make_ts_distributions.py
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#! /usr/bin/env python
# ==========================================================================
# This script generates TS distributions for the standard energy bands that
# are also used for sensitivity computation. The script makes use of the
# "processing" Python module, if available, that can be used for parallel
# computing on multiple cores/CPUs.
#
# This script provides an illustration of how a ctools or cscript can be
# used from within Python. It further illustrates the possibility of
# parallel computing using the "processing" Python module.
#
# Required 3rd party modules:
# - processing (optional)
#
# Copyright (C) 2011-2015 Jurgen Knodlseder
#
# 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 time
from cscripts import cstsdist
# Try importing processing module
has_processing = False
try:
import processing
has_processing = True
except:
pass
# ====================== #
# Create TS distribution #
# ====================== #
def create_ts(loge, emin, emax, ntrials=100, duration=180000.0,
enumbins=0, log=False):
"""
Create TS distribution.
Parameters:
loge - Logarithm of mean energy in TeV
emin - Minimum energy (TeV)
emax - Maximum energy (TeV)
Keywords:
log - Create log file(s)
"""
# Generate output filename
outfile = "ts_"+str(loge)+".dat"
# Setup cstsdist tool
tsdist = cstsdist()
tsdist["inmodel"] = "$CTOOLS/share/models/crab.xml"
tsdist["srcname"] = "Crab"
tsdist["outfile"] = outfile
tsdist["ntrials"] = ntrials
tsdist["caldb"] = "prod2"
tsdist["irf"] = "South_50h"
tsdist["ra"] = 83.63
tsdist["dec"] = 22.01
tsdist["emin"] = emin
tsdist["emax"] = emax
tsdist["enumbins"] = enumbins
tsdist["tmin"] = 0
tsdist["tmax"] = duration
tsdist["rad"] = 5.0
tsdist["npix"] = 200
tsdist["binsz"] = 0.05
#tsdist["debug"].boolean(True)
# Optionally open the log file
if log:
tsdist.logFileOpen()
# Run tool
tsdist.run()
# Return
return
# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':
# Get input arguments
usage = "make_ts_distributions [-n ntrials] [-e enumbins] [-m max_threads]"
if len(sys.argv) < 1:
print(usage)
sys.exit()
# Set default parameters
ntrials = 100
enumbins = 0
duration = 180000.0
max_threads = 1
# Parameter dictionnary
pars = [{'option': '-n', 'value': ntrials},
{'option': '-e', 'value': enumbins},
{'option': '-d', 'value': duration},
{'option': '-m', 'value': max_threads}]
# Gather parameters from command line
i = 1
while i < len(sys.argv):
# Search for options
for par in pars:
if sys.argv[i] == par['option']:
if len(sys.argv) > i+1:
i += 1
try:
par['value'] = int(sys.argv[i])
except:
print(usage)
sys.exit()
else:
print(usage)
sys.exit()
# Next item
i += 1
# Recover parameters
ntrials = pars[0]['value']
enumbins = pars[1]['value']
duration = pars[2]['value']
max_threads = pars[3]['value']
# Loop over energy bands. The energy bands are those that are also
# used for sensitivity computation.
for ieng in range(20):
# Set energies
loge = -1.7 + ieng * 0.2
emean = pow(10.0, loge)
emin = pow(10.0, loge-0.1)
emax = pow(10.0, loge+0.1)
if loge < 0:
loge = "m"+str(abs(loge))
else:
loge = "p"+str(abs(loge))
# Processing support?
if has_processing:
# Wait until one thread has finished
while len(processing.activeChildren()) >= max_threads:
time.sleep(10)
# Set arguments
args = (loge, emin, emax)
kwargs = {'ntrials': ntrials, 'enumbins': enumbins,
'duration': duration}
# Generate pull distribution
p = processing.Process(target=create_ts, args=args, kwargs=kwargs)
p.start()
print("Process emin=%.4f emax=%.4f started." % (emin, emax))
# Wait a short time to allow process to start
time.sleep(1)
# ... no
else:
create_ts(loge, emin, emax, ntrials=ntrials, enumbins=enumbins,
duration=duration)
# Processing support
if has_processing:
# Wait until all threads finished
while len(processing.activeChildren()) > 0:
time.sleep(10)