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docs: docstrings and comments cleaned up
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2 changed files with 3 additions and 7 deletions
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@ -39,7 +39,7 @@ class Corr:
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region indentified for this correlator.
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"""
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if isinstance(data_input, np.ndarray): # Input is an array of Corrs
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if isinstance(data_input, np.ndarray):
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# This only works, if the array fulfills the conditions below
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if not len(data_input.shape) == 2 and data_input.shape[0] == data_input.shape[1]:
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@ -95,7 +95,6 @@ class Corr:
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# An undefined timeslice is represented by the None object
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self.content = [None] * padding[0] + self.content + [None] * padding[1]
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self.T = len(self.content)
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self.prange = prange
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self.gamma_method()
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@ -160,9 +159,6 @@ class Corr:
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raise Exception("Vectors are of wrong shape!")
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if normalize:
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vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
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# if (not (0.95 < vector_r @ vector_r < 1.05)) or (not (0.95 < vector_l @ vector_l < 1.05)):
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# print("Vectors are normalized before projection!")
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newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
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else:
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@ -1301,7 +1301,7 @@ def correlate(obs_a, obs_b):
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Keep in mind to only correlate primary observables which have not been reweighted
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yet. The reweighting has to be applied after correlating the observables.
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Currently only works if ensembles are identical. This is not really necessary.
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Currently only works if ensembles are identical (this is not strictly necessary).
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"""
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if sorted(obs_a.names) != sorted(obs_b.names):
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@ -1462,7 +1462,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
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def pseudo_Obs(value, dvalue, name, samples=1000):
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"""Generate a pseudo Obs with given value, dvalue and name
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"""Generate an Obs object with given value, dvalue and name for test purposes
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Parameters
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----------
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