_bounds_truncate

Author: dweindl

petab/v1/priors.py

@@ -59,8 +59,8 @@ 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.
+    :param _bounds_truncate: **deprecated**
+        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.
@@ -75,7 +75,7 @@ def __init__(
         parameters: tuple,
         bounds: tuple = None,
         transformation: str = C.LIN,
-        bounds_truncate: bool = True,
+        _bounds_truncate: bool = True,
     ):
         if transformation not in C.PARAMETER_SCALES:
             raise ValueError(
@@ -97,7 +97,7 @@ def __init__(
         self._parameters = parameters
         self._bounds = bounds
         self._transformation = transformation
-        self._bounds_truncate = bounds_truncate
+        self._bounds_truncate = _bounds_truncate
 
         truncation = bounds
         if truncation is not None:
@@ -267,14 +267,14 @@ def neglogprior(
     def from_par_dict(
         d,
         type_=Literal["initialization", "objective"],
-        bounds_truncate: bool = True,
+        _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.
+        :param _bounds_truncate: Whether the generated prior will be truncated
+            at the bounds. **deprecated**.
         :return: A distribution object.
         """
         dist_type = d.get(f"{type_}PriorType", C.PARAMETER_SCALE_UNIFORM)
@@ -300,7 +300,7 @@ def from_par_dict(
             parameters=params,
             bounds=(d[C.LOWER_BOUND], d[C.UPPER_BOUND]),
             transformation=pscale,
-            bounds_truncate=bounds_truncate,
+            _bounds_truncate=_bounds_truncate,
         )
 
 
@@ -329,6 +329,12 @@ def priors_to_measurements(problem: Problem):
     - `measurement`: the PDF location
     - `noiseFormula`: the PDF scale
 
+    .. warning::
+
+       This function does not account for the truncation of the prior by
+       the bounds in the parameter table. The resulting observable will
+       not be truncated, and the PDF will not be rescaled.
+
     Arguments
     ---------
     problem:

petab/v1/sampling.py

@@ -32,7 +32,6 @@ def sample_from_prior(
         tuple(p_params),
         bounds=tuple(bounds),
         transformation=scaling,
-        bounds_truncate=True,
     )
     return prior.sample(shape=(n_starts,))
 
@@ -79,7 +78,8 @@ def sample_parameter_startpoints(
     return np.array(
         [
             Prior.from_par_dict(
-                row, type_="initialization", bounds_truncate=True
+                row,
+                type_="initialization",
             ).sample(n_starts, x_scaled=True)
             for row in par_to_estimate.to_dict("records")
         ]

tests/v1/test_priors.py

@@ -196,7 +196,7 @@ def apply_parameter_values(row):
         Prior.from_par_dict(
             petab_problem_priors.parameter_df.loc[par_id],
             type_="objective",
-            bounds_truncate=False,
+            _bounds_truncate=False,
         )
         for par_id in parameter_ids
     ]