def test_update_events(self):
m = equilibrium_model()
con = InitialValueExperiment(Model().add('@(B < 2) B = 0'))
sim = simulate(m, con)
self.assertTrue(sim.matrix_values(0.7, 'B') < 1)
def test_simulate_system2(self):
m = equilibrium_model()
con = InitialValueExperiment(equilibrium_dose_variant())
sim = m.simulate(con)
self.assertEqual(sim.system_values(0, 'kf'), 0.5)
self.assertEqual(sim.system_values(0, 'A'), 10)
self.assertLess(sim.system_values(1, 'A'), 10)
def test_simulate_system2(self):
m = equilibrium_model()
con = InitialValueExperiment(equilibrium_dose_variant())
sim = simulate(m, con)
self.assertEqual(sim.matrix_values(0, 'kf'), 0.5)
self.assertEqual(sim.matrix_values(0, 'A'), 10)
self.assertLess(sim.matrix_values(1, 'A'), 10)
def test_log_probability_dk(self):
m = equilibrium_model()
con = InitialValueExperiment()
unc = AffineMeasurementUncertainty(2, 0)
obs = LinearWeightedSumOfSquaresObservation([0], ['A'], unc)
assert_log_probability_dk(m, con, obs, m['A0'].value, atol=1e-6)
assert_log_probability_dk(m, con, obs, m['A0'].value + 2)
obs = LinearWeightedSumOfSquaresObservation([0, 0, 0], ['A', 'B', 'C'],
unc)
assert_log_probability_dk(
m, con, obs, [m['A0'].value + 0.1, m['B0'].value + 0.2, 0.3])
def test_simulate_system(self):
m = equilibrium_model()
con = InitialValueExperiment()
sim = m.simulate(con)
self.assertEqual(sim.system_values(0, 'kf'), 0.5)
self.assertEqual(sim.system_values(0, 'A'), 10)
self.assertLess(sim.system_values(1, 'A'), 10)
self.assertTrue(array_equal(sim.system_values(0, ['A', 'B']), array([10, 5])))
self.assertTrue(array_equal(sim.system_values([0, 1], 'kf'), array([0.5, 0.5])))
self.assertTrue(array_equal(sim.system_values([0, 1, 2], ['kr', 'kf']),
array([[0.2, 0.5], [0.2, 0.5], [0.2, 0.5]])))
def test_simulate_single(self):
m = equilibrium_model()
sim = simulate(m)
self.assertEqual(sim.vector_values(0, 'A'), m['A0'].value)
assert_allclose(sim.vector_values(0, ['A', 'A0']),
[m['A0'].value, m['A0'].value])
self.assertEqual(sim.matrix_values(0, 'kf'), 0.5)
self.assertEqual(sim.matrix_values(0, 'A'), 10)
self.assertLess(sim.matrix_values(1, 'A'), 10)
assert_allclose(sim.matrix_values(0, ['A', 'B']), np.asarray([10, 5]))
assert_allclose(sim.matrix_values([0, 1], 'kf'), np.asarray([0.5,
0.5]))
assert_allclose(sim.matrix_values([0, 1, 2], ['kr', 'kf']),
np.asarray([[0.2, 0.5], [0.2, 0.5], [0.2, 0.5]]))
def test_simulate_sensitivity(self):
m = equilibrium_model()
con = InitialValueExperiment()
sim = simulate(m, con, parameters=['A0', 'kr'])
assert_allclose(sim.matrix_sensitivities(0.0, 'A'), [1.0, 0.0])
self.assertEqual(sim.matrix_sensitivities(0.0, 'A').shape, (2, ))
assert_allclose(sim.matrix_sensitivities([0.0, 10.0], 'A0'),
[[1.0, 0.0], [1.0, 0.0]])
self.assertEqual(
sim.matrix_sensitivities([0.0, 10.0], 'A0').shape, (2, 2))
assert_allclose(sim.matrix_sensitivities(0.0, ['A', 'A0']),
[[1.0, 0.0], [1.0, 0.0]])
self.assertEqual(
sim.matrix_sensitivities(0.0, ['A', 'A0']).shape, (2, 2))
assert_allclose(sim.matrix_sensitivities([0.0, 10.0], ['A0', 'B0']),
[[[1.0, 0.0], [0.0, 0.0]], [[1.0, 0.0], [0.0, 0.0]]])
self.assertEqual(
sim.matrix_sensitivities([0.0, 10.0], ['A', 'A0']).shape,
(2, 2, 2))
def test_log_probability(self):
m = equilibrium_model()
con = InitialValueExperiment()
sim = simulate(m, con)
unc = AffineMeasurementUncertainty(2, 0)
obs = LinearWeightedSumOfSquaresObservation([0], ['A'], unc)
self.assertAlmostEqual(obs.probability(sim, m['A0'].value),
norm.pdf(m['A0'].value, m['A0'].value, 2))
self.assertAlmostEqual(obs.log_probability(sim, m['A0'].value),
norm.logpdf(m['A0'].value, m['A0'].value, 2))
obs = LinearWeightedSumOfSquaresObservation([0], ['A'], unc)
self.assertAlmostEqual(obs.probability(sim, m['A0'].value + 2),
norm.pdf(m['A0'].value + 2, m['A0'].value, 2))
self.assertAlmostEqual(
obs.log_probability(sim, m['A0'].value + 2),
norm.logpdf(m['A0'].value + 2, m['A0'].value, 2))
self.assertEqual(obs.sample(sim).size, 1)
obs = LinearWeightedSumOfSquaresObservation([0, 0, 0], ['A', 'B', 'C'],
unc)
ics = [m['A0'].value, m['B0'].value, 0]
self.assertAlmostEqual(
obs.probability(sim, ics),
multivariate_normal.pdf([0, 0, 0], [0, 0, 0], 2**2))
self.assertAlmostEqual(
obs.log_probability(sim, ics),
multivariate_normal.logpdf([0, 0, 0], [0, 0, 0], 2**2))
self.assertAlmostEqual(
obs.log_probability(sim, ics),
multivariate_normal.logpdf([0, 0, 0], [0, 0, 0],
np.asarray([2, 2, 2])**2))
self.assertEqual(obs.sample(sim).size, 3)
