def test_poisson_noise(self):
out = utils.poisson_noise(1000)
self.assertEqual(type(out), SherpaFloat)
self.assertGreater(out, 0.0)
for x in (-1000, 0):
out = utils.poisson_noise(x)
self.assertEqual(type(out), SherpaFloat)
self.assertEqual(out, 0.0)
out = utils.poisson_noise([1001, 1002, 0.0, 1003, -1004])
self.assertEqual(type(out), numpy.ndarray)
self.assertEqual(out.dtype.type, SherpaFloat)
ans = numpy.flatnonzero(out > 0.0)
assert_equal(ans, numpy.array([0, 1, 3]))
self.assertRaises(ValueError, utils.poisson_noise, 'ham')
self.assertRaises(TypeError, utils.poisson_noise, [1, 2, 'ham'])
def test_poisson_noise(self):
out = utils.poisson_noise(1000)
self.assertEqual(type(out), SherpaFloat)
self.assertGreater(out, 0.0)
for x in (-1000, 0):
out = utils.poisson_noise(x)
self.assertEqual(type(out), SherpaFloat)
self.assertEqual(out, 0.0)
out = utils.poisson_noise([1001, 1002, 0.0, 1003, -1004])
self.assertEqual(type(out), numpy.ndarray)
self.assertEqual(out.dtype.type, SherpaFloat)
ans = numpy.flatnonzero(out > 0.0)
assert_equal(ans, numpy.array([0, 1, 3]))
self.assertRaises(ValueError, utils.poisson_noise, 'ham')
self.assertRaises(TypeError, utils.poisson_noise, [1, 2, 'ham'])
def calculate(nullfit, altfit, proposal, null_vals, alt_vals):
# FIXME: only null perturbed?
nullfit.model.thawedpars = proposal
# Fake using poisson_noise with null
fake = poisson_noise(nullfit.data.eval_model(nullfit.model))
# Set faked data for both nullfit and altfit
nullfit.data.set_dep(fake)
# Start the faked fit at initial null best-fit values
#nullfit.model.thawedpars = null_vals
# Fit with null model
nullfr = nullfit.fit()
debug(nullfr.format())
null_stat = nullfr.statval
debug("statistic null = " + repr(null_stat))
# nullfit and altfit BOTH point to same faked dataset
assert ( id(nullfit.data) == id(altfit.data) )
assert (nullfit.data.get_dep() == altfit.data.get_dep()).all()
# Start the faked fit at the initial alt best-fit values
#altfit.model.thawedpars = alt_vals
debug("proposal: " + repr(proposal))
debug("alt model")
debug(str(altfit.model))
# Set alt model and fit
altfr = altfit.fit()
debug(altfr.format())
debug(str(altfit.model))
alt_stat = altfr.statval
debug("statistic alt = " + repr(alt_stat))
LR = -(alt_stat - null_stat)
debug("LR = " + repr(LR))
return [null_stat, alt_stat, LR]
def calculate(nullfit, altfit, proposal, null_vals, alt_vals):
# FIXME: only null perturbed?
nullfit.model.thawedpars = proposal
# Fake using poisson_noise with null
fake = poisson_noise(nullfit.data.eval_model(nullfit.model))
# Set faked data for both nullfit and altfit
nullfit.data.set_dep(fake)
# Start the faked fit at initial null best-fit values
#nullfit.model.thawedpars = null_vals
# Fit with null model
nullfr = nullfit.fit()
debug(nullfr.format())
null_stat = nullfr.statval
debug("statistic null = " + repr(null_stat))
# nullfit and altfit BOTH point to same faked dataset
assert (id(nullfit.data) == id(altfit.data))
assert (nullfit.data.get_dep() == altfit.data.get_dep()).all()
# Start the faked fit at the initial alt best-fit values
#altfit.model.thawedpars = alt_vals
debug("proposal: " + repr(proposal))
debug("alt model")
debug(str(altfit.model))
# Set alt model and fit
altfr = altfit.fit()
debug(altfr.format())
debug(str(altfit.model))
alt_stat = altfr.statval
debug("statistic alt = " + repr(alt_stat))
LR = -(alt_stat - null_stat)
debug("LR = " + repr(LR))
return [null_stat, alt_stat, LR]
def setup_data1d_fit():
"""Create a 1D dataset for fitting a gaussian-like profile.
This sets the random seed to a fixed value, so use the
reset_seed fixture.
"""
x = np.linspace(2300, 2400, 51)
cpt = ui.voigt1d('cpt')
cpt.pos = 2345
cpt.fwhm_g = 20
cpt.fwhm_l = 15
cpt.ampl = 480
np.random.seed(72383)
y = poisson_noise(cpt(x))
ui.load_arrays(1, x, y, ui.Data1D)
ui.set_stat('leastsq')
ui.set_method('simplex')
ui.delete_model_component('cpt')
def test_poisson_noise():
out = utils.poisson_noise(1000)
assert type(out) == SherpaFloat
assert out > 0.0
for x in (-1000, 0):
out = utils.poisson_noise(x)
assert type(out) == SherpaFloat
assert out == 0.0
out = utils.poisson_noise([1001, 1002, 0.0, 1003, -1004])
assert type(out) == numpy.ndarray
assert out.dtype.type == SherpaFloat
ans = numpy.flatnonzero(out > 0.0)
assert (ans == numpy.array([0, 1, 3])).all()
with pytest.raises(ValueError):
utils.poisson_noise('ham')
with pytest.raises(TypeError):
utils.poisson_noise(1, 2, 'ham')