make_cube_analysis.py
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#! /usr/bin/env python
# ==========================================================================
# This script illustrates how to perform a stacked cube analysis.
#
# Copyright (C) 2014 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/>.
#
# ==========================================================================
import ctools
import gammalib
from ctools import obsutils
# ================= #
# Analysis pipeline #
# ================= #
def pipeline(caldb = "dummy", \
irf = "cta_dummy_irf", \
model = "$CTOOLS/share/models/crab.xml"):
"""
Analysis pipeline for a stacked cube analysis. The pipeline writes
intermediate results to FITS files on disk. Also log files of all
individual ctools will be written.
Keywords:
caldb - Calibration database repository
irf - Name of instrument response
model - Source model
"""
# Set pointing pattern (4 pointings)
pointings = obsutils.set_obs_patterns("four", offset=2.0)
# Set CTA observations (default: 1800 sec per pointing)
obs = obsutils.set_obs_list(pointings, caldb=caldb, irf=irf)
# Load models
models = gammalib.GModels(model)
obs.models(models)
# Run ctobssim (simulates 4 event lists)
sim = ctools.ctobssim(obs)
sim.logFileOpen()
sim.run()
# Run ctbin (builds stacked event cube)
bin = ctools.ctbin(sim.obs())
bin.logFileOpen()
bin["outcube"].filename("cntmap.fits")
bin["ebinalg"].string("LOG")
bin["emin"].real(0.1)
bin["emax"].real(100.0)
bin["enumbins"].integer(20)
bin["usepnt"].boolean(False)
bin["nxpix"].integer(300)
bin["nypix"].integer(300)
bin["binsz"].real(0.02)
bin["coordsys"].string("CEL")
bin["proj"].string("CAR")
bin["xref"].real(83.6331)
bin["yref"].real(22.0145)
bin.execute()
# Run ctexpcube (builds exposure cube)
expcube = ctools.ctexpcube(sim.obs())
expcube.logFileOpen()
expcube["incube"].filename("cntmap.fits")
expcube["outcube"].filename("expcube.fits")
expcube["caldb"].string(caldb)
expcube["irf"].string(irf)
expcube.execute()
# Run ctpsfcube (builds PSF cube)
psfcube = ctools.ctpsfcube(sim.obs())
psfcube.logFileOpen()
psfcube["incube"].filename("NONE")
psfcube["outcube"].filename("psfcube.fits")
psfcube["caldb"].string(caldb)
psfcube["irf"].string(irf)
psfcube["ebinalg"].string("LOG")
psfcube["emin"].real(0.1)
psfcube["emax"].real(100.0)
psfcube["enumbins"].integer(20)
psfcube["nxpix"].integer(5)
psfcube["nypix"].integer(5)
psfcube["binsz"].real(2.0)
psfcube["coordsys"].string("CEL")
psfcube["proj"].string("CAR")
psfcube["xref"].real(83.6331)
psfcube["yref"].real(22.0145)
psfcube["amax"].real(0.3)
psfcube["anumbins"].integer(60)
psfcube.execute()
# Run ctbkgcube (builds background cube)
bkgcube = ctools.ctbkgcube(sim.obs())
bkgcube.logFileOpen()
bkgcube["incube"].filename("cntmap.fits")
bkgcube["inmodel"].filename(model)
bkgcube["outcube"].filename("bkgcube.fits")
bkgcube["outmodel"].filename("bkgcube.xml")
bkgcube.execute()
# Create observation container for stacked cube analysis
obs.clear()
cta = gammalib.GCTAObservation("cntmap.fits", "expcube.fits", "psfcube.fits")
obs.append(cta)
obs.models(gammalib.GModels("bkgcube.xml"))
# Run ctlike
like = ctools.ctlike(obs)
like.logFileOpen()
like["debug"].boolean(True)
like["outmodel"].filename("cube_results.xml")
like.execute()
# Return
return
#==========================#
# Main routine entry point #
#==========================#
if __name__ == '__main__':
"""
"""
# Dump header
print("*************************************")
print("* CTA stacked cube analysis *")
print("*************************************")
# Run pipeline
pipeline()