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params_2d_obs_plot.py
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import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from adds import t0_gamma_from_bs_corr, get_contours, findNearest
from adds import read_predictions, t0_gamma_from_bs, get_t0_gamma_values_str
from adds import t0_gamma_to_sim_model, joint_conf, t0_gamma
import os
import os.path
#from matplotlib.ticker import FormatStrFormatter
from cov_models import get_cov
font = {'family' : 'serif', 'weight' : 'normal','size' : 34}
matplotlib.rc('font', **font)
lw = 2
skewer_length = 20
hubble = 0.678
realizations = 1000
trim = 16
los = 0
dir_dataset = 'bh2igm/dataset_files/'
dir_output = 'bh2igm/'
dataset_file_filter = 'ml_outputs_J'
file_list = os.listdir(dir_output)
dir_output_folder_list = [filename for filename in file_list
if dataset_file_filter in filename]
redshifts = [3.96, 4.0, 4.04,
4.20, 4.24, 4.35,
4.4, 4.45, 4.55,
4.6, 4.65,
4.75, 4.8]
#redshifts = [4.04, 4.24, 4.45, 4.65]
quasar_list = ['J021043', 'J025019', 'J145147','J120523',
'J004054', 'J025019', 'J030722', 'J033829']
cl = ['red', 'blue', 'orange','purple', 'olive', 'tan', 'green','magenta']
###############################################################################
#load UVB models
###############################################################################
fg20 = np.loadtxt('UVB_Models/Thermal_Parameter_FG20.txt', dtype=np.float32)
hm12 = np.loadtxt('UVB_Models/Thermal_Parameter_HM12.txt', dtype=np.float32)
ks19 = np.loadtxt('UVB_Models/Thermal_Parameter_KS19.txt', dtype=np.float32)
oh17 = np.loadtxt('UVB_Models/Thermal_Parameter_OH17.txt', dtype=np.float32)
p19 = np.loadtxt('UVB_Models/Thermal_Parameter_P19.txt', dtype=np.float32)
b19 = np.loadtxt('Observation/boera_2019.txt', dtype=np.float32)
w18 = np.loadtxt('Observation/walther_2018.txt', dtype=np.float32,
usecols = (0,2,3,4,5,6,7))
g21 = np.loadtxt('Observation/gaikwad_2021.txt', dtype=np.float32,
usecols = (0,2,3,4,5,6,7))
g20 = np.loadtxt('Observation/gaikwad_2020.txt', dtype=np.float32,
usecols = (0,2,3,4,5,6,7))
###############################################################################
#fig, ax = plt.subplots(1, 2, figsize=(16, 8))
fig, ax = plt.subplots(1, len(redshifts), figsize=(10*len(redshifts), 8))
fig.subplots_adjust(wspace=0.0, hspace=.0)
fig2, ax2 = plt.subplots(1, 2, figsize=(20, 8))
fig2.subplots_adjust(wspace=0.3, hspace=.0)
combined_densityw_mean = [ [] for _ in range(len(redshifts)) ]
combined_tempw_mean = [ [] for _ in range(len(redshifts)) ]
combined_densityw_std = [ [] for _ in range(len(redshifts)) ]
combined_tempw_std = [ [] for _ in range(len(redshifts)) ]
combined_corr = [ [] for _ in range(len(redshifts)) ]
combined_t0 = [ [] for _ in range(len(redshifts)) ]
combined_gamma = [ [] for _ in range(len(redshifts)) ]
combined_mask = [ [] for _ in range(len(redshifts)) ]
initialized = [False] * len(redshifts)
for file_folder in dir_output_folder_list:
string_split = (file_folder).split('_')
quasar = string_split[2]
redshift = np.float32(string_split[7][1:])
zi = findNearest(redshifts, redshift)[0]
qso_index = quasar_list.index(quasar)
qs_color = cl[qso_index]
filename = dir_dataset +"/"+ quasar + '_z'+"{:.2f}".format(redshift) + ".npy"
data = np.load(filename,'rb')
flux = data["flux_obs"]
# update mean for flux, fwhm and noise
mean_flux = data["mean_flux"]
fwhm = data["fwhm"]
noise = data["noise"]
bins = data["bins"]
flux_level = data["flux_level"]
noise_level = data["noise_level"]
noise_obs = data["noise_obs"]
snr = 1/np.mean(noise[noise!=0])
print()
print()
if snr>=0:# and quasar!='J145147': #and redshift==4.0:
_, densityw_mean, densityw_upper_1sigma , densityw_lower_1sigma, \
tempw_mean, tempw_upper_1sigma , tempw_lower_1sigma = \
read_predictions(dir_dataset+"/", dir_output+"/"+file_folder+"/",
quasar, redshift, False
)
densityw_std = densityw_upper_1sigma - densityw_mean
tempw_std = tempw_upper_1sigma - tempw_mean
mean_concat = np.hstack((densityw_mean, tempw_mean))
std_concat = np.hstack((densityw_std, tempw_std))
# remove trimmed pixels
flux = flux[trim:]
noise_obs = noise_obs[trim:]
t0 = np.full(realizations, np.nan)
gamma = np.full(realizations, np.nan)
#mask for valid flux pixels
mask = np.logical_or(flux > noise_obs, flux!=1)
t0_noncorr, gamma_noncorr = t0_gamma_from_bs(
densityw_mean[los][mask[los]], densityw_std[los][mask[los]],
tempw_mean[los][mask[los]], tempw_std[los][mask[los]])
t0m, gammam, tempstr, gammastr = get_t0_gamma_values_str(
t0_noncorr, gamma_noncorr
)
print('without corr = ', tempstr, gammastr)
sim_model = t0_gamma_to_sim_model(dir_dataset,
np.median(t0_noncorr), np.median(gamma_noncorr), redshift)
#sim_model = 'planck1_20_1024'
filename = dir_output+"/"+file_folder+"/"+'corr_'+sim_model+'.npy'
if os.path.isfile(filename):
corr = np.load(filename)
#print('file for correlation..', corr)
else:
corr = get_cov(dir_dataset+"/", dir_output+"/"+file_folder+"/", sim_model, redshift)
pixels = densityw_mean.shape[1]
concat_real_orig = np.random.multivariate_normal(
np.zeros(pixels*2), corr, realizations)
concat_real = mean_concat[los] + concat_real_orig * std_concat[los]
t0 = np.full(realizations, np.nan)
gamma = np.full(realizations, np.nan)
for ri in range(realizations):
t0[ri], gamma[ri] = t0_gamma(concat_real[ri, :pixels][mask],
concat_real[ri, pixels:][mask])
crl, crm, cru = np.quantile(concat_real, [0.16, 0.5, 0.84], axis=0)
t0m, gammam, tempstr, gammastr = get_t0_gamma_values_str(t0, gamma)
print('with corr = ', tempstr, gammastr)
#print(zi, qso_index, tempstr, gammastr, qs_color)
#print('densityw_mean', densityw_mean.shape)
if initialized[zi] == False:
initialized[zi] = True
combined_densityw_mean[zi] = densityw_mean
combined_tempw_mean[zi] = tempw_mean
combined_densityw_std[zi] = densityw_std
combined_tempw_std[zi] = tempw_std
combined_corr[zi] = corr
combined_mask[zi] = mask
#each LOS is an independent measurement, find joint PDF and than KDE
combined_t0[zi] = t0
combined_gamma[zi] = gamma
else:
combined_densityw_mean[zi] = np.vstack((combined_densityw_mean[zi], densityw_mean))
combined_tempw_mean[zi] = np.vstack((combined_tempw_mean[zi], tempw_mean))
combined_densityw_std[zi] = np.vstack((combined_densityw_std[zi], densityw_std))
combined_tempw_std[zi] = np.vstack((combined_tempw_std[zi], tempw_std))
combined_corr[zi] = np.dstack((combined_corr[zi], corr))
#each LOS is an independent measurement, find joint PDF and than KDE
combined_t0[zi] = np.vstack((combined_t0[zi], t0))
combined_gamma[zi] = np.vstack((combined_gamma[zi], gamma))
combined_mask[zi] = np.vstack((combined_mask[zi], mask))
#######################################################################
X,Y,Z,conf = get_contours(t0, gamma)
##ax[zi].scatter(10**t0[:1000], gamma[:1000], marker='.', alpha=0.04, c=qs_color)
ax[zi].contour(X,Y,Z, conf, colors=qs_color, alpha=0.6, linestyles='solid')
#ax.yaxis.get_major_locator().set_params(integer=True)
ax[zi].set_xlim(2999, 14900)
ax[zi].set_ylim(.89, 2.)
ax[zi].text(0.65, 0.04, r'$z=$'+"{:.2f}".format(redshift),
fontsize=28, transform = ax[zi].transAxes,
color='black')
# ax[zi].text(0.1, 1.08+qso_index*0.06, r'${\rm S/N=}$'+str(snr)+ r', $T_{\rm 0}$=' +
# tempstr + r'$, \gamma$=' + gammastr,
# fontsize=20, transform = ax[zi].transAxes, color=qs_color)
ax[zi].text(0.04,0.04+qso_index*0.06,
quasar+r', $T_{\rm 0}$=' +\
str(int(np.mean(10**t0)))+r'${\rm K}$' + ', ' + r'$\gamma=$'\
+str(np.round(np.mean(gamma), 2))\
+', '+r'${\rm S/N}=$'+str(int(snr))+\
', '+r'${\rm \langle F \rangle}=$'+str(np.round(np.mean(flux[mask]), 2)),
fontsize=20, transform = ax[zi].transAxes, color=qs_color)
ax[zi].set_xlabel(r'$T_{\rm 0 }{\rm [K]}$')
ax[zi].legend(frameon=False, fontsize=30, handlelength=1, loc='upper left')
ax[zi].tick_params(which='both',direction="in", width=1.5)
ax[zi].tick_params(which='major',length=14, top=True, left=True, right=True)
ax[zi].tick_params(which='minor',length=10, top=True, left=True, right=True)
ax[zi].minorticks_on()
if zi==0:
ax[zi].set_ylabel(r'$\gamma$')
else:
ax[zi].set_yticklabels([])
ax2[0].errorbar(redshift, t0m[0], yerr=
np.array([np.abs(t0m[2]),t0m[1]]).reshape(2,1),
fmt='o', color=qs_color, ecolor=qs_color, alpha=0.4)
ax2[0].set_xlabel(r'$z$')
ax2[0].set_ylabel(r'$T_{\rm 0}{\rm [K]}$')
ax2[0].set_xlim(3.4, 5.8)
ax2[1].errorbar(redshift, gammam[0],
yerr=np.array([np.abs(gammam[2]), np.abs(gammam[1])]).reshape(2,1),
fmt='o', color=qs_color, ecolor=qs_color, alpha=0.4)
ax2[1].set_xlabel(r'$z$')
ax2[1].set_ylabel(r'$\gamma$')
ax2[1].set_xlim(3.4, 5.8)
for zi, redshift in enumerate(redshifts):
if len(combined_densityw_mean[zi])>0:
t0, gamma = t0_gamma_from_bs_corr(
combined_densityw_mean[zi], combined_densityw_std[zi],
combined_tempw_mean[zi], combined_tempw_std[zi],
combined_corr[zi], combined_mask[zi], 10000)
#X, Y, Z, conf, t0, gamma = joint_conf(combined_t0[zi], combined_gamma[zi])
X,Y,Z,conf = get_contours(t0, gamma)
t0m, gammam, tempstr, gammastr = get_t0_gamma_values_str(t0, gamma, False)
t0_error=np.abs(np.array([t0m[2], t0m[1]]).reshape((2,1)))
gamma_error=np.abs(np.array([gammam[2], gammam[1]]).reshape((2,1)))
#ax[zi].scatter(10**t0[:1000], gamma[:1000], marker='.', alpha=0.04, c='black')
ax[zi].contour(X,Y,Z, conf, colors='black', alpha=1, linestyles='solid')
ax[zi].text(0.04, 0.9, r'$T_{\rm 0}$=' + tempstr+r'$\gamma=$'+gammastr,
fontsize=28, transform = ax[zi].transAxes, color='black')
if zi==0:
bar_label='This work'
else:
bar_label=''
# ax2[0].errorbar(redshift-0.01, t0m[0], yerr=t0_error,
# fmt='o', ecolor='black', color='black', alpha=1,
# markersize='14', elinewidth=4, label=bar_label)
# ax2[1].errorbar(redshift-0.01, gammam[0], yerr=gamma_error,
# fmt='o', ecolor='black', color='black', alpha=1,
# label=bar_label, markersize='14', elinewidth=4)
# #renormlize the heating rates from Gaikwad2021
# # fg20[:, 1] /= 0.7
# # p19[:, 1] /= 0.9
# # oh17[:, 1] /= 0.8
ax2[0].plot(fg20[:,0], fg20[:,1], color='blue',
linestyle='-', label=r'${\rm FG20 x 0.7}$', linewidth=lw)
ax2[0].plot(p19[:,0], p19[:,1], color='red',
linestyle='--', label=r'${\rm P19 x 0.9}$', linewidth=lw)
ax2[0].plot(hm12[:,0], hm12[:,1], color='purple',
linestyle=':', label=r'${\rm HM12}$', linewidth=lw)
ax2[0].plot(ks19[:,0], ks19[:,1], color='black',
linestyle='-.', label=r'${\rm KS19}$', linewidth=lw)
ax2[0].plot(oh17[:,0], oh17[:,1], color='green',
linestyle='-', label=r'${\rm O17 x 0.8}$', linewidth=lw)
ax2[0].errorbar(b19[:,0]-0.01, b19[:,2], yerr=[b19[:,3],b19[:,4]], fmt='^',
color='blue', ecolor='blue', alpha=1, label='Boera2019',
markersize='10', elinewidth=2)
ax2[0].errorbar(w18[:,0]+0.01, w18[:,1], yerr=[w18[:,2],w18[:,3]], fmt='*',
color='red', ecolor='red', alpha=1,label='Walther2018',
markersize='10', elinewidth=2)
ax2[0].errorbar(g21[:,0]-0.01, g21[:,1], yerr=[g21[:,2],g21[:,3]], fmt='o',
color='orange', ecolor='orange', label='Gaikwad2021',
markersize='10', elinewidth=2, alpha=1)
ax2[0].errorbar(g20[:,0]-0.01, g20[:,1], yerr=[g20[:,2],g20[:,3]], fmt='<',
color='purple', ecolor='purple', label='Gaikwad2020',
markersize='10', elinewidth=2, alpha=1)
ax2[0].set_ylim(5000, 14000)
ax2[0].legend(loc='upper center', fontsize=28, handlelength=1,
bbox_to_anchor=(1.1, 1.3), ncol=5, fancybox=True)
ax2[1].plot(fg20[:,0], fg20[:,2], color='blue', linestyle='-', linewidth=lw)
ax2[1].plot( p19[:,0], p19[:,2], color='red', linestyle='--', linewidth=lw)
ax2[1].plot(hm12[:,0], hm12[:,2], color='purple',linestyle=':', linewidth=lw)
ax2[1].plot(ks19[:,0], ks19[:,2], color='black', linestyle='-.', linewidth=lw)
ax2[1].plot(oh17[:,0], oh17[:,2], color='green', linestyle='-', linewidth=lw)
ax2[1].errorbar(b19[:,0]-0.01, b19[:,5], yerr=[b19[:,6],b19[:,7]], fmt='^',
color='blue', ecolor='blue', alpha=1, markersize='10',
elinewidth=2)
ax2[1].errorbar(w18[:,0]+0.01, w18[:,4], yerr=[w18[:,5],w18[:,6]], fmt='*',
color='red', ecolor='red', alpha=1, markersize='10',
elinewidth=2)
ax2[1].errorbar(g21[:,0]-0.01, g21[:,4], yerr=[g21[:,5],g21[:,6]], fmt='o',
color='orange', ecolor='orange', alpha=1, markersize='10',
elinewidth=2)
ax2[1].errorbar(g20[:,0]+0.01, g20[:,4], yerr=[g20[:,5],g20[:,6]], fmt='<',
color='purple', ecolor='purple', alpha=1, markersize='10',
elinewidth=2)
ax2[1].set_ylim(.99, 2.)
###############################################################################
for pi in range(2):
ax2[pi].tick_params(which='both',direction="in", width=1.5)
ax2[pi].tick_params(which='major',length=14, top=True, left=True, right=True)
ax2[pi].tick_params(which='minor',length=10, top=True, left=True, right=True)
ax2[pi].minorticks_on()
###############################################################################
fig.savefig('thermal_params_obs_joint'+'.pdf',
format='pdf', dpi=90, bbox_inches = 'tight')
fig2.savefig('evolution_obs_joint'+'.pdf',
format='pdf', dpi=90, bbox_inches = 'tight')