pipeline_stacked_disk.py
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#! /usr/bin/env python
# ==========================================================================
# Perform stacked 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 sys
import gammalib
import ctools
from cscripts 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=180.0,
deadc=0.95, caldb='prod2', irf='South_0.5h'):
"""
Returns an observation container
Parameters
----------
pattern : str, optional
Pointing pattern, either 'single' or 'four'
ra : float, optional
Right Ascension of pattern centre (deg)
dec : float, optional
Declination of pattern centre (deg)
offset : float, optional
Offset between observations of pattern (deg)
emin : float, optional
Minimum energy (TeV)
emax : float, optional
Maximum energy (TeV)
rad : float, optional
ROI radius used for analysis (deg)
duration : float, optional
Duration of one CTA observation (s)
deadc : float, optional
Deadtime correction factor
caldb : str, optional
Calibration database path
irf : str, optional
Instrument response function
Returns
-------
obs : `~gammalib.GObservations`
Observation container
"""
# 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='data/crab.xml'):
"""
Setup model for analysis
Parameters
----------
obs : `~gammalib.GObservations`
Observation container
model : str, optional
Model definition XML file
Returns
-------
obs : `~gammalib.GObservations`
Observation container
"""
# 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=83.63, dec=22.01, emin=0.1, emax=100.0,
enumbins=20, nxpix=200, nypix=200, binsz=0.02,
coordsys='CEL', proj='CAR',
model='data/crab.xml',
caldb='prod2', irf='South_0.5h',
debug=False):
"""
Simulation and stacked analysis pipeline
Parameters
----------
obs : `~gammalib.GObservations`
Observation container
ra : float, optional
Right Ascension of counts cube centre (deg)
dec : float, optional
Declination of Region of counts cube centre (deg)
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
model : str, optional
Model definition XML file
caldb : str, optional
Calibration database path
irf : str, optional
Instrument response function
debug : bool, optional
Debug function
"""
# Simulate events
sim = ctools.ctobssim(obs)
sim['debug'] = debug
sim['outevents'] = 'obs.xml'
sim.execute()
# Bin events into counts map
bin = ctools.ctbin()
bin['inobs'] = 'obs.xml'
bin['outcube'] = 'cntcube.fits'
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['proj'] = proj
bin['xref'] = ra
bin['yref'] = dec
bin['debug'] = debug
bin.execute()
# Create exposure cube
expcube = ctools.ctexpcube()
expcube['inobs'] = 'obs.xml'
expcube['incube'] = 'cntcube.fits'
expcube['outcube'] = 'expcube.fits'
expcube['caldb'] = caldb
expcube['irf'] = irf
expcube['ebinalg'] = 'LOG'
expcube['emin'] = emin
expcube['emax'] = emax
expcube['enumbins'] = enumbins
expcube['nxpix'] = nxpix
expcube['nypix'] = nypix
expcube['binsz'] = binsz
expcube['coordsys'] = coordsys
expcube['proj'] = proj
expcube['xref'] = ra
expcube['yref'] = dec
expcube['debug'] = debug
expcube.execute()
# Create PSF cube
psfcube = ctools.ctpsfcube()
psfcube['inobs'] = 'obs.xml'
psfcube['incube'] = 'NONE'
psfcube['outcube'] = 'psfcube.fits'
psfcube['caldb'] = caldb
psfcube['irf'] = irf
psfcube['ebinalg'] = 'LOG'
psfcube['emin'] = emin
psfcube['emax'] = emax
psfcube['enumbins'] = enumbins
psfcube['nxpix'] = 10
psfcube['nypix'] = 10
psfcube['binsz'] = 1.0
psfcube['coordsys'] = coordsys
psfcube['proj'] = proj
psfcube['xref'] = ra
psfcube['yref'] = dec
psfcube['debug'] = debug
psfcube.execute()
# Create background cube
bkgcube = ctools.ctbkgcube()
bkgcube['inobs'] = 'obs.xml'
bkgcube['inmodel'] = model
bkgcube['incube'] = 'NONE'
bkgcube['outcube'] = 'bkgcube.fits'
bkgcube['outmodel'] = 'model_bkg.xml'
bkgcube['caldb'] = caldb
bkgcube['irf'] = irf
bkgcube['ebinalg'] = 'LOG'
bkgcube['emin'] = emin
bkgcube['emax'] = emax
bkgcube['enumbins'] = enumbins
bkgcube['nxpix'] = 10
bkgcube['nypix'] = 10
bkgcube['binsz'] = 1.0
bkgcube['coordsys'] = coordsys
bkgcube['proj'] = proj
bkgcube['xref'] = ra
bkgcube['yref'] = dec
bkgcube['debug'] = debug
bkgcube.execute()
# Perform maximum likelihood fitting
like = ctools.ctlike()
like['inobs'] = 'cntcube.fits'
like['inmodel'] = 'model_bkg.xml'
like['outmodel'] = 'fit_results.xml'
like['expcube'] = 'expcube.fits'
like['psfcube'] = 'psfcube.fits'
like['bkgcube'] = 'bkgcube.fits'
like['caldb'] = caldb
like['irf'] = irf
like['debug'] = True # Switch this always on for results in console
like.execute()
# Return
return
# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':
# Dump header
print('********************************************')
print('* CTA stacked analysis pipeline *')
print('********************************************')
# Get optional arguments
if len(sys.argv) == 2:
datadir = sys.argv[1]
else:
datadir = 'data'
# Setup observations
obs = setup_observations()
# Setup model
obs = setup_model(obs, model=datadir+'/crab.xml')
# Run analysis pipeline
run_pipeline(obs, model=datadir+'/crab.xml')