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qso_SDSS_correlation_plotting.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Oct 15 15:55:24 2014
@author: suberlak
Plotting histograms of javelin SDSS vs Chelsea SDSS from
chelsea_results_SDSS_load.py
"""
import numpy as np
import matplotlib.pyplot as plt
from math import isinf
band = 'u'
# set_prior = FALSE in Javelin
#results = 's82drw/javelin_SDSS_chelsea_comparison_'+band+'_band_TEST1_with_z.txt' # TEST is with NO PRIOR
# set_prior = TRUE in Javelin
results='s82drw/javelin_SDSS_chelsea_comparison_u_band_with_z.txt' # standard setting is WITH PRIOR
#results = 's82drw/javelin_SDSS_chelsea_comparison_'+band+'_band_MEAN_SUB_with_z.txt' # no prior, mean-subtracted fitting, with a column for z
fname3='sdss_'+band+'_band-log_sigma_ratio_vs_log_tau_ratio_NO_PRIOR_MEAN_SUB.png'
fname4='sdss_'+band+'_band-log_sigma_ratio_vs_log_tau_ratio_NO_PRIOR_MEAN_SUB_redshift.png'
fname5='sdss_'+band+'_band-log_sigma_vs_log_tau_CHELSEA.png'
#fname6='sdss_'+band+'_band-log_sigma_vs_log_tau_NO_PRIOR_MEAN_SUB.png'
output = np.loadtxt(results, dtype='str')
ra_jav = output[:,1].astype(np.float)
ra_ch = output[:,2].astype(np.float)
dec_jav= output[:,3].astype(np.float)
dec_ch= output[:,4].astype(np.float)
tau_jav= output[:,5].astype(np.float)
tau_ch= output[:,6].astype(np.float)
sigma_jav= output[:,7].astype(np.float)
sigma_ch= output[:,8].astype(np.float)
sig_rat= output[:,9].astype(np.float)
redshift = output[:,10].astype(np.float)
# HISTOGRAM OF TAU AND SIGMA RATIOS : no redshift correction
#
#print '\n Plotting coloured hist for log_tau_ratio vs log_sigma_ratio'
#plt.clf()
#fig1 = plt.figure()
#x = np.log10(tau_jav / tau_ch)
#y = np.log10(sigma_jav / sigma_ch)
#
#xinf = np.asarray(map(isinf,x),dtype=bool)
#yinf = np.asarray(map(isinf,y),dtype=bool)
#ttlinf = xinf + yinf
## ttlwh = np.where(ttlinf == True) list of good indices
#gi = -ttlinf # good_indices
#non_inf = len(np.where(gi == True)[0])
#print 'Out of ', len(ra_jav),' rows, we have ', non_inf, ' of those that do not',\
#' have any infinities, and only those are used for plotting '
#
#plt.plot(x[gi],y[gi],'.r')
#nbins =150
#
#H, xedges,yedges = np.histogram2d(x[gi],y[gi],bins=nbins)
#H = np.rot90(H)
#H = np.flipud(H)
#Hmasked = np.ma.masked_where(H==0,H)
#fig2 = plt.figure()
#plt.pcolormesh(xedges, yedges, Hmasked)
#plt.xlim((-2,2))
#plt.ylim((-1.5,1.5))
#
#title1 = 'SDSS quasars : Javelin vs Chelsea, '+band+' band for '+str(non_inf)+ ' objects'
#plt.title(title1)
#plt.axhline(0)
#plt.axvline(0)
#plt.xlabel('log(tau_jav / tau_ch)')
#plt.ylabel('log(sigma_jav / sigma_ch)')
#cbar = plt.colorbar()
#cbar.ax.set_ylabel('Counts')
#plt.savefig(fname3)
#
# HISTOGRAM OF TAU AND SIGMA RATIOS : with redshift correction
#print '\n Plotting coloured hist for log_tau_ratio vs log_sigma_ratio with redshift correction'
#plt.clf()
#fig1 = plt.figure()
#x = np.log10(tau_jav / ((1+redshift)*tau_ch))
#y = np.log10(sigma_jav / sigma_ch)
#
#xinf = np.asarray(map(isinf,x),dtype=bool)
#yinf = np.asarray(map(isinf,y),dtype=bool)
#ttlinf = xinf + yinf
## ttlwh = np.where(ttlinf == True) list of good indices
#gi = -ttlinf # good_indices
#non_inf = len(np.where(gi == True)[0])
#print 'Out of ', len(ra_jav),' rows, we have ', non_inf, ' of those that do not',\
#' have any infinities, and only those are used for plotting '
#
#plt.plot(x[gi],y[gi],'.r')
#nbins =200
#
#H, xedges,yedges = np.histogram2d(x[gi],y[gi],bins=nbins)
#H = np.rot90(H)
#H = np.flipud(H)
#Hmasked = np.ma.masked_where(H==0,H)
#fig2 = plt.figure()
#plt.pcolormesh(xedges, yedges, Hmasked)
#plt.xlim((-2,2))
#plt.ylim((-1.5,1.5))
#title = 'SDSS quasars : Javelin vs Chelsea, '+band+' band for '+str(non_inf)+ ' objects \n redshift corrected '
#plt.title(title)
#plt.axhline(0)
#plt.axvline(0)
#plt.xlabel('log(tau_jav / tau_ch)')
#plt.ylabel('log(sigma_jav / sigma_ch)')
#cbar = plt.colorbar()
#cbar.ax.set_ylabel('Counts')
#plt.savefig(fname4)
##
#
#print ' Files saved are',fname3, ' and ', fname4
#
# HISTOGRAM OF LOG SIGMA VS LOG TAU, LIKE FIG 13 FROM MCLEOD+2011
print '\n Plotting coloured hist for log_tau vs log_sigma for javelin fitting '
plt.clf()
fig1 = plt.figure()
x = np.log10(sigma_ch)
y = np.log10(tau_ch)
xinf = np.asarray(map(isinf,x),dtype=bool)
yinf = np.asarray(map(isinf,y),dtype=bool)
ttlinf = xinf + yinf
# ttlwh = np.where(ttlinf == True) list of good indices
gi = -ttlinf # good_indices
non_inf = len(np.where(gi == True)[0])
print 'Out of ', len(ra_jav),' rows, we have ', non_inf, ' of those that do not',\
' have any infinities, and only those are used for plotting '
plt.plot(x[gi],y[gi],'.r')
nbins =100
H, xedges,yedges = np.histogram2d(x[gi],y[gi],bins=nbins)
H = np.rot90(H)
H = np.flipud(H)
Hmasked = np.ma.masked_where(H==0,H)
fig2 = plt.figure()
plt.pcolormesh(xedges, yedges, Hmasked)
plt.xlim((-1.5,0.1))
plt.ylim((-1,4.3))
title = 'SDSS quasars : Chelsea results, '+band+' band for '+str(non_inf)+ ' objects '
plt.title(title)
#plt.axhline(0)
#plt.axvline(0)
plt.ylabel(r'$\log_{10}{ \, \tau_{ch}}$',fontsize=15)
plt.xlabel(r'$\log_{10}{ \, \sigma_{ch}}$',fontsize=15)
cbar = plt.colorbar()
cbar.ax.set_ylabel('Counts')
plt.savefig(fname5)
print 'File saved is ', fname5
### SINGLE HISTOGRAMS
#
#for i in range(5,6):
# print '\n Plotting histograms for',names[i]
# pct1sig = len(stats[i][0:divide]) * np.array([0.25,0.50,0.75])
# medlowhig =pct1sig.astype(np.int32) # expresses the pointers above as integers
# vsort = np.sort(stats[i][0:divide]) # sorts the array along either sigma or tau dimension
# hpdb = vsort[medlowhig] # picks out values at the positions for the
# print names[i], ': 25, 50, 75 percentiles for bad tau ', hpdb
#
# hist_minb = hpdb[1] - 2*(hpdb[2]-hpdb[0])
# hist_maxb = hpdb[1] +2*(hpdb[2]-hpdb[0])
# print 'hist_min, hist_max', hist_minb, hist_maxb
#
#
# pct1sig = len(stats[i][divide:]) * np.array([0.25,0.50,0.75])
# medlowhig =pct1sig.astype(np.int32) # expresses the pointers above as integers
# vsort = np.sort(stats[i][divide:]) # sorts the array along either sigma or tau dimension
# hpdg = vsort[medlowhig] # picks out values at the positions for the
# print names[i], ': 25, 50, 75 percentiles for good tau', hpdg
#
# hist_ming = hpdg[1] - 2*(hpdg[2]-hpdg[0])
# hist_maxg = hpdg[1] +2*(hpdg[2]-hpdg[0])
# print 'hist_min, hist_max', hist_ming, hist_maxg
#
#
# plt.clf()
# plt.hist(stats[i][0:divide],bins=50)
# plt.xlim((hist_minb,hist_maxb))
# plot_title = names[i] + 'distribution, bad tau ratio (<-2.5)'
# plt.title(plot_title)
# plt.xlabel(names[i])
# plt.ylabel('Frequency')
# fname1 = 'corr_h1d-'+names[i]+'-bad_tau.png'
# plt.savefig(fname1)
#
# plt.clf()
# plt.hist(stats[i][divide:],bins=50)
# plt.xlim((hist_ming,hist_maxg))
# plot_title = names[i] + 'distribution, good tau ratio (>-2.5)'
# plt.title(plot_title)
# plt.xlabel(names[i])
# plt.ylabel('Frequency')
# fname2 = 'corr_h1d-'+names[i]+'-good_tau.png'
# plt.savefig(fname2)
# COLOURED HISTOGRAMS
#for i in range(len(stats)):
#
# pct1sig = len(stats[i][0:divide]) * np.array([0.25,0.50,0.75])
# medlowhig =pct1sig.astype(np.int32) # expresses the pointers above as integers
# vsort = np.sort(stats[i][0:divide]) # sorts the array along either sigma or tau dimension
# hpdb = vsort[medlowhig] # picks out values at the positions for the
# print '\n', names[i], ': 25, 50, 75 percentiles for bad tau ', hpdb
#
# hist_minb = hpdb[1] - 2*(hpdb[2]-hpdb[0])
# hist_maxb = hpdb[1] +2*(hpdb[2]-hpdb[0])
# print 'hist_min, hist_max', hist_minb, hist_maxb
#
#
# pct1sig = len(stats[i][divide:]) * np.array([0.25,0.50,0.75])
# medlowhig =pct1sig.astype(np.int32) # expresses the pointers above as integers
# vsort = np.sort(stats[i][divide:]) # sorts the array along either sigma or tau dimension
# hpdg = vsort[medlowhig] # picks out values at the positions for the
# print '\n',names[i], ': 25, 50, 75 percentiles for good tau', hpdg
#
# hist_ming = hpdg[1] - 2*(hpdg[2]-hpdg[0])
# hist_maxg = hpdg[1] +2*(hpdg[2]-hpdg[0])
# print 'hist_min, hist_max', hist_ming, hist_maxg
#
# for j in range(len(stats)):
# if (i != j) :
# print '\n Plotting coloured hist for',names[i], ' vs ', names[j], 'for bad tau'
# plt.clf()
# fig1 = plt.figure()
# x=stats[i][0:divide]
# y=stats[j][0:divide]
# plt.plot(x,y,'.r')
# plt.xlim((hist_minb,hist_maxb))
# #plt.xlabel(names[i])
# #plt.ylabel(names[j])
# nbins =100
# H, xedges,yedges = np.histogram2d(x,y,bins=nbins)
# H = np.rot90(H)
# H = np.flipud(H)
# Hmasked = np.ma.masked_where(H==0,H)
# fig2 = plt.figure()
# plt.pcolormesh(xedges, yedges, Hmasked)
# plt.xlim((hist_minb,hist_maxb))
# plt.title('Sample with log (tau ratio) <-2')
# plt.xlabel(names[i])
# plt.ylabel(names[j])
# cbar = plt.colorbar()
# cbar.ax.set_ylabel('Counts')
# fname3='corr_h2d-'+names[i]+'_vs_'+names[j]+'-bad_tau.png'
# plt.savefig(fname3)
#
# print '\n Plotting coloured hist for',names[i], ' vs ', names[j], 'for good tau'
# plt.clf()
# fig1 = plt.figure()
# x=stats[i][divide:]
# y=stats[j][divide:]
# plt.plot(x,y,'.r')
# plt.xlim((hist_ming,hist_maxg))
# #plt.xlabel(names[i])
# #plt.ylabel(names[j])
# nbins =100
# H, xedges,yedges = np.histogram2d(x,y,bins=nbins)
# H = np.rot90(H)
# H = np.flipud(H)
# Hmasked = np.ma.masked_where(H==0,H)
# fig2 = plt.figure()
# plt.pcolormesh(xedges, yedges, Hmasked)
# plt.xlim((hist_ming,hist_maxg))
# plt.title('Sample with log (tau ratio) >-2')
# plt.xlabel(names[i])
# plt.ylabel(names[j])
# cbar = plt.colorbar()
# cbar.ax.set_ylabel('Counts')
# fname3='corr_h2d-'+names[i]+'_vs_'+names[j]+'-good_tau.png'
# plt.savefig(fname3)
# AVG ERR TTL
# figuring out what are the percentiles of the avg_err_ttl distribution
#print '\n Plotting histograms for avg err ttl '
#
#pct1sig = len(avg_err_ttl[0:divide]) * np.array([0.25,0.50,0.75])
#medlowhig =pct1sig.astype(np.int32) # expresses the pointers above as integers
#vsort = np.sort(avg_err_ttl[0:divide]) # sorts the array along either sigma or tau dimension
#hpdb = vsort[medlowhig] # picks out values at the positions for the
#print 'avg_err_ttl for bad tau 25, 50, 75 percentiles ', hpdb
#
#hist_minb = hpdb[1] - 2*(hpdb[2]-hpdb[0])
#hist_maxb = hpdb[1] +2*(hpdb[2]-hpdb[0])
#print hist_maxb, hist_minb
#
#pct1sig = len(avg_err_ttl[divide:]) * np.array([0.25,0.50,0.75])
#medlowhig =pct1sig.astype(np.int32) # expresses the pointers above as integers
#vsort = np.sort(avg_err_ttl[divide:]) # sorts the array along either sigma or tau dimension
#hpdg = vsort[medlowhig] # picks out values at the positions for the
#print 'avg_err_ttl for good tau 25, 50, 75 percentiles ', hpdg
#
#hist_ming = hpdg[1] - 2*(hpdg[2]-hpdg[0])
#hist_maxg = hpdg[1] +2*(hpdg[2]-hpdg[0])
#print hist_maxg, hist_ming
#
#
#
#plt.clf()
#plt.hist(avg_err_ttl[0:divide],bins=40)
#plt.xlim((hist_minb,hist_maxb))
#plt.title('Average error distribution, bad tau ratio (<-2)')
#plt.xlabel('Average total error ')
#plt.ylabel('Frequency')
#fname1 = 'qso_corr-avg_ttl_err-bad_tau-hist.png'
#plt.savefig(fname1)
#
#
#plt.clf()
#plt.hist(avg_err_ttl[divide:],bins=40)
#plt.xlim((hist_ming,hist_maxg))
#plt.title('Average error distribution, good tau ratio (>-2)')
#plt.xlabel('Average total error ')
#plt.ylabel('Frequency')
#fname2 = 'qso_corr-avg_ttl_err-good_tau-hist.png'
#plt.savefig(fname2)
#print '\n plotting avg_err_ttl for bad tau vs log_sigma_ration as a colour-coded histogram'
#
#plt.clf()
#fig1 = plt.figure()
#x=avg_err_ttl[0:divide]
#y=log_sigma_ratio[0:divide]
#plt.plot(x,y,'.r')
#plt.xlim((hist_minb,hist_maxb))
#plt.xlabel('Average of quasar error on magnitude')
#plt.ylabel('Log (sigma_jav / sigma_chelsea) ')
#nbins =100
#H, xedges,yedges = np.histogram2d(x,y,bins=nbins)
#H = np.rot90(H)
#H = np.flipud(H)
#Hmasked = np.ma.masked_where(H==0,H)
#fig2 = plt.figure()
#plt.pcolormesh(xedges, yedges, Hmasked)
#plt.xlim((hist_minb,hist_maxb))
#plt.title('Sample with log (tau ratio) <-2')
#plt.xlabel('Average of quasar error on magnitude')
#plt.ylabel('Log (sigma_jav / sigma_chelsea) ')
#cbar = plt.colorbar()
#cbar.ax.set_ylabel('Counts')
#fname3='qso_corr-avg_ttl_err-log_sig_ratio-bad_tau-hist.png'
#plt.savefig(fname3)
#print '\n plotting avg_err_ttl for good tau vs log_sigma_ration as a colour-coded histogram'
#
#plt.clf()
#fig1 = plt.figure()
#x=avg_err_ttl[divide:]
#y=log_sigma_ratio[divide:]
#plt.plot(x,y,'.r')
#plt.xlim((hist_ming,hist_maxg))
#plt.xlabel('Average of quasar error on magnitude')
#plt.ylabel('Log (sigma_jav / sigma_chelsea) ')
#nbins =100
#H, xedges,yedges = np.histogram2d(x,y,bins=nbins)
#H = np.rot90(H)
#H = np.flipud(H)
#Hmasked = np.ma.masked_where(H==0,H)
#fig2 = plt.figure()
#plt.pcolormesh(xedges, yedges, Hmasked)
#plt.xlim((hist_ming,hist_maxg))
#plt.title('Sample with log (tau ratio) >-2')
#plt.xlabel('Average of quasar error on magnitude')
#plt.ylabel('Log (sigma_jav / sigma_chelsea) ')
#cbar = plt.colorbar()
#cbar.ax.set_ylabel('Counts')
#fname3='qso_corr-avg_ttl_err-log_sig_ratio-good_tau-hist.png'
#plt.savefig(fname3)