def test_cstat_rmfpha():
"""What does CSTAT calculate when there is an RMF+PHA instrument model.
This includes the AREASCAL when evaluating the model.
See Also
--------
test_cstat_nophamodel, test_cstat_arfpha, test_cstat_rsppha
"""
dset, mdl, expected = setup_likelihood(scale=True)
# use the full channel grid; the energy grid has to be
# "the same" as the channel values since the model
# has a dependency on the independent axis
#
egrid = 1.0 * np.concatenate((dset.channel, [dset.channel.max() + 1]))
rmf = make_ideal_rmf(egrid[:-1], egrid[1:])
mdl_ascal = RMFModelPHA(rmf, dset, mdl)
stat = CStat()
sval_ascal = stat.calc_stat(dset, mdl_ascal)
assert_allclose(sval_ascal[0], expected)
def test_rmfmodelpha_delta_call(ignore):
"""What happens calling an rmf (delta) with a pha?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
estep = 0.025
egrid = np.arange(0.1, 0.8, estep)
elo = egrid[:-1]
ehi = egrid[1:]
rdata = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
nchans = elo.size
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
de = estep * 0.9
e0 = egrid[ignore]
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RMFModelPHA(rdata, pha, 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).
#
# Note that the filter doesn't change the grid.
#
de = egrid[1:] - egrid[:-1]
expected = constant * de
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rmfmodelpha_matrix_call(ignore):
"""What happens calling an rmf (matrix) with a pha?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
rdata = create_non_delta_rmf()
elo = rdata.e_min
ehi = rdata.e_max
nchans = elo.size
constant = 12.2
slope = 0.01
mdl = Polynom1D('not-flat')
mdl.c0 = constant
mdl.c1 = slope
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
e0 = elo[ignore]
e1 = ehi[ignore]
de = 0.9 * (e1 - e0)
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RMFModelPHA(rdata, pha, mdl)
# Note that the evaluation ignores any filter we've applied.
# and the exposure time is not used.
#
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)
def test_rmf1d_delta_pha_zero_energy_bin():
"What happens when the first bin starts at 0, with replacement"
ethresh = 2e-7
egrid = np.asarray([0.0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.8])
elo = egrid[:-1]
ehi = egrid[1:]
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
rdata = create_delta_rmf(elo, ehi, ethresh=ethresh)
validate_zero_replacement(ws, 'RMF', 'delta-rmf', ethresh)
exposure = 2.4
channels = np.arange(1, 7, dtype=np.int16)
counts = np.ones(6, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
tmdl = PowLaw1D()
wrapped = RMFModelPHA(rdata, pha, mdl)
out = wrapped([0.1, 0.2])
elo[0] = ethresh
expected = tmdl(elo, ehi)
assert_allclose(out, expected)
assert not np.isnan(out[0])