def test_pha_get_xerr_all_bad_energy_group():
"""get_xerr handles all bad values [energy]
The behavior with grouping is different, presumably because
we assume we have grouping when we have a quality array.
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1],
grouping=[1, 1, 1],
quality=[2, 2, 2])
ebins = np.asarray([3.0, 5., 8.0, 12.0])
rlo = ebins[:-1]
rhi = ebins[1:]
rmf = create_delta_rmf(rlo, rhi, e_min=rlo, e_max=rhi)
pha.set_rmf(rmf)
pha.units = 'energy'
assert pha.get_xerr() == pytest.approx([2.0, 3.0, 4.0])
assert pha.grouped
pha.ignore_bad()
# Should this error out or not?
assert pha.get_filter() == ''
# with pytest.raises(DataErr) as de:
# pha.get_filter()
# assert str(de.value) == 'mask excludes all data'
assert pha.get_xerr() == pytest.approx([])
def test_416_c():
"""The third test case from issue #416
This used to use channels but it has been changed to add an RMF so
we can filter in energy space, as it is not clear what non-integer
channels should mean.
"""
x = np.asarray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = np.asarray([0, 0, 0, 2, 1, 1, 0, 0, 0, 0])
pha = DataPHA('416', x, y)
rmf = create_delta_rmf(x, x + 1, e_min=x, e_max=x + 1,
name='416')
pha.set_arf(rmf)
pha.set_analysis('energy')
# When using channels this used notice(3.5, 6.5)
# but using energy space we need to use a different
# range to match the ones the original channel filter
# used.
#
pha.notice(4.5, 6.5)
# this should be ~pha.mask
tabstops = [True] * 3 + [False] * 3 + [True] * 4
assert ~pha.mask == pytest.approx(tabstops)
pha.group_counts(3, tabStops=~pha.mask)
pha.ignore_bad()
grouping = [0] * 3 + [1, -1, 1] + [0] * 4
assert pha.grouping == pytest.approx(grouping)
# the second grouped bin has a quality of 2 as
# it only contains 1 count
quality = np.zeros(10, dtype=int)
quality[5] = 2
assert pha.quality == pytest.approx(quality)
dep = pha.get_dep(filter=False)
assert dep == pytest.approx(y)
# It is not at all obvious why we get 8 bins returned
# here. The ignore_bad has removed any existing
# filters, but why do we get 8, not 10, values?
# Well, one bin has been removed (quality=2)
# and two bins have merged into 1. Hence the 8.
#
dep = pha.get_dep(filter=True)
exp = np.zeros(8)
exp[3] = 3
assert dep == pytest.approx(exp)
def test_pha_ignore_bad_no_quality():
"""Just check we error out"""
chans = np.arange(1, 4)
counts = np.ones_like(chans)
pha = DataPHA("dummy", chans, counts)
with pytest.raises(DataErr) as de:
pha.ignore_bad()
assert str(de.value) == "data set 'dummy' does not specify quality flags"
def make_data(data_class):
"""Create a test data object of the given class.
Using a string means it is easier to support the various PHA
"types" - eg basic, grouping, grouping+quality.
"""
x0 = np.asarray([1, 3, 7, 12])
y = np.asarray([2, 3, 4, 5])
if data_class == "1d":
return Data1D('x1', x0, y)
if data_class == "1dint":
return Data1DInt('xint1', x0, np.asarray([3, 5, 8, 15]), y)
chans = np.arange(1, 5)
if data_class == "pha":
return DataPHA('pha', chans, y)
# We want to provide PHA tests that check out the grouping and
# quality handling (but it is not worth trying all different
# variants), so we have "grp" for grouping and no quality [*], and
# "qual" for grouping and quality.
#
# [*] by which I mean we have not called ignore_bad, not that
# there is no quality array.
#
grp = np.asarray([1, -1, 1, 1])
qual = np.asarray([0, 0, 2, 0])
pha = DataPHA('pha', chans, y, grouping=grp, quality=qual)
if data_class == "grp":
return pha
if data_class == "qual":
pha.ignore_bad()
return pha
x0 = np.asarray([1, 2, 3] * 2)
x1 = np.asarray([1, 1, 1, 2, 2, 2])
y = np.asarray([2, 3, 4, 5, 6, 7])
if data_class == "2d":
return Data2D('x2', x0, x1, y, shape=(2, 3))
if data_class == "2dint":
return Data2DInt('xint2', x0, x1, x0 + 1, x1 + 1, y, shape=(2, 3))
if data_class == "img":
return DataIMG('img', x0, x1, y, shape=(2, 3))
if data_class == "imgint":
return DataIMGInt('imgi', x0, x1, x0 + 1, x1 + 1, y, shape=(2, 3))
assert False
def test_416_b(caplog):
"""The second test case from issue #416
This is to make sure this hasn't changed.
This used to use channels but it has been changed to add an RMF so
we can filter in energy space, as it is not clear what non-integer
channels should mean.
"""
x = np.asarray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = np.asarray([0, 0, 0, 2, 1, 1, 0, 0, 0, 0])
pha = DataPHA('416', x, y)
rmf = create_delta_rmf(x, x + 1, e_min=x, e_max=x + 1,
name='416')
pha.set_arf(rmf)
pha.set_analysis('energy')
pha.notice(3.5, 6.5)
pha.group_counts(3)
with caplog.at_level(logging.INFO, logger='sherpa'):
pha.ignore_bad()
# It's not obvious why this has switched to a boolean
assert pha.mask
# Mask is also interesting (currently just reporting
# this behavior)
mask = [True] * 5 + [False] * 5
assert pha.get_mask() == pytest.approx(mask)
grouping = [1, -1, -1, -1, -1, 1, -1, -1, -1, -1.]
assert pha.grouping == pytest.approx(grouping)
quality = [0, 0, 0, 0, 0, 2, 2, 2, 2, 2]
assert pha.quality == pytest.approx(quality)
dep = pha.get_dep(filter=True)
assert dep == pytest.approx([3])
# check captured log
#
emsg = 'filtering grouped data with quality flags, previous filters deleted'
assert caplog.record_tuples == [
('sherpa.astro.data', logging.WARNING, emsg)
]
def test_pha_get_xerr_all_bad_channel_no_group():
"""get_xerr handles all bad values [channel]
It's not obvious what it is meant to be doing here.
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1],
quality=[2, 2, 2])
assert pha.get_xerr() == pytest.approx([1, 1, 1])
pha.ignore_bad()
assert pha.get_filter() == ''
assert pha.get_xerr() == pytest.approx([1, 1, 1])
def test_pha_get_xerr_all_bad_channel_group():
"""get_xerr handles all bad values [channel]
The behavior with grouping is different, presumably because
we assume we have grouping when we have a quality array.
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1],
grouping=[1, 1, 1],
quality=[2, 2, 2])
assert pha.get_xerr() == pytest.approx([1, 1, 1])
assert pha.grouped
pha.ignore_bad()
assert pha.get_filter() == ''
assert pha.get_xerr() == pytest.approx([])
def test_pha_get_xerr_all_bad_energy_no_group():
"""get_xerr handles all bad values [energy]
It's not obvious what it is meant to be doing here.
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1],
quality=[2, 2, 2])
ebins = np.asarray([3.0, 5., 8.0, 12.0])
rlo = ebins[:-1]
rhi = ebins[1:]
rmf = create_delta_rmf(rlo, rhi, e_min=rlo, e_max=rhi)
pha.set_rmf(rmf)
pha.units = 'energy'
assert pha.get_xerr() == pytest.approx([2.0, 3.0, 4.0])
pha.ignore_bad()
assert pha.get_filter() == ''
assert pha.get_xerr() == pytest.approx([2.0, 3.0, 4.0])
def test_416_c():
"""The third test case from issue #416
"""
x = np.asarray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = np.asarray([0, 0, 0, 2, 1, 1, 0, 0, 0, 0])
pha = DataPHA('416', x, y)
pha.notice(3.5, 6.5)
# this should be ~pha.mask
tabstops = [True] * 3 + [False] * 3 + [True] * 4
assert ~pha.mask == pytest.approx(tabstops)
pha.group_counts(3, tabStops=~pha.mask)
pha.ignore_bad()
grouping = [0] * 3 + [1, -1, 1] + [0] * 4
assert pha.grouping == pytest.approx(grouping)
# the second grouped bin has a quality of 2 as
# it only contains 1 count
quality = np.zeros(10, dtype=np.int)
quality[5] = 2
assert pha.quality == pytest.approx(quality)
dep = pha.get_dep(filter=False)
assert dep == pytest.approx(y)
# It is not at all obvious why we get 8 bins returned
# here. The ignore_bad has removed any existing
# filters, but why do we get 8, not 10, values?
# Well, one bin has been removed (quality=2)
# and two bins have merged into 1. Hence the 8.
#
dep = pha.get_dep(filter=True)
exp = np.zeros(8)
exp[3] = 3
assert dep == pytest.approx(exp)
def test_416_b(caplog):
"""The second test case from issue #416
This is to make sure this hasn't changed.
"""
x = np.asarray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = np.asarray([0, 0, 0, 2, 1, 1, 0, 0, 0, 0])
pha = DataPHA('416', x, y)
pha.notice(3.5, 6.5)
pha.group_counts(3)
with caplog.at_level(logging.INFO, logger='sherpa'):
pha.ignore_bad()
# It's not obvious why this has switched to a boolean
assert pha.mask
# Mask is also interesting (currently just reporting
# this behavior)
mask = [True] * 5 + [False] * 5
assert pha.get_mask() == pytest.approx(mask)
grouping = [1, -1, -1, -1, -1, 1, -1, -1, -1, -1.]
assert pha.grouping == pytest.approx(grouping)
quality = [0, 0, 0, 0, 0, 2, 2, 2, 2, 2]
assert pha.quality == pytest.approx(quality)
dep = pha.get_dep(filter=True)
assert dep == pytest.approx([3])
# check captured log
#
emsg = 'filtering grouped data with quality flags, previous filters deleted'
assert caplog.record_tuples == [
('sherpa.astro.data', logging.WARNING, emsg)
]
