annotate env/share/doc/networkx-2.5/examples/graph/plot_words.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

"""
==================
Words/Ladder Graph
==================

Generate  an undirected graph over the 5757 5-letter words in the datafile
`words_dat.txt.gz`.  Two words are connected by an edge if they differ in one
letter, resulting in 14,135 edges. 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/graph/words_dat.txt.gz
"""

import gzip
from string import ascii_lowercase as lowercase

import networkx as nx


def generate_graph(words):
    G = nx.Graph(name="words")
    lookup = {c: lowercase.index(c) for c in lowercase}

    def edit_distance_one(word):
        for i in range(len(word)):
            left, c, right = word[0:i], word[i], word[i + 1 :]
            j = lookup[c]  # lowercase.index(c)
            for cc in lowercase[j + 1 :]:
                yield left + cc + right

    candgen = (
        (word, cand)
        for word in sorted(words)
        for cand in edit_distance_one(word)
        if cand in words
    )
    G.add_nodes_from(words)
    for word, cand in candgen:
        G.add_edge(word, cand)
    return G


def words_graph():
    """Return the words example graph from the Stanford GraphBase"""
    fh = gzip.open("words_dat.txt.gz", "r")
    words = set()
    for line in fh.readlines():
        line = line.decode()
        if line.startswith("*"):
            continue
        w = str(line[0:5])
        words.add(w)
    return generate_graph(words)


G = words_graph()
print("Loaded words_dat.txt containing 5757 five-letter English words.")
print("Two words are connected if they differ in one letter.")
print(f"Graph has {nx.number_of_nodes(G)} nodes with {nx.number_of_edges(G)} edges")
print(f"{nx.number_connected_components(G)} connected components")

for (source, target) in [("chaos", "order"), ("nodes", "graph"), ("pound", "marks")]:
    print(f"Shortest path between {source} and {target} is")
    try:
        sp = nx.shortest_path(G, source, target)
        for n in sp:
            print(n)
    except nx.NetworkXNoPath:
        print("None")