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docs: formatting of docstrings improved.
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2 changed files with 15 additions and 5 deletions
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@ -161,6 +161,8 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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This can take a while as the full correlation matrix
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has to be calculated (default False).
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Notes
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-----
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Based on the orthogonal distance regression module of scipy
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'''
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@ -580,9 +582,13 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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def fit_lin(x, y, **kwargs):
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"""Performs a linear fit to y = n + m * x and returns two Obs n, m.
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y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
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x can either be a list of floats in which case no xerror is assumed, or
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a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
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Parameters
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----------
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x : list
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Can either be a list of floats in which case no xerror is assumed, or
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a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
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y : list
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List of Obs, the dvalues of the Obs are used as yerror for the fit.
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"""
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def f(a, x):
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@ -600,8 +606,8 @@ def fit_lin(x, y, **kwargs):
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def qqplot(x, o_y, func, p):
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""" Generates a quantile-quantile plot of the fit result which can be used to
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check if the residuals of the fit are gaussian distributed.
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"""Generates a quantile-quantile plot of the fit result which can be used to
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check if the residuals of the fit are gaussian distributed.
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"""
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residuals = []
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@ -1300,6 +1300,8 @@ def correlate(obs_a, obs_b):
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obs_b : Obs
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Second observable
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Notes
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-----
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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 strictly necessary).
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@ -1496,6 +1498,8 @@ def merge_obs(list_of_obs):
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list_of_obs : list
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list of the Obs object to be combined
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Notes
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-----
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It is not possible to combine obs which are based on the same replicum
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"""
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replist = [item for obs in list_of_obs for item in obs.names]
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