Merge pull request #3219 from jarrodmillman/python3

3.7 default and drop 2.7 for CI

.appveyor.yml

@@ -10,14 +10,6 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\tools\\appveyor\\run_with_env.cmd"
 
   matrix:
-    - PYTHON: "C:\\Python27"
-      PYTHON_VERSION: "2.7.15"
-      PYTHON_ARCH: "32"
-
-    - PYTHON: "C:\\Python27-x64"
-      PYTHON_VERSION: "2.7.15"
-      PYTHON_ARCH: "64"
-
     - PYTHON: "C:\\Python35"
       PYTHON_VERSION: "3.5.3"
       PYTHON_ARCH: "32"

.travis.yml

@@ -12,7 +12,9 @@ cache:
 matrix:
   include:
     - os: linux
-      python: 2.7
+      python: 3.7
+      dist: xenial
+      sudo: true
       env:
       - OPTIONAL_DEPS=1
       - BUILD_DOCS=1
@@ -26,7 +28,9 @@ matrix:
           - texlive-latex-extra
           - latexmk
     - os: linux
-      python: 2.7
+      python: 3.7
+      dist: xenial
+      sudo: true
       env:
       - OPTIONAL_DEPS=1
       - MINIMUM_REQUIREMENTS=1
@@ -36,33 +40,13 @@ matrix:
           packages:
           - libgdal-dev
           - graphviz
-    - os: linux
-      python: 2.7
-      env:
-      - OPTIONAL_DEPS=1
-      - PIP_FLAGS="--pre"
-      addons:
-        apt:
-          packages:
-          - libgdal-dev
-          - graphviz
-    - os: linux
-      python: 3.7
-      dist: xenial
-      sudo: true
-      env: OPTIONAL_DEPS=1
-      addons:
-        apt:
-          packages:
-          - libgdal-dev
-          - graphviz
     - os: linux
       python: 3.7
       dist: xenial
       sudo: true
       env:
       - OPTIONAL_DEPS=1
-      - MINIMUM_REQUIREMENTS=1
+      - PIP_FLAGS="--pre"
       addons:
         apt:
           packages:
@@ -78,7 +62,6 @@ matrix:
     - os: osx
       language: generic
       env: TRAVIS_PYTHON_VERSION=3.7
-    - python: 2.7
     - python: 3.5
     - python: 3.6
     - python: 3.7

INSTALL.rst

@@ -1,7 +1,7 @@
 Install
 =======
 
-NetworkX requires Python 2.7, 3.5, 3.6, or 3.7.  If you do not already
+NetworkX requires Python 3.5, 3.6, or 3.7.  If you do not already
 have a Python environment configured on your computer, please see the
 instructions for installing the full `scientific Python stack
 <https://scipy.org/install.html>`_.
@@ -87,14 +87,14 @@ Optional packages
 
 The following optional packages provide additional functionality.
 
-- `NumPy <http://www.numpy.org/>`_ (>= 1.15.0) provides matrix representation of
+- `NumPy <http://www.numpy.org/>`_ (>= 1.15.4) provides matrix representation of
   graphs and is used in some graph algorithms for high-performance matrix
   computations.
 - `SciPy <http://scipy.org/>`_ (>= 1.1.0) provides sparse matrix representation
   of graphs and many numerical scientific tools.
 - `pandas <http://pandas.pydata.org/>`_ (>= 0.23.3) provides a DataFrame, which
   is a tabular data structure with labeled axes.
-- `Matplotlib <http://matplotlib.org/>`_ (>= 2.2.2) provides flexible drawing of
+- `Matplotlib <http://matplotlib.org/>`_ (>= 3.0.2) provides flexible drawing of
   graphs.
 - `PyGraphviz <http://pygraphviz.github.io/>`_ (>= 1.5) and
   `pydot <https://github.com/erocarrera/pydot>`_ (>= 1.2.4) provide graph drawing

doc/reference/introduction.rst

@@ -317,7 +317,7 @@ edges $(A, B)$ and $(B, C)$.
    >>> G.add_edge('A', 'B')
    >>> G.add_edge('B', 'C')
    >>> print(G.adj)
-   {'A': {'B': {}}, 'C': {'B': {}}, 'B': {'A': {}, 'C': {}}}
+   {'A': {'B': {}}, 'B': {'A': {}, 'C': {}}, 'C': {'B': {}}}
 
 The data structure gets morphed slightly for each base graph class.
 For DiGraph two dict-of-dicts-of-dicts structures are provided, one

doc/release/api_1.0.rst

@@ -45,7 +45,7 @@ keyword arguments.
 >>> G=nx.Graph(region='Africa')
 >>> G.graph['color']='green'
 >>> G.graph
-{'color': 'green', 'region': 'Africa'}
+{'region': 'Africa', 'color': 'green'}
 
 Node attributes
 ---------------

doc/release/migration_guide_from_1.x_to_2.0.rst

@@ -81,9 +81,10 @@ views.
     >>> H = nx.Graph()
     >>> H.add_nodes_from([1, 'networkx', '2.0'])
     >>> G.nodes & H.nodes  # finding common nodes in 2 graphs
-    set([1])
-    >>> G.nodes | H.nodes  # union of nodes in 2 graphs
-    set([0, 1, 2, 3, 4, 'networkx', '2.0'])
+    {1}
+    >>> # union of nodes in 2 graphs
+    >>> G.nodes | H.nodes  # doctest: +SKIP
+    {0, 1, 2, 3, 4, 'networkx', '2.0'}
 
 Similarly, ``G.edges`` now returns an EdgeView instead of a list of edges and it
 also supports set operations.
@@ -131,7 +132,7 @@ for ``out_degree`` and ``degree``
     [1, 0]
 
     >>> dict(DG.in_degree([1, 3])).values()    # works for nx-1 and nx-2
-    [1, 0]
+    dict_values([1, 0])
     >>> # DG.in_degree(nlist) creates a restricted view for only nodes in nlist.
     >>> # but see the fourth option above for using lookup instead.
     >>> list(d for n, d in DG.in_degree([1, 3]))
@@ -148,7 +149,7 @@ If ``n`` is a node in ``G``, then ``G.neighbors(n)`` returns an iterator.
 
     >>> n = 1
     >>> G.neighbors(n)
-    <dictionary-keyiterator object at ...>
+    <dict_keyiterator object at ...>
     >>> list(G.neighbors(n))
     [0, 2, 3, 4]
 
@@ -169,9 +170,9 @@ DiGraphViews behave similar to GraphViews, but have a few more methods.
     >>> D.out_degree[2]
     2
     >>> D.in_edges
-    InEdgeView([(1, 2), (1, 3), (2, 3), (2, 4)])
+    InEdgeView([(1, 2), (2, 3), (1, 3), (2, 4)])
     >>> list(D.in_edges())
-    [(1, 2), (1, 3), (2, 3), (2, 4)]
+    [(1, 2), (2, 3), (1, 3), (2, 4)]
     >>> D.out_edges(2)
     OutEdgeDataView([(2, 3), (2, 4)])
     >>> list(D.out_edges(2))
@@ -181,11 +182,11 @@ DiGraphViews behave similar to GraphViews, but have a few more methods.
     >>> list(D.in_degree)
     [(1, 0), (2, 1), (3, 2), (4, 1)]
     >>> D.successors(2)
-    <dictionary-keyiterator object at ...>
+    <dict_keyiterator object at ...>
     >>> list(D.successors(2))
     [3, 4]
     >>> D.predecessors(2)
-    <dictionary-keyiterator object at ...>
+    <dict_keyiterator object at ...>
     >>> list(D.predecessors(2))
     [1]
 

doc/release/release_2.0.rst

@@ -59,7 +59,7 @@ API Changes
     >>> G.nodes[3]
     {'color': 'blue'}
     >>> G.nodes & {3, 4, 5}
-    set([3, 4])
+    {3, 4}
 
   The following methods have changed:
 

doc/tutorial.rst

@@ -137,7 +137,7 @@ better in other contexts.
 .. nbplot::
 
     >>> list(G.nodes)
-    ['a', 1, 2, 3, 'spam', 'm', 'p', 's']
+    [1, 2, 3, 'spam', 's', 'p', 'a', 'm']
     >>> list(G.edges)
     [(1, 2), (1, 3), (3, 'm')]
     >>> list(G.adj[1])  # or list(G.neighbors(1))
@@ -296,7 +296,7 @@ Add node attributes using ``add_node()``, ``add_nodes_from()``, or ``G.nodes``
     {'time': '5pm'}
     >>> G.nodes[1]['room'] = 714
     >>> G.nodes.data()
-    NodeDataView({1: {'room': 714, 'time': '5pm'}, 3: {'time': '2pm'}})
+    NodeDataView({1: {'time': '5pm', 'room': 714}, 3: {'time': '2pm'}})
 
 Note that adding a node to ``G.nodes`` does not add it to the graph, use
 ``G.add_node()`` to add new nodes. Similarly for edges.
@@ -450,7 +450,7 @@ functions such as:
     >>> G.add_edges_from([(1, 2), (1, 3)])
     >>> G.add_node("spam")       # adds node "spam"
     >>> list(nx.connected_components(G))
-    [set([1, 2, 3]), set(['spam'])]
+    [{1, 2, 3}, {'spam'}]
     >>> sorted(d for n, d in G.degree())
     [0, 1, 1, 2]
     >>> nx.clustering(G)
@@ -463,7 +463,7 @@ These are easily stored in a `dict` structure if you desire.
 
     >>> sp = dict(nx.all_pairs_shortest_path(G))
     >>> sp[3]
-    {1: [3, 1], 2: [3, 1, 2], 3: [3]}
+    {3: [3], 1: [3, 1], 2: [3, 1, 2]}
 
 See :doc:`/reference/algorithms/index` for details on graph algorithms
 supported.

examples/basic/plot_read_write.py

@@ -21,10 +21,10 @@
 import networkx as nx
 
 G = nx.grid_2d_graph(5, 5)  # 5x5 grid
-try:  # Python 2.6+
-    nx.write_adjlist(G, sys.stdout)  # write adjacency list to screen
-except TypeError:  # Python 3.x
-    nx.write_adjlist(G, sys.stdout.buffer)  # write adjacency list to screen
+
+# print the adjacency list
+for line in nx.generate_adjlist(G):
+    print(line)
 # write edgelist to grid.edgelist
 nx.write_edgelist(G, path="grid.edgelist", delimiter=":")
 # read edgelist from grid.edgelist

examples/drawing/plot_unix_email.py

@@ -26,7 +26,6 @@
 #    All rights reserved.
 #    BSD license.
 
-import email
 from email.utils import getaddresses, parseaddr
 import mailbox
 import sys
@@ -35,25 +34,18 @@
 import networkx as nx
 
 # unix mailbox recipe
-# see https://docs.python.org/2/library/mailbox.html
-
+# see https://docs.python.org/3/library/mailbox.html
 
 def mbox_graph():
-    try:
-        fh = open("unix_email.mbox", 'rb')
-    except IOError:
-        print("unix_email.mbox not found")
-        raise
-
-    mbox = mailbox.UnixMailbox(fh, email.message_from_file)  # parse unix mailbox
+    mbox = mailbox.mbox("unix_email.mbox")  # parse unix mailbox
 
     G = nx.MultiDiGraph()  # create empty graph
 
     # parse each messages and build graph
     for msg in mbox:  # msg is python email.Message.Message object
         (source_name, source_addr) = parseaddr(msg['From'])  # sender
         # get all recipients
-        # see https://docs.python.org/2/library/email.html
+        # see https://docs.python.org/3/library/email.html
         tos = msg.get_all('to', [])
         ccs = msg.get_all('cc', [])
         resent_tos = msg.get_all('resent-to', [])

examples/graph/plot_erdos_renyi.py

@@ -21,8 +21,6 @@
 #    All rights reserved.
 #    BSD license.
 
-import sys
-
 import matplotlib.pyplot as plt
 from networkx import nx
 
@@ -36,11 +34,9 @@
 for v in nx.nodes(G):
     print('%s %d %f' % (v, nx.degree(G, v), nx.clustering(G, v)))
 
-# print the adjacency list to terminal
-try:
-    nx.write_adjlist(G, sys.stdout)
-except TypeError:  # Python 3.x
-    nx.write_adjlist(G, sys.stdout.buffer)
+# print the adjacency list
+for line in nx.generate_adjlist(G):
+    print(line)
 
 nx.draw(G)
 plt.show()

networkx/__init__.py

@@ -65,8 +65,8 @@
 from __future__ import absolute_import
 
 import sys
-if sys.version_info[:2] < (2, 7):
-    m = "Python 2.7 or later is required for NetworkX (%d.%d detected)."
+if sys.version_info[:2] < (3, 5):
+    m = "Python 3.5 or later is required for NetworkX (%d.%d detected)."
     raise ImportError(m % sys.version_info[:2])
 del sys
 

networkx/drawing/nx_pylab.py

@@ -1126,7 +1126,7 @@ def apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):
         # rgba_colors) is the same as the number of elements, resize the array,
         # to avoid it being interpreted as a colormap by scatter()
         if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):
-            rgba_colors.resize((len(elem_list), 4))
+            rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))
             rgba_colors[1:, 0] = rgba_colors[0, 0]
             rgba_colors[1:, 1] = rgba_colors[0, 1]
             rgba_colors[1:, 2] = rgba_colors[0, 2]

networkx/release.py

@@ -210,8 +210,6 @@ def get_info(dynamic=True):
     'Intended Audience :: Science/Research',
     'License :: OSI Approved :: BSD License',
     'Operating System :: OS Independent',
-    'Programming Language :: Python :: 2',
-    'Programming Language :: Python :: 2.7',
     'Programming Language :: Python :: 3',
     'Programming Language :: Python :: 3.4',
     'Programming Language :: Python :: 3.5',

requirements/doc.txt

@@ -1,6 +1,6 @@
-sphinx>=1.7.6
-sphinx_rtd_theme>=0.4.1
+sphinx>=1.8.1
+sphinx_rtd_theme>=0.4.2
 sphinx-gallery>=0.2.0
-pillow>=5.2.0
+pillow>=5.3.0
 nb2plots>=0.6
-texext>=0.6
+texext>=0.6.1

requirements/extras.txt

@@ -1,7 +1,7 @@
-numpy>=1.15.0
+numpy>=1.15.4
 scipy>=1.1.0
 pandas>=0.23.3
-matplotlib>=2.2.2
+matplotlib>=3.0.2
 pygraphviz>=1.5
 pydot>=1.2.4
 pyyaml>=3.13

requirements/test.txt

@@ -1,4 +1,4 @@
 nose>=1.3.7
 nose-ignore-docstring>=0.2
-coverage>=4.5.1
+coverage>=4.5.2
 codecov>=2.0.15

setup.py

@@ -19,8 +19,8 @@
     print("To install, run 'python setup.py install'")
     print()
 
-if sys.version_info[:2] < (2, 7):
-    print("NetworkX requires Python 2.7 or later (%d.%d detected)." %
+if sys.version_info[:2] < (3, 5):
+    print("NetworkX requires Python 3.5 or later (%d.%d detected)." %
           sys.version_info[:2])
     sys.exit(-1)