def test_full_likelihood():
"""
Simple test of likelihood, test against known values for high and low signal cases. Check
That full calculation and the gaussian approx become equal at high signal
"""
spe = 0.5 # Single photo-electron width
pedestal = 1 # width of the pedestal distribution
image_small = [0,1,2]
expectation_small = [1,1,1]
full_like_small = poisson_likelihood_full(image_small, expectation_small, spe, pedestal)
# Check against known values
assert np.sum(np.abs(full_like_small - np.asarray([2.75630505, 2.62168656, 3.39248449]))) < 1e-6
image_large = [40,50,60]
expectation_large = [50,50,50]
full_like_large = poisson_likelihood_full(image_large, expectation_large, spe, pedestal)
# Check against known values
assert np.sum(np.abs(full_like_large - np.asarray([7.45489137, 5.99305388, 7.66226007]))) < 1e-6
gaus_like_large = poisson_likelihood_gaussian(image_large, expectation_large, spe, pedestal)
# Check thats in large signal case the full expectation is equal to the gaussian approximation (to 5%)
assert np.all(np.abs((full_like_large-gaus_like_large)/full_like_large) < 0.05)
def test_full_likelihood():
"""
Simple test of likelihood, test against known values for high and low signal cases. Check
That full calculation and the gaussian approx become equal at high signal
"""
spe = 0.5 # Single photo-electron width
pedestal = 1 # width of the pedestal distribution
image_small = [0, 1, 2]
expectation_small = [1, 1, 1]
full_like_small = poisson_likelihood_full(image_small, expectation_small,
spe, pedestal)
# Check against known values
assert np.sum(
np.abs(full_like_small -
np.asarray([2.75630505, 2.62168656, 3.39248449]))) < 1e-6
image_large = [40, 50, 60]
expectation_large = [50, 50, 50]
full_like_large = poisson_likelihood_full(image_large, expectation_large,
spe, pedestal)
# Check against known values
assert np.sum(
np.abs(full_like_large -
np.asarray([7.45489137, 5.99305388, 7.66226007]))) < 1e-6
gaus_like_large = poisson_likelihood_gaussian(image_large,
expectation_large, spe,
pedestal)
# Check thats in large signal case the full expectation is equal to the gaussian approximation (to 5%)
assert np.all(
np.abs((full_like_large - gaus_like_large) / full_like_large) < 0.05)