Add arrays test for Wald, remove useless test and simplify sampling f…

…rom numpy wald

pymc3/distributions/continuous.py

@@ -891,7 +891,7 @@ class WaldRV(RandomVariable):
 
     @classmethod
     def rng_fn(cls, rng, mu, lam, alpha, size):
-        return getattr(np.random.RandomState, cls.name)(rng, mu, lam, size) + alpha
+        return rng.wald(mu, lam, size=size) + alpha
 
 
 wald = WaldRV()

pymc3/tests/test_distributions.py

@@ -2807,21 +2807,6 @@ def test_lower_bounded_broadcasted(self):
         assert lower_interval.value == -1
         assert upper_interval is None
 
-    def test_rich_context(self):
-        with Model() as model:
-            sigma = TruncatedNormal("lower_bounded", mu=2, sigma=1.5, lower=0, upper=None)
-            mu = TruncatedNormal("upper_bounded", mu=0, sigma=2, lower=None, upper=3)
-            Normal("normal", mu=mu, sigma=sigma, observed=[1.3, -1.4, 2.0])
-        (
-            (_, _, lower, upper),
-            lower_interval,
-            upper_interval,
-        ) = self.get_dist_params_and_interval_bounds(model, "upper_bounded")
-        assert lower.value == -np.inf
-        assert upper.value == 3
-        assert lower_interval is None
-        assert upper_interval.value == 3
-
 
 @pytest.mark.xfail(reason="LaTeX repr and str no longer applicable")
 class TestStrAndLatexRepr:

pymc3/tests/test_distributions_random.py

@@ -681,6 +681,31 @@ class TestWaldMuPhi(TestWald):
     ]
 
 
+class TestTruncatedNormalUpperArray(BaseTestDistribution):
+    pymc_dist = pm.TruncatedNormal
+    lower, upper, mu, tau = (
+        np.array([-np.inf, -np.inf]),
+        np.array([3, 2]),
+        np.array([0, 0]),
+        np.array(
+            [
+                1,
+                1,
+            ]
+        ),
+    )
+    size = (15, 2)
+    tau, sigma = get_tau_sigma(tau=tau, sigma=None)
+    pymc_dist_params = {"mu": mu, "tau": tau, "upper": upper}
+    expected_rv_op_params = {"mu": mu, "sigma": sigma, "lower": lower, "upper": upper}
+    reference_dist_params = {"loc": mu, "scale": sigma, "a": lower, "b": (upper - mu) / sigma}
+    reference_dist = seeded_scipy_distribution_builder("truncnorm")
+    tests_to_run = [
+        "check_pymc_params_match_rv_op",
+        "check_pymc_draws_match_reference",
+    ]
+
+
 class TestSkewNormal(BaseTestDistribution):
     pymc_dist = pm.SkewNormal
     pymc_dist_params = {"mu": 0.0, "sigma": 1.0, "alpha": 5.0}