def main():
usage = "usage: %(prog)s [config file]"
description = "Run fermipy analysis chain."
parser = argparse.ArgumentParser(usage=usage,description=description)
parser.add_argument('--config', default = 'sample_config.yaml')
parser.add_argument('--source', default = None)
args = parser.parse_args()
gta = GTAnalysis(args.config)
if args.source is None:
src_name = gta.roi.sources[0].name
gta.setup()
gta.optimize()
if (gta.roi[src_name]['ts'] > 1000. and
gta.roi[src_name]['SpectrumType'] == 'PowerLaw'):
gta.set_source_spectrum(src_name, spectrum_type='LogParabola',
spectrum_pars={'beta' : {'value' : 0.0, 'scale' : 1.0,
'min' : 0.0, 'max' : 2.0}})
gta.free_source(src_name)
gta.fit()
gta.free_source(src_name, False)
loc = gta.localize(src_name, free_radius=1.0, update=True, make_plots=True)
model = {'Index' : 2.0, 'SpatialModel' : 'PointSource'}
srcs = gta.find_sources(model=model, sqrt_ts_threshold=5.0,
min_separation=0.5)
sed = gta.sed(src_name, free_radius=1.0, make_plots=True)
gta.tsmap(make_plots=True)
gta.tsmap(prefix='excludeSource', exclude=[src_name], make_plots=True)
gta.write_roi('fit0')
lc = gta.lightcurve(src_name, binsz=86400.*28.0, free_radius=3.0, use_scaled_srcmap=True,
multithread=False)
class ExtensionFit:
def __init__(self, configFile):
self.gta = GTAnalysis(configFile, logging={'verbosity': 3})
self.target = None
self.targetRadius = None
self.distance = None
self.catalog = fits.getdata('/users-data/mfalxa/code/gll_psch_v13.fit',
1)
def setSourceName(self, sourceObject, newName):
self.gta.delete_source(sourceObject['name'])
self.gta.add_source(newName, sourceObject)
''' INITIALIZE '''
def initialize(self, sizeROI, rInner, addToROI, TSMin, debug):
self.gta.setup()
if self.gta.config['selection']['emin'] >= 10000:
self.gta.set_parameter('galdiff', 'Scale', 30000)
if debug == True:
self.gta.make_plots('startAll')
self.gta.residmap(prefix='startAll', make_plots=True)
# Get model source names
sourceList = self.gta.get_sources(exclude=['isodiff', 'galdiff'])
# Delete sources unassociated with TS < 50
for i in range(len(sourceList)):
if sourceList[i]['catalog']['TS_value'] < TSMin and self.catalog[
'CLASS'][self.catalog['Source_Name'] == sourceList[i]
['name']][0] == '':
self.gta.delete_source(sourceList[i]['name'])
closests = self.gta.get_sources(distance=rInner,
exclude=['isodiff', 'galdiff'])
# Delete all unidentified sources
for i in range(len(closests)):
if self.catalog['CLASS'][self.catalog['Source_Name'] == closests[i]
['name']][0].isupper() == False:
self.gta.delete_source(closests[i]['name'])
if self.catalog['CLASS'][self.catalog['Source_Name'] == closests[i]
['name']][0] == 'SFR':
self.target = closests[i]
self.setSourceName(self.target, 'TESTSOURCE')
# If debug, save ROI and make plots
if debug == True:
self.gta.write_roi('startModel')
self.gta.residmap(prefix='start', make_plots=True)
self.gta.make_plots('start')
# Optmize spectral parameters for sources with npred > 1
self.gta.optimize(npred_threshold=1, skip=['isodiff'])
# Get model source names
sourceList = self.gta.get_sources(distance=sizeROI + addToROI,
square=True,
exclude=['isodiff', 'galdiff'])
# Iterate source localizing on source list
for i in range(len(sourceList)):
if sourceList[i].extended == False:
self.gta.localize(sourceList[i]['name'],
write_fits=False,
write_npy=False,
update=True)
# Free sources within ROI size + extra distance from center
self.gta.free_sources(distance=sizeROI + addToROI, square=True)
# Re-optimize ROI
self.gta.optimize(skip=['isodiff'])
# Save and make plots if debug
if debug == True:
self.gta.write_roi('modelInitialized')
self.gta.residmap(prefix='initialized', make_plots=True)
self.gta.make_plots('initialized')
# Lock sources
self.gta.free_sources(free=False)
''' OUTER REGION '''
def outerRegionAnalysis(self, sizeROI, rInner, sqrtTsThreshold,
minSeparation, debug):
self.gta.free_sources(distance=sizeROI,
pars='norm',
square=True,
free=True)
self.gta.free_sources(distance=rInner, free=False)
self.gta.free_source('galdiff', free=True)
self.gta.free_source('isodiff', free=False)
# Seek new sources until none are found
sourceModel = {
'SpectrumType': 'PowerLaw',
'Index': 2.0,
'Scale': 30000,
'Prefactor': 1.e-15,
'SpatialModel': 'PointSource'
}
newSources = self.gta.find_sources(sqrt_ts_threshold=sqrtTsThreshold,
min_separation=minSeparation,
model=sourceModel,
**{
'search_skydir':
self.gta.roi.skydir,
'search_minmax_radius':
[rInner, sizeROI]
})
if len(newSources) > 0:
for i in range(len(newSources)):
if newSources['sources'][i]['ts'] > 100.:
self.gta.set_source_spectrum(
newSources['sources'][i]['name'],
spectrum_type='LogParabola')
self.gta.free_source(newSources['sources'][i]['name'])
self.gta.fit()
self.gta.free_source(newSources['sources'][i]['name'],
free=False)
# Optimize all ROI
self.gta.optimize(skip=['isodiff'])
# Save sources found
if debug == True:
self.gta.residmap(prefix='outer', make_plots=True)
self.gta.write_roi('outerAnalysisROI')
self.gta.make_plots('outer')
''' INNER REGION '''
def innerRegionAnalysis(self, sizeROI, rInner, maxIter, sqrtTsThreshold,
minSeparation, dmMin, TSm1Min, TSextMin, debug):
self.gta.free_sources(distance=sizeROI, square=True, free=False)
self.gta.free_sources(distance=rInner, free=True, exclude=['isodiff'])
# Keep closest source if identified with star forming region in catalog or look for new source closest to center within Rinner
if self.target != None:
print('Closest source identified with star forming region : ',
self.target['name'])
self.gta.set_source_morphology('TESTSOURCE',
**{'spatial_model': 'PointSource'})
else:
closeSources = self.gta.find_sources(sqrt_ts_threshold=2.,
min_separation=minSeparation,
max_iter=1,
**{
'search_skydir':
self.gta.roi.skydir,
'search_minmax_radius':
[0., rInner]
})
dCenter = np.array([])
for i in range(len(closeSources['sources'])):
dCenter = np.append(
dCenter,
self.gta.roi.skydir.separation(
closeSources['sources'][i].skydir).value)
self.target = closeSources['sources'][np.argmin(dCenter)]
print('Target name : ', self.target['name'])
self.setSourceName(self.target, 'TESTSOURCE')
for i in [
x for x in range(len(closeSources['sources']))
if x != (np.argmin(dCenter))
]:
self.gta.delete_source(closeSources['sources'][i]['name'])
self.gta.optimize(skip=['isodiff'])
# Initialize n sources array
nSources = []
# Save ROI without extension fit
self.gta.write_roi('nSourcesFit')
if debug == True:
self.gta.make_plots('innerInit')
self.gta.residmap(prefix='innerInit', make_plots=True)
# Test for extension
extensionTest = self.gta.extension('TESTSOURCE',
make_plots=True,
write_npy=debug,
write_fits=debug,
spatial_model='RadialDisk',
update=True,
free_background=True,
fit_position=True)
extLike = extensionTest['loglike_ext']
TSext = extensionTest['ts_ext']
print('TSext : ', TSext)
extAIC = 2 * (len(self.gta.get_free_param_vector()) -
self.gta._roi_data['loglike'])
self.gta.write_roi('extFit')
if debug == True:
self.gta.residmap(prefix='ext0', make_plots=True)
self.gta.make_plots('ext0')
self.gta.load_roi('nSourcesFit', reload_sources=True)
for i in range(1, maxIter + 1):
# Test for n point sources
nSourcesTest = self.gta.find_sources(
sources_per_iter=1,
sqrt_ts_threshold=sqrtTsThreshold,
min_separation=minSeparation,
max_iter=1,
**{
'search_skydir': self.gta.roi.skydir,
'search_minmax_radius': [0., rInner]
})
if len(nSourcesTest['sources']) > 0:
if nSourcesTest['sources'][0]['ts'] > 100.:
self.gta.set_source_spectrum(
nSourcesTest['sources'][0]['name'],
spectrum_type='LogParabola')
self.gta.free_source(nSourcesTest['sources'][0]['name'])
self.gta.fit()
self.gta.free_source(nSourcesTest['sources'][0]['name'],
free=False)
if debug == True:
self.gta.make_plots('nSources' + str(i))
nSources.append(nSourcesTest['sources'])
self.gta.localize(nSourcesTest['sources'][0]['name'],
write_npy=debug,
write_fits=debug,
update=True)
nAIC = 2 * (len(self.gta.get_free_param_vector()) -
self.gta._roi_data['loglike'])
self.gta.free_source(nSourcesTest['sources'][0]['name'],
free=True)
self.gta.residmap(prefix='nSources' + str(i), make_plots=True)
self.gta.write_roi('n1SourcesFit')
# Estimate Akaike Information Criterion difference between both models
dm = extAIC - nAIC
print('AIC difference between both models = ', dm)
# Estimate TS_m+1
extensionTestPlus = self.gta.extension(
'TESTSOURCE',
make_plots=True,
write_npy=debug,
write_fits=debug,
spatial_model='RadialDisk',
update=True,
free_background=True,
fit_position=True)
TSm1 = 2 * (extensionTestPlus['loglike_ext'] - extLike)
print('TSm+1 = ', TSm1)
if debug == True:
self.gta.residmap(prefix='ext' + str(i), make_plots=True)
self.gta.make_plots('ext' + str(i))
if dm < dmMin and TSm1 < TSm1Min:
self.gta.load_roi('extFit', reload_sources=True)
break
else:
# Set extension test to current state and save current extension fit ROI and load previous nSources fit ROI
extensionTest = extensionTestPlus
extLike = extensionTestPlus['loglike_ext']
TSext = extensionTestPlus['ts_ext']
print('TSext : ', TSext)
extAIC = 2 * (len(self.gta.get_free_param_vector()) -
self.gta._roi_data['loglike'])
self.gta.write_roi('extFit')
self.gta.load_roi('n1SourcesFit', reload_sources=True)
self.gta.write_roi('nSourcesFit')
else:
if TSext > TSextMin:
self.gta.load_roi('extFit', reload_sources=True)
break
else:
self.gta.load_roi('nSourcesFit', reload_sources=True)
break
self.gta.fit()
# Get source radius depending on spatial model
endSources = self.gta.get_sources()
for i in range(len(endSources)):
if endSources[i]['name'] == 'TESTSOURCE':
self.target = endSources[i]
self.distance = self.gta.roi.skydir.separation(
endSources[i].skydir).value
if endSources[i].extended == True:
self.targetRadius = endSources[i]['SpatialWidth']
else:
self.targetRadius = endSources[i]['pos_r95']
''' CHECK OVERLAP '''
def overlapDisk(self, rInner, radiusCatalog):
print('Target radius : ', self.targetRadius)
# Check radius sizes
if radiusCatalog < self.targetRadius:
r = float(radiusCatalog)
R = float(self.targetRadius)
else:
r = float(self.targetRadius)
R = float(radiusCatalog)
# Estimating overlapping area
d = self.distance
print('Distance from center : ', d)
if d < (r + R):
if R < (r + d):
area = r**2 * np.arccos(
(d**2 + r**2 - R**2) / (2 * d * r)) + R**2 * np.arccos(
(d**2 + R**2 - r**2) / (2 * d * R)) - 0.5 * np.sqrt(
(-d + r + R) * (d + r - R) * (d - r + R) *
(d + r + R))
overlap = round((area / (np.pi * r**2)) * 100, 2)
else:
area = np.pi * r**2
overlap = 100.0
else:
area = 0.
overlap = 0.
print('Overlapping surface : ', area)
print('Overlap : ', overlap)
if overlap > 68. and self.distance < rInner:
associated = True
else:
associated = False
return associated
''' CHECK UPPER LIMIT '''
def upperLimit(self, name, radius):
sourceModel = {
'SpectrumType': 'PowerLaw',
'Index': 2.0,
'Scale': 30000,
'Prefactor': 1.e-15,
'SpatialModel': 'RadialDisk',
'SpatialWidth': radius,
'glon': self.gta.config['selection']['glon'],
'glat': self.gta.config['selection']['glat']
}
self.gta.add_source(name, sourceModel, free=True)
self.gta.fit()
self.gta.residmap(prefix='upperLimit', make_plots=True)
print('Upper limit : ', self.gta.get_sources()[0]['flux_ul95'])
def _get_fermipy_instance(configuration, likelihood_model):
"""
Generate a 'model' configuration section for fermipy starting from a likelihood model from astromodels
:param configuration: a dictionary containing the configuration for fermipy
:param likelihood_model: the input likelihood model from astromodels
:type likelihood_model: astromodels.Model
:return: a dictionary with the 'model' section of the fermipy configuration
"""
# Generate a new 'model' section in the configuration which reflects the model
# provided as input
# Get center and radius of ROI
ra_center = float(configuration["selection"]["ra"])
dec_center = float(configuration["selection"]["dec"])
roi_width = float(configuration["binning"]["roiwidth"])
roi_radius = old_div(roi_width, np.sqrt(2.0))
# Get IRFS
irfs = evclass_irf[int(configuration["selection"]["evclass"])]
log.info(f"Using IRFs {irfs}")
if "gtlike" in configuration and "irfs" in configuration["gtlike"]:
if irfs.upper() != configuration["gtlike"]["irfs"].upper():
log.critical(
"Evclass points to IRFS %s, while you specified %s in the "
"configuration" % (irfs, configuration["gtlike"]["irfs"]))
else:
if not "gtlike" in configuration:
configuration["gtlike"] = {}
configuration["gtlike"]["irfs"] = irfs
# The fermipy model is just a dictionary. It corresponds to the 'model' section
# of the configuration file (http://fermipy.readthedocs.io/en/latest/config.html#model)
fermipy_model = {}
# Find Galactic and Isotropic templates appropriate for this IRFS
# (information on the ROI is used to cut the Galactic template, which speeds up the
# analysis a lot)
# NOTE: these are going to be absolute paths
galactic_template = str(
sanitize_filename(
findGalacticTemplate(irfs, ra_center, dec_center, roi_radius),
True # noqa: F821
))
isotropic_template = str(
sanitize_filename(findIsotropicTemplate(irfs), True)) # noqa: F821
# Add them to the fermipy model
fermipy_model["galdiff"] = galactic_template
fermipy_model["isodiff"] = isotropic_template
# Now iterate over all sources contained in the likelihood model
sources = []
# point sources
for point_source in list(likelihood_model.point_sources.values()
): # type: astromodels.PointSource
this_source = {
"Index": 2.56233,
"Scale": 572.78,
"Prefactor": 2.4090e-12
}
this_source["name"] = point_source.name
this_source["ra"] = point_source.position.ra.value
this_source["dec"] = point_source.position.dec.value
# The spectrum used here is unconsequential, as it will be substituted by a FileFunction
# later on. So I will just use PowerLaw for everything
this_source["SpectrumType"] = "PowerLaw"
sources.append(this_source)
# extended sources
for extended_source in list(likelihood_model.extended_sources.values()
): # type: astromodels.ExtendedSource
raise NotImplementedError("Extended sources are not supported yet")
# Add all sources to the model
fermipy_model["sources"] = sources
# Now we can finally instance the GTAnalysis instance
configuration["model"] = fermipy_model
gta = GTAnalysis(configuration) # noqa: F821
# This will take a long time if it's the first time we run with this model
gta.setup()
# Substitute all spectra for point sources with FileSpectrum, so that we will be able to control
# them from 3ML
energies_keV = None
for point_source in list(likelihood_model.point_sources.values()
): # type: astromodels.PointSource
# Fix this source, so fermipy will not optimize by itself the parameters
gta.free_source(point_source.name, False)
# This will substitute the current spectrum with a FileFunction with the same shape and flux
gta.set_source_spectrum(point_source.name,
"FileFunction",
update_source=False)
# Get the energies at which to evaluate this source
this_log_energies, _flux = gta.get_source_dnde(point_source.name)
this_energies_keV = (10**this_log_energies * 1e3
) # fermipy energies are in GeV, we need keV
if energies_keV is None:
energies_keV = this_energies_keV
else:
# This is to make sure that all sources are evaluated at the same energies
if not np.all(energies_keV == this_energies_keV):
log.critical(
"All sources should be evaluated at the same energies.")
dnde = point_source(energies_keV) # ph / (cm2 s keV)
dnde_per_MeV = dnde * 1000.0 # ph / (cm2 s MeV)
gta.set_source_dnde(point_source.name, dnde_per_MeV, False)
# Same for extended source
for extended_source in list(likelihood_model.extended_sources.values()
): # type: astromodels.ExtendedSource
raise NotImplementedError("Extended sources are not supported yet")
return gta, energies_keV
def main():
usage = "usage: %(prog)s -c config.yaml"
description = "Run the lc analysis"
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('-c', '--conf', required=True)
parser.add_argument('-i',
required=False,
default=0,
help='Set local or scratch calculation',
type=int)
parser.add_argument('--state',
help='analysis state',
choices=['avgspec', 'setup'],
default='avgspec')
parser.add_argument('--forcepl',
default=0,
help='Force the target source to have power-law shape',
type=int)
parser.add_argument('--createsed',
default=0,
help='Create SED from best fit model',
type=int)
parser.add_argument(
'--adaptive',
default=0,
help='Use adaptive binning for minute scale light curves',
type=int)
parser.add_argument('--srcprob', default = 0,
help='Calculate the source probability for the photons,' \
' only works when no sub orbit time scales are used',
type=int)
parser.add_argument(
'--mincounts',
default=2,
help='Minimum number of counts within LC bin to run analysis',
type=int)
parser.add_argument('--simulate', default = None,
help='None or full path to yaml file which contains src name' \
'and spec to be simulated',
)
parser.add_argument(
'--make_plots',
default=0,
type=int,
help='Create sed plot',
)
parser.add_argument(
'--randomize',
default=1,
help=
'If you simulate, use Poisson realization. If false, use Asimov data set',
type=int)
args = parser.parse_args()
utils.init_logging('DEBUG')
config = yaml.load(open(args.conf))
tmpdir, job_id = lsf.init_lsf()
if not job_id:
job_id = args.i
logging.info('tmpdir: {0:s}, job_id: {1:n}'.format(tmpdir, job_id))
os.chdir(tmpdir) # go to tmp directory
logging.info('Entering directory {0:s}'.format(tmpdir))
logging.info('PWD is {0:s}'.format(os.environ["PWD"]))
# copy the ft1,ft2 and ltcube files
#for k in ['evfile','scfile','ltcube']:
# don't stage them, done automatically by fermipy if needed
# config[k] = utils.copy2scratch(config[k], tmpdir)
# set the scratch directories
logging.debug(config['data'])
config['fileio']['scratchdir'] = tmpdir
# set the log file
logdir = copy.deepcopy(config['fileio']['logfile'])
config['fileio']['logfile'] = path.join(tmpdir, 'fermipy.log')
# debugging: all files will be saved (default is False)
#config['fileio']['savefits'] = True
# if simulating an orbit, save fits files
if args.simulate is not None:
config['fileio']['savefits'] = True
# copy all fits files already present in outdir
# run the analysis
lc_config = copy.deepcopy(config['lightcurve'])
fit_config = copy.deepcopy(config['fit_pars'])
# remove parameters from config file not accepted by fermipy
for k in ['configname', 'tmp', 'log', 'fit_pars']:
config.pop(k, None)
if 'adaptive' in config['lightcurve'].keys():
config['lightcurve'].pop('adaptive', None)
# set the correct time bin
config['selection']['tmin'], config['selection']['tmax'], nj = set_lc_bin(
config['selection']['tmin'],
config['selection']['tmax'],
config['lightcurve']['binsz'],
job_id - 1 if job_id > 0 else 0,
ft1=config['data']['evfile'])
logging.debug('setting light curve bin' + \
'{0:n}, between {1[tmin]:.0f} and {1[tmax]:.0f}'.format(job_id, config['selection']))
if args.adaptive:
config['fileio']['outdir'] = utils.mkdir(
path.join(config['fileio']['outdir'],
'adaptive{0:.0f}/'.format(lc_config['adaptive'])))
if args.state == 'setup':
config['fileio']['outdir'] = utils.mkdir(
path.join(config['fileio']['outdir'],
'setup{0:05n}/'.format(job_id if job_id > 0 else 1)))
else:
config['fileio']['outdir'] = utils.mkdir(
path.join(config['fileio']['outdir'],
'{0:05n}/'.format(job_id if job_id > 0 else 1)))
logging.info('Starting with fermipy analysis')
logging.info('using fermipy version {0:s}'.format(fermipy.__version__))
logging.info('located at {0:s}'.format(fermipy.__file__))
if config['data']['ltcube'] == '':
config['data'].pop('ltcube', None)
compute_sub_gti_lc = False
if type(config['lightcurve']['binsz']) == str:
if len(config['lightcurve']['binsz'].strip('gti')):
compute_sub_gti_lc = True
if config['lightcurve']['binsz'].find('min') > 0:
config['lightcurve']['binsz'] = float(
config['lightcurve']['binsz'].strip('gti').strip(
'min')) * 60.
logging.info("set time bin length to {0:.2f}s".format(
config['lightcurve']['binsz']))
else:
config['lightcurve']['binsz'] = 3. * 3600.
try:
gta = GTAnalysis(config, logging={'verbosity': 3})
except Exception as e:
logging.error("{0}".format(e))
config['selection']['target'] = None
gta = GTAnalysis(config, logging={'verbosity': 3})
sep = gta.roi.sources[0]['offset']
logging.warning(
"Source closets to ROI center is {0:.3f} degree away".format(sep))
if sep < 0.1:
config['selection']['target'] = gta.roi.sources[0]['name']
gta.config['selection']['target'] = config['selection']['target']
logging.info("Set target to {0:s}".format(
config['selection']['target']))
# stage the full time array analysis results to the tmp dir
# do not copy png images
files = [
fn for fn in glob(fit_config['avgspec'])
if fn.find('.xml') > 0 or fn.find('.npy') > 0
]
files += [config['data']['evfile']]
utils.copy2scratch(files, gta.workdir)
# we're using actual data
if args.simulate is None:
# check before the analysis start if there are any events in the master file
# in the specified time range
logging.info('Checking for events in initial ft1 file')
t = Table.read(path.join(gta.workdir,
path.basename(config['data']['evfile'])),
hdu='EVENTS')
logging.info("times in base ft1: {0} {1} {2}".format(
t["TIME"].max(), t["TIME"].min(),
t["TIME"].max() - t["TIME"].min()))
m = (t["TIME"] >= config['selection']['tmin']) & (
t["TIME"] <= config['selection']['tmax'])
if np.sum(m) < args.mincounts + 1:
logging.error(
"*** Only {0:n} events between tmin and tmax! Exiting".format(
np.sum(m)))
assert np.sum(m) > args.mincounts
else:
logging.info("{0:n} events between tmin and tmax".format(
np.sum(m)))
# check how many bins are in each potential light curve bin
if compute_sub_gti_lc:
# select time of first and last
# photon instead of GTI time
m = (t["TIME"] >= config['selection']['tmin']) & \
(t["TIME"] <= config['selection']['tmax'])
tmin = t["TIME"][m].min() - 1.
tmax = t["TIME"][m].max() + 1.
logging.info("There will be up to {0:n} time bins".format(np.ceil(
(tmax - tmin) / \
config['lightcurve']['binsz'])))
bins = np.arange(tmin, tmax, config['lightcurve']['binsz'])
bins = np.concatenate([bins, [config['selection']['tmax']]])
counts = calc_counts(t, bins)
# remove the starting times of the bins with zero counts
# and rebin the data
logging.info("Counts before rebinning: {0}".format(counts))
mincounts = 10.
mc = counts < mincounts
if np.sum(mc):
# remove trailing zeros
if np.any(counts == 0.):
mcounts_post, mcounts_pre = rm_trailing_zeros(counts)
counts = counts[mcounts_post & mcounts_pre]
bins = np.concatenate([
bins[:-1][mcounts_post & mcounts_pre],
[bins[1:][mcounts_post & mcounts_pre].max()]
])
bins = rebin(counts, bins)
logging.info("Bin lengths after rebinning: {0}".format(
np.diff(bins)))
logging.info("Bin times after rebinning: {0}".format(bins))
counts = calc_counts(t, bins)
logging.info("Counts after rebinning: {0}".format(counts))
else:
logging.info("Regular time binning will be used")
bins = list(bins)
logging.info('Running fermipy setup')
try:
gta.setup()
except (RuntimeError, IndexError) as e:
logging.error(
'Caught Runtime/Index Error while initializing analysis object')
logging.error('Printing error:')
logging.error(e)
if e.message.find("File not found") >= 0 and e.message.find(
'srcmap') >= 0:
logging.error("*** Srcmap calculation failed ***")
if e.message.find("NDSKEYS") >= 0 and e.message.find('srcmap') >= 0:
logging.error(
"*** Srcmap calculation failed with NDSKEYS keyword not found in header ***"
)
logging.info("Checking if there are events in ft1 file")
ft1 = path.join(gta.workdir, 'ft1_00.fits')
f = glob(ft1)
if not len(f):
logging.error(
"*** no ft1 file found at location {0:s}".format(ft1))
raise
t = Table.read(f[0], hdu='EVENTS')
if not len(t):
logging.error("*** The ft1 file contains no events!! ***".format(
len(t)))
else:
logging.info("The ft1 file contains {0:n} event(s)".format(len(t)))
return
# end here if you only want to calulate
# intermediate fits files
if args.state == 'setup':
return gta
logging.info('Loading the fit for the average spectrum')
gta.load_roi('avgspec') # reload the average spectral fit
logging.info('Running fermipy optimize and fit')
# we're using actual data
if args.simulate is None:
if args.forcepl:
gta = set_src_spec_pl(
gta, gta.get_source_name(config['selection']['target']))
# to do add EBL absorption at some stage ...
# gta = add_ebl_atten(gta, gta.get_source_name(config['selection']['target']), fit_config['z'])
# make sure you are fitting data
gta.simulate_roi(restore=True)
if compute_sub_gti_lc:
if args.adaptive:
# do import only here since root must be compiled
from fermiAnalysis import adaptivebinning as ab
# compute the exposure
energy = 1000.
texp, front, back = ab.comp_exposure_phi(gta, energy=1000.)
# compute the bins
result = ab.time_bins(
gta,
texp,
0.5 * (front + back),
#critval = 20., # bins with ~20% unc
critval=lc_config['adaptive'],
Epivot=None, # compute on the fly
# tstart = config['selection']['tmin'],
# tstop = config['selection']['tmax']
)
# cut the bins to this GTI
mask = result['tstop'] > config['selection']['tmin']
mask = mask & (result['tstart'] < config['selection']['tmax'])
# try again with catalog values
if not np.sum(mask):
logging.error(
"Adaptive bins outside time window, trying catalog values for flux"
)
result = ab.time_bins(
gta,
texp,
0.5 * (front + back),
critval=lc_config['adaptive'], # bins with ~20% unc
Epivot=None, # compute on the fly
forcecatalog=True,
# tstart = config['selection']['tmin'],
# tstop = config['selection']['tmax']
)
# cut the bins to this GTI
mask = result['tstop'] > config['selection']['tmin']
mask = mask & (result['tstart'] <
config['selection']['tmax'])
if not np.sum(mask):
logging.error(
"Adaptive bins do not cover selected time interval!"
)
logging.error("Using original bins")
else:
bins = np.concatenate((result['tstart'][mask],
[result['tstop'][mask][-1]]))
bins[0] = np.max(
[config['selection']['tmin'], bins[0]])
bins[-1] = np.min(
[config['selection']['tmax'], bins[-1]])
bins = list(bins)
# removing trailing zeros
counts = calc_counts(t, bins)
mcounts_post, mcounts_pre = rm_trailing_zeros(counts)
logging.info(
"count masks: {0} {1}, bins: {2}, counts: {3}".
format(mcounts_post, mcounts_pre, bins, counts))
counts = counts[mcounts_post & mcounts_pre]
bins = np.concatenate([
np.array(bins)[:-1][mcounts_post & mcounts_pre],
[
np.array(bins)[1:][mcounts_post
& mcounts_pre].max()
]
])
logging.info(
"Using bins {0}, total n={1:n} bins".format(
bins,
len(bins) - 1))
logging.info("bins widths : {0}".format(np.diff(bins)))
logging.info("counts per bin: {0} ".format(
calc_counts(t, bins)))
bins = list(bins)
# TODO: test that this is working also with GTIs that have little or no counts
lc = gta.lightcurve(
config['selection']['target'],
binsz=config['lightcurve']['binsz'],
free_background=config['lightcurve']['free_background'],
free_params=config['lightcurve']['free_params'],
free_radius=config['lightcurve']['free_radius'],
make_plots=False,
multithread=True,
nthread=4,
#multithread = False,
#nthread = 1,
save_bin_data=True,
shape_ts_threshold=16.,
use_scaled_srcmap=True,
use_local_ltcube=True,
write_fits=True,
write_npy=True,
time_bins=bins,
outdir='{0:.0f}s'.format(config['lightcurve']['binsz']))
else:
# run the fitting of the entire time and energy range
try:
o = gta.optimize() # perform an initial fit
logging.debug(o)
except RuntimeError as e:
logging.error("Error in optimize: {0}".format(e))
logging.info("Trying to continue ...")
gta = set_free_pars_lc(gta, config, fit_config)
f = gta.fit()
if 'fix_sources' in fit_config.keys():
skip = fit_config['fix_sources'].keys()
else:
skip = []
gta, f = refit(gta,
config['selection']['target'],
f,
fit_config['ts_fixed'],
skip=skip)
gta.print_roi()
gta.write_roi('lc')
if args.createsed:
if args.make_plots:
init_matplotlib_backend()
gta.load_roi('lc') # reload the average spectral fit
logging.info('Running sed for {0[target]:s}'.format(
config['selection']))
sed = gta.sed(config['selection']['target'],
prefix = 'lc_sed',
free_radius = None if config['sed']['free_radius'] == 0. \
else config['sed']['free_radius'],
free_background= config['sed']['free_background'],
free_pars = fa.allnorm,
make_plots = args.make_plots,
cov_scale = config['sed']['cov_scale'],
use_local_index = config['sed']['use_local_index'],
bin_index = config['sed']['bin_index']
)
# debugging: calculate sed and resid maps for each light curve bin
#logging.info('Running sed for {0[target]:s}'.format(config['selection']))
#sed = gta.sed(config['selection']['target'], prefix = 'lc')
#model = {'Scale': 1000., 'Index' : fit_config['new_src_pl_index'], 'SpatialModel' : 'PointSource'}
#resid_maps = gta.residmap('lc',model=model, make_plots=True, write_fits = True, write_npy = True)
if args.srcprob:
logging.info("Running srcprob with srcmdl {0:s}".format('lc'))
gta.compute_srcprob(xmlfile='lc', overwrite=True)
# we are simulating a source
else:
# TODO: I probably have to run the setup here. Do on weekly files, i.e., no time cut? Only do that later?
with open(args.simulate) as f:
simsource = np.load(f, allow_pickle=True).flat[0]
# set the source to the simulation value
gta.set_source_spectrum(
simsource['target'],
spectrum_type=simsource['spectrum_type'],
spectrum_pars=simsource['spectrum_pars'][job_id - 1])
logging.info("changed spectral parameters to {0}".format(
gta.roi.get_source_by_name(simsource['target']).spectral_pars))
# simulate the ROI
gta.simulate_roi(randomize=bool(args.randomize))
gta = set_free_pars_lc(gta, config, fit_config)
# fit the simulation
f = gta.fit()
gta, f = refit(gta, config['selection']['target'], f,
fit_config['ts_fixed'])
gta.print_roi()
gta.write_roi('lc_simulate_{0:s}'.format(simsource['suffix']))
return gta
def _process_lc_bin(itime, name, config, basedir, workdir, diff_sources, const_spectrum, roi, lck_params,
**kwargs):
i, time = itime
roi = copy.deepcopy(roi)
config = copy.deepcopy(config)
config['selection']['tmin'] = time[0]
config['selection']['tmax'] = time[1]
# create output directories labeled in MET vals
outdir = basedir + 'lightcurve_%.0f_%.0f' % (time[0], time[1])
config['fileio']['outdir'] = os.path.join(workdir, outdir)
config['logging']['prefix'] = 'lightcurve_%.0f_%.0f ' % (time[0], time[1])
config['fileio']['logfile'] = os.path.join(config['fileio']['outdir'],
'fermipy.log')
utils.mkdir(config['fileio']['outdir'])
yaml.dump(utils.tolist(config),
open(os.path.join(config['fileio']['outdir'],
'config.yaml'), 'w'))
xmlfile = os.path.join(config['fileio']['outdir'], 'base.xml')
try:
from fermipy.gtanalysis import GTAnalysis
gta = GTAnalysis(config, roi, loglevel=logging.DEBUG)
gta.logger.info('Fitting time range %i %i' % (time[0], time[1]))
gta.setup()
except:
print('Analysis failed in time range %i %i' %
(time[0], time[1]))
print(sys.exc_info()[0])
raise
return {}
gta._lck_params = lck_params
# Recompute source map for source of interest and sources within 3 deg
if gta.config['gtlike']['use_scaled_srcmap']:
names = [s.name for s in
gta.roi.get_sources(distance=3.0, skydir=gta.roi[name].skydir)
if not s.diffuse]
gta.reload_sources(names)
# Write the current model
gta.write_xml(xmlfile)
# Optimize the model
gta.optimize(skip=diff_sources,
shape_ts_threshold=kwargs.get('shape_ts_threshold'))
fit_results = _fit_lc(gta, name, **kwargs)
gta.write_xml('fit_model_final.xml')
srcmodel = copy.deepcopy(gta.get_src_model(name))
numfree = gta.get_free_param_vector().count(True)
max_ts_thresholds = [None, 4, 9]
for max_ts in max_ts_thresholds:
if max_ts is not None:
gta.free_sources(minmax_ts=[None, max_ts], free=False, exclude=[name])
# rerun fit using params from full time (constant) fit using same
# param vector as the successful fit to get loglike
specname, spectrum = const_spectrum
gta.set_source_spectrum(name, spectrum_type=specname,
spectrum_pars=spectrum,
update_source=False)
gta.free_source(name, free=False)
const_fit_results = gta.fit()
if not const_fit_results['fit_success']:
continue
const_srcmodel = gta.get_src_model(name)
# rerun using shape fixed to full time fit
# for the fixed-shape lightcurve
gta.free_source(name, pars='norm')
fixed_fit_results = gta.fit()
if not fixed_fit_results['fit_success']:
continue
fixed_srcmodel = gta.get_src_model(name)
break
# special lc output
o = {'flux_const': const_srcmodel['flux'],
'loglike_const': const_fit_results['loglike'],
'fit_success': fit_results['fit_success'],
'fit_success_fixed': fixed_fit_results['fit_success'],
'fit_quality': fit_results['fit_quality'],
'fit_status': fit_results['fit_status'],
'num_free_params': numfree,
'config': config}
# full flux output
if fit_results['fit_success'] == 1:
for k in defaults.source_flux_output.keys():
if not k in srcmodel:
continue
o[k] = srcmodel[k]
o[k+'_fixed'] = fixed_srcmodel[k]
gta.logger.info('Finished time range %i %i' % (time[0], time[1]))
return o
def _process_lc_bin(itime, name, config, basedir, workdir, diff_sources, const_spectrum, roi, lck_params,
**kwargs):
i, time = itime
roi = copy.deepcopy(roi)
config = copy.deepcopy(config)
config['selection']['tmin'] = time[0]
config['selection']['tmax'] = time[1]
# create output directories labeled in MET vals
outdir = basedir + 'lightcurve_%.0f_%.0f' % (time[0], time[1])
config['fileio']['outdir'] = os.path.join(workdir, outdir)
config['logging']['prefix'] = 'lightcurve_%.0f_%.0f ' % (time[0], time[1])
config['fileio']['logfile'] = os.path.join(config['fileio']['outdir'],
'fermipy.log')
utils.mkdir(config['fileio']['outdir'])
yaml.dump(utils.tolist(config),
open(os.path.join(config['fileio']['outdir'],
'config.yaml'), 'w'))
xmlfile = os.path.join(config['fileio']['outdir'], 'base.xml')
try:
from fermipy.gtanalysis import GTAnalysis
gta = GTAnalysis(config, roi, loglevel=logging.DEBUG)
gta.logger.info('Fitting time range %i %i' % (time[0], time[1]))
gta.setup()
except:
print('Analysis failed in time range %i %i' %
(time[0], time[1]))
print(sys.exc_info()[0])
raise
return {}
gta._lck_params = lck_params
# Recompute source map for source of interest and sources within 3 deg
if gta.config['gtlike']['use_scaled_srcmap']:
names = [s.name for s in
gta.roi.get_sources(distance=3.0, skydir=gta.roi[name].skydir)
if not s.diffuse]
gta.reload_sources(names)
# Write the current model
gta.write_xml(xmlfile)
# Optimize the model
gta.optimize(skip=diff_sources,
shape_ts_threshold=kwargs.get('shape_ts_threshold'))
fit_results = _fit_lc(gta, name, **kwargs)
gta.write_xml('fit_model_final.xml')
srcmodel = copy.deepcopy(gta.get_src_model(name))
numfree = gta.get_free_param_vector().count(True)
const_srcmodel = gta.get_src_model(name).copy()
fixed_fit_results = fit_results.copy()
fixed_srcmodel = gta.get_src_model(name).copy()
fixed_fit_results['fit_success'],fixed_srcmodel['fit_success'] = [False,False]
fixed_fit_results['fit_quality'],fixed_srcmodel['fit_quality'] = [0,0]
max_ts_thresholds = [None, 4, 9, 16, 25]
for max_ts in max_ts_thresholds:
if max_ts is not None:
gta.free_sources(minmax_ts=[None, max_ts], free=False, exclude=[name])
# rerun fit using params from full time (constant) fit using same
# param vector as the successful fit to get loglike
specname, spectrum = const_spectrum
gta.set_source_spectrum(name, spectrum_type=specname,
spectrum_pars=spectrum,
update_source=False)
gta.free_source(name, free=False)
const_fit_results = gta.fit()
if not const_fit_results['fit_success']:
continue
const_srcmodel = gta.get_src_model(name)
# rerun using shape fixed to full time fit
# for the fixed-shape lightcurve
gta.free_source(name, pars='norm')
fixed_fit_results = gta.fit()
if not fixed_fit_results['fit_success']:
continue
fixed_srcmodel = gta.get_src_model(name)
break
# special lc output
o = {'flux_const': const_srcmodel['flux'],
'loglike_const': const_fit_results['loglike'],
'fit_success': fit_results['fit_success'],
'fit_success_fixed': fixed_fit_results['fit_success'],
'fit_quality': fit_results['fit_quality'],
'fit_status': fit_results['fit_status'],
'num_free_params': numfree,
'config': config}
# full flux output
if fit_results['fit_success'] == 1:
for k in defaults.source_flux_output.keys():
if not k in srcmodel:
continue
o[k] = srcmodel[k]
o[k+'_fixed'] = fixed_srcmodel[k]
gta.logger.info('Finished time range %i %i' % (time[0], time[1]))
return o
