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_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 setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile1 = self.make_path('3c273.pi')
infile2 = self.make_path('9774.pi')
ui.load_pha(1, infile1)
ui.load_pha(2, infile2)
# Since 9774.pi isn't grouped, group it. Note that this
# call groups the background to 20 counts per bin. In this
# case we do not want that; instead we want to use the same
# grouping scheme as the source file.
#
# Note: this is related to issue 227
#
ui.group_counts(2, 20)
ui.set_grouping(2, bkg_id=1, val=ui.get_grouping(2))
# There's no need to have the same model in both datasets,
# but assume the same source model can be used, with a
# normalization difference.
#
ui.set_source(1, ui.powlaw1d.pl1)
ui.set_source(2, ui.const1d.c2 * ui.get_source(1))
# The powerlaw slope and normalization are
# intended to be "a reasonable approximation"
# to the data, just to make sure that any statistic
# calculation doesn't blow-up too much.
#
# Note: the model values for 3c273 are slighly different
# to the single-PHA-file case, so stat results are
# slightly different
#
ui.set_par("pl1.gamma", 1.7)
ui.set_par("pl1.ampl", 1.6e-4)
ui.set_par("c2.c0", 45)
def load(self, filename):
self.modelFile = filename
with open(filename, 'r') as f:
self.pca = json.load(f)
for k, v in self.pca.items():
self.pca[k] = np.array(v)
nactivedata = self.pca['ihi'] - self.pca['ilo']
assert self.pca['hi'].shape == (
nactivedata,
), 'spectrum has different number of channels: %d vs %s' % (len(
self.pca['hi']), self.ndata)
assert self.pca['lo'].shape == self.pca['hi'].shape
assert self.pca['mean'].shape == self.pca['hi'].shape
assert len(self.pca['components']) == nactivedata
assert nactivedata <= self.ndata
ilo = int(self.pca['ilo'])
ihi = int(self.pca['ihi'])
self.cts = self.data[ilo:ihi]
self.ncts = self.cts.sum(
) # 'have ncts background counts for deconvolution
self.x = np.arange(ihi - ilo)
self.ilo = ilo
self.ihi = ihi
# Only notice the channels between ilo + 1 and ihi (channel starts from 1, while index from 0).
# The stat value will be affected, for assessment of goodness-of-fit for background.
self.grouping0 = ui.get_grouping()
ui.set_analysis('channel')
# The channel filters, filter0 and filter_chan are all native channels.
# ui.get_filter() will, instead, reture the binned channels if the spectrum is grouped.
if self.grouping0 is not None:
ui.ungroup()
self.filter0 = ui.get_filter()
ui.ignore()
ui.notice(
self.ilo + 1, self.ihi
) # ui.notice(a, b), from channel a to channel b, including channels a, b.
self.filter_chan = ui.get_filter()
ui.set_analysis('energy')
if self.grouping0 is not None:
ui.group()
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile1 = self.make_path('3c273.pi')
infile2 = self.make_path('9774.pi')
ui.load_pha(1, infile1)
ui.load_pha(2, infile2)
# Since 9774.pi isn't grouped, group it. Note that this
# call groups the background to 20 counts per bin. In this
# case we do not want that; instead we want to use the same
# grouping scheme as the source file.
#
# Note: this is related to issue 227
#
ui.group_counts(2, 20)
ui.set_grouping(2, bkg_id=1, val=ui.get_grouping(2))
# There's no need to have the same model in both datasets,
# but assume the same source model can be used, with a
# normalization difference.
#
ui.set_source(1, ui.powlaw1d.pl1)
ui.set_source(2, ui.const1d.c2 * ui.get_source(1))
# The powerlaw slope and normalization are
# intended to be "a reasonable approximation"
# to the data, just to make sure that any statistic
# calculation doesn't blow-up too much.
#
# Note: the model values for 3c273 are slighly different
# to the single-PHA-file case, so stat results are
# slightly different
#
ui.set_par("pl1.gamma", 1.7)
ui.set_par("pl1.ampl", 1.6e-4)
ui.set_par("c2.c0", 45)