def test_fake_pha_bkg_model(reset_seed):
"""Test background model
"""
np.random.seed(5329853)
data = DataPHA("any", channels, counts, exposure=1000.)
bkg = DataPHA("bkg", channels, bcounts, exposure=2000, backscal=1.)
data.set_background(bkg, id="used-bkg")
data.set_arf(arf)
data.set_rmf(rmf)
bkg.set_arf(arf)
bkg.set_rmf(rmf)
mdl = Const1D("mdl")
mdl.c0 = 0
bmdl = Const1D("bmdl")
bmdl.c0 = 2
# With no background model the simulated source counts
# are 0.
#
fake_pha(data,
mdl,
is_source=True,
add_bkgs=False,
bkg_models={"used-bkg": bmdl})
assert data.counts == pytest.approx([0, 0, 0])
# Check we have created source counts this time.
#
fake_pha(data,
mdl,
is_source=True,
add_bkgs=True,
bkg_models={"used-bkg": bmdl})
assert data.exposure == pytest.approx(1000.0)
assert (data.channel == channels).all()
assert data.name == "any"
assert data.get_arf().name == "user-arf"
assert data.get_rmf().name == "delta-rmf"
# The background itself is unchanged
assert data.background_ids == ["used-bkg"]
bkg = data.get_background("used-bkg")
assert bkg.name == "bkg"
assert bkg.counts == pytest.approx(bcounts)
assert bkg.exposure == pytest.approx(2000)
# Apply a number of regression checks to test the output. These
# can expect to change if the randomization changes (either
# explicitly or implicity). There used to be a number of checks
# that compares the simulated data to the input values, but these
# could occasionally fail, and so the seed was fixed for these
# tests.
#
# For reference the predicted signal is
# [200, 400, 400]
# but, unlike in the test above, this time it's all coming
# from the background.
#
assert data.counts == pytest.approx([186, 411, 405])
# Now add a second set of arf/rmf for the data.
# However, all the signal is background, so this does not change
# any of the results.
data.set_arf(arf, 2)
data.set_rmf(rmf, 2)
fake_pha(data,
mdl,
is_source=True,
add_bkgs=True,
bkg_models={"used-bkg": bmdl})
assert data.counts == pytest.approx([197, 396, 389])
def test_fake_pha_basic(has_bkg, is_source, reset_seed):
"""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 with default settings
"""
np.random.seed(4276)
data = DataPHA("any", channels, counts, exposure=1000.)
if has_bkg:
bkg = DataPHA("bkg", channels, bcounts, exposure=2000, backscal=0.4)
data.set_background(bkg, id="unused-bkg")
data.set_arf(arf)
data.set_rmf(rmf)
mdl = Const1D("mdl")
mdl.c0 = 2
fake_pha(data, mdl, is_source=is_source, add_bkgs=False)
assert data.exposure == pytest.approx(1000.0)
assert (data.channel == channels).all()
assert data.name == "any"
assert data.get_arf().name == "user-arf"
assert data.get_rmf().name == "delta-rmf"
if has_bkg:
assert data.background_ids == ["unused-bkg"]
bkg = data.get_background("unused-bkg")
assert bkg.name == "bkg"
assert bkg.counts == pytest.approx(bcounts)
assert bkg.exposure == pytest.approx(2000)
else:
assert data.background_ids == []
if is_source:
# check we've faked counts (the scaling is such that it is
# very improbable that this condition will fail)
assert (data.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 data.counts.sum() > 500
assert data.counts.sum() < 1500
# This is more likely to fail by chance, but still very unlikely
assert data.counts[1] > data.counts[0]
else:
# No multiplication with exposure time, arf binning, etc.
# so we just expect very few counts
assert data.counts.sum() < 10
assert data.counts.sum() >= 2
# Essentially double the exposure by having two identical arfs
data.set_arf(arf, 2)
data.set_rmf(rmf, 2)
fake_pha(data, mdl, is_source=is_source, add_bkgs=False)
if is_source:
assert data.counts.sum() > 1200
assert data.counts.sum() < 3000
assert data.counts[1] > data.counts[0]
else:
assert data.counts.sum() < 20
assert data.counts.sum() >= 4
def test_fake_pha_bkg_model():
"""Test background model
"""
data = DataPHA('any', channels, counts, exposure=1000.)
bkg = DataPHA('bkg', channels, bcounts, exposure=2000, backscal=1.)
data.set_background(bkg, id='used-bkg')
data.set_arf(arf)
data.set_rmf(rmf)
bkg.set_arf(arf)
bkg.set_rmf(rmf)
mdl = Const1D('mdl')
mdl.c0 = 0
bmdl = Const1D('bmdl')
bmdl.c0 = 2
fake_pha(data,
mdl,
is_source=True,
add_bkgs=True,
bkg_models={'used-bkg': bmdl})
assert data.exposure == pytest.approx(1000.0)
assert (data.channel == channels).all()
assert data.name == 'any'
assert data.get_arf().name == 'user-arf'
assert data.get_rmf().name == 'delta-rmf'
# The background itself is unchanged
assert data.background_ids == ['used-bkg']
bkg = data.get_background('used-bkg')
assert bkg.name == 'bkg'
assert bkg.counts == pytest.approx(bcounts)
assert bkg.exposure == pytest.approx(2000)
# check we've faked counts (the scaling is such that it is
# very improbable that this condition will fail)
assert (data.counts > counts).all()
# For reference the predicted signal is
# [200, 400, 400]
# but, unlike in the test above, this time it's all coming
# from the background.
#
# 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 data.counts.sum() > 500
assert data.counts.sum() < 1500
# This is more likely to fail by chance, but still very unlikely
assert data.counts[1] > 1.5 * data.counts[0]
# Now add a second set of arf/rmf for the data.
# However, all the signal is background, so this does not change
# any of the results.
data.set_arf(arf, 2)
data.set_rmf(rmf, 2)
fake_pha(data,
mdl,
is_source=True,
add_bkgs=True,
bkg_models={'used-bkg': bmdl})
assert data.counts.sum() > 500
assert data.counts.sum() < 1500
assert data.counts[1] > 1.5 * data.counts[0]
