mirror of
https://github.com/triqs/dft_tools
synced 2024-12-27 06:43:40 +01:00
71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
|
|
################################################################################
|
|
#
|
|
# TRIQS: a Toolbox for Research in Interacting Quantum Systems
|
|
#
|
|
# Copyright (C) 2011 by M. Ferrero, O. Parcollet
|
|
#
|
|
# TRIQS is free software: you can redistribute it and/or modify it under the
|
|
# terms of the GNU General Public License as published by the Free Software
|
|
# Foundation, either version 3 of the License, or (at your option) any later
|
|
# version.
|
|
#
|
|
# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
|
|
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
|
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
|
|
# details.
|
|
#
|
|
# You should have received a copy of the GNU General Public License along with
|
|
# TRIQS. If not, see <http://www.gnu.org/licenses/>.
|
|
#
|
|
################################################################################
|
|
|
|
from scipy.optimize import leastsq
|
|
import numpy as np, inspect as ins
|
|
|
|
class Fit:
|
|
"""
|
|
A simple general functional fit of a X,Y plot
|
|
|
|
Given a function f(x, p0,p1,p2 ...) with parameters p0, ..., p2, and an init guess
|
|
it adjust the parameters with least square method.
|
|
|
|
The fitting is done at construction
|
|
|
|
`self.param` is the tuple of adjusted parameters.
|
|
|
|
The object is callable : `self(x) = f(x, *self.param)`, so it can be plotted e.g.
|
|
|
|
"""
|
|
def __init__ (self, x_array, y_array, fitter, p0 = None ) :
|
|
"""
|
|
:param x_array,y_array: curve to fit, as two 1d numpy arrays
|
|
:param fitter: a tuple (F, name, init_value_default) where :
|
|
|
|
* F is a function : `(x, *param_tuple)` -> y, which act on numpy arrays x and y
|
|
* name is string for which name%param_tuple gives the TeX representation of the function
|
|
* init_value_default is the default init point of the minimization
|
|
|
|
:param p0: init guess of the fit. If None, uses the init_value_default of the function.
|
|
|
|
"""
|
|
self.function, self.fname, p00 = fitter
|
|
assert len(ins.getargspec(self.function)[0])== len(p00) + 1, "error in number of parameters"
|
|
assert len(y_array) == len(x_array)
|
|
assert len(y_array) > len (p00)
|
|
errfunc = lambda x : np.abs ( self.function(x_array,*x) - y_array[:])
|
|
self.param, success = leastsq(errfunc, p0 if p0 else p00 )
|
|
|
|
def __str__ (self) : return (self.fname%tuple(self.param) or 'Fit').replace("+ -","-")
|
|
def __repr__ (self) : return str(self)
|
|
def __repr_tex__ (self) : return str(self)
|
|
def __call__ (self,x) : return self.function(x,*self.param)
|
|
|
|
|
|
# a collection of useful fit ...
|
|
|
|
linear = lambda X, a,b : a * X + b, r"$%f x + %f$" , (1,1)
|
|
quadratic = lambda X, a,b,c : (a * X + b)*X + c, r"$%f x^2 + %f x + %f$" , (0,1,1)
|
|
|
|
|