fittheory
: Fit theory definition¶
This module defines the FitTheory
object that is used by
silx.math.fit.FitManager
to define fit functions and background
models.

class
FitTheory
(function, parameters, estimate=None, configure=None, derivative=None, description=None, pymca_legacy=False, is_background=False)[source]¶ This class defines a fit theory, which consists of:
a model function, the actual function to be fitted
parameters names
an estimation function, that return the estimated initial parameters that serve as input for
silx.math.fit.leastsq()
an optional configuration function, that can be used to modify configuration parameters to alter the behavior of the fit function and the estimation function
an optional derivative function, that replaces the default model derivative used in
silx.math.fit.leastsq()

function
¶ Regular fit functions must have the signature
f(x, *params) > y
, where x is a 1D array of values for the independent variable, params are the parameters to be fitted and y is the output array that we want to have the best fit to a series of data points.Background functions used by
FitManager
must have a slightly different signature:f(x, y0, *params) > bg
, where y0 is the array of original data points and bg is the background signal that we want to subtract from the data array prior to fitting the regular fit function.The number of parameters must be the same as in
parameters
, or a multiple of this number if the function is defined as a sum of a variable number of base functions and ifestimate
is designed to be able to estimate the number of needed base functions.

parameters
¶ List of parameters names.
This list can contain the minimum number of parameters, if the function takes a variable number of parameters, and if the estimation function is responsible for finding the number of required parameters

estimate
¶ The estimation function should have the following signature:
f(x, y) > (estimated_param, constraints)
Parameters:
x
is a sequence of values for the independent variabley
is a sequence of the same length asx
containing the data to be fitted
Return values:
estimated_param
is a sequence of estimated fit parameters to be used as initial values for an iterative fit.constraints
is a sequence of shape (n, 3), where n is the number of estimated parameters, containing the constraints for each parameter to be fitted. Seesilx.math.fit.leastsq()
for more explanations about constraints.

configure
¶ The optional configuration function must conform to the signature
f(**kw) > dict
(i.e it must accept any named argument and return a dictionary). It can be used to modify configuration parameters to alter the behavior of the fit function and the estimation function.

derivative
¶ The optional derivative function must conform to the signature
model_deriv(xdata, parameters, index)
, where parameters is a sequence with the current values of the fitting parameters, index is the fitting parameter index for which the the derivative has to be provided in the supplied array of xdata points.

description
¶ Optional description string for this particular fit theory.

pymca_legacy
¶ This attribute can be set to True to indicate that the theory is a PyMca legacy theory.
This tells
silx.math.fit.fitmanager
that the signature of the estimate function is:f(x, y, bg, xscaling, yscaling) > (estimated_param, constraints)

is_background
¶ Flag to indicate that the theory is background theory.
A background function is an secondary function that needs to be added to the main fit function to better fit the original data. If this flag is set to True, modules using this theory are informed that
function
has the signaturef(x, y0, *params) > bg
, instead of the usual fit function signature.