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(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(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)
residuals /= m
pylab.imshow(residuals,
origin='lower',
interpolation="nearest",
aspect=aspect)
pylab.title("(data-model)/model")
#pylab.cool()
#pylab.savefig("graph.eps")
pylab.colorbar()
pylab.savefig("graph.png")
#pylab.show()
def plot_weights(w, isos, aspect=0.4):
graph = iso.computefehage(w, isos)
import pylab
pylab.figure()
pylab.imshow(graph, origin='lower', interpolation="nearest", aspect=aspect)
pylab.title("isochrones weights")
#pylab.cool()
#pylab.savefig("graph.eps")
pylab.colorbar()
pylab.savefig("graph-weights.png")
#pylab.show()
print "likelihood: %r, toms: %r\n" % (utils.loglikelihood(
model, data), utils.tomslikelihood(model, data))
plot_residuals(data, model)
plot_weights(isow, isos)
pylab.title("data-model")
pylab.colorbar()
pylab.subplot(224)
residuals = d-m
residuals /= m
pylab.imshow(residuals,origin='lower',interpolation="nearest",aspect=aspect)
pylab.title("(data-model)/model")
#pylab.cool()
#pylab.savefig("graph.eps")
pylab.colorbar()
pylab.savefig("graph.png")
#pylab.show()
def plot_weights(w,isos,aspect=0.4):
graph=iso.computefehage(w,isos)
import pylab
pylab.figure()
pylab.imshow(graph,origin='lower',interpolation="nearest",aspect=aspect)
pylab.title("isochrones weights")
#pylab.cool()
#pylab.savefig("graph.eps")
pylab.colorbar()
pylab.savefig("graph-weights.png")
#pylab.show()
print "likelihood: %r, toms: %r\n"%(utils.loglikelihood(model,data),
utils.tomslikelihood(model,data))
plot_residuals(data,model)
plot_weights(isow,isos)
