apply black

Author: michaelosthege

pymc3/tests/test_memo.py

@@ -20,6 +20,7 @@
 def test_memo():
     def fun(inputs, suffix="_a"):
         return str(inputs) + str(suffix)
+
     inputs = ["i1", "i2"]
     assert fun(inputs) == "['i1', 'i2']_a"
     assert fun(inputs, "_b") == "['i1', 'i2']_b"
@@ -28,7 +29,9 @@ def fun(inputs, suffix="_a"):
     assert hasattr(fun, "cache")
     assert isinstance(fun.cache, dict)
     assert len(fun.cache) == 0
-    
+
+    # call the memoized function with a list input
+    # and check the size of the cache!
     assert funmem(inputs) == "['i1', 'i2']_a"
     assert funmem(inputs) == "['i1', 'i2']_a"
     assert len(fun.cache) == 1
@@ -49,10 +52,7 @@ def test_hashing_of_rv_tuples():
         mu = pm.Normal("mu", 0, 1)
         sd = pm.Gamma("sd", 1, 2)
         dd = pm.DensityDist(
-            "dd",
-            pm.Normal.dist(mu, sd).logp,
-            random=pm.Normal.dist(mu, sd).random,
-            observed=obs,
+            "dd", pm.Normal.dist(mu, sd).logp, random=pm.Normal.dist(mu, sd).random, observed=obs,
         )
         print()
         for freerv in [mu, sd, dd] + pmodel.free_RVs:

pymc3/tests/test_sampling.py

@@ -73,22 +73,14 @@ def test_sample(self):
             for cores in test_cores:
                 for steps in [1, 10, 300]:
                     pm.sample(
-                        steps,
-                        tune=0,
-                        step=self.step,
-                        cores=cores,
-                        random_seed=self.random_seed,
+                        steps, tune=0, step=self.step, cores=cores, random_seed=self.random_seed,
                     )
 
     def test_sample_init(self):
         with self.model:
             for init in ("advi", "advi_map", "map"):
                 pm.sample(
-                    init=init,
-                    tune=0,
-                    n_init=1000,
-                    draws=50,
-                    random_seed=self.random_seed,
+                    init=init, tune=0, n_init=1000, draws=50, random_seed=self.random_seed,
                 )
 
     def test_sample_args(self):
@@ -108,11 +100,7 @@ def test_sample_args(self):
     def test_iter_sample(self):
         with self.model:
             samps = pm.sampling.iter_sample(
-                draws=5,
-                step=self.step,
-                start=self.start,
-                tune=0,
-                random_seed=self.random_seed,
+                draws=5, step=self.step, start=self.start, tune=0, random_seed=self.random_seed,
             )
             for i, trace in enumerate(samps):
                 assert i == len(trace) - 1, "Trace does not have correct length."

pymc3/util.py

@@ -261,7 +261,7 @@ def enhanced(*args, **kwargs):
 def dataset_to_point_dict(ds: xarray.Dataset) -> List[Dict[str, np.ndarray]]:
     warnings.warn(
         "dataset_to_point_dict was renamed to dataset_to_point_list and will be removed!.",
-        DeprecationWarning
+        DeprecationWarning,
     )
     return dataset_to_point_list(ds)
 
@@ -289,8 +289,7 @@ def chains_and_samples(data: Union[xarray.Dataset, arviz.InferenceData]) -> Tupl
         dataset = data.posterior
     else:
         raise ValueError(
-            "Argument must be xarray Dataset or arviz InferenceData. Got %s",
-            data.__class__,
+            "Argument must be xarray Dataset or arviz InferenceData. Got %s", data.__class__,
         )
 
     coords = dataset.coords