lint

Author: OlaRonning

pyro/distributions/sine_bivariate_von_mises.py

@@ -45,9 +45,9 @@ class SineBivariateVonMises(TorchDistribution):
 
             \frac{\rho^2}{\kappa_1\kappa_2} \rightarrow 1
 
-        because the distribution becomes increasingly bimodal. To avoid inefficient sampling use the 
-        `weighted_correlation` parameter with a skew away from one (e.g., 
-        `TransformedDistribution(Beta(5,5), AffineTransform(loc=-1, scale=2))`). The `weighted_correlation` 
+        because the distribution becomes increasingly bimodal. To avoid inefficient sampling use the
+        `weighted_correlation` parameter with a skew away from one (e.g.,
+        `TransformedDistribution(Beta(5,5), AffineTransform(loc=-1, scale=2))`). The `weighted_correlation`
         should be in [-1,1].
 
     .. note:: The correlation and weighted_correlation params are mutually exclusive.
@@ -141,7 +141,8 @@ def norm_const(self):
             - m * torch.log(4 * torch.prod(conc, dim=-1))
         )
         num_I1terms = torch.maximum(
-            torch.tensor(501), torch.max(self.phi_concentration) + torch.max(self.psi_concentration)
+            torch.tensor(501),
+            torch.max(self.phi_concentration) + torch.max(self.psi_concentration),
         ).int()
 
         fs += log_I1(m.max(), conc, num_I1terms).sum(-1)

tests/distributions/test_sine_bivariate_von_mises.py

@@ -131,14 +131,15 @@ def guide(data):
 
         assert_equal(expected[k].squeeze(), actual.squeeze(), 9e-2)
 
+
 @pytest.mark.parametrize("conc", [1.0, 10.0, 1000.0, 10000.0])
 def test_sine_bivariate_von_mises_norm(conc):
     dist = SineBivariateVonMises(0, 0, conc, conc, 0.0)
     num_samples = 500
     x = torch.linspace(-torch.pi, torch.pi, num_samples)
     y = torch.linspace(-torch.pi, torch.pi, num_samples)
-    mesh = torch.stack(torch.meshgrid(x, y, indexing='ij'), axis=-1)
+    mesh = torch.stack(torch.meshgrid(x, y, indexing="ij"), axis=-1)
     integral_torus = (
         torch.exp(dist.log_prob(mesh)) * (2 * torch.pi) ** 2 / num_samples**2
     ).sum()
-    assert torch.allclose(integral_torus, torch.tensor(1.0), rtol=1e-2)
+    assert torch.allclose(integral_torus, torch.tensor(1.0), rtol=1e-2)