test_cslightcrv.py
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#! /usr/bin/env python
# ==========================================================================
# This scripts performs unit tests for the cslightcrv script.
#
# Copyright (C) 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 cscripts
# ================================ #
# Test class for cslightcrv script #
# ================================ #
class Test(gammalib.GPythonTestSuite):
"""
Test class for cslightcrv script.
This test class makes unit tests for the cslightcrv script by using it
from the command line and from Python.
"""
# Constructor
def __init__(self):
"""
Constructor.
"""
# Call base class constructor
gammalib.GPythonTestSuite.__init__(self)
# Set members
self._events_name = "data/crab_events.fits"
self._model_name = "data/crab.xml"
# Return
return
# Set test functions
def set(self):
"""
Set all test functions.
"""
# Set test name
self.name("cslightcrv")
# Append tests
self.append(self._test_cmd, "Test cslightcrv on command line")
self.append(self._test_python, "Test cslightcrv from Python")
# Return
return
# Test cslightcrv on command line
def _test_cmd(self):
"""
Test cslightcrv on the command line.
"""
# Kluge to set the command (installed version has no README file)
if os.path.isfile("README"):
cslightcrv = "../cscripts/cslightcrv.py"
else:
cslightcrv = "cslightcrv"
# Setup cslightcrv command
cmd = cslightcrv+' inobs="'+self._events_name+'"'+ \
' inmodel="'+self._model_name+'"'+ \
' srcname="Crab" caldb="prod2" irf="South_0.5h"'+ \
' tbinalg="LIN" tmin=51544.50 tmax=51544.53'+ \
' tbins=3 enumbins=0 emin=0.1 emax=100.0'+ \
' outfile="lightcurve_cmd1.fits"'
# Execute cslightcrv, make sure we catch any exception
try:
rc = os.system(cmd+" >/dev/null 2>&1")
except:
pass
# Check if execution was successful
self.test_assert(rc == 0,
"Successful cslightcrv execution on command line")
# Check light curve
self._check_light_curve("lightcurve_cmd1.fits", 3)
# Setup cslightcrv command
cmd = cslightcrv+' inobs="events_that_do_not_exist.fits"'+ \
' inmodel="'+self._model_name+'"'+ \
' srcname="Crab" caldb="prod2" irf="South_0.5h"'+ \
' tbinalg="LIN" tmin=51544.50 tmax=51544.53'+ \
' tbins=3 enumbins=0 emin=0.1 emax=100.0'+ \
' outfile="lightcurve_cmd1.fits"'
# Execute cslightcrv, make sure we catch any exception
try:
rc = os.system(cmd+" >/dev/null 2>&1")
except:
pass
# Check if execution failed
self.test_assert(rc != 0,
"Failure of cslightcrv execution on command line")
# Return
return
# Test cslightcrv from Python
def _test_python(self):
"""
Test cslightcrv from Python.
"""
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv["inobs"] = self._events_name
lcrv["inmodel"] = self._model_name
lcrv["srcname"] = "Crab"
lcrv["caldb"] = "prod2"
lcrv["irf"] = "South_0.5h"
lcrv["tbinalg"] = "LIN"
lcrv["tmin"] = 51544.50
lcrv["tmax"] = 51544.53
lcrv["tbins"] = 3
lcrv["enumbins"] = 0
lcrv["emin"] = 0.1
lcrv["emax"] = 100.0
lcrv["outfile"] = "lightcurve_py1.fits"
# Run cslightcrv script and save light curve
lcrv.run()
lcrv.save()
# Check light curve
self._check_light_curve("lightcurve_py1.fits", 3)
# Now use FILE as time bin algorithm. For this we need first to
# create an ASCII file. We use now 6 time bins. The ASCII file
# is saved into the file "lightcurve_py2.dat".
csv = gammalib.GCsv(2,2)
tmin = 51544.50
tdelta = 0.01
for i in range(csv.nrows()):
csv[i,0] = "%.5f" % (tmin + i * tdelta)
csv[i,1] = "%.5f" % (tmin + (i+1) * tdelta)
csv.save("lightcurve_py2.dat", " ", True)
# Set-up unbinned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv["inobs"] = self._events_name
lcrv["inmodel"] = self._model_name
lcrv["srcname"] = "Crab"
lcrv["caldb"] = "prod2"
lcrv["irf"] = "South_0.5h"
lcrv["tbinalg"] = "FILE"
lcrv["tbinfile"] = "lightcurve_py2.dat"
lcrv["enumbins"] = 0
lcrv["emin"] = 0.1
lcrv["emax"] = 100.0
lcrv["outfile"] = "lightcurve_py2.fits"
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve("lightcurve_py2.fits", 2)
# Now we setup an observation container on input. We attached the
# model to the observation container so that cslightcrv should
# no longer query for the parameter.
cta = gammalib.GCTAObservation(self._events_name)
obs = gammalib.GObservations()
obs.append(cta)
obs.models(self._model_name)
# Set-up unbinned cslightcrv from observation container. Now use
# the GTI algorithm so that we test all timing algorithms.
lcrv = cscripts.cslightcrv(obs)
lcrv["srcname"] = "Crab"
lcrv["caldb"] = "prod2"
lcrv["irf"] = "South_0.5h"
lcrv["tbinalg"] = "GTI"
lcrv["enumbins"] = 0
lcrv["emin"] = 0.1
lcrv["emax"] = 100.0
lcrv["outfile"] = "lightcurve_py3.fits"
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve("lightcurve_py3.fits", 1)
# Finally we set-up a binned cslightcrv
lcrv = cscripts.cslightcrv()
lcrv["inobs"] = self._events_name
lcrv["inmodel"] = self._model_name
lcrv["srcname"] = "Crab"
lcrv["caldb"] = "prod2"
lcrv["irf"] = "South_0.5h"
lcrv["tbinalg"] = "LIN"
lcrv["tmin"] = 51544.50
lcrv["tmax"] = 51544.53
lcrv["tbins"] = 2
lcrv["emin"] = 0.1
lcrv["emax"] = 100.0
lcrv["enumbins"] = 10
lcrv["coordsys"] = "CEL"
lcrv["proj"] = "TAN"
lcrv["xref"] = 83.63
lcrv["yref"] = 22.01
lcrv["nxpix"] = 20
lcrv["nypix"] = 20
lcrv["binsz"] = 0.02
lcrv["outfile"] = "lightcurve_py4.fits"
# Execute cslightcrv script
lcrv.execute()
# Check light curve
self._check_light_curve("lightcurve_py4.fits", 2)
# Return
return
# Check light curve result file
def _check_light_curve(self, filename, bins):
"""
Check light curve file.
"""
# Expected column names
cols = ["MJD", "e_MJD", "Prefactor", "e_Prefactor",
"Index", "e_Index", "TS", "UpperLimit"]
# Open FITS file
fits = gammalib.GFits(filename)
# Check FITS file structure
self.test_value(fits.size(), 2,
'Check for 2 extensions in light curve FITS file')
self.test_assert(fits.contains("LIGHTCURVE"),
'FITS file contains "LIGHTCURVE" extension')
# Get LIGHTCURVE table
table = fits["LIGHTCURVE"]
# Check FITS table structure
self.test_value(table.ncols(), len(cols),
'Check for %d columns in light curve FITS table' % len(cols))
self.test_value(table.nrows(), bins,
'Check for %d rows in light curve FITS table' % bins)
for col in cols:
self.test_assert(table.contains(col),
'FITS file contains "'+col+'" column')
# Close FITS file
fits.close()
# Return
return