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Plot_Table_Routine.py
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"""
@file Plot_Table_Routine.py
@date September 6, 2023
@authors Fabrizio Cogato <fabrizio.cogato@inaf.it>
Michele Moresco <michele.moresco@unibo.it>
Please remember to cite: https://ui.adsabs.harvard.edu/abs/2023arXiv230901375C
"""
###################
#### Libraries ####
###################
from operator import truth
import os
import numpy as np
import random
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import pandas as pd
import emcee
from astropy.table import Table
from astropy.cosmology import FlatLambdaCDM, LambdaCDM, FlatwCDM, wCDM, Flatw0waCDM, w0waCDM
from scipy.stats import norm
from chainconsumer import ChainConsumer
##############################
#### Functions Definition ####
##############################
# Selecting Column Function
def column(matrix, i):
return [row[i] for row in matrix]
def plot_bestfitMCMC(cosmo, probe, chain_dim, nwalk, par_dim, dir_chain, dir_plot):
os.chdir(dir_chain+probe)
filename = "Chain_FlatPrior_{0}_{1}.txt".format(cosmo,probe)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
if cosmo=='FLCDM':
parstep = [samples[:,:,0], samples[:,:,1], samples[:,:,2], samples[:,:,3], samples[:,:,4], samples[:,:,5], samples[:,:,6]]
labels = ["H0", "Om", "M", "a", "b", "int", "rd"]
N=len(labels)
ndim=N
if cosmo=='LCDM':
parstep = [samples[:,:,0], samples[:,:,1], samples[:,:,2], samples[:,:,3], samples[:,:,4], samples[:,:,5], samples[:,:,6], samples[:,:,7]]
labels = ["H0", "Om", "OL", "M", "a", 'b' , 'int', "rd"]
N= len(labels)
ndim=N
if cosmo=='FwCDM':
parstep = [samples[:,:,0], samples[:,:,1], samples[:,:,2], samples[:,:,3], samples[:,:,4], samples[:,:,5], samples[:,:,6], samples[:,:,7]]
labels = ["H0", "Om", "w", "M", "a", 'b' , 'int', "rd"]
N= len(labels)
ndim=N
if cosmo=='wCDM':
parstep = [samples[:,:,0], samples[:,:,1], samples[:,:,2], samples[:,:,3], samples[:,:,4], samples[:,:,5], samples[:,:,6], samples[:,:,7], samples[:,:,8]]
labels = ["H0", "Om", "OL", "w", "M", "a", 'b' , 'int', "rd"]
N= len(labels)
ndim=N
if cosmo=='Fw0waCDM' :
parstep = [samples[:,:,0], samples[:,:,1], samples[:,:,2], samples[:,:,3], samples[:,:,4], samples[:,:,5], samples[:,:,6], samples[:,:,7], samples[:,:,8]]
labels = ["H0", "Om", "w0", "wa", "M", "a", 'b' , 'int', "rd"]
N= len(labels)
ndim=N
if cosmo=='w0waCDM':
parstep = [samples[:,:,0], samples[:,:,1], samples[:,:,2], samples[:,:,3], samples[:,:,4], samples[:,:,5], samples[:,:,6], samples[:,:,7], samples[:,:,8], samples[:,:,9]]
labels = ["H0", "Om", "OL", "w0$", "wa", "M", "a", 'b' , 'int', "rd"]
N= len(labels)
ndim=N
############### Create Plots
# Par vs. Step
fig, axes = plt.subplots(ndim, figsize=(10, 7), sharex=True)
plt.suptitle(f'ParVsStep')
for i in range(ndim):
ax = axes[i]
ax.plot(parstep[i], "k", alpha=0.3, zorder=1)
ax.set_xlim(0, len(samples))
ax.set_ylabel(labels[i], fontsize=16)
ax.yaxis.set_label_coords(-0.1, 0.5)
ax.tick_params(axis="x", labelsize=14)
ax.tick_params(axis="y", labelsize=14)
axes[-1].set_xlabel("Step Number", fontsize=16)
os.chdir(dir_plot)
plt.savefig(r'ParStep_FlatPrior_{0}_{1}.png'.format(cosmo, probe), dpi=350)
os.chdir(dir_home)
plt.close()
#Corner
color='#b2967d'
c = ChainConsumer()
c.add_chain(flat_samples, parameters=labels, color=color, shade=True, shade_alpha=1, bar_shade=True, linewidth=1)
c.configure(statistics='cumulative', diagonal_tick_labels=True, serif=True, usetex=False, summary=False, tick_font_size=16, label_font_size=28, max_ticks=4)
# Save
os.chdir(dir_plot)
c.plotter.plot(filename=f'Corner_{cosmo}_{probe}', parameters=labels, figsize="page")
os.chdir(dir_home)
plt.close()
def table_paper(probe, chain_dim, nwalk, dir_chain, dir_out):
dirchain=dir_chain+probe
os.chdir(dirchain)
FLCDM = "Chain_FlatPrior_FLCDM_{0}.txt".format(probe)
with open(FLCDM, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, 7))
flcdm = samples.reshape(-1, 7)
LCDM = "Chain_FlatPrior_LCDM_{0}.txt".format(probe)
with open(LCDM, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, 8))
lcdm = samples.reshape(-1, 8)
FwCDM = "Chain_FlatPrior_FwCDM_{0}.txt".format(probe)
with open(FwCDM, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, 8))
fwcdm = samples.reshape(-1, 8)
wCDM = "Chain_FlatPrior_wCDM_{0}.txt".format(probe)
with open(wCDM, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, 9))
wcdm = samples.reshape(-1, 9)
Fw0waCDM = "Chain_FlatPrior_Fw0waCDM_{0}.txt".format(probe)
with open(Fw0waCDM, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, 9))
fw0wacdm = samples.reshape(-1, 9)
W0WaCDM = "Chain_FlatPrior_w0waCDM_{0}.txt".format(probe)
with open(W0WaCDM, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, 10))
w0wacdm = samples.reshape(-1, 10)
perc = [0.15, 2.3, 15.85, 50, 84.15, 97.7, 99.85]
sH0_flcdm, sOm_flcdm, sM_flcdm, sa_flcdm, sb_flcdm, sint_flcdm, srd_flcdm = map(lambda v: (v[3], v[4]-v[3], v[3]-v[2], v[5]-v[3], v[3]-v[1], v[6]-v[3], v[3]-v[0]),
zip(*np.percentile(flcdm, perc, axis=0)))
H0_flcdm = str(np.around(sH0_flcdm[0],1))
H0errup_flcdm = str(np.around(sH0_flcdm[1],1))
H0errdw_flcdm = str(np.around(sH0_flcdm[2],1))
stringH0_flcdm = "$"+H0_flcdm+"^{+"+H0errup_flcdm+"}_{-"+H0errdw_flcdm+"}$"
Om_flcdm = str(np.around(sOm_flcdm[0],3))
Omerrup_flcdm = str(np.around(sOm_flcdm[1],3))
Omerrdw_flcdm = str(np.around(sOm_flcdm[2],3))
stringOm_flcdm = "$"+Om_flcdm+"^{+"+Omerrup_flcdm+"}_{-"+Omerrdw_flcdm+"}$"
m=column(flcdm,2)
M=[]
for i in range(len(m)):
M.append(-1*m[i])
M_flcdm=str(np.around((np.percentile(M,50)),1))
Merrup_flcdm = str(np.around((np.percentile(M,84.15)-np.percentile(M,50)),1))
Merrdw_flcdm = str(np.around((np.percentile(M,50)-np.percentile(M,15.85)),1))
stringM_flcdm = "$"+M_flcdm+"^{+"+Merrup_flcdm+"}_{-"+Merrdw_flcdm+"}$"
a_flcdm = str(np.around(sa_flcdm[0],3))
aerrup_flcdm = str(np.around(sa_flcdm[1],3))
aerrdw_flcdm = str(np.around(sa_flcdm[2],3))
stringa_flcdm = "$"+a_flcdm+"^{+"+aerrup_flcdm+"}_{-"+aerrdw_flcdm+"}$"
b_flcdm = str(np.around(sb_flcdm[0],2))
berrup_flcdm = str(np.around(sb_flcdm[1],2))
berrdw_flcdm = str(np.around(sb_flcdm[2],2))
stringb_flcdm = "$"+b_flcdm+"^{+"+berrup_flcdm+"}_{-"+berrdw_flcdm+"}$"
int_flcdm = str(np.around(sint_flcdm[0],3))
interrup_flcdm = str(np.around(sint_flcdm[1],3))
interrdw_flcdm = str(np.around(sint_flcdm[2],3))
stringint_flcdm = "$"+int_flcdm+"^{+"+interrup_flcdm+"}_{-"+interrdw_flcdm+"}$"
rd_flcdm = str(np.around(srd_flcdm[0],1))
rderrup_flcdm = str(np.around(srd_flcdm[1],1))
rderrdw_flcdm = str(np.around(srd_flcdm[2],1))
stringrd_flcdm = "$"+rd_flcdm+"^{+"+rderrup_flcdm+"}_{-"+rderrdw_flcdm+"}$"
sH0_lcdm, sOm_lcdm, sOl_lcdm, sM_lcdm, sa_lcdm, sb_lcdm, sint_lcdm, srd_lcdm = map(lambda v: (v[3], v[4]-v[3], v[3]-v[2], v[5]-v[3], v[3]-v[1], v[6]-v[3], v[3]-v[0]),
zip(*np.percentile(lcdm, perc, axis=0)))
H0_lcdm = str(np.around(sH0_lcdm[0],1))
H0errup_lcdm = str(np.around(sH0_lcdm[1],1))
H0errdw_lcdm = str(np.around(sH0_lcdm[2],1))
stringH0_lcdm = "$"+H0_lcdm+"^{+"+H0errup_lcdm+"}_{-"+H0errdw_lcdm+"}$"
Om_lcdm = str(np.around(sOm_lcdm[0],3))
Omerrup_lcdm = str(np.around(sOm_lcdm[1],3))
Omerrdw_lcdm = str(np.around(sOm_lcdm[2],3))
stringOm_lcdm = "$"+Om_lcdm+"^{+"+Omerrup_lcdm+"}_{-"+Omerrdw_lcdm+"}$"
Ol_lcdm = str(np.around(sOl_lcdm[0],3))
Olerrup_lcdm = str(np.around(sOl_lcdm[1],3))
Olerrdw_lcdm = str(np.around(sOl_lcdm[2],3))
stringsOl_lcdm = "$"+Ol_lcdm+"^{+"+Olerrup_lcdm+"}_{-"+Olerrdw_lcdm+"}$"
Om=np.array(column(lcdm,1))
OL=np.array(column(lcdm,2))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
Ok_lcdm=str(np.around((np.percentile(Ok,50)),3))
Okerrup_lcdm = str(np.around((np.percentile(Ok,84.15)-np.percentile(Ok,50)),3))
Okerrdw_lcdm = str(np.around((np.percentile(Ok,50)-np.percentile(Ok,15.85)),3))
stringOk_lcdm = "$"+Ok_lcdm+"^{+"+Okerrup_lcdm+"}_{-"+Okerrdw_lcdm+"}$"
m=column(lcdm,3)
M=[]
for i in range(len(m)):
M.append(-1*m[i])
M_lcdm=str(np.around((np.percentile(M,50)),1))
Merrup_lcdm = str(np.around((np.percentile(M,84.15)-np.percentile(M,50)),1))
Merrdw_lcdm = str(np.around((np.percentile(M,50)-np.percentile(M,15.85)),1))
stringM_lcdm = "$"+M_lcdm+"^{+"+Merrup_lcdm+"}_{-"+Merrdw_lcdm+"}$"
a_lcdm = str(np.around(sa_lcdm[0],3))
aerrup_lcdm = str(np.around(sa_lcdm[1],3))
aerrdw_lcdm = str(np.around(sa_lcdm[2],3))
stringa_lcdm = "$"+a_lcdm+"^{+"+aerrup_lcdm+"}_{-"+aerrdw_lcdm+"}$"
b_lcdm = str(np.around(sb_lcdm[0],2))
berrup_lcdm = str(np.around(sb_lcdm[1],2))
berrdw_lcdm = str(np.around(sb_lcdm[2],2))
stringb_lcdm = "$"+b_lcdm+"^{+"+berrup_lcdm+"}_{-"+berrdw_lcdm+"}$"
int_lcdm = str(np.around(sint_lcdm[0],3))
interrup_lcdm = str(np.around(sint_lcdm[1],3))
interrdw_lcdm = str(np.around(sint_lcdm[2],3))
stringint_lcdm = "$"+int_lcdm+"^{+"+interrup_lcdm+"}_{-"+interrdw_lcdm+"}$"
rd_lcdm = str(np.around(srd_lcdm[0],1))
rderrup_lcdm = str(np.around(srd_lcdm[1],1))
rderrdw_lcdm = str(np.around(srd_lcdm[2],1))
stringrd_lcdm = "$"+rd_lcdm+"^{+"+rderrup_lcdm+"}_{-"+rderrdw_lcdm+"}$"
sH0_fwcdm, sOm_fwcdm, sw_fwcdm, sM_fwcdm, sa_fwcdm, sb_fwcdm, sint_fwcdm, srd_fwcdm = map(lambda v: (v[3], v[4]-v[3], v[3]-v[2], v[5]-v[3], v[3]-v[1], v[6]-v[3], v[3]-v[0]),
zip(*np.percentile(fwcdm, perc, axis=0)))
H0_fwcdm = str(np.around(sH0_fwcdm[0],1))
H0errup_fwcdm = str(np.around(sH0_fwcdm[1],1))
H0errdw_fwcdm = str(np.around(sH0_fwcdm[2],1))
stringH0_fwcdm = "$"+H0_fwcdm+"^{+"+H0errup_fwcdm+"}_{-"+H0errdw_fwcdm+"}$"
Om_fwcdm = str(np.around(sOm_fwcdm[0],3))
Omerrup_fwcdm = str(np.around(sOm_fwcdm[1],3))
Omerrdw_fwcdm = str(np.around(sOm_fwcdm[2],3))
stringOm_fwcdm = "$"+Om_fwcdm+"^{+"+Omerrup_fwcdm+"}_{-"+Omerrdw_fwcdm+"}$"
w_fwcdm = str(np.around(sw_fwcdm[0],2))
werrup_fwcdm = str(np.around(sw_fwcdm[1],2))
werrdw_fwcdm = str(np.around(sw_fwcdm[2],2))
stringw_fwcdm = "$"+w_fwcdm+"^{+"+werrup_fwcdm+"}_{-"+werrdw_fwcdm+"}$"
m=column(fwcdm,3)
M=[]
for i in range(len(m)):
M.append(-1*m[i])
M_fwcdm=str(np.around((np.percentile(M,50)),1))
Merrup_fwcdm = str(np.around((np.percentile(M,84.15)-np.percentile(M,50)),1))
Merrdw_fwcdm = str(np.around((np.percentile(M,50)-np.percentile(M,15.85)),1))
stringM_fwcdm = "$"+M_fwcdm+"^{+"+Merrup_fwcdm+"}_{-"+Merrdw_fwcdm+"}$"
a_fwcdm = str(np.around(sa_fwcdm[0],3))
aerrup_fwcdm = str(np.around(sa_fwcdm[1],3))
aerrdw_fwcdm = str(np.around(sa_fwcdm[2],3))
stringa_fwcdm = "$"+a_fwcdm+"^{+"+aerrup_fwcdm+"}_{-"+aerrdw_fwcdm+"}$"
b_fwcdm = str(np.around(sb_fwcdm[0],2))
berrup_fwcdm = str(np.around(sb_fwcdm[1],2))
berrdw_fwcdm = str(np.around(sb_fwcdm[2],2))
stringb_fwcdm = "$"+b_fwcdm+"^{+"+berrup_fwcdm+"}_{-"+berrdw_fwcdm+"}$"
int_fwcdm = str(np.around(sint_fwcdm[0],3))
interrup_fwcdm = str(np.around(sint_fwcdm[1],3))
interrdw_fwcdm = str(np.around(sint_fwcdm[2],3))
stringint_fwcdm = "$"+int_fwcdm+"^{+"+interrup_fwcdm+"}_{-"+interrdw_fwcdm+"}$"
rd_fwcdm = str(np.around(srd_fwcdm[0],1))
rderrup_fwcdm = str(np.around(srd_fwcdm[1],1))
rderrdw_fwcdm = str(np.around(srd_fwcdm[2],1))
stringrd_fwcdm = "$"+rd_fwcdm+"^{+"+rderrup_fwcdm+"}_{-"+rderrdw_fwcdm+"}$"
sH0_wcdm, sOm_wcdm, sOl_wcdm, sw_wcdm, sM_wcdm, sa_wcdm, sb_wcdm, sint_wcdm, srd_wcdm = map(lambda v: (v[3], v[4]-v[3], v[3]-v[2], v[5]-v[3], v[3]-v[1], v[6]-v[3], v[3]-v[0]),
zip(*np.percentile(wcdm, perc, axis=0)))
H0_wcdm = str(np.around(sH0_wcdm[0],1))
H0errup_wcdm = str(np.around(sH0_wcdm[1],1))
H0errdw_wcdm = str(np.around(sH0_wcdm[2],1))
stringH0_wcdm = "$"+H0_wcdm+"^{+"+H0errup_wcdm+"}_{-"+H0errdw_wcdm+"}$"
Om_wcdm = str(np.around(sOm_wcdm[0],3))
Omerrup_wcdm = str(np.around(sOm_wcdm[1],3))
Omerrdw_wcdm = str(np.around(sOm_wcdm[2],3))
stringOm_wcdm = "$"+Om_wcdm+"^{+"+Omerrup_wcdm+"}_{-"+Omerrdw_wcdm+"}$"
Ol_wcdm = str(np.around(sOl_wcdm[0],3))
Olerrup_wcdm = str(np.around(sOl_wcdm[1],3))
Olerrdw_wcdm = str(np.around(sOl_wcdm[2],3))
stringsOl_wcdm = "$"+Ol_wcdm+"^{+"+Olerrup_wcdm+"}_{-"+Olerrdw_wcdm+"}$"
Om=np.array(column(wcdm,1))
OL=np.array(column(wcdm,2))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
Ok_wcdm=str(np.around((np.percentile(Ok,50)),3))
Okerrup_wcdm = str(np.around((np.percentile(Ok,84.15)-np.percentile(Ok,50)),3))
Okerrdw_wcdm = str(np.around((np.percentile(Ok,50)-np.percentile(Ok,15.85)),3))
stringOk_wcdm = "$"+Ok_wcdm+"^{+"+Okerrup_wcdm+"}_{-"+Okerrdw_wcdm+"}$"
w_wcdm = str(np.around(sw_wcdm[0],2))
werrup_wcdm = str(np.around(sw_wcdm[1],2))
werrdw_wcdm = str(np.around(sw_wcdm[2],2))
stringw_wcdm = "$"+w_wcdm+"^{+"+werrup_wcdm+"}_{-"+werrdw_wcdm+"}$"
m=column(wcdm,4)
M=[]
for i in range(len(m)):
M.append(-1*m[i])
M_wcdm=str(np.around((np.percentile(M,50)),1))
Merrup_wcdm = str(np.around((np.percentile(M,84.15)-np.percentile(M,50)),1))
Merrdw_wcdm = str(np.around((np.percentile(M,50)-np.percentile(M,15.85)),1))
stringM_wcdm = "$"+M_wcdm+"^{+"+Merrup_wcdm+"}_{-"+Merrdw_wcdm+"}$"
a_wcdm = str(np.around(sa_wcdm[0],3))
aerrup_wcdm = str(np.around(sa_wcdm[1],3))
aerrdw_wcdm = str(np.around(sa_wcdm[2],3))
stringa_wcdm = "$"+a_wcdm+"^{+"+aerrup_wcdm+"}_{-"+aerrdw_wcdm+"}$"
b_wcdm = str(np.around(sb_wcdm[0],2))
berrup_wcdm = str(np.around(sb_wcdm[1],2))
berrdw_wcdm = str(np.around(sb_wcdm[2],2))
stringb_wcdm = "$"+b_wcdm+"^{+"+berrup_wcdm+"}_{-"+berrdw_wcdm+"}$"
int_wcdm = str(np.around(sint_wcdm[0],3))
interrup_wcdm = str(np.around(sint_wcdm[1],3))
interrdw_wcdm = str(np.around(sint_wcdm[2],3))
stringint_wcdm = "$"+int_wcdm+"^{+"+interrup_wcdm+"}_{-"+interrdw_wcdm+"}$"
rd_wcdm = str(np.around(srd_wcdm[0],1))
rderrup_wcdm = str(np.around(srd_wcdm[1],1))
rderrdw_wcdm = str(np.around(srd_wcdm[2],1))
stringrd_wcdm = "$"+rd_wcdm+"^{+"+rderrup_wcdm+"}_{-"+rderrdw_wcdm+"}$"
sH0_fw0wacdm, sOm_fw0wacdm, sw0_fw0wacdm, wsa_fw0wacdm, sM_fw0wacdm, sa_fw0wacdm, sb_fw0wacdm, sint_fw0wacdm, srd_fw0wacdm = map(lambda v: (v[3], v[4]-v[3], v[3]-v[2], v[5]-v[3], v[3]-v[1], v[6]-v[3], v[3]-v[0]),
zip(*np.percentile(fw0wacdm, perc, axis=0)))
H0_fw0wacdm = str(np.around(sH0_fw0wacdm[0],1))
H0errup_fw0wacdm = str(np.around(sH0_fw0wacdm[1],1))
H0errdwsa_fw0wacdm = str(np.around(sH0_fw0wacdm[2],1))
stringH0_fw0wacdm = "$"+H0_fw0wacdm+"^{+"+H0errup_fw0wacdm+"}_{-"+H0errdwsa_fw0wacdm+"}$"
Om_fw0wacdm = str(np.around(sOm_fw0wacdm[0],3))
Omerrup_fw0wacdm = str(np.around(sOm_fw0wacdm[1],3))
Omerrdwsa_fw0wacdm = str(np.around(sOm_fw0wacdm[2],3))
stringOm_fw0wacdm = "$"+Om_fw0wacdm+"^{+"+Omerrup_fw0wacdm+"}_{-"+Omerrdwsa_fw0wacdm+"}$"
w0_fw0wacdm = str(np.around(sw0_fw0wacdm[0],2))
w0errup_fw0wacdm = str(np.around(sw0_fw0wacdm[1],2))
w0errdw_fw0wacdm = str(np.around(sw0_fw0wacdm[2],2))
stringw0_fw0wacdm = "$"+w0_fw0wacdm+"^{+"+w0errup_fw0wacdm+"}_{-"+w0errdw_fw0wacdm+"}$"
wa_fw0wacdm = str(np.around(wsa_fw0wacdm[0],2))
waerrup_fw0wacdm = str(np.around(wsa_fw0wacdm[1],2))
waerrdw_fw0wacdm = str(np.around(wsa_fw0wacdm[2],2))
stringwa_fw0wacdm = "$"+wa_fw0wacdm+"^{+"+waerrup_fw0wacdm+"}_{-"+waerrdw_fw0wacdm+"}$"
m=column(fw0wacdm,4)
M=[]
for i in range(len(m)):
M.append(-1*m[i])
M_fw0wacdm=str(np.around((np.percentile(M,50)),1))
Merrup_fw0wacdm = str(np.around((np.percentile(M,84.15)-np.percentile(M,50)),1))
Merrdw_fw0wacdm = str(np.around((np.percentile(M,50)-np.percentile(M,15.85)),1))
stringM_fw0wacdm = "$"+M_fw0wacdm+"^{+"+Merrup_fw0wacdm+"}_{-"+Merrdw_fw0wacdm+"}$"
a_fw0wacdm = str(np.around(sa_fw0wacdm[0],3))
aerrup_fw0wacdm = str(np.around(sa_fw0wacdm[1],3))
aerrdwsa_fw0wacdm = str(np.around(sa_fw0wacdm[2],3))
stringa_fw0wacdm = "$"+a_fw0wacdm+"^{+"+aerrup_fw0wacdm+"}_{-"+aerrdwsa_fw0wacdm+"}$"
b_fw0wacdm = str(np.around(sb_fw0wacdm[0],2))
berrup_fw0wacdm = str(np.around(sb_fw0wacdm[1],2))
berrdwsa_fw0wacdm = str(np.around(sb_fw0wacdm[2],2))
stringb_fw0wacdm = "$"+b_fw0wacdm+"^{+"+berrup_fw0wacdm+"}_{-"+berrdwsa_fw0wacdm+"}$"
int_fw0wacdm = str(np.around(sint_fw0wacdm[0],3))
interrup_fw0wacdm = str(np.around(sint_fw0wacdm[1],3))
interrdwsa_fw0wacdm = str(np.around(sint_fw0wacdm[2],3))
stringint_fw0wacdm = "$"+int_fw0wacdm+"^{+"+interrup_fw0wacdm+"}_{-"+interrdwsa_fw0wacdm+"}$"
rd_fw0wacdm = str(np.around(srd_fw0wacdm[0],1))
rderrup_fw0wacdm = str(np.around(srd_fw0wacdm[1],1))
rderrdwsa_fw0wacdm = str(np.around(srd_fw0wacdm[2],1))
stringrd_fw0wacdm = "$"+rd_fw0wacdm+"^{+"+rderrup_fw0wacdm+"}_{-"+rderrdwsa_fw0wacdm+"}$"
sH0_w0wacdm, sOm_w0wacdm, sOl_w0wacdm, sw0_w0wacdm, wsa_w0wacdm, sM_w0wacdm, sa_w0wacdm, sb_w0wacdm, sint_w0wacdm, srd_w0wacdm = map(lambda v: (v[3], v[4]-v[3], v[3]-v[2], v[5]-v[3], v[3]-v[1], v[6]-v[3], v[3]-v[0]),
zip(*np.percentile(w0wacdm, perc, axis=0)))
H0_w0wacdm = str(np.around(sH0_w0wacdm[0],1))
H0errup_w0wacdm = str(np.around(sH0_w0wacdm[1],1))
H0errdwsa_w0wacdm = str(np.around(sH0_w0wacdm[2],1))
stringH0_w0wacdm = "$"+H0_w0wacdm+"^{+"+H0errup_w0wacdm+"}_{-"+H0errdwsa_w0wacdm+"}$"
Om_w0wacdm = str(np.around(sOm_w0wacdm[0],3))
Omerrup_w0wacdm = str(np.around(sOm_w0wacdm[1],3))
Omerrdwsa_w0wacdm = str(np.around(sOm_w0wacdm[2],3))
stringOm_w0wacdm = "$"+Om_w0wacdm+"^{+"+Omerrup_w0wacdm+"}_{-"+Omerrdwsa_w0wacdm+"}$"
Ol_w0wacdm = str(np.around(sOl_w0wacdm[0],3))
Olerrup_w0wacdm = str(np.around(sOl_w0wacdm[1],3))
Olerrdwsa_w0wacdm = str(np.around(sOl_w0wacdm[2],3))
stringsOl_w0wacdm = "$"+Ol_w0wacdm+"^{+"+Olerrup_w0wacdm+"}_{-"+Olerrdwsa_w0wacdm+"}$"
Om=np.array(column(w0wacdm,1))
OL=np.array(column(w0wacdm,2))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
Ok_w0wacdm=str(np.around((np.percentile(Ok,50)),3))
Okerrup_w0wacdm = str(np.around((np.percentile(Ok,84.15)-np.percentile(Ok,50)),3))
Okerrdw_w0wacdm = str(np.around((np.percentile(Ok,50)-np.percentile(Ok,15.85)),3))
stringOk_w0wacdm = "$"+Ok_w0wacdm+"^{+"+Okerrup_w0wacdm+"}_{-"+Okerrdw_w0wacdm+"}$"
w0_w0wacdm = str(np.around(sw0_w0wacdm[0],2))
w0errup_w0wacdm = str(np.around(sw0_w0wacdm[1],2))
w0errdwsa_w0wacdm = str(np.around(sw0_w0wacdm[2],2))
stringw0_w0wacdm = "$"+w0_w0wacdm+"^{+"+w0errup_w0wacdm+"}_{-"+w0errdwsa_w0wacdm+"}$"
wa_w0wacdm = str(np.around(wsa_w0wacdm[0],2))
waerrup_w0wacdm = str(np.around(wsa_w0wacdm[1],2))
waerrdw_w0wacdm = str(np.around(wsa_w0wacdm[2],2))
stringwa_w0wacdm = "$"+wa_w0wacdm+"^{+"+waerrup_w0wacdm+"}_{-"+waerrdw_w0wacdm+"}$"
m=column(w0wacdm,5)
M=[]
for i in range(len(m)):
M.append(-1*m[i])
M_w0wacdm=str(np.around((np.percentile(M,50)),1))
Merrup_w0wacdm = str(np.around((np.percentile(M,84.15)-np.percentile(M,50)),1))
Merrdw_w0wacdm = str(np.around((np.percentile(M,50)-np.percentile(M,15.85)),1))
stringM_w0wacdm = "$"+M_w0wacdm+"^{+"+Merrup_w0wacdm+"}_{-"+Merrdw_w0wacdm+"}$"
a_w0wacdm = str(np.around(sa_w0wacdm[0],3))
aerrup_w0wacdm = str(np.around(sa_w0wacdm[1],3))
aerrdwsa_w0wacdm = str(np.around(sa_w0wacdm[2],3))
stringa_w0wacdm = "$"+a_w0wacdm+"^{+"+aerrup_w0wacdm+"}_{-"+aerrdwsa_w0wacdm+"}$"
b_w0wacdm = str(np.around(sb_w0wacdm[0],2))
berrup_w0wacdm = str(np.around(sb_w0wacdm[1],2))
berrdwsa_w0wacdm = str(np.around(sb_w0wacdm[2],2))
stringb_w0wacdm = "$"+b_w0wacdm+"^{+"+berrup_w0wacdm+"}_{-"+berrdwsa_w0wacdm+"}$"
int_w0wacdm = str(np.around(sint_w0wacdm[0],3))
interrup_w0wacdm = str(np.around(sint_w0wacdm[1],3))
interrdwsa_w0wacdm = str(np.around(sint_w0wacdm[2],3))
stringint_w0wacdm = "$"+int_w0wacdm+"^{+"+interrup_w0wacdm+"}_{-"+interrdwsa_w0wacdm+"}$"
rd_w0wacdm = str(np.around(srd_w0wacdm[0],1))
rderrup_w0wacdm = str(np.around(srd_w0wacdm[1],1))
rderrdwsa_w0wacdm = str(np.around(srd_w0wacdm[2],1))
stringrd_w0wacdm = "$"+rd_w0wacdm+"^{+"+rderrup_w0wacdm+"}_{-"+rderrdwsa_w0wacdm+"}$"
os.chdir(dir_out)
name_output=r'Table_{0}_Paper.dat'.format(probe)
fileout=open(name_output,"w")
fileout.write("\\begin{table}[H] \n")
fileout.write("\\renewcommand{\\arraystretch}{1.4} \n")
fileout.write(" \\begin{center}\n")
fileout.write(" \\begin{tabular}{ccccccc}\n")
fileout.write(" \hline\n")
fileout.write(" & Flat $\Lambda$CDM & $\Lambda$CDM & Flat $w$CDM & $w$CDM & Flat $w_0w_a$CDM & $w_0w_a$CDM \\\ \n")
fileout.write(" \hline\n")
fileout.write(" \hline\n")
fileout.write(" $H_0$ & "+stringH0_flcdm+" & "+stringH0_lcdm+" & "+stringH0_fwcdm+" & "+stringH0_wcdm+" & "+stringH0_fw0wacdm+" & "+stringH0_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" $\Omega_m$ & "+stringOm_flcdm+" & "+stringOm_lcdm+" & "+stringOm_fwcdm+" & "+stringOm_wcdm+" & "+stringOm_fw0wacdm+" & "+stringOm_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" $\Omega_{\Lambda}$ & -- & "+stringsOl_lcdm+" & -- & "+stringsOl_wcdm+" & -- & "+stringsOl_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" $\Omega_k$ & -- & "+stringOk_lcdm+" & -- & "+stringOk_wcdm+" & -- & "+stringOk_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" $w_0$ & -- & -- & "+stringw_fwcdm+" & "+stringw_wcdm+" & "+stringw0_fw0wacdm+" & "+stringw0_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" $w_a$ & -- & -- & -- & -- & "+stringwa_fw0wacdm+" & "+stringwa_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" M & "+stringM_flcdm+" & "+stringM_lcdm+" & "+stringM_fwcdm+" & "+stringM_wcdm+" & "+stringM_fw0wacdm+" & "+stringM_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" a & "+stringa_flcdm+" & "+stringa_lcdm+" & "+stringa_fwcdm+" & "+stringa_wcdm+" & "+stringa_fw0wacdm+" & "+stringa_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" b & "+stringb_flcdm+" & "+stringb_lcdm+" & "+stringb_fwcdm+" & "+stringb_wcdm+" & "+stringb_fw0wacdm+" & "+stringb_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" $\sigma_{int}$ & "+stringint_flcdm+" & "+stringint_lcdm+" & "+stringint_fwcdm+" & "+stringint_wcdm+" & "+stringint_fw0wacdm+" & "+stringint_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" $r_d$ & "+stringrd_flcdm+" & "+stringrd_lcdm+" & "+stringrd_fwcdm+" & "+stringrd_wcdm+" & "+stringrd_fw0wacdm+" & "+stringrd_w0wacdm+" \\\ [0.3ex] \n")
fileout.write(" \hline\n")
fileout.write(" \hline\n")
fileout.write(" \end{tabular}\n")
fileout.write(" \end{center}\n")
fileout.write("\end{table}\n")
return fileout.close()
def Table_MCMC(cosmo, chain_dim, nwalk, par_dim, cosmolatex, dir_chain, dir_out):
### 4 ####
os.chdir(dir_chain+'BAOCCSNGRB')
filename = 'Chain_FlatPrior_{0}_BAOCCSNGRB.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
BAO_CC_SN_GRB = flat_samples
if cosmo=='LCDM':
H0=np.array(column(BAO_CC_SN_GRB,0))
Om=np.array(column(BAO_CC_SN_GRB,1))
OL=np.array(column(BAO_CC_SN_GRB,2))
M=np.array(column(BAO_CC_SN_GRB,3))
a=np.array(column(BAO_CC_SN_GRB,4))
b=np.array(column(BAO_CC_SN_GRB,5))
scat=np.array(column(BAO_CC_SN_GRB,6))
rd=np.array(column(BAO_CC_SN_GRB,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(BAO_CC_SN_GRB,0))
Om=np.array(column(BAO_CC_SN_GRB,1))
OL=np.array(column(BAO_CC_SN_GRB,2))
w=np.array(column(BAO_CC_SN_GRB,3))
M=np.array(column(BAO_CC_SN_GRB,4))
a=np.array(column(BAO_CC_SN_GRB,5))
b=np.array(column(BAO_CC_SN_GRB,6))
scat=np.array(column(BAO_CC_SN_GRB,7))
rd=np.array(column(BAO_CC_SN_GRB,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(BAO_CC_SN_GRB,0))
Om=np.array(column(BAO_CC_SN_GRB,1))
OL=np.array(column(BAO_CC_SN_GRB,2))
w0=np.array(column(BAO_CC_SN_GRB,3))
wa=np.array(column(BAO_CC_SN_GRB,4))
M=np.array(column(BAO_CC_SN_GRB,5))
a=np.array(column(BAO_CC_SN_GRB,6))
b=np.array(column(BAO_CC_SN_GRB,7))
scat=np.array(column(BAO_CC_SN_GRB,8))
rd=np.array(column(BAO_CC_SN_GRB,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
### 3 ###
os.chdir(dir_chain+'BAOCCGRB')
filename = 'Chain_FlatPrior_{0}_BAOCCGRB.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
BAO_CC_GRB = flat_samples
if cosmo=='LCDM':
H0=np.array(column(BAO_CC_GRB,0))
Om=np.array(column(BAO_CC_GRB,1))
OL=np.array(column(BAO_CC_GRB,2))
M=np.array(column(BAO_CC_GRB,3))
a=np.array(column(BAO_CC_GRB,4))
b=np.array(column(BAO_CC_GRB,5))
scat=np.array(column(BAO_CC_GRB,6))
rd=np.array(column(BAO_CC_GRB,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_GRB = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(BAO_CC_GRB,0))
Om=np.array(column(BAO_CC_GRB,1))
OL=np.array(column(BAO_CC_GRB,2))
w=np.array(column(BAO_CC_GRB,3))
M=np.array(column(BAO_CC_GRB,4))
a=np.array(column(BAO_CC_GRB,5))
b=np.array(column(BAO_CC_GRB,6))
scat=np.array(column(BAO_CC_GRB,7))
rd=np.array(column(BAO_CC_GRB,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_GRB = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(BAO_CC_GRB,0))
Om=np.array(column(BAO_CC_GRB,1))
OL=np.array(column(BAO_CC_GRB,2))
w0=np.array(column(BAO_CC_GRB,3))
wa=np.array(column(BAO_CC_GRB,4))
M=np.array(column(BAO_CC_GRB,5))
a=np.array(column(BAO_CC_GRB,6))
b=np.array(column(BAO_CC_GRB,7))
scat=np.array(column(BAO_CC_GRB,8))
rd=np.array(column(BAO_CC_GRB,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_GRB = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
os.chdir(dir_chain+'BAOSNGRB')
filename = 'Chain_FlatPrior_{0}_BAOSNGRB.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
BAO_SN_GRB = flat_samples
if cosmo=='LCDM':
H0=np.array(column(BAO_SN_GRB,0))
Om=np.array(column(BAO_SN_GRB,1))
OL=np.array(column(BAO_SN_GRB,2))
M=np.array(column(BAO_SN_GRB,3))
a=np.array(column(BAO_SN_GRB,4))
b=np.array(column(BAO_SN_GRB,5))
scat=np.array(column(BAO_SN_GRB,6))
rd=np.array(column(BAO_SN_GRB,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(BAO_SN_GRB,0))
Om=np.array(column(BAO_SN_GRB,1))
OL=np.array(column(BAO_SN_GRB,2))
w=np.array(column(BAO_SN_GRB,3))
M=np.array(column(BAO_SN_GRB,4))
a=np.array(column(BAO_SN_GRB,5))
b=np.array(column(BAO_SN_GRB,6))
scat=np.array(column(BAO_SN_GRB,7))
rd=np.array(column(BAO_SN_GRB,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(BAO_SN_GRB,0))
Om=np.array(column(BAO_SN_GRB,1))
OL=np.array(column(BAO_SN_GRB,2))
w0=np.array(column(BAO_SN_GRB,3))
wa=np.array(column(BAO_SN_GRB,4))
M=np.array(column(BAO_SN_GRB,5))
a=np.array(column(BAO_SN_GRB,6))
b=np.array(column(BAO_SN_GRB,7))
scat=np.array(column(BAO_SN_GRB,8))
rd=np.array(column(BAO_SN_GRB,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
os.chdir(dir_chain+'CCSNGRB')
filename = 'Chain_FlatPrior_{0}_CCSNGRB.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
CC_SN_GRB = flat_samples
if cosmo=='LCDM':
H0=np.array(column(CC_SN_GRB,0))
Om=np.array(column(CC_SN_GRB,1))
OL=np.array(column(CC_SN_GRB,2))
M=np.array(column(CC_SN_GRB,3))
a=np.array(column(CC_SN_GRB,4))
b=np.array(column(CC_SN_GRB,5))
scat=np.array(column(CC_SN_GRB,6))
rd=np.array(column(CC_SN_GRB,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
CC_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(CC_SN_GRB,0))
Om=np.array(column(CC_SN_GRB,1))
OL=np.array(column(CC_SN_GRB,2))
w=np.array(column(CC_SN_GRB,3))
M=np.array(column(CC_SN_GRB,4))
a=np.array(column(CC_SN_GRB,5))
b=np.array(column(CC_SN_GRB,6))
scat=np.array(column(CC_SN_GRB,7))
rd=np.array(column(CC_SN_GRB,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
CC_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(CC_SN_GRB,0))
Om=np.array(column(CC_SN_GRB,1))
OL=np.array(column(CC_SN_GRB,2))
w0=np.array(column(CC_SN_GRB,3))
wa=np.array(column(CC_SN_GRB,4))
M=np.array(column(CC_SN_GRB,5))
a=np.array(column(CC_SN_GRB,6))
b=np.array(column(CC_SN_GRB,7))
scat=np.array(column(CC_SN_GRB,8))
rd=np.array(column(CC_SN_GRB,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
CC_SN_GRB = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
os.chdir(dir_chain+'BAOCCSN')
filename = 'Chain_FlatPrior_{0}_BAOCCSN.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
BAO_CC_SN = flat_samples
if cosmo=='LCDM':
H0=np.array(column(BAO_CC_SN,0))
Om=np.array(column(BAO_CC_SN,1))
OL=np.array(column(BAO_CC_SN,2))
M=np.array(column(BAO_CC_SN,3))
a=np.array(column(BAO_CC_SN,4))
b=np.array(column(BAO_CC_SN,5))
scat=np.array(column(BAO_CC_SN,6))
rd=np.array(column(BAO_CC_SN,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_SN = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(BAO_CC_SN,0))
Om=np.array(column(BAO_CC_SN,1))
OL=np.array(column(BAO_CC_SN,2))
w=np.array(column(BAO_CC_SN,3))
M=np.array(column(BAO_CC_SN,4))
a=np.array(column(BAO_CC_SN,5))
b=np.array(column(BAO_CC_SN,6))
scat=np.array(column(BAO_CC_SN,7))
rd=np.array(column(BAO_CC_SN,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_SN = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(BAO_CC_SN,0))
Om=np.array(column(BAO_CC_SN,1))
OL=np.array(column(BAO_CC_SN,2))
w0=np.array(column(BAO_CC_SN,3))
wa=np.array(column(BAO_CC_SN,4))
M=np.array(column(BAO_CC_SN,5))
a=np.array(column(BAO_CC_SN,6))
b=np.array(column(BAO_CC_SN,7))
scat=np.array(column(BAO_CC_SN,8))
rd=np.array(column(BAO_CC_SN,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC_SN = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
### 2 ###
os.chdir(dir_chain+'BAOCC')
filename = 'Chain_FlatPrior_{0}_BAOCC.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
BAO_CC = flat_samples
if cosmo=='LCDM':
H0=np.array(column(BAO_CC,0))
Om=np.array(column(BAO_CC,1))
OL=np.array(column(BAO_CC,2))
M=np.array(column(BAO_CC,3))
a=np.array(column(BAO_CC,4))
b=np.array(column(BAO_CC,5))
scat=np.array(column(BAO_CC,6))
rd=np.array(column(BAO_CC,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(BAO_CC,0))
Om=np.array(column(BAO_CC,1))
OL=np.array(column(BAO_CC,2))
w=np.array(column(BAO_CC,3))
M=np.array(column(BAO_CC,4))
a=np.array(column(BAO_CC,5))
b=np.array(column(BAO_CC,6))
scat=np.array(column(BAO_CC,7))
rd=np.array(column(BAO_CC,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(BAO_CC,0))
Om=np.array(column(BAO_CC,1))
OL=np.array(column(BAO_CC,2))
w0=np.array(column(BAO_CC,3))
wa=np.array(column(BAO_CC,4))
M=np.array(column(BAO_CC,5))
a=np.array(column(BAO_CC,6))
b=np.array(column(BAO_CC,7))
scat=np.array(column(BAO_CC,8))
rd=np.array(column(BAO_CC,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_CC = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
os.chdir(dir_chain+'BAOSN')
filename = 'Chain_FlatPrior_{0}_BAOSN.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
BAO_SN = flat_samples
if cosmo=='LCDM':
H0=np.array(column(BAO_SN,0))
Om=np.array(column(BAO_SN,1))
OL=np.array(column(BAO_SN,2))
M=np.array(column(BAO_SN,3))
a=np.array(column(BAO_SN,4))
b=np.array(column(BAO_SN,5))
scat=np.array(column(BAO_SN,6))
rd=np.array(column(BAO_SN,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_SN = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(BAO_SN,0))
Om=np.array(column(BAO_SN,1))
OL=np.array(column(BAO_SN,2))
w=np.array(column(BAO_SN,3))
M=np.array(column(BAO_SN,4))
a=np.array(column(BAO_SN,5))
b=np.array(column(BAO_SN,6))
scat=np.array(column(BAO_SN,7))
rd=np.array(column(BAO_SN,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_SN = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(BAO_SN,0))
Om=np.array(column(BAO_SN,1))
OL=np.array(column(BAO_SN,2))
w0=np.array(column(BAO_SN,3))
wa=np.array(column(BAO_SN,4))
M=np.array(column(BAO_SN,5))
a=np.array(column(BAO_SN,6))
b=np.array(column(BAO_SN,7))
scat=np.array(column(BAO_SN,8))
rd=np.array(column(BAO_SN,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_SN = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
os.chdir(dir_chain+'CCSN')
filename = 'Chain_FlatPrior_{0}_CCSN.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
CC_SN = flat_samples
if cosmo=='LCDM':
H0=np.array(column(CC_SN,0))
Om=np.array(column(CC_SN,1))
OL=np.array(column(CC_SN,2))
M=np.array(column(CC_SN,3))
a=np.array(column(CC_SN,4))
b=np.array(column(CC_SN,5))
scat=np.array(column(CC_SN,6))
rd=np.array(column(CC_SN,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
CC_SN = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(CC_SN,0))
Om=np.array(column(CC_SN,1))
OL=np.array(column(CC_SN,2))
w=np.array(column(CC_SN,3))
M=np.array(column(CC_SN,4))
a=np.array(column(CC_SN,5))
b=np.array(column(CC_SN,6))
scat=np.array(column(CC_SN,7))
rd=np.array(column(CC_SN,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
CC_SN = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(CC_SN,0))
Om=np.array(column(CC_SN,1))
OL=np.array(column(CC_SN,2))
w0=np.array(column(CC_SN,3))
wa=np.array(column(CC_SN,4))
M=np.array(column(CC_SN,5))
a=np.array(column(CC_SN,6))
b=np.array(column(CC_SN,7))
scat=np.array(column(CC_SN,8))
rd=np.array(column(CC_SN,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
CC_SN = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
os.chdir(dir_chain+'BAOGRB')
filename = 'Chain_FlatPrior_{0}_BAOGRB.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
BAO_GRB = flat_samples
if cosmo=='LCDM':
H0=np.array(column(BAO_GRB,0))
Om=np.array(column(BAO_GRB,1))
OL=np.array(column(BAO_GRB,2))
M=np.array(column(BAO_GRB,3))
a=np.array(column(BAO_GRB,4))
b=np.array(column(BAO_GRB,5))
scat=np.array(column(BAO_GRB,6))
rd=np.array(column(BAO_GRB,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_GRB = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':
H0=np.array(column(BAO_GRB,0))
Om=np.array(column(BAO_GRB,1))
OL=np.array(column(BAO_GRB,2))
w=np.array(column(BAO_GRB,3))
M=np.array(column(BAO_GRB,4))
a=np.array(column(BAO_GRB,5))
b=np.array(column(BAO_GRB,6))
scat=np.array(column(BAO_GRB,7))
rd=np.array(column(BAO_GRB,8))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_GRB = np.transpose(np.array([H0,Om,OL,Ok,w,M,a,b,scat,rd]))
if cosmo=='w0waCDM':
H0=np.array(column(BAO_GRB,0))
Om=np.array(column(BAO_GRB,1))
OL=np.array(column(BAO_GRB,2))
w0=np.array(column(BAO_GRB,3))
wa=np.array(column(BAO_GRB,4))
M=np.array(column(BAO_GRB,5))
a=np.array(column(BAO_GRB,6))
b=np.array(column(BAO_GRB,7))
scat=np.array(column(BAO_GRB,8))
rd=np.array(column(BAO_GRB,9))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
BAO_GRB = np.transpose(np.array([H0,Om,OL,Ok,w0,wa,M,a,b,scat,rd]))
os.chdir(dir_chain+'CCGRB')
filename = 'Chain_FlatPrior_{0}_CCGRB.txt'.format(cosmo)
with open(filename, 'r') as file:
lines = file.read().splitlines()
data = np.array([list(map(float, line.split())) for line in lines])
samples = data.reshape((chain_dim, nwalk, par_dim))
flat_samples = samples.reshape(-1, par_dim)
CC_GRB = flat_samples
if cosmo=='LCDM':
H0=np.array(column(CC_GRB,0))
Om=np.array(column(CC_GRB,1))
OL=np.array(column(CC_GRB,2))
M=np.array(column(CC_GRB,3))
a=np.array(column(CC_GRB,4))
b=np.array(column(CC_GRB,5))
scat=np.array(column(CC_GRB,6))
rd=np.array(column(CC_GRB,7))
Ok=[]
for i in range(len(Om)):
Ok.append(1-Om[i]-OL[i])
CC_GRB = np.transpose(np.array([H0,Om,OL,Ok,M,a,b,scat,rd]))
if cosmo=='wCDM':