def test_plots(self):
"""
"""
model = xpeInterstellarAbsorptionModel()
plt.figure()
model.xsection.plot(logx=True, logy=True, show=False)
save_current_figure('gabs_xsection.png', clear=False)
plt.figure()
model.xsection_ecube().plot(logx=True, show=False)
save_current_figure('gabs_xsection_ecube.png', clear=False)
plt.figure()
ra, dec = 10.684, 41.269
column_density = mapped_column_density_HI(ra, dec, 'LAB')
trans = model.transmission_factor(column_density)
trans.plot(logx=True, show=False, label='$n_H = $%.3e' % column_density)
plt.legend(loc='upper left')
save_current_figure('gabs_trans_lab.png', clear=False)
plt.figure()
for column_density in [1.e20, 1.e21, 1.e22, 1.e23]:
trans = model.transmission_factor(column_density)
trans.plot(logx=True, show=False, label='$n_H = $%.1e' %\
column_density)
plt.legend(loc='upper left')
save_current_figure('gabs_trans_samples.png', clear=False)
if sys.flags.interactive:
plt.show()
def scale_flux(energy, flux, emin=2., emax=8.):
ism_model = xpeInterstellarAbsorptionModel()
ism_trans = ism_model.transmission_factor(GAL_NH)
flux *= ism_trans(energy)
norm = xInterpolatedUnivariateSplineLinear(energy,energy*flux).integral(
emin, emax)
norm = keV2erg(norm)
scale_factor = FLUX/norm
return flux*scale_factor
def setUpClass(cls):
"""Setup.
"""
cls.irf_name = "xipe_baseline"
cls.aeff = load_arf(cls.irf_name)
cls.modf = load_mrf(cls.irf_name)
cls.emin = 2.0
cls.emax = 8.0
cls.ebinning = numpy.linspace(cls.emin, cls.emax, 4)
cls.ism_model = xpeInterstellarAbsorptionModel()
def test_transmission(self):
"""
"""
model = xpeInterstellarAbsorptionModel()
for nh, label in [(1e21, '1e21'), (1e22, '1e22'), (1e23, '1e23')]:
trans = model.transmission_factor(nh)
file_name = 'xspec_wabs_%s.txt' % label
file_path = os.path.join(XIMPOL_SRCMODEL, 'ascii', file_name)
elo, ehi, txspec = numpy.loadtxt(file_path, unpack=True)
energy = numpy.sqrt(elo*ehi)
_mask = (txspec > 1.e-2)
energy = energy[_mask]
txspec = txspec[_mask]
tximpol = trans(energy)
delta = abs(txspec - tximpol)/txspec
delta_ave = numpy.average(delta)
self.assertTrue(delta_ave < 0.01, 'average difference %s' %\
delta_ave)
def _pdf(E, t):
"""Return the convolution between the effective area and
the input photon spectrum.
Note that, due the way this callable is used within the constructor
of xUnivariateAuxGenerator at this point E and t are two numpy
arrays whose shape is `(num_time_samples, num_energy_samples)`.
Particularly, `E[0]` is the array of energy values that we use for
sampling the spectrum (nominally the same sampling that we use
for the effective area).
We start from all the stuff that is energy-independent, e.g., the
effective area and the interstellar absorption and do the proper
convolutions in energy space. Then we tile the values horizontally
for as many times as the length of the array sampling the time,
and at the end we multiply the resulting bidimensional array by
an equal-size array containing the value of the differential
energy spectrum on the time-energy grid.
"""
# Grab the one-dimensional array used to sample the energy.
_energy = E[0]
# Calculate the effective area on the array.
_vals = aeff(_energy)
# Multiply by the scale factor.
_vals *= scale_factor
# If needed, fold in the transmission factor for the interstellar
# absorption.
if column_density > 0.:
logger.info('Applying interstellar absorption (nH = %.3e)' %\
column_density)
ism_model = xpeInterstellarAbsorptionModel()
ism_trans = ism_model.transmission_factor(column_density)
_vals *= ism_trans(_energy)
# Tile the one dimensional vector horizontally.
_vals = numpy.tile(_vals, (t.shape[0], 1))
# And multiply by the source spectrum---we're done.
_vals *= source_spectrum(E*(1 + redshift), t)
return _vals
