def test_parameterized_condition_table(minimal_sbml_model):
condition_df = pd.DataFrame(
data={
CONDITION_ID: ['condition1', 'condition2', 'condition3'],
CONDITION_NAME: ['', 'Condition 2', ''],
'dynamicParameter1':
['dynamicOverride1_1', 'dynamicOverride1_2', 0]
})
condition_df.set_index(CONDITION_ID, inplace=True)
measurement_df = pd.DataFrame(
data={
SIMULATION_CONDITION_ID:
['condition1', 'condition2', 'condition3'],
OBSERVABLE_ID: ['obs1', 'obs2', 'obs1'],
OBSERVABLE_PARAMETERS: '',
NOISE_PARAMETERS: '',
})
parameter_df = pd.DataFrame(
data={
PARAMETER_ID: ['dynamicOverride1_1', 'dynamicOverride1_2'],
PARAMETER_NAME: ['', '...'],
ESTIMATE: [1, 1]
})
parameter_df.set_index(PARAMETER_ID, inplace=True)
document, model = minimal_sbml_model
model.createParameter().setId('dynamicParameter1')
assert petab.get_model_parameters(model) == ['dynamicParameter1']
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
sbml_model=model)
expected = [({}, {
'dynamicParameter1': 'dynamicOverride1_1'
}, {}, {
'dynamicParameter1': LIN
}),
({}, {
'dynamicParameter1': 'dynamicOverride1_2'
}, {}, {
'dynamicParameter1': LIN
}),
({}, {
'dynamicParameter1': 0
}, {}, {
'dynamicParameter1': LIN
})]
assert actual == expected
def test_parameterized_condition_table(minimal_sbml_model):
condition_df = pd.DataFrame(
data={
'conditionId': ['condition1', 'condition2', 'condition3'],
'conditionName': ['', 'Condition 2', ''],
'dynamicParameter1':
['dynamicOverride1_1', 'dynamicOverride1_2', 0]
})
condition_df.set_index('conditionId', inplace=True)
measurement_df = pd.DataFrame(
data={
'simulationConditionId':
['condition1', 'condition2', 'condition3'],
'observableId': ['obs1', 'obs2', 'obs1'],
'observableParameters': '',
'noiseParameters': '',
})
parameter_df = pd.DataFrame(
data={
'parameterId': ['dynamicOverride1_1', 'dynamicOverride1_2'],
'parameterName': ['', '...'], # ...
})
parameter_df.set_index('parameterId', inplace=True)
document, model = minimal_sbml_model
model.createParameter().setId('dynamicParameter1')
assert petab.get_model_parameters(model) == ['dynamicParameter1']
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
sbml_model=model)
expected = [({}, {
'dynamicParameter1': 'dynamicOverride1_1'
}), ({}, {
'dynamicParameter1': 'dynamicOverride1_2'
}), ({}, {
'dynamicParameter1': 0
})]
assert actual == expected
def test_parameterized_condition_table_changed_scale(
minimal_sbml_model):
"""Test overriding a dynamic parameter `overridee` with
- a log10 parameter to be estimated (condition 1)
- lin parameter not estimated (condition2)
- log10 parameter not estimated (condition 3)
- constant override (condition 4)"""
# overridden parameter
overridee_id = "overridee"
# set up model
document, model = minimal_sbml_model
p = model.createParameter()
p.setId(overridee_id)
p.setValue(2.0)
assert petab.get_model_parameters(model) == [overridee_id]
assert petab.get_model_parameters(model, with_values=True) \
== {overridee_id: 2.0}
# set up condition table
condition_df = pd.DataFrame(data={
CONDITION_ID:
['condition1', 'condition2', 'condition3', 'condition4'],
overridee_id:
['dynamicOverrideLog10', 'fixedOverrideLin',
'fixedOverrideLog10', 10.0]
})
condition_df.set_index('conditionId', inplace=True)
# set up measurement table
measurement_df = pd.DataFrame(data={
SIMULATION_CONDITION_ID:
['condition1', 'condition2', 'condition3', 'condition4'],
OBSERVABLE_ID:
['obs1', 'obs2', 'obs1', 'obs2'],
OBSERVABLE_PARAMETERS: '',
NOISE_PARAMETERS: '',
})
# set up parameter table
parameter_df = pd.DataFrame(data={
PARAMETER_ID: ['dynamicOverrideLog10',
'fixedOverrideLin',
'fixedOverrideLog10'],
ESTIMATE: [1, 0, 0],
NOMINAL_VALUE: [np.nan, -2, 1000],
PARAMETER_SCALE: [LOG10, LIN, LOG10]
})
parameter_df.set_index(PARAMETER_ID, inplace=True)
# test without preequilibration condition; unscaled known parameters
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
sbml_model=model
)
expected = [
({}, {overridee_id: 'dynamicOverrideLog10'},
{}, {overridee_id: LOG10}),
({}, {overridee_id: -2.0}, {}, {overridee_id: LIN}),
# not scaled:
({}, {overridee_id: 1000.0}, {}, {overridee_id: LIN}),
({}, {overridee_id: 10.0}, {}, {overridee_id: LIN})
]
assert actual == expected
# test without preequilibration condition; scaled known parameters
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
sbml_model=model,
scaled_parameters=True
)
expected = [
({}, {overridee_id: 'dynamicOverrideLog10'},
{}, {overridee_id: LOG10}),
({}, {overridee_id: -2.0}, {}, {overridee_id: LIN}),
# scaled fixedOverrideLog10:
({}, {overridee_id: 3.0}, {}, {overridee_id: LOG10}),
({}, {overridee_id: 10.0}, {}, {overridee_id: LIN})
]
assert actual == expected
# Add preeq condition
measurement_df[PREEQUILIBRATION_CONDITION_ID] = \
['condition1', 'condition1', 'condition3', 'condition3']
actual = petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
sbml_model=model)
expected = [
({overridee_id: 'dynamicOverrideLog10'},
{overridee_id: 'dynamicOverrideLog10'},
{overridee_id: LOG10}, {overridee_id: LOG10}),
({overridee_id: 'dynamicOverrideLog10'}, {overridee_id: -2.0},
{overridee_id: LOG10}, {overridee_id: LIN}),
# not rescaled:
({overridee_id: 1000.0}, {overridee_id: 1000.0},
{overridee_id: LIN}, {overridee_id: LIN}),
({overridee_id: 1000.0}, {overridee_id: 10.0},
{overridee_id: LIN}, {overridee_id: LIN})]
assert actual == expected
def test_parameterized_condition_table_changed_scale(minimal_sbml_model):
"""Test overriding a dynamic parameter `overridee` with
- a log10 parameter to be estimated (condition 1)
- lin parameter not estimated (condition2)
- log10 parameter not estimated (condition 3)
- constant override (condition 4)"""
document, model = minimal_sbml_model
model.createParameter().setId('overridee')
assert petab.get_model_parameters(model) == ['overridee']
condition_df = pd.DataFrame(
data={
'conditionId':
['condition1', 'condition2', 'condition3', 'condition4'],
'conditionName':
'',
'overridee': [
'dynamicOverrideLog10', 'fixedOverrideLin',
'fixedOverrideLog10', 10.0
]
})
condition_df.set_index('conditionId', inplace=True)
measurement_df = pd.DataFrame(
data={
'simulationConditionId':
['condition1', 'condition2', 'condition3', 'condition4'],
'observableId': ['obs1', 'obs2', 'obs1', 'obs2'],
'observableParameters':
'',
'noiseParameters':
'',
})
parameter_df = pd.DataFrame(
data={
'parameterId': [
'dynamicOverrideLog10', 'fixedOverrideLin',
'fixedOverrideLog10'
],
'parameterName':
'',
'estimate': [1, 0, 0],
'nominalValue': [np.nan, 2, -2],
'parameterScale': ['log10', 'lin', 'log10']
})
parameter_df.set_index('parameterId', inplace=True)
actual_par_map = \
petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
sbml_model=model
)
actual_scale_map = petab.get_optimization_to_simulation_scale_mapping(
parameter_df=parameter_df,
measurement_df=measurement_df,
mapping_par_opt_to_par_sim=actual_par_map)
expected_par_map = [
({}, {
'overridee': 'dynamicOverrideLog10'
}),
({}, {
'overridee': 2.0
}),
# rescaled:
({}, {
'overridee': 0.01
}),
({}, {
'overridee': 10.0
})
]
expected_scale_map = [({}, {
'overridee': 'log10'
}), ({}, {
'overridee': 'lin'
}), ({}, {
'overridee': 'lin'
}), ({}, {
'overridee': 'lin'
})]
assert actual_par_map == expected_par_map
assert actual_scale_map == expected_scale_map
# Add preeq condition
measurement_df['preequilibrationConditionId'] = \
['condition1', 'condition1', 'condition3', 'condition3']
actual_par_map = \
petab.get_optimization_to_simulation_parameter_mapping(
measurement_df=measurement_df,
condition_df=condition_df,
parameter_df=parameter_df,
sbml_model=model
)
actual_scale_map = petab.get_optimization_to_simulation_scale_mapping(
parameter_df=parameter_df,
measurement_df=measurement_df,
mapping_par_opt_to_par_sim=actual_par_map)
expected_par_map = [
({
'overridee': 'dynamicOverrideLog10'
}, {
'overridee': 'dynamicOverrideLog10'
}),
({
'overridee': 'dynamicOverrideLog10'
}, {
'overridee': 2.0
}),
# rescaled:
({
'overridee': 0.01
}, {
'overridee': 0.01
}),
({
'overridee': 0.01
}, {
'overridee': 10.0
})
]
expected_scale_map = [({
'overridee': 'log10'
}, {
'overridee': 'log10'
}), ({
'overridee': 'log10'
}, {
'overridee': 'lin'
}), ({
'overridee': 'lin'
}, {
'overridee': 'lin'
}), ({
'overridee': 'lin'
}, {
'overridee': 'lin'
})]
assert actual_par_map == expected_par_map
assert actual_scale_map == expected_scale_map