yapf

deepchem/utils/hash_utils.py

@@ -7,6 +7,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 def hash_ecfp(ecfp, size):
   """
   Returns an int < size representing given ECFP fragment.
@@ -28,6 +29,7 @@ def hash_ecfp(ecfp, size):
   ecfp_hash = int(digest, 16) % (size)
   return (ecfp_hash)
 
+
 def hash_ecfp_pair(ecfp_pair, size):
   """Returns an int < size representing that ECFP pair.
 
@@ -52,9 +54,8 @@ def hash_ecfp_pair(ecfp_pair, size):
   ecfp_hash = int(digest, 16) % (size)
   return (ecfp_hash)
 
-def vectorize(hash_function,
-              feature_dict=None,
-              size=1024):
+
+def vectorize(hash_function, feature_dict=None, size=1024):
   """Helper function to vectorize a spatial description from a hash.
 
   Hash functions are used to perform spatial featurizations in
@@ -80,8 +81,7 @@ def vectorize(hash_function,
   feature_vector = np.zeros(size)
   if feature_dict is not None:
     on_channels = [
-        hash_function(feature, size)
-        for key, feature in feature_dict.items()
+        hash_function(feature, size) for key, feature in feature_dict.items()
     ]
     feature_vector[on_channels] += 1
 

deepchem/utils/test/test_hash_utils.py

@@ -2,13 +2,16 @@
 import numpy as np
 from deepchem.utils import hash_utils
 
+
 def random_string(length, chars=None):
   import string
   if chars is None:
     chars = list(string.ascii_letters + string.ascii_letters + '()[]+-.=#@/\\')
   return ''.join(np.random.choice(chars, length))
 
+
 class TestHashUtils(unittest.TestCase):
+
   def test_hash_ecfp(self):
     for power in (2, 16, 64):
       for _ in range(10):