def b(params,value,iter):
"Print status everytime a best fit is found."
m=iso.computeCMD(params,isos)
C=sum(data.flat)/sum(m.flat)
x=array(params)
bestfit[0] = x*C
file("bestfit117","w").write(str(list(bestfit[0])))
print "henry:",value,"tom:",2.0*(value+llhC),"iter:",iter,"norm:",C
def b(params,value,iter):
"Print status everytime a best fit is found."
m=iso.computeCMD(params,isos)
C=sum(data.flat)/sum(m.flat)
x=array(params)
bestfit[0] = x*C
file("bestfit117","w").write(str(list(bestfit[0])))
print "henry:",value,"tom:",2.0*(value+llhC),"iter:",iter,"norm:",C
def f(par):
params.setvalues(par)
w = utils.calculateweights(t, sfr(t, params))
#isow=iso.getisosweights(w,10.**t,metallicity(t,params),isos)
isow = iso.getisosweights_gauss(w, 10.**t, metallicity(t, params),
isos, params.sigma)
m = iso.computeCMD(isow, isos)
m = utils.normalize(m, sum(data.flat))
return utils.loglikelihood(m, data)
def f(par):
params.setvalues(par)
w=utils.calculateweights(t,sfr(t,params))
isow=iso.getisosweights(w,10.**t,metallicity(t,params),isos)
#isow=iso.getisosweights_gauss(w,10.**t,metallicity(t,params),isos,
# params.sigma)
m=iso.computeCMD(isow,isos)
m=utils.normalize(m,ndata)
return utils.loglikelihood(m,data)
def f(params):
w = utils.calculateweights(t, sfr(t, params))
isow = iso.getisosweights(w, 10.**t, metallicity(t, params), isos)
#a3=time.time()
m = iso.computeCMD(isow, isos)
#a4=time.time()
m = utils.normalize(m, ndata)
l = utils.loglikelihood(m, data)
#print a4-a3
return l
def f(params):
w=utils.calculateweights(t,sfr(t,params))
isow=iso.getisosweights(w,10.**t,metallicity(t,params),isos)
#a3=time.time()
m=iso.computeCMD(isow,isos)
#a4=time.time()
m=utils.normalize(m,ndata)
l= utils.loglikelihood(m,data)
#print a4-a3
return l
def f(params):
global iter
m = iso.computeCMD(params, isos)
C = ndata / sum(m.flat)
x = numarray.array(params)
# out.write(str(list(x*C))+"\n")
pickle.dump(x * C, out, protocol=-1)
value = utils.loglikelihood(m * C, data)
# out.write("henry: %r tom: %r iter: %r norm: %r\n"
# %(value,2.0*(value+llhC),iter,C))
pickle.dump((value, 2.0 * (value + llhC), iter, C), out, protocol=-1)
#out.flush()
iter += 1
return value
def f(params):
global iter
m=iso.computeCMD(params,isos)
C=ndata/sum(m.flat)
x=numarray.array(params)
# out.write(str(list(x*C))+"\n")
pickle.dump(x*C,out,protocol=-1)
value=utils.loglikelihood(m*C,data)
# out.write("henry: %r tom: %r iter: %r norm: %r\n"
# %(value,2.0*(value+llhC),iter,C))
pickle.dump((value,2.0*(value+llhC),iter,C),out,protocol=-1)
#out.flush()
iter+=1
return value
def f(params):
m=utils.normalize(iso.computeCMD(params,isos),ndata)
return utils.loglikelihood(m,data)
def f(params):
m = utils.normalize(iso.computeCMD(params, isos), ndata)
return utils.loglikelihood(m, data)
#! /usr/bin/env python
"""Writes all the information about the best fit to the file "protocol" """
import sys
from chemev import iso, isodir, utils
print "reading isochrones"
data = iso.readfits(isodir + "/117/halo/datarr.fits")
isos = iso.readisos(isodir + "/117/halo")
isow = eval(open("bestfit117").readline())
model = iso.computeCMD(isow, isos)
model = utils.normalize(model, sum(data.flat))
def plot_residuals(d, m, aspect=0.2):
"""
todo: the same color range for both data and model. the rediduals are ok
"""
import pylab
pylab.figure()
pylab.subplot(221)
pylab.imshow(d, origin='lower', interpolation="nearest", aspect=aspect)
pylab.title("data")
pylab.colorbar()
pylab.subplot(222)
pylab.imshow(m, origin='lower', interpolation="nearest", aspect=aspect)
pylab.title("model")
pylab.colorbar()
pylab.subplot(223)
residuals = d - m
#! /usr/bin/env python
"""Writes all the information about the best fit to the file "protocol" """
import sys
from chemev import iso, isodir, utils
print "reading isochrones"
data = iso.readfits(isodir+"/117/halo/datarr.fits")
isos = iso.readisos(isodir+"/117/halo")
isow = eval(open("bestfit117").readline())
model = iso.computeCMD(isow,isos)
model = utils.normalize(model,sum(data.flat))
def plot_residuals(d,m,aspect=0.2):
"""
todo: the same color range for both data and model. the rediduals are ok
"""
import pylab
pylab.figure()
pylab.subplot(221)
pylab.imshow(d,origin='lower',interpolation="nearest",aspect=aspect)
pylab.title("data")
pylab.colorbar()
pylab.subplot(222)
pylab.imshow(m,origin='lower',interpolation="nearest",aspect=aspect)
pylab.title("model")
pylab.colorbar()
pylab.subplot(223)
residuals = d-m
pylab.imshow(residuals,origin='lower',interpolation="nearest",aspect=aspect)
