def test_get_priors_log_prob (self):
""" Given theta, we should be able to get
log {p(theta)}. """
priors_file = 'input/kolch_model.priors'
theta = read_priors_file (priors_file)
# theta has been randomly generated by the joint distribution,
# and there's a high (but of course not 1) probability of the
# log of the pdf being representable in floating point (it
# could not be if the value is really small).
log_prior = theta.get_log_p ()
assert (not self.is_nan (log_prior))
assert (log_prior > float ('-inf'))
def test_read_lognormal(self):
""" Tests if the module can correctly read a prior definition
file. """
priors = read_priors_file('input/lognormal.priors')
for x in priors:
distribution = x.get_distribution()
if x.name == 'k1':
expected_mean = np.exp(3)
self.assertEqual(distribution.mean(), expected_mean)
elif x.name == 'd1':
expected_mean = np.exp(10)
self.assertEqual(distribution.mean(), expected_mean)
elif x.name == "Sigma" or x.name == "Noise":
pass
else:
self.fail()
def test_reader(self):
""" Tests if the module can correctly read a prior definition
file. """
priors = read_priors_file('input/simple_enzymatic.priors')
for x in priors:
distribution = x.get_distribution()
if x.name == 'k1':
self.assertEqual(distribution.get_a(), 2.0)
self.assertEqual(distribution.get_b(), 0.01)
elif x.name == 'd1' or x.name == 'kcat':
self.assertEqual(distribution.get_a(), 2.0)
self.assertEqual(distribution.get_b(), 0.1)
elif x.name == "Noise":
pass
else:
self.fail()
def test_write_priors(self):
""" Tests if it is possible to write a priors file. """
priors = read_priors_file('input/simple_enzymatic.priors')
write_priors_file('input/simple_enzymatic_out.priors', priors)
assert (filecmp.cmp ('input/simple_enzymatic_out.priors', \
'input/simple_enzymatic.priors'))
def test_sigma_prior(self):
""" Tests if the module can read the experimental error
prior. """
priors = read_priors_file('input/simple_enzymatic.priors')
sigma = priors.get_experimental_error()
assert (sigma > 0)