pipeline_stacked_mem.py
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#! /usr/bin/env python
# ==========================================================================
# This script illustrates how to perform a stacked CTA analysis from the
# simulation to the analysis. You may use and adapt this script to
# implement your own pipeline. You can run the example by typing
#
# ./pipeline_stacked_mem.py
#
# 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 gammalib
import ctools
# ================== #
# Setup observations #
# ================== #
def setup_observations(pattern="four", ra=83.6331, dec=22.0145, offset=1.5, \
emin=0.1, emax=100.0, rad=5.0, duration=1800.0, \
deadc=0.95, \
caldb="dummy", irf="cta_dummy_irf"):
"""
Returns an observation container.
Keywords:
pattern - Pointing pattern, either "single" or "four"
ra - RA of pattern centre [deg] (default: 83.6331)
dec - DEC of pattern centre [deg] (default: 22.0145)
offset - Offset between observations of pattern [deg] (default: 1.5)
emin - Minimum energy [TeV] (default: 0.1)
emax - Maximum energy [TeV] (default: 100.0)
rad - ROI radius used for analysis [deg] (default: 5.0)
duration - Duration of one CTA observation [seconds] (default: 1800.0)
deadc - Deadtime correction factor (default: 0.95)
caldb - Calibration database path (default: "dummy")
irf - Instrument response function (default: cta_dummy_irf)
"""
# Set list of observations
obs_def_list = ctools.obsutils.set_obs_patterns(pattern, \
ra=ra, \
dec=dec, \
offset=offset)
# Get observation container
obs = ctools.obsutils.set_obs_list(obs_def_list, \
duration=duration, \
emin=emin, \
emax=emax, \
rad=rad, \
caldb=caldb, \
irf=irf)
# Return observation container
return obs
# =========== #
# Setup model #
# =========== #
def setup_model(obs, model="${CTOOLS}/share/models/crab.xml"):
"""
Setup model for analysis.
Keywords:
model - Model Xml file
"""
# Append model from file to observation container
obs.models(gammalib.GModels(model))
# Return observation container
return obs
# ================================ #
# Simulation and analysis pipeline #
# ================================ #
def run_pipeline(obs, ra=83.6331, dec=22.0145, emin=0.1, emax=100.0, \
enumbins=20, nxpix=200, nypix=200, binsz=0.02, \
coordsys="CEL", proj="CAR", debug=False):
"""
Simulation and stacked analysis pipeline.
Keywords:
ra - RA of cube centre [deg] (default: 83.6331)
dec - DEC of cube centre [deg] (default: 22.0145)
emin - Minimum energy of cube [TeV] (default: 0.1)
emax - Maximum energy of cube [TeV] (default: 100.0)
enumbins - Number of energy bins in cube (default: 20)
nxpix - Number of RA pixels in cube (default: 200)
nypix - Number of DEC pixels in cube (default: 200)
binsz - Spatial cube bin size [deg] (default: 0.02)
coordsys - Cube coordinate system (CEL or GAL)
proj - Cube World Coordinate System (WCS) projection
"""
# Simulate events
sim = ctools.ctobssim(obs)
sim["debug"].boolean(debug)
sim.run()
# Bin events into counts map
bin = ctools.ctbin(sim.obs())
bin["ebinalg"].string("LOG")
bin["emin"].real(emin)
bin["emax"].real(emax)
bin["enumbins"].integer(enumbins)
bin["nxpix"].integer(nxpix)
bin["nypix"].integer(nypix)
bin["binsz"].real(binsz)
bin["coordsys"].string(coordsys)
bin["proj"].string(proj)
bin["xref"].real(ra)
bin["yref"].real(dec)
bin["debug"].boolean(debug)
bin.run()
# Create exposure cube
expcube = ctools.ctexpcube(sim.obs())
expcube["cntmap"].filename("NONE")
expcube["ebinalg"].string("LOG")
expcube["emin"].real(emin)
expcube["emax"].real(emax)
expcube["enumbins"].integer(enumbins)
expcube["nxpix"].integer(nxpix)
expcube["nypix"].integer(nypix)
expcube["binsz"].real(binsz)
expcube["coordsys"].string(coordsys)
expcube["proj"].string(proj)
expcube["xref"].real(ra)
expcube["yref"].real(dec)
expcube["debug"].boolean(debug)
expcube.run()
# Create PSF cube
psfcube = ctools.ctpsfcube(sim.obs())
psfcube["cntmap"].filename("NONE")
psfcube["ebinalg"].string("LOG")
psfcube["emin"].real(emin)
psfcube["emax"].real(emax)
psfcube["enumbins"].integer(enumbins)
psfcube["nxpix"].integer(10)
psfcube["nypix"].integer(10)
psfcube["binsz"].real(1.0)
psfcube["coordsys"].string(coordsys)
psfcube["proj"].string(proj)
psfcube["xref"].real(ra)
psfcube["yref"].real(dec)
psfcube["debug"].boolean(debug)
psfcube.run()
# Create background cube
bkgcube = ctools.ctbkgcube(sim.obs())
bkgcube["cntmap"].filename("NONE")
bkgcube["ebinalg"].string("LOG")
bkgcube["emin"].real(emin)
bkgcube["emax"].real(emax)
bkgcube["enumbins"].integer(enumbins)
bkgcube["nxpix"].integer(10)
bkgcube["nypix"].integer(10)
bkgcube["binsz"].real(1.0)
bkgcube["coordsys"].string(coordsys)
bkgcube["proj"].string(proj)
bkgcube["xref"].real(ra)
bkgcube["yref"].real(dec)
bkgcube["debug"].boolean(debug)
bkgcube.run()
# Attach background model to observation container
bin.obs().models(bkgcube.models())
# Set Exposure and Psf cube for first CTA observation
# (ctbin will create an observation with a single container)
bin.obs()[0].response(expcube.expcube(), psfcube.psfcube())
# Perform maximum likelihood fitting
like = ctools.ctlike(bin.obs())
like["debug"].boolean(True) # Switch this always on for results in console
like.run()
# Return
return
# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':
"""
"""
# Dump header
print("********************************************")
print("* CTA stacked analysis pipeline *")
print("********************************************")
# Setup observations
obs = setup_observations()
# Setup model
obs = setup_model(obs)
# Run analysis pipeline
run_pipeline(obs)