def stat_sum(self):
"""Total likelihood given the current model parameters."""
counts, npred = self._counts_data, self.npred().data
if self.mask is not None:
return cash_sum_cython(counts[self.mask.data],
npred[self.mask.data])
else:
return cash_sum_cython(counts.ravel(), npred.ravel())
def f_cash(x, counts, background, model):
"""Wrapper for cash statistics, that defines the model function.
Parameters
----------
x : float
Model amplitude.
counts : `~numpy.ndarray`
Count image slice, where model is defined.
background : `~numpy.ndarray`
Background image slice, where model is defined.
model : `~numpy.ndarray`
Source template (multiplied with exposure).
"""
return cash_sum_cython(counts.ravel(),
(background + x * FLUX_FACTOR * model).ravel())
def test_cash_sum_cython(test_data):
counts = np.array(test_data["n_on"], dtype=float)
npred = np.array(test_data["mu_sig"], dtype=float)
stat = stats.cash_sum_cython(counts=counts, npred=npred)
ref = stats.cash(counts, npred).sum()
assert_allclose(stat, ref)
def stat_sum(self, norm):
"""Stat sum"""
return cash_sum_cython(self.counts.ravel(), self.npred(norm).ravel())
