.. _sphx_glr_gallery_plot_cities.py:


======
Cities
======

`miles_graph()` returns an undirected graph over the 128 US cities from
the datafile `miles_dat.txt`. The cities each have location and population
data.  The edges are labeled with the distance between the two cities.

This example is described in Section 1.1 in Knuth's book (see [1]_ and [2]_).

References.
-----------

.. [1] Donald E. Knuth,
   "The Stanford GraphBase: A Platform for Combinatorial Computing",
   ACM Press, New York, 1993.
.. [2] http://www-cs-faculty.stanford.edu/~knuth/sgb.html






.. image:: /gallery/images/sphx_glr_plot_cities_001.png
    :align: center


.. rst-class:: sphx-glr-script-out

 Out::

    Loaded miles_dat.txt containing 128 cities.
    digraph has 128 nodes with 8128 edges
    Subgraph has 6 nodes with 15 edges
    ['San Diego, CA', 'San Francisco, CA', 'Worcester, MA', 'Spokane, WA', 'Tucson, AZ', 'Saint Augustine, FL']




|


.. code-block:: python

    # Based on example from NetworkX

    import re
    import sys

    import matplotlib.pyplot as plt
    import networkx as nx
    import grave


    def miles_graph():
        """ Return the cites example graph in miles_dat.txt
            from the Stanford GraphBase.
        """
        # open file miles_dat.txt.gz (or miles_dat.txt)
        import gzip
        fh = gzip.open('knuth_miles.txt.gz', 'r')

        G = nx.Graph()
        G.position = {}
        G.population = {}

        cities = []
        for line in fh.readlines():
            line = line.decode()
            if line.startswith("*"):  # skip comments
                continue

            numfind = re.compile("^\d+")

            if numfind.match(line):  # this line is distances
                dist = line.split()
                for d in dist:
                    G.add_edge(city, cities[i], weight=int(d))
                    i = i + 1
            else:  # this line is a city, position, population
                i = 1
                (city, coordpop) = line.split("[")
                cities.insert(0, city)
                (coord, pop) = coordpop.split("]")
                (y, x) = coord.split(",")

                G.add_node(city)
                # assign position - flip x axis for matplotlib, shift origin
                G.position[city] = (-int(x) + 7500, int(y) - 3000)
                G.population[city] = float(pop) / 1000.0
        return G


    if __name__ == '__main__':

        G = miles_graph()

        print("Loaded miles_dat.txt containing 128 cities.")
        print("digraph has %d nodes with %d edges"
              % (nx.number_of_nodes(G), nx.number_of_edges(G)))
        cities = ['San Diego, CA',
                  'San Francisco, CA',
                  'Saint Augustine, FL',
                  'Spokane, WA',
                  'Worcester, MA',
                  'Tucson, AZ']

        # make subgraph of cities
        H = G.subgraph(cities)
        print("Subgraph has %d nodes with %d edges" % (len(H), H.size()))
        print(H.nodes)

        # draw with grave
        plt.figure(figsize=(8, 8))
        # create attribute for label
        nx.set_edge_attributes(H,
                               {e: G.edges[e]['weight'] for e in H.edges},
                               'label')

        # create stylers
        def transfer_G_layout(network):
            return {n: G.position[n] for n in network}

        def elabel_base_style(attr):
            return {'font_size': 4,
                    'font_weight': .1,
                    'font_family': 'sans-serif',
                    'font_color': 'b',
                    'rotate': True,  # TODO: make rotation less granular
                    }

        elabel_style = grave.style_merger(grave.use_attributes('label'),
                                          elabel_base_style)

        grave.plot_network(H, transfer_G_layout,
                           node_style=dict(node_size=20),
                           edge_label_style=elabel_style,
                           node_label_style={})

        # scale the axes equally
        plt.xlim(-5000, 500)
        plt.ylim(-2000, 3500)

        plt.show()

**Total running time of the script:** ( 0 minutes  0.081 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_cities.py <plot_cities.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_cities.ipynb <plot_cities.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_