-
Notifications
You must be signed in to change notification settings - Fork 0
/
plotdistribution.py
146 lines (113 loc) · 6.32 KB
/
plotdistribution.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
binstep=0.1
dfr=pd.read_csv('shortGRBsDetectedByIRT.txt',header=None)
dfx=pd.read_csv('shortGRBsDetectedBySXIXGIS.txt',header=None)
zr=dfr[0]
zx=dfx[0]
zmax=20
l=int((zmax-0)/binstep)
bins=[]
for i in range(0,l):
bins.append(round(binstep*i,1))
#bins.append(round(binstep*i,2))
#hist_x_density=plt.hist(zx,bins=bins,density=True,color='blue',linewidth=5,histtype='step',alpha=0)
hist_x_density=plt.hist(zx,bins=bins,density=True,color='blue',linewidth=5,alpha=0)
#hist_x_density=plt.hist(zx,bins=bins,density=True,alpha=0)
hist_x_number=plt.hist(zx,bins=bins,alpha=0)
# distribuzione delle simulazioni (400)
#Fx=hist_x_density[0]
zbinx=hist_x_density[1]
Nx=hist_x_number[0]
Fx=Nx/sum(Nx) # Fx con stepbin =1 riproduce le percentuali del MOS quindi ok
# con plt.hist credo si deve moltiplicare per stebin ogni bin (con 1 viene automatico ma con 0.3 no)
# percentuale di casi per ciascun zbin fino a z=3
#PGRBinz_x=Fx[0:10]
hist_r_density=plt.hist(zr,bins=bins,density=True,alpha=0)
hist_r_number=plt.hist(zr,bins=bins,alpha=0)
# distribuzione delle simulazioni (400)
# percentuale di casi per ciascun zbin
#Fr=hist_r_density[0]
zbin_r=hist_r_density[1]
Nr=hist_r_number[0]
Fr=Nr/sum(Nr)
frac=len(zr)/len(zx)
#PGRBinz_r=Fr[0:10]
#PGRBinz_xr=PGRBinz_x*PGRBinz_r
# original redshift grid by Stefan
#x1=np.asarray([0,0.3,0.6,0.9,1.2,1.5,1.8,2.1,2.4,2.7])
#x2=np.asarray([0.3,0.6,0.9,1.2,1.5,1.8,2.1,2.4,2.7,3.0])
#Pbns_ETCE=(np.asarray([0.115,0.125,0.118,0.08,0.055,0.04,0.03,0.02,0.01,0.01])/0.125)
#Pbns_ET=(np.asarray([0.115,0.12,0.07,0.025,0.021,0.005,0.01,0.0,0.0,0.0])/0.125)
# efficienza di detection in bin di ampiezza 0.1 (ogni valore della prob. è ripetuto 3 volte)
#Pbns_ETCE=(np.asarray([0.115,0.115,0.115,0.125,0.125,0.125,0.118,0.118,0.118,0.08,0.08,0.08,0.055,0.055,0.055,0.04,0.04,0.04,0.03,0.03,0.03,0.02,0.02,0.02,0.01,0.01,0.01,0.01,0.01,0.01])/0.125)
#Pbns_ET=(np.asarray([0.115,0.115,0.115,0.12,0.12,0.12,0.07,0.07,0.07,0.025,0.025,0.025,0.021,0.021,0.021,0.005,0.005,0.005,0.01,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])/0.125)
# efficienza di detection in bin di ampiezza 0.3 per gli on-axis (5deg)
#Pbns_ETCE_03=(np.asarray([0.115,0.125,0.118,0.08,0.055,0.04,0.03,0.02,0.01,0.01])/0.125)
#Pbns_ET_03=(np.asarray([0.115,0.12,0.07,0.025,0.021,0.005,0.01,0.0,0.0,0.0])/0.125)
Pbns_ET2CE_03=(np.asarray([1,1,1,1,0.9,0.6,0.3,0.1,0.0,0.0]))
Pbns_ETCE_03=(np.asarray([1,1,1,0.8,0.6,0.4,0.05,0.01,0.0,0.0]))
Pbns_ET_03=(np.asarray([1,1,0.7,0.4,0.4,0.05,0.01,0.0,0.0,0.0]))
# efficienza di detection in bin di ampiezza 0.02
#Pbns_ET2CE=np.repeat(Pbns_ET2CE_03,15)
#Pbns_ETCE=np.repeat(Pbns_ETCE_03,15)
#Pbns_ET=np.repeat(Pbns_ET_03,15)
# efficienza di detection in bin di ampiezza 0.1
Pbns_ET2CE=np.repeat(Pbns_ET2CE_03,3)
Pbns_ETCE=np.repeat(Pbns_ETCE_03,3)
Pbns_ET=np.repeat(Pbns_ET_03,3)
#x1=x1[0:6]
#x2=x2[0:6]
# percentuale vista anche con GW
#P_TH_ETCE=PGRBinz_x*Pbns_ETCE
#P_TH_ET=PGRBinz_x*Pbns_ET
P_TH_ET2CE=Fx[0:30]*Pbns_ET2CE
P_TH_ETCE=Fx[0:30]*Pbns_ETCE
P_TH_ET=Fx[0:30]*Pbns_ET
#P_TH_ETCE=Fx[0:150]*Pbns_ETCE
#P_TH_ET=Fx[0:150]*Pbns_ET
#P_TH_ETCE=Pbns_ETCE
#P_TH_ET=Pbns_ET
# plot histogram
#plt.bar((x1+x2)/2,Pbns_ETCE,align='center',width=(x2-x1),label='THESEUS+ET+CE') # A bar chart
#plt.hist(zr,bins=bins,density=True,color='red',linewidth=8,histtype='step',alpha=0.7,label='THESEUS short GRB with IRT detection')
#plt.bar(zbinx[0:10],100*PGRBinz_x,width=binstep,align='edge',log=False,linewidth=4,color="None",edgecolor='blue',alpha=0.9,label='THESEUS short GRB')
#plt.bar(zbin_r[0:10],100*PGRBinz_xr,width=binstep,align='edge',log=False,linewidth=4,color="None",edgecolor='red',alpha=0.9,label='THESEUS short GRB with IRT')
plt.bar(zbinx[0:-1],100*Fx,width=binstep,align='edge',log=False,linewidth=2,color="None",edgecolor='cornflowerblue',alpha=0.9,label='THESEUS short GRB')
#plt.bar((x1+x2)/2.,100*P_TH_ETCE,width=(x2-x1),log=False,align='center',hatch='\\',color='magenta',alpha=0.5,label='THESEUS+ET+CE short GRB')
#plt.bar((x1+x2)/2.,100*P_TH_ET,width=(x2-x1),log=False,align='center',hatch='/',color='lime',alpha=0.5,label='THESEUS+ET short GRB')
#plt.bar(zbinx[0:10],100*P_TH_ETCE,width=(x2-x1),log=False,align='edge',hatch='\\',color='magenta',alpha=0.5,label='THESEUS+ET+CE short GRB')
#plt.bar(zbinx[0:10],100*P_TH_ET,width=(x2-x1),log=False,align='edge',hatch='/',color='lime',alpha=0.5,label='THESEUS+ET short GRB')
plt.bar(zbinx[0:30],100*P_TH_ET2CE,width=binstep,log=False,align='edge',hatch='-',color='violet',alpha=0.5,label='THESEUS+ET+2CE short GRB')
plt.bar(zbinx[0:30],100*P_TH_ETCE,width=binstep,log=False,align='edge',hatch='\\',color='magenta',alpha=0.5,label='THESEUS+ET+CE short GRB')
plt.bar(zbinx[0:30],100*P_TH_ET,width=binstep,log=False,align='edge',hatch='/',color='lime',alpha=0.5,label='THESEUS+ET short GRB')
#plt.bar(zbinx[0:150],100*P_TH_ETCE,width=binstep,log=False,align='edge',hatch='\\',color='magenta',alpha=0.5,label='THESEUS+ET+CE short GRB')
#plt.bar(zbinx[0:150],100*P_TH_ET,width=binstep,log=False,align='edge',hatch='/',color='lime',alpha=0.5,label='THESEUS+ET short GRB')
plt.bar(zbin_r[0:-1],100*Fr*frac,width=binstep,align='edge',log=False,linewidth=2,color="None",edgecolor='indigo',alpha=0.9,label='THESEUS short GRB with IRT')
#plt.xlim(0.,13)
plt.xlim(0.,6)
plt.ylim(0,7.5)
plt.xlabel('redshift')
plt.ylabel(' short GRB [% in each redshift bin]')
#plt.legend(fontsize='small')
plt.legend()
plt.show()
#plt.savefig('SGRB_hist_ET_CE_onaxis_bin01.pdf')
#plt.savefig('SGRB_hist_ET_CE_onaxis_bin01.png',dpi=500)
plt.savefig('SGRB_hist_ET_2CE_onaxis_bin01.png',dpi=500)
onaxisGRBperyear=Fx*12
print('')
print('THESEUS+ET',100*sum(P_TH_ET),'%')
print('THESEUS+ET+CE',100*sum(P_TH_ETCE),'%')
print('THESEUS+ET+2CE',100*sum(P_TH_ET2CE),'%')
print('')
print('Total GRB THESEUS (on axis) in 1(3.45)yr:', 12,'(',12*3.45,')')
print('Total GRB THESEUS+ET (on axis) in 1(3.45)yr:', 12*sum(P_TH_ET),'(',12*3.45*sum(P_TH_ET),')')
print('Total GRB THESEUS+ET+CE (on axis) in 1(3.45)yr:', 12*sum(P_TH_ETCE),'(',12*3.45*sum(P_TH_ETCE),')')
print('Total GRB THESEUS+ET+2CE (on axis) in 1(3.45)yr:', 12*sum(P_TH_ET2CE),'(',12*3.45*sum(P_TH_ET2CE),')')
# only if bin <=0.1
print('')
#print('Total GRB THESEUS+2G (at z<0.1) in 3.45 yr: ',sum(40*Fx[0]))
#print('Total GRB THESEUS+2G (at z<0.08) in 3.45 yr: ',sum(40*Fx[0]))
#print('Total GRB THESEUS+2G (at z<0.06) in 3.45 yr: ',sum(40*Fx[0]))