test_python.py
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#! /usr/bin/env python
# ===========================================================================================#
# This script illustrates how to build a unbinned analysis chain using CTAtools.
#
# If matplotlib is installed, the spectrum will be displayed on the screen.
# ===========================================================================================#
from ctatools import *
from gammalib import *
from math import *
import os
import glob
import sys
# ============================= #
# Analysis pipeline - version 1 #
# ============================= #
def pipeline_v1():
"""
Unbinned analysis pipeline - save intermediate results.
This function implements an analysis pipeline that successively calls
ctobssim, ctselect and ctlike by saving the intermediate results as FITS
files on disk. This replicates the way how the analysis would be done
in a ftools-like approach.
"""
# Set script parameters
model_name = "data/crab.xml"
events_name = "events.fits"
selected_events_name = "selected_events.fits"
result_name = "results.xml"
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
# Initialise timing
wall_seconds = 0.0
cpu_seconds = 0.0
# Simulate events
sim = ctobssim()
sim.logFileOpen()
sim["infile"].filename(model_name)
sim["outfile"].filename(events_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.execute()
sys.stdout.write(".")
# Select events
select = ctselect()
select.logFileOpen()
select["infile"].filename(events_name)
select["outfile"].filename(selected_events_name)
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.execute()
sys.stdout.write(".")
# Perform maximum likelihood fitting
like = ctlike()
like.logFileOpen()
like["evfile"].filename(selected_events_name)
like["srcmdl"].filename(model_name)
like["outmdl"].filename(result_name)
like["method"].string("UNBINNED")
like["caldb"].string(caldb)
like["irf"].string(irf)
like.execute()
sys.stdout.write(".")
# Return
return
# ============================= #
# Analysis pipeline - version 2 #
# ============================= #
def pipeline_v2():
"""
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
model_name = "data/crab.xml"
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
# 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()
sys.stdout.write(".")
# 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()
sys.stdout.write(".")
# Perform maximum likelihood fitting
like = ctlike(select.obs())
like.run()
sys.stdout.write(".")
# Return
return
#==========================#
# Main routine entry point #
#==========================#
if __name__ == '__main__':
"""
Test ctatools Python modules.
"""
# Dump header
print ""
print "*************************************"
print "* ctatools Python interface testing *"
print "************************************"
# Remove any existing result files
list = [glob.glob("*.fits"), glob.glob("*.log"), glob.glob("*.xml")]
for files in list:
for file in files:
os.remove(file)
# Save intermediate results on disk
print "Test executable analysis: ",
pipeline_v1()
print " ok"
# Keep results in memory
print "Test in-memory analysis: ",
pipeline_v2()
print " ok"