def test_all_override(condition_df_2_conditions, minimal_sbml_model):
# Condition-specific parameters overriding original parameters
condition_df = condition_df_2_conditions
_, sbml_model = minimal_sbml_model
add_global_parameter(sbml_model, 'dynamicParameter1')
add_global_parameter(sbml_model, 'dynamicParameter2')
add_global_parameter(sbml_model, 'observableParameter1_obs1')
add_global_parameter(sbml_model, 'observableParameter2_obs1')
add_global_parameter(sbml_model, 'observableParameter1_obs2')
measurement_df = pd.DataFrame(
data={
'observableId': ['obs1', 'obs2', 'obs1', 'obs2'],
'simulationConditionId':
['condition1', 'condition1', 'condition2', 'condition2'],
'preequilibrationConditionId': ['', '', '', ''],
'observableParameters': [
'obs1par1override;obs1par2cond1override',
'obs2par1cond1override',
'obs1par1override;obs1par2cond2override',
'obs2par1cond2override'
],
'noiseParameters': ['', '', '', '']
})
expected = [({}, {
'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'observableParameter1_obs1': 'obs1par1override',
'observableParameter2_obs1': 'obs1par2cond1override',
'observableParameter1_obs2': 'obs2par1cond1override',
}),
({}, {
'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'observableParameter1_obs1': 'obs1par1override',
'observableParameter2_obs1': 'obs1par2cond2override',
'observableParameter1_obs2': 'obs2par1cond2override'
})]
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
sbml_model=sbml_model)
assert actual == expected
def test_assert_model_parameters_in_condition_or_parameter_table():
document = libsbml.SBMLDocument(3, 1)
model = document.createModel()
model.setTimeUnits("second")
model.setExtentUnits("mole")
model.setSubstanceUnits('mole')
sbml.add_global_parameter(model, 'parameter1')
sbml.add_global_parameter(model, 'noiseParameter1_')
sbml.add_global_parameter(model, 'observableParameter1_')
lint.assert_model_parameters_in_condition_or_parameter_table(
model, pd.DataFrame(columns=['parameter1']), pd.DataFrame()
)
lint.assert_model_parameters_in_condition_or_parameter_table(
model, pd.DataFrame(), pd.DataFrame(index=['parameter1']))
with pytest.raises(AssertionError):
lint.assert_model_parameters_in_condition_or_parameter_table(
model,
pd.DataFrame(columns=['parameter1']),
pd.DataFrame(index=['parameter1']))
lint.assert_model_parameters_in_condition_or_parameter_table(
model, pd.DataFrame(), pd.DataFrame())
sbml.create_assigment_rule(model, assignee_id='parameter1',
formula='parameter2')
lint.assert_model_parameters_in_condition_or_parameter_table(
model, pd.DataFrame(), pd.DataFrame())
def test_no_condition_specific(condition_df_2_conditions,
minimal_sbml_model):
# Trivial case - no condition-specific parameters
condition_df = condition_df_2_conditions
measurement_df = pd.DataFrame(
data={
'observableId': ['obs1', 'obs2'],
'simulationConditionId': ['condition1', 'condition2'],
'preequilibrationConditionId': ['', ''],
'observableParameters': ['', ''],
'noiseParameters': ['', '']
})
_, sbml_model = minimal_sbml_model
add_global_parameter(sbml_model, 'dynamicParameter1')
add_global_parameter(sbml_model, 'dynamicParameter2')
add_global_parameter(sbml_model, 'dynamicParameter3')
expected = [({}, {
'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3'
}),
({}, {
'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3'
})]
actual = petab.get_optimization_to_simulation_parameter_mapping(
sbml_model=sbml_model,
measurement_df=measurement_df,
condition_df=condition_df,
)
assert actual == expected
def test_partial_override(condition_df_2_conditions,
minimal_sbml_model):
# Condition-specific parameters, keeping original parameters
condition_df = condition_df_2_conditions
_, sbml_model = minimal_sbml_model
add_global_parameter(sbml_model, 'dynamicParameter1')
add_global_parameter(sbml_model, 'observableParameter1_obs1')
add_global_parameter(sbml_model, 'observableParameter2_obs1')
add_global_parameter(sbml_model, 'observableParameter1_obs2')
measurement_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1', 'obs2', 'obs1', 'obs2'],
SIMULATION_CONDITION_ID: ['condition1', 'condition1',
'condition2', 'condition2'],
PREEQUILIBRATION_CONDITION_ID: ['', '', '', ''],
OBSERVABLE_PARAMETERS: ['obs1par1override;obs1par2cond1override',
'',
'obs1par1override;obs1par2cond2override',
'obs2par1cond2override'],
NOISE_PARAMETERS: ['', '', '', '']
})
parameter_df = pd.DataFrame(data={
PARAMETER_ID: [
'dynamicParameter1', 'obs1par1override',
'obs1par2cond1override', 'obs1par2cond2override',
'obs2par1cond2override'],
ESTIMATE: [1, 1, 1, 1, 1],
})
parameter_df.set_index(PARAMETER_ID, inplace=True)
expected = [({},
{'fixedParameter1': 1.0,
'dynamicParameter1': 'dynamicParameter1',
'observableParameter1_obs1': 'obs1par1override',
'observableParameter2_obs1': 'obs1par2cond1override',
'observableParameter1_obs2': np.nan,
},
{},
{'fixedParameter1': LIN,
'dynamicParameter1': LIN,
'observableParameter1_obs1': LIN,
'observableParameter2_obs1': LIN,
'observableParameter1_obs2': LIN}),
({},
{'fixedParameter1': 2.0,
'dynamicParameter1': 'dynamicParameter1',
'observableParameter1_obs1': 'obs1par1override',
'observableParameter2_obs1': 'obs1par2cond2override',
'observableParameter1_obs2': 'obs2par1cond2override'
},
{},
{'fixedParameter1': LIN,
'dynamicParameter1': LIN,
'observableParameter1_obs1': LIN,
'observableParameter2_obs1': LIN,
'observableParameter1_obs2': LIN}),
]
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
sbml_model=sbml_model, parameter_df=parameter_df
)
# Comparison with NaN containing expected results fails after pickling!
# Need to test first for correct NaNs, then for the rest.
assert np.isnan(expected[0][1]['observableParameter1_obs2'])
assert np.isnan(actual[0][1]['observableParameter1_obs2'])
expected[0][1]['observableParameter1_obs2'] = 0.0
actual[0][1]['observableParameter1_obs2'] = 0.0
assert actual == expected
def test_all_override(condition_df_2_conditions,
minimal_sbml_model):
# Condition-specific parameters overriding original parameters
condition_df = condition_df_2_conditions
_, sbml_model = minimal_sbml_model
add_global_parameter(sbml_model, 'dynamicParameter1')
add_global_parameter(sbml_model, 'dynamicParameter2')
measurement_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1', 'obs2', 'obs1', 'obs2'],
SIMULATION_CONDITION_ID: ['condition1', 'condition1',
'condition2', 'condition2'],
PREEQUILIBRATION_CONDITION_ID: ['', '', '', ''],
OBSERVABLE_PARAMETERS: ['obs1par1override;obs1par2cond1override',
'obs2par1cond1override',
'obs1par1override;obs1par2cond2override',
'obs2par1cond2override'],
NOISE_PARAMETERS: ['', '', '', '']
})
parameter_df = pd.DataFrame(data={
PARAMETER_ID: [
'dynamicParameter1', 'dynamicParameter2', 'obs1par1override',
'obs1par2cond1override', 'obs1par2cond2override',
'obs2par1cond1override', 'obs2par1cond2override'
],
ESTIMATE: [1] * 7
})
parameter_df.set_index(PARAMETER_ID, inplace=True)
expected = [
(
{},
{'fixedParameter1': 1.0,
'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'observableParameter1_obs1': 'obs1par1override',
'observableParameter2_obs1': 'obs1par2cond1override',
'observableParameter1_obs2': 'obs2par1cond1override',
},
{},
{'fixedParameter1': LIN,
'dynamicParameter1': LIN,
'dynamicParameter2': LIN,
'observableParameter1_obs1': LIN,
'observableParameter2_obs1': LIN,
'observableParameter1_obs2': LIN
}
),
(
{},
{'fixedParameter1': 2.0,
'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'observableParameter1_obs1': 'obs1par1override',
'observableParameter2_obs1': 'obs1par2cond2override',
'observableParameter1_obs2': 'obs2par1cond2override'
},
{},
{'fixedParameter1': LIN,
'dynamicParameter1': LIN,
'dynamicParameter2': LIN,
'observableParameter1_obs1': LIN,
'observableParameter2_obs1': LIN,
'observableParameter1_obs2': LIN
}
)
]
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
sbml_model=sbml_model, parameter_df=parameter_df)
assert actual == expected
# For one case we test parallel execution, which must yield the same
# result
os.environ[petab.ENV_NUM_THREADS] = "4"
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
sbml_model=sbml_model, parameter_df=parameter_df)
assert actual == expected
def test_no_condition_specific(condition_df_2_conditions,
minimal_sbml_model):
# Trivial case - no condition-specific parameters
condition_df = condition_df_2_conditions
measurement_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1', 'obs2'],
SIMULATION_CONDITION_ID: ['condition1', 'condition2'],
PREEQUILIBRATION_CONDITION_ID: ['', ''],
OBSERVABLE_PARAMETERS: ['', ''],
NOISE_PARAMETERS: ['', '']
})
_, sbml_model = minimal_sbml_model
add_global_parameter(sbml_model, 'dynamicParameter1').setValue(1.0)
add_global_parameter(sbml_model, 'dynamicParameter2').setValue(2.0)
add_global_parameter(sbml_model, 'dynamicParameter3').setValue(3.0)
# add species, which will have initial concentration in condition table
# but which should not show up in mapping
s = sbml_model.createSpecies()
s.setId("someSpecies")
condition_df["someSpecies"] = [0.0, 0.0]
# Test without parameter table
expected = [({},
{'dynamicParameter1': 1.0,
'dynamicParameter2': 2.0,
'dynamicParameter3': 3.0,
'fixedParameter1': 1.0},
{},
{'dynamicParameter1': LIN,
'dynamicParameter2': LIN,
'dynamicParameter3': LIN,
'fixedParameter1': LIN}),
({},
{'dynamicParameter1': 1.0,
'dynamicParameter2': 2.0,
'dynamicParameter3': 3.0,
'fixedParameter1': 2.0},
{},
{'dynamicParameter1': LIN,
'dynamicParameter2': LIN,
'dynamicParameter3': LIN,
'fixedParameter1': LIN}
)]
actual = petab.get_optimization_to_simulation_parameter_mapping(
sbml_model=sbml_model,
measurement_df=measurement_df,
condition_df=condition_df,
)
assert actual == expected
# Test with parameter table
parameter_df = pd.DataFrame(data={
PARAMETER_ID: ['dynamicParameter1', 'dynamicParameter2',
'dynamicParameter3'],
ESTIMATE: [0, 1, 1],
NOMINAL_VALUE: [11.0, 12.0, None],
PARAMETER_SCALE: [LOG, LOG10, LIN],
})
parameter_df.set_index(PARAMETER_ID, inplace=True)
expected = [({},
{'dynamicParameter1': 11.0,
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3',
'fixedParameter1': 1.0},
{},
{'dynamicParameter1': LIN,
'dynamicParameter2': LOG10,
'dynamicParameter3': LIN,
'fixedParameter1': LIN}),
({},
{'dynamicParameter1': 11.0,
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3',
'fixedParameter1': 2.0},
{},
{'dynamicParameter1': LIN,
'dynamicParameter2': LOG10,
'dynamicParameter3': LIN,
'fixedParameter1': LIN})
]
actual = petab.get_optimization_to_simulation_parameter_mapping(
sbml_model=sbml_model,
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df
)
assert actual == expected
# Test with applied scaling
expected = [
({},
{'dynamicParameter1': np.log(11.0),
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3',
'fixedParameter1': 1.0},
{},
{'dynamicParameter1': LOG,
'dynamicParameter2': LOG10,
'dynamicParameter3': LIN,
'fixedParameter1': LIN}),
({},
{'dynamicParameter1': np.log(11.0),
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3',
'fixedParameter1': 2.0},
{},
{'dynamicParameter1': LOG,
'dynamicParameter2': LOG10,
'dynamicParameter3': LIN,
'fixedParameter1': LIN}),
]
actual = petab.get_optimization_to_simulation_parameter_mapping(
sbml_model=sbml_model,
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
scaled_parameters=True
)
assert actual == expected
# Test without fixed overrides
expected = [
({},
{'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3',
'fixedParameter1': 1.0},
{},
{'dynamicParameter1': LOG,
'dynamicParameter2': LOG10,
'dynamicParameter3': LIN,
'fixedParameter1': LIN}),
({},
{'dynamicParameter1': 'dynamicParameter1',
'dynamicParameter2': 'dynamicParameter2',
'dynamicParameter3': 'dynamicParameter3',
'fixedParameter1': 2.0},
{},
{'dynamicParameter1': LOG,
'dynamicParameter2': LOG10,
'dynamicParameter3': LIN,
'fixedParameter1': LIN}),
]
actual = petab.get_optimization_to_simulation_parameter_mapping(
sbml_model=sbml_model,
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
fill_fixed_parameters=False
)
assert actual == expected
