[chore] Stricter ruff rules

.github/workflows/docs.yml

@@ -14,7 +14,7 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: "3.12"
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
       - name: Updated documentation
         run: |
           git config --global user.email "${{ github.actor }}@users.noreply.github.com"

.github/workflows/examples.yml

@@ -21,7 +21,7 @@ jobs:
 
     steps:
       - name: Checkout source
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Setup python
         uses: actions/setup-python@v5

.github/workflows/pytest.yml

@@ -26,7 +26,7 @@ jobs:
 
     steps:
       - name: Checkout source
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Setup python
         uses: actions/setup-python@v5

.github/workflows/release.yml

@@ -10,7 +10,7 @@ jobs:
     name: Build sdist and wheel
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
       name: Checkout repository
 
     - uses: actions/setup-python@v5

.github/workflows/ruff.yml

@@ -9,7 +9,7 @@ jobs:
   ruff:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-      - uses: astral-sh/ruff-action@v2
+      - uses: actions/checkout@v6
+      - uses: astral-sh/ruff-action@v3
         with:
           src: "./pyerrors"

CONTRIBUTING.md

@@ -19,14 +19,14 @@ Please add docstrings to any new function, class or method you implement. The do
 
 ### Tests
 When implementing a new feature or fixing a bug please add meaningful tests to the files in the `tests` directory which cover the new code.
-We follow the [PEP8](https://peps.python.org/pep-0008/) code style which is checked by `flake8`.
+We follow the [PEP8](https://peps.python.org/pep-0008/) code style which is checked by `ruff`.
 For all pull requests tests are executed for the most recent python releases via
 ```
 pytest -vv -Werror
 pytest --nbmake examples/*.ipynb
-flake8 --ignore=E501,W503 --exclude=__init__.py pyerrors
+ruff check pyerrors
 ```
-The tests require `pytest`, `pytest-cov`, `pytest-benchmark`, `hypothesis`, `nbmake` and `flake8`. To install the test dependencies one can run `pip install pyerrors[test]`.
+The tests require `pytest`, `pytest-cov`, `pytest-benchmark`, `hypothesis` and `nbmake`. To install the test dependencies one can run `pip install pyerrors[test]`. Linting is done with `ruff`, which can be installed via `pip install ruff` or run ad-hoc with `uvx ruff check pyerrors`.
 Please make sure that all tests pass for a new pull requests.
 
 To get a coverage report in html run

pyerrors/__init__.py

@@ -477,16 +477,15 @@ def func(a, x):
 
 Julia I/O routines for the json.gz format, compatible with [ADerrors.jl](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl), can be found [here](https://github.com/fjosw/ADjson.jl).
 '''
-from .obs import *
-from .correlators import *
-from .fits import *
-from .misc import *
 from . import dirac as dirac
 from . import input as input
+from . import integrate as integrate
 from . import linalg as linalg
 from . import mpm as mpm
 from . import roots as roots
-from . import integrate as integrate
 from . import special as special
-
+from .correlators import *
+from .fits import *
+from .misc import *
+from .obs import *
 from .version import __version__ as __version__

pyerrors/correlators.py

@@ -1,14 +1,17 @@
+import itertools
 import warnings
 from itertools import permutations
-import numpy as np
+
 import autograd.numpy as anp
 import matplotlib.pyplot as plt
+import numpy as np
 import scipy.linalg
-from .obs import Obs, reweight, correlate, CObs
-from .misc import dump_object, _assert_equal_properties
+
+from . import linalg
 from .fits import least_squares
+from .misc import _assert_equal_properties, dump_object
+from .obs import CObs, Obs, correlate, reweight
 from .roots import find_root
-from . import linalg
 
 
 class Corr:
@@ -40,9 +43,9 @@ class Corr:
     the temporal extent of the correlator and N is the dimension of the matrix.
     """
 
-    __slots__ = ["content", "N", "T", "tag", "prange"]
+    __slots__ = ["N", "T", "content", "prange", "tag"]
 
-    def __init__(self, data_input, padding=[0, 0], prange=None):
+    def __init__(self, data_input, padding=None, prange=None):
         """ Initialize a Corr object.
 
         Parameters
@@ -58,6 +61,9 @@ def __init__(self, data_input, padding=[0, 0], prange=None):
             region identified for this correlator.
         """
 
+        if padding is None:
+            padding = [0, 0]
+
         if isinstance(data_input, np.ndarray):
             if data_input.ndim == 1:
                 data_input = list(data_input)
@@ -77,7 +83,7 @@ def __init__(self, data_input, padding=[0, 0], prange=None):
                 for t in range(T):
                     if any([(item.content[t] is None) for item in data_input.flatten()]):
                         if not all([(item.content[t] is None) for item in data_input.flatten()]):
-                            warnings.warn("Input ill-defined at different timeslices. Conversion leads to data loss.!", RuntimeWarning)
+                            warnings.warn("Input ill-defined at different timeslices. Conversion leads to data loss.!", RuntimeWarning, stacklevel=2)
                         input_as_list.append(None)
                     else:
                         array_at_timeslace = np.empty([N, N], dtype="object")
@@ -178,14 +184,14 @@ def projected(self, vector_l=None, vector_r=None, normalize=False):
             if not vector_l.shape == vector_r.shape == (self.N,):
                 raise ValueError("Vectors are of wrong shape!")
             if normalize:
-                vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
+                vector_l, vector_r = vector_l / np.sqrt(vector_l @ vector_l), vector_r / np.sqrt(vector_r @ vector_r)
             newcontent = [None if _check_for_none(self, item) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
 
         else:
             # There are no checks here yet. There are so many possible scenarios, where this can go wrong.
             if normalize:
                 for t in range(self.T):
-                    vector_l[t], vector_r[t] = vector_l[t] / np.sqrt((vector_l[t] @ vector_l[t])), vector_r[t] / np.sqrt(vector_r[t] @ vector_r[t])
+                    vector_l[t], vector_r[t] = vector_l[t] / np.sqrt(vector_l[t] @ vector_l[t]), vector_r[t] / np.sqrt(vector_r[t] @ vector_r[t])
 
             newcontent = [None if (_check_for_none(self, self.content[t]) or vector_l[t] is None or vector_r[t] is None) else np.asarray([vector_l[t].T @ self.content[t] @ vector_r[t]]) for t in range(self.T)]
         return Corr(newcontent)
@@ -228,7 +234,7 @@ def symmetric(self):
 
         if self.content[0] is not None:
             if np.argmax(np.abs([o[0].value if o is not None else 0 for o in self.content])) != 0:
-                warnings.warn("Correlator does not seem to be symmetric around x0=0.", RuntimeWarning)
+                warnings.warn("Correlator does not seem to be symmetric around x0=0.", RuntimeWarning, stacklevel=2)
 
         newcontent = [self.content[0]]
         for t in range(1, self.T):
@@ -250,7 +256,7 @@ def anti_symmetric(self):
         test = 1 * self
         test.gamma_method()
         if not all([o.is_zero_within_error(3) for o in test.content[0]]):
-            warnings.warn("Correlator does not seem to be anti-symmetric around x0=0.", RuntimeWarning)
+            warnings.warn("Correlator does not seem to be anti-symmetric around x0=0.", RuntimeWarning, stacklevel=2)
 
         newcontent = [self.content[0]]
         for t in range(1, self.T):
@@ -342,7 +348,7 @@ def GEVP(self, t0, ts=None, sort="Eigenvalue", vector_obs=False, **kwargs):
                 raise ValueError("ts has to be larger than t0.")
 
         if "sorted_list" in kwargs:
-            warnings.warn("Argument 'sorted_list' is deprecated, use 'sort' instead.", DeprecationWarning)
+            warnings.warn("Argument 'sorted_list' is deprecated, use 'sort' instead.", DeprecationWarning, stacklevel=2)
             sort = kwargs.get("sorted_list")
 
         if self.is_matrix_symmetric():
@@ -381,7 +387,7 @@ def _get_mat_at_t(t, vector_obs=vector_obs):
 
         elif sort in ["Eigenvalue", "Eigenvector"]:
             if sort == "Eigenvalue" and ts is not None:
-                warnings.warn("ts has no effect when sorting by eigenvalue is chosen.", RuntimeWarning)
+                warnings.warn("ts has no effect when sorting by eigenvalue is chosen.", RuntimeWarning, stacklevel=2)
             all_vecs = [None] * (t0 + 1)
             for t in range(t0 + 1, self.T):
                 try:
@@ -439,7 +445,7 @@ def Hankel(self, N, periodic=False):
 
         array = np.empty([N, N], dtype="object")
         new_content = []
-        for t in range(self.T):
+        for _t in range(self.T):
             new_content.append(array.copy())
 
         def wrap(i):
@@ -569,7 +575,7 @@ def T_symmetry(self, partner, parity=+1):
                 if not t_slice[0].is_zero_within_error(5):
                     t_slices.append(x0)
         if t_slices:
-            warnings.warn("T symmetry partners do not agree within 5 sigma on time slices " + str(t_slices) + ".", RuntimeWarning)
+            warnings.warn("T symmetry partners do not agree within 5 sigma on time slices " + str(t_slices) + ".", RuntimeWarning, stacklevel=2)
 
         return (self + T_partner) / 2
 
@@ -663,7 +669,7 @@ def second_deriv(self, variant="symmetric"):
                 if (self.content[t - 1] is None) or (self.content[t + 1] is None):
                     newcontent.append(None)
                 else:
-                    newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
+                    newcontent.append(self.content[t + 1] - 2 * self.content[t] + self.content[t - 1])
             if (all([x is None for x in newcontent])):
                 raise ValueError("Derivative is undefined at all timeslices")
             return Corr(newcontent, padding=[1, 1])
@@ -978,7 +984,7 @@ def show(self, x_range=None, comp=None, y_range=None, logscale=False, plateau=No
             ax1.set_ylabel(ylabel)
         ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5])
 
-        handles, labels = ax1.get_legend_handles_labels()
+        _handles, labels = ax1.get_legend_handles_labels()
         if labels:
             ax1.legend()
 
@@ -1004,8 +1010,8 @@ def spaghetti_plot(self, logscale=True):
         if self.N != 1:
             raise ValueError("Correlator needs to be projected first.")
 
-        mc_names = list(set([item for sublist in [sum(map(o[0].e_content.get, o[0].mc_names), []) for o in self.content if o is not None] for item in sublist]))
-        x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
+        mc_names = list(set([item for sublist in [list(itertools.chain.from_iterable(map(o[0].e_content.get, o[0].mc_names))) for o in self.content if o is not None] for item in sublist]))
+        x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content, strict=True) if o is not None]
 
         for name in mc_names:
             data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T
@@ -1091,6 +1097,8 @@ def __eq__(self, y):
             comp = np.asarray(y)
         return np.asarray(self.content, dtype=object) == comp
 
+    __hash__ = None
+
     def __add__(self, y):
         if isinstance(y, Corr):
             if ((self.N != y.N) or (self.T != y.T)):
@@ -1396,7 +1404,7 @@ def prune(self, Ntrunc, tproj=3, t0proj=2, basematrix=None):
         if basematrix is None:
             basematrix = self
         if Ntrunc >= basematrix.N:
-            raise ValueError('Cannot truncate using Ntrunc <= %d' % (basematrix.N))
+            raise ValueError(f'Cannot truncate using Ntrunc <= {basematrix.N}')
         if basematrix.N != self.N:
             raise ValueError('basematrix and targetmatrix have to be of the same size.')
 
@@ -1420,7 +1428,7 @@ def _sort_vectors(vec_set_in, ts):
     """Helper function used to find a set of Eigenvectors consistent over all timeslices"""
 
     if isinstance(vec_set_in[ts][0][0], Obs):
-        vec_set = [anp.vectorize(lambda x: float(x))(vi) if vi is not None else vi for vi in vec_set_in]
+        vec_set = [anp.vectorize(float)(vi) if vi is not None else vi for vi in vec_set_in]
     else:
         vec_set = vec_set_in
     reference_sorting = np.array(vec_set[ts])

pyerrors/covobs.py

@@ -32,7 +32,7 @@ def __init__(self, mean, cov, name, pos=None, grad=None):
                     raise Exception('Have to specify position of cov-element belonging to mean!')
             else:
                 if pos > self.N:
-                    raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
+                    raise Exception(f'pos {pos} too large for covariance matrix with dimension {self.N}x{self.N}!')
             self._grad = np.zeros((self.N, 1))
             self._grad[pos] = 1.
         else:
@@ -72,7 +72,7 @@ def _set_cov(self, cov):
         for i in range(self.N):
             for j in range(i):
                 if not self._cov[i][j] == self._cov[j][i]:
-                    raise Exception('Covariance matrix is non-symmetric for (%d, %d' % (i, j))
+                    raise Exception(f'Covariance matrix is non-symmetric for ({i}, {j}')
 
         evals = np.linalg.eigvalsh(self._cov)
         for ev in evals:

pyerrors/dirac.py

@@ -1,6 +1,5 @@
 import numpy as np
 
-
 gammaX = np.array(
     [[0, 0, 0, 1j], [0, 0, 1j, 0], [0, -1j, 0, 0], [-1j, 0, 0, 0]],
     dtype=complex)