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[Fix] Docstrings and small issues in dirac and obs

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Fabian Joswig 2024-12-24 16:59:37 +01:00
parent 3eac9214b4
commit be22624f83
2 changed files with 27 additions and 21 deletions
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8
pyerrors/dirac.py
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@ -32,8 +32,8 @@ def epsilon_tensor(i, j, k):
elem : int
Element (i,j,k) of the epsilon tensor of rank 3
"""
test_set = set((i, j, k))
if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))):
test_set = {i, j, k}
if not (test_set <= {1, 2, 3} or test_set <= {0, 1, 2}):
raise ValueError("Unexpected input", i, j, k)
return (i - j) * (j - k) * (k - i) / 2
@ -50,8 +50,8 @@ def epsilon_tensor_rank4(i, j, k, o):
elem : int
Element (i,j,k,o) of the epsilon tensor of rank 4
"""
test_set = set((i, j, k, o))
if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))):
test_set = {i, j, k, o}
if not (test_set <= {1, 2, 3, 4} or test_set <= {0, 1, 2, 3}):
raise ValueError("Unexpected input", i, j, k, o)
return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12

40
pyerrors/obs.py
View file

@ -112,7 +112,7 @@ class Obs:
else:
self.idl[name] = list(idx)
else:
raise TypeError('incompatible type for idl[%s].' % (name))
raise TypeError('incompatible type for idl[%s].' % name)
else:
for name, sample in sorted(zip(names, samples)):
self.idl[name] = range(1, len(sample) + 1)
@ -388,7 +388,7 @@ class Obs:
if self.tag is not None:
print("Description:", self.tag)
if not hasattr(self, 'e_dvalue'):
print('Result\t %3.8e' % (self.value))
print('Result\t %3.8e' % self.value)
else:
if self.value == 0.0:
percentage = np.nan
@ -446,7 +446,7 @@ class Obs:
my_string_list.append(my_string)
print('\n'.join(my_string_list))
def reweight(self, weight):
def reweight(self, weight, all_configs=False):
"""Reweight the obs with given rewighting factors.
Parameters
@ -459,7 +459,7 @@ class Obs:
the reweighting factor on all configurations in weight.idl and not
on the configurations in obs[i].idl. Default False.
"""
return reweight(weight, [self])[0]
return reweight(weight, [self], all_configs=all_configs)[0]
def is_zero_within_error(self, sigma=1):
"""Checks whether the observable is zero within 'sigma' standard errors.
@ -1078,7 +1078,7 @@ def _expand_deltas(deltas, idx, shape, gapsize):
are found in idx, the data is expanded.
"""
if isinstance(idx, range):
if (idx.step == gapsize):
if idx.step == gapsize:
return deltas
ret = np.zeros((idx[-1] - idx[0] + gapsize) // gapsize)
for i in range(shape):
@ -1188,6 +1188,10 @@ def derived_observable(func, data, array_mode=False, **kwargs):
of func. Use cautiously, supplying the wrong derivative will
not be intercepted.
array_mode: bool
If True, the function is applied to the full array of data.
Default: False
Notes
-----
For simple mathematical operations it can be practical to use anonymous
@ -1210,7 +1214,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
for name in o.cov_names:
if name in allcov:
if not np.allclose(allcov[name], o.covobs[name].cov):
raise Exception('Inconsistent covariance matrices for %s!' % (name))
raise Exception('Inconsistent covariance matrices for %s!' % name)
else:
allcov[name] = o.covobs[name].cov
@ -1260,7 +1264,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
for mc_name in obs.mc_names:
mc_idl_d = [name for name in obs.idl if name.startswith(mc_name + '|')]
new_mc_idl_d = [name for name in new_idl_d if name.startswith(mc_name + '|')]
if len(mc_idl_d) > 0 and len(mc_idl_d) < len(new_mc_idl_d):
if 0 < len(mc_idl_d) < len(new_mc_idl_d):
scalef_d[mc_name] = sum([len(new_idl_d[name]) for name in new_mc_idl_d]) / sum([len(new_idl_d[name]) for name in mc_idl_d])
return scalef_d
@ -1386,7 +1390,7 @@ def _reduce_deltas(deltas, idx_old, idx_new):
return np.array(deltas)[indices]
def reweight(weight, obs, **kwargs):
def reweight(weight, obs, all_configs=False):
"""Reweight a list of observables.
Parameters
@ -1417,7 +1421,7 @@ def reweight(weight, obs, **kwargs):
new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name]))
tmp_obs = Obs(new_samples, sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
if kwargs.get('all_configs'):
if all_configs:
new_weight = weight
else:
new_weight = Obs([w_deltas[name] + weight.r_values[name] for name in sorted(obs[i].names)], sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
@ -1471,8 +1475,8 @@ def correlate(obs_a, obs_b):
return o
def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
r'''Calculates the error covariance matrix of a set of observables.
def covariance(obs, visualize=False, correlation=False, smooth=None):
r"""Calculates the error covariance matrix of a set of observables.
WARNING: This function should be used with care, especially for observables with support on multiple
ensembles with differing autocorrelations. See the notes below for details.
@ -1503,7 +1507,7 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
For observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.
$$\tau_{\mathrm{int}, ij}=\sqrt{\tau_{\mathrm{int}, i}\times \tau_{\mathrm{int}, j}}$$
This construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).
'''
"""
length = len(obs)
@ -1557,7 +1561,7 @@ def invert_corr_cov_cholesky(corr, inverrdiag):
if condn > 0.1 / np.finfo(float).eps:
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)
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % condn, RuntimeWarning)
chol = np.linalg.cholesky(corr)
chol_inv = scipy.linalg.solve_triangular(chol, inverrdiag, lower=True)
@ -1714,6 +1718,8 @@ def import_jackknife(jacks, name, idl=None):
the N jackknife samples as first to Nth entry.
name : str
name of the ensemble the samples are defined on.
idl : list, optional
list of ranges or lists on which the samples are defined
"""
length = len(jacks) - 1
prj = (np.ones((length, length)) - (length - 1) * np.identity(length))
@ -1789,7 +1795,7 @@ def cov_Obs(means, cov, name, grad=None):
Parameters
----------
mean : list of floats or float
means : list of floats or float
N mean value(s) of the new Obs
cov : list or array
2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
@ -1821,7 +1827,7 @@ def cov_Obs(means, cov, name, grad=None):
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 ValueError('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
@ -1843,14 +1849,14 @@ def _determine_gap(o, e_content, e_name):
def _check_lists_equal(idl):
'''
"""
Use groupby to efficiently check whether all elements of idl are identical.
Returns True if all elements are equal, otherwise False.
Parameters
----------
idl : list of lists, ranges or np.ndarrays
'''
"""
g = groupby([np.nditer(el) if isinstance(el, np.ndarray) else el for el in idl])
if next(g, True) and not next(g, False):
return True