvy8YIgFBaHjfl8svA0GDWR0pbx4FQV6OlUYWpgZB1ePPwbuTB0HtkLrJw+cHQUMxjB853gdBBq8KkM\\u002fdB0Fgz21svFcIQXljypDYpglBCnCFRh34C0EWSXlDrD8PQcp8dMcpnRFBTUuRqC2+E0EMxcb+KccVQbF3nJtbiRdByKP43bryGEEfZfWLIxEaQcTYyncbBBtB9hPxvQDjG0EI\\u002f8diV6YcQRYzRSSbIh1BYUM4Bp8YHUF0EVVjjFIcQcmhRttEvhpBJWM91355GEEs1yvKzMwVQY2BRzvJGRNBBwMOFMjCEEF87dhRniMOQQNydFjwSAxBC5NbXQLGC0HfcZf0DA8MQfh\\u002fPWU8agxBf+ffeBAuDEEf6lZ\\u002fbfYKQcqonxR0ughBAQ1j92O9BUH\\u002fzSyKfmcCQTc0igdROv5AtK7JbapL+EBZNEUaGE7zQN6CiyMbse5Au+t2Ga3E6EBWWHht65\\u002fkQOHH7JQ18eFAbcjNKtdU4ECuHJnsLcbeQFEwVVokgd1AcXp+DhtR3EAv7YsrudraQAvKTcE35thAX27Rc4pY1kC0mJV1sTbTQBpxTdMoV89Aw9sSyMUGyECeB4f96y7BQCqUGJwey7ZArJOAVVLqq0AsozrmuXafQLs5Uk6RSJBA52juCjbxfkCqlWLLZjhrQEuqR3zy5FdAs4HThbD8TkAjtJkhA6SHQBJh4akjWo1AMAVnwvJvl0But3IISjOjQHS8VerToa1AYTYUZBYdtUDAxyiZe8W7QKdkFfmPAMFAnJ5u6JW+w0AiiKYCPnrGQPO0M1QtQ8pArGoL5epf0ED3ITpXtt7VQHk7I0iYQt5APmTYS6nO5ECiMudBR6frQNnkqIzzjfFA87kqw+FO9UDE3Qxw4\\u002fH4QMCZyodpg\\u002fxA1RNRvw8WAEFzTdnhHgECQfLQYmDy7wNBS+oOU4avBUEMmiDYJQIHQZOm3k\\u002fuwAdBdSPSswv2B0HKeNaBjuEHQROQVfkZ6AdBTyKjOop1CEG\\u002fdi0Xut8JQVrkX2XiUAxBMGdWfU+8D0GXqeKWp+8RQX27j1qFKBRBLO+rTpNQFkHRjb84Qz4YQeCN1Oxh4xlBqnUAHplLG0FNwQI9J4scQeQWs6qlph1B8kzxlNiCHkHMQXmayOceQapkc8uzlh5BYBGbrBtnHUHHcJGVKlwbQdKX4HauqRhBb4Gl7AmpFUHJQwOwJ8QSQRpJoIv2XBBBGeTfmZpsDUG3JuwyYcULQSqPSvOXfAtBlP5ovCXxC0FCmT06VVoMQVg7+wPHCAxBMbLwS8ScCkFa0jtUHhsIQRMysREh2gRBNhuwtDBUAUGNBofkWvH7QDYGLTQoH\\u002fZAsNz7\\u002fIFr8UBwIUvUu6brQPQJX42UbeZAhVjw3wva4kA7cH7H2JTgQIZo4A2JgN5AYk09IoT23ECllZVa5tjbQFLGnbZpndpAfyvSBSXv2EBba8CI3KvWQIKkTkiU2dNAMgIbzN+c0EBhLJT5HWLKQGh0\\u002fhl4u8NAyB0+MQ2du0CfckhipfixQH3rbXi8p6VAjo5\\u002fFpkRmEBbEmkpDZyIQIgKQ258IHdAgkhapvMiZECsg5q1nX1RQNmB0oLihkZATKhaEYtxjUCkefdLMTSSQJxr0jm715xAW8cWmipvp0CdZf8D7OyxQLIXoeGdSblAJ7YAZ7xrwECjWaSBM8PDQLb4a09ZZMZAeio+kqCgyEAsizOiDJTLQBdQyJLbhNBAaE074rKC1UAomu8RTYfdQPdBzXnFYORAPStscxBT60D4T98cUIfxQMNiYzHVfvVAkU9aQ6ph+UA0EOtPUiz9QCSLhZZpegBBNC8Vs2NhAkH\\u002fckye0DYEQRdLvlJrzgVB3L+uXGb3BkGav5yRCZgHQTiqVEsuwQdBRncaL9qvB0Hsp2C+kL4HQYanGX7ETghBQcoT6\\u002fqxCUFIImLyRRYMQZ0ttXCVeA9B7a\\u002fzNtjQEUFKfSfirBkUQfJ1C8+zYhZB9pBCGOyDGEEM1sTwrGsaQdJvwQ7wGhxBRSljdKeUHUFHo6zqZcoeQWsozqLIkx9BaAFoxei3H0G7fwexqwQfQSJWioddZx1BeBrplkP6GkF\\u002fJd2CSQIYQRyVgBwN4BRBtYxd\\u002ffn6EUEgKqvuk1cPQb\\u002fJFJ\\u002feUgxBUL5b3aP2CkHVOMrKreoKQaGO1kwyhAtBU5DZGxH3C0HQV5yC6ZULQTT4R1XyBwpBLonJDxhdB0E08G0zwfgDQamKsDSjYABBJEB6e3UT+kAvsbi\\u002fK3T0QF\\u002f9WNolDfBA7673DoeO6UAjD65jm+XkQBj2RELDv+FADFw6hZiM30De85eeZkPdQPJWTQW+6NtAoxGO9nvH2kBKDznc51LZQGHlQ0xhQNdAfApYYaeJ1EA40ywjxFrRQLL3d8138ctAVvEGpTFYxUCEZkoj9sq+QIE9Eyi73bRAVyESuQ5tqkARxW2QCh+fQPihBBH895BA7XITlaQTgUBegvy+tqpvQN6HsYoFP1tA\\u002fUhX6HNlR0Bwm15PNOo9QFERwPOoBZFALUvyfAHtlEDUHDRn\\u002fWWgQCx0ec4mXqpAQy1LBn73s0AMJUv1xd+7QBPJjbOf48FAyC1M3sc4xUBeAXWXjZbHQF4RoArLQ8lAVshrg3pZy0Db2HTTbqbPQDnJnbNdHdRAuhHD84Zc20BYxY9vLeziQKzo6WvJlOlASCKIk8uX8EBkrGCqS5n0QPT7cFxanfhAzPo2WfSR\\u002fEBDPQFGgDsAQY5d0AhMIAJB+rVLrobgA0HWrLkNQlcFQQHMdKi\\u002fYQZBVntY+wzzBkHqD8KnfR8HQTdFHkarGwdBadGujosyB0E7TawEAbgHQY898i7D+QhBbiGkDRIuC0Gy0AbfpmIOQW+uCEHeORFBoPZ0Y8yJE0GuoqZQfPEVQY5EBG9YRxhB9jVuoFNwGkHEMA4NdFwcQXXV1SES+h1B0IH29MIpH0FqLJp+Wr0fQci6ku4mhR9BVJL\\u002fLMRkHkGbAnBG9GMcQd8LeQ36shlBQq\\u002flW76hFkG5ldBDNI8TQYaaSFxH1xBB1igQoTuFDUHvg3eTxPEKQbd\\u002fBzqa7QlBMQX+mj8XCkHlNUcbS8UKQfsq6UKxMwtBWSYAvb2\\u002fCkEeILSiNBsJQR9Mc38sXwZBzAGe\\u002fQn2AkHzJjVQmtL+QACSdYgjW\\u002fhAXvUO49EI80CVWleD4+7tQPJAd4xqBOhA6hvzw9Dg40Cqw8hBXx7hQOaFgIAN0d5A9p4dM5rW3EADFhO1jpPbQFETzCtXUNpAs27pvImD2EAwrv3aRvTVQGiUSiqRvNJAsLalNH5azkAMHNtYu0zHQOrYnx8278BA8Ik62xNGt0AQ5ITJ4imuQNyw1qNwXaJAm4Vs03LrlEB4S5b7MTGGQAzGxvO11XVAZHhhkpffY0AlDCKGYtVQQDEzBxzefzxARd137IkPMkCgDWoQCUeSQBP38Hk3T5ZArHujkJ1AoUAUkWRqyWSrQH6CMw7fgLRAxesbMQJMvED\\u002fSp5ffvHBQJr4rEyt\\u002f8RAtOfkFejzxkDBDk01\\u002fgzIQF8S6dLaUclARPpEAe90zECBwRJaBLXRQIetK6ZV5tdAqjjDNDeU4EBXyFD8SqDmQG5TyqUGve1AAvTHV7G88kBlzJvcZLv2QAcWY5rRvPpA3ZnKkUqr\\u002fkDlWwTdqTMBQf4zQkI\\u002f3wJBcFApJe86BEHpRKhBSjEFQfmEZk0CwAVB8cjEmMj7BUFad8GJug0GQSOA+tsSLwZBQngvPgOlBkHUvUEcLrkHQcwnm+6lqglBzhq6XeCZDEG9F6zZ3zwQQRFzpZcgiBJB7lskzBEEFUHz2h1WWIQXQS2GN5wM4hlB5WR\\u002fwDb6G0G3Ki1N7aYdQdV040Njux5BVsYxD44JH0GalBO4zHAeQTQFzCnI7RxByY+OZkCiGkFlHppeg9AXQZwjmKd9zRRBOsGtZCrwEUHhXwZpTQcPQQtmCeH\\u002feQtBnOTOA4hmCUHaULC29rIIQfGQk9Dm+ghBh2ccE+WfCUHGqKTOlPAJQVzbT8uiXQlBtiaOkYSnB0HYf2\\u002fv3O4EQXKG5q26ngFBJx+\\u002fJpd4\\u002fEAbvQuLiGr2QInb8H7Uh\\u002fFAE9ZarkXA60AgDNx0VI3mQFeYgnLk\\u002feJAJuVpQ5Cq4EDVkEeBzn7eQJE3MVP30dxApjykSA+W20B1lDDdrRnaQA2DzFOX49dAZbBEKhnW1EBVucihEyzRQP\\u002fX\\u002fbt0pspA1MqAT352w0CPS13P78i6QE5v0mKLY7FAdbAsfBBQpUA9ZGomD6OYQH2XeUjcy4pA4Cwg4hZTe0DMSGfCAwRqQCGFGNjfDFdA3qcWxiwYQ0DPr706v7IvQBWhJzwt2iNAaE5yueZckkCz3KQOejqWQIeTcRyR7KBAqMYIJDh6qkD+Ux50H42zQLDtqzg2orpAwKAAu9+qwEB8MSyLejvDQOI1hgb0rsRAVljDUog+xUA2Q65IldPFQNkdXN7668dAllpF+cdAzUAVtoe9U6XTQL03wk\\u002fFaNtAXn\\u002fgqsvt4kCI74S0pkXpQAwYaKi9NPBAapehQ2ID9EDug6N99On3QPcrXxLbwvtAtlbla3dg\\u002f0B1c8EiUUcBQQM2sNrnjwJBlo2jA7F+A0Eljw8ywxcEQdFiSG\\u002f1bARB5ggYRhGbBEF33rnFqMoEQYayZWtWMwVBdB8z+gwaBkHNGv8XM8QHQcKWQ7T3YQpBhJaiAAX7DUGZIP2cfzQRQV5jyT38sBNBp3UoYPBEFkELH85kUb8YQaPGS\\u002fUB7RpBCLnS2PiXHEFv1Tf5oYodQetlxQh1mh1B3VNgTla2HEHsTp+h5fAaQZw4\\u002fGMqgBhBt07LohWyFUELoDGtX9sSQYu\\u002fQx37RxBBi4kyCW5iDEF9BxBg53EJQdH1qHbSzQdBbQ3P+dxMB0FHvQTEd40HQVPaZxa2AwhBj2t1q0UZCEElwbjII1kHQcKGGTd6lQVBRdkdlT\\u002f0AkHH2vkRRbX\\u002fQC6C1jj4gvlA\\u002fBuPK8MS9EAJbbxTFovvQDroohUvQelApwhpeufn5ECQlyXCRAbiQJvuqAK5NOBAh1AZiLY63kA2CbRsDdfcQFC0moTxjNtAdXJcAum\\u002f2UClmYGSPBfXQGnZUeREmdNAULcu3V89z0AoM2aMQ0vHQC+ycUu6PcBAMqDKQsM0tUCmhVH4kP2pQKUfckym+p1AFWHCMM5NkEBVpRLVdruAQIVoXiVIL3BAF9UPlfxuXUAmJhn5BRZJQBihIFjVHTRAi9nOVpU6IEBWse4ZJvwTQIHxSMTUa5FARkuIrgLllED5lu0jGkOfQBjnh7JmC6hAYk1eqXt7sUCTDVsqLne3QCzdgnKa8LxAi4vm5EpxwEBOUGzTJ2PBQOKEuOTfgsFArRvBdMaVwUBkp\\u002fi2N9jCQGquIpXYt8ZAg1U6oF54zkA5NbJEh3HVQH+QRJ9TD95AU3uDRCtv5EB58LYKFbvqQPkHIO9o1fBAusF2qkF69EARiC1JRSD4QBPzXdW1lPtAoOK0kFWl\\u002fkDlIEyqq5YAQanGHsB7jwFBg0Kd1mJBAkFMXuiZ+LcCQajQrqCXBANB\\u002fdONhvJCA0GGz\\u002fEjdqADQaBqic+SXQRBij80\\u002fkTCBUHzxu4hYwgIQTa9CySXRgtBrZS01fNmD0Fy4R7lzxUSQZx\\u002fNxJRnRRB75lIq+QTF0HQvGjvID0ZQQvd59\\u002f22RpBM67vIiGxG0E4073OfZwbQfgWdFE8lhpB+1\\u002fHZ6S+GEFGXr9BTVYWQZXCFj2+rhNBesfbDi4YEUFU1L+bpqYNQRCxZTbnFApBQbQ1EoygB0E2cJz9GkIGQdx+unc4xwVBH\\u002fX\\u002fum7WBUFfbsAaQfsFQQlVTgYFvwVBUNCIRkvKBEHwK5+iTwEDQcD7N1kMjABB5365k2OH+0C5crvEShz2QGCNTTJldPFAdathLe6z60DyFlISWZTmQGJDjfcOLONAjTKfT5kH4UDuJo0Fvn\\u002ffQHdaKwIq9t1Ajqfsl4XG3EC2FaZP7E7bQH+FJ9QfF9lAeeKK5TP11UA+zYyjFhnSQPXz42r85stAV+mHkVMLxEBlwMTvgc66QHN5aP1jsrBAsi4Ns9dpo0DD6xmJEiGVQPY2cA68mIVAdK4jZnjKdEBZ7jvz5uJiQKSgBs9\\u002fMVBA7ocM5TIwOkAdsmEGdg4kQAag9\\u002fihDA9AvV1J8jikAkD1Pf461W2PQNsQYfSWqpJAvROu1vBom0BoVL6QFrOkQJ6gkc\\u002fomK1Au220F3uLs0B5W82He7i3QBSDCvF+g7pAQnrmTJWLu0CLH52aszG7QIvXRaattLpALrl1pkkFvEBEhA+eQrDAQPIiaFbQacZASHQdC4rpz0Adt4dlFMHWQAF8r2a0kN9A2MTA6AgZ5UCLTFvBVynrQG7wNl5P3\\u002fBA93KW1jVF9EDdplVtgJf3QM4Phnqap\\u002fpAc2Zsq4pR\\u002fUAz\\u002frTAyYH\\u002fQEa+RKKgmQBBEMo57zs1AUHjht32zZsBQbcVejfD4QFBjUU+vqgvAkHEHhxUGcMCQUWmN9174wNB20ccTnjNBUFTtTdP754IQeulnL\\u002fRTAxBTVH3MmtSEEEoGCBYvKoSQdV6tnfX+hRB7jtQZdQFF0GDDQBUC4wYQe4ztAZ9VRlB\\u002f4oWcKM\\u002fGUH8uDl4H0kYQdGhBAoBlRZBrO0d8hNjFEE\\u002fnKo1GwASQbvs2b3saA9B9SyCmJ9uC0F+jlSwAFIIQXgolgLOJgZBmBpsS+jZBEHRR9310zcEQZ+Qq5qY8wNBrt61r\\u002fKxA0FQje+2AhwDQTiuGXLG9gFBPpuxv\\u002fk1AEGLOdk+Rv\\u002f7QPkLzECZOPdA58YHAbG18kDuEJ68\\u002f9ftQGjWx47HHehADPvrMmos5EBNmyqh5K7hQLE41N0LPOBAerl7gbfd3kA\\u002fEF6l59PdQLAmhUsnr9xALjrcxvnk2kBdxZLgTyvYQLVgdvjkjdRA1BTke+Vl0ECiJhlNz2jIQMeqlxgj5cBAttMq4P63tUD2efQWsuypQFjJY1aEw5xAx+IBI4+5jUDZz8xZc7F8QPuMpomf9WlACYazipgWVkCN7K7\\u002f4LlBQPu3Shyr4ypAsoLsLAZlE0BXM2kIKFv8PzQFgG+XafA\\u002flvaMm83\\u002fikB+1JMjtcWPQIKOvoyT5pZAt0funPj6oEC+HUBEuN6nQCeSol7bA69At9u7xfSEskAoUqDQCV20QN\\u002fgFYQfy7RAmIR3sQIktECgMnS6Ql2zQEDy8vZQ6bNAKtbQ5hVyt0ArXROZ1ou\\u002fQBDOzKVAvMZAEQ0CRHGC0EBphkixT2jXQLOM4sGKB+BAd51TZgwu5UAvyrKDRQnrQFMNqA5osvBAXfxU2hj780CoNLaGeTL3QL3MzFvpK\\u002fpAvn1EVB\\u002fA\\u002fEDQ01LXo9D+QMr0Be7pJQBBMe6\\u002fkNGaAEGri9PnaNoAQTya\\u002fnhZDgFBhj+02R10AUGC6TWwiFACQTo60hb13ANBc92VaXs2BkHgnHkaSFYJQbREGA+6Ew1B7lj79HiVEEEy5ZgbPKESQSDSVKIfeBRBTZT94bLhFUGn2IFVc6sWQSGMaesWtRZB10\\u002f\\u002fP836FUEvWKyIOpgUQekm\\u002fAoywhJBfFnJQJy5EEFmpyheWHkNQRaknzGr9glB7eNx0yAjB0FgAuoIJg4FQdWYW07CpANBMsvWtBW6AkFD7UnllhACQVlo+EIkZQFBO4MXBQ59AEFtF2a3U2r+QM8BJVuOGPtAeFO9xr5F90ASiuTHyFzzQD0FHKkTlu9AkrnkAoC76UBtGHTYI2blQNsiVP1IgOJAXUNM1oHD4ECKz1n08avfQA+RQzkSst5AgAkMRwPf3UCbymUeS47cQIAxWnVuUdpAL5xTlXAN10AJA9bI0f\\u002fSQJE43LUqRs1AddQu\\u002fJ7+xEAhuxzzE\\u002fe7QBTQyoFARrFA51SKfzHJo0DvIOMgpwSVQFZScSOzuoRA3pdguw8Ec0Aozr5fYURgQMAiAUWoCUpAyvVja\\u002fWQM0B1b6ruwbcbQDWtiSoJpwJAXCYMab5i6T8msOuZDPjbPxl7FMKz\\u002f4VAFRCnsaOsiUC37VWOnjOSQPi2+cXijJpAmCpD1F9hokAKwVViXounQHniOanwvKtAR8JzgNAarkCx+nVuDlmuQB9PJJP1\\u002f6xABpS+4nBzq0BRcRwCXr2rQHW8cH04G7BA23cxJ6GYtUCnq61MMWG\\u002fQGcUNxgiLMdA2E6bXYnQ0EDaNnuN7rLXQKEwLxhGL+BAu56JAj9u5UCSTsBPp4PrQLyVMmJtHfFALd8Wq5qc9ECune6sUQL4QO86URS7CvtAc0AJ2J14\\u002fUCpA8eiSCX\\u002fQEvNpoH6BwBBa\\u002fQSagg0AEHwoW\\u002fpVEYAQR7MFRhXfgBB4Mm4Z7ocAUEHkyt2iFICQbOSBe4oNQRBUbCoBsu7BkGKULoXzsQJQWT96ib0HA1By4sCpYtBEEEI12G3iNQRQWGAs8rcGxNBezZOsIDtE0GcJgJUbyoUQZorEv2xxxNBTaMkUKvSEkHjU1pagG4RQZiBHDVIlw9BUa9L2GU5DEG9\\u002fMrYbxgJQZYQIp\\u002frawZBA\\u002fNfykFJBEGl8xNO46YCQVBsafJiZAFB2RfSyfRUAEEL9V8nfZX+QOtLlLSeQ\\u002fxAiZF+C8uR+UCPy9rOj4r2QIbCQuASX\\u002fNAySAxd51T8ECCJcBimk7rQLeImYzXA+dAPyqBJTjd40AraYMdAMPhQKJF\\u002fUY8geBAIrKiyWCm30CRerFEOdDeQO65o0MJ1d1AVl+08kQh3EATO0hHVGPZQGhsb\\u002f64ptVA39TzSg5M0UBdOPUPzb\\u002fJQOLIXqiPzMFAeDPJhNrPtkBn\\u002f9MnnBSrQJSaEjT5xJ1AiwbPXlRPjkD0biSQbpl8QHaFLCDhCGlAVfDPwORcVEDAvf6cm9k+QEYxJMv90yVAzqeMr1L2DEDqDd1t1yjyP6nFreHa1dY\\u002f7v06LJuzxz\\u002f7mmbON+CAQE9FuZqbkYNASr\\u002fMENFki0Bshf89pryTQMBmEOfEDJtAs3\\u002fcbKIvoUBA4x5b2iKkQLXmAEeuzaVAYEWIXu4BpkDsp9kVPR2lQN8UYm8WCaRAjBOr2g4ZpEA1bPWkeNymQE1JCe8\\u002f+q1AA7bqUS2TtUB0tpbCfQ3AQANy5EA70sdAZgmm7u1c0UB+mtbCU7vYQLXMyKJiKOFAfIqws4Mb50AXnkL\\u002f5RPuQLVEyec21\\u002fJAOgjyZGel9kAqyoYfyhP6QHFaIdqMyfxANjfcWg+R\\u002fkDyS+MFAmz\\u002fQD5DOnyDmP9A5t\\u002fTsXuE\\u002f0A2Vl1DJrL\\u002fQBztTZHySgBBg59VrZA8AUG65JaqxrYCQe54+SRvqgRB03MQCKj6BkGIOPkyu4UJQXjxwivZJgxBtcWGg5WxDkHZk7hyAXcQQbq45yckThFBawUA1HXAEUEj0b2Aa7oRQTwfXXbnOBFBCTmW5BBLEEGqkgmQUB4OQSzunaz4VgtBraDTIHaLCEGaOhFWYfYFQVsBDZYeuANBTNFG5HbVAUGJ6+I3hj0AQXBE\\u002fNJ2qP1AZ2yVIpD7+kAQKQKZW0\\u002f4QLtIYTBlmvVA36lLhwzt8kD3oMqVCmfwQFKfNHsbUuxA88Vlu6eR6EDCOWsNUJnlQOelPsz+XONABeE2xu3D4UCRu+T4nK7gQIE++pBW5t9AaBgCQymw3kA8ER5sfTLdQNs6oGES9tpAJQ10SObD10Axz1LJrb7TQFL6ApGoqM5AB3VbJHAfxkCeLiqewpS9QPZ\\u002fZ2dkS7JAnd2AzDbrpEBWrK50ZRyWQOwncOhJnIVAVXMDK6GKc0A\\u002fMQuqsVxgQD7ydNwSZklACRXnu+9MMkDyNCnuJIgYQLqfgtdMrf4\\u002fpNRDLcYC4j8sxtkiVfXEP8oZZ333abQ\\u002f1vBJzho7eEB8hExxwAB8QPnZl761fINAQYznH1z9i0DKCXmivDOTQDKc3Y\\u002f4kJhARkF3wm0xnUCe1oGj3y2gQOPpTmAo5KBAZuN1pfjloEAAD9YBqK2gQGElxgV\\u002f+aBArbZLhz2+okA5h9gXSCSnQJx4V2BUma9Ay9EiGT\\u002f+tkA3ohf\\u002fYDfBQCk++C+r6slAGKzrmKpQ00DXwXeRby3cQH+9eBen6eNAUOA6SKoI60ArfrHDhIPxQOvdBe8Fk\\u002fVAudfZxaM9+UBQ6QRIphn8QPgmQmTT6\\u002f1APIeftRi9\\u002fkBpfWmtbdn+QOyCxntluP5A1SmBNHPY\\u002fkBZ15J9Jpr\\u002fQK0YimFXlABBcOGI2kq8AUF1yj0JGS4DQbjR65bK0ARB2bJuG7WTBkE0e8ePCG4IQdASO7HLVApBXQKHn0gvDEGHNK0oktINQQm6t5unBw9BwE95+GyZD0FrLUPUUGQPQfbmp7SrYQ5BgmP4r\\u002f2qDEGbyC715HMKQVKmViWi\\u002fAdBub1tYLKBBUGoQNBaQS4DQQFJoWQQFgFBhxEgl3tw\\u002fkArkJ2bKRH7QC68UzAQ9fdAt7B9ZcAR9UCSaCAIx27yQF+oiSztHfBAmU96fNRc7EDkTrmiGUXpQLLmI+t72uZAchVMhzPw5EBnkjAbZlvjQHRXMva\\u002fAeJAT2WNNwbY4EB4BLDYy6XfQAB1BQanr91AxZrANxB520C2Smr1HbDYQAsgbBO7M9VAv3WYEJMs0UAg25k1ov7JQG3Zq4c5RMJACOVVgZm\\u002ft0ATZTDlWYGsQML7hPSUj59AuvycekYdkEAL2anK6Fx+QATsxBQhaGpA5Ae4f+E3VUAOQCSF44g\\u002fQHiFndyMsiVAd2dlwc+vC0DsxSc6w2nwP82uwJ7AK9I\\u002fXjs6C8y0sz8L9Xs6Vw6iPwN32dw1UnBAM\\u002fZx4xzqckC6B+Nxm456QMaVc1GtYYNAKW5t4NBHi0DFs4rFqh2SQOMw4DQQqpZAkvqJfdbXmkB8n\\u002fHq0lKeQABqVKVZfaBAXiHfb657oUCedF\\u002f7xmeiQPDD9wtfwqNAREf\\u002fc25cpkDMCG4XSXarQOpYLpCAerJAXwUWr37VukCmbr45PGPEQN5TJSonYM9Ax5uuR8nM10ARtoUhznPhQKpXbNctbehAQHlyONAw8EBKa6ylPkP0QLXldhVB8vdAk0OhypDR+kAjZL7Bl6n8QAlGkAE7i\\u002f1ASxeNFdzI\\u002fUD7OjRf3df9QOS05l+TJ\\u002f5AcUXJ4q39\\u002fkCEnQouyjIAQceaYwSiHAFB1qPu9cIfAkEftZMp\\u002fCcDQREwOexUNARBzSbuwkhUBUGjCPIdV5kGQYh1a2W+BQhBSnqtrBGCCUFuaTvse94KQW3HijBe3gtBIyZrnkBJDEHzUWeyMfsLQTWswBdX7wpBX\\u002fRt\\u002fRZBCUGtn\\u002fRCPSMHQR9MNShq0ARBb3yFlRh7AkF+UHjLqkMAQZX84uaOb\\u002fxA2i2kKAqz+EBlO0f3P1L1QGpeYie8VvJAYtjm7ASm70B3niNJWa\\u002frQEvSE+Ljy+hAQzAF7xfR5kDmxWBkhG\\u002flQAvv6ebNTeRAqwt55g0o40BpF5p8\\u002feHhQPjz8x4YguBA\\u002fF9MFJo13kASm7THmmXbQKy5Gv9AethAVl6r\\u002fK5J1UD5c46Wn8LRQCw6aickC8xAVDj3jYe6xEDjYPmDwXm8QJSAflWjFrJA0E+ETWsxpUAf+wJtqd2WQMQ1L2rCtIZAvawZR7HAdEBzpGmKGndhQGh7aQObFktAsUVMAOZfM0DyFHDK35YZQKgSksHAPv8\\u002f8hf2pLSp4T\\u002fG8PhFzo\\u002fCPzlg\\u002fPb\\u002f66I\\u002fUp6fx+5mkD\\u002fFB2gnnB1lQPJfvfIp3WhA05+eHsQkckAZOXNZ0Ol7QISerM4v+4RAJ7sb\\u002fLgojkBWVfZqZ6SUQN5LykU355pAmFaK6E6xoEC6oMKp0LujQMgEwChbSKZA3Jz+5g80qED7pY+AyqipQF\\u002f9nHaFPKtAN9dd4LYRrkBnPdQxNgSyQLACEKhRAbhAOsiUTmKFwUAMXJNpjtTKQCmPJoXVmNRACGvEeKKz3kCUqdrIZs3lQNIiTVK7O+1AVPay2i5y8kBFQQR1NfD1QCoN+PkOtvhAfliWyNOV+kAkS24nB6X7QBoL33lsMfxAtMj5hH6g\\u002fECSw\\u002fc21Ef9QGLlGD2hTv5AzC4hPnuk\\u002f0AZyKkEyYgAQbWLGdXjLAFBdbRkoO6wAUFsVxNnaCECQe8DzAUPoAJBhtP\\u002fDe5SA0EGEBtlUFAEQcmz6coekQVBL2swB7DuBkEt98OtnysIQXE\\u002fWXD3AwlBmb0I07M\\u002fCUG\\u002f3aDqJMIIQWkI7WL\\u002fkAdBN4VEsVrQBUEYcrO7VbUDQQV4giJxdQFBegrPegl0\\u002fkDfHDMi8jj6QDI79UF5VfZAL\\u002f+u6ADe8kB2c3GxEtPvQPjbdNbgH+tAvvOg1DK950Af9aEsDZjlQLKsPRrbZuRAJmrB2B2540BCFfCmOBrjQIjgHJfANuJAj1ydDmrx4EDAkRGuGLveQHB71TOnSdtApYLsF9TT10D5gUNFbnPUQBUfbmcyIdFAm05ER2uqy0C1n7MaTUDFQJFwmPOLsL5An8743g2ltEAXTgIjnLepQIR0nTn9i51A27xBbM88j0DBGYuMe1p+QClJ01K0GGtAi3yLhlE5VkAHDQyx8sBAQP2L\\u002fia2OydA11E3f\\u002f2oDUArVjBNlXLxP8WBzmc78dI\\u002fDIeGPkoJsz8\\u002flRoI2meSP7eckjlKZn4\\u002fM5\\u002fRuULjW0B5Fg+9mRFhQHUsI2i0EmtAyALE1wjhdkDRaMKf6++CQK\\u002fjAGyh7I1AAyAKrXVelkARLAGHeH+fQJhOKb3N1aRAW7RpMc7jqUAm6qE3XUuuQNf8Rh5oy7BA0zGUtdLXsUC1L230V4GyQCdD+QZ+TLNASrTAItwdtUBX6F9ZrFW5QMhXn3ON8cBA75QRtV2TyEC3644cfV7SQEFrgZNAFdtArbJmGDcq40DogT91V7HpQCSGqHbZPfBAZL0XNeJj80Cu1j0lEfv1QCcS2lGq4PdAjqYEsiop+UClGkDZgxP6QNx\\u002ful5R6\\u002fpA\\u002fRzzXm\\u002fn+0BEngO6IhP9QIioSmOPS\\u002f5AR1moGoRT\\u002f0AqzPjKC\\u002fb\\u002fQLxLwNnuEwBBBUx\\u002fWx8LAEHwANMEPA0AQbpHrwG+SwBBvkzUaP3oAEHAu\\u002fw\\u002fwOkBQQVc0tvEMANBFYH55ZGEBEHDYJU+bp0FQb2OM3P3NwZBTK228SwnBkHB+mhoyl8FQSbGaIF++ANBdP00gkEfAkEQ7jd1PgkAQV8OYs3myftAsOP8Ctin90ALn9FT1NjzQPmPZGuoffBAqecr\\u002fSRm60BD1iDMOyDnQD13FG21OeRAvHyxDXCR4kBWDutFp9DhQG1Ks+fpfOFArvIe3Vwb4UBXfkoG8FXgQMSKXd1qId5ArExDi9zK2kAF7kdCqhPXQBHGbb\\u002fMYdNAtlX4r2ngz0CeK+n7lpzJQP9AW+K278NA1j2IN620vUCi81kdm+u0QH3m3RLakKtAzSE\\u002ffRncoEDfKL7k2QuTQI2lmmk4zoNAow0\\u002fG63rckBzJQWfqpZgQJCnOA05r0pAqvmXTIOwM0AWLUrwOKoaQKg5AdlFlABAtK58evnx4j+J147O++nDPz2ECwCsTaM\\u002fpd1MhsDggT\\u002fhhlT+MVxsP8XEwVBjmVRABS44QEvAWkDL6dsiQaNnQHloOfpzCnZA2vY4+6rLg0DPmDxOMraQQCWHQUcCU5pA7Qk7p9lKo0CUQZ96OlCqQJeicPbMvLBAq4M7Udv0s0Br71S9ani2QNeQ68q\\u002fJrhA3WcwDj8nuUBqsjq6yu+5QNe3qlLPTrtAQKVQ0i5\\u002fvkC2EWEbuJrCQHFMs5HpwMhAQfCFVIhZ0UDARw6NO4jYQH+zutLv7OBAw63sk5Ve5kDD3+irxxnsQCyVhoN2xfBAytJY+jYd80BCbtg1cfr0QLtTb\\u002f5Wa\\u002fZACvEo6k2a90BpE\\u002fXGRLb4QLuNriX+2flAueetYlL9+kAL6SNuV\\u002ff7QGfQSDnWkfxAX+vgWNim\\u002fECxZzx+DDv8QMviwuZzh\\u002ftAN\\u002fM2MDrs+kArSuEm7NH6QAo3ucn+hftAGgP0nXcf\\u002fUD8YFLXH3P\\u002fQL0HyYC7DAFBv1fkO8FCAkHEQdVpExQDQT4xl7czSwNBPTkVon\\u002fSAkFhsoNBL7cBQSgTyyblIABB+zqIGmGF\\u002fEBRAdpUv5n4QG6cKeEoyvRA+na+c\\u002f5K8UDJB21TkoDsQB+3G2GMh+dALoCPaGDK40A3k\\u002fded0rhQMNcWSAPwN9AZCMRfPNx3kCjuBTFGNDdQLxrsLEUA91AW95R67F020CgDk29ufXYQA3dTQ34uNVAqBvIIl8o0kBEPoEwJVzNQA2PIjEjHcdA8DhIoWbDwUCHjncp2Iu6QNzpAjthGrNAm9uh41cpqkDFl06nINqgQBLZgM5zPJRACmruk7F\\u002fhkALtQ66lA53QK41KpLRt2VAhXInFULFUkBTZWIxbb49QNqXNYc6mCVA2FsKYF+7DEA1sTZMq4TxPxtkl11TltM\\u002fsDetDxYYtD8\\u002fpBDHjPOSP2pGFemx+3A\\u002fJiPpxoUaWj8w6+CXksdRQO4bpF5YW1hAxJK3w4piZ0Dw8si+yzJ3QHzuoKtpwoVAJmpUv7\\u002fxkkBNLaxJOYKeQMkFBLi2uqZAjxzFd2Jqr0DW9m1tZDu0QNV7DMUdc7hASod5NgX5u0DnoOBP6KO+QInNbu2kSMBAxpC3kG0SwUBFjI4q3gXCQJ9w7HcrpcNAecLz0VaixkDcgvE71tDLQA\\u002f8+aK3\\u002fNFAxN8gyZ3K10A0bUt7QUTfQEflArZWAORAtne8pbul6ECMahh3xirtQIelb5ZznfBAMYsrTwhX8kDxJs7WTsfzQHyiywUPBPVAtfq015oj9kDN+kBt9yz3QJvfGoDXD\\u002fhAp85NYNeo+EBB2BAmgtH4QCgU28hIdvhAfn+s5MGo90AGtpCl6KT2QNUxOlGDxfVAzaZZGnVr9UAtDMMet+D1QE5PEhXfP\\u002fdAwPhT6ppm+UCk5mzRVvf7QBRnpDEZbP5AJ282AvobAEEYRCTdq3YAQYCl6\\u002fxxLQBBednxITWT\\u002fkCaOFO\\u002fOd77QJgCCS5JnfhANZLV0Yov9UA0EqLIj+DxQCm546Wixu1A85jEsBmr6EBxriIKEIvkQPOymUP1a+FAFp1y\\u002fJl73kCp69Kl7rLbQOWHi5I2AtpA5N5ol+7P2ECqzSkO34XXQB4QODKludVApK0qpUFJ00AFGfVMVlvQQMnM2ZnshMpAJi+HwlKoxEBq\\u002fawg6ya\\u002fQHPWfWge3bZAdZu0AEdXsEBLqrvBCZ2mQFb\\u002fMHf3+51AGmnNZ0HTkkAp16UklyaGQOqQov8uOXhACddiAtJ5aEAExBZs9sJWQCnNYqTybkNAb+rakWhrLkBeUyzVes4VQJqFfKXSn\\u002fw\\u002fYOjX\\u002fdcy4T8hiJ7UJOzCP3GrX9dmEaM\\u002fJXfypCmggT8hJ6rmLtZeP7l4b0tAJkc\\u002fk6wZPajcUEA9j8dmU8tXQKqDl0fwy2dAm\\u002fAsdhk9eEBcuhdBphyHQF2mub4bV5RAwIHB4rCBoEDlcHGdP8OoQNuLKkkpPrFAXpdLVjxvtkAZEYbN64C7QCStUPKEDcBA6XR9EkoOwkBZ5rvaP9DDQJwfAazpesVAPN+JM9FOx0CsUeX9j6rJQEOTSfO7Dc1Ax51j0jMG0UAvztvegJbUQCtWXVGGXNlA9hi6\\u002fFBK30Blf9BlWAzjQKkavVjWqeZANxSnirM76kBemodXq4ntQP2vH2NaOfBA6VfmPxx48UBJdgzNeobyQEpBLDPeafNA5YmG11wf9EAjGbBWP5j0QJZLhwMIvvRA3shPvGV99EBJNM7PJNTzQAr3QFEf3PJA8ba7TJ\\u002fN8UDXi9inu\\u002fbwQCVbSS9UqfBAAxqtIkgk8UD2MQPZuX3yQCXhl+SzlPRAansENZQQ90CDDUFhTXL5QAHYEsEWNftA6mYeAMrz+0C4siAB8YP7QGMcRFTI+\\u002flApK0AvRCj90AYGKwoLtj0QHOQ5IHV9fFAxJXTqgCD7kC+aiiZT8npQK5wloJE3eVAFU6tQme34kB17FgSxzvgQGWSn1KOkdxAYidJS+B82UBquOVvSvzWQHaXD7iZz9RAAURTceS10kBtmCIcF3zQQME8BEWpHcxA\\u002fIhtGU8Cx0BbkUaLGgLCQFwW6bC+AbtAZTv7eY2Cs0D\\u002fSTPBSVWrQO\\u002f6nO2ZoqJArfMWS\\u002fyvmEBAAcgA74mPQILmGduTN4NAea27EnoWdkDGeNQ7oLZnQF9xSr72mldAUvKs88usRUAWc+51t0syQJkdUt\\u002fMVBxAOb3PlAIYBED352Bk2BbqP8+rohwn\\u002fs4\\u002fAPF\\u002f7EzXsD8BAsQ5qb6QP+FCAqY7gG4\\u002fauWNmIA3Sj9J+\\u002fmfPlkzPw5BLppP9U9A4WLi\\u002fRzMVkDKhwRaIh5nQObArThIu3dABgexDmm5hkAqHz2fIA6UQEtCi5iIUqBAggv6PRyYqEDoRWgX20CxQClHIollubZAxPdVjxhjvEBivET\\u002fhgTBQDBvJaue0sNAbDuo9yCtxkBvp2ruWbHJQMCsDGnqBM1A+nMVs8ls0EAgYe1aObjSQK1CVU7PiNVA2ifvWff62EDbMFEKuhbdQF2sSUpU5OBA+vAFi+px40C9sdphOhXmQCzwlP5OrOhAlOQDnC4X60A4JkUw\\u002fzvtQKlUFKY5Ce9Apa42XzY68ED\\u002fOdXTwLrwQI4ZIN+lAPFA2XSJQOkD8UA9OuyQurzwQL3yR3JFKfBAgwnrqaGp7kDpNoL4i7rsQNrkVx5\\u002f6OpAUNKACDiz6UC86OZk7p3pQJROMnC\\u002fCutAuoKulK0S7kDNPEAYQjTxQNBfg9rGq\\u002fNAYPlD9Rb09UCMgM\\u002f0qpH3QK+bnPi5LfhAROuQoNet90AVvavLgDX2QNPzPeXJE\\u002fRAdUDGJc2n8UDw0i4vcY7uQMXIbDj6WupAfvRvAAfq5kCwk\\u002fxCADPkQOgrXIulA+JAgNZWTtIc4EAQ29\\u002fedZvcQNZPijOQBNlAM0Ywm6J\\u002f1UBS2Xi4GizSQFOQErZyQc5A6eSSfj3CyEC7Gy538szDQPKoDAUwtr5AUp9QnEfytkDejEgEuniwQFyZ23PKwaZAqB2\\u002fSdtmnkCMImGNoLuTQMThmwn77YhAFlZbgaGHfkA21FrtJvZxQA7V+46iFGRAsrk+V6gZVUANDne5U6dEQCo+ECJFtTJAAa7Cejk4H0CRc5AFUOwHQDJu1fyWzfA\\u002fR0XyuSKc1T9K\\u002fsX28Gy5P4aXJZB5W5s\\u002fNBl2017qej\\u002fEWY8CdzxYP2sz1p9QjzQ\\u002fY9gj9XX3HT+XiEmsz3hMQOHEtY4FWlRAVoOac7atZEDqS\\u002fvmbDx1QCENOOw0VYRAiVEdKlD0kUCZH\\u002fGV+UqdQFfvMoudMKZAiaDjwft0r0DR2v3mJxO1QI+45EMcBLtAXSppoJLHwED+Aylj1mzEQOUJ6BtfjMhAeecQP4k6zUARd2TQFD\\u002fRQPPrakOKKdRAbCKL1MRX10Bs1Xd6rsHaQIGdpL9cWN5Abos\\u002fWi0C4UD\\u002frVGSJtXiQHeWiiLEmeRAzwTx8j5J5kBDiwO8zd7nQO\\u002f9Rj0sUulArnMXSxKU6kCiBJ5UnY\\u002frQLjSvLdCL+xA6hUfoqJh7EBi\\u002fe2rNxzsQLYaij\\u002fAXOtAEJW9lUQr6kCruT8ZnJ3oQI8pUt0E3OZAmpgqBHYl5UCCF8EAENHjQDZr4cEzSONA1yBWwC3040B5X9rW5h3mQGxld8SzxulAu8Y9Y2yL7kCOpBkULNPxQGRUOl0wDfRAnoo2U0p+9UDzLjRLvt71QOvjtFKVKPVAe77UiU6T80BZG9gMNnzxQJ2qSG\\u002f0j+5Ax3DK5N+R6kBD5u3JsV7nQApi1ktkCOVA\\u002f8tmO9pb40AfsKO3iPzhQBb0vdnhi+BAPUcEQ6mc3UCi85Q7JXvZQDWwgJM\\u002fAdVAsctjvlGi0ECoWfLZb33JQJWrcDKnC8NA+QO3KE3ju0DTKxXYyf+zQHpAFWLx96tAIwMp8Jn3okCP7yogqeCYQJpTv\\u002f2GlI9AojCTEhR3g0A3kEn\\u002fdl53QNhBDR99TGtAvX5udXvXXkDK48zLo65QQFkX2yzwFUFA\\u002fH7JUKBlMECFIYh5xz8dQJ5c1UzaGQhAbbrRp69F8j8WpOGSoGzZPxs\\u002fwmESNMA\\u002f8gEuTVrmoj+EGMKP\\u002fCiEP6ClsCW8qGM\\u002frOyOBs6JQT+xiJGHf3IdP85J060lQAU\\u002fjnULuUI0R0DWGbPAJpBQQEWq\\u002fQw2ymBA1+mc3n01cUCISdxfE3aAQNqEIY3lGI1AtEK+hcvUl0AAa6K4MzaiQJGcGm6mOapA7QeNYpMCskD4vmb3buq3QMbTZSRqFb9AJjfLDF3uw0C6RxcwUkfJQO1HYHNgos9ATyicGchy00AWg60Zn2jXQCqiqFnmhNtAse8qWg2R30BdcfCVi6nhQO1DAHpSS+NAfRafVo2d5EAqXWGBI6DlQOVTABkHY+ZAknmosPD85kCm5x5wb33nQDqjYTEN4+dAY4nGsXMb6EAkJNWSUAzoQOmBXXvTnudAEf7TM0nI5kAH23utBY7lQNltu4CgBeRAFIELoHtT4kCLh6FMsqngQCr\\u002fTzvTkd5Aa++J16MD3UAq1jozsWPdQHUEXdkvM+BAvax33Hw840CsXzhu97fnQG99ax17Ju1ATQh83SFT8UBl9A53xZPzQFMSmVrW3\\u002fRA4yszDTv79EAb6+a8pfTzQMboq7g6G\\u002fJAKeI4m\\u002fK\\u002f70BsHRDAhmTrQARoqPyVyOdAR\\u002fTXiN445UD5pRSkDqbjQI4ToBtCtuJAQz8nUa3p4UAjIa7UgcrgQBLabLsBL95ArFMS8k6t2UD\\u002f2VofBJDUQL1mVNG8CM9AC8+5SGU1xkAJHdZk03O+QCCBexSNPLRAUWZH0jJRqkB6qJrT9tCgQB389EqoF5VAFszFP77ciUANegXGS+d+QC9DfS3e\\u002fHFAWHHFDE1uZED0zDYum6ZWQOLv6UQobUhAtlD6abhqOUDU+Zf\\u002f5z0pQDnrxM4DqBdANyY7\\u002fdy3BEAJRvzmUNTwP7wQUN8SOdk\\u002fol31Z8ZfwT8r5vYk\\u002fvKlP9vVZ22zY4k\\u002fh2ieCIzdaj+KZ0evSf1JP8IvqOuH\\u002fyY\\u002fmIzeqzMjAz8EYkW5rGXrPsUuMpFwHkFAJ3rxVf1gSEBjKWR6g6BYQANAhn5SMGlAvpIdNFsXeED4NgbuglqFQJ+\\u002f+HINn5FAoWwrOrNSm0BEknw7ZyWkQKUsW3yGqKxAQ5FNO0zus0CVFMmgiFu7QG0Rwc97ksJAFo+PBUjYyEDFw\\u002fCjWULQQCUgZhz9rtRAQCwnU4112UCgVs7FykHeQOKAudLjWeFAaFKQ8z0340DowNwI85TkQDcwy9CiY+VA2vEGLUuv5UBG+Bn3cpzlQBbJWjC7WeVAeqw4Ze8L5UCHECQqiL\\u002fkQCDtcIRCZ+RAkQRCpuDm40BBW9JWviLjQLGLuAKoDOJAPuxrAD2q4EBrV8\\u002f9FyreQEpyiJPx7dpA2rn7xT0N2EBXgp9lZg3WQM\\u002fhu7Y2j9VATiUBHExJ10Cw4x2AnebbQAxfMtwX4OFAaHptPS5C50Aj5+vSjX3tQKQbtOMnx\\u002fFA6h5nn7cj9ECMFMeA8Fj1QHHsY57NMPVADaVaH8HL80AvgUiGGJHxQF9kWjRrEe5ApdV0e5hh6UCb2vQkC77lQF+AUOeHZeNA5DzDCs8t4kB8LfusPp\\u002fhQOlhrkcyIuFAYnh9zfIz4EDDVfeyISTdQEvZIfS4lNhAM8TXQmRS00C3un+B0EXMQGfbtkv3UMNAO2D9GfrUuEDqm+0r3l6uQB04djW36KFA9FBn2kWhlECk744ueGmHQE8JNJAnPnpAgTxQPLr\\u002fbEAcTylUc39fQCfwI46IyVBAGjByGQ2MQUDILp3uRfExQDlFGpFh1iFAqWFS5hoSEUCUqtgAkRb\\u002fP\\u002fRJF0+Aouo\\u002fUO71sf5F1T9ZFlx7cHa\\u002fPwiRoBvwb6U\\u002fcwqSKGnUij9uGs7oTMZuP2VfvG50J1A\\u002fnu3BF7oELz+VQhThMj4LP3wDzb18feY+EYdhbVf3zz6oUPz7hMg2QHucYYSnLkBACizMMc5LUEDC+25D86ZgQIYQAx7V629AjQOpRzN8fEDnmoMiS9SHQAJ40RQJ6ZJAyBigTCLknEBIhF\\u002fl9Y6lQCoXSvKLwq9AUVfWfekot0CnFctLaaPAQB2NvVbJVsdA3aQnvKqrz0C8eg9zkqbUQJzICJZPytlAcf0PdjPQ3kBQQSoSNZ7hQK0fg7MmT+NAdKixoSFU5EAmzM6Y36LkQNeFWQklU+RAvwbpXEOZ40AYWG2jf7TiQMYBxkYr2OFAPbbkPNga4UDViP\\u002fXu3PgQALBhe11i99Apg4SBYvh3UAqusgfbMLbQKEBXKSzMNlAJbdoLwdb1kC66CiqmJTTQNY3ddTiTNFAGpfov\\u002fAN0EAVYS+Vd3\\u002fQQJIi2LD8X9NAx0\\u002f6awVf2UBod8l8jmfhQPUAjx5UoudAfEpJoRWr7kCUiZ0CMa7yQN7OFznRM\\u002fVAZ6klATNg9kDIy1Au0P31QBS9HW8MOvRAItESMc2R8UA5BRd5nUftQMhEXjK5\\u002fedAN8SyPor940AhhcUmi37hQMCbEUBnROBAHOLC\\u002fKCK30CZi\\u002fWvHL3eQF9ROsRIFd1AlYGxZU4S2kAU8aGg0dPVQFf2pFbG8NBAMd40ryVPyEB63zaUtSLAQOHTzbr95LNALI6MXx\\u002fzpkBJgQO+LguZQHfCLptPOIpAUYtgTzy9ekABNCBQleVqQPL+vxP73lpAWEB8zK+wSkCXu8EUtEw6QLpD6Ll9oilAieFYUBmiGEAtFBI13zQHQPqiscgTPvU\\u002fBXawTqis4j9\\u002fUmvjCSbPPx1lHMI7YLg\\u002foZUg\\u002fMe+oT8KKWA0HOKHP12Hu63ulW0\\u002fP217s1PRUD+Z2fDmsoMxP7S7FmBrsBA\\u002fZ18uvmca7T7eknQij9jHPo6b6Ilq1bA+1\\u002fJDaONUK0CJY8XjkmAzQNEJ+IA6fENAD0dV8MDyU0B0FUUU0D9jQLdRzC1Ra3FAQRamIEHcfUAi49yfV5mIQL3ntGt+x5NAkZof8DBnn0CMjh1RN7GoQBE8wo4zILNAjKGjjGzgvECdOef\\u002fRQPFQLoqzD6DO81AG3oXqDBY00C5V+sjiVDYQKcoRU2gCN1AxHUqtc194EAYUgZdEdzhQJoqY3XDfOJADA9Rg6hd4kBqy+L9w5\\u002fhQKeNHgu+f+BAONz0x+eG3kDp6h6lmkbcQL9Ydbx0c9pA4RqW8nAA2UCICmTPS7PXQDWam729RtZAiH104omK1EAS3Tdg\\u002fnbSQPhrgHV5MdBAL5XJMFgNzED2RuxuP8fIQGWzv86\\u002fqcdAV39HVx8TykC80CPVzcrQQDwRlFrB0ddAekBNGpU54UCciayqZBvoQBFFUpE1ye9A5qILNvKB80AAFSbMvC\\u002f2QPQRx1YuXPdAlG0bXWrO9kDoID9qPrv0QGPtVby7r\\u002fFA6kUZuzq77EATzyCJP8XmQKgwJ0WKQuJABPfXqtXQ3kAvIEMzQeLbQBcELu2tl9pAZZ8BRzGs2UBb28bTuB\\u002fYQL6KjrrFedVAf\\u002fW\\u002fcGjU0UDuR8qnCWLLQL8f94wNYMNA2Rq1Z709uUC7lqwTkVCuQKFk8Muw2KBAVPOjqNlxkUDeP6WYKAGBQNCDmWaApW9AAvMeL5eUXECEnwgeWXRJQLotZ0mfnTZAH4WyUKgkJEC7Fsyc3\\u002fgRQEIpd04s\\u002f\\u002f8\\u002fI\\u002fEtcwk97D\\u002fruGrUNnzYP6wwI6MkncQ\\u002fDvnTTs2gsD8h9UnU9GCZP7CHAMS8III\\u002fezRNRVgLaD8Go8HBnnBNP+QZg1ujkjA\\u002fEuyp\\u002fAcdET+dDh6HdC7wPkWw1KhZA8w+DWhg3czIpj5ALE+0IvaPPs7XJ5pemh1AyxPR0y39JEAgiNTzkyo1QLKot+Cu2kVAatEUmohyVUAPndWgJfljQLJOnk3D4XFAR8mRw+RAf0BPg4MQHPWKQGsdQ+gFApdADPCUg7JNo0D\\u002ffiBmpnevQNmRHa40nrhAwNB5Xx1RwkDJtqJHJcjJQIkJqxfwH9FAfAIahDl51UB4tX5hyHTZQBonsoW\\u002fl9xAZp6zek+B3kBqJT88ogDfQPaLvhPXIt5AoxCmqcwz3EB5ATPQ0qzZQJVFRy6dEtdAT+W\\u002f487N1EAVNl944Q7TQIKV+wc2ydFAJLpRvh7F0ECgPu9L2nzPQDuRAedqBM1ArCO0olwEykBbTVR5grXGQHGCZ71en8NATP1SLgiPwUAPLqSoz5jBQJlhi5cGH8VAQ4UcHZ6+zUCM\\u002fS3niHvWQNnPF4fr3uBAedkXtKEW6ECTylzNNg3wQMW4aRfAz\\u002fNAQ38JY7CU9kBkGZh5JcL3QOVNJXD+GvdAGg9OMEzW9ECnVSXas4nxQDZ3HlK75OtASrXXZgNv5UC18NTnEIDgQKWGnJhPmdpADnT2E7kX10AJcBsrvVzVQEKTj6qKNNRAuXnbT5S10kBZ6q\\u002fhvnPQQBv9CB0sAMtATZND6kR8xEDJRxAPHpy8QPoRGh5pWrJA5KeJE1KhpUCJuD9O+HOXQJBM\\u002flC3eYdAox+b6lrSdUDhHIj4kQNjQLJwUsVyek9A0KDIRdsuOUCFn+zn\\u002fNYjQDeAQWk\\u002fSQ9AdqWal7vs+D\\u002frwpfAQxjkPwvZ1kRQUNA\\u002fFaEXvThruj\\u002f7\\u002f8l9DxClP+qLaTaxSpA\\u002f38EXSLYXeD9cIc6OJ89gP1JcaP2E50U\\u002f8keN06R0Kj+SCCUBuXUNPxUItTXjI+4+OjCIKF9EzD4Nj7rQ9kioPmHDNVk2m4M+3i4MxNtTaz6Nshc8BScNQK9NETL3yBRAB\\u002fBy4zVGJUDH9HjgnYk2QAjUC9i4CUdAv9zUTFjBVkAbL2ms7fplQKBOnO\\u002fF7XRA7JDjQlqgg0BzKWCNbPeRQHLJjcxVtp9A9WPQeFmrqkBfRxZU\\u002fzC1QPZaqty8qb9APV5jsmIyxkAIowVENTbNQJ153YrtEtJARpGZZWMV1UDvJITdBEDXQEMGBEpTTthAQoMrZego2EBVy5MGPuzWQM0lX3215NRA89vHCMB90kABguW6FSbQQONYnCf6YsxAaEld\\u002fzSIyUDQQ95kN5\\u002fHQG+o5qYgRMZAwMybl5j9xEDiEtSTam7DQNoohfhjd8FA0Tk\\u002fYCCIvkBqht\\u002f96pC6QBNaPSMBe7hAYmHUM\\u002fxMukBLtYuG41jBQOEevvqcaMpAsMS\\u002fz4ro1ED10tlsexLgQLgaUWcxPOdAM3zenfss70Da9GiX70\\u002fzQC1\\u002fzryLDfZAz\\u002feowaA090B5mkfo9IX2QJtwC9v3NvRAReV8wVTd8EDq2XKC7m7qQMszkuz92uNACSgDNcmW3UB7POdMouPWQLW4TaP1CdNAFA1KCmP40EBnUrd1nCzPQE5c6toIMcxAD3gg661JyECVr0Qz4o7DQCXsqmUFJr1AmzGKpJr\\u002fs0Cj\\u002fgugPTSpQBHfNR8HJp1A\\u002fEC35+DwjkCGr\\u002f122i9+QGaaYe2zJWtAsw5vXDOeVkB90xDTI5pBQDbAmkox6SlADuY1LVJZEkCCEDV0w4f5P+Fmu6w90OE\\u002fPOH40jVOyT+T8n131WKyPyKxa8VbLZs\\u002fKOrxnisqhD8q7FDAKIhtP2Fbalrw9lQ\\u002fBAfrEUNkPD8Qm0RZlxgiP7Dc69x3fwU\\u002fPbOClUai5z65LmCT0+3HPsyci5ZjPqY+\\u002fvJP0az1gj6XQk4wm2FePugm48ArCUU+vG3L+h3E+j+7jyJNv4MDQLh6l8T+9xRAyth\\u002fwCvGJ0AGg8kuOY86QMc90EhVIE1AlSQQFbZUX0Cs1Z6VuWhwQFCF767rioBApoF6AaG4j0AE433HkaacQKR3h3bsOahAe5zSNbgds0ChZlW7jiC8QHSrrR2ZT8NAljtjkrDMyEBiepYAl+DNQLwoUIYt8dBAD8PJv3om0kC6Ue\\u002fhRG3SQHvUBd1kyNFAG9Ll4MVf0EAWh\\u002fA7UPLMQPiR2XoB1MhAdPJWt7cHxUCRiCT36wDCQJo\\u002fUgq0079AseNySsREvUBgW3cFDq67QFYKfRvcS7pA7nOHACuIuEBVmT1IRC+2QKQk8H+JhrNARV+Xn+VOsUCc59kWrMewQHsZ\\u002fysEwLNAb7SxUranvEAk+EeBwzTHQMi2Mx038tJAoahvWRiJ3UCc3pjC5nzlQEBB55KE7exAXq+6KUH08UDOGiNs94X0QDIMtKuQmfVAWAxgjxby9ECqPTsaF8HyQGwGjXt4KO9A3JIROt9A6ECWg2XwogniQFhS1ITAgdpA7Y1hjSsf1EACediZ0VfQQLieM5TQV8xA6jM8BXtGyUCWIqlmBy3GQPXMZupTicJAJ81\\u002fa736vECsi2Jey\\u002fq0QOgGiQas\\u002fqtAXs1cDbgroUBBcb3vP1eTQG27u0DL\\u002foNAL2plgpf6ckCXtj8dlY5gQL6YqPm\\u002fnEpAZIEFZ6zIM0DfpA+8kmYbQEaJoMO53wFAdhyDqX9V5j81sIDvIFTLPzGIJlG2ybA\\u002fFXLkolMmlT\\u002fArLvLoJN7P0lQGmVOgGI\\u002f7iwO090cST9COXMRLN4wPxfUnQmx9BU\\u002fOrzkJmI0+z6Hc4asgajfPrdfLbh0IsE+I8uFnNgjoT6XkkKlJY1\\u002fPsj14vINqlo+FgD7nHUxNT4gb3YQ6SYdPlKD4tVF1eg\\u002frPlyIQ5U8z+JqedxW2YHQMf6upmXPx5AdraavOlLM0BZQQ6hdtlHQM0g84bpLVxAateMvEJ0b0Bem\\u002fRfPm6AQJhCS0Cg7o9AeHkFr8nBnECURwsg\\u002ffanQFgz0dh6e7JAqNo\\u002fQSprukCrVzSCHYrBQC7pSQiitMVAMxSpnokiyUC20m4LAFvLQErH42LzGsxAlO7Y3bRey0CmD4g8XlvJQD4ExsjPb8ZArwB\\u002f1NcSw0APkpfca36\\u002fQK7etTLKurlAIY+x4fddtUBJaKuIlIWyQJ0KUXuJ7rBAxv2bSDwWsEAMUETlDdmuQBJRmRTLC61ANz5T6h2AqkAciddfkqinQApwMREmvaVAxMIbhsHPpkB3XRAHL\\u002fatQNyX0gjqtLdABsUwqEwQxEANMwjf46nQQKQVrH7fHtpAft5vzzoH40BmpWYxBZ7pQFXdCWSDyu9AYmKQEkcq8kB1oxdclxzzQET1x\\u002fNRhvJAx8867UuU8ED6XSxr8ovrQIqLOU0\\u002fe+VABlh50gwR4EAQQ66eDuHXQO1il+CiZ9JAY1MisaY7zkCi5RDxzyzKQJ0Nthm298ZAeUeTSA2dw0AK5mKHhbW\\u002fQO2BPT\\u002fS47dARclMMAClsEBYfD4ku12lQGAQn+BiOJlA5qCBN7BZi0AUIGYDcz17QHSM0cJ96mhAjrezJgnwVEB9C833vi1AQP01Pp4XCCdAWbJkefpMDkDUa8Wdo4byP6FXbuKCQtU\\u002fXLvcq91Atz9nVTDrH82YP87okinMjXo\\u002fHv1nuzpcXT8dzrPGnwdBP0lQPZ3IoCQ\\u002fgTR0qpeHCT+OGu1ND2bvPkaNZhPFtdI+Kym2TtwwtT4ag47jJX+WPiP91mC8LXY+GSXqSVcuVD70Urcw3uQwPrKjBCIYoAo+cGHyLP0u8j2cTz7kLM3bP\\u002fxkKRCPZOg\\u002fpBATigh6AUBRlTEPJN8ZQKp6f7JKSTJAY+EznThASEBH\\u002f4lxw+ZdQAunPgTGCnFAmj6X79HlgUCLJUWocUmRQLz8Ds+osZ5AVY72kPINqUCKG7jeANOyQCp+2oeCFbpA7JVkuEu1wEAWXLNeldvDQFPU\\u002f6EA\\u002fcVAiXQT3NTLxkBQPQ0oOj\\u002fGQPedEFXyi8RAB+\\u002f1aqYMwkBEn2liT1C+QGEWJaS\\u002ffbhAfn22CNg\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5WGFsUBcHUtgbJivQMP0Gpx2JapAuOmsYsLno0CtVwppzPibQBrcIq3iN5JAkJeKEBAihkACVB7FBlB5QFmycb1WtWtArWjk\\u002fA8IXkA7dykU7DdRQH0XxdAW6EZAMSJfGpGaQkD8P5BA7+9BQKHXqtzAKERAf4coO4TBS0Cj6aCJGOdXQHVVC0qgkWdA214oQEbad0CMUuddq02HQLdyuhZgdZVAOo9GxSx7okCJca1x8rStQK6YnExoQrZAUHvnbO0Rv0BpN0hc8yvEQNCa4ynKVchAN+T2onhCy0DiDojS5GLMQFgiNHZlnctAg9TdY1BmyUBaENK8NJvGQO5dzs0mK8RAjlra+0u0wkBepaRtG0fCQPqEvaK5bMJAAQUliqRqwkC5hK3G0J7BQJ1YRokgi79ARt8TQ1sVukCOclgghsyzQBAzDwd1gatAnfcn+Fl1oUD2YpwSpTyUQKZ1tyeeaYVAWQIzEKStdEDQPCw\\u002fcTliQB5DFpDfUE1AjOK+HqSENUD3B1SrDNQcQKNRkQ6inwFAhD9wAUSq4z9F5glyzQbEP26w9ZspnaI\\u002f7gRBy+iUfz+HhlBHW3RYP8HkSZPvSDE\\u002fNJmspXBOBj\\u002fwcd4slEjaPjC2RKvZSKw+ZL90J5TQez6r7\\u002fDVBgdJPhWTbRBmqxQ+HNiH3uyW3z0RFiqR886mPUPS6nFNXnA9euuDsOKtOT0e5ZJTgDIIPXPWPVQ6nds8hifLkbEqwDz8erdxOe\\u002fEP\\u002ft0kJmBRNU\\u002fIvofQ5iJ8T9xxr48a9QLQNHj5X8KOCRAEWO3UMHQOkCCmndfOjpQQMBDKfFb7GFAeWrIv\\u002foQckDHGBcAsJ6AQEXX1x\\u002fM6ItAFv\\u002f4o+5jlUBScHUx3+6dQKtoZLe1H6NAVNC7u6JRpkBuw9x08cynQGDc1TNQNKdAAIqMkkGzpECOPJhPl+qgQLLPgIl2XplAM1aL13d+kUBAF4tbPkKGQOROBHQOP3pA2aqpQGvhbEAi7KKUgQZeQEYRGWw7RU5ASg39z7pUP0AtO2LXIHEyQHgBRMUMcStAm66CFhCuKkBvJ96VLP0wQIPAH+v2RzxASM8cVWpCTEAdziIEjexdQGzR8szjAW9At0cq5ERpfkCZGMY\\u002fcuyLQAr5\\u002fQ2U75dAG4Mc1dEko0CCmcXDzo+sQGxxNlRW27NAIteUdTGzuUA+Quz4guy+QKtBL0bkScFAah2KxhP+wUAcTGgtJ4jBQJcBvFJtN8BAPjeHS1M3vUAcu+NPL5a6QMf+S2cuRblAuxUUltI\\u002fuUB4j+nUGdK5QIlacd4x+LlAArJe\\u002fgPcuEBq\\u002fIOT6zC2QAspdYgfRbJAucrhQsSXq0DpizBuXg+jQNUve79UDphAhVlNxm+4i0C7XhgD\\u002fid9QOj6mUmx\\u002fGtA9P6+ASWEWECxY9UdBZlDQL\\u002fanZ6ClyxAowzitGAIE0ALupZufB\\u002f3P1vRM+23otk\\u002fAEKS36DvuT+umzCKHPKXP2WQHGjpLHQ\\u002f9dyCgJ8GTz\\u002fGauBfZcUlP3Pw7AFr4vs+Zf68czZM0D7IKKB\\u002fbWOhPv14+a1I73A+dIFRNtwdPj5OohntbnYIPucKXYBnLNI9n0+Jhk\\u002fHmD007rI0J0lfPXRSYvf5vyI9Y3XWgKes5jw5bQe7uTWvPBiRB3Egq4k8Sqzde0O2uz\\u002fkDZuK3ALMP9QwUG\\u002fH8+Y\\u002fYOE+X8YaAkCvrnLBbicaQBipjwQUPjFAF+3ZyiK\\u002fREDVhd7ppsdWQDutHW9Y02ZArszyclDfdEAilZvlNmuBQBarkzgCiYpAQvcjY9NykkDUSdOSE2uXQHZsvIyGJJtATQPt74W7nEDeEmGjNMqbQGWy4zxSkphANIie0pXgk0CWIELpy3SNQIl3mprPBYRAOpDP1TAHeUDeQbkRrdtsQDy7Fjaw2F5AeTT1X+vVTkBjgoL4bF49QL0ZaV+52ytATIn5YmESHUBHo29kNqITQKNEfezHtBNAb9NHYjWcHUBXuAXQzkotQPV+xZxHIUBACol2UyC8UUCiG+7uapJiQMmaOTnTOnJAmnWg3t6zgEBub1q8RI6MQK509b19x5ZAMbZOmED0oEC+DuhU\\u002fYanQGhkiDriZa5A02M1WNZCskDUpPkjR2W0QGVT6BEeOLVArGOHTG60tEAgXQZCsj2zQCPFl\\u002fIAgbFA2v28vuEtsEA94KiBYVOvQBTmi07TyK9AbZ8QYF5lsECLAWET5YiwQBffZJm9nK9AsH80NPAdrEDx4diF6wmnQMg\\u002f6L8fTqFAbn7kdjLGl0AepdJMq9WNQLa8zKcoF4FAaB9tegXfcUBJ+tpx7A1hQGq3LoobtE1Ath4E68yaN0BZrqcf\\u002fR0hQI+rM3lMpwZAcrx05ZZb6z\\u002fQDWkqDCbOP\\u002fE8HN9XUa4\\u002fNegLyiPSiz9r+aKI5UtnP0xQ5OMyzUE\\u002f9lpUNHXTGD9jBCIMK5jvPvwq\\u002f7GWWMI+dSEeTn1xkz6+kI7ZFc5iPnhlolg8mTA+O\\u002fxLKn2++j0W74t5GqvDPfhlkbL7a4o9REBi9OQ6UD2943Idf00SPdktUmuEJtM8eblwiEs+kzwLwb+t+axjPB\\u002fvPW2hf7A\\u002fj08hWJ+XwD8oZb1TyQTbPyd9Ukf+L\\u002fU\\u002f0gcuKTRuDkBHCxXsFPIjQKZTMXEa3DdAnt8jmqcLSkAYSJ5S7PFZQAkgRtDIlWdAaMXPjNeQc0D\\u002fq4FRaKB9QOq\\u002fqTTMeIRA42GyMfDSiUBeXTFCzLyNQFXZqwNBRI9ATm0\\u002fmCAGjkCwEXyIP1eKQI8TN6AOIIVAL+nsjsr\\u002ffkAqkjcAjNR0QFQCayzQrWlAXA59j7saXUC8quftE29OQO\\u002fXJ\\u002f8eij1A7IfcNzf0KkBoJw8C0+AXQFiP5I6rcAZAUF3za7G2+z9X5GtPkaH9P7TQ8oT0eQpAX7bb8I2uHUBfWym9RUIxQOaVj2qDUkNA6WKHh69SVECA8\\u002ftB1\\u002fJjQNtPK44VQnJAohf5sZwuf0AYJM8Xk9qIQOjkJEt5fJJAon8Ai1OjmUAZJjOHJI2gQNdI5zvY3qNA+k0yFkstpkAcsX0u7xCnQLAvZqarh6ZAYSIHtJ0DpUC8I9vmZkSjQHoskIrkBqJA1FfdA+KwoUDKVr2QCSSiQKP4eAQi0KJAnCnvySH7okAQenxQJRmiQAiA13aYB6BAgh12iS8jmkDP9TcNyIaTQG6twPH3rIpAWayFPICkgEAZKmKYKPVyQLDLuG6dtWNA433z5TGzUkC5vPqcyjBAQPyOeXVZlSlAWvlZHr9xEkA9yYgdnET4P7dAz2EEI90\\u002f9FW7dPDrvz\\u002f9cmFnremfPxnq4kDVHH0\\u002fdbkCiAw8WD8hjCrgq2gyP6SkwK47hQk\\u002fuRXCH18k4D7Cjxr+QqKyPmQsUqrpoIM+neR+NCfeUj5c0uwT2owgPq\\u002fmawLbfuo9Uk15s8Jasz1yW0PgaM55Pe3ZLRCsaD89fBCOBtt0AT03iNOubb\\u002fBPDUAQHlxn4A8tIkD\\u002f+nPTDxNCtbo7qqhP39av1ecrbE\\u002fzt9RBqSazD\\u002f\\u002fWz5psEzmP96Z1D3B1\\u002f8\\u002fAb9jgjbAFEBVte0Z8q0oQD7DhErDyDpABwloH52GSkCT+WPT7\\u002fhXQE6ii\\u002fA5xWNAlTBnPWfCbUBVyS23zXB0QAaOZoC\\u002foHlAFyEjqxJUfUCaAry5k6N+QMccoQqAOX1AR4hP3UB1eUCQOOsA2UJ0QJ\\u002ftFZfeem1Amodgl5GeY0BG5NMEUepXQBW8rFxjvUpAY4sqd6B9O0C9Rfp7NRgqQCkmLtanEBdADLHfHyhsA0CeLaD6VtPwP+3FCe6Mc+M\\u002fhC0smhS\\u002f5j+UJFY2zoH3P0IEwpR6bwxAHyxRd7X+IEBvuTYT0C4zQLlnUNKFNkRAJy3rkvfXU0CWOVQwjyliQNW\\u002fFxbGBm9A4tLFCbS9eEDVbJxFL2mCQI5WGZlXiolAVLtL+cB8kEB7K8ZRE8mTQMNDnECLEZZAqPPwV7nxlkDB8R6SlGyWQIPZS5lH+ZRALQb07pBZk0Ds+NFtKEeSQBQIKztMH5JAk0GYSEC4kkCU8c9o2neTQLoxbQyWn5NAfz7wJDCmkkA1kbrNR3GQQMMTtkL5q4pAlXyHqQLRg0Cf1PW7Yet6QDzmHNjzsnBAD2IMM7npYkCGQkwmzoxTQI2L3YYmcUJA3RS2TSHAL0BFpyg8xvAYQOZOtNyL4AFAUFEuJs9i5z\\u002fkNTQxZurLP0\\u002fDMgovaK4\\u002fGyV21QA5jj8NQ9UGPmlrP6YNm5GRr0Y\\u002fLBbyG+EhIT+xsgFf05z3PhrJGoNRss0+WCXk1EgKoT5BMERlO9hxPvQp3TpaDUE+APGH9Qu9DT6V\\u002ffFfsanXPUBi5YRvLqE9PMJESKLEZj31PtReeYgrPdQZPSvWYu48jjJXY2Ccrjzun3scdzxsPNwV+8GctTc8yo7vx94DkT+4HjuAbPCgP9oGou7qO7s\\u002fv1BbxYAb1T8U3MV1c\\u002fftP\\u002fKDpVllagNAudobuE71FkDjjMnuscUoQNq8H80pZDhAeBb8HlfqRUD0\\u002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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A8QVywB8hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQGfXrTe3IQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A8QVywB8hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQEHhGT2xIQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BxopkEZSEPQAhC3HqHIQ9AQgwwIh0hD0B12aP1byEPQEGzRqw0IQ9A1GYA8EEhD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BxopkEZSEPQBRhacuHIQ9ACo0FgVkhD0CGsSshXSEPQEGzRqw0IQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0AasKbUZSEPQBRhacuHIQ9ATL3N1S4hD0CGsSshXSEPQEGzRqw0IQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"BgAD0MqRvr+m5x+msCu+v8pkuZhLQ76\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fv0unV87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fQw9Bb7uIvb8wAVFTefm9v52rhV\\u002fHCb6\\u002fhYY8lDW0vb8ChPTWRHO9v2zB8r3efr2\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fkDHQk7qIvb8wAVFTefm9v9qxEqjHCb6\\u002fhYY8lDW0vb8ChPTWRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002fkDHQk7qIvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002f8NfH+Nrm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr\\u002fRhCsMs0W+v2LsgnYpDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v\\u002fIGgj0mDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v7sBnnCWsLy\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb+HFjgXXOu9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v\\u002fCT6KGYsLy\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v7Sa1mbW4by\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr96flRLR3O9vw7qaQZQR76\\u002fXtCiUADbvr\\u002fNxLe0FBu9v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr9sP678RHO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8vwfAQNZPDb6\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8v+NJ43ynXr2\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fXtCiUADbvr8D+Q6PjbK8v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fj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qI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51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6TxAdAbC1gRa4KB0Cjmijw58UEQA5Sikv8YwFA0hK+qk0D+z+YH580hHPzP5iXzWsw9uk\\u002fs4W1CqoN4D9N7MxQy2TSPwsm3f++h8M\\u002fde0jg6U+sz9j5\\u002fjbF7qhP3aW1m2Ll48\\u002fb7HYzRI2fz9JkFoYjAZ3P9sdKltHSXs\\u002fKkBGVV9fgz+XMdesTEeLP6qY0saCDpI\\u002f+ekro5k2lj+VyyDEgFSZPyUE\\u002fgFNwpo\\u002f9eOLLlovmj9KAf3c4ruXPw59d\\u002fIc7ZM\\u002fz39XZi3+jj84VeKl61OGP2Zs1XrjzX0\\u002f3MMrNHBucj+my5RVeCBlPz\\u002fUUpsIc1Y\\u002f\\u002f\\u002fd+Go0fRj\\u002fMImIXTzs0P+QvdC8qLyE\\u002fALseeK0lCz\\u002fdGgc8ufrzPuq4dQvhe9s+fIWc2o3CwT5PR\\u002fBk3cilPqHl1edn5ok+9KuX2Mwjbz4glEX56ldUPvDDYWP0gj8+vRQAbdatLz6g7gutIiooPgQrkhilMrY\\u002fCEAyWIGgtz9wWw4JiPG7P\\u002feoOo1ElcE\\u002fFNoBdemTxj8uWv+smMTMP2JB+z\\u002fp9tE\\u002fWdwBv9cF1j+npU\\u002f8sdbaP\\u002ftkeljwkuA\\u002fuOYZT9MY5T8zgB1x+sfrP8RWhFYZsPI\\u002fYyxlOQQh+T88QYhjd4QAQPEGxeCY3QRA5RPjbXMHCUC5Honvp0kMQDmnNrAl+A1AQeA+gsmpDUCGef7mWF4LQPVLuuJ3gQdAT1V\\u002fiCLIAkA44S54k\\u002fD7P+ULB0q2ZvM\\u002fgTDAK2Vf6T\\u002f3qWlQowDgPzKOhUxsttQ\\u002fUL66tqcczz\\u002fl2fjqBI7OP4UJUnnjCdM\\u002fKHsSm4N\\u002f2j8qFfYJ+d\\u002fiPzuls8cEoOo\\u002fSUZDUSVv8j9CZRS2jQD5P8tYhABQjQBAWwdTsBJCBUDky3u\\u002fm0EKQI\\u002fNQR4\\u002f6A5AD4jth9wyEUBAA5eaY\\u002fsRQAhHIQmRlBFAyquEGvMEEEAFNhPHQSYLQK4+QFAzXQVAkvrCX4Ux\\u002fz8YeIrASh31PyTTekRRfuo\\u002fRJVwzcbO3j8\\u002ftyyjSpvQPxQtX6Eno8A\\u002funw26pBVrz\\u002fA8bRopjSdPx5Yk5HwL5A\\u002feFQYW4uUjD+8V5Ry5++SP1fzS4elBZw\\u002f23LQ3ZsepD9NfK9ABQurPzg2VRqJ4LA\\u002flbYCccyGsz\\u002f6ApozlO60P\\u002fzSy21rybQ\\u002fSb49pk8fsz96y2mwrEuwP+m7W7Owuqk\\u002fNOabVpLRoj\\u002fIyAdy\\u002f4GZP\\u002fZmZDOVBZA\\u002fmzsQWsWogj\\u002f9w7Mapyd0P12TfMVANGQ\\u002f6A8IxCDQUj+Rddyi4UpAP07q6ZJXSyo\\u002fgSBBiVXTEz9jN\\u002f5PBQ\\u002f8PoMh7MQEyOI+khgls\\u002f0kyD5jU57zVbeuPq8mGBz8aZQ+6JkLc6+Afj5hkLNFEi5rPnWjPWvRZV4+8AUse2EuWD4RmjzrLJ7NP9QBNCoPbM8\\u002fdio+LNVw0j84MojHagTXP5yBl5fjVd0\\u002fQEbrhjKI4j840UH+odXmP2GKYLUHUes\\u002flIwB2w\\u002fr7z9hSS8vmXzyPyZGxxAvpvU\\u002fBx9QQkEK+j+Vc01uaSIAQCsKc41IWgRAz8JG5JeaCUC62\\u002fT6c3cPQDlDCuWjlRJAh1xrhSrcFED+Gf9WehQWQPYC5HrK7RVAt6gMfRheFEDAoi9+J6gRQF373wH6jQxA464FwnOPBUCPRolTOor+P7ZKtOBhiPQ\\u002f7FvuzeP76j+ewEsOEXriP28Yr+olUN0\\u002fDSM8iR5E3T8V97eIgcrhPyQNQ5gH0+c\\u002flkM8sX5S8D\\u002f2jsfOsiX2P0+tqP3Adf0\\u002fdyp9iK8uA0CrRCAkk3MIQCpj1+03bA5AjOiynL1bEkA36pY0+E4VQOOrrhagkhdAc3jwfQeoGECikRZNyToYQE2HK1T7QxZAIftWxSgSE0AcdOajMGIOQHXiXcVDegZArZFph77b\\u002fj+unPtB0aTzP5XppevlMOc\\u002fgREWShJo2T\\u002ftwT+vS+7JPwGVRXljG7k\\u002fjtUGrgLmqD\\u002fUvwMZadqfP59qP+fJqaA\\u002fEk+Xei7\\u002fpz9OAxUSD0qyP2cr9xtDsbo\\u002fxCVdYCExwj+QWzS+qgbHP1N8h5TRBcs\\u002ffOgzxMpjzT\\u002fzwGAr5p3NP2cNfWrRpss\\u002fPLM0OEnrxz8Q9fjEqyvDP4n0q6fUebw\\u002foqkseReasz8+PScnLwWpP3Xe8uFVn50\\u002f\\u002f8GXYYNGkD8VM1JwSp2AP8kZNBiGjW8\\u002foWHoEr3rWz+Y8sIhzhVHP4WaKaOb5zE\\u002ff8B1Wxo4Gj\\u002fKBShGzVMCPzO+yR\\u002fj\\u002fOg+9QOykuRI0T6H+VfyS8q5Pq5GfAk7CKY+v5sKECcblj7Wd3VU7MmKPvAM7jZY9oU+gzADcs7g4T+p5v6SkOjiP4sjPuBjCOY\\u002fqhGqchRK6z8ngs7+k0jxP9WAvZ9LufU\\u002f6P7KHYeV+j8zXBjj\\u002f2X\\u002fP+R42xti5wFAiCMQh+jjA0AjVh0aq98FQHwPPj8NRghAqN9ELPWUC0A1PZPBeBQQQNHzcec5BBNA3nYRF61gFkDOU11tYbQZQCS+JKiiYhxAKg2T6ULPHUAwTU9ZHI4dQP0wEYPTiBtAk4uzMiYKGEB7n2cyQagTQNevQIl2LQ5AHrMuBgroBUBaJd91VJr+Pyy2pD4XUfU\\u002ffzPSnQqI7z+JTt8LJ8XqP8eIUpQzNOs\\u002fVKq2KeEH8D8Mpn+venr0P6VkVEx11vo\\u002fIqoGWPF9AUB7SliHLWAGQI+d8xLT\\u002fAtA048bJNEfEUAoBtMS+IEUQHPssWnP+BdAmnKqJZUuG0Av9FidEqUdQOQGRkVp0R5AEByJH6JLHkBF7jed8\\u002fsbQESrCYonLRhAG6V3LuB4E0A3leuQIS4NQHxezx05UARApyG7NhZC+j+ZY9Hx7YPvPy\\u002f9p1VnluE\\u002fJ6rYna5h0j\\u002fJRSVJoITCPyIqLCwoD7Q\\u002fO6NAU9lGrj\\u002f62aU\\u002fKkGyP8EAvySZxbs\\u002fgvvLVEqUxT+SKx3KzuTPPx+q5EU+AtY\\u002fG1Ws8bw53D+VRtRZq8rgP8u+L\\u002fUIhuI\\u002fz3\\u002fmE4Pw4j+W\\u002f8IhJ\\u002fLhP82aJXoyhN8\\u002fiFdamOml2T+4yyfD\\u002f1jTP5Nj8yW\\u002fEMs\\u002falHt9Z6PwT9eQQjlPSe1P3yUr0eZrKc\\u002frnt6s2GmmD8zgMtlEOyHP20HqQjJsXU\\u002f3kwjI9lzYj+BOhpV8plNP7AMlYAbljY\\u002fnWb6CeGlID9ytjEvkWIIP5oMDzaqmvI+DUCOdn1b3z7MF2SND0TOPoMqz+A4ssA+8Icqir1+tT6jV9F2IQayPuAufiH+hPM\\u002fviyFybuV9D+WXQxOOdL3P8+2hMD4SP0\\u002fG1f0\\u002fidxAkDvnGHs9BcHQIiJ9UOZKgxAlqWzEnGJEEBY89ZV\\u002f58SQATfoZUxMRRAbvn8QEpMFUAyLufQezgWQJ\\u002frAl8LXBdAYpJYqTsSGUA6t0KO0YAbQFT2VWLQfR5AdsBdMq\\u002fHIEAFy0QTjQMiQAoq7MwYmSJAv0bPU05IIkA\\u002ffl\\u002fpjgAhQO\\u002fGQBmj0B1ANtQ9vhqlGEBgUfrDNUsTQH43p9t99wxAKLrxbylgBUCj5OD3tygAQF7l9pAOXPo\\u002fuMWJ6vci+D8CkOrLze74P12qJIr2Uvw\\u002fuMQV7DEVAUDISL5CMzEFQJfdHXBuYApAU+VSd74zEEAGku1vxHwTQPxcG9ED5xZAn7u\\u002fq2pTGkCdS4D8dJwdQID+KmYaQSBA6TdTctVPIUBWGPA24bwhQJkXJcpQVCFAAyUBnSwEIEAUp0iFTtUbQC9byIBzohZAW56rNwYtEUD408Pl\\u002fEcIQG8Z57jq8\\u002f8\\u002fOsiRKYGV8z986DZ33HDmP\\u002f3O17+TV9g\\u002fNE+iVxAmyj9YdgHjGwTAP\\u002fnJGDSvfrw\\u002ffqkrNzLkwj9kodnA4n3NP908WmxQGtc\\u002fDyx0Yf004T\\u002fxvREmVfznP4L1xcSXH+8\\u002fPENTiI7B8j+DHaa50fn0P0aoXlExwfU\\u002fBLsdnhTr9D\\u002fZcpT9qKXyP7ZPGLHA0u4\\u002fNowHKwug5z8teFc64czgP2\\u002ffZ\\u002fbYLtY\\u002fZRA0fvs3yz8o3+KTxhO\\u002fPyqo35ktirA\\u002fdKYzFIV0oD87c1YP4bWOP6fGrwbZAns\\u002fe0y3EYyNZj947E4CwhRSP6djE5lcZTw\\u002fCIid5baaJj+hmZka\\u002fS4TP\\u002fCrp239MAI\\u002fXN+cNcN68z64\\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\\u002f9uwyj7nL6z8A3q68oCHgP9IrQpjLPdM\\u002fmxekej1Lyz\\u002f5QbfWrFzLP3lKZUPCuNI\\u002ffagAwHkJ3T870QeAL5vmPwCwpLbQ2PA\\u002fKdjy9Sif9z+fjy\\u002fVqe\\u002f+PxuSdfBq2wJAw5rElE5cBUB8ByWj9XUGQIxIxp+k6QVAkFnJFdHUA0CdLQupmKYAQNTHMIb08vk\\u002fG0qLkdTG8j\\u002fkACGH\\u002f0HpP3HBno6nn98\\u002f28ZtNhN20j\\u002ftAr8e7ibEP+QAUqoeo7Q\\u002fXgUDEMzqoz\\u002fnK7AIIzqSP+tGuFkn6n8\\u002fqKB8hM4baz8Hec5Vh+JWPzvr8jx\\u002f9UM\\u002fd2aVUXncMj\\u002fTmbAen+UjP6WDkx7mMhc\\u002fQbE3q9I9DT8GwDP2V3wEP\\u002fTGjvK+vQE\\u002f3v\\u002fJboMtEUDA6Vd8pgcSQDJk0cZHnhRAqtT9XKX\\u002fGEBhW5SeKSIfQGB0MJN1XSNA0RwanPONJ0DXuQovfZorQDC5C4fJ5C5AV0gMs010MEBlEgLYZ7YwQJFTGc6STTBASTAMV6rvLkAl55kaSwYtQLf45ZIPXytAed5O4mQ0KkB4sAovk3IpQDGG6jLtzShAq04vQ3TpJ0CybFyFin0mQJ15QuClciRAsLbRLtPpIUAJ\\u002fuqaK2QeQNxHIGhnXRlAz3rHc4xuFUAuwoNHPPoSQOSMNugPABJAJJnBaggtEkBuFTYyrwoTQKFM0DgSNxRABHJAK3mLFUCtKYB6sx8XQEW+OOOvKRlAy6q61EvMG0Dh7cS+8PEeQFiHTaVIJCFArP4EVeivIkBwcvFIQuojQB3NO2yqtiRAR7wQqVsMJUABSLsO8ekkQH7XfZlnSSRAL1oaNkgfI0C7mdue+mUhQBms52K3WB5Afcl+F1E4GUDwze1EwuUTQM+LtdX2ww1AXp5VfvgiBUCr\\u002ftfsgKT8P56plwycu\\u002fI\\u002fyUNFqRtI6D9yDdYNJ4zgPwZbq+7Wcdo\\u002f63T5rkZ82z+R9zgUDyXiP2MvOsgk+Oo\\u002f8qY0hvZ29D8948kadi3+Pww4NWXLIgVAR1j43v7RC0AxSj81ihsRQK\\u002fr2Ir7mBNA+t2P2tXfFECePlhQmqcUQLf5IMV6+hJAhvDnCcMxEEAQ9ILqe60JQESLYH8F7gJAhblRW9z8+T8q4UHXKKTwP62IGwjk7uM\\u002fqgmsIClo1j\\u002fOlDwVfr3HP2DwNuCL1rc\\u002fzUzVvCrZpj+tjDTujh6VP7XYh9xTI4M\\u002fKBA35ux5cT+WMAUKjr5gP9ckchmQhVE\\u002fVyrgCQhERD8au9gPvVE5P0\\u002fKf\\u002fcovDA\\u002fB6+hS+EzKD+gBIkhVzklP66Y7lx1qhtAfCq8Xrn9HEA9SgP28IAgQE8yN9m+5CNAg5JtBUukKEB6pnCMEowuQORpI23LjjJAyr\\u002fqgePDNUD04sYA0GM4QNslSCrn9zlANbxVEMI9OkAFcdJSQUE5QIkAX1kEWDdAejVVPWn\\u002fNEA6dQptIa4yQHqC\\u002f32OrzBAwqr88VQuLkAglQJyKpgrQDE0qJwxRClALzaLqhbmJkDsSlzgQF8kQA0uaDIbySFAz7T22sfTHkDU3fAu\\u002fTMbQJAHciLRRBlAMwntN3A+GUCvgFvnuOIaQFBeJfXMjR1Ac4vtKKk4IEDonmj653EhQGieM+xGUCJAkV4LVgLsIkA58pwkbIMjQGQnP4nTUyRAShCgC5V0JUCw6Dxl\\u002f8cmQFutpMHiCChA5BCGVtvqKECSi2qkizkpQNuPvaUw5ihA8woFKTQAKEDjXE6muqImQASW1IKN5SRAwwOeR6bZIkD5g4bKU48gQBaOqgBoOxxAZKXlgBtJF0CZtpZkBI8SQG\\u002fmMPb0mQxATOg5UCxgBUBb0rHb2T3\\u002fP5l3Ebjzv\\u002fY\\u002fbh3H8UYv8T\\u002fFBXfpL9vsP6CpseZD9ew\\u002fforIC3iR8T9EPGgRRy74P0eWzm9PdAFAi6+us1gMCUAVrlA2SFYRQGJpwBBPxhZA+a6wrp8hHEDDSh7HD0AgQNfRNGOPgSFAjRAl3UWOIUDR9HUmbGAgQE0bfp1baxxARCeZ0wDzFkActxadWEQRQJ3LqOfUQAhAI\\u002fNyHdng\\u002fz9VPsSzjqvzP0mZ8cL04uY\\u002f0\\u002feTMr872T9k+Zlv24XKP2kPWwtQyLo\\u002fMs47tPxFqj9wZnowUYGZP8tx1Uz8P4k\\u002fZHgW3ayEej9mUOFGUHBuP\\u002fOH3HAiB2M\\u002fR9cV15MoWT+iIEpPzk1RP7qw7oZNuUk\\u002fqK4DFhnPRj9yBc5YpxwkQAW+frUDDiVA+OPM4pDmJ0BSsr377q0sQM5xe8g4rTFAIYMkv7vVNUDvZU0T1346QPiaQaXiGj9ABdlHliN4QUD4eCgKAKRCQAc\\u002flq1T1UJAxvtv4S8HQkAhviCc+m1AQICgnNXGxjxAHm7l57mLOEB0J2FQGLc0QOIVviFtiTFAUxSOBYn\\u002fLUD2L62we+QpQGdE\\u002fbL\\u002fayZAGpDuPXZpI0AJ8V+a3OkgQC7yPBWuUh5A5\\u002fOROW7sHEDsjXlyHhYeQNOZ6k0N6yBAlh0vA+bNI0CKHJKeDCcnQBHTBVxVSypAaRnwN9ypLEB99TNyTQAuQL3W0F2HcC5AQhvm7EFqLkC7VJqGxXEuQASJOCvu3y5AzkOJrHu7L0DjStP3jl4wQIEeuDvmuzBAkdWYeFbGMEBHh9A1xGcwQN7bwIDTTS9Ai+z7b088LUC45ZEDsdoqQBaDfsypXChAnQRDCczfJUDswkyOXm0jQLvacYQWAyFAncdZMUs7HUDzuFLS1YIYQIbRuhBq\\u002fRNAHdLLQTbBD0DDe4Vg99QIQDoeRdjipANAl7P8MzeAAECdkUYDsjb\\u002fP4rM7UU4JwFAMBt1alBoBUChzGAvFLQMQCrt3NDcoRNAeliZK81wGkAEaSlSwyQhQOYm3CwDHSVAEzU8d5t8KEA6Lhn8a5sqQKs4KPPNAStA3CAmNRiSKUAV+VK+iZMmQG9NZmkomSJAC7VPmgeiHECuAK60qKQUQEjlhd959wtAuBnN3rzeAUDSADNzq6T1P+Q9ozcw+eg\\u002fF9E9mOSd2z97Xn1oAn7NP+f5\\u002f7jExr4\\u002fhkbMSP8DsD+660IojCqhP1CzSghPp5M\\u002fS7MNGxl8iD+rWd7aXliAP+MFKkIgqXY\\u002fHJeZ32ohcD94\\u002fM0eqpNoP2Z\\u002fKMvmAmY\\u002fBE7TMMZjKkB78dW9+Z4rQMjQ6iJuUS9AtiWb6z69MkC9fKgW2QM3QGQDZK2UVjxAPWsSYGQrQUCFEO9j1ixEQBeaH9npuUZAiHqK5PNWSED3wBGq06lIQOqAOj0tnEdAElM7f79lRUCO5tZNPHhCQP5aPJVprT5AYMtTJ5HZOEAc3fkpne4zQLi9B4yrCDBAmV76jBEQKkDbWPMR\\u002fHglQN6V6aN9DCJAPZvFglGQH0DxGGPlz78dQLfEJ0plPB9AiSyVtJA2IkA1H15QWZcmQARCZaOQSyxAZ2UNZWA\\u002fMUDdhgmz6RU0QMQDMENMNjZAnrmNemNlN0AZh783jbU3QEJL5ER6fDdAthY4ifwrN0A5vxogBCA3QIE4vibEeTdAvtOWwWcYOEDIl57mt684QArRABls7jhAluLpvpagOEAaq\\u002fri9783QBfdbXv1bjZATjGpOqvkNEDu9yAr3FQzQMjbk7CY3TFA45A98nmAMEByWESIEVEuQCSxmoC2citADaorvh9BKEAbwSWPzcMkQCjUXQ+eMSFARhzUpQu+G0DtTYp1CkQWQOjycvJcdRJAurCOB6qVEEBijVIDorsQQF77kCvG7xJAYJCV1P9AF0DUdjvs1LodQATjVSr6HCNALluVDPQYKECBQtLuyUMtQKOLBHcB6TBAW6SaLAhzMkDtr9YJ9ugyQE\\u002fwQhJAKTJATco28RVUMEAYCJz4HYArQD6CwNcGuiVAMBsoxtclIEArXT\\u002fCaakWQJMQmR1SJw5AtCn9PHAbA0AmdQ7U+Sv3P9y7FeulB+s\\u002fwTAoJK2M3j\\u002fJZEcDIPHQP40h+rPX3cI\\u002fvrmNSYzQtT8in5lpaQqrP3WP5TWHGaI\\u002fT8AG7CuOmT\\u002f22AEC33KSP6JsqTi0E4s\\u002fvhlOSKcYhT\\u002fTspCktBGDP+pcc4plPy9AUDxdNzRdMECLfHVX7JIyQIWruT0COTZAO2E+GOw+O0BEQVs7DL1AQDIFJpdWQ0RAsfwHntLVR0DppEFTCO9KQMMqb6Or+kxAEDx+\\u002fMiCTUBJHN7d6FtMQDXAR1mfuElAhhbqI8sYRkBQ32kXmhxCQPJnXELYozxA2X\\u002fJG7glNkB9guW0f\\u002f8wQCGsR4GKPipA+\\u002fS29VimJEB8ea4uIeggQNE2BT287B1AxCcHBO4HHkAhojW0S2IhQGiFSvRTSiZAGFgSrTmgLUCrLTDGmWMzQGFJTaSRTzhAK3xT0jrePECHi88mcDRAQH0n6IwESUFA\\u002fAXyw6+0QUBom6NoSbJBQOMnOTtflkFAo1zH10mrQUCkw19DYxNCQAd+xi6hvkJABZ0yD5V2Q0CuNZinFflDQDc6+7JPFERA+WctkXa4Q0C7DvCohPlCQMNo6K+TAkJAz0gHfnMBQUAhFJf5PBNAQOlAlg9jcz5A3D7DbDO7PEDGa46GwLk6QG0kDqKZOzhAPeGBe2s8NUDtpr3fFu8xQCuiyYXYWS1Agg+3ylWsJ0Akh9kFXmgjQIDJPWkk1SBA9oVVIz\\u002frH0DLDSLJ\\u002f6cgQEjqi540xiJAlfX9ngwzJkAaHFYW+8sqQF2RIIbuIzBALjO7oxwMM0CxGq01ybI1QPIuugo2mjdARP4mDGRVOEAhACgibKw3QJt8nDHMsDVAD+pLdGq3MkBAqNcMRXcuQOr3oQlmcydA77b2vpAhIUDFl737p9cXQA4hq6Y5uQ9AARwqPwU\\u002fBECADWOKFOX4PzYZUbSctO0\\u002f2PfNiKVz4T8Owrn7crjUP4dPnbekusk\\u002fviMLL1IlwT\\u002fafGo+OnO4P8s32FsgHbI\\u002fmMs3KIQZqz\\u002f7QMhsmHGkP50swrmMQ6A\\u002f9l3foeGnnT9NZxN0oLIwQJ9yoskVhTFAE6knd\\u002fr1M0CbZ5DyrvQ3QDus9jM1aD1Au\\u002ftQxmIRQkBJgbm6fuFFQP4MmGoiyUlAJvLMZGM\\u002fTUBy39O7h6RPQCmGivsXOFBAXq6h8l5jT0CCKaeNJKRMQNVM6Ba7skhA+hjxVOU8RECyRGI8XMw\\u002fQOJ\\u002f247YPThABPywOJMwMkCbZSZyhkorQAJ0rOCW4SRANokIYFHbIEDEyqsfm18eQOOsVPdwICBA6kH8MawUJECmHp\\u002f2IForQMMkqvPA7DJAYUDNcWJtOUC+l2YEkT5AQAkmDoDymUNAesb5+4laRkAriK8WTj5IQG8cOUalSUlA6sLOU03GSUDMTsTJeSRKQBVxBLX8ykpAiv1rs2DsS0BoFd43OXRNQBrhbgaCE09AxD2pPhwyUEDJymcSkIlQQDg4enoYf1BAjKCuGGMeUEAuZfAkLhBPQD5lBIsZxE1ADUgBQFOMTEAVz5ksdW5LQCZqDNjTQUpAfc2cvzrGSEA+TK6bC8dGQJBuVQ22OURAAdZFaahIQUDDrcbVR4w8QDFfuUOzHTdA3bsoTxDXMkAV+1Gi+vwvQOxo0TcCHi1A61DFHwuoLEBMzUP6HRkuQNR59flfgzBAwtxL6VySMkBjcEjOeBM1QAdI884iyzdAL4CYqdxbOkAcKDArWE48QPPyJw9ZLj1A1tg+lQ+wPEA1f3i5XMs6QNtfCoAXvzdA\\u002fkGoglf7M0D0JZZD6vwvQKzfSt\\u002fjaShAcvnS7tHOIUB2FtdXveAYQDXqHea3rBBABwX3kACHBUCGG\\u002frzyv36P6DQKXIvufA\\u002f6xrdcGAW5T+tIj69s\\u002f7bP9xOHFw97tM\\u002fruLjAEYNzj9XvJr4JjnHP9zD0Y+w7sE\\u002f4VFpRBXBuz\\u002fNf2ncMoS2PynfNJLbsbQ\\u002fu+x7aWAeMECyLUPP9PYwQIPJZbSldzNAH7ubv56JN0B5+HxSFQ49QNUEc\\u002fdz60FAYsAQEYLFRUCn5obFnsFJQNUiP7KVXk1AlXfAqI3\\u002fT0CwfyyydYtQQKkITnZ7LFBAV3qjFRPcTUDmCDFw4RNKQNEK2SrZqEVAFnmLhRVBQUDEuJU6pak6QFG9myELPjRAO59HqqylLkCVEMiBo5snQF8OHODbMyNAucv6bw+KIUBBKtqTiwcjQK+mpy3VPShAz0ZPOQvQMEAWgc6ZRpY3QJ0MqmDJCEBAbN0SR1S8REB9Q+NhG11JQBHLv0KJZE1ArC2uS4w+UEDCU+koWlJRQH2rIGh+F1JAqZYr8gfRUkDlDsSixr1TQM2vkc86\\u002fFRAHqmD\\u002fLt8VkCAuJhCmwdYQPM4dp2yU1lAIppr6g0jWkA+tJMnv1laQFoekTkeBlpAzmDUrKlXWUBpH02uXohYQOo3+hnawVdAJMlQfAIKV0DH6jR\\u002foEBWQBPeHN4IMFVAPHotg5ipU0B3jeiqP6BRQMO47C7YZ05ABhSuBP1QSUD3fQSeOptEQJ723lCpx0BADRarsu0sPEDHc618UAU5QES1SQDlrjdA8PejQtufN0DQ4KjJWmI4QCA33aSvqjlAHgRz2U1OO0Ct2j4viyg9QJlJAUorAD9AfBPmnxg\\u002fQEBoSfcNGJ5AQNN7Y6aFcUBA\\u002f\\u002fBOks1JP0B7o2zzloQ8QJJFT1BF3ThAnNsURau7NEANKt6\\u002f64kwQI08s\\u002fGoPylAXnVMv2lyIkAsxOGnP9QZQNnbp6fxZRFAVU6fWFfKBkDsuX0XS6b9P+tGigGdyPM\\u002fsQ4tCzj36z\\u002f4euOzuSXlP0BIkuy7wOA\\u002fkz5rlfrg2j8CCbA8w1zVP1pEq38j7tA\\u002f54GnrR38yz9pBPnt++rJPxQgXgvbJyxApZPhrbjGLUBW2XjlhkgxQCjGDzp2LDVAEcMR5S5xOkB\\u002fsm9GkHZAQFMo\\u002f8LBKURAMNIt8bIJSEAvDP7yz6NLQCpbeX38aE5AtmMdcM\\u002fST0DUBj2UP5JPQB85TPLHr01AjHFhm2CKSkCUoEvPzbRGQNO+Ga2Nw0JAlhS3MdpIPkCfh4rGTyA4QAHI34sfLDNAbCV01E\\u002fULkDtyBnGc8wpQEwd5sYTpidAmkSBWF8TKUC0IapYAeguQOsRGnRk5zRAcGRneEr3PEATV8cpX6ZDQDEgm1YKjElAGVNTgkKYT0C+0ossM5lSQF+iYvEP+VRAf5TUDlLkVkB8MirKwYNYQFGbRlMxHlpAfU9JQ1j3W0BgY\\u002fQheC5eQOOjztohVmBApPZgqSmUYUB404CwyaFiQI3d04AWVmNA01o7jwSeY0DD0vZz0YJjQA\\u002fQo9vbJGNAFQBb4aCsYkBXuk\\u002foyjdiQCV8b8+kymFAs46gzkFNYUA0U23hx5ZgQLk2I3ahBF9At1+TdOcGXEAceYVUamBYQGITRizldlRAgvkjpubFUEBwCKad\\u002fGhLQN8y2DvL70ZAN+HSixEbREDrMOs4VpRCQJ0pY7ol5kFAWcIYJ3KpQUAjrX8z+p1BQIHKYwWuqkFAfE6pxTHNQUDpMugbNwRCQJdYVh+0P0JAcP+MpWZdQkCDX8eAjTFCQHKfruEHlkFAciLH69R3QEAttO\\u002ftPLo9QPPcBH5kxTlA2s97k5BnNUDJeowxRAAxQIWw0WhpzSlA8H2fMSq6IkCPzW9nmCQaQFej4VvbyRFAXrigWMUzCEDAVE8YjgoBQKJ7azrWjfk\\u002fxwLrkqJq9D+e3qVssefwP3AGyOFLDOw\\u002fu\\u002fL0hU3k5j\\u002fDXz0PXo7iP9jRoD1fON8\\u002fPhIR\\u002ffkf3T9tTcOiZFUmQNmw5yv3ySdAvX1QVd0aLEAX00WgCpQxQNHijcTKYzZAUsCmlK9dPECRcUWcAKJBQJWeCd7RTkVA67AYAKLTSEAppbMEgrRLQDIsDGWbek1Ae2H6GFDcTUAY+7bWVdlMQJb8W80gukpAxynogufxR0C8olI3svNEQMe4YGRNEUJAGQbw0YvlPkA3HMH1Z0o6QK+\\u002fHfDkZjZA3nKdb5ZzM0DSLw\\u002fcY9kxQCKyxVgGJjJAwlT\\u002fWg3wNEDEg+jdMbE6QC9+Zra5zUFABFOP3fi4R0COJwVLvrtOQCrw2TfmIVNAFgBkFmDYVkCzAYrAWERaQG0QDYKoUl1AjgvTJDAQYEDB0IAqi3VhQOfZdX8R+GJAFyOTyqqkZEA4tm6p9WtmQH3YGvAjJGhADHbvE9CWaUBL8KyVHJVqQKra29bCCWtAD712\\u002fWEBa0CB8chR6KRqQJx\\u002ftTdPJ2pA54YUG4ytaUD1WdjFUztpQE\\u002f597CsrmhAmsWDr1LOZ0AAKDVMqGVmQBnRp9U4YWRAGxZNs4vdYUDj6z1CW0FeQKlXqNacA1lAFJRVYlKWVEDpc+gAPEhRQCjlfmijLk5A99TkXtCJS0D1x6B8E\\u002fRJQAoexF6t00hAsYgYz0PLR0ABXulmfr9GQAhANAiWwkVAV0l1T9L0RECaFukkTWhEQHr0RhuZEURASZ6d+nLIQ0ANTLM+V1VDQMLvLMifhEJAYCYUzSY3QUBF8eT0H9Y+QLcpd06jdjpA9npUn4CvNUCLP\\u002fVqtPEwQFEn6xFDSylAGV6d1+YrIkCAbpCsWpAZQCKzWQFFIRJAaUTuFwTgCkDq2tPpZ0wFQGaHcz4S5AFAicDpKWbT\\u002fj+xmtR+qVz6P5YUSv49DvY\\u002fhoHgFmtE8j8EmwGZpELvP4fkNgFpXe0\\u002fQ80+54Y6IEAa6kpgzXghQA0I1b8eMCVAiAAoOHZYK0Dhez\\u002fK9PIxQOydllpzXDdAKE+eoYu7PUDn\\u002fmgkzlZCQNbK4lHFx0VA1MneZsjISEBUlCu2RvZKQI\\u002fDC7odEkxApQgsknYZTECiveEdlkFLQHxwFkQi3ElA+SfcvrUySECEnkDk2W5GQOsKSTDBmkRAnUBL8fa2QkATf80DStVAQAXDPA\\u002fsUT5AotJ6wRALPED+4jWXQaE7QLWlxfzL3T1AG5JML5CxQUAzCSfZPkVGQD3B6UhgokxABJQJ\\u002f\\u002fpBUkAo0AvY1LZWQITXcIS4Y1tAraRYYYMCYEAGFsUjrjpiQPXuKlTBW2RAJtQ1Bpd2ZkCFhepsXJ5oQI0LVxPO2GpAnsfpEq0TbUBSTqxGQCVvQIlf8AVabHBAZgs79tUAcUAVRPL5Y0dxQBHun25KSXFA7jP5fcMecUBydOO\\u002fvuRwQITGXmkSr3BAUNyBVkN9cEDT7a97+TdwQMwrCd+lc29A+UztkFrBbUBqu4NEJT9rQF4dc9G2EGhAYiSF7tSPZECJdCHBdC1hQKutRj8klVxA9iPnaLs1WED0FoB\\u002fQjlVQKc6+VNSS1NAg2B1L0r3UUA3oPdBb9dQQDtzVB4tYk9AqZRr0kjyTEDE1KOy74xKQC1lu2Z6eEhACadzV\\u002fHqRkBmHOw5\\u002fe9FQNgYwBPpYkVAAkKlyT37REDRHBIfJ2VEQHOw6xECXENAS7\\u002fSwxO+QUC3ed2+8yc\\u002fQG+Vp+FHFjpAzqhetlvNNEColpPO37AvQCX2D2SgSidAQPpFfnvdIEC0IbllVN8YQG+HD+iiWxNA8GE6R8snEEBr2qSIqGcMQFnwf8syXAlADrOtTPhMBkCT\\u002fcs1VhkDQBb21esoJgBAulMB6jMX\\u002fD\\u002fUnUSLB5D6P+dEuUl+CRZAZqknTfAcGEDPVBjmxmQeQCiYOlEbgyRAEYw21TQWLED7UsimUfYyQE4CDEG95ThA2KqdseCJP0AMsL4OfC1DQLHHQlS9VkZA6SxSg4vwSECwHd32YctKQORQpYCo50tAKelaJTduTEDKfcJsYpRMQEmB7urGe0xACYsnRiYiTECMdlGm+2tLQCZbRrRAQ0pAUo9UUEy3SEAoxx3lWQtHQOZU876qrUVACVS9DmgfRUC2pdxUU9tFQC83hcZoREhAB7VP9RacTEDzViXnRX1RQIBARbhdo1VAajiQ3oyZWkD892VR2xFgQGJvduS3\\u002fmJAyhUl5JP1ZUDp02MbkeNoQJfUG+VBwWtAHM14k0uMbkB0BM0Mmp5wQEY36GqN33FAlcfeRmv3ckAoiOm309FzQCw59sNrX3RA1cSOmr2ddEAQp3R9\\u002fpp0QFbcYU+qcnRAJl4mU8hCdEDXXepjdR10QPRA8IJF\\u002fXNAQQBBEDfDc0Baqu8gT0FzQPrzVGWwTnJAK+WeP1XccEBsyNF1RgBuQKMnucyP4GlAJ6EFL1fhZUBN6sYXjnZiQJGU1v8BuV9AHXri49AbXEDaqAvHWKhZQGNkyLuPzFdASg4wiHYNVkCyhl1raSpUQP+MTN5\\u002fIVJApHazYIUcUECv3Nj6CqhMQIyEVyki6UlAcHIrCW8bSEBCC6T77hpHQDY4cMSFjkZA8+D1wN0FRkCCey4UOBxFQMAmc5a5lENA7PsUiO9nQUCmbIb3IIA9QHHFuYXgzjdAh2sCKuhgMkA6CH3RVXwrQKsepxNcaCRAC+Pe3201H0ChfBFuLG8ZQDDUltbEQxZASDWlEjBlFEBcZ42Qi84SQP1e+2dO+BBApWhYkJW4DUBI7gfDUaMJQPo3\\u002fl8BpwZAD8fpoXCOBUAk8olWWi0NQF67qz0bWxBAidUKsbXHFUCMnNiZ5i8fQGpoQGE8fCZAagA7FoWsL0DoQLPqvYI1QIQycrl6ATxA88me60NvQUCWycksd8VEQOPqYs4txEdAomz8RSlLSkDmFSVt1WJMQF7OHplbME5ABlwAxN7XT0BtsFdG1K5QQMK86qxLTVFA4rPE4fumUUAZ77KgWKJRQKrVznHTP1FA44ZqtL+hUEBuKiAKUAVQQOYAK411ZE9AOY0vWS3WT0BaEh6XouVQQEr+McDYylJAlJPuL+e4VUDlt\\u002fTDHMFZQE1\\u002fB9jh4F5ACkHfPDR9YkAJ6nC6s+llQNPSJKVrjmlA6p8bT3hAbUBq1GpxKmxwQPiOZ5KTGnJA250EXdadc0DKWmSRcel0QDO8XOIz8XVAJB27tveqdkA3sDVhAhN3QD59LWn7MHdAMCwIkfsZd0C2BIScjOx2QCdWfBj8xXZAZlkQWEq0dkCiyLXfR6t2QG2FrmrNg3ZAWmYl\\u002fzcIdkAaHkZOqgp1QN99ssaFe3NADPZ80vt0cUDsxcs5TGpuQNvZkURWEWpAUa+77INfZkAKmFt61ZNjQJZbHohIoWFAztcZ1vdCYEBCaEEP\\u002f0JeQH6Uasfp7VtAoD1RYOhGWUDARUj5kVxWQCc36dhNcVNAc\\u002fyaI7LVUEBQXLjLCY9NQHOiCXVRvEpAw9cE+i0NSUAa39i\\u002fZRlIQNQyNPmFWkdAkxp2puhURkDxNL19N7lEQMAZJzkIdUJAqOZJxv9eP0CamTnUuGs5QNgRTOTCwDNAcWBmf8XjLUDOZH7wnagmQLAm0upv5yFApdwEoHdyHkD1tl1dp9UbQPkAfANLdRpA97AodsEfGUBIGVnwAzkXQA6bgktSxhRArWtaVpRFEkAzsZIAU2QQQM6Nz5JOYw9AeSo08BZJBECStWt1RX4HQJnNwuHWuxBAUE5cZfqQGUBEOfqTkmYjQND3RwJIZSxA\\u002fRQmHHDWM0Dysvql9WE6QHMBb0FptUBAUdHbybg5REBh+zibM4tHQO7f+VFml0pA5w6YFzlyTUDUQsYHSyNQQDg5rmkjm1FAKQ1cFfQcU0Acn9sFuIpUQGfiDyUYtlVAh\\u002fojtV92VkAVZrO+uL5WQFGDLw88qFZAoy\\u002fpWc9qVkAMiMMQm0pWQHjVyYl5hlZAv0nniXhRV0AmwNC4P9hYQBkCLNPkSVtAArPCBB\\u002fZXkAeda\\u002frNdhhQNmwRQCf7mRAOlgGPpaeaEBb5kvyEbxsQJZATeIOg3BAc5jZ0b+ZckC61tkJbYB0QKyKOhhvHXZAXERyw\\u002flgd0CguPM1a0R4QMshWOgsyXhAWyIU3N34eEAq8IzTi+Z4QLXRQr+SrnhAOGFwkBNyeEAH475O2kx4QIHWGypiSHhAQGj\\u002f1jdSeEDhm5Bylzx4QGjUyc1mzHdA7Do55NbQdkBRsIzbtTp1QNlJ6M3hJ3NAv3dhVRbccECN7WR6sVJtQF0y\\u002fpMopWlAQzWGhYTrZkAQab+qeA1lQNgqNZx2tmNAqkZncA+BYkCPmPAdVCBhQCuqOnSG7V5ASLRlbeArW0C8ookw\\u002fFlXQOE+IVo44lNAOMwS78YTUUDUUU2L8hpOQILvU+5zd0tAnmkQlbLKSUAV4bWxYYFIQIGquGhpFEdAVVhcxzcsRUBgD7ZGZLFCQOJRl5qMkD9AzDb1Uk14OUBx26ImdcYzQD0S1OLgEy5AqK6DqlolJ0DPR4FQjM0iQIibJB+2mCBAPFnMEWuIH0CXdJ+SPvoeQM6L8ptrNh5An7jnVfaQHEC2RIyjOxYaQEiIqDqmYRdAN\\u002fXZp5RJFUCszgRAE4AUQHIT+SYpBwBAVwUWa5QtA0DFt31+LQ8NQHEFc2QJZhdAQ\\u002f7YJbJuIkAkwZwk46wrQHA38wIKqzNAWR1qqpx1OkDZyhzlIOVAQPp+aF34l0RADXLTUFAnSEBiroH8lYtLQM9PtmZP5k5A+p\\u002faQ2o4UUCPdAY57CxTQCc4iT8DUVVA2VZDFw6GV0ABlrKZhZZZQCEizcinS1tA\\u002fWbIW9OFXECYeDoIQkpdQC8vzi8Kv11Axgt9B7IZXkDfYr7bW41eQCr2C9sbRV9A32GM4KA2YEDsCYOjFCFhQK7d0GasimJAhhraI1SfZEAQ\\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\\u002fnxbIEDxrFxmqlggQBB3V\\u002fywlB9AyjP9kdtrHUCADrjTMtwaQCFFjJVXzhhAj0gcQ9gFGEDMcyr\\u002fJE39P5OB5w5s6QFAcC8\\u002fG7EvDEAoPwCiz18XQAXqaTPcxSJAqBjZHsWHLEDQytnz4ms0QLwiLr+NkztAb5dqgvaiQUDnGY6MjoJFQNCQWaPNPklA+uAiiLLYTEArx\\u002f0EhkBQQMvhRpA3Q1JAjjMwKSyYVECCIO5QWktXQIQn4mz6RFpA7H2RCi9NXUCV73iBkQ9gQABuCwthQWFADUBHX+MvYkCHmEeDRuBiQHq6EJ7KYmNASAEPgIrKY0Ac1W1jPSpkQKdopbfNmGRAZLNMVtY5ZUAsyFcGkUFmQHRlC7ca7WdAQISN57RuakC8xKjWqtVtQC\\u002fIEr7e\\u002fnBA5K6hS95Hc0Bzb8Semop1QBrCaA\\u002fxi3dAi+88f+gfeUBTyxALKTJ6QJDqtT+fxXpAX9ExqLbtekBrgz5NNMZ6QKcfsiq2bXpAFYqJ4zYDekB7UWZjnaN5QJnRAewVZHlAUeMHnDBJeUCFui0vQD95QNi1c\\u002f6dGnlAizmz5u+jeEDFZYTmDa53QNbClCxtLHZAwekOc4c+dEDd1O0Q0yhyQNPd+YnNPHBAIWjEXd90bUBv+6d+CnJrQCL199BjQ2pAKHn7nhp6aUD2BenoZJhoQK0EIYpgQmdAFa26LpJZZUAm5qHw3PtiQCULJDidbGBAHNFaqC7sW0BOsVjpE6FXQISak7zBK1RAMo2+mDeEUUCMyMY\\u002fPvFOQArdDPyOjEtAGYmDbwpiSEBAYYmYUiZFQOQGETqWxkFAcjJ3YV\\u002fBPECHFD1Yv1w2QIu9y5332TBAanAJJdQmKUAZNznapVojQIzOTfq0IyBAjIJTHyPsHUCV6r+BqBceQINfNAXjKh9Afhb1RXPVH0BicRmMB2EfQBJv8DrTzR1AAZWBjpmzG0BaoIREJfAZQKRIZdYJQRlAVDNz3p6i\\u002fT+n+QtjlD8CQL3KMvDsEA1AmIRwh1xQGEADdKtVEaAjQFgFcteP5S1ANLIToAxpNUAJm441K+M8QB2J\\u002fvNxcUJA86v1BaNyRkCJSZBV\\u002fU1KQD\\u002fEh3yWDk5A4zNRPvH7UEC\\u002fxxbybTlTQPlJuhjm8lVAkIq2JolCWUCydhcyiRhdQKqdXnaknGBA4lNVRaKmYkAmw\\u002f9ZUH5kQKAwmt3iBGZAs1lHO1wvZ0C9LhLkjARoQBW2GV4OlmhAT3uXOQv7aEB8s09+fFFpQA1JHQEzxGlAWR\\u002fepDmOakAdz18eoPNrQFzzP\\u002f2iLW5AXojeUnOocEBHpWBxNJ1yQIBr6O1nx3RArRbHgOLndkDWwZzCCsB4QClcnMexI3pAnHuJfg0De0DbjPjyUml7QED7SshtcntAdLu9h3o+e0BKnKkZZOl6QJg\\u002fy3LBiHpA5YUU\\u002fQwtekBNMPOHROF5QEfoI+9ppnlAct7ylltteUCZk\\u002fJvIhV5QGYXVJDucnhA3RMesHRjd0BD7GN3id51QL\\u002fq6dRdAnRALgWJe34OckAe8V4vrE5wQGQUTFq0\\u002fG1A4VjI8vNjbEAjrGPOM6FrQOljv\\u002fJ0QGtAuSCjkHS8akAEH09TY7BpQMm0h+yu9WdAN0PE2kOlZUD826gnxQFjQKSnN9WtWGBArIlgpH\\u002fQW0BAztrF\\u002f6NXQAThqMRuNFRAKS\\u002fD7p1gUUCaZ\\u002foJiepNQEbRWsCRhUlAIFHzIkhXRUC7K7oeTVJBQNho4e0IHjtAgFOLLPxxNECzfXUMNeotQAR0\\u002ffKstiVAyT5Us3NUIEBOd4VipOcaQEQBphbP\\u002fhhABODwgUp3GUCY1oGUNtcaQB3aUW\\u002fF7BtAo3cyD48KHEBHronVGCEbQBNV3iiGpxlA6tMnjxxXGEBoAvzRwdEXQM2qky7o\\u002fP8\\u002f4wFD1aScA0DOEJrPOfwOQI6oiIK+uBlAx1aefseeJEBdHSGQIDcvQNA26B8jOTZAkKSTJAfSPUDQxNcuh\\u002fJCQHnvUKlr+kZA8pZWgBfjSkAEpyYWoclOQLxqjofchVFAel+sAQkZVECSugNINWFXQNJXS9HIg1tATCrkJSc5YEBhy1XG6vFiQCAsH7ufsWVAamNJH303aEAAka8qEU9qQIITbszS3WtA0CSuVqDmbECBZu3Q5oNtQIjPnutf3m1ARlSgz\\u002fcmbkB\\u002fSxlFyZVuQDaKR+XKaW9ATV9PWQdxcEC8hPfyjJZxQJovv8xkKXNAt++qpv0RdUCWjnCv3B13QEgTlzpQC3lAEskGceqcekCXvTiouqx7QNtRHW18NnxAr1E0WBxUfEDh12xp9S58QMpnfnng7XtAnGen4gGpe0Aka+R3vGd7QL0YU4p9JntAvjiYGsrdekAxImYh04R6QPzrWL7iDnpAGeuGtTxoeUD2FrzJgXh4QDnXH3z7LHdAehHbUNiGdUBi143V0aRzQPftQZh1wHFAl8GLe2cdcEDoRGsi1OFtQPrW9NDemWxAGOtOrqIxbEDq+nEWTTZsQDNbbtaXH2xAl9+o9qJ\\u002fa0Ap7ms7bSJqQM2UEScKE2hAhaYFcvyJZUAKFt4r\\u002ftBiQGyGowN9KWBAiYNFabd7W0CChMB0Yj1XQJvj4yYtkVNAdXM9WGFbUECc+VyQrPtKQE6iZ+xGyUVAtefyrMAVQUBF15KnI+A5QLuxnBqn4zJAI7NO2pC\\u002fKkBobTTZ\\u002f78iQPBTbbNCLBtAayhmimKgFUDTciT2o6UTQPHJNr7O4xNANJZAIHwTFUDci+okJy8WQFZfAg\\u002fSmxZA9YhodE47FkBz4ULfl1sVQCrUEGgZgxRATEmxfOMqFEBkwBz7JmcCQGOBcSutSwZAEkWdLv83EUAIl7BPAf4bQMDyijepCCZAeOt8NGxoMEDvg2KvmwU3QMBjmPYxfj5AyszqYGwqQ0Ba0h\\u002fU0hBHQEbpHuIo6kpAn7ii2nDzTkAa0yIRuthRQKsrcWM76lRAIrZL1z4AWUDjsht09kReQP37CIaZT2JAawn2+4jUZUCMPgZRH2BpQN1s1RnvlmxA9vI\\u002fudAsb0AKX30jPn1wQPfMsKp4A3FA812Q7RhBcUChlm369lpxQHKL550\\u002fenFAngDLrjzGcUCNfGvdiGByQDf+znNhYXNAPyGx7\\u002f\\u002fRdEBGTKujsaV2QJaT\\u002fxGdtXhAptbLnyTEekAqsApCSIp8QADLkT41zH1Aofm1Jy5tfkDKq\\u002fplanl+QMOs8WA6IH5Ac\\u002fsGDkegfUBQ1xe9+y59QAgs1lOO5nxA7eb5nQPCfECXeehmiqZ8QLsSate1cnxAwAlN7J4KfEC2GhqiJVx7QNV0YyABXXpAKtAetGMIeUDYmtIYB2F3QN5xvs2Ld3VArtuUzQVwc0BGDovuMoBxQHhYl7bMxm9AQyDpLQyPbUAZJwQd8nZsQJ\\u002f4u\\u002feTT2xAnk75LUGrbEBsjmfbYwJtQEBzVQ\\u002fl3mxAhNEdUhD8a0BIBTHvc1BqQPq6PgqfAmhAdE17MTZSZUBMcymE1oBiQHHx3n8AhV9AANaP4idvWkBjvrulptxVQHtTnsHg0FFAvANvRmKGTEDPypOERFNGQHpqn4KI\\u002fUBAeNx0oXIIOUDtfyYH+tIxQG3U\\u002fscCpyhAZFB3\\u002fAjYIEAkDPcnZZoXQLW\\u002fHNfB8RFA578lJFv9DkC\\u002f3v8+2yEOQCywpWHnNw9AzLRA8k9PEEBEAU3dBqsQQJNIxxmXiBBA0SZxvlQPEECmoJVkXSMPQJb\\u002fYqCtug5AssyzqiylBkDU77gwuw0LQHEv\\u002fiW+UhRA8Kk\\u002fHXQRIEBFlcxauqUoQE7vaBtT5zFAQAXcyPOGOEACK+MWHMM\\u002fQICXCj+HkENAqRorNXcsR0AP\\u002fzYNIs5KQIc6dfkp4k5AMZdZBUcSUkBTVoZxSLxVQHPqyW0Q0VpAuxLoX++8YEBsK\\u002fC4V8NkQLnYWDxeMmlAIqK44YWabUAwZVtmScBwQAceiy3QP3JA\\u002fC3bP5ozc0AZIwR1BqVzQBEXIpsvvHNAsYlmqoC0c0B1vhxgvc1zQOefQxnJPnRAkrqnl+osdUBeLtwqDqd2QCPRvj8Mo3hACiaPiyL8ekBE3nHhVnR9QGzPPA4DvH9AVnTLxL\\u002fAgEDmFbBQYkOBQG4qkdD7WoFASUUsBbsVgUC3IuyKSpeAQGa3LXDYDIBAN5dNc506f0A\\u002fe3uC8bh+QKDwUE3mjH5A3RHrrRCJfkA3bChGG3B+QNqtQ3Q2C35AhfFTgCo4fUDQu0sCjOx7QC+g8SHVMXpAn6aRqFcgeEAaXm+XkNx1QMBhCdvxlXNARHAo1lODcUCrl742G7RvQIfkpzMagW1A3zjd2dSCbEANNJ3sw4psQGnTDq5TMG1Arq7WEhbubUAsfW\\u002fjAEduQG9ALB4j5W1AiEF9ILOnbEDTssdVC59qQMoH1N2z\\u002fGdA8pTk3D8AZUDbB8jMoudhQGUOfiQwy11AqCn8B1w7WECDnM\\u002f23EhTQO6L+D6XCE5Anc0qi4jbRkCPArY3B\\u002flAQNaBtSm+jzhAqcEqAuVVMUA1EkLTHwQoQMAsIUwxkSBAZT4hdI9eF0DM+QvezHYRQGa3aBmXgwxAqgqP7R2ICUABL4i3PWwIQOpfuufFCwhAyyDuJBuwB0C3Y\\u002ffWChAHQAION+R5QAZAyZB+rSaOBUBV1P250EcFQBg9wS\\u002f+KQ1ADKAdyCo9EUBwH23241YZQNkJ+Mq\\u002fjyNAGRjglExHLUA1crOjOLo0QDnt1gLRojtAGYlrdahlQUBpoxaKgdZEQNS1xN\\u002fRDUhAtBLVCt1JS0CEYXN6yjFPQLFKITePY1JAQXImQqyWVkB05W843adcQATH6oZAX2JAHDJtYfVGZ0CrdGoWs6ZsQIU8mmAW9nBAvowMN9I8c0AIT4lSxuh0QBi53Zhr3nVA9icQWgAvdkCtzKYu1RR2QPxUbI7j43VAkB39Z3v0dUD7ilg9I492QImnXVa43ndASOFy9SHqeUCqxP8QBpR8QJnaWaFvnn9AzqfVX\\u002fNYgUALD3GHo7SCQByHXCTRsYNAIe\\u002fpImUthEArDVEAYByEQN4YLQmKkYNAOOGu3TO6gkCa\\u002fTx\\u002fXdGBQJSHzx8vDYFAUhysNvKNgEDmCeEjdlWAQDivpPHrSYBArV+3N\\u002f1BgEBQi4GCMBSAQEAr\\u002f6K5Rn9As8Z0\\u002fTLJfUC3Bs07e717QJ8UROH8S3lAhwnxlimtdkBKlhc0ByB0QFNgwi\\u002f64XFA0pM7nFUmcEBwfF52expuQLq2Nq56N21Axb2vSANxbUAh9g+7BGBuQCPn308CgG9AOV1bKu0mcED8B4mV8zFwQFnLperbim9Af6Kcl3W8bUAH9+3r7BprQAnAQe3S4mdAfY0wxEBcZECbwVDe\\u002fs1gQDvGVMFZ51pAsHv0PyDvVEAM2+yyTcJPQK5RvwElkUdAB6nPpl8uQUD6uSFKV8A4QBvQD2cuwTFAIkC8G3SiKUCkiY0zY9kiQMXunEgNhhxA6Nzb9LdQFkBlGqSRTwYSQBvbyB5p5w1AVtHaudpUCUAMmMDvqdEFQKRKrTWqEANAxYqHLT3pAECNj\\u002fS4XaD+P1q4Dpaynvw\\u002fsnnHU1ju+z+H9TvYG+YSQOtSbadMORZA6EqdScIkIEAOhN8d6pQoQJCXu5QBHDJA+chzzaAtOUACVOV9h25AQNm3UVS2LkRApZEWRPSAR0D58gkLwFFKQEWPkOai\\u002fkxAIPSFncM0UECgu8Rq4O9SQDhMia7BZ1dAglJ6dskrXkD8AGaFo7NjQOI2fwLXWmlAMnALp\\u002fuNb0AfnQXnssxyQJz0XO8aX3VAw+mW\\u002frczd0DMehoW4St4QNwG+8vQX3hAHqSQtcUZeEAWRNSd9cJ3QEFryQ4EyXdACa07CniEeEC+1tKz0yZ6QLPeXzTlsnxAgsJSoBX+f0AHejzW4duBQKPbbQU4uoNAIkSdoTVehUCAQjVKaI+GQN8RE8wnJIdAk69U6MsMh0DgfHBNq1qGQKu8MIWOPoVASmflDLz8g0DCuhUtVtmCQIoR7FnxA4JAuEsLHFyKgUCdqwskmleBQAB32GauPoFAwAH4mP4LgUA0V2IcP5aAQHGpeC+zkn9ABPYAQWpSfUAr0giaQpt6QOsStQjxtHdA5968hibudEClEfx\\u002fB45yQKVwoOaAyXBAANjzLYZ4b0CrZweVRspuQMeOVPrGTG9A2kUpV7JKcEA9zjY71w5xQBThi2JGrnFApTRUk3jvcUBkujmJ66xxQBPHT98l2XBAEPzxTd\\u002f7bkCq6GV1CXBrQJmJl+spYWdA\\u002fPsHmC4rY0DRe4G87kdeQMNYxHyEHFdAzkQb8QQkUUD42\\u002fdCZO5IQIVv977wA0JAVo2QxLhPOkDlUsEwJ8MzQFjsKgH55i5Ah5iKyAomKUA39aJi2QclQGeUEAOZuCFAxISdlAqJHUDI0WdwoQYYQJhYJQdk9xJAB+f1yKQaDUAj3xi6uegFQNZLndtYfQBA1jUI8ESW+T9WgKlDUVf1Pz4omFZf9vM\\u002fh0mVCr3PF0BUmb70pPQbQM7Yyb85OyRAesE9EH2nLkD4D2wmdXA2QBZghhtD7z5AOsTearn1Q0ALxMUq7iRIQFAVrGpKjUtAMJtSMewMTkDxhqhYJAhQQJqKAoa+U1FA2JNhGLeyU0ByH4mfhgJYQJtAmE438l5A7UklYQpeZEBa4uazlHlqQCY62577nnBAZS4wzVfvc0C5VP2mEsF2QIw35EdSv3hAw1DrKTLHeUD366hVBvR5QB8M3jd7mXlAHhkMD9wteUCZqvTZKyx5QFonFt6P93lAkgfMqHfHe0AEtAZ1gJ9+QO3JcE7bKIFA2cOvpcVDg0CNErZvG2eFQKiZ7BK1UodAGlK\\u002fn5fIiECeCJ6Y1JeJQA1jheEcqIlAUV\\u002fl1oUCiUCbqt6g1dKHQOckJIE+XoZAPoVabbbwhEBCkKuU5saDQHsI9f0H\\u002fYJA8pTmWkCJgkAWEDcuHUOCQDi4fuZm9IFANArUImJsgUBhHLz8wY6AQPV+S7yLtH5AGRZrlBzNe0BtX3A+JLd4QJPvI2jszHVAH+Bo8Zhec0B4XU943KNxQDEZC+IStXBAuqxYiKGKcEAeKKjOwgBxQNq7I5NV3nFAXwUxTt7dckDWfOkc47dzQHwNbMlOLXRAknDPFT4QdEABgPhF7ElzQP7T57EY3XFA1EdhWmPJb0BD\\u002fWHggR1rQEp4jAv2J2ZAbYxnZYdaYUBt\\u002fqCwNyZaQDGxvmU8G1NAPOIQNhl+S0APgIsMPfNDQMnOp+E9Fz5AJ7jHE1UoOEBzm9wmNLQ0QPJTZYMijDJAY8IjR77TMEAx2zap0A8uQF7Yiyrs7ClANGjYa05VJUBsLGfQ96UgQDm8zeD3mhhArMCej4FHEUDa\\u002fHJqd1sHQEfN0yGraf8\\u002fS8Uh7LGs9j9mAjM6d9rzPwBR8aWUlBxAlEWyXK7GIEAm1+9Mq0coQP8Goy+BZDJAAe37t9TpOkAYcZwZ84VCQE3VNG0y00dAkjQCMYakTECpIcA4PStQQNoMRlcbVFFAeQpygNj\\u002fUUD35\\u002fsMO8JSQDsllcjPfVRAtFtYakIxWECn7FpBga1eQKM6G4\\u002fOI2RA5goTf9ZPakDZE6uYd6VwQJE6nVokH3RAuVyUUnUgd0C9fMYItEx5QEHCfukqe3pAnK5lCIvEekCLCNQYxXx6QBXq91wZHHpAxnuDXfcfekAiAq4q9O16QK7eYMh5wHxAgcT2xsOff0BMGue2fLKBQFB8G6Rt4YNAM\\u002fSkyBgohkA6lVpkgEmIQP2m62sxB4pA\\u002fGxpubAqi0AI58kzD5GLQEMtL7CeNYtANN5rBcs2ikBMis8409CIQPOt5vb4TYdAbf4wFeLwhUALW25XQuGEQFHmLB1MIoRAQ8WlDiyWg0Dpb+PoYAyDQBbur6bWVIJA2wZXr8BQgUCqP99uAvh\\u002fQAnDFhln2nxA0+pfDn2beUDz\\u002fJymNpt2QHoBSZ9ULnRAYKQnqAONckBeNG0FXctxQPWty3G72nFA+EjeRIeQckCnsmZjEa9zQFGFGld87nRAtHffci4FdkCq4JCI9q92QBLRw1PsuXZAHIBkzZEDdkDSqjdhIoh0QM4AjRSBX3JA6JtM\\u002f2t2b0AkitvwwLtpQNa2DxDbHGRA\\u002fctSf30pXkAxFNGP0OxVQIG+WL+gjk9Ap\\u002f61cMhDR0AVKiyhTElCQIi59tqTXT9Awr6bdCP7PEDAvnNeRsQ7QPZE+sXphjpAYlH87aqVOEDBaLWmEL81QHshZrmeMTJAneoCKRGnLEAtnSYxry8lQGxDFY17cB1A8hIiQERbE0CuUS3Gdb4IQL1GXuhisQBAh5iOMMYp\\u002fD9UjLTsazYgQFn\\u002f91vnBiNA3cV\\u002fSGqJK0AdEbwaZOE0QN8DL0p5mz5ArteEhRwbRUDd6x0M+C5LQNUhzPkyVlBA\\u002ffaUjwRiUkCd6R6QOYZTQGLZA2nN6FNAWcET\\u002fMYbVEDKV25gXwxVQM+5Yw8Az1dAbl6IrnRSXUBZymJn6AZjQNUGK\\u002fwj4WhAHasfuSu1b0B46yw1nVlzQJDjDaXpeHZAtQPsBvLXeEB5PflIi0d6QAtClLNV2HpAjeEGET\\u002fVekBCHYgj\\u002fKx6QCLUtLK203pAD7kyQdOne0CKTkoXaGF9QJtS1wYJB4BAQKMF1MHKgUBB7RQ3e+CDQMRjAMadIIZAcIQqyp9ZiECWE5QxY1OKQGIM3AYM1otAoxab9vC0jEBiRVNvBtuMQMWnfnxnU4xA9VwChqNIi0C+azUf+viJQAZ4VH4Eo4hA1EooJVJyh0DJ35aYwXOGQNBWiP\\u002fNlYVAIxJKItGzhEBRPgngEKeDQIGlgnz1VoJADzEerfnCgECdChQdeQd+QGmyeF20h3pAdA3IuZJnd0AxkI3Qb\\u002fp0QEJOAcHEc3NAqsNzD\\u002fPfckDthMM1eSdzQNnWDDJdGHRABYsA4dtwdUDU9jPaLeh2QEKaX0HgNHhAk09jZywSeUCiA+7BEkZ5QGvJEl7qqHhANfvkwkYtd0C9xiZQdOV0QLXc7iFPA3JAgRssnxahbUDAxplcxkBnQOg+1M5FfWFARJ\\u002fZ8pmFWUC3jP6A04ZSQIY0Vx864UtAAG5sliK+RkBpua\\u002fASH1EQMq8Z5Hf4kNAD9rmLLTXQ0BanW4oFI1DQKgcv7C3jEJASf3iyzm3QEBs7vwrX2c8QMSh5VnpqDZAdl0q6Q\\u002fwMEB\\u002fAYkd3LknQLn5oPckUh9Ac36dEvb4E0C4mSjE1rYKQDld8F14ZQZAjWUvGvZjIUD0YZeG\\u002f14kQLUIEJH9Zi1ATDLjm2xHNkB80lq3rVlAQHZtpJfgm0ZAUelPGDs2TUDVNO6J95pRQLHChDtZ1VNAY5tg8PYBVUCIS9oa+DNVQFjXLBl771RACgcVGkwcVUBAnPwJqtRWQCXiwKSIGltArWVCwRlDYUDh6hBIAoNmQHNI1AqX42xA43p4\\u002fBHWcUBE6FmqOgF1QNf2biZ7lHdAETwM\\u002fNBbeUAXMEqX9Vx6QNnYXQMg03pAxhmHVy0ae0CgV7HCppJ7QCwT0Sb3iXxAuT4dxF4ufkBfizQvp0eAQMNLS65Z0oFA00uEX8Wqg0BitDEvWbyFQKNQOsFc6IdAMks5V3QEikBEb\\u002fB5n92LQG9fb3SEQY1AhI7YXtkLjkBZsyRrKDKOQFrPpZOXx41AK+AFwKn1jEC10tPZc+yLQEvR1usw0YpA0qjkI9myiUAEdSiBKomIQCKmxrOQPYdAhslMIVK5hUAiTA66\\u002ffKDQILkwrc59oFArY04XV3Hf0AHGXsuWtN7QD7KEq\\u002f8cHhAbkCK4gntdUA5KR6fC3B0QKfUUSd3+XNAoGBJxLtldEDn0uvldnp1QMGggPLF8nZAeKlyQpOHeEDKwzHsVfN5QD4Wo9nS83pAoixdumZNe0AOasi\\u002f9NF6QK2BX9ytanlAWMWidg0hd0Ct\\u002fd2LOyJ0QIx7GYduuXBAsp5y2PyCakCrZKN1oCNkQIsG6Vdps11A0uo0dmfWVUDs7GmtZ7hQQF8nkUEK7UtAF7zZuSvYSUDrIz9ecbxJQCAgP6huQEpAJdyLsLxZSkAVEPpQcGhJQK3CWj0jPEdArornCB4EREDm4TALzy5AQG7Mb64rgjhAnknwww5iMUBNmyVQhDonQMXT3GXm+B1APU3yU3VAFED9yTpMQg8RQDMQCOPwuCFA98tf0aurJEAOeFsfN54tQFZ6IzCUVzZAeYr\\u002fVP9gQEAqC++RSa5GQDTv+cHoZE1AspFq12\\u002fIUUC+z+v9nhpUQGv+gLZtU1VAccJLGq9yVUAdyg6SUuZUQBJDDvQnglRAqT5Xa+1UVUACoqHZNWRYQBjOY3NpYF5Ac42wyACzY0DVpun8vnBpQIfNDcgc029AMKHV3oYOc0CPFYlsstB1QKcFeIGE+3dArUCT9OqKeUDokxvDHah6QIYM3OrolntAe3c\\u002fAMacfECYhR8kWe19QOx4bYLuo39AdZXzy+XkgECZGP0P\\u002fjCCQLR+rT1ZuINALfBc4+Z7hUCkOzdS3HSHQBvUGyydjYlAY5b326yfi0BA+CiL8nmNQJK+OErP7Y5AnUDn5Qrdj0DiBoNztCCQQJCk1qHuFJBAbFyb5Ayvj0Bb3mRLfOWOQDUhxA2s1I1A4nnJYy13jEBKPGDxwMKKQGz06mbUs4hARs5cAtlWhkDkgKvEocuDQDlT2NfBQYFAxECJV8refUBZeHLaawt6QLMPW3zoTHdAIGSapNC7dUAlgS29kEV1QA1dA23WtnVAFaLKuzTLdkAcnETXNDt4QDQ8GnwCxHlAbb6h3DQoe0BM43zDTi18QLrXRl4sm3xA3nEie\\u002ftAfEBe1KULlf96QJpPQWMu1XhALKEax3HkdUBLyU5KQHJyQBsGluhnsm1AG1OAmvjsZkDFNCdJ9CxhQMruTjXLnFlAg5Of9yvPU0AyC2M7RKJQQJm2PCix5E5A8HuyQrPpTkAPzkUxJMZPQArvAMw1G1BApGXyM3BqT0Dg1awlwBRNQPN8gP4OX0lA9EeIj7vHREBWDpUC5eM\\u002fQHfiE3Zq7TZAWojIhM8SL0BxCtCMFVokQHqyLLZv6htACLgKv7SwF0Az+vh1eEUhQK8Y7VInASRAcOci+X9NLEAGmmzu5yU1QGKFzUXj2j5AHxa5+jVWRUDpA\\u002f3oV7FLQNGvb1xZz1BAPgicX2IWU0DvY2MT6FRUQCRXumLRe1RABB7hH6\\u002ffU0DZ8Q\\u002fm0zVTQCDG9Qj4cVNAOmqIRjmMVUBSstitRD5aQDNuWnjc5GBAq6lr0h7tZUBOtxfnCclrQB0e0Pc28XBARuj8HYDUc0DKJs5biF92QIBi1zpRiXhA+1TRp5NrekCF\\u002fd6EojF8QKfg\\u002foNhAn5AoFSTPAnxf0BWuPZfQ\\u002f6AQDlyYHFjDoJAbFfpdnUqg0A3ZiSSBl+EQKUSf\\u002foIwYVAIcls6Z5jh0D5s58kaEuJQAjptt57ZotAdH7QggmOjUCPJvLZO5GPQI7pJ4jOoZBApFrf0qdDkUCea2byJaeRQNmv6IrMyZFArNFKyiupkUDBmPB5o0CRQGNcGKcBi5BATijpoRcOj0CRpvc5lHiMQDyPBtDZeolATgXcwQtOhkAC2nuTADaDQAmCIwjGdIBAUgEIS9R8fEBvCMBnbmB5QMrcCO7+mndAd9m\\u002fQTAFd0Brdql9hVl3QD2wyW8FSHhAq3SyUFuGeUBOQFCPJNd6QEuxP2L3B3xACsCNc5XqfEAMdt8Y6k99QDF+uw3NCX1AuUDWtDH0e0DZWyKV5gF6QFmwvv6fRndAR82kTs74c0AdNeR8L2lwQBjhwFjB5GlAn6iRmqrLY0CN5p+luvBdQBj9AQYvNFdAQ6J40lE9U0CexMJuO4FRQKlSphXMOVFAg46Ksd6TUUC4\\u002fJrvb9dRQBbaS4c3hFFAvpvgs59fUECDYt\\u002fY8uVMQKJSsFvY9kdAvg7Rpz2hQkBOGrMhEyg7QGoczccqrDJAoicZ\\u002fcrZKEDIuc1g6VAhQDEmmmpknx1AvY6EMoouIECo8\\u002f\\u002ftapMiQD7jyAFx1SlAtFw9kqEGM0CRQDXg7n87QJ0AqWV37EJARLGKluKHSEDwS9x0m9VNQFu2sR5LAlFAHnrJmNU4UkAroMmk1ndSQGSM8cc2\\u002fFFAqJmnjM5XUUDCZ7EQWVhRQIaPx4Ry2VJAXemmJ3SNVkCXPuXOAsxcQGgG4yLjumJAL4W95BX9Z0D9q\\u002fImvsFtQH3KceyNz3FAYAHXQBSodEBNoBi1pWN3QHJtqZirEnpAAuXD1f3LfECsymCrsZh\\u002fQGtnO7csNIFAh2anx+yKgkDaZ+16Ab6DQLoj5kvax4RA0arKqmm4hUC0DPIWfrOGQAfrA\\u002fqL5IdAvWhvVj1tiUDeLDo8JFeLQMsfxGhDj41A\\u002fnLZ85nsj0D3jhMxax6RQGPHE8rOJ5JA7faQOY39kkD8qZ3nDI2TQF0xIcZmxJNAVhK7UAiTk0C\\u002f5JGEqe2SQKU\\u002fveEC1JFA+XXGnRxVkECcMnx6Fx+NQH1BgqTwWYlAC0bYoNi0hUCug3Yl9H+CQCaG9P8z6H9AFExTVB9XfEDZQWWgiEN6QAOVzPHlcnlAFwEOKm6OeUCb5+udojp6QLr5u2icKXtAZFIlv48ifEBLn9S1\\u002f\\u002f18QEwvXqWLm31A1vjL5d3YfUBCyp4MqY99QL+ncQ0bnHxAQntXQIroekA0E0HMrHh4QKYXWfASb3VADQFrfB0KckB58Cd11TFtQH\\u002fyuL\\u002fs1mZA+0W3cSuFYUBEdmR+8RBbQI53Em8y21VA9k+L03QPU0CHDOegDgNSQPxSAEsa6FFAZeaaIRX7UUDiK5BlD6dRQKsRNMimmlBA9NxpfuqWTUBr4UcuVdJIQF16gCemikNAnD4Aco3fPECKgEYbDyU0QHsbaoM\\u002fPitANCaKUPpJI0AX\\u002f5\\u002ffDaEgQMTCDcyOMh1A4kB9ZZiaIECwmdL8BakmQGyEpAMwZjBAFOY3JVJlN0BwJcFfuOw\\u002fQLtVhcXsmURANIDH0x4LSUAaGXpD1qVMQL7QYqes5U5A01Mi8EWeT0BmJulLvCRPQOQ6KbJRV05AczZXWgh8TkBjqJMaZH5QQJ5gs15DiVNAnfiwyGq7WECgAkZhRQ1gQECQncwzrGRATT8KWy36aUBppPmNArlvQDgGLlZ443JAU+1nbaAUdkDaP0VG9X55QFomgW\\u002fkK31AoNE6XMKFgEDJgjuSeXWCQJLeBa+3PoRAxsdTL9W7hUDPFcu8MdmGQCNUn53eoYdAEUazHbpAiEAooS17UvSIQPMZjuVg+olAgbENMcJ7i0ASDtzfF4GNQPx\\u002fHj4e849AGANGd7tRkUCk5aNMlKuSQFGbGCq85pNAHsB41VXhlEACGrumaHmVQFSg1r2pkJVAmDi7viITlUAU+u9fe\\u002f6TQAiut0g6ZpJAmTU74D1ykEBlmLpCrq6MQJkZ0AsimYhAvrcNwXwBhUBXTLoTTCaCQCxJ2DcCIYBAwmyLymTQfUC6UZOUarB8QCHuTS1Kf3xAabEudtvYfEB\\u002fRIKelWt9QN\\u002fL3jLWAH5Acf5pyK94fkC7p0D9Gb5+QIkzEwpLu35AGsMd\\u002f4NUfkCMO9P0G2t9QHVQGWRG5ntAmrMnoCS9eUCHMKpVIv52QKtEQ0cO0XNAsvR6oNZycEAwPxpdmVZqQCDTAF4tfmRAwUzIFVSGX0AaOawZ66tYQMpNF2k\\u002fVlRA0yrn1QAKUkDl+DYZQxBRQKZOntztplBA0ZGJSV4rUEAWEOHDUmxOQG+QVZCASEtAmpV5SgMdR0DzvOYEfGlCQAuhO7cKkDtA1Cgh1W2CM0CZ9dxG684qQFzG8KG\\u002fSCNAVx3xpfLAIEDlS3fABEoZQBiuZZCRixxAfqiir88pI0C\\u002f7eG9BEcrQHd8Q\\u002fiSLzNA7\\u002fI8tfTmOUCcYUWl3ZlAQNTFUlYpIURAE\\u002fF\\u002fxysUR0A8RRGbqRRJQG8HA01FA0pAox7FQj8eSkADaRM5XQlKQHfD92iKuEpAwuspN7tATUDrB6f7pk1RQFckJ8ndtlVAe6rE08v0W0BjRSXYcfVhQOi+RPFtsWZAX5niAFUbbED1ocG5ER1xQMvWDWh+mHRAMAFbsPaUeEA\\u002fgE+I0hZ9QMyL9zuB\\u002foBA4Ddvzvx7g0DlWtq4q8qFQJ6b9GsrsIdAbmVVYQ8GiUCXXwOlG8uJQJa+RTh4KIpAdqfutL1nikDeRrj5g92KQAX1XA8X0YtAqlHomWtqjUD8O7d4QquPQJjZunAtOZFAQFvJZrvBkkAx1axXnEeUQMvP6H\\u002ffnJVAs0LYGnORlkBxdg7c2PmWQKp1LHNVuJZAYvOTzNvFlUCNbQkxnzaUQGjUFuxxN5JAelfIjD4EkEBNwtxU87WLQKDN0ywP34dAFRn7eMnHhEB\\u002fR4Y4M4uCQPhcgH1iH4FAvkUlbG5fgEA8VUHyhxiAQGMdNMc9F4BAEi\\u002fOmb8xgEBupliQgEyAQE1yZcijWIBAbi6BfFVOgEBecrnexCaAQEqf9ACNsX9Au6je2+2xfkD33t9vmTh9QMqbZeoYN3tA5I5M6jateEAzl1ppK651QM+0oJuaYnJA4AyuTFAKbkCOrLi5KbFnQFTFECXMMmJA6fTpCca\\u002fW0Bq+mh5X6BVQKl9nYUTxlFA2aQOx6o8T0DSzh2fF+pMQI1aIJidRktAPv347l1hSUBHrAxIRMNGQLQ9qEPfZ0NA52Vju7c4P0DZlByxo6Y3QPHrFnkk+TBAnA+Efm6wJ0Cc\\u002fuW3ZE4hQB58eUrISx5A0zsqQwjmFECZgtvNw30XQKBQ5My5PB9AKK8WtKL7JUB4uJ20o5YuQGsrj51WdDRAR1ft6nEKOkDgXacwoHs\\u002fQIIcZfEqF0JAGRNznsvXQ0AQo50edvNEQGhVsli7nkVALAMqqjdTRkDRDZUxN8BHQKdZHWU4qkpACerVIUrCT0AAMm8jdcFTQHElgJTeEFlAv3LQog7RX0BYMB5gUQJkQN83VM1w6GhANVfgOQ\\u002fKbkCScw0er\\u002fZyQBP6f8+uSXdANo7aQhZlfEDYK2A9\\u002fg+BQAtxoqzOC4RAJJkAgdXdhkDE2xR2zDiJQB3dbp1J4opA0R\\u002fp+hHIi0DJIT6G4AmMQG3cHR4f84tAehbhNrDmi0AIVfyndUSMQEUopFbtUY1AfOsyC2gsj0BJJRF6AuOQQGaDr3QSdZJAtm+5ZIkilECsExESlLaVQHd4rCeD95ZAotDH1ZWvl0DtTze++LeXQAIpoj0BA5dAbi9xo2ChlUDBbG8LVr+TQJMWfTFImpFALSn\\u002fmR3ljkBsCrQu5fyKQOQxAqASxIdACqtHA7xZhUAO1TSSXbiDQAPAJIW+v4JAK77v3TFBgkDeZeYW2guCQFsKuo1\\u002f9oFA\\u002fUxfP6TkgUBCN6C\\u002f+8WBQML3LOjqkYFAf\\u002fPMhltCgUBzT6zco9CAQEkSRyoaNYBAEpHxcVjQfkB+RPLhosd8QOB1HHiYSnpA81PikmVfd0ABdalxkh10QC5lxhKlsHBAXGmp92OnakBru2eEjopkQBvccFSM315Aky0JoG0WV0BhcbHGBLBRQDu1GwIriExAg7MAXYhtSED\\u002fxJNHpc9FQJPPjo3UrUNA+sHlT8drQUDoMFQSG6Y9QG1553Ud9zdAYRspre1QMkDnUW51KJoqQOJuv8ma1CJA7CHqzQrqG0Ab1nOMrpoYQM9WJMvmXBBAXReFXdplEkBzmVs0G3UYQA1LLBBuLSFAsA4suYjRJ0CCp0PJj7ovQHF33Dj5IjRATxvMAONWOEBa\\u002fsUONBs8QDPoz3jFNz9AZ5o32mzdQEA+j7qftAZCQCR+iQsaeENAkUtQr\\u002f6uRUA9pc2vwixJQFQS2uzTW05AS9De0yG\\u002fUkB6UxouyFhXQN8NPHlWB11AwjvRHYf4YUDGIEid0zlmQIXr+MS8mGtAfQPV56s+cUCHCfOOT6J1QMgU8uTtBntA88TnUmOigECZYrd7lPiDQO77UUzdNIdATPBopBD8iUA5wYbhYwKMQPeCNYOsI41AW43l70txjUAYOb1tNjCNQNOzVhGbx4xAkvfvFWymjEDcX1v\\u002fuiiNQECFVpB8hI5AKcJRt3pgkEBuOLkbv9uRQIAACd8UjZNAw5m+cPo+lUDVJFrxYbSWQG+AXWw8spdAdu3uTwkLmECf1CIMrKmXQFWAF6trl5ZAYUnCawf6lEC6TFggBgqTQOpXdYLdBJFA4qc9Qg1AjkAVgFYhlwGLQH5Zxm+Oc4hA2Ly+bHeZhkB9ZXwYdF2FQIDv9CBamoRAewfWpPUlhEBRLQ5N39mDQE3jV0znmINASocs0OZPg0C5WMLe8vKCQMF6H1O1eYJAspPWmWHdgUD9n3j4nRiBQGXBq0zwJ4BA8v\\u002fgXvcSfkCVdKikLXl7QCtDCWaYhXhAzVgB3ItFdUC+wMRvz9ZxQKP9cJbO0GxAW\\u002f7sNnllZkCWWgp8C9NgQC7F7838t1hAvSTTCSofUkAegt0UXTZLQHpXsSdVXkVA8N31qxqaQUAkzxflXuQ9QDqS3kvMdTlAgyk0ZXI3NUBBZI6ecwAxQPfSjV8t+ilAqB6NTUf3IkDbJF9tIRwbQCycvtrYURRAgL5TWM8CEkA358vZbiQIQBvgTXvDRwtAGWjPDcBQEkCeQE11s\\u002fUZQIXL\\u002frWJHSJAF4196+0+KEBf7j6WjeguQKfn9pbmyTJAZp5LdrDpNUD7bafd18M4QJeliM3GhTtAruPee++hPkAqgn+Q0GBBQKJy51yVUERAUSd02D5wSEC91Y52kO1NQPYd87qJZ1JACpgiYcSDVkCADQLGOFJbQFEJviGYg2BASTZmFcEQZECJCmtGV7loQBfURblcAG9AWRof7Leuc0Cjy7MIiQN5QA06F1c4X39AdHY15Rstg0DcxSVU56aGQPyXzhTpuolAe\\u002f3qkJQRjEDWiobEf3eNQBcJdNZV7o1A1C8PV+6ujUARYCIAqxuNQLCZ1tPKp4xACtQ\\u002f8M27jEAcmdgs\\u002fJ6NQMb9GBHsao9AwsFNFLYEkUBHouoGAZ2SQJuGlxP4TZRAgNvQBNrdlUCkSrnUSxKXQEVPDRHwupdA3MJsQ8u7l0D6ckeW8BOXQIWBgXZf3ZVAeLEccJ1FlEBZUUg734GSQCAuksJOw5BA50zVRz1ejkDl5JcuGLeLQOX2OotJoYlAzV6ijesViED\\u002fdyn86\\u002f2GQPD7u0+MOYZABb\\u002f91cinhUBQa3B7\\u002fyuFQBzXX05DsIRApRrna4gkhEDmWe2WxHyDQCgSv5g7sIJAjhPu1z+6gUB0ZyQGvZqAQBUQywUNqn5Agmf4Eu7ae0C4a9CwT9B4QNxKSPvIlHVAD7+uX+k7ckAJlMffJs1tQAI5xM0hgGdAVZZcI97nYUBSee7xG4daQDv8z9IbVVNAbiOUwpAuTEAJ23F87OREQN76SoTy1j9AaoRfiVviOECf+NUwibAzQIVHz7XI+i5AxXQuwa3UJ0CvOMv5zbohQGP4Z2IHgRlAFms7Dx4eEkC9t9+CHigLQN3np9HMGAhA8seur0+6AED29MbnvxQDQBuLOr4SIQpAGB+alj\\u002fhEkC29VQmj8kaQNrPlvFXLiJAqKeLcLN6J0A5oq9Opu4sQKmRA1CTJjFAtA6a4mvTM0AlLk+rWb82QGAQR\\u002fHRZjpAApIDo2hrP0B0CyDjITRDQDCBekwn30dAAOWm+oK3TUCselqcz0dSQGNi\\u002fijiFFZAfCtYg085WkDqrWEYOOVeQADi7qRyUGJAUdOUV68pZkBvWzeK1pBrQDBcTXBwiHFA05itqId7dkDxDpBTz458QA9j6jFls4FAXAFARaoyhUAoCNBkxWOIQCLcYfKd6opAOxFkf8uJjEC3hlB1HTWNQGgSbkzqFo1AKAxKWzuGjEDzLZZND\\u002fGLQFJDhdS+wYtASx8sxJRHjED7slPkfqiNQNFydRN43Y9Aw4n52KRbkUDcDiej5POSQEp0RaqahpRAo6mAcuPflUBG\\u002frOCDdGWQP42XJ5dOZdARYxRsOQMl0AR3lxvh1aWQJEmKsYBNJVAB9DeaXXNk0CkFj4pAEySQAVxp2yd0pBA2Cyb90D1jkDcYzw7GKaMQLW3GMWuwopA2jox\\u002fqFFiUDy\\u002foTWTB2IQBvsmhczMYdABwl9W5JnhkBeiOnKMKmFQF+kWC6p4oRAr2Xj2G4EhECGEGfVaQODQHWd\\u002f9+R2oFAvmFfUvCLgECnzZCwdj5+QAZGy8d4PHtA8gijktEieEB9E1e7Uf50QPERApYO3HFAbJjgZvadbUCMp+vhSOJnQOiapgZKvWJAWAShoke1XEDHMx+9I5lVQPF3PdIgE1BArP3GfnTQR0B\\u002fD\\u002faAHZtBQJ9T3OsT8zlA2g1MHjH6MkA1IU\\u002ftRGArQBNMmmdTWyNAPd0Iety5GkB6EqZrLhASQNy1mwwEcAhA8I9bU1WyAUCbg52xe\\u002f3+P0\\u002fyRpRqwfU\\u002f3Ar7NpIl+T\\u002f\\u002fn8EQVLIBQAYNsXFoSQpA0COQqoobE0Btoym3cIIaQINkPtGIeCFA7rg7hQ35JUDWbuhcQ6QqQDOdJoMIpC9Aj\\u002fM4JofGMkCBcyap8K82QGAfHeAALDxAf+j\\u002fsfriQUCEvKtRzd5GQAL+fM+S70xAhsB1nRfiUUBnt0ojcXtVQOMsW4oFJ1lAc0SKXXgFXUCFxl4abcpgQKlgLDEI3GNAb39US95LaEAjjg2ifKxuQAlAjieVrXNAwfM8BCcmeUCKm5VQ0nV\\u002fQLIkw+V4CINA4G\\u002fmwZshhkCIjjx146yIQLB807E8aYpAJsg2IpVAi0C+uO3sRE+LQCYR7mCg3YpAfEqngbFNikA7\\u002fJimBgOKQF+NNBBDTIpAtVG6OM5Ui0BTyWa5CyGNQJbclr74ko9AL2rwZ0s5kUBYqf3eRbuSQDSefEJCJpRAP2HF+o5RlUARUF+PFxuWQLXGWcGPbZZAIfHkjktElkC3pHtJ7qqVQBftBEoYuZRAVndO+iyMk0CKbDBxvEGSQI9WbqDq85BAFWzfih1vj0CMFL5yKjaNQEsq1ahsS4tAY43tzd2uiUAMB7NKglSIQMPPiYMHKYdA1ranNpcWhkBIvL26QwiFQFX28OUg7INA\\u002fsVyBjy1gkBU02Nh512BQAfJSMg70n9AGX3vW4PCfECI4IeXF6h5QIYmoNuLm3ZAyIYyKC2tc0CC6q1KC+ZwQPTFeD+bmWxApL97xdDSZ0DSdFKyf4ljQIXfr+jllF9A2cleEXIuWUAnW9ZDbstTQCbKPbDGlU5AqqAxe0cZR0A835P3XvJAQO3pCMjRBDhAyP+6AANgMEA8TuF1\\u002fXElQO025SzkBBtAfEZsa2yCEEBnoY2AxhwEQOCa0Djee\\u002fo\\u002fUtsdP\\u002fMy9j+DV16e7JzqP6gphJJFQ+8\\u002fMZVIj1K89j9C+WDQb3IBQEQw68ijHgpADdowCm+dEkBaTkmXxi0ZQKKCG2TtPyBAdmL3vbVAJECamX9NK84oQI9o+SK+ey5ApZIbKC8fM0CRKBp22584QHDxtyhIIEBAK6VbkSUaRUCHY\\u002feTrBpLQLKcY3Gf4VBAWaeW+hhNVEDqu9S+H6VXQJBidERC+FpA9yA9PlyuXkDVt+XC0MhhQMCamIYWWmVAI80ll6KXakB8vlrK1fFwQDAu\\u002fbp4o3VA\\u002fqQw99Eoe0Bbo3D77oaAQCrdex7wWoNA3ody4WG\\u002fhUBXRVIyZnWHQOG\\u002fsYzOYYhAdGZCGn6ViEBcjFrctEmIQM7RXQ5M0YdAqWWOebmDh0BqmHKw16iHQKYAaevLa4hA\\u002fQIp\\u002ft3XiUCdcC1bWt2LQMOJrltvWY5AR3bciTaOkEB4ZCROp\\u002faRQGkuOgOARpNAlPflazlelEAtiGiv8SKVQFS2HJYdg5VAv3DR0i95lUC7+FKkMguVQLO8JVEOSJRAze353ChEk0CbLu25khaSQH\\u002f3qFDD1pBAK7wehXw0j0DymF3o0+OMQKvsXXyez4pAlDDPd6X8iEAuUee5m2KHQLbKmorO8IVAiZB6Q0KThED3iGAXDTeDQJ893Br+zYFApLVNH69RgEBfjcMGTYp9QNG8+TluZ3pA3i+X585Zd0Ax8XSdKn50QD4Z\\u002fM7s5nFAWgO8REc0b0C+\\u002f6D67yhrQFrdZpUim2dA\\u002ftUacOR1ZEBlwNzsG6JhQDgkPC0dEV5ALdmi6KMsWUCr2yWQDIVUQEDZGI0DI1BAcrp7SfxJSECaiKzTsGFBQHr+KHlpjDdA6pTnjlsgLkBkZctMcDMiQO6tOxia4xRAYDQPFulSB0BURxnWtK\\u002f7P9rWQ+wi6vU\\u002fPPBiOdT13j8QsWsArIPiPx2UmdXy5+s\\u002ftue8vwUu9j+ZwROxnyQBQLrXJdSsMAlA915rFhCPEUA0II7qd2EXQDW7a783Hh5Arn8kqbcaI0D8Xp+YGFgoQPg8J95unS9AJF6xG973NEDzFi1ULxo8QNHHDm2xskJAojg+VntOSEDgag1E+IlOQPdc5FdueFJAhpe0UfqTVUAbCmcsaJlYQMnNLouM11tAYo+BImL0X0DmupYvVOxiQEPTW5RGPWdA4pkbUzVIbUCEpLFmBZByQMFABeqXOXdAtk050TZEfEDpjUsx65aAQHCqdqixtYJAE0eFvutFhEDluRDMpSuFQBrjZ8Tfb4VATDAR0cU+hUCQ1cAQE9yEQItQ9AB2kYRA8C1iTRmdhEDbonR0SSaFQJmxlSYxO4ZAx8wt7hTWh0D7gVzC8+SJQLL\\u002fnYhVToxA5JUtRnHxjkB1UrlVEtKQQInVtKaTGZJADWzgVI0zk0DPF6HdogaUQFV0oFIRgJRA8KHSWCyWlECNapjfu0iUQIAQ\\u002fM0WoJNAflxRreWrkkDwXJ2jm4GRQC0DadUCOpBA0dnJOhTbjUCibiL8KWCLQIn55\\u002f+nG4lA6AfRyLYUh0Bo01StGESFQCo8SR7UmYNAGaNQqo4DgkDd32C0PHKAQGKpXpbPvH1AExiKd3iUekB1VcUEGIF3QJugAr2qoXRAW8XBdZ4TckA529jrK9NvQHu8pitSTmxAl0Ymy4uDaUDIW8Mm2khnQH67RZwFaGVA+XIpBS+nY0AMMxnahNJhQKRan1lyjl9A8S12ry75WkDytpm7yAxWQHG+W3K9G1FA58Cc\\u002fwwOSUAxgajsmD5BQGCJ5liCQTZAt4vVsvToKkDWwNK2Z5YeQNHkudnhqxBAdgQ1o+0FA0CRr4SK41X9PyTTNknb8dE\\u002fn42GoBDW1T8KbbnDywXhP5Q3tSu+\\u002fOs\\u002f2WtEKBxX9j9MvPY52fQAQLureTO\\u002fdghAZ5D3lhXmEEDWCe8QV6MWQBAYUfm94R1AjuY4nue+I0DZd2e292UqQIoq+aNO2jFAr5qjPJoyOED4E7+gNzRAQE9KvkNRK0VAKjxMcsS\\u002fSkCYUvXUb0xQQAaxfp3ENlNAzksZP5sdVkDTiiBhdztZQEZ1pHprEF1AIvixK+QpYUBKtOa4geVkQBOl+WcyBmpAyk6CilNPcEBqfim0fDh0QAzi9x\\u002f3dHhAnXbr2+qafECbk88aTBuAQA5IAB2Nc4FA61iain48gkA0tiTZ43qCQJSxM7xVUYJAKfIO7BP3gUAIsAhpXKmBQIMvknWRnIFApv3XM5nygUDmdRW3aLmCQNoEOoqD8INAsvaC9KaQhUBklmbxNJCHQEW\\u002fF4nT4YlA84aH8ytvjEDSbVPKkxSPQCXedwPb0JBAljFY4ejvkUD21ftDWM2SQOrwvfNHVJNAgdDb5YZ3k0CLXZK3zjKTQKNt7\\u002fxoi5JAFnDMPD2QkUBzfQh3dFiQQA0Mpx\\u002ft\\u002f41Afqj+LFlEi0C2X1EecamIQMFfHqw3SIZAYUXvOqsmhECCPs3sRDyCQFsJGl1NeYBABy4qnuGcfUD643FYTGV6QONwXFhSTHdAvBpAQeJjdEDcKVCNEMhxQB22LUwiKW9AqQjKrzu2a0Cd6aVutTtpQJxqZ7urnGdA8otwkLCfZkCRt4aEPvdlQIK5fwRUTWVArDvy4rBSZECj1zJWNdBiQLqE1tA8tWBADeP5ZH44XEDYmc7ZCIRWQP2BD3QD5FBAUadNu4O\\u002fR0DYUT\\u002fi0zg\\u002fQCzQLLpoLTNAeKo7PKgUJkBvQjZ2jUoYQCme\\u002fJi92AtARIp2I699BUCrVgfMtxzIP+yXcxpmms0\\u002fBCFzupqB1z8c3HYod7vjP\\u002fQpzXRDIPA\\u002fJse2pSYp+T9HgTNIAb4CQBl2RWcS2gpA6WtsUwSxEkDq8ov8M5UZQGotRqKGYyFAWNCwzladJ0ARotUPVQMwQAsIxVaYkDVA4NbkgT2aPEBXRkR8r4lCQF3ObPUPXUdA8FqchwGXTEAWrjPtIwlRQGjVrsei7FNA6WA0JlUkV0BMXwOOuQ9bQKH865A6G2BAiK3yAHSVY0CL81iF9C1oQFG0gRaA8G1ANb3BXo5UckAcunCLXu91QPefhshHcXlAMjSgTMJ5fEBNQn\\u002fx87Z+QJ5SEQxw\\u002fH9A1WBI2pAogECbftwF6O5\\u002fQEPiog8+M39AVfk\\u002fTfuFfkCN+HKleT9+QKDSbCD7l35Afk1S7AOmf0CBXLqm9bSAQNgpX2b87oFAElBZfiSAg0DJxRqAFmiFQP\\u002fikHaSoIdAj3EOaDMWikCvwwv6RqWMQML0Ip2\\u002fHI9A102gzM6ikECEBVLsp3WRQKgN\\u002fLc\\u002f8ZFAev5iowYHkkDVTxohprGRQOOZirLD9pBAsyQjGeTOj0C+Ki+PGTyNQFJzVnV3copAH180pwKrh0ArxoLQag+FQHhdYH3GtIJAAfpqgiSdgEDXjQjSU3x9QGxgk9IvE3pAZwQkrH\\u002fpdkDNs5yxw\\u002flzQFwzbZ9XT3FAlSSw908BbkCxgG7XaEtqQLWtD7dwnWdA1HicHJr7ZUAtpVWyp0hlQNOujccBR2VAgR8xVr6eZUD9xnC4CullQAX0f2vQwWVAPPeWrabdZECBtGO2qB1jQIXmlnKQmGBAQdEEKvUmW0D6baSyENhUQGqzQwGo8k1Ayhxtf5QYRECuMGPRHi45QHYAvj5cji1AE2vXphqTIEBOat26Vl4TQJhiPW3SNA5Aen4\\u002fW0Gmxz+7bitMa9LMP52nBelGm9Y\\u002fE1+QVo3T4j+ZOccB0LXuP1n4dEGHDvg\\u002f\\u002fYT67WoZAkCovRPzaE8KQE8IXk3bnBJAbHxZjGDQGUAl428bYaMhQNGEy0vLzidAtyLRAHC7L0ARyVxe3tY0QJiLVfCk5zpADsAJetMJQUAFbMtpEChFQGeBXoxNyElA5uMjltrvTkAm8pwupGBSQAauk3t6x1VAqV2dG7\\u002fyWUAFlCu+WUFfQHsPPW0MDGNA+iOUY3JgZ0DOdHGFVKFsQOg1GNTOT3FAfmu7ANJ5dEDprf5TJoR3QKDmI6wiHHpA9+STSYv7e0DnE8jZ0vx8QBzdLA3TJ31Ap\\u002fsoiZmxfEDV07lgWe57QM7lrGAKOntA3DRvpG3gekCXdGN54w17QIS3JwzIzntADzXamR8cfUBoPTWHjO1+QNijyZfkooBAdcvNlhoZgkBu3o6V5d6DQPb+Pwqo74VA2P52NH82iEC\\u002fCbwO\\u002fY2KQP1ai6xOxYxAMTDyi8CnjkA\\u002fICiy2AGQQDQrXVsXWJBAgVEtXyRJkEAItrhevaKPQK4XuZZa8o1AwZIFVNepi0DsT2jk8QKJQMg+7A0LPIZAqyaRta6Kg0Bu6nC9IxKBQEQ\\u002f2f\\u002fewX1Az2IQKt3peUAwn5MVdIZ2QIkfvda\\u002fgHNAXUphaV3McEC3Dh84\\u002fdZsQGktg5fB12hAxVrj87u+ZUCMoMktOadjQLtyZIkHl2JAUoy+QNF2YkBRednGgA9jQMXDmb+EDGRA4REA9HMEZUAnXs23y4llQNxla3ZqQmVAHw05M0L\\u002fY0AAjumKgcphQHhzTBQ+yV1AVujetiVgV0AhR3\\u002fNoydRQL9GkCZFgkdALZw5KFwSPkDkRsqTgAUyQIctU7l7qSRAfQhFGlS0GEDFs3XqEYITQACz\\u002ffV6cNA\\u002frtd2ltjQ0z9fPZBnhWnePz4alqBLxeg\\u002f1qVwB23J8z+8UepJcWb+P+fHLdVodQZABAlVM+MLEEDbR2jnLlMWQKH+SDrjaB5AXwJQ7UdYJEDh3+tMp8MqQJvINYHhSzFAhBLF2WryNUAdDC6Fr1k7QNckG05bxkBA5oAEcLxURECxWBimlXRIQKvw4UqOUU1AWqWakIqTUUBfeiu3iSFVQDl9RLD0hFlAOadXPIj6XkATSmkgdeBiQORf0UMnAmdAe8TwcSnga0AXnEIScKhwQAtnoqzXfXNAH1JyJgQwdkAHXckm2nZ4QOLFt8reFHpAXY4HbTzqekCEllvqFwF7QJLkWfx9jXpA6Vojx4\\u002ffeUDXljJ1OEx5QBGCvlIqFXlAlvKI1dlZeUCXInjMpBd6QKsbvdppOHtA+VXd5rSnfEBYImD9LGN+QC7sg7y3PoBA2cuKkPyIgUCZ5ZzlfBiDQBp6e2L+5oRAG+LKbODchkAvO\\u002fopNNSIQG+TvTtDn4pA17dznQsPjEDmT0I\\u002fN\\u002fiMQNRk47CmN41ArPovAy24jEAy18mh\\u002fniLQFwNsU+lkYlAom7l5dkvh0Dw9SkCUo6EQPHNrXV454FA95MzBo3SfkAJq6I2Q116QJhWfUJVf3ZAFRujaMIuc0Ax01DCwFZwQEzT1gU6ymtAVR\\u002feej2jZ0DNhfLxZj1kQDmo4SJdqmFAS9hi+v73X0CTxbScA3FeQMgynmS6ol5Aj6oW0XAdYECrhZ9WCldhQOZf+w0poGJABv3NfZuPY0DRr4aUrcZjQB6f2PzSCWNAIsFRUqFSYUBkLP0bi6RdQGcc7J4UxFdA0c47QgLQUUDi5K+SxuxIQHJvnlUeR0BAGPGyW0buM0DdiaydWGAnQE60xmbpnhxAGjS0UuzkFkAjnDFPQFXZP2HEo3xIZ94\\u002faU4DuhwX5z8gGk5RyovyP8CCSxW3HP0\\u002fvQPOeS\\u002fiBUC388Rey4APQNZxJapM1xVAggRgZz1jHUCZApCc5FAjQHNZbu0v7ChAqAs5B6yeL0An456v37czQL\\u002fi\\u002fh5GKThACqlhg6QdPUBN9Kg181FBQAKsQeASekRAlHKhCJw7SEAHnc\\u002fFR91MQGW8pLumVVFAzU+nKQ\\u002f2VECz9geS8W5ZQL2l3Sji3V5AdaE4dPqvYkBVfBEY2oRmQOFw3ctd6WpA8uUqZ0O9b0C26z0QLl1yQE5lYI9hu3RA2j+aOfK7dkBqF3aczyl4QNcnixNv6nhAASIu3FsKeUAP+52Qqr14QFYh3\\u002fNBUnhA57HSqOcXeEAhaxmcDEd4QHZks23c8HhAMBW7A8X\\u002feUCkI\\u002ftBdEh7QOnQ8yJao3xAkQIkijoCfkCoIq57GHd\\u002fQO1FzdKmlIBAUfZyNH6fgUCpKsTSZ+OCQJE+vS5YVoRA4GS3yVvehUCzK4NTpVeHQOnIJhPqmohAK6IEjnyBiUBrNRac9+eJQH9po\\u002fMpsolAmgYvvYTRiEAdknCr5EuHQGNiJS1ePoVAlBX0Ee\\u002fYgkC3F0iDzFOAQHPauLkaxXtA5eBoF5RUd0Dhm6NLoHtzQN40Ui\\u002fXP3BAuGfOVbEla0AWQ425Vr9mQJ0aVUxYKWNApzJf8RZQYECvc57snF1cQGqk25a3kVlARsGflrBBWECCUTm2UF9YQF1BgxHauFlAhECCX4TtW0DLH65EWmxeQA\\u002fcqiMFQmBAtfOtOa3CYECuHrNIFXhgQMcZk4MTml5A\\u002fJw6v8G8WkBlB4FWt+NVQBG8xk1nwFBAKdPgLc\\u002fuR0CrjPKWk+s\\u002fQARex5g49zNAMXy8et71J0C0qW2smAoeQHCh\\u002fg6bVhhAqKr73X7D4j\\u002fVwSpMPYjmP7h3U9kzGvE\\u002fpDGQMCJb+z9wV+ZxdEcFQP+x+z1jiA9ASs17+4Y7FkBOvDIzjP4dQLlE9vIUgiNAPnL+lFqrKEDb7Hwfe4suQEOkD1VumjJASvr\\u002fuvVWNkD2ul+os3Y6QL47BIDM9z5Aa42hpRj5QUBJg4Z98ddEQF0aGslSWkhAnwkHWYHUTECUFzLrektRQIdyKp0f7FRAD+OUZ1tWWUDAoCk8kIheQBvwS\\u002fSbP2JA4tHKH5icZUCaUCIy\\u002f1ZpQIi5gahgW21AgvZbpoi9cEByFFdGdbRyQD5nyB7qYnRA4Ii8rWaedUAQ3OIHoVJ2QCzTws5bjnZAoKmxB9uEdkAumdgg84B2QPgUQxrhzHZAhNDHidaXd0ChRolCieR4QLZwwSpaiXpAxkaTv0NDfECBJmYRz9N9QEvF9DFhHH9ALzBPDPYUgEBlJYjz1pWAQEB1ck8uLYFAWNadcmzugUApwNLx0tuCQMHDiDhx5oNAxWydfiz0hECJbvaasOaFQEb0XhxOn4ZAlDidHs3\\u002fhkAGRvEsleuGQKwZk3pPTIZAHEURP5kZhUDWhdtPp1+DQEAmI5ciQIFAXAQ\\u002fCxTXfUAMywLDQit5QMuDmL6+03RAOF0VGlcIcUBLfiMXQr5rQExxyS4Rp2ZAGw\\u002fa7MKjYkASGlyHsAlfQBWp6zszQ1pAqvZPajW9VkA00zE1FVtUQE0R1VNlEVNAbd0eCp7XUkC4e4h81ZZTQIlAKHUtGVVAFSY\\u002frl8AV0BlKhCStcpYQPUu3FDl6VlAOOF9TwznWUCPzGEOkIZYQBpN8vfs21VA6ZcDyHpDUkBq3H2FlIhMQHmZGgrSzkRAjuiuxS1WPECPE920zRsyQN6ZtgdjPCZA3eAUCR6LHEDeZefWuWkXQNa9E5thiuk\\u002fmR2NHsrE7j+LflukHXj3PwD7W8FFzgJA0kJdFCcwDUD3OkxwL3gVQCFMCimo4B1AROWM84XAI0ANeP0Ek\\u002f0oQN\\u002fH5hGxhC5AK4bAwVomMkAsCvjMhTI1QBY3jhdPdDhAsu3U88j5O0Bk1lECENc\\u002fQIkXJURzHEJA7OsEHFa9REC3hA5WPBZIQIOnQSLMfExAJ7aTBIccUUAjoG89UbRUQLu0ddIp+VhAfVPMsjzKXUAygbC+0IFhQKlrejP4RmRAeh7RVN0vZ0BqTSq0TzZqQCWNrRKFRW1AUwM+IdsYcEAuMB2nqF5xQM3nUwMMV3JAt04rQZ71ckDzLEa9P0tzQMFAaxXKiHNAGxiAb+Xzc0BZpjyjAdB0QANXjnufQ3ZAZ2tkh3NFeEBppiUlNJt6QDr9lDnk7HxAutHOvM3lfkAGIKYpeCqAQCvHc8rznoBAhZjl8zzpgEDDhroyXCyBQJOimOuqhYFAwSMMh+cBgkCMvoSkdZuCQDDAJ05IQINAbTaV2s3Zg0AWlJJmc1GEQNeIqT2BkIRAB1aH3ap+hEB6FXuE7AOEQJ9XELpJD4NA6Il1OGGfgUB62WZW5o5\\u002fQD1cS6JXWntA2TzhUXT\\u002fdkBALv9ph9hyQJzRfwMLUm5ACVAkFOcpaEBwy4qAd0VjQMnP4hDnDl9Atj7BJu96WUDv+KZO0WNVQCqYC1Vcb1JAuc5cC11fUEAjReD6NyROQCAB3k1X9ExAxEniwNIeTUDex8Ahz3lOQHoXDh8pUlBAmdE43R5\\u002fUUAzqwj1h19SQE5MfA\\u002fKk1JAvjBGlw\\u002feUUAh880h6TZQQOcRI+Jon0tAOirXBxwDRkAZeFQijmFAQOwfeKLMxTZATfPNUaO7LUAEeAk3BK0iQG0xd8QmiRhAdomEJU9eFEB5IJdsT4nvP75UBmpaFvM\\u002f2ee7sTlc\\u002fT+knWM1NqoHQNjg33xCaBJAYtYCpjMIG0DniAGWQK8iQA+Jd0dKZChA\\u002fROEDqw\\u002fLkBB+ZIN0fUxQIre+pzPnjRAv241W1MhN0DntieEfZs5QP1pZGONOjxAGLZHmRU4P0D549uxPXFBQEKtRYI11UNAJ0jWAfwRR0CVp5osa3VLQI0A3fdtmlBAvv84nUUnVEC5LQYd9T1YQFLo\\u002fKxMoVxAKhOtJBmIYECnnlNn3q5iQFCRwddOvmRARlOKFMS6ZkBSKFWw3aZoQNGUohJvd2pAMhbTMMkRbEA\\u002fiyNA+VhtQOuUfMzLRW5A9DMbhbD9bkAI+QvtS9pvQF6hGw4trHBAzgNhCov3cUA\\u002fWDRKxu5zQMw0uZjAgXZA3119Il1teUCHcaCy4Ux8QEVWcYsfu35AJQJnGfM6gEAD5\\u002fBJ3rmAQOaPbETc8IBAp01aWYkFgUCrDh7jqRuBQG7qXUgBSIFADgsHS3+MgUDzwAanZt2BQKcm7gfuKYJA+eHrkvJhgkBaJKnmBnaCQDZkVbVuVIJAziwdlT3ogUCefrPD3h2BQDtbdQ0j139Ab7FQxpyxfED2Vk7fjf94QBqFbsN8D3VADYGUh\\u002fI1cUACLbiMVHNrQA3xV5yLjmVAMDZx25vdYEASJTFQX6NaQPX8nI0KfVVAN\\u002f4aShjYUUClYh81KpFOQHyBMWmf8UpAoLv1xGRjSEBTh1n+KrNGQG34HV2I1EVArQrov\\u002f\\u002fBRUA+le5rN1xGQKh5JDRWVkdA2EF4SO45SEDJW2vlEYNIQKr0g3huyUdA6JNI\\u002fQHkRUCBzGNh4PZCQBJk6PA5yj5AQjv2ItVcN0ClLO1deZEwQF23Ct4kFyZAI1o\\u002f9dxhHEBcJETaShITQKr6MBcTBBBAeFbqZbuV8T+iFcyWrGn1P9SQ7XjmpABAP4WPtmoQC0B5JRTIwCsVQO+fXV\\u002fYKh9AV6RP41yDJUCF1tnFbugrQDoWsuPSFjFA1QLWaDHkM0BXn4brbzQ2QNyssq1dCzhAUsHDipOYOUDHtn4J4yw7QI2T3aHiLj1AkSDAbD4LQEBxidE3tTZCQLM8kJ\\u002fTY0VAt1x91sjSSUDWYnG7XZ9PQPMVAl2ZUlNATL5IjVc6V0AYqi6TXDNbQACnnYvQ5l5ACJWPPqMLYUBA5VLwWVliQAIZHMSiZ2NAUtvc9W9KZECXvYPXyxBlQJSDqIP8vWVAM2C2a3lPZkDpNM7dEs5mQDx8AJsxYWdA\\u002fFdDvbVXaEA20etGwxxqQI07pZ8dF21AY45JOk2+cEAMk1V\\u002fBJZzQIdhAjPS0HZAcjGHXuEIekAGajAsW9J8QPH1pcef3n5AtRkCrqYKgEBo+Xz4rkuAQB7OS8WVVYBA\\u002fR8PfO1NgED9\\u002fRNHUk6AQHMGW093XoBAovtobKp3gEAYk1iORo2AQJxrb1Z6k4BAYZnto3CAgEChwYy+EkmAQPFAp3N5vH9AmbgN3khdfkBVHEhJS1t8QEw3ixAYtnlAP2dNB9iNdkClYOj3JR5zQNlDsY6EXW9A+AxsujMDaUBSduJx2IpjQDIRCdwxQl5AjSdRtOKLV0B9fFUMrbJSQPUK3C0OqE5AC6Mx3n0KSkDX08I1I89GQD1z\\u002fHJWakRAdISx61eMQkA900GNCxpBQEwbPPmzFkBAYW\\u002fhOEcNP0DwdTQbaq0+QK9PiD0\\u002fpj5AQf9kLipjPkBOOF8pBFM9QOhc8WcDHTtA2JSwBqzAN0Dq3p9s7ZIzQLWeLeLmOC5ARGG\\u002f26bWJUCXzdJse7UdQOWgA6zrgRNA+g\\u002fEPk\\u002fMCkBoFSCJ\\u002fcAGQCHrSZ0SrfE\\u002fvJIsAoqr9T96SAh0fA0BQEiwfUSsBgxArw87q1saFkDY2R5sqFwgQFn07fcqpiZA9a9vEtBZLUBQP2Gqj90xQG1lhkmNijRAOlGp5UR7NkAx+eUTAa83QMzfcVF9YjhAmoe1RIUFOUAZet7h8Sg6QCyEsOnibTxAq5MLUtw7QECeifaFi2xDQH+Mgzwi+UdArumFs7ngTUB+iaqqzG1SQIdCbWtJK1ZA1dnHMZrFWUA1vAk3e9lcQBlFTodgIV9Ayw39j9REYECE\\u002fK\\u002fff5hgQJ9xtil+qmBAXALkZjWbYEBkWs0Y7IJgQE70x3xOcmBAGfPuyl99YEDLpPP0icpgQEeLlhTOm2FAbgQQ4CJJY0CnQw0O1yhmQN+A96qHa2pATUJkbYv5b0C7z4ucpzFzQHcTHNK+eHZA2x2RVWtqeUCj13+bvLJ7QCRbR05zK31AT0IhSFjkfUD7jtYHAxZ+QDDjQHn\\u002fBX5ANGYsSYLqfUBR1C0fBNt9QNf5ztif0n1AxojIflu+fUDYG2iok4t9QD2wemNILX1A+YNWSaeXfEAUG1XxHbl7QCUsXkWeeXpAK177xBjDeECBVvbkhY52QIhkorwl7XNAynXX0xsIcUDjjAQHMi1sQKf8VoqFn2ZANrLnAjW9YUCAU03q0W5bQBm0M4MONFVAu7tZDZOmUECQzpKoX\\u002flKQEFZRx24tkZAn612Ij\\u002fQQ0AzVsoonq5BQEFPvgdW1T9AbBkaF\\u002fCkPEB7qzhj0L45QMJ68f3hQTdAw\\u002fDTnk5JNUDYwCBz\\u002fMozQECuPR4WkTJAZXpYYOdNMUBwVLNu7nkvQP2Vz1vAfitAp9t9lyHMJkASRxlI8s4hQECpt0WjIhpAthyjK8YZEkDXt\\u002fvfNkMIQB01hHrUAwFAwFVcac0x\\u002fT9mvwmZKf3vP1bwJQlMwfM\\u002fGxMR4EuE\\u002fz8TlNp3SzcKQDbMLYQp4hRAZ7BVdAcrH0AiQY2H6bIlQF1a6KmVNCxAbv1qjiooMUA+Dc+VDZ4zQHFaVeaqODVAiSIIdRDyNUAHZScLwAw2QKGcohsBDDZAcloLNECfNkB8hcYPpYg4QNrrFeJQgjxAyel+QIWPQUDucU6SGVFGQJrvPeG5aUxAX8dTM\\u002f+3UUC6IMbzj1hVQBN8jGYAqFhAWhCbx3k9W0CCNCb\\u002f\\u002f9JcQGFyD6ifWF1AEAio7vb0XECYyFlMRvNbQLLL\\u002fkiZqFpAPM+q5Q9eWUBzJjNJQktYQAtRCOXKoldAoeoKctGoV0DbbWbuyMRYQGrgtVFpf1tARniQpWwyYECh0vXcsOdjQESx0HN41mhAcmewhGytbkBMFHieMGxyQHOmZcawUHVAN0Z5rQuyd0Dh5NTUll95QG1lZ1G2VnpAU8FDDEu9ekB\\u002frQNi7sx6QK6wWoeTuXpAoB2fHOefekAZOJAjcIJ6QOhamc9qU3pA3uKBVnICekAwadBIsIR5QB1EYGUh1HhAXiWjNtLpd0D9XD8kQ7p2QLDAzkJ0OHVA6QZtUThec0BsyZOzMzRxQIKZbt5oqG1Atum3nNTIaEDyGaldWxxkQLfJIRYN019Ai0Ntf9i9WEC6NRlwwh9TQBdVy2ge3k1AW+p5v5f7R0AacNBV2A5EQDFfyfx+fEFA6fm9jrVhP0BqZ3diaG08QO+U2YYmjDlAHYudWa2MNkDatB7exIUzQBe\\u002fYwV7rjBA\\u002f2Q1wltsLEDXupmHLlgoQLox2Mc7+iRASl41Id0GIkCLMsdFInAeQDLsTVQl2RhAjK21y6ZdE0CqPAvpmJsMQD60BoVhEARAWFWlAR9b+z\\u002f+HXZRR4rzP5dbYbJW6\\u002fA\\u002fO30+nJMI6j+aSDR7yTTwPzsHt9bvO\\u002fo\\u002flEC41\\u002f8cBkDVMfIpItERQPQTyYai2hpAi8gSX3LZIkBoRQQHJacoQDNcj2eWGC5AXNQ\\u002fdFA0MUASJ74SaoYyQNxrnZKE+DJA48QNr0rLMkAcx7WaG4cyQBesy+tS6TJA7J+kFdPHNECI+7EVF+44QGCnztsc8z9APTtKMlwERUBcg1NL1GZLQG6on5SpTVFAPCVgMEnpVEAoeIaFUw1YQDcbjFXRTFpAuI6fVfVjW0At+L6rmklbQF4q03sCLlpANIYvhCZoWEABLCN+yVpWQFOezbiJXVRAzgICT1azUkA3CqD8qJBRQPkN1YpGLFFAIbIsKg3QUUCUx4cEJd5TQFCWYsM9wldAbGi\\u002fnCjOXUBRlt+WygVjQLpTOoIfC2hAW8yfrVuKbUCv5ZGbW3dxQBVWKwic0HNA7A2yJjiadUAhSWBkmsF2QCwtUmzbWXdAnZGOQrOOd0Ck\\u002fEPrX493QC8u2Abze3dA8lrJC2pdd0CkxKL\\u002fQCp3QB5fBr0l0nZALtkp4YdIdkC3F\\u002fMdcIh1QDgU\\u002ftrOkXRA7wryjN9kc0ChxcMQcwByQECCT6OXZHBAsJhJFJ4ubUBqpABQQ09pQBrdkpukWWVAYkrjUTOEYUAhHXo0dARcQKUPjyup9VVAWsY1Y3kIUUAGj3GVrYxKQDsnQxk5M0VA+LV8MVuoQUBwujK0IeA+QJewucB8BzxA7RElF3HTOUCXpvbmMJQ3QNTJngGD9DRAJE5g98b1MUBWjdWlwqYtQCK13K4XtSdA6GVATMGOIkCsug59wswcQO1z7VkeWRZAdxPkHwtZEUCoW4guvLQKQLQaXMziGgRApicLNIU+\\u002fT\\u002fDzcPJGHr0PxaDPXdwJOw\\u002f7DI3MKVb5D97w+u4PMDhP4e6ewbqCuM\\u002fLpC1Kcjo5z+5AAPdZabzP8Y6IvhOzABA4Odm\\u002fA1oC0AesxxBreIUQIJ4b3ninB1AYjuB462HI0B0PsCu1P8nQFlbBTgljStAX0Fg3cayLUCahfHgdlkuQK\\u002f4xnCc8y1Ananc0d6BLUC8PZKEz3guQOH4Day0RjFAKHlv3IK1NUBFvh615yg9QCRymo\\u002f46ENAFeForvueSkAB8iBDtQ9RQGLja5wawlRA7SoFF8HiV0CrTn+Sav1ZQGr\\u002fQL7GzFpANOHObTVMWkAWqODOXLVYQIO71Q3Ma1ZAOoHr8UfgU0A+VmkXmHdRQIRvO9vZ+k5Ax7ijkpdLTED2lV8\\u002f+DJLQI1lOVghDExAHTu\\u002f5mNRT0CZzgEvvMtSQFRIqEYMs1dAhV0XvOt5XkD1kRPpQ3djQEfvrdG8PmhAjf6meDofbUBQwxPluclwQKDuunUFlXJAnOxCv5rTc0Bl9fjKj4l0QKh0Wp621nRAkq\\u002fd+8\\u002fldEA6P4UgfNl0QJC9XxAHwHRAMbbH\\u002feKSdEA\\u002fF8tk9z90QEh+BuQMtnNAikZBNVHtckBsc2KbvehxQB8Vnav\\u002fsXBACE3TVE2obkC7DvfMVrFrQFyM9L2Dj2hANsGEjwNVZUBPREQrbxliQPL+yDL6811AQAgNomcsWEDrYX2drxVTQOHp+MpvtE1AU7UjNlUdR0AxkvabMF5CQPT6VZzbfj5ABXO6SALFOkCt8oOIJqQ4QEQFHKIrMDdAIB1VKwKqNUDhn8XRL6IzQBFjZhT\\u002fAjFAvqHQoVb+K0DbI3ULVNslQM0BqMczRSBAWqFb8xNRF0BjIMs2\\u002fUsQQJb+9DsdiAZAtBDiv4Ea\\u002fz+tqodwNnT1P9DAt7BdYu0\\u002f25mIl27f4z+8jeM5denaP3qLrG0Qb9M\\u002fXV7a07\\u002f40D\\u002faVnBEvQbZP55bI44ms98\\u002fAfpYd\\u002fh36j\\u002fwiR7WCPb2Pyrc63d\\u002f+gJAkULLO1tHDUCwx1puvP0UQBM5BzOu+BtAVC3ncPZWIUC7f2NQPQ0kQL+i5U8NvCVAmE6dN8ROJkBOMh4CYCMmQMB6UOY1GCZAl2J3ohWAJ0AUwdkOo\\u002fwrQBFlTmj2nTJAOJFS9nVKOkBrk91fOKJCQA1UAhjskUlAtBSDKcStUEAdnvOI6YZUQE3Y7TTexldAm+\\u002fKpWruWUDcwnePoa9aQB74zhX6AlpAEqUki5YmWEAIB1cYbolVQKR8oKAwrFJACYdHRj4EUED8jJ68jNNLQCcQbXxCKElAHDhtHlZESECKfkO3kFJJQDZgCv2TiExAs05DAtkaUUBKsaggCFtVQGgc5m\\u002fXIltAyHZXzbovYUBuc2aAgVhlQIg2zkXFsmlAQYaWRq7LbUBx12duj5pwQHcB0+b\\u002f03FA+m0sxQeNckBBX1MUtN1yQOphF9Lk7XJAWJwD6TrickC+SEovscxyQPOZ3ib\\u002fp3JAhwB+KMxeckAuaewejNlxQHSIVREvC3FAZrxICnPtb0BlMTRuNFltQJcEaKzrhWpAl4kk06ibZ0AMsOx8s7VkQAs2M5HZ4mFAr7+f3xBXXkAw61h7ZDFZQOXCOIIxb1RAturEcHUyUEDnmDOuGDpJQJKObHgRjkNAd51CX5vbPkCRcz2DTHc5QIFY1XkmYzZABeWoq9XZNEB9I7wHfwM0QD5huusGIzNANCR45Jm8MUA7qwqiYVAvQDYeOmxTEypAUiROFDpmJEBIxa4MPfwdQI82TgMpxxRAF+QHcL1ZC0AMdffbXksBQPF\\u002flhgzU\\u002fU\\u002f\\u002f1VWrIYI6j8sKRRsvtffP08dBZuertM\\u002fF\\u002fQFRP8NyT9LGdJOtnbBPxZxPbj2Jr4\\u002fyR258zOJzT8byfbbgeDSP4aybVUpBeA\\u002fw6aPUmY37D+4ZvSTxKT3P7lz2bdOeQJAG1MLrGrQCkCi+D1SOxISQKnUMD7VpBZAuQytlcpwGkC\\u002fQg1xmuscQD\\u002fENVvw+B1A9nBeqdIqHkB0T0rpDugeQLapV0+5NyFACylxrHrXJUAAlr+VXu8uQEwy\\u002fSNh8zZAmxKBFjDcQECOwwWyj8BHQEGAsT6DnU9A9aib8g\\u002fEU0AHm7govS5XQLxTGTLhhFlAPek5XaFqWkAHt7+twcxZQKyMdGr55FdAHj8qapMnVUA5+KhvQSNSQOeCX2Hvv05AB0W9j2eOSkAMgMKodjRIQN7JxCUR60dAAL49IDK7SUAZsjntsp9NQKhFTf0L0VFAq5TvK5TxVUBR9Ace7EFbQLzXr8Af3GBAn1gbRyGJZEBdao9T8WJoQBnGzD+kCGxAq7og1jkWb0Cz+A8yqqJwQIcRKvMUQ3FAv94mCleBcUCoC2o6L4RxQNEXpvhUcXFAIrNAs0NccUDe7\\u002f+ADz9xQLBjP7Y2AHFA4uogaYuBcECnMZ0SG2BvQGz6AakFGm1Ad57jDfdZakDBLC+IuV1nQB+MXNTwYGRAqyMWwYeMYUD4mk2iN+RdQILURzthJFlAAbHhM4LOVEBOGuslPt5QQLVXM8EBu0pABHx2BWPBREC0nRJIy\\u002fE\\u002fQCOdzbYg7DhAx7pI9FRWNEC\\u002fdBlrRssxQPu4pknbqzBAagi9UsY+MED3VV6RhrAvQJCaMdgT\\u002fS1A+1y7Iin+KkBg81zW1tomQPEIbGA5GiJAcNcWtc\\u002fEGkBhXaRnEXsSQFpV2yE74wdA9CsgByQO\\u002fT\\u002fdGeBw0sXwP6ugYqJinuI\\u002feExT2ds31D+KPVO5C\\u002fjFP96Kfge9xbg\\u002fdpxr1Hhsrz9YvIuv2xmqP8RWN8NfS78\\u002fGhmVD14wxD\\u002fchpaHdGvRPw9Up28OKN8\\u002fVkME0zR66j92tfHWhPf0Pyd2dpvZ0\\u002f4\\u002fyyidCQcJBUB484nwZ6wKQNGevc38gQ9AaWcDORRwEUBsnm8zs1USQBCDlhNg3hJA+fqO3usZFEDFnKKXZNEXQGZbmm\\u002fAPiBAu71jds6DKEDo7Cceqw4zQBXA7xPJ9jxAJ\\u002fQ5oLDxREDFZcW+QH5MQPWj2WLgJ1JANQOdk12nVUBR+kpUXC9YQLPYqla\\u002fVllA6xbPGET5WEBGxPp50kJXQCWWIUi9olRAa2nJ44atUUDSCuXAFfNNQJ9qIK01BEpAN2hTOMguSEDjn1En7rdIQHUsIdwOm0tAxUb3KPJUUEBnQ88a7NdTQD5Qcva0RFhAac+FRD6QXUCX4NzoMdJhQOkBfcvjImVA0hLi4g+BaEDAWomNPJxrQAmgrnOaIG5AeumxMxHUb0CsZQbL+lZwQMT2D9WgbXBAY\\u002fzT2ihVcEDCcqfcNjNwQLW7LnNtGnBAUwM8lHoCcEC3vvKM55pvQJTrZ7LKrm5AsnIUbAwQbUCojFefJ7xqQLVjtuaJ32dAMVRwPgLDZEC3Dpyk1LBhQLz8hAwMvV1Aczu8+4rHWEBWSi7+J35UQPo+IEjexFBAWAw+pqcAS0DFheiQLklFQC8OLqYhbEBAc7qJBqoDOUB2Kma7ezczQDOVcU9dzy5ArH3zRF+aKkB8Kqvg8NwoQCW0FU\\u002fSbihAdvwM4tAxKECzB+9Jl04nQMaBUM9FXCVA7SDMqrVlIkA6ptnrM5gdQJWCFQLGKRZAti52hGfgDkB2FeU2LQMEQPpPiMQKLvg\\u002f4pUqsp9T6z+WconOoxDdP\\u002fxVA6emZc0\\u002feInomPrKvD+Q25Va3WCsP6ECM98uK58\\u002fYaBPnCkOmD9xz0fJ78KtP97BMNSGYrM\\u002f8UOJXV8CwT8xMrqDRubOP5vdoC6Ro9o\\u002fmF3iVTxj5T\\u002f4Hh8UAN\\u002fvP+kBjmvPCPY\\u002frTe1\\u002fqVN\\u002fD+Qtvl1u+8AQPB8aNPsBANArpubGoBhBEAZDV2uJ6AFQH6pKJ30RAhA0Pz8cB76DkDTrTuIEtQWQCtDp0QVTyJAvS7uR2+4LUATK222ylA3QA3Z849iTUFA9Zjv+OwUSEA745F1l1ZPQJeqTJIFDlNAKMTz4xWrVUDwQVMxlBNXQM8W5axSE1dA9SFG\\u002fH\\u002fCVUCLEM5vR4BTQEQvAXff2VBAB3LciV3RTEAHKigFbGZJQLsQHoGdMEhA9i0QMPaNSUAE8nhcI4NNQHX37rGW6VFA3DSOAxsQVkBchMwmLgRbQM00BC7xS2BAaAP3XPNLY0Aap6peK11mQBx11k0tSWlAcoApAP\\u002fLa0BNUOjt\\u002fKRtQBXXTBgBr25ANqrmijPzbkAmej+ODqpuQDEGobZiJm5A9TyctTCybUCJ8P1Q0G1tQAD3PkyzQW1AEIfPSPzpbEDfZotINRZsQMg8NpDNjWpAG9BJ3ZtIaEAvgvFKmHFlQLfYsUULVWJAg00QWxyJXkCiWZmxTPxYQEVdUC\\u002fzP1RAkQNJ9jdVUEBKpunCgzRKQHziedN\\u002fzERA87PgsCc5QEDFWFfUJck4QOZEkXelojJATMAEbR0bLEDQAf\\u002fEQAEmQG9vfiPrlCJAbLUWEoQnIUBVvwmKFd4gQAgHRraO3iBALOFsawR9IEBA\\u002f22LybYeQLk8nWYh4xpASQPw2QD4FUBiGqrjabIQQBlD83l\\u002fkgdA1eS3Q7vk\\u002fj+tpEXSoczyP8IizcNjReU\\u002f1nQs4KBt1j\\u002fCHV3m3SLGP3FO6Y7tpLQ\\u002fCqFGXPisoj\\u002fl0b8oOxaSP\\u002f\\u002fdnAS7t4k\\u002fu2VNBpFmmT\\u002fAsGlMBLWgP878PXUs0q0\\u002fftbxc\\u002fSDuz8pnMNN1hHIP7P8OqVRmdM\\u002fg3LKILOc3T8DZZI5JMHkPwYHKKoaB+s\\u002f8acQgXxo8D+DisQ6iL3yPyGAbhB1kfQ\\u002fdIK0R1+29j\\u002f0E\\u002fZ+9jX7PzpS4Nog5QJA\\u002ffnezb8bDkC6oBaKyY4ZQOUin8H6kyVAFdcAtmRxMUAPBE1UUo46QJv6u4Nl6kJAjjZGXQEqSUDRtg8XvUFPQJBVotu8I1JARwN03oO0U0CTCZKISxZUQPFB8qQ8TVNAXB70rP+fUUDAj9m\\u002fhwxPQDtkedXfGEtAtvkvishtSEAUYKwro9hHQBpZW5q600lAUuUYZhyBTkCMOvJoZdpSQLjWDaHIhFdAioO8Jxf8XEDuR\\u002fABanlhQEUMZ4VqiGRAyh62Bat2Z0DVhjna6gZqQEmGsSlX+mtALLFAZvEgbUDu3T3tOW1tQLC4yvM6AG1AfhBCAlQkbECDARmuEjVrQAH8GB28fGpAvYBEzMQWakAbCHL3M+VpQE0GejlInmlAWUIvbkLsaEDlbD\\u002fgApFnQPV0JdMofmVALcRwwazYYkAQByR7ndFfQOTRHtsU\\u002f1lA8Wbizoy7VEAH0Ega\\u002fUxQQJjdLySBeklA6VF\\u002fRNzbQ0DGQCQzkco+QGKwUVrvlTdAeWme8TS\\u002fMUAYpq0\\u002fcUMqQHaorHBBZCNAZrgQD0GFHUAMJCb6Ei0YQJ5Hc9GR0RVA4Zfgg1RHFUAvwISlo14VQKXrclgqHxVAFPIullT0E0ADd9FzmLwRQKVRX4T\\u002fbg1AboqgqAO2BkCj92AKRUQAQJUqq4EAnfU\\u002fPwD9f4Oh6j+NTHbT62\\u002feP7x9KhyXJdA\\u002fFioZh87cvz9+mhl8e12tP4yb5F1Qtpk\\u002f94WsJAdYhz8y7zxyxYF\\u002fP+a0\\u002fiv6c4M\\u002fnMvY36PYiT8iw9jP6HaXP8IPZuGw\\u002fqU\\u002fVBthzZmGsz93I2IxySDAP6kYuoAHt8g\\u002fLiP0O5SR0T\\u002fE0kK9YzbXP31qEz3Wodw\\u002fNr+iaSCt4D8loDHhPdviPwd9\\u002fI7y5+U\\u002ffS1u79Rk7D8dzHBMaJf1P+qRSEwPiwJAhajbZbCSEEAk2Ge1oQIdQNCjnGpSIChAU3NWCOHUMkAB6USPHXg7QElxMz\\u002fUskJAIglhEUXER0Bqn83QsDxMQElrHQ+AbE9AyoiZkn1uUEDPbqGJAjxQQIf8pbU6k05A4F3KpTLcS0ADLoPl0zBJQMXjvlLnc0dAz5WLmMJpR0AxcDz7uqBJQO911hDEY05A5mv8U5vaUkBpsfyyYKhXQBiqi+UjWV1A96NNF03HYUArDITmZehkQIY4ybgayWdANCEzeRseakD8ZPCfuKRrQCZ7dgGjNWxAieDwgijWa0B1aQ+wD75qQERQ7whhS2lAZ1x5s6vlZ0CqTflUGttmQGJHFVbRRWZAyPgCdRAFZkDXxdbb78xlQHB0iMG1RWVAzoyLf3UtZEBkvF\\u002fDp25iQIobLdM5JGBA+IKDLP4XW0CHAgPsw95VQD85FDZ5GVFAPnx0KdMlSkB1qTuL2L5DQLFSntQRqz1AZP6+Pvk2NkB1QOZtaoQwQNSEIk2DPyhAvTa\\u002f4sCJIUBed+aFvT0ZQCAbxrf1lhJAouxclftLDUC5ccXliZEJQN6so\\u002fzhfAhAVHoES3qICEBX4gy\\u002fZG4IQN0R+07VXQdAatAzFD8RBUAvY8\\u002fcFL4BQCVxme87y\\u002fs\\u002fn3gAwYo09D\\u002f7\\u002fnQdcj3rP10IN1f+BOE\\u002flI98PAu20z8jXJ28hSnFP9mOkDWPE7U\\u002f8e\\u002fYejeFoz8RdQr+8weRP+JopD8zXX4\\u002fRzqyT3YcdD\\u002f5rw3bD7xqP8WVhSXn8HE\\u002fj55l9+qRgD8+\\u002f+2db4+PP4M3\\u002f\\u002faZb5w\\u002fUtcZ\\u002fIzUpz\\u002fqm05BE4ayP1OZVw2TuLo\\u002fRy\\u002fquEzswT93FCbxeX7GP8VDXMbu0Mo\\u002fxMdEUO14zz9\\u002f9QQPvXrTP3m1HhrDp9s\\u002fRYbQEeoT5z+y\\u002fLktvT71P+YFkyp34gNAio94YSD9EUBYSX1Rr78eQOdxpYKJnChAq7IqndBlMkBA84qc3645QPBQm03zwUBAE6Dztjl8RED6VBwA5YhHQBmQK7qnh0lAzFumfSFTSkBI88BDvQxKQP6YQYZaFElAzgE3ElnzR0BxBeiw+UVHQMThzJxzqUdAPCWdi6mtSUCKlh7zxcVNQNfe1zmyGlJAHzmWlU1+VkD5g85ksONbQKnm15sE+WBAuwH1n0kRZEBwMqjxX+VmQAYVHl4wGmlAZhJDtmRiakB6b0V5V5VqQOg5qhKIwGlAbt8NKeMpaECDXkr8Sj5mQIz5xsjAcGRAcMqU11EWY0A\\u002fgqhs\\u002fExiQI0lScYQ92FAxQhqMcPLYUD4hNAMH3VhQDm1ipEMr2BAXFIhHDm4XkCVm6d2ahRbQDQ5m7Rk0FZA94iYkOFlUkBD1hZAZYhMQCzzEOzdbEVAvjKtoQNuP0ADYijy5LU2QKx6jw1OQzBAC97zFL0eJ0B7DvYqwUcgQKV7mIxfrRZAKLTJy2JvD0ARhbOQDy8GQNpG5De9owBAS\\u002fPSoce3+z8zzWealL\\u002f5P183PPsthfk\\u002f\\u002f7ZF77x9+T9LW2wGp6D4Pxd3d1ybf\\u002fY\\u002fESclccI48z+\\u002fkJLBDY\\u002fuP0V7yGoejOY\\u002fuAH5UY7Z3j+e6U4u747TPzmn\\u002fGhM+sY\\u002f+YlS3RYDuT9NX1WSGDypP8DKIr2No5c\\u002f8PutYgnNhD99Qw2AbpxyPyWrPK++pGg\\u002fuT35y3N7UD\\u002fSVgbplFlWPx8DTkdBAGU\\u002fUQmL79lRdD8qmKUMR5WCP47nVvrtmo8\\u002fELJjmOPtmD+nfNl4XkCiP2+B8SNy4ag\\u002fHtMG\\u002f2TVrz+1Px\\u002f\\u002fK4KzP\\u002fkL7CGS\\u002frc\\u002ftA+xX8cNwD\\u002fpy4sF5zTJP9JNFo+aBtc\\u002f9z\\u002f8o6KP5j\\u002fv8mIYtgH2Pyy1wsJTiQRAlHvE7hQJEkBV9oVj6aQdQNjADa8sxiZARK1idbdfMEC5OvN1KBc2QDnCxibBETxAshtLXgDiQECk7h4Z81lDQNW1MjOQS0VAuzBQTwqvRkD0dddTwpZHQCcz2JN+KkhAM5N9EwaoSED\\u002flXr0LGlJQMTPNx8n50pAfzutTqWwTUBmO5NvDCdRQGKrUH53jFRA5MI9i4QIWUBvoHLMcl5eQED8h9HxB2JAHJKACBa0ZEClqY85u81mQHbzHBq6+2dAOP3dcrwMaECtVSqhswpnQHgtKy3DPGVAIlXsfAIUY0AgiOsy+whhQAaN1ygX7l5AWOYu0WQHXUB0EuQ4NTRcQCX5GqrZ9FtASzwu2N+pW0D31V70q8taQKutFQl6EFlA4QPXPKd3VkBEkEdnVjxTQPkb4KLvck9AcSVe30aXSEAKlGWmH3VCQJk6zBaWujpASFpJwtvHMkCA4hevesQpQB6oV0v5WCFACQtlrB0BF0Bk7AZ78yIOQKU\\u002ft97QowNAUn4d+nH5+T+xwVLGmyXyP54U90oZNew\\u002fVxluNtXq6D9bF9AQWx7oP3fxMBoOAeg\\u002ffoh5gwRb5z+55z75HpflP9\\u002fGdHqIs+I\\u002fTNXaeUoq3j\\u002fPIg95VJbWP7GU8zVdXc8\\u002fZyOldyEuxD9ezKsQ5A64P2gNJczmkao\\u002f0dMZH20wmz\\u002fCGeaBLdKJP6pRuRAfBXc\\u002fV+76eSTaZD+RJ2Yfus5bPwoZkbiWOzI\\u002feAkVvmj7OD\\u002fkl+9XMuJHPz1M+7V+elc\\u002fgqWukbPLZT8xq1qNS89yP2oDzPv9HX4\\u002fFzRx1mxkhj+6dG4YoQ2PPxuk8VhATpQ\\u002fUnwLGYy\\u002fmT8qYugDvMygP3icmSDAqqg\\u002ff41OaTCHtT9oT90HXWTFPxTiJO0ZJtY\\u002fefpC3Jdv5j\\u002fpGJLgjpb1P+x7jStjhANAwtGWZKqIEECxWUjAtkYaQNSir1p+pSNAVuYZBz7GK0C9XYJl56syQE6C6QojCThAWjSN7tfQPUCtN12Eu+hBQCVq0JNm40RAA8FAz3OoR0BM2BPbyv9JQCmosC3DvktAuZXnYDDoTEDcSUp30cZNQLFJs+Xd705AzpaC51eSUEAFPfy7x4xSQPAt3SqcmVVAT58BCP6nWUBNl6wXQ1deQC0BsvTNf2FApjNvl2RpY0B548qgWohkQC\\u002f\\u002fYFwIoWRARUamv7qxY0BlnIWoiPZhQJn0avLfr19AakackkSUW0AL2G9lqlNYQPSe2aXmR1ZA4D\\u002fUGqxfVUDxNcDrvTZVQLW6McVeQ1VAnW\\u002fMqMIFVUDVO1MR+SlUQHXNbg06lVJAhuCkqilgUEC2KN7TTopLQMZ6rLocFkZAD90KEOTlQEAS8H8pV7E4QPrLdi55QjFAo8XLJe4kJ0CPUNmh9uAdQI3EonTlpxJA51Kr6nitBkDD3w9c5Br7PwMl1SfuPvA\\u002f2Ndgm8hR5D\\u002fQlPucRy\\u002fcP+EDX8McutY\\u002f7x+bmxvq1D8UTQz67nfUPyG+z4d08tM\\u002fS4UuZWGe0j9z\\u002fVF0LVbQP+K5MkbWuco\\u002fyuxf609OxD9HPooijZ28P\\u002fyrGRpur7I\\u002ffdEE\\u002fmSbpj9l+nSCPlaZP2APId4UT4o\\u002fQqKR9MpZeT\\u002fAqKFMqO9mP8U1krjYGVU\\u002fhZtUUaFqTD\\u002fZWyvEGBgSPwQ2eX\\u002fvDRk\\u002fQnvWmP9eKD8W8ZIAR1c4P\\u002fut0CIw8EY\\u002fggVBpJcXVD8JyOswglRgP44+IVPQrGg\\u002fZUayDV5ucT9j\\u002f6jElGN3P\\u002fizaJs8834\\u002fzRJPLC3AhT9hTw6hr8KRPyn3dsEROqE\\u002febMCyk53sj\\u002f8vX89ZhbEP0LonhxiG9U\\u002fzC3u1Sb+5D\\u002fjqMb8g6XzP29rvog7TwFAnsWhqOjRDEC2BVwB88sWQFgNJTkVPyFAUhaUzz4eKUCzgsOQ8rAxQNYMYBnBJThAB3EY1xfjP0BlaUMF0UdEQMu+AnfGtkhAY3rDcre2TEBAmD\\u002fteLtPQKwN\\u002fEattVBA79olXeHoUEAuNjlZa7lQQOC6KqSQmFBAu628kk0FUUBPOoG612FSQJmhBTRZzlRAmHuHmMkUWEBdJ1eQFa1bQIuX9XhG215AWoP7Sy9xYEC1u\\u002fwQEp9gQNoQ+E68z19Asu8gTmnqXEBce+myRDlZQPvZxnqkh1VA\\u002fldawJKCUkCfEWUhK4tQQGiH8wnDT09Ar+Xsv3YoT0CniBn9dcZPQMx43fH9JFBA0t8wyWADUECCZWnXkaBOQH7AayEQDUxAb7rP+nWDSEBhszENxmNEQGTH3LRvHEBAxdfeFb4lOEAOj8b3gCQxQBClh4KxCydA6qaKT5lZHUCuqmILRL0RQIqcfo4fbwRAwyCPAJGZ9j9GWIGHQl7oP9V0q\\u002fJ\\u002fd9o\\u002fin8\\u002fm1bazj+zWMPyHPvEP1nDEg4LNcE\\u002fJc29XloAwD+\\u002fUl4NXM6+P634nk6q47w\\u002f0S4GtPWhuT+NFZ5ofkC1Pw5qYwVVYbA\\u002fnnX7S4Jspz\\u002fE24YZiAufP9lgtEpXD5M\\u002fwZg0TOKthT+wqJ5\\u002fd9h2P4ALBxGrV2Y\\u002ffimth+qGVD+UpvC0rTNDPx4NUT0fIzo\\u002fvWY\\u002fS9Eb8D4SLe1fQYv2Poxn5FX7TwY\\u002fSX8MqhukFj82ph3EBKklPy0yrxJqQjM\\u002f\\u002fbmsziTLPz\\u002fcST5BlW1IP0ynXXdrnlE\\u002fy+C5\\u002fyxjWD\\u002fyD0yz1QlhPy5ynyFyPmo\\u002f1qcnKvwReD9VAswrd9GJP3cIj99wn50\\u002fcRBrRu\\u002fisD9uXvwlJHbCP3wPSTWjH9M\\u002fqOkzw6+\\u002f4j9CE+VHrXbxPxvYgWswGP8\\u002fSkJo5bugCkBiKAtjlwsWQLnEFpfzriFA2wCjxFByK0AnI3TFCIM0QC5VbaJMVT1AxNQisY7uQ0AB9azLSpdJQFE+UmV36U5AuxWcpHqHUUAdRkcKgq5SQDwqPd53zVJAzpeG2uwTUkBDbYwn+\\u002fRQQOZGxt\\u002fyBFBAtZLiEEuTT0A5zWwoYI5QQFi1H5XLSVJA9OZ79ESdVEBCWUelM+5WQBxlwlIzk1hAP\\u002fhZDWQIWUBLiheerBlYQA8EN5YF81VAkQz6KPoPU0DqIgcmKhFQQEnVs7rYEktAK9UR5zqsR0Aq6lWOjR1GQKoXWdiCGUZAFFmfR60ER0CueyLNsStIQAM1clyi70hAzoXHbADhSEB2Q8RkgspHQPj2JLAqsEVAYjgtokvGQkC7QGVUNsQ+QB9FLtrhyjdAghK\\u002ffbxSMUBEEFnHm7QnQI6BLy67ch5AHFlkBFJZEkCZIa9cYsUEQMuszgu8JfY\\u002fvYNdmJxl5j8sItY8TtfVP\\u002fuLib6AYsU\\u002f4wf2B7Xctj9HnIp5O7KtP0RN6No89qc\\u002f0+TCy9LVpT\\u002fBjMZs\\u002f0GkP27BOK55FaI\\u002fpiY8IkNYnj+QI7G8yLaXP8YO6xnlM5E\\u002fD5SqBBkjhz904BCdCNV8P05ccmMxpHA\\u002f8JnET1vMYT9uSD\\u002fjGqtRPxECFgnifEA\\u002fWk0nCt9gLz82uFdjSpclPx4lRMLfusk+F8rH+AQz0j7ZYqjC0FPiPh3i+If55PI+MzcwlPVZAj\\u002fjT3p8GpEQPwVYP29TyRs\\u002fltrEReG8JT+IcYjVDA8wPy2gLXSmHDc\\u002fDXp\\u002f4qhRQT9h5PFG3MNNP1ApHiGcz14\\u002f8N5G1OElcj8AYP92OjKGP4KAf3kXnJo\\u002f3RuYApCQrj9JahmYNL3APzwmbo7liNE\\u002fNNqhxrup4T\\u002fS9rZlCjLxP1MUXQhTOABALK6g4FKeDUDikAMoeA8aQEB9c8se9CVAlKIdRbWUMUCIsQomCJg6QLqufkFO5UJAk8cu4rIgSUCm06TY4DBPQO9r+ta2DFJAXqV0mcp8U0CKSX91Ja9TQKxxTJhou1JAfXZYfWUIUUB0dNJvElpOQDG4RC6fgEtATah4wQ9VSkDcdWG9NQ9LQOPSgFNmRE1AOaqwCm4HUECSbhfBXixRQA3mO0AdmlFAtC6AtZgSUUBi+N\\u002fFkUNPQE5qi4n4KktAcHxWoQbARkBCWDnes+RCQHc9OhMMOUBA\\u002fGOaL7\\u002f3PUBJznH5Ohw+QKZuy5HEDUBAJxl+ZKZ4QUBiod4PF8tCQGZLUVINlkNAay0F3bKNQ0AGlMChW5JCQDcm5xWMs0BA2p64zGlVPEDRg1mR4pg2QD4ksWhI5jBA0mcXSsGmJ0Cc+fBCDuweQAMsnOEd3RJA4fX\\u002fXx14BUDupKuqms32P\\u002f5S73WjpeY\\u002fWSk5Cb4l1T+WWuj1JtrCP4mErtv7uLA\\u002fwMkhq1U4oD9v+hQo6YuTP4ZbaRreAo4\\u002fw48ar2gnij+jncPmJBOHPyb7mE5le4M\\u002frV0xCyDSfj8i97BBDKx2PzCMChxy8G4\\u002fj8pnMCCQYz8\\u002f4nxeOOtWP2OsJETM4Eg\\u002fPmkI1NIROT9h45\\u002f60cMnPw+FIEDdARc\\u002fqXaRrnEBED+izY\\u002fuAm+iPtmUpqavXKo+T4yHcxQDuz4vesod4UrMPiyIuLF+5ts+eY1J5xWU6T4op5RG+c71PtXQcwtFZwE\\u002fQangVTJ3Cj9PMeLqDAgUP2EjcGKSbCA\\u002fVCiY+sUEMD+qPI1y37pCP5GsFWjfIFg\\u002fkE\\u002fNL\\u002f6Nbz+FCVs\\u002f\\u002fh6EPxjv6aMRuZg\\u002fbkUnhpVErT+b\\u002fXr6o7\\u002fAP1OleM16k9I\\u002fjOUR8a3z4z8Be2m8F6v0P4OHbTHMhARA1O0b08lhE0CBX14Nh00hQPU4UgZ9CC1ARiLhBt\\u002fKNkAVHBeC57BAQAs1G\\u002f1TwUZA1hgvjATXTEDlDoV9KPtQQKZvNd9GllJAGd47lT3xUkBOa4H\\u002fTAxSQLSm\\u002f2HzN1BAzMSLKJP4S0D89v198NpHQP\\u002fWLmqF\\u002fURAsWQf9g7IQ0D6JfbUmxNEQFZ1dvyeRkVABmFRj6ePRkBcErhLLCtHQJiJlCUvn0ZAbgTpu8jaRECMoUb7CjFCQNpkfGlzaD5AHkQGITcEOUCsZHOrnys1QM+WM5QZWzNAfgRaTK6OM0CGAlkmLlw1QIOWHEMPGjhAPT8Yg3wEO0BVvfTvdFw9QIPqdzCdgz5Ao2WFv24WPkCBnrxaRwA8QMmQmf5AgDhA9fhzZj4YNEApD\\u002fx7wtEuQJYsSiruDSZA1KwQwgJpHUA9DRX1SEASQFCtXDRMEQVA1jJKjTOc9j8RzRIo9pDmP9T8RWMl+dQ\\u002fvu8EWcY6wj\\u002fX7g0sOQauP0jo9A+kVpg\\u002fivgaZRZZhT88NbtbHVt3P4zCRamonXA\\u002fqOiJw1Q5az+FR0FdyaVmP\\u002fv\\u002fYlYRBGI\\u002f1CGCJ2TTWj9KXuLClY9SPy4OVBrf0Ec\\u002fgxILcKlQPD8PVuNlxTEvP50XcIoB6R8\\u002fKwHWDga7Dj+rgNwoTEX+PmZBFncwSvU+cBKB2ouxdz7pGRGjFyGBPhT3MQ7T25E+5n\\u002fJjg4Boz5PziJMtQezPoafkkxPucE+I0ubO1LCzj6vvTf4PBzZPrT6yZMAzeM+rxb+fCUI8D7B0F9dg3z9PiplgME+kxA\\u002fjFv0TpnpJT95ZfxrOPY+P1RukNc561U\\u002fNfoOa21Rbj9SjgfQ71uEPyKn5zuwiJo\\u002fdWT\\u002fvuvBsD9XmR00um7EP2ajXg5p5tc\\u002f60kfTl2g6j9vAEwyIQ\\u002f8P5oCIniFzgtAyDsAEArMGUBm0wSfaVQmQGTOCKPV\\u002fTFAimNq3JLxOkDUFXTU1LpCQID4SRLtKEhABWNlkW3oTEDsFojx7QtQQPpIdzHMi1BAV20xLEHFT0B+PnAGl41MQOxSf2QDS0hApe7\\u002fM+X+Q0BUWLBNT4NAQLxepwqmpDxALrAV\\u002fmblOkDPFPnYTxM7QMb1b73vCzxARdQ42k2oPEBzW6KzcBc8QO+PH2hlEjpA2cGgMlXeNkDur9w6gSIzQG+X0OUqTi9A1Uj1fWEuKkA\\u002fpQOYU7MnQLmpOA7MCyhAjRcmxNfaKkDXzLbLd1wvQJcJ5zrERTJAM6zyRGyjNEDPGvEj8D02QLvhce3HrDZAigWeRYvANUDSVSu1UZAzQGGybFmSczBAx3atv37QKUDk9JYgU90iQCN3rZEBphlA5KY5CU00EEBYXW3wAwQDQKfZEwX0tvQ\\u002fRaobZWLx5D\\u002fY14TLgKfTP\\u002fo7VqVeI8E\\u002fB9pTZRjdqz\\u002fNEskOb1iVPwrqR5rQ3X8\\u002f7tdPdeVRaT\\u002fpNq6Rt9hYP4T5tIlLFlA\\u002f9NrziQl8SD\\u002fvaKonhRlDPy6VDFExkDw\\u002fu0w5yNP+Mz8OTSkM2wMqP9R7yzrjYR8\\u002f6k9rHz+LET\\u002fpLb902DcCP3lWGm4c0\\u002fE+VYBn\\u002fond4T7lWE2lU2fZPiqHm7GiUUs+tysMXHT4Uz6F8XbXcy9lPtsDzX+y53Y++wVgu35Lhz59pMy9uQ2WPuoF2MUegqM+jZlN4F5esD55efL4bRq7PuKgr8snE8g+fRdt0Mqa2T4IAvKLjNnwPgOK0XgFVAk\\u002f9zifK3PaIz+DRiXh3fk+P61LC2kOpVc\\u002fiyQvUfKKcT+kalNIXyqJP6ReLqMdVqE\\u002fb568OzfGtj89MPn+XVPMPxKvE9rdk+A\\u002f6irB89Es8j+b2NQ6XJkCQEMVR7SCuBFAsndJpatiH0CbVaZCa80pQFib9q90rTNAE3ukW9\\u002fTO0Byd8GgHz1CQLZSeHDOKEZAyEVnM1j1SEBX9LT1hxNKQHAVvFyRT0lAyjbONcPmRkCweZi\\u002f1XRDQPMs2z2kgT9ATkSTYYf\\u002fOEDGGvSHNko0QBH2m2MonjFAR7LJmA+gMEBjz9CXd5cwQBttoC4CuDBAVeTCme1hMEDqZkNaypIuQGatTO8H+ypAOp+2qBalJkDLhVaJj3ciQJLmPxZ3lB5AKoa9CTRiG0A44V30gc4bQOo\\u002fWtDXox9A1wprLhwOI0Chuj2NEwAnQNuF\\u002fgmH5SpAIyAfUfnwLUABSEOKEnEvQO70SEE6\\u002fC5AZcfc7YKPLEBHKBoeiZAoQLDRpRLorSNAOwV36HZUHUCu6MRuTU8UQAk+6cnPHQpA3uW40Q8q\\u002fz+x2biQVz\\u002fxP+S9ouwPtOE\\u002fZ\\u002fCAFUvZ0D8TDYXPlb29P+mOEvLvW6g\\u002ftXupoZiQkj9t\\u002fig+1JB6PygRi1TAW2I\\u002fgqfEjCeHSj8W7Q1Cbjw3PxlD4+kNECs\\u002fEa9iWLT3Ij\\u002fpUAkvzqMbP928u2aFZRM\\u002fdY7fa++ECT9uPjk12DX\\u002fPkkAwnUOtPE+uAjSX0Gp4j5J5cdD04zSPjIGLMIg68I+6QlmiPkwuz4v2jIogEEcPqSID8pd4yQ+kuQz4AiMNj61oDL468RIPg9W+Pqsl1k+zG91TtymaD64Q6\\u002fhNEt2PsqPPCTGYoM+VPsrpwk2kT5ttqQbCXChPhnE\\u002fZcARLY+ODKjtVxt0T4iYHOkawzuPp7gUyN6bQo\\u002fS2NvrSLrJj92UOTSXE9DP5YPDtH4UV8\\u002ffSTRRzpAeD\\u002fFis6jz8uRPzpUx\\u002f+cmag\\u002fyN0ttNTevz+bq4ijGkfTP6tnZqZRuOU\\u002fRMbgOea\\u002f9j85nUi8ph8GQJuaMQqM9RNARUOz0PCxIEAc5UAf1eIpQGvVeq+JmDJAS1VlkAvBOEDJpYIYn4c+QOL+y2D6cUFA3BLOiVh7QkCfuyiKJitCQCspqFb5m0BA64b3CA9gPECxvQ9GC+E2QDHpBbdGxTFAGtK0QouOK0A62zm9mF4mQIE23s5jtCNA+CpAfE67IkBmwTPl+W8iQLy\\u002f4dQ+9iFAJFqBLwjQIEDnEegvHtkdQCMMjzRaKBlAP56IYQN+FECQWePOYNEQQE0S1Mfkvg1AvO8Qa84WDkClrvRMV1kRQNpi3CFgdBVAwM8bbAO3GkCmiQYwZRwgQPuxmjfcdSJABkjdUcHnI0CRTYDfwhgkQKq7NhHh8CJAiuLbg4OhIEAj3TMDHisbQJ8GUfMHnxRAgCkneqMQDUCHbqqhswEDQEO+XhVKDvc\\u002fxr7bD1Hu6T87q1nBuwjbP57ZttkIH8o\\u002faqLGxOZjtz\\u002ff\\u002fA3sommjP1qYuK0w4o0\\u002fOx2mVIZgdT8XLvzM3KFcP4A6PWBlYkI\\u002fGYOxVDZCKD\\u002fAnDTDT\\u002fUSP1VW7dXktQM\\u002f2NjLHmJG+T55EF\\u002flfyXxPpXzR58Vj+Y+PLApJk\\u002fh2z6yGxFP6QfQPkVcLf4iJsE+LkiezLFRsT56YeDKAPmhPkvwj6KZG5o+DBKgtEo36j17Gg0Gx5nzPat6zwddhwU+H88Eq8wIGD4Xm8+6Mz4pPl\\u002f3VupWyDg+4FVU6nEJRz5wAb9DPxhVPnxQmCHt4GQ+KSw4mruSeT6mI5pyI1mUPrHwqCOJErM+YAnNW2rW0j7nonWaTIDyPjyvIiq9nxE\\u002fhT2tdioTMD\\u002fHsZnz+dRLP8EOa2HKtmY\\u002fYXQXnIFigT\\u002fPgGOMd9yYP12byBOmj7A\\u002fEckl8R2ExD8pebeA35nXP1dH7a1sMOk\\u002fwmxsEXjt+D+Z\\u002fwXjSd0GQMdep2PSbhNARDqDJnmaHkBdOHhzrlImQHFbc1iYKi5Akp4kVZPhMkCJG6pKeeU1QCjRf2wBiDdADjtbRARzN0B9NgoRM7M1QDyu3\\u002fdqtTJAONSsUU1ELkDcP7y4XE8nQD6uFl1RkyFAvCCd\\u002fBgbG0ANZSsko2MWQCLnF2nsJBRAaH\\u002fd4ywzE0DJAcLtR3wSQO4nTSBjThFASGeHZ6ThDkA+zUwYJSgKQALLusuYUAVAZr4eHSZcAUDLCWe6ukX+P4vB1ZyqVv4\\u002fb8H9iBmdAUCTkSvgZj4GQJIPbQSbcwxAOXz1\\u002fHCiEUByoFm5R7wUQNYXedci5hZA3POodHKiF0Cw6RuIi7wWQGTQ4OfzWRRAsswXkeTtEED46aJq+yYKQJ\\u002f2\\u002fFCGvgJAxtDUBMLr+D8sjdFQK7juPxaiRPWzjOE\\u002fkW+9nByV0j8AsWbOVDvCP7+ar4a3krA\\u002fCRKQ02bqmz9p8EfDxsiFP7B\\u002fb8GphG8\\u002fH\\u002f1Hy3wsVT+gvhqBx5M6P0LmSPInyh8\\u002f1sZYcdc6Az+NBbNsY9\\u002fqPtfCYxO80tg+HRb+xS\\u002fzzD7Fm5AL4jvCPhnqagJQd7Y+iBIUYegVqj7yjwbTeUmcPs+BkYYJBI0+V3osmtOifj57UMfaf3x2PveoIpKx0bU9x1mX0D6AwD0HRo8AnHHSPcClIjg67uQ9EnnpjJNe9j2OHO7Za3YGPqbBBY78tRU+8AYW0xSnJT4NZHv4HZ45PhD8ksEdS1Q++y0OX5lZdD6Kym8uvGeWPp0uOwfTybg+WAxgoy2N2j5E+a99hBf7Puo8BM1uGRo\\u002f2cLuN26ZNz+X5dv3BvJTP1NZbeypa28\\u002fyW1FS5IEhz8jgqKlaVKfP+mCKe0hxrM\\u002fVzXlRdsmxz8e6XRkBCDZPzp1z+gkROk\\u002fh\\u002femqiqK9z9\\u002fnHSs5lAEQIcnIKG\\u002fPRBAlD3cmUwNGEDB2bbl3X4gQMlO9yUY9SRAGyjzevupKEDdy1AWdeQqQGk4i3DLLStABYs+Lol8KUAQ230QrTkmQPt6FpooHSJAYmf4whvkG0CZrpccqLgUQIhvuzP31Q5AMSNLGc8eCEDIBTrzToUEQHp7oZQc0AJAmyedFDTLAUD070EjPJsAQG7VLFjuv\\u002f0\\u002fQKrXZAVV+T\\u002fsj097Tq70PzXQsFlwvfA\\u002fC+8eM3nA7D9JHmcAsmXsP4TiFXLDe\\u002fA\\u002fGUxlA4Ei9T+rniRI5ab7P0YgQSrfjgFAKf4W0dEhBUDv8+99AtoHQIlDfefXHwlA3lGj3xGjCEDdSVDw8nQGQBF5K5Q2AgNAsranaMTc\\u002fT8lKwxJpsH1P1RbuVqGZO0\\u002fo3xoskFn4j+OcaLqRVvVP\\u002fxZQ1699sY\\u002fKuPT5lzgtj8SevNjpBylP4\\u002fT0n1lDJI\\u002fBLFOF\\u002fyVfD9Bii1v4PhkP1hkKyQVhUw\\u002fPYlXV3H\\u002fMT8g+PaQpi8VPymocBdbpfc+s\\u002fL8aRRT2j55fODuWIDAPixOHW+fA6s+P2lgJKSAnD4lrq\\u002f82Z6QPkTw8voIK4M+NpNOFQ74dD4oAMdVKNFlPpTNpkaKbVc+xeji9KFfUT6PG6mPSEqAPZqQNDkd7Yg9hokHyH5bnD3DKKgW4F2wPXmW6s0u18E9I1N2JF550j1sQqGdrRPjPb46pdcyPfY9N\\u002fFEucpUET4Ab\\u002fPwLEAyPnUuoXLRKlY+jTfkheNwez64sLHlbW6gPjXs\\u002fsUiqsI+iIM7fnbu4z5hNcoKt+gDP8nI+OWLiiI\\u002fXe89J1gQQD8wt5MzQttZP\\u002fPHl1eTT3M\\u002f5KO7\\u002fdG\\u002fij8nPK+NTSyhPxUK5XDobrQ\\u002f82XR7WWGxj+M2zXDnQDXP0yqrX0hwuU\\u002fCSWYKDAQ8z8A5fCP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kDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDPxpYQhUMiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkC+QPFbfTwiQDBLfR7kLSJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAG7HklUVOIkBpWmsIGVsiQPzuTuDUUyJAtuXgzFUvIkAGWp945DQiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAkLg4cTodIkC+QPFbfTwiQDBLfR7kLSJACrKdTVJhIkCy8ZhSVEMiQOcTNQ+ISyJAgqUnuscpIkBpWmsIGVsiQPzuTuDUUyJAtuXgzFUvIkAGWp945DQiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAox8dfScYIkC+QPFbfTwiQDBLfR7kLSJAf6CAE1FhIkCy8ZhSVEMiQOcTNQ+ISyJARWy0Wv5SIkBpWmsIGVsiQPzuTuDUUyJAtuXgzFUvIkBxx6NLsykiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAhZlH07FPIkC+QPFbfTwiQDBLfR7kLSJAf6CAE1FhIkC3ba9fOE4iQOcTNQ+ISyJARWy0Wv5SIkBpWmsIGVsiQCTdglnaZCJAz4fPPFUvIkBxx6NLsykiQDioWXjRSSJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAGWOlH9JWIkC+QPFbfTwiQDBLfR7kLSJAf6CAE1FhIkC3ba9fOE4iQOcTNQ+ISyJARWy0Wv5SIkBpWmsIGVsiQCTdglnaZCJAz4fPPFUvIkB9xZ3isSkiQDioWXjRSSJAdMcbPSxfIkCz+0Ek7UciQHqFvW0xSyJAGWOlH9JWIkAOhGhMejwiQDBLfR7kLSJAYDt3cYZJIkC3ba9fOE4iQOcTNQ+ISyJA4MG0A41cIkBpWmsIGVsiQCTdglnaZCJAm3e8bug+IkB9xZ3isSkiQLE6mC40TCJAdMcbPSxfIkCz+0Ek7UciQP9ScLcvSyJA4Xx8fN1VIkAOhGhMejwiQI7hqPniLSJAhAJUNE4cIkC3ba9fOE4iQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJAm3e8bug+IkB9xZ3isSkiQLE6mC40TCJAdMcbPSxfIkCz+0Ek7UciQJpB4Z30JyJA4Xx8fN1VIkAOhGhMejwiQDWwUmSoQCJAhAJUNE4cIkBrTabGzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJAm3e8bug+IkB9xZ3isSkiQBndUz1DOSJAa6ralSRQIkCz+0Ek7UciQJpB4Z30JyJA4Xx8fN1VIkAOhGhMejwiQEXt7Qf2OiJAVS6cvr83IkDiYWl9zFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAa6ralSRQIkCz+0Ek7UciQJpB4Z30JyJA4Xx8fN1VIkDR5AtBeDwiQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"BgAD0MqRvr+m5x+msCu+v8pkuZhLQ76\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fv0unV87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fQw9Bb7uIvb8wAVFTefm9v52rhV\\u002fHCb6\\u002fhYY8lDW0vb8ChPTWRHO9v2zB8r3efr2\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+vwEshQxuZb6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9vx6Aa5Cd672\\u002fkDHQk7qIvb8wAVFTefm9v9qxEqjHCb6\\u002fhYY8lDW0vb8ChPTWRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002fCfqOwM7m+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002fkDHQk7qIvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr+m5x+msCu+v2LsgnYpDL6\\u002fn5kRfBp2vr\\u002f8NfH+Nrm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb8wAVFTefm9v00hw9McT72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fZYwhd86Rvr\\u002fRhCsMs0W+v2LsgnYpDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fsjs\\u002fKYtb2\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fhYY8lDW0vb+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v\\u002fIGgj0mDL6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v7sBnnCWsLy\\u002ffkPYs2RUvb9QvUb1dvm9v0NECkeRg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fiab5Af3avr\\u002fRhCsMs0W+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb\\u002fXJ\\u002f+4V+u9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v4djDc8fZr6\\u002fZxNLL87Ivb+HFjgXXOu9v\\u002fCT6KGYsLy\\u002ffkPYs2RUvb9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr91HekAObm+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v\\u002fCT6KGYsLy\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr+\\u002fMZPuRHO9vw7qaQZQR76\\u002fXtCiUADbvr911jhJskW+v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v6h+3IiIY76\\u002fZxNLL87Ivb9mIOmxP4G9v7Sa1mbW4by\\u002fgDfzRJKrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr96flRLR3O9vw7qaQZQR76\\u002fXtCiUADbvr\\u002fNxLe0FBu9v+ZE9A1SDb6\\u002fn5kRfBp2vr8JcPjwl9m+v4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fCsDt\\u002f7Qqvr9sP678RHO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8vwfAQNZPDb6\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v7Sa1mbW4by\\u002f1X2xS5OrvL9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9vw7qaQZQR76\\u002fXtCiUADbvr8D+Q6PjbK8v+NJ43ynXr2\\u002fn5kRfBp2vr9QabOiq7S\\u002fv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fXtCiUADbvr8D+Q6PjbK8v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv4O9dIuHY76\\u002fZxNLL87Ivb+EhhUhf\\u002fS8v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqvr9j2N8ZRnO9v3+hRohOR76\\u002fOdRfhvbnvr9Pdou7TCy9v5NgbcqoXr2\\u002fn5kRfBp2vr8EMxiInjjAv7J3A0IbHr6\\u002fZxNLL87Ivb\\u002fTEOkjUTm9v+zbmTqlNL6\\u002fteo2u8FCvb9QvUb1dvm9v3l1kiyVg72\\u002fjzp8drYqv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zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P9UwnWfMg7I\\u002fog+G5\\u002fV\\u002ftD+CW0L4UXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD+IPruMV422P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9SzDykVY22P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj9zqHjQD8y1PwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762Pwsgb9WiW7I\\u002fqqpyhYhzsz9d2oQaF1GyPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002feUpi1BAXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fhzk0nO8etD9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fUdacTvEetD8TMHXFtYa1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz\\u002f6jNnAmXyxPxCfr3bfM7E\\u002fUdacTvEetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\u002fSeHsoLRItT9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT+Rswun7tW1Pwsgb9WiW7I\\u002f3kSRwYpzsz\\u002fMHvG7a1qzPxCfr3bfM7E\\u002fHTYIv\\u002fIetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\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DKwrrvL87VA7AF2G\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v1efHtiFOb6\\u002fo5ZDgWlZvr\\u002f6X\\u002fhZIvm+v+mEmUnr176\\u002fBp5YUB\\u002fjvL+1O2AFGVC+v9fdl\\u002f3Qqb+\\u002futG9XDqsvb8GwqpG6eO8vxsdHkBjfr6\\u002fDgPDKwrrvL+jVJOEFmG\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002f2\\u002fPfkDGwv795aUHyJ+S+v+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9fdl\\u002f3Qqb+\\u002fDfUXY7ffu7+pVBoT8pm+vxsdHkBjfr6\\u002fDgPDKwrrvL+jVJOEFmG\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002faGcifi+wv795aUHyJ+S+v+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v0aZKQ7Oqb+\\u002fQchJ0dO2u7+pVBoT8pm+vxsdHkBjfr6\\u002fhF95bf\\u002ffvL+jVJOEFmG\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002faGcifi+wv78MY0tDNi3Av+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v0aZKQ7Oqb+\\u002fQchJ0dO2u78\\u002fONbIe\\u002fO+vxsdHkBjfr6\\u002fhF95bf\\u002ffvL+XMPBhF2G\\u002fv+HpO\\u002fCdwr2\\u002fVINi048Cvb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002faGcifi+wv78MY0tDNi3Av+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fQchJ0dO2u78\\u002fONbIe\\u002fO+vxsdHkBjfr6\\u002feZIzIphlv7\\u002fDyuPfGWG\\u002fv+HpO\\u002fCdwr2\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fb9hjWC4QvL9OpDvtuq+\\u002fv+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fdwlVfrtqvr8\\u002fONbIe\\u002fO+v9uL6bZifr6\\u002feZIzIphlv7\\u002fDyuPfGWG\\u002fv+HpO\\u002fCdwr2\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fb9hjWC4QvL8\\u002fjnXLua+\\u002fv+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fgXE8r7pDvr8\\u002fONbIe\\u002fO+v9uL6bZifr6\\u002feZIzIphlv78P4veGSh+\\u002fv\\u002fNoQHwNYb6\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fb9hjWC4QvL8\\u002fjnXLua+\\u002fv+mEmUnr176\\u002ftTP8Lhdfvb+1O2AFGVC+v9kro0Z4HL+\\u002fgXE8r7pDvr9Wl2ipz3m9v9uL6bZifr6\\u002feZIzIphlv78BCXMfLJm+v\\u002fNoQHwNYb6\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fcbRc0OvUur9h6Xg+vK+\\u002fv\\u002fbSTZUfJ72\\u002fH5GMvhYku7+VPaYjzt++v9kro0Z4HL+\\u002fgXE8r7pDvr9Wl2ipz3m9v9uL6bZifr6\\u002feZIzIphlv78BCXMfLJm+v\\u002fNoQHwNYb6\\u002fmlCBbbjevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fcbRc0OvUur9h6Xg+vK+\\u002fv\\u002fbSTZUfJ72\\u002fH5GMvhYku78cXtyoPxi+v9kro0Z4HL+\\u002fgXE8r7pDvr9Wl2ipz3m9v8WdtfTqs76\\u002feZIzIphlv78BCXMfLJm+v0s3Qd0NYb6\\u002fpIWMabvevb\\u002fXS0\\u002fMDmy8v\\u002fvr2+OFOb6\\u002fcbRc0OvUur9gJgXW1AHAv\\u002fbSTZUfJ72\\u002fH5GMvhYku78cXtyoPxi+vw91DJ+qxL6\\u002ffnjP9\\u002fatvr9Wl2ipz3m9v8WdtfTqs76\\u002feZIzIphlv78BCXMfLJm+v0s3Qd0NYb6\\u002fpIWMabvevb90loINRsG7v5UvxXiX4r2\\u002fQIWhTskcvb9gJgXW1AHAv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fktd0LKhuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v0s3Qd0NYb6\\u002fIIInD7zevb+hkl5ewyG8v5UvxXiX4r2\\u002fQIWhTskcvb9gJgXW1AHAv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fktd0LKhuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v2YbMBj4QL2\\u002fIIInD7zevb+hkl5ewyG8v5UvxXiX4r2\\u002fQIWhTskcvb9kHrR8RpXAv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fESNXladuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v2YbMBj4QL2\\u002fIIInD7zevb9QKDgvxP+7v5UvxXiX4r2\\u002fQIWhTskcvb\\u002fm7UmmuWm\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vw91DJ+qxL6\\u002fESNXladuvL9Wl2ipz3m9v8WdtfTqs76\\u002fYWYfaxMIwL+8qN5FyNm+v2YbMBj4QL2\\u002fIIInD7zevb9QKDgvxP+7v5UvxXiX4r2\\u002fljdLOUpUvL\\u002fm7UmmuWm\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v6x58HGmxL6\\u002fESNXladuvL9Wl2ipz3m9v5LOcqnHU7+\\u002fYWYfaxMIwL\\u002f8EgCHxdm+v2YbMBj4QL2\\u002fNkdcSbnevb\\u002f7k\\u002fqQGN28v5UvxXiX4r2\\u002fljdLOUpUvL\\u002fm7UmmuWm\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v6x58HGmxL6\\u002fESNXladuvL9Wl2ipz3m9v4Sev6qMGL6\\u002fYWYfaxMIwL\\u002f8EgCHxdm+v2YbMBj4QL2\\u002fS+n60bfevb+C65p7KKK8v5UvxXiX4r2\\u002fljdLOUpUvL+MDh\\u002fyu2m\\u002fv\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vwTYoverxL6\\u002fESNXladuvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fYWYfaxMIwL93STKd3q69v2YbMBj4QL2\\u002fS+n60bfevb+C65p7KKK8v5UvxXiX4r2\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+vzMN6YPo27y\\u002fESNXladuvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fjqediUlvwL93STKd3q69v37XYSb2QL2\\u002fS+n60bfevb+C65p7KKK8v\\u002fJn6RjK0b+\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v\\u002fIF5JT0kb2\\u002fluJSjwxPvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fqCks6Cmdv793STKd3q69v1AGMJf3QL2\\u002fS+n60bfevb+C65p7KKK8v5XgxHYCAMC\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002fyd\\u002fm1HCyu78cXtyoPxi+v\\u002fIF5JT0kb2\\u002fluJSjwxPvL9Wl2ipz3m9v9Mw9x+MGL6\\u002fqCks6Cmdv793STKd3q69v1AGMJf3QL2\\u002fS+n60bfevb+C65p7KKK8v5XgxHYCAMC\\u002fLX\\u002fIUj2hvL+7wdNwbsK+v\\u002fbSTZUfJ72\\u002f19OmGkR8vL8\\u002fecALPBi+v\\u002fIF5JT0kb2\\u002fuhUo8myOvr9Wl2ipz3m9v9Mw9x+MGL6\\u002fqCks6Cmdv793STKd3q69v1caeH0wBcC\\u002fS+n60bfevb+C65p7KKK8v5XgxHYCAMC\\u002fLX\\u002fIUj2hvL+7wdN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02fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP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hD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"u7uf++cs5D\\u002fe0PSAMSLkPxAE\\u002f5S+PuQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002fQEgwcpEQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002flnzOUnpD5D8MQsHvNCvkPxrIavggKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP6jhY2yUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002f5OXkhpMQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002fu71xi7ov5D8MQsHvNCvkP5DOktoiKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002fu71xi7ov5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D+Uop4+FBfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002f2LWk47hTkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkP71runVCUOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP9lKWq7rM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D85zSncJS3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP5JdXovsM+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP2qI3WoiL+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D+hZttwQBfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+T9ri1kwzkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D86e\\u002ftxQhfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkP409VM4dTeQ\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPxNhQyesS+Q\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se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13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4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GeE1OX2GLkJ5nzT6oYF4G0xLMyewhmcvT+YxuyyUX08ucTAyUy8eoO\\u002foLnqscwdDDVEtJ24GHKv5pSv1QMWWsq\\u002fqKSH9M4iaiR4oMMwi\\u002fNF2QCZd93q4krpQJP1tEkdZBFAiBIHBT7rmPCCC\\u002fSy0CpIYHnvXn77zruMb3wtdNJe3nRlyFgUadSNFF32hiZ5mVtwUD9mptu\\u002flYRKCweIyBU7VD5aeVmWC5TcN\\u002fwwoi85BiQqqNlmboSfJB1\\u002fdN12DmwcFOTykttR1fDGaFFr9ezPUMsCugrESBis0XnAmrn8IcTU1cj\\u002faHTykNg4dOVdlp8Y3vvvjEi7n1zgAB0n6wMXXOVOdl2\\u002fLScY6c+UfmXK0ozsnDDAHSW9wPJklG8QK4ik9dpFofVBF0z0DpNs36zRrPgtRH7gDT\\u002fI+MLD0Ma+\\u002fZz\\u002fN167vqlPOP+QVdx6pySNAPhr1v2PLa0A8qMVrc\\u002fylQCvNHtrl3NNA1mHb\\u002fruY9EAaCDDIHToKQRslkfBlyBdBwHU5k+RuIEGObMIacBMgQeOr5IG8EBdBG3xZtMuCEkFxSij\\u002fG+AaQcbOfQgSCShBiakp3Sx\\u002fMUF4Anpwm0EzQYVeCGHFtjJBU7r2gLg\\u002fNkF0NUkbBwk+QQQQW5DJ30BB1L8aijwSPkFTkyG37qU3QezVIJQQ2TBBSCSTKzSfJEGjwJXAR5sXQSG2s5OiVw9BN9pU0fMnDEGvairZirUJQc645wP7IgBBBwDPDS4k6EAweI+q3orEQHZF9OYL6ZJAgQZBn97aUUA\\u002f4322ZqEAQNzqjVlL4Z0\\u002fnhW+dliXKT8ReXhf08akPtIMaX+b4w8+QbgpU5wZZz0bWd3NBJGvPFaNfkjOVeQ7EqnH1V6yCDt0FiQZ70QcOir\\u002fc\\u002fbgfh45G48WWncADzjwOyHkGLPtNtWMyq62z7o1N210uNrOdjSEcNLpgEgiM\\u002fIL6s0Dn7sxnHT6LxSpQzBnrbAsnV+6Lnk+KZ0yqyAtoPfMPcDacytpd8QJ3ki2KSQNKNCvkecnfzZv5EN9BybEoHHoIg8WJD3JROBPhRMiBQSFARxHACCJncNGrZTZHaJLgJDH8KIbSj8sHv5tWhnSTTo\\u002fRGABF40XW4yBh5UUcMI6b98iGRIrh4OKdKeLDwGAtlE4q+wMQMFxe4UBPAp3+AZMpceJB33D8BLdaDQw4DluRghrnDFeepXas6QCM4FoJPsIDVc00YcyxX7bmjVuTRcuPn3NNgHHYl2ug+434X08DEHC\\u002fTgbmrIO8Vr7OVhN8quftOc66VLDnw5fwztE1sGX492NPC8LG3y+vEU9kf+r6LLp7T3IFTTKtYGDPnXehR3+Ogg\\u002ft3c5yH3xfD\\u002fF\\u002fcRKAPDgPypzrG72FTRAPhr3kytaeUC+\\u002fxea1+CxQFzmkmYA09xAgwfsNVjg+kCGYFPRECoPQc32NLAhLhpBiRh7d9XsIEG2V3TmwUQfQVSqnx90PxZBtb71Y70bFEGElZyjL3YgQVgSsgwtHy1BjookGVndM0FohR73Qss0QQIjgPMRPDVBg1JPVcSVO0HHv0DrtidCQeDNI1NrBENBlXPGrTvZP0HDOiCv0ac3QUu5Y36EDTBBg66oAcZnI0Fsrf0KMroWQccsCx6CRA5BWudZX4TvCUF1NpfEbmgFQZ9fzfmhHfhASTmEiNxt4EDP1t5WWMS5QExI\\u002fN4t9IVAAv8E3yAxQ0Cw3ReGa4rwPyvbcCE\\u002ffIs\\u002fSykdFMLDFT+4sG9csVWQPkxBn\\u002f7GLPc9NxaaB7wITz1iXOP6VJmTPAOk+lXeVsc7vgXuNt8y6jrlxR3MpLf7OY9UrK27ovs4M\\u002foydG\\u002f36TftbZ4r4P3GNoOxVUYIL5M1RgfS8ZsqTjTxAz9Qp1n2MiEao40yNY8xzZu4lueHFDCak\\u002fEDfXSJLiwz+i1Eve0sOXTR9bleQCsf3RbiYvuAKWnD8paxmbAnrk4DJwWVziUXyh1YcIvaIxPot9L9tdUhyn24i5i7wB8YaTbW2E2YHYwovqrZoWAbdApevmFzFRkqXa8dpRG6Fmz4SuR82k0UQnHXgIEb0BGK+5qu8GBAD3cOyskXY58Mos5M1MNW\\u002fAn2NYpak3fdLk7cjMokH1QwJ6h8jSHlyTFITusEI2cvM3k5JnVu8YE09zVs9ZxSwzVwGA\\u002fK\\u002f5vzNvU4OxcgwRI4FYQqHa7nIDkS7xj127kcOjR6mv5EAgc7P4RH3Phg4Tuw23LHQ8SoPK4mWQ0sqWA96t\\u002fe640wBT7vK0iR8YiZPit0lMMOSR0\\u002fnssdr9oekD+BLafGj1LxPyuaeVnzuUJAnaVL0wRWhUA\\u002f+ffqBfe6QL3zRAp\\u002fguNAaoPlYgd\\u002fAEEIQqC4qZoRQQnLuUwXkBtBoQAo7e+nIEEWTNutwiQdQc7yBy7X9hRBuUxwXUHnFUHT1NWjreojQdwDznsYWjFBI6X+hVxsNkGkINV88Nw2QTodlTCAFzlBLIcsWiorQUFo5WhrZYNFQfFuOjrFKUVBnqgdkHXzQEGGhZV3y\\u002fk3QUxZRI+eFC9B5GdgXq3zIkFVdW+vvk4XQYNfhEMsYQ9BG\\u002fEhg4HQCEFl9KHucKoBQeCUJGvZOfFAclvMJH\\u002fm1ECME9rll9mtQHGJMgf9ZndA4vm0nybjMkC5SgrbnhTeP2Y9Qil7GHc\\u002fW9Qn5t3mAD9od4exfXJ3PoQi9DDBvt49QBYfZ7kGMz22CzyI\\u002fzV2PJAuV7xDcqg76wRfC+RcyTrc6zj39czYOX+mqj7H2tY4121eYAPZwzcfmx12JT6gNlA3YHZVDWk11TdWcI00IjRvtH\\u002f94u7IMoTDqBC1FmAx5fWYRLqQ4y\\u002frV4xxp2tWLs1MHHTENbgs9fruz1CiCCu0L9mziZ5HKezXPcj2VnUnVyjnY+8qkiWvMRYPTCadIz+ymdQdCZYhTyInobNkfx8cX\\u002fLrfhJVHZ9FXFoUqBob2CW952\\u002fGzxjgMP5NedhxFjfyXrlt4wIUqUj2qa\\u002fWghGwaqy\\u002fc7TxDgUjGiPSW18MZGoe0dBpdS2EM\\u002f3eF6\\u002f8LilBuSZzGoIwEsR9dXGI9TE30bitmiJYM6NAvZbtfak0vi+Xom5f6TVwAEIIkcwXN1rfUSqnCTU4ncAkCvaGQTlgIy8FPIc7OqBEZB5cYSQ7n2NBaet0\\u002fDvsjBMaGL7CPB6KeS78Tnc9BWO12l1mGz4aHHTVSoOuPnOBQk4bKTA\\u002ff2KV1AFpoD83oINxNTcAQOnJ81VMB1BAoIcslmmQkEAiZEN+kt\\u002fCQGfdCLiBr+hA19LfmPcXA0GX97xor+0SQQQBJddSwBtBITYjzEFnH0GF601aTkMaQaDuAMe\\u002fpBNBGAIyN1MjGEEKmVAb07onQUmkwspZQjRBehavwAQkOUEI4vGDf6Y5QYKpGe21Wj5BlQnDihoTRUGCkTz+EfJIQcUqqpdGX0dBjYev3cEyQkHVfZ\\u002fOd6w4QfT5kc91rS5BLWHjrVUfI0Hv\\u002fCUluucYQUdeY2fMDxFBoRw6Leq+CEEEZY3Akrn9QGxbq5twSOhAS3VApzhxyUCcVMqWGjSgQMNFbG8xGmdAA15I7TwaIUBfFxht2RPJP8CaMjYVwmE\\u002f37E+3GX+5z7Hdm9iM75ePk21OO0kn8I9RZQyOAJMFT1oMY6Js\\u002flWPMUv0tHXXoc7v7BKibZopjqAx66JgEC0OdjgKO65P7E4a1P5yIWwmzfMNzqtl\\u002fF0NkJsvNEQ2z01qzJ59XkN9DPUW4mHCWKZMjjz0OjPRi4xh5Y2Yd8DsS8ZbQ9GcQUiLtCn6XFU\\u002fIEsqVhPpBvq0CovG9Zth\\u002foNKXY6HJyhCDknusLhbMWyUyU8RTbTIzZdIyBPDx\\u002fnaFQhOn8sY+rfOh9IDaH3O6wQHaFwNEBhftMaWZwtaw56hRhz2MS+4UsmFjgvCXPQz7UT6AV\\u002fv0UbNBHNDBzYF3exDpNEoRYFVP8rUILkvKaUlC2Sz8gW\\u002fXopL1gchEIRuq0wjKX1wBdXIDLeXEoHfO2AMwZEmiVrhtA0H34dNH1nDjbsl\\u002fU\\u002f0Vs6NwvvlaAoiVU4Gh6\\u002fJGKVYDmrtwO2+hJYOtXdd\\u002fRSeUA7vaKxJfJCFTy9LNuzXuPZPNhVGVl6wo09gM6mwRorMD7g55MDKqTAPlvveuY2SUA\\u002fHga4uj+Hrj89vaJXXcgLQMMqMD4GMFlA+xDJv9+8l0CH0FCUbpTIQNvtY4WzWe1AJHm2m+T+BEGMPkffmYATQYkx9fRk1hpBcUSuRjSEHEH4ksPFgC4XQe9Sk7fRuBJBzIP\\u002fyLMFG0E3L6rViLkrQfPfJHRjFDdBvN57vLHtO0F\\u002fA2+5Jjc9QWOQAJXgZ0JBvls+tno5SUGvjvBp1kpMQbSG\\u002fjFvoklB+iLQQ42bQ0F28joGCpk5QT8hW1CErS5BGbexPRyeI0HfyXCinO8aQdIDmEUNzxJBk063y4hPCUHKIzt4\\u002f\\u002fX5QLQhaMJvmuFA9SAXQ3fzvkBXIe+gWQqRQCODpUmBlVVA1gW\\u002fDj3iDECybf6FW1OzPzwNvH73HEk\\u002fGmfN5JI6zz67mBoZCHJCPscsZEZun6Q9P6l6OanH9TwuN7FBhbQ1PMeuH8w5ZmQ71GbpjiETgjpsWAlU6TCOOcohd0Ksw4c4kWDJF+OgcTex204Ig6VINuXUDqNoPBA1NvU05UsoxDN0KduLIJVnMmJonhiG\\u002f\\u002fkw4X77MfwBey97pxn+HHDqLQ0cbXkUY0gsHE8eDoYylSpCNTdPoVzRKPin6CC7zPomwy5R25l9EyXqz8FR1LYaI98B\\u002fLpLQBEh25acV7X+9B46TXbytBPIHK0ORA6xBIoawP8JkXR+OhiCpYd49WvZFQeMqipG\\u002fGYTxH9nvWWV8xAzVcTxrBF3KmR2iJ6vuh0sw\\u002fI5qgsNwi1VGStF3KdUL6gYNr7VRdYwywoqfZOhRjKRO918R6ulM5bOQCgwjfM0\\u002fsloqMGfMDbmLenUIaRaN8dCNXhDHnQ40mqdSUSjfDnHgoYZfDZzOt5jfgXpTVg7k3ox1XP+LDxpgWc\\u002fAFHwPIffyACZVqE9+0xvgV9qQT5T8QgUuJHQPhb6+gjf+E0\\u002f+W8L4l3yuT+mYlFsG8gVQIVbN21nLGJA7+a3ij9yn0C6gWB98OnNQHk5iQhCivBAlJa5VZIyBkEXKBCyJ3YTQQmpzCokGRlBFSc7PDAgGUHxLYBMcWIUQWNvuSX5jBJB2IbIhcqgHkF76vA0dZovQYDlj7qqjzlBTlJiXGikPkEcDM2qo7dAQULtBLI+\\u002fEVB1zoUV2xETUGAkDnVoFxPQcBKpi67zEtBJYVGP4L1REGVsObyKWs6QdhjWNMMtC5BIma9RFocJEEj8dSOedUcQaqTuR2FbBRBUvmz2L\\u002fmCUGUbB3Q+of3QASFf5gxR9tAWL9Yp4oXtEC7fWkRSY+CQGY9oNdjHURAmH9eWTSX9z\\u002fy1gcnQTScP2oCfwJnljA\\u002fm83qjRfTsj7DrXm3R2UkPmqaeEgB+oQ9o6sfhVBq1Dxn+HdR18QSPE6JwaigSEA7Na5zBRimWjpnbLgNJY9kOWm0EGYJ5104R0P0Vfp+RDdUU3BS3noaNpgAnnPDHuA0zn20mOp+kjPhD33Le\\u002f8zMghk02kWYMQwC7ygtf+PQy\\u002f1f7DN3bKxLQWA28lzLQ4syC84YEU+WCoUjJzeMFqSKAQvqZ2\\u002fLromxiCWCXCZ0SQyiDo6dEvWIjFw0jQCncogg8dmLHzvrR7B40M1xrp\\u002fHJX6h5HXsD8aFkS07mfT7RdWOH5Cc3OKFSXQm53UGiYToAzsU5oZ4SiMPI4PZJ2VKsAPL\\u002frLvkksyx0rZVzl7C1JbYsUpY9+L7XX6Jwpdf4w8lWibYqabDK3rpJzNFDJM\\u002fhN6QAaHBU1ZIr82u6WUDZqVggvjZJ4NzL0W06dJpE4M8F\\u002fha+Qljn3+ykRQPyLOhhOjn5uXHA7+IQYrYwKQjwrWuXXEsUCPTAhPpoWcLI9oDOXLwYfUT5RsyyjfB7ePmScyuFJL1k\\u002f40Rxo2MnxD+aNOkfdEUfQBGzGArxHGhAgnkiK0lRo0CzlRfccSLRQM3+DHQf5PFA4TszLbDlBkFKGZaPHQgTQXJQ0y1l3BZBE\\u002fy455CqFUFUkdg\\u002fUjkSQaiFqx0QVhNBlPnvB0JpIUFNnqrZb34xQRtMyQf0djtBEyGpvnWOQEGzUyJNcwBDQdmLydQEkklBKPztrdFrUEGUyQxeafVQQUl5gm67kk1BBjmQdYruRUFuOgh8Y8I6QWUTnpcyZy5BXAeRlyVfJEHYjPEK\\u002fC0eQTUqf58LexVByxQxJBUICkHJ4u5stun1QGkwpsapC9dAG\\u002foGBqC8rUBR5Wb33SF3QG\\u002fbGUhLwzRA7Efogkth5D8QePGkn96EP0bxW4qVgxU\\u002fBEJjjwTIlT469yOo9U8FPuCGb7hS9GM9jfOBoTDFsTxpa+G0Cf\\u002ftO6NBKI8r8Rc7uzNLE3EMMjoikZQPFq45OUbqauNwOzE4nn6w06XOFTeHzF++TgTqNZWU648CQq00YyYDhvUCXzPQc98Vqfr+MSppxcIzKo0wMKUnUgDgCS\\u002foo4YxGqJ1LY+h0LtJC9ErRezrDu9OGSpLnxThyrRRKPowtuGlWHcmOnfvLNwBjSQWkxwLHvuQIseCMtkCvIIgzTqO4\\u002f95Yx58j+VtdhQzHGV5JYTtnPEZU\\u002f3yTECknheBJ3QCHh5JFV1dDCbkhDknDKJ1CpCj\\u002fyiNBb9ESHvCKtcHs72PWHQs+nROIy0bFS7FqTxAtKGkL6x6ygUhASMxmCa2P+t+kDI+BRkFLfzqM2i8QWSrzDQ1ALYwcy03bjZXX4Td0a6UNx6JFm+Ir6o4DNw1hc45sDkV\\u002fdbjj5miOgGL6goiGoQ78\\u002f4Jm019VDyzzcCAi7QTPVRp1iQB5cE9P0DU7eu5Xj4GEJpj1P3oPn4N9x41VGM\\u002frHf5EP+gzD+lmZI4kJQkQIpVh7jNgW1A8PWmsP4npkC5IgXiarLSQLsRWNg23vJAV7inI3ZhB0GWG70cBmoSQY2tKy7vaBRBAxUUpzB8EkEIvwNjsOkQQdsNb1AxHBVBTuJcrG+kI0ECr4dko8QyQV6yNGTCpjxBxR9hG7SZQUFDVJeUWj9FQRKCOF56yExBos4cOwnQUUGKf1+RvNdRQXeeSDuFl05BupvZY+k4RkHK8enEXlo6QYxRGlfOnC1BYwblevhVJEEXy8YhQcseQQkXLFL0yxVBgR\\u002f2IMqdCUEETKoe5OX0QJWe78moP9VATMXNsb+vqUDnnls9ablxQHJvzlZT2ipABnknl9qt1T8LQD1HrD5yP41ZESU8Yv8+Ek\\u002fknKAQez6pK3H4hALnPSR30syFBkM9OvZLV7xLjjyYZ+5ANxHHO4Efuz3Ct\\u002fA6lqpvIGj+BjquV\\u002f0ZZvINOXgpapWbcAI4ZzLnOrRz5TatRrVAEI+3NS4RAsqNaXg0c64fFlPcJzNri3Dg8P3FMaIC\\u002f1FPHFMwyCEjV1xPzy7\\u002f46nBFy04LTq3qnNAmJErjtg8PPUi2CnHG2rKVDQPKJCy0P9SAjMmsgMdokTTRSR1cTwCPZ1HIgThb7yhEzggRBJb9\\u002fIhFx7akebpl\\u002fHkG0qoZslH3qEZFo22sYi9XBeMbwt5ASuDJQnAxp8\\u002fT1cnhyY\\u002fh+W1KikwKH5Of9fsKsV\\u002fGJSnWJ0sedyePhAjPC79vlMWz2vJL5fCQLV8pEUxD4NfCOBcsTLfUUuGiUAKNJpc3e21s1I1JmXF\\u002f2AciTY4w9ttHcavNzig9NcS8sI4ZTvv6nNLxTmmaPILBZC2OhFBg6cfipY7b0ER4ns8ZTw7ETssSuEiPVuA87ncs889wHDybDkraT78QzG87e\\u002fyPmxaj1ohHms\\u002fgZN2DRKf0j9V\\u002f3dyy+ooQI3Td1saxHBAzEXdHb0HqEA6DwEWOMzTQH+HqbfHwfNAZ0z1QC\\u002fnB0FFW8VhmroRQYZ8\\u002f34W9xFBEUQqCAGuD0H9JOpyX4gQQe8A1RTuuxdBuJWofZ\\u002fPJUGvCPltJYwzQeb3nJJ7KD1B2oYfmL5rQkFiRyb+EjlHQYHTX4XvRk9BLrbi8BKtUkEz0oM\\u002fzzJSQV+cseGkjE5BCKovMTmpRUGIz9WJ+SU5QYkmKKLOaixB48mDQtIbJEEsnDsNEMseQe8MTz3riRVB9Uv9M1wPCUEAnOoSR6X0QO9LHPUBONVAG3EEWoMqqUBTpRO5HUtwQMIGVD7lLyZAV5ozqGAUzz8VzeLvUBtmPzWoKoOUpe8+ZHCALVObZj7tUlSTzPjPPRRr4LqFNCY93WMV4rgTbjzFyNdJ\\u002f7yjOynaGLD798g68ltsgPBN3jmZ7mxnPpThOHzQPH2gb9M3s669b9pttDa9aF68sl+ENfeZc6mzP0M0unOsvhw18TIZkyhBDBWNMXYjrDDcNhcwW+QFf2d+kS6Ys26oRuH4LKOrDFGSsFAr79XJPekdlSmV+TQzTjHJJ2QBg8+jVewlfnf7mWIK\\u002fiO+uVs1IQX+IfhBK+BLRuwfJP6e+h8ZyR1nVxIn+f6UG4nOLIaxlWAZ5fKcd3D8vCO1YHSwdkmhJQ03+yoIboMnCUO+wwqUVCljj4y8hYkUK4JmGs5MUMMsydE\\u002fnnsdYS5OysboeZXsL0YCb3bxfWYxQJrfE3St0DKWAZMmYE4nNGC1bccLsW41aQ+WX0YLozbZ9M0lukXGN5FFsVKVjNg4IS2kvAWB2Tmk9p\\u002f\\u002fIfrIOi1OwC1VEKc77VOJOV8WdDwB0\\u002fDApYIwPXvRKle9otk9gxatXMHTcj56O1vPzDr6Pmvuzf3IanE\\u002fNP6GRbVE1j9gdEdRaPgrQOgSCRcg93FACwiiIdw\\u002fqUDG3aKGn+fUQKfcv7Wb7fRAreev1nqjCEHA\\u002fi3EZAgRQRjMuCdpaA9Br9i8PWDMC0FsOiIzdgkRQRKl7Na78RpBgeukSXvEJ0HJ\\u002fzwam+czQfZ+iLV2Nj1BwsG9kyYHQ0Fkq5S\\u002fl7dIQQA61EvfY1BB6YkDSG3qUkGeBzHdNu1RQepdMpCdT01BnibGQcxFREHsmc5OaVE3QcpE6WjBGytBoZv+1q7rI0F2hdgIDJQeQRIQbC4IMRVBQV+neFEWCUHg1ElU73v1QLPsmo7RodZAkfJWC6WeqkBH34ZSgZNwQKlHMyL2OCVAyiPzxr5vyz\\u002f5A4tVoK5hP8LzffGwfuY+NaE\\u002fH04EXD4c26ay7vnAPearw83h7hM96d\\u002f51AueVjwfTseIUsOIO4MwULX2I6o6BIsYukGXujmxj1OLKgy6OJG2EY69i6g3pY\\u002fwEl84hjaRXau9yElTNXJGNHWhBRA09Cz8\\u002f5ZquTKhOkrYfjVTMWVtzAOlnNsv4BJ\\u002f5vXWUi4ZEF\\u002f4nV64LPUy+2jt1w0rHgvOuk1IUSlEQ6wsZ+qCJ7m6laZBjaMl4FHtxmETsyOwyOMUcY+xIdCcTAvnfp4fofLg1xn5eB1dGY08+nxTG7ASegTeD+YhF4Jp25\\u002fP2SP+Mk\\u002f+cnTMJT8tS\\u002fZCj60nckGqFIDvfCmYTrp3wbA6K8E9lj\\u002f7Mucsro2BA04Agy5moIkRglQNMEImPy7BVIUxjkPLA9A87TKVxJBkveFCNAm+Ncth+4Y1pxkFr0lcujZ7WJ3klH7cN+3kayZ3B+04K9CsDA3g6znWNxfvxDvZOhx1GtjfibU7VdBEn6NXgTwf3mwMZ1w6Paq3rRma7uI9DTIz0Ee\\u002feT4F8ApRKaMAPxZSc64PlHQ\\u002fmF\\u002fdyla22D+pyX0NQactQPw22eLHunJAnBHzqs3CqkCYXsEwesLWQF6hOHQEyPZABX2n8r2zCUFfIS1+nV4QQTdh0Jj5iwtB\\u002fzltuqh0CUHfVtKv3UISQTjrUcMsbR5BIvXoXJd5KUEYfLY2Zg80QbJ34ks3Kj1B9rUH069zQ0GoVxilqpBJQQqMwBpVj1BBv+dtvlZ7UkEIUxU1tf9QQSi49nYx90pB5iWL9FRBQkGRExvKTi01QYhaGlxaECpB3H6ViE0MJEEsDrhsxrYeQW1EznBAXhVBWq8VPvZiCkHy281ozpn3QPfzKgLoKNlAO6PNtPkKrUC5idYSl2pxQM8Q0WRvPyVAS5ei9dj4yT+8YYKJy2NfPxE7VQ0CiuI+\\u002fr\\u002fl\\u002f6czVT6W3JI0yU+3PV0kKEephgg9ARELBnOeSDxhaNKD6Yp3O0bWUJhocJU6ez0jc3CZojnfcI7mTMaeOO5FQ5gXUIg32QncXDVfYjZnLGH7Y5kqNfceY37AeOIzhWEfISOiiDJz7qIbNI0fMYnv2iZ+ZqMv2EMSHRDiFi6rsfgsYtl5LAP9qIK06csqbUl8O4DBDCnAzC3V8TQ8J+MvTcPKS1ol9I4tjM9CZyOkk9\\u002fLJH9jIauDpqXx8U4fI\\u002fDwgTWMOB0owACtPecAIBn5jH6fZQMirmVEwWn5BCRbG3TlC1\\u002f1JVwGHYv3hNQnYIbXtpWQoinSKCQJkqdfK1jbwfYObgktm5A8Fi1Aoy6sbB8d6nYrMPlexjccdqIxYbjivDljBzMoZvHod+tbNHp+G4moaJ81pX\\u002fDbR+m0DZNoj0fSKLwN0qk3uR+U\\u002f84ngGtR+fN+znN\\u002fINPCkTnOuSLn1DzW8I7wvKbZZRWizxp5kGxzThDPRdEyphzkek9DgKwVnYigD4CnHUICHADP1IvbODdnnY\\u002fdqMwoXwH2j8S3VhVN+EuQNJp1QNdDnRApn6DvgAXrkBGJNOQ5UHaQFq7wqVDr\\u002flAjhhtFoksC0HPs108JZYPQfa2LF1xiwhBwR0i\\u002fQWLCEHMwHG\\u002fZ\\u002fQTQRi0RBSg8iBBHn+MbK8FK0HmWxXjJU80QXkWVWfqWD1Ba2NWVDO1Q0FpOTDPWqpJQSInMV4eH1BB2ce7iUNjUUGEhG20p\\u002fVOQSvMvGSBy0dBFDYdnsHLP0GudmDBWw8zQWqM3a3dmilBXN7MLju1JEEnvY7SeLEfQfwxkOx7hhZBwkVG\\u002ffE6DUGbdPObRcn6QAQVRadvOdxA\\u002fADvEYCBr0DyaEu\\u002fcxRyQKrGs5+y\\u002fSRAaGFNkOhNyD\\u002fXrn\\u002fzO65bP7ct1o5ro94+MlApCrhSUD7kBLf+LaGwPQS+03S2HwA9FN4m6HGrPTz26i02attpO\\u002fG1lowTU4U6bMh6G8aikDmoJeBB6Y2IOHMQP0w7JnE3Ogz0y96vRjboNrbBBHQMNf+HQGz+78AzqTW3tRosYzK4fEVWtav0MLYgAaexRHUvz2n4YJTs5C1fqNRG27dDLBLhWa\\u002fu0pEqPTh5J6fvziim01Zorsf5JiZW\\u002fROGnhQlQsEYtyyfHyN\\u002f2TiLnkAXIXR3WdjIohQf3wypF9ESCh7NlBvh1VgdIK0nejssIC8iNkmpKoAbLyS+tro3oUsdJk9727BH\\u002f\\u002fknRCX7Lj29xSmrGLmzOyGBK18AmUwjcCkt2ITpsFrMwS4pikoMFXhHMOxQEXRdKb0xM0gaHo4SITN8CMvEV9ZyNEvfhzkdlrM1+QC+AyMx4zYsSxm7DrkBOMJsqi7R2Q45IHhzRsFPCToWk4NonpTzOhW4GZp+k8w7jWhxGm+vkzwJRib6ZqRJPTzszLVWrO89\\u002f4m3t02ogj4cNXGcLTUFP1kMMMEbxnc\\u002fdI4rC4BJ2z+zFm39td8wQBZ31OvTfHdA7GEW3ZWHskDJwdYhlhzgQCvWzifB3P1A4f50f3APDUGcnzEZVK4OQY\\u002fSAuWLbwZBngRGfFS7CEHkHbCIm9UVQTv1s1ZkkyJBWGFo0JSTLEHijWYIz+c0QWppViti7T1B0GDdi3LFQ0HlIfeb7\\u002f9IQb3ydaayPU5BDJw5j5F0T0HD4JnG3AtLQaGZDhrZLkRBUcTStC\\u002f4OkEmdGKFKDcxQVLd27f33ilBPwrnRzvxJUEdEvLqGNUgQaDvDvT3tRhBOdL7lXSfEEGSg3cMqlv+QCV0aLUt+d5A3L+4O7qGsEC3+91wCwhyQETREa9v0yNAjKl4OPmmxT9I9wkE6ShXP+6ZLouj+dc+QeJ9uVvORz5EkxE+VIimPUY48dEOP\\u002fQ8ixylVqM5MTxWDJEL+rRbOyXVA\\u002fxpC3U6E06pcJ8sfjlEETQuoml0OETxKEr8Dlo368NoDdVjLzZ6Q+2p7dfxNJZqRDz4JqMzj2aqsMhqQzIto+LfFpvSMCWi9Xkx3VAv7+LH1kHyvC2keyv+Po0XLDTPNYSbMWIq8ehZ4OW7mihGdAypM7fCJjwaOa33BNkkruhJhpgR4CL\\u002fKPnS6ejwID3rnj4kPgQcmLP6qAxZJh44mnX8lD9HIKcyceYsylYiEzJn5BsNVST8F9h6x1JCJtaMJIS8Dh4odtMjkRM75ynHr25JKuugK5ZTZb+sOEctz0c9oo0I3i70dVEqLU1iMPKPqe2lBNUxoDqoYPi+NjMsWx5NTTKHNDK03j3fSsY15ymZA48w9DYmLbKKsjsROAmY85rcuhs5buX1x5UIFTrzDNT9RRj+OvNyrAZqUtQ7XQJoswDymTwNJGyTPmlPPfnT8ySSJPI9LoEzYcA+hD4UBudgulMGP99Q7SzeQXk\\u002f0zTY9Yyv3j9t1F7Rrqw0QGUrcG8JzX5Aae0DcQShuEBr0RmGSonkQIak1Z\\u002fLmwFBPQzZkYUxD0HFXG80qfYNQXer8LgOEAVBU8iIjpuOCUGTCpDpHqUXQegCShvoDCRBQ7n8B3tNLkGj+ExKl\\u002fM1QdTPpqsN0z5BtSbpVkCWQ0GILrL376ZHQRe5yCEGXEtB9apTfg9bS0HD+l9b4K5GQVcbl\\u002fALg0BB\\u002fduPaZVzNkEJThBXNIAvQUQ9+M61vSpBKP\\u002fOQAOJJ0GgiJxnsCEiQSrYF1BxeBtBK0W6Zu+5EkFOBmaJracAQeq7a\\u002fB2POBAdeT26BZxsECfpFipQfdwQChebJBLlSFAMKKIB\\u002fIHwj\\u002f+igBDWgpSP8xpzZlFadE+ye+LB20TQD5Sum\\u002fW5T2cPeEcnAoUhOc8w\\u002fCiLhiFIjxYkApiRY1LO9m1\\u002f3k4V2M6yA3XFK6caTnt7AECa\\u002ftfOOri+xB700I3IlNnePvkFDY\\u002fPMvvmdzVNAzOJlN5kIUzlRXUVxcOJDI+6IfcY5axMBXoUiHCFy0vlTfPTB6zli3R1\\u002fZSxbbwK2HlKvyrPDcqIJZ1XS+Ebij8EB1tI\\u002fGSJkHozkwGXqYkg+zBdqkKyCKmw+\\u002fy66PvGcOYsCiAISEcFwPLxdd6UR4DDrjBjs5wIGKaY9hudH4i9ZDVYTAAeiR4+pAm1epkJuocwDO1tj8oX\\u002fC2qnqnBio3D\\u002f3Vgn++KwmOKSk0WGMt0j8mlc8f9y4Vi684Qgx6MHM3Cc8TpusxclaqRM6nSzO5OtCbRRGaNOox2tGkJ9c1ViWAyTpiAzd3Pg7vspYeOEQp7XfVwCY5xyJ+x6fsHzrGDGg+uCMFO9kKrvIxeNo7rAl0JadxnzwG58wy+NdRPZK75ZUynvM9xWk0X15thT79P8akKSsIPxk8sHGhYH0\\u002f9sSKPvOo4z\\u002floUFmFow8QHKeZzDht4VADSD3\\u002f8XSwEAnL8FyhTXqQBoRVTzmlARBEUS9rWyUEEFYqfPpCDQNQWQ41c1rIARBuNN3QC+KCkHZQlFgUjQZQZPeZe9+XyVB1EUAA6okMEF3t6Sf0Vg3QaB1G5l8vj9B\\u002f8+Myb4bQ0FNdAIxatBFQbOU1PIy+kdBKFDmmrzxRkGNVHOjEFJCQSVXuQ\\u002fDKzpBHKmE5bd4MkG+onQSBkMtQSZbh+rf1CtBxUBcL08FKUG8sfZvw1QjQVUJ2ykD+R1B0WXTXGxIFEHuCQFqXFEBQbtop8JOAuBA3xgvXmGgrkD\\u002faeMtuMVtQINUHf5i+RxAMvLforDMuz\\u002fZ5w2+2u9JPxHDu5fDR8c+VfyW\\u002fnDzMz4lbwGFSj6QPRpR9lhtDNk852ImSkBBEjzg2jpLjx45O7oThFLfTVA6MGW5EE\\u002f1UznThfbZPAhHOCjIkze7DSk3ehXjVoiv+TUyC9sBh9G4NCG9ggCtmWYzn62RDFZlAzJ2wfnvOWCPMM+IsDEV6wcvucMuf34vcS1eX6zjR0fHK+d+r1F3uQ0qfqkO85LkQSh4MbRp42lkJiQumyzpg5QkNfmGZUTkyBdlg8+nD3AKGqRyx5yudUocQoXySij0eB7Vf5at+CyWID9raiThkaIi7tsiwGVOnSTjXQj4b8qFJiSsM3MaiV4oIRP7jBMpJCpd+LJRyxXZK9M\\u002fQP56aX0t6hj8QHc\\u002fEC+lt0C3e+qQMPD9L4B\\u002fmAAy3iGalnavXjOnF1Ux0buqNFPERrAi8+U1GRlEkW38EDfhtwfsscgoOH1ybECGDTE5MlQpuBMnJjo1CCkXyzcLO01ACRVBwd87Kq5bYti7oTxaxYeydURTPeoJMo6g8vQ9uUZqdTW9hz6dlmLjPSgNP8SNI+ohn4M\\u002f1dn5Yiw67D\\u002fd2\\u002f3A0+FEQDx3MjX93o5AL8jooI2CxkCRZ7gidTTwQDBt4qGgTgdBgIlN1lstEUGQTi5NoBIMQbJv6kFUSwNBk2p2waNLC0HZ2fwtnWsaQf1s4yJ+kyZBtt7rpnc+MUE0xU2479E4Qe0BtiU9KEBBALZpS0dYQkHhxG4kM8JDQfvtQN7siURBTEJhxIa1QkEsmlcP+q88QYSATrC7MTRBp38KMCAzLkEh\\u002fA0rxXIrQRk9rO8llyxBuP88I1jQKUEkzURabO4jQe4yfPKoTh9BhFoDS+K5FEE5D1AI8tgAQSbKm3KXaN1AXOYosfh8qkA26Z4xni9oQB6HQGLBCRZACCPvMVO7sz+tr3MxSiFBP+crJM4Hjbw+qjhdcKiuJj6gQIUqCxqBPdIPozLlZ8g8aeqo\\u002fPBzADz5W9xno+8kO8SoDeewIDk61oDqOVZvPDmITYj\\u002f5VQuOFH7\\u002friufg43RNyL\\u002fuTk3DX1Hy0v3syZNLfPBjbAtUUz0cRohQ034THaBEj4RLppMJZvvaPQHeIu6AL7HRILSC3\\u002f+LBc7RGeK6UROMqIuOEpoiV06+fEEyh6Pnh2ulJSJrClg6hdtpMVdIB+UCiJ5BfegVT0wSg0GtzIPVLSpXIcFFc1FhxBoB46\\u002f1G+iLO6IIBgjiN+qsQiiacleNokviQ2FKQTSbekJjnJ40+j1Hoo9hufdmJfQCpXUYfQNtTyK7Ukbk2UZ5QtZzfU3yXWJC\\u002ffFTJD3wykMF1Uo7leLhIyzOGI3G0SbzMVXQGovgW5NCduZg4X\\u002fvI1PZAXXEsuGzdRu57XaFgyOO3QDOnpYTc5LfXHwfgzLDrPU+di4CsQO5\\u002fGJOltzeE7+JmVQoUqozywc6tfmNRUPWFNxzsP2Pc9ucgLc4K9jT4P6UKEYTcUPze1MEEz\\u002fow\\u002fM7fUNTkT9T8qksksFTVOQCg+4+nvEZVAt9zM9KeSzEDtrzwYvPryQGmpDaq2HQlB5REZv9YXEUHeosO3Nk0KQe0yZ+StTgJBpzuT05eXC0HWtNrtYEcbQSjFBDRusidBHKSZBtNdMkE9ktaN3wY6QZK2GUXkIEBBOPzJFc1hQUG80VDowcdBQRBBUty0dEFBPuzjRTsaPkFJrWK1sAQ2QcAd06WRii5BsI8uOYufKEHcO0CBy7kpQYYOBy42eixBvzTX+EZtKUEjm5DLsYsjQTMJCMLs1B5BDpR6qqrGE0HElt+c\\u002fnT+QKFRE0\\u002fZBtlADgZHerospUCZbitaAR5iQJ6b8TPu1w5AS4EaUmy4qT9sAdZFzL40P0f3Z0F3CLA+g3li7LCYFz55ncuISndwPaRoNrrFvLU8P4h0gEka6zvw3h9O3+IPOyIe0d92sCE6+f0nJuWAIjn7yi5MbT4SOGa2IYf98\\u002fA2iCzZo1axvTXLAgiEGoF4NDjlB8x8DiMzujFCNUPuuzEGbPedwElDMFFd32tdGrku8M+HZVXJHi0NYRePMspxK6WmEANlcrMp5CGcM7yoAChk7moRuW1PE5wFdnujDrAVMlMdYvbrDhhOjrn1Ng5cGutXn15+\\u002fJcckCYE8vVSwx5qT9Ah71bdIFTZ48\\u002fp\\u002fOQiD0AHZnZL3CS3LKbA5\\u002fjBJkAP9kiWg5Uonucu05BEWCoDllrue8sJLHsAufYU1qkt4u4fyZJiOC+hhW3+KLC1MAY1fZp6LSIyjy9Zi123fDOf8vADDGHFNGxBcNkqA\\u002f414cgLQ2PfIze1vwGqeds4OKsvNacfcj05wDUMeBOcMDoSh4h0ygwSO57pPmqeTuM7YukOa44OpTy03XQVpnBYPdzB\\u002f2JgBf89PhtBQlpPlT7RGjBG0oAeP8OrkFBq35U\\u002foVOBb\\u002fGh\\u002fj8f0+rborFUQMwtgiB755pAFh0A3aHn0EB+tHcE\\u002fcP0QPJa3RPJcQlB8JDk54UqEEGTqjXKjM8HQaaxQY2jCgFBXXoBtcpZC0GrDzhMotAbQZCJk42YvihB10j9TlZeM0FdlgnBIqw6QSXG0ZXCgD9BZ4941aVcQEFOCjyxoB9AQSqwq01sCD5BKe4eZBRpOEFBExjG89gwQS9WLi\\u002f51SZB0\\u002fmvekIYJEF7virXK9MnQWBDBEl+KitBsCeMZQysJ0GobbV2WQ8iQaXZPBJHahxBbVj9KpOKEUHdVzhnh4H5QHTjCCVCsNNAdfqwT5M7n0CXnCEikf1YQHyk1WcR1gNA2kHabi3Enj\\u002fYNvXpVAcnPzvo6+zdfaA+KsPoDc13Bj6TSI7qRgJdPbmCKZ8FtaE8e8m\\u002ftVlp1Duqw\\u002f9M1jL2OrZ35MQMxAY6JTIr+5YCBjl\\u002foHawmQ70Nz1Q5eq4OdE2H8mLUwvimzUO7HbVNERVNOMX2MTHkf4yrUiwlTS0lDFbP+qcSW0aMIWZy6s5yY8ueTKuUXkD8iynvHmNV01DK2hYzTq1sZ4pJo2HMHA4+RDNbXeT90ZpE7Q2X\\u002fNT39cV0BRn5ak+NRi\\u002fNYh2x9CBGuePdkLbJ7wclINVCgb35B6EkIrMfmv9IDP9w5WKcwMj6scUMKk8+CStXpKKInXcJr76vdI\\u002ffK8opnzKpKppcCo9l4nezB8gLJ2uIwKj2r0t5ww5hy4LSi8+pUxsAmnFMIcTPjJ6ljAy+bi5kCc6iDNo28PJiK7QNP+Z5ODurAU2EjzLTBSdKjdugT4M9\\u002fk+OAE6JPhINEE5sgHbpG18MjoUvqbCErMTOzgCVKndouU7NyRsup+IqTyM5Fkoln5gPWOFwG4j3gY+vkyVr9ZRoD5BN0mmFhwnP8AZWuNGu58\\u002fmu9DdrHkBEAofdUoPU5aQFS9K8kMvZ9AShGfqVx70kAPvLOj6A\\u002f1QPqo6Do7EAhBSIJXWY7lDEF7BjJiub4EQS\\u002ftxmiLAP9AoLZrFOicCkFPYIQjmRUcQXxILTuaqylBibWXVW4UNEFUG9Z2KZo6QbFNB\\u002fz0Mj5Bct7HcebcPkG71lpartk9QbQfPIsPqTpBudilHtdsNEHYD3pHYT8qQY\\u002fodLz4GyFBfcgtql2AIEHL0TCGQqMlQR5FDGIVrChB3fb3RJu+JEF9jUmQ71ofQRVx0EVcehhBe9rm\\u002fmjwDEHEGfivWM\\u002fzQHgTKY+krMxA9xDlhjtIlUBJxVOC8MpPQKfJ7lKNfvc\\u002feusW2xnskD8NZcyrOHwXP6Xc2yIlJY8+ZGmTdseg8z2wSRWHGW9HPXm53AdAcoo8yryilDItvDuj2PCJ2VHcOl+VT3bm1uo5lJ7GSY365zhdlwHQHzHUN9a6SK1IBrA2qSe8ZCz4dzVOMt5buOQwNDiRAHyTcNYybJO6B40WbDFI1Hd8spDwL\\u002fnx4b6MaWIuP\\u002f7R0dxWwyzkva9ziqcsKxFLBW51X5QOwlmvPhYHFBEpTlY7Z42SE58vCZeyMQAW9ZBx\\u002fd+jWhgvdA1+26WkGgBYLVcFKd4cSDV4sarBBB9a70hW0+saIYXJReV6cyAjkNOBryjyEiUEI9q1bI\\u002f0JlNKNvFcBsUoji7VXXdChCoXEk\\u002ftbGUyLMNNuudZe88tB18bMa1iWS9PKe1jp0rTMC8Tii6HozsyfrxvloOrkjPSsffDDs7XNNDA7xqCsgw2K3vZpetqMDdwJSYlN\\u002flBOOWO0t4+IUM5lRggv\\u002fRdNDo6Dbtu0pkWO6Tw8K4ZK+s70PC8++vYsTzm2lcivuhoPQ\\u002fYDU3qsRE+oFwQSkW8qD7snXSIGawwP2Z+r42Ed6U\\u002feipttx5RCkAugveXor9eQMjCQ5b3MqFAcFrluuKW0kBflm\\u002fTubrzQMvXBKdILgVBUYn9SIVgCEGEDzXPXmUBQdlw0KjHjPtAnvFgZH2CCUHcTWaj7iQcQQrrfAwCXCpBWk0dmR5XNEEfGNsBUtQ5QXezzh8knTxBdwE5wtNgPUGLWKWK\\u002fWI8QZsCpaReqDhB6xtSCSTnMUGD85QMFD0lQbwxdlJrMxpBpKJm87SEG0HAUozHozwjQe57dVbkViVBDH5hUeYkIUHkMkgqPa0ZQbtYm1sX0xNBgZ5ehy5aBkGbLzmnMb\\u002fsQBVEOUwHecNAlatWQ4AAi0ASipN8ostCQD+mcIJC0uk\\u002fqjCRBes\\u002fgT+U\\u002fFH3BSsGPy5Ud5CoMXs+DvZXfWyv3z1DYu1qbXoxPRzC3jckOXI8SIfiN27voTuBbTkSt6bAOq5lJu9HJ805DDZbwgIPyDjEM\\u002f9UqbayN4O9\\u002foDXb4s2K3gqjX\\u002f0UjWb4uJjfa4INEww3kktSa4ymjCmaZeCQTEE8vReixTDLzyCJPDJozMu\\u002fxqrF5Uuqywd+WRR9JAgDC3F32OA8q8OTA\\u002fLEvIGPRE7oXseKdq4EziepfXPDCQWSYUG4yl8fhi\\u002f13ffltbFGiwtlZOae\\u002f0cGDb8jeHAIh+9nh3yGns2IQA6azfxZDkjwHnolsAHKyWRp\\u002f16mRwLJ9wv9zjon9kowx8cJ3rSlioFJ9GHeidDLKJEhL+QTN4tyF5U3\\u002feVZi9VelaW\\u002fr3fMOu9+qlcCUUy8QDfK+5SmjOnPpaRjyrfNJT8aZeeiRE24DLvUX3xMjcfHhNE0QJEOL+Zx9VQWUU5gJOYzKUBODr53ZUl1msdOwUf59psnvM7oBWpDpF9uzxerc4652ZzPagasg9VwRo+RJbM84u2sT57WUbzwVU2P8BAlyx7vao\\u002f8LVfGZxnDkB+6IMywHZgQAaoOtVvFaFAB0DAG2Yr0UADA3Ep7QvxQC6yYgZDWwFBUiqU+x52A0FspNRecSf8QOxCRP1p\\u002ffdAZRvBJa0+CEHAENbIdwscQSPVRgy2pipBHIGMeJwKNEFEyxrGjn84QQ\\u002fqPf2iATtBHn9c5UxPPEEMxn0r5qU7QTU7\\u002f8lvoDdBNw032FlpMEGIob3k2hEiQZkn3p6lCBVBukDZtmqWF0HByt3HQM8gQXNW8tEksCFBzp9j0Cb4GkFBoa1qrR4UQRC4AFZatg5B7ZOk4QN4AEFQonQKJd7jQA5Xe1wUKrlAO728C4BGgEDTLS5iHBQ1QPjxCU\\u002fB3do\\u002fBLCfg3qdcD9tUtlx3rzzPnTHYXFsXGY+mxAE6JgMyD0n+Lbp5HsYPVSyP9fnjVc8FuAXrb1jhTultTPns1KiOn35Lw8Gmq05bfAVfMeKpji20\\u002f\\u002fISy6QN8RJHm\\u002fm5GU2oJCpbJ3rKzWxR4fVIMfgM7j2nQ7IAIMyLI6xka1IFDFJ4adPCXKUL0LGCyFRaxouk+c5XtMemwmBswDBQ6Y5DC7uDgvR29YOxHNxWzQyYxGZJCdYbWHeE7y96ShYp0YW9mhxYbXVnxgfiqmY6xPlGlnJhb4CTRodgdOHOLDsPh\\u002f6E0kpsiFRIYJ2ARbV4lEjrqmOhKyYQSUSmBtrD1AgJxHF24dBgOwohiuGyGZ2pyqdMD66wjNSLE0oYXVvnuotxrSPEX9Zci87Qr\\u002fOIt3nMApbWJCwT00yoaJfrOYLoTOcDi+JxeHiNIjGS2gSJhQ2hgWwOO4rNTf5rE762rNGOGSXAuE\\u002f70k5oDySUeozQDpTPtG1+OElOwtPXcYRs\\u002f47gFawC5B8xTyM1W3W\\u002fjR9PehyRjd6\\u002fiI+kvpy5lF5tz4pSYzk6n07Pwz3At8Ogq4\\u002fhywUCFcREECTQgS8ySBgQI6wjzI0E59AXhUG5ZMczUA39LdibjDrQKVy984WkPpAcE4caXKQ\\u002fUDeXjxBUg\\u002f2QIbruvLSofRAUfo0aywUB0GBdG2UItIbQe\\u002f0kyIKYypBD5r+ZJEoM0H+ob8FQtA2QZV5\\u002fe5dhzlBboOHp9SLO0FamXpky0g7QbGa3kOSDjdBUh\\u002fch338LkFgQsO7Ki4gQapD5dRpIhJBCj48EIcAFUG8bqEOBRcdQWpyhRoabhxBpd1bAruVFEHM5+LoURwPQTW3CKzzlAdBxzlJXMUQ+ECUxlFgE0PbQLwXb6LjJrBAd1SDU\\u002fV6c0AQo7F80W4nQJ9ZdY\\u002fwpMs\\u002fqQegoFaRXz97TfHjeEjhPqOE0pAuCFI+1VeqeDDasT1tZXw7cLoAPaFbGZvGnz08FCCD2hbDaDv8TBaW+IaDOnWbCAiQC4058liZ9e9dhDhVJg+WZ+1qN\\u002f31DjiLx0A2feTdrxK2AzUmVFvNL9O1MwknbzXSxlYyxdSkxadw5jAT7i\\u002fehOZ6L5X\\u002f7ay3WAYHyVjastK6tAm4ifm11R5iDImVC6bv2f0OoYBnhmkrhxEqnqF3IvIAFDMN06KLW2cWIdUR63dWvhgZ3WwwsJACG4Rq59syaTUdJiOi86dEVx+odNXU39NnIRkut5Uo\\u002fmYjAy1H2kboVCUa61ubIuoxJ548aMNH7vwo4jytRvQDtioQEZul2ZRfLPQf\\u002fciIW\\u002fUtRPxW1kdBey9AHmlkwW7wMNm7SCnJyFIyO3elW4x+pDMUi3Ch6aLlNFG\\u002fDwxwqxY27ID0EhSCODdrZqZhrX9MOK5Ybj5+I1I5srvPFFi9SDqM3dlQCFQxO8m8KGWgAwg8UO00k5cT0Dy\\u002fu1Osx4mEPThm5Ydu4yg+r7bSkcmKvD7m6vhqS\\u002fc+P9Hjc8ku0a8\\u002fEHZxr1wKD0ACvmJ74eNcQFgMUbjd35lAd7Ohnr6qxkDUnmEf\\u002fA\\u002fkQKvASHwjFvNAaRVMl72A9UAlBekEQM3wQLoqGYQW1PFAK+Vh7ZRKBkH9yB4Ej3obQf4B4PXJdilB247WHxfDMUGqKLMcTPk0QYzhbk6dMzhBVf133TrhOkFi3+I9yOc6QRSFHXN5ezZBCJyGSqWXLUFA8\\u002fjbuUseQcmRuNhV7xBBdeORhQdvE0E0Qdq0ORUZQb6B5JSifhZBToOm6SWFD0EH6lD+qvUIQeWeHjxc+wJBtzxdOA2I8kAebZYMY9LTQPI7m6i9DqZADjEjw4jWaEDtTgaXvbobQPC+YeF+Nr4\\u002fWuSsBMLBTz9wi7YMS\\u002fHPPvFHfXiLlD4+uiJAa0zEmz0yIarU1tvnPPmTd8dlXyM8nxYB5smzTTtY8oe6SXxlOqA1dULaUW05REShrqXdYjjbFv\\u002fUauRGN9WT4LqIMBo2k8gi5Cs\\u002f3DTS+r2leLiMM1WslyDAjysy9q4LB8w4zjD5JBBGAIliBCOZG7Jm3SAHhUPAWFvr3AkpmyZPS12HDA+lHnWRySEPTe937D2FqRGBtsXKX0AhFKsC7wJO+oUW3LhQgidi2hiHOWNmR9gdG1U4L4AC0E8dAFcqIBv0bx9uOh83WT5+IRQvOdtO+Xoj29xfPpmrZiWeGoRSjPRBJ+Yr5sgDzgop8C+70RLcwiqgzL9elQZpLFpmlhApV\\u002f8tzRj+eQGOgi+oOngZq9r0MFcLtDNWblYyBwergd53pzNu1J9KgpXoNMOLJWv\\u002f3xo2hptD8FraPzdWcoRwaJ9UOLueEPS0Ulw5AU59SYjCUzq7PUaYKxI7OzypQ0DR1hE8b8BtbbNh1jytP6Cj1pmKPbh2foDA4y0+iN54Ffu8vz7rhQsKTN4\\u002fPwi2NVlWT64\\u002fLt5j42ZkC0CI57K4GKhXQEYaWXhzu5NAuShZ9Es8wEANOp319YLbQK\\u002fO4H8D8+lASnPUmwkh7kD0WhqJzRTpQE4Cs0b66+9A7pBCdYIdBkFUHi2jzPsaQVULobqZ4CdBO372oGABMEHOrPqmyCIzQY5XMJVB9DZB1MszikwUOkF7YgstAi46QVQaDwotmjVBxtmXIFdGLEHJQo4ymmUdQf28hKXG4BBBWYSu9M9yEkGrvQvxu5IVQT1DyEkPzxFBjMaj2RRKCUHThah+oekFQdUbwhdH3ABBiEZ4rUG\\u002f70CkLJ7LXynQQL0mCnQe+6BA61j9S6\\u002fmYUAOn4umcZASQPXcE74+rbI\\u002f7EEWk7MNQj+qPSkdc6jAPsn5OpJuNy0+bp4GsyVIiD1KSU6SCRfTPNPGaKA5XAw8bWH8bGfjMzsKdBCQTVJKOhR9m+kvblA5A\\u002fNH5XVYQziLIThosHolNzT4Gw+WfPY1\\u002fPak6S8ytjQy72I6xatkMxi\\u002fIqbEeBQySe\\u002fL\\u002f57zrgGLc5nwgJ97BKdJ40vkOkcHyuU70pNoAgqxj9NF932rDKdMAfUsWEMPzLCzvvimyREqZIIW7wZAFNnIHZIU36IWdsUubj7w9BgDBvhVOuQ1G04rhatEkWUdiceelroFhB\\u002fL53tQDISRIQIyVZAE4YwjrxE22mhvdiVzSV7xNG1QJ\\u002fs3VvTyrBYpvzqUhn+FzSocvneViiRyLIUqIr2OGQUuTQtWgkJUhy\\u002fLLeOQwsP4MJVeRHgCulkybKapu2oFqzPm66Pak\\u002fPtNP5PCV0jGiI2PeS4MCzLRzeVupGjZmdgOFRjtdVAv2Y5h89tppLEXjrOvtRKOPJDOyMraM6ukhg8B5k4o8il3DwyHbjjEY2PPTynLHwOaDA+pme\\u002fmDQewD7r2bHI8PI9P2+TgKBDXqo\\u002fQ97V0VsVBkDfMnL39LZRQMlDt2Fll4tAI5X3HWx0tUBXeZCBTaPRQByEkBUb1OBAgaN6OAJv5EBmjLACIcbiQOT4PKz3yu5AOI5mSfGpBkGV8nBx1EIaQb4WqB0buyVByRz5X3AuLEFuTYjoj2gxQYKAMChlsDVB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ZAQdTfSEPQNBKIA1NIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9A8WWTinchD0BZpKoyqCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AuLlSsHQhD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0BAJ3lkXCEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0C5EEHHmCEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9AJERJ9TUhD0D4TPPdYyEPQJ306PN5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0By0pUWmiEPQFRD19V1IQ9Ae81UMnAhD0D\\u002fpgEdViEPQOotJYx\\u002fIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9Ad4zcbUshD0Avb+G\\u002ffSEPQPImiX1KIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9Ae81UMnAhD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQB12SMu0IQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0C\\u002f3BVXZCEPQFoROjbeIQ9ACqAIkCQhD0Daw2sFZSEPQDmxI5dsIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9ACqAIkCQhD0AdBEiLdyEPQBr6zm5\\u002fIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0AdBEiLdyEPQFFmtHlcIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AimUL7G8hD0DEhrPoVyEPQGGqxqt9IQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AkNmNUTshD0A4r7SZdyEPQJNBj\\u002fNuIQ9AUSeZ2FUhD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ARz5YRnkhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQB8hd6O0IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9ApsL9GpIhD0BcxGMaryEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9A+H\\u002fnzC8hD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQLbTSMhlIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AFoExeY8hD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DrPRBEniEPQJHLyVeIIQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9A9t\\u002fE3IwhD0DO98j5dSEPQGGqxqt9IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AU6AADighD0AQ87XqliEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AhODVD8ghD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0DExVmWgyEPQLwmcw2FIQ9AsziSlmwhD0AX\\u002fRn4ESEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9A3wFTRzIhD0B+F3JCiiEPQJNBj\\u002fNuIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AvyR5Q3shD0A99s34kiEPQJWCmdxtIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9ApwMEdAkhD0A4\\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\\u002fOM8zIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AGeTo+UshD0B07vNELSEPQHMxlemqIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9AqqLMRIchD0BHiO4qqyEPQMy\\u002fOM8zIQ9AiYuYdGUhD0BtWAOkWCEPQHbAsJ6jIQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AuTOwQCEhD0CjxLauXiEPQAhC3HqHIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0BHiO4qqyEPQMy\\u002fOM8zIQ9A1GYA8EEhD0BtWAOkWCEPQAPibOibIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9AoClUL3MhD0DxQ7eQYCEPQAhC3HqHIQ9A8QVywB8hD0CaVbgwbyEPQMy\\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\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AE+u9woAhD0BEdnfnViEPQBRhacuHIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AjXNiwW4hD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9ADuZdabMhD0BEdnfnViEPQLYs8eSFIQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0A86S8vMSEPQFM2EJlZIQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AAV\\u002fxH3UhD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0CgZLkKYyEPQEgm\\u002fgF\\u002fIQ9AjykGUWAhD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0Dv2VHzViEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9Awci7uGEhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9ADQ1S11AhD0A6FF\\u002fAgCEPQIjDOtWaIQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ArpWZ32whD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A93tnJ3MhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9AYn49s3whD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9AaY7X5X0hD0A6FF\\u002fAgCEPQIjDOtWaIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AaO8Y4GUhD0BtWAOkWCEPQD2Bims1IQ9AZJHvVHshD0DXYCbjmiEPQCXvRNx7IQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9AaY7X5X0hD0A0nhg4giEPQIjDOtWaIQ9ACxtIFE4hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQMSsx0jCIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9AtDe0fo0hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9AkNxaoHkhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQB962sp4IQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9A5GBfd34hD0DXYCbjmiEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0A0nhg4giEPQIXmejJ7IQ9A+9cMV8ghD0DACuHDViEPQEgm\\u002fgF\\u002fIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9A1XA26qQhD0D0v563VyEPQEgm\\u002fgF\\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\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQDiVehi6IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0C2QCm\\u002fUCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQAy2A126IQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQPtq3Dt6IQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQFXWFPGVIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AOMFGhqkhD0Dbx7mIVSEPQP0UbKGNIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9AsNoJoIshD0Dbx7mIVSEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9Ah+USdIohD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQL6GL\\u002fA\\u002fIQ9ATEM5oBQhD0A801ZmTCEPQHD5QEF5IQ9AgpbYYBIhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9AV+A9SZYhD0Di0eXbSCEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQEtX5KM2IQ9Asc0uIhIhD0A801ZmTCEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQLpTd+6XIQ9Atv3GdiUhD0ACqAFsSSEPQFfjQt6nIQ9A86MR94shD0DjKG3DXyEPQInprCg2IQ9Asc0uIhIhD0CCZeT4jSEPQHD5QEF5IQ9AEvtaPTQhD0DANIp+liEPQHcY1mpwIQ9AmU++kSghD0DsuMVyeSEPQKe2TYiPIQ9Atv3GdiUhD0ADARbEjyEPQPaQtjPmIA9A\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"u7uf++cs5D\\u002fe0PSAMSLkPxAE\\u002f5S+PuQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002fQEgwcpEQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002flnzOUnpD5D8MQsHvNCvkPxrIavggKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP6jhY2yUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002f5OXkhpMQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002fu71xi7ov5D8MQsHvNCvkP5DOktoiKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002fu71xi7ov5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D+Uop4+FBfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002f2LWk47hTkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkP71runVCUOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP9lKWq7rM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D85zSncJS3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP5JdXovsM+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP2qI3WoiL+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D+hZttwQBfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+T9ri1kwzkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D86e\\u002ftxQhfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkP409VM4dTeQ\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPxNhQyesS+Q\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPxv3JX0eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D\\u002fX5YqTtjfkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q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qI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51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D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPxv3JX0eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D\\u002fX5YqTtjfkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5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mgWya3qT86oEpq\\u002fcCyPyeqAjD7XLk\\u002fSqSetkXOvz\\u002fXJUNyyn3CP\\u002fGcz6zp78M\\u002f0jdCMvDuwz85utZmv3vCPzjDZGXWzb8\\u002fBPXCq0FsuT9Yr7Cyh\\u002fiyPy4Y3JYf3ao\\u002flYgNrqIIoz+eN4RhSfWeP8aTnh81QqE\\u002fTVKNS2D\\u002fqT8lvHIPCDW2P6ulcnA1NsM\\u002fyKaEH3k90D805wvUdLDaP8PyAR2QXuU\\u002f4BjYlyaz8D9mAxYvD275P9kvrECYwwJAmB9\\u002fFB+jCkDPJcUimAsSQPzwPK0OLBdApwA+GwYLHECs9kofV9ofQMFZWm9L7iBArAhl1c\\u002fQIECDrTW4Ji4fQA9VYCR9+xpAylv7y4XPFUDCYgW+un8QQHcue1AwcwdAnuO13EaP\\u002fz95R\\u002f4KtnL0P5wSt3WVZeo\\u002flJp4VU3\\u002f4T+9XkWg\\u002f6\\u002fbP9sX1ecjgtg\\u002f6OUvFtLJ1z9UiDpKjaTXP42KmmmK8dY\\u002f4ade+yE71T\\u002fZrNgkNZHSPzq4ycMhrM4\\u002fNx4RrIMOyD9Wzm0ARCDCPybr55kfwbo\\u002f\\u002fwBZh4Xdsz\\u002fHP3yIGoOuP8yb\\u002fXGXmKg\\u002fSi7x27mjpD+MoH0PDJGhPxkMlvGsc50\\u002fhRtpYKzClz915M1uPiuSP8Swkbv1GIo\\u002fwu4XHeCCgT8WFWBDzON1P5B7dksQcmk\\u002f43NDLeR6Wz9W\\u002fRhVy45LP65iJ1wYqDk\\u002fvna44+gsJj9tuqqX1csRP4p+ELojivo+ZOm2Sux94j65nAEq95rJPn3z+VenSrw+yI5K1uH6oT7V40ovI7mzPk19JyjgINI+SJctQ3gv8D63XBoHyt0KP5SdHHmCrSQ\\u002fJNalb1CDPT8jPcQdVodTP4lKfB2O9mc\\u002fph7M0qpDez\\u002furziJNsOMPxGDUtJ2Ipw\\u002fMat2KHOEqT+04TILi3W1P7bENJyeu8A\\u002fhVpwKicyyD9qhRz3eTjQP5WSYYdgKtQ\\u002f0stVnfI+1z+29p54FdnYP3FydAmCodg\\u002fByNcdpCl1j+QUBhwV1PTP0kkg\\u002f5cq84\\u002fAu9I5a\\u002fExj8\\u002fIJVKxyPAP1xQClzSRrc\\u002f85X7UzDKsz+am\\u002f6OfyW3P8uzQLtdpcE\\u002f5dhsJ2eWzT8KGciP3bnYP8TeIU87+eM\\u002f3YZpErcP7z89aKiwjFb3Pxot\\u002faa0BwFAqQf5yb02CEB0S8Gsu8MQQAiaNLMShRZA1H8zVeAoHUBrdjPr\\u002fxAiQPHhlRNrRyVAL\\u002fKDfNqwJ0DGYo9VqtQoQMLhTfiGbyhARfd0GQGMJkA8n4OQuoAjQDqiltvuqR9AfdCxtdkwGEDVEEmm034RQJTU4ySWPQhAut6KyIJ4AEAnGMFsxur2PwSZdjUPU\\u002fE\\u002fPn+Lgwq17T92xDX8nXjsPx9NlwMXyew\\u002fPDr4rHcL7T9q97vXr0fsP+i96iBKKOo\\u002fZsREBHbe5j+qiz4tO+7iP29G8jr9490\\u002fO3F1NqfQ1j\\u002fL0tvrozHRP0jtRqzjRco\\u002fbl9G++HUxD\\u002f4k8DEJUXBP9FfMK\\u002fDh70\\u002fx72wniZauT8KWsqMOUe1PwGCANUqHLE\\u002fF8oB59AEqj\\u002fLX6u7Bo+iP19vlmvguJg\\u002fOgqpbrerjj\\u002flUy70MrGBP7WomS8r93I\\u002f04x84RTiYj\\u002fuMD5L8HVRPwo5L+3M\\u002fT0\\u002fRlB7diDuJz9q3i1CKsERPy4H\\u002fRtUpPg+wlIxhQoD4T5Dqa+M\\u002fMXSPj9QZaMwYbg+5u8iasqKyj4Yg7wIWizoPiRDh5ZRZgU\\u002f6FccFaOcIT+EfxHsBOI6P7gjmjAgBlM\\u002frlfEIRf3aD8yEpAam2B+P31uw+TgIpE\\u002fgSjWiiTtoT97y+QGUGOxP471l+G7Rr8\\u002f8NJYbeIUyj8+3oYPqyrUP9UX2SWp6tw\\u002fE6fxEQw54z\\u002fpCyAMprLnP5cHstvzFus\\u002fA9KeE1m37D\\u002fG7LAUVTvsPx+KPH4SwOk\\u002fRE696kbP5T\\u002fsBjQsrjLhPyWeN6tBddk\\u002fjE6E+Ggl0j8jYzpVn9bKPwtYp+9YBMg\\u002fQ9hc1pBrzT+6C+N14afWP9bWvCh5tuI\\u002f\\u002fO27piF57j9nWdGyOM\\u002f3P1CkZrJexQFA+sekqldsCUAI9IIrO4cRQJrdMWhebhdA\\u002fnyHaad7HkDU4X7e3k8jQAm3Jm+UxCdAZDFkvDxDLECAq4iYaiEwQICZnFN2kDFAjT5BuHAmMkD\\u002fVIie1LsxQKPJJv1AWTBAi\\u002fqyWYBtLECWEkC8jFYnQFFhY6xBKCJAqgmALcP6GkCVGyZk+G4TQOL3Ikss7gtAOv7hXMb2BEA9HZLfe0kBQGeP3Qmz7\\u002f8\\u002fJtMTi3IDAEBDEDVeGYMAQPdMq6kTwQBAfHF4uXRQAECJI20UZSf+PxXBnQeQYfo\\u002fTpG7SYns9T9J0I1bN3bxP0uw9mowEes\\u002fCir19\\u002fPc5D\\u002fs6mQMGmXgP0\\u002fs0m3Ewdo\\u002fmsGEVAGz1j\\u002fIHZxwba3TP+vwCPiq+9A\\u002fMezd3WR9zD83mgZwvNDGP7AJUHDbPsE\\u002f86x4EshvuD8VW15ZWimwP4ooGxOH6KM\\u002fXr8fa\\u002fXOlj\\u002fShTkRdEmIP9qoYYuoB3g\\u002f71XKe7IXZj9DLNzP3eBSPxECNrt\\u002fAD4\\u002fElJNG2gxJj+h872vfcEOP1I4yOOcPvU+aBq1es975z5iHwIXQWbOPlgqYAaKbOA+Jg13ubSk\\u002fT7fyT3APgUaPwEsvlBTPDU\\u002fxpJ5Y1kSUD\\u002fcjUlwc45mP4zO+b2CWn0\\u002f6n5Q1ZO1kT+2mXNiJdCjP3DxIYCpjbQ\\u002fsy7hpPvEwz+pTdZ2pqHRP7th9+8MKd0\\u002fUSdDMuJb5j+uPmc94crvP1OqIQFe9fQ\\u002fcnXlCWyf+T\\u002fPoTGEQQz9PyLObPhGiv4\\u002fjmUqcPHI\\u002fT9he2vD5fX6P+K8ik5qrvY\\u002f7luEezXN8T\\u002fkRShDsVvqP2UBQs3vA+M\\u002fSIVeMV4j3T9JqWYd6p3bP1xwLVaMoOE\\u002f7N4O2TVF6z\\u002f2R8QGLi72P1Vg36j1qAFAGcb26ADgCkDQ4+fcj3ATQNSDn9wTyBpAoInQjFCnIUCjocNMCmkmQJcv2FObkCtASOl8rNOAMEA1JROjAEYzQEqaVAvF6zVAVxBa6rotOEA8j22IY7c5QDaOtqqeOTpARiYulKKFOUBmxR1LAKE3QDaNRgPFyDRARvdCVC5iMUCpc6Eyb8ArQDzn1stZUiVAaiDFQ\\u002fQJIED8anadvlsYQNMmOHIAghNAY1SvD4QiEUAZP0SlipIQQNzLR\\u002fKIBRFAx4VrxtayEUAoWppCivkRQFCzrShLexFAcainC9UlEECSYqinak8MQCoHMhsZqgdADCjX4rAOA0CtzPVd3Q7+Pzcw0H2kuPc\\u002fS4IGRlwn8z9mzVavZwjwP7loPOSBt+s\\u002fD2YgeW5F6D+DfAShAgLlPyo1Q55IluE\\u002fhCX+ReoJ3D9wJKosVBHVP9NRIkhgqM0\\u002fvhuz1c57wz+dGr6v09e3P7F6YV81Jas\\u002fxotQIce8nD8YqdEvWUmMP2CX5ESq5Hk\\u002fJxTiC60NZj9NPk19TH1RPw0gr0fB3Tk\\u002f8JJbsKf0IT+vyNdswe0IP7mG0tv9s\\u002fs+2yOFa1Rs4T7Y3qzVjq\\u002fyPtBNvLl4tRA\\u002fIrRq9fEVLT9PpI5T6YlHP9iffFx1qmE\\u002fh8WXRDOWeD+vavNRc7qPP0mVfU1q+6I\\u002fbbTvb0YPtT+hM+yv\\u002f6nFP4BwmfnrqdQ\\u002ff7+CnW9G4j+ZtkpDAfntP4AsLQxEyvY\\u002f7gPT714RAEDTfmFVPQIFQKriJZfSeAlAvtCcYgakDEBiM3F\\u002f7d4NQMNp7rmj6AxApWVUaFD8CUAZsVDKOb4FQFzDyRUYCAFAWuVTugBd+T8UzmHu3rTyP9sv6xN2Fe4\\u002fT7QiDAtQ7j9vjK7Od\\u002frzP0kdWUn3yP4\\u002fthLiRBGTCEBjp\\u002fdoQiQTQHPtYAYCdxxABgh0CDsTJEDLNQF+XNwqQK+JwymhGjFAqlYE3fjYNECxDBPXs3w4QAb6\\u002fu0i6jtAhM\\u002fsqM4QP0DPZRwKoO1AQCrM9K5BD0JAPV9po\\u002frKQkDvN4\\u002fZ7PhCQOMFKTIPekJAGQto2NlGQUDLd0q08O0+QP1Lal\\u002fTfDpAk\\u002fHtUGPDNUBY5cdnl0wxQFoVUidlEytAtJER9FGBJUAQKNB3kwsiQMWVe2KVfSBAg1B6FcZZIED3S28tcfggQE4c2kBLrSFASvEuOs3sIUDeogbsymchQEzxodzWFCBAA4zvbixKHEBLKXoEdNQXQIi5YUwscRNA84oYbXw\\u002fD0CM06HPSEAJQPsNQlW\\u002f6ARAxtQO2HLjAUCjlSNHWWL\\u002fP6ocS5c3rfs\\u002fjeEx9M\\u002f69z+0arN\\u002f6gT0P58Ah1MBw+8\\u002f6tvwGvK55z+IibZP+5jgP9\\u002fnutC4rNU\\u002f36mCAwJfyj89dy63qt29Py\\u002fnPL6Lea8\\u002fVsvMbffenj\\u002ft2QymeTGMP66iSMs3\\u002fnc\\u002fyi9DPjQMYz+Kpb++hENMP2yji2ymvTM\\u002feMDlGlWxGz+ejeZhTxIPP7N5Yu8IXPI+P+5vwgWLAz8FSXO101AhP+ZqfOko4z0\\u002fLmdoGDX8Vz\\u002f+P5WrvNlxP6TyJKbeoog\\u002fJ6kf2s+Gnz9P34xNMLSyP8eYs0\\u002fik8Q\\u002f+gzKJND91D8Kn8IX5drjP8zS6jXUafE\\u002fGxFy+jtS\\u002fD88fvxb6VoFQA+dYzy+3A1Akp+Xx3lcE0CaqK6fTkgXQN9gAEwI+BlAnieSQCTgGkAxpM2ZgtMZQLBJ3yXUFBdAT5YDMGBBE0CDPVr1nkAOQI4V4VhQ3QZArugW24h\\u002fAUD4NqVx2fb9P9Ngm3u5CABAuI5LJKSJBUByL8mUxFkQQOiCuDNieRlAJybMjFJfI0BZeKAY7SssQA2k4GgjbzNAnC4MLH1lOUDd3l\\u002fg1n0\\u002fQElVz0Hvm0JA3zl\\u002fMfwZRUD\\u002f1fD\\u002f\\u002fSVHQOdQ0uo5yEhAk8H85u4USkBt0jmjwxZLQOA5XA0Ev0tApRKFihTlS0CEEgxHXFZLQBEhB28M70lAET6aZr+vR0A\\u002fk2HSEMZEQCffm3YPhUFAUNYWOAqjPEAqEsv99BM3QGsWUjhO5DJArMmsnURNMEBt+DsErmkuQC0iQ8Fhdi5ABcwU+YesL0BL2LjKUXUwQMkPrAHIozBAV9vy5OYhMEClaC\\u002frT9gtQFEnEBzcYCpALZveqNZtJkBl8YvPBpEiQPOlX+x5bR5ApXwgfm4nGUCOzLhQYU0VQAuUFgeajhJAgMJjjlB2EEBP5ont6CoNQPR6TaVzQwlAnVO7CJsFBUCqEZOop5gAQDn1e+81qfg\\u002fxFQ+qCoo8T9y6wR3B0rmP1U4as2M\\u002fto\\u002fGX0WMBF1zj+T6F\\u002fEwgHAP7BkX16IXK8\\u002fimdvTgConD+qYNpgb3OIP8Td\\u002f019gnM\\u002fN1lkeLMtXT9QsMAnj5xEP8yHAa3bYy0\\u002f5ooVRRS4ID90oqV8ycgBP1imlnQwyhI\\u002fUVrXVvh+MD9W08dzUDtMP2\\u002fnt8qOd2Y\\u002faV9MFbuUgD+2dXxVYbGWPzM\\u002fjn8WzKw\\u002fo05bFiHxwD+cELGGy3vSP3+7srKnsuI\\u002fBWpKPsGJ8T8rO3yAoIH+P8UY0Fz3mQhAbKXaOnllEkBS27AxOYMZQHQ7EpDtZyBAhYt9UA+SI0DfTYU2C6klQPm+\\u002fNTNQSZA4gHndehCJUAk\\u002fuOaZO8iQGb4aLlTox9AydZJQ50iGUDhq81cDZETQAqXm9zcsQ9AP8NGpzFDDUBjSZkxyYEQQBO08UkWPBZAo\\u002ftpYR1uIEDfOEYg7MkoQIrHYE2DUzJARJRPY7UFOkCflTmLDZNBQIpw8YPsf0ZAqWhMukRPS0CfnP2vVYhPQEBPy5ABbVFAjc+C1xSaUkD4vV9r8mJTQP\\u002fCnAl871NAhkhm78xgVECYuaRYir1UQCljbrGo61RAeSr99xq7VED2zFUqd\\u002fxTQG\\u002fZCa7rmFJAjq8Be8WgUEDkGcmzmJVMQJbnDLl8y0dAuKbC8QyFQ0Cr79xwrT5AQJ1YifKQZzxAnz5qmRGuOkAOf4vZ5ro6QHqKJxmurjtAd5w5YHGfPEBHjLVEl9M8QDeOpe4M6TtA\\u002fuYCVtPeOUCm7+c6egE3QIFcOkxgxTNA9kxaf1ieMEAiRivkPcMrQPSXromjbydAACe8N947JECJC5U1++EhQHKITIpd\\u002fR9ALbw9AWRqHEC8\\u002fbd1SpUYQJVHplDvYhRAY\\u002fP2HQkGEEAQKX6U4bIHQCjmLCwqagBAD8MbPo0+9T8j1KoYBKjpPzZ\\u002fjXwi59w\\u002fGRD+psRhzj\\u002fc5b5iydK9P3v+rbWrXqs\\u002fxP\\u002f7woaElz\\u002fgnK+X6POCPxCjAOIeuGw\\u002fdQYBHsCgVD8Nk519Sg8+P2gMDOkEZjE\\u002fwrVjuUCsDz8HsIcCfpsgP6zKV7CO5Dw\\u002fAZAsRs6DWD8D\\u002fKtIklhzPyAKW6ERUYw\\u002fGibCsSQ3oz\\u002foAPcbQS64P4IJtlDeNsw\\u002fH3Xs61+G3j\\u002f4jjdUD5\\u002fuP0YyoeWWe\\u002fw\\u002fMT4ECuaQCECdv2U\\u002fk6UTQGnRFHAJJB1A6U7H67gKJEAAvjZ165EpQCmj1tB\\u002fRC5AEIPeem2hMEDA+FItf\\u002fwwQLlv+l5PKjBALHySx9jPLEBQhz96XksoQHE97QLzyiNArbbLwsY0IEC0+xSZW2EcQGnPo5dJZRxAqlTA2EamIEB9Nvae3h8mQJjeJwcocS9A1mbNEFjDNkCuu3VNFUJAQFP0DlgMdkZAiyfToKeqTUBQBXQSgZ9SQDcRbGW7MVZAvpFEKbwqWUB+Z7Dk1lBbQJTrgdypplxA9YIwPXpnXUDxKR1VDOpdQAqIhnWwdV5A7lbaJkcdX0B+wVc8UrJfQAFMHogU119AcVUD5dsnX0DI7ZHepmhdQJJKcd33olpAPr4+zP0nV0CPlajqOHhTQJYvGYd5HFBAGXrRlbP\\u002fSkD0yQKi7K1HQDVGKcQnM0ZAUkvEtlAaRkBBbswmO7VGQBrsykMwUEdAzqsxVFdfR0AAhsRGp5pGQOusAsPHAkVAqoaNZv7QQkBerek59FpAQL35A7T\\u002f6jtAo\\u002fztQw++N0BLJbmREGo0QHZ7SwBd6DFABPTXDyIBMEAZXjUIzsssQLcNoCYVnSlAHqSN7uUfJkCIaXXweEoiQHe\\u002f6QAdphxA8maz28YcFUCJacKOIioNQHMTQH4I1wJAWMnPpPG+9j+Mc6lIw6npP7BEuxudE9s\\u002fnd347ta+yj+jy5gg5cS4P3cAHfzRiqU\\u002f0d7OfTagkT+wxaEE\\u002fzF7P8\\u002fFB\\u002fjc8mM\\u002f0x58ZhLQTT9AXarAh5VBPzDSt2Fk7Rk\\u002f5LJyH9b8Kj9MsaeDr0JHP1DCpDamkWM\\u002fRA1sOaGgfj8NWbJ9OjqWPxHQr5gu6q0\\u002fiyMaPlGqwj+W02pT5pjVP76imAbWK+c\\u002fSfk5nw8N9z9TaUmSX0MFQN\\u002fFDQ0EMBJAP5H9cCraHEB9c6A7OzklQAEBOycd9ixANQ0PRG1VMkDFnZvWbIw1QHh+FQbXiTdAPOs9swfzN0Af8mxjLcw2QL+0SipkezRAfo0qb7apMUBA\\u002fA4rhyEuQC\\u002f5Xv9umSpAREn4gwKOKUATZBQG6oIrQFpXPDuzeTBACH8Uw+tPNUBJs+Arq9Y8QGUghLtL20NAcz1T60kxS0CSUwVr4SxSQMasTDXrZldApBHWhuHJXEA6lPUQMd1gQNUwqKxk12JAkpD5IQQxZEAXqCNyOvhkQBqRitZXZmVAYe+m\\u002fjXGZUA2vQvDnlBmQBM\\u002fcbDUDmdAOLWyeabRZ0D0sx+mTkJoQKHP4wwzBmhA1oosP6jlZkC315gpzORkQOVTTculRGJA+XYHErXbXkAdwM1G\\u002fZ5ZQHzvd\\u002fc9f1VAVfMTJrjPUkB1TKSfsoNRQF1u7gtqQlFA+5aiMdOKUUDRLBRYttpRQIX6T1uszlFAZ1L\\u002fihk0UUDspnGM3QpQQLWXDjrF8kxADnE2frxySUBJi1lsuAhGQAILcOpNCkNA7jQ9+UOgQECDFKNgPYo9QG1vBPY5nzpABv9z0a0LOEBIAu2ttGY1QCjI7lGidTJAVHG3QJRzLkAwlBE7DcsnQE01x3FWgSFA\\u002fXQjiYIrGEA5AkL4FUMPQFC1qDxR7gJAFdz3uzN69T+X0M0xGNnmP1S72PSg0tY\\u002fWI+3rGBwxT9\\u002fuEkVrPeyP03kzGT7pp8\\u002fFh44RjzziD+CLQ0Hc7tyP\\u002f3Czssou1w\\u002futSxKLlCUT\\u002fJ2AzHG4MjP5iKIiS9KDQ\\u002fccD8Jww3UT8Reo+BZbhsPwQGBhZ9SYY\\u002f47D\\u002fAhgKoD8JfOuCD2i1P4EYW+yLfco\\u002fwcBPVHdl3j903b+5mCvwP9FUhH8C6P8\\u002f8EWUXWIwDUB6GBhNRcMYQBLtXF+meyNAiWlVSfpvLEB75IqFmkEzQOOClyfvNThAxIV6\\u002fUFKPEDTc6dsX8k+QLRaKRB5VD9AgFqC8MQMPkBO+ia3BZE7QAvITCxjzjhAxlYStI65NkBFAUsrQhA2QHg9yJijPjdAu9BKuzhzOkCg5qyfZdY\\u002fQEYnXl7F40NAt68fXkV8SUDjn7aU2pFQQONdKDQolFVABL2OKIK3W0CMmhu+aU9hQJljdmXlz2RAtISITgf2Z0CTsiwF0GtqQCxjn8XFD2xAuVqoxHgGbUC60y05E65tQH4Me5VadW5AjP6ZZUumb0CqXaStTp5wQLFAFJx8bnFA6NOtN6r5cUAI2cg0QPZxQHtwfsL1OHFAvXE94LuNb0CorCJ3GqlrQIavQEImaGdAV+1mFy91Y0AiGb7Z9FBgQNen8PYDb1xA6ENathQ8WkDIC3D59YhZQPZ0VMGSoVlAmlAkL63UWUALeX1OIJ1ZQJOZqrHEtlhAUruvdoMeV0AilPrxdAJVQJu1JjcDqlJA4cWlr\\u002fVdUECLYOykeKtMQOEvJ3jfWElAgYZfFbW8RkCMtypsFZ5EQNT+lQe8rEJAmPa66+CgQEDwbiF79qk8QJ3wlBjQnjdABlg1kCJzMkA0E0\\u002fAOiwrQGhSSzUszyJAC9nZcHJyGEAN57nqO9QNQBJD3ItxGAFAsGz+Ntpt8j9ZXnLLY7XiPxvdls8c6NE\\u002foTGy3rUswD86qBCan5erP4GEimykPpY\\u002fGZ4fLBsWgT+mRZyBldpqP5aX2BgcaGA\\u002fgIUYlLgBKz9ubquAQrE7P4Eb5tiPbVc\\u002fNWE6DElgcz83a9wAydGNP1vNvwLmR6U\\u002fV7R\\u002fkzIqvD9KQhWeKUjRPzjeUoRfquM\\u002fhMebDCPA9D8NPCnGyE0EQCRhw8l8bBJAiIrvtKACH0CEXf5JgDUoQMnsTnSIiTFA747AYzyXN0AyDGW+5X49QEb0onN8LEFAcRGFjcSyQkB1IDwDayxDQIFepG6tw0JAGCP5WbPsQUCca12ktUNBQCTni3wrXUFAin56lICcQkAS1phPhSNFQPpVFQaj4khAeJfE7ojDTUA7zB5Y\\u002fuxRQDpyVREiv1VAuRfANEGcWkB5mh3w+11gQBcr+A\\u002fZC2RAgdM4488iaEAxes\\u002fC\\u002fz1sQKwh\\u002fsmO5G9AEj5lGjFccUDCbjpchlFyQGAkUcpL9HJA2x55hIKIc0DIv43WSlZ0QKT4b3xehXVAqGH+wtECd0Ctv5M82354QNGqIcvphXlAZOquLXCreUDTqFDlF7R4QAKQT\\u002f42rXZAU1gH5GToc0A3XTM79N5wQF9IxC0KFmxAfpa+rkqMZ0BgYom+UHBkQH7GK01bsGJAseGvom71YUCvVjoy3MlhQCD12jidvGFAJYW1Za15YUD8\\u002fti1BdVgQOHY4U1Bk19AOBhPHjrgXEDjRvVZoOJZQPcnOuYL8VZA15\\u002fgC+hPVEBms9Cg7SFSQArvnlvDYlBAtl\\u002fNYuTaTUDPZrKnvxpLQCs3b+T2J0hAhd+lRbTUREDSJQtiFi9BQH\\u002fTOFRy6DpAjZcUzTXmM0Cj71OEJ7srQDZyH4+JMCJA4Q2OkkR3FkCrUGSIZyQKQAldM\\u002fmbr\\u002fw\\u002fTvXayui07T+OlXJyHgzdP6ZN0QHS0co\\u002fIu\\u002fvQWlgtz+4kR1pST6jP3ySP+IELY4\\u002fSIqEPDxBeD8iHgc2nR5uP7U9sOWXNDE\\u002fMmPMoJ6AQT8mu7jot1NdPyhyQt4DC3g\\u002fjw6R1upXkj8CA9KhFfapP18UvK01CcE\\u002feVnYXZm71D\\u002fQXg9g3GXnP8enrdS8fPg\\u002frWPBgWvEB0A6DT6JjmUVQO9aJaXP3iFArGaAf+izK0AdAfGjsPAzQG9fInzkrzpAix4G1gWjQEAN9ppEqmVDQKL\\u002fNf6JS0VAFFmyiFZJRkBA6kGjJ7FGQDFdiSKsJEdAx400CYNmSEAxvRjPOBtLQLPaadgzk09AA2K25tbaUkD3EvdX1YxWQGzbaqaVo1pAGeIW6+gCX0Dg6YDU+ONhQA\\u002fyPTAFoWRASanYyfviZ0BNEAAYD7ZrQJ27tZ6I7m9AQUyDwmUUckBu2VxhefVzQKlUUv25bnVAQUn9Qud\\u002fdkATSsrLO113QJpenWAvXnhAWEchstDVeUBPhffl\\u002fOR7QOkQiSpTXH5AbjFvyVlfgED3lyStczOBQEe5+F4oYYFAi6jXDObDgEA455xdN9N+QPbrMeP0GntAK23e2UYGd0AJGFRGLThzQCXefa5hJHBAvcTloXb3a0DdObMXwGFpQF2JTXyEGWhA0pQmVEaPZ0AYJX+eIDxnQDlF9ycYvGZAFG\\u002fYWaLYZUA1KoZ16YZkQN+ydIAo3mJAYVoJdO8JYUDZHIPOYXZeQErNf6utNFtAADE6eVp5WEDGJxxZhj9WQALxKhthWVRALyN+YVKFUkAtAcQ0volQQCcAhb4Jl0xA5TIl15+rR0B\\u002fzynp8qJCQKKWE5qVyTtApk2lJZCSM0AoVdYwrQcqQI2cV0EwVyBAEk3p4bpfE0BHwEuRgLIFQPI4qmye8\\u002fY\\u002fYllJwRzs5j8xPIW4CZnVP0iZPAMfL8M\\u002fpqTVDh0TsD9S6VYbqq2ZP\\u002fPYHhq8I4U\\u002fzr0wNlPBej9PkDIIiUU0PwuAS4AJbEQ\\u002fjtf9oHnqYD8FYDy3Knh7P00fcnmwxJQ\\u002fzEXfGwUjrT+68Nr2\\u002f\\u002fTCP42A2nnX4NY\\u002fdxIasqib6T+GL6CKF5b6P7CyNVzrmglAy\\u002fuSq73hFkDJCHxpNvsiQCcqJtTKQC1A0OLiVBr3NEBqXZXWbQI8QHgXYEOEgUFArfvlLsWcREDvpwO40yBHQGa\\u002f530bMklAz25mUdZaS0CakEymknJOQLK+yJ\\u002fKr1FAGvKI\\u002fWdiVUBcrbZKjl1aQOOUFdqJLWBA1SEMJ4NqY0AXvvG+iZtmQNsZGjyLjmlAmtc928BAbED9MpkytN9uQMAksrOi13BAb4IhdIFvckCf\\u002fIqj7DJ0QJNulDAp\\u002fnVAC2xOnNOfd0B7HpvzQ\\u002fh4QFIHWltNFnpA8FBaRQQ\\u002fe0BT3fGNxtZ8QCOXBKI4MX9AKQ4xm0sugUDtdZeDSwGDQAl4fvz0uIRAKQrq09PohUDdillQxzWGQOiDnj\\u002fxdoVAmh7pFk3Eg0CcEUlnTm2BQELJS5\\u002feuX1AXy4CEWPweEDWOP\\u002fhGwl1QCq8GIG8O3JAkiNMHF52cEB\\u002ffgWEAfFuQK2F5Oii4W1AgCRcNBQobUCK8KQh+lNsQHhBgDilJGtAUs0RjHqHaUA+cPR9iI9nQFRPgealaGVA6bYN07NHY0D1wQnxLVphQBzugYgPcV9AcQZOqiq\\u002fXEAK8o1k32daQBf\\u002f\\u002fPceHVhAeQCFR+eaVUAkF7Wp\\u002fMFSQNLLdsbsQU9A\\u002fnwdnPTTSED6fR7KmblCQFjkfWpswjpArYtSwYsYMkD8nrJlWCYnQM4upjrS\\u002fxtAyZg2ihz\\u002fD0DB6CFf+0EBQAnBqBqMjfE\\u002fsRERoGLQ4D+TABjocVLOP1Sq+AYdy7k\\u002fAzKOsrMCpT9CFjGzI+CRP8zLsM16SIc\\u002fIYCoc4x9Nj8tHpNHU0dGP\\u002fjYQQ6iH2I\\u002fnaAXEBgKfT9Y3NMkYbWVPxJReVQ3Jq4\\u002fW\\u002ftnYQtvwz+TM6PC5z\\u002fXP1KwX65hz+k\\u002fYrgC9eGW+j8cJ07ZCW0JQJgVwPwhlBZAVxsw+oGiIkBTO9wPtqAsQHGkWW2GgzRACT7q6QuJO0AnGxRg+mxBQAVZwCVwB0VANBkjZvqcSECjd66RCJhMQFviiQBk5VBAg2GKn1GkVEAQGWhf+QlaQDW1UaoWrWBAuY4YqL82ZUCd1ETCElBqQC1XOhH9dm9Aa6knMe4OckCXtXk4su1zQOtC5f6lSHVA9+QZP6A6dkCDT7e4\\u002ffl2QNiyd0ZNvXdAqlw4SJ6heECuv3yl16F5QA+IotvppXpAuvMZY5eie0BWzeSz8rZ8QIU\\u002fJ6jnMn5AYY7ABy8+gECsaCZLHu2BQGV7Jb1mHoRAAAVx3GOQhkAtvSzumNCIQOkJ0KW9W4pA3nLfdCHJikAomrT9L++JQBHZ3fxS8YdAZzXPrt8xhUBrREL6ny+CQJOLQA2rvX5AqsZlvW4aekArAFZQWLF2QEtX9d+NbXRAqT2losMJc0D1wO8WoDByQFBS+m+Vk3FAyGzDjN32cECybUD1yTVwQFNSf15whW5AH\\u002foH4UNIbEBb5Zal1N1pQCTATzFGfGdAMkVRbUVTZUD6Vuuv4ntjQDxQiyXB8GFAZCnnu\\u002fuRYECSX+X502ZeQIrn1XM9XltA51gZOAroV0CkWWdLkxVUQPklefO1IFBAB8vMdk+oSEAx8sfT7uVBQJAMR3lOoThAm7\\u002f5p8AKMEAGjMuxDcEjQOHQsWAp9hZAcvAGRLMnCUDhXs6cxu\\u002f5P\\u002frYode1Jek\\u002fKZ\\u002fZhDzy1j\\u002fhlnAA99HDPyKkCNI5mbA\\u002fb\\u002fEgYzzVnT\\u002f8yORpKl+UP7mI5DILIjk\\u002fk8OUvWXeRz+2hugBu5hiPyGoQz54+Xw\\u002f6y7gveU4lT9\\u002fOoPauAKtP+aUlTY5dsI\\u002fjGo17Cfa1T\\u002fGy9G0PgroP6kHHmTjk\\u002fg\\u002fylXdfNhbB0D9tnWIoacUQLiqCry5BCFA0xtQ4UYzKkCAkMzeIOwyQAmMaik20jlAYTKLGoTSQEDdbPoBzUFFQK9TAGIvkkpAdtp3G2fDUECuFo3zUqJVQKXEIDVpjVxAtcslrEsGY0DEarvM1iJpQGzfceChMHBAxHt8ITAXdEB0OYVHndl3QI94IUWaBHtA4aFLoJI\\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\\u002fvjVACKsdts8hK0Cf0fLbZOkfQKMPRuSSqBFALgCzLFFeAkAvrYiCyPbxPxjkODp6lOA\\u002fvZKEPfxCzT8V4182GrG5PyEsWDvlTqk\\u002frJf9XN2Ooj9Q\\u002fDUUMPVAP5upeXmkJ00\\u002fdzhuusxLZD\\u002fE7ciEqVV9P\\u002fu4c7DsYZQ\\u002fzPHG4E7Vqj8gWDGaJp\\u002fAP9flJpD3S9M\\u002fE5ABqjPw5D+xtNctQDX1P0FgmdgFDQRA6Q2ZKgi3EUABu6I8v1MdQAlav9hG2CZAGMuLaP\\u002fcMEDKbQj2LtU3QHKkw\\u002fwNWEBAcvxPQpUbRkClvHUC7fFNQKiorlgGgFRAA3EcftheXECTjAssnKpjQJfT95AL82pAJ41ZWNABckB+OvlmCTd3QALZw2gFqHxAfUqhJODbgEDP6CuUHuCCQMFGD150IIRA7FnQbGqDhECa499uTB6EQDIEXnu2LINA9PxrUoP7gUClo+h2Z9GAQEccF9MDwX9AoyQpBmCNfkBJD2VtEzN+QN8BUeCP535AcWtoX7F4gED\\u002fAdGk2keCQMAqyj4g5YRAiriHASchiEANEfOzJZGLQLJKoIw8n45ALlLMc5ZYkECHUIkosquQQAOZGr9HOJBA4Iiu8yEyjkCSChi1uxKLQOFOeWFMoYdAJw25IGtbhEAc\\u002fYKDzJKBQE5DCaNkz35AvP7qVAmne0CQ9JnME3N5QMsqbzmk6ndA2fanL1XHdkC\\u002fFh0Gi851QI5Z6gm21nRAVML2pWLKc0B8neUr96dyQF\\u002fRrUK7fHFABPtkdeJbcEA04azKLaluQHu0OYtV1WxAGm0v2OIoa0CBEth5SXxpQPI4nO5aomdA1U7nnb94ZUBAoZwNRfRiQHKJpt78JGBA47t+TxFjWkCuKb++P5hUQI+YV9lwlk5AIEtUIfmKRUBbJUSE17I8QIiBFY6RCDJAp3gKhhVWJUBcs3O0ZbsXQOtNWOEd0AhAfp8SkiJ0+D9HQV+14u7mPyyFbWyBANU\\u002f6dDtu4UAxD+Yy1dVAIy2P9CHuUtvB7I\\u002f1KyDUKjIUD9Qtwh1WFxZP9pF07lJV20\\u002fVc1J+2Uvgj+T\\u002fAUV1EyWP1jX8Ov5n6o\\u002fDddPhmWuvj\\u002ft3k9NrOzQP0dx9IUrweE\\u002fQzlOAIyg8T9RbwKlsIYAQPDS1lsjRg1AJ27e6MiRGEBUJJUyB6gjQOMBmxe0PS5Anv8Bo7ycNkAY6lige6BAQB+uHOcISEhAWGorg\\u002fWyUUAEHkp4br1ZQJow5fFGkGJAtKTR1MRTakBUlyupIjByQCYhywdeSHhAL5W6iLEef0CyJ5V5YwyDQPzbHH0jNIZACnt1Qh2biEBTMaiPOu6JQGpjKgH+C4pARhfB51EPiUC5VbGGtUaHQNOLwTmuG4VADOADr9\\u002f1gkDde8EWiyaBQAVWiIkmx39AZC+KIrKffkBAYyV2eQ5\\u002fQEa6IE8Qo4BA4SWhRA+xgkCE6xtzDaCFQIRQW06FLYlAfTy3Y3DhjEDH+a51RRCQQKdOA0soKZFAodvWmoOGkUBTN7rjpRqRQLnnLKWhAZBA8T8B4cnrjEB11Hyp83iJQCrg206BI4ZAvAHCG3o7g0CzGoH4IOSAQF2aHreVO35AWA9VaF2oe0DiK2WXYdR5QNmOhWOHgnhAhy4+7tl7d0BKjEVpQpZ2QKwDEfjsuHVAobn+SivcdEBOYqF76gN0QGrSCJDiNnNAWh6JH4B2ckAFgNA+yrpxQJsrQxUt83BAAVacSfcKcECEcutu9t5tQKHAdxzIK2tACBgWZHn+Z0DcXvzvLnNkQKcC3MvBu2BAidv+PDYrWkA1d5NgK3lTQCsiVdtce0tAOVG22M1UQkAwof4q5Q43QN3tv\\u002f4pTCtAQ3VzqL9lHkBIm1M1seYPQNnqFUTHyf8\\u002f4ctbB2St7j+55Yqhpv\\u002fdPyQqSUBTK9A\\u002fIRUUhcRexT+EImzn+obCP8oCedM1D2Y\\u002ftWqr8lfmbj\\u002ffVF5b9iF\\u002fP0UWo8PXlJA\\u002fOL2KkHhRoT+AElggcKGxP9IM7Q0Tk8E\\u002f9z5cSYUy0T\\u002fbZk9p7X7gPxmGvy9O5O4\\u002fXhwjRp0c\\u002fD+IKLjPVswIQF5ueIlKNhVA0XIu4VWmIUD+WQit1bUsQCD\\u002fLy2P8DZA1+zByVYRQkBtXyD2\\u002fBNMQAokSw\\u002fje1VAtLbjEUUgYECcgzWZuKJnQEmK23l903BArwMS8qAod0B369rxL6x+QDnJ\\u002fmGLd4NAcfIUDd6Zh0BkZDZb\\u002fkGLQEJzR+5j8Y1AeJq7S7dGj0AX\\u002fm5awRqPQCyemaOVjY1Alp1pK+f9ikBO85u9M+6HQHNrJbmK44RAyqTy7otLgkAOUCMG7HGAQHkVYXBECX9AIXiDOmU8f0CrNoGWfcuAQN5rKr3qBoNA55ibcvcqhkAr1Vp0geWJQLgkBM1Wt41AWm2ltHSDkEBZf79Fr6GRQBTlvWDHBZJAc76MYU6kkUA4zhjBQJiQQJ6qtgscMI5A5NXtp5THikAxeV7nq2iHQJX6hV6wYoRA4q1GHczegUCVoA+az85\\u002fQAeG\\u002fIS\\u002f5XxAGELUF\\u002fHYekCNiC8MZ3V5QLKDOqKGiHhAP79BzV7od0AZ9ZYHUXh3QIupcgK\\u002fJ3dA\\u002f+bxnXHrdkDnkcSw3rV2QK1gQHLzcnZApoKFG0IJdkA\\u002fP5cX9F51QKkqcD5mX3RAd59HAlD\\u002fckBx\\u002fdOPHD9xQPNuMmDHV25AMFAhH+i9aUA2bqTldfZkQKkorFY3UGBA1ewUO\\u002fopWECH1VU\\u002fU\\u002fhQQJEdbtlniEZAQzY2khU4PEAUgMcA2aUwQJudvRAUhyJACUG8USyQE0A8ARjQmekDQAYYU3UdQPQ\\u002fju8Mvj\\u002f55T9vc5QrEKPbPyYHnM3zIdU\\u002fFuU2q6Nu0z8FqliP0eR+P88B\\u002f4t+B4U\\u002fMr6Dkef\\u002fkz+arkME2cqjP04\\u002f3PugzbI\\u002fkyrBc7L+wD8F98iazXLNP82nzcrxxdg\\u002fy8wk8UOK5D8DuWLEfQXxP8ZFm3+Bd\\u002fw\\u002f1ewRO0kXCEBLy16Ty5EUQFGnjvK8mSFAXEgx49vyLUBVh1GQiSU5QPOgp7eftERA6HymBS2iUECBxkxNXPlZQAhIdX\\u002f5p2NAs8PEU2TGbEAKD+JReVV0QHhQIflLsHtAHHDrGxAfgkDgVrSU672GQEY8LJEGS4tAo3WsW1lAj0BW4NDztAqRQD5yKgG0sJFAphOo1+98kUCYoe1v0ICQQFFmsqUu4Y1AFg9uuJ4rikApvDjBIXuGQBo02AvqUoNARdzUzbQPgUCivzKuaM9\\u002fQJGGMe7V5X9AlIAgoEMygUCmsyrA946DQB+VcE3h0oZA5iKEYAShikANklmd6XeOQNlrbzHL4ZBAYVNzvVT+kUAygVH\\u002f8WSSQL0iJSshC5JA\\u002f+IZlxcJkUA\\u002f3A4+EiCPQBG4CtzEtYtAEP2M6uE\\u002fiEBh\\u002fecUJxCFQBIkTNqlWIJAUp7FzJIvgEChelBOTix9QFpzzPu+\\u002fnpAOWucUkqseUBQA1yhSQZ5QFleICbm4nhAS\\u002fIW2I4geUC9xJFXOKJ5QJ\\u002f+t2umR3pAFHbEo\\u002frnekDrBHkuIlN7QG6zsvW4WXtAhJijhcDWekDI3GpgRbZ5QIS\\u002fz1Mt93dARWn6QK6pdUAHHuFctuxyQAtnL88K1m9A2jLHDxCuaUDoLAHpBcljQC3\\u002fJp+zAl1AXhngIt4rVECeGyjAiIhKQN6Avlgze0BAbLXrBmhYM0DH+9HrAo0lQAhT8KwJFRdAfxsSQ5h6CEA+N8TWuSP7PysRWbCs4vA\\u002fOb7Tx4ew6D+g+AuJsfvkPw7e2Jwn\\u002feM\\u002f5bhT3Ky7lD9GeYeQ5uSbP8SqyKf966k\\u002f893AJofvuD9PIAJAW9PGPy2BduoQnNM\\u002fhclVJ9qz3z\\u002f2bNAa6VPoPw\\u002f7UPsH\\u002fvE\\u002fYQlTvPc6+j9JpvmcjmADQEPszWWVwA1AyCSwHeH7F0CQKJ1\\u002f3RskQDLS9dLuKDFAb3NtYwQoPUANKkT1W0NIQGT92AiTllNAApzUhlGJXkA+UygADfBmQNl4\\u002fpbumHBA5Pywy\\u002foid0DDqNPQWQ5\\u002fQH3anRzDDIRAxGRDMaDciEBeCz\\u002f8ZIuNQM1mysaRyZBAJky6QPE1kkD9wDsYNtaSQH+4TDthlJJAM3uo+CeBkUABGO4Hp6KPQEPjn+5YootALUGcLqalh0CoU0zYIzyEQA5beKJWzYFAJNcnlL+UgEBIQ+XUOKiAQKkMHNvi\\u002f4FASKkxDd94hECGo8KAFtKHQJF4GiGKqItAC9z7MEF7j0BPGih+\\u002fl2RQH6pl3lfdZJABJV3HoLZkkBq2\\u002fd+eX+SQMzTK8JMfJFAWGf7ZaL5j0DyO7lMcnGMQJTS1hYmzIhAIavrd7phhUCPsDNJ8G6CQEcfMy5WFYBATqB1WT2+fEB64aJ4uYl6QEaNjhnPaHlAfvepGnUveUCIfT9GOLN5QH79LCMuzHpADFCFKyZOfEDOdX6sOQB+QB1TeiyYmn9A45wsiS5ngEBeWeIYKqqAQP5xRDKhfYBAWhX4sI6vf0A2sWV2KXt9QBunon67gHpApi+QuJj1dkApo70WlxtzQP\\u002f57Aivc25AlI\\u002fU348nZ0A2PqQsJr9gQLw9Ys8O+1ZAGVYMeAzdTUBuajKSm19CQDI5shnLejVAHagDzf0bKECWep87PaAaQNzXujchTQ5ATDXj1frsAkCIRy1fDiv7P1Jsp6\\u002fOTPY\\u002fEwiSxxsw9D8ZcnnZo5bzP+lSYbQv26k\\u002fMjpOPcpDsT\\u002fIkp3p76i\\u002fPypxIc57Bc4\\u002fopPvWk4H2z9uyJ8AoLzmPytykVp43fE\\u002fr1DacJVZ+j+O3DMlNmsCQB72vuMD1ghAA4T2fpOaEECLvl\\u002frBdEWQD\\u002fEt9DEnSBAgLuzDqDWKUCqf5rs\\u002fQU1QJb2KzBxWkFAJMgEym9RTEBDbBeqQ3tWQAw7w4\\u002f2O2FAZsjuhZ5yaUBTdAJjTBdyQC6E7azSyHhAIRV1ecRdgEBRRx+cFdWEQMTpVny3h4lAe\\u002f\\u002fhcwISjkDcApNHMvyQQOe2vqgsXpJANUKUL9v8kkDTMymatcGSQLZvfy5mupFA812gvdoXkEA45tIfNEeMQEmNgoFrXohAMwBNIsUHhUB4hgxO\\u002fq6CQMSUPdEzkoFAHqTMe3HGgUAzOXOEyD+DQH6EvL\\u002fR1YVAVt4jFc5CiUCc9kFRviGNQFpGcBRheZBAaE\\u002f0clwVkkDx3j+QVSaTQPrzeAlzgpNA06pmdJwdk0Ar8P51mwqSQLLsSZFNdJBAlb4aFmMkjUD+I+iLWTmJQBsowa1giIVAefiyBZBYgkChjBCKcKh\\u002fQLOB1+AzFnxAR1uAFNXweUAyf0UeExZ5QGitwuaDWnlACeuNGe6RekBU4PQbIY18QM23YCVREH9APvmrDEjlgEAdIwZkhiuCQL1dDv1LJYNA1Un0LPyjg0Dyt9TN5YiDQByQHwqxyYJAEIY\\u002fmAdwgUB8OZpdnyx\\u002fQG3gIr+JxnpACDx2CuMJdkAGexvkjFVxQGtMQsI5\\u002fGlAqr8K8yaEYkCKTMLMUwtZQJ2HJojMEVBAEJ1ca4udQ0DYVxrFL\\u002fg2QKwOtYRHVSpAxWE1nYnAHkB2+G1jj18TQH47Rn2wnAtASB5+ADdIBkBAkPdQdKgDQHgITAU0YAJAYfwnynf6AUAgr328mbu9P1Ez1ZUrusM\\u002fnIc9DmDj0T\\u002fSVSj6ycfgP2JfGUkg4u0\\u002f8v6g3WHU+D8ZcczETzgDQK8N3ag6zAtAcTYHSh\\u002fmEkDm8BrnVXMYQBRhC53iux5Ajzex8bBgI0B9W9vQEXUpQJckfy5n4TFAcQvgrIPHOkArxb8tpc5EQI9B2lBUQVBAmW0AUFj3WECLxhscRJ5iQOkyTx3U1GpAhYSiXkCpckB+3k5AiBF5QPPU3AchRoBASkwowgZthEAzkGKnHsSIQB\\u002fYaKOT9oxAdyT2CgFNkEB4lTxyV5+RQNU+LaghRJJAxazY2BwkkkA4Du0aGkiRQNJTn673s49AqPp5AcY4jEBYIjrLoreIQKzLNSeBvYVAqzlSYE60g0DwzSBrP9qCQBgKz8oLRYNASlbyzH3phEDYXuVd85+HQBmG2iWII4tAN\\u002fioowIQj0CI6drC73KRQBQxSAHQDJNAaeOvEYUWlEAvy1d9NmWUQJYlGyvE65NAT1ca74K8kkAr2FIhIwORQDn4ZwRW841Apdd4YAa5iUCfFrkfY8GFQHVsKQlqW4JA54ycbrRsf0Dgq3aefcd7QPKCjTk4vXlAjinmbxgqeUAmHcK2DeF5QAOwyR+wsntAr\\u002fMbDm5ofkDk7gb7kNyAQHl8xWSNoIJAA+0VSw5DhEBghGlIP4SFQHaqtcBILYZA5RtA3UwahkCXQJBkkz+FQHsHsP3CqYNA7qsvjbl6gUCDZ+WbkMh9QGJxlIQ6QnhALbt7iirYckC4GjrE\\u002ftxrQAtZD+9ck2NAOdkKeq0hWkA1UQaeg5pQQEFLuoZdOURAQanGkjoCOEC0u\\u002frjgrksQC0USGahPiJAbZqLdQ7eGUAw4cbfPasUQEyWVwg3AhJAq7TZkIV8EEBOwiSBtjkPQBqS7wT8oQ5A\\u002fj342q16zz9shbFzxMHUP5f1cJszn+I\\u002fgHzpWWlL8T+IlQuVoYD+P9VGItlUFwlADmmq9GA1E0BZwJ9n02obQFaB3+Q4UiJAMr7d5KYeJ0C6ee00hvcrQDgowjKhnzBA6MrOflAaNECsMCjNTpY5QCrVqhAoZkFABOmtUVX0SEDqwtAq21VSQDjcBrho4FpAZcpFsiBRY0AYoaFwlgFrQDlvNoZ5T3JA2DjUKpIUeECJHmzo1Lt+QI+NYQUWCoNAgTHtMrnihkBUWv4J96SKQK5cyXKz+Y1Ap52aXpBAkECfHdTzkvOQQAOU+ybI\\u002fZBA5bkZaZxikED3UovFV4eOQPXR4yEstYtA79JkyNTXiEA0RiV+gnCGQKdbvloc4oRA9SQR\\u002f1VohECZt9erRxqFQArOgB\\u002fu8IZAB6hpPhTKiUDO4nEcmWWNQKb02S\\u002flsJBA62CTdGqfkkC5sCeCVTeUQPXQWebbN5VAgixb9+V0lUBqFHiSKuGUQFDOowREkJNAX3QoH5SwkUDcT5K4uP+OQGwbWYQifYpAra88p2dKhkBCzB5D8bqCQDsBmUfi\\u002fn9A+NL9OzdRfEBh7OleLF96QNQc6uUVAXpAA64JWBsGe0DsRisL9Dl9QIrbbQJoL4BALqElQ+kQgkA2n2HzJwqEQHUxryS02IVAz6x\\u002fbA44h0D9089Hdu2HQGUSk54U0odAapN7Ld\\u002fYhkAOCKPg+Q+FQE3m4oZinoJA1QTzbo58f0CpGXxIImh5QE0VCSYmhnNADpkoJYCJbEAuwXI6CtRjQHBnsM3OPVpAgTM9oSKhUEAqAnVufXREQIpPjLb9FzlAOAWoQhIQMEBah++iK5UmQBbFZppy0SFA4wunRRvJHkAsB13ny+obQIcxT34g6hlArvwYMNqXGEBkLAH6Lh4YQJ5R9Xwbsd4\\u002f0ECLBkMc5D9yExhzzNjxP5ZC2YnGaABAttB\\u002fUE+rDEB9zI7ChFwXQDZj4pIxtSFA+a1Ls3j\\u002fKEB8hYw3znswQFFz+QYRbzRAaijCJ94TOEALPUcxi3s7QFJjEYjhQj9Ap4yy26FTQkC7Z5b2brhGQIXyPYHi201AWskaeA1mVEBb3l0UmDtcQORZnh9yZWNAqdc+AhgpakAlLozGmzxxQJnQO1tKK3ZAJQbQZKfVe0D3E64w0A6BQKUt8nZ3Y4RA7IHZnce2h0DbxFD4v8KKQD63OamGNY1AOgZ+mJ\\u002fCjkBYzG87PzmPQD7yvx+Cl45A4KYHwZoRjUBsC1\\u002fxWQiLQPscsB0f8ohAtC1uNNM\\u002fh0CGNOl9xkiGQF\\u002fySikrQ4ZAG9pS89VGh0DwsJUDYlKJQBgns\\u002fh1S4xAn7W0XwL5j0Cvrsq6s\\u002f6RQAGHdQ2w7JNAEjX65Ih+lUDa6XYNZHKWQNK398vYm5ZApHyJ6HrulUBtRnuW2n+UQG9MWsr8gJJA1gamsWgykEAgQToa8q2LQH+TBmKtUYdAm1aD5fKkg0AVuQPxZ9iAQA6SayRb931ASF7C3JsMfECc01jE08R7QI2P2eaz6XxADahHyK8\\u002ff0CMSA5SSkCBQE6114H3KINAyG1N9zYghUAFCfG0M+OGQOtyJN92LohApZAqPafIiEAWp7nd84uIQIqypjAmbIdA5FRcdPN4hUC5M4LXetyCQOaftSPoqn9Ag\\u002fC1hOdZeUCBTvJnz1JzQDC1KHZCCWxAoeb0FiViY0CYJgu+v6hZQDj86n4RblBAtS1i6\\u002fvJREDk59jWhfQ6QBBlORN+xTJAvvyzIHEDLUBbyHiM8aooQJAYbf1qMSZAxV9RdoJnJEAX7RSQBvAiQDKcIs+Y5SFA9lASkoyDIUD+rPcZC5brPwLD9kvD9PE\\u002f2KkzNTaC\\u002fz+oBDdvp6sMQMo9FEhdzhhASai1OTgGJEBq2aWoCxEuQIOtMNCeATVA7gnLSxNiO0B6r94OzrpAQM4bfvZ\\u002fU0NAQS7M+ARrRUBSL5cs\\u002fktHQFXCouTXoElA0agGht5YTUAH3enM7btRQNzJal9yclZAb5pumhwmXUA3I8JETwdjQKYJCSK+p2hAf3WOCm6Cb0A8Nx+O7dFzQBL5+R7wh3hAiMuyU9vXfUBDw6jUHM6BQIQNUggHxYRAWnIpbR6Xh0Dr7CErav2JQOFprSL0sotApQRLWVeJjEA1zx26rHqMQIKTrPJgsItAfPWG6LV6ikCOrWLhGjyJQHpZYRStUYhAGzsdMOECiECu7QVe33yIQN+uFMEv1olAYrcWgn8SjEB8OvrS4x+PQPPphWPpZpFAY6hXY5Bik0BJGcIKQkWVQFc2IANNyJZAaL8c4fKql0BFXDTsE8KXQLTRj5eRApdANHTRjeyClUCpUQhffnSTQJ5CKJaeF5FAy\\u002foGV59djUCMJd5SOuiIQJzyKJekJIVA5Idx8O5EgkD4z31bUFqAQHd5US9Hun5A3pfBxittfkBfqm6LU5B\\u002fQKJj30Kg7oBARsq1\\u002f5mAgkCEOFqd2kuEQOTyvS\\u002fjFIZAXE8nLwabh0BO67WxTKCIQIHNVgqK8ohAutxRi\\u002f9yiEA6Rl8OSRuHQNK7r\\u002fJE\\u002f4RAgTU3FXxLgkDRyeFtSX5+QFr0rFdMQ3hAWpE1AjtrckDvjCNF87RqQCuWHdwRj2JAnMOUDevsWEAGGPKUEW5QQDKl4RPZ1kVAmrZNOilkPkCR8bVzWO42QPSYZV\\u002fk7zJAP0CffLC2MED6H2mtImwuQKbSohm94CtAGtimggqlKUBfNIxAeAkoQGZKJO32cSdAMoMlEiXn9j8PWM4rDJr9PyAgENv\\u002fpglAmeRlVEkVF0CahwRzlMUjQOgja2w7nS9AoCekncaBN0BW9Ihys0JAQBgsxkvp90RAiGcw65tMSUDq1JXvfsFMQFOKtIH9Jk9ALPITVFZiUEBsswLOrCtRQGkxo1+lcVJAvVY4fJG3VEC6WPAPJWRYQM5gmh77tV1ATVVif9xmYkAptL+JuuJmQGXUioj+YmxAIrUoELqCcUBu9ZNqQHN1QPzl3y4wBXpAJMMShnAgf0A8u\\u002fyTxUOCQLNFqAw364RAsJnssjdIh0CsxWoXyh6JQNbFSPOWR4pA0xEI6La+ikB6EBDS1aeKQDzKo6vuRIpAlTUky6DjiUBVtk9kDcqJQNr10nqzK4pAYbEuhN0oi0CgAbZg29OMQHr1JUqrM49AYcV6vUEfkUAxmqsU\\u002f+aSQLi7AdGpypRAtjMRyKGRlkCb19L+9PmXQBOVDlYHx5hAvLGWboLQmEAg5Wa\\u002f6QuYQGPPAI3ojZZAF60ZJB2ElEBh+UCyliqSQGxwzUYkgY9A5EhPnuH9ikDl4XXnSiOHQGX0xUcRKYRAIzRSRfUmgkAQlynzIBqBQCP1Hzu67IBAjiqn4Wl8gUDOGF1\\u002f0p2CQFYCuZFVHYRASWG8Ho2\\u002fhUAc3cAGI0SHQNU2teP5a4hAzczMvzwBiUDJprWss96IQD6iQURr9IdAnx1DJVhJhkDLMt54e\\u002fqDQKqOuHuaN4FAyvH+JuJ5fEAq2SCFopN2QGatjlXLK3FAPfs2OishaUAaaYXsps1hQGL6bDhzs1hA7xPbrlYWUUDFVWHv5C9IQMZq3ssjBEJAY8KmYWDDPEDyU0l2ZYE4QO2HLXsgxTVAW1TevrikM0DD68wD2rwxQCTG6kS8EjBAzyOUEpzKLUBgwTKQFu8sQIsIMWAIoAFA\\u002f0J7mkKYBkB52VbAn04TQJYfAufIKCFAGvZrapsTLUDjq+wLmQI3QHiY+Fax8EBA5AXOXPczR0C\\u002fDJpT0JtNQCTjfSOip1FArUGueWnJU0BF4rEk5ApVQCNftjaEkFVA1JsJi2HGVUD295nKSD9WQOVIg0fdh1dAWmqffVsEWkDaZshvUeldQP7qtDMSqGFAqKe7n08sZUCC0BR8zqBpQDEJHBydM29AK\\u002f6uaGUIc0DQ7yEbbiN3QKzRTlxT1XtAwo70s8xxgEBVnd24JvmCQGNJOFumSoVAr8CPPjQ2h0Bt3AYutaCIQHCkUirHjYlASiBv9UkfikAetxOXQ4qKQKwDhMZFBotApR\\u002f\\u002fzYm\\u002fi0B0qtDuB9KMQDVNf5GxTY5AEc1zMdQekEDXjzeeIlWRQAZ5ihLVyJJA95rJbf1slEC\\u002fpgViGCOWQMzbAv5au5dAr6D12ZL8mECKQj6OMbGZQOzl6lNstJlAZAJ6NJz6mECPoGnDvZKXQBcAzWgJopVA447pFkBck0BRheWgFvuQQBPV+yxYbI1ASZfpNap4iUDCxtEt6V2GQEeFaLD4P4RABOS6HRQlg0DmJyf0W\\u002fqCQDNOtIf\\u002fmINAsOo2BlDKhEDsTmBaL0uGQIyYi4La0IdAkV5iEqkQiUANTfp4LcqJQFOuo7rX0IlAwI7GFrsRiUAbAU0l2ZOHQIdJKtjsc4VAsBGRwRHegkBiov+bEAeAQAFrdAJoS3pAJIArAvjXdEDe9Wq8jgFwQKbH4JYG7mdALDYzeCyNYUAHN6ot1I1ZQD+14RRovlJAa3yfg2svTECEsQe2aQ1GQKNFkahnE0JALg\\u002f+J1XcPkB83A2sEgI7QNS8BRq61DdAYwrZo54GNUAQMOtdjKsyQIhANvYwDjFAg0jQbg56MEBluZI7a0sJQDryekkvDhBAyS1uJTn7GkCo3TQbUKInQLy5APMzxzNAcxi1oIr0PkBdTW09vYxGQKt14z2zkU5APodjqyZNU0DE4DMlX8NWQA6FMj\\u002fJMFlAtE18RTJfWkAZghLvKHdaQCeoLP4w9llAfq\\u002fwkk2EWUBHtmZFRb5ZQOjJfMA+EFtAm00wXmWyXUCwoTq7ieJgQIvzImnVu2NAf4sc1Z2PZ0BulNUti5VsQLwhgaTvfXFAo+7niKtkdUDDH6LMrOJ5QC9ypLaOuX5A3COLX4PJgUBxVRvpEwuEQKxWxxamAIZAjiF2zXufh0Cj\\u002fzr3LPaIQEhqIT+eJopAaOGk0+RYi0Ah+R+1OK2MQAZqp8L2M45AL2ueDV3vj0DHerTiWu6QQH7\\u002fwvrf\\u002fpFAfjNstFktk0D41s8H8HyUQBVv6kJv6JVAoxiStQ1cl0AKSRs9g7SYQH5xsmPMxJlAbJVAQmtgmkCXiDap8WWaQP4F+4AuxplAcKGRZKuGmECsAbmoUsCWQEizt6+6m5RAE98j0OxLkkDqjiqQ2weQQLsqHP0KBoxAts1nti3NiEA\\u002fhfVhSJmGQJqE3wKTfIVARJWyDF5ohUBUucezkTCGQPQXKmE8kIdAJyY3TuoviUCRk34Woa+KQC+87HAQtYtA9VxZ0XP7i0BYvxsVE2CLQEwtZnKP5olAX2LKfX6zh0BeDX0rIACFQMKiu6dgDIJA0kZW0iYnfkAkWVj+Qot4QEfWgGIeiHNAa9iaRD+GbkAZlyiMi41nQP7C5a2\\u002fEGJArraT0TW\\u002fW0CRjwefdX1VQHBs9\\u002fzM5lBAhFayhq4lS0B6f\\u002fJyIFFGQFx2UgcLu0JA+piJ0T\\u002fcP0BV5f0gAjk7QJondqAkTDdA9QmPKG8pNEAEYOBQCxcyQM+NgcVoXDFAp1\\u002fWY8UXEUC4L3hl+24VQGa25Qe7oSFApN+cWXFWLkC0DVdzFQQ5QIX9PZWZU0NAdnHK9nTUS0DwhNQ\\u002fPqhSQNE9TQheT1dAZM4Z1OQxW0BceNTzH79dQOQnB9MiuV5A4ZR4GBBQXkBSWS9QpxNdQLKpRMtewVtAs0u4OdQIW0CmIZL5M2VbQOJrGl3aHF1AfV\\u002fbUYQwYEAa9DqpKbpiQIcA24pUXWZApJTKr5xRa0BW3s5SytxwQLruH2PbwnRAi\\u002fHrhokyeUBQ0R1dZed9QHtVgxIoSYFAsGahC9R4g0CJemaNfHOFQG487OplQIdAOQIUy6L5iECoVOlctcCKQHVmoxgfsIxAauQncXXQjkDTTK9hLYuQQEOFl9E1tZFAIVBXGGrbkkDnSghkcfmTQMoaJ+beEpVApCYnnxgvlkA6BLjz1lGXQHf0cf84dJhANZn0Qw2CmUDRFgcekFyaQFDug7oY4ZpA6eLgwMPwmkBeQ4PQxHaaQO3gA7OsbJlAgNttnPrcl0C7Hj9Z9uOVQPicj1rnrZNAaMQA5LdxkUBTl91f6tCOQHQycquxh4tA6UZMZCRNiUAFMt7azD+IQIpB\\u002fgf3VIhAWojYNXtaiUBg0N9wq\\u002fuKQFSPhf+ayoxAqf7gZatPjkCRf29p6B6PQKx7gnB27o5AgzY6z12mjUAdA3GCHmOLQBmw7Qi+aYhA7Zf9HF4ShUCimLZ067GBQAoSqvGDFH1AOMmBsnOHd0DkELFGT91yQM0ObMMJJW5A4573+dEaaECF5he8vlVjQH2AhjSKH19AGuuExPYcWUAhMDHni01UQJggk2eEdFBASOusUCfHSkAZ5z2m8+NFQI\\u002f5KbMv9UFAhr8zR+iBPUBxDS6GUVM4QCKS1KMJYzRAjBMku0bhMUARQF2\\u002fvgMxQNvayOhyGBZACsWIRjBFG0ASdeQiwtElQD7CAcBkVjJAT0g8NTGvPUD9Z10AX5RGQCI3\\u002fpPXCVBAVc340a0+VUCUj8itej9aQPJP3Rl0TF5Az6eo\\u002fVllYEA2PuRrSb5gQPDlpMvfTWBAZ6ceiOvGXkBRvYEiAcxcQCTddrYOa1tADpHzfMgwW0AEuT09uHdcQHyLCGHAhl9AnGDDoM9YYkACWUharC9mQKd8RUKUdWtAhcZIE0wbcUDUVGbkmyJ1QJsZN908m3lA6lbZA\\u002fI8fkCUm8mozWGBQGfseKfwg4NA4whgGFGHhUDMlhpen4OHQBmmCXv2m4lAT4a6zo\\u002fvi0DCr26qNYuOQBc9J\\u002fjhsJBAPndNraInkkD\\u002fTq0k0pKTQN\\u002fUkoPA3pRAjbJQONoBlkAUUtQ3vP6WQPGxVJzF4JdAkd3qvuC0mEAQ1YAB2oGZQHzty8htQ5pAcps4RYHpmkA2s2w9+FqbQFHCRPWieptA0YT\\u002f9yQtm0C0sIT6fV+aQM2qW+3ADZlA\\u002fObyzldIl0AjUfQJPzWVQDS7lU1jC5NAUxdJGcQIkUCNPOnK88yOQISOFnrAnIxAtbnBBzipi0CRyaFMA+qLQIWTLPSmJo1AUtDpze\\u002f7jkCG1kJTA3SQQMCGYdaSL5FAyvxju7dzkUAbvJBjghqRQH3snPYaHpBAq0mPtM0xjUCoJLcjp3iJQNJPCzH3g4VAR8kdLvi5gUDyzlJvecF8QBQsEhqTMXdA2\\u002f1HEcPFckCiLQvEca1uQBNF43RGV2lAGr\\u002fstl4YZUD9KktTwZZhQIlLOnPbLl1AAyB1MQH4V0BiPaqqA3NTQPzkpLmBNE9AjBpHBijOSEDCtHqBipZDQMVnKOg30j5AoM6LsclROEAN\\u002f+livpYzQGG7RiF2rjBA7EInaK5kL0AZrk5xcOwbQAcxKlZ34yBA1mbkrroQKkA6e64kUj01QEBexu\\u002fWx0BAvLbuBeMISUBgsIJKrn5RQKoQUQXt2lZApVtS4jDnW0BMW9qwe+BfQFdl4E+CF2FAmMX26mhOYUD4jEIIV7ZgQJP5wdmaRV9A+UUFyo\\u002f9XEBysORofF9bQPxQ85HdCFtABTTxpn1qXEBW5Mwx9+BfQE92bXBj5WJArNfd4xRCZ0CI+++SpCFtQG2Q+MKrOXJA\\u002f0xd6qt1dkAVzTVGoAV7QP5w5zDVon9AMsAOiUAMgkDb35yo5iyEQPdi1OjORIZARDNH+293iEA07DbfSeyKQAclvPr2vY1AZIR3iG92kEB\\u002fI05vay2SQBFmCADN6JNAyVrN3PuIlUD5iyVdJPOWQKzrtqnDGJhArpsCt9T6mEC\\u002fDBinm6eZQHAKSQgcNJpAHfRdvMqzmkBMpLg5UDGbQIISCIM7qptAcGlaczMOnEDZpKWLfUGcQDNxOje8IpxAm6iY26+Tm0BjdVX4poOaQKLbfGF9+JhASJEBwjUSl0DBcr4RvgaVQD4jGAjUFpNAgNfbvwaAkUAPYKg1bHCQQEXx58Vt\\u002fY9AN1G0ysgmkEAuxepBu8mQQCt\\u002f8hsNsZFA2jtBDeqVkkDMnr4Cay2TQMhv8gCTOJNAIZ5TaNCTkkApAsdYOUCRQBF1WOSIxI5AjEI9sQ1sikDX68lBPPuFQBuXs7Q24YFAhn9hAZXHfEC57TCpUTd3QLKV0YbT+nJA2GLd01Chb0DWD04tw9JqQCqsz4Mi+WZAWkTQTx6pY0DvZcC1OqRgQPRIA0BWp1tAGcXcpYd2VkB+ws1YxNNRQIQctB7LsktAORdrCXEjRUBHqGlOCd4\\u002fQJA3T9bF+DdAKJzBXJJpMkBF0Z9LuS8uQMhYiOiF+StAqCNDQNuwIUAOte6BUuYkQE9zK5Xx2i5AtfYKH4osOEAvUAr1joRCQLregvLA9EpASEgHJMl3UkCN0ZAQaL5XQEEnQ+VAn1xAmsZU17cwYEAiz0rtdT1hQCUZ+Xr3YGFArO2SbPi\\u002fYEBICOQaMF1fQCp53\\u002fkpMF1AaZDGp0HKW0DigjcBENlbQNOf3Yrk411AieDMitssYUBks5EK18xkQBlmIWjc8mlAs0im9RFQcECjG+Y0U1J0QHNeVizLzXhARPpa3Mh\\u002ffUC06fPMExWBQMxT8mObVYNAnYIelOiGhUCYsCw1i8SHQNLiNyUGN4pAxbCy05MEjUC0cwbJpyCQQIAThFMq8pFAwcyBasTik0DdhNi+eNKVQAjpYtTMnJdAcJzLAushmUBwVZmew06aQIFt5NiiIZtAy2N0KbOpm0DqlrcKjgGcQGHVnl6iRpxAIk4\\u002fzQyQnEB3NSEPPuecQEDnp2j\\u002fQ51AsRyxvtKMnUArh1scYJydQPLOyADSS51AyUMWehSAnEDewk4FqTWbQFOv\\u002fsARhplARV3N2fyjl0BzrmD2bM+VQF2Kuul+RpRAPn7bG\\u002fo3k0B4UsQgrrqSQKgmLDvXyZJANFtXK31Fk0BX99t1YvaTQCHq6SU8lpRA1Ewmk7fclEB6MwPhsY+UQAdGbRQxkpNAoLL7m2vskUAPijoPC5KPQDSguXEs0IpASJxrjA4ahkDi46dac9yBQOkes\\u002fmqrnxAJ9JeR9g3d0B39TrULi5zQM5nRTvlQXBAGZTznPotbECE1BbObbVoQMJCwVT\\u002fn2VA2YadR8qnYkA2sEBj8W1fQBmmKz4ztFlADwrY1j5cVECo0xbIG0BPQJc2SAw2S0dA+PvqaR70QEBspJQxVGA4QP\\u002fXO7+ByDFArczUuqvWK0BXAmggKkspQLAyvNHC7SZAlFGX\\u002ft9vKkDmclAXm5gyQI4AALn02jtAi8pf9lOTREDpMTBOnhhNQDLQfZBWe1NAAfPp9kObWEDJiTQWTEddQOma517ea2BAjButtGxqYUB6LxDA35BhQGiLm8sHBmFALrxAMfIcYEA3rmmnq4BeQH1lgciauV1ADURXPTKlXkBudkcP0vBgQNoVyVgK9WNALRt2eBSIaECmtGmWc7RuQOuOroARLXNA6r7Al46Ud0DdtMzfC1Z8QGsDGvqImYBArezndNb+gkCkMf8JH1WFQHsxKtdEq4dAWH3Br\\u002fMgikByhBDvV9yMQIwjsjrl\\u002fI9AmhcMfqHHkUCoiXw2zMKTQEQTPR8K25VABjiifi7wl0Cxq0AEvNyZQNzl2+EUfptALTKRTfS8nEB336aimZOdQFgfYnRdD55AFON2CNdMnkAR08yYnm+eQCF639gPmJ5AnmNYtbXZnkA8OMJLozSfQLDyIIibk59ArPPj4XvQn0A\\u002f74tVFL+fQDucGLMIPJ9AosKBaag6nkB8Cip+\\u002fsucQGVHj9TgG5tA3U2k1EVlmUD5BJZZyOKXQJTtpH3cwJZAeBrD+AUVlkCBZU9\\u002fg9qVQGG5nyho85VAs6620GktlkDM\\u002fZyUg0qWQO4esJZbDZZA2dXbGqRHlUBrGszNgeaTQOVNpNM8+JFAH\\u002f+VwL1Qj0BSJmnHimaKQPOpIgPNq4VA3CeDMp5\\u002fgUAtYAyoXi58QAzX+PJn+nZA+AOKwhw3c0Dt52J+8ZFwQJS1j6h1V21An\\u002fMge85YakATgKviDaBnQGdWJGrC2mRABX66LHDsYUDn3PCtb8ddQAwM2rcy0FdATOlTQTNNUkBSwGRCHA1LQMhLWhKkSkNAMX+GiLbTOkCeGPWCzLQyQFBAqqlh8ytAbOoL+5zZKEC94C5S84ouQCiK9ALkPDFA1VdEtyYyN0AloHCd059AQIsYHPOZq0dAUnhFLro+UEABjGinqj9VQItYaBTxXVpAi6Qaz4L+XkD9v5OdnURhQB2+25kNS2JAGWF5eX6JYkDzq7uC6ChiQNcRYfNDfWFAfRFxM670YEDktnA7gwRhQKVQqic2GmJAmlah+ZWQZEAmJBm2baZoQDSUP1cbdG5ATL7PhwnxckBODUjpSFN3QGCImC2OKHxABJdi71qZgECtdYah0R+DQIwEYF2RnIVA6BfZZRAWiEAxngZ6EKKKQHPrEvOOXo1AqRfhvGM0kEDU7CNtg+qRQNVLM8I\\u002f1JNAmx1T74DqlUA09MSnVxuYQIv3Yd6MSppA0bkiNQBVnEASpb55OxeeQO9JUAX9dZ9A5Cxr3PcyoEDujuvlwXegQGIK4xSZlqBA8XwqpkaioEAH2HzeDa6gQCSGU2dCyKBA2BA6zsn1oEB78ETAzy+hQBiuj7XbZKFAMLN64Ll9oUA4xyoHH2WhQPYmsYZTD6FAJVt3Wbt+oECBkOKpr4afQEi5qeho651A338aijNfnEB4vR7OngqbQJNWM3+AAppAMmAKfeNEmUA06W0JvbqYQHg4rnhGPZhAWh9+jECel0DNBmtRWbKWQATUAx4CXJVAV0rBBdmTk0BniFtPkWuRQM94CT+PE45AMBgQLOk\\u002fiUDKMevOV7yEQGctNAjz0YBAph7O06JQe0BTfSBh14p2QB4IOHzvKHNA6ILOWO\\u002fecEBwvh8knqJuQDvJzLYXS2xA\\u002fE1FH+sfakA9lpZHYLtnQESGNGQ08mRAZRdnieXOYUBMatmvsQJdQIACs5PTkVZA+YFP47nAUEARg7h0zcRHQCSi+\\u002fc1QEBAOYIhtCUDNkDeeqzJtcIvQBvDii5WvStA7JN\\u002f31OmNEA0VnrdzPA2QMWIWWrn0D1AuHPisNqcRECGIMZOanNMQBm0YlPaCFNA9OvbDmpmWECozQm8\\u002f9JdQJNvX1V+WmFAEdwroBw9Y0CkEiunWWFkQHpMr0dFwmRAUnbmoJONZEAKRyABfh5kQJsl0VYl72NAzCC6ye+FZECs5TiTdWJmQLnOhpBk6mlAkIj51EdWb0BHjkFSwFBzQOqzA2d4wndAZAdDBAfCfEDxSR2XdgOBQLeiOFM6qoNA8xhyUFlGhkBR1uToC9yIQP7w+OAbf4tAbbk0EQZKjkAewrs4T6qQQKMXR69AV5JAs0zW0xwvlEC\\u002fIdJ3HzGWQEKOi1HuV5hAd5Ebv1yXmkDX5k\\u002fpBdqcQH+tMhx1AZ9Agoh7\\u002fFd1oECbCmUzPjuhQISpABEzyaFARnR1snMfokAIEoueMUiiQHQwOepWVaJAHP7YuStcokAORkDbw2+iQEcY3MJXm6JAEhC+yhreokBoZ99iXSqjQHtHzEQYaaNAsbU65DKBo0AQRNiD3F+jQAo6DxiJ\\u002fqJA7zER6DtkokC+TScgE6KhQI6jMsMMzaBAgbkRBl3vn0CY63rLKVueQCk5t1Fq45xA+KVw3jB+m0BGbW1TsRaaQMbnbzUolJhAfmLlnDzhlkDs2L39d\\u002fKUQFwgzT9mypJAgFrmAix6kEBz6pqkcDyMQE7crkMUsIdAaCX8hTyOg0CTyv5JfQaAQG\\u002fnkKznZXpAAf8HbccudkAipe\\u002f5YUdzQBjidUQCcHFA4pxP1NlTcEAV58\\u002fLzStvQKtv+GAKwG1AkAckzjTja0Dokie1eFNpQMsBsQS8EmZALEHu2cBZYkB3of2lyP5cQPArpQJ2ulVA0djn74fpTkAWffSdNwZFQD9019NCFDxAwBDchTncM0BFpCXkqigxQJBcBpR6vTtA5KEt1\\u002fJ8PkC8W\\u002f6eoFdDQAo9t9bNEUpArobXd0mYUUB6r8ZKdh1XQCEN56RRM11AjPP\\u002fBPelYUA8gAJl3GBkQI46pgJ6fWZA06VbY1TLZ0ByjvFLokloQCoc1dowMGhAMbApHSLrZ0CkI+D3vwtoQLOaHORvMmlAArXkyFv0a0AoVwF\\u002fEV9wQBX7pyas23NAoI3asf9XeEA7rw00K5J9QCQGEVuWl4FAXaYVRcVrhEBXkBptryqHQM2bC+390olA+0Vxku56jEAqY+j2SUSPQMBBr7loJpFAqyRNJdDRkkC\\u002f\\u002fFcXSKSUQED15WHmmZZA2aFxtRKvmECw\\u002fbarQuGaQLh4W4VDK51A0Xi3aKJ\\u002fn0CohAJjwuKgQDGuJ+R97aFAHfUxSFLOokDtGd4HyXejQCqmpvjc5KNATMP2wCIbpEC\\u002fAwIWYiqkQA40oreUKaRAx324IYExpEDtscynwFWkQEY87x4SnqRA8nXxFKICpUDjCWoZZm2lQBxwpUOXwKVAaNUnUdvfpUC4MchPYbilQGQyZTjuRKVAzWUF5PeMpEAFmHUwFqCjQA6B23ctkKJABa4cA5BsoUDlJtPsZD+gQEISbb4GG55A0O0shCCwm0B+NLZxjj2ZQIW6yLnww5ZAf0BKDnRHlEAnX1evi9CRQGb3MevZ1o5AXG9BiYhNikCIda8gCCSGQMWMjuQ9d4JAiqrE+ZK9fkAVjhRcJNR5QMiYSXzaOnZAV+r7Ztbec0BuDHQ9VY5yQEvmj35\\u002f+nFAE7Wiif\\u002fAcUB5jZmYIX1xQJC096Rb3HBA5oDD2Q5fb0C8jSywNuVrQHJTP+CCkGdAkut7\\u002fvTWYkAZA3ahXXRcQJ1Nv5WaTlRAOQfyJBKUS0CSM9OVBkpCQA\\u002fnXCMymTlAAXMvO6\\u002f7NUBkg\\u002fI94h9CQAxHSXa8x0NApb\\u002fWGPCzSEBwv3+cSF5QQLhw3bQQxlVAQ6kRrKJDXECVGEqoFa5hQLkkZ7ZFNGVANhIVMeZUaECpFW8fPLlqQAEUBZpnLWxANCTFU9i2bEAvp21RpZ5sQCBdGxs2bGxA6TWgs\\u002fvRbEDPnH53EZBuQKBSNyTvJ3FAit4j+Rs+dEDH6x+JEI94QFT0XVNI5n1ANpcTUaXvgUDmZrlKKgGFQI4zFQGK9odAzpDB6WS7ikAIUvuM3l6NQP2kdFNYBZBASnhsWUR3kUAo6D+\\u002fQxWTQCngYY094pRAx3nkoWvWlkCTBReZNueYQLJouvWTDptAJ2X2zqpMnUCDwR1mqaKfQNg1GegvBaFA6S6Pixc4okCVqBURllmjQBvJvknXVaRALP6KQcwapUAxsZLncp2lQAInrrrs3aVArvUJQ2vppUAzAhx5\\u002fdilQIbp8gZ+zKVAxilnF\\u002f\\u002fhpUB9wFlA9yumQO2RIYPsqaZA53QZ+wNHp0A8L2lKet+nQEAdrgHoSqhAxBsTuzZnqEAkReYmVyCoQP88gWyycqdAivAvLK9opkBWi5ceTRWlQDrilFlajqNA4Hp+9TnooUAOZsvsBzSgQJ3QwwhR\\u002f5xAvzocgyutmUBxP8LuqoSWQK8APQd8lJNAa9fNvezlkEBymCjbkPmMQIvPH0iisYhAd\\u002fz0zt\\u002fwhEARpNBnx7aBQD4kYXt8EH5AIVkYGi\\u002fceUBQayGsA992QAoNF7BZFHVAVX\\u002fPFX1WdEDyHYRA8Fh0QEtF5muwr3RApUM8I1vjdEAYCneUh4x0QPSewBfcbHNAZMzDMJx8cUCc6ihfvc9tQDajX7l2+mdA04HvM5QqYkDz+9WL5PBZQG17aAxRklFA+6mhdS4tR0ABRjDmqRhAQMEsiw2DhztAxgpzlXOrRkA3iNpmcZ9IQPJT\\u002fr\\u002f5a05AYux9lDDuU0BhAc\\u002frA0JaQJCMgnrL6GBAuPvISBcEZUBI2Q+lIA9pQPdpM7mWmWxASREkHfA\\u002fb0CY+8vddWVwQPr6EWYHpXBAK3aTC12PcEBNxedpp3lwQCO4rxUo0HBA8KYpMD8CckAKwzD3kWp0QDdmCPG2NnhAP6xQOZRUfUAvQ9aDNreBQDQaKoev\\u002foRApzcPQBI5iEAXFj4t1zOLQKmhS6Tq5I1ASM00Je02kEAFQsqt3YWRQGfYwLx7\\u002fZJAR9cFh7qulEDUCjfAUZiWQA2BxdLNq5hA8PldlrHYmkA9MsYWNhadQBt9IaCZZZ9AupAQHwfmoED61Jy+QiSiQER\\u002f9GjAZaNA1tHq45GapEAzu\\u002fx7GK2lQGKPLP0Ch6ZAPRaZ\\u002fn0Xp0CWs7eWU1mnQNmtKJZsV6dAelHFzJ8tp0BAignP8QOnQG2e4Y0oBKdAMo7F6G9Mp0CmYAXlBuSnQCoOW6PNtqhAqFdN1syZqUC2ppoW8VaqQOa605phu6pAMbebxGOjqkAFBfI5cgCqQAiOBN7Y2KhAMXMXLnxCp0CfHjF9p1ulQEGw5O2bRKNAb3RKsGkboUCZhCw\\u002fHvSdQFjH\\u002f8aS65lAqV1WrfE8lkBAKrwklPuSQJLDLoFwLZBAgZwn4vOYi0ADI3M66JWHQD0qM1g9M4RABaDVWu9agUChV7Hjcwt+QM92gQ98c3pAdbQtpzIEeEAkgleVOMV2QEehAxS7mHZAnFGNlfkvd0A9xSRigRF4QBmvxcEtsXhAukbonrSSeEAc0jhycmp3QOb+5QUcMHVAW5kjDIYcckBskm3fqihtQC2+H3LNEWZAdSSAniRuX0B0LnLJ5DFVQMcENKLoxktAkpsAobElQ0AtBWJ3DU9AQFwaP1Gb2EpAd8MQEBgPTUBwNue3ONBRQK5ZYHJqJ1dAqNE0FFBNXkCdBufln2hjQFWF3llyAmhA4KWJmvd\\u002fbED7hoHkFzBwQEKgAPUImnFAtqdlbvxhckDr3DEl85ZyQMcRXSn9d3JAvlowC91sckD7nu05dvNyQCcCn+S6hXRAuTYqM4Z8d0ALH2zB6PR7QCm3MCvo34BAZNtSYrkyhEBqwmanRKGHQMQAHtm53YpAfqDVFDO6jUBr2+4Qex2QQPuhhARsSpFAX2ruK0yLkkD3KlXPlgOUQCxRma7SxJVAKinuOkjKl0AAin2e7v+ZQL3+xUOWT5xACKBbrSesnkBtq+6XX4qgQEJxH9lrx6FAaC4cfeANo0D9YyZHplekQOt9Y8\\u002fulaVA5T4AQJGypkCoOJX+bZSnQK3LXtwxJqhAgBF1g1ZeqEBMjFywKUaoQK2cwJWS\\u002fKdAbVMBE5exp0D41zBsF5qnQAhecAgE36dAJTksVEKOqEBmGjmog5OpQLd\\u002fGYMtvKpACck3x83Eq0CIhwXg6GqsQCxUnaHgfKxAtu0B6Uzjq0BcYcIAMqKqQJqDvjGI06hAeCn4\\u002fl+epkAzXpHPHS6kQM9BNSsLrKFAFi7EUlV3nkDiS6bAyfGZQDwJbcvd7ZVA1kjlMBF\\u002fkkAuYDK3PkyPQPX\\u002f32B0qIpA0Bbb0TbjhkCZvfBtv8uDQAwP7gXmPoFAs5x0pt5dfkC+dazpWUN7QKWmhdOOS3lAp+jvU1uGeEC9FVyGD9l4QAStE9oy73lACFau0\\u002fBAe0BSTAxzRi98QJPy3g4nLXxAqLtkd57mekDpwHUK8VZ4QO+HBHSLxXRApT94oySscEDVTIY+AiNpQGaQz3UAz2FAbeWCG2\\u002feV0B\\u002fAnGDXQtPQMzoEAiVOUVAgcZ1nacAQkDCzrxrXeRNQDaTMPkMI1BAWNn7lyysU0DHafjaoWxZQHAqWX18j2BAanJMulcjZUAqNeAW+hBqQJAiNZSl125AFbv0GCF1cUDQWYzqrelyQLt7Snnbr3NAbfTsXo\\u002ffc0Aq04gOU8ZzQEBCNcsm3HNAg14JvyCrdEBfFVojTK92QCq0khChNXpAHQ5ySeFBf0AEGzQXHsKCQNVhtzlsM4ZA4ahbuOKaiUCYqXq0e6yMQFGbA0slRo9A2M1t9DG\\u002fkEAmB\\u002fBwk86RQAWY6JUAAJNAx3EsU8d6lEC2tmYwgVCWQEuhUPZteZhADbP7ay7cmkDIK1cbqVudQCNyElU4459AGA9xA6M1oUB0jTuFrHqiQBNOlzB0waNAgtnR3eMFpUDvwxfV\\u002fjumQJHtj3qbT6dAyLi0fIsnqEDhI0kBYayoQBHbWEbb0ahAT23L9RqgqEDSRkqiLTioQJ4uBlrtz6dACdAgqlGkp0CXfa8s8OWnQJnpyUXzpqhAJmWvp\\u002fnRqUDxiWkfzS2rQOncQruEbKxA5XFx5TpArUBGQcIg\\u002f22tQNY43IRT2axANLOQExaGq0DQnleUXJKpQAEx1FcQLKdAV+M66GCGpEAV7y2iw9GhQHLU655dbp5Avmx2O3mrmUB3MARFHYOVQLkuYSriBpJA+l00vTFmjkB1vNnPsuaJQAzAYCqAVIZA5\\u002frMrGR1g0Cy7a8j0B+BQPno02lIhn5AIkEQitLKe0Ahhv25fC56QJriM+quxHlAemjy1DlyekAFeOIDUNx7QICHrDEBcH1ArRWwNxOCfkDAKgVR6nx+QDH7+b5kC31Al5TxGvovekCDOWmajD92QGjEhglfw3FArniHmgCfakBp6OleDbxiQMgW72gJ7FhAK7pXJowRUECRflVz38ZFQPENJF0bY0JAF1Lv80QqT0BwCQsqiMtQQOYrfvj5ZVRAIYx3h\\u002fxEWkBYg1DVnRFhQMijtQpTwGVAgsL5foXJakAWLLkSxaZvQJAuUeA\\u002f43FAgORUFvdZc0DYPM4mkCJ0QCikj4lNXnRA7rJczfhmdEAF22jCdL90QEOflfme9XVAyhptAxp9eEAQI4J+oYx8QJN61cfoA4FAt9oOwBU\\u002fhEBu7dh2NaKHQOqraibT0opA4lziUbKPjUDfsaydtMmPQLs0ZVla1pBAJAm5eFPJkUCifmn5QPKSQIdE6c2ZepRAc6O4vRtzlkBbTa6HbtCYQDCBSwSTcptATKk51I4ynkAy77Fzy3egQOmwmLI2y6FAa+HjTpgQo0Dpw591iEikQEIw4nt7cqVAhV9jIT6IpkB+M+VcYHunQGvYRdFaNqhA8oQz2JWiqEArIi4HlrKoQC2u0UjabKhAld17vP\\u002fxp0A6ZVuAU3mnQAF0y4ejQqdAOPZQ2FqBp0ClaqAo5kioQBAw9vaagqlA5+uM9IzxqkAUN1b120KsQD55zyUVJK1AbWHLyIlXrUCw9V3wW8CsQJu\\u002fkpoOZKtAaOPUE31jqUBbyLH0he+mQNAisknUPaRAyB3KhY2AoUD\\u002fkqU1+cKdQClEUgCd\\u002f5hAt2HxdFXelEDZY2kgMW+RQKmSaIKoWY1AGhcECIsCiUC2fiuKbJuFQNerdDiJ54JAWe9jcC67gEA\\u002ftYGtoQh+QMLgk2fBkHtAyP6d6NQvekCMGrCy0fh5QO62uQOTznpAV+Z3UrtSfECbamcMku59QCsSty0c9X5AzziA6t3SfkBBlbnjAjl9QKE9\\u002fFHpMnpA328yQDAedkBACXkXB4pxQBNS+u5oGGpAAftzzSs5YkCq+FUIRgpYQNRVjLKwtk5Ai7eq4JCdRECYAsLpp1JBQBGHoh6UXU5ARdJoxAxaUEDumPl7HNVTQMfXRw\\u002fbhVlAlOP2lOqWYEDwSvtJrSllQAvxCLSTGGpA8lrDo3vfbkC+aav6U3hxQNdeJ5Rv73JATa98hXPGc0BOHcp4Fid0QPwSIBtXc3RAyYgH99ExdUBGsdi6o+p2QHfDQ2vQ\\u002fnlALOZ\\u002fYqCGfkCwlxnSOCGCQMKjTeO7UoVAr7O5kIB9iEBRJebd+k6LQF\\u002fOX7j+mI1AhaQmOP5kj0AXFJ2+4nqQQKgxPgSvWpFAcSqe\\u002fjaKkkAsc\\u002f6lQTOUQHp+k8wbZJZAWV0T\\u002fQMMmUCyQRvrhwGcQHwYU3SvD59AlMpJgWsCoUDB8m3UKF+iQEFWY3jIlqNA9B14p9CqpECCrUgQ8KClQINUPhmHfKZAWpRTKgk5p0C0yYbyWMinQD8yikxNF6hAZF6usOYXqECN6\\u002fRQU82nQAA1crucU6dAxv+NqordpkCFlHceQKemQHxT51mi4aZAN4lkP72ep0BItWswo8eoQAHahxRcIKpAHOmXPFpYq0CAO385yCCsQEepXQFNQKxASy40KAmeq0Df0GfN+kGqQCnM9YY2TahAUEB\\u002f15vupUB4EMd7YFijQDbnfgbTuKBArU7b++9snEDag0DXotyXQHUE12Ir5pNArmQ6VseZkEAYN7DVPOeLQB7J+mJiv4dAT023KLiBhEAuJQKRQ\\u002fOBQBcvuYDS0H9AUL\\u002fT5PKbfED0gE63WE96QCysVvWVCHlAlKQDSM7XeEBos+93rZ55QGvz5NSMAHtAbK887ntsfEA\\u002f+KsZgD99QHIjIBDA8nxAQxGWW35De0BtRhTZAkV4QHGA+R8nVnRAKDRHy2QAcEBkICS2zp1nQB4sV+u6WmBAB9J0Vh9iVUALw3BC6A1LQF1juIXf+kFAT+yLMIAQPkDXihfzkKhLQPAyVStyzk1A6Wcgl4McUkAQHQcJ9WJXQL9vgtMeiV5ARwY541aSY0A1I6ZmikBoQFWUViYr1GxA+9JDS\\u002fxkcEC7NcwWiOBxQGmiEnP+1HJA\\u002fuQoc3pyc0A8dwDGRB50QLCIsaJXW3VAq9zSHISjd0D3XB1Nkj17QELDh7KgD4BA7agvzLDzgkBU0SZqifqFQLNz7HS7y4hAi9G7FPsji0Ba5lpsEO6MQFGciqDZTo5AFt2Al5afj0CgTsVEu6uQQH4+QzRm9ZFAQzLKWsfUk0B5nJ5tdlOWQJe2CTOBWJlAABiXrIytnEBsKMkf+AWgQP1n4zcml6FA6WuU0NHwokDUg1HJSgikQC0bI39F4qRAVBBqQdaNpUBVNmdxdxumQMAlo0LTk6ZAXAdfqtHypkAm8mYhDyqnQNDM7EeKKqdAraQctmfxpkAVp7UtO5GmQEbknav\\u002fMaZAck6wnewFpkDbfen\\u002f3DamQNcOXUIh1KZAraIb4vbIp0B2a2bbHeCoQKvW2YgM06lAerbMgz9eqkDyRNu\\u002fl1KqQHWRMLYTnqlAuQTkxLlKqEDqPOiypHamQPWOw0\\u002fpSaRArNSIHjfuoUDADd1vlBOfQGVEQm84eppA9Elff6JDlkBEVmn3fJOSQPnIkP6z945AySZeDjP5iUCMT37roguGQDZ4\\u002fw10+4JADiwHVtGRgEC\\u002f3q6VD0l9QKlZHqIhP3pAXgoJXMsIeEBul+tUY752QKUBvLmJbHZAPAao5NP1dkBYqUHiMAV4QNbQyeSEGHlAtm29\\u002f8qgeUAMWOTVgCt5QOveMXG6hHdAEbxygFnEdEA2lv9mI0JxQEo5JBxf7mpA\\u002fCRjXBy0Y0DWeuQYsAxbQIzA2dw0hVFAajUleO\\u002fxRUCvgH\\u002fJN988QM6eBdM8AzhAw4RSVA6hR0Dwi7xrZYhJQEWYZYA\\u002fP09AzWi5O9FcVEARCgSHS9taQIfhF9LfYmFAe0QWIsO\\u002fZUA8ETpmYxZqQEhP5CRk8W1AEev7zOx+cED42\\u002fwDTJxxQOvP192LhHJAHl7GqHibc0Bgd8V2Xlp1QDbcJXwDJ3hAZF6Qj6YqfECSYbDH0ZyAQIWfKEXZa4NAGFAET0wthkA9ke+lr5CIQEk66erXZopASd0XsMy1i0C\\u002fHQcHK72MQEji3P+p6Y1AdPMULnK7j0BZ9ek9\\u002fVKRQKbTvMtweJNAHvxJc1pQlkBluWoty7aZQBslVpUDZp1AlsTL2DGCoECjr1MjyxyiQEUonxWyYqNA1aiNyTpIpEBnDfGgBdikQGRurQoILaVAaVeQPbZmpUDUA2pLHZylQPGAli+f06VAe0rAZTcDpkD0Uv2iURmmQBEpXUYGCqZALuIrahTapUAluKzHnKGlQDoXmSlnhKVARWg3j6yipUCnJ5QOWQmmQL603ExIqaZAk4mNK7VZp0Bw1dqIqOSnQAx28a6ZF6hAtCj+qxzRp0BDYHmQ1QanQJMBqbE5w6VAg7rKoFEepEBFdyP\\u002f3jWiQEDz06fzJ6BAjLgTByIhnEADT+4QARKYQC8wVAVZTpRAX0J7Ojz4kEA3DPD1EUiMQHq4XgWnq4dAPQeKWY0EhEBAEPefKieBQABq6NuwxH1A7YDCP7UdekB0lUmk7DF3QAUVgpS2AHVAh4J+Giedc0Dvm9ESpBBzQN47GIiuQHNAbsjgnSjkc0BtonvAzo10QLtENJtbyHRAk6qP4gI5dECfyaKQLbpyQI5OSar\\u002fY3BAwyCG1cMAa0DFAL8Q4+FkQKmui\\u002fSVSF5A83bA6xqZVEB8WTKbNW5KQAhxBUA3YkBA2hXSkbhUNUADNdmWUKUxQBprzsFZFkNAkCd+Tgy\\u002fREARrTk4OcJJQOdGJWizFVFAFejk1qLuVkDPD91yxCxeQLbGjHhBJmNAn0Nwp2tGZ0BurtiICA9rQJqznztrOW5Am7ZKKn9lcEAlb+nDQ5ZxQDPkjcGHDXNAf\\u002fjP80o3dUDQwHZOfGN4QJmjELabn3xAuoYdinvSgEDrmLtkJXODQIZNN+aa3YVAbSRpPKHNh0DOwcRypimJQJM0EvMhEYpAL2TQXhjaikD1RBWsPf+LQPxjSrU+BI5AJk9k0VSrkED5rpfHfBiTQOnZvLdVQpZArrS+E3T4mUC\\u002fZhWv4OWdQB\\u002fBvKNb0KBAb0rOeHthokCLMKum\\u002f4KjQO7qFjt7K6RAgKHIUpttpEBKeHlSw3CkQGWoZP9+YqRA1q5rE4FmpEB+SAmvqoukQIx2s59ryqRAon1+p6wMpUBv+PcYQzulQCmZKAqISqVAUFDH7i5ApUCLLEVYuS+lQDwHi4l3L6VAbGnfRbNLpUCTJ9rRfH6lQDeDgGgOsKVAfY7Ani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K1GpPvdOfEA4mJ7mMZd2QDnfn086WnFAqCfrRFuraUCJoU6pI1ZiQMkj4W5Ve1lATYdvd\\u002fR2UUBbGpB9WCtIQNWYBO8EaEFAp3TUJAPDOkA1W3M2IgM2QL4qYVvP6TJAl3MHX6xkMEAC6BaFFtIrQOGRj9qJpyZAShAFK4N\\u002fIUBaZiiSr3kZQG\\u002fKRP5TaxFAHsLyfNdaBkBJpM9XiQb7P158noU\\u002fce8\\u002fDAVVufAs4z\\u002fRvCDxW6feP7QBnH7iwihA\\u002f+8OkKK0LUCUo06w+Hw2QN8g8WwQ4kFAkDNhUEF6S0Cmzi1W6eFTQC0rHNHo8FpAdKr4o6MZYUBgoSPXW2tkQDFqPAC6HGdAzdaO\\u002fsAbaUBxvlviSqlqQMZ+h8dxTGxA0F532HulbkCrm5f58RdxQPxn+zI+hnNAKwBCuK51dkCR19dE7pB5QBIZXkLXc3xAAD9j9QbTfkDHTzZytk6AQL4lZEInBYFAKCbxhMTGgUCuI12j99qCQKgcPkncgIRAbo\\u002fzNb3bhkBXmFh2eOeJQD7uiUhnd41AsvfUlcqfkEDhL+QJCHOSQJybM+9HDJRApBaW+rFQlUAVOPQnETuWQGTdKU3u3ZZALoAG6MZfl0DLms9nS\\u002fKXQDhuqbV2xphAw9oSi3v\\u002fmUCJi8\\u002fxoqabQCB1igfaop1AEr1ztF63n0AA4Z\\u002fQMsagQFviI+HhYKFA320yoqiDoUBEQ2uGnxmhQBbt7RszJ6BAsxh\\u002fC0qSnUCzgFZkL1qaQLSSJwE0DZdAQ6r85JkIlEDnykh\\u002fs4+RQMnue+Oxh49AiI18VKVIjUBz51UZcSmMQPHbclzczotATt5waYTSi0CyRMODQNaLQFxTXYyfj4tA3EefOH3LikAQIoA3S22JQPUV9mlCbodAPpFfO+zdhEDcqxdI2uGBQDosprtTYn1A7utstkYVd0Dj\\u002fMGR509xQKIv+w6fymhANCnG59v6YEDMul5aU1xWQILoMZTYmUxA1F6d8RUbQkA\\u002ffurLNmI3QOGpcr8+7y9A3M+sVdWZJ0A6QXl4erkiQEga2pOI0h5AwTTvbPlTGUCaxiwW6jIUQAU\\u002f+epIvA5ApGVcKtcZBkBSu2qlR+X9P7F9iupq\\u002fPI\\u002fP8r+Geu35j+BSh9F4CXaP+MGuYsxis8\\u002fy+R2IPEOyT8ZXBwd9EYnQGwkWV3jBSxAP3OAlS9TNUC073GpGf9AQKmbv+5\\u002fGUpAvxlAckLWUkBbLfC4QGxZQM\\u002fc6EZyDWBAGgMQdS8LY0C1nryrFmJlQHTSAYXkAmdAgP0jjXYwaEBo0lzlonVpQKQ4AP\\u002fXe2tA7o6gb2zPbkAlWV6x09NxQAL9TW2V5HRAy2CPBJtIeECOIkwPzJR7QA8h9Exfan5Aw1g6kmJNgECGd595ex2BQB6ZPTkCz4FAJg\\u002fgqpecgkA\\u002f8STakb6DQNiVF2yRWYVAA3qD0a50h0Bajl6TNfmJQJlko0OKuoxA++R74fyCj0C2HjgJ6RCRQC\\u002fqDfiUO5JAp6\\u002fA5q09k0BUG9wTZCCUQGiz24M8+JRApdO\\u002fQCrhlUDQ4rJBgviWQBoQlMGmVJhAXhHVMAz7mUDiOxtbFtebQFF5YV4Qtp1A4OG9fF1Mn0AfEp5egyKgQOc3E0gELKBAluhW0eJfn0CbzC6IaWadQGhHVCsxpJpAzJCOpU5zl0D85tNslzqUQLDtMDIiWJFAn92xGBEhjkCDRQsVpQuLQHIWsl4NbYlAKmsAY0cHiUA0zTuRPHaJQAE8ft58R4pAbBxzf4MOi0DKydrLuXGLQDDKGDlnMItA287PodokikDhBcSbF0aIQPOtbhlbqYVAWEx9R6GAgkBJcrUMBSZ+QP05YEeWXXdA5puco3MycUC2iNGYogZoQGjhs9I24l9AxusexbImVEAgt97q8WlIQAaihkGVtDxAZ9jb2xnCMECujijVPSQkQEzQ3ykh8hlAZsVz235HEkBkrXp9fK4LQKNuHnt0mwVAtY9DgSq1AEAkK14TQuz4P4K8RKLtqfE\\u002fME6+bjmc5z8nyHubrqrdP8gCJyiDkNE\\u002f1vp2ilX\\u002fwz96PfmmB9q3PzbXGLbB1bI\\u002fVTko8ABQJUBSDx93YacpQIrA2eN2gDNAyPTVZwgEP0BY3+opNbxHQA6R\\u002f1vCD1FAePyyftTrVkCHp55+bcxcQKzKWb23\\u002fGBA+ux8ES32YkD877hOxkpkQClR9xhmQWVAMPvbbVZnZkCUdpeLcmpoQDoSGyvt32tAOC49FXmFcED0BGCnhdxzQLv\\u002fpsB5ondAmUForOhhe0By50S89Kd+QB\\u002fBzlxklYBALI1hYO1wgUBcUPyFJwOCQMC9TGpDfoJAjaUKgN8Yg0CdWl6nFv6DQGOnzsMZRIVACXbpz83qhkBgl037puCIQMIL27edCotAr7GstmdMjUBjk\\u002fVMLY+PQB3Q34oM45BAYfPucHz4kUDIagMgoA6TQDUUUmxlMpRA5TPvXS90lUAE6GJu6OGWQF3Jf4cifphA2+YkuuA2mkC5cw3rm+CbQEIfOnvXOZ1ABF5Fo434nUB\\u002fX\\u002fyjJt6dQLEbmL0cy5xAPK5bSQHLmkDzxtTykRSYQLSgQfI4\\u002fpRADD\\u002fIUD3rkUBK+SWx9m6OQHuQUIR8TYpAl+TT5ry1h0As5h3Ya6OGQNQZvAKA1IZAbItqm5jdh0Ang+kxV0KJQAzj1yJAi4pAaYmPffBUi0B33XYDS1iLQLyo7Io+bopAPjWDCNuRiEDs6EsyzuCFQAbq\\u002fWu9l4JAMRUn6hoQfkBk1UnZpg93QNFHw1nowHBAObKWMW4HZ0COHAdYXPFdQM5AF4OxblJA3bA4nE+NRUDSwLSfuRY4QLnlY0mMFSpAjJ7UgAkQHEBnlWZftzcPQPzSLatZwwJASZP+Vb\\u002fZ+D8qF1\\u002feUbfxP1reVugG9uk\\u002fOlQqXKnD4j9SWw5FUhPaP5lZv\\u002fnBLdE\\u002fD3vG87JUxT+i4Z8Xwvu4PzuUoyl0Iaw\\u002fuBB5kT6WoD+XqlTP5AiaPworv6ABnyJAHtHGoxtgJkAbUqAaufIwQNnbcm7+2DpAtwSKhH52REB+yGe9iExNQNV081+tmVNAAbiQdfaGWED5CQtCCtRcQDL7L\\u002figC2BA87\\u002fyA2QqYUCeYqtUvxBiQLiJntuQSmNAfpxOxeKCZUBOkWZb7E1pQIcNy838725A64IC3TAbc0A7R1ouRDp3QOA9UlYyVntAyBJtIfzrfkA4cPyZeM6AQILFVMZkpYFA3KHwRX0OgkCpsQ7\\u002fmzeCQDcRIpRuWYJAxi4TXUGogkB40jcxtkmDQE7vrxrJUIRA\\u002f691e9m+hUCudcnQnYeHQKU\\u002fYUhmlolAmS28Q83Ti0DwZtdnWyuOQMIUdG5USJBAWf0zQpCBkUAAEPgbYMeSQDrujKfLI5RAJjJDlkWflUDOyGz6aTiXQE+zBk3h25hA\\u002f0Ziy4lgmkDPAXRPX4ubQCIOsG28G5xAs09a68bcm0A7FU6SLbaaQCJotWhGtphAubBBXRYSlkArpufichuTQM4FeTJ+MJBAEtoIPExSi0DoI28fII6HQK6qeVVRVoVAq7wg9walhEC1iDTWJjWFQLVpqEwyloZAWXAQX4tFiEDOIzEYP8aJQOWEdnBJsopABwCtHgjEikDRGtWQ3dmJQFEdynCw9odALxgXIgBAhUCWpoyPBPiBQJrznT+T5HxA04g0DaIHdkDUq2l7mstvQBfnvb5nr2VAFNcQm73uW0AestSbZPxQQJ2PP058hUNAJre+vN1ANUCSSzaoLwwmQH+M2zsgERZAZ\\u002fBDlObWBUA8JgnKdEL2P1l+l8BsgOg\\u002fqz506s7U3T9E8CjBkqfTP9Gc8Y0Stco\\u002fAD1A5SPrwT8z+ySxpRy3P5Iv77urRaw\\u002f8h8+1cBaoD81p9A6kTOSP+GUJlBUN4U\\u002fAAIHhZeLgD\\u002fbB\\u002f7Ei70eQFVsFk9GbiJAL5rB+L\\u002fOK0DCsE2fK\\u002fA1QNtzvNNTqUBA+oBP82THR0AC5wT9MrlPQNvIzQmLzVNA\\u002fnoipk4\\u002fV0BaiW5I9edZQHtQ9QHy3ltAVeHN8T67XUDdNS8y00NgQB+5E370wmJAoOQzsbXlZkD18yno6ulsQDYbydxSTHJA9+D\\u002fefWfdkCgWnbtfet6QCCq3lmuon5Aa21BPxKtgEDeV2\\u002fN9XKBQN7tnxp0sYFAIYbKme2UgUCi1iYV+FmBQASpoUwwPoFAfsw4Swl0gUAUCVCkbhuCQBGWZDkwP4NAReiwoKnWhEDwhxBJGsuGQBLmS4O+\\u002f4hASu3D3uJai0A2sk2Ohs2NQD9KYjiTK5BAV8fVH\\u002feBkUDNUqaUVvKSQD\\u002fhOH\\u002fRgJRAGl2vVsMllkBbBlVtHsiXQN3+fZmPPJlA9v8a7y5LmkCkzq4YLbuaQL5FdnZwYZpAN1DsjaotmUA6IlfIwjGXQF6YxzdJoZRA11nfYRHJkUAnUJOdRAKOQBxsARejOIlA5ZRyVESyhUD6M5O4L66DQGZHKCB0JYNA2zxgDgjSg0Du5qKoMEKFQEza40eU8oZAxL9Eg4NniEBDac3xcT+JQH0GrqlmPIlAyfiUYwJGiEDn2Dj5OWeGQA6XkgGnyYNAy9X2BDOugED5m0baiMR6QEiatZpVZHRA5YTvGlxvbUBImN59NRdkQBn0KqCV5llAUkONEQmBT0DkTzpmXRFCQLi7XKfaizNA\\u002fJqDUR75I0BJ65S4qFsTQBVhBCbC+AFAzU8RFPdQ8D8R0iDARhHeP8P7Nbz9is0\\u002fbbfPz9oHwD\\u002fM+xmBkwazP5oTXQ1Slac\\u002f5mpXzkcenT+liJpbg12RPxEucItFwYM\\u002fwEvXYjmwdT+Shj+bRvdoPxjcz90ZV2M\\u002fddGvM6vlF0CykKP3J5scQC7vm\\u002fh7giVAlyO5AXXrMEDyENPZq6M5QGyK4T0nQ0JAYT\\u002fDU4tVSED\\u002frulBqV9OQJ52geJ021FANpfxM8kAVEDOp7nH9sVVQGwWkANEvVdARelNF2rbWkAyV\\u002f7fhyFgQOvH9w0tdmRA3pvRWxOgakAn405AuDJxQI+IhCfiinVAilBc7r7UeUDRuMqaJIJ9QPvUyzMjEYBAXKmIc9TAgEBZ5U2Qk9uAQJ0neeObjYBAdn\\u002fYLYAXgEBCkud5y3x\\u002fQIxOwL2Ven9AZL4yaTE6gECtWuDZ3kCBQO+ZS+E2xIJArYoWwCamhEBFyObSK8SGQNNqsoVwBIlAH2FJoKtei0BPyJzJJt2NQFtzIoKNSpBAD5QqD1bMkUBiv5Dlh3OTQFZfNuYfMJVADfTkqWjglkAlKJyoK1SYQGwRYmDPVJlAgpHbLYqwmUDADeJz5EWZQOy2Zoq0DJhATCEXSHIalkCVy3BT2KCTQA8dMLXq5pBA8gu0F\\u002fh6jEA\\u002fArliW+KHQFOmswNDeYRAZSb3UNp7gkDqqJLW\\u002fOOBQEt3tQGaboJAweMbCkWug0AA9S8qTSWFQChNeHB8X4ZA0l44D60Eh0C+ggKXReGGQFNayMgL5oVAX4UEow0jhECTh0rcxMCBQF2XRC7p8H1A6ygVU6gWeECDU1i1DHFyQIlmprCNzWpAIv+LAVlyYkCRrDPSzf9XQLk\\u002frZXidU1AiV52MF4JQUDsSOoVJosyQM68Bkil+yJAbfS2HVZIEkBygHFUqJoAQIA0rq5soOw\\u002fCvRPnQLH1z8UPbQUcprDP7l+FKzxz7A\\u002fyQN8xut3nz\\u002fKJPKdlkeQP19kmcGq\\u002foE\\u002flm8Cih4zdD\\u002fBYGah7ThmP2ma9dR941c\\u002f4YzRCSYRSz8+C4XFVs5EP9OEqcY5hRFAB6hbGb3zFEBMzbjKM3QfQF7o6oh1tShAeKzhFgK1MkBrGtdPKKU6QCCxe6F3w0FA9oWxWWQ3RkAMo+ifkD1KQDk0BVRyqk1Aw0OD061tUEA3QrgzxG9SQCyLK8CbtFVA58QFIzM7W0A26cLrMO5hQPjES7OF+GdAp0QJetCEb0CGIb09OvZzQJ1i4wPgGXhA2A9jh7Kie0BcK\\u002fguCx9+QAHHSYgkV39AlCta4aldf0A42a+bE4t+QK\\u002fK+XyNZn1A\\u002fxfVxzqEfEBsrk3mMGJ8QI3Hu9zOTH1AigotSYxRf0ABpk6AUyKBQLFuVZGN7IJAwvjA073hhECBSefPSO2GQInl4Nz3E4lAW51MDTlwi0DFldbAUiOOQCoGGN+doJBAUducr3xfkkDctFZ7HzWUQNNYLxSp9pVAaZ93IN5ul0C7RmQwfWiYQBBk5UWsuJhAdX0Oqi5HmEC2FxuLPROXQDO00UzSNJVA+QT9VYnakkCzGVU\\u002fK0SQQDKX9Bbhc4tAxJ7uC1wCh0C6amYpaaCDQKvruIxpiIFAypb6\\u002fES4gEAQUf8E9vSAQBNopPtS24FAZMu2D+X4gkDtTgjA4eSDQDZ6XJgNUoRAt+byNxkWhEC8o8VHuSeDQGJi7Vepl4FAJU2xWG8Rf0B9SpzcllF6QBCEK0ZTVXVAJepdyjh\\u002fcECOoO44EkRoQJ3JE+dO7GBA85WSUh1SVkCPZ\\u002fM1B8VLQHVRZRokQkBA1pJeSVPiMUA786uCqHMiQNNHG5M91hFAo3IXbTMmAECIFlMhkWrrP1LuxusM59U\\u002f4yV+8rmgwD9QSAhhmH+oP06ECZqSMpI\\u002fBqqGKJG8fD9tM3Gl6BVpP1HxecxIBVg\\u002fEPUDmWwkSD+2TLRm87Q4P0AQhBIOMys\\u002fTVS26MuoJD811KPEl1QIQPbqJ6cmGA1A9ZdCyirXFUBCkMdaRyohQHIpJycTBCpA7NU6y0COMkC0G4zW2sw4QH9EJ2MdJD9AXKL0JZyFQkCbH96TNTZFQHhP2SlCB0hA+OoS4QLsS0B9aaXs3S5RQPeXtl84i1ZAd3HZgCbJXkATt012tBxlQE9pm725PWxAxhHUkKgYckAE8QTy5wZ2QGatyFTgZHlA1VhiapzBe0Dtu+BV3uF8QK9MWxNY1HxAtBMrPcTwe0C72+70wcF6QNMgD0Me4HlAwdjkPXDKeUBANaDMJcV6QNtYrotkzXxA07g\\u002fIjOkf0AvNuegrneBQBWrXfI8MoNAwq8TZUf2hEA0QqLDB9iGQDK1xEoeAolARQwrOy+gi0Cz8r\\u002fqMMaOQN+0Fs9XL5FAGFqVFZETk0Au3BArp92UQFAKzZBhVZZAI9FdwC5Il0DbHe8UWJKXQCd1Nk\\u002f2I5dAWhz+sQkClkA9+JhTLUWUQMWUXzMKF5JAboT9LBZdj0C53mFv+5OKQP8KaOs3TYZAwT+DirLsgkCpckqXyayAQMcskfMEJn9AmIT9YBbifkCOtvRuFeV\\u002fQAm9ac9ns4BA4BOqUg1XgUD\\u002fXwUTaZmBQDH7fbrLVYFAUA24iXyEgEA30qH4WGZ+QFtgH1PR+XpAe0pyg0QId0AZjO5oct1yQBOf0vsiim1A69sCO28FZkBOliRm2iRfQKBSgw0F0VRAchwWZXY4SkAPro0gcwk\\u002fQDszEAsQOTFAC3KnpofjIUAP5RfXDV0RQENFYwpwd\\u002f8\\u002fF7wognKa6j+pWv3IZ\\u002f7UP7Ljk1oe\\u002fr4\\u002fz++4ynaFpT82a49HG36MP2JyenjUeXI\\u002f7LfzB7GWWD\\u002f\\u002fo72egb5BP9gQatYbmCw\\u002fznPl7ZZ7GT\\u002fzBPzXAPgJP+54pBKDKgM\\u002fEA5gcIMWAEAu4Np3GUEDQF2+sV7H9QxAYurjC+rOFkB\\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\\u002fXtAC4ubCmFVfEDh6fgqR0V9QP8OKSf0J35ArWoY9FeAfkD4\\u002fKeh\\u002fAd+QMmXIX7Vq3xAgkZjdUZ+ekAOmYbHy6d3QBh2RUJgXHRAXcbiTx3WcECPUStdQKNqQIIVwzZqEWRAteFvCv6qXEBQAXvGz1VTQGHEhyOXikhAmUnXSYg5PUD8SuqLikkwQPwrODDc9SBA+djPqcl5EEDJd4a73dP9P+YvTuugI+k\\u002fe63InhK40z9xMElgN8u8PzpENL\\u002fPlqM\\u002fKuDoz7HqiD8Jan+CHNxtP\\u002fqcAqOGJlE\\u002fDfnfsx6LMz92yUC9qVkXP9xZX4t3RP8+7OcAjJPP6T5CKnE7fHHhPs+J8o70XfQ\\u002fx6jd+29p+D\\u002fkMgdC0mkCQJyai9SjFw1AFeTYwTkqFkC1XOL95s8fQPZM6ID+bSVA4vn7gS89K0CzYpILWIswQEs\\u002fJ05bqzNAch\\u002fi+Le6N0ANjdrlMDQ+QKfb3EhZqURAXtneaePiTUAWpGyPlAJWQDrW2\\u002fQ33l9AMw66HfMoZkAzogOg2TxtQJY+mOtzMnJAr9uAY4tWdUCTJxZxeJp3QCL0fLKUvHhAXKSW\\u002fUTEeEBwh2JHwQV4QB27PAX+CndAuV5vnYFodkDytXsovo12QPGyov48o3dAKRQNjUyEeUB7lnCYhtd7QAKsXJUVPX5A0yGMNu8\\u002fgEBjFvzODFqBQEzSIv5LmoJA8mxc4\\u002fc+hECfoqgICnuGQAtaCF4AXIlAjGz0cb+8jEBSt6S5VCWQQHbxHQ4QzZFAeC8Yhb4gk0D+4kXs+\\u002feTQGeEwO6qPJRASSaYt77qk0AUx+kYUwyTQDA7UFJ3tZFAICQQquwBkEASxYiieCmMQIO0C7orL4hAwibFKU9vhECXz8zwVD6BQBCVbbZ2tH1Azc1R7\\u002fC4ekBgmnwawGh5QAnhJsLGWXlALrJdzt\\u002f3eUDaHTKCvKt6QPt9K7k9\\u002fHpAnmmDB0+gekCeSnYDH395QNYSzy9JpHdAtvz+QwgxdUCbfF2PclFyQLvZ196jbm5AwO3hVAcvaEBGF5wt2ktiQBJ95c0DO1pAALHFvg+9UUCsaNgh84xGQGSTYkGZ3TpAf7cIt0fsLUD+MLDD\\u002fhkfQNddKKXOHw5AybXaAi0p+z8UQMSCMcfmP6BwPLgNwtE\\u002fsyt02Dq7uT\\u002fczgbqDFOhP5MJksLxsIU\\u002fqj+m0exLaT8xyVwjSZlLP14bWdUadSw\\u002fTvH2TYtdDD+wCcy337DsPheQLST\\u002fvdA+09S0BGk9wj548rK5WcPoP01uXTn2uu0\\u002ffscpzB9\\u002f9j\\u002fmI6z8h9MBQC4bJ53RPQtA2IgcL6qcE0AkJzTZ74oaQJ1cEaG0ACFAM\\u002fVuotv3JEAInEhEWaQpQMRqYlhmNDBAIMdV0iz0NUB+ePCUqgZAQO+UEmiUeEhA6gIiNEW\\u002fUkCurhIlLehbQDRl2GtyzGNAMoig0oGFakA8hcrQ8rNwQMFvmzW8xHNA+DqB8qQGdkCNukF3Ujd3QKT358RDXHdA+tGvr+zGdkAWHxDFU\\u002fx1QGLBZ9Txh3VA2ID0coLLdUADn7gitt92QPUU+8Qik3hASYFfTUqIekBrCjU0X2Z8QJ7cE9IICH5AvbhSYIaTf0CX5VXqT7iAQLsMxBmxD4JAFtJHWy0AhECMVolFvpKGQGQyquJ7n4lAv\\u002fuN13fVjEBrrToHFtCPQOgI+nBfGJFA0LTAYITZkUB3dF4SmRqSQJr\\u002fjAh02pFArAUaftQikUCbzP2b8gOQQOuei\\u002feJJY1AXoEBv6rTiUB8i49xulSGQMShbPfg9YJAYhWIq9cEgEA0wVrWwoB7QChwg7IvknhAj\\u002f1S5uoxd0B4MSDUEwt3QNpCVoLnmHdAlka3MSpLeEC4iQbpxah4QE4oU\\u002fQ7ZXhAHrddSnZkd0AOU65Qv7B1QJ69nPiXa3NA80\\u002fjEGHBcEB1aVjTi8VrQAm5a0NhA2ZAyppkRrubYEC1ltc6FL1XQFA6y0Ju\\u002f09AwYnyMLpCREAxLitNJwg4QI9vqW6AoipAgKcOITuIG0DOLd68voEKQBQktEdovvc\\u002fF5Tkxv3F4z9ixpv925nOP+4S3FMg\\u002frU\\u002fMp9rXIFanT8BiwPVPjCCP+hE80Yf72Q\\u002f40MniKllRj9bJtvftFMmP\\u002fUWqizH2wQ\\u002fb+A\\u002fIRWM4j6sbffIK9nAPokebDVhWqw+lJmQy3zq3D8GL\\u002fJ6c2HhP9bzMuHVXeo\\u002fFJ6WNCXw9D8FCPoS+wcAQDQkd8cpJQdACOs7qs56D0A5TjITLGAUQDNR9AUurRlAb5RSf5lZIEA375x78gMmQLBsvPOxGTBAGH0qTQk8OUCbF7PM11JEQOIKA\\u002fE\\u002fJVBATFd9j\\u002fCgWEAPliodlMZhQKSYk7QgHmhAx7JM5VOubkAiFduSS01yQLpTPVcGiHRAiKj43JXHdUAk0PghDhF2QP80OI6isXVA1d0deFUldUAzUGJeyel0QNSVtvwPUHVAy8NEKqlhdkBLkwkNpeR3QDQz7ZkYfXlAz0nOhDHdekC7LUjx2O57QCmQe6KT5HxAtRUWeOUpfkA0WuLsrByAQHBUuoO2tIFAXxVnaIrig0DCccSlmn+GQC7tbYBUQolAGnIzYAvSi0DBO\\u002f5SgN2NQMLgPo+kK49AQ7pW17Shj0BgHyWnHT+PQOhIKnAnFI5AywPMlfI5jECZywrWENCJQH1t+Elz\\u002f4ZAErqXaUj+g0BykDozSQ+BQO4nV\\u002fHW8nxA4vYwlbvyeEDX7JVDBGR2QFY5rmKuRHVA9SSR\\u002fdFKdUAHt\\u002f6Wyfl1QPj2FqMlxXZAV8g7hHM0d0Da5fL7Uft2QHanQgaU\\u002f3VAVQOuOeNPdEAb0F+pbRNyQLToOerm9m5AKu82u7N1aUCmKnsMzARkQL5kTIZ+911Ai+jpOKQ\\u002fVUDw+9d2q2tMQLQjgP1820FATpYsoXcENUCd9mGqbBwnQDTW2GTFshdAxDJsyvegBkBtdo3ulBn0Py3jF5BdmOA\\u002f7rFfsvt0yT\\u002fNgfMNnCGyP1UpP7Pk+Zc\\u002fQNsMREptfT+X\\u002fEVPasJgP3xVM6rjt0E\\u002fwOY508hkIT8IU6WT3b\\u002f\\u002fPiEmjfR6Dds++eO+3uxbtj5MLx+YLnWgPgMcF67RKNA\\u002flb9jw6Rv0z\\u002fMYsnii4LdP9dPgM5tc+c\\u002fuT2shhD58T8ueoDHZgP6P7FBlvmezgFA54eI4ztrB0Bsz4nEzYMOQF0L410NnxRAFwN7fMQqHkAAeJ4IiA4oQIBbcy+GNDRA3BwNanEMQUBy6f1bduRLQI9HjA9JrVVAO4Pyms+xX0BK6xIIrLRlQNgoL1lW0mtASyUQ85W0cEDPk3ojR9xyQLrjicBbJnRAFpu1M1GYdEDmDqKN6Xh0QM8Z5+0BN3RAoId6sZ4+dECPo93S+c50QLnx56Ov5HVA4AAlxe5Bd0A1ybJYp5B4QCz3r1sJj3lALm3rWX4yekAbZujdC7J6QCbz2y8FdHtAVyGkCAvnfEADE+HWZlZ\\u002fQHdQjc5iZYFA+VH7LjCBg0A42r+05sCFQMVT4Fiu2odAcxK\\u002f3dGKiUAmTCSMLaGKQEm1DmAfBotAh9n9B1+3ikBMGa8HqsCJQNTj2aV6NYhAee+IblgvhkC2oUHon9CDQI\\u002fQb9QaSIFA4n7EZzGhfUALpdQmQFN5QGBKbTVOFHZAqrn1\\u002frgjdEANGKnzF35zQDmUjmWv23NAb3oTG8vDdECJnjw3i651QMWAqzeGKHZA+euTEkzrdUDDOAHNw+N0QGP6wc5KKHNAZ543TSfncEAbIE\\u002fVPK1sQIMYnPnlVmdAbiuRisIpYkBiJf3ay+laQN6k8ijl5FJAOwH9YbcGSUBNL++NOSY\\u002fQDHUChZfKDJAFYrpSUzHI0Ch85UgABcUQAq8CaFPAANAfiGRsGe38D\\u002fwGPncS1bbPxa+y75xw8Q\\u002f0RttKkhJrT8MADFB3yuTP1sdGnIcS3c\\u002fZik6X8RCWj8DlqemE3g7P1lvTiG6qBo\\u002fLPvPBsMC+D4gYRVxJhnUPgma28nHGLA+87cpiOyolj4FIVS4vy\\u002fBP+0v1DF4qcQ\\u002fQg8KVWlXzz+npEKiHePYP9rIy\\u002fCJFOM\\u002fFK\\u002fApEi26z\\u002fm31hyhi3zP3MNeK7I5Pk\\u002fm14Z4jfHAUDy0qoV6hsKQBmpVk6jERVAPk7QJSdOIkBgIOdApk4wQA1ETBbEgzxAzPMGC5HRR0DhtYymiMBSQIdlbm4tqVtAhQPhWjkSY0A6ciowXpZoQNdSBSR1sm1AQ9hwUYHfcEA\\u002fhjHKPyxyQON6JmNTxXJAl1HsC9vockA4JNUmrPVyQJ\\u002ftsVGxRHNADxLhEa8EdEDjEkD4aCl1QATcNkfXdXZAwti2mCecd0CmvRt3b2R4QFOHS0PRyHhAXKN2TI\\u002f7eEDTnEf2oVV5QHSa0Vb0NXpAdbpJK5Xee0Ci+SZii1x+QNxuwgXcwIBA\\u002ft0Ue314gkAn67C8SxuEQBaM50PMcYVArHef5adRhkBNs6S\\u002fv6KGQFp+Bvt3XoZApclrnYuLhUAv7HjG5DmEQANtVSAegYJAR88vvNqBgECGRmw7\\u002f858QLeCBWOGzXhAowAc1I9tdUA1mnwr3ApzQDY\\u002fnMQu1XFAbFgNki2\\u002fcUCaBXjIK35yQFjBgpYZnXNAdVFcLaecdEDKKzPwpBR1QAIPVxlCynRA4Yfx\\u002f5W0c0C9N7OznfFxQNNrGOmDaG9AmfCRUgRpakCI8RbslExlQN1YKCHba2BAFBNkdKQaWEBuBCHWLcNQQLUVX6oN\\u002fkVA3cr2I8UcO0DtupaAL04vQJxtIIZ44iBAwBd1MIX7EEDYM8i0f87\\u002fP1Jt5nvMs+s\\u002fVqG7tDps1j8mJB9CftvAP9czExVfiKc\\u002f3hhVKrx9jj9dHMc\\u002fl1RyP04WeuXNclQ\\u002fYHPIWYEpNT+TXrn76lAUP7XTSAm\\u002fF\\u002fI+7Jt9DnTrzT4rDdEzVpenPtkF2rfUT5A+J\\u002fhFqUVGsT9z8WBEOr20P\\u002fizkdvwYL8\\u002fbVnkjbPfyD8z1qFKAhbTP3JavKoS6ds\\u002fjeiWmrGz4z9x+m1lC83rP2pHdG6Po\\u002fQ\\u002flJoA5mPSAEAo+W6nNAgOQOsrEsDeEhxAYt5\\u002fJMEhKkBMjr7hVmk3QIodthgV1ENA4J6\\u002fRuh9T0Dpp+F78F5XQLwUisShMmBAKenWEIL9ZEDW6JBAh39pQClEy7XnLW1AaZOMffO2b0A68GvvDZNwQAwyqlft8XBAiNvi8UpGcUCs7iJAZtdxQISw9Kpyx3JAsgate4sGdECKZt39dVx1QNMitWDYg3ZAochP4+NJd0BlzNOtt6R3QMKIq\\u002fFBuHdALk2VGnPJd0BOXrp6NSZ4QPTL38ODC3lAiXYUEA2SekDSXMBbaKZ8QN8jTbKmDX9AVY584sC5gEDHc5XFm76BQIaK0znKbYJAOc8nfdqrgkBFhmoZNmyCQJa\\u002fLS+ksIFAHkgHgUGHgEC1EIInXBF+QPZzYigirnpAz+bYtOQ5d0C+Mxkn7BN0QBIlBGwvmHFAxrYY6OQLcEDmWSqarxNvQDFgTXpd7W9AiSsmuOQDcUA1\\u002fGqW1EJyQCNw4BobQnNAP3Nj0Xaqc0BDDcIUaVBzQA6S1XiNNXJAoShcYpt8cECGVifuxrBsQNGN14PA+2dAIKjIYJU5Y0DBaVx6f3RdQFggzTlleFVA0mtXkpekTUCV5SNAL0tDQCKui+CWljdAY\\u002fIrf9T\\u002fKkDdp8NWXNwcQOgIZ2K\\u002fwQxAbfbm+NOr+j\\u002f3dWA3iADnP5ZzsDbKbtI\\u002fVypSYG9vuz9NJ0OKKfSiP2+aCGfyTYg\\u002fKSKYXvLqbD9oTUX2lutPP7Uw+qenVzA\\u002fV5LpWh4LDz94gXOPFVnrPp3i1cjpXMY+ipWUYDRroT69Rzi3rM6HPoaVx7AOTaA\\u002friQMjrqHoz9Wp5OtIHKtP2hf3N8ATrc\\u002fJ\\u002f2GeXfxwT+m6l78153KP\\u002fPx+WJCedM\\u002f\\u002f+Pl9uJ53T+vErOoXU3oP3Qi237JLvY\\u002fj0wcgkKjBUBra7BF4lwVQMnusG86fCRAXSWBiaylMkAo7ubR9eA\\u002fQO9PEKX9eElAg9ehfMH\\u002fUkDBCbTtW3ZaQBcF1L44PGFA59XRRLcQZUACDs+afkpoQAeiPUgLqWpAkEHkGcY2bEDlLTp09UhtQIb+DLBWXm5AotHlz6rpb0AD0lFkYRBxQF7Byp1Vc3JA4Srvetfsc0CBFEUFTD51QMJunPfTM3ZArwClktm2dkD0H5whCNV2QLw6hnktu3ZAzCSfnjindkBqcog8HdZ2QJjw0NV0cndATFwKrPuHeEDinIcuxv55QNHcca1Jn3tAAAz\\u002fjDMefUCUe1W3+Sx+QB7ozsu6in5A1CkD7RsRfkCIAcwGh7p8QOYsKrTDoXpA0TFdTu38d0ByJ92OFhZ1QC+9nkOWQnJA2MWMQcqwb0CTeiZTjEFsQJXdfWaBkmpAPNClsXitakDcx8JJkEBsQNyke+rYq25AMja8NJOTcEAh+XsvcXpxQIA4EWCyx3FARcuRPx5gcUDapMCX3E1wQLB\\u002f8JJzaG1Ai++AThGCaUA6XptEBERlQFT16pHq\\u002f2BABQ\\u002fJMw72WUD+CnGSy9hSQMiwQf8b5ElAMncgVnvAQEDtvfmUj1c0QC7Vsjf9GydA8r1XmsOAGEBD8\\u002fAtsjQIQGqksCPuP\\u002fY\\u002ftjI5kf0C4z8tRJk7di7OP5nVW7FzPrY\\u002fNz6lM8punj\\u002f4Hi65YVGDPw4QkpDlwGY\\u002fUHtt52\\u002fcSD+dQ36PNjIpP+TJMW8vrwc\\u002fwODbMgGm5D4KXzDjILXAPuGWlxjOvJk+ybKo1NZogT6Z8ca6ZLmMPweg+9WuKpE\\u002fGLJ5aerKmT9a1jgp7mukPxSuBJXiv68\\u002f4yPUtmk5uD\\u002fi+tSCpNbCP6hoJmL7i88\\u002fpoK7Fntg3T8jX2uexMPtPyG6dbZcEP8\\u002fch5e\\u002f47TD0A+l2WIAB0fQOnkXVignyxAxqS2efqhOEBgtmwiDslDQHDx95q2p01AjBHD\\u002f\\u002fLAVEDWpZf6ky5bQOhIIluqt2BAUDX6MmxtY0DAxwYsh4dlQAAQ0outD2dADmRaiEFLaEAo4PwwC6JpQGAS8cVrdGtAIhy6lYDzbUDYK2YhLIZwQFaZi4+sNnJA\\u002fdM8UILQc0BMnOjNHBl1QBzEe+P\\u002f5nVA9DZkn2MtdkBnmrg9aAB2QDwxjZfqkHVAFg5AdkcgdUAX3gmhYu50QPdWQXWQJnVASCfkPDHRdUB3\\u002fxlB5M52QOkdzbRW33dAb0XmZOqxeEDtunvJKvt4QBShzuOEiHhA+cKqoqBMd0CmY1dntGF1QEk6HrfDAnNAnBIxxiZ\\u002fcEBYypQkCFhsQD0FRAU2rGhAb5THFtlpZkBxxPYxc8BlQERD6SzFk2ZAYEIqzxJ+aECbkdwzquRqQPxEy\\u002fn3G21AcaTJd5aQbkCMKx2PzeZuQA+DS3GSBW5AtZztWfsLbEBaE1MiPjppQHQyJisp22VAQ4M6sa42YkDWxMHweRtdQGESEk6LM1ZADl937Q8VUEDsc\\u002fRaigRGQFiqf1cjXDxAXJhj\\u002fPMeMUCGjtIYaVIjQML2BaBlVhRAZFK4ucXuA0DQsuA5ayvyP6zuZXwvyN4\\u002f2lsNDjI3yD81oXjyI6+xP6loW\\u002fEu+Jc\\u002f8QZmVLkkfj\\u002f1uiz2NpVhP7Fn1mbLBkM\\u002f7pJoNYcYIz9nfJRoS8YBP+iud+AKsN4+x2682piVuD4N7L9o3bySPt1MQkHzG3k+6DGUgUCIdz8CGhSTmhF8P2BQei40DIU\\u002fMFbhSN3BkD+cb6sQcKCaPwf1sjlOZqU\\u002fV1aXmXtKsj\\u002fHFGPXd2DBP4CieLbAOtI\\u002fmTVkfu4N5D\\u002foSB74kuv1P0AcLRRaAAdAfPjSRBnGFkCFr40\\u002fBh0lQM17UxqhRTJApza4cuR+PUB\\u002fBB9\\u002fjzVGQASQ37MLPU9Awd8vTw+RVEBeoifdjHNZQPNOopAdyl1AbFXNiwWnYECdlb7Q3AliQNM6WzaYR2NAi2ulxRy4ZECytMwwRrFmQAw\\u002f0bTFZWlAJ87mlT7PbEAnxhPeE1VwQCbDgGeQQXJA5SwG67Toc0B\\u002fHT9qdQx1QEphXfmEhnVAvkclg5xVdUDHPduje6F0QEgsKMpGsnNAi7v0E0ncckAMpM8kI2ZyQDpbT7NAcnJAnEm9aIbzckAPIa3TH7FzQJd5KVgNWHRAHGVIGYSUdED13lLazyp0QKcYfJf2B3NANNvLfLJFcUBdq0J8HkRuQBsgHU3X3WlAEyIUYDr+ZUD2XRB5uytjQIkWXiTptWFAK+ARouGoYUCggq5t3MxiQHxFZYzSsmRAEDiMgJnNZkBsQIUpfJJoQN3hMhbcl2lAvHLXwYmnaUAQFJdzUL9oQMEsvsCLAmdAb5ljnESmZEBEjyT54OFhQPH71U3a0V1Ax3vpDeHWV0AWk0RUky1SQITWxebQS0pAaKKz5AHzQUDPmD3gEgY3QF2A184NpytAHhXVJbEDH0AEBtkrmjUQQAr1hnEnif8\\u002fTN8mUsyE7D8b55leTPXXP\\u002f4CEsvtr8I\\u002fxmZlV24Oqz\\u002fic9wokyySP8FuKt18pnY\\u002fVqrEhUEvWj+\\u002f+qshSxM8P9r11jYB6xs\\u002fnrc\\u002f1de++T7lXWc4fQTWPlw1j00UebE+4PvbeERfij6UwhZs\\u002f4JxPgku9SNS4WE\\u002fXrWOvIlOZT9sa2rljgRwP3QbBsle7Hk\\u002fFurmiUOEhT\\u002fzKAXRwNuSP\\u002f4b9OpkT6I\\u002fXuWXLs3asz\\u002fHqbYAjfnGP8KTah9mwto\\u002fbTg3Do8g7j\\u002fc+wpWJQ0AQOSjWxK9CBBAf61dtEzpHUCgqtQ1fgMqQDRzFtAoFzVAp+SJ6xjmP0CGnuCXMohGQOAk+xUXzk1AYg25huaIUkAYAs5LjNJVQK9HjuQ2lVhAEI\\u002flhszoWkBJudbfbjJdQLf5U480BmBAX4L2wfMKYkBgGP6IPd1kQMu2NFd7gWhAmA\\u002f5vV++bEAgBwEK3I9wQAjaPv8uhXJAD7f7mB\\u002fwc0D2Dd5ozpZ0QOZlUXjMaHRA+r72qZCIc0AWGWl\\u002fYkRyQOQFHEuC\\u002f3BA3cMRfjwTcEChrla1LGZvQBTq+8Wlu29AigOaWfNfcEBC2k1fIuZwQCmTHTnhGHFA5eLY5Ci4cEBvbzSRU1xvQN6d3+C3J2xAVAtSWctOaECpLtssZXJkQKd52XaPLWFAyEWaK57lXUAZWIIXFe9bQOrMiTh8XVxA7QT2IsaiXkCf4Srt7fFgQHYC6cr7lmJAKVrp5\\u002frWY0D4e0+79WxkQJKjIq0mQGRANuL+yjhdY0A21KLzjeZhQPW47klqBGBAjAcQOhq4W0DD5e+C5x1XQOHL8hUVfFJAStXv2YYsTEBUPm9YlldEQMqc5VBkrztAOA2zDw2uMUCgDwev\\u002fh4lQMigfy3ZihdAHlyZNphwCEBLxHBNYpn3P1ovrqAULeU\\u002fbkHc9I+l0T\\u002fknRX\\u002fH027P309ocv6mKM\\u002fkZjk0Ecaij+UMv30AiBwP6Hw\\u002fKpyelI\\u002foUNpJWmjMz\\u002fDyMBo\\u002fVoTP7jOLRDFsPE+1OGAhNP8zT4tSfymhZWnPiDXLbVDoYE+rMavCpI2Zz5k6tZKvS1JP7AsBT\\u002fADk4\\u002fvNMaq2reVj\\u002fgxP5QQjJjP4zEo\\u002fE3PXE\\u002fAE35\\u002fR8kgT8lq2UDajSTP5FVhe+6V6c\\u002fBsTw0P3xvD9R3rm1\\u002fHnRP+HuyMNUCuQ\\u002fPlIkXQqS9T\\u002fRWNwTmK8FQJ2Z1PQTUxRACXK9yVq\\u002fIUDpbtFeseEsQEHrK0cm6jVA4xevGd8OP0CVeKflZZtEQCs2G2cNuElA3n\\u002fzDipqTkC4x+a7Tz5RQPoEolkFFFNAEfhEuskQVUCvBU4O8r1XQEdZO2AFr1tAwZtuUHioYEBw2BbImFxkQNh2AR04wWhAzyNaIWJibUCW\\u002fVchg81wQIsR7gFVXXJAxyqK6nkcc0CvLkJH0\\u002fJyQMn16xIpAHJA\\u002f+cHuz+VcEBVJorLyjZuQJQpSA2v5mtAEgv2sHqvakDVHrNlr5pqQJJMws2nSmtACHNbb50fbEAfygb7QW9sQEAUV+YivGtA6hFmtwfbaUBLj\\u002fgwd\\u002ftmQDmqeeFkk2NAylfMhDA4YEDrBbp0CuhaQI7X+bZ\\u002fSVdAzRmoD4jWVUCUrqqwfldWQDXuaJrtNlhAVxLmwmOuWkBSQTCWJvlcQKZwy3UFgF5AjerGWW\\u002fyXkD6r9pJnUZeQCUT30t9pFxAA7DwZQpIWkCqHshbB2pXQCPrraPRN1RA4AbXZ3HYUEDxpzRC0epKQEgUO057e0RA8UibSWuAPUCGj0XWSgA0QNXVA1mGbClAD6s79DszHkAKl9B0MLkQQFwNfg1sPQFAxHkhZxOG8D8IXfSX2mzdP+S\\u002f3NBZU8g\\u002fG99ip+ipsj88jVavk5KaP5UXikRHjIE\\u002f0U94dj9\\u002fZT+vQDNslmxIP3w5HOsYvCk\\u002fYHdzczwlCT\\u002fcAaZhn8jmPoXcmRecJMM+FdHdhAjZnT6wHybtuht2Pm9Zh\\u002f9O3lw+vTcqABZ0MD\\u002f+1yYf4MozP6vpcGisDz8\\u002ftTRK83IDTD+geVMr+ndcP\\u002f+hHfgeenA\\u002ffWMo4moFhT\\u002fjaFgW8bubPx6AXIdP9LE\\u002fSBoboa4mxj8tJlY0aa\\u002fZP\\u002fbLLO+u0+s\\u002fNhzXXmIZ\\u002fD9vycF\\u002fUGwKQFf\\u002fu0eHIhdA8fbOJ7bdIkCR66K3q64sQCt6lZkaWzRAV332nzQNO0CSDNi6iulAQPLutXN8EERA2l5Zvs7lRkDGRvd0TKhJQIx6FwRY+UxAIICwt6HkUEA\\u002fTN674Y9UQKeim9s05FlAvZZ9vg99YEB+oGzj1LpkQD5pV39YPGlAaptrYY9fbUDU666+aDpwQK\\u002fbBp+G+XBAWFMTrArUcEBZY6yWwc9vQFu58j5sBW1Axc0owJYTakD4LlzmGbRnQLS+B2WXWGZADjBBxrANZkBsWB2JRYBmQMH2hwtMIGdALhX5MzZUZ0CSh9VrmqtmQHnEPsLVAGVAoIKof8p\\u002fYkC1shN28iNfQAqP0FeSc1lAQQ0Nqu3WVECjdPQ6aeBRQJB5VAqZs1BAJpsGQRENUUDTYoIBFWJSQMOdMAFbDFRAXqCzebR1VUC\\u002fxLcWTzhWQMn9KquBK1ZAjjzLZZRbVUA4MiFhGfNTQLnOyw\\u002f5IVJANKOIrJ4NUEAwB\\u002frpup1LQFOkJYM580ZAwOVvsSdLQkB6Itgli8M7QFAhR+Zb7DNAUwl2WqbkKkDU\\u002fdBPJgAhQDw6I\\u002fczEhRAkn1MRXYUBkCMBJSOUJn2P\\u002fguG3BPf+U\\u002fyfKJ8f390j82evu6oia\\u002fP\\u002fKkngnStKc\\u002fHZkKQ7W8kD8UCtNACOx1PxeAnO5woVo\\u002fQugH9oMAPj9pzE+pOFgfP1+gio30Xf4+AJX8K95H2z4SNWuK27m2PnSxQ4DCkJE+QZUV5tDJaT7meRW4JbNQPt3T8XZjEhQ\\u002fhAt6axmgGD+nSN95daIkP0xMYZ2LBDU\\u002fsmvXSW4OST+cmL9Mq8NgP\\u002fsEX+SjhXc\\u002fIqvJnSZSkD+qxGCW+aelP6hsoV9mCrs\\u002fIvOWb9eNzz9CnIbjCSjhPy9rQlimXfE\\u002fdcSyoxJbAEBWwLdUGKsMQEOodPCTYxdAo4g3IZPHIUDBPMCR0zopQICSylrFwjBAZBFG6xr4NEBDovD6vfI4QKCY0W09sjxAE+Gu839aQEBp0oy6DQhDQFp6EZUaK0dAEf8ZXruhTUBtTC4Qy4xTQFZ1fs3n3VlAAa1Wosu4YED3PVlhA8BkQKEYB96+cmhAJxkyaoEza0ClAUzXoohsQO4VG9oeR2xAYPf5sqilakAz1A0s9C1oQIO\\u002f\\u002fAEAkWVAxsgkB3JvY0CDaxsUlCpiQGduWyoTzWFAalh0F7oQYkCbxbfPw3xiQIrXGD2HkWJAqYwGvu3yYUD9dDSECIRgQHbGfCww11xA5a51VRwEWEDDUu2XqGNTQPjylbQLWk9ACXYPWDeXSkCTPQyT8JxIQNB4C7lp50hAxSojEpeNSkAXPrrmyoxMQGOe9HFIDU5AYfaPQSWOTkCMwL4M3+5NQBGKf2\\u002fkWExALzWU0EkXSkAgr1iUv3BHQF5KCdqhk0RAh+VozuSXQUBf1SQldBw9QEjYu4oWHTdANUFjBM93MUDlhLr2PfQoQN8q8ZiuwCBAFw4Ikt8NFUBoSbpEwbEIQOsV60zB+fo\\u002f9j1Xufdn6z8C23TD697ZP8wyxTklrcY\\u002f0s4ovKNysj\\u002fNmfSZj9mbP1IHKY5BgIM\\u002fIFuO3qBUaT+ut5kQ3oNOP++NSBueCzE\\u002fXsSpPaSoET+11dA1z\\u002fbwPjTEBPIcOc4+hh0Wt+32qD6OC5bS1yGDPm+guWr31ls+wYLzyQ3iQT5BQSWqfSz3PiRdzNTZrf0+coCRl+wLDD9kUU9eauggP016GjgJqDc\\u002fVfYJ8DujUT+JyzvuoDhqP7O0RD0qs4I\\u002fWf+HHa8gmT9Jx0N6rI+vPxjTVfeJd8I\\u002fS1bWVHsb1D+b\\u002f1k1KFzkP4MV7ZZkK\\u002fM\\u002fxt5UpADJAEDOCPptw1kLQMx8K4pFwRRAXR2d9ZtlHUDQEHvGDoAjQOvnSKOUZShAaX2Xv5UiLUAdXbhPePIwQESZSiXxyDNAvlpTngD1N0AWsKtBGLQ+QLpfTIU2sURAMF3WBAKHTEABaeujU3tTQEPBYqMruVlA05l6ujIrYEDx+GYuYDFjQC8Gldocb2VAbjBFyXyDZkD6rkl+lU5mQPtr8LYm\\u002f2RACDyjVfAEY0A8TmdAwuxgQAEny0b6aF5AJ7mMZRtOXEBhFw1PB5dbQGxyq8A31VtATMMF\\u002fONNXEDGUy6fHj5cQEU6vaoqHFtAwKoQHeLAWEAFeKrNumxVQJtl9MLZqVFAXZHA\\u002fWozTEAGPHX3OIVGQFQeDBbX20JA8wm0cYY8QUCo+ctN3zZBQGk0aYfTF0JAYpHZgnwkQ0BXyhkvYMtDQHiAUivbv0NAnJhZ8uP6QkDFfhLCmKVBQBjniorV8z9AvoI7tklQPEBsfxbglpY4QLX2h7wr2jRA+4fqrz0kMUCWnGwCxBErQBxXLbT9WCRA6ONsP4LnHEAOl4uqhkkTQMBz2LznFAhAH\\u002fTCs6YM\\u002fD99SKAk2WruP8pWPlNFq94\\u002fVbxX18G5zD\\u002fAIqzvh\\u002fq4P7l9pywoKKQ\\u002fah57oIQujj8u6CePw\\u002fV0P7dwa+8gAFs\\u002fgGlswdUgQD+Rvd8cLt4hP2a+ln0sWwI\\u002fZwQME4R84T705ZPFcOS+PnthzpSYTZk+DvJx+HE6cz5fDVqRwbtLPkj7NgYOrDE+0qzwprsP2j4YwUqNNhviPoS7g33CfPQ+9T2Mlu2JDT\\u002ffIDXl2FknP7UrD4DqlEI\\u002fTSEDTVN4XD9fQYxxyZF0P9rwn8l0yYs\\u002fyZLqhoJ7oT\\u002fe0mmr6HW0P6Q0ZSEXQsY\\u002fxZ2+2IKA1j+QoBmOBSTlP+lrE39rdvI\\u002fblkgw1L+\\u002fT++42kI8K8GQHkvQEGFBBBAZ6\\u002fVl4w0FUB4oGww64oaQNQbdh2p5R9A+bXIs\\u002fHfIkBoSRMPZMgmQEmj8wRm\\u002fixABVD62zyqM0DtDX2wAuI7QIXFOtoW80NAgGZeqkHpS0Dxs\\u002fn1ALFSQBqULbzDrldAhK0D7Pg4XEDynnTLMJJfQPFfqAw7l2BAyRtXPd9xYEDBDGHTDvteQBB9lkq1GVxAsQ0lgq4LWUBYxDyZf4RWQLNp9ZMQ7VRAVOVLrk9NVEBp7BdrdlVUQD9T34Y5gVRA22R5g3FIVECJ1UMyP01TQNU19TiOeFFAUXRpBWP4TUAEn5w+LXhIQIQtH0UiU0NA70ygV9d+PkDZg3G+4zI5QCHNuFsDtDZA\\u002fJVuZBxWNkAVcrquPxw3QI\\u002fAgHmxCzhAvOQeYbtsOEA6J2NU6ek3QBlMD\\u002fodijZA\\u002freGpp6ONECLjv2keUcyQM\\u002frWE594y9AkYKOGPRVK0CwQDHNt\\u002fEmQMK1WzeHtCJA+m4BS0hUHUDV14DWquYVQDd2C\\u002flU6A5AiHJhETV8BEDmwGBk+mX5P\\u002fbsKiYcXu0\\u002fiUm5FmSb3z\\u002f9t22qNZ\\u002fPP3XsDJlXYr0\\u002fHNFeoqNYqT\\u002fpWbqMl0mUPx0YtevnIH4\\u002fYFeLjyzAZD\\u002fe0lmMcYJKP69ncEQraC8\\u002fRbQov5tAET8ffqbpb5PxPnry44dtmtA+tbn7azUWrT7o4qy07Z+HPvldMFVXzWE+47MjSZFzOT7rC3AkpRYgPpNRT6qOAL4+v3JBZoynxz7bw+jkXm3gPngaVcD7Q\\u002fs+gkfwiw4wFz90wsPozxAzP1WdIhf3mU0\\u002f1PsWhFd8ZT\\u002frVcm2Awx9Pzlqh9ByQZI\\u002f9Xj0myhSpT9moIaoSSG3P3Iuwg16Tsc\\u002fVqdj76XQ1T\\u002fFkon\\u002fRfniP+XVRWDhsO4\\u002f7Hi9yoEd9z+yiGz6U0IAQNssnCR7ewVAV04PRMjyCkBHCmfK\\u002fl0QQIdG5M9d3hNA2sOTfqwQGUC30Qibi+YgQL47S6+tRyhAQYh0nkL\\u002fMUAbud2FEoI6QIxa7xy52kJABdA7Xip5SUDdVtZlLTRQQHT6cQ6JV1NAwibtRqumVUCTH7C0XsVWQFENs7x\\u002fmlZAapcKaG9ZVUCYTrJ7K3FTQMSGpsMkaFFAgRp0XUhmT0BCCWSDAytNQCI33akaJ0xAhAkPU2zyS0AtzCRRdOJLQND1noLUSUtAmUmJtC20SUBhilehQgpHQC6RVDPqkkNANnYGKbKmP0DRuq+RK7w4QInXjjmjSTNAXPP4+89yL0ACgyV66+krQNbA+9kgCStAN\\u002f8m\\u002flGHK0At+FdeliwsQMuu0OVKICxAA2ziDzkJK0DHJgOG3\\u002f4oQHngM056WiZAnWbTNhiAI0DtiIIpBbcgQHCEjF\\u002fMOhxAaKK2sjpnF0B\\u002fIf9GIOMSQIOHOMV\\u002fXA1AazeJ\\u002f3\\u002fBBUAHdjBZ63n+P1u35n8fDfQ\\u002fvWQ5j\\u002fSs6D928nc8w1DcP2slxgoWPc4\\u002f9xOmEqsDvj8rbe9uyqqrPzHj4hwKrJc\\u002ffkQKVu7Kgj+SlMK5561rP\\u002fKmn6oH6FI\\u002f2a5yigT0Nz+bLQSKeCQcPzSy\\u002foouqf4+fVQMHrT53j5L86lTUgS9Pra34bhfNJk+j3gQVedMdD5iGn33d1ZOPrrqE1dIfyU+SbezuQH2Cj5RvlJ5HL+iPquc2NccHLE+wc4Vh9EpzD5rEoQ\\u002fJ2\\u002fpPmapDB6EagY\\u002f3rSRhXCuIj8AXd\\u002f9UR49PynLHJJMIVU\\u002fOTFLMyqBbD9n0nw7qduBP5XsN1L4xpQ\\u002fLXMFL5xxpj\\u002fhw+Pwd4K2PxWrWdHa9sQ\\u002ff5mAZZcj0j+1jkoP4S\\u002fdP97JaErm3+U\\u002ffye7OSao7j+PHN\\u002fRCTz0PxXP+p8\\u002fhfk\\u002fIsH7qy+L\\u002fz8zPvnjrtcDQKBZPiYhcApAscKy4rz3EkB7W11AlbscQKpp7a94EyZAk5lUHsiYMEDEqiUOmNs3QMxAbpABMUBAT6H6PyyiRECMzpdmU6RIQKiKx0cwmUtAM\\u002fotOzgRTUBMN9TERfBMQO2btEDCdktAIIjcXGMrSUA2BrqsMq9GQAFlockZjURAnn766x8VQ0CXLq92\\u002fUxCQCjGXWku+EFAkFwRF3CzQUCWGPVukhpBQBqlKUA92D9A45K2oQI9PEAdOA9XXb83QCQYIdkkAjNAq6QVY0hmLUBQbfAat6gmQEAdYUkXOiJAHRQicTDgH0DEnmPF2WQeQFU8WFZrdx5AAnRgwpCwHkBrZ0QhWyIeQCy1NBG7dRxA6\\u002fE+00DSGUBjIwkrEKQWQIaSY5DiXBNAYMjFx2VJEEBQg2QvFQwLQFzMwWn2GgZAsdyotkOhAUA33SLMfyL7P0HkPnMP7vM\\u002f+yk+B\\u002few6z9oHGSiVBPiP5EDocQGEtY\\u002fu+MAV40gyT96copk+Z66P+EFNeM4Nqo\\u002fYMaoFG73lz+E0n+pq1aEP\\u002fGDs4iKA3A\\u002fqsm8cVNkVz+L7xIz87A\\u002fP6A3Rmqm6CM\\u002fEgttyjkyBz\\u002fIppYZvQ\\u002fpPiF566GCG8k+OxSPl9xSpz5HHA6\\u002fFheEPrOlI0HDC2A+W4nSR2XHNz6yz2mY27MQPjq3VC7Yx\\u002fQ97N8Dyxgdij7o6QxHgd6aPqCcsWUWjbg+rQ\\u002fgy8cX1z6iXc40kKP0PmxQ62nSQRE\\u002fa6T+QR7eKj\\u002fzou7MnW9DP\\u002fcHAh6QHFo\\u002fGLughTNIcD\\u002f9GArqw9iCPyq6uXlnP5Q\\u002fYnLNXu8wpD8GbgWLpbGyP0YNBMj2E8A\\u002f8Y8GsoG4yT+yn9bT2y3TP0dUF9CCy9o\\u002fDd4YrGK14T8Baa8WZJHmP+QcT5ETo+w\\u002f+mUjUBvi8j8rrYVEb8j6P4uiOgbqawRAeJNCYYAtEEBYi+YjfYgZQGDpD4VMdSNAhei3jdsiLECV45fjESIzQLX4HlNwZDhAP119MKojPUCkJBrVG1ZAQLoW8VO0QEFAxDePjjVCQUCUHgfoGH5AQPiF0Ouhez5AwLw7Lq2xO0CPLtOWwzU5QHPI9a7vYDdAavhGFiA9NkA5hq7oFo01QGl4mkV16TRAyVRdHqvpM0CkxHzsBksyQAeJdgTDBzBAemeztNmrKkAlCkklxiAlQCqn57ujKiBALljgDuuhGECj3xgrwI0TQOWrAmkJ2RBAJ+dfS1KkD0Al8G6LxT0PQJLkD5iE\\u002fw5AWfHVSLb2DUCX\\u002fE6fDtQLQPEXmKIyywhAdqIDX+dRBUByZ4V6Ct4BQC7G9jHCdv0\\u002fjoWkje4E+D\\u002fU9HMXv1HzP9GH6fe6Z+4\\u002f45iU2+ki5z+1USCZ6NPgP6FyjsahLNc\\u002fvQKT\\u002fkMAzj9xbBFQnSrCPwO0WDzog7Q\\u002ffjE+CiCPpT9g7VNCJQ6VPxFax3cvGIM\\u002fB\\u002fiN5S8ScD8\\u002fCdwvUxlZP1DSzV4GLkI\\u002fcbWh4PVsKD9VjFcKFW8OP6gF4dYClfE+nvhh0G7W0j5Abgx4\\u002fbayPsJC0SR4PZE+BHumm7tzbT6E7SAPp1NHPsPC7yO6IyE+wbTSNEDe9z0KD1amd3XdPd+w+621o3M+YZTkBEmzhT67cADyMNukPjIZ696Z7MM+eG4Axtfb4T7vWCCC3s79PuoLk2\\u002fmHBc\\u002fSQ9MUVCjMD+AxhhrHjtGP2ftN2hMkFs\\u002fC0jPfCe2bz+12riFVe2AP8Tm6IdNxZA\\u002f8\\u002fM4ad3Znj\\u002fspFJI2FyqPxEji5Tj9bQ\\u002f0T+tKJscvz8B3AfrNbLFP+rXAPkg0cw\\u002fgMwQxE6v0j\\u002fOGxz2y5rYP7iXqgZzMOE\\u002foSi3fYYJ6j9CoTiaqvj0P86FfltFNQFAPlEHOsSqC0B860l8\\u002f0EVQJizZ3Ff0x5AoqXbiUb5JEDX1y7a7r0qQE8EHjP++C9AD7qOjpn3MUBtAPJSXQ8zQCy6742CMjNAxMcSq4+CMkCX1fns0kYxQE5iVtp6qi9ArVT3jnzzLEBB8+0fs8YqQIYZv3CNNilAllkJT2UPKECurannZvImQHYcvlW8fSVAoX5u7QB0I0AwUnwUzNMgQGvQnPzFrBtAC3k3FdCvFUCNAzh0Y2oQQAHdYm4WuQhAh+wgYNJeA0DmSsVDW3MAQAch1Y31cP4\\u002f83tseoaf\\u002fT9hLvm7Xfn8P+dkk0mPmPs\\u002fd8RD38Q5+T\\u002f\\u002f6Ap8Rhb2P3\\u002fl7sguovI\\u002fyWRBghWa7j\\u002fY1RCu+rboP\\u002fybdRBKweM\\u002fB3MQVyg63z\\u002fS2460tzXYP\\u002f2yOIl3MdI\\u002fQG8WqNYuyj+c6gK3jtvBP2+\\u002f3gsF6rY\\u002flZNILe2Dqz+HOXuppdCeP4EHQCjiDpA\\u002fXchXy50bfz++os+CjPprP+gXQrGuWlc\\u002f7TGchfoVQj\\u002fglIpdnPspP5VMmvxMTxE\\u002fIeLljmRj9T5XwULruIHYPpq0QrezCbo+8vfTGACnmT7glC6FC293PnZTdsVw2VM+W7OMJAYuLz5vvawxx7cGPlnOmKZiXN89VjN4aeUywz18x\\u002f8eBBJePg0XBR68KXE+IEsYFdfQkD70LqlJDh2wPlRhDxKI0Mw+EhdFX8fv5z519bv+E3UCP1PVYrSmaRo\\u002ffRuDRJyIMT\\u002fO78Mmr5hFPy74Fd+erVg\\u002fA6ffO+opaj\\u002fj8uEjuL15P1a5sNoVg4c\\u002fUaC3pi31kz80FNwzIY2fP155jAesU6c\\u002fU7PI6XlHsD\\u002fSWO38Jte1P39neOWpFb0\\u002fpQY+pxMbxD+T57CtNP7NP7E63f1rNNg\\u002f+F\\u002fQXWZn5D+CHwsisC3xP2c8m4Cm8\\u002fs\\u002fm3zg\\u002fZOSBUBKdkAh4lAPQM36HMdETxVAT3eXL5kvG0DgCpImv0ggQNP8yfBhYCJAp5Vi4CifI0DqUUhC2fAjQLs7YDQ6byNANHgR3\\u002fdWIkCPmurXJPQgQFs9HgdsGh9A2n+1uuqjHEBIwfDtGKQaQFieBQnP+hhA3joYi5ddF0BhgwsxSXsVQAlnDlXmIBNAT\\u002f8IJmRQEEByCaGE9H8KQHe8j\\u002fwFiARA1CI\\u002fgCG9\\u002fj9ilkEAz+D2P6q7L9ylsfE\\u002fGvKfKrGh7T8EUgH2WQbrP4yyb+9n7+k\\u002fHbPRKwoG6T90FRPDnIDnP\\u002fRhJ2DYKeU\\u002fIQuHipc54j++\\u002fUXxzC\\u002feP2y\\u002fuFZ+Stg\\u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kDHWtt6REsiQMFubWDLSCJAjazWjqI5IkA4trUkS0ciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAT3\\u002fsVitXIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDU73tBaT0iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkC5VQ4NTUciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAvQyn\\u002fiBRIkDN7abVyEgiQMTGzmsuVyJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkAdrezhTkciQGLLYJuqTyJAkgivCkk+IkDiv2HVKD4iQEsSnuKvRSJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAGlBmIGFdIkDpgZVquT0iQEboULIbRSJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJAkgivCkk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQAHnrA36SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAT7HdVyEgiQArvfV65QSJAQwjUdbNhIkBOhZ\\u002feqkEiQObh3mN5RyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAQwjUdbNhIkALPjLMd1kiQAm0MM4NTyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkALPjLMd1kiQLagkKWKOyJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAkudSZUk+IkAaC5M0EDciQCqH4OCwRSJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAWYZEz49pIkCsIMiLdVkiQEeNqTaKOyJAVdIqpZ00IkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAdJH77IVAIkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQPGVsNe5QSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAIIJygTo1IkCuhjhCMDUiQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAvjGaL09xIkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQF9MkAzvVCJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJAbeb4yY4wIkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkChLmUFiD8iQNObl8oPSCJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAv3kf+0Q0IkCfL\\u002f0yEB0iQCqH4OCwRSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAGFnIBZ88IkDNhz\\u002fjKjYiQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAqttQNwA2IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkCIuMSUATMiQDINUv8NSCJAvvrgkdgvIkDaFd3mpT4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJADSU0+O9GIkCxNC0\\u002fICciQEeNqTaKOyJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkCPh7uDPj4iQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkCiylRyICciQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAYp0cWkg3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAaxanuo9UIkAdkJZ9ox0iQMeOFE4LTyJAjFxkqiZPIkDBEtfjolIiQJOXHVzFXSJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAvvrgkdgvIkAXiI7Bpj4iQLeDp2bkRyJAWnCL2s9gIkD3NOkjlkQiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAO69lUJ9SIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAxIP\\u002fd2llIkACzuKdQSgiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJAsReEas9gIkAlL4nRd2UiQEi1LaS2QSJAxIP\\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\\u002fLLQQ7WiJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAc4OfdyZPIkDBEtfjolIiQNbicV0PNyJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkB9YkJ38T4iQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\u002fRtWSJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAroIaLxgiIkAASBDzS0kiQNbicV0PNyJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJA206amhQ8IkBpWmsIGVsiQMOh\\u002f\\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\\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\\u002fAss0ONyJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAycIymiokIkBkh68GIzkiQKyG9fjGSyJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAng\\u002ftlD0dIkD5PlON9DgiQMgfSh4PNyJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBkh68GIzkiQKyG9fjGSyJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJAW7fFOEYWIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkAr5vxXi1UiQOcTNQ+ISyJA6SDl\\u002f5FMIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJASnuy+zodIkDHCagiVFciQMgfSh4PNyJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJA88\\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\\u002fzFYiQOcTNQ+ISyJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkAU7+dQMVciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQIEsfIFYbCJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQOIi4cTjRCJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJAz4KhECJQIkDPjHs\\u002f4CciQJpB4Z30JyJA4Xx8fN1VIkCgCoVWMlciQBqUTmflRCJAPElOiL83IkAR3IsMa0wiQAuNoop9PSJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAmNcEIX9BIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJAsH32LnhTIkADjSuZq0siQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkC0xdwPa0wiQAuNoop9PSJASMtHM3hTIkDTuy4MA1AiQBUCFRMYaiJA+XE9J6YpIkDg1F2AJ0QiQBndUz1DOSJAgoAUjexFIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkCQlyU6sEkiQBqUTmflRCJAPElOiL83IkBa9amk4EYiQAuNoop9PSJASMtHM3hTIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQItaEiovKCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkAaNVhGA1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQHb1Am0hHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQAuNoop9PSJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQKd7cXsgHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAnGYaNr83IkCzaQ1O4EYiQFjH8K0oNyJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJAXCpsW8lLIkCzaQ1O4EYiQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAawbp3y5DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQDCSvB7gMiJAB5It2tNEIkDjGPn1A1AiQBUCFRMYaiJA3N4AzI0uIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJAuzziAeczIkASUrGfwkoiQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQNp7OUEtMSJAuts3vdREIkDjGPn1A1AiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQMH0l1wfHCJA4sRhl1AqIkA60rRpVi8iQBqUTmflRCJA17UqhchLIkCKMiSFT0ciQMgAP\\u002fssMSJAuts3vdREIkBKqXre11MiQBUCFRMYaiJAQ2Mp1oYzIkDg1F2AJ0QiQBndUz1DOSJAiN2bBS9DIkDPjHs\\u002f4CciQJvFct0fHCJA4sRhl1AqIkBZlpMBWTYiQCM+lrxqRCJA\"},\"y\":{\"dtype\":\"f8\",\"bdata\":\"u7uf++cs5D\\u002fe0PSAMSLkPxAE\\u002f5S+PuQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002fQEgwcpEQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002flnzOUnpD5D8MQsHvNCvkPxrIavggKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP6jhY2yUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP9o2S6juEOQ\\u002f5OXkhpMQ5D8nxKqQJi3kP+5XFhKXFOQ\\u002fu71xi7ov5D8MQsHvNCvkP5DOktoiKeQ\\u002fVLR9T2g25D\\u002fFXFWIsSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D810BeRExfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002fu71xi7ov5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002fe0PSAMSLkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D+Uop4+FBfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8MQsHvNCvkPxTpdsotOuQ\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fVTr4cegs5D\\u002f2LWk47hTkP7CzC\\u002fgASOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kPxJBw36WFOQ\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fVLR9T2g25D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkP71runVCUOQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP9lKWq7rM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkPxmSEDgNN+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002fuE94Sjcz5D\\u002f2LWk47hTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D8nxKqQJi3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP0vt8DlsHeQ\\u002f5OXkhpMQ5D85zSncJS3kP5JdXovsM+Q\\u002f+9Bvsrkv5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D8DGo\\u002fiEhfkP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP5JdXovsM+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D\\u002fBY5h3sSDkP7j2DuKUOuQ\\u002ftNSdmjcz5D+7UfDw7BTkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP41H4YwlGuQ\\u002f5OXkhpMQ5D+TEcFucDTkP2qI3WoiL+Q\\u002fuAVB3GYz5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D+hZttwQBfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+T9ri1kwzkPzvBEH0bTeQ\\u002f\\u002f6QFJsoY5D+LpiR6lw3kP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fUc8AX2om5D86e\\u002ftxQhfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkP409VM4dTeQ\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP2qI3WoiL+Q\\u002fZF3YE\\u002fkw5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP7j2DuKUOuQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPxNhQyesS+Q\\u002f\\u002f6QFJsoY5D9q9\\u002fPa4hrkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002ftNSdmjcz5D+Bq+4P\\u002fDDkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP4XbxPN3EeQ\\u002f5OXkhpMQ5D8CUQFDSDPkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyJn2lGqS+Q\\u002f\\u002f6QFJsoY5D8lFddLUBzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd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Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3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zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEHk0lQOHuD9JABVJwNO3P37JvbMPebQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f\\u002fRCcy5ULuD9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD\\u002fIz9wEnMi0P8R39IOJzbI\\u002f5P2NYa+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD\\u002fu\\u002fVbk1Su5PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fyTW2oL3KtT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fFo0QcwOHuD9O9LouJhi2PyUZECT90rI\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002f2kl4qQ+2tT9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fE7+iJJdItT9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj+F4foHPNW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fdxB+LrSJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fEUx3n8zEtD9O9LouJhi2PyUZECT90rI\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fz8t12De9tD9VYzcnvlq0P8scrWxKprM\\u002f8qGMXbKJtj8l7l5zdbS1P4FssAXomrY\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fEUx3n8zEtD8Bwc1HJhi2PyUZECT90rI\\u002fYXVmgqAZtD\\u002fDwLHmfcW0P8R39IOJzbI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azP6c9nUK9ybU\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002fD1jGfOCRsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002flQINpFBOtT\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P5u\\u002fiKdO17I\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f57gx9KB5tD8l7l5zdbS1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD8Bwc1HJhi2P9UwnWfMg7I\\u002fog+G5\\u002fV\\u002ftD+CW0L4UXSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002fSOuJ1d+htD+IPruMV422P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzP8R39IOJzbI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9SzDykVY22P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P2u\\u002f2lyP27I\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762P0L1k3qkW7I\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002fsY1KZrvltj9zqHjQD8y1PwpkBvzaaLU\\u002fSOuJ1d+htD9pIjdD4762Pwsgb9WiW7I\\u002fqqpyhYhzsz9d2oQaF1GyPxCfr3bfM7E\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002feUpi1BAXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fhzk0nO8etD9u9j7apJy1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz8GJdFBGFGyPxCfr3bfM7E\\u002fUdacTvEetD8TMHXFtYa1P8Ae8tSDMrM\\u002f6pAACaxTsz+aiYVtnsK1P+9VO+3T5bQ\\u002fyEgcHbeztz9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT857M44KGS3Pwsgb9WiW7I\\u002fqqpyhYhzsz\\u002f6jNnAmXyxPxCfr3bfM7E\\u002fUdacTvEetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\u002fSeHsoLRItT9zqHjQD8y1P4XhguzbaLU\\u002fAJKA4xIXtT+Rswun7tW1Pwsgb9WiW7I\\u002f3kSRwYpzsz\\u002fMHvG7a1qzPxCfr3bfM7E\\u002fHTYIv\\u002fIetD9xmKvWt4a1P8Ae8tSDMrM\\u002fW25iD6GysT+aiYVtnsK1P+9VO+3T5bQ\\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fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+I+eZ6XzDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkP42KEhOGPuQ\\u002f\\u002f8cQg9Mz5D8TCFcwrCvkPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002faIG5Rawq5D9IAZ4svTjkP59Qjz3qKeQ\\u002f2VHc4DUk5D+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02fJ\\u002fKT7FkKWBafAFPqNIIcGv\\u002fuA9hB8Bd2aztz1f8v3aYcGNPYmxT+dIDGE9G5Hp9KwVQT3ScQKC0FhUPxBHexosxGI\\u002fFqSvPtEIez993Ux7LKiSPzUgGoGRSqc\\u002faZyZ3LYtuj8KyPaxSHvKP4lzg5nZG9g\\u002fNjXesdjA4z8TkO9kviHtPwtBDptBVfM\\u002fGqEQChoY9z9cUMM\\u002fNNT4P615DkUaB\\u002fg\\u002fp2WyVc7v9D\\u002flFE4uhnHwP1h5jllGW+c\\u002f16jWtStP3j\\u002fDQmtawIjSP7woQcelNMc\\u002fHMHPCklIwT+XoZzW8\\u002fDAP1rF\\u002fXJKWMQ\\u002faTMgOWG2yj9T7RrJWCrSP43MKEncCtk\\u002fmzuCa8oj4T8ctzjnjL7mP+dKoBfboew\\u002fCrqmRHTW8D+aJzHNp1ryP5J8SiMAjPI\\u002f22hhRACa8T+wU9Kc5EnwP5rCpO0lwe8\\u002fEF0eywQD8j+gBviNvsP4P2XydAgGgwNAxqu4NjpGEED8+ahorDobQDUXtvn2GCZA8x+0zSb+MEDsyBHgy1Y4QAQxgeJ7CUBAec08B5xKQ0BUmGjMoxVFQFOCwEk+40RAdBqfmYq+QkCVtiWyF4E+QKqN0MbkkjZAwXl8bsCSLkBgQFT6+isjQOUi02y+xBZAcietl\\u002fWmCkBmrZCOy1gAQJnAezPbXvY\\u002fC\\u002f7n6zVc8T9FiavK7y3tP74KBs8i1Og\\u002ftGqXtPVQ5D\\u002fNikbzOQPfP0bWmO8euNU\\u002fqsBk5QKtyz\\u002f0tS9buvK\\u002fP95S4odHrrA\\u002f2n0wycKLnz9WL+fUxyGLPzikeJmyc3U\\u002f7gG9McHbXz9wd5BcJgtHP\\u002ftCZeKhADE\\u002fcMCNhwJjGj93vOIuHk0FP\\u002ftStx2\\u002fJfE+VTPgxyNc2j7h8B8KkMrCPkaM14aXdag+Bsf2lpTXjD5oHwEpNLNuPn1SQcHdck0+ZNJbBjJwKT400TzWDMgDPsImRqH8sNs9P8CyNLFysT2H7xgP8hGEPcquZs2qL2Q9RnIKTHaQZz+bYXveZc11P0tTqTSRiY8\\u002fDms7opvYpT9fYeEFhF+7P+Ll72sg4c4\\u002fau1\\u002fs\\u002flZ3z9AXwIcpKXsP0JSsEXIjvc\\u002f2C9sOFhvAUCOjNd30jkHQFHz7CeZ2AtAzlFWhEkMDkAW2BCewS8NQO2aVjiMhwlAw17wxHciBEBbgkJRE8D8P+k1wJjNz\\u002fI\\u002fShq3sDZm5z8NbLz9KFPePyYlVySmvdc\\u002fYO2cy4Ea2D\\u002fL6G71qQPdP\\u002fE4ydcgqeI\\u002fQcye+FWY6D+YDkmPvGXwP61VMyGl0PU\\u002fno8ge\\u002fpd\\u002fD9vI0rSqqUBQOIXHYtQqgRAT\\u002f7SNziPBkAYg4Oc4usGQIPnYH1A5AVASNKfHdpBBEDoQwgfQlQDQHlxS\\u002fFxsgRA1SwsHwsRCkCUxv7T+68SQCYyFrFnmRxA29OirBxFJkCAhzUo7B8xQGYCQLnzaDlANOqNgjjUQUCLpn0poElHQDWs4niV\\u002fktAE1ozxjvBTkBQY17eyMNOQLenACRz\\u002fktAEkCaZpM0R0CkzV0y9plBQFSxMsmypThAWL8A3M4uMEDo0cBkbnokQD\\u002fbTLF3ERpASNzZARqhEUC1e56X3mkKQI7Y\\u002f9KV1QVAq159qMn\\u002fAkBO2QthpXMAQOqa1quULvs\\u002frkrvBJTg9D+5CspaaWPtP3fIz8RXz+I\\u002f+QlHCRbR1T+XWbTN+ejGP+91paED1bU\\u002fNr5m5FsDoz9JsTdDVryOP6WW6TAUvHc\\u002fLq3lbmxJYj8jrb4yJVNNPzSk0Hh8uTg\\u002fZYcwZEFBJT\\u002fJA1PsAsoRP++jn95E8\\u002fs+neLxugAs5D6Ntz2C2XTKPrRpSiF4Xa8+3yWCwgPEkD7BEjmDKSVwPvPFpLoC\\u002f0s+bL\\u002fz2FvZJT55mulK37H+PfMa5G69aNM9ZPzP5z5opj3sHvY8QJ2GPd56kdnPYnk\\u002fq81G+zKPhz\\u002fgtBE5aRuhP4EyGL8+ybc\\u002f2O4pOuzozT8DoN5uLe7gP6TsJdcJQPE\\u002fAVQlgtGi\\u002fz++CxwVohsKQKUA+8cNZBNAD56xT0zsGUA2P8I1szAfQKxYOpJt4yBAbucvYLN2IED1ZwG7FekcQNJf3NXl5RZA+90By6ZwEECWogXtZrgFQBeluSOJjvs\\u002fJLHFEYaR8j8+44TX4qHuP9yCgzTMFfA\\u002fLP3ln8Zq8z\\u002faQMFSuIr4P\\u002fRTt3sxdP8\\u002fFqPlh7VTBEBQ\\u002f\\u002fS1G0wKQIjbn+CrvxBADAss99SWFECaCNUFFAMYQFXdtKNKRxpAAXeooH\\u002flGkCuw5CyZe0ZQC5U\\u002feU7HRhAy34kcwnCFkD64gJekmcXQD2wge2khxtAPffB5Cc5IkCgElGa9cUpQJhyJZ8YrDJAVBsw4rIKO0BOcpeRjjNDQFoX2mutOUpANT5lXvToUEDYraQfOUpUQKb61uvLbVZACG1nA8S3VkCL0LeipQxVQKrXFuuQ31FAlQe54t74S0Czh8j2a2NEQJHXnwWRMDxADgBtGOwEM0AZZ+NtFxQqQEEv\\u002fFlGACNALzhngsk+HkDFNNrSGwAaQFPeh9lpHxdAzCvPmqlDFECv8XCIq90QQPd7r3jcDApAi5nhcsxrAkBMdX3LGrD3P0Zu94GqnOs\\u002fcIjlbH0u3T9ZWMv8uhPMP0lNgjM027g\\u002fqFQxMQ2xpD8hYiCnb86QP4GrQXn35Hs\\u002foelJW+paaD+shjBGEBlWP1lgB6vL9UM\\u002fYBtflz4xMT9SYw8uqnIbP6r+MuHj\\u002fgM\\u002fcIHUq1pj6j4Krr6voW7PPlhVeFGr3rA+iT0F5npOkD4xfNQz62BsPqEJWkj\\u002fOUY+9y5QR\\u002fFVHz53B1OJaOLzPeYTvJUGCsc9DHe5gexUpz2nDffK1zGJP8USMLHic5c\\u002fp\\u002fj9+r4YsT\\u002f2aggBadvHP+SrL7biGt4\\u002f4I3pWdUZ8T9DfCuLPHwBQAPTGuISFxBAXYJXzZOmGkA6m6uyHd0jQIR0IDsipipAxflyMKsWMED2h+c\\u002fHnwxQAUDx+9sGzFAjDJhhvknLkAYbRmk3P0nQGF4\\u002fPZxVSFAnvULjaQpF0AEsprW+BgOQJta+LxnNwVA46HFzmN8AkDz31A\\u002fowwEQO6ZAIkJQwhAHunuD20vDkBkTm+mYdsSQNpN+vWeqxdAyOBKhyLNHUD4ebdzDpgiQGJuHaA9liZAiJABreBAKkC42t6KjdgsQPEHaowvzi1A4Dhs3WsULUCJtVRcTUorQGPM9QuDpClAqdpsTheaKUDH3Cjd0XwsQLfWvaUtnTFA1zLhFuY6N0Az+mbr\\u002f3U\\u002fQPiM8nbDbkVA55HjeCH+TEB90gVOdCtTQHUHc\\u002fSlU1hA7NBnNCUdXUCmqjt2iDdgQPL\\u002fzTbkr2BAAMY9BgShX0BTAxiLMqZbQAYZUUGqbVZAFgq228oSUUCq8jiFMddIQJg3BY6TvUFAQmSMBVzIOUCezy2CmckzQIQojRzPXzBA8\\u002f\\u002fbSkXcLEB+Vz9XoAsqQAebj\\u002fhmCCdAtWEKdHxKI0CtUWTcIfIdQOJB9t+7RRVA7nSHwYR7C0CtiHy5NxsAQHjvcu5CJ\\u002fE\\u002f2PEwDAe24D+wwaU95j\\u002fOP2h0S+7SMLo\\u002fDUENpriopj\\u002fn1EMJu2uUP5SMW\\u002fyYWIM\\u002feKQLKPqpcj+Z9fhjCYJhP0NjSGiPzk4\\u002ff3T7\\u002fXHjOD\\u002fwbC4PK0QiP+a\\u002fl7nTOwg\\u002fF5lAPp797D5sb4JRVz3PPgjSxzvGTq4+AE7CtJp3ij4Mvoerms1kPq5D1UrAbj0+ZD2tWya+Ej6PegZsm8vlPVjfszLnJcY9MUzTyT8Clz90q\\u002fylmHulP4KdLanJcb8\\u002fNcMJ5pcE1j\\u002f+6C1U3uHrPyK6bqjHyf8\\u002fAyk+lgBPEEC\\u002f7iGc0R4eQLOfjO5cCClAMC4hijq5MkDwAjGYPzU5QO8IgoqMiz5AsFJsNFCoQECWq55dk1tAQCmX7mo08zxAbQ6Fu8gkN0DcB58A3dcwQD4IPKaH0iZAzdRNRr+JHkBuqHMj8qwWQO4gnRhX6BRAp2PGmD5cF0DqfcmZmE4cQNY7WaavWSFASD5wEMosJUC7hsQKYt0pQMNk2Twyuy9AJwG6Uu5oM0C\\u002flWaSblE3QLSQUMlEDDtAb82WIqDkPUC\\u002fjIszaEA\\u002fQBYB7dyo7z5ACPZbf3VhPUCg2\\u002fn+OZ07QD7CK4ij+DpAoqBRACmoPEBjq4nBirNAQI6vN6yHuURA6eo8p+lySkA8YtPUFglRQLTb7h43+FVA6ykI5mEWXEDL5upItINhQLZavvL86GRApZ6nmM2DZ0Bn1FnAEqVoQPkKtWeG9GdAPAF4B4maZUBuIbCv5CtiQF2mBxV3z1xA\\u002fNTIzRfjVUDmWPfE3VNQQGZAZu+1s0hAM+XplSaYQ0Au47r9NZtAQCUFOtqtuz1A6vbJwZcbO0AmuvjdSiQ4QFNHe29NVDRAwjTXz1K0L0DkF4oyYZ8mQCiBofECXx1A0VYj6KJSEUDfOQKfqqACQBp2dXZVc\\u002fI\\u002fwK05boI04T8fU8xEzVrPP\\u002fiyyqY2O70\\u002fXyHSecqurD8sXbdHvTidP\\u002fNj7c5EmY0\\u002fn5cyy2WPfD9GK35fYIZpP2e7wB73z1Q\\u002fWcmD0rO8Pj+EEF7fNnwkP23jTy8Mmwg\\u002faXiQJjCd6j6ijDiZ6OnJPo\\u002f5DbnxtaY+B\\u002fo+qO7pgT5CKe2YMG9ZPo3Td4sHQTA+Hbz2Cmz4Aj5spgfSMVjjPVuQI7QnVaM\\u002fdi+esfAasj9OIjxS3prKPwkYNU02suI\\u002f5vw5H2bC9z+iI9fr5i4LQIHob+KO\\u002fRtAfAL07WDwKUBgkEJkNaI1QBbDlTMEPUBAcpp1cIPwRUBSy4KL461KQCZHzBUmNE1Avxc8lEDJTEBaiTSXZZRJQICNSyE\\u002fj0RAsorz4uQvPkDJV6wg3c80QBry1Jen4CxAs1svg8\\u002fAJkBA5wmqbTYmQHG\\u002fS9K0eylAhhvtBdjeLkAJouLhLKYyQLysuKNtQzZAiaNJgk+COkDI\\u002fOGgXr0\\u002fQM\\u002f7GR1tD0NAE4MQI+KtRkDfsRo3I01KQBwtUmfLTE1AJNWlfMUOT0CwGJ9YjkBPQKLVphyyGE5Anr5X6EpjTECUvh\\u002fUc0lLQNbTFS8l5ktAnNjzG8XhTkAtexIJXCtSQPR\\u002f7liTDFZAX2LnXL0XW0APAW+s18JgQCiPBQhPyWRAQq\\u002ffdSmEaUB2tbyxQmtuQB6niupVR3FAGtbhn8dyckBnexcIR2ByQPPvLbFMDXFA3bEbzxGYbUDBLQ21HjtoQE6FoFsyAGNAJZE9xvcuXUCh+V84Tp9WQHYs0ehFSVJAxD02NRlrT0CFA6YvlmVMQAqzXeABEkpATYPmhQFYR0ClILQxP79DQOYfnEo77T5AgEIys0cqNkAI6qgcT+4sQOlc0K5iMSFAPj9WnE+4EkAih6ns6fUCQJpawm\\u002fuZfI\\u002f5DVRmADe4T8ziwTj7hfSP0CO0IqFQcM\\u002fV8wZOiLYtD\\u002f9zh075+mlP8C8GjAdl5U\\u002fmtvaRmGGgz8FSjaukwlwP6BAkQdPz1c\\u002faRbQyZXfPz87CiorYTcjP0XlsuJ43AQ\\u002fVh3f551i5D6p3cp61+3BPvWTt3hqYpw+MN95gMk4dD4lpsiMJvBJPs483xavYh4+zD1jK48Y\\u002fz08KeVf9uOtP19V+KryE7w\\u002fe4uqWv+11D\\u002f6jyt2kDbtPyhnjzDBoAJAPe83iTpjFUA2F5ArvxkmQIFel\\u002fOdjTRA\\u002fihvMdMzQUB\\u002fRruhhOpJQP1zt2bekVFARYx5iTNxVUA2Vq8wpY5XQDmHx++HT1dA1tuyjKzOVEA4\\u002fjJo3NNQQOixNADi+UhA3q3BkQicQUB0y9cgFI85QC4W\\u002f\\u002fcLhjVA84OItPw4NkDHnDTZqBI6QEQ9yMP3gj9A5tXtzt7BQkAd6arxu+tFQJ+c6I4xeklAWxCkBh\\u002fRTUBMinBDmJtRQLYZTYR3ylRAW8sBjpYoWED46N7sYy9bQLwXRIksRF1AuiXkWEr4XUB8Th9grktdQLUDQIQRy1tAmbb7YhBsWkAkLIZiei5aQEkXqkTjtVtAsmKkGbAeX0BLYwHpmhhiQM1qwJUyaGVAm\\u002fc+iw+jaUB0ReoG\\u002fQlvQHgtq+pU0XJAn5AsyMZsdkDoA26NeLZ5QNEEq7XY5HtADXgdzvBYfEANGK+s4eV6QL4p94mG4ndAq4mWZJ7\\u002fc0CRkTTGxwBwQAT7zD\\u002fX\\u002f2hAfT9Ni5ShY0C1Jdg4ugNgQETSJ8jAsVtAO0\\u002fZsPknWUCCaXS9OjFXQO\\u002f0GKYz2FRASyqihA2yUUCYwPO74tBLQCkGSDu0BURADYLZUgBKOkCLsZQVSoovQFxJZ5PtdCFAfGKUSIU4EkBdt6jIm50CQCMkKwkfffM\\u002f6B5yUyJ85T+Ok4xZtZLYP1KY6zSc6Ms\\u002fGfe4w4cqvj+DekUGCS+uP\\u002fNLtQ+8i5s\\u002fq7exm2bDhj8bGd87B\\u002fpwP1+TkwNh0VY\\u002f9YM2c+qdOz\\u002f4hSqh6hYeP07MzKIJgv0+aMlhf3oL2j4NcBQWlbC0PuRKDoPflY0+aNZXrBwLYz4\\u002fEY4cLmU2PpdTxir3ARc+Cii40LVCtT9GDCfDRQjEPywdJoZQq90\\u002fp7fk0Pz\\u002f9D\\u002f9t3r8Z+AKQE6xVo669x5Abbrar4kOMEBOeS\\u002fdo\\u002fg9QKYKxfujLElAnbno\\u002fN0HU0Cn5SsKeeVZQNVaOdoJuF9AgY0ZBkJ9YUC+xuuMgGBhQFJ3S1QbLV9A6vE2ipVmWUDTox3EFh1TQD2g+iQBwEtA3IyYUQtJRUCDYlXRNUZDQNq9fwFJ+0RAWtao+gQQSUCe\\u002ffGJiSdOQGyIERjJrFFA\\u002fsiN\\u002fJU5VEBqYOQ\\u002fUfZWQGb6en+sTFpAD2K3bnWYXkCdMP6IbPZhQF4dF\\u002fWb9WRAsy8hwDveZ0CkojwPJCBqQDY9CVozPGtACT+FmhcHa0CeVwwK7dNpQOnfOSRcY2hAp4fEMGeVZ0Avp0rdDgloQFMALNpY6WlArix\\u002fmiYNbUBtXXusbKlwQEj0CW\\u002fEdXNAAytf+ikkd0DDpu6FQ857QEM4KU6kkIBAoshujfYfg0BX9I1w5\\u002fyEQMrzXnkTooVA49P4RpXXhEDlIBbgCMqCQA8lhgqq539A3RJDdCDVeUBxk+dPPlt0QD5syyt+E3BAUYnP4+ZOakAO5kWhPMhmQEubT2O7t2RAUENDyDQgY0BerMU8CDhhQJ3g3Jt4TF1AlLNYsDMYV0B6p\\u002fasorFQQNZGWktlEUZA6JN882TLOkA8Lqnf5VIuQMiNUmNJeCBAr39fNXjwEUBR55cytWYEQP79uESjYfg\\u002fN1TV032b7T9sGp27rnDhP9oNSYfCPdM\\u002fTUUJMEJ6wz\\u002fcPwC9nOexP\\u002fLs5sYpv50\\u002fl+xLAatHhj+f9vRgxA9uPw7m3M1GQlI\\u002fZBjE+hj3Mz\\u002ffUvwtMaYTP98hMzivZ\\u002fE+1UjYapjAyz6\\u002fDfcYS+qjPgn2qmF3vHk+eyoyfnllTj7qVTkFO18vPg4ihzdz1Ls\\u002fjbTnknhNyj\\u002f7OR0fIY7jPxbKQ7JryPs\\u002foFB1E6bXEUANp5+Hu6EkQEaCBgzGeDVAL1wPFnscREAPY4baB\\u002fRQQDFkTPJ4uVlAyxntzf6QYUB4nXLAnJhlQJLWdzKh6GdAKhUzjB7cZ0D5qxiawYZlQFrKUtWps2FAmpjBehwiW0DKgpYVp3ZUQBDCMzT6x1BANdlx6SpmUEBhf0C05LlSQDGRcFPqqlZA6S1upP4XW0BG2P+7eEBfQJOC6kEAgWFApV5VgclqY0DMdqN7mMdlQGy2gjKv\\u002fWhA9HDiJYw8bUA0ZO5OjilxQChRnJ1RznNA+jXaxC0NdkDauaMElmR3QK6mtLPljndAk5V3iECydkADzjdS7111QLGVrQ5uTXRA+23fxsMTdEBnKlBgb+d0QGOXCoUDs3ZAfjnQ3bVbeUCeZp12EAJ9QFNwynTZ\\u002foBAOPrf9GtChEC5gDk52BqIQDPvcmbe7otAfcsCqLrejkCCsEh58AyQQMW2C5wYOY9AvwHpWV5pjEConDpmX1OIQJsz4M571INA62AgK55lf0D0WjUvuNh4QO9GbmKcUnRAQmpZye6PcUCREZa0ydtvQMz1uBxFWW1AL51Fq8xkakAvQAPobHVmQAxXHnKru2FAbyd\\u002fMyK9WUChe22mACZRQK\\u002fiViP1JUVAfmrUafumOEDeinJbVzEsQE8pv\\u002fwwjSBAf7u1c8V7FEC+tlJZ0kkKQJUVDhsTvABAj9yQ\\u002fJVC9D\\u002fYCBEdzbHmPxRs7L8oLtc\\u002fNaDbP9JvxT+EpU8Y5eOxP6kJGAI16Jo\\u002fXPjTTHE4gj8btrrtxzZmPxDkZkeIYEg\\u002fjjNbocgTKD+wAyLSm2cFP\\u002fyX8knjIOE+nk+T2fCtuD5lvN2vCgSQPtmIqYGwBGM+YkdGmfXBQz78w8LeRMPAPzFYtja4yM8\\u002f83xQc5+55z8nyFhlXOoAQPPWUuwdzhVAStxsQjJOKUBhCluveG46QMGzxfff2EhAU9wPkb0FVUBV7y3nMQJgQODAxoEW8mVANKHldLYVa0C0rsQiSBxuQP3U1wSIMm5AvfvFBupua0Bw85jEeNVmQBB9VFQa8GFALDaM4J9vXEAy9apO5DVZQDL4LW3MlVpA1F6YCsKgX0BDaH+3pk9jQKjyJ7+\\u002f42ZAEpK7GDr2aUDIFdl+6nVsQDqUo12I125ARcThmknxcEBfnNxkzDBzQO+tSPGqZnZA79+lK0OAekAvWmNGRwR\\u002fQKPaGacqj4FAPToXIDzwgkBUU2zr9FaDQGDGVW7Ay4JA7DNtnKazgUATwhJWLaOAQGZG\\u002fSuXGIBAr55S90dMgEA3ODciMzeBQOJ45j9qxoJAE1nHJpIOhUBoit3te06IQDzYRNEbs4xAwNEc6\\u002fQAkUDs4yvquLWTQKucniFh2pVANa5RGJrWlkDbr+ZoJFiWQCHmRgz4dJRAV0UyuRiekUD103ZqsN6MQMpqJx7p8IZAlU59rjAvgkCboc5idrR9QNvGN2gxjXlA+ZnnexQGd0CO46MbVhJ1QAirgTrK2nJAsKVqOen+b0CFuCkDTEJpQJQpoDQ+ZWJAvwgyPFy\\u002fWEANWL1HeyZPQM7F0tEL4EJAg778u9L\\u002fNkClaUjnn1EtQOnNICWlpiNA+1jmJubAGkD4QHSxj6cRQI1CBqDkzgVAIijo+p61+D9WJEyacGvpPyZ7Nr8lodc\\u002ftNYQ6uTNwz8Ufpl7meatPwfDrMSBUpQ\\u002fEIQ2H7jdeD8t3LmD0mJbPxDoiSr2JTs\\u002fGl16q+s5GD\\u002fXQV8t\\u002fnbzPrP8IUqfLMw+7555xDVjoj51a3FGEAR2Pp4hye7YIlc+B1V+pd2Wwj8giUz\\u002f8a3RPwM5+EJUgOo\\u002fbD5nhTn3AkCy+LD07IkYQFyt8+CAlSxAxeChyMj3PUCUaMCdLEdMQN1ifwg5BFhAXWwpYgJcYkCws20Ds0RpQE1E6DPhUG9Ak96XEvl8cUAUFHPvIqRxQFXWV2Q4K3BA8UQ7xLZca0CbHlhCfTtmQIy0Hs01xmJAx75s6oUmYkBBZeFu+o5kQPv3k1tkR2lAfXY8amoDb0B\\u002fjTOqJTRyQB8CoUbsS3RAD\\u002fLmwn\\u002fFdUDIYW89nAp3QMAtzsUzynhAXWxMRrG5e0B7iq6n2CiAQNiCoCp7R4NAtQOfI9jihkC04gzculSKQJwssuJ41oxARmPAmdvVjUCDhYmuOESNQLWuqMu0qItAKw6HXIngiUADLSECI7OIQL8YIhP\\u002fgohA1TTfKmtPiUD0ISJgIf2KQABxqxEGpI1Ac6Jc4vjMkED6qw88J5KTQFtkdoITAZdAijCOUHyUmkA3k8HN83qdQMYTgyw75J5ATgYcQ1xZnkBDhAW9yO2bQO8tkPp8MZhAZzVgmVXyk0BwM0DiruOPQMGYsnOJYYlACKvpsCq6hECxCcQVjriBQJ3eDlVDl39AYHOcSnOPfEAy5hbBJkl5QDlA3qxBTHVA7luJbfLCcEB2G78\\u002fNHpoQLY4zaTvp2BAjIV+gO6HVUD\\u002fpqjn8GlLQNjxVAJx+kFAZUN1QMvoOEDJVJSSkugxQLxRADoaiilAnWAQ1UlPIUCB12m+9LMVQLbQqA3bzghAFgMWTUCs+T+JIa7xlPnnP+RIhBKsLNQ\\u002fpmFjVP6Svj+uPCo9I9ukP\\u002fOhVGS7nIk\\u002f9bPtoPxPbD+vM+2u4yxMPweqMoGqPyk\\u002f5x+0KsRiBD8LQQgRdLHdPnDS6x8xjrM+e\\u002ffd69HHhz5PSZbjtJZpPlPgb+Hl\\u002fMI\\u002fky14kSQe0j\\u002fAUDC180XrP9nFhhwwmANA7N9JJ2dzGUAJrTNCN8MtQKK3lu3IUz9AdK7Iwf2tTUB3OY89ZE9ZQAaUqtz5bWNAbLmDv7vbakBmfnFK0LhwQKVYQ6+Lx3JA1PyABn8Vc0D6ywegybNxQLDUAViKp25AWdQhQO4SakCSFqvoHstnQLt\\u002fcFIBK2lAc+TIGXZXbkBe9JxhIh5zQD\\u002fnorz7endAqPjMdnRHe0DpOi3AEOh9QKMDJv99Zn9A11iVJEk6gEDsaG8jiReBQNW27QdF34JAA48p0xf5hUDZcTSNPXCKQKbaQHPpz49AlwAiV2qSkkD+e5Zk7J+UQBHNdfVwmZVAr5Lw62dllUDMfY56p1mUQK2ZDYhgEJNA16Wb+NsZkkDtYYUXN8CRQNdyhF++BJJAax5Z1MDRkkCxBAziCDOUQOTugKPeYZZAP2CObsSTmUA1OsFFKaydQH3ksPVsBqFAf4zsXpXYokDTLqdqMsejQE5IMCFPgqNABEewpyIQokBVD+H3RIyfQGAnVpj1RJpAa5dYXXE6lUC8L09ujw2RQKo5Q9L98YtAxGcDuW6+h0Bldu1s+tiEQEPiDLDJfYJAWsJZ61cUgEByu7VnEbh6QIGMnlHi3nRA\\u002fV1rCZ2BbkCLMAId0QtlQF1q4YTWI1xAWcjmBPsDU0DF9aucbwFLQK3sPEGnOURA\\u002fse+g6nOPkB4kfP2a8A2QFfoJ9yMcy9AKvDIwynwI0Bg7F42g\\u002fMWQBvW0Otk3wdA6n0QsmNj9j9jPbMZfOniP3sD75V3xMw\\u002fzzmqDDKysz8vDzi8c0eYP68F4xge8no\\u002fRSO71SDvWj\\u002fJRUqx1kM4P0tR0VcyuxM\\u002fePOZgS0M7T6WXaFXnXjDPpObPUcgeZg+jEm8\\u002fzi0ez5VxlILKuLBP94EOcCjH9E\\u002fp\\u002fqhbLrk6T91L9\\u002fWeq4CQAkw2RgOXhhAn24way6eLEC6RlF2H0E+QG74pFK1ykxAGSosyqWqWEDeopd94gZjQP3+PC7db2pA93NHMOqNcEDeTWn3B7lyQEgTrIf4N3NA7kdEG3gickByMBXeCTlwQC2Mj0AYUm1AEM0XbM4\\u002fbUAiGOAO1dpwQCVCJAVCW3VA\\u002ftlHmD9ae0BS\\u002fFZ70sOAQDWcOXEmRYNA3EhVyRvKhEAN56ajcGOFQOBM87tRm4VAmUCggGpIhkA8LIi7EEmIQM62s6aXO4xArag5YgIekUCicYEzJ9mUQKfk9NUZqZhAcs9ohbK5m0Cn3Dbq1V2dQNzMXgO\\u002fZJ1AhUjfujM2nECJiRtDzpqaQE2246CAUZlAn0UXQxS7mEDgrvEmyNOYQEJ+uHVVdplAer4gCiermkCepXSkLr6cQDRM5v+JAaBA8j\\u002fuzxc4okDl13qZaq6kQJtsQpjdzaZAqeA70kX2p0C\\u002fttOiYsKnQO246Nj\\u002fMKZArPiZR62eo0ClrllZKJmgQF70tVG0UJtAiwCn8oROlkAn7FE7H2+SQANTAqU+LI9A6VBl2IPfikB3tmPq\\u002fDiHQEOOHn2+qYNAMsV\\u002f4Ez3f0Aeg5wDE6F4QKwj5+1c93FAccb8VvIuaUDw2PBoZ4lhQE8bUziwWVlAmQ7kQkJ7U0AhyxymSDdPQKTVQdTZ3EhA4mT5JjPXQkAqyhsZ3Ws6QBbs5n6N5DBA4mgXL+SOI0ACEp9MhW8UQDX+CjPTPQNAnA6pSsNQ8D\\u002fOHYP1OunYP7qyPEMkH8E\\u002f1qDLnWwxpT\\u002fLlUu6waGHP1WMAU\\u002fEwmc\\u002fzcEhoguVRT+zuMNZXMYhP4VD3nGjvPo+7\\u002fJEpGSd0j46CfAn9RipPuEb5oSyWI8+7+DNQAYgvz+cOnw9SOvNPzgKI3h7vOY\\u002fLigVDnd7AEBUgM\\u002fyYpoVQJ\\u002fgQcJ+fylAXktq6GEYO0Crlk0keuxJQGzqnJ+XVVZAIlFLg8FUYUCgVyicNj1oQNCMgTe4lm5AsrjOsf93cUBdLUc2KzJyQEpIJ35dm3FAM6hTqHR9cEDeVE\\u002fVzxNwQDzp1s\\u002f8m3FAHAfa+k\\u002fXdUAuOHRYu6p8QIDqWpzvgoJABLvvZZSWhkA2uqL44K+JQECgrKtZTotAAZtjTGmUi0DIAb79V0iLQA4BD3PDlYtAQ5NvxIGvjUBAsC5BFziRQC491PunApVA9RA\\u002fht\\u002fYmUCetH5rKueeQO7TiZopjqFArvMOqGrKokAD9jS17QOjQE1dVSSWdqJAWHuVCwGcoUC2MMvAkOegQLBlh5iyj6BAi0z1OIKJoECjeXzA8LSgQF2J\\u002fvBXEqFAbkazDwnUoUD3dmJMDjujQAjMQWKTV6VAcAwgVmLcp0D2jTRcDCmqQG7eh3L+iKtAFsmI3maCq0BdNDuALweqQKXQghkrcKdAuuMib6VMpEAJ3nE3fSahQFlQLj0espxAXXM3WisNmEC1qMDO0UmUQI6fy0WkJpFAVPlO0PW6jEDznqNyFn+HQLOO95WPhIJATvdpAZbue0AmNK8DrUB0QL+puJsC3mxAYKABFFALZUAtFURLFE5gQBtbgEga7FpAry3ecRHEVkDrQlysmr1SQLTwdk4+7kxAfgncXcF\\u002fREAIY7kPA2M6QPswN\\u002fcWsy5AMGDW2NkaIEDFhyfv5HEOQLVy2R0H6vk\\u002fPiPumLPc4z9TzQ1K\\u002fGvLP3A9EkjmDrE\\u002fmMBntswlkz\\u002fqnNI6NHBzP6UXlLFg7lE\\u002fPucY8HhULj+ji2Sq2OQHP8A4jDRE\\u002f+E+GTInTQ6Zuz7TJSkedvejPjEEphOXH7k\\u002fPHff40hDyD9SU1GsFo3iPykB94vnDfs\\u002fENemkSjWEUDWB457RS8lQFBu8Kn5qDZA5mhKfs\\u002fURUAYOBgdzvJSQOQ4SIxTpV1Az\\u002ft8JNjrZEBkyBqYn7FqQIyrzYt78G5A0bEvm3l5cECinSIjVIpwQDMM+HM1eXBADWcyVLOEcUBbLsWgsOx0QHTi6pHmaHtAb3kMmmtbgkDfN\\u002fXLPbuHQBwdyBo5wIxAR2UP2FgskEAwhb2Pp\\u002feQQGM83wbS3pBAcGkKmoBlkEAZAvTXW02QQKfRbOmrWZFAuqO+k9oTlECE3UrjV5mYQO5bmfSneZ5AZ9ygYqJcokAqf5GSxwmlQG4j8EMwvKZAhc+DVNBFp0ACBRuV5OimQPH7aYHBLqZAMzJjl2mWpUDQ3r\\u002fJ5lKlQCkXzcJeR6VAnCO278A\\u002fpUAGi7dEWDalQCivZxVwbaVADA7Gb9ZGpkCN6QI+uPWnQH6bCSvOQ6pAG4oUwDqVrEDmgkc7riquQDcvHLIreK5A129xGptcrUDS497mFB+rQHe4KSNvPahAffNbkiAwpUDVGKGnfESiQJqm2Z\\u002fYL59AaL+9HhlSmkCOD4LaUNmVQDDtcl\\u002fDtpFA4qFfbOfai0DR2gURMyKFQBBP7GoI+X5A9E4Zjrc1dkCCcP+LcxRwQKFdPlg1jmhA46+lO3xJZEBlaim9tsFhQLwqrXB5Pl9AtUXMyeNPWkAtlNSUEZFUQNlTDAMyXU1AlCHL\\u002fC3\\u002fQkCR\\u002f+Pw5TE2QGJwydadYCdAfPipk7kuFkD9SZ2nkvUCQJf68s2vMu0\\u002fB40CuRZF1D\\u002fzKb\\u002fZgWm5Py4F4TAQ25w\\u002fDSYz9fTafT8ec1B0uHVcPyWyo3yweTk\\u002fTpg555cFFj+QmdQpQ\\u002f\\u002fyPjZjqdGjWNE+tS1OAWsovT7HDRu6t\\u002fGyPwUu1Sv3Z8I\\u002fmOlhvzFf3D+1Yke4S9n0P+8UehhztgtAi1BtyASZIECx0Fx0J+oxQGH385BUbUFAM4h+AgeSTkAWlvhsvjFYQMz2ZpwbTmFAqbhTmdxyZkDnrDlQVZtqQJTQVHPMSW1ABlSVie30bkD2mXn1U51wQOek0ZQnRXNABY5xmyKleEAvU7kH7K2AQBcIoqQVe4ZATa8kkfLjjEB9ZhVHzlORQGflvaTkR5NA6vKLKLT+k0CVCuMXAaOTQEnmBEE915JAzvF0OJWDkkBGg8uVno2TQL\\u002f4wSsik5ZAse6sG32zm0D8pz4gQjehQMOWrGI81qRAoKMVT0EEqEADsQ7ykSyqQAFW86uZHKtA7t\\u002fvAcMaq0AR0dM+XLeqQMkbsefycKpAjGMA9EprqkDd3paDOG6qQKH0Wez2K6pA\\u002fW7ljGmXqUA8HQrfcwapQLy+lEx3BKlA9vhJQe70qUDDcNDVWMirQH3wysTI9q1AbT1fIIbBr0BsrDp6OkewQBioXZMdErBAVu0xJb6prkBLHvLQcXasQKQze+FY26lAcMDGIA0Hp0AYffcU6QukQPtLIfbO9qBAhKwI4y\\u002fAm0AVkrhpatmVQGlOEFvCiJBASqqRc6sbiEA6d3LsFBmBQPyjKLjBJ3hAL1RN34+1cUAa5nDMmydsQLpf\\u002fRvzeGhAeKcy6SVRZkAyIm5qmSJkQH5ccT7FMGFAT+3CuxcUW0ATtd2i52xTQDNFWKFtO0lAytOwUZuWPUDGKV2mt0gvQCyXwICwzx1Acy8wJ5CbCUDXOy49cdjzP8vLl\\u002fIqzts\\u002fI+l97FmtwT8hXDLdJY2kP66y32NfJYY\\u002fvMxzqeKdZj8yzdiooY5GP5Q6HiLFmiY\\u002flE7bXkENBz98yVpNVa7oPlGvi3uV\\u002f9Y+Ks1yFtAFqz+OWUkdrnm6PyvPM2bUodQ\\u002fS94fSgCn7j86mT3lmpoEQG3FgZmi+hhAdwnLgVRPK0BbSZNt5u86QCS+Mdia\\u002fEdAJ7emdvJOU0AEycl9MCxcQA+un1HyuWJA6W5BuDjuZkCPHMJ5cW1qQAoD16aDyG1AacU5EVtLcUB+mPVMcJJ1QACSfLZFz3xAluTMk\\u002fm6g0DgpXyB5W2KQDyzEk+LxZBA2Ht8My7bk0CEYxxj5dWVQC7sg0I+apZABuxoMgzTlUCzpIRvjMuUQGOL+iJmVpRAS+FSxBVtlUB6Rt56B7WYQHYM4Hu+SJ5AyAG8pfDNokBzPwjTPMWmQJGBzDZlVKpAKgDFEJXmrEAumXFrXkuuQNkeCPDEyq5A7cX8voDxrkBQYfeb\\u002fC+vQE4Xh2YLj69AV5cIXCa0r0AbKh8HOjWvQLBcKtU6\\u002fq1AqaziuXJ7rEDxf8OGVWerQIl1CzV6XqtAJXjSSfeDrECI8L4rvnCuQOlyXTbPOLBAV2PiCOnxsEB6QoHmgjmxQOl+VbEeDbFAH9t7BfZ7sEDq4eEREyGvQKeXLYutlaxA+4totiFXqUBRatVtAoalQK8Q7CmMa6FAOfo9aTjQmkBDPVOq96mTQBJT\\u002fRups4tAFbEMq4cTg0AbSTJeUJB6QE6gdlt9pHNALiOa7dgNcEDAQYesp8VsQBKCuLMXyWpAPsvVROpraEBdGXulIvNkQAVxmgD8imBA+xQftqrJV0CngFsAnfpOQEs8ZkQrOUJALRh3JTFZM0Dek8s9yYkiQPLNS6YwCxBAYhzWBbkk+T9W4605PO3hP90iZOP7ecc\\u002fFdaM3CazrD\\u002fZc7ck3sqQP39i+IywZHM\\u002fUYKN4XmfVj\\u002fgu5TMW686P\\u002f0eMGv0Oh8\\u002fi1k1usJ8Aj92Duu4iS7yPm1nFBmml6I\\u002fAxDtIoNusj+MRYDHZi3NPyXTI681AeY\\u002fsiZG5OgN\\u002fj\\u002fJ\\u002fgOBp4YSQIL31IGQniRARnVqkUu7NEBsnVy859lCQG0GU892EU9ARWx69slKV0C0QuZG+QFgQGMb+cmqdWRABlNRM+nuaEDaC8+yLhVuQDGJHxTYt3JAET45OAmJeEBBuCQfI7KAQLH+DaaMwIZAu6IdsG78jUAv9nIOF66SQD6KSuBqwJVAKBsObH+bl0Ak4mJ\\u002fLgOYQK4yf0gYRJdAh0UBWC8plkCXB54o376VQE6EYfe6\\u002f5ZAhwU55mmJmkBYdPSpITWgQAsD09EZB6RAE+8fXj4rqECHTr4qfvWrQP8lxlvP3a5A1FKYDkResEAV6dukKeywQLYJjDzHV7FAkDr439nLsUDX63a\\u002f4TqyQCVk+cgrZbJA4G9y14kIskDJnIgd7RixQJBEuaSvsK9A119IZ1Z3rUATFL1w71ysQJcojhu1u6xA4FkM7R5VrkCC7cldEEWwQGCPXPLLWrFA2xVMF5I0skB7vWB377ayQBGoQiSXzrJApQmlIN1gskB4nzLZw1CxQH8PA3cFKa9A04owULWQqkDaal9UPlylQHure7hKMaBAZJ6jItxBl0C9tGH7YgSQQNOQvoihq4VADOS+fMn8fUC5acT6fVV2QAUFn0vyenJANWfvp3WqcECkuQ8O0\\u002fluQIQ4lM1MFmxAt\\u002f6SLpT3Z0B7n\\u002fbJztxiQHqyri2EGFtAw86nmSeqUUC4Y5mJAtpEQKOG47rHQzZA0RQK5XSFJUC3uiI4ReISQK4N0OkbQ\\u002f4\\u002f6LbNvFhk5j8eXJ3l4i7PP7t2ZWjp\\u002fbQ\\u002fv8fvjMMfnD87Ii+0EA6DP3xCaV1h62k\\u002fzFrLC9U+UT8SR8bw\\u002fOA1Py8i3lhDPhs\\u002fzw3Tltx4Cz\\u002fGUt7iDFeZP+8Oc4ndhak\\u002fA\\u002f1FQEKfxD8LHJreyrrfP6F7mqcuHvY\\u002fD6M2Z0HcC0BnbLCxRLgfQPSKBJ8jVjBAwLTQV8l\\u002fPkAw7R4ctuBJQDMTr93FEFRATenCxHi2XEAmPoT4JEVjQH5LgRJr4mhADPtWgKvqb0CZudbGHuR0QElIPrY2HnxAA6wmAw0kg0DEwadMgqaJQGWhgAZ7gpBA17HVm1IXlEDngGkzGO6WQKSA8L7shJhAsFSnhBq6mEA\\u002fvyF0Fe2XQD4aWmcx7pZA9nm5fJrBlkBsQMwSik+YQO5d\\u002fr4CH5xABGdYGRUWoUDo+j4YL\\u002fCkQPIPYMohGalAaH6\\u002fCCz6rEC9WFGVYhGwQONeupyJPLFA723rxpofskB\\u002fVzFE2u2yQG2JTHuQv7NAciA57QhztEBMCPZidre0QMf70\\u002ffaQLRAIgb\\u002fq5oDs0BFsiFYTk2xQKPk2UFlT69AlbMbymowrUBbMCpPK8usQKFrbnygCq5A\\u002f1IrT2s0sEC2A43\\u002fR6KxQGZ+\\u002fkwFDbNADO0c+dlAtEDwvmcG1Aa1QDQRUakYHbVA4HEWfw9ItEDPwR4cJHOyQEBfR4QPj69Ae+G1ES5LqUCK0sKVsAajQDmvNjS7EZtAE2JEFtaCkkC7ewp9lQyJQJBQmTYufoFA\\u002fZmbTz1aekCjTWAUOc51QBTHOxF2WnNATnYmI1+OcUDpvHg7UBxvQInpFydWFmpAd66eV5NOZEBsa5tYmgVdQJ8HoVt561JAyr3wBnxwRkCE0SLY5TM4QHaWot59zSdAvX3bYgx9FUCMhRKp+wgCQNmLOvKBuuw\\u002fs4sqAA9P1j8iwJuGy1DBP+KIArtVGqs\\u002fa01hzOIYlT\\u002fLVd2Dw8x\\u002fP7AlO9xbmWY\\u002f59R23jTQTT+MnCLprwAzP3+3sKogZCM\\u002fx7YGaZmNkT+w86tmfAWiP6\\u002fSg2kB170\\u002fJF\\u002fVokx+1z+NPLgNiMTwPwdWJPIApQVAuPGBQJlIGUAcBl4EpMMqQDeMmZQevDlAG+uM+hWRRkBWf6uI7ypSQIfjtjKhJltAY0ZDe4klY0AEF37Jng9qQArQSx5rh3FA8ivkm0+vd0DYt5hs0hSAQCKPs6tZpYVAjMlKdm1UjECH77UI3rqRQHg1vXS9\\u002f5RA3MRA95Rsl0APMJPw5aOYQAmlyGyLpphAh3HvGkPml0BbQWNL3y6XQFJ0Fm1pbJdAON8Q1XVkmUDOLpEl9nudQIQ7kkfHzKFAM4zcqNOUpUBw\\u002f1jMo6KpQCzANIUKfa1A5k8gLgVpsECI7E7lIsyxQBbr5HAZBbNAMbEQgMY3tEBNzi0MCme1QFw8asn8XLZA+b8XujC7tkC3dKQHtTC2QBH7SqBHtbRAe35J5sCgskAB4vYJaIywQNpEFjRJIa5AytTWyWUQrUDvV7pWOfWtQC9biJjTMbBA+4t3NcPhsUBHbwtIpsGzQIK5tXCxibVAvywC887jtkDNG1smS2u3QOXVg7d6ybZAJ6ozsNPgtEBDoFh73eaxQHBtRqiurqxAGN8keQyNpUAr8X8pDbeeQGyQCY42M5VAWpjGyltHjUCGZHXTWQaFQBkS1r\\u002fRKIBATGYOweahekAkzj1sHPR2QLYhSCV3+nNAFr2\\u002fWO0CcUCcgRi4dKlrQP9wmTmDG2VAUKNWFQDiXUCJZAkhY3xTQDw1ZqXVVkdA4IhZoSG0OUDuTze0DzEqQKq+ERNIAxlAMMkM9sPXBkD99BEmSHH0P5kQGrg6RuI\\u002f4EHKEMtb0D9E4c2+Zdq8P\\u002fSSjLd5cKg\\u002fgsFKyIJrkz+3k0bBanl8P6dZNceMIGM\\u002fXsBt2UKrSD+hUI7lRFY5P1DiDKRGC4k\\u002f+jQ77TI8mj+r3K8\\u002fS0a2PwtYmf\\u002fS9dE\\u002ftdvc1+FE6j\\u002fiwxRrGGQBQPFJ4FWZ3hRAlOeenC++JkCeIOlNx5M2QGv6YPnjg0RAq4D8WW0wUUC5q+0EkdlaQN7W5HY71mNAU\\u002fhPj582bEDE+9R0\\u002f59zQJuThFff8npAEy9nJhY9gkA6AWjo7xiIQBR7h+iGr45A2529rmqhkkBc6TJ7lW+VQIAwEEAVVpdA9CN0ysYimEDLvWllyfuXQD5BGKEHYpdAjBQUr0cUl0BWomY47tqXQBj7LDMuTppAzqW203aqnkB0DJF+TmCiQGy1YVO6AqZAz7OK\\u002fJLeqUAMiSy1tZytQJhZyX5ThrBAdhNHhecaskAwRYNaQKSzQIzEdzwZNbVAkX5\\u002f\\u002fWK6tkC1V\\u002f\\u002fpNey3QNcpGFtZY7hAuGXdpynPt0AB1TFzLC22QEwVN0Tz3LNAGNUmZzaAsUC6hfVEYHKvQNLOY+vT3K1ALsKn4oRorkCIhg8T7V+wQEhiw\\u002fCEK7JAg6YcUwNMtEAPhMFtEG22QNU0HKo5I7hAw5+OU37xuEA5HcChxG64QFyKf1h0eLZATlUAhxpOs0BzGfLZT\\u002f6uQBHlmYCxZ6dAV\\u002f8K81HjoECl0DWFquaXQCKPqbqJKJFAfiDIUz+\\u002fiUDL\\u002fOVgQWKEQELNqdCZy4BAi+FjYfQbfEBI39Lyk1t3QJgfUoPF8XJANMhF8+KVbUCsVtUDZftlQHUIBiY9yF5Ard8pthctVEBAV+YI07NIQMvLL2L0VjxA5+ErntDELkDhQCEEQxEgQPaG2RpUcxBA9Z9+a+C6AEBrkVfhqOLwP6Mtin27p+A\\u002f6uLXHYhozz9ak\\u002fEC87u7P9dT9HmWlqY\\u002fshkjbw7OkD9t2cmNz8l2P7Bwj80vmF0\\u002fXKzdGW+HTj9gBXpa4UiCP+VZzQxGh5M\\u002fBdIOiO78sD+LyRn09wvMP1rExQXYBeU\\u002fLYYG3TeV\\u002fD9BsKYzJacRQBn25kBw3CNAg5Arb1NtNEAVpDKRcE1DQF3HpbPd31BA\\u002ffZlQYmLW0BpgjYcPDllQIPAX2l1Pm9AWirTPpwxdkAhxPHkAJl+QN31W2NedYRA7Ynl1jJlikBYsapqdE+QQGCQ239qMZNA+JepiiZylUBX19+iD8uWQCf+dgkpNpdAW2pmXUv7lkBzSAvXWaSWQNtdhxrz25ZAdMRcWzpAmEB8WSKFLzebQG8A6hv10p9A0j8ElCHookCdZVAmNFimQPqTMn9T86lAjK+ggDSFrUA6uyvdEn+wQJ09YEZaOrJAtBOtkuQGtEA2a4xlpOa1QCCsd1jGsrdAXpUI9rQVuUCvXNpWD6W5QJlf84A\\u002fFblAeMXJtdFqt0Ct7UZ4Twm1QAV3fJK+krJAPziRifyosEBIsKUtummvQAcGn9JXoa9An0n7aHjZsECubxvtDpCyQCmF4vECrLRAqldp+gLWtkDpd2876Jq4QG95noaTdLlAibodsd7zuECgbwqLZva2QEvCV0f2wbNA362dVzDdr0Bqg1DADlWoQLaaX4d99qFA33JbrX1umkDI77ijRQGUQEwm64tXqo9ANGuReL76iUBztDHSTZGFQNKjPV4EtYFAxoEfL2JdfEAseB4Vtgl2QDj7tmHPkXBANdHCDkgLaECRedu3VcNgQG7mt01nXVZANRYAMjmBTECWkoz3mWhBQDpU9o\\u002fwjDRAIyRhJse0J0BQx4HR8u4aQFIS0ChcGQ5AuxU1Ya1ZAEAgrckoIffwP98YTQ31g+A\\u002fKCwzite8zT\\u002fF42MK2oK4P19cQB8uYaI\\u002fJz70cHsRiT+0t+D5KlxwPxjJ\\u002fWfR72A\\u002f\\u002f\\u002fnbMQbfej\\u002fYORIGmUCNP8tD3W\\u002fxFKo\\u002f6SCnPVATxj\\u002fcgp9SWQHhP7cUzLL31Pc\\u002fOisA5T9yDkAWfdRCNcchQJcm7tJRCzNAlwfDAqLKQkAVh5nC+CpRQBYb9aj0OF1ARtgopqdUZ0AJD7\\u002f0o5lxQACaDwMIQXlAP+YgDB9PgUDm5pXL+LSGQKEtJN9Qe4xACPWzDFwKkUBAMgzT+WqTQBRRAYEdFpVADEL0ef\\u002ftlUDUnKMDshSWQCU3AQgp65VA73KvV3z7lUCjEJJTNNaWQIxHS7mM7JhA8NMo4\\u002fZxnECTJ6V+qaegQMfEkxuNl6NA2s3Hqk\\u002fQpkAwu8Y8uiKqQD\\u002fpoC4We61ALb0I6XRzsEDuGNxyfkSyQCG5dK97PbRAaPSabQdStkCwn9yjXUy4QA7lntqUz7lAk0gqu1l0ukAlNT0nHPi5QPAN4ktgZrhAOJ1iPfMhtkB6nVPutMSzQFTg+U3t5bFAWr0x0ODmsEDXrnPE89+wQCZ2bcZesbFAYuin0QUis0Dgx9ysofC0QJcI\\u002fk5BzbZAGMM\\u002fr2BMuEAGCFjf3e+4QFNV2npAULhAgrTdRNRQtkAzd00VATmzQH1d3gMYOq9ASti9CJ1EqEBNM++ODoSiQGkijwYAqJxAiXvhV+sNl0DB3208DUyTQHdv7w3FbpBAIKVxMeOti0BxsIMf35qGQAbnPcvexIFALpEKynXyekCLE75ArNhzQLF+9OVZk2xAn3X5Zz4nZECx3ysNlMlbQFu\\u002fnqrIqFJA5rfYk49fSECho6l4FgU\\u002fQF\\u002fNy\\u002fb9SDNAgNqH1appJ0Ax8o2\\u002fHI8bQIsrrZ35DQ9A8y2XnfR\\u002fAECkgGd4dE7wPzj25bVIpt0\\u002fZL5LUsyayD\\u002fiL09krouyP8IfSWYTaJk\\u002fur89AxqngD+r15JCuEtxP1JEiKaYqnM\\u002fsF7hBqPfhT8aKPHr\\u002fRCkP4GZxERSgME\\u002fWUwsOrbg2z9dHDmXR0X0P4fTxWny9gpAPZno3aFwIEDsqxUQpmoyQEeOY+Og\\u002f0JAMUMxxJ8YUkA5q2qo1e1fQLtWKPVxL2pAauE1OZkMdEDGFRveaMx8QHiCrvuXfYNABXq2whf2iECFuZFKqFaOQNP+sN3yiZFAjq80hcJXk0B5zQn\\u002f1HGUQO6j1OQA5ZRAdG0xWsT0lEAZQT9tIxCVQJ0PicG3t5VASqVeaiJdl0BluIW3fkSaQOqQSiF8cJ5AAd5\\u002f4iHRoUAvPGgILrmkQCRxVAk+vqdA6nNSU97FqkB9ED11lditQE1aa7PrjbBAbv6XnY5dskBEcXCHG2O0QBciVsXYh7ZASzfvrNiNuEABR31Zoxi6QIcTmJ51yrpAzP1ycB1tukBezvwlSxK5QCYcv+lAF7dAoFw0U+4FtUD\\u002fudDmuGGzQIdf9IQEe7JACq\\u002fzQEpgskAyxTvXdO+yQCCGzBLv87NACqAoP0M3tUCLWxJLdHq2QBbkf4cPaLdA\\u002fqHpA9eZt0AVJwBeYry2QKneXaItvbRAf9VvN5XgsUA0Of1h0FetQCQyAFrDXadAFBAjDoOZokBoBGG6r4WeQOnb3y21KJpAkjIrNzsXl0C6hZiMOl+UQNLDydmpepFAXnjyxXS6jEC6Zz1I+46GQDEfZZ2jDoFATK2YgwYseUCwwOOKdVpyQI7akTVNl2pALMPnXk4TY0AVXy+Dm+JaQLcol62Ve1JARUrXzlu1SEAMHerLMglAQJozicu5JTRAu2v538JRKEDPwcagFeIbQHhvpkck+g1AYuZ2rWLd\\u002fT98v+LD6U\\u002frP+9uiP7cxdY\\u002fSnn0PHU8wT+FkuqvVrSnP1Qn4V5PM48\\u002f8rq6iM9AgD\\u002fHNHQ10f5sP7ABqqBAjYA\\u002fKMX7lolxnz9XXphFTXK8P7apGWXXk9c\\u002fRPL85cbj8T\\u002f1Q3aNtN8IQJ+\\u002fpR9OtR9AwP+Ww9OJMkB4+S1vq+RDQOBY51aEmlNAFoYjhLvCYUBSrlsQoqRtQPqwP4as2XZAodg7WkBVgED2X2\\u002fcb8GFQO+YEs3iJotAlEXmAfDxj0C\\u002fVt+YVNWRQFVKTmGaCpNAMmu6yw+kk0DyLdclr9mTQPmfrhtlCZRA9lQoic+llEBIAdr1lx2WQPGERignwJhADTbIkGelnEBbhqzRntCgQJBjgg24pqNAFH9\\u002fWz+XpkCv0x74D3SpQISCr4rqM6xA912ZtX\\u002f2rkBn3tnl0PmwQNCvfHIVrrJAtozoJx6ctECQwIwvaae2QLL7TQQIkbhAmUqw06AEukCVH+P887O6QFThI1kCeLpAf7uvyRRouUA7dzfPXNi3QPnShpQdPbZAkXoGbK3+tEA6UG4HyVS0QH9m1rOlPLRAWyX5pEKMtEBe9iPkLRG1QDIOxt3wobVAPylP\\u002f70XtkDPm+dKVz62QFfw4N1o07VA698N6YWgtEA\\u002fONA2F6CyQKijytaJD7BA8zwSMlS6qkC3GBoPgPWlQNctutQLYKJACViAkFUKoEC7r0uaWDGdQH0YJXimAJtAnDONEGOWmEC3UdWpV4aVQErLyo7N7ZFAWdZolBN5jEDZb1yd5s6FQBVerOZcY4BA+BRFtW2FeEB9DFYCSVlyQFJTKj9nSGtAMrsP5XrqY0DOt9OvU0ZcQIPIJLSJbFNAeGbx7ubGSUCRHvlJXHhAQFFwuHVdITRA7gaAMyJKJ0DCX0lB3DQZQDQYevhuPglAuJYbyTAw9z8C3vo1QGnjP4HM8rhPfs0\\u002fXHBx2PZatD8sCZDj5eWaP1pfexMXHIw\\u002fdpmSLeGJZj8oKpMLVXN6P6iLD01xDZo\\u002fadTcRzZ4uD\\u002fCAvzKVxjVP7r+ZHTgqPA\\u002fFxiUDhsXCEBiWWh+4d0fQDajiijOQzNAfO0yOsBGRUDhqYgGDnVVQCRot7QbxGNA3gxm+kimcECq9SANeLN5QM7mBqpWPIJAmVC9kmXmh0BbqTy9aByNQEO3iM47nJBAD\\u002fKD3XjxkUADJSut2ZeSQKCyAjyfzJJACxyJfY3xkkDkF5fd63iTQAxBo\\u002fJU0ZRA3riYGTtSl0BZj2ULpiebQLompqlwH6BA3viv2SAfo0BVLe8gbkumQGCNWzGTWqlAQVQRa34frEA3fYKtfp6uQPGxjW4MhbBA7o66lCPUsUCQUGO4n1azQJPD3qn4DbVACAsvFrjbtkAx+mYKXoW4QML9Ds8BxLlAVZE8J8FcukCHZc6j0Tm6QJkuFlqReLlAKqC+QmFjuEBV93Hrvla3QB\\u002ftXlJEnLZAOiphvO1NtkC3KIjN7VG2QJPi5q04cbZADODvDHB4tkCQwiymRku2QLhPylbV3rVA6jhQWFsmtUAOtc2qFAq0QO+1IwoKdbJAiHkEwlRvsEB2lMmuqlmsQHA2I5eUAahAdBiIK8Z3pEBa56kE4xKiQNUn5VFMvqBAYxjo\\u002fCIMoECCLOW7m9ieQDGcY8it15xA2VhIn3a+mUDXkjzU58yVQDsXqa\\u002fBm5FAgGqfqleGi0Cbk6Jj+TOFQJuLdgKGUoBAd1HXqaMoeUBLw+yf1zhzQCgC3SLBuWxAmc2ub1XIZECwEAghgv1cQKfk84Nad1NA12GAAEMbSUB0TdcSFeg+QDu4+TYE+zFAWVTXy3qNI0AQrVAIvakTQBo4jD6KIQJAoBE\\u002fxV537j8EgdimXTrXP2muG0etFsA\\u002fHYNHvLJXpT8\\u002f4mPJHF+WPwHT+dDTDmQ\\u002fPv+nj+Dedz8TB4SnAf6XP8dYZ5G9C7c\\u002fwItNkkha1D\\u002fQEFIM7njwPwjhEPMHZAhAUU5pmhd9IED8u9sawVI0QKR1016fzkZApvL4ZvtIV0C0aecI059lQJNgeir\\u002fRnJAhOwmk3gpfEA4OJh99tODQLJCp4VSn4lArlemIQCTjkAtF7bXzv+QQMpgl7MV2pFA6w92RA4LkkCadCUiPACSQKtxbOIwQZJAo\\u002fZeIK9Pk0Ao4jG9ipKVQCVXT6bmSJlAkdCbMvN8nkAgLO2M23miQOSUUGlcE6ZAL40VfBiuqUACxSUd8\\u002fGsQMHxCz8TsK9AR\\u002fmcFsL7sEDYkZi\\u002flgWyQHZS4nHXHrNAiSW4RnhjtEAa24tTstO1QEUyhDw4UbdA08IGttenuEAqBz5h9J65QPoYjxMkD7pAwdyjrlH0uUCsH45dyXO5QL0vsNwK0bhAZh0MgG9TuECPUGGrqSa4QGi7O+hlRbhAkQ8\\u002fkml7uEAVA9AEi4C4QMx8UIQLHbhAF8DLId4\\u002ft0CaUmdr5fm1QAXWBHD6ZrRAvX\\u002fuMhebskAMAJs48aKwQJXL887HJq1ADz5j3P8qqUBG6sjcL8KlQI92j2EmTqNAOUcdam3yoUDpQ\\u002fxOXnyhQFoX1Ni\\u002fcaFAuZzUifw\\u002foUBw\\u002fs\\u002fbiXagQKIRoj6v4Z1AfGQVvFq2mUCV9gereiOVQMdfJ00o3pBATjZ1W36cikCboapr+P+EQBacl8a\\u002fj4BA+WgS+xTNeUDsQheOOZNzQIR4bZlOqWxAadhGKRItZEBjbVpB2EtbQP8dt\\u002fCKtVFAp4XlOzXpRUBSUOSJUZs5QFcMi17d9CtAdzSYEBg4HEB63W0THR0KQB3XvvRYBPY\\u002fWO+JNDDY4D86uZpxkmrHP1m2x3+1Lq8\\u002fz8ykFEhkoD9oYAdkav9lPz75yJdkw3k\\u002f4eCALAeEmT+CcnmiT0u4P2MCn\\u002fFVW9U\\u002fxwHYeXlD8T9smCeCxpcJQItq\\u002f34sWSFAGEhJsol0NUDvxb+\\u002fFShIQDBsaFaNuVhAyq9DCpf8ZkAgr5PfDWlzQORfQuAlzH1AB\\u002f1kmVLUhEDE7Taa0ZyKQBbDuuE4QY9Al+GC4NAIkUAsUqY\\u002ft4GRQPlmi5kOYpFAKoNVfk5AkUA1v1bQDMORQCPuhQyyd5NALi3mcUm9lkDl6x1Pub2bQHHWgTfYMqFAn6sqA7gqpUB0JNl8Y22pQNdrzeikh61A1BH56IqIsEDqX+4kb+uxQPny4nyZ+LJAft6ew57Zs0Bz6kCLAr+0QI8xfG4xyLVATGUB66n1tkBt+VRKWCm4QAlSVHTvMrlArZa4ZJ\\u002fjuUAJ6iSj\\u002fiK6QJoSy0QM\\u002fblAWJ0qd82huUA463+YQlS5QNjeL57kTblA0ih6ytmhuUDL4fa2ly26QI2o+kebobpAeKrVzPahukAD6ki5OO+5QCGuKLA\\u002fgLhAPQrq+o1+tkCVBT\\u002fVbyu0QJWr3+pRw7FAmNldkNLirkDDeHW+MqqqQHQ8+oC+GKdAEE1OkKJrpEB6cz4nfs2iQCEZgzbSOKJAF21bqCdmokBxMX3YftWiQMz4JHLh9KJAbC5AHMZVokBlzYqqO9egQKz7xlC1WZ1A8oei4DZ7mEBS8v1ZlN2TQFOgLzUg7I9A8PA8S2yniUBOZ1v0ZY+EQIVLjRmnOYBAADoA+ffeeECiFyB7iVpyQNhFxyRBAmpAIeHnCO2wYUBTJIWa7RBXQCfAmhySqUxAdz3txJDQQED0FdIJ\\u002fWwyQFogk2cwqiJATLe6LfhUEUBywSvoQlP9P2+7Q3oDg+Y\\u002fcGF1XOxmzz8GZU+On\\u002fy0P9XRY4n1IKY\\u002f1wtUEa5dbT\\u002fYGftAvI6APxfPtgpqYJ8\\u002frRrTV7W+vD+HrFGaTm3YP9csBNd5MPM\\u002frB7cOF\\u002fNC0BqG8JJ+IMiQAXMP8I\\u002fmTZAZfCaUvUySUAEKrkgZZtZQMfivAfEq2dAtMG3rlDic0DhyHDoT1p+QERrOLgTEoVA92DSe5WtikAxG2zorfeOQEWZ2ehApJBAMFa8lzbbkEA+7BS\\u002fa5KQQJBgzPDcfZBAF\\u002fQ2ZWNakUCFkHYPOL6TQHwVzX20A5hAu+LeJfxFnkCNfMY7dy2jQH7cRsNg4adAwRZHwMPQrEClMJd55riwQOj6RKVKpbJADoNof20RtED89RTmxA21QAwWOsbuzLVAciI35+KItkBiZacfYGe3QI2zIUPTarhAnUA088NzuUBqL\\u002fby5U+6QMdqMLdT0bpAtF5agkHmukDIsbjg3aa6QJrI4yakULpAfyz96GUuukCgcCacHna6QMh5qkYiK7tAboaOZ7wSvECk0HCLk8O8QGgZ17r0zLxAqWsujUzlu0AFWZN45Qe6QCFk8nE7crdACmBXR0GFtEDmG37vc5+xQO6Qgd+zCa5AjcOgdQu4qUC1nH4b\\u002fG+mQCyZDtgwQ6RALxrYJCowo0A6In1e\\u002fxOjQDC6lLnzm6NA533BJLRIpECbhK7zVoykQDcguv4E+6NA9FEXdp91okB6CLbiWjOgQM3SxloZQ5tAcDfmtDBZlkCpWZ8bqieSQHKR0Mv9fo1AnMEg0vndh0DvFAiS1vyCQK7AH+a+TX1AZ4yY6L3AdUB\\u002f\\u002f4QDpv1uQI1oxo0YL2VA01r0FxS\\u002fW0BWegG8y09RQHoUWg8GZERAlfy2fexsNkDXZVXPdMsmQPECsevIPBVAR2Aw2XsGAkDcVO3OpMPrPxZ+WYqMbdM\\u002fZYbxNMcPuj8mCZTDc46rP8L5HI1O3XU\\u002fhNYV4cnEhz9uAw0IKnSlP8feucyOuMI\\u002fwMsNfwBN3j9abcqp6Lf2P90+94fNjQ9APecDladDJEAEg58qTwE4QEcahB8kJEpApYXFmVAVWkDw3eu0zsZnQN95AgfUwXNA5kNVOFvkfUBVtekW0pWEQLoBUFq62olABpICbAvAjUDGP9AOKq2PQORKL6AB0Y9ATlPbadIuj0DVFAb5n0+PQFkQ7UTK45BAopq3xNfok0BsRcXA\\u002fhGZQOCG27AoOqBAD1nQd4LrpEB7JQXy40+qQOu+OPbU5a9AWbSCvZSGskA1O36F+Zy0QAt0heHkFrZAPCOHUVEJt0BvN0xwV7G3QO9HMadJVbhAdgGGFYUkuUAEly9K5iS6QKdiRd4FM7tAZPjIey4TvECrweVVsY28QAkXbBAZjbxALuao6BkwvEDmiUXVEcO7QBbgko6DoLtACcTelPMFvEAm5NMhHvG8QOHxWPwXFr5A+ynFchb1vkBXLu1vggm\\u002fQPIK9r\\u002fs\\u002fr1AQTeJF7fSu0DqLkmW0dG4QPSdQM\\u002fmdbVAs8us3B03skD+1a8GJtWuQOkycJDGcqpAxNuNBfVWp0CqpKLvuG+lQNaELBybm6RAI04nbkKmpEDFvensvDqlQBH\\u002fZ9gr4KVAau1l5REQpkD\\u002fAIad7mKlQHY4jLVZu6NALQCP3y9SoUAE++f67i+dQPkd4fws+pdAdE1T\\u002fXOHk0DeEfTcA9OPQBDzPEri1olAPAluIRWjhEBo6SOdQf1\\u002fQPKfa9F23HdAxtUbDIcVcUBWeo1x7XlnQIinCruW5l5A40iqUANeU0DdyEk\\u002fmudGQKZM5n9ISDlA2TrMhNTIKUAyVB10JBkYQBk1fWPcgwRAcyIQg1ix7z+\\u002fFFy2Qj7WP11Ulbia770\\u002fAiv2T9S7rz9axO37H7CAPy0Xd7h+tJE\\u002fnYozpzDXrj\\u002fcWQHwitnJPys2Ozt\\u002f\\u002feM\\u002f53b5PSGM\\u002fD9WPhch1t0SQCRFSYsPHCdAzsmdDXI5OkBS+tU1nYVLQJIqyX+io1pAE3n94Z+0Z0BuyG7exFVzQIEY43AU13xA9wNrDbSkg0Coj19OJXSIQHYKMsN27YtARsv2OAqMjUCsAWElQY6NQCzLDglgAo1ADkOC0ix7jUCahUnRWUyQQB5mWlxv0pNAzrSlGD+ymUBHQlyIGAKhQIKEqm6HRKZAj2zx06tIrECWwRXSdz2xQIN09gOuErRAwXsOYv5UtkBHP\\u002fnGFuK3QODb0vrn07hA8Xmd341zuUA4xcoZUha6QORIFRDI97pAFeda2PQhvECEwPbLu2q9QDfoyidMhr5ALuVumMApv0C2nIeJaDK\\u002fQMZt95b2vr5AxlWQSqspvkD6n8KDv+G9QNvSzjxhNb5AQH+pmq4mv0Cr+dgyUjDAQIv\\u002fc2YyqcBAV9FeoVGzwEAgPCpW2B\\u002fAQAGdj+mm3L1AFfsWY2SWukBvuQfz\\u002fPe2QBtYql9GibNAO\\u002fp6n6qmsEAUHc\\u002fNGuusQPSVmyLa46lAecWO4TcKqEAOJQrfEiqnQI0ogNlwCqdAD3RXny9dp0DsjYxO57SnQOvey1kSladABPhcTPmbpkBW08DOgq6kQL4c6ZCRBqJA7W84lR0snkB8ZvzMkJ6YQE7v63G565NAyH\\u002fQIB0lkECtXnBbwCOKQB511Se534RACeZJz9E3gECX8sb\\u002fYU14QI1Kw53Sf3FAqFmsoi8yaECC5HJXDAVgQAGFmSQuMFRAh0nO1K\\u002f7R0BbzgCBU5I6QAv\\u002f5shCLytAvZckxBV6GUD\\u002frWmLZ74FQGwvV08U1vA\\u002fZCTygayw1z+xXwGJ7fe\\u002fP2fmGRLB+rA\\u002fkRL26IHTiD+RxvvvGwGaP9vNMN1pMLY\\u002f9y6YreQg0j+OxiaUTCjrP8vz9DybqgJA5O\\u002fVVaqhF0COl942dKQrQD+zFAID8j1At1gSB6QSTkCT1bt2XvtbQFmkWQtYFGhAks+ruS8dc0AXqV7cb+p7QEzY9oY8t4JAF7lb1FwIh0Bz1cjklBeKQNPn3TqddYtABoijX5loi0DjvuyH2fqKQC5+5vOYuYtAw99rlTpJj0DoV\\u002ff3a4OTQNWX3UjW6ZlA\\u002f3H56OF9oUCVa4bUAj2nQEAMq1zZ0a1A6h3UWNlLskDVTrGZxGG1QK254QET07dA1g8uDNV6uUDsG1wWy3i6QJQkF21pI7tALVM8xWfgu0BW23rGSfi8QNfTIuNheL5AqpAAeZIWwEAjP2PgIdvAQFd27MfvV8FAdNnGH61xwUBO0oYQfTTBQP3ergJ20sBAP\\u002fhEg9iNwECl1AB10JjAQLwUJXHz+sBAlkJwxceJwUCpm2tJO\\u002fjBQFfIH5lL9sFA13MfVJNSwUC9UPloOQ7AQD8tZ8uStLxAzbKq3T8CuUBCFBRdc5G1QGXyuzIVurJAmw+QL92VsEDb8F0JkiauQDk5Bcm+JKxAUvh7S53uqkDhee8HVVWqQJd+rj+PG6pAjpeH5uDjqUBMV2EdtDypQJ753q7TyadAGPkfEh5ypUD9DZ66EnCiQJE3nux+bJ5AIvspiPtvmECoAm8\\u002fwXOTQJkSfMn7EY9ACFd4c5fkiEBm411V+8iDQBECngmlw35AVveFC+8od0BuMznyr8xwQJpVEyxObGdApEunZcpDX0D72zJKc9VTQK08TNl7r0dAFB4sqpJXOkCKZhPzJQYrQCoh8UvaYBlAtdS0y\\u002fyxBUC\\u002faidXsNLwPwwQ4BxqtNc\\u002fcR347t0EwD8FZ176nAmxP4JMUsu\\u002fjpE\\u002f8U8bOI9Moj+477Bg4PG+Pw\\u002fEs2BU9dg\\u002fdrjdcJpd8j9ErAvwf6oIQPocIkVxVR5A369QUqQkMUCwQlLd0d9BQBq3YdhOPlFAJZ9BdHjXXkCe6zyHSpVpQD0awbENq3NAe6p6ekT7e0COCA2pVmKCQG4btUTRS4ZA\\u002f2P9LhYDiUD3sIQygCuKQANEtGQSDopAFLqRCluriUC1z6Arl4eKQJiG1BSdTY5ApUH3ou86k0BD5\\u002f3PhwSaQLYtbkPE26FAdCCXfnILqEBQX+9mRyivQKclBmRWPrNA8a1jC+KUtkDH0TkBvji5QGmPRr73A7tAeyKMfr4dvEAn+ZOA7eq8QBQVBz5s4b1A\\u002fX6mUL5Uv0Dga7cmOqnAQDnwiS1lzcFAeJgK+K3bwkDUMi1nFJXDQHBKoH\\u002fz0sNAB0\\u002fLMX+bw0CPVmXIeSLDQLoTYyW6ssJA\\u002fRwKmCyKwkCEDBgdabrCQHcmwWwaHsNA7fnVTdFow0CBvUXCp0nDQJVQaHfOj8JARLKi5kI\\u002fwUCan+xllhi\\u002fQLzne1KWg7tAX6jVM9RCuEC4D4Hmkpy1QAbLUbLllbNAOLfLSMMNskDp6grTh9+wQEHzS34C7K9A76N\\u002fPnKOrkCYg1+9PYWtQBxeWQJyh6xAzxfxm68tq0A\\u002fKQRtYB6pQALsg3u4P6ZAUaKwYmfLokA+5YxLjmieQHBt1OYi3pdAaubj7AmEkkBXLTRhLNmMQJKKA1htrIZARTkPhM3VgUAv3bb\\u002f+rF7QMmw47m4+HRAekW9qSC9bkBgqhPraq9lQBQ2LCLFRF1AieHKTKW7UkAME8BYfoJGQKk4OgUbITlAgZzs2b7UKUCLJRJgDkcYQANxk2zAwQRAjyCMBIoX8D8Vk8Xs0KrWP9TW6XM+oL4\\u002fU5Z6B5RIsD+rQ80a012XP8AAfUgyWag\\u002fX8liweKMxD9mJn3QyYHgP5bgNDbVH\\u002fg\\u002fYSpdbMQHEEDJ6U1sLW0jQDEwZKBtijVAyMZPFbfvRUDZF0B2+pdUQKjxUfi04WFAIgAVluPKbEB44SDMEIV1QEOgZq\\u002fV3X1Akv89OyA7g0B+cORTPvmGQLWzAorFgYlANALUK0qDikDEXB5R20eKQOtJdVoBy4lAvAk2jnWSikC65xDZC12OQH95MSRuZZNAyThzqieJmkDG+CF4JG6iQA98IVBWD6lAJBstD85ZsECcq336qUm0QBap+73x3rdAPtwd3tm1ukByNguB4aq8QMNW7RWQ771ABaZOLRH4vkBToSbYNCTAQA4V\\u002f6wvHcFAIp7pMDNrwkB+8n5duebDQJLb2ljnScVAW+XO5ilKxkCnYyPNH7bGQOQ6U\\u002f8xjMZAG\\u002fdUn9n9xUDCx1xUsVrFQJ4c4aQ168RA4MEUt37NxEAnNdTRrebEQLv4JbNB8cRAd2ne3PygxEBecYYfV8nDQJ2QSIpMcsJAZdbz8pPRwEB5oVTr9lu+QHvmNJLefLtADO8N6uMtuUC6VspDEFe3QE9xBfH5xbVA+eFdr7BVtEBF\\u002fmFdpv+yQKXnUGamz7FAG1s0Wx\\u002fJsEBIO7EXVqivQL7NiPvegq1A0fi88f7FqkAVTyMnLVKnQKyJjQoVXaNAPn38Dw2ynkByz1KNGHqXQDbgGqGqpZFAs1cENlyXikDPMVy73FCEQLkvqxPBa39ARZ3MkrtNeEDeqmS58YlyQF9wrpZ1j2tA5LIrp0rDY0CxFm69FhJbQI8h4+cMhVFAyVXbROUyRUCY9CZGw743QDGuenbIbChAJRPtG43uFkDL7Nu+2ZADQEdjL51YQu4\\u002fOIKPT4lA1T8fkghrjqC8P+roN6hnX64\\u002fmtu9DTAtnT+MTI1RGX2uP0EfUFJrzsk\\u002fdvra7SDD5D\\u002fImBOSAVr+PwLbbnK0IxRA2ilYy7JRKECHbOFbGM06QODdSkDGDEtApAr4T5sYWUCGzJzbintlQBqWO8DXBXFAnUVFjuwIeUAFICImpRuBQPZcP2EivoVAfpelwi+1iUCofbQ\\u002fDlWMQGQqIX15So1A0rnU01PgjEBcVlofShuMQPImp7j0moxAShcgEx8mkED96CkuXYSUQDSgkZ0SG5xACw96iXmZo0AHVqTl1ryqQPsZQscDeLFArn2QnOOqtUAeCe1jVne5QFng+EBXebxA7+9KXLiVvkAmUPU7fgbAQLqak07WscBAfnkVTDSUwUBn09jdK9rCQNFu\\u002fDHtgcRA7+7RD7FaxkCg5x0cHRXIQF\\u002f2O+T4XclAr3pa62L7yUCMa1ynVuPJQDf6zlVbQclAbYyKSDtlyEAMIQPfap\\u002fHQJnA38WpG8dAXFzp6CnOxkChgbW\\u002fin7GQLEKf+7U6sVA1DKbcD3uxEC+q5udWZXDQNOqbqAdFcJAYDExPvGrwEB4iZ56pwG\\u002fQGqQ0LYlKL1AHWjis9uQu0C8Jx6ZKve5QLW2eBkOObhAecrzIDJktkBs4ULFHqC0QNZjU17uCLNAuEEOxJSVsUBTbyM5rxqwQFs7bC+szaxApqMFkL7IqEAmJFbqZVOkQKSeNOpuup9AJE3k2\\u002fG\\u002fl0B9n5W5nFWRQElgSjOrN4lAWijBwpKhgkBvHtPzWit8QDxBWJ+XpHVAW7CHBsKjcEAACOKXvidpQEM9Q+wrY2JAltm4k2acWUDgpL0YrMVQQAlpWgeobURAFghjiYnuNkC8pA+vOJAnQDm\\u002feXiTDRZAxYIfjrC7AkDlpha1LtPsP+NvCQKvItQ\\u002f\\u002fCCxC3r2uj9qHsheRHqsP8BmtMjxHaE\\u002fangKx6L5sT+J7CNb15zOP8s5hctbw+g\\u002fpBg+FHkxAkDGwVjmJ0IYQFtcFD8lai1A7ZgrszFDQECT+I05eXNQQLiy6Vbpjl5AvXhgGAQnakBKGDodvrF0QBvHG+POXX5AoJKxELiyhED+fi+bTT6KQIch\\u002fHde+Y5AMCrHafELkUAnHSZPXJiRQAqgdPzYRpFABtg6xCqokECcA9UC5qqQQF06GGXib5JA7X8dP58Nl0Cx2zOj3k6fQAOY4PHquaVAOODy11V\\u002frUASz2\\u002f7\\u002fCezQBUE3hnTmLdA9bwtPGiMu0DESVNFmaS+QLV8NLtca8BAuxFPRFM8wUDjvtgdYhDCQFAQguoZMsNAwb2zavHJxEB4qfO\\u002fe8zGQLp5yV9N\\u002fshAnvhtWysIy0AoKESv5JHMQP+VXq2AXM1AAae5ymxWzUDI5XM736LMQNS+ZWKVjctAn7v0uHNrykD8WEy8K3XJQCYtJc+JsMhARaW3w8X2x0DtbpZqdRTHQHzcmuvl7sVAeD4HtBSXxEBWTpJ5+j7DQAsI4kOOGcJAmOagEEs7wUAfNTB8aY\\u002fAQOghpN15zr9AnUjkBscvvkA8eTdO5CS8QJC3tJLF0LlAH4UoaeB3t0AUstGknVC1QNFq\\u002fzp8YrNA3Ol5rN2GsUAtJZugIxCvQHcvooSYkKpAfXVUBLKtpUDsjTXaRtCgQGTw2hvC5phAOcdvsBDZkUBpkvYsQUyJQAYLgU1RJ4JAGqyY+e\\u002fRekCm7ivuu2l0QGKIB4Tqi29AdnNRJaUvaEA2RdtcP\\u002flhQA1P6Z3vYllA0USqQ17HUEDNgY4MK4ZEQMsYL61QCzdAZ\\u002f6y0TidJ0BUq0jNTv8VQDw+xs+dkwJAY58LuQVm7D9pesZvdLPTP6IfE\\u002f46Lro\\u002fiyR3MB99qz\\u002fOp4xyIPSiP2IBql0CCbQ\\u002f0rYiB0Q00T+i8wFJ6w\\u002fsP9XYA+ApywRA4exAUpf5G0CviEBiux4xQKouSixlHUNA6vNa63aHU0Dx+tkIvVFiQGKDFN3jqW9ATnz4Q1xKeUCkHQgm1LeCQA8WJb\\u002fKuIlAoGy5+alskECwCRVoJoKTQFfL\\u002fLCJlZVAAmDfbllZlkBhbfmU\\u002f+2VQNQrSHMf\\u002fJRAGQIyhryllEBmIxeXdU6WQClNNhGDSJtAlFtBn5I8okC9ZhpMnAOpQJaqA54+yrBAqMmh2MGKtUCzGouEBzK6QB4QGpaaNL5A3vHO4FmgwEADw7tbtrLBQLge+wenisJAaj0BHkR9w0DxLzT9itbEQL1eAI6Rt8ZA3sjXipAIyUCFTQvQ0ILLQOfWmwgpyc1AM8KdtUaCz0D2T77UFzfQQIj9Ud4YO9BAuNdHx8a0z0CKuOKUlm7OQMIxTWbv+MxAuUiA4X+Wy0ATFNu2AV7KQDmXlP6GO8lAThXx9N8LyEBG\\u002fc5mBL\\u002fGQI6iHHZaacVAtFJTkyc4xECieWzhA1LDQDoIqLHXucJAJrh9\\u002fHVIwkCE2fnV3MDBQP2xBYti8cBA2hbdb4mZv0AQ4Nor3Nm8QNjns8YeALpA5tVcDERat0CvzmvMtQC1QGt56SPL07JAtfG4IEubsEB3js1HImKsQBxPZ2NfNqdAJB4yVMsPokAhS3uV28yaQPOFkjRlKZNAaJBrRTfpikAiHaoK4QODQFpU34wtoXtAW1YzGPTMdEDRZmInhQpwQNy+Vp6Lt2hAxCVbbq2AYkBod6+axEtaQIVwBt6QcFFAuQBmmZ5XRUC2JWkt7uk3QIJFMEgMaShA3PT\\u002f2JifFkDMSrl4iv4CQHjS2kk22+w\\u002fQsVz\\u002fGbj0z+505wwfj26P\\u002foKf03LZas\\u002fT5Z1lv\\u002froz+rgjGGKDq1P6zAd+mRatI\\u002ffQJreZxZ7j9VZg8v3bwGQAMkbn3Z9B5AJw9Ki3wwM0BzeeaU\\u002fbhFQHUONgJThVZA9WdSSpJzZUCBAR0v29RyQOkcNAR2jn5Al7fpLCv2hkBs1AATDgGQQBO6ZKRUtZRADFI6CiXimEC5xn9eZ8ybQJ4UokgX\\u002fJxAXRAfFEmEnECL8lFj0CqbQEbezAQzWJpAjnjDo+7Mm0ClbVYhnJagQFFi5CkzwKVAN85rgl5jrUAbge2OsnKzQAV2\\u002f5yok7hA5CRtTvVnvUCZPhBup67AQHZE0SKvGcJA23rTfY0Nw0BwfG+9E9XDQIpdQikj0MRAcfla5eZKxkB91nPpKV3IQPBel1O\\u002f4cpAjapZ7z6HzUAZEzXP1ezPQKl+p5PQ3dBAzvPgSGtb0UAA1W4uJWPRQEANsV2r\\u002ftBA0bPZsRFM0EAIjURtVOjOQDIZnciENc1AN72HYyujy0AaCE\\u002fosC3KQJ6UETG2wMhAu18Sr4FVx0A\\u002fMG5ESgLGQL6jj7nB7sRAO9CXJno2xEDV6fGKG8\\u002fDQCreDGZxhcNA5eTSnScUw0A+EZDj3kXCQKY3I6zkDcFA4uOAb2MUv0AiIgULWt+7QIq4ZuLx2rhATs1RvPEutkAaKWdnpMWzQKbG9SODaLFAWblUJsnWrUBpmXeSpJSoQDOB9GeQVKNALu3Jai0JnUCP7E+C6v6UQP24LizRsY1Agtc5OPn6hEBuol7NC0N+QKM16YBxh3ZApg6jVYwxcUDT55ubGExqQIS3cVKjl2NA944kumu\\u002fW0Agf+GUWVdSQPrNKdi2XEZAh8iG+I3zOEArim9FR1opQDlJwXcoYhdAQgnBbCmJA0CDPrP0YojtP5rXXwMMQdQ\\u002fnPfSRfGUuj9fVFfyOKSrP0yK0deP\\u002f6M\\u002fvwHVlQiBtT9p7IN1xuDSP+AYgXabe+8\\u002flKQqHsTjB0B3PFhWTn0gQChCPzKuwDRAMur+8h3gR0Ad3dj1yC5ZQKBcogFobGhA5GN+oSbXdUCVo8g4xwyCQG37o+6LnotAu1KEPL6Uk0DcZpjpoLyZQPNPOnN\\u002fXJ9AO78uOkO6oUDdAZRoZqeiQBtroFiIcaJAcH5ffDiJoUDE8xYYF8ygQJZD8cHSU6FAqIeJq88mpED5dWD5MuCpQI3l0\\u002fZyNbFA72+koAN2tkCnwb5NV\\u002fi7QJcwIDwmd8BAIB28yjtewkCwKZSLApvDQP7iB2r4WMRAFB3pOvX1xECw7cZAt9zFQJ7ZFurkWMdATge4hNd4yUDpkBXEJQvMQBQ2amnrtc5AiYZKVqiL0ECj\\u002feVe8W7RQGPUTZTc6dFAUrw7PKfx0UDnL+Ak943RQFBGVYip2NBAfBkQu7rtz0DmVKCLPhfOQJcVRnYyV8xAKE\\u002fQsMi0ykDdeb+tmCbJQKIncAiIrMdAtVoodeFcxkDhHm5JvlnFQJuKlOeYtsRATL6KwyNixEAP68ZidyXEQPK7xYuCusNAeXsUOCfswkD7CaT5Pa3BQASs4DS0GsBA7rkkvjvTvEDc2216eZi5QHJRHuOruLZAbC6E2p8qtECNV13H6L6xQCrn2QtBj65Ag5x5nR5vqUC3rm1Qnk+kQLdiF0jzGJ9AXXfQAl7zlkDhuXCuCIaQQMielk+2mYdAhGYIDUgEgUA7yPDpOg95QE4FN+esy3JAGnBrJKM6bEB+0NHAIrRkQDqv0jqB9VxAiWEy3w31UkDAPjDRgu9GQK6VJcnKbTlAELKxpr+yKUD1eBvIM5cXQJ1ctTx9oANAnaxOUGmP7T8E8wp6RzTUPyXyGTCSbbo\\u002fxg26dSRqqz9coQxHZESjP1ssmGcT7bQ\\u002f0\\u002fSL2N2b0j9Xo57IWnDvP\\u002fUmvzvIMAhAeAjVzjzzIEBVoKhfU641QHYUx50VYUlAvCiD0TVDW0ArwrCbQPJqQOkagjp9kHhAFIG5SCGxhEAsCkZSJyCQQGphf8ojRJdA1wzqNCUTn0BRbXnLHTWjQIsZHO2T\\u002fKVADJ4lRXtdp0CuQQCWlT2nQC\\u002f28z0IFqZAU07J2RvupEA0guKWPB6lQMjU1a4H6adA8sfP4IMIrkCjDMvwWauzQNzXCANhXLlAoED4nVMyv0DleZ+koh3CQGqr4t1S6MNA6vqCTsHrxEBq5QxgaGXFQBWVekuYxMVAl+fqQz99xkAN+L4D4NnHQLCHslvW4MlAcSowXNRXzEDxOEEnVuDOQMDSvWFLjdBAMXXsvHZe0UDIxrEohc3RQBjTlYHb0tFAHAolf9F10UCjuJcugczQQEKNRx917c9AIaIaSNMmzkDQkLjxWmzMQKn2\\u002fGo7ycpAl0T5DmE6yUBmlSTxnMPHQLieJakiesZADWw0sBx7xUB0V85xL9XEQLX3nzEqdsRAxGFlUjQrxEDofCp+obTDQELwakqu4cJA8BMeeoukwUD2F9dZHhXAQIrN172+xbxAMTrFJjV8uUAbSyHw2Ie2QMTKIwzG7LNA622fazaHsUDkUEVphliuQB0W61T+jqlAq960Y2LJpEALj73Qq0mgQFAGb72ZqphAvJ2d66UvkkB2weuj6maKQLuP0Y0RG4NArq+YgtPUe0Dnp8GyEGx0QEAt2Eyw4G1AuaxA2JhgZUAAI3GRzUtdQPHjPc6K4FJAeNjkaWaRRkBj9YKdJMw4QGN8VNed4yhApTumvT26FkD2YW4Qw9QCQEDS7z0pRew\\u002fvmse0exF0z8U5yUTCyi5P+t2eexWD6o\\u002fk981OOPboT+zpLf8LJqzP\\u002f7A6g08q9E\\u002fdT4Jr0hC7j+byW15dZ8HQF77V6X0zyBAi1\\u002f\\u002fxJfdNUCM+MIAvQxKQKKBCLIpgFxAnHtecBO0bEAB\\u002fqprlal6QBPzL\\u002ftJ4IZAX8F\\u002fe\\u002fwkkkCQrgOXUp2aQPpLgqp9C6JAjyoKRAKdpkDggormqjGqQEAahSK3GqxAkTysE1QgrECGv\\u002f2tA7yqQL+M\\u002fJfuF6lAYYnc4JXHqEAfkyqCSkqrQISGn9kQv7BAU\\u002fheSwWbtUA8uz2ZK5K7QMy3ro9EzcBAMizBjWdRw0BQhl2hwfzEQGWUHyRlyMVAXLMY+E7\\u002fxUDIhfS9BR3GQF7l78SEmsZACHHgYvW\\u002fx0AijgdoHo7JQJN7Gi7DxstAuiUpjvoLzkCh4k9Z1wHQQNzqJMXxttBAz6WuK7kU0UCJAKtM7RfRQPM6AJ7lyNBATjGvDFM60EC2Rs6EggrPQFht1KK8gs1AwFoV8Cb7y0BXa\\u002fsS+HzKQIBwit0PCMlA5GlJCA6jx0DQXgHXVGLGQMtNyktZX8VARI0tzkumxEBCcVd34SfEQLPlua4eusNALNYkDHUow0CAiNboyknCQHG\\u002f6XuhEMFAM6+GYKocv0Dqsx6f\\u002fdK7QEjJdAB9mbhAPsV6vZurtUDfDn88ERmzQLOKhDaYy7BAZYsxRms9rUA7XSqPmPKoQE32WZ\\u002fds6RAGIekcCOloEBSEF7uM+6ZQHKfdxA\\u002foJNAsG2krgsGjUAVGJVrax2FQCmo+wR5b35AhXhDo8\\u002fLdUDY8RwMYONuQF13t9dyZWVAsSIv3CGBXEAA0b+OFvVRQJjw60ysGkVArvptc5blNkAcJ34\\u002frMQmQBy2exYyphRA\\u002fnZVxhwGAUB9mukxBHfpP\\u002f0TU57uUNE\\u002fBeNnDJmPtj+EOZelM1mnP7Ttquik1J8\\u002fd8Vf3LqosT9Qt2CjlCPQPzGBlMNiBuw\\u002fK4xVGEk0BkA+BRjKtA0gQB3MRP\\u002fHOjVADR1goH28SUCwBIcwe6pcQBavQK8ZZW1AbDPYw1HLe0D6HEcjXUKIQKoEQCskjpNAqfqag+sdnUCIq8H1QwOkQFZfzBI1Y6lAzayGb7q4rUC4jb9YPRWwQMFpoA3IL7BAebGSpLPIrkA2k9dtm6WsQMb48xjLtqtA+RNy9FqurUABYDlV8saxQCvNQLLMlrZAzdrhyKKSvECQx04OJk3BQFZCtu+BxMNAjAF4HiJTxUCRQtjzdvXFQPOCgR3H\\u002fMVAhAHSL1HoxUDlkUCHly\\u002fGQMVdK\\u002f4wFsdATmCEIdGZyECcFZ8O6X3KQNlY8+Qga8xAz6otCigRzkAeWd\\u002f5dTvPQH+6z9Wz1M9AFvbX3eLfz0AVE0pFi3DPQPQpb8C5pM5AHsxcUNydzUAfcT4XcnfMQEjrdwtCQMtA5sDkd+37yUDbUAn+qqzIQDpxMkqZXcdAwghXN7MkxkCoCHMVlRnFQM8ltFLwRsRABiOUCtagw0Ajp23iwQbDQBxH2SduUMJAZWGP+ylewUAoSsAKrCTAQCdnYG4kYL1AP50XByE8ukDse5RWPiS3QKoArO2OULRAqFVHm2bYsUDtZznHvGKvQPavAhuZfKtAU49gRgLHp0BTzskD7CekQDa7xAVXpaBARip+oAOxmkDeLOtk0r2UQEZedy0vQI9AyrAgBbffhkBhp1QqZFSAQF0\\u002fUpJ90nZAA7EZa1U7b0CnVvwy49BkQLAVIfjHvlpAzcnRq7hXUEDud\\u002fRFPMBCQMLP3fxy+jNAEqpuozGaI0A8xfoHFJsRQHUUt+iy0\\u002fw\\u002fqNiKexN05T+oQk80mxPNPyJJxDjA5bI\\u002fex535KaIoz\\u002foZeKKrS6bPyuw3cmVfq4\\u002fiUOzSlRDzD+JftqcB+PoP5Vt+0y5AwRApcBHWQJnHUDUjzJ1JMQzQKv+0msTYEhAg5NCvJ2fW0BJDAyFm9BsQCkJuRNZs3tAn0fa+SOPiEC9oECYIBaUQGcsT2NDTp5APYNM5g8UpUAT4UQFawWrQDgywU70669AkKhz+ZZmsUCX2NJX8ZixQKnZZYlQvbBAv\\u002fE2ci3rrkA0V8KA8FmtQN+RmkVPl65Ak7hAdQvgsUDvjT+LPV62QLZgwR\\u002fYE7xAUb8G0lLvwECfsBej\\u002fUvDQCparPDfw8RAE5eht89TxUBCWQKuD0vFQJxn29SPIsVA23bg+IxIxUAVRdeN9\\u002fjFQOuvoutQMMdAg3DC59S4yEBbEiJve0fKQCc\\u002f6TrzmctAyLip5ReJzEC0rN8umAvNQJ7sgtVdLc1A6N1YCKUDzUCT5xk57qLMQCyhtaFIGMxAve8r88lmy0BSGkqIjYrKQNnaOvu6gMlAvjt+HkpQyEBSf9y6cQ3HQLOi8LRj1MVA27O92oy9xEBjsydo+9HDQL5e1P6wBsNAbWlTj2pBwkDkSXM75GPBQHuZUXawV8BAMTQBQqMrvkCOIGfbxVC7QMUM9d62TbhACEJVaH5ctUB31JSK4a6yQB5UyLGUXrBAbblD7AnRrEA2Q+IvK2qpQNScwqtVTqZAuW0+uZNRo0CwJW01mGCgQMLmX9+q\\u002fZpAMVyrMQh\\u002flUAhYLeE6XqQQAHZoS18SIhAaVx6Njg1gUCXPcmwuYV3QDGt3sPYF29AdIp7AAzfY0AGRon3J3RYQO\\u002fWwEy5u0xApWNEt9fmP0Dywsnea48wQP7KmZc83R9ARv\\u002fRx3U0DEDvGudy7Nj2P4wIXfn33+A\\u002fQKcTPuS\\u002fxj\\u002fluM81x3WtP6xPrncIZJ4\\u002fyM035oQjlj+4hc5RPR6pP05iXT\\u002fQncc\\u002fkJTBIuMY5T+uS3eAKzoBQL5u\\u002fEBQtxlABhW1q6+TMUBoEMskGQtGQO2ub+bZZllAvTovjxfuakDIv9\\u002fh0Ul6QKoxCZWqo4dAxImQPFCVk0ATee2Le+KdQNWK0dd0\\u002fqRAG4CtCIMlq0A7PrMvJiewQO62pinFtrFA0a3vd4v7sUAufxzs2xuxQOhEvpMRZa9Ayoic+mlPrUCiIaATrMmtQPyy3Pwr\\u002frBAGmbswa3ytEB8WdGGrB+6QGARXMOQdb9A6cUytXTvwUAs4oL\\u002fwFbDQAsqtN\\u002f268NA0z5WJgf1w0A9pdswedvDQLQk6+MS\\u002fcNAMmpSeO+KxEBq8zTvO4HFQCx\\u002fLDiItMZAvmyvIXrqx0ApbavNYfLIQCpMFVN9tMlAxlM6VaszykA7P8VkW4PKQBDJrJHrt8pAW92RE9bYykCwy+jM0drKQCBzNxX0pMpAzWKEg9AeykCmBQKdJkDJQIxNZFLlGMhAtC5DQITMxkBkHb2TUYPFQMvIAJjyWcRAQzIDVIpXw0Cu\\u002fOykcG7CQOllTcR1hcFABtbHcuuDwEC6LcxPu7W+QPIiPoEHErxA0Hz8NCc0uUAa2GvxJ0a2QNInGXM7ebNAUS5WbLb3sEAaZKI9NLGtQC8jCyikNKpABY90PJ5Op0DoHcSscsKkQDoOaRJsWKJAtqvb5HzXn0Bu6Z7w\\u002fd6aQHIhBESU25VAOYxZ\\u002foIEkUAQet3iHD+JQCE2yvvXz4FASHG1\\u002f4Ptd0BJ5gBDjq5uQO1oaKDC0mJAn+PAlC8cVkACvqWpospIQE3hpjlkYDpAl3nOGllqKkCWygQzI7EYQLazIouiYAVAkQLpgz8I8T+5+iH6NtvYP9V0VJffm8A\\u002f1Pd\\u002fPc9epT\\u002fsl\\u002f6dH\\u002feVP\\u002fQ0ONxrHZE\\u002f9ujKbRemoz\\u002f2o9xDsMDCP6Moa4d0AeE\\u002fX8gWh602\\u002fD+qYrUn4mcVQEuXKwQmwS1AKNRcQ4j4QkAvICBWsTdWQE5FcoDc6mdAVqb+zMutd0B8gyBFXZCFQJyknmI4EJJACcSWBHXUm0DWwDORpbWjQHtPhfLQqKlAy39giiW1rkDEB\\u002fFSVumwQDOA6f0jN7FAvnzZO8lgsEDDXc3Kq+StQMFLpZhqhqtApzvwmvFeq0BoGu32A4+uQFkR6DbKlLJA5idHUigOt0C8CTVx9sa7QP2gTpsHy79AFU6ufok\\u002fwUBTnjrpwerBQE67BPo+H8JA37GcwjwxwkCI0PeZX2nCQPehQLWg68JAuNdz3a6zw0CPa2\\u002fhoqHEQEM1eCe5jcVAy5GALu5axkA\\u002fz+aOXALHQJCZXm8\\u002fk8dAtCFKahAnyEA1+KKuec7IQOyMfWuOgMlAVlJypaYWykDJxIwg4lnKQE0SYybzHMpAm9GzWdpTyUCVDzW2OxvIQLjhnyYIq8ZAljEFNfo8xUDSOXYJkvXDQE+08\\u002fCh2sJAKYj28DrZwUB6F8SLm9TAQGjtGTwTar9Aa7e7vLLgvEBNMOQYNBa6QMzuDcEeKrdABaRsBP9HtEDHZ4BkeZyxQBa7acgNla5AdXAGTFTFqkCUB5PFE7+nQDpewtLQU6VAQDkMecRDo0CBtMiNglGhQKCB1IvKn55AohJeJK5SmkB\\u002fGRdJAcSVQI7inKogKZFA3lgfqRqiiUClQD9ulBCCQBaT2eRf\\u002f3dAeKuROvwTbkDW9sbUHdRhQDG59tCFClRAveAk9A1jRUAB17z\\u002fOaQ1QCfxDTj9ryRArAT6bwSTEkBEo3swdR7\\u002fP4zYqdEOKeg\\u002fxoh\\u002fizxJ0T8E8rOOycO2P\\u002f84Y1sb95w\\u002fcUX5Il+TjT\\u002fWHuVaIQaJP0i4xyA1GJ0\\u002fORwHHr02vD8LAyzHyP7ZP2HbExct7PU\\u002frbQWkHjqEEAoe6oC8OknQMwA70X1\\u002fz5AFk6OZh1wUkDYtjNpvCJkQGC4AKxAMXRAFAJnIFOYgkBTEbMRqnKPQODTRRo1aphArs4LqXJmoUC8+Rj7TsSmQDXgd8RoWqtAuU3kpnY3rkCEajUXTtCuQA7SDpUlT61AdJH3VF+kqkD8PJOmGEioQMlbeJkLv6dADG4uWX0PqkDpZazDRluvQEYNoauRZbNAp2C\\u002fkLRut0AZL2IVwf+6QCs\\u002fsqLGlr1AEX9C3J8jv0DjXesuufi\\u002fQInO2NeLSMBA9+Np7eCowECUVO5HgzPBQHo\\u002fPUyv4sFA2WZfia6gwkDBQXh2KVfDQERvd6lg\\u002fMNA21n+SXeaxEAf9hbS5EzFQPI4rtYOMsZAunMkYBFVx0DT+15pC5vIQLn3HruWwclAUHEH96V0ykDsTKB\\u002fPHPKQMArkaaerslAS\\u002fPkiB9RyECP5ttHw6jGQKjdksM9A8VAbr+cedOPw0BROQObCVXCQG88rO64OsFAC39Z1ZIewEADmXJyjs29QH5rncSQFrtAlB7gxYMquEA4hu69zTG1QMxlvwYjXLJAEB6ECQ6or0AvnlqRB2urQPVnT543EahAIo6soMKBpUCF5uZpoIWjQDV5M+ie2aFASs8xMec\\u002foEAH11NpPRedQHgcknQxTZlACamByGUmlUBIRnOzhNSQQH5hJ1\\u002fpTIlALQfp37TcgUD5BSzJ15x3QGjlS72HMW1AVSxahXXkYEAD+e8jW1ZSQBVEHcgKuEJAVZ6qwQQBMkBR1tRutFIgQGeShVmQ2wtA4t\\u002f+vbhL9j\\u002fCz687qKfgP9vPim3AGcc\\u002fP7rD4R2wrT9ff0gcoIWSP9SLwOzUr4I\\u002ffaOuIPRCgT8eVrDuP1mUPyz7z7Q3E7Q\\u002f5WMfcQfQ0j+emEjtYiTwP7JtofXsWAlAWA7roig5IkAc+M7o2AE4QOpwpkJj\\u002fExAjDu8bRUKYECwxvtEV0VwQCZUI5L\\u002fQX5A3TwqjlTJiUBqg0KHWSSUQHIFw\\u002fqk15xAGBrygr\\u002fuokDmrrgPC82mQAJfE7maOqlAAxXFgFXAqUAYAeBzFX6oQJ1uS8ZBOKZAXS7oQTQkpEBIhyEwuoOjQFVcWqGcNKVADb3+CKheqUATYgj5vmKvQH9V\\u002fMlhDLNAe+dKKDYktkCHEfJeD5O4QGDH4ZmTSbpAkXCmE\\u002fSAu0CHNMl5W4y8QJphNpK6qb1AQbAaU9vrvkATDJuOxCDAQJ50rIxdxsBAyyKn2bddwUA7ZTpJOe3BQJ+nX2JejsJAbbi55\\u002f9nw0A24nJ\\u002fy5zEQHJwiTA7MsZA\\u002fYoBRQL9x0BMZkTmj6LJQICg3BcYtspAA1XrxOflykD7xeKIUiDKQH0GDDFPmshAaxIZnU20xkAAvfmRN87EQNM7ixdCI8NAEqlLa9a9wUCp7IdJz4PAQFEH8xrdnb5AQSmvlLQFvEDXWjtThC+5QGciaDhoMrZAJfkYYjg8s0DiSciSGH+wQCQKAUQeRqxAs1C9wpl6qEC6Gmw9EJqlQOH8oZXae6NAFz4IuQ\\u002ffoUDrSFp\\u002fCH6gQJn0b\\u002fSfPZ5AvIBXSzY3m0B+KgLrzciXQMUz7IaZ+5NArLK1U5T6j0B2Z4vYOSqIQDjRsQYJIIFASKsNWMWldkBwLg1bydtrQFxfj0qx1V9AAb5rNVXnUEBuxIWambVAQGfNZu6S0i5A3MUMt+6WGkAoaI1\\u002fPn4FQIx1k3t3SfA\\u002fD6cmF6Qc1z9+JZPJMqC+PxpyyIqG7qI\\u002fEKaUl+XZhj\\u002fBBmsmDpB2PwhOZ0IHdXY\\u002fo6DLLPPgij\\u002f5Sa\\u002fR4wOrPwTw3u2oxsk\\u002fu1JxmL6E5j\\u002fOWhwjjP4BQDvF8h7hTxpAHMDBjGmaMUD9KQZHXo1FQB3aWkhvJFhAqcc2uHu9aED2l4DHLTF3QNXB0r6X44NAL\\u002fksDmk2j0D60cHokGqWQIUa6CglfJ1AgaMt2RXGoUCsUezjuK2jQEOIfRbmGKRAEjyEfrAho0BNuGJ+pWGhQLMVgFgdkp9ALr2C23qjnkAtQIyNyqmgQKIzr3js9aNA8Ymd3MnEqEBaoLUCiD2uQMnj7iFfvbFApWoSxwL0s0AUP612Hri1QPC8puc3MrdAEAaCNMuYuEDGhK7isgy6QDTP42nei7tADUyh8Hb8vEDVwwJPYES+QDLEstwyYL9AqVJFpj05wEBECJC09uLAQLLoqzex3cFAlgdToRBQw0BXH3BvqjvFQBvx412GacdAxeArNHxuyUBIoAL+Cc3KQJjoIPKJJ8tA\\u002fwN14i5oykAPaRFc4sfIQBfu2eLXsMZARw0zSfmOxEDxa4r4RqjCQOv9ulmVDsFAy9oS3LFWv0D8iEaqMLK8QPy4VV339blA\\u002fi1FrFYRt0AJi5gT9Bq0QG5C3cnUP7FAf8PVI+herUBR5MjQMhmpQNZGeppZyqVAnK96EUBho0Be4SpnZqihQGm+Bve5V6BA4oFtiHtTnkBBS8CGONebQF+ZMz1pBZlAmWA8wsLSlUAvxJrKoVKSQPFjsJ+WXo1AY7fq8d5GhkArictIiLp\\u002fQKmAqUmkEnVAFo7rjaP5aUAaRXKIwJ1dQKtmnqmKL09AtDHJO9ZSPkBZFPHunUErQLvukQ8UsRZAyP3P1HWKAUBXHzkC1j3pP0ODmKuv79A\\u002fzEvx3Zo2tT+EK6y2YtqYP4DuKuI3fnw\\u002fK1PiO6gfaz+v1MJf5ZJrP\\u002fOcaPUcx4A\\u002fpKef0aIzoT8WpxtWZ7nAP3nhdVIfw90\\u002fl4acK+Ez+D++MfdBJPwRQOtMJjdZayhA4rEH1TJIPkA2DMNL8yRRQNzqE+YLuWFA5FkKjtm5cEBK3kl3yNN8QMI8PU4nsYZAcU1ntX9TkEAobPwejX2VQEieV4Dn6ZlAbQ7VD7eynECSLSkiz1idQBbCY6dnC5xAT4L0SwCymUCMqThIMq6XQOQbIGIsa5dAgWXahw\\u002fkmUAXZCGqVE2fQGS+k2E9gqNAyNdP2XLqp0C8VTYsrD6sQLLUIz0wDrBAEsk9k6e\\u002fsUAhxhU7QVWzQIAM\\u002fsyi9bRAzaJTmAawtkALf4TXHHO4QBA7zjvhGbpA7ZAV0X6Eu0A3F8\\u002fBGLG8QHVh8Emgy71AWcs9SV8tv0Bdkz3PmqLAQOlZxfLtM8JAE4EJHEdLxEBLcer\\u002f5azGQFKnPKkx58hAwyQqC0V2ykAmj6jUkPbKQOK9H221TMpAOtrCEwmtyEAsfcBQ93\\u002fGQGIC5rAzNMRAlo++ukEXwkDAJOl1NkXAQN+qCILUYr1AG3u5irl6ukBq00\\u002f2lZ63QJKwErgNvbRAzKEmxm\\u002fnsUALZyDGf4WuQB8KU76J6qlAn\\u002fFCVjoxpkARtBr+gGWjQMqpgnGbaqFADaBk2XYEoECy7G9sgNOdQJzKbMNlsptAP7eqcJpSmUCEkVQRpZeWQBgETOcBjZNAc00FZflRkECGtxp7dBuKQPV59l932INADvmSD9NsfEA9M6ujDgZzQE2VZPg+o2dAqjKUtpIhW0Dz3X47a6xMQOZtIXBz2jtAbgz6yPjaKEBxTxmdgmIUQGuIhAXLxP4\\u002fmMQZjVFo5T8fKoGdkYXLPz7fHtPLYbA\\u002fL5\\u002ftzx0ikj8L8MTQJ4JzPw0iwXxTo2E\\u002fiHJDtugAYD9+4MENXNNzP+G9PZjowJQ\\u002fBkLp2EiRtD\\u002fWYjP+9aLSP\\u002fdmLx2+0+4\\u002fTid19adCB0DQd8ORKgEgQLPsFSM8EzRAcV88mwzyRkAMIb8VF+VXQIae5aVurGZAbuAvhUacc0CnBUv4PPB+QAlxA29JR4ZAvjD3qCJUjUBpLbtcMK+RQPP6Uv9knJNA7Hza82UmlEB4k+kZwnuTQDzzqLnyRpJAibzd0kR\\u002fkUAEfmFQxxmSQNUnZ6lCt5RAxq1ljpNsmUAgRAO6R7yfQFscK560ZaNAPz3KRL3hpkD+xppo1hqqQH3I8DIWI61AVW5Sww4gsECBTyJz3dqxQJFSUhF2ybNA24TWPE\\u002fQtUDaqIvbhL23QOI6l7bOY7lA\\u002f1FOZsO3ukB5UEA1a+O7QEDxgIZ2Rb1AeQpaSidVv0D2yCeEATfBQIFELELTSsNAvPl\\u002fOL6sxUAaPZBSGu\\u002fHQIndJpsOkslAG9DOGGYyykDVMqy8Pq7JQLl8UDprLchAHsXy6zMLxkATYlwsTK\\u002fDQFEkWAssasFAFWY77AXHvkCTxeEidTu7QCmy6jIlDrhA3fMZc0MZtUD+3KFONkyyQI+TZZNqXq9Adkw7ytixqkDAcAACvcCmQPxiD52upKNASn4qrPtYoUDmdCNie3SfQNsB3s2vG51A3TNJItwhm0Du\\u002fSAcpBaZQMDIykQyvJZAdYN8HyYMlEA8Xr8fiySRQFZ3QOxoW4xAOmD5gKSVhkAimfZ6CDKBQPgxhbuqzXhAR4kKuD7JcEAU6zaP\\u002fSRlQMKDtI6QoFhAPzjKkFdmSkALzu3wwPc5QBXKMdtPZSdAuk1rEitKE0DH2fWooRr9P8ic5giXGOQ\\u002f8iQJWMhryT+j0KlIhn2tPwd0np6HeY8\\u002faQ8gbBQZcD9\\u002fhfMIG7tbP82IPrfQmFE\\u002f1JDEaV41Zj9MHEyAFr6HP6ToT70++qc\\u002fNgaWj8Mbxj\\u002fn97xtgJPiP5T1v+tObfw\\u002fVudB8qLLE0Dc3iYNVxQpQIlyj2dY5TxAYuzspZ5GTkC4FO5TZdpcQCxnB2UEBmlAK\\u002fHeW17Ec0AokQ1VyHt8QNCcLQbcwYJAf0+ic0uphkC03gFI30aJQCgWk23dWYpAj6ZMxRVEikAFHf2VjwCKQFM\\u002fdtAi2YpAyjoJnpj3jUD2HKiQaP6RQJiR6yBDYpZAet1QK\\u002fW3m0CUaanH8a+gQJkTo4cdaqNAq78M0Tv4pUDrQI0M8oWoQHFwTVJ9ZqtAa0rNHtLirkAlKIv+v4OxQHHvHGwIyLNARoo54vH+tUCkSH8tV+m3QEGfjyE6a7lAu9pMSr6kukDaaW4EqfK7QHZDWJCNz71Avbuxh2JOwEA7zh6epTTCQN2jMlqYacRA\\u002fk7M\\u002f9aLxkBtz0Ut9ifIQB8kfCNw4MhAccP\\u002fweiMyEC+5HoeXEPHQO0Cj+mMScVAYcd0hDT4wkA\\u002fHjSwC57AQO57zffR27xAJTSYkxb4uEDGw2LLiY61QOCvDmS5jLJAhODkOdPIr0C0jjXM+iSrQDevXQPwNadAVi\\u002fNM8MCpECJydhDnYahQBBniplUWZ9AXmeAq5KgnED+PeDgGoGaQAzUrl8RkphAZTSRrAx\\u002flkDnAoRBXyCUQF9XMPJ5f5FATbQakPuHjUCM8vb\\u002fqzCIQFVXhpYkMoNAcxCRK8BVfUDmf7kT7GB1QLNsws+eZm1AuRaF2sfiYkAW8w5rEnxWQD39c7hmqUhABK\\u002fhSZ\\u002fROEDR9W+i29omQFD4hW1UORNA8n230UaB\\u002fT\\u002fcc8lkZaTkP2jhlET6Uco\\u002f3ZjGTfKTrj+PkpU1ETaQP\\u002fWuQj+ySnA\\u002fZzxFjCtwWz\\u002fjyiR0mltCPy4G6YD1mlc\\u002fyzaRtVzCeT9S5vJX03uaPwG7N04D0bg\\u002fYRPSaOAm1T+dCTOkzmLwP57c\\u002fs6dDwdAf86Mn9Z3HUCxAZe\\u002fYxcxQIrOXhah\\u002f0FA\\u002fJLo+582UUB1qWPil+xdQMfOb+22q2dAt5gI5K4TcUBWfxX6Uo52QMFsAzivdHtAcT6DKZgsf0AQ7X8zid2AQDqAblDK+4FAuoVaD5Swg0Annvl78c2GQK8jjcFQ5otATQ2HKqqGkUAieFhwUuOVQPlsSg17lppAlgLWKMYln0DSX44M2qShQNx41nCsj6NA0+3kaY2ZpUD8qPVAGSioQCBQPPyLj6tA3ZnzQ5Dfr0BmgsLfEmWyQJj2zkW027RAbg0Z6hICt0Dr1YSDbam4QOL0WxtU5LlATohk8CIJu0B244yP25G8QJVKSco75L5AoeKfT5UNwUDdCQ9vG\\u002fbCQHroJPx028RAPwcS1KZbxkBROfrndSLHQDyAKhAiAsdAhmpZchb9xUBXJI3crD\\u002fEQARG8AP3DsJA1Y4XRANov0A12NGUYtW6QGLCGkz8sbZADrKio+0es0Av01Avzh+wQGzWWcUMVKtA338jOhpdp0CDy+Cq5zmkQLeJj2T6xqFA\\u002fGEU8B+\\u002fn0Bwh98L6sCcQPYbLlOUUZpAPaWh0J4wmEASpUjRnR+WQIvUCe5e7pNAOAvHr0iLkUDE20Pm0w+OQK73s4fNFIlATTEb8TFshECLa6zx+DmAQFAr7rSJBnlAY68otMGJckBB6sXlLw5qQNXzzsCKKmFA8aZUSMwAVUAvcMvqmbFHQFSK7JsJhjhAkJm5BZA1J0DyLcD0ewkUQOtYwklfgf8\\u002fgbzMZKWG5j8FgvnutEPNPxExgVHIQbE\\u002fBrCAqad+kj\\u002fGqGS2ELByPy4fZY11el8\\u002fpprRS3ouMj+W9H++jM1HP0LBlTmpemo\\u002fztjlTzCsiz9ynsFSx06qP+5ksnPqtMY\\u002fsInozMTH4T9j41LxqD\\u002f5P83qWf7sPxBAQJrkOCn1IkA4a1Td2gw0QF08YY5iPENArfdCl5vCUEBo+jF58JRaQIihrimwQmNA54e\\u002fKguwaUDBmTsiRvNvQAfPgdSx9XJA7i3UpA44dkA2JQZQ\\u002frR6QM72WpCh04BA2jhf7dQGhkA3M4n4GxGNQDYU2W4DxZJAtVrBMxdMl0CdMPPxuZybQGvNGRezU59AIoaD1jQuoUCBd\\u002fVkooeiQMKX9U+0FaRA+AEUpLRPpkAdqHVvN5SpQBYe0yid9a1Ain8CtgePsUAF8h5cbzC0QPJmYXPnfbZAFNsA7SA4uEAO7x1IVWS5QApVTwCZULpAODa6C4hzu0CT8DhkLjW9QBzKRPQjvL9Av0QJHYVrwUChQqibKQXDQCHrWH\\u002f2W8RAQiwPcLQmxUAxuPFrLTTFQBKruavsdMRA\\u002ft8Gpn78wkCQEWR0LvvAQFECad9bY71AEsMKQF3DuEBpOW5lf4C0QK3ENn+n3LBA+OyKeETiq0Cnu\\u002fwUt2+nQC5pcUK3LaRAOtnhqNvToUDC6gkcHBWgQLISYKhTXJ1A9bLZdKnfmkB8Q0rBXIGYQMfJSX2lK5ZAhC90prXTk0AeMsFMPnORQAoR7RVeHI5ATnW7m7NuiUDSE4TFlRKFQJG26MIAL4FA+r6lGxyVe0BjyeJJTaZ1QFcQSF1eanBAX2X9GwmyZ0DnVVW5uQ5gQF7awwHDNlRAxf+YjWRzR0BIrX3n\\u002fO84QJEJniuQNyhAq3dGSI9rFUDNXGoMJTkBQPbgxd78I+k\\u002fq8v5K5Ci0D8dEp7ak\\u002fCzPyz8dRYNq5U\\u002fZItCUkwndj9siSXFPs5iPxNYB3gQFSE\\u002fKh\\u002fNmQ29Nj+HhRFJt7xZP6j6TgfgSXs\\u002faqT5lkZFmj+C99GO0uu2P3m4LX4MHdI\\u002fYGIj+Cbr6T9kiCV67sgAQGj8aJ\\u002f\\u002frRNAxa0ae47lJEAe2vyr0R00QFDiXhLal0FA3ttF2ssRTEBJCVTCoJVUQCFnFBZ6KVxA8BdOXYBuYkBjasSdqPNnQIEA\\u002fGz6CnBAcpQcQIKAdkD+sEaF1DqAQC5SsfxQPodAk+YUxSIHkECeWfCY9+uUQFF0IdjLs5lARZnuiBbKnUBvP6MRWG2gQCpbpSMPfqFA0uy0OMldokDGyGpAA4KjQEC979Ufb6VAM+CJndCIqEBwBMAyt+CsQK2MOSM9DbFAwcmyePa9s0CENeMdfBe2QJwUcpYxz7dAKscZ6cXfuEBPoHRSt465QLuEM2bTTLpA5EB1eIeBu0Ckhc6EfVy9QCkfKvPNwr9ALElHQHAtwUB0Y9AFFlXCQGbWkUZZGsNA4zXddPJKw0AcjcF2osnCQJD8plwYlcFArSrv\\u002fCKVv0B2ZJWW\\u002fEG7QL096y9AuLZAzPQ4Wbd3skAvniNZs7mtQGt4h3pQKKhAODopfm45pEAc4ghZ26ehQG6KLvzXCKBAGBxbO0HUnUCQI21syNmbQHdfo9sPsJlA2KCcbZA2l0B6jRDPBIaUQBuQ6T1Ey5FAhYCJFlFZjkAaJSt\\u002fgYaJQKB0b82xPYVADmTgq3ePgUA1GaVK4O18QApFRdb8rndANc\\u002f3\\u002fzYTc0ANV4yuEsBtQDvxTDdTFGZAXFfHHd26XkBgLHaSK9RTQFijrRTsikdAKhAtxXyUOUAeKohcqlcpQACMnT9o1RZACki0i6isAkAbDUmKg6\\u002frP2TQE5A1lNI\\u002fuVAE4NSNtj9u5erQ7smYP9hyt1jdnHk\\u002f73sdYzDqZT8z7BsLHmMOP8g0n9qAhiQ\\u002fCw3oB\\u002fiVRz\\u002fup+KW9FFpP0JKoStSo4g\\u002f9Ajo5d6xpT\\u002fWim40OEfBPz+SZpGl4tg\\u002fTR8RSDU08D959LPmOhYDQEM5b7mkWhRARfljKYawI0Aqo8KPLlkxQA7JU+cCEzxAQ8\\u002fTBnQxRUDyBB7Uq7FOQK5XXPX3MFZAxdD5HcWkYECvBCpMnS5qQCNA6k+0D3VAFLVyAnqmgEDx\\u002fil+Qg+JQLdYuFTpopFA5ixn5O4Rl0DoDwC9dxScQMYD5neUAaBAvyf9iBRKoUBG+2azeQCiQICXzevAgKJABno1IXpQo0Cx5DThcPqkQAfDye0N4adAHJIEdtIRrEAVj8t5SpawQJeQlTG0ObNAIIeoAzmFtUAQHfuPeim3QLUWYC86GrhA3tm74k6UuEByMkVfCAC5QJNPLI19wLlA6WCy\\u002fVEKu0AhuR62KNS8QLRltjdz4L5A8DV0onJpwEBPBAfIOiLBQGv\\u002fa0OaacFA54QltJwZwUCklN0rqyHAQPsthXW2Hr1AQlR4GRgfuUAQ14MvD8m0QOfQKbCEqLBAD1sYNH5ZqkBPy+o4xSOlQN0L\\u002foSOuaFAkBKjpHOVn0B23YLog6OdQFUnEJhdepxAmM+kRRksm0Ce08jFkTSZQAU+8l6lhJZABQarWhVik0C2T0UoxjCQQOokjgwRiIpA5zZsa2eUhUCL+zFpJJ6BQB1DT7lNGn1AQBBJ3NZYeEC\\u002fM3jRyHR0QAEr9be893BAaIWI87g3a0ArDAeCCLVkQEhjdWW1cV1A+eX061tbU0CEUNuBp11HQGnZ+h0exTlAFCqnuaDhKUCqhhfWBp0XQODy615SiQNAPU6P4N9F7T8+a+vvR9bTP5B7lvJcTLg\\u002foPEz4HLtmj\\u002fP5fxU9gt8P0rjU9wIJmg\\u002fHtCsNZeB+T6JkpwT\\u002f3IRP\\u002fIYY0lhTzQ\\u002fyOGO65YIVj\\u002faHi\\u002fOaKJ1P3yvNVXXMpM\\u002f+sEdFu\\u002fIrj+jehDNFkzGP6\\u002fhrCydL90\\u002fdUViSjpG8T+lrgDAAYcCQJH8QRL\\u002fExJA+eIeJDAvIEBzExGqDgkrQBWtEsAhtDVAoPtlHNt5QUANCzAliFtNQBRR\\u002f8HO6llALzAMcjJUZ0CTUcTcEIJ0QOhLWtgiCYFASM33R2dDikAYfWM2waaSQBke6WoEZphA0EL8goSFnUBTppPHPKigQCpACq+8waFAFAqNha43okASsvowpHOiQEoVttstA6NALz4TtV1wpEC13tg5YRenQFEIAri9AKtADDQrAaDNr0CrJE4x0GOyQMxHJRbBjbRA+SHQxacWtkCWziWtPe62QDDtLtniR7dAwF1TMymAt0D4ySlFv\\u002fG3QEn2IeXk0bhAWBWGroIjukCflOUSor67QPPhTKIqX71AVxuOE5ywvkCD0EsScVe\\u002fQDftqEvg\\u002f75Aj9g48oN1vUDm9ZVKE7y6QO58A9IrGrdA0r1WcuwLs0DPn7rvbkGuQKHxZCMppqdAYyCWvUPWokBvuHmjjtCfQPld70RVEJ1AjOl+yiM8nEDXDtRhc\\u002f+bQBUMNs3HQZtAGUz1XSBsmUDk+NaW4XuWQJIKTDkn3pJADrJEEppTjkAtGUO+OruHQNq9qbD+e4JAcc81JM5SfUB+R4aelw14QPUZKkjZZHRAkXfbGb+QcUCEv1yUJe9tQC2Nwc2DlWhARTdn8MUQY0Af5RryLodbQBaAIERxU1JAWFzikkJcRkCJ31Qd1OU4QPkQPUB3OSlAnkZyp4QzF0AMKKfOclcDQHx9KT9BMO0\\u002f1HxWSsXp0z+t5NL+8Yu4PzvStO73XZs\\u002fsgilA4mtfD+9qLplHtBoP7JCX94zIeQ+sNVqouHW+z7u3HSZi2AgP+IdIBGN7EE\\u002fUAOrne+7YT+48a46bK9\\u002fP4judaS3jZk\\u002fK8omA7Sbsj9nfRpWGX3IP8S3uv53LN0\\u002fPx1vGduW7z8nT0zwlVv\\u002fP\\u002fXr8Y\\u002f9CA1AExyvTeTeGUALEMGgFTInQAiVXXEEyTVAH7jUFeSMRUD+O6qxerRVQK\\u002f+bEqmRWVAv\\u002f2QD1qdc0BxDl0ZgrSAQOuB7Z5aE4pAWb5w4+GekkDRGQrL5GOYQFzY7HEGdp1AumSh9sWNoECHkTQJj4mhQGP8VOeX2qFAxWLYfIPtoUCi6spqRU6iQD9Radrhf6NAK77\\u002ftUTWpUCFJTYtrFepQAK4To8nrq1AmTj9ehsbsUDot8MtwBizQP8AqN3lh7RAw8oDCYtVtUAgaoj7xqm1QMl778Lz0bVAWvIYHLgctkCmDfszeLy2QD641mFAvLdAW0FpfWAEuUDp3ucBlWO6QFaa+vselbtA615DBA5GvECcuoAI3SG8QKTPC4I26rpAJNYgPZSSuEBlkRqielC1QETJrx8alLFAvuCjRGfTq0CaknXApKGlQFmqHZ3CNKFA9t0Qhu1inUD3ScWKU42bQOkd\\u002f0gTpZtAG0TO6Z4wnEAbg7HfmPGbQEjOsP9CO5pA9wZCg+wPl0BLP3zuVvuSQHa4UswfgY1ABeOLDYcNhkCCRX\\u002fygFKAQKEVphb0zXhAo5LkETTnc0BZyqfFf+VwQG3N2I8akG1AoQMNR2ataUAuYCFFPXJlQHF5Q0Tx2WBAGghR1CGTWECPFlB\\u002fMn1QQDZzZRmjQERAABElRxuvNkBmZu75uBsnQEIbxK7kXRVAlmCu2DbnAUCuaaig6CbrPwOviE0QndI\\u002fh0dW35MNtz+orPpYL9KZP0yr+GVGMHs\\u002f13U8VUWeZz8401XDzMLNPhrWOXfYw+Q+efffYcqmCD+EjQq\\u002fZy4rP9Gq6p\\u002ftEUs\\u002fKkloXGZTaD\\u002ffTVkxGLqDP9dnXzyf5Jw\\u002fsYmsZyMksz8zu0JgywjHP90CrVLtZNk\\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\\u002fSTNAq9hlF2S3I0CU35iFmksSQO0TQPwlxf4\\u002fNED79zlr5z+FjhPIQB3QP7ZRg8htCLQ\\u002fUFLGIyOGlj\\u002fnXdI0adB3P+zviCUewmQ\\u002fMnsFosWItD5BkygW0t7MPpInY2NwRPE+S2yoOVcpEz8QmqBwxjEzP39BWQkwWFE\\u002fq0AVHilLbD8b63HLmN2EP6wMB5Rp75s\\u002fc8Lx2tsesT+ciTbeMY7DP3\\u002fGHYpdgNU\\u002fxJSoQNra5z8r7NpDnNr7PxPFOTxSMBFAwdrKZ0eaJUBjA7oV9Vo6QEoilJT7JU5ARQXZduW6X0D8\\u002fadcp3ZuQAX1D5\\u002fPmnpA56WhAjYkhUCWnAtGvKSOQBKiBwMOVpRARUnSHNLdmEDr32TW\\u002f0GcQI0nKp7jN55AhY53+\\u002f3ynkB91NqG+ROfQM+d+Jf3cp9AhHuOtblvoED+\\u002fv08Y\\u002fShQF1PJnbnWqRADn\\u002fP8zl4p0AK9Q1GxuiqQIAC\\u002fDFzLK5AoKDyY85psEC7oH\\u002fFn1SxQE8FOI624bFAl7oBq\\u002fY4skCv6p5+AYyyQJ+RkdTRA7NAiylMkS+2s0A4hY00lKK0QBe0CWEDsrVAGRMZbBe3tkBP\\u002fCVgV3C3QN9mnjkDkrdAGJosqDratkBV+Ga8DSu1QIfKxRW\\u002fnrJAzT+NgqwTr0BZIsIDac2oQNKF6WspZKNA5RaeB\\u002fIbn0AeeqkVcxybQBCBYaNdVppAb4Lje1+Om0CK779KKyqdQF917N3Ns51AEyvYEehNnEDZwRT\\u002fWeuYQP0\\u002fRnC1MpRAIoUmaqw\\u002fjkB\\u002fasEosi6FQAlGL1zjfHxAEaUrk7FEc0AjZcbpBshrQGA3FVETD2ZA7Do49ezaYkBO8yrc0GhgQJRdiAhusFtA31hDiob7VUBAYTlEpipQQOQzfSL42EVAbmK6Ner\\u002fOkAKbaaWOWsuQJcCef7HKx9AOKxTToj\\u002fDEAEju1Qz3P4P\\u002fGnvLA+quI\\u002fJHb0fAXEyT+ISvA1NRGwP13NPMcjIJI\\u002fNg6mivA7cz88fKF\\u002fjtFgPxb9VsH3XZo+Dwlt4WSnsj4\\u002fUmuLwnXWPscg0UoAEfk+Ig9UV3E\\u002fGT+kGIPPZfI2P406gQ1q2VI\\u002fSgoG6GYYbD9os1WrkSiDPyhoAdv0VJg\\u002fJkpcd7S6rT9Zjm5YhlPCP4kpk8mCzdc\\u002fSAyMGEJd8D\\u002fSbr1P5+8GQCGSCB7sNx9AgzumR4rqM0BUz4kDpF5HQCTNsSPs\\u002f1hAU3dFq1dOaECemAaVwHl1QPbUQSO8RYFA003QmSteiUDXm6ZMnBWRQMlPTtvOO5VAmRlO7YCOmECCa1\\u002fI7rqaQLKbq6mDz5tAFIk04Zc2nEAqpiZO25KcQCKVGyh1kJ1A+uKdKne5n0CI7cZqJqmhQB8JnD+AIKRARmmuis0Ep0BXUUgeUPapQFhaVu8vk6xAtEF5YvqarkCIa3M+DgKwQAh759+qfLBAWnzrJs3hsECeJY370laxQO6N2QhF9rFAm9tPtHrIskBBNfd6wL+zQIw+uJkZt7RAM7XZOqp0tUCZtjdfvbK1QO8ox8V8MbVABLrkhYDOs0BB3fPv+JixQNdDQIgerq1AIfBa2Wntp0B1ZoKd+tyiQEpGW9usa55A6l3BupuTmkDN8ncLdeKZQDYNqylwOJtA1PFVpnkEnUAEQ\\u002furdsedQJ2lAB74kJxAQejqc9JCmUBjj0rna32UQAqyNivyhY5AAQzZV+cLhUABOy3nm2l7QLz7YB05jXFAtNJIjNqAZ0CEAZf8AGBhQA6a8e8cU1xA76gHzSIZWEDsVzShOCpUQCAAATs1809ACOEaP2J\\u002fR0APSMhHSsc\\u002fQKNuA\\u002f9VqDNA3t4DEVMtJkAzhfO9SsMWQJaDnhafNwVA0RJa9Vnu8T+zEob\\u002fN3HbP\\u002f0Q+P8B\\u002fsI\\u002fJwhRtU3Bpz8G\\u002f\\u002fBMX+GKPyAtOiU9n2w\\u002fJaMy1+0ZWT8eAkE3omx\\u002fPjcSgKv0WpY+1mTpChYTuz58kRbfZ2HePtiH8EvPxv4+YcOvG44rHD938fkoSWI3Px0jXOs2wFE\\u002fFt4UfaQTaT+Fxy83awaBP7em47zZT5c\\u002fhZ8+XCHSsD\\u002fWZg5aILzJP2HP7qZUGeQ\\u002f3JWtlHuD\\u002fj9+xplbALkVQGwt5\\u002fZ1cixAzYVbqBb7QEDPkgTRH2xSQFN7vjo\\u002fKGJA4Jv3INNHcEDg+8k4yqB6QMiGO3a97oNASjFyCepxi0Ahl5oe1XuRQIbqU8kaxpRA8aHwVRQ\\u002fl0AksLkt1c2YQE6Sm4uDoJlARBZerBIemkBRIaSTGMyaQKbnVCxIMJxAmKQ4IWavnkC4JkGLKzahQGqmGt4ymaNALVUJp589pkCDDPfF\\u002fMuoQEhdVtfS96pA8h7sJDibrEA2yrWH3cGtQHuCdEcsoK5AWIWmqXR8r0CJnnB\\u002fHEqwQNmCL1wcA7FALreKKtLisUCorNeNbMuyQEeUuQ1\\u002firNAtSQdpx3hs0CIcgJdypKzQIDRRgMBerJAruBvoKCbsEBHFXnN\\u002fF2sQAnJxVx6J6dAcgG8tvduokBKwk7pSNadQDVQ0bt+85lAILoIK6gOmUBEcc1EQzGaQNCiGYPz5ptAqzr71eO5nEBPDQVr5rCbQNtmXw8Ym5hAceZTJFUGlEDgGsObiNONQGxCRSBfbIRALvjG870SekBHTCDAC\\u002f9vQCKUx6Sl8mNA\\u002fexOGzYIW0CFauSzXn1UQDZ2s1hMtFBAIxETENhpS0BveX4gsYxFQNtuKNFEnD9AoWKM\\u002fSRcNUC3wrhwVW4qQJQpyKc+1x1A5AJ95zaqDkD\\u002fIwWbh6D8P5PJT0YvPeg\\u002ftIpC47yW0j+IcvguC8u5P0e0eR6MLKA\\u002fUx94pc5agj9rzDermJtjP5qgONSdPlE\\u002fzZK3AZVYYT4gmkPr3814Pm+pbrA7NZ4+TqKVi28LwT71\\u002fXl1WGPhPgQNAwPgFAA\\u002f7nEEAggwGz\\u002f3ANka6l01P984vfsYH1A\\u002fKI882qWBaD8XEGwhVqGDP1MnE5OWsaA\\u002fBoW6jtgKvT+G39BLqJbYPwZqzUc6ivM\\u002fa1+WFxaUDEBlh4UgNA8jQGGZEoLtGzdAtRdLEER2SUAeswDQ14ZZQJTzPigoV2dA4Usbh9iGc0AhSK+\\u002fAQR+QGHLbYVWTIVALOpLTVARjEASKvCrXUqRQEcGRI4PEpRAFA\\u002fOY2AllkDDfUorHHqXQP5qhHYpPJhAWjQ2IarImEAQIVrYWZ2ZQDLTshnMOZtAOgp+EkP1nUCs+DIZjOugQNAEBcKwQ6NAJSOFaH+zpUBfJCde9eSnQEPGQ37\\u002fnKlAgY39hCzRqkCbPVrD+6erQP3Yr\\u002fIFZaxATC6v0FVLrUDl+V5S6oKuQF5VWpftBLBA7\\u002fARyenYsEBwYsZqD5OxQKQW0jxi\\u002fLFAmGvhxNjcsUCKtdZKlwyxQJjREf7NDK9ACrJJ2YDkqkCrAMXp+T+mQMdw46Tt46FASF\\u002f3\\u002fDL\\u002fnECGH1HtK\\u002fKYQJ+c8c5lopdASfph305MmEAgOrfNsamZQLg\\u002fDnhTYZpA5Iw9wNJ+mUBpSQ0Uz8CWQExE3UspmZJArPGlo17Hi0DLpTRSrv6CQIJ11QGT\\u002fndAUGa984GabEDl8Neu\\u002fM5gQNztsDlv1FRA3VFnVNDHTEDiSkV8HvpFQG1kWXI1bEFAqyD91+X5OkC7s7h3iKkzQMgl4HmEgipA5AlMVhpkIED+4LlIg4ISQN5aB\\u002fZNCQNA\\u002ftUUgjTL8T9H\\u002fbXqti7eP+WVtS9uMcc\\u002fnre787QgsD8TKMdJFUeUP0REKDFFE3c\\u002fN4PAWX+6WD8CnXDf2s5FP0j1JoRwtkE+vjdEUIxzWT4BPL\\u002flTC1\\u002fPqvMSaiStqE+d0Ub7mVBwj6PEXDx7y\\u002fhPuEgfY35D\\u002f4+7IdOczU1GT+bF\\u002fpsfEM1P+UsZpqq5VI\\u002fe0W6BJfacT\\u002fF6qD0hkqRP5kjNtD0U7A\\u002fspDeJDv6zD9NemyMMa7nP006rW3ZqAFAcNhLEyD1F0DcPF2hzY0tQJBuO7XUmEBAkxD79t8CUUB8nIdoSetfQGGvAAjpgmtARrTFhajgdUDbBwCGnyCAQDCQkfU0J4ZAC3H1Xvl\\u002fjEAi3+ufvUKRQMZgxsfAy5NArgS5HyyklUAVwB14BsSWQAowWY0+YJdAjXg36efnl0DsubYXF+mYQHQcsPCU35pAOFoBArn+nUCY4dab1gihQOWoaYLbQ6NAV6ciGD5WpUCNib0cffmmQMxncOj0FqhAm1g4Aw\\u002fNqEDInI46g1ypQKmLcAFACapARDQEbPT+qkC3ZV5tSkOsQOVCGh0fta1AftQAxuQRr0DTXfc\\u002fz\\u002f2vQJYVQ0KQCbBA7Jhmwer9rkAVMfxDlp+sQI0\\u002frVQeKalAhKTV8MQZpUB+LD68mB2hQNWEM+T4sZtA7kIb\\u002f3ttl0CNq3ZqEpSVQFUzVRJqmJVA\\u002fi2IMZ5tlkAa7g8iheaWQFrdmjTSIJZAyzXsBtPPk0DdP0CYiUSQQMQevjjHZohAsQHhZf23gECXEp6VHwl1QFS5n\\u002fQmpWhAhW\\u002fFCLO9W0C1GdSebMJPQJizS3G6z0NAumVX5T60O0Aca0j+ttA0QLlXFtdPZi9A0ctB\\u002fRShJkCtxop\\u002fjl4eQAGqKlrTvhJAzlvSxmwpBUAQTlC7KMb1PzBWE+mmX+Q\\u002fjaUqCPxN0T\\u002fK\\u002f6WCyKS6P77YNNYxkaI\\u002fKY3KGcJmhz+G8i4rfLNqP85UPcDDs0w\\u002fc8fMwW9hOT9OC700ubYgPoZ6ojLDIzg+9eeWSE3HXT5wdtpwyRaBPsyT8mbz66E+00mPsoJxwT6H2x5aXUHgPpDV+6AMbv4+KvQ+Kdn9HT\\u002fkQ6AFxnc\\u002fP4O6Oz5d9mA\\u002fY7f9gdjdgT9JI3\\u002fPebWhP\\u002f7pIkxELsA\\u002fhXtfxXgB2z+YjMqR+oP0P27RxckvXgxAlhVtgJPfIUBr13g3Q5I0QPkitF9UsEVA6lWiadAEVUClvloJU8tiQP+vEnKUIm9AG0PzKHf8d0BSPh5ucECBQPdCfdtFP4dANkXWQl9vjUCM8cHIZpCRQOwJPPCR1pNAIVknMKFalUAD8iqIwSyWQMnrocBxppZAkK1ztsxUl0CJvOCSYMSYQGin1HpRQptADCA5IHaynkAE\\u002f9GIKkqhQFGuZMatHKNAPedjTC6JpEDrJgeN8HWlQLglW7tl\\u002faVA\\u002f3kTibRbpkAafweYe9CmQFBCLu\\u002fxhadAJ9nreq+GqED9awlF1b6pQD40D387\\u002f6pAf2MSAfP+q0BXv7FuFmKsQBlVjdiWz6tAy+UZ8qYUqkBzE40Yz0SnQDuOddZVwaNAuqFlNHgfoEBxW8xxBfGZQCsesUgDc5VAS22g7WEJk0Bi76HiM1mSQFeTEPvukJJA0Jvuhxu3kkBuBIMe8QSSQGxPAP29KJBA3h9OfpylikBofg\\u002fdNxWEQKdBH9D0oHtA6M9iaktjcUBHg1fyWDJkQFPeK4XEGlZAOIW\\u002fTZrKR0AXD6GOfuo6QPPvdSBt8TBAWJk3sGuWJ0CoCzIoZBEhQL\\u002fG6AHnIhhAQ0aLybIQEEA7SbZct8QDQHDwposwS\\u002fY\\u002fiAjLlNnw5j8NG3fAcXvVP8OgqU4PRcI\\u002f6liQInItrD9STp0XGK2TPx0NeyJe23g\\u002fBH5LInRvXD+iLpNwj6g+P02hxaWeLis\\u002fcWh\\u002fbFMj\\u002fT0b3HTSqiwVPjdkCwipYjo+3E9sTJ7NXj4GfFpEM6+APq6gzjuiR6E+Yxp6TPDxwT5L+hTVPpzjPhkL4CNC2AY\\u002fVLQC7QBqKz+NxVXxehtQP\\u002f8lECf41nE\\u002ffLenXCk8kj8Ek00IPgqxP9fIUfncBM0\\u002fiu7y\\u002f8aC5j8IPda\\u002fO9z\\u002fP4elRM9JnRRAsX8gNGV1KEDx7VleCbE6QFdMSWnE4UpA0ULwRhASWUA5Wlrdq7hlQCnUCOKWiHFAK6nEPe1uekAngvdOt6OCQKvObX0JoIhAykk\\u002fD\\u002fOFjkDygNSkU8yRQJ+EsSompJNA6v0PbeS8lEAQFAXJJFaVQPk+BjeJ7ZVASKMVRMMMl0Cu3vEXGwmZQBt9Yc8X1ptADl1sh74Gn0DvT86GEQChQOxUHbp5IaJAOkbMuMXRokAIx505ySmjQEpaeY72XqNA8npKsGSoo0AvLoNhQCukQO4nwNJk9aRA+SzfB9H\\u002fpUDIO25ZEC6nQAzAMaEBSKhA\\u002fHtjj4j4qEBhul2ACd+oQLalPKidtKdAyatVmtRypUC86+JG3mKiQIXkUzIiEp5Ac0wQEAbul0C6nSFx\\u002fzaTQEF\\u002fM+soSJBANgA1uo7ejUBFbODpQR6NQGwNExu9wIxA3PSHw2V2i0DLzvEA\\u002f5yIQOmRRwZdXIRAhK9qfOLXfkAgICWXv1J1QPjzQms87GpAPq+ijAAxX0AAx1AzPMxQQCA006cXUkFASg25m\\u002fAMMkCeHTwAu1skQDOODxdvrhlA+Zfjz8x2EUDfBOO1T\\u002fEHQGOyA1NBcP8\\u002f1eA2T7A78z8Z+M96+6TlP1xW1H5tQ9Y\\u002fHZhm67nbxD9xW4O4P8KxPxO33GGmbps\\u002fvyeZMVYxgz\\u002fAPUWf7UxoP\\u002fGsfONs3ks\\u002fHk1QY9wiLj\\u002fUA0xJ1skaP12t8OM7etc9nbNCg5Q38T3EvnCGM9IVPoX2Fxv4Sjo++64OYetDXj6e3Gafnm+BPpe90x7nH6U+v2CGQSdSyz4A+KqDnT\\u002fyPrHyjwoz7hc\\u002f8Eg9sQuhPT\\u002fqQMVRIfNgPz5G3ViTv4E\\u002fHwZ93NvzoD8rFhClHYm9P\\u002f68e25hf9c\\u002fS+3pJh8c8T+SlodMsd0GQOhxD3WlHxxAFqc4OnbtL0CFYdOSlMZAQJe6F65VXFBAGo2QGU6uXUC6Dic4CRVpQB2zXW2jxHNAHM3lCgoUfUDysUitdfeDQFFNZitXn4lA9US3ChzMjkCHtP4NDW6RQGiOkyQpxpJAIu78d+GTk0DsV0jSLzuUQF7TTDsOM5VA9bEZrifKlkD9lxsdMfyYQPGkVtAUcptAOwgiI8aunUCSwSo4NE+fQF69XWocHKBAuTDrejVNoEB5Ik\\u002f+MGigQHpKmsiBmaBAQE3LGuj\\u002foEBXjMCbzquhQJ9HnK3doaJAmUHYR3LVo0ARcetelxqlQLVtNsvwHaZAiRMYbBx0pkB03+wco8KlQBp18rv\\u002f7aNAL1N5m0cvoUA226wdlgScQARPccto7ZVA\\u002fgSyo6j8kEAlxn54yjqLQIQ8fp0dc4dA8I05WTW2hUA+gA6jvceEQCv3l6pVkYNAhIKMyhJ5gUD9LeTZyfZ8QP9NYTczCnZAEDi8CaqkbkDk9NDZC3BjQBAZ58ADjVZAd5gWZHMcSEARebn7YjU4QCn9Spw9vCdAuyO80Fo1GEAIBO9CdEULQInDmpHKAwFAM00NvVw59j8aiSlIm4bsP75y9KE\\u002fSOE\\u002fCgoss8df0z+eQPUAgefDP5twfYx7prI\\u002fYlj3EDHJnz\\u002fCLiQ5sJWIP2XQXRlVO3E\\u002fY2QMl1TdVT8rtGAQ8iI5P5v\\u002f20KLQhs\\u002fGMDyNSJLCD8rcqyX9oaxPXsGHIlbG8o948UDykUS8T3hPH\\u002fWyNMVPhaOjzOY5zs+iWUbAdW9Yj79uBsdf+mKPsCFps+0A7Q+xHyxJNxM3T7kSf80mEkEPy9OM8A4\\u002fyk\\u002fZnviOEuGTj9oonR+f11wP5jbfsehBZA\\u002f61bhsqKurD\\u002fj8L8sP4XHP0KM51mYtuE\\u002fOya1LGCQ+D9TDZzg+HAPQJvRTjW1nSJAI+BCxNFvNEDOUskWi9VEQHLVQ\\u002f9Jv1NABPNxbSZqYUA446ov25VsQL7te9Vg1XVA4RHFZaAKf0BrbExdqI2EQETM3\\u002fT1aYlAnJQslMGAjUC1qltiKT6QQFqE\\u002fFKDPZFArqQkZYAEkkAl+fJVwO2SQGD9B3xGN5RAAyNwPhbflUBlSpAeGaSXQHM\\u002fDKCgK5lA50IDLTo0mkD7OH7giraaQCd16tvN4ppAjQ0legMDm0BGthoiQFubQHIuK5pXHZxAaor5Cg9vnUBnVQLmAW2fQDF3v0FsCqFA8GGlJ8GNokDk7oi8u+mjQHxM29TApaRAjfUEI1NVpEAqLIC6Qs2iQFxKPspcPqBAN\\u002fRN7bdHmkAI\\u002fqMCZSKUQP+mjK8F9I1Alk\\u002fZEOmIhkA1iDrvv\\u002feBQBpqyacZC39Ahh4YeWdffEDad5lO0xl6QBeYMzDiHHdAt0d\\u002fmConc0CHgz928UJtQHeYH+jDdGRA6uDVn5kZWkBXHHM822VOQGIghqO7OUBA0nZn9O8OMEAhjyfAHT0eQAcJ\\u002f2EHdAxAigKQgeac\\u002fD+x6nJ+ywbwPykY9zezfeM\\u002fwteLwF0d2D8e3pFq\\u002fb3MP4cEHQViAMA\\u002fmBd+xh5msD+hVkjGqreeP+jpVTsAMYo\\u002fe6mgNVxIdD+OpDTIHntcP01HrPctG0I\\u002fG04kUm7dJD\\u002fAXcWV\\u002fbAGP8gpSkXPRfQ+b9qI7Q1kiD0P9mMh6LGiPYck5E+S78k9lXXGz9Zi8j0LoxkvsGIbPpe5jtHO1UU+ldhmAjMWcj4Yd\\u002feqk5KdPnwWoukk6sY+cQCCb0508D687lzA7q0VPyX\\u002fvUiGHTo\\u002fsiili2TBXD\\u002fdoJvqNPp8P6xFhtJ1xpo\\u002fi7oc9NG7tj97cUKOWMbRP5Zc1W31pek\\u002fnH2deXEbAUAuSEjOnCEVQBIufQeBMyhA8B6gDxG5OUDM02SqEGNJQFhdLyC7RFdAjjIg9WPNY0A78g95oklvQJPMy3gu8nZAwh6ouMNGf0BG1MzWON2DQLGF3UekpodAL9IsGYypikBGwKFqIeSMQIgfyWCYqY5Aw1AzBIs4kEBWJ0lyMkaRQP82suoxepJAvWmHrhCnk0BA6DmUEJOUQNVAYo79HJVAax6NV\\u002fdPlUBp5Pqms1uVQJg1Scmre5VAcy5ebgXjlUAsHrgiUruWQDwkeHg5L5hArEabs21pmkAyb3+AO3WdQGO6\\u002fq9PhaBAezCgR5s3okBU8omF3UujQEWL4xfoRqNAqdufQB3zoUD2M0GOHv2eQNPOr2v30JhAKiS7k+qVkkBz5zFQA4iKQPF7i6rTr4JAiEUuxy9Me0BPDBOZLJJ1QNOSRgCdcnJArBAeyu5ZcEAj1Qv1IIJsQOOkmY5WimdAAD2f4vYGYkD8Meecb1ZZQGYquWpmQ1BA8heHzwYLQ0AKHm+Ng180QHDtGx3xCiRASRE5C1tsEkAS4E7MMl8AQK1T5hna0u0\\u002f2t7TfBOq3T9QvdiAiWDQP+RT87bmI8M\\u002f3hfOikAytj+gfgNyxGyoPwaJDn6W6pg\\u002fE8ArZbNNhz82V27edt9zP0K6TW5Zz14\\u002fZJD520aqRT9AmsWgmpkrP7S\\u002feW3K4A8\\u002fzEs9gMlh8T4918YWeyLfPt\\u002fsjw+JBWA9aN3BscjveT2sBMAU8\\u002fSjPa8FXMvyedA9KO\\u002flCt0v\\u002fT0Mo\\u002fiKhPAqPq8TqGV1nFg+ivKkLeBahT5kCZWsmzKxPiIUlmjCcdk+yEt\\u002fAKw6AT\\u002fyPCzAqlklP63us+g6PEg\\u002fGdPqZdQ9aT853hfzRiqIP6ActeZ3TaU\\u002fNMmnvO5RwT\\u002fzHbcSaQPaPz2\\u002fNAvaEPI\\u002f7hw6Xdw6B0DY\\u002fpQWpKwbQEqchxVsjy5A3DZWsCBJP0Bah4ZR669NQOh9IHMvGlpAV5d3sZ5CZUAOps5FuQtwQHpzjFSfeXZAwBOFah1PfUAxkcZnDOeBQEuB4BUysYRAJBy\\u002fWIDxhkCBiIddLs2IQG6zi3Wzg4pAHZtvsBg+jEBBEa3Hve6NQFme6HbsWY9AggBmlCQhkECQ4f6\\u002flkyQQCt8Nv5FSJBARevryXxCkEBDNU3mUGuQQJr4USga6pBAXNcAOsDjkUBnFKUE64aTQIKMtS2nBpZACa2D3B90mUAwW7\\u002f2OIWdQFfVMTn5uaBAyDfY63oRokDrpHX86kSiQG0g15kKGaFACgUnd9p2nUAfAc9B9V2XQBzaJvJSJZFAwBEygZSXh0DNt1EaFEN\\u002fQPB5WNEx33RACy28nZmubUAUCy\\u002fh8ipnQHjzmZ8zYmNAsVAFOY1wYECUKsRBQOdaQD42o3sLm1RAJglvo\\u002fwWTUAbivlHKMlCQMEDTEbTITZA7R6vGjDIJ0A8ZjEK7mEXQMKBEl5WOwVAfOJVDvsx8j\\u002fG\\u002fO\\u002fpF7zeP0yuEKliSss\\u002fvuQkNvjRuj8t4A7Q5N2sPwtOcCejA6A\\u002fHsiYz1NFkT8JNbpIEHmBP9I\\u002fU+coS3A\\u002f5SGtgWDGWz\\u002f6LD4bcotFP2P5tOZOVy4\\u002fAOtk2o9cEz8ol3oQa2n2Pr+nItyWgdg+FXjBJSQAxj53gmdYXGE0PYrG6m\\u002f5LFI9Kkdsv\\u002fdUgD3dQGDmJBKwPSvKoQ+yieA9jrd0EADlED4TsZ1CVm1APgDo6NnesW0+T1s6yHCvmD6XA\\u002fpO6c3CPp5EfbhSPeo+xQ3\\u002f6UPIED9smtRYqLQzP4cSD\\u002fwzRVU\\u002fzVAz2WEidT\\u002fZdCo+j1qTP2MxNyg4WrA\\u002fPcyFdWGFyT9hSZKLvWjiPyBqA8xPkfg\\u002firmv5RxYDkCwwR8B8lghQGqkeAAXXDJAb\\u002fKvHgP7QUD2F1Hn9UlQQECz3CJcTFtADDozfecrZUAZjTRK+nNuQCdDavRiY3RA+Af45vmTeUBJLn\\u002fgv1p+QJ8AnpcOQIFAlZF5J8kHg0BVEfGeG5qEQLS+dbYkAIZASK4mW2Amh0AJYo2sjueHQC6wTVmELIhAXI\\u002fMiMIIiEDoalA4YL2HQKolHqD+oIdARofj2agCiEBG0PgyNiKJQGnZVwajRItAfR6AhQTLjkCm4p4DdhCSQLCmtHcFtZVArR9leDYHmkCT+Pqr8D2eQJy4fnJunKBABzGu9Xr2oEDY0JEiROGfQG\\u002fRvFdsbptAD7Clk8CXlUAyZASpMjGPQDBv3fcX2oRAWRGYukBJekBCsuxDUTBwQK7Swqdqm2RAuJCY7A2rXEDTY3r8RQVWQAiQ4oYj1VFA2o9Adq+lTEAnw6NxDttFQDmLWegb8T5Ajtt1UUEYNECvnTK5ltMnQH3kWCddvxlAgWxUfTpiCUDBh\\u002fxz5fH2Pyqom7ymQ+M\\u002fAoMm5Kfrzj\\u002fE2xZk2P+4P427Swpnv6U\\u002fcg6F72wElT+9XK8+9suFPzxAMtEzu3Y\\u002fqkGsZSqqZj9jrrxDAQRVPxoQQDHD4UE\\u002fjZxoe+C+Kz9nc4rU6o4TP55RqOPUAPk+v30eALsA3T4LX4ETs8u\\u002fPlBMx+KZnKw+cwn07rEwCj3J9LbbmxArPZfby9XtK109Z36Z\\u002fvy9kD030tvcmyPDPU3HRqyY3vQ9wEZ++Y4vJT5VokUaDc5TPnEim4uE+4A+En4mU12yqj7VjnZSBT\\u002fTPrN0TDVcevk+c3Ur+tX\\u002fHj\\u002fnu2uOo1lBP8PrHcs\\u002f4mE\\u002fQ\\u002fck+P\\u002f8gD8QRvvvrMSdP3owlW0ME7g\\u002foovQ\\u002fBT80T802jh1+tXoP6uyhQPgtf8\\u002fGNg+n9y3EkCE4tJDom0kQCd0n0pKmjRAMTyqVIoyQ0DY93aOyYdQQMy++x\\u002f5VVpAKerFqmN0Y0ATwbZRwcRqQB3qJl2NQ3FABtWxWvoPdUCUXh0N\\u002fpB4QFy0QvY4r3tAWSpxfcNhfkBPQ9X7fEmAQC46POoXC4FAX6Xhe1BfgUDtd9RhkUWBQMOqzH0c4oBAz3cW\\u002fcB4gEDW4sSTbFaAQL+OK5+IvIBAHWqbkUjhgUDtch60rAWEQL0f+\\u002fN0iIdAl5utfdjQjEBIgo2VSP+RQPSLf\\u002foUP5ZAKPvA23NomkDbJif5rWqdQO0\\u002faeZZSZ5ABr5xYf6QnEC873Wj4ZKYQP\\u002fs17MMQZNALKbPSLqFi0C\\u002fhbpASQmCQA6xmgCM7nVAZYEd3tBbaUAs2rm+njJdQKTfob314FFALMwq+jt0SEAnjRbuGmRCQNo9rWb6izxAyuqVhDOKNUAqyBowRH4uQCs6BKMI5SNAYDQbr7a8F0C6Rfrbtc8JQAyV8c51kPk\\u002f\\u002fj+DLyYd5z8CrX0JdDfTP3Jnvupl570\\u002fogn6lfCXpj\\u002fSD1VHs5yRP0cO0eiwFn4\\u002fGjMsfgZibD\\u002f9U6Fj+w1cP2q3eC0fSEs\\u002fWODqdGUJOT+YxoxKpTklP+SPyPF0dBA\\u002fYU3pWgU39z4XppdHsbjdPhvv3XuWRcE+oXDNX4n7oj7lgIKO6B6RPoKoMNmR5+E83boNFK0OBj3vErIxT\\u002fY7Pdn3H0XZ5XE9lKEsx1DnpT2LsSSqH\\u002f7YPZPTtDlNSgo+rkV0acRjOT6omQN9Z31mPjWwq8vzRpI+jYRf7hRGuz4sAobPFbLiPkEw5EApkAc\\u002ffbx1PX5RKz+q3rGjDidNP5BA6eLCp2w\\u002ftJDmuNj2iT\\u002fsIM2bHLSlP4+fxU6cv8A\\u002fG1kCaCbg1z8ufvCme3HvP85f52xYIANAkBpRNg99FUAs4FgzHkomQGz2iBGHWDVANg5C81TiQkACYJ0s1OlOQIToHV8EeldA+axzdtOdYEA0uo+ntA9mQAQbRp8DsWtAC1RQV1aTcEBgZQoQ6xNzQK2F63krOXVAoVQOpAXcdkBupWEfcdF3QGpmduc7\\u002f3dAE2yZjtZ4d0BelHtHFo12QKvI1gDhtXVAhvD5PjtzdUBRNJP6lC92QAYWFRSWR3hAWGhm5IAxfEDgrVTkr0yBQFU9MVQFG4ZA+13IAMadjED2L17oWiiSQC4LGRz88JVAlJJPvuK4mEAuunCcmaGZQGMBRN3cQ5hA1x+nq4TklEC6zCWnC1aQQDyw7dACNodAsGO9mMkMfkB6lMe2DdlxQHvMRXPWwGNAWK6Y\\u002fD0JVUDSQotxBNpGQJ2kJvF+OTtA5qDc6GRcMkA\\u002fi6dyk9kqQNJ0yWGFySNAEpvwLoDdG0BK82m0RDoSQNUjj0XV3gVAwuAhj8zu9z8uBDp\\u002fTdbnP7XAzk83ntU\\u002fceDVP+zqwT\\u002fOO7LKBGyrP1mDqxNl1ZM\\u002fI06JuEpkfD9uM\\u002fspbXplP8PTFe53AFI\\u002fr+HwZCltQD8OxslEb6YuP0mCwe1Knhs\\u002fvgSc6WI\\u002fBz879s6r4fvxPi8O2+O3X9k+rTf8HgBDwD4zuVN2Ke+iPnPF3D0Q3IQ+OVSPl\\u002f3acj5XgOz8XuO6PN7Eu9t7auM8qrkgMzilGz3WlFbKEwBTPRWFi\\u002fXCYog90ddWnsXhvD0OW6dqQG3vPQM8DQqGYB8+AbBys26+TD4z+Dbn9yp4PivslkNEqKI+KESPFVB1yj6o\\u002fCW+6jzxPtKIFCcqphQ\\u002fJhsxlxbBNj9A+1I7qBVXPz2ZiaGllHU\\u002fH3ryDSmbkj8PMQKjuZutPwzPrHjXv8U\\u002fhP7\\u002fGHWA3T\\u002fXmTqqgnjyP1TjLbxsWAVAfh3wMVLDFkCx8MC\\u002fLGgmQEZ\\u002fzOX8YDRAGnEYJywoQUCB5oJSytJKQPC624nqjVNAIwg9to29WkBDp2f\\u002fnUNhQP1ST3\\u002fPMWVAZ13tFu3haEDnQzkUOAhsQEJJ0K\\u002f8U25AKAFQMVd9b0Avy743ZmZvQO+9kF6VP25ALZkSfv+RbECo1sZaaCBrQOPhBuywsGpA2XWjLuDoa0Av7fQZBWJvQKjNLKsY83JAYt3RN19LeEBZ9K4tFCiAQO59ueMolYVAJwNTGkcAjEC7CNhG9SqRQK7kbBBqh5NAZJS5TjdflED0dla8AVyTQEiel8M2s5BAFgHLsvocikD2R14Kf4CCQMMNJdxey3dAvPFf5Cjha0AYBgv9xgZeQIMDR0cOU05As0yHrWTzPUDs82R05hMvQG2GPA2aNCJAF6vzWlAzGEC2I+kmIQ0RQCr6JG1eqQdA5MjLihbs\\u002fj+EP65FXaHyP8IXEIKQguQ\\u002flqmc3hWN1D8pf9swRrnCP0vlEdcFEa8\\u002fflGe0eiYlz96NFa7NKSAP6Wkp7peeGY\\u002fRq8llo2wTj+x7eDqkqU2PxQZR\\u002fdOjiI\\u002fblVo6rs4ED+JYBnaFFX8PiCWsqPgiec+n8Wj7bEf0j5WEfvKvou5Plq6oWIhYqA+ONEh0xUbgz6YUn1WqhhlPvQLqaQ2HFM+1bFP0nHnlTydMz1xX77BPN2Dba3WQfs8UKPnq+KvMz32nP9IBURqPYYCX4njGqA9dluB\\u002f\\u002fAf0j0ydpXjVrYCPvkxrCThuDE+2ESwYwXMXj4Ft6aCbI2IPnm1SeHq9rE+yrkbXjMj2D4K5MM9Rcz9PvG+5TsE6iA\\u002fUQj7dQOtQT9igdXOVgVhP\\u002fbCa+kcO34\\u002fEbW0Qt\\u002fFmD\\u002f7k8JupLyyP7M0+uLDKMo\\u002fXtrurMrZ4D874sIbnwf0P5qN1jIc+AVA33Bbh2Q\\u002fFkCFTI5hm9MkQAtM2agjDzJA8otPUngYPUC0YoHMFtxFQJTibaALyE5AFxbCsgxrVEAdnOYDX6dZQKPaaSkWp15A5aoCVZ50YUDW6qjE6fRiQLIourNsn2NA9OCyOsFjY0BON0nYzWtiQHVxuMB+HmFA4rCOLSAIYEDQ\\u002fxuaBWxfQLZm5q72oWBAy31uttZCY0A0qCGpmThoQKnktfsgMnBAKH4+mdlVdkBkUIc8oaZ+QA+pKD6VQIRAZVVLDW4jiUCiRYJCdtSMQE29f55WPo5A2aAPNVbajEDo8SGCMfSIQE+dNyzkiINA57g2LQ2se0DE+0xEW75xQGe4fiRcpGRA8jFYehrkVUBdmC0a02dFQHsuqS68zzNA1MN0LttTIkDkptLj5m0SQO10w5DZZwVApv6JFQXm+z9D9pcoQsHyP7RcGkDNUOg\\u002fL6kK3INZ3T8f9YzQEz3QP6VT\\u002fqxAXsA\\u002ffG21mTb8rT+ooJKSrvSYP07zX3sC7II\\u002fwdmOCqJYaj8vqoo9ESlRP20mdlk9vzU\\u002fbsV\\u002fO8+NHD9YDr0KG50EP1NT0TDJaPA+N6xQjzw62z64VVflVBrGPk4kGeMx3bA+HnuVM+azlz5MRmdbXGZ+PttnFihRv2E+LBUJEBmjQz7gBIs5HNMxPtttKVPImXI8OzqKf1dAoDyHTXWnaU\\u002faPD2GJxcHxxM9gzl1zV5WSz0yh8b0YFaBPeC5d9pVKrQ95FNlR0OA5T2PrrshJgMVPrX2UJ0c0kI+EKxlyxrobj6agkMzz0WXPvuthxM5FcA+q\\u002fE4zpVr5D69t51PaNgHP7qI1fuIoyk\\u002fZFNgssJoST\\u002fdq4tFaTpnPxoQumZsmIM\\u002fsP821GWDnj9RPa5Viuy1PyOnmi8DEs0\\u002faxCiWLHI4T8OmBSjjRX0P3eqTLy+8wRAK60kF2w3FEC\\u002fdazNYxMiQG+h54LiBi5AIOW\\u002flaM9N0ACSW9wRdNAQHBU\\u002fYAX4UZA+5udfnpVTUC3Pqn4MslRQC40GjB2b1RAt2Lm5xRBVkAeSCHOTPhWQOpGGdI2hFZAyh67pyYhVUApj2tToFhTQOihUd9b4lFAgokYi114UUD7seah58BSQN8RU2QcZ1ZA19VIAnVUXUAT9T4aQGZkQOnuiUY5C21AP9Qut5tjdEBTQV5uxlx7QOlIwPSbKIFAOCWX6gPSg0Bj5BKS4OaEQJn6KAFfBYRA8PXf9XRegUACGkEHGkF7QDcSZPGFU3NAs31IFJHGaEBrg32OIb1cQEv8OFW2PU5AGNt2bcwLPUCofxgNrtgpQHWbxt7bDxZAXDS7HktdA0D8kys9ISbzP4AOuivsHOY\\u002fNRvZjWMA3D93UK\\u002fT2MfRP63e\\u002fB3gZMU\\u002fFEOnowrBtz\\u002fBs+KE8RKoP2cBaOx3LJY\\u002ffCYUFUCJgj\\u002fPmAckbSZsP4rl8JLhflM\\u002fRM6GpublOD9HLmjXWQ4eP6D8820s\\u002fwE\\u002f+dgn0uTh5j7RoiipaSnQPmEt1u52zLg+CqYVqdRaoz72drBWdwuNPtoXoFyhSXQ+oQNIh4P7WT4ToGyYO1s+Pi2\\u002fBR4e0yA+PCwJlhmaDj7pLGNvdsFPPCovfT8LPH08trYyAdupuDwHARUTTjbzPBSIrewodSs9XTx2siz7YT2wSgPPB5KVPX\\u002fAwSNNscc9PDmpOxDT9z1pv\\u002fmWje8lPsHIhidWgFI+yCQn2bucfD7u9KMuP06kPqI84ChwfMo+l3OfM3\\u002fK7z4WxoAqBZQRP4KCQBhF7TE\\u002fp6FwiNreUD+uXmwVFU9tP3jj02cGgIc\\u002fdNXUOjJkoT\\u002fqdXLgEMK3P1o3KFym9c0\\u002f9HE\\u002fpsdx4T\\u002ffdXEiC8byPzv478TVsAJAg1lq+AM9EUDsPcArVoIdQN9EE4IEfidAX4hisF5xMUAz+hgrvDs4QIMg9xTfmD9AkfQHBFZiQ0D6jWMH92pGQDNJ3lnic0hA3Gf\\u002f2okqSUAam6X1FH9IQNTaJzjfvUZAlhj6weeMREA4Zi4h78dCQLClJKxnUEJATv+vqTT4Q0B0jkw\\u002fhqJIQJU01jWbxFBAH2\\u002faRV4tWEA+2jZCJ7JhQMr0VflYVWlABYDSKUU5cUAk5ffmL8x1QH0ERZokWHlAMUK5rdLcekBxMK9lJNZ5QAs4vrTDfXZAU+mVPi6ycUCSbVFnoiZpQJvVn3oQJGBAiQr4iYe3UkAK1Jo4S6JDQFkJmA0GsTJACrEuQiRHIEBf8GqfmnQKQApcbpLxAvU\\u002fvxWeDnbF4T+hEbla9H3RP2O3BD3PAsQ\\u002fAc1\\u002fVTFWuD9gLdwAIuysP6Fny2mHDqA\\u002fxmacUblXkD9ZIvJRMkR+P+COihExbWk\\u002fS7MOosReUz\\u002fDPtGGy886PxKV7ddP9SA\\u002fJyCraY7lAz8lqTCf81zmPgHszIeGgsk+lTWCg86Yrz4\\u002floZBY8SVPhJK7t7\\u002f5n8+LAb1eFE+Zz5QIwHrrQtQPj0C6tLDeDQ+UK7cCOXoFz5EudQDIor6PWjV\\u002f813Leg9Ktzk8jiqKjzwKnXtipFZPCQujYh\\u002fW5Y8Qu7MgHv+0Tzf6ld48oUKPWyry4v24kE9+V2uxI4Qdj3q50EF+uOoPQZriGc\\u002frtk9ocy4jQU\\u002fCD6gUNXfkfc0PpV1I8GUoGA+RbtITPI3iD5zfQK46DiwPqJOVecrBNQ+qZPyIAvG9j7Es9woWukXP0Y63Hn6LTc\\u002fK6r\\u002fHMC\\u002fVD8tiXFqjiZxP0E2pyn6LYo\\u002fRdzfVThzoj8BNSvQMQS4P0vGGWr34Mw\\u002f0yGaWZwL4D+vzX5xRnzwP0HDhyG0V\\u002f8\\u002f1LmbBz6YC0BEUs4lpIgWQM95IDv0GSFABfQzGV4vKECfR8ii5fQvQFLOOT75xjNAjZPSX1j5NkB9C1xsXxI5QOR\\u002fnBfDuTlAo4qKcJHiOEA0bFmN0eM2QBGrdwMWdDRASyCdjESEMkCjFDzh9g8yQKs3ak3ACzRAjjMfAmmNOUBOoKif9gxCQMdi6sLh4UpAUd70pmooVEDNcFe\\u002fsFhdQGwSoWvALmRAkqJfX0HAaUDEr9d4EiBuQBCxeptOC3BAmnOiNyH+bkD9QHa0WxRrQFmjKTAaYGVA6NOowhR3XkBMSQtkHJhTQJua07IPwEZA46s\\u002fd1TbN0C3eqAakaEmQPSOgDcmgBNAP7CKg3TZ\\u002fj+t3KbpjQLnP7MdTTaRNNE\\u002fr5cKMN2DvD\\u002fYyRYHdB2sPxhDh3f1j58\\u002fs8zGQI41kj\\u002f\\u002flCzncRaEP3wyqxVhfHQ\\u002faMTVFLcOYz+5ocU8FhdQP6IF9Nsbnjg\\u002fhMfHxc8RIT\\u002ffyb\\u002fyUIUFP4R43gC\\u002f3+g+CXXw30ziyj4FNnyBrk+sPj0SIkfJBo8+W\\u002fpWxQXJcj42nXGSxxhZPtvvXQVyaEE+N\\u002f65m0GGJz4iryfVdMYNPloW31UbWvE9Emlzis1F0z0jfAcCH5XBPevG7LVyxgU8ngjO2SmdNTwQ4yiKBoNzPN8VxBdKLLA8Vez\\u002fGueB6Dzo0spT2fYgPRazPXb9c1U9kyxvbHrJiD3gNL1apC66PYinC6MaT+k98EfJXE9rFj4CwHrORjpCPm29uYf3P2s+g3oiHqrBkj62sL3rEc+3PqvENGEz5ds+DvwWiJIv\\u002fj4OeM6AfS0eP5qBLzH53zs\\u002fLf1BO9nJVz\\u002f0NK0aOMFyP+NqaFtJUYs\\u002fC+EFsUFgoj8ZMvx+5dS2P6OA8wlwMco\\u002f6ojCTNS92z+zHXzE6x\\u002frP5x17+A\\u002ffvg\\u002fTvWLvLFxBEC4z1tFOJkPQE9BN9YxqRZA16gYtrM+HkAf++ctSdUiQEtbdMoV7yVA6benravsJ0C7TrpgZHgoQPun2n25iSdAyOFwABN7JUBxIQLrWQQjQFvBs3opGCFA8Nqb4EK5IEDvjYkfuO4iQLfDS6uu8ShAHsX\\u002fcLo\\u002fMkCOBYSGKvs7QFplt6Elb0VA0CX2kp6rT0D36i+g5gFWQKk02jPiSlxA1c66tbelYEBUb+1t0NFhQDyZEsUOSWFAnVSwQcFSXkD0PSW5TgVYQJFudf0xLFFAqpQbJtgmRkDK8JzN9sY5QOMh1crcECtAU0dWZ6ipGUA7qb9FbwQGQNuWGqg+L\\u002fE\\u002f666f+mHB2D8oQlC8zhHBP+z2dnYMY6g\\u002f9JhHETcZlD95Xf7pyNKDP4Wg8SqqjHU\\u002fsypkQEVTZz9pw3R8Q7pXP1ZmCJYtJEY\\u002f7NB3973HMj9hIGpS59wcP0eSkX8TFAQ\\u002fOxE4yeNR6T4QYwB6ARLNPhP\\u002f7FEBva4+fE6qZEu6jj50WQo5Zo5uPlTcb63BN1A+5FCiMLE0Mz4h\\u002fzFVu64YPt\\u002fU+ixYEQA+gWtYa5wH5D2syRf\\u002f8znHPd\\u002fpqErdx6k9Im5vhMCKlz1AODNeDSrhO\\u002ffNa795jhE8ARc\\u002f6b1MUDzZx64+xbqLPFVqAdi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02fMYvP\\u002fI61tj+rAIpqWs2zP96OV0Omh7U\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj\\u002fuH8VKvWW2P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P5l+wb4dxrM\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f8eHlT1NnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+cqFK0Ln+1P0kCgyJWd7U\\u002fMYvP\\u002fI61tj+rAIpqWs2zPxm4WbefMLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P\\u002fAtowY2brQ\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT8bhW9pRQq3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002fQgCCUlJnsT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT\\u002f\\u002fVVMtFyO3PxE3aGVYd7U\\u002fMYvP\\u002fI61tj++PoKIXM2zP9tXLOfGoLY\\u002fHnYa\\u002f3n9tD+0HHPceRW1P1yaEICF+LM\\u002flm5pJrO7tj9OIhtEgfW1P+EMeMqDKLU\\u002f5HamtFsgsz+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002fhTofgto8tD++PoKIXM2zP9tXLOfGoLY\\u002fd2QMoIuotT+0HHPceRW1P1yaEICF+LM\\u002fxDrVnU4Mtz9OIhtEgfW1P+EMeMqDKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fvnCFDk56tT+1xLBnPyK3PxE3aGVYd7U\\u002f4u\\u002f4S9o8tD++PoKIXM2zPziC\\u002f0rlkrY\\u002fd2QMoIuotT+0HHPceRW1P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0P0MFxF0g7LU\\u002fFKmZPcoGuD+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002fd2QMoIuotT98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz9OIhtEgfW1P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj98YujQLsC0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P1hi9D6DKLU\\u002fMmCbZJ84sT+J9XBLsZW0PzNlgXsh7LU\\u002fq72UXjQ1tz+1xLBnPyK3P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj\\u002fOSl8PL8C0P\\u002fdS7N2E+LM\\u002fJPtO5k0Mtz\\u002frpz9A9xS2P\\u002fFUEPuDKLU\\u002fMmCbZJ84sT+Nr+duspW0P0TwPTV8vbU\\u002fq72UXjQ1tz9q48vVhWq1P59d1u322LU\\u002f4u\\u002f4S9o8tD+sxCexa52zPziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj+ukkZNHhq2PypYcjCJarQ\\u002fJPtO5k0Mtz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002fq72UXjQ1tz\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1Pzw8Y8hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT+Nr+duspW0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002f5uC38ecO1P784\\u002fXCXvbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+1lakkQHi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f+XyBAQ+Ktj\\u002fRefbCebi1P0ig4RP2VbY\\u002f4u\\u002f4S9o8tD8OlBEmT7+3PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fNJ8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002fRefbCebi1P6YgDUi+ULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002fDphXPvWtsz+eQ5lVPHi1Pwo9SL+CKLU\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fqJ3IpF2xtT\\u002f2GM+Nebi1PyW5CAXBULY\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002frd\\u002ffpJf5tj\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP+nHIumiTbU\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fkF9vyX5\\u002ftD\\u002fhWKuV63OzP69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fyr5fwS8Vtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1PzC8vW3kkrY\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002fxYUfw6\\u002f9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT+GAu3oYEa1P69oGe9oi7U\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fh1PghLD9sz\\u002fT3sGtd6C2Pwo9SL+CKLU\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fp76yVzAVtz9C58DJLEW3PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fB9GYm9eatD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD9kWAU4twu2P0fKNt1pi7U\\u002fWNyD5bkFtz9iZU4aC6a2P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fIif9bIDOsj+m6F0q5dy2PyW5CAXBULY\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD9jZHQ2\\u002f1u2P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002fBJ8+C\\u002fKHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002fITuoo7arsD95DtwDPEe3P8mr3j6S\\u002fbM\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P3ohtruqnrU\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fWNyD5bkFtz+SfO1uFYO1P\\u002ffJ1odKCLY\\u002f3p33T4FTtD8i\\u002fcOwN46yP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fRSaSsvPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz+UNq2WMN24P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fnXb0DINTtD9N+\\u002fxVRzuyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzPzELZ\\u002fALiLY\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002fFCRHQbDktz+ea4jbuZC1P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1P+WrbL2snrU\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fy2yjViDrQ\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fFuO6mfPGsj948kmIq+GzP1CveSSkabQ\\u002fi0g\\u002fFaLiuD9VYzcnvlq0P5ZMebkJFLQ\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002figi8k6UOsj\\u002fU3dZlxZm1PxCCFNQPebQ\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT948kmIq+GzP1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj+7ek5yxJm1PxCCFNQPebQ\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002fhlOfqmyXtz9VYzcnvlq0P\\u002f4uP1TPSrc\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002f1RjsOhTHtT8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2PxCCFNQPebQ\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002fhlOfqmyXtz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9bMJ06IX+2P9Kh92sQebQ\\u002fOGCnQrHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fExcmEbHLtD+QuVRcsEi4P7CwNIKkabQ\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fArJRox\\u002futD9JABVJwNO3P9Kh92sQebQ\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002fp8\\u002f30R8\\u002ftj\\u002fiATe0xYGzP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D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+I+eZ6XzDkP2s9+JuPPOQ\\u002fMrD+1WYp5D+wQoVtES\\u002fkP42KEhOGPuQ\\u002f\\u002f8cQg9Mz5D8TCFcwrCvkPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\u002fP0D91NLeQ\\u002faIG5Rawq5D9IAZ4svTjkP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D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPxv3JX0eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002f5OXkhpMQ5D\\u002fX5YqTtjfkP\\u002fOHd\\u002fRvNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D+bQwadQxfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D\\u002fX5YqTtjfkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP6Z11JvwMOQ\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fZbwUe+cs5D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkPyoCy94eO+Q\\u002f\\u002f6QFJsoY5D9zSKZ\\u002fThzkP3WCgPbjIeQ\\u002fl8u7AeYe5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP65duGRlH+Q\\u002fwmu6ih8z5D9JxfganTvkP1Qys7IeO+Q\\u002f\\u002f6QFJsoY5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkPxyTLldyNuQ\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D9JxfganTvkP1Qys7IeO+Q\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fs5buORc65D8VYkvnMyvkP21se\\u002fLLL+Q\\u002fS9Kiy2om5D8dI39NQRfkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fqckX+2MU5D8kMaWmThzkP3WCgPbjIeQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fS9Kiy2om5D9cxcWm2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fdj8RThIw5D8vp0m51yjkP1BDi8zDO+Q\\u002fzJ4\\u002fKBQp5D8VYkvnMyvkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP+VkdrRkH+Q\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D98HhckBSDkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP7ieZjTDO+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fAXeQLRMw5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+HgLiqDzTkP+uGcisNHuQ\\u002fClk7EbMi5D\\u002fm\\u002fJUJ2B\\u002fkP\\u002f6eyKfsKOQ\\u002fHBLi1r045D+fxEAINUDkPyXSwpzoLeQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D+s\\u002f2+JDjTkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fjBvVZZAo5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D\\u002f7YIxiIjnkP1rJaxAPHuQ\\u002fClk7EbMi5D+FoEBS6iHkPwArmzPsKOQ\\u002fHBLi1r045D+fxEAINUDkP8f98PDSLOQ\\u002fxNfmL2IU5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fJ9CzfY8o5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fKGaSgFMo5D+FoEBS6iHkPwArmzPsKOQ\\u002fUrPDl88w5D+fxEAINUDkP8f98PDSLOQ\\u002fI2yINwIg5D\\u002fx6dpgEiLkP398MDdlNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002f7vpYCJUs5D9+qV88dS\\u002fkP1rJaxAPHuQ\\u002fGZdiD1Eo5D+FoEBS6iHkP3yab5bsKOQ\\u002fUrPDl88w5D+fxEAINUDkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP4wsQFZMJ+Q\\u002fgliASCMz5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fUrPDl88w5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D\\u002fx6dpgEiLkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8sTM9Wtz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP2SwhFNjNOQ\\u002fiab95mAe5D8vp0m51yjkP6aeR6ZLJ+Q\\u002fQmw94GYk5D9+qV88dS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D+oVhR\\u002ftz\\u002fkP36rhpDSLOQ\\u002fiGbC0h8q5D+dbClmRB\\u002fkP6q2dmJiNOQ\\u002fiab95mAe5D95iEr21yjkPwIvMOY6LeQ\\u002fQmw94GYk5D9sMSrOdS\\u002fkP8YLsZcsHeQ\\u002fGZdiD1Eo5D\\u002fp\\u002f52hBiLkP3yab5bsKOQ\\u002fNBQslvwr5D8WQNiKDEbkP96lH6v0KeQ\\u002fiGbC0h8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fQmw94GYk5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP80sN74tROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D95iEr21yjkP0d1oGMRQuQ\\u002fHdQY8EsZ5D\\u002f\\u002fh7nHUi7kP+m\\u002fHn3jKeQ\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D\\u002fJ7b+IRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fHdQY8EsZ5D9iHhpFUy7kP7o9p042K+Q\\u002fGZdiD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D9iHhpFUy7kP+bghWVvHuQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002fkTrKSx8q5D9Qa70HRB\\u002fkP+laLJ9hNOQ\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzK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rIwjO0BAaRfcG7U1QGCJSW4L9DFAwvzAR87FLkAODwvfMhQrQD8J\\u002f03eGihA14BovFhfJUAAVmEPvKAiQMcm0+zMrx9A0zxHZbN0GkBraBCeRXcWQAP7sCChIRVAKXsU6gdnGECyjf32L0ohQF8lqhTrzCpAsSWpFlFuNEAd7zt6ggc9QAvkESGazUJAKOZZh0sGRkDWypKefEBHQA8\\u002fBGT6GEZAB6HqMC7lQkD3VHE6lRE9QNKO2QjTHDRAF5HmQqMIKUCYM5onrAYcQCByiMKgOAxAhGU5Up6P+T+fKb6sDtXkP1wE2C8htM4\\u002fNlTxe7SytT8cM\\u002fJoEdenP+8X6IHoRZk97ZA8OTjKqD1dWh4+BFHHPfEZcOMSTuw9mi9i2H9\\u002fFD5vXGcolrs\\u002fPvvgmyqBFWg+xNNgaVkpkT7VjRVuloi2PlRpC+rUDNs+Suz1Vfqf\\u002fT46QxsEXKYdP4dzx+7mQTs\\u002fGe63YuE2Vz\\u002fN60HTHIdyP5qTQkEMEIw\\u002fu88nWz1dpD9Wb1A3B2m8P6zmkMuz9NI\\u002f63eSCCLv5z+tpypbvTv8P33delRlxQ5AWqe9wyG9HkDVqzVVQAUsQAVyGx4vSzdA2cwdr526QUBnsUnkgvZIQPXHkvQzkVBA8vWXqNZSVUAnKml4\\u002fnJbQIQmw83A\\u002fWFAJoEtFkbYZ0C1viIx7RxvQOWkgPqNZnNAVhG5hk+edkAnVFfk4lR4QCA8BxVaAnhAh2URTtm7dUDeIkg0szByQJW5rj18xmxAI5n40LyLZkAeoQ5tbtliQItm6amV32FAwG\\u002fTYuD5YkBdajB1fQ5lQK6V0axH+2ZAQKH8SFfsZ0AZIpJBWYVnQAArDBw12mVAmbg9hQlHY0B\\u002f3RHQLUFgQGMGF6KvaVpA5uBQ3\\u002fjnVECxHtdSv2BQQKS8Ndrn60lA\\u002fbzp9O4gRUCSJURA\\u002f+hBQHfAikzNcD9AK7zI\\u002f\\u002foxPEDw0z43GnM5QL1ZfgYA0zZA018vwLoiNECapM5\\u002fsmExQKT\\u002fkDpUgS1A5yeqDi1KKUAvfva+yl0nQE2epZgReylA1j7WlObaMEC4LHnSSOc4QOxRq9vbf0JAuDxIxfnuSUCb5w7M+qpQQCXdRTJva1NAhQfamuBpVEBocMSdLlNTQHxeM3eadlBA1gpq5VI8SUC7h94p22VBQOHw8OzRkzVAj3hKngoSKEDgBarS1yYYQPBYA1UXzAVAfkbJUo2z8T++U+QejP7ZPxNMRKP3S8I\\u002ffOAoVboKtD8UjdrLnD3HPTkDIrXdX9U9VV\\u002focjFH8T1+FhMB5fgQPqMTMmCZNDQ+7zT3rBB\\u002fWz6zgffVbWGDPjtQD0iRcao+DiBLtBLh0D5KghzjW9nzPjcms5ECWxU\\u002fq96Hger7ND8TOR+hYd5SP485jZucOW8\\u002fxqCmsZ79hz++d1Y\\u002fo1GhPwJ+GEXGyLc\\u002fcnb\\u002fwsxXzz\\u002f8pIAdX9njPxa3ozmeAvg\\u002f92grWMRrC0AWsFsjLTMdQIRI\\u002fWSqsSxAajAs4c7XOUC1vGy8xEVFQJqAw4BmCFBAFxMBcCpNVkAatHRa7xVdQHh0rp17PWJAQmNVmKOvZkA+3jQvup9sQKtbCZI1U3JAAqQnRCNMd0DqNCssgZB8QMneKyCffYBAcjS44ZKrgUAaLF94BXWBQD3kLF841H9AaIyUmkEOe0BvHykV1Qd2QC+CchpwHHJAw54sxqsfcECORpftLitwQOt8DuActHFAvZbtUp3Sc0AhVZD\\u002fA5p1QNgk56u4YHZARSQzSzjidUA\\u002fNY\\u002flQzh0QFnhtfbouXFAWUm88MCqbUCtIOv4vuVnQG5t1dDzxWJAjlaIeHhVXUAjXOZdZ1lXQCka6xeZUVNA15gZgLC0UEAoRsosDOVNQL0hs3a6OUtA03hsOg7hSEDI1XbbpJRGQJZNGH3eNURA8d\\u002fyVbjFQUBoQLJbFs8+QLCn77sfzjpA6bt0P9aDOECZ8nvO2V85QIC0M5BtJD9AnebhjSmqRUCfeNfV5xVPQGEZTxeoZVVAmU\\u002foAa87W0Ce71Ny1IdfQJMmWr55fmBAZY6rwCAaX0D3jMGPc2VaQF7ulCYZKFRAbdWmykCxS0A6C4uSaRxBQCCNw7cSBTNAvvOVBxcEI0A8gsDJ2hkRQHI1HugQrfs\\u002fExpXbkQ\\u002f5D+jMacJpWPMPwbdp3KcAL8\\u002fuzAJmoEL9D1\\u002fSpe7M8MBPuOpBfjTHxo+ijjtOHLKNT5cTr1M6ixVPogahsjvSng+8NXUbrWinj6G8ZousojDPv2dceeA5uc+uwNOET1PCz8FVWxDHcEsPx9zP2X5uUs\\u002fuxpTPnNyaD8W37ShjsGDP76ZTTXlcJ0\\u002fkoGY3LhrtD9iVItrA7HKP3kz6QcEouA\\u002f9USo08vq8z83YZyqLuoGQMZDy+LyIhlAmsQde3b6KUAIyogGD\\u002f44QKznQhfOLEZA0VrufVYNUkC\\u002fw5eKPvVaQKvtvms\\u002fkWJAo4cg5VbqZ0C1XnYEJnltQM6vAqKk5nFAPFr\\u002f2Hf6dUBxbEnZQGx7QADX+FhDFYFAkS6rAI6qhEDr5b6e6q+HQFu\\u002fX542U4lAUaGWvUsWiUBC9gIkNhGHQP0Nya8o8oNAL3B4O+O7gEBOAHt41sh8QIgIyUudAHtAQP\\u002fPZ0M0fEC0hRCAWH5\\u002fQJOEjXFAtYFADI5J5V5Ag0BN+gr1I9yDQDsnud5WVoNAl4c4NwfJgUCUiYrqBAJ\\u002fQPEdTB4EwXlAZMr9UzOOdEAlanHeZABwQNSj4uUX3mhA8D56narVY0Bju3TOsJRgQKw6yge5G11AShcS\\u002fN1uWkAiI\\u002fqk\\u002fWBYQO2yeuKUiVZA157Pb126VEDFhPCE7uBSQDLkQHTu+lBAHOE2TcwwTkDXYXoBzclKQPUEI5Rra0hAF7Kw86IrSEB1OkfZlHNLQA9hgx0Yw1FAf5KQ6jJVWECTIse1oFZgQMUDmgvphWRAnLnAshuUZ0DofIJ+SIloQGlYpQ1nCGdAHQcyw614Y0Bced8+ZqBdQN9zxvn7RlRAncIDyNz3SEAtUapndqc7QC2UEtu\\u002fjCtA\\u002f22HOuKvGEBAKkkFvucDQK1we4y8BO0\\u002f32kNKUlD1D9WuJ6Bfw\\u002fGP37lCPHmESA+ycNsBlPXKz70glVw6lJDPugajGvX3Fw+jcFHwoGhdz7SMxzeYH6WPvBKZAgIerg+sF9xqBBJ3D4bUif\\u002fXjwAPydd6R4syCE\\u002fzZe9K64sQj88NjT2YiFhPz5E4z7Um30\\u002fc\\u002fZqf2Zulz9mE6JeSAaxP4UQ6gix3MY\\u002fpgewUtSo3D+MCn5cB\\u002frwP8bfg4jOOQNA9bKAjJ3xFECVXzIKreglQASKeW9i0DVAcZc9v4NsREAXeBwQ38dRQA5yyepCkVxAa7HOO5weZUAdHyYtINdsQBk1svq8ZXJA2fzuz9pkdkCWfUSjjMJ6QH6ioB28HIBAJdi+m5m5g0BinTEH7DOIQKYzAlTo+4xANQ12OGuGkECieVHnRaiRQMVCX2EzkJFA7e8Gg3hPkED4TY\\u002fx\\u002fbmMQB3wuQ5u1IhAoZEd93xBhkDMhOostsuFQCCGhpsvdodAXmeYtf+KikACcbHKQuqNQPV+33f\\u002fOZBAu2\\u002fEJ+WvkEB3rX3MTDKQQNoKdZLhr41AHUmLjLnAiUBzfNZeFj2FQPws\\u002fEg8y4BAMGx+M2bheUCHJJxGePNzQOU4WLAzxG9AXgviYEGzakDMnqzIiaxnQAS\\u002fn\\u002f5JvGVA9qIMU3BCZECgXi88tPJiQOcZ+jXosWFAWCdiQS11YEA+0+cnmWdeQNMXNY1r0ltATXLzkkJCWUB6pKigOxRXQDBH6\\u002fq8CVZA\\u002f83JtaI4V0Bk0rXSn6lbQB3VbvGY2GFAy9E0k9MpZ0AjGsTdJ5RsQG6U4mvxQHBAFC3+BFnOcECsNB2kimZvQMzFpgF7b2pAfyr7XMEIZECYhJV4klJbQEU5DWmywlBAdQzWqSZ\\u002fQkB3BHRSYFwyQOx3XE7kZCBA2Mh+UrlXCkCoz\\u002fl7uCHzP6YqBvqenNo\\u002fwrZIxWTizD+hNK0gAdhHPh4nv00yWVQ+8ghrv3k7az78C0eby++CPsIyaa0LV5s+LmJvNiz2tT4Fw33PAT\\u002fUPmrQaA7TlfQ+hHJeRaGbFT8s1K1cMUA2P7rti8EQyVU\\u002fOlXtvkzocz+dORhIesuQP9JE+rP3BKo\\u002f8wsoGJV8wj\\u002fjKPzeDSvYPwTAN8mJRu0\\u002fUoiqwuiaAEBzjTjQrdwRQOjE6xV2aiJA83aLbppIMkDNZLKxU2tBQHpBsQLeiU9ADVNbj9zOWkB19UgMpi9lQAF63wdE+W5AVkzKxR34dEBXEOiWTYt6QO8MQ9pOAoBA71cLyS3ogkBpajJLfHKGQBIdwekpFotANYJRkJZrkEAivV4mL4KTQPEPNhozKpZAOe6Do+6yl0BG5vm+trKXQHK0kiBAQZZATIzjHZX6k0C8YplDxsiRQJcJjeTYjJBA4Vde4pvLkEDUJc6+Tn+SQMQJtSIcH5VAmYYSRZPWl0C3CO3vUdKZQB5zqgLNf5pAVwumokGqmUDDjoD+LnWXQMO88SqkQ5RAEUVWjXqakEBsO65\\u002fsASKQGRlpefu1INADLKzQE1GfkAmOyF5xPh3QKHA02X2KnRAuV8hTRT8cUAvL5sZRaRwQHb7C3ygT29ADNTGHaulbUCqkcz0bi9sQF8jTZPK3mpA\\u002faFhiRKVaUCePK7+ziZoQEcwRsFLeGZAmXak04uoZEBksTI+rTNjQO9mBA9k6WJA6j6khgagZEBtYjOVzrVoQOKgEh6FoG5A3oEM98BtckA7uTSdtq50QM8s3BDzNnVA+it6nVS1c0DD4nXIa4RwQP7GZlFA72hAkDIg4C3wYEDLeP\\u002fKQrRUQEPD+XcExEZACw\\u002fu71mENkC2wdn3awgkQA\\u002fg1vd9CRBAHRoEn5o19z8CJtBi1RPgP0gaNvylZdE\\u002feYJYlN1UcD6SZs6M7JR7PlhktZZtApI+1Nyfkdjnpz7khTp\\u002frqO\\u002fPolOSmWGM9Y+NqM5WOh08T6wN0jm77kOPxD1iWA00Cw\\u002f9\\u002fZRMepKSz9LmuyX\\u002fSRpP92pmb+N\\u002fIU\\u002fgAuG\\u002fcX0oT8yqIDu3x27P37nLoHt1NI\\u002frwmGjL0J6D\\u002fmjUc6vUz8Pzakub4Z+A5AvEvJhxzcH0AsOHnFXTMvQN6OWs7qXj1AkoRVzdumSkD85tMRrDBXQJrz7KAcJWNAEt2NV2CpbUBtOxKrQWZ1QKvXUNTNuHxAuH3qKsgOgkDq09isq6KFQPlZzsPsVYlAfLem8cfBjUCTd125hr6RQC3jVAIdS5VASIb0V9YemUCeWaBzKHScQAUzS50Kdp5AeLKvCHqlnkDDjJg9qiGdQE\\u002f2cNHVrZpAuXH6CXxwmEDXQ2n3pYaXQLnoTlHvlphAiOns9nKQm0AGxCEC7bCfQL5WToAo56FAJlQng\\u002f1fo0B9oXC3qNujQHi\\u002fNfx\\u002fNKNA7mxnO\\u002fSDoUBRbuZSUSaeQHbQRBHbjZhA415\\u002f5TYSk0Ddlz1P+cGMQEAyhKyPtYVA4XhNtKMNgUABpoGH4p58QKk8\\u002faqmlHlA4uEe0JrMd0ByhoIagZR2QDVf4kOgq3VASZjqFC4IdUB6ltOtVJ90QMeoiEJpSXRAz8fbS8bFc0B0U6d76thyQFPYM4TSeHFAyGXO\\u002frneb0DCOj9YaqltQESTNxBKp21AGCN5Ta5LcEDegqlhdwJzQGhOEY64GHZAPmmpnCxXeECMIzHlObR4QAq3a70iy3ZA1fzGVEUBc0AHy9JNr5BsQIp76zrDU2NA44F+A9eIV0DJW3k6NshJQO3hMf1JaDlA71RuXsuFJkBNwR1+F\\u002fcRQM979w2f5\\u002fk\\u002fzSCFenXf4T+pC2yb0EfTPyhPBQryoJQ+EU8StAxIoT4OX+TXrC22PpkKLR\\u002fThcw+yd6pVhbI4T6FSBPW1JD2PmnyNOhz+g4\\u002fRS4AnlukJz8UrAa535BDP147xoo\\u002fv2A\\u002fDE7W7sB+fD8S1AQW43GXP2U4vm\\u002fGS7I\\u002fA2GY9tqzyj8QADFpLQziP0rZPMSte\\u002fY\\u002fI3ssT8nNCUA5KEz5imYbQEVVzTXOJytAp4hpprFrOUC2tYZH+cFGQIZLMj6spVNA1+8WRuxdYEBmGDRWsSBqQHNb6cbwxHNAkG\\u002fNejkZfEBKwNlPqa+CQAt0ZkN6XYdAyviczwvei0B9YmknOjmQQL6Q7WYI5pJAbIfv7YNPlkB\\u002fy6KTNIKaQK1UScZ5Cp9Aa3TQYjSEoUD3gd4oCcWiQJT0btU9AKNAD3UAZOJJokD+piMIPhahQH1cqeF7FaBAUbtmue3mn0C6pWuTABShQJAerLN5Z6NAMfjhadRrpkDA7XMTY1qpQIJO8TIxbKtAknENdgYVrEAr0HwGVCCrQCQ2TpFisKhApnlvpisspUAAhplw6CShQCETmSXfaZpAtaT3J5a1k0B2DNzOdG6NQF33Bq4J6oZA8NX0k4wkg0DX\\u002fH8ujRyBQMhRJpx89n9ASeIsJRKTfkA\\u002f6e2gD8d9QMf\\u002fhgkUk31AQH7n7uTkfUBgLQIsNWh+QCVsjMq\\u002fjn5AwIe4eaDJfUDeRZiHr9t7QGzRBY1FF3lAA+XoRjRXdkAkJP\\u002fVKqR0QP+ELg8CrXRAlZtvJKNbdkD+TyYwgMd4QAP0clV5lXpAKkshXKaWekAGlm\\u002f9llB4QCH9cqXfJXRABGRvXEkibkAP88GI+0xkQPdCe3IEn1hAytKCBYjeSkCMw+lGSWE6QG2hg\\u002fWLTCdA5hhcVE6EEkC+AH2gF5r6P0YOI2u+R+I\\u002fPQKnn8Oo0z9nirg99wG4PnDPuwlO\\u002fMM+f95TZzlR2T7KOilh29bvPilBTeadCQM\\u002fOvdYdfVyFj\\u002fWAk8dYqMrP5t\\u002fkZLDjkI\\u002fAzhE6xwkWz9zDFdFF9Z0P0iBx0tGJ5A\\u002fCagFz3GbqD+U5wNdXQ\\u002fCP7mWgj6XJdk\\u002f251UIZVl8D9Xc1K3utgDQPncghH+MRZAyPxTcf7wJkAmqluWOQg2QENYoMie2kNANLTggZ4AUUAAHFnIKAFcQN3CdSqcUWZAcxej4d4tcUDq0us1i1N5QBCZlqwWtIFAJwPgTbVVh0Bdd\\u002fpOsw6NQN8ivPQzS5FA8sUmyO8UlEAsHBPy8kKXQCz99X+WN5tAQuQ4SPEAoEA+KLt3CZOiQJsj48AC2qRAJ5ByCfdUpkBEbcnZhLmmQP6QXVOgIaZAr6YmZtQRpUA\\u002f+Wzs6VKkQLpcD0VcqqRAJrAxRZ2KpkBHmQuNDdypQCnKS2ML+q1A7pukJy70sECRoBSNw1OyQP9mpsw\\u002fwLJAoRU610sWskAePzZyNGywQAPNFhwdD6xAT\\u002f2DIZyXpkDBjqfasUWhQOx\\u002fktHXiplAuhfW31PkkkCtvYpMuDGNQBquN7ahSohAWB71aBy1hUBBxP8ecFaEQDdv6gPbm4NASXFUsnVkg0Aq9n3k8rqDQDyKYCRpkIRAFPxYO6yXhUBsFUI7b02GQAUXxGLWKYZAGQiH47LlhEAX100SrqyCQC40v1cxGIBArtH5k6\\u002fZe0Ai2gMj93Z5QEiw6uiUNHlA6XIByDg5ekCetxJf6Bh7QK5qGQXFiXpAq7Gjpbr4d0BLE5TVTbRzQI8A27qMS21A\\u002fqany4KjY0BN0lAx37ZXQHL1RlhLxklAlEKqxwI1OUBACjyU2C0mQON\\u002fTm+3jxFA\\u002frCyKKUi+T\\u002fN3Jao3TPhPyVqFKVhcdI\\u002f1KG8IhW72T7rocxo5U7lPjZ9PUNAuPo+jUQI+iCHED\\u002fWwSgClS4jPzV38HB7bzU\\u002fGl7wx448SD9dsILXjitdP8wM5pJTB3M\\u002foe1078lIij9TSmeYgIOiP2asyFH\\u002f3Lk\\u002fQZgZNYea0T8kh0qdzgnnP2B\\u002fOvZ+nvw\\u002f+Sn0xkGuEECidyOCZBgiQHzCysWjMjJA1mctOkL9QEBpwiMOraVNQIWqIBUjc1hAf7vcUbZPY0DUutHFsYZtQK4MymiS63VA06Hk0Ot0f0DPAevdqJ6FQKh0oksJQ4xAWoEAEVCPkUDp7g4NAO2UQNboKXy3UphAaAqDulUbnEAuKvFuVFOgQFjq\\u002fuGI\\u002f6JAEk2WKO3QpUCPsoavqkyoQPzIkOED8qlAGa5xTsh6qkB+uYHyeAmqQDBxxcQoMKlABR\\u002f5+STKqEAUSLWJpbCpQEdv5MbIYaxAqyaSqxNfsED9c82p7f+yQJwsDbZ3erVA6xzv42Axt0CTU4MK77G3QMXN4t\\u002fwzrZAnYHR6xqltEBTHvtQJ4+xQDtJiyOsHaxAYyKBORNapUC\\u002f+Y4KOFufQMQrC9A+DpdA4YZswxfBkUATXkAny4eNQFoAsyw7bYpA2zXlL\\u002fDWiECshUNubB+IQF55ZK2fM4hAwv7HVQwwiUD7t55bof6KQP0hbRSEIo1AomkknBrHjkCsWE3aQg6PQAF74atQeo1AbnF+yjk4ikBGIWsOcBeGQH\\u002frOkiCLIJAvW\\u002fkGHKjfkAJ49xe4ph7QKHNv2YDh3pASGIvPvcDekB6uQvcK7V4QEVp50nL63VARS\\u002fFLkbRcUDNezLSqkhqQGyHW++gg2FArxo9AMoKVUCp5aSCcMRGQJFQ5YpWLDZAHQ\\u002fuNKtuI0DMCidgo6cOQCQnoX2t2vU\\u002f2zgds4rK3T\\u002fN+ifHttbPP5fV3tGPY\\u002fk+ZI0Z6jfuBD\\u002fZCTJosQcaP3zBXGGqzS8\\u002frboAa+QNQj9zRyoB4WNTPy1KHUUghmQ\\u002fJLWQMCqJdj9bHzV0fomKP14yk5jTnqA\\u002fzMS3DgpltT\\u002fq8YWB5W7LP3bVxvgcNuE\\u002fRc2BBJTs9D8glkiRP2wIQD918SQsFhtALno+nddBLEAMgemTs4Y7QLU0L9cg+UhArHVhZ3EqVUDL7WpsfOdgQCK6kJ4OxWlAg6H7DGr9ckCZtV8PP0V7QO8cyX\\u002fpEYNAxH1DCX\\u002fLiUDy\\u002fDwDo76QQIRobFKk0JRABbjBty7rmECg6J+5LhydQG6oMwsq2KBAwTyjezh7o0CJJNtRB3SmQAj3bsDre6lAmqcGNA8ZrEDpEnahp9KtQChZsESSbq5ASm9dILEbrkCew1cVoXetQEqZuiT2aa1AtsfB0QXZrkDR793JpSWxQJw4SGhtzbNAhAq9yVLytkAJjHtUauK5QFkLreel47tAu1kt0D1svEDrFrn91kW7QM2qxf1\\u002flbhAL0PVnJrNtEDHKdXXm4+wQIUNeQ3hBKlA5c80fBROokAstzoUyOqaQGLLOCdM0JRAsbCERgBrkUDfs4C9DVePQDlcoK9Ck41AmX6Gx+3hjEB6+rmYbUyNQPqgMqtrCo9Av4wfvaX\\u002fkECZ+B7K17qSQIB0BnxqHJRA8gha2dyClECaO5ttz4+TQCMSdXYwWpFAg77n83fPjEAKXyYVQ96GQHVbHXGuDYJAvLlTf3O2fUDyd76IERR6QMM5I2\\u002fw23dAPPaV6K6rdUCM8kbb7LZyQLOc0Ywb621AiGPwa93SZUANBgRj1dlcQP7IbfW6OFFAVSfbMMSIQkC2Xp\\u002ftIfcxQFaUec1MWh9AHeIr+u6gCEDr\\u002f7ep83zxP1dNDN0Tvdc\\u002f4kKLctxJyT8WCrIZ+w4XP1JVIfft7iI\\u002f4QqE6+thNz+ff+u\\u002fYEpMP9IFyWd1mF8\\u002feczze\\u002f54cD\\u002f+nluyO5KAPzSBM5nO3ZA\\u002fiXeGJOEgoj+TFgTF\\u002fsC0P62DWxh2nsg\\u002f53vnPbM33T8GLK23S\\u002ffwP6KzdP4tGgNANaQq4p7BFECLVlVHyqMlQOFjGyTgdzVAOi7HQSsYREA9LtDD56RRQHbfpv9XDF1AjFFnUd2JZkDgZb1lCqpwQHmP4oXMzHdADtfi6yGYgEA\\u002fBpghOKyGQLKJqiVKOY5AmV36xLSDk0CeOEmuF1GYQL7NCNsGVJ1AdAdoi0NHoUBFYmb8nRqkQMSz5MsJO6dAYz84AOWUqkCDH3+SvNetQPoSY0jgRbBAYZ4FadgisUArFQTsFW+xQHMQ\\u002f9UpSrFAJt03ixsIsUCM6waxPiCxQB04g49KCbJA6iljhycJtECkAWjCpAq3QGTLicvdkLpAZMQI9PrUvUByuFM74QHAQDGkig6cP8BAjh3MfmwKv0D\\u002f47HpK9W7QFMHEx+wardAp+9wh66KskByANiYG+urQA0Xmqtyc6RAK1F3hWhRnkA4\\u002fuP8kMiXQHH\\u002fPzWRNZRAT3sHTCdfkkDUpfKaMWyRQKnTxyisEJFATBaCHnVnkUDeyi65zJ6SQFvCyl6zppRARZzZYn4Hl0C8K5G1VPWYQJ\\u002fIdfiJm5lAGcuqqUV8mEDULLe\\u002f866VQCdTYeSc1pFASPdVPi2qi0BhpGuVv9KEQJQphIatu39ACZjPYSxeeUDUZlEp01B1QO6xsGfnMHJAc49c9xsrbkAp4jQRHIBnQMWBndrQ2mBAsfOGm4EFVkAY76AGrQ9KQL6XQkKv2jtAMVCSfvfXKkBHdG+6LU4XQNJqVzs5OQJAKXErwbLE6T\\u002fAZA\\u002f3O2nRPydSxZVQfMI\\u002fh2EKsOxFMz8wgMWD1Yg\\u002fP7\\u002fpyoWuWlM\\u002fmSFPflk7Zz8xIJ\\u002fK1J55P40VU6b4IIo\\u002f1koo7utHmT9mCMaDDjGoP6F1r9i6\\u002fbc\\u002fBMIYpAJCyT923uPWS8rbP5wkcVAk0u4\\u002fDrii58C8AEBgRDHXqpERQKLJyIb+xCFAkj+1WIlNMUBOB03VqCpAQMJcnN5YzkxAh3FZP9hPWEA6OC\\u002fxUF9jQMDYJbmWNW1Ajy6CFq0BdUC+5ieZvS99QIjG5ofh04NA2CLcjJ6EikCAL3oPyW6RQDbFTQsyZpZAjlkHhyP\\u002fm0Bmuyzi5QehQKfzQgoOSKRA1I7RBRPLp0AreytoiJCrQM5SjxZhbK9AcMd7Ckt\\u002fsUDGCqZz4OeyQP40MnIdwrNARAAkrVYFtEDAfyvCG9qzQCXE5e\\u002f6mLNAvYt20ba6s0AdEG3bG7e0QOoIytBz1bZAiY85i4\\u002f\\u002fuUCc5Yu7U7G9QMxN45WeisBAVDdJ7t+iwUDJRkIdYcvBQPNBfJWa5MBA90\\u002fy2rYZvkCkjZrbLCu5QL9knteg1rNACWiOYhPcrUCqYvQrKwmmQMbvs4DMoKBAAO73LhLDmkAWpk3tzkqXQAfQzgFyfpVA0FkioYV\\u002flEDr23kuTxSUQHVLF25gdJRA26Q\\u002fxOHjlUDEIUsejlOYQMxiIlh7MptA3FgPOoWJnUBM1ciiFFieQDIZIvjnBZ1A4HQd1O+qmUBmHP0LUwKVQNFh231ZEZBA9ov8ctNxh0COcknRlNuAQMQKRAhlx3hAjozjLyD6ckCoRun0Y+VtQBzcKko+YGdAGZoQ\\u002fYd\\u002fYUCaD5sjYnBYQEEcKh3lWU9AWtx3fjZPQkCzLzg6kmAzQJTLyaGehyJAjooc4DL4D0BWkt91f9z4P+t+g6N\\u002ffeE\\u002fxO6z3ueExz\\u002fHmnPLU+G4PxulQ2rfpU0\\u002f\\u002fhyydv4rWD9B0MG6QYJtPyvJIJrgl4E\\u002fJRscGPA1kz+eP3pF0EOjP9XSDvc9GLI\\u002fPP+EY6p7wD\\u002fVd7PKa3\\u002fOP+13iQ\\u002fOsd0\\u002ffk5JsXBp7j+CObPedrr\\u002fPw6SRX32SBBARXrlW5UfIEDkTuKnIJ4uQFN5tzWX6jtAGU0Js797SEAw4n1rq59UQO8obP9znWBA5LyG1HmAaUAJYrIU96JyQAHKQ5qLE3pAv3GqWuylgUCW6TXiVGSHQD9DmGh8n45AUfQbNh\\u002fUk0B8oun270yZQIThGYWeqZ9AB+wTbFVoo0CwivZq\\u002flqnQLCK\\u002fYIXqKtAyvl8ePwZsEAzCHPGalqyQL\\u002fjpxNAW7RA9jnLMpLftUDTelIGesC2QM+MceBb\\u002f7ZAsjKAHVnMtkD+zfMzmIK2QFSCd3\\u002f8mbZAMf1hKh2Jt0B8v00YWJe5QLTXPwm2rrxAsuZPXW8kwEAtGXomIcXBQCJdJ8W2wsJAPZ4wh8jEwkDx8dZlqq3BQKxubyn0RL9AlDx7QpP8uUBHawD6B2y0QNjI9u\\u002fw2K5A83cdB9UYp0D\\u002fGsEln+mhQHuUA6jc1Z1Ao8tOYpK9mkBWLpjkoBqZQBdZX5bOFJhAqQZAlfmLl0AX7WGY3taXQA0eSRiyT5lAYZz+t\\u002ffpm0CsgWRVBgOfQKSNBrTCwqBAZxfsUrQpoUCRknxzuWGgQDzkvGUb7pxAvziLvNWdl0AGIU1Mm+6RQEbdiohQsIlA3uCgjG3EgUDR49A91XJ4QJkOeJgeKHFATC7h266uaEBq8iTFBNxhQChZYj\\u002f1N1lA0FelepnkUEADBdhA6QtFQG4JDju3EzhA9934q\\u002fwYKUBf1+4lLroXQAPO1eJeSQRAtYi\\u002flhFT7z\\u002f53AIz2ebVPxAPvN\\u002feR70\\u002fpG+CKuXYrj\\u002fFNEtDpvtkPxlsWFk7DXE\\u002fJ43FWjO2hD\\u002fzuYt+MY6YP31btatXmao\\u002fhEOsLqJUuj9QOprS4i3IP9iVfD02MdU\\u002fBMmJr4l+4j+cRNA3lMPwP\\u002fTjFhsEBABAgo0uYg6HD0CfxLTkNc4eQBaW1hn7FC1Ap6bIaeE7OkCHHJrMz5xGQKyEcqImtFJARnX9uGTJXUBXnuqv\\u002f9RmQEAVAtlTz3BAuGIX8gS\\u002fd0AimRWTwyKAQLJwl90AQ4VAt1VxK6F1i0DvV4M+5ouRQHrwh5QvSZZAIbwEbbAYnEDbNwC32IShQM2WZKTDjqVAabSdTf4mqkBWtPbZJzyvQKFTMxkzTLJAIyCxGhLqtEBfBhF7OjO3QBm1F4eL6LhAgJsgoq\\u002fnuUA8VJPLUDi6QO8Z4AqqDbpAFq+CPUbBuUAIIxOsnMa5QD8UZgchkLpAcIt8mHxjvEDB1P1p9iu\\u002fQNyL33leM8FA6ttd1IeewkBgEzVllmPDQBKnDiLKLcNAvlxUiX7kwUB3+evgA2a\\u002fQPCnauEl9LlAwHed\\u002fHRitECAUPj7fAuvQJz1lD7cvqdA1K7GvdEOo0DSJRf+3HygQAs\\u002fniYrcp5AJy0TgxIQnUCMXFFhGQucQJ8wAC3mXptADKs4nYx9m0AVk\\u002fHIaNKcQBKKa\\u002f8BVJ9AjP5uUqkroUDdzTXVHl2iQF7DnbgjqqJAshhCKb67oUAfn+JoUD2fQBExWBpfdZlA4WMpd5RDk0AYxuHJLVOLQHXdVINwdIJAyrRB9RpBeEA9PSlpyLRvQLQnLbyB42RAl4+BDJ2oW0DyVg+IMBRSQGNGAiiqy0ZANMwv7okkO0BqzGiYywkuQPrwcdyqjx5A19OXRv1gDEByR9sbEfD3P+j\\u002f7S7qSeI\\u002f5oSdFSdcyT+MU3b4bNOwP5tuZyTDoqE\\u002fBiDY1QZUez8zyCQIbiOGP+qQnXNBw5o\\u002f+V97AUeRrz84mlq5zvvAP8SsnoRYpNA\\u002fWX99htkK3j9FG7Nc8o7pP6Zgo3b7RPU\\u002fxA9d2HcYAkBSb0Vy0SsQQGx1ml3KDR5AKMeaIf0JLEDiztW7n2s5QKuEhpz+AkZAgEbvtesjUkBfJIcEMpNcQPUd5jR8o2VA0Vdy502ib0DmT\\u002fM\\u002fzlZ2QG5tRJsnd35AzbpTgWQVhECGnQzVaLqJQCl5GditJpBArL\\u002fu\\u002fHMQlEBnQrAbU9WYQJ60VcZZsp5A4MBNIrrtokB0DQrrXzunQO5cV8csSKxAPTSfRUT+sEB9ofuKfQe0QLuJdLZXALdAOgUWSnicuUB2M+Ey1Zq7QMazYma22rxA2aZoaCBkvUAcU2ZSI2a9QCUTiADWMb1A0LJ2AKgwvUADIExQkMy9QIGX+mzpRr9AU0ve2o\\u002fGwEBZCDT0NRPCQNepAlyjJsNArZRjPH2Xw0Cvn056JhvDQAC0xOjVoMFAnccKVJqwvkBXRwJFWUW5QGz51RoJ57NAwNPk1AW2rkC0Naud\\u002fB6oQDxc4050FaRA4zsdcIwDokA4XRXPGRGhQEH+MqXnhqBARpNC57oLoEBgij6mF1KfQLDJzHkpQJ9AyQN23vQpoEC2D4rtQkKhQLCMtXTkk6JAZhdL9J2Qo0BWgZNzEqmjQKzRNgadiqJArY7iwlNDoEAgZvAGi3KaQCl5dg1d95NAH67KvNIojEAcWuFu7b6CQPK421I+6HdAdsLBl1GvbUBjreMUGy9iQM4D96FPGFZASIpk2e57SkAYzV5cveU+QFrWCSPWQDFAiGhtxM8oIkCkV9Xmm8gRQO5r2e0WDABAZhr27CN\\u002f6j8wKN6F9OrTP2cFGH4gRrs\\u002f4ETL0NvnoT+Arc7JXKOSP+MyVce2X5A\\u002fEHeEx5lymj+7i+xitNOvPxIujmzNrsI\\u002f4QD4bPv80z\\u002f+7BnUG23jP0swtSCITfE\\u002f5PmC8dbG\\u002fD+qmEb14w8HQDAaU6zJlhJAIPR6D1U5H0D4BguIt2grQLPUmch0bjhABtoKlplXRUCEx5cFCttRQOMvYDXMY1xAoD6js0l\\u002fZUD31Q1klzlvQILTMZyj5XVAXGy\\u002fPqDEfUD5awm5tqGDQD5JWofuIYlAIWxB0JROj0BkypVT4xWTQEZzCIqa+JZAISMZsp+Im0BxfC9SyoqgQPlCOT6p\\u002fKNA3ajUahJDqEA4qZrqonOtQNES82e1vLFAkjO25T4BtUD9tUQzfUK4QGcIjVo9MbtABp1JWnmMvUCYzWQslDK\\u002fQMX2\\u002ffeuEsBAbvb2on1DwEAMvSs2mEvAQGZcznHXVsBAfoFAFQGTwEDgREpJ\\u002fxzBQGU3n9k17cFAjGMq39rPwkA8R1FoGXHDQFoTR\\u002f9yecNAdtDPQRyuwkBR8V7o7AfBQPa8gXnEcL1A+dYcvF02uEALWyYWITOzQDrJRznnJ65A82Q46fJhqEDOEefPLwOlQDsrtztPaqNAGPQcivXCokDtiu1feGGiQHA8yX\\u002fz96FAxf8qJuyVoUAmoqAldHyhQPZOIw8v4aFAdsT8p+m9okBanJvmzMGjQMEuBb6yaaRA1TqFL080pEBTiOOt0tmiQOD7yJJ\\u002fa6BASDsrmC+WmkArf\\u002fzoVQCUQFLdu7I9EIxAujivz5t8gkCej18YPxh3QNPxbYsrqmtAeQB6JE4NYEBo79HlIipSQAhY5MMTEERAi4fCpYGGNUAg7y88FTwmQIDu+8It3xVA0DthvPlCBED+V9fqUX\\u002fxP+xI+XaM7ds\\u002fsXzcnFB1xD\\u002fNzr00IHerPzcAh\\u002fxCvpE\\u002f1pOqbNFHgj9qk5Bucw2iP60tIlFaEq0\\u002fS3S4AFlqwT+49NnIWFvUP7IjpppCq+U\\u002f8ov2NMjq9D+t1s8lD3ACQPBVU+0zIg5A4AuskNVzF0C0tQYuYxUiQHqWExuSvSxAKHb\\u002fFzziN0C5bgT\\u002fMVdEQPYlFIvxIlFAgzg0BTnKW0Cbm\\u002fWzdWxlQNXTHLtvZ29AQAFNoa0HdkCYTgX4x9R9QI46iB5ynYNA8X5jYY4diUAOw0ZMT1CPQPuhspaBA5NAZ3S\\u002feCOOlkCcYpoYDE6aQLNU6B5kcZ5ASyAM+1WpoUD3NB6VKbekQDkVP5vooqhAm1djURWXrUDSwsONfMWxQFkyHZvGGLVAk8NaJ3WCuECW3gzY6La7QOLuOdBXeL5Au1CJloNRwECELKsmkRbBQLm2ntbMkcFAFkR5SL3VwUD0hg+\\u002f7ADCQBfdnIqKNsJAUZNVMEOOwkAErRU4QwPDQI3GN5ggbsNAhUFCyIKPw0AFw8dKUyfDQOwGkXITDsJAXH2mYENGwEBXPEVkbP+7QPiBsCq8FrdAYJUhO1mGskBbFq0brbqtQNU+1m75vKhA72SeQ8vvpUCgJQdtI7WkQKYKNYdnSKRAiqIwMXUOpEDfqm2PEcKjQDEzQoDRcKNAbdn0yFFSo0DR5HBf\\u002fZGjQHPjHUD2JaRA\\u002fyAGw8fEpEC3B4Zm1fykQMHY2aYlYqRA024ym269okBvgiYLyCWgQFisA+sZ8JlA6Kn7li9ik0BDaeX7S\\u002f+KQOtHLbe2mIFALmvY7nGcdUCSQmYkKDNpQAvkM7eRGlxA0U9GOXMiTkB+peO7jCI\\u002fQF5eVKeg9y5Auis54FWMHUAOrqyoI+gKQNJehWdaO\\u002fc\\u002fS8AZ\\u002f23h4j9bB5dpOqrMP\\u002fyuy+uCLbQ\\u002fPNFJCppBmj+7vPzAJ4mAP2JW2hGjw3A\\u002fd4pCjtxPsj\\u002fGp+iB\\u002fWa9P+P0Ql6oidE\\u002fYe12nB9q5D9XnXxpZKH1Pym5BxtSwgRADjsY\\u002fmglEkCMIh3WyEIdQCXIdVNRRCZAcGzFMOWSMEBAyr1h+yA5QAP13R+p2kNABybjN\\u002fYrUEDui3O31UhaQEsXP3IprWRALj6vR5H8bkAJwJ0neA92QNWgJN4ABn5APlcQvDG2g0C4\\u002fBn0jy2JQAD+\\u002fM9Wao9AvveZM\\u002fElk0DCI7BtOsWWQE3ZqF6+aJpA8Oy2s8b5nUCUVrXWsMigQJ01cd\\u002frxKJAhkywHLM8pUDmwGqY+IaoQO4f+FpX5qxAy4lssVkzsUDUAa+NRGa0QA1ig10V0rdAIhfZEO80u0Bgp9KD+Fe+QPzdLlriisBAaETsbvSqwUDFSo4VHIXCQA8GOmaHGsNASaNF10t4w0C615zh9rPDQAU3rEEx38NAj4NRbcj5w0BEYoPpWuzDQK3ntjPwj8NAp5aPFWS+wkBt5vrrxmPBQDfkQGVaFL9AhrB\\u002f+im4ukBhtquGgze2QCEhh2pLIrJAna0BcuzRrUA9ZEMg+nmpQAOfWdB1FKdARiHRbcIUpkAg8rjyss6lQGMpJwWZuKVAfcs1Qk2QpUCGv\\u002fBO+FilQHUhtiZHOaVAmYv9F2lOpUC9geLVuIqlQPwMUjLrrqVA6aBgHzNfpUDOCtf+kUmkQNyRO2JyS6JArP\\u002fKoy4Jn0CQ+Em1UpuYQArFh41QLpJAifizmdQIiUCKgvF4uRmAQC\\u002fVF2u+bnNA56XsMbwhZkB0n7nr5elXQPuPnqtInEhAsx7dUckpOEBfACoEWJ8mQGn7BBLPKBRAvZGx2fQOAUB83lnzQFfrP5Mo6keYr9Q\\u002f94UQW65uvT+\\u002fHvJhKJejP+E4lHFeVYg\\u002fao79YQN3bT9mx\\u002f2JLR5dPw\\u002fw\\u002fof7FsE\\u002fcx25+BNcyz9AsYK8EUDgPyZBzvgK1\\u002fI\\u002fG6deuG3gA0CoV6ovkPoSQH2mQiTdeSBAzXgbDD9KKkDNeIecOqozQGDAgxFIezxAD\\u002fqgnXDGRED1c1Ai22dPQC57qR6niFhA4DPIQ5k\\u002fY0CYnlE7e2RtQABlInzLbHVAoR3zbxisfUCCGbrh5J6DQCl7I6JLCIlA03PGZ9gfj0DmB6vLH\\u002fmSQLwQR3zHspZAH5\\u002f7Z5aRmkBQJJTy\\u002fFSeQJpoCX+k4qBAssPMZhJuokB\\u002f+rlNkvKjQI9kK5HvwKVAboxk7NZBqEAaa8lNadCrQBc1fmN4SbBAqUgG34E0s0B6dxnk5H22QDjRsssa8rlAn4FQVIBkvUCuaI105lfAQJS+NV2O1sFAYRQo73MYw0AyTi3bDgvEQArrB9Udp8RAxskZ+pzyxEAds+pXc\\u002fnEQOU3NDRwwsRAEVdrfitJxED4LN3uj4DDQOlRIy9xW8JAHO9s8VrWwEBDC8DdLP69QLOVfhPt7rlAFdqjE1HitUDnDD\\u002fHLESyQDTpORIF1K5AgsIehqH0qkAaG6NGFsuoQC1g6Qby5adAxESbMTm0p0A3GmLLvrmnQBpDVPIesKdAgtLbhKeJp0DduQPDRVmnQH8D4MHYLqdAg7onA8j7pkB6RV77ro2mQMLJZJ9xoKVASetiUZL9o0At9MGNA5mhQNYrYHg2Op1ATKOoaHm+lkCMpBTPL4WQQLlk0gAjX4ZA5NM43nVFfECmq+I+ibhwQOQuiabSl2JAzUqs1EmFU0D93ItGqGlDQMYc25PSTjJA\\u002fp2p+uJbIEBDAoivSKMLQP6fC0CuBPY\\u002fa7Y\\u002f25mD4D+QewSNFkzHP2XFkebc4q4\\u002fSxg9gFU3kz\\u002f+XQ9UEnJ2P6hCFzuWsVk\\u002fAYEvRshtSD+4M7X28FjNP8cGuEBubNc\\u002fnM3O3Va16z\\u002fdw6MQoP\\u002f\\u002fP+zz2sKozxBAM\\u002fZxdaH0H0C58voDdJQrQJIbNtD60TVAoqSAIXsbQECUKaKv4NdGQLZLjWlkKVBAt7Q0MoeKV0BKrs10nLdhQCvu4Qje5WpAq8tV0O7zc0BpjCLLeVB8QBn18V2rFINAnpkOTyqIiEDSMC\\u002fVEGyOQHhcUqMLbJJAjJJJl5L6lUA+hsYPb+KZQE7P8yCc+p1A+ZvkyPn4oEDQaEa6L7eiQJfSzZMMIKRAAow39WlJpUDnoj1NHoCmQIKyzhJIN6hAwvUi\\u002fCPgqkCND5JBCr6uQEe12pGk6LFAPhg5j9bytEA5iLFAv1q4QL1+RX4SALxAevhk\\u002f0XAv0BE3uTbb7TBQIyOjZhwWcNAG+9MXMqoxEDj8asADIbFQN6Q6qA758VAmdmNG4zTxUDhzQtF5lrFQGRfRhYvjcRAIDUXIPR1w0CSY2XE5hzCQIRwsqGjicBAmIh3ykWSvUBGeMS3zeS5QE2nmGP5TbZA5BiktmMas0AU59ilcYuwQEbbDJvahq1AVLQ1j0R4q0BRLas5hZeqQFLcoZKSaqpA0SbK1b96qkC8OK2ok3aqQLcUZc2sPapA76ca\\u002fAjTqUCepuElB0CpQN+pcEMNe6hAXaeieSlhp0CErvBNMMWlQH06Yf8XiqNAUamUZf64oEA10EGnSg2bQK8zst6hipRAAY+ERbcojUBUpZDU402DQJERqhb513dAoIE1F0KHa0BXjevDMc9dQIA2QXxLZE5AAyNWwmBDPUA0l+hgkqQqQEkomcjN6RZAqUN+nfSPAkDB0YkvtjvsP56jAiJJGdQ\\u002fEoP+mLa+uj\\u002flnlQy4aCgP6J+EJWMVoM\\u002fODtPklUYZT+0pdqCOohGP1Bu3WUlVjQ\\u002fDcwECuku1z8i7lE0RnPiP6TNBmsXvPU\\u002f8H789fIACUA\\u002f7HhhlCsaQBoCP7RXxChAc0+\\u002f5ExENUDfF14Q6rVAQPnxjXdSa0hAOTTXklsKUUDFZD\\u002fW+oxXQCoquzOhpWBA1S7qotFGaEBoWeNvluRxQLJXt0KK2HlAncELrPDfgUA8u3s2WXeHQEar70dsWI1AV8K6DlawkUBY7ZtNo96UQBVBU0aEY5hAe1z2PgNanEADM9gJ4FOgQDs9L47ceaJAGkmD6lphpEDACrhRZdmlQNlWfCEG36ZAuLibH0Owp0CCayvNdMGoQA9+ZhEblapAWz7j\\u002fk+LrUCdIARiieOwQET8jJsvnLNAIlUuWaDdtkBSkfJclZS6QN7Z3q3Xo75AbQzYdxNpwUC4lEr3YWHDQKU48wcFA8VABKihc9kdxkAKsaD1\\u002fZjGQA6sHHQCeMZAhJx+KIDUxUD2RyhnbdHEQI0q\\u002fE76jsNABGs+ZqAjwkDoX2NQGpzAQCcJIjItAr5AaGwcbZy9ukDcpQ8YJZO3QNQSj6WJuLRAmACEJd9iskBQCQrPMrWwQM69CeVEZ69AROahFsqArkDNCllfcUmuQNirOkZZRq5A9\\u002fDdGi8XrkDXf5QMWY2tQAd\\u002fNEWYpqxARhBwc7Nxq0BtWaFKGPKpQEu8nJ4HFqhA10F9gL\\u002fDpUBcSPhd8\\u002fGiQPwndVH1c59A8ohWRUqwmEDN9txp3DSSQCXELiwBHolAjt0ZXNYqgEDfngRAxWlzQDsH1uq\\u002fxWVArwxGmBnfVkAewqN4fpVGQHjLBDRwBzVAERhxqhp\\u002fIkCUszdtorcOQDefdhXxAvg\\u002fNNfYPauY4T\\u002fFYwoZiBPIP6+QVYPZp64\\u002fShxQ1owikj\\u002f25N1\\u002f9\\u002fFzP0F2R8iUdlQ\\u002fBbJ43+JpND+sLhXdgmkhP3MB3Ixj2eA\\u002fP\\u002fWVT+e96j\\u002f7AFUJUV\\u002f\\u002fP5ZT6tEp+hFAqJiEjrG+IkBzKjsCM6sxQHtloHGOND5AF3uqKiaZR0BhoSAQcRpRQGoV76balFdAqz3VxqYBYEDsrz10lRtmQFe\\u002f3Znfa29AVNEF5RWWdkCf2ARdwd1\\u002fQK3NCam1iIVA6eUfVMeXi0BlawjqI8+QQB1DWsdeuZNAn81MlmuxlkBmvCyrBASaQOuKaAD98J1AlCwwq\\u002fQ7oUByubrfpaCjQJRw3yQ82qVAs+3rkwycp0BC77w9+MKoQLCDpUX2c6lAvMhTibsaqkBGxYAMbUSrQETGHbcta61ASdHsuP1psEDYArx+3siyQLzZJlvcz7VAo4ft8Ql4uUDePg4ncau9QGedtEbcF8FA\\u002fKI12HlNw0D97\\u002fx30THFQAZsZqSlhMZAFPybHC4gx0CRL1bXggTHQPwFV8r6UsZAM32I2kc9xUBMbZQm8fHDQJDidVGaj8JARiomYoMjwUBtDTz3aWK\\u002fQBu+i0ILerxAHXU\\u002fXDymuUCTGd+j\\u002fg+3QLQdmVSZ5rRAYmPMCiZPs0BoXZUiaFSyQA9c+qvM3bFAd1RPEcuzsUCbA9z4qJKxQHCMG8R4Q7FAOXJeuzOtsEAKanS9B6ivQB7AVMWNkq1Arvct1q8yq0CvEUNjJoioQD9gQGqXhqVAD981bbMvokDRqW3A5UmdQMtzTFaCSZZA+fjLoI\\u002fZj0A8+plskEeFQMSkgDfMhnpAmxRQJenUbkBXFi6vdrlgQB0tzGJj+lBA65+71N8uQEAHZtqLehEtQPYx4Ud8qBhACPCa\\u002fqvDA0BujQyFbuDtP4ixljvRNdU\\u002fOdEivN8nvD+tlZzG5GahP+SGTPL8+4M\\u002f+qGAY3JSZT+j4Z3+rzhFP2Z7bxNhjSQ\\u002fCSUwmYQpET8HkAupZYjmP6mOL\\u002fSL1PE\\u002fWowTP\\u002frUBEDyByhUFckXQHDF0P\\u002fJtShA6rGXqvczN0DUFlM4R8BDQESVC\\u002fHxtU5ADHDnKYEdVkCRXnjcDDZeQJQ9KQzvO2RAu8P7HjZ1a0DhGrMeox9zQER6XCJ\\u002f8HpAkj3VPDOigkBO5ygYQbGIQKW\\u002flgKz\\u002fI5A4SxjWoB0kkCdgMx4lCaVQHRnPX1Kz5dAFS4P2DHfmkCoybSWP7+eQASgodCOx6FAxI1fWhmApEA5n+yOJzGnQJtFxI+fcqlAFtIWCGX9qkDeaU4JytWrQEduc23PVaxAMFrVVacNrUDdoZuY9IyuQChsLCIqnLBAvEOtC4GhskDmH47rvmG1QBjf7Fab4LhA\\u002fa3D\\u002fkkQvUC3uy\\u002fCbN3AQDdiV\\u002fmaOcNADcaJC\\u002f9QxUB6DO7rRdjGQFr7RkoWn8dAG+0ietSgx0B8ZPPf3wHHQJZbM\\u002fd\\u002f+8VAjyegRt\\u002fCxEBRkRa76HjDQNA8UXxPKcJALQoWJdfUwEDNxw2dQ\\u002fu+QKFm76OOXLxAEW+FENrzuUBPMtrRYu63QL3VZvVRcLZA8oumaDeDtUB+ZvluXwq1QIpBRvJSxrRAj2LdM8tqtEAwagc0c72zQHRdC\\u002f0Jq7JAfB5aYLZFsUCZreTUbl+vQGXBieeOBqxABEz3kICQqECWy3wa0\\u002fakQP3WG1njO6FAkyERMcP+mkBKKO0SePWTQNp+lmBHrYtAknOXw8jrgUB\\u002fRnKS+aB1QJO++WCmUWhAbjAukcqAWUAeMsVJUQFJQATNc6cgADdAX28f4LbsI0Dofb4qyk8QQEGLqA27S\\u002fk\\u002f5\\u002f3O0fKV4j9PQUraldrJP4Mb9tQ3ALE\\u002fclSwV1MjlT8HfdtwjOh4P\\u002fCz2wYQ91s\\u002f\\u002fU6JhUIxPj8CdFh6AYUgP8j0cZXAHQ8\\u002fTJhNzmW56z\\u002fg74lLTOD1PzZIGsB+dAlAbaxjERP1HEDJ8A11x\\u002fktQCtQG1PJCzxAdCzzx6bIR0Db0RTs0mlSQMdeJtdlYlpAtY4GncDlYUDwyxfjyL1nQGsAqYopzW9ADjSV30PQdUAM0SVPtD5+QJ45g\\u002flrmIRAlLX9PIveikDe\\u002fTtnp5aQQGpxycbEbJNAlLAHPTTilUDzVanG+UWYQEjLpUHPKJtA+etZQvQXn0Bk5lGYiiiiQENfSRHUSKVAjeY8Or2MqECktyikfXSrQIXNg0H4la1AeOjh53\\u002fRrkCiAtTsi2ivQIEyNCXZ5q9A7X9lv\\u002ft1sEDst0WLaXyxQOxxhm\\u002f+LrNAB0kM2VuktUCvh9yXgOi4QDRjz8WS9bxAdUagEkHPwECjuzUpdj7DQLN8x3M1fMVAUJh4T6g3x0Cf1iUMJTjIQADD\\u002fbPzcchAPWH\\u002fYtsFyEBvWEsO+ivHQE3Eo84zGMZAiBtNNlLpxED0YpIE8qnDQOXKyVr1XMJAc2cYG5UJwUAGqKS0oH+\\u002fQOMgqbJ3K71A3rNKxs1Cu0AcUSXcyuK5QC7F4KzfCblAE7EcUKyNuECx5uS9RSG4QAn76L4HcLdA8h7lf11BtkAh4tk2c4+0QEt4zKi2gbJA6FVxfiRQsEBSlATa6UisQDCp5wgoGqhAsIeJeBcMpEDXDAX26BegQNP+A+9lo5hA8j909xzNkUDnYUTM0xqIQHoqi51jdX5ArGPCgOzscUCNVhTyBahjQLmNWNDDHFRA\\u002fLSwDHRBQ0C3MI+o81AxQI54pO0BZR1A9KAkpxqvB0DpH8B4XD3yPxUu4i28Eds\\u002f99e0K2uIwz967LUNm6urP0UWnybwXpM\\u002fbw4++CbTej+I7urTdDxiPyCCS77jG0g\\u002fhG1kCN8oMD8Ik3XC2YQhP8u2yemTYu8\\u002fen147hCy+D\\u002fLAdC0254MQOUzjAuJOCBANE80QeK7MEAhe09DYjU\\u002fQLQd74GrYEpA3ZneqAdaVEBYCyQ4CA1dQLQTYkjAnGNAjTGkIUvYaUCKc\\u002feTaSdxQE3nh7gCRndAzMSamezif0CN4N63mHWFQBb2Gzdqr4tA6t5k6AvqkEBZ7FV\\u002fM5+TQIGgJzCj8pVAe+mvVPZEmEAFeB5CSTybQOZ37Z7be59Ah9pCmBWqokA4swHOCUamQKCec6pELapAAd2jjdDOrUCOSxy1oFGwQAhtd5NANLFADnQRwx+isUCraZLy7tuxQIg2DAquNrJAcrxNnRb\\u002fskAP9ULQcGq0QLTic8NhmLZACaquyWKauUCId1YrTXG9QJf4hRbv\\u002fcBAN52IYFZxw0DRCvLRDszFQHOVztwUvcdAffIZdoEFyUAviGk\\u002fwY\\u002fJQGMWsOMKcslAA2yTiLjayEDjPaJdevXHQMY95lYr3MZAbI5mkkaaxUApmtc7WjrEQO6wUXGhz8JA1bgE02d1wUAmYFg3x0fAQAbDM5S5t75AKXGS0hJzvUBiyhTxTqy8QG5asFOGJLxAYgTn+fB+u0DARnfN3WC6QFxF+7SkmLhAS7nX9EsztkBui4BtVXGzQPvq6NwZorBAC1ZJBCj9q0DkhQm6QzSnQO7K6Q2M2qJA+hHtzUaxnUApAtbiWl6WQBQUAmUB449AffmONjJXhUC+VCPkR7d6QJs\\u002fxTQpQG9A7jcZSpIZYUByWWwBw45RQF37Eeew+0BAoblvD\\u002fkeL0BC6a9LHzcbQLv9fsXc9gZAc15Y32758j92EVX42DffP0YAodB55Mk\\u002fk+96diKstT9MiO857hCiP8jgyHsjWI0\\u002f06OTqrW0dj+2WE4w8oVgP5IG\\u002f\\u002f1yvkc\\u002fmFBnTGW3Oj81s\\u002fDvgVjwP8aWKZw1p\\u002fk\\u002faAX\\u002fzk+dDUCvPsT+ErogQMREW1yXMzFA\\u002fMENDZb8P0AbfZ8qpfZKQMQBg28ywVRABz44LQOQXUADBQ+9RuljQP6c5wPHJmpA8OJXjxlEcUB7E\\u002fi+mEB3QFDjeEv1lX9ARH5bLV0VhUB3Qa3WQAaLQOB9oqfYc5BA4dsAEn4ak0D1eDQx\\u002fIOVQE1Ey7wDHphAloiMpqmUm0BXQ9vk8kWgQJfQv7XrqaNAX2c+rpvZp0CeLzXB8m2sQLCnxH4RZrBANZ3LPTksskBqakUxZ1qzQHcfp2Vd9rNARK6T+mY6tEAePMNksH60QKYtZDgnGrVA1hHbS99MtkDHlpMPrzy4QOR\\u002fsy1e\\u002fbpADxhWdHyUvkCkohHa+XbBQFyNXEas48NAa9RdBLZTxkDldi4Fj3rIQNNH0AQkFcpAIDVoRO8By0C3fhxh2EbLQDpZq\\u002fyDActAogx5\\u002fUJQykCqBKotbUbJQBL3D05R8sdAAZJy\\u002fEJqxkBCpM3U\\u002fdHEQJ+Wy4CSU8NA9\\u002fo88rsSwkAh6ka4QSPBQFo4ZruWhMBAV0xFRWUgwEAchW0Eg5i\\u002fQHE1zyBipb5A\\u002fXTt7UEKvUCXQm3Bnp+6QJezGH16hrdA4gkBLlkXtEB4K9j50rawQEqp55h3VqtAYkxH7BgXpkBshqkc9pahQDARDFwPVZtA6AvURtJ0lEDARUkIsyqNQNzYqP2SqYNAOgGA6lX+eEAnAp\\u002fM\\u002fvZtQAMpHsuP\\u002fGBA3FyE3oNIUkBTypehysVCQIb8h86lgTJAI11k66iuIUDNVqBiQZcQQFmSnjjpBv8\\u002fH1Nu\\u002fCk17T\\u002fItclCUqPbP3SH9tQF6Mk\\u002fJu1bg4qItz+PnTS6hkakP5Bi0ItdS5A\\u002fgHBS6t1FeD\\u002futLCOk7BhP6oNdt9VCVQ\\u002fFEfCai9W7z+Km38GmYb4P+gEfUWVNQxAoQTBu7PEH0D\\u002foX1wvkowQJwGKqghOz5AuSGZ+HpySUCISnE455NTQGJksba35ltAWjQqgw3RYkB11+GO9rxoQBh9xLopUXBAYx\\u002feiODjdUCok9HMW5F9QPPkl1+vo4NAGIjdMc4jiUCFasHnc8SOQEqKTDCfHpJAqBhAUJ\\u002fslEApuQ8QuUKYQN5xLJKdwpxAAArxZ098oUCtR6mc342lQP1Frdv\\u002fcKpA5M6mK9m6r0DLDrB0ZmayQIy1z86tgLRAPDw7yZj0tUBaN8Nqfr62QP9UDkWCE7dAMgbZChBOt0DjwP6d1cy3QJ5\\u002f3WFO17hAv54gD7KVukDjVDUJixq9QIAnaMfxNsBAPvki+5lEwkDVt67vhaHEQDCgukvgHsdA1vG2X5N4yUAjKLuEtmnLQLnMToEJxMxAw0GMOHF5zUCSCbNCK5LNQBDZXgjFG81APP4qOA8izEDR+8Ss97bKQA67Uuji\\u002fchAspkAC+wrx0Ax5LBENXrFQIzhwHEnFMRA47CsXnQMw0C3HDaqClvCQJi7UZHT38FAYVowvktowUAe9aBO7rrAQDiCU\\u002f\\u002fSU79A1+uPogZNvEAqoJfvUJK4QG5g2kgBjrRAD0yzyFe0sEC+USpSmKuqQP37jC9OGKVAAmY832uNoEAePnqYapeZQL9C3z2lRZNAhpxxrG37i0CQTcBCXXSDQPi9b2WE13lAuL0YYHJmcED+UNcSgutjQJA35ANPMldA5ExfYk74SUBL6BCtlhk8QDTrVstIpS1ABdqMI2TYHkCqlxaFGfQPQO35th6NgABATCAUXtPU8D\\u002fCfejmqKfgP7CBl0axW88\\u002fSEdv+z+euz8QRtyd13umP\\u002fcFxdMf3pA\\u002f8Mg2XaiteD9vZoQmev1rP+FjnRacpus\\u002fLwMUXwaX9T8RnHr9\\u002fMAIQHjvxPWozxtAJtYUSB17LEAIrcB0Umk6QBkPh+0+QUZA7JJBGp8sUUDYwCN7MpxYQH7IKvffuGBA\\u002fYdJltEnZkDf4n74bl9tQEpF9YoNtXNAN0Hg4QmKekBvy7xTSZiBQPXONVYAoIZAwPj3MqghjEB9w5xhAhWRQIA3UejhoZRAFTxJ9S80mUBAvMMAFVWfQGQQpofIsKNAuenUmS2uqEDTAA0QRmquQJKbbH7nOLJAltCz\\u002fAUTtUCfJbrjTnG3QG\\u002fS7zujGrlASj\\u002fTbWgIukDUkTcaVW66QN+je0kRqbpA0gJ+b6gbu0BZAv1pvQ+8QIvpOT+wqr1A5bTQrPz5v0CTwhPfJIPBQGfMYbB2a8NAW511Ay6vxUBKPEcLGDDIQHvCOGUotspAzyehD3T9zEDj+Ib3k8vOQB+\\u002f5pKM\\u002fM9ARzMREyFA0EBQCP+rmSjQQNGZGxyecs9AlT4uNGr5zUBNEX2CmhTMQMI+c4UgCMpASF13+XoZyEBp7QXjUXrGQKDK3+E4PcVAhpJCzElWxECaThj4caHDQF7uDaVv6sJAUTHoIqb4wUDLXBB55aDAQGMfpmuEr71AZrcfPWByuUA8mQksJvy0QPAnh62ZyLBAHGHsPJFcqkB8Mdfv\\u002fZKkQKp3FWmxDaBAAJSe4xT7mECyh+q98TaTQPiQrCxK8YxAT55YZv8vhUC2dlVflgJ+QMEauSVDfnRAp70ZKMPqakBWq7iS2\\u002flgQOdxj0KhllRAJPIZpeMhSEAraTmZj5Q7QIa5VnHdBi9AF7OsyFxDIUAkccSQgO0SQOziwO36MARAR8Cy5W2a9D92mFTakcfjP72q6tqQoNE\\u002f\\u002fQoa4d3kvD\\u002fdRZlUwcClP3gQ2Sgw348\\u002fR6qd7hUUgj9qTEonZnnmP9iVpcwYhPE\\u002fFQo4oWAKBECiv3pTKX8WQBlFWB8ODidAaV9DZgRxNUCx\\u002fLrExCxCQBD+l483VUxAH9p8IxCZVEAfXD359IFcQFUGTaLgPGNAv6UP2qrZaUA\\u002fi74cOW9xQBekWtsbd3dAl1PMSiwkf0A3JQBoKj2EQHkBm1Je3YlAork7e1dzkEBU\\u002foazxxeVQJP2TBdEXptAM4re7orWoUBhijEG7A6nQFqLZWyJMq1AVjqkdrz0sUCuzetZuVi1QBzXpunRdrhA1rb3keUCu0Ag2bAxzMq8QFFvrT9szb1AYI6oh1RCvkCIFYY9coi+QFSde2E8Ar9AUWNksBX0v0CjjsGs9LzAQCW1q+opzMFAYhsuxs8rw0DV4MHVBOfEQELXviBmBsdA7UmE2\\u002f9+yUBmzXu0YSjMQOUqXynwws5ABi8sH86E0EAlzm4\\u002f02DRQIR5XRkr4NFAE74v8IT10UCmvOR+j53RQDOABv+C49BA+c07QoHGz0C\\u002f7v8x2ofNQAZuhZo2WMtAbbZbztJsyUCLjbbYUtnHQFBs+gNhk8ZAE50dvi97xUATZSkBRGPEQH6aTpzoGcNAs04B0Dl4wUC+DYfKuOa+QDZZlTJrSrpAx7Rp5aKItUDZqCV+gRuxQFeoYwSutqpAaj8E0dvNpEB4DI25OVWgQL5ve6r75plAKk\\u002fZNPGdlEAUJTewPU6QQID+vFTrW4lAqCirlLk0g0Cpp\\u002f+GZRt8QOvUkbT3wHNAT2TRaN2ZakCJUIRl9jBhQNnHOIcXdlVAmOldsZ8aSkBhXYr7dSg\\u002fQI+rTX0ORDJAmA3n+zHiJEDpOMgZ+PcWQMCEvLen7AdASibZTN5E9z9SRMhlMubkP3jTGWEHNNE\\u002fjh7c0gf1uT9X48HEuAajP0fp3TJMlJU\\u002fR9eE8qvZ4D+8gHq\\u002fdz\\u002fqP0Fw8FEmCf4\\u002f5YdQr\\u002fblEEC1SszBRWkhQIkeRAeeWTBAMETG+3EmPEA1qxIthHFGQCv3gizfz1BAABnwpZQWWEBes07FMs1gQL\\u002fUUFy1HWdA+uSGQNmBb0AFbasQnDt1QGG4yDFlQXxA\\u002f0YrWxWmgkBW1VTakLCIQBEXcJXFkZBAFkXyXuyTlkDWMnpRheaeQI9XIs5X46RAANsZYz5+q0Bsa5Vgi3axQAHzDhs9V7VAlosUkKYVuUBxhBKjVmW8QGdlnSK+Bb9AFs+lMjFqwEADxHT5P+\\u002fAQINlcpxVMsFA+b310IxjwUCIkdF3erDBQNzU7sUfNMJA7o+WGAL0wkBQZTbiRezDQOyz8pPpIMVAAdSTDnulxkDkwrV\\u002fm5PIQFV23F8x9spALChC5Qa4zUCAibMGslDQQKs9Y4c6stFAZSRZN43X0kA19f5G3pzTQCDirTsw6dNAd4T6UZWz00BuUttaawfTQPfCLg2iA9JAsQuBtj7S0EC7wMK1DDbPQEibWdVb8cxA4HKmoMzrykDWDqNw3CHJQPd9D1dFgMdALSIvELHoxUAIOSav+TXEQEP+IzbuRsJAVQSSvZ0OwECaxjQTjT+7QPXX2SBgUrZAmCNIVyHGsUBvi2ZuQOKrQOfCiJWb6aVAH7bF12h\\u002foUCYZ0awM4ycQPH1R00Zq5dA5ApP2bayk0CsU2m2djaQQItBv2KYBopAffTQ1nolhECPFx6VluB9QBOMNDPqLnVAymW2TFPQbED9TYwNvu1iQKvPMuTBNVhA83fCllBHTkDJ8zYXGnlCQD54wW+3yTVA8FQNqd+AKECY7hAv8ecZQF0mdAgUbwlAdVXvJJH49j\\u002fteCQWFvfiPwUMvVnOpsw\\u002f4zH+qlQAtT+VrJEfn86nPz3lOtjAYdc\\u002fY\\u002fUaNWE+4j9cbVTv4Pr0P+eFq02n0QdA0yctPYbnGEDWK6GLjuonQJpAAkr\\u002fQTVAsvZJ1DWuQUAtwUD03dxLQFjumS5RC1VA7pljbXi8XkCwaie60s1lQA+GJCg7H25AHQp6s4hYdEBahhKUECd7QDLqgdBIM4JA5BYiKjXsiEDw2RcQqoaRQAZ6YH7cB5lAOjLpTCHDoUC3luzPwomoQInmZaoTSLBA72VFacOktEAobPis+QK5QAhR0k+IC71APgYfPcg6wEDJlUjHlYjBQP2OgCiUacJAGEkhlDnvwkCFjlItGUDDQIdycoaui8NAIp4hzvn3w0AiN36v\\u002fZPEQFbbBP8wWcVA4+ECFCE8xkDjB8q1\\u002fUDHQBUzB4XnhMhAUNBLAww0ykAd1\\u002f0IdXHMQFCIaCxpPs9A6ZdVZUE30UBSXvqlodbSQKttTlaCSNRAz6o3f79Z1UAFfcXSuOTVQGqJa7Nk2tVAG0AfTwFG1UBRKZdDykjUQMguxG\\u002f4DdNAsRdK31O80UA2SqH9Bm3QQI+NFtAQV85AMXUDtUz2y0B2Byfxe7bJQGkTxuPkjcdAiqHdgZBnxUD\\u002fVuzq+CjDQOpRLeqowcBAkZGnQap0vEDNjhTMt223QL6ezVJ00LJAsjfq0izerUB4M298WdynQFq0CfXpgKNAbHaA71duoEDVyMmf5GCcQFGW\\u002fjE\\u002fvZhAfu\\u002fkfbpdlUAPSXMfbvmRQEpqy7PtGY1ADxxA7YuGhkDj3sr5H6mAQEM5QHK7pXdApJQEb402cEBXdRwD9JxlQJ7fw5M8EFxAl64L4heqUUBryH\\u002flSlZFQHwv8jq2ZzhAwhnBnuYXKkDVaLcobcwZQOr1dNdOZQdA1chlyx5b8z\\u002fTd8dT\\u002f0LdP+eWznGycMU\\u002fBYAfTohJuD\\u002fHFFaZLjvOP2Z1KERuwNc\\u002fmg4\\u002f8qO36z8IGz5MzhMAQG\\u002fSFmO7VxFAlDywriJiIUDDKxCLUFIwQHfrpJpQ8zxA21ip6gdnSEBRDcCZO5tTQJx4uIqlCl5A1QygGDHzZUC+vaDH4rNuQKq1bR9fx3RANPcsyvDFe0C+e3cm0NCCQPnoJgbAXYpA2D9ehiwak0AaU7fW1wicQEU5bL4URaRApZYU2DpDrEAwO5OB38ayQOLdxtQVsrdAdPUQFdt0vEC+\\u002fJAwqFbAQOP4pGQNDMJAalspHdVMw0DWU1lVjyHEQOnXXijHqMRArrgtJ\\u002f0PxUADCzo6S4TFQOw5yUpfIMZAGtNXbAvjxkATSC6ZtbbHQA791Esyh8hAyKc6vyJZyUCkJulp7lPKQM4mV7gRt8tAc8zYc+y+zUCFas4rskLQQAwA8uYj9tFANVPxLIvN00Aa5c87DInVQC9YdQY25tZAE+aNk3iy10CJ+VpSatjXQFGCxSH1YtdAalH2hC511kBozGUomzrVQJUDRpXa19NAT734FJ1j0kDOR36cxunQQE+0hj695c5AtTMEWh0PzEDBaa0Y5FrJQC9AfH1lxMZA5MJapQE4xECU2GGINaLBQEXbaAZSBL5A7DjnXTziuEDKz+FBBTK0QKXD4lpQQrBAUh76yYh3qkA3NJcp9CymQCr3nO\\u002fESKNAz3p4TAhKoUBwWEpYzmGfQBM7yoItNpxAdqan6WWpmEBl\\u002fnwDkbWUQB40FJnlnJBAqpPLqT94iUA4uCrn7r2CQN1RvJbTpHpA\\u002fu0uRfhgckBMJeNMr5loQOy\\u002fkmlhv19Abp8jCl+JU0BvtrHXX6RGQJyALpiRazhA6vlMlmhGKEBoA7M71hQWQKyQ3AVcSwJARGaa6u6p6z9ImCyodUHUPzF9HhPm7MY\\u002fiodIqI55wj\\u002fuHotcIofNPwoU\\u002fXlHzOE\\u002fJM8mKjaL9T9vgkq2V4wIQJXmJkWgSxpAGFAWsmaVKkCoXBstb2Y5QN\\u002fZQo4n5kZAPdtrovdpU0DE8GunM+BeQA9YS6HCC2dAa7Re71VCcEDbuwxtmQl2QMPff4YAd31AS48K0YUOhEBiIQEH7meMQABYjUzN2ZRAaKB2i\\u002fzunkBMczwEYYGmQE09p0+mba9A1VOX3rvUtEDxh1CE\\u002fSS6QNKbpc+xJr9APH2DSNu3wUChxzFivWHDQA\\u002fj9yvwjcRA0XnsOOpTxUDjagBi6d\\u002fFQOOdlZAjZsZA7fiZ6cYPx0AXhuwSkunHQKysKiJd38hAxvS\\u002f4HfKyUB0PHfbEo3KQLD9N8AELMtA+LwV3LLYy0D2x0QzNOXMQLY6SO6Fps5AWoqj8bSn0EB5R1fi9WjSQB7CeSuratRAnyc1uC1k1kD8Sy1MiwXYQGC9iz0QENlAtubQ+bVn2UD+n4ETORbZQJ\\u002fDNL+sP9hApK4FkHkP10ArsDILQqjVQBN\\u002fKNejHtRAGslknN1+0kBfm0S5edXQQGweZqEOZM5AJ3GMDJZDy0AnUVWcH1HIQL7alvQhf8VAYfcrB2C5wkAzSoUb3vK\\u002fQMCalYS9o7pA9PH3lNDQtUA0yZ6yc8ixQP754qWeca1AkvQUsc0+qUChbMqSu5umQHm73c3k9qRA2aDlhQm0o0BodovKxFKiQI9Vx9dciqBAePyVqdqlnEAZL0IzabCXQDkSeQQeuZJASNBf2YBrjECvMboZnNOEQNDRrzqTkH1AGukrXzVJdEBpqdjKP7ZqQGYY1Sj5r2BAeAxqqI+LU0CLFfpXDzpFQNmx1iKwMDVAgS3tvdFRI0B1Ux+3YAUQQOa1tL9yOfg\\u002fuVwPmKy44T\\u002f04vSOgQnUP5KfiM1987U\\u002fozmBf6smwj+d2BlGJzbXP595rgkHEO4\\u002fAaTVasB4AkAlwIdnMmYVQHklLBtYTCdAhOBle+65N0DHH\\u002fKv53tGQHFokW8LwFNAyqTdPaUQYECmz8t45khoQB2GNNNHOnFAn77R1O9id0AthMzORUh\\u002fQG6oAzFIUoVAe+rKutpEjkAq7WsDs0SWQCQbv\\u002fPGiKBAzzf8ghALqEAc3vXsn8CwQPdg72NzIbZAcJafe4qku0Do1w4mGl7AQECi8\\u002fFPfMJAWvDITsMRxEDhXfd6DyXFQOcEiXuD28VA01F2SF5uxkBDebapEhjHQBxcgJpW\\u002fcdAasbujjUcyUA90k1DqkzKQGfsOvAaVstAVADDNGAQzECADfXcs3\\u002fMQEI02qUD3sxAdWUmXISNzUAA2w3NtvjOQAtZn0WMtNBAX03\\u002fNPVw0kDJo3BWtYfUQH2UfVVmq9ZANgiZBG+C2EClMiwg7cPZQEI4fsK0TNpAen2dNTcj2kAWO8uClmrZQHhyzILSTdhAJ0Vg3BTv1kCaedLE\\u002f2LVQP+Gy\\u002fjrttNA42\\u002fcZnb50UDo0q0KpzzQQIf20C14IM1Afpt2ycL0yUA54\\u002f8y0uzGQL4iKs4h98NAH8QS3xAOwUC2gjaV24e8QAuvpfzkfbdASgOxV01Rs0BWmsG6BDewQFt7MBetYaxABpR7R4gbqkAEthFnM\\u002fSoQK8hSjrWLahAig2X+fskp0Du\\u002fk5KZnelQEomIwdxFKNAxMz8JIMwoEAA9lIWo0SaQMJYQtMHeJRAJ8dv8zbJjkB\\u002fU3TwE2KGQIPbqzfYXH9AUEJMVxL\\u002fdEDFrMnj8I9qQKVntmHhYV9Aef\\u002fg50ojUUAAEsg0jipBQBXa8\\u002fgdWy9A1hARKTsEGkBj3+nnp6kDQKQbrFPYvOw\\u002fHUHQRpU64D+sAfMH44aqPzmSBMrGL7c\\u002flcJKNwcg0D9y3kP5O6jmP7yuD4lqEf4\\u002fxJYzWDiiEkCSG3V\\u002fvW0lQEMQS9gmvTZAZrV4nPUtRkA4G1VFZNtTQDdYqwwwWGBAdNclb0DiaECbzuDU\\u002fLpxQBBT5WycJHhA8xcZ1B0vgEBbaeWe4hWGQDO6VdEcVI9AUyZJ7fb8lkAc304okAKhQJ7yiiujpqhA5aPnaJ0fsUCfYYbBLY62QNwGNmpuFbxAPPxY+FeQwEBuKAa4AZ7CQCJhYSFhGsRAxI3l7TgTxUBQigRRPLnFQD1RXyksUcZA9skyonwbx0Bem73ROjnIQMCc84sIm8lAvkh2KiEGy0D1uCLAgjDMQD5rn\\u002fpw58xAkqLVZXUtzUBWMpUYtULNQOwqGbV2lc1A0xcs9GCgzkBHl0Eek1\\u002fQQCL+0MzqAtJATOF0nbsV1EB6KUhpPUvWQCeP\\u002fheaRdhA1bpCxYq02UAsKiGV0GzaQBvf5aw0btpAB4D1mNvX2UDFLiZU7dPYQE8P7XCUhtdA8vJvF\\u002foH1kCqM7GVVmjUQKL8wk2QttJAtI+nJyAC0UCIhXgQTq7OQBibIGZfc8tAVQbKLpBLyEDy9NEfbCvFQGoiPyHOFMJAAleu7j1BvkBPDXcx+\\u002fS4QHGg7EYnobRAUnp90ryBsUDgaSZMTjWvQBLY\\u002ftIna61A0w2JT7TjrEC9W04YU72sQCT0Hh8NMKxA8eFt\\u002fxa+qkDYHxn0I0uoQJ5K\\u002fapXEaVAqzpNq+R5oUDzEgtptdubQGs+RvWka5VAM6LtOvbLj0BwNd90Mq2GQHUEGyQ5zH5AXwMYeRuwc0BtEJ3QdW5nQDgZqsxwtllAfSjGZmjTSUA3lIvKCp43QEhwzP3ImSNAIg\\u002f14bucDUDKhsCaBZ31P1F5DoL9Yeg\\u002fyAVvEmgmoT+npzln1AuwP2SKnZ1tXcg\\u002fSCAtKztg4j9vzzrNjND5P+OE0NHZtBBAgnl9JPLRI0C+gh8bBXw1QNTfyIGsQEVAc0kkmlc0U0CLEpmj1tJfQE4VxJJJXGhALtI52Bl1cUB5Eo0bn+t3QEeANecmIYBAduGY9toWhkBh6H38B1OPQELPEneC55ZA+f57\\u002f\\u002f7eoEAxwsnWXluoQCU6HIey4LBANFRTZEMztkDYD+6ZtJy7QFbdvm3+QsBA2WYONqk7wkAUlMVjwp7DQKM0DzXXfcRAaXQwobkRxUCnV\\u002fvctqjFQJxG7bRHicZAlVxlTrXSx0BmM1w8M2vJQBnwkzwvCMtAVcB330RQzEDXXgyK4gbNQNB0sjE4LM1AQIcO6BsEzUBB+D4h6APNQLwdumpbr81ABFk+Hr5tz0D6gwNc2jDRQNOYs9wCKdNAgIPt+6lY1UD2TdeplmPXQI5rShBB9dhAK8MxjWXZ2UB0Dc8McAXaQOz79sAYkdlA1ZMuFh2l2EBuO7z2VWrXQLPptKlvANZAclUjk\\u002fZ81ECw4PgnNe7SQN4rI4QXXdFAI8xnO\\u002f2Zz0Awke+2r3fMQL7iWJriSclAbWkzUCENxkBNOvE\\u002feM\\u002fCQMlE5oKWar9AQ4Kz0avluUCDOdVuyHe1QFmJrWmRYrJAu1zq5HOpsEDQDOeO\\u002fA6wQGJUh6qkJbBAqMz2jW5ssEAp8cEcMW+wQL\\u002f1a7WtxK9AKBMzTMZirUClPjJM9\\u002fSpQHURk4Bh8qVAvViaJvXWoUCXQcr\\u002faPmbQN3jnMydJJVAtqVtgiGijkDioOs5LRCFQAPE3k\\u002faLXtASlneHK9GcEBIWScqpu5hQCnzuosvDVJA741mBQiGQEBlqT7LYW0rQNyyD5o9tBRA20CqbMcu\\u002fj9kK\\u002feNEQHxP2gDHbdGUJg\\u002f34Pvx7kiqD9MgEpifo\\u002fDP7+kMOPJ3N4\\u002faXA33yRg9j+XiiCfnpgNQDzE+jtQzyFA4ZYBw\\u002f58M0C6R0Q3vWdDQC8z+jiooVFAPDRSQ41jXUDE60q3JKpmQP2r\\u002fDBVZnBA+A\\u002fhYRW7dkD\\u002fNIe80QB\\u002fQEug5hCZZYVA12t+om5ojkDT77zvGS2WQNyjKSXcQ6BAXRgbyXhnp0AbYQV73zGwQHGMwPWeTrVAewRrXlCHukDZa45HzEW\\u002fQGOx5HYPh8FAqKzRSDDUwkBP79LmNpzDQBkFlbmBHMRAzUYbYoCqxEC0BTGTb5LFQNWS+db+88ZA5sK1h+GuyEBOGqjmKW3KQJGOcsnTyctAG\\u002f2gNs6AzEBI+VcZX5DMQGuFXc7yPcxAIn0UfbIAzEAVq2pceF3MQL4cC0Dvvs1AvaULge0o0EBXUV1UgPfRQNwh67jLDtRAWoW5VT8Z1kCOzYdDN8HXQGZq63lBydhANSYOVNMZ2UCF\\u002fj2xksDYQIbXE8A\\u002f5NdAvpGEdlS01kA5mC51JVvVQDWgIWrw9dNA1e7QxLGS0kCjUP5iFTLRQCC5CR8pmM9Afi96UT2szEDw+zp815LJQNkjltIbUMZA0ny8ME3\\u002fwkAbD+Rv7qK\\u002fQNd1xB+GB7pA7L0LkZedtUCiyPlGjKmyQPaCEeIZLLFAGKzZGM3gsEDD3T0cQlCxQGFUqwNJ7rFATK0ZQ6w7skBxfjLLl+OxQF50fQz1y7BAhBJz\\u002fzQkrkCOzVKs9umpQMQ0Zk8GcKVAEfLHjGgboUDdW3mpA0qaQFkk3FIfUZNAMIhm0kbdikBdMqJks3mBQLwnyywEDHVA09k+U7ZEZ0BnCeUmXHdXQGvMMVyHfkVAUXw\\u002fgvPVMUA6SDZKt+caQNS36sZKlQNAJeW2Wm4J9j8U8XfxvKCSPyUTxo1tI6M\\u002fCQRpFzsJwD9o4lH9YM\\u002fZP\\u002fru+u0q8vI\\u002fJS4R6epACUCDcq+6kIseQOH1RX7\\u002fxjBAvp\\u002fJVvDGQECMP8jm\\u002fapOQKQg6Wq3yFlAkb4R2m8iZEAoKC+LhJ5tQGbx\\u002f6lp63RAYdAFa4AOfUDoF7CRd1CEQJIkxxbzAo1Ale6ZYxYflUAJKRgS9tmeQNwDZcKBH6ZAAi6tAWqWrkAKq4RJYyW0QBJFn9fOJLlA+E21bqe2vUCvEChAoK3AQPUk2JC96cFA2GBnEUWfwkC26WiZnwzDQPXElpVNi8NAahszGA5sxEDo1lJJY9DFQMEFADd9lcdAJpApk5RfyUBB\\u002fmHIlMPKQBhEGz0uestAocDBvZqBy0AQV2miex\\u002fLQEErw6C0x8pARglMwA34ykBfsVV2exXMQAUL7pEoUM5AcasKb53I0EBII5\\u002fKKLzSQPrq\\u002f0A3t9RAwhbUiRhm1kCIs\\u002fxanoPXQEPteC8Y69dAm7lGIj+f10Cm+5ZMY8TWQFV2yFIQkdVApGHDIWk71EB6JCtoxOnSQLnSVWkeq9FAdsfu4N140EBkZtRmDIDOQCHqxJJ\\u002f2stAEOc2WVTtyEA62zmXXMHFQM962bz5e8JAOQJVav+vvkBjIH5MwDG5QK9+uSNA87RAW0DwTyQ4skDjdzZiZv2wQJWgYfr5+bBAUinJ0USysUD1CbH7dJayQK7DbRO8I7NAiZEGM18Bs0BRgJcHmRGyQIInROQ1b7BAvqXYtymwrEANAnivIx+oQGm53wrKkaNAfs3\\u002f27CKnkAGqzPshb2WQOrmHNVc9I9APd+aMkPxhEDbIHaaHFl5QKZHplRwG2xA0VV8onVjXEDiSpI4eANKQL\\u002fXTIXskzVA+oa3AWRCIEA3C7\\u002fn56EHQK2x01Szjvo\\u002fDoqmP0WKjj9qbJlgYIKfPxQyE3gVcro\\u002fR4EkSAJA1T+EYF3utR3vPyuw+4ctsARAIS4DBeP+GEBqcitu6X0rQBZNeBhLnjtAR3WiPh15SUAfPmy04LpVQLIdpu19UWFAYTMq\\u002fwYiakBisxXtxPlyQEm5r0BsBntAzUBa8bQ3g0AGK\\u002fUJTJ6LQKIcJsABEJRAz30D5bUjnUBiXa8CyMukQFSlWaDtsaxAQqEEw6jpskCWdNQjc6m3QH0ouTGBC7xA5nyHOdqOv0Bxy09R0fbAQAcgH\\u002fa7n8FACoDK3Xf+wUC03CbnvmzCQOToqSYEPcNAlUy5l3aSxEAqOq8a+ErGQBRcs1XZCshA7SAtivBnyUChSRwMmx3KQMLvQ4EcLcpAwbPxHY\\u002fbyUALujnc8pTJQE5hnDBSyMlASQhM+9bLykC7y87Nx8rMQCDxBbxSuc9AR7E+3zin0UDsw\\u002fo\\u002fQIXTQMXr89oRKNVAf3HhYDNG1kBqy60njLDWQLvQY0g0YNZAIU9Mjrl21UDASi8EUDDUQEJj01SEzdJAYjgw5J590UD5B+60UlLQQPBtELW8gc5AHLjekdRczECngn3N3wDKQGTitcC5UsdA6EVgFCdbxEAsEBxFeEPBQIjvQrzylLxAXwCUu6lqt0DR+iGrxXyzQL3\\u002f\\u002f78nC7FAFJeetZcPsEB+nuSTkUCwQOp8GHROJbFAMJRFYugyskAxifwUQeuyQOnIMRbo97JAh2DyfhM6skDjzwp3psiwQIYiadact61AJC4TzaphqUBqGZp\\u002fi+ykQMhunPqxkqBABHZJP\\u002fH9mEBSxBVd+7qRQMNEIgeHZodAEouRliBzfEDXSbu1rqBvQHgTPe5a+19Au9fp765PTUBQgEgMvkw4QF2RZKdHSiJAfeex\\u002fzyKCkBoWH1FQcj9PzamJVJAZIw\\u002fnBAai49cnD\\u002f0bb24yvC2P\\u002fQ\\u002fh5O46NE\\u002f6SsbqF2g6T8PBBOWc78AQBULiGUQBBRAuayHwKDtJUD4EuHZ2Bw2QDpSHH57pURA\\u002fb4voWj8UUD1v9aQK4JdQPYes640CWdAo66OjrlQcUBmo10WhGJ5QIS0QCMTYYJAQMVFQcqNikDULEiRJDSTQOl0371Np5tAMbpqGYGUo0AyBnvhieKqQIzt8wB9sLFAu3w9+VoltkCFN8uAfEy6QFpe19OTqL1AF7kySQLxv0C8IkTa+5rAQAWS1cJk88BAFcAGGEZXwUAzc2gpYhbCQAcvX66BU8NAV+Ni3v7uxEAzvf34x5LGQJNgTnsR3cdAJk\\u002fB2JKSyEAnCkuYN7vIQOzZQiA2m8hAa5KrF8SSyEAR3GquyfvIQMZYJhC9FcpAulBdo4L+y0BBLRwcrK3OQC0fqkhy9tBA89JenCeq0kARkh6xZivUQGBMiJiHMdVAQgSrKzGI1UDrVmvD3SHVQE0iEecGHdRAm5aftXO40kBaDyKYYjvRQF42ToVpuM9AEyH6j3lhzUCimJE+SVzLQMMghFihbMlA+vKmRqhUx0CV8x39j\\u002fHEQKBnNWLmRsJAV+gNp0v1vkAYs+zzm5G5QF0MXTKx5rRAVcSt20pisUAO1S4rqn2uQMYDudFQ6axAcWNjVN18rUAAFlw7zFmvQMyQNPPAwbBAsGoZNvqKsUDOJsZRK7qxQOcoBd9GMrFA4gaBJmcFsEBhGY\\u002fTUcSsQPnC65449ahA+RhLxvHnpEBZuEwmqc2gQOF3BUkipplA66I4oStdkkAPKnuB+2OIQIEHvt6NxX1AOtzEW7aVcEAjdM3j+8hgQPx5MpDFw05AHMN1v1x8OUCng7T0pCgjQPfnSnMmwAtANzZ4hfgX\\u002fz\\u002fNw7uEB+GQP5Kt+D0jnp8\\u002fi6\\u002fy8Rehtz\\u002f05B3itETRP8xJjg3dWec\\u002fMqOw6J8k\\u002fT\\u002fk57fDsdMQQPMPZCffDCJA8nuu4lcUMkADLnwZNgNBQEMw1f0SRk5AaRZ+Yp2fWUBVmKMT0L1kQCf96rptJHBAKSINL8lQeECRtKFsDeCBQOxkP54e5IlAgaeq+s+WkkBNL50OnHOaQFHHggsWgqJARU2cMlsuqUCbvgKo9HiwQOhvaGRVkLRAdFHzVp9nuEDPDmIWTIy7QFvZWVL+ub1Anj2H06D8vkCBGINXJ7e\\u002fQJXFOa+3QcBAgqqix2T4wEASk5riPB7CQGsB1vQVlsNAeM+gfp8TxUCZnWGn+EPGQLWjchFI+8ZAHN6I2WJLx0BJBhtCZ3fHQIKPpY730MdAX2c9VJGYyEAC9VnPb\\u002fHJQF3\\u002frowS5ctAB1FqhptmzkBq8cB3HqbQQOOr\\u002fkJnItJAVgmNBr5s00D\\u002fkslbLkPUQI\\u002fqB6NpctRArEIDyg\\u002fq00BUaRQYB8XSQJa2uwd9QNFAUrEJFkVKz0D1Yvm6cFrMQBUhveJ65slAwdprcSDgx0CqrHPtFg7GQHKTZ8QVMMRA1e8\\u002fK2wcwkBHXt6GyZq\\u002fQBBEA7LkvrpAXcC3BRgMtkDQSJBpJ\\u002fixQGMFfddIzK1Ac8KkAUcVqkC4TGwQRr2oQFkvGwGDSalAfDAtICv4qkDnm72SePCsQA2NeeoAca5AkLD+qID1rkAXLBUO0kuuQKsQD9UGjqxAtOQSLY0BqkBgKyQA3e2mQNObbmJMhaNAjK0QRyPXn0CRNf3Er5aYQNoi3MTxwZFAhzPQDO24h0DxVnIFyg59QByWZ+tON3BAoVAAhL5rYEAL\\u002f0PgXBZOQBQzWOqs5jhAcYQ1vU6xIkDR\\u002fGUv3QULQOe+JjbEOv4\\u002fwlE1C7cnmj\\u002finpbighenP5JAZ2YZxb8\\u002fkszEC+pt1T\\u002fixdZQr8LqP6nrU6cy8\\u002f4\\u002fT0GNIi+yEEA3poXJIvUgQApYh69EXzBAwWMgysRPPkBBioW\\u002fHg1LQGmOM1TYTVdAT8AT2Q5YY0CBADoDheBuQPN6QY61tXdA\\u002fTAkweiagUCadxh1RH6JQJiG9txEJJJAvgE8+8V0mUB\\u002flV5XqYqhQHopVVAuiqdAgeS8mqZ2rkDEZNhak96yQAPaL0wyS7ZA3lwH4WUhuUC0B8Rysym7QDRB4TYUcbxAEFZGbSxNvUCGqozQRz++QCeC3jrCu79A7BfamTD3wECdHn4RRU\\u002fCQJ6XemGOpcNAp8S0f126xEBc2DWDZ3XFQHCo0qyE9MVAasdOcaJ5xkDqnVKMnUbHQJrfx4gTgchA4iYGFNItykAIjQdB2T3MQA5FSIvQmc5AVZBRbWOP0EAZzMDrYcjRQONV5ME4ytJAt2He1xlf00AnNUnxrlrTQNlCuK4yrdJAaCxv\\u002fjBt0UB\\u002f4\\u002fUUf6PPQGIIsv6VPsxAakSQnPUfyUCYJ0jL8ITGQEIR89joa8RA83ciUC+mwkDmUfrL9\\u002fbAQC\\u002fyw5AjX75Aix8yAGp+ukDblmnAaGq2QGgk3NwkcrJAQBEBQKHxrUAXVR97PqqoQJ94JMsgbKVAUkD000QtpEAD5nQlioakQN\\u002foIFcP16VAsrYnnphup0Dv8XMLU7OoQH6FNwD\\u002fPalAeoWn00vnqEDF6FTI4b+nQP0otdag9KVAOXbbP2evo0An31sw3gmhQB1SK4dbMpxA1Gc7nvIFlkBTIpP0qgiQQKCFQ60qiIVA4xO9FTZ0ekDep1cA\\u002fpBtQEIWZmM68l1Af1Z8dBdrS0AyCllibao2QNyzpVzC\\u002fCBAuyWBTJCBCEA466CD8V37P2POldzlC6c\\u002fmMC6hrO3sz9IvPjUYdXJP1jdd2N0g+A\\u002fJn6fMxJj8z+pHd9Hf+gEQLs7VhB+6BRAqk63pzuoI0BMhmFnGK0xQMtBq3Lg7T5AsBZwYoWgSkCoi9UcQJZWQMztdDbhvWJA3\\u002f7at0chbkBV9VxIllJ3QF\\u002fyPDHBZIFAPWPhdn4kiUDu5ZNwsr2RQDhFG5Y5jJhA+RYugtGgoECBsW+VLuylQD6I6AxB6qtArgyfhS4QsUBBauhRGfmzQN+wBcrCabZA+V1H6a0\\u002fuEDdVTAa1465QBqsAoKroLpA63RcD3DZu0DHJfnqWIq9QDIfhVZCxb9A2JIcfwYnwUBDSgohK13CQDDpS+CyWcNAHrLmWBgZxEBOg\\u002fA+psXEQOk5jCwjoMVAPH8PN1XbxkAjw382oYPIQATJE+fjgMpAY41aPiSrzECrMS0wft3OQHTaHS72e9BAuS3H8xBp0UDTrwUU2BfSQBdVQLuSYtJAJ1N0Ijso0kA+lIj9C1zRQGaQGi85D9BAOGjqhHndzEANeXN\\u002fPGvJQHAN8anwM8ZA1phdjn16w0AgReFrKUzBQOVKFluJE79AbnKAgWz9u0AcP6CQQfS4QEIiyMbOwbVAkt2xt3NpskCsFfxGfUGuQAkOMZjFbqhAzH5RhAbvo0CzY\\u002fH7NBWhQBu9fycBtp9AmW00kOzXn0Dxzs5g\\u002fMagQC+PRQWz46FANg\\u002f81xLQokAvvA8I3kKjQIVQZksVIaNA5x465\\u002fRzokCNMaNoqlKhQAQFZ3d4mp9AplmIlofQm0Bsia4Q4leXQP+9zTICbpJA2l83h9wIi0C1oIxRaTyCQDkXlme1dXZAgIuQlSggaUAUvLCb4HFZQOb32sHHRkdA285P6gs3M0CVmZ6TJsAcQGYCtT7UsARAI+7fqE0Q9z\\u002fEi2IW4Vy0P+E4v0ewL8E\\u002fGauO1TUH1j85miK8eXPrP7JVqJEuNv8\\u002fFYnnQ74nEEDjQo7ubqseQKnIm1V+BytAYE2\\u002foPWINkANiHqs9TNCQH\\u002fuerN6Jk1AEOiCBBFrV0BLhY4Blc5iQJGFiMQ5yW1AdesjBeHzdkCcmMNDdhqBQFuPwGo3q4hAMm7WlFlKkUC3NWEHRKGXQPfApQRtdp9A\\u002fwMaMMVSpEAVxSLis1ipQAp3y+u6aK5AXc8jR1qKsUAeql8K7IWzQGbhO9PnHrVAi5yzOBB1tkDdiq9bFMi3QFBIzVeDXrlAINHYKORiu0Ck67IkEMi9QAfjOOOQJMBAsc181+RGwUCU3NCL4jHCQMB7pw3Y9MJAVKomcB7Fw0CUYLnFceLEQBy3XRzpcsZA0hqAXhluyEC96CfutKPKQIUOn0LN1cxA+A2XChPSzkDGiMPJvzzQQLV2PsPl2tBAE5v94Rg10UBFTQ5ZbTfRQNrQqORGztBAZIj5XNzhz0BlA\\u002fwWnlTNQHprYIjsNMpAT3UCMbzexkBSkh0szq\\u002fDQBO5inMG7cBAZMttpTBkvUClUSA\\u002fceK5QLJZUckuArdAORw8CNdjtEDkBEs8IcKxQNov+GnFEq5AROCQsTWxqEAtD6XPcNujQLM511G1DaBAGHl1mO8cm0DlfnDSjK6YQMp78TctQphASUpSQIMKmUAisrkpDz6aQHAcniUhQ5tAXE6C72\\u002fBm0AhtXdybJ2bQJID9NS\\u002f4JpAQtxIacCYmUBNykhAvL6XQGLVLiJlPZVAMViBCy0RkkA4AfR3F9CMQBYk5nS\\u002fRYVAmj1kcljPfECOZT8hW8ZxQNyJznTU5GNAX6uXt+0iVECVfpoNIGZCQLJYVO7bUy5AwYGyOcekFkA77JFKd0EAQMX4LVGcFfI\\u002fyA6Qam0JwT+7vTmPEpLMP2M7nqzvHuI\\u002fjZmf14VQ9j9OFQAKVv0IQOXSCxHZWBlAkMJDwuxiJ0AEtRvWDM0zQFEyB3EoPD9APMoBS2l7R0CS7\\u002ffLxVJRQL4tqdhYv1lAzwdeHBJ7Y0CTtRqD6cFtQMOTvfDIinZAWZW9taGxgEBwzuZJYAKIQEkzKKNGvpBARlcwgWmllkBMW45aTqmdQEXLnFJ1waJAoCgOST7XpkBFBOXTncWqQL+hFPjuSq5A971RktyosEA\\u002fWKVCHwCyQGmf1yroWLNAuRbPtyzstECl09Lw9OW2QMmZTatSTLlAQXIemxnzu0DZ\\u002fVvhYYm+QDlXq\\u002fYeYcBAhM28aFxDwUDJYQ+W7AjCQDV\\u002f9uUs78JAyEpVj8Q0xEBTfAyYkvbFQC5Cv8uIHchAIQb5KS9oykAB+8EFQYjMQMvLhfa9QM5AVFEE81Bzz0DJwd4wygzQQG49VvAOGtBALS0sBlC9z0BfyHD1VqjOQJYYXNCX68xANERT8VePykBBlPwVsbfHQFHwiZdZo8RAZznAgfudwUCJVuxiAda9QC2mAgsIZLlAdtHu7ojrtUA+S\\u002fUj1zKzQFwoqsB24rBA4JOoXK1brUDqirFbqOOoQMLM7qtpeaRAG6J12vx7oEAjllaUGaKaQJLwei1iXpZAq5E1++ARlEB2Fb87mDiTQLlojQbdKpNAKBAgeWtbk0AxWlwDwHSTQJFZdQXlVpNAGLzBDSIDk0BV64NudoGSQHPbkI2yyZFA8evHLMm7kEAf1YIMgl6OQGdHA6kfJYpAfngedbMHhUAVWaLKFDp\\u002fQJ7iDg0SNnVAeaQVfdk0akDaYDmBmVRdQPk03NGDqU1A841RlicQO0D30wxAS0MmQI8PSF72lRBAC8tBVCvA9z\\u002fiskeK\\u002fl3qP8Ctm\\u002f54d8o\\u002fEhuZJaAZ1j83io+trdvrP8FeKkeCCQFASczUEVzsEkB1R81sh\\u002fwiQIcdTnUtQDFAE1bON2mNPEDX+JiWL8FFQOgesTR1Fk9AQ39GFI5rVUCUihqxkHFdQB1Alw+8u2RARE9NRkQVbkAgfLqoEyZ2QFtH5TXkM4BALIBza+svh0CHY5b+eheQQIlnlcxrkZVAztHI48DTm0CLx6gJQjyhQGXuSJXfeqRAhTzF4DNmp0AbiLiieOKpQKMwWqrTDKxA\\u002f9meYtA6rkDF4l20ynCwQAZ8urWwNbJACAzcXvKDtEDa9c8vvUS3QPqtzr9HMrpArcicUL\\u002fvvEDu3pipEDW\\u002fQHqSl6ODfcBAnPT9z1NGwUACT7KKKDbCQOx6Lx8cisNA0lqdtmVZxUAsdfLb5YTHQKGQFw+6wclAijtt+Cm4y0BjOwBtyCTNQPl6P5mc6s1AUfIpIVQQzkDHyrdWzK7NQKyZkKoa3cxARra4bZKky0CP4wD2igHKQFvB+LE578dA7Oap7pN3xUBmZ9YCkr3CQAuliBzP9L9AP0WaIrbaukCZvAPeD4y2QMLVtIELKbNAJlCiQVOPsEANJRIcI+esQEvzggDOE6lAnGuXmjRRpUDpahyqyaihQJs0usjFyZxAdNRPy4+rl0BVGknirDqUQGRCRKmFPJJAVtggUKojkUDxHNolE16QQF5OJdoZIo9AvdPZnCNPjUB0SI4POXWLQIUf4mJa2IlACSn2nTmTiEAcxV9bcn2HQEPhTIQYOIZAAWbgDktbhEDp8\\u002fvmxq+BQKbrUNg5pnxAe0i4ktBcdUATUL9b4hVtQEJ7OymN+mFAaj1hdrccVEACX\\u002fcFgFBEQDkiHJOIgDJAbOc9SHVgHkBKyiLnmpMGQFkQAFh5HvA\\u002flfSCmuba4T8VCXEQkffSPw0Ohbu0kd8\\u002f2CXnjtDM8z+gbiflkBkIQBnkbfm2nhpAp7+TL7mGKkBJmLwUIeE3QJtRUkI8f0NACoeZTCcZTUD01DupcCBUQCdvBykcblpAAmCdLBsNYUB9c3H78m5mQH\\u002fXyLYGwG5A88+OwrXUdUBaeHFz6Ft\\u002fQAetJdNuP4ZA8uiiwP2vjkBW0usBHmOUQEI4y6Ja9JlARDrxhd2Qn0C+anGsVFaiQObEuB5pcaRAShBT8QsZpkBUMYmWxI2nQAaLKeMoRqlA3SxhhGTKq0Dux5wa94GvQN\\u002fgt4JKQrJALsz3mJFAtUBUG9bl+2W4QMBKm6lQTbtAjnFQ9reuvUAncbxIn4e\\u002fQDPTyl0Vk8BALhwgTJWFwUAoAXun09XCQDxJpiQfmMRAsMJYlt2qxkAKf99otsDIQCVESnYmf8pApuxhFPOgy0CDCSx5jQvMQAP7w55+z8tAZAfAXiIXy0BrzIqREg7KQNzihtEpzchAnyMa9R1Ux0CkWtL1UJPFQKEXVAgZgMNAC\\u002fhBMFInwUDzOFACs2S9QF\\u002fhmHpQtbhA22AMgkCmtECv18DkdGixQI500guI161A28i\\u002fBjflqUCgx0WLPnGmQPCdGTwuLKNAxQxg7b8aoEBtVTwUBfWaQK6RfyD7DZdA9yDH5LGPlEA8uBV86g2TQMYHH6xI35FAEFy\\u002fnvh5kED7d7ZETV+NQC45rv8sV4lA2D8iNrWFhUCHL1dqunmCQOSQOybOY4BA8OcBCJ4efkD2l3Q3FhF8QI6UI4DurXlAuK7JzOhjdkA0thcEZzRyQDjcv9FlOGtABN2hKkeNYkBT\\u002fkjC+u5WQDltehpSoUlATqefxcPYOUBFSIvXRn4nQJH5NeawPRNALL2xe+OG\\u002fD99VIiMcE7kP6+F\\u002fLfZcNY\\u002fo3QuxmwI2T8OD622ssTkP7f9oqb67\\u002fk\\u002f8wX54RVwD0B8ljMXDkkhQDDQndD1IjFAGAAFo0aoPkAEvzHWfNBIQC0Z8xGcR1JABAqzGXrIWECMHpDc0YpfQKaCBaLXdGNApp77M\\u002f08aECWZtNslodvQEB9hnkpinVAfAbF295AfkDZcD5rbjWFQBsbbHtLBo1AzJuuayAgk0DgYjS1HxeYQPvE9XGa5JxAyGd+K15\\u002foEAokWdlMgiiQD56QeQmGqNADk3eFQYHpEA20BhTj1WlQJoy+Z1HmKdAy9SN0CE4q0CT1gw\\u002f7yKwQNJHoaSxMrNAb66wwNJwtkBYyydclXW5QAcZwABy97tA6xphhgvwvUAr4HhI7aW\\u002fQK+\\u002f29QHyMBAlY8cra0NwkAQEA9UrbbDQPxDvgaHosVAI38pT1GGx0DHVEHR4gnJQHzCM50r6slA7ZZTEyUQykC+60FtN5PJQBIfQkpJqMhAJj3zzpiGx0CZ\\u002fse3Lk\\u002fGQKRTvuOZAsVARzQG2fyJw0CpaY4L\\u002fc3BQOQZQkNCm79AeGfVEg5Ou0DvHN4y\\u002fRm3QOcN9qxcYbNA\\u002f1csGA5asEC3lgVSKf2rQIfSbJwgPahASue2T2IEpUCiHSFX6xWiQLf2N0r\\u002f+55AlnPwH8LnmkBJCAmG2jyYQHmIhpqJzJZAey7OoqHqlUBhH9ard8SUQE5OoofZz5JAaVkDdgMIkEDoTNfAp7OJQARsJLgVs4NAH1y2SGTdfUCBcp8jJ2Z3QNJzq5oWiHNAUe8Z4I42cUCaVfVoW71uQMWRztrhl2pAeHLptd+WZUBZT719+iNgQMTy6SUD\\u002f1VACeq1HPwqS0DDARu+YlE+QKOIW0vNgy5ASWUCa1WsG0Bxjw7XcZsGQNL\\u002fFLuAtvA\\u002fBSdNJ\\u002fi31z9Ie4WewyXKP+R205OkZ94\\u002fonpGX14m6T8mHBt8E0f\\u002fPyyGxm6c4RJAirjGHiSuJEAp7xnk+Wk0QN1SpOSEK0JAvY0EMIs7TUA1Ke+fMFpVQI88\\u002feWolVxAf2sDOU\\u002fWYUAqCbt8fF5lQLUJZybPm2lAXiKLu131b0CJn8Iezhh1QBQT8Vfx+XxARsLl9ssOhEBUMdANLz+LQA+BTUjQ2pFAT0uetgZdlkAGxvLHeKOaQKhdifydJp5AwyclN7RJoEDPWrTOvwKhQKR8\\u002fNaakKFAVTTplVZ7okB6hSOQmFikQMdZ0DO1lqdATvXYTGpOrEAwWuQNIRexQNErsSJrRrRAKX3d+1BRt0C235gtJO25QN8G85odC7xAZyZXA1fjvUCRZ07RDN2\\u002fQEL2yj\\u002fzLMFAovY6Sxi+wkBrhtLetoPEQM+bXKx+N8ZAVlwugXyGx0DCkEtKhTLIQCAMqCI6KchAwLl3s+CGx0BTGBCKUIXGQKPlndS4X8VAKJrbgcI4xEAP0YjI2Q\\u002fDQE3EHTbzysFAJ0RKx3hOwEBb726p1Si9QM31\\u002fnV+ablAOwldJ2q2tUDfpLvMI2WyQKi+2P\\u002fQSa9AeK5YxMLlqkAF9teI5FOnQLtndTjYQ6RAXckchzWVoUAcj8k5wsieQE7nRyAWzZtAWBJR4NFbmkAQEyUGLQOaQMqlI5zr0JlAjvwKjFHEmEBsvKnBC1CWQFoA5phnl5JAJEX4B1KPjEBAagt5GWaEQBMibwwZx3tAErek4zbgckCz65ONAiFrQDIaK8y0SGVADf\\u002faKiLHYUBA3ElNs9ddQGx5xWbZ8FdAbrvZxNvQUUCYS0FN7zhIQPhg\\u002f4lN3D1Ato5hgZigMEBqAVHeaLIgQEcox5IkNQ5Aj\\u002fbM\\u002fF6c+D8It21XNSTiP2Kwu9hHqck\\u002frNqoDow3vD+e8gU2U\\u002f3gP5KoTXgTBuw\\u002fK7POzaRaAUBIqiYEgt8UQCzyJjifxiZAESpaGhNlNkANpXz6WdlDQGODnLKVx09AMx8Y0r0SV0Ai1mCPbaJeQPy9pj6U4mJArjpD9a43ZkAlHqnvHvhpQLDXJSIPf29ADHBw2eBFdECEcPBJXWB7QMGAqu38yYJAqTJyGhp1iUApfwjKurKQQKNbD1yH+JRA1dfScygNmUBh2zlrQmWcQGhukSuGn55AKw5hQQjBn0BOkHncGiugQNdKMu6Kr6BAynnpgB8DokAwDC9biJ2kQHZIRLN+qqhAYfdEaX\\u002fyrUA82YK3fvOxQCoOknuN6bRAUWNZJ4mRt0Dv9TDrOtO5QJdRYkTA1btAM1QP1NntvUANWws2tjfAQFmiWyuUu8FA3kYfmoJlw0D8g3yoWPPEQNrkbrzsF8ZAkM3P+mmbxkA\\u002frn+BU3HGQHyOG4w5usVASRYLR5GxxEAcslW\\u002fLpHDQHGcciOheMJA\\u002fZAVdvZkwUB5mAcpjDzAQBgCW7dDzb1A8x2Zut++ukDYZp744nm3QGAfOt42R7RABnf+pW9osUCHkKsV6vmtQMq5Mtmc+6lAY9BNIFydpkBRTdAyPrCjQPMA\\u002fdd+NaFAXP\\u002fy4pG7nkDoYiE0gLycQPXcK5jKapxAFf8N1boUnUCmfF\\u002fxgIedQHlIY04RmpxArKk94F2+mUCG2FAkiTyVQJ9UUQOH9I9Av7qEUAL9hUAOGgJl5Al8QDaZPUlLFHFAOVloyKwDZUBa32u3CulbQKIXMjAFi1RAutn0gOQRUEClp+lVc\\u002fhIQJoSU\\u002fMdVUJAHPm+2LDFOEDsu1S1620uQN5p6g5m5SBAC70O\\u002fLTrEEC0McW+AYf+Px7a8TGvzOg\\u002f86jUDJ450j+sbh8xBrG5P4utf4D+LKw\\u002fvUSnpf134T\\u002frcOJD4brsP8pEgesBuAFAfPLFTYo7FUB+NHSXbBUnQGASWY0XnTZAwQVHXGL3Q0A9P0QTO9ZPQJGTcIQXAldAc2HXD0lgXkA3rZEi35ViQG\\u002f9MofbomVAyStVCLPvaECV4meRmMRtQPcNMSQX6XJAdQUXPvxfeUC+PhUPJHCBQKAQGmgAzodAsj7jk1CXj0B4m\\u002fJ1JRuUQFcASETIVphAD37zWvTjm0B1uFgq3kKeQD2fLvPCUZ9Auj+sizR5n0Adi98fvqOfQK6lrMBdfqBAxA3s8+lGokBqaIDwZHClQL3HmBvA5KlAuzOcxqc3r0B1FAasamayQB9otGsaC7VAXUCBTaJpt0C23YfLG5m5QH4H9XUC3LtAYYmmUyN3vkBZBV2xG7\\u002fAQLJ1f1J3WsJAblCcA3vNw0CIjZzq0s\\u002fEQCJNxWCZMMVAfjj4YJzqxECX5vOXdSPEQDPsELQoF8NApx8Wmoz7wUDtN3rIi+rAQJPKknC4vL9ASjtS6F6AvUBgpro78\\u002fy6QN5gs9\\u002fiN7hAKi8KSOldtUAf4f8+B6myQAQGAdfBQrBASVf5DKRqrECfKGW3beKoQOqSjdHuwKVApI57qgP3okASvg1OU6egQP6uaqujLZ5AkyZaw572nECT\\u002fK2pdYWdQJhXevzL9Z5ASSv7Bxjmn0DZ4kFRaRSfQEUxtFxm95tAggEpX3j0lkD4KJV5yRiRQFywGUrHGodA\\u002f1eZtVp8fECjVoG6WkdwQBM5ngfm4GFASy8j6tAbVEDKg68e4dZIQOBrWXl6G0FAb36hBcfTOEAUfbdJFqwxQP\\u002fHMLfwjidAil+k+h2\\u002fHECLHQSzm8oPQJ6ZuWp8u\\u002f8\\u002fkeHW+k6J7D+l87yQYBzXP40Wt2kl7sA\\u002fwhU1yDXIpz8EW+wZygKaP+fE+DNkhuA\\u002fLLOUbFAZ6z9ZlmYWFKUAQHcyweFJ3xNAqLmDAP6GJUAvtejWdgM1QMjx0lmffEJAfYF9+LleTUAuwiRGMyVVQCUMyQfhzVtApPp86HTvYEBMXRldQ51jQAovIaxQfGZANO+fBqO+akDE8LDhOgNxQOVzGdttB3dA5HNO3PoUgEAPpkZk52yGQGvyw8A+eY5AS9c9eBbak0Ciylsbr4eYQG+cIvC\\u002fjpxA6Z4X+OdEn0BOj\\u002fn6cy2gQJYe8Mj\\u002fCqBAVuJJV\\u002f1Qn0DsK1YMJD+fQGYLmTzihKBAk51+C7m3okBXfPM3lkOmQDXjr68Y3qpANgA2mv8BsEB4+EAXmpeyQIGmtbjoCbVARxXnNwJjt0ClYh5gZNW5QNXTt128lrxAnMlf+cGuv0CDv\\u002f653GzBQHcfeW0ly8JA+xmeOnOtw0Bsw9y0MerDQFoViZXyhMNAL4U+lLSpwkCAIL7IYJXBQMAr\\u002fro3ecBAEr7WBrbRvkDAXD1Rp7e8QGqpoRR0frpAYWn\\u002fdysUuEA6L2KvM421QKhzZuTKF7NAXfvMSdfesEDwfw8xIeatQOzRunuMkKpA2Pzy4z+Jp0COsh3\\u002fUqykQHH1TlphBaJAL0WIE+inn0AgX+fKA92cQJ07it2hGJxAlguU0pMhnUCT5PqtefeeQA0Y8R+mEqBApcvu5nJln0Bpydp23zicQPJaN5qfFZdAPyWlmLkXkUCOC1cWT+GGQH1GNh\\u002fGvHtAotZxTLWtbkD9LhD9LoxfQHGpushmcE9AyyN4Y1RKQECqYXeRscsyQGooMT2zJihA55f86I8kIEDtMbqIMusUQBVD\\u002fq+TMwlAbX+hr9Gv+z\\u002f0xSWu84PrPyNrmOIFqNg\\u002fHWn\\u002fvwrnwz\\u002fpEqlJPhCtP8ptY0G2VpQ\\u002fLbfpUdIuhj9ifCUJjcPcP7Zw+zMphOc\\u002f53T1JnbF\\u002fD9JxYdysBwRQJhP1bi1eCJA77SUkKv3MUBMno5DMIE\\u002fQOn4mCLw8EhAp\\u002fRWjg7mUUDjtP2NSHdXQHL8CmYhhVxATpdt5ZCAYEBh5a9TZPdiQMtC+UhRw2ZAHbF98TJ9bUAHINwibIF0QHNyBHCFmH1A07N\\u002f13xchUBOXHefvv2NQO\\u002fXL37SF5RAZeOMNZJgmUBqvwOBnP2dQK9KGJb7iqBArtslei8YoUCDyXKiOLygQLS4Gh6GxJ9Afw+3fVhXnkBz+R30enqeQEKy\\u002fOxejaBAqlxHX25Do0ARwcZF6TCnQCSO0S5856tAase4o3t0sEDB3Ku1P\\u002fGyQF\\u002fSo3a5arVArdSLcpwGuEDLa6f+gey6QMkBRLRvFr5A9x3bauyawED29UrvpuLBQEuENJ6joMJA6s+aZzmzwkAguChJ4ifCQM2WnP7xMcFAcGstVbEPwEADWPIySty9QK4FNH9\\u002ftrtA31qZowqZuUDB324B\\u002f2W3QNvL4pKYGrVA4K721QnXskDtdvR7wsiwQPSQ7aYmF65AKzuDUcAyq0DZqRP5NKOoQKdmCLmtIKZAxLP6BiiLo0Crks324wGhQKFxx1KutJ1A5moi+IjxmkArXdubmS+aQO3OW5azMZtATHG\\u002f70D3nEDaV\\u002fR\\u002fGxaeQAiFB+5fWp1ApU+hFn9SmkDz2J+jFHWVQLZkBc\\u002fOoo9AzECZjikMhUAkEgv72Ud5QAZgd0LHfmtA1zi2XExSW0D+QetZLlxJQKYUWbqxDjdABJUBJAwnJkBufS6wyO0XQC\\u002fj2vaOlgxAb4y71hyDAUD2APS3qJL0PxKOqQvgVuY\\u002f9CrrXG4P1j\\u002frqyEug67DP+SpBp9WqK8\\u002f3k4nsLoIlz97A1P8HQ+AP9gvVqwZd3E\\u002frT0Y1AcI1z+KkDTqcMbiP3PZnt+s4PY\\u002f4SVorjAdC0BBOhAeOiodQJGpJHZdRSxACBji4r6zOEBF5eS2V35DQKR+veI+5ktAWh7OMz5AUkCItaDBcSxWQPiYSJRQv1lAzTwWBaPrXUCrTsm3V2FiQJ9lPMc8tmhAjukOhqn9cUDW3vAKLDh7QFFgijwsgIRAquwg+YbOjUDoI3DxrYCUQNdxhcDqXZpALcV7LmN+n0CXORk3ZHChQJ9F1pbi9qFALcC0sX9ioUA+hnPvPiGgQDhm1b4Eu51ANm196\\u002f6RnEDOBe15W9SdQBqzlnYC+6BAaA6pa7JfpEDSJI2mcrioQBvjvGpija1AhYKbdRpEsUCtFTcUDM+zQNl3pp+fg7ZAmEclORp9uUA6\\u002fKznAqi8QGfNVd6wqr9A3b59bQf+wEAUieHAC5DBQHSRH85zcsFAJJsgQuG8wEDpp0PSLVO\\u002fQLN3Fvuj77xAm8azXGWhukB4Y87\\u002fQ3u4QMmHVQAyZrZAHNb0JEFLtEAhgSt0cjKyQKQWk1n6QbBAEEbtHXBFrUDx5VsnbMOqQLievFkey6hAZM9co376pkAdLdWsr\\u002fikQIAYCVpNpqJAGE4Na9suoEBfOfB1Du2bQMQr4Qt22JhAGHc\\u002ffBSil0AtZKSVbRuYQIkSugFfXplAKzpJatklmkCP0JA2xV+ZQJLad8Cwp5ZAWVuqX6BkkkAlV6gNcQGLQJrZSCoO4oFAfcFgL6dZdUCEq1ArkQBnQPX4FWwZdFZAOE64FqoSREB02qvn2t0wQAipfqGUHRxAypKqeMEpCUCo2aKdB3f5P9o9HdEuMew\\u002f+FkjbluI3z8xQ99UNb\\u002fQP7XI+RdZXcA\\u002fb7+9qXQGrT9md8VSCz6XP+kh6+nz2IA\\u002fvXWjgYBmZz9+z5Ig\\u002f19ZP1YCmMpf99A\\u002fbbIAEwOV2z+TcgzNgrzwPyFplQ5iwwNAKNgJK\\u002f8uFUBRzK6RxXYkQIQ2+hlj0jFAraSlu\\u002fsKPEBYrHp6tgREQICebQzwKEpAbHlWE67WT0DIivLHIKRSQA3xb7EUIlZA0aAIKp5RXEC6Zp2siydkQLX0QBwYNG9ANFKyo\\u002fbseEB6tgPR3JeDQIlHqJoUYY1AfosrqoiglEAMfgsrVdqaQMup40QtIaBARNh0PmrfoUDIAik3q1WiQBhSNYn8k6FAZQ+DziINoECyTOWtQuOcQKV94j9W8ppAPI4tumRhm0BnAqHUe7SeQDP3X3tNY6JAbY4EqnZ0pkCqgmB3thqrQFpmUCVN\\u002f69AEmV\\u002fd0yIskAtMzR6cjy1QFir8PglLbhABJkT5pE7u0DrggZwAAa+QBeamDXcAcBAaRWiluFhwEBE80U4YRPAQGt5gGIGbr5Ac04qGrIXvEBJx05bzaC5QFHky+2LU7dAh3LfRxY8tUBNSO9egEOzQE636iLmWLFAxjQ1AswUr0BegM\\u002fyp\\u002fyrQH\\u002fE3RzfqqlAwXdPlWQdqED4lsoJ8AKnQESCOi2J4aVAxv2VMsNTpEC+5f5F+ziiQER4gZL0hZ9ApwnkQYW9mkBorFyvrAeXQOWwRxwg+pRAnnVZr8+ElEBHKhVok++UQOWfVZL8KpVAL36U1n5OlEB4zDu7Uv+RQFAUo7fOEI1AsSkjfJA9hUD9blRr7gF8QEZefseIo3BAUebWY6DQYUCPF4zbXTdRQLJnVVjKLj5AYJD63+JOKEDTPkfrtYoSQM8pF3ledfw\\u002fm+s9BkXQ5z\\u002fwl8ntzJDWP0CekBJrEsc\\u002fzgk2Fwt9tz8pNo28XoSmPyCkhIwByJM\\u002fd7N6q1SAfz9YLGpRY7xmP9y4FBD8ck8\\u002fJLEwC\\u002fr\\u002fQD\\u002fmm2KpWP\\u002fGPzBQWPe\\u002fo9I\\u002feHwBZmSH5j+HAaXep4H6P\\u002f7irYHwTwxAtee1D\\u002fpBG0ACrfloe6knQBbqrnYpkDJApJGUTNZyOkBGnRH7mkdBQKSa0aOLIEVAH8leAFQeSUAWAGYPdeROQK6rPWoS\\u002flRASZ71wwwZYECFX7LGl7VqQMC7ZGDIf3ZAhBK76DVYgkDr1WO+VyiMQETV9YjRCpRAKEnqQFFFmkA9tpqRnp+fQHrLk6uwfqFA1yVYB2bfoUAwTtfE9wWhQNsGcSFyz55AgifvS7tom0DtYkbNA0CZQH6m3W5\\u002fZ5lA+md\\u002fhCpgnEDVZTeUUAShQJHpDgla3aRA969hOvJMqUB88at\\u002fzv6tQABrSYY+b7FA4MIHNCAGtEDtxQbEK822QNEcQa3vnblATTCrV1kUvEDteF39Wq69QCtWIPwlCr5AEkEAe04avUAEOFG17iy7QNIPNoKpwrhA0b+nQvNRtkDA7V0Z7xu0QDWw1m8vKLJA+RFFFPJhsED7YQF\\u002fWHutQJta9qf5l6pA+kl2OBNeqEAIGGtUjPqmQL9dW0HfUaZAv0\\u002fvWz78pUC4XjQKenClQAfasL+5QaRAS3sCQwFOokChnIYyHZOfQMoJzAseTJpAxp8JXyPElUByVRofjKiSQGQPFg6SEJFA6y7iz\\u002fx6kEBfQMORBBOQQJa8rjJxMo5A1u15J4FvikBsn6xDTS6FQPPjID1xyH5AsxF+dTYzdEB5X\\u002fYQ3+RnQEff7KwldVlA0kn5zP9wSEApHtbjvjA1QID+gz2tsCBAPPjH57dACEA+DX9ygdfwP0tUP1535tc\\u002fJOrNbH6\\u002fwj\\u002fJL\\u002f7GBZywP9+\\u002fl69HQ58\\u002fbOUglM\\u002fljD9pPEddDvh4P8apHI+ptmM\\u002fGjxkH5FNTD89oHaMVHwzP6bfKPG1\\u002fyQ\\u002fodMWiN6tvD\\u002fePwlWkC3HP5hglVeW5ts\\u002fwpiy2oxa8D\\u002fZNQ2wN2gBQNmJ1dPVsxBAnxwGkEvoHEChH5V2Pp4mQKFHmq\\u002fWFjBAPnwRWaQNNUBhUCtl8vY5QAzIe6UHpj9ADI84TBCFREAOAmwrcjpOQBTAKE71PllA\\u002f0Tahi5oZkBG9tC6nr5zQEVNECZbjIBAyQMKMYPNiUATvz\\u002fmd4OSQA4aOAbqVphAc4FdebZKnUC5oZL0PCugQEtOKIrGdqBArBv+53tBn0DwPe4lNjecQD8ailVEGplA1W1YkKs3l0C9cSh4+oeXQFR+zhMqe5pAzBrK063qn0DAYEdFCpujQL+cGUdSxqdA7AQDEsAnrEA7aNBANlWwQGV63ACLs7JAzDXt\\u002f4YytUDMJhFyAam3QCwffoSct7lATzvu37jpukD9rAovoe+6QFavBwvzyrlAXdFj+wjPt0Cp9CZ8tXa1QJtkJA6VLLNAd4j5HcQnsUCNSC7cxtmuQO8ONCBy1atANJiHI9U0qUBnP8jukBmnQJTbBCVMu6VA1ilvI98xpUB7IaZ9x0ylQFmAa0mSlaVAnPbp6AJ8pUCmIaH4U5GkQG1ZNsics6JA0TGVrKIXoEDb81umS2GaQHB\\u002fO9M\\u002fBJVA7bNaoSHckEAfGq2OVE+MQDtuQTrxNYlA5Z5oFpouh0CNddfG1QCFQBLACQcJA4JATVgHbqiEfECzegTulZB0QPxQGCfC1mpARj5\\u002fqSGZX0AkyNQD08FQQC60lRW+AUBA6bVhrqSPK0BfwltMPHEVQPX+rcShXv4\\u002fxfU2qufv4z9reHaj\\u002f0jJP4t6P5SJp7A\\u002f2ERFLqOImD84GHkaIEaEP6JkewKUgXE\\u002fcBsslxdTXT8LpLP6RtJGPxX03NuzQTA\\u002f3vP7OgxDFj+kATvEFegHP9Ccjqe1c7A\\u002fK0eV1SOEuj+qVjS8OMrPPxzmN5zRkOI\\u002fA2aZnHSx8z+hRdXeV9UCQBRo1U6QPxBASXQzg9FdGUBbyvWGQggiQFarUzszrydA9JHeMkilLUDCWj1I28cyQPicRFgbOjpAiLmRIzZIRUDuEHL0jl5TQIAQayLRN2JATwvMzU6ccED\\u002fd0+SqV58QKxx44IjVIZAvcAe84wVkECIsw1woiiVQCsTRHp3bplAqp8ECowEnEC9lTR\\u002fQHqcQL1sTUZjB5tAkJzV8jh+mEAqGvj1YQWWQDaCIsnyvpRApLyVVEt+lUAsXtpmnJuYQIg0MuOf6J1AXokgGpZlokCShA7jFUCmQBeavR7dOapAIshz2cU9rkA7ck3hPy+xQIExLiL9TrNAKqWNvs1XtUD5oh0xL\\u002fa2QCc9\\u002f7d6yLdATjTr9b6Rt0B5LZKBkV22QCxC5PEyfLRAV5bpeSFbskBhJACmV1WwQEzGg6yTL61A\\u002fsse\\u002f81KqkDdbrVrvOGnQL1CCZby7aVAN0uica6OpECi2AjEcu6jQM4battUE6RAksdY\\u002fHS+pEBF7apD93KlQKynDbtYoaVALbXq82XepEBUuXmEcgqjQMdgN0C\\u002fWaBAUASQcih8mkAjeNM\\u002fdHiUQJc46n\\u002fi945Adc3lu1HKh0DRwl26WyiDQLBfHvEhLIBAaW0MhjSoe0ApiO3VTet2QI06Cwdpy3FAOTbYGjRfaUA+g9IzG29gQAc4AogGPlNABuOizmFQREAss3JX31AzQDcL5pepiiBAhbMOTtWKCUDCqPj4TtbxP5X8qcxsu9Y\\u002fmBjqP8oDuz+sfvVJaGOfP9091NVIQ4M\\u002fSmhmy0Kpaj9R3cD15H1UPzjNfReSL0A\\u002fkEfWzBeIKD84n7kDgEERP1a\\u002fmSqUdPc+x9GNHVQV6T7Kox19pV2hP2sn3i2w6Ks\\u002fRjsvSISpwD\\u002faOhoxLmTTPzxk3fqKf+Q\\u002fpATekBGK8z87jnD3Dc8AQNpMvYrmMApAPf\\u002fzXDKhEkAWZqbMX6YYQOXATlvWmx9ATMxgmc40JUDBVv2yiEswQCPsT\\u002fFuVz1AnZtD8\\u002fXUTECf\\u002fZlp61tcQDYuVqc8eWpAfUuVfWTfdkDl3Wea+BeCQABDQ5hxGopAQhRhDrEokUB3UfJ2+ZeUQGpvf59jqJZA03UXMQgIl0BYvTikoO+VQFbxHXNcFJRANYb8rRxtkkBIb2oGaumRQHlS3TNWNJNA84zJibOOlkD6GVyzT8SbQGqyfXAHIaFAT8WDPzqopEA+ap2r6DCoQGFurLOBo6tAiDlBtIkNr0DWNl9Q+zixQGxR2zW7yrJAK4zG4Ij6s0CR14Q4\\u002fny0QLJZt94aJrRAoc5m\\u002fx4Fs0AO1AeFqF+xQDQPbDpnIa9Ax1qWsGjEq0AI1u1TAvCoQBAVuQCUqKZAW5iscRvapEAU0upFO4KjQE5aSkPfu6JA5bC0xU6mokDrT2iSzDujQDOpPlxvN6RA\\u002fC0vhpUepUBs\\u002fC7Tx2mlQL4UK6BStqRA43sHY7HookBbSXZ+ezOgQNeaf6rFBppAb7RVMrGyk0B\\u002flPLK5YOMQMcyC44ETIRA9H+5n6V2fUCkAGr1r1h2QFTtldtgjnFAqEtw\\u002fGWKa0BYXIyvY7VkQKFbAPx5\\u002flxAruJOne+TUkBIiHCnC5lFQPP7ik60rTZAN\\u002fZfMdV2JUAI0esDyksSQIULBxsbGPw\\u002fvdcWSz904z\\u002fGrRkl0WXIPw3nOcqiAKw\\u002ffr5wE1Uujj+eQYp3ix5wP+v9RMijflI\\u002fN8+7e94KOD\\u002fgk+hxxSEhPxqQcNbesQg\\u002fmdfq9pz68D7tFex5SNDWPtjKqRjcQsg+HY6Uiz3dkD9vaVluhQabP8KzjS7LEbA\\u002fR0da+0+iwj\\u002f6Sbb3aKHTPzvVHUQWp+I\\u002fuV6q8eYA8D8+tCp9uef4P1voZR73wgFA5azbmGzPB0BqI\\u002fk6VLYPQEuTJwpIDRdABWemc7TRI0ApCij6ErQzQGnadlLRlURAoDaET63lVEBsyvhxuc1jQJjmZxcSOXFA8HbTXhNPe0AVLE6hpLKDQE8pMNt03olAwIqAHAgDj0B19eXWlQ6RQHXZRbzfYJFAPc1l0SKtkEBVZYjUQg2PQP\\u002f4Q2hzZI1AR3+JDuzcjUBFaOdJ4cKQQBV3rIiyXpRA5yCPvFGLmUAy97jFB7ufQDD+OYYdHaNAE\\u002fAcuT07pkCYfMlq8RmpQCjUHnuDwqtAawUv0Q9ArkBiCoU8qTmwQBd6JWQYA7FARr8RKOFHsUDznW3CL+mwQEqIABBE569ASoIuAHM5rUBDSTsmelWqQIo2WuM8r6dAYBppsk2ApUCWPHGQJM2jQIxOTJkLhqJAOSaN1dyooUAklZw2kkqhQJZEULQGgqFARPfOi35EokAsUr8cuE+jQHK1\\u002fTmJM6RAFkuMmNB2pEB3Suh1E8OjQEfUq5H4AaJAlVtqkZLFnkAbeMYwMZKYQAUV5XxBWJJApUAzlnDJiUAXnx2k3VqBQMcjDg\\u002faCXdA1erJIoY5b0BjIkWKWAJmQHhYpaGB0F9AoWUpEh63VkAfIhyerN1OQE1md9E9cUNASUwXVyNfNkBaPOGQdlQnQMd2N9fx9hVAya6PksWiAkB2DWNI1XrsP5YuYM2xmtM\\u002fWnjl1uBauD8UHELgJ3CbP28xv0A8ZHw\\u002f0EWWSNbQWz\\u002fDo+RedWo7P3adzfdBfB0\\u002fmbgizUr6AT+BJZ0Nq63nPt5a+xYdMc8+BIQa4QaJtD5R4FWwv6alPuogldRnIn4\\u002f1oStrbETiD\\u002fRblqVVoScP0m5Gal3ebA\\u002fe1IxOghMwT\\u002f0ae84lmLQP8iKNw40Ddw\\u002fVeUrW\\u002fDS5T\\u002fmInzaw0\\u002fvP8s1oxHYc\\u002fU\\u002fYjZAvs8\\u002f\\u002fj+UU3MJGWUIQGkSvey0mRdA1v42rGWHKUDPZ0ZW8OM7QKUB+FOR50xAtSfEZc+iW0DauvBTsxxoQLCNKEgaIHNAgTKe6MORe0ADSh+lqxWCQNsbsoCSq4VAoWJJ+kveh0ATyt5hjXCIQPsu5fEYvodASHgVr\\u002fSrhkAAlaxfx2qGQL0gj9cKJ4hAJ9Lywny8jEBfXegmRDuSQI31pFGOdJdANl8yCBx9nUBGokOiucyhQJQSlTg6kqRAolgSi4jmpkDSYEUyQdCoQJnCx3efaapA6jIMdsC3q0Bs1DFZgpWsQORx\\u002fuV4xKxAm6sn+tIXrEAmJz3LLpmqQPJrQZyrjahAMPSOMYVZpkBHjErPGVakQJWkZdwntKJATfdvEt15oUB7otV6n5mgQIai9PsoDqBAd64GNnvKn0BbLJCk0i+gQDH2sMsE5KBAt3m5+W7JoUBZdlo81ICiQJGW1F\\u002fTo6JAkvx\\u002fRj7qoUALNs0S5EKgQEu\\u002ftw\\u002fCr5tAVga1ev7\\u002flUDr+hi990aQQBepKHN0dYZAwJAmB1YifUADEuZQDxtyQLUvzn2hO2ZAmXyHl0bgW0A3B2SVVRhSQNDVUdeG5EdAxPoqjNrrPkDWgqvTK\\u002fAyQNkJfMWJcSVA8hHWdNokFkCGNy9trbUEQKTv6XHtevE\\u002fzgyUf+uW2j+R7UhFmjbCP+Ka0FqKe6Y\\u002fSvLttFsPiT\\u002fx6aSb3F9pP8\\u002fe4nFUs0c\\u002f1t+IQ2ApJT+Hl\\u002fiiVVEDP1bN9GKQg+M+p75dS9dHxj4oBAiF4yGrPivpRHKYM5E+pIKZyvvdgT44QiehFsVoP1jSrkMLvHM\\u002f9xKIeJBHhz+RpIGAJc2aP4stWPriC6w\\u002fpVSPzdV9uj\\u002fm3viTX6PGP0bBL2QWpNE\\u002fgA+ocpGX2T\\u002fOT+uF1CniP0I3omolxus\\u002fAcOvQA5P+T+S4WGEYlYLQGVhe5mbjR9AqfU2zojDMUDHsuvZxKRCQAFfyy7H6FFAXOERYEVJX0AbyUHr581oQHl2JZdS3HFAnJh6c5Vqd0D83SPNsxJ8QK9jjngiBH9AY0CnWSwDgEAHd9KHkbJ\\u002fQNNYHaO5ZX9AzVllC3R7gEChKKGLoCaDQEaX6q6jcohAZIEoikNTkECEUC+Z8ayVQGHAp9gstJtASYURZjLNoEAYmukVRk6jQKBXVljGJ6VALHIFc+pfpkBF6z00USWnQHnHhiJlpadAZe87n57qp0DiGCP24dmnQOQ2pD3JTadAnmKMS9M5pkAGmZmA1rykQDeNvLbPF6NA+6R9zKCQoUAepV\\u002fZe1SgQBxIA7Ef2p5A9YVQ3rabnUArQDSCYNmcQFnV4ikSn5xAh4oCghoGnUAYg8ZHVAOeQKfcoTH7QZ9AgNAsnCoWoEDZJZXpeQ+gQB2\\u002fnqXUpp5A5BZIEn6qm0AXRAB50nCXQCWy3LMOiZJAvy\\u002fJbJQ9i0CFQKyqDJGCQD5EY4kfiHdAf4N+kMkAbEDOcNu9lPFfQEddxNx5BlJAmXF\\u002fz7TEREBD4XAfPa44QDl+IW\\u002f7oi1A1EJytcZVIUAVCuvnwR0TQFo790AtcQNAVGDu2VgD8j+JzLN1VDbePwpD0x1p3MY\\u002fd4LqAQQsrz83QKryKiSTPwkWm0wDMHU\\u002fNFzutrcxVT92mGWdt0wzP\\u002fr+plCxSxA\\u002fPP9uiwOZ6j71M4vRT5LGPhELeLPjcaU+s\\u002fI8gmjxhj7KuP4WzCBrPge1S9qAZls+K9f0KFq7Uj9f9oJ9bMNdP61BvDb+e3E\\u002fCcJkKN0OhD\\u002fbGBCBLeyUP+hz+59AtqM\\u002fPMBPq\\u002fTUsD\\u002fqDbxoAlq6P7mL7wK7fMM\\u002fpqzgVwM0zT\\u002fCoQITddfYPx9GxLbLvOk\\u002f0PyozQF6\\u002fj+C4VZMTWkSQK\\u002f3UW8LISVAn\\u002feRwmRVNkAOYzFLUH9FQBJIswNexlJAmgboNWC+XUD0EQ++LWZlQHDKAjywDWxA84Ik7FjYcECcfhANRrZyQIo\\u002fG26dknNAKRjnmI7rc0CLEb1gJM10QATY8Ku3rndA8WDI9fIwfkBKtpMhctuEQKEjo6qba41AJ8kd+uUxlED0DF6Sr0aaQFnN71OiBqBA1Ww199BQokBr\\u002fLHCJcCjQKcL1yELXaRAI9DRW1BspEDmlgZ2nkGkQBisnMoOEKRAUmizYE3Wo0CTcHsc126jQMafD48vtaJA7lddxgamoUC5CqGHX2agQE66AdTBXp5A\\u002fVjKQL9dnEA7PJmAg+eaQP\\u002fTRfEc5plA8skmNcM7mUD6dD66meWYQNk+Y16A9ZhAZ8wk3VJqmUBP7rObYQyaQC2TajXAbppAtfW4nRQWmkCcAi1hsKqYQHO8jjYoGpZAmIulv8mckkC4d4T6okSNQKFOLA7gXoVA+HnO1CbffEA8wtvvqwVyQDh7EQyk3WRAkqB7KxypVkDIII2GdZtHQGHbJi8zXjhAk85jbheuKUA+1l67x9IbQEklmX6pTA5A6c+Cfvz8\\u002fz+CCzpgALvvP+rgnJq9+dw\\u002f1KcRjOQUyD+FmYfikxuyP0tlL6zekJg\\u002ftfruhc4Gfj+57pMdiYdgPzDWDnmPaEA\\u002faDSpHgJ4HT9Aj\\u002fk6oib4Phy1sq0xd9I+bwBQzeSiqz5EBsqQyuGFPm2zSjg8nWM+5LRP2g+VRD6mNbjdprgzPu2d\\u002fpm4EDo\\u002fX1ayfz6mRD9XxkLx4ClYP\\u002fxHnzSln2s\\u002fbEkxfva5fD8gLeGFIgKLP1cOtRmrFJc\\u002f06hujyg8oj96KzqkaNirP\\u002fDJjHnLkrY\\u002fUvSZbobPxT8YSulTk3jZP3G48vuJKfA\\u002fm3cjsngfBEBzWao+uVoXQCPSiFSxxShAvVv29JnaN0ANa7RBGtFEQCksVisceVBAsV4dfu2wV0DHh1pywhRfQPp4wUCYuWJAOp0woyP7ZEDgHsnLKWNmQCZkkeKmwmdAHpks7kbCakBOypfVs+lwQHptT+5A8XdAbQkWR1rogUB\\u002f4StRD5WKQPFbl4PGzZJA2SBlvCPamEDkKOAoj3SeQH7KBuvvT6FAtJ7WpmlkokBRcVUMY4SiQKBiiIMSCKJAb\\u002fTxG\\u002f9goUCw710UO9+gQBibUPz2kKBA9aoTCiVMoEBYJMgRLa6fQOT2IWa0J55ATJmmEsQmnEAwHrnZmQ6aQCw\\u002fCwyeQJhAs4pP6gnnlkDvjQ8BpPGVQECtpwvNPJVAFqSB9Xe5lEB7kXCRCHKUQN2nzmNnbJRAeJ9V0BmJlEB6uUw\\u002fdX6UQHPUQJ0P9JNApa3CyMipkkC2RMPvo5KQQJ4\\u002fyz0XsotAjkt4rROfhUBKRVvOFFp\\u002fQM4\\u002fSXXmAXVAtVFunLXvaUArUoVRB39dQEdDLEOJDE9AGET6zUOlPkBMGU3vdQwtQHrEe2ulVBtA3CxYCZpCCkC3AiE8F9\\u002f5P6oQKK73gOk\\u002fVYlliwBG2D+E9BBXmKXFP7OmwOd+vrE\\u002fxnluhFJ0mj9zwZhytNWBP+crq9CYsGU\\u002fDkU06dbDRz9Tu5J+KnUnP2rlGFPH4gQ\\u002fEBw11pzX4D5z6YzR\\u002fN24PoDiEb1zQJE+27vjz2HCZz41ehdCxKRBPmrceFmPIh8+\\u002fUX7QcgsCz5DiQYXOK8gP6YHYc5zXCo\\u002fDaFbwoG5Pj+ThkzojIBRPzdcjCF0JmI\\u002f1Tp\\u002f2ygLcT\\u002fl\\u002fjpfmTp9P9NTKKuue4c\\u002fl7JwW53akj8PrzH7pv2gP0BAWuYrzbI\\u002fMtNk86RAyD9ZTGeRSSXgP8zNT4VKevQ\\u002fKdoU6CfpB0B45wOj2WQZQIwUDWpgcChAqyYxigVONUD7i96jUthAQEAFNtzyOUhA0DE3H1rXT0D+K+xuKEpTQOWw1jAG6VVALKroXXQdWECPZlurtERbQEBhKRPSCmFAIS6x+bBhaEC28gOQLylzQMazCI2Run5AZeGpqujEh0CCcMcpKDWRQHDI4xWTAJdAtuhtdbtCnEDJ5Q+nWvafQF10KUrawqBAOunOrRGKoEAzW19jm2qfQGjgAXRrjZ1A22vN8ic6nEAUx5pxkZmbQOp3yhqhTJtAoaztnq\\u002fCmkB+IkxZh52ZQO4AJaG05ZdASbJGrX71lUCmluaq6jOUQHuh4vHy1pJA6TozdZLWkUDg8BHKRw6RQH13fRDGZpBAkWnPY6vFj0Cq4CtdQRWPQN17ka4Kno5AOzWAFisCjkDo\\u002fHPMYciMQKS0Bd6QlopADsetdzRbh0Dmu4WziFODQKJKrQQZ531ATyJOnyx+dUC22YPRGY1sQCweTdF7c2FAl8G3lCmWU0AiGL3CYzNEQFZKTQzcQjNA4Iv16GU5IUAQiFym1aENQC6j4eMUWvk\\u002fFmH7wUop5j\\u002f8Z0eIV9HTP6t7chWvqcE\\u002fAO9GyNhJrj\\u002favYqbYEaYPwF24ADI24E\\u002fiRHO4PngZz+PFIbWwN1MPysLeUxGeC8\\u002f7KaFwdDoDj8eWrRjw1rrPir8Ws9G2cU+edJL6EOonz6ckYYSERN1PrcNNgw1mEo+H86VK4nvID5HepCrW334PVoQ0HzzheI9Y0QvdbKmAz+OAq3ZN\\u002fYOP2gMMEfu+CE\\u002fBCe4mKBoND+eIqg3Wx1FPw9esqeE1FM\\u002fcEHosEIiYT++0dZl11NsP64+mZ0IgXg\\u002fgQIS5MQfiT8jCT\\u002fUjpefP48C+Rzs87U\\u002fKuqUQG8mzj9r6MDEJFPjP54jhCmWoPY\\u002f2nkc2TQHCEBFCHnKrRkXQNlI+7WnHSRACu+GEQfLL0AJ8dzjHOE2QAh9d3n5Kz5Af2dByY5xQkCxuj8ZNGlFQAzZ0N3Tv0hAJMdC9eu6TkCEhULjaOxVQL94e\\u002fJ31GFAuqUR0EysbkBkU\\u002fYGUfB5QCObmbNYqYRA1hdXG+9hjkB6gZb\\u002fJXKUQPEYrVmpIZlA48qnKjJHnEDX9R4Mg1idQLJwEyIfgZxA5vx+9fqOmkAzrBOXc4mYQCXwfOeAL5dAQcCHjiaolkBFxMP8m42WQPJl\\u002f8s5Q5ZAMmEEtv9blUDoPttOU9OTQC3qxp2A\\u002fJFAhdRcNQNAkEBWu4lhy7eNQLlbNmEqootA0P+1nkD9iUB542iZtJKIQCNgHkRJWodAwo1dhnxghkDFz\\u002f7FRpiFQAiGe7rUx4RAyTfD6NSeg0AjkfnPb+CBQHOZiTSOA39AuEoIu31beUB8vHhFu2RzQL7Cfqoek2tAHges8osfYkA12iTQE+xVQCQ888rGUUhANhk1mxO1OECi5UHqfQcnQNo7Cay01RNAZq8Br+QIAECP9MpMBv3oPwchhYjzaNM\\u002fbrFi+xHUvj8Uj0zOI\\u002f6oP7JJrSYpH5Q\\u002f7SppeIMRfz8y3ll4Kl9mP6pj+ZPdiU0\\u002f3seopdG1MT8L9GqPsDETP3vAfb0NwvI+hL3UuyKE0D5cYH8mSDaqPmbMDbEAyYI+ZI8nZj53WD5iaWcXXmktPrhdcw5r7wA+6DffRxmM1D20WmYftA+6PY3DgTtYS+U+LixPAZq68D7vNAC6oFgDP0OwDx7a5hU\\u002fK+qMzWOfJj8oRwz0UEw1P00loeqIq0I\\u002f20l0plcoUD9dTHXvnPheP8KVuu1aRHI\\u002fREMzKh2QiT8glHO65bKiP52L+uxqK7o\\u002fYkLKbQbe0D8W5fz3wMPjPz997vZB+PQ\\u002fBBGUYIwiBEBGSDaFYoQRQGj9mc1ArxtA3dks7Fz1I0BjIlTceHgqQDpJivD1azBApZUPRprBM0DwKD\\u002fOF6k4QFOTASmMaUFAMSKfpUvHTEAyEKTfkzhaQJJ1KIc5IWhANI57OA0ndUD39jilMiqBQAbZDZ4oeIlAq45NcIcxkUCUFZaj8BuVQE4NnyXDopdAA\\u002f78N\\u002ftRmEAKsBaA3liXQM1vTw7NdpVAbozOu1ibk0BYF3SSpnCSQBFL3LnME5JAgCgxBuofkkCdMM8gov2RQGQ8RIMPQpFAy9og6V\\u002fIj0Bn2sQHy16MQEKExbV3DolAGp+oR\\u002fxXhkCAZPRQNEyEQNtjg\\u002fjbtYJAeUt4qrtegUBzIHOpUzSAQN5dG5ERdn5AqxIDkuPXfED3EymYdUZ7QMGIr4D3WXlA4Ay6IJvDdkCcrwvruHdzQMlOcyl1Ym9AEF+ZFIiqZ0B8MpBBW5hgQK2HRtHDh1VAIVV\\u002fT8S4SUB0ksr\\u002f\\u002fi88QAJ0TqqPQixAZq9r\\u002fEjnGUAKyqa4ssMFQA1l8TlD5vA\\u002fWL8qevOp2D+m2eYzTmPBPxtoTjbxf6g\\u002fgwSMwoKokT99NfrxD+95P4mVaWtR2WI\\u002fCbrJYsMxSj+vr1G2f\\u002fEwP6StvwW6EhQ\\u002frgu7QNaV9T6mfJ7LFPnUPkZkBiGnX7I+eLV6LX8BjT4yl1h\\u002f2qNkPsIUa5UJjTo+Kxvw6lEaDz4WOYtrmungPaEgO4DGKbI9kTGBUl6Ckz0tzefnxDrFPjIBQlHrodA+c3WNmKQp4z46fOPx1aL1PiYyAonMVgY\\u002fyazTvYUpFT8l4LS8QAYjP8BKkPrhpTE\\u002fi9KVyR85Qz8G2+GMD\\u002fFZP5CJGjOSrHM\\u002f3EuEEzi\\u002fjT\\u002fSmZkaOAqlP3b\\u002ff9LsL7s\\u002fMZgH4crXzz9\\u002ffSSL5d7gP9o\\u002fPyTWLfA\\u002fXU20Xhoi\\u002fD+OZglYAD8GQDM3w\\u002f+aFxBAINehY8qMFUDM9kwH\\u002fGgbQFVyZ9UofCFAhOa43d13KEDd1BWQxTE0QOoLWptyKENADhFTiXMFU0Dz1YShdU1iQNwCXjldZnBAbclKZITlekBx46XVnQqEQOBIN1bVEotAIJM0VC+VkEBOwKNvf3uSQI8ziITI45JAUyQapMP7kUD9UvTrdWWQQCX8wVlFzY1AWAPa6hIPjEAO8kvEnLGLQAh+P7oi\\u002fYtA2Plfcpvoi0DftnsIM7qKQKwUeJ5yYYhAqIpSe\\u002fZghUAcRchoOGmCQG7KwALc5X9AXrEZwxcwfEBFrwH\\u002fD2N5QPvt6SgqHndAQSMV9J8xdUBrFcDTH5ZzQMf4sRy1PnJAD10JZA\\u002f9cECexEKtkh5vQHtMrgQriWtARG\\u002f01ocwZ0AbKlkaP2RiQFYQILFkRltAklajiK3OUkBEcrIVRgJIQFUetAMlQDxATGRkFbCFLkC7nRk7tC4eQLpO+okuQwtAKL3EVeJ99j8BIDjzHAHhP6acjlAyxsc\\u002fgarl5\\u002fJJrz+0EYz+e\\u002fGTPwElI6BdeHk\\u002f\\u002fTaw8m+mYD98KuAyoCJGP2rLPbNcCC0\\u002fG9gooMsmEj9riVPL\\u002fBb1Pl3B7ovFatY+pEjQr8edtT6pv5z3UtSSPofeLZUgk20+9sYqFaztRD5u6Aw4PLYaPj2oolAm2e495Ad2ZptEwD3JYNS\\u002f70KQPTBoKopR7G49TZ9SbQB5oz7D\\u002fNnkNW6uPlofIGq7d8E+JR2sDKmu0z6LpmCpzlrkPoyI286zhfM+VH7vAJFKAj\\u002fJSMSlJ7USP\\u002fv0zgkwfyc\\u002fqahS3lPGQT+ioTe4VYBcP5y2Q2ng+nU\\u002fF\\u002fjj9qxDjz+MvO\\u002f8fjWkP48ytfOgpLc\\u002f9g5fb2EEyT+E2X6kOPfXP013EHZU1eQ\\u002fc53Gn6+A8D8yydkyLgX4P6qvhnl\\u002fWgBAnTOGQIezBUBbDLK412YOQFQ\\u002fvhaBxhhA+FMq1Fj8J0DiMsy7CV85QLzfnWQGqUpAPE0hZiFYWkDRWY5JYORnQERW5p3ZsHNAK9QdRJdhfUAi+S08wtKDQAMWR6MaN4hA9MSNySfgikDpfOcG5lKLQAIDXrXC3olAAvP4Aol+h0AiQBMBB1yFQGYPdQbEPIRAuoZFnFUshEDXXIUkBpGEQKRktMPCloRAgrzyqaCrg0B5qTQtCsWBQNSuJrp8m35Avnq+o3SteUAjsJ5BSo51QL+rsEwAd3JAj6X6xzozcEB7UCerLulsQLjcL5BFFGpAxv4uZPDDZ0ANBx0VL9plQHjg6T0WGGRAl7QyM3ArYkAE4RWWVrVfQFcbEXRHSVpA1HGEDMh7VEBxCZ9S6M1NQJCbLgGlJkRAej0odaU6OUDdXuLnvSQtQNd8kbsb9B5AjFN4zTEhDkDZMjGMYM76PzQZp7PYw+U\\u002f\\u002fEdmV2Ui0D\\u002f0BQKoiOy1P\\u002fGWONdZkps\\u002fzMn4HtpZgD+ZUEh5U9ZiP5ZHqCMfzEU\\u002fGbNUs+PVKT9bCR4WPxMPP4S+YWR8YfI+WXIYnMis1D7QcSyiSZK1PjtXN\\u002fsMkpQ+LR1mv2HJcT6loEyTlsdLPgTwGuqfjiM+kDTrHtXQ+D1eIvS07mjMPeRkIwZeeJ09lt7lqbdWbD01uoo9mgRJPQVNn4rrboA+LCuUklKdiT7uJkVp7U+dPjUspxaVf7A+HiNzWbUlwT5XquZ0btDQPnBvr+ARy+A+ibaQlc518z7fs5LkshwMP2xM4IqCMyc\\u002fbGoV6QlHQz9GbnIA1RFeP1ikdcs+b3U\\u002fb8X5xRexiz\\u002few2BZ0CygP5yQWPJgF7E\\u002fccD9skdcwD8JqIua9HXMP\\u002fOQT+zpn9Y\\u002fEAJ44\\u002fGk4D9mMEW6WVbnP8putOtiofA\\u002f4ZTbtWy2+j8V6srH6d8JQHdRteVxrBxAnJIAPcVSMEBP8SzCLL1BQNLiaAGHyFFAVmXtPZY4YEBVGu4LosdqQGH4cDFF93NAKongapLiekCNG8c9rF6AQE7ex0RzGYJAc5cQJgZUgkBfZjoayEyBQHcE5no3a39AWYqeiUO3fECuutm8p4F7QO8fQxuUxHtAhEmfYG6TfEBwKA7lW7Z8QNi+3lpRXHtAu\\u002fayvVt8eEBGMGN10bl0QKzvwiJS8XBAJpowgCuIa0ALmyMQ2ctmQPT0f2nlbGNAq3lf9ED2YED7ZCGetyNeQCcM\\u002fgYhKVtA87mZrFK+WEAakcoi+4tWQCBnTkbsLVRAYw0E0LpjUUC6f6z6O2VMQC0GdYALu0VAUlAiH7b6PkBw1XL2t300QJ+It3RnFilA59IK1\\u002fhaHEDMl+1puIENQJ9oqdkPMPw\\u002ftx0bU7Wm6D9rltM\\u002fzrDTP\\u002fjBu3Visrw\\u002fTPXWdHcXoz+7aB1KLUuHP1mP1l+gVWo\\u002fxbCx0oMjTD+rKLk0NUotP\\u002fCDjjqUsw4\\u002f3+QaxCN98D4J83qIwO\\u002fRPric+VqqIbM+mu1c\\u002fXJakz7B7mFzcCFyPrMqmTQaBU8+e0l11esMKD4uWsMuV9YAPlcWG9uoQNU9DwMCotItqD2Fy+yZBNZ4PdEdY1ptZEc914SXRMHTIz0Fjdj8IYVZPsJDjwI\\u002f12M+JAG\\u002fYimldj6vlXKSVoOJPl3G65Pdy5o++zgqn25Eqz5Nn\\u002fYrztm9Pvx9lzDR8tM+w34MKXVE8D49Wz7uMYcMPy8S0mtwOCg\\u002feTkq7MH\\u002fQj\\u002f0FUkPghhbP2\\u002ff6DbqenE\\u002fdiVOoHVjhD\\u002f5mI30bISVPwrjyr91l6Q\\u002fi63Dg1PxsT8n6Xc+D7i8P2InKpddi8U\\u002f\\u002f1h7xtPNzz8csYWw+WDZPyTLvjoLO+g\\u002fUCm3IdOR+z9OYKVAuNAQQBX0SxgaAiRAkxhzPjkkNkClCEfHnFlGQB8iJ7jvbFRAw3j6OCHaYED7AXmRDBVpQLmkTZeb13BAf7RxetVxdECTBL+mjYd2QPrCf5kewHZAm+B0vKZ0dUAj376P2YxzQAveTceyC3JACuHnw3mMcUDn3Qa9bvtxQNVToQmnsHJADZMifKjYckAMEC4YFOpxQDC+52NByW9AFJ25krB5akDxzb5dfRxlQHYo0FT6mGBANi9H+0aCWkB6BW+kZd9VQC3MM+x0p1JAHM55sGZUUECYkDB\\u002fRi1NQEXek55obUpAjXP8+WTyR0CN6eKYM0ZFQFZlwmGtJEJAasHc7lk3PUB3chvCkfg1QA9KM0Znri5A85vKd8TUI0DE\\u002fpP2mLEXQJ0hWtX4IApAf9YLdh6N+j9MTFBjCc\\u002foP3\\u002fbvuohQ9U\\u002f3G3W7WeswD9f5qx85+CnP253jxVoMo8\\u002fBa+OkYaccj9pmfp4l2BUP6LmpIW\\u002fszQ\\u002f\\u002fJ3G7pvsEz+TVvlyrLzyPsFVE5HnydE+xRNnALxPsT6kxEGPVQaRPvoGH3kmXnA+pvhB9ajGTT49JarXLgspPvXQCNTCNwM+n9Kp7l+42j2qHfllHMWwPSlQlZf2+YI9Z8eNSF1cUz2FyBEUXgUiPU+8Rmex4\\u002f08dkpGxao7Mj7kzBojGko8PpSyVsjyHVA+yTcp9Vc6Yj5ObPtauoRzPr9KUpSlDYU+qTtMUsT+mT4h65T2TQ+0Pq0TYpFU+NE+e46\\u002fpNVi8D6t9JL0YCoMPw6Ve89+JSY\\u002fWvov9FWPPz+J+mrq0FNUP6GCmv9crGc\\u002fAgGCUDD2eD\\u002fZNKtvm+aHP352C7FH6JQ\\u002fkDnrvqvxoD9YKyBqT1eqP4WQpn1mNrU\\u002fPhY8I4nYwz95oqorGbLWP0y1FWS2Nu0\\u002fMJSk4c\\u002fnAkCLsy0ubwoXQHVfJfWluClADLyHf9AGOkDFAB2D3cZHQKj+f8MylVNARQbwYskSXUBE+NPh7XZjQKs4+jHqjWdAvHO6wv3haUAcyR3X8xdqQCO3HPwOpGhAOPwxGNubZkC7lQ5RWitlQDop77iU+mRAQpPdcajcZUBxqCeMVfVmQCKtniJVO2dARhZq3WsIZkDnPqX2tmRjQBmiZLwR1l9A3bjk6WDNWEA6nzZZDuFSQOoLHfOFCU1AP76hPHYgR0A0RriT9TZDQDEZONnlkEBAz1UCJfdkPUDyh\\u002fPJuow6QOaDHk2T\\u002fzdAUD0NeYM4NUAupi6RgvMxQCVNYAKRjyxArqAs\\u002fx0jJUDsvckH7vAcQN+BbSOlSBJAmchY+25NBUBxBhWKduH2P2OuuqgnpeY\\u002f9lyqrAyh1D9Yo2p4okTBPw6bnDLkgao\\u002fSNHLKPmckj\\u002fG\\u002fTs+AN93PyYQ0TmQ71s\\u002fbMjDxkHfPT9etncEmlYdP0X7dS1Px\\u002fo+GQX9njs21z6wKX23qrizPhxcN\\u002f4Q9ZA+Q1HepkHlbT4QBUDzPpJKPnf6PpMMBSc+zLCsqOrSAj7XG3iO0WzcPRbhPLDKkLM9+jJS7khkiD33K6MWrXJbPXORdCL+2Cs9Erl2QrC4+Tzgcxd52xHVPOo+eziY+Qc+LR94gBiREj7tpI7cjSclPu8++tDUIjg+gwE3uhu2Sj7jYwiVtmFfPgqq04ZLUHY+czoypv+Qkz4JO8oNRrqyPpcTlJvWfNE+oRC7WKw\\u002f7j752MnpDssHPxwuPfXz7SA\\u002ffZsxYmTFNT8zXlsTEVJJP7aK1WR3sFo\\u002fY32tG8qaaT9zY5wmnZJ2P7iGOCsMu4I\\u002fOZYjoOkEjz91fvOXepScP2FrWYpgILA\\u002fOiOQI9Z8xT+hBhgCRffdP5QxfPv\\u002fCfQ\\u002fE9TbNwy2CED9W0qeh60bQN7wKomnACxA683Hg+iJOUDd+w0SYvtEQHYVvCuHD09AW3RydTO7VEAYs+O3XANZQJs8p+RUbFtABqPLCGyiW0A80\\u002fXFnzBaQMhrDB4bSVhAzXQ4bvMqV0B2gBQdDXpXQOMadc6j6lhAs+I5u0JxWkCbDkXHeNlaQCzwQPaYaVlARQtY4A02VkASd19qMf5RQD4su9pxcUtAWS3PulFARECKrg2c3\\u002fg9QElIZBQa+TZA2M94M3SIMkAvgjWydW0vQAroKhYstitAr1MVpVwDKUAx7BemCJwmQI+J5AE08iNAv5y\\u002fJs\\u002fFIEChjArj\\u002fGsaQGrE8diiSBNAMjY1rBzwCUDR7aU2TAoAQPQvNcT4PPI\\u002fevptqD0T4z+aBh8Xu1zSPyfWW9gpRcA\\u002fuA55nM+Fqj9LY2AWw9uTP4INUiv5Q3s\\u002fICtDl30gYT8FEM0yrqdDP7gus7+OlyQ\\u002fgnVfXcO5Az+RWMK8mF\\u002fhPpN8TY6Efbw+dgemPQI+lj4IRJFsBBNxPpfVg5Gll0o+LFMHZXg6JT6agjcyFQwBPjvMf5oNnNo9vj8tLP+Osz31smTMtoKKPfaoZ80FXmA9eyuYUYVLMj2YhLyfQXUCPeenzwYx8tA876eVNK2LqzwkWx2+qQPdPSI7NDtJcuY9Dcj1epmk+T36xsffU8ANPvthWUf\\u002fWiE+FMe\\u002f+YHeNj59sYIuZtNSPgneCVRYQ3I+eVbLFW9Akj6zxVjJjEaxPiu+F3sq9s0+NXTUMlyM5z6tpNeoALoAP0S85XODeRU\\u002f2YnEcn\\u002fzKD\\u002fT3SiqslA6P\\u002fOhIOsHXE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Acp5D8LIdl7GijkP1I5BZYuNeQ\\u002fi3U0xAsk5D87IJLtUi7kPykt5aFqLOQ\\u002fGZdiD1Eo5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fNBQslvwr5D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP1I5BZYuNeQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fwH4bwl405D8YJxiIeEjkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fE+9fhyQf5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkPxC2iVXrKOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002fBk2kK8ox5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D8mmZOx7EHkP75+LdnfNuQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fB6vUWA0w5D9w784DzyrkP+laLJ9hNOQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fwA1IlH4b5D+0I9dKajvkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fSbgage1F5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D9I15aUeDfkPzjUH0ffNuQ\\u002fzEOHwEQm5D\\u002fkRKDC0xjkP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fOY62NoIZ5D9lzBc5DzTkPykt5aFqLOQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D84G4aWlzbkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fcOEIZ0YN5D9mnp5DtTDkP8iEB3LpKuQ\\u002fKG7zw4Iu5D8cZN2DhybkPxl15nPOKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP2jCQz2hPOQ\\u002fMoeTHuMZ5D\\u002ft4cgT4BbkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002feG+qKBE15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fzFvffTYi5D+4PDOt\\u002fDzkP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fBuJ2St4Z5D\\u002f5QTCnERjkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f+tXwvaNeQ\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002foGzC00I45D+5oX44LhfkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP68\\u002fz4\\u002fNKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkPy8ye2s7PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002fNOaKUyAt5D9N+dhyniPkP5ibHhuoOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkPy1CzU+OMeQ\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002f30DDac4p5D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002frKUVlDgh5D9N+dhyniPkP0xRJ96pOuQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fIo+rSDEQ5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002frKUVlDgh5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+5ZgeKmyjkP2xDiSyNMeQ\\u002frfJOpAAe5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fLSkWpVwb5D+qQ8OHVizkP96atD4xG+Q\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fKZXUsMos5D\\u002fLF5QqhCHkP2xDiSyNMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002f42fnc9Qq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fkQE7I9E95D\\u002fLF5QqhCHkP0qElKmOMeQ\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002flS2VMQEt5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D89DRN4fTDkP8XHh8jeLuQ\\u002fGsIqdlYn5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D94SBtKhCHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002fET9G\\u002flA15D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002ftGxkF1Eh5D+T62SrMjHkPwu+JjJnPOQ\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fTpbZboU05D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fnnyXGKYo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fkPZrN4Ay5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002f2yxvAOgo5D+T62SrMjHkPwu+JjJnPOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002fd5qs4To25D9N+dhyniPkP9TI9I1tFeQ\\u002fr0nIRoIy5D9KpoUvhjzkP7l0wZ7IN+Q\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002f2yxvAOgo5D\\u002fP5mA0LzHkPwu+JjJnPOQ\\u002f2X+WelUa5D8gLbVngh3kP8XHh8jeLuQ\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkPyuiZubDQ+Q\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002f\\u002fPI0QB4z5D8gLbVngh3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fnY8J1qEr5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP9d22qp1LuQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f80NkFX8w5D9KpoUvhjzkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5SCC17MMOQ\\u002fhO\\u002fkAWE\\u002f5D\\u002fo2Dn4Zy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kP8XHh8jeLuQ\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fMrD+1WYp5D\\u002f8ei4KMTHkPzoLCxWGPuQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002fsO6u1A4t5D\\u002fFFI8Bziv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-       "    const normalizedHex = hexMatch[1].length === 3\n",
-       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
-       "        : hexMatch[1];\n",
-       "    return {\n",
-       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
-       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
-       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
-       "    };\n",
-       "};\n",
-       "\n",
-       "const resolveLegendButtonFill = function (opacity) {\n",
-       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
-       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
-       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
-       "        ? graphDiv._fullLayout.font.color\n",
-       "        : null;\n",
-       "    const parsedColor = (\n",
-       "        parseColor(referenceFill)\n",
-       "        || parseColor(fontColor)\n",
-       "        || {red: 68, green: 68, blue: 68}\n",
-       "    );\n",
-       "    return (\n",
-       "        'rgba('\n",
-       "        + parsedColor.red\n",
-       "        + ', '\n",
-       "        + parsedColor.green\n",
-       "        + ', '\n",
-       "        + parsedColor.blue\n",
-       "        + ', '\n",
-       "        + opacity\n",
-       "        + ')'\n",
-       "    );\n",
-       "};\n",
-       "\n",
-       "const updateLegendButtonAppearance = function (legendVisible) {\n",
-       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
-       "    if (!legendButton) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const legendIconPath = legendButton.querySelector('path');\n",
-       "    if (!legendIconPath) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    legendButton.classList.toggle('active', legendVisible);\n",
-       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
-       "    legendIconPath.setAttribute(\n",
-       "        'style',\n",
-       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
-       "    );\n",
-       "};\n",
-       "\n",
-       "const applyLegendVisibility = function (legendVisible) {\n",
-       "    const legend = graphDiv.querySelector('.legend');\n",
-       "    if (legend) {\n",
-       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
-       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
-       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.layout) {\n",
-       "        graphDiv.layout.showlegend = legendVisible;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv._fullLayout) {\n",
-       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "const readLegendVisibility = function () {\n",
-       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
-       "        return true;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
-       "        return false;\n",
-       "    }\n",
-       "\n",
-       "    const legend = graphDiv.querySelector('.legend');\n",
-       "    if (legend) {\n",
-       "        return (\n",
-       "            window.getComputedStyle(legend).display !== 'none'\n",
-       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
-       "        );\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
-       "        return graphDiv.layout.showlegend;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
-       "        return graphDiv._fullLayout.showlegend;\n",
-       "    }\n",
-       "\n",
-       "    return true;\n",
-       "};\n",
-       "\n",
-       "const syncLegendVisibility = function (legendVisible) {\n",
-       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
-       "        ? legendVisible\n",
-       "        : readLegendVisibility();\n",
-       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
-       "    applyLegendVisibility(resolvedLegendVisible);\n",
-       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
-       "    return resolvedLegendVisible;\n",
-       "};\n",
-       "\n",
-       "const toggleLegend = function (event) {\n",
-       "    if (event) {\n",
-       "        event.preventDefault();\n",
-       "        event.stopPropagation();\n",
-       "    }\n",
-       "\n",
-       "    const currentValue = readLegendVisibility();\n",
-       "    const nextValue = !currentValue;\n",
-       "    syncLegendVisibility(nextValue);\n",
-       "};\n",
-       "\n",
-       "const installLegendToggleButton = function () {\n",
-       "    const modebar = graphDiv.querySelector('.modebar');\n",
-       "    if (!modebar) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    if (!modebar.querySelector('.modebar-group')) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
-       "    if (!legendButton) {\n",
-       "        const legendButtonGroup = document.createElement('div');\n",
-       "        legendButtonGroup.className = 'modebar-group';\n",
-       "\n",
-       "        legendButton = document.createElement('a');\n",
-       "        legendButton.className = 'modebar-btn';\n",
-       "        legendButton.href = 'javascript:void(0)';\n",
-       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
-       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
-       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
-       "        legendButton.setAttribute('role', 'button');\n",
-       "        legendButton.setAttribute('tabindex', '0');\n",
-       "        legendButton.innerHTML = [\n",
-       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
-       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
-       "            + ' aria-hidden=\"true\">',\n",
-       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
-       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
-       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
-       "            '</svg>',\n",
-       "        ].join('');\n",
-       "\n",
-       "        legendButtonGroup.appendChild(legendButton);\n",
-       "        modebar.appendChild(legendButtonGroup);\n",
-       "    }\n",
-       "\n",
-       "    legendButton.onclick = toggleLegend;\n",
-       "    legendButton.onkeydown = function (event) {\n",
-       "        if (event.key === 'Enter' || event.key === ' ') {\n",
-       "            toggleLegend(event);\n",
-       "        }\n",
-       "    };\n",
-       "\n",
-       "    syncLegendVisibility();\n",
-       "};\n",
-       "\n",
-       "if (graphDiv.on) {\n",
-       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
-       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
-       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
-       "            syncLegendVisibility(eventData.showlegend);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        syncLegendVisibility();\n",
-       "    });\n",
-       "}\n",
-       "syncLegendVisibility();\n",
-       "window.requestAnimationFrame(installLegendToggleButton);\n",
-       "}\n",
-       "                        })                };            </script>        </div>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.display.plotter.plot_posterior_pairs()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "54",
+   "id": "53",
    "metadata": {},
    "source": [
     "The one-dimensional posterior distributions below make it easier to\n",
@@ -3034,1101 +666,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "55",
+   "execution_count": null,
+   "id": "54",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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LlKl\\u002fGoIQ9AdSEvuTshD0C2VT4iViEPQNO0cm9\\u002fIQ9AOSaFg6shD0DAijCVeyEPQPhM891jIQ9A82tk9Z0hD0Co4s5HeSEPQLvmmP5VIQ9Aj4fhwnkhD0BVbyRPSyEPQLRL7JCgIQ9ACJirzVchD0B469HgcCEPQAx\\u002f\\u002fhlTIQ9AHQb4em0hD0BauK4hiCEPQFmkqjKoIQ9AQAVqgXchD0C5SpfxqCEPQHUhL7k7IQ9AtlU+IlYhD0DTtHJvfyEPQDkmhYOrIQ9A1QpPOHEhD0D4TPPdYyEPQIXmek2eIQ9AqOLOR3khD0C2vx+WZSEPQOVeFgOQIQ9AVW8kT0shD0AY\\u002fswjoSEPQAiYq81XIQ9AeOvR4HAhD0AUxkfDZiEPQB0G+HptIQ9AWriuIYghD0BZpKoyqCEPQEAFaoF3IQ9AuUqX8aghD0B1IS+5OyEPQEAneWRcIQ9A07Ryb38hD0A8F3+PwCEPQNUKTzhxIQ9A+Ezz3WMhD0AlJnBFdyEPQKjizkd5IQ9Atr8flmUhD0DlXhYDkCEPQFVvJE9LIQ9AGP7MI6EhD0AImKvNVyEPQHjr0eBwIQ9AFMZHw2YhD0D2ADERRSEPQFq4riGIIQ9AWaSqMqghD0BABWqBdyEPQNVObNzGIQ9AdSEvuTshD0BAJ3lkXCEPQNO0cm9\\u002fIQ9APBd\\u002fj8AhD0DvXtt7RCEPQPhM891jIQ9AJSZwRXchD0Co4s5HeSEPQLa\\u002fH5ZlIQ9A3Ni9P5EhD0DQSiANTSEPQBj+zCOhIQ9ACJirzVchD0Ao\\u002fHFYXCEPQNXO8SBlIQ9A9gAxEUUhD0BauK4hiCEPQFmkqjKoIQ9AjnxxT3QhD0DVTmzcxiEPQHUhL7k7IQ9AQCd5ZFwhD0DTtHJvfyEPQDwXf4\\u002fAIQ9A717be0QhD0D4TPPdYyEPQCUmcEV3IQ9AqOLOR3khD0C2vx+WZSEPQPT5iZigIQ9A0EogDU0hD0AY\\u002fswjoSEPQAiYq81XIQ9AKPxxWFwhD0DVzvEgZSEPQKCUoIgiIQ9AWriuIYghD0BZpKoyqCEPQI58cU90IQ9A1U5s3MYhD0B1IS+5OyEPQEAneWRcIQ9A07Ryb38hD0A8F3+PwCEPQAFmtQNgIQ9A+Ezz3WMhD0AlJnBFdyEPQKjizkd5IQ9Atr8flmUhD0AZAQdTfSEPQNBKIA1NIQ9AGP7MI6EhD0B35P1bViEPQCj8cVhcIQ9A1c7xIGUhD0CglKCIIiEPQPFlk4p3IQ9AWaSqMqghD0COfHFPdCEPQKXXRf\\u002fWIQ9AoNa3ilkhD0BAJ3lkXCEPQNO0cm9\\u002fIQ9ABgewmcUhD0AkREn1NSEPQPhM891jIQ9AJSZwRXchD0Co4s5HeSEPQLa\\u002fH5ZlIQ9AGQEHU30hD0DQSiANTSEPQBj+zCOhIQ9AUSeZ2FUhD0Ao\\u002fHFYXCEPQKJGJYFnIQ9AoJSgiCIhD0DxZZOKdyEPQFmkqjKoIQ9AVEPX1XUhD0Cl10X\\u002f1iEPQHvNVDJwIQ9AQCd5ZFwhD0CR7m3fgCEPQAYHsJnFIQ9AJERJ9TUhD0D4TPPdYyEPQCUmcEV3IQ9AqOLOR3khD0C4uVKwdCEPQBkBB1N9IQ9A8iaJfUohD0AY\\u002fswjoSEPQFEnmdhVIQ9A\\u002f5p\\u002f3FohD0CiRiWBZyEPQKCUoIgiIQ9A8WWTinchD0C5EEHHmCEPQFRD19V1IQ9A4U7EC9chD0B7zVQycCEPQEAneWRcIQ9Ake5t34AhD0AGB7CZxSEPQCRESfU1IQ9A+Ezz3WMhD0Cd9OjzeSEPQKjizkd5IQ9Ad4zcbUshD0AZAQdTfSEPQPImiX1KIQ9AGP7MI6EhD0BRJ5nYVSEPQP+af9xaIQ9AokYlgWchD0CglKCIIiEPQEc+WEZ5IQ9AuRBBx5ghD0BUQ9fVdSEPQOFOxAvXIQ9Ae81UMnAhD0D\\u002fpgEdViEPQJHubd+AIQ9ABgewmcUhD0AkREn1NSEPQPhM891jIQ9AnfTo83khD0Co4s5HeSEPQHeM3G1LIQ9AGQEHU30hD0DyJol9SiEPQBj+zCOhIQ9AUSeZ2FUhD0D\\u002fmn\\u002fcWiEPQKJGJYFnIQ9AxqJTiTQhD0BHPlhGeSEPQHLSlRaaIQ9AVEPX1XUhD0DhTsQL1yEPQHvNVDJwIQ9A\\u002f6YBHVYhD0DqLSWMfyEPQAYHsJnFIQ9AJERJ9TUhD0C\\u002f3BVXZCEPQF2MQDqZIQ9AqOLOR3khD0B3jNxtSyEPQC9v4b99IQ9A8iaJfUohD0CjkZbafyEPQFEnmdhVIQ9A\\u002f5p\\u002f3FohD0CiRiWBZyEPQMaiU4k0IQ9ARz5YRnkhD0AjMHOCpSEPQEQl\\u002fsRwIQ9A4U7EC9chD0B7zVQycCEPQN2R15FWIQ9AYarGq30hD0AGB7CZxSEPQMRQlOtEIQ9Av9wVV2QhD0BaETo23iEPQKjizkd5IQ9Ad4zcbUshD0Daw2sFZSEPQDmxI5dsIQ9AAykSjoIhD0BRJ5nYVSEPQEB6SWSCIQ9AokYlgWchD0DGolOJNCEPQEc+WEZ5IQ9AiqD2b6YhD0AddkjLtCEPQOFOxAvXIQ9AimUL7G8hD0DdkdeRViEPQGGqxqt9IQ9ABgewmcUhD0DEUJTrRCEPQL\\u002fcFVdkIQ9AWhE6Nt4hD0Co4s5HeSEPQAqgCJAkIQ9A2sNrBWUhD0A5sSOXbCEPQFPSJn+BIQ9AUSeZ2FUhD0BAeklkgiEPQKJGJYFnIQ9AxqJTiTQhD0BHPlhGeSEPQIqg9m+mIQ9A\\u002fErdrbIhD0AVna4+iyEPQIplC+xvIQ9A3ZHXkVYhD0BhqsarfSEPQAYHsJnFIQ9AxFCU60QhD0Ap6osYYiEPQFoROjbeIQ9AqOLOR3khD0AKoAiQJCEPQB0ESIt3IQ9AGvrObn8hD0BT0iZ\\u002fgSEPQFEnmdhVIQ9AQHpJZIIhD0CiRiWBZyEPQCmVkh9lIQ9ARz5YRnkhD0CKoPZvpiEPQPxK3a2yIQ9AFZ2uPoshD0CKZQvsbyEPQCD7tWxVIQ9AYarGq30hD0AGB7CZxSEPQMRQlOtEIQ9AKeqLGGIhD0BaETo23iEPQKjizkd5IQ9A8U\\u002f7dCMhD0AdBEiLdyEPQFFmtHlcIQ9AOxXZ4YEhD0BRJ5nYVSEPQGbGaEG+IQ9AokYlgWchD0AplZIfZSEPQEc+WEZ5IQ9AiqD2b6YhD0D8St2tsiEPQBWdrj6LIQ9AimUL7G8hD0Ag+7VsVSEPQGGqxqt9IQ9ABgewmcUhD0Bd1aD4diEPQCnqixhiIQ9AHyF3o7QhD0Co4s5HeSEPQJDZjVE7IQ9AHQRIi3chD0BRZrR5XCEPQDsV2eGBIQ9AUSeZ2FUhD0BmxmhBviEPQKJGJYFnIQ9AKZWSH2UhD0BHPlhGeSEPQIqg9m+mIQ9A\\u002fErdrbIhD0CAcWIEnyEPQIplC+xvIQ9AxIaz6FchD0BhqsarfSEPQAYHsJnFIQ9AXdWg+HYhD0Ap6osYYiEPQB8hd6O0IQ9AqOLOR3khD0CQ2Y1ROyEPQDivtJl3IQ9Ak0GP824hD0BodoZelyEPQFEnmdhVIQ9AZsZoQb4hD0C200jIZSEPQCmVkh9lIQ9ARz5YRnkhD0CKoPZvpiEPQJHLyVeIIQ9AgHFiBJ8hD0AWgTF5jyEPQH0df+NKIQ9AYarGq30hD0AaKx0akCEPQBkDOVtEIQ9AnAJAy1IhD0AfIXejtCEPQKjizkd5IQ9AkNmNUTshD0Dm1fvgdyEPQJNBj\\u002fNuIQ9AaHaGXpchD0CL+397eiEPQGbGaEG+IQ9AttNIyGUhD0Bs1ikDZSEPQEc+WEZ5IQ9AXMRjGq8hD0CRy8lXiCEPQIBxYgSfIQ9AFoExeY8hD0B9HX\\u002fjSiEPQGGqxqt9IQ9AGisdGpAhD0AZAzlbRCEPQJwCQMtSIQ9AHyF3o7QhD0Co4s5HeSEPQJDZjVE7IQ9A5tX74HchD0CTQY\\u002fzbiEPQPL932iOIQ9Ai\\u002ft\\u002fe3ohD0BmxmhBviEPQLbTSMhlIQ9AbNYpA2UhD0Cmwv0akiEPQFzEYxqvIQ9AkcvJV4ghD0CAcWIEnyEPQBaBMXmPIQ9ANwsBjHMhD0BhqsarfSEPQOaIQ5h3IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AqOLOR3khD0D4f+fMLyEPQFc5XqqYIQ9Ak0GP824hD0Dy\\u002fd9ojiEPQDGovRZzIQ9AZsZoQb4hD0C200jIZSEPQILwrntGIQ9ABl06u3IhD0DrPRBEniEPQJHLyVeIIQ9AgHFiBJ8hD0AWgTF5jyEPQDcLAYxzIQ9AYarGq30hD0DmiEOYdyEPQBkDOVtEIQ9AnAJAy1IhD0D9dMbOsSEPQKjizkd5IQ9AU6AADighD0BXOV6qmCEPQJNBj\\u002fNuIQ9A8v3faI4hD0AxqL0WcyEPQGbGaEG+IQ9AcJYs72YhD0C5PQvUSSEPQMGnjFqGIQ9A6z0QRJ4hD0CRy8lXiCEPQBSSEl1+IQ9A9t\\u002fE3IwhD0A3CwGMcyEPQGGqxqt9IQ9A5ohDmHchD0AZAzlbRCEPQJwCQMtSIQ9A\\u002fXTGzrEhD0BCtILpUiEPQFOgAA4oIQ9AEPO16pYhD0CTQY\\u002fzbiEPQPL932iOIQ9AMai9FnMhD0BmxmhBviEPQHCWLO9mIQ9A9UO1J0chD0DBp4xahiEPQMTFWZaDIQ9AkcvJV4ghD0AUkhJdfiEPQPbfxNyMIQ9AzvfI+XUhD0BhqsarfSEPQOaIQ5h3IQ9AGQM5W0QhD0CcAkDLUiEPQP10xs6xIQ9AVe1dT28hD0BToAAOKCEPQBDzteqWIQ9Ak0GP824hD0Dy\\u002fd9ojiEPQDGovRZzIQ9AZsZoQb4hD0CVgpncbSEPQPVDtSdHIQ9AwaeMWoYhD0DExVmWgyEPQJHLyVeIIQ9AFJISXX4hD0CzOJKWbCEPQGbY7QJWIQ9AYarGq30hD0DmiEOYdyEPQFaHp0VFIQ9AnAJAy1IhD0D9dMbOsSEPQFXtXU9vIQ9A3wFTRzIhD0CRK83KhiEPQJNBj\\u002fNuIQ9A8v3faI4hD0AxqL0WcyEPQGbGaEG+IQ9AlYKZ3G0hD0B13q1TUSEPQITg1Q\\u002fIIQ9AxMVZloMhD0C8JnMNhSEPQBSSEl1+IQ9AsziSlmwhD0Bm2O0CViEPQGGqxqt9IQ9A5ohDmHchD0BWh6dFRSEPQJwCQMtSIQ9A\\u002fXTGzrEhD0BV7V1PbyEPQN8BU0cyIQ9AfhdyQoohD0CTQY\\u002fzbiEPQIml8PmMIQ9AMai9FnMhD0BmxmhBviEPQJWCmdxtIQ9ApIZS9WYhD0BcHbdTvSEPQMTFWZaDIQ9AvCZzDYUhD0AUkhJdfiEPQLM4kpZsIQ9AF\\u002f0Z+BEhD0CPmu8\\u002fqCEPQH5KWcF4IQ9AVoenRUUhD0CcAkDLUiEPQP10xs6xIQ9AVe1dT28hD0DfAVNHMiEPQH4XckKKIQ9Ak0GP824hD0C4Jq4fiCEPQDGovRZzIQ9AZsZoQb4hD0CVgpncbSEPQKSGUvVmIQ9AXB23U70hD0BHiO4qqyEPQLwmcw2FIQ9ABbjOHnkhD0CzOJKWbCEPQGT3EygVIQ9Aj5rvP6ghD0B+SlnBeCEPQFaHp0VFIQ9AnAJAy1IhD0D9dMbOsSEPQFXtXU9vIQ9ApwMEdAkhD0A4\\u002fd9siSEPQE789LJrIQ9AuCauH4ghD0C\\u002fJHlDeyEPQD32zfiSIQ9AlYKZ3G0hD0CkhlL1ZiEPQFwdt1O9IQ9AR4juKqshD0C8JnMNhSEPQAW4zh55IQ9AsziSlmwhD0Bk9xMoFSEPQI+a7z+oIQ9AfkpZwXghD0BWh6dFRSEPQJwCQMtSIQ9A\\u002fXTGzrEhD0BV7V1PbyEPQKcDBHQJIQ9AOP3fbIkhD0BO\\u002fPSyayEPQLgmrh+IIQ9AvyR5Q3shD0CjxLauXiEPQJWCmdxtIQ9ApIZS9WYhD0BcHbdTvSEPQEeI7iqrIQ9ALClO\\u002fZshD0AFuM4eeSEPQLM4kpZsIQ9AZPcTKBUhD0Bq7G+xpyEPQH5KWcF4IQ9Av92a4UkhD0CcAkDLUiEPQP10xs6xIQ9AVe1dT28hD0CnAwR0CSEPQCAdvMeGIQ9ATvz0smshD0ASglnBcCEPQL8keUN7IQ9Ao8S2rl4hD0AhWDREcCEPQKSGUvVmIQ9AmSuYop0hD0BHiO4qqyEPQCwpTv2bIQ9ABbjOHnkhD0CzOJKWbCEPQGT3EygVIQ9AauxvsachD0BnHy4YeCEPQL\\u002fdmuFJIQ9ATC9cqgshD0BzMZXpqiEPQFXtXU9vIQ9AdIR0j1EhD0AgHbzHhiEPQE789LJrIQ9AEoJZwXAhD0C5M7BAISEPQKPEtq5eIQ9AIVg0RHAhD0CkhlL1ZiEPQPITYKOKIQ9AR4juKqshD0AsKU79myEPQAW4zh55IQ9APFn0hJAhD0Bj3AbsLSEPQGrsb7GnIQ9AZx8uGHghD0BtfQhPSyEPQEBxBBz2IA9AczGV6aohD0BV7V1PbyEPQHSEdI9RIQ9AIB28x4YhD0BO\\u002fPSyayEPQM\\u002fxPtJnIQ9AuTOwQCEhD0CjxLauXiEPQCFYNERwIQ9ApIZS9WYhD0DyE2CjiiEPQEeI7iqrIQ9ALClO\\u002fZshD0C5fhoSfyEPQDxZ9ISQIQ9AY9wG7C0hD0DYSqfVoyEPQGcfLhh4IQ9AbX0IT0shD0BAcQQc9iAPQHMxlemqIQ9A89q4y00hD0B0hHSPUSEPQOWEMjirIQ9ATvz0smshD0AobgVVaCEPQLkzsEAhIQ9Ao8S2rl4hD0AhWDREcCEPQKSGUvVmIQ9AqqLMRIchD0BHiO4qqyEPQCwpTv2bIQ9AuX4aEn8hD0A8WfSEkCEPQGPcBuwtIQ9A2Eqn1aMhD0BnHy4YeCEPQG19CE9LIQ9Awwyy5QYhD0BzMZXpqiEPQPPauMtNIQ9AdIR0j1EhD0DOl9o49iAPQE789LJrIQ9AKG4FVWghD0C5M7BAISEPQKPEtq5eIQ9AIVg0RHAhD0CkhlL1ZiEPQKqizESHIQ9AR4juKqshD0AsKU79myEPQLl+GhJ\\u002fIQ9APFn0hJAhD0Bj3AbsLSEPQNhKp9WjIQ9AibFqrXghD0BtfQhPSyEPQMMMsuUGIQ9AczGV6aohD0AhQrNQUCEPQMXJ8rdHIQ9AzpfaOPYgD0BO\\u002fPSyayEPQChuBVVoIQ9AuTOwQCEhD0CjxLauXiEPQHmzwcduIQ9A\\u002fwiX0iYhD0CqosxEhyEPQEeI7iqrIQ9APBmeY30hD0C5fhoSfyEPQDxZ9ISQIQ9Al\\u002fiKZTAhD0DYSqfVoyEPQImxaq14IQ9AlFvdRUwhD0DSw5hLLCEPQHMxlemqIQ9A+Z2DHGQhD0DFyfK3RyEPQM6X2jj2IA9ATvz0smshD0BqtmsBaCEPQLkzsEAhIQ9Ao8S2rl4hD0B5s8HHbiEPQP8Il9ImIQ9AqqLMRIchD0BHiO4qqyEPQB6D3s6HIQ9AuX4aEn8hD0A8WfSEkCEPQJf4imUwIQ9A2Eqn1aMhD0CJsWqteCEPQBnk6PlLIQ9AdO7zRC0hD0BzMZXpqiEPQPmdgxxkIQ9Axcnyt0chD0BiA5bK9iAPQE789LJrIQ9AS980H1EhD0C5M7BAISEPQKPEtq5eIQ9AzgxhGXAhD0D\\u002fCJfSJiEPQKqizESHIQ9AR4juKqshD0DMvzjPMyEPQLl+GhJ\\u002fIQ9APFn0hJAhD0BtWAOkWCEPQNhKp9WjIQ9AibFqrXghD0AZ5Oj5SyEPQHTu80QtIQ9AczGV6aohD0D5nYMcZCEPQHqsXI+5IQ9AgKsI0vcgD0BO\\u002fPSyayEPQEvfNB9RIQ9AuTOwQCEhD0CjxLauXiEPQER4ThBjIQ9A\\u002fwiX0iYhD0CqosxEhyEPQEeI7iqrIQ9AzL84zzMhD0C5fhoSfyEPQImLmHRlIQ9AbVgDpFghD0B2wLCeoyEPQImxaq14IQ9AGeTo+UshD0B07vNELSEPQExMIfyrIQ9A+Z2DHGQhD0B6rFyPuSEPQICrCNL3IA9ATvz0smshD0CfRHjGPSEPQLkzsEAhIQ9Ao8S2rl4hD0AIQtx6hyEPQP8Il9ImIQ9AndTIkWQhD0BHiO4qqyEPQMy\\u002fOM8zIQ9AuX4aEn8hD0DUZgDwQSEPQG1YA6RYIQ9A\\u002fCvAJpkhD0Bm7uAhyiEPQBnk6PlLIQ9AdO7zRC0hD0BTNhCZWSEPQHP92tyFIQ9Aeqxcj7khD0CAqwjS9yAPQE789LJrIQ9An0R4xj0hD0C5M7BAISEPQKPEtq5eIQ9ACELceochD0D\\u002fCJfSJiEPQJ3UyJFkIQ9AR4juKqshD0DMvzjPMyEPQLl+GhJ\\u002fIQ9A1GYA8EEhD0BtWAOkWCEPQPwrwCaZIQ9AZu7gIcohD0AZ5Oj5SyEPQHTu80QtIQ9AUzYQmVkhD0BSMmPBXyEPQHqsXI+5IQ9AgKsI0vcgD0BO\\u002fPSyayEPQJ9EeMY9IQ9A8LpBA5IhD0DxQ7eQYCEPQAhC3HqHIQ9A\\u002fwiX0iYhD0DxBXLAHyEPQEeI7iqrIQ9AzL84zzMhD0C5fhoSfyEPQNRmAPBBIQ9AbVgDpFghD0AD4mzomyEPQGbu4CHKIQ9AGeTo+UshD0B07vNELSEPQFM2EJlZIQ9AUjJjwV8hD0B6rFyPuSEPQICrCNL3IA9ATvz0smshD0CfRHjGPSEPQKApVC9zIQ9A8UO3kGAhD0AIQtx6hyEPQP8Il9ImIQ9A8QVywB8hD0CaVbgwbyEPQMy\\u002fOM8zIQ9AuX4aEn8hD0DUZgDwQSEPQG1YA6RYIQ9AA+Js6JshD0Bm7uAhyiEPQBnk6PlLIQ9AdO7zRC0hD0BTNhCZWSEPQFIyY8FfIQ9Av74yU8QhD0BwZY+XJCEPQE789LJrIQ9AYOpoEpwhD0CgKVQvcyEPQHGimQRlIQ9ACELceochD0D\\u002fCJfSJiEPQPEFcsAfIQ9Addmj9W8hD0BBs0asNCEPQLl+GhJ\\u002fIQ9A1GYA8EEhD0BtWAOkWCEPQGfXrTe3IQ9AmDNGp\\u002fchD0A3gXyLcyEPQDzpLy8xIQ9AUzYQmVkhD0BSMmPBXyEPQL++MlPEIQ9AcGWPlyQhD0BO\\u002fPSyayEPQGDqaBKcIQ9AoClUL3MhD0BxopkEZSEPQAhC3HqHIQ9A\\u002fwiX0iYhD0DxBXLAHyEPQHXZo\\u002fVvIQ9AQbNGrDQhD0C5fhoSfyEPQNRmAPBBIQ9AbVgDpFghD0BB4Rk9sSEPQJgzRqf3IQ9AN4F8i3MhD0A86S8vMSEPQFM2EJlZIQ9AUjJjwV8hD0CFyt+PwCEPQIagfQMkIQ9ATvz0smshD0BOSzuCVCEPQBPrvcKAIQ9AcaKZBGUhD0AIQtx6hyEPQP8Il9ImIQ9AQgwwIh0hD0B12aP1byEPQEGzRqw0IQ9AuX4aEn8hD0DUZgDwQSEPQG1YA6RYIQ9AQeEZPbEhD0CYM0an9yEPQGOXqO+EIQ9APOkvLzEhD0BTNhCZWSEPQFIyY8FfIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AMNeLQF8hD0AT673CgCEPQHGimQRlIQ9AFGFpy4chD0D\\u002fCJfSJiEPQAqNBYFZIQ9AhrErIV0hD0BBs0asNCEPQLl+GhJ\\u002fIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AmDNGp\\u002fchD0Bjl6jvhCEPQDzpLy8xIQ9AUzYQmVkhD0BSMmPBXyEPQIXK34\\u002fAIQ9AhqB9AyQhD0BO\\u002fPSyayEPQGOQR7ZaIQ9AE+u9woAhD0AasKbUZSEPQBRhacuHIQ9A\\u002fwiX0iYhD0BMvc3VLiEPQIaxKyFdIQ9AQbNGrDQhD0C5fhoSfyEPQALJ8DBfIQ9AbVgDpFghD0BB4Rk9sSEPQJgzRqf3IQ9AY5eo74QhD0A86S8vMSEPQFM2EJlZIQ9AUjJjwV8hD0CFyt+PwCEPQIagfQMkIQ9ATvz0smshD0BjkEe2WiEPQBPrvcKAIQ9ARHZ351YhD0AUYWnLhyEPQJ\\u002fthkhMIQ9ATL3N1S4hD0DsFKP5WSEPQEgm\\u002fgF\\u002fIQ9AwAL9gHAhD0ACyfAwXyEPQG1YA6RYIQ9AQeEZPbEhD0CYM0an9yEPQI1zYsFuIQ9APOkvLzEhD0BTNhCZWSEPQFIyY8FfIQ9Ahcrfj8AhD0CGoH0DJCEPQE789LJrIQ9AY5BHtlohD0AT673CgCEPQER2d+dWIQ9AFGFpy4chD0Cf7YZITCEPQEy9zdUuIQ9A7BSj+VkhD0BIJv4BfyEPQMAC\\u002fYBwIQ9AAsnwMF8hD0BtWAOkWCEPQEHhGT2xIQ9AmDNGp\\u002fchD0CNc2LBbiEPQDzpLy8xIQ9AUzYQmVkhD0BSMmPBXyEPQIXK34\\u002fAIQ9AhqB9AyQhD0BO\\u002fPSyayEPQGOQR7ZaIQ9AEC5HHIMhD0BEdnfnViEPQKPTMCaGIQ9A8otr7nUhD0BMvc3VLiEPQOwUo\\u002flZIQ9ASCb+AX8hD0DAAv2AcCEPQI8pBlFgIQ9AbVgDpFghD0BB4Rk9sSEPQJgzRqf3IQ9APv5wi2khD0A86S8vMSEPQFM2EJlZIQ9AUjJjwV8hD0CFyt+PwCEPQIagfQMkIQ9ATvz0smshD0BjkEe2WiEPQA7mXWmzIQ9ARHZ351YhD0C2LPHkhSEPQPKLa+51IQ9ATL3N1S4hD0AJnF2BZiEPQEgm\\u002fgF\\u002fIQ9AwAL9gHAhD0CPKQZRYCEPQG1YA6RYIQ9AQeEZPbEhD0CYM0an9yEPQAFf8R91IQ9APOkvLzEhD0BTNhCZWSEPQEjvBJ51IQ9AFUO\\u002f8dEhD0CGoH0DJCEPQE789LJrIQ9A948Ugy0hD0B4FOzLUSEPQEld\\u002f9pVIQ9Atizx5IUhD0Dyi2vudSEPQEy9zdUuIQ9AoGS5CmMhD0BIJv4BfyEPQMAC\\u002fYBwIQ9AjykGUWAhD0BtWAOkWCEPQEHhGT2xIQ9AmDNGp\\u002fchD0ABX\\u002fEfdSEPQKc6sHUuIQ9AUzYQmVkhD0BI7wSedSEPQOiBbKGrIQ9AhqB9AyQhD0BO\\u002fPSyayEPQPePFIMtIQ9AeBTsy1EhD0BJXf\\u002faVSEPQLYs8eSFIQ9A8otr7nUhD0BMvc3VLiEPQKBkuQpjIQ9ASCb+AX8hD0Drt0uBdyEPQI8pBlFgIQ9AbVgDpFghD0C70yXQcSEPQJgzRqf3IQ9A6vBh83whD0CnOrB1LiEPQFM2EJlZIQ9ACmLNVoAhD0DogWyhqyEPQIagfQMkIQ9ATvz0smshD0D3jxSDLSEPQHgU7MtRIQ9ASV3\\u002f2lUhD0C2LPHkhSEPQPKLa+51IQ9ATL3N1S4hD0B897emlSEPQEgm\\u002fgF\\u002fIQ9A67dLgXchD0BB8ayMriEPQG1YA6RYIQ9Au9Ml0HEhD0CYM0an9yEPQOrwYfN8IQ9ApzqwdS4hD0BTNhCZWSEPQApizVaAIQ9A6IFsoashD0CGoH0DJCEPQE789LJrIQ9A948Ugy0hD0B4FOzLUSEPQO\\u002fZUfNWIQ9AiMM61ZohD0Dyi2vudSEPQEy9zdUuIQ9AfPe3ppUhD0BIJv4BfyEPQOu3S4F3IQ9AQfGsjK4hD0BtWAOkWCEPQLvTJdBxIQ9AY5F3feIhD0Dq8GHzfCEPQKc6sHUuIQ9AUzYQmVkhD0AKYs1WgCEPQDyd382aIQ9AhqB9AyQhD0BO\\u002fPSyayEPQPePFIMtIQ9AeBTsy1EhD0A6FF\\u002fAgCEPQIjDOtWaIQ9A8otr7nUhD0BMvc3VLiEPQHz3t6aVIQ9ASCb+AX8hD0BpGFyLGiEPQMHIu7hhIQ9AbVgDpFghD0C70yXQcSEPQPlapEzUIQ9AYn49s3whD0DnPh2SPyEPQCXvRNx7IQ9Ag8POBlQhD0A8nd\\u002fNmiEPQIagfQMkIQ9ATvz0smshD0D3jxSDLSEPQA0NUtdQIQ9AOhRfwIAhD0CIwzrVmiEPQPKLa+51IQ9ATL3N1S4hD0BJpwcWaSEPQEgm\\u002fgF\\u002fIQ9ACwOdpHwhD0CulZnfbCEPQG1YA6RYIQ9Au9Ml0HEhD0D5WqRM1CEPQGJ+PbN8IQ9A5z4dkj8hD0Al70TceyEPQIPDzgZUIQ9APJ3fzZohD0CGoH0DJCEPQE789LJrIQ9A948Ugy0hD0D3e2cncyEPQDoUX8CAIQ9AiMM61ZohD0Dyi2vudSEPQFNkWcAvIQ9ASacHFmkhD0BIJv4BfyEPQJRB0pSEIQ9AaO8Y4GUhD0BtWAOkWCEPQLvTJdBxIQ9A+VqkTNQhD0Bifj2zfCEPQJ1p9TKcIQ9AJe9E3HshD0A\\u002flIvaVCEPQDyd382aIQ9AhqB9AyQhD0BO\\u002fPSyayEPQPePFIMtIQ9AApALFzUhD0A6FF\\u002fAgCEPQIjDOtWaIQ9AgulIjmQhD0BTZFnALyEPQMPOQx5VIQ9ASCb+AX8hD0CUQdKUhCEPQGjvGOBlIQ9AbVgDpFghD0A9gYprNSEPQKomChrYIQ9AZJHvVHshD0CdafUynCEPQCXvRNx7IQ9ATgPxZVghD0A8nd\\u002fNmiEPQIagfQMkIQ9ATvz0smshD0D3jxSDLSEPQGmO1+V9IQ9AOhRfwIAhD0CIwzrVmiEPQAU8Z\\u002flnIQ9AU2RZwC8hD0DDzkMeVSEPQEgm\\u002fgF\\u002fIQ9AlEHSlIQhD0Bo7xjgZSEPQG1YA6RYIQ9APYGKazUhD0CqJgoa2CEPQGSR71R7IQ9A12Am45ohD0Al70TceyEPQA4lYnxXIQ9AkNxaoHkhD0CGoH0DJCEPQBE3ZYBtIQ9A948Ugy0hD0BpjtflfSEPQDSeGDiCIQ9AiMM61ZohD0AFPGf5ZyEPQAsbSBROIQ9Aw85DHlUhD0BIJv4BfyEPQJRB0pSEIQ9AErW6FikhD0BtWAOkWCEPQD2Bims1IQ9AqiYKGtghD0DkYF93fiEPQNdgJuOaIQ9A5GfaCD8hD0CTshkaViEPQJDcWqB5IQ9AhqB9AyQhD0DErMdIwiEPQPePFIMtIQ9ArmZybH0hD0A0nhg4giEPQEvY+3SZIQ9ABTxn+WchD0C0N7R+jSEPQMPOQx5VIQ9ASCb+AX8hD0CUQdKUhCEPQOmlqyJHIQ9AbVgDpFghD0DZCFY\\u002fbyEPQC4dM2ugIQ9A5GBfd34hD0DXYCbjmiEPQORn2gg\\u002fIQ9Ak7IZGlYhD0CQ3FqgeSEPQIagfQMkIQ9AAVMtDb0hD0BhhBeeKyEPQK5mcmx9IQ9ANJ4YOIIhD0AfetrKeCEPQAU8Z\\u002flnIQ9AtDe0fo0hD0DVCFltVyEPQEgm\\u002fgF\\u002fIQ9AlEHSlIQhD0DppasiRyEPQG1YA6RYIQ9A2QhWP28hD0AuHTNroCEPQORgX3d+IQ9A12Am45ohD0B3GNZqcCEPQDYKfKM9IQ9AcoNpZmYhD0CGoH0DJCEPQAFTLQ29IQ9AYYQXnishD0CuZnJsfSEPQDSeGDiCIQ9AheZ6MnshD0AFPGf5ZyEPQPvXDFfIIQ9AwArhw1YhD0BIJv4BfyEPQJRB0pSEIQ9A6aWrIkchD0BtWAOkWCEPQNkIVj9vIQ9AByx6c50hD0CAFB6efyEPQMA0in6WIQ9AdxjWanAhD0A2CnyjPSEPQHKDaWZmIQ9AhqB9AyQhD0ABUy0NvSEPQGGEF54rIQ9ArmZybH0hD0AmJo5BgiEPQP6RBeCTIQ9ABTxn+WchD0DVcDbqpCEPQPS\\u002fnrdXIQ9ASCb+AX8hD0CUQdKUhCEPQOmlqyJHIQ9AbVgDpFghD0DZCFY\\u002fbyEPQDTp4X+pIQ9AgBQenn8hD0DANIp+liEPQHcY1mpwIQ9ANgp8oz0hD0DdnlufaSEPQCKoSUd4IQ9AAVMtDb0hD0BhhBeeKyEPQK5mcmx9IQ9AXINj7YIhD0D+kQXgkyEPQAU8Z\\u002flnIQ9A1XA26qQhD0D0v563VyEPQPtq3Dt6IQ9AlEHSlIQhD0DppasiRyEPQNbQjzxoIQ9A2QhWP28hD0A06eF\\u002fqSEPQIAUHp5\\u002fIQ9AwDSKfpYhD0B3GNZqcCEPQDYKfKM9IQ9A3Z5bn2khD0AiqElHeCEPQAFTLQ29IQ9AYYQXnishD0CuZnJsfSEPQAcuEVeGIQ9A\\u002fpEF4JMhD0AFPGf5ZyEPQNVwNuqkIQ9A9L+et1chD0D7atw7eiEPQJRB0pSEIQ9A6aWrIkchD0DW0I88aCEPQHD5QEF5IQ9ANOnhf6khD0CAFB6efyEPQMA0in6WIQ9AdxjWanAhD0A2CnyjPSEPQN2eW59pIQ9A7LjFcnkhD0ABUy0NvSEPQGGEF54rIQ9ArmZybH0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TsTQiQFeuK5pISSJAn8tklANWIkAPAroZPz4iQCjxXx9GSyJAGK+eCaQyIkBW5rcW+EsiQIT42gB0OyJAOLa1JEtHIkB04rYdlEAiQHjeGHymRCJAO9V8lslVIkDDLqe1EzoiQJsC5N1uOCJASXXeNyVHIkBnt+cPOiwiQM3tptXISCJA5ZhVNcFDIkCWjoWQH1EiQPxd\\u002fKkFaiJANXMaE7E0IkBXriuaSEkiQCvZATcDViJAbqcQpRc5IkDNhoy6RUsiQBivngmkMiJA0fq8v\\u002fZLIkCE+NoAdDsiQDi2tSRLRyJAdOK2HZRAIkB43hh8pkQiQDvVfJbJVSJAwy6ntRM6IkCbAuTdbjgiQEl13jclRyJAs\\u002fGTOWtDIkDN7abVyEgiQHdnZVcwSiJAlo6FkB9RIkCFgQ10bm0iQGlWW2KxNCJAV64rmkhJIkCLr4enqlgiQG6nEKUXOSJAzYaMukVLIkBDIG1zQ0siQNH6vL\\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\\u002fYXtBRiJAi6+Hp6pYIkAoJJF3mzQiQMda23pESyJA8IRvHkJLIkC8TJU5jzQiQI2s1o6iOSJAOLa1JEtHIkAgyxQPrEIiQKweQAGoMiJAvCGU7qtLIkDU73tBaT0iQJsC5N1uOCJAKRb58+RIIkC9DKf+IFEiQM3tptXISCJAVG8aajBXIkCWjoWQH1EiQIWBDXRubSJA+jN3ILo9IkDrv2F7QUYiQIuvh6eqWCJAVdIqpZ00IkDHWtt6REsiQMFubWDLSCJAvEyVOY80IkCNrNaOojkiQDi2tSRLRyJAYstgm6pPIkCsHkABqDIiQJIIrwpJPiJA1O97QWk9IkAD8N0wjjgiQCkW+fPkSCJAvQyn\\u002fiBRIkDN7abVyEgiQFRvGmowVyJAlo6FkB9RIkBPf+xWK1ciQPozdyC6PSJARuhQshtFIkCLr4enqlgiQFXSKqWdNCJAIRpvgD1LIkDBbm1gy0giQLxMlTmPNCJAjazWjqI5IkC5VQ4NTUciQGLLYJuqTyJA0\\u002fXAtqoyIkCSCK8KST4iQNTve0FpPSJAA\\u002fDdMI44IkApFvnz5EgiQL0Mp\\u002f4gUSJAze2m1chIIkDExs5rLlciQJaOhZAfUSJAGlBmIGFdIkD6M3cguj0iQEboULIbRSJAi6+Hp6pYIkBV0iqlnTQiQCEab4A9SyJAwW5tYMtIIkC8TJU5jzQiQHSR++yFQCJAuVUODU1HIkBiy2Cbqk8iQNP1wLaqMiJAkgivCkk+IkDiv2HVKD4iQAPw3TCOOCJAKRb58+RIIkC9DKf+IFEiQM3tptXISCJAxMbOay5XIkCWjoWQH1EiQBpQZiBhXSJA+jN3ILo9IkBG6FCyG0UiQIuvh6eqWCJAVdIqpZ00IkAhGm+APUsiQMFubWDLSCJA1GhIdA9JIkB0kfvshUAiQB2t7OFORyJAYstgm6pPIkDT9cC2qjIiQJIIrwpJPiJA4r9h1Sg+IkBLEp7ir0UiQCkW+fPkSCJAvQyn\\u002fiBRIkAT7HdVyEgiQIzOr6akQSJAlo6FkB9RIkAaUGYgYV0iQOmBlWq5PSJARuhQshtFIkCQGz1A4FgiQFXSKqWdNCJAIRpvgD1LIkDBbm1gy0giQNRoSHQPSSJAdJH77IVAIkA+PCVUwTUiQJLh1LL6SyJA0\\u002fXAtqoyIkCSCK8KST4iQGKOh5cpPiJAKofg4LBFIkApFvnz5EgiQKf68Pr2VyJAE+x3VchIIkAK731euUEiQJaOhZAfUSJAGlBmIGFdIkBOhZ\\u002feqkEiQObh3mN5RyJAwAyySuFYIkBV0iqlnTQiQJOoaeH0RCJAwW5tYMtIIkDUaEh0D0kiQHSR++yFQCJARf1UPmg+IkAB56wN+ksiQNP1wLaqMiJAkudSZUk+IkBijoeXKT4iQCqH4OCwRSJAKRb58+RIIkCn+vD69lciQBPsd1XISCJACu99XrlBIkCWjoWQH1EiQEMI1HWzYSJAToWf3qpBIkDm4d5jeUciQFyMDiR2VyJAVdIqpZ00IkCTqGnh9EQiQMFubWDLSCJA1GhIdA9JIkB0kfvshUAiQEX9VD5oPiJAcL1M9PlLIkCUCLiPgCsiQJLnUmVJPiJAYo6Hlyk+IkAqh+DgsEUiQCkW+fPkSCJAp\\u002frw+vZXIkAN9FxUvEoiQArvfV65QSJAlo6FkB9RIkBDCNR1s2EiQAs+Msx3WSJACbQwzg1PIkBcjA4kdlciQFXSKqWdNCJAk6hp4fREIkDBbm1gy0giQCkxqwi4MSJAdJH77IVAIkBF\\u002fVQ+aD4iQHC9TPT5SyJAlAi4j4ArIkCS51JlST4iQAkJjDkRNyJAKofg4LBFIkApFvnz5EgiQKf68Pr2VyJADfRcVLxKIkAK731euUEiQJaOhZAfUSJAdjGj+7NhIkALPjLMd1kiQLagkKWKOyJAAAsukzFVIkBV0iqlnTQiQJeE13oEKiJAwW5tYMtIIkApMasIuDEiQHSR++yFQCJARf1UPmg+IkBwvUz0+UsiQJQIuI+AKyJAkudSZUk+IkAJCYw5ETciQCqH4OCwRSJAKRb58+RIIkDP\\u002fDnFcUgiQA30XFS8SiJA8ZWw17lBIkCWjoWQH1EiQFmGRM+PaSJACz4yzHdZIkC2oJClijsiQAALLpMxVSJAVdIqpZ00IkCXhNd6BCoiQMFubWDLSCJAKTGrCLgxIkB0kfvshUAiQEX9VD5oPiJAcL1M9PlLIkDjiX\\u002fe0TYiQJLnUmVJPiJAGguTNBA3IkAqh+DgsEUiQCkW+fPkSCJAz\\u002fw5xXFIIkAN9FxUvEoiQPGVsNe5QSJAlo6FkB9RIkBZhkTPj2kiQKwgyIt1WSJAR42pNoo7IkCCLtHV3UQiQFXSKqWdNCJAl4TXegQqIkBfTJAM71QiQCkxqwi4MSJAdJH77IVAIkBF\\u002fVQ+aD4iQNObl8oPSCJA44l\\u002f3tE2IkBt5vjJjjAiQHiwAG7pLCJAKofg4LBFIkDaGSFNXUQiQK6kfmKrWyJAoxapdrVCIkDxlbDXuUEiQJaOhZAfUSJAWYZEz49pIkCu\\u002forhVE0iQEeNqTaKOyJAgi7R1d1EIkDGZlJWDUMiQJeE13oEKiJAX0yQDO9UIkCeYSdkYi0iQHSR++yFQCJAroY4QjA1IkDTm5fKD0giQOOJf97RNiJAbeb4yY4wIkB4sABu6SwiQCqH4OCwRSJA2hkhTV1EIkCupH5iq1siQKMWqXa1QiJA8ZWw17lBIkCWjoWQH1EiQFmGRM+PaSJArv6K4VRNIkBHjak2ijsiQHJOMR4pQSJAxmZSVg1DIkCXhNd6BCoiQF9MkAzvVCJAnmEnZGItIkAggnKBOjUiQK6GOEIwNSJA05uXyg9IIkDjiX\\u002fe0TYiQG3m+MmOMCJAa4SWuBEdIkAqh+DgsEUiQGvMutllOSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAlo6FkB9RIkC+MZovT3EiQKoO1ORBMCJAR42pNoo7IkByTjEeKUEiQIssxzC\\u002fPiJAl4TXegQqIkBfTJAM71QiQOksocVWOyJAK0s0JtpVIkChLmUFiD8iQNObl8oPSCJA44l\\u002f3tE2IkBt5vjJjjAiQGuElrgRHSJAKofg4LBFIkBrzLrZZTkiQK6kfmKrWyJAoxapdrVCIkCANw\\u002fSuUEiQJaOhZAfUSJAGFnIBZ88IkCqDtTkQTAiQEeNqTaKOyJAck4xHilBIkCLLMcwvz4iQJeE13oEKiJA9K1L0u9UIkAKABT92TsiQLVZf5q+TSJAoS5lBYg\\u002fIkDTm5fKD0giQLdFYE7TNiJAv3kf+0Q0IkBrhJa4ER0iQCqH4OCwRSJAa8y62WU5IkCupH5iq1siQKMWqXa1QiJAgDcP0rlBIkDmo9+FW1ciQBhZyAWfPCJAzYc\\u002f4yo2IkBHjak2ijsiQHJOMR4pQSJAiyzHML8+IkCXhNd6BCoiQPStS9LvVCJAGrKQndg7IkC1WX+avk0iQIi4xJQBMyJA05uXyg9IIkC3RWBO0zYiQL95H\\u002ftENCJAny\\u002f9MhAdIkAqh+DgsEUiQGvMutllOSJArqR+YqtbIkCjFql2tUIiQIA3D9K5QSJAPmKbzZNMIkAYWcgFnzwiQM2HP+MqNiJAR42pNoo7IkByTjEeKUEiQIssxzC\\u002fPiJAl4TXegQqIkDP6z4TlloiQBqykJ3YOyJAtVl\\u002fmr5NIkCIuMSUATMiQNObl8oPSCJAt0VgTtM2IkC++uCR2C8iQLYxy2ATKiJAKofg4LBFIkBrzLrZZTkiQKHuNLHPYCJAoxapdrVCIkCANw\\u002fSuUEiQD5im82TTCJADSU0+O9GIkA1Fp\\u002fXICciQEeNqTaKOyJAck4xHilBIkCLLMcwvz4iQJeE13oEKiJAz+s+E5ZaIkAwol31RUAiQKrbUDcANiJAiLjElAEzIkAyDVL\\u002fDUgiQLdFYE7TNiJAvvrgkdgvIkC2MctgEyoiQCqH4OCwRSJAa8y62WU5IkCh7jSxz2AiQKMWqXa1QiJAgDcP0rlBIkA+YpvNk0wiQA0lNPjvRiJAsTQtPyAnIkBHjak2ijsiQLTIMejHLCJAiyzHML8+IkCXhNd6BCoiQM\\u002frPhOWWiJAFFH6PhU7IkBinRxaSDciQIi4xJQBMyJAMg1S\\u002fw1IIkC3RWBO0zYiQL764JHYLyJA2hXd5qU+IkBBX8N540ciQLUlSBQ+KyJAoe40sc9gIkCjFql2tUIiQIA3D9K5QSJAPmKbzZNMIkANJTT470YiQLE0LT8gJyJAR42pNoo7IkDbjrGSxiwiQIssxzC\\u002fPiJAl4TXegQqIkDP6z4TlloiQBRR+j4VOyJAYp0cWkg3IkB9YkJ38T4iQDINUv8NSCJAf65nzfVLIkC++uCR2C8iQBeIjsGmPiJAQV\\u002fDeeNHIkC1JUgUPisiQKHuNLHPYCJAoxapdrVCIkCANw\\u002fSuUEiQD5im82TTCJAaxanuo9UIkCiylRyICciQMeOFE4LTyJA246xksYsIkCMXGSqJk8iQI+Hu4M+PiJAz+s+E5ZaIkAUUfo+FTsiQGKdHFpINyJAfWJCd\\u002fE+IkAyDVL\\u002fDUgiQH+uZ831SyJAvvrgkdgvIkAXiI7Bpj4iQEFfw3njRyJAtSVIFD4rIkCh7jSxz2AiQKMWqXa1QiJAgDcP0rlBIkA+YpvNk0wiQGsWp7qPVCJAospUciAnIkDHjhROC08iQNuOsZLGLCJAjFxkqiZPIkDBEtfjolIiQM\\u002frPhOWWiJAFFH6PhU7IkBinRxaSDciQH1iQnfxPiJAVrHUH45TIkB\\u002frmfN9UsiQL764JHYLyJAF4iOwaY+IkC3g6dm5EciQLUlSBQ+KyJAWnCL2s9gIkCjFql2tUIiQIA3D9K5QSJAPmKbzZNMIkBrFqe6j1QiQB2Qln2jHSJAx44UTgtPIkDAKnzl90EiQIxcZKomTyJAwRLX46JSIkCTlx1cxV0iQBRR+j4VOyJAdUKxDUk3IkB9YkJ38T4iQFax1B+OUyJAf65nzfVLIkC++uCR2C8iQBeIjsGmPiJAt4OnZuRHIkDAf7Z5PisiQFpwi9rPYCJA9zTpI5ZEIkBItS2ktkEiQD5im82TTCJAxIP\\u002fd2llIkAdkJZ9ox0iQMeOFE4LTyJAwCp85fdBIkBzg593Jk8iQMES1+OiUiJAk5cdXMVdIkAUUfo+FTsiQDuvZVCfUiJAfWJCd\\u002fE+IkBWsdQfjlMiQH+uZ831SyJAVTQWtLA+IkBrfHj8yjUiQLeDp2bkRyJAwH+2eT4rIkCxF4Rqz2AiQNO3YGvHVCJASLUtpLZBIkA+YpvNk0wiQMSD\\u002f3dpZSJAHZCWfaMdIkDHjhROC08iQKTHQsRURyJAc4OfdyZPIkDBEtfjolIiQJOXHVzFXSJAFFH6PhU7IkA7r2VQn1IiQH1iQnfxPiJAVrHUH45TIkCBqozg0UIiQFU0FrSwPiJAa3x4\\u002fMo1IkAZ5wwKO1oiQMB\\u002ftnk+KyJAsReEas9gIkDTt2Brx1QiQEi1LaS2QSJAA8uYonJWIkDEg\\u002f93aWUiQALO4p1BKCJAx44UTgtPIkBaa1K1zUUiQHODn3cmTyJAwRLX46JSIkCTlx1cxV0iQBRR+j4VOyJA2U3j+qBSIkB9YkJ38T4iQFax1B+OUyJAgaqM4NFCIkBVNBa0sD4iQGt8ePzKNSJAGecMCjtaIkDAf7Z5PisiQLEXhGrPYCJAJS+J0XdlIkBItS2ktkEiQAPLmKJyViJAxIP\\u002fd2llIkAKsF08UjsiQMeOFE4LTyJAWmtStc1FIkBzg593Jk8iQMES1+OiUiJAk5cdXMVdIkAUUfo+FTsiQNlN4\\u002fqgUiJAfWJCd\\u002fE+IkBWsdQfjlMiQIGqjODRQiJAVTQWtLA+IkBrfHj8yjUiQBnnDAo7WiJADD1hET8rIkCxF4Rqz2AiQCUvidF3ZSJASLUtpLZBIkAhTxqakSgiQFK41NfZWSJACrBdPFI7IkDHjhROC08iQFprUrXNRSJAc4OfdyZPIkDBEtfjolIiQBmhkfrGXSJAU\\u002frhkSdGIkDZTeP6oFIiQH1iQnfxPiJACBf6+21LIkCBqozg0UIiQFU0FrSwPiJA7MVAXuU2IkAZ5wwKO1oiQAw9YRE\\u002fKyJAIJXuHs1gIkC0XXi8dT4iQEi1LaS2QSJA0Tc51go6IkBSuNTX2VkiQAqwXTxSOyJAx44UTgtPIkC4IVKczUUiQHODn3cmTyJAwRLX46JSIkAZoZH6xl0iQFP64ZEnRiJA2U3j+qBSIkB9YkJ38T4iQEPQ6TgoRiJAgaqM4NFCIkBVNBa0sD4iQOzFQF7lNiJAGecMCjtaIkAMPWERPysiQL3AlOTjNCJA\\u002fmfpPnU+IkBItS2ktkEiQNE3OdYKOiJAUrjU19lZIkCNmJQBUzsiQMeOFE4LTyJAO3Ety1lYIkBzg593Jk8iQMES1+OiUiJAXTtv620rIkBT+uGRJ0YiQNlN4\\u002fqgUiJAfWJCd\\u002fE+IkAR6MhsJ0YiQIGqjODRQiJAVTQWtLA+IkBpWmsIGVsiQBnnDAo7WiJADD1hET8rIkC9wJTk4zQiQP5n6T51PiJASLUtpLZBIkDRNznWCjoiQAtcjvl2VSJANXJu7VA7IkDHjhROC08iQDtxLctZWCJAc4OfdyZPIkDBEtfjolIiQNOYfiA6TyJAU\\u002frhkSdGIkDZTeP6oFIiQH1iQnfxPiJAEejIbCdGIkCBqozg0UIiQATNmfA5PSJAaVprCBlbIkDvyy0EO1oiQAw9YRE\\u002fKyJAvcCU5OM0IkD+Z+k+dT4iQDNLwcq1QSJA0Tc51go6IkALXI75dlUiQDVybu1QOyJAx44UTgtPIkA7\\u002fNk4DkoiQHODn3cmTyJAwRLX46JSIkDW4nFdDzciQFP64ZEnRiJAR3aqJp9SIkB9YkJ38T4iQBHoyGwnRiJAgaqM4NFCIkDbTpqaFDwiQGlaawgZWyJAJEauhZZWIkBug9TLPSsiQL3AlOTjNCJA\\u002fmfpPnU+IkCshvX4xksiQNSb9igYOSJAC1yO+XZVIkA1cm7tUDsiQMeOFE4LTyJAO\\u002fzZOA5KIkBzg593Jk8iQMES1+OiUiJA1uJxXQ83IkBT+uGRJ0YiQEd2qiafUiJAfWJCd\\u002fE+IkAR6MhsJ0YiQIGqjODRQiJA206amhQ8IkBpWmsIGVsiQCRGroWWViJAboPUyz0rIkC9wJTk4zQiQP5n6T51PiJArIb1+MZLIkD3Vj3DnkUiQAtcjvl2VSJANXJu7VA7IkDHjhROC08iQDv82TgOSiJAXcfX8EsfIkAASBDzS0kiQNbicV0PNyJAU\\u002frhkSdGIkBBYrFU3FkiQH1iQnfxPiJAEejIbCdGIkCBqozg0UIiQNtOmpoUPCJAaVprCBlbIkDDof\\u002f0bVkiQG6D1Ms9KyJAvcCU5OM0IkD+Z+k+dT4iQKyG9fjGSyJA91Y9w55FIkALXI75dlUiQDVybu1QOyJAx44UTgtPIkA7\\u002fNk4DkoiQK6CGi8YIiJAAEgQ80tJIkDW4nFdDzciQFP64ZEnRiJAQWKxVNxZIkBzrWkTGzQiQBHoyGwnRiJAgaqM4NFCIkDbTpqaFDwiQGlaawgZWyJAw6H\\u002f9G1ZIkBug9TLPSsiQL3AlOTjNCJA\\u002fmfpPnU+IkCshvX4xksiQPdWPcOeRSJAUNcrgjE8IkCktVa1bkciQMeOFE4LTyJAnyRFSNE\\u002fIkCughovGCIiQOdEGeJ+PCJA1uJxXQ83IkBT+uGRJ0YiQEFisVTcWSJAcKUPHGFeIkA3tEymKEYiQIGqjODRQiJA206amhQ8IkBpWmsIGVsiQPWHIkduWSJARwmQbNg3IkAwo\\u002fJ5cC0iQGSHrwYjOSJArIb1+MZLIkD3Vj3DnkUiQFDXK4IxPCJApLVWtW5HIkDHjhROC08iQJ8kRUjRPyJAroIaLxgiIkDnRBnifjwiQNbicV0PNyJAU\\u002frhkSdGIkBBYrFU3FkiQHClDxxhXiJAN7RMpihGIkCBqozg0UIiQNtOmpoUPCJAaVprCBlbIkBMleMYcEIiQEcJkGzYNyJAMKPyeXAtIkBkh68GIzkiQKyG9fjGSyJA91Y9w55FIkB95wvlJFUiQLP7QSTtRyJAx44UTgtPIkAmuqv6rEsiQO7chduVGyJA50QZ4n48IkDW4nFdDzciQFP64ZEnRiJA5+iKwNtZIkBwpQ8cYV4iQDe0TKYoRiJAgaqM4NFCIkDbTpqaFDwiQGlaawgZWyJATJXjGHBCIkBHCZBs2DciQM3LwC1tOSJAZIevBiM5IkCshvX4xksiQPdWPcOeRSJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAPmsO7gtEIkDu3IXblRsiQOdEGeJ+PCJAuqYwsQ03IkBT+uGRJ0YiQCk\\u002fwiJTYSJAPgXpKiZlIkA3tEymKEYiQIGqjODRQiJAMvd8Q5JMIkBpWmsIGVsiQEyV4xhwQiJARwmQbNg3IkDNy8AtbTkiQGSHrwYjOSJArIb1+MZLIkD3Vj3DnkUiQH3nC+UkVSJAs\\u002ftBJO1HIkDHjhROC08iQH+9usYLRCJA7tyF25UbIkAEryNHfjwiQLqmMLENNyJAU\\u002frhkSdGIkAKsp1NUmEiQD4F6SomZSJAN7RMpihGIkCBqozg0UIiQDL3fEOSTCJAaVprCBlbIkBMleMYcEIiQEcJkGzYNyJAzcvALW05IkBkh68GIzkiQKyG9fjGSyJA91Y9w55FIkB95wvlJFUiQLP7QSTtRyJAx44UTgtPIkB\\u002fvbrGC0QiQO7chduVGyJA+T5TjfQ4IkC6pjCxDTciQBPSiNcFPSJACrKdTVJhIkD2syRqT2IiQOcTNQ+ISyJAUPmwFVlAIkAy93xDkkwiQGlaawgZWyJATJXjGHBCIkBHCZBs2DciQL9FZqXUMCJAZIevBiM5IkCshvX4xksiQPdWPcOeRSJAfecL5SRVIkCz+0Ek7UciQMeOFE4LTyJAf726xgtEIkDu3IXblRsiQPk+U430OCJAuqYwsQ03IkAT0ojXBT0iQAqynU1SYSJA9rMkak9iIkDnEzUPiEsiQFD5sBVZQCJAMvd8Q5JMIkBpWmsIGVsiQEyV4xhwQiJARwmQbNg3IkC\\u002fRWal1DAiQGSHrwYjOSJArIb1+MZLIkD3Vj3DnkUiQH3nC+UkVSJAs\\u002ftBJO1HIkDHjhROC08iQH+9usYLRCJAU60okpYbIkD5PlON9DgiQO\\u002fAss0ONyJAZCcR0z9UIkAKsp1NUmEiQPazJGpPYiJA5xM1D4hLIkBQ+bAVWUAiQOkg5f+RTCJAaVprCBlbIkBMleMYcEIiQEcJkGzYNyJAycIymiokIkBkh68GIzkiQKyG9fjGSyJA91Y9w55FIkB95wvlJFUiQLP7QSTtRyJAx44UTgtPIkB\\u002fvbrGC0QiQJ4P7ZQ9HSJA+T5TjfQ4IkDIH0oeDzciQGQnEdM\\u002fVCJACrKdTVJhIkCiGGasyGQiQOcTNQ+ISyJAUPmwFVlAIkDpIOX\\u002fkUwiQGlaawgZWyJATJXjGHBCIkBHCZBs2DciQFu3xThGFiJAZIevBiM5IkCshvX4xksiQLC4qbOeRSJAgiXHjmNCIkCz+0Ek7UciQMeOFE4LTyJAbqF3j4syIkBKe7L7Oh0iQMcJqCJUVyJAyB9KHg83IkBkJxHTP1QiQAqynU1SYSJAK+b8V4tVIkDnEzUPiEsiQFD5sBVZQCJA6SDl\\u002f5FMIkBpWmsIGVsiQEyV4xhwQiJARwmQbNg3IkBbt8U4RhYiQEvCGcHURyJArIb1+MZLIkCwuKmznkUiQPPPzpQ+NiJAs\\u002ftBJO1HIkDHjhROC08iQG6hd4+LMiJASnuy+zodIkDHCagiVFciQMgfSh4PNyJAZCcR0z9UIkAKsp1NUmEiQCvm\\u002fFeLVSJA5xM1D4hLIkBETu1BHkQiQOkg5f+RTCJAaVprCBlbIkD87k7g1FMiQEcJkGzYNyJA2x5rUK0lIkBLwhnB1EciQKyG9fjGSyJAMFRs\\u002fRFAIkDzz86UPjYiQLP7QSTtRyJAx44UTgtPIkBuoXePizIiQEp7svs6HSJAxwmoIlRXIkDIH0oeDzciQGQnEdM\\u002fVCJACrKdTVJhIkBHWmR1oFEiQOcTNQ+ISyJARE7tQR5EIkC4F2VZG0YiQGlaawgZWyJA\\u002fO5O4NRTIkBHCZBs2DciQNsea1CtJSJAS8IZwdRHIkCshvX4xksiQDBUbP0RQCJA88\\u002fOlD42IkCz+0Ek7UciQMeOFE4LTyJAbqF3j4syIkBKe7L7Oh0iQM\\u002fGlhCFQyJAMEt9HuQtIkBkJxHTP1QiQAqynU1SYSJAR1pkdaBRIkDnEzUPiEsiQERO7UEeRCJAuBdlWRtGIkBpWmsIGVsiQPzuTuDUUyJAZAanlehDIkDbHmtQrSUiQEvCGcHURyJArIb1+MZLIkAwVGz9EUAiQM0Ka70+NiJAs\\u002ftBJO1HIkDHjhROC08iQG6hd4+LMiJASnuy+zodIkC+QPFbfTwiQDBLfR7kLSJAZCcR0z9UIkAKsp1NUmEiQEdaZHWgUSJA5xM1D4hLIkAS+ILegVIiQBux5JVFTiJAaVprCBlbIkD87k7g1FMiQIqSs0gPSSJAtuXgzFUvIkAGWp945DQiQDioWXjRSSJAsMmJ1\\u002f5KIkDNCmu9PjYiQLP7QSTtRyJAx44UTgtPIkBuoXePizIiQJC4OHE6HSJAvkDxW308IkAwS30e5C0iQGQnEdM\\u002fVCJACrKdTVJhIkCy8ZhSVEMiQOcTNQ+ISyJAw7mnmBxRIkCCpSe6xykiQGlaawgZWyJA\\u002fO5O4NRTIkCKkrNID0kiQLbl4MxVLyJABlqfeOQ0IkA4qFl40UkiQLDJidf+SiJAzQprvT42IkCz+0Ek7UciQMeOFE4LTyJAbqF3j4syIkCjHx19JxgiQL5A8Vt9PCJAMEt9HuQtIkBkJxHTP1QiQH+ggBNRYSJAsvGYUlRDIkDnEzUPiEsiQOJjfDzDSiJARWy0Wv5SIkBpWmsIGVsiQPzuTuDUUyJAipKzSA9JIkC25eDMVS8iQHHHo0uzKSJAOKhZeNFJIkDGZjBt\\u002fEoiQM0Ka70+NiJAs\\u002ftBJO1HIkDHjhROC08iQG6hd4+LMiJAhZlH07FPIkC+QPFbfTwiQDBLfR7kLSJAz39up+JQIkB\\u002foIATUWEiQLdtr184TiJA5xM1D4hLIkDiY3w8w0oiQEVstFr+UiJAaVprCBlbIkAk3YJZ2mQiQH2F\\u002fRz2QCJAz4fPPFUvIkBxx6NLsykiQDioWXjRSSJA3pRdAvtKIkDNCmu9PjYiQLP7QSTtRyJAx44UTgtPIkBuoXePizIiQBljpR\\u002fSViJAvkDxW308IkAwS30e5C0iQO\\u002fjc96uTyJAf6CAE1FhIkC3ba9fOE4iQOcTNQ+ISyJA4mN8PMNKIkBFbLRa\\u002flIiQGlaawgZWyJAJN2CWdpkIkB9hf0c9kAiQM+HzzxVLyJAfcWd4rEpIkA4qFl40UkiQCVGvyb6SiJAdMcbPSxfIkCz+0Ek7UciQHqFvW0xSyJAbqF3j4syIkAZY6Uf0lYiQA6EaEx6PCJAMEt9HuQtIkDv43Perk8iQGA7d3GGSSJAt22vXzhOIkDnEzUPiEsiQOJjfDzDSiJA4MG0A41cIkBpWmsIGVsiQCTdglnaZCJAfYX9HPZAIkCbd7xu6D4iQH3FneKxKSJAsTqYLjRMIkDUeuNx+EoiQHTHGz0sXyJAs\\u002ftBJO1HIkD\\u002fUnC3L0siQG6hd4+LMiJA4Xx8fN1VIkAOhGhMejwiQI7hqPniLSJA7+Nz3q5PIkCEAlQ0ThwiQLdtr184TiJA5xM1D4hLIkDiY3w8w0oiQPS2JjedUCJAaVprCBlbIkCBLHyBWGwiQLiypnAWRCJAm3e8bug+IkB9xZ3isSkiQLE6mC40TCJA1HrjcfhKIkB0xxs9LF8iQLP7QSTtRyJAmkHhnfQnIkBNJqDGjDIiQOF8fHzdVSJADoRoTHo8IkA1sFJkqEAiQO\\u002fjc96uTyJAhAJUNE4cIkBrTabGzFYiQOcTNQ+ISyJA4mN8PMNKIkD0tiY3nVAiQGlaawgZWyJAgSx8gVhsIkC4sqZwFkQiQJt3vG7oPiJAfcWd4rEpIkAZ3VM9QzkiQA2hJytDQSJAa6ralSRQIkCz+0Ek7UciQJpB4Z30JyJATSagxowyIkDhfHx83VUiQA6EaEx6PCJARe3tB\\u002fY6IkDv43Perk8iQFUunL6\\u002fNyJA4mFpfcxWIkDnEzUPiEsiQOJjfDzDSiJA9LYmN51QIkBpWmsIGVsiQIEsfIFYbCJAklxsB5FUIkDvVsPIwSgiQODUXYAnRCJAGd1TPUM5IkANoScrQ0EiQGuq2pUkUCJAs\\u002ftBJO1HIkCaQeGd9CciQE0moMaMMiJA4Xx8fN1VIkDR5AtBeDwiQOIi4cTjRCJA7+Nz3q5PIkA8SU6IvzciQK+j57\\u002fMViJA5xM1D4hLIkDiY3w8w0oiQPS2JjedUCJAaVprCBlbIkCBLHyBWGwiQIA2ophsSyJA71bDyMEoIkDg1F2AJ0QiQBndUz1DOSJADaEnK0NBIkDPgqEQIlAiQE\\u002f99XbhJyJAmkHhnfQnIkBNJqDGjDIiQOF8fHzdVSJAFO\\u002fnUDFXIkDiIuHE40QiQO\\u002fjc96uTyJAPElOiL83IkCvo+e\\u002fzFYiQAuNoop9PSJA4mN8PMNKIkD0tiY3nVAiQAONK5mrSyJAgSx8gVhsIkCANqKYbEsiQO9Ww8jBKCJA4NRdgCdEIkAZ3VM9QzkiQA2hJytDQSJAz4KhECJQIkBP\\u002ffV24SciQJpB4Z30JyJATSagxowyIkDhfHx83VUiQKAKhVYyVyJA4iLhxONEIkDv43Perk8iQDxJToi\\u002fNyJAr6Pnv8xWIkALjaKKfT0iQOJjfDzDSiJA9LYmN51QIkADjSuZq0siQBUCFRMYaiJAgDaimGxLIkDvVsPIwSgiQODUXYAnRCJAGd1TPUM5IkANoScrQ0EiQM+CoRAiUCJAz4x7P+AnIkCaQeGd9CciQE0moMaMMiJA4Xx8fN1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-       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
-       "        legendButton.setAttribute('role', 'button');\n",
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-       "        legendButton.innerHTML = [\n",
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-       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
-       "            + ' aria-hidden=\"true\">',\n",
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-       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
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-       "        legendButtonGroup.appendChild(legendButton);\n",
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J8tAp44sT8HTeG\\u002fpAS0P5KCAOafTbU\\u002fH4BT11a4sj\\u002f5fIEBD4q2P9F59sJ5uLU\\u002fSKDhE\\u002fZVtj9nxU4e6n61P+Lv+EvaPLQ\\u002fDpQRJk+\\u002ftz84gv9K5ZK2P86Ua3UamrU\\u002frd\\u002ffpJf5tj8lSoyvFB+1PyIs35hVXLQ\\u002flgUzRPAosz8OmFc+9a2zP8mX4+M9eLU\\u002fCj1Iv4IotT8I9JsH6AC2PzSfLQKeOLE\\u002fB03hv6QEtD+SggDmn021Px+AU9dWuLI\\u002f8jH7AQ6Ktj\\u002fRefbCebi1P6YgDUi+ULY\\u002fdaWaPuFztT\\u002fi7\\u002fhL2jy0PwqC0VwOl7U\\u002fOIL\\u002fSuWStj\\u002fOlGt1Gpq1P63f36SX+bY\\u002fJUqMrxQftT8iLN+YVVy0P5YFM0TwKLM\\u002fDphXPvWtsz\\u002fJl+PjPXi1Pwo9SL+CKLU\\u002fCPSbB+gAtj\\u002f6zw6b3MywPwdN4b+kBLQ\\u002f6cci6aJNtT8fgFPXVriyP6idyKRdsbU\\u002f0Xn2wnm4tT+mIA1IvlC2P3Wlmj7hc7U\\u002f4u\\u002f4S9o8tD8KgtFcDpe1PziC\\u002f0rlkrY\\u002fzpRrdRqatT+t39+kl\\u002fm2PyVKjK8UH7U\\u002fIizfmFVctD9o5IalKDOxPw6YVz71rbM\\u002fnkOZVTx4tT8KPUi\\u002fgii1Pwj0mwfoALY\\u002f+s8Om9zMsD8HTeG\\u002fpAS0P+nHIumiTbU\\u002fH4BT11a4sj+oncikXbG1P\\u002fYYz415uLU\\u002fJbkIBcFQtj9K9\\u002fza9Y60P+Lv+EvaPLQ\\u002fCoLRXA6XtT8wvL1t5JK2P86Ua3UamrU\\u002frd\\u002ffpJf5tj8lSoyvFB+1P69oGe9oi7U\\u002faOSGpSgzsT\\u002fFhR\\u002fDr\\u002f2zPwLYith4oLY\\u002fCj1Iv4IotT8rf1won8i2P3yy42PWI7M\\u002fEzM0dNEzsz\\u002fpxyLpok21Px+AU9dWuLI\\u002fqJ3IpF2xtT8Dcbk4LAu3PyW5CAXBULY\\u002fSvf82vWOtD90yDBrD32zPwqC0VwOl7U\\u002fMLy9beSStj+qpt+liHu1P63f36SX+bY\\u002f4Virletzsz+vaBnvaIu1P2jkhqUoM7E\\u002fxYUfw6\\u002f9sz8C2IrYeKC2Pwo9SL+CKLU\\u002fK39cKJ\\u002fItj98suNj1iOzPxMzNHTRM7M\\u002f6cci6aJNtT8fgFPXVriyP6idyKRdsbU\\u002fA3G5OCwLtz8luQgFwVC2P\\u002fVDblSeBbM\\u002fdMgwaw99sz8KgtFcDpe1PzC8vW3kkrY\\u002fqqbfpYh7tT+QX2\\u002fJfn+0P+FYq5Xrc7M\\u002fr2gZ72iLtT9o5IalKDOxP8WFH8Ov\\u002fbM\\u002fJepcAXegtj8KPUi\\u002fgii1Pxm8VrbvBrc\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002fH4BT11a4sj\\u002fKvl\\u002fBLxW3PzBDBS8sRbc\\u002fJbkIBcFQtj\\u002f1Q25UngWzP8z\\u002fpcUqGbQ\\u002fCoLRXA6XtT8wvL1t5JK2P+EONuJW9bU\\u002fsxp0pge1sT+GAu3oYEa1P69oGe9oi7U\\u002faOSGpSgzsT\\u002fFhR\\u002fDr\\u002f2zPyXqXAF3oLY\\u002fCj1Iv4IotT8ZvFa27wa3P3yy42PWI7M\\u002fEzM0dNEzsz\\u002f1\\u002fiQ4ok21Px+AU9dWuLI\\u002fp76yVzAVtz8wQwUvLEW3PyW5CAXBULY\\u002f9UNuVJ4Fsz\\u002fM\\u002f6XFKhm0PwqC0VwOl7U\\u002fy8Y9VuGStj+gGNv0V\\u002fW1PwdV63qKiLE\\u002fhgLt6GBGtT+vaBnvaIu1P8guSxUqM7E\\u002fh1PghLD9sz8l6lwBd6C2Pwo9SL+CKLU\\u002fGbxWtu8Gtz98suNj1iOzPxMzNHTRM7M\\u002f9f4kOKJNtT+216RKdGa0P6e+slcwFbc\\u002fQufAySxFtz8luQgFwVC2P\\u002fVDblSeBbM\\u002fzP+lxSoZtD8KgtFcDpe1P8vGPVbhkrY\\u002fc3Qa5VX1tT8HVet6ioixP2RYBTi3C7Y\\u002fr2gZ72iLtT\\u002fILksVKjOxP4dT4ISw\\u002fbM\\u002f097BrXegtj8KPUi\\u002fgii1Pxm8VrbvBrc\\u002ffLLjY9Yjsz8TMzR00TOzP\\u002fX+JDiiTbU\\u002f0Gi8qZSOsz+nvrJXMBW3P0LnwMksRbc\\u002fJbkIBcFQtj\\u002f1Q25UngWzP8z\\u002fpcUqGbQ\\u002fCoLRXA6XtT9nw04TBvG1P3N0GuVV9bU\\u002fB1XreoqIsT9kWAU4twu2P69oGe9oi7U\\u002fyC5LFSozsT9Y3IPluQW3P30vkuJ2oLY\\u002fCj1Iv4IotT8ZvFa27wa3Pxi\\u002fu2+EU7Q\\u002fEzM0dNEzsz\\u002f1\\u002fiQ4ok21P9BovKmUjrM\\u002fIif9bIDOsj\\u002fTd9Rw5dy2PyW5CAXBULY\\u002f9UNuVJ4Fsz\\u002fM\\u002f6XFKhm0PwqC0VwOl7U\\u002fZ8NOEwbxtT9RNzMAcC20PwfRmJvXmrQ\\u002fZFgFOLcLtj9HyjbdaYu1P8guSxUqM7E\\u002fWNyD5bkFtz99L5LidqC2Pwo9SL+CKLU\\u002fGbxWtu8Gtz8Yv7tvhFO0PxMzNHTRM7M\\u002f9f4kOKJNtT\\u002fQaLyplI6zPyIn\\u002fWyAzrI\\u002fpuhdKuXctj8luQgFwVC2P6vMBhruxrI\\u002fzP+lxSoZtD8KgtFcDpe1P2fDThMG8bU\\u002foOGATHOLtj8Enz4L8oe0P2RYBTi3C7Y\\u002fR8o23WmLtT\\u002fILksVKjOxP1jcg+W5Bbc\\u002fYmVOGgumtj+I9yywSwi2Py81bihXbLY\\u002fGL+7b4RTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002f0Gi8qZSOsz8iJ\\u002f1sgM6yP6boXSrl3LY\\u002fJbkIBcFQtj870XPCoza1P8z\\u002fpcUqGbQ\\u002fCoLRXA6XtT9nw04TBvG1P6DhgExzi7Y\\u002fBJ8+C\\u002fKHtD948kmIq+GzP0fKNt1pi7U\\u002fubt68CkzsT9Y3IPluQW3P5J87W4Vg7U\\u002fiPcssEsItj8vNW4oV2y2Pxi\\u002fu2+EU7Q\\u002fEzM0dNEzsz\\u002f1\\u002fiQ4ok21P9BovKmUjrM\\u002fITuoo7arsD9Yc6qa5Ny2P8mr3j6S\\u002fbM\\u002fO9FzwqM2tT9oAt4iz+CwP2NkdDb\\u002fW7Y\\u002fZ8NOEwbxtT+g4YBMc4u2PwSfPgvyh7Q\\u002fePJJiKvhsz9HyjbdaYu1P7m7evApM7E\\u002fWNyD5bkFtz+SfO1uFYO1P4j3LLBLCLY\\u002fLzVuKFdstj8Yv7tvhFO0PxMzNHTRM7M\\u002f9f4kOKJNtT\\u002fQaLyplI6zPyE7qKO2q7A\\u002fWHOqmuTctj\\u002fJq94+kv2zPzvRc8KjNrU\\u002faALeIs\\u002fgsD\\u002fU3dZlxZm1P2fDThMG8bU\\u002foOGATHOLtj8Enz4L8oe0P3jySYir4bM\\u002fAq9UTq2XtT+5u3rwKTOxP1jcg+W5Bbc\\u002fknztbhWDtT\\u002f3ydaHSgi2Py81bihXbLY\\u002f3p33T4FTtD8TMzR00TOzP\\u002fX+JDiiTbU\\u002f0Gi8qZSOsz8hO6ijtquwP3kO3AM8R7c\\u002fyavePpL9sz\\u002fOhLJC7ua0P2gC3iLP4LA\\u002f1N3WZcWZtT96Iba7qp61P6DhgExzi7Y\\u002fE9r7HPOHtD948kmIq+GzPwKvVE6tl7U\\u002fubt68CkzsT9Y3IPluQW3P5J87W4Vg7U\\u002f98nWh0oItj9D7mfgVWy2P96d90+BU7Q\\u002fIv3DsDeOsj\\u002f8tso1Yg60P9BovKmUjrM\\u002faoX\\u002fhJMPsz95DtwDPEe3P8mr3j6S\\u002fbM\\u002fzoSyQu7mtD+KCLyTpQ6yP9Td1mXFmbU\\u002feiG2u6qetT+g4YBMc4u2P0UmkrLzxrI\\u002fePJJiKvhsz8Cr1ROrZe1P7m7evApM7E\\u002fFCRHQbDktz87RJQhu5C1P\\u002ffJ1odKCLY\\u002fQ+5n4FVstj+ddvQMg1O0Py4poj0ZQrI\\u002f\\u002fLbKNWIOtD\\u002fQaLyplI6zP2qF\\u002f4STD7M\\u002feQ7cAzxHtz\\u002fJq94+kv2zP0T\\u002fZuNl3rM\\u002figi8k6UOsj\\u002fU3dZlxZm1P3ohtruqnrU\\u002foOGATHOLtj9FJpKy88ayP3jySYir4bM\\u002fAq9UTq2XtT+ipcEvW4SxPxQkR0Gw5Lc\\u002fO0SUIbuQtT+DCY32CxS0P0PuZ+BVbLY\\u002fnXb0DINTtD8uKaI9GUKyP\\u002fy2yjViDrQ\\u002f\\u002fIXPv5eDtT9qhf+Ekw+zP5Q2rZYw3bg\\u002fyavePpL9sz8IrXv1fsazP4oIvJOlDrI\\u002f1N3WZcWZtT96Iba7qp61P6DhgExzi7Y\\u002fFuO6mfPGsj948kmIq+GzPwKvVE6tl7U\\u002foqXBL1uEsT8UJEdBsOS3PztElCG7kLU\\u002fgwmN9gsUtD9D7mfgVWy2P5129AyDU7Q\\u002fTfv8VUc7sj\\u002f8tso1Yg60P\\u002fyFz7+Xg7U\\u002faoX\\u002fhJMPsz\\u002f2mczF5BC3P8mr3j6S\\u002fbM\\u002fCK179X7Gsz+KCLyTpQ6yP9Td1mXFmbU\\u002feiG2u6qetT+g4YBMc4u2PxbjupnzxrI\\u002fePJJiKvhsz8Cr1ROrZe1P6KlwS9bhLE\\u002fFCRHQbDktz87RJQhu5C1P4MJjfYLFLQ\\u002fPsahIksttT+ddvQMg1O0P037\\u002fFVHO7I\\u002f\\u002fLbKNWIOtD+KAeP0mIO1P2vfQRjoPLI\\u002f9pnMxeQQtz\\u002fJq94+kv2zPwite\\u002fV+xrM\\u002figi8k6UOsj\\u002fU3dZlxZm1P4ZOrBqtnrU\\u002fqJ9pRUZxtj8W47qZ88ayP3jySYir4bM\\u002fMQtn8AuItj+ipcEvW4SxPxQkR0Gw5Lc\\u002fnmuI27mQtT+DCY32CxS0Pz7GoSJLLbU\\u002fxt1Gu4NTtD9aU8GXo9qyP\\u002fy2yjViDrQ\\u002f+5QQjvCYtj9r30EY6DyyP\\u002faZzMXkELc\\u002fyavePpL9sz\\u002feEmbXfcazP4oIvJOlDrI\\u002f1N3WZcWZtT+GTqwarZ61P6ifaUVGcbY\\u002fFuO6mfPGsj948kmIq+GzP1W22ZCkabQ\\u002foqXBL1uEsT8UJEdBsOS3P55riNu5kLU\\u002fgwmN9gsUtD8+xqEiSy21P7MZOz2EU7Q\\u002fLiQ5XKPasj\\u002f8tso1Yg60P\\u002fuUEI7wmLY\\u002fa99BGOg8sj\\u002fgoAVC4hC3P8mr3j6S\\u002fbM\\u002f48+nZ91ztD+KCLyTpQ6yP9Td1mXFmbU\\u002f5atsvayetT+on2lFRnG2PxbjupnzxrI\\u002fePJJiKvhsz9Qr3kkpGm0P6KlwS9bhLE\\u002fFCRHQbDktz9VYzcnvlq0P4MJjfYLFLQ\\u002fPsahIksttT+zGTs9hFO0Py4kOVyj2rI\\u002f\\u002fLbKNWIOtD\\u002f7lBCO8Ji2PxKT\\u002fu39trI\\u002fcG9ppO3MtT\\u002fJq94+kv2zP+PPp2fdc7Q\\u002figi8k6UOsj\\u002fU3dZlxZm1PxZ\\u002fe8f1brQ\\u002fqJ9pRUZxtj8W47qZ88ayP3jySYir4bM\\u002fUK95JKRptD+ipcEvW4SxP4tIPxWi4rg\\u002fVWM3J75atD+WTHm5CRS0Pz7GoSJLLbU\\u002fsxk7PYRTtD8uJDlco9qyP+d+MRhlCrc\\u002f+5QQjvCYtj8Sk\\u002f7t\\u002fbayP3BvaaTtzLU\\u002fyavePpL9sz+Xv7guoqO0P4oIvJOlDrI\\u002f1N3WZcWZtT8QghTUD3m0P6ifaUVGcbY\\u002fr1I8Pc6nsj948kmIq+GzP1CveSSkabQ\\u002foqXBL1uEsT+GU5+qbJe3P1VjNye+WrQ\\u002fHvGhj+Hbsz\\u002fUgYhsSi21P7MZOz2EU7Q\\u002fLiQ5XKPasj\\u002f79igkUaK3P6jqZ4\\u002fwmLY\\u002fEpP+7f22sj9wb2mk7cy1P8mr3j6S\\u002fbM\\u002fl7+4LqKjtD+KCLyTpQ6yP9Td1mXFmbU\\u002fEIIU1A95tD+on2lFRnG2P69SPD3Op7I\\u002fePJJiKvhsz9Qr3kkpGm0P6KlwS9bhLE\\u002fhlOfqmyXtz9VYzcnvlq0Px7xoY\\u002fh27M\\u002f1IGIbEottT+zGTs9hFO0Py4kOVyj2rI\\u002f+\\u002fYoJFGitz+lRblFqO+0PxKT\\u002fu39trI\\u002fcG9ppO3MtT\\u002fJq94+kv2zP5e\\u002fuC6io7Q\\u002fPJygxTPhsj+7ek5yxJm1PxCCFNQPebQ\\u002fqJ9pRUZxtj\\u002fVGOw6FMe1P3jySYir4bM\\u002fUK95JKRptD+ipcEvW4SxP4ZTn6psl7c\\u002fVWM3J75atD\\u002f+Lj9Uz0q3P9SBiGxKLbU\\u002fsxk7PYRTtD8uJDlco9qyP\\u002fv2KCRRorc\\u002fpUW5RajvtD8Sk\\u002f7t\\u002fbayP3BvaaTtzLU\\u002fyavePpL9sz+Xv7guoqO0P9zx\\u002fFOBTLY\\u002fu3pOcsSZtT8QghTUD3m0P6ifaUVGcbY\\u002f1RjsOhTHtT9\\u002fGRB59US1P1CveSSkabQ\\u002foqXBL1uEsT+GU5+qbJe3P1VjNye+WrQ\\u002f\\u002fi4\\u002fVM9Ktz\\u002fUgYhsSi21P7MZOz2EU7Q\\u002fLiQ5XKPasj\\u002f79igkUaK3P6VFuUWo77Q\\u002fs\\u002f51oi4wtD+Wvgm1xWC0P8mr3j6S\\u002fbM\\u002fwX\\u002f2wp03sz\\u002fc8fxTgUy2P1swnTohf7Y\\u002fEIIU1A95tD+on2lFRnG2P9UY7DoUx7U\\u002fOuNLPrebuD+wsDSCpGm0P6KlwS9bhLE\\u002fhlOfqmyXtz9VYzcnvlq0Pxg+aQfPSrc\\u002f1wfVILwBtj9tgN+kDoG1P+IBN7TFgbM\\u002f+\\u002fYoJFGitz+lRblFqO+0P7P+daIuMLQ\\u002flr4JtcVgtD\\u002fJq94+kv2zP8F\\u002f9sKdN7M\\u002f3PH8U4FMtj9bMJ06IX+2PxCCFNQPebQ\\u002fqJ9pRUZxtj\\u002fVGOw6FMe1PzrjSz63m7g\\u002fsLA0gqRptD+ipcEvW4SxP4ZTn6psl7c\\u002fVWM3J75atD8Fa4rJz0q3P9cH1SC8AbY\\u002fbYDfpA6BtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fpUW5RajvtD\\u002fS\\u002fF45YhezP+gMnIL7lrM\\u002fyavePpL9sz9FhVR5WSWzPwKyUaMf7rQ\\u002fWzCdOiF\\u002ftj8QghTUD3m0P6ifaUVGcbY\\u002fzfO7T\\u002fI3tj8640s+t5u4P7CwNIKkabQ\\u002foqXBL1uEsT+GU5+qbJe3P1VjNye+WrQ\\u002fBWuKyc9Ktz\\u002fXB9UgvAG2PxDu1cmdibU\\u002f4gE3tMWBsz\\u002f79igkUaK3P6VFuUWo77Q\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002frMZ7lYoasz8CslGjH+60P1swnTohf7Y\\u002f0qH3axB5tD+on2lFRnG2Pzhgp0Kxy7Q\\u002fkLlUXLBIuD+wsDSCpGm0P6KlwS9bhLE\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002f1wfVILwBtj8Q7tXJnYm1P+IBN7TFgbM\\u002f+\\u002fYoJFGitz+lRblFqO+0P9L8XjliF7M\\u002f6AycgvuWsz\\u002fJq94+kv2zP5M\\u002fdtGKGrM\\u002fArJRox\\u002futD95tuTOIn+2P9Kh92sQebQ\\u002fqJ9pRUZxtj8TFyYRscu0P5C5VFywSLg\\u002fsLA0gqRptD+ipcEvW4SxP0\\u002fPrA6ui7c\\u002fVWM3J75atD8Fa4rJz0q3P9cH1SC8AbY\\u002fEO7VyZ2JtT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fpUW5RajvtD\\u002fS\\u002fF45YhezP+gMnIL7lrM\\u002fyavePpL9sz+TP3bRihqzPwKyUaMf7rQ\\u002fSQAVScDTtz\\u002fSofdrEHm0P0bo2KUqpbU\\u002fExcmEbHLtD+hGkO9Avq3P8R39IOJzbI\\u002fi\\u002fy0MnYStT9Pz6wOrou3P1VjNye+WrQ\\u002fBWuKyc9Ktz\\u002fXB9UgvAG2P6fP99EfP7Y\\u002f4gE3tMWBsz\\u002f79igkUaK3P6VFuUWo77Q\\u002f0vxeOWIXsz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fkz920Yoasz8CslGjH+60P0kAFUnA07c\\u002f0qH3axB5tD9G6NilKqW1PxMXJhGxy7Q\\u002foRpDvQL6tz\\u002fEd\\u002fSDic2yP4v8tDJ2ErU\\u002fT8+sDq6Ltz9VYzcnvlq0PwVrisnPSrc\\u002f1wfVILwBtj+nz\\u002ffRHz+2P+IBN7TFgbM\\u002f+\\u002fYoJFGitz+lRblFqO+0P9L8XjliF7M\\u002f6AycgvuWsz\\u002fJq94+kv2zP5M\\u002fdtGKGrM\\u002fDnpQz07Gtj9JABVJwNO3P9Xn+yIQebQ\\u002fOtNjKxOStD8TFyYRscu0P6EaQ70C+rc\\u002fxHf0g4nNsj+L\\u002fLQydhK1P+T9jWGvi7c\\u002fVWM3J75atD8Fa4rJz0q3P9cH1SC8AbY\\u002fowPzWDgjuT\\u002fiATe0xYGzP\\u002fv2KCRRorc\\u002fpUW5RajvtD\\u002fS\\u002fF45YhezP+gMnIL7lrM\\u002fyavePpL9sz+TP3bRihqzPxB5NJUDh7g\\u002fSQAVScDTtz9+yb2zD3m0PzrTYysTkrQ\\u002fExcmEbHLtD\\u002f8qEdS5mu0P8R39IOJzbI\\u002fi\\u002fy0MnYStT\\u002fk\\u002fY1hr4u3P1VjNye+WrQ\\u002fBWuKyc9Ktz\\u002fXB9UgvAG2P\\u002f0QnMuVC7g\\u002f4gE3tMWBsz\\u002f79igkUaK3P72pP1eo77Q\\u002fNjoPKpJ3sz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fJ2S33siLtD\\u002fF7JiUA4e4P7Hew9+\\u002f07c\\u002ffsm9sw95tD8602MrE5K0PxMXJhGxy7Q\\u002fyM\\u002fcBJzItD\\u002fEd\\u002fSDic2yP4v8tDJ2ErU\\u002f5P2NYa+Ltz9VYzcnvlq0PwVrisnPSrc\\u002f1wfVILwBtj\\u002f9EJzLlQu4P3EzC3LgbLI\\u002f+\\u002fYoJFGitz+9qT9XqO+0P2oRNM1+XLM\\u002f6AycgvuWsz\\u002fJq94+kv2zPydkt97Ii7Q\\u002fxeyYlAOHuD+x3sPfv9O3P37JvbMPebQ\\u002fOtNjKxOStD8TFyYRscu0P8jP3AScyLQ\\u002fxHf0g4nNsj9Q0\\u002f\\u002fRdZmxP+T9jWGvi7c\\u002fVWM3J75atD+SasPRXZ62P9cH1SC8AbY\\u002fqJqL8Q3dtj9xMwty4GyyP\\u002fv2KCRRorc\\u002feCOFAIldtT9qETTNflyzP+gMnIL7lrM\\u002fyavePpL9sz8nZLfeyIu0P8XsmJQDh7g\\u002fsd7D37\\u002fTtz9+yb2zD3m0PzrTYysTkrQ\\u002fExcmEbHLtD9uCazDO9W0P8R39IOJzbI\\u002fUNP\\u002f0XWZsT\\u002fNbXE6r4u3P1VjNye+WrQ\\u002fkmrD0V2etj\\u002fXB9UgvAG2P6iai\\u002fEN3bY\\u002fcTMLcuBssj\\u002f79igkUaK3P3gjhQCJXbU\\u002fahE0zX5csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fJ2S33siLtD\\u002fF7JiUA4e4P+79VuTVK7k\\u002fJRkQJP3Ssj8602MrE5K0PxMXJhGxy7Q\\u002fbgmswzvVtD\\u002fEd\\u002fSDic2yP1DT\\u002f9F1mbE\\u002fzW1xOq+Ltz9VYzcnvlq0P5Jqw9FdnrY\\u002fcpfvSudetj+omovxDd22P3EzC3LgbLI\\u002f+\\u002fYoJFGitz94I4UAiV21P7cxbvB\\u002fXLM\\u002f6AycgvuWsz\\u002fJq94+kv2zPydkt97Ii7Q\\u002fxeyYlAOHuD9O9LouJhi2PyUZECT90rI\\u002fOtNjKxOStD8TFyYRscu0P24JrMM71bQ\\u002fxHf0g4nNsj8VwSeMdtmwP8k1tqC9yrU\\u002fVWM3J75atD+SasPRXZ62Py7uhQptabc\\u002fdxB+LrSJtj\\u002fSykCoHN2yP4FssAXomrY\\u002ft6B7FCsFsz+3MW7wf1yzP+gMnIL7lrM\\u002fyavePpL9sz8nZLfeyIu0PxaNEHMDh7g\\u002fTvS6LiYYtj8lGRAk\\u002fdKyPzrTYysTkrQ\\u002fExcmEbHLtD+F4foHPNW0P8R39IOJzbI\\u002fbUHKM6INtD\\u002faSXipD7a1P1VjNye+WrQ\\u002fkmrD0V2etj8u7oUKbWm3P3cQfi60ibY\\u002f0spAqBzdsj+BbLAF6Jq2P7egexQrBbM\\u002ftzFu8H9csz\\u002foDJyC+5azP8mr3j6S\\u002fbM\\u002fJ2S33siLtD8Tv6Ikl0i1P070ui4mGLY\\u002fJRkQJP3Ssj8602MrE5K0P+MQ4Vuj2LI\\u002fheH6BzzVtD\\u002fEd\\u002fSDic2yPwEzPa56qLI\\u002fz8t12De9tD9VYzcnvlq0P5Jqw9FdnrY\\u002fLu6FCm1ptz93EH4utIm2P1da5EF2tLU\\u002fgWywBeiatj9Nxrh\\u002fKgWzP7cxbvB\\u002fXLM\\u002f6AycgvuWsz\\u002fJq94+kv2zPydkt97Ii7Q\\u002fU9rstTVetD9O9LouJhi2PyUZECT90rI\\u002f1Sxay8jFsz\\u002fjEOFbo9iyP8PAseZ9xbQ\\u002fxHf0g4nNsj8BMz2ueqiyP8\\u002fLddg3vbQ\\u002fVWM3J75atD\\u002fLHK1sSqazP4sZHFuxHrc\\u002f8qGMXbKJtj9XWuRBdrS1P4FssAXomrY\\u002fmqvdRy0Fsz+3MW7wf1yzP+gMnIL7lrM\\u002fyavePpL9sz8nZLfeyIu0PxFMd5\\u002fMxLQ\\u002fTvS6LiYYtj8lGRAk\\u002fdKyP2k7JG4LwbM\\u002f4xDhW6PYsj\\u002fDwLHmfcW0P8R39IOJzbI\\u002fATM9rnqosj\\u002fPy3XYN720P1VjNye+WrQ\\u002fyxytbEqmsz+LGRxbsR63P\\u002fKhjF2yibY\\u002fJe5ec3W0tT+BbLAF6Jq2P3GnvlwuBbM\\u002flQINpFBOtT\\u002foDJyC+5azP\\u002fR\\u002fBBZvGrU\\u002fJ2S33siLtD8RTHefzMS0PwHBzUcmGLY\\u002fJRkQJP3Ssj9pOyRuC8GzP2F1ZoKgGbQ\\u002fw8Cx5n3FtD\\u002fEd\\u002fSDic2yPwEzPa56qLI\\u002frKmeFj2Zsj9VYzcnvlq0P8scrWxKprM\\u002fixkcW7Eetz\\u002fnuDH0oHm0PyXuXnN1tLU\\u002fUk4qly0ctT9cZoeXLAWzP5UCDaRQTrU\\u002f6AycgvuWsz+nPZ1Cvcm1Pydkt97Ii7Q\\u002fSOuJ1d+htD8Bwc1HJhi2P2w2EeL90rI\\u002faTskbgvBsz8PWMZ84JGyP8PAseZ9xbQ\\u002fxHf0g4nNsj8BMz2ueqiyP\\u002fJeoJ60k7I\\u002fVWM3J75atD9rv9pcj9uyP8wxtj0fUrg\\u002f57gx9KB5tD8l7l5zdbS1P1JOKpctHLU\\u002fXGaHlywFsz+VAg2kUE61P+gMnIL7lrM\\u002fCmQG\\u002fNpotT\\u002fxFdlJx4u0P0jridXfobQ\\u002fAcHNRyYYtj+bv4inTteyP2k7JG4LwbM\\u002fD1jGfOCRsj8yFDOGUXSzP8R39IOJzbI\\u002fATM9rnqosj\\u002fyXqCetJOyP1VjNye+WrQ\\u002fa7\\u002faXI\\u002fbsj\\u002fMMbY9H1K4P+e4MfSgebQ\\u002fJe5ec3W0tT\\u002fvVTvt0+W0P\\u002f6VdE7AM7M\\u002fK6w01Lrltj\\u002foDJyC+5azPwpkBvzaaLU\\u002f8RXZSceLtD9I64nV36G0PwHBzUcmGLY\\u002f1TCdZ8yDsj9pOyRuC8GzP6IPhuf1f7Q\\u002fgltC+FF0sz\\u002fEd\\u002fSDic2yPwEzPa56qLI\\u002f8l6gnrSTsj9VYzcnvlq0P2u\\u002f2lyP27I\\u002fGbq1AB5SuD\\u002f9PMzvzFK0P5qJhW2ewrU\\u002f71U77dPltD\\u002f+lXROwDOzPyusNNS65bY\\u002f6AycgvuWsz8KZAb82mi1P\\u002fEV2UnHi7Q\\u002fSOuJ1d+htD+IPruMV422P0L1k3qkW7I\\u002faTskbgvBsz+qqnKFiHOzP\\u002fqdu9ZSdLM\\u002fxHf0g4nNsj8BMz2ueqiyP\\u002fJeoJ60k7I\\u002fVWM3J75atD9rv9pcj9uyP8lWlUNV0Lc\\u002f\\u002fTzM78xStD+aiYVtnsK1P+9VO+3T5bQ\\u002f\\u002fpV0TsAzsz+xjUpmu+W2P4AgUs9ei7M\\u002fCmQG\\u002fNpotT\\u002fxFdlJx4u0P0jridXfobQ\\u002fUsw8pFWNtj9C9ZN6pFuyP2k7JG4LwbM\\u002fqqpyhYhzsz\\u002f6nbvWUnSzPxCfr3bfM7E\\u002fATM9rnqosj\\u002fyXqCetJOyP272PtqknLU\\u002fa7\\u002faXI\\u002fbsj\\u002fJVpVDVdC3P\\u002f08zO\\u002fMUrQ\\u002fmomFbZ7CtT\\u002fvVTvt0+W0P\\u002f6VdE7AM7M\\u002fsY1KZrvltj+AIFLPXouzPwpkBvzaaLU\\u002f8RXZSceLtD9I64nV36G0P2kiN0PjvrY\\u002fQvWTeqRbsj9pOyRuC8GzP6qqcoWIc7M\\u002f+p271lJ0sz8Qn6923zOxPwEzPa56qLI\\u002f8l6gnrSTsj9u9j7apJy1P8Ae8tSDM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iD1Eo5D\\u002f4Nps1XizkP3yab5bsKOQ\\u002fIf\\u002fp1Agc5D80FCyW\\u002fCvkP6ZvNWo2ReQ\\u002fu\\u002fsXTytE5D\\u002fkdeGYnSrkP5E6yksfKuQ\\u002fye2\\u002fiEUf5D\\u002fpWiyfYTTkPxlRzEXUQ+Q\\u002fWGns2Agi5D8LIdl7GijkP0d1oGMRQuQ\\u002fxOOs3MIu5D8d1BjwSxnkP2IeGkVTLuQ\\u002fuj2nTjYr5D9UacNI5TDkPxmXYg9RKOQ\\u002f+DabNV4s5D98mm+W7CjkPwguT40iK+Q\\u002fNBQslvwr5D+mbzVqNkXkP7v7F08rROQ\\u002f5HXhmJ0q5D+ROspLHyrkP2oFoXBFH+Q\\u002f6Vosn2E05D8ZUcxF1EPkP1hp7NgIIuQ\\u002fCyHZexoo5D9HdaBjEULkP8TjrNzCLuQ\\u002fo8yrnpcT5D9iHhpFUy7kP+bghWVvHuQ\\u002fSPC4qFcx5D8Zl2IPUSjkPyzkRybuQ+Q\\u002ffJpvluwo5D8ILk+NIivkPzQULJb8K+Q\\u002fpm81ajZF5D+7+xdPK0TkP+R14ZidKuQ\\u002fkTrKSx8q5D9qBaFwRR\\u002fkP+laLJ9hNOQ\\u002fGVHMRdRD5D\\u002fv\\u002fbCgBynkPwsh2XsaKOQ\\u002fUjkFli415D\\u002fE46zcwi7kP4t1NMQLJOQ\\u002fYh4aRVMu5D\\u002fm4IVlbx7kP0jwuKhXMeQ\\u002fGZdiD1Eo5D8s5Ecm7kPkP3yab5bsKOQ\\u002fCC5PjSIr5D80FCyW\\u002fCvkP6ZvNWo2ReQ\\u002fu\\u002fsXTytE5D\\u002fh6TNZuzPkP5E6yksfKuQ\\u002fUGu9B0Qf5D\\u002fpWiyfYTTkPxlRzEXUQ+Q\\u002f7\\u002f2woAcp5D8LIdl7GijkP1I5BZYuNeQ\\u002fxOOs3MIu5D+LdTTECyTkPzsgku1SLuQ\\u002fKS3loWos5D+w0NzYZjPkPxmXYg9RKOQ\\u002fLORHJu5D5D8QtolV6yjkPwguT40iK+Q\\u002fNBQslvwr5D+mbzVqNkXkP75+LdnfNuQ\\u002f4ekzWbsz5D8GTaQryjHkP3YwUBJNF+Q\\u002f6Vosn2E05D+zM+VrEj3kP0R08osuIeQ\\u002fNUjYcvUn5D9SOQWWLjXkP8TjrNzCLuQ\\u002fi3U0xAsk5D\\u002f\\u002feDp8yi\\u002fkPykt5aFqLOQ\\u002fsNDc2GYz5D\\u002fVRAhWwC7kPyzkRybuQ+Q\\u002fELaJVeso5D+4NGqbaivkPzQULJb8K+Q\\u002fGCcYiHhI5D++fi3Z3zbkP+HpM1m7M+Q\\u002fBk2kK8ox5D92MFASTRfkP+laLJ9hNOQ\\u002fszPlaxI95D9EdPKLLiHkPzVI2HL1J+Q\\u002fUjkFli415D\\u002fE46zcwi7kP4t1NMQLJOQ\\u002f\\u002f3g6fMov5D8pLeWhaizkP\\u002fYdsLsvLeQ\\u002f1UQIVsAu5D8s5Ecm7kPkPxC2iVXrKOQ\\u002fuDRqm2or5D\\u002fAfhvCXjTkPxgnGIh4SOQ\\u002fvn4t2d825D\\u002fh6TNZuzPkPwZNpCvKMeQ\\u002fG9oM79Eq5D\\u002fpWiyfYTTkP6o\\u002fuiPhLeQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fxOOs3MIu5D8T71+HJB\\u002fkP2L\\u002f7YpsO+Q\\u002fKS3loWos5D\\u002f2HbC7Ly3kP2GipUswKuQ\\u002fLORHJu5D5D8QtolV6yjkPzpGpUY+JOQ\\u002f8wXqHkwp5D8mmZOx7EHkP75+LdnfNuQ\\u002f4ekzWbsz5D8GTaQryjHkPxvaDO\\u002fRKuQ\\u002f6Vosn2E05D+qP7oj4S3kP0R08osuIeQ\\u002fNUjYcvUn5D9J1lOmKzXkP8TjrNzCLuQ\\u002fwA1IlH4b5D9i\\u002f+2KbDvkPykt5aFqLOQ\\u002f9h2wuy8t5D9hoqVLMCrkPyzkRybuQ+Q\\u002fx5RD9+oo5D+oC9rjPSTkP9\\u002fzMMJhMeQ\\u002fJpmTsexB5D++fi3Z3zbkP7H+S3LuMOQ\\u002fB6vUWA0w5D8b2gzv0SrkP+laLJ9hNOQ\\u002fqj+6I+Et5D9EdPKLLiHkPzVI2HL1J+Q\\u002fSdZTpis15D\\u002frQSv5QCbkP8ANSJR+G+Q\\u002ftCPXSmo75D8pLeWhaizkP\\u002fYdsLsvLeQ\\u002fYaKlSzAq5D8s5Ecm7kPkP8eUQ\\u002ffqKOQ\\u002fgxnmnz4k5D\\u002ff8zDCYTHkP0jXlpR4N+Q\\u002fvn4t2d825D+x\\u002fkty7jDkPwer1FgNMOQ\\u002fcO\\u002fOA88q5D\\u002fpWiyfYTTkP6o\\u002fuiPhLeQ\\u002fRHTyiy4h5D81SNhy9SfkP0nWU6YrNeQ\\u002fd3QNbXIy5D\\u002fADUiUfhvkP7Qj10pqO+Q\\u002fKS3loWos5D\\u002f2HbC7Ly3kP2GipUswKuQ\\u002fLORHJu5D5D+PgMRBOC3kP4MZ5p8+JOQ\\u002f3\\u002fMwwmEx5D9I15aUeDfkP75+LdnfNuQ\\u002fsf5Lcu4w5D\\u002fMQ4fARCbkP6QM9tfPKuQ\\u002f6Vosn2E05D+qP7oj4S3kPyzlazUcJeQ\\u002fNUjYcvUn5D9J1lOmKzXkP3d0DW1yMuQ\\u002fOY62NoIZ5D9f7QoqDTTkPykt5aFqLOQ\\u002f9h2wuy8t5D9hoqVLMCrkPyzkRybuQ+Q\\u002fj4DEQTgt5D9WuiiypCbkP0m4GoHtReQ\\u002fSNeWlHg35D841B9H3zbkP7H+S3LuMOQ\\u002fzEOHwEQm5D+kDPbXzyrkP+laLJ9hNOQ\\u002fqj+6I+Et5D8s5Ws1HCXkPzVI2HL1J+Q\\u002fSdZTpis15D93dA1tcjLkPzmOtjaCGeQ\\u002fZcwXOQ805D8pLeWhaizkP0z6xWQ3MeQ\\u002fYaKlSzAq5D8s5Ecm7kPkP4+AxEE4LeQ\\u002fjHWHKSsq5D\\u002fmT+Hp40HkP0jXlpR4N+Q\\u002fONQfR9825D+x\\u002fkty7jDkP8xDh8BEJuQ\\u002f5ESgwtMY5D+nEzr\\u002foTzkP0RCVACzK+Q\\u002fLOVrNRwl5D81SNhy9SfkP0nWU6YrNeQ\\u002fd3QNbXIy5D85jrY2ghnkP2XMFzkPNOQ\\u002fKS3loWos5D\\u002fX3isBUCrkP2GipUswKuQ\\u002fLORHJu5D5D+PgMRBOC3kP4x1hykrKuQ\\u002f5k\\u002fh6eNB5D+I4SgYCD\\u002fkPzjUH0ffNuQ\\u002f5dd1R+4w5D\\u002fMQ4fARCbkPyBebBMxDeQ\\u002fpxM6\\u002f6E85D9EQlQAsyvkPyzlazUcJeQ\\u002fNUjYcvUn5D9J1lOmKzXkP3d0DW1yMuQ\\u002fcOEIZ0YN5D+O8mt1DTTkP8iEB3LpKuQ\\u002f194rAVAq5D8obvPDgi7kPzgbhpaXNuQ\\u002fj4DEQTgt5D+MdYcpKyrkP+ZP4enjQeQ\\u002fiOEoGAg\\u002f5D841B9H3zbkP+XXdUfuMOQ\\u002fzEOHwEQm5D8gXmwTMQ3kP6cTOv+hPOQ\\u002fREJUALMr5D8s5Ws1HCXkPzVI2HL1J+Q\\u002fSdZTpis15D93dA1tcjLkP3DhCGdGDeQ\\u002fjvJrdQ005D\\u002fIhAdy6SrkP9feKwFQKuQ\\u002fKG7zw4Iu5D8cZN2DhybkP4+AxEE4LeQ\\u002fjHWHKSsq5D\\u002fmT+Hp40HkP4jhKBgIP+Q\\u002f\\u002fSxEP+c85D\\u002fl13VH7jDkP8xDh8BEJuQ\\u002fIF5sEzEN5D9owkM9oTzkP0RCVACzK+Q\\u002fMoeTHuMZ5D81SNhy9SfkP0nWU6YrNeQ\\u002fd3QNbXIy5D9w4QhnRg3kP2aenkO1MOQ\\u002fyIQHcukq5D9+qmGkHSfkPyhu88OCLuQ\\u002fHGTdg4cm5D8ZdeZzzirkP4x1hykrKuQ\\u002f1HH\\u002ffw085D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002f5dd1R+4w5D\\u002fMQ4fARCbkPyBebBMxDeQ\\u002faMJDPaE85D\\u002fgBJiPsivkPzKHkx7jGeQ\\u002f7eHIE+AW5D8vMntrOz7kP3d0DW1yMuQ\\u002fzFvffTYi5D9mnp5DtTDkP8iEB3LpKuQ\\u002ffqphpB0n5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002fGXXmc84q5D+MdYcpKyrkP3hvqigRNeQ\\u002fiOEoGAg\\u002f5D\\u002f9LEQ\\u002f5zzkP+XXdUfuMOQ\\u002foGzC00I45D9qbApWMRfkP2jCQz2hPOQ\\u002f4ASYj7Ir5D8G4nZK3hnkP3wIzcnID+Q\\u002fLzJ7azs+5D93dA1tcjLkP8xb3302IuQ\\u002fZp6eQ7Uw5D\\u002fIhAdy6SrkP3p7FXUnJ+Q\\u002f\\u002fYioyl0T5D8cZN2DhybkPxl15nPOKuQ\\u002fjHWHKSsq5D94b6ooETXkP4jhKBgIP+Q\\u002f\\u002fSxEP+c85D\\u002ffcUIlujDkP6BswtNCOOQ\\u002famwKVjEX5D9DA1rFqDrkP+AEmI+yK+Q\\u002fBuJ2St4Z5D98CM3JyA\\u002fkPy8ye2s7PuQ\\u002fapAUEl8m5D\\u002fMW999NiLkP7g8M638POQ\\u002fyIQHcukq5D87Tdq2lCfkP\\u002f2IqMpdE+Q\\u002fHGTdg4cm5D8ZdeZzzirkP4x1hykrKuQ\\u002fhARAnRA15D+I4SgYCD\\u002fkP\\u002f0sRD\\u002fnPOQ\\u002f33FCJbow5D+gbMLTQjjkP2psClYxF+Q\\u002fQwNaxag65D\\u002fgBJiPsivkPwbidkreGeQ\\u002f+UEwpxEY5D8vMntrOz7kP2qQFBJfJuQ\\u002fzFvffTYi5D\\u002fS3MnEig7kP8iEB3LpKuQ\\u002fO03atpQn5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002fGXXmc84q5D+MdYcpKyrkP4QEQJ0QNeQ\\u002fiOEoGAg\\u002f5D\\u002f9LEQ\\u002f5zzkP99xQiW6MOQ\\u002foGzC00I45D9qbApWMRfkP0MDWsWoOuQ\\u002fAD3g+rEr5D8G4nZK3hnkP\\u002flBMKcRGOQ\\u002fLzJ7azs+5D\\u002f92LAFXibkP4Vw1nf7IeQ\\u002f0tzJxIoO5D\\u002fIhAdy6SrkPztN2raUJ+Q\\u002f\\u002fYioyl0T5D8cZN2DhybkP3woRMzOKuQ\\u002fXp\\u002fHJUUX5D+EBECdEDXkP4jhKBgIP+Q\\u002f\\u002f61fC9o15D\\u002ffcUIlujDkP6BswtNCOOQ\\u002fuaF+OC4X5D9DA1rFqDrkPwA94PqxK+Q\\u002fvJ7lKN4Z5D\\u002fdP+POdyXkPy8ye2s7PuQ\\u002fVBlikZsx5D+FcNZ3+yHkP9LcycSKDuQ\\u002fyIQHcukq5D\\u002fYI63QlSfkP\\u002f2IqMpdE+Q\\u002fHGTdg4cm5D98KETMzirkP16fxyVFF+Q\\u002fhARAnRA15D+I4SgYCD\\u002fkP0oPu3diM+Q\\u002f33FCJbow5D+gbMLTQjjkP7mhfjguF+Q\\u002fQwNaxag65D8APeD6sSvkP0j3Ra\\u002feGeQ\\u002fgA5WznQl5D8vMntrOz7kP1QZYpGbMeQ\\u002fhXDWd\\u002fsh5D\\u002fKHfmAiw7kP8iEB3LpKuQ\\u002fV+dk+h4i5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002frz\\u002fPj80q5D9en8clRRfkP4QEQJ0QNeQ\\u002fiOEoGAg\\u002f5D\\u002fMyTUPMRvkP99xQiW6MOQ\\u002foGzC00I45D9N+dhyniPkP0MDWsWoOuQ\\u002fAD3g+rEr5D9I90Wv3hnkP4AOVs50JeQ\\u002fLzJ7azs+5D9UGWKRmzHkP1TfG6MuROQ\\u002fin64GY0O5D\\u002fIhAdy6SrkP1fnZPoeIuQ\\u002f\\u002fYioyl0T5D8cZN2DhybkP4qz0fAGKuQ\\u002fXp\\u002fHJUUX5D+EBECdEDXkP4jhKBgIP+Q\\u002fzMk1DzEb5D\\u002ffcUIlujDkPzTmilMgLeQ\\u002fTfnYcp4j5D+Ymx4bqDrkPwA94PqxK+Q\\u002fSPdFr94Z5D+ADlbOdCXkP+p5VZA8PuQ\\u002fVBlikZsx5D9U3xujLkTkP4p+uBmNDuQ\\u002fyIQHcukq5D\\u002fJpRJPKBnkP\\u002f2IqMpdE+Q\\u002fHGTdg4cm5D8tQs1PjjHkP16fxyVFF+Q\\u002fBeY37sMn5D+I4SgYCD\\u002fkP8zJNQ8xG+Q\\u002f33FCJbow5D+spRWUOCHkP0352HKeI+Q\\u002fXeoVDKk65D\\u002f\\u002fw5TrCUnkP0j3Ra\\u002feGeQ\\u002fgA5WznQl5D+5rFDEsyrkP\\u002f9R4PqcMeQ\\u002fVN8boy5E5D+KfrgZjQ7kP8iEB3LpKuQ\\u002fyaUSTygZ5D\\u002f9iKjKXRPkPxxk3YOHJuQ\\u002fLULNT44x5D9en8clRRfkPwXmN+7DJ+Q\\u002fiOEoGAg\\u002f5D\\u002fMyTUPMRvkP99xQiW6MOQ\\u002frKUVlDgh5D9N+dhyniPkP13qFQypOuQ\\u002f\\u002f8OU6wlJ5D9I90Wv3hnkP4AOVs50JeQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP1TfG6MuROQ\\u002fin64GY0O5D\\u002fIhAdy6SrkP8mlEk8oGeQ\\u002fRUHeAco45D9zW\\u002fFIhibkPy1CzU+OMeQ\\u002fXp\\u002fHJUUX5D8ij6tIMRDkP4jhKBgIP+Q\\u002fzMk1DzEb5D\\u002ffcUIlujDkP6ylFZQ4IeQ\\u002fTfnYcp4j5D9MUSfeqTrkP\\u002f\\u002fDlOsJSeQ\\u002fSPdFr94Z5D+ADlbOdCXkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D9U3xujLkTkP4p+uBmNDuQ\\u002fyIQHcukq5D\\u002fJpRJPKBnkP99Aw2nOKeQ\\u002fc1vxSIYm5D8tQs1PjjHkP16fxyVFF+Q\\u002fIo+rSDEQ5D\\u002f27AZUZTLkP8zJNQ8xG+Q\\u002f33FCJbow5D+spRWUOCHkP0352HKeI+Q\\u002fTFEn3qk65D\\u002f\\u002fw5TrCUnkP0j3Ra\\u002feGeQ\\u002fgA5WznQl5D+5rFDEsyrkP\\u002fy\\u002fZxDBIeQ\\u002f81dXwRRH5D8nr4bNGBPkP8iEB3LpKuQ\\u002fD4WWlrEy5D\\u002ffQMNpzinkP7lmB4qbKOQ\\u002fLULNT44x5D9en8clRRfkPyKPq0gxEOQ\\u002fR+cG\\u002fLo05D\\u002femrQ+MRvkP99xQiW6MOQ\\u002frKUVlDgh5D9N+dhyniPkP8T4ChWEQ+Q\\u002fE1VG3wZN5D9ERZitKirkP\\u002fNsz6DTJOQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP\\u002fNXV8EUR+Q\\u002fJ6+GzRgT5D\\u002fIhAdy6SrkPw+FlpaxMuQ\\u002f30DDac4p5D+5ZgeKmyjkPy1CzU+OMeQ\\u002fXp\\u002fHJUUX5D8ij6tIMRDkP0fnBvy6NOQ\\u002f3pq0PjEb5D\\u002ffcUIlujDkP6ylFZQ4IeQ\\u002fTfnYcp4j5D8nQAJeg0PkPxNVRt8GTeQ\\u002fREWYrSoq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D\\u002fu6t7woUfkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D8khVx14BzkPymV1LDKLOQ\\u002fuWYHipso5D8tQs1PjjHkP16fxyVFF+Q\\u002fz47boJoU5D9H5wb8ujTkP96atD4xG+Q\\u002f33FCJbow5D+spRWUOCHkP0352HKeI+Q\\u002fJ0ACXoND5D8TVUbfBk3kPxdKQbhcMeQ\\u002f82zPoNMk5D+5rFDEsyrkP\\u002fy\\u002fZxDBIeQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fVWG6cnIn5D8pldSwyizkP7lmB4qbKOQ\\u002fbEOJLI0x5D9en8clRRfkP63yTqQAHuQ\\u002fqkPDh1Ys5D\\u002femrQ+MRvkP99xQiW6MOQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fE1VG3wZN5D8XSkG4XDHkP\\u002fNsz6DTJOQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP+7q3vChR+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkPx24pM1xJ+Q\\u002fKZXUsMos5D+17gL3mSjkP2xDiSyNMeQ\\u002fXp\\u002fHJUUX5D8tKRalXBvkP6pDw4dWLOQ\\u002f3pq0PjEb5D\\u002ffcUIlujDkPyJ4xldUJ+Q\\u002fTfnYcp4j5D8nQAJeg0PkPxNVRt8GTeQ\\u002fF0pBuFwx5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D\\u002fu6t7woUfkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D8duKTNcSfkPymV1LDKLOQ\\u002fyxeUKoQh5D9sQ4ksjTHkPztW6fs4G+Q\\u002fLSkWpVwb5D81H1eV2CvkP8XHh8jeLuQ\\u002fhmni0yUt5D8ieMZXVCfkP0352HKeI+Q\\u002fJ0ACXoND5D8TVUbfBk3kP+Nn53PUKuQ\\u002f82zPoNMk5D+5rFDEsyrkP\\u002fy\\u002fZxDBIeQ\\u002f7ure8KFH5D+RolFmfxHkP8iEB3LpKuQ\\u002fHbikzXEn5D8pldSwyizkP8sXlCqEIeQ\\u002fbEOJLI0x5D87Vun7OBvkPy0pFqVcG+Q\\u002fNR9Xldgr5D\\u002fFx4fI3i7kP4Zp4tMlLeQ\\u002fInjGV1Qn5D9N+dhyniPkPydAAl6DQ+Q\\u002fE1VG3wZN5D\\u002fjZ+dz1CrkP\\u002fNsz6DTJOQ\\u002fuaxQxLMq5D\\u002f8v2cQwSHkP+7q3vChR+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkPx24pM1xJ+Q\\u002fn5+sOMss5D\\u002fLF5QqhCHkPwG8HmONMeQ\\u002fY4Z7abAr5D8tKRalXBvkPzUfV5XYK+Q\\u002fxceHyN4u5D+GaeLTJS3kPxrCKnZWJ+Q\\u002fTfnYcp4j5D8nQAJeg0PkPxNVRt8GTeQ\\u002fAiPs5pUq5D\\u002fzbM+g0yTkP7msUMSzKuQ\\u002f\\u002fL9nEMEh5D\\u002fu6t7woUfkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D8duKTNcSfkP5EBOyPRPeQ\\u002fyxeUKoQh5D9KhJSpjjHkP2OGe2mwK+Q\\u002fLSkWpVwb5D\\u002fgo7NLay\\u002fkP8XHh8jeLuQ\\u002fhmni0yUt5D8awip2VifkP0352HKeI+Q\\u002fJ0ACXoND5D8TVUbfBk3kP5UtlTEBLeQ\\u002f82zPoNMk5D+5rFDEsyrkP9C+1yMuKeQ\\u002fM5MSb6dM5D+RolFmfxHkP8iEB3LpKuQ\\u002fzxWO1hYV5D9q2vxyUSHkPy\\u002fbQ5OEIeQ\\u002fSoSUqY4x5D9jhntpsCvkPy0pFqVcG+Q\\u002fPQ0TeH0w5D\\u002fFx4fI3i7kP4Zp4tMlLeQ\\u002fGsIqdlYn5D9N+dhyniPkPydAAl6DQ+Q\\u002fE1VG3wZN5D+VLZUxAS3kP7OM3LWVIuQ\\u002fuaxQxLMq5D\\u002fQvtcjLinkP1KBtUUfP+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkP88VjtYWFeQ\\u002fatr8clEh5D8v20OThCHkP0qElKmOMeQ\\u002fY4Z7abAr5D8tKRalXBvkPz0NE3h9MOQ\\u002fxceHyN4u5D\\u002f0RmBPPy\\u002fkPxrCKnZWJ+Q\\u002fTfnYcp4j5D+B0bhwSzPkPxNVRt8GTeQ\\u002fTERCPnIz5D+zjNy1lSLkP7msUMSzKuQ\\u002fVn1mSA425D9SgbVFHz\\u002fkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D\\u002fPFY7WFhXkP2ra\\u002fHJRIeQ\\u002fL9tDk4Qh5D9KhJSpjjHkP2OGe2mwK+Q\\u002fLSkWpVwb5D+mHhWV9znkP8XHh8jeLuQ\\u002f9EZgTz8v5D9j+Sj47ETkP0352HKeI+Q\\u002fgdG4cEsz5D8TVUbfBk3kP0xEQj5yM+Q\\u002fs4zctZUi5D+5rFDEsyrkP1Z9ZkgONuQ\\u002fUoG1RR8\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fzxWO1hYV5D9q2vxyUSHkP3hIG0qEIeQ\\u002fC74mMmc85D9jhntpsCvkPy0pFqVcG+Q\\u002fph4Vlfc55D\\u002fFx4fI3i7kP\\u002fRGYE8\\u002fL+Q\\u002fY\\u002fko+OxE5D9N+dhyniPkP4HRuHBLM+Q\\u002fXZS\\u002fgbpF5D9MREI+cjPkP7OM3LWVIuQ\\u002fuaxQxLMq5D9WfWZIDjbkP9wz6BAgP+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkP88VjtYWFeQ\\u002fatr8clEh5D+T62SrMjHkPwu+JjJnPOQ\\u002fY4Z7abAr5D8tKRalXBvkP6YeFZX3OeQ\\u002fxceHyN4u5D9MgC8yJhbkPxE\\u002fRv5QNeQ\\u002fTfnYcp4j5D+B0bhwSzPkP1ZGUojSQOQ\\u002fkPZrN4Ay5D+y038FZyfkP7l0wZ7IN+Q\\u002fZSv8RXwi5D\\u002fcM+gQID\\u002fkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D\\u002fPFY7WFhXkP7RsZBdRIeQ\\u002fk+tkqzIx5D8LviYyZzzkP2OGe2mwK+Q\\u002fLSkWpVwb5D9Ki8mfmy3kP8XHh8jeLuQ\\u002fc1KKC7Us5D9OltluhTTkP0352HKeI+Q\\u002fgdG4cEsz5D9WRlKI0kDkP5D2azeAMuQ\\u002fstN\\u002fBWcn5D+5dMGeyDfkP2Ur\\u002fEV8IuQ\\u002f3DPoECA\\u002f5D+RolFmfxHkP8iEB3LpKuQ\\u002fzxWO1hYV5D+efJcYpijkP5PrZKsyMeQ\\u002fC74mMmc85D9jhntpsCvkPyaoiSPZD+Q\\u002fSovJn5st5D\\u002fFx4fI3i7kP9xFRCkYL+Q\\u002fd5qs4To25D9N+dhyniPkP4HRuHBLM+Q\\u002fVkZSiNJA5D+Q9ms3gDLkP8xalUyGPOQ\\u002fuXTBnsg35D+87w4wfCLkP9wz6BAgP+Q\\u002fkaJRZn8R5D\\u002fIhAdy6SrkP88VjtYWFeQ\\u002f8H007n4P5D+T62SrMjHkPwu+JjJnPOQ\\u002fZLEJU7Er5D8mqIkj2Q\\u002fkPyAttWeCHeQ\\u002fxceHyN4u5D\\u002fcRUQpGC\\u002fkP3earOE6NuQ\\u002fTfnYcp4j5D\\u002fUyPSNbRXkP6QgDejRQOQ\\u002fr0nIRoIy5D\\u002fMWpVMhjzkP7l0wZ7IN+Q\\u002f2utkHH0i5D\\u002fcM+gQID\\u002fkP5GiUWZ\\u002fEeQ\\u002fyIQHcukq5D\\u002fPFY7WFhXkP9ssbwDoKOQ\\u002fk+tkqzIx5D8LviYyZzzkP3sUeBmRLOQ\\u002fJqiJI9kP5D8gLbVngh3kP8XHh8jeLuQ\\u002f3EVEKRgv5D93mqzhOjbkP0352HKeI+Q\\u002f1Mj0jW0V5D+kIA3o0UDkP69JyEaCMuQ\\u002fSqaFL4Y85D+5dMGeyDfkP2aIdy59IuQ\\u002fnY8J1qEr5D+RolFmfxHkP\\u002fE9CsTcJuQ\\u002fzxWO1hYV5D\\u002fbLG8A6CjkP8\\u002fmYDQvMeQ\\u002fC74mMmc85D97FHgZkSzkP9l\\u002flnpVGuQ\\u002fIC21Z4Id5D\\u002fFx4fI3i7kP9xFRCkYL+Q\\u002fXBMlaYgg5D9N+dhyniPkP9TI9I1tFeQ\\u002fpCAN6NFA5D\\u002fzQ2QVfzDkP0qmhS+GPOQ\\u002fqRnJXUIi5D\\u002fK1S2KfiLkP52PCdahK+Q\\u002fkaJRZn8R5D8rombmw0PkP88VjtYWFeQ\\u002fMrD+1WYp5D\\u002fP5mA0LzHkP5xZl89nPOQ\\u002fexR4GZEs5D\\u002f88jRAHjPkPyAttWeCHeQ\\u002fxceHyN4u5D\\u002fcRUQpGC\\u002fkP7DurtQOLeQ\\u002fTfnYcp4j5D8EUJ465yzkP3fUgK6lMuQ\\u002f80NkFX8w5D9KpoUvhjzkP6kZyV1CIuQ\\u002fytUtin4i5D+djwnWoSvkP5GiUWZ\\u002fEeQ\\u002faz34m4885D8fr4nGFhXkPzKw\\u002ftVmKeQ\\u002fz+ZgNC8x5D\\u002fXdtqqdS7kP3sUeBmRLOQ\\u002f\\u002fPI0QB4z5D8\\u002fHALEbBzkP8XHh8jeLuQ\\u002f3EVEKRgv5D+w7q7UDi3kP0352HKeI+Q\\u002fBFCeOucs5D931ICupTLkP\\u002fNDZBV\\u002fMOQ\\u002fSqaFL4Y85D+fUI896inkPwM\\u002f4JBwF+Q\\u002fvgYyc1Ir5D+RolFmfxHkP2s9+JuPPOQ\\u002fH6+JxhYV5D8ysP7VZinkP8\\u002fmYDQvMeQ\\u002flIILXsww5D97FHgZkSzkP4Tv5AFhP+Q\\u002f6Ng5+Gct5D\\u002fFx4fI3i7kP9xFRCkYL+Q\\u002fsO6u1A4t5D9N+dhyniPkPwRQnjrnLOQ\\u002fJ0Nsw6gy5D\\u002fvGA2wVjfkP0gBniy9OOQ\\u002fn1CPPeop5D8DP+CQcBfkP74GMnNSK+Q\\u002fkaJRZn8R5D9rPfibjzzkPx+vicYWFeQ\\u002fMrD+1WYp5D92R9ZoMDHkPzoLCxWGPuQ\\u002fexR4GZEs5D\\u002f\\u002fxxCD0zPkPwYcjuxnLeQ\\u002fxceHyN4u5D\\u002fcRUQpGC\\u002fkP7DurtQOLeQ\\u002fTfnYcp4j5D8EUJ465yzkP1kavgWqMuQ\\u002f7xgNsFY35D9IAZ4svTjkP59Qjz3qKeQ\\u002fAz\\u002fgkHAX5D+Ixd4HICzkP59k3L5gMOQ\\u002faz34m4885D8fr4nGFhXkPzKw\\u002ftVmKeQ\\u002f\\u002fHouCjEx5D86CwsVhj7kP3sUeBmRLOQ\\u002f\\u002f8cQg9Mz5D8GHI7sZy3kPyp28cO+LOQ\\u002f3EVEKRgv5D+w7q7UDi3kP8UUjwHOK+Q\\u002fBFCeOucs5D9ZGr4FqjLkP+8YDbBWN+Q\\u002fSAGeLL045D+fUI896inkPwM\\u002f4JBwF+Q\\u002fiMXeByAs5D+fZNy+YDDkP2s9+JuPPOQ\\u002fH6+JxhYV5D8ysP7VZinkP7BChW0RL+Q\\u002fOgsLFYY+5D97FHgZkSzkP\\u002f\\u002fHEIPTM+Q\\u002fBhyO7Gct5D8qdvHDvizkP9xFRCkYL+Q\\u002fsO6u1A4t5D\\u002fFFI8BzivkP\\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-       "    }\n",
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-       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
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-       "        legendButton.className = 'modebar-btn';\n",
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-       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
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-       "        legendButton.innerHTML = [\n",
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-       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
-       "            + ' aria-hidden=\"true\">',\n",
-       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
-       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
-       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
-       "            '</svg>',\n",
-       "        ].join('');\n",
-       "\n",
-       "        legendButtonGroup.appendChild(legendButton);\n",
-       "        modebar.appendChild(legendButtonGroup);\n",
-       "    }\n",
-       "\n",
-       "    legendButton.onclick = toggleLegend;\n",
-       "    legendButton.onkeydown = function (event) {\n",
-       "        if (event.key === 'Enter' || event.key === ' ') {\n",
-       "            toggleLegend(event);\n",
-       "        }\n",
-       "    };\n",
-       "\n",
-       "    syncLegendVisibility();\n",
-       "};\n",
-       "\n",
-       "if (graphDiv.on) {\n",
-       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
-       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
-       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
-       "            syncLegendVisibility(eventData.showlegend);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        syncLegendVisibility();\n",
-       "    });\n",
-       "}\n",
-       "syncLegendVisibility();\n",
-       "window.requestAnimationFrame(installLegendToggleButton);\n",
-       "}\n",
-       "                        })                };            </script>        </div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "for param in project.free_parameters:\n",
     "    project.display.plotter.plot_param_distribution(param)"
@@ -4136,7 +677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56",
+   "id": "55",
    "metadata": {},
    "source": [
     "Finally, the posterior predictive plot propagates the sampled parameter\n",
@@ -4147,236 +688,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "57",
+   "execution_count": null,
+   "id": "56",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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qWh0hZUBiuFUtmiBlQH6\\u002f0JUXIGVAp23QkZUfZUD1shUfFB9lQFeSbTuTHmVAqNiw5BIeZUDe1cMYkx1lQFgYltUTHWVAKSoiGZUcZUBXUG3hFhxlQP1LhyyZG2VASB2K+BsbZUAuyJlDnxplQPAa5AsjGmVANnagT6cZZUDclg8NLBllQEZhe0KxGGVARa427jYYZUByGZ0OvRdlQADREqJDF2VA+WYEp8oWZUDSo+YbUhZlQFdaNv\\u002fZFWVA6jx4T2IVZUDxszgL6xRlQJ61CzF0FGVAy56Mv\\u002f0TZUAUDV61hxNlQA+6KRESE2VAnFeg0ZwSZUBZbXn1JxJlQBY3c3uzEWVAWYRSYj8RZUDxmOKoyxBlQGkO9U1YEGVAnrZhUOUPZUAmfwavcg9lQLpVx2gAD2VAhg2OfI4OZUBWRUrpHA5lQMNO8a2rDWVAHhZ+yToNZUBNC\\u002fE6ygxlQIILUAFaDGVAtUumG+oLZUAPRASJegtlQBScf0gLC2VAoRczWZwKZUC9hD66LQplQDaqxmq\\u002fCWVACjf1aVEJZUCcsvi24whlQLFtBFF2CGVASHRQNwkIZUAegBlpnAdlQCXsoOUvB2VAqqgsrMMGZUBVMAe8VwZlQP99fxTsBWVARAPptIAFZUAIoJucFQVlQLqa88qqBGVAeZlRP0AEZUAhnBr51QNlQCP3t\\u002fdrA2VAX0+XOgIDZUDZlirBmAJlQF8K6IovAmVAMzBKl8YBZUCq18\\u002flXQFlQNUZ\\u002fHX1AGVAO1tWR40AZUCqTmpZJQBlQC75x6u9\\u002f2RAKrcDPlb\\u002fZEC9QrYP7\\u002f5kQF27fCCI\\u002fmRAz674byH+ZEB\\u002fI9D9uv1kQEikrclU\\u002fWRAyk1A0+78ZEBR3TsaifxkQGjBWJ4j\\u002fGRAOCxUX777ZECwJ\\u002fBcWftkQLmr85b0+mRAabYqDZD6ZEBsZma\\u002fK\\u002fpkQL0Xfa3H+WRAz4JK12P5ZEBW3q88APlkQNEDlN2c+GRA\\u002fJbjuTn4ZEBwMJHR1vdkQIeLlSR092RA47fvshH3ZECuTqV8r\\u002fZkQPOrwoFN9mRARCxbwuv1ZEATb4k+ivVkQPmdb\\u002fYo9WRAYrk36sf0ZEDn6hMaZ\\u002fRkQOTdPoYG9GRAox78LqbzZEC0gJgURvNkQPyMajfm8mRAEPjSl4byZECIIT02J\\u002fJkQAidHxPI8WRA0sX8LmnxZECjXWOKCvFkQPc37yWs8GRAn\\u002fJJAk7wZEDavCsg8O9kQEUuXICS72RA7C6zIzXvZEAP8hkL2O5kQFgFjDd77mRARnYYqh7uZED2D+Njwu1kQISzJWZm7WRAh80xsgrtZECF63FJr+xkQGJ0ay1U7GRAY4fAX\\u002fnrZEBxBTLinutkQATJobZE62RAfxEV3+rqZEA2J7ddkepkQEA+3DQ46mRAp54EZ9\\u002fpZECJGeD2hulkQJjTUecu6WRAeW50O9foZECzm572f+hkQFMlaBwp6GRA33mvsNLnZEAgy5+3fOdkQFjRtzUn52RA3kbRL9LmZEDeMimrfeZkQEEdaa0p5mRAXEmxPNblZECHGaRfg+VkQJTCch0x5WRAI3zrfd\\u002fkZECkYYmJjuRkQOM\\u002fhkk+5GRAkpTux+7jZEBLELgPoONkQOj42ixS42RAp9luLAXjZEA8AcscueJkQCdkqw1u4mRAaJRaECTiZEBfn+E32+FkQF3JPpmT4WRAvkykS03hZEDpesBoCOFkQDfiEA3F4GRAm29CWIPgZEBLZ0FxQ+BkQJTaxoAF4GRAy9X5rsnfZECDPnYtkN9kQEIUITRZ32RA3YMXAiXfZEAEE9De895kQA96dhvG3mRAAEeLFJzeZECNClYxdt5kQFGh2tpU3mRAXtL\\u002frDjeZECVOCFJIt5kQLHAtWoS3mRAEjCI7AneZEC9xDDPCd5kQBGtKkET3mRAusX9qCfeZEDb1h2ySN5kQE8fWV143mRA8Ev\\u002fFbneZEBrWlrNDd9kQHpDtB5632RAdPAQfwLgZEAtfjF9rOBkQMZQ\\u002fhh\\u002f4WRApYdZP4PiZECHJWqJxONkQMfVK5xS5WRABN\\u002fzTkTnZEBDDW0FwOlkQGgPrCIS7WRAqedWw+TxZEAI7buJjflkQPDD5JMQB2VAMQunf2QgZUANIzDJV09lQAaEvVdjoWVA9ofVTX4kZkA0rqfnQOBmQKs3p034zGdAmINzOO3KaECN1uCcQKFpQNXk7mfVD2pA41UAxcHqaUCnQgnGKUJpQL0qPrH6UGhAcF83gBhVZ0AViIpRs3tmQBswDtdr2WVAdoDyHuRtZUArzEioci1lQCZ3ENS6CWVAnnMERID2ZEBl5cCC1utkQPYUbaJD5WRA4WaHSsHgZECcYSFmV91kQOdOxTiV2mRA5KIv30XYZECl9klnTNZkQKGjECmW1GRAK87awhXTZECub68WwdFkQF\\u002fYz0yQ0GRAyJkSOn3PZEAYN8j2gs5kQKgN+JKdzWRAUJB73snMZED\\u002f6C0\\u002fBcxkQNycSpFNy2RA9LRCD6HKZEAOdBk\\u002f\\u002fslkQEl75ONjyWRAa1xs8tDIZEAHUi+IRMhkQDT9OOS9x2RAOotlYTzHZEAwjMBxv8ZkQDvTwppGxmRATN9ActHFZEAc4uWbX8VkQIx3H8fwxGRA8jBkrYTEZEAgvMMQG8RkQJ4Bs7qzw2RAAFsJe07DZEC6KyYn68JkQA3ZN5mJwmRA9XOerynCZEDTd2VMy8FkQNLY0FRuwWRAkEr6sBLBZEAeM3xLuMBkQB0vKBFfwGRAhmfH8AbAZEAmROPar79kQAVDlcFZv2RATO9cmAS\\u002fZEBuG\\u002ftTsL5kQMWkUepcvmRAUCNHUgq+ZEDw\\u002f62DuL1kQFx\\u002fLndnvWRApmAzJhe9ZEA7vNiKx7xkQAHc3J94vGRAVM+SYCq8ZEAChtbI3LtkQLBEAtWPu2RAT0vlgUO7ZEBPi7vM97pkQC5QJrOsumRAdsAlM2K6ZEDxIBNLGLpkQGFxV\\u002fnOuWRA6KTaOIa5ZECuEkgNPrlkQCi4Lnb2uGRA4CNjc6+4ZECH+PwEabhkQNXVVCsjuGRA+J8C5923ZECTH9w4mbdkQCD18yFVt2RAfduYoxG3ZEAlNlW\\u002fzrZkQEro7naMtmRA0XJnzEq2ZECdV\\u002fzBCbZkQGTBJ1rJtWRAuG+hl4m1ZECO5199SrVkQBjpmQ4MtWRASSzITs60ZED1ZadBkbRkQAaYOutUtGRA3a\\u002fNTxm0ZECNdvhz3rNkQDzXoVyks2RAsIADD2uzZEDV562QMrNkQK1Dguj6smRAe1JQIsSyZEBJE4k+jrJkQIq+TERZsmRAI04tOyWyZECldTUr8rFkQBU\\u002f8BzAsWRA7VpxGY+xZEDnM14qX7FkQAvZ91kwsWRApNIlswKxZEA9+YFB1rBkQJVoZRGrsGRAeqv2L4GwZEA7QTmrWLBkQDGhHpIxsGRAmOWY9AuwZEABTK\\u002fj569kQK+\\u002flHHFr2RA26jAsaSvZECjQwq5ha9kQFDKxp1or2RAsMjqd02vZEAh+i5hNK9kQFohOXUdr2RApVjJ0QivZEBoa+yW9q5kQNHeM+fmrmRABmj059muZEC2qovBz65kQFY8rp\\u002fIrmRAdw+\\u002fscSuZECDmDErxK5kQHiZ8UPHrmRAFc6zOM6uZECRhidM2a5kQAGy6MbormRAtJZu+PyuZEC7ANk3Fq9kQG4e2uQ0r2RANrfBaFmvZEBkSK83hK9kQPui8dG1r2RAtO6bxe6vZEAwiVmwL7BkQFsljEF5sGRAXe7BPMywZEAf4iM+tLFkQD4wI3mnsmRAU5IZAaezZEAGH2AMtLRkQG3kz\\u002fnPtWRAQ6RLV\\u002fy2ZEAnraTEOrhkQPCWNFWNuWRAuNYfZ\\u002fa6ZEBGwlyieLxkQE3IPxgXvmRApMWzV9W\\u002fZEAyHBuGt8FkQImpHH7Cw2RA8AYF9vvFZECsE+6vashkQM9im7YWy2RAYiX\\u002fqgnOZEALCMgnT9FkQDELXEL11GRAJ0uFMw3ZZEBwYEE2rN1kQNcnULHs4mRA6m28ye\\u002foZEDS5NaN3+9kQDQZ+hDy92RAsIkgUW4BZUDNzMWBtQxlQNywqyxZGmVAYVzmW1UrZUDfoJ7kuEFlQON0KGqqYWVAJD+H0\\u002f6UZUCqUAakR\\u002fFlQPq3\\u002f6mQnmZAMtBO8lzcZ0BPejsm+vppQLKxi6ckQG1AzlPKsI7bcEACYXLgbX1zQD58R7mj\\u002fnVAim2KbAuxd0BxiIvOK\\u002ft3QDNSUZv2tHZA4D6Ts1RodEDyUaO+M71xQAKrkYgBqW5AZJRtF0H2akBDkCa4MXhoQI7iGBlI9mZAhC5dOv0fZkCgwFDoYa5lQPyYPT9YcWVAi1aCPH1NZUDWtKta7TVlQGvxVRXOJGVAmlj0ZH8XZUCFgZeNywxlQH0GGD4HBGVA\\u002fA2Hc8X8ZEDcHNh6uPZkQIgmn5Gl8WRA06GFYl\\u002ftZEAGf+hxwulkQC7+u6qy5mRAVUEHnBnkZEB3uygu5eFkQJlBrawG4GRA6oMBDXLeZEDKAIBhHd1kQNiYM20A3GRA9p\\u002f3TxTbZEAee+xEU9pkQOgz12642WRA9\\u002fEprz\\u002fZZEBWwkeF5dhkQB1YMPSm2GRA234xbYHYZEBSjY++cthkQLl5VAV52GRAlAekoZLYZEANABctvthkQMd0unL62GRAQO2KCkbZZEDNnphvoNlkQOF+bOAI2mRAKc98tH7aZEA0uJ5cAdtkQI3N8Y6Q22RABPDv7CvcZEDTaTfs0txkQGCvi0uF3WRAyTmp2ELeZEDW+RFvC99kQCLFC\\u002ffe32RAKjPKZL3gZECqgr63puFkQDoRCfqa4mRAR7kIQJrjZEBjFAWopORkQFIw8Vm65WRA78BEh9vmZEDtQ+pqCOhkQETpQElB6WRAVmYxcIbqZEAIJVU32OtkQOCELwA37WRA3CR5NqPuZEBLan1QHfBkQBRkOM+l8WRAbDB6Pz3zZEDMOIY65PRkQJiKGmab9mRAXMiEdWP4ZEDEu34qPfpkQPZpJVYp\\u002fGRADPwM2ij+ZEAvKnSpPABlQMc\\u002fmsplAmVApUM7WKUEZUDHUjaD\\u002fAZlQNzRYpRsCWVAF8SZ7vYLZUBxWfkQnQ5lQGCmaZlgEWVA+XlqR0MUZUA+cjP\\u002fRhdlQA3JMM1tGmVALufo6bkdZUABoVi+LSFlQLQl1ujLJGVA\\u002fi2NQpcoZUCb7KnlkixlQEe\\u002fSzTCMGVAJr5c4Cg1ZUC2KnD0yjllQAGPz92sPmVAb0zld9NDZUBwpTsZREllQGdCUaIET2VAvTGQjhtVZUCu48IHkFtlQNY6dPxpYmVARTi9ObJpZUDS\\u002fByJcnFlQLQCGNO1eWVATxiDR4iCZUDivI+M94tlQDzQVNwSlmVAto3HoOqgZUAZfqMdk6xlQCVkAl4iuWVA+eoEbbHGZUDman3WXNVlQPqtaEFF5WVADO5FKpD2ZUBn1gTFaAlmQC1daBABHmZAcGKVJ5M0ZkBhm0viYk1mQFB0V9m\\u002faGZAM2Si6weHZkDiIplpqqhmQM8XcRkszmZADh6lWCz4ZkAfMAC4aydnQF4LBJPUXGdAjARmVoaZZ0Bt6XZz5N5nQAZIS2yqLmhAz99kDweLaEDFjV0nwvZoQE7SrKVgdWlAr1f957YLakA+1JzFS8BqQFe7jPW7nGtAygu5692xbEBI3c3bnSJuQOYyjvf0G3BALwD0W0vQcUBQeCTwhut0QIhwtnbYzHpAZLb10kjMgkDD0e4k7fWLQDi\\u002fkfF89ZRA41BX0BJLnkDMf\\u002fbCW2+kQODBjTt0JqlA7PMnZHLiq0C8vYwYl42rQAwW1yNuSKhA\\u002fURiSD9So0A\\u002fbN52uSScQF0dfP9sRZNAwR3iVrGgiUDgsWYGj1aBQMYChPoEFXlA+cggv\\u002fHpc0APUF7hLy9xQCFp9OawU29AxkaXbuFvbUDU8I+7jR5sQItEFz6DHWtAEP371EtQakCXwV9uIqhpQLt9Y+w+HGlAa6WQ6p+maECuSVkw10JoQI5Hj8OC7WdArn55TQykZ0AP+glDZWRnQKbP7pnrLGdAPVebO1D8ZkBbwSwuhNFmQHNEAYSqq2ZABzZavg2KZkCjA3a1F2xmQNdWs11LUWZAxfHo8D85ZkAVKk8knSNmQM0DwioYEGZAJpDVU3H+ZUD1MT4kcu5lQAZoz8zr32VAem\\u002fB67XSZUBbPbGIrcZlQNUPXT+0u2VA9pbNj6+xZUBb8KNMiKhlQF8w0iEqoGVAvv41L4OYZUBJlXmzg5FlQH\\u002fsV8Qdi2VA3XvxEUWFZUBu8FCz7n9lQLyUmfoQe2VAFSOhT6N2ZUBWY\\u002fAPnnJlQFxeVHP6bmVAqXtPdLJrZUCIYNi7wGhlQBxU7Y8gZmVANGCXxM1jZUDTRgmvxGFlQDlklBoCYGVA6EI5P4NeZUDPdqO5RV1lQB2haIRHXGVA85Np8oZbZUC6qDqqAltlQH2wfaK5WmVARJ4cH6taZUANTFiv1lplQKGjoiw8W2VAZ3dW2NpbZUAELmLNs1xlQJ\\u002fSeffHXWVA3F0UbRhfZUDBg4Xvp2BlQHP59LB2YmVAPMXOtIZkZUAmwrxc2mZlQEF9gW90aWVAP+8cIVhsZUBT5mQciW9lQMGoQ44Lc2VARAvoMOR2ZUB2XOReGHtlQNr69SSuf2VAlvEkU6yEZUDPq72WGoplQCr0h5YBkGVAHcLBE2uWZUBjlsEQYp1lQKBmVv7ypGVASsQ58SutZUBufjfhHLZlQJMIEfTXv2VA\\u002f3eg1nHKZUBs6V4nAtZlQFFNP\\u002faj4mVAJ1HXXnbwZUCG5idDnf9lQLxtGC9CEGZAr4QHb5UiZkDS8+VmzzZmQPuoajsyTWZAY0d35AtmZkD5DkcUuIFmQNymPEChoGZAMKHYhU7DZkCZ46fkXupmQDqGQpKTFmdA8+ueHNlIZ0C4uPF+U4JnQJtiZ0luxGdA+jZ1fvIQaEAxWXecJGpoQHfJj+ru0mhA8N4t5vtOaUDnHR4nu+NpQLVMxKVImGpAh+ZZhEV4a0B+96McKZpsQMywmeNMLm5AvO1RzmhOcEDCzxrDVXByQFKaDdYidXZAglQsX2cCfkCbFTwNBayFQNU\\u002fvvuDKZBAMKhaKmCsl0ACDU8tlWCgQMbvrt7oyaRAR6oNWGHap0DTY7XAO2KoQD4QWxccKaZA83lWa7cYokBIf0y\\u002fAfCaQD0WuZnxuZJAGADv4d4kiUA7QkClxRGBQL08Jya6p3hAnPy0bWCDc0B9R14Cos5wQH7h2vYJom5AKVWLFivSbEBckY61DZNrQG91lIvqoWpAcH77Al7iaUBz1Z\\u002fA8UVpQKpKPcEhxGhAwajHqy1XaED0r2Du2PpnQJpEWJb6q2dATDn0Vh5oZ0BF2YI1VS1nQMIF5GcX+mZA5bmzvivNZkA0\\u002fw6XlaVmQMNo62yHgmZAKqBywVhjZkBkYcptfkdmQO7FxL6ELmZAQoue5AoYZkCZR1hivwNmQIR1\\u002fD5d8WVA\\u002fBJSy6ngZUAoxBnactFlQGCPYFGNw2VA4aueAdS2ZUCLouezJqtlQOBJ1mNpoGVANwtsnIOWZUBg\\u002fwjxX41lQOgiG43rhGVAuw1E1hV9ZUDoCpQd0HVlQL0sJ10Nb2VA6OP4\\u002f8FoZUAlji2y42JlQM4IZjhpXWVA8Jf0TEpYZUApNgCCf1NlQLuOzicCT2VACLmPNsxKZUDpByM72EZlQEw5Y0YhQ2VAphmb3qI\\u002fZUA7F9PyWDxlQI3ytM8\\u002fOWVAmTfNFVQ2ZUB84fqwkjNlQGzL5ND4MGVAyqJS4oMuZUCEJUyJMSxlQNzB5Zv\\u002fKWVAoU2lHewnZUB5oGw79SVlQAt\\u002f2kcZJGVAiIgUuFYiZUCVze4grCBlQNltZzQYH2VAIhxuv5kdZUD9w\\u002fCnLxxlQIHHJ+vYGmVACHMdnJQZZUA1X23iYRhlQNKQOfk\\u002fF2VAmURULi4WZUAKhZ7hKxVlQDT4nIQ4FGVAatJFmlMTZUBEngy3fBJlQKysMoGzEWVA4KNlsfcQZUDT+bgTSRBlQO1sC4mnD2VAfB3vCBMPZUBmPDKkiw5lQDrB7l4XDmVAm0ScsLANZUABGN4NWA1lQIFi+RgODWVAFkIuq9MMZUDvWubgqQxlQNb+rSmSDGVAqeFOXY4MZUCXwfHXoAxlQDQh\\u002f5\\u002fMDGVAYyG3mRUNZUBuZIDOgA1lQPo93+QUDmVAo62Iv9oOZUD\\u002fL1gI3g9lQBhn0dovEWVAfV1HLOwSZUBJl2ClSRVlQLqtl8jDGGVAEUZVg3weZUDh0BuN8ihlQBloQP3CPGVAE6GkurphZUCCDKcV\\u002fKFlQBVzTQIlBmZAPxMOM+KOZkAqfblowStnQOPdacJXumdA3Ucpy4kMaEBjrWCcqgBoQKnYTKSDnGdALWpbIFAGZ0CZoXHHu2tmQH336hIl62VAeBYdOmOPZUBm4YTuuVVlQIKgZ28BNWVAHncLfpcjZUDxnzHxXRplQK3FuiQhFWVAuUsHeccRZUB\\u002fVZWbYQ9lQPuAmJSJDWVAXZjqGREMZUDgJ\\u002f5s3wplQPR1B9PkCWVA30GGJRYJZUDSsfcRawhlQEx0wDrdB2VARZ2brmcHZUDGDY2LBgdlQJl3o7y2BmVAkYHTyXUGZUCUqHu0QQZlQFNr7NwYBmVAffFy7vkFZUA\\u002fXiXQ4wVlQLLjMpnVBWVA0LvTh84FZUC16TP6zQVlQPbf32jTBWVAUK5ZYt4FZUBwDZSH7gVlQDkkIYkDBmVAwQPvJB0GZUDw1XQkOwZlQLZ0OltdBmVAUjOppYMGZUBHgBjorQZlQFj+Cw7cBmVA1P+aCQ4HZUAlHfvSQwdlQCINKWh9B2VAuyvdzLoHZUBS7+oN\\u002fAdlQFXviDVBCGVA3iRaV4oIZUBMH3eM1whlQDfdmvMoCWVAvoNjsX4JZUAlwKfw2AllQHcg4uI3CmVACDi0wJsKZUBF+YPKBAtlQPZUNklzC2VAC+4Kj+cLZUAXkJ34YQxlQAweE+7iDGVAaep45GoNZUCo5l5f+g1lQFrntvKRDmVA+HEFRTIPZUCLAuwR3A9lQHxniSyQEGVA+hoRhU8RZUCK4vcqGxJlQAitnVL0EmVAAvHfW9wTZUBJLZfX1BRlQCxrW5HfFWVA1iXFmf4WZUAyM8VSNBhlQFAo5H6DGWVAmJnzU+8aZUAdARuSexxlQDfecaAsHmVABJy5sAcgZUD9\\u002fE3sEiJlQH+qGK1VJGVAUkVRxtgmZUCzPTbipillQH6q4vvMLGVAMFg5\\u002f1owZUAwmfqcZDRlQKDMLGcCOWVAyCclU1M+ZUCje+\\u002fMfkRlQAnz5aG3S2VAqX3sRkBUZUBELxTLcV5lQKpYHg\\u002fFamVAoGlt\\u002fQV6ZUDcdRmMqY1lQHDn57fEqGVAtD6ABb7SZUDm1rzaShtmQMxtr+Bdo2ZAtz79zd6iZ0BsYksYkmVpQI3iw2RKM2xAw4GROSoQcEB5Ya3x82NyQGM9r8OqmHRA6DQWrkAGdkBeTY5Ibw92QHJqgfbztHRAYDA1nMeGckDceamemy9wQNXdmc0YaWxAhmwfPLCLaUDlWxi2HrpnQJkpnhUbsGZA9vSFph0iZkBvFQRhB9dlQAnNnIukrGVAETXbFbyRZUDqP4+3i35lQKP22JnKb2VA6HIEnP5jZUC7xd0kXlplQCARfGRpUmVAj453A8ZLZUCwQTPSMEZlQPZn1Il2QWVA3pC1bG89ZUDEKZFR\\u002fDllQNrsV4YEN2VAIWyuR3Q0ZUCbyyigOzJlQGXqPpFNMGVAvZVacZ8uZUC3sZxwKC1lQNqyLTrhK2VAxbXSqsMqZUBgkoOXyillQMSUKKDxKGVAtjioCzUoZUBAgSarkSdlQC8C3sIEJ2VAxEBd94smZUD48js+JSZlQEH+kdHOJWVAovKiJYclZUDcsU\\u002fgTCVlQBaQ99EeJWVAxF+E7\\u002fskZUCj4mpN4yRlQIYEdBvUJGVADdEroc0kZUA60Nk6zyRlQInJ61bYJGVAsirAc+gkZUAus8Ad\\u002fyRlQA2\\u002fwO0bJWVA676Uhz4lZUBZM9qYZiVlQOb16NeTJWVAGsrnAsYlZUAcKv\\u002fe\\u002fCVlQJkQpjc4JmVAXisF3ncmZUB8b22ouyZlQDh933EDJ2VATaShGU8nZUB+q+KCnidlQJHDZ5TxJ2VAZ0hEOEgoZUAwJJpboihlQNvSYe7\\u002fKGVAfyc542ApZUAJFDgvxSllQKrNysksKmVA076QrJcqZUAZyz\\u002fTBStlQNp5izt3K2VAPqsP5esrZUCohz7RYyxlQMdkUQPfLGVAPGU8gF0tZUBFoKRO3y1lQHem2HZkLmVAbj\\u002fLAu0uZUBJQhD+eC9lQGpw23UIMGVA5T4BeZswZUCRf\\u002fkXMjFlQBzf42TMMWVAWzGOc2oyZUDpiHxZDDNlQNUa8y2yM2VAlfIBClw0ZUDofZIICjVlQOX7dka8NWVArN174nI2ZUD6Knv9LTdlQEUAcrrtN2VA8z+YPrI4ZUATlHqxezllQDLiFj1KOmVAZlf7DR47ZUBEN2hT9ztlQL+ddD\\u002fWPGVAV2k2B7s9ZUAZie3ipT5lQIfwMg6XP2VAv3oryI5AZUAEDr9TjUFlQMpZ1PeSQmVA5pKR\\u002f59DZUBEnKK6tERlQIYWhX3RRWVA\\u002fN3aofZGZUBxjcOGJEhlQGWsPZFbSWVAE0KQLJxKZUAEm73K5ktlQDQmAOU7TWVA22hS\\u002fJtOZUASJQOaB1BlQLHwVlB\\u002fUWVA\\u002fJ44uwNTZUDOCfqAlVRlQMT2JlM1VmVADwts7+NXZUAR\\u002fpMgolllQCSBnb9wW2VApqLstFBdZUDczZr5Ql9lQKvx6JhIYWVA58\\u002fXsWJjZUDJAOt4kmVlQEvUGzrZZ2VAPPIBWzhqZUCmbDhdsWxlQCftBuFFb2VAb8FXqPdxZUCb1QSayHRlQCwsh8W6d2VAfyUWZ9B6ZUAX+EXsC35lQJEmN\\u002flvgWVAuJlrbv+EZUDMYllvvYhlQFMm12mtjGVAheuCHtOQZUDFr0mqMpVlQOvZPJHQmWVAUbzqyrGeZUBTCnnQ26NlQHLdy6xUqWVAKhcSDyOvZUB6KyFgTrVlQNZrH9veu2VAPrEVqt3CZUB0+iAHVcplQHS\\u002fI2NQ0mVAQlgHk9zaZUBncNoECORlQCxuZf7i7WVAf1Et53\\u002f4ZUAjl1ag8wNmQKnAc+1VEGZAqWgQ8sEdZkDF7cfIVixmQMw6ADo4PGZAaDsPmo9NZkB6zMbYjGBmQDJ0T89ndWZAvhEh3WGMZkChvyjqx6VmQNhSS+v0wWZAHnY2EFXhZkBx1vHOaQRnQGuAtRnRK2dAkwtWE0hYZ0Cxat7ytIpnQIWiBrE0xGdAR5nLYykGaEAsspIjT1JoQApNzinWqmhAsVK+tIcSaUCHCvqQEI1pQGpdMW1DH2pAbESdkJrPakAepCTghKdrQOyfJxTGt2xAzeAvGkshbkBorowQXRZwQJe8ssSSwXFAgNIdHEbFdEDc7rXVDW16QB5c1gxoXoJAn4hw7v0Ei0DTufVXoO+TQMpH0Ta5N5xA73vs\\u002fKSUokC+NUJa+EumQFYD1a7GxqdAsXhnJu5VpkDmm5ENPLeiQAhwIHKPcZxAPH7dGxkXlECEpOuGvzSLQILRL0GOfYJAWfzvrA6MekBHNd2fM850QH7ex9M8wHFApP44pIEOcEBRsWN5qw1uQBB1zAnFo2xAZHFFAxOUa0BL9wq27bxqQBk81sHUDWpAI\\u002fSvtwd9aUCix8Ij5gNpQGoXEA6XnWhA3cpMWnFGaEAzWm20o\\u002ftnQNe5A4f7umdAUwfVF72CZ0DUK5bXhlFnQAVz12g8JmdAU6fiF\\u002ff\\u002fZkCIgNA3+t1mQFfEnFKqv2ZAfxmKxoakZkDwjMe6IoxmQM3q3oUkdmZAM2AarD9iZkDn25U0M1BmQM2gh6bHP2ZAV1DBbM0wZkDNz+2HGyNmQIgaP3+OFmZA07iWggcLZkCMViS0awBmQKUlrpGj9mVA2c1md5rtZUC8kX83PuVlQJBypMJ+3WVAYRdW3k3WZUCwzq\\u002fmns9lQG1poZlmyWVAQpIE6prDZUBfbEHZMr5lQIkUc1YmuWVAxG4tIm60ZUBZDi22A7BlQBX7WTDhq2VA3ymgQAGoZUAO6TUZX6RlQIk3+mD2oGVAycajJ8OdZUAt5IXbwZplQI05u0Dvl2VADLOPaUiVZUB0RQuwypJlQEwuh7BzkGVAyPY+RUGOZUAncdaCMYxlQGKV1rVCimVApDAtYXOIZUDJpMc9woZlQNuncDsuhWVAwL8tg7aDZUAblHV7WoJlQCTPv84ZgWVA3rwndfR\\u002fZUBkySzC6n5lQLc4FXj9fWVAZz4w4y19ZUBIbmQAfnxlQKWL17Twe2VAyVYHI4p7ZUAlB1NAUHtlQKlQ9R1Me2VAS4QiU417ZUAEmVl5M3xlQCv9CH2GfWVAWiEXRyOAZUAWYucIUYVlQJf1WD7Bj2VAvzsRkUSjZUC7KL2MQcRlQOHfLCBO9mVARhpkvmk3ZkBGvdTmcX9mQJw2kT5FvWZA1ijyY7zXZkCU6AdMxMRmQH63P\\u002ffai2ZAvuZCwDVDZkDA1Dwo4P5lQLs93fryyGVA4ixSvw2kZUBPh5Rsf41lQCzyKKvqgGVAZbRaoBx6ZUCGFVbyRHZlQCJmNj\\u002fsc2VAXiKpHlFyZUD+atUOHnFlQPYqqGotcGVAn5sDyGxvZUD\\u002fTqxx0W5lQIYScDZUbmVAhqIP2+9tZUDge8FsoG1lQDpPBOJibWVAG5U23TRtZUD0B+eBFG1lQA1SN1YAbWVAQoGHLPdsZUDgG\\u002fgS+GxlQAfFGkcCbWVA4R6sLBVtZUDKKYlGMG1lQDQSTjFTbWVAqaI1n31tZUAVdO1Ur21lQDByJyfobWVA7\\u002f2\\u002f+CduZUCQqFq5bm5lQKViX2S8bmVA2t9HABFvZUA6bjGebG9lQE7\\u002fqlnPb2VAyO65WDlwZUAiZRLMqnBlQFY3gu8jcWVAYfmOCqVxZUBOu0lxLnJlQLSxXIXAcmVAYvJYt1tzZUDqjUyIAHRlQFbCq4uvdGVAcOuaaWl1ZUDpWKrhLnZlQEOFGc4Ad2VAn4i8J+B3ZUD4ZKUKznhlQEpbu7vLeWVAcm11r9p6ZUDoS\\u002fuR\\u002fHtlQOU6AlEzfWVArmTTJ4F+ZUACiwiu6H9lQNrQtelsgWVAbzfvZRGDZUDuyuNO2oRlQI4QL5XMhmVAIzGOGu6IZUAk0unqRYtlQO+Et4XcjWVAHug5PbyQZUA5FFOz8ZNlQPycxH6Ml2VASHVdCKCbZUDDZZG2RKBlQBGp+YiZpWVAmrsgWcarZUDMzTTKAbNlQEbVWAWRu2VAHf1EpNPFZUBTSMdgTdJlQE47YhET4mVAjibKq1L3ZUBU87WNAhdmQFiEL1wdTGZAOZY4ifmrZkCfg27ax1tnQE\\u002fweuAmkGhA8ABpUmR8akDx0pXDYi9tQJJdHM3cPHBAZVIzZMHkcUAJX8FejQxzQEjEobWaMXNAm6DhQf1RckDqeO5BPcpwQHahHpUXNG5AxjVHIHNJa0CG0Srq2SFpQNYg87nDuWdARt6Nh0LnZkACFS9tCXVmQKvyD9MDOGZAeCoLzpkVZkBhpFfzr\\u002f9lQDMBXn2p8GVA7bXh3ablZUAJqbRgQN1lQPsWRIi+1mVAVoSn7LLRZUDnlfplz81lQFW4+7ndymVAsk1UwbbIZUAc3SZeOsdlQGuOQ79PxmVAkM0zqOPFZUBW9GsQ58VlQIH3BT5OxmVALA4pFBDHZUBpL3iOJchlQHvgcl2JyWVA0u5EmzfLZUCu4vSSLc1lQFHBlZVpz2VAOP1U2urRZUDLAhpnsdRlQDTrDAC+12VAL\\u002f3PHBLbZUBPc5Dir95lQP9aVyKa4mVAzo85W9TmZUBjbTLAYutlQEc7m0FK8GVALj1UmpD1ZUAg7NVgPPtlQNkcgxxVAWZAZ5u5X+MHZkBp+0fn8A5mQKgtH8CIFmZAHl5PdLceZkCoyaRAiydmQNyEjlQUMWZAPppjHmU7ZkB1\\u002fqCmkkZmQL1AZf20UmZALBpJvudfZkAZjs2vSm5mQAs8BoUCfmZABpYFyjmPZkDQnQgGIqJmQJDipyH1tmZAHFm5I\\u002ffNZkCocYBdeOdmQE4W5SbYA2dACQ+TVogjZ0AlCWexEUdnQPWz8PEXb2dAQ3nCF1ecZ0B4sOWr0c9nQCJCGNq4CmhA+xFuv4lOaEDal833JJ1oQHgTfBjv+GhAYys5i\\u002f5kaUCZXW67X+VpQGwIFWiHf2pADzBGwz47a0AB7uCr+CRsQP5DDn8tVG1AEuth7737bkBeYio3UsdwQPzR5LpgAXNAe89BtSUTd0BSzsmAC3F+QIJl7jmpeoVACjz35N0Oj0DTH9JczgGWQC7KzSg5iJ1AwJNgmDw8okCUEBfdQD6kQEgGJvhS8qNArwVEEZd5oUCf2jyPusKbQAqZG8oXbJRAPGOOCnypjEBUjSl8vNqDQE3QJnA3cHxAmZqNdGHxdUDfLtMiomVyQFsBtmpEc3BAzCTfemKUbkDwMtk+0QptQGrIooch7GtAmRAGusoPa0ARnxWVkl5qQIJKYFEOzWlABPH39f9TaUCNS\\u002fTnKu5oQMeNhofFl2hAY2uOUOtNaEDQIf8ZXQ5oQCC6+61W12dAmTefzHCnZ0Cp9WNhi31nQGOLXG29WGdAGtRsAkk4Z0D12kYvkhtnQL77WBIYAmdA2f1vhG\\u002frZkAf2TH0PtdmQHCAxyc7xWZAOPb1rSS1ZkC9Mh\\u002fWxaZmQL\\u002f85RDxmWZAoAWqon+OZkB5FJKWUIRmQFE18ORHe2ZA3mH9t0pzZkBkTnFgR2xmQLc28MwtZmZAMLje6u9gZkDJ4xY8glxmQKQ6\\u002fJzbWGZASVzeGPVVZkDUA2zEyVNmQEHFt6lWUmZA76i865pRZkBGLGqRl1FmQAeK3qlPUmZAjjnRZ8hTZkB5L5tKCVZmQHxiwVYcWWZABcqfYA5dZkAhDLts72FmQEZtZyrTZ2ZAVeX0jtFuZkC5f4yZB3dmQMwMkkqYgGZA\\u002fEny3K2LZkCcGapUe5hmQH0WlHw+p2ZAfav1dkK4ZkBKMTuG1ctmQLLo2zRz4mZAYlSke6f8ZkC7GkngHxtnQPUV46C2PmdAJgjG34BoZ0DXaJFo4plnQAvHh1Wq1GdAJzhlc0AbaEBDNV0W+3BoQBsyG7Ho2mhABapDtehhaUDSk8fOLxhqQBuX\\u002fXBFJmtAEupzjp\\u002fkbEBOpKBZVwFwQPTpppIr1nJAsEKJ\\u002fkrCd0D6oYk2K55\\u002fQNoufh7TaoVANzRfheJojEBJiWzyg9aRQNa94\\u002fGDgpRAKbEdKNFUlUDgPxQ04uWTQDwt5pDC1JBAzSXAOMZ0ikD2seGBMtGDQF7L4d11V31AJMbuaYA\\u002fdkAP0Szl6+1xQF4R9UJy+25AQK9nSxpObEBGuX+l48dqQKqeASit0GlA3dXXcjwlaUBBZYjoSqRoQDgZNtXkPmhADhdGbETsZ0C8kiUb0KdnQHHs92VYbmdA5dVGqKE9Z0CjOMVn\\u002fRNnQHu54TUg8GZAZzL8uQbRZkD8qa6u47VmQMRutPoSnmZAvCK5OxCJZkAEogm+b3ZmQOqVWTLZZWZADUljqANXZkCxeNF4sklmQFKaSOGyPWZAHj9XJtoyZkDPSf8aBClmQKnqAfYRIGZAuOMtY+kXZkBCkFnDcxBmQO+l7ZCdCWZA66v8g1QDZkDUcyLhiv1lQPm5mXA0+GVAQwdi5kXzZUAtCYJUte5lQMvIo\\u002fl56mVA+oanF4zmZUA2SsDQ5OJlQOA09Ql+32VA+GkbUlLcZUCjJInMXNllQPVf5h6Z1mVAHgqcYQPUZUDYsqYNmNFlQAaP3\\u002f9Tz2VA1O8yYDTNZUCaOPmdNstlQO+Q2mhYyWVAcfaVqpfHZUB3XbmB8sVlQMzJND1nxGVAi0O5WPTCZUAfoNt5mMFlQE\\u002fC+m1SwGVAf9fxKCG\\u002fZUDVDKrEA75lQFdyrYH5vGVAmgnwyAG8ZUBR\\u002fx4vHLtlQIkK7XlIumVApHcNqIa5ZUDnZ+\\u002f81rhlQKT5CxI6uGVARZyl8bC3ZUDK2JVFPbdlQHX\\u002fNrvhtmVARDcAAKO2ZUBHnLFdirZlQBRqjyOptmVA1uAlhSm3ZUAwjl3xdbhlQApkX+4cu2VAMb9mJi\\u002fAZUB07TtdBsllQCbFQK7Q1mVAPw+ePM\\u002fpZUD5XRwadgBmQEYR83i\\u002fFWZANiRqg3gjZkC8Q\\u002fPfoiRmQMIV6GGKGGZA\\u002fjVeMMwDZkCGo+Co++xlQPWQYcut2GVAeqoQZBvJZUCsEp+0mr5lQGt9YHg2uGVA6CGazY+0ZUB0TqQngrJlQO+DdlFLsWVA\\u002ff9PrYSwZUBbYGYN+69lQDKt++2Vr2VAF2iG60mvZUCuDG0sEa9lQE0uZzLormVAxIO+oMyuZUCtBYrBvK5lQBRlQE63rmVAa+kBUbuuZUAiGcgQyK5lQCS\\u002fogPdrmVA3TPfxPmuZUB3sOYNHq9lQKknDrFJr2VADqzRlXyvZUCbgKaPtq9lQMFQgM73r2VAj3jCbECwZUCgC0uSkLBlQMV9GXXosGVA6CpKWUixZUCcvGORsLFlQPUF8X4hsmVAMXhlk5uyZUAzZE1RH7NlQN0Qzk2ts2VAf3V8Mka0ZUCSTJS\\u002f6rRlQPtdnM6btWVAh3yIVVq2ZUA8721qJ7dlQLoU5EcEuGVAnkIxUvK4ZUC0i2sd87llQOSkvnQIu2VAFyUUYzS8ZUDjlWo9eb1lQEY5O6\\u002fZvmVAt19nyVjAZUCsdUYU+sFlQEgBmKXBw2VAeBjAaLPFZUDB2QYC1cdlQCB6BWQtymVAJIxUIMTMZUAPYKwpos9lQKBXEiPS0mVA8npnxGDWZUDYzSRbXdplQDTTk3Da3mVA1qdiou7jZUC2RZW\\u002ftellQGv+VENS8GVA67RZUu\\u002f3ZUCHno1xwwBmQHNXAE4UC2ZA9aOkQDwXZkBvZG05syVmQFxnHxAgN2ZAl1nUWoFMZkBwxjEpl2dmQJLIEsLsi2ZAPWyDcyzBZkAmyKShgBdnQCxM0Tj5rWdAoQGPvmi4aEDRi1xORn5qQHxJInj4SW1A8WdYikKicEAM5Hl60SJzQLjnI8DZx3VA+uHbMIDJd0AyKHxej6l4QDQTgEBUAnhAASGkHYYYdkDKZPb4iZRzQJccEEGKFnFAoy1d7o4HbkDh97waTP5qQA9hItCYCGlAMFjuQ1DeZ0DJElv8BjVnQI25mYin02ZAoPflr7mYZkASX2+ozXFmQOtTgf0kVWZAA3xrvxQ\\u002fZkBuPHLjWi1mQDeL3inNHmZAE4KAuqgSZkBB6kA+awhmQDLTq\\u002fu1\\u002f2VAILtVa0D4ZUA1Kbw80vFlQDIam\\u002fE+7GVAkajUAGPnZUAG38NbIeNlQCN0Ippg32VAXqDdzRDcZUDeI4EWItllQAH8\\u002f0SH1mVAovbESDXUZUA324vuItJlQPu6BmJI0GVAF3j6+57OZUBISN0NIc1lQEonwbfJy2VAPI9MxpTKZUCqAgyXfsllQD1JzAGEyGVAEj\\u002f5RaLHZUC\\u002fhjb71sZlQKEnkwQgxmVAtafahXvFZUCNDqDa58RlQP1Vs45jxGVAeH\\u002fBV+3DZUCm2usPhMNlQP7ILLEmw2VAGQhoUdTCZUDZugofjMJlQAtfJF5NwmVAYdzlZRfCZUDqFXee6cFlQPgIFn\\u002fDwWVAHqp0jKTBZUD9c0xXjMFlQEwOIHt6wWVANqYknW7BZUD2jk1raMFlQPaUdZtnwWVAGx6i6mvBZUB4xVwcdcFlQOacIPqCwWVAZqfYUpXBZUDGd236q8FlQPApYMnGwWVAJC1xnOXBZUDVi1FUCMJlQJeMXdUuwmVAi69fB1nCZUBhLlvVhsJlQPRCXS24wmVAeu1k9+zCZUB7EesgJcNlQCz5m69gw2VAW+46nJ\\u002fDZUAKjP7h4cNlQEIIgH4nxGVAJ2KucXDEZUDdQ8W9vMRlQIRyR2cMxWVARLD8dF\\u002fFZUC9\\u002ffLvtcVlQOIwg+MPxmVAC+BYXW3GZUAzqH1tzsZlQOfdZyYzx2VA38AMnZvHZUDjUvboB8hlQNj7XCR4yGVAQy5FbOzIZUD2SaHgZMllQHYFeKThyWVA37IP3mLKZUAOwh636MplQHrwAV1zy2VARKf4AAPMZUArGmjYl8xlQDTOJR0yzWVAy0XKDdLNZUDiqQzud85lQANlKAckz2VAgcZNqNbPZUA56h4nkNBlQO1MOuBQ0WVAQKfUNxnSZUAj42Oa6dJlQOFBXX3C02VA9BC3QaTUZUAjLIJSj9VlQApd03yE1mVAoCzOYoTXZUAjJb20j9hlQG6YgTKn2WVAI7IwrcvaZUBNPOQI\\u002fttlQJ+Hxj4\\u002f3WVAbyNiX5DeZUBfhz+V8t9lQIyN3Cdn4WVA+q4Lf+\\u002fiZUCtb8smjeRlQKJjqdNB5mVAIs7HZw\\u002foZUBiNqH49+llQMiTqtX962VA2CL7jyPuZUBi0igDbPBlQEfikV\\u002fa8mVA5zhYNnL1ZUBeuGGHN\\u002fhlQAc9w9Eu+2VACOkSJ13+ZUBbQDtCyAFmQGkRjaJ2BWZAUHf8q28JZkAcarDNuw1mQOJPWLBkEmZALz0dbnUXZkAHYoTW+hxmQImKP8EDI2ZA0nXFc6EpZkACU7Ee6DBmQNXvcXnvOGZApUbkhNNBZkAWuUiAtUtmQGor5R+9VmZAakILGhpjZkDZq8omBnFmQPlrUZnHgGZAGUxoybSSZkBoOzCaOKdmQA==\"},\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"},{\"fill\":\"tonexty\",\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PoB9lQAfzeOweH2VARZ792J0eZUC2KcRSHR5lQBXUsledHWVAXwm75R0dZUDjINn6nhxlQFcdFJUgHGVAsW59sqIbZUDitTBRJRtlQEmKU2+oGmVA2EAVCywaZUDutK4isBllQLkSYrQ0GWVAOKN6vrkYZUC0mUw\\u002fPxhlQLviNDXFF2VAhPSYnksXZUC\\u002foOZ50hZlQLPnk8VZFmVAvswegOEVZUAKLA2oaRVlQJuR7DvyFGVAfxFSOnsUZUA5IdqhBBRlQFRyKHGOE2VAFs7nphgTZUBR8slBoxJlQERvh0AuEmVAj4bfobkRZUAtC5hkRRFlQG5CfYfREGVA+MVhCV4QZUC5Zh7p6g9lQMoQkiV4D2VATbChvQUPZUApFziwkw5lQLrjRfwhDmVAXmjBoLANZUDfk6acPw1lQMba9u7ODGVAfSG5ll4MZUBSp\\u002fmS7gtlQEzyyeJ+C2VA07tAhQ8LZUAx3nl5oAplQNhClr4xCmVAitG7U8MJZUBAYBU4VQllQOmj0mrnCGVA9CEo63kIZUCsIk+4DAhlQGSkhdGfB2VAbU8ONjMHZUDrajDlxgZlQG3SN95aBmVAZux0IO8FZUB+oTyrgwVlQLdU6H0YBWVActzVl60EZUBffGf4QgRlQD\\u002fgA5\\u002fYA2VArRcWi24DZUC5kg28BANlQJEfXjGbAmVAHOl\\u002f6jECZUCRdu\\u002fmyAFlQDGsLSZgAWVA+8y\\u002fp\\u002fcAZUCPfS9rjwBlQDHICnAnAGVA\\u002fiHktb\\u002f\\u002fZEBncVI8WP9kQPEV8QLx\\u002fmRAUvFfCYr+ZED1cUNPI\\u002f5kQPieRNS8\\u002fWRAvSURmFb9ZEAQaVua8PxkQAOS2tqK\\u002fGRAnqJKWSX8ZEBkimwVwPtkQOI8Bg9b+2RAT8riRfb6ZEBuetK5kfpkQL7pqmot+mRAMylHWMn5ZECT4IeCZflkQJxzU+kB+WRAKSqWjJ74ZECPW0JsO\\u002fhkQEmdUIjY92RAW\\u002fW\\u002f4HX3ZEB8EJZ1E\\u002fdkQGt830ax9mRAruavVE\\u002f2ZEABYCKf7fVkQOekWSaM9WRAnWuA6ir1ZEDmt8nryfRkQCY1cSpp9GRAL5e7pgj0ZEBmAvdgqPNkQKl7e1lI82RAvmCrkOjyZEDY6fMGifJkQPi0zbwp8mRACly9ssrxZECEFlTpa\\u002fFkQJVnMGEN8WRA79n+Gq\\u002fwZEBIynoXUfBkQABCb1fz72RAL+O325XvZEC750GlOO9kQDI1DbXb7mRAR4ctDH\\u002fuZEAPs8urIu5kQEcFJ5XG7WRAQL6WyWrtZEBDrotKD+1kQJX1kRm07GRAp+tSOFnsZEBRMZeo\\u002futkQHrySGyk62RABlx2hUrrZEB4S1T28OpkQGc+QcGX6mRAf4jI6D7qZEDH16Vv5ulkQLgPyViO6WRAyoRapzbpZEBto79e3+hkQJwOoIKI6GRAEUTrFjLoZEDG1d4f3OdkQKRKDaKG52RAwbplojHnZEBBPjwm3eZkQEdJUzOJ5mRAMhPmzzXmZECsK7QC4+VkQFhlDtOQ5WRAvkPlSD\\u002flZEAyIdls7uRkQI9JTEie5GRAWFB35U7kZEAx9H9PAORkQPXukpKy42RAyiEBvGXjZEB1n2DaGeNkQL4tsv3O4mRA6\\u002fGLN4XiZECdHEqbPOJkQKCQRj714WRAd68YOK\\u002fhZECir96iauFkQKomkpsn4WRA+sVpQubgZEBzskq7puBkQJ1fTC5p4GRA5XZSyC3gZED3GcG79N9kQD\\u002fHUEG+32RA3V4ImYrfZEBwT2ULWt9kQHLku+os32RAQyzclAPfZEArHAt13t5kQGy3Ywa+3mRA7li51qLeZED5QBuKjd5kQPnEIt9+3mRA6MJBtHfeZEC4ZFcOed5kQLJn5yCE3mRA20RuWJreZECEB3Znvd5kQOIaSVfv3mRA6+ZwnTLfZEC0O6A3it9kQMZsTdD532RA2kcw74XgZEDiyko6NOFkQGaBs84L4mRAgcRHvhXjZECsqJHRXeRkQJBQN+vz5WRAe9d7Ne\\u002fnZEBrojWNdupkQCFXvibX7WRAUuOs2bzyZEDelJwovPpkQMxX58JJCWVAZm8174wkZUBoL8Dhp1ZlQEaKrK8WrWVAEDtSSyo1ZkBwgtEh1fRmQOMLZzZl4mdA4SseKR7faEB6NkkEnrhpQLvKpCi3KmpA7H3dCHYHakBymnNpnl1pQFwcNCO2aWhA6n6le9hqZ0DG1yy3bI9mQHe76qbR6GVAvzUjBXd4ZUDxhIBy9zNlQINxL9BRDWVAGuS0b1\\u002f4ZEAzg1mY2+xkQCyRtwYb5mRATDLZLoLhZEC+k266CN5kQB24G70622RA6f396+HYZEBJs6am4NZkQA+Y7uAj1WRA+WX49Z3TZECywl2WRNJkQFiNb8QP0WRA\\u002fsdKN\\u002fnPZEBmb6rv+85kQLDvr+oTzmRAY9ft6D3NZEDQMMxDd8xkQKCkTc29y2RAF4Zvtw\\u002fLZEC0WSyBa8pkQNDqtefPyWRANv7e2jvJZEDjufJzrshkQBPyae4myGRAL7wSoqTHZEAuDVn+JsdkQLKVcoatxmRAYHk+zjfGZEBBSbR3xcVkQEfMxTBWxWRA6E+dsenEZECD9Sa7f8RkQFAW1xUYxGRAZaqikLLDZEBY0yAAT8NkQF1lzj3twmRAn6ZtJ43CZEBSkX2eLsJkQInBxIfRwWRAfOjsynXBZEA1LSxSG8FkQEZV+gnCwGRAzO3P4GnAZEDM+O3GEsBkQGnhLK68v2RAiqzRiWe\\u002fZECrhWhOE79kQKXqo\\u002fG\\u002fvmRAGtY\\u002fam2+ZEBnYOivG75kQE9jI7vKvWRAWL08hXq9ZEA24DQIK71kQLdhsT7cvGRAlVDvI468ZECZF7ezQLxkQGfAUerzu2RA9m1\\u002fxKe7ZEDb624\\u002fXLtkQDY0tlgRu2RALNJLDse6ZEAJCoFefbpkQF6z\\u002fEc0umRAzLO2yeu5ZECZC\\u002fTio7lkQAJnQ5NcuWRACCl62hW5ZED25bG4z7hkQC9FRi6KuGRAFkPTO0W4ZEDnzDPiALhkQEaxgCK9t2RAQuEP\\u002fnm3ZEA6\\u002fnN2N7dkQOIxfI31tmRASk40RbS2ZEBoNOWfc7ZkQFSAFaAztmRA5XqKSPS1ZED6UEmctbVkQFKRmJ53tWRASfIBUzq1ZECBYVS9\\u002fbRkQPdfpuHBtGRAsq1YxIa0ZEDFSBlqTLRkQAzE5tcStGRAz\\u002foTE9qzZEATJ0whorNkQE1hlwhrs2RADJFfzzSzZED61nV8\\u002f7JkQOt5GBfLsmRAhmH5ppeyZECmKkU0ZbJkQMriqsczsmRAnnlkagOyZEBT+T8m1LFkQKCYqQWmsWRAQLu2E3mxZECR+DFcTbFkQHFCqOsisWRA8Ul3z\\u002fmwZEDhQt0V0rBkQEwrCs6rsGRAy8AyCIewZECKUqXVY7BkQDOl4EhCsGRAiSStdSKwZEBxpjhxBLBkQEIMNVLor2RARRn6MM6vZEB74aontq9kQExBX1Kgr2RAo95Rz4yvZEB8RhO\\u002fe69kQPzOwkRtr2RAP\\u002f9NhmGvZEDRWresWK9kQJ+QZeRSr2RA+jR8XVCvZECgXD9MUa9kQOOlg+lVr2RAuH0sc16vZEBcu7osa69kQIEI7l98r2RArvh7XZKvZECJNOB9ra9kQKK5SSLOr2RA6+qptfSvZEB5EOutIbBkQIj3VI1VsGRAZLEn5JCwZEBBB3ZS1LBkQC8sS4ogsWRADp0pUnaxZEChl4NJYbJkQOI1dKdXs2RA5m9tg1q0ZECl414Ya7VkQG8hScqKtmRALmPdLLu3ZEBZzmQL\\u002frhkQBhkOXJVumRApR4uusO7ZEBj2FyWS71kQIpn8STwvmRAzYC0A7XAZEBNJFJpnsJkQMa5o0SxxGRADNysY\\u002fPGZECLh4Wka8lkQC\\u002fbKzMizGRAOZdA2CDPZEDN8iNec9JkQMzG8xMo1mRA\\u002fiPYeFDaZEBNIEceAt9kQGKQK9ZX5GRAJPSVS3PqZEDizPY1f\\u002fFkQDyvmn+y+WRAzPGlN1UDZUA6YmDmyQ5lQFMSyaijHGVACBWWTOItZUBqVlW5lURlQGsyepSZZWVAFebX7H2cZUBG2pzS+P9lQMaBaC3ruWZAn+KYnYwKaED62yLv+UBqQHK9pgeonm1A5lttLIEQcUCZ\\u002fZqxhq1zQBVYlb34NnZABXrWs9z6d0BIv3DCEkl4QGJfUpGG\\u002fXZA93KYY9WidEB3Fjr6SvJxQMZ4LEQnFG9AYbYc+FNOa0B4sYKfb7VoQJxL14yuG2dABaP72H80ZkDYgkXG1LhlQJ8sIDqWdmVAQAA6wQxRZUAJgakm8DhlQBRUS0t0J2VAbxTr2eEZZUBlUMFk9w5lQDiz79oGBmVA965KtqD+ZED7qKhVdfhkQKj+g85I82RAQMoj6uzuZEDsSVp9PetkQO8ejegd6GRAj4ZGTHflZEDS5f43N+NkQK86Ja9O4WRA9\\u002fQzbLHfZEBm1wBRVd5kQA5PZfgx3WRA0Se+YEDcZEA5WhupettkQC42oNzb2mRA1IXByF\\u002faZEAt6efbAtpkQLb1mQrC2WRANerEuZrZZEARABCtitlkQFfuZfiP2WRAr\\u002fMP9KjZZEC15uEy1NlkQOthEnoQ2mRAf1BvulzaZECFgq8KuNpkQNetrqIh22RAkjpr15jbZEC1FKUXHdxkQBj0Aumt3GRALX+o5UrdZEDfoiu6891kQHKp2iOo3mRA+i1H72ffZECYIQz3MuBkQJzNxiIJ4WRAISU8ZurhZECJ4aTA1uJkQMXbGzzO42RArOIq7dDkZEDh+3Ly3uVkQGaQbXT45mRA14NEpR3oZECaor\\u002fATulkQNMyRgyM6mRA5sjz1tXrZEDDzL95LO1kQIpjt1eQ7mRA2LJJ3gHwZEC0saaFgfFkQDr4MNEP82RAFzwDUK30ZECFZYqdWvZkQHxoNWIY+GRAH1E8VOf5ZEBMOoA4yPtkQCgzhuO7\\u002fWRA5m2POsP\\u002fZEBocNE03wFlQM9o0dwQBGVALD7mUVkGZUBce+XJuQhlQKnJ\\u002f5IzC2VA413SFcgNZUDMgLPXeBBlQB4\\u002fQX1HE2VA6VM6zTUWZUAwj6qzRRllQEZadUV5HGVAJ5RKxNIfZUC93BOjVCNlQPiS6ooBJ2VAxlapYNwqZUCX3y5L6C5lQAOBa7ooM2VAg+RWb6E3ZUCUc\\u002fCEVjxlQP7QcnpMQWVAk8bpPohGZUBsdGI+D0xlQK2l93DnUWVAIVYIbBdYZUC6JPV1pl5lQOxA05ycZWVAQBSZ0AJtZUAecGMB43RlQEeXkkJIfWVANpym8z6GZUA4PfTv1I9lQOYxicYZmmVAxxXk+x6lZUA3FYVX+LBlQMDt1j+8vWVATzSLJ4TLZUBHK0sQbdplQCUgnSeY6mVAq1knhCv8ZUDDkyULUw9mQPBoFohBJGZAGB+JAzI7ZkDUdcppaVRmQKWvUpc4cGZA2xrI5\\u002f6OZkCwAt9tLbFmQGzPYgdL12ZAg87GkvkBZ0C7wPqk\\u002fDFnQO+yPUJCaGdAEBdXUO6lZ0DgMM\\u002fDaexnQBjESPl2PWhAO8riWE2baEBzH7uPvwhpQPi\\u002fg1xyiWlAW+IEQDIiakCEvkHrk9lqQDP3XLlvuWtAgFO92fHSbECYEYylx0luQJWviyHhOnBAzsFrbNUNckC9daiE72t1QIeKMiKbvntAMmMtEYeWg0BbHyiaXhKNQLI2i2gZppVAjYq0o94In0D6IMYuSsekQE0fDOJRoalABY5Y+0uArEA0HCF9HSysQD9UiWaryahAQo3rRZS1o0C\\u002fgjc+auWcQBn3RSqd+5NA1nbTpI+8ikBsyzK1gBOCQDjwihj\\u002f8HlAO09Ui9lcdECSzC8YjGdxQGRLCB1Ljm9Ajvwsc1qcbUBECzlQP0NsQKGTWmYaPWtAX5GR0sZrakArXpGeQMBpQACVQgiVMWlAVjPUHaO5aED+3w685lNoQJv6Mdfu\\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\\u002fHu4nHJlQA55n6ssdmVA2D3xQRR6ZUDXcxTrWH5lQP28vMkAg2VAdVW7yBKIZUC1qIazlo1lQMfh2FKVk2VAkTEjjhiaZUAEhLySK6FlQAo65QHbqGVADO34JjWxZUC7xHY3SrplQAkd7Z4sxGVAwzRUWfHOZUBDbwVfsNplQE4oTCaF52VAVbOYP4\\u002f1ZUDDkbkS8wRmQKwYRcXaFWZA\\u002fK6zVHcoZkCH7sPxAT1mQMNK5q69U2ZA4kUDmflsZkCsC4dZE4lmQH4WCYt6qGZAwMdX+LTLZkB5v+gRZPNmQEOJTgJMIGdAJLVQ81xTZ0An\\u002fa9Qv41nQI5jFi3k0GdA9qbAcZseaEDjQXBWMnloQITf5Dme42hAz+nnLbxhaUBGpPvsu\\u002fhpQKg+cVsAsGpABgVIqWaTa0CLAtdxrLlsQNjycyd4VW5AXP83J7t0cEBuD\\u002frYR8NyQM80EhWDHndAblrXO5wsf0AKfwPEqYmGQDlJGQ\\u002fDtJBA8lX3oFtBmEBP4HHoW6agQBiRrtzFKKVA1T0OF\\u002ftcqEBXq+p9w\\u002fOoQFDGZFEso6ZA7u39mvxwokAm5l7ia4GbQAwv\\u002faaeS5NA6U+y0fMWikCso7iJhbqBQJGy9TfEcnlAplq\\u002fJaLvc0AYjoylBwRxQMIR4yZu2m5A9RAt1jr8bECEMwzBPbVrQNSGwh4kv2pAqFDfy6z7aUDSHTcGDFxpQGFTAT2X12hAYP+gsG9oaECAG0NKSQpoQDrRQpDhuWdASNk6nbN0Z0DCEqGOxjhnQHSaesiKBGdAvCgJ38DWZkDEScMqZ65mQI1JDRWsimZAkshLyuNqZkCGuf5kgE5mQH6fC+sLNWZAl0T7pSMeZkAWm\\u002f9\\u002fdAlmQIti0ia49mVAkoL\\u002fxbLlZUCc7Ag2MdZlQJ30aoYHyGVAJaDTzg+7ZUCC7oU4Ka9lQEgRWTQ3pGVAu7Fd1CCaZUDPgytC0JBlQI65WUwyiGVAIu3LBjaAZUCaC2F6zHhlQNoJQ2HocWVAE9Sg7X1rZUDM+wiZgmVlQLPy8frsX2VA1T1BpbRaZUCt49kF0lVlQGNhZkw+UWVAS\\u002fu1U\\u002fNMZUDDByKO60hlQKFCh\\u002fQhRWVA0V1y95FBZUCyui5yNz5lQAwpc58OO2VASjxzDxQ4ZUBlqSSfRDVlQOqFj3CdMmVABnAG5BswZUBu2CiSvS1lQHQGlkaAK2VA3SE7+2EpZUC0sinUYCdlQBi35ht7JWVAg70kQK8jZUCKcd3O+yFlQIDHwHNfIGVAoH3x9dgeZUCRCgg2Zx1lQCtTViwJHGVAU61n570aZUCq1bmKhBllQLyZrE1cGGVAiRunekQXZUBiyHJuPBZlQNhxzZdDFWVAanw1d1kUZUCV4fSefRNlQM\\u002fscbOvEmVAdUjPa+8RZUCnUOiSPBFlQLf2ugiXEGVAgiFWxP4PZUCL4GrWcw9lQPJjZ0P8DmVARX6Xg5IOZUD1pgUNNw5lQHZqCYTqDWVACc6\\u002fxK0NZUCRG2LvgQ1lQE4Ac3hoDWVAc7AcPmMNZUAelaqkdA1lQBvG3byfDWVAMOgeeOgNZUDiN6DwUw5lQBPRl8\\u002foDmVAMM3c5q8PZUAxX885tRBlQABhkjsKEmVAsyqIw8sTZUAmYoTTMhZlQE1pJa\\u002fAGWVA0f8M2qIfZUDc6jdwvyplQNEQ6bjpP2VAg6DN\\u002fCNnZUBQOFiptqllQK4re7YSD2ZAxOUmhM+XZkCC5hgfGzVnQFrA1QXMx2dApXD3FBAfaEC\\u002fNXco9xNoQNbLCm64q2dACjtVwEkSZ0DF9TZ2cnVmQEAgKD4E9GVAPmiH2lCWZUCBf2UVaFplQPry41bYN2VAsdcTvjolZUC6JOiPfRtlQI6KQrgQFmVAfJo+HZ0SZUC+15RaJxBlQHjvZBVEDmVAlKkm08IMZUCS23\\u002fviQtlQGk7fEKJCmVAr3MoZbUJZUAoFljYBQllQB+vDR10CGVAGKpGKPsHZUDUvt0DlwdlQEht6opEB2VADwGeOAEHZUD8tAwEywZlQEm\\u002fKkWgBmVAOS9uoH8GZUDHiUv3ZwZlQGyrSFxYBmVAf1+9CVAGZUBHJ5laTgZlQOLOssRSBmVAlZpG1FwGZUCZA18obAZlQGjX9G+ABmVA2O6fZ5kGZUAV4LrXtgZlQFjs4pLYBmVAXZjCdP4GZUDvTxlhKAdlQFV89UJWB2VAOc4YDIgHZUC3UoC0vQdlQC5gCzr3B2VAMJM9oDQIZUDHCBnwdQhlQPjODji7CGVAVTIEjAQJZUBnKGwFUgllQCOhdMOjCWVAzQxI6\\u002fkJZUCy42OoVAplQB2ABS20CmVAKiWvshgLZUDlo8d6ggtlQHq5V8\\u002fxC2VA5gLpA2cMZUDMRIt24gxlQFrRBZFkDWVA+BU8yu0NZUCG3M2nfg5lQJuh\\u002fb8XD2VAmKDpu7kPZUDD+yZaZRBlQATW0XEbEWVAqnwp9twRZUDSHtX6qhJlQB9W9LiGE2VA4FsnlXEUZUARu8UmbRVlQLZgiEB7FmVAcRb++p0XZUC9ETnB1xhlQFYtQ2ArGmVAOScPGpwbZUB2aNO8LR1lQEdnA8DkHmVAQq5+aMYgZUBFFBL22CJlQElQH90jJWVAbCQ+ELAnZUAuYBBfiCplQL8VfvG5LWVAP\\u002fto6VQxZUAt2wo5bTVlQCcjUMIbOmVAMvq33H8\\u002fZUDJD+ttwUVlQBwZPdwTTWVAbCRGZbpVZUDS96lAD2BlQNDFYxqSbGVApa9C6wx8ZUA152O5+49lQJ8Su8ikq2VAv2kpEn3XZUD7jUUIsyVmQIoGVDftuGZAmUpL6s\\u002fIZ0Avac8TTp1pQEHjIMwLdWxAAYlmF6IucEA9A9t2V4NyQLq7EIGLyHRAynr+10lHdkD2r38q61d2QLQNvjR38XRA5AWzoX6zckBjdWIun1VwQLj4oTsSqmxA2CyvOCm\\u002faUCwdHuew9xnQNvqn+5+xGZA38888AMtZkA0F46JrNxlQMcdn1aJsGVA2oCQgMGUZUDNH2G9EIFlQP1f01P7cWVACDJeYfBlZUAT5JKSHVxlQFeINlv\\u002fU2VAM842WDlNZUBzN+nqhkdlQEbpVr+zQmVAHNx\\u002fTJc+ZUC9WNPJETtlQMzKDQcKOGVANL\\u002fh22s1ZUAA83ABJzNlQGPI8zYuMWVAq\\u002faInHYvZUB56YE19y1lQMSozIeoLGVA4Y0PUYQrZUC0PBhMhSplQM3QsAKnKWVAOQH1qOUoZUCK7f3\\u002fPShlQEB0RD6tJ2VABn1+\\u002fDAnZUBZ+gUmxyZlQB7YD+xtJmVAKLsiuyMmZUC+CF0y5yVlQIDEMRy3JWVAS1VWaJIlZUA3kqkmeCVlQBqc54JnJWVAZsAGwV8lZUDifyA6YCVlQN1Cz1loJWVA0Zftm3clZUDJTqeKjSVlQIiLz7ypJWVAtiZx1MslZUA5iJB98yVlQDCgF20gJmVAF9rlX1ImZUAq5P4ZiSZlQAT302XEJmVAw\\u002fajEwQnZUAbV+\\u002f4RydlQJsj\\u002fe+PJ2VAnPFu19snZUBe1eGRKyhlQGO6mgV\\u002fKGVA2Lo8HNYoZUAWRIjCMCllQFMBIuiOKWVA0algf\\u002fApZUAX7yB9VSplQGPinti9KmVAZD9UiykrZUANHtuQmCtlQEqe1OYKLGVAYi\\u002fTjIAsZUB1IUiE+SxlQAE7dNB1LWVAGRZbdvUtZUDWEbl8eC5lQB+s++v+LmVA7h48zogvZUBkIjwvFjBlQIi6ZBynMGVAr\\u002fzGpDsxZUBKvR7Z0zFlQBUb18tvMmVAzOAQkQ8zZUBRuqo+szNlQAM+S+xaNGVAcs5sswY1ZUBCXGuvtjVlQI8TlP1qNmVAiAM3vSM3ZUC+0roP4TdlQCmWshijOGVAzeT1\\u002fWk5ZUDnRbvnNTplQMEbtQAHO2VA3jExdt07ZUDkGTt4uTxlQASIwTmbPWVAIOW+8II+ZUA3U2XWcD9lQBtnTidlQGVAFeKuI2BBZUDlvY4PYkJlQJzmBTNrQ2VAqgd+2ntEZUA13PlWlEVlQBd\\u002fYv60RmVAM0TbK95HZUDpsxxAEEllQBNS16FLSmVAv+wevpBLZUDTQ98I4ExlQEXyWv05TmVA9pu1Hp9PZUAagYr4D1FlQH65kB+NUmVA\\u002fH9OMhdUZUASId3ZrlVlQDVQv8pUV2VAet7LxQlZZUCJCy+ZzlplQFDxhCGkXGVArNsPS4teZUBaug0ThWBlQGhFMImSYmVADuY70bRkZUC\\u002fAdIk7WZlQO7la9U8aWVA0UyMTqVrZUCWSS4YKG5lQCRjednGcGVAdsTDW4NzZUA5ueyNX3ZlQCQ0G4hdeWVAjuXtj398ZUAogCwdyH9lQIQ+DN85g2VA7aAcwteGZUAxxfT2pIplQO7FvfmkjmVAV1y7mtuSZUBvuPkHTZdlQEVZTtj9m2VAguDhF\\u002fOgZUCjtoJWMqZlQFo4DLjBq2VAYYs8B6ixZUBvt2TL7LdlQEj5c2GYvmVAC2v5GLTFZUBwRtdVSs1lQFW\\u002firdm1WVAi8AcRxbeZUARSA2tZ+dlQJga2HBr8WVA\\u002fXISRDT8ZUDKAJxb1wdmQFKx+dlsFGZAJam4ThAiZkBSVLlP4TBmQN9AkDIEQWZAIhLe7aJSZkDsVrws7mVmQBf9UqEee2ZAVm+dp3aSZkBf7bZORKxmQK7VO+bjyGZAXbUtN8PoZkDLZ2+dZQxnQKT9AEppNGdAUqcQEY5hZ0D1W2pNvpRnQJTgZZoaz2dA0tPcdgkSaEADLOhcTF9oQBkzA5QcuWhAanXkN1MiaUAm4+5kop5pQJpgp27sMmpA9e12aN7lakDu5dSJScFrQOIvvwrE1WxANGJrqcZEbkAQdi4NjC9wQHoSuG9h8XFAgirKGkwpdUCbM9KFFit7QOUFPYm89oJAkj1SA13Ni0CdeEGF\\u002f2OUQGEO19dIuJxA6JhmqubnokAmg9Ogvq6mQIVZrq0ZP6hAlVCSaSnQpkBehw2u1BejQAsbd+FiEp1ATe7Voa2RlEAkETpezu6LQBNpKTqQAoNAJubP3yIwe0BTiJwceCV1QKx7vU9J7XFAElNT6TgtcEBy3fby+jtuQJbFnmORyWxA5BU7o\\u002fKza0B+B1vMYdhqQCl6sbzHJWpAWLCxbBySaUA9cmhhlxZpQLKxsSpGrmhAa6SWQWxVaEB1Xl2JKQloQFIpdPU\\u002fx2dAqw7ZyuqNZ0Du8mRVwVtnQP37kH6hL2dAYe2Z8p8IZ0BOs9E\\u002f\\u002fOVmQBaeFtgXx2ZA8lSrL2+rZkBge3JtlJJmQBtmzkcrfGZANEvYxOVnZkCsiQ6ogVVmQDUffmXGRGZAAlt1fIM1ZkAYCCEjjydmQMsS2jHFGmZAsknrQAYPZkC68pbuNgRmQB+rZ0U\\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\\u002fdWvP9ZtZUA2CE+v8G1lQF2lnBMTbmVAOKU6HT1uZUBpL5iPbm5lQIhZdj6nbmVAuX8EDOduZUDfkXXnLW9lQGS698t7b2VA0cf9v9BvZUAMWM7ULHBlQLZVUCaQcGVAfDMP2\\u002fpwZUCcr3MkbXFlQCP2Lz\\u002fncWVAntXfc2lyZUDdg98X9HJlQG1BXY6Hc2VAQxqsSSR0ZUDbKuDMynRlQHdOv617dWVA7g4Ulzd2ZUA3QHRL\\u002f3ZlQCsVkajTd2VAPPArq7V4ZUAxD9JzpnllQO3jiUynemVAdAqor7l7ZUB5HRBQ33xlQNg8OCMafmVAMF5ebWx\\u002fZUADc37Q2IBlQMLUwl5igmVA0xphsQyEZUAm+yEF3IVlQPzqOl7Vh2VALC2vtf6JZUArSjQzX4xlQNreq3f\\u002fjmVASITO\\u002femRZUA+H9iYK5VlQI1DORzUmGVAE2kHPPecZUD1oPm8raFlQEnK4hUXp2VA7oqNuFutZUDak9NmsLRlQAK5R41bvWVAGJoLqr\\u002fHZUBRI2OUdNRlQGQdX6eM5GVAxLIHZk36ZUAZ6FHjYRtmQBScoYcHVGZADI1SjiS7ZkCqEsDaCndnQBCqQgVsumhAYKnXjla0akDs6RntwXFtQIFTgfmtYnBA9s6g8fUHckACITYjpjVzQPY0wH1CanNAbhL0bp9\\u002fckCx9qTyne5wQPylXjaCdG5AvOdPrnx\\u002fa0CF67nlH0ppQHSkI1mU1WdAB++Bzpb4ZkC5uygCn35mQPdeWi0xPWZA9o2g9yQZZkA+f9xD5QJmQB6DR7ak82VAntYzzmToZUBBG1\\u002fgyN9lQFEWe5sb2WVA6PItk+3TZUArKVOp8c9lQAvdszLvzGVAqdhpZbvKZUD087V9NcllQOuMpitEyGVAE1Ezw9PHZUAOfGXt1MdlQLBUN7M7yGVAQP80yP7IZUDKjR4DF8plQOOTMfh+y2VAg45mrDLNZUCO2W9bL89lQKB\\u002fBkxz0WVAjEtMr\\u002f3TZUB45+uIztZlQC1nQ57m2WVAbD9ea0fdZUCnWt4c8+BlQJXBOY7s5GVA3pfpSzfpZUDrpVSZ1+1lQJ3OZnrS8mVAd13qwC34ZUAfTtod8P1lQL2GBzchBGZADGKQwckKZkAuzNOh8xFmQJDOuRGqGWZALUVlzvkhZkBqv6hO8SpmQPo17wOhNGZAIl61pxs\\u002fZkB1nTiYdkpmQFe\\u002frUfKVmZA5kMvwjJkZkBdLbFP0HJmQNL3uzjIgmZA7SWXtkWUZkBi4g0ae6dmQLPVTTejvGZAbmXYKQPUZkDBEomJ7O1mQAza8DHACmdAUzWeyPEqZ0Ai3a4+DE9nQIM+MaC3d2dA+eBWo8ClZ0A3k7mWItpnQB4Y148UFmhAChRXHBtbaEBBdthMIKtoQMEwUuK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ATwRxHk4AZUCCzM6i9\\u002f9kQLSULCeh\\u002f2RA5lyKq0r\\u002fZEAYJegv9P5kQErtRbSd\\u002fmRAfbWjOEf+ZECvfQG98P1kQOFFX0Ga\\u002fWRAEw69xUP9ZEBF1hpK7fxkQHieeM6W\\u002fGRAqmbWUkD8ZEDcLjTX6ftkQA73kVuT+2RAQL\\u002fv3zz7ZEBzh01k5vpkQKVPq+iP+mRA1xcJbTn6ZEAJ4Gbx4vlkQDuoxHWM+WRAbnAi+jX5ZECgOIB+3\\u002fhkQNIA3gKJ+GRABMk7hzL4ZEA2kZkL3PdkQGlZ94+F92RAmyFVFC\\u002f3ZEDN6bKY2PZkQP+xEB2C9mRAMXpuoSv2ZEBkQswl1fVkQJYKKqp+9WRAyNKHLij1ZED6muWy0fRkQCxjQzd79GRAXyuhuyT0ZECR8\\u002f4\\u002fzvNkQMO7XMR382RA9YO6SCHzZEAnTBjNyvJkQFoUdlF08mRAjNzT1R3yZEC+pDFax\\u002fFkQPBsj95w8WRAIjXtYhrxZEBV\\u002fUrnw\\u002fBkQIfFqGtt8GRAuY0G8BbwZEDrVWR0wO9kQB0ewvhp72RAUOYffRPvZECCrn0Bve5kQLR224Vm7mRA5j45ChDuZEAYB5eOue1kQEvP9BJj7WRAfZdSlwztZECvX7AbtuxkQOEnDqBf7GRAE\\u002fBrJAnsZEBGuMmosutkQHiAJy1c62RAqkiFsQXrZEDcEOM1r+pkQA7ZQLpY6mRAQaGePgLqZEBzafzCq+lkQKUxWkdV6WRA1\\u002fm3y\\u002f7oZEAJwhVQqOhkQDyKc9RR6GRAblLRWPvnZECgGi\\u002fdpOdkQNLijGFO52RABKvq5ffmZEA3c0hqoeZkQGk7pu5K5mRAmwMEc\\u002fTlZEDNy2H3neVkQP+Tv3tH5WRAMlwdAPHkZEBkJHuEmuRkQJbs2AhE5GRAyLQ2je3jZED6fJQRl+NkQC1F8pVA42RAXw1QGuriZECR1a2ek+JkQMOdCyM94mRA9WVpp+bhZEAoLscrkOFkQFr2JLA54WRAjL6CNOPgZEC+huC4jOBkQPBOPj024GRAIxecwd\\u002ffZEBV3\\u002flFid9kQIenV8oy32RAuW+1TtzeZEDrNxPThd5kQB4AcVcv3mRAUMjO29jdZECCkCxggt1kQLRYiuQr3WRA5iDoaNXcZEAZ6UXtftxkQEuxo3Eo3GRAfXkB9tHbZECvQV96e9tkQOEJvf4k22RAFNIag87aZEBGmngHeNpkQHhi1osh2mRAqio0EMvZZEDc8pGUdNlkQA+77xge2WRAQYNNncfYZEBzS6shcdhkQKUTCaYa2GRA19tmKsTXZEAKpMSubddkQDxsIjMX12RAbjSAt8DWZECg\\u002fN07atZkQNLEO8AT1mRABY2ZRL3VZEA3VffIZtVkQGkdVU0Q1WRAm+Wy0bnUZEDNrRBWY9RkQAB2btoM1GRAMj7MXrbTZEBkBirjX9NkQJbOh2cJ02RAyJbl67LSZED7XkNwXNJkQC0nofQF0mRAX+\\u002f+eK\\u002fRZECRt1z9WNFkQMN\\u002fuoEC0WRA9kcYBqzQZEAoEHaKVdBkQFrY0w7\\u002fz2RAjKAxk6jPZEC+aI8XUs9kQPEw7Zv7zmRAI\\u002flKIKXOZEBVwaikTs5kQIeJBin4zWRAuVFkraHNZEDsGcIxS81kQB7iH7b0zGRAUKp9Op7MZECCctu+R8xkQLQ6OUPxy2RA5wKXx5rLZEAZy\\u002fRLRMtkQEuTUtDtymRAfVuwVJfKZECvIw7ZQMpkQOLra13qyWRAFLTJ4ZPJZEBGfCdmPclkQHhEhermyGRAqgzjbpDIZEDd1EDzOchkQA+dnnfjx2RAQWX8+4zHZEBzLVqANsdkQKX1twTgxmRA2L0ViYnGZEAKhnMNM8ZkQDxO0ZHcxWRAbhYvFobFZECg3oyaL8VkQNOm6h7ZxGRABW9Io4LEZEA3N6YnLMRkQGn\\u002fA6zVw2RAm8dhMH\\u002fDZEDOj7+0KMNkQABYHTnSwmRAMiB7vXvCZEBk6NhBJcJkQJawNsbOwWRAyXiUSnjBZED7QPLOIcFkQC0JUFPLwGRAX9Gt13TAZECRmQtcHsBkQMRhaeDHv2RA9inHZHG\\u002fZEAo8iTpGr9kQFq6gm3EvmRAjILg8W2+ZEC\\u002fSj52F75kQPESnPrAvWRAI9v5fmq9ZEBVo1cDFL1kQIdrtYe9vGRAujMTDGe8ZEDs+3CQELxkQB7EzhS6u2RAUIwsmWO7ZECCVIodDbtkQLUc6KG2umRA5+RFJmC6ZEAZraOqCbpkQEt1AS+zuWRAfT1fs1y5ZECwBb03BrlkQOLNGryvuGRAFJZ4QFm4ZEBGXtbEArhkQHgmNEmst2RAq+6RzVW3ZEDdtu9R\\u002f7ZkQA9\\u002fTdaotmRAQUerWlK2ZEBzDwnf+7VkQKbXZmOltWRA2J\\u002fE5061ZEAKaCJs+LRkQDwwgPChtGRAbvjddEu0ZEChwDv59LNkQNOImX2es2RABVH3AUizZEA3GVWG8bJkQGnhsgqbsmRAnKkQj0SyZEDOcW4T7rFkQAA6zJeXsWRAMgIqHEGxZEBkyoeg6rBkQJeS5SSUsGRAyVpDqT2wZED7IqEt569kQC3r\\u002frGQr2RAX7NcNjqvZECSe7q6465kQMRDGD+NrmRA9gt2wzauZEAo1NNH4K1kQFqcMcyJrWRAjWSPUDOtZEC\\u002fLO3U3KxkQPH0SlmGrGRAI72o3S+sZEBVhQZi2atkQIhNZOaCq2RAuhXCaiyrZEDs3R\\u002fv1apkQB6mfXN\\u002fqmRAUG7b9yiqZECDNjl80qlkQLX+lgB8qWRA58b0hCWpZEAZj1IJz6hkQEtXsI14qGRAfh8OEiKoZECw52uWy6dkQOKvyRp1p2RAFHgnnx6nZEBGQIUjyKZkQHkI46dxpmRAq9BALBumZEDdmJ6wxKVkQA9h\\u002fDRupWRAQSlauRelZEB08bc9waRkQKa5FcJqpGRA2IFzRhSkZEAKStHKvaNkQDwSL09no2RAb9qM0xCjZEChoupXuqJkQNNqSNxjomRABTOmYA2iZEA3+wPltqFkQGrDYWlgoWRAnIu\\u002f7QmhZEDOUx1ys6BkQAAce\\u002fZcoGRAMuTYegagZEBlrDb\\u002fr59kQJd0lINZn2RAyTzyBwOfZED7BFCMrJ5kQC3NrRBWnmRAYJULlf+dZECSXWkZqZ1kQMQlx51SnWRA9u0kIvycZEAotoKmpZxkQFt+4CpPnGRAjUY+r\\u002fibZEC\\u002fDpwzoptkQPHW+bdLm2RAI59XPPWaZEBWZ7XAnppkQIgvE0VImmRAuvdwyfGZZEDsv85Nm5lkQB6ILNJEmWRAUVCKVu6YZECDGOjal5hkQLXgRV9BmGRA56ij4+qXZEAZcQFolJdkQEw5X+w9l2RAfgG9cOeWZECwyRr1kJZkQOKReHk6lmRAFFrW\\u002feOVZEBHIjSCjZVkQHnqkQY3lWRAq7LviuCUZEDdek0PipRkQA9Dq5MzlGRAQgsJGN2TZEB002achpNkQKabxCAwk2RA2GMipdmSZEAKLIApg5JkQD303a0skmRAb7w7MtaRZEChhJm2f5FkQNNM9zopkWRABRVVv9KQZEA43bJDfJBkQGqlEMglkGRAnG1uTM+PZEDONczQeI9kQAD+KVUij2RAM8aH2cuOZEBljuVddY5kQJdWQ+IejmRAyR6hZsiNZED75v7qcY1kQC6vXG8bjWRAYHe688SMZECSPxh4boxkQMQHdvwXjGRA9s\\u002fTgMGLZEApmDEFa4tkQFtgj4kUi2RAjSjtDb6KZECoctpT8opkQMO8x5kmi2RA3ga131qLZED4UKIlj4tkQBObj2vDi2RALuV8sfeLZEBJL2r3K4xkQGR5Vz1gjGRAf8NEg5SMZECZDTLJyIxkQLRXHw\\u002f9jGRAz6EMVTGNZEDq6\\u002fmaZY1kQAU25+CZjWRAIIDUJs6NZEA6ysFsAo5kQFUUr7I2jmRAcF6c+GqOZECLqIk+n45kQKbydoTTjmRAwTxkygePZEDbhlEQPI9kQPbQPlZwj2RAERssnKSPZEAsZRni2I9kQEevBigNkGRAYvnzbUGQZEB8Q+GzdZBkQJeNzvmpkGRAste7P96QZEDNIamFEpFkQOhrlstGkWRAA7aDEXuRZEAdAHFXr5FkQDhKXp3jkWRAU5RL4xeSZEBu3jgpTJJkQIkoJm+AkmRApHITtbSSZEC+vAD76JJkQNkG7kAdk2RA9FDbhlGTZEAPm8jMhZNkQCrltRK6k2RARS+jWO6TZEBgeZCeIpRkQHrDfeRWlGRAlQ1rKouUZECwV1hwv5RkQMuhRbbzlGRA5usy\\u002fCeVZEABNiBCXJVkQBuADYiQlWRANsr6zcSVZEBRFOgT+ZVkQGxe1VktlmRAh6jCn2GWZECi8q\\u002fllZZkQLw8nSvKlmRA14aKcf6WZEDy0He3MpdkQA0bZf1ml2RAKGVSQ5uXZEBDrz+Jz5dkQF35LM8DmGRAeEMaFTiYZECTjQdbbJhkQK7X9KCgmGRAySHi5tSYZEDka88sCZlkQP61vHI9mWRAGQCquHGZZEA0Spf+pZlkQE+UhETamWRAat5xig6aZECFKF\\u002fQQppkQJ9yTBZ3mmRAurw5XKuaZEDVBiei35pkQPBQFOgTm2RAC5sBLkibZEAm5e5zfJtkQEEv3Lmwm2RAW3nJ\\u002f+SbZEB2w7ZFGZxkQJENpItNnGRArFeR0YGcZEDHoX4XtpxkQOLra13qnGRA\\u002fDVZox6dZEAXgEbpUp1kQDLKMy+HnWRATRQhdbudZEBoXg67751kQIOo+wAknmRAnfLoRlieZEC4PNaMjJ5kQNOGw9LAnmRA7tCwGPWeZEAJG55eKZ9kQCRli6Rdn2RAPq946pGfZEBZ+WUwxp9kQHRDU3b6n2RAj41AvC6gZECq1y0CY6BkQMUhG0iXoGRA32sIjsugZED6tfXT\\u002f6BkQBUA4xk0oWRAMErQX2ihZEBLlL2lnKFkQGbequvQoWRAgCiYMQWiZECbcoV3OaJkQLa8cr1tomRA0QZgA6KiZEDsUE1J1qJkQAebOo8Ko2RAIuUn1T6jZEA8LxUbc6NkQFd5AmGno2RAcsPvptujZECNDd3sD6RkQKhXyjJEpGRAw6G3eHikZEDd66S+rKRkQPg1kgThpGRAE4B\\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\\u002fJKr2RAbEPFOH+vZECHjbJ+s69kQKHXn8Tnr2RAvCGNChywZEDXa3pQULBkQPK1Z5aEsGRADQBV3LiwZEAoSkIi7bBkQEKUL2ghsWRAXd4crlWxZEB4KAr0ibFkQJNy9zm+sWRArrzkf\\u002fKxZEDJBtLFJrJkQONQvwtbsmRA\\u002fpqsUY+yZEAZ5ZmXw7JkQDQvh933smRAT3l0IyyzZEBqw2FpYLNkQIQNT6+Us2RAn1c89cizZEC6oSk7\\u002fbNkQNXrFoExtGRA8DUEx2W0ZEALgPEMmrRkQCbK3lLOtGRAQBTMmAK1ZEBbXrneNrVkQHaopiRrtWRAkfKTap+1ZECsPIGw07VkQMeGbvYHtmRA4dBbPDy2ZED8GkmCcLZkQBdlNsiktmRAMq8jDtm2ZEBN+RBUDbdkQGhD\\u002fplBt2RAgo3r33W3ZECd19glqrdkQLghxmvet2RA02uzsRK4ZEDutaD3RrhkQAkAjj17uGRAI0p7g6+4ZEA+lGjJ47hkQFneVQ8YuWRAdChDVUy5ZECPcjCbgLlkQKq8HeG0uWRAxAYLJ+m5ZEDfUPhsHbpkQPqa5bJRumRAFeXS+IW6ZEAwL8A+urpkQEt5rYTuumRAZcOayiK7ZECADYgQV7tkQJtXdVaLu2RAtqFinL+7ZEDR60\\u002fi87tkQOw1PSgovGRAB4Aqbly8ZEAhyhe0kLxkQDwUBfrEvGRAV17yP\\u002fm8ZEByqN+FLb1kQI3yzMthvWRAqDy6EZa9ZEDChqdXyr1kQN3QlJ3+vWRA+BqC4zK+ZEATZW8pZ75kQC6vXG+bvmRASflJtc++ZEBjQzf7A79kQH6NJEE4v2RAmdcRh2y\\u002fZEC0If\\u002fMoL9kQM9r7BLVv2RA6rXZWAnAZEAEAMeePcBkQB9KtORxwGRAOpShKqbAZEBV3o5w2sBkQHAofLYOwWRAi3Jp\\u002fELBZEClvFZCd8FkQMAGRIirwWRA21Axzt\\u002fBZED2mh4UFMJkQBHlC1pIwmRALC\\u002f5n3zCZEBGeeblsMJkQGHD0yvlwmRAfA3BcRnDZECXV663TcNkQLKhm\\u002f2Bw2RAzeuIQ7bDZEDoNXaJ6sNkQAKAY88exGRAHcpQFVPEZEA4FD5bh8RkQFNeK6G7xGRAbqgY5+\\u002fEZECJ8gUtJMVkQKM883JYxWRAvobguIzFZEDZ0M3+wMVkQPQau0T1xWRAD2WoiinGZEAqr5XQXcZkQET5ghaSxmRAX0NwXMbGZEB6jV2i+sZkQJXXSugux2RAsCE4LmPHZEDLayV0l8dkQOW1ErrLx2RAAAAAAADIZEAbSu1FNMhkQDaU2otoyGRAUd7H0ZzIZEBsKLUX0chkQIZyol0FyWRAobyPoznJZEC8Bn3pbclkQNdQai+iyWRA8ppXddbJZEAN5US7CspkQCcvMgE\\u002fymRAQnkfR3PKZEBdwwyNp8pkQHgN+tLbymRAk1fnGBDLZECuodReRMtkQMjrwaR4y2RA4zWv6qzLZED+f5ww4ctkQBnKiXYVzGRANBR3vEnMZEBPXmQCfsxkQGqoUUiyzGRAhPI+jubMZECfPCzUGs1kQLqGGRpPzWRA1dAGYIPNZEDwGvSlt81kQAtl4evrzWRAJa\\u002fOMSDOZEBA+bt3VM5kQFtDqb2IzmRAdo2WA73OZECR14NJ8c5kQKwhcY8lz2RAxmte1VnPZEDhtUsbjs9kQPz\\u002fOGHCz2RAF0omp\\u002fbPZEAylBPtKtBkQE3eADNf0GRAZyjueJPQZECCctu+x9BkQJ28yAT80GRAuAa2SjDRZEDTUKOQZNFkQO6akNaY0WRACOV9HM3RZEAjL2tiAdJkQD55WKg10mRAWcNF7mnSZEB0DTM0ntJkQI9XIHrS0mRAqaENwAbTZEDE6\\u002foFO9NkQN816Etv02RA+n\\u002fVkaPTZEAVysLX19NkQDAUsB0M1GRASl6dY0DUZEBlqIqpdNRkQIDyd++o1GRAmzxlNd3UZEC2hlJ7EdVkQNHQP8FF1WRA7BotB3rVZEAGZRpNrtVkQCGvB5Pi1WRAPPn02BbWZEBXQ+IeS9ZkQHKNz2R\\u002f1mRAjde8qrPWZECnIarw59ZkQMJrlzYc12RA3bWEfFDXZED4\\u002f3HChNdkQBNKXwi512RALpRMTu3XZEBI3jmUIdhkQGMoJ9pV2GRAfnIUIIrYZECZvAFmvthkQLQG76vy2GRAz1Dc8SbZZEDpmsk3W9lkQATltn2P2WRAHy+kw8PZZEA6eZEJ+NlkQFXDfk8s2mRAcA1slWDaZECKV1nblNpkQKWhRiHJ2mRAwOszZ\\u002f3aZEDbNSGtMdtkQPZ\\u002fDvNl22RAEcr7OJrbZEArFOl+zttkQEZe1sQC3GRAYajDCjfcZEB88rBQa9xkQK36XG2l3GRA3gIJit\\u002fcZEAPC7WmGd1kQEETYcNT3WRAchsN4I3dZECjI7n8x91kQNQrZRkC3mRABTQRNjzeZEA2PL1Sdt5kQGhEaW+w3mRAmUwVjOreZEDKVMGoJN9kQPtcbcVe32RALGUZ4pjfZEBdbcX+0t9kQI51cRsN4GRAwH0dOEfgZEDxhclUgeBkQCKOdXG74GRAU5YhjvXgZECEns2qL+FkQLWmecdp4WRA5q4l5KPhZEAYt9EA3uFkQEm\\u002ffR0Y4mRAescpOlLiZECrz9VWjOJkQNzXgXPG4mRADeAtkADjZEA\\u002f6NmsOuNkQHDwhcl042RAofgx5q7jZEDSAN4C6eNkQAMJih8j5GRANBE2PF3kZEBlGeJYl+RkQJchjnXR5GRAyCk6kgvlZED5MeauReVkQCo6kst\\u002f5WRAW0I+6LnlZECMSuoE9OVkQL5SliEu5mRA71pCPmjmZEAgY+5aouZkQFFrmnfc5mRAgnNGlBbnZECze\\u002fKwUOdkQOSDns2K52RAFoxK6sTnZEBHlPYG\\u002f+dkQHicoiM56GRAqaROQHPoZEDarPpcrehkQAu1pnnn6GRAPL1SliHpZEBuxf6yW+lkQJ\\u002fNqs+V6WRA0NVW7M\\u002fpZEAB3gIJCupkQDLmriVE6mRAY+5aQn7qZECV9gZfuOpkQMb+snvy6mRA9wZfmCzrZEAoDwu1ZutkQFkXt9Gg62RAih9j7trrZEC7Jw8LFexkQO0vuydP7GRAHjhnRInsZEBPQBNhw+xkQIBIv3397GRAsVBrmjftZEDiWBe3ce1kQBRhw9Or7WRARWlv8OXtZEB2cRsNIO5kQKd5xyla7mRA2IFzRpTuZEAJih9jzu5kQDqSy38I72RAbJp3nELvZECdoiO5fO9kQM6qz9W272RA\\u002f7J78vDvZEAwuycPK\\u002fBkQGHD0ytl8GRAk8t\\u002fSJ\\u002fwZEDE0ytl2fBkQPXb14ET8WRAJuSDnk3xZEBX7C+7h\\u002fFkQIj029fB8WRAufyH9PvxZEDrBDQRNvJkQBwN4C1w8mRATRWMSqryZEB+HThn5PJkQK8l5IMe82RA4C2QoFjzZEARNjy9kvNkQEM+6NnM82RAdEaU9gb0ZEClTkATQfRkQNZW7C979GRAB1+YTLX0ZEA4Z0Rp7\\u002fRkQGpv8IUp9WRAm3ecomP1ZEDMf0i\\u002fnfVkQP2H9NvX9WRALpCg+BH2ZEBfmEwVTPZkQJCg+DGG9mRAwqikTsD2ZEDzsFBr+vZkQCS5\\u002fIc092RAVcGopG73ZECGyVTBqPdkQLfRAN7i92RA6dms+hz4ZEAa4lgXV\\u002fhkQEvqBDSR+GRAfPKwUMv4ZECt+lxtBflkQN4CCYo\\u002f+WRADwu1pnn5ZEBBE2HDs\\u002flkQHIbDeDt+WRAoyO5\\u002fCf6ZEDUK2UZYvpkQAU0ETac+mRANjy9Utb6ZEBnRGlvEPtkQJlMFYxK+2RAylTBqIT7ZED7XG3FvvtkQCxlGeL4+2RAXW3F\\u002fjL8ZECOdXEbbfxkQMB9HTin\\u002fGRA8YXJVOH8ZEAijnVxG\\u002f1kQFOWIY5V\\u002fWRAhJ7Nqo\\u002f9ZEC1pnnHyf1kQOauJeQD\\u002fmRAGLfRAD7+ZEBJv30deP5kQHrHKTqy\\u002fmRAq8\\u002fVVuz+ZEDc14FzJv9kQA3gLZBg\\u002f2RAP+jZrJr\\u002fZEBw8IXJ1P9kQKH4MeYOAGVA0gDeAkkAZUADCYofgwBlQDQRNjy9AGVAZRniWPcAZUCXIY51MQFlQMgpOpJrAWVA+THmrqUBZUAqOpLL3wFlQFtCPugZAmVAjErqBFQCZUC9UpYhjgJlQO9aQj7IAmVAIGPuWgIDZUBRa5p3PANlQIJzRpR2A2VAs3vysLADZUDkg57N6gNlQBaMSuokBGVAR5T2Bl8EZUB4nKIjmQRlQKmkTkDTBGVA2qz6XA0FZUALtaZ5RwVlQDy9UpaBBWVAbsX+srsFZUCfzarP9QVlQNDVVuwvBmVAAd4CCWoGZUAy5q4lpAZlQGPuWkLeBmVAlfYGXxgHZUDG\\u002frJ7UgdlQPcGX5iMB2VAKA8LtcYHZUBZF7fRAAhlQIofY+46CGVAuycPC3UIZUDtL7snrwhlQB44Z0TpCGVAT0ATYSMJZUCASL99XQllQLFQa5qXCWVA4lgXt9EJZUATYcPTCwplQEVpb\\u002fBFCmVAdnEbDYAKZUCneccpugplQNiBc0b0CmVACYofYy4LZUA6kst\\u002faAtlQGyad5yiC2VAnaIjudwLZUDOqs\\u002fVFgxlQP+ye\\u002fJQDGVAMLsnD4sMZUBhw9MrxQxlQJLLf0j\\u002fDGVAxNMrZTkNZUD129eBcw1lQCbkg56tDWVAV+wvu+cNZUCI9NvXIQ5lQLn8h\\u002fRbDmVA6wQ0EZYOZUAcDeAt0A5lQE0VjEoKD2VAfh04Z0QPZUCvJeSDfg9lQOAtkKC4D2VAETY8vfIPZUBDPujZLBBlQHRGlPZmEGVApU5AE6EQZUDWVuwv2xBlQAdfmEwVEWVAOGdEaU8RZUBqb\\u002fCFiRFlQJt3nKLDEWVAzH9Iv\\u002f0RZUD9h\\u002fTbNxJlQC6QoPhxEmVAX5hMFawSZUCQoPgx5hJlQMKopE4gE2VA87BQa1oTZUAkufyHlBNlQFXBqKTOE2VAhslUwQgUZUC30QDeQhRlQOjZrPp8FGVAGuJYF7cUZUBL6gQ08RRlQHzysFArFWVArfpcbWUVZUDeAgmKnxVlQA8LtabZFWVAQRNhwxMWZUByGw3gTRZlQKMjufyHFmVA1CtlGcIWZUAFNBE2\\u002fBZlQDY8vVI2F2VAZ0Rpb3AXZUCZTBWMqhdlQMpUwajkF2VA+1xtxR4YZUAsZRniWBhlQF1txf6SGGVAjnVxG80YZUDAfR04BxllQPGFyVRBGWVAIo51cXsZZUBTliGOtRllQISezarvGWVAtaZ5xykaZUDmriXkYxplQBi30QCeGmVASb99HdgaZUB6xyk6EhtlQKvP1VZMG2VA3NeBc4YbZUAN4C2QwBtlQD7o2az6G2VAcPCFyTQcZUCh+DHmbhxlQNIA3gKpHGVAAwmKH+McZUA0ETY8HR1lQGUZ4lhXHWVAliGOdZEdZUDIKTqSyx1lQPkx5q4FHmVAKjqSyz8eZUBbQj7oeR5lQIxK6gS0HmVAvVKWIe4eZUDvWkI+KB9lQCBj7lpiH2VAUWuad5wfZUCCc0aU1h9lQLN78rAQIGVA5IOezUogZUAVjErqhCBlQEeU9ga\\u002fIGVAeJyiI\\u002fkgZUCppE5AMyFlQNqs+lxtIWVAC7WmeachZUA8vVKW4SFlQG7F\\u002frIbImVAn82qz1UiZUDQ1VbsjyJlQAHeAgnKImVAMuauJQQjZUBj7lpCPiNlQJT2Bl94I2VAxv6ye7IjZUD3Bl+Y7CNlQCgPC7UmJGVAWRe30WAkZUCKH2PumiRlQLsnDwvVJGVA7S+7Jw8lZUAeOGdESSVlQE9AE2GDJWVAgEi\\u002ffb0lZUCxUGua9yVlQOJYF7cxJmVAE2HD02smZUBFaW\\u002fwpSZlQHZxGw3gJmVAp3nHKRonZUDYgXNGVCdlQAmKH2OOJ2VAOpLLf8gnZUBsmnecAihlQJ2iI7k8KGVAzqrP1XYoZUD\\u002fsnvysChlQDC7Jw\\u002frKGVAYcPTKyUpZUCSy39IXyllQMTTK2WZKWVA9dvXgdMpZUAm5IOeDSplQFfsL7tHKmVAiPTb14EqZUC5\\u002fIf0uyplQOoENBH2KmVAHA3gLTArZUBNFYxKaitlQH4dOGekK2VAryXkg94rZUDgLZCgGCxlQBE2PL1SLGVAQz7o2YwsZUB0RpT2xixlQKVOQBMBLWVA1lbsLzstZUAHX5hMdS1lQDhnRGmvLWVAaW\\u002fwhektZUCbd5yiIy5lQMx\\u002fSL9dLmVA\\u002fYf025cuZUAukKD40S5lQF+YTBUML2VAkKD4MUYvZUDBqKROgC9lQPOwUGu6L2VAJLn8h\\u002fQvZUBVwaikLjBlQIbJVMFoMGVAt9EA3qIwZUDo2az63DBlQBriWBcXMWVAS+oENFExZUB88rBQizFlQK36XG3FMWVA3gIJiv8xZUAPC7WmOTJlQEATYcNzMmVAchsN4K0yZUCjI7n85zJlQNQrZRkiM2VABTQRNlwzZUA2PL1SljNlQGdEaW\\u002fQM2VAmUwVjAo0ZUDKVMGoRDRlQPtcbcV+NGVALGUZ4rg0ZUBdbcX+8jRlQI51cRstNWVAv30dOGc1ZUDxhclUoTVlQCKOdXHbNWVAU5YhjhU2ZUCEns2qTzZlQLWmeceJNmVA5q4l5MM2ZUAYt9EA\\u002fjZlQEm\\u002ffR04N2VAescpOnI3ZUCrz9VWrDdlQNzXgXPmN2VADeAtkCA4ZUA+6NmsWjhlQHDwhcmUOGVAofgx5s44ZUDSAN4CCTllQAMJih9DOWVANBE2PH05ZUBlGeJYtzllQJYhjnXxOWVAyCk6kis6ZUD5MeauZTplQCo6ksufOmVAW0I+6Nk6ZUCMSuoEFDtlQL1SliFOO2VA71pCPog7ZUAgY+5awjtlQFFrmnf8O2VAgnNGlDY8ZUCze\\u002fKwcDxlQOSDns2qPGVAFYxK6uQ8ZUBHlPYGHz1lQHicoiNZPWVAqaROQJM9ZUDarPpczT1lQAu1pnkHPmVAPL1SlkE+ZUBtxf6yez5lQJ\\u002fNqs+1PmVA0NVW7O8+ZUAB3gIJKj9lQDLmriVkP2VAY+5aQp4\\u002fZUCU9gZf2D9lQMb+snsSQGVA9wZfmExAZUAoDwu1hkBlQFkXt9HAQGVAih9j7vpAZUC7Jw8LNUFlQOwvuydvQWVAHjhnRKlBZUBPQBNh40FlQIBIv30dQmVAsVBrmldCZUDiWBe3kUJlQBNhw9PLQmVARWlv8AVDZUB2cRsNQENlQKd5xyl6Q2VA2IFzRrRDZUAJih9j7kNlQDqSy38oRGVAa5p3nGJEZUCdoiO5nERlQM6qz9XWRGVA\\u002f7J78hBFZUAwuycPS0VlQGHD0yuFRWVAkst\\u002fSL9FZUDE0ytl+UVlQPXb14EzRmVAJuSDnm1GZUBX7C+7p0ZlQIj029fhRmVAufyH9BtHZUDqBDQRVkdlQBwN4C2QR2VATRWMSspHZUB+HThnBEhlQK8l5IM+SGVA4C2QoHhIZUARNjy9skhlQEM+6NnsSGVAdEaU9iZJZUClTkATYUllQNZW7C+bSWVAB1+YTNVJZUA4Z0RpD0plQGlv8IVJSmVAm3ecooNKZUDMf0i\\u002fvUplQP2H9Nv3SmVALpCg+DFLZUBfmEwVbEtlQJCg+DGmS2VAwaikTuBLZUDzsFBrGkxlQCS5\\u002fIdUTGVAVcGopI5MZUCGyVTByExlQLfRAN4CTWVA6Nms+jxNZUAa4lgXd01lQEvqBDSxTWVAfPKwUOtNZUCt+lxtJU5lQN4CCYpfTmVADwu1pplOZUBAE2HD005lQHIbDeANT2VAoyO5\\u002fEdPZUDUK2UZgk9lQAU0ETa8T2VANjy9UvZPZUBnRGlvMFBlQJhMFYxqUGVAylTBqKRQZUD7XG3F3lBlQCxlGeIYUWVAXW3F\\u002flJRZUCOdXEbjVFlQL99HTjHUWVA8YXJVAFSZUAijnVxO1JlQFOWIY51UmVAhJ7Nqq9SZUC1pnnH6VJlQOauJeQjU2VAF7fRAF5TZUBJv30dmFNlQHrHKTrSU2VAq8\\u002fVVgxUZUDc14FzRlRlQA3gLZCAVGVAPujZrLpUZUBw8IXJ9FRlQKH4MeYuVWVA0gDeAmlVZUADCYofo1VlQDQRNjzdVWVAZRniWBdWZUCWIY51UVZlQMgpOpKLVmVA+THmrsVWZUAqOpLL\\u002f1ZlQFtCPug5V2VAjErqBHRXZUC9UpYhrldlQO9aQj7oV2VAIGPuWiJYZUBRa5p3XFhlQIJzRpSWWGVAs3vysNBYZUDkg57NClllQBWMSupEWWVAR5T2Bn9ZZUB4nKIjuVllQKmkTkDzWWVA2qz6XC1aZUALtaZ5Z1plQDy9UpahWmVAbcX+sttaZUCfzarPFVtlQNDVVuxPW2VAAd4CCYpbZUAy5q4lxFtlQGPuWkL+W2VAlPYGXzhcZUDG\\u002frJ7clxlQPcGX5isXGVAKA8LteZcZUBZF7fRIF1lQIofY+5aXWVAuycPC5VdZUDsL7snz11lQB44Z0QJXmVAT0ATYUNeZUCASL99fV5lQLFQa5q3XmV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fZUCXIvJhWh9lQDqnYx\\u002fZHmVAXyz8algeZUBwTZtC2B1lQFL9K6RYHWVAHkOkjdkcZUDN+AT9WhxlQNaLWfDcG2VAl7+3ZV8bZUCVcT9b4hplQHNfGs9lGmVAmu57v+kZZUCH9aAqbhllQLGGzw7zGGVA87xWangYZUCDiY47\\u002fhdlQE+D14CEF2VA2beaOAsXZUBgfUlhkhZlQHJGXfkZFmVAw3ZX\\u002f6EVZUBHOcFxKhVlQJdXK0+zFGVAfhIuljwUZUC8+2hFxhNlQOvQgltQE2VAglcp19oSZUD4ORG3ZRJlQPHl9fnwEWVAfWuZnnwRZUBZXcSjCBFlQC+yRQiVEGVA26byyiEQZUCdoabqrg9lQD4WQ2Y8D2VAK2uvPMoOZUBo39hsWA5lQH9xsvXmDWVAPsc01nUNZUBeFl4NBQ1lQAIOMpqUDGVADcG5eyQMZUBXkQOxtAtlQKsbIzlFC2VAnyQxE9YKZUA9hks+ZwplQHEelbn4CWVAWL41hIoJZUBMGlqdHAllQMW6MwSvCGVABO74t0EIZUCQuuS31AdlQHrSNgNoB2VAeoczmfsGZUDNvyN5jwZlQPjrVKIjBmVAR\\u002f0YFLgFZUA9XcbNTAVlQMTlt87hBGVAS9pMFncEZUC74eijDARlQE8B9HaiA2VAYJjajjgDZUANXQ3rzgJlQOtZAYtlAmVAn+wvbvwBZUCBxRaUkwFlQEvoN\\u002fwqAWVA0a0ZpsIAZUDfxkaRWgBlQDRATr3y\\u002f2RAq4fDKYv\\u002fZECscj7WI\\u002f9kQN1FW8K8\\u002fmRAKb667VX+ZEAzGwJY7\\u002f1kQDsr2wCJ\\u002fWRAj1j05yL9ZECWuAANvfxkQIgcuG9X\\u002fGRA9yPXD\\u002fL7ZEApUR\\u002ftjPtkQHcfVwco+2RAvhtKXsP6ZED\\u002f\\u002fsjxXvpkQF\\u002fLqcH6+WRAkuvHzZb5ZED0VAQWM\\u002flkQGCsRZrP+GRABm54Wmz4ZEBhGI9WCfhkQItago6m92RAMUZRAkT3ZEBMhQGy4fZkQPeTn51\\u002f9mRAr\\u002f4+xR32ZEAvpfoovPVkQGwC9cha9WRA3XlYpfn0ZECdqle+mPRkQL\\u002fILRQ49GRAWP0ep9fzZEC8zXh3d\\u002fNkQIaKkoUX82RA+cbN0bfyZEB+2ZZcWPJkQNplZSb58WRAD\\u002fK8L5rxZECkhi15O\\u002fFkQGVbVAPd8GRAnpHczn7wZED4\\u002fH\\u002fcIPBkQEf8By3D72RArWNOwWXvZECReT6aCO9kQDwI1rir7mRA04UmHk\\u002fuZED4VFbL8u1kQCYgosGW7WRAgFJeAjvtZEDRsPiO3+xkQOEV+miE7GRApVUIkinsZEAfS+gLz+tkQEkVgNh062RA2IfZ+RrrZEA81SRywepkQOp3u0No6mRAqWEjcQ\\u002fqZEBweBL9tulkQHRpcupe6WRA09tkPAfpZEDWDUj2r+hkQNLouxtZ6GRAkpmnsALoZEDcvD+5rOdkQMgxDTpX52RAJab0NwLnZEDG8z64reZkQP9oosBZ5mRAPxpNVwbmZEARX\\u002fCCs+VkQPuhzUph5WRAf7DEtg\\u002flZECNv2PPvuRkQMRg+Z1u5GRAia6oLB\\u002fkZEAzAICG0ONkQN2EkreC42RAWTMVzTXjZEBykH\\u002fV6eJkQCvjsOCe4mRAjIkaAFXiZECWQPBGDOJkQABZX8rE4WRAHgHOoX7hZEA2BSTnOeFkQOKsHrf24GRAmbCxMbXgZEBxqnd6deBkQCrlNLk34GRA5gpwGvzfZEAc+iTQwt9kQCH9lhKM32RA6tVIIVjfZEBOlSJEJ99kQOcjz8z53mRABNpdGNDeZEAhrjeRqt5kQBKYe7GJ3mRAHhfbBW7eZEB+vRYxWN5kQFPOQ\\u002fBI3mRA7SYQIEHeZEBS90nDQd5kQHnKBgtM3mRAtE3jYGHeZEDQZv5zg95kQDo1i0m03mRAo44lUvbeZEAH04WFTN9kQKWf04a632RAs4jF1ETgZECG2SwK8eBkQHBTHDbG4WRAL2bTWM3iZEC+kPMkEuRkQFNmQ2Ok5WRAycshJJvnZECdUgI3HepkQFRz++d37WRAaAWwX1fyZEDqptOaQPpkQOSVp+NsCGVAz8zHm\\u002fkiZUDemIxb5FNlQHMYt9SbqGVAdyyPJZ0uZkBZ34YuhuxmQP+6HzBo2WdAalkOMUrWaEBYggYMk61pQLsiu\\u002fqmHWpAv3L4kv75aUD65MzSp1BpQD5tdoPKXWhAcnLDZ+tgZ0DkEjaeCYZmQELQyPdb4WVAyalE91NzZUCOFFPi1jBlQMh\\u002fUcGZC2VAHPZRw333ZEAn5UElYuxkQB8yyImv5WRA53sEiyDhZEA7NImwrt1kQOAGN9Dm2mRAgNmpAJPYZEA+qS7gldZkQBIAmJDc1GRAlKcJkFnTZED\\u002fKwiqAtJkQGJvvvXP0GRApwuWOrvPZEBL5yeGv85kQJ8N19\\u002fYzWRA4DV2EATNZECgDh94PsxkQEybTe6Fy2RAl52BqdjKZEDGJXMsNcpkQJFRczeayWRAhiD0vAbJZED2iHfYechkQDOEV8fyx2RAApX84nDHZEBRZzKc88ZkQPZfXXd6xmRASDdiCQXGZECyZhv1ksVkQLI+QOkjxWRASJmnnrfEZED7ytTWTcRkQAEQwVrmw2RAmH7W+YDDZEC6tROJHcNkQFI1UeK7wmRATKai41vCZEDyas9u\\u002fcFkQEuq3migwWRAFLizuUTBZEDCR7hL6sBkQJ1KkguRwGRAmrXj5zjAZEC3thLR4b9kQGQfGbmLv2RANvxZkza\\u002fZECue3xU4r5kQMxoS\\u002fKOvmRAjpqYYzy+ZECh0SOg6r1kQHqRhKCZvWRA2ZIWXkm9ZEDYe+jS+bxkQJiVrPmqvGRAtkGrzVy8ZEA5+rZKD7xkQPetIW3Cu2RAvVGzMXa7ZECpg6GVKrtkQPUiiJbfumRALsJiMpW6ZEBO3YZnS7pkQCjAnjQCumRADwylmLm5ZEDkzeCScblkQLcX4iIquWRAxRJ\\u002fSOO4ZEAkgNEDnbhkQNifNFVXuGRADndDPRK4ZECQb9e8zbdkQCFLB9WJt2RAlGYmh0a3ZEAmScTUA7dkQDx9rL\\u002fBtmRAkrDmSYC2ZEBNGrd1P7ZkQB4mn0X\\u002ftWRANmRevL+1ZEBGvvPcgLVkQHnynqpCtWRAtlXiKAW1ZEAv34RbyLRkQLh\\u002flEaMtGRAB8ho7lC0ZECj4aVXFrRkQMveP4fcs2RAe2Z+gqOzZEBZwgBPa7NkQCBVwvIzs2RAEIEfdP2yZEDIBtvZx7JkQATmIyuTsmRA58qbb1+yZECkE16vLLJkQPR7B\\u002fP6sWRAEH2+Q8qxZED7cjyrmrFkQJSZ1zNssWRAUvaN6D6xZEDzRRHVErFkQBwI1AXosGRAQcYXiL6wZECOtvxplrBkQKrfkrpvsGRA0+bsiUqwZEDotjTpJrBkQAo1wuoEsGRAPT40ouSvZEA4MYwkxq9kQJZQTIipr2RAslOZ5Y6vZEBqiF9Wdq9kQJj1e\\u002fZfr2RAuv3p40uvZECfE\\u002fY+Oq9kQO4ndiorr2RAHo8IzB6vZEAjPFpMFa9kQJdNddcOr2RAmBIZnQuvZEDH2hzRC69kQDsb36sPr2RAvrLCahevZECuY7xQI69kQKX08qYzr2RA0NR0vUivZEDjoQbsYq9kQGaIDpOCr2RADjGhHKivZEDCzrX9069kQOzwh7cGsGRAHQkv2UCwZEAWJ3UBg7BkQNxd+ODNsGRAQ6akPCKxZEA3hyayC7JkQIJonXYAs2RAbaFBnwG0ZEBk5oFkELVkQDeKiycutmRAbx3deFy3ZEBDGR4gnbhkQOAqhSXyuWRAPuop3V27ZEBQtbf04rxkQJ\\u002f4F4SEvmRA+APVIUbAZECmTi\\u002f8K8JkQB3gKvg6xGRALqtA2HjGZECiVups7MhkQN8B\\u002fdGdy2RAlp7OvJbOZECZLpHg4tFkQITEVnCQ1WRAncQXybDZZEA9ykhSWd5kQArZxamk42RASxaJObTpZED3egNnsvBkQLAGHrXV+GRA46WZs2UCZUBEm05cxA1lQAmstl6EG2VAlMdoyaUsZUBhX56POkNlQMRRKND7Y2VA4XmA2KGZZUBGPqB4nfplQFaX0rg1sGZA4OpetW\\u002f6Z0BUgZg3DihqQO3xy\\u002fJCem1ArvzP87v6cEDk8NIoUphzQJ+8qnXgHXZADpPjaXjad0BygPsOHCV4QIbk7+xk23ZAClYwClCEdEDA0pwKfdtxQHrieAe94m5A0BwEo88ja0AuW8rRqpZoQF8ssLlsCGdAEURt6NgpZkC7jJvJebNlQEhHaHoEdGVA+BFH\\u002fk5PZUAr5viFajdlQIubDy4aJmVAKGMhqqoYZUAvymGe3A1lQPMTNWMDBWVA8MpkhLD9ZED700gzlfdkQBGW2iZ28mRAMwO9pCXuZECIP1Xif+pkQHhJy4to52RAt\\u002fWx\\u002fcjkZEBMOYj3juJkQL0gf6Or4GRAG3KZ2xLfZEDCaYWbut1kQB3fcJOa3GRA8WRv06vbZED+ylyJ6NpkQA4NycxL2mRAq6SkddHZZEB\\u002fZjr7ddlkQGWdn1k22WRAbhM1\\u002fA\\u002fZZEDgAiisANlkQMXCIIIG2WRAlT982h\\u002fZZEBTF5FLS9lkQOV6nJ2H2WRA7uwHxNPZZEC6K8nXLtpkQAEwqhKY2mRAtAlRyw7bZEBdKedxkttkQEHMRY0i3GRAWyeSuL7cZED22DehZt1kQOhPMwUa3mRAXmSgsdjeZEB5cIOBot9kQGnoxFx34GRArNVYN1fhZEDJvowQQuJkQDR5d\\u002fI342RADi+H8TjkZEA1jiosReVkQC+okspc5mRAFYeL\\u002fn\\u002fnZEAW6GkDr+hkQH7qDB7q6WRAotbynDHrZECZa2DYhexkQIBomjLn7WRABkcxGFbvZEBMW18A0\\u002fBkQIjJeW1e8mRAdv507fjzZECflH0ao\\u002fVkQAvQpptd92RAqhywJSn5ZEASQ+N7BvtkQHNUDXH2\\u002fGRAdqOU6Pn+ZEA\\u002ffa7XEQFlQHO+t0Y\\u002fA2VAWdWzUoMFZUDxRvYu3wdlQD5j+yZUCmVAy4B1oOMMZUDz25Qdjw9lQEkYkT9YEmVAyWZ8yUAVZUBYgGmjShhlQP0A7913G2VA60UUtsoeZUBQybWZRSJlQLkfcSzrJWVA9UIrTb4pZUAk0EYcwi1lQJ1gowL6MWVApkqDuWk2ZUCbAXpTFTtlQD8li0YBQGVAxUipdzJFZUDS0ctHrkplQPJS3aJ6UGVALNXPEJ5WZUCoGDPJH11lQE+EuskHZGVA8QU28F5rZUC2eJsYL3NlQPl43j+De2VAE2J8rGeEZUAqhNMd6o1lQPb1mQMamGVAzAwVvwijZUAqyBLwya5lQBgEINBzu2VACoQQnx\\u002fJZUDpzLIk6tdlQF6Yhkz052VACFmT4WP5ZUCjIiVyZAxmQPmfYmUoIWZA9IOMT+o3ZkCl\\u002fYWV7lBmQATlX3WFbGZA4K2EkA2LZkASjIcd96xmQHRbhPTH0mZAwazvuiD9ZkC64eWMwyxnQFhJxaWcYmdA2\\u002fIuvc2fZ0B2fzQYveVnQNlbSL0pNmhA5qOF5UaTaEA+C6D04f9oQMRGIvqYf2lAvOTNOzAXakAbn1ZhM81qQJwnqcB0q2tAnFinBSnDbECe8PaRLDhuQHuwVpmCLnBA5aRskhL3cUAGLOJMKzt1QJoiJLpvY3tANvyS8qtKg0ARZvFVr62MQDelxFgrZpVA\\u002fXmAaiG6nkCyX1GmaaKkQL7imGLdbKlA4GGaoNA9rEAqJfYEu+SrQOIioZ6iiKhAu6Vzo1aCo0BCkhK0l42cQDsLuJknoZNA2oxu7YcoikDEUa397a2BQLHLnN\\u002fOeHlAYxG+CxQedEB4TIIToElxQD\\u002fAbreccm9ARPlXt02GbUCi3fURfTBsQGw9Lsf0LGtA\\u002fnrY2L5dakBoFAAR9LNpQDZQXei2JmlAecgArvavaECzXRSpO0toQHvRLkse9WdArlSoZvqqZ0CShWzDvGpnQITJ6Jq\\u002fMmdAL7Sq07ABZ0Bf3loBf9ZmQPwbejVLsGZAwi99Sl6OZkC\\u002faM25IHBmQI2Q4VIUVWZAaL2bWc88ZkDsZY2z+CZmQD6gXeNEE2ZAIS9jonMBZkDMnKX0TfFlQMx+TJyk4mVAta7\\u002f107VZUBK5ohcKcllQG\\u002fPnX0VvmVATFBtfPizZUDTppf0uqplQGbmyWFIomVAHutpuY6aZUDlua4UfpNlQKJnO2gIjWVAW4fkRiGHZUBZIrutvYFlQHFm09fTfGVAE9uFGFt4ZUCpGyS7S3RlQMnqSOeecGVAZFMRiU5tZUBPV6s8VWplQJvQvzyuZ2VAnc9RU1VlZUAZx77MRmNlQJ34mGx\\u002fYWVAQFwhZPxfZUCAJTBKu15lQNRmYxS6XWVA1ntyEfdcZUB7C4vkcFxlQCDVoYEmXGVAXD6mKhdcZUAy4ottQlxlQJpTISOoXGVAYPqubkhdZUANhFy+I15lQOfrXsw6X2VAhLX0oI5gZUCJtjmVIGJlQBjQ31byY2VAhF3e7AVmZUCJBC+9XWhlQP0itJP8amVApntrquVtZUC5NhezHHFlQNMMkOKldGVATdb9\\u002fYV4ZUB5IUBqwnxlQCZe3z1hgWVAfDjxVWmGZUCer3Bu4otlQA9IpT7VkWVAga9VmkuYZUBs2aqYUJ9lQHYa28HwpmVAf8LzRDqvZUBkpmY3PbhlQL6HZOALwmVAE1uLErvMZUCEFxCXYthlQIC2Wa4d5WVAN78MqwvzZUBUuN+tUAJmQP3VU4oWE2ZATPq+3o0lZkBytidt7zlmQHXykMZ9UGZAgl7eX4dpZkCUMAUvaYVmQKYdlvmRpGZAD10FjIbHZkAX7yol5+5mQGAR4312G2dAprdr\\u002fSJOZ0BdNCHmEohnQGHiHp20ymdAJRaWstQXaEAMkzclvHFoQNzxa\\u002fha22hAAeFwgodYaUBzmbmHae5pQABWEpRcpGpAbDSdBj6Ga0AkYALf5KpsQHOhWGRIRW5A3i6F3hZmcEB4zkT9zKJyQMYqV+Jn2XZAk0IKoeGzfkAd50rvGDSGQHCtdyaKgZBA6X5utVAImEAH7sWu9YigQB6Ij8i8AKVA+E8jUtcmqEDtISIH7LWoQNyjAZbmZ6ZAIETAI15AokAp+CSpUjubQK33z28uAZNAgJ583hCViUAkkLwyml2BQF5XOQ7IAXlAjLNZKcyzc0BGd1Qyl+dwQFUcy6rUwG5AQZ+KebHnbECFedS54qNrQAp2AyNHsGpASe+vMdHuaUAq1Qjf0lBpQJ8rULq2zWhAEo9R661faEDkfklneAJoQIP9adzcsmdAXuM5aV1uZ0B6ZC+aBjNnQNYExRRN\\u002f2ZARBuhy\\u002fTRZkBMKKbI\\u002fqlmQJDjaqGbhmZAkDTaRiFnZkA55IZGA0tmQAC52NjMMWZAa6I\\u002fRhwbZkAPBkNOnwZmQMerN1IQ9GVAq\\u002fC2FTTjZUBuJqny19NlQOOQNGfQxWVAdnkb6ve4ZUCnwaz2La1lQC5N10RWomVAOkqDJFiYZUCt50b1HY9lQPccCLWUhmVA9+5Aoat+ZUBQFX3nU3dlQDk4V2KAcGVAcCfFYCVqZUA8VO51OGRlQFM3HVCwXmVA9C6glYRZZUBypKTGrVRlQC7cQiMlUGVAjvsTleRLZUAzT8ib5kdlQGwMSzwmRGVA87wT8p5AZUCDEVWiTD1lQDunxZArOmVAv+PJVTg3ZUCM1s7VbzRlQNVgrTnPMWVAxgTz51MvZUCk4PJ++yxlQGmuhc\\u002fDKmVAWUpj2KooZUDWXwPCriZlQF+E9drNJGVAvFGjlAYjZUCDCnGAVyFlQG0OM02\\u002fH2VAV+rvxDweZUCgLejKzhxlQJ1t3ll0G2VALwmbgiwaZUCzW6hq9hhlQCcoRUvRF2VA9iWMcLwWZUCWy9A4txVlQCnFMhTBFGVATQdrhNkTZUDYNNccABNlQA40yoI0EmVAnHgrbnYRZUBF4nGqxRBlQHhdCxgiEGVAgApIrosPZUDNDOd9Ag9lQPWuK4uMDmVADET2TSQOZUAHQps7yg1lQOzcZPd+DWVAQMn6W0MNZUBFaaOHGA1lQPerVOz\\u002fDGVAIgftZPsMZUDL23tRDQ1lQEz8U704DWVAl7HkkoENZUA1AGXj7A1lQJkIekyBDmVAgABWkUcPZUDnjTakSxBlQI\\u002fIr+meEWVAPwcyP14TZUB2HIPtwhVlQMl1sQZPGWVA6kwcfTEfZUCjbsjR\\u002fCllQNAQ2oyZPmVA9t2Pnd1kZUD8IaE7k6ZlQIgv0B+4C2ZAtgZjUiaUZkDg0p0xGTFnQDO2hoDKwWdAzuAegU0XaEAEynsffwtoQBBe\\u002frzso2dAXNWhEhIMZ0A7SsTfBXFmQBwY2LeP72VAMO4NGamSZUCTjfBq7VdlQPMJ1btnNmVAsFmHYH0kZUDZ+dMS\\u002fhplQEezEM6eFWVAOrELYTQSZUDCLAeQxQ9lQE300NXnDWVA1ZTgG2sMZUAauvf6NQtlQDbUiXY4CmVA2bzkR2cJZUBv2CAIughlQLeSr0oqCGVAJzq2ErMHZUA\\u002f0g51UAdlQNhOdFX\\u002fBmVADvXyNb0GZUBSxRwTiAZlQDmUVUleBmVAztevgD4GZUDMsJWdJwZlQAKg\\u002fbQYBmVAox1FAxEGZUCfiArlDwZlQBJ8jNEUBmVAjGI0Vh8GZUA9BQkTLwZlQF9+1rdDBmVAxTPlAV0GZUAikSK6egZlQHsDpbOcBmVAQ9Z5ysIGZUDjeK\\u002fi7AZlQPSxkecaB2VABZgPy0wHZUB9+USFggdlQCROIhS8B2VAf3Uve\\u002fkHZUBwd2bDOghlQBVGJPt\\u002fCGVAQCwuNskIZUAQK8uNFgllQKYS8SBoCWVAqqOFFL4JZUBlhrSTGAplQBNmW9B3CmVAEgiOA9wKZUBdzDRuRQtlQK2syFm0C2VA1Y8wGSkMZUCFp8QJpAxlQO+XfZQlDWVASmZWL64NZUB2qulePg5lQHhSVLjWDmVAtoFq43cPZUBo1k2dIhBlQO7Id7vXEGVACBpPL5gRZUCDmmQKZRJlQG9ReoM\\u002fE2VAIY6A\\u002fCgUZUDbVb8JIxVlQKKVcHovFmVAL4whY1AXZUAlVkkqiBhlQJQ9oZfZGWVAC+7z5kcbZUDPfk7f1hxlQH9Xxu+KHmVAGExoU2kgZUBk82k+eCJlQGSvbBe\\u002fJGVAHVOfwEYnZUAWF+\\u002f1GSplQG5\\u002fccdFLWVAE\\u002fAFOtowZUAvFUwc6zRlQNsUICSROWVAMS35ces+ZUCvHCSmIUVlQCuB8M5mTGVAfsvQuv1UZUB0iI0bQF9lQN9W2hita2VAgR70jA57ZUDPvBbU4Y5lQBa30+lxqmVA\\u002fL2Ubm\\u002fVZUAudiPv8SBmQOL66uyxrmZAeK6oOda2Z0BKZQgOpYNpQKq4MkzLWGxA7lh8ZFwicEA6aKTVEXZyQLxIGuRktHRA7dKaGgcsdkCgDMBd7Dh2QAWoN8QB1HRAeJyJRX+bckAJJyF8JkRwQICAM+MfjGxA1HxIs6elaUA7fdekHstnQMvxLu4xumZAaKbARs4nZkAI6BiSUNplQKAN9xHArmVAGQogXUKTZUCZKHkyx39lQAqJCj7bcGVAIVA8ZfFkZUBzU2F4OVtlQKU3s1sxU2VAkZMxt31MZUA7EiWz2kZlQFSzA5IUQmVAtob\\u002fPQM+ZUCMQtNHhzplQA1KPsWHN2VA1xFtxfA0ZUDvGOUtsjJlQKHfteG+MGVA7iQsHgwvZUCIU4r+kC1lQHVghxxGLGVA50U\\u002fRiUrZUBe8UZEKSplQIJsAqxNKWVAl6tfu44oZUAIGNM76SdlQId6+mpaJ2VAF0Su598mZUDKsJKidyZlQPwMcdEfJmVAdJrK5NYlZUCeyTV\\u002fmyVlQDdNLm5sJWVAu9sSpEglZUCtmRkzLyVlQJArD0kfJWVAfw69KxglZUBnoNo1GSVlQE+hcdQhJWVAMD6jhDElZUDgIb7RRyVlQIjImFNkJWVAUoolrYYlZUDznjeLriVlQOTScaPbJWVAFtpYsw0mZUC6J4R\\u002fRCZlQF8C6dJ\\u002fJmVASjc8fr8mZUBrXmdXAydlQA4XDzlLJ2VAdwgoApcnZUDaxJiV5idlQO7y5tk5KGVAgl3uuJAoZUAnuqAf6yhlQLAizf1IKWVACWHuRaopZUADTP\\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\\u002fUCBPZUAFs1GajlBlQLxrqRcJUmVAVnHIZZBTZUAYwm0sJVVlQOa3qh7IVmVAKe3L+3lYZUDoj1iQO1plQJyeKrcNXGVAsdqiWvFdZUDUmPt1519lQHoAvhbxYWVAycRdXg9kZUDf6\\u002f+DQ2ZlQBXZcdaOaGVAM4dWvvJqZUAwtJDAcG1lQPW28YAKcGVAgNU1xcFyZUDhPlh4mHVlQLJQSq6QeGVAQJUbqKx7ZUBa+6HY7n5lQP84tOlZgmVAmykLwvCFZUDAXOKLtollQJkOdLyujWVAc4hxHN2RZUBGlp7RRZZlQMWHvWntmmVAlFYB59ifZUAZVEXODaVlQIaPVTeSqmVAaXGh32ywZUAzasM\\u002fpbZlQDzFXaRDvWVAY6rmSlHEZUCXSh2D2MtlQPapCdbk02VAW3KZM4PcZUAwRSkowuVlQLt9lhuy72VAD6HVm2X6ZUCsnYO18QVmQBsmg11uEmZASxh+7\\u002fYfZkCFxiLGqi5mQDiwQPOtPmZAxiSZHypQZkCGR3GcT2NmQGxI5LNWeGZASuzhSIGPZkDs+A3dHKlmQAp64BmFxWZAcV84BCflZkAFiiUPhQhnQHtsplU8MGdAUdfcXQtdZ0A0gXzu2o9nQLe+f7bJyWdA9u8r2DsMaEBhVQrh71hoQLsfknEbsmhAXJqAC5MaaUAy1GL3ApZpQGpm7DhGKWpAmRDyOwHbakAQJVAI\\u002fbRrQJx88JHOx2xAQsU1HQM1bkA\\u002fMt6ZhyRwQMDF1\\u002fSz2XFAZtWmgznzdEBXZqVa6cN6QAsysaHTpIJADGlyQyVki0D\\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\\u002fNzQipZUBrUIbXXaVlQGETwtPsoWVAXSknybGeZUAznNEdqZtlQC5aaY7PmGVALH16JiKWZUAJAfE5npNlQHFMoV9BkWVAtq\\u002fObAmPZUDp\\u002fKhx9IxlQNwbw7YAi2VAGqCOuyyJZUB8yfQ1d4dlQDAqNRPfhWVA5yFHemOEZUD08hfQA4NlQLogJ76\\u002fgWVAf6g7PJeAZUAlMT+ein9lQEpuxqeafmVATj6Ip8h9ZUAjIjieFn1lQFryonaHfGVAEmCdWx98ZUDIi4NP5HtlQKL6pXrfe2VA8l6ZpCB8ZUAlm3DWx3xlQARfbfodfmVAnmZFicqAZUDotfScMIZlQO+ZYyffkGVA7sg\\u002fn7ykZUANho\\u002fcUsZlQEH3TTvA+GVAITXQ5Z86ZkDvvgA8KINmQESbdEfVwGZATURqr6TcZkDY9KP7w8hmQHKQV2H4jmZA7uamqBdGZkA2NoXUPgFmQP2C05bUymVAVzUr7G2lZUC9W9B4hI5lQPzYn0mggWVAhfnmUaV6ZUA0CKtrwnZlQGno6BhjdGVAGCIWjsNyZUCt3coxjXFlQIs9feaZcGVAE5aLC9dvZUCa3crQOW9lQNWsUfS6bmVAnMoXL1VuZUCJ5YCFBG5lQE1kQ+fFbWVA7uZz8ZZtZUBBt3bDdW1lQL6NHuBgbWVAmG4mF1dtZUDgDo90V21lQLgLNDRhbWVA0RtxuHNtZUARtAeDjm1lQJ7NsS+xbWVAYhr4b9ttZUBLGf8HDW5lQNgkE8xFbmVAHmvKnoVuZUCzhp5vzG5lQIti5zkab2VADAUnBG9vZUC9Zprfym9lQG0AB+gtcGVAJJC\\u002fQ5hwZUAg7NwjCnFlQGTCqcSDcWVA9ftBbgVyZUDcQGh1j3JlQB\\u002fnlTwic2VAIYNLNb5zZUBfdKrhY3RlQLpCYtYTdWVAHo\\u002f\\u002fvM51ZUA75q1WlXZlQEAggn9od2VALWVoMkl4ZUAMv9eNOHllQAPJddk3emVArxfgjEh7ZUAsM99XbHxlQCRyWSylfWVASSd0SvV+ZUDBWHFPX4BlQCRjAkjmgWVAVVAAx42DZUDhwMYBWoVlQMvT1fNPh2VAELrti3WJZUC1+5vl0YtlQBEVR5NtjmVAcBpN\\u002flKRZUBhHfzkjpRlQMTtWgExmGVAGvlp6EycZUCQE5E4+6BlQEn1CDlbpmVARFP4H5WsZUCeVvJj3bNlQJXsOhJ6vGVAIVtWMc3GZUCgEScHbtNlQJb60UZu42VAQKOuqBL5ZUBVKem7hhlmQHj7HvgvUGZAJ3mUSpqzZkDmmyiEwWlnQBzYCasSpmhAPvUaygSaakB\\u002f5ZJvTlZtQPLBWIQBU3BAIL\\u002fE5I35cUDePZVZ\\u002fiJzQCiBNwUkUHNAlqi4ZA5qckAlk+NcydtwQDXExNfgVW5AeqVc2xpla0A+ll7+ZjVpQMH02ldnx2dAymFteefvZkB6LRAi2nlmQI03Y\\u002fOOOmZAALqvlRQXZkALPbP0JQFmQNsRgqYX8mVAYYAHj\\u002frmZUBnw52aeN5lQGhrPG3f12VAwec5JsHSZUBp\\u002fOOd0c5lQHsgbtXYy2VAb99sgqzJZUDgYLpBLMhlQEL0jA0\\u002fx2VAA5BGc9HGZUBDVqhI1MZlQMEtoLk7x2VAhUUQlf7HZUCXoAnHFcllQHRCR\\u002fR7ymVAk8NRLi3MZUAZcSy6Js5lQPsgJuVm0GVARx+f5OzSZUDBP3S+uNVlQHG6YDjL2GVAabAfzSXcZUBv12+myt9lQNTGYJu842VAnQ6IMv\\u002fnZUBYQOmnluxlQFHTgvaH8WVApwaV5dj2ZUBYzNkZkPxlQI5ZByu1AmZABBQcvlAJZkB5LxylbBBmQEyUGQUUGGZAiyeYg1MgZkBsZaZ8OSlmQIdgWEPWMmZACrS+bjw9ZkC0aPo1gUhmQHLvut68VGZASM9PQgtiZkDLIZRujHBmQNLCa2llgGZASUhuH8GRZkAeoNqI0aRmQEOIPxPRuWZAwY+3YgTRZkCbcJqDvOpmQDf6uK1ZB2dAlICDxU4nZ0DZyS7XJUtnQPC+\\u002fN2Fc2dAU2cpSTqhZ0BpmBHdPNVnQKJuztDCEGhA1W6caU5VaEBYrxHpxqRoQN7ylZSZAWlAKwKyeOhuaUDSqsOF0PBpQF317l\\u002fhjGpAYGUwAxtLa0CXrXz+dzhsQJGAOcfTbW1AYzYUunIhb0C8kDVpvuZwQJiqbW8iOnNAVLyqk9l7d0CsEdnqNiV\\u002fQADAG83jAoZAATR4efHIj0Agk07xhW+WQPDnu7Hg851AJRaBVIJzokAkOQlgkoGkQLIs30YpNKRAZ7d7zECyoUCgpRo1SR6cQBZPR4nyu5RAwB3eIZEgjUAK9vJ3NzOEQN64NEAI8HxAB2HCSA0+dkCjhysrOI1yQFr9cLmrhXBAiLRKjnKqbkCdyv4AJx1tQGKYy6vK\\u002fWtACCbQIx8fa0B0VnZH6mtqQNxvT3Ss2GlAGqJ44BdeaUCynB2UA\\u002fdoQOI83FSXn2hAyBGTmeJUaEBwuDUEnRRoQEVoA2H73GdARzXoFZGsZ0C7N3PpOYJnQBEz6J0JXWdAonWtsz88Z0BCSBMsPh9nQKoG+32CBWdAS6D1KaDuZkAkddCEPNpmQE1WYmwLyGZAzrfGrsy3ZkCWwbv6SalmQJjoUDpVnGZAWB60QMeQZkDDRYe5foZmQIxHUUxffWZAHJ686VB1ZkAWQLE6P25mQAtuJSsZaGZAraPli9BiZkDcPKvHWV5mQMCWt6irWmZAkubdLL9XZkCTc3xmj1VmQLezZ2kZVGZAUrs+Q1xTZkCeSw\\u002f\\u002fWFNmQFr6nrQSVGZAZvYkpI5VZkAiCMRf1FdmQPUarwTuWmZAjXuehuheZkBswSYR1GNmQPxjroPEaWZAmTVFDtJwZkDl6qP3GXlmQHLcSZe\\u002fgmZAlAxRku2NZkA63oZu15pmQOYyMpa7qWZA+HmA8OW6ZkBI0hNAs85mQDpLdIyV5WZAlHEG9xkAZ0BKRA2F8B5nQCuKn6X2QmdAnE7wm0VtZ0AQRDOqRp9nQFQofpHQ2mdAEoU6cVciaEDqwSfHSnloQOTZMoz45GhAX1p4NutuaUBpKewUBytqQIQLaAaIRWtAZX4iaJwdbUBNdfU4BjZwQHyiX21hMnNAiGyHXYZUeED8YtjVSDWAQNISCdzL5YVA3c+olKzjjEBpOAPjbwySQN\\u002fYRCii0JRAKHSwqaiolUDYnfXKvyqUQAnDKxO+CpFABOXA2XzIikAG3+jbpBmEQNB4zqN\\u002fxH1ARGObuQCZdkDsgssLcylyQIFW7OJbPm9ArBnxD+5ubEDsRcaQ5NZqQPiy2qtJ22lAx+ZtVIsuaUAV8OPYA61oQLix+SV6RmhA9lRT\\u002ft7yZ0A20ANbhq1nQC9jYIVSc2dA0ll3\\u002fwBCZ0BVPr3K3BdnQCW1S8aU82ZAtPMnPyHUZkDKsM6fsbhmQK0AuUOfoGZAwO\\u002fBzmOLZkBRuvP\\u002fkXhmQCtYvUzQZ2ZAwQE6yNRYZkDY2Nf+YUtmQPGRvYdEP2ZAzLu0HlE0ZkC105UlYypmQGQ64HRbIWZAHeRhaR8ZZkAu0lUhmBFmQJWnrd6xCmZAB7mqhlsEZkBKo7s4hv5lQLo\\u002f8Pck+WVAz6VdYyz0ZUDt8pR6ku9lQP7U6WtO62VAUv28aljnZUAtRFuMqeNlQEe8Sqo74GVAcvsXSQndZUCTAeOCDdplQGEYD\\u002fVD12VAr9GVsKjUZUAagZQsONJlQBhovjrvz2VA7jtt\\u002fcrNZUApdBbfyMtlQF6K9ormyWVAn4DM5iHIZUAHFIgNecZlQHFY1ErqxGVA0n5vF3TDZUDHo0cWFcJlQK82XBLMwGVAhnNs\\u002fZe\\u002fZUCRZIbvd75lQL8gmCdrvWVAcEU4DXG8ZUBrq\\u002fUzibtlQItapWCzumVA6r9gke+5ZUBEUUkJPrllQFtP+GGfuGVAamLSphS4ZUBFfyeGn7dlQDOlFb1Ct2VAXq4LKwO3ZUCS0Eqj6rZlQP\\u002f9Ib4Ot2VALTTtEp+3ZUA9TTNG\\u002fbhlQBC7eF\\u002fau2VA9R09wEXBZUAxeWXqicplQDB4IUO22GVA760HD93rZUDtpHj4RAJmQKOSsm7YF2ZAt\\u002fOzh0AmZkBnPUgEZSdmQKTJ7GWZGmZAHYbOjlgFZkBGCkkiPO5lQFCBxk3M2WVAcdqspyXKZUCFwZuee79lQJuQ+zDguGVADs7qgAu1ZUD\\u002fV8w+4LJlQF5sJBmcsWVAFevK08+wZUBBFWlpQ7BlQLRy8rzcr2VAB5Fsr4+vZUALMkUeVq9lQPOS8XEsr2VAzlA9RRCvZUDi9+\\u002fe\\u002f65lQLO9u\\u002fb5rmVAkwGJlf2uZUCZ4GQBCq9lQGjbqq8er2VASwkjOzuvZUDzKNpcX69lQDmf7OaKr2VAZBi+wL2vZUBs20Dk969lQF3JC1w5sGVAA1YRQoKwZUASaNe+0rBlQHvcGQkrsWVAWp\\u002fIZYuxZUBgw1co9LFlQBZLXLNlsmVAVN5zeeCyZUDAtnj+ZLNlQOriBdnzs2VAschStI20ZUDKt29SM7VlQLCM8I7ltWVAXwYWYqW2ZUAdtYrkc7dlQCWHzVNSuGVAhTZqF0K5ZUBskSfHRLplQOQzXTJcu2VA2mGuaIq8ZUC5AnfE0b1lQBNMS\\u002fc0v2VA+sQDGbfAZUBnxO+5W8JlQMLV8vgmxGVA2OGInh3GZUAJPPY9RchlQNM4Sl6kymVASNJdrULNZUA1\\u002fag\\u002fKdBlQGonu+Bi02VAOqNwefzWZUDao86SBdtlQEnE+\\u002f6Q32VAJ59qtbXkZUAmYYP0j+plQLY7vcJC8WVAJr2x9Pn4ZUD+XA7x7AFmQF6DM5NiDGZAv25D+rYYZkBLZjxxZSdmQDGGdwAdOWZAMLX4xfJOZkDKOWYm42pmQAXakQgKkWZA0DnxgznKZkBUFdwkiChnQFSn7bRlzWdAA3ky5CnuaEBA287ap9BqQF\\u002fTPUZbuG1ABKER\\u002fyzhcEDJ190OqlxzQHpeFOR4+HVA7LoQwT0beECdATqdXAd5QNbmj0wLU3hAjjhXnSFRdkDu9GUjC71zQHrn+vY1OHFAwDVoeHlCbkDIAjI8QDNrQFt\\u002fJbXALmlAQEag41n1Z0CejETi9kBnQKS3vqX02WZA4qX49lKcZkBVHWtqCXRmQO4BxfQYV2ZA1szqVctAZkAEdV\\u002fX3S5mQE+4DzIdIGZAU7JHEM0TZkDlzB15awlmQNhBIFCYAGZA9BIuAgr5ZUDlSqEsh\\u002fJlQB9Qynvi7GVAgYYlw\\u002ffnZUDHj7DfqeNlQOOyQSbh32VAmg3sNYrcZUDg1hQTldllQEGk\\u002fnf01mVAHgQSTZ3UZUDYg\\u002fs+htJlQO\\u002foZGun0GVAJzn2HvrOZUB9daigeM1lQB4CcQcezGVA1dEFGObKZUCnBgMpzcllQMFjIAzQyGVA5TJx++vHZUBO6eKJHsdlQCPBWpZlxmVAwSj0QL\\u002fFZUCb5vvhKcVlQB0BWAKkxGVA3EAcVSzEZUDsiReywcNlQPoVMBFjw2VAL2Jshg\\u002fDZUA024w+xsJlQOpOH3yGwmVA0TD5lE\\u002fCZUB4\\u002fwjwIMJlQFi\\u002fcQP6wWVAzqjlUtrBZUCx7DZuwcFlQHzoFvCuwWVAVVf9fKLBZUAcDTLCm8FlQEad9HSawWVA3gC+UZ7BZUDc45gbp8FlQMu+jZu0wWVAK0sgoMbBZUAAOtz83MFlQJBf74n3wWVAmsfPIxbCZUAnXOyqOMJlQC34ZgNfwmVAjOfWFInCZUDH+BLKtsJlQAtjAhHowmVAQt1z2hzDZUDlWfoZVcNlQEjvzsWQw2VAzoW31s\\u002fDZUBV9fFHEsRlQBRJIxdYxGVA+O1KRKHEZUAIm7nR7cRlQAPOC8Q9xWVARsAnIpHFZUBhwT7158VlQJXt0UhCxmVAHz+6KqDGZUCoATSrAcdlQJ637dxmx2VA1oka1c\\u002fHZUCWYoirPMhlQFveuXqtyGVAWkYEYCLJZUAJ07F7m8llQDWAKPEYymVAGMgV55rKZUB8qJ+HIctlQE9jmwCty2VAlnzKgz3MZUDoiR5H08xlQNt7BIVuzWVAJyG4fA\\u002fOZUAHvaByts5lQEuot7Bjz2VA9hX6hhfQZUDsOudL0tBlQDhEDF2U0WVA5bmfH17SZUCzJi4BMNNlQPYfWngK1GVAexayBe7UZUBGs54029VlQHTrbJzS1mVAJXB34dTXZUB9rXO24thlQPQx59382WVAgxrLKyTbZUCTBWSHWdxlQM4dVu2d3WVAMA7+cfLeZUA8LRlEWOBlQJ\\u002fxyK\\u002fQ4WVA6d7\\u002fIV3jZUDKn2gs\\u002f+RlQGYM24m45mVA\\u002fYN2I4voZUA6bn0WeeplQPcXE7uE7GVAKasDrLDuZUBOBMbP\\u002f\\u002fBlQJX98GJ182VAfeppBBX2ZUBTBqHD4vhlQMRMQzHj+2VAepfgchv\\u002fZUC6PyJakQJmQIeiU4BLBmZATgotZ1EKZkDwtA2gqw5mQFADH\\u002ftjE2ZAttM+wIUYZkCmDxH0HR5mQHe\\u002fQqw7JGZAoI7pdvAqZkCMGhXaUDJmQPIGN\\u002fJ0OmZAyFwiOHlDZkBUakF6f01mQN\\u002fYkhiwWGZAvf92mDtlZkD8BAGtXHNmQInZX9tag2ZALJ+v8o2VZkCh61CmYqpmQA==\"},\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"},{\"hoverlabel\":{\"bordercolor\":\"white\",\"font\":{\"color\":\"white\"}},\"hovertemplate\":\"%{text}\",\"marker\":{\"color\":\"rgb(255, 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2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (2 2 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 70.96\\u003cbr\\u003eMiller indices: (3 0 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 75.38\\u003cbr\\u003eMiller indices: (3 1 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 79.72\\u003cbr\\u003eMiller indices: (3 1 1)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 83.99\\u003cbr\\u003eMiller indices: (2 2 2)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 88.23\\u003cbr\\u003eMiller indices: (3 2 0)\\u003cbr\\u003e\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"lbco\\u003cbr\\u003ex: 92.45\\u003cbr\\u003eMiller indices: (3 2 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02f2RK8DQiu5GS2suQLBROCaGZypAAEhM4O+wyD\\u002fAiNoGJboYQBAAoyqBqSvAgDYhatlhG8Awao3h2EYqQIDfGV257vE\\u002fACjFbjsKuj+gcILslSkiQAD0p5kCDf+\\u002fQHTzy+kkHEAgD24wUPQXwHB5sTOK+CVAAJXKKTKqB0CAZF9GCCHxv4DZELEBmRdA4PByGI9DIsCAGkcVlqLyv0D0VVvNaAZAoOJw0f\\u002fvFMCgfmHJD3UvQCBYMR9eYSFAgFdKw5gzDUDgjPLzRG8aQAB4WdaMISdAoI\\u002frfJ4KIUCgJQX9Z3k5QIBnYygZ2ihAUAxYlDhgNEAQtiZYzqUiQFD\\u002fkkUmiiBAgNX4k7a1CUBw5+UgzCcxQNC4Ku+YMCRAYDdKiZ7vL8Dw0+d6HREgwGgMQsoF5jJAgKzqWanB8r\\u002fwkJsQDYIpQIDP2L4JlQLAwDJgHeg4C8CABpI1cTj4P7g9dwM+bTxAcJM5aPOrJEAA6p8lV+0JQDDwDyGCSChAoEJ3PakmFEAAKrk2s0gcwEDuRxzLegnAUC+00uSbJsDgfskd75E9QBj75OovcDJAQKuWz8gyHUDAZoDeVMUGwPBTsmgUACzAwM6tFWKzDkDAxYUGN6oWQJCX6hbVgzLAoFaBeQ9LNEBAxZhJ\\u002fugzwIjdK7LB3z5AeNBPfgilM0Bgi7eJ8TIlwDg4DAHmIztAQHvn4BTmBkBQIZzuUG86wKCgYMZffiBA0MyrA93zR0Cwvml\\u002fsIk0QIBBAAI9ty\\u002fAgAIhxOdHCsBAOrMsifoFwMAL05CJ\\u002fhzAwDdl8+A0MEDQ1a3duDkhQHAA1ql57S9AABj45mAr0D\\u002fYEtBTi3gwQICg\\u002fKuukBHAAHgrIWTDAsAgwXjYcc8gwMBIureA0yhA8DMFUfQtJEDAyENjjmcWQICVVFzKKSLAYFvepbf5E8DA7o8Go7cUwHBCeitknSBAgAaXnkYuC0AgTP+14AEkQIDYSEQf8xfAINqQIX5GMEBg49MqbKYVwIAM2+pnkR5AcGKdFqSMQMDwhLePZME1wNiDtcoU9EXAKCdlk5S4RsAA6E93zFLTPwCz7u0iSAZAoDzQ1kBVQkDwnumgCiVGQJCstXNK3VpAYMDwr9K7RkAAZL+iTNMcwOArjinWbWHAAB6mCQp9YsCASok9wS5DwODR7RXWlWhAoEiLkSt1WEDg29a+hOZcQAA7C7iZJxXAgDajW\\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\\u002fcvjeijAgM7nqKmq8r9wjhM0k8slQDB2cDYJ6C1AAB713hwANUAAUPu5ligcQMCSybleJC5AoELY6I4wIkCgkJ5u43EcQACu4uWXPSZAQBqcVY0+IEA4d9BJ9g5AQDBj9ea+GjRAgBZERd4KQkAwvzcaxB0oQJBBETpTDCJA4FNa+Ir7M0Bwl2Ss9t0vQID+ApLawClAwCcaMqGfJUDgbh9aSPQaQEC\\u002fCO0kvwLAYLq3plreKsBwlIzONO4gQEDL\\u002fFuH1gJAMJsiIKB3KEDg0Pu3smcYQEA7FETY0x9A4MOJr0ozPMCA5yucm\\u002fgKwCD+VitYxRFACHyt0lzCMMBgy1bFhPo4wGCnsR0MbyzACOxh2C2GNUDAmZWJLHwoQEAIb\\u002fODvhdAaCelayeaMUDAh\\u002f6O1nkmQAAwyTRKXBNAgJtfhtWF+b+A9QhE6rUTQIABFda6dPY\\u002fQFsQFCKnJ0BwTMKWhDA8QGCx4S1H8BFAEPTy\\u002fjpMMsBACVPwklYkwNASTzNw2zVA8NBVwGh4LMCQDlGtjREvQADUnUMwkRxAQAuJUqA7FkAwuuyd0X4nQPDZES62VC9AALbT05pW0D+AYfKmhDcewAASB8qDUvI\\u002fEBAOJ3iII8BgLjP3MM0RwAAAKwlKLvK\\u002fYKPpNPAxN8BAAibwTa4kwJgLxSJDKjfA8HYp9LYwQsCA50zUzywwwMBY5Ur8LCnAMCmkEBo+NsBgHKMW4nw+QAAphZhd511A4CIwUul1Q0AA8iN0ohQWQID4HdzN0EJAAP9pROpaWcDQHiJywH5lwIB7NMDS\\u002fFHAAPjuD3foQUCA64Ntq1YiQACt989vLh3AAMCwwZB3FUBAAskro9lFwPC6ynFAjkfAYJzNSmEeRcDA6I5K5vImwFDFsaxKDSrAEPSpmBd7JsBgz3DFqIMbQLBP5OfGfTFAcAsB5ewSJUDAWuUepOUdQBBG\\u002fVqUJCtAQOIxar31H8DAEWiLedgtQOgTsBwZaTZAAKIcxpai4T9Aj+xF02AewIDKvs66LABAwEvuRbPgIkBAe511E2AlQACOo9KLLBdAgFsuyfXGPEA43sjL5ac0QACQi43NHCnAVHYB546TQUAQn88bC5YtQJBDhdz6viJAYBXetoJmEMAAyWyrBhT2P6DjbRnzRRNAoGhIXoFwKkAgy2cqQVoeQABwabomsrK\\u002fQEdpkAQLF8AwhZGrIA4jQIB7DoRaSvO\\u002fkBDx60UVKkAAVl0QgxUVQMjBuRIDhDHAgMTW+dSuPECAeDVC8XgXQNCKLP76FCpAgN4FtJIwE8DguLWVI7UkQEiP9HJrv0JAIEfArrZBL0Bg5gl50\\u002fwYwKCcpx3O3jlAgHneCXlP8L9An7tq16wIQCjG0nmjLj5A4Hc8uQrnGsAAXUqMCuQKQFj5lDKGcTFAQGefAYMVHkDg+mgIJJg+QIDLKD0Ynvo\\u002fOK3lPKm6NkCgAsrfE5UhQAB8EdZrN8O\\u002fwBs1SmkwIsDQV+\\u002f4h+opQGAynlkWCBBAgNQKiJ3h8D8AJn1REzMoQIAxyRDTRfI\\u002f4CVhU5BFE8DA7Baqmj0GwCD7WpfWBRVAQEF77nEoEUCwXIbGua0\\u002fwEBKsQxFMAXAmMWn3DNhOUCATXPNATAhwHCgURakkTDAQHZIHZnoJkBQJB6nWgwhwMDrWsAQgTPAAGD9bRQdDECAmXkMwX74P2AhippuLhpAgIHIvonEGcAADoQ2d5Tbv6AY2UQIlDFAAGxTQMorJUBAUdroIUYLwHC\\u002fSsX+zyrAAG98iU2C+T9geXsvqqEZwMDEP9WL0ybAIM1po81PEkCoASgbZ28zwIBMBD2mQPe\\u002fACCAVeTRC8Bw3mhDugQnwBB3A2UR5iBACMZvBg5lMkBgxzHYLlwrwLhRdMqHNTlAUGfi6Iv5NcAARt2Qo\\u002fnTvwA0hDZjpg\\u002fAUBCBExPZNUCAPyR0Z9IUwIAH\\u002fQJ+RCdAAKV8ztbs9T8AXLozJiMWwDBjawioHCrAwBncI7DpEsCAQHnv428VwADh5c\\u002fLPibAgKs6VEKCHcCguHYHRN8RQAAHBvbt4wvAAB0hb27VLkBok3uolEA3QNBgr0KEmSJAAMoU71NwG0BwYsDSHlAmQABymd5jwtQ\\u002fYOxE77ncIkCAMMsBZPELwGiih64+bzPAAKopwTfW2L+goXuvX7MmwLChTrTDUTHAcDK0gYtpKUBA5Mn3fdENQJDUBlVbfSNAkF2c1M6EJUBAcEu84isGQGA70VX9m0PAIK\\u002fQoCyUJ0AgVcwxgWgswHC8pmobmitAgPP1K6CPAcDADFvZeWIowEAAtJ8WwxDAwGhH0UCEAcAg7K5eAz4fQOA+OOeynitAgK5zORU8E0DQU5Aw8WIgwAChgSlIvOk\\u002foHimPKDLGMBAJFJEV88QwMDeQOkspwnA0JtiWNOTIUCg4xCqYyITQEAfG3mucSTAoADzYZnpEMBg0GBXrw8bQACJkwgF7wHA4K\\u002fuZS\\u002f\\u002fHMAAuDuzYR30v1QYkez\\u002fIUDAACAWF5tk9L+AWFlutEgwwGAqEQ+CliLAgBWX3nqQDUCwmbfEdl4lQADtmaQviOU\\u002fgASC4wC3CsBAFzONW9ECwKCVFTmLdhnAYAXu2dx6GkCg8JQ4gZQZwHi\\u002f7KMsaTfAUDKzenFtNMBgp5ru5eMmwACxdfUoJR5AwDZQbfzuGcCAZt3UKQIvwCBxg0iE8iJAQNB4esGMNcAY1CRTKJM2wIBI10I+YPY\\u002fEOIIyI9CK8CAElUge271PxCthNN7sTrA8MIYMgZ1LcAosUpSQcQywKDsCb3MzjfAWHCfNz\\u002faMcDg2c\\u002fp25obwPB3Zfyu6C\\u002fAAOizl6yW7z8gm0\\u002fzwRMwwED2ynbxSyrAoGPqE9joGMCwuHivz1A0wLj9xR0XNUHAMAFfoKMNJ8B4qWDiWKc5wNimACGJzDHAIKYlP27dE8DglYPENKQYwKDa7yEmcxJA8FuG1u2nNMAAHWLjBtAHwPBtpY3RuijA8Be+MBdPLkDAM68FdbgDwGBxO50epyzA4O+ldHurOsBwsRt5jwcywEBcX5091h3AADHqyjglFUBgq3kfr8UtQKCKK8O0jCXAQOJ0cHO0KUAwvtxScc9EQBDdlm9sblVAmGgpK8efQUAAm\\u002f8RnThNQNi\\u002fQt7xX0tAgDhmpwtIM0C4x\\u002fYezF5HQAD8Y+YAnzVAAFDzIc2exr\\u002fY6bsm9Vg3wMByvIt7jAbAQDMFNnI+MsAAAR1DEkoTwAC04T4C2BXA4Lz+W7SXHUDIRMmTOf47wMDerj6Y5BlA+H4d1nTYP0AwNxWGl7MnwKD0ZnYrZhbAIHIy5raPGcDsSJTMaptBwEA1OyBJISrAwNbwv2fAH8CArpcFF68UQNCg5FN8eCHAIMVdUudhEUCIONeQbmoxQCD0uzbcFxbAkNieDp+HMkAAcUrRH7IZwGDxE288dx5AkKELnKtNJ0DgFW9ElKIqwECQTYC1KRnASKMCJYq7NEAAAWN6J\\u002f0cwMgrLKgsxTBAAFwQuZ5\\u002fy7+gDCspemciwACCeUcX8Pw\\u002fQKepTG5dIsBAzEYZkEwTQOCmycWNrRjAUETSvnWrJUBgysycGVobQACOGt4WXBNAIDBeiPpcH0DI\\u002fCpRNtcwQPAUGkcdUijAgA7m2nPWMkAAPMQ3+qgcwICIjDlFViLAcK3TlsrTOkDQEIZMF6UhQOBoceQi0TJAYKGNNdtgIMCge0L4R2QowBDab9ECmCtAACjd8Pj9yr9Y8zq7GjgzwJBHhzemxTlAIPH+uh8NF0CgTYxZaX4iwAB3efPsHPS\\u002f8BONOHq7NUCQXfQFTnEnQABYkeZ+pPS\\u002fcO8ZoVtlJ0AwwPTP7qAmwEDdSKVupyLAiB7auxFXMsAAugLyl1f6P8CHZnis8ALA4FU+xn3DIsAQtFMRBcsowEC7KjP7tARAqC3WPVltPcBAQb6omnQEQLhNxyObdTPAAMCrDjYMI0Do+e2GSn4wwACWpbzxTN8\\u002fMPuy8irxLECAfCDpSxgnwKBVBawDvB1AkE5rD+0rJcAwQ5T0GTYpwAAHHYcyBPq\\u002fICfDuDNLL8AABlMxhDvVPyhfh0mssDbAMKeNx9NsIcCgxdjj0A4VQABACTB51s4\\u002fuM3W44DIMMBAai8jWjsTwOBZRz1VVRfAwK5ShAWQEEBg8Lbpp8UjwNB8sRopFjJAkE3Ixu0dKECgn1yfkwc7QIA9IUkKABzAgBfRQ8ngF0Dg15Klnx8qwMCUZ+YsYBDAAPga8e37BkAA6XBzu0kJwMADyltKNxNAWBv2BGc7O8BgnLFB50Q2wKDaX8c6xR5AcPgY6G5OI0AQOP6SMGM9wBhIjsoSkj9AgHkDwNdv+D+AgtkoTUf4v+BdS71IkDvAsIpDLoOcOMAADOE2lpL6vzC\\u002fVfVAtjjAkNK+vJ+HLcBAf8SC3NE3wMDpqXLbwCnA4Kmkq9RBHECAGnO\\u002f7vwfwIAegK9FfAjAIJm4y+uBHMAQQgHAx5shQFg3cUyJuzFAYObQyldeHcAAvfT28ygjQGAhyeGv\\u002fh1AAKyuKQ6WOMDgI\\u002fj08EwdQJCFUnKFxDXAAFDlBirrDsCgtR+K7e4vwBBwvGRh2iFAwGbKgjB8AcAANNpBl2QhQHAWvzgUJSdAmEOM4+iOM8DAyNIzusgawKD4sDetXRvAAKVVwDn2D8CA81XdGHn9PzB8rLqRVDDAkBYX+s0GIcBgRm34p9QSwMBT3EU61CPAoLMqo9Z1G0CQVivOd0ItQEDByj5lgxFA2JLLnBnkMcAArWt93aMrQLDbZ7kh+yDAQLyXCplmAUCwdg7WxhdAwEB5Np9oZAtAoPY8ECIBFEDYy+nOqYo1QAAHFmi+1RfAoLNtBl79KEBgeBTH+TQqwGBDIp+1wjbAsJ3wuPWDMECgOJAXGbc2QAD2hR\\u002fmeuo\\u002f4BH0eB9bF0Bgv04bXu8SQLDHZlrFAy\\u002fAgFeUEVF68T9AE8jnrv0owLj1\\u002fbNNTjLAYP++gr3DJMDwqVrvAXEkQGD3QzJuQxrAwKUJuDCpIcAgQy12L2AtwKBmxo5jlC7AQCaE\\u002fE7ABsAAECVQCP3kvwAnH3yk8wnAIFRc0zFQLcDwI+OdeUgzwABcfE0\\u002fmzjAMKs2HcwZQcBwZVaqlT4zwLAgExs6zULAwJAmN1RCTMCA\\u002fzndW3MuwCDUNl0A3kjAgLimatCWVMAAiPucBwsBwAApQR3NpznAID2JphQjVkAALvr3Xz1jQJCDfC+5b15AgCeSWlbfKUBwrPZOQYxBQIAgbM7Fb0rAIFtJPkBAOcCwZHAt9GlCwMCPLJkCfTdAAOcY4dUiNUA8PM3jN\\u002fFDQOAjvWdgTxVA+M\\u002f57jnoMUAAyUyxT3ICwJhE2FlCNzNAgFiO9yLIOkCA0wXYSjADwID0P+Xa0zlAQFhKk3BNGkAAM0wscd4vQEhLCTQt+TBAKB8puTe\\u002fPECQDyhD\\u002fJgiQIBXh7BAsArAAOeuwKQIAcBAtdGiXYoHQEC6a9MAaBjAcBFM5zGCJUD4Jt9h\\u002fkJBQABPSmOih\\u002fy\\u002f8G3CITuwJ0AALDba9MXiv2B7lcJ3uRtAAE03XeZiGsAARkKhWlEJwCgjcwsnOzBAgJbI+NhgFkAA4ygBst3+P0BL2HLTPDRAgMhHgi4uBECAr49X2LUBwAAglE8MSp8\\u002fgOz1nwoPCkCAd3R7u74DQICFfk\\u002f7oPo\\u002fWF+xRtvYOUCACXf2FSQIQPCssnGOWiZAoIbjKXOyGsBw\\u002fAdC6REvwECjqpF5wwRAMIOUkbm3MkAAla95KKLqPwibEJ5mDzPA6Eo5SY71M8BghjO6I3UXwMBFK83xGRPAgKVPz0TEFsCQEBCf4zkjwBDHFPoVFC\\u002fAoAQVM2kPKUBI50eNo2c1wAAJOU\\u002fSPw1AYP4VRzRtLUAAXzJ9zd0BwACjUjPxXSDAyPDGLeTcP0AAvfwF9MAIwDD6xg4C8jJAIPCKeBjtE0BQEvPjqfgtwADbyByssvA\\u002fkA2+w7oRO0DAiAiOp0UPwDBt6ErEGzNAQAKsc+uDG8CAvDj4RL4vwCCqX6r3vSHAkPfKJAXhNsDYJntMwRk7QEDw1abf4SvA4GlWlKgMLMBAr6XnhDE2wIgw48QGeThAwB3555OXGMBgPg9upDUXQPCLIEv8cyZA8K38ogFWKEDw7gvAgzImwOAlW8RV+T9AYHa3EmprFEBAWeAi4LkxQMDxDdXzIBZA4G5uinphIsBgY1NI7RMkwAADfSxpK+U\\u002fwFXNCntbCcAYIn053IsyQEBwAmb75SlA2DPIcNUqM0DAfE9F2ZMiQLi8uDjnXDBAAEPEwnFYHsAwJVLjLOcqQMBiT595JgzAwITl4sL1C8BAs1sZOucRwFDNGkWv6yrAALIadGjV+D\\u002fgTuNPbz8WQJjd5MXQkTVAkMhgdEiTMkDAL1Iij6IEQODb6AEO1iTAwHQzubhqOMAA3OGR7qoZwECiX172lDBAAC\\u002fkjkeM4j\\u002fgfQmfL04SQMCzOfYlthHA4Fl+AEkiLUCg1hwAXz4aQIBNMsFc5CrAgMeasmehC8DQlBcGORMhQFgUO8\\u002fReDHAwFBwQvD2IsBAr5nmt\\u002fILwJj8x7+QfjZAgATydwgTHsAACDv3iEIEwEAmnEo8GCfAoH4\\u002fyK1AHsAA3EBodY\\u002fiv8DHeSWPyATAIGRwqcZ3EsD4olMNH6M1wACj3s4UdvU\\u002fIA4HMBSjJkDAjnmrVaUFwAD83Qd4iSBAgGnNy2bb8z+gB0KROzY3QKDfRtEEuRhAiEL\\u002fmLslNUCAmhkEdXIcQCCRy2Ip7TLAQNIJnVK9B8AgGCvu6QscwABAMiaAZK6\\u002fgCoogOPG8b\\u002fg4yfHv1QXQKCGRYUBFh9AAEK3fbEuEcBA7f5mefQKwHhXx2WTjDFAAJxGk79UBMDgU3xgI24VQEDcrhUQgy3AYG5geTE7KEAAjt3tmOAfQAAk1SPkbMm\\u002fQDSF\\u002f1HJJMAgltrAKcIkQGBtori9cBxA+CZNdZbJMUAAGnFy4DYhwMBSP9rIbRzAAOaKumo3OECwam1BlGcmQIBIEH4A\\u002fSZAyDZcqy1jPUBgvolCGJwuwADkJ\\u002fZU7ek\\u002fEFYor9kvMUAI1GiDjE09QJAPxiIMGEHAAOBAOxty9j\\u002fg3VOZ5S8wwIASeFNAAjnAsETFJXNQRsAomRznSl5CwEANMfE1eA3AMNQaJSnXM0CAcFpoQKYCQBBMr311dijAwMZT0hADFkAwlH7vLjE2QMAcMidDXAlAAGAEpbaOKkAA9cJMC9ruPyhx78tCbzdA+PzDhyvIMUDgbLhTE88TwABtjaft+xLAEHyeYxIsI8C4HOAQc4kxQMAeiFs19grA4O2bTZA1FcAAHEw3iAUZwOC7MCcPjCFAgAFIo7lo879Ab+rV7UoOQAA\\u002f0l9GxOg\\u002f2NN9VwvAMEBwCZpwXJEgwMDYi7tAWClAaONxjZaeMEBw7jhdlB0jQGAfxKTcDBrAQC442MZEA0D4AV4MOlI+QPBY9HlMcypAkAb70VzCIcAgEgUyqwYUQABLzifZEA9AwFj8XTMADkCABZR+askowPDK0peA5yhA4NSgshzbGsAA6oyJ+dsIQMAx62ClVhzA4F8fD3q1OEDgEHpca\\u002fIdQEC7Zq6\\u002ffiRAqMM+42P9PUCArcyULycdwJD3ecqb0i5AYL8UFDksJkBguixQ9rs6QMARoihkaBFAAEB6fhJasL9Q470WOfcgQODSQ2mp+StA3N5Ggvu4Q0Ag\\u002flVy57IrQGAI8WciOhfA+IFD6tx3OUCAsscyvqH6PyQXHEmZYkBAYPvj04nFMUBwYhONok0rQAARNCCixyBAoBXT2rbYH0C4PHMXGVY6QCDq5gwtDCvAUO3GmeZUJcCA9RqRbkwkwBBpUFsz8zJAsCIgi4fuJMDApYpH7+UZQKCr3v0rnBXAACuDGdhYM8CAy1Y+\\u002fzv8v\\u002fD2Z4zDIiFAjNlQ6MoFQMAAL2RNmq8RwICp2vYmojrAYB2qYjOhSEBA5W5Skaw8QAAAiFyGoz5A8L98aOjwQ0AAnC3WQY9HQAACg8jpA0NAQIlFIJVgR8CAJCcBTDJSwECW5dsoBVTAAM5u95iBEMAATKtc2TZFQACow+LaNfA\\u002fALjDOyQSNcCgYC9\\u002fdzNLwPDGpQFCgEDAOAgTRmrwRcBg9HBlBacpwGAq8GhEpTRAALyl2rjG+j8Yqwn4xxYxQMDzjIaqRhxA4Ge\\u002fcIODS0BgTE3EraA3QOB+e6JjxTLAMO86\\u002fEAPNEBwGhNf40cyQPAYHllFAzhAgOPOZtaxLkAA8kh5X2wZQNi95PckGDBAyFW+UHebMkCwezOXniMkwOCd+ULMXhxA8FKUYgIeMED43rJPB4NEQLDKJxDs0zhAUPulYP0VJUAASR00IY8OwACzqZ2T9Oc\\u002fQAYpJ2oGFUBQ8yEqsLUwQEC7eC1WHTFAQA1f+sV5PECgRxfPKDAbQKDDdZ0FFT9AIA8bsnhVMkBgARSr8+MuwKiP1KY2vzRATHFp0L10QEAgGabCMQ0yQACUhnRFVShAcMsQmQZCMEC0MQ5mwilDQJDEhGlpviRAgCtRMO8oC0DAc+nhH2sSwIBpQdhS+wpA0EzY3opTNEAAvO93QFfQP1goh9qPKDJAzBGG5V2ESEDAFmwSQT0iwCDgjOLbsTdAnCnrxrc8SkCAcvXR+4z8vziO2KIB9UFAYJt3bZOQOEDAOCQvEqUQQNBobk4jsj5AQDD8e9AoMUCAJD0BNOsqwNBZomOsLDLAYHPMRzD\\u002fMkCw3ZXXewg+QFBdB6aV0ClAQBb7QKYrKUAAQTSjavQtwADW60C3l\\u002fI\\u002f4K5X7IjaKUBAPfjkWCkTwACHSfPcwQ5AILT0MJkiGkCQlsJOcbAuwOCjv8y\\u002f0zpAsBnYfTkmIkBAU109joERwADCLlBJzwZAgDeJJ5q3MkCAXwy7Gqk0wMCl3X6ERjxAwAjM1doUI0AgDWdg\\u002fA1BwIBvbCIUUSjAgL34ZHV1VkAA7M565V8twOCeh5o9KENAgF\\u002fjx2TwPUAAYPCNvk3KvwDmMIYLcCtAQDS2mQuQMsAAu+DywlwawPCXOtFBGipAACpzB\\u002fiI+D+g0M1520g0QCA0lVDZkkNAQE6G5CpdDEBYf+A44ZczQEByMtAuzDZAQGGVNSCkFUBA+YWfNE8WQERzfuq1MkZAQDrr7h9AEMBgNVioZgQdQFBSu2RMOSfAwMSADey9IECweYoBczoyQGDq34gX7BNAAAThE8NJI0B4LWIAcDs\\u002fQKD6VGj2BRNAgB\\u002f\\u002f4puV8z8AyglKgCcNQODBTQhvFxhAAPMurUcUBcCA2unKkjH1v0CmA063FSbAwPiGpyW4CUCQblH1PnMyQGANSyrEqRpAIN+oKo90F0BAaXQXx9AfwAAj4AeEbfM\\u002fEKMlyjm9LEDARMNaYRsEQEBgxYIylhJAwFVgqPIUF0CA6V3SOSv+P+CIV0l1BxDAcCRAt7UXMkCgzZ+jT74YQODzHF6BFxVAiHJRe7paN0CAud\\u002fa9HEDQICB5qvz\\u002firA4L7T1q\\u002fcIMCA68V0kBv6PyBal1CXYS9AEPY3k+F9JUCAAwrEhjzzv4DH05Xaiv0\\u002fIO33dkE3LsCQw4Wbrt4rQIhKHm2f+ThAQI9xUgANGEAQSWb4fucjwCDo+wybUhhAgNQ9lnccMECgkkqB2Y4UQEBdqdYnUwlAYAH06QZhIkAA8Lra2gfLP7D1hB\\u002f2iSPAoCYmbUqBIcCwC4idB4YmQGjWUhfqRTFAQDSKnpofHUCQhFYaVbc+wMB\\u002ff4ivww3AaKFpl\\u002fi8N8AwLMucK3AqQACe6BC0hBxAQH1HD3BRCMDg2VCzwq4SQIDBCxmyxTzAAN5bDx66179gYuvwYLcXQACjkrJu2Oe\\u002fcDs\\u002fewhkKsA460EiKDs5wMBlM6FpViZA6DB0dsQqOMCAkUKSCI8LwEAFGjcxZ0HAgMeSKSzt+j+gMHjTc+8XwCh7I3j+ODdAkI+p+KNXMEBAAOpM8EcDQMCLjSSDMxrAsE5RwwLzIECAX3VLS973P6CVk+flfjPA4CCS7fXxFcBQ36wbngorQDAvGR\\u002fH8iTAAJyheoog3r8gfv\\u002fu\\u002fe8owMBs7659vgvAoM3fTk0gEkAAZx+b\\u002fvXgPwC0bdVXj\\u002fe\\u002fYClhZGfnGcBgHkWb6+sVwJDzyW6u+CLAwHke9CMEI0CwbQOR3z9AwABdyQtcOeC\\u002f6E917+17MEDA\\u002fRIlqOUZQLDHnZGwEifAQOsTuWwxHsAANnyFQh8jwMBiiWu2TB7AAJX3XB64DMAAUNJhwCYDQMCiQ9+EgR1AgKeb1iW59T+gfPsmNVMpwAAsI7xjeQ3A4MvAQqzUHsCAcaW6DUb0P0DJYfOUFAbAgEIbtQuh\\u002fL9A6YaNs1siQEA+0yXDtSHAwMQz6rJuHEAgV+COOLoXwKB9lhZhGRRA0CceyLgFMcDAGfF77pYAwOCj0nCQvSlAAMXjLkw8G0CQwGPfU4QmwDCNo+RFqibAgCTd1SrUIMBQ04\\u002f6kwIrwEDtZBdcbB7A0BmFy+O3OsAAmHDFtOnDP7jdIQh4AzRAIBusUklpF0CASdgg\\u002faMCwGC70yssFC\\u002fAQFvIacHgEEBAYDTeYcIbQPAanJkUYzTA8Os2pG+LK8DAkhlPXNkBwPCch\\u002fgvbiZAALTSgU5DBEDAxjgzm6MaQMB+idt9vQtAAMbYgc+4EkCAVX1k++4WQMCKJbGkKSlAYCBPhjzFFcAAJokpwXo2QCCURTiX9BBA4PdyB5h2QkAg+brbQZUlwEDPi4IcDyfAwK4LEdyzPMDoDNDp5DpHwGBt\\u002fsi0MDPAIHeMKubtMUDAnb5sZKEpwEDvXN++BifAAEDKl4eG7T+ALCDDAzQlwMDWX0ktsAPAAGQEOp5VJ8AgNrfbkfAvQIALKUhrwRRAgIfWQULrAEBQ1bGmlkAjwAAJf52Fc\\u002fQ\\u002fgMpMhSrND0DArwiKIhIhQIg9gm8W\\u002fzJAAFqbcN9l2D\\u002fYmBE3pLQ1QADF9\\u002fv17BdAgKE9ur\\u002feHECwUes1jdcgQCD8tYawYxZA8JenzYOALUAAOXBPIFbsP+ho8s32ADZAAJoN7DWK7L8Abk0xUZkvwEAQqf8dvQ3A4EEg0dRJL8AAe3Dfx1ASwMA7OtnaKQTAcJJj76HvLsDQV4cKitcswAAPgbgDD\\u002fa\\u002fqI4uwDBXO8A4NRhIaU4\\u002fwAgeA2GARjjAKJeJ218\\u002fM8DglC6e6HEswBgJ1rIsMzPAgJ\\u002frhV8g\\u002fT8oyyDwsNY3QGDc\\u002f7R\\u002faxNAwA3EUcVCKsAAFHboTT7sP9CvgIkYGzHAgBcxNsOH8b+AZpvRx1gYwIC60wefoQjAgGiYfMon8b+ACHD\\u002f8N0hQMD6jRvQDzrA4IxaLqUdLMBALLuNW3AIwIAP3QLeNRzAWEUVGOzyNEAA5PLNPWTuP9AVW1gs8zlAAJH\\u002fINcw\\u002fz8AJBxn5FjuP6gJkiNb8jNAUE37\\u002fZXjL0AA0OHm5w40wAD59Z54HyDAoHn8PGIhJMBwwsWuiiMgwNCCbzbwJSjAQIeRsm7XIUA4xpdQthU5wMDCmEAEugjAINQ9p80xIsBgw7TAXJUbQIALEaPzxitAIKOHlALDI0BQVR9\\u002fJEEowECRNHKBRSbAgN+uRBRKIsCAT2bkIrEhQKA\\u002fhn5HWB9AMAI+EYksMsAQFuxTf14uwFS2RyMJmUHA8PGjqwJqIsCAGkCzGnAkwCCGJDGSiSNAYJ2oUf18LsBgKYa4yoMuwNjyztVrRTPAoGVEACaSKMBImI7d20wxwFADiBKPoSzAAAO54lxTGcCAD\\u002fXzMGQZwHga2wRoXTfAYMmnPNjDKMCAnuhxNM0mwLC9R1Do1iDAcBKCy\\u002ffgKsA46AWVs3UwwFhCvYUdezfAYF+hb3gBJ8CAohiDtuURQIBDxNBFGSHAKM\\u002f9\\u002fPCSM8Awa+ISADMtwLD\\u002f0MJToDbAUJjepB+xIkAAdJnClknUP0C3zsYpbSvAUAJ3F059IcDoa52zFcc4wIDBGUNkwBBA0FdOQ71NIkCYLCA7zOI3wADnDqv2zv6\\u002fAOPgHyfvJcAAsbT57ukHQABhDjdQL+4\\u002fkO79H9I1KsCA8icayz8BwDBj2E7ENTXAMMCXyE13IUDouPVZ5KlAwJCAzk60NyFASFkdYIV1M8AA2f0cGID3P1DZDy9WNynAQJ96GkGFBcCgNd+Lp+MQQIBnlteZgxRAoHcJLrfxJcAARsDdpW7tPzgUnQJcMjXAcFJoOYtSPcAAT9sAu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-       "\n",
-       "        syncLegendVisibility();\n",
-       "    });\n",
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-       "syncLegendVisibility();\n",
-       "window.requestAnimationFrame(installLegendToggleButton);\n",
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-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.display.plotter.plot_posterior_predictive(expt_name='hrpt')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "58",
+   "id": "57",
    "metadata": {},
    "source": [
     "A final zoomed measured-vs-calculated plot is useful for checking how\n",
@@ -4386,240 +708,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "id": "59",
+   "execution_count": null,
+   "id": "58",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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'hrpt'\"},\"legend\":{\"bgcolor\":\"rgba(255, 255, 255, 0.5)\",\"xanchor\":\"right\",\"x\":1.0,\"yanchor\":\"top\",\"y\":1.0},\"height\":600.0},                        {\"displayModeBar\": true, \"displaylogo\": false, \"modeBarButtonsToRemove\": [\"select2d\", \"lasso2d\", \"zoomIn2d\", \"zoomOut2d\", \"autoScale2d\"], \"responsive\": true}                    ).then(function(){\n",
-       "                            {\n",
-       "const graphDiv = document.getElementById('eafdb772-2ad2-46ec-955e-1b143a986a54');\n",
-       "if (!graphDiv) {\n",
-       "    return;\n",
-       "}\n",
-       "\n",
-       "const parseColor = function (colorValue) {\n",
-       "    if (!colorValue) {\n",
-       "        return null;\n",
-       "    }\n",
-       "\n",
-       "    const rgbMatch = colorValue.match(/^rgba?\\(([^)]+)\\)$/);\n",
-       "    if (rgbMatch) {\n",
-       "        const channels = rgbMatch[1].split(',').slice(0, 3).map((value) => Number(value.trim()));\n",
-       "        if (channels.every((value) => Number.isFinite(value))) {\n",
-       "            return {red: channels[0], green: channels[1], blue: channels[2]};\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    const hexMatch = colorValue.match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);\n",
-       "    if (!hexMatch) {\n",
-       "        return null;\n",
-       "    }\n",
-       "\n",
-       "    const normalizedHex = hexMatch[1].length === 3\n",
-       "        ? hexMatch[1].split('').map((value) => value + value).join('')\n",
-       "        : hexMatch[1];\n",
-       "    return {\n",
-       "        red: Number.parseInt(normalizedHex.slice(0, 2), 16),\n",
-       "        green: Number.parseInt(normalizedHex.slice(2, 4), 16),\n",
-       "        blue: Number.parseInt(normalizedHex.slice(4, 6), 16),\n",
-       "    };\n",
-       "};\n",
-       "\n",
-       "const resolveLegendButtonFill = function (opacity) {\n",
-       "    const referencePath = graphDiv.querySelector('.modebar-btn path');\n",
-       "    const referenceFill = referencePath ? window.getComputedStyle(referencePath).fill : null;\n",
-       "    const fontColor = graphDiv._fullLayout && graphDiv._fullLayout.font\n",
-       "        ? graphDiv._fullLayout.font.color\n",
-       "        : null;\n",
-       "    const parsedColor = (\n",
-       "        parseColor(referenceFill)\n",
-       "        || parseColor(fontColor)\n",
-       "        || {red: 68, green: 68, blue: 68}\n",
-       "    );\n",
-       "    return (\n",
-       "        'rgba('\n",
-       "        + parsedColor.red\n",
-       "        + ', '\n",
-       "        + parsedColor.green\n",
-       "        + ', '\n",
-       "        + parsedColor.blue\n",
-       "        + ', '\n",
-       "        + opacity\n",
-       "        + ')'\n",
-       "    );\n",
-       "};\n",
-       "\n",
-       "const updateLegendButtonAppearance = function (legendVisible) {\n",
-       "    const legendButton = graphDiv.querySelector('[data-legend-toggle=\"true\"]');\n",
-       "    if (!legendButton) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    const legendIconPath = legendButton.querySelector('path');\n",
-       "    if (!legendIconPath) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    legendButton.classList.toggle('active', legendVisible);\n",
-       "    legendButton.setAttribute('aria-pressed', String(legendVisible));\n",
-       "    legendIconPath.setAttribute(\n",
-       "        'style',\n",
-       "        'fill: ' + resolveLegendButtonFill(legendVisible ? 0.7 : 0.3) + ';',\n",
-       "    );\n",
-       "};\n",
-       "\n",
-       "const applyLegendVisibility = function (legendVisible) {\n",
-       "    const legend = graphDiv.querySelector('.legend');\n",
-       "    if (legend) {\n",
-       "        legend.style.display = legendVisible ? 'inline' : 'none';\n",
-       "        legend.style.visibility = legendVisible ? 'visible' : 'hidden';\n",
-       "        legend.style.pointerEvents = legendVisible ? '' : 'none';\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.layout) {\n",
-       "        graphDiv.layout.showlegend = legendVisible;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv._fullLayout) {\n",
-       "        graphDiv._fullLayout.showlegend = legendVisible;\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "const readLegendVisibility = function () {\n",
-       "    if (graphDiv.dataset.legendVisible === 'true') {\n",
-       "        return true;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.dataset.legendVisible === 'false') {\n",
-       "        return false;\n",
-       "    }\n",
-       "\n",
-       "    const legend = graphDiv.querySelector('.legend');\n",
-       "    if (legend) {\n",
-       "        return (\n",
-       "            window.getComputedStyle(legend).display !== 'none'\n",
-       "            && window.getComputedStyle(legend).visibility !== 'hidden'\n",
-       "        );\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv.layout && typeof graphDiv.layout.showlegend === 'boolean') {\n",
-       "        return graphDiv.layout.showlegend;\n",
-       "    }\n",
-       "\n",
-       "    if (graphDiv._fullLayout && typeof graphDiv._fullLayout.showlegend === 'boolean') {\n",
-       "        return graphDiv._fullLayout.showlegend;\n",
-       "    }\n",
-       "\n",
-       "    return true;\n",
-       "};\n",
-       "\n",
-       "const syncLegendVisibility = function (legendVisible) {\n",
-       "    const resolvedLegendVisible = typeof legendVisible === 'boolean'\n",
-       "        ? legendVisible\n",
-       "        : readLegendVisibility();\n",
-       "    graphDiv.dataset.legendVisible = String(resolvedLegendVisible);\n",
-       "    applyLegendVisibility(resolvedLegendVisible);\n",
-       "    updateLegendButtonAppearance(resolvedLegendVisible);\n",
-       "    return resolvedLegendVisible;\n",
-       "};\n",
-       "\n",
-       "const toggleLegend = function (event) {\n",
-       "    if (event) {\n",
-       "        event.preventDefault();\n",
-       "        event.stopPropagation();\n",
-       "    }\n",
-       "\n",
-       "    const currentValue = readLegendVisibility();\n",
-       "    const nextValue = !currentValue;\n",
-       "    syncLegendVisibility(nextValue);\n",
-       "};\n",
-       "\n",
-       "const installLegendToggleButton = function () {\n",
-       "    const modebar = graphDiv.querySelector('.modebar');\n",
-       "    if (!modebar) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    if (!modebar.querySelector('.modebar-group')) {\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    let legendButton = modebar.querySelector('[data-legend-toggle=\"true\"]');\n",
-       "    if (!legendButton) {\n",
-       "        const legendButtonGroup = document.createElement('div');\n",
-       "        legendButtonGroup.className = 'modebar-group';\n",
-       "\n",
-       "        legendButton = document.createElement('a');\n",
-       "        legendButton.className = 'modebar-btn';\n",
-       "        legendButton.href = 'javascript:void(0)';\n",
-       "        legendButton.setAttribute('data-title', 'Toggle legend');\n",
-       "        legendButton.setAttribute('data-legend-toggle', 'true');\n",
-       "        legendButton.setAttribute('aria-label', 'Toggle legend');\n",
-       "        legendButton.setAttribute('role', 'button');\n",
-       "        legendButton.setAttribute('tabindex', '0');\n",
-       "        legendButton.innerHTML = [\n",
-       "            '<svg viewBox=\"0 0 1000 1000\"'\n",
-       "            + ' class=\"icon\" height=\"1em\" width=\"1em\"'\n",
-       "            + ' aria-hidden=\"true\">',\n",
-       "            '<path d=\"M120 160H240V280H120z M120 440H240V560H120z '\n",
-       "            + 'M120 720H240V840H120z M320 200H880V240H320z '\n",
-       "            + 'M320 480H880V520H320z M320 760H880V800H320z\"></path>',\n",
-       "            '</svg>',\n",
-       "        ].join('');\n",
-       "\n",
-       "        legendButtonGroup.appendChild(legendButton);\n",
-       "        modebar.appendChild(legendButtonGroup);\n",
-       "    }\n",
-       "\n",
-       "    legendButton.onclick = toggleLegend;\n",
-       "    legendButton.onkeydown = function (event) {\n",
-       "        if (event.key === 'Enter' || event.key === ' ') {\n",
-       "            toggleLegend(event);\n",
-       "        }\n",
-       "    };\n",
-       "\n",
-       "    syncLegendVisibility();\n",
-       "};\n",
-       "\n",
-       "if (graphDiv.on) {\n",
-       "    graphDiv.on('plotly_afterplot', installLegendToggleButton);\n",
-       "    graphDiv.on('plotly_relayout', function (eventData) {\n",
-       "        if (eventData && typeof eventData.showlegend === 'boolean') {\n",
-       "            syncLegendVisibility(eventData.showlegend);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        syncLegendVisibility();\n",
-       "    });\n",
-       "}\n",
-       "syncLegendVisibility();\n",
-       "window.requestAnimationFrame(installLegendToggleButton);\n",
-       "}\n",
-       "                        })                };            </script>        </div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=45.4, x_max=46.3)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "395b00f6-635c-43b9-9d86-aa00608a4b9b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -4627,23 +722,6 @@
    "cell_metadata_filter": "-all",
    "main_language": "python",
    "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.14.4"
   }
  },
  "nbformat": 4,

From f1039da1ae650f1cb45bf6bcc98079927dd042a8 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 00:00:31 +0200
Subject: [PATCH 069/106] Fix docstring errors in Plotter class

---
 src/easydiffraction/display/plotting.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 733e54e6..5567aca6 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -851,7 +851,7 @@ def plot_posterior_predictive(
         x_min : float | None, default=None
             Lower bound for the x-axis range.
         x_max : float | None, default=None
-            include_draws=style in {'draws', 'band+draws'},
+            Upper bound for the x-axis range.
         show_residual : bool | None, default=None
             Whether to include the residual row in the composite plot.
         x : object | None, default=None
@@ -3921,7 +3921,9 @@ def _plot_meas_data(
             Object with x-axis arrays (``two_theta``,
             ``time_of_flight``, ``d_spacing``) and ``meas`` array.
         expt_name : str
-            Experiment name for the title. *,
+            Experiment name for the title.
+        expt_type : object
+            Experiment type with scattering/beam enums.
         x_min : object, default=None
             Optional minimum x-axis limit.
         x_max : object, default=None

From a79bd6600f00f9ee2a4617f8d2b717c774457229 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 08:49:59 +0200
Subject: [PATCH 070/106] Standardize axis title font size

---
 .../analysis/minimizers/bumps_dream.py        |  3 +--
 .../display/plotters/plotly.py                | 26 ++++++++++++++++---
 src/easydiffraction/display/plotting.py       | 10 ++++---
 .../display/plotters/test_plotly.py           | 11 ++++++++
 .../easydiffraction/display/test_plotting.py  | 15 +++++++++++
 5 files changed, 56 insertions(+), 9 deletions(-)

diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 7eab3f23..f7060d12 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -763,8 +763,7 @@ def _build_success_result(
 
         if not convergence_diagnostics.get('converged', True):
             log.warning(
-                'DREAM sampling completed, but convergence diagnostics indicate '
-                'the posterior may be poorly mixed.'
+                'Convergence diagnostics indicate the posterior may be poorly mixed.'
             )
 
         return OptimizeResult(
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 764c5a0f..30164d10 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -65,6 +65,7 @@
 COMPOSITE_MARGIN_RIGHT = 30
 COMPOSITE_MARGIN_TOP = 40
 COMPOSITE_MARGIN_BOTTOM = 45
+AXIS_TITLE_FONT_SIZE = 12
 PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.26)'
 PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
 PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
@@ -1005,14 +1006,20 @@ def _get_layout(
                 'y': 1.0,
             },
             xaxis={
-                'title_text': axes_labels[0],
+                'title': {
+                    'text': axes_labels[0],
+                    'font': {'size': AXIS_TITLE_FONT_SIZE},
+                },
                 'showline': True,
                 'linecolor': cls._axis_frame_color(),
                 'mirror': True,
                 'zeroline': False,
             },
             yaxis={
-                'title_text': axes_labels[1],
+                'title': {
+                    'text': axes_labels[1],
+                    'font': {'size': AXIS_TITLE_FONT_SIZE},
+                },
                 'showline': True,
                 'linecolor': cls._axis_frame_color(),
                 'mirror': True,
@@ -1574,6 +1581,7 @@ def _configure_powder_composite_axes(
         fig.update_xaxes(showticklabels=(layout.row_count == 1), row=1, col=1)
         fig.update_yaxes(
             title_text=plot_spec.axes_labels[1],
+            title_font={'size': AXIS_TITLE_FONT_SIZE},
             range=list(main_y_range),
             row=1,
             col=1,
@@ -1591,7 +1599,12 @@ def _configure_powder_composite_axes(
             return
 
         terminal_row = layout.bragg_row if layout.bragg_row is not None else 1
-        fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=terminal_row, col=1)
+        fig.update_xaxes(
+            title_text=plot_spec.axes_labels[0],
+            title_font={'size': AXIS_TITLE_FONT_SIZE},
+            row=terminal_row,
+            col=1,
+        )
 
     def _configure_shared_composite_axes(
         self,
@@ -1667,7 +1680,12 @@ def _configure_residual_axes(
             row=layout.residual_row,
             col=1,
         )
-        fig.update_xaxes(title_text=plot_spec.axes_labels[0], row=layout.residual_row, col=1)
+        fig.update_xaxes(
+            title_text=plot_spec.axes_labels[0],
+            title_font={'size': AXIS_TITLE_FONT_SIZE},
+            row=layout.residual_row,
+            col=1,
+        )
 
     @staticmethod
     def _get_predictive_band_traces(
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 5567aca6..4da2f63b 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -146,7 +146,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 PAIR_PLOT_SUBPLOT_SPACING = 0.01
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
 POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = 12
-POSTERIOR_PAIR_TITLE_FONT_SIZE = 16
+POSTERIOR_PAIR_TITLE_FONT_SIZE = POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE
 POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 16
 POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS = 10
 POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS = 2
@@ -155,8 +155,8 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_PAIR_FIXED_ASPECT_META_KEY = 'fixed_aspect_wrapper'
 POSTERIOR_PAIR_LEFT_MARGIN_PIXELS = 58
 POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS = 10
-POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 26
-POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 42
+POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 40
+POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 24
 POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
 POSTERIOR_PAIR_SAMPLE_MARKER_SIZE = 6
 POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE = 6
@@ -2219,6 +2219,8 @@ def _apply_posterior_distribution_layout(
                     'y': 1.0,
                 },
             )
+            fig.update_xaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
+            fig.update_yaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
         if x_axis_range is not None:
             fig.update_xaxes(range=list(x_axis_range))
         if y_axis_range is not None:
@@ -3010,6 +3012,8 @@ def _plot_posterior_predictive_summary(
             xaxis_title=axes_labels[0],
             yaxis_title=axes_labels[1],
         )
+        fig.update_xaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
+        fig.update_yaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
         fig.show()
 
     def _plot_posterior_predictive_data(
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index 54c2d5d6..bfb3105f 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -13,6 +13,15 @@ def test_module_import():
     assert expected_module_name == actual_module_name
 
 
+def test_get_layout_sets_axis_title_font_size():
+    import easydiffraction.display.plotters.plotly as pp
+
+    layout = pp.PlotlyPlotter._get_layout('Title', ['x axis', 'y axis'])
+
+    assert layout.xaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
+    assert layout.yaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
+
+
 def test_get_trace_and_plot(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
@@ -775,6 +784,8 @@ def fake_show_figure(self, fig):
     assert fig.layout.xaxis2.matches == 'x'
     assert fig.layout.yaxis2.title.text is None
     assert fig.layout.xaxis2.title.text == '2θ (degree)'
+    assert fig.layout.yaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
+    assert fig.layout.xaxis2.title.font.size == pp.AXIS_TITLE_FONT_SIZE
     assert [trace.name for trace in fig.data] == [
         'Measured (Imeas)',
         'Total calculated (Icalc)',
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 7ee026fa..09ccf6f6 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -307,8 +307,11 @@ def test_correlation_from_posterior_samples_returns_labeled_dataframe():
 
 
 def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE
     from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_MARKER_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TOP_MARGIN_PIXELS
 
     plotter, _, _ = _make_bayesian_plotter_fixture()
 
@@ -330,12 +333,15 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
         'broad_gauss_v',
         'twotheta_offset',
     ]
+    assert figure.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
     assert figure.layout.annotations[0].xshift == -plotter._square_matrix_title_left_shift([
         'length_a',
         'broad_gauss_u',
         'broad_gauss_v',
         'twotheta_offset',
     ])
+    assert figure.layout.margin.t == POSTERIOR_PAIR_TOP_MARGIN_PIXELS
+    assert figure.layout.margin.b == POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
     subplot = figure.get_subplot(1, 1)
     bottom_subplot = figure.get_subplot(4, 1)
     assert subplot.yaxis.showticklabels is False
@@ -1294,6 +1300,10 @@ def test_plot_param_correlations_renders_plotly_heatmap(monkeypatch):
     import numpy as np
 
     import easydiffraction.display.plotters.plotly as plotly_mod
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TOP_MARGIN_PIXELS
     from easydiffraction.display.plotting import Plotter
 
     captured = {}
@@ -1357,10 +1367,15 @@ class Project:
         'phase.<br>scale',
         'phase.<br>cell.<br>length_c',
     ]
+    assert fig.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
     assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift([
         'phase.<br>scale',
         'phase.<br>cell.<br>length_c',
     ])
+    assert fig.layout.margin.t == POSTERIOR_PAIR_TOP_MARGIN_PIXELS
+    assert fig.layout.margin.b == (
+        POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS + 2 * POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
+    )
     assert fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio'] == '1 / 1'
     assert fig.layout.xaxis.domain[1] < fig.layout.xaxis2.domain[0]
     assert fig.layout.xaxis.showline is False

From 9731a7aa4f6d5de23a3c1c231e36695e41575551 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 08:50:48 +0200
Subject: [PATCH 071/106] Improve Bayesian fit summary display

---
 .../analysis/fit_helpers/bayesian.py          | 44 +++++++++++++------
 .../display/plotters/plotly.py                |  7 ++-
 src/easydiffraction/display/plotting.py       | 29 +++++++++---
 .../analysis/fit_helpers/test_bayesian.py     | 44 +++++++++++++++++++
 .../display/plotters/test_plotly.py           |  4 +-
 .../easydiffraction/display/test_plotting.py  |  4 ++
 6 files changed, 110 insertions(+), 22 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 243ce872..7e455893 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -330,19 +330,24 @@ def _print_table_notes(self) -> None:
 
     def _display_summary_header(self) -> None:
         """Render the high-level Bayesian fit summary."""
-        status_icon = '✅' if self.success else '❌'
+        status_icon, overall_status = _format_bayesian_overall_status(
+            success=self.success,
+            sampler_completed=self.sampler_completed,
+            convergence_diagnostics=self.convergence_diagnostics,
+        )
         fitting_time = _format_optional_float(self.fitting_time, suffix=' seconds')
         goodness_of_fit = _format_optional_float(self.reduced_chi_square)
 
         console.paragraph('Bayesian fit results')
-        console.print(f'{status_icon} Success: {self.success}')
+        console.print(f'{status_icon} Overall status: {overall_status}')
         if self.message:
-            console.print(f'i Status: {self.message}')
+            console.print(f'💬 Sampler status: {self.message}')
         console.print(f'🧪 Sampler: {self.sampler_name}')
         console.print(
             f'🎯 Committed point estimate: {_format_point_estimate_name(self.point_estimate_name)}'
         )
-        console.print(f'🔁 Sampler completed: {self.sampler_completed}')
+        sampler_completed = 'yes' if self.sampler_completed else 'no'
+        console.print(f'🔁 Sampler completed: {sampler_completed}')
         console.print(f'⏱️ Fitting time: {fitting_time}')
         console.print(f'📏 Goodness-of-fit (reduced χ²): {goodness_of_fit}')
         if self.best_log_posterior is not None:
@@ -566,6 +571,24 @@ def _format_point_estimate_name(point_estimate_name: str) -> str:
     return point_estimate_name.replace('_', ' ').title()
 
 
+def _format_bayesian_overall_status(
+    *,
+    success: bool,
+    sampler_completed: bool,
+    convergence_diagnostics: dict[str, object],
+) -> tuple[str, str]:
+    """Return icon and text for Bayesian run status."""
+    if not success:
+        return '❌', 'failed'
+
+    converged = convergence_diagnostics.get('converged')
+    if converged is False:
+        return '⚠️', 'completed with warnings'
+    if sampler_completed:
+        return '✅', 'completed'
+    return '✅', 'posterior available'
+
+
 def _format_convergence_summary(convergence_diagnostics: dict[str, object]) -> str | None:
     if not convergence_diagnostics:
         return None
@@ -573,8 +596,8 @@ def _format_convergence_summary(convergence_diagnostics: dict[str, object]) -> s
     parts: list[str] = []
     converged = convergence_diagnostics.get('converged')
     if converged is not None:
-        status = 'yes' if converged else '[red]failed[/red]'
-        parts.append(f'converged={status}')
+        status = 'passed' if converged else '[red]failed[/red]'
+        parts.append(f'status={status}')
 
     max_r_hat = _maybe_scalar(convergence_diagnostics.get('max_r_hat'))
     if max_r_hat is not None:
@@ -639,8 +662,6 @@ def _render_posterior_summary_table(
         'entry',
         'parameter',
         'median',
-        'std',
-        '68% interval',
         '95% interval',
         'r_hat',
         'ess_bulk',
@@ -655,8 +676,6 @@ def _render_posterior_summary_table(
         'right',
         'right',
         'right',
-        'right',
-        'right',
         'left',
     ]
     rows = [
@@ -679,16 +698,15 @@ def _build_posterior_summary_row(
     datablock = getattr(identity, 'datablock_entry_name', 'N/A')
     category = getattr(identity, 'category_code', 'N/A')
     entry = getattr(identity, 'category_entry_name', '') or ''
+    parameter_name = getattr(parameter, 'name', summary.display_name)
     units = getattr(parameter, 'units', 'N/A')
 
     return [
         datablock,
         category,
         entry,
-        summary.display_name,
+        parameter_name,
         f'{summary.median:.4f}',
-        f'{summary.standard_deviation:.4f}',
-        _format_interval(summary.interval_68),
         _format_interval(summary.interval_95),
         _format_r_hat(summary.r_hat),
         _format_ess_bulk(summary.ess_bulk),
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 30164d10..0366e961 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -65,6 +65,7 @@
 COMPOSITE_MARGIN_RIGHT = 30
 COMPOSITE_MARGIN_TOP = 40
 COMPOSITE_MARGIN_BOTTOM = 45
+TITLE_FONT_SIZE = 14
 AXIS_TITLE_FONT_SIZE = 12
 PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.26)'
 PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
@@ -997,6 +998,7 @@ def _get_layout(
             },
             title={
                 'text': title,
+                'font': {'size': TITLE_FONT_SIZE},
             },
             legend={
                 'bgcolor': cls._legend_background_color(),
@@ -1552,7 +1554,10 @@ def _configure_powder_composite_layout(
                 't': COMPOSITE_MARGIN_TOP,
                 'b': COMPOSITE_MARGIN_BOTTOM,
             },
-            title={'text': plot_spec.title},
+            title={
+                'text': plot_spec.title,
+                'font': {'size': TITLE_FONT_SIZE},
+            },
             legend={
                 'bgcolor': self._legend_background_color(),
                 'xanchor': 'right',
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 4da2f63b..c9e8be42 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -31,6 +31,10 @@
 from easydiffraction.display.plotters.base import BraggTickSet
 from easydiffraction.display.plotters.base import PowderMeasVsCalcSpec
 from easydiffraction.display.plotters.base import XAxisType
+from easydiffraction.display.plotters.plotly import (
+    AXIS_TITLE_FONT_SIZE as PLOTLY_AXIS_TITLE_FONT_SIZE,
+)
+from easydiffraction.display.plotters.plotly import TITLE_FONT_SIZE as PLOTLY_TITLE_FONT_SIZE
 from easydiffraction.display.plotters.plotly import PlotlyPlotter
 from easydiffraction.display.tables import TableRenderer
 from easydiffraction.utils.environment import in_jupyter
@@ -145,17 +149,17 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS = 980
 PAIR_PLOT_SUBPLOT_SPACING = 0.01
 POSTERIOR_PAIR_AXIS_LINE_WIDTH = 1.2
-POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = 12
-POSTERIOR_PAIR_TITLE_FONT_SIZE = POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE
+POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE = PLOTLY_AXIS_TITLE_FONT_SIZE
+POSTERIOR_PAIR_TITLE_FONT_SIZE = PLOTLY_TITLE_FONT_SIZE
 POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 16
 POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS = 10
-POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS = 2
+POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS = 8
 POSTERIOR_PAIR_GUIDE_LINE_COLOR = 'rgba(125, 140, 173, 0.18)'
 POSTERIOR_PAIR_FIXED_ASPECT_RATIO = '1 / 1'
 POSTERIOR_PAIR_FIXED_ASPECT_META_KEY = 'fixed_aspect_wrapper'
 POSTERIOR_PAIR_LEFT_MARGIN_PIXELS = 58
 POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS = 10
-POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 40
+POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 26
 POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 24
 POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
 POSTERIOR_PAIR_SAMPLE_MARKER_SIZE = 6
@@ -2205,10 +2209,18 @@ def _apply_posterior_distribution_layout(
     ) -> None:
         """Apply layout settings to the distribution plot."""
         if callable(layout_factory):
-            fig.update_layout(title={'text': title})
+            fig.update_layout(
+                title={
+                    'text': title,
+                    'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+                }
+            )
         else:
             fig.update_layout(
-                title=title,
+                title={
+                    'text': title,
+                    'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+                },
                 xaxis_title=label,
                 yaxis_title='Probability density',
                 legend={
@@ -3008,7 +3020,10 @@ def _plot_posterior_predictive_summary(
             )
         )
         fig.update_layout(
-            title=f"Posterior predictive for experiment 🔬 '{expt_name}'",
+            title={
+                'text': f"Posterior predictive for experiment 🔬 '{expt_name}'",
+                'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
+            },
             xaxis_title=axes_labels[0],
             yaxis_title=axes_labels[1],
         )
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
index 58fee092..708d8a6f 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -150,21 +150,65 @@ def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(cap
         ],
         best_log_posterior=-12.34,
     )
+    results.message = 'DREAM sampling completed'
 
     results.display_results(y_obs=[10.0, 20.0], y_calc=[9.5, 19.5])
 
     out = capsys.readouterr().out
     assert 'Bayesian fit results' in out
+    assert 'Overall status: completed with warnings' in out
+    assert 'Sampler status: DREAM sampling completed' in out
     assert 'Sampler: dream' in out
+    assert 'Sampler completed: yes' in out
     assert 'random_seed=1313900679' in out
     assert 'steps=200' in out
+    assert 'status=failed' in out
     assert 'max_r_hat=1.107' in out
     assert 'min_ess_bulk=125.9' in out
     assert 'Posterior parameter summaries:' in out
+    assert 'Success: True' not in out
+    assert 'datablock' in out
+    assert 'category' in out
+    assert 'entry' in out
+    assert '95% interval' in out
+    assert '68% interval' not in out
+    assert 'std' not in out
 
     monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
 
 
+def test_build_posterior_summary_row_restores_identifier_columns():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _build_posterior_summary_row
+
+    parameter = Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    summary = PosteriorParameterSummary(
+        unique_name='a',
+        display_name='a',
+        map_value=1.2,
+        median=1.15,
+        standard_deviation=0.05,
+        interval_68=(1.1, 1.2),
+        interval_95=(1.0, 1.3),
+        r_hat=1.107,
+        ess_bulk=125.9,
+    )
+
+    row = _build_posterior_summary_row(summary, {'a': parameter})
+
+    assert row == [
+        'db',
+        'cat',
+        'entry',
+        'a',
+        '1.1500',
+        '[1.0000, 1.3000]',
+        '[red]1.107[/red]',
+        '[red]125.9[/red]',
+        'arb',
+    ]
+
+
 def test_posterior_table_notes_split_failed_diagnostics():
     from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
     from easydiffraction.analysis.fit_helpers.bayesian import _posterior_table_notes
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index bfb3105f..cb81844e 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -13,11 +13,12 @@ def test_module_import():
     assert expected_module_name == actual_module_name
 
 
-def test_get_layout_sets_axis_title_font_size():
+def test_get_layout_sets_title_and_axis_title_font_sizes():
     import easydiffraction.display.plotters.plotly as pp
 
     layout = pp.PlotlyPlotter._get_layout('Title', ['x axis', 'y axis'])
 
+    assert layout.title.font.size == pp.TITLE_FONT_SIZE
     assert layout.xaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
     assert layout.yaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
 
@@ -784,6 +785,7 @@ def fake_show_figure(self, fig):
     assert fig.layout.xaxis2.matches == 'x'
     assert fig.layout.yaxis2.title.text is None
     assert fig.layout.xaxis2.title.text == '2θ (degree)'
+    assert fig.layout.title.font.size == pp.TITLE_FONT_SIZE
     assert fig.layout.yaxis.title.font.size == pp.AXIS_TITLE_FONT_SIZE
     assert fig.layout.xaxis2.title.font.size == pp.AXIS_TITLE_FONT_SIZE
     assert [trace.name for trace in fig.data] == [
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 09ccf6f6..acc1c9d6 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -311,6 +311,7 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
     from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE
     from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_MARKER_SIZE
     from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_TOP_MARGIN_PIXELS
 
     plotter, _, _ = _make_bayesian_plotter_fixture()
@@ -334,6 +335,7 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
         'twotheta_offset',
     ]
     assert figure.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
+    assert figure.layout.annotations[0].yshift == POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
     assert figure.layout.annotations[0].xshift == -plotter._square_matrix_title_left_shift([
         'length_a',
         'broad_gauss_u',
@@ -1303,6 +1305,7 @@ def test_plot_param_correlations_renders_plotly_heatmap(monkeypatch):
     from easydiffraction.display.plotting import POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
+    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_TOP_MARGIN_PIXELS
     from easydiffraction.display.plotting import Plotter
 
@@ -1368,6 +1371,7 @@ class Project:
         'phase.<br>cell.<br>length_c',
     ]
     assert fig.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
+    assert fig.layout.annotations[0].yshift == POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
     assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift([
         'phase.<br>scale',
         'phase.<br>cell.<br>length_c',

From 511a5e5c4053fa4ae334c93cca75173d455caf4c Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 09:58:27 +0200
Subject: [PATCH 072/106] Improve Bayesian fit result display

---
 docs/docs/tutorials/ed-21.py                  |   4 +-
 .../analysis/fit_helpers/bayesian.py          |  14 +--
 .../analysis/fit_helpers/reporting.py         |   6 +-
 src/easydiffraction/display/plotters/base.py  |   1 +
 .../display/plotters/plotly.py                |   5 +-
 src/easydiffraction/display/plotting.py       |  28 +++--
 .../analysis/fit_helpers/test_bayesian.py     | 116 ++++++++++++++++++
 .../analysis/fit_helpers/test_reporting.py    |  64 ++++++++++
 .../display/plotters/test_plotly.py           |  43 +++++++
 .../easydiffraction/display/test_plotting.py  | 104 ++++++++++++++++
 10 files changed, 365 insertions(+), 20 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 1fadc54f..4bdbc0cf 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -331,4 +331,6 @@
 # after the Bayesian run.
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=45.4, x_max=46.3)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=92, x_max=93)
+
+# %%
diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 7e455893..78fa637b 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -621,10 +621,10 @@ def _render_committed_parameter_table(parameters: list[object]) -> None:
         'category',
         'entry',
         'parameter',
+        'units',
         'start',
         'max posterior',
         'uncertainty',
-        'units',
         'change',
     ]
     alignments = [
@@ -632,10 +632,10 @@ def _render_committed_parameter_table(parameters: list[object]) -> None:
         'left',
         'left',
         'left',
+        'left',
         'right',
         'right',
         'right',
-        'left',
         'right',
     ]
     rows = [_build_parameter_row(parameter) for parameter in parameters]
@@ -661,22 +661,22 @@ def _render_posterior_summary_table(
         'category',
         'entry',
         'parameter',
+        'units',
         'median',
         '95% interval',
-        'r_hat',
-        'ess_bulk',
-        'units',
+        'r-hat',
+        'ess bulk',
     ]
     alignments = [
         'left',
         'left',
         'left',
         'left',
+        'left',
         'right',
         'right',
         'right',
         'right',
-        'left',
     ]
     rows = [
         _build_posterior_summary_row(summary, parameters_by_name)
@@ -706,11 +706,11 @@ def _build_posterior_summary_row(
         category,
         entry,
         parameter_name,
+        units,
         f'{summary.median:.4f}',
         _format_interval(summary.interval_95),
         _format_r_hat(summary.r_hat),
         _format_ess_bulk(summary.ess_bulk),
-        units,
     ]
 
 
diff --git a/src/easydiffraction/analysis/fit_helpers/reporting.py b/src/easydiffraction/analysis/fit_helpers/reporting.py
index 0e867156..aefad27b 100644
--- a/src/easydiffraction/analysis/fit_helpers/reporting.py
+++ b/src/easydiffraction/analysis/fit_helpers/reporting.py
@@ -127,10 +127,10 @@ def display_results(
             'category',
             'entry',
             'parameter',
+            'units',
             'start',
             'fitted',
             'uncertainty',
-            'units',
             'change',
         ]
         alignments = [
@@ -138,10 +138,10 @@ def display_results(
             'left',
             'left',
             'left',
+            'left',
             'right',
             'right',
             'right',
-            'left',
             'right',
         ]
 
@@ -223,10 +223,10 @@ def _build_parameter_row(param: object) -> list[str]:
         param._identity.category_code,
         param._identity.category_entry_name or '',
         name,
+        units,
         start,
         fitted,
         uncertainty,
-        units,
         relative_change,
     ]
 
diff --git a/src/easydiffraction/display/plotters/base.py b/src/easydiffraction/display/plotters/base.py
index 6f041161..d4be0d67 100644
--- a/src/easydiffraction/display/plotters/base.py
+++ b/src/easydiffraction/display/plotters/base.py
@@ -64,6 +64,7 @@ class PowderMeasVsCalcSpec:
     predictive_upper_95: np.ndarray | None = None
     predictive_draws: np.ndarray | None = None
     y_calc_name: str | None = None
+    y_calc_line_dash: str | None = None
 
 
 class XAxisType(StrEnum):
diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 0366e961..16383473 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -1461,6 +1461,8 @@ def _add_main_intensity_traces(
         )
         if plot_spec.y_calc_name is not None:
             calc_trace.name = plot_spec.y_calc_name
+        if plot_spec.y_calc_line_dash is not None:
+            calc_trace.line.dash = plot_spec.y_calc_line_dash
         fig.add_trace(calc_trace, row=1, col=1)
 
     def _add_predictive_draw_traces(
@@ -1718,9 +1720,10 @@ def _get_predictive_band_traces(
             line={'color': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1},
             fill='tonexty',
             fillcolor=PREDICTIVE_BAND_COLOR,
-            name='Posterior predictive 95% CI',
+            name='95% interval',
             hoverinfo='skip',
             legendgroup='predictive_band',
+            legendrank=35,
         )
         return lower_trace, upper_trace
 
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index c9e8be42..375787a6 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -94,10 +94,13 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH = 1
 POSTERIOR_HISTOGRAM_FILL_COLOR = 'rgba(120, 120, 120, 0.38)'
 POSTERIOR_HISTOGRAM_LINE_COLOR = 'rgba(120, 120, 120, 0.24)'
-POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(140, 140, 140, 0.08)'
+POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(214, 39, 40, 0.26)'
 POSTERIOR_INTERVAL_68_FILL_COLOR = 'rgba(140, 140, 140, 0.16)'
 POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(80, 80, 80)'
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
+POSTERIOR_POINT_ESTIMATE_TRACE_NAME = 'Max posterior'
+POSTERIOR_POINT_ESTIMATE_LINE_DASH = 'dot'
+POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME = '95% interval'
 POSTERIOR_DRAW_LINE_COLOR = 'rgba(140, 140, 140, 0.18)'
 POSTERIOR_SCATTER_MARKER_COLOR = 'rgba(140, 140, 140, 0.20)'
 POSTERIOR_CONTOUR_FILL_COLORSCALE = [
@@ -2191,9 +2194,9 @@ def _add_posterior_distribution_reference_traces(
             self._posterior_reference_line_trace(
                 x_value=summary.map_value,
                 y_axis_range=y_axis_range,
-                trace_name='Max posterior',
+                trace_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
                 color=POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
-                dash='dot',
+                dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH,
             )
         )
 
@@ -2976,9 +2979,10 @@ def _plot_posterior_predictive_summary(
                     mode='lines',
                     line={'color': 'rgba(0, 0, 0, 0)'},
                     fill='tonexty',
-                    fillcolor='rgba(214, 39, 40, 0.18)',
-                    name='Posterior predictive 95% CI',
+                    fillcolor=POSTERIOR_INTERVAL_95_FILL_COLOR,
+                    name=POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME,
                     hoverinfo='skip',
+                    legendrank=30,
                 )
             )
 
@@ -2998,6 +3002,7 @@ def _plot_posterior_predictive_summary(
                         line={'color': POSTERIOR_DRAW_LINE_COLOR, 'width': 1},
                         name='Posterior draw' if index == 0 else None,
                         showlegend=index == 0,
+                        legendrank=40,
                     )
                 )
 
@@ -3008,6 +3013,7 @@ def _plot_posterior_predictive_summary(
                 mode='lines+markers',
                 line={'color': 'rgb(31, 119, 180)', 'width': 1.5},
                 name='Measured',
+                legendrank=10,
             )
         )
         fig.add_trace(
@@ -3015,8 +3021,13 @@ def _plot_posterior_predictive_summary(
                 x=summary.x,
                 y=summary.map_prediction,
                 mode='lines',
-                line={'color': POSTERIOR_POINT_ESTIMATE_LINE_COLOR, 'width': 2},
-                name='Max posterior prediction',
+                line={
+                    'color': POSTERIOR_POINT_ESTIMATE_LINE_COLOR,
+                    'width': 2,
+                    'dash': POSTERIOR_POINT_ESTIMATE_LINE_DASH,
+                },
+                name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
+                legendrank=20,
             )
         )
         fig.update_layout(
@@ -3136,7 +3147,8 @@ def _plot_posterior_predictive_data(
             predictive_lower_95=predictive_lower_95,
             predictive_upper_95=predictive_upper_95,
             predictive_draws=predictive_draws,
-            y_calc_name='Max posterior prediction',
+            y_calc_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME,
+            y_calc_line_dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH,
         )
         self._backend.plot_powder_meas_vs_calc(plot_spec=plot_spec)
 
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
index 708d8a6f..55d8dacc 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -201,12 +201,128 @@ def test_build_posterior_summary_row_restores_identifier_columns():
         'cat',
         'entry',
         'a',
+        'arb',
         '1.1500',
         '[1.0000, 1.3000]',
         '[red]1.107[/red]',
         '[red]125.9[/red]',
+    ]
+
+
+def test_render_committed_parameter_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_committed_parameter_table([
+        Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    ])
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'max posterior',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [[
+        'db',
+        'cat',
+        'entry',
+        'a',
         'arb',
+        '1.0000',
+        '1.2000',
+        '0.0500',
+        '20.00 % ↑',
+    ]]
+
+
+def test_render_posterior_summary_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_posterior_summary_table(
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+    )
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'median',
+        '95% interval',
+        'r-hat',
+        'ess bulk',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
     ]
+    assert captured['columns_data'] == [[
+        'db',
+        'cat',
+        'entry',
+        'a',
+        'arb',
+        '1.1500',
+        '[1.0000, 1.3000]',
+        '[red]1.107[/red]',
+        '[red]125.9[/red]',
+    ]]
 
 
 def test_posterior_table_notes_split_failed_diagnostics():
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
index ee8014f2..d46f267b 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
@@ -59,3 +59,67 @@ def __init__(self, start, value, uncertainty, name='p', units='u'):
     assert 'Fitted parameters:' in out
     # Table border: accept common border glyphs from Rich/tabulate
     assert any(ch in out for ch in ('╒', '┌', '+', '─'))
+
+
+def test_fitresults_display_results_places_units_after_parameter(monkeypatch):
+    class Identity:
+        def __init__(self):
+            self.datablock_entry_name = 'db'
+            self.category_code = 'cat'
+            self.category_entry_name = 'entry'
+
+    class Param:
+        def __init__(self):
+            self._identity = Identity()
+            self._fit_start_value = 1.0
+            self.value = 1.2
+            self.uncertainty = 0.05
+            self.name = 'a'
+            self.units = 'arb'
+
+    from easydiffraction.analysis.fit_helpers import reporting
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(reporting, 'render_table', fake_render_table)
+
+    reporting.FitResults(success=True, parameters=[Param()]).display_results()
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'fitted',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [[
+        'db',
+        'cat',
+        'entry',
+        'a',
+        'arb',
+        '1.0000',
+        '1.2000',
+        '0.0500',
+        '20.00 % ↑',
+    ]]
diff --git a/tests/unit/easydiffraction/display/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py
index cb81844e..5c3c8dd5 100644
--- a/tests/unit/easydiffraction/display/plotters/test_plotly.py
+++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py
@@ -795,6 +795,49 @@ def fake_show_figure(self, fig):
     ]
 
 
+def test_plot_powder_meas_vs_calc_styles_predictive_max_posterior_and_band(monkeypatch):
+    import easydiffraction.display.plotters.plotly as pp
+
+    from easydiffraction.display.plotters.base import PowderMeasVsCalcSpec
+
+    captured = {}
+
+    def fake_show_figure(self, fig):
+        captured['fig'] = fig
+
+    monkeypatch.setattr(pp.PlotlyPlotter, '_show_figure', fake_show_figure)
+
+    plotter = pp.PlotlyPlotter()
+    plotter.plot_powder_meas_vs_calc(
+        plot_spec=PowderMeasVsCalcSpec(
+            x=np.array([1.0, 2.0, 3.0]),
+            y_meas=np.array([10.0, 12.0, 11.0]),
+            y_calc=np.array([9.0, 11.0, 10.5]),
+            y_resid=np.array([1.0, 1.0, 0.5]),
+            bragg_tick_sets=(),
+            axes_labels=['2θ (degree)', 'Intensity (arb. units)'],
+            title='Powder',
+            residual_height_fraction=0.25,
+            bragg_peaks_height_fraction=0.15,
+            height=None,
+            predictive_lower_95=np.array([8.0, 9.0, 10.0]),
+            predictive_upper_95=np.array([10.0, 11.0, 12.0]),
+            y_calc_name='Max posterior',
+            y_calc_line_dash='dot',
+        ),
+    )
+
+    fig = captured['fig']
+    predictive_band_trace = next(trace for trace in fig.data if trace.name == '95% interval')
+    max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
+    residual_trace = next(trace for trace in fig.data if trace.name == 'Residual (Imeas - Icalc)')
+
+    assert predictive_band_trace.fillcolor == pp.PREDICTIVE_BAND_COLOR
+    assert predictive_band_trace.legendrank == 35
+    assert max_posterior_trace.line.dash == 'dot'
+    assert predictive_band_trace.legendrank < residual_trace.legendrank
+
+
 def test_plot_powder_meas_vs_calc_keeps_exact_residual_scale_match(monkeypatch):
     import easydiffraction.display.plotters.plotly as pp
 
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index acc1c9d6..0a8b45cb 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -557,6 +557,8 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
+    from easydiffraction.display.plotting import POSTERIOR_INTERVAL_95_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_POINT_ESTIMATE_LINE_DASH
 
     plotter, fit_results, _ = _make_bayesian_plotter_fixture()
     parameter = fit_results.parameters[0]
@@ -574,11 +576,15 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     }
     marginal_trace = next(trace for trace in figure.data if trace.name == 'Marginal density')
     histogram_trace = next(trace for trace in figure.data if trace.name == 'Posterior histogram')
+    interval_trace = next(trace for trace in figure.data if trace.name == '95% credible interval')
+    max_posterior_trace = next(trace for trace in figure.data if trace.name == 'Max posterior')
     assert marginal_trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
     assert marginal_trace.line.width == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
     assert marginal_trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
     assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
     assert histogram_trace.xbins.size is not None
+    assert interval_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
+    assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
     assert figure.layout.xaxis.range is not None
     assert tuple(figure.layout.xaxis.range) == (
         float(marginal_trace.x[0]),
@@ -587,6 +593,104 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert figure.layout.yaxis.range is not None
 
 
+def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(monkeypatch):
+    from types import SimpleNamespace
+
+    from easydiffraction.display.plotting import POSTERIOR_INTERVAL_95_FILL_COLOR
+    from easydiffraction.display.plotting import POSTERIOR_POINT_ESTIMATE_LINE_DASH
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+
+    def fake_show(self):
+        captured['fig'] = self
+
+    monkeypatch.setattr('plotly.graph_objects.Figure.show', fake_show)
+
+    Plotter._plot_posterior_predictive_summary(
+        expt_name='hrpt',
+        summary=SimpleNamespace(
+            x=np.array([1.0, 2.0, 3.0]),
+            lower_95=np.array([8.0, 9.0, 10.0]),
+            upper_95=np.array([10.0, 11.0, 12.0]),
+            map_prediction=np.array([9.0, 10.0, 11.0]),
+        ),
+        y_meas=np.array([9.5, 10.5, 11.5]),
+        axes_labels=['2θ (degree)', 'Intensity (arb. units)'],
+        show_band=True,
+        show_draws=False,
+    )
+
+    fig = captured['fig']
+    upper_band_trace = fig.data[1]
+    measured_trace = next(trace for trace in fig.data if trace.name == 'Measured')
+    max_posterior_trace = next(trace for trace in fig.data if trace.name == 'Max posterior')
+
+    assert upper_band_trace.name == '95% interval'
+    assert upper_band_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
+    assert upper_band_trace.legendrank == 30
+    assert measured_trace.legendrank == 10
+    assert max_posterior_trace.legendrank == 20
+    assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
+
+
+def test_plot_posterior_predictive_data_uses_max_posterior_label_and_dash(monkeypatch):
+    from types import SimpleNamespace
+
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotting import XAxisType
+
+    captured: dict[str, object] = {}
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.BRAGG})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Pattern:
+        two_theta = np.array([1.0, 2.0, 3.0])
+        intensity_meas = np.array([10.0, 12.0, 11.0])
+        intensity_bkg = np.array([1.0, 1.0, 1.0])
+
+    class Experiment:
+        type = ExptType()
+        data = Pattern()
+
+    plotter = Plotter()
+    plotter.engine = 'plotly'
+    plotter._backend = SimpleNamespace(
+        plot_powder_meas_vs_calc=lambda *, plot_spec: captured.setdefault('plot_spec', plot_spec)
+    )
+
+    monkeypatch.setattr(
+        Plotter,
+        '_get_or_build_posterior_predictive_summary',
+        lambda self, **kwargs: SimpleNamespace(
+            x=np.array([1.0, 2.0, 3.0]),
+            lower_95=np.array([8.0, 9.0, 10.0]),
+            upper_95=np.array([10.0, 11.0, 12.0]),
+            map_prediction=np.array([9.0, 11.0, 10.5]),
+            draws=None,
+        ),
+    )
+    monkeypatch.setattr(Plotter, '_extract_bragg_tick_sets', lambda self, **kwargs: ())
+
+    plotter._plot_posterior_predictive_data(
+        experiment=Experiment(),
+        expt_name='hrpt',
+        plot_options=SimpleNamespace(x_min=None, x_max=None, show_residual=None, x=None),
+        x_axis=XAxisType.TWO_THETA,
+        style='band',
+    )
+
+    plot_spec = captured['plot_spec']
+    assert plot_spec.y_calc_name == 'Max posterior'
+    assert plot_spec.y_calc_line_dash == 'dot'
+
+
 def test_build_param_distribution_plot_accepts_unique_name_string():
     plotter, _, _ = _make_bayesian_plotter_fixture()
 

From 46791e27c5b6404e037770df2a2075590f5348eb Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 09:59:01 +0200
Subject: [PATCH 073/106] Adjust posterior and band fill transparency

---
 src/easydiffraction/display/plotters/plotly.py      | 2 +-
 src/easydiffraction/display/plotting.py             | 4 ++--
 tests/unit/easydiffraction/display/test_plotting.py | 3 +++
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/easydiffraction/display/plotters/plotly.py b/src/easydiffraction/display/plotters/plotly.py
index 16383473..cb4914c3 100644
--- a/src/easydiffraction/display/plotters/plotly.py
+++ b/src/easydiffraction/display/plotters/plotly.py
@@ -67,7 +67,7 @@
 COMPOSITE_MARGIN_BOTTOM = 45
 TITLE_FONT_SIZE = 14
 AXIS_TITLE_FONT_SIZE = 12
-PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.26)'
+PREDICTIVE_BAND_COLOR = 'rgba(214, 39, 40, 0.14)'
 PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)'
 PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)'
 PREDICTIVE_DRAW_PLOT_CAP = 50
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 375787a6..a3076463 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -94,8 +94,8 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH = 1
 POSTERIOR_HISTOGRAM_FILL_COLOR = 'rgba(120, 120, 120, 0.38)'
 POSTERIOR_HISTOGRAM_LINE_COLOR = 'rgba(120, 120, 120, 0.24)'
-POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(214, 39, 40, 0.26)'
-POSTERIOR_INTERVAL_68_FILL_COLOR = 'rgba(140, 140, 140, 0.16)'
+POSTERIOR_INTERVAL_95_FILL_COLOR = 'rgba(214, 39, 40, 0.14)'
+POSTERIOR_INTERVAL_68_FILL_COLOR = 'rgba(214, 39, 40, 0.26)'
 POSTERIOR_MEDIAN_LINE_COLOR = 'rgb(80, 80, 80)'
 POSTERIOR_POINT_ESTIMATE_LINE_COLOR = 'rgb(214, 39, 40)'
 POSTERIOR_POINT_ESTIMATE_TRACE_NAME = 'Max posterior'
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 0a8b45cb..4870d299 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -554,6 +554,7 @@ def test_build_posterior_pairs_plot_rejects_unknown_style():
 
 
 def test_build_param_distribution_plot_returns_plotly_figure():
+    from easydiffraction.display.plotting import POSTERIOR_INTERVAL_68_FILL_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
     from easydiffraction.display.plotting import POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_WIDTH
@@ -576,6 +577,7 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     }
     marginal_trace = next(trace for trace in figure.data if trace.name == 'Marginal density')
     histogram_trace = next(trace for trace in figure.data if trace.name == 'Posterior histogram')
+    interval_68_trace = next(trace for trace in figure.data if trace.name == '68% credible interval')
     interval_trace = next(trace for trace in figure.data if trace.name == '95% credible interval')
     max_posterior_trace = next(trace for trace in figure.data if trace.name == 'Max posterior')
     assert marginal_trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR
@@ -583,6 +585,7 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     assert marginal_trace.fillcolor == POSTERIOR_PAIR_MARGINAL_DENSITY_FILL_COLOR
     assert marginal_trace.hovertemplate == 'length_a: %{x:.4f}<br>density: %{y:.4f}<extra></extra>'
     assert histogram_trace.xbins.size is not None
+    assert interval_68_trace.fillcolor == POSTERIOR_INTERVAL_68_FILL_COLOR
     assert interval_trace.fillcolor == POSTERIOR_INTERVAL_95_FILL_COLOR
     assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
     assert figure.layout.xaxis.range is not None

From f3f5accbd01bb3aab00b169435ada0f654932c38 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 10:17:53 +0200
Subject: [PATCH 074/106] Update fit bounds and plot range in ed-21

---
 docs/docs/tutorials/ed-21.ipynb | 4 ++--
 docs/docs/tutorials/ed-21.py    | 4 +---
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index 0a198508..305ece8d 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -513,7 +513,7 @@
    "outputs": [],
    "source": [
     "for param in project.free_parameters:\n",
-    "    param.set_fit_bounds_from_uncertainty(multiplier=3)"
+    "    param.set_fit_bounds_from_uncertainty(multiplier=3.5)"
    ]
   },
   {
@@ -713,7 +713,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=45.4, x_max=46.3)"
+    "project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=92, x_max=93)"
    ]
   }
  ],
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 4bdbc0cf..5cb44557 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -243,7 +243,7 @@
 
 # %%
 for param in project.free_parameters:
-    param.set_fit_bounds_from_uncertainty(multiplier=3)
+    param.set_fit_bounds_from_uncertainty(multiplier=3.5)
 
 # %% [markdown]
 # Displaying the free parameters again is a convenient way to confirm
@@ -332,5 +332,3 @@
 
 # %%
 project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=92, x_max=93)
-
-# %%

From 1f3e1ae6cd609069636a87c846147edc0c56504f Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 10:39:21 +0200
Subject: [PATCH 075/106] Improve posterior table diagnostic notes

---
 .../analysis/fit_helpers/bayesian.py          |  8 ++--
 .../analysis/minimizers/bumps_dream.py        |  4 +-
 .../analysis/fit_helpers/test_bayesian.py     | 48 ++++++++++---------
 .../analysis/fit_helpers/test_reporting.py    | 24 +++++-----
 .../easydiffraction/display/test_plotting.py  |  4 +-
 5 files changed, 47 insertions(+), 41 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 78fa637b..db3bd220 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -758,12 +758,12 @@ def _posterior_table_notes(
     notes: list[str] = []
     if has_failed_r_hat:
         notes.append(
-            f'[red]r_hat[/red]: exceeds {R_HAT_CONVERGENCE_THRESHOLD:.2f} '
-            '(consider longer sampling, tighter bounds, or reparameterization).'
+            f'[red]r-hat[/red]: exceeds {R_HAT_CONVERGENCE_THRESHOLD:.2f} '
+            'Consider longer sampling, better initialization, or reparameterization.'
         )
     if has_failed_ess_bulk:
         notes.append(
-            f'[red]ess_bulk[/red]: less than {ESS_BULK_CONVERGENCE_THRESHOLD:.0f} '
-            '(consider longer sampling, tighter bounds, or reparameterization).'
+            f'[red]ess bulk[/red]: less than {ESS_BULK_CONVERGENCE_THRESHOLD:.0f} '
+            'Consider longer sampling or reparameterization.'
         )
     return notes
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index f7060d12..82226547 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -762,9 +762,7 @@ def _build_success_result(
         )
 
         if not convergence_diagnostics.get('converged', True):
-            log.warning(
-                'Convergence diagnostics indicate the posterior may be poorly mixed.'
-            )
+            log.warning('Convergence diagnostics indicate the posterior may be poorly mixed.')
 
         return OptimizeResult(
             x=map_values,
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
index 55d8dacc..020b87fe 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -247,17 +247,19 @@ def fake_render_table(*, columns_headers, columns_alignment, columns_data):
         'right',
         'right',
     ]
-    assert captured['columns_data'] == [[
-        'db',
-        'cat',
-        'entry',
-        'a',
-        'arb',
-        '1.0000',
-        '1.2000',
-        '0.0500',
-        '20.00 % ↑',
-    ]]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
 
 
 def test_render_posterior_summary_table_places_units_after_parameter(monkeypatch):
@@ -312,17 +314,19 @@ def fake_render_table(*, columns_headers, columns_alignment, columns_data):
         'right',
         'right',
     ]
-    assert captured['columns_data'] == [[
-        'db',
-        'cat',
-        'entry',
-        'a',
-        'arb',
-        '1.1500',
-        '[1.0000, 1.3000]',
-        '[red]1.107[/red]',
-        '[red]125.9[/red]',
-    ]]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.1500',
+            '[1.0000, 1.3000]',
+            '[red]1.107[/red]',
+            '[red]125.9[/red]',
+        ]
+    ]
 
 
 def test_posterior_table_notes_split_failed_diagnostics():
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
index d46f267b..bb824f82 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py
@@ -112,14 +112,16 @@ def fake_render_table(*, columns_headers, columns_alignment, columns_data):
         'right',
         'right',
     ]
-    assert captured['columns_data'] == [[
-        'db',
-        'cat',
-        'entry',
-        'a',
-        'arb',
-        '1.0000',
-        '1.2000',
-        '0.0500',
-        '20.00 % ↑',
-    ]]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 4870d299..66fee2a1 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -577,7 +577,9 @@ def test_build_param_distribution_plot_returns_plotly_figure():
     }
     marginal_trace = next(trace for trace in figure.data if trace.name == 'Marginal density')
     histogram_trace = next(trace for trace in figure.data if trace.name == 'Posterior histogram')
-    interval_68_trace = next(trace for trace in figure.data if trace.name == '68% credible interval')
+    interval_68_trace = next(
+        trace for trace in figure.data if trace.name == '68% credible interval'
+    )
     interval_trace = next(trace for trace in figure.data if trace.name == '95% credible interval')
     max_posterior_trace = next(trace for trace in figure.data if trace.name == 'Max posterior')
     assert marginal_trace.line.color == POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR

From 01dc0057fd0fdabc015a23207553f0ad2055b89b Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 10:59:37 +0200
Subject: [PATCH 076/106] Add parallel DREAM sampling

---
 docs/docs/tutorials/ed-21.ipynb               |  2 +-
 docs/docs/tutorials/ed-21.py                  |  2 +-
 .../analysis/fit_helpers/bayesian.py          |  4 +-
 .../analysis/minimizers/bumps_dream.py        | 50 ++++++++++++++-----
 4 files changed, 42 insertions(+), 16 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index 305ece8d..82aa0c0e 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -586,7 +586,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 50  # 1000"
+    "project.analysis.fit.minimizer.steps = 100  # 1000"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 5cb44557..1d59516f 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -278,7 +278,7 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 50  # 1000
+project.analysis.fit.minimizer.steps = 100  # 1000
 
 # %%
 project.analysis.fit()
diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index db3bd220..907cb53f 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -758,12 +758,12 @@ def _posterior_table_notes(
     notes: list[str] = []
     if has_failed_r_hat:
         notes.append(
-            f'[red]r-hat[/red]: exceeds {R_HAT_CONVERGENCE_THRESHOLD:.2f} '
+            f'[red]r-hat > {R_HAT_CONVERGENCE_THRESHOLD:.2f}[/red]: '
             'Consider longer sampling, better initialization, or reparameterization.'
         )
     if has_failed_ess_bulk:
         notes.append(
-            f'[red]ess bulk[/red]: less than {ESS_BULK_CONVERGENCE_THRESHOLD:.0f} '
+            f'[red]ess bulk < {ESS_BULK_CONVERGENCE_THRESHOLD:.0f}[/red]: '
             'Consider longer sampling or reparameterization.'
         )
     return notes
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 82226547..c23d923c 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -12,6 +12,8 @@
 from bumps.fitters import FITTERS
 from bumps.fitters import FitDriver
 from bumps.fitters import monitor as bumps_monitor
+from bumps.mapper import MPMapper
+from bumps.mapper import can_pickle
 from scipy.optimize import OptimizeResult
 
 from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
@@ -34,6 +36,7 @@
 DEFAULT_MIN_BURN = 50
 DEFAULT_THIN = 1
 DEFAULT_POP = 4
+DEFAULT_PARALLEL = 0
 DEFAULT_INIT = DreamPopulationInitializationEnum.EPS
 DEFAULT_ALPHA = 0.0
 DEFAULT_OUTLIER_TEST = 'none'
@@ -264,6 +267,7 @@ def __init__(
         self._burn: int | None = None
         self._thin: int = DEFAULT_THIN
         self._pop: int = DEFAULT_POP
+        self._parallel: int = DEFAULT_PARALLEL
         self._init: DreamPopulationInitializationEnum = DEFAULT_INIT
 
     @property
@@ -305,6 +309,15 @@ def pop(self) -> int:
     def pop(self, value: int) -> None:
         self._pop = self._validated_positive_integer('pop', value)
 
+    @property
+    def parallel(self) -> int:
+        """DREAM parallel worker count; ``0`` uses all CPUs."""
+        return self._parallel
+
+    @parallel.setter
+    def parallel(self, value: int) -> None:
+        self._parallel = self._validated_non_negative_integer('parallel', value)
+
     @property
     def init(self) -> DreamPopulationInitializationEnum:
         """DREAM population initializer."""
@@ -495,26 +508,24 @@ def _resolved_burn(self, steps: int) -> int:
             raise ValueError(msg)
         return burn
 
-    @staticmethod
     def _sampler_settings(
+        self,
         *,
         random_seed: int,
         steps: int,
         burn: int,
-        thin: int,
-        pop: int,
-        init: DreamPopulationInitializationEnum,
         n_parameters: int,
     ) -> dict[str, object]:
         """Build the sampler settings dictionary recorded in results."""
-        samples = steps * pop * n_parameters
+        samples = steps * self.pop * n_parameters
         return {
             'random_seed': int(random_seed),
             'steps': int(steps),
             'burn': int(burn),
-            'thin': int(thin),
-            'pop': int(pop),
-            'init': init.value,
+            'thin': int(self.thin),
+            'pop': int(self.pop),
+            'parallel': int(self.parallel),
+            'init': self.init.value,
             'samples': int(samples),
             'alpha': float(DEFAULT_ALPHA),
             'outliers': DEFAULT_OUTLIER_TEST,
@@ -591,9 +602,6 @@ def _prepare_run_context(
             random_seed=random_seed,
             steps=steps,
             burn=burn,
-            thin=self.thin,
-            pop=self.pop,
-            init=init,
             n_parameters=len(bumps_params),
         )
         driver = self._build_driver(
@@ -634,6 +642,7 @@ def _build_driver(
     ) -> FitDriver:
         """Build and clip the BUMPS DREAM driver."""
         total_generations = int(steps + burn + 1)
+        problem = FitProblem(fitness)
         progress_monitor = _DreamProgressMonitor(
             tracker=self.tracker,
             n_points=fitness.numpoints(),
@@ -641,10 +650,12 @@ def _build_driver(
             total_generations=total_generations,
             burn_steps=int(burn),
         )
+        mapper = self._build_mapper(problem)
         driver = FitDriver(
             fitclass=fitclass,
-            problem=FitProblem(fitness),
+            problem=problem,
             monitors=[progress_monitor],
+            mapper=mapper,
             steps=steps,
             burn=burn,
             thin=self.thin,
@@ -658,6 +669,20 @@ def _build_driver(
         driver.clip()
         return driver
 
+    def _build_mapper(self, problem: FitProblem) -> object | None:
+        """Return a DREAM mapper for the configured parallel setting."""
+        if self.parallel == 1:
+            return None
+
+        if not can_pickle(problem):
+            log.warning(
+                'DREAM parallel evaluation requires a picklable '
+                'problem; falling back to serial execution.'
+            )
+            return None
+
+        return MPMapper.start_mapper(problem, [], cpus=self.parallel)
+
     @staticmethod
     def _execute_driver(*, driver: FitDriver, random_seed: int) -> _DreamDriverResult:
         """
@@ -678,6 +703,7 @@ def _execute_driver(*, driver: FitDriver, random_seed: int) -> _DreamDriverResul
                 error=error,
             )
         finally:
+            MPMapper.stop_mapper()
             numpy_rng.set_state(numpy_state)
             random.setstate(python_state)
 

From 83ef1534009d9513a0129f090041547a783bd42e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 11:06:25 +0200
Subject: [PATCH 077/106] Add parallel=0 description to DREAM tutorial

---
 docs/docs/tutorials/ed-21.ipynb | 15 ++++++++-------
 docs/docs/tutorials/ed-21.py    | 15 ++++++++-------
 2 files changed, 16 insertions(+), 14 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index 82aa0c0e..d2a23c71 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -550,13 +550,14 @@
     "of steps (`steps`) and often the burn-in (`burn`) as well. When\n",
     "needed, the DREAM API also lets you tune how chains are initialized\n",
     "through the `init` setting. Other sampler settings such as `thin` and\n",
-    "`pop` can be adjusted  as well, but here we keep them at their\n",
-    "defaults.\n",
+    "`pop` can be adjusted as well. The current EasyDiffraction default\n",
+    "also uses `parallel=0`, which tells BUMPS DREAM to use all available\n",
+    "CPUs for population evaluations.\n",
     "\n",
-    "Default `steps` is 1000, which is often need to be increased for a\n",
-    "real analysis to ensure good convergence and sampling of the posterior\n",
-    "distribution. Here we use much smaller value to speed up the tutorial,\n",
-    "but this is not recommended for a real analysis."
+    "The default `steps` value is 1000, and real analyses often need more\n",
+    "to achieve good convergence and posterior sampling. Here we use a much\n",
+    "smaller value to keep the tutorial fast, but this is not recommended\n",
+    "for production analysis."
    ]
   },
   {
@@ -586,7 +587,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "project.analysis.fit.minimizer.steps = 100  # 1000"
+    "project.analysis.fit.minimizer.steps = 100  # lower than the default 1000"
    ]
   },
   {
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index 1d59516f..ccfdb965 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -263,13 +263,14 @@
 # of steps (`steps`) and often the burn-in (`burn`) as well. When
 # needed, the DREAM API also lets you tune how chains are initialized
 # through the `init` setting. Other sampler settings such as `thin` and
-# `pop` can be adjusted  as well, but here we keep them at their
-# defaults.
+# `pop` can be adjusted as well. The current EasyDiffraction default
+# also uses `parallel=0`, which tells BUMPS DREAM to use all available
+# CPUs for population evaluations.
 #
-# Default `steps` is 1000, which is often need to be increased for a
-# real analysis to ensure good convergence and sampling of the posterior
-# distribution. Here we use much smaller value to speed up the tutorial,
-# but this is not recommended for a real analysis.
+# The default `steps` value is 1000, and real analyses often need more
+# to achieve good convergence and posterior sampling. Here we use a much
+# smaller value to keep the tutorial fast, but this is not recommended
+# for production analysis.
 
 # %%
 project.analysis.fit.show_minimizer_types()
@@ -278,7 +279,7 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 100  # 1000
+project.analysis.fit.minimizer.steps = 100  # lower than the default 1000
 
 # %%
 project.analysis.fit()

From b2ac66af6fd1e4a7df5e700c80966c683a2f7708 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 11:09:12 +0200
Subject: [PATCH 078/106] Fix test expectations for r-hat and parallel=0

---
 .../analysis/fit_helpers/test_bayesian.py     |   4 +-
 .../analysis/minimizers/test_bumps_dream.py   |   8 +-
 tmp/bumps_dream/ed-2-bayesian-new-API.py      | 333 ------------------
 3 files changed, 7 insertions(+), 338 deletions(-)
 delete mode 100644 tmp/bumps_dream/ed-2-bayesian-new-API.py

diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
index 020b87fe..278a48ce 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -348,5 +348,5 @@ def test_posterior_table_notes_split_failed_diagnostics():
     ])
 
     assert len(notes) == 2
-    assert 'r_hat' in notes[0]
-    assert 'ess_bulk' in notes[1]
+    assert 'r-hat' in notes[0]
+    assert 'ess bulk' in notes[1]
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index 9fa85263..8e800cb4 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -110,17 +110,19 @@ def test_sampler_settings_include_init_and_sample_count():
     from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
 
     minimizer = BumpsDreamMinimizer()
+    minimizer.thin = 1
+    minimizer.pop = 4
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+
     settings = minimizer._sampler_settings(
         random_seed=7,
         steps=10,
         burn=2,
-        thin=1,
-        pop=4,
-        init=DreamPopulationInitializationEnum.LHS,
         n_parameters=3,
     )
 
     assert settings['random_seed'] == 7
+    assert settings['parallel'] == 0
     assert settings['init'] == 'lhs'
     assert settings['samples'] == 120
 
diff --git a/tmp/bumps_dream/ed-2-bayesian-new-API.py b/tmp/bumps_dream/ed-2-bayesian-new-API.py
deleted file mode 100644
index 571cd01f..00000000
--- a/tmp/bumps_dream/ed-2-bayesian-new-API.py
+++ /dev/null
@@ -1,333 +0,0 @@
-# %% [markdown]
-# # Deterministic and Bayesian Refinement: LBCO, HRPT
-#
-# This tutorial demonstrates a practical two-stage workflow for powder
-# diffraction analysis with EasyDiffraction.
-#
-# In the first stage, we run a fast local refinement to obtain a sensible
-# point estimate and parameter uncertainties. In the second stage, we use
-# these refined values to define fit bounds and then sample the posterior
-# distribution with DREAM.
-#
-# The example uses constant-wavelength neutron powder diffraction data
-# for La0.5Ba0.5CoO3 measured on HRPT at PSI.
-#
-# The goal is not only to obtain a good fit, but also to answer Bayesian
-# questions such as:
-#
-# - Which parameter values are most probable?
-# - How broad are the credible intervals?
-# - Which parameters are strongly correlated?
-# - How much uncertainty propagates into the calculated diffraction
-#   pattern?
-
-# %% [markdown]
-# ## Import Library
-
-# %%
-import easydiffraction as ed
-
-# %% [markdown]
-# ## Step 1: Create a Project Container
-#
-# The project object keeps structures, experiments, fit settings, and
-# plotting utilities together in a single place. We will build the full
-# workflow inside this object.
-
-# %%
-project = ed.Project()
-
-# %% [markdown]
-# ## Step 2: Build the Structural Model
-#
-# We define a simple cubic perovskite model for LBCO. La and Ba share the
-# same crystallographic site with equal occupancy, while Co and O occupy
-# the remaining ideal perovskite positions.
-
-# %%
-project.structures.create(name='lbco')
-
-# %%
-structure = project.structures['lbco']
-
-# %%
-structure.space_group.name_h_m = 'P m -3 m'
-structure.space_group.it_coordinate_system_code = '1'
-
-# %%
-structure.cell.length_a = 3.88
-
-# %% [markdown]
-# The atom-site definitions below form the starting structural model. The
-# parameters are intentionally reasonable rather than fully optimized,
-# because the refinement step will improve them.
-
-# %%
-structure.atom_sites.create(
-    label='La',
-    type_symbol='La',
-    fract_x=0,
-    fract_y=0,
-    fract_z=0,
-    wyckoff_letter='a',
-    adp_type='Biso',
-    adp_iso=0.5151,
-    occupancy=0.5,
-)
-structure.atom_sites.create(
-    label='Ba',
-    type_symbol='Ba',
-    fract_x=0,
-    fract_y=0,
-    fract_z=0,
-    wyckoff_letter='a',
-    adp_type='Biso',
-    adp_iso=0.5151,
-    occupancy=0.5,
-)
-structure.atom_sites.create(
-    label='Co',
-    type_symbol='Co',
-    fract_x=0.5,
-    fract_y=0.5,
-    fract_z=0.5,
-    wyckoff_letter='b',
-    adp_type='Biso',
-    adp_iso=0.2190,
-)
-structure.atom_sites.create(
-    label='O',
-    type_symbol='O',
-    fract_x=0,
-    fract_y=0.5,
-    fract_z=0.5,
-    wyckoff_letter='c',
-    adp_type='Biso',
-    adp_iso=1.3916,
-)
-
-# %% [markdown]
-# ## Step 3: Define the Diffraction Experiment
-#
-# Next we download the measured powder pattern, create a neutron powder
-# experiment, and configure the instrument, profile, background, and
-# excluded regions.
-
-# %% [markdown]
-# #### Download the Measured Data
-
-# %%
-data_path = ed.download_data(id=3, destination='data')
-
-# %% [markdown]
-# #### Create the Experiment Object
-
-# %%
-project.experiments.add_from_data_path(
-    name='hrpt',
-    data_path=data_path,
-    sample_form='powder',
-    beam_mode='constant wavelength',
-    radiation_probe='neutron',
-)
-
-# %%
-experiment = project.experiments['hrpt']
-
-# %% [markdown]
-# #### Set Instrument and Peak-Profile Parameters
-#
-# These values provide the initial instrument description for the local
-# refinement. Later, a subset of them will be refined.
-
-# %%
-experiment.instrument.setup_wavelength = 1.494
-experiment.instrument.calib_twotheta_offset = 0.0
-
-# %%
-experiment.peak.broad_gauss_u = 0.1
-experiment.peak.broad_gauss_v = -0.1
-experiment.peak.broad_gauss_w = 0.1204
-experiment.peak.broad_lorentz_y = 0.0844
-
-# %% [markdown]
-# #### Add Background Points and Excluded Regions
-#
-# The line-segment background is defined by a few anchor points. We also
-# exclude regions that are not intended to contribute to the fit.
-
-# %%
-experiment.background.create(id='1', x=10, y=168.5585)
-experiment.background.create(id='2', x=30, y=164.3357)
-experiment.background.create(id='3', x=50, y=166.8881)
-experiment.background.create(id='4', x=110, y=175.4006)
-experiment.background.create(id='5', x=165, y=174.2813)
-
-# %%
-experiment.excluded_regions.create(id='1', start=0, end=30)
-experiment.excluded_regions.create(id='2', start=70, end=180)
-
-# %% [markdown]
-# #### Link the Structural Phase to the Experiment
-
-# %%
-experiment.linked_phases.create(id='lbco', scale=9.1351)
-
-# %% [markdown]
-# ## Step 4: Run an Initial Local Refinement
-#
-# Before Bayesian sampling, it is useful to run a deterministic fit. This
-# gives us:
-#
-# - a good point estimate near the best-fit region,
-# - uncertainties from the local optimizer,
-# - a quick check that the model and experiment are configured
-#   sensibly.
-#
-# In this tutorial we refine only a small set of parameters that are easy
-# to interpret in the later Bayesian stage.
-
-# %%
-structure.cell.length_a.free = True
-experiment.peak.broad_gauss_u.free = True
-experiment.peak.broad_gauss_v.free = True
-experiment.peak.broad_gauss_w.free = True
-experiment.instrument.calib_twotheta_offset.free = True
-
-# %% [markdown]
-# We choose the BUMPS Levenberg-Marquardt minimizer as a fast local
-# optimizer. Its main purpose here is to provide a stable starting point
-# and uncertainty estimates for the Bayesian run.
-
-# %%
-project.analysis.fit.show_minimizer_types()
-project.analysis.fit.minimizer_type = 'bumps (lm)'
-
-# %%
-project.analysis.fit()
-
-# %%
-project.analysis.display.fit_results()
-
-# %% [markdown]
-# The correlation plot shows how strongly the fitted parameters move
-# together in the local refinement. The measured-vs-calculated plots show
-# how well the refined model reproduces the data globally and in a zoomed
-# region.
-
-# %%
-project.display.plotter.plot_param_correlations(show_diagonal=True)
-
-# %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
-
-# %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)
-
-# %% [markdown]
-# ## Step 5: Prepare for Bayesian Sampling
-#
-# DREAM requires finite bounds for the free parameters. Instead of
-# setting them manually, we derive them from the uncertainties estimated
-# in the local refinement.
-#
-# The helper method `set_fit_bounds_from_uncertainty` centers the bounds
-# on the current parameter value and expands them by a chosen multiple of
-# the reported uncertainty.
-
-# %%
-project.analysis.display.free_params()
-
-# %%
-structure.cell.length_a.set_fit_bounds_from_uncertainty(multiplier=4)
-experiment.peak.broad_gauss_u.set_fit_bounds_from_uncertainty(multiplier=4)
-experiment.peak.broad_gauss_v.set_fit_bounds_from_uncertainty(multiplier=4)
-experiment.peak.broad_gauss_w.set_fit_bounds_from_uncertainty(multiplier=4)
-experiment.instrument.calib_twotheta_offset.set_fit_bounds_from_uncertainty(multiplier=4)
-
-# %% [markdown]
-# Displaying the free parameters again is a convenient way to confirm
-# that the fit bounds have been assigned as expected before launching the
-# sampler.
-
-# %%
-project.analysis.display.free_params()
-
-# %% [markdown]
-# ## Step 6: Configure and Run DREAM
-#
-# We now switch from the local minimizer to the Bayesian DREAM sampler.
-#
-# The settings below are intentionally small so the tutorial runs
-# quickly. For production analysis you would usually increase the number
-# of steps and often the burn-in as well. When needed, the DREAM API
-# also lets you tune how chains are initialized through the `init`
-# setting.
-
-# %%
-project.analysis.fit.show_minimizer_types()
-
-# %%
-project.analysis.fit.minimizer_type = 'bumps (dream)'
-
-# %%
-project.analysis.fit.minimizer.steps = 200 #1000
-project.analysis.fit.minimizer.burn = 40 #200
-project.analysis.fit.minimizer.thin = 1
-project.analysis.fit.minimizer.pop = 4
-
-# %%
-project.analysis.fit()
-
-# %% [markdown]
-# ## Step 7: Inspect Bayesian Results
-#
-# The fit-results display now includes sampler settings, convergence
-# diagnostics, committed parameter values, and posterior summary
-# statistics.
-
-# %%
-project.analysis.display.fit_results()
-
-# %% [markdown]
-# The correlation and posterior-pair plots are complementary:
-#
-# - `plot_param_correlations` summarizes pairwise structure in a compact
-#   matrix.
-# - `plot_posterior_pairs` shows marginal densities on the diagonal and
-#   posterior contours off-diagonal.
-
-# %%
-project.display.plotter.plot_param_correlations(show_diagonal=True)
-
-# %%
-project.display.plotter.plot_posterior_pairs()
-
-# %% [markdown]
-# The one-dimensional posterior distributions below make it easier to
-# inspect individual parameters in isolation, including asymmetry or
-# multimodality.
-
-# %%
-project.display.plotter.plot_param_distribution(structure.cell.length_a)
-project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_u)
-project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_v)
-project.display.plotter.plot_param_distribution(experiment.peak.broad_gauss_w)
-project.display.plotter.plot_param_distribution(experiment.instrument.calib_twotheta_offset)
-
-# %% [markdown]
-# Finally, the posterior predictive plot propagates the sampled parameter
-# uncertainty into the calculated diffraction pattern. Comparing this to
-# the zoomed measured-vs-calculated view helps assess whether the sampled
-# model family explains the data in the region of interest.
-
-# %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
-
-# %% [markdown]
-# A final zoomed measured-vs-calculated plot is useful for checking how
-# the posterior-supported model behaves in a narrow region of the pattern
-# after the Bayesian run.
-
-# %%
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', x_min=65, x_max=68)

From 72093d739c24194b8879aee0da42c46a81a41bf3 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 11:14:50 +0200
Subject: [PATCH 079/106] Add Bayesian DREAM to architecture

---
 docs/dev/architecture.md | 58 ++++++++++++++++++++++++++++++++++++++--
 1 file changed, 56 insertions(+), 2 deletions(-)

diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md
index 8c30e378..48a41161 100644
--- a/docs/dev/architecture.md
+++ b/docs/dev/architecture.md
@@ -826,14 +826,45 @@ workflow:
   only `single`; >1 → all three).
 - Joint-fit weights: `joint_fit_experiments` (`CategoryCollection` of
   per-experiment weight entries); sibling of `fit`, not a child.
+- Fit results: `analysis.fit_results` stores the latest runtime result
+  object. This is `FitResults` for deterministic fits and
+  `BayesianFitResults` for Bayesian DREAM runs.
 - Parameter tables: `show_all_params()`, `show_fittable_params()`,
   `show_free_params()`, `how_to_access_parameters()`
 - Fitting: `fit()` dispatches single/joint through the callable `fit`
   category; `fit_sequential()` handles sequential mode (sets `fit.mode`
-  to `'sequential'` internally). `display.fit_results()` shows results.
+  to `'sequential'` internally). `fit()` accepts optional `random_seed`
+  for stochastic minimizers; deterministic minimizers reject non-`None`
+  seeds. `display.fit_results()` dispatches through the active runtime
+  result object.
 - Aliases and constraints (single-type categories; no public `_type`
   getter or setter)
 
+#### 6.4.1 Bayesian DREAM Runtime Results
+
+Bayesian sampling is integrated as a normal minimizer selection with tag
+`'bumps (dream)'`. It does not create a parallel `Analysis` stack or a
+new persisted results category.
+
+- `BayesianFitResults` extends `FitResults` with runtime-only posterior
+  state such as `posterior_samples`, `posterior_parameter_summaries`,
+  `posterior_predictive`, `diagnostics`, and `sampler_settings`.
+- Posterior arrays and predictive caches remain runtime-only; they are
+  not serialized into CIF or project directories.
+- `sampler_settings` records the resolved stochastic settings actually
+  used for the run, including `random_seed`, `steps`, `burn`, `thin`,
+  `pop`, and `parallel`.
+- The current user-facing DREAM controls live on the active minimizer
+  object, for example `project.analysis.fit.minimizer.steps`, `burn`,
+  `thin`, `pop`, `parallel`, and `init`.
+- `plot_param_correlations()` uses posterior samples when available and
+  otherwise falls back to deterministic covariance or engine-derived
+  correlations.
+- Bayesian-only plotting methods are exposed explicitly rather than by
+  overloading deterministic plot calls: `plot_posterior_pairs()`,
+  `plot_param_distribution(param)`, and
+  `plot_posterior_predictive(expt_name, ...)`.
+
 ---
 
 ## 7. Project — The Top-Level Façade
@@ -895,7 +926,9 @@ project_dir/
 saved project re-opens with the same display backends. Per-experiment
 calculator selection (`_calculation.calculator_type`) lives in each
 experiment file, and fit configuration (`_fit.minimizer_type`,
-`_fit.mode`) lives in `analysis/analysis.cif`.
+`_fit.mode`) lives in `analysis/analysis.cif`. Runtime fit outputs,
+including `analysis.fit_results`, posterior chains, posterior predictive
+summaries, and convergence diagnostics, are not serialized.
 
 ### 7.3 Verbosity
 
@@ -1053,6 +1086,27 @@ project.display.plotter.plot_meas_vs_calc(expt_name='hrpt', show_residual=True)
 project.save()
 ```
 
+### 8.4.1 Bayesian Refinement
+
+```python
+# Deterministic pre-fit remains explicit
+project.analysis.fit.minimizer_type = 'bumps (lm)'
+project.analysis.fit()
+
+# Switch to Bayesian sampling using the same entry point
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+project.analysis.fit.minimizer.steps = 1000
+project.analysis.fit.minimizer.parallel = 0
+project.analysis.fit(random_seed=11)
+
+# Runtime-only Bayesian summaries and plots
+project.analysis.display.fit_results()
+project.display.plotter.plot_param_correlations()
+project.display.plotter.plot_posterior_pairs()
+project.display.plotter.plot_param_distribution(param)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+```
+
 ### 8.5 TOF Experiment (tutorial ed-7)
 
 ```python

From d901adcbe2448cfe9a65c87b450a66cc665489ff Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 11:16:27 +0200
Subject: [PATCH 080/106] Clean up

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 docs/dev/plan_bayesian-analysis.md   | 334 ------------
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diff --git a/docs/dev/bayesian-analysis-design.md b/docs/dev/bayesian-analysis-design.md
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-# Bayesian Analysis Design
-
-**Status:** Design proposal **Date:** 2026-05-07
-
-## Goal
-
-Add Bayesian analysis to the existing fitting workflow with the smallest
-user-facing API change possible. The first implementation target is the
-BUMPS DREAM sampler for diffraction refinement, with sampled parameters
-bounded by each parameter's `fit_min` and `fit_max` attributes.
-
-The word `DREAM` in this document means the BUMPS Markov-chain Monte
-Carlo sampler. It does not refer to the ESS DREAM instrument.
-
-## Design Principles
-
-- Keep `project.analysis.fit()` as the single execution entry point.
-- Add `'bumps (dream)'` as a normal minimizer tag.
-- Store large posterior outputs in runtime result objects, not in CIF
-  categories.
-- Commit one coherent sampled parameter vector back to the project after
-  sampling.
-- Keep deterministic fitting and Bayesian sampling behavior explicit.
-- Add Bayesian display and plotting behavior without changing existing
-  deterministic semantics.
-
-## Current Code Anchors
-
-The design should extend the existing seams instead of adding a parallel
-analysis stack:
-
-- `src/easydiffraction/analysis/categories/fit/default.py` owns
-  `fit.minimizer_type` and `fit.mode`.
-- `src/easydiffraction/analysis/fitting.py` collects free parameters,
-  builds the residual objective, and calls `MinimizerBase.fit()`.
-- `src/easydiffraction/analysis/minimizers/base.py` defines the
-  minimizer lifecycle and returns a `FitResults` instance.
-- `src/easydiffraction/analysis/minimizers/bumps.py` already adapts an
-  EasyDiffraction residual function into BUMPS through
-  `_EasyDiffractionFitness`.
-- `src/easydiffraction/analysis/fit_helpers/reporting.py` owns
-  `FitResults` display behavior.
-- `src/easydiffraction/display/plotting.py` currently builds parameter
-  correlations from `analysis.fit_results.engine_result`.
-- `pyproject.toml` already declares `arviz`, so Bayesian plotting does
-  not need a new dependency decision.
-
-## User-Facing API
-
-The intended workflow stays aligned with the current fitting API:
-
-```python
-project.analysis.fit.minimizer_type = 'bumps (lm)'
-project.analysis.fit()
-
-project.analysis.fit.minimizer_type = 'bumps (dream)'
-project.analysis.fit()
-
-project.analysis.display.fit_results()
-project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')
-project.display.plotter.plot_param_correlations()
-```
-
-Additional Bayesian-specific plotting methods should be added rather
-than forcing every existing plotting method to accept Bayesian-specific
-options:
-
-```python
-project.display.plotter.plot_posterior_pairs()
-project.display.plotter.plot_param_distribution(param)
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
-```
-
-### Explicit Two-Step Workflow
-
-`'bumps (dream)'` must not implicitly run a deterministic
-Levenberg-Marquardt pre-fit. Users should call `'bumps (lm)'` first when
-they want a better starting point.
-
-Reasons:
-
-- `fit()` stays explicit and predictable.
-- Hidden pre-fitting can add significant runtime.
-- Deterministic failures stay separate from sampling failures.
-- The behavior matches the existing minimizer-selection model.
-
-## Scope
-
-### In Scope For The First Implementation
-
-- Register `MinimizerTypeEnum.BUMPS_DREAM` with tag `'bumps (dream)'`.
-- Add `BumpsDreamMinimizer`.
-- Add `BayesianFitResults` and small posterior value objects.
-- Require finite bounds for every sampled free parameter.
-- Use bounded uniform priors implied by `fit_min` and `fit_max`.
-- Use the existing residual objective and BUMPS likelihood convention.
-- Store posterior chains runtime-only in `analysis.fit_results`.
-- Display posterior summaries from `analysis.display.fit_results()`.
-- Add posterior correlation support to `plot_param_correlations()`.
-- Add posterior marginal and posterior pair plots.
-- Add posterior predictive plotting with capped draw evaluation.
-
-### Out Of Scope Initially
-
-- Automatic deterministic pre-fitting.
-- Persistent Bayesian configuration categories.
-- CIF serialization of posterior chains.
-- Export of posterior chains.
-- Model comparison across multiple Bayesian fits.
-- User-facing DREAM hyperparameter controls.
-- Unbounded posterior predictive storage for every draw and every
-  experiment.
-- Sequential Bayesian fitting.
-
-## Resolved Design Decisions
-
-- `plot_posterior_predictive(...)` is required for the first useful
-  Bayesian implementation.
-- Posterior chains remain runtime-only and do not need an export API in
-  phase 1.
-- `project.analysis.fit(random_seed=...)` should be supported for
-  `'bumps (dream)'` because stochastic scientific workflows need
-  reproducibility. If omitted, the minimizer should generate a seed and
-  record it in `BayesianFitResults.sampler_settings`.
-- Deterministic minimizers should reject a non-`None` `random_seed` with
-  a clear error rather than silently ignoring it.
-- `BayesianFitResults.success` should mean that DREAM completed and
-  produced usable posterior samples. Convergence quality should be
-  stored separately in diagnostics, displayed prominently, and warned on
-  when poor. It should set `success=False` only when there are no usable
-  samples or the sampler itself fails.
-- Posterior predictive plots should evaluate a capped subset of
-  posterior draws. Start with an internal default cap of 200 draws,
-  record the effective draw count in the result, and expose the cap only
-  later if users need control.
-
-## Statistical Semantics
-
-The existing residual objective returns the residual vector consumed by
-deterministic minimizers. The first Bayesian implementation should treat
-the BUMPS negative log likelihood as:
-
-```python
-nllf = 0.5 * sum(residuals**2)
-```
-
-This assumes residuals are already scaled consistently with the
-experimental uncertainty model. If an experiment does not provide valid
-uncertainties, the posterior is only as meaningful as the residual
-weighting currently used by deterministic fitting.
-
-Initial priors are uniform inside finite `fit_min` and `fit_max` bounds
-and zero outside those bounds. No Gaussian, log-normal, or
-domain-specific priors are part of the first implementation.
-
-## Core Architectural Decision
-
-### Keep Full Results In `analysis.fit_results`
-
-Bayesian results should be stored in a new runtime result object, not in
-a new heavy `Analysis` category.
-
-Recommended model:
-
-- `FitResults` remains the base deterministic result container.
-- `BayesianFitResults` extends `FitResults` for posterior-specific data.
-- `analysis.fit_results` stores either `FitResults` or
-  `BayesianFitResults`.
-
-This keeps the existing public access pattern unchanged:
-
-```python
-project.analysis.fit_results
-```
-
-### Why Not A New Results Category Under `Analysis`
-
-Current `Analysis` categories are lightweight, structured, project-owned
-configuration or control objects:
-
-- `fit`
-- `aliases`
-- `constraints`
-- `joint_fit_experiments`
-
-These are serialized with the project. Full Bayesian results are a poor
-fit for this category model because they are:
-
-- runtime products rather than project configuration
-- potentially large arrays
-- not natural CIF content
-- often disposable or recomputable
-
-Posterior chains, posterior predictive draws, and pair-plot inputs
-should therefore remain runtime-only objects attached to
-`BayesianFitResults`.
-
-## Optional Future Category
-
-A small `Analysis` category may still be useful later, but only for
-Bayesian configuration, not for heavy result storage.
-
-Possible future category name:
-
-- `analysis.sampling`
-- `analysis.bayesian`
-
-Possible responsibilities:
-
-- sampler type
-- point estimate policy
-- default highest-density interval levels
-- default posterior predictive draw cap
-- public DREAM hyperparameters
-- persistent default random seed policy, if users need saved sampling
-  preferences later
-
-This category should stay lightweight and serializable. It must not
-store raw chains.
-
-## Runtime Type Contracts
-
-### `BumpsDreamMinimizer`
-
-Add a new minimizer implementation registered through `MinimizerFactory`
-with tag:
-
-```python
-'bumps (dream)'
-```
-
-Responsibilities:
-
-- Reuse `_EasyDiffractionFitness` or a small subclass of it.
-- Build bounded BUMPS parameters from EasyDiffraction parameters.
-- Preserve parameter order from `Fitter.fit()` through all arrays.
-- Validate finite bounds before starting the sampler.
-- Run DREAM with internal defaults.
-- Collect posterior samples, log likelihood values, and sampler
-  diagnostics where BUMPS exposes them.
-- Choose and commit the MAP or best posterior sample after sampling.
-- Build `BayesianFitResults`.
-
-The existing `BumpsMinimizer` handles BUMPS parameter ordering carefully
-because `FitProblem` can sort parameters internally. DREAM must preserve
-the same guarantee: result arrays and summaries must be mapped back to
-the original EasyDiffraction parameter order.
-
-### `BayesianFitResults`
-
-Subclass `FitResults` and add Bayesian-specific fields. Inherited fields
-should keep deterministic-compatible meanings:
-
-- `success`: sampler completed and produced usable posterior samples
-- `parameters`: EasyDiffraction parameters updated to the committed MAP
-  vector
-- `starting_parameters`: an immutable snapshot of starting values, not
-  the same mutable parameter objects after fitting
-- `reduced_chi_square`: reduced chi-square at the committed MAP vector,
-  when available
-- `engine_result`: raw or lightly wrapped BUMPS DREAM result
-- `fitting_time`: elapsed sampling time
-
-Recommended Bayesian fields:
-
-- `sampler_name`
-- `point_estimate_name`
-- `posterior_samples`
-- `posterior_parameter_summaries`
-- `posterior_predictive`
-- `credible_interval_levels`
-- `diagnostics`
-- `sampler_settings`
-
-`sampler_settings` should record the internal defaults actually used.
-That keeps a runtime audit trail even before the settings are public
-configuration.
-
-For DREAM, `sampler_settings` must include `random_seed`, whether the
-seed was user-provided or generated, and the posterior predictive draw
-cap used for summaries.
-
-### `PosteriorSamples`
-
-Recommended fields:
-
-- `parameter_names`: minimizer-safe names, matching sampled array order
-- `parameter_labels`: user-facing labels for tables and plots
-- `values`: NumPy array shaped `(chain, draw, parameter)`
-- `flat_values`: optional cached array shaped `(sample, parameter)`
-- `log_likelihood`: optional NumPy array shaped `(chain, draw)`
-- `log_posterior`: optional NumPy array shaped `(chain, draw)`
-- `bounds`: mapping from parameter name to `(fit_min, fit_max)`
-- `start_values`: starting values in parameter order
-- `map_values`: committed MAP values in parameter order
-- `map_chain`: chain index of the MAP sample, if known
-- `map_draw`: draw index of the MAP sample, if known
-
-The object should provide one conversion helper:
-
-```python
-posterior_samples.to_arviz()
-```
-
-This keeps public EasyDiffraction methods independent of ArviZ while
-letting plotting and summaries use ArviZ internally.
-
-### `PosteriorParameterSummary`
-
-Recommended fields:
-
-- `parameter_name`
-- `parameter_label`
-- `datablock`
-- `category`
-- `entry`
-- `parameter`
-- `start`
-- `map`
-- `mean`
-- `median`
-- `std`
-- `hdi`
-- `fit_min`
-- `fit_max`
-- `units`
-
-`hdi` should be a mapping keyed by interval probability, for example:
-
-```python
-{
-    0.68: (lower_68, upper_68),
-    0.95: (lower_95, upper_95),
-}
-```
-
-### `PosteriorPredictiveSummary`
-
-Posterior predictive data can be expensive for diffraction patterns, so
-the first implementation should store summaries rather than every draw.
-
-Recommended fields:
-
-- `experiment_name`
-- `x`
-- `y_observed`
-- `y_map`
-- `intervals`
-- `draw_count`
-
-`intervals` should be keyed by interval probability:
-
-```python
-{
-    0.95: (lower_y, upper_y),
-}
-```
-
-Posterior predictive summaries are required for
-`plot_posterior_predictive(...)`, but they should be based on a capped
-subset of posterior draws. The default cap is an internal implementation
-constant, initially 200 draws.
-
-## Point Estimate Policy
-
-After a DREAM run, project parameters should be updated to the MAP or
-best posterior sample, not to independent marginal medians.
-
-Reasons:
-
-- Per-parameter medians can produce a parameter vector that was never
-  sampled jointly.
-- MAP corresponds to one coherent sampled state.
-- The calculated profile shown after Bayesian fitting should correspond
-  to one actual parameter set.
-
-Posterior tables should still report mean, median, standard deviation,
-and HDIs.
-
-If the sampler fails before producing usable samples, the minimizer
-should restore starting parameter values instead of leaving the project
-at an arbitrary last sampled state.
-
-## Parameter Bounds
-
-DREAM should require finite bounds for every sampled free parameter.
-
-Validation rules:
-
-- Use `fit_min` and `fit_max`.
-- Bounds must be finite.
-- `fit_min` must be strictly less than `fit_max`.
-- The starting value must be inside the bounds.
-- Validate only sampled parameters: free, unconstrained parameters
-  collected by `Fitter.fit()`.
-
-Failure behavior:
-
-- Stop before running BUMPS.
-- Raise a clear `ValueError`.
-- List every offending parameter and the specific problem.
-
-This is stricter than some deterministic minimizers, but appropriate for
-bounded posterior sampling.
-
-### Interaction With Physical Limits
-
-`Analysis.fit(use_physical_limits=True)` currently allows deterministic
-minimizers to fill unbounded `fit_min` and `fit_max` from parameter
-value spec physical limits. DREAM should use the same pre-processing
-path.
-
-If `use_physical_limits=True` still leaves any sampled parameter
-unbounded, DREAM must fail with the same finite-bound error.
-
-## DREAM Defaults And Reproducibility
-
-For the first implementation, sampling parameters should stay internal
-and use library defaults or small wrapper constants in `bumps_dream.py`.
-
-Examples:
-
-- `n_steps`
-- `n_burn`
-- `thin`
-- `pop`
-- `alpha`
-
-These should not be user-facing API initially. They should be recorded
-in `BayesianFitResults.sampler_settings` so users can inspect how a
-runtime result was produced.
-
-The exception is `random_seed`, which should be accepted as an optional
-keyword by `project.analysis.fit(...)` when the active minimizer is
-`'bumps (dream)'`. This keeps stochastic results reproducible without
-creating a persistent sampling category in phase 1.
-
-Tests may monkeypatch the internal defaults to keep unit and integration
-tests fast. That test hook should not become public API.
-
-## Display Behavior
-
-### `project.analysis.display.fit_results()`
-
-Keep the existing entry point and dispatch by result type.
-
-Deterministic fit:
-
-- keep the current summary
-- keep the current fitted-parameter table
-
-Bayesian fit:
-
-- show a sampling summary section
-- show sampler settings that materially affect the result
-- show diagnostics when available
-- show posterior parameter summaries instead of only fitted values and
-  covariance-derived uncertainties
-
-Recommended Bayesian table columns:
-
-- datablock
-- category
-- entry
-- parameter
-- start
-- MAP
-- mean
-- median
-- posterior std
-- 68% HDI
-- 95% HDI
-- units
-
-Display code should not require ArviZ at render time if summaries were
-already computed by the minimizer.
-
-## Plotting Behavior
-
-### `plot_meas_vs_calc(expt_name=...)`
-
-Keep the existing method name.
-
-Deterministic fit:
-
-- current measured vs calculated behavior unchanged
-
-Bayesian fit:
-
-- measured pattern
-- MAP calculated line
-- one shaded 95% credible interval band when `posterior_predictive` is
-  available
-
-If predictive intervals are unavailable, the method should plot the MAP
-calculated line and warn that posterior predictive intervals were not
-computed.
-
-This is the clearest default for diffraction patterns. A full posterior
-predictive distribution at every x value is useful but visually denser,
-so it should not be the default first view.
-
-### `plot_param_correlations()`
-
-Keep the current method name and purpose.
-
-Deterministic fit:
-
-- covariance- or engine-derived correlation matrix
-
-Bayesian fit:
-
-- posterior-sample correlation matrix computed from flattened posterior
-  samples
-
-Implementation detail:
-
-- Check for `BayesianFitResults.posterior_samples` first.
-- Fall back to the existing covariance path for deterministic results.
-- Keep the current thresholding and lower-triangle display behavior.
-
-This preserves the meaning of the method while making it work for
-posterior samples.
-
-### New `plot_posterior_pairs()`
-
-Add a Bayesian-specific method for pairwise posterior exploration.
-
-Intended behavior:
-
-- ArviZ-backed implementation first
-- Plotly backend where practical
-- pairwise scatter or density panels
-- marginal distributions on the diagonal
-- optional parameter subset support
-
-This method should serve the role of an interactive corner or pair plot.
-When the active result is deterministic, it should log a clear warning
-instead of trying to infer a posterior from covariance.
-
-### New `plot_param_distribution(param)`
-
-Add a one-dimensional marginal posterior plot for a selected parameter.
-
-Intended behavior:
-
-- ArviZ-backed implementation first
-- posterior histogram or density
-- MAP marker
-- median marker
-- HDI overlay
-
-Parameter selection should accept the same identifiers users see in
-fit-result tables where possible:
-
-- parameter object
-- unique parameter name
-- user-facing label
-
-Ambiguous string matches should fail with a clear error listing matching
-parameters.
-
-### New `plot_posterior_predictive(...)`
-
-Add a dedicated method for posterior predictive views. This is required
-for phase 1 because `plot_meas_vs_calc(...)` should stay a concise
-measured-versus-current-calculation view.
-
-Initial API:
-
-```python
-project.display.plotter.plot_posterior_predictive(
-    expt_name='hrpt',
-    style='band',
-)
-```
-
-Possible styles:
-
-- `band`
-- `draws`
-- `distribution`
-
-Initial behavior:
-
-- default to `style='band'`
-- show measured pattern
-- show MAP calculated line
-- show a 95% posterior predictive band by default
-- support capped individual posterior predictive draws with
-  `style='draws'`
-
-For the first implementation, richer views may be delegated to ArviZ
-where practical, but diffraction-specific x/y plotting should remain
-clear and domain-oriented.
-
-## Data Ownership
-
-Experiment `data` categories should remain responsible for:
-
-- measured pattern
-- current calculated pattern
-- current background pattern
-
-Posterior-specific data should remain analysis-owned through
-`BayesianFitResults`, because it is:
-
-- fit-result scoped
-- potentially joint across experiments
-- used mainly for reporting and plotting
-
-When the MAP vector is committed, normal category update behavior should
-make the current calculated pattern correspond to the MAP values.
-Posterior predictive summaries should not mutate experiment data
-categories for every posterior draw.
-
-## Persistence And Serialization
-
-For the first implementation:
-
-- Do not serialize full posterior chains to CIF.
-- Do not force posterior arrays into project save files.
-- Let project auto-save persist the current MAP parameter values and
-  `fit.minimizer_type`, as it already does for deterministic fitting.
-- Treat `analysis.fit_results` as runtime-only.
-
-Possible future export surfaces:
-
-- explicit posterior export methods
-- CSV summaries
-- ArviZ NetCDF export
-- NumPy archive export
-
-## Failure And Warning Policy
-
-Hard failures should stop before expensive sampling when possible:
-
-- missing finite bounds
-- invalid bound order
-- start value outside bounds
-- no sampled parameters
-- BUMPS DREAM backend unavailable
-
-Soft warnings should allow a result to be returned:
-
-- convergence diagnostics unavailable
-- convergence diagnostics outside recommended thresholds
-- posterior predictive intervals not computed
-- MAP value close to a fit bound
-
-`success` should mean that the sampler completed and usable posterior
-samples were produced. Convergence diagnostics should be shown and
-stored separately. Poor convergence should set a diagnostic flag and log
-a warning, but it should not turn the result into a hard failure unless
-there are no usable samples.
-
-## Testing Requirements
-
-Unit tests should cover:
-
-- `MinimizerTypeEnum.BUMPS_DREAM` and factory registration.
-- Finite-bound validation with all offending parameters reported.
-- Start-value-inside-bounds validation.
-- `random_seed` threading and recording for DREAM.
-- Non-`None` `random_seed` rejection for deterministic minimizers.
-- Preservation of parameter order between EasyDiffraction parameters,
-  BUMPS parameters, posterior arrays, and summaries.
-- MAP commit behavior.
-- Restore-start-values behavior on sampler failure.
-- `BayesianFitResults.display_results()` table content.
-- Posterior correlation calculation from flattened posterior samples.
-- Posterior predictive summary generation with capped draw evaluation.
-- Deterministic correlation behavior remaining unchanged.
-- Project serialization not including posterior arrays.
-
-Integration tests should cover:
-
-- A small bounded synthetic refinement with `'bumps (dream)'`.
-- Optional deterministic pre-fit followed by DREAM as two explicit
-  `fit()` calls.
-- `analysis.display.fit_results()` after DREAM.
-- `plot_posterior_predictive(...)` after DREAM.
-- Bayesian plotting smoke tests using a small posterior fixture.
-
-## Implementation Plan
-
-The detailed implementation plan is
-`docs/dev/plan_bayesian-analysis.md`. It follows
-`.github/copilot-instructions.md`: phase 1 is implementation only, phase
-2 is verification, and every completed phase 1 implementation step must
-be staged with explicit paths and committed locally before the next step
-starts.
-
-High-level implementation order:
-
-- register the DREAM minimizer
-- add Bayesian result models
-- thread optional `random_seed` through fitting
-- implement DREAM sampling and MAP commit behavior
-- add Bayesian result display
-- add posterior correlations
-- add posterior distribution plots
-- add posterior predictive summaries and plots
-
-## Acceptance Criteria
-
-The first useful implementation is complete when:
-
-- `project.analysis.fit.show_minimizer_types()` lists `'bumps (dream)'`.
-- `project.analysis.fit.minimizer_type = 'bumps (dream)'` runs through
-  the same `project.analysis.fit()` path as other minimizers.
-- Missing finite bounds fail before sampling with a clear parameter
-  list.
-- Successful sampling stores `BayesianFitResults` in
-  `project.analysis.fit_results`.
-- Project parameters are left at the MAP sample after a successful run.
-- `project.analysis.display.fit_results()` renders posterior summaries.
-- `plot_param_correlations()` works from posterior samples.
-- `plot_posterior_predictive(expt_name=...)` shows measured data, MAP
-  calculation, and a posterior predictive band.
-- Saving a project does not serialize posterior chains.
-
-## Implementation Discovery
-
-During implementation, inspect which BUMPS DREAM result object fields
-are stable enough to store directly in `engine_result`. Normalize
-posterior samples, log-likelihood values, diagnostics, and summaries
-into EasyDiffraction-owned value objects first; keep `engine_result` as
-an opaque backend escape hatch.
-
-## Summary
-
-The recommended design is to add `'bumps (dream)'` as a normal minimizer
-type, keep Bayesian analysis inside the existing `fit()` workflow, store
-heavy posterior data in `BayesianFitResults`, and keep
-`analysis.fit_results` as the main public result entry point. A
-lightweight serialized `Analysis` category can be added later if
-Bayesian settings need to become persistent and user-configurable.
diff --git a/docs/dev/plan_bayesian-analysis.md b/docs/dev/plan_bayesian-analysis.md
deleted file mode 100644
index 4a5697b8..00000000
--- a/docs/dev/plan_bayesian-analysis.md
+++ /dev/null
@@ -1,334 +0,0 @@
-# Bayesian Analysis Implementation Plan
-
-**Status:** Planned **Feature name:** `bayesian-analysis` **Suggested
-branch:** `feature/bayesian-analysis` **Design doc:**
-`docs/dev/bayesian-analysis-design.md`
-
-## Context
-
-This plan follows `.github/copilot-instructions.md`. The instructions
-refer to `docs/dev/architecture.md`, but this checkout does not contain
-that file. The matching living architecture document used for this plan
-is `docs/dev/architecture.md`.
-
-Relevant current seams:
-
-- `src/easydiffraction/analysis/categories/fit/default.py`
-- `src/easydiffraction/analysis/analysis.py`
-- `src/easydiffraction/analysis/fitting.py`
-- `src/easydiffraction/analysis/minimizers/base.py`
-- `src/easydiffraction/analysis/minimizers/bumps.py`
-- `src/easydiffraction/analysis/minimizers/enums.py`
-- `src/easydiffraction/analysis/minimizers/factory.py`
-- `src/easydiffraction/analysis/minimizers/__init__.py`
-- `src/easydiffraction/analysis/fit_helpers/reporting.py`
-- `src/easydiffraction/display/plotting.py`
-
-## Decisions
-
-- Add Bayesian sampling as a normal minimizer tag: `'bumps (dream)'`.
-- Keep `project.analysis.fit()` as the execution entry point.
-- Keep posterior chains runtime-only in `BayesianFitResults`.
-- Do not add posterior chain export in phase 1.
-- Add `plot_posterior_predictive(...)` in phase 1.
-- Add optional `random_seed` support for `'bumps (dream)'`.
-- Generate and record a seed when the user does not provide one.
-- Reject non-`None` `random_seed` for deterministic minimizers.
-- Treat sampler completion and convergence quality separately.
-- Use `success=False` only when DREAM fails or no usable posterior
-  samples exist.
-- Store and display convergence diagnostics; warn on poor convergence.
-- Start with an internal posterior predictive draw cap of 200.
-
-## Scope
-
-Phase 1 delivers implementation only. Do not add or run tests in phase 1
-unless the user explicitly asks. Stop after phase 1 and request review.
-
-Phase 2 delivers verification only. Add/update tests, then run the
-commands listed in this plan.
-
-## Agent Execution Rule
-
-When an AI agent follows this plan, every completed phase 1
-implementation step must be staged with explicit paths and committed
-locally before moving to the next implementation step or the phase 1
-review gate. Commits must be atomic, single-purpose, and aligned with
-the step just completed.
-
-If implementation uncovers a serious requirement, risk, design issue, or
-scope change not covered by this plan, stop and ask for clarification or
-approval before proceeding.
-
-## Status Checklist
-
-- [x] Repository context gathered.
-- [x] Design decisions recorded.
-- [ ] Phase 1 implementation complete.
-- [ ] Phase 1 review approved.
-- [ ] Phase 2 tests added.
-- [ ] Phase 2 verification commands completed.
-
-## Phase 1: Implementation
-
-### Step 1: Register The DREAM Minimizer
-
-- [ ] Add `BUMPS_DREAM = 'bumps (dream)'` to `MinimizerTypeEnum`.
-- [ ] Add a human-readable enum description.
-- [ ] Add `src/easydiffraction/analysis/minimizers/bumps_dream.py`.
-- [ ] Register `BumpsDreamMinimizer` with `MinimizerFactory`.
-- [ ] Import `BumpsDreamMinimizer` from
-      `src/easydiffraction/analysis/minimizers/__init__.py`.
-- [ ] Update `docs/dev/architecture.md` minimizer tag tables if the
-      implementation changes the documented supported tags.
-
-Likely files:
-
-- `src/easydiffraction/analysis/minimizers/enums.py`
-- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
-- `src/easydiffraction/analysis/minimizers/__init__.py`
-- `docs/dev/architecture.md`
-
-Suggested commit message:
-
-```text
-Register BUMPS DREAM minimizer
-```
-
-### Step 2: Add Bayesian Result Models
-
-- [ ] Add `BayesianFitResults`.
-- [ ] Add `PosteriorSamples`.
-- [ ] Add `PosteriorParameterSummary`.
-- [ ] Add `PosteriorPredictiveSummary`.
-- [ ] Keep public constructors explicit; do not use `**kwargs`.
-- [ ] Add NumPy-style docstrings for public classes and methods.
-- [ ] Provide `PosteriorSamples.to_arviz()` for plotting integration.
-
-Likely files:
-
-- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
-- `src/easydiffraction/analysis/fit_helpers/reporting.py`
-- `src/easydiffraction/analysis/fit_helpers/__init__.py`
-
-Suggested commit message:
-
-```text
-Add Bayesian fit result models
-```
-
-### Step 3: Thread Random Seed Through Fit Execution
-
-- [ ] Add optional `random_seed: int | None = None` to `Fit.run(...)`
-      and `Fit.__call__(...)`.
-- [ ] Thread `random_seed` through `Analysis._run_fit(...)`,
-      `_fit_single(...)`, `_fit_joint(...)`, and `Fitter.fit(...)`.
-- [ ] Thread `random_seed` through `MinimizerBase.fit(...)`.
-- [ ] Reject non-`None` `random_seed` in deterministic minimizers with a
-      clear `ValueError`.
-- [ ] Let `BumpsDreamMinimizer` generate a seed when `random_seed` is
-      `None`.
-- [ ] Record the user-provided or generated seed in
-      `BayesianFitResults.sampler_settings`.
-
-Likely files:
-
-- `src/easydiffraction/analysis/categories/fit/default.py`
-- `src/easydiffraction/analysis/analysis.py`
-- `src/easydiffraction/analysis/fitting.py`
-- `src/easydiffraction/analysis/minimizers/base.py`
-- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
-
-Suggested commit message:
-
-```text
-Thread random seed through fitting
-```
-
-### Step 4: Implement DREAM Sampling
-
-- [ ] Reuse `_EasyDiffractionFitness` or extract a shared BUMPS helper
-      only if needed.
-- [ ] Build BUMPS parameters from sampled EasyDiffraction parameters.
-- [ ] Validate finite bounds, bound ordering, and start values before
-      sampling.
-- [ ] Preserve parameter order across BUMPS parameters, posterior
-      arrays, summaries, and MAP commit.
-- [ ] Run BUMPS DREAM with internal defaults.
-- [ ] Commit the MAP or best posterior sample to project parameters.
-- [ ] Restore starting values if the sampler fails before producing
-      usable samples.
-- [ ] Store raw BUMPS output as `engine_result` only after stable fields
-      have been normalized into EasyDiffraction-owned result objects.
-
-Likely files:
-
-- `src/easydiffraction/analysis/minimizers/bumps.py`
-- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
-- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
-
-Suggested commit message:
-
-```text
-Implement BUMPS DREAM sampling
-```
-
-### Step 5: Add Bayesian Display
-
-- [ ] Make `BayesianFitResults.display_results(...)` render sampler
-      status, diagnostics, settings, and posterior parameter summaries.
-- [ ] Keep deterministic `FitResults.display_results(...)` unchanged.
-- [ ] Ensure `analysis.display.fit_results()` continues to dispatch
-      through the active result object.
-- [ ] Display poor convergence prominently without hiding usable
-      posterior summaries.
-
-Likely files:
-
-- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
-- `src/easydiffraction/analysis/fit_helpers/reporting.py`
-
-Suggested commit message:
-
-```text
-Display Bayesian fit summaries
-```
-
-### Step 6: Add Posterior Correlations
-
-- [ ] Extend `plot_param_correlations()` to detect
-      `BayesianFitResults.posterior_samples`.
-- [ ] Compute the correlation matrix from flattened posterior samples.
-- [ ] Preserve deterministic covariance and engine-derived behavior.
-- [ ] Preserve current thresholding and lower-triangle display behavior.
-
-Likely files:
-
-- `src/easydiffraction/display/plotting.py`
-- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
-
-Suggested commit message:
-
-```text
-Plot posterior parameter correlations
-```
-
-### Step 7: Add Bayesian Posterior Plots
-
-- [ ] Add `plot_posterior_pairs(...)`.
-- [ ] Add `plot_param_distribution(param, ...)`.
-- [ ] Use `PosteriorSamples.to_arviz()` internally where practical.
-- [ ] Accept parameter objects, unique names, and user-facing labels for
-      one-parameter selection.
-- [ ] Fail ambiguous string matches with a clear list of matches.
-- [ ] Warn clearly when Bayesian-only plots are called on deterministic
-      results.
-
-Likely files:
-
-- `src/easydiffraction/display/plotting.py`
-- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
-
-Suggested commit message:
-
-```text
-Add posterior distribution plots
-```
-
-### Step 8: Add Posterior Predictive Plots
-
-- [ ] Generate posterior predictive summaries from a capped subset of
-      posterior draws.
-- [ ] Store summaries in `BayesianFitResults.posterior_predictive`.
-- [ ] Add `plot_posterior_predictive(expt_name, style='band', ...)`.
-- [ ] Support `style='band'` and `style='draws'` initially.
-- [ ] Extend `plot_meas_vs_calc(...)` to show a 95% credible band when
-      posterior predictive summaries are available.
-- [ ] Keep experiment data categories responsible only for measured,
-      current calculated, and current background patterns.
-
-Likely files:
-
-- `src/easydiffraction/display/plotting.py`
-- `src/easydiffraction/analysis/minimizers/bumps_dream.py`
-- `src/easydiffraction/analysis/fit_helpers/bayesian.py`
-
-Suggested commit message:
-
-```text
-Add posterior predictive plotting
-```
-
-## Phase 1 Review Gate
-
-- [ ] Inspect `git status --short`.
-- [ ] Inspect the diff for unrelated changes.
-- [ ] Confirm every phase 1 step has an atomic local commit.
-- [ ] Present the implementation summary for review.
-- [ ] Do not start phase 2 until the user approves verification work.
-
-## Phase 2: Verification
-
-### Test Additions
-
-- [ ] Add unit tests for enum and factory registration.
-- [ ] Add unit tests for finite-bound validation.
-- [ ] Add unit tests for start-value-inside-bounds validation.
-- [ ] Add unit tests for `random_seed` behavior.
-- [ ] Add unit tests for deterministic minimizer seed rejection.
-- [ ] Add unit tests for parameter order preservation.
-- [ ] Add unit tests for MAP commit and restore-on-failure behavior.
-- [ ] Add unit tests for Bayesian display output.
-- [ ] Add unit tests for posterior sample correlation calculation.
-- [ ] Add unit tests for posterior predictive summary generation.
-- [ ] Add plotting smoke tests using small posterior fixtures.
-- [ ] Add integration coverage for a small bounded DREAM refinement.
-- [ ] Add integration coverage for explicit LM pre-fit followed by
-      DREAM.
-- [ ] Add serialization coverage proving posterior chains are not saved.
-
-Likely test files:
-
-- `tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py`
-- `tests/unit/easydiffraction/analysis/minimizers/test_enums.py`
-- `tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py`
-- `tests/unit/easydiffraction/display/test_plotting.py`
-- `tests/integration/fitting/test_bayesian_dream.py`
-- `tests/integration/fitting/test_project_load.py`
-
-Suggested commit message:
-
-```text
-Test Bayesian DREAM workflow
-```
-
-### Verification Commands
-
-- [ ] `pixi run test-structure-check`
-- [ ] `pixi run fix`
-- [ ] `pixi run check`
-- [ ] `pixi run unit-tests`
-- [ ] `pixi run integration-tests`
-- [ ] `pixi run script-tests`
-
-If `pixi run fix` regenerates package-structure docs, accept the
-generated changes and include them only in the verification commit where
-they were produced.
-
-## Non-Blocking Implementation Discovery
-
-The BUMPS DREAM return object must be inspected during implementation.
-If the stable public fields are insufficient for posterior samples,
-log-likelihood values, or diagnostics, normalize the available fields
-into EasyDiffraction-owned value objects and keep `engine_result`
-opaque. If this prevents a required feature, stop and ask before
-changing scope.
-
-## Suggested Pull Request
-
-**Title:** Add Bayesian DREAM sampling and posterior plots
-
-**Description:** Adds Bayesian refinement through the BUMPS DREAM
-sampler, including reproducible runs, posterior parameter summaries, and
-posterior predictive plots so users can inspect uncertainty in fitted
-diffraction models.

From c7f3a6d11497befd08190352ff358c74cd35f1d5 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 11:56:22 +0200
Subject: [PATCH 081/106] Fix Bayesian diagnostics and DREAM cleanup edge cases

---
 .../analysis/fit_helpers/bayesian.py          |  26 ++--
 .../analysis/minimizers/base.py               |  13 +-
 .../analysis/minimizers/bumps_dream.py        |  62 +++++---
 src/easydiffraction/display/plotting.py       | 146 ++++++++++++++++--
 .../analysis/fit_helpers/test_bayesian.py     |  31 ++++
 .../analysis/minimizers/test_base.py          |  39 +++++
 .../analysis/minimizers/test_bumps_dream.py   |  53 +++++++
 .../easydiffraction/display/test_plotting.py  | 118 +++++++++++++-
 8 files changed, 433 insertions(+), 55 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index 907cb53f..aa087fcb 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -383,10 +383,15 @@ def compute_convergence_diagnostics(posterior_samples: PosteriorSamples) -> dict
     r_hat_by_parameter = _dataset_to_scalar_dict(rhat_dataset)
     ess_bulk_by_parameter = _dataset_to_scalar_dict(ess_dataset)
 
-    max_r_hat = max(r_hat_by_parameter.values(), default=None)
-    min_ess_bulk = min(ess_bulk_by_parameter.values(), default=None)
+    finite_r_hat = [value for value in r_hat_by_parameter.values() if value is not None]
+    finite_ess_bulk = [value for value in ess_bulk_by_parameter.values() if value is not None]
 
-    converged = True
+    max_r_hat = max(finite_r_hat, default=None)
+    min_ess_bulk = min(finite_ess_bulk, default=None)
+
+    converged = len(finite_r_hat) == len(r_hat_by_parameter) and len(finite_ess_bulk) == len(
+        ess_bulk_by_parameter
+    )
     if max_r_hat is not None and max_r_hat > R_HAT_CONVERGENCE_THRESHOLD:
         converged = False
     if min_ess_bulk is not None and min_ess_bulk < ESS_BULK_CONVERGENCE_THRESHOLD:
@@ -500,17 +505,20 @@ def standard_deviations_from_summaries(
     return np.array([summary.standard_deviation for summary in summaries], dtype=float)
 
 
-def _dataset_to_scalar_dict(dataset: object) -> dict[str, float]:
-    values: dict[str, float] = {}
+def _dataset_to_scalar_dict(dataset: object) -> dict[str, float | None]:
+    values: dict[str, float | None] = {}
     for name, data_array in dataset.data_vars.items():
-        values[name] = float(np.asarray(data_array).reshape(-1)[0])
+        values[name] = _maybe_scalar(np.asarray(data_array).reshape(-1)[0])
     return values
 
 
 def _maybe_scalar(value: object) -> float | None:
     if value is None:
         return None
-    return float(value)
+    scalar = float(value)
+    if not np.isfinite(scalar):
+        return None
+    return scalar
 
 
 def _format_sampler_settings(sampler_settings: dict[str, object]) -> str | None:
@@ -719,7 +727,7 @@ def _format_interval(interval: tuple[float, float]) -> str:
 
 
 def _format_r_hat(value: float | None) -> str:
-    if value is None:
+    if value is None or not np.isfinite(value):
         return 'N/A'
     formatted = f'{value:.3f}'
     if value > R_HAT_CONVERGENCE_THRESHOLD:
@@ -728,7 +736,7 @@ def _format_r_hat(value: float | None) -> str:
 
 
 def _format_ess_bulk(value: float | None) -> str:
-    if value is None:
+    if value is None or not np.isfinite(value):
         return 'N/A'
     formatted = f'{value:.1f}'
     if value < ESS_BULK_CONVERGENCE_THRESHOLD:
diff --git a/src/easydiffraction/analysis/minimizers/base.py b/src/easydiffraction/analysis/minimizers/base.py
index 142ed569..bbc639f1 100644
--- a/src/easydiffraction/analysis/minimizers/base.py
+++ b/src/easydiffraction/analysis/minimizers/base.py
@@ -338,12 +338,13 @@ def fit(
 
         self._start_tracking(minimizer_name, verbosity=verbosity)
 
-        solver_args = self._prepare_solver_args(parameters)
-        if resolved_random_seed is not None:
-            solver_args['random_seed'] = resolved_random_seed
-        raw_result = self._run_solver(objective_function, **solver_args)
-
-        self._stop_tracking()
+        try:
+            solver_args = self._prepare_solver_args(parameters)
+            if resolved_random_seed is not None:
+                solver_args['random_seed'] = resolved_random_seed
+            raw_result = self._run_solver(objective_function, **solver_args)
+        finally:
+            self._stop_tracking()
 
         return self._finalize_fit(parameters, raw_result)
 
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index c23d923c..cc141d32 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -41,6 +41,7 @@
 DEFAULT_ALPHA = 0.0
 DEFAULT_OUTLIER_TEST = 'none'
 DEFAULT_TRIM = False
+MAX_RANDOM_SEED = int(np.iinfo(np.uint32).max)
 BURN_IN_PROGRESS_POINTS = 5
 SAMPLING_PROGRESS_POINTS = 20
 DREAM_SAMPLE_ARRAY_NDIM = 3
@@ -345,9 +346,24 @@ def _resolve_random_seed(self, random_seed: int | None) -> int:
             generator = np.random.default_rng()
             random_seed = int(generator.integers(0, np.iinfo(np.int32).max))
 
-        self._resolved_random_seed = int(random_seed)
+        integer_seed = self._validated_random_seed_value(random_seed)
+
+        self._resolved_random_seed = integer_seed
         return self._resolved_random_seed
 
+    @staticmethod
+    def _validated_random_seed_value(random_seed: object) -> int:
+        """Validate and normalize a DREAM random seed."""
+        if isinstance(random_seed, bool):
+            msg = f'DREAM random_seed must be an integer between 0 and {MAX_RANDOM_SEED}.'
+            raise TypeError(msg)
+
+        integer_seed = int(random_seed)
+        if integer_seed != random_seed or integer_seed < 0 or integer_seed > MAX_RANDOM_SEED:
+            msg = f'DREAM random_seed must be an integer between 0 and {MAX_RANDOM_SEED}.'
+            raise ValueError(msg)
+        return integer_seed
+
     @staticmethod
     def _tracking_mode() -> str:
         """Use sampler-style progress reporting for DREAM runs."""
@@ -651,23 +667,28 @@ def _build_driver(
             burn_steps=int(burn),
         )
         mapper = self._build_mapper(problem)
-        driver = FitDriver(
-            fitclass=fitclass,
-            problem=problem,
-            monitors=[progress_monitor],
-            mapper=mapper,
-            steps=steps,
-            burn=burn,
-            thin=self.thin,
-            pop=self.pop,
-            init=init.value,
-            samples=sampler_settings['samples'],
-            alpha=DEFAULT_ALPHA,
-            outliers=DEFAULT_OUTLIER_TEST,
-            trim=DEFAULT_TRIM,
-        )
-        driver.clip()
-        return driver
+        try:
+            driver = FitDriver(
+                fitclass=fitclass,
+                problem=problem,
+                monitors=[progress_monitor],
+                mapper=mapper,
+                steps=steps,
+                burn=burn,
+                thin=self.thin,
+                pop=self.pop,
+                init=init.value,
+                samples=sampler_settings['samples'],
+                alpha=DEFAULT_ALPHA,
+                outliers=DEFAULT_OUTLIER_TEST,
+                trim=DEFAULT_TRIM,
+            )
+            driver.clip()
+        except Exception:
+            MPMapper.stop_mapper()
+            raise
+        else:
+            return driver
 
     def _build_mapper(self, problem: FitProblem) -> object | None:
         """Return a DREAM mapper for the configured parallel setting."""
@@ -691,9 +712,10 @@ def _execute_driver(*, driver: FitDriver, random_seed: int) -> _DreamDriverResul
         numpy_rng = np.random.mtrand._rand
         numpy_state = numpy_rng.get_state()
         python_state = random.getstate()
-        numpy_rng.seed(random_seed)
-        random.seed(random_seed)
         try:
+            validated_seed = BumpsDreamMinimizer._validated_random_seed_value(random_seed)
+            numpy_rng.seed(validated_seed)
+            random.seed(validated_seed)
             best_values, best_nllf = driver.fit()
         except DREAM_DRIVER_FAILURES as error:  # pragma: no cover - backend-specific
             return _DreamDriverResult(
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index a3076463..6998640d 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -84,6 +84,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 DEFAULT_BRAGG_ROW = DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION
 DEFAULT_POSTERIOR_PREDICTIVE_DRAWS = 200
 DEFAULT_POSTERIOR_PREDICTIVE_DRAW_PLOT_CAP = 50
+FULL_POSTERIOR_PAIR_COVARIANCE_RANK = 2
 POSTERIOR_FLATTENED_SAMPLE_NDIM = 2
 MIN_POSTERIOR_PARAMETER_COUNT = 2
 MIN_POSTERIOR_SAMPLE_COUNT = 2
@@ -906,26 +907,85 @@ def plot_posterior_predictive(
             )
             return
 
-        summary = self._get_or_build_posterior_predictive_summary(
+        self._plot_non_bragg_posterior_predictive(
             experiment=experiment,
             expt_name=expt_name,
+            plot_options=plot_options,
             x_axis=x_axis,
-            include_draws=style in {'draws', 'band+draws'},
+            sample_form=sample_form,
+            scattering_type=scattering_type,
+            style=style,
         )
-        if summary is None:
-            return
 
+    def _plot_non_bragg_posterior_predictive(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        plot_options: _MeasVsCalcPlotOptions,
+        x_axis: object,
+        sample_form: object,
+        scattering_type: object,
+        style: str,
+    ) -> None:
+        """Render non-Bragg posterior predictive summaries."""
         pattern = intensity_category_for(experiment)
         y_meas = getattr(pattern, 'intensity_meas', None)
         if y_meas is None:
             log.warning(f'No measured data available for experiment {expt_name}.')
             return
 
+        ctx = self._prepare_powder_context(
+            pattern,
+            expt_name,
+            experiment.type,
+            plot_options.x_min,
+            plot_options.x_max,
+            plot_options.x,
+        )
+        if ctx is None:
+            return
+
+        if plot_options.show_residual:
+            log.warning(
+                'Posterior predictive residuals are unavailable for non-Bragg '
+                'summary plots; ignoring show_residual=True.'
+            )
+
+        summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+            include_draws=style in {'draws', 'band+draws'},
+        )
+        if summary is None:
+            return
+
+        filtered_summary = self._filtered_posterior_predictive_summary(
+            summary=summary,
+            x_min=ctx['x_min'],
+            x_max=ctx['x_max'],
+            include_draws=style in {'draws', 'band+draws'},
+        )
+        if filtered_summary is None:
+            log.warning(
+                f'No posterior predictive data available within the requested x-range '
+                f'for experiment {expt_name}.'
+            )
+            return
+
+        filtered_y_meas = self._filtered_y_array(
+            y_meas,
+            ctx['x_array'],
+            ctx['x_min'],
+            ctx['x_max'],
+        )
+
         axes_labels = self._get_axes_labels(sample_form, scattering_type, x_axis)
         self._plot_posterior_predictive_summary(
             expt_name=expt_name,
-            summary=summary,
-            y_meas=np.asarray(y_meas, dtype=float),
+            summary=filtered_summary,
+            y_meas=filtered_y_meas,
             axes_labels=axes_labels,
             show_band=style in {'band', 'band+draws'},
             show_draws=style in {'draws', 'band+draws'},
@@ -2557,16 +2617,24 @@ def _posterior_pair_density_surface(
         if np.allclose(x_data, x_data[0]) and np.allclose(y_data, y_data[0]):
             return None
 
+        pair_data = np.vstack([x_data, y_data])
+        covariance = np.cov(pair_data)
+        if np.linalg.matrix_rank(covariance) < FULL_POSTERIOR_PAIR_COVARIANCE_RANK:
+            return None
+
         x_grid = np.linspace(x_bounds[0], x_bounds[1], num=grid_size)
         y_grid = np.linspace(y_bounds[0], y_bounds[1], num=grid_size)
         mesh_x, mesh_y = np.meshgrid(x_grid, y_grid)
-        density = cls._evaluate_reflected_2d_kde(
-            gaussian_kde(np.vstack([x_data, y_data])),
-            mesh_x=mesh_x,
-            mesh_y=mesh_y,
-            x_bounds=x_bounds,
-            y_bounds=y_bounds,
-        )
+        try:
+            density = cls._evaluate_reflected_2d_kde(
+                gaussian_kde(pair_data),
+                mesh_x=mesh_x,
+                mesh_y=mesh_y,
+                x_bounds=x_bounds,
+                y_bounds=y_bounds,
+            )
+        except (np.linalg.LinAlgError, ValueError):
+            return None
         if not np.any(np.isfinite(density)):
             return None
         return x_grid, y_grid, density
@@ -2947,8 +3015,8 @@ def _get_posterior_samples_and_fit_results(
 
         return posterior_samples, fit_results
 
-    @staticmethod
     def _plot_posterior_predictive_summary(
+        self,
         *,
         expt_name: str,
         summary: object,
@@ -3040,7 +3108,55 @@ def _plot_posterior_predictive_summary(
         )
         fig.update_xaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
         fig.update_yaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
-        fig.show()
+        self._show_plot_figure(fig)
+
+    def _filtered_posterior_predictive_summary(
+        self,
+        *,
+        summary: PosteriorPredictiveSummary,
+        x_min: float,
+        x_max: float,
+        include_draws: bool,
+    ) -> PosteriorPredictiveSummary | None:
+        """Return a predictive summary filtered to an x-range."""
+        x_filtered = self._filtered_y_array(summary.x, summary.x, x_min, x_max)
+        if np.asarray(x_filtered).size == 0:
+            return None
+
+        draws = None
+        if include_draws and summary.draws is not None:
+            draws = np.asarray(
+                [self._filtered_y_array(draw, summary.x, x_min, x_max) for draw in summary.draws],
+                dtype=float,
+            )
+
+        return PosteriorPredictiveSummary(
+            experiment_name=summary.experiment_name,
+            x_axis_name=summary.x_axis_name,
+            x=x_filtered,
+            map_prediction=self._filtered_y_array(summary.map_prediction, summary.x, x_min, x_max),
+            lower_95=(
+                None
+                if summary.lower_95 is None
+                else self._filtered_y_array(summary.lower_95, summary.x, x_min, x_max)
+            ),
+            upper_95=(
+                None
+                if summary.upper_95 is None
+                else self._filtered_y_array(summary.upper_95, summary.x, x_min, x_max)
+            ),
+            lower_68=(
+                None
+                if summary.lower_68 is None
+                else self._filtered_y_array(summary.lower_68, summary.x, x_min, x_max)
+            ),
+            upper_68=(
+                None
+                if summary.upper_68 is None
+                else self._filtered_y_array(summary.upper_68, summary.x, x_min, x_max)
+            ),
+            draws=draws,
+        )
 
     def _plot_posterior_predictive_data(
         self,
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
index 278a48ce..5fcfbf1a 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -72,6 +72,37 @@ def test_posterior_samples_to_arviz_validates_shapes():
         posterior_samples.to_arviz()
 
 
+def test_compute_convergence_diagnostics_treats_non_finite_values_as_not_converged(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import compute_convergence_diagnostics
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((4, 2, 1), dtype=float),
+    )
+
+    fake_dataset = type('FakeDataset', (), {'data_vars': {'a': np.array([np.nan], dtype=float)}})
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.rhat',
+        lambda inference_data: fake_dataset,
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.ess',
+        lambda inference_data, method='bulk': type(
+            'FakeDataset', (), {'data_vars': {'a': np.array([4000.0], dtype=float)}}
+        ),
+    )
+
+    diagnostics = compute_convergence_diagnostics(posterior_samples)
+
+    assert diagnostics['converged'] is False
+    assert diagnostics['r_hat_by_parameter'] == {'a': None}
+    assert diagnostics['ess_bulk_by_parameter'] == {'a': 4000.0}
+    assert diagnostics['max_r_hat'] is None
+    assert diagnostics['min_ess_bulk'] == pytest.approx(4000.0)
+
+
 def test_summarize_posterior_parameters_preserves_order_and_display_names():
     from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
     from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_base.py b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
index 23d385e7..0ad0ca12 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_base.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_base.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import numpy as np
+import pytest
 
 
 class _DummyParam:
@@ -119,3 +120,41 @@ def _compute_residuals(
     )
     out = f({})
     assert np.allclose(out, np.array([1.0, 2.0, 3.0]))
+
+
+def test_minimizer_base_fit_stops_tracking_when_solver_prep_fails():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class M(MinimizerBase):
+        def __init__(self):
+            super().__init__(name='dummy', method='m', max_iterations=5)
+            self.started = False
+            self.stopped = False
+
+        def _start_tracking(self, minimizer_name, verbosity=None):
+            self.started = True
+
+        def _stop_tracking(self):
+            self.stopped = True
+
+        def _prepare_solver_args(self, parameters):
+            msg = 'prep failed'
+            raise ValueError(msg)
+
+        def _run_solver(self, objective_function, **kwargs):
+            msg = 'should not run solver'
+            raise AssertionError(msg)
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            pass
+
+        def _check_success(self, raw_result):
+            return True
+
+    minimizer = M()
+
+    with pytest.raises(ValueError, match='prep failed'):
+        minimizer.fit(parameters=[_DummyParam(1.0)], objective_function=lambda _: np.array([0.0]))
+
+    assert minimizer.started is True
+    assert minimizer.stopped is True
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index 8e800cb4..6ee5e1f4 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -87,6 +87,19 @@ def test_resolve_random_seed_returns_provided_or_generated(monkeypatch):
     assert minimizer._resolved_random_seed == 123456
 
 
+@pytest.mark.parametrize('seed', [-1, np.iinfo(np.uint32).max + 1])
+def test_resolve_random_seed_rejects_out_of_range_values(seed):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with pytest.raises(
+        ValueError,
+        match=r'DREAM random_seed must be an integer between 0 and 4294967295\.',
+    ):
+        minimizer._resolve_random_seed(seed)
+
+
 def test_resolved_burn_uses_auto_or_explicit_and_validates():
     from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
 
@@ -286,3 +299,43 @@ def best(self):
     assert result.sampler_settings['init'] == 'lhs'
     assert result.sampler_settings['random_seed'] == 17
     assert summarize_mock.call_args.kwargs['parameter_names'] == ['beta', 'alpha']
+
+
+def test_build_driver_stops_mapper_when_driver_clip_fails():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with (
+        patch.object(minimizer, '_build_mapper', return_value='mapper'),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitProblem', return_value='problem'),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch('easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper') as stop_mapper,
+    ):
+        mock_driver_cls.return_value.clip.side_effect = RuntimeError('clip failed')
+
+        with pytest.raises(RuntimeError, match='clip failed'):
+            minimizer._build_driver(
+                fitclass=object(),
+                fitness=SimpleNamespace(numpoints=lambda: 10),
+                steps=10,
+                burn=2,
+                init=minimizer.init,
+                sampler_settings={'samples': 40},
+                n_parameters=1,
+            )
+
+    stop_mapper.assert_called_once()
+
+
+def test_execute_driver_stops_mapper_when_seed_is_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    driver = SimpleNamespace(fit=MagicMock(), fitter=SimpleNamespace(state=None))
+
+    with patch('easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper') as stop_mapper:
+        result = BumpsDreamMinimizer._execute_driver(driver=driver, random_seed=-1)
+
+    assert isinstance(result.error, ValueError)
+    driver.fit.assert_not_called()
+    stop_mapper.assert_called_once()
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 66fee2a1..0b21b53d 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -607,12 +607,10 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
 
     captured: dict[str, object] = {}
 
-    def fake_show(self):
-        captured['fig'] = self
-
-    monkeypatch.setattr('plotly.graph_objects.Figure.show', fake_show)
+    plotter = Plotter()
+    plotter._backend = SimpleNamespace(_show_figure=lambda figure: captured.setdefault('fig', figure))
 
-    Plotter._plot_posterior_predictive_summary(
+    plotter._plot_posterior_predictive_summary(
         expt_name='hrpt',
         summary=SimpleNamespace(
             x=np.array([1.0, 2.0, 3.0]),
@@ -639,6 +637,36 @@ def fake_show(self):
     assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
 
 
+@pytest.mark.parametrize(
+    ('x_values', 'y_values', 'x_bounds', 'y_bounds'),
+    [
+        (np.ones(8), np.arange(8, dtype=float), (0.5, 1.5), (0.0, 7.0)),
+        (
+            np.arange(8, dtype=float),
+            2.0 * np.arange(8, dtype=float) + 1.0,
+            (0.0, 7.0),
+            (1.0, 15.0),
+        ),
+    ],
+)
+def test_posterior_pair_density_surface_returns_none_for_rank_deficient_samples(
+    x_values,
+    y_values,
+    x_bounds,
+    y_bounds,
+):
+    from easydiffraction.display.plotting import Plotter
+
+    surface = Plotter._posterior_pair_density_surface(
+        x_values=np.asarray(x_values, dtype=float),
+        y_values=np.asarray(y_values, dtype=float),
+        x_bounds=x_bounds,
+        y_bounds=y_bounds,
+    )
+
+    assert surface is None
+
+
 def test_plot_posterior_predictive_data_uses_max_posterior_label_and_dash(monkeypatch):
     from types import SimpleNamespace
 
@@ -984,6 +1012,86 @@ def fake_plot_posterior_predictive_data(
     assert captured['show_residual'] is None
 
 
+def test_plot_posterior_predictive_non_bragg_filters_x_range_and_warns_for_residual(
+    monkeypatch,
+):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorPredictiveSummary
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+    from easydiffraction.datablocks.experiment.item.enums import SampleFormEnum
+    from easydiffraction.datablocks.experiment.item.enums import ScatteringTypeEnum
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+    warnings: list[str] = []
+
+    class ExptType:
+        sample_form = type('SF', (), {'value': SampleFormEnum.POWDER})()
+        scattering_type = type('S', (), {'value': ScatteringTypeEnum.TOTAL})()
+        beam_mode = type('B', (), {'value': BeamModeEnum.CONSTANT_WAVELENGTH})()
+
+    class Pattern:
+        x = np.array([1.0, 2.0, 3.0])
+        two_theta = np.array([1.0, 2.0, 3.0])
+        intensity_meas = np.array([10.0, 20.0, 30.0])
+
+    class Experiment:
+        type = ExptType()
+        data = Pattern()
+
+    class Project:
+        experiments = {'pdf': Experiment()}
+
+    plotter = Plotter()
+    plotter.engine = 'plotly'
+    plotter._set_project(Project())
+
+    monkeypatch.setattr(Plotter, '_update_project_categories', lambda self, expt_name: None)
+    monkeypatch.setattr(
+        Plotter,
+        '_get_or_build_posterior_predictive_summary',
+        lambda self, **kwargs: PosteriorPredictiveSummary(
+            experiment_name='pdf',
+            x_axis_name='two_theta',
+            x=np.array([1.0, 2.0, 3.0]),
+            map_prediction=np.array([9.0, 19.0, 29.0]),
+            lower_95=np.array([8.0, 18.0, 28.0]),
+            upper_95=np.array([10.0, 20.0, 30.0]),
+        ),
+    )
+    monkeypatch.setattr('easydiffraction.display.plotting.log.warning', warnings.append)
+
+    def fake_plot_summary(
+        self,
+        *,
+        expt_name,
+        summary,
+        y_meas,
+        axes_labels,
+        show_band,
+        show_draws,
+    ):
+        captured['expt_name'] = expt_name
+        captured['summary'] = summary
+        captured['y_meas'] = y_meas
+        captured['axes_labels'] = axes_labels
+        captured['show_band'] = show_band
+        captured['show_draws'] = show_draws
+
+    monkeypatch.setattr(Plotter, '_plot_posterior_predictive_summary', fake_plot_summary)
+
+    plotter.plot_posterior_predictive('pdf', x_min=1.5, x_max=2.5, show_residual=True)
+
+    assert captured['expt_name'] == 'pdf'
+    np.testing.assert_allclose(captured['summary'].x, np.array([2.0]))
+    np.testing.assert_allclose(captured['summary'].map_prediction, np.array([19.0]))
+    np.testing.assert_allclose(captured['summary'].lower_95, np.array([18.0]))
+    np.testing.assert_allclose(captured['summary'].upper_95, np.array([20.0]))
+    np.testing.assert_allclose(captured['y_meas'], np.array([20.0]))
+    assert captured['show_band'] is True
+    assert captured['show_draws'] is False
+    assert any('ignoring show_residual=True' in warning for warning in warnings)
+
+
 def test_extract_bragg_tick_sets_uses_derived_d_spacing_for_cwl_ticks():
     import numpy as np
 

From 798b0bc9a1136aea8ee68518e8b0b520dacb34d3 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 13:03:20 +0200
Subject: [PATCH 082/106] Fix square matrix plot spacing and aspect ratio

---
 src/easydiffraction/display/plotting.py       | 340 +++++++++++++++---
 .../analysis/minimizers/test_bumps_dream.py   |  12 +-
 .../easydiffraction/display/test_plotting.py  | 182 ++++++++--
 3 files changed, 454 insertions(+), 80 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 6998640d..7e351abb 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -76,7 +76,8 @@ class PosteriorPairPlotStyleEnum(StrEnum):
     FULL = 'full'
 
 
-DEFAULT_CORRELATION_THRESHOLD = 0.0
+DEFAULT_CORRELATION_THRESHOLD: float | None = None
+DEFAULT_CORRELATION_MAX_PARAMETERS = 5
 EXPECTED_COVAR_NDIM = 2
 DEFAULT_RESIDUAL_HEIGHT_FRACTION = 0.25
 DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION = 0.10
@@ -742,9 +743,9 @@ def plot_param_correlations(
         ----------
         threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
             Minimum absolute off-diagonal correlation required for a
-            parameter to be shown. Parameters are kept only if they
-            participate in at least one pair with ``abs(correlation) >=
-            threshold``. Set to ``None`` or ``0`` to show the full
+            parameter to be shown. When omitted, an automatic cutoff is
+            chosen so the displayed matrix stays at or below ``5 x 5``
+            parameters when possible. Set to ``0`` to show the full
             matrix.
         precision : int, default=2
             Number of decimal places to show in the table fallback.
@@ -756,14 +757,17 @@ def plot_param_correlations(
         if corr_df is None:
             return
 
-        corr_df = self._filter_correlation_dataframe(corr_df, threshold=threshold)
+        corr_df, resolved_threshold = self._resolve_correlation_filter(
+            corr_df,
+            threshold=threshold,
+        )
         if corr_df is None:
             return
 
         corr_df = self._mask_correlation_lower_triangle(corr_df)
         title = 'Refined parameter correlation matrix'
-        if threshold is not None and threshold > 0:
-            title += f' with |correlation| >= {threshold:.2f}'
+        if resolved_threshold > 0:
+            title += f' with |correlation| >= {resolved_threshold:.2f}'
 
         is_graphical = self._backend._supports_graphical_heatmap
         display_corr_df, row_numbers, col_numbers = self._trim_correlation_display_dataframe(
@@ -776,7 +780,7 @@ def plot_param_correlations(
             self._plot_correlation_heatmap(
                 display_corr_df,
                 title,
-                threshold=threshold,
+                threshold=resolved_threshold,
                 precision=precision,
             )
             return
@@ -787,11 +791,55 @@ def plot_param_correlations(
                 display_corr_df,
                 row_numbers=row_numbers,
                 col_numbers=col_numbers,
-                threshold=threshold,
+                threshold=resolved_threshold,
                 precision=precision,
             )
         )
 
+    @classmethod
+    def _resolve_correlation_filter(
+        cls,
+        corr_df: pd.DataFrame,
+        *,
+        threshold: float | None,
+    ) -> tuple[pd.DataFrame | None, float]:
+        """Return a filtered matrix and effective threshold."""
+        if threshold is not None:
+            filtered_corr_df = cls._filter_correlation_dataframe(corr_df, threshold=threshold)
+            return filtered_corr_df, float(threshold)
+        return cls._auto_filtered_correlation_dataframe(
+            corr_df,
+            max_parameters=DEFAULT_CORRELATION_MAX_PARAMETERS,
+        )
+
+    @staticmethod
+    def _auto_filtered_correlation_dataframe(
+        corr_df: pd.DataFrame,
+        *,
+        max_parameters: int,
+    ) -> tuple[pd.DataFrame, float]:
+        """Return an auto-limited matrix for default display."""
+        if corr_df.shape[0] <= max_parameters:
+            return corr_df, 0.0
+
+        abs_corr = np.abs(corr_df.to_numpy(copy=True))
+        np.fill_diagonal(abs_corr, 0.0)
+        positive_values = np.unique(abs_corr[abs_corr > 0.0])
+        for candidate in np.sort(positive_values):
+            keep_mask = (abs_corr >= candidate).any(axis=0)
+            if 0 < int(keep_mask.sum()) <= max_parameters:
+                labels = corr_df.index[keep_mask]
+                return corr_df.loc[labels, labels], float(candidate)
+
+        if positive_values.size == 0:
+            return corr_df.iloc[:max_parameters, :max_parameters], 0.0
+
+        parameter_strength = np.max(abs_corr, axis=0)
+        top_indices = np.argsort(-parameter_strength, kind='stable')[:max_parameters]
+        top_indices.sort()
+        labels = corr_df.index[top_indices]
+        return corr_df.loc[labels, labels], 0.0
+
     def plot_posterior_pairs(
         self,
         parameters: list[object] | None = None,
@@ -1696,11 +1744,46 @@ def _square_matrix_title_left_shift(annotation_labels: list[str]) -> int:
         return POSTERIOR_PAIR_LEFT_MARGIN_PIXELS + extra_margin
 
     @staticmethod
-    def _posterior_pair_layout_meta() -> dict[str, object]:
-        """Return layout metadata used by the Plotly HTML wrapper."""
+    def _square_matrix_gap_data_width(n_parameters: int) -> float:
+        """Return the gap width matching pair-plot spacing."""
+        if n_parameters <= 1:
+            return 0.0
+
+        denominator = 1.0 - PAIR_PLOT_SUBPLOT_SPACING * (n_parameters - 1)
+        if denominator <= 0:
+            return 0.0
+        return PAIR_PLOT_SUBPLOT_SPACING * n_parameters / denominator
+
+    @classmethod
+    def _square_matrix_plot_extent(cls, n_parameters: int) -> float:
+        """Return the inner plot extent for one square matrix plot."""
+        gap_width = cls._square_matrix_gap_data_width(n_parameters)
+        return float(n_parameters + max(0, n_parameters - 1) * gap_width)
+
+    @classmethod
+    def _square_matrix_target_plot_size_pixels(cls, n_parameters: int) -> float:
+        """Return the target inner size for one square matrix plot."""
+        cell_size = cls._posterior_pair_cell_size_pixels(
+            n_parameters,
+            available_width_pixels=PAIR_PLOT_ESTIMATED_CONTAINER_WIDTH_PIXELS,
+        )
+        return cell_size * cls._square_matrix_plot_extent(n_parameters)
+
+    @classmethod
+    def _square_matrix_layout_meta(
+        cls,
+        *,
+        n_parameters: int,
+        annotation_labels: list[str],
+    ) -> dict[str, object]:
+        """Return wrapper metadata for square matrix plots."""
+        margins = cls._posterior_pair_layout_margin(annotation_labels)
+        plot_size = cls._square_matrix_target_plot_size_pixels(n_parameters)
+        aspect_width = round(plot_size + int(margins['l']) + int(margins['r']))
+        aspect_height = round(plot_size + int(margins['t']) + int(margins['b']))
         return {
             POSTERIOR_PAIR_FIXED_ASPECT_META_KEY: {
-                'aspect_ratio': POSTERIOR_PAIR_FIXED_ASPECT_RATIO,
+                'aspect_ratio': f'{aspect_width} / {aspect_height}',
             }
         }
 
@@ -1722,7 +1805,10 @@ def _finalize_posterior_pairs_figure(
                 *subplot_title_annotations,
             ],
             shapes=subplot_border_shapes,
-            meta=self._posterior_pair_layout_meta(),
+            meta=self._square_matrix_layout_meta(
+                n_parameters=context.n_parameters,
+                annotation_labels=context.annotation_labels,
+            ),
             legend={
                 'bgcolor': 'rgba(0, 0, 0, 0)',
                 'xanchor': 'right',
@@ -3702,7 +3788,7 @@ def _plot_correlation_heatmap(
         precision: int,
     ) -> None:
         """
-        Render a Plotly correlation matrix with pair-plot styling.
+        Delegate correlation heatmap rendering to the backend.
 
         Parameters
         ----------
@@ -3731,9 +3817,8 @@ def _build_correlation_heatmap_plot(
         threshold: float | None,
         precision: int,
     ) -> object:
-        """Build a Plotly correlation matrix with pair-plot geometry."""
-        make_subplots = __import__('plotly.subplots', fromlist=['make_subplots']).make_subplots
-
+        """Build a compact pair-plot-style correlation heatmap."""
+        go = __import__('plotly.graph_objects', fromlist=['Figure', 'Heatmap'])
         context = _CorrelationHeatmapContext(
             corr_df=corr_df,
             row_labels=self._posterior_pair_axis_title_labels(corr_df.index.tolist()),
@@ -3741,27 +3826,40 @@ def _build_correlation_heatmap_plot(
             threshold=threshold,
             precision=precision,
         )
-        subplot_title_annotations: list[dict[str, object]] = []
-        subplot_border_shapes: list[dict[str, object]] = []
-        fig = make_subplots(
-            rows=context.n_rows,
-            cols=context.n_cols,
-            shared_xaxes='columns',
-            shared_yaxes='rows',
-            horizontal_spacing=PAIR_PLOT_SUBPLOT_SPACING,
-            vertical_spacing=PAIR_PLOT_SUBPLOT_SPACING,
+        plot_extent = self._square_matrix_plot_extent(context.n_cols)
+        x_edges = self._correlation_heatmap_edges(context.n_cols)
+        y_edges = self._correlation_heatmap_edges(context.n_rows)
+        x_centers = self._correlation_heatmap_centers(context.n_cols)
+        y_centers = self._correlation_heatmap_centers(context.n_rows)
+
+        heatmap = go.Heatmap(
+            z=self._correlation_heatmap_values(context.corr_df),
+            x=x_edges,
+            y=y_edges,
+            customdata=self._correlation_heatmap_customdata(context.corr_df),
+            zmin=-1.0,
+            zmax=1.0,
+            zmid=0.0,
+            colorscale=self._plot_correlation_colorscale(),
+            showscale=False,
+            hoverongaps=False,
+            hovertemplate=(
+                '%{customdata[0]}<br>'
+                '%{customdata[1]}<br>'
+                f'correlation: %{{z:.{context.precision}f}}<extra></extra>'
+            ),
         )
-
-        for row_index in range(context.n_rows):
-            for col_index in range(context.n_cols):
-                self._populate_correlation_heatmap_panel(
-                    fig=fig,
-                    context=context,
-                    row_index=row_index,
-                    col_index=col_index,
-                    subplot_title_annotations=subplot_title_annotations,
-                    subplot_border_shapes=subplot_border_shapes,
-                )
+        label_trace = PlotlyPlotter._get_correlation_label_trace(
+            context.corr_df,
+            x_centers=x_centers,
+            y_centers=y_centers,
+            threshold=context.threshold,
+            precision=context.precision,
+        )
+        traces = [heatmap]
+        if label_trace is not None:
+            traces.append(label_trace)
+        fig = go.Figure(data=traces)
 
         fig.update_layout(
             autosize=True,
@@ -3769,19 +3867,169 @@ def _build_correlation_heatmap_plot(
                 *context.row_labels,
                 *context.col_labels,
             ]),
-            annotations=[
-                self._square_matrix_title_annotation(
-                    title,
-                    [*context.row_labels, *context.col_labels],
-                ),
-                *subplot_title_annotations,
-            ],
-            shapes=subplot_border_shapes,
-            meta=self._posterior_pair_layout_meta(),
+            annotations=self._correlation_heatmap_annotations(
+                title=title,
+                context=context,
+                plot_extent=plot_extent,
+                x_centers=x_centers,
+                y_centers=y_centers,
+            ),
+            shapes=self._correlation_heatmap_grid_shapes(context),
+            meta=self._square_matrix_layout_meta(
+                n_parameters=context.n_cols,
+                annotation_labels=[*context.row_labels, *context.col_labels],
+            ),
             showlegend=False,
         )
+        fig.update_xaxes(
+            range=[0.0, plot_extent],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            constrain='domain',
+        )
+        fig.update_yaxes(
+            range=[plot_extent, 0.0],
+            showline=False,
+            mirror=False,
+            zeroline=False,
+            showgrid=False,
+            ticks='',
+            ticklen=0,
+            tickwidth=0,
+            showticklabels=False,
+            title_text=None,
+            layer='above traces',
+            constrain='domain',
+            scaleanchor='x',
+            scaleratio=1,
+        )
         return fig
 
+    @classmethod
+    def _correlation_heatmap_edges(cls, n_parameters: int) -> np.ndarray:
+        """Return expanded heatmap edges with pair-plot-like gaps."""
+        if n_parameters <= 0:
+            return np.asarray([0.0], dtype=float)
+
+        gap_width = cls._square_matrix_gap_data_width(n_parameters)
+        if np.isclose(gap_width, 0.0):
+            return np.arange(n_parameters + 1, dtype=float)
+
+        widths = [1.0 if index % 2 == 0 else gap_width for index in range(2 * n_parameters - 1)]
+        return np.concatenate(([0.0], np.cumsum(np.asarray(widths, dtype=float))))
+
+    @classmethod
+    def _correlation_heatmap_centers(cls, n_parameters: int) -> np.ndarray:
+        """Return visible-cell centers for a gapped heatmap."""
+        gap_width = cls._square_matrix_gap_data_width(n_parameters)
+        return np.arange(n_parameters, dtype=float) * (1.0 + gap_width) + 0.5
+
+    @staticmethod
+    def _correlation_heatmap_values(corr_df: pd.DataFrame) -> np.ndarray:
+        """Return a gapped heatmap array for correlation cells."""
+        n_rows, n_cols = corr_df.shape
+        expanded = np.full((2 * n_rows - 1, 2 * n_cols - 1), np.nan, dtype=float)
+        expanded[0::2, 0::2] = corr_df.to_numpy(dtype=float)
+        return expanded
+
+    @staticmethod
+    def _correlation_heatmap_customdata(
+        corr_df: pd.DataFrame,
+    ) -> np.ndarray:
+        """Return hover labels for a correlation heatmap."""
+        n_rows, n_cols = corr_df.shape
+        expanded = np.empty((2 * n_rows - 1, 2 * n_cols - 1, 2), dtype=object)
+        expanded[..., 0] = ''
+        expanded[..., 1] = ''
+        for row_index, row_label in enumerate(corr_df.index):
+            for col_index, col_label in enumerate(corr_df.columns):
+                expanded[2 * row_index, 2 * col_index, 0] = str(col_label)
+                expanded[2 * row_index, 2 * col_index, 1] = str(row_label)
+        return expanded
+
+    def _correlation_heatmap_annotations(
+        self,
+        *,
+        title: str,
+        context: _CorrelationHeatmapContext,
+        plot_extent: float,
+        x_centers: np.ndarray,
+        y_centers: np.ndarray,
+    ) -> list[dict[str, object]]:
+        """Return pair-plot-like title and axis annotations."""
+        annotations = [
+            self._square_matrix_title_annotation(
+                title,
+                [*context.row_labels, *context.col_labels],
+            )
+        ]
+        for row_index, row_label in enumerate(context.row_labels):
+            annotations.append({
+                'x': 0.0,
+                'xref': 'paper',
+                'xanchor': 'right',
+                'xshift': -POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS,
+                'y': 1.0 - (float(y_centers[row_index]) / plot_extent),
+                'yref': 'paper',
+                'yanchor': 'middle',
+                'text': row_label,
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'textangle': -90,
+                'showarrow': False,
+            })
+        for col_index, col_label in enumerate(context.col_labels):
+            annotations.append({
+                'x': float(x_centers[col_index]) / plot_extent,
+                'xref': 'paper',
+                'xanchor': 'center',
+                'y': 0.0,
+                'yref': 'paper',
+                'yanchor': 'top',
+                'yshift': -POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS,
+                'text': col_label,
+                'align': 'center',
+                'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+                'showarrow': False,
+            })
+        return annotations
+
+    def _correlation_heatmap_grid_shapes(
+        self,
+        context: _CorrelationHeatmapContext,
+    ) -> list[dict[str, object]]:
+        """Return per-cell borders for the visible lower triangle."""
+        axis_frame_color = self._plot_axis_frame_color()
+        gap_width = self._square_matrix_gap_data_width(context.n_cols)
+        return [
+            {
+                'type': 'rect',
+                'xref': 'x',
+                'yref': 'y',
+                'x0': float(col_index * (1.0 + gap_width)),
+                'x1': float(col_index * (1.0 + gap_width) + 1.0),
+                'y0': float(row_index * (1.0 + gap_width)),
+                'y1': float(row_index * (1.0 + gap_width) + 1.0),
+                'layer': 'above',
+                'line': {
+                    'color': axis_frame_color,
+                    'width': POSTERIOR_PAIR_AXIS_LINE_WIDTH,
+                },
+                'fillcolor': 'rgba(0, 0, 0, 0)',
+            }
+            for row_index in range(context.n_rows)
+            for col_index in range(context.n_cols)
+            if col_index <= row_index
+        ]
+
     def _populate_correlation_heatmap_panel(
         self,
         *,
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index 6ee5e1f4..9ac35b57 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -308,9 +308,13 @@ def test_build_driver_stops_mapper_when_driver_clip_fails():
 
     with (
         patch.object(minimizer, '_build_mapper', return_value='mapper'),
-        patch('easydiffraction.analysis.minimizers.bumps_dream.FitProblem', return_value='problem'),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.FitProblem', return_value='problem'
+        ),
         patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
-        patch('easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper') as stop_mapper,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+        ) as stop_mapper,
     ):
         mock_driver_cls.return_value.clip.side_effect = RuntimeError('clip failed')
 
@@ -333,7 +337,9 @@ def test_execute_driver_stops_mapper_when_seed_is_invalid():
 
     driver = SimpleNamespace(fit=MagicMock(), fitter=SimpleNamespace(state=None))
 
-    with patch('easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper') as stop_mapper:
+    with patch(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+    ) as stop_mapper:
         result = BumpsDreamMinimizer._execute_driver(driver=driver, random_seed=-1)
 
     assert isinstance(result.error, ValueError)
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 0b21b53d..889e805a 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -322,7 +322,18 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
     assert figure.layout.autosize is True
     assert figure.layout.width is None
     assert figure.layout.height is None
-    assert figure.layout.meta['fixed_aspect_wrapper']['aspect_ratio'] == '1 / 1'
+    assert (
+        figure.layout.meta['fixed_aspect_wrapper']['aspect_ratio']
+        == plotter._square_matrix_layout_meta(
+            n_parameters=4,
+            annotation_labels=[
+                'length_a',
+                'broad_gauss_u',
+                'broad_gauss_v',
+                'twotheta_offset',
+            ],
+        )['fixed_aspect_wrapper']['aspect_ratio']
+    )
     assert [annotation.text for annotation in figure.layout.annotations] == [
         'Posterior pair plot',
         'length_a',
@@ -608,7 +619,9 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
     captured: dict[str, object] = {}
 
     plotter = Plotter()
-    plotter._backend = SimpleNamespace(_show_figure=lambda figure: captured.setdefault('fig', figure))
+    plotter._backend = SimpleNamespace(
+        _show_figure=lambda figure: captured.setdefault('fig', figure)
+    )
 
     plotter._plot_posterior_predictive_summary(
         expt_name='hrpt',
@@ -1561,25 +1574,26 @@ class Project:
     p.plot_param_correlations()
 
     fig = captured['fig']
+    heatmap = fig.data[0]
+    text_trace = fig.data[1]
+    gap_width = Plotter._square_matrix_gap_data_width(2)
     assert len(fig.data) == 2
-    assert fig.data[0].type == 'heatmap'
-    assert list(fig.data[0].x) == [0.0, 1.0]
-    assert list(fig.data[0].y) == [0.0, 1.0]
-    assert fig.data[0].xgap in (None, 0)
-    assert fig.data[0].ygap in (None, 0)
-    assert fig.data[0].showscale is False
-    assert (
-        fig.data[0].hovertemplate
-        == 'phase.scale<br>phase.cell.length_c<br>correlation: %{z:.2f}<extra></extra>'
+    assert heatmap.type == 'heatmap'
+    assert list(heatmap.x) == pytest.approx([0.0, 1.0, 1.0 + gap_width, 2.0 + gap_width])
+    assert list(heatmap.y) == pytest.approx([0.0, 1.0, 1.0 + gap_width, 2.0 + gap_width])
+    assert heatmap.showscale is False
+    assert heatmap.hovertemplate == (
+        '%{customdata[0]}<br>%{customdata[1]}<br>correlation: %{z:.2f}<extra></extra>'
     )
-    assert pytest.approx(fig.data[0].z[0][0], rel=1e-9) == -0.5
-    assert fig.data[1].type == 'scatter'
-    assert fig.data[1].mode == 'text'
-    assert list(fig.data[1].x) == [0.5]
-    assert list(fig.data[1].y) == [0.5]
-    assert list(fig.data[1].text) == ['-0.50']
-    assert fig.data[1].textposition == 'middle center'
-    assert fig.data[1].hoverinfo == 'skip'
+    assert list(heatmap.customdata[2][0]) == ['phase.scale', 'phase.cell.length_c']
+    assert pytest.approx(np.nanmin(np.asarray(heatmap.z, dtype=float)), rel=1e-9) == -0.5
+    assert text_trace.type == 'scatter'
+    assert text_trace.mode == 'text'
+    assert list(text_trace.x) == [0.5]
+    assert list(text_trace.y) == pytest.approx([1.5 + gap_width])
+    assert list(text_trace.text) == ['-0.50']
+    assert text_trace.textposition == 'middle center'
+    assert text_trace.hoverinfo == 'skip'
     assert [annotation.text for annotation in fig.layout.annotations] == [
         'Refined parameter correlation matrix',
         'phase.<br>scale',
@@ -1597,24 +1611,30 @@ class Project:
     assert fig.layout.margin.b == (
         POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS + 2 * POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
     )
-    assert fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio'] == '1 / 1'
-    assert fig.layout.xaxis.domain[1] < fig.layout.xaxis2.domain[0]
+    assert (
+        fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio']
+        == Plotter._square_matrix_layout_meta(
+            n_parameters=2,
+            annotation_labels=[
+                'phase.<br>scale',
+                'phase.<br>cell.<br>length_c',
+                'phase.<br>scale',
+                'phase.<br>cell.<br>length_c',
+            ],
+        )['fixed_aspect_wrapper']['aspect_ratio']
+    )
     assert fig.layout.xaxis.showline is False
     assert fig.layout.xaxis.mirror is False
-    assert fig.layout.xaxis.layer == 'above traces'
-    assert fig.layout.xaxis.showticklabels is False
-    assert fig.layout.xaxis.title.text is None
     assert fig.layout.yaxis.showline is False
     assert fig.layout.yaxis.mirror is False
-    assert fig.layout.yaxis.layer == 'above traces'
+    assert fig.layout.xaxis.showticklabels is False
     assert fig.layout.yaxis.showticklabels is False
+    assert fig.layout.xaxis.title.text is None
     assert fig.layout.yaxis.title.text is None
     assert fig.layout.paper_bgcolor is None
     assert fig.layout.plot_bgcolor is None
     assert len(fig.layout.shapes) == 3
     assert all(shape.type == 'rect' for shape in fig.layout.shapes)
-    assert all(shape.xref == 'paper' for shape in fig.layout.shapes)
-    assert all(shape.yref == 'paper' for shape in fig.layout.shapes)
 
 
 def test_plot_param_correlations_plotly_labels_respect_threshold(monkeypatch):
@@ -1673,11 +1693,111 @@ class Project:
     fig = captured['fig']
     heatmap_traces = [trace for trace in fig.data if trace.type == 'heatmap']
     text_traces = [trace for trace in fig.data if trace.type == 'scatter' and trace.mode == 'text']
-    assert len(heatmap_traces) == 10
-    assert [trace.text[0] for trace in text_traces] == ['-0.91', '0.83', '-0.89', '0.82']
+    assert len(heatmap_traces) == 1
+    assert len(text_traces) == 1
+    assert list(text_traces[0].text) == ['-0.91', '0.83', '-0.89', '0.82']
+    assert len(fig.layout.shapes) == 15
+
+
+def test_plot_param_correlations_limits_default_table_to_five_parameters(monkeypatch):
+    from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.tables import TableRenderer
+
+    captured = {}
+
+    class FakeTabler:
+        def render(self, df):
+            captured['df'] = df
+
+    monkeypatch.setattr(TableRenderer, 'get', staticmethod(lambda: FakeTabler()))
+
+    class Param:
+        def __init__(self, uid, unique_name):
+            self._minimizer_uid = uid
+            self.unique_name = unique_name
+
+    class RawResult:
+        covar = None
+        var_names = ['p1', 'p2', 'p3', 'p4', 'p5', 'p6']
+
+        class ParamResult:
+            def __init__(self, correl):
+                self.correl = correl
+
+        params = {
+            'p1': ParamResult({'p2': 0.95}),
+            'p2': ParamResult({'p1': 0.95, 'p3': 0.94}),
+            'p3': ParamResult({'p2': 0.94, 'p4': 0.93}),
+            'p4': ParamResult({'p3': 0.93, 'p5': 0.92}),
+            'p5': ParamResult({'p4': 0.92, 'p6': 0.91}),
+            'p6': ParamResult({'p5': 0.91}),
+        }
+
+    class FitResults:
+        engine_result = RawResult()
+        parameters = [
+            Param('p1', 'phase.scale'),
+            Param('p2', 'phase.cell.length_a'),
+            Param('p3', 'phase.background'),
+            Param('p4', 'phase.profile.u'),
+            Param('p5', 'phase.profile.v'),
+            Param('p6', 'phase.profile.w'),
+        ]
+
+    class Analysis:
+        fit_results = FitResults()
+
+    class Project:
+        analysis = Analysis()
+
+    p = Plotter()
+    p.engine = 'asciichartpy'
+    p._set_project(Project())
+    p.plot_param_correlations()
+
+    df = captured['df']
+    assert [column.strip() for column in df.columns.get_level_values(0)] == [
+        'parameter',
+        '1',
+        '2',
+        '3',
+        '4',
+        '5',
+    ]
+    assert list(df.index) == [0, 1, 2, 3, 4]
+    assert df.iloc[0, 0] == 'phase.scale'
+    assert df.iloc[0, 1] == ''
+    assert df.iloc[0, 2] == ''
+    assert df.iloc[0, 3] == ''
+    assert df.iloc[0, 4] == ''
+    assert df.iloc[0, 5] == ''
+    assert df.iloc[1, 0] == 'phase.cell.length_a'
+    assert _strip_markup(df.iloc[1, 1]).strip() == '0.95'
+    assert df.iloc[1, 2] == ''
+    assert df.iloc[1, 3] == ''
+    assert df.iloc[1, 4] == ''
+    assert df.iloc[1, 5] == ''
+    assert df.iloc[2, 0] == 'phase.background'
+    assert df.iloc[2, 1] == ''
+    assert _strip_markup(df.iloc[2, 2]).strip() == '0.94'
+    assert df.iloc[2, 3] == ''
+    assert df.iloc[2, 4] == ''
+    assert df.iloc[2, 5] == ''
+    assert df.iloc[3, 0] == 'phase.profile.u'
+    assert df.iloc[3, 1] == ''
+    assert df.iloc[3, 2] == ''
+    assert _strip_markup(df.iloc[3, 3]).strip() == '0.93'
+    assert df.iloc[3, 4] == ''
+    assert df.iloc[3, 5] == ''
+    assert df.iloc[4, 0] == 'phase.profile.v'
+    assert df.iloc[4, 1] == ''
+    assert df.iloc[4, 2] == ''
+    assert df.iloc[4, 3] == ''
+    assert _strip_markup(df.iloc[4, 4]).strip() == '0.92'
+    assert df.iloc[4, 5] == ''
 
 
-def test_plot_param_correlations_shows_full_table_by_default(monkeypatch):
+def test_plot_param_correlations_shows_full_table_when_threshold_is_zero(monkeypatch):
     from easydiffraction.display.plotting import Plotter
     from easydiffraction.display.tables import TableRenderer
 
@@ -1725,7 +1845,7 @@ class Project:
     p = Plotter()
     p.engine = 'asciichartpy'
     p._set_project(Project())
-    p.plot_param_correlations()
+    p.plot_param_correlations(threshold=0)
 
     df = captured['df']
     assert [column.strip() for column in df.columns.get_level_values(0)] == [

From 39683b5001b6c7b2b224954640a885520946f705 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 23:05:32 +0200
Subject: [PATCH 083/106] Apply the latest templates

---
 .copier-answers.yml                       |   2 +-
 .github/workflows/coverage.yml            |   2 -
 .github/workflows/issues-labels.yml       | 149 +++-------------------
 .github/workflows/pr-labels.yml           |  74 ++++-------
 .github/workflows/test.yml                |   6 +-
 docs/docs/installation-and-setup/index.md |  13 +-
 docs/mkdocs.yml                           |   4 +-
 7 files changed, 62 insertions(+), 188 deletions(-)

diff --git a/.copier-answers.yml b/.copier-answers.yml
index a807c84d..b7376e06 100644
--- a/.copier-answers.yml
+++ b/.copier-answers.yml
@@ -1,6 +1,6 @@
 # WARNING: Do not edit this file manually.
 # Any changes will be overwritten by Copier.
-_commit: v0.11.1-12-gbb9bb30
+_commit: v0.11.2-5-gd120259
 _src_path: gh:easyscience/templates
 app_docs_url: https://easyscience.github.io/diffraction-app
 app_doi: 10.5281/zenodo.18163581
diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml
index 95c190c6..d96e5b87 100644
--- a/.github/workflows/coverage.yml
+++ b/.github/workflows/coverage.yml
@@ -5,8 +5,6 @@ on:
   push:
     branches:
       - develop
-    # Do not run on version tags (those are handled by other workflows)
-    tags-ignore: ['v*']
   # Trigger the workflow on pull request
   pull_request:
   # Allows you to run this workflow manually from the Actions tab
diff --git a/.github/workflows/issues-labels.yml b/.github/workflows/issues-labels.yml
index 56ab5b19..4fbf4fab 100644
--- a/.github/workflows/issues-labels.yml
+++ b/.github/workflows/issues-labels.yml
@@ -1,6 +1,5 @@
-# Verifies if the current issue has at least one real `[scope]` label and one
-# real `[priority]` label. If either is missing, the workflow adds a reminder
-# label with a warning emoji.
+# Verifies if the current issue has at least one `[scope]` label and one
+# `[priority]` label. If either is missing, the workflow adds a reminder label.
 
 name: Issue labels check
 
@@ -20,130 +19,22 @@ jobs:
       cancel-in-progress: true
 
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@v6
-
-      - name: Sync missing-label reminders
-        uses: ./.github/actions/github-script
+      - name: Ensure [scope] label
+        uses: Rindrics/expect-label-prefix@v1.2.1
         with:
-          script: |
-            const fs = require('fs');
-
-            const issueNumber = context.issue.number;
-            const action = context.payload.action;
-            const changedLabel = context.payload.label?.name ?? null;
-            const labels = context.payload.issue.labels.map(({ name }) => name);
-            const requirements = [
-              {
-                prefix: '[scope] ',
-                reminder: '[scope] ⚠️ label needed',
-              },
-              {
-                prefix: '[priority] ',
-                reminder: '[priority] ⚠️ label needed',
-              },
-            ];
-
-            const labelsToAdd = [];
-            const labelsToRemove = [];
-            const evaluations = [];
-
-            console.log(`::group::Issue label check for #${issueNumber}`);
-            console.log(`Event action: ${action}`);
-            if (changedLabel) {
-              console.log(`Event label: ${changedLabel}`);
-            }
-            console.log(
-              `Current labels: ${labels.length > 0 ? labels.join(', ') : '(none)'}`,
-            );
-
-            for (const { prefix, reminder } of requirements) {
-              const matchingRealLabels = labels.filter(
-                (name) => name.startsWith(prefix) && name !== reminder,
-              );
-              const hasRealLabel = matchingRealLabels.length > 0;
-              const hasReminderLabel = labels.includes(reminder);
-
-              evaluations.push({
-                prefix,
-                reminder,
-                matchingRealLabels,
-                hasReminderLabel,
-              });
-
-              if (hasRealLabel && hasReminderLabel) {
-                labelsToRemove.push(reminder);
-              } else if (!hasRealLabel && !hasReminderLabel) {
-                labelsToAdd.push(reminder);
-              }
-            }
-
-            for (const evaluation of evaluations) {
-              if (evaluation.matchingRealLabels.length > 0) {
-                console.log(
-                  `Found required ${evaluation.prefix}label(s): ${evaluation.matchingRealLabels.join(', ')}`,
-                );
-              } else {
-                console.log(`Missing required ${evaluation.prefix}label.`);
-              }
-
-              if (evaluation.hasReminderLabel) {
-                console.log(`Reminder label already present: ${evaluation.reminder}`);
-              }
-            }
-
-            if (labelsToAdd.length > 0) {
-              console.log(`Adding reminder labels: ${labelsToAdd.join(', ')}`);
-              await github.rest.issues.addLabels({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: context.issue.number,
-                labels: labelsToAdd,
-              });
-            }
-
-            for (const name of labelsToRemove) {
-              console.log(`Removing reminder label: ${name}`);
-              await github.rest.issues.removeLabel({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                issue_number: context.issue.number,
-                name,
-              });
-            }
-
-            if (labelsToAdd.length === 0 && labelsToRemove.length === 0) {
-              console.log('No label changes required.');
-            }
-
-            console.log('::endgroup::');
-
-            if (process.env.GITHUB_STEP_SUMMARY) {
-              const summaryLines = [
-                '### Issue Label Check',
-                `- Issue: #${issueNumber}`,
-                `- Event: ${action}`,
-                `- Trigger label: ${changedLabel ?? '(none)'}`,
-                `- Current labels: ${labels.length > 0 ? labels.join(', ') : '(none)'}`,
-                '',
-                '#### Requirement status',
-                ...evaluations.map((evaluation) => {
-                  const status =
-                    evaluation.matchingRealLabels.length > 0
-                      ? `found ${evaluation.matchingRealLabels.join(', ')}`
-                      : 'missing';
-                  const reminder = evaluation.hasReminderLabel
-                    ? `reminder present: ${evaluation.reminder}`
-                    : `reminder absent: ${evaluation.reminder}`;
-                  return `- ${evaluation.prefix}: ${status}; ${reminder}`;
-                }),
-                '',
-                `- Labels to add: ${labelsToAdd.length > 0 ? labelsToAdd.join(', ') : '(none)'}`,
-                `- Labels to remove: ${labelsToRemove.length > 0 ? labelsToRemove.join(', ') : '(none)'}`,
-              ];
-
-              fs.appendFileSync(
-                process.env.GITHUB_STEP_SUMMARY,
-                `${summaryLines.join('\n')}\n`,
-              );
-            }
+          repository_full_name: ${{ github.repository }}
+          token: ${{ github.token }}
+          label_prefix: '[scope]'
+          label_separator: ' '
+          add_label: 'true'
+          default_label: '[scope] ⚠️ label needed'
+
+      - name: Ensure [priority] label
+        uses: Rindrics/expect-label-prefix@v1.2.1
+        with:
+          repository_full_name: ${{ github.repository }}
+          token: ${{ github.token }}
+          label_prefix: '[priority]'
+          label_separator: ' '
+          add_label: 'true'
+          default_label: '[priority] ⚠️ label needed'
diff --git a/.github/workflows/pr-labels.yml b/.github/workflows/pr-labels.yml
index 25710633..fcb8a78c 100644
--- a/.github/workflows/pr-labels.yml
+++ b/.github/workflows/pr-labels.yml
@@ -1,17 +1,9 @@
 # Verifies if a pull request has at least one label from a set of valid
 # labels before it can be merged.
 #
-# NOTE:
-# This workflow may be triggered twice in quick succession when a PR is
-# created:
-#   1) `opened`  — when the pull request is initially created
-#   2) `labeled` — if labels are added immediately after creation
-# (e.g. by manual labeling, another workflow, or GitHub App).
-#
-# These are separate GitHub events, so two workflow runs can be started.
-# The `concurrency` configuration below ensures that only the latest run
-# for the same PR remains active, canceling any previous in-progress
-# run.
+# The label validation is delegated to `mheap/github-action-required-labels`,
+# which checks the current PR labels via the GitHub API and can add or update a
+# PR comment when the required label set is missing.
 
 name: PR labels check
 
@@ -19,45 +11,35 @@ on:
   pull_request_target:
     types: [opened, labeled, unlabeled, synchronize]
 
-concurrency:
-  group: pr-labels-${{ github.event.pull_request.number }}
-  cancel-in-progress: true
-
 permissions:
-  pull-requests: read
+  issues: write
+  pull-requests: write
 
 jobs:
   check-labels:
     runs-on: ubuntu-latest
 
     steps:
-      - name: Check for valid labels
-        run: |
-          PR_LABELS=$(echo '${{ toJson(github.event.pull_request.labels.*.name) }}' | jq -r '.[]')
-
-          echo "Current PR labels: $PR_LABELS"
-          VALID_LABELS=(
-            "[bot] release"
-            "[scope] bug"
-            "[scope] documentation"
-            "[scope] enhancement"
-            "[scope] maintenance"
-            "[scope] significant"
-          )
-
-          found=false
-          for label in "${VALID_LABELS[@]}"; do
-            if echo "$PR_LABELS" | grep -Fxq "$label"; then
-              echo "✅ Found valid label: $label"
-              found=true
-              break
-            fi
-          done
-
-          if [ "$found" = false ]; then
-            echo "ERROR: PR must have at least one of the following labels:"
-            for label in "${VALID_LABELS[@]}"; do
-              echo "       - $label"
-            done
-            exit 1
-          fi
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Setup easyscience[bot]
+        id: bot
+        uses: ./.github/actions/setup-easyscience-bot
+        with:
+          app-id: ${{ vars.EASYSCIENCE_APP_ID }}
+          private-key: ${{ secrets.EASYSCIENCE_APP_KEY }}
+
+      - uses: mheap/github-action-required-labels@v5
+        with:
+          token: ${{ steps.bot.outputs.token }}
+          add_comment: true
+          mode: minimum
+          count: 1
+          labels: |
+            [bot] release
+            [scope] bug
+            [scope] documentation
+            [scope] enhancement
+            [scope] maintenance
+            [scope] significant
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index d0298f95..86bfa606 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -248,10 +248,10 @@ jobs:
               exit 1
             fi
 
-            whl_url="file://$(python -c 'import os,sys; print(os.path.abspath(sys.argv[1]))' "${whl_path[0]}")"
+            whl_abs_path="$(python -c 'import os,sys; print(os.path.abspath(sys.argv[1]))' "${whl_path[0]}")"
 
-            echo "Adding easydiffraction from: $whl_url"
-            pixi add --pypi "easydiffraction[dev] @ ${whl_url}"
+            echo "Adding easydiffraction from: $whl_abs_path"
+            pixi add --pypi "easydiffraction[dev] @ ${whl_abs_path}"
 
             echo "Exiting pixi project directory"
             cd ..
diff --git a/docs/docs/installation-and-setup/index.md b/docs/docs/installation-and-setup/index.md
index 41036f1d..ee86679b 100644
--- a/docs/docs/installation-and-setup/index.md
+++ b/docs/docs/installation-and-setup/index.md
@@ -254,8 +254,8 @@ once using the command line, as shown below.
   ```txt
   pixi run easydiffraction download-all-tutorials
   ```
-- Start JupyterLab in the `tutorials/` directory to access the
-  notebooks:
+- Start the JupyterLab server in the `tutorials/` directory to access
+  the notebooks:
   ```txt
   pixi run jupyter lab tutorials/
   ```
@@ -278,13 +278,14 @@ once using the command line, as shown below.
   ```txt
   python -m easydiffraction download-all-tutorials
   ```
-- Launch the Jupyter Notebook server (opens browser automatically at
-  `http://localhost:8888/`):
+- Start the Jupyter Notebook server in the `tutorials/` directory to
+  access the notebooks:
   ```txt
   jupyter notebook tutorials/
   ```
-- Open one of the `*.ipynb` files and select the
-  `EasyDiffraction Python kernel` to get started.
+- Your web browser should open automatically. Click on one of the
+  `*.ipynb` files and select the `EasyDiffraction Python kernel` to get
+  started.
 
 ### Run Tutorials via Google Colab
 
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 6ad6b770..42294197 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -148,8 +148,10 @@ plugins:
         python:
           paths: ['src'] # Change 'src' to your actual sources directory
           options:
+            annotations_path: source
             docstring_style: numpy
-            group_by_category: false
+            group_by_category: true
+            members_order: source
             heading_level: 1
             show_root_heading: true
             show_root_full_path: false

From 078842cc8df7909f5d39f0da3b5fef02fdd905f4 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 23:12:55 +0200
Subject: [PATCH 084/106] Bump setup-pixi to v0.9.5

---
 .github/actions/setup-pixi/action.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/actions/setup-pixi/action.yml b/.github/actions/setup-pixi/action.yml
index ec7d7ba7..dc3232c6 100644
--- a/.github/actions/setup-pixi/action.yml
+++ b/.github/actions/setup-pixi/action.yml
@@ -33,7 +33,7 @@ inputs:
 runs:
   using: 'composite'
   steps:
-    - uses: prefix-dev/setup-pixi@v0.9.4
+    - uses: prefix-dev/setup-pixi@v0.9.5
       with:
         environments: ${{ inputs.environments }}
         activate-environment: ${{ inputs.activate-environment }}

From ab3843bfbd2d0f57bdd9c7c67baf902e48e4ff12 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 23:13:38 +0200
Subject: [PATCH 085/106] Bump dependencies

---
 pixi.lock | 1065 ++++++++++++++++++++++++++---------------------------
 1 file changed, 531 insertions(+), 534 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 3ac082e7..0d0a058d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -76,32 +76,32 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20260107.1-cxx17_h7b12aa8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-6_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-7_h4a7cf45_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.2.0-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-6_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-7_h0358290_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.0-hecca717_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_18.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.32-pthreads_h94d23a6_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.33-pthreads_h94d23a6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.21-h280c20c_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_18.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42-h5347b49_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py314h67df5f8_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/msgspec-0.21.1-py314h5bd0f2a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda
@@ -161,7 +161,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.6.3-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.7.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda
@@ -195,8 +195,8 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/98/ba/fc20faf5b2bb04615fa906f8daecfe896e6f7a56b34debd93c4a4d63e6e5/copier-9.15.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/22/e5/06b1f88f42a5a99df42ce61208bdec3bddb3d261412874280a19796fc09c/coverage-7.13.5-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
-      - pypi: https://files.pythonhosted.org/packages/b2/e6/65abe97bbd42eb6ef73d3a58566ce89b097ae049511b7d9708288714a798/crysfml-0.6.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/d7/51/ec641c26e6dca1b25a7d2035ba6ecb7c884ef1a100a9e42fbe4ce4405139/coverage-7.14.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
+      - pypi: https://files.pythonhosted.org/packages/46/b4/0887c88ddfaba1d7140ea335144eb904af97550786ee58bdb295ff10d255/crysfml-0.6.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/96/5d/0c59079aa7ef34980a5925a06a90ad2b7c94e486c194b3527d557cabb042/cryspy-0.11.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/42/d9/27b13bc9419bf5dae02905b348f16ca827646cd76244ddd326f1a8139a6a/cyclebane-24.10.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl
@@ -239,16 +239,16 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/f4/a4/61adb19f3c74b0dc0e411de4f06ebef564b1f179928f9dffcbd4b378f2ef/jupyter_notebook_parser-0.1.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/ab/b5/36c712098e6191d1b4e349304ef73a8d06aed77e56ceaac8c0a306c7bda1/jupyterlab_widgets-3.0.16-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/b3/52/bc858b1665d0dec3a2511f4e6f5c18ea85c0977563d624d597c95d6d0fd7/jupyterquiz-2.9.6.4-py2.py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/16/5a/736dd2f4535dbf3bf26523f9158c011389ef88dd06ec2eef67fd744f1c7b/jupytext-1.19.1-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/4c/65/b4b86e5fa07543bfbbcdc6c9f7f9f561e66a5f3539992e3009973f2b1314/jupytext-1.19.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/e7/f9/b06c934a6aa8bc91f566bd2a214fd04c30506c2d9e2b6b171953216a65b6/kiwisolver-1.5.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
       - pypi: https://files.pythonhosted.org/packages/8a/a1/8d812e53a5da1687abb10445275d41a8b13adb781bbf7196ddbcf8d88505/lazy_loader-0.5-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/38/7e/7b91c89a4cf0f543a83be978657afb20c86af6d725253e319589dcc4ce52/lmfit-1.3.4-py3-none-any.whl
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From 45b2fc56a24b2dc4e5cfddd47852b6ebe5351f17 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Mon, 11 May 2026 23:14:57 +0200
Subject: [PATCH 086/106] Bump pixi.lock from v6 to v7

---
 pixi.lock | 21812 ++++++++++++++++++++++++++--------------------------
 1 file changed, 10903 insertions(+), 10909 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 0d0a058d..73f8d611 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,4 +1,8 @@
-version: 6
+version: 7
+platforms:
+- name: linux-64
+- name: osx-arm64
+- name: win-64
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+  - twine ; extra == 'dev'
+  - markdown ; extra == 'dev'
+  - flake8 ; extra == 'dev'
+  - wheel ; extra == 'dev'
+- pypi: https://files.pythonhosted.org/packages/f9/58/6e1b8f52fdc3184b47dc5037f5070d83a3d11042db1594b02d2a44d786c8/coverage-7.14.0-cp314-cp314-win_amd64.whl
+  name: coverage
+  version: 7.14.0
+  sha256: 45e0f79d8351fa76e256716df91eab12890d32678b9590df7ae1042e4bd4cf5d
+  requires_dist:
+  - tomli ; python_full_version <= '3.11' and extra == 'toml'
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/fa/22/4cc31a62a6c7b74a8730e31a4274c5dc80e005751e277a2ce38e675e4923/numpy-2.4.4-cp314-cp314-win_amd64.whl
+  name: numpy
+  version: 2.4.4
+  sha256: 715d1c092715954784bc79e1174fc2a90093dc4dc84ea15eb14dad8abdcdeb74
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/fa/bc/8b8ec5a4bfc5b9cf3ce27a118339e994f88410be5677c96493e0ea28e76d/dunamai-1.26.1-py3-none-any.whl
+  name: dunamai
+  version: 1.26.1
+  sha256: 2727d939c5b4257cb01ea404372803b477f5176e5a347c43beaf89cd5072e853
+  requires_dist:
+  - importlib-metadata>=1.6.0 ; python_full_version < '3.8'
+  - packaging>=20.9
+  requires_python: '>=3.5'
+- pypi: https://files.pythonhosted.org/packages/fb/12/5911ae3eeec47800503a238d971e51722ccea5feb8569b735184d5fcdbc0/toolz-1.1.0-py3-none-any.whl
+  name: toolz
+  version: 1.1.0
+  sha256: 15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl
+  name: aiosignal
+  version: 1.4.0
+  sha256: 053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e
+  requires_dist:
+  - frozenlist>=1.1.0
+  - typing-extensions>=4.2 ; python_full_version < '3.13'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fc/b6/6b8de4c0a7d7ab3004c439c80c5c1e0a3e8d78bbae19379b01960383d9e5/pydantic_core-2.46.4-cp314-cp314-win_amd64.whl
+  name: pydantic-core
+  version: 2.46.4
+  sha256: 811ff8e9c313ab425368bcbb36e5c4ebd7108c2bbf4e4089cfbb0b01eff63fac
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fd/7b/122376b1fd3c62c1ed9dc80c931ace4844b3c55407b6fb2d199377c9736f/pydantic-2.13.4-py3-none-any.whl
+  name: pydantic
+  version: 2.13.4
+  sha256: 45a282cde31d808236fd7ea9d919b128653c8b38b393d1c4ab335c62924d9aba
+  requires_dist:
+  - annotated-types>=0.6.0
+  - pydantic-core==2.46.4
+  - typing-extensions>=4.14.1
+  - typing-inspection>=0.4.2
+  - email-validator>=2.0.0 ; extra == 'email'
+  - tzdata ; python_full_version >= '3.9' and sys_platform == 'win32' and extra == 'timezone'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl
+  name: contourpy
+  version: 1.3.3
+  sha256: cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77
+  requires_dist:
+  - numpy>=1.25
+  - furo ; extra == 'docs'
+  - sphinx>=7.2 ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - bokeh ; extra == 'bokeh'
+  - selenium ; extra == 'bokeh'
+  - contourpy[bokeh,docs] ; extra == 'mypy'
+  - bokeh ; extra == 'mypy'
+  - docutils-stubs ; extra == 'mypy'
+  - mypy==1.17.0 ; extra == 'mypy'
+  - types-pillow ; extra == 'mypy'
+  - contourpy[test-no-images] ; extra == 'test'
+  - matplotlib ; extra == 'test'
+  - pillow ; extra == 'test'
+  - pytest ; extra == 'test-no-images'
+  - pytest-cov ; extra == 'test-no-images'
+  - pytest-rerunfailures ; extra == 'test-no-images'
+  - pytest-xdist ; extra == 'test-no-images'
+  - wurlitzer ; extra == 'test-no-images'
+  requires_python: '>=3.11'
+- pypi: https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
+  name: multidict
+  version: 6.7.1
+  sha256: 7eee46ccb30ff48a1e35bb818cc90846c6be2b68240e42a78599166722cea709
+  requires_dist:
+  - typing-extensions>=4.1.0 ; python_full_version < '3.11'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/ff/1c/a28b27effb13a381fe077ea3e3e78f6debd6315f2b3edff67bbb93d0ef51/gemmi-0.7.5-cp314-cp314-win_amd64.whl
+  name: gemmi
+  version: 0.7.5
+  sha256: 419c36d9ea0f28dda0ff0d6db17035170d0888ca78aff82a0f9f604613aec58f
+  requires_python: '>=3.9'

From e2c913b07236a9bfd44ada25799442f37a4f7217 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 08:31:29 +0200
Subject: [PATCH 087/106] Add correlation-based limits to posterior pair plots

---
 src/easydiffraction/display/plotting.py | 141 +++++++++++++++++++++---
 1 file changed, 125 insertions(+), 16 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 7e351abb..fc09f82e 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -77,7 +77,7 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 
 
 DEFAULT_CORRELATION_THRESHOLD: float | None = None
-DEFAULT_CORRELATION_MAX_PARAMETERS = 5
+DEFAULT_CORRELATION_MAX_PARAMETERS = 6
 EXPECTED_COVAR_NDIM = 2
 DEFAULT_RESIDUAL_HEIGHT_FRACTION = 0.25
 DEFAULT_BRAGG_PEAKS_HEIGHT_FRACTION = 0.10
@@ -210,6 +210,7 @@ class _PosteriorPairsContext:
     parameter_names: list[str]
     labels: list[str]
     annotation_labels: list[str]
+    title: str
     density_samples: np.ndarray
     scatter_samples: np.ndarray
     show_contours: bool
@@ -726,6 +727,7 @@ def plot_param_correlations(
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
         precision: int = 2,
         *,
+        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
         show_diagonal: bool = True,
     ) -> None:
         """
@@ -744,11 +746,14 @@ def plot_param_correlations(
         threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
             Minimum absolute off-diagonal correlation required for a
             parameter to be shown. When omitted, an automatic cutoff is
-            chosen so the displayed matrix stays at or below ``5 x 5``
-            parameters when possible. Set to ``0`` to show the full
-            matrix.
+            chosen so the displayed matrix stays at or below
+            ``max_parameters x max_parameters`` when possible. Set to
+            ``0`` to show the full matrix.
         precision : int, default=2
             Number of decimal places to show in the table fallback.
+        max_parameters : int, default=DEFAULT_CORRELATION_MAX_PARAMETERS
+            Maximum number of parameters to display when ``threshold``
+            is omitted. Ignored when ``threshold`` is provided.
         show_diagonal : bool, default=True
             Whether to retain blank diagonal cells in the displayed
             lower-triangle matrix.
@@ -760,14 +765,16 @@ def plot_param_correlations(
         corr_df, resolved_threshold = self._resolve_correlation_filter(
             corr_df,
             threshold=threshold,
+            max_parameters=max_parameters,
         )
         if corr_df is None:
             return
 
         corr_df = self._mask_correlation_lower_triangle(corr_df)
-        title = 'Refined parameter correlation matrix'
-        if resolved_threshold > 0:
-            title += f' with |correlation| >= {resolved_threshold:.2f}'
+        title = self._correlation_filtered_title(
+            'Refined parameter correlation matrix',
+            resolved_threshold,
+        )
 
         is_graphical = self._backend._supports_graphical_heatmap
         display_corr_df, row_numbers, col_numbers = self._trim_correlation_display_dataframe(
@@ -802,21 +809,44 @@ def _resolve_correlation_filter(
         corr_df: pd.DataFrame,
         *,
         threshold: float | None,
+        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
+        min_parameters: int = 1,
     ) -> tuple[pd.DataFrame | None, float]:
         """Return a filtered matrix and effective threshold."""
         if threshold is not None:
             filtered_corr_df = cls._filter_correlation_dataframe(corr_df, threshold=threshold)
             return filtered_corr_df, float(threshold)
+        validated_max_parameters = cls._validated_max_parameter_count(
+            max_parameters,
+            minimum=min_parameters,
+        )
         return cls._auto_filtered_correlation_dataframe(
             corr_df,
-            max_parameters=DEFAULT_CORRELATION_MAX_PARAMETERS,
+            max_parameters=validated_max_parameters,
+            min_parameters=min_parameters,
         )
 
+    @staticmethod
+    def _validated_max_parameter_count(
+        max_parameters: int,
+        *,
+        minimum: int,
+    ) -> int:
+        """Return a validated parameter-count limit."""
+        if not isinstance(max_parameters, int) or isinstance(max_parameters, bool):
+            msg = 'max_parameters must be an integer.'
+            raise ValueError(msg)
+        if max_parameters < minimum:
+            msg = f'max_parameters must be at least {minimum}.'
+            raise ValueError(msg)
+        return max_parameters
+
     @staticmethod
     def _auto_filtered_correlation_dataframe(
         corr_df: pd.DataFrame,
         *,
         max_parameters: int,
+        min_parameters: int = 1,
     ) -> tuple[pd.DataFrame, float]:
         """Return an auto-limited matrix for default display."""
         if corr_df.shape[0] <= max_parameters:
@@ -827,7 +857,7 @@ def _auto_filtered_correlation_dataframe(
         positive_values = np.unique(abs_corr[abs_corr > 0.0])
         for candidate in np.sort(positive_values):
             keep_mask = (abs_corr >= candidate).any(axis=0)
-            if 0 < int(keep_mask.sum()) <= max_parameters:
+            if min_parameters <= int(keep_mask.sum()) <= max_parameters:
                 labels = corr_df.index[keep_mask]
                 return corr_df.loc[labels, labels], float(candidate)
 
@@ -840,10 +870,20 @@ def _auto_filtered_correlation_dataframe(
         labels = corr_df.index[top_indices]
         return corr_df.loc[labels, labels], 0.0
 
+    @staticmethod
+    def _correlation_filtered_title(base_title: str, threshold: float) -> str:
+        """Return a plot title with a correlation cutoff."""
+        if threshold <= 0:
+            return base_title
+        return f'{base_title} with |correlation| ≥ {threshold:.2f}'
+
     def plot_posterior_pairs(
         self,
         parameters: list[object] | None = None,
         style: PosteriorPairPlotStyleEnum | str = 'auto',
+        *,
+        threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
+        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
     ) -> None:
         """
         Plot posterior pair relationships for sampled parameters.
@@ -852,14 +892,29 @@ def plot_posterior_pairs(
         ----------
         parameters : list[object] | None, default=None
             Optional subset of sampled parameters to include. When
-            ``None``, all sampled parameters are shown.
+            provided, ``threshold`` and ``max_parameters`` are ignored.
         style : PosteriorPairPlotStyleEnum | str, default='auto'
             Pair-plot rendering mode. Defaults to ``'auto'``. ``'auto'``
             keeps contours for compact plots and disables them for wide
             grids. ``'fast'`` always skips contours. ``'full'`` always
             renders contours.
+        threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
+            Minimum absolute off-diagonal correlation required for a
+            parameter to be auto-selected. When omitted, an automatic
+            cutoff keeps the plot at or below ``max_parameters``
+            parameters when possible. Set to ``0`` to show all sampled
+            parameters.
+        max_parameters : int, default=DEFAULT_CORRELATION_MAX_PARAMETERS
+            Maximum number of parameters to auto-select when
+            ``parameters`` is omitted and ``threshold`` is ``None``.
+            Must be at least ``2``.
         """
-        plot = self._build_posterior_pairs_plot(parameters=parameters, style=style)
+        plot = self._build_posterior_pairs_plot(
+            parameters=parameters,
+            style=style,
+            threshold=threshold,
+            max_parameters=max_parameters,
+        )
         if plot is None:
             return
         self._show_plot_figure(plot)
@@ -1233,6 +1288,8 @@ def _build_posterior_pairs_plot(
         *,
         parameters: list[object] | None,
         style: PosteriorPairPlotStyleEnum | str = 'auto',
+        threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
+        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
     ) -> object | None:
         """
         Build a Plotly posterior pair plot.
@@ -1243,6 +1300,10 @@ def _build_posterior_pairs_plot(
             Optional subset of sampled parameters to include.
         style : PosteriorPairPlotStyleEnum | str, default='auto'
             Posterior pair-plot rendering mode. Defaults to ``'auto'``.
+        threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
+            Absolute-correlation cutoff for auto-selected parameters.
+        max_parameters : int, default=DEFAULT_CORRELATION_MAX_PARAMETERS
+            Maximum number of auto-selected parameters.
 
         Returns
         -------
@@ -1250,7 +1311,12 @@ def _build_posterior_pairs_plot(
             Plotly figure, or ``None`` when posterior plotting is
             unavailable.
         """
-        context = self._posterior_pairs_context(parameters, style=style)
+        context = self._posterior_pairs_context(
+            parameters,
+            style=style,
+            threshold=threshold,
+            max_parameters=max_parameters,
+        )
         if context is None:
             return None
 
@@ -1291,6 +1357,8 @@ def _posterior_pairs_context(
         parameters: list[object] | None,
         *,
         style: PosteriorPairPlotStyleEnum | str = 'auto',
+        threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
+        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
     ) -> _PosteriorPairsContext | None:
         """Return the resolved inputs for a posterior pair plot."""
         posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
@@ -1299,9 +1367,11 @@ def _posterior_pairs_context(
 
         plot_style = self._validated_posterior_pair_plot_style(style)
 
-        parameter_names = self._resolve_posterior_parameter_names(
+        parameter_names, resolved_threshold = self._resolved_posterior_pair_parameter_names(
             fit_results=fit_results,
             parameters=parameters,
+            threshold=threshold,
+            max_parameters=max_parameters,
         )
         if parameter_names is None:
             return None
@@ -1339,6 +1409,7 @@ def _posterior_pairs_context(
             parameter_names=parameter_names,
             labels=self._posterior_plot_labels(fit_results, parameter_names),
             annotation_labels=self._posterior_pair_axis_title_labels(parameter_names),
+            title=self._correlation_filtered_title('Posterior pair plot', resolved_threshold),
             density_samples=density_samples,
             scatter_samples=scatter_samples,
             show_contours=show_contours,
@@ -1351,6 +1422,38 @@ def _posterior_pairs_context(
             ),
         )
 
+    def _resolved_posterior_pair_parameter_names(
+        self,
+        *,
+        fit_results: object,
+        parameters: list[object] | None,
+        threshold: float | None,
+        max_parameters: int,
+    ) -> tuple[list[str] | None, float]:
+        """Return pair-plot names and the effective cutoff."""
+        parameter_names = self._resolve_posterior_parameter_names(
+            fit_results=fit_results,
+            parameters=parameters,
+        )
+        if parameter_names is None:
+            return None, 0.0
+        if parameters is not None:
+            return parameter_names, 0.0
+
+        corr_df = self._posterior_correlation_dataframe(fit_results)
+        if corr_df is None:
+            return parameter_names, 0.0
+
+        filtered_corr_df, resolved_threshold = self._resolve_correlation_filter(
+            corr_df.loc[parameter_names, parameter_names],
+            threshold=threshold,
+            max_parameters=max_parameters,
+            min_parameters=MIN_POSTERIOR_PARAMETER_COUNT,
+        )
+        if filtered_corr_df is None:
+            return None, 0.0
+        return list(filtered_corr_df.index), resolved_threshold
+
     def _posterior_pair_axis_ranges(
         self,
         *,
@@ -1730,10 +1833,13 @@ def _square_matrix_title_annotation(
         }
 
     @staticmethod
-    def _posterior_pair_title_annotation(annotation_labels: list[str]) -> dict[str, object]:
+    def _posterior_pair_title_annotation(
+        title: str,
+        annotation_labels: list[str],
+    ) -> dict[str, object]:
         """Return the outer title annotation for the pair plot."""
         return Plotter._square_matrix_title_annotation(
-            'Posterior pair plot',
+            title,
             annotation_labels,
         )
 
@@ -1801,7 +1907,10 @@ def _finalize_posterior_pairs_figure(
             margin=self._posterior_pair_layout_margin(context.annotation_labels),
             bargap=0.05,
             annotations=[
-                self._posterior_pair_title_annotation(context.annotation_labels),
+                self._posterior_pair_title_annotation(
+                    context.title,
+                    context.annotation_labels,
+                ),
                 *subplot_title_annotations,
             ],
             shapes=subplot_border_shapes,

From 09da4695af26086f893b4f35c64745a939c48a50 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 09:25:43 +0200
Subject: [PATCH 088/106] Improve posterior summary plot styling

---
 src/easydiffraction/display/plotting.py | 37 +++++++++++++++++++++++--
 1 file changed, 35 insertions(+), 2 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index fc09f82e..dbd360b5 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -2059,6 +2059,13 @@ def _plot_axis_frame_color(self) -> str:
             return axis_frame_color()
         return PlotlyPlotter._axis_frame_color()
 
+    def _plot_legend_background_color(self) -> str:
+        """Return the shared legend background for Plotly plots."""
+        legend_background_color = getattr(self._backend, '_legend_background_color', None)
+        if callable(legend_background_color):
+            return legend_background_color()
+        return PlotlyPlotter._legend_background_color()
+
     def _posterior_contour_traces(
         self,
         *,
@@ -3222,6 +3229,7 @@ def _plot_posterior_predictive_summary(
     ) -> None:
         """Render posterior predictive summaries using Plotly."""
         go = __import__('plotly.graph_objects', fromlist=['Figure', 'Scatter'])
+        axis_frame_color = self._plot_axis_frame_color()
 
         fig = go.Figure()
         if show_band:
@@ -3298,11 +3306,36 @@ def _plot_posterior_predictive_summary(
                 'text': f"Posterior predictive for experiment 🔬 '{expt_name}'",
                 'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
             },
+            margin={
+                'autoexpand': True,
+                'r': 30,
+                't': 40,
+                'b': 45,
+            },
+            legend={
+                'bgcolor': self._plot_legend_background_color(),
+                'xanchor': 'right',
+                'x': 1.0,
+                'yanchor': 'top',
+                'y': 1.0,
+            },
             xaxis_title=axes_labels[0],
             yaxis_title=axes_labels[1],
         )
-        fig.update_xaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
-        fig.update_yaxes(title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE})
+        fig.update_xaxes(
+            title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+            showline=True,
+            linecolor=axis_frame_color,
+            mirror=True,
+            zeroline=False,
+        )
+        fig.update_yaxes(
+            title_font={'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE},
+            showline=True,
+            linecolor=axis_frame_color,
+            mirror=True,
+            zeroline=False,
+        )
         self._show_plot_figure(fig)
 
     def _filtered_posterior_predictive_summary(

From 1bb174bbc785f23e20f6a9969ffd464079aaf766 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 09:25:57 +0200
Subject: [PATCH 089/106] Add single-crystal posterior predictive scatter plot

---
 src/easydiffraction/display/plotting.py       | 166 +++++++++++++++++-
 .../easydiffraction/display/test_plotting.py  |  15 ++
 2 files changed, 174 insertions(+), 7 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index dbd360b5..836b6130 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -835,7 +835,7 @@ def _validated_max_parameter_count(
         """Return a validated parameter-count limit."""
         if not isinstance(max_parameters, int) or isinstance(max_parameters, bool):
             msg = 'max_parameters must be an integer.'
-            raise ValueError(msg)
+            raise TypeError(msg)
         if max_parameters < minimum:
             msg = f'max_parameters must be at least {minimum}.'
             raise ValueError(msg)
@@ -949,7 +949,7 @@ def plot_posterior_predictive(
         x: object | None = None,
     ) -> None:
         """
-        Plot posterior predictive curves for a powder experiment.
+        Plot posterior predictive checks for supported experiments.
 
         Parameters
         ----------
@@ -958,13 +958,15 @@ def plot_posterior_predictive(
         style : str, default='band'
             ``'band'`` shows the 95% credible interval, ``'draws'``
             shows sampled predictive curves, and ``'band+draws'`` shows
-            both together.
+            both together. Single-crystal plots currently render only
+            the interval-based reflection check.
         x_min : float | None, default=None
             Lower bound for the x-axis range.
         x_max : float | None, default=None
             Upper bound for the x-axis range.
         show_residual : bool | None, default=None
-            Whether to include the residual row in the composite plot.
+            Whether to include the residual row in supported powder
+            composite plots.
         x : object | None, default=None
             Optional explicit x-axis data to override stored values.
 
@@ -989,9 +991,6 @@ def plot_posterior_predictive(
         self._update_project_categories(expt_name)
         experiment = self._project.experiments[expt_name]
         x_axis, _, sample_form, scattering_type, _ = self._resolve_x_axis(experiment.type, x)
-        if sample_form != SampleFormEnum.POWDER:
-            log.warning('Posterior predictive plots currently support powder experiments only.')
-            return
 
         plot_options = _MeasVsCalcPlotOptions(
             x_min=x_min,
@@ -1000,6 +999,21 @@ def plot_posterior_predictive(
             x=x,
         )
 
+        if sample_form == SampleFormEnum.SINGLE_CRYSTAL:
+            self._plot_single_crystal_posterior_predictive(
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=x_axis,
+                scattering_type=scattering_type,
+                plot_options=plot_options,
+                style=style,
+            )
+            return
+
+        if sample_form != SampleFormEnum.POWDER:
+            log.warning('Posterior predictive plots currently support powder experiments only.')
+            return
+
         if scattering_type == ScatteringTypeEnum.BRAGG:
             self._plot_posterior_predictive_data(
                 experiment=experiment,
@@ -1020,6 +1034,87 @@ def plot_posterior_predictive(
             style=style,
         )
 
+    def _plot_single_crystal_posterior_predictive(
+        self,
+        *,
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+        scattering_type: object,
+        plot_options: _MeasVsCalcPlotOptions,
+        style: str,
+    ) -> None:
+        """Render a single-crystal posterior predictive scatter plot."""
+        if scattering_type != ScatteringTypeEnum.BRAGG:
+            log.warning(
+                'Single-crystal posterior predictive plots currently support '
+                'Bragg data only.'
+            )
+            return
+        if x_axis not in {XAxisType.INTENSITY_CALC, 'intensity_calc'}:
+            log.warning(
+                'Single-crystal posterior predictive plots currently support '
+                "x='intensity_calc' only."
+            )
+            return
+        if plot_options.show_residual:
+            log.warning(
+                'Posterior predictive residuals are unavailable for '
+                'single-crystal plots; ignoring show_residual=True.'
+            )
+        if style != 'band':
+            log.warning(
+                'Single-crystal posterior predictive plots currently support '
+                'style="band" only; rendering the 95% interval.'
+            )
+
+        summary = self._get_or_build_posterior_predictive_summary(
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+            include_draws=False,
+        )
+        if summary is None:
+            return
+
+        pattern = intensity_category_for(experiment)
+        y_meas_raw = getattr(pattern, 'intensity_meas', None)
+        if y_meas_raw is None:
+            log.warning(f'No measured data available for experiment {expt_name}.')
+            return
+        y_meas = np.asarray(y_meas_raw, dtype=float)
+        if y_meas.shape != np.asarray(summary.map_prediction).shape:
+            log.warning(
+                'Single-crystal posterior predictive values do not match the '
+                'measured reflection array shape.'
+            )
+            return
+
+        y_meas_su_raw = getattr(pattern, 'intensity_meas_su', None)
+        if y_meas_su_raw is None:
+            log.warning(f'No measurement uncertainties for experiment {expt_name}')
+            y_meas_su = np.zeros_like(y_meas)
+        else:
+            y_meas_su = np.asarray(y_meas_su_raw, dtype=float)
+            if y_meas_su.shape != y_meas.shape:
+                log.warning(
+                    'Single-crystal posterior predictive uncertainties do not '
+                    'match the measured reflection array shape.'
+                )
+                return
+
+        self._plot_single_crystal_posterior_predictive_summary(
+            expt_name=expt_name,
+            summary=summary,
+            y_meas=y_meas,
+            y_meas_su=y_meas_su,
+            axes_labels=self._get_axes_labels(
+                SampleFormEnum.SINGLE_CRYSTAL,
+                ScatteringTypeEnum.BRAGG,
+                XAxisType.INTENSITY_CALC,
+            ),
+        )
+
     def _plot_non_bragg_posterior_predictive(
         self,
         *,
@@ -3338,6 +3433,63 @@ def _plot_posterior_predictive_summary(
         )
         self._show_plot_figure(fig)
 
+    def _plot_single_crystal_posterior_predictive_summary(
+        self,
+        *,
+        expt_name: str,
+        summary: PosteriorPredictiveSummary,
+        y_meas: np.ndarray,
+        y_meas_su: np.ndarray,
+        axes_labels: list[str],
+    ) -> None:
+        """Render single-crystal posterior predictive checks."""
+        if summary.lower_95 is None or summary.upper_95 is None:
+            log.warning(
+                'Single-crystal posterior predictive plots require 95% '
+                'predictive intervals.'
+            )
+            return
+
+        map_prediction = np.asarray(summary.map_prediction, dtype=float)
+        lower_95 = np.asarray(summary.lower_95, dtype=float)
+        upper_95 = np.asarray(summary.upper_95, dtype=float)
+        if lower_95.shape != map_prediction.shape or upper_95.shape != map_prediction.shape:
+            log.warning(
+                'Single-crystal posterior predictive interval arrays have '
+                'invalid shapes.'
+            )
+            return
+
+        go = __import__('plotly.graph_objects', fromlist=['Figure'])
+        trace = PlotlyPlotter._get_single_crystal_trace(
+            x_calc=map_prediction,
+            y_meas=y_meas,
+            y_meas_su=y_meas_su,
+        )
+        trace.error_x = {
+            'type': 'data',
+            'array': np.maximum(0.0, upper_95 - map_prediction),
+            'arrayminus': np.maximum(0.0, map_prediction - lower_95),
+            'visible': True,
+        }
+        trace.customdata = np.column_stack((lower_95, upper_95, y_meas_su))
+        trace.hovertemplate = (
+            'Predicted I²: %{x:,.2f}<br>'
+            '95% interval: [%{customdata[0]:,.2f}, %{customdata[1]:,.2f}]<br>'
+            'Measured I²: %{y:,.2f}<br>'
+            'su(I²meas): %{customdata[2]:,.2f}<extra></extra>'
+        )
+
+        fig = go.Figure(
+            data=[trace],
+            layout=PlotlyPlotter._get_layout(
+                f"Posterior predictive reflection check for experiment 🔬 '{expt_name}'",
+                axes_labels,
+                shapes=[PlotlyPlotter._get_diagonal_shape()],
+            ),
+        )
+        self._show_plot_figure(fig)
+
     def _filtered_posterior_predictive_summary(
         self,
         *,
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 889e805a..16786b19 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -615,6 +615,7 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
     from easydiffraction.display.plotting import POSTERIOR_INTERVAL_95_FILL_COLOR
     from easydiffraction.display.plotting import POSTERIOR_POINT_ESTIMATE_LINE_DASH
     from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotters.plotly import PlotlyPlotter
 
     captured: dict[str, object] = {}
 
@@ -648,6 +649,20 @@ def test_plot_posterior_predictive_summary_uses_consistent_labels_and_styles(mon
     assert measured_trace.legendrank == 10
     assert max_posterior_trace.legendrank == 20
     assert max_posterior_trace.line.dash == POSTERIOR_POINT_ESTIMATE_LINE_DASH
+    assert fig.layout.legend.x == 1.0
+    assert fig.layout.legend.y == 1.0
+    assert fig.layout.legend.bgcolor == PlotlyPlotter._legend_background_color()
+    assert fig.layout.margin.r == 30
+    assert fig.layout.margin.t == 40
+    assert fig.layout.margin.b == 45
+    assert fig.layout.xaxis.showline is True
+    assert fig.layout.xaxis.mirror is True
+    assert fig.layout.xaxis.zeroline is False
+    assert fig.layout.xaxis.linecolor == PlotlyPlotter._axis_frame_color()
+    assert fig.layout.yaxis.showline is True
+    assert fig.layout.yaxis.mirror is True
+    assert fig.layout.yaxis.zeroline is False
+    assert fig.layout.yaxis.linecolor == PlotlyPlotter._axis_frame_color()
 
 
 @pytest.mark.parametrize(

From 72a99ec9c3e0214e293d13f2d928dde78d0dc05c Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 09:36:58 +0200
Subject: [PATCH 090/106] Add Tb2TiO7 Bayesian tutorial

---
 docs/docs/tutorials/ed-22.ipynb         | 427 ++++++++++++++++++++++++
 docs/docs/tutorials/ed-22.py            | 144 ++++++++
 docs/docs/tutorials/index.md            |   7 +
 docs/mkdocs.yml                         |   1 +
 src/easydiffraction/display/plotting.py |  11 +-
 5 files changed, 582 insertions(+), 8 deletions(-)
 create mode 100644 docs/docs/tutorials/ed-22.ipynb
 create mode 100644 docs/docs/tutorials/ed-22.py

diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb
new file mode 100644
index 00000000..64561a9c
--- /dev/null
+++ b/docs/docs/tutorials/ed-22.ipynb
@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {
+    "tags": [
+     "hide-in-docs"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Check whether easydiffraction is installed; install it if needed.\n",
+    "# Required for remote environments such as Google Colab.\n",
+    "import importlib.util\n",
+    "\n",
+    "if importlib.util.find_spec('easydiffraction') is None:\n",
+    "    %pip install easydiffraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Deterministic and Bayesian Refinement: Tb2TiO7, HEiDi\n",
+    "\n",
+    "This tutorial demonstrates a practical two-stage workflow for single-crystal\n",
+    "diffraction analysis with EasyDiffraction.\n",
+    "\n",
+    "In the first stage, we run a fast local refinement to obtain a sensible\n",
+    "point estimate and parameter uncertainties. In the second stage, we use\n",
+    "these refined values to define fit bounds and then sample the posterior\n",
+    "distribution with BUMPS-DREAM.\n",
+    "\n",
+    "The example uses constant-wavelength neutron single-crystal diffraction data\n",
+    "for Tb2TiO7 measured on HEiDi at FRM II."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Import Library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import easydiffraction as ed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "## Step 1: Create a Project Container"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project = ed.Project()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Step 2: Build the Structural Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure_path = ed.download_data(id=20, destination='data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.structures.add_from_cif_path(structure_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure = project.structures['tbti']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "## Step 3: Define the Diffraction Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = ed.download_data(id=19, destination='data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.experiments.add_from_data_path(\n",
+    "    name='heidi',\n",
+    "    data_path=data_path,\n",
+    "    sample_form='single crystal',\n",
+    "    beam_mode='constant wavelength',\n",
+    "    radiation_probe='neutron',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment = project.experiments['heidi']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_crystal.id = 'tbti'\n",
+    "experiment.linked_crystal.scale = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.instrument.setup_wavelength = 0.793"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.extinction.mosaicity = 35000\n",
+    "experiment.extinction.radius = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17",
+   "metadata": {},
+   "source": [
+    "## Step 4: Run an Initial Local Refinement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure.atom_sites['O1'].fract_x.free = True\n",
+    "\n",
+    "structure.atom_sites['Ti'].occupancy.free = False\n",
+    "structure.atom_sites['O1'].occupancy.free = False\n",
+    "structure.atom_sites['O2'].occupancy.free = False\n",
+    "\n",
+    "structure.atom_sites['Tb'].adp_iso.free = True\n",
+    "structure.atom_sites['Ti'].adp_iso.free = True\n",
+    "structure.atom_sites['O1'].adp_iso.free = True\n",
+    "structure.atom_sites['O2'].adp_iso.free = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "experiment.linked_crystal.scale.free = True\n",
+    "experiment.extinction.radius.free = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_correlations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_meas_vs_calc(expt_name='heidi')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25",
+   "metadata": {},
+   "source": [
+    "## Step 5: Prepare for Bayesian Sampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for param in project.free_parameters:\n",
+    "    param.set_fit_bounds_from_uncertainty(multiplier=1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.free_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29",
+   "metadata": {},
+   "source": [
+    "## Step 6: Configure and Run BUMPS-DREAM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.show_minimizer_types()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer_type = 'bumps (dream)'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit.minimizer.steps = 500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.analysis.display.fit_results()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_param_correlations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_pairs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for param in project.free_parameters:\n",
+    "    project.display.plotter.plot_param_distribution(param)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "project.display.plotter.plot_posterior_predictive(expt_name='heidi')"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
new file mode 100644
index 00000000..22cd3f49
--- /dev/null
+++ b/docs/docs/tutorials/ed-22.py
@@ -0,0 +1,144 @@
+# %% [markdown]
+# # Deterministic and Bayesian Refinement: Tb2TiO7, HEiDi
+#
+# This tutorial demonstrates a practical two-stage workflow for single-crystal
+# diffraction analysis with EasyDiffraction.
+#
+# In the first stage, we run a fast local refinement to obtain a sensible
+# point estimate and parameter uncertainties. In the second stage, we use
+# these refined values to define fit bounds and then sample the posterior
+# distribution with BUMPS-DREAM.
+#
+# The example uses constant-wavelength neutron single-crystal diffraction data
+# for Tb2TiO7 measured on HEiDi at FRM II.
+
+# %% [markdown]
+# ## Import Library
+
+# %%
+import easydiffraction as ed
+
+# %% [markdown]
+# ## Step 1: Create a Project Container
+
+# %%
+project = ed.Project()
+
+# %% [markdown]
+# ## Step 2: Build the Structural Model
+
+# %%
+structure_path = ed.download_data(id=20, destination='data')
+
+# %%
+project.structures.add_from_cif_path(structure_path)
+
+# %%
+structure = project.structures['tbti']
+
+# %% [markdown]
+# ## Step 3: Define the Diffraction Experiment
+
+# %%
+data_path = ed.download_data(id=19, destination='data')
+
+# %%
+project.experiments.add_from_data_path(
+    name='heidi',
+    data_path=data_path,
+    sample_form='single crystal',
+    beam_mode='constant wavelength',
+    radiation_probe='neutron',
+)
+
+# %%
+experiment = project.experiments['heidi']
+
+# %%
+experiment.linked_crystal.id = 'tbti'
+experiment.linked_crystal.scale = 1.0
+
+# %%
+experiment.instrument.setup_wavelength = 0.793
+
+# %%
+experiment.extinction.mosaicity = 35000
+experiment.extinction.radius = 10
+
+# %% [markdown]
+# ## Step 4: Run an Initial Local Refinement
+
+# %%
+structure.atom_sites['O1'].fract_x.free = True
+
+structure.atom_sites['Ti'].occupancy.free = False
+structure.atom_sites['O1'].occupancy.free = False
+structure.atom_sites['O2'].occupancy.free = False
+
+structure.atom_sites['Tb'].adp_iso.free = True
+structure.atom_sites['Ti'].adp_iso.free = True
+structure.atom_sites['O1'].adp_iso.free = True
+structure.atom_sites['O2'].adp_iso.free = True
+
+# %%
+experiment.linked_crystal.scale.free = True
+experiment.extinction.radius.free = True
+
+# %%
+project.analysis.fit.show_minimizer_types()
+
+# %%
+project.analysis.fit()
+
+# %%
+project.analysis.display.fit_results()
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_meas_vs_calc(expt_name='heidi')
+
+# %% [markdown]
+# ## Step 5: Prepare for Bayesian Sampling
+
+# %%
+project.analysis.display.free_params()
+
+# %%
+for param in project.free_parameters:
+    param.set_fit_bounds_from_uncertainty(multiplier=1.5)
+
+# %%
+project.analysis.display.free_params()
+
+# %% [markdown]
+# ## Step 6: Configure and Run BUMPS-DREAM
+
+# %%
+project.analysis.fit.show_minimizer_types()
+
+# %%
+project.analysis.fit.minimizer_type = 'bumps (dream)'
+
+# %%
+project.analysis.fit.minimizer.steps = 500
+
+# %%
+project.analysis.fit()
+
+# %%
+project.analysis.display.fit_results()
+
+# %%
+project.display.plotter.plot_param_correlations()
+
+# %%
+project.display.plotter.plot_posterior_pairs()
+
+# %%
+for param in project.free_parameters:
+    project.display.plotter.plot_param_distribution(param)
+
+# %%
+project.display.plotter.plot_posterior_predictive(expt_name='heidi')
diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md
index 18c2e143..1f4c82f0 100644
--- a/docs/docs/tutorials/index.md
+++ b/docs/docs/tutorials/index.md
@@ -106,6 +106,13 @@ The tutorials are organized into the following categories:
   tutorial covers the use of Markov Chain Monte Carlo (MCMC) sampling to
   explore the posterior distribution of the refined parameters,
   providing insights into parameter uncertainties and correlations.
+- [Tb2TiO7 Bayesian](ed-22.ipynb) – Demonstrates how to perform a
+  Bayesian analysis of the Tb2TiO7 crystal structure using constant
+  wavelength neutron single crystal diffraction data from HEiDi at FRM
+  II. This tutorial covers the use of Markov Chain Monte Carlo (MCMC)
+  sampling to explore the posterior distribution of the refined
+  parameters, providing insights into parameter uncertainties and
+  correlations.
 
 ## Workshops & Schools
 
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 42294197..50eb7449 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -218,6 +218,7 @@ nav:
           - BEER McStas: tutorials/ed-20.ipynb
       - Bayesian Analysis:
           - LBCO Bayesian: tutorials/ed-21.ipynb
+          - Co2SiO4 Bayesian: tutorials/ed-22.ipynb
       - Workshops & Schools:
           - DMSC Summer School: tutorials/ed-13.ipynb
   - API Reference:
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 836b6130..3082897a 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -1047,8 +1047,7 @@ def _plot_single_crystal_posterior_predictive(
         """Render a single-crystal posterior predictive scatter plot."""
         if scattering_type != ScatteringTypeEnum.BRAGG:
             log.warning(
-                'Single-crystal posterior predictive plots currently support '
-                'Bragg data only.'
+                'Single-crystal posterior predictive plots currently support Bragg data only.'
             )
             return
         if x_axis not in {XAxisType.INTENSITY_CALC, 'intensity_calc'}:
@@ -3445,8 +3444,7 @@ def _plot_single_crystal_posterior_predictive_summary(
         """Render single-crystal posterior predictive checks."""
         if summary.lower_95 is None or summary.upper_95 is None:
             log.warning(
-                'Single-crystal posterior predictive plots require 95% '
-                'predictive intervals.'
+                'Single-crystal posterior predictive plots require 95% predictive intervals.'
             )
             return
 
@@ -3454,10 +3452,7 @@ def _plot_single_crystal_posterior_predictive_summary(
         lower_95 = np.asarray(summary.lower_95, dtype=float)
         upper_95 = np.asarray(summary.upper_95, dtype=float)
         if lower_95.shape != map_prediction.shape or upper_95.shape != map_prediction.shape:
-            log.warning(
-                'Single-crystal posterior predictive interval arrays have '
-                'invalid shapes.'
-            )
+            log.warning('Single-crystal posterior predictive interval arrays have invalid shapes.')
             return
 
         go = __import__('plotly.graph_objects', fromlist=['Figure'])

From 6b6f6220fa29c8f76da83ff0795e8dca87f0c424 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 09:49:50 +0200
Subject: [PATCH 091/106] Set default correlation and pair-plot limit to six

---
 src/easydiffraction/display/plotting.py       | 15 +++--
 .../easydiffraction/display/test_plotting.py  | 66 +++++++++++--------
 2 files changed, 48 insertions(+), 33 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 3082897a..1206a0a3 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -809,13 +809,15 @@ def _resolve_correlation_filter(
         corr_df: pd.DataFrame,
         *,
         threshold: float | None,
-        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
+        max_parameters: int | None = DEFAULT_CORRELATION_MAX_PARAMETERS,
         min_parameters: int = 1,
     ) -> tuple[pd.DataFrame | None, float]:
         """Return a filtered matrix and effective threshold."""
         if threshold is not None:
             filtered_corr_df = cls._filter_correlation_dataframe(corr_df, threshold=threshold)
             return filtered_corr_df, float(threshold)
+        if max_parameters is None:
+            return corr_df, 0.0
         validated_max_parameters = cls._validated_max_parameter_count(
             max_parameters,
             minimum=min_parameters,
@@ -1383,7 +1385,7 @@ def _build_posterior_pairs_plot(
         parameters: list[object] | None,
         style: PosteriorPairPlotStyleEnum | str = 'auto',
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
-        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
+        max_parameters: int | None = None,
     ) -> object | None:
         """
         Build a Plotly posterior pair plot.
@@ -1396,8 +1398,9 @@ def _build_posterior_pairs_plot(
             Posterior pair-plot rendering mode. Defaults to ``'auto'``.
         threshold : float | None, default=DEFAULT_CORRELATION_THRESHOLD
             Absolute-correlation cutoff for auto-selected parameters.
-        max_parameters : int, default=DEFAULT_CORRELATION_MAX_PARAMETERS
-            Maximum number of auto-selected parameters.
+        max_parameters : int | None, default=None
+            Maximum number of auto-selected parameters. ``None`` keeps
+            the full posterior parameter set.
 
         Returns
         -------
@@ -1452,7 +1455,7 @@ def _posterior_pairs_context(
         *,
         style: PosteriorPairPlotStyleEnum | str = 'auto',
         threshold: float | None = DEFAULT_CORRELATION_THRESHOLD,
-        max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS,
+        max_parameters: int | None = None,
     ) -> _PosteriorPairsContext | None:
         """Return the resolved inputs for a posterior pair plot."""
         posterior_samples, fit_results = self._get_posterior_samples_and_fit_results()
@@ -1522,7 +1525,7 @@ def _resolved_posterior_pair_parameter_names(
         fit_results: object,
         parameters: list[object] | None,
         threshold: float | None,
-        max_parameters: int,
+        max_parameters: int | None,
     ) -> tuple[list[str] | None, float]:
         """Return pair-plot names and the effective cutoff."""
         parameter_names = self._resolve_posterior_parameter_names(
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 16786b19..3b434c8d 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -1714,7 +1714,7 @@ class Project:
     assert len(fig.layout.shapes) == 15
 
 
-def test_plot_param_correlations_limits_default_table_to_five_parameters(monkeypatch):
+def test_plot_param_correlations_limits_default_table_to_six_parameters(monkeypatch):
     from easydiffraction.display.plotting import Plotter
     from easydiffraction.display.tables import TableRenderer
 
@@ -1778,38 +1778,50 @@ class Project:
         '3',
         '4',
         '5',
+        '6',
+    ]
+    assert list(df.index) == [0, 1, 2, 3, 4, 5]
+    assert list(df.iloc[:, 0]) == [
+        'phase.scale',
+        'phase.cell.length_a',
+        'phase.background',
+        'phase.profile.u',
+        'phase.profile.v',
+        'phase.profile.w',
     ]
-    assert list(df.index) == [0, 1, 2, 3, 4]
-    assert df.iloc[0, 0] == 'phase.scale'
     assert df.iloc[0, 1] == ''
-    assert df.iloc[0, 2] == ''
-    assert df.iloc[0, 3] == ''
-    assert df.iloc[0, 4] == ''
-    assert df.iloc[0, 5] == ''
-    assert df.iloc[1, 0] == 'phase.cell.length_a'
     assert _strip_markup(df.iloc[1, 1]).strip() == '0.95'
-    assert df.iloc[1, 2] == ''
-    assert df.iloc[1, 3] == ''
-    assert df.iloc[1, 4] == ''
-    assert df.iloc[1, 5] == ''
-    assert df.iloc[2, 0] == 'phase.background'
-    assert df.iloc[2, 1] == ''
     assert _strip_markup(df.iloc[2, 2]).strip() == '0.94'
-    assert df.iloc[2, 3] == ''
-    assert df.iloc[2, 4] == ''
-    assert df.iloc[2, 5] == ''
-    assert df.iloc[3, 0] == 'phase.profile.u'
-    assert df.iloc[3, 1] == ''
-    assert df.iloc[3, 2] == ''
     assert _strip_markup(df.iloc[3, 3]).strip() == '0.93'
-    assert df.iloc[3, 4] == ''
-    assert df.iloc[3, 5] == ''
-    assert df.iloc[4, 0] == 'phase.profile.v'
-    assert df.iloc[4, 1] == ''
-    assert df.iloc[4, 2] == ''
-    assert df.iloc[4, 3] == ''
     assert _strip_markup(df.iloc[4, 4]).strip() == '0.92'
-    assert df.iloc[4, 5] == ''
+    assert _strip_markup(df.iloc[5, 5]).strip() == '0.91'
+    assert df.iloc[5, 6] == ''
+
+
+def test_plot_posterior_pairs_uses_default_max_parameter_limit(monkeypatch):
+    from easydiffraction.display.plotting import DEFAULT_CORRELATION_MAX_PARAMETERS
+    from easydiffraction.display.plotting import Plotter
+
+    captured: dict[str, object] = {}
+
+    plotter = Plotter()
+
+    def fake_build(self, *, parameters, style, threshold, max_parameters):
+        captured['parameters'] = parameters
+        captured['style'] = style
+        captured['threshold'] = threshold
+        captured['max_parameters'] = max_parameters
+        return object()
+
+    monkeypatch.setattr(Plotter, '_build_posterior_pairs_plot', fake_build)
+    monkeypatch.setattr(Plotter, '_show_plot_figure', lambda self, figure: None)
+
+    plotter.plot_posterior_pairs()
+
+    assert captured['parameters'] is None
+    assert captured['style'] == 'auto'
+    assert captured['threshold'] is None
+    assert captured['max_parameters'] == DEFAULT_CORRELATION_MAX_PARAMETERS
 
 
 def test_plot_param_correlations_shows_full_table_when_threshold_is_zero(monkeypatch):

From 7d3341b19c70503d66cf97fc2d1fe0ed93b92c8e Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 13:30:22 +0200
Subject: [PATCH 092/106] Bump copier template to v0.11.3

---
 .copier-answers.yml | 2 +-
 CONTRIBUTING.md     | 6 ++++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/.copier-answers.yml b/.copier-answers.yml
index b7376e06..c1c59d2a 100644
--- a/.copier-answers.yml
+++ b/.copier-answers.yml
@@ -1,6 +1,6 @@
 # WARNING: Do not edit this file manually.
 # Any changes will be overwritten by Copier.
-_commit: v0.11.2-5-gd120259
+_commit: v0.11.3
 _src_path: gh:easyscience/templates
 app_docs_url: https://easyscience.github.io/diffraction-app
 app_doi: 10.5281/zenodo.18163581
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e8dc420f..9e5b93cf 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -249,6 +249,12 @@ or to run only Python linting checks:
 pixi run py-lint-check
 ```
 
+To add missing license headers:
+
+```bash
+pixi run spdx-add
+```
+
 Some formatting issues can be fixed automatically:
 
 ```bash

From 2f1f65f8621007f70d84f1a33dc20eab88db9e0c Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 13:39:30 +0200
Subject: [PATCH 093/106] Update tutorials

---
 docs/docs/tutorials/ed-21.ipynb |  2 +-
 docs/docs/tutorials/ed-21.py    |  2 +-
 docs/docs/tutorials/ed-22.ipynb |  2 +-
 docs/docs/tutorials/ed-22.py    |  2 +-
 docs/docs/tutorials/index.json  | 14 ++++++++++++++
 docs/docs/tutorials/index.md    | 12 ++++++------
 docs/mkdocs.yml                 |  6 +++---
 7 files changed, 27 insertions(+), 13 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.ipynb b/docs/docs/tutorials/ed-21.ipynb
index d2a23c71..405bc858 100644
--- a/docs/docs/tutorials/ed-21.ipynb
+++ b/docs/docs/tutorials/ed-21.ipynb
@@ -24,7 +24,7 @@
    "id": "1",
    "metadata": {},
    "source": [
-    "# Deterministic and Bayesian Refinement: LBCO, HRPT\n",
+    "# Bayesian Analysis: LBCO, HRPT\n",
     "\n",
     "This tutorial demonstrates a practical two-stage workflow for powder\n",
     "diffraction analysis with EasyDiffraction.\n",
diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index ccfdb965..fe0cf405 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Deterministic and Bayesian Refinement: LBCO, HRPT
+# # Bayesian Analysis: LBCO, HRPT
 #
 # This tutorial demonstrates a practical two-stage workflow for powder
 # diffraction analysis with EasyDiffraction.
diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb
index 64561a9c..cc880314 100644
--- a/docs/docs/tutorials/ed-22.ipynb
+++ b/docs/docs/tutorials/ed-22.ipynb
@@ -24,7 +24,7 @@
    "id": "1",
    "metadata": {},
    "source": [
-    "# Deterministic and Bayesian Refinement: Tb2TiO7, HEiDi\n",
+    "# Bayesian Analysis: Tb2TiO7, HEiDi\n",
     "\n",
     "This tutorial demonstrates a practical two-stage workflow for single-crystal\n",
     "diffraction analysis with EasyDiffraction.\n",
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
index 22cd3f49..68f447d2 100644
--- a/docs/docs/tutorials/ed-22.py
+++ b/docs/docs/tutorials/ed-22.py
@@ -1,5 +1,5 @@
 # %% [markdown]
-# # Deterministic and Bayesian Refinement: Tb2TiO7, HEiDi
+# # Bayesian Analysis: Tb2TiO7, HEiDi
 #
 # This tutorial demonstrates a practical two-stage workflow for single-crystal
 # diffraction analysis with EasyDiffraction.
diff --git a/docs/docs/tutorials/index.json b/docs/docs/tutorials/index.json
index dcd0cb87..40145a15 100644
--- a/docs/docs/tutorials/index.json
+++ b/docs/docs/tutorials/index.json
@@ -131,5 +131,19 @@
     "title": "Instrument calibration: BEER at ESS",
     "description": "Instrument calibration of BEER at ESS using neutron powder diffraction data simulated with McStas",
     "level": "intermediate"
+  },
+  "21": {
+    "url": "https://easyscience.github.io/diffraction-lib/{version}/tutorials/ed-21/ed-21.ipynb",
+    "original_name": "",
+    "title": "Bayesian Analysis: LBCO, HRPT",
+    "description": "Bayesian analysis of the La0.5Ba0.5CoO3 crystal structure - Markov Chain Monte Carlo (MCMC) sampling.",
+    "level": "advanced"
+  },
+  "22": {
+    "url": "https://easyscience.github.io/diffraction-lib/{version}/tutorials/ed-22/ed-22.ipynb",
+    "original_name": "",
+    "title": "Bayesian Analysis: Tb2TiO7, HEiDi",
+    "description": "Bayesian analysis of the Tb2TiO7 crystal structure - Markov Chain Monte Carlo (MCMC) sampling.",
+    "level": "advanced"
   }
 }
diff --git a/docs/docs/tutorials/index.md b/docs/docs/tutorials/index.md
index 1f4c82f0..a11c73e6 100644
--- a/docs/docs/tutorials/index.md
+++ b/docs/docs/tutorials/index.md
@@ -106,13 +106,13 @@ The tutorials are organized into the following categories:
   tutorial covers the use of Markov Chain Monte Carlo (MCMC) sampling to
   explore the posterior distribution of the refined parameters,
   providing insights into parameter uncertainties and correlations.
-- [Tb2TiO7 Bayesian](ed-22.ipynb) – Demonstrates how to perform a
-  Bayesian analysis of the Tb2TiO7 crystal structure using constant
-  wavelength neutron single crystal diffraction data from HEiDi at FRM
-  II. This tutorial covers the use of Markov Chain Monte Carlo (MCMC)
-  sampling to explore the posterior distribution of the refined
+- [Tb2TiO7 Bayesian](ed-22.ipynb) – Another example of a Bayesian
+  analysis. This tutorial focuses on the Tb2TiO7 crystal structure using
+  constant wavelength neutron single crystal diffraction data from HEiDi
+  at FRM II. Similar to the LBCO Bayesian tutorial, it covers the use of
+  MCMC sampling to explore the posterior distribution of the refined
   parameters, providing insights into parameter uncertainties and
-  correlations.
+  correlations in the context of single crystal diffraction data.
 
 ## Workshops & Schools
 
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 50eb7449..16091a5d 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -218,7 +218,7 @@ nav:
           - BEER McStas: tutorials/ed-20.ipynb
       - Bayesian Analysis:
           - LBCO Bayesian: tutorials/ed-21.ipynb
-          - Co2SiO4 Bayesian: tutorials/ed-22.ipynb
+          - Tb2TiO7 Bayesian: tutorials/ed-22.ipynb
       - Workshops & Schools:
           - DMSC Summer School: tutorials/ed-13.ipynb
   - API Reference:
@@ -234,5 +234,5 @@ nav:
       - project: api-reference/project.md
       - summary: api-reference/summary.md
       - utils: api-reference/utils.md
-  - Command-Line Interface:
-      - Command-Line Interface: cli/index.md
+  - Command-Line:
+      - Command-Line: cli/index.md

From 213a63ca835b32ee6d0e0e477eff2a7952b00270 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 19:24:57 +0200
Subject: [PATCH 094/106] Adjust posterior pair plot margins and title shift

---
 src/easydiffraction/display/plotting.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 1206a0a3..7f737f2b 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -158,14 +158,14 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_PAIR_TITLE_FONT_SIZE = PLOTLY_TITLE_FONT_SIZE
 POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 16
 POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS = 10
-POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS = 8
+POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS = 12
 POSTERIOR_PAIR_GUIDE_LINE_COLOR = 'rgba(125, 140, 173, 0.18)'
 POSTERIOR_PAIR_FIXED_ASPECT_RATIO = '1 / 1'
 POSTERIOR_PAIR_FIXED_ASPECT_META_KEY = 'fixed_aspect_wrapper'
-POSTERIOR_PAIR_LEFT_MARGIN_PIXELS = 58
-POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS = 10
-POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 26
-POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 24
+POSTERIOR_PAIR_LEFT_MARGIN_PIXELS = 40
+POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS = 24
+POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 40
+POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 40
 POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
 POSTERIOR_PAIR_SAMPLE_MARKER_SIZE = 6
 POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE = 6
@@ -1944,7 +1944,7 @@ def _posterior_pair_title_annotation(
     def _square_matrix_title_left_shift(annotation_labels: list[str]) -> int:
         """Return the title shift that cancels left margin."""
         extra_margin = Plotter._posterior_pair_extra_axis_title_margin(annotation_labels)
-        return POSTERIOR_PAIR_LEFT_MARGIN_PIXELS + extra_margin
+        return max(0, POSTERIOR_PAIR_LEFT_MARGIN_PIXELS + extra_margin - 14)
 
     @staticmethod
     def _square_matrix_gap_data_width(n_parameters: int) -> float:

From 156ec249d390c1a8dcefd5ef9d8a94da9c6e614c Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 19:28:00 +0200
Subject: [PATCH 095/106] Generalize pair-plot layout to square matrix

---
 src/easydiffraction/display/plotting.py | 76 +++++++++++++------------
 1 file changed, 41 insertions(+), 35 deletions(-)

diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 7f737f2b..758831c7 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -158,15 +158,16 @@ class PosteriorPairPlotStyleEnum(StrEnum):
 POSTERIOR_PAIR_TITLE_FONT_SIZE = PLOTLY_TITLE_FONT_SIZE
 POSTERIOR_PAIR_Y_TITLE_XSHIFT_PIXELS = 16
 POSTERIOR_PAIR_X_TITLE_YSHIFT_PIXELS = 10
-POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS = 12
+SQUARE_MATRIX_TITLE_YSHIFT_PIXELS = 12
 POSTERIOR_PAIR_GUIDE_LINE_COLOR = 'rgba(125, 140, 173, 0.18)'
-POSTERIOR_PAIR_FIXED_ASPECT_RATIO = '1 / 1'
-POSTERIOR_PAIR_FIXED_ASPECT_META_KEY = 'fixed_aspect_wrapper'
-POSTERIOR_PAIR_LEFT_MARGIN_PIXELS = 40
-POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS = 24
-POSTERIOR_PAIR_TOP_MARGIN_PIXELS = 40
-POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS = 40
-POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
+SQUARE_MATRIX_FIXED_ASPECT_RATIO = '1 / 1'
+SQUARE_MATRIX_FIXED_ASPECT_META_KEY = 'fixed_aspect_wrapper'
+SQUARE_MATRIX_LEFT_MARGIN_PIXELS = 40
+SQUARE_MATRIX_RIGHT_MARGIN_PIXELS = 24
+SQUARE_MATRIX_TOP_MARGIN_PIXELS = 40
+SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS = 40
+SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS = 18
+SQUARE_MATRIX_TITLE_LEFT_PADDING_PIXELS = 14
 POSTERIOR_PAIR_SAMPLE_MARKER_SIZE = 6
 POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE = 6
 
@@ -1505,7 +1506,7 @@ def _posterior_pairs_context(
             fit_results=fit_results,
             parameter_names=parameter_names,
             labels=self._posterior_plot_labels(fit_results, parameter_names),
-            annotation_labels=self._posterior_pair_axis_title_labels(parameter_names),
+            annotation_labels=self._square_matrix_axis_title_labels(parameter_names),
             title=self._correlation_filtered_title('Posterior pair plot', resolved_threshold),
             density_samples=density_samples,
             scatter_samples=scatter_samples,
@@ -1923,7 +1924,7 @@ def _square_matrix_title_annotation(
             'y': 1.0,
             'yref': 'paper',
             'yanchor': 'bottom',
-            'yshift': POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS,
+            'yshift': SQUARE_MATRIX_TITLE_YSHIFT_PIXELS,
             'text': title,
             'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE},
             'showarrow': False,
@@ -1942,9 +1943,14 @@ def _posterior_pair_title_annotation(
 
     @staticmethod
     def _square_matrix_title_left_shift(annotation_labels: list[str]) -> int:
-        """Return the title shift that cancels left margin."""
-        extra_margin = Plotter._posterior_pair_extra_axis_title_margin(annotation_labels)
-        return max(0, POSTERIOR_PAIR_LEFT_MARGIN_PIXELS + extra_margin - 14)
+        """Return the title shift relative to the shared left margin."""
+        extra_margin = Plotter._square_matrix_extra_axis_title_margin(annotation_labels)
+        return max(
+            0,
+            SQUARE_MATRIX_LEFT_MARGIN_PIXELS
+            + extra_margin
+            - SQUARE_MATRIX_TITLE_LEFT_PADDING_PIXELS,
+        )
 
     @staticmethod
     def _square_matrix_gap_data_width(n_parameters: int) -> float:
@@ -1980,12 +1986,12 @@ def _square_matrix_layout_meta(
         annotation_labels: list[str],
     ) -> dict[str, object]:
         """Return wrapper metadata for square matrix plots."""
-        margins = cls._posterior_pair_layout_margin(annotation_labels)
+        margins = cls._square_matrix_layout_margin(annotation_labels)
         plot_size = cls._square_matrix_target_plot_size_pixels(n_parameters)
         aspect_width = round(plot_size + int(margins['l']) + int(margins['r']))
         aspect_height = round(plot_size + int(margins['t']) + int(margins['b']))
         return {
-            POSTERIOR_PAIR_FIXED_ASPECT_META_KEY: {
+            SQUARE_MATRIX_FIXED_ASPECT_META_KEY: {
                 'aspect_ratio': f'{aspect_width} / {aspect_height}',
             }
         }
@@ -2001,7 +2007,7 @@ def _finalize_posterior_pairs_figure(
         """Apply final layout settings to the posterior pair plot."""
         fig.update_layout(
             autosize=True,
-            margin=self._posterior_pair_layout_margin(context.annotation_labels),
+            margin=self._square_matrix_layout_margin(context.annotation_labels),
             bargap=0.05,
             annotations=[
                 self._posterior_pair_title_annotation(
@@ -2026,30 +2032,30 @@ def _finalize_posterior_pairs_figure(
         )
 
     @staticmethod
-    def _posterior_pair_layout_margin(annotation_labels: list[str]) -> dict[str, int | bool]:
-        """Return outer margins sized for multiline pair-plot labels."""
-        extra_margin = Plotter._posterior_pair_extra_axis_title_margin(annotation_labels)
+    def _square_matrix_layout_margin(annotation_labels: list[str]) -> dict[str, int | bool]:
+        """Return outer margins sized for multiline matrix labels."""
+        extra_margin = Plotter._square_matrix_extra_axis_title_margin(annotation_labels)
         return {
             'autoexpand': False,
-            'l': POSTERIOR_PAIR_LEFT_MARGIN_PIXELS + extra_margin,
-            'r': POSTERIOR_PAIR_RIGHT_MARGIN_PIXELS,
-            't': POSTERIOR_PAIR_TOP_MARGIN_PIXELS,
-            'b': POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS + extra_margin,
+            'l': SQUARE_MATRIX_LEFT_MARGIN_PIXELS + extra_margin,
+            'r': SQUARE_MATRIX_RIGHT_MARGIN_PIXELS,
+            't': SQUARE_MATRIX_TOP_MARGIN_PIXELS,
+            'b': SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS + extra_margin,
         }
 
     @staticmethod
-    def _posterior_pair_extra_axis_title_margin(annotation_labels: list[str]) -> int:
+    def _square_matrix_extra_axis_title_margin(annotation_labels: list[str]) -> int:
         """Return extra margin needed for multiline axis labels."""
         if not annotation_labels:
             return 0
 
         max_line_count = max(
-            Plotter._posterior_pair_axis_title_line_count(label) for label in annotation_labels
+            Plotter._square_matrix_axis_title_line_count(label) for label in annotation_labels
         )
-        return max(0, max_line_count - 1) * POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
+        return max(0, max_line_count - 1) * SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS
 
     @staticmethod
-    def _posterior_pair_axis_title_line_count(label: str) -> int:
+    def _square_matrix_axis_title_line_count(label: str) -> int:
         """Return the number of display lines in one axis title."""
         if not label:
             return 1
@@ -3862,15 +3868,15 @@ def _posterior_plot_labels(
         return labels
 
     @staticmethod
-    def _posterior_pair_axis_title_labels(
+    def _square_matrix_axis_title_labels(
         parameter_names: list[str],
     ) -> list[str]:
-        """Return compact multiline labels for pair-plot axes."""
-        return [Plotter._posterior_pair_axis_title_label(name) for name in parameter_names]
+        """Return compact multiline labels for square-matrix axes."""
+        return [Plotter._square_matrix_axis_title_label(name) for name in parameter_names]
 
     @staticmethod
-    def _posterior_pair_axis_title_label(unique_name: str) -> str:
-        """Return one compact multiline axis title for a pair plot."""
+    def _square_matrix_axis_title_label(unique_name: str) -> str:
+        """Return one compact multiline axis title."""
         normalized_name = unique_name.strip()
         if not normalized_name or '.' not in normalized_name:
             return normalized_name
@@ -4113,8 +4119,8 @@ def _build_correlation_heatmap_plot(
         go = __import__('plotly.graph_objects', fromlist=['Figure', 'Heatmap'])
         context = _CorrelationHeatmapContext(
             corr_df=corr_df,
-            row_labels=self._posterior_pair_axis_title_labels(corr_df.index.tolist()),
-            col_labels=self._posterior_pair_axis_title_labels(corr_df.columns.tolist()),
+            row_labels=self._square_matrix_axis_title_labels(corr_df.index.tolist()),
+            col_labels=self._square_matrix_axis_title_labels(corr_df.columns.tolist()),
             threshold=threshold,
             precision=precision,
         )
@@ -4155,7 +4161,7 @@ def _build_correlation_heatmap_plot(
 
         fig.update_layout(
             autosize=True,
-            margin=self._posterior_pair_layout_margin([
+            margin=self._square_matrix_layout_margin([
                 *context.row_labels,
                 *context.col_labels,
             ]),

From 006ca6ae8fddb4b4ed14ed608838d75ee4e0aff8 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 20:28:00 +0200
Subject: [PATCH 096/106] Switch DREAM defaults and refine Bayesian fit
 displays

---
 .../analysis/fit_helpers/bayesian.py          |  2 +-
 .../analysis/minimizers/bumps_dream.py        | 41 +++++++++++++--
 src/easydiffraction/core/variable.py          |  4 +-
 src/easydiffraction/display/plotting.py       |  2 +-
 .../analysis/fit_helpers/test_bayesian.py     |  4 +-
 .../analysis/minimizers/test_bumps_dream.py   | 17 +++++-
 .../easydiffraction/core/test_parameters.py   | 19 +++++++
 .../easydiffraction/display/test_plotting.py  | 52 +++++++++++--------
 8 files changed, 108 insertions(+), 33 deletions(-)

diff --git a/src/easydiffraction/analysis/fit_helpers/bayesian.py b/src/easydiffraction/analysis/fit_helpers/bayesian.py
index aa087fcb..e10d9c69 100644
--- a/src/easydiffraction/analysis/fit_helpers/bayesian.py
+++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py
@@ -527,7 +527,7 @@ def _format_sampler_settings(sampler_settings: dict[str, object]) -> str | None:
 
     parts = [
         f'{key}={sampler_settings[key]}'
-        for key in ('random_seed', 'steps', 'burn', 'thin', 'pop', 'samples')
+        for key in ('steps', 'burn', 'thin', 'pop', 'init', 'samples')
         if key in sampler_settings
     ]
     return ', '.join(parts) if parts else None
diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index cc141d32..5c1681b6 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -37,13 +37,12 @@
 DEFAULT_THIN = 1
 DEFAULT_POP = 4
 DEFAULT_PARALLEL = 0
-DEFAULT_INIT = DreamPopulationInitializationEnum.EPS
+DEFAULT_INIT = DreamPopulationInitializationEnum.LHS
 DEFAULT_ALPHA = 0.0
 DEFAULT_OUTLIER_TEST = 'none'
 DEFAULT_TRIM = False
 MAX_RANDOM_SEED = int(np.iinfo(np.uint32).max)
-BURN_IN_PROGRESS_POINTS = 5
-SAMPLING_PROGRESS_POINTS = 20
+TOTAL_PROGRESS_POINTS = 25
 DREAM_SAMPLE_ARRAY_NDIM = 3
 DREAM_DRIVER_FAILURES = (ArithmeticError, RuntimeError, TypeError, ValueError)
 
@@ -92,15 +91,19 @@ def __init__(
         self._n_parameters = n_parameters
         self._total_generations = max(1, total_generations)
         self._burn_steps = max(0, burn_steps)
+        burn_target_count, sampling_target_count = self._phase_progress_point_counts(
+            total_generations=self._total_generations,
+            burn_steps=self._burn_steps,
+        )
         self._burn_targets = self._progress_targets(
             start=1,
             stop=self._burn_steps,
-            target_count=BURN_IN_PROGRESS_POINTS,
+            target_count=burn_target_count,
         )
         self._sampling_targets = self._progress_targets(
             start=self._burn_steps + 1,
             stop=self._total_generations,
-            target_count=SAMPLING_PROGRESS_POINTS,
+            target_count=sampling_target_count,
         )
         self._next_burn_target_index = 0
         self._next_sampling_target_index = 0
@@ -175,6 +178,34 @@ def _progress_targets(
             unique_targets.append(stop)
         return unique_targets
 
+    @staticmethod
+    def _phase_progress_point_counts(
+        *,
+        total_generations: int,
+        burn_steps: int,
+    ) -> tuple[int, int]:
+        """Return proportional burn and sampling progress counts."""
+        total_points = min(TOTAL_PROGRESS_POINTS, max(1, total_generations))
+        burn_generations = min(max(0, burn_steps), total_generations)
+        sampling_generations = max(total_generations - burn_generations, 0)
+
+        if burn_generations == 0:
+            return 0, total_points
+        if sampling_generations == 0:
+            return total_points, 0
+
+        burn_target_count = round(total_points * burn_generations / total_generations)
+        burn_target_count = min(
+            max(burn_target_count, 1),
+            burn_generations,
+            total_points - 1,
+        )
+        sampling_target_count = min(
+            max(total_points - burn_target_count, 1),
+            sampling_generations,
+        )
+        return burn_target_count, sampling_target_count
+
     def _should_report(self, generation: int) -> bool:
         """Return whether the current generation should be rendered."""
         clamped_generation = min(max(1, generation), self._total_generations)
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index a09f502f..4e0fb499 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -429,7 +429,7 @@ def fit_max(self, v: float) -> None:
 
     def set_fit_bounds_from_uncertainty(
         self,
-        multiplier: float = 8.0,
+        multiplier: float = 4.0,
         *,
         clip_to_limits: bool = True,
     ) -> None:
@@ -438,7 +438,7 @@ def set_fit_bounds_from_uncertainty(
 
         Parameters
         ----------
-        multiplier : float, default=8.0
+        multiplier : float, default=4.0
             Positive finite factor applied symmetrically to the current
             parameter uncertainty.
         clip_to_limits : bool, default=True
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 758831c7..a7d6ece5 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -2360,7 +2360,7 @@ def _posterior_distribution_context(
             parameter_name=parameter_name,
             values=samples[:, 0],
             label=label,
-            title=f'Posterior distribution: {label}',
+            title=f'Posterior distribution: {parameter_name}',
             summary=self._posterior_summary_by_name(fit_results).get(parameter_name),
         )
 
diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
index 5fcfbf1a..3b641e6d 100644
--- a/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
+++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py
@@ -157,6 +157,7 @@ def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(cap
             'burn': 50,
             'thin': 1,
             'pop': 4,
+            'init': 'lhs',
             'samples': 3200,
         },
         convergence_diagnostics={
@@ -191,8 +192,9 @@ def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(cap
     assert 'Sampler status: DREAM sampling completed' in out
     assert 'Sampler: dream' in out
     assert 'Sampler completed: yes' in out
-    assert 'random_seed=1313900679' in out
     assert 'steps=200' in out
+    assert 'init=lhs' in out
+    assert 'random_seed=1313900679' not in out
     assert 'status=failed' in out
     assert 'max_r_hat=1.107' in out
     assert 'min_ess_bulk=125.9' in out
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index 9ac35b57..5c3dd60b 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -49,10 +49,25 @@ def test_type_info_and_default_init():
     minimizer = BumpsDreamMinimizer()
 
     assert minimizer.type_info.tag == MinimizerTypeEnum.BUMPS_DREAM
-    assert minimizer.init is DreamPopulationInitializationEnum.EPS
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
     assert minimizer.steps == 1000
 
 
+def test_dream_progress_monitor_allocates_rows_by_phase_ratio():
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamProgressMonitor
+
+    monitor = _DreamProgressMonitor(
+        tracker=MagicMock(),
+        n_points=100,
+        n_parameters=3,
+        total_generations=100,
+        burn_steps=40,
+    )
+
+    assert len(monitor._burn_targets) == 10
+    assert len(monitor._sampling_targets) == 15
+
+
 def test_init_accepts_enum_or_string_and_rejects_invalid():
     from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
     from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
diff --git a/tests/unit/easydiffraction/core/test_parameters.py b/tests/unit/easydiffraction/core/test_parameters.py
index 0a4c95cc..a6c5b7ad 100644
--- a/tests/unit/easydiffraction/core/test_parameters.py
+++ b/tests/unit/easydiffraction/core/test_parameters.py
@@ -123,6 +123,25 @@ def test_parameter_set_fit_bounds_from_uncertainty_sets_bounds_and_returns_none(
     assert np.isclose(p.fit_max, 3.0)
 
 
+def test_parameter_set_fit_bounds_from_uncertainty_uses_default_multiplier():
+    from easydiffraction.core.validation import AttributeSpec
+    from easydiffraction.core.variable import Parameter
+    from easydiffraction.io.cif.handler import CifHandler
+
+    p = Parameter(
+        name='default_multiplier',
+        value_spec=AttributeSpec(default=0.0),
+        cif_handler=CifHandler(names=['_param.default_multiplier']),
+    )
+    p.value = 2.0
+    p.uncertainty = 0.25
+
+    p.set_fit_bounds_from_uncertainty()
+
+    assert np.isclose(p.fit_min, 1.0)
+    assert np.isclose(p.fit_max, 3.0)
+
+
 def test_parameter_set_fit_bounds_from_uncertainty_clips_to_physical_limits():
     from easydiffraction.core.validation import AttributeSpec
     from easydiffraction.core.validation import DataTypes
diff --git a/tests/unit/easydiffraction/display/test_plotting.py b/tests/unit/easydiffraction/display/test_plotting.py
index 3b434c8d..c0ef1615 100644
--- a/tests/unit/easydiffraction/display/test_plotting.py
+++ b/tests/unit/easydiffraction/display/test_plotting.py
@@ -307,12 +307,12 @@ def test_correlation_from_posterior_samples_returns_labeled_dataframe():
 
 
 def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE
     from easydiffraction.display.plotting import POSTERIOR_PAIR_SAMPLE_MARKER_SIZE
     from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_TOP_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TOP_MARGIN_PIXELS
 
     plotter, _, _ = _make_bayesian_plotter_fixture()
 
@@ -346,15 +346,15 @@ def test_build_posterior_pairs_plot_hides_diagonal_ticks_and_uses_annotations():
         'twotheta_offset',
     ]
     assert figure.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
-    assert figure.layout.annotations[0].yshift == POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
+    assert figure.layout.annotations[0].yshift == SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
     assert figure.layout.annotations[0].xshift == -plotter._square_matrix_title_left_shift([
         'length_a',
         'broad_gauss_u',
         'broad_gauss_v',
         'twotheta_offset',
     ])
-    assert figure.layout.margin.t == POSTERIOR_PAIR_TOP_MARGIN_PIXELS
-    assert figure.layout.margin.b == POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
+    assert figure.layout.margin.t == SQUARE_MATRIX_TOP_MARGIN_PIXELS
+    assert figure.layout.margin.b == SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
     subplot = figure.get_subplot(1, 1)
     bottom_subplot = figure.get_subplot(4, 1)
     assert subplot.yaxis.showticklabels is False
@@ -431,8 +431,8 @@ def test_build_posterior_pairs_plot_sign_colors_contours_and_marginals():
 
 
 def test_build_posterior_pairs_plot_formats_dotted_axis_titles_multiline():
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_LEFT_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_LEFT_MARGIN_PIXELS
 
     plotter, fit_results, _ = _make_bayesian_plotter_fixture()
     dotted_parameter_names = [
@@ -452,8 +452,8 @@ def test_build_posterior_pairs_plot_formats_dotted_axis_titles_multiline():
     assert 'hrpt.<br>peak.<br>broad_gauss_u' in annotation_texts
     assert 'hrpt.<br>instrument.<br>twotheta_offset' in annotation_texts
     assert all('None broad_gauss_u' not in text for text in annotation_texts)
-    assert figure.layout.margin.l > POSTERIOR_PAIR_LEFT_MARGIN_PIXELS
-    assert figure.layout.margin.b > POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
+    assert figure.layout.margin.l > SQUARE_MATRIX_LEFT_MARGIN_PIXELS
+    assert figure.layout.margin.b > SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
 
 
 def test_build_posterior_pairs_plot_uses_full_names_in_hovertemplates():
@@ -753,20 +753,28 @@ class Experiment:
 
 
 def test_build_param_distribution_plot_accepts_unique_name_string():
-    plotter, _, _ = _make_bayesian_plotter_fixture()
+    plotter, fit_results, posterior_samples = _make_bayesian_plotter_fixture()
+    unique_name = 'phase.cell.length_a'
+    posterior_samples.parameter_names[0] = unique_name
+    fit_results.parameters[0].unique_name = unique_name
+    fit_results.posterior_parameter_summaries[0].unique_name = unique_name
 
-    figure = plotter._build_param_distribution_plot('length_a')
+    figure = plotter._build_param_distribution_plot(unique_name)
 
-    assert figure.layout.title.text == 'Posterior distribution: length_a'
+    assert figure.layout.title.text == f'Posterior distribution: {unique_name}'
 
 
 def test_build_param_distribution_plot_accepts_user_facing_label_string():
-    plotter, fit_results, _ = _make_bayesian_plotter_fixture()
+    plotter, fit_results, posterior_samples = _make_bayesian_plotter_fixture()
+    unique_name = 'phase.cell.length_a'
+    posterior_samples.parameter_names[0] = unique_name
+    fit_results.parameters[0].unique_name = unique_name
+    fit_results.posterior_parameter_summaries[0].unique_name = unique_name
     fit_results.posterior_parameter_summaries[0].display_name = 'Cell a'
 
     figure = plotter._build_param_distribution_plot('Cell a')
 
-    assert figure.layout.title.text == 'Posterior distribution: length_a'
+    assert figure.layout.title.text == f'Posterior distribution: {unique_name}'
 
 
 def test_resolve_posterior_parameter_names_warns_on_ambiguous_label(monkeypatch):
@@ -1547,12 +1555,12 @@ def test_plot_param_correlations_renders_plotly_heatmap(monkeypatch):
     import numpy as np
 
     import easydiffraction.display.plotters.plotly as plotly_mod
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS
     from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_FONT_SIZE
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
-    from easydiffraction.display.plotting import POSTERIOR_PAIR_TOP_MARGIN_PIXELS
     from easydiffraction.display.plotting import Plotter
+    from easydiffraction.display.plotting import SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
+    from easydiffraction.display.plotting import SQUARE_MATRIX_TOP_MARGIN_PIXELS
 
     captured = {}
 
@@ -1617,14 +1625,14 @@ class Project:
         'phase.<br>cell.<br>length_c',
     ]
     assert fig.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE
-    assert fig.layout.annotations[0].yshift == POSTERIOR_PAIR_TITLE_YSHIFT_PIXELS
+    assert fig.layout.annotations[0].yshift == SQUARE_MATRIX_TITLE_YSHIFT_PIXELS
     assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift([
         'phase.<br>scale',
         'phase.<br>cell.<br>length_c',
     ])
-    assert fig.layout.margin.t == POSTERIOR_PAIR_TOP_MARGIN_PIXELS
+    assert fig.layout.margin.t == SQUARE_MATRIX_TOP_MARGIN_PIXELS
     assert fig.layout.margin.b == (
-        POSTERIOR_PAIR_BOTTOM_MARGIN_PIXELS + 2 * POSTERIOR_PAIR_AXIS_TITLE_LINE_HEIGHT_PIXELS
+        SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS + 2 * SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS
     )
     assert (
         fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio']

From 043e7fd27c2b6af9aa2226a8ffa03b52e72cda56 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 20:49:58 +0200
Subject: [PATCH 097/106] Show uncertainty multiplier in pair plot titles

---
 .../analysis/minimizers/bumps_dream.py        |  2 +-
 src/easydiffraction/core/variable.py          | 26 +++++++---
 src/easydiffraction/display/plotting.py       | 50 ++++++++++++++++++-
 3 files changed, 70 insertions(+), 8 deletions(-)

diff --git a/src/easydiffraction/analysis/minimizers/bumps_dream.py b/src/easydiffraction/analysis/minimizers/bumps_dream.py
index 5c1681b6..c9af1012 100644
--- a/src/easydiffraction/analysis/minimizers/bumps_dream.py
+++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py
@@ -31,7 +31,7 @@
 from easydiffraction.utils.logging import log
 
 DEFAULT_METHOD = 'dream'
-DEFAULT_MAX_ITERATIONS = 1000
+DEFAULT_MAX_ITERATIONS = 3000
 DEFAULT_BURN_FRACTION = 0.2
 DEFAULT_MIN_BURN = 50
 DEFAULT_THIN = 1
diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index 4e0fb499..b804889e 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -22,6 +22,8 @@
 
 # ======================================================================
 
+FIT_BOUNDS_FROM_UNCERTAINTY_DEFAULT_MULTIPLIER = 4.0
+
 
 class GenericDescriptorBase(GuardedBase):
     """
@@ -280,6 +282,7 @@ def __init__(
         self._fit_min = self._fit_min_spec.default
         self._fit_max_spec = AttributeSpec(data_type=DataTypes.NUMERIC, default=np.inf)
         self._fit_max = self._fit_max_spec.default
+        self._fit_bounds_uncertainty_multiplier: float | None = None
         self._start_value_spec = AttributeSpec(data_type=DataTypes.NUMERIC, default=0.0)
         self._start_value = self._start_value_spec.default
         self._constrained_spec = self._BOOL_SPEC_TEMPLATE
@@ -414,6 +417,7 @@ def fit_min(self, v: float) -> None:
         self._fit_min = self._fit_min_spec.validated(
             v, name=f'{self.unique_name}.fit_min', current=self._fit_min
         )
+        self._fit_bounds_uncertainty_multiplier = None
 
     @property
     def fit_max(self) -> float:
@@ -426,10 +430,18 @@ def fit_max(self, v: float) -> None:
         self._fit_max = self._fit_max_spec.validated(
             v, name=f'{self.unique_name}.fit_max', current=self._fit_max
         )
+        self._fit_bounds_uncertainty_multiplier = None
+
+    @property
+    def fit_bounds_uncertainty_multiplier(self) -> float | None:
+        """
+        Multiplier used for uncertainty-derived fit bounds, if known.
+        """
+        return self._fit_bounds_uncertainty_multiplier
 
     def set_fit_bounds_from_uncertainty(
         self,
-        multiplier: float = 4.0,
+        multiplier: float = FIT_BOUNDS_FROM_UNCERTAINTY_DEFAULT_MULTIPLIER,
         *,
         clip_to_limits: bool = True,
     ) -> None:
@@ -438,7 +450,7 @@ def set_fit_bounds_from_uncertainty(
 
         Parameters
         ----------
-        multiplier : float, default=4.0
+        multiplier : float, default=FIT_BOUNDS_FROM_UNCERTAINTY_DEFAULT_MULTIPLIER
             Positive finite factor applied symmetrically to the current
             parameter uncertainty.
         clip_to_limits : bool, default=True
@@ -459,10 +471,11 @@ def set_fit_bounds_from_uncertainty(
             msg = f'Cannot set fit bounds for {name}: current value is missing or invalid.'
             raise ValueError(msg)
 
-        if isinstance(multiplier, bool) or not np.isfinite(float(multiplier)):
+        resolved_multiplier = float(multiplier)
+        if isinstance(multiplier, bool) or not np.isfinite(resolved_multiplier):
             msg = 'multiplier must be a positive finite number.'
             raise ValueError(msg)
-        if float(multiplier) <= 0:
+        if resolved_multiplier <= 0:
             msg = 'multiplier must be a positive finite number.'
             raise ValueError(msg)
 
@@ -470,8 +483,8 @@ def set_fit_bounds_from_uncertainty(
             msg = f'Cannot set fit bounds for {name}: uncertainty is missing or invalid.'
             raise ValueError(msg)
 
-        lower = float(value) - float(multiplier) * float(uncertainty)
-        upper = float(value) + float(multiplier) * float(uncertainty)
+        lower = float(value) - resolved_multiplier * float(uncertainty)
+        upper = float(value) + resolved_multiplier * float(uncertainty)
 
         if clip_to_limits:
             physical_lower = float(self._physical_lower_bound())
@@ -490,6 +503,7 @@ def set_fit_bounds_from_uncertainty(
 
         self.fit_min = lower
         self.fit_max = upper
+        self._fit_bounds_uncertainty_multiplier = resolved_multiplier
 
 
 # ======================================================================
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index a7d6ece5..65961dd5 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -880,6 +880,47 @@ def _correlation_filtered_title(base_title: str, threshold: float) -> str:
             return base_title
         return f'{base_title} with |correlation| ≥ {threshold:.2f}'
 
+    @staticmethod
+    def _posterior_pair_title(multiplier: float | None) -> str:
+        """
+        Return the posterior pair title with its displayed bound scale.
+        """
+        if multiplier is None:
+            return 'Posterior pair plot'
+        return f'Posterior pair plot in ±{multiplier:g}×uncertainty region'
+
+    @staticmethod
+    def _posterior_pair_uncertainty_multiplier(
+        fit_results: object,
+        parameter_names: list[str],
+    ) -> float | None:
+        """
+        Return a shared uncertainty-bound multiplier for a pair plot.
+        """
+        parameters_by_name = {
+            getattr(parameter, 'unique_name', ''): parameter
+            for parameter in fit_results.parameters
+        }
+        multiplier: float | None = None
+
+        for parameter_name in parameter_names:
+            parameter = parameters_by_name.get(parameter_name)
+            if parameter is None:
+                return None
+
+            current = getattr(parameter, 'fit_bounds_uncertainty_multiplier', None)
+            if current is None or not np.isfinite(float(current)):
+                return None
+
+            current_value = float(current)
+            if multiplier is None:
+                multiplier = current_value
+                continue
+            if not np.isclose(multiplier, current_value):
+                return None
+
+        return multiplier
+
     def plot_posterior_pairs(
         self,
         parameters: list[object] | None = None,
@@ -1501,13 +1542,20 @@ def _posterior_pairs_context(
             selected_samples,
             max_points=POSTERIOR_PAIR_SCATTER_MAX_POINTS,
         )
+        uncertainty_multiplier = self._posterior_pair_uncertainty_multiplier(
+            fit_results,
+            parameter_names,
+        )
 
         return _PosteriorPairsContext(
             fit_results=fit_results,
             parameter_names=parameter_names,
             labels=self._posterior_plot_labels(fit_results, parameter_names),
             annotation_labels=self._square_matrix_axis_title_labels(parameter_names),
-            title=self._correlation_filtered_title('Posterior pair plot', resolved_threshold),
+            title=self._correlation_filtered_title(
+                self._posterior_pair_title(uncertainty_multiplier),
+                resolved_threshold,
+            ),
             density_samples=density_samples,
             scatter_samples=scatter_samples,
             show_contours=show_contours,

From 3db44d1729d9245227772e369a763ee656d50573 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 21:12:53 +0200
Subject: [PATCH 098/106] Refine Bayesian tutorial examples

---
 docs/docs/tutorials/ed-21.py            |  65 +++++++-----
 docs/docs/tutorials/ed-22.py            | 129 +++++++++++++++++++++++-
 src/easydiffraction/display/plotting.py |   2 +-
 3 files changed, 170 insertions(+), 26 deletions(-)

diff --git a/docs/docs/tutorials/ed-21.py b/docs/docs/tutorials/ed-21.py
index fe0cf405..d91173c3 100644
--- a/docs/docs/tutorials/ed-21.py
+++ b/docs/docs/tutorials/ed-21.py
@@ -114,13 +114,16 @@
 # excluded regions.
 
 # %% [markdown]
-# #### Download the Measured Data
+# Download the measured data from the repository. Alternatively, you
+# could use your own data file by providing the path to it instead of
+# downloading from the repository.
 
 # %%
 data_path = ed.download_data(id=3, destination='data')
 
 # %% [markdown]
-# #### Create the Experiment Object
+# Create the experiment object and specify the sample form, beam mode,
+# and radiation probe.
 
 # %%
 project.experiments.add_from_data_path(
@@ -135,7 +138,13 @@
 experiment = project.experiments['hrpt']
 
 # %% [markdown]
-# #### Set Instrument and Peak-Profile Parameters
+# Link the structural phase to the experiment.
+
+# %%
+experiment.linked_phases.create(id='lbco', scale=9.1351)
+
+# %% [markdown]
+# Set instrument and peak profile parameters.
 #
 # These values provide the initial instrument description for the local
 # refinement. Later, a subset of them will be refined.
@@ -151,7 +160,7 @@
 experiment.peak.broad_lorentz_y = 0.0844
 
 # %% [markdown]
-# #### Add Background Points and Excluded Regions
+# Add background points and excluded regions.
 #
 # The line-segment background is defined by a few anchor points. We also
 # exclude regions that are not intended to contribute to the fit.
@@ -166,12 +175,6 @@
 experiment.excluded_regions.create(id='1', start=0, end=10)
 experiment.excluded_regions.create(id='2', start=100, end=180)
 
-# %% [markdown]
-# #### Link the Structural Phase to the Experiment
-
-# %%
-experiment.linked_phases.create(id='lbco', scale=9.1351)
-
 # %% [markdown]
 # ## Step 4: Run an Initial Local Refinement
 #
@@ -235,15 +238,23 @@
 # on the current parameter value and expands them by a chosen multiple of
 # the reported uncertainty.
 #
-# Default `multiplier` is 8 to give a wide range for the sampler to
-# explore, but here we use 3 to speed up the tutorial.
+# The default `multiplier` is 4. If the local refinement is very tight,
+# or if you expect a broader posterior, increase it explicitly.
+#
+# Show unset fit bounds before setting them from the local refinement uncertainties.
 
 # %%
 project.analysis.display.free_params()
 
+# %% [markdown]
+# Set fit bounds for all free parameters using the default multiplier of
+# 4. In this tutorial that means the posterior pair plot will later
+# refer to a `±4 × uncertainty` region in its title. To use a different
+# region, pass another value, for example `multiplier=6`.
+
 # %%
 for param in project.free_parameters:
-    param.set_fit_bounds_from_uncertainty(multiplier=3.5)
+    param.set_fit_bounds_from_uncertainty()
 
 # %% [markdown]
 # Displaying the free parameters again is a convenient way to confirm
@@ -263,14 +274,15 @@
 # of steps (`steps`) and often the burn-in (`burn`) as well. When
 # needed, the DREAM API also lets you tune how chains are initialized
 # through the `init` setting. Other sampler settings such as `thin` and
-# `pop` can be adjusted as well. The current EasyDiffraction default
-# also uses `parallel=0`, which tells BUMPS DREAM to use all available
-# CPUs for population evaluations.
+# `pop` can be adjusted as well. The current EasyDiffraction defaults
+# use `steps=3000`, `init='lhs'`, and `parallel=0`, which tells
+# BUMPS-DREAM to use all available CPUs for population evaluations.
 #
-# The default `steps` value is 1000, and real analyses often need more
-# to achieve good convergence and posterior sampling. Here we use a much
-# smaller value to keep the tutorial fast, but this is not recommended
-# for production analysis.
+# The `burn` setting is auto-resolved when left unset. With the default
+# `steps=3000` this gives `burn=600`, but if you override `steps` and
+# keep `burn=None`, the effective burn-in is recomputed automatically.
+# Here we use a much smaller step count to keep the tutorial fast, but
+# this is not recommended for production analysis.
 
 # %%
 project.analysis.fit.show_minimizer_types()
@@ -279,7 +291,7 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 100  # lower than the default 1000
+project.analysis.fit.minimizer.steps = 300  # lower than the default 3000
 
 # %%
 project.analysis.fit()
@@ -300,7 +312,10 @@
 # - `plot_param_correlations` summarizes pairwise structure in a compact
 #   matrix.
 # - `plot_posterior_pairs` shows marginal densities on the diagonal and
-#   posterior contours off-diagonal.
+#   posterior contours off-diagonal. In this tutorial its title also
+#   reminds you that the display region follows the `±4 × uncertainty`
+#   bounds defined above, while numeric subplot ranges are omitted to
+#   keep the grid readable.
 
 # %%
 project.display.plotter.plot_param_correlations()
@@ -332,4 +347,8 @@
 # after the Bayesian run.
 
 # %%
-project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=92, x_max=93)
+project.display.plotter.plot_posterior_predictive(
+    expt_name='hrpt',
+    x_min=92,
+    x_max=93,
+)
diff --git a/docs/docs/tutorials/ed-22.py b/docs/docs/tutorials/ed-22.py
index 68f447d2..aaa72718 100644
--- a/docs/docs/tutorials/ed-22.py
+++ b/docs/docs/tutorials/ed-22.py
@@ -11,6 +11,15 @@
 #
 # The example uses constant-wavelength neutron single-crystal diffraction data
 # for Tb2TiO7 measured on HEiDi at FRM II.
+#
+# The goal is not only to obtain a good fit, but also to answer Bayesian
+# questions such as:
+#
+# - Which parameter values are most probable?
+# - How broad are the credible intervals?
+# - Which parameters are strongly correlated?
+# - How much uncertainty propagates into the calculated reflection
+#   intensities?
 
 # %% [markdown]
 # ## Import Library
@@ -20,12 +29,21 @@
 
 # %% [markdown]
 # ## Step 1: Create a Project Container
+#
+# The project object keeps structures, experiments, fit settings, and
+# plotting utilities together in a single place. We will build the full
+# workflow inside this object.
 
 # %%
 project = ed.Project()
 
 # %% [markdown]
 # ## Step 2: Build the Structural Model
+#
+# For this example we start from a CIF file describing the Tb2TiO7
+# pyrochlore structure. Loading the structure from CIF is convenient
+# because it preserves a realistic starting
+# model without rebuilding the full structure by hand.
 
 # %%
 structure_path = ed.download_data(id=20, destination='data')
@@ -38,6 +56,10 @@
 
 # %% [markdown]
 # ## Step 3: Define the Diffraction Experiment
+#
+# Next we download the measured reflection data, create a neutron
+# single-crystal experiment, and configure the crystal link,
+# wavelength, and extinction model.
 
 # %%
 data_path = ed.download_data(id=19, destination='data')
@@ -54,10 +76,18 @@
 # %%
 experiment = project.experiments['heidi']
 
+# %% [markdown]
+# Link the crystal structure to the experiment and set its scale factor.
+
 # %%
 experiment.linked_crystal.id = 'tbti'
 experiment.linked_crystal.scale = 1.0
 
+# %% [markdown]
+# Set the instrument wavelength and starting extinction parameters.
+# These values provide the initial experiment description for the local
+# refinement.
+
 # %%
 experiment.instrument.setup_wavelength = 0.793
 
@@ -67,6 +97,17 @@
 
 # %% [markdown]
 # ## Step 4: Run an Initial Local Refinement
+#
+# Before Bayesian sampling, it is useful to run a deterministic fit. This
+# gives us:
+#
+# - a good point estimate near the best-fit region,
+# - uncertainties from the local optimizer,
+# - a quick check that the model and experiment are configured
+#   sensibly.
+#
+# In this tutorial we refine a small set of structural and extinction
+# parameters while keeping occupancies fixed.
 
 # %%
 structure.atom_sites['O1'].fract_x.free = True
@@ -84,15 +125,30 @@
 experiment.linked_crystal.scale.free = True
 experiment.extinction.radius.free = True
 
+# %% [markdown]
+# We keep using the default LMFIT Levenberg-Marquardt minimizer as a fast local
+# optimizer. Its main purpose here is to provide a stable starting point
+# and uncertainty estimates for the Bayesian run.
+
 # %%
 project.analysis.fit.show_minimizer_types()
 
 # %%
 project.analysis.fit()
 
+# %% [markdown]
+# The fit-results display summarizes the locally refined values and their
+# estimated uncertainties.
+
 # %%
 project.analysis.display.fit_results()
 
+# %% [markdown]
+# The correlation plot shows how strongly the refined parameters move
+# together in the local refinement. The measured-vs-calculated plot shows
+# how well the refined crystal model reproduces the measured reflection
+# intensities.
+
 # %%
 project.display.plotter.plot_param_correlations()
 
@@ -101,19 +157,60 @@
 
 # %% [markdown]
 # ## Step 5: Prepare for Bayesian Sampling
+#
+# DREAM requires finite bounds for the free parameters. Instead of
+# setting them manually, we derive them from the uncertainties estimated
+# in the local refinement.
+#
+# The helper method `set_fit_bounds_from_uncertainty` centers the bounds
+# on the current parameter value and expands them by a chosen multiple of
+# the reported uncertainty.
+#
+# The default `multiplier` is 4. In this single-crystal tutorial we use
+# a tighter value of `1.5` to keep the sampling window closer to the
+# locally refined solution.
+#
+# Show unset fit bounds before setting them from the local refinement
+# uncertainties.
 
 # %%
 project.analysis.display.free_params()
 
+# %% [markdown]
+# Set fit bounds for all free parameters using `multiplier=1.5`. In this
+# tutorial that means the posterior pair plot will later refer to a
+# `±1.5 × uncertainty` region in its title. To widen the sampling window,
+# increase the multiplier explicitly.
+
 # %%
 for param in project.free_parameters:
     param.set_fit_bounds_from_uncertainty(multiplier=1.5)
 
+# %% [markdown]
+# Displaying the free parameters again is a convenient way to confirm
+# that the fit bounds have been assigned as expected before launching the
+# sampler.
+
 # %%
 project.analysis.display.free_params()
 
 # %% [markdown]
-# ## Step 6: Configure and Run BUMPS-DREAM
+# ## Step 6: Configure and Run DREAM
+#
+# We now switch from the local minimizer to the Bayesian DREAM sampler.
+#
+# The settings below are intentionally small so the tutorial runs
+# quickly. For production analysis you would usually increase the number
+# of steps (`steps`) and often the burn-in (`burn`) as well. When
+# needed, the DREAM API also lets you tune how chains are initialized
+# through the `init` setting. Other sampler settings such as `thin` and
+# `pop` can be adjusted as well. The current EasyDiffraction defaults
+# use `steps=3000`, `init='lhs'`, and `parallel=0`, which tells
+# BUMPS-DREAM to use all available CPUs for population evaluations.
+#
+# The `burn` setting is auto-resolved when left unset. Here we override
+# `steps` with a smaller value to keep the tutorial fast, and the
+# effective burn-in is recomputed automatically.
 
 # %%
 project.analysis.fit.show_minimizer_types()
@@ -122,23 +219,51 @@
 project.analysis.fit.minimizer_type = 'bumps (dream)'
 
 # %%
-project.analysis.fit.minimizer.steps = 500
+project.analysis.fit.minimizer.steps = 500  # lower than the default 3000
 
 # %%
 project.analysis.fit()
 
+# %% [markdown]
+# ## Step 7: Inspect Bayesian Results
+#
+# The fit-results display now includes sampler settings, convergence
+# diagnostics, committed parameter values, and posterior summary
+# statistics.
+
 # %%
 project.analysis.display.fit_results()
 
+# %% [markdown]
+# The correlation and posterior-pair plots are complementary:
+#
+# - `plot_param_correlations` summarizes pairwise structure in a compact
+#   matrix.
+# - `plot_posterior_pairs` shows marginal densities on the diagonal and
+#   posterior contours off-diagonal. In this tutorial its title also
+#   reminds you that the display region follows the `±1.5 × uncertainty`
+#   bounds defined above, while numeric subplot ranges are omitted to
+#   keep the grid readable.
+
 # %%
 project.display.plotter.plot_param_correlations()
 
 # %%
 project.display.plotter.plot_posterior_pairs()
 
+# %% [markdown]
+# The one-dimensional posterior distributions below make it easier to
+# inspect individual parameters in isolation, including asymmetry or
+# multimodality.
+
 # %%
 for param in project.free_parameters:
     project.display.plotter.plot_param_distribution(param)
 
+# %% [markdown]
+# Finally, the posterior predictive plot propagates the sampled
+# parameter uncertainty into the calculated single-crystal reflection
+# intensities.
+
 # %%
 project.display.plotter.plot_posterior_predictive(expt_name='heidi')
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index 65961dd5..34b0da10 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -887,7 +887,7 @@ def _posterior_pair_title(multiplier: float | None) -> str:
         """
         if multiplier is None:
             return 'Posterior pair plot'
-        return f'Posterior pair plot in ±{multiplier:g}×uncertainty region'
+        return f'Posterior pair plot in ±{multiplier:g} × uncertainty region'  # noqa: RUF001
 
     @staticmethod
     def _posterior_pair_uncertainty_multiplier(

From a862edc76ecc95c582957766236d467d92c6d753 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 21:26:58 +0200
Subject: [PATCH 099/106] Shorten fit-bounds multiplier constant name

---
 src/easydiffraction/core/variable.py                        | 6 +++---
 .../easydiffraction/analysis/minimizers/test_bumps_dream.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py
index b804889e..303c5bbe 100644
--- a/src/easydiffraction/core/variable.py
+++ b/src/easydiffraction/core/variable.py
@@ -22,7 +22,7 @@
 
 # ======================================================================
 
-FIT_BOUNDS_FROM_UNCERTAINTY_DEFAULT_MULTIPLIER = 4.0
+DEFAULT_FIT_BOUNDS_MULTIPLIER = 4.0
 
 
 class GenericDescriptorBase(GuardedBase):
@@ -441,7 +441,7 @@ def fit_bounds_uncertainty_multiplier(self) -> float | None:
 
     def set_fit_bounds_from_uncertainty(
         self,
-        multiplier: float = FIT_BOUNDS_FROM_UNCERTAINTY_DEFAULT_MULTIPLIER,
+        multiplier: float = DEFAULT_FIT_BOUNDS_MULTIPLIER,
         *,
         clip_to_limits: bool = True,
     ) -> None:
@@ -450,7 +450,7 @@ def set_fit_bounds_from_uncertainty(
 
         Parameters
         ----------
-        multiplier : float, default=FIT_BOUNDS_FROM_UNCERTAINTY_DEFAULT_MULTIPLIER
+        multiplier : float, default=DEFAULT_FIT_BOUNDS_MULTIPLIER
             Positive finite factor applied symmetrically to the current
             parameter uncertainty.
         clip_to_limits : bool, default=True
diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
index 5c3dd60b..eea9f089 100644
--- a/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
+++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py
@@ -50,7 +50,7 @@ def test_type_info_and_default_init():
 
     assert minimizer.type_info.tag == MinimizerTypeEnum.BUMPS_DREAM
     assert minimizer.init is DreamPopulationInitializationEnum.LHS
-    assert minimizer.steps == 1000
+    assert minimizer.steps == 3000
 
 
 def test_dream_progress_monitor_allocates_rows_by_phase_ratio():

From 8a21d9424e658d1ca286ddc11a33dad0c5c8693c Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Tue, 12 May 2026 21:27:23 +0200
Subject: [PATCH 100/106] Bump dependencies

---
 pixi.lock | 159 ++++++++++++++++++++++++++++--------------------------
 1 file changed, 84 insertions(+), 75 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 73f8d611..04890b1c 100644
--- a/pixi.lock
+++ b/pixi.lock
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-  sha256: d1a71cf58f2f9228fff23a1f6ec15d39785c6b32e03658d104974247145edd35
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   name: mdurl
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From d17b4797ededfc7e95056392050afcbf16f5a806 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Wed, 13 May 2026 14:48:02 +0200
Subject: [PATCH 101/106] Add ADR documents

---
 docs/dev/adr_analysis-cif-fit-state.md        | 221 ++++++
 .../adr_parameter-correlation-persistence.md  | 114 +++
 docs/dev/adr_parameter-posterior-summary.md   | 684 ++++++++++++++++++
 docs/dev/adr_undo-fit.md                      | 163 +++++
 4 files changed, 1182 insertions(+)
 create mode 100644 docs/dev/adr_analysis-cif-fit-state.md
 create mode 100644 docs/dev/adr_parameter-correlation-persistence.md
 create mode 100644 docs/dev/adr_parameter-posterior-summary.md
 create mode 100644 docs/dev/adr_undo-fit.md

diff --git a/docs/dev/adr_analysis-cif-fit-state.md b/docs/dev/adr_analysis-cif-fit-state.md
new file mode 100644
index 00000000..bf4af6f6
--- /dev/null
+++ b/docs/dev/adr_analysis-cif-fit-state.md
@@ -0,0 +1,221 @@
+# ADR: Analysis CIF Fit State
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+`analysis/analysis.cif` currently persists analysis configuration such
+as `_fit.minimizer_type`, `_fit.mode`, aliases, constraints, and
+joint-fit weights. It does not persist analysis-owned fit state such as
+fit bounds, bound provenance, pre-fit scalar snapshots, or latest
+fit-status metadata.
+
+At the same time, parameter CIF serialization already carries the
+committed parameter `value`, the current `free` state, and the current
+`uncertainty` via CIF bracket notation. That data belongs to the model
+and should remain in structure or experiment CIF files.
+
+The missing piece is analysis-owned fit state:
+
+- fit controls that apply to parameters during fitting but are not part
+  of the model itself
+- latest fit-status metadata shown by `display.fit_results()`
+- deterministic and Bayesian fit metadata that should survive project
+  reloads and command-line workflows
+
+This separation matters because:
+
+- Bayesian plotting after reload needs `fit_min`, `fit_max`, and bound
+  provenance even when raw posterior arrays are absent
+- command-line users need a saved pre-fit starting state to recover from
+  a poor minimization run
+- `analysis.fit_results` already changes by fit type, but its persisted
+  projection should have a stable analysis-owned home
+
+The current architecture document still describes fit results as
+runtime-only. This ADR proposes a narrower persisted projection of the
+latest fit state, not a direct dump of backend runtime objects.
+
+## Decision
+
+### 1. `analysis/analysis.cif` becomes the home of analysis-owned fit state
+
+The analysis CIF file will persist:
+
+- fit configuration
+- aliases and constraints
+- joint-fit weights
+- per-parameter fit controls owned by analysis
+- latest fit-status metadata common to deterministic and Bayesian fits
+- fit-type-specific extensions defined in separate ADRs
+
+Committed parameter values remain in structure or experiment CIF files.
+They are not duplicated into `analysis/analysis.cif`.
+
+### 2. Add a real `_fit_parameter` loop
+
+Introduce a new analysis-owned loop category:
+
+```cif
+loop_
+_fit_parameter.param_unique_name
+_fit_parameter.fit_min
+_fit_parameter.fit_max
+_fit_parameter.fit_bounds_uncertainty_multiplier
+_fit_parameter.start_value
+_fit_parameter.start_uncertainty
+cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+```
+
+Fields:
+
+- `param_unique_name`
+- `fit_min`
+- `fit_max`
+- `fit_bounds_uncertainty_multiplier`
+- `start_value`
+- `start_uncertainty`
+
+Rationale:
+
+- `fit_min` and `fit_max` are required to restore deterministic and
+  Bayesian fit controls faithfully.
+- `fit_bounds_uncertainty_multiplier` preserves the provenance of
+  uncertainty-derived bounds for restored Bayesian plot annotations.
+- `start_value` and `start_uncertainty` capture the last committed
+  pre-fit scalar state and enable fit recovery workflows, especially in
+  command-line usage.
+- `start_uncertainty` preserves a user-visible pre-fit uncertainty
+  instead of treating it as disposable fit residue.
+
+### 3. Add a generic `_fit_result` single-item category
+
+Persist the latest fit-status metadata shared across fit types in a
+single analysis-owned category:
+
+- `result_kind`
+- `success`
+- `message`
+- `iterations`
+- `fitting_time`
+- `reduced_chi_square`
+
+Suggested CIF fragment:
+
+```cif
+_fit_result.result_kind deterministic
+_fit_result.success yes
+_fit_result.message "Fit converged"
+_fit_result.iterations 37
+_fit_result.fitting_time 1.82
+_fit_result.reduced_chi_square 1.031
+```
+
+`result_kind` distinguishes the latest persisted fit projection, for
+example `deterministic` or `bayesian`.
+
+### 4. Persist only stable fit-status fields here
+
+The `_fit_result` category is for generic status fields that are stable
+across engines and already belong to the result model.
+
+It should not persist backend runtime objects or arbitrary engine
+payloads.
+
+Metrics such as R-factors shown by `display.fit_results()` are derived
+from observed and calculated data and can be recomputed after load when
+needed. They do not need to be part of the first persisted fit-state
+schema.
+
+### 5. Fit-type-specific extensions build on this ADR
+
+This ADR defines the common `analysis.cif` contract for deterministic
+and Bayesian fitting.
+
+Fit-type-specific extensions are layered on top:
+
+- Bayesian persistence extends this with `_bayesian_*` categories and an
+  HDF5 sidecar, as described in
+  `adr_parameter-posterior-summary.md`.
+- Future fit-specific summaries should follow the same pattern: generic
+  shared fields in `_fit_result`, specialized fields in separate
+  categories.
+
+### 6. Restore order is analysis-first, fit-type-second
+
+Load order should be:
+
+1. standard analysis configuration
+2. aliases and constraints
+3. joint-fit weights
+4. `_fit_parameter`
+5. `_fit_result`
+6. fit-type-specific extensions such as `_bayesian_*`
+
+This ensures that generic fit controls are available before restoring
+specialized fit summaries.
+
+### 7. Suggested full `analysis.cif` example
+
+```cif
+_fit.minimizer_type "bumps (dream)"
+_fit.mode single
+
+loop_
+_alias.label
+_alias.param_unique_name
+biso_Co1 cosio.atom_site.Co1.adp_iso
+biso_Co2 cosio.atom_site.Co2.adp_iso
+
+loop_
+_constraint.expression
+"biso_Co2 = biso_Co1"
+
+loop_
+_fit_parameter.param_unique_name
+_fit_parameter.fit_min
+_fit_parameter.fit_max
+_fit_parameter.fit_bounds_uncertainty_multiplier
+_fit_parameter.start_value
+_fit_parameter.start_uncertainty
+cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+
+_fit_result.result_kind bayesian
+_fit_result.success yes
+_fit_result.message "Sampler converged"
+_fit_result.iterations 3000
+_fit_result.fitting_time 82.4
+_fit_result.reduced_chi_square 1.031
+
+# Bayesian-specific extension categories follow here.
+```
+
+## Consequences
+
+### Positive
+
+- `analysis.cif` becomes the single analysis-owned source of fit state.
+- Deterministic and Bayesian persistence share one common base schema.
+- Fit bounds, bound provenance, and start values survive project
+  reloads.
+- Command-line workflows gain a persisted pre-fit starting state.
+
+### Trade-offs
+
+- The architecture document must be updated because fit state is no
+  longer entirely runtime-only.
+- Analysis persistence becomes more stateful and must be kept in sync
+  with live parameter objects.
+- Some existing serializer assumptions will need refactoring so that
+  `analysis.cif` owns fit metadata rather than individual parameters.
+
+## Deferred Work
+
+- Bayesian-specific categories and HDF5 sidecar details remain in
+  `adr_parameter-posterior-summary.md`.
+- Undo semantics for `start_value` and `start_uncertainty` are defined
+  in a separate ADR.
+- Correlation-matrix persistence is defined in a separate ADR.
diff --git a/docs/dev/adr_parameter-correlation-persistence.md b/docs/dev/adr_parameter-correlation-persistence.md
new file mode 100644
index 00000000..5d7b6888
--- /dev/null
+++ b/docs/dev/adr_parameter-correlation-persistence.md
@@ -0,0 +1,114 @@
+# ADR: Parameter Correlation Persistence
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+`plot_param_correlations()` can currently visualize either:
+
+- deterministic parameter correlations derived from engine covariance
+- Bayesian correlations derived from posterior samples
+
+After project reload, this correlation information is not available
+unless the underlying runtime objects are rebuilt. For Bayesian fits,
+full posterior samples may not always be restored. For deterministic
+fits, engine covariance is typically not persisted at all.
+
+The correlation matrix is an analysis-owned summary, not model state.
+It therefore belongs in `analysis/analysis.cif`, not in structure or
+experiment CIF files.
+
+## Decision
+
+### 1. Add a `_fit_parameter_correlation` loop category
+
+Persist pairwise parameter correlations in a new analysis-owned loop:
+
+- `source_kind`
+- `param_unique_name_i`
+- `param_unique_name_j`
+- `correlation`
+
+Suggested example:
+
+```cif
+loop_
+_fit_parameter_correlation.source_kind
+_fit_parameter_correlation.param_unique_name_i
+_fit_parameter_correlation.param_unique_name_j
+_fit_parameter_correlation.correlation
+posterior cosio.atom_site.Co1.adp_iso cosio.atom_site.Co2.adp_iso 0.87
+```
+
+`source_kind` records how the correlation was obtained, for example:
+
+- `deterministic`
+- `posterior`
+
+### 2. Store only the upper triangle excluding the diagonal
+
+Each row stores one unordered parameter pair with
+`param_unique_name_i < param_unique_name_j` in a stable ordering.
+
+The diagonal is omitted because it is always 1.0 and can be rebuilt on
+load.
+
+This keeps the CIF loop compact while remaining lossless for the
+correlation matrix.
+
+### 3. Treat the loop as a summary, not a replacement for raw samples
+
+For Bayesian fits, `_fit_parameter_correlation` is a persisted summary.
+It does not replace posterior samples or posterior pair data.
+
+This means:
+
+- correlation heatmaps can be restored from the loop alone
+- posterior pair plots still require posterior samples
+
+### 4. Deterministic and Bayesian fits share the same loop schema
+
+The same loop category is used for both deterministic and Bayesian fit
+results. The distinction is carried by `source_kind`, not by separate
+loop names.
+
+### 5. Suggested restore behavior
+
+On load:
+
+- if `_fit_parameter_correlation` is present, correlation summaries are
+  restored into a lightweight analysis-owned correlation structure
+- if it is absent, correlation plots fall back to whatever live runtime
+  information is available
+
+### 6. Suggested user-facing behavior
+
+```python
+# Restored from analysis.cif when available
+project.display.plotter.plot_param_correlations()
+```
+
+If only the correlation loop is restored, the user still gets the
+correlation heatmap without needing raw posterior samples.
+
+## Consequences
+
+### Positive
+
+- Deterministic and Bayesian correlation summaries survive reload.
+- Correlation heatmaps no longer depend entirely on runtime-only data.
+- The schema is compact and fit-type-agnostic.
+
+### Trade-offs
+
+- The correlation loop is a derived summary, so it must be kept in sync
+  with the latest fit result.
+- Restored correlation data is not enough for posterior pair plots or
+  predictive summaries.
+
+## Deferred Work
+
+- optional storage of covariance matrices in addition to correlations
+- multiple named correlation sources for the same saved project
+- full correlation restoration for pair-plot density surfaces
diff --git a/docs/dev/adr_parameter-posterior-summary.md b/docs/dev/adr_parameter-posterior-summary.md
new file mode 100644
index 00000000..5469a2bd
--- /dev/null
+++ b/docs/dev/adr_parameter-posterior-summary.md
@@ -0,0 +1,684 @@
+# ADR: Parameter-Level Posterior Projection and Bayesian Persistence
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+`GenericParameter` already stores fit-adjacent helper data such as
+`uncertainty`, `fit_min`, and `fit_max`, while `value` remains the
+single scalar used by calculations.
+
+Bayesian DREAM currently keeps posterior state only on
+`analysis.fit_results` via `BayesianFitResults`, including
+`posterior_samples`, `posterior_parameter_summaries`,
+`posterior_predictive`, diagnostics, and sampler settings. The current
+architecture document describes this state as runtime-only and not
+serialized.
+
+`analysis.fit_results` already changes by analysis type: deterministic
+fits use `FitResults`, while posterior-capable fits such as DREAM use
+`BayesianFitResults`. This ADR preserves that result-model split.
+
+The user-facing need is more local: when a posterior-capable fit has
+completed, each parameter should expose a compact Bayesian summary for
+inspection without forcing users to traverse `analysis.fit_results`.
+At the same time, `parameter.value` must remain the only scalar used by
+the live model, minimizer setup, constraints, and category updates.
+
+There is also a staleness problem. After a manual parameter edit or a
+new fit, fit-derived helper data must be cleared or replaced as a
+group. Keeping old posterior summaries after the active parameter state
+changes would mislead users.
+
+One more implementation constraint matters: minimizers currently apply
+final fitted values through `_set_value_from_minimizer(...)` and then
+write `param.uncertainty = ...` directly. If `uncertainty` and
+posterior metadata become read-only fit outputs and the public `value`
+setter starts clearing stale fit metadata on manual edits, fit-result
+application must move to a dedicated internal update path so that valid
+fit outputs are installed atomically instead of being cleared
+accidentally.
+
+## Decision
+
+### 1. Add one optional posterior object to each parameter
+
+Each `GenericParameter` will gain a read-only
+`posterior` property whose default value is `None`.
+
+This property is convenience metadata only. It does not participate in
+calculations. `parameter.value` remains the only scalar used by the
+live model.
+
+### 2. Reuse the existing Bayesian summary container
+
+Do not add separate flat parameter attributes such as `median`,
+`map_estimate`, `interval_95`, `r_hat`, or `ess_bulk`.
+
+Instead, the parameter-level projection reuses the existing
+`PosteriorParameterSummary` object already produced for
+`BayesianFitResults`. This keeps one grouped summary shape for display,
+inspection, and later persistence.
+
+The summary object currently provides the right level of detail:
+
+- `map_estimate`
+- `median`
+- `uncertainty`
+- `interval_68`
+- `interval_95`
+- `r_hat`
+- `ess_bulk`
+
+`unique_name` and `display_name` remain redundant but acceptable when
+the object is attached to a parameter.
+
+Different communities use different names for this scalar spread term:
+`standard deviation`, `estimated standard deviation` (`e.s.d.`), and
+`standard uncertainty` (`s.u.` / `su`). The public EasyDiffraction API
+selects `uncertainty` as the canonical name because it already matches
+the existing parameter API, aligns better with CIF terminology, and can
+be used consistently for both deterministic and Bayesian results. For
+Bayesian summaries, this `uncertainty` value is computed from the
+posterior standard deviation.
+
+The internal field names stay compact and code-oriented. User-facing
+tables, summaries, and plot annotations should use these friendly
+labels:
+
+- `map_estimate` -> `MAP estimate`
+- `median` -> `Median`
+- `uncertainty` -> `Standard uncertainty`
+- `interval_68` -> `68% credible interval`
+- `interval_95` -> `95% credible interval`
+- `r_hat` -> `R-hat`
+- `ess_bulk` -> `Bulk ESS`
+
+Asymmetric interval information remains explicit through the stored
+lower and upper bounds. For example, `parameter.posterior.interval_95`
+returns a `(lower, upper)` tuple rather than one symmetric uncertainty
+value.
+
+Example user access:
+
+```python
+param = project.phases['lbco'].cell.length_a
+
+current_value = param.value
+current_uncertainty = param.uncertainty
+
+if param.posterior is not None:
+  map_estimate = param.posterior.map_estimate
+  median = param.posterior.median
+  uncertainty = param.posterior.uncertainty
+  low95, high95 = param.posterior.interval_95
+  r_hat = param.posterior.r_hat
+  ess_bulk = param.posterior.ess_bulk
+```
+
+### 3. Make fit outputs read-only for the user
+
+`uncertainty` becomes a read-only fit output on the public parameter
+API. `posterior` is also read-only.
+
+Users may inspect these properties, but only internal fit-application
+paths may set or clear them.
+
+This keeps the writable public parameter state focused on user intent:
+`value`, `free`, `fit_min`, `fit_max`, and related configuration.
+
+### 4. Populate it only for posterior-capable fit methods
+
+Only fit methods that actually produce posterior summaries may populate
+`parameter.posterior`.
+
+At present this means only `bumps (dream)`.
+
+Do not add a generic minimizer-capability abstraction yet. That would
+introduce a new abstraction before there is a second concrete posterior
+fit method. When another posterior-capable minimizer exists, the shared
+contract can be extracted then.
+
+### 5. Treat parameter-level posterior data as a projection, not the canonical result
+
+The canonical Bayesian result remains `analysis.fit_results`.
+
+`parameter.posterior` is a synchronized projection of the current fit
+result for user convenience. It must never be the only place where
+Bayesian information lives, because it cannot represent joint posterior
+arrays, predictive summaries, or cross-parameter correlations.
+
+### 6. Clear or replace fit-derived metadata as a group
+
+Fit-derived helper data on a parameter must be coherent.
+
+The following policy applies:
+
+- Manual edit of `parameter.value` clears `parameter.uncertainty` and
+  `parameter.posterior`.
+- A deterministic fit replaces `value` and `uncertainty`, then clears
+  `posterior`.
+- A posterior fit replaces `value`, `uncertainty`, and `posterior`
+  together.
+
+Manual user edits of `uncertainty` and `posterior` are not supported,
+because both are read-only fit outputs.
+
+Configuration attributes such as `free`, `fit_min`, `fit_max`, units,
+and `fit_bounds_uncertainty_multiplier` are not cleared by this policy.
+They are parameter configuration or user intent, not posterior output.
+
+### 7. Add a dedicated internal fit-application path
+
+To support the clearing policy above, fit application must not rely on
+public setters alone.
+
+Implementation should add private helpers on `GenericParameter` to set
+and clear fit-derived state explicitly, for example:
+
+- `_set_uncertainty(...)`
+- `_set_posterior(...)`
+- `_clear_fit_metadata()`
+- `_apply_deterministic_fit_update(...)`
+- `_apply_posterior_fit_update(...)`
+
+The exact helper names can be refined during implementation, but the
+design requirement is fixed: manual user edits clear stale metadata,
+while internal fit application installs fresh metadata atomically.
+
+### 8. Commit MAP to `parameter.value` after Bayesian fits
+
+After a posterior-capable fit, `parameter.value` is committed from the
+maximum-a-posteriori estimate.
+
+MAP is chosen because it is a coherent joint point estimate across all
+free parameters. Marginal medians remain available on
+`parameter.posterior`, but they are summary data rather than the active
+live model state.
+
+### 9. Keep `uncertainty` as a convenience scalar after Bayesian fits
+
+`parameter.uncertainty` remains a single convenience scalar even after
+posterior fits.
+
+For posterior-capable fits, populate it from posterior standard
+deviation and expose it as `uncertainty`. This preserves one scalar API
+term across deterministic and Bayesian results while keeping the
+calculation itself statistically conventional.
+
+Asymmetric interval information is not squeezed into
+`parameter.uncertainty`; it remains available only via
+`parameter.posterior`.
+
+### 10. Persist canonical Bayesian state at analysis level
+
+Canonical Bayesian state is owned by `analysis.fit_results`, not by
+individual parameters.
+
+When the active result is a `BayesianFitResults` instance, persistence
+must save enough data to restore two distinct capability levels:
+
+- summary-only restore for parameter inspection and tables
+- full restore for posterior plots and predictive plots
+
+`parameter.posterior` is never serialized as a per-parameter property.
+It is always rebuilt from the canonical analysis-level persisted data.
+
+### 11. Persist fit-control and Bayesian metadata in `analysis/analysis.cif`
+
+The existing `analysis/analysis.cif` file remains the text metadata
+entry point for analysis persistence.
+
+The persisted fit-control and Bayesian metadata is split into explicit
+CIF categories.
+
+#### 11.1 `_fit_parameter` loop
+
+Stores analysis-owned per-parameter fit metadata that is not currently
+covered by parameter CIF serialization.
+
+This loop exists because the committed parameter CIF representation
+already carries the active `value`, current `free` state, and current
+`uncertainty`, but it does not carry fit bounds, bound provenance, or
+the pre-fit uncertainty snapshot needed by undo. Those fields are
+required for Bayesian plot ranges, pair-plot bound annotations, and
+clean fit rollback after project reload.
+
+Fields:
+
+- `param_unique_name`
+- `fit_min`
+- `fit_max`
+- `fit_bounds_uncertainty_multiplier`
+- `start_value`
+- `start_uncertainty`
+
+`fit_min` and `fit_max` are required for restored Bayesian plotting.
+`fit_bounds_uncertainty_multiplier` is required if restored plots should
+preserve the uncertainty-derived bound annotation exactly.
+`start_value` and `start_uncertainty` are required for clean
+cross-session undo. If omitted, restored fit reports may show `N/A` for
+`start` and `change`, and undo may need to clear uncertainty as a
+compatibility fallback.
+
+#### 11.2 `_bayesian_result` single item
+
+Stores one saved Bayesian result header with these fields:
+
+- `schema_version`
+- `sampler_name`
+- `point_estimate_name`
+- `success`
+- `sampler_completed`
+- `reduced_chi_square`
+- `fitting_time`
+- `best_log_posterior`
+- `credible_interval_inner`
+- `credible_interval_outer`
+- `has_posterior_samples`
+- `has_posterior_predictive`
+- `sidecar_file`
+
+For the current design, `point_estimate_name` is always `map`.
+
+#### 11.3 `_bayesian_sampler` single item
+
+Stores resolved sampler settings actually used for the run:
+
+- `steps`
+- `burn`
+- `thin`
+- `pop`
+- `parallel`
+- `init`
+- `random_seed`
+
+This persists the existing runtime `sampler_settings` in an explicit,
+schema-driven form rather than as an open-ended key/value mapping.
+
+#### 11.4 `_bayesian_convergence` single item
+
+Stores top-level convergence and shape metadata:
+
+- `converged`
+- `max_r_hat`
+- `min_ess_bulk`
+- `n_draws`
+- `n_chains`
+- `n_parameters`
+
+Per-parameter `r_hat` and `ess_bulk` remain in the parameter summary
+loop described below.
+
+#### 11.5 `_bayesian_parameter_posterior` loop
+
+Stores one canonical posterior summary row per sampled parameter.
+These rows are the source used to rebuild `parameter.posterior` on
+load.
+
+Fields:
+
+- `order_index`
+- `unique_name`
+- `display_name`
+- `map_estimate`
+- `median`
+- `uncertainty`
+- `interval_68_lower`
+- `interval_68_upper`
+- `interval_95_lower`
+- `interval_95_upper`
+- `ess_bulk`
+- `r_hat`
+
+`order_index` defines the parameter order used by posterior sample
+columns in the sidecar arrays.
+
+#### 11.6 `_bayesian_predictive_dataset` loop
+
+Stores one manifest row per persisted posterior predictive summary.
+
+Fields:
+
+- `experiment_name`
+- `x_axis_name`
+- `x_path`
+- `map_prediction_path`
+- `lower_95_path`
+- `upper_95_path`
+- `lower_68_path`
+- `upper_68_path`
+- `draws_path`
+- `n_x`
+- `n_draws_cached`
+
+This loop tells the loader which arrays to read from the sidecar file
+for each experiment-level predictive summary.
+
+#### 11.7 Suggested CIF fragments
+
+The active parameter value remains in the structure or experiment CIF as
+it does today, for example:
+
+```cif
+_atom_site_U_iso_or_equiv 0.0319(21)
+```
+
+Analysis-owned fit-control and Bayesian metadata then lives in
+`analysis/analysis.cif`, for example:
+
+```cif
+_fit.minimizer_type "bumps (dream)"
+_fit.mode single
+
+loop_
+_fit_parameter.param_unique_name
+_fit_parameter.fit_min
+_fit_parameter.fit_max
+_fit_parameter.fit_bounds_uncertainty_multiplier
+_fit_parameter.start_value
+_fit_parameter.start_uncertainty
+cosio.atom_site.Co1.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+cosio.atom_site.Co2.adp_iso 0.0000 0.1200 4.0 0.0312 0.0021
+
+_bayesian_result.schema_version 1
+_bayesian_result.sampler_name dream
+_bayesian_result.point_estimate_name map
+_bayesian_result.success yes
+_bayesian_result.sampler_completed yes
+_bayesian_result.reduced_chi_square 1.031
+_bayesian_result.fitting_time 82.4
+_bayesian_result.best_log_posterior -1542.77
+_bayesian_result.credible_interval_inner 0.68
+_bayesian_result.credible_interval_outer 0.95
+_bayesian_result.has_posterior_samples yes
+_bayesian_result.has_posterior_predictive yes
+_bayesian_result.sidecar_file "bayesian_data.h5"
+
+_bayesian_sampler.steps 3000
+_bayesian_sampler.burn 600
+_bayesian_sampler.thin 1
+_bayesian_sampler.pop 20
+_bayesian_sampler.parallel 0
+_bayesian_sampler.init lhs
+_bayesian_sampler.random_seed 12345
+
+_bayesian_convergence.converged yes
+_bayesian_convergence.max_r_hat 1.01
+_bayesian_convergence.min_ess_bulk 812.4
+_bayesian_convergence.n_draws 2400
+_bayesian_convergence.n_chains 20
+_bayesian_convergence.n_parameters 2
+
+loop_
+_bayesian_parameter_posterior.order_index
+_bayesian_parameter_posterior.unique_name
+_bayesian_parameter_posterior.display_name
+_bayesian_parameter_posterior.map_estimate
+_bayesian_parameter_posterior.median
+_bayesian_parameter_posterior.uncertainty
+_bayesian_parameter_posterior.interval_68_lower
+_bayesian_parameter_posterior.interval_68_upper
+_bayesian_parameter_posterior.interval_95_lower
+_bayesian_parameter_posterior.interval_95_upper
+_bayesian_parameter_posterior.ess_bulk
+_bayesian_parameter_posterior.r_hat
+0 cosio.atom_site.Co1.adp_iso "Co1 ADP" 0.0319 0.0317 0.0021 0.0298 0.0339 0.0278 0.0361 812.4 1.01
+1 cosio.atom_site.Co2.adp_iso "Co2 ADP" 0.0320 0.0318 0.0020 0.0300 0.0338 0.0281 0.0359 830.7 1.00
+
+loop_
+_bayesian_predictive_dataset.experiment_name
+_bayesian_predictive_dataset.x_axis_name
+_bayesian_predictive_dataset.x_path
+_bayesian_predictive_dataset.map_prediction_path
+_bayesian_predictive_dataset.lower_95_path
+_bayesian_predictive_dataset.upper_95_path
+_bayesian_predictive_dataset.lower_68_path
+_bayesian_predictive_dataset.upper_68_path
+_bayesian_predictive_dataset.draws_path
+_bayesian_predictive_dataset.n_x
+_bayesian_predictive_dataset.n_draws_cached
+hrpt ttheta /predictive/hrpt/x /predictive/hrpt/map_prediction /predictive/hrpt/lower_95 /predictive/hrpt/upper_95 /predictive/hrpt/lower_68 /predictive/hrpt/upper_68 /predictive/hrpt/draws 2500 200
+```
+
+### 12. Persist bulk arrays in `analysis/bayesian_data.h5`
+
+Large numerical arrays are stored outside CIF text in a single sidecar
+file:
+
+- `analysis/bayesian_data.h5`
+
+The sidecar is optional. Summary-only restore remains valid without it.
+
+HDF5 is selected instead of NPZ because the persisted Bayesian payload
+is a structured collection of named datasets with heterogeneous shapes,
+optional groups, and potentially large predictive arrays. HDF5 provides
+explicit hierarchical storage, dataset metadata, selective reads, and a
+better long-term path for compression or chunking. NPZ is simpler, but
+it is flatter and less suitable once the saved Bayesian state grows
+beyond a small set of arrays.
+
+#### 12.1 Required core HDF5 dataset paths when posterior samples are saved
+
+- `posterior_parameter_samples`
+- `posterior_log_posterior`
+- `posterior_draw_index`
+
+Expected array shapes:
+
+- `posterior_parameter_samples`: `(n_draws, n_chains, n_parameters)`
+- `posterior_log_posterior`: `(n_draws, n_chains)` when available
+- `posterior_draw_index`: `(n_draws,)` when available
+
+If `posterior_log_posterior` or `posterior_draw_index` are unavailable,
+their corresponding header flags remain false and the arrays may be
+omitted.
+
+#### 12.2 Predictive dataset keys
+
+Posterior predictive arrays are addressed through the
+`_bayesian_predictive_dataset` manifest rather than inferred from file
+ordering.
+
+Recommended HDF5 dataset naming is:
+
+- `predictive__<experiment>__x`
+- `predictive__<experiment>__map_prediction`
+- `predictive__<experiment>__lower_95`
+- `predictive__<experiment>__upper_95`
+- `predictive__<experiment>__lower_68`
+- `predictive__<experiment>__upper_68`
+- `predictive__<experiment>__draws`
+
+The manifest, not the naming convention, is the source of truth.
+
+Recommended HDF5 group layout is:
+
+- `/posterior/parameter_samples`
+- `/posterior/log_posterior`
+- `/posterior/draw_index`
+- `/predictive/<experiment>/x`
+- `/predictive/<experiment>/map_prediction`
+- `/predictive/<experiment>/lower_95`
+- `/predictive/<experiment>/upper_95`
+- `/predictive/<experiment>/lower_68`
+- `/predictive/<experiment>/upper_68`
+- `/predictive/<experiment>/draws`
+
+The manifest remains the canonical mapping used by the loader, so this
+layout is recommended rather than mandatory.
+
+#### 12.3 What is not persisted in the sidecar
+
+Do not persist backend-specific runtime objects such as `engine_result`,
+the DREAM driver, or ArviZ `InferenceData`.
+
+Those objects can be rebuilt from canonical saved arrays when needed,
+or left unavailable after load.
+
+### 13. Restore flow and partial-availability policy
+
+Persistence must support both full and partial restore.
+
+#### 13.1 Save flow
+
+When `Project.save()` sees `analysis.fit_results` as a
+`BayesianFitResults` instance:
+
+1. It writes standard analysis configuration to `analysis/analysis.cif`.
+2. It appends `_fit_parameter`, `_bayesian_result`,
+  `_bayesian_sampler`, `_bayesian_convergence`, and
+  `_bayesian_parameter_posterior`.
+3. If posterior predictive summaries are available, it also writes the
+  `_bayesian_predictive_dataset` manifest.
+4. If posterior sample arrays or predictive arrays are available, it
+  writes `analysis/bayesian_data.h5`.
+
+#### 13.2 Load flow
+
+When `Project.load()` restores `analysis/analysis.cif`:
+
+1. Standard analysis configuration is restored first.
+2. If `_fit_parameter` is present, fit bounds, bound provenance, and
+  optional pre-fit scalar snapshots are restored by matching
+  `param_unique_name` to live parameters.
+3. If Bayesian categories are present, a lightweight
+  `BayesianFitResults` instance is rebuilt from persisted metadata and
+  parameter summary rows.
+4. `parameter.posterior` is rebuilt by matching summary rows to live
+  parameters via `unique_name`.
+5. `parameter.value` and `parameter.uncertainty` continue to come from
+  the normal project serialization path; Bayesian restore does not
+  overwrite them.
+6. If `analysis/bayesian_data.h5` exists and matches the manifest,
+  `posterior_samples` and `posterior_predictive` are restored.
+7. If the sidecar is missing or incomplete, the restore degrades to
+  summary-only mode without failing the whole project load.
+
+#### 13.3 Partial restore behavior
+
+The chosen partial-restore policy is:
+
+- `parameter.posterior` and `display.fit_results()` remain available
+  from saved metadata and summaries.
+- Bayesian-only plots requiring canonical posterior arrays remain
+  unavailable unless those arrays were restored successfully.
+- Missing sidecar data should produce a clear warning, not a hard load
+  failure.
+
+Example user access after load:
+
+```python
+project = Project.load('/path/to/project')
+
+param = project.phases['lbco'].cell.length_a
+posterior = param.posterior
+
+if posterior is not None:
+    print(posterior.map_estimate)
+    print(posterior.uncertainty)
+    print(posterior.interval_68)
+
+project.analysis.display.fit_results()
+```
+
+Example plotting behavior after load:
+
+```python
+# Works with summary-only restore
+project.analysis.display.fit_results()
+
+# Requires canonical posterior arrays from analysis/bayesian_data.h5
+project.display.plotter.plot_posterior_pairs()
+project.display.plotter.plot_param_distribution(param)
+project.display.plotter.plot_posterior_predictive(expt_name='hrpt')
+```
+
+### 14. Keep parameter posterior data rebuilt, not duplicated
+
+`parameter.posterior` is always rebuilt from the canonical
+`_bayesian_parameter_posterior` loop in `analysis/analysis.cif`.
+
+Do not serialize posterior summaries again inside each parameter's own
+CIF representation. Duplicating the same posterior summary data across
+structure and experiment files would create multiple sources of truth.
+
+## Consequences
+
+### Positive
+
+- Users get a compact Bayesian overview directly from each parameter.
+- The parameter API stays tidy because posterior helpers are grouped
+  into one optional object rather than many flat attributes.
+- `uncertainty` and posterior metadata become clearly fit-owned rather
+  than mixed user-editable and fit-editable state.
+- `analysis.fit_results` remains the canonical source for full Bayesian
+  state.
+- `analysis/analysis.cif` becomes the home for fit-control metadata
+  that does not belong in structure or experiment CIF files.
+- Bayesian save/load gains a clear split between text metadata in
+  `analysis/analysis.cif` and bulk numerical arrays in
+  `analysis/bayesian_data.h5`.
+- Partial restore works for summaries even when full posterior arrays
+  are absent.
+- The design matches the current rule that `value` is the only active
+  scalar used for calculations.
+
+### Trade-offs
+
+- `GenericParameter` now holds an optional reference to analysis-derived
+  metadata.
+- Any manual user edit invalidates fit-derived metadata more eagerly
+  than today.
+- `uncertainty` becomes a read-only public property, so fit-result
+  application code must be updated to use dedicated internal helpers
+  rather than a mix of `_set_value_from_minimizer(...)` and public
+  uncertainty assignment.
+- Bayesian persistence now spans both CIF metadata and a binary sidecar,
+  so save/load code must validate consistency between the two.
+
+## Layering and Ownership
+
+To keep `core/` free of Bayesian computations, the parameter object must
+not compute posterior summaries itself.
+
+The summary object is created by posterior-capable fit code and then
+attached to parameters as already-computed metadata. `core/variable.py`
+should avoid eager runtime imports of Bayesian helper modules; a
+type-only import is acceptable.
+
+## Deferred Work
+
+This ADR now defines persistence for one canonical saved Bayesian run.
+
+It still defers:
+
+- support for multiple saved Bayesian runs per project
+- plot-ready cache layers beyond canonical posterior and predictive data
+- persistence-time compression or chunking strategies beyond the first
+  HDF5 sidecar implementation
+- persistence for future posterior-capable minimizers beyond DREAM
+- enabling currently unsupported single-crystal predictive draw plots
+
+## Implementation Notes
+
+- The first implementation should only populate
+  `parameter.posterior` for DREAM.
+- Existing `PosteriorParameterSummary` instances should be reused rather
+  than copied into a second summary type unless implementation reveals a
+  concrete layering problem that cannot be resolved cleanly.
+- `parameter.posterior` should always be rebuilt from restored canonical
+  Bayesian data rather than serialized redundantly at parameter level.
+- The sidecar reader and writer should be isolated behind explicit
+  serializer helpers instead of being implemented inline in
+  `Project.save()` and `Project.load()`.
+
+## Chosen Defaults
+
+- `parameter.value` remains committed to MAP after posterior fits.
+- If a project is loaded without full posterior arrays, restoring only
+  `parameter.posterior` is acceptable for table display and parameter
+  inspection.
+- Posterior plotting remains unavailable unless the canonical Bayesian
+  containers needed by those plots are also restored.
diff --git a/docs/dev/adr_undo-fit.md b/docs/dev/adr_undo-fit.md
new file mode 100644
index 00000000..e2aa46e0
--- /dev/null
+++ b/docs/dev/adr_undo-fit.md
@@ -0,0 +1,163 @@
+# ADR: Undo Fit
+
+**Status:** Proposed  
+**Date:** 2026-05-13
+
+## Context
+
+The new `_fit_parameter.start_value` and
+`_fit_parameter.start_uncertainty` fields in `analysis/analysis.cif`
+capture the last committed pre-fit scalar state for each fitted
+parameter. This is useful when a minimization run produces a poor
+result and the user wants to return to the state from immediately
+before the fit.
+
+This need is especially important in command-line workflows, where the
+user may save a project after a bad fit and reopen it later expecting a
+simple way to roll back to the pre-fit state.
+
+However, these snapshots alone do not define undo semantics. The API
+owner, rollback scope, and interaction with fit-derived metadata must
+be explicit.
+
+## Decision
+
+### 1. Add an analysis-owned `undo_fit()` operation
+
+The rollback operation belongs on `Analysis`, for example:
+
+```python
+project.analysis.undo_fit()
+```
+
+`Analysis` owns the fit lifecycle, fit metadata, and persisted
+`analysis.cif` state, so it is the correct owner for this operation.
+
+### 2. Initial undo scope is scalar rollback plus posterior clear
+
+The first undo implementation restores each fitted parameter's saved
+pre-fit scalar state and clears fit state that belongs only to the
+discarded fit.
+
+It does not attempt to restore every possible runtime detail of the
+previous fit result.
+
+After `undo_fit()`:
+
+- `parameter.value` is restored from `_fit_parameter.start_value`
+- `parameter.uncertainty` is restored from
+  `_fit_parameter.start_uncertainty`
+- `parameter.posterior` is cleared
+- `analysis.fit_results` is cleared
+
+This gives the user a safe, predictable return to the pre-fit visible
+parameter state without pretending to restore a full historical result.
+
+If an older saved project lacks `start_uncertainty`, clearing
+`parameter.uncertainty` remains an acceptable compatibility fallback.
+
+### 3. Undo does not roll back user configuration
+
+The initial undo operation does not revert:
+
+- aliases
+- constraints
+- fit bounds
+- minimizer type
+- fit mode
+- joint-fit weights
+
+These are analysis configuration, not fit output.
+
+### 4. Undo is single-level for now
+
+Only the latest saved pre-fit state is addressable.
+
+The initial API does not create a stack of historical fits. Supporting
+multiple undo levels would require a dedicated snapshot history design
+and is deferred.
+
+### 5. Persisted scalar snapshots are the rollback anchors
+
+The minimum persisted state required for clean cross-session undo is the
+pair of `_fit_parameter.start_value` and
+`_fit_parameter.start_uncertainty` defined in
+`adr_analysis-cif-fit-state.md`.
+
+If a parameter has no saved `start_value`, `undo_fit()` leaves that
+parameter unchanged.
+
+If a parameter has no saved `start_uncertainty`, `undo_fit()` may clear
+that parameter's uncertainty as a compatibility fallback for older
+saved projects.
+
+### 6. Suggested user flow
+
+```python
+project.analysis.fit()
+
+# Decide that the latest fit should be discarded.
+project.analysis.undo_fit()
+
+# Save the recovered state if desired.
+project.save()
+```
+
+### 7. Add a top-level `undo-fit` CLI command
+
+Because command-line recovery is one of the main motivations for this
+feature, undo must also be exposed through the existing top-level CLI.
+
+Suggested command:
+
+```bash
+python -m easydiffraction undo-fit PROJECT_DIR
+```
+
+This command should:
+
+- load the saved project from `PROJECT_DIR`
+- execute `project.analysis.undo_fit()`
+- save the recovered state back to the same project directory by
+  default
+- support `--dry` to perform the rollback in memory without overwriting
+  project files
+- emit a clear message describing whether the latest fit snapshot was
+  successfully discarded
+
+Suggested dry-run form:
+
+```bash
+python -m easydiffraction undo-fit PROJECT_DIR --dry
+```
+
+If the project does not contain a usable undo snapshot, the command
+should fail with a clear non-zero exit status instead of silently doing
+nothing.
+
+## Consequences
+
+### Positive
+
+- Users gain a simple recovery path after a poor fit.
+- The feature works naturally with saved projects and both Python and
+  command-line workflows.
+- The initial scope stays small and does not require full historical
+  fit snapshots.
+
+### Trade-offs
+
+- Undo restores pre-fit scalar parameter state, not a full historical
+  `fit_results` object.
+- Older saved projects that do not carry `start_uncertainty` may still
+  fall back to clearing uncertainty.
+- Multi-level undo remains unsupported.
+
+## Deferred Work
+
+- exact restoration of previous posterior-derived projections beyond the
+  scalar parameter snapshot
+- multi-level undo / redo
+- user-facing confirmation or preview APIs
+- rollback of fit-type-specific persisted summaries beyond parameter
+  values

From 9d03c30d5f26fccd26677c94313f62997bab1bd8 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Wed, 13 May 2026 14:50:39 +0200
Subject: [PATCH 102/106] Improve CI and codecov settings

---
 .codecov.yml                                  | 26 ++++++
 .copier-answers.yml                           |  2 +-
 .github/workflows/dashboard.yml               |  6 +-
 codecov.yml                                   | 13 ---
 docs/dev/adr_analysis-cif-fit-state.md        |  3 +-
 .../adr_parameter-correlation-persistence.md  |  4 +-
 docs/dev/adr_parameter-posterior-summary.md   | 79 +++++++++----------
 docs/dev/adr_undo-fit.md                      | 21 +++--
 8 files changed, 83 insertions(+), 71 deletions(-)
 create mode 100644 .codecov.yml
 delete mode 100644 codecov.yml

diff --git a/.codecov.yml b/.codecov.yml
new file mode 100644
index 00000000..d001c41d
--- /dev/null
+++ b/.codecov.yml
@@ -0,0 +1,26 @@
+# Codecov configuration
+# https://docs.codecov.com/docs/codecov-yaml
+
+github_checks:
+  annotations: true
+
+comment:
+  layout: 'reach, diff, flags, files'
+  behavior: default
+  require_changes: false
+  require_base: false
+  require_head: true
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 1%
+        # Make project coverage informational (won't block PR)
+        informational: true
+    patch:
+      default:
+        target: auto
+        # Require patch coverage but with threshold
+        threshold: 1%
diff --git a/.copier-answers.yml b/.copier-answers.yml
index c1c59d2a..009e6acc 100644
--- a/.copier-answers.yml
+++ b/.copier-answers.yml
@@ -1,6 +1,6 @@
 # WARNING: Do not edit this file manually.
 # Any changes will be overwritten by Copier.
-_commit: v0.11.3
+_commit: v0.11.4
 _src_path: gh:easyscience/templates
 app_docs_url: https://easyscience.github.io/diffraction-app
 app_doi: 10.5281/zenodo.18163581
diff --git a/.github/workflows/dashboard.yml b/.github/workflows/dashboard.yml
index 9d1f2b0b..573e3086 100644
--- a/.github/workflows/dashboard.yml
+++ b/.github/workflows/dashboard.yml
@@ -32,9 +32,13 @@ jobs:
       - name: Set up pixi
         uses: ./.github/actions/setup-pixi
 
+      # Install badgery into the active Pixi environment without modifying
+      # pixi.toml/pixi.lock. Using `pixi add` here re-solves the whole project
+      # and rebuilds the local editable package, which can cause intermittent
+      # Linux CI failures (`Text file busy`, os error 26).
       - name: Install badgery
         shell: bash
-        run: pixi add --pypi --git https://github.com/enhantica/badgery badgery
+        run: pixi run python -m pip install git+https://github.com/enhantica/badgery
 
       - name: Run docstring coverage and code complexity/maintainability checks
         run: |
diff --git a/codecov.yml b/codecov.yml
deleted file mode 100644
index f62b13ab..00000000
--- a/codecov.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-# Codecov configuration
-# https://docs.codecov.com/docs/codecovyml-reference
-
-coverage:
-  status:
-    project:
-      default:
-        # Make project coverage informational (won't block PR)
-        informational: true
-    patch:
-      default:
-        # Require patch coverage but with threshold
-        threshold: 1%
diff --git a/docs/dev/adr_analysis-cif-fit-state.md b/docs/dev/adr_analysis-cif-fit-state.md
index bf4af6f6..c39fc192 100644
--- a/docs/dev/adr_analysis-cif-fit-state.md
+++ b/docs/dev/adr_analysis-cif-fit-state.md
@@ -137,8 +137,7 @@ and Bayesian fitting.
 Fit-type-specific extensions are layered on top:
 
 - Bayesian persistence extends this with `_bayesian_*` categories and an
-  HDF5 sidecar, as described in
-  `adr_parameter-posterior-summary.md`.
+  HDF5 sidecar, as described in `adr_parameter-posterior-summary.md`.
 - Future fit-specific summaries should follow the same pattern: generic
   shared fields in `_fit_result`, specialized fields in separate
   categories.
diff --git a/docs/dev/adr_parameter-correlation-persistence.md b/docs/dev/adr_parameter-correlation-persistence.md
index 5d7b6888..9c494908 100644
--- a/docs/dev/adr_parameter-correlation-persistence.md
+++ b/docs/dev/adr_parameter-correlation-persistence.md
@@ -15,8 +15,8 @@ unless the underlying runtime objects are rebuilt. For Bayesian fits,
 full posterior samples may not always be restored. For deterministic
 fits, engine covariance is typically not persisted at all.
 
-The correlation matrix is an analysis-owned summary, not model state.
-It therefore belongs in `analysis/analysis.cif`, not in structure or
+The correlation matrix is an analysis-owned summary, not model state. It
+therefore belongs in `analysis/analysis.cif`, not in structure or
 experiment CIF files.
 
 ## Decision
diff --git a/docs/dev/adr_parameter-posterior-summary.md b/docs/dev/adr_parameter-posterior-summary.md
index 5469a2bd..dd7999ba 100644
--- a/docs/dev/adr_parameter-posterior-summary.md
+++ b/docs/dev/adr_parameter-posterior-summary.md
@@ -22,20 +22,20 @@ fits use `FitResults`, while posterior-capable fits such as DREAM use
 
 The user-facing need is more local: when a posterior-capable fit has
 completed, each parameter should expose a compact Bayesian summary for
-inspection without forcing users to traverse `analysis.fit_results`.
-At the same time, `parameter.value` must remain the only scalar used by
-the live model, minimizer setup, constraints, and category updates.
+inspection without forcing users to traverse `analysis.fit_results`. At
+the same time, `parameter.value` must remain the only scalar used by the
+live model, minimizer setup, constraints, and category updates.
 
 There is also a staleness problem. After a manual parameter edit or a
-new fit, fit-derived helper data must be cleared or replaced as a
-group. Keeping old posterior summaries after the active parameter state
-changes would mislead users.
+new fit, fit-derived helper data must be cleared or replaced as a group.
+Keeping old posterior summaries after the active parameter state changes
+would mislead users.
 
 One more implementation constraint matters: minimizers currently apply
 final fitted values through `_set_value_from_minimizer(...)` and then
-write `param.uncertainty = ...` directly. If `uncertainty` and
-posterior metadata become read-only fit outputs and the public `value`
-setter starts clearing stale fit metadata on manual edits, fit-result
+write `param.uncertainty = ...` directly. If `uncertainty` and posterior
+metadata become read-only fit outputs and the public `value` setter
+starts clearing stale fit metadata on manual edits, fit-result
 application must move to a dedicated internal update path so that valid
 fit outputs are installed atomically instead of being cleared
 accidentally.
@@ -44,12 +44,12 @@ accidentally.
 
 ### 1. Add one optional posterior object to each parameter
 
-Each `GenericParameter` will gain a read-only
-`posterior` property whose default value is `None`.
+Each `GenericParameter` will gain a read-only `posterior` property whose
+default value is `None`.
 
 This property is convenience metadata only. It does not participate in
-calculations. `parameter.value` remains the only scalar used by the
-live model.
+calculations. `parameter.value` remains the only scalar used by the live
+model.
 
 ### 2. Reuse the existing Bayesian summary container
 
@@ -256,11 +256,10 @@ Fields:
 
 `fit_min` and `fit_max` are required for restored Bayesian plotting.
 `fit_bounds_uncertainty_multiplier` is required if restored plots should
-preserve the uncertainty-derived bound annotation exactly.
-`start_value` and `start_uncertainty` are required for clean
-cross-session undo. If omitted, restored fit reports may show `N/A` for
-`start` and `change`, and undo may need to clear uncertainty as a
-compatibility fallback.
+preserve the uncertainty-derived bound annotation exactly. `start_value`
+and `start_uncertainty` are required for clean cross-session undo. If
+omitted, restored fit reports may show `N/A` for `start` and `change`,
+and undo may need to clear uncertainty as a compatibility fallback.
 
 #### 11.2 `_bayesian_result` single item
 
@@ -313,9 +312,8 @@ loop described below.
 
 #### 11.5 `_bayesian_parameter_posterior` loop
 
-Stores one canonical posterior summary row per sampled parameter.
-These rows are the source used to rebuild `parameter.posterior` on
-load.
+Stores one canonical posterior summary row per sampled parameter. These
+rows are the source used to rebuild `parameter.posterior` on load.
 
 Fields:
 
@@ -514,8 +512,8 @@ layout is recommended rather than mandatory.
 Do not persist backend-specific runtime objects such as `engine_result`,
 the DREAM driver, or ArviZ `InferenceData`.
 
-Those objects can be rebuilt from canonical saved arrays when needed,
-or left unavailable after load.
+Those objects can be rebuilt from canonical saved arrays when needed, or
+left unavailable after load.
 
 ### 13. Restore flow and partial-availability policy
 
@@ -527,13 +525,12 @@ When `Project.save()` sees `analysis.fit_results` as a
 `BayesianFitResults` instance:
 
 1. It writes standard analysis configuration to `analysis/analysis.cif`.
-2. It appends `_fit_parameter`, `_bayesian_result`,
-  `_bayesian_sampler`, `_bayesian_convergence`, and
-  `_bayesian_parameter_posterior`.
+2. It appends `_fit_parameter`, `_bayesian_result`, `_bayesian_sampler`,
+   `_bayesian_convergence`, and `_bayesian_parameter_posterior`.
 3. If posterior predictive summaries are available, it also writes the
-  `_bayesian_predictive_dataset` manifest.
+   `_bayesian_predictive_dataset` manifest.
 4. If posterior sample arrays or predictive arrays are available, it
-  writes `analysis/bayesian_data.h5`.
+   writes `analysis/bayesian_data.h5`.
 
 #### 13.2 Load flow
 
@@ -541,20 +538,20 @@ When `Project.load()` restores `analysis/analysis.cif`:
 
 1. Standard analysis configuration is restored first.
 2. If `_fit_parameter` is present, fit bounds, bound provenance, and
-  optional pre-fit scalar snapshots are restored by matching
-  `param_unique_name` to live parameters.
+   optional pre-fit scalar snapshots are restored by matching
+   `param_unique_name` to live parameters.
 3. If Bayesian categories are present, a lightweight
-  `BayesianFitResults` instance is rebuilt from persisted metadata and
-  parameter summary rows.
+   `BayesianFitResults` instance is rebuilt from persisted metadata and
+   parameter summary rows.
 4. `parameter.posterior` is rebuilt by matching summary rows to live
-  parameters via `unique_name`.
+   parameters via `unique_name`.
 5. `parameter.value` and `parameter.uncertainty` continue to come from
-  the normal project serialization path; Bayesian restore does not
-  overwrite them.
+   the normal project serialization path; Bayesian restore does not
+   overwrite them.
 6. If `analysis/bayesian_data.h5` exists and matches the manifest,
-  `posterior_samples` and `posterior_predictive` are restored.
+   `posterior_samples` and `posterior_predictive` are restored.
 7. If the sidecar is missing or incomplete, the restore degrades to
-  summary-only mode without failing the whole project load.
+   summary-only mode without failing the whole project load.
 
 #### 13.3 Partial restore behavior
 
@@ -615,8 +612,8 @@ structure and experiment files would create multiple sources of truth.
   than mixed user-editable and fit-editable state.
 - `analysis.fit_results` remains the canonical source for full Bayesian
   state.
-- `analysis/analysis.cif` becomes the home for fit-control metadata
-  that does not belong in structure or experiment CIF files.
+- `analysis/analysis.cif` becomes the home for fit-control metadata that
+  does not belong in structure or experiment CIF files.
 - Bayesian save/load gains a clear split between text metadata in
   `analysis/analysis.cif` and bulk numerical arrays in
   `analysis/bayesian_data.h5`.
@@ -663,8 +660,8 @@ It still defers:
 
 ## Implementation Notes
 
-- The first implementation should only populate
-  `parameter.posterior` for DREAM.
+- The first implementation should only populate `parameter.posterior`
+  for DREAM.
 - Existing `PosteriorParameterSummary` instances should be reused rather
   than copied into a second summary type unless implementation reveals a
   concrete layering problem that cannot be resolved cleanly.
diff --git a/docs/dev/adr_undo-fit.md b/docs/dev/adr_undo-fit.md
index e2aa46e0..29974130 100644
--- a/docs/dev/adr_undo-fit.md
+++ b/docs/dev/adr_undo-fit.md
@@ -8,17 +8,17 @@
 The new `_fit_parameter.start_value` and
 `_fit_parameter.start_uncertainty` fields in `analysis/analysis.cif`
 capture the last committed pre-fit scalar state for each fitted
-parameter. This is useful when a minimization run produces a poor
-result and the user wants to return to the state from immediately
-before the fit.
+parameter. This is useful when a minimization run produces a poor result
+and the user wants to return to the state from immediately before the
+fit.
 
 This need is especially important in command-line workflows, where the
 user may save a project after a bad fit and reopen it later expecting a
 simple way to roll back to the pre-fit state.
 
 However, these snapshots alone do not define undo semantics. The API
-owner, rollback scope, and interaction with fit-derived metadata must
-be explicit.
+owner, rollback scope, and interaction with fit-derived metadata must be
+explicit.
 
 ## Decision
 
@@ -88,8 +88,8 @@ If a parameter has no saved `start_value`, `undo_fit()` leaves that
 parameter unchanged.
 
 If a parameter has no saved `start_uncertainty`, `undo_fit()` may clear
-that parameter's uncertainty as a compatibility fallback for older
-saved projects.
+that parameter's uncertainty as a compatibility fallback for older saved
+projects.
 
 ### 6. Suggested user flow
 
@@ -118,8 +118,7 @@ This command should:
 
 - load the saved project from `PROJECT_DIR`
 - execute `project.analysis.undo_fit()`
-- save the recovered state back to the same project directory by
-  default
+- save the recovered state back to the same project directory by default
 - support `--dry` to perform the rollback in memory without overwriting
   project files
 - emit a clear message describing whether the latest fit snapshot was
@@ -142,8 +141,8 @@ nothing.
 - Users gain a simple recovery path after a poor fit.
 - The feature works naturally with saved projects and both Python and
   command-line workflows.
-- The initial scope stays small and does not require full historical
-  fit snapshots.
+- The initial scope stays small and does not require full historical fit
+  snapshots.
 
 ### Trade-offs
 

From 303a4190d1ee3819417c4ca60b8d782899dc0fe8 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Wed, 13 May 2026 21:18:45 +0200
Subject: [PATCH 103/106] Fix structure factor support for X-ray

---
 .../analysis/calculators/cryspy.py            | 10 +++++--
 .../analysis/calculators/test_cryspy.py       | 29 +++++++++++++++++++
 2 files changed, 37 insertions(+), 2 deletions(-)

diff --git a/src/easydiffraction/analysis/calculators/cryspy.py b/src/easydiffraction/analysis/calculators/cryspy.py
index 3c2da252..7bac2738 100644
--- a/src/easydiffraction/analysis/calculators/cryspy.py
+++ b/src/easydiffraction/analysis/calculators/cryspy.py
@@ -315,13 +315,19 @@ def _powder_refln_core_arrays(
         try:
             indices = np.asarray(phase_block['index_hkl'], dtype=int)
             sin_theta_over_lambda = np.asarray(phase_block['sthovl'], dtype=float)
-            f_nucl = np.asarray(phase_block['f_nucl'])
         except KeyError:
             return None
 
+        structure_factor = phase_block.get('f_nucl')
+        if structure_factor is None:
+            structure_factor = phase_block.get('f_charge')
+        if structure_factor is None:
+            return None
+        structure_factor = np.asarray(structure_factor)
+
         if indices.shape[0] != EXPECTED_HKL_INDEX_ROWS:
             return None
-        return indices, sin_theta_over_lambda, f_nucl
+        return indices, sin_theta_over_lambda, structure_factor
 
     @staticmethod
     def _powder_refln_d_spacing(
diff --git a/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py b/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py
index 5fdda3ee..6cebe4c3 100644
--- a/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py
+++ b/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py
@@ -173,3 +173,32 @@ def test_last_powder_refln_records_reads_tof_time_and_d_spacing():
     assert records[0].d_spacing == pytest.approx(3.21)
     assert records[0].f_calc == pytest.approx(6.0)
     assert records[0].f_squared_calc == pytest.approx(36.0)
+
+
+def test_last_powder_refln_records_reads_xray_charge_structure_factor():
+    from easydiffraction.analysis.calculators.cryspy import CryspyCalculator
+    from easydiffraction.datablocks.experiment.item.enums import BeamModeEnum
+
+    calculator = CryspyCalculator()
+    calculator._last_powder_phase_blocks = {
+        'phase_exp': {
+            'index_hkl': np.array([[1], [1], [1]]),
+            'sthovl': np.array([0.2]),
+            'ttheta_hkl': np.array([np.pi / 3]),
+            'f_charge': np.array([8.0 - 6.0j]),
+        }
+    }
+    structure = SimpleNamespace(name='phase')
+    experiment = SimpleNamespace(
+        name='exp',
+        type=SimpleNamespace(beam_mode=SimpleNamespace(value=BeamModeEnum.CONSTANT_WAVELENGTH)),
+    )
+
+    records = calculator.last_powder_refln_records(structure, experiment, phase_id='phase-x')
+
+    assert len(records) == 1
+    assert records[0].phase_id == 'phase-x'
+    assert records[0].two_theta == pytest.approx(60.0)
+    assert records[0].d_spacing == pytest.approx(2.5)
+    assert records[0].f_calc == pytest.approx(10.0)
+    assert records[0].f_squared_calc == pytest.approx(100.0)

From ebfa81ff11e2486b1aa7c5c8d7efcfe1f041f156 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Wed, 13 May 2026 22:32:55 +0200
Subject: [PATCH 104/106] Increase integration coverage for Bayesian fit
 helpers

---
 .../test_analysis_and_fit_category_support.py | 352 ++++++++++
 .../fitting/test_bayesian_helper_support.py   | 662 ++++++++++++++++++
 .../fitting/test_bayesian_tracker_and_base.py | 451 ++++++++++++
 .../fitting/test_bumps_dream_support.py       | 559 +++++++++++++++
 .../fitting/test_cli_entrypoints.py           | 173 +++++
 5 files changed, 2197 insertions(+)
 create mode 100644 tests/integration/fitting/test_analysis_and_fit_category_support.py
 create mode 100644 tests/integration/fitting/test_bayesian_helper_support.py
 create mode 100644 tests/integration/fitting/test_bayesian_tracker_and_base.py
 create mode 100644 tests/integration/fitting/test_bumps_dream_support.py
 create mode 100644 tests/integration/fitting/test_cli_entrypoints.py

diff --git a/tests/integration/fitting/test_analysis_and_fit_category_support.py b/tests/integration/fitting/test_analysis_and_fit_category_support.py
new file mode 100644
index 00000000..f476b0d4
--- /dev/null
+++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py
@@ -0,0 +1,352 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import pytest
+
+
+def _make_project():
+    class ExpCol:
+        def __init__(self) -> None:
+            self._names: list[str] = []
+
+        @property
+        def names(self):
+            return self._names
+
+        @property
+        def parameters(self):
+            return []
+
+        @property
+        def fittable_parameters(self):
+            return []
+
+        @property
+        def free_parameters(self):
+            return []
+
+    class Project:
+        experiments = ExpCol()
+        structures = ExpCol()
+        _varname = 'proj'
+        verbosity = 'full'
+
+    return Project()
+
+
+def _make_project_with_names(names: list[str]):
+    class ExpCol:
+        def __init__(self, names):
+            self._names = names
+
+        def __len__(self):
+            return len(self._names)
+
+        @property
+        def names(self):
+            return self._names
+
+        @property
+        def parameters(self):
+            return []
+
+        @property
+        def fittable_parameters(self):
+            return []
+
+        @property
+        def free_parameters(self):
+            return []
+
+    class Project:
+        experiments = ExpCol(names)
+        structures = ExpCol([])
+        _varname = 'proj'
+        verbosity = 'full'
+
+    return Project()
+
+
+def test_fit_mode_enum_members_default_and_descriptions():
+    from easydiffraction.analysis.categories.fit.enums import FitModeEnum
+
+    assert FitModeEnum.SINGLE == 'single'
+    assert FitModeEnum.JOINT == 'joint'
+    assert FitModeEnum.SEQUENTIAL == 'sequential'
+    assert FitModeEnum.default() is FitModeEnum.SINGLE
+    assert all(member.description() for member in FitModeEnum)
+
+
+def test_fit_instantiation_defaults_and_run_paths():
+    from easydiffraction.analysis.categories.fit.default import Fit
+    import easydiffraction.analysis.categories.fit.default as fit_mod
+
+    fit = Fit()
+
+    assert fit._identity.category_code == 'fit'
+    assert fit.mode.value == 'single'
+    assert fit.minimizer_type.value == 'lmfit (leastsq)'
+    assert fit.minimizer is None
+
+    with pytest.raises(
+        RuntimeError,
+        match=r'Fit category is not attached to an Analysis object\.',
+    ):
+        fit.run()
+
+    calls: list[tuple[str | None, bool, int | None]] = []
+
+    class Parent:
+        fitter = object()
+
+        @staticmethod
+        def _run_fit(
+            verbosity: str | None = None,
+            *,
+            use_physical_limits: bool = False,
+            random_seed: int | None = None,
+        ) -> None:
+            calls.append((verbosity, use_physical_limits, random_seed))
+
+    fit._parent = Parent()
+    fit(verbosity='silent', use_physical_limits=True, random_seed=7)
+
+    assert calls == [('silent', True, 7)]
+
+    class ParentWithMinimizer:
+        fitter = type('FitterHolder', (), {'minimizer': 'MIN'})()
+
+    fit._parent = ParentWithMinimizer()
+    assert fit.minimizer == 'MIN'
+
+    shown: list[str] = []
+    monkeypatch = pytest.MonkeyPatch()
+    monkeypatch.setattr(fit_mod.MinimizerFactory, 'show_supported', lambda: shown.append('shown'))
+    Fit.show_available_minimizers()
+    monkeypatch.undo()
+
+    assert shown == ['shown']
+
+
+def test_fit_from_cif_warns_on_invalid_minimizer(monkeypatch):
+    import easydiffraction.analysis.categories.fit.default as fit_mod
+    from easydiffraction.analysis.categories.fit.default import Fit
+
+    fit = Fit()
+    fit._minimizer_type._value = 'bad-minimizer'
+
+    class Parent:
+        fitter = None
+
+    warnings: list[str] = []
+    fit._parent = Parent()
+    monkeypatch.setattr(fit_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    monkeypatch.setattr(
+        fit_mod,
+        'Fitter',
+        lambda value: (_ for _ in ()).throw(ValueError('bad minimizer')),
+    )
+    monkeypatch.setattr(fit_mod.log, 'warning', lambda message: warnings.append(message))
+
+    fit.from_cif(object())
+
+    assert warnings == ['bad minimizer']
+
+
+def test_fit_fallback_paths_without_parent_or_project(capsys, monkeypatch):
+    import easydiffraction.analysis.categories.fit.default as fit_mod
+    from easydiffraction.analysis.categories.fit.default import Fit
+
+    fit = Fit()
+
+    assert fit.minimizer is None
+
+    fit.show_modes()
+    out = capsys.readouterr().out
+    assert 'single' in out
+    assert 'joint' in out
+    assert 'sequential' in out
+
+    monkeypatch.setattr(fit_mod.CategoryItem, 'from_cif', lambda self, block, idx=0: None)
+    fit.from_cif(object())
+
+
+def test_fit_show_modes_for_single_and_multiple_experiments(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    single = Analysis(project=_make_project_with_names(['e1']))
+    single.fit.show_modes()
+    out_single = capsys.readouterr().out
+    assert 'Fit modes' in out_single
+    assert 'single' in out_single
+    assert 'joint' not in out_single
+
+    multi = Analysis(project=_make_project_with_names(['e1', 'e2']))
+    multi.fit.show_modes()
+    out_multi = capsys.readouterr().out
+    assert 'joint' in out_multi
+    assert 'sequential' in out_multi
+
+
+def test_show_minimizer_types_prints(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project_with_names([]))
+    analysis.fit.show_minimizer_types()
+    out = capsys.readouterr().out
+    assert 'Minimizer types' in out
+    assert 'lmfit (leastsq)' in out
+
+
+def test_analysis_help_and_mode_switching(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project_with_names(['e1', 'e2']))
+    assert analysis.fit.mode.value == 'single'
+    analysis.fit.mode = 'joint'
+    assert analysis.fit.mode.value == 'joint'
+    assert len(analysis.joint_fit_experiments) == 0
+
+    analysis.help()
+    out = capsys.readouterr().out
+    assert "Help for 'Analysis'" in out
+    assert 'fit' in out
+    assert 'display' in out
+    assert 'Properties' in out
+    assert 'Methods' in out
+    assert 'fit_sequential()' in out
+
+
+def test_display_fit_results_warns_when_no_results(capsys):
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project_with_names([]))
+    analysis.display.fit_results()
+    out = capsys.readouterr().out
+    assert 'No fit results available' in out
+
+
+def test_display_fit_results_calls_process_fit_results(monkeypatch):
+    from easydiffraction.analysis.analysis import Analysis
+
+    process_called = {'called': False, 'args': None}
+
+    def fake_process_fit_results(structures, experiments):
+        process_called['called'] = True
+        process_called['args'] = (structures, experiments)
+
+    class Project:
+        structures = object()
+        _varname = 'proj'
+        verbosity = 'full'
+
+        class Experiments:
+            names: list[str] = []
+
+            @staticmethod
+            def values():
+                return []
+
+            @property
+            def parameters(self):
+                return []
+
+            @property
+            def fittable_parameters(self):
+                return []
+
+            @property
+            def free_parameters(self):
+                return []
+
+        experiments = Experiments()
+
+    analysis = Analysis(project=Project())
+    analysis.fit_results = object()
+    monkeypatch.setattr(analysis.fitter, '_process_fit_results', fake_process_fit_results)
+
+    analysis.display.fit_results()
+
+    assert process_called['called'] is True
+
+
+def test_analysis_display_as_cif_and_constraints(monkeypatch, capsys):
+    import easydiffraction.analysis.analysis as analysis_mod
+    from easydiffraction.analysis.analysis import Analysis
+
+    analysis = Analysis(project=_make_project())
+    rendered: dict[str, str] = {}
+
+    monkeypatch.setattr(analysis_mod, 'render_cif', lambda text: rendered.setdefault('text', text))
+    analysis.display.as_cif()
+    assert 'text' in rendered
+
+    analysis.display.constraints()
+    assert 'No constraints' in capsys.readouterr().out
+
+    class FakeExpr:
+        value = 'x = y + 1'
+
+    class FakeConstraint:
+        expression = FakeExpr()
+
+    analysis.constraints._items = [FakeConstraint()]
+    captured: dict[str, object] = {}
+    monkeypatch.setattr(analysis_mod, 'render_table', lambda **kwargs: captured.update(kwargs))
+    analysis.display.constraints()
+    out = capsys.readouterr().out
+    assert 'User defined constraints' in out
+    assert captured['columns_data'][0][0] == 'x = y + 1'
+
+
+def test_discover_helpers_and_snapshot_params():
+    from easydiffraction.analysis.analysis import Analysis
+    from easydiffraction.analysis.analysis import _discover_method_rows
+    from easydiffraction.analysis.analysis import _discover_property_rows
+
+    class Demo:
+        @property
+        def alpha(self):
+            """Alpha property."""
+            return 1
+
+        @property
+        def beta(self):
+            """Beta property."""
+            return 2
+
+        @beta.setter
+        def beta(self, value):
+            return None
+
+        def do_thing(self):
+            """Do a thing."""
+
+        def _private(self):
+            return None
+
+    property_rows = _discover_property_rows(Demo)
+    method_rows = _discover_method_rows(Demo)
+
+    assert len(property_rows) == 2
+    assert 'alpha' in [row[1] for row in property_rows]
+    assert next(row for row in property_rows if row[1] == 'beta')[2] == '✓'
+    assert 'do_thing()' in [row[1] for row in method_rows]
+    assert '_private()' not in [row[1] for row in method_rows]
+
+    analysis = Analysis(project=_make_project())
+
+    class FakeParam:
+        unique_name = 'p1'
+        value = 1.23
+        uncertainty = 0.01
+        units = 'A'
+
+    class FakeResults:
+        parameters = [FakeParam()]
+
+    analysis._snapshot_params('expt1', FakeResults())
+    assert analysis._parameter_snapshots['expt1']['p1']['value'] == 1.23
+    assert analysis._parameter_snapshots['expt1']['p1']['uncertainty'] == 0.01
diff --git a/tests/integration/fitting/test_bayesian_helper_support.py b/tests/integration/fitting/test_bayesian_helper_support.py
new file mode 100644
index 00000000..8a5fed2f
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_helper_support.py
@@ -0,0 +1,662 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+
+class Identity:
+    def __init__(self) -> None:
+        self.datablock_entry_name = 'db'
+        self.category_code = 'cat'
+        self.category_entry_name = 'entry'
+
+
+class Param:
+    def __init__(self, unique_name: str, start: float, value: float, uncertainty: float) -> None:
+        self._identity = Identity()
+        self._fit_start_value = start
+        self.unique_name = unique_name
+        self.name = unique_name
+        self.value = value
+        self.uncertainty = uncertainty
+        self.units = 'arb'
+
+
+def test_posterior_samples_flatten_and_to_arviz():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a', 'b'],
+        parameter_samples=np.array(
+            [
+                [[1.0, 10.0], [2.0, 20.0]],
+                [[3.0, 30.0], [4.0, 40.0]],
+            ],
+            dtype=float,
+        ),
+        log_posterior=np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float),
+    )
+
+    flattened = posterior_samples.flattened()
+    inference_data = posterior_samples.to_arviz()
+
+    assert flattened.shape == (4, 2)
+    np.testing.assert_allclose(flattened[:, 0], np.array([1.0, 2.0, 3.0, 4.0]))
+    np.testing.assert_allclose(flattened[:, 1], np.array([10.0, 20.0, 30.0, 40.0]))
+    assert set(inference_data.posterior.data_vars) == {'a', 'b'}
+    assert inference_data.posterior['a'].shape == (2, 2)
+    assert inference_data.sample_stats['lp'].shape == (2, 2)
+
+
+def test_posterior_samples_to_arviz_validates_shapes():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.array([1.0, 2.0]),
+    )
+
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior sample array must have shape \(n_draws, n_chains, n_parameters\)\.',
+    ):
+        posterior_samples.to_arviz()
+
+
+def test_posterior_samples_to_arviz_validates_name_and_log_posterior_lengths():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+
+    wrong_names = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((2, 2, 2), dtype=float),
+    )
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior sample array does not match the parameter name list length\.',
+    ):
+        wrong_names.to_arviz()
+
+    wrong_log_posterior = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((2, 2, 1), dtype=float),
+        log_posterior=np.ones((3, 2), dtype=float),
+    )
+    with pytest.raises(
+        ValueError,
+        match=r'Log-posterior array must match the first two posterior sample axes\.',
+    ):
+        wrong_log_posterior.to_arviz()
+
+
+def test_compute_convergence_diagnostics_treats_non_finite_values_as_not_converged(
+    monkeypatch,
+):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import compute_convergence_diagnostics
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['a'],
+        parameter_samples=np.ones((4, 2, 1), dtype=float),
+    )
+
+    fake_dataset = type('FakeDataset', (), {'data_vars': {'a': np.array([np.nan], dtype=float)}})
+
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.rhat',
+        lambda inference_data: fake_dataset,
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.fit_helpers.bayesian.az.ess',
+        lambda inference_data, method='bulk': type(
+            'FakeDataset', (), {'data_vars': {'a': np.array([4000.0], dtype=float)}}
+        ),
+    )
+
+    diagnostics = compute_convergence_diagnostics(posterior_samples)
+
+    assert diagnostics['converged'] is False
+    assert diagnostics['r_hat_by_parameter'] == {'a': None}
+    assert diagnostics['ess_bulk_by_parameter'] == {'a': 4000.0}
+    assert diagnostics['max_r_hat'] is None
+    assert diagnostics['min_ess_bulk'] == pytest.approx(4000.0)
+
+
+def test_summarize_posterior_parameters_preserves_order_and_display_names():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['beta', 'alpha'],
+        parameter_samples=np.array(
+            [
+                [[2.0, 1.0], [2.2, 1.2]],
+                [[1.8, 0.8], [2.1, 1.1]],
+            ],
+            dtype=float,
+        ),
+    )
+
+    summaries = summarize_posterior_parameters(
+        parameter_names=['beta', 'alpha'],
+        posterior_samples=posterior_samples,
+        map_values=np.array([2.05, 1.05]),
+        parameter_display_names=['Beta width', 'Alpha shift'],
+        convergence_diagnostics={
+            'r_hat_by_parameter': {'beta': 1.02, 'alpha': 1.0},
+            'ess_bulk_by_parameter': {'beta': 120.0, 'alpha': 800.0},
+        },
+    )
+
+    assert [summary.unique_name for summary in summaries] == ['beta', 'alpha']
+    assert [summary.display_name for summary in summaries] == ['Beta width', 'Alpha shift']
+    assert summaries[0].r_hat == pytest.approx(1.02)
+    assert summaries[0].ess_bulk == pytest.approx(120.0)
+    assert summaries[1].r_hat == pytest.approx(1.0)
+    assert summaries[1].ess_bulk == pytest.approx(800.0)
+
+
+def test_summarize_posterior_parameters_validates_display_name_length():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorSamples
+    from easydiffraction.analysis.fit_helpers.bayesian import summarize_posterior_parameters
+
+    posterior_samples = PosteriorSamples(
+        parameter_names=['alpha'],
+        parameter_samples=np.ones((2, 2, 1), dtype=float),
+    )
+
+    with pytest.raises(
+        ValueError,
+        match=r'Posterior display-name list must match the sampled parameter name list length\.',
+    ):
+        summarize_posterior_parameters(
+            parameter_names=['alpha'],
+            posterior_samples=posterior_samples,
+            map_values=np.array([1.0]),
+            parameter_display_names=['Alpha', 'Extra'],
+        )
+
+
+def test_standard_deviations_from_summaries_returns_float_array():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import standard_deviations_from_summaries
+
+    values = standard_deviations_from_summaries([
+        PosteriorParameterSummary(
+            unique_name='a',
+            display_name='A',
+            map_value=1.0,
+            median=1.0,
+            standard_deviation=0.2,
+            interval_68=(0.9, 1.1),
+            interval_95=(0.8, 1.2),
+        ),
+        PosteriorParameterSummary(
+            unique_name='b',
+            display_name='B',
+            map_value=2.0,
+            median=2.0,
+            standard_deviation=0.3,
+            interval_68=(1.9, 2.1),
+            interval_95=(1.8, 2.2),
+        ),
+    ])
+
+    np.testing.assert_allclose(values, np.array([0.2, 0.3]))
+    assert values.dtype == float
+
+
+def test_bayesian_format_helpers_cover_edge_cases():
+    from easydiffraction.analysis.fit_helpers.bayesian import _calculate_fit_quality_metrics
+    from easydiffraction.analysis.fit_helpers.bayesian import _dataset_to_scalar_dict
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_bayesian_overall_status
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_convergence_summary
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_point_estimate_name
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_sampler_settings
+    from easydiffraction.analysis.fit_helpers.bayesian import _maybe_scalar
+
+    dataset = type(
+        'FakeDataset',
+        (),
+        {'data_vars': {'a': np.array([np.nan], dtype=float), 'b': np.array([3.0], dtype=float)}},
+    )
+
+    assert _maybe_scalar(None) is None
+    assert _maybe_scalar(float('inf')) is None
+    assert _maybe_scalar(3.0) == pytest.approx(3.0)
+    assert _dataset_to_scalar_dict(dataset) == {'a': None, 'b': 3.0}
+    assert _format_sampler_settings({}) is None
+    assert (
+        _format_sampler_settings({'steps': 10, 'burn': 2, 'samples': 40})
+        == 'steps=10, burn=2, samples=40'
+    )
+    assert _format_point_estimate_name('map') == 'Max posterior'
+    assert _format_point_estimate_name('best_sample') == 'Best Sample'
+    assert _format_bayesian_overall_status(
+        success=False,
+        sampler_completed=False,
+        convergence_diagnostics={},
+    ) == ('❌', 'failed')
+    assert _format_bayesian_overall_status(
+        success=True,
+        sampler_completed=False,
+        convergence_diagnostics={'converged': False},
+    ) == ('⚠️', 'completed with warnings')
+    assert _format_bayesian_overall_status(
+        success=True,
+        sampler_completed=True,
+        convergence_diagnostics={'converged': True},
+    ) == ('✅', 'completed')
+    assert _format_bayesian_overall_status(
+        success=True,
+        sampler_completed=False,
+        convergence_diagnostics={},
+    ) == ('✅', 'posterior available')
+    assert _format_convergence_summary({}) is None
+    assert _format_convergence_summary({
+        'converged': False,
+        'max_r_hat': 1.02,
+        'min_ess_bulk': 200.0,
+        'n_draws': 30,
+        'n_chains': 8,
+    }) == (
+        'status=[red]failed[/red], max_r_hat=[red]1.020[/red], '
+        'min_ess_bulk=[red]200.0[/red], draws=30, chains=8'
+    )
+
+    metrics = _calculate_fit_quality_metrics(
+        y_obs=[10.0, 20.0],
+        y_calc=[9.5, 19.5],
+        y_err=[1.0, 1.0],
+        f_obs=[5.0, 6.0],
+        f_calc=[5.1, 5.9],
+    )
+
+    assert metrics['rf'] is not None
+    assert metrics['rf2'] is not None
+    assert metrics['wr'] is not None
+    assert metrics['br'] is not None
+
+
+def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(capsys, monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import BayesianFitResults
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.utils.logging import Logger
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.WARN, raising=True)
+
+    results = BayesianFitResults(
+        success=True,
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        reduced_chi_square=1.2345,
+        fitting_time=0.9876,
+        sampler_name='dream',
+        sampler_completed=True,
+        sampler_settings={
+            'random_seed': 1313900679,
+            'steps': 200,
+            'burn': 50,
+            'thin': 1,
+            'pop': 4,
+            'init': 'lhs',
+            'samples': 3200,
+        },
+        convergence_diagnostics={
+            'converged': False,
+            'max_r_hat': 1.107,
+            'min_ess_bulk': 125.9,
+            'n_draws': 200,
+            'n_chains': 16,
+        },
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+        best_log_posterior=-12.34,
+    )
+    results.message = 'DREAM sampling completed'
+
+    results.display_results(y_obs=[10.0, 20.0], y_calc=[9.5, 19.5])
+
+    out = capsys.readouterr().out
+    assert 'Bayesian fit results' in out
+    assert 'Overall status: completed with warnings' in out
+    assert 'Sampler status: DREAM sampling completed' in out
+    assert 'Sampler: dream' in out
+    assert 'Sampler completed: yes' in out
+    assert 'steps=200' in out
+    assert 'init=lhs' in out
+    assert 'random_seed=1313900679' not in out
+    assert 'status=failed' in out
+    assert 'max_r_hat=1.107' in out
+    assert 'min_ess_bulk=125.9' in out
+    assert 'Posterior parameter summaries:' in out
+    assert 'Success: True' not in out
+    assert 'datablock' in out
+    assert 'category' in out
+    assert 'entry' in out
+    assert '95% interval' in out
+    assert '68% interval' not in out
+    assert 'std' not in out
+
+    monkeypatch.setattr(Logger, '_reaction', Logger.Reaction.RAISE, raising=True)
+
+
+def test_render_posterior_summary_table_without_summaries_prints_notice(capsys):
+    from easydiffraction.analysis.fit_helpers import bayesian
+
+    bayesian._render_posterior_summary_table(parameters=[], posterior_parameter_summaries=[])
+
+    assert 'No posterior parameter summaries available.' in capsys.readouterr().out
+
+
+def test_build_posterior_summary_row_restores_identifier_columns():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _build_posterior_summary_row
+
+    parameter = Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    summary = PosteriorParameterSummary(
+        unique_name='a',
+        display_name='a',
+        map_value=1.2,
+        median=1.15,
+        standard_deviation=0.05,
+        interval_68=(1.1, 1.2),
+        interval_95=(1.0, 1.3),
+        r_hat=1.107,
+        ess_bulk=125.9,
+    )
+
+    row = _build_posterior_summary_row(summary, {'a': parameter})
+
+    assert row == [
+        'db',
+        'cat',
+        'entry',
+        'a',
+        'arb',
+        '1.1500',
+        '[1.0000, 1.3000]',
+        '[red]1.107[/red]',
+        '[red]125.9[/red]',
+    ]
+
+
+def test_render_committed_parameter_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_committed_parameter_table([
+        Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)
+    ])
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'max posterior',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
+
+
+def test_render_posterior_summary_table_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import bayesian
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(bayesian, 'render_table', fake_render_table)
+
+    bayesian._render_posterior_summary_table(
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+        posterior_parameter_summaries=[
+            PosteriorParameterSummary(
+                unique_name='a',
+                display_name='a',
+                map_value=1.2,
+                median=1.15,
+                standard_deviation=0.05,
+                interval_68=(1.1, 1.2),
+                interval_95=(1.0, 1.3),
+                r_hat=1.107,
+                ess_bulk=125.9,
+            )
+        ],
+    )
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'median',
+        '95% interval',
+        'r-hat',
+        'ess bulk',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.1500',
+            '[1.0000, 1.3000]',
+            '[red]1.107[/red]',
+            '[red]125.9[/red]',
+        ]
+    ]
+
+
+def test_posterior_table_notes_split_failed_diagnostics():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _posterior_table_notes
+
+    notes = _posterior_table_notes([
+        PosteriorParameterSummary(
+            unique_name='a',
+            display_name='a',
+            map_value=1.0,
+            median=1.0,
+            standard_deviation=0.1,
+            interval_68=(0.9, 1.1),
+            interval_95=(0.8, 1.2),
+            r_hat=1.02,
+            ess_bulk=100.0,
+        )
+    ])
+
+    assert len(notes) == 2
+    assert 'r-hat' in notes[0]
+    assert 'ess bulk' in notes[1]
+
+
+def test_bayesian_helpers_cover_non_warning_and_default_display_paths():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.fit_helpers.bayesian import _build_posterior_summary_row
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_ess_bulk
+    from easydiffraction.analysis.fit_helpers.bayesian import _format_r_hat
+    from easydiffraction.analysis.fit_helpers.bayesian import _posterior_table_notes
+
+    summary = PosteriorParameterSummary(
+        unique_name='missing',
+        display_name='Missing',
+        map_value=1.0,
+        median=1.0,
+        standard_deviation=0.1,
+        interval_68=(0.9, 1.1),
+        interval_95=(0.8, 1.2),
+        r_hat=1.0,
+        ess_bulk=500.0,
+    )
+
+    row = _build_posterior_summary_row(summary, {})
+
+    assert row == [
+        'N/A',
+        'N/A',
+        '',
+        'Missing',
+        'N/A',
+        '1.0000',
+        '[0.8000, 1.2000]',
+        '1.000',
+        '500.0',
+    ]
+    assert _format_r_hat(None) == 'N/A'
+    assert _format_r_hat(1.0) == '1.000'
+    assert _format_ess_bulk(None) == 'N/A'
+    assert _format_ess_bulk(500.0) == '500.0'
+    assert _posterior_table_notes([]) == []
+    assert _posterior_table_notes([summary]) == []
+
+
+def test_fitresults_display_results_prints_and_table(capsys):
+    from easydiffraction.analysis.fit_helpers.reporting import FitResults
+
+    params = [Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)]
+
+    results = FitResults(
+        success=True,
+        parameters=params,
+        reduced_chi_square=1.2345,
+        fitting_time=0.9876,
+    )
+
+    results.display_results(
+        y_obs=[10.0, 20.0],
+        y_calc=[9.5, 19.5],
+        y_err=[1.0, 1.0],
+        f_obs=[5.0, 6.0],
+        f_calc=[5.1, 5.9],
+    )
+
+    out = capsys.readouterr().out
+    assert 'Fit results' in out
+    assert 'Success: True' in out
+    assert 'reduced χ²' in out
+    assert 'R-factor (Rf)' in out
+    assert 'R-factor squared (Rf²)' in out
+    assert 'Weighted R-factor (wR)' in out
+    assert 'Bragg R-factor (BR)' in out
+    assert 'Fitted parameters:' in out
+    assert any(char in out for char in ('╒', '┌', '+', '─'))
+
+
+def test_fitresults_display_results_places_units_after_parameter(monkeypatch):
+    from easydiffraction.analysis.fit_helpers import reporting
+
+    captured: dict[str, object] = {}
+
+    def fake_render_table(*, columns_headers, columns_alignment, columns_data):
+        captured['columns_headers'] = columns_headers
+        captured['columns_alignment'] = columns_alignment
+        captured['columns_data'] = columns_data
+
+    monkeypatch.setattr(reporting, 'render_table', fake_render_table)
+
+    reporting.FitResults(
+        success=True,
+        parameters=[Param(unique_name='a', start=1.0, value=1.2, uncertainty=0.05)],
+    ).display_results()
+
+    assert captured['columns_headers'] == [
+        'datablock',
+        'category',
+        'entry',
+        'parameter',
+        'units',
+        'start',
+        'fitted',
+        'uncertainty',
+        'change',
+    ]
+    assert captured['columns_alignment'] == [
+        'left',
+        'left',
+        'left',
+        'left',
+        'left',
+        'right',
+        'right',
+        'right',
+        'right',
+    ]
+    assert captured['columns_data'] == [
+        [
+            'db',
+            'cat',
+            'entry',
+            'a',
+            'arb',
+            '1.0000',
+            '1.2000',
+            '0.0500',
+            '20.00 % ↑',
+        ]
+    ]
diff --git a/tests/integration/fitting/test_bayesian_tracker_and_base.py b/tests/integration/fitting/test_bayesian_tracker_and_base.py
new file mode 100644
index 00000000..f63cb111
--- /dev/null
+++ b/tests/integration/fitting/test_bayesian_tracker_and_base.py
@@ -0,0 +1,451 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import pytest
+
+
+class DummyParam:
+    def __init__(self, value: float) -> None:
+        self.value = value
+        self.fit_min = -np.inf
+        self.fit_max = np.inf
+        self.unique_name = f'param_{value}'
+
+    def _physical_lower_bound(self) -> float:
+        return -np.inf
+
+    def _physical_upper_bound(self) -> float:
+        return np.inf
+
+
+def test_tracker_terminal_flow_prints_and_updates_best(monkeypatch, capsys):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+
+    tracker = FitProgressTracker()
+    tracker.start_tracking('dummy')
+    tracker.start_timer()
+
+    tracker.track(np.array([2.0, 1.0]), parameters=[1])
+    out = capsys.readouterr().out
+    assert 'Goodness-of-fit' in out
+
+    tracker.track(np.array([1.9, 1.0]), parameters=[1])
+    tracker.track(np.array([0.1, 0.1]), parameters=[1])
+
+    tracker.stop_timer()
+    tracker.finish_tracking()
+
+    out = capsys.readouterr().out
+    assert 'Best goodness-of-fit' in out
+    assert tracker.best_iteration is not None
+
+
+def test_tracker_sampler_progress_renders_and_completes(monkeypatch, capsys):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+    from easydiffraction.analysis.fit_helpers.tracking import SamplerProgressUpdate
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+
+    tracker = FitProgressTracker()
+    tracker.start_tracking('dream', mode='sampling')
+    tracker.start_timer()
+    tracker.track_sampler_progress(
+        SamplerProgressUpdate(
+            iteration=1,
+            total_iterations=10,
+            phase='burn-in',
+            progress_percent=10.0,
+            log_posterior=-12.0,
+            reduced_chi2=5.0,
+            elapsed_time=0.0,
+            force_report=True,
+        )
+    )
+    tracker.track_sampler_progress(
+        SamplerProgressUpdate(
+            iteration=10,
+            total_iterations=10,
+            phase='sampling',
+            progress_percent=100.0,
+            log_posterior=-3.0,
+            reduced_chi2=1.0,
+            elapsed_time=6.0,
+            force_report=False,
+        )
+    )
+    tracker.stop_timer()
+    tracker.finish_tracking()
+
+    out = capsys.readouterr().out
+    assert 'Bayesian sampling progress' in out
+    assert 'Bayesian sampling complete.' in out
+    assert tracker.best_chi2 == pytest.approx(1.0)
+    assert tracker.best_iteration == 10
+
+
+def test_tracker_helper_error_paths_and_short_mode(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+    from easydiffraction.utils.enums import VerbosityEnum
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+
+    tracker = FitProgressTracker()
+    tracker._verbosity = VerbosityEnum.SHORT
+    tracker.stop_timer()
+    tracker.start_tracking('dream', mode='sampling')
+    tracker.finish_tracking()
+
+    with pytest.raises(RuntimeError, match='Sampler progress is unavailable'):
+        FitProgressTracker()._resolved_final_sampler_progress()
+    with pytest.raises(RuntimeError, match='Sampler iteration labels require'):
+        FitProgressTracker()._sampler_iteration_label(1)
+
+    assert FitProgressTracker._rows_match_on_columns(['1', 'a'], ['1', 'b'], (0,)) is True
+
+
+def test_tracker_final_sampler_row_replaces_last_row(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    monkeypatch.setattr(tracking_mod, 'render_table', lambda **kwargs: None)
+
+    tracker = FitProgressTracker()
+    tracker._tracking_mode = 'sampling'
+    tracker._sampler_total_iterations = 10
+    tracker._last_iteration = 10
+    tracker._last_sampler_progress_percent = 100.0
+    tracker._last_sampler_log_posterior = -3.0
+    tracker._last_sampler_phase = 'sampling'
+    tracker._last_sampler_elapsed_time = 5.0
+    tracker._df_rows = [['10/10', '90.0%', '4.00', '-3.00', 'sampling']]
+
+    tracker.finish_tracking()
+
+    assert tracker._df_rows[-1] == ['10/10', '100.0%', '5.00', '-3.00', 'sampling']
+
+
+def test_make_display_handle_uses_terminal_live_when_available(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+
+    class FakeLive:
+        def __init__(self, *, console, auto_refresh):
+            self.console = console
+            self.auto_refresh = auto_refresh
+            self.started = False
+            self.stopped = False
+
+        def start(self):
+            self.started = True
+
+        def stop(self):
+            self.stopped = True
+
+    monkeypatch.setattr(tracking_mod, 'in_jupyter', lambda: False)
+    monkeypatch.setattr(tracking_mod, 'Live', FakeLive)
+    monkeypatch.setattr(tracking_mod.ConsoleManager, 'get', lambda: 'console')
+
+    handle = tracking_mod._make_display_handle()
+
+    assert isinstance(handle, tracking_mod._TerminalLiveHandle)
+    assert handle._live.console == 'console'
+    assert handle._live.auto_refresh is True
+    assert handle._live.started is True
+
+    handle.close()
+
+    assert handle._live.stopped is True
+
+
+def test_tracker_misc_helper_paths(monkeypatch):
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    render_calls: list[dict[str, object]] = []
+    monkeypatch.setattr(tracking_mod, 'render_table', lambda **kwargs: render_calls.append(kwargs))
+    monkeypatch.setattr(
+        tracking_mod, 'calculate_reduced_chi_square', lambda residuals, n_params: 3.0
+    )
+
+    tracker = FitProgressTracker()
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    tracker._previous_chi2 = 5.0
+    tracker._best_chi2 = 5.0
+
+    residuals = np.array([1.0, -1.0], dtype=float)
+
+    assert np.array_equal(tracker.track(residuals, [1.0]), residuals)
+    assert tracker.best_chi2 == pytest.approx(3.0)
+
+    tracker.start_timer()
+    tracker.stop_timer()
+    assert tracker.fitting_time is not None
+
+    tracker.reset()
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    assert tracker._headers() == tracking_mod.SAMPLER_HEADERS
+    assert tracker._alignments() == tracking_mod.SAMPLER_ALIGNMENTS
+    assert tracker._current_elapsed_time() is None
+    assert tracker._format_elapsed_time() == ''
+
+    tracker._replace_last_tracking_row(['1'])
+
+    assert tracker._df_rows == [['1']]
+    assert len(render_calls) == 1
+
+
+def test_tracker_final_rows_cover_fallbacks_and_close_suppression():
+    import easydiffraction.analysis.fit_helpers.tracking as tracking_mod
+    from easydiffraction.analysis.fit_helpers.tracking import FitProgressTracker
+
+    tracker = FitProgressTracker()
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_SAMPLER
+    tracker._sampler_total_iterations = 10
+    tracker._last_iteration = 8
+    tracker._last_sampler_elapsed_time = 2.5
+
+    assert tracker._final_sampler_tracking_row() == ['8/10', '80.0%', '2.50', '', 'sampling']
+
+    tracker._tracking_mode = tracking_mod.TRACKING_MODE_FIT
+    tracker._fitting_time = 1.5
+    assert tracker._final_fit_tracking_row() == ['8', '1.50', '', '']
+
+    class BadHandle:
+        @staticmethod
+        def close() -> None:
+            message = 'boom'
+            raise RuntimeError(message)
+
+    tracker._display_handle = BadHandle()
+    tracker._close_display_handle()
+
+
+def test_minimizer_base_fit_flow_and_finalize():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    @dataclass
+    class DummyResult:
+        success: bool = True
+
+    class DummyMinimizer(MinimizerBase):
+        def __init__(self) -> None:
+            super().__init__(name='dummy', method='m', max_iterations=5)
+            self.synced = False
+
+        def _prepare_solver_args(self, parameters):
+            return {'engine_parameters': {'ok': True}}
+
+        def _run_solver(self, objective_function, **kwargs):
+            residuals = objective_function(kwargs.get('engine_parameters'))
+            self.tracker.track(residuals=np.array(residuals), parameters=[1])
+            return DummyResult(success=True)
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            self.synced = True
+            if parameters:
+                parameters[0].value = 42
+
+        def _check_success(self, raw_result):
+            return getattr(raw_result, 'success', False)
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            assert engine_params == {'ok': True}
+            return np.array([0.0, 0.0])
+
+    minimizer = DummyMinimizer()
+    params = [DummyParam(1.0), DummyParam(2.0)]
+    objective = minimizer._create_objective_function(
+        parameters=params,
+        structures=None,
+        experiments=None,
+        calculator=None,
+    )
+
+    result = minimizer.fit(parameters=params, objective_function=objective)
+
+    assert result.success is True
+    assert minimizer.synced is True
+    assert isinstance(result.parameters, list)
+    assert result.parameters[0].value == 42
+    assert minimizer.tracker.fitting_time is not None
+    assert minimizer.tracker.fitting_time >= 0.0
+
+
+def test_minimizer_base_create_objective_function_uses_compute_residuals():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class Minimizer(MinimizerBase):
+        def _prepare_solver_args(self, parameters):
+            return {}
+
+        def _run_solver(self, objective_function, **kwargs):
+            return None
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            return None
+
+        def _check_success(self, raw_result):
+            return True
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            return np.array([1.0, 2.0, 3.0])
+
+    minimizer = Minimizer()
+    objective = minimizer._create_objective_function(
+        parameters=[],
+        structures=None,
+        experiments=None,
+        calculator=None,
+    )
+
+    np.testing.assert_allclose(objective({}), np.array([1.0, 2.0, 3.0]))
+
+
+def test_minimizer_base_fit_stops_tracking_when_solver_prep_fails():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class Minimizer(MinimizerBase):
+        def __init__(self) -> None:
+            super().__init__(name='dummy', method='m', max_iterations=5)
+            self.started = False
+            self.stopped = False
+
+        def _start_tracking(self, minimizer_name, verbosity=None):
+            self.started = True
+
+        def _stop_tracking(self):
+            self.stopped = True
+
+        def _prepare_solver_args(self, parameters):
+            message = 'prep failed'
+            raise ValueError(message)
+
+        def _run_solver(self, objective_function, **kwargs):
+            message = 'should not run solver'
+            raise AssertionError(message)
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            return None
+
+        def _check_success(self, raw_result):
+            return True
+
+    minimizer = Minimizer()
+
+    with pytest.raises(ValueError, match='prep failed'):
+        minimizer.fit(parameters=[DummyParam(1.0)], objective_function=lambda _: np.array([0.0]))
+
+    assert minimizer.started is True
+    assert minimizer.stopped is True
+
+
+def test_minimizer_base_applies_physical_limits_and_warns(monkeypatch):
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    warnings: list[str] = []
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.base.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    class BoundaryParam(DummyParam):
+        def __init__(self) -> None:
+            super().__init__(5.0)
+            self.unique_name = 'boundary'
+
+        def _physical_lower_bound(self) -> float:
+            return 0.0
+
+        def _physical_upper_bound(self) -> float:
+            return 10.0
+
+    class DummyResult:
+        success = True
+
+    class Minimizer(MinimizerBase):
+        def __init__(self) -> None:
+            super().__init__(name='dummy', method='m', max_iterations=5)
+
+        def _prepare_solver_args(self, parameters):
+            return {'engine_parameters': {}}
+
+        def _run_solver(self, objective_function, **kwargs):
+            residuals = objective_function(kwargs.get('engine_parameters'))
+            self.tracker.track(residuals=np.array(residuals), parameters=[1])
+            return DummyResult()
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            parameters[0].value = 11.0
+
+        def _check_success(self, raw_result):
+            return True
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            return np.array([0.0, 0.0])
+
+    minimizer = Minimizer()
+    parameter = BoundaryParam()
+    objective = minimizer._create_objective_function(
+        parameters=[parameter],
+        structures=None,
+        experiments=None,
+        calculator=None,
+    )
+
+    result = minimizer.fit(
+        parameters=[parameter],
+        objective_function=objective,
+        use_physical_limits=True,
+    )
+
+    assert result.success is True
+    assert parameter.fit_min == 0.0
+    assert parameter.fit_max == 10.0
+    assert parameter._outside_physical_limits is True
+    assert any('upper bound' in message for message in warnings)
+    assert any('physical upper limit' in message for message in warnings)
+
+
+def test_minimizer_base_rejects_random_seed_when_not_supported():
+    from easydiffraction.analysis.minimizers.base import MinimizerBase
+
+    class Minimizer(MinimizerBase):
+        def _prepare_solver_args(self, parameters):
+            return {'engine_parameters': {}}
+
+        def _run_solver(self, objective_function, **kwargs):
+            return object()
+
+        def _sync_result_to_parameters(self, parameters, raw_result):
+            return None
+
+        def _check_success(self, raw_result):
+            return True
+
+        def _compute_residuals(
+            self, engine_params, parameters, structures, experiments, calculator
+        ):
+            return np.array([0.0])
+
+    minimizer = Minimizer(name='dummy')
+
+    with pytest.raises(
+        ValueError,
+        match=r"Minimizer 'dummy' does not support random_seed\.",
+    ):
+        minimizer.fit(parameters=[], objective_function=lambda _: np.array([0.0]), random_seed=7)
diff --git a/tests/integration/fitting/test_bumps_dream_support.py b/tests/integration/fitting/test_bumps_dream_support.py
new file mode 100644
index 00000000..bf87549a
--- /dev/null
+++ b/tests/integration/fitting/test_bumps_dream_support.py
@@ -0,0 +1,559 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+
+class FakeParam:
+    def __init__(
+        self,
+        uid: str,
+        value: float,
+        uncertainty: float | None = None,
+        *,
+        fit_min: float | None = 0.0,
+        fit_max: float | None = 1.0,
+    ) -> None:
+        self._minimizer_uid = uid
+        self.unique_name = uid
+        self.name = uid.upper()
+        self.value = value
+        self.uncertainty = uncertainty
+        self.fit_min = fit_min
+        self.fit_max = fit_max
+
+    def _set_value_from_minimizer(self, value: float) -> None:
+        self.value = value
+
+
+def test_type_info_and_default_init():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+    from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer.type_info.tag == MinimizerTypeEnum.BUMPS_DREAM
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
+    assert minimizer.steps == 3000
+
+
+def test_dream_progress_monitor_allocates_rows_by_phase_ratio():
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamProgressMonitor
+
+    monitor = _DreamProgressMonitor(
+        tracker=MagicMock(),
+        n_points=100,
+        n_parameters=3,
+        total_generations=100,
+        burn_steps=40,
+    )
+
+    assert len(monitor._burn_targets) == 10
+    assert len(monitor._sampling_targets) == 15
+
+
+def test_dream_progress_monitor_helper_edge_cases():
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamProgressMonitor
+
+    monitor = _DreamProgressMonitor(
+        tracker=MagicMock(),
+        n_points=2,
+        n_parameters=3,
+        total_generations=10,
+        burn_steps=2,
+    )
+
+    assert _DreamProgressMonitor._progress_targets(start=3, stop=2, target_count=2) == []
+    assert _DreamProgressMonitor._progress_targets(start=1, stop=5, target_count=0) == []
+    assert _DreamProgressMonitor._phase_progress_point_counts(
+        total_generations=10, burn_steps=0
+    ) == (0, 10)
+    assert _DreamProgressMonitor._phase_progress_point_counts(
+        total_generations=10, burn_steps=10
+    ) == (10, 0)
+    assert _DreamProgressMonitor._population_mean_log_posterior(
+        SimpleNamespace(population_values=[], value=[2.5])
+    ) == pytest.approx(-2.5)
+    assert _DreamProgressMonitor._population_mean_log_posterior(
+        SimpleNamespace(population_values=[np.array([np.nan, np.inf])], value=[1.5])
+    ) == pytest.approx(-1.5)
+    assert monitor._reduced_chi_square_from_nllf(4.0) == pytest.approx(8.0)
+
+
+def test_init_accepts_enum_or_string_and_rejects_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+    assert minimizer.init is DreamPopulationInitializationEnum.LHS
+
+    minimizer.init = 'random'
+    assert minimizer.init is DreamPopulationInitializationEnum.RANDOM
+
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'init' must be one of: eps, cov, lhs, random\.",
+    ):
+        minimizer.init = 'bad-init'
+
+
+def test_resolve_random_seed_returns_provided_or_generated(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolve_random_seed(17) == 17
+    assert minimizer._resolved_random_seed == 17
+
+    generator = SimpleNamespace(integers=lambda *args, **kwargs: 123456)
+    monkeypatch.setattr(np.random, 'default_rng', lambda: generator)
+
+    assert minimizer._resolve_random_seed(None) == 123456
+    assert minimizer._resolved_random_seed == 123456
+
+
+def test_dream_numeric_validators_reject_boolean_inputs():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with pytest.raises(
+        TypeError,
+        match=r"DREAM setting 'steps' must be a positive integer\.",
+    ):
+        minimizer.steps = True
+    with pytest.raises(
+        TypeError,
+        match=r"DREAM setting 'parallel' must be a non-negative integer\.",
+    ):
+        minimizer.parallel = True
+    with pytest.raises(TypeError, match='DREAM random_seed must be an integer'):
+        minimizer._validated_random_seed_value(random_seed=True)
+
+
+@pytest.mark.parametrize('seed', [-1, np.iinfo(np.uint32).max + 1])
+def test_resolve_random_seed_rejects_out_of_range_values(seed):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with pytest.raises(
+        ValueError,
+        match=r'DREAM random_seed must be an integer between 0 and 4294967295\.',
+    ):
+        minimizer._resolve_random_seed(seed)
+
+
+def test_resolved_burn_uses_auto_or_explicit_and_validates():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    assert minimizer._resolved_burn(steps=200) == 50
+
+    minimizer.burn = 20
+    assert minimizer._resolved_burn(steps=200) == 20
+
+    minimizer.burn = 200
+    with pytest.raises(
+        ValueError,
+        match=r"DREAM setting 'burn' must be smaller than 'steps'\.",
+    ):
+        minimizer._resolved_burn(steps=200)
+
+
+def test_sampler_settings_include_init_and_sample_count():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.enums import DreamPopulationInitializationEnum
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.thin = 1
+    minimizer.pop = 4
+    minimizer.init = DreamPopulationInitializationEnum.LHS
+
+    settings = minimizer._sampler_settings(
+        random_seed=7,
+        steps=10,
+        burn=2,
+        n_parameters=3,
+    )
+
+    assert settings['random_seed'] == 7
+    assert settings['parallel'] == 0
+    assert settings['init'] == 'lhs'
+    assert settings['samples'] == 120
+
+
+@pytest.mark.parametrize(
+    ('fit_min', 'fit_max', 'value', 'message'),
+    [
+        (None, 1.0, 0.5, r'fit_min must be finite'),
+        (0.0, np.inf, 0.5, r'fit_max must be finite'),
+        (2.0, 1.0, 1.5, r'fit_min \(2\.0\) must be smaller than fit_max \(1\.0\)'),
+        (0.0, 1.0, 2.0, r'starting value 2\.0 is outside \[0\.0, 1\.0\]'),
+    ],
+)
+def test_prepare_solver_args_rejects_invalid_dream_bounds(fit_min, fit_max, value, message):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameter = FakeParam('alpha', value, fit_min=fit_min, fit_max=fit_max)
+
+    with pytest.raises(ValueError, match=message):
+        minimizer._prepare_solver_args([parameter])
+
+
+def test_prepare_solver_args_lists_all_offending_dream_parameters():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [
+        FakeParam('alpha', 2.0, fit_min=0.0, fit_max=1.0),
+        FakeParam('beta', 0.5, fit_min=None, fit_max=1.0),
+    ]
+
+    with pytest.raises(
+        ValueError,
+        match=r'alpha: .*outside \[0\.0, 1\.0\][\s\S]*beta: .*fit_min',
+    ):
+        minimizer._prepare_solver_args(parameters)
+
+
+def test_sync_result_to_parameters_restores_starting_values_on_failure():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    parameters = [FakeParam('a', 10.0, uncertainty=0.7), FakeParam('b', 20.0, uncertainty=0.8)]
+    raw_result = SimpleNamespace(
+        x=np.array([99.0, 88.0]),
+        success=False,
+        starting_values=np.array([1.5, 2.5]),
+        starting_uncertainties=[0.1, None],
+    )
+
+    minimizer._sync_result_to_parameters(parameters, raw_result)
+
+    assert parameters[0].value == 1.5
+    assert parameters[0].uncertainty == 0.1
+    assert parameters[1].value == 2.5
+    assert parameters[1].uncertainty is None
+
+
+def test_build_mapper_falls_back_for_serial_and_unpicklable(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.parallel = 1
+    assert minimizer._build_mapper('problem') is None
+
+    warnings: list[str] = []
+    minimizer.parallel = 0
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.can_pickle', lambda problem: False
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    assert minimizer._build_mapper('problem') is None
+    assert any('falling back to serial execution' in message for message in warnings)
+
+
+def test_run_solver_preserves_parameter_order_and_forwards_init():
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+    minimizer.steps = 4
+    minimizer.burn = 1
+    minimizer.thin = 1
+    minimizer.pop = 2
+    minimizer.init = 'lhs'
+
+    draw_index = np.array([0.0, 1.0])
+    parameter_samples = np.array(
+        [
+            [[1.0, 10.0], [2.0, 20.0]],
+            [[3.0, 30.0], [4.0, 40.0]],
+        ],
+        dtype=float,
+    )
+    log_posterior = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=float)
+
+    class FakeState:
+        labels = ['uid_a', 'uid_b']
+
+        def chains(self):
+            return draw_index, parameter_samples, log_posterior
+
+        def best(self):
+            return np.array([11.0, 22.0]), 3.5
+
+    fake_fitter = SimpleNamespace(id='dream')
+
+    with (
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitProblem'),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FITTERS', [fake_fitter]),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.compute_convergence_diagnostics',
+            return_value={'converged': True},
+        ),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.summarize_posterior_parameters',
+            return_value=[
+                PosteriorParameterSummary(
+                    unique_name='beta',
+                    display_name='Beta',
+                    map_value=22.0,
+                    median=21.0,
+                    standard_deviation=0.4,
+                    interval_68=(20.5, 21.5),
+                    interval_95=(20.0, 22.0),
+                ),
+                PosteriorParameterSummary(
+                    unique_name='alpha',
+                    display_name='Alpha',
+                    map_value=11.0,
+                    median=10.5,
+                    standard_deviation=0.3,
+                    interval_68=(10.0, 11.0),
+                    interval_95=(9.5, 11.5),
+                ),
+            ],
+        ) as summarize_mock,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.standard_deviations_from_summaries',
+            return_value=np.array([0.4, 0.3]),
+        ),
+    ):
+        driver_instance = mock_driver_cls.return_value
+        driver_instance.clip = MagicMock()
+        driver_instance.fit.return_value = (np.array([22.0, 11.0]), 0.25)
+        driver_instance.fitter = SimpleNamespace(state=FakeState())
+
+        result = minimizer._run_solver(
+            lambda values: np.array([0.0, 0.0]),
+            bumps_params=[
+                SimpleNamespace(name='uid_a', value=1.0),
+                SimpleNamespace(name='uid_b', value=2.0),
+            ],
+            parameter_names=['beta', 'alpha'],
+            parameter_display_names=['Beta', 'Alpha'],
+            parameter_uids=['uid_b', 'uid_a'],
+            random_seed=17,
+            starting_uncertainties=[0.01, 0.02],
+        )
+
+    assert mock_driver_cls.call_args.kwargs['init'] == 'lhs'
+    np.testing.assert_allclose(result.x, np.array([22.0, 11.0]))
+    np.testing.assert_allclose(result.dx, np.array([0.4, 0.3]))
+    assert result.posterior_samples.parameter_names == ['beta', 'alpha']
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 0], parameter_samples[:, :, 1]
+    )
+    np.testing.assert_allclose(
+        result.posterior_samples.parameter_samples[:, :, 1], parameter_samples[:, :, 0]
+    )
+    assert result.sampler_settings['init'] == 'lhs'
+    assert result.sampler_settings['random_seed'] == 17
+    assert summarize_mock.call_args.kwargs['parameter_names'] == ['beta', 'alpha']
+
+
+def test_build_driver_stops_mapper_when_driver_clip_fails():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    minimizer = BumpsDreamMinimizer()
+
+    with (
+        patch.object(minimizer, '_build_mapper', return_value='mapper'),
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.FitProblem', return_value='problem'
+        ),
+        patch('easydiffraction.analysis.minimizers.bumps_dream.FitDriver') as mock_driver_cls,
+        patch(
+            'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+        ) as stop_mapper,
+    ):
+        mock_driver_cls.return_value.clip.side_effect = RuntimeError('clip failed')
+
+        with pytest.raises(RuntimeError, match='clip failed'):
+            minimizer._build_driver(
+                fitclass=object(),
+                fitness=SimpleNamespace(numpoints=lambda: 10),
+                steps=10,
+                burn=2,
+                init=minimizer.init,
+                sampler_settings={'samples': 40},
+                n_parameters=1,
+            )
+
+    stop_mapper.assert_called_once()
+
+
+def test_execute_driver_stops_mapper_when_seed_is_invalid():
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+
+    driver = SimpleNamespace(fit=MagicMock(), fitter=SimpleNamespace(state=None))
+
+    with patch(
+        'easydiffraction.analysis.minimizers.bumps_dream.MPMapper.stop_mapper'
+    ) as stop_mapper:
+        result = BumpsDreamMinimizer._execute_driver(driver=driver, random_seed=-1)
+
+    assert isinstance(result.error, ValueError)
+    driver.fit.assert_not_called()
+    stop_mapper.assert_called_once()
+
+
+def test_run_solver_failure_paths_return_failure_results(monkeypatch):
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamDriverResult
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamRunContext
+
+    minimizer = BumpsDreamMinimizer()
+    context = _DreamRunContext(
+        driver=object(),
+        parameter_names=['alpha'],
+        parameter_display_names=['Alpha'],
+        parameter_uids=['uid_alpha'],
+        sampler_settings={'random_seed': 7},
+        starting_values=np.array([1.0]),
+        starting_uncertainties=[0.1],
+    )
+
+    monkeypatch.setattr(
+        minimizer,
+        '_prepare_run_context',
+        lambda *, objective_function, kwargs: context,
+    )
+    monkeypatch.setattr(
+        minimizer,
+        '_execute_driver',
+        lambda *, driver, random_seed: _DreamDriverResult(
+            best_values=None,
+            best_nllf=None,
+            raw_state='state',
+            error=RuntimeError('boom'),
+        ),
+    )
+
+    failed = minimizer._run_solver(lambda _: np.array([0.0]))
+
+    assert failed.success is False
+    assert failed.message == 'DREAM sampling failed: boom'
+    assert failed.sampler_completed is False
+
+    monkeypatch.setattr(
+        minimizer,
+        '_execute_driver',
+        lambda *, driver, random_seed: _DreamDriverResult(
+            best_values=np.array([1.0]),
+            best_nllf=0.5,
+            raw_state=None,
+            error=None,
+        ),
+    )
+
+    unusable = minimizer._run_solver(lambda _: np.array([0.0]))
+
+    assert unusable.success is False
+    assert unusable.message == 'DREAM sampling did not produce usable posterior samples.'
+    assert unusable.sampler_completed is False
+
+
+def test_build_success_result_handles_invalid_samples_and_warns_when_not_converged(monkeypatch):
+    from easydiffraction.analysis.fit_helpers.bayesian import PosteriorParameterSummary
+    from easydiffraction.analysis.minimizers.bumps_dream import BumpsDreamMinimizer
+    from easydiffraction.analysis.minimizers.bumps_dream import _DreamRunContext
+
+    minimizer = BumpsDreamMinimizer()
+    context = _DreamRunContext(
+        driver=object(),
+        parameter_names=['alpha'],
+        parameter_display_names=['Alpha'],
+        parameter_uids=['uid_alpha'],
+        sampler_settings={'random_seed': 7},
+        starting_values=np.array([1.0]),
+        starting_uncertainties=[0.1],
+    )
+
+    class BadState:
+        labels = ['uid_alpha']
+
+        @staticmethod
+        def chains():
+            return np.array([0.0]), np.array([1.0]), np.array([0.0])
+
+        @staticmethod
+        def best():
+            return np.array([1.0]), 0.5
+
+    failed = minimizer._build_success_result(context=context, raw_state=BadState(), best_nllf=0.5)
+
+    assert failed.success is False
+    assert failed.sampler_completed is True
+
+    class GoodState:
+        labels = ['uid_alpha']
+
+        @staticmethod
+        def chains():
+            return (
+                np.array([0.0, 1.0]),
+                np.ones((2, 2, 1), dtype=float),
+                np.zeros((2, 2), dtype=float),
+            )
+
+        @staticmethod
+        def best():
+            return np.array([1.0]), 0.5
+
+    warnings: list[str] = []
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.compute_convergence_diagnostics',
+        lambda posterior_samples: {'converged': False},
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.summarize_posterior_parameters',
+        lambda **kwargs: [
+            PosteriorParameterSummary(
+                unique_name='alpha',
+                display_name='Alpha',
+                map_value=1.0,
+                median=1.0,
+                standard_deviation=0.2,
+                interval_68=(0.9, 1.1),
+                interval_95=(0.8, 1.2),
+            )
+        ],
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.standard_deviations_from_summaries',
+        lambda summaries: np.array([0.2]),
+    )
+    monkeypatch.setattr(
+        'easydiffraction.analysis.minimizers.bumps_dream.log.warning',
+        lambda message: warnings.append(message),
+    )
+
+    successful = minimizer._build_success_result(
+        context=context,
+        raw_state=GoodState(),
+        best_nllf=0.5,
+    )
+
+    assert successful.success is True
+    assert successful.sampler_completed is True
+    assert any('poorly mixed' in message for message in warnings)
diff --git a/tests/integration/fitting/test_cli_entrypoints.py b/tests/integration/fitting/test_cli_entrypoints.py
new file mode 100644
index 00000000..636c0ca4
--- /dev/null
+++ b/tests/integration/fitting/test_cli_entrypoints.py
@@ -0,0 +1,173 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+from typer.testing import CliRunner
+
+runner = CliRunner()
+
+
+def test_cli_version_invokes_show_version(monkeypatch):
+    import easydiffraction as ed
+    import easydiffraction.__main__ as main_mod
+
+    called = {'ok': False}
+
+    def fake_show_version() -> None:
+        print('VERSION_OK')
+        called['ok'] = True
+
+    monkeypatch.setattr(ed, 'show_version', fake_show_version)
+
+    result = runner.invoke(main_mod.app, ['--version'])
+
+    assert result.exit_code == 0
+    assert called['ok'] is True
+    assert 'VERSION_OK' in result.stdout
+
+
+def test_cli_help_shows_and_exits_zero():
+    import easydiffraction.__main__ as main_mod
+
+    result = runner.invoke(main_mod.app, ['--help'])
+
+    assert result.exit_code == 0
+    assert 'EasyDiffraction command-line interface' in result.stdout
+
+
+def test_cli_subcommands_call_utils(monkeypatch):
+    import easydiffraction as ed
+    import easydiffraction.__main__ as main_mod
+
+    calls: list[str] = []
+    monkeypatch.setattr(ed, 'list_tutorials', lambda: calls.append('LIST'))
+    monkeypatch.setattr(
+        ed,
+        'download_all_tutorials',
+        lambda destination='tutorials', overwrite=False: calls.append('DOWNLOAD_ALL'),
+    )
+    monkeypatch.setattr(
+        ed,
+        'download_tutorial',
+        lambda id, destination='tutorials', overwrite=False: calls.append(f'DOWNLOAD_{id}'),
+    )
+
+    list_result = runner.invoke(main_mod.app, ['list-tutorials'])
+    download_all_result = runner.invoke(main_mod.app, ['download-all-tutorials'])
+    download_one_result = runner.invoke(main_mod.app, ['download-tutorial', '1'])
+
+    assert list_result.exit_code == 0
+    assert download_all_result.exit_code == 0
+    assert download_one_result.exit_code == 0
+    assert calls == ['LIST', 'DOWNLOAD_ALL', 'DOWNLOAD_1']
+
+
+def test_cli_fit_loads_and_fits(monkeypatch, tmp_path):
+    import easydiffraction.__main__ as main_mod
+    from easydiffraction.project.project import Project
+
+    calls: list[str] = []
+
+    class FakeInfo:
+        _path = '/some/path'
+
+    class FakeExperiment:
+        name = 'exp1'
+
+    class FakeProject:
+        info = FakeInfo()
+        experiments = [FakeExperiment()]
+
+        class _analysis:
+            @staticmethod
+            def fit() -> None:
+                calls.append('FIT')
+
+            class display:
+                @staticmethod
+                def fit_results() -> None:
+                    calls.append('DISPLAY')
+
+        analysis = _analysis()
+
+        class _display:
+            class _plotter:
+                @staticmethod
+                def plot_param_correlations() -> None:
+                    calls.append('PLOT_CORR')
+
+                @staticmethod
+                def plot_meas_vs_calc(expt_name: str, *, show_residual: bool = False) -> None:
+                    calls.append(f'PLOT_{expt_name}_{show_residual}')
+
+            plotter = _plotter()
+
+        display = _display()
+
+    fake_project = FakeProject()
+
+    project_dir = tmp_path / 'proj'
+    project_dir.mkdir()
+    (project_dir / 'project.cif').write_text('_project.id test\n')
+
+    monkeypatch.setattr(Project, 'load', staticmethod(lambda path: fake_project))
+
+    result = runner.invoke(main_mod.app, ['fit', str(project_dir)])
+
+    assert result.exit_code == 0
+    assert calls == ['FIT', 'DISPLAY', 'PLOT_CORR', 'PLOT_exp1_True']
+
+
+def test_cli_fit_dry_clears_path(monkeypatch, tmp_path):
+    import easydiffraction.__main__ as main_mod
+    from easydiffraction.project.project import Project
+
+    class FakeInfo:
+        _path = '/some/path'
+
+    class FakeExperiment:
+        name = 'exp1'
+
+    class FakeProject:
+        info = FakeInfo()
+        experiments = [FakeExperiment()]
+
+        class _analysis:
+            @staticmethod
+            def fit() -> None:
+                return None
+
+            class display:
+                @staticmethod
+                def fit_results() -> None:
+                    return None
+
+        analysis = _analysis()
+
+        class _display:
+            class _plotter:
+                @staticmethod
+                def plot_param_correlations() -> None:
+                    return None
+
+                @staticmethod
+                def plot_meas_vs_calc(expt_name: str, *, show_residual: bool = False) -> None:
+                    return None
+
+            plotter = _plotter()
+
+        display = _display()
+
+    fake_project = FakeProject()
+
+    project_dir = tmp_path / 'proj'
+    project_dir.mkdir()
+    (project_dir / 'project.cif').write_text('_project.id test\n')
+
+    monkeypatch.setattr(Project, 'load', staticmethod(lambda path: fake_project))
+
+    result = runner.invoke(main_mod.app, ['fit', '--dry', str(project_dir)])
+
+    assert result.exit_code == 0
+    assert fake_project.info._path is None

From c22525e7df427abc9c1b2c4400ef55e90cc23f12 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Wed, 13 May 2026 22:52:29 +0200
Subject: [PATCH 105/106] Remove unused benchmark dependency and track serial
 benchmarks

---
 docs/dev/issues_open.md | 24 ++++++++++++++++++++++++
 pixi.lock               | 36 ------------------------------------
 pyproject.toml          |  1 -
 3 files changed, 24 insertions(+), 37 deletions(-)

diff --git a/docs/dev/issues_open.md b/docs/dev/issues_open.md
index 014aa467..21976afd 100644
--- a/docs/dev/issues_open.md
+++ b/docs/dev/issues_open.md
@@ -180,6 +180,30 @@ minimiser.
 
 ---
 
+## 16. 🟢 Add Serial Pattern-Generation Benchmarks
+
+**Type:** Performance
+
+The dev environment previously installed `pytest-benchmark`, but the
+repository does not currently define any benchmark tests. At the same
+time, the integration, script, and notebook pytest tasks all run with
+`pytest-xdist`, so benchmark plugins only add warning noise and do not
+provide reliable performance regression coverage.
+
+Performance regressions are still worth tracking, especially for single
+diffraction-pattern calculation where backend or profile changes can
+quietly slow interactive workflows.
+
+**Fix:** add a dedicated serial benchmark task outside the normal
+parallel pytest suite. Benchmark representative single-pattern
+calculations on fixed datasets and calculators, run without `-n auto`,
+and define regression thresholds only after measurements are stable on a
+controlled runner.
+
+**Depends on:** nothing.
+
+---
+
 ## 17. 🟢 Use PDF-Specific CIF Names for Total Scattering
 
 **Type:** Naming
diff --git a/pixi.lock b/pixi.lock
index 04890b1c..f1532e2b 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -209,7 +209,6 @@ environments:
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-      - pypi: https://files.pythonhosted.org/packages/33/29/e756e715a48959f1c0045342088d7ca9762a2f509b945f362a316e9412b7/pytest_benchmark-5.2.3-py3-none-any.whl
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@@ -329,7 +328,6 @@ environments:
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-      - pypi: https://files.pythonhosted.org/packages/e0/a9/023730ba63db1e494a271cb018dcd361bd2c917ba7004c3e49d5daf795a2/py_cpuinfo-9.0.0-py3-none-any.whl
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@@ -549,7 +547,6 @@ environments:
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-      - pypi: https://files.pythonhosted.org/packages/33/29/e756e715a48959f1c0045342088d7ca9762a2f509b945f362a316e9412b7/pytest_benchmark-5.2.3-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/33/6b/e0547afaf41bf2c42e52430072fa5658766e3d65bd4b03a563d1b6336f57/distlib-0.4.0-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/33/b2/986d1220f6ee931e338d272bc1f3ec02cfe5f9b5fad84e95afdad57f1ebc/format_docstring-0.2.7-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/33/f0/3fe8c6e69135a845f4106f2ff8b6805638d4e85c264e70114e8126689587/tokenize_rt-6.2.0-py2.py3-none-any.whl
@@ -3057,7 +3040,6 @@ environments:
       - pypi: https://files.pythonhosted.org/packages/dc/b7/901d837999a9350a7773289f7760cb473d4ba01fdc6ebae0ff2553d269ac/ncrystal_python-4.4.2-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/de/15/545e2b6cf2e3be84bc1ed85613edd75b8aea69807a71c26f4ca6a9258e82/email_validator-2.3.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/de/1f/77fa3081e4f66ca3576c896ae5d31c3002ac6607f9747d2e3aa49227e464/markdown-3.10.2-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/e0/a9/023730ba63db1e494a271cb018dcd361bd2c917ba7004c3e49d5daf795a2/py_cpuinfo-9.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/e0/bf/52f25716bbe93745595800f36fb17b73711f14da59ed0bb2eba141bc9f0f/multidict-6.7.1-cp314-cp314-win_amd64.whl
       - pypi: https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/e4/bc/daa30c02069eeac5b9198985ba42f5d65ca71bed6705b18329e51d352b7c/plopp-26.4.2-py3-none-any.whl
@@ -8209,7 +8191,6 @@ packages:
   - pre-commit ; extra == 'dev'
   - pydoclint ; extra == 'dev'
   - pytest ; extra == 'dev'
-  - pytest-benchmark ; extra == 'dev'
   - pytest-cov ; extra == 'dev'
   - pytest-xdist ; extra == 'dev'
   - pyyaml ; extra == 'dev'
@@ -9280,19 +9261,6 @@ packages:
   - prettytable
   - ply
   - numpy
-- pypi: https://files.pythonhosted.org/packages/33/29/e756e715a48959f1c0045342088d7ca9762a2f509b945f362a316e9412b7/pytest_benchmark-5.2.3-py3-none-any.whl
-  name: pytest-benchmark
-  version: 5.2.3
-  sha256: bc839726ad20e99aaa0d11a127445457b4219bdb9e80a1afc4b51da7f96b0803
-  requires_dist:
-  - pytest>=8.1
-  - py-cpuinfo
-  - aspectlib ; extra == 'aspect'
-  - pygal ; extra == 'histogram'
-  - pygaljs ; extra == 'histogram'
-  - setuptools ; extra == 'histogram'
-  - elasticsearch ; extra == 'elasticsearch'
-  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/33/6b/e0547afaf41bf2c42e52430072fa5658766e3d65bd4b03a563d1b6336f57/distlib-0.4.0-py2.py3-none-any.whl
   name: distlib
   version: 0.4.0
@@ -12796,10 +12764,6 @@ packages:
   - mkdocs-section-index ; extra == 'docs'
   - mkdocs-literate-nav ; extra == 'docs'
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/e0/a9/023730ba63db1e494a271cb018dcd361bd2c917ba7004c3e49d5daf795a2/py_cpuinfo-9.0.0-py3-none-any.whl
-  name: py-cpuinfo
-  version: 9.0.0
-  sha256: 859625bc251f64e21f077d099d4162689c762b5d6a4c3c97553d56241c9674d5
 - pypi: https://files.pythonhosted.org/packages/e0/bf/52f25716bbe93745595800f36fb17b73711f14da59ed0bb2eba141bc9f0f/multidict-6.7.1-cp314-cp314-win_amd64.whl
   name: multidict
   version: 6.7.1
diff --git a/pyproject.toml b/pyproject.toml
index 3bab31f7..f81f2303 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,6 @@ dev = [
   'pytest',                          # Testing
   'pytest-cov',                      # Test coverage
   'pytest-xdist',                    # Enable parallel testing
-  'pytest-benchmark',                # Benchmarking tests
   'ruff',                            # Linting and formatting code
   'radon',                           # Code complexity and maintainability
   'validate-pyproject[all]',         # Validate pyproject.toml

From d2f53fba71189ae7d228cea71b61383fa6a28054 Mon Sep 17 00:00:00 2001
From: Andrew Sazonov <andrew.sazonov@icloud.com>
Date: Wed, 13 May 2026 23:23:21 +0200
Subject: [PATCH 106/106] Normalize styled console output in Windows
 integration tests

---
 .../test_analysis_and_fit_category_support.py        | 10 +++++++++-
 .../fitting/test_bayesian_helper_support.py          | 12 ++++++++++--
 2 files changed, 19 insertions(+), 3 deletions(-)

diff --git a/tests/integration/fitting/test_analysis_and_fit_category_support.py b/tests/integration/fitting/test_analysis_and_fit_category_support.py
index f476b0d4..7a52c63d 100644
--- a/tests/integration/fitting/test_analysis_and_fit_category_support.py
+++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py
@@ -3,8 +3,16 @@
 
 from __future__ import annotations
 
+import re
+
 import pytest
 
+ANSI_ESCAPE_RE = re.compile(r'\x1b\[[0-?]*[ -/]*[@-~]')
+
+
+def _unstyled_output(text: str) -> str:
+    return ANSI_ESCAPE_RE.sub('', text)
+
 
 def _make_project():
     class ExpCol:
@@ -210,7 +218,7 @@ def test_analysis_help_and_mode_switching(capsys):
     assert len(analysis.joint_fit_experiments) == 0
 
     analysis.help()
-    out = capsys.readouterr().out
+    out = _unstyled_output(capsys.readouterr().out)
     assert "Help for 'Analysis'" in out
     assert 'fit' in out
     assert 'display' in out
diff --git a/tests/integration/fitting/test_bayesian_helper_support.py b/tests/integration/fitting/test_bayesian_helper_support.py
index 8a5fed2f..a4eef900 100644
--- a/tests/integration/fitting/test_bayesian_helper_support.py
+++ b/tests/integration/fitting/test_bayesian_helper_support.py
@@ -3,9 +3,17 @@
 
 from __future__ import annotations
 
+import re
+
 import numpy as np
 import pytest
 
+ANSI_ESCAPE_RE = re.compile(r'\x1b\[[0-?]*[ -/]*[@-~]')
+
+
+def _unstyled_output(text: str) -> str:
+    return ANSI_ESCAPE_RE.sub('', text)
+
 
 class Identity:
     def __init__(self) -> None:
@@ -329,7 +337,7 @@ def test_bayesian_fit_results_display_results_prints_sampler_and_convergence(cap
 
     results.display_results(y_obs=[10.0, 20.0], y_calc=[9.5, 19.5])
 
-    out = capsys.readouterr().out
+    out = _unstyled_output(capsys.readouterr().out)
     assert 'Bayesian fit results' in out
     assert 'Overall status: completed with warnings' in out
     assert 'Sampler status: DREAM sampling completed' in out
@@ -596,7 +604,7 @@ def test_fitresults_display_results_prints_and_table(capsys):
         f_calc=[5.1, 5.9],
     )
 
-    out = capsys.readouterr().out
+    out = _unstyled_output(capsys.readouterr().out)
     assert 'Fit results' in out
     assert 'Success: True' in out
     assert 'reduced χ²' in out