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qso_CRTS_SDSS_matched_plotting.py
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qso_CRTS_SDSS_matched_plotting.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Dec 22 13:58:42 2014
@author: astronomy
modified qso_DRW_plotting...
meant to plot the log(sigma) vs log(tau) for Chelsea S82 results
matched to Javelin fits of CRTS data, from chelsea_results_load_crts.py
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from math import isinf
######################
# lOAD DATA #
######################
dir_in = 'QSO_CRTS_analysis/'
dir_out = 'QSO_CRTS_analysis/'
results_file = 'javelin_CRTS_err_w_Chelsea_s82drw_r_compare.txt'
output = dir_in+ results_file
data = np.loadtxt(output,dtype='str' )
qso_name = data[:,0]
ra_crts = data[:,1].astype(float) # ra and dec in degrees
ra_sdss = data[:,2].astype(float)
dec_crts = data[:,3].astype(float)
dec_sdss = data[:,4].astype(float)
tau_med_jav_crts = data[:,5].astype(float)
tau_chelsea_sdss = data[:,6].astype(float)
sigma_med_jav_crts = data[:,7].astype(float)
sigma_hat_jav_crts = sigma_med_jav_crts * np.sqrt(tau_med_jav_crts / (2.0*365))
sigma_chelsea_sdss = data[:,8].astype(float)
sigma_hat_chelsea_sdss = sigma_chelsea_sdss * np.sqrt(tau_chelsea_sdss /(2.0*365) )
xmin = 0.001 # sigma limits
xmax = 5
ymin = 1 # tau limits
ymax = 70000
xlim = [xmin, xmax]
ylim = [ymin, ymax]
##########################
# SELECT POINTS TO USE #
##########################
good_LC = np.loadtxt(dir_in + 'good_err_LC.txt', dtype='str')
good_LC_cut = np.empty(0, dtype=str)
for i in range(len(good_LC)):
good_LC_cut = np.append(good_LC_cut, good_LC[i][4:-4])
good_LC_mask = np.zeros_like(qso_name, dtype='bool')
for i in range(len(qso_name)):
print '\nComparison in progress...', str((float(i) / float(len(qso_name)) )*100.0)[:5], '%'
good_LC_mask[i] = qso_name[i] in good_LC_cut
print 'Out of ', len(qso_name), 'objects, we use ', good_LC_mask.sum()
###########################
# DEFINE NEEDED FUNCTION #
###########################
def load_x_y(x_arr, y_arr, x_limits, y_limits):
print '\n Loading x and y ... '
x = x_arr
y = y_arr
# sieve out suspiciously bad values , based only on x and y
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
def histogram2D(x_arr, y_arr, number, percent, xlim, ylim, title, dir_out):
# args could include javelin results_file , from which you can
# take the info about the prior
font = 20
x = np.log10(x_arr)
y = np.log10(y_arr)
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)
ax1 = fig1.add_subplot(gs[:,:90])
pcObject1 = ax1.pcolormesh(xedges, yedges, Hmasked)
ax1.tick_params(axis='x', labelsize=font)
ax1.tick_params(axis='y', labelsize=font)
xmin = np.log10(xlim[0])
xmax = np.log10(xlim[1])
ymin = np.log10(ylim[0])
ymax = np.log10(ylim[1])
plt.xlim((xmin, xmax))
plt.ylim((ymin, ymax))
x_label_ch = r'$\log_{10}{ \, \left( \hat\sigma_{ch} \right)}$'
y_label_ch = r'$\log_{10}{ \, \left( \tau_{ch} \right)}$'
x_label_jav = r'$\log_{10}{ \, \left( \hat\sigma_{jav} \right)}$'
y_label_jav = r'$\log_{10}{ \, \left( \tau_{jav} \right)}$'
if title == 'ch' :
plt.ylabel(y_label_ch,fontsize=font+5)
plt.xlabel(x_label_ch,fontsize=font+5)
title_hist = 'S82 SDSS Chelsea results, '+ str(number) + ', i.e. ' + str(percent)[:5]+ '% points'
fname = dir_out + 'Chelsea_s82_SDSS_matched_sigma_hat_tau.png'
if title == 'jav' :
plt.ylabel(y_label_jav,fontsize=font+5)
plt.xlabel(x_label_jav,fontsize=font+5)
title_hist = 'CRTS Javelin results, '+ str(number) + ', i.e. ' + str(percent)[:5]+ '% points'
fname = dir_out + 'CRTS_Javelin_matched_sigma_hat_tau.png'
if title == 'ss' :
plt.xlabel(x_label_ch,fontsize=font+5)
plt.ylabel(x_label_jav,fontsize=font+5)
title_hist = 'S82 SDSS Chelsea vs CRTS JAVELIN, '+str(number) + ', i.e. ' + str(percent)[:5]+ '% points'
fname = dir_out + 'CRTS_SDSS_matched_sigma_hat_sigma_hat.png'
if title == 'tt' :
plt.xlabel(y_label_ch, fontsize=font+5)
plt.ylabel(y_label_jav,fontsize=font+5)
title_hist = 'S82 SDSS Chelsea vs CRTS JAVELIN, '+str(number) + ', i.e. ' + str(percent)[:5]+ '% points'
fname = dir_out + 'CRTS_SDSS_matched_tau_tau.png'
plt.title(title_hist, fontsize = font)
# Add the colorbar
axC = fig1.add_subplot(gs[:,95:])
axC.tick_params(axis='y', labelsize=font)
cbar = fig1.colorbar(pcObject1,ax=ax1, cax=axC, orientation='vertical')
cbar.ax.set_ylabel('Counts', fontsize=font)
plt.savefig(fname)
print 'File saved is ', fname
# Make log(sigma_hat) vs log(tau) histogram for Chelsea
#x_arr, y_arr, number, percent = load_x_y(sigma_hat_chelsea_sdss[good_LC_mask], tau_chelsea_sdss[good_LC_mask], xlim, ylim)
#histogram2D(x_arr, y_arr, number, percent, xlim, ylim, 'ch', dir_out)
##
### Make log(sigma_hat) vs log(tau) histogram for Javelin CRTS
#x_arr, y_arr, number, percent = load_x_y(sigma_hat_jav_crts, tau_med_jav_crts, xlim, ylim)
#histogram2D(x_arr, y_arr, number, percent, xlim, ylim, 'jav', dir_out)
##
### Make log(sigma_hat) vs log(sigma_hat) histogram
##
x_arr, y_arr, number, percent = load_x_y(sigma_hat_chelsea_sdss[good_LC_mask], sigma_med_jav_crts[good_LC_mask], xlim, xlim)
#histogram2D(x_arr, y_arr, number, percent, xlim, xlim, 'ss', dir_out)
##
### Make log(tau) vs log(tau) histogram
##
#x_arr, y_arr, number, percent = load_x_y(tau_chelsea_sdss[good_LC_mask], tau_med_jav_crts[good_LC_mask], ylim, ylim)
#histogram2D(x_arr, y_arr, number, percent, ylim, ylim, 'tt', dir_out)
def median_and_rms(array):
median = np.median(array)
rms = np.percentile(array, 75) - np.percentile(array,25)
print median, rms
print '\nMedian and RMs from IQR (75-25%) range for Chelsea sigma hat is ', median_and_rms(x_arr)
print '\nMedian and RMs from IQR (75-25%) range for Javelin CRTS sigma hat is ', median_and_rms(y_arr)