def plot_distribution(pseudo_redshifts):
z_min = 1e-5
z_max = 1e1
z_bin = 1e0
width = 1.0
ymin = 00
ymax = 40
x, y = mf.my_histogram_according_to_given_boundaries(
pseudo_redshifts, z_bin, z_min, z_max)
plt.ylim(ymin, ymax)
plt.bar(x,
y,
width=width,
color='w',
edgecolor='k',
hatch='//',
label=r'$ \rm{ pseudo } $')
x, y = mf.my_histogram_according_to_given_boundaries(
common_redshift, z_bin, z_min, z_max)
plt.bar(x,
y,
width=width,
color='c',
alpha=0.5,
label=r'$ \rm{ observed } $')
plt.xlabel(r'$ z $', fontsize=size_font + 2)
plt.legend()
def discrepancy_and_redchisqrd(A, eta):
numerator__delta_pseudo_z = numerator__delta_pseudo_GRB__first_term + eta * numerator__delta_pseudo_GRB__second_term # defined for each GRB
denominator__delta_pseudo_z = denominator__delta_pseudo_zsim__first_term + (
(eta + 2) / (1 + z_sim)) # defined for each simulated redshift
denominator__F = (1 + z_sim)**eta
F_Fermi_sim = numerator__F / denominator__F
F_Fermi_sim = F_Fermi_sim * (cm_per_Mpc**2) / L_norm
RHSs = (A / common_Fermi_flux) * (common_Epeak_in_MeV**eta)
pseudo_redshifts = np.zeros(RHSs.size)
pseudo_redshifts_error = np.zeros(RHSs.size)
for j, RHS in enumerate(RHSs):
array = np.abs(F_Fermi_sim - RHS)
ind = np.where(array == array.min())[0][0]
pseudo_redshift = z_sim[ind]
pseudo_redshift_error = numerator__delta_pseudo_z[
j] / denominator__delta_pseudo_z[ind]
pseudo_redshifts[j] = pseudo_redshift
pseudo_redshifts_error[j] = pseudo_redshift_error
z_min = 1e-5
z_max = 1e1
x1, y_pseudo = mf.my_histogram_according_to_given_boundaries(
pseudo_redshifts, z_bin, z_min, z_max)
x2, y_common = mf.my_histogram_according_to_given_boundaries(
common_redshift, z_bin, z_min, z_max)
# print ( x1 - x2 == 0 ).all()
discrepancy = np.sum((y_pseudo - y_common)**2)
chisquared, dof, redchisqrd = mf.reduced_chisquared(
common_redshift, pseudo_redshifts, pseudo_redshifts_error, 2)
return pseudo_redshifts, pseudo_redshifts_error, discrepancy, chisquared, dof, redchisqrd
####################### CO-ADDED LIGHTCURVES #######################
lc = np.zeros( int(t_max // time_res) )
lt = np.zeros( int(t_max // time_res) )
start_time = t.time()
for j, end in enumerate( bunch_ends[:-1] ):
# To loop over the bunches.
start = bunch_starts[j+1]
if ( start - end > 0 ):
times, [] = mf.window( end, start, time, [] )
if times.size > 0:
# To make the light-curve from the data within the bunches.
x, y = mf.my_histogram_according_to_given_boundaries( times-end, time_res, 0, t_max )
lc = lc + y
ind = np.where( y > 0 )[0]
z = np.zeros( int(t_max // time_res) )
z[ind] = 1
lt = lt + z
print '\n{:.3f} mins elapsed to make co-added LCs from raw data.'.format( (t.time() - start_time) / 60 )
non_zero = np.where( lc != 0 )[0]
plt.xlabel( r'$ \rm{ \tau \; [20 \, \mu s] } $', fontsize = size_font )
plt.ylabel( r'$ \rm{ Coadded \; Data } $', fontsize = size_font )
plt.xticks( range(10, 100, 10) )
plt.plot( x[non_zero]/time_res, lc[non_zero]/lt[non_zero], color = 'k' )
plt.savefig( path_save + 'Q{:d}--LC--raw_data.png'.format(i) )
plt.savefig( path_save + 'Q{:d}--LC--raw_data.pdf'.format(i) )
common_Epeak_error = common_GRBs_table['Epeak_error'].data # in keV.
common_alpha = common_GRBs_table['alpha'].data
common_alpha_error = common_GRBs_table['alpha_error'].data
common_beta = common_GRBs_table['beta'].data
common_beta_error = common_GRBs_table['beta_error'].data
common_Luminosity = common_GRBs_table['Luminosity'].data
common_Luminosity_error = common_GRBs_table['Luminosity_error'].data
common_num = common_ID.size
####################################################################################################################################################
####################################################################################################################################################
## To extract the average parameters of the Fermi GRBs.
hist = mf.my_histogram_according_to_given_boundaries(np.log10(Fermi_Epeak),
0.125, 1, 4)
hxF = hist[0]
hyF = hist[1]
fits = mf.fit_a_gaussian(hxF, hyF)
f0 = fits[0]
f1 = fits[1]
f2 = fits[2]
print 'Fermi spectral parameters -- statistics :'
print 'Epeak : ', np.median(Fermi_Epeak), mf.std_via_mad(Fermi_Epeak)
print 'alpha : ', np.median(Fermi_alpha), mf.std_via_mad(Fermi_alpha)
print 'beta : ', np.median(Fermi_beta), mf.std_via_mad(Fermi_beta)
print 'Epeak, fit : ', 10**f0, 10**f1
print '\n\n\n\n'
####################################################################################################################################################
# To loop over the Quadrants.
print '##########################################################\n'
print 'Quadrant {:d}...'.format(i), '\n\n'
Q_data = ascii.read( path_read + 'data_before_DPHclean--Q{:d}.txt'.format(i), format = 'fixed_width' )
time = Q_data['Time'].data
detx = Q_data['detx'].data
dety = Q_data['dety'].data
energy = Q_data['Energy'].data
veto = Q_data['Veto'].data
########################### DPHclean ###############################
print '..........................................................\n'
print 'DPHclean...', '\n'
x_t, y_c = mf.my_histogram_according_to_given_boundaries( time, t_look, t_start, t_stop )
y_c = y_c / t_look
# To find the n-sigma peaks in the light-curve at defined time-bin.
peaks_index, peaks_time, peaks_countrate, peaks_significance = mf.detect_peaks( x_t, y_c, T_search, cutoff )
how_many_outliers = peaks_time.size
print '\nThese many outliers: ', how_many_outliers
total_flaginds = np.array( [] )
flag_indices = np.array( [] )
times_to_flag = np.array( [] )
check_flagged = np.array( [] )
check_unflagged = np.array( [] )
# To make dph-investigation of the peaks in the light-curve.
for j, ind_peaks in enumerate( peaks_index ):
# The j-th peak found in the light-curve.