flake8 canonical.py

dedupe/canonical.py

@@ -1,63 +1,71 @@
 import numpy
 from affinegap import normalizedAffineGapDistance as comparator
 
+
 def getCentroid(attribute_variants, comparator):
-    """ 
+    """
     Takes in a list of attribute values for a field,
     evaluates the centroid using the comparator,
     & returns the centroid (i.e. the 'best' value for the field)
     """
-    
+
     n = len(attribute_variants)
-    
-    distance_matrix = numpy.zeros([n,n])
-    
+
+    distance_matrix = numpy.zeros([n, n])
+
     # populate distance matrix by looping through elements of matrix triangle
-    for i in range (0,n):
-        for j in range (0, i):
+    for i in range(0, n):
+        for j in range(0, i):
             distance = comparator(attribute_variants[i], attribute_variants[j])
-            distance_matrix[i,j] = distance_matrix[j,i] = distance
-    
+            distance_matrix[i, j] = distance_matrix[j, i] = distance
+
     average_distance = distance_matrix.mean(0)
-    
+
     # there can be ties for minimum, average distance string
-    min_dist_indices = numpy.where(average_distance==average_distance.min())[0]
-
+    min_dist_indices = numpy.where(
+        average_distance == average_distance.min())[0]
+
     if len(min_dist_indices) > 1:
         centroid = breakCentroidTie(attribute_variants, min_dist_indices)
-    else :
+    else:
         centroid_index = min_dist_indices[0]
         centroid = attribute_variants[centroid_index]
 
-    return centroid    
+    return centroid
+
 
 def breakCentroidTie(attribute_variants, min_dist_indices):
     """
-    Finds centroid when there are multiple values w/ min avg distance 
-    (e.g. any dupe cluster of 2) right now this selects the first among a set of 
-    ties, but can be modified to break ties in strings by selecting the longest string
+    Finds centroid when there are multiple values w/ min avg distance
+    (e.g. any dupe cluster of 2) right now this selects the first
+    among a set of ties, but can be modified to break ties in strings
+    by selecting the longest string
+
     """
     return attribute_variants[min_dist_indices[0]]
 
+
 def getCanonicalRep(record_cluster):
     """
-    Given a list of records within a duplicate cluster, constructs a canonical representation
-    of the cluster by finding canonical values for each field
+    Given a list of records within a duplicate cluster, constructs a
+    canonical representation of the cluster by finding canonical
+    values for each field
+
     """
     canonical_rep = {}
 
     keys = record_cluster[0].keys()
-
 
-    for key in record_cluster[0].keys():
+    for key in keys:
         key_values = []
-        for record in record_cluster :
-            # assume non-empty values always better than empty value for canonical record
+        for record in record_cluster:
+            # assume non-empty values always better than empty value
+            # for canonical record
             if record[key]:
                 key_values.append(record[key])
         if key_values:
             canonical_rep[key] = getCentroid(key_values, comparator)
         else:
             canonical_rep[key] = ''
-    
+
     return canonical_rep