def doplot(bf, yrange=None, fitter_type='mcmc'):
import biggles
plt=biggles.FramedPlot()
color1='red'
color2='blue'
g1pts=biggles.Points(bf.g1true, bf.g1diff, color=color1,type='filled circle')
gerr1pts=biggles.SymmetricErrorBarsY(bf.g1true, bf.g1diff, bf.g1err, color=color1)
g1pts.label=r'$\gamma_1$'
g2pts=biggles.Points(bf.g2true, bf.g2diff, color=color2,type='filled circle')
gerr2pts=biggles.SymmetricErrorBarsY(bf.g2true, bf.g2diff, bf.g2err, color=color2)
g2pts.label=r'$\gamma_2$'
ply1=bf.get_g1poly()
ply2=bf.get_g2poly()
g1c=biggles.Curve(bf.g1true, ply1(bf.g1true),color=color1)
g2c=biggles.Curve(bf.g2true, ply2(bf.g2true),color=color2)
res1=bf.g1fit.get_result()
res2=bf.g2fit.get_result()
m1_mess=r'$m1: %.4g \pm %.4g$' % (res1['pars'][0],res1['perr'][0])
c1_mess=r'$c1: %.4g \pm %.4g$' % (res1['pars'][1],res1['perr'][1])
m2_mess=r'$m2: %.4g \pm %.4g$' % (res2['pars'][0],res2['perr'][0])
c2_mess=r'$c2: %.4g \pm %.4g$' % (res2['pars'][1],res2['perr'][1])
y=0.3
dec=-0.05
m1lab=biggles.PlotLabel(0.1,y,m1_mess,halign='left')
y += dec
c1lab=biggles.PlotLabel(0.1,y,c1_mess,halign='left')
y += dec
m2lab=biggles.PlotLabel(0.1,y,m2_mess,halign='left')
y += dec
c2lab=biggles.PlotLabel(0.1,y,c2_mess,halign='left')
key=biggles.PlotKey(0.9,0.15,[g1pts,g2pts],halign='right')
plt.add(g1pts, gerr1pts, g1c, g2pts, gerr2pts, g2c, key)
plt.add(m1lab,c1lab,m2lab,c2lab)
plt.xlabel=r'$\gamma_{true}$'
plt.ylabel=r'$\Delta \gamma$'
plt.aspect_ratio=1
if yrange is not None:
plt.yrange=yrange
plt.show()
if fitter_type=='mcmc':
import mcmc
names=['m','c']
plt1=mcmc.plot_results(bf.g1fit.trials,names=names,show=False)
plt2=mcmc.plot_results(bf.g2fit.trials,names=names,show=False)
plt1.title='g1'
plt2.title='g2'
plt1.show()
plt2.show()
def fit_input_g(g, show=False, **keys):
"""
Fit only the g prior
"""
import esutil as eu
import biggles
import mcmc
# do the fit
binsize=0.02
hdict=get_norm_hist(g, min=0, binsize=binsize)
ivar = numpy.ones(hdict['center'].size)
w,=numpy.where(hdict['hist_norm_err'] > 0.0)
if w.size > 0:
ivar[w] = 1.0/hdict['hist_norm_err'][w]**2
gpfitter=GPriorFitterErf(hdict['center'],
hdict['hist_norm'],
ivar,**keys)
gpfitter.do_fit()
gpfitter.print_result()
res=gpfitter.get_result()
# make a plot
yrange=[0.0, 1.1*(hdict['hist_norm']+hdict['hist_norm_err']).max()]
plt=biggles.FramedPlot()
#plt.aspect_ratio=1
plt.aspect_ratio=1.0/1.618
plt.xlabel='e'
plt.ylabel='p(e)'
plt.yrange=yrange
pts=biggles.Points(hdict['center'], hdict['hist_norm'],
type='filled circle')
pts.label='COSMOS'
perr=biggles.SymmetricErrorBarsY(hdict['center'],
hdict['hist_norm'],
hdict['hist_norm_err'])
plt.add(pts,perr)
gvals=numpy.linspace(0.0, 1.0)
model=gpfitter.get_model_val(res['pars'], g=gvals)
crv=biggles.Curve(gvals, model, color='red')
crv.label='model'
plt.add(crv)
key=biggles.PlotKey(0.9,0.9,[pts,crv],halign='right')
plt.add(key)
trials_plt = mcmc.plot_results(gpfitter.trials,show=False)
if show:
plt.show()
trials_plt.show()
return gpfitter, plt, trials_plt
def do_make_plots(self, trials, glike):
import mcmc
tab=mcmc.plot_results(trials, names=['g1','g2'])
if self.sampler_type != 'true':
import biggles
import esutil as eu
# add some points drawn from true distribution
sh=trials.shape
true_trials=numpy.zeros( sh )
true_trials[:,0],true_trials[:,1]=glike.sample(sh[0])
key=raw_input('hit a key (q to quit): ')
if key=='q':
stop
def fit_gprior_m_style(cat_type, version=None,
a=0.25, g0=0.1, gmax=0.87, gmax_min=None, Awidth=1.0,
binsize=0.02, doplot=False):
"""
cat_type should be "galfit" or "ngmix-exp" "ngmix-dev" "ngmix-bdf"
If cat_type=="galfit" then fit to the shapes from the sersic fits.
If cat=="ngmix-exp" use my fits, same for dev. Must send version= as well
This works much better than an lm fitter
for all cosmos galaxies I get
[840.0, 1.05, 0.087, 0.810]
"""
import mcmc
import emcee
import esutil as eu
from esutil.random import srandu
g=get_shapes(cat_type, version=version)
bs=eu.stat.Binner(g)
bs.dohist(binsize=binsize)
bs.calc_stats()
xdata=bs['center']
ydata=bs['hist']
nwalkers=200
burnin=500
nstep=100
print 'fitting exp'
A=ydata.sum()*(xdata[1]-xdata[0])
pcen=[A,a,g0,gmax]
npars=4
guess=numpy.zeros( (nwalkers,npars) )
guess[:,0] = pcen[0]*(1.+0.1*srandu(nwalkers))
guess[:,1] = pcen[1]*(1.+0.1*srandu(nwalkers))
guess[:,2] = pcen[2]*(1.+0.1*srandu(nwalkers))
guess[:,3] = pcen[3]*(1.+0.1*srandu(nwalkers))
ivar = numpy.ones(xdata.size)
w,=numpy.where(ydata > 0)
ivar[w] = 1./ydata[w]
gfitter=GPriorMFitter(xdata, ydata, ivar, Aprior=A, Awidth=Awidth, gmax_min=gmax_min)
print 'pcen:',pcen
sampler = emcee.EnsembleSampler(nwalkers,
npars,
gfitter.get_lnprob,
a=2)
pos, prob, state = sampler.run_mcmc(guess, burnin)
sampler.reset()
pos, prob, state = sampler.run_mcmc(pos, nstep)
trials = sampler.flatchain
pars,pcov=mcmc.extract_stats(trials)
d=numpy.diag(pcov)
perr = numpy.sqrt(d)
res={'A':pars[0],
'A_err':perr[0],
'a':pars[1],
'a_err':perr[1],
'g0':pars[2],
'g0_err':perr[2],
'gmax': pars[3],
'gmax_err':perr[3],
'pars':pars,
'pcov':pcov,
'perr':perr}
fmt="""
A: %(A).6g +/- %(A_err).6g
a: %(a).6g +/- %(a_err).6g
g0: %(g0).6g +/- %(g0_err).6g
gmax: %(gmax).6g +/- %(gmax_err).6g
""".strip()
print fmt % res
if doplot:
import mcmc
import ngmix
mcmc.plot_results(trials,names=['A','a','g0','gmax'],
title=cat_type)
p=ngmix.priors.GPriorM(pars)
gsamp=p.sample1d(g.size)
plt=eu.plotting.bhist(g, binsize=binsize, show=False)
eu.plotting.bhist(gsamp, binsize=binsize,
plt=plt, color='blue',
xlabel='|g|',
xrange=[0.,1.],
title=cat_type)
return res
def fit_g_prior(run, model, prior_name, type='m-erf', **keys):
"""
Fit only the g prior
"""
import esutil as eu
import biggles
import mcmc
fl=read_field_list(run, model, prior_name, **keys)
comb=make_combined_pars_subtract_mean_shape(fl)
g=comb[:,0]
binsize=0.01
#binsize=0.005
hdict=get_norm_hist(g, min=0, binsize=binsize)
yrange=[0.0, 1.1*(hdict['hist_norm']+hdict['hist_norm_err']).max()]
plt=eu.plotting.bscatter(hdict['center'],
hdict['hist_norm'],
yerr=hdict['hist_norm_err'],
yrange=yrange,
show=False)
ivar = ones(hdict['center'].size)
w,=where(hdict['hist_norm_err'] > 0.0)
if w.size > 0:
ivar[w] = 1.0/hdict['hist_norm_err'][w]**2
if type=='great-des':
prior=ngmix.priors.GPriorGreatDES()
prior.dofit(hdict['center'],
hdict['hist_norm'],
show=True)
return
elif type=='m-erf':
prior=ngmix.priors.GPriorMErf()
prior.dofit(hdict['center'],
hdict['hist_norm'],
show=True)
return
elif type=='m-erf2':
prior=ngmix.priors.GPriorMErf2()
prior.dofit(hdict['center'],
hdict['hist_norm'],
show=True)
return
elif type=='ba':
prior=ngmix.priors.GPriorBA()
prior.dofit(hdict['center'],
hdict['hist_norm'],
show=True)
return
elif type=='bdf':
print("bdf")
gpfitter=GPriorFitterAlt(hdict['center'],
hdict['hist_norm'],
ivar)
elif type=='exp':
print("exp")
gpfitter=GPriorFitterExp(hdict['center'],
hdict['hist_norm'],
ivar)
gpfitter.do_fit()
gpfitter.print_result()
res=gpfitter.get_result()
gvals=numpy.linspace(0.0, 1.0)
model=gpfitter.get_model_val(res['pars'], g=gvals)
crv=biggles.Curve(gvals, model, color='red')
plt.add(crv)
mcmc.plot_results(gpfitter.trials)
plt.show()
def fit_gprior_2gauss_cut(xdata, ydata, ivar):
"""
This works much better than the lm fitter
Input is the histogram data.
"""
import mcmc
import emcee
nwalkers=800
burnin=1000
nstep=100
A=ydata.sum()#*(xdata[1]-xdata[0])
A1 = 0.6*A
A2 = 0.4*A
sigma1 = 0.02
sigma2 = 0.3
pcen = numpy.array([A1,sigma1,A2,sigma2])
npars=pcen .size
guess=zeros( (nwalkers,npars) )
guess[:,0] = pcen[0]*(1.+0.2*srandu(nwalkers))
guess[:,1] = pcen[1]*(1.+0.2*srandu(nwalkers))
guess[:,2] = pcen[2]*(1.+0.2*srandu(nwalkers))
guess[:,3] = pcen[3]*(1.+0.2*srandu(nwalkers))
gfitter=GPrior2GaussCutFitter(xdata, ydata, ivar)
print 'pcen:',pcen
sampler = emcee.EnsembleSampler(nwalkers,
npars,
gfitter.get_lnprob,
a=2)
pos, prob, state = sampler.run_mcmc(guess, burnin)
sampler.reset()
pos, prob, state = sampler.run_mcmc(pos, nstep)
arate = sampler.acceptance_fraction.mean()
print 'arate:',arate
trials = sampler.flatchain
mcmc.plot_results(trials, ptypes=['log','linear','log','linear'])
pars,pcov=mcmc.extract_stats(trials)
d=diag(pcov)
perr = sqrt(d)
gprior=GPrior2GaussCut(pars)
res={'A1':pars[0],
'A1_err':perr[0],
'sigma1':pars[1],
'sigma1_err':perr[1],
'A2':pars[2],
'A2_err':perr[2],
'sigma2':pars[3],
'sigma2_err':perr[3],
'pars':pars,
'pcov':pcov,
'perr':perr}
fmt="""
A1: %(A1).6g +/- %(A1_err).6g
sigma1: %(sigma1).6g +/- %(sigma1_err).6g
A2: %(A2).6g +/- %(A2_err).6g
sigma2: %(sigma2).6g +/- %(sigma2_err).6g
""".strip()
print fmt % res
return gprior,res