def test_sourceplot_facn(factor, caplog, make_basic_datapha):
"""Change plot factor for test_sourceplot"""
data = make_basic_datapha
data.units = "energy"
data.plot_fac = factor
assert data.rate
# 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, factor=factor)
def test_sourceplot_channels(caplog, make_basic_datapha):
"""Although we ask for channels we get energy units"""
data = make_basic_datapha
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_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(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_sourceplot_wavelength(caplog, make_basic_datapha):
"""Check we get wavelength units"""
data = make_basic_datapha
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_wavelength_counts(caplog, make_basic_datapha):
"""test_sourceplot_wavelength but when rate=False is chosen"""
data = make_basic_datapha
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_counts(caplog, make_basic_datapha):
"""test_sourceplot but when rate=False is chosen"""
data = make_basic_datapha
data.units = "energy"
data.rate = False
# 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_energy(sp)
def test_sourceplot(self):
sp = SourcePlot()
sp.prepare(self.data, self.src)
def test_sourceplot(self):
sp = SourcePlot()
sp.prepare(self.data, self.src)