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docs: docstrings and comments cleaned up

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This commit is contained in:
Fabian Joswig 2022-02-09 11:33:16 +00:00
parent 080e09a07f
commit 0a1a9ce1a1
2 changed files with 3 additions and 7 deletions
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6
pyerrors/correlators.py
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@ -39,7 +39,7 @@ class Corr:
region indentified for this correlator. region indentified for this correlator.
""" """
if isinstance(data_input, np.ndarray): # Input is an array of Corrs if isinstance(data_input, np.ndarray):
# This only works, if the array fulfills the conditions below # This only works, if the array fulfills the conditions below
if not len(data_input.shape) == 2 and data_input.shape[0] == data_input.shape[1]: if not len(data_input.shape) == 2 and data_input.shape[0] == data_input.shape[1]:
@ -95,7 +95,6 @@ class Corr:
# An undefined timeslice is represented by the None object # An undefined timeslice is represented by the None object
self.content = [None] * padding[0] + self.content + [None] * padding[1] self.content = [None] * padding[0] + self.content + [None] * padding[1]
self.T = len(self.content) self.T = len(self.content)
self.prange = prange self.prange = prange
self.gamma_method() self.gamma_method()
@ -160,9 +159,6 @@ class Corr:
raise Exception("Vectors are of wrong shape!") raise Exception("Vectors are of wrong shape!")
if normalize: 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)
# if (not (0.95 < vector_r @ vector_r < 1.05)) or (not (0.95 < vector_l @ vector_l < 1.05)):
# print("Vectors are normalized before projection!")
newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content] newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
else: else:

4
pyerrors/obs.py
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@ -1301,7 +1301,7 @@ def correlate(obs_a, obs_b):
Keep in mind to only correlate primary observables which have not been reweighted Keep in mind to only correlate primary observables which have not been reweighted
yet. The reweighting has to be applied after correlating the observables. yet. The reweighting has to be applied after correlating the observables.
Currently only works if ensembles are identical. This is not really necessary. Currently only works if ensembles are identical (this is not strictly necessary).
""" """
if sorted(obs_a.names) != sorted(obs_b.names): if sorted(obs_a.names) != sorted(obs_b.names):
@ -1462,7 +1462,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
def pseudo_Obs(value, dvalue, name, samples=1000): def pseudo_Obs(value, dvalue, name, samples=1000):
"""Generate a pseudo Obs with given value, dvalue and name """Generate an Obs object with given value, dvalue and name for test purposes
Parameters Parameters
---------- ----------