comparison env/share/doc/networkx-2.5/examples/drawing/plot_knuth_miles.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

0:4f3585e2f14b

"""
===========
Knuth Miles
===========

`miles_graph()` returns an undirected graph over the 128 US cities from.  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 of

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

The data file can be found at:

- https://github.com/networkx/networkx/blob/master/examples/drawing/knuth_miles.txt.gz
"""

import gzip
import re

import matplotlib.pyplot as plt
import networkx as nx


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)

    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(r"^\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


G = miles_graph()

print("Loaded miles_dat.txt containing 128 cities.")
print(f"digraph has {nx.number_of_nodes(G)} nodes with {nx.number_of_edges(G)} edges")

# make new graph of cites, edge if less then 300 miles between them
H = nx.Graph()
for v in G:
    H.add_node(v)
for (u, v, d) in G.edges(data=True):
    if d["weight"] < 300:
        H.add_edge(u, v)

# draw with matplotlib/pylab
plt.figure(figsize=(8, 8))
# with nodes colored by degree sized by population
node_color = [float(H.degree(v)) for v in H]
nx.draw(
    H,
    G.position,
    node_size=[G.population[v] for v in H],
    node_color=node_color,
    with_labels=False,
)

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

plt.show()