Implement sampling for v2 prior distributions (#461)

Author: dweindl

petab/v1/distributions.py

@@ -508,6 +508,9 @@ def _cdf_untransformed_untruncated(self, x) -> np.ndarray | float:
     def _ppf_untransformed_untruncated(self, q) -> np.ndarray | float:
         return cauchy.ppf(q, loc=self._loc, scale=self._scale)
 
+    def _sample(self, shape=None) -> np.ndarray | float:
+        return cauchy.rvs(loc=self._loc, scale=self._scale, size=shape)
+
     @property
     def loc(self) -> float:
         """The location parameter of the underlying distribution."""
@@ -541,14 +544,16 @@ class ChiSquare(Distribution):
 
     def __init__(
         self,
-        dof: int,
+        dof: int | float,
         trunc: tuple[float, float] | None = None,
         log: bool | float = False,
     ):
-        if not dof.is_integer() or dof < 1:
-            raise ValueError(
-                f"`dof' must be a positive integer, but was `{dof}'."
-            )
+        if isinstance(dof, float):
+            if not dof.is_integer() or dof < 1:
+                raise ValueError(
+                    f"`dof' must be a positive integer, but was `{dof}'."
+                )
+            dof = int(dof)
 
         self._dof = dof
         super().__init__(log=log, trunc=trunc)
@@ -565,6 +570,9 @@ def _cdf_untransformed_untruncated(self, x) -> np.ndarray | float:
     def _ppf_untransformed_untruncated(self, q) -> np.ndarray | float:
         return chi2.ppf(q, df=self._dof)
 
+    def _sample(self, shape=None) -> np.ndarray | float:
+        return chi2.rvs(df=self._dof, size=shape)
+
     @property
     def dof(self) -> int:
         """The degrees of freedom parameter."""
@@ -602,6 +610,9 @@ def _cdf_untransformed_untruncated(self, x) -> np.ndarray | float:
     def _ppf_untransformed_untruncated(self, q) -> np.ndarray | float:
         return expon.ppf(q, scale=self._scale)
 
+    def _sample(self, shape=None) -> np.ndarray | float:
+        return expon.rvs(scale=self._scale, size=shape)
+
     @property
     def scale(self) -> float:
         """The scale parameter of the underlying distribution."""
@@ -650,6 +661,9 @@ def _cdf_untransformed_untruncated(self, x) -> np.ndarray | float:
     def _ppf_untransformed_untruncated(self, q) -> np.ndarray | float:
         return gamma.ppf(q, a=self._shape, scale=self._scale)
 
+    def _sample(self, shape=None) -> np.ndarray | float:
+        return gamma.rvs(a=self._shape, scale=self._scale, size=shape)
+
     @property
     def shape(self) -> float:
         """The shape parameter of the underlying distribution."""
@@ -700,6 +714,9 @@ def _cdf_untransformed_untruncated(self, x) -> np.ndarray | float:
     def _ppf_untransformed_untruncated(self, q) -> np.ndarray | float:
         return rayleigh.ppf(q, scale=self._scale)
 
+    def _sample(self, shape=None) -> np.ndarray | float:
+        return rayleigh.rvs(scale=self._scale, size=shape)
+
     @property
     def scale(self) -> float:
         """The scale parameter of the underlying distribution."""

tests/v1/test_distributions.py

@@ -34,6 +34,11 @@
         Normal(2, 1, log=10),
         Laplace(1, 2, trunc=(1, 2)),
         Laplace(1, 0.5, log=True, trunc=(0.5, 8)),
+        Cauchy(2, 1),
+        ChiSquare(4),
+        Exponential(1),
+        Gamma(3, 5),
+        Rayleigh(3),
     ],
 )
 def test_sample_matches_pdf(distribution):