def estimate_flux(dnde,energy,emin,emax, e_weight):
""" estimate the emin to emax flux for a source with prefactor
dnde at the given energy. assuming the source has a
spectral index of 2.
Note, for our situation dnde has units [ph/cm^2/s/TeV]
and energy has units [TeV], but the ouptut of the
i_flux function is correct. If e_weight=0,
the return has units [ph/cm^2/s]. If e_weight=1,
the return has units [TeV/cm^2/s]. """
model = PowerLaw(index=2, norm=dnde, e0=energy)
return model.i_flux(emin=emin, emax=emax, e_weight=e_weight)
def estimate_flux(dnde, energy, emin, emax, e_weight):
""" estimate the emin to emax flux for a source with prefactor
dnde at the given energy. assuming the source has a
spectral index of 2.
Note, for our situation dnde has units [ph/cm^2/s/TeV]
and energy has units [TeV], but the ouptut of the
i_flux function is correct. If e_weight=0,
the return has units [ph/cm^2/s]. If e_weight=1,
the return has units [TeV/cm^2/s]. """
model = PowerLaw(index=2, norm=dnde, e0=energy)
return model.i_flux(emin=emin, emax=emax, e_weight=e_weight)
def test_ff(self):
""" Simulate from a filefunction object and test that the best
fit flux
is consistent with the simulated flux. """
name = 'ff'
model = PowerLaw(index=2)
model.set_flux(1e-6)
simdir = path.expand('$SIMDIR/%s' % name)
if not os.path.exists(simdir):
os.makedirs(simdir)
filename = abspath(join(simdir, 'file_function.txt'))
model.save_profile(filename, 10, 1e6)
ff = FileFunction(file=filename)
center = SkyDir(0, 0)
ps = PointSource(name='source', skydir=center, model=ff)
point_sources = [ps]
diffuse_sources = None
roi = PointlikeTest.get_roi(name,
center,
point_sources,
diffuse_sources,
emin=1e2,
emax=1e5,
binsperdec=4)
if PointlikeTest.VERBOSE:
roi.print_summary()
print roi
roi.fit(use_gradient=PointlikeTest.USE_GRADIENT)
if PointlikeTest.VERBOSE:
roi.print_summary()
print roi
fit, error = ff.i_flux(1e2, 1e5, error=True)
true = model.i_flux(1e2, 1e5, error=False)
self.assertPull(fit, true, error, 'flux')
