def test_cache_copy(self):
# fake up a PHA data set
chans = numpy.arange(1, 11, dtype=numpy.int8)
counts = numpy.ones(chans.size)
# bin width is not 0.1 but something slightly different
ebins = numpy.linspace(0.1, 1.2, num=chans.size + 1)
elo = ebins[:-1]
ehi = ebins[1:]
dset = ui.DataPHA('test', chans, counts)
# make sure ARF isn't 1 to make sure it's being applied
arf = ui.create_arf(elo, ehi, specresp=0.7 * numpy.ones(chans.size))
rmf = ui.create_rmf(elo, ehi, e_min=elo, e_max=ehi)
ui.set_data(1, dset)
ui.set_arf(1, arf)
ui.set_rmf(1, rmf)
ui.set_source(ui.const1d.mdl)
# again not 1
mdl.c0 = 8
# Copy the values from the plot structures, since get_xxx_plot
# returns the same object so m1.y == m2.y will not note a difference.
#
d1y = ui.get_data_plot().y.copy()
m1y = ui.get_model_plot().y.copy()
s1y = ui.get_source_plot().y.copy()
d2y = ui.get_data_plot().y.copy()
m2y = ui.get_model_plot().y.copy()
s2y = ui.get_source_plot().y.copy()
rtol = 1.0e-4
atol = 1.0e-4
numpy.testing.assert_allclose(d1y, d2y, rtol, atol)
numpy.testing.assert_allclose(m1y, m2y, rtol, atol)
numpy.testing.assert_allclose(s1y, s2y, rtol, atol)
def _get_plot_data(ids, emin, emax):
all_model = []
all_emodel = []
all_data = []
all_dataxerr = []
all_datayerr = []
all_edata = []
all_ratio = []
all_ratioerr = []
# Get data and model for each spectrum
for sid in ids:
d = shp.get_data_plot(sid)
m = shp.get_model_plot(sid)
e = (m.xhi + m.xlo) / 2
bins = np.concatenate((d.x - d.xerr / 2, [d.x[-1] + d.xerr[-1]]))
model = m.y
model_de = model * (m.xhi - m.xlo)
model_binned, foo1, foo2 = binned_statistic(
e, model_de, bins=bins, statistic="sum"
)
model_binned = model_binned / d.xerr
# delchi = resid/d.yerr
ratio = d.y / model_binned
mask_data = np.logical_and(d.x + d.xerr / 2 >= emin, d.x - d.xerr / 2 <= emax)
mask_model = np.logical_and(e >= emin, e <= emax)
all_model.append(model[mask_model])
all_emodel.append(e[mask_model])
all_data.append(d.y[mask_data])
all_dataxerr.append(d.xerr[mask_data])
all_datayerr.append(d.yerr[mask_data])
all_edata.append(d.x[mask_data])
all_ratio.append(ratio[mask_data])
all_ratioerr.append(d.yerr[mask_data] / model_binned[mask_data])
return (
all_model,
all_emodel,
all_data,
all_dataxerr,
all_datayerr,
all_edata,
all_ratio,
all_ratioerr,
)
def qq_export(id=None, bkg=False, outfile='qq.txt', elow=None, ehigh=None):
"""
Export Q-Q plot into a file for plotting.
:param id: spectrum id to use (see get_bkg_plot/get_data_plot)
:param bkg: whether to use get_bkg_plot or get_data_plot
:param outfile: filename to write results into
:param elow: low energy limit
:param ehigh: low energy limit
Example::
qq.qq_export('bg', outfile='my_bg_qq', elow=0.2, ehigh=10)
"""
# data
d = ui.get_bkg_plot(id=id) if bkg else ui.get_data_plot(id=id)
e = d.x
mask = logical_and(e >= elow, e <= ehigh)
data = d.y[mask].cumsum()
d = ui.get_bkg_model_plot(id=id) if bkg else ui.get_model_plot(id=id)
e = d.xlo
mask = logical_and(e >= elow, e <= ehigh)
e = e[mask]
model = d.y[mask].cumsum()
last_stat = ui.get_stat()
ui.set_stat(ksstat)
ks = ui.calc_stat()
ui.set_stat(cvmstat)
cvm = ui.calc_stat()
ui.set_stat(adstat)
ad = ui.calc_stat()
ui.set_stat(last_stat)
ad = ui.calc_stat()
ui.set_stat('chi2gehrels')
chi2 = ui.calc_stat()
ui.set_stat('cstat')
cstat = ui.calc_stat()
ui.set_stat(last_stat)
stats = dict(ks=ks, cvm=cvm, ad=ad, cstat=cstat, chi2=chi2)
numpy.savetxt(outfile, numpy.transpose([e, data, model]))
json.dump(stats, open(outfile + '.json', 'w'), indent=4)
def qq_export(id=None, bkg=False, outfile='qq.txt', elow=None, ehigh=None):
"""
Export Q-Q plot into a file for plotting.
:param id: spectrum id to use (see get_bkg_plot/get_data_plot)
:param bkg: whether to use get_bkg_plot or get_data_plot
:param outfile: filename to write results into
:param elow: low energy limit
:param ehigh: low energy limit
Example::
qq.qq_export('bg', outfile='my_bg_qq', elow=0.2, ehigh=10)
"""
# data
d = ui.get_bkg_plot(id=id) if bkg else ui.get_data_plot(id=id)
e = d.x
mask = logical_and(e >= elow, e <= ehigh)
data = d.y[mask].cumsum()
d = ui.get_bkg_model_plot(id=id) if bkg else ui.get_model_plot(id=id)
e = d.xlo
mask = logical_and(e >= elow, e <= ehigh)
e = e[mask]
model = d.y[mask].cumsum()
last_stat = ui.get_stat()
ui.set_stat(ksstat)
ks = ui.calc_stat()
ui.set_stat(cvmstat)
cvm = ui.calc_stat()
ui.set_stat(adstat)
ad = ui.calc_stat()
ui.set_stat(last_stat)
ad = ui.calc_stat()
ui.set_stat('chi2gehrels')
chi2 = ui.calc_stat()
ui.set_stat('cstat')
cstat = ui.calc_stat()
ui.set_stat(last_stat)
stats = dict(ks=ks, cvm=cvm, ad=ad, cstat=cstat, chi2=chi2)
numpy.savetxt(outfile, numpy.transpose([e, data, model]))
json.dump(stats, open(outfile + '.json', 'w'), indent=4)
def check_bad_grouping(exp_xlo, exp_xhi, exp_counts, lo1, hi1, lo2, hi2):
"""Common tests from test_grouped_pha_all_badXXX
Sending in two ranges is a bit excessive but easiest
thing to implement
"""
cts = ui.get_counts()
assert cts == pytest.approx([exp_counts])
dplot = ui.get_data_plot()
assert dplot.xlo == pytest.approx([exp_xlo])
assert dplot.xhi == pytest.approx([exp_xhi])
assert dplot.y == pytest.approx([exp_counts])
# ignore all the data
ui.ignore(lo1, hi1)
# can still plot
cts = ui.get_counts()
assert cts == pytest.approx([exp_counts])
cts = ui.get_counts(filter=True)
assert len(cts) == 0
dplot = ui.get_data_plot()
assert len(dplot.xlo) == 0
assert len(dplot.xhi) == 0
assert len(dplot.y) == 0
# ignore does not fail
#
ui.ignore(lo2, hi2)
# we can restore the data
ui.notice(None, None)
cts = ui.get_counts()
assert cts == pytest.approx([exp_counts])
dplot = ui.get_data_plot()
assert dplot.xlo == pytest.approx([exp_xlo])
assert dplot.xhi == pytest.approx([exp_xhi])
assert dplot.y == pytest.approx([exp_counts])
# now ignore the bad channels (ie everything)
#
ui.ignore_bad()
cts = ui.get_counts()
assert len(cts) == 0
dplot = ui.get_data_plot()
assert len(dplot.xlo) == 0
assert len(dplot.xhi) == 0
assert len(dplot.y) == 0
# there's nothing to notice (this line is an example of #790)
ui.notice(lo1, hi1)
cts = ui.get_counts()
assert len(cts) == 0
dplot = ui.get_data_plot()
assert len(dplot.xlo) == 0
assert len(dplot.xhi) == 0
assert len(dplot.y) == 0