def test_updating(self):
m = equilibrium_model()
con1 = InitialValueExperiment(equilibrium_dose_variant())
con2 = InitialValueExperiment(Model().add('A0 = 15', 'kf = 0.3'))
active_parameters = ActiveParameters.from_model_experiments(
m, [con1, con2], ['kr'], [[], ['A0', 'kf']])
updated_parameters = active_parameters.update_parameters(
[4.0, 1.3, 2.0])
self.assertEqual(
updated_parameters,
ActiveParameters(OrderedDict([
('kr', 4.0)
]), [OrderedDict(),
OrderedDict([('A0', 1.3), ('kf', 2.0)])]))
active_parameters = ActiveParameters.from_model_experiments(
m, [con2, con2], ['kr'], [['kf'], ['A0', 'kf']])
updated_parameters = active_parameters.update_parameters(
[4.0, 3.4, 1.3, 2.0])
self.assertEqual(
updated_parameters,
ActiveParameters(OrderedDict([('kr', 4.0)]), [
OrderedDict([('kf', 3.4)]),
OrderedDict([('A0', 1.3), ('kf', 2.0)])
]))
def test_extraction(self):
m = equilibrium_model()
con1 = InitialValueExperiment(equilibrium_dose_variant())
con2 = InitialValueExperiment(Model().add('A0 = 15', 'kf = 0.3'))
active_parameters = ActiveParameters.from_model_experiments(
m, [con1], ['kf', 'kr'], [[]])
self.assertEqual(
active_parameters,
ActiveParameters(OrderedDict([('kf', 0.5), ('kr', 0.2)]),
[OrderedDict()]))
active_parameters = ActiveParameters.from_model_experiments(
m, [con1, con2], ['kf', 'kr'], [])
self.assertEqual(
active_parameters,
ActiveParameters(OrderedDict([('kf', 0.5), ('kr', 0.2)]),
[OrderedDict(), OrderedDict()]))
active_parameters = ActiveParameters.from_model_experiments(
m, [con1, con2], ['kr', 'kf'], [])
self.assertEqual(
active_parameters,
ActiveParameters(OrderedDict([('kr', 0.2), ('kf', 0.5)]),
[OrderedDict(), OrderedDict()]))
active_parameters = ActiveParameters.from_model_experiments(
m, [con1, con2], ['kr', 'kf'], [])
self.assertEqual(
active_parameters,
ActiveParameters(OrderedDict([('kr', 0.2), ('kf', 0.5)]),
[OrderedDict(), OrderedDict()]))
active_parameters = ActiveParameters.from_model_experiments(
m, [con1, con2], [], [[], ['A0']])
self.assertEqual(
active_parameters,
ActiveParameters(
OrderedDict(),
[OrderedDict(), OrderedDict([('A0', 15)])]))
active_parameters = ActiveParameters.from_model_experiments(
m, [con1, con2], [], [[], ['A0', 'kf']])
self.assertEqual(
active_parameters,
ActiveParameters(
OrderedDict(),
[OrderedDict(),
OrderedDict([('A0', 15), ('kf', 0.3)])]))
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_empty(self):
m = Model()
con = InitialValueExperiment()
sim = simulate(m, con)
self.assertEqual(sim.matrix_values(10).shape, (0, ))
self.assertEqual(sim.matrix_values([10, 20]).shape, (2, 0))
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_basic_fitting(self):
m = Model().add('k = 10')
con = InitialValueExperiment()
obj = LinearWeightedSumOfSquaresObservation(
[0], ['k'], AffineMeasurementUncertainty(1, 0)).objective([20])
fitted_parameters = fit_parameters(m, [con], [obj],
model_parameters='k')
self.assertAlmostEqual(fitted_parameters.model_parameters['k'], 20)
def test_events(self):
m = Model()
m = m.add("A* = 0", "A' = 1", "@(A > 1) A = 0")
con = InitialValueExperiment()
sim = simulate(m, con)
self.assertTrue(0.9 < sim.matrix_values(0.95, 'A') < 1)
self.assertTrue(0 < sim.matrix_values(1.05, 'A') < 0.1)
self.assertTrue(0.9 < sim.matrix_values(1.95, 'A') < 1)
self.assertTrue(0 < sim.matrix_values(2.05, 'A') < 0.1)
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_dose(self):
m = dose_step()
con = InitialValueExperiment()
sim = simulate(m, con)
self.assertEqual(sim.matrix_values(0, 'A'), 0.0)
self.assertEqual(sim.matrix_values(1, 'A'), 2.0)
self.assertEqual(sim.matrix_values(1.5, 'A'), 2.0)
self.assertEqual(sim.matrix_values(2, 'A'), 3.0)
self.assertEqual(sim.matrix_values(0, 'B'), 0.0)
self.assertAlmostEqual(sim.matrix_values(0.5, 'B'), 2.0)
self.assertAlmostEqual(sim.matrix_values(1, 'B'), 3.0)
self.assertAlmostEqual(sim.matrix_values(2, 'B'), 7.0)
self.assertAlmostEqual(sim.matrix_values(3, 'B'), 16.0)
def test_simulate_system(self):
m = equilibrium_model()
con = InitialValueExperiment()
sim = simulate(m, con)
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)
from biolucia.parser import * from biolucia.experiment import InitialValueExperiment from biolucia.simulation import simulate import matplotlib.pyplot as plt import numpy as np filename = '../models/discontinuity.txt' m = read_model(filename) con = InitialValueExperiment() sim = simulate(m, con) times = np.linspace(0, 10, 1000) values = sim.matrix_values(times) lines = plt.plot(times, values) plt.show()