optional

Author: dweindl

petab/v1/priors.py

@@ -40,9 +40,6 @@
 
 __all__ = ["priors_to_measurements"]
 
-# TODO: does anybody really rely on the old behavior?
-USE_PROPER_TRUNCATION = True
-
 
 class Prior:
     """A PEtab parameter prior.
@@ -61,6 +58,15 @@ class Prior:
         on the `parameter_scale` scale).
     :param bounds: The untransformed bounds of the sample (lower, upper).
     :param transformation: The transformation of the distribution.
+    :param bounds_truncate: Whether the generated prior will be truncated
+        at the bounds.
+        If ``True``, the probability density will be rescaled
+        accordingly and the sample is generated from the truncated
+        distribution.
+        If ``False``, the probability density will not account for the
+        bounds, but any parameter samples outside the bounds will be set to
+        the value of the closest bound. In this case, the PDF might not match
+        the sample.
     """
 
     def __init__(
@@ -69,6 +75,7 @@ def __init__(
         parameters: tuple,
         bounds: tuple = None,
         transformation: str = C.LIN,
+        bounds_truncate: bool = True,
     ):
         if transformation not in C.PARAMETER_SCALES:
             raise ValueError(
@@ -91,7 +98,7 @@ def __init__(
         self._bounds = bounds
         self._transformation = transformation
 
-        truncation = bounds if USE_PROPER_TRUNCATION else None
+        truncation = bounds if bounds_truncate else None
         if truncation is not None:
             # for uniform, we don't want to implement truncation and just
             #  adapt the distribution parameters
@@ -235,12 +242,16 @@ def neglogprior(self, x):
 
     @staticmethod
     def from_par_dict(
-        d, type_=Literal["initialization", "objective"]
+        d,
+        type_=Literal["initialization", "objective"],
+        bounds_truncate: bool = True,
     ) -> Prior:
         """Create a distribution from a row of the parameter table.
 
         :param d: A dictionary representing a row of the parameter table.
         :param type_: The type of the distribution.
+        :param bounds_truncate: Whether the generated prior will be truncated
+            at the bounds.
         :return: A distribution object.
         """
         dist_type = d.get(f"{type_}PriorType", C.PARAMETER_SCALE_UNIFORM)
@@ -268,6 +279,7 @@ def from_par_dict(
             parameters=params,
             bounds=(d[C.LOWER_BOUND], d[C.UPPER_BOUND]),
             transformation=pscale,
+            bounds_truncate=bounds_truncate,
         )
 
 

tests/v1/test_priors.py

@@ -156,7 +156,9 @@ def apply_parameter_values(row):
     ]
     priors = [
         Prior.from_par_dict(
-            petab_problem_priors.parameter_df.loc[par_id], type_="objective"
+            petab_problem_priors.parameter_df.loc[par_id],
+            type_="objective",
+            bounds_truncate=False,
         )
         for par_id in parameter_ids
     ]