def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', numpy.arange(46, dtype=float)+1.,
numpy.zeros(46),
bin_lo = self._emin, bin_hi = self._emax )
self.pha.units="energy"
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)
class test_filter_energy_grid(SherpaTestCase):
_notice = numpy.ones(46, dtype=bool)
_notice[44:46]=False
_ignore = numpy.zeros(46, dtype=bool)
_ignore[14:33]=True
_emin = numpy.array([
1.46000006e-03, 2.48199999e-01, 3.06600004e-01, 4.67200011e-01,
5.69400012e-01, 6.42400026e-01, 7.00800002e-01, 7.44599998e-01,
7.88399994e-01, 8.17600012e-01, 8.61400008e-01, 8.90600026e-01,
9.49000001e-01, 9.92799997e-01, 1.03659999e+00, 1.09500003e+00,
1.13880002e+00, 1.19719994e+00, 1.28480005e+00, 1.40160000e+00,
1.47459996e+00, 1.60599995e+00, 1.69360006e+00, 1.81040001e+00,
1.89800000e+00, 1.94180000e+00, 2.02940011e+00, 2.08780003e+00,
2.19000006e+00, 2.27760005e+00, 2.39439988e+00, 2.58419991e+00,
2.71560001e+00, 2.86159992e+00, 3.08060002e+00, 3.38720012e+00,
3.56240010e+00, 3.79600000e+00, 4.02960014e+00, 4.24860001e+00,
4.71579981e+00, 5.02239990e+00, 5.37279987e+00, 5.89839983e+00,
6.57000017e+00, 9.86960030e+00], numpy.float)
_emax = numpy.array([
0.2482 , 0.3066 , 0.46720001, 0.56940001, 0.64240003,
0.7008 , 0.7446 , 0.78839999, 0.81760001, 0.86140001,
0.89060003, 0.949 , 0.9928 , 1.03659999, 1.09500003,
1.13880002, 1.19719994, 1.28480005, 1.4016 , 1.47459996,
1.60599995, 1.69360006, 1.81040001, 1.898 , 1.9418 ,
2.02940011, 2.08780003, 2.19000006, 2.27760005, 2.39439988,
2.58419991, 2.71560001, 2.86159992, 3.08060002, 3.38720012,
3.5624001 , 3.796 , 4.02960014, 4.24860001, 4.71579981,
5.0223999 , 5.37279987, 5.89839983, 6.57000017, 9.8696003 ,
14.95040035], numpy.float)
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', numpy.arange(46, dtype=float)+1.,
numpy.zeros(46),
bin_lo = self._emin, bin_hi = self._emax )
self.pha.units="energy"
def tearDown(self):
logger.setLevel(self.old_level)
def test_notice(self):
#clear mask
self.pha.notice()
self.pha.notice(0.0, 6.0)
#self.assertEqual(self._notice, self.pha.mask)
assert (self._notice==numpy.asarray(self.pha.mask)).all()
def test_ignore(self):
#clear mask
self.pha.notice()
self.pha.ignore(0.0, 1.0)
self.pha.ignore(3.0, 15.0)
#self.assertEqual(self._ignore, self.pha.mask)
assert (self._ignore==numpy.asarray(self.pha.mask)).all()
def setUp(self):
self.pha = DataPHA('',
np.arange(204, dtype=float) + 1.,
np.zeros(204),
bin_lo=self._emin,
bin_hi=self._emax)
self.pha.units = "energy"
def test_rsp_normf_error(analysis):
"""Check that an error is raised on set_analysis
"""
exposure = 200.1
# rdata is only used to define the grids
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)
with pytest.raises(DataErr) as exc:
pha.set_analysis(analysis)
emsg = "response incomplete for dataset test-pha, " + \
"check the instrument model"
assert str(exc.value) == emsg
def test_pha_get_ylabel_yfac1(override_plot_backend):
"""Basic check
The label depends on the backend, so we just want the dummy
backend used here. **UNFORTUNATELY** - either because the
override_plot_backend fixture is not well written, the current
approach to setting up the plot backend does not handle it being
swapped out (e.g. see #1191), or a combination of the two - the
test doesn't work well if there is a non-dummy backend loaded.
"""
chans = np.arange(1, 4)
counts = np.ones_like(chans)
pha = DataPHA("dummy", chans, counts)
pha.plot_fac = 1
# This is ugly - hopefully #1191 will fix this
#
ylabel = pha.get_ylabel()
if backend.__name__.endswith('.dummy_backend'):
assert ylabel == 'Counts/channel X Channel^1'
else:
assert ylabel.startswith('Counts/channel X Channel')
assert "1" in ylabel
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
class test_filter_wave_grid(SherpaTestCase):
_notice = np.ones(16384, dtype=bool)
_notice[8465:16384] = False
_ignore = np.zeros(16384, dtype=bool)
_ignore[14064:15984] = True
_emin = np.arange(205.7875, 0.9875, -0.0125)
_emax = _emin + 0.0125
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', np.arange(16384, dtype=float) + 1,
np.zeros(16384),
bin_lo=self._emin,
bin_hi=self._emax)
def tearDown(self):
logger.setLevel(self.old_level)
def test_notice(self):
self.pha.units = 'wavelength'
self.pha.notice()
self.pha.notice(100.0, 225.0)
assert (self._notice == np.asarray(self.pha.mask)).all()
def test_ignore(self):
self.pha.units = 'wavelength'
self.pha.notice()
self.pha.ignore(30.01, 225.0)
self.pha.ignore(0.1, 6.0)
assert (self._ignore == np.asarray(self.pha.mask)).all()
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_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_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)
class test_filter_energy_grid(SherpaTestCase):
_notice = numpy.ones(46, dtype=bool)
_notice[44:46]=False
_ignore = numpy.zeros(46, dtype=bool)
_ignore[14:33]=True
_emin = numpy.array([
1.46000006e-03, 2.48199999e-01, 3.06600004e-01, 4.67200011e-01,
5.69400012e-01, 6.42400026e-01, 7.00800002e-01, 7.44599998e-01,
7.88399994e-01, 8.17600012e-01, 8.61400008e-01, 8.90600026e-01,
9.49000001e-01, 9.92799997e-01, 1.03659999e+00, 1.09500003e+00,
1.13880002e+00, 1.19719994e+00, 1.28480005e+00, 1.40160000e+00,
1.47459996e+00, 1.60599995e+00, 1.69360006e+00, 1.81040001e+00,
1.89800000e+00, 1.94180000e+00, 2.02940011e+00, 2.08780003e+00,
2.19000006e+00, 2.27760005e+00, 2.39439988e+00, 2.58419991e+00,
2.71560001e+00, 2.86159992e+00, 3.08060002e+00, 3.38720012e+00,
3.56240010e+00, 3.79600000e+00, 4.02960014e+00, 4.24860001e+00,
4.71579981e+00, 5.02239990e+00, 5.37279987e+00, 5.89839983e+00,
6.57000017e+00, 9.86960030e+00], numpy.float)
_emax = numpy.array([
0.2482 , 0.3066 , 0.46720001, 0.56940001, 0.64240003,
0.7008 , 0.7446 , 0.78839999, 0.81760001, 0.86140001,
0.89060003, 0.949 , 0.9928 , 1.03659999, 1.09500003,
1.13880002, 1.19719994, 1.28480005, 1.4016 , 1.47459996,
1.60599995, 1.69360006, 1.81040001, 1.898 , 1.9418 ,
2.02940011, 2.08780003, 2.19000006, 2.27760005, 2.39439988,
2.58419991, 2.71560001, 2.86159992, 3.08060002, 3.38720012,
3.5624001 , 3.796 , 4.02960014, 4.24860001, 4.71579981,
5.0223999 , 5.37279987, 5.89839983, 6.57000017, 9.8696003 ,
14.95040035], numpy.float)
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', numpy.arange(46, dtype=float)+1.,
numpy.zeros(46),
bin_lo = self._emin, bin_hi = self._emax )
self.pha.units="energy"
def tearDown(self):
logger.setLevel(self.old_level)
def test_notice(self):
#clear mask
self.pha.notice()
self.pha.notice(0.0, 6.0)
#self.assertEqual(self._notice, self.pha.mask)
assert (self._notice==numpy.asarray(self.pha.mask)).all()
def test_ignore(self):
#clear mask
self.pha.notice()
self.pha.ignore(0.0, 1.0)
self.pha.ignore(3.0, 15.0)
#self.assertEqual(self._ignore, self.pha.mask)
assert (self._ignore==numpy.asarray(self.pha.mask)).all()
def calc_wstat_sherpa(mu_sig, n_on, n_off, alpha):
import sherpa.stats as ss
from sherpa.astro.data import DataPHA
from sherpa.models import Const1D
wstat = ss.WStat()
model = Const1D()
model.c0 = mu_sig
data = DataPHA(counts=np.atleast_1d(n_on),
name='dummy',
channel=np.atleast_1d(1),
backscal=1,
exposure=1)
background = DataPHA(counts=np.atleast_1d(n_off),
name='dummy background',
channel=np.atleast_1d(1),
backscal=np.atleast_1d(1. / alpha),
exposure=1)
data.set_background(background, 1)
# Docstring for ``calc_stat``
# https://github.com/sherpa/sherpa/blob/fe8508818662346cb6d9050ba676e23318e747dd/sherpa/stats/__init__.py#L219
stat = wstat.calc_stat(model=model, data=data)
print("Sherpa stat: {}".format(stat[0]))
print("Sherpa fvec: {}".format(stat[1]))
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('',
np.arange(16384, dtype=float) + 1,
np.zeros(16384),
bin_lo=self._emin,
bin_hi=self._emax)
def test_can_not_group_ungrouped():
"""Does setting the grouping setting fail with no data?"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1])
assert not pha.grouped
with pytest.raises(DataErr) as exc:
pha.grouped = True
assert str(exc.value) == "data set 'name' does not specify grouping flags"
def test_error_on_invalid_channel_ungrouped(chan):
"""Does channel access fail when outside the bounds?
For ungrouped data it currently does not, but just
acts as an identity function.
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1])
assert pha._from_channel(chan) == chan
def test_pha_get_ylabel_yfac0():
"""This does not depend on the backend"""
chans = np.arange(1, 4)
counts = np.ones_like(chans)
pha = DataPHA("dummy", chans, counts)
assert pha.plot_fac == 0
assert pha.get_ylabel() == 'Counts/channel'
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_set_analysis_rate_invalid():
"""Just check we error out"""
chans = np.arange(1, 4)
counts = np.ones_like(chans)
pha = DataPHA("dummy", chans, counts)
with pytest.raises(DataErr) as de:
pha.set_analysis("channel", type=None)
assert str(de.value) == "unknown plot type 'None', choose 'rate' or 'counts'"
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_error_on_invalid_channel_grouped(chan, exp1, exp2):
"""Does channel access fail when outside the bounds?
It is not clear what _from_channel is doing here, so
just check the responses.
"""
pha = DataPHA('name', [1, 2, 3], [1, 1, 1],
grouping=[1, -1, 1])
assert pha.grouped
assert pha._from_channel(chan) == exp2
def make_data(data_class):
"""Create a test data object of the given class.
Using a string means it is easier to support the various PHA
"types" - eg basic, grouping, grouping+quality.
"""
x0 = np.asarray([1, 3, 7, 12])
y = np.asarray([2, 3, 4, 5])
if data_class == "1d":
return Data1D('x1', x0, y)
if data_class == "1dint":
return Data1DInt('xint1', x0, np.asarray([3, 5, 8, 15]), y)
chans = np.arange(1, 5)
if data_class == "pha":
return DataPHA('pha', chans, y)
# We want to provide PHA tests that check out the grouping and
# quality handling (but it is not worth trying all different
# variants), so we have "grp" for grouping and no quality [*], and
# "qual" for grouping and quality.
#
# [*] by which I mean we have not called ignore_bad, not that
# there is no quality array.
#
grp = np.asarray([1, -1, 1, 1])
qual = np.asarray([0, 0, 2, 0])
pha = DataPHA('pha', chans, y, grouping=grp, quality=qual)
if data_class == "grp":
return pha
if data_class == "qual":
pha.ignore_bad()
return pha
x0 = np.asarray([1, 2, 3] * 2)
x1 = np.asarray([1, 1, 1, 2, 2, 2])
y = np.asarray([2, 3, 4, 5, 6, 7])
if data_class == "2d":
return Data2D('x2', x0, x1, y, shape=(2, 3))
if data_class == "2dint":
return Data2DInt('xint2', x0, x1, x0 + 1, x1 + 1, y, shape=(2, 3))
if data_class == "img":
return DataIMG('img', x0, x1, y, shape=(2, 3))
if data_class == "imgint":
return DataIMGInt('imgi', x0, x1, x0 + 1, x1 + 1, y, shape=(2, 3))
assert False
def test_pha_ignore_bad_no_quality():
"""Just check we error out"""
chans = np.arange(1, 4)
counts = np.ones_like(chans)
pha = DataPHA("dummy", chans, counts)
with pytest.raises(DataErr) as de:
pha.ignore_bad()
assert str(de.value) == "data set 'dummy' does not specify quality flags"
def test_288_b():
"""The issue from #288 which was failing"""
channels = np.arange(1, 6)
counts = np.asarray([5, 5, 10, 10, 2])
grouping = np.asarray([1, -1, 1, -1, 1], dtype=np.int16)
pha = DataPHA('x', channels, counts, grouping=grouping)
assert pha.mask
pha.ignore(3.1, 4)
assert pha.mask == pytest.approx([True, False, True])
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_288_a_energy():
"""The issue from #288 which was working
test_288_a but with a response so we test energy filters
"""
channels = np.arange(1, 6)
counts = np.asarray([5, 5, 10, 10, 2])
grouping = np.asarray([1, -1, 1, -1, 1], dtype=np.int16)
pha = DataPHA('x', channels, counts, grouping=grouping)
rlo = channels
rhi = channels + 1
rmf = create_delta_rmf(rlo, rhi, e_min=rlo, e_max=rhi)
pha.set_arf(rmf)
pha.set_analysis('energy')
assert pha.mask
pha.ignore(3, 4)
# I use approx because it gives a nice answer, even though
# I want equality not approximation in this test. Fortunately
# with bools the use of approx is okay (it can tell the
# difference between 0 and 1, aka False and True).
#
assert pha.mask == pytest.approx([True, False, True])
def test_416_a():
"""The first test case from issue #416"""
# if y is not a numpy array then group_counts errors out
# with a strange error. Another reason why DataPHA needs
# to validate input
#
x = np.asarray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = np.asarray([0, 0, 0, 2, 1, 1, 0, 0, 0, 0])
pha = DataPHA('416', x, y)
pha.notice(3.5, 6.5)
mask = [False, False, False, True, True, True, False, False, False, False]
assert pha.mask == pytest.approx(mask)
pha.group_counts(3)
# We have a simplified mask
mask = [True, True]
assert pha.mask == pytest.approx(mask)
# the "full" mask can be retrieved with get_mask
mask = [True] * 10
assert pha.get_mask() == pytest.approx(mask)
grouping = [1, -1, -1, -1, -1, 1, -1, -1, -1, -1.]
assert pha.grouping == pytest.approx(grouping)
quality = [0, 0, 0, 0, 0, 2, 2, 2, 2, 2]
assert pha.quality == pytest.approx(quality)
dep = pha.get_dep(filter=True)
assert dep == pytest.approx([3, 1])
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_arfmodelpha_call(ignore):
"""What happens calling an arf with a pha?
The ignore value indicates what channel to ignore (0 means
nothing is ignored). The aim is to check edge effects,
and as there are only a few channels, it was decided to
test all channels.
"""
# Note: the exposure is set in the PHA and ARF, but should not be
# used when evaluating the model; it's value has been
# set to a value that the test will fail it it is.
#
exposure = 200.1
estep = 0.01
egrid = np.arange(0.01, 0.06, estep)
svals = [1.1, 1.2, 1.3, 1.4]
specresp = np.asarray(svals)
adata = create_arf(egrid[:-1], egrid[1:], specresp, exposure=exposure)
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
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,
exposure=exposure)
pha.set_arf(adata)
# 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, True, True, True]
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = ARFModelPHA(adata, pha, mdl)
# The model is evaluated on the ARF 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 * np.asarray(svals) * de
out = wrapped([4, 5])
assert_allclose(out, expected)
def test_xspecvar_zero_handling(bexp, yexp, dyexp):
"""How does XSPEC variance handle 0 in source and/or background?
The values were calculated using XSPEC 12.10.1m (HEASOFT 6.26.1)
using the following commands to create the file foo.dat which
contains (after three 'header' lines) the data 'x 0.5 y dy'
data foo.fits
iplot data
wplot foo.dat
quit
where foo.fits is a fake PHA file set up to have the channel/count
values used below (a CSC-style PHA file was used so that source
and background were in the same file but a separate bgnd PHA
file could also have been used).
"""
stat = Chi2XspecVar()
chans = numpy.arange(1, 10, dtype=numpy.int16)
scnts = numpy.asarray([0, 0, 0, 1, 3, 1, 1, 3, 3], dtype=numpy.int16)
bcnts = numpy.asarray([0, 1, 3, 0, 0, 1, 3, 1, 3], dtype=numpy.int16)
s = DataPHA('src', chans, scnts, exposure=1)
b = DataPHA('bkg', chans, bcnts, exposure=bexp)
s.set_background(b)
s.subtract()
y, dy, other = s.to_fit(staterrfunc=stat.calc_staterror)
assert other is None
assert y == pytest.approx(yexp)
assert dy == pytest.approx(dyexp)
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_pha_no_bkg(subtract):
"""Just check we error out
Given the way the code works, it errors out both ways.
"""
chans = np.arange(1, 4)
counts = np.ones_like(chans)
pha = DataPHA("dummy", chans, counts)
with pytest.raises(DataErr) as de:
pha.subtracted = subtract
assert str(de.value) == "data set 'dummy' does not have any associated backgrounds"
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', np.arange(16384, dtype=float) + 1,
np.zeros(16384),
bin_lo=self._emin,
bin_hi=self._emax)
def setUp(self):
self.old_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
self.pha = DataPHA('', numpy.arange(46, dtype=float)+1.,
numpy.zeros(46),
bin_lo = self._emin, bin_hi = self._emax )
self.pha.units="energy"
def test_arfmodelpha_call(ignore):
"""What happens calling an arf with a pha?
The ignore value indicates what channel to ignore (0 means
nothing is ignored). The aim is to check edge effects,
and as there are only a few channels, it was decided to
test all channels.
"""
# Note: the exposure is set in the PHA and ARF, but should not be
# used when evaluating the model; it's value has been
# set to a value that the test will fail it it is.
#
exposure = 200.1
estep = 0.01
egrid = np.arange(0.01, 0.06, estep)
svals = [1.1, 1.2, 1.3, 1.4]
specresp = np.asarray(svals)
adata = create_arf(egrid[:-1], egrid[1:], specresp,
exposure=exposure)
constant = 2.3
mdl = Const1D('flat')
mdl.c0 = constant
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,
exposure=exposure)
pha.set_arf(adata)
# 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, True, True, True]
mask[ignore] = False
assert (pha.mask == mask).all()
wrapped = ARFModelPHA(adata, pha, mdl)
# The model is evaluated on the ARF 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 * np.asarray(svals) * 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_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_arf1d_pha_zero_energy_bin():
"What happens when the first bin starts at 0, with replacement"
ethresh = 1.0e-10
# Note: the two exposures are different to check which is
# used (the answer is neither, which seems surprising)
#
exposure1 = 0.1
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)
arf = ARF1D(adata)
exposure2 = 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=exposure2)
pha.set_arf(adata)
pha.set_analysis('energy')
mdl = MyPowLaw1D()
tmdl = PowLaw1D()
wrapped = ARFModelPHA(arf, 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_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(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_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_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_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_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 get_data(mu_sig, n_on, n_off, alpha):
from sherpa.astro.data import DataPHA
from sherpa.models import Const1D
model = Const1D()
model.c0 = mu_sig
data = DataPHA(
counts=np.atleast_1d(n_on),
name="dummy",
channel=np.atleast_1d(1),
backscal=1,
exposure=1,
)
background = DataPHA(
counts=np.atleast_1d(n_off),
name="dummy background",
channel=np.atleast_1d(1),
backscal=np.atleast_1d(1. / alpha),
exposure=1,
)
data.set_background(background, 1)
return model, data
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_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_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 read_pha(arg, use_errors=False, use_background=False):
"""Create a DataPHA object.
Parameters
----------
arg
The name of the file or a representation of the file
(the type depends on the I/O backend) containing the
PHA data.
use_errors : bool, optional
If the PHA file contains statistical error values for the
count (or count rate) column, should it be read in. This
defaults to ``False``.
use_background : bool, optional
Should the background PHA data (and optional responses) also
be read in and associated with the data set?
Returns
-------
data : sherpa.astro.data.DataPHA
"""
datasets, filename = backend.get_pha_data(arg,
use_background=use_background)
phasets = []
output_once = True
for data in datasets:
if not use_errors:
if data['staterror'] is not None or data['syserror'] is not None:
if data['staterror'] is None:
msg = 'systematic'
elif data['syserror'] is None:
msg = 'statistical'
if output_once:
wmsg = "systematic errors were not found in " + \
"file '{}'".format(filename)
warning(wmsg)
else:
msg = 'statistical and systematic'
if output_once:
imsg = msg + " errors were found in file " + \
"'{}' \nbut not used; ".format(filename) + \
"to use them, re-read with use_errors=True"
info(imsg)
data['staterror'] = None
data['syserror'] = None
dname = os.path.dirname(filename)
albl = 'ARF'
rlbl = 'RMF'
if use_background:
albl = albl + ' (background)'
rlbl = rlbl + ' (background)'
arf = _read_ancillary(data, 'arffile', albl, dname, read_arf,
output_once)
rmf = _read_ancillary(data, 'rmffile', rlbl, dname, read_rmf,
output_once)
backgrounds = []
if data['backfile'] and data['backfile'].lower() != 'none':
try:
if os.path.dirname(data['backfile']) == '':
data['backfile'] = os.path.join(os.path.dirname(filename),
data['backfile'])
bkg_datasets = []
# Do not read backgrounds of backgrounds
if not use_background:
bkg_datasets = read_pha(data['backfile'], use_errors, True)
if output_once:
info('read background file {}'.format(
data['backfile']))
if numpy.iterable(bkg_datasets):
for bkg_dataset in bkg_datasets:
if bkg_dataset.get_response() == (None, None) and \
rmf is not None:
bkg_dataset.set_response(arf, rmf)
backgrounds.append(bkg_dataset)
else:
if bkg_datasets.get_response() == (None, None) and \
rmf is not None:
bkg_datasets.set_response(arf, rmf)
backgrounds.append(bkg_datasets)
except:
if output_once:
warning(str(sys.exc_info()[1]))
for bkg_type, bscal_type in izip(('background_up', 'background_down'),
('backscup', 'backscdn')):
if data[bkg_type] is not None:
b = DataPHA(filename,
channel=data['channel'],
counts=data[bkg_type],
bin_lo=data['bin_lo'],
bin_hi=data['bin_hi'],
grouping=data['grouping'],
quality=data['quality'],
exposure=data['exposure'],
backscal=data[bscal_type],
header=data['header'])
b.set_response(arf, rmf)
if output_once:
info("read {} into a dataset from file {}".format(
bkg_type, filename))
backgrounds.append(b)
for k in ['backfile', 'arffile', 'rmffile', 'backscup', 'backscdn',
'background_up', 'background_down']:
data.pop(k, None)
pha = DataPHA(filename, **data)
pha.set_response(arf, rmf)
for id, b in enumerate(backgrounds):
if b.grouping is None:
b.grouping = pha.grouping
b.grouped = (b.grouping is not None)
if b.quality is None:
b.quality = pha.quality
pha.set_background(b, id + 1)
# set units *after* bkgs have been set
pha._set_initial_quantity()
phasets.append(pha)
output_once = False
if len(phasets) == 1:
phasets = phasets[0]
return phasets
class test_filter_energy_grid_reversed(SherpaTestCase):
_notice = numpy.zeros(204, dtype=bool)
_notice[0:42]=True
_ignore = numpy.ones(204, dtype=bool)
_ignore[66:70]=False
_ignore[0:17]=False
_emin = numpy.array([
2.39196181, 2.35973215, 2.34076023, 2.30973101, 2.2884388 ,
2.25861454, 2.22371697, 2.20662117, 2.18140674, 2.14317489,
2.12185216, 2.09055495, 2.06256914, 2.04509854, 2.02788448,
2.00133967, 1.97772908, 1.96379483, 1.93868744, 1.91855776,
1.89444292, 1.87936974, 1.85819471, 1.84568763, 1.82923627,
1.78920078, 1.77360916, 1.76206875, 1.74499893, 1.73006463,
1.70084822, 1.6883322 , 1.67772949, 1.65171933, 1.63476169,
1.59687376, 1.5745424 , 1.55736887, 1.54051399, 1.52546024,
1.50043869, 1.48890531, 1.47329199, 1.46072423, 1.44289041,
1.43344045, 1.41616774, 1.40441585, 1.3979584 , 1.38773119,
1.37138033, 1.35170007, 1.33725214, 1.33249414, 1.31839108,
1.30797839, 1.29657102, 1.28310275, 1.26550889, 1.25471842,
1.24513853, 1.23672664, 1.22944438, 1.21509433, 1.21003771,
1.20401597, 1.19705439, 1.18722582, 0.90194935, 0.89519638,
0.88912934, 0.88492262, 0.87837797, 0.87366825, 0.8689999 ,
0.86437255, 0.85693878, 0.84793305, 0.84404182, 0.83580172,
0.82876647, 0.82395256, 0.81865752, 0.81185687, 0.80004948,
0.79450154, 0.78852075, 0.77920061, 0.77340651, 0.76626247,
0.76202762, 0.75783074, 0.75413191, 0.74727529, 0.74321008,
0.73474538, 0.73166627, 0.72687 , 0.71785438, 0.71488959,
0.71068853, 0.70199603, 0.69832331, 0.69387686, 0.68788701,
0.68354762, 0.67847627, 0.67117327, 0.66512167, 0.66175646,
0.65620857, 0.6518243 , 0.64605182, 0.64142239, 0.63754696,
0.63128632, 0.62478495, 0.62006336, 0.61440694, 0.60915887,
0.60591549, 0.60078359, 0.5938406 , 0.59103745, 0.58488411,
0.58124125, 0.57883304, 0.57406437, 0.57023615, 0.56442606,
0.56041539, 0.55701393, 0.55392498, 0.55030966, 0.54346251,
0.53728294, 0.53515989, 0.5291304 , 0.52448714, 0.51990861,
0.51589233, 0.50996011, 0.50509953, 0.49889025, 0.49512967,
0.49003205, 0.48888513, 0.48524383, 0.48164544, 0.47720695,
0.47283325, 0.46916556, 0.46660379, 0.46280268, 0.45925769,
0.45514211, 0.45290345, 0.44987884, 0.44589564, 0.44333643,
0.44099477, 0.43790293, 0.43446559, 0.43088335, 0.42605683,
0.42131537, 0.41826019, 0.41506338, 0.41155648, 0.40895697,
0.40502119, 0.40400422, 0.40164718, 0.39864835, 0.39584854,
0.39389083, 0.39130434, 0.38890362, 0.38526753, 0.38292497,
0.38075879, 0.37891743, 0.37648395, 0.37557775, 0.37347662,
0.37154216, 0.36742872, 0.3641032 , 0.36167556, 0.35983625,
0.35634032, 0.35248783, 0.35085678, 0.34843227, 0.34669766,
0.34418666, 0.33912122, 0.33720407, 0.33505177, 0.33279634,
0.33081138, 0.32847831, 0.32592943, 0.3111549 ], numpy.float)
_emax = numpy.array([
3.06803656, 2.39196181, 2.35973215, 2.34076023, 2.30973101,
2.2884388 , 2.25861454, 2.22371697, 2.20662117, 2.18140674,
2.14317489, 2.12185216, 2.09055495, 2.06256914, 2.04509854,
2.02788448, 2.00133967, 1.97772908, 1.96379483, 1.93868744,
1.91855776, 1.89444292, 1.87936974, 1.85819471, 1.84568763,
1.82923627, 1.78920078, 1.77360916, 1.76206875, 1.74499893,
1.73006463, 1.70084822, 1.6883322 , 1.67772949, 1.65171933,
1.63476169, 1.59687376, 1.5745424 , 1.55736887, 1.54051399,
1.52546024, 1.50043869, 1.48890531, 1.47329199, 1.46072423,
1.44289041, 1.43344045, 1.41616774, 1.40441585, 1.3979584 ,
1.38773119, 1.37138033, 1.35170007, 1.33725214, 1.33249414,
1.31839108, 1.30797839, 1.29657102, 1.28310275, 1.26550889,
1.25471842, 1.24513853, 1.23672664, 1.22944438, 1.21509433,
1.21003771, 1.20401597, 1.19705439, 1.18722582, 0.90194935,
0.89519638, 0.88912934, 0.88492262, 0.87837797, 0.87366825,
0.8689999 , 0.86437255, 0.85693878, 0.84793305, 0.84404182,
0.83580172, 0.82876647, 0.82395256, 0.81865752, 0.81185687,
0.80004948, 0.79450154, 0.78852075, 0.77920061, 0.77340651,
0.76626247, 0.76202762, 0.75783074, 0.75413191, 0.74727529,
0.74321008, 0.73474538, 0.73166627, 0.72687 , 0.71785438,
0.71488959, 0.71068853, 0.70199603, 0.69832331, 0.69387686,
0.68788701, 0.68354762, 0.67847627, 0.67117327, 0.66512167,
0.66175646, 0.65620857, 0.6518243 , 0.64605182, 0.64142239,
0.63754696, 0.63128632, 0.62478495, 0.62006336, 0.61440694,
0.60915887, 0.60591549, 0.60078359, 0.5938406 , 0.59103745,
0.58488411, 0.58124125, 0.57883304, 0.57406437, 0.57023615,
0.56442606, 0.56041539, 0.55701393, 0.55392498, 0.55030966,
0.54346251, 0.53728294, 0.53515989, 0.5291304 , 0.52448714,
0.51990861, 0.51589233, 0.50996011, 0.50509953, 0.49889025,
0.49512967, 0.49003205, 0.48888513, 0.48524383, 0.48164544,
0.47720695, 0.47283325, 0.46916556, 0.46660379, 0.46280268,
0.45925769, 0.45514211, 0.45290345, 0.44987884, 0.44589564,
0.44333643, 0.44099477, 0.43790293, 0.43446559, 0.43088335,
0.42605683, 0.42131537, 0.41826019, 0.41506338, 0.41155648,
0.40895697, 0.40502119, 0.40400422, 0.40164718, 0.39864835,
0.39584854, 0.39389083, 0.39130434, 0.38890362, 0.38526753,
0.38292497, 0.38075879, 0.37891743, 0.37648395, 0.37557775,
0.37347662, 0.37154216, 0.36742872, 0.3641032 , 0.36167556,
0.35983625, 0.35634032, 0.35248783, 0.35085678, 0.34843227,
0.34669766, 0.34418666, 0.33912122, 0.33720407, 0.33505177,
0.33279634, 0.33081138, 0.32847831, 0.32592943], numpy.float)
def setUp(self):
#self.old_level = logger.getEffectiveLevel()
#logger.setLevel(logging.ERROR)
self.pha = DataPHA('', numpy.arange(204, dtype=float)+1.,
numpy.zeros(204),
bin_lo = self._emin, bin_hi = self._emax )
self.pha.units="energy"
def tearDown(self):
#logger.setLevel(self.old_level)
pass
def test_notice(self):
#clear mask
self.pha.notice()
self.pha.notice(4., 8.3)
assert (self._notice==numpy.asarray(self.pha.mask)).all()
def test_ignore(self):
#clear mask
self.pha.notice()
self.pha.ignore(10.3, 13.8)
self.pha.ignore(4.6, 6.2)
assert (self._ignore==numpy.asarray(self.pha.mask)).all()
def read_pha(arg, use_errors=False, use_background=False):
"""
read_pha( filename [, use_errors=False [, use_background=False]] )
read_pha( PHACrate [, use_errors=False [, use_background=False]] )
"""
datasets, filename = backend.get_pha_data(arg,
use_background=use_background)
phasets = []
output_once = True
for data in datasets:
if not use_errors:
if data['staterror'] is not None or data['syserror'] is not None:
if data['staterror'] is None:
msg = 'systematic'
elif data['syserror'] is None:
msg = 'statistical'
if output_once:
wmsg = "systematic errors were not found in " + \
"file '{}'".format(filename)
warning(wmsg)
else:
msg = 'statistical and systematic'
if output_once:
imsg = msg + " errors were found in file " + \
"'{}' \nbut not used; ".format(filename) + \
"to use them, re-read with use_errors=True"
info(imsg)
data['staterror'] = None
data['syserror'] = None
dname = os.path.dirname(filename)
albl = 'ARF'
rlbl = 'RMF'
if use_background:
albl = albl + ' (background)'
rlbl = rlbl + ' (background)'
arf = _read_ancillary(data, 'arffile', albl, dname, read_arf,
output_once)
rmf = _read_ancillary(data, 'rmffile', rlbl, dname, read_rmf,
output_once)
backgrounds = []
if data['backfile'] and data['backfile'].lower() != 'none':
try:
if os.path.dirname(data['backfile']) == '':
data['backfile'] = os.path.join(os.path.dirname(filename),
data['backfile'])
bkg_datasets = []
# Do not read backgrounds of backgrounds
if not use_background:
bkg_datasets = read_pha(data['backfile'], use_errors, True)
if output_once:
info('read background file {}'.format(
data['backfile']))
if numpy.iterable(bkg_datasets):
for bkg_dataset in bkg_datasets:
if bkg_dataset.get_response() == (None, None) and \
rmf is not None:
bkg_dataset.set_response(arf, rmf)
backgrounds.append(bkg_dataset)
else:
if bkg_datasets.get_response() == (None, None) and \
rmf is not None:
bkg_datasets.set_response(arf, rmf)
backgrounds.append(bkg_datasets)
except:
if output_once:
warning(str(sys.exc_info()[1]))
for bkg_type, bscal_type in izip(('background_up', 'background_down'),
('backscup', 'backscdn')):
if data[bkg_type] is not None:
b = DataPHA(filename,
channel=data['channel'],
counts=data[bkg_type],
bin_lo=data['bin_lo'],
bin_hi=data['bin_hi'],
grouping=data['grouping'],
quality=data['quality'],
exposure=data['exposure'],
backscal=data[bscal_type],
header=data['header'])
b.set_response(arf, rmf)
if output_once:
info("read {} into a dataset from file {}".format(
bkg_type, filename))
backgrounds.append(b)
for k in ['backfile', 'arffile', 'rmffile', 'backscup', 'backscdn',
'background_up', 'background_down']:
data.pop(k, None)
pha = DataPHA(filename, **data)
pha.set_response(arf, rmf)
for id, b in enumerate(backgrounds):
if b.grouping is None:
b.grouping = pha.grouping
b.grouped = (b.grouping is not None)
if b.quality is None:
b.quality = pha.quality
pha.set_background(b, id + 1)
# set units *after* bkgs have been set
pha._set_initial_quantity()
phasets.append(pha)
output_once = False
if len(phasets) == 1:
phasets = phasets[0]
return phasets
def test_rsp_normf_call(arfexp, phaexp):
"""Check out Response1D with no RMF.
analysis is the analysis setting
arfexp determines whether the arf has an exposure time
phaexp determines whether the PHA has an exposure time
This only uses the channel setting
"""
# Chose different exposure times for ARF and PHA to see which
# gets picked up.
#
if arfexp:
arf_exposure = 200.1
else:
arf_exposure = None
if phaexp:
pha_exposure = 220.9
else:
pha_exposure = None
if phaexp:
exposure = pha_exposure
mdl_label = '({} * flat)'.format(exposure)
elif arfexp:
exposure = arf_exposure
mdl_label = '({} * flat)'.format(exposure)
else:
exposure = 1.0
mdl_label = 'flat'
# rdata is only used to define the grids
rdata = create_non_delta_rmf()
specresp = create_non_delta_specresp()
adata = create_arf(rdata.energ_lo,
rdata.energ_hi,
specresp,
exposure=arf_exposure)
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_arf(adata)
rsp = Response1D(pha)
wrapped = rsp(mdl)
assert isinstance(wrapped, ArithmeticModel)
expname = 'apply_arf({})'.format(mdl_label)
assert wrapped.name == expname
expected = exposure * constant * specresp
pha.set_analysis('channel')
out = wrapped([4, 5])
assert_allclose(out, expected)
def read_pha(arg, use_errors=False, use_background=False):
"""
read_pha( filename [, use_errors=False [, use_background=False]] )
read_pha( PHACrate [, use_errors=False [, use_background=False]] )
"""
datasets, filename = backend.get_pha_data(arg, use_background=use_background)
phasets = []
output_once = True
for data in datasets:
if not use_errors:
if data["staterror"] is not None or data["syserror"] is not None:
if data["staterror"] is None:
msg = "systematic"
elif data["syserror"] is None:
msg = "statistical"
if output_once:
wmsg = "systematic errors were not found in " + "file '{}'".format(filename)
warning(wmsg)
else:
msg = "statistical and systematic"
if output_once:
imsg = (
msg
+ " errors were found in file "
+ "'{}' \nbut not used; ".format(filename)
+ "to use them, re-read with use_errors=True"
)
info(imsg)
data["staterror"] = None
data["syserror"] = None
dname = os.path.dirname(filename)
albl = "ARF"
rlbl = "RMF"
if use_background:
albl = albl + " (background)"
rlbl = rlbl + " (background)"
arf = _read_ancillary(data, "arffile", albl, dname, read_arf, output_once)
rmf = _read_ancillary(data, "rmffile", rlbl, dname, read_rmf, output_once)
backgrounds = []
if data["backfile"] and data["backfile"].lower() != "none":
try:
if os.path.dirname(data["backfile"]) == "":
data["backfile"] = os.path.join(os.path.dirname(filename), data["backfile"])
bkg_datasets = []
# Do not read backgrounds of backgrounds
if not use_background:
bkg_datasets = read_pha(data["backfile"], use_errors, True)
if output_once:
info("read background file {}".format(data["backfile"]))
if numpy.iterable(bkg_datasets):
for bkg_dataset in bkg_datasets:
if bkg_dataset.get_response() == (None, None) and rmf is not None:
bkg_dataset.set_response(arf, rmf)
backgrounds.append(bkg_dataset)
else:
if bkg_datasets.get_response() == (None, None) and rmf is not None:
bkg_datasets.set_response(arf, rmf)
backgrounds.append(bkg_datasets)
except:
if output_once:
warning(str(sys.exc_info()[1]))
for bkg_type, bscal_type in izip(("background_up", "background_down"), ("backscup", "backscdn")):
if data[bkg_type] is not None:
b = DataPHA(
filename,
channel=data["channel"],
counts=data[bkg_type],
bin_lo=data["bin_lo"],
bin_hi=data["bin_hi"],
grouping=data["grouping"],
quality=data["quality"],
exposure=data["exposure"],
backscal=data[bscal_type],
header=data["header"],
)
b.set_response(arf, rmf)
if output_once:
info("read {} into a dataset from file {}".format(bkg_type, filename))
backgrounds.append(b)
for k in ["backfile", "arffile", "rmffile", "backscup", "backscdn", "background_up", "background_down"]:
data.pop(k, None)
pha = DataPHA(filename, **data)
pha.set_response(arf, rmf)
for id, b in enumerate(backgrounds):
if b.grouping is None:
b.grouping = pha.grouping
b.grouped = b.grouping is not None
if b.quality is None:
b.quality = pha.quality
pha.set_background(b, id + 1)
# set units *after* bkgs have been set
pha._set_initial_quantity()
phasets.append(pha)
output_once = False
if len(phasets) == 1:
phasets = phasets[0]
return phasets
def setUp(self):
self.pha = DataPHA('', np.arange(204, dtype=float) + 1.,
np.zeros(204),
bin_lo=self._emin,
bin_hi=self._emax)
self.pha.units = "energy"
