def test_calc_int_flux_sensitivity():
ltc = LTCube.create_from_obs_time(3.1536E8)
c = SkyCoord(10.0, 10.0, unit='deg', frame='galactic')
ebins = 10**np.linspace(2.0, 5.0, 8 * 3 + 1)
gdiff = Map.create_from_fits(galdiff_path)
iso = np.loadtxt(
os.path.expandvars('$FERMIPY_ROOT/data/iso_P8R2_SOURCE_V6_v06.txt'),
unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins, 'P8R2_SOURCE_V6',
[['FRONT', 'BACK']])
fn = spectrum.PowerLaw([1E-13, -2.0], scale=1E3)
o = scalc.int_flux_threshold(c, fn, 25.0, 3.0)
assert_allclose(o['eflux'], 1.0719847971553671e-06, rtol=3E-3)
assert_allclose(o['flux'], 1.550305083355546e-09, rtol=3E-3)
assert_allclose(o['npred'], 511.16725330021416, rtol=3E-3)
assert_allclose(o['dnde'], 1.5518569402958423e-14, rtol=3E-3)
assert_allclose(o['e2dnde'], 1.5518569402958427e-07, rtol=3E-3)
assert_allclose(
o['bins']['flux'],
np.array([
3.88128407e-10, 2.91055245e-10, 2.18260643e-10, 1.63672392e-10,
1.22736979e-10, 9.20397499e-11, 6.90200755e-11, 5.17577549e-11,
3.88128407e-11, 2.91055245e-11, 2.18260643e-11, 1.63672392e-11,
1.22736979e-11, 9.20397499e-12, 6.90200755e-12, 5.17577549e-12,
3.88128407e-12, 2.91055245e-12, 2.18260643e-12, 1.63672392e-12,
1.22736979e-12, 9.20397499e-13, 6.90200755e-13, 5.17577549e-13
]),
rtol=1E-3)
def test_calc_int_flux_sensitivity():
ltc = LTCube.create_from_obs_time(3.1536E8)
c = SkyCoord(10.0, 10.0, unit='deg', frame='galactic')
ebins = 10**np.linspace(2.0, 5.0, 8 * 3 + 1)
gdiff = Map.create_from_fits(galdiff_path)
iso = np.loadtxt(
os.path.expandvars('$FERMI_DIFFUSE_DIR/iso_P8R3_SOURCE_V3_v1.txt'),
unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins, 'P8R3_SOURCE_V3',
[['FRONT', 'BACK']])
fn = spectrum.PowerLaw([1E-13, -2.0], scale=1E3)
o = scalc.int_flux_threshold(c, fn, 25.0, 3.0)
assert_allclose(o['eflux'], 1.15296479181e-06, rtol=3E-3)
assert_allclose(o['flux'], 1.66741840222e-09, rtol=3E-3)
assert_allclose(o['npred'], 549.71066228, rtol=3E-3)
assert_allclose(o['dnde'], 1.66908748971e-14, rtol=3E-3)
assert_allclose(o['e2dnde'], 1.66908748971e-07, rtol=3E-3)
assert_allclose(o['bins']['flux'],
np.array([
4.180875e-10, 3.135214e-10, 2.351078e-10, 1.763060e-10,
1.322108e-10, 9.914417e-11, 7.434764e-11, 5.575286e-11,
4.180876e-11, 3.135216e-11, 2.351078e-11, 1.763060e-11,
1.322108e-11, 9.914417e-12, 7.434764e-12, 5.575286e-12,
4.180875e-12, 3.135214e-12, 2.351078e-12, 1.763060e-12,
1.322108e-12, 9.914417e-13, 7.434764e-13, 5.575286e-13
]),
rtol=1E-3)
def test_calc_diff_flux_sensitivity(create_diffuse_dir):
ltc = LTCube.create_from_obs_time(3.1536E8)
c = SkyCoord(10.0, 10.0, unit='deg', frame='galactic')
ebins = 10**np.linspace(2.0, 5.0, 8 * 3 + 1)
gdiff = Map.create_from_fits(galdiff_path)
iso = np.loadtxt(os.path.expandvars('$FERMI_DIFFUSE_DIR/iso_P8R3_SOURCE_V3_v1.txt'),
unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins,
'P8R3_SOURCE_V3', [['FRONT', 'BACK']])
fn = spectrum.PowerLaw([1E-13, -2.0], scale=1E3)
o = scalc.diff_flux_threshold(c, fn, 25.0, 3.0)
assert_allclose(o['eflux'],
np.array([9.93077714e-07, 8.59371115e-07, 7.44896026e-07, 6.61397166e-07,
5.67842613e-07, 4.93802411e-07, 4.22501374e-07, 3.69299411e-07,
3.16648674e-07, 2.81050363e-07, 2.52941925e-07, 2.35549281e-07,
2.27269823e-07, 2.33109741e-07, 2.28212061e-07, 2.22473996e-07,
2.26623213e-07, 2.46539227e-07, 2.61423960e-07, 3.04766386e-07,
3.35688695e-07, 4.09265051e-07, 4.79776973e-07, 5.93001330e-07]),
rtol=3E-3)
assert_allclose(o['flux'],
np.array([8.62941353e-09, 5.59988099e-09, 3.63993562e-09, 2.42359683e-09,
1.56036437e-09, 1.01753944e-09, 6.52869186e-10, 4.27933870e-10,
2.75153929e-10, 1.83139573e-10, 1.23600112e-10, 8.63137188e-11,
6.24510606e-11, 4.80350743e-11, 3.52644113e-11, 2.57796669e-11,
1.96925718e-11, 1.60651237e-11, 1.27744860e-11, 1.11677353e-11,
9.22432852e-12, 8.43340011e-12, 7.41374161e-12, 6.87153420e-12]),
rtol=3E-3)
def create(skydir, ltc, event_class, event_types, energies, cth_bins=None,
ndtheta=500, use_edisp=False, fn=None, nbin=64):
"""Create a PSFModel object. This class can be used to evaluate the
exposure-weighted PSF for a source with a given observing
profile and energy distribution.
Parameters
----------
skydir : `~astropy.coordinates.SkyCoord`
ltc : `~fermipy.irfs.LTCube`
energies : `~numpy.ndarray`
Grid of energies at which the PSF will be pre-computed.
cth_bins : `~numpy.ndarray`
Bin edges in cosine of the inclination angle.
use_edisp : bool
Generate the PSF model accounting for the influence of
energy dispersion.
fn : `~fermipy.spectrum.SpectralFunction`
Model for the spectral energy distribution of the source.
"""
if isinstance(event_types, int):
event_types = bitmask_to_bits(event_types)
if fn is None:
fn = spectrum.PowerLaw([1E-13, -2.0])
dtheta = np.logspace(-4, 1.75, ndtheta)
dtheta = np.insert(dtheta, 0, [0])
log_energies = np.log10(energies)
egy_bins = 10**utils.center_to_edge(log_energies)
if cth_bins is None:
cth_bins = np.array([0.2, 1.0])
if use_edisp:
psf = create_wtd_psf(skydir, ltc, event_class, event_types,
dtheta, egy_bins, cth_bins, fn, nbin=nbin)
wts = calc_counts_edisp(skydir, ltc, event_class, event_types,
egy_bins, cth_bins, fn, nbin=nbin)
else:
psf = create_avg_psf(skydir, ltc, event_class, event_types,
dtheta, energies, cth_bins)
wts = calc_counts(skydir, ltc, event_class, event_types,
egy_bins, cth_bins, fn)
exp = calc_exp(skydir, ltc, event_class, event_types,
energies, cth_bins)
return PSFModel(dtheta, energies, cth_bins, np.squeeze(exp), np.squeeze(psf),
np.squeeze(wts))
def test_calc_diff_flux_sensitivity():
ltc = LTCube.create_from_obs_time(3.1536E8)
c = SkyCoord(10.0, 10.0, unit='deg', frame='galactic')
ebins = 10**np.linspace(2.0, 5.0, 8 * 3 + 1)
gdiff = Map.create_from_fits(galdiff_path)
iso = np.loadtxt(
os.path.expandvars('$FERMIPY_ROOT/data/iso_P8R2_SOURCE_V6_v06.txt'),
unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins, 'P8R2_SOURCE_V6',
[['FRONT', 'BACK']])
fn = spectrum.PowerLaw([1E-13, -2.0], scale=1E3)
o = scalc.diff_flux_threshold(c, fn, 25.0, 3.0)
assert_allclose(
o['eflux'],
np.array([
9.46940878e-07, 8.18350327e-07, 7.08228859e-07, 6.25785181e-07,
5.45241744e-07, 4.80434705e-07, 4.16747935e-07, 3.68406513e-07,
3.16719850e-07, 2.79755007e-07, 2.50862769e-07, 2.31349646e-07,
2.16286209e-07, 2.08381335e-07, 2.02673929e-07, 2.05372045e-07,
2.14355673e-07, 2.29584275e-07, 2.53220397e-07, 2.80878974e-07,
3.19989251e-07, 3.74686765e-07, 4.49321103e-07, 5.48865583e-07
]),
rtol=3E-3)
assert_allclose(
o['flux'],
np.array([
8.22850449e-09, 5.33257909e-09, 3.46076145e-09, 2.29310172e-09,
1.49825986e-09, 9.89993669e-10, 6.43978699e-10, 4.26899204e-10,
2.75215778e-10, 1.82295486e-10, 1.22584132e-10, 8.47748218e-11,
5.94328933e-11, 4.29394879e-11, 3.13181379e-11, 2.37979404e-11,
1.86265760e-11, 1.49602959e-11, 1.23736187e-11, 1.02924147e-11,
8.79292634e-12, 7.72087281e-12, 6.94312304e-12, 6.36010146e-12
]),
rtol=3E-3)
def run_flux_sensitivity(**kwargs):
index = kwargs.get('index', 2.0)
sedshape = kwargs.get('sedshape', 'PowerLaw')
cutoff = kwargs.get('cutoff', 1e3)
curvindex = kwargs.get('curvindex', 1.0)
beta = kwargs.get('beta', 0.0)
emin = kwargs.get('emin', 10**1.5)
emax = kwargs.get('emax', 10**6.0)
nbin = kwargs.get('nbin', 18)
glon = kwargs.get('glon', 0.0)
glat = kwargs.get('glat', 0.0)
ltcube_filepath = kwargs.get('ltcube', None)
galdiff_filepath = kwargs.get('galdiff', None)
isodiff_filepath = kwargs.get('isodiff', None)
galdiff_fit_filepath = kwargs.get('galdiff_fit', None)
isodiff_fit_filepath = kwargs.get('isodiff_fit', None)
wcs_npix = kwargs.get('wcs_npix', 40)
wcs_cdelt = kwargs.get('wcs_cdelt', 0.5)
wcs_proj = kwargs.get('wcs_proj', 'AIT')
map_type = kwargs.get('map_type', None)
spatial_model = kwargs.get('spatial_model', 'PointSource')
spatial_size = kwargs.get('spatial_size', 1E-2)
obs_time_yr = kwargs.get('obs_time_yr', None)
event_class = kwargs.get('event_class', 'P8R2_SOURCE_V6')
min_counts = kwargs.get('min_counts', 3.0)
ts_thresh = kwargs.get('ts_thresh', 25.0)
nside = kwargs.get('hpx_nside', 16)
output = kwargs.get('output', None)
event_types = [['FRONT', 'BACK']]
if sedshape == 'PowerLaw':
fn = spectrum.PowerLaw([1E-13, -index], scale=1E3)
elif sedshape == 'PLSuperExpCutoff':
fn = spectrum.PLSuperExpCutoff([1E-13, -index, cutoff, curvindex],
scale=1E3)
elif sedshape == 'LogParabola':
fn = spectrum.LogParabola([1E-13, -index, beta], scale=1E3)
log_ebins = np.linspace(np.log10(emin), np.log10(emax), nbin + 1)
ebins = 10**log_ebins
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
c = SkyCoord(glon, glat, unit='deg', frame='galactic')
if ltcube_filepath is None:
if obs_time_yr is None:
raise Exception('No observation time defined.')
ltc = LTCube.create_from_obs_time(obs_time_yr * 365 * 24 * 3600.)
else:
ltc = LTCube.create(ltcube_filepath)
if obs_time_yr is not None:
ltc._counts *= obs_time_yr * 365 * \
24 * 3600. / (ltc.tstop - ltc.tstart)
gdiff = skymap.Map.create_from_fits(galdiff_filepath)
gdiff_fit = None
if galdiff_fit_filepath is not None:
gdiff_fit = skymap.Map.create_from_fits(galdiff_fit_filepath)
if isodiff_filepath is None:
isodiff = utils.resolve_file_path('iso_%s_v06.txt' % event_class,
search_dirs=[
os.path.join(
'$FERMIPY_ROOT', 'data'),
'$FERMI_DIFFUSE_DIR'
])
isodiff = os.path.expandvars(isodiff)
else:
isodiff = isodiff_filepath
iso = np.loadtxt(isodiff, unpack=True)
iso_fit = None
if isodiff_fit_filepath is not None:
iso_fit = np.loadtxt(isodiff_fit_filepath, unpack=True)
scalc = SensitivityCalc(gdiff,
iso,
ltc,
ebins,
event_class,
event_types,
gdiff_fit=gdiff_fit,
iso_fit=iso_fit,
spatial_model=spatial_model,
spatial_size=spatial_size)
# Compute Maps
map_diff_flux = None
map_diff_npred = None
map_int_flux = None
map_int_npred = None
map_nstep = 500
if map_type == 'hpx':
hpx = HPX(nside, True, 'GAL', ebins=ebins)
map_diff_flux = HpxMap(np.zeros((nbin, hpx.npix)), hpx)
map_diff_npred = HpxMap(np.zeros((nbin, hpx.npix)), hpx)
map_skydir = map_diff_flux.hpx.get_sky_dirs()
for i in range(0, len(map_skydir), map_nstep):
s = slice(i, i + map_nstep)
o = scalc.diff_flux_threshold(map_skydir[s], fn, ts_thresh,
min_counts)
map_diff_flux.data[:, s] = o['flux'].T
map_diff_npred.data[:, s] = o['npred'].T
hpx = HPX(nside, True, 'GAL')
map_int_flux = HpxMap(np.zeros((hpx.npix)), hpx)
map_int_npred = HpxMap(np.zeros((hpx.npix)), hpx)
map_skydir = map_int_flux.hpx.get_sky_dirs()
for i in range(0, len(map_skydir), map_nstep):
s = slice(i, i + map_nstep)
o = scalc.int_flux_threshold(map_skydir[s], fn, ts_thresh,
min_counts)
map_int_flux.data[s] = o['flux']
map_int_npred.data[s] = o['npred']
elif map_type == 'wcs':
wcs_shape = [wcs_npix, wcs_npix]
wcs_size = wcs_npix * wcs_npix
map_diff_flux = Map.create(c,
wcs_cdelt,
wcs_shape,
'GAL',
wcs_proj,
ebins=ebins)
map_diff_npred = Map.create(c,
wcs_cdelt,
wcs_shape,
'GAL',
wcs_proj,
ebins=ebins)
map_skydir = map_diff_flux.get_pixel_skydirs()
for i in range(0, len(map_skydir), map_nstep):
idx = np.unravel_index(np.arange(i, min(i + map_nstep, wcs_size)),
wcs_shape)
s = (slice(None), idx[1], idx[0])
o = scalc.diff_flux_threshold(map_skydir[slice(i, i + map_nstep)],
fn, ts_thresh, min_counts)
map_diff_flux.data[s] = o['flux'].T
map_diff_npred.data[s] = o['npred'].T
map_int_flux = Map.create(c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj)
map_int_npred = Map.create(c, wcs_cdelt, wcs_shape, 'GAL', wcs_proj)
map_skydir = map_int_flux.get_pixel_skydirs()
for i in range(0, len(map_skydir), map_nstep):
idx = np.unravel_index(np.arange(i, min(i + map_nstep, wcs_size)),
wcs_shape)
s = (idx[1], idx[0])
o = scalc.int_flux_threshold(map_skydir[slice(i, i + map_nstep)],
fn, ts_thresh, min_counts)
map_int_flux.data[s] = o['flux']
map_int_npred.data[s] = o['npred']
o = scalc.diff_flux_threshold(c, fn, ts_thresh, min_counts)
cols = [
Column(name='e_min', dtype='f8', data=scalc.ebins[:-1], unit='MeV'),
Column(name='e_ref', dtype='f8', data=o['e_ref'], unit='MeV'),
Column(name='e_max', dtype='f8', data=scalc.ebins[1:], unit='MeV'),
Column(name='flux', dtype='f8', data=o['flux'], unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', data=o['eflux'],
unit='MeV / (cm2 s)'),
Column(name='dnde',
dtype='f8',
data=o['dnde'],
unit='ph / (MeV cm2 s)'),
Column(name='e2dnde',
dtype='f8',
data=o['e2dnde'],
unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', data=o['npred'], unit='ph')
]
tab_diff = Table(cols)
cols = [
Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV'),
Column(name='e_ref', dtype='f8', unit='MeV'),
Column(name='e_max', dtype='f8', unit='MeV'),
Column(name='flux', dtype='f8', unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', unit='ph'),
Column(name='ebin_e_min', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='ebin_e_ref', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='ebin_e_max', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='ebin_flux',
dtype='f8',
unit='ph / (cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_eflux',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_dnde',
dtype='f8',
unit='ph / (MeV cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_e2dnde',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='ebin_npred', dtype='f8', unit='ph', shape=(len(ectr), ))
]
cols_ebounds = [
Column(name='E_MIN', dtype='f8', unit='MeV', data=ebins[:-1]),
Column(name='E_MAX', dtype='f8', unit='MeV', data=ebins[1:]),
]
tab_int = Table(cols)
tab_ebounds = Table(cols_ebounds)
index = np.linspace(1.0, 5.0, 4 * 4 + 1)
for g in index:
fn = spectrum.PowerLaw([1E-13, -g], scale=10**3.5)
o = scalc.int_flux_threshold(c, fn, ts_thresh, 3.0)
row = [g]
for colname in tab_int.columns:
if colname == 'index':
continue
if 'ebin' in colname:
row += [o['bins'][colname.replace('ebin_', '')]]
else:
row += [o[colname]]
tab_int.add_row(row)
hdulist = fits.HDUList()
hdulist.append(fits.table_to_hdu(tab_diff))
hdulist.append(fits.table_to_hdu(tab_int))
hdulist.append(fits.table_to_hdu(tab_ebounds))
hdulist[1].name = 'DIFF_FLUX'
hdulist[2].name = 'INT_FLUX'
hdulist[3].name = 'EBOUNDS'
if map_type is not None:
hdu = map_diff_flux.create_image_hdu()
hdu.name = 'MAP_DIFF_FLUX'
hdulist.append(hdu)
hdu = map_diff_npred.create_image_hdu()
hdu.name = 'MAP_DIFF_NPRED'
hdulist.append(hdu)
hdu = map_int_flux.create_image_hdu()
hdu.name = 'MAP_INT_FLUX'
hdulist.append(hdu)
hdu = map_int_npred.create_image_hdu()
hdu.name = 'MAP_INT_NPRED'
hdulist.append(hdu)
hdulist.writeto(output, clobber=True)
def test_powerlaw_spectrum():
params = [1E-13,-2.3]
fn = spectrum.PowerLaw(params, scale=2E3)
def main():
usage = "usage: %(prog)s [options]"
description = "Calculate the LAT point-source flux sensitivity."
parser = argparse.ArgumentParser(usage=usage, description=description)
parser.add_argument('--ltcube',
default=None,
help='Set the path to the livetime cube.')
parser.add_argument('--galdiff',
default=None,
required=True,
help='Set the path to the galactic diffuse model.')
parser.add_argument(
'--isodiff',
default=None,
help='Set the path to the isotropic model. If none then the '
'default model will be used for the given event class.')
parser.add_argument('--ts_thresh',
default=25.0,
type=float,
help='Set the detection threshold.')
parser.add_argument('--min_counts',
default=3.0,
type=float,
help='Set the minimum number of counts.')
parser.add_argument(
'--joint',
default=False,
action='store_true',
help='Compute sensitivity using joint-likelihood of all event types.')
parser.add_argument('--event_class',
default='P8R2_SOURCE_V6',
help='Set the IRF name (e.g. P8R2_SOURCE_V6).')
parser.add_argument('--glon',
default=0.0,
type=float,
help='Galactic longitude.')
parser.add_argument('--glat',
default=0.0,
type=float,
help='Galactic latitude.')
parser.add_argument('--index',
default=2.0,
type=float,
help='Source power-law index.')
parser.add_argument('--emin',
default=10**1.5,
type=float,
help='Minimum energy in MeV.')
parser.add_argument('--emax',
default=10**6.0,
type=float,
help='Maximum energy in MeV.')
parser.add_argument(
'--nbin',
default=18,
type=int,
help='Number of energy bins for differential flux calculation.')
parser.add_argument('--output',
default='output.fits',
type=str,
help='Output filename.')
parser.add_argument(
'--obs_time_yr',
default=None,
type=float,
help=
'Rescale the livetime cube to this observation time in years. If none then the '
'calculation will use the intrinsic observation time of the livetime cube.'
)
args = parser.parse_args()
event_types = [['FRONT', 'BACK']]
fn = spectrum.PowerLaw([1E-13, -args.index], scale=1E3)
log_ebins = np.linspace(np.log10(args.emin), np.log10(args.emax),
args.nbin + 1)
ebins = 10**log_ebins
ectr = np.exp(utils.edge_to_center(np.log(ebins)))
c = SkyCoord(args.glon, args.glat, unit='deg', frame='galactic')
if args.ltcube is None:
if args.obs_time_yr is None:
raise Exception('No observation time defined.')
ltc = irfs.LTCube.create_from_obs_time(args.obs_time_yr * 365 * 24 *
3600.)
else:
ltc = irfs.LTCube.create(args.ltcube)
if args.obs_time_yr is not None:
ltc._counts *= args.obs_time_yr * 365 * \
24 * 3600. / (ltc.tstop - ltc.tstart)
gdiff = skymap.Map.create_from_fits(args.galdiff)
if args.isodiff is None:
isodiff = utils.resolve_file_path('iso_%s_v06.txt' % args.event_class,
search_dirs=[
os.path.join(
'$FERMIPY_ROOT', 'data'),
'$FERMI_DIFFUSE_DIR'
])
isodiff = os.path.expandvars(isodiff)
else:
isodiff = args.isodiff
iso = np.loadtxt(isodiff, unpack=True)
scalc = SensitivityCalc(gdiff, iso, ltc, ebins, args.event_class,
event_types)
o = scalc.diff_flux_threshold(c, fn, args.ts_thresh, args.min_counts)
cols = [
Column(name='e_min', dtype='f8', data=scalc.ebins[:-1], unit='MeV'),
Column(name='e_ref', dtype='f8', data=o['e_ref'], unit='MeV'),
Column(name='e_max', dtype='f8', data=scalc.ebins[1:], unit='MeV'),
Column(name='flux', dtype='f8', data=o['flux'], unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', data=o['eflux'],
unit='MeV / (cm2 s)'),
Column(name='dnde',
dtype='f8',
data=o['dnde'],
unit='ph / (MeV cm2 s)'),
Column(name='e2dnde',
dtype='f8',
data=o['e2dnde'],
unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', data=o['npred'], unit='ph')
]
tab_diff = Table(cols)
cols = [
Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV'),
Column(name='e_ref', dtype='f8', unit='MeV'),
Column(name='e_max', dtype='f8', unit='MeV'),
Column(name='flux', dtype='f8', unit='ph / (cm2 s)'),
Column(name='eflux', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='dnde', dtype='f8', unit='ph / (MeV cm2 s)'),
Column(name='e2dnde', dtype='f8', unit='MeV / (cm2 s)'),
Column(name='npred', dtype='f8', unit='ph')
]
cols_ebin = [
Column(name='index', dtype='f8'),
Column(name='e_min', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='e_ref', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='e_max', dtype='f8', unit='MeV', shape=(len(ectr), )),
Column(name='flux',
dtype='f8',
unit='ph / (cm2 s)',
shape=(len(ectr), )),
Column(name='eflux',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='dnde',
dtype='f8',
unit='ph / (MeV cm2 s)',
shape=(len(ectr), )),
Column(name='e2dnde',
dtype='f8',
unit='MeV / (cm2 s)',
shape=(len(ectr), )),
Column(name='npred', dtype='f8', unit='ph', shape=(len(ectr), ))
]
tab_int = Table(cols)
tab_int_ebin = Table(cols_ebin)
index = np.linspace(1.0, 5.0, 4 * 4 + 1)
for g in index:
fn = spectrum.PowerLaw([1E-13, -g], scale=10**3.5)
o = scalc.int_flux_threshold(c, fn, args.ts_thresh, 3.0)
row = [g]
for colname in tab_int.columns:
if not colname in o:
continue
row += [o[colname]]
tab_int.add_row(row)
row = [g]
for colname in tab_int.columns:
if not colname in o:
continue
row += [o['bins'][colname]]
tab_int_ebin.add_row(row)
hdulist = fits.HDUList()
hdulist.append(fits.table_to_hdu(tab_diff))
hdulist.append(fits.table_to_hdu(tab_int))
hdulist.append(fits.table_to_hdu(tab_int_ebin))
hdulist[1].name = 'DIFF_FLUX'
hdulist[2].name = 'INT_FLUX'
hdulist[3].name = 'INT_FLUX_EBIN'
hdulist.writeto(args.output, clobber=True)
