dont wrap np.pi

Author: williambdean

pymc/dims/distributions/custom.py

@@ -104,9 +104,7 @@ def logitnormal_dist(mu, sigma):
 
         def normal_logp(value, mu, sigma):
             v = value.values  # strip dims for tensor ops
-            return pt.sum(
-                pt.pow(v - mu, 2) / (2 * sigma**2) + pt.log(sigma * pt.sqrt(2 * pt.constant(np.pi)))
-            )
+            return pt.sum(pt.pow(v - mu, 2) / (2 * sigma**2) + pt.log(sigma * pt.sqrt(2 * np.pi)))
 
 
         pmd.CustomDist("y", mu, sigma, logp=normal_logp, observed=y, dims="city")

tests/dims/distributions/test_custom.py

@@ -73,9 +73,7 @@ def test_logp_basic(self):
 
         def normal_logp(value, mu, sigma):
             v = value.values
-            return pt.sum(
-                -0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * pt.constant(np.pi)))
-            )
+            return pt.sum(-0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * np.pi)))
 
         coords = {"city": range(5)}
         rng = np.random.default_rng(42)
@@ -101,9 +99,7 @@ def test_random_logp(self):
 
         def normal_logp(value, mu, sigma):
             v = value.values
-            return pt.sum(
-                -0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * pt.constant(np.pi)))
-            )
+            return pt.sum(-0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * np.pi)))
 
         def normal_random(mu, sigma, rng=None, size=None):
             return rng.normal(loc=mu, scale=sigma, size=size)
@@ -135,9 +131,7 @@ def test_logcdf(self):
 
         def normal_logp(value, mu, sigma):
             v = value.values
-            return pt.sum(
-                -0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * pt.constant(np.pi)))
-            )
+            return pt.sum(-0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * np.pi)))
 
         def normal_logcdf(value, mu, sigma):
             v = value.values
@@ -169,9 +163,7 @@ def test_mu_as_model_var(self):
 
         def normal_logp(value, mu, sigma):
             v = value.values
-            return pt.sum(
-                -0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * pt.constant(np.pi)))
-            )
+            return pt.sum(-0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * np.pi)))
 
         coords = {"city": range(5)}
         rng = np.random.default_rng(42)
@@ -197,9 +189,7 @@ def test_support_point(self):
 
         def normal_logp(value, mu, sigma):
             v = value.values
-            return pt.sum(
-                -0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * pt.constant(np.pi)))
-            )
+            return pt.sum(-0.5 * ((v - mu) / sigma) ** 2 - pt.log(sigma * pt.sqrt(2 * np.pi)))
 
         def custom_support_point(rv, size, mu, sigma):
             return pt.full_like(rv, mu)