test_python.py
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#! /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(model_name="data/crab.xml", \
ra_sim=83.63, dec_sim=22.01, rad_sim=10.0, \
ra_select=83.63, dec_select=22.01, rad_select=3.0, \
tstart=0.0, tstop=1800.0, \
emin=0.1, emax=100.0, \
ref={}):
"""
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
# Simulate events
sim = ctobssim()
sim["infile"].filename(model_name)
sim["caldb"].string(caldb)
sim["irf"].string(irf)
sim["ra"].real(ra_sim)
sim["dec"].real(dec_sim)
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)
select["dec"].real(dec_select)
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(".")
# Analyse results
errors = 0
models = like.obs().models()
#print(models)
for model in models:
for key in ref:
if key == model.name():
for par in model:
for value in ref[key]:
if value == par.name():
if abs(float(ref[key][value])-par.value()) < 0.0001:
sys.stdout.write(".")
else:
sys.stdout.write("E")
errors += 1
# Return number of errors
return errors
#==========================#
# Main routine entry point #
#==========================#
if __name__ == '__main__':
"""
Test ctools Python modules.
"""
# Dump header
print("")
print("***********************************")
print("* ctools Python interface testing *")
print("***********************************")
# 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("*.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()
print(" ok")
# Keep results in memory
sys.stdout.write("Test in-memory analysis: ")
err = pipeline_v2()
if err == 0:
print(" ok")
else:
print(" NOK")
# Test diffuse map cube with constant spectrum
sys.stdout.write("Test diffuse map cube with constant analysis: ")
ref = {'CTABackgroundModel': {'Value': 1.03547}}
err = pipeline_v2(model_name="data/model_background_cube_const.xml", \
ra_sim=84.17263, dec_sim=22.01444, rad_sim=2.0, \
ra_select=83.80, dec_select=22.20, rad_select=1.0, \
tstart=0.0, tstop=1.0, \
emin=0.3, emax=8.0, \
ref=ref)
if err == 0:
print(" ok")
else:
print(" NOK")
# Test diffuse map cube with power law spectrum
sys.stdout.write("Test diffuse map cube with power law analysis: ")
ref = {'CTABackgroundModel': {'Prefactor': 1.02396, 'Index': -0.00710087}}
err = pipeline_v2(model_name="data/model_background_cube.xml", \
ra_sim=84.17263, dec_sim=22.01444, rad_sim=2.0, \
ra_select=84.00, dec_select=22.20, rad_select=1.0, \
tstart=0.0, tstop=1.0, \
emin=0.3, emax=8.0, \
ref=ref)
if err == 0:
print(" ok")
else:
print(" NOK")