def example_pha_data():
"""Create an example data set."""
etime = 1201.0
d = DataPHA('example',
_data_chan.copy(),
_data_counts.copy(),
exposure=etime,
backscal=0.2)
a = DataARF('example-arf',
_energies_lo.copy(),
_energies_hi.copy(),
_arf.copy(),
exposure=etime)
r = create_delta_rmf(_energies_lo.copy(),
_energies_hi.copy(),
e_min=_energies_lo.copy(),
e_max=_energies_hi.copy(),
offset=1,
name='example-rmf')
d.set_arf(a)
d.set_rmf(r)
return d
def test_rmfmodelpha_delta_no_ebounds(analysis, caplog):
"""What happens calling an rmf with a pha and no EBOUNDS is set
Ensure we can't filter on energy or wavelength since there's no
EBOUNDS information. This behavior was seen when writing
test_rmfmodelpha_call, so a test was written for it.
The code used to raise a DataErr but now just displays a
logged warning.
"""
estep = 0.01
egrid = np.arange(0.01, 0.06, estep)
rdata = create_delta_rmf(egrid[:-1], egrid[1:])
channels = np.arange(1, 5, dtype=np.int16)
counts = np.asarray([10, 5, 12, 7], dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts)
pha.set_rmf(rdata)
pha.set_analysis(analysis)
with caplog.at_level(logging.INFO, logger='sherpa'):
pha.notice(0.025, 0.045, ignore=False)
assert len(caplog.records) == 1
log_name, log_level, message = caplog.record_tuples[0]
assert log_name == 'sherpa.astro.data'
assert log_level == logging.INFO
assert message == 'Skipping dataset test-pha: RMF does not specify energy bins'
def test_stats_calc_stat_wstat_diffbins():
"""wstat statistic fails when src/bg bin sizes do not match"""
statobj = WStat()
data, model = setup_single_pha(True, False, background=True)
# Tweak data to have one-less bin than the background. This
# used to be easy but with data validation we need to
# create a new object.
#
data2 = DataPHA("faked",
channel=data.channel[:-1],
counts=data.counts[:-1],
staterror=data.staterror[:-1],
grouping=data.grouping[:-1],
exposure=data.exposure,
backscal=data.backscal,
areascal=data.areascal)
# We might expect the ARF/RMF calls to fail if we add validation
# (to check the ARF/RMF is valid for the PHA dataset).
#
data2.set_arf(data.get_arf())
data2.set_rmf(data.get_rmf())
data2.set_background(data.get_background())
# There is no Sherpa error for this, which seems surprising
with pytest.raises(TypeError) as err:
statobj.calc_stat(data2, model)
assert str(
err.value) == "input array sizes do not match, data: 5 vs group: 4"
def test_has_pha_response():
"""Check the examples from the docstring"""
exposure = 200.1
rdata = create_non_delta_rmf()
specresp = create_non_delta_specresp()
adata = create_arf(rdata.energ_lo,
rdata.energ_hi,
specresp,
exposure=exposure)
nchans = rdata.e_min.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_arf(adata)
pha.set_rmf(rdata)
rsp = Response1D(pha)
m1 = Gauss1D()
m2 = PowLaw1D()
assert not has_pha_response(m1)
assert has_pha_response(rsp(m1))
assert not has_pha_response(m1 + m2)
assert has_pha_response(rsp(m1 + m2))
assert has_pha_response(m1 + rsp(m2))
# reflexivity check
assert has_pha_response(rsp(m1) + m2)
assert has_pha_response(rsp(m1) + rsp(m2))
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_rspmodelpha_delta_call(ignore):
"""What happens calling a rsp with a pha (RMF is a delta fn)?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
estep = 0.025
egrid = np.arange(0.1, 0.8, estep)
elo = egrid[:-1]
ehi = egrid[1:]
specresp = 2.4 * np.ones(elo.size, dtype=np.float32)
specresp[2:5] = 0.0
specresp[16:19] = 3.2
adata = create_arf(elo, ehi, specresp, exposure=exposure)
rdata = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
nchans = elo.size
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
de = estep * 0.9
e0 = egrid[ignore]
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# The model is evaluated on the RMF grid, not whatever
# is sent in. It is also integrated across the bins,
# which is why there is a multiplication by the
# grid width (for this constant model).
#
# Note that the filter doesn't change the grid.
#
de = egrid[1:] - egrid[:-1]
expected = constant * specresp * de
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rsp_no_arf_matrix_call(analysis, phaexp):
"""Check out Response1D with matrix but no ARF
analysis is the analysis setting
arfexp determines whether the arf has an exposure time
phaexp determines whether the PHA has an exposure time
"""
if phaexp:
pha_exposure = 220.9
else:
pha_exposure = None
if phaexp:
exposure = pha_exposure
mdl_label = '({} * flat)'.format(exposure)
else:
exposure = 1.0
mdl_label = 'flat'
rdata = create_non_delta_rmf()
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
# Turn off integration on this model, so that it is not integrated
# across the bin width.
#
mdl.integrate = False
nchans = rdata.e_min.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=pha_exposure)
pha.set_rmf(rdata)
rsp = Response1D(pha)
wrapped = rsp(mdl)
assert isinstance(wrapped, ArithmeticModel)
expname = 'apply_rmf({})'.format(mdl_label)
assert wrapped.name == expname
modvals = exposure * constant * np.ones(rdata.energ_lo.size)
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
pha.set_analysis(analysis)
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rspmodelpha_delta_call(ignore):
"""What happens calling a rsp with a pha (RMF is a delta fn)?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
estep = 0.025
egrid = np.arange(0.1, 0.8, estep)
elo = egrid[:-1]
ehi = egrid[1:]
specresp = 2.4 * np.ones(elo.size, dtype=np.float32)
specresp[2:5] = 0.0
specresp[16:19] = 3.2
adata = create_arf(elo, ehi, specresp, exposure=exposure)
rdata = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
nchans = elo.size
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
de = estep * 0.9
e0 = egrid[ignore]
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# The model is evaluated on the RMF grid, not whatever
# is sent in. It is also integrated across the bins,
# which is why there is a multiplication by the
# grid width (for this constant model).
#
# Note that the filter doesn't change the grid.
#
de = egrid[1:] - egrid[:-1]
expected = constant * specresp * de
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rsp_no_arf_matrix_call(analysis, phaexp):
"""Check out Response1D with matrix but no ARF
analysis is the analysis setting
arfexp determines whether the arf has an exposure time
phaexp determines whether the PHA has an exposure time
"""
if phaexp:
pha_exposure = 220.9
else:
pha_exposure = None
if phaexp:
exposure = pha_exposure
mdl_label = '({} * flat)'.format(exposure)
else:
exposure = 1.0
mdl_label = 'flat'
rdata = create_non_delta_rmf()
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
# Turn off integration on this model, so that it is not integrated
# across the bin width.
#
mdl.integrate = False
nchans = rdata.e_min.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=pha_exposure)
pha.set_rmf(rdata)
rsp = Response1D(pha)
wrapped = rsp(mdl)
assert isinstance(wrapped, ArithmeticModel)
expname = 'apply_rmf({})'.format(mdl_label)
assert wrapped.name == expname
modvals = exposure * constant * np.ones(rdata.energ_lo.size)
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
pha.set_analysis(analysis)
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rsp1d_matrix_pha_zero_energy_bin():
"""What happens when the first bin starts at 0, with replacement.
Unlike test_rsp1d_delta_pha_zero_energy_bin this directly
calls Response1D to create the model.
"""
ethresh = 1.0e-5
rdata = create_non_delta_rmf()
# hack the first bin to have 0 energy
rdata.energ_lo[0] = 0.0
# PHA and ARF have different exposure ties
exposure_arf = 0.1
exposure_pha = 2.4
specresp = create_non_delta_specresp()
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
adata = create_arf(rdata.energ_lo,
rdata.energ_hi,
specresp,
exposure=exposure_arf,
ethresh=ethresh)
validate_zero_replacement(ws, 'ARF', 'user-arf', ethresh)
nchans = rdata.e_min.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure_pha)
pha.set_rmf(rdata)
pha.set_arf(adata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
rsp = Response1D(pha)
wrapped = rsp(mdl)
# Evaluate the statistic / model. The value was calculated using
# commit a65fb94004664eab219cc09652172ffe1dad80a6 on a linux
# system (Ubuntu 17.04).
#
f = Fit(pha, wrapped)
ans = f.calc_stat()
assert ans == pytest.approx(37971.8716151947)
def test_rspmodelpha_matrix_call(ignore):
"""What happens calling a rsp with a pha (RMF is a matrix)?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
rdata = create_non_delta_rmf()
specresp = create_non_delta_specresp()
elo = rdata.energ_lo
ehi = rdata.energ_hi
adata = create_arf(elo, ehi, specresp, exposure=exposure)
nchans = rdata.e_min.size
constant = 22.3
slope = -1.2
mdl = Polynom1D('sloped')
mdl.c0 = constant
mdl.c1 = slope
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
e0 = rdata.e_min[ignore]
e1 = rdata.e_max[ignore]
de = 0.9 * (e1 - e0)
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# The filter does not change the grid
modvals = specresp * mdl(rdata.energ_lo, rdata.energ_hi)
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rmfmodelpha_matrix_call(ignore):
"""What happens calling an rmf (matrix) with a pha?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
rdata = create_non_delta_rmf()
elo = rdata.e_min
ehi = rdata.e_max
nchans = elo.size
constant = 12.2
slope = 0.01
mdl = Polynom1D('not-flat')
mdl.c0 = constant
mdl.c1 = slope
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
e0 = elo[ignore]
e1 = ehi[ignore]
de = 0.9 * (e1 - e0)
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RMFModelPHA(rdata, pha, mdl)
# Note that the evaluation ignores any filter we've applied.
# and the exposure time is not used.
#
modvals = mdl(rdata.energ_lo, rdata.energ_hi)
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rsp1d_matrix_pha_zero_energy_bin():
"""What happens when the first bin starts at 0, with replacement.
Unlike test_rsp1d_delta_pha_zero_energy_bin this directly
calls Response1D to create the model.
"""
ethresh = 1.0e-5
rdata = create_non_delta_rmf()
# hack the first bin to have 0 energy
rdata.energ_lo[0] = 0.0
# PHA and ARF have different exposure ties
exposure_arf = 0.1
exposure_pha = 2.4
specresp = create_non_delta_specresp()
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
adata = create_arf(rdata.energ_lo,
rdata.energ_hi,
specresp,
exposure=exposure_arf,
ethresh=ethresh)
validate_zero_replacement(ws, 'ARF', 'user-arf', ethresh)
nchans = rdata.e_min.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure_pha)
pha.set_rmf(rdata)
pha.set_arf(adata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
rsp = Response1D(pha)
wrapped = rsp(mdl)
# Evaluate the statistic / model. The value was calculated using
# commit a65fb94004664eab219cc09652172ffe1dad80a6 on a linux
# system (Ubuntu 17.04).
#
f = Fit(pha, wrapped)
ans = f.calc_stat()
assert ans == pytest.approx(37971.8716151947)
def test_rspmodelpha_matrix_call(ignore):
"""What happens calling a rsp with a pha (RMF is a matrix)?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
rdata = create_non_delta_rmf()
specresp = create_non_delta_specresp()
elo = rdata.energ_lo
ehi = rdata.energ_hi
adata = create_arf(elo, ehi, specresp, exposure=exposure)
nchans = rdata.e_min.size
constant = 22.3
slope = -1.2
mdl = Polynom1D('sloped')
mdl.c0 = constant
mdl.c1 = slope
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
e0 = rdata.e_min[ignore]
e1 = rdata.e_max[ignore]
de = 0.9 * (e1 - e0)
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# The filter does not change the grid
modvals = specresp * mdl(rdata.energ_lo, rdata.energ_hi)
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rsp1d_delta_pha_zero_energy_bin():
"What happens when the first bin starts at 0, with replacement"
ethresh = 2.0e-7
# PHA and ARF have different exposure ties
exposure1 = 0.1
exposure2 = 2.4
egrid = np.asarray([0.0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.8])
elo = egrid[:-1]
ehi = egrid[1:]
specresp = np.asarray([10.2, 9.8, 10.0, 12.0, 8.0, 10.0])
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
adata = create_arf(elo,
ehi,
specresp,
exposure=exposure1,
ethresh=ethresh)
validate_zero_replacement(ws, 'ARF', 'user-arf', ethresh)
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
rdata = create_delta_rmf(elo, ehi, ethresh=ethresh)
validate_zero_replacement(ws, 'RMF', 'delta-rmf', ethresh)
channels = np.arange(1, 7, dtype=np.int16)
counts = np.ones(6, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure2)
pha.set_rmf(rdata)
pha.set_arf(adata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
tmdl = PowLaw1D()
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
out = wrapped([0.1, 0.2])
elo[0] = ethresh
expected = specresp * tmdl(elo, ehi)
assert_allclose(out, expected)
assert not np.isnan(out[0])
def test_rmfmodelpha_matrix_call(ignore):
"""What happens calling an rmf (matrix) with a pha?
The ignore value gives the channel to ignore (counting from 0).
"""
exposure = 200.1
rdata = create_non_delta_rmf()
elo = rdata.e_min
ehi = rdata.e_max
nchans = elo.size
constant = 12.2
slope = 0.01
mdl = Polynom1D('not-flat')
mdl.c0 = constant
mdl.c1 = slope
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
# force energy units (only needed if ignore is set)
pha.set_analysis('energy')
if ignore is not None:
e0 = elo[ignore]
e1 = ehi[ignore]
de = 0.9 * (e1 - e0)
pha.notice(lo=e0, hi=e0 + de, ignore=True)
# The assert are intended to help people reading this
# code rather than being a useful check that the code
# is working.
mask = [True] * nchans
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = RMFModelPHA(rdata, pha, mdl)
# Note that the evaluation ignores any filter we've applied.
# and the exposure time is not used.
#
modvals = mdl(rdata.energ_lo, rdata.energ_hi)
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rspmodelpha_matrix_call_xspec():
"""Check XSPEC constant is invariant to wavelength/energy setting.
As XSPEC models internally convert from Angstrom to keV,
do a simple check here.
"""
exposure = 200.1
rdata = create_non_delta_rmf()
specresp = create_non_delta_specresp()
adata = create_arf(rdata.energ_lo,
rdata.energ_hi,
specresp,
exposure=exposure)
constant = 2.3
mdl = XSconstant('flat')
mdl.factor = constant
nchans = rdata.e_min.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
# The set_arf call isn't necessary, but leave in
pha.set_arf(adata)
pha.set_rmf(rdata)
# The XSPEC models are evaluated on an energy grid, even when
# the analysis setting is wavelength. Also, unlike the Sherpa
# Constant model, the XSPEC XSconstant model is defined
# over the integrated bin, so no correction is needed for the
# bin width.
#
modvals = constant * specresp
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
pha.set_analysis('wave')
out_wl = wrapped([4, 5])
assert_allclose(out_wl, expected)
pha.set_analysis('energy')
out_en = wrapped([4, 5])
assert_allclose(out_en, expected)
def test_rspmodelpha_matrix_call_xspec():
"""Check XSPEC constant is invariant to wavelength/energy setting.
As XSPEC models internally convert from Angstrom to keV,
do a simple check here.
"""
exposure = 200.1
rdata = create_non_delta_rmf()
specresp = create_non_delta_specresp()
adata = create_arf(rdata.energ_lo,
rdata.energ_hi,
specresp,
exposure=exposure)
constant = 2.3
mdl = XSconstant('flat')
mdl.factor = constant
nchans = rdata.e_min.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
# The set_arf call isn't necessary, but leave in
pha.set_arf(adata)
pha.set_rmf(rdata)
# The XSPEC models are evaluated on an energy grid, even when
# the analysis setting is wavelength. Also, unlike the Sherpa
# Constant model, the XSPEC XSconstant model is defined
# over the integrated bin, so no correction is needed for the
# bin width.
#
modvals = constant * specresp
matrix = get_non_delta_matrix()
expected = np.matmul(modvals, matrix)
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
pha.set_analysis('wave')
out_wl = wrapped([4, 5])
assert_allclose(out_wl, expected)
pha.set_analysis('energy')
out_en = wrapped([4, 5])
assert_allclose(out_en, expected)
def test_rsp1d_delta_pha_zero_energy_bin():
"What happens when the first bin starts at 0, with replacement"
ethresh = 2.0e-7
# PHA and ARF have different exposure ties
exposure1 = 0.1
exposure2 = 2.4
egrid = np.asarray([0.0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.8])
elo = egrid[:-1]
ehi = egrid[1:]
specresp = np.asarray([10.2, 9.8, 10.0, 12.0, 8.0, 10.0])
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
adata = create_arf(elo, ehi, specresp, exposure=exposure1,
ethresh=ethresh)
validate_zero_replacement(ws, 'ARF', 'user-arf', ethresh)
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
rdata = create_delta_rmf(elo, ehi, ethresh=ethresh)
validate_zero_replacement(ws, 'RMF', 'delta-rmf', ethresh)
channels = np.arange(1, 7, dtype=np.int16)
counts = np.ones(6, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure2)
pha.set_rmf(rdata)
pha.set_arf(adata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
tmdl = PowLaw1D()
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
out = wrapped([0.1, 0.2])
elo[0] = ethresh
expected = specresp * tmdl(elo, ehi)
assert_allclose(out, expected)
assert not np.isnan(out[0])
def test_rspmodelpha_delta_call_wave():
"""What happens calling a rsp with a pha (RMF is a delta fn)? Wavelength.
Unlike the energy case no bins are ignored, as this code path
has already been tested.
"""
exposure = 200.1
estep = 0.025
egrid = np.arange(0.1, 0.8, estep)
elo = egrid[:-1]
ehi = egrid[1:]
specresp = 2.4 * np.ones(elo.size, dtype=np.float32)
specresp[2:5] = 0.0
specresp[16:19] = 3.2
adata = create_arf(elo, ehi, specresp, exposure=exposure)
rdata = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
nchans = elo.size
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
pha.set_analysis('wave')
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# Note that this is a Sherpa model, so it's normalization is
# per unit x axis, so when integrated here the bins are in
# Angstroms, so the bin width to multiply by is
# Angstroms, not keV.
#
dl = (DataPHA._hc / elo) - (DataPHA._hc / ehi)
expected = constant * specresp * dl
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rspmodelpha_delta_call_channel():
"""What happens calling a rsp with a pha (RMF is a delta fn)? Channels.
I am not convinced I understand the bin width calculation here,
as it doesn't seem to match the wavelength case.
"""
exposure = 200.1
estep = 0.025
egrid = np.arange(0.1, 0.8, estep)
elo = egrid[:-1]
ehi = egrid[1:]
specresp = 2.4 * np.ones(elo.size, dtype=np.float32)
specresp[2:5] = 0.0
specresp[16:19] = 3.2
adata = create_arf(elo, ehi, specresp, exposure=exposure)
rdata = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
nchans = elo.size
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
pha.set_analysis('channel')
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# Since this is channels you might expect the bin width to be 1,
# but it is actually still dE.
#
de = ehi - elo
expected = constant * specresp * de
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_rspmodelpha_delta_call_wave():
"""What happens calling a rsp with a pha (RMF is a delta fn)? Wavelength.
Unlike the energy case no bins are ignored, as this code path
has already been tested.
"""
exposure = 200.1
estep = 0.025
egrid = np.arange(0.1, 0.8, estep)
elo = egrid[:-1]
ehi = egrid[1:]
specresp = 2.4 * np.ones(elo.size, dtype=np.float32)
specresp[2:5] = 0.0
specresp[16:19] = 3.2
adata = create_arf(elo, ehi, specresp, exposure=exposure)
rdata = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
nchans = elo.size
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
pha.set_analysis('wave')
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# Note that this is a Sherpa model, so it's normalization is
# per unit x axis, so when integrated here the bins are in
# Angstroms, so the bin width to multiply by is
# Angstroms, not keV.
#
dl = (DataPHA._hc / elo) - (DataPHA._hc / ehi)
expected = constant * specresp * dl
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_1209_response(make_data_path):
"""Do we pick up the header keywords from the response?
This is related to issue #1209
"""
# We could set up channels and counts, but let's not.
#
d = DataPHA("dummy", None, None)
assert d.header["TELESCOP"] == "none"
assert d.header["INSTRUME"] == "none"
assert d.header["FILTER"] == "none"
infile = make_data_path(RMFFILE)
rmf = io.read_rmf(infile)
d.set_rmf(rmf)
assert d.header["TELESCOP"] == "ROSAT"
assert d.header["INSTRUME"] == "PSPCC"
assert d.header["FILTER"] == "NONE"
def test_rspmodelpha_delta_call_channel():
"""What happens calling a rsp with a pha (RMF is a delta fn)? Channels.
I am not convinced I understand the bin width calculation here,
as it doesn't seem to match the wavelength case.
"""
exposure = 200.1
estep = 0.025
egrid = np.arange(0.1, 0.8, estep)
elo = egrid[:-1]
ehi = egrid[1:]
specresp = 2.4 * np.ones(elo.size, dtype=np.float32)
specresp[2:5] = 0.0
specresp[16:19] = 3.2
adata = create_arf(elo, ehi, specresp, exposure=exposure)
rdata = create_delta_rmf(elo, ehi, e_min=elo, e_max=ehi)
nchans = elo.size
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
pha.set_analysis('channel')
wrapped = RSPModelPHA(adata, rdata, pha, mdl)
# Since this is channels you might expect the bin width to be 1,
# but it is actually still dE.
#
de = ehi - elo
expected = constant * specresp * de
out = wrapped([4, 5])
assert_allclose(out, expected)
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])
def test_rmfmodelpha_matrix_mismatch(analysis):
"""Check that an error is raised if there's a mismatch.
"""
exposure = 200.1
rdata = create_non_delta_rmf()
# nchans should be rdata.e_min.size for the sizes to match
nchans = rdata.energ_lo.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
with pytest.raises(DataErr) as exc:
pha.set_analysis(analysis)
emsg = "RMF 'non-delta-rmf' is incompatible with PHA dataset 'test-pha'"
assert str(exc.value) == emsg
def test_rmfmodelpha_matrix_mismatch(analysis):
"""Check that an error is raised if there's a mismatch.
"""
exposure = 200.1
rdata = create_non_delta_rmf()
# nchans should be rdata.e_min.size for the sizes to match
nchans = rdata.energ_lo.size
channels = np.arange(1, nchans + 1, dtype=np.int16)
counts = np.ones(nchans, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
with pytest.raises(DataErr) as exc:
pha.set_analysis(analysis)
emsg = "RMF 'non-delta-rmf' is incompatible with PHA dataset 'test-pha'"
assert str(exc.value) == emsg
def test_rmfmodelpha_delta_no_ebounds(analysis):
"""What happens calling an rmf with a pha and no EBOUNDS is set
Ensure we can't filter on energy or wavelength since there's no
EBOUNDS information. This behavior was seen when writing
test_rmfmodelpha_call, so a test was written for it.
"""
estep = 0.01
egrid = np.arange(0.01, 0.06, estep)
rdata = create_delta_rmf(egrid[:-1], egrid[1:])
channels = np.arange(1, 5, dtype=np.int16)
counts = np.asarray([10, 5, 12, 7], dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts)
pha.set_rmf(rdata)
pha.set_analysis(analysis)
with pytest.raises(DataErr) as exc:
pha.notice(0.025, 0.045, ignore=False)
assert str(exc.value) == 'RMF does not specify energy bins'
def test_rmfmodelpha_delta_no_ebounds(analysis):
"""What happens calling an rmf with a pha and no EBOUNDS is set
Ensure we can't filter on energy or wavelength since there's no
EBOUNDS information. This behavior was seen when writing
test_rmfmodelpha_call, so a test was written for it.
"""
estep = 0.01
egrid = np.arange(0.01, 0.06, estep)
rdata = create_delta_rmf(egrid[:-1], egrid[1:])
channels = np.arange(1, 5, dtype=np.int16)
counts = np.asarray([10, 5, 12, 7], dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts)
pha.set_rmf(rdata)
pha.set_analysis(analysis)
with pytest.raises(DataErr) as exc:
pha.notice(0.025, 0.045, ignore=False)
assert str(exc.value) == 'RMF does not specify energy bins'
def test_rmf1d_delta_pha_zero_energy_bin():
"What happens when the first bin starts at 0, with replacement"
ethresh = 2e-7
egrid = np.asarray([0.0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.8])
elo = egrid[:-1]
ehi = egrid[1:]
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
rdata = create_delta_rmf(elo, ehi, ethresh=ethresh)
validate_zero_replacement(ws, 'RMF', 'delta-rmf', ethresh)
exposure = 2.4
channels = np.arange(1, 7, dtype=np.int16)
counts = np.ones(6, dtype=np.int16)
pha = DataPHA('test-pha',
channel=channels,
counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
tmdl = PowLaw1D()
wrapped = RMFModelPHA(rdata, pha, mdl)
out = wrapped([0.1, 0.2])
elo[0] = ethresh
expected = tmdl(elo, ehi)
assert_allclose(out, expected)
assert not np.isnan(out[0])
def test_fake_pha_background_pha(reset_seed):
"""Sample from background pha"""
np.random.seed(1234)
data = DataPHA("any", channels, counts, exposure=1000.)
bkg = DataPHA("bkg", channels, bcounts, exposure=2000, backscal=2.5)
data.set_background(bkg, id="used-bkg")
data.set_arf(arf)
data.set_rmf(rmf)
mdl = Const1D("mdl")
mdl.c0 = 0
# Just make sure that the model does not contribute
fake_pha(data, mdl, is_source=True, add_bkgs=False)
assert data.counts.sum() == 0
fake_pha(data, mdl, is_source=True, add_bkgs=True)
# expected is [200, 200, 200]
assert data.counts.sum() > 400
assert data.counts.sum() < 1000
# Add several more backgrounds. Actual background should be average.
# We add 5 background with half the exposure time as this first oned
# and essentially 0 counts. So, we should find 1/11 of the counts
# we found in the last run.
for i in range(5):
bkg = DataPHA("bkg",
channels,
np.ones(3, dtype=np.int16),
exposure=1000,
backscal=2.5)
data.set_background(bkg, id=i)
fake_pha(data, mdl, is_source=True, add_bkgs=True)
# expected is about [18, 18, 18]
assert data.counts.sum() > 10
assert data.counts.sum() < 200
def test_1209_response(mode, make_data_path):
"""Do we pick up the header keywords from the response?
This is related to issue #1209
"""
# We could set up channels and counts, but let's not.
#
d = DataPHA("dummy", None, None)
assert d.header["TELESCOP"] == "none"
assert d.header["INSTRUME"] == "none"
assert d.header["FILTER"] == "none"
# We do not care about the warning messages here from
# ENERG_LO replacement.
#
if "arf" in mode:
infile = make_data_path(ARFFILE)
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("ignore")
arf = io.read_arf(infile)
d.set_arf(arf)
if "rmf" in mode:
infile = make_data_path(RMFFILE)
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("ignore")
rmf = io.read_rmf(infile)
d.set_rmf(rmf)
# The PHA file contains a FILTER keyword but the responses do not.
#
assert d.header["TELESCOP"] == "SWIFT"
assert d.header["INSTRUME"] == "XRT"
assert d.header["FILTER"] == "none"
def test_rmf1d_delta_pha_zero_energy_bin():
"What happens when the first bin starts at 0, with replacement"
ethresh = 2e-7
egrid = np.asarray([0.0, 0.1, 0.2, 0.4, 0.5, 0.7, 0.8])
elo = egrid[:-1]
ehi = egrid[1:]
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always")
rdata = create_delta_rmf(elo, ehi, ethresh=ethresh)
validate_zero_replacement(ws, 'RMF', 'delta-rmf', ethresh)
exposure = 2.4
channels = np.arange(1, 7, dtype=np.int16)
counts = np.ones(6, dtype=np.int16)
pha = DataPHA('test-pha', channel=channels, counts=counts,
exposure=exposure)
pha.set_rmf(rdata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
tmdl = PowLaw1D()
wrapped = RMFModelPHA(rdata, pha, mdl)
out = wrapped([0.1, 0.2])
elo[0] = ethresh
expected = tmdl(elo, ehi)
assert_allclose(out, expected)
assert not np.isnan(out[0])
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]
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_has_valid_ogip_keywords_all_fake(tmp_path, reset_seed):
"""See #1209
When everything is faked, what happens?
"""
np.random.seed(5)
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})
outfile = tmp_path / "sim.pha"
io.write_pha(str(outfile), data, ascii=False)
inpha = io.read_pha(str(outfile))
assert inpha.channel == pytest.approx(channels)
# it is not required that we check counts (that is, we can drop this
# if it turns out not to be repeatable across platforms), but for
# now keep the check.
#
assert inpha.counts == pytest.approx([188, 399, 416])
for field in [
"staterror", "syserror", "bin_lo", "bin_hi", "grouping", "quality"
]:
assert getattr(inpha, field) is None
assert inpha.exposure == pytest.approx(1000.0)
assert inpha.backscal == pytest.approx(1.0)
assert inpha.areascal == pytest.approx(1.0)
assert not inpha.grouped
assert not inpha.subtracted
assert inpha.response_ids == []
assert inpha.background_ids == []
hdr = inpha.header
assert hdr["TELESCOP"] == "none"
assert hdr["INSTRUME"] == "none"
assert hdr["FILTER"] == "none"
for key in [
"EXPOSURE", "AREASCAL", "BACKSCAL", "ANCRFILE", "BACKFILE",
"RESPFILE"
]:
assert key not in hdr
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 setup_single_pha(stat, sys, background=True, areascal="none"):
"""Return a single data set and model.
This is aimed at wstat calculation. The data set is grouped
which is a bit against the ethos of WSTAT (fit all the channels).
Parameters
----------
stat, sys : bool
Should statistical and systematic errors be explicitly set
(True) or taken from the statistic (False)?
background : bool
Should a background data set be included (True) or not (False)?
The background is *not* subtracted when True.
areascal : {'none', 'scalar', 'array'}
Is the AREASCAL set and, if so, to a scalar or array value?
If background is True then it is also applied to the background
data set.
Returns
-------
data, model
DataPHA and Model objects.
"""
# For the array of areascals, ensure that areascal is not
# constant within at least one group
#
areascals = {
'source': {
'none': None,
'scalar': 1.0,
'array': np.asarray([0.9, 0.9, 0.8, 0.9, 0.7], dtype=np.float32)
},
'background': {
'none': None,
'scalar': 0.8,
'array': np.asarray([1.2, 1.2, 1.2, 1.1, 1.4], dtype=np.float32)
}
}
# If used the same bins as setup_single_1dint then could
# re-use the results, but the bins are different, and it
# is useful for the Data1DInt case to test non-consecutive
# histogram bins.
#
channels = np.arange(1, 6, dtype=np.int16)
counts = np.asarray([10, 13, 9, 17, 21], dtype=np.int16)
rlo = channels - 0.5
rhi = channels + 0.5
if stat:
staterror = np.asarray([3.0, 4.0, 3.0, 4.0, 5.0])
else:
staterror = None
if sys:
syserror = 0.2 * counts
else:
syserror = None
grouping = np.asarray([1, -1, 1, -1, 1], dtype=np.int16)
# quality = np.asarray([0, 0, 0, 0, 0], dtype=np.int16)
quality = None
exposure = 150.0
backscal = 0.01
ascal = areascals['source'][areascal]
# does not set areascal or header
data = DataPHA(name='tstpha',
channel=channels,
counts=counts,
staterror=staterror,
syserror=syserror,
grouping=grouping,
quality=quality,
exposure=exposure,
backscal=backscal,
areascal=ascal)
rmf = create_delta_rmf(rlo, rhi, e_min=rlo, e_max=rhi)
data.set_rmf(rmf)
data.units = 'energy'
if background:
bgcounts = np.asarray([2, 1, 0, 2, 2], dtype=np.int16)
if stat:
bgstaterror = np.asarray([0.2, 0.4, 0.5, 0.3, 0.2])
else:
bgstaterror = None
if sys:
bgsyserror = 0.3 * bgcounts
else:
bgsyserror = None
bggrouping = None
bgquality = None
bgexposure = 550.0
bgbackscal = np.asarray([0.05, 0.06, 0.04, 0.04, 0.07])
bgascal = areascals['background'][areascal]
bgdata = DataPHA(name='bgpha',
channel=channels,
counts=bgcounts,
staterror=bgstaterror,
syserror=bgsyserror,
grouping=bggrouping,
quality=bgquality,
exposure=bgexposure,
backscal=bgbackscal,
areascal=bgascal)
data.set_background(bgdata)
# Trying a multi-component model, even though this actual
# model is degenerate (cnst.c0 and poly.c0)
cnst = Const1D('cnst')
poly = Polynom1D('poly')
cnst.c0 = 1.2
poly.c0 = 7.9
poly.c1 = 2.1
poly.c1.frozen = False
mdl = cnst + poly
return data, mdl
