Update styles PEP8

Signed-off-by: Stanislav Filin <stasfilinmusic@gmail.com>

examples/pysparnn_utils.py

@@ -5,9 +5,10 @@
 # LICENSE-examples file in the root directory of this source tree.
 import numpy as np
 
+
 # code that will measure query time and recall
 def recall(query, full_set):
-    ret =  []
+    ret = []
     for r_items, t_items in zip(query, full_set):
         result = 0.0
         for r in np.unique(r_items):

pysparnn/cluster_index.py

@@ -6,15 +6,16 @@
 # of patent rights can be found in the PATENTS file in the same directory.
 """Defines a cluster pruing search structure to do K-NN Queries"""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
+from __future__ import absolute_import, division, print_function, unicode_literals
+
 import collections as _collections
 import random as _random
+
 import numpy as _np
+
 import pysparnn.matrix_distance
 
+
 def _k_best(tuple_list, k):
     """For a list of tuples [(distance, value), ...] - Get the k-best tuples by
     distance.
@@ -27,6 +28,7 @@ def _k_best(tuple_list, k):
 
     return tuple_lst
 
+
 def _filter_unique(tuple_list):
     """For a list of tuples [(distance, value), ...] - filter out duplicate
     values.
@@ -111,7 +113,7 @@ def __init__(self, features, records_data,
 
         self.matrix_size = matrix_size
 
-        num_levels = _np.log(num_records)/_np.log(self.matrix_size)
+        num_levels = _np.log(num_records) / _np.log(self.matrix_size)
 
         if num_levels <= 1.4:
             self.is_terminal = True
@@ -131,7 +133,7 @@ def __init__(self, features, records_data,
                                  list(_np.arange(clusters_selection.shape[0])))
 
             root.remove_near_duplicates()
-            root = distance_type(root.matrix, 
+            root = distance_type(root.matrix,
                                  list(_np.arange(root.matrix.shape[0])))
 
             rng_step = self.matrix_size
@@ -162,7 +164,6 @@ def __init__(self, features, records_data,
 
             self.root = distance_type(cluster_keeps, clusters)
 
-
     def insert(self, feature, record):
         """Insert a single record into the index.
 
@@ -185,8 +186,6 @@ def insert(self, feature, record):
 
         cluster_index._reindex(feature, record)
 
-
-
     def _get_child_data(self):
         """Get all of the features and corresponding records represented in the
         full tree structure.
@@ -229,8 +228,7 @@ def _reindex(self, feature=None, record=None):
             flat_rec.append(record)
 
         self.__init__(self.distance_type.vstack(features), flat_rec, self.distance_type,
-                self.desired_matrix_size, self.parent)
-
+                      self.desired_matrix_size, self.parent)
 
     def _search(self, features, k=1, k_clusters=1):
         """Find the closest item(s) for each feature_list in.
@@ -277,7 +275,7 @@ def _search(self, features, k=1, k_clusters=1):
             return ret
 
     def search(self, features, k=1, k_clusters=1,
-            return_distance=True):
+               return_distance=True):
         """Find the closest item(s) for each feature_list in the index.
 
         Args:
@@ -423,7 +421,6 @@ class docstring for a description of the method.
                 at the cost of memory.
         """
 
-
         self.indexes = []
         for _ in range(num_indexes):
             self.indexes.append((ClusterIndex(features, records_data,

pysparnn/matrix_distance.py

@@ -6,15 +6,15 @@
 # of patent rights can be found in the PATENTS file in the same directory.
 """Defines a distance search structure"""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
+from __future__ import absolute_import, division, print_function, unicode_literals
+
 import abc as _abc
+
 import numpy as _np
 import scipy.sparse as _sparse
 import scipy.spatial.distance as _spatial_distance
 
+
 class MatrixMetricSearch(object):
     """A matrix representation out of features."""
     __metaclass__ = _abc.ABCMeta
@@ -152,7 +152,7 @@ def __init__(self, features, records_data):
         m_c = self.matrix.copy()
         m_c.data **= 2
         self.matrix_root_sum_square = \
-                _np.sqrt(_np.asarray(m_c.sum(axis=1)).reshape(-1))
+            _np.sqrt(_np.asarray(m_c.sum(axis=1)).reshape(-1))
 
     @staticmethod
     def features_to_matrix(features):
@@ -186,13 +186,14 @@ def _distance(self, a_matrix):
         a_c.data **= 2
         a_root_sum_square = _np.asarray(a_c.sum(axis=1)).reshape(-1)
         a_root_sum_square = \
-                a_root_sum_square.reshape(len(a_root_sum_square), 1)
+            a_root_sum_square.reshape(len(a_root_sum_square), 1)
         a_root_sum_square = _np.sqrt(a_root_sum_square)
 
         magnitude = 1.0 / (a_root_sum_square * self.matrix_root_sum_square)
 
         return 1 - dprod.multiply(magnitude).toarray()
 
+
 class UnitCosineDistance(MatrixMetricSearch):
     """A matrix that implements cosine distance search against it.
 
@@ -210,7 +211,7 @@ class UnitCosineDistance(MatrixMetricSearch):
     def __init__(self, features, records_data):
         super(UnitCosineDistance, self).__init__(features, records_data)
         self.matrix_root_sum_square = \
-                _np.sqrt(_np.asarray(self.matrix.sum(axis=1)).reshape(-1))
+            _np.sqrt(_np.asarray(self.matrix.sum(axis=1)).reshape(-1))
 
     @staticmethod
     def features_to_matrix(features):
@@ -242,13 +243,14 @@ def _distance(self, a_matrix):
 
         a_root_sum_square = _np.asarray(a_matrix.sum(axis=1)).reshape(-1)
         a_root_sum_square = \
-                a_root_sum_square.reshape(len(a_root_sum_square), 1)
+            a_root_sum_square.reshape(len(a_root_sum_square), 1)
         a_root_sum_square = _np.sqrt(a_root_sum_square)
 
         magnitude = 1.0 / (a_root_sum_square * self.matrix_root_sum_square)
 
         return 1 - dprod.multiply(magnitude).toarray()
 
+
 class SlowEuclideanDistance(MatrixMetricSearch):
     """A matrix that implements euclidean distance search against it.
     WARNING: This is not optimized.
@@ -286,6 +288,7 @@ def _distance(self, a_matrix):
 
         return _spatial_distance.cdist(a_matrix, self.matrix, 'euclidean')
 
+
 class DenseCosineDistance(MatrixMetricSearch):
     """A matrix that implements cosine distance search against it.
 
@@ -300,7 +303,7 @@ def __init__(self, features, records_data):
         super(DenseCosineDistance, self).__init__(features, records_data)
 
         self.matrix_root_sum_square = \
-                _np.sqrt((self.matrix**2).sum(axis=1).reshape(-1))
+            _np.sqrt((self.matrix ** 2).sum(axis=1).reshape(-1))
 
     @staticmethod
     def features_to_matrix(features):
@@ -330,7 +333,7 @@ def _distance(self, a_matrix):
         # what is the implmentation of transpose? can i change the order?
         dprod = self.matrix.dot(a_matrix.transpose()).transpose() * 1.0
 
-        a_root_sum_square = (a_matrix**2).sum(axis=1).reshape(-1)
+        a_root_sum_square = (a_matrix ** 2).sum(axis=1).reshape(-1)
         a_root_sum_square = a_root_sum_square.reshape(len(a_root_sum_square), 1)
         a_root_sum_square = _np.sqrt(a_root_sum_square)
 

setup.py

@@ -1,30 +1,30 @@
 from distutils.core import setup
 
-#This is a list of files to install, and where
-#(relative to the 'root' dir, where setup.py is)
-#You could be more specific.
+# This is a list of files to install, and where
+# (relative to the 'root' dir, where setup.py is)
+# You could be more specific.
 files = []
 
-setup(name = "pysparnn",
-    version = "0.4",
-    description = "Sparse (approximate) nearest neighbor search for python!",
-    author = "Spencer Beecher",
-    author_email = "spencebeecher@gmail.com",
-    #url = "",
-    #Name the folder where your packages live:
-    #(If you have other packages (dirs) or modules (py files) then
-    #put them into the package directory - they will be found
-    #recursively.)
-    packages = ['pysparnn'],
-    #'package' package must contain files (see list above)
-    #I called the package 'package' thus cleverly confusing the whole issue...
-    #This dict maps the package name =to=> directories
-    #It says, package *needs* these files.
-    #package_data = {},
-    #'runner' is in the root.
-    #scripts = [],
-    long_description = """Sparse (approximate) nearest neighbor search for python!"""
-    #
-    #This next part it for the Cheese Shop, look a little down the page.
-    #classifiers = []
-)
+setup(name="pysparnn",
+      version="0.4",
+      description="Sparse (approximate) nearest neighbor search for python!",
+      author="Spencer Beecher",
+      author_email="spencebeecher@gmail.com",
+      # url = "",
+      # Name the folder where your packages live:
+      # (If you have other packages (dirs) or modules (py files) then
+      # put them into the package directory - they will be found
+      # recursively.)
+      packages=['pysparnn'],
+      # 'package' package must contain files (see list above)
+      # I called the package 'package' thus cleverly confusing the whole issue...
+      # This dict maps the package name =to=> directories
+      # It says, package *needs* these files.
+      # package_data = {},
+      # 'runner' is in the root.
+      # scripts = [],
+      long_description="""Sparse (approximate) nearest neighbor search for python!"""
+      #
+      # This next part it for the Cheese Shop, look a little down the page.
+      # classifiers = []
+      )