Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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48 lines
1.1 KiB
48 lines
1.1 KiB
4 years ago
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"""
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========
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Football
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========
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Load football network in GML format and compute some network statistcs.
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Shows how to download GML graph in a zipped file, unpack it, and load
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into a NetworkX graph.
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Requires Internet connection to download the URL
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http://www-personal.umich.edu/~mejn/netdata/football.zip
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"""
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import urllib.request as urllib
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import io
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import zipfile
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import matplotlib.pyplot as plt
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import networkx as nx
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url = "http://www-personal.umich.edu/~mejn/netdata/football.zip"
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sock = urllib.urlopen(url) # open URL
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s = io.BytesIO(sock.read()) # read into BytesIO "file"
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sock.close()
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zf = zipfile.ZipFile(s) # zipfile object
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txt = zf.read("football.txt").decode() # read info file
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gml = zf.read("football.gml").decode() # read gml data
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# throw away bogus first line with # from mejn files
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gml = gml.split("\n")[1:]
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G = nx.parse_gml(gml) # parse gml data
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print(txt)
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# print degree for each team - number of games
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for n, d in G.degree():
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print(f"{n:20} {d:2}")
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options = {
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"node_color": "black",
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"node_size": 50,
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"linewidths": 0,
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"width": 0.1,
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}
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nx.draw(G, **options)
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plt.show()
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