Fix PyMC and PyTensor deprecation warnings (#130)

* Add tests for CeleriteNormalRV random variable and support point

* Update `CeleriteNormalRV` to use signature

* Replace `moment` by `support_point` in `CeleriteNormalRV`

* Add coments to test

Needed a commit to trigger Github Actions

python/celerite2/pymc/distribution.py

@@ -31,8 +31,7 @@ def safe_celerite_normal(rng, mean, norm, t, c, U, W, d, size=None):
 
 class CeleriteNormalRV(RandomVariable):
     name = "celerite_normal"
-    ndim_supp = 1
-    ndims_params = [1, 0, 1, 1, 2, 2, 1]
+    signature = "(i_mean),(),(i_t),(i_c),(i_U1,i_U2),(i_W1,i_W2),(i_d)->(i)"
     dtype = "floatX"
     _print_name = ("CeleriteNormal", "\\operatorname{CeleriteNormal}")
 
@@ -46,12 +45,15 @@ def _supp_shape_from_params(self, dist_params, param_shapes=None):
 
     @classmethod
     def rng_fn(cls, rng, mean, norm, t, c, U, W, d, size):
+        # Hardcoded because no longer set as class attribute
+        # Ref: https://github.com/pymc-devs/pytensor/issues/866
+        ndims_params = [1, 0, 1, 1, 2, 2, 1]
         if any(
             x.ndim > n
-            for n, x in zip(cls.ndims_params, [mean, norm, t, c, U, W, d])
+            for n, x in zip(ndims_params, [mean, norm, t, c, U, W, d])
         ):
             mean, norm, t, c, U, W, d = broadcast_params(
-                [mean, norm, t, c, U, W, d], cls.ndims_params
+                [mean, norm, t, c, U, W, d], ndims_params
             )
             size = tuple(size or ())
 
@@ -111,12 +113,12 @@ def dist(cls, mean, norm, t, c, U, W, d, **kwargs):
         mean = pt.broadcast_arrays(mean, t)[0]
         return super().dist([mean, norm, t, c, U, W, d], **kwargs)
 
-    def moment(rv, size, mean, *args):
-        moment = mean
+    def support_point(rv, size, mean, *args):
+        support_point = mean
         if not rv_size_is_none(size):
-            moment_size = pt.concatenate([size, [mean.shape[-1]]])
-            moment = pt.full(moment_size, mean)
-        return moment
+            support_size = pt.concatenate([size, [mean.shape[-1]]])
+            support_point = pt.full(support_size, mean)
+        return support_point
 
     def logp(value, mean, norm, t, c, U, W, d):
         ok = pt.all(pt.gt(d, 0.0))

python/test/pymc/test_pymc_distribution.py

@@ -0,0 +1,44 @@
+import numpy as np
+import pytest
+
+pytest.importorskip("celerite2.pymc")
+
+try:
+    from pymc.testing import assert_support_point_is_expected
+
+    from celerite2.pymc import GaussianProcess, terms
+    from celerite2.pymc.distribution import CeleriteNormalRV
+except (ImportError, ModuleNotFoundError):
+    pass
+
+
+def test_celerite_normal_rv():
+    # Test that ndims_params and ndim_supp have the expected value
+    # now that they are created from signature
+    celerite_normal = CeleriteNormalRV()
+    assert celerite_normal.ndim_supp == 1
+    assert tuple(celerite_normal.ndims_params) == (1, 0, 1, 1, 2, 2, 1)
+
+
+@pytest.mark.parametrize(
+    "t, mean, size, expected",
+    [
+        (np.arange(5, dtype=float), 0.0, None, np.full(5, 0.0)),
+        (
+            np.arange(5, dtype=float),
+            np.arange(5, dtype=float),
+            None,
+            np.arange(5, dtype=float),
+        ),
+    ],
+)
+def test_celerite_normal_support_point(t, mean, size, expected):
+    # Test that support point has the expected shape and value
+    pm = pytest.importorskip("pymc")
+
+    with pm.Model() as model:
+        term = terms.SHOTerm(S0=1.0, w0=0.5, Q=3.0)
+        gp = GaussianProcess(term, t=t, mean=mean)
+        # NOTE: Name must be "x" for assert function to work
+        gp.marginal("x", size=size)
+    assert_support_point_is_expected(model, expected)