def src_detect(inskymap,
outmodel,
outds9file,
threshold=5.0,
maxsrcs=20,
avgrad=1.0,
corr_rad=0.2,
exclrad=0.2,
logfile=None,
silent=False):
"""
Detect sources from a map
See http://cta.irap.omp.eu/ctools/users/reference_manual/cssrcdetect.html
Parameters
----------
- inskymap (str): input skymap file
- outmodel (str): output xml model filename
- outds9file (str): output ds9 region filename
- threshold (float): treshold (sigma gaussian) for detection
- maxsrcs (int): maximum number of sources
- avgrad (float): averaging radius for significance computation (degrees)
- corr_rad (float): correlation kernel (deg)
- exclrad (float): Radius around a detected source that is excluded
from further source detection (degrees).
- silent (bool): print information or not
Outputs
--------
- Write xml model file
- Write a DS9 region file
"""
srcdet = cscripts.cssrcdetect()
srcdet['inmap'] = inskymap
srcdet['outmodel'] = outmodel
srcdet['outds9file'] = outds9file
srcdet[
'srcmodel'] = 'POINT' # For the moment, only point source + power law
srcdet['bkgmodel'] = 'NONE' # NONE|IRF|AEFF|CUBE|RACC
srcdet['threshold'] = threshold
srcdet['maxsrcs'] = maxsrcs
srcdet['avgrad'] = avgrad
srcdet['corr_rad'] = corr_rad
srcdet['corr_kern'] = 'GAUSSIAN' #<NONE|DISK|GAUSSIAN>
srcdet['exclrad'] = exclrad
srcdet['fit_pos'] = True
srcdet['fit_shape'] = True
if logfile is not None: srcdet['logfile'] = logfile
if logfile is not None: srcdet.logFileOpen()
srcdet.execute()
if logfile is not None: srcdet.logFileClose()
if not silent:
print(srcdet)
print('')
return srcdet
def _test_python(self):
"""
Test cssrcdetect from Python
"""
# Allocate empty cssrcdetect script
srcdetect = cscripts.cssrcdetect()
# Check that saving does not nothing
srcdetect['outmodel'] = 'cssrcdetect_py0.xml'
srcdetect['outds9file'] = 'cssrcdetect_py0.reg'
srcdetect['logfile'] = 'cssrcdetect_py0.log'
srcdetect.logFileOpen()
srcdetect.save()
# Check than an empty model definition file has been created
self._check_model_file('cssrcdetect_py0.xml', 0)
# Check that clearing does not lead to an exception or segfault
srcdetect.clear()
# Now set cssrcdetect parameters
srcdetect['inmap'] = self._map
srcdetect['outmodel'] = 'cssrcdetect_py1.xml'
srcdetect['outds9file'] = 'cssrcdetect_py1.reg'
srcdetect['srcmodel'] = 'POINT'
srcdetect['bkgmodel'] = 'NONE'
srcdetect['threshold'] = 8.0
srcdetect['corr_rad'] = 0.1
srcdetect['logfile'] = 'cssrcdetect_py1.log'
srcdetect['chatter'] = 4
# Run cssrcdetect script and save models
srcdetect.logFileOpen() # Make sure we get a log file
srcdetect.run()
srcdetect.save()
# Check model file
self._check_model_file('cssrcdetect_py1.xml', 2)
# Execute cssrcdetect script again, now with a higher chatter level
# than before
srcdetect['outmodel'] = 'cssrcdetect_py2.xml'
srcdetect['outds9file'] = 'cssrcdetect_py2.reg'
srcdetect['bkgmodel'] = 'IRF'
srcdetect['threshold'] = 8.0
srcdetect['logfile'] = 'cssrcdetect_py2.log'
srcdetect['chatter'] = 3
srcdetect.logFileOpen() # Needed to get a new log file
srcdetect.execute()
# Check model file
self._check_model_file('cssrcdetect_py2.xml', 3)
# Return
return
def _test_python(self):
"""
Test cssrcdetect from Python
"""
# Allocate empty cssrcdetect script
srcdetect = cscripts.cssrcdetect()
# Check that saving does not nothing
srcdetect['outmodel'] = 'cssrcdetect_py0.xml'
srcdetect['outds9file'] = 'cssrcdetect_py0.reg'
srcdetect['logfile'] = 'cssrcdetect_py0.log'
srcdetect.logFileOpen()
srcdetect.save()
# Check than an empty model definition file has been created
self._check_model_file('cssrcdetect_py0.xml', 0)
# Check that clearing does not lead to an exception or segfault
srcdetect.clear()
# Now set cssrcdetect parameters
srcdetect['inmap'] = self._map
srcdetect['outmodel'] = 'cssrcdetect_py1.xml'
srcdetect['outds9file'] = 'cssrcdetect_py1.reg'
srcdetect['srcmodel'] = 'POINT'
srcdetect['bkgmodel'] = 'NONE'
srcdetect['threshold'] = 10.0
srcdetect['logfile'] = 'cssrcdetect_py1.log'
srcdetect['chatter'] = 2
# Run cssrcdetect script and save models
srcdetect.logFileOpen() # Make sure we get a log file
srcdetect.run()
srcdetect.save()
# Check model file
self._check_model_file('cssrcdetect_py1.xml', 2)
# Execute cssrcdetect script again, now with a higher chatter level
# than before
srcdetect['outmodel'] = 'cssrcdetect_py2.xml'
srcdetect['outds9file'] = 'cssrcdetect_py2.reg'
srcdetect['bkgmodel'] = 'IRF'
srcdetect['logfile'] = 'cssrcdetect_py2.log'
srcdetect['chatter'] = 3
srcdetect.logFileOpen() # Needed to get a new log file
srcdetect.execute()
# Check model file
self._check_model_file('cssrcdetect_py2.xml', 3)
# Return
return
def gw_simulation(sim_in, config_in, model_xml, fits_model, counter):
"""
:param sim_in:
:param config_in:
:param model_xml:
:param fits_model:
:param counter:
:return:
"""
src_name = fits_model.split("/")[-1][:-5]
run_id, merger_id = src_name.split('_')
fits_header_0 = fits.open(fits_model)[0].header
ra_src = fits_header_0['RA']
dec_src = fits_header_0['DEC']
coordinate_source = SkyCoord(ra=ra_src * u.deg, dec=dec_src * u.deg, frame="icrs")
src_yaml = sim_in['source']
point_path = create_path(src_yaml['pointings_path'])
opt_point_path = f"{point_path}/optimized_pointings"
ctools_pipe_path = create_path(config_in['exe']['software_path'])
ctobss_params = sim_in['ctobssim']
seed = int(counter)*10
# # PARAMETERS FROM THE CTOBSSIM
sim_e_min = u.Quantity(ctobss_params['energy']['e_min']).to_value(u.TeV)
sim_e_max = u.Quantity(ctobss_params['energy']['e_max']).to_value(u.TeV)
sim_rad = ctobss_params['radius']
output_path = create_path(sim_in['output']['path'] + f"/{src_name}/seed-{seed:03}")
irf_dict = sim_in['IRF']
site = irf_dict['site']
detection = sim_in['detection']
significance_map = detection['skymap_significance']
srcdetect_ctlike = detection['srcdetect_likelihood']
save_simulation = ctobss_params['save_simulation']
try:
mergers_data = pd.read_csv(
f"{point_path}/BNS-GW-Time_onAxis5deg.txt",
sep=" ")
except FileNotFoundError:
print("merger data not present. check that the text file with the correct pointings is in the 'pointings' folder!")
sys.exit()
filter_mask = (mergers_data["run"] == run_id) & (mergers_data["MergerID"] == f"Merger{merger_id}")
merger_onset_data = mergers_data[filter_mask]
time_onset_merger = merger_onset_data['Time'].values[0]
with open(f"{output_path}/GW-{src_name}_seed-{seed:03}_site-{site}.txt", "w") as f:
f.write(f"GW_name\tRA_src\tDEC_src\tseed\tpointing_id\tsrc_to_point\tsrc_in_point\tra_point\tdec_point\tradius\ttime_start\ttime_end\tsignificanceskymap\tsigmasrcdetectctlike\n")
try:
file_name = f"{opt_point_path}/{run_id}_Merger{merger_id}_GWOptimisation_v3.txt"
pointing_data = pd.read_csv(
file_name,
header=0,
sep=",")
except FileNotFoundError:
print("File not found\n")
sys.exit()
RA_data = pointing_data['RA(deg)']
DEC_data = pointing_data['DEC(deg)']
times = pointing_data['Observation Time UTC']
durations = pointing_data['Duration']
# LOOP OVER POINTINGS
for index in range(0, len(pointing_data)):
RA_point = RA_data[index]
DEC_point = DEC_data[index]
coordinate_pointing = SkyCoord(
ra=RA_point * u.degree,
dec=DEC_point * u.degree,
frame="icrs"
)
src_from_pointing = coordinate_pointing.separation(coordinate_source)
t_in_point = Time(times[index])
obs_condition = Observability(site=site)
obs_condition.set_irf(irf_dict)
obs_condition.Proposal_obTime = 10
obs_condition.TimeOffset = 0
obs_condition.Steps_observability = 10
condition_check = obs_condition.check(RA=RA_point, DEC=DEC_point, t_start=t_in_point)
# once the IRF has been chosen, the times are shifted
# this is a quick and dirty solution to handle the times in ctools...not elegant for sure
t_in_point = (Time(times[index]) - Time(time_onset_merger)).to(u.s)
t_end_point = t_in_point + durations[index] * u.s
if len(condition_check) == 0:
print(f"Source Not Visible in pointing {index}")
f.write(
f"{src_name}\t{ra_src}\t{dec_src}\t{seed}\t{index}\t{src_from_pointing.value:.2f}\t{src_from_pointing.value < sim_rad}\t{RA_point}\t{DEC_point}\t{sim_rad}\t{t_in_point.value:.2f}\t{t_end_point.value:.2f}\t -1 \t -1\n")
continue
name_irf = condition_check['IRF_name'][0]
irf = condition_check['IRF'][0]
# model loading
if irf.prod_number == "3b" and irf.prod_version == 0:
caldb = "prod3b"
else:
caldb = f'prod{irf.prod_number}-v{irf.prod_version}'
# simulation
sim = ctools.ctobssim()
sim['inmodel'] = model_xml
sim['caldb'] = caldb
sim['irf'] = name_irf
sim['ra'] = RA_point
sim['dec'] = DEC_point
sim['rad'] = sim_rad
sim['tmin'] = t_in_point.value
sim['tmax'] = t_end_point.value
sim['emin'] = sim_e_min
sim['emax'] = sim_e_max
sim['seed'] = seed
if save_simulation:
event_list_path = create_path(f"{ctobss_params['output_path']}/{src_name}/seed-{seed:03}/")
sim['outevents'] = f"{event_list_path}/event_list_source-{src_name}_seed-{seed:03}_pointingID-{index}.fits"
sim.execute()
f.write(
f"{src_name}\t{ra_src}\t{dec_src}\t{seed}\t{index}\t{src_from_pointing.value:.2f}\t{src_from_pointing.value < sim_rad}\t{RA_point}\t{DEC_point}\t{sim_rad}\t{t_in_point.value:.2f}\t{t_end_point.value:.2f}\t -1 \t -1\n"
)
continue
else:
sim.run()
obs = sim.obs()
obs.models(gammalib.GModels())
# ctskymap
sigma_onoff = -1
sqrt_ts_like = -1
if significance_map:
pars_skymap = detection['parameters_skymap']
scale = float(pars_skymap['scale'])
npix = 2 * int(sim_rad / scale)
fits_temp_title = f"{output_path}/GW-skymap_point-{index}_{seed}.fits"
skymap = ctools.ctskymap(obs.copy())
skymap['proj'] = 'CAR'
skymap['coordsys'] = 'CEL'
skymap['xref'] = RA_point
skymap['yref'] = DEC_point
skymap['binsz'] = scale
skymap['nxpix'] = npix
skymap['nypix'] = npix
skymap['emin'] = sim_e_min
skymap['emax'] = sim_e_max
skymap['bkgsubtract'] = 'RING'
skymap['roiradius'] = pars_skymap['roiradius']
skymap['inradius'] = pars_skymap['inradius']
skymap['outradius'] = pars_skymap['outradius']
skymap['iterations'] = pars_skymap['iterations']
skymap['threshold'] = pars_skymap['threshold']
skymap['outmap'] = fits_temp_title
skymap.execute()
input_fits = fits.open(fits_temp_title)
datain = input_fits['SIGNIFICANCE'].data
datain[np.isnan(datain)] = 0.0
datain[np.isinf(datain)] = 0.0
sigma_onoff = np.max(datain)
if pars_skymap['remove_fits']:
os.remove(fits_temp_title)
if srcdetect_ctlike:
pars_detect = detection['parameters_detect']
scale = float(pars_detect['scale'])
npix = 2 * int(sim_rad / scale)
skymap = ctools.ctskymap(obs.copy())
skymap['proj'] = 'TAN'
skymap['coordsys'] = 'CEL'
skymap['xref'] = RA_point
skymap['yref'] = DEC_point
skymap['binsz'] = scale
skymap['nxpix'] = npix
skymap['nypix'] = npix
skymap['emin'] = sim_e_min
skymap['emax'] = sim_e_max
skymap['bkgsubtract'] = 'NONE'
skymap.run()
# cssrcdetect
srcdetect = cscripts.cssrcdetect(skymap.skymap().copy())
srcdetect['srcmodel'] = 'POINT'
srcdetect['bkgmodel'] = 'NONE'
srcdetect['corr_kern'] = 'GAUSSIAN'
srcdetect['threshold'] = pars_detect['threshold']
srcdetect['corr_rad'] = pars_detect['correlation']
srcdetect.run()
models = srcdetect.models()
# if there's some detection we can do the likelihood.
# Spectral model is a PL and the spatial model is the one from cssrcdetect
if len(models) > 0:
hotspot = models['Src001']
ra_hotspot = hotspot['RA'].value()
dec_hotspot = hotspot['DEC'].value()
models_ctlike = gammalib.GModels()
src_dir = gammalib.GSkyDir()
src_dir.radec_deg(ra_hotspot, dec_hotspot)
spatial = gammalib.GModelSpatialPointSource(src_dir)
spectral = gammalib.GModelSpectralPlaw()
spectral['Prefactor'].value(5.5e-16)
spectral['Prefactor'].scale(1e-16)
spectral['Index'].value(-2.6)
spectral['Index'].scale(-1.0)
spectral['PivotEnergy'].value(50000)
spectral['PivotEnergy'].scale(1e3)
model_src = gammalib.GModelSky(spatial, spectral)
model_src.name('PL_fit_GW')
model_src.tscalc(True)
models_ctlike.append(model_src)
spectral_back = gammalib.GModelSpectralPlaw()
spectral_back['Prefactor'].value(1.0)
spectral_back['Prefactor'].scale(1.0)
spectral_back['Index'].value(0)
spectral_back['PivotEnergy'].value(300000)
spectral_back['PivotEnergy'].scale(1e6)
back_model = gammalib.GCTAModelIrfBackground()
back_model.instruments('CTA')
back_model.name('Background')
back_model.spectral(spectral_back.copy())
models_ctlike.append(back_model)
xmlmodel_PL_ctlike_std = f"{output_path}/model_PL_ctlike_std_seed-{seed}_pointing-{index}.xml"
models_ctlike.save(xmlmodel_PL_ctlike_std)
like_pl = ctools.ctlike(obs.copy())
like_pl['inmodel'] = xmlmodel_PL_ctlike_std
like_pl['caldb'] = caldb
like_pl['irf'] = name_irf
like_pl.run()
ts = -like_pl.obs().models()[0].ts()
if ts > 0:
sqrt_ts_like = np.sqrt(ts)
else:
sqrt_ts_like = 0
if pars_detect['remove_xml']:
os.remove(xmlmodel_PL_ctlike_std)
f.write(
f"{src_name}\t{ra_src}\t{dec_src}\t{seed}\t{index}\t{src_from_pointing.value:.2f}\t{src_from_pointing.value < sim_rad}\t{RA_point:.2f}\t{DEC_point:.2f}\t{sim_rad}\t{t_in_point:.2f}\t{t_end_point:.2f}\t{sigma_onoff:.2f}\t{sqrt_ts_like}\n")
import ctools import cscripts debug = True skymap = ctools.ctskymap() skymap['inobs'] = 'obs_selected.xml' skymap['outmap'] = 'skymap_bkgsubtract.fits' skymap['emin'] = 0.1 skymap['emax'] = 100.0 skymap['nxpix'] = 600 skymap['nypix'] = 600 skymap['binsz'] = 0.02 skymap['coordsys'] = 'GAL' skymap['proj'] = 'CAR' skymap['xref'] = 0.0 skymap['yref'] = 0.0 skymap['bkgsubtract'] = 'IRF' skymap["debug"] = debug skymap.execute() detect = cscripts.cssrcdetect() detect["inmap"] = 'skymap_bkgsubtract.fits' detect["outmodel"] = 'stacked_models.xml' detect["outds9file"] = 'ds9_1.reg' detect["srcmodel"] = 'POINT' detect["bkgmodel"] = 'CUBE' detect["threshold"] = 15.0 detect["debug"] = debug detect.execute()