def tests_presimulation(self):
self.model.getFixedParameterNames()
combos = itertools.product([(10, 5), (5, 10), ()], repeat=3)
cases = dict()
for icombo, combo in enumerate(combos):
cases[f'{icombo}'] = {
'fixedParameters': combo[0],
'fixedParametersPreequilibration': combo[1],
'fixedParametersPresimulation': combo[2],
}
for case in cases:
with self.subTest(**cases[case]):
for fp in cases[case]:
setattr(self.edata[0], fp, cases[case][fp])
df_edata = amici.getDataObservablesAsDataFrame(
self.model, self.edata)
edata_reconstructed = amici.getEdataFromDataFrame(
self.model, df_edata)
for fp in [
'fixedParameters', 'fixedParametersPreequilibration',
'fixedParametersPresimulation'
]:
if fp != 'fixedParameters' or cases[case][fp] is not ():
self.assertTupleEqual(
getattr(self.edata[0], fp),
getattr(edata_reconstructed[0], fp),
)
self.assertTupleEqual(
cases[case][fp],
getattr(edata_reconstructed[0], fp),
)
self.assertTupleEqual(
getattr(self.edata[0], fp),
cases[case][fp],
)
else:
self.assertTupleEqual(
self.model.getFixedParameters(),
getattr(edata_reconstructed[0], fp),
)
self.assertTupleEqual(
self.model.getFixedParameters(),
getattr(edata_reconstructed[0], fp),
)
self.assertTupleEqual(
getattr(self.edata[0], fp),
cases[case][fp],
)
def test_steadystate_simulation(model_steadystate_module):
model = model_steadystate_module.getModel()
model.setTimepoints(np.linspace(0, 60, 60))
solver = model.getSolver()
solver.setSensitivityOrder(amici.SensitivityOrder.first)
rdata = amici.runAmiciSimulation(model, solver)
edata = [amici.ExpData(rdata, 1, 0)]
rdata = amici.runAmiciSimulations(model, solver, edata)
# check roundtripping of DataFrame conversion
df_edata = amici.getDataObservablesAsDataFrame(model, edata)
edata_reconstructed = amici.getEdataFromDataFrame(model, df_edata)
assert np.isclose(
amici.ExpDataView(edata[0])['observedData'],
amici.ExpDataView(edata_reconstructed[0])['observedData']).all()
assert np.isclose(
amici.ExpDataView(edata[0])['observedDataStdDev'],
amici.ExpDataView(edata_reconstructed[0])['observedDataStdDev']).all()
if len(edata[0].fixedParameters):
assert list(edata[0].fixedParameters) \
== list(edata_reconstructed[0].fixedParameters)
else:
assert list(model.getFixedParameters()) \
== list(edata_reconstructed[0].fixedParameters)
assert list(edata[0].fixedParametersPreequilibration) == \
list(edata_reconstructed[0].fixedParametersPreequilibration)
df_state = amici.getSimulationStatesAsDataFrame(model, edata, rdata)
assert np.isclose(rdata[0]['x'],
df_state[list(model.getStateIds())].values).all()
df_obs = amici.getSimulationObservablesAsDataFrame(model, edata, rdata)
assert np.isclose(rdata[0]['y'],
df_obs[list(model.getObservableIds())].values).all()
amici.getResidualsAsDataFrame(model, edata, rdata)
solver.setRelativeTolerance(1e-12)
solver.setAbsoluteTolerance(1e-12)
check_derivatives(model,
solver,
edata[0],
assert_fun,
atol=1e-3,
rtol=1e-3,
epsilon=1e-4)
# Run some additional tests which need a working Model,
# but don't need precomputed expectations.
_test_set_parameters_by_dict(model_steadystate_module)
def test_pandas_import_export(sbml_example_presimulation_module, case):
"""TestCase class for testing csv import using pandas"""
# setup
model = sbml_example_presimulation_module.getModel()
model.setTimepoints(np.linspace(0, 60, 61))
solver = model.getSolver()
rdata = amici.runAmiciSimulation(model, solver)
edata = [amici.ExpData(rdata, 0.01, 0)]
# test copy constructor
_ = amici.ExpData(edata[0])
for fp in case:
setattr(edata[0], fp, case[fp])
df_edata = amici.getDataObservablesAsDataFrame(model, edata)
edata_reconstructed = amici.getEdataFromDataFrame(model, df_edata)
for fp in [
'fixedParameters', 'fixedParametersPreequilibration',
'fixedParametersPresimulation'
]:
if fp != 'fixedParameters' or case[fp] != ():
assert getattr(edata[0], fp) == getattr(edata_reconstructed[0], fp)
assert case[fp] == getattr(edata_reconstructed[0], fp)
else:
assert model.getFixedParameters() \
== getattr(edata_reconstructed[0], fp)
assert model.getFixedParameters() == \
getattr(edata_reconstructed[0], fp)
assert getattr(edata[0], fp) == case[fp]
def test_steadystate_scaled(self):
"""
Test SBML import and simulation from AMICI python interface
"""
def assert_fun(x):
return self.assertTrue(x)
sbmlFile = os.path.join(os.path.dirname(__file__), '..', 'python',
'examples', 'example_steadystate',
'model_steadystate_scaled.xml')
sbmlImporter = amici.SbmlImporter(sbmlFile)
observables = amici.assignmentRules2observables(
sbmlImporter.sbml,
filter_function=lambda variable:
variable.getId().startswith('observable_') and
not variable.getId().endswith('_sigma')
)
outdir = 'test_model_steadystate_scaled'
sbmlImporter.sbml2amici('test_model_steadystate_scaled',
outdir,
observables=observables,
constantParameters=['k0'],
sigmas={'observable_x1withsigma':
'observable_x1withsigma_sigma'})
sys.path.insert(0, outdir)
import test_model_steadystate_scaled as modelModule
model = modelModule.getModel()
model.setTimepoints(np.linspace(0, 60, 60))
solver = model.getSolver()
solver.setSensitivityOrder(amici.SensitivityOrder_first)
rdata = amici.runAmiciSimulation(model, solver)
edata = [amici.ExpData(rdata, 1, 0)]
rdata = amici.runAmiciSimulations(model, solver, edata)
# check roundtripping of DataFrame conversion
df_edata = amici.getDataObservablesAsDataFrame(model, edata)
edata_reconstructed = amici.getEdataFromDataFrame(model, df_edata)
self.assertTrue(
np.isclose(
amici.ExpDataView(edata[0])
['observedData'],
amici.ExpDataView(edata_reconstructed[0])
['observedData'],
).all()
)
self.assertTrue(
np.isclose(
amici.ExpDataView(edata[0])
['observedDataStdDev'],
amici.ExpDataView(edata_reconstructed[0])
['observedDataStdDev'],
).all()
)
if len(edata[0].fixedParameters):
self.assertListEqual(
list(edata[0].fixedParameters),
list(edata_reconstructed[0].fixedParameters),
)
else:
self.assertListEqual(
list(model.getFixedParameters()),
list(edata_reconstructed[0].fixedParameters),
)
self.assertListEqual(
list(edata[0].fixedParametersPreequilibration),
list(edata_reconstructed[0].fixedParametersPreequilibration),
)
df_state = amici.getSimulationStatesAsDataFrame(model, edata, rdata)
self.assertTrue(
np.isclose(
rdata[0]['x'],
df_state[list(model.getStateIds())].values
).all()
)
df_obs = amici.getSimulationObservablesAsDataFrame(model, edata, rdata)
self.assertTrue(
np.isclose(
rdata[0]['y'],
df_obs[list(model.getObservableIds())].values
).all()
)
amici.getResidualsAsDataFrame(model, edata, rdata)
solver.setRelativeTolerance(1e-12)
solver.setAbsoluteTolerance(1e-12)
check_derivatives(model, solver, edata[0], assert_fun, atol=1e-3,
rtol=1e-3, epsilon=1e-4)
def test_steadystate_scaled(self):
'''
Test SBML import and simulation from AMICI python interface
'''
sbmlFile = os.path.join(os.path.dirname(__file__), '..', 'python',
'examples', 'example_steadystate',
'model_steadystate_scaled.xml')
sbmlImporter = amici.SbmlImporter(sbmlFile)
observables = amici.assignmentRules2observables(
sbmlImporter.sbml,
filter_function=lambda variable: variable.getId().startswith(
'observable_') and not variable.getId().endswith('_sigma'))
outdir = 'test_model_steadystate_scaled'
sbmlImporter.sbml2amici(
'test_model_steadystate_scaled',
outdir,
observables=observables,
constantParameters=['k0'],
sigmas={'observable_x1withsigma': 'observable_x1withsigma_sigma'})
sys.path.insert(0, outdir)
import test_model_steadystate_scaled as modelModule
model = modelModule.getModel()
model.setTimepoints(amici.DoubleVector(np.linspace(0, 60, 60)))
solver = model.getSolver()
rdata = amici.runAmiciSimulation(model, solver)
edata = [amici.ExpData(rdata, 0.01, 0)]
rdata = amici.runAmiciSimulations(model, solver, edata)
# check roundtripping of DataFrame conversion
df_edata = amici.getDataObservablesAsDataFrame(model, edata)
edata_reconstructed = amici.getEdataFromDataFrame(model, df_edata)
self.assertTrue(
np.isclose(
amici.edataToNumPyArrays(edata[0])['observedData'],
amici.edataToNumPyArrays(
edata_reconstructed[0])['observedData'],
).all())
self.assertTrue(
np.isclose(
amici.edataToNumPyArrays(edata[0])['observedDataStdDev'],
amici.edataToNumPyArrays(
edata_reconstructed[0])['observedDataStdDev'],
).all())
if edata[0].fixedParameters.size():
self.assertListEqual(
list(edata[0].fixedParameters),
list(edata_reconstructed[0].fixedParameters),
)
else:
self.assertListEqual(
list(model.getFixedParameters()),
list(edata_reconstructed[0].fixedParameters),
)
self.assertListEqual(
list(edata[0].fixedParametersPreequilibration),
list(edata_reconstructed[0].fixedParametersPreequilibration),
)
df_state = amici.getSimulationStatesAsDataFrame(model, edata, rdata)
self.assertTrue(
np.isclose(rdata[0]['x'],
df_state[list(model.getStateIds())].values).all())
df_obs = amici.getSimulationObservablesAsDataFrame(model, edata, rdata)
self.assertTrue(
np.isclose(rdata[0]['y'],
df_obs[list(model.getObservableIds())].values).all())
amici.getResidualsAsDataFrame(model, edata, rdata)