[chore] Furture lint rules and removal of flake8

.github/workflows/flake8.yml

@@ -1,26 +0,0 @@
-name: flake8
-
-on:
-  push:
-    branches:
-      - master
-      - develop
-  pull_request:
-
-jobs:
-  flake8-lint:
-    runs-on: ubuntu-latest
-    name: Lint
-    steps:
-      - name: Check out source repository
-        uses: actions/checkout@v4
-      - name: Set up Python environment
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-      - name: flake8 Lint
-        uses: py-actions/flake8@v2
-        with:
-          ignore: "E501,W503"
-          exclude: "__init__.py, input/__init__.py"
-          path: "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/correlators.py

@@ -1,3 +1,4 @@
+import itertools
 import warnings
 from itertools import permutations
 
@@ -42,7 +43,7 @@ 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=None, prange=None):
         """ Initialize a Corr object.
@@ -983,7 +984,7 @@ class Corr:
             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()
 
@@ -1009,7 +1010,7 @@ class Corr:
         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]))
+        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:
@@ -1096,6 +1097,8 @@ class Corr:
             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)):
@@ -1425,7 +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/fits.py

@@ -18,6 +18,8 @@ from odrpack import odr_fit
 
 from .obs import Obs, cov_Obs, covariance, derived_observable, invert_corr_cov_cholesky
 
+_rng = np.random.default_rng()
+
 
 class Fit_result(Sequence):
     """Represents fit results.
@@ -946,6 +948,6 @@ def _construct_prior_obs(i_prior, i_n):
         return i_prior
     elif isinstance(i_prior, str):
         loc_val, loc_dval = _extract_val_and_dval(i_prior)
-        return cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}")
+        return cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{_rng.integers(2147483647):010d}")
     else:
         raise TypeError("Prior entries need to be 'Obs' or 'str'.")

pyerrors/input/bdio.py

@@ -506,7 +506,7 @@ def read_mesons(file_path, bdio_path='./libbdio.so', **kwargs):
             else:
                 deltas = tmp_data[:, t]
             tmp_corr.append(Obs([deltas], [ensemble_name], idl=[idl_target]))
-        result[(corr_name[c], corr_source[c]) + tuple(corr_kappa[c])] = tmp_corr
+        result[(corr_name[c], corr_source[c], *corr_kappa[c])] = tmp_corr
 
     # Check that all data entries have the same number of configurations
     if len(set([o[0].N for o in list(result.values())])) != 1:

pyerrors/input/dobs.py

@@ -289,7 +289,7 @@ def _import_array(arr):
 
 
 def _import_rdata(rd):
-    name, idx, mask, deltas = _import_array(rd)
+    name, idx, _mask, deltas = _import_array(rd)
     return deltas, name, idx
 
 

pyerrors/input/json.py

@@ -302,7 +302,7 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
                     if len(rep_name) > len(ens["id"]):
                         if rep_name[len(ens["id"])] != "|":
                             tmp_list = list(rep_name)
-                            tmp_list = tmp_list[:len(ens["id"])] + ["|"] + tmp_list[len(ens["id"]):]
+                            tmp_list = [*tmp_list[:len(ens["id"])], "|", *tmp_list[len(ens["id"]):]]
                             rep_name = ''.join(tmp_list)
                     retd['names'].append(rep_name)
                     retd['idl'].append([di[0] for di in rep['deltas']])

pyerrors/input/openQCD.py

@@ -219,7 +219,7 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
 
     if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
         raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
-    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+    stepsizes = [next(iter(np.unique(np.diff(cl)))) for cl in configlist]
     if np.any([step != 1 for step in stepsizes]):
         warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning, stacklevel=2)
 
@@ -408,7 +408,7 @@ def _extract_flowed_energy_density(path, prefix, dtr_read, xmin, spatial_extent,
 
     if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
         raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
-    stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
+    stepsizes = [next(iter(np.unique(np.diff(cl)))) for cl in configlist]
     if np.any([step != 1 for step in stepsizes]):
         warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning, stacklevel=2)
 

pyerrors/input/sfcf.py

@@ -276,9 +276,7 @@ def read_sfcf_multi(path, prefix, name_list, quarks_list=None, corr_type_list=No
                 if isinstance(files, list):
                     if all(isinstance(f, list) for f in files):
                         files = files[i]
-                    elif all(isinstance(f, str) for f in files):
+                    elif not all(isinstance(f, str) for f in files):
-                        files = files
-                    else:
                         raise TypeError("files has to be of type list[list[str]] or list[str]!")
                 else:
                     raise TypeError("files has to be of type list[list[str]] or list[str]!")

pyerrors/misc.py

@@ -10,6 +10,8 @@ import scipy
 from .obs import Obs
 from .version import __version__
 
+_rng = np.random.default_rng()
+
 
 def print_config():
     """Print information about version of python, pyerrors and dependencies."""
@@ -120,7 +122,7 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
         return Obs([np.zeros(samples) + value], [name])
     else:
         for _ in range(100):
-            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
+            deltas = [_rng.normal(0.0, dvalue * np.sqrt(samples), samples)]
             deltas -= np.mean(deltas)
             deltas *= dvalue / np.sqrt(np.var(deltas) / samples) / np.sqrt(1 + 3 / samples)
             deltas += value
@@ -163,7 +165,7 @@ def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
         raise Exception('All integrated autocorrelations have to be >= 0.5.')
 
     a = (2 * tau - 1) / (2 * tau + 1)
-    rand = np.random.multivariate_normal(np.zeros_like(means), cov * samples, samples)
+    rand = _rng.multivariate_normal(np.zeros_like(means), cov * samples, samples)
 
     # Normalize samples such that sample variance matches input
     norm = np.array([np.var(o, ddof=1) / samples for o in rand.T])

pyerrors/obs.py

@@ -2,6 +2,7 @@ import hashlib
 import pickle
 import warnings
 from itertools import groupby
+from typing import ClassVar
 
 import autograd.numpy as anp  # Thinly-wrapped numpy
 import matplotlib.pyplot as plt
@@ -14,7 +15,7 @@ from scipy.stats import kurtosis, kurtosistest, skew, skewtest
 from .covobs import Covobs
 
 # Improve print output of numpy.ndarrays containing Obs objects.
-np.set_printoptions(formatter={'object': lambda x: str(x)})
+np.set_printoptions(formatter={'object': str})
 
 
 class Obs:
@@ -46,18 +47,40 @@ class Obs:
         Dictionary for N_sigma values. If an entry for a given ensemble exists
         this overwrites the standard value for that ensemble.
     """
-    __slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
+    __slots__ = [
-                 'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
+        'N',
-                 'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
+        'N_sigma',
-                 'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
+        'S',
-                 'idl', 'tag', '_covobs', '__dict__']
+        '__dict__',
+        '_covobs',
+        '_dvalue',
+        '_value',
+        'ddvalue',
+        'deltas',
+        'e_ddvalue',
+        'e_drho',
+        'e_dtauint',
+        'e_dvalue',
+        'e_n_dtauint',
+        'e_n_tauint',
+        'e_rho',
+        'e_tauint',
+        'e_windowsize',
+        'idl',
+        'names',
+        'r_values',
+        'reweighted',
+        'shape',
+        'tag',
+        'tau_exp',
+    ]
 
     S_global = 2.0
-    S_dict = {}
+    S_dict: ClassVar[dict] = {}
     tau_exp_global = 0.0
-    tau_exp_dict = {}
+    tau_exp_dict: ClassVar[dict] = {}
     N_sigma_global = 1.0
-    N_sigma_dict = {}
+    N_sigma_dict: ClassVar[dict] = {}
 
     def __init__(self, samples, names, idl=None, **kwargs):
         """ Initialize Obs object.
@@ -502,7 +525,7 @@ class Obs:
             fig = plt.figure()
             plt.xlabel(r'$W$')
             plt.ylabel(r'$\tau_\mathrm{int}$')
-            length = int(len(self.e_n_tauint[e_name]))
+            length = len(self.e_n_tauint[e_name])
             if self.tau_exp[e_name] > 0:
                 base = self.e_n_tauint[e_name][self.e_windowsize[e_name]]
                 x_help = np.arange(2 * self.tau_exp[e_name])
@@ -541,7 +564,7 @@ class Obs:
             fig = plt.figure()
             plt.xlabel('W')
             plt.ylabel('rho')
-            length = int(len(self.e_drho[e_name]))
+            length = len(self.e_drho[e_name])
             plt.errorbar(np.arange(length), self.e_rho[e_name][:length], yerr=self.e_drho[e_name][:], linewidth=1, capsize=2)
             plt.axvline(x=self.e_windowsize[e_name], color='r', alpha=0.25, ls='--', marker=',')
             if self.tau_exp[e_name] > 0:
@@ -919,7 +942,7 @@ class Obs:
 
 class CObs:
     """Class for a complex valued observable."""
-    __slots__ = ['_real', '_imag', 'tag']
+    __slots__ = ['_imag', '_real', 'tag']
 
     def __init__(self, real, imag=0.0):
         self._real = real
@@ -1021,6 +1044,8 @@ class CObs:
     def __eq__(self, other):
         return self.real == other.real and self.imag == other.imag
 
+    __hash__ = None
+
     def __str__(self):
         return '(' + str(self.real) + int(self.imag >= 0.0) * '+' + str(self.imag) + 'j)'
 
@@ -1305,12 +1330,12 @@ def derived_observable(func, data, array_mode=False, **kwargs):
             d_extracted[name] = []
             ens_length = len(new_idl_d[name])
             for dat in data:
-                d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas.get(name, np.zeros(ens_length)), o.idl.get(name, new_idl_d[name]), o.shape.get(name, ens_length), new_idl_d[name], _compute_scalefactor_missing_rep(o).get(name.split('|')[0], 1)) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
+                d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas.get(name, np.zeros(ens_length)), o.idl.get(name, new_idl_d[name]), o.shape.get(name, ens_length), new_idl_d[name], _compute_scalefactor_missing_rep(o).get(name.split('|')[0], 1)) for o in dat.reshape(np.prod(dat.shape))]).reshape((*dat.shape, ens_length)))
         for name in new_cov_names:
             g_extracted[name] = []
             zero_grad = _Zero_grad(new_covobs_lengths[name])
             for dat in data:
-                g_extracted[name].append(np.array([o.covobs.get(name, zero_grad).grad for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (new_covobs_lengths[name], 1)))
+                g_extracted[name].append(np.array([o.covobs.get(name, zero_grad).grad for o in dat.reshape(np.prod(dat.shape))]).reshape((*dat.shape, new_covobs_lengths[name], 1)))
 
     for i_val, new_val in np.ndenumerate(new_values):
         new_deltas = {}
@@ -1320,11 +1345,11 @@ def derived_observable(func, data, array_mode=False, **kwargs):
                 ens_length = d_extracted[name][0].shape[-1]
                 new_deltas[name] = np.zeros(ens_length)
                 for i_dat, dat in enumerate(d_extracted[name]):
-                    new_deltas[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
+                    new_deltas[name] += np.tensordot(deriv[(*i_val, i_dat)], dat)
             for name in new_cov_names:
                 new_grad[name] = 0
                 for i_dat, dat in enumerate(g_extracted[name]):
-                    new_grad[name] += np.tensordot(deriv[i_val + (i_dat, )], dat)
+                    new_grad[name] += np.tensordot(deriv[(*i_val, i_dat)], dat)
         else:
             for j_obs, obs in np.ndenumerate(data):
                 scalef_d = _compute_scalefactor_missing_rep(obs)
@@ -1781,7 +1806,7 @@ def merge_obs(list_of_obs):
     """
     replist = [item for obs in list_of_obs for item in obs.names]
     if (len(replist) == len(set(replist))) is False:
-        raise ValueError(f'list_of_obs contains duplicate replica: {str(replist)}')
+        raise ValueError(f'list_of_obs contains duplicate replica: {replist!s}')
     if any([len(o.cov_names) for o in list_of_obs]):
         raise ValueError('Not possible to merge data that contains covobs!')
     new_dict = {}

pyerrors/special.py

@@ -34,10 +34,39 @@ from autograd.scipy.special import (
     yn,
 )
 
-__all__ = ["beta", "betainc", "betaln",
+__all__ = [
-           "polygamma", "psi", "digamma", "gamma", "gammaln", "gammainc", "gammaincc", "gammasgn", "rgamma", "multigammaln",
+    "beta",
-           "kn", "j0", "y0", "j1", "y1", "jn", "yn", "i0", "i1", "iv", "ive",
+    "betainc",
-           "erf", "erfc", "erfinv", "erfcinv", "logit", "expit", "logsumexp"]
+    "betaln",
+    "digamma",
+    "erf",
+    "erfc",
+    "erfcinv",
+    "erfinv",
+    "expit",
+    "gamma",
+    "gammainc",
+    "gammaincc",
+    "gammaln",
+    "gammasgn",
+    "i0",
+    "i1",
+    "iv",
+    "ive",
+    "j0",
+    "j1",
+    "jn",
+    "kn",
+    "logit",
+    "logsumexp",
+    "multigammaln",
+    "polygamma",
+    "psi",
+    "rgamma",
+    "y0",
+    "y1",
+    "yn",
+]
 
 
 @primitive

pyproject.toml

@@ -6,5 +6,11 @@ build-backend = "setuptools.build_meta"
 target-version = "py310"
 
 [tool.ruff.lint]
-extend-select = ["I", "B", "PLE", "UP"]
+extend-select = ["E", "W", "I", "B", "PIE", "PLE", "PLW", "UP", "NPY", "RUF"]
-ignore = ["F403", "E501", "PLC0415"]
+ignore = [
+    "F403",     # star imports in __init__ files are intentional
+    "E501",     # line too long
+    "PLC0415",  # import outside top level
+    "PLW2901",  # redefined loop name (too noisy)
+    "RUF002",   # ambiguous unicode in docstrings (Greek letters)
+]

setup.py

@@ -26,7 +26,7 @@ setup(name='pyerrors',
       packages=find_packages(),
       python_requires='>=3.10.0',
       install_requires=['numpy>=2.0', 'autograd>=1.7.0', 'numdifftools>=0.9.41', 'matplotlib>=3.9', 'scipy>=1.13', 'iminuit>=2.28', 'h5py>=3.11', 'lxml>=5.0', 'python-rapidjson>=1.20', 'pandas>=2.2', 'odrpack>=0.5'],
-      extras_require={'test': ['pytest', 'pytest-cov', 'pytest-benchmark', 'hypothesis', 'nbmake', 'flake8']},
+      extras_require={'test': ['pytest', 'pytest-cov', 'pytest-benchmark', 'hypothesis', 'nbmake']},
       classifiers=[
           'Development Status :: 5 - Production/Stable',
           'Intended Audience :: Science/Research',