jean perrot / ctools

#! /usr/bin/env python
# ==========================================================================
# This script illustrates how to build an unbinned analysis pipeline using
# the ctools.
#
# Copyright (C) 2011 Jurgen Knodlseder
#
# 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 - 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                  = "cta_dummy_irf"
	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["infile"].filename(selected_events_name)
	like["srcmdl"].filename(model_name)
	like["outmdl"].filename(result_name)
	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         = "cta_dummy_irf"
	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 ctools Python modules.
	"""
	# Dump header
	sys.stdout.write("\n")
	sys.stdout.write("***********************************\n")
	sys.stdout.write("* ctools Python interface testing *\n")
	sys.stdout.write("***********************************\n")
	
	# Set PFILES environment variable
	try:
		os.mkdir("pfiles")
	except:
		pass
	os.system("cp -r ../src/*/*.par pfiles/")
	os.environ['PFILES'] = "pfiles"
	
	# 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
	sys.stdout.write("Test executable analysis: ")
	pipeline_v1()
	sys.stdout.write(" ok\n")

	# Keep results in memory
	sys.stdout.write("Test in-memory analysis: ")
	pipeline_v2()
	sys.stdout.write(" ok\n")