Blacken changed files.

pymc3/distributions/dist_math.py

@@ -16,7 +16,7 @@
 Created on Mar 7, 2011
 
 @author: johnsalvatier
-'''
+"""
 import numpy as np
 import scipy.linalg
 import theano.tensor as tt
@@ -409,11 +409,7 @@ def incomplete_beta_cfe(a, b, x, small):
     qkm1 = one
     r = one
 
-    def _step(
-            _i,
-            pkm1, pkm2, qkm1, qkm2,
-            k1, k2, k3, k4, k5, k6, k7, k8, r
-    ):
+    def _step(_i, pkm1, pkm2, qkm1, qkm2, k1, k2, k3, k4, k5, k6, k7, k8, r):
         xk = -(x * k1 * k2) / (k3 * k4)
         pk = pkm1 + pkm2 * xk
         qk = qkm1 + qkm2 * xk

pymc3/distributions/distribution.py

@@ -710,8 +710,7 @@ def draw_values(params, point=None, size=None):
             if (next_, size) in drawn:
                 # If the node already has a givens value, add it to givens
                 givens[next_.name] = (next_, drawn[(next_, size)])
-            elif isinstance(next_, (theano_constant,
-                                    tt.sharedvar.SharedVariable)):
+            elif isinstance(next_, (theano_constant, tt.sharedvar.SharedVariable)):
                 # If the node is a theano.tensor.TensorConstant or a
                 # theano.tensor.sharedvar.SharedVariable, its value will be
                 # available automatically in _compile_theano_function so
@@ -750,9 +749,13 @@ def draw_values(params, point=None, size=None):
                     # The node failed, so we must add the node's parents to
                     # the stack of nodes to try to draw from. We exclude the
                     # nodes in the `params` list.
-                    stack.extend([node for node in named_nodes_parents[next_]
-                                  if node is not None and
-                                  getattr(node, "name", None) not in givens])
+                    stack.extend(
+                        [
+                            node
+                            for node in named_nodes_parents[next_]
+                            if node is not None and getattr(node, "name", None) not in givens
+                        ]
+                    )
 
         # the below makes sure the graph is evaluated in order
         # test_distributions_random::TestDrawValues::test_draw_order fails without it
@@ -774,10 +777,7 @@ def draw_values(params, point=None, size=None):
                     evaluated[param_idx] = drawn[(param, size)]
                 else:
                     try:  # might evaluate in a bad order,
-                        value = _draw_value(param,
-                                            point=point,
-                                            givens=givens.values(),
-                                            size=size)
+                        value = _draw_value(param, point=point, givens=givens.values(), size=size)
                         evaluated[param_idx] = drawn[(param, size)] = value
                         givens[param.name] = (param, value)
                     except theano.gof.fg.MissingInputError:

pymc3/tests/test_sampling.py

@@ -916,12 +916,15 @@ def test_bounded_dist(self):
             prior_trace = pm.sample_prior_predictive(5)
             assert prior_trace["x"].shape == (5, 3, 1)
 
+
 # Added this test because the NormalMixture distribution did not support
 # component shape identification, causing prior predictive sampling to
 # error out.
 def test_prior_sampling_mixture():
-    old_faithful_df = pd.read_csv(pm.get_data('old_faithful.csv'))
-    old_faithful_df['std_waiting'] = (old_faithful_df.waiting - old_faithful_df.waiting.mean()) / old_faithful_df.waiting.std()
+    old_faithful_df = pd.read_csv(pm.get_data("old_faithful.csv"))
+    old_faithful_df["std_waiting"] = (
+        old_faithful_df.waiting - old_faithful_df.waiting.mean()
+    ) / old_faithful_df.waiting.std()
     N = old_faithful_df.shape[0]
     K = 30
 
@@ -931,15 +934,16 @@ def stick_breaking(beta):
         return result / tt.sum(result, axis=-1, keepdims=True)
 
     with pm.Model() as model:
-        alpha = pm.Gamma('alpha', 1., 1.)
-        beta = pm.Beta('beta', 1., alpha, shape=K)
-        w = pm.Deterministic('w', stick_breaking(beta))
-
-        tau = pm.Gamma('tau', 1., 1., shape=K)
-        lambda_ = pm.Gamma('lambda_', 10., 1., shape=K)
-        mu = pm.Normal('mu', 0, tau=lambda_ * tau, shape=K)
-        obs = pm.NormalMixture('obs', w, mu, tau=lambda_ * tau,
-                               observed=old_faithful_df.std_waiting.values)
+        alpha = pm.Gamma("alpha", 1.0, 1.0)
+        beta = pm.Beta("beta", 1.0, alpha, shape=K)
+        w = pm.Deterministic("w", stick_breaking(beta))
+
+        tau = pm.Gamma("tau", 1.0, 1.0, shape=K)
+        lambda_ = pm.Gamma("lambda_", 10.0, 1.0, shape=K)
+        mu = pm.Normal("mu", 0, tau=lambda_ * tau, shape=K)
+        obs = pm.NormalMixture(
+            "obs", w, mu, tau=lambda_ * tau, observed=old_faithful_df.std_waiting.values
+        )
 
         pm.sample_prior_predictive()