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fitters.py
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fitters.py
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import os
import sys
import h5py as h5
import lsqfit
import gvar as gv
import numpy as np
import datetime
import string
def fit_data(data,fit_range,particles,Pcm,nstates,nsinks,cov=None, print_fit = True):
if nsinks ==1:
if print_fit:
print "fitting with "+str(nstates)+" states and smeared data"
else:
pass
else:
if print_fit:
print "fitting with "+str(nstates)+" states and point and smeared data"
else:
pass
x = fit_range
y = data
if cov is None:
cov_matrix = gv.evalcov(y)
#cov_matrix = gv.evalcov(y)/len(y)
#cov_matrix = np.identity(len(x))
if print_fit:
print np.shape(cov_matrix)
else:
pass
else:
cov_matrix = cov
y = gv.mean(y)
#you'll need to update the pipi function to deal with number of sinks as well as nstates
if particles == "pipi":
p = pipi_priors(nstates,nsinks,Pcm)
#fitc = pipi_fit_function(nstates=nstates,T=len(data))
if nsinks == 1:
fitc = pipi_fit_function(nstates=nstates,T=48,sinks = ["s"])
else:
fitc = pipi_fit_function(nstates=nstates,T=48,sinks = ["s","p"])
elif particles == "pion":
p = pion_priors(nstates,nsinks,Pcm)
p0 = pion_priors(nstates,nsinks,Pcm)
if nsinks == 1:
fitc = pion_fit_function(nstates=nstates,T=48,sinks = ["s"])
else:
fitc = pion_fit_function(nstates=nstates,T=48,sinks = ["s","p"])
#fit = lsqfit.nonlinear_fit(data=(x,y,cov_matrix),prior=p0,fcn=fitc.full_func)
fit = lsqfit.nonlinear_fit(data=(x,y,cov_matrix),prior=p,fcn=fitc.full_func)
if print_fit:
print fit
else:
pass
return fit
def pion_priors(nstates,nsinks,Pcm):
p = dict()
#p['Cs0'] = [0.0,1.0]
p['Es0'] = [0.237,0.002]
p['Bs1'] = [-1000.,1005.0]
p['Bs2'] = [-1.0E6,5.0]
p['As0'] = [0.001,0.0001]
p['As1'] = [0.,8E-4]
p['As2'] = [0.,8E-6]
#p['Cp0'] = [0.0,1.0]
p['Ep0'] = [0.25,0.05]
p['Bp1'] = [-100,50]
p['Bp2'] = [-100,50]
p['Ap0'] = [9E-4,0.001]
p['Ap1'] = [0.,8E-4]
p['Ap2'] = [0.,8E-6]
prior = dict()
if nsinks == 1:
#sinks = ['s']
sinks = ['s']
else:
sinks = ['s','p']
for n in range(nstates):
for snk in sinks:
for k in p.keys():
if int(k[-1]) == n and k[-2:-1] == snk:
prior[k] = gv.gvar(p[k][0], p[k][1])
else: pass
return prior
class pion_fit_function():
def __init__(self,nstates,T,sinks):
self.sinks = sinks
self.T = T
self.nstates = nstates
return None
def A(self,n,snk,p):
return p['A%s%s' %(snk,n)]
def E(self,n,snk,p):
E = p['E%s0' %snk]
for ns in range(1,n+1):
E += np.exp(p['B%s%s' %(snk,ns)])
return E
def C(self,n,snk,p):
return p['C%s%s' %(snk,n)]
def func(self,t_dict,p):
r = 0
t = t_dict["x"]
snk = t_dict["sink"]
#r += self.C(0,snk,p)*np.ones_like(t)
for n in range(0,self.nstates):
An = self.A(n,snk,p)
En = self.E(n,snk,p)
r += An * np.exp(-En*t) + An*np.exp(-En*(self.T-t))
return r
def full_func(self,t,p):
r = []
for snk in self.sinks:
t_dict = {"x":t,"sink":snk}
r = np.concatenate((np.array(r),self.func(t_dict,p)))
#print r
return r
def pipi_priors(nstates,nsinks,Pcm):
p = dict()
p['Cs0'] = [1E-10,5E-11]
p['Es0'] = [0.45,0.05]
p['Bs1'] = [-5.0,3.0]
p['Bs2'] = [-5,5.0]
p['As0'] = [0.0000008,0.0000008]
p['As1'] = [0.00000,0.0000008]
p['As2'] = [0.,0.0000008]
#p['Cp0'] = [0.0,1.0]
p['Ep0'] = [0.5,0.2]
p['Bp1'] = [-100,50]
p['Bp2'] = [-100,50]
p['Ap0'] = [9E-4,0.001]
p['Ap1'] = [0.,8E-4]
p['Ap2'] = [0.,8E-6]
prior = dict()
if nsinks == 1:
#sinks = ['s']
sinks = ['s']
else:
sinks = ['s','p']
for n in range(nstates):
for snk in sinks:
for k in p.keys():
if int(k[-1]) == n and k[-2:-1] == snk:
prior[k] = gv.gvar(p[k][0], p[k][1])
else: pass
return prior
class pipi_fit_function():
def __init__(self,nstates,T,sinks):
self.sinks = sinks
self.T = T
self.nstates = nstates
return None
def A(self,n,snk,p):
return p['A%s%s' %(snk,n)]
def E(self,n,snk,p):
E = p['E%s0' %snk]
for ns in range(1,n+1):
E += np.exp(p['B%s%s' %(snk,ns)])
return E
def C(self,n,snk,p):
return p['C%s%s' %(snk,n)]
def func(self,t_dict,p):
t = t_dict["x"]
snk = t_dict["sink"]
r = self.C(0,snk,p)*np.ones_like(t)
for n in range(0,self.nstates):
An = self.A(n,snk,p)
En = self.E(n,snk,p)
r += An * np.exp(-En*t) + An*np.exp(-En*(self.T-t))
return r
def full_func(self,t,p):
r = []
for snk in self.sinks:
t_dict = {"x":t,"sink":snk}
r = np.concatenate((np.array(r),self.func(t_dict,p)))
#print r
return r