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"""
This module defines the :class:`FitTheory` object that is used by
:class:`silx.math.fit.FitManager` to define fit functions and background
models.
"""
__authors__ = ["P. Knobel"]
__license__ = "MIT"
__date__ = "09/08/2016"
[docs]
class FitTheory(object):
"""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 :func:`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 :func:`silx.math.fit.leastsq`
"""
def __init__(
self,
function,
parameters,
estimate=None,
configure=None,
derivative=None,
description=None,
pymca_legacy=False,
is_background=False,
):
"""
:param function function: Actual function. See documentation for
:attr:`function`.
:param list[str] parameters: List of parameter names for the function.
See documentation for :attr:`parameters`.
:param function estimate: Optional estimation function.
See documentation for :attr:`estimate`
:param function configure: Optional configuration function.
See documentation for :attr:`configure`
:param function derivative: Optional custom derivative function.
See documentation for :attr:`derivative`
:param str description: Optional description string.
See documentation for :attr:`description`
:param bool pymca_legacy: Flag to indicate that the theory is a PyMca
legacy theory. See documentation for :attr:`pymca_legacy`
:param bool is_background: Flag to indicate that the theory is a
background theory. This has implications regarding the function's
signature, as explained in the documentation for :attr:`function`.
"""
self.function = 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 :class:`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 :attr:`parameters`, or
a multiple of this number if the function is defined as a sum of a
variable number of base functions and if :attr:`estimate` is designed
to be able to estimate the number of needed base functions.
"""
self.parameters = 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 """
self.estimate = 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 variable
- ``y`` is a sequence of the same length as ``x`` 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. See :func:`silx.math.fit.leastsq` for more
explanations about constraints."""
if estimate is None:
self.estimate = self.default_estimate
self.configure = 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."""
self.derivative = 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."""
self.description = description
"""Optional description string for this particular fit theory."""
self.pymca_legacy = pymca_legacy
"""This attribute can be set to *True* to indicate that the theory
is a PyMca legacy theory.
This tells :mod:`silx.math.fit.fitmanager` that the signature of
the estimate function is::
f(x, y, bg, xscaling, yscaling) -> (estimated_param, constraints)
"""
self.is_background = 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 :attr:`function` has the signature ``f(x, y0, *params) -> bg``,
instead of the usual fit function signature."""
[docs]
def default_estimate(self, x=None, y=None, bg=None):
"""Default estimate function. Return an array of *ones* as the
initial estimated parameters, and set all constraints to zero
(FREE)"""
estimated_parameters = [1.0 for _ in self.parameters]
estimated_constraints = [[0, 0, 0] for _ in self.parameters]
return estimated_parameters, estimated_constraints
