diff --git a/.codecov.yml b/.codecov.yml new file mode 100644 index 000000000..d001c41da --- /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 a807c84da..009e6acc4 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: v0.11.4 _src_path: gh:easyscience/templates app_docs_url: https://easyscience.github.io/diffraction-app app_doi: 10.5281/zenodo.18163581 diff --git a/.github/actions/setup-pixi/action.yml b/.github/actions/setup-pixi/action.yml index ec7d7ba7b..dc3232c61 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 }} diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index 18b01c478..0a54cc941 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//.py` → `tests/unit/easydiffraction//test_.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; @@ -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/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 95c190c63..d96e5b87d 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/dashboard.yml b/.github/workflows/dashboard.yml index 9d1f2b0bd..573e30868 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/.github/workflows/issues-labels.yml b/.github/workflows/issues-labels.yml index 56ab5b19a..4fbf4fab1 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: ${{ github.token }} + label_prefix: '[priority]' + label_separator: ' ' + add_label: 'true' + default_label: '[priority] ⚠️ label needed' diff --git a/.github/workflows/pr-labels.yml b/.github/workflows/pr-labels.yml index 25710633b..fcb8a78cb 100644 --- a/.github/workflows/pr-labels.yml +++ b/.github/workflows/pr-labels.yml @@ -1,17 +1,9 @@ # Verifies if a pull request has at least one label from a set of valid # labels before it can be merged. # -# NOTE: -# This workflow may be triggered twice in quick succession when a PR is -# created: -# 1) `opened` — when the pull request is initially created -# 2) `labeled` — if labels are added immediately after creation -# (e.g. by manual labeling, another workflow, or GitHub App). -# -# These are separate GitHub events, so two workflow runs can be started. -# The `concurrency` configuration below ensures that only the latest run -# for the same PR remains active, canceling any previous in-progress -# run. +# The label validation is delegated to `mheap/github-action-required-labels`, +# which checks the current PR labels via the GitHub API and can add or update a +# PR comment when the required label set is missing. name: PR labels check @@ -19,45 +11,35 @@ on: pull_request_target: types: [opened, labeled, unlabeled, synchronize] -concurrency: - group: pr-labels-${{ 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: ${{ steps.bot.outputs.token }} + add_comment: true + mode: minimum + count: 1 + labels: | + [bot] release + [scope] bug + [scope] documentation + [scope] enhancement + [scope] maintenance + [scope] significant diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index d0298f95b..86bfa6063 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -248,10 +248,10 @@ jobs: exit 1 fi - whl_url="file://$(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/CONTRIBUTING.md b/CONTRIBUTING.md index e8dc420f2..9e5b93cff 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 diff --git a/codecov.yml b/codecov.yml deleted file mode 100644 index f62b13ab4..000000000 --- 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/architecture/package-structure-full.md b/docs/architecture/package-structure-full.md deleted file mode 100644 index be35bb076..000000000 --- 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 ba2a0b33b..000000000 --- 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/docs/dev/adr_analysis-cif-fit-state.md b/docs/dev/adr_analysis-cif-fit-state.md new file mode 100644 index 000000000..c39fc192b --- /dev/null +++ b/docs/dev/adr_analysis-cif-fit-state.md @@ -0,0 +1,220 @@ +# 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 000000000..9c494908b --- /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 000000000..dd7999ba1 --- /dev/null +++ b/docs/dev/adr_parameter-posterior-summary.md @@ -0,0 +1,681 @@ +# 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____x` +- `predictive____map_prediction` +- `predictive____lower_95` +- `predictive____upper_95` +- `predictive____lower_68` +- `predictive____upper_68` +- `predictive____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//x` +- `/predictive//map_prediction` +- `/predictive//lower_95` +- `/predictive//upper_95` +- `/predictive//lower_68` +- `/predictive//upper_68` +- `/predictive//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 000000000..299741308 --- /dev/null +++ b/docs/dev/adr_undo-fit.md @@ -0,0 +1,162 @@ +# 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 diff --git a/docs/dev/architecture.md b/docs/dev/architecture.md index 390aa77bd..48a411619 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` | @@ -825,14 +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 @@ -894,7 +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 @@ -1052,6 +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 diff --git a/docs/dev/issues_open.md b/docs/dev/issues_open.md index 014aa4675..21976afde 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/docs/dev/package-structure-full.md b/docs/dev/package-structure-full.md index ae233b21d..8083f39b0 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 @@ -48,10 +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 @@ -65,12 +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 @@ -169,9 +182,6 @@ │ │ │ │ │ ├── 🏷️ class PdDataBase │ │ │ │ │ ├── 🏷️ class PdCwlData │ │ │ │ │ └── 🏷️ class PdTofData -│ │ │ │ ├── 📄 bragg_sc.py -│ │ │ │ │ ├── 🏷️ class Refln -│ │ │ │ │ └── 🏷️ class ReflnData │ │ │ │ ├── 📄 factory.py │ │ │ │ │ └── 🏷️ class DataFactory │ │ │ │ └── 📄 total_pd.py @@ -257,6 +267,20 @@ │ │ │ │ │ └── 🏷️ 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 @@ -352,7 +376,13 @@ │ │ └── 🏷️ class RendererFactoryBase │ ├── 📄 plotting.py │ │ ├── 🏷️ class PlotterEngineEnum +│ │ ├── 🏷️ class PosteriorPairPlotStyleEnum │ │ ├── 🏷️ class _MeasVsCalcPlotOptions +│ │ ├── 🏷️ class _PowderMeasVsCalcSeries +│ │ ├── 🏷️ class _PosteriorDistributionContext +│ │ ├── 🏷️ class _PosteriorPairsContext +│ │ ├── 🏷️ class _CorrelationHeatmapContext +│ │ ├── 🏷️ 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 30b4daf72..9ce283eb5 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 @@ -85,7 +87,6 @@ │ │ │ ├── 📁 data │ │ │ │ ├── 📄 __init__.py │ │ │ │ ├── 📄 bragg_pd.py -│ │ │ │ ├── 📄 bragg_sc.py │ │ │ │ ├── 📄 factory.py │ │ │ │ └── 📄 total_pd.py │ │ │ ├── 📁 diffrn @@ -128,6 +129,11 @@ │ │ │ │ ├── 📄 tof_mixins.py │ │ │ │ ├── 📄 total.py │ │ │ │ └── 📄 total_mixins.py +│ │ │ ├── 📁 refln +│ │ │ │ ├── 📄 __init__.py +│ │ │ │ ├── 📄 bragg_pd.py +│ │ │ │ ├── 📄 bragg_sc.py +│ │ │ │ └── 📄 factory.py │ │ │ └── 📄 __init__.py │ │ ├── 📁 item │ │ │ ├── 📄 __init__.py diff --git a/docs/docs/installation-and-setup/index.md b/docs/docs/installation-and-setup/index.md index 41036f1d9..ee86679ba 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/docs/tutorials/ed-13.ipynb b/docs/docs/tutorials/ed-13.ipynb index 4d605962c..69eae9aa9 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 000000000..405bc8589 --- /dev/null +++ b/docs/docs/tutorials/ed-21.ipynb @@ -0,0 +1,730 @@ +{ + "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": [ + "# Bayesian Analysis: 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": null, + "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": null, + "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": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "project.structures.create(name='lbco')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "structure = project.structures['lbco']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "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": null, + "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": null, + "id": "15", + "metadata": {}, + "outputs": [], + "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": null, + "id": "17", + "metadata": {}, + "outputs": [], + "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": null, + "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": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.instrument.setup_wavelength = 1.494\n", + "experiment.instrument.calib_twotheta_offset = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": {}, + "outputs": [], + "source": [ + "experiment.excluded_regions.create(id='1', start=0, end=10)\n", + "experiment.excluded_regions.create(id='2', start=100, end=180)" + ] + }, + { + "cell_type": "markdown", + "id": "25", + "metadata": {}, + "source": [ + "#### Link the Structural Phase to the Experiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "id": "28", + "metadata": {}, + "outputs": [], + "source": [ + "structure.cell.length_a.free = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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" + ] + }, + { + "cell_type": "markdown", + "id": "30", + "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": null, + "id": "31", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit.show_minimizer_types()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit.minimizer_type = 'bumps (lm)'" + ] + }, + { + "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": "markdown", + "id": "35", + "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": null, + "id": "36", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.plotter.plot_param_correlations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.plotter.plot_meas_vs_calc(expt_name='hrpt')" + ] + }, + { + "cell_type": "markdown", + "id": "38", + "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.\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": null, + "id": "39", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.display.free_params()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40", + "metadata": {}, + "outputs": [], + "source": [ + "for param in project.free_parameters:\n", + " param.set_fit_bounds_from_uncertainty(multiplier=3.5)" + ] + }, + { + "cell_type": "markdown", + "id": "41", + "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": null, + "id": "42", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.display.free_params()" + ] + }, + { + "cell_type": "markdown", + "id": "43", + "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 (`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. The current EasyDiffraction default\n", + "also uses `parallel=0`, which tells BUMPS DREAM to use all available\n", + "CPUs for population evaluations.\n", + "\n", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit.show_minimizer_types()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit.minimizer_type = 'bumps (dream)'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit.minimizer.steps = 100 # lower than the default 1000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.fit()" + ] + }, + { + "cell_type": "markdown", + "id": "48", + "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": "49", + "metadata": {}, + "outputs": [], + "source": [ + "project.analysis.display.fit_results()" + ] + }, + { + "cell_type": "markdown", + "id": "50", + "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": "51", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.plotter.plot_param_correlations()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.plotter.plot_posterior_pairs()" + ] + }, + { + "cell_type": "markdown", + "id": "53", + "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": "54", + "metadata": {}, + "outputs": [], + "source": [ + "for param in project.free_parameters:\n", + " project.display.plotter.plot_param_distribution(param)" + ] + }, + { + "cell_type": "markdown", + "id": "55", + "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": "56", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.plotter.plot_posterior_predictive(expt_name='hrpt')" + ] + }, + { + "cell_type": "markdown", + "id": "57", + "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": "58", + "metadata": {}, + "outputs": [], + "source": [ + "project.display.plotter.plot_posterior_predictive(expt_name='hrpt', x_min=92, x_max=93)" + ] + } + ], + "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-21.py b/docs/docs/tutorials/ed-21.py new file mode 100644 index 000000000..d91173c37 --- /dev/null +++ b/docs/docs/tutorials/ed-21.py @@ -0,0 +1,354 @@ +# %% [markdown] +# # Bayesian Analysis: 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 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 and specify the sample form, beam mode, +# and radiation probe. + +# %% +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] +# 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. + +# %% +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.excluded_regions.create(id='1', start=0, end=10) +experiment.excluded_regions.create(id='2', start=100, end=180) + +# %% [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.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 + +# %% [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() + +# %% +project.display.plotter.plot_meas_vs_calc(expt_name='hrpt') + +# %% [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. 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() + +# %% [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 (`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. 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() + +# %% +project.analysis.fit.minimizer_type = 'bumps (dream)' + +# %% +project.analysis.fit.minimizer.steps = 300 # 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 `±4 × 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 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_posterior_predictive( + expt_name='hrpt', + x_min=92, + x_max=93, +) diff --git a/docs/docs/tutorials/ed-22.ipynb b/docs/docs/tutorials/ed-22.ipynb new file mode 100644 index 000000000..cc880314e --- /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": [ + "# Bayesian Analysis: 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 000000000..aaa727180 --- /dev/null +++ b/docs/docs/tutorials/ed-22.py @@ -0,0 +1,269 @@ +# %% [markdown] +# # Bayesian Analysis: 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. +# +# 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 + +# %% +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 +# +# 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') + +# %% +project.structures.add_from_cif_path(structure_path) + +# %% +structure = project.structures['tbti'] + +# %% [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') + +# %% +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'] + +# %% [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 + +# %% +experiment.extinction.mosaicity = 35000 +experiment.extinction.radius = 10 + +# %% [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 + +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 + +# %% [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() + +# %% +project.display.plotter.plot_meas_vs_calc(expt_name='heidi') + +# %% [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 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() + +# %% +project.analysis.fit.minimizer_type = 'bumps (dream)' + +# %% +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/docs/docs/tutorials/index.json b/docs/docs/tutorials/index.json index dcd0cb870..40145a15e 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 69b9d0ca9..a11c73e60 100644 --- a/docs/docs/tutorials/index.md +++ b/docs/docs/tutorials/index.md @@ -98,6 +98,22 @@ 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. +- [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 in the context of single crystal diffraction data. + ## Workshops & Schools - [DMSC Summer School](ed-13.ipynb) – A workshop tutorial that diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 45c2ce5fb..16091a5d6 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 @@ -214,6 +216,9 @@ nav: - Simulated Data: - LBCO+Si McStas: tutorials/ed-9.ipynb - BEER McStas: tutorials/ed-20.ipynb + - Bayesian Analysis: + - LBCO Bayesian: tutorials/ed-21.ipynb + - Tb2TiO7 Bayesian: tutorials/ed-22.ipynb - Workshops & Schools: - DMSC Summer School: tutorials/ed-13.ipynb - API Reference: @@ -229,5 +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 diff --git a/pixi.lock b/pixi.lock index 4ff7fdb61..f1532e2b2 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 environments: default: channels: @@ -6,49 +10,91 @@ environments: - url: https://conda.anaconda.org/conda-forge/ indexes: - https://pypi.org/simple - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda + - 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'pytest-benchmark', # Benchmarking tests 'ruff', # Linting and formatting code 'radon', # Code complexity and maintainability 'validate-pyproject[all]', # Validate pyproject.toml diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py index c132e409c..23b9fcf3f 100644 --- a/src/easydiffraction/analysis/analysis.py +++ b/src/easydiffraction/analysis/analysis.py @@ -184,9 +184,11 @@ 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 = 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.') @@ -513,7 +515,13 @@ 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 +550,8 @@ 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 +573,20 @@ 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 +605,7 @@ def _fit_joint( experiments: object, *, use_physical_limits: bool, + random_seed: int | None, ) -> None: """ Run joint fitting across all experiments with weights. @@ -599,6 +620,8 @@ 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 +645,7 @@ def _fit_joint( analysis=self, verbosity=verb, use_physical_limits=use_physical_limits, + random_seed=random_seed, ) # After fitting, get the results @@ -634,6 +658,7 @@ def _fit_single( experiments: object, *, use_physical_limits: bool, + random_seed: int | None, ) -> None: """ Run single-mode fitting for each experiment independently. @@ -648,6 +673,8 @@ 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 +693,7 @@ 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/calculators/cryspy.py b/src/easydiffraction/analysis/calculators/cryspy.py index 3c2da252c..7bac27385 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/src/easydiffraction/analysis/categories/fit/default.py b/src/easydiffraction/analysis/categories/fit/default.py index c002fe399..7a9ebcb12 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/fit_helpers/__init__.py b/src/easydiffraction/analysis/fit_helpers/__init__.py index 4e798e209..c7c53862a 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 # 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 000000000..e10d9c698 --- /dev/null +++ b/src/easydiffraction/analysis/fit_helpers/bayesian.py @@ -0,0 +1,777 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause +"""Bayesian fit result models and posterior data containers.""" + +from __future__ import annotations + +from dataclasses import dataclass + +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 +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.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: + r""" + Posterior summary statistics for one fitted parameter. + + Attributes + ---------- + 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. + + Attributes + ---------- + 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 + 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_axis_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. + + Attributes + ---------- + 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. + """ + posterior_array = np.asarray(self.parameter_samples, dtype=float) + if posterior_array.ndim != POSTERIOR_SAMPLE_NDIM: + 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))} + + data = {'posterior': posterior_dict} + if sample_stats is not None: + data['sample_stats'] = sample_stats + + 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. + + Attributes + ---------- + 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 : SummaryList, default=None + Posterior summaries for each sampled parameter. + posterior_predictive : PredictiveMap, default=None + Posterior predictive summaries keyed by experiment name. + credible_interval_levels : IntervalLevels, default=DEFAULT_CI_LEVELS + Interval levels available in the summaries. + sampler_settings : SettingsMap, default=None + Sampler settings recorded for reproducibility. + 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. + best_log_posterior : float | None, default=None + Best log-posterior value reported by the sampler. + """ + + 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=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.posterior_parameter_summaries = ( + list(self.posterior_parameter_summaries) + if self.posterior_parameter_summaries is not None + else [] + ) + self.posterior_predictive = ( + dict(self.posterior_predictive) if self.posterior_predictive is not None else {} + ) + self.sampler_settings = dict(self.sampler_settings) if self.sampler_settings else {} + self.convergence_diagnostics = ( + dict(self.convergence_diagnostics) if self.convergence_diagnostics is not None else {} + ) + + 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. + """ + 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, 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} Overall status: {overall_status}') + if 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)}' + ) + 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: + 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(Text(f'⚙️ Sampler settings: {sampler_settings}')) + + convergence_summary = _format_convergence_summary(self.convergence_diagnostics) + if convergence_summary is not None: + console.print(Text.from_markup(f'📊 Convergence: {convergence_summary}')) + + +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) + + 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] + + 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: + 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, + parameter_display_names: list[str] | None = None, + 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. + 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. + + 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) + 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 = {} + 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()) + 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=display_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 | None]: + values: dict[str, float | None] = {} + for name, data_array in dataset.data_vars.items(): + 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 + scalar = float(value) + if not np.isfinite(scalar): + return None + return scalar + + +def _format_sampler_settings(sampler_settings: dict[str, object]) -> str | None: + if not sampler_settings: + return None + + parts = [ + f'{key}={sampler_settings[key]}' + for key in ('steps', 'burn', 'thin', 'pop', 'init', '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('_', ' ') + if normalized_name == 'map': + return 'Max posterior' + 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 + + parts: list[str] = [] + converged = convergence_diagnostics.get('converged') + if converged is not None: + 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: + 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', + 'units', + 'start', + 'max posterior', + 'uncertainty', + 'change', + ] + alignments = [ + 'left', + 'left', + 'left', + 'left', + 'left', + 'right', + 'right', + 'right', + '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', + 'units', + 'median', + '95% interval', + 'r-hat', + 'ess bulk', + ] + alignments = [ + 'left', + 'left', + 'left', + 'left', + 'left', + 'right', + 'right', + 'right', + 'right', + ] + 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 '' + parameter_name = getattr(parameter, 'name', summary.display_name) + units = getattr(parameter, 'units', 'N/A') + + return [ + datablock, + 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), + ] + + +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 or not np.isfinite(value): + return 'N/A' + formatted = f'{value:.3f}' + if value > R_HAT_CONVERGENCE_THRESHOLD: + return f'[red]{formatted}[/red]' + return formatted + + +def _format_ess_bulk(value: float | None) -> str: + if value is None or not np.isfinite(value): + return 'N/A' + formatted = f'{value:.1f}' + if value < ESS_BULK_CONVERGENCE_THRESHOLD: + 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 [] + + notes: list[str] = [] + if has_failed_r_hat: + notes.append( + 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 < {ESS_BULK_CONVERGENCE_THRESHOLD:.0f}[/red]: ' + 'Consider longer sampling or reparameterization.' + ) + return notes diff --git a/src/easydiffraction/analysis/fit_helpers/reporting.py b/src/easydiffraction/analysis/fit_helpers/reporting.py index a83ad7bf5..aefad27b6 100644 --- a/src/easydiffraction/analysis/fit_helpers/reporting.py +++ b/src/easydiffraction/analysis/fit_helpers/reporting.py @@ -108,8 +108,10 @@ 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}') + 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: @@ -125,10 +127,10 @@ def display_results( 'category', 'entry', 'parameter', + 'units', 'start', 'fitted', 'uncertainty', - 'units', 'change', ] alignments = [ @@ -136,10 +138,10 @@ def display_results( 'left', 'left', 'left', + 'left', 'right', 'right', 'right', - 'left', 'right', ] @@ -221,10 +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, ] @@ -248,3 +250,29 @@ 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}' diff --git a/src/easydiffraction/analysis/fit_helpers/tracking.py b/src/easydiffraction/analysis/fit_helpers/tracking.py index 99f9c8b68..b5cfb11a2 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 @@ -29,8 +30,29 @@ from easydiffraction.utils.logging import ConsoleManager SIGNIFICANT_CHANGE_THRESHOLD = 0.01 # 1% threshold -DEFAULT_HEADERS = ['iteration', 'χ²', 'improvement [%]'] -DEFAULT_ALIGNMENTS = ['center', 'center', 'center'] +SAMPLER_PROGRESS_UPDATE_SECONDS = 5.0 +TRACKING_MODE_FIT = 'fit' +TRACKING_MODE_SAMPLER = 'sampling' +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'] + + +@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: @@ -97,10 +119,20 @@ 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 + 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 + 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._last_sampler_elapsed_time: float | None = None self._df_rows: list[list[str]] = [] self._display_handle: object | None = None @@ -112,9 +144,19 @@ 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 + 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, @@ -140,6 +182,16 @@ 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 @@ -150,6 +202,7 @@ def track( row = [ str(self._iteration), + self._format_elapsed_time(), f'{reduced_chi2:.2f}', '', ] @@ -164,6 +217,7 @@ def track( row = [ str(self._iteration), + self._format_elapsed_time(), f'{reduced_chi2:.2f}', f'{change_in_percent:.1f}% ↓', ] @@ -175,7 +229,7 @@ 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 @@ -185,6 +239,46 @@ def track( return residuals + def track_sampler_progress(self, update: SamplerProgressUpdate) -> None: + """ + Update progress from a sampler monitor. + + Parameters + ---------- + update : SamplerProgressUpdate + Sampler iteration, phase, timing, and fit-quality payload. + """ + self._iteration = update.iteration + self._tracking_mode = TRACKING_MODE_SAMPLER + self._sampler_total_iterations = max(1, update.total_iterations) + + 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 = update.phase + self._last_sampler_progress_percent = clamped_progress + self._last_sampler_log_posterior = update.log_posterior + self._last_sampler_elapsed_time = update.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 = update.reduced_chi2 + self._last_iteration = update.iteration + @property def best_chi2(self) -> float | None: """Best recorded reduced chi-square value or None.""" @@ -208,13 +302,17 @@ 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 - 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. @@ -222,14 +320,23 @@ def start_tracking(self, minimizer_name: str) -> None: ---------- minimizer_name : str Name of the minimizer used for the run. + mode : str, default=TRACKING_MODE_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 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('📈 Goodness-of-fit progress:') # Reset rows and create an environment-appropriate handle self._df_rows = [] @@ -237,8 +344,8 @@ 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, ) @@ -250,41 +357,291 @@ 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: + 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.""" - # Add last iteration as last row - row: list[str] = [ + if self._tracking_mode == TRACKING_MODE_SAMPLER: + self._finalize_sampler_tracking_row() + else: + self._finalize_fit_tracking_row() + + if self._verbosity is not VerbosityEnum.FULL: + return + + 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 '', '', ] - self.add_tracking_info(row) - if self._verbosity is not VerbosityEnum.FULL: - return + 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}' - # Close terminal live if used + 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() - # Print best result + def _print_completion_summary(self) -> None: + if self._tracking_mode == TRACKING_MODE_SAMPLER: + console.print('✅ Bayesian sampling complete.') + return + 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 + + 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/analysis/fitting.py b/src/easydiffraction/analysis/fitting.py index 9b0360143..db5590e1b 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/__init__.py b/src/easydiffraction/analysis/minimizers/__init__.py index 7dddb75ee..1006eefb7 100644 --- a/src/easydiffraction/analysis/minimizers/__init__.py +++ b/src/easydiffraction/analysis/minimizers/__init__.py @@ -4,8 +4,10 @@ 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.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/base.py b/src/easydiffraction/analysis/minimizers/base.py index c0b39eb33..bbc639f10 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( @@ -60,7 +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: @@ -68,6 +69,11 @@ def _stop_tracking(self) -> None: self.tracker.stop_timer() self.tracker.finish_tracking() + @staticmethod + def _tracking_mode() -> str: + """Return the tracker mode for the current minimizer.""" + return 'fit' + @abstractmethod def _prepare_solver_args(self, parameters: list[Any]) -> dict[str, Any]: """ @@ -124,7 +130,38 @@ 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, @@ -132,7 +169,6 @@ def _finalize_fit( starting_parameters=parameters, fitting_time=self.tracker.fitting_time, ) - return self.result @staticmethod def _warn_boundary_parameters(parameters: list[object]) -> None: @@ -231,6 +267,33 @@ 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 +301,7 @@ def fit( verbosity: VerbosityEnum = VerbosityEnum.FULL, *, use_physical_limits: bool = False, + random_seed: int | None = None, ) -> FitResults: """ Run the full minimization workflow. @@ -255,6 +319,8 @@ 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 ------- @@ -264,16 +330,21 @@ 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})' self._start_tracking(minimizer_name, verbosity=verbosity) - solver_args = self._prepare_solver_args(parameters) - 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 new file mode 100644 index 000000000..c9af1012b --- /dev/null +++ b/src/easydiffraction/analysis/minimizers/bumps_dream.py @@ -0,0 +1,948 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause +"""Bumps minimizer variant using the DREAM sampler.""" + +from __future__ import annotations + +import random +from dataclasses import dataclass + +import numpy as np +from bumps.fitproblem import FitProblem +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 +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.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 +from easydiffraction.core.metadata import TypeInfo +from easydiffraction.utils.logging import log + +DEFAULT_METHOD = 'dream' +DEFAULT_MAX_ITERATIONS = 3000 +DEFAULT_BURN_FRACTION = 0.2 +DEFAULT_MIN_BURN = 50 +DEFAULT_THIN = 1 +DEFAULT_POP = 4 +DEFAULT_PARALLEL = 0 +DEFAULT_INIT = DreamPopulationInitializationEnum.LHS +DEFAULT_ALPHA = 0.0 +DEFAULT_OUTLIER_TEST = 'none' +DEFAULT_TRIM = False +MAX_RANDOM_SEED = int(np.iinfo(np.uint32).max) +TOTAL_PROGRESS_POINTS = 25 +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. + """ + + def __init__( + self, + *, + tracker: object, + n_points: int, + n_parameters: int, + total_generations: int, + burn_steps: int, + ) -> None: + self._tracker = tracker + self._n_points = n_points + 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_target_count, + ) + self._sampling_targets = self._progress_targets( + start=self._burn_steps + 1, + stop=self._total_generations, + target_count=sampling_target_count, + ) + self._next_burn_target_index = 0 + self._next_sampling_target_index = 0 + + @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) + + 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) + self._tracker.track_sampler_progress( + 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: + """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) + best_nllf = float(best['value']) + reduced_chi2 = self._reduced_chi_square_from_nllf(best_nllf) + self._tracker.track_sampler_progress( + 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 + 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({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 + + @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) + 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) + 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 + + @staticmethod + def _population_mean_log_posterior(history: object) -> float: + """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]) + + 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 + 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.""" + + 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, + ) + 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 + 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) + + @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.""" + 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. + + 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)) + + 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.""" + return 'sampling' + + 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. + """ + 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'] = [ + 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 + + @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 '' + + @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.""" + if isinstance(value, bool): + msg = f"DREAM setting '{name}' must be a positive integer." + raise TypeError(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: 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 TypeError(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 + + @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: + 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, + n_parameters: int, + ) -> dict[str, object]: + """Build the sampler settings dictionary recorded in results.""" + samples = steps * self.pop * n_parameters + return { + 'random_seed': int(random_seed), + 'steps': int(steps), + 'burn': int(burn), + '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, + 'trim': DEFAULT_TRIM, + } + + 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``. + """ + 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 = int(kwargs.get('random_seed')) + starting_uncertainties = kwargs.get('starting_uncertainties') + + fitness = _EasyDiffractionFitness(bumps_params, objective_function) + fitness.nllf() + fitclass = next(cls for cls in FITTERS if cls.id == self.method) + steps = self.steps + burn = self._resolved_burn(steps) + init = self.init + sampler_settings = self._sampler_settings( + random_seed=random_seed, + steps=steps, + burn=burn, + n_parameters=len(bumps_params), + ) + driver = self._build_driver( + fitclass=fitclass, + fitness=fitness, + steps=steps, + burn=burn, + 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) + problem = FitProblem(fitness) + progress_monitor = _DreamProgressMonitor( + tracker=self.tracker, + n_points=fitness.numpoints(), + n_parameters=n_parameters, + total_generations=total_generations, + burn_steps=int(burn), + ) + mapper = self._build_mapper(problem) + 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.""" + 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: + """ + 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() + 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( + best_values=None, + best_nllf=None, + raw_state=getattr(driver.fitter, 'state', None), + error=error, + ) + finally: + MPMapper.stop_mapper() + numpy_rng.set_state(numpy_state) + random.setstate(python_state) + + 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, + ) + + 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.', + raw_state=raw_state, + sampler_completed=True, + ) + + 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 context.parameter_uids], dtype=float) + posterior_samples = PosteriorSamples( + 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=context.parameter_names, + posterior_samples=posterior_samples, + map_values=map_values, + parameter_display_names=context.parameter_display_names, + convergence_diagnostics=convergence_diagnostics, + ) + posterior_standard_deviations = standard_deviations_from_summaries( + posterior_parameter_summaries + ) + + if not convergence_diagnostics.get('converged', True): + log.warning('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=context.parameter_names, + posterior_samples=posterior_samples, + posterior_parameter_summaries=posterior_parameter_summaries, + convergence_diagnostics=convergence_diagnostics, + sampler_settings=context.sampler_settings, + sampler_completed=True, + raw_state=raw_state, + best_log_posterior=float(best_log_posterior), + starting_values=context.starting_values, + starting_uncertainties=context.starting_uncertainties, + ) + + @staticmethod + def _sync_result_to_parameters( + 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 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 = raw_result + 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(fit_results.sampler_settings.get('steps', self.steps)) + fit_results.result = raw_result + return fit_results diff --git a/src/easydiffraction/analysis/minimizers/enums.py b/src/easydiffraction/analysis/minimizers/enums.py index cdfe4ecb8..0d5bb086d 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)' @@ -45,7 +46,17 @@ 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'), } return descriptions.get(self, '') + + +class DreamPopulationInitializationEnum(StrEnum): + """Supported DREAM population initializers.""" + + EPS = 'eps' + COV = 'cov' + LHS = 'lhs' + RANDOM = 'random' diff --git a/src/easydiffraction/core/variable.py b/src/easydiffraction/core/variable.py index 84bbd2f42..303c5bbe5 100644 --- a/src/easydiffraction/core/variable.py +++ b/src/easydiffraction/core/variable.py @@ -22,6 +22,8 @@ # ====================================================================== +DEFAULT_FIT_BOUNDS_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: @@ -426,6 +430,80 @@ 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 = DEFAULT_FIT_BOUNDS_MULTIPLIER, + *, + clip_to_limits: bool = True, + ) -> None: + """ + Set fit bounds from the current standard uncertainty. + + Parameters + ---------- + multiplier : float, default=DEFAULT_FIT_BOUNDS_MULTIPLIER + 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. + + 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) + + 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 resolved_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) - resolved_multiplier * float(uncertainty) + upper = float(value) + resolved_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 + self._fit_bounds_uncertainty_multiplier = resolved_multiplier # ====================================================================== diff --git a/src/easydiffraction/display/plotters/ascii.py b/src/easydiffraction/display/plotters/ascii.py index a0c95fe11..4aeb5577a 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 b1a19a274..d4be0d67a 100644 --- a/src/easydiffraction/display/plotters/base.py +++ b/src/easydiffraction/display/plotters/base.py @@ -60,6 +60,11 @@ 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 + 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 887879dd3..cb4914c3f 100644 --- a/src/easydiffraction/display/plotters/plotly.py +++ b/src/easydiffraction/display/plotters/plotly.py @@ -65,6 +65,15 @@ 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, 0.14)' +PREDICTIVE_BAND_EDGE_COLOR = 'rgba(214, 39, 40, 0.45)' +PREDICTIVE_DRAW_COLOR = 'rgba(140, 140, 140, 0.18)' +PREDICTIVE_DRAW_PLOT_CAP = 50 +PREDICTIVE_DRAW_ARRAY_NDIM = 2 +FIXED_ASPECT_WRAPPER_META_KEY = 'fixed_aspect_wrapper' +FIXED_ASPECT_WRAPPER_CLASS_NAME = 'ed-fixed-aspect-plotly-wrapper' @dataclass(frozen=True) @@ -157,6 +166,11 @@ 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.""" @@ -208,7 +222,7 @@ def plot_correlation_heatmap( 'yanchor': 'middle', }, hoverongaps=False, - hovertemplate=f'x: %{{x}}
y: %{{y}}
corr: %{{z:.{precision}f}}', + hovertemplate=f'%{{x}}
%{{y}}
correlation: %{{z:.{precision}f}}', ) label_trace = self._get_correlation_label_trace( corr_df, @@ -461,12 +475,13 @@ 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}
' 'Imeas: %{customdata[0]:,.2f}
' - 'Icalc: %{customdata[1]:,.2f}
' - 'Imeas - Icalc: %{customdata[2]:,.2f}' + f'{calc_label}: %{{customdata[1]:,.2f}}
' + f'Imeas - {calc_label}: %{{customdata[2]:,.2f}}' '' ) @@ -474,8 +489,8 @@ def _powder_meas_vs_calc_hover_template(plot_spec: PowderMeasVsCalcSpec) -> str: 'x: %{x:,.2f}
' 'Imeas: %{customdata[0]:,.2f}
' 'Ibkg: %{customdata[1]:,.2f}
' - 'Icalc: %{customdata[2]:,.2f}
' - 'Imeas - Icalc: %{customdata[3]:,.2f}' + f'{calc_label}: %{{customdata[2]:,.2f}}
' + f'Imeas - {calc_label}: %{{customdata[3]:,.2f}}' '' ) @@ -778,6 +793,90 @@ 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 _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 _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 _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 None + + wrapper = meta.get(FIXED_ASPECT_WRAPPER_META_KEY) + if not isinstance(wrapper, dict): + return None + + aspect_ratio = wrapper.get('aspect_ratio') + if not isinstance(aspect_ratio, str): + return None + + 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 ( + '\n\n' + f'
\n' + f'{html_fig}\n' + '
' + ) + @staticmethod def _get_figure( data: object, @@ -855,9 +954,7 @@ 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', @@ -865,6 +962,7 @@ def _show_figure( config=config, post_script=post_script, ) + html_fig = self._wrap_html_figure(fig, html_fig) display(HTML(html_fig)) @classmethod @@ -900,6 +998,7 @@ def _get_layout( }, title={ 'text': title, + 'font': {'size': TITLE_FONT_SIZE}, }, legend={ 'bgcolor': cls._legend_background_color(), @@ -909,14 +1008,22 @@ 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, 'zeroline': False, }, @@ -1165,11 +1272,7 @@ 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) + 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)) @@ -1180,6 +1283,49 @@ 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.""" @@ -1221,11 +1367,40 @@ 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, @@ -1233,58 +1408,146 @@ def plot_powder_meas_vs_calc( row_heights=layout.row_heights, ) - 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), + 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 + + 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: + bkg_trace = self._get_powder_trace( + plot_spec.x, + plot_spec.y_bkg, + 'bkg', + customdata=hover_data, + hovertemplate=hover_template, ) + fig.add_trace(bkg_trace, row=1, col=1) + + calc_trace = self._get_powder_trace( + plot_spec.x, + plot_spec.y_calc, + 'calc', + customdata=hover_data, + hovertemplate=hover_template, ) - for label, y_values in main_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( + 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( - self._get_powder_trace( - plot_spec.x, - y_values, - label, - customdata=hover_data, - hovertemplate=hover_template, + 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
x: %{x:,.2f}
y: %{y:,.2f}' + ), ), 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, - ), - row=layout.bragg_row, - 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={ @@ -1293,7 +1556,10 @@ def plot_powder_meas_vs_calc( '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', @@ -1303,10 +1569,64 @@ 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], + title_font={'size': AXIS_TITLE_FONT_SIZE}, + 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], + title_font={'size': AXIS_TITLE_FONT_SIZE}, + 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': axis_frame_color, 'mirror': True, 'zeroline': False, 'tickformat': ',.6~g', @@ -1317,6 +1637,7 @@ def plot_powder_meas_vs_calc( fig.update_xaxes(row=row_idx, col=1, **x_axis_kwargs) fig.update_yaxes( showline=True, + linecolor=axis_frame_color, mirror=True, zeroline=False, tickformat=',.6~g', @@ -1325,50 +1646,86 @@ 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) + 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], + title_font={'size': AXIS_TITLE_FONT_SIZE}, + row=layout.residual_row, + col=1, + ) - 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': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1}, + hoverinfo='skip', + showlegend=False, + legendgroup='predictive_band', + ) + upper_trace = go.Scatter( + x=x, + y=upper, + mode='lines', + line={'color': PREDICTIVE_BAND_EDGE_COLOR, 'width': 1}, + fill='tonexty', + fillcolor=PREDICTIVE_BAND_COLOR, + name='95% interval', + hoverinfo='skip', + legendgroup='predictive_band', + legendrank=35, + ) + return lower_trace, upper_trace def plot_single_crystal( self, diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py index 61e93f683..34b0da107 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 @@ -30,6 +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 @@ -63,12 +68,108 @@ def description(self) -> str: return '' -DEFAULT_CORRELATION_THRESHOLD = 0.7 +class PosteriorPairPlotStyleEnum(StrEnum): + """Available posterior pair-plot rendering modes.""" + + AUTO = 'auto' + FAST = 'fast' + FULL = 'full' + + +DEFAULT_CORRELATION_THRESHOLD: float | None = None +DEFAULT_CORRELATION_MAX_PARAMETERS = 6 EXPECTED_COVAR_NDIM = 2 DEFAULT_RESIDUAL_HEIGHT_FRACTION = 0.25 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 +FULL_POSTERIOR_PAIR_COVARIANCE_RANK = 2 +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_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(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' +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 = [ + [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)'], +] +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)'], + [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)'], +] +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 +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 +PAIR_PLOT_MARGIN_PIXELS = 120 +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 = 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 +SQUARE_MATRIX_TITLE_YSHIFT_PIXELS = 12 +POSTERIOR_PAIR_GUIDE_LINE_COLOR = 'rgba(125, 140, 173, 0.18)' +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 @dataclass(frozen=True) @@ -90,6 +191,70 @@ 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] + annotation_labels: list[str] + title: 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]] + + @property + def n_parameters(self) -> int: + """Return the number of plotted parameters.""" + 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.""" + + show_density: bool = True + show_scatter: bool = True + show_contour: bool = True + + class Plotter(RendererBase): """User-facing plotting facade backed by concrete plotters.""" @@ -562,6 +727,9 @@ def plot_param_correlations( self, threshold: float | None = DEFAULT_CORRELATION_THRESHOLD, precision: int = 2, + *, + max_parameters: int = DEFAULT_CORRELATION_MAX_PARAMETERS, + show_diagonal: bool = True, ) -> None: """ Plot the parameter correlation matrix from the latest fit. @@ -570,44 +738,57 @@ 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 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 ---------- 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 - matrix. + parameter to be shown. When omitted, an automatic cutoff is + 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. """ corr_df = self._get_param_correlation_dataframe() 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, + 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 threshold is not None and threshold > 0: - title += f' with |correlation| >= {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( corr_df, preserve_all_rows=not is_graphical, + show_diagonal=show_diagonal, ) if is_graphical: self._plot_correlation_heatmap( display_corr_df, title, - threshold=threshold, + threshold=resolved_threshold, precision=precision, ) return @@ -618,11 +799,439 @@ 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, + 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, + ) + return cls._auto_filtered_correlation_dataframe( + corr_df, + 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 TypeError(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: + 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 min_parameters <= 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 + + @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}' + + @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' # noqa: RUF001 + + @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, + 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. + + Parameters + ---------- + parameters : list[object] | None, default=None + Optional subset of sampled parameters to include. When + 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, + threshold=threshold, + max_parameters=max_parameters, + ) + if plot is None: + return + self._show_plot_figure(plot) + + def plot_param_distribution( + self, + param: object, + ) -> None: + """ + Plot the posterior distribution for one sampled parameter. + + Parameters + ---------- + param : object + 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: + return + self._show_plot_figure(plot) + + def plot_posterior_predictive( + self, + expt_name: str, + style: str = 'band', + x_min: float | None = None, + x_max: float | None = None, + *, + show_residual: bool | None = None, + x: object | None = None, + ) -> None: + """ + Plot posterior predictive checks for supported experiments. + + Parameters + ---------- + expt_name : str + Experiment name to plot. + style : str, default='band' + ``'band'`` shows the 95% credible interval, ``'draws'`` + shows sampled predictive curves, and ``'band+draws'`` shows + 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 supported powder + composite plots. + 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'." + 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 + + 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) + + plot_options = _MeasVsCalcPlotOptions( + x_min=x_min, + x_max=x_max, + show_residual=show_residual, + 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, + expt_name=expt_name, + plot_options=plot_options, + x_axis=x_axis, + style=style, + ) + return + + self._plot_non_bragg_posterior_predictive( + experiment=experiment, + expt_name=expt_name, + plot_options=plot_options, + x_axis=x_axis, + sample_form=sample_form, + scattering_type=scattering_type, + 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, + *, + 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=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'}, + ) + @staticmethod def _filter_correlation_dataframe( corr_df: pd.DataFrame, @@ -697,6 +1306,7 @@ 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. @@ -711,6 +1321,8 @@ 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 ------- @@ -722,7 +1334,7 @@ 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: @@ -739,16 +1351,19 @@ 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 - raw_result, var_names, fit_results = result - 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._posterior_correlation_dataframe(fit_results) + if corr_df is not None: + return corr_df - corr_df = self._get_param_correlation_dataframe_from_engine_params( + 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, ) @@ -757,21 +1372,2587 @@ 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( + def _posterior_correlation_dataframe( self, - ) -> tuple[object, list[str], object] | None: - """ - Validate and return the raw fit result for correlation. + fit_results: object, + ) -> pd.DataFrame | None: + """Return posterior-sample correlations when available.""" + posterior_samples = getattr(fit_results, 'posterior_samples', None) + if posterior_samples is None: + return None + return self._correlation_from_posterior_samples(posterior_samples) - Returns + @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) + 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 + 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, + getattr(raw_result, 'var_names', None), + parameters, + ) + return self._get_param_correlation_dataframe_from_engine_params( + raw_result=raw_result, + parameters=parameters, + ) + + def _build_posterior_pairs_plot( + self, + *, + parameters: list[object] | None, + style: PosteriorPairPlotStyleEnum | str = 'auto', + threshold: float | None = DEFAULT_CORRELATION_THRESHOLD, + max_parameters: int | None = None, + ) -> object | None: + """ + Build a Plotly posterior pair plot. + + Parameters + ---------- + parameters : list[object] | None + 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 | None, default=None + Maximum number of auto-selected parameters. ``None`` keeps + the full posterior parameter set. + + Returns + ------- + object | None + Plotly figure, or ``None`` when posterior plotting is + unavailable. + """ + context = self._posterior_pairs_context( + parameters, + style=style, + threshold=threshold, + max_parameters=max_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, + *, + style: PosteriorPairPlotStyleEnum | str = 'auto', + threshold: float | None = DEFAULT_CORRELATION_THRESHOLD, + 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() + if posterior_samples is None or fit_results is None: + return None + + plot_style = self._validated_posterior_pair_plot_style(style) + + 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 + if len(parameter_names) < MIN_POSTERIOR_PARAMETER_COUNT: + log.warning('Posterior pair plots require at least two sampled parameters.') + return 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, + ) + 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( + self._posterior_pair_title(uncertainty_multiplier), + resolved_threshold, + ), + density_samples=density_samples, + 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=selected_samples, + ), + ) + + def _resolved_posterior_pair_parameter_names( + self, + *, + fit_results: object, + parameters: list[object] | None, + threshold: float | None, + max_parameters: int | None, + ) -> 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, + *, + 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, + ) + axis_ranges.append( + self._posterior_axis_bounds( + density_samples[:, index], + lower_bound=lower_bound, + upper_bound=upper_bound, + ) + ) + return axis_ranges + + 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, + ) + + @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) + + 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': POSTERIOR_PAIR_MARGINAL_DENSITY_LINE_COLOR}, + showlegend=False, + hovertemplate=self._posterior_pair_density_hovertemplate( + context.parameter_names[parameter_index] + ), + ), + row=row, + col=col, + ) + 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 + 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 = 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 = 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( + x=x_scatter_values, + y=y_scatter_values, + mode='markers', + marker={ + 'color': POSTERIOR_SCATTER_MARKER_COLOR, + 'size': POSTERIOR_PAIR_SAMPLE_MARKER_SIZE, + }, + 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': POSTERIOR_PAIR_SAMPLE_HOVER_MARKER_SIZE, + }, + 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, + showgrid=False, + layer='above traces', + linecolor=context.axis_frame_color, + linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH, + ticks='', + ticklen=0, + tickwidth=0, + showticklabels=False, + row=row, + col=col, + ) + fig.update_yaxes( + showline=True, + mirror=True, + zeroline=False, + showgrid=False, + layer='above traces', + linecolor=context.axis_frame_color, + linewidth=POSTERIOR_PAIR_AXIS_LINE_WIDTH, + 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) + if is_diagonal: + fig.update_yaxes( + title_text=None, + row=row, + col=col, + ) + fig.update_xaxes(title_text=None, 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) + 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': 0.5 * (subplot.yaxis.domain[0] + subplot.yaxis.domain[1]), + 'yref': 'paper', + 'yanchor': 'middle', + 'text': context.annotation_labels[row_index], + 'align': 'center', + 'font': {'size': POSTERIOR_PAIR_AXIS_TITLE_FONT_SIZE}, + 'textangle': -90, + 'showarrow': False, + }) + 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.annotation_labels[col_index], + 'align': 'center', + '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 _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': SQUARE_MATRIX_TITLE_YSHIFT_PIXELS, + 'text': title, + 'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE}, + 'showarrow': False, + } + + @staticmethod + 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( + title, + annotation_labels, + ) + + @staticmethod + def _square_matrix_title_left_shift(annotation_labels: list[str]) -> int: + """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: + """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._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 { + SQUARE_MATRIX_FIXED_ASPECT_META_KEY: { + 'aspect_ratio': f'{aspect_width} / {aspect_height}', + } + } + + 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=self._square_matrix_layout_margin(context.annotation_labels), + bargap=0.05, + annotations=[ + self._posterior_pair_title_annotation( + context.title, + context.annotation_labels, + ), + *subplot_title_annotations, + ], + shapes=subplot_border_shapes, + 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', + 'x': 0.995, + 'yanchor': 'top', + 'y': 0.995, + 'groupclick': 'togglegroup', + }, + ) + + @staticmethod + 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': 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 _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._square_matrix_axis_title_line_count(label) for label in annotation_labels + ) + return max(0, max_line_count - 1) * SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS + + @staticmethod + 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 + return label.count('
') + 1 + + @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, + ) + + @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 = f'style must be one of {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. + """ + 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 _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, + *, + fit_results: object, + x_parameter_name: str, + 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. + """ + 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], + grid_size=grid_size, + ) + if density_surface is None: + 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) + 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=fill_density, + contours={ + 'coloring': 'fill', + 'showlabels': False, + 'showlines': False, + 'start': contour_start, + 'end': contour_end, + 'size': contour_size, + }, + colorscale=fill_colorscale, + zmin=contour_start, + zmax=contour_end, + connectgaps=False, + hoverinfo='skip', + showscale=False, + showlegend=False, + zorder=1, + ) + 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, + }, + colorscale=line_colorscale, + zmin=contour_start, + zmax=contour_end, + line={'width': 0.9}, + hoverinfo='skip', + showscale=False, + showlegend=False, + zorder=2, + ) + return fill_trace, line_trace + + def _build_param_distribution_plot( + self, + param: object, + ) -> object | None: + """ + Build a Plotly posterior distribution plot for one parameter. + + Parameters + ---------- + param : object + Parameter descriptor to plot. + + Returns + ------- + object | None + 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, + ) + 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='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( + fig=fig, + summary=context.summary, + y_axis_range=y_axis_range, + ) + self._add_posterior_distribution_histogram( + 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( + 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, + x_axis_range=x_axis_range, + 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 + + parameter_names = self._resolve_posterior_parameter_names( + fit_results=fit_results, + parameters=[param], + ) + if parameter_names is None: + return None + + parameter_name = parameter_names[0] + samples = self._selected_posterior_samples(posterior_samples, [parameter_name]) + if samples is None: + return None + + label = self._posterior_plot_labels(fit_results, [parameter_name])[0] + return _PosteriorDistributionContext( + fit_results=fit_results, + parameter_name=parameter_name, + values=samples[:, 0], + label=label, + title=f'Posterior distribution: {parameter_name}', + 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): + 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, + histogram_bin_edges: np.ndarray | None, + ) -> tuple[float, float] | None: + """Return the y-axis range for a posterior distribution plot.""" + 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)) + + @staticmethod + def _posterior_distribution_x_axis_range( + *, + 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: + 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 ( + 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 float(np.min(finite_values)), float(np.max(finite_values)) + + @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, + *, + 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 + + 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, + ) + ) + + @staticmethod + 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, + histnorm='probability density', + marker={ + 'color': POSTERIOR_HISTOGRAM_FILL_COLOR, + 'line': {'color': POSTERIOR_HISTOGRAM_LINE_COLOR, 'width': 1}, + }, + opacity=0.82, + name='Posterior histogram', + hovertemplate='sample=%{x:.4f}
density: %{y:.2f}', + **histogram_kwargs, + ) + ) + + @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 = 'Marginal 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 None: + return + + fig.add_trace( + self._posterior_reference_line_trace( + x_value=summary.map_value, + y_axis_range=y_axis_range, + trace_name=POSTERIOR_POINT_ESTIMATE_TRACE_NAME, + color=POSTERIOR_POINT_ESTIMATE_LINE_COLOR, + dash=POSTERIOR_POINT_ESTIMATE_LINE_DASH, + ) + ) + + @staticmethod + def _apply_posterior_distribution_layout( + *, + fig: object, + 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.""" + if callable(layout_factory): + fig.update_layout( + title={ + 'text': title, + 'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE}, + } + ) + else: + fig.update_layout( + title={ + 'text': title, + 'font': {'size': POSTERIOR_PAIR_TITLE_FONT_SIZE}, + }, + xaxis_title=label, + yaxis_title='Probability density', + legend={ + 'bgcolor': 'rgba(0, 0, 0, 0)', + 'xanchor': 'right', + 'x': 1.0, + 'yanchor': 'top', + '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: + fig.update_yaxes(range=list(y_axis_range)) + + 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() + + @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_density_hovertemplate(parameter_name: str) -> str: + """Return the hover template for a marginal density trace.""" + return f'{parameter_name}: %{{x:.4f}}
density: %{{y:.4f}}' + + @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}}
{y_parameter_name}: %{{y:.4f}}' + + @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, + *, + 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': POSTERIOR_DENSITY_LINE_COLOR, 'width': 2}, + fill='tozeroy', + fillcolor=POSTERIOR_DENSITY_FILL_COLOR, + name=trace_name, + showlegend=False, + hovertemplate='%{x:.4f}
density: %{y:.2f}', + ) + + @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 + 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 = min(0.0, data_min) + return lower, data_max + upper_padding + + @staticmethod + def _posterior_interval_band_trace( + *, + x0: float, + x1: float, + y_axis_range: tuple[float, float], + trace_name: str, + color: str, + ) -> object: + """Return a hideable credible-interval band trace.""" + go = __import__('plotly.graph_objects', fromlist=['Scatter']) + + 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], + ], + mode='lines', + fill='toself', + fillcolor=color, + line={'color': color, 'width': 0}, + name=trace_name, + showlegend=True, + hoverinfo='skip', + ) + + @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}}', + ) + + @staticmethod + def _posterior_parameter_bounds( + *, + fit_results: object, + parameter_name: str, + ) -> tuple[float | None, float | None]: + """Return finite fit bounds for a posterior parameter.""" + 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_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, + 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 < MIN_POSTERIOR_SAMPLE_COUNT: + 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: + kde = gaussian_kde(data) + density = cls._evaluate_reflected_1d_kde( + kde, + grid, + lower_bound=lower_bound, + upper_bound=upper_bound, + ) + + area = np.trapezoid(density, grid) + if area <= 0: + 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 < 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]): + 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) + 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 + + @staticmethod + def _reflection_positions_1d( + values: np.ndarray, + *, + lower_bound: float | None, + upper_bound: float | None, + ) -> list[np.ndarray]: + """Return mirrored 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 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, + *, + experiment: object, + expt_name: str, + x_axis: object, + include_draws: bool = True, + ) -> object | None: + """Return a cached or built 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) + 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 + + summary = self._build_posterior_predictive_summary( + fit_results=fit_results, + experiment=experiment, + expt_name=expt_name, + x_axis=x_axis, + include_draws=include_draws, + ) + 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, + include_draws: bool = True, + ) -> object | None: + """Build posterior predictive summaries from posterior draws.""" + 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 if include_draws else None, + ) + + @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 != 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 + } + sampled_parameters: list[object] = [] + 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) + return sampled_parameters + + 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] + 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: + self._restore_posterior_predictive_parameters( + sampled_parameters=sampled_parameters, + original_values=original_values, + original_uncertainties=original_uncertainties, + expt_name=expt_name, + ) + + 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, + *, + 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, + *, + include_draws: bool = True, + ) -> str: + """Return the cache key for a posterior predictive summary.""" + key_suffix = 'draws' if include_draws else 'band' + return f'{expt_name}:{x_axis_name}:{key_suffix}' + + 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 + + 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_summary( + self, + *, + expt_name: str, + summary: object, + y_meas: np.ndarray, + axes_labels: list[str], + show_band: bool, + show_draws: bool, + ) -> 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: + 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=POSTERIOR_INTERVAL_95_FILL_COLOR, + name=POSTERIOR_PREDICTIVE_INTERVAL_TRACE_NAME, + hoverinfo='skip', + legendrank=30, + ) + ) + + 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, + legendrank=40, + ) + ) + + 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', + legendrank=10, + ) + ) + fig.add_trace( + go.Scatter( + x=summary.x, + y=summary.map_prediction, + mode='lines', + 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( + title={ + '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}, + 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 _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}
' + '95% interval: [%{customdata[0]:,.2f}, %{customdata[1]:,.2f}]
' + 'Measured I²: %{y:,.2f}
' + 'su(I²meas): %{customdata[2]:,.2f}' + ) + + 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, + *, + 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, + *, + experiment: object, + expt_name: str, + plot_options: _MeasVsCalcPlotOptions, + x_axis: object, + style: str, + ) -> None: + """Render posterior predictive curves on the powder layout.""" + pattern = intensity_category_for(experiment) + 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 + + 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 + + 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=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) + + @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) + + 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 + 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( + 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 != POSTERIOR_FLATTENED_SAMPLE_NDIM: + 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 _square_matrix_axis_title_labels( + parameter_names: list[str], + ) -> list[str]: + """Return compact multiline labels for square-matrix axes.""" + return [Plotter._square_matrix_axis_title_label(name) for name in parameter_names] + + @staticmethod + 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 + + name_parts = [part.strip() for part in normalized_name.split('.') if part.strip()] + if not name_parts: + return normalized_name + + return '
'.join([*(f'{part}.' for part in name_parts[:-1]), name_parts[-1]]) + + @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: + """ + 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 +3962,46 @@ 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 != 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] < MIN_POSTERIOR_SAMPLE_COUNT: + 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( @@ -938,12 +4147,437 @@ 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 compact pair-plot-style correlation heatmap.""" + go = __import__('plotly.graph_objects', fromlist=['Figure', 'Heatmap']) + context = _CorrelationHeatmapContext( + corr_df=corr_df, + 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, + ) + 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]}
' + '%{customdata[1]}
' + f'correlation: %{{z:.{context.precision}f}}' + ), + ) + 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, + margin=self._square_matrix_layout_margin([ + *context.row_labels, + *context.col_labels, + ]), + 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, + *, + 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'{context.corr_df.columns[col_index]}
' + f'{context.corr_df.index[row_index]}
' + f'correlation: %{{z:.{context.precision}f}}' + ) + 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( @@ -1283,6 +4917,7 @@ def _plot_powder_bragg_meas_vs_calc( x_min=ctx['x_min'], x_max=ctx['x_max'], ) + plot_spec = PowderMeasVsCalcSpec( x=ctx['x_filtered'], y_meas=series.y_meas, diff --git a/src/easydiffraction/project/project.py b/src/easydiffraction/project/project.py index 7dc42cf6d..58d7e97fb 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/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 000000000..7a52c63d8 --- /dev/null +++ b/tests/integration/fitting/test_analysis_and_fit_category_support.py @@ -0,0 +1,360 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +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: + 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 = _unstyled_output(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_dream.py b/tests/integration/fitting/test_bayesian_dream.py new file mode 100644 index 000000000..23e0de589 --- /dev/null +++ b/tests/integration/fitting/test_bayesian_dream.py @@ -0,0 +1,178 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# 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/integration/fitting/test_bayesian_helper_support.py b/tests/integration/fitting/test_bayesian_helper_support.py new file mode 100644 index 000000000..a4eef9007 --- /dev/null +++ b/tests/integration/fitting/test_bayesian_helper_support.py @@ -0,0 +1,670 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# SPDX-License-Identifier: BSD-3-Clause + +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: + 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 = _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 + 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 = _unstyled_output(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 000000000..f63cb111f --- /dev/null +++ b/tests/integration/fitting/test_bayesian_tracker_and_base.py @@ -0,0 +1,451 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# 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 000000000..bf87549a4 --- /dev/null +++ b/tests/integration/fitting/test_bumps_dream_support.py @@ -0,0 +1,559 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# 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 000000000..636c0ca4c --- /dev/null +++ b/tests/integration/fitting/test_cli_entrypoints.py @@ -0,0 +1,173 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# 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 diff --git a/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py b/tests/unit/easydiffraction/analysis/calculators/test_cryspy.py index 5fdda3ee4..6cebe4c33 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) 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 000000000..3b641e6d6 --- /dev/null +++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_bayesian.py @@ -0,0 +1,385 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# 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_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_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_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] diff --git a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py index ee8014f23..bb824f829 100644 --- a/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py +++ b/tests/unit/easydiffraction/analysis/fit_helpers/test_reporting.py @@ -59,3 +59,69 @@ 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/analysis/minimizers/test_base.py b/tests/unit/easydiffraction/analysis/minimizers/test_base.py index 23d385e7c..0ad0ca127 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.py b/tests/unit/easydiffraction/analysis/minimizers/test_bumps.py index 668524eaf..1a73fdbcd 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 000000000..eea9f0898 --- /dev/null +++ b/tests/unit/easydiffraction/analysis/minimizers/test_bumps_dream.py @@ -0,0 +1,362 @@ +# SPDX-FileCopyrightText: 2026 EasyScience contributors +# 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, + *, + 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_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.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_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 + + +@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_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() diff --git a/tests/unit/easydiffraction/analysis/minimizers/test_enums.py b/tests/unit/easydiffraction/analysis/minimizers/test_enums.py index 23e44e5bd..5551b9b7a 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 7351d84b2..406a5e093 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 e7d102d9d..a6c5b7adf 100644 --- a/tests/unit/easydiffraction/core/test_parameters.py +++ b/tests/unit/easydiffraction/core/test_parameters.py @@ -103,6 +103,90 @@ 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_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 + 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/plotters/test_plotly.py b/tests/unit/easydiffraction/display/plotters/test_plotly.py index 3905ee278..5c3c8dd50 100644 --- a/tests/unit/easydiffraction/display/plotters/test_plotly.py +++ b/tests/unit/easydiffraction/display/plotters/test_plotly.py @@ -13,62 +13,19 @@ def test_module_import(): assert expected_module_name == actual_module_name -def test_default_template_name_prefers_jupyter_theme(monkeypatch): +def test_get_layout_sets_title_and_axis_title_font_sizes(): 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) + layout = pp.PlotlyPlotter._get_layout('Title', ['x axis', 'y axis']) - 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)' + 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 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 +40,6 @@ 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 +78,6 @@ 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') @@ -278,6 +233,57 @@ def __init__(self, html): assert captured['displayed_html'] == '
plot
' +def test_show_figure_wraps_fixed_aspect_html(monkeypatch): + import easydiffraction.display.plotters.plotly as pp + + monkeypatch.setattr(pp, 'in_pycharm', lambda: False) + + captured = {} + + class DummyLayout: + def __init__(self): + self.meta = { + 'fixed_aspect_wrapper': { + 'aspect_ratio': '1 / 1', + } + } + 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 '
plot
' + + 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'] is None + assert 'aspect-ratio: 1 / 1;' in captured['displayed_html'] + assert 'ed-fixed-aspect-plotly-wrapper' in captured['displayed_html'] + assert '
plot
' in captured['displayed_html'] + + def test_plotly_single_crystal_trace_and_plot(monkeypatch): import easydiffraction.display.plotters.plotly as pp @@ -779,6 +785,9 @@ 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] == [ 'Measured (Imeas)', 'Total calculated (Icalc)', @@ -786,6 +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 5e1cc4daf..c0ef16158 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 +237,897 @@ 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(): + 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 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() + + figure = plotter._build_posterior_pairs_plot(parameters=None) + + assert figure.layout.title.text is None + 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'] + == 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', + 'broad_gauss_u', + 'broad_gauss_v', + 'twotheta_offset', + 'length_a', + 'broad_gauss_u', + 'broad_gauss_v', + 'twotheta_offset', + ] + assert figure.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE + 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 == 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 + 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 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(): + 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_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 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 = [ + '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.
peak.
broad_gauss_u' in annotation_texts + assert 'hrpt.
instrument.
twotheta_offset' in annotation_texts + assert all('None broad_gauss_u' not in text for text in annotation_texts) + 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(): + 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}
hrpt.peak.broad_gauss_u: %{y:.4f}' + ) in hovertemplates + assert ( + 'hrpt.instrument.twotheta_offset: %{x:.4f}
density: %{y:.4f}' + ) 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 + + cell_size = Plotter._posterior_pair_cell_size_pixels(8, available_width_pixels=980) + + 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(): + 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 + 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] + + 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', + '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') + 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 + 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}
density: %{y:.4f}' + 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 + assert tuple(figure.layout.xaxis.range) == ( + float(marginal_trace.x[0]), + float(marginal_trace.x[-1]), + ) + 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 + from easydiffraction.display.plotters.plotly import PlotlyPlotter + + captured: dict[str, object] = {} + + plotter = Plotter() + plotter._backend = SimpleNamespace( + _show_figure=lambda figure: captured.setdefault('fig', figure) + ) + + 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 + 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( + ('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 + + 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, 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(unique_name) + + assert figure.layout.title.text == f'Posterior distribution: {unique_name}' + + +def test_build_param_distribution_plot_accepts_user_facing_label_string(): + 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 == f'Posterior distribution: {unique_name}' + + +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 + + 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_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 + 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_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 @@ -641,7 +1536,7 @@ 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)] == [ @@ -660,7 +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_TITLE_FONT_SIZE 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 = {} @@ -694,44 +1594,70 @@ 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'] + 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].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}
y: %{y}
corr: %{z:.2f}' - 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 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 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]}
%{customdata[1]}
correlation: %{z:.2f}' + ) + 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.
scale', + 'phase.
cell.
length_c', + 'phase.
scale', + 'phase.
cell.
length_c', + ] + assert fig.layout.annotations[0].font.size == POSTERIOR_PAIR_TITLE_FONT_SIZE + assert fig.layout.annotations[0].yshift == SQUARE_MATRIX_TITLE_YSHIFT_PIXELS + assert fig.layout.annotations[0].xshift == -Plotter._square_matrix_title_left_shift([ + 'phase.
scale', + 'phase.
cell.
length_c', + ]) + assert fig.layout.margin.t == SQUARE_MATRIX_TOP_MARGIN_PIXELS + assert fig.layout.margin.b == ( + SQUARE_MATRIX_BOTTOM_MARGIN_PIXELS + 2 * SQUARE_MATRIX_AXIS_TITLE_LINE_HEIGHT_PIXELS + ) + assert ( + fig.layout.meta['fixed_aspect_wrapper']['aspect_ratio'] + == Plotter._square_matrix_layout_meta( + n_parameters=2, + annotation_labels=[ + 'phase.
scale', + 'phase.
cell.
length_c', + 'phase.
scale', + 'phase.
cell.
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 list(fig.layout.yaxis.tickvals) == [0.5] - assert list(fig.layout.yaxis.ticktext) == ['phase.cell.length_c'] 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.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) def test_plot_param_correlations_plotly_labels_respect_threshold(monkeypatch): @@ -785,17 +1711,128 @@ 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) == 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_filters_by_default_threshold(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 + + 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', + '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 df.iloc[0, 1] == '' + assert _strip_markup(df.iloc[1, 1]).strip() == '0.95' + assert _strip_markup(df.iloc[2, 2]).strip() == '0.94' + assert _strip_markup(df.iloc[3, 3]).strip() == '0.93' + assert _strip_markup(df.iloc[4, 4]).strip() == '0.92' + 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 easydiffraction.display.plotting import Plotter from easydiffraction.display.tables import TableRenderer @@ -843,18 +1880,28 @@ 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)] == [ '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): @@ -909,7 +1956,7 @@ 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)] == [ diff --git a/tests/unit/easydiffraction/project/test_project.py b/tests/unit/easydiffraction/project/test_project.py index 92907f66c..090ce5404 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 # 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] diff --git a/tools/generate_package_docs.py b/tools/generate_package_docs.py index 671a4f0ec..1d860557e 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 = {