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

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/share/doc/networkx-2.5/examples/drawing/plot_knuth_miles.py	Mon Mar 22 18:12:50 2021 +0000
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+"""
+===========
+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()