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_pha_get_filter_checks_ungrouped(chtype, expected, args):
"""Check we get the filter we expect
chtype is channel, energy, or wavelength
expected is the expected response
args is a list of 3-tuples of (flag, loval, hival) where
flag is True for notice and False for ignore; they define
the filter to apply
"""
chans = np.arange(1, 11, dtype=int)
counts = np.ones(10, dtype=int)
pha = DataPHA('data', chans, counts)
# Use an ARF to create a channel to energy mapping
# The 0.2-2.2 keV range maps to 5.636-61.992 Angstrom
#
egrid = 0.2 * np.arange(1, 12)
arf = DataARF('arf', egrid[:-1], egrid[1:], np.ones(10))
pha.set_arf(arf)
pha.units = chtype
for (flag, lo, hi) in args:
if flag:
pha.notice(lo, hi)
else:
pha.ignore(lo, hi)
assert pha.get_filter(format='%.1f') == expected
def test_sourceplot_wavelength_counts(caplog):
"""test_sourceplot_wavelength but when rate=False is chosen"""
bins = np.arange(0.1, 10.1, 0.1)
data = DataPHA('',
np.arange(10),
np.ones(10),
bin_lo=bins[:-1].copy(),
bin_hi=bins[1:].copy())
data.units = "wave"
data.rate = False
# use a model that is "okay" to use with keV bins
#
m1 = Const1D('bgnd')
m2 = Gauss1D('abs1')
src = 100 * m1 * (1 - m2) * 10000
m1.c0 = 0.01
m2.pos = 5.0
m2.fwhm = 4.0
m2.ampl = 0.1
sp = SourcePlot()
with caplog.at_level(logging.INFO, logger='sherpa'):
sp.prepare(data, src)
assert len(caplog.records) == 0
check_sourceplot_wavelength(sp)
def test_sourceplot_wavelength_facn(factor, caplog):
"""Change plot factor for test_sourceplot_wavelength"""
bins = np.arange(0.1, 10.1, 0.1)
data = DataPHA('',
np.arange(10),
np.ones(10),
bin_lo=bins[:-1].copy(),
bin_hi=bins[1:].copy())
data.units = "wavelength"
data.plot_fac = factor
assert data.rate
m1 = Const1D('bgnd')
m2 = Gauss1D('abs1')
src = 100 * m1 * (1 - m2) * 10000
m1.c0 = 0.01
m2.pos = 5.0
m2.fwhm = 4.0
m2.ampl = 0.1
sp = SourcePlot()
with caplog.at_level(logging.INFO, logger='sherpa'):
sp.prepare(data, src)
assert len(caplog.records) == 0
check_sourceplot_wavelength(sp, factor=factor)
def test_sourceplot_wavelength(caplog):
"""Check we get wavelength units"""
bins = np.arange(0.1, 10.1, 0.1)
data = DataPHA('',
np.arange(10),
np.ones(10),
bin_lo=bins[:-1].copy(),
bin_hi=bins[1:].copy())
data.units = "wave"
# Note that the model evaluation in done in Angstroms
#
m1 = Const1D('bgnd')
m2 = Gauss1D('abs1')
src = 100 * m1 * (1 - m2) * 10000
m1.c0 = 0.01
m2.pos = 5.0
m2.fwhm = 4.0
m2.ampl = 0.1
sp = SourcePlot()
with caplog.at_level(logging.INFO, logger='sherpa'):
sp.prepare(data, src)
assert len(caplog.records) == 0
check_sourceplot_wavelength(sp)
def test_sourceplot_channels(caplog):
"""Although we ask for channels we get energy units"""
bins = np.arange(0.1, 10.1, 0.1)
data = DataPHA('',
np.arange(10),
np.ones(10),
bin_lo=bins[:-1].copy(),
bin_hi=bins[1:].copy())
data.units = "channel"
# use a model that is "okay" to use with keV bins
#
m1 = Const1D('bgnd')
m2 = Gauss1D('abs1')
src = 100 * m1 * (1 - m2) * 10000
m1.c0 = 0.01
m2.pos = 5.0
m2.fwhm = 4.0
m2.ampl = 0.1
sp = SourcePlot()
with caplog.at_level(logging.INFO, logger='sherpa'):
sp.prepare(data, src)
assert len(caplog.records) == 1
lname, lvl, msg = caplog.record_tuples[0]
assert lname == 'sherpa.astro.plot'
assert lvl == logging.WARN
assert msg == 'Channel space is unappropriate for the PHA unfolded source model,\nusing energy.'
check_sourceplot_energy(sp)
def test_sourceplot(caplog):
bins = np.arange(0.1, 10.1, 0.1)
data = DataPHA('',
np.arange(10),
np.ones(10),
bin_lo=bins[:-1].copy(),
bin_hi=bins[1:].copy())
data.units = "energy"
assert data.rate
assert data.plot_fac == 0
# use a model that is "okay" to use with keV bins
#
m1 = Const1D('bgnd')
m2 = Gauss1D('abs1')
src = 100 * m1 * (1 - m2) * 10000
m1.c0 = 0.01
m2.pos = 5.0
m2.fwhm = 4.0
m2.ampl = 0.1
sp = SourcePlot()
with caplog.at_level(logging.INFO, logger='sherpa'):
sp.prepare(data, src)
assert len(caplog.records) == 0
check_sourceplot_energy(sp)
def test_sourceplot():
bins = np.arange(0.1, 10.1, 0.1)
data = DataPHA('', np.arange(10), np.ones(10),
bin_lo=bins[:-1].copy(),
bin_hi=bins[1:].copy())
data.units = "energy"
# use a model that is "okay" to use with keV bins
#
m1 = Const1D('bgnd')
m2 = Gauss1D('abs1')
src = 100 * m1 * (1 - m2) * 10000
m1.c0 = 0.01
m2.pos = 5.0
m2.fwhm = 4.0
m2.ampl = 0.1
sp = SourcePlot()
sp.prepare(data, src)
# add in several asserts to check that something has been
# added to the object
#
assert sp.xlabel == 'Energy (keV)'
# the following depends on the backend
# assert sp.ylabel == 'f(E) Photons/sec/cm$^2$/keV'
assert sp.title == 'Source Model of '
assert sp.xlo == pytest.approx(bins[:-1])
assert sp.xhi == pytest.approx(bins[1:])
# The check of the values is just to check that things are going
# as expected, so the model values have been adjusted so that
# an "integer" check can be used with enough precision to make
# sure that the model is being evaluated correctly, but without
# a very-high-precision check
#
yexp = np.asarray([9998, 9997, 9997, 9997, 9996, 9996, 9995, 9994,
9994, 9993, 9992, 9991, 9990, 9988, 9987, 9985,
9983, 9982, 9980, 9977, 9975, 9973, 9970, 9967,
9964, 9961, 9958, 9955, 9951, 9948, 9944, 9941,
9937, 9934, 9930, 9927, 9923, 9920, 9917, 9914,
9911, 9909, 9907, 9905, 9903, 9902, 9901, 9900,
9900, 9900, 9900, 9901, 9902, 9903, 9905, 9907,
9909, 9911, 9914, 9917, 9920, 9923, 9927, 9930,
9934, 9937, 9941, 9944, 9948, 9951, 9955, 9958,
9961, 9964, 9967, 9970, 9973, 9975, 9977, 9980,
9982, 9983, 9985, 9987, 9988, 9990, 9991, 9992,
9993, 9994, 9994, 9995, 9996, 9996, 9997, 9997,
9997, 9998, 9998])
assert (sp.y.astype(np.int) == yexp).all()
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 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
