def test_stats_calc_stat_wstat_nobg(usestat, usesys):
"""wstat statistic fails with no background"""
statobj = WStat()
data, model = setup_single(usestat, usesys)
with pytest.raises(StatErr):
statobj.calc_stat(data, model)
def test_stats_calc_stat_wstat_pha_nobg():
"""wstat statistic fails with no background: PHA dataset"""
statobj = WStat()
data, model = setup_single_pha(False, False, background=False)
with pytest.raises(StatErr):
statobj.calc_stat(data, model)
def test_stats_calc_stat_wstat_diffbins():
"""wstat statistic fails when src/bg bin sizes do not match"""
statobj = WStat()
data, model = setup_single_pha(True, False, background=True)
# Tweak data to have one-less bin than the background. This
# used to be easy but with data validation we need to
# create a new object.
#
data2 = DataPHA("faked",
channel=data.channel[:-1],
counts=data.counts[:-1],
staterror=data.staterror[:-1],
grouping=data.grouping[:-1],
exposure=data.exposure,
backscal=data.backscal,
areascal=data.areascal)
# We might expect the ARF/RMF calls to fail if we add validation
# (to check the ARF/RMF is valid for the PHA dataset).
#
data2.set_arf(data.get_arf())
data2.set_rmf(data.get_rmf())
data2.set_background(data.get_background())
# There is no Sherpa error for this, which seems surprising
with pytest.raises(TypeError) as err:
statobj.calc_stat(data2, model)
assert str(
err.value) == "input array sizes do not match, data: 5 vs group: 4"
def test_stats_calc_stat_wstat_diffbins():
"""wstat statistic fails when src/bg bin sizes do not match"""
statobj = WStat()
data, model = setup_single_pha(True, False, background=True)
# Tweak data to have one-less bin than the background
data.channel = data.channel[:-1]
data.counts = data.channel[:-1]
for attr in ['staterror', 'syserror', 'grouping', 'quality', 'backscal']:
val = getattr(data, attr)
if val is not None:
try:
setattr(data, attr, val[:-1])
except TypeError:
# assume a scalar, so leave be
pass
# There is no Sherpa error for this, which seems surprising
with pytest.raises(TypeError):
statobj.calc_stat(data, model)