fixes variale name error

src/py/crankshaft/crankshaft/clustering/kmeans.py

@@ -33,27 +33,37 @@ class Kmeans(object):
         labels = km.fit_predict(zip(xs, ys))
         return zip(ids, labels)
 
-    def spatial_balanced(self, query, no_clusters, no_init=20, target_per_cluster = None, value_column = None):
-        params = { "subquery": query,
-                   "geom_col": "the_geom",
-                   "id_col": "cartodb_id",
-                   "value_column" : value_column }
+    def spatial_balanced(self, query, no_clusters, no_init=20,
+                         target_per_cluster=None, value_column=None):
+        params = {
+            "subquery": query,
+            "geom_col": "the_geom",
+            "id_col": "cartodb_id",
+            "value_column": value_column,
+        }
 
         data = self.data_provider.get_spatial_balanced_kmeans(params)
 
-        xs = data[0]['xs']
-        ts = data[0]['ys']
+        lons = data[0]['xs']
+        lats = data[0]['ys']
         ids = data[0]['ids']
         values = data[0]['values']
 
-        total_value  = np.sum(values)
+        total_value = np.sum(values)
 
         if target_per_cluster is None:
-           target_per_cluster = total_value / float(no_clusters)
-
-        km = BalancedGroupsKMeans(n_clusters=17,max_iter=100, max_cluster_size=target_per_cluster)
-        labels = km.fit_predict(zip(xs,ys), values=values)
-        return zip(ids,labels)
+            target_per_cluster = total_value / float(no_clusters)
+
+        bal_kmeans = BalancedGroupsKMeans(
+            n_clusters=17,
+            max_iter=100,
+            max_cluster_size=target_per_cluster
+        )
+        labels = bal_kmeans.fit_predict(
+            zip(lons, lats),
+            values=values
+        )
+        return zip(ids, labels)
 
     def nonspatial(self, subquery, colnames, no_clusters=5,
                    standardize=True, id_col='cartodb_id'):