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