pipeline_binned_mem.py
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#! /usr/bin/env python
# ==========================================================================
# Perform binned in-memory analysis of simulated CTA data.
#
# Copyright (C) 2015-2016 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
import cscripts
# ================================ #
# 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
Parameters
----------
obs : `~gammalib.GObservations`
Observation container
emin : float, optional
Minimum energy (TeV)
emax : float, optional
Maximum energy (TeV)
enumbins : int, optional
Number of energy bins
nxpix : int, optional
Number of pixels in X axis
nypix : int, optional
Number of pixels in Y axis
binsz : float, optional
Pixel size (deg)
coordsys : str, optional
Coordinate system
proj : str, optional
Coordinate projection
debug : bool, optional
Debug function
"""
# 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
# ============================= #
# Run binned in-memory pipeline #
# ============================= #
def pipeline_binned_mem():
"""
Run binned in-memory pipeline
"""
# Set usage string
usage = 'pipeline_binned_mem.py [-d datadir]'
# Set default options
options = [{'option': '-d', 'value': 'data'}]
# Get arguments and options from command line arguments
args, options = cscripts.ioutils.get_args_options(options, usage)
# Extract script parameters from options
datadir = options[0]['value']
# Setup observations
obs = cscripts.obsutils.set_observations(83.63, 22.01, 5.0, 0.0, 180.0,
0.1, 100.0, 'South_0.5h', 'prod2',
pattern='four', offset=1.5)
# Setup model
obs.models(gammalib.GModels(datadir+'/crab.xml'))
# Run analysis pipeline
run_pipeline(obs, enumbins=10, nxpix=40, nypix=40, binsz=0.1)
# Return
return
# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':
# Run binned in-memory pipeline
pipeline_binned_mem()