example_models.py
3.02 KB
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
#! /usr/bin/env python
# ===========================================================================================#
# This script illustrates how to build a unbinned analysis chain using CTAtools.
# ===========================================================================================#
from ctatools import *
from gammalib import *
from math import *
import os
import glob
import sys
# ================= #
# Analysis pipeline #
# ================= #
def pipeline(model_name):
"""
Unbinned analysis pipeline - keep intermediate results in memory.
This function implements an analysis pipeline that successively calls
ctobssim, ctselect and ctlike without saving the intermediate results as
FITS files on disk. All data is only hold in memory.
"""
# Set script parameters
caldb = "irf"
irf = "kb_E_50h_v3"
ra = 83.63
dec = 22.01
rad_sim = 10.0
tstart = 0.0
tstop = 1800.0
emin = 0.1
emax = 100.0
rad_select = 3.0
# Write model name
print "Model: "+model_name
# Initialise timing
wall_seconds = 0.0
cpu_seconds = 0.0
# Simulate events
sim = ctobssim()
sim["infile"].filename(model_name)
sim["caldb"].string(caldb)
sim["irf"].string(irf)
sim["ra"].real(ra)
sim["dec"].real(dec)
sim["rad"].real(rad_sim)
sim["tmin"].real(tstart)
sim["tmax"].real(tstop)
sim["emin"].real(emin)
sim["emax"].real(emax)
sim.run()
# Select events
select = ctselect(sim.obs())
select["ra"].real(ra)
select["dec"].real(dec)
select["rad"].real(rad_select)
select["tmin"].real(tstart)
select["tmax"].real(tstop)
select["emin"].real(emin)
select["emax"].real(emax)
select.run()
# Perform maximum likelihood fitting
like = ctlike(select.obs())
like.run()
# Show model fitting results
print like.obs().models()[0]
# Return
return
#==========================#
# Main routine entry point #
#==========================#
if __name__ == '__main__':
"""
Perform unbinned analyses for various models.
This script implements the analysis steps to perform an unbinned analysis.
The steps include:
- Event simulation
- Event selection
- Binned maximum likelihood fitting
"""
# Initialise flags
need_help = False
show_data = True
# Test for command line arguments
print sys.argv[0]
if (len(sys.argv) > 1):
if sys.argv[1] == "-h":
need_help = True
elif sys.argv[1] == "-noshow":
show_data = False
else:
need_help = True
# Print help if needed and exit
if need_help:
print "Usage: example_models.py [OPTIONS]"
print " -h Display this usage message"
print " -noshow Do not show data using matplotlib"
sys.exit()
# Dump header
print "********************************"
print "* CTA unbinned analysis script *"
print "********************************"
# Perform analysis for Crab model
pipeline("data/crab.xml")
# Perform analysis for disk model
pipeline("data/disk.xml")
# Perform analysis for Gaussian model
pipeline("data/gauss.xml")
# Perform analysis for shell model
pipeline("data/shell.xml")