--amend

Author: dweindl

petab/v1/distributions.py

@@ -150,6 +150,15 @@ def pdf(self, x):
 
         return self._pdf(x) * coeff
 
+    def neglogprior(self, x):
+        """Negative log-prior at x.
+
+        :param x: The value at which to evaluate the negative log-prior.
+            ``x`` is assumed to be on the parameter scale.
+        :return: The negative log-prior at ``x``.
+        """
+        return -np.log(self.pdf(x))
+
     @staticmethod
     def from_par_dict(
         d, type_=Literal["initialization", "objective"]

tests/v1/test_priors.py

@@ -5,10 +5,10 @@
 import numpy as np
 import pandas as pd
 import pytest
-from scipy.stats import norm
 
 import petab.v1
-from petab.v1 import get_simulation_conditions
+from petab.v1 import ESTIMATE, get_simulation_conditions
+from petab.v1.distributions import Distribution
 from petab.v1.priors import priors_to_measurements
 
 
@@ -102,33 +102,34 @@ def test_priors_to_measurements(problem_id):
 
     # get prior objective function contribution
     parameter_ids = petab_problem_priors.parameter_df.index.values[
-        (petab_problem_priors.parameter_df[petab.v1.ESTIMATE] == 1)
+        (petab_problem_priors.parameter_df[ESTIMATE] == 1)
         & petab_problem_priors.parameter_df[
             petab.v1.OBJECTIVE_PRIOR_TYPE
         ].notna()
     ]
-    priors = petab.v1.get_priors_from_df(
-        petab_problem_priors.parameter_df,
-        mode="objective",
-        parameter_ids=parameter_ids,
-    )
+    priors = [
+        Distribution.from_par_dict(
+            petab_problem_priors.parameter_df.loc[par_id], type_="objective"
+        )
+        for par_id in parameter_ids
+    ]
     prior_contrib = 0
+    x_scaled_dict = dict(
+        zip(
+            petab_problem_priors.x_free_ids,
+            petab_problem_priors.x_nominal_free_scaled,
+            strict=True,
+        )
+    )
     for parameter_id, prior in zip(parameter_ids, priors, strict=True):
-        prior_type, prior_pars, par_scale, par_bounds = prior
-        if prior_type == petab.v1.PARAMETER_SCALE_NORMAL:
-            prior_contrib += norm.logpdf(
-                petab_problem_priors.x_nominal_free_scaled[
-                    petab_problem_priors.x_free_ids.index(parameter_id)
-                ],
-                loc=prior_pars[0],
-                scale=prior_pars[1],
-            )
+        if prior.type == petab.v1.PARAMETER_SCALE_NORMAL:
+            prior_contrib -= prior.neglogprior(x_scaled_dict[parameter_id])
         else:
             # enable other models, once libpetab has proper support for
             #  evaluating the prior contribution. until then, two test
             #  problems should suffice
             assert problem_id == "Raimundez_PCB2020"
-            pytest.skip(f"Prior type {prior_type} not implemented")
+            pytest.skip(f"Prior type {prior.type} not implemented")
 
     assert np.isclose(
         llh_priors + prior_contrib, llh_measurements, rtol=1e-3, atol=1e-16