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Fabian Joswig 2021-11-07 21:44:22 +00:00
parent a23a97aed1
commit effccb1cc8
8 changed files with 173 additions and 159 deletions
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52
pyerrors/fits.py
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@ -59,7 +59,7 @@ class Fit_result(Sequence):
def least_squares(x, y, func, priors=None, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x).
Arguments:
Parameters
----------
x : list
list of floats.
@ -87,22 +87,23 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
enough.
silent : bool, optional
If true all output to the console is omitted (default False).
Keyword arguments
-----------------
initial_guess -- can provide an initial guess for the input parameters. Relevant for
initial_guess : list
can provide an initial guess for the input parameters. Relevant for
non-linear fits with many parameters.
method -- can be used to choose an alternative method for the minimization of chisquare.
The possible methods are the ones which can be used for scipy.optimize.minimize and
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
Reliable alternatives are migrad, Powell and Nelder-Mead.
resplot -- If true, a plot which displays fit, data and residuals is generated (default False).
qqplot -- If true, a quantile-quantile plot of the fit result is generated (default False).
expected_chisquare -- If true prints the expected chisquare which is
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
method : str
can be used to choose an alternative method for the minimization of chisquare.
The possible methods are the ones which can be used for scipy.optimize.minimize and
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
Reliable alternatives are migrad, Powell and Nelder-Mead.
resplot : bool
If true, a plot which displays fit, data and residuals is generated (default False).
qqplot : bool
If true, a quantile-quantile plot of the fit result is generated (default False).
expected_chisquare : bool
If true prints the expected chisquare which is
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
"""
if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
@ -254,6 +255,8 @@ def odr_fit(x, y, func, silent=False, **kwargs):
def total_least_squares(x, y, func, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
Parameters
----------
x : list
list of Obs, or a tuple of lists of Obs
y : list
@ -276,15 +279,14 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
silent : bool, optional
If true all output to the console is omitted (default False).
Based on the orthogonal distance regression module of scipy
Keyword arguments
-----------------
initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear
fits with many parameters.
expected_chisquare -- If true prints the expected chisquare which is
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
initial_guess : list
can provide an initial guess for the input parameters. Relevant for non-linear
fits with many parameters.
expected_chisquare : bool
If true prints the expected chisquare which is
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
"""
output = Fit_result()