def test_cstat_rsppha():
"""What does CSTAT calculate when there is an RSP+PHA instrument model.
This includes the AREASCAL when evaluating the model.
See Also
--------
test_cstat_nophamodel, test_cstat_arfpha, test_cstat_rmfpha
"""
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]))
arf = make_arf(energ_lo=egrid[:-1], energ_hi=egrid[1:])
rmf = make_ideal_rmf(e_min=egrid[:-1], e_max=egrid[1:])
mdl_ascal = RSPModelPHA(arf, rmf, dset, mdl)
stat = CStat()
sval_ascal = stat.calc_stat(dset, mdl_ascal)
assert_allclose(sval_ascal[0], expected)
def test_cstat_rsppha():
"""What does CSTAT calculate when there is an RSP+PHA instrument model.
This includes the AREASCAL when evaluating the model.
See Also
--------
test_cstat_nophamodel, test_cstat_arfpha, test_cstat_rmfpha
"""
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]))
arf = make_arf(energ_lo=egrid[:-1],
energ_hi=egrid[1:])
rmf = make_ideal_rmf(e_min=egrid[:-1], e_max=egrid[1:])
mdl_ascal = RSPModelPHA(arf, rmf, dset, mdl)
stat = CStat()
sval_ascal = stat.calc_stat(dset, mdl_ascal)
assert_allclose(sval_ascal[0], expected)
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 = create_delta_rmf(egrid[:-1], egrid[1:])
mdl_ascal = RMFModelPHA(rmf, dset, mdl)
stat = CStat()
sval_ascal = stat.calc_stat(dset, mdl_ascal)
assert sval_ascal[0] == pytest.approx(expected)
def test_cstat_nophamodel():
"""What does CSTAT calculate when there is no PHA instrument model.
The value here is technically wrong, in that the AREASCAL value
is not being included in the calculation, but is included as a
test to validate the current approach.
See Also
--------
test_cstat_arfpha, test_cstat_rmfpha, test_cstat_rsppha
"""
dset, mdl, expected = setup_likelihood(scale=False)
stat = CStat()
sval_noascal = stat.calc_stat(dset, mdl)
assert_allclose(sval_noascal[0], expected)
def test_cstat_nophamodel():
"""What does CSTAT calculate when there is no PHA instrument model.
The value here is technically wrong, in that the AREASCAL value
is not being included in the calculation, but is included as a
test to validate the current approach.
See Also
--------
test_cstat_arfpha, test_cstat_rmfpha, test_cstat_rsppha
"""
dset, mdl, expected = setup_likelihood(scale=False)
stat = CStat()
sval_noascal = stat.calc_stat(dset, mdl)
assert_allclose(sval_noascal[0], expected)
def test_cstat_arfpha():
"""What does CSTAT calculate when there is an ARF+PHA instrument model.
The value here is technically wrong, in that the AREASCAL value
is not being included in the calculation, but is included as a
test to validate this use case.
See Also
--------
test_cstat_nophamodel, test_cstat_rmfpha, test_cstat_rsppha
"""
dset, mdl, expected = setup_likelihood(scale=True)
# Use the channel grid as the "energy axis".
#
arf = make_arf(energ_lo=dset.channel, energ_hi=dset.channel + 1)
mdl_ascal = ARFModelPHA(arf, dset, mdl)
stat = CStat()
sval_ascal = stat.calc_stat(dset, mdl_ascal)
assert_allclose(sval_ascal[0], expected)
def test_cstat_arfpha():
"""What does CSTAT calculate when there is an ARF+PHA instrument model.
The value here is technically wrong, in that the AREASCAL value
is not being included in the calculation, but is included as a
test to validate this use case.
See Also
--------
test_cstat_nophamodel, test_cstat_rmfpha, test_cstat_rsppha
"""
dset, mdl, expected = setup_likelihood(scale=True)
# Use the channel grid as the "energy axis".
#
arf = make_arf(energ_lo=dset.channel,
energ_hi=dset.channel + 1)
mdl_ascal = ARFModelPHA(arf, dset, mdl)
stat = CStat()
sval_ascal = stat.calc_stat(dset, mdl_ascal)
assert_allclose(sval_ascal[0], expected)