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effhist.py
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effhist.py
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#!/usr/bin/env python
import numpy
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import pickle
fracincrease_min = 0.1
fracincrease_max = 10.
stonmin=4.
stonmax=10.5
npar=7
def effmodel(ston, frac, pars, minlnFI, maxlnFI):
logfrac = numpy.log(frac)
logfrac = (logfrac-minlnFI)/(maxlnFI-minlnFI)
if logfrac < 0:
logfrac = 0
elif logfrac>1:
logfrac=1
pterm = (1-logfrac)**pars[1]
aterm = 1+(pars[0]-1)*pterm
bterm = pars[2]+(pars[3]-pars[2])*pterm
cterm = pars[4]+(pars[5]-pars[4])*pterm
dum = numpy.zeros(len(ston))
ok = (ston-bterm) >0
argterm = (ston[ok]-bterm)/cterm
tanhterm = numpy.tanh(argterm)**pars[6]
dum[ok] = aterm *tanhterm
return dum
def loadpars(emcee=True):
if not emcee:
f = open('stan2.pkl','rb')
fit,minlogFI, maxlogFI = pickle.load(f)
pars = numpy.append(numpy.mean(fit['a1']), numpy.mean(fit['a2']))
pars = numpy.append(pars, numpy.mean(fit['b'],axis=0))
pars = numpy.append(pars, numpy.mean(fit['c'],axis=0))
# pars = numpy.append(pars, numpy.mean(fit['pop']))
else:
f = open('emcee.pkl','rb')
fit,minlogFI, maxlogFI = pickle.load(f)
f.close()
fit = fit[:,600:,:].reshape((-1,npar))
pars = numpy.mean(fit,axis=0)
print pars
return pars, fit, minlogFI, maxlogFI
# n=3
# pars = numpy.append(numpy.mean(fit['a'][n+0:n+100,:],axis=0), numpy.mean(fit['b'][n+0:n+100,:],axis=0))
# pars = numpy.append(pars, numpy.mean(fit['c'][n+0:n+100,:],axis=0))
# pars = numpy.append(pars, numpy.mean(fit['pop'][n+0:n+100]))
def surfaceplot():
from matplotlib import colors, ticker, cm
from matplotlib.ticker import FormatStrFormatter
pars, fit, minlnFI, maxlnFI =loadpars()
frac = numpy.arange(fracincrease_min, fracincrease_max, 0.02)
ston = numpy.arange(4,10, 0.02)
ans = numpy.zeros((len(frac),len(ston)))
for i in xrange(len(frac)):
ans[i,:] = effmodel(ston, frac[i], pars, minlnFI, maxlnFI)
totaleff=1-(1-ans)**3
X, Y = numpy.meshgrid(ston, frac)
fig1, ax1 = plt.subplots()
# cs = ax1.imshow(X, Y, totaleff,norm=colors.LogNorm(vmin=0.9, vmax=1),interpolation='nearest')
cs = ax1.pcolor(X, Y, totaleff,norm=colors.LogNorm(vmin=0.9, vmax=1),rasterized=True)
ax1.set_xlabel('STON')
ax1.set_ylabel('Fraction Increase')
ax1.set_yscale('log')
ax1.yaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
ax1.set_ylim((0.3,10))
ax1.set_xscale('log')
ax1.set_xlim((4,10))
ax1.xaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
ax1.set_title('Discovery Efficiency Given Three Opportunities')
cbar = fig1.colorbar(cs, ax=ax1)
cbar.set_ticks(numpy.arange(0.9,1.01,.01),update_ticks=True)
cbar.set_ticklabels(numpy.arange(0.9,1.01,.01),update_ticks=True)
cbar.set_label('Discovery Efficiency')
with PdfPages('efficiency_3.pdf') as pdf:
pdf.savefig()
plt.close()
def readdataforfit():
import pickle
input = open('data.pkl', 'rb')
[ston, fracincrease, found]=pickle.load(input)
input.close()
# prune sample
use = numpy.logical_and(fracincrease > fracincrease_min, fracincrease <fracincrease_max)
use = numpy.logical_and(use, ston > stonmin)
use = numpy.logical_and(use, ston < stonmax)
# use = numpy.logical_and(use, numpy.logical_or(ston < 10, ston > 16))
ston = ston[use]
fracincrease = fracincrease[use]
found = found[use]
lnFI = numpy.log(fracincrease)
minlogFI = lnFI.min()
maxlogFI = lnFI.max()
lnFI = (lnFI-minlogFI)/(maxlogFI-minlogFI)
return ston, found ,lnFI, minlogFI, maxlogFI
def fit():
import pystan
ston, found, lnFI, minlogFI, maxlogFI = readdataforfit()
# input = open('data.pkl', 'rb')
# [ston, fracincrease, found]=pickle.load(input)
# input.close()
# # prune sample
# use = numpy.logical_and(fracincrease > 0.01, fracincrease <10)
# ston = ston[use]
# fracincrease = fracincrease[use]
# found = found[use]
# lnFI = numpy.log(fracincrease)
# minlogFI = lnFI.min()
# maxlogFI = lnFI.max()
# lnFI = (lnFI-minlogFI)/(maxlogFI-minlogFI)
data = {'D': len(ston), 'ston': ston, 'lnFI': lnFI, 'found': found.astype('int')}
sm = pystan.StanModel(file='effmodel.stan')
# indict = {'a': [0.996,.999999], 'b': [.1, 4], 'c': [2, 1.],'pop':5.}
indict = {'a1': 0.6 , 'a2': 4.5, 'b': [3.3, 2.5], 'c': [1.2, 3.5],'pop':4.5}
fit = sm.sampling(data=data, iter=500, chains=4, init=[indict, indict, indict, indict])
output = open('stan2.pkl','wb')
pickle.dump([fit.extract(),minlogFI, maxlogFI], output)
output.close()
def fit_emcee():
import sys
import emcee
nwalkers=npar*2
ston, found, lnFI, minlogFI, maxlogFI = readdataforfit()
xedges = stonmin * (2**numpy.arange(0,2.5,.25)) #numpy.arange(2,20.5,1)
yedges = fracincrease_min*(2**numpy.arange(8)) #(4**numpy.arange(6))/64. #numpy.arange(0,210,50)
fracincrease = numpy.exp(lnFI*(maxlogFI-minlogFI) + minlogFI)
H, _,_ = numpy.histogram2d(ston, fracincrease, bins=[xedges, yedges])
xbin = numpy.digitize(ston,xedges)
ybin = numpy.digitize(fracincrease,yedges)
xbin[xbin == xedges.shape] = xedges.shape[0]-1
ybin[ybin == yedges.shape] = yedges.shape[0]-1
invH = [H[i-1,j-1] for i,j in zip(xbin,ybin)]
invH = numpy.array(invH)
invH= 1.*len(invH)/invH
# input = open('data.pkl', 'rb')
# [ston, fracincrease, found]=pickle.load(input)
# input.close()
# # prune sample
# use = numpy.logical_and(fracincrease > 0.01, fracincrease <10)
# ston = ston[use]
# fracincrease = fracincrease[use]
# found = found[use]
# lnFI = numpy.log(fracincrease)
# minlogFI = lnFI.min()
# maxlogFI = lnFI.max()
# lnFI = (lnFI-minlogFI)/(maxlogFI-minlogFI)
foundpm = (-1)**found
def lnprob(p, oneminuslnFI, found, foundpm, invH):
if (p[0]<0.98 or p[0]>1 or p[1]<1 or p[2]<=1 or p[3]<=1 or p[4]<=0 or p[5]<=0 or p[6] < 1):
return -numpy.inf
pterm = oneminuslnFI**p[1];
t = (ston-(p[2] + (p[3]-p[2])* pterm)) / (p[4] + (p[5]-p[4])* pterm)
eff=numpy.zeros(len(t))
ok = t>0
eff[ok] = numpy.tanh(t[ok]) **p[6]
eff[ok] = (1+(p[0]-1)*pterm[ok]) *eff[ok]
# eff[t<=0] = 0.
ans= (1-found) -foundpm*eff
ans[ans<=0] = 1e-4
return (invH*numpy.log(ans)).sum()
# return numpy.log(ans).sum()
p0 = []
# pars = numpy.array([ .987 , 1.5 , 4. , 4. , .8 , 1.3 , 1])
avg = numpy.array([0.985,1.3,3.8,3.8,0.9,1.3, 1.3])
for i in range(nwalkers):
p0.append(avg+numpy.random.uniform(-.5,.5,size=npar)*numpy.array([0.005,.1,.1,.1,.1,.1,.2]))
sampler = emcee.EnsembleSampler(nwalkers, npar, lnprob, args=(1-lnFI, found, foundpm, invH))
sampler.run_mcmc(p0, 50000, thin=50)
output = open('emcee.pkl','wb')
chain = numpy.array(sampler.chain)
pickle.dump([chain,minlogFI, maxlogFI], output)
output.close()
def savedata():
import glob
zp=31.4
dirname = '/Users/akim/Downloads/DESY1_forcePhoto_fake_snana_text/des_fake_*.dat'
indeces = [2, 4, 5, 6, 7, 9, 16]
filts=numpy.array(['g','r','i','z'])
# VARLIST: MJD FLT FIELD FLUXCAL FLUXCALERR PHOTFLAG PHOTPROB ZPFLUX PSF SKYSIG
# SKYSIG_T GAIN XPIX YPIX SIM_MAGOBS MASKFRAC NITE
data = []
for fn in glob.glob(dirname):
# print fn
with open(fn) as f:
# f.readline()
# varnames= f.readline().split()
# varnames = numpy.array(varnames)
# print varnames[indeces]
for line in f:
if line.strip():
values = line.split()
values = numpy.array(values)
if values[0] == 'HOSTGAL_SB_FLUXCAL:':
galsb=values[1:5]
galsb=galsb.astype('float')
galsbneg = numpy.logical_and(galsb >-800,galsb<=0)
galsb[galsbneg]=1e-12
galsbpos = galsb>0
galsb[galsbpos] = -2.5*numpy.log10(galsb[galsbpos]) + zp
# galsb = -2.5*numpy.log10(galsb)+zp
# print galsb
if values[0] == 'OBS:':
mfrac = float(values[indeces[6]])
emask = int(values[indeces[3]])
gsb = galsb[filts == values[indeces[0]]][0]
if mfrac >= 0 and mfrac <= 0.1 and (emask & 240)==0 and gsb > -800:
temp = [values[indeces[0]],values[indeces[5]],gsb, values[indeces[1]],values[indeces[2]],values[indeces[3]],values[indeces[4]],values[indeces[6]]]
data.append(numpy.array(temp))
# # data = []
filename = 'Y1reprocFakes/FAKEMATCH_ALL.OUT'
indeces = [3, 18, 31, 50, 51, 52, 53, 24]
with open(filename) as f:
f.readline()
varnames= f.readline().split()
varnames = numpy.array(varnames)
# print varnames[indeces]
for line in f:
if line.strip():
values = line.split()
values = numpy.array(values)
values = values[indeces]
data.append(values)
data = numpy.array(data)
band= data[:, 0]
psf= data[:, 1].astype('float')
galsb = data[:, 2].astype('float')
flux = data[:, 3].astype('float')
fluxerr = data[:, 4].astype('float')
errmask = data[:, 5].astype('int')
autoscan = data[:,6].astype('float')
maskfrac = data[:,7].astype('float')
bad = numpy.bitwise_and(errmask, 240)
good = numpy.where(numpy.logical_and(bad ==0, maskfrac<0.1))[0]
ston = flux[good]/fluxerr[good]
galflux = numpy.pi*psf[good]**2 * 10**(-0.4*(galsb[good]-zp))
fracincrease = flux[good]/galflux
galsb = galsb[good]
found = autoscan[good] >=0.5
import pickle
output = open('data.pkl', 'wb')
pickle.dump([ston, fracincrease, found],output)
output.close()
def plotdata(ston, fracincrease, found):
lnFI = numpy.log(fracincrease)
# lnFImin=lnFI.min()
# lnFImax=lnFI.max()
with PdfPages('multipage_pdf.pdf') as pdf:
xedges = numpy.arange(2,20.5,1)
yedges = 0.1*(2**numpy.arange(8)) #(2**numpy.arange(6))/8. #numpy.arange(0,210,50)
H, _, _ = numpy.histogram2d(ston, fracincrease, bins=[xedges, yedges])
xticks = (0.5*(xedges+numpy.roll(xedges,-1)))[:-1]
yticks = (0.5*(yedges+numpy.roll(yedges,-1)))[:-1]
# plt.hist2d(ston, numpy.log(fracincrease), bins=[xedges, numpy.log(yedges)])
# plt.colorbar()
# plt.title("Number of Fakes Per Bin")
# plt.xlabel('STON')
# plt.ylabel('log(Fraction Flux Increase)')
# pdf.savefig()
# plt.close()
eff = numpy.array(H)
effston = numpy.zeros(len(xedges)-1)
eff[:]=0.
for i in xrange(len(xedges)-1):
inx = numpy.logical_and(ston > xedges[i], ston < xedges[i+1])
if inx.sum() !=0:
effston[i] = 1.*found[inx].sum() / inx.sum()
for j in xrange(len(yedges)-1):
iny = numpy.logical_and(fracincrease > yedges[j], fracincrease <= yedges[j+1])
if H[i,j] !=0:
inxy = numpy.logical_and(inx, iny)
eff[i,j] = found[inxy].sum()/H[i,j]
pars, fit, minlnFI, maxlnFI = loadpars()
# pars[0]=pars[0]/1.4
# pars[1]=pars[1]*1.2
# print pars
# pars = numpy.array([ .987 , 1.5 , 4. , 4. , .8 , 1.3 , 1])
# pars[0]=0.985
# pars[2]=3.5
# pars[3]=3.5
# pars[4]=1.4
# pars[5]=0.8
for i in xrange(len(yedges)-1):
plt.plot(xticks, eff[:,i], label="Frac. Inc. [{:.2f}, {:.2f}]".format(yedges[i],yedges[i+1]))
# dum=(pars[0]+(pars[1]-pars[0])*logyticks[i])*numpy.tanh((xticks-(pars[2]+(pars[3]-pars[2])*logyticks[i]))/(pars[4]+(pars[5]-pars[4])*logyticks[i]))
dum = effmodel(xticks, yticks[i], pars, minlnFI, maxlnFI)
plt.plot(xticks, dum, linestyle=':',linewidth=3)
# plt.plot(xticks, effston, label='all')
plt.xlabel('STON')
plt.ylabel('eff')
# plt.yscale('log')
# plt.title('Fraction Increase in Seeing Disk')
plt.ylim([0.1,1.05])
plt.legend(loc=4)
plt.show()
# pdf.savefig()
# plt.close()
# xedges = numpy.arange(2,20.5,1)
# yedges = numpy.arange(17,28,2)
# H, xedges, yedges = numpy.histogram2d(ston, galsb, bins=[xedges, yedges])
# eff = numpy.array(H)
# effston = numpy.zeros(len(xedges)-1)
# eff[:]=0.
# for i in xrange(len(xedges)-1):
# inx = numpy.logical_and(ston > xedges[i], ston < xedges[i+1])
# if inx.sum() !=0:
# effston[i] = 1.*found[inx].sum() / inx.sum()
# for j in xrange(len(yedges)-1):
# iny = numpy.logical_and(galsb > yedges[j], galsb <= yedges[j+1])
# if H[i,j] !=0:
# inxy = numpy.logical_and(inx, iny)
# eff[i,j] = found[inxy].sum()/H[i,j]
# xticks = (0.5*(xedges+numpy.roll(xedges,-1)))[:-1]
# for i in xrange(len(yedges)-1):
# plt.plot(xticks, eff[:,i], label="[{:.1f}, {:.1f}]".format(yedges[i],yedges[i+1]))
# plt.plot(xticks, effston, label='all')
# plt.ylim([0,1.05])
# plt.xlabel('STON')
# plt.ylabel('eff')
# plt.title('Host Surface Brightness')
# plt.legend(loc=4)
# pdf.savefig()
# plt.close()
fit_emcee()
# surfaceplot()
# fit()
# wefwe
# lookatfit()
from matplotlib import colors, ticker, cm
from matplotlib.ticker import FormatStrFormatter
input = open('data.pkl', 'rb')
[ston, fracincrease, found]=pickle.load(input)
input.close()
use = numpy.logical_and(fracincrease > 0.1, fracincrease < 10)
use = numpy.logical_and(use, ston > 4)
ston = ston[use]
fracincrease = fracincrease[use]
found = found[use]
plotdata(ston, fracincrease, found)
# xedges = stonmin * (2**numpy.arange(0,2,.05)) #numpy.arange(2,20.5,1)
# yedges = fracincrease_min*(2**numpy.arange(0,8,.1)) #(4**numpy.arange(6))/64. #numpy.arange(0,210,50)
# fracincrease = numpy.exp(lnFI*(maxlogFI-minlogFI) + minlogFI)
# H, _,_ = numpy.histogram2d(ston, fracincrease, bins=[xedges, yedges])
# xbin = numpy.digitize(ston,xedges)
# ybin = numpy.digitize(fracincrease,yedges)
# eff = numpy.array(H)
# eff[:,:]=0.
# for i in xrange(len(found)):
# eff[xbin[i]-1,ybin[i]-1] += found[i]
# eff /= H
# xticks = (0.5*(xedges+numpy.roll(xedges,-1)))[:-1]
# yticks = (0.5*(yedges+numpy.roll(yedges,-1)))[:-1]
# X, Y = numpy.meshgrid(yticks, xticks)
# fig1, ax1 = plt.subplots()
# # cs = ax1.imshow(X, Y, totaleff,norm=colors.LogNorm(vmin=0.9, vmax=1),interpolation='nearest')
# cs = ax1.pcolor(X, Y, eff,norm=colors.LogNorm(vmin=0.9, vmax=1),rasterized=True)
# ax1.set_xlabel('STON')
# ax1.set_ylabel('Fraction Increase')
# ax1.set_yscale('log')
# ax1.yaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
# ax1.set_ylim((0.3,10))
# ax1.set_xscale('log')
# ax1.set_xlim((4,10))
# ax1.xaxis.set_minor_formatter(FormatStrFormatter("%.1f"))
# ax1.set_title('Discovery Efficiency Given Three Opportunities')
# cbar = fig1.colorbar(cs, ax=ax1)
# cbar.set_ticks(numpy.arange(0.9,1.01,.01),update_ticks=True)
# cbar.set_ticklabels(numpy.arange(0.9,1.01,.01),update_ticks=True)
# cbar.set_label('Discovery Efficiency')
# plt.show()