Formatting and linting.

pyro/distributions/stable.py

@@ -107,7 +107,7 @@ class Stable(TorchDistribution):
 
     This implements a reparametrized sampler :meth:`rsample` , and a relatively
     expensive :meth:`log_prob` calculation by numerical integration which makes
-    inference slow (compared to other distributions) , but with better 
+    inference slow (compared to other distributions) , but with better
     convergence properties especially for :math:`\alpha`-stable distributions
     that are skewed (see the ``skew`` parameter below). Faster
     inference can be performed using either likelihood-free algorithms such as

pyro/infer/reparam/stable.py

@@ -44,7 +44,11 @@ def apply(self, msg):
         is_observed = msg["is_observed"]
 
         fn, event_dim = self._unwrap(fn)
-        assert isinstance(fn, dist.Stable) and fn.coords == "S0" and not isinstance(fn, dist.StableWithLogProb)
+        assert (
+            isinstance(fn, dist.Stable)
+            and fn.coords == "S0"
+            and not isinstance(fn, dist.StableWithLogProb)
+        )
         if is_observed:
             raise NotImplementedError(
                 f"At pyro.sample({repr(name)},...), "
@@ -101,7 +105,11 @@ def apply(self, msg):
         is_observed = msg["is_observed"]
 
         fn, event_dim = self._unwrap(fn)
-        assert isinstance(fn, dist.Stable) and fn.coords == "S0" and not isinstance(fn, dist.StableWithLogProb)
+        assert (
+            isinstance(fn, dist.Stable)
+            and fn.coords == "S0"
+            and not isinstance(fn, dist.StableWithLogProb)
+        )
         if is_validation_enabled():
             if not (fn.skew == 0).all():
                 raise ValueError("SymmetricStableReparam found nonzero skew")

tests/distributions/conftest.py

@@ -515,13 +515,18 @@ def __init__(self, von_loc, von_conc, skewness):
                 "skew": 0.0,
                 "scale": 2.0,
                 "loc": -2.0,
-                "test_data": [10.0, -10.0]
+                "test_data": [10.0, -10.0],
             },
         ],
         scipy_arg_fn=lambda stability, skew, scale, loc: (
             (),
-            {"alpha": np.array(stability), "beta": np.array(skew), "scale": np.array(scale), "loc": np.array(loc)}
-        )
+            {
+                "alpha": np.array(stability),
+                "beta": np.array(skew),
+                "scale": np.array(scale),
+                "loc": np.array(loc),
+            },
+        ),
     ),
     Fixture(
         pyro_dist=dist.MultivariateStudentT,

tests/distributions/test_distributions.py

@@ -171,7 +171,7 @@ def test_mean(continuous_dist):
         "SineBivariateVonMises",
         "VonMises",
         "ProjectedNormal",
-        "Stable"
+        "Stable",
     ]:
         pytest.xfail(reason="Euclidean mean is not defined")
     for i in range(continuous_dist.get_num_test_data()):

tests/distributions/test_stable_log_prob.py

@@ -10,8 +10,7 @@
 import pyro
 import pyro.distributions
 import pyro.distributions.stable_log_prob
-from pyro.distributions import Stable
-from pyro.distributions import constraints
+from pyro.distributions import Stable, constraints
 from pyro.infer import SVI, Trace_ELBO
 from pyro.infer.autoguide import AutoNormal
 from tests.common import assert_close