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qso_DRW_plotting_chelsea_javelin_ratio.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Nov 18 17:48:01 2014
@author: suberlak
plotting log(tau) vs log(sigma) for the
simulated DRW sample
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from math import isinf
# set_prior = TRUE in Javelin
#results='qso_drw_analysis/javelin_drw_test_chain_results_with_prior_all.txt'
# set_prior = FALSE in Javelin
#results='qso_drw_analysis/javelin_drw_test_chain_results_no_prior_603.txt'
prior = 'yes' # or 'no'
length = 'medium' # or 'medium'
n_errors= 2 # also need to change number of error when calling function err_rows_extract below
# Define plot size
x_min = -0.15
x_max = 2
y_min = -0.3
y_max = 2
if prior== 'yes':
if length == 'short' : # results with Javelin Prior , short LC length
results_jav = 'qso_drw_analysis/javelin_drw_test_chain_results_with_prior_all.txt'
results_ch='qso_drw_S_M_Chelsea_results/fitsshort.dat'
n_errors = 3
if length == 'medium' : # results with Javelin Prior , medium LC length
results_jav = 'qso_drw_medium_analysis/javelin_drw_test_chain_results_with_prior_all.txt'
results_ch='qso_drw_S_M_Chelsea_results/fitsmed.dat'
n_errors = 2
if prior =='no':
if length == 'short' : # results without Javelin Prior , short LC length
results_jav = 'qso_drw_analysis/javelin_drw_test_chain_results_no_prior_all.txt'
results_ch='qso_drw_S_M_Chelsea_results/fitsshort.dat'
n_errors = 3
if length == 'medium' : # results without Javelin Prior , medium LC length
results_jav = 'qso_drw_medium_analysis/javelin_drw_test_chain_results_no_prior_all.txt'
results_ch='qso_drw_S_M_Chelsea_results/fitsmed.dat'
n_errors = 2
fig_name=['qso_drw_S_M_Chelsea_results/drw_'+length+'_prior-'+prior+'_err','_log_sigma_ratio_vs_log_tau_ratio.png']
print 'Using Chelsea results from', results_ch,' and Javelin results from ', results_jav, 'n_errors=,', n_errors
output_ch = np.loadtxt(results_ch, dtype='str')
output_jav = np.loadtxt(results_jav , dtype='str')
# CHELSEA RESULTS
name_ch_raw =output_ch[:,0].astype(str)
log_10_tau = output_ch[:,2].astype(np.float) # NOTE : already log_10
log_10_sigma_hat = output_ch[:,3].astype(np.float)
tau = np.power(10,log_10_tau)
sigma_hat = np.power(10,log_10_sigma_hat)
sigma = sigma_hat * np.sqrt(tau / (2.0*365.0))
log_10_sigma = np.log10(sigma)
# JAVELIN RESULTS
name_jav =output_jav[:,0].astype(str)
sigma_max_jav = output_jav[:,1].astype(np.float) # extracted as max value of the chain 2D histogram
tau_max_jav = output_jav[:,2].astype(np.float)
sigma_l_jav= output_jav[:,3].astype(np.float)
sigma_m_jav_raw = output_jav[:,4].astype(np.float) # extracted as the median value of the chain
sigma_m_jav = sigma_m_jav_raw
sigma_h_jav= output_jav[:,5].astype(np.float)
tau_l_jav = output_jav[:,6].astype(np.float)
tau_m_jav = output_jav[:,7].astype(np.float)
tau_h_jav = output_jav[:,8].astype(np.float)
# transform chelsea naming into javelin names
name_ch = name_ch_raw
for i in range(len(name_ch)):
name_ch[i] = name_ch_raw[i][:-6]
def name_matching():
matched_sigma_ch = np.empty(0, dtype=float)
matched_tau_ch = np.empty(0, dtype=float)
matched_name_ch = np.empty(0, dtype=str)
a = min(len(name_ch),len(name_jav)) < len(name_ch)
b = len(name_ch) == len(name_jav)
if a or b :
# case where either javelin data shorter than Chelsea, or they have same
# length
print '\n Matching Chelsea to Javelin...'
for i in range(len(name_jav)):
name = name_jav[i]
index = np.where(name_ch == name)[0][0]
matched_sigma_ch = np.append(matched_sigma_ch,log_10_sigma[index] )
matched_tau_ch = np.append(matched_tau_ch,log_10_tau[index] )
matched_name_ch = np.append(matched_name_ch, name_ch[index])
if not a : # case where chelsea data shorter than Javelin
print 'Something is wrong : Chelsea shorter than Javelin...'
return matched_sigma_ch, matched_tau_ch, matched_name_ch
def err_rows_extract(name_list, err_pos, n_errors):
'''
Since Javelin and Chelsea data is not ordered , name lists are
different for each set
Form a three-column list of indices for each error:
col0 are indices of rows with err1
col1 are indices of rows with err2
col2 are indices of rows with err3
Handle separately two cases, whether we have data with
err1,2,3 or only err1,2
'''
if n_errors == 3 :
ind=[0,0,0]
name = name_list
#upprint len(name)
for i in range(0,len(name)):
err = name[i][err_pos] # take err id value (1,2, or 3) from filename
for j in range(1,4):
if err == str(j) :
ind[j-1] = np.append(ind[j-1],i)
err1 = ind[0][1:]
err2 = ind[1][1:]
err3 = ind[2][1:]
upind = [err1,err2,err3]
if n_errors == 2:
ind=[0,0]
name = name_list
# print len(name)
for i in range(0,len(name)):
err = name[i][err_pos] # take err id value (1 or 2) from filename
for j in range(1,3):
if err == str(j) :
ind[j-1] = np.append(ind[j-1],i)
err1 = ind[0][1:]
err2 = ind[1][1:]
upind = [err1,err2]
return upind
def load_x_y(x_arr, y_arr, err_indices, ka , x_limits, y_limits):
indices = err_indices
print '\n Loading x and y ... '
x = x_arr[indices]
y = y_arr[indices]
# sieve out suspiciously bad values , based only on x and y
if ka < 0 :
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])
else : #(ALWAYS)
# separate treatment of the high error test
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
ysmall = np.where(y < y_limits[0])
ylarge = np.where(y > y_limits[1])
xsmall = np.where(x < x_limits[0])
xlarge = np.where(x > x_limits[1])
gi[xsmall] = False
gi[ysmall] = False
gi[xlarge] = False
gi[ylarge] = False
non_inf = len(np.where(gi == True)[0])
percent = (float(non_inf) / float(len(x))) * 100.0
print 'Out of ', len(x),' rows, we have ', non_inf, ' of those that match', \
'the criteria of ', x_limits[0],' < x <', x_limits[1],' and ', y_limits[0],\
' < y < ',y_limits[1], 'and only those are used for plotting ... '
return x[gi], y[gi], non_inf, percent
log_sigma_ch , log_tau_ch , matched_name_ch = name_matching()
for k in range(1,n_errors+1): #n_errors+1
# looping over err1, err2 , err3, selecting appropriate rows
print '\nFor err', k
x_lim = [x_min, x_max]
y_lim = [y_min,y_max]
# We only load x and y that are within the limits of my histogram, to have the same
# pixel size for both distributions... (regardless of how much I'm removing by my boundaries)
jav_indices = err_rows_extract(name_jav,-1,n_errors)
assert len(jav_indices) == n_errors
# Check that Chelsea matched rows are exactly the ones that correspond to javelin
truth = matched_name_ch[jav_indices[0]] == name_jav[jav_indices[0]]
assert len(np.where(truth == False)[0]) == 0
# And since I have matched Chelsea to Javelin results, now the rows with corresponding
# err1,2,3, are the same
ratio_sigma = np.log10( sigma_m_jav / np.power(10,log_sigma_ch) )
ratio_tau = np.log10( tau_m_jav / np.power(10,log_tau_ch) )
# Remove all values that are not within bounds specified
# since values plotted are logs of ratios, I'm feeding here
# already logs of ratios
x , y, num, percent = load_x_y(ratio_sigma ,ratio_tau,jav_indices[k-1], k, x_lim, y_lim)
print '\n Plotting coloured hist for log_tau vs log_sigma for Chelsea fitting... '
assert len(log_sigma_ch) == len(log_tau_ch) ==len(sigma_m_jav) ==len(tau_m_jav)
# Define number of bins at the beginning, especially if it is shared between the histograms...
nbins =50
plt.clf()
fig1 = plt.figure()
# Define the canvas to work on and the grid
fig1 = plt.figure(figsize=[10,8])
gs = GridSpec(100,100,bottom=0.18,left=0.18,right=0.88)
# First histogram : Chelsea results
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)
# First histogram : make axis, and plot all that is needed
ax1 = fig1.add_subplot(gs[:,:90]) #
pcObject1 = ax1.pcolormesh(xedges, yedges, Hmasked)
#plt.xlim((x_min,x_max))
#plt.ylim((y_min,y_max))
title = 'DRW '+ length +', err'+str(k)+', prior='+prior+', cross-matched, '+str(percent)[:5]+'% points'
plt.title(title)
plt.axhline(0, color = 'r', lw=2)
plt.axvline(0,color='r',lw=2)
plt.ylabel(r'$\log_{10}{ \, \left( \tau_{jav} / \tau_{ch} \right)}$',fontsize=15)
plt.xlabel(r'$\log_{10}{ \, \left( \sigma_{jav} / \sigma_{ch} \right)}$',fontsize=15)
# Add the colorbar
axC = fig1.add_subplot(gs[:,95:])
cbar = fig1.colorbar(pcObject1,ax=ax1, cax=axC, orientation='vertical')
# cbar.ax.set_ylabel('Counts')
fname2 = fig_name[0]+str(k)+fig_name[1]
plt.savefig(fname2)
print 'File saved is ', fname2