example_models.py
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#! /usr/bin/env python
# ==========================================================================
# This script performs an unbinned maximum likelihood analysis for a
# variety of models. This allows checking the models.
#
# Copyright (C) 2011-2012 Juergen Knoedlseder
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# ==========================================================================
from ctools 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")