def test_default_background_issue_fit(clean_astro_ui):
"""Test issue #943 with fit
See https://github.com/sherpa/sherpa/issues/943#issuecomment-696119982
"""
ui.set_default_id('x')
# use least-square as we don't really care about the fit
ui.set_stat('leastsq')
ui.load_arrays('x', [1, 2, 3, 4], [5, 4, 3, 4], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3, 4]), [1, 1, 0, 1])
arf = ui.create_arf(np.asarray([0.1, 0.2, 0.3, 0.4]),
np.asarray([0.2, 0.3, 0.4, 0.5]))
ui.set_arf(arf)
bkg.set_arf(arf)
ui.set_bkg(bkg)
# The model being fitted is a constant to 1,1,0,1 for
# the background, so that should be 0.75 / 0.1 (as the
# bin width is constant), and for the source it is
# 5,4,3,4 - <0.75> [here ignoring the bin-width],
# so [4.25,3.25,2.25,3.25] -> 13 / 4 -> 3.25
#
ui.set_source(ui.const1d.mdl1)
ui.set_bkg_source(ui.const1d.mdl2)
# Prior to #943 this would give a confusing error.
#
ui.fit()
assert mdl1.c0.val == pytest.approx(3.25 / 0.1)
assert mdl2.c0.val == pytest.approx(0.75 / 0.1)
def test_fake_pha_background_model(clean_astro_ui, reset_seed):
"""Check we can add a background component.
See also test_fake_pha_basic.
For simplicity we use perfect responses.
"""
np.random.seed(27347)
id = 'qwerty'
channels = np.arange(1, 4, dtype=np.int16)
counts = np.ones(3, dtype=np.int16)
bcounts = 100 * counts
ui.load_arrays(id, channels, counts, ui.DataPHA)
ui.set_exposure(id, 100)
ui.set_backscal(id, 0.1)
bkg = ui.DataPHA('bkg', channels, bcounts, exposure=200, backscal=0.4)
ebins = np.asarray([1.1, 1.2, 1.4, 1.6])
elo = ebins[:-1]
ehi = ebins[1:]
arf = ui.create_arf(elo, ehi)
rmf = ui.create_rmf(elo, ehi, e_min=elo, e_max=ehi)
mdl = ui.create_model_component('const1d', 'mdl')
mdl.c0 = 0
bkgmdl = ui.create_model_component('const1d', 'mdl')
bkgmdl.c0 = 2
ui.set_source(id, mdl)
ui.set_bkg(id, bkg)
ui.set_bkg_source(id, bkgmdl)
ui.set_arf(id, arf, bkg_id=1)
ui.set_rmf(id, rmf, bkg_id=1)
ui.fake_pha(id, arf, rmf, 1000.0, bkg='model')
faked = ui.get_data(id)
assert faked.exposure == pytest.approx(1000.0)
assert (faked.channel == channels).all()
# check we've faked counts (the scaling is such that it is
# very improbable that this condition will fail)
assert (faked.counts > counts).all()
# For reference the predicted source signal is
# [200, 400, 400]
# and the background signal is
# [125, 125, 125]
# so, even with randomly drawn values, the following
# checks should be robust.
#
predicted_by_source = 1000 * mdl(elo, ehi)
predicted_by_bkg = (1000 / 200) * (0.1 / 0.4) * bcounts
assert (faked.counts > predicted_by_source).all()
assert (faked.counts > predicted_by_bkg).all()
def test_get_syserror_missing(bid):
"""Does get_syserror error out?"""
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
with pytest.raises(DataErr) as exc:
ui.get_syserror(bkg_id=bid)
assert str(exc.value) == "data set '1' does not specify systematic errors"
def test_set_filter_mismatch(f, bid):
"""Does set_filter error when there's a mis-match?
"""
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
with pytest.raises(DataErr) as exc:
ui.set_filter(f, bkg_id=bid)
assert str(exc.value) == 'size mismatch between 3 and 2'
def test_fix_background_id_error_checks2():
"""Check error handling of background id"""
ui.load_arrays(2, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(2, bkg)
with pytest.raises(IdentifierErr) as exc:
ui.get_bkg_source(id=2, bkg_id='bkg')
assert str(exc.value) == "identifier 'bkg' is a reserved word"
def test_fix_background_id_error_checks1():
"""Check error handling of background id"""
ui.load_arrays(2, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(2, bkg)
with pytest.raises(ArgumentTypeErr) as exc:
ui.get_bkg_source(id=2, bkg_id=bkg)
assert str(exc.value) == 'identifiers must be integers or strings'
def test_save_xxx_nodata(func, etype, emsg):
"""Does save_xxx error out if there's no data to save? DataPHA
"""
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
with pytest.raises(etype) as exc:
func("temp-file-that-should-not-be-created")
assert str(exc.value) == emsg
def test_save_image_nodata():
"""Does save_image error out if there's no data to save? DataPHA
Unlike the other calls, this requires a FITS library
"""
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
with pytest.raises(IOErr) as exc:
ui.save_image("temp-file-that-should-not-be-created")
assert str(exc.value) == "data set '' does not contain an image"
def test_bkg_id_get_bkg_source(clean_astro_ui):
"""Check the error message when the background model has not been set (issue #943)"""
ui.set_default_id('x')
ui.load_arrays('x', [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
with pytest.raises(ModelErr) as exc:
ui.get_bkg_source()
assert str(
exc.value) == 'background model 1 for data set x has not been set'
def test_save_xxx_bkg_nodata(func, etype, emsg):
"""Does save_xxx error out if there's no data to save? DataPHA + bkg
Note that save_image does not support a bkg_id parameter so is
not tested here.
"""
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
with pytest.raises(etype) as exc:
func("temp-file-that-should-not-be-created", bkg_id=1)
assert str(exc.value) == emsg
def test_save_filter_ignored(bid):
"""Does save_filter error out if everything is masked?
We should be able to write out the filter in this case,
as it's easy (all False).
"""
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
ui.ignore(None, None)
with pytest.raises(DataErr) as exc:
ui.save_filter("temp-file-that-should-not-be-created", bkg_id=bid)
assert str(exc.value) == "mask excludes all data"
def test_set_filter_mismatch_with_filter(f, bid):
"""Does set_filter error when there's a mis-match after a filter?
test_set_filter_mismatch checks when .mask is a scalar,
so now check when it's a NumPy array.
"""
ui.load_arrays(1, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(bkg)
ui.ignore(3, None) # set the .mask attribute to an array
with pytest.raises(DataErr) as exc:
ui.set_filter(f, bkg_id=bid)
assert str(exc.value) == 'size mismatch between 3 and 2'
def test_delete_bkg_model_with_bkgid(id, clean_astro_ui):
"""Check we call delete_bkg_model with non-default bkg_id"""
ui.load_arrays(id, [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
ui.set_bkg(id, bkg, bkg_id=2)
ui.set_bkg_source(id, ui.const1d.bmdl, bkg_id=2)
assert ui.list_model_components() == ['bmdl']
assert ui.get_bkg_source(id, 2).name == 'const1d.bmdl'
ui.delete_bkg_model(id, bkg_id=2)
assert ui.list_model_components() == ['bmdl']
with pytest.raises(ModelErr) as exc:
ui.get_bkg_source(id, 2)
emsg = 'background model 2 for data set {} has not been set'.format(id)
assert str(exc.value) == emsg
def test_show_bkg_model_issue943(clean_astro_ui):
"""Test issue #943
We do not check that show_bkg_model is creating anything
useful, just that it can be called.
See https://github.com/sherpa/sherpa/issues/943#issuecomment-696119982
"""
ui.set_default_id('x')
ui.load_arrays('x', [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
arf = ui.create_arf(np.asarray([0.1, 0.2, 0.3]),
np.asarray([0.2, 0.3, 0.4]))
bkg.set_arf(arf)
ui.set_bkg(bkg)
ui.set_bkg_source(ui.const1d.mdl2)
ui.show_bkg_model()
def test_default_background_issue(clean_astro_ui):
"""Test issue #943"""
ui.set_default_id('x')
# use least-square as we don't really care about the fit
ui.set_stat('leastsq')
ui.load_arrays('x', [1, 2, 3], [5, 4, 3], ui.DataPHA)
bkg = ui.DataPHA('bkg', np.asarray([1, 2, 3]), [1, 1, 0])
arf = ui.create_arf(np.asarray([0.1, 0.2, 0.3]),
np.asarray([0.2, 0.3, 0.4]))
bkg.set_arf(arf)
ui.set_bkg(bkg)
ui.set_bkg_source(ui.const1d.mdl2)
# Ensure we can fit the background model. Prior to #943 being
# fixed the fit_bkg call would error out.
#
ui.fit_bkg()
assert mdl2.c0.val == pytest.approx(2 / 3 / 0.1)
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])
def test_fake_pha_basic(id, has_bkg, clean_astro_ui):
"""No background.
See also test_fake_pha_add_background
For simplicity we use perfect responses.
A background dataset can be added, but it should
not be used in the simulation.
"""
channels = np.arange(1, 4, dtype=np.int16)
counts = np.ones(3, dtype=np.int16)
bcounts = 100 * counts
ui.load_arrays(id, channels, counts, ui.DataPHA)
ui.set_exposure(id, 100)
if has_bkg:
bkg = ui.DataPHA('bkg', channels, bcounts, exposure=200, backscal=0.4)
ui.set_bkg(id, bkg, bkg_id='faked-bkg')
ebins = np.asarray([1.1, 1.2, 1.4, 1.6])
elo = ebins[:-1]
ehi = ebins[1:]
arf = ui.create_arf(elo, ehi)
rmf = ui.create_rmf(elo, ehi, e_min=elo, e_max=ehi)
mdl = ui.create_model_component('const1d', 'mdl')
mdl.c0 = 2
ui.set_source(id, mdl)
ui.fake_pha(id, arf, rmf, 1000.0)
faked = ui.get_data(id)
assert faked.exposure == pytest.approx(1000.0)
assert (faked.channel == channels).all()
assert faked.name == 'faked'
assert faked.get_arf().name == 'test-arf'
assert faked.get_rmf().name == 'delta-rmf'
if has_bkg and id is not None:
assert faked.background_ids == ['faked-bkg']
bkg = ui.get_bkg(id, 'faked-bkg')
assert bkg.name == 'bkg'
assert bkg.counts == pytest.approx(bcounts)
assert bkg.exposure == pytest.approx(200)
else:
assert faked.background_ids == []
# check we've faked counts (the scaling is such that it is
# very improbable that this condition will fail)
assert (faked.counts > counts).all()
# For reference the predicted source signal is
# [200, 400, 400]
#
# What we'd like to say is that the predicted counts are
# similar, but this is not easy to do. What we can try
# is summing the counts (to average over the randomness)
# and then a simple check
#
assert faked.counts.sum() > 200
