def test_ARFModelPHA(self):
from sherpa.astro import ui
ui.load_pha(self.make_path("3c120_meg_1.pha"))
# remove the RMF to ensure this is an ARF-only analysis
# (which is what is needed to trigger the bug that lead to #699)
ui.get_data().set_rmf(None)
ui.group_counts(20)
ui.notice(0.5, 6)
ui.subtract()
ui.set_model(ui.xsphabs.abs1 * (ui.xsapec.bubble + ui.powlaw1d.p1))
ui.set_xsabund('angr')
ui.set_xsxsect('vern')
abs1.nh = 0.163
abs1.nh.freeze()
p1.ampl = 0.017
p1.gamma = 1.9
bubble.kt = 0.5
bubble.norm = 4.2e-5
tol = 1.0e-2
ui.set_method_opt('ftol', tol)
ui.fit()
result = ui.get_fit_results()
assert result.numpoints == self._fit_using_ARFModelPHA['numpoints']
assert result.dof == self._fit_using_ARFModelPHA['dof']
def test_plot_pvalue(make_data_path, clean_astro_ui, hide_log_output):
fname = make_data_path("qso.pi")
ui.load_pha(fname)
ui.set_stat('cstat')
ui.set_method("neldermead")
ui.group_counts(10)
ui.notice(0.3, 8)
ui.set_model("xsphabs.abs1*xspowerlaw.p1")
ui.set_model("abs1*(p1+gauss1d.g1)")
# move the fit close to the best fit to save a small amount
# of time.
abs1.nh = 0.05
p1.phoindex = 1.28
p1.norm = 2e-4
g1.ampl = 1.8e-5
g1.pos = 3.
ui.freeze(g1.pos)
g1.fwhm = 0.1
ui.freeze(g1.fwhm)
ui.fit()
ui.plot_pvalue(p1, p1 + g1, num=100)
tmp = ui.get_pvalue_results()
assert tmp.null == pytest.approx(210.34566845619273)
assert tmp.alt == pytest.approx(207.66618095925094)
assert tmp.lr == pytest.approx(2.679487496941789)
def test_rsp(self):
fname = self.make_path("qso.pi")
ui.load_pha(fname)
ui.set_stat("chi2xspecvar")
ui.set_method("neldermead")
ui.group_counts(10)
ui.notice(0.3, 8)
ui.set_model("xsphabs.abs1*xspowerlaw.p1")
ui.set_model("abs1*(p1+gauss1d.g1)")
g1.pos = 3.
ui.freeze(g1.pos)
g1.fwhm = 0.1
ui.freeze(g1.fwhm)
ui.set_stat('cstat')
ui.fit()
ui.plot_pvalue(p1, p1 + g1, num=100)
tmp = ui.get_pvalue_results()
expected = [210.34566845619273, 207.66618095925094, 2.679487496941789]
self.compare_results(expected, [tmp.null, tmp.alt, tmp.lr])
def group_setup(make_data_path):
ui.set_stat('wstat')
infile = make_data_path('9774.pi')
ui.load_pha(1, infile)
ui.group_counts(1, 20)
# Unlike the test_wstat_two_scalar case, the grouping
# is not copied over.
# ui.set_grouping(1, bkg_id=1, val=ui.get_grouping(1))
ui.set_source(1, ui.const1d.c1 * ui.powlaw1d.pl1)
# These should be the same as test_wstat_two_scalar
#
ui.set_par("pl1.gamma", 1.7)
ui.set_par("pl1.ampl", 1.6e-4)
ui.set_par("c1.c0", 45)
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile1 = self.make_path('3c273.pi')
infile2 = self.make_path('9774.pi')
ui.load_pha(1, infile1)
ui.load_pha(2, infile2)
# Since 9774.pi isn't grouped, group it. Note that this
# call groups the background to 20 counts per bin. In this
# case we do not want that; instead we want to use the same
# grouping scheme as the source file.
#
# Note: this is related to issue 227
#
ui.group_counts(2, 20)
ui.set_grouping(2, bkg_id=1, val=ui.get_grouping(2))
# There's no need to have the same model in both datasets,
# but assume the same source model can be used, with a
# normalization difference.
#
ui.set_source(1, ui.powlaw1d.pl1)
ui.set_source(2, ui.const1d.c2 * ui.get_source(1))
# The powerlaw slope and normalization are
# intended to be "a reasonable approximation"
# to the data, just to make sure that any statistic
# calculation doesn't blow-up too much.
#
# Note: the model values for 3c273 are slighly different
# to the single-PHA-file case, so stat results are
# slightly different
#
ui.set_par("pl1.gamma", 1.7)
ui.set_par("pl1.ampl", 1.6e-4)
ui.set_par("c2.c0", 45)
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile1 = self.make_path('3c273.pi')
infile2 = self.make_path('9774.pi')
ui.load_pha(1, infile1)
ui.load_pha(2, infile2)
# Since 9774.pi isn't grouped, group it. Note that this
# call groups the background to 20 counts per bin. In this
# case we do not want that; instead we want to use the same
# grouping scheme as the source file.
#
# Note: this is related to issue 227
#
ui.group_counts(2, 20)
ui.set_grouping(2, bkg_id=1, val=ui.get_grouping(2))
# There's no need to have the same model in both datasets,
# but assume the same source model can be used, with a
# normalization difference.
#
ui.set_source(1, ui.powlaw1d.pl1)
ui.set_source(2, ui.const1d.c2 * ui.get_source(1))
# The powerlaw slope and normalization are
# intended to be "a reasonable approximation"
# to the data, just to make sure that any statistic
# calculation doesn't blow-up too much.
#
# Note: the model values for 3c273 are slighly different
# to the single-PHA-file case, so stat results are
# slightly different
#
ui.set_par("pl1.gamma", 1.7)
ui.set_par("pl1.ampl", 1.6e-4)
ui.set_par("c2.c0", 45)
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile = self.make_path('9774.pi')
ui.load_pha(1, infile)
ui.group_counts(1, 20)
# Unlike the test_wstat_two_scalar case, the grouping
# is not copied over.
# ui.set_grouping(1, bkg_id=1, val=ui.get_grouping(1))
ui.set_source(1, ui.const1d.c1 * ui.powlaw1d.pl1)
# These should be the same as test_wstat_two_scalar
#
ui.set_par("pl1.gamma", 1.7)
ui.set_par("pl1.ampl", 1.6e-4)
ui.set_par("c1.c0", 45)
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile = self.make_path('9774.pi')
ui.load_pha(1, infile)
ui.group_counts(1, 20)
# Unlike the test_wstat_two_scalar case, the grouping
# is not copied over.
# ui.set_grouping(1, bkg_id=1, val=ui.get_grouping(1))
ui.set_source(1, ui.const1d.c1 * ui.powlaw1d.pl1)
# These should be the same as test_wstat_two_scalar
#
ui.set_par("pl1.gamma", 1.7)
ui.set_par("pl1.ampl", 1.6e-4)
ui.set_par("c1.c0", 45)
def test_bug38(self):
ui.load_pha('3c273', self.pha3c273)
ui.notice_id('3c273', 0.3, 2)
ui.group_counts('3c273', 30)
ui.group_counts('3c273', 15)
def test_more_ui_bug38(make_data_path):
ui.load_pha('3c273', make_data_path('3c273.pi'))
ui.notice_id('3c273', 0.3, 2)
ui.group_counts('3c273', 30)
ui.group_counts('3c273', 15)
def test_bug38(self):
ui.load_pha('3c273', self.pha3c273)
ui.notice_id('3c273', 0.3, 2)
ui.group_counts('3c273', 30)
ui.group_counts('3c273', 15)
def test_bug38(self):
ui.load_pha("3c273", self.pha3c273)
ui.notice_id("3c273", 0.3, 2)
ui.group_counts("3c273", 30)
ui.group_counts("3c273", 15)
def test_load_pha2_compare_meg_order1(make_data_path):
"""Do we read in the MEG +/-1 orders?"""
# The MEG -1 order is dataset 9
# The MEG +1 order is dataset 10
#
pha2file = make_data_path('3c120_pha2')
meg_p1file = make_data_path('3c120_meg_1.pha')
meg_m1file = make_data_path('3c120_meg_-1.pha')
ui.load_pha('meg_p1', meg_p1file)
ui.load_pha('meg_m1', meg_m1file)
orig_ids = set(ui.list_data_ids())
assert 'meg_p1' in orig_ids
assert 'meg_m1' in orig_ids
ui.load_pha(pha2file)
for n, lbl in zip([9, 10], ["-1", "1"]):
h = '3c120_meg_{}'.format(lbl)
ui.load_arf(n, make_data_path(h + '.arf'))
ui.load_rmf(n, make_data_path(h + '.rmf'))
# check that loading the pha2 file doesn't overwrite existing
# data
new_ids = set(ui.list_data_ids())
for i in range(1, 13):
orig_ids.add(i)
assert orig_ids == new_ids
# Check that the same model gives the same statistic
# value; this should check that the data and response are
# read in, that grouping and filtering work, and that
# model evaluation is the same, without having to
# check these steps individually.
#
# The model is not meant to be physically meaningful,
# just one that reasonably represents the data and
# can be evaluated without requiring XSPEC.
#
pmdl = ui.create_model_component('powlaw1d', 'pmdl')
pmdl.gamma = 0.318
pmdl.ampl = 2.52e-3
ncts = 20
for i in [9, 10, "meg_m1", "meg_p1"]:
ui.set_analysis(i, 'wave')
ui.group_counts(i, ncts)
ui.notice_id(i, 2, 12)
ui.set_source(i, pmdl)
ui.set_stat('chi2datavar')
s9 = ui.calc_stat(9)
s10 = ui.calc_stat(10)
sm1 = ui.calc_stat('meg_m1')
sp1 = ui.calc_stat('meg_p1')
# Since these should be the same, we use an equality test
# rather than approximation. At least until it becomes
# a problem.
#
assert s9 == sm1
assert s10 == sp1
# The values were calculated using CIAO 4.9, Linux64, with
# Python 3.5.
#
assert s9 == pytest.approx(1005.4378559390879)
assert s10 == pytest.approx(1119.980439489647)
def test_plot_pvalue(make_data_path, clean_astro_ui, hide_logging):
"""Check plot_pvalue with PHA data."""
fname = make_data_path("qso.pi")
ui.load_pha(fname)
ui.set_stat('cstat')
ui.set_method("neldermead")
ui.group_counts(10)
ui.notice(0.3, 8)
ui.set_model("xsphabs.abs1*(xspowerlaw.p1 +gauss1d.g1)")
# move the fit close to the best fit to save a small amount
# of time.
abs1.nh = 0.05
p1.phoindex = 1.28
p1.norm = 2e-4
g1.ampl = 1.8e-5
g1.pos = 3.
ui.freeze(g1.pos)
g1.fwhm = 0.1
ui.freeze(g1.fwhm)
# Could we reduce the number of bins to save evaluation time?
# We do want a non-default num value when checking the shapes
# of the output attributes.
#
ui.fit()
ui.plot_pvalue(p1, p1 + g1, num=100, bins=20)
tmp = ui.get_pvalue_results()
assert tmp.null == pytest.approx(210.34566845619273)
assert tmp.alt == pytest.approx(207.66618095925094)
assert tmp.lr == pytest.approx(2.679487496941789)
# Have we returned the correct info?
#
# Is it worth checking the stored data (aka how randomised is this
# output)?
#
assert tmp.samples.shape == (100, 2)
assert tmp.stats.shape == (100, 2)
assert tmp.ratios.shape == (100, )
# Check the plot
#
tmp = ui.get_pvalue_plot()
assert tmp.lr == pytest.approx(2.679487496941789)
assert tmp.xlabel == 'Likelihood Ratio'
assert tmp.ylabel == 'Frequency'
assert tmp.title == 'Likelihood Ratio Distribution'
# It would be nice to check the values here
#
assert tmp.ratios.shape == (100, )
assert tmp.xlo.shape == (21, )
assert tmp.xhi.shape == (21, )
assert tmp.y.shape == (21, )
