[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

setup.py

@@ -1,4 +1,5 @@
 """Setup module."""
+
 import setuptools
 
 if __name__ == "__main__":

src/pecanpy/__init__.py

@@ -1,4 +1,5 @@
 """PecanPy: parallelized, efficient, and accelerated node2vec."""
+
 from . import graph
 from . import pecanpy
 

src/pecanpy/cli.py

@@ -12,6 +12,7 @@
         $ pecanpy --help
 
 """
+
 import argparse
 import warnings
 

src/pecanpy/experimental.py

@@ -1,4 +1,5 @@
 """Experimental features."""
+
 import numpy as np
 from numba import njit
 from pecanpy.pecanpy import Base

src/pecanpy/graph.py

@@ -1,4 +1,5 @@
 """Lite graph objects used by pecanpy."""
+
 import warnings
 
 import numpy as np

src/pecanpy/pecanpy.py

@@ -1,4 +1,5 @@
 """Different strategies for generating node2vec walks."""
+
 import numpy as np
 from gensim.models import Word2Vec
 from numba import njit

src/pecanpy/rw/__init__.py

@@ -1,4 +1,5 @@
 """Graph objects equipped with random walk transition functions."""
+
 from .dense_rw import DenseRWGraph
 from .sparse_rw import SparseRWGraph
 

src/pecanpy/rw/dense_rw.py

@@ -1,4 +1,5 @@
 """Dense Graph object equipped with random walk computation."""
+
 import numpy as np
 from numba import njit
 
@@ -106,9 +107,9 @@ def get_extended_normalized_probs(
             alpha = 1 / q + (1 - 1 / q) * t
 
             # suppress noisy edges
-            alpha[
-                unnormalized_probs[out_ind] < noise_threshold_ary[cur_idx]
-            ] = np.minimum(1, 1 / q)
+            alpha[unnormalized_probs[out_ind] < noise_threshold_ary[cur_idx]] = (
+                np.minimum(1, 1 / q)
+            )
             unnormalized_probs[out_ind] *= alpha  # apply out biases
             unnormalized_probs[prev_idx] /= p  # apply the return bias
 

src/pecanpy/rw/sparse_rw.py

@@ -1,4 +1,5 @@
 """Sparse Graph equipped with random walk computation."""
+
 import numpy as np
 from numba import boolean
 from numba import njit
@@ -119,9 +120,9 @@ def get_extended_normalized_probs(
             alpha = 1 / q + (1 - 1 / q) * t[out_ind]
 
             # suppress noisy edges
-            alpha[
-                unnormalized_probs[out_ind] < noise_threshold_ary[cur_idx]
-            ] = np.minimum(1, 1 / q)
+            alpha[unnormalized_probs[out_ind] < noise_threshold_ary[cur_idx]] = (
+                np.minimum(1, 1 / q)
+            )
             unnormalized_probs[out_ind] *= alpha  # apply out biases
             unnormalized_probs[prev_ptr] /= p  # apply the return bias
 

src/pecanpy/typing.py

@@ -1,4 +1,5 @@
 """Type annotations."""
+
 from typing import Any
 from typing import Callable
 from typing import Dict

src/pecanpy/wrappers.py

@@ -1,4 +1,5 @@
 """Wrappers used by pecanpy."""
+
 import time