Remove trailing spaces

28 files changed, 204 insertions, 204 deletions

diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 3d950bc3..b8c3edc1 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -44,7 +44,7 @@ Contributor Guide
       PYTHONPATH=. pytest networkx
 
    * Running the tests locally *before* submitting a pull request helps catch
-     problems early and reduces the load on the continuous integration 
+     problems early and reduces the load on the continuous integration
      system. To ensure you have a properly-configured development environment
      for running the tests, see `Build environment setup`_.
 
diff --git a/CONTRIBUTORS.rst b/CONTRIBUTORS.rst
index e60033f5..09f8af11 100644
--- a/CONTRIBUTORS.rst
+++ b/CONTRIBUTORS.rst
@@ -127,13 +127,13 @@ is partially historical, and now, mostly arbitrary.
 - Julien Klaus
 - Peter C. Kroon, Github: `pckroon <https://github.com/pckroon>`_
 - Weisheng Si, Github: `ws4u <https://github.com/ws4u>`_
-- Haakon H. Rød, Gitlab: `haakonhr <https://gitlab.com/haakonhr>`_, `<https://haakonhr.gitlab.io>`_ 
+- Haakon H. Rød, Gitlab: `haakonhr <https://gitlab.com/haakonhr>`_, `<https://haakonhr.gitlab.io>`_
 - Efraim Rodrigues, GitHub `<https://github.com/efraimrodrigues>`_, LinkedIn `<https://linkedin.com/in/efraim-rodrigues/>`_
 - Erwan Le Merrer
 - Søren Fuglede Jørgensen, GitHub: `fuglede <https://github.com/fuglede>`_
 - Salim BELHADDAD, LinkedIn `<https://www.linkedin.com/in/salymdotme/>`_
 - Jangwon Yie, GitHub `<https://github.com/jangwon-yie>`_, LinkedIn `<https://www.linkedin.com/in/jangwon-yie-a7960065/>`_
-- ysitu 
+- ysitu
 - Tomas Gavenciak
 - Luca Baldesi
 - Yuto Yamaguchi
@@ -141,11 +141,11 @@ is partially historical, and now, mostly arbitrary.
 - Minas Gjoka
 - Drew Conway
 - Alex Levenson
-- Haochen Wu 
+- Haochen Wu
 - Erwan  Le Merrer
 - Alex Roper
 - P C Kroon
-- Christopher Ellison   
+- Christopher Ellison
 - D. Eppstein
 - Federico Rosato
 - Aitor Almeida
@@ -155,8 +155,8 @@ is partially historical, and now, mostly arbitrary.
 - Nanda H Krishna
 - Nicholas Mancuso
 - Fred Morstatter
-- Ollie Glass       
-- Rodrigo Dorantes-Gilardi 
+- Ollie Glass
+- Rodrigo Dorantes-Gilardi
 - Pranay Kanwar
 - Balint Tillman
 - Diederik van Liere
diff --git a/RELEASE.rst b/RELEASE.rst
index ba4831c6..e1a386c8 100644
--- a/RELEASE.rst
+++ b/RELEASE.rst
@@ -64,7 +64,7 @@ How to make a new release of ``networkx``
     Assuming you are at the top-level of the ``documentation`` repo::
 
       # FIXME - use eol_banner.html
-      cp -a latest networkx-<major>.<minor> 
+      cp -a latest networkx-<major>.<minor>
       ln -sfn networkx-<major>.<minor> stable
       git add networkx-<major>.<minor> stable
       git commit -m "Add <major>.<minor> docs"
diff --git a/doc/Makefile b/doc/Makefile
index 833d1399..73a8b210 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -113,7 +113,7 @@ latexpdf: latex
 
 docs: clean html latexpdf
 	cp build/latex/networkx_reference.pdf build/html/_downloads/.
-	
+
 gitwash-update:
 	python ../tools/gitwash_dumper.py developer networkx \
 	--project-url=http://networkx.github.io \
diff --git a/doc/bibliography.rst b/doc/bibliography.rst
index 16dc95b0..f619091a 100644
--- a/doc/bibliography.rst
+++ b/doc/bibliography.rst
@@ -2,24 +2,24 @@ Bibliography
 ============
 
 .. [BA02] R. Albert and A.-L. Barabási, "Statistical mechanics of complex
-   networks", Reviews of Modern Physics, 74, pp. 47-97, 2002. 
+   networks", Reviews of Modern Physics, 74, pp. 47-97, 2002.
    https://arxiv.org/abs/cond-mat/0106096
 
 .. [Bollobas01] B. Bollobás, "Random Graphs", Second Edition,
    Cambridge University Press, 2001.
 
 .. [BE05] U. Brandes and T. Erlebach, "Network Analysis:
-   Methodological Foundations", Lecture Notes in Computer Science, 
+   Methodological Foundations", Lecture Notes in Computer Science,
    Volume 3418, Springer-Verlag, 2005.
 
-.. [CL1996] G. Chartrand and L. Lesniak, "Graphs and Digraphs", 
+.. [CL1996] G. Chartrand and L. Lesniak, "Graphs and Digraphs",
    Chapman and Hall/CRC, 1996.
 
-.. [choudum1986] S.A. Choudum. "A simple proof of the Erdős-Gallai theorem on 
-   graph sequences." Bulletin of the Australian Mathematical Society, 33, 
+.. [choudum1986] S.A. Choudum. "A simple proof of the Erdős-Gallai theorem on
+   graph sequences." Bulletin of the Australian Mathematical Society, 33,
    pp 67-70, 1986. https://doi.org/10.1017/S0004972700002872
 
-.. [Diestel97] R. Diestel, "Graph Theory", Springer-Verlag, 1997. 
+.. [Diestel97] R. Diestel, "Graph Theory", Springer-Verlag, 1997.
    http://diestel-graph-theory.com/index.html
 
 .. [DM03] S.N. Dorogovtsev and J.F.F. Mendes, "Evolution of Networks",
@@ -28,27 +28,27 @@ Bibliography
 .. [EppsteinPads] David Eppstein.
    PADS, A library of Python Algorithms and Data Structures.
    http://www.ics.uci.edu/~eppstein/PADS
-   
+
 .. [EG1960] Erdős and Gallai, Mat. Lapok 11 264, 1960.
 
-.. [hakimi1962] Hakimi, S. "On the Realizability of a Set of Integers as 
+.. [hakimi1962] Hakimi, S. "On the Realizability of a Set of Integers as
    Degrees of the Vertices of a Graph." SIAM J. Appl. Math. 10, 496-506, 1962.
 
-.. [havel1955] Havel, V. "A Remark on the Existence of Finite Graphs" 
+.. [havel1955] Havel, V. "A Remark on the Existence of Finite Graphs"
    Casopis Pest. Mat. 80, 477-480, 1955.
-   
+
 .. [Langtangen04] H.P. Langtangen, "Python Scripting for Computational
     Science.", Springer Verlag Series in Computational Science and
-    Engineering, 2004. 
+    Engineering, 2004.
 
 .. [Martelli03]  A. Martelli, "Python in a Nutshell", O'Reilly Media
    Inc, 2003.
 
 .. [Newman03] M.E.J. Newman, "The Structure and Function of Complex
-   Networks", SIAM Review, 45, pp. 167-256, 2003. 
+   Networks", SIAM Review, 45, pp. 167-256, 2003.
    http://epubs.siam.org/doi/abs/10.1137/S003614450342480
 
-.. [Sedgewick02] R. Sedgewick, "Algorithms in C: Parts 1-4: 
+.. [Sedgewick02] R. Sedgewick, "Algorithms in C: Parts 1-4:
    Fundamentals, Data Structure, Sorting, Searching", Addison Wesley
    Professional, 3rd ed., 2002.
 
diff --git a/doc/developer/gitwash/configure_git.rst b/doc/developer/gitwash/configure_git.rst
index 3a172d5b..7059bde1 100644
--- a/doc/developer/gitwash/configure_git.rst
+++ b/doc/developer/gitwash/configure_git.rst
@@ -152,19 +152,19 @@ and it gives graph / text output something like this (but with color!):
 
     * 6d8e1ee - (HEAD, origin/my-fancy-feature, my-fancy-feature) NF - a fancy file (45 minutes ago) [Matthew Brett]
     *   d304a73 - (origin/placeholder, placeholder) Merge pull request #48 from hhuuggoo/master (2 weeks ago) [Jonathan Terhorst]
-    |\  
+    |\
     | * 4aff2a8 - fixed bug 35, and added a test in test_bugfixes (2 weeks ago) [Hugo]
-    |/  
+    |/
     * a7ff2e5 - Added notes on discussion/proposal made during Data Array Summit. (2 weeks ago) [Corran Webster]
     * 68f6752 - Initial implimentation of AxisIndexer - uses 'index_by' which needs to be changed to a call on an Axes object - this is all very sketchy right now. (2 weeks ago) [Corr
     *   376adbd - Merge pull request #46 from terhorst/master (2 weeks ago) [Jonathan Terhorst]
-    |\  
+    |\
     | * b605216 - updated joshu example to current api (3 weeks ago) [Jonathan Terhorst]
     | * 2e991e8 - add testing for outer ufunc (3 weeks ago) [Jonathan Terhorst]
     | * 7beda5a - prevent axis from throwing an exception if testing equality with non-axis object (3 weeks ago) [Jonathan Terhorst]
     | * 65af65e - convert unit testing code to assertions (3 weeks ago) [Jonathan Terhorst]
     | *   956fbab - Merge remote-tracking branch 'upstream/master' (3 weeks ago) [Jonathan Terhorst]
-    | |\  
+    | |\
     | |/
 
 Thanks to Yury V. Zaytsev for posting it.
diff --git a/doc/developer/gitwash/development_workflow.rst b/doc/developer/gitwash/development_workflow.rst
index bedc49fd..644edc22 100644
--- a/doc/developer/gitwash/development_workflow.rst
+++ b/doc/developer/gitwash/development_workflow.rst
@@ -24,7 +24,7 @@ In what follows we'll refer to the upstream networkx ``master`` branch, as
 * Name your branch for the purpose of the changes - e.g.
   ``bugfix-for-issue-14`` or ``refactor-database-code``.
 * If you can possibly avoid it, avoid merging trunk or any other branches into
-  your feature branch while you are working.  
+  your feature branch while you are working.
 * If you do find yourself merging from trunk, consider :ref:`rebase-on-trunk`
 * Ask on the `networkx mailing list`_ if you get stuck.
 * Ask for code review!
diff --git a/doc/developer/values.rst b/doc/developer/values.rst
index 8d74dd33..8d5c2f4d 100644
--- a/doc/developer/values.rst
+++ b/doc/developer/values.rst
@@ -34,7 +34,7 @@ Our values
   (especially dicts) due to their consistent, intuitive interface and amazing
   performance capabilities. We include interfaces to other data structures,
   especially NumPy arrays and SciPy sparse matrices for algorithms that more
-  naturally use arrays and matrices or where time or space requirements are 
+  naturally use arrays and matrices or where time or space requirements are
   significantly lower. Sometimes we provide two algorithms for the same result,
   one using each data structure, when pedagogy or space/time trade-offs justify
   such multiplicity.
diff --git a/doc/news.rst b/doc/news.rst
index 0dde232d..1539912e 100644
--- a/doc/news.rst
+++ b/doc/news.rst
@@ -61,7 +61,7 @@ Release date: 20 September 2017
 Support for Python 3.6 added, drop support for Python 3.3.
 
 See :doc:`release/migration_guide_from_1.x_to_2.0`.
-   
+
 Release notes
 ~~~~~~~~~~~~~
 
@@ -104,7 +104,7 @@ Highlights
 - pyparsing dependence removed from GML reader/parser
 - improve flow algorithms
 - new generators related to expander graphs.
-- new generators for multipartite graphs, nonisomorphic trees, 
+- new generators for multipartite graphs, nonisomorphic trees,
   circulant graphs
 - allow graph subclasses to use dict-like objects in place of dicts
 - added ordered graph subclasses
@@ -197,7 +197,7 @@ Highlights
 ~~~~~~~~~~
 
 - New functions for k-clique community finding, flow hierarchy,
-  union, disjoint union, compose, and intersection operators that work on 
+  union, disjoint union, compose, and intersection operators that work on
   lists of graphs, and creating the biadjacency matrix of a bipartite graph.
 
 - New approximation algorithms for dominating set, edge dominating set,
@@ -252,7 +252,7 @@ Highlights
 
 New features
 ~~~~~~~~~~~~
- - Algorithms for :mod:`generating <networkx.generators.bipartite>` 
+ - Algorithms for :mod:`generating <networkx.generators.bipartite>`
    and :mod:`analyzing <networkx.algorithms.bipartite>` bipartite graphs
  - :mod:`Maximal independent set <networkx.algorithms.mis>` algorithm
  - :mod:`Erdős-Gallai graphical degree sequence test <networkx.generators.degree_seq>`
@@ -260,10 +260,10 @@ New features
  - More memory efficient :mod:`Dijkstra path length <networkx.algorithms.shortest_paths.weighted>` with cutoff parameter
  - :mod:`Weighted clustering coefficient <networkx.algorithms.cluster>`
  - Read and write version 1.2 of :mod:`GEXF reader <networkx.readwrite.gexf>` format
- - :mod:`Neighbor degree correlation <networkx.algorithms.neighbor_degree>` 
+ - :mod:`Neighbor degree correlation <networkx.algorithms.neighbor_degree>`
    that handle subsets of nodes
- - :mod:`In-place node relabeling <networkx.relabel>` 
- - Many 'weighted' graph algorithms now take optional parameter to use 
+ - :mod:`In-place node relabeling <networkx.relabel>`
+ - Many 'weighted' graph algorithms now take optional parameter to use
    specified edge attribute (default='weight')
    (ticket https://networkx.lanl.gov/trac/ticket/509)
 
@@ -335,10 +335,10 @@ New features
 
 API changes
 ~~~~~~~~~~~
- - :mod:`gnp_random_graph() <networkx.generators.random_graphs>` now takes a 
-   directed=True|False keyword instead of create_using 
- - :mod:`gnm_random_graph() <networkx.generators.random_graphs>` now takes a 
-   directed=True|False keyword instead of create_using 
+ - :mod:`gnp_random_graph() <networkx.generators.random_graphs>` now takes a
+   directed=True|False keyword instead of create_using
+ - :mod:`gnm_random_graph() <networkx.generators.random_graphs>` now takes a
+   directed=True|False keyword instead of create_using
 
 Bug fixes
 ~~~~~~~~~
@@ -358,14 +358,14 @@ New features
  - Works with Python versions 2.6, 2.7, 3.1, and 3.2 (but not 2.4 and 2.5).
  - :mod:`Minimum cost flow algorithms <networkx.algorithms.flow>`
  - :mod:`Bellman-Ford shortest paths <networkx.algorithms.shortest_paths.weighted>`
- - :mod:`GraphML reader and writer <networkx.readwrite.graphml>` 
- - :mod:`More exception/error types <networkx.exception>` 
+ - :mod:`GraphML reader and writer <networkx.readwrite.graphml>`
+ - :mod:`More exception/error types <networkx.exception>`
  - Updated many tests to unittest style.  Run with: "import networkx; networkx.test()" (requires nose testing package)
  - and more, see  https://networkx.lanl.gov/trac/query?status=closed&group=milestone&milestone=networkx-1.3
 
 API changes
 ~~~~~~~~~~~
- - :mod:`minimum_spanning_tree() now returns a NetworkX Graph (a tree or forest) <networkx.algorithms.mst>` 
+ - :mod:`minimum_spanning_tree() now returns a NetworkX Graph (a tree or forest) <networkx.algorithms.mst>`
 
 Bug fixes
 ~~~~~~~~~
@@ -381,11 +381,11 @@ See: https://networkx.lanl.gov/trac/timeline
 
 New features
 ~~~~~~~~~~~~
- - :mod:`Ford-Fulkerson max flow and min cut <networkx.algorithms.flow>` 
- - :mod:`Closeness vitality <networkx.algorithms.vitality>` 
- - :mod:`Eulerian circuits <networkx.algorithms.euler>` 
- - :mod:`Functions for isolates <networkx.algorithms.isolates>` 
- - :mod:`Simpler s_max generator <networkx.generators.degree_seq>` 
+ - :mod:`Ford-Fulkerson max flow and min cut <networkx.algorithms.flow>`
+ - :mod:`Closeness vitality <networkx.algorithms.vitality>`
+ - :mod:`Eulerian circuits <networkx.algorithms.euler>`
+ - :mod:`Functions for isolates <networkx.algorithms.isolates>`
+ - :mod:`Simpler s_max generator <networkx.generators.degree_seq>`
  - Compatible with IronPython-2.6
  - Improved testing functionality: import networkx; networkx.test() tests
    entire package and skips tests with missing optional packages
@@ -402,21 +402,21 @@ See: https://networkx.lanl.gov/trac/timeline
 
 New features
 ~~~~~~~~~~~~
- - :mod:`Algorithm for finding a basis for graph cycles <networkx.algorithms.cycles>` 
- - :mod:`Blockmodeling <networkx.algorithms.block>` 
- - :mod:`Assortativity and mixing matrices <networkx.algorithms.mixing>` 
- - :mod:`in-degree and out-degree centrality <networkx.algorithms.centrality.degree>` 
- - :mod:`Attracting components <networkx.algorithms.components.attracting>` 
+ - :mod:`Algorithm for finding a basis for graph cycles <networkx.algorithms.cycles>`
+ - :mod:`Blockmodeling <networkx.algorithms.block>`
+ - :mod:`Assortativity and mixing matrices <networkx.algorithms.mixing>`
+ - :mod:`in-degree and out-degree centrality <networkx.algorithms.centrality.degree>`
+ - :mod:`Attracting components <networkx.algorithms.components.attracting>`
    and  :mod:`condensation <networkx.algorithms.components.strongly_connected>`.
  - :mod:`Weakly connected components <networkx.algorithms.components.weakly_connected>`
- - :mod:`Simpler interface to shortest path algorithms <networkx.algorithms.shortest_paths.generic>` 
- - :mod:`Edgelist format to read and write data with attributes <networkx.readwrite.edgelist>` 
- - :mod:`Attribute matrices <networkx.linalg.spectrum>` 
- - :mod:`GML reader for nested attributes <networkx.readwrite.gml>` 
- - Current-flow (random walk) 
-   :mod:`betweenness <networkx.algorithms.centrality.current_flow_betweenness>` 
-   and 
-   :mod:`closeness <networkx.algorithms.centrality.current_flow_closeness>`. 
+ - :mod:`Simpler interface to shortest path algorithms <networkx.algorithms.shortest_paths.generic>`
+ - :mod:`Edgelist format to read and write data with attributes <networkx.readwrite.edgelist>`
+ - :mod:`Attribute matrices <networkx.linalg.spectrum>`
+ - :mod:`GML reader for nested attributes <networkx.readwrite.gml>`
+ - Current-flow (random walk)
+   :mod:`betweenness <networkx.algorithms.centrality.current_flow_betweenness>`
+   and
+   :mod:`closeness <networkx.algorithms.centrality.current_flow_closeness>`.
  - :mod:`Directed configuration model <networkx.generators.degree_seq>`,
    and  :mod:`directed random graph model <networkx.generators.random_graphs>`.
  - Improved documentation of drawing, shortest paths, and other algorithms
@@ -430,35 +430,35 @@ Returning dictionaries
 Several of the algorithms and the degree() method now return dictionaries
 keyed by node instead of lists.  In some cases there was a with_labels
 keyword which is no longer necessary.  For example,
- 
+
 >>> G=nx.Graph()
 >>> G.add_edge('a','b')
 >>> G.degree()  # doctest: +SKIP
 {'a': 1, 'b': 1}
- 
+
 Asking for the degree of a single node still returns a single number
- 
+
 >>> G.degree('a')
 1
 
 The following now return dictionaries by default (instead of lists)
 and the with_labels keyword has been removed:
- 
- - :meth:`Graph.degree`, 
+
+ - :meth:`Graph.degree`,
    :meth:`MultiGraph.degree`,
-   :meth:`DiGraph.degree`, 
-   :meth:`DiGraph.in_degree`, 
+   :meth:`DiGraph.degree`,
+   :meth:`DiGraph.in_degree`,
    :meth:`DiGraph.out_degree`,
-   :meth:`MultiDiGraph.degree`, 
-   :meth:`MultiDiGraph.in_degree`, 
+   :meth:`MultiDiGraph.degree`,
+   :meth:`MultiDiGraph.in_degree`,
    :meth:`MultiDiGraph.out_degree`.
- - :func:`clustering`, 
+ - :func:`clustering`,
    :func:`triangles`
- - :func:`node_clique_number`, 
-   :func:`number_of_cliques`, 
+ - :func:`node_clique_number`,
+   :func:`number_of_cliques`,
    :func:`cliques_containing_node`
  - :func:`eccentricity`
-   
+
 
 The following now return dictionaries by default (instead of lists)
 
@@ -483,11 +483,11 @@ Examples
 Bug fixes
 ~~~~~~~~~
  - Support graph attributes with union, intersection, and other graph operations
- - Improve subgraph speed (and related algorithms such as 
+ - Improve subgraph speed (and related algorithms such as
    connected_components_subgraphs())
- - Handle multigraphs in more operators (e.g. union)   
+ - Handle multigraphs in more operators (e.g. union)
  - Handle double-quoted labels with pydot
- - Normalize betweenness_centrality for undirected graphs correctly 
+ - Normalize betweenness_centrality for undirected graphs correctly
  - Normalize eigenvector_centrality by l2 norm
  - :func:`read_gml` now returns multigraphs
 
@@ -515,7 +515,7 @@ to allow graph, node, and edge attributes.
 See http://networkx.lanl.gov/reference/api_changes.html
 
  - Update Graph, DiGraph, and MultiGraph classes to allow attributes.
- - Default edge data is now an empty dictionary (was the integer 1)   
+ - Default edge data is now an empty dictionary (was the integer 1)
  - Difference and intersection operators
  - Average shortest path
  - A* (A-Star) algorithm
@@ -558,7 +558,7 @@ See http://networkx.lanl.gov/reference/api_changes.html
 
 Bug fixes
 ~~~~~~~~~
- - handle root= option to draw_graphviz correctly 
+ - handle root= option to draw_graphviz correctly
 
 Examples
 ~~~~~~~~
@@ -589,20 +589,20 @@ New features
 
 Bug fixes
 ~~~~~~~~~
- - Better edge data handling with GML writer 
+ - Better edge data handling with GML writer
  - Edge betweenness fix for XGraph with default data of None
  - Handle Matplotlib version strings (allow "pre")
  - Interface to PyGraphviz (to_agraph()) now handles parallel edges
  - Fix bug in copy from XGraph to XGraph with multiedges
- - Use SciPy sparse lil matrix format instead of coo format 
+ - Use SciPy sparse lil matrix format instead of coo format
  - Clear up ambiguous cases for Barabasi-Albert model
  - Better care of color maps with Matplotlib when drawing colored nodes
-   and edges 
+   and edges
  - Fix error handling in layout.py
 
 Examples
 ~~~~~~~~
- - Ubigraph examples showing 3D drawing 
+ - Ubigraph examples showing 3D drawing
 
 
 NetworkX 0.36
@@ -615,15 +615,15 @@ See: https://networkx.lanl.gov/trac/timeline
 
 New features
 ~~~~~~~~~~~~
-  - GML format graph reader, tests, and example (football.py)	
+  - GML format graph reader, tests, and example (football.py)
   - edge_betweenness() and load_betweenness()
 
 Bug fixes
 ~~~~~~~~~
-  - remove obsolete parts of pygraphviz interface 
+  - remove obsolete parts of pygraphviz interface
   - improve handling of Matplotlib version strings
   - write_dot() now writes parallel edges and self loops
-  - is_bipartite() and bipartite_color() fixes 
+  - is_bipartite() and bipartite_color() fixes
   - configuration model speedup using random.shuffle()
   - convert with specified nodelist now works correctly
   - vf2 isomorphism checker updates
@@ -649,7 +649,7 @@ See: https://networkx.lanl.gov/trac/timeline
 New features
 ~~~~~~~~~~~~
   - algorithms for strongly connected components.
-  - Brandes betweenness centrality algorithm (weighted and unweighted versions) 
+  - Brandes betweenness centrality algorithm (weighted and unweighted versions)
   - closeness centrality for weighted graphs
   - dfs_preorder, dfs_postorder, dfs_tree, dfs_successor, dfs_predecessor
   - readers for GraphML, LEDA, sparse6, and graph6 formats.
@@ -673,7 +673,7 @@ See: https://networkx.lanl.gov/trac/timeline
 
 New features
 ~~~~~~~~~~~~
-  - benchmarks for graph classes	
+  - benchmarks for graph classes
   - Brandes betweenness centrality algorithm
   - Dijkstra predecessor and distance algorithm
   - xslt to convert DIA graphs to NetworkX
@@ -684,9 +684,9 @@ New features
 
 Bug fixes
 ~~~~~~~~~
-  - speedups of neighbors() 	
+  - speedups of neighbors()
   - simplified Dijkstra's algorithm code
-  - better exception handling for shortest paths   
+  - better exception handling for shortest paths
   - get_edge(u,v) returns None (instead of exception) if no edge u-v
   - floyd_warshall_array fixes for negative weights
   - bad G467, docs, and unittest fixes for graph atlas
@@ -695,8 +695,8 @@ Bug fixes
   - remove extra kwds arguments in many places
   - no multi counting edges in conversion to dict of lists for multigraphs
   - allow passing tuple to get_edge()
-  - bad parameter order in node/edge betweenness 
-  - edge betweenness doesn't fail with XGraph 
+  - bad parameter order in node/edge betweenness
+  - edge betweenness doesn't fail with XGraph
   - don't throw exceptions for nodes not in graph (silently ignore instead)
     in edges_* and degree_*
 
@@ -716,13 +716,13 @@ New features
   - include documentation in source package (doc)
   - tests can now be run with
      >>> import networkx
-     >>> networkx.test()    
+     >>> networkx.test()
 
 Bug fixes
 ~~~~~~~~~
   - read_gpickle now works correctly with Windows
   - refactored large modules into smaller code files
-  - degree(nbunch) now returns degrees in same order as nbunch 
+  - degree(nbunch) now returns degrees in same order as nbunch
   - degree() now works for multiedges=True
   - update node_boundary and edge_boundary for efficiency
   - edited documentation for graph classes, now mostly in info.py
@@ -747,12 +747,12 @@ New features
   - Generators and functions for bipartite graphs
   - Experimental classes for trees and forests
   - Support for new pygraphviz update (in nx_agraph.py) , see
-    http://networkx.lanl.gov/pygraphviz/ for pygraphviz details 
+    http://networkx.lanl.gov/pygraphviz/ for pygraphviz details
 
 Bug fixes
 ~~~~~~~~~
   - Handle special cases correctly in triangles function
-  - Typos in documentation  
+  - Typos in documentation
   - Handle special cases in shortest_path and shortest_path_length,
     allow cutoff parameter for maximum depth to search
   - Update examples: erdos_renyi.py, miles.py, roget,py, eigenvalues.py
@@ -780,7 +780,7 @@ New features
 Bug fixes
 ~~~~~~~~~
    - Allow drawing graphs with no edges using pylab
-   - Use faster heapq in dijkstra 
+   - Use faster heapq in dijkstra
    - Don't complain if X windows is not available
 
 Examples
@@ -798,10 +798,10 @@ See: https://networkx.lanl.gov/trac/timeline
 
 New features
 ~~~~~~~~~~~~
-   - update to work with Python 2.5 
-   - bidirectional version of shortest_path and Dijkstra 
+   - update to work with Python 2.5
+   - bidirectional version of shortest_path and Dijkstra
    - single_source_shortest_path and all_pairs_shortest_path
-   - s-metric and experimental code to generate  maximal s-metric graph 
+   - s-metric and experimental code to generate  maximal s-metric graph
    - double_edge_swap and connected_double_edge_swap
    - Floyd's algorithm for all pairs shortest path
    - read and write unicode graph data to text files
@@ -817,13 +817,13 @@ Bug fixes
    - function name changes in shortest_path routines
    - saner internal handling of nbunch (node bunches), raise an
      exception if an nbunch isn't a node or iterable
-   - better keyword handling in io.py allows reading multiple graphs 
+   - better keyword handling in io.py allows reading multiple graphs
    - don't mix Numeric and numpy arrays in graph layouts and drawing
    - avoid automatically rescaling matplotlib axes when redrawing graph layout
 
 Examples
 ~~~~~~~~
-   - unicode node labels 
+   - unicode node labels
 
 
 NetworkX 0.29
@@ -836,14 +836,14 @@ See: https://networkx.lanl.gov/trac/timeline
 New features
 ~~~~~~~~~~~~
    - Algorithms for betweenness, eigenvalues, eigenvectors, and
-     spectral projection for threshold graphs  
+     spectral projection for threshold graphs
    - Use numpy when available
    - dense_gnm_random_graph generator
    - Generators for some directed graphs: GN, GNR, and GNC by Krapivsky
-     and Redner 
+     and Redner
    - Grid graph generators now label by index tuples.  Helper
      functions for manipulating labels.
-   - relabel_nodes_with_function 
+   - relabel_nodes_with_function
 
 
 Bug fixes
@@ -867,9 +867,9 @@ See: https://networkx.lanl.gov/trac/timeline
 New features
 ~~~~~~~~~~~~
   - Option to construct Laplacian with rows and columns in specified order
-  - Option in convert_node_labels_to_integers to use sorted order   
+  - Option in convert_node_labels_to_integers to use sorted order
   - predecessor(G,n) function that returns dictionary of
-    nodes with predecessors from breadth-first search of G 
+    nodes with predecessors from breadth-first search of G
     starting at node n.
     https://networkx.lanl.gov/trac/ticket/26
 
@@ -878,24 +878,24 @@ Examples
   - Formation of giant component in binomial_graph:
   - Chess masters matches:
   - Gallery https://networkx.github.io/documentation/latest/auto_examples/index.html
-  
+
 Bug fixes
 ~~~~~~~~~
   - Adjusted names for random graphs.
-     + erdos_renyi_graph=binomial_graph=gnp_graph: n nodes with 
+     + erdos_renyi_graph=binomial_graph=gnp_graph: n nodes with
        edge probability p
      + gnm_graph: n nodes and m edges
-     + fast_gnp_random_graph: gnp for sparse graphs (small p)   
+     + fast_gnp_random_graph: gnp for sparse graphs (small p)
   - Documentation contains correct spelling of Barabási, Bollobás,
     Erdős, and Rényi in UTF-8 encoding
   - Increased speed of connected_components and related functions
     by using faster BFS algorithm in networkx.paths
-    https://networkx.lanl.gov/trac/ticket/27     
+    https://networkx.lanl.gov/trac/ticket/27
   - XGraph and XDiGraph with multiedges=True produced error on delete_edge
   - Cleaned up docstring errors
   - Normalize names of some graphs to produce strings that represent
     calling sequence
-  
+
 NetworkX 0.27
 -------------
 
@@ -917,7 +917,7 @@ New features
     See https://networkx.lanl.gov/trac/ticket/24
   - Addition of out_edges, in_edges and corresponding out_neighbors
     and in_neighbors for digraphs.  For digraphs edges=out_edges.
-   
+
 Examples
 ~~~~~~~~
   - Minard's data for Napoleon's Russian campaign
@@ -925,7 +925,7 @@ Examples
 Bug fixes
 ~~~~~~~~~
    - XGraph(multiedges=True) returns a copy of the list of edges
-     for get_edge() 
+     for get_edge()
 
 NetworkX 0.26
 -------------
@@ -941,7 +941,7 @@ New features
     rows/columns in matrix
   - optional pygraphviz and pydot interface to graphviz is now callable as
     "graphviz" with pygraphviz preferred.  Use draw_graphviz(G).
-   
+
 Examples
 ~~~~~~~~
   - Several new examples showing how draw to graphs with various
@@ -981,9 +981,9 @@ Bug fixes
 ~~~~~~~~~
    - use create_using= instead of result= keywords for graph types
      in all cases
-   - missing weights for degree 0 and 1 nodes in clustering     
+   - missing weights for degree 0 and 1 nodes in clustering
    - configuration model now uses XGraph, returns graph with identical
-     degree sequence as input sequence	   
+     degree sequence as input sequence
    - fixed Dijkstra priority queue
    - fixed non-recursive toposort and is_directed_acyclic graph
 
@@ -999,7 +999,7 @@ Bug fixes
    - Changed to list comprehension in DiGraph.reverse() for python2.3
      compatibility
    - Barabasi-Albert graph generator fixed
-   - Attempt to add self loop should add node even if parallel edges not 
+   - Attempt to add self loop should add node even if parallel edges not
      allowed
 
 NetworkX 0.23
@@ -1042,7 +1042,7 @@ Documentation
 
 Bug fixes
 ~~~~~~~~~
-   - Fixed logic in io.py for reading DiGraphs.  
+   - Fixed logic in io.py for reading DiGraphs.
    - Path based centrality measures (betweenness, closeness)
      modified so they work on graphs that are not connected and
      produce the same result as if each connected component were
@@ -1068,20 +1068,20 @@ Examples
   - Kevin Bacon movie actor graph: Examples/kevin_bacon.py
   - Compute eigenvalues of graph Laplacian: Examples/eigenvalues.py
   - Atlas of small graphs: Examples/atlas.py
-  
+
 Documentation
 ~~~~~~~~~~~~~
   - Rewrite of setup scripts to install documentation and
-    tests in documentation directory specified 
+    tests in documentation directory specified
 
 Bug fixes
 ~~~~~~~~~
    - Handle calls to edges() with non-node, non-iterable items.
    - truncated_tetrahedral_graph was just plain wrong
    - Speedup of betweenness_centrality code
-   - bfs_path_length now returns correct lengths 
+   - bfs_path_length now returns correct lengths
    - Catch error if target of search not in connected component of source
    - Code cleanup to label internal functions with _name
    - Changed import statement lines to always use "import NX" to
-     protect name-spaces   
+     protect name-spaces
    - Other minor bug-fixes and testing added
diff --git a/doc/reference/algorithms/assortativity.rst b/doc/reference/algorithms/assortativity.rst
index fe7af8fb..9c8853f3 100644
--- a/doc/reference/algorithms/assortativity.rst
+++ b/doc/reference/algorithms/assortativity.rst
@@ -7,7 +7,7 @@ Assortativity
    :toctree: generated/
 
 Assortativity
--------------   
+-------------
 .. autosummary::
    :toctree: generated/
 
@@ -17,7 +17,7 @@ Assortativity
    degree_pearson_correlation_coefficient
 
 Average neighbor degree
------------------------   
+-----------------------
 .. autosummary::
    :toctree: generated/
 
diff --git a/doc/reference/algorithms/clique.rst b/doc/reference/algorithms/clique.rst
index 751752e9..c2ac6e9c 100644
--- a/doc/reference/algorithms/clique.rst
+++ b/doc/reference/algorithms/clique.rst
@@ -9,9 +9,9 @@ Clique
    enumerate_all_cliques
    find_cliques
    make_max_clique_graph
-   make_clique_bipartite        
+   make_clique_bipartite
    graph_clique_number
-   graph_number_of_cliques      
+   graph_number_of_cliques
    node_clique_number
    number_of_cliques
    cliques_containing_node
diff --git a/doc/reference/algorithms/clustering.rst b/doc/reference/algorithms/clustering.rst
index 98475126..afedff14 100644
--- a/doc/reference/algorithms/clustering.rst
+++ b/doc/reference/algorithms/clustering.rst
@@ -9,6 +9,6 @@ Clustering
    triangles
    transitivity
    clustering
-   average_clustering   
+   average_clustering
    square_clustering
    generalized_degree
diff --git a/doc/reference/algorithms/community.rst b/doc/reference/algorithms/community.rst
index 31d08afb..dbdca0c4 100644
--- a/doc/reference/algorithms/community.rst
+++ b/doc/reference/algorithms/community.rst
@@ -27,7 +27,7 @@ Modularity-based communities
 .. automodule:: networkx.algorithms.community.modularity_max
 .. autosummary::
    :toctree: generated/
-   
+
    greedy_modularity_communities
 
 Tree partitioning
diff --git a/doc/reference/algorithms/graph_hashing.rst b/doc/reference/algorithms/graph_hashing.rst
index 42856ad8..0fd908be 100644
--- a/doc/reference/algorithms/graph_hashing.rst
+++ b/doc/reference/algorithms/graph_hashing.rst
@@ -6,4 +6,4 @@ Graph Hashing
 .. autosummary::
    :toctree: generated/
 
-   weisfeiler_lehman_graph_hash 
+   weisfeiler_lehman_graph_hash
diff --git a/doc/reference/algorithms/isomorphism.ismags.rst b/doc/reference/algorithms/isomorphism.ismags.rst
index b8c4c510..8ca55fae 100644
--- a/doc/reference/algorithms/isomorphism.ismags.rst
+++ b/doc/reference/algorithms/isomorphism.ismags.rst
@@ -1,7 +1,7 @@
 .. _ismags:
 
 ****************
-ISMAGS Algorithm 
+ISMAGS Algorithm
 ****************
 
 .. automodule:: networkx.algorithms.isomorphism.ismags
diff --git a/doc/reference/algorithms/isomorphism.vf2.rst b/doc/reference/algorithms/isomorphism.vf2.rst
index 6a4b5a89..a5c2ad42 100644
--- a/doc/reference/algorithms/isomorphism.vf2.rst
+++ b/doc/reference/algorithms/isomorphism.vf2.rst
@@ -1,7 +1,7 @@
 .. _vf2:
 
 *************
-VF2 Algorithm 
+VF2 Algorithm
 *************
 
 .. automodule:: networkx.algorithms.isomorphism.isomorphvf2
@@ -12,7 +12,7 @@ Graph Matcher
 
 .. autosummary::
    :toctree: generated/
-   
+
     GraphMatcher.__init__
     GraphMatcher.initialize
     GraphMatcher.is_isomorphic
diff --git a/doc/reference/algorithms/shortest_paths.rst b/doc/reference/algorithms/shortest_paths.rst
index 539719a4..daf1c2d3 100644
--- a/doc/reference/algorithms/shortest_paths.rst
+++ b/doc/reference/algorithms/shortest_paths.rst
@@ -80,5 +80,5 @@ A* Algorithm
    :toctree: generated/
 
    astar_path
-   astar_path_length   
+   astar_path_length
 
diff --git a/doc/reference/randomness.rst b/doc/reference/randomness.rst
index d45a0276..4ca72802 100644
--- a/doc/reference/randomness.rst
+++ b/doc/reference/randomness.rst
@@ -7,18 +7,18 @@ Randomness
 Random Number Generators (RNGs) are often used when generating, drawing
 and computing properties or manipulating networks. NetworkX provides
 functions which use one of two standard RNGs: NumPy's package `numpy.random`
-or Python's built-in package `random`. They each provide the same 
+or Python's built-in package `random`. They each provide the same
 algorithm for generating numbers (Mersenne Twister). Their interfaces
-are similar (dangerously similar) and yet distinct. 
+are similar (dangerously similar) and yet distinct.
 They each provide a global default instance of their generator that
 is shared by all programs in a single session.
 For the most part you can use the RNGs as NetworkX has them set up and
 you'll get reasonable pseudorandom results (results that are statistically
-random, but created in a deterministic manner). 
+random, but created in a deterministic manner).
 
 Sometimes you want more control over how the numbers are generated.
 In particular, you need to set the `seed` of the generator to make
-your results reproducible -- either for scientific publication or 
+your results reproducible -- either for scientific publication or
 for debugging. Both RNG packages have easy functions to set the seed
 to any integer, thus determining the subsequent generated values.
 Since this package (and many others) use both RNGs you may need to
@@ -48,7 +48,7 @@ RNG package to use, and whether to use a global or local RNG.
     >>> pos = random_layout(G, seed=None)  # use (either) global default RNG
     >>> pos = random_layout(G, seed=42)  # local RNG just for this call
     >>> pos = random_layout(G, seed=numpy.random)  # use numpy global RNG
-    >>> random_state = numpy.random.RandomState(42) 
+    >>> random_state = numpy.random.RandomState(42)
     >>> pos = random_layout(G, seed=random_state)  # use/reuse your own RNG
 
 Each NetworkX function that uses an RNG was written with one RNG package
@@ -59,7 +59,7 @@ can use a `numpy.random` RNG even if the function is written for `random`.
 It works as follows.
 
 The default behavior (when `seed=None`) is to use the global RNG
-for the function's preferred package. 
+for the function's preferred package.
 If seed is set to an integer value,
 a local RNG is created with the indicated seed value and
 is used for the duration of that function (including any
@@ -69,20 +69,20 @@ the global numpy RNG is used whether the function expects it or not.
 Finally, you can provide a numpy RNG to be used by the function.
 The RNG is then available to use in other functions or even other
 package like sklearn.
-In this way you can use a single RNG for all random numbers 
+In this way you can use a single RNG for all random numbers
 in your project.
 
 While it is possible to assign `seed` a `random`-style RNG for
 NetworkX functions written for the `random` package API,
-the numpy RNG interface has too 
+the numpy RNG interface has too
 many nice features for us to ensure a `random`-style RNG will work in
 all functions. In practice, you can do most things using only `random`
 RNGs (useful if numpy is not available). But your experience will be
 richer if numpy is available.
 
 To summarize, you can easily ignore the `seed` argument and use the global
-RNGs. You can specify to use only the numpy global RNG with 
+RNGs. You can specify to use only the numpy global RNG with
 `seed=numpy.random`. You can use a local RNG by providing an integer
 seed value. And you can provide your own numpy RNG, reusing it for all
-functions. It is easier to use numpy RNGs if you want a single RNG for 
+functions. It is easier to use numpy RNGs if you want a single RNG for
 your computations.
diff --git a/doc/release/api_0.99.rst b/doc/release/api_0.99.rst
index 0d6a29f6..e2d4f07b 100644
--- a/doc/release/api_0.99.rst
+++ b/doc/release/api_0.99.rst
@@ -5,10 +5,10 @@ Version 0.99 API changes
 The version networkx-0.99 is the penultimate release before
 networkx-1.0.  We have bumped the version from 0.37 to 0.99 to
 indicate (in our unusual version number scheme) that this is a major
-change to NetworkX.  
+change to NetworkX.
 
 We have made some significant changes, detailed below, to NetworkX
-to improve  performance, functionality, and clarity. 
+to improve  performance, functionality, and clarity.
 
 Version 0.99 requires Python 2.4 or greater.
 
@@ -18,8 +18,8 @@ http://groups.google.com/group/networkx-discuss
 Changes in base classes
 =======================
 
-The most significant changes are in the graph classes. 
-We have redesigned the Graph() and DiGraph() classes 
+The most significant changes are in the graph classes.
+We have redesigned the Graph() and DiGraph() classes
 to optionally allow edge data.
 This change allows Graph and DiGraph to naturally represent
 weighted graphs and to hold arbitrary information on edges.
@@ -31,7 +31,7 @@ weighted graphs and to hold arbitrary information on edges.
 
  - The Graph and DiGraph classes now allow self loops.
 
- - The XGraph and XDiGraph classes are removed and replaced with 
+ - The XGraph and XDiGraph classes are removed and replaced with
    MultiGraph and MultiDiGraph. MultiGraph and MultiDiGraph
    optionally allow parallel (multiple) edges between two nodes.
 
@@ -56,7 +56,7 @@ edges()
 
 delete_node()
 ^^^^^^^^^^^^^
-   The preferred name is now remove_node().        
+   The preferred name is now remove_node().
 
 
 delete_nodes_from()
@@ -79,37 +79,37 @@ delete_edges_from()
 add_edge()
 ^^^^^^^^^^
    The add_edge() method no longer accepts an edge tuple (u,v)
-   directly.  The tuple must be unpacked into individual nodes. 
+   directly.  The tuple must be unpacked into individual nodes.
 
    >>> import networkx as nx
    >>> u='a'
    >>> v='b'
    >>> e=(u,v)
    >>> G=nx.Graph()
-   
+
    Old
 
-   >>> # G.add_edge((u,v))  # or G.add_edge(e) 
+   >>> # G.add_edge((u,v))  # or G.add_edge(e)
 
-   New 
+   New
 
-   >>> G.add_edge(*e) # or G.add_edge(*(u,v)) 
+   >>> G.add_edge(*e) # or G.add_edge(*(u,v))
 
    The * operator unpacks the edge tuple in the argument list.
 
    Add edge now has
    a data keyword parameter for setting the default (data=1) edge
    data.
-   
+
    >>> # G.add_edge('a','b','foo')  # add edge with string "foo" as data
    >>> # G.add_edge(1,2,5.0)  # add edge with float 5 as data
-   
+
 
 
 add_edges_from()
 ^^^^^^^^^^^^^^^^
    Now can take list or iterator of either 2-tuples (u,v),
-   3-tuples (u,v,data) or a mix of both.  
+   3-tuples (u,v,data) or a mix of both.
 
    Now has data keyword parameter (default 1) for setting the edge data
    for any edge in the edge list that is a 2-tuple.
@@ -118,17 +118,17 @@ add_edges_from()
 has_edge()
 ^^^^^^^^^^
    The has_edge() method no longer accepts an edge tuple (u,v)
-   directly.  The tuple must be unpacked into individual nodes. 
+   directly.  The tuple must be unpacked into individual nodes.
 
-   Old: 
+   Old:
 
    >>> # G.has_edge((u,v))  # or has_edge(e)
 
-   New: 
+   New:
 
-   >>> G.has_edge(*e) # or has_edge(*(u,v)) 
+   >>> G.has_edge(*e) # or has_edge(*(u,v))
    True
-   
+
    The * operator unpacks the edge tuple in the argument list.
 
 get_edge()
@@ -136,7 +136,7 @@ get_edge()
    Now has the keyword argument "default" to specify
    what value to return if no edge is found.  If not specified
    an exception is raised if no edge is found.
-   
+
    The fastest way to get edge data for edge (u,v) is to use G[u][v]
    instead of G.get_edge(u,v)
 
@@ -144,12 +144,12 @@ get_edge()
 degree_iter()
 ^^^^^^^^^^^^^
    The degree_iter method now returns an iterator over pairs of (node,
-   degree).  This was the previous behavior of degree_iter(with_labels=true)    
+   degree).  This was the previous behavior of degree_iter(with_labels=true)
    Also there is a new keyword weighted=False|True for weighted degree.
 
 subgraph()
 ^^^^^^^^^^
-   The argument inplace=False|True has been replaced with copy=True|False.     
+   The argument inplace=False|True has been replaced with copy=True|False.
 
    Subgraph no longer takes create_using keyword.  To change the graph
    type either make a copy of
@@ -173,11 +173,11 @@ __getitem__()
 
    >>> G.neighbors(0)  # doctest: +SKIP
    [1]
-   
+
    This change allows algorithms to use the underlying dict-of-dict
-   representation through G[v] for substantial performance gains.  
+   representation through G[v] for substantial performance gains.
    Warning: The returned dictionary should not be modified as it may
-   corrupt the graph data structure.  Make a copy G[v].copy() if you 
+   corrupt the graph data structure.  Make a copy G[v].copy() if you
    wish to modify the dict.
 
 
@@ -200,13 +200,13 @@ node_boundary()
 ^^^^^^^^^^^^^^^
    now a function
 
-edge_boundary() 
-^^^^^^^^^^^^^^^ 
+edge_boundary()
+^^^^^^^^^^^^^^^
    now a function
 
-is_directed() 
+is_directed()
 ^^^^^^^^^^^^^
-   use the directed attribute 
+   use the directed attribute
 
    >>> G=nx.DiGraph()
    >>> # G.directed
@@ -216,8 +216,8 @@ G.out_edges()
 ^^^^^^^^^^^^^
    use G.edges()
 
-G.in_edges() 
-^^^^^^^^^^^^ 
+G.in_edges()
+^^^^^^^^^^^^
    use
 
    >>> G = nx.DiGraph()
@@ -251,12 +251,12 @@ Imports
 -------
 Some of the code modules were moved into subdirectories.
 
-Import statements such as:: 
+Import statements such as::
 
   import networkx.centrality
   from networkx.centrality import *
 
-may no longer work (including that example). 
+may no longer work (including that example).
 
 Use either
 
@@ -269,7 +269,7 @@ or
 Self-loops
 ----------
 For Graph and DiGraph self loops are now allowed.
-This might affect code or algorithms that add self loops 
+This might affect code or algorithms that add self loops
 which were intended to be ignored.
 
 Use the methods
@@ -285,7 +285,7 @@ Copy
 Copies of NetworkX graphs including using the copy() method
 now return complete copies of the graph.  This means that all
 connection information is copied--subsequent changes in the
-copy do not change the old graph.  But node keys and edge 
+copy do not change the old graph.  But node keys and edge
 data in the original and copy graphs are pointers to the same data.
 
 prepare_nbunch
@@ -296,20 +296,20 @@ Used internally - now called nbunch_iter and returns an iterator.
 Converting your old code to Version 0.99
 ========================================
 
-Mostly you can just run the code and python will raise an exception 
+Mostly you can just run the code and python will raise an exception
 for features that changed.  Common places for changes are
 
     - Converting XGraph() to either Graph or MultiGraph
     - Converting XGraph.edges()  to  Graph.edges(data=True)
     - Switching some rarely used methods to attributes (e.g. directed)
       or to functions (e.g. node_boundary)
-    - If you relied on the old default edge data being None, you will 
+    - If you relied on the old default edge data being None, you will
       have to account for it now being 1.
 
-You may also want to look through your code for places which could 
+You may also want to look through your code for places which could
 improve speed or readability.  The iterators are helpful with large
 graphs and getting edge data via G[u][v] is quite fast.   You may also
-want to change G.neighbors(n) to G[n] which returns the dict keyed by 
+want to change G.neighbors(n) to G[n] which returns the dict keyed by
 neighbor nodes to the edge data.  It is faster for many purposes but
 does not work well when you are changing the graph.
 
diff --git a/doc/release/api_1.4.rst b/doc/release/api_1.4.rst
index 64aef6b7..0f809860 100644
--- a/doc/release/api_1.4.rst
+++ b/doc/release/api_1.4.rst
@@ -4,7 +4,7 @@ Version 1.4 notes and API changes
 
 We have made some API changes, detailed below, to add clarity.
 This page reflects changes from networkx-1.3 to networkx-1.4.
-For changes from earlier versions to networkx-1.0 see 
+For changes from earlier versions to networkx-1.0 see
 :doc:`Version 1.0 API changes <api_1.0>`.
 
 Please send comments and questions to the networkx-discuss mailing list:
diff --git a/doc/release/api_1.5.rst b/doc/release/api_1.5.rst
index f15e3ead..bda3d36a 100644
--- a/doc/release/api_1.5.rst
+++ b/doc/release/api_1.5.rst
@@ -10,19 +10,19 @@ http://groups.google.com/group/networkx-discuss .
 Weighted graph algorithms
 -------------------------
 
-Many 'weighted' graph algorithms now take optional parameter to 
+Many 'weighted' graph algorithms now take optional parameter to
 specify which edge attribute should be used for the weight
 (default='weight') (ticket https://networkx.lanl.gov/trac/ticket/509)
 
 In some cases the parameter name was changed from weighted_edges,
-or weighted, to weight.  Here is how to specify which edge attribute 
+or weighted, to weight.  Here is how to specify which edge attribute
 will be used in the algorithms:
 
 - Use weight=None to consider all weights equally (unweighted case)
 
 - Use weight=True or weight='weight' to use the 'weight' edge attribute
 
-- Use weight='other' to use the 'other' edge attribute 
+- Use weight='other' to use the 'other' edge attribute
 
 Algorithms affected are:
 
@@ -41,7 +41,7 @@ single_source_dijkstra_path_basic, astar_path, astar_path_length
 Random geometric graph
 ----------------------
 
-The random geometric graph generator has been simplified.  
-It no longer supports the create_using, repel, or verbose parameters.  
+The random geometric graph generator has been simplified.
+It no longer supports the create_using, repel, or verbose parameters.
 An optional pos keyword was added to allow specification of node positions.
 
diff --git a/doc/release/api_1.6.rst b/doc/release/api_1.6.rst
index 9e3be994..9d45d621 100644
--- a/doc/release/api_1.6.rst
+++ b/doc/release/api_1.6.rst
@@ -12,14 +12,14 @@ Graph Classes
 
 The degree* methods in the graph classes (Graph, DiGraph, MultiGraph,
 MultiDiGraph) now take an optional weight= keyword that allows computing
-weighted degree with arbitrary (numerical) edge attributes.  Setting 
+weighted degree with arbitrary (numerical) edge attributes.  Setting
 weight=None is equivalent to the previous weighted=False.
 
 
 Weighted graph algorithms
 -------------------------
 
-Many 'weighted' graph algorithms now take optional parameter to 
+Many 'weighted' graph algorithms now take optional parameter to
 specify which edge attribute should be used for the weight
 (default='weight') (ticket https://networkx.lanl.gov/trac/ticket/573)
 
@@ -30,11 +30,11 @@ how to specify which edge attribute will be used in the algorithms:
 
 - Use weight='weight' to use the 'weight' edge attribute
 
-- Use weight='other' to use the 'other' edge attribute 
+- Use weight='other' to use the 'other' edge attribute
 
 Algorithms affected are:
 
-to_scipy_sparse_matrix, 
+to_scipy_sparse_matrix,
 clustering,
 average_clustering,
 bipartite.degree,
@@ -95,13 +95,13 @@ Other
 
 * condensation
 
-  The condensation algorithm now takes a second argument (scc) and returns a   
+  The condensation algorithm now takes a second argument (scc) and returns a
   graph with nodes labeled as integers instead of node tuples.
 
 * degree connectivity
 
-  average_in_degree_connectivity and average_out_degree_connectivity have 
-  have been replaced with 
+  average_in_degree_connectivity and average_out_degree_connectivity have
+  have been replaced with
 
   average_degree_connectivity(G, source='in', target='in')
 
@@ -111,8 +111,8 @@ Other
 
 * neighbor degree
 
-  average_neighbor_in_degree and  average_neighbor_out_degreey have 
-  have been replaced with 
+  average_neighbor_in_degree and  average_neighbor_out_degreey have
+  have been replaced with
 
   average_neighbor_degree(G, source='in', target='in')
 
diff --git a/doc/release/release_2.4.rst b/doc/release/release_2.4.rst
index b6e0d3e0..face96eb 100644
--- a/doc/release/release_2.4.rst
+++ b/doc/release/release_2.4.rst
@@ -65,7 +65,7 @@ Improvements
 - fix unionfind; betweenness_subset; lexico-topo-sort; A*;
   inverse_line_graph; async label propagation; edgelist reading;
   Gomory-Hu flow method; label_propagation; partial_duplication;
-  shell_layout with 1 node in shell; from_pandas_edgelist  
+  shell_layout with 1 node in shell; from_pandas_edgelist
 - Documentation improvement and fixes
 
 
diff --git a/networkx/algorithms/d_separation.py b/networkx/algorithms/d_separation.py
index fd8ab191..30d76098 100644
--- a/networkx/algorithms/d_separation.py
+++ b/networkx/algorithms/d_separation.py
@@ -16,13 +16,13 @@ Examples
 --------
 
 >>> import networkx as nx
->>> 
+>>>
 >>> # HMM graph with five states and observation nodes
 ... g= nx.DiGraph()
 >>> g.add_edges_from([('S1', 'S2'), ('S2', 'S3'), ('S3', 'S4'), ('S4', 'S5'),
 ...                   ('S1', 'O1'), ('S2', 'O2'), ('S3', 'O3'), ('S4', 'O4'),
 ...                   ('S5', 'O5')])
->>> 
+>>>
 >>> # states/obs before 'S3' are d-separated from states/obs after 'S3'
 ... nx.d_separated(g, {'S1', 'S2', 'O1', 'O2'}, {'S4', 'S5', 'O4', 'O5'}, {'S3'})
 True
diff --git a/networkx/algorithms/flow/tests/test_maxflow_large_graph.py b/networkx/algorithms/flow/tests/test_maxflow_large_graph.py
index 29185601..73f2e0a5 100644
--- a/networkx/algorithms/flow/tests/test_maxflow_large_graph.py
+++ b/networkx/algorithms/flow/tests/test_maxflow_large_graph.py
@@ -59,7 +59,7 @@ def validate_flows(G, s, t, soln_value, R, flow_func):
     excess = {u: 0 for u in flow_dict}
     for u in flow_dict:
         for v, flow in flow_dict[u].items():
-            assert flow <= G[u][v].get("capacity", float("inf")), errmsg 
+            assert flow <= G[u][v].get("capacity", float("inf")), errmsg
             assert flow >= 0, errmsg
             excess[u] -= flow
             excess[v] += flow
diff --git a/networkx/classes/function.py b/networkx/classes/function.py
index 4ee94f64..8e199e9a 100644
--- a/networkx/classes/function.py
+++ b/networkx/classes/function.py
@@ -526,7 +526,7 @@ def create_empty_copy(G, with_data=True):
 
 def info(G, n=None):
     """Return a summary of information for the graph G or a single node n.
- 
+
     The summary includes the number of nodes and edges (or neighbours for a single
     node), and their average degree.
 
diff --git a/networkx/tests/README b/networkx/tests/README
index 4ab00d73..965a2cd6 100644
--- a/networkx/tests/README
+++ b/networkx/tests/README
@@ -4,7 +4,7 @@ https://pytest.org
 
 The tests also demonstrate the usage of many of the features of NetworkX.
 
-There are a few ways to run the tests. 
+There are a few ways to run the tests.
 
 The simplest way is to import networkx and run the test() function.
 
diff --git a/networkx/utils/tests/test.txt b/networkx/utils/tests/test.txt
index 20b88ee7..63b8d6ea 100644
--- a/networkx/utils/tests/test.txt
+++ b/networkx/utils/tests/test.txt
@@ -1 +1 @@
-Blah... BLAH BLAH!!!! 
\ No newline at end of file
+Blah... BLAH BLAH!!!!
\ No newline at end of file