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LoopSEDLikelihood.py
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LoopSEDLikelihood.py
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#!/usr/bin/env python
import sys
#import os
import numpy as np
from scipy.stats import poisson
#import yaml
#import datetime
from array import array
import math
from math import cos, sin, tan, acos, asin, atan, radians, degrees, pi
import click
import ROOT
from ROOT import gROOT, gDirectory, gPad, gSystem, gStyle, kTRUE, kFALSE, TH1, TH2
ROOT.gROOT.SetBatch()
from pColor import *
#from pMETandMJD import *
import ModelPointSource
import ExtrapolateFlux
def LoopSEDLikelihood(name, observed, tpl_dnde_log, tpl_index, eref, ra, dec, king, livetime, suffix, nside, redshift, addregular, dnde_pri_min, dnde_pri_max, idx_pri_min, idx_pri_max, binw=0.294, enr_min=4.00, nbin=4, t_start=770., t_stop=8233., nmaxevt_ebin=20):
#t_stop will be included in the time window.
TPL_CLASS = ('CalOnlyR100',) #, 'CalOnlyR30', 'CalOnlyR10')
NREBIN = 1
#SCALE_FLUX = 1.0e-13
prob_skip = 1.0E-5
FILE_IN = ROOT.TFile(observed, 'READ')
HTG_OBS = FILE_IN.Get('spectrum_observed')
print HTG_OBS, 'has been found.'
HTG_OBS_ENR = HTG_OBS.ProjectionY('{0}_projEnr'.format(HTG_OBS.GetName()), HTG_OBS.GetXaxis().FindBin(t_start), HTG_OBS.GetXaxis().FindBin(t_stop))
nobs = HTG_OBS_ENR.Integral(HTG_OBS_ENR.GetXaxis().FindBin(enr_min), HTG_OBS_ENR.GetXaxis().FindBin(enr_min+binw*nbin))
print nobs, 'observed events.'
HTG_OBS_ENR.Rebin(NREBIN)
HTG_OBS_ENR.SetLineWidth(0)
HTG_OBS_ENR.SetLineColor(ROOT.kRed)
HTG_OBS_ENR.SetMarkerColor(ROOT.kRed)
HTG_OBS_ENR.SetMarkerStyle(20)
HTG_OBS_ENR.SetFillStyle(0)
PATH_FILE_OUT = 'LoopLikelihood_{0}{1}'.format(name, suffix)
FILE_OUT = ROOT.TFile('{0}.root'.format(PATH_FILE_OUT), 'RECREATE')
FILE_OUT.cd()
# Histogram for results
#xaxis = array('d', tpl_dnde)
xaxis = np.array(tpl_dnde_log+(2.*tpl_dnde_log[-1]-tpl_dnde_log[-2],), dtype=float)
#xaxis_scaled = xaxis/SCALE_FLUX
#yaxis = array('d', tpl_index)
yaxis = np.array(tpl_index+(2.*tpl_index[-1]-tpl_index[-2],), dtype=float)
dct_htg_likeresult = {}
dct_htg_likeratio = {}
dct_htg_likerfrac = {}
dct_htg_unlikerfrac = {}
dct_htg_likecoverage = {}
dct_cvs_likeresult = {}
dct_cvs_likeratio = {}
likelihood_max = {}
xlocmax = {}
ylocmax = {}
for cla in TPL_CLASS:
#dct_htg_likeresult[cla] = ROOT.TH2D('htg_likeresult', 'Likelihood;log_{{10}}dN/dE at {0:1.2e} MeV;PWL index'.format(eref), len(tpl_dnde_log), xaxis, len(tpl_index), yaxis)
dct_htg_likeresult[cla] = ROOT.TGraph2D()
dct_htg_likeresult[cla].SetName('htg_likeresult')
dct_htg_likeresult[cla].SetTitle('Likelihood for data')
dct_htg_likeresult[cla].GetXaxis().SetTitle('log_{{10}}dN/dE at {0:1.2e} MeV')
dct_htg_likeresult[cla].GetYaxis().SetTitle('PWL index'.format(eref))
# dct_htg_likeratio[cla] = ROOT.TH2D('htg_likeratio', 'Likelihood Ratio;log_{{10}}dN/dE at {0:1.2e} MeV;PWL index'.format(eref), len(tpl_dnde_log), xaxis, len(tpl_index), yaxis)
dct_htg_likeratio[cla] = ROOT.TGraph2D()
dct_htg_likeratio[cla].SetName('htg_likeratio')
dct_htg_likeratio[cla].SetTitle('Likelihood ratio with physically possible ideal case')
dct_htg_likeratio[cla].GetXaxis().SetTitle('log_{{10}}dN/dE at {0:1.2e} MeV')
dct_htg_likeratio[cla].GetYaxis().SetTitle('PWL index'.format(eref))
dct_htg_likerfrac[cla] = ROOT.TGraph2D()
dct_htg_likerfrac[cla].SetName('htg_likerfrac')
dct_htg_likerfrac[cla].SetTitle('Fraction of cases liker than data')
dct_htg_likerfrac[cla].GetXaxis().SetTitle('log_{{10}}dN/dE at {0:1.2e} MeV')
dct_htg_likerfrac[cla].GetYaxis().SetTitle('PWL index'.format(eref))
dct_htg_unlikerfrac[cla] = ROOT.TGraph2D()
dct_htg_unlikerfrac[cla].SetName('htg_unlikerfrac')
dct_htg_unlikerfrac[cla].SetTitle('Fraction of cases unliker than data')
dct_htg_unlikerfrac[cla].GetXaxis().SetTitle('log_{{10}}dN/dE at {0:1.2e} MeV')
dct_htg_unlikerfrac[cla].GetYaxis().SetTitle('PWL index'.format(eref))
dct_htg_likecoverage[cla] = ROOT.TGraph2D()
dct_htg_likecoverage[cla].SetName('htg_likecoverage')
dct_htg_likecoverage[cla].SetTitle('Fraction of cases covered by calculation')
dct_htg_likecoverage[cla].GetXaxis().SetTitle('log_{{10}}dN/dE at {0:1.2e} MeV')
dct_htg_likecoverage[cla].GetYaxis().SetTitle('PWL index'.format(eref))
dct_cvs_likeresult[cla] = ROOT.TCanvas('cvs_likeresult_{0}'.format(cla), '{0} Likelihood'.format(cla), 750, 750)
dct_cvs_likeratio[cla] = ROOT.TCanvas('cvs_likeratio_{0}'.format(cla), '{0} Likelihood Ratio'.format(cla), 750, 750)
likelihood_max[cla] = 0.0
xlocmax[cla] = 0.0
ylocmax[cla] = 0.0
likelihood_ceil = math.exp(-HTG_OBS_ENR.Integral())
for ienr in range(1, HTG_OBS_ENR.GetNbinsX()+1):
ni = HTG_OBS_ENR.GetBinContent(ienr)
likelihood_ceil = likelihood_ceil * math.pow(ni, ni)/math.factorial(ni)
print 'Ideal maximum likelihood =', likelihood_ceil
# Possible ideal likelihood (independent for model)
nda_likelihood_bestpossible = []
nda_likelihood_best_directprod = np.ones(nmaxevt_ebin)
for ienr in range(1, HTG_OBS_ENR.GetNbinsX()+1):
print 'Energy range (observed): 10^{0} - 10^{1}'.format(HTG_OBS_ENR.GetXaxis().GetBinLowEdge(ienr), HTG_OBS_ENR.GetXaxis().GetBinUpEdge(ienr))
nda_likelihood_bestpossible.append(np.ones(nmaxevt_ebin))
for mevt in range(nmaxevt_ebin):
nda_likelihood_bestpossible[-1][mevt] = nda_likelihood_bestpossible[-1][mevt] * math.exp(-mevt)*math.pow(mevt, mevt)/math.factorial(mevt)
# Make a direct product array
nda_likelihood_bestpossible_t = nda_likelihood_bestpossible[-1] # Transposing matrix
if ienr>1:
for jenr in range(ienr-1):
nda_likelihood_bestpossible_t = nda_likelihood_bestpossible_t[:, np.newaxis]
nda_likelihood_best_directprod = nda_likelihood_best_directprod * nda_likelihood_bestpossible_t # Broadcasting of np array
#print nda_likelihood_best_directprod
print 'Likelihood of physically ideal cases:'
print nda_likelihood_best_directprod # This array's indeces are corresponding to observable count for each energy bin
# Loop over dN/dE and PWL-index
for (ix, dnde_log) in enumerate(tpl_dnde_log):
dnde = 10**dnde_log
print '===================='
print 'dN/dE = {0:1.2e} at {1:1.1e} MeV'.format(dnde, eref)
for (iy, idx_pl) in enumerate(tpl_index):
print '--------------------'
print 'PWL index = {0}'.format(idx_pl)
lst_flux_itgl = ExtrapolateFlux.ExtrapolateFlux(eref, dnde, idx_pl, binw, enr_min, nbin, redshift)
htg_flux = ROOT.TH1D('htg_flux', 'Integral flux', nbin, enr_min, enr_min+nbin*binw)
for ibin in range(1, htg_flux.GetNbinsX()+1):
htg_flux.SetBinContent(ibin, lst_flux_itgl[ibin-1])
htg_flux.SetBinError(ibin, 0)
str_fp = 'dNdE{0:0>12d}_PWL{1}{2:0>3d}'.format(int(dnde*1e20+0.5), "n" if idx_pl<0 else "p", int(idx_pl*100+0.5))
suffix_fp = suffix + str_fp
dct_htg_model = ModelPointSource.ModelPointSource(name, htg_flux, ra, dec, king, livetime, suffix_fp, nside, addregular)
print dct_htg_model
hs = ROOT.THStack('spectrum_{0}'.format(str_fp), 'log_{{10}}dN/dE={0:.2f} at {1} MeV, PWL-index={2:+f};log_{{10}}Energy [MeV];[counts]'.format(dnde_log, eref, idx_pl))
hs.Add(HTG_OBS_ENR)
for (icla,cla) in enumerate(TPL_CLASS):
print cla
htg_model = dct_htg_model[cla]
htg_model.Rebin(NREBIN)
htg_model.SetLineWidth(2)
htg_model.SetLineColor(ROOT.kGray)
htg_model.SetLineStyle(icla+1)
htg_model.SetMarkerColor(ROOT.kGray)
hs.Add(htg_model)
factor_expected_total = math.exp(-htg_model.Integral())
likelihood_data = factor_expected_total
likelihood_data_highcut = poisson.cdf(nobs-1, htg_model.Integral())
likelihood_data_lowcut = poisson.sf(nobs-1, htg_model.Integral())
if likelihood_data_highcut<prob_skip or likelihood_data_lowcut<prob_skip:
print 'Detaction probability of', nobs, 'events is smaller than', min(likelihood_data_highcut, likelihood_data_lowcut)*100, '%.'
print 'Calculation is skipped...'
dct_htg_likeresult[cla].SetPoint(dct_htg_likeresult[cla].GetN(), dnde_log, idx_pl, 0.)
dct_htg_likeratio[cla].SetPoint(dct_htg_likeratio[cla].GetN(), dnde_log, idx_pl, 0.)
dct_htg_likerfrac[cla].SetPoint(dct_htg_likerfrac[cla].GetN(), dnde_log, idx_pl, 1.-prob_skip)
dct_htg_unlikerfrac[cla].SetPoint(dct_htg_unlikerfrac[cla].GetN(), dnde_log, idx_pl, 0.+prob_skip)
dct_htg_likecoverage[cla].SetPoint(dct_htg_likecoverage[cla].GetN(), dnde_log, idx_pl, 1.-prob_skip)
continue
nda_likelihood_allpossible = []
nda_likelihood_allpossible_t = []
nda_likelihood_all_directprod = np.ones(nmaxevt_ebin)
for ienr in range(1, htg_model.GetNbinsX()+1):
print 'Energy range (model): 10^{0} - 10^{1}'.format(htg_model.GetXaxis().GetBinLowEdge(ienr), htg_model.GetXaxis().GetBinUpEdge(ienr))
print 'Energy range (observed): 10^{0} - 10^{1}'.format(HTG_OBS_ENR.GetXaxis().GetBinLowEdge(ienr), HTG_OBS_ENR.GetXaxis().GetBinUpEdge(ienr))
mi = htg_model.GetBinContent(ienr)
ni = HTG_OBS_ENR.GetBinContent(ienr)
likelihood_data = likelihood_data * math.pow(mi, ni)/math.factorial(ni)
# For likelihood RATIO ordering
nda_likelihood_allpossible.append(np.ones(nmaxevt_ebin))
for mevt in range(nmaxevt_ebin):
nda_likelihood_allpossible[-1][mevt] = nda_likelihood_allpossible[-1][mevt] * math.pow(mi, mevt)/math.factorial(mevt)
# Make a direct product array
nda_likelihood_allpossible_t = nda_likelihood_allpossible[-1] # Transposing matrix
if ienr>1:
for jenr in range(ienr-1):
nda_likelihood_allpossible_t = nda_likelihood_allpossible_t[:, np.newaxis]
nda_likelihood_all_directprod = nda_likelihood_all_directprod * nda_likelihood_allpossible_t # Broadcasting of np array
#print nda_likelihood_all_directprod
print 'Likelihood of model and data =', likelihood_data
nda_likelihood_all_directprod = nda_likelihood_all_directprod * factor_expected_total
print 'Possible likelihood values:'
print nda_likelihood_all_directprod # Array indeces are corresponding to observable count for each energy bin
nda_likelihood_ratio_directprod = nda_likelihood_all_directprod / nda_likelihood_best_directprod
print 'Possible likelihood ratio:'
print nda_likelihood_ratio_directprod
likelihood_ratio_data = likelihood_data / likelihood_ceil
fprob_liker = 0.
fprob_unliker = 0.
for itpl, rvalue in enumerate(nda_likelihood_ratio_directprod.flat):
if rvalue > likelihood_ratio_data:
fprob_liker+=nda_likelihood_all_directprod.flat[itpl]
else:
fprob_unliker+=nda_likelihood_all_directprod.flat[itpl]
print 'Data is', fprob_liker*100., '% likest case and excluded from acceptance interval by', fprob_unliker*100., '%.'
fprob_coverage = fprob_liker + fprob_unliker
print 'Calculation covers', fprob_coverage*100., '% of total possibility.'
dct_htg_likeresult[cla].SetPoint(dct_htg_likeresult[cla].GetN(), dnde_log, idx_pl, likelihood_data)
dct_htg_likeratio[cla].SetPoint(dct_htg_likeratio[cla].GetN(), dnde_log, idx_pl, likelihood_data/likelihood_ceil)
dct_htg_likerfrac[cla].SetPoint(dct_htg_likerfrac[cla].GetN(), dnde_log, idx_pl, fprob_liker)
dct_htg_unlikerfrac[cla].SetPoint(dct_htg_unlikerfrac[cla].GetN(), dnde_log, idx_pl, fprob_unliker)
dct_htg_likecoverage[cla].SetPoint(dct_htg_likecoverage[cla].GetN(), dnde_log, idx_pl, fprob_liker+fprob_unliker)
if likelihood_data>likelihood_max[cla]:
likelihood_max[cla] = likelihood_data
xlocmax[cla] = dnde_log
ylocmax[cla] = idx_pl
FILE_OUT.cd()
hs.Write()
del dct_htg_model
del htg_flux
FILE_OUT.cd()
#likelihood_max = 0.0
#likelihood_temp = 0.0
#xlocmax = ROOT.Long()
#ylocmax = ROOT.Long()
#zlocmax = ROOT.Long()
for cla in TPL_CLASS:
dct_htg_likeresult[cla].Write()
dct_htg_likeratio[cla].Write()
dct_htg_likerfrac[cla].Write()
dct_htg_unlikerfrac[cla].Write()
dct_htg_likecoverage[cla].Write()
dct_cvs_likeresult[cla].cd()
dct_cvs_likeresult[cla].SetLogz()
dct_htg_likeresult[cla].Draw("colz")
#likelihood_max = dct_htg_likeresult[cla].GetMaximum()
#dct_htg_likeresult[cla].GetMaximumBin(xlocmax, ylocmax, zlocmax)
print '===== Maximum likelihood ====='
print 'dNdE =', xlocmax[cla], 'at', eref, 'MeV'
print 'PWL-index =', ylocmax[cla]
dct_htg_likeresult[cla].GetZaxis().SetRangeUser(0.001*likelihood_max[cla], likelihood_max[cla])
dct_cvs_likeresult[cla].Write()
dct_cvs_likeratio[cla].cd()
dct_cvs_likeratio[cla].SetLogz()
dct_htg_likeratio[cla].Draw("colz")
dct_htg_likeratio[cla].GetZaxis().SetRangeUser(0.05, 0.68)
dct_cvs_likeratio[cla].Write()
return dct_htg_likeresult
@click.command()
@click.argument('name', type=str)
@click.argument('ra', type=float)
@click.argument('dec', type=float)
@click.argument('observed', type=str)
@click.argument('livetime', type=str)
@click.option('--king', type=str, default='/nfs/farm/g/glast/u/mtakahas/v20r09p09_G1haB1/Dispersion/AG_dispersion.root')
@click.option('--suffix', '-s', type=str, default='')
@click.option('--nside', '-n', type=int, default=256)
@click.option('--addregular', type=str, default=None, help='Set a file containing htgEaRegular')
@click.option('--redshift', '-z', type=float, default=0.)
@click.option('--dndeprimin', type=float, default=-20.)
@click.option('--dndeprimax', type=float, default=-10.)
@click.option('--indexprimin', type=float, default=-3.)
@click.option('--indexprimax', type=float, default=6.)
def main(name, ra, dec, observed, king, livetime, suffix, nside, redshift, addregular, dndeprimin, dndeprimax, indexprimin, indexprimax):
FACTOR_RANGE_INDEX = 10
INDEX_MIN = int(-3.0*FACTOR_RANGE_INDEX-0.5)
INDEX_MAX = int(3.0*FACTOR_RANGE_INDEX+0.5)
INDEX_STEP = int(0.2*FACTOR_RANGE_INDEX+0.5)
TPL_INDEX = tuple([ float(x)/FACTOR_RANGE_INDEX for x in range(INDEX_MIN, INDEX_MAX+1, INDEX_STEP)])
FACTOR_RANGE_FLUX = 10
FLUX_LOG_MIN = int(-20.0*FACTOR_RANGE_FLUX-0.5)
FLUX_LOG_MAX = int(-10.0*FACTOR_RANGE_FLUX-0.5)
FLUX_LOG_STEP = int(0.2*FACTOR_RANGE_FLUX+0.5)
#TPL_FLUX = tuple([ 10**(float(x)/FACTOR_RANGE_FLUX) for x in range(FLUX_LOG_MIN, FLUX_LOG_MAX+1, FLUX_LOG_STEP)])
TPL_FLUX = tuple([ float(x)/FACTOR_RANGE_FLUX for x in range(FLUX_LOG_MIN, FLUX_LOG_MAX+1, FLUX_LOG_STEP)]) # In log scale
if suffix!="":
suffix = "_" + suffix
LoopSEDLikelihood(name, observed, TPL_FLUX, TPL_INDEX, 10000., ra, dec, king, livetime, suffix, nside, redshift, addregular, dndeprimin, dndeprimax, indexprimin, indexprimax)
if __name__ == '__main__':
main()