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better docstrings (#144)

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* first example of returns statement in docstring

* added a some return statements for pandas API

* last return statements in pandas input

* added returns to bdio docstrings

* few returns statements added to docstring

* finished docstrings for hadrons submodule

* also finished docstrings for json submodule

* finished misc submodule

* added returns in docstrings in openqQCD

* made some cosmetic chanes to dostrings

* added return nad return statement in docstring

* linting

* Improved docstrings of mpm, fits, roots, misc to have return statements

returns added for misc.py

returns added for mpm.py

reutrns added for fits.py

* linting...

* Some polishing of docstrings
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Justus Kuhlmann 2023-01-16 15:57:22 +01:00 • committed by GitHub
parent b9cdebd442
commit 26447d658c
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14 changed files with 276 additions and 6 deletions
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pyerrors/fits.py
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@ -129,6 +129,11 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
If True, a quantile-quantile plot of the fit result is generated (default False).
num_grad : bool
Use numerical differentation instead of automatic differentiation to perform the error propagation (default False).
Returns
-------
output : Fit_result
Parameters and information on the fitted result.
'''
if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
@ -180,7 +185,12 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
Notes
-----
Based on the orthogonal distance regression module of scipy
Based on the orthogonal distance regression module of scipy.
Returns
-------
output : Fit_result
Parameters and information on the fitted result.
'''
output = Fit_result()
@ -668,6 +678,11 @@ def fit_lin(x, y, **kwargs):
a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
y : list
List of Obs, the dvalues of the Obs are used as yerror for the fit.
Returns
-------
fit_parameters : list[Obs]
LIist of fitted observables.
"""
def f(a, x):
@ -687,6 +702,10 @@ def fit_lin(x, y, **kwargs):
def qqplot(x, o_y, func, p):
"""Generates a quantile-quantile plot of the fit result which can be used to
check if the residuals of the fit are gaussian distributed.
Returns
-------
None
"""
residuals = []
@ -711,7 +730,12 @@ def qqplot(x, o_y, func, p):
def residual_plot(x, y, func, fit_res):
""" Generates a plot which compares the fit to the data and displays the corresponding residuals"""
"""Generates a plot which compares the fit to the data and displays the corresponding residuals
Returns
-------
None
"""
sorted_x = sorted(x)
xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
@ -741,7 +765,13 @@ def residual_plot(x, y, func, fit_res):
def error_band(x, func, beta):
"""Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
"""Calculate the error band for an array of sample values x, for given fit function func with optimized parameters beta.
Returns
-------
err : np.array(Obs)
Error band for an array of sample values x
"""
cov = covariance(beta)
if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
@ -765,6 +795,10 @@ def ks_test(objects=None):
----------
objects : list
List of fit results to include in the analysis (optional).
Returns
-------
None
"""
if objects is None: