PEP8 fixes (no code change).

Author: exhuma

cluster/method/hierarchical.py

@@ -49,8 +49,8 @@ def __init__(self, data, distance_function, linkage=None):
         """
         if not linkage:
             linkage = 'single'
-        logger.info("Initializing HierarchicalClustering object with linkage method %s",
-                    linkage)
+        logger.info("Initializing HierarchicalClustering object with linkage "
+                    "method %s", linkage)
         BaseClusterMethod.__init__(self, data, distance_function)
         self.set_linkage_method(linkage)
         self.__cluster_created = False
@@ -73,7 +73,7 @@ def set_linkage_method(self, method):
             self.linkage = self.uclus_distance
         else:
             raise ValueError('distance method must be one of single, '
-                    'complete, average of uclus')
+                             'complete, average of uclus')
 
     def uclus_distance(self, x, y):
         """
@@ -228,7 +228,7 @@ def cluster(self, matrix=None, level=None, sequence=None):
                     # if we are not on the diagonal (which is always 0)
                     # and if this cell represents a new minimum...
                     if ((rowindex != cellindex) and
-                        (cell < mindistance or smallestpair is None)):
+                            (cell < mindistance or smallestpair is None)):
                         smallestpair = (rowindex, cellindex)
                         mindistance = cell
                     cellindex += 1
@@ -237,7 +237,7 @@ def cluster(self, matrix=None, level=None, sequence=None):
             sequence += 1
             level = matrix[smallestpair[1]][smallestpair[0]]
             cluster = Cluster(level, self._data[smallestpair[0]],
-                    self._data[smallestpair[1]])
+                              self._data[smallestpair[1]])
 
             # maintain the data, by combining the the two most similar items
             # in the list we use the min and max functions to ensure the
@@ -247,9 +247,9 @@ def cluster(self, matrix=None, level=None, sequence=None):
             # value of the second "remove" call, but we don't know the order
             # in which they come. The max and min approach clarifies that
             self._data.remove(self._data[max(smallestpair[0],
-                smallestpair[1])])  # remove item 1
+                                             smallestpair[1])])  # remove item 1
             self._data.remove(self._data[min(smallestpair[0],
-                smallestpair[1])])  # remove item 2
+                                             smallestpair[1])])  # remove item 2
             self._data.append(cluster)  # append item 1 and 2 combined
 
         # all the data is in one single cluster. We return that and stop
@@ -277,4 +277,4 @@ def getlevel(self, threshold):
         if not self.__cluster_created:
             self.cluster()
 
-        return self._data[0].getlevel(threshold)
+        return self._data[0].getlevel(threshold)

cluster/method/kmeans.py

@@ -57,13 +57,13 @@ def __init__(self, data, distance=None, equality=None):
             for item in data[1:]:
                 if len(item) != control_length:
                     raise ValueError("Each item in the data list must have "
-                            "the same amount of dimensions. Item %r was out "
-                            "of line!" % (item,))
+                                     "the same amount of dimensions. Item "
+                                     "%r was out of line!" % (item,))
         # now check if we need and have a distance function
         if (len(data) > 1 and not isinstance(data[0], tuple) and
                 distance is None):
             raise ValueError("You supplied non-standard items but no "
-                    "distance function! We cannot continue!")
+                             "distance function! We cannot continue!")
         # we now know that we have tuples, and assume therefore that it's
         # items are numeric
         elif distance is None:
@@ -81,7 +81,8 @@ def getclusters(self, count):
         # only proceed if we got sensible input
         if count <= 1:
             raise ClusteringError("When clustering, you need to ask for at "
-                    "least two clusters! You asked for %d" % count)
+                                  "least two clusters! "
+                                  "You asked for %d" % count)
 
         # return the data straight away if there is nothing to cluster
         if (self.__data == [] or len(self.__data) == 1 or
@@ -90,9 +91,10 @@ def getclusters(self, count):
 
         # It makes no sense to ask for more clusters than data-items available
         if count > self.__initial_length:
-            raise ClusteringError("Unable to generate more clusters than "
-                    "items available. You supplied %d items, and asked for "
-                    "%d clusters." % (self.__initial_length, count) )
+            raise ClusteringError(
+                "Unable to generate more clusters than "
+                "items available. You supplied %d items, and asked for "
+                "%d clusters." % (self.__initial_length, count))
 
         self.initialise_clusters(self.__data, count)
 
@@ -119,8 +121,8 @@ def assign_item(self, item, origin):
         """
         closest_cluster = origin
         for cluster in self.__clusters:
-            if self.distance(item, centroid(cluster)) < self.distance(item,
-                    centroid(closest_cluster)):
+            if self.distance(item, centroid(cluster)) < self.distance(
+                    item, centroid(closest_cluster)):
                 closest_cluster = cluster
 
         if id(closest_cluster) != id(origin):