Mercurial > repos > shellac > sam_consensus_v3
annotate env/share/doc/networkx-2.5/examples/drawing/plot_knuth_miles.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 """ |
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2 =========== |
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3 Knuth Miles |
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4 =========== |
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5 |
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6 `miles_graph()` returns an undirected graph over the 128 US cities from. The |
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7 cities each have location and population data. The edges are labeled with the |
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8 distance between the two cities. |
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9 |
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10 This example is described in Section 1.1 of |
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11 |
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12 Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial |
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13 Computing", ACM Press, New York, 1993. |
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14 http://www-cs-faculty.stanford.edu/~knuth/sgb.html |
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15 |
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16 The data file can be found at: |
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17 |
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18 - https://github.com/networkx/networkx/blob/master/examples/drawing/knuth_miles.txt.gz |
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19 """ |
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20 |
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21 import gzip |
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22 import re |
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23 |
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24 import matplotlib.pyplot as plt |
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25 import networkx as nx |
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26 |
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27 |
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28 def miles_graph(): |
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29 """ Return the cites example graph in miles_dat.txt |
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30 from the Stanford GraphBase. |
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31 """ |
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32 # open file miles_dat.txt.gz (or miles_dat.txt) |
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33 |
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34 fh = gzip.open("knuth_miles.txt.gz", "r") |
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35 |
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36 G = nx.Graph() |
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37 G.position = {} |
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38 G.population = {} |
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39 |
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40 cities = [] |
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41 for line in fh.readlines(): |
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42 line = line.decode() |
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43 if line.startswith("*"): # skip comments |
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44 continue |
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45 |
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46 numfind = re.compile(r"^\d+") |
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47 |
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48 if numfind.match(line): # this line is distances |
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49 dist = line.split() |
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50 for d in dist: |
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51 G.add_edge(city, cities[i], weight=int(d)) |
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52 i = i + 1 |
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53 else: # this line is a city, position, population |
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54 i = 1 |
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55 (city, coordpop) = line.split("[") |
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56 cities.insert(0, city) |
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57 (coord, pop) = coordpop.split("]") |
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58 (y, x) = coord.split(",") |
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59 |
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60 G.add_node(city) |
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61 # assign position - flip x axis for matplotlib, shift origin |
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62 G.position[city] = (-int(x) + 7500, int(y) - 3000) |
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63 G.population[city] = float(pop) / 1000.0 |
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64 return G |
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65 |
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66 |
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67 G = miles_graph() |
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68 |
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69 print("Loaded miles_dat.txt containing 128 cities.") |
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70 print(f"digraph has {nx.number_of_nodes(G)} nodes with {nx.number_of_edges(G)} edges") |
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71 |
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72 # make new graph of cites, edge if less then 300 miles between them |
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73 H = nx.Graph() |
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74 for v in G: |
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75 H.add_node(v) |
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76 for (u, v, d) in G.edges(data=True): |
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77 if d["weight"] < 300: |
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78 H.add_edge(u, v) |
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79 |
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80 # draw with matplotlib/pylab |
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81 plt.figure(figsize=(8, 8)) |
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82 # with nodes colored by degree sized by population |
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83 node_color = [float(H.degree(v)) for v in H] |
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84 nx.draw( |
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85 H, |
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86 G.position, |
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87 node_size=[G.population[v] for v in H], |
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88 node_color=node_color, |
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89 with_labels=False, |
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90 ) |
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91 |
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92 # scale the axes equally |
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93 plt.xlim(-5000, 500) |
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94 plt.ylim(-2000, 3500) |
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95 |
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96 plt.show() |