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Diffstat (limited to 'silx/math/fit/fittheory.py')
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diff --git a/silx/math/fit/fittheory.py b/silx/math/fit/fittheory.py deleted file mode 100644 index fa42e6b..0000000 --- a/silx/math/fit/fittheory.py +++ /dev/null @@ -1,161 +0,0 @@ -# coding: utf-8 -#/*########################################################################## -# -# Copyright (c) 2004-2018 European Synchrotron Radiation Facility -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. -# -########################################################################### */ -""" -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" - - -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.""" - - 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. for _ in self.parameters] - estimated_constraints = [[0, 0, 0] for _ in self.parameters] - return estimated_parameters, estimated_constraints |