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QuantileGRBs.py
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QuantileGRBs.py
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#!/usr/bin/env python
import sys
import os
import numpy as np
import pandas as pd
import click
import ReadLATCatalogueInfo
#import ReadLTFCatalogueInfo
import ReadGBMCatalogueInfo
import FindGoodstatPeriods
import pickle_utilities
import pIntegratePowerlawLightcurve
##### PATH of Catalogue #####
GRB_CATALOGUE_LTF = '/nfs/farm/g/glast/u/mtakahas/FermiAnalysis/GRB/Regualr/catalogue/LAT2CATALOG-v1-LTF.fits'
def quantile_grbs(output, longonly, tolerr, suffix):
str_output = '{lo}{te}{suf}'.format(lo='_longonly' if longonly==True else '', te='tolerr'.format(int(tolerr*10)) if tolerr<180 else '', suf='_'+suffix if suffix!='' else suffix)
tb_lat = ReadLATCatalogueInfo.open_table()
tb_gbm = ReadGBMCatalogueInfo.open_table()
lst_lat = ReadLATCatalogueInfo.read_all(tb_lat, tb_gbm)
lst_lat = ReadLATCatalogueInfo.select_gbm_exist(lst_lat)
if longonly==True:
lst_lat = ReadLATCatalogueInfo.select_long(lst_lat)
lst_lat = ReadLATCatalogueInfo.select_small_error(lst_lat, tolerr)
grbs_skipped = ('130427324', '150314205') #, '141022087'
ngrb_lat = len(lst_lat)-len(grbs_skipped)
print 'Number of GRBs:', ngrb_lat
charas = ['GRBNAME', 'GBM_NAME', 'GBM_T90', 'GBM_T90_START', 'GBM_FLUENCE', 'GBM_FLUX_1024', 'GBM_FLUX_64', 'RA', 'DEC', 'NCOUNTS', 'LC_INDEX']
arrays = {}
for chara in charas:
if chara in ('GRBNAME', 'GBM_NAME'):
arrays[chara] = np.chararray(ngrb_lat, itemsize=9 if chara=='GRBNAME' else 12)
else:
arrays[chara] = np.zeros(ngrb_lat)
#spec_index = np.zeros(ngrb_lat)
#flux_gev = np.zeros(ngrb_lat)
#lc_index = np.zeros(ngrb_lat)
nevent = 0
nevent_validlc = 0
lc_phases = ('prompt', 'T95to03ks', '03ksto100ks')
integrated_lc = {}
scaled_lc = {}
lc_indices = (-1.0, -1.3)
jgrb = 0
for igrb, grb_lat in enumerate(lst_lat):
print 'No.{0} {1}'.format(igrb, grb_lat['GRBNAME'])
if grb_lat['GRBNAME'] in grbs_skipped:
print 'Skipped...'
continue
path_lc_fit = '/nfs/farm/g/glast/u/mtakahas/FermiAnalysis/GRB/Regualr/HighestFluenceGRBs/LatAlone/{name}/E0000100-0100000MeV/r12deg/lightcurve/LightCurve_{name}_indexfreeLAT_N10in2deg_fit.pickle'.format(name=grb_lat['GRBNAME'])
lc_fit = pickle_utilities.load(path_lc_fit)
path_gtifile = '{name}/E0000100-0100000MeV/r12deg/unified/PowerLaw2/Unbinned/GRB{name}_P8_P302_BASE_T00-999-101000_r030_ft1_filtered_gti.fits'.format(name=grb_lat['GRBNAME'])
integrated_lc[grb_lat['GRBNAME']] = pIntegratePowerlawLightcurve.integrate_time_phases(grb_lat['GRBNAME'], path_gtifile, indices=lc_indices, tpl_phases=lc_phases)
for chara in charas:
if chara=='NCOUNTS':
arrays[chara][jgrb] = FindGoodstatPeriods.get_entries_roi(path_gtifile, None, None, rlim=5.0, ra=grb_lat['RA'], dec=grb_lat['DEC'])
elif chara[:4]=='GBM_':
arrays[chara][jgrb] = grb_lat['GBM'][chara[4:]]
elif chara=='LC_INDEX':
if len(lc_fit['fit']['flux']['lightcurve'].keys())>0:
arrays[chara][jgrb] = lc_fit['fit']['flux']['lightcurve']['index']['value']
#if not grb_lat['GRBNAME'] in grbs_skipped:
nevent_validlc += arrays['NCOUNTS'][jgrb]
else:
arrays[chara][jgrb] = 0
else:
arrays[chara][jgrb] = grb_lat[chara]
#if not grb_lat['GRBNAME'] in grbs_skipped:
nevent += arrays['NCOUNTS'][jgrb]
# print ' Skipping this GRB...'
# continue
#ncounts_validlc = np.zeros_like(gbm_fluence)
#ncounts_validintermittent = np.zeros_like(gbm_fluence)
#ncounts_validaf = np.zeros_like(gbm_fluence)
#afterglow = pickle_utilities.load('/u/gl/mtakahas/work/FermiAnalysis/GRB/Regualr/HighestFluenceGRBs/LatAlone/{name}/Summary_{name}_afterglow_Eth100MeV_r12deg.pickle'.format(name=grb))
#if afterglow['lower_energies']['TS']>0:
# ncounts_validaf[jgrb] = ncounts[jgrb]
# spec_index[jgrb] = afterglow['lower_energies']['Index']['value']
# flux_gev[jgrb] = afterglow['lower_energies']['flux']['value']
#else:
# ncounts_validaf[jgrb] = 0
# spec_index[jgrb] = 0
# flux_gev[jgrb] = 0
#if (gbm_t90_start[jgrb]+gbm_t90[jgrb])-(gbm_t50_start[jgrb]+1.5*gbm_t50[jgrb])>=10:
# ncounts_validintermittent[jgrb] = ncounts[jgrb]
#else:
# ncounts_validintermittent[jgrb] = 0
#lc = pickle_utilities.load('/u/gl/mtakahas/work/FermiAnalysis/GRB/Regualr/HighestFluenceGRBs/LatAlone/{name}/E0000100-0100000MeV/r12deg/lightcurve/LightCurve_{name}_indexfree_fit.pickle'.format(name=grb))
#if 'index' in lc['fit']['flux']['lightcurve'] and lc['fit']['flux']['lightcurve']['index']['value']==lc['fit']['flux']['lightcurve']['index']['value']:
# lc_index[jgrb] = lc['fit']['flux']['lightcurve']['index']['value']
# ncounts_validlc[jgrb] = ncounts[jgrb]
#else:
# lc_index[jgrb] = 0
# ncounts_validlc[jgrb] = 0
#print ' {0} events'.format(ncounts[jgrb])
jgrb += 1
#nevent = sum(arrays['NCOUNTS'])
nevent_onethird = nevent/3.
nevent_validlc_onehalf = nevent_validlc/2.
#nevent_validlc = sum(ncounts_validlc)
#nevent_validlc_onehalf = nevent_validlc/2.
#nevent_validintermittent = sum(ncounts_validintermittent)
#nevent_validintermittent_onehalf = nevent_validintermittent/2.
#nevent_validaf = sum(ncounts_validaf)
#nevent_validaf_onethird = nevent_validaf/3.
categories = {}
gbm_fluence_category_sum = {}
fluence_scaled_sum = {}
#gbm_fluence_category_sum_err = {}
charas_quant = ('GBM_T90', 'GBM_FLUENCE', 'GBM_FLUX_1024', 'GBM_FLUX_64', 'LC_INDEX')
for chara in charas_quant:
categories[chara] = [[], [], []]
gbm_fluence_category_sum[chara] = [{'value':0, 'error':0}, {'value':0, 'error':0}, {'value':0, 'error':0}]
fluence_scaled_sum[chara] = [np.zeros((len(lc_indices), len(lc_phases))),np.zeros((len(lc_indices), len(lc_phases))),np.zeros((len(lc_indices), len(lc_phases)))]
# categories = {'gbm_t90':[[], [], []],
# 'lat_count':[[], [], []],
# 'gbm_fluence':[[], [], []],
# 'gbm_flux1024':[[], [], []],
# 'gbm_flux64':[[], [], []]
# #'epeak_band':[[], [], []],
# #'gbm_intermittent':[[], []],
# #'gbm_fluence_per_t50':[[], [], []],
# #'spec_index':[[], [], []],
# #'flux_gev':[[], [], []],
# #'lc_index':[[], [], []]
# }
dct_df = {}
for chara in charas:
dct_df[chara] = arrays[chara]
df = pd.DataFrame(dct_df)
# df = pd.DataFrame({ 'name' : names,
# #'redshift' : redshift,
# 'gbm_t90' : gbm_t90,
# 'gbm_t90_start' : gbm_t90_start,
# 'gbm_t50' : gbm_t50,
# 'gbm_t50_start' : gbm_t50_start,
# 'gbm_flux1024': gbm_flux1024,
# 'gbm_flux64': gbm_flux64,
# #'epeak_band': epeak_band,
# #'gbm_intermittent' : (gbm_t90_start+gbm_t90)-(gbm_t50_start+1.5*gbm_t50),
# #'gbm_fluence_per_t50': gbm_fluence/gbm_t50,
# 'lat_count' : ncounts,
# 'gbm_fluence' : gbm_fluence
# #'spec_index' : spec_index,
# #'flux_gev' : flux_gev,
# #'lc_index': lc_index
# })
# GBM fluence, T90
for col in charas_quant: #('gbm_fluence', 'gbm_t90', 'gbm_flux1024', 'gbm_flux64'): #, 'spec_index', 'flux_gev', 'epeak_band', 'gbm_fluence_per_t50'):
print '#####', col, '#####'
df_sorted = df.sort_values(by=col, ascending=True if col in ('LC_INDEX') else False)
df_sorted.reset_index( drop = True, inplace=True )
print df_sorted
mcount = 0
print '----- Category 1 -----'
for irow in range(len(df_sorted.index)):
name = df_sorted.loc[irow,['GRBNAME']][0]
if name in grbs_skipped:
print ' Skipping this GRB...'
continue
lst_lat_one = ReadLATCatalogueInfo.select_one_by_name(tb_lat, name, tb_gbm)
gbm_fluence_value = lst_lat_one['GBM']['FLUENCE']
gbm_fluence_error = lst_lat_one['GBM']['FLUENCE_ERROR']
scaled_lc[name] = integrated_lc[name]*gbm_fluence_value
lat_count = df_sorted.loc[irow,['NCOUNTS']][0]
if col in ('spec_index', 'flux_gev'): # Requires LAT low-E results
if df_sorted.loc[irow,['spec_index']][0]!=0:
mcount += lat_count
if mcount < nevent_validaf_onethird:
ncategory = 1
elif mcount < 2*nevent_validaf_onethird:
if len(categories[col][1])<1:
print '----- Category 2 -----'
ncategory = 2
else:
if len(categories[col][2])<1:
print '----- Category 3 -----'
ncategory = 3
categories[col][ncategory-1].append(name)
gbm_fluence_category_sum[col][ncategory-1]['value'] += gbm_fluence_value
gbm_fluence_category_sum[col][ncategory-1]['error'] += pow(gbm_fluence_error,2)
fluence_scaled_sum[col][ncategory-1] += scaled_lc[name]
elif col in ('LC_INDEX'):
if df_sorted.loc[irow,[col]][0]!=0:
mcount += lat_count
if mcount < nevent_validlc_onehalf:
ncategory = 1
else:
if len(categories[col][1])<1:
print '----- Category 2 -----'
ncategory = 2
else:
if len(categories[col][2])<1:
print '----- Category 3 -----'
ncategory = 3
categories[col][ncategory-1].append(name)
gbm_fluence_category_sum[col][ncategory-1]['value'] += gbm_fluence_value
gbm_fluence_category_sum[col][ncategory-1]['error'] += pow(gbm_fluence_error,2)
fluence_scaled_sum[col][ncategory-1] += scaled_lc[name]
else:
mcount += lat_count
if mcount < nevent_onethird:
ncategory = 1
elif mcount < 2*nevent_onethird:
if len(categories[col][1])<1:
print '----- Category 2 -----'
ncategory = 2
else:
if len(categories[col][2])<1:
print '----- Category 3 -----'
ncategory = 3
categories[col][ncategory-1].append(name)
gbm_fluence_category_sum[col][ncategory-1]['value'] += gbm_fluence_value
gbm_fluence_category_sum[col][ncategory-1]['error'] += pow(gbm_fluence_error,2)
fluence_scaled_sum[col][ncategory-1] += scaled_lc[name]
print '{name}: {col} (LAT count: {cnt})'.format(name=name, cnt=lat_count, col=df_sorted.loc[irow,[col]][0])
for icat in (1, 2, 3):
print 'Category {0}: {1}'.format(icat, categories[col][icat-1])
print '{0} GRBs'.format(len(categories[col][icat-1]))
gbm_fluence_category_sum[col][icat-1]['error'] = np.sqrt(gbm_fluence_category_sum[col][icat-1]['error'])
print 'Summed GBM fluence: {0} +/- {1}'.format(gbm_fluence_category_sum[col][icat-1]['value'], gbm_fluence_category_sum[col][icat-1]['error'])
print ''
# col = 'gbm_intermittent'
# print '#####', 'GBM intermittent time', '#####'
# df_sorted = df.sort_values(by=col, ascending=False)
# df_sorted.reset_index( drop = True, inplace=True )
# print df_sorted
# mcount = 0
# print '----- Category 1 -----'
# for irow in range(len(df_sorted.index)):
# name = df_sorted.loc[irow,['name']][0]
# if name in ('130427324', '141022087'):
# print ' Skipping this GRB...'
# continue
# lat_count = df_sorted.loc[irow,['lat_count']][0]
# time_intermittent = df_sorted.loc[irow,['gbm_intermittent']][0]
# if time_intermittent>=10:
# #mcount += lat_count
# # if mcount < nevent_validintermittent_onehalf:
# categories[col][0].append(name)
# #else:
# # if len(categories[col][1])<1:
# # print '----- Category 2 -----'
# # categories[col][1].append(name)
# else:
# if len(categories[col][1])<1:
# print '----- Category 2 -----'
# categories[col][1].append(name)
# print '{name}: {col} (LAT count: {cnt})'.format(name=name, cnt=lat_count, col=df_sorted.loc[irow,[col]][0])
# for icat in (1, 2):
# print 'Category {0}: {1}'.format(icat, categories[col][icat-1])
# print '{0} GRBs'.format(len(categories[col][icat-1]))
# col = 'lc_index'
# print '#####', 'lightcurve index', '#####'
# df_sorted = df.sort_values(by=col, ascending=False)
# df_sorted.reset_index( drop = True, inplace=True )
# print df_sorted
# mcount = 0
# print '----- Category 1 -----'
# for irow in range(len(df_sorted.index)):
# name = df_sorted.loc[irow,['name']][0]
# if name in ('130427324', '141022087'):
# print ' Skipping this GRB...'
# continue
# lat_count = df_sorted.loc[irow,['lat_count']][0]
# lc_index = df_sorted.loc[irow,['lc_index']][0]
# if lc_index!=0:
# mcount += lat_count
# if mcount < nevent_validlc_onehalf:
# categories[col][0].append(name)
# else:
# if len(categories[col][1])<1:
# print '----- Category 2 -----'
# categories[col][1].append(name)
# else:
# if len(categories[col][2])<1:
# print '----- Category 3 -----'
# categories[col][2].append(name)
# print '{name}: {col} (LAT count: {cnt})'.format(name=name, cnt=lat_count, col=df_sorted.loc[irow,[col]][0])
# for icat in (1, 2, 3):
# print 'Category {0}: {1}'.format(icat, categories[col][icat-1])
# print '{0} GRBs'.format(len(categories[col][icat-1]))
pickle_utilities.dump('{dire}/QuantiledGRBs{suf}.pickle'.format(dire=output, suf=str_output ),
{'categories':categories,
'fluence_summed':gbm_fluence_category_sum,
'fluence_scaled_gbm':{'sum':fluence_scaled_sum,
'normalized':integrated_lc,
'scaled': scaled_lc,
'indices':lc_indices,
'phases':lc_phases}
}
)
@click.command()
@click.option('--output', '-o', type=str, default='.')
@click.option('--tolerr', '-t', type=float, default=180.)
@click.option('--suffix', '-s', type=str, default='')
@click.option('--longonly', '-l', is_flag=True)
def main(output, longonly, tolerr, suffix): #name, sed, ra, dec, king, acceptance, livetime, suffix, nside):
quantile_grbs(output, longonly, tolerr, suffix)
if __name__ == '__main__':
main()