def test_grouped_pha_all_bad_response_bg_warning(elo, ehi, nbins, bkg_id,
caplog, make_data_path,
clean_astro_ui):
"""Check we get the warning messages with background filtering"""
ui.load_pha('check', make_data_path('3c273.pi'))
ui.set_quality('check', 2 * numpy.ones(1024, dtype=numpy.int16), bkg_id=1)
ui.ignore_bad('check', bkg_id=1)
with caplog.at_level(logging.INFO, logger='sherpa'):
ui.notice_id('check', elo, ehi, bkg_id=bkg_id)
# filtering has or hasn't happened
nsrc = ui.get_dep('check', filter=True).size
nback = ui.get_dep('check', filter=True, bkg_id=1).size
if bkg_id is None:
assert nsrc == nbins
assert nback == 0
else:
assert nsrc == 46 # ie no filter
assert nback == 0
# did we get a warning message from the background?
assert len(caplog.records) == 1
name, lvl, msg = caplog.record_tuples[0]
assert name == 'sherpa.astro.data'
assert lvl == logging.INFO
assert msg.startswith('Skipping dataset ')
assert msg.endswith('/3c273_bg.pi: mask excludes all data')
def test_dataspace1d_datapha(clean_astro_ui):
"""Explicitly test dataspace1d for DataPHA"""
assert ui.list_data_ids() == []
# Note the grid is ignored, other than the number of bins
ui.dataspace1d(20, 30, step=2.5, id='x', dstype=ui.DataPHA)
assert ui.list_data_ids() == ['x']
assert ui.get_data('x').name == 'dataspace1d'
grid = ui.get_indep('x')
assert len(grid) == 1
expected = numpy.asarray([1, 2, 3, 4, 5])
assert grid[0] == pytest.approx(expected)
y = ui.get_dep('x')
assert y == pytest.approx(numpy.zeros(5))
assert ui.get_exposure('x') is None
assert ui.get_grouping('x') is None
assert ui.get_quality('x') is None
assert ui.get_data('x').subtracted is False
with pytest.raises(IdentifierErr):
ui.get_bkg('x')
def test_filter_bad_grouped(make_data_path, clean_astro_ui):
"""Check behavior when the data is grouped.
This is a test of the current behavior, to check that
values still hold. It may be necessary to change this
test if we change the quality handling.
"""
infile = make_data_path('q1127_src1_grp30.pi')
ui.load_pha(infile)
pha = ui.get_data()
assert pha.quality_filter is None
assert pha.mask is True
assert ui.get_dep().shape == (439, )
assert pha.quality_filter is None
assert pha.mask is True
# The last group is marked as quality=2 and so calling
# ignore_bad means we lose that group.
#
ui.ignore_bad()
assert ui.get_dep().shape == (438, )
assert pha.mask is True
expected = np.ones(1024, dtype=bool)
expected[996:1025] = False
assert pha.quality_filter == pytest.approx(expected)
# What happens when we filter the data? Unlike #1169
# we do change the noticed range.
#
ui.notice(0.5, 7)
assert pha.quality_filter == pytest.approx(expected)
# The mask has been filtered to remove the bad channels
# (this is grouped data)
expected = np.ones(438, dtype=bool)
expected[0:15] = False
expected[410:438] = False
assert pha.mask == pytest.approx(expected)
expected = np.ones(996, dtype=bool)
expected[0:34] = False
expected[481:996] = False
assert pha.get_mask() == pytest.approx(expected)
def test_filter_bad_ungrouped(make_data_path, clean_astro_ui):
"""Check behavior when the data is ungrouped.
This is a test of the current behavior, to check that
values still hold. It may be necessary to change this
test if we change the quality handling.
"""
infile = make_data_path('q1127_src1_grp30.pi')
ui.load_pha(infile)
pha = ui.get_data()
assert pha.quality_filter is None
assert pha.mask is True
assert ui.get_dep().shape == (439, )
ui.ungroup()
assert ui.get_dep().shape == (1024, )
assert pha.quality_filter is None
assert pha.mask is True
ui.ignore_bad()
assert ui.get_dep().shape == (1024, )
assert pha.quality_filter is None
expected = np.ones(1024, dtype=bool)
expected[996:1025] = False
assert pha.mask == pytest.approx(expected)
# At this point we've changed the mask array so Sherpa thinks
# we've applied a filter, so a notice is not going to change
# anything. See issue #1169
#
ui.notice(0.5, 7)
assert pha.mask == pytest.approx(expected)
# We need to ignore to change the mask.
#
ui.ignore(None, 0.5)
ui.ignore(7, None)
expected[0:35] = False
expected[479:1025] = False
assert pha.mask == pytest.approx(expected)
def check_chi2():
"""Execute this function after fitting to see if the
best-fit chi2 reported matches the formula coded here"""
import sherpa.astro.ui as sau
chi2 = sau.get_fit_results().statval
print('chi2 from fit: {0}'.format(chi2))
data = sau.get_dep()
model = sau.get_model_plot().y
error = np.where(model > data, sau.get_syserror(), sau.get_staterror())
chi = ((data - model) / error) # Chi per bin
chi2 = chi ** 2 # Chi^2 per bin
print('chi2 re-computed: {0}'.format(chi2.sum()))
def check_chi2():
"""Execute this function after fitting to see if the
best-fit chi2 reported matches the formula coded here"""
import sherpa.astro.ui as sau
chi2 = sau.get_fit_results().statval
print('chi2 from fit: {0}'.format(chi2))
data = sau.get_dep()
model = sau.get_model_plot().y
error = np.where(model > data, sau.get_syserror(), sau.get_staterror())
chi = ((data - model) / error) # Chi per bin
chi2 = chi**2 # Chi^2 per bin
print('chi2 re-computed: {0}'.format(chi2.sum()))
def test_group_already_grouped(idval):
"""Does group still work if the data is already grouped?"""
x = [1, 2, 3]
y = [0, 4, 3]
if idval is None:
ui.load_arrays(1, x, y, ui.DataPHA)
ui.set_grouping([1, -1, 1])
else:
ui.load_arrays(idval, x, y, ui.DataPHA)
ui.set_grouping(idval, [1, -1, 1])
data = ui.get_data(idval)
assert not data.grouped
ui.group(idval)
assert data.grouped
assert ui.get_dep(idval) == pytest.approx([2, 3])
ui.group(idval)
assert ui.get_dep(idval) == pytest.approx([2, 3])
assert data.grouped
def test_dataspace1d_datapha_bkg(clean_astro_ui):
"""Explicitly test dataspace1d for DataPHA (background)"""
# list_bkg_ids will error out until the dataset exists
assert ui.list_data_ids() == []
# We don't use the grid range or step size since numbins has been
# given.
ui.dataspace1d(20, 30, step=2.5, numbins=10, id='x', dstype=ui.DataPHA)
assert ui.list_data_ids() == ['x']
assert ui.list_bkg_ids('x') == []
ui.dataspace1d(20,
30,
step=2.5,
numbins=10,
id='x',
bkg_id=2,
dstype=ui.DataPHA)
assert ui.list_data_ids() == ['x']
assert ui.list_bkg_ids('x') == [2]
assert ui.get_data('x').name == 'dataspace1d'
# I've explicitly not chosen the default background identifier
with pytest.raises(IdentifierErr):
ui.get_bkg('x')
assert ui.get_bkg('x', 2).name == 'bkg_dataspace1d'
grid = ui.get_indep('x', bkg_id=2)
assert len(grid) == 1
expected = numpy.asarray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
assert grid[0] == pytest.approx(expected)
y = ui.get_dep('x', bkg_id=2)
assert y == pytest.approx(numpy.zeros(10))
assert ui.get_exposure('x', bkg_id=2) is None
assert ui.get_grouping('x', bkg_id=2) is None
assert ui.get_quality('x', bkg_id=2) is None
assert ui.get_bkg('x', bkg_id=2).subtracted is False
# check we can subtract the dataset; as the data is all zeros
# we don't bother checking the result.
#
ui.subtract('x')
def test_load_grouping(idval, clean_astro_ui, tmp_path):
"""Simple grouping check"""
x = [1, 2, 3]
y = [0, 4, 3]
if idval is None:
ui.load_arrays(1, x, y, ui.DataPHA)
else:
ui.load_arrays(idval, x, y, ui.DataPHA)
path = tmp_path / 'group.dat'
path.write_text('1\n-1\n1')
data = ui.get_data(idval)
assert data.grouping is None
if idval is None:
ui.load_grouping(str(path))
else:
ui.load_grouping(idval, str(path))
assert not data.grouped
assert data.grouping is not None
ui.group(idval)
assert data.grouped
grps = ui.get_grouping(idval)
assert grps.shape == (3, )
# It's not clear what requirements load_grouping makes of the
# data, so do not enforce a data type. At a minimum there
# would be potential backend differences.
#
# assert grps.dtype == np.int16
assert grps == pytest.approx([1, -1, 1])
# Note that get_dep is returning the sum per group / channel width
# (since we have no instrument response).
#
y = ui.get_dep(idval)
assert y.shape == (2, )
assert y == pytest.approx([2, 3])
def test_dataspace1d_data1dint(clean_astro_ui):
"""Explicitly test dataspace1d for Data1DInt"""
assert ui.list_data_ids() == []
ui.dataspace1d(20, 30, step=2.5, id='x', dstype=ui.Data1DInt)
assert ui.list_data_ids() == ['x']
assert ui.get_data('x').name == 'dataspace1d'
grid = ui.get_indep('x')
assert len(grid) == 2
expected = numpy.asarray([20, 22.5, 25, 27.5, 30.0])
assert grid[0] == pytest.approx(expected[:-1])
assert grid[1] == pytest.approx(expected[1:])
y = ui.get_dep('x')
assert y == pytest.approx(numpy.zeros(4))
def test_subtract_already_subtracted(idval):
"""Does subtract still work if the data is already subtracted?"""
x = [1, 2, 3]
y = [0, 4, 3]
ui.load_arrays('bgnd', x, y, ui.DataPHA)
bkg = ui.get_data('bgnd')
if idval is None:
ui.load_arrays(1, x, y, ui.DataPHA)
ui.set_bkg(bkg)
else:
ui.load_arrays(idval, x, y, ui.DataPHA)
ui.set_bkg(idval, bkg)
data = ui.get_data(idval)
assert not data.subtracted
ui.subtract(idval)
assert data.subtracted
ui.subtract(idval)
assert ui.get_dep(idval) == pytest.approx([0, 0, 0])