smpl.fit

Simplified Fitting.

Uses scipy.curve_fit (no x errors) or scipy.odr (with x errors).

Submodules

• smpl.fit.fit
• smpl.fit.minuit
• smpl.fit.scipy

Package Contents

Classes

Fitter

Different implementations to perform a fit.

Functions

R2(datax, datay, function, ff, **kwargs)
Chi2(datax, datay, function, ff, **kwargs)
auto(datax, datay[, funcs])	Automatically loop over functions and fit the best one.
data_split(datax, datay, **kwargs)	Split data + errors
fit
fit_kwargs(kwargs)	Set default fit_kwargs if not set.
fit_split(datax, datay, **kwargs)	Splits datax and datay into (x,y,xerr,yerr).

smpl.fit.R2(datax, datay, function, ff, **kwargs)

smpl.fit.Chi2(datax, datay, function, ff, **kwargs)

class smpl.fit.Fitter

Bases: enum.Enum

Different implementations to perform a fit.

AUTO = 0

SCIPY_CURVEFIT = 1

SCIPY_ODR = 2

MINUIT_LEASTSQUARES = 3

smpl.fit.auto(datax, datay, funcs=None, **kwargs)

Automatically loop over functions and fit the best one.

Parameters

funcsfunction array

functions to consider as fit. Default all smpl.functions.

**kwargsoptional

see fit_kwargs().

Returns

The best fit function and it’s parameters and a lambda where the parameters are already applied to the function.

smpl.fit.data_split(datax, datay, **kwargs)

Split data + errors

smpl.fit.fit(datax, datay, function, **kwargs)

Returns a fit of function to datax and datay.

Parameters

dataxarray_like

X data either as unp.uarray or np.array or list

datayarray_like

Y data either as unp.uarray or np.array or list

functionfunc

Fit function with parameters: x, params

**kwargsoptional

see fit_kwargs().

smpl.fit.fit_kwargs(kwargs)

Set default fit_kwargs if not set.

smpl.fit.fit_split(datax, datay, **kwargs)

Splits datax and datay into (x,y,xerr,yerr).

Parameters

**kwargsoptional

see fit_kwargs().