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

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author shellac
date Mon, 22 Mar 2021 18:12:50 +0000
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1 """
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2 ===============
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3 Giant Component
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4 ===============
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5
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6 This example illustrates the sudden appearance of a
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7 giant connected component in a binomial random graph.
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8 """
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9
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10 import math
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11
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12 import matplotlib.pyplot as plt
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13 import networkx as nx
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14
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15 # This example needs Graphviz and either PyGraphviz or pydot.
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16 # from networkx.drawing.nx_pydot import graphviz_layout as layout
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17 from networkx.drawing.nx_agraph import graphviz_layout as layout
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18
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19 # If you don't have pygraphviz or pydot, you can do this
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20 # layout = nx.spring_layout
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21
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22
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23 n = 150 # 150 nodes
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24 # p value at which giant component (of size log(n) nodes) is expected
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25 p_giant = 1.0 / (n - 1)
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26 # p value at which graph is expected to become completely connected
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27 p_conn = math.log(n) / float(n)
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28
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29 # the following range of p values should be close to the threshold
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30 pvals = [0.003, 0.006, 0.008, 0.015]
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31
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32 region = 220 # for pylab 2x2 subplot layout
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33 plt.subplots_adjust(left=0, right=1, bottom=0, top=0.95, wspace=0.01, hspace=0.01)
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34 for p in pvals:
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35 G = nx.binomial_graph(n, p)
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36 pos = layout(G)
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37 region += 1
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38 plt.subplot(region)
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39 plt.title(f"p = {p:.3f}")
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40 nx.draw(G, pos, with_labels=False, node_size=10)
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41 # identify largest connected component
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42 Gcc = sorted(nx.connected_components(G), key=len, reverse=True)
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43 G0 = G.subgraph(Gcc[0])
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44 nx.draw_networkx_edges(G0, pos, edge_color="r", width=6.0)
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45 # show other connected components
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46 for Gi in Gcc[1:]:
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47 if len(Gi) > 1:
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48 nx.draw_networkx_edges(
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49 G.subgraph(Gi), pos, edge_color="r", alpha=0.3, width=5.0,
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50 )
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51 plt.show()