def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile = self.make_path('3c273.pi')
ui.load_pha(1, infile)
# Change the backscale value slightly so that the
# results are different to other runs with this file.
#
nbins = ui.get_data(1).get_dep(False).size
bscal = 0.9 * np.ones(nbins) * ui.get_backscal(1)
ui.set_backscal(1, backscale=bscal)
ui.set_source(1, ui.powlaw1d.pl)
# 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.
#
ui.set_par("pl.gamma", 1.7)
ui.set_par("pl.ampl", 1.7e-4)
def setUp(self):
self._old_logger_level = logger.getEffectiveLevel()
logger.setLevel(logging.ERROR)
ui.set_stat('wstat')
infile = self.make_path('3c273.pi')
ui.load_pha(1, infile)
# Change the backscale value slightly so that the
# results are different to other runs with this file.
#
nbins = ui.get_data(1).get_dep(False).size
bscal = 0.9 * np.ones(nbins) * ui.get_backscal(1)
ui.set_backscal(1, backscale=bscal)
ui.set_source(1, ui.powlaw1d.pl)
# 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.
#
ui.set_par("pl.gamma", 1.7)
ui.set_par("pl.ampl", 1.7e-4)
def test_fake_pha_background_model(clean_astro_ui, reset_seed):
"""Check we can add a background component.
See also test_fake_pha_basic.
For simplicity we use perfect responses.
"""
np.random.seed(27347)
id = 'qwerty'
channels = np.arange(1, 4, dtype=np.int16)
counts = np.ones(3, dtype=np.int16)
bcounts = 100 * counts
ui.load_arrays(id, channels, counts, ui.DataPHA)
ui.set_exposure(id, 100)
ui.set_backscal(id, 0.1)
bkg = ui.DataPHA('bkg', channels, bcounts, exposure=200, backscal=0.4)
ebins = np.asarray([1.1, 1.2, 1.4, 1.6])
elo = ebins[:-1]
ehi = ebins[1:]
arf = ui.create_arf(elo, ehi)
rmf = ui.create_rmf(elo, ehi, e_min=elo, e_max=ehi)
mdl = ui.create_model_component('const1d', 'mdl')
mdl.c0 = 0
bkgmdl = ui.create_model_component('const1d', 'mdl')
bkgmdl.c0 = 2
ui.set_source(id, mdl)
ui.set_bkg(id, bkg)
ui.set_bkg_source(id, bkgmdl)
ui.set_arf(id, arf, bkg_id=1)
ui.set_rmf(id, rmf, bkg_id=1)
ui.fake_pha(id, arf, rmf, 1000.0, bkg='model')
faked = ui.get_data(id)
assert faked.exposure == pytest.approx(1000.0)
assert (faked.channel == channels).all()
# check we've faked counts (the scaling is such that it is
# very improbable that this condition will fail)
assert (faked.counts > counts).all()
# For reference the predicted source signal is
# [200, 400, 400]
# and the background signal is
# [125, 125, 125]
# so, even with randomly drawn values, the following
# checks should be robust.
#
predicted_by_source = 1000 * mdl(elo, ehi)
predicted_by_bkg = (1000 / 200) * (0.1 / 0.4) * bcounts
assert (faked.counts > predicted_by_source).all()
assert (faked.counts > predicted_by_bkg).all()
def single_array_setup(make_data_path):
ui.set_stat('wstat')
infile = make_data_path('3c273.pi')
ui.load_pha(1, infile)
# Change the backscale value slightly so that the
# results are different to other runs with this file.
#
nbins = ui.get_data(1).get_dep(False).size
bscal = 0.9 * np.ones(nbins) * ui.get_backscal(1)
ui.set_backscal(1, backscale=bscal)
ui.set_source(1, ui.powlaw1d.pl)
# 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.
#
ui.set_par("pl.gamma", 1.7)
ui.set_par("pl.ampl", 1.7e-4)