Apply black code formatting and fix missing config imports for style compliance

Author: Vansh-Sharmaa

stumpy/config.py

@@ -43,7 +43,6 @@
 RNG = np.random.default_rng()
 
 
-
 def _reset(var=None):
     """
     Reset the value of a configuration variable(s) to their default value(s)

tests/test_aamp_mmotifs.py

@@ -51,9 +51,7 @@
 
 
 def test_aamp_mmotifs_default_parameters():
-    motif_distances_ref = np.array(
-        [[0.0, 0.07497313, 0.35610208]]
-    )
+    motif_distances_ref = np.array([[0.0, 0.07497313, 0.35610208]])
     motif_indices_ref = np.array([[17, 57, 25]])
     motif_subspaces_ref = [np.array([1])]
     motif_mdls_ref = [

tests/test_cache.py

@@ -1,5 +1,4 @@
 import numba
-import numpy as np
 
 from stumpy import cache, config
 from stumpy.stump import stump

tests/test_gpu_aamp_ostinato.py

@@ -12,7 +12,7 @@
 import naive
 import pytest
 
-from stumpy import core
+from stumpy import core, config
 
 if cuda.is_available():
     from stumpy.gpu_aamp_ostinato import gpu_aamp_ostinato

tests/test_gpu_aamp_stimp.py

@@ -1,6 +1,7 @@
 from unittest.mock import patch
 
 import numpy as np
+from stumpy import config
 import numpy.testing as npt
 from numba import cuda
 

tests/test_gpu_aampdist.py

@@ -1,6 +1,7 @@
 from unittest.mock import patch
 
 import numpy as np
+from stumpy import config
 import numpy.testing as npt
 from numba import cuda
 

tests/test_gpu_mpdist.py

@@ -2,6 +2,7 @@
 from unittest.mock import patch
 
 import numpy as np
+from stumpy import config
 import numpy.testing as npt
 from numba import cuda
 

tests/test_gpu_ostinato.py

@@ -14,7 +14,7 @@
 import naive
 import pytest
 
-from stumpy import core
+from stumpy import core, config
 
 if cuda.is_available():
     from stumpy.gpu_ostinato import gpu_ostinato

tests/test_gpu_stimp.py

@@ -2,6 +2,7 @@
 from unittest.mock import patch
 
 import numpy as np
+from stumpy import config
 import numpy.testing as npt
 from numba import cuda
 

tests/test_motifs.py

@@ -228,8 +228,12 @@ def test_motifs_two_motifs():
         [0.0, 0.0, np.nan],
         [
             0.0,
-            naive.distance(core.z_norm(T[idx[0] : idx[0] + m]), core.z_norm(T[idx[1] : idx[1] + m])),
-            naive.distance(core.z_norm(T[idx[0] : idx[0] + m]), core.z_norm(T[idx[2] : idx[2] + m])),
+            naive.distance(
+                core.z_norm(T[idx[0] : idx[0] + m]), core.z_norm(T[idx[1] : idx[1] + m])
+            ),
+            naive.distance(
+                core.z_norm(T[idx[0] : idx[0] + m]), core.z_norm(T[idx[2] : idx[2] + m])
+            ),
         ],
     ]