[Fix] Ruff rules and more precise Excpetion types (fjosw#248)

* [Fix] Fix test for membership should be 'not in' (E713)

* [Fix] Fix module imported but unused (F401)

* [Fix] More precise Exception types in dirac, obs and correlator

pyerrors/__init__.py

@@ -481,12 +481,12 @@ def func(a, x):
 from .correlators import *
 from .fits import *
 from .misc import *
-from . import dirac
-from . import input
-from . import linalg
-from . import mpm
-from . import roots
-from . import integrate
-from . import special
-
-from .version import __version__
+from . import dirac as dirac
+from . import input as input
+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 .version import __version__ as __version__

pyerrors/dirac.py

@@ -34,7 +34,7 @@ def epsilon_tensor(i, j, k):
     """
     test_set = set((i, j, k))
     if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))):
-        raise Exception("Unexpected input", i, j, k)
+        raise ValueError("Unexpected input", i, j, k)
 
     return (i - j) * (j - k) * (k - i) / 2
 
@@ -52,7 +52,7 @@ def epsilon_tensor_rank4(i, j, k, o):
     """
     test_set = set((i, j, k, o))
     if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
-        raise Exception("Unexpected input", i, j, k, o)
+        raise ValueError("Unexpected input", i, j, k, o)
 
     return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12
 
@@ -92,5 +92,5 @@ def Grid_gamma(gamma_tag):
     elif gamma_tag == 'SigmaZT':
         g = 0.5 * (gamma[2] @ gamma[3] - gamma[3] @ gamma[2])
     else:
-        raise Exception('Unkown gamma structure', gamma_tag)
+        raise ValueError('Unkown gamma structure', gamma_tag)
     return g

pyerrors/input/__init__.py

@@ -5,11 +5,11 @@
 For comparison with other analysis workflows `pyerrors` can also generate jackknife samples from an `Obs` object or import jackknife samples into an `Obs` object.
 See `pyerrors.obs.Obs.export_jackknife` and `pyerrors.obs.import_jackknife` for details.
 '''
-from . import bdio
-from . import dobs
-from . import hadrons
-from . import json
-from . import misc
-from . import openQCD
-from . import pandas
-from . import sfcf
+from . import bdio as bdio
+from . import dobs as dobs
+from . import hadrons as hadrons
+from . import json as json
+from . import misc as misc
+from . import openQCD as openQCD
+from . import pandas as pandas
+from . import sfcf as sfcf

pyerrors/input/dobs.py

@@ -79,7 +79,7 @@ def _dict_to_xmlstring_spaces(d, space='  '):
             o += space
         o += li + '\n'
         if li.startswith('<') and not cm:
-            if not '<%s' % ('/') in li:
+            if '<%s' % ('/') not in li:
                 c += 1
         cm = False
     return o
@@ -671,7 +671,7 @@ def _dobsdict_to_xmlstring_spaces(d, space='  '):
             o += space
         o += li + '\n'
         if li.startswith('<') and not cm:
-            if not '<%s' % ('/') in li:
+            if '<%s' % ('/') not in li:
                 c += 1
         cm = False
     return o

pyerrors/input/hadrons.py

@@ -113,7 +113,7 @@ def read_hd5(filestem, ens_id, group, attrs=None, idl=None, part="real"):
     infos = []
     for hd5_file in files:
         h5file = h5py.File(path + '/' + hd5_file, "r")
-        if not group + '/' + entry in h5file:
+        if group + '/' + entry not in h5file:
             raise Exception("Entry '" + entry + "' not contained in the files.")
         raw_data = h5file[group + '/' + entry + '/corr']
         real_data = raw_data[:].view("complex")
@@ -186,7 +186,7 @@ def _extract_real_arrays(path, files, tree, keys):
     for hd5_file in files:
         h5file = h5py.File(path + '/' + hd5_file, "r")
         for key in keys:
-            if not tree + '/' + key in h5file:
+            if tree + '/' + key not in h5file:
                 raise Exception("Entry '" + key + "' not contained in the files.")
             raw_data = h5file[tree + '/' + key + '/data']
             real_data = raw_data[:].astype(np.double)

pyerrors/input/openQCD.py

@@ -47,7 +47,7 @@ def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
         Reweighting factors read
     """
     known_oqcd_versions = ['1.4', '1.6', '2.0']
-    if not (version in known_oqcd_versions):
+    if version not in known_oqcd_versions:
         raise Exception('Unknown openQCD version defined!')
     print("Working with openQCD version " + version)
     if 'postfix' in kwargs:

pyerrors/obs.py

@@ -222,7 +222,7 @@ def _parse_kwarg(kwarg_name):
                 tmp = kwargs.get(kwarg_name)
                 if isinstance(tmp, (int, float)):
                     if tmp < 0:
-                        raise Exception(kwarg_name + ' has to be larger or equal to 0.')
+                        raise ValueError(kwarg_name + ' has to be larger or equal to 0.')
                     for e, e_name in enumerate(self.e_names):
                         getattr(self, kwarg_name)[e_name] = tmp
                 else:
@@ -291,7 +291,7 @@ def _compute_drho(i):
                 texp = self.tau_exp[e_name]
                 # Critical slowing down analysis
                 if w_max // 2 <= 1:
-                    raise Exception("Need at least 8 samples for tau_exp error analysis")
+                    raise ValueError("Need at least 8 samples for tau_exp error analysis")
                 for n in range(1, w_max // 2):
                     _compute_drho(n + 1)
                     if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
@@ -620,7 +620,7 @@ def plot_piechart(self, save=None):
         if not hasattr(self, 'e_dvalue'):
             raise Exception('Run the gamma method first.')
         if np.isclose(0.0, self._dvalue, atol=1e-15):
-            raise Exception('Error is 0.0')
+            raise ValueError('Error is 0.0')
         labels = self.e_names
         sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
         fig1, ax1 = plt.subplots()
@@ -659,7 +659,7 @@ def dump(self, filename, datatype="json.gz", description="", **kwargs):
             with open(file_name + '.p', 'wb') as fb:
                 pickle.dump(self, fb)
         else:
-            raise Exception("Unknown datatype " + str(datatype))
+            raise TypeError("Unknown datatype " + str(datatype))
 
     def export_jackknife(self):
         """Export jackknife samples from the Obs
@@ -676,7 +676,7 @@ def export_jackknife(self):
         """
 
         if len(self.names) != 1:
-            raise Exception("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
+            raise ValueError("'export_jackknife' is only implemented for Obs defined on one ensemble and replicum.")
 
         name = self.names[0]
         full_data = self.deltas[name] + self.r_values[name]
@@ -711,7 +711,7 @@ def export_bootstrap(self, samples=500, random_numbers=None, save_rng=None):
             should agree with samples from a full bootstrap analysis up to O(1/N).
         """
         if len(self.names) != 1:
-            raise Exception("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
+            raise ValueError("'export_boostrap' is only implemented for Obs defined on one ensemble and replicum.")
 
         name = self.names[0]
         length = self.N
@@ -1267,7 +1267,7 @@ def _compute_scalefactor_missing_rep(obs):
     if 'man_grad' in kwargs:
         deriv = np.asarray(kwargs.get('man_grad'))
         if new_values.shape + data.shape != deriv.shape:
-            raise Exception('Manual derivative does not have correct shape.')
+            raise ValueError('Manual derivative does not have correct shape.')
     elif kwargs.get('num_grad') is True:
         if multi > 0:
             raise Exception('Multi mode currently not supported for numerical derivative')
@@ -1333,7 +1333,7 @@ def __init__(self, N):
         new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad}
 
         if not set(new_covobs.keys()).isdisjoint(new_deltas.keys()):
-            raise Exception('The same name has been used for deltas and covobs!')
+            raise ValueError('The same name has been used for deltas and covobs!')
         new_samples = []
         new_means = []
         new_idl = []
@@ -1374,15 +1374,15 @@ def _reduce_deltas(deltas, idx_old, idx_new):
         Has to be a subset of idx_old.
     """
     if not len(deltas) == len(idx_old):
-        raise Exception('Length of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
+        raise ValueError('Length of deltas and idx_old have to be the same: %d != %d' % (len(deltas), len(idx_old)))
     if type(idx_old) is range and type(idx_new) is range:
         if idx_old == idx_new:
             return deltas
     if _check_lists_equal([idx_old, idx_new]):
         return deltas
     indices = np.intersect1d(idx_old, idx_new, assume_unique=True, return_indices=True)[1]
     if len(indices) < len(idx_new):
-        raise Exception('Error in _reduce_deltas: Config of idx_new not in idx_old')
+        raise ValueError('Error in _reduce_deltas: Config of idx_new not in idx_old')
     return np.array(deltas)[indices]
 
 
@@ -1404,12 +1404,12 @@ def reweight(weight, obs, **kwargs):
     result = []
     for i in range(len(obs)):
         if len(obs[i].cov_names):
-            raise Exception('Error: Not possible to reweight an Obs that contains covobs!')
+            raise ValueError('Error: Not possible to reweight an Obs that contains covobs!')
         if not set(obs[i].names).issubset(weight.names):
-            raise Exception('Error: Ensembles do not fit')
+            raise ValueError('Error: Ensembles do not fit')
         for name in obs[i].names:
             if not set(obs[i].idl[name]).issubset(weight.idl[name]):
-                raise Exception('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
+                raise ValueError('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
         new_samples = []
         w_deltas = {}
         for name in sorted(obs[i].names):
@@ -1446,14 +1446,14 @@ def correlate(obs_a, obs_b):
     """
 
     if sorted(obs_a.names) != sorted(obs_b.names):
-        raise Exception(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
+        raise ValueError(f"Ensembles do not fit {set(sorted(obs_a.names)) ^ set(sorted(obs_b.names))}")
     if len(obs_a.cov_names) or len(obs_b.cov_names):
-        raise Exception('Error: Not possible to correlate Obs that contain covobs!')
+        raise ValueError('Error: Not possible to correlate Obs that contain covobs!')
     for name in obs_a.names:
         if obs_a.shape[name] != obs_b.shape[name]:
-            raise Exception('Shapes of ensemble', name, 'do not fit')
+            raise ValueError('Shapes of ensemble', name, 'do not fit')
         if obs_a.idl[name] != obs_b.idl[name]:
-            raise Exception('idl of ensemble', name, 'do not fit')
+            raise ValueError('idl of ensemble', name, 'do not fit')
 
     if obs_a.reweighted is True:
         warnings.warn("The first observable is already reweighted.", RuntimeWarning)
@@ -1555,7 +1555,7 @@ def invert_corr_cov_cholesky(corr, inverrdiag):
 
     condn = np.linalg.cond(corr)
     if condn > 0.1 / np.finfo(float).eps:
-        raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
+        raise ValueError(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
     if condn > 1e13:
         warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
     chol = np.linalg.cholesky(corr)
@@ -1636,7 +1636,7 @@ def _smooth_eigenvalues(corr, E):
         Number of eigenvalues to be left substantially unchanged
     """
     if not (2 < E < corr.shape[0] - 1):
-        raise Exception(f"'E' has to be between 2 and the dimension of the correlation matrix minus 1 ({corr.shape[0] - 1}).")
+        raise ValueError(f"'E' has to be between 2 and the dimension of the correlation matrix minus 1 ({corr.shape[0] - 1}).")
     vals, vec = np.linalg.eigh(corr)
     lambda_min = np.mean(vals[:-E])
     vals[vals < lambda_min] = lambda_min
@@ -1768,9 +1768,9 @@ 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 Exception('list_of_obs contains duplicate replica: %s' % (str(replist)))
+        raise ValueError('list_of_obs contains duplicate replica: %s' % (str(replist)))
     if any([len(o.cov_names) for o in list_of_obs]):
-        raise Exception('Not possible to merge data that contains covobs!')
+        raise ValueError('Not possible to merge data that contains covobs!')
     new_dict = {}
     idl_dict = {}
     for o in list_of_obs:
@@ -1821,7 +1821,7 @@ def covobs_to_obs(co):
     for i in range(len(means)):
         ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad)))
     if ol[0].covobs[name].N != len(means):
-        raise Exception('You have to provide %d mean values!' % (ol[0].N))
+        raise ValueError('You have to provide %d mean values!' % (ol[0].N))
     if len(ol) == 1:
         return ol[0]
     return ol
@@ -1837,7 +1837,7 @@ def _determine_gap(o, e_content, e_name):
 
     gap = min(gaps)
     if not np.all([gi % gap == 0 for gi in gaps]):
-        raise Exception(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
+        raise ValueError(f"Replica for ensemble {e_name} do not have a common spacing.", gaps)
 
     return gap
 

tests/correlators_test.py

@@ -129,7 +129,7 @@ def test_m_eff():
     with pytest.warns(RuntimeWarning):
         my_corr.m_eff('sinh')
 
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         my_corr.m_eff('unkown_variant')
 
 
@@ -140,7 +140,7 @@ def test_m_eff_negative_values():
         assert m_eff_log[padding + 1] is None
         m_eff_cosh = my_corr.m_eff('cosh')
         assert m_eff_cosh[padding + 1] is None
-        with pytest.raises(Exception):
+        with pytest.raises(ValueError):
             my_corr.m_eff('logsym')
 
 
@@ -155,7 +155,7 @@ def test_correlate():
     my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
     corr1 = my_corr.correlate(my_corr)
     corr2 = my_corr.correlate(my_corr[0])
-    with pytest.raises(Exception):
+    with pytest.raises(TypeError):
         corr3 = my_corr.correlate(7.3)
 
 
@@ -176,9 +176,9 @@ def f(a, x):
     assert fit_res[0] == my_corr[0]
     assert fit_res[1] == my_corr[1] - my_corr[0]
 
-    with pytest.raises(Exception):
+    with pytest.raises(TypeError):
         my_corr.fit(f, "from 0 to 3")
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         my_corr.fit(f, [0, 2, 3])
 
 
@@ -256,11 +256,11 @@ def test_prange():
     corr = pe.correlators.Corr(corr_content)
 
     corr.set_prange([2, 4])
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         corr.set_prange([2])
-    with pytest.raises(Exception):
+    with pytest.raises(TypeError):
         corr.set_prange([2, 2.3])
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         corr.set_prange([4, 1])
 
 

tests/dirac_test.py

@@ -30,7 +30,7 @@ def test_grid_dirac():
                   'SigmaYZ',
                   'SigmaZT']:
         pe.dirac.Grid_gamma(gamma)
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         pe.dirac.Grid_gamma('Not a gamma matrix')
 
 
@@ -44,7 +44,7 @@ def test_epsilon_tensor():
              (1, 1, 3) : 0.0}
     for key, value in check.items():
         assert pe.dirac.epsilon_tensor(*key) == value
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         pe.dirac.epsilon_tensor(0, 1, 3)
 
 
@@ -59,5 +59,5 @@ def test_epsilon_tensor_rank4():
              (1, 2, 3, 1) : 0.0}
     for key, value in check.items():
         assert pe.dirac.epsilon_tensor_rank4(*key) == value
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         pe.dirac.epsilon_tensor_rank4(0, 1, 3, 4)