def test_high_photon(self):
"""Test if function raises a ``ValueError`` if ``photon_number`` is so high that it
cannot correspond to a sample given the constraints of ``max_count_per_mode`` and
``modes``"""
with pytest.raises(ValueError,
match="No valid samples can be generated."):
similarity.event_to_sample(5, 1, 4)
def get_event_probability_mc(self, event: tuple, samples: int = 1000):
"""
Calculate approximate probability of the event with Monte Carlo.
Similar to function sfs.prob_event_mc()
:param event: a specification of an event. Tuple of length 2
:param samples: number of Monte Carlo samples.
:return: the mc approximate probability of the given event
"""
photons, max_photons_per_mode = event
prob = 0
for _ in range(samples):
sample = sfs.event_to_sample(photons, max_photons_per_mode, self.mode_count)
prob += self.get_probability(sample)
prob = prob * sfs.event_cardinality(photons, max_photons_per_mode, self.mode_count) / samples
return prob
def test_sample_max_count(self, photon_num, modes_dim, count):
"""Test if function returns a sample that has maximum element not exceeding ``count``."""
samp = similarity.event_to_sample(photon_num, count, modes_dim)
assert max(samp) <= count
def test_sample_sum(self, photon_num, modes_dim, count):
"""Test if function returns a sample that has the correct number of photons."""
samp = similarity.event_to_sample(photon_num, count, modes_dim)
assert sum(samp) == photon_num
def test_sample_length(self, photon_num, modes_dim, count):
"""Test if function returns a sample that is of correct length ``modes_dim``."""
samp = similarity.event_to_sample(photon_num, count, modes_dim)
assert len(samp) == modes_dim
def test_low_count(self):
"""Test if function raises a ``ValueError`` if ``max_count_per_mode`` is negative."""
with pytest.raises(ValueError, match="Maximum number of photons"):
similarity.event_to_sample(2, -1, 5)
