formatting

test/dataset/test_common.py

@@ -14,6 +14,7 @@
 # First-party imports
 from pts.dataset import FieldName
 
+
 def test_dataset_fields():
     assert (
         "feat_static_cat" == FieldName.FEAT_STATIC_CAT

test/dataset/test_multivariate_grouper.py

@@ -12,6 +12,7 @@
 # permissions and limitations under the License.
 
 import numpy as np
+
 # Standard library imports
 import pytest
 
@@ -116,8 +117,7 @@ def test_multivariate_grouper_test(
     univariate_ts, multivariate_ts, test_fill_rule, max_target_dim
 ) -> None:
     univariate_ds = ListDataset(univariate_ts, freq="1D")
-    multivariate_ds = ListDataset(
-        multivariate_ts, freq="1D", one_dim_target=False)
+    multivariate_ds = ListDataset(multivariate_ts, freq="1D", one_dim_target=False)
 
     grouper = MultivariateGrouper(
         test_fill_rule=test_fill_rule, num_test_dates=2, max_target_dim=max_target_dim,

test/model/test_deepvar.py

@@ -46,28 +46,13 @@ metadata = dataset.metadata
 estimator = DeepVAREstimator
 
 
-#@pytest.mark.timeout(10)
+# @pytest.mark.timeout(10)
 @pytest.mark.parametrize(
     "distr_output, num_batches_per_epoch, Estimator, use_marginal_transformation",
     [
-        (
-            NormalOutput(dim=target_dim),
-            10,
-            estimator,
-            True,
-        ),
-        (
-            NormalOutput(dim=target_dim),
-            10,
-            estimator,
-            False,
-        ),
-        (
-            LowRankMultivariateNormalOutput(dim=target_dim, rank=2),
-            10,
-            estimator,
-            True,
-        ),
+        (NormalOutput(dim=target_dim), 10, estimator, True,),
+        (NormalOutput(dim=target_dim), 10, estimator, False,),
+        (LowRankMultivariateNormalOutput(dim=target_dim, rank=2), 10, estimator, True,),
         (
             LowRankMultivariateNormalOutput(dim=target_dim, rank=2),
             10,
@@ -75,18 +60,8 @@ estimator = DeepVAREstimator
             False,
         ),
         (None, 10, estimator, True),
-        (
-            MultivariateNormalOutput(dim=target_dim),
-            10,
-            estimator,
-            True,
-        ),
-        (
-            MultivariateNormalOutput(dim=target_dim),
-            10,
-            estimator,
-            False,
-        ),
+        (MultivariateNormalOutput(dim=target_dim), 10, estimator, True,),
+        (MultivariateNormalOutput(dim=target_dim), 10, estimator, False,),
     ],
 )
 def test_deepvar(

test/modules/test_distribution_output.py

@@ -190,7 +190,7 @@ def test_independent_normal() -> None:
 
     loc = np.arange(0, dim) / float(dim)
     diag = np.arange(dim) / dim + 0.5
-    Sigma = diag**2
+    Sigma = diag ** 2
 
     distr = Independent(Normal(loc=torch.Tensor(loc), scale=torch.Tensor(diag)), 1)
 

test/test_transform.py

@@ -21,6 +21,7 @@ import pytest
 import torch
 
 from pts import transform
+
 # First-party imports
 from pts.dataset import (
     ProcessStartField,