test_CTA.py
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# ==========================================================================
# This module performs unit tests for the GammaLib CTA module.
#
# Copyright (C) 2012-2016 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 os
import gammalib
import math
# ================================== #
# Test class for GammaLib CTA module #
# ================================== #
class Test(gammalib.GPythonTestSuite):
"""
Test class for GammaLib CTA module.
"""
# Constructor
def __init__(self):
"""
Constructor.
"""
# Call base class constructor
gammalib.GPythonTestSuite.__init__(self)
# Set test directories
self._data = os.environ['TEST_CTA_DATA']
self._caldb = self._data + '/../caldb'
# Return
return
# Set test functions
def set(self):
"""
Set all test functions.
"""
# Set test name
self.name('CTA')
# Append tests
self.append(self.test_aeff, 'Test CTA effective area classes')
self.append(self.test_psf, 'Test CTA PSF classes')
self.append(self.test_edisp, 'Test CTA energy dispersion classes')
self.append(self.test_response, 'Test CTA response classes')
self.append(self.test_onoff, 'Test CTA On/Off analysis')
# Return
return
# Test effective area response
def test_aeff(self):
"""
Test GCTAAeff classes.
"""
# Test GCTAAeff2D file constructor
filename = self._caldb + '/prod1_gauss.fits'
aeff = gammalib.GCTAAeff2D(filename)
# Test Aeff values
self.test_value(aeff(0.0, 0.0), 5535774176.75, 0.1,
'Test reference effective area value')
self.test_value(aeff(1.0, 0.0), 20732069462.7, 0.1,
'Test reference effective area value')
self.test_value(aeff(0.0, 0.01745), 5682897797.76, 0.1,
'Test reference effective area value')
self.test_value(aeff(1.0, 0.01745), 18446656815.1, 0.1,
'Test reference effective area value')
# Test that Aeff values outside boundaries are zero
self.test_value(aeff(-1.80001, 0.0), 0.0, 1.0e-6,
'Test that effective area is zero for energy below'
' minimum energy')
self.test_value(aeff(+2.20001, 0.0), 0.0, 1.0e-6,
'Test that effective area is zero for energy above'
' maximum energy')
self.test_value(aeff(0.0, -0.00001), 0.0, 1.0e-6,
'Test that effective area is zero for offset angle'
' below minimum offset angle')
self.test_value(aeff(0.0, 0.13963), 0.0, 1.0e-6,
'Test that effective area is zero for offset angle'
' above maximum offset angle')
# Test GCTAAeffPerfTable file constructor
filename = self._caldb + '/cta_dummy_irf.dat'
aeff = gammalib.GCTAAeffPerfTable(filename)
# Test Aeff values
self.test_value(aeff(0.0, 0.0), 2738898000.0, 0.1)
self.test_value(aeff(1.0, 0.0), 16742420500.0, 0.1)
self.test_value(aeff(0.0, 0.01745), 2590995083.29, 0.1)
self.test_value(aeff(1.0, 0.01745), 15838314971.2, 0.1)
# Test GCTAAeffArf file constructor
filename = self._caldb + '/dc1/arf.fits'
aeff = gammalib.GCTAAeffArf(filename)
# Test Aeff values
self.test_value(aeff(0.0, 0.0), 1607246236.98, 0.1)
self.test_value(aeff(1.0, 0.0), 4582282342.98, 0.1)
self.test_value(aeff(0.0, 0.01745), 1607246236.98, 0.1)
self.test_value(aeff(1.0, 0.01745), 4582282342.98, 0.1)
# Return
return
# Test point spread function response
def test_psf(self):
"""
Test GCTAPsf classes.
"""
# Test GCTAPsf2D file constructor
filename = self._caldb + '/prod1_gauss.fits'
psf = gammalib.GCTAPsf2D(filename)
# Test PSF values
self.test_value(psf(0.0, 0.0, 0.0), 163782.469465, 1.0e-6)
self.test_value(psf(0.001, 0.0, 0.0), 97904.9307797, 1.0e-6)
self.test_value(psf(0.0, 1.0, 0.0), 616076.98558, 1.0e-6)
self.test_value(psf(0.001, 1.0, 0.0), 88932.681708, 1.0e-6)
self.test_value(psf(0.0, 1.0, 0.01745), 433247.309504, 1.0e-6)
self.test_value(psf(0.001, 1.0, 0.01745), 111075.0692681, 1.0e-6)
# Test GCTAPsfKing file constructor
filename = self._caldb + '/prod1_king.fits'
psf = gammalib.GCTAPsfKing(filename)
# Test PSF values
self.test_value(psf(0.0, 0.0, 0.0), 213666.253408, 1.0e-6)
self.test_value(psf(0.001, 0.0, 0.0), 90939.5585827, 1.0e-6)
self.test_value(psf(0.0, 1.0, 0.0), 1127688.29309, 1.0e-5)
self.test_value(psf(0.001, 1.0, 0.0), 54916.6796886, 1.0e-6)
self.test_value(psf(0.0, 1.0, 0.01745), 660973.856068, 1.0e-6)
self.test_value(psf(0.001, 1.0, 0.01745), 80272.5530008, 1.0e-6)
# Test GCTAPsfPerfTable file constructor
filename = self._caldb + '/cta_dummy_irf.dat'
psf = gammalib.GCTAPsfPerfTable(filename)
# Test PSF values
self.test_value(psf(0.0, 0.0, 0.0), 537853.354917, 1.0e-6)
self.test_value(psf(0.001, 0.0, 0.0), 99270.360144, 1.0e-6)
self.test_value(psf(0.0, 1.0, 0.0), 1292604.7473727, 1.0e-6)
self.test_value(psf(0.001, 1.0, 0.0), 22272.4258111, 1.0e-6)
self.test_value(psf(0.0, 1.0, 0.01745), 1292604.7473727, 1.0e-6)
self.test_value(psf(0.001, 1.0, 0.01745), 22272.4258111, 1.0e-6)
# Test GCTAPsfVector file constructor
filename = self._caldb + '/dc1/psf_magic.fits'
psf = gammalib.GCTAPsfVector(filename)
# Print
self.test_value(psf(0.0, -1.0, 0.0), 42263.9572394, 1.0e-6)
self.test_value(psf(0.001, -1.0, 0.0), 37008.8652966, 1.0e-6)
self.test_value(psf(0.0, 0.0, 0.0), 208989.164294, 1.0e-6)
self.test_value(psf(0.001, 0.0, 0.0), 108388.031915, 1.0e-6)
self.test_value(psf(0.0, 0.0, 0.01745), 208989.164294, 1.0e-6)
self.test_value(psf(0.001, 0.0, 0.01745), 108388.031915, 1.0e-6)
# Return
return
# Test energy dispersion
def test_edisp(self):
"""
Test GCTAEdisp classes.
"""
# Test GCTAEdispRmf file constructor
filename = self._caldb + '/dc1/rmf.fits'
edisp = gammalib.GCTAEdispRmf(filename)
# Test energy dispersion values
self.test_value(edisp(math.log10(30),math.log10(1)), 0.0, 1.0e-9)
self.test_value(edisp(math.log10(1),math.log10(30)), 0.0, 1.0e-9)
# Test GCTAEdispPerfTable file constructor
filename = self._caldb + '/cta_dummy_irf.dat'
edisp = gammalib.GCTAEdispPerfTable(filename)
# Test energy dispersion values
self.test_value(edisp(0.0, 0.0), 9.99019627861, 1.0e-6)
self.test_value(edisp(0.001, 0.0), 9.9870644077, 1.0e-6)
self.test_value(edisp(0.01, 0.0), 9.68182, 1.0e-6)
self.test_value(edisp(0.1, 0.0), 0.434382, 1.0e-6)
self.test_value(edisp(1.0, 1.0), 18.064868197, 1.0e-6)
self.test_value(edisp(1.001, 1.0, 0.0), 18.0463571212, 1.0e-6)
# Test GCTAResponseIrf file constructor
db = gammalib.GCaldb(self._caldb)
irf = 'cta_dummy_irf'
rsp = gammalib.GCTAResponseIrf(irf, db)
# Test nedisp computations
#dir = gammalib.GSkyDir()
#pnt = gammalib.GCTAPointing()
#time = gammalib.GTime()
#self.test_value(rsp.nedisp(dir, GEnergy(3.7, "TeV"), time, pnt,
# GEbounds(GEnergy(0.1, "TeV"),
# GEnergy(10.0, "TeV"))),
# 1.0, 0.005)
#self.test_value(rsp.nedisp(dir, GEnergy(3.7, "TeV"), time, pnt,
# GEbounds(GEnergy(2.72345, "TeV"),
# GEnergy(5.026615, "TeV"))),
# 1.0, 0.005)
#self.test_value(rsp.nedisp(dir, GEnergy(3.7, "TeV"), time, pnt,
# GEbounds(GEnergy(3.7, "TeV"),
# GEnergy(10.0, "TeV"))),
# 0.5, 0.005)
# Test response
def test_response(self):
"""
Test response classes
"""
# Load 1DC CTA observation (ARF, PSF, RMF)
filename = self._data + '/irf_1dc.xml'
obs = gammalib.GObservations(filename)
# Return
return
# Test On/Off analysis
def test_onoff(self):
"""
Test On/Off analysis.
"""
# Create On region
crab = gammalib.GSkyDir()
crab.radec_deg(83.6331, 22.0145)
on = gammalib.GSkyRegions()
on.append(gammalib.GSkyRegionCircle(crab, 0.2))
# Create Off region
crab.radec_deg(83.6331, 23.5145)
off = gammalib.GSkyRegions()
off.append(gammalib.GSkyRegionCircle(crab, 0.5))
# Set energy binning
etrue = gammalib.GEbounds(10, gammalib.GEnergy(0.1, 'TeV'),
gammalib.GEnergy(10.0, 'TeV'))
ereco = gammalib.GEbounds(10, gammalib.GEnergy(0.1, 'TeV'),
gammalib.GEnergy(10.0, 'TeV'))
# Create On/Off observations from CTA observations
filename = self._data + '/irf_unbinned.xml'
inobs = gammalib.GObservations(filename)
outobs = gammalib.GObservations()
for run in inobs:
onoff = gammalib.GCTAOnOffObservation(run, etrue, ereco, on, off)
outobs.append(onoff)
# Load model container and attach it to the observations
models = gammalib.GModels(self._data + '/onoff_model.xml')
outobs.models(models)
# Perform maximum likelihood fit
lm = gammalib.GOptimizerLM()
outobs.optimize(lm)
outobs.errors(lm)
#print(lm)
#print(outobs)
#print(outobs.models())
# Test On/Off model fitting results
sky = outobs.models()['Crab']
bgd = outobs.models()['Background']
self.test_value(sky['Prefactor'].value(), 5.95777e-16, 1.0e-20,
'Check sky model prefactor value')
self.test_value(sky['Prefactor'].error(), 2.02034e-17, 1.0e-20,
'Check sky model prefactor error')
self.test_value(sky['Index'].value(), -2.50932, 1.0e-4,
'Check sky model index value')
self.test_value(sky['Index'].error(), 0.0304839, 1.0e-4,
'Check sky model index error')
self.test_value(bgd['Prefactor'].value(), 1.09509, 1.0e-4,
'Check background model prefactor value')
self.test_value(bgd['Prefactor'].error(), 0.140468, 1.0e-4,
'Check background model prefactor error')
self.test_value(bgd['Index'].value(), 0.54909, 1.0e-4,
'Check background model index value')
self.test_value(bgd['Index'].error(), 0.0872644, 1.0e-4,
'Check background model index error')
# Save PHA, ARF and RMFs
for run in outobs:
run.on_spec().save('test_cta_onoff_pha_on.fits', True)
run.off_spec().save('test_cta_onoff_pha_off.fits', True)
run.arf().save('test_cta_onoff_arf.fits', True)
run.rmf().save('test_cta_onoff_rmf.fits', True)
# Save On/Off observations
outobs.save('test_cta_onoff.xml')
# Return
return