def test_get_filter_is_empty():
# Need to send in numpy arrays otherwise the code fails as it
# assumes a numpy array. This should be addressed upstream.
#
# pha = DataPHA('name', [1, 2, 3], [1, 1, 1])
pha = DataPHA('name', np.asarray([1, 2, 3]), [1, 1, 1])
assert pha.get_filter() == '1:3'
pha.ignore()
assert pha.get_filter() == 'No noticed bins'
def test_need_numpy_channels():
"""We didn't used to convert channels to a NumPy array which broke
this logic - the ignore line would error out due to an operation on
self.channel
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1])
assert pha.get_filter() == '1:3'
pha.ignore()
assert pha.get_filter() == 'No noticed bins'
def test_need_numpy_channels():
"""We need to convert the input to NumPy arrrays.
This should be done in the constructor to DataPHA, but
for now error out if not set.
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1])
assert pha.get_filter() == '1:3'
# This errors out with a TypeError on 'elo = self.channel - 0.5'
pha.ignore()
assert pha.get_filter() == 'No noticed bins'
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_pha_get_filter_checks_ungrouped(chtype, expected, args):
"""Check we get the filter we expect
chtype is channel, energy, or wavelength
expected is the expected response
args is a list of 3-tuples of (flag, loval, hival) where
flag is True for notice and False for ignore; they define
the filter to apply
"""
chans = np.arange(1, 11, dtype=int)
counts = np.ones(10, dtype=int)
pha = DataPHA('data', chans, counts)
# Use an ARF to create a channel to energy mapping
# The 0.2-2.2 keV range maps to 5.636-61.992 Angstrom
#
egrid = 0.2 * np.arange(1, 12)
arf = DataARF('arf', egrid[:-1], egrid[1:], np.ones(10))
pha.set_arf(arf)
pha.units = chtype
for (flag, lo, hi) in args:
if flag:
pha.notice(lo, hi)
else:
pha.ignore(lo, hi)
assert pha.get_filter(format='%.1f') == expected
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])
