def test_calculate_llh(models): # pylint: disable=W0621
"""Test calculate.calculate_llh."""
for i_model, model in enumerate(models):
print(f"Model {i_model}")
(measurement_df, observable_df, parameter_df, simulation_df, _, _,
expected_llh) = model
llh = calculate_llh(measurement_df, simulation_df, observable_df,
parameter_df)
assert llh == pytest.approx(expected_llh) or expected_llh is None
def petab_llh(pp, sim):
"""Computes the llh based on the simulation data frame
:param pp: petab problem
:type pp: petab.Problem
:param sim: simulation data frame
:type sim: pd.DataFrame
:return: the llh as calculated by petab
"""
return petab.calculate_llh(pp.measurement_df, sim, pp.observable_df,
pp.parameter_df)
# write files
write_problem(test_id=test_id,
parameter_df=parameter_df,
condition_dfs=[condition_df],
observable_dfs=[observable_df],
measurement_dfs=[measurement_df])
# solutions ------------------------------------------------------------------
simulation_df = measurement_df.copy(deep=True).rename(
columns={MEASUREMENT: SIMULATION})
simulation_df[SIMULATION] = [
analytical_a(10, 1, 0, 0.8, 0.6),
analytical_b(10, 1, 0, 0.8, 0.6),
analytical_a(10, 1, 0, 0.8, 0.6) + analytical_b(10, 1, 0, 0.8, 0.6)
]
chi2 = petab.calculate_chi2(measurement_df, simulation_df, observable_df,
parameter_df)
llh = petab.calculate_llh(measurement_df, simulation_df, observable_df,
parameter_df)
print(llh)
# write files
write_solution(test_id=test_id,
chi2=chi2,
llh=llh,
simulation_dfs=[simulation_df])
