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This commit is contained in:
Roman Krivonos
2024-03-18 14:46:38 +03:00
parent 4e5f88e166
commit afe3862cda
24 changed files with 55736 additions and 236 deletions
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scripts/06_plot.py
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@@ -45,10 +45,10 @@ import glob
from os.path import dirname
import inspect
import uds
from scipy.stats import norm
import matplotlib.pyplot as plt
import pandas as pd
from uds.utils import *
from uds.config import *
@@ -76,7 +76,13 @@ outkey="tm0"
outfile_srctool="{}_SrcTool_".format(outkey)
do_flux_distr = False
do_sens_curve = True
do_sens_curve = False
do_4xmm_ratio = False
do_euds_radec_err = False
do_euds_dr12_diff = False
do_euds_dr12_stat = True
do_print_ecf = False
index=0
@@ -87,8 +93,9 @@ if not (os.path.isfile(catalog)==True):
sys.exit()
if(do_flux_distr==True):
data, logbins = get_log_distr(infile=catalog, field='ml_rate', minval=1e-3, maxval=2)
data, logbins, mean, median = get_log_distr(infile=catalog, field='ml_rate', minval=1e-3, maxval=2)
fig, ax = plt.subplots()
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(1)
@@ -101,9 +108,9 @@ if(do_flux_distr==True):
plt.yscale('log')
plt.savefig(catalog.replace("main.selected.csv", "ml_rate.png"), bbox_inches='tight')
plt.close(fig)
data, logbins = get_log_distr(infile=catalog, field='ml_flux', minval=1e-15, maxval=2e-12)
data, logbins, mean, median = get_log_distr(infile=catalog, field='ml_flux', minval=1e-15, maxval=2e-12)
fig, ax = plt.subplots()
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(1)
@@ -143,11 +150,12 @@ if(do_sens_curve==True):
ax.set_xlim([3e-16,1e-13])
ax.set_ylim([0.3,160])
plt.plot(limflux_dl10,area_dl10, color="black", linestyle='solid',label="eUDS (DL10)")
plt.plot(limflux_dl6,area_dl6, color="black", linestyle='dashed', label="eUDS (DL6)")
df = pandas.read_csv("../data/surveys/eFEDS.dat",header=None,names=['limflux','area'])
plt.plot(df['limflux'],df['area'], color="brown", linestyle='solid',label="eFEDS (DL6)")
plt.plot(limflux_dl10,area_dl10, color="black", linestyle='solid',label="eUDS (DL10)")
plt.plot(limflux_dl6,area_dl6, color="black", linestyle='dashed', label="eUDS (DL6)")
df = pandas.read_csv("../data/surveys/cosmos-legacy.dat",header=None,names=['limflux','area'])
plt.plot(df['limflux'],df['area'], color="blue", linestyle='solid',label="COSMOS Legacy")
@@ -187,3 +195,251 @@ if(do_sens_curve==True):
Model Flux 0.00028334 photons (3.4012e-13 ergs/cm^2/s) range (0.30000 - 2.3000 keV)
Model Flux 0.0001619 photons (2.4336e-13 ergs/cm^2/s) range (0.50000 - 2.0000 keV)
"""
if(do_4xmm_ratio==True):
filename="../products/eUDS_4XMM-DR12.flux.csv"
data, logbins, mean, median = get_log_distr(infile=filename, field='ratio', minval=8e-2, maxval=60, nbin=60)
print("Median {}".format(median))
print(" Mean {}".format(mean))
print("Ntotal {}".format(len(data)))
fig, ax = plt.subplots()
#plt.figure(figsize=(5,5))
#plt.figure().set_figheight(3.6)
#plt.rcParams['figure.figsize'] = [4, 4]
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(1)
ax.tick_params(axis="both", width=1, labelsize=14)
plt.hist(data, bins=logbins, histtype='step', color='blue', linewidth=1, linestyle='solid')
plt.axvline(x = median, color = 'black', label = 'Median value', linestyle='dashed')
plt.xlabel('Energy flux ratio',fontsize=14, fontweight='normal')
plt.ylabel('Number',fontsize=14, fontweight='normal')
plt.grid(visible=True)
plt.xscale('log')
plt.yscale('log')
plt.savefig(filename.replace(".csv", ".distr.png"), bbox_inches='tight')
plt.close(fig)
df = pandas.read_csv(filename)
fig, ax = plt.subplots()
#plt.rcParams['figure.figsize'] = [4, 4]
#plt.figure(figsize=(5,5))
#plt.figure().set_figheight(4)
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(1)
ax.tick_params(axis="both", width=1, labelsize=14)
plt.plot(np.array([1e-17,1e-11]), np.array([1e-17,1e-11]), linestyle = 'solid', color='black')
plt.plot(np.array([1e-17,1e-11]), 10*np.array([1e-17,1e-11]), linestyle = 'dashed', color='black')
plt.plot(np.array([1e-17,1e-11]), 0.1*np.array([1e-17,1e-11]), linestyle = 'dotted', color='black')
#plt.plot(np.array([1e-17,1e-11]), median*np.array([1e-17,1e-11]), linestyle = 'dashed', color='blue')
plt.errorbar(df['dr12_flux'],df['euds_flux'], yerr=df['euds_flux_err'], xerr=df['dr12_flux_err'], linestyle='None', color='black', label='Errors')
plt.plot(df['dr12_flux'],df['euds_flux'], marker="o", linewidth=1, linestyle='None', markerfacecolor='Gold',markeredgecolor="black",)
#plt.axvline(x = median, color = 'black', label = 'Median value', linestyle='dashed')
plt.xlabel('4XMM-DR12 Energy flux (erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
plt.ylabel('eUDS Energy flux (erg s$^{-1}$ cm$^{-2}$)',fontsize=14, fontweight='normal')
plt.grid(visible=True)
plt.xlim(2e-16,1e-12)
plt.ylim(1e-15,2e-12)
plt.xscale('log')
plt.yscale('log')
plt.savefig(filename.replace(".csv", ".png"), bbox_inches='tight')
plt.close(fig)
if(do_euds_radec_err==True):
filename="../products/eUDS.fits"
ecat={}
hdul = fits.open(filename)
etab = hdul[1].data
hdul.close()
x=[]
y=[]
for s in etab:
if ("XMM" in s['DR12_IAU_NAME']):
print("Skip ",s['DR12_IAU_NAME'])
continue
if (s['EXT_LIKE']>0.0):
print("Skip extended ",s['ID_SRC'])
continue
x.append(s['DET_LIKE'])
y.append(s['RADEC_ERR'])
fig, ax = plt.subplots()
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(1)
ax.tick_params(axis="both", width=1, labelsize=14)
ax.set_xlim([10,10000])
ax.set_ylim([0.1,40])
plt.plot(x,y,".", color="gold", markersize=12, markeredgewidth=1.5, markeredgecolor="black")
plt.xlabel('DET_LIKE',fontsize=14, fontweight='normal')
plt.ylabel('RADEC_ERR (arcsec)',fontsize=14, fontweight='normal')
plt.grid(visible=True)
plt.xscale('log')
plt.yscale('log')
png="../products/en0_radec_err.png"
plt.savefig(png, bbox_inches='tight')
plt.close(fig)
stat={}
hdul = fits.open("../products/eUDS.dr12.cross.fits")
tab = hdul[1].data
hdul.close()
print(len(np.unique(tab['ID'])))
for rec in tab:
sid=rec['ID']
if sid in stat.keys():
stat[sid]=stat[sid]+1
else:
stat[sid]=1
unique=0
double=0
triple=0
for key in stat.keys():
if(stat[key]==1):
unique=unique+1
if(stat[key]==2):
double=double+1
if(stat[key]==3):
triple=triple+1
print("unique: {}, double: {}. triple: {}".format(unique, double, triple))
if(do_euds_dr12_diff==True):
# read forced first
# fieldnames = ['euds_det_like', 'euds_flux', 'euds_flux_err', 'dr12_flux', 'dr12_flux_err', 'ratio']
filename="../products/eUDS_4XMM-DR12.flux.csv"
df = pandas.read_csv(filename)
# calculate difference similar to Bruner
threshold=5e-14
# forced catalog contains CONF flag, take it to remove from histogram
hdul = fits.open("../products/eUDS_4XMM-DR12.fits")
forced = hdul[1].data
fcat={}
for rec in forced:
key=rec['DR12_SRCID']
fcat[key]={'conf':rec['CONF'],}
# read cross-match results (not forced)
hdul = fits.open('../products/eUDS.dr12.cross.fits')
tb = hdul[1].data
cross_diff=[]
cross_ratio=[]
mean=0.0
count=0
for ind,s in enumerate(tb):
key=rec['DR12_SRCID']
#if not (tb[ind]['dr12_flux']>threshold and tb[ind]['src_flux']>threshold):
if not (tb[ind]['dr12_flux']>threshold):
continue
ape_flux=(tb[ind]['ape_cts']-tb[ind]['ape_bkg'])/tb[ind]['ape_exp']/ecf[index]
cross_dr12_flux=(tb[ind]['dr12_flux'])
cross_dr12_flux_error=(tb[ind]['dr12_flux_error'])
cross_euds_flux=(tb[ind]['src_flux'])
#cross_euds_flux=ape_flux
cross_euds_flux_error=(tb[ind]['src_flux_error'])
print(cross_euds_flux,cross_euds_flux_error,tb[ind]['ape_cts'],tb[ind]['det_like'])
d=(cross_dr12_flux - cross_euds_flux) / np.sqrt(cross_dr12_flux_error**2 + cross_euds_flux_error**2)
#if(d < -15):
# continue
r=cross_euds_flux/cross_dr12_flux
cross_diff.append(d)
cross_ratio.append(r)
#if(d < -15.0):
#print("GGG",d,r,cross_euds_flux,cross_dr12_flux)
#print(tb[ind]['DR12_NAME'],tb[ind]['DR12_RA'],tb[ind]['DR12_DEC'])
mean=mean + (cross_euds_flux/cross_dr12_flux)
count=count+1
print("min={:.2f}, max={:.2f}, mean={:.2f}, median={:.2f}, std={:.2f} N={}".format(min(cross_diff),
max(cross_diff),
np.mean(cross_diff),
np.median(cross_diff),
np.std(cross_diff),
len(cross_diff)))
print("ratio mean",mean/count)
print("ratio median",np.median(cross_ratio))
nbin=50
bins=np.linspace(-30,30, nbin)
fig, ax = plt.subplots()
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(1)
ax.tick_params(axis="both", width=1, labelsize=14)
plt.hist(cross_diff, bins=bins,histtype='step', color='green', linewidth=1, linestyle='solid', density=True)
#x = np.arange(-10, 10, 0.001)
#plot normal distribution with mean 0 and standard deviation 1
#plt.plot(x, norm.pdf(x, 0, 1), color='red', linewidth=2)
plt.ylabel('Relative fraction',fontsize=14, fontweight='normal')
plt.xlabel('(F$_{XMM}$-F$_{eUDS}$)/$\sqrt{\Delta F_{XMM}^{2}+\Delta F_{eUDS}^{2}}$',fontsize=14, fontweight='normal')
plt.grid(visible=True)
plt.xscale('linear')
plt.yscale('linear')
ax.set_xlim([-31, 31])
ax.set_ylim([0, 0.2])
plt.savefig("../products/cross-match_diff.png", bbox_inches='tight')
plt.close(fig)
nbin=200
bins=np.linspace(-1,1.0, nbin)
fig, ax = plt.subplots()
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_linewidth(1)
ax.tick_params(axis="both", width=1, labelsize=14)
plt.hist(cross_ratio, bins=bins, histtype='step', color='green', linewidth=1, linestyle='solid', density=False)
#x = np.arange(-10, 10, 0.001)
#plot normal distribution with mean 0 and standard deviation 1
#plt.plot(x, norm.pdf(x, 0, 1), color='red', linewidth=2)
plt.xlabel('eUDS and 4XMM-DR12 flux ratio',fontsize=14, fontweight='normal')
plt.ylabel('Relative fraction',fontsize=14, fontweight='normal')
plt.grid(visible=True)
plt.xscale('linear')
plt.yscale('linear')
plt.savefig("../products/cross-match_ratio.png", bbox_inches='tight')
plt.close(fig)
if(do_print_ecf==True):
filename='../data/ECF/ecf_tbabspow_g2nh0.02.pkl'
with open(filename, 'rb') as f:
ecf_table = pickle.load(f)
"""
for key in table.keys():
print("{} --> {}".format(key,table[key]))
"""
print(ecf_table[(0.3,2.3)])
print(ecf_table[(0.3,0.6)])
print(ecf_table[(0.6,2.3)])
print(ecf_table[(2.3,5.0)])
print(ecf_table[(5.0,8.0)])
print()
print(ecf_table[(0.5,1.0)]) # 4XMM-DR12 EP2 band
print(ecf_table[(1.0,2.0)]) # 4XMM-DR12 EP3 band
if(do_euds_dr12_stat==True):
dr12_stat(infile='../products/eUDS_4XMM-DR12.fits',
xmmslim='../data/4XMM-DR12/4XMM_DR12cat_slim_v1.0_UDS.fits.catalog',
xmmlim=None,
outfile_reg='../products/dr12_fluxlim.reg')