/usr/lib/python2.7/dist-packages/PyMca/Gefit.py is in pymca 4.7.4+dfsg-1build1.
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# Copyright (C) 2004-2013 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed at
# the ESRF by the Software group.
#
# This file is free software; you can redistribute it and/or modify it
# under the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 2 of the License, or (at your option)
# any later version.
#
# This file 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 Lesser General Public License for more
# details.
#
# Please contact the ESRF industrial unit (industry@esrf.fr) if this license
# is a problem for you.
#############################################################################*/
__author__ = "V.A. Sole - ESRF Software Group"
__license__ = "LGPL"
import numpy
from numpy.linalg import inv
import time
# codes understood by the routine
CFREE = 0
CPOSITIVE = 1
CQUOTED = 2
CFIXED = 3
CFACTOR = 4
CDELTA = 5
CSUM = 6
CIGNORED = 7
ONED = 0
def LeastSquaresFit(model, parameters0, data=None, maxiter = 100,constrains=None,
weightflag = 0,model_deriv=None,deltachi=None,fulloutput=0,
xdata=None,ydata=None,sigmadata=None,linear=None):
"""
Typical use:
LeastSquaresFit(model_function, parameters, xdata=xvalues, ydata=yvalues)
model_function - it has the form model_function(parameters, x) where parameters is a sequence
containing the parameters to be refined and x is the array of values in which
the function is to be evaluated.
parameters - sequence with the initial values to be refined
xdata - array with the x axis data points
ydata - array with the y axis data points
Additional keywords:
sigmadata - array with the uncertainties associated to ydata (default is sqrt(y) )
weightflag - 0 Means no weighted fit 1 means weighted fit
constrains - if provided, it is a 2D sequence of dimension (3, n_parameters) where, for each
parameter denoted by the index i, the meaning is
constrains[0][i] -> 0 - Free (Gefit.CFREE)
1 - Positive (Gefit.CPOSITIVE)
2 - Quoted (Gefit.CQUOTED)
3 - Fixed (Gefit.CFIXED)
4 - Factor (Gefit.CFACTOR)
5 - Delta (Gefit.CDELTA)
6 - Sum (Gefit.CSUM)
constrains[1][i] -> Ignored if constrains[0][i] is 0, 1, 3
Min value of the parameter if constrains[0][i] is Gefit.CQUOTED
Index of fitted parameter to which it is related
constrains[2][i] -> Ignored if constrains[0][i] is 0, 1, 3
Max value of the parameter if constrains[0][i] is Gefit.CQUOTED
Factor to apply to related parameter with index constrains[1][i]
Difference with parameter with index constrains[1][i]
Sum obtained when adding parameter with index constrains[1][i]
model_deriv - function providing the derivatives of the fitting function respect to the fitted parameters.
It will be called as model_deriv(parameters, index, x) where parameters are the current values
of the fitting parameters, index is the fitting parameter index of which the the derivative has
to be provided in the supplied array of x points.
linear - Flag to indicate a linear fit instead of a non-linear. Default is non-linear fit (=false)
maxiter - Maximum number of iterations (default is 100)
Output:
fitted_parameters, reduced_chi_square, uncertainties
"""
if constrains is None:
constrains = []
parameters = numpy.array(parameters0, dtype=numpy.float, copy=False)
if linear is None:linear=0
if deltachi is None:
deltachi = 0.01
if ONED:
data0 = numpy.array(data)
x = data0[0:2,0]
#import SimplePlot
#SimplePlot.plot([data[:,0],data[:,1]],yname='Received data')
else:
if xdata is None:
x=numpy.array([y[0] for y in data])
else:
x=xdata
if linear:
return LinearLeastSquaresFit(model,parameters,
data,maxiter,
constrains,weightflag,model_deriv=model_deriv,
deltachi=deltachi,
fulloutput=fulloutput,
xdata=xdata,
ydata=ydata,
sigmadata=sigmadata)
elif len(constrains) == 0:
try:
model(parameters,x)
constrains = [[],[],[]]
for i in range(len(parameters0)):
constrains[0].append(0)
constrains[1].append(0)
constrains[2].append(0)
return RestreinedLeastSquaresFit(model,parameters,
data,maxiter,
constrains,weightflag,
model_deriv=model_deriv,
deltachi=deltachi,
fulloutput=fulloutput,
xdata=xdata,
ydata=ydata,
sigmadata=sigmadata)
except TypeError:
print("You should reconsider how to write your function")
raise TypeError("You should reconsider how to write your function")
else:
return RestreinedLeastSquaresFit(model,parameters,
data,maxiter,
constrains,weightflag,model_deriv=model_deriv,
deltachi=deltachi,
fulloutput=fulloutput,
xdata=xdata,
ydata=ydata,
sigmadata=sigmadata)
def LinearLeastSquaresFit(model0,parameters0,data0,maxiter,
constrains0,weightflag,model_deriv=None,deltachi=0.01,fulloutput=0,
xdata=None,
ydata=None,
sigmadata=None):
#get the codes:
# 0 = Free 1 = Positive 2 = Quoted
# 3 = Fixed 4 = Factor 5 = Delta
# 6 = Sum 7 = ignored
constrains = [[],[],[]]
if len(constrains0) == 0:
for i in range(len(parameters0)):
constrains[0].append(0)
constrains[1].append(0)
constrains[2].append(0)
else:
for i in range(len(parameters0)):
constrains[0].append(constrains0[0][i])
constrains[1].append(constrains0[1][i])
constrains[2].append(constrains0[2][i])
for i in range(len(parameters0)):
if type(constrains[0][i]) == type('string'):
#get the number
if constrains[0][i] == "FREE":
constrains[0][i] = CFREE
elif constrains[0][i] == "POSITIVE":
constrains[0][i] = CPOSITIVE
elif constrains[0][i] == "QUOTED":
constrains[0][i] = CQUOTED
elif constrains[0][i] == "FIXED":
constrains[0][i] = CFIXED
elif constrains[0][i] == "FACTOR":
constrains[0][i] = CFACTOR
constrains[1][i] = int(constrains[1][i])
elif constrains[0][i] == "DELTA":
constrains[0][i] = CDELTA
constrains[1][i] = int(constrains[1][i])
elif constrains[0][i] == "SUM":
constrains[0][i] = CSUM
constrains[1][i] = int(constrains[1][i])
elif constrains[0][i] == "IGNORED":
constrains[0][i] = CIGNORED
elif constrains[0][i] == "IGNORE":
constrains[0][i] = CIGNORED
else:
#I should raise an exception
#constrains[0][i] = 0
raise ValueError("Unknown constraint %s" % constrains[0][i])
if (constrains[0][i] == CQUOTED):
raise ValueError("Linear fit cannot handle quoted constraint")
# make a local copy of the function for an easy speed up ...
model = model0
parameters = numpy.array(parameters0, dtype=numpy.float, copy=False)
if data0 is not None:
selfx = numpy.array([x[0] for x in data0])
selfy = numpy.array([x[1] for x in data0])
else:
selfx = xdata
selfy = ydata
selfweight = numpy.ones(selfy.shape,numpy.float)
# nr0 = len(selfy)
if data0 is not None:
nc = len(data0[0])
else:
if sigmadata is None:
nc = 2
else:
nc = 3
if weightflag == 1:
if nc == 3:
#dummy = abs(data[0:nr0:inc,2])
if data0 is not None:
dummy = abs(numpy.array([x[2] for x in data0]))
else:
dummy = abs(numpy.array(sigmadata))
selfweight = 1.0 / (dummy + numpy.equal(dummy,0))
selfweight = selfweight * selfweight
else:
selfweight = 1.0 / (abs(selfy) + numpy.equal(abs(selfy),0))
#linear fit, use at own risk since there is no check for the
#function being linear on its parameters.
#Only the fixed constrains are handled properly
x=selfx
y=selfy
weight = selfweight
iiter = maxiter
niter = 0
newpar = parameters.__copy__()
while (iiter>0):
niter+=1
chisq0, alpha0, beta,\
n_free, free_index, noigno, fitparam, derivfactor =ChisqAlphaBeta(
model,newpar,
x,y,weight,constrains,model_deriv=model_deriv,
linear=1)
nr, nc = alpha0.shape
fittedpar = numpy.dot(beta, inv(alpha0))
#check respect of constraints (only positive is handled -force parameter to 0 and fix it-)
error = 0
for i in range(n_free):
if constrains [0] [free_index[i]] == CPOSITIVE:
if fittedpar[0,i] < 0:
#fix parameter to 0.0 and re-start the fit
newpar[free_index[i]] = 0.0
constrains[0][free_index[i]] = CFIXED
error = 1
if error:continue
for i in range(n_free):
newpar[free_index[i]] = fittedpar[0,i]
newpar=numpy.array(getparameters(newpar,constrains))
iiter=-1
yfit = model(newpar,x)
chisq = (weight * pow(y-yfit , 2)).sum()
sigma0 = numpy.sqrt(abs(numpy.diag(inv(alpha0))))
sigmapar = getsigmaparameters(newpar,sigma0,constrains)
lastdeltachi = chisq
if not fulloutput:
return newpar.tolist(), chisq/(len(y)-len(sigma0)), sigmapar.tolist()
else:
return newpar.tolist(), chisq/(len(y)-len(sigma0)), sigmapar.tolist(),niter,lastdeltachi
def RestreinedLeastSquaresFit(model0,parameters0,data0,maxiter,
constrains0,weightflag,model_deriv=None,deltachi=0.01,fulloutput=0,
xdata=None,
ydata=None,
sigmadata=None):
#get the codes:
# 0 = Free 1 = Positive 2 = Quoted
# 3 = Fixed 4 = Factor 5 = Delta
# 6 = Sum 7 = ignored
constrains=[[],[],[]]
for i in range(len(parameters0)):
constrains[0].append(constrains0[0][i])
constrains[1].append(constrains0[1][i])
constrains[2].append(constrains0[2][i])
for i in range(len(parameters0)):
if type(constrains[0][i]) == type('string'):
#get the number
if constrains[0][i] == "FREE":
constrains[0][i] = CFREE
elif constrains[0][i] == "POSITIVE":
constrains[0][i] = CPOSITIVE
elif constrains[0][i] == "QUOTED":
constrains[0][i] = CQUOTED
elif constrains[0][i] == "FIXED":
constrains[0][i] = CFIXED
elif constrains[0][i] == "FACTOR":
constrains[0][i] = CFACTOR
constrains[1][i] = int(constrains[1][i])
elif constrains[0][i] == "DELTA":
constrains[0][i] = CDELTA
constrains[1][i] = int(constrains[1][i])
elif constrains[0][i] == "SUM":
constrains[0][i] = CSUM
constrains[1][i] = int(constrains[1][i])
elif constrains[0][i] == "IGNORED":
constrains[0][i] = CIGNORED
elif constrains[0][i] == "IGNORE":
constrains[0][i] = CIGNORED
else:
#I should raise an exception
#constrains[0][i] = 0
raise ValueError("Unknown constraint %s" % constrains[0][i])
# make a local copy of the function for an easy speed up ...
model = model0
parameters = numpy.array(parameters0, dtype=numpy.float, copy=False)
if ONED:
data = numpy.array(data0)
x = data[1:2,0]
fittedpar = parameters.__copy__()
flambda = 0.001
iiter = maxiter
niter = 0
if ONED:
selfx = data [:,0]
selfy = data [:,1]
else:
if data0 is not None:
selfx = numpy.array([x[0] for x in data0])
selfy = numpy.array([x[1] for x in data0])
else:
selfx = xdata
selfy = ydata
selfweight = numpy.ones(selfy.shape,numpy.float)
if ONED:
nr0, nc = data.shape
else:
nr0 = len(selfy)
if data0 is not None:
nc = len(data0[0])
else:
if sigmadata is None:
nc = 2
else:
nc = 3
if weightflag == 1:
if nc == 3:
#dummy = abs(data[0:nr0:inc,2])
if ONED:
dummy = abs(data [:,2])
else:
if data0 is not None:
dummy = abs(numpy.array([x[2] for x in data0]))
else:
dummy = abs(numpy.array(sigmadata))
selfweight = 1.0 / (dummy + numpy.equal(dummy,0))
selfweight = selfweight * selfweight
else:
selfweight = 1.0 / (abs(selfy) + numpy.equal(abs(selfy),0))
n_param = len(parameters)
index = numpy.arange(0,nr0,2)
while (iiter > 0):
niter = niter + 1
if (niter < 2) and (n_param*3 < nr0):
x=numpy.take(selfx,index)
y=numpy.take(selfy,index)
weight=numpy.take(selfweight,index)
else:
x=selfx
y=selfy
weight = selfweight
chisq0, alpha0, beta,\
n_free, free_index, noigno, fitparam, derivfactor =ChisqAlphaBeta(
model,fittedpar,
x,y,weight,constrains,model_deriv=model_deriv)
nr, nc = alpha0.shape
flag = 0
lastdeltachi = chisq0
while flag == 0:
newpar = parameters.__copy__()
if(1):
alpha = alpha0 + flambda * numpy.identity(nr) * alpha0
deltapar = numpy.dot(beta, inv(alpha))
else:
#an attempt to increase accuracy
#(it was unsuccessful)
alphadiag=numpy.sqrt(numpy.diag(alpha0))
npar = len(numpy.sqrt(alphadiag))
narray = numpy.zeros((npar,npar),numpy.float)
for i in range(npar):
for j in range(npar):
narray[i,j] = alpha0[i,j]/(alphadiag[i]*alphadiag[j])
narray = inv(narray + flambda * numpy.identity(nr))
for i in range(npar):
for j in range(npar):
narray[i,j] = narray[i,j]/(alphadiag[i]*alphadiag[j])
deltapar = numpy.dot(beta, narray)
pwork = numpy.zeros(deltapar.shape, numpy.float)
for i in range(n_free):
if constrains [0] [free_index[i]] == CFREE:
pwork [0] [i] = fitparam [i] + deltapar [0] [i]
elif constrains [0] [free_index[i]] == CPOSITIVE:
#abs method
pwork [0] [i] = fitparam [i] + deltapar [0] [i]
#square method
#pwork [0] [i] = (numpy.sqrt(fitparam [i]) + deltapar [0] [i]) * \
# (numpy.sqrt(fitparam [i]) + deltapar [0] [i])
elif constrains [0] [free_index[i]] == CQUOTED:
pmax=max(constrains[1] [free_index[i]],
constrains[2] [free_index[i]])
pmin=min(constrains[1] [free_index[i]],
constrains[2] [free_index[i]])
A = 0.5 * (pmax + pmin)
B = 0.5 * (pmax - pmin)
if (B != 0):
pwork [0] [i] = A + \
B * numpy.sin(numpy.arcsin((fitparam[i] - A)/B)+ \
deltapar [0] [i])
else:
print("Error processing constrained fit")
print("Parameter limits are",pmin,' and ',pmax)
print("A = ",A,"B = ",B)
newpar [free_index[i]] = pwork [0] [i]
newpar=numpy.array(getparameters(newpar,constrains))
workpar = numpy.take(newpar,noigno)
#yfit = model(workpar.tolist(), x)
yfit = model(workpar,x)
chisq = (weight * pow(y-yfit, 2)).sum()
if chisq > chisq0:
flambda = flambda * 10.0
if flambda > 1000:
flag = 1
iiter = 0
else:
flag = 1
fittedpar = newpar.__copy__()
lastdeltachi = (chisq0-chisq)/(chisq0+(chisq0==0))
if (lastdeltachi) < deltachi:
iiter = 0
chisq0 = chisq
flambda = flambda / 10.0
#print "iter = ",iter,"chisq = ", chisq
iiter = iiter -1
sigma0 = numpy.sqrt(abs(numpy.diag(inv(alpha0))))
sigmapar = getsigmaparameters(fittedpar,sigma0,constrains)
if not fulloutput:
return fittedpar.tolist(), chisq/(len(yfit)-len(sigma0)), sigmapar.tolist()
else:
return fittedpar.tolist(), chisq/(len(yfit)-len(sigma0)), sigmapar.tolist(),niter,lastdeltachi
def ChisqAlphaBeta(model0, parameters, x,y,weight, constrains,model_deriv=None,linear=None):
if linear is None:linear=0
model = model0
#nr0, nc = data.shape
n_param = len(parameters)
n_free = 0
fitparam=[]
free_index=[]
noigno = []
derivfactor = []
for i in range(n_param):
if constrains[0] [i] != CIGNORED:
noigno.append(i)
if constrains[0] [i] == CFREE:
fitparam.append(parameters [i])
derivfactor.append(1.0)
free_index.append(i)
n_free += 1
elif constrains[0] [i] == CPOSITIVE:
fitparam.append(abs(parameters[i]))
derivfactor.append(1.0)
#fitparam.append(numpy.sqrt(abs(parameters[i])))
#derivfactor.append(2.0*numpy.sqrt(abs(parameters[i])))
free_index.append(i)
n_free += 1
elif constrains[0] [i] == CQUOTED:
pmax=max(constrains[1] [i],constrains[2] [i])
pmin=min(constrains[1] [i],constrains[2] [i])
if ((pmax-pmin) > 0) & \
(parameters[i] <= pmax) & \
(parameters[i] >= pmin):
A = 0.5 * (pmax + pmin)
B = 0.5 * (pmax - pmin)
if 1:
fitparam.append(parameters[i])
derivfactor.append(B*numpy.cos(numpy.arcsin((parameters[i] - A)/B)))
else:
help0 = numpy.arcsin((parameters[i] - A)/B)
fitparam.append(help0)
derivfactor.append(B*numpy.cos(help0))
free_index.append(i)
n_free += 1
elif (pmax-pmin) > 0:
print("WARNING: Quoted parameter outside boundaries")
print("Initial value = %f" % parameters[i])
print("Limits are %f and %f" % (pmax, pmin))
print("Parameter will be kept at its starting value")
fitparam = numpy.array(fitparam, numpy.float)
alpha = numpy.zeros((n_free, n_free),numpy.float)
beta = numpy.zeros((1,n_free),numpy.float)
delta = (fitparam + numpy.equal(fitparam,0.0)) * 0.00001
nr = x.shape[0]
##############
# Prior to each call to the function one has to re-calculate the
# parameters
pwork = parameters.__copy__()
for i in range(n_free):
pwork [free_index[i]] = fitparam [i]
newpar = getparameters(pwork.tolist(),constrains)
newpar = numpy.take(newpar,noigno)
if n_free == 0:
raise ValueError("No free parameters to fit")
for i in range(n_free):
if model_deriv is None:
#pwork = parameters.__copy__()
pwork [free_index[i]] = fitparam [i] + delta [i]
newpar = getparameters(pwork.tolist(),constrains)
newpar=numpy.take(newpar,noigno)
f1 = model(newpar, x)
pwork [free_index[i]] = fitparam [i] - delta [i]
newpar = getparameters(pwork.tolist(),constrains)
newpar=numpy.take(newpar,noigno)
f2 = model(newpar, x)
help0 = (f1-f2) / (2.0 * delta [i])
help0 = help0 * derivfactor[i]
pwork [free_index[i]] = fitparam [i]
#removed I resize outside the loop:
#help0 = numpy.resize(help0,(1,nr))
else:
newpar = getparameters(pwork.tolist(),constrains)
help0=model_deriv(pwork,free_index[i],x)
help0 = help0 * derivfactor[i]
if i == 0 :
deriv = help0
else:
deriv = numpy.concatenate((deriv,help0), 0)
#line added to resize outside the loop
deriv=numpy.resize(deriv,(n_free,nr))
if linear:
pseudobetahelp = weight * y
else:
yfit = model(newpar, x)
deltay = y - yfit
help0 = weight * deltay
for i in range(n_free):
derivi = numpy.resize(deriv [i,:], (1,nr))
if linear:
if i==0:
beta = numpy.resize(numpy.sum((pseudobetahelp * derivi),1),(1,1))
else:
beta = numpy.concatenate((beta, numpy.resize(numpy.sum((pseudobetahelp * derivi),1),(1,1))), 1)
else:
help1 = numpy.resize(numpy.sum((help0 * derivi),1),(1,1))
if i == 0:
beta = help1
else:
beta = numpy.concatenate((beta, help1), 1)
help1 = numpy.inner(deriv,weight*derivi)
if i == 0:
alpha = help1
else:
alpha = numpy.concatenate((alpha, help1),1)
if linear:
#not used
chisq = 0.0
else:
chisq = (help0 * deltay).sum()
return chisq, alpha, beta, \
n_free, free_index, noigno, fitparam, derivfactor
def getparameters(parameters,constrains):
# 0 = Free 1 = Positive 2 = Quoted
# 3 = Fixed 4 = Factor 5 = Delta
newparam=[]
#first I make the free parameters
#because the quoted ones put troubles
for i in range(len(constrains [0])):
if constrains[0][i] == CFREE:
newparam.append(parameters[i])
elif constrains[0][i] == CPOSITIVE:
#newparam.append(parameters[i] * parameters[i])
newparam.append(abs(parameters[i]))
elif constrains[0][i] == CQUOTED:
if 1:
newparam.append(parameters[i])
else:
pmax=max(constrains[1] [i],constrains[2] [i])
pmin=min(constrains[1] [i],constrains[2] [i])
A = 0.5 * (pmax + pmin)
B = 0.5 * (pmax - pmin)
newparam.append(A + B * numpy.sin(parameters[i]))
elif abs(constrains[0][i]) == CFIXED:
newparam.append(parameters[i])
else:
newparam.append(parameters[i])
for i in range(len(constrains [0])):
if constrains[0][i] == CFACTOR:
newparam[i] = constrains[2][i]*newparam[int(constrains[1][i])]
elif constrains[0][i] == CDELTA:
newparam[i] = constrains[2][i]+newparam[int(constrains[1][i])]
elif constrains[0][i] == CIGNORED:
newparam[i] = 0
elif constrains[0][i] == CSUM:
newparam[i] = constrains[2][i]-newparam[int(constrains[1][i])]
return newparam
def getsigmaparameters(parameters,sigma0,constrains):
# 0 = Free 1 = Positive 2 = Quoted
# 3 = Fixed 4 = Factor 5 = Delta
n_free = 0
sigma_par = numpy.zeros(parameters.shape,numpy.float)
for i in range(len(constrains [0])):
if constrains[0][i] == CFREE:
sigma_par [i] = sigma0[n_free]
n_free += 1
elif constrains[0][i] == CPOSITIVE:
#sigma_par [i] = 2.0 * sigma0[n_free]
sigma_par [i] = sigma0[n_free]
n_free += 1
elif constrains[0][i] == CQUOTED:
pmax = max(constrains [1] [i], constrains [2] [i])
pmin = min(constrains [1] [i], constrains [2] [i])
# A = 0.5 * (pmax + pmin)
B = 0.5 * (pmax - pmin)
if (B > 0) & (parameters [i] < pmax) & (parameters [i] > pmin):
sigma_par [i] = abs(B * numpy.cos(parameters[i]) * sigma0[n_free])
n_free += 1
else:
sigma_par [i] = parameters[i]
elif abs(constrains[0][i]) == CFIXED:
sigma_par[i] = parameters[i]
for i in range(len(constrains [0])):
if constrains[0][i] == CFACTOR:
sigma_par [i] = constrains[2][i]*sigma_par[int(constrains[1][i])]
elif constrains[0][i] == CDELTA:
sigma_par [i] = sigma_par[int(constrains[1][i])]
elif constrains[0][i] == CSUM:
sigma_par [i] = sigma_par[int(constrains[1][i])]
return sigma_par
def fitpar2par(fitpar,constrains,free_index):
newparam = []
for i in range(len(constrains [0])):
if constrains[0][free_index[i]] == CFREE:
newparam.append(fitpar[i])
elif constrains[0][free_index[i]] == CPOSITIVE:
newparam.append(fitpar[i] * fitpar [i])
elif abs(constrains[0][free_index[i]]) == CQUOTED:
pmax=max(constrains[1] [free_index[i]],constrains[2] [free_index[i]])
pmin=min(constrains[1] [free_index[i]],constrains[2] [free_index[i]])
A = 0.5 * (pmax + pmin)
B = 0.5 * (pmax - pmin)
newparam.append(A + B * numpy.sin(fitpar[i]))
return newparam
def gauss(param0,t0):
param=numpy.numpy.array(param0)
t=numpy.numpy.array(t0)
dummy=2.3548200450309493*(t-param[3])/param[4]
return param[0] + param[1] * t + param[2] * myexp(-0.5 * dummy * dummy)
def myexp(x):
# put a (bad) filter to avoid over/underflows
# with no python looping
return numpy.exp(x*numpy.less(abs(x),250))-1.0 * numpy.greater_equal(abs(x),250)
def test(npoints):
xx = arange (npoints)
xx=numpy.resize(xx,(npoints,1))
#yy = 1000.0 * exp (- 0.5 * (xx * xx) /15)+ 2.0 * xx + 10.5
yy = gauss([10.5,2,1000.0,20.,15],xx)
yy=numpy.resize(yy,(npoints,1))
sy = numpy.sqrt(abs(yy))
sy=numpy.resize(sy,(npoints,1))
data = numpy.concatenate((xx, yy, sy),1)
parameters = [0.0,1.0,900.0, 25., 10]
stime = time.time()
if 0:
#old fashion
fittedpar, chisq, sigmapar = LeastSquaresFit(gauss,parameters,data)
else:
#easier to handle
fittedpar, chisq, sigmapar = LeastSquaresFit(gauss,parameters,
xdata=xx.reshape((-1,)),
ydata=yy.reshape((-1,)),
sigmadata=sy.reshape((-1,)))
etime = time.time()
print("Took ",etime - stime, "seconds")
print("chi square = ",chisq)
print("Fitted pars = ",fittedpar)
print("Sigma pars = ",sigmapar)
if __name__ == "__main__":
import profile
profile.run('test(10000)',"test")
import pstats
p=pstats.Stats("test")
p.strip_dirs().sort_stats(-1).print_stats()
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