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qso_CRTS_plot_stats.py
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qso_CRTS_plot_stats.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 23 22:12:01 2014
@author: Chris
Plotting stats for CRTS stars, to see if there is anything weird in those
that have sigma < 0 ...
I leave it open to also plot these same stats for QSO,
because it's so much quicker, to test the program ...
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from math import isinf
#args = sys.argv
#err = int(args[1])
ch = 0
dir_in_out = ['QSO_CRTS_analysis/', 'stars_CRTS_analysis/']
files = ['javelin_CRTS_chain_results_err_w.txt', 'javelin_CRTS_stars_err_w_chain_results.txt']
chain_results = dir_in_out[ch]+ files[ch]
data = np.loadtxt(chain_results,dtype='str' )
############################
# READ IN THE CHAIN VALUES#
############################
fname = np.empty(0,dtype=float)
sigma_m = np.empty(0,dtype=float)
tau_m = np.empty(0,dtype=float)
print 'Reading in all the values ... '
for i in range(len(data[:,0])):
try:
fname = np.append(fname, data[i,0])
sigma_m = np.append(sigma_m, float(data[i,2]))
tau_m = np.append(tau_m, float(data[i,5]))
except ValueError:
pass
if len(sigma_m) != len(tau_m) :
m = min(len(tau_m), len(sigma_m))
sigma_m = sigma_m[0:m]
tau_m = tau_m[0:m]
fname = fname[0:m]
assert len(sigma_m) == len(tau_m)
print '\nOut of ', len(data[:,0]), ' rows we were able to read in ', len(tau_m)
############################
# SELECTING POINTS TO USE #
############################
def sel_points_stars(dir_in_out, fname):
good_LC = np.loadtxt(dir_in_out + '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:-8])
good_LC_mask = np.zeros_like(fname, dtype='bool')
for i in range(len(fname)):
print '\nComparison in progress...', str((float(i) / float(len(fname)) )*100.0)[:5], '%'
good_LC_mask[i] = fname[i][4:] in good_LC_cut
print 'Out of ', len(fname), 'objects, we use ', good_LC_mask.sum()
return good_LC_mask
def sel_points_qso(dir_in_out, fname):
good_LC = np.loadtxt(dir_in_out + '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(fname, dtype='bool')
for i in range(len(fname)):
print '\nComparison in progress...', str((float(i) / float(len(fname)) )*100.0)[:5], '%'
good_LC_mask[i] = fname[i][:-10] in good_LC_cut
print 'Out of ', len(fname), 'objects, we use ', good_LC_mask.sum()
return good_LC_mask
good_LC_mask = sel_points_qso(dir_in_out[ch], fname)
fname = fname[good_LC_mask]
sigma_m = sigma_m[good_LC_mask]
tau_m = tau_m[good_LC_mask]
sigma_hat = sigma_m * np.sqrt(tau_m / (2.0 * 365.0))
############################
# READ IN STATS FOR LC'S #
############################
stats = np.loadtxt(dir_in_out[ch]+'LC_stats.txt', dtype='str')
lc_names = stats[:,0]
ind = np.zeros(len(fname), dtype=int)
for i in range(len(fname)):
print '\nChecking', i, ' of ', len(fname)
for j in range(len(lc_names)):
if lc_names[j][4:-4] == fname[i][:-10] :
ind[i] = j
mjd_span = stats[:,1]
mag_rms = stats[:,2]
mag_mean = stats[:,3]
err_mean = stats[:,4]
N_lines = stats[:,5]
# choose only those matching from stats - now their order matches the fname order
lc_names = lc_names[ind]
mjd_span = mjd_span[ind]
mag_rms = mag_rms[ind]
mag_mean = mag_mean[ind]
err_mean = err_mean[ind]
N_lines = N_lines[ind]
print 'These should match: ', fname[0], lc_names[0]
DAT = np.column_stack((fname,lc_names, sigma_m, tau_m, sigma_hat, mjd_span, mag_rms, mag_mean, err_mean, N_lines))
newDAT=DAT[DAT[:,2].argsort()] # sort according to sigma_m
##################
# PLOTTING STATS #
##################
sigma_neg = [sigma_hat< 0.0]
sigma_pos = [sigma_hat>0]