def create_parameter_table(problem: petab.Problem, nominal_parameters):
"""Create PEtab parameter table"""
df = petab.create_parameter_df(problem.sbml_model,
problem.condition_df,
problem.observable_df,
problem.measurement_df,
include_optional=True,
lower_bound=1e-3,
upper_bound=1e5)
df['hierarchicalOptimization'] = 0
df.loc['scaling_x1_common', 'hierarchicalOptimization'] = 1
df.loc['offset_x2_batch_0', 'hierarchicalOptimization'] = 1
df.loc['offset_x2_batch_1', 'hierarchicalOptimization'] = 1
df.loc['x1withsigma_sigma', 'hierarchicalOptimization'] = 1
for pid, val in nominal_parameters.items():
if pid in df.index:
df.loc[pid, ptc.NOMINAL_VALUE] = val
df.loc[pid, ptc.PARAMETER_SCALE] = ptc.LOG10
df.loc[pid, ptc.ESTIMATE] = 1
elif pid.startswith('noiseParameter') \
or pid.startswith('observableParameter'):
continue
else:
print("extra parameter", pid, val)
# offsets can be negative: adapt scaling and bounds:
offsets = df.index.str.startswith('offset_')
df.loc[offsets, ptc.PARAMETER_SCALE] = ptc.LIN
problem.parameter_df = df
def test_create_parameter_df(condition_df_2_conditions):
document = libsbml.SBMLDocument(3, 1)
model = document.createModel()
model.setTimeUnits("second")
model.setExtentUnits("mole")
model.setSubstanceUnits('mole')
s = model.createSpecies()
s.setId('x1')
petab.sbml.add_global_parameter(model,
parameter_id='fixedParameter1',
parameter_name='FixedParameter1',
value=2.0)
petab.sbml.add_global_parameter(model,
parameter_id='p0',
parameter_name='Parameter 0',
value=3.0)
petab.sbml.add_model_output_with_sigma(sbml_model=model,
observable_id='obs1',
observable_name='Observable 1',
observable_formula='x1')
petab.add_model_output_with_sigma(sbml_model=model,
observable_id='obs2',
observable_name='Observable 2',
observable_formula='2*x1')
measurement_df = pd.DataFrame(
data={
'observableId': ['obs1', 'obs2'],
'observableParameters': ['', 'p1;p2'],
'noiseParameters': ['p3;p4', 'p5']
})
parameter_df = petab.create_parameter_df(model, condition_df_2_conditions,
measurement_df)
# first model parameters, then row by row noise and sigma overrides
expected = ['p0', 'p3', 'p4', 'p1', 'p2', 'p5']
actual = parameter_df.index.values.tolist()
assert actual == expected
assert parameter_df.loc['p0', 'nominalValue'] == 3.0
def test_create_parameter_df(
minimal_sbml_model, # pylint: disable=W0621
condition_df_2_conditions): # pylint: disable=W0621
"""Test petab.create_parameter_df."""
_, model = minimal_sbml_model
s = model.createSpecies()
s.setId('x1')
petab.sbml.add_global_parameter(
model,
parameter_id='fixedParameter1',
parameter_name='FixedParameter1',
value=2.0)
petab.sbml.add_global_parameter(
model,
parameter_id='p0',
parameter_name='Parameter 0',
value=3.0)
observable_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1', 'obs2'],
OBSERVABLE_FORMULA: ['x1', '2*x1']
}).set_index(OBSERVABLE_ID)
# Add assignment rule target which should be ignored
petab.add_global_parameter(sbml_model=model,
parameter_id='assignment_target')
petab.create_assigment_rule(sbml_model=model,
assignee_id='assignment_target', formula='1.0')
measurement_df = pd.DataFrame(data={
OBSERVABLE_ID: ['obs1', 'obs2'],
OBSERVABLE_PARAMETERS: ['', 'p1;p2'],
NOISE_PARAMETERS: ['p3;p4', 'p5']
})
parameter_df = petab.create_parameter_df(
model,
condition_df_2_conditions,
observable_df,
measurement_df)
# first model parameters, then row by row noise and sigma overrides
expected = ['p3', 'p4', 'p1', 'p2', 'p5']
actual = parameter_df.index.values.tolist()
assert actual == expected
# test with condition parameter override:
condition_df_2_conditions.loc['condition2', 'fixedParameter1'] \
= 'overrider'
expected = ['p3', 'p4', 'p1', 'p2', 'p5', 'overrider']
parameter_df = petab.create_parameter_df(
model,
condition_df_2_conditions,
observable_df,
measurement_df)
actual = parameter_df.index.values.tolist()
assert actual == expected
# test with optional parameters
expected = ['p0', 'p3', 'p4', 'p1', 'p2', 'p5', 'overrider']
parameter_df = petab.create_parameter_df(
model,
condition_df_2_conditions,
observable_df,
measurement_df,
include_optional=True)
actual = parameter_df.index.values.tolist()
assert actual == expected
assert parameter_df.loc['p0', NOMINAL_VALUE] == 3.0