Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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107 lines
2.6 KiB
107 lines
2.6 KiB
4 years ago
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"""
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Read graphs in LEDA format.
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LEDA is a C++ class library for efficient data types and algorithms.
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Format
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------
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See http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
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"""
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# Original author: D. Eppstein, UC Irvine, August 12, 2003.
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# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
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__all__ = ["read_leda", "parse_leda"]
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import networkx as nx
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from networkx.exception import NetworkXError
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from networkx.utils import open_file
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@open_file(0, mode="rb")
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def read_leda(path, encoding="UTF-8"):
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"""Read graph in LEDA format from path.
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Parameters
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----------
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path : file or string
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File or filename to read. Filenames ending in .gz or .bz2 will be
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uncompressed.
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Returns
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-------
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G : NetworkX graph
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Examples
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--------
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G=nx.read_leda('file.leda')
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References
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----------
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.. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
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"""
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lines = (line.decode(encoding) for line in path)
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G = parse_leda(lines)
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return G
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def parse_leda(lines):
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"""Read graph in LEDA format from string or iterable.
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Parameters
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----------
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lines : string or iterable
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Data in LEDA format.
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Returns
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-------
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G : NetworkX graph
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Examples
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--------
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G=nx.parse_leda(string)
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References
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----------
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.. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
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"""
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if isinstance(lines, str):
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lines = iter(lines.split("\n"))
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lines = iter(
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[
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line.rstrip("\n")
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for line in lines
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if not (line.startswith("#") or line.startswith("\n") or line == "")
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]
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)
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for i in range(3):
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next(lines)
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# Graph
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du = int(next(lines)) # -1=directed, -2=undirected
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if du == -1:
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G = nx.DiGraph()
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else:
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G = nx.Graph()
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# Nodes
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n = int(next(lines)) # number of nodes
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node = {}
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for i in range(1, n + 1): # LEDA counts from 1 to n
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symbol = next(lines).rstrip().strip("|{}| ")
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if symbol == "":
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symbol = str(i) # use int if no label - could be trouble
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node[i] = symbol
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G.add_nodes_from([s for i, s in node.items()])
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# Edges
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m = int(next(lines)) # number of edges
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for i in range(m):
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try:
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s, t, reversal, label = next(lines).split()
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except BaseException as e:
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raise NetworkXError(f"Too few fields in LEDA.GRAPH edge {i+1}") from e
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# BEWARE: no handling of reversal edges
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G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
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return G
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