def print_values_gammalib():
"""Print some Gammalib model values that can be used for unit tests.
Gammalib uses normalised PDFs, i.e. eval() returns probability / steradian
so that the integral is one.
"""
import numpy as np
import gammalib
# We need some dummy variables
center = gammalib.GSkyDir()
center.radec_deg(0, 0)
energy = gammalib.GEnergy()
time = gammalib.GTime()
FWHM_TO_SIGMA = 1. / np.sqrt(8 * np.log(2))
g = gammalib.GModelSpatialRadialGauss(
center, PIX_TO_DEG * FWHM_TO_SIGMA * GAUSS_FWHM)
d = gammalib.GModelSpatialRadialDisk(center, PIX_TO_DEG * DISK_R0)
s = gammalib.GModelSpatialRadialShell(center, PIX_TO_DEG * SHELL_R0,
PIX_TO_DEG * SHELL_WIDTH)
models = [('g', g), ('d', d), ('s', s)]
for name, model in models:
for theta in THETAS:
theta_radians = np.radians(PIX_TO_DEG * theta)
gammalib_value = model.eval(theta_radians, energy, time)
sherpa_value = INTEGRAL * gammalib_value * np.radians(
PIX_TO_DEG)**2
print name, theta, sherpa_value
def append_fhl(models, bmax,
bin_models, bin_dict, bin_distx, bin_disty, bin_radr, bin_frlog,
snr_models, snr_dict, snr_distx, snr_disty, snr_radr, snr_frlog,
isnr_models, isnr_dict, isnr_distx, isnr_disty, isnr_radr, isnr_frlog,
dist_sigma=3., sig50_thresh=3., eph_thresh=100.):
"""
Append missing models from Fermi high-energy catalog to gammalib model container
:param models: ~gammalib.GModels, gammalib model container
:param bmax: float, maximum latitude of models to include
:param dist_sigma: float, minimum distance in sigma's from pre-existing source to add as new
:param sig50_thresh: float, threshold sigma on significance above 50 GeV to apply
:param eph_thresh: float, minimum energy of detected photons required
:return: result: dictionary
"""
# filter FHL table based on latitude and hardness
# hard sources are seleted based on significance above 50 GeV and highest photon energy
# calculate significance above 50 GeV
sig50 = np.sqrt(np.sum(np.power(fhl['Sqrt_TS_Band'][:,2:], 2),axis=1))
# filter
fsources = fhl[(np.abs(fhl['GLAT']) <= bmax) & (sig50 >= sig50_thresh) & (
fhl['HEP_Energy'] >= eph_thresh)]
# prepare new gammalib model container
newpt = 0
newext = 0
n_bin_del = 0
n_snr_del = 0
n_isnr_del = 0
newmodels = gammalib.GModels()
# keep track also of artificial cutoffs
ecut_pwn = []
ecut_snr = []
ecut_unid = []
ecut_agn = []
n_ecut_pwn = 0
n_ecut_snr = 0
n_ecut_agn = 0
n_ecut_unid = 0
msg = ''
# loop over fermi sources
for fsource in fsources:
# assume source is new
new = 1
# fermi source position as gammalib.GSkyDir
fdir = gammalib.GSkyDir()
ra = np.double(fsource['RAJ2000'])
dec = np.double(fsource['DEJ2000'])
fdir.radec_deg(ra, dec)
######################################### treat pointlike sources for Fermi ###########
if fsource['Extended_Source_Name'] == '':
# case of sources pointlike for Fermi
# loop over gammalib container and determine closest neighbor
# initialize at 1000
dist_min = 1000.
for source in models:
dir = get_model_dir(source)
dist = fdir.dist_deg(dir)
dist_min = np.minimum(dist, dist_min)
if dist_min < dist_sigma * fsource['Conf_95_SemiMajor'] / 2:
# closeby source foud in container, source will not be added
new = 0
else:
# source will be added to container, set spatial model
spatial = gammalib.GModelSpatialPointSource(fdir)
newpt += 1
######################################### treat extended sources for Fermi ############
else:
# retrieve Fermi extended source radius
ext_fsource = ext_fhl[ext_fhl['Source_Name'] == fsource['Extended_Source_Name']][0]
fradius = np.double(ext_fsource['Model_SemiMajor'])
# "corrected" distance, i.e, centre distance - radius of source
# initialize at 1000
dist_corr = 1000.
for source in models:
dir = get_model_dir(source)
dist = fdir.dist_deg(dir)
# get source radius according to model used
radius = get_model_radius(source)
# use as radius the max between Fermi and model
radius = np.maximum(fradius,radius)
# subtract from distance the max radius
dist -= radius
dist_corr = np.minimum(dist,dist_corr)
if dist_corr < 0.:
# overlapping source foud in container, source will not be added
new = 0
else:
# source will be added to container, set spatial model
fradius2 = np.double(ext_fsource['Model_SemiMinor'])
fpangle = np.double(ext_fsource['Model_PosAng'])
# retrieve Fermi spatial model to set it in container
if ext_fsource['Model_Form'] == 'Disk':
if fradius2 == fradius:
spatial = gammalib.GModelSpatialRadialDisk(fdir,fradius)
else:
spatial = gammalib.GModelSpatialEllipticalDisk(fdir, fradius, fradius2, fpangle)
elif ext_fsource['Model_Form'] == '2D Gaussian':
if fradius2 == fradius:
spatial = gammalib.GModelSpatialRadialGauss(fdir,fradius)
else:
spatial = gammalib.GModelSpatialEllipticalGauss(fdir, fradius, fradius2, fpangle)
elif ext_fsource['Model_Form'] == 'Ring':
spatial = gammalib.GModelSpatialRadialShell(fdir,fradius, fradius2)
elif ext_fsource['Model_Form'] == 'Map':
print('{} modeled by spatial template, which is not implemented, skip'.format(fsource['Source_Name']))
new = 0
else:
print('{} modeled by model type {}, which is not implemented, skip'.format(fsource['Source_Name'],ext_fsource['Model_Form']))
new = 0
if new == 1:
newext +=1
######################################### spectra #####################################
if new == 1:
# spectral model
eref = gammalib.GEnergy(np.double(fsource['Pivot_Energy']), 'GeV')
norm = np.double(fsource['Flux_Density'])
norm *= 1.e-3 # ph GeV-1 -> ph MeV-1
# use power law model only if signifcance of curvature < 1
# this avoids extrapolating hard power laws not justified by the data
if fsource['Signif_Curve'] < 1:
index = np.double(fsource['PowerLaw_Index'])
if index < 2.4:
# correction for fake pevatrons
if fsource['CLASS'] == 'PWN' or fsource['CLASS'] == 'pwn':
# dummy model to obtain search radius
mod = gammalib.GModelSky(spatial, gammalib.GModelSpectralPlaw())
# search radius
rad = get_model_radius(mod) + 0.2
# set cutoff
ecut = get_cutoff(ra, dec, 'PSR', rad_search=rad)
ecut_pwn.append(ecut)
n_ecut_pwn += 1
elif fsource['CLASS'] == 'SNR' or fsource['CLASS'] == 'snr':
gname = fsource['ASSOC1']
if 'SNR' in gname:
pass
else:
gname = None
# compute cutoff
# we use default value for particle spectral index because measurements are highly uncertain
ecut = get_cutoff(ra, dec, 'SNR', name=gname)
ecut_snr.append(ecut)
n_ecut_snr += 1
elif fsource['CLASS'] == 'bll' or fsource['CLASS'] == 'bcu' or fsource['CLASS'] == 'fsrq':
ecut = get_cutoff(ra, dec, 'AGN', z = fsource['Redshift'])
ecut_agn.append(ecut)
n_ecut_agn += 1
else:
if fsource['CLASS'] == '' or fsource['CLASS'] == 'unknown':
pass
else:
# set warning if we have hard source of unexpected type
msg += 'FHL source {} of type {} has an unxepctedly hard spectrum ' \
'with index {}. We are setting a random artificial cutoff\n'.format(
fsource['Source_Name'], fsource['CLASS'], index)
print(msg)
ecut = get_cutoff(ra, dec, 'UNID')
ecut_unid.append(ecut)
n_ecut_unid += 1
ecut = gammalib.GEnergy(np.double(ecut), 'TeV')
spectral = gammalib.GModelSpectralExpPlaw(norm, -index, eref, ecut)
else:
spectral = gammalib.GModelSpectralPlaw(norm, -index, eref)
else:
index = np.double(fsource['Spectral_Index'])
curvature = np.double(fsource['beta'])
spectral = gammalib.GModelSpectralLogParabola(norm, -index, eref, curvature)
# assemble model and append to container
model = gammalib.GModelSky(spatial, spectral)
model.name(fsource['Source_Name'])
newmodels.append(model)
# delete synthetic sources as needed
if fsource['CLASS'] == 'HMB' or fsource['CLASS'] == 'hmb' or fsource['CLASS'] == 'BIN' or fsource['CLASS'] == 'bin':
rname, bin_dict, distx, disty, radr, frlog = find_source_to_delete(bin_dict, fdir.l_deg(),
fdir.b_deg(), get_model_radius(model),
flux_Crab(model,1.,1000.))
bin_models.remove(rname)
n_bin_del += 1
bin_distx.append(distx)
bin_disty.append(disty)
bin_radr.append(radr)
bin_frlog.append(frlog)
elif fsource['CLASS'] == 'PWN' or fsource['CLASS'] == 'pwn' or fsource['CLASS'] == 'spp':
# implement synthetic PWNe here
pass
elif fsource['CLASS'] == 'SNR' or fsource['CLASS'] == 'snr':
# determine if snr is interacting
assoc = snr_class[snr_class['ASSOC1'] == fsource['ASSOC1']]
if assoc[0]['is int'] == 'yes':
# interacting
rname, isnr_dict, distx, disty, radr, frlog = find_source_to_delete(isnr_dict,
fdir.l_deg(),
fdir.b_deg(), get_model_radius(model),
flux_Crab(model, 1.,1000.))
isnr_models.remove(rname)
n_isnr_del += 1
isnr_distx.append(distx)
isnr_disty.append(disty)
isnr_radr.append(radr)
isnr_frlog.append(frlog)
else:
# young
rname, snr_dict, distx, disty, radr, frlog = find_source_to_delete(snr_dict,
fdir.l_deg(),
fdir.b_deg(), get_model_radius(model),
flux_Crab(model,1.,1000.))
snr_models.remove(rname)
n_snr_del += 1
snr_distx.append(distx)
snr_disty.append(disty)
snr_radr.append(radr)
snr_frlog.append(frlog)
else:
pass
else:
# source already present, skip
pass
# add new models to original container
for model in newmodels:
models.append(model)
# assemble output in dictionary
result = { 'models' : models, 'newpt' : newpt, 'newext' : newext,
'ecut_pwn' : ecut_pwn, 'ecut_snr' : ecut_snr, 'ecut_agn' : ecut_agn,
'ecut_unid' : ecut_unid, 'n_ecut_pwn' : n_ecut_pwn, 'n_ecut_snr' : n_ecut_snr,
'n_ecut_agn' : n_ecut_agn, 'n_ecut_unid' : n_ecut_unid, 'msg' : msg,
'bin_models' : bin_models, 'bin_dict' : bin_dict, 'n_bin_del' : n_bin_del,
'bin_distx' : bin_distx, 'bin_disty' : bin_disty, 'bin_radr' : bin_radr, 'bin_frlog' : bin_frlog,
'snr_models': snr_models, 'snr_dict': snr_dict, 'n_snr_del': n_snr_del,
'snr_distx': snr_distx, 'snr_disty': snr_disty, 'snr_radr': snr_radr, 'snr_frlog': snr_frlog,
'isnr_models': isnr_models, 'isnr_dict': isnr_dict, 'n_isnr_del': n_isnr_del,
'isnr_distx': isnr_distx, 'isnr_disty': isnr_disty, 'isnr_radr': isnr_radr, 'isnr_frlog': isnr_frlog
}
return result
def set_source_model(srcmodel,
spec='plaw',
alpha=1.0,
mapname='../map_RXJ1713.fits'):
"""
Set source model
Parameters
----------
srcmodel : str
Source model name
spec : str, optional
Spectral model
alpha : float, optional
Map scaling factor
mapname : str, optional
Sky map name
Returns
-------
source : `~gammalib.GModelSky()`
Source model
"""
# Set spectral component
if spec == 'plaw':
spectral = gammalib.GModelSpectralPlaw(1.0e-17, -2.0,
gammalib.GEnergy(1.0, 'TeV'))
spectral['Prefactor'].min(1.0e-25)
elif spec == 'eplaw':
spectral = gammalib.GModelSpectralExpPlaw(
1.0e-17, -2.0, gammalib.GEnergy(1.0, 'TeV'),
gammalib.GEnergy(10.0, 'TeV'))
spectral['CutoffEnergy'].min(1.0e6)
spectral['CutoffEnergy'].max(1.0e8)
spectral['Prefactor'].min(1.0e-25)
elif spec == 'inveplaw':
spectral = gammalib.GModelSpectralExpInvPlaw(
1.0e-17, -2.0, gammalib.GEnergy(1.0, 'TeV'),
gammalib.GEnergy(10.0, 'TeV'))
spectral['Prefactor'].min(1.0e-25)
elif spec == 'logparabola':
spectral = gammalib.GModelSpectralLogParabola(
1.0e-17, -2.0, gammalib.GEnergy(1.0, 'TeV'), -0.3)
spectral['Prefactor'].min(1.0e-25)
elif spec == 'abdalla2018':
spectral = gammalib.GModelSpectralExpPlaw(
2.3e-17, -2.06, gammalib.GEnergy(1.0, 'TeV'),
gammalib.GEnergy(12.9, 'TeV'))
spectral['Prefactor'].fix()
spectral['Index'].fix()
spectral['CutoffEnergy'].fix()
elif spec == 'abdalla2018Ec':
spectral = gammalib.GModelSpectralExpPlaw(
2.3e-17, -2.06, gammalib.GEnergy(1.0, 'TeV'),
gammalib.GEnergy(12.9, 'TeV'))
spectral['CutoffEnergy'].fix()
spectral['Prefactor'].min(1.0e-25)
elif spec == 'aharonian2007':
spectral = gammalib.GModelSpectralLogParabola(
2.06e-17, -2.02, gammalib.GEnergy(1.0, 'TeV'), -0.29)
spectral['Prefactor'].fix()
spectral['Index'].fix()
spectral['Curvature'].fix()
# Set spatial component
if 'map' in srcmodel:
filename = '%s' % mapname
map = gammalib.GSkyMap(filename)
map = scale_map(map, alpha)
filename = 'map_RXJ1713_%.2f.fits' % alpha
map.save(filename, True)
spatial = gammalib.GModelSpatialDiffuseMap(filename)
elif 'disk' in srcmodel:
dir = gammalib.GSkyDir()
dir.radec_deg(258.3, -39.7)
spatial = gammalib.GModelSpatialRadialDisk(dir, 0.5)
spatial['RA'].free()
spatial['DEC'].free()
elif 'gauss' in srcmodel:
dir = gammalib.GSkyDir()
dir.radec_deg(258.3, -39.7)
spatial = gammalib.GModelSpatialRadialGauss(dir, 0.5)
spatial['RA'].free()
spatial['DEC'].free()
elif 'shell' in srcmodel:
dir = gammalib.GSkyDir()
dir.radec_deg(258.3, -39.7)
spatial = gammalib.GModelSpatialRadialShell(dir, 0.4, 0.2)
spatial['RA'].free()
spatial['DEC'].free()
# Set source model
source = gammalib.GModelSky(spatial, spectral)
source.name('RX J1713.7-3946')
source.tscalc(True)
# Return source model
return source
msg = 'WARNING: elliptical source {} from gamma-cat has spatial model frame of type {} which is not implemented\n'.format(
source['common_name'], source['morph_pa_frame'])
print(msg)
outfile.write(msg)
skip = True
elif source['morph_type'] == 'shell':
if np.isnan(source['morph_sigma2']):
# only average radius known, assume with of shell is 30% of radius
rad = np.double(source['morph_sigma'])
r_inner = rad * 0.85
width = 0.3 * rad
else:
r_inner = np.double(source['morph_sigma'])
r_outer = np.double(source['morph_sigma2'])
width = r_outer - r_inner
spatial = gammalib.GModelSpatialRadialShell(
src_dir, r_inner, width)
radius = r_inner + width
else:
msg = 'WARNING: source {} from gamma-cat has spatial model of type {} which is not implemented\n'.format(
source['common_name'], source['morph_type'])
print(msg)
outfile.write(msg)
skip = True
# retrieve source spectral model
if source['spec_type'] == 'pl' or source['spec_type'] == 'pl2':
# treat together to handle fake PeVatron correction
# index
if source['spec_type'] == 'pl':
index = source['spec_pl_index']
norm = source['spec_pl_norm']
eref = gammalib.GEnergy(np.double(source['spec_pl_e_ref']),