def __init__(self, n, model, rmf):
self.n = n
RMFModelNoPHA.__init__(self, rmf=rmf, model=model)
self.elo = numpy.arange(n)
self.ehi = numpy.arange(n)
self.lo = numpy.arange(n)
self.hi = numpy.arange(n)
self.xlo = numpy.arange(n)
self.xhi = numpy.arange(n)
def __init__(self, n, model, rmf): self.n = n RMFModelNoPHA.__init__(self, rmf=rmf, model=model) self.elo = numpy.arange(n) self.ehi = numpy.arange(n) self.lo = numpy.arange(n) self.hi = numpy.arange(n) self.xlo = numpy.arange(n) self.xhi = numpy.arange(n)
def test_rmfmodelnopha_matrix_call():
"What happens calling an rmf (matrix) with no pha?"
rdata = create_non_delta_rmf()
# Do not use a flat model as it is not as useful a check
# that the RMF is doing its job.
#
constant = 2.3
slope = -0.1
mdl = Polynom1D('mdl')
mdl.c0 = constant
mdl.c1 = slope
wrapped = RMFModelNoPHA(rdata, mdl)
# Calculate the model analytically.
#
modvals = mdl(rdata.energ_lo, rdata.energ_hi)
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
out = wrapped([4, 5])
assert_allclose(out, expected)
# the RMF convolution shouldn't lose flux, although
# given the previous check it's not clear if this really
# adds any extra confidence.
#
assert out.sum() == pytest.approx(modvals.sum())
def test_rmfmodelnopha_delta_call():
"What happens calling an rmf (delta function) with no pha?"
egrid = np.arange(0.01, 0.06, 0.01)
rdata = create_delta_rmf(egrid[:-1], egrid[1:])
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
wrapped = RMFModelNoPHA(rdata, mdl)
# The model is evaluated on the RMF grid, not whatever
# is sent in. It is also integrated across the bins,
# which is why there is a multiplication by the
# grid width (for this constant model).
#
de = egrid[1:] - egrid[:-1]
expected = constant * de
out = wrapped([4, 5])
assert_allclose(out, expected)