pipeline_binned_mem.py
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#! /usr/bin/env python
# ==========================================================================
# This script illustrates how to perform a binned CTA analysis based on
# simulated CTA data. You may use and adapt this script to implement your
# own pipeline.
#
# Usage:
# ./pipeline_binned_mem.py
#
# ==========================================================================
#
# Copyright (C) 2015 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
from ctools import obsutils
# ================== #
# Setup observations #
# ================== #
def setup_observations(pattern="four", ra=83.63, dec=22.01, offset=1.5, \
emin=0.1, emax=100.0, rad=5.0, duration=1800.0, \
deadc=0.95, \
caldb="prod2", irf="South_50h"):
"""
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 = obsutils.set_obs_patterns(pattern, \
ra=ra, \
dec=dec, \
offset=offset)
# Get observation container
obs = 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, emin=0.1, emax=100.0, \
enumbins=20, nxpix=200, nypix=200, binsz=0.02, \
coordsys="CEL", proj="CAR", debug=False):
"""
Simulation and binned analysis pipeline.
Keywords:
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
debug - Enable debugging (default: False)
"""
# Simulate events
sim = ctools.ctobssim(obs)
sim["debug"] = debug
sim.run()
# Bin events by looping over all observations in the container
obs = gammalib.GObservations()
obs.models(sim.obs().models())
for run in sim.obs():
# Create container with a single observation
container = gammalib.GObservations()
container.append(run)
# Bin events for that observation
bin = ctools.ctbin(container)
bin["ebinalg"] = "LOG"
bin["emin"] = emin
bin["emax"] = emax
bin["enumbins"] = enumbins
bin["nxpix"] = nxpix
bin["nypix"] = nypix
bin["binsz"] = binsz
bin["coordsys"] = coordsys
bin["usepnt"] = True
bin["proj"] = proj
bin.run()
# Append result to observations
obs.extend(bin.obs())
# Perform maximum likelihood fitting
like = ctools.ctlike(obs)
like["debug"] = 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 binned analysis pipeline *")
print("********************************************")
# Setup observations
obs = setup_observations()
# Setup model
obs = setup_model(obs)
# Run analysis pipeline
run_pipeline(obs)