obsutils.py
33.5 KB
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# ==========================================================================
# Utility functions for observation handling
#
# Copyright (C) 2011-2020 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 math
import gammalib
import ctools
import cscripts
# ===================== #
# Simulate observations #
# ===================== #
def sim(obs, log=False, debug=False, chatter=2, edisp=False, seed=0,
emin=None, emax=None, nbins=0, onsrc=None, onrad=0.2, addbounds=False,
binsz=0.05, npix=200, proj='TAN', coord='GAL', nthreads=0):
"""
Simulate events for all observations in the container
Simulate events for all observations using ctobssim. If the number of
energy bins is positive, the events are binned into a counts cube using
ctbin. If multiple observations are simulated, the counts cube is a
stacked cube and the corresponding response cubes are computed using
ctexpcube, ctpsfcube, ctbkgcube and optionally ctedispcube. The response
cubes are attached to the first observation in the container, which
normally is the observation with the counts cube.
Parameters
----------
obs : `~gammalib.GObservations`
Observation container without events
log : bool, optional
Create log file(s)
debug : bool, optional
Create console dump?
chatter : int, optional
Chatter level
edisp : bool, optional
Apply energy dispersion?
seed : int, optional
Seed value for simulations
emin : float, optional
Minimum energy of counts cube for binned (TeV)
emax : float, optional
Maximum energy of counts cube for binned (TeV)
nbins : int, optional
Number of energy bins (0=unbinned)
onsrc : str, optional
Name of source for On region (None if no On/Off obs. is used)
onrad : float, optional
Radius for On region (deg)
addbounds : bool, optional
Add boundaries at observation energies
binsz : float, optional
Pixel size for binned simulation (deg/pixel)
npix : int, optional
Number of pixels in X and Y for binned simulation
proj : str, optional
Projection for binned simulation
coord : str, optional
Coordinate system for binned simulation
nthreads : str, optional
Number of parallel processes for On/Off spectra computation (0=all available CPUs)
Returns
-------
obs : `~gammalib.GObservations`
Observation container filled with simulated events
"""
# Allocate ctobssim application and set parameters
obssim = ctools.ctobssim(obs)
obssim['seed'] = seed
obssim['edisp'] = edisp
obssim['nthreads'] = nthreads
obssim['chatter'] = chatter
obssim['debug'] = debug
# Optionally open the log file
if log:
obssim.logFileOpen()
# Run ctobssim application. This will loop over all observations in the
# container and simulation the events for each observation. Note that
# events are not added together, they still apply to each observation
# separately.
obssim.run()
# Binned option?
if nbins > 0:
# If energy boundaries are not given then determine the minimum and
# the maximum energies from all observations and use these values
# as energy boundaries. The energy boundaries are given in TeV.
if emin == None or emax == None:
emin = 1.0e30
emax = 0.0
for run in obssim.obs():
emin = min(run.events().ebounds().emin().TeV(), emin)
emax = max(run.events().ebounds().emax().TeV(), emax)
# If a On source is specified then create On/Off observations
if onsrc != None:
# Extract source position from model
model = obssim.obs().models()[onsrc]
ra = model['RA'].value()
dec = model['DEC'].value()
# Allocate csphagen application and set parameters
phagen = cscripts.csphagen(obssim.obs())
phagen['inmodel'] = 'NONE'
phagen['srcname'] = onsrc
phagen['ebinalg'] = 'LOG'
phagen['emin'] = emin
phagen['emax'] = emax
phagen['enumbins'] = nbins
phagen['srcshape'] = 'CIRCLE'
phagen['coordsys'] = 'CEL'
phagen['ra'] = ra
phagen['dec'] = dec
phagen['rad'] = onrad
phagen['stack'] = False
phagen['inexclusion'] = 'NONE'
phagen['bkgmethod'] = 'REFLECTED'
phagen['nthreads'] = nthreads
# Optionally open the log file
if log:
phagen.logFileOpen()
# Run csphagen application
phagen.run()
# Make a deep copy of the observation that will be returned
# (the csphagen object will go out of scope one the function is
# left)
obs = phagen.obs().copy()
# ... otherwise use binned observations
else:
# Allocate ctbin application and set parameters
binning = ctools.ctbin(obssim.obs())
binning['ebinalg'] = 'LOG'
binning['emin'] = emin
binning['emax'] = emax
binning['enumbins'] = nbins
binning['usepnt'] = True # Use pointing for map centre
binning['nxpix'] = npix
binning['nypix'] = npix
binning['binsz'] = binsz
binning['coordsys'] = coord
binning['proj'] = proj
binning['nthreads'] = nthreads
binning['chatter'] = chatter
binning['debug'] = debug
# Optionally open the log file
if log:
binning.logFileOpen()
# Run ctbin application. This will loop over all observations in
# the container and bin the events in counts maps
binning.run()
# If we have multiple input observations then create stacked response
# cubes and append them to the observation
if len(obssim.obs()) > 1:
# Get counts cube. The counts cube is needed to obtained a
# proper background cube.
cntcube = binning.cube()
# Get stacked response
response = get_stacked_response(obssim.obs(), cntcube,
edisp=edisp,
addbounds=addbounds,
log=log, debug=debug,
chatter=chatter)
# Set stacked response
if edisp:
binning.obs()[0].response(response['expcube'],
response['psfcube'],
response['edispcube'],
response['bkgcube'])
else:
binning.obs()[0].response(response['expcube'],
response['psfcube'],
response['bkgcube'])
# Set new models
binning.obs().models(response['models'])
# Make a deep copy of the observation that will be returned
# (the ctbin object will go out of scope one the function is
# left)
obs = binning.obs().copy()
else:
# Make a deep copy of the observation that will be returned
# (the ctobssim object will go out of scope one the function is
# left)
obs = obssim.obs().copy()
# Delete the simulation
del obssim
# Return observation container
return obs
# ======================= #
# Set one CTA observation #
# ======================= #
def set_obs(pntdir, tstart=0.0, duration=1800.0, deadc=0.98, \
emin=0.1, emax=100.0, rad=5.0, \
instrument='CTA', irf='South_50h', caldb='prod2', \
obsid='000000', mjdref=51544.5):
"""
Set a single CTA observation
The function sets a single CTA observation containing an empty CTA
event list. By looping over this function CTA observations can be
added to the observation container.
Parameters
----------
pntdir : `~gammalib.GSkyDir`
Pointing direction
tstart : float, optional
Start time (s)
duration : float, optional
Duration of observation (s)
deadc : float, optional
Deadtime correction factor
emin : float, optional
Minimum event energy (TeV)
emax : float, optional
Maximum event energy (TeV)
rad : float, optional
ROI radius used for analysis (deg)
instrument : str, optional
Name of Cherenkov Telescope
irf : str, optional
Instrument response function
caldb : str, optional
Calibration database path
obsid : str, optional
Observation identifier
Returns
-------
obs : `~gammalib.GCTAObservation`
CTA observation
"""
# Allocate CTA observation
obs = gammalib.GCTAObservation()
# Set mission
mission = gammalib.toupper(instrument)
obs.instrument(mission)
# Set CTA calibration database
db = gammalib.GCaldb()
if (gammalib.dir_exists(caldb)):
db.rootdir(caldb)
else:
db.open(mission, caldb)
# Set pointing direction for CTA observation
pnt = gammalib.GCTAPointing()
pnt.dir(pntdir)
obs.pointing(pnt)
# Set ROI
roi = gammalib.GCTARoi()
instdir = gammalib.GCTAInstDir()
instdir.dir(pntdir)
roi.centre(instdir)
roi.radius(rad)
# Set GTI
ref = gammalib.GTimeReference(mjdref,'s','TT','LOCAL')
gti = gammalib.GGti(ref)
gti.append(gammalib.GTime(tstart, ref),
gammalib.GTime(tstart+duration, ref))
# Set energy boundaries
ebounds = gammalib.GEbounds(gammalib.GEnergy(emin, 'TeV'),
gammalib.GEnergy(emax, 'TeV'))
# Allocate event list
events = gammalib.GCTAEventList()
# Set ROI, GTI and energy boundaries for event list
events.roi(roi)
events.gti(gti)
events.ebounds(ebounds)
# Set the event list as the events for CTA observation
obs.events(events)
# Set instrument response for CTA observation
obs.response(irf, db)
# Set ontime, livetime, and deadtime correction factor for CTA observation
obs.ontime(duration)
obs.livetime(duration*deadc)
obs.deadc(deadc)
obs.id(obsid)
# Return CTA observation
return obs
# ============================ #
# Set list of CTA observations #
# ============================ #
def set_obs_list(obsdeflist, tstart=0.0, duration=1800.0, deadc=0.98, \
emin=0.1, emax=100.0, rad=5.0, \
irf='South_50h', caldb='prod2'):
"""
Returns an observation container filled with a list of CTA observations
The list is defined by the obsdeflist parameter which is a dictionnary
containing the mandatory keywords 'ra' and 'dec' that specify the
pointing direction for a given observation. Optional keyword give control
over other observation proposerties, such as duration, deadtime correction,
energy range, etc. If an optional keyword is not specified, the function
keyword is used instead.
Parameters
----------
obsdeflist : list of dict
Observation definition list
tstart : float, optional
Start time (s)
duration : float, optional
Duration of observation (s)
deadc : float, optional
Deadtime correction factor
emin : float, optional
Minimum event energy (TeV)
emax : float, optional
Maximum event energy (TeV)
rad : float, optional
ROI radius used for analysis (deg)
irf : str, optional
Instrument response function
caldb : str, optional
Calibration database path
Returns
-------
obs : `~gammalib.GObservations`
Observation container filled with CTA observation
"""
# Initialise empty observation container
obs = gammalib.GObservations()
# Initialise first time and identifier
obs_start = tstart
obs_id = 1
# Loop over observation definition list
for obsdef in obsdeflist:
# Set pointing direction
pntdir = gammalib.GSkyDir()
pntdir.radec_deg(obsdef['ra'], obsdef['dec'])
# Generate identifier string
obsid = '%6.6d' % obs_id
# Set one CTA observation
obs_cta = set_obs(pntdir,
tstart=obs_start,
duration=obsdef.setdefault('duration', duration),
deadc=obsdef.setdefault('deadc', deadc),
emin=obsdef.setdefault('emin', emin),
emax=obsdef.setdefault('emax', emax),
rad=obsdef.setdefault('rad', rad),
irf=obsdef.setdefault('irf', irf),
caldb=obsdef.setdefault('caldb', caldb),
obsid=obsid)
# Append to container
obs.append(obs_cta)
# Update start time and identifier
obs_start += duration
obs_id += 1
# Return observation container
return obs
# ============================ #
# Set CTA observation patterns #
# ============================ #
def set_obs_patterns(pattern, ra=83.6331, dec=22.0145, offset=1.5):
"""
Sets a number of standard patterns
Parameters
----------
pattern : str
Observation pattern ("single", "four")
ra : float, optional
Right Ascension of pattern centre (deg)
dec : float, optional
Declination of pattern centre (deg)
offset : float, optional
Offset from pattern centre (deg)
Returns
-------
obsdeflist : list
Observation definition list
"""
# Initialise observation definition list
obsdeflist = []
# If the pattern is a single observation then append the Right Ascension
# and Declination to the observation definition list
if pattern == 'single':
obsdef = {'ra': ra, 'dec': dec}
obsdeflist.append(obsdef)
# ... otherwise, if the pattern is four observations then append four
# observations offset by a certain amount from the pattern centre to the
# observation definition list
elif pattern == 'four':
# Set pattern centre
centre = gammalib.GSkyDir()
centre.radec_deg(ra, dec)
# Append pointings
for phi in [0.0, 90.0, 180.0, 270.0]:
pntdir = centre.copy()
pntdir.rotate_deg(phi, offset)
obsdeflist.append({'ra': pntdir.ra_deg(), 'dec': pntdir.dec_deg()})
# ... otherwise raise an exception since we have an unknown pattern
else:
msg = 'Observation pattern "%s" not recognized.' % (pattern)
raise RuntimeError(msg)
# Return observation definition list
return obsdeflist
# ====================================================== #
# Set observation container filled with CTA observations #
# ====================================================== #
def set_observations(ra, dec, rad, tstart, duration, emin, emax, irf, caldb,
deadc=0.98, pattern='single', offset=1.5):
"""
Set an observation container filled with CTA observations
Parameters
----------
ra : float
Right Ascension of pattern centre (deg)
dec : float
Declination of pattern centre (deg)
rad : float
ROI radius used for analysis (deg)
tstart : float
Start time of observation (s)
duration : float
Duration of each observation (s)
emin : float, optional
Minimum event energy (TeV)
emax : float, optional
Maximum event energy (TeV)
irf : str
Instrument response function
caldb : str
Calibration database path
deadc : float, optional
Deadtime correction factor
pattern : str, optional
Observation pattern ("single", "four")
offset : float, optional
Offset from pattern centre (deg)
Returns
-------
obs : `~gammalib.GObservations()
Observation container
"""
# Setup observation definition list
obsdeflist = set_obs_patterns(pattern, offset=offset, ra=ra, dec=dec)
# Create list of observations
obs = set_obs_list(obsdeflist, tstart=tstart, duration=duration,
emin=emin, emax=emax, rad=rad, irf=irf, caldb=caldb,
deadc=deadc)
# Return observation container
return obs
# ==================== #
# Get stacked response #
# ==================== #
def get_stacked_response(obs, cntcube, edisp=False, addbounds=False,
log=False, debug=False, chatter=2):
"""
Get stacked response cubes
The number of energies bins are set to at least 30 bins per decade, unless
the counts cube has more energy bins per decade.
Parameters
----------
obs : `~gammalib.GObservations`
Observation container
cntcube : `~gammalib.GCTAEventCube`
Counts cube
edisp : bool, optional
Apply energy dispersion?
addbounds : bool, optional
Add boundaries at observation energies
log : bool, optional
Create log file(s)
debug : bool, optional
Create console dump?
chatter : int, optional
Chatter level
Returns
-------
result : dict
Dictionary of response cubes
"""
# Derive binning parameters from counts cube
xref = cntcube.counts().projection().crval(0)
yref = cntcube.counts().projection().crval(1)
binsz = cntcube.counts().projection().cdelt(1)
coordsys = cntcube.counts().projection().coordsys()
proj = cntcube.counts().projection().code()
nxpix = cntcube.nx()
nypix = cntcube.ny()
emin = cntcube.emin().TeV()
emax = cntcube.emax().TeV()
ebins = cntcube.ebins()
# Translate coordinate system
if coordsys == 'EQU':
coordsys = 'CEL'
# Set energy limits, with larger etrue limits for the case that energy
# dispersion is requested
if edisp:
emin *= 0.5
emax *= 1.5
# Set number of energy bins to at least 30 per energy decade
enumbins = int((math.log10(emax) - math.log10(emin)) * 30.0)
if ebins > enumbins:
enumbins = ebins
# Compute spatial binning for point spread function and energy dispersion
# cubes. The spatial binning is 10 times coarser than the spatial binning
# of the exposure and background cubes. At least 2 spatial are required.
psf_binsz = 10.0 * binsz
psf_nxpix = max(nxpix // 10, 2) # Make sure result is int
psf_nypix = max(nypix // 10, 2) # Make sure result is int
# Create exposure cube
expcube = ctools.ctexpcube(obs)
expcube['incube'] = 'NONE'
expcube['ebinalg'] = 'LOG'
expcube['xref'] = xref
expcube['yref'] = yref
expcube['binsz'] = binsz
expcube['nxpix'] = nxpix
expcube['nypix'] = nypix
expcube['enumbins'] = enumbins
expcube['emin'] = emin
expcube['emax'] = emax
expcube['coordsys'] = coordsys
expcube['proj'] = proj
expcube['addbounds'] = addbounds
expcube['debug'] = debug
expcube['chatter'] = chatter
if log:
expcube.logFileOpen()
expcube.run()
# Create point spread function cube
psfcube = ctools.ctpsfcube(obs)
psfcube['incube'] = 'NONE'
psfcube['ebinalg'] = 'LOG'
psfcube['xref'] = xref
psfcube['yref'] = yref
psfcube['binsz'] = psf_binsz
psfcube['nxpix'] = psf_nxpix
psfcube['nypix'] = psf_nypix
psfcube['enumbins'] = enumbins
psfcube['emin'] = emin
psfcube['emax'] = emax
psfcube['coordsys'] = coordsys
psfcube['proj'] = proj
psfcube['addbounds'] = addbounds
psfcube['debug'] = debug
psfcube['chatter'] = chatter
if log:
psfcube.logFileOpen()
psfcube.run()
# Create background cube. Note that we use the same energy binning as
# for the counts cube since no interpolation should be actually done
# for the background cube.
bkgcube = ctools.ctbkgcube(obs)
bkgcube.cntcube(cntcube)
bkgcube['debug'] = debug
bkgcube['chatter'] = chatter
if log:
bkgcube.logFileOpen()
bkgcube.run()
# If energy dispersion is requested then create energy dispersion cube
if edisp:
edispcube = ctools.ctedispcube(obs)
edispcube['incube'] = 'NONE'
edispcube['ebinalg'] = 'LOG'
edispcube['xref'] = xref
edispcube['yref'] = yref
edispcube['binsz'] = psf_binsz
edispcube['nxpix'] = psf_nxpix
edispcube['nypix'] = psf_nypix
edispcube['enumbins'] = enumbins
edispcube['emin'] = emin
edispcube['emax'] = emax
edispcube['coordsys'] = coordsys
edispcube['proj'] = proj
edispcube['addbounds'] = addbounds
edispcube['debug'] = debug
edispcube['chatter'] = chatter
if log:
edispcube.logFileOpen()
edispcube.run()
# Build response dictionary
response = {}
response['expcube'] = expcube.expcube().copy()
response['psfcube'] = psfcube.psfcube().copy()
response['bkgcube'] = bkgcube.bkgcube().copy()
response['models'] = bkgcube.models().copy()
if edisp:
response['edispcube'] = edispcube.edispcube().copy()
# Return response cubes
return response
# ================================= #
# Get stacked observation container #
# ================================= #
def get_stacked_obs(cls, obs, nthreads=0):
"""
Bin an observation and return an observation container with a single
binned observation
Parameters
----------
cls : `~ctools.cscript`
cscript class
obs : `~gammalib.GObservations`
Observation container
nthreads : str, optional
Number of parallel processes for On/Off spectra computation (0=all available CPUs)
Returns
-------
obs : `~gammalib.GObservations`
Observation container where the first observation is a binned observation
"""
# Write header
if cls._logExplicit():
cls._log.header3('Binning events')
# Bin events
cntcube = ctools.ctbin(obs)
cntcube['usepnt'] = False
cntcube['ebinalg'] = 'LOG'
cntcube['xref'] = cls['xref'].real()
cntcube['yref'] = cls['yref'].real()
cntcube['binsz'] = cls['binsz'].real()
cntcube['nxpix'] = cls['nxpix'].integer()
cntcube['nypix'] = cls['nypix'].integer()
cntcube['enumbins'] = cls['enumbins'].integer()
cntcube['emin'] = cls['emin'].real()
cntcube['emax'] = cls['emax'].real()
cntcube['coordsys'] = cls['coordsys'].string()
cntcube['proj'] = cls['proj'].string()
cntcube['nthreads'] = nthreads
cntcube.run()
# Store counts cube so that we can use it to build the background
# cube
cube = cntcube.cube()
# Write header
if cls._logExplicit():
cls._log.header3('Creating stacked response')
# Get stacked response
response = get_stacked_response(obs, cube, edisp=cls['edisp'].boolean())
# Retrieve a new oberservation container
new_obs = cntcube.obs().copy()
# Set stacked response
if cls['edisp'].boolean():
new_obs[0].response(response['expcube'], response['psfcube'],
response['edispcube'], response['bkgcube'])
else:
new_obs[0].response(response['expcube'], response['psfcube'],
response['bkgcube'])
# Get new models
models = response['models']
# Set models for new oberservation container
new_obs.models(models)
# Return new oberservation container
return new_obs
# ================================ #
# Get On/Off observation container #
# ================================ #
def get_onoff_obs(cls, obs, nthreads=0, ra = None, dec = None, srcname = ''):
"""
Create On/Off observations container from given observations
Parameters
----------
cls : `~ctools.cscript`
cscript class
obs : `~gammalib.GObservations`
Observation container
nthreads : str, optional
Number of parallel processes for On/Off spectra computation (0=all available CPUs)
ra : float, optional
R.A. of source region centre
dec : float, optional
Dec. of source region centre
srcname : str, optional
Source name
Returns
-------
onoff_obs : `~gammalib.GObservations`
Observation container with On/Off observations
"""
# Write header
if cls._logExplicit():
cls._log.header3('Creating On/Off observations')
# Initialise inmodel and use_model_bkg
inmodel = 'NONE'
use_model_bkg = True
# Set inmodel, srcname, and use_model_bkg if they are available
if cls.has_par('inmodel') and obs.models().size() == 0:
if cls['inmodel'].is_valid():
inmodel = cls['inmodel'].value()
if srcname == '' and cls.has_par('srcname'):
srcname = cls['srcname'].value()
if cls.has_par('use_model_bkg'):
use_model_bkg = cls['use_model_bkg'].boolean()
# Create On/Off observations
phagen = cscripts.csphagen(obs)
phagen['inmodel'] = inmodel
phagen['srcname'] = srcname
phagen['emin'] = cls['emin'].real()
phagen['emax'] = cls['emax'].real()
phagen['enumbins'] = cls['enumbins'].integer()
phagen['ebinalg'] = 'LOG'
phagen['srcshape'] = cls['srcshape'].string()
# User has specified custom centre for the source region
if ra != None and dec != None:
phagen['coordsys'] = 'CEL'
phagen['ra'] = ra
phagen['dec'] = dec
# Otherwise use default in class
else:
phagen['coordsys'] = cls['coordsys'].string()
if cls['coordsys'].string() == 'CEL':
phagen['ra'] = cls['xref'].real()
phagen['dec'] = cls['yref'].real()
elif cls['coordsys'].string() == 'GAL':
phagen['glon'] = cls['xref'].real()
phagen['glat'] = cls['yref'].real()
if cls['srcshape'].string() == 'CIRCLE':
phagen['rad'] = cls['rad'].real()
elif cls['srcshape'].string() == 'RECT':
phagen['width'] = cls['width'].real()
phagen['height'] = cls['height'].real()
phagen['posang'] = cls['posang'].real()
phagen['bkgmethod'] = cls['bkgmethod'].string()
if cls['bkgmethod'].string() == 'REFLECTED':
phagen['bkgregmin'] = cls['bkgregmin'].integer()
phagen['use_model_bkg'] = use_model_bkg
phagen['maxoffset'] = cls['maxoffset'].real()
phagen['stack'] = True
phagen['etruemin'] = cls['etruemin'].real()
phagen['etruemax'] = cls['etruemax'].real()
phagen['etruebins'] = cls['etruebins'].integer()
phagen['chatter'] = cls['chatter'].integer()
phagen['clobber'] = cls['clobber'].boolean()
phagen['debug'] = cls['debug'].boolean()
phagen['nthreads'] = nthreads
# Set exclusion map
# Initialise exclusion map flag to False
use_excl_map = False
# Initialise inexclusion par flag to False
use_inexclusion = False
# Do we have valid exclusion map in memory?
if hasattr(cls, 'exclusion_map'):
exclusion_map = cls.exclusion_map()
if exclusion_map is not None:
# Set use exclusion map flag to True
use_excl_map = True
# Set exclusion map
phagen.exclusion_map(exclusion_map)
# If we do not have valid exclusion map in memory ...
if not use_excl_map:
# ... do we have an inxeclusion parameter?
if cls.has_par('inexclusion'):
# If the inexclusion parameter is valid
if cls['inexclusion'].is_valid():
# Set use inexclusion flag to True
use_inexclusion = True
# Set inexclusion parameter
inexclusion = cls['inexclusion'].value()
phagen['inexclusion'] = inexclusion
# If there is no valid exclusion map in memory
# nor valid inexclusion parameter
if not use_excl_map and not use_inexclusion:
# Set inexclusion for csphagen to None
phagen['inexclusion'] = 'NONE'
# Run csphagen
phagen.run()
# Clone resulting observation container
onoff_obs = phagen.obs().copy()
# On/Off observations are created with CSTAT as default statistic
# We will change this to the user choice
if cls['statistic'].string() != 'DEFAULT':
for observation in onoff_obs:
observation.statistic(cls['statistic'].string())
# Return On/Off oberservation container
return onoff_obs
# ========================================= #
# Calculate residuals from counts and model #
# ========================================= #
def residuals(cls, counts, model):
"""
Calculate residuals given counts and models, according to algorithm
specified by user.
Can handle GSkyMap or GNdarray objects
Parameters
----------
cls : `~ctools.cscript`
cscript class
counts : `~gammalib.GSkyMap/~gammalib.GNdarray'
Data counts
model : `~gammalib.GSkyMap/~gammalib.GNdarray'
Model counts
Returns
-------
residuals : `~gammalib.GSkyMap/~gammalib.GNdarray'
Residuals
"""
# Find type of objects we are manipulating and set size to iterate later
if counts.classname() == 'GNdarray':
nelem = counts.size()
elif counts.classname() == 'GSkyMap':
nelem = counts.npix()
else:
msg = 'cscripts.obsutils.residuals only handles ' \
+ 'gammalib.GNdarray or gammalib.GSkyMap objects.\n'
raise RuntimeError(msg)
# Copy counts to initialise residuals
residuals = counts.copy()
# Get residual map algorithm type
algorithm = cls['algorithm'].string()
# Subtract
if algorithm == 'SUB':
residuals -= model
# Subtract and divide by model
elif algorithm == 'SUBDIV':
residuals -= model
residuals /= model
# Subtract and divide by sqrt of model
elif algorithm == 'SUBDIVSQRT':
residuals -= model
residuals /= model.sqrt()
# Calculate significance from Li&Ma
elif algorithm == 'SIGNIFICANCE':
# Compute sign
sign = (residuals - model).sign()
# Loop over every bin
for i in range(nelem):
# If the model value > 0.0 do the computation as normal ...
model_val = model[i]
if model_val > 0.0:
# If the data value is also > 0 then compute the
# significance^2 and save it ...
data_val = residuals[i]
if data_val > 0.0:
log_val = math.log(data_val / model_val)
residuals[i] = (data_val * log_val) + model_val - data_val
if residuals[i] < 0.0: # See glitch issue #2765
residuals[i] = 0.0
# ... otherwise compute the reduced value of the above
# expression. This is necessary to avoid computing log(0).
else:
residuals[i] = model_val
# ... otherwise hard-code the significance to 0
else:
residuals[i] = 0.0
# Compute significance map
residuals *= 2.0
residuals = residuals.sqrt()
residuals *= sign
# Raise exception if algorithm is unknown
else:
raise TypeError('Algorithm "' + algorithm + '" not known')
# Return
return residuals
# ================= #
# Create counts map #
# ================= #
def create_counts_cube(cls, obs):
"""
Create counts cube from observations
Parameters
----------
cls : `~ctools.cscript`
cscript class
obs : `~gammalib.GObservations`
Observation container
Returns
-------
cntcube : `~gammalib.GCTAEventCube`
Counts cube
"""
# Initialise ctbin
ctbin = ctools.ctbin(obs)
# Set parameters
ctbin['xref'] = cls['xref'].real()
ctbin['yref'] = cls['yref'].real()
ctbin['proj'] = cls['proj'].string()
ctbin['coordsys'] = cls['coordsys'].string()
ctbin['ebinalg'] = cls['ebinalg'].string()
ctbin['nxpix'] = cls['nxpix'].integer()
ctbin['nypix'] = cls['nypix'].integer()
ctbin['binsz'] = cls['binsz'].real()
if cls['ebinalg'].string() == 'FILE':
ctbin['ebinfile'] = cls['ebinfile'].filename().file()
else:
ctbin['enumbins'] = cls['enumbins'].integer()
ctbin['emin'] = cls['emin'].real()
ctbin['emax'] = cls['emax'].real()
if cls['ebinalg'].string() == 'POW':
ctbin['ebingamma'] = cls['ebingamma'].real()
ctbin['chatter'] = cls['chatter'].integer()
ctbin['clobber'] = cls['clobber'].boolean()
ctbin['debug'] = cls['debug'].boolean()
# Run ctbin
ctbin.run()
# Retrieve counts cube
cntcube = ctbin.cube().copy()
# Return counts cube
return cntcube