1053 lines
38 KiB
Python
1053 lines
38 KiB
Python
|
"""
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*******
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GraphML
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*******
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Read and write graphs in GraphML format.
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.. warning::
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This parser uses the standard xml library present in Python, which is
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insecure - see :external+python:mod:`xml` for additional information.
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Only parse GraphML files you trust.
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This implementation does not support mixed graphs (directed and unidirected
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edges together), hyperedges, nested graphs, or ports.
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"GraphML is a comprehensive and easy-to-use file format for graphs. It
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consists of a language core to describe the structural properties of a
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graph and a flexible extension mechanism to add application-specific
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data. Its main features include support of
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* directed, undirected, and mixed graphs,
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* hypergraphs,
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* hierarchical graphs,
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* graphical representations,
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* references to external data,
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* application-specific attribute data, and
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* light-weight parsers.
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Unlike many other file formats for graphs, GraphML does not use a
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custom syntax. Instead, it is based on XML and hence ideally suited as
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a common denominator for all kinds of services generating, archiving,
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or processing graphs."
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http://graphml.graphdrawing.org/
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Format
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------
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GraphML is an XML format. See
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http://graphml.graphdrawing.org/specification.html for the specification and
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http://graphml.graphdrawing.org/primer/graphml-primer.html
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for examples.
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"""
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import warnings
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from collections import defaultdict
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import networkx as nx
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from networkx.utils import open_file
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__all__ = [
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"write_graphml",
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"read_graphml",
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"generate_graphml",
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"write_graphml_xml",
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"write_graphml_lxml",
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"parse_graphml",
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"GraphMLWriter",
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"GraphMLReader",
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]
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@open_file(1, mode="wb")
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def write_graphml_xml(
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G,
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path,
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encoding="utf-8",
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prettyprint=True,
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infer_numeric_types=False,
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named_key_ids=False,
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edge_id_from_attribute=None,
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):
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"""Write G in GraphML XML format to path
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Parameters
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----------
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G : graph
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A networkx graph
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path : file or string
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File or filename to write.
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Filenames ending in .gz or .bz2 will be compressed.
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encoding : string (optional)
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Encoding for text data.
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prettyprint : bool (optional)
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If True use line breaks and indenting in output XML.
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infer_numeric_types : boolean
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Determine if numeric types should be generalized.
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For example, if edges have both int and float 'weight' attributes,
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we infer in GraphML that both are floats.
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named_key_ids : bool (optional)
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If True use attr.name as value for key elements' id attribute.
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edge_id_from_attribute : dict key (optional)
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If provided, the graphml edge id is set by looking up the corresponding
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edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
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the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
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Examples
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--------
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>>> G = nx.path_graph(4)
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>>> nx.write_graphml(G, "test.graphml")
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Notes
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-----
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This implementation does not support mixed graphs (directed
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and unidirected edges together) hyperedges, nested graphs, or ports.
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"""
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writer = GraphMLWriter(
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encoding=encoding,
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prettyprint=prettyprint,
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infer_numeric_types=infer_numeric_types,
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named_key_ids=named_key_ids,
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edge_id_from_attribute=edge_id_from_attribute,
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)
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writer.add_graph_element(G)
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writer.dump(path)
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@open_file(1, mode="wb")
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def write_graphml_lxml(
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G,
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path,
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encoding="utf-8",
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prettyprint=True,
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infer_numeric_types=False,
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named_key_ids=False,
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edge_id_from_attribute=None,
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):
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"""Write G in GraphML XML format to path
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This function uses the LXML framework and should be faster than
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the version using the xml library.
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Parameters
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----------
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G : graph
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|
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A networkx graph
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path : file or string
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File or filename to write.
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|
|
Filenames ending in .gz or .bz2 will be compressed.
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encoding : string (optional)
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|
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Encoding for text data.
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prettyprint : bool (optional)
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|
If True use line breaks and indenting in output XML.
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|
|
infer_numeric_types : boolean
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|
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Determine if numeric types should be generalized.
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||
|
|
For example, if edges have both int and float 'weight' attributes,
|
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|
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we infer in GraphML that both are floats.
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|
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named_key_ids : bool (optional)
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If True use attr.name as value for key elements' id attribute.
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edge_id_from_attribute : dict key (optional)
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|
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If provided, the graphml edge id is set by looking up the corresponding
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|
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edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
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the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
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Examples
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--------
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>>> G = nx.path_graph(4)
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>>> nx.write_graphml_lxml(G, "fourpath.graphml")
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Notes
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||
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-----
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|
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This implementation does not support mixed graphs (directed
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and unidirected edges together) hyperedges, nested graphs, or ports.
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"""
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try:
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import lxml.etree as lxmletree
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except ImportError:
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return write_graphml_xml(
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G,
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path,
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encoding,
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prettyprint,
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infer_numeric_types,
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named_key_ids,
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edge_id_from_attribute,
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)
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writer = GraphMLWriterLxml(
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path,
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graph=G,
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encoding=encoding,
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prettyprint=prettyprint,
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infer_numeric_types=infer_numeric_types,
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named_key_ids=named_key_ids,
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edge_id_from_attribute=edge_id_from_attribute,
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)
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writer.dump()
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def generate_graphml(
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G,
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encoding="utf-8",
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prettyprint=True,
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named_key_ids=False,
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edge_id_from_attribute=None,
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):
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|
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"""Generate GraphML lines for G
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|
|
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Parameters
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||
|
|
----------
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|
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G : graph
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|
|
A networkx graph
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|
|
encoding : string (optional)
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||
|
|
Encoding for text data.
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||
|
|
prettyprint : bool (optional)
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||
|
|
If True use line breaks and indenting in output XML.
|
||
|
|
named_key_ids : bool (optional)
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||
|
|
If True use attr.name as value for key elements' id attribute.
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||
|
|
edge_id_from_attribute : dict key (optional)
|
||
|
|
If provided, the graphml edge id is set by looking up the corresponding
|
||
|
|
edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
|
||
|
|
the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> G = nx.path_graph(4)
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>>> linefeed = chr(10) # linefeed = \n
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>>> s = linefeed.join(nx.generate_graphml(G))
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>>> for line in nx.generate_graphml(G): # doctest: +SKIP
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|
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... print(line)
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||
|
|
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||
|
|
Notes
|
||
|
|
-----
|
||
|
|
This implementation does not support mixed graphs (directed and unidirected
|
||
|
|
edges together) hyperedges, nested graphs, or ports.
|
||
|
|
"""
|
||
|
|
writer = GraphMLWriter(
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||
|
|
encoding=encoding,
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||
|
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prettyprint=prettyprint,
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||
|
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named_key_ids=named_key_ids,
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|
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edge_id_from_attribute=edge_id_from_attribute,
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||
|
|
)
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||
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writer.add_graph_element(G)
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yield from str(writer).splitlines()
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||
|
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||
|
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@open_file(0, mode="rb")
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||
|
|
@nx._dispatchable(graphs=None, returns_graph=True)
|
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|
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def read_graphml(path, node_type=str, edge_key_type=int, force_multigraph=False):
|
||
|
|
"""Read graph in GraphML format from path.
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||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
path : file or string
|
||
|
|
File or filename to write.
|
||
|
|
Filenames ending in .gz or .bz2 will be compressed.
|
||
|
|
|
||
|
|
node_type: Python type (default: str)
|
||
|
|
Convert node ids to this type
|
||
|
|
|
||
|
|
edge_key_type: Python type (default: int)
|
||
|
|
Convert graphml edge ids to this type. Multigraphs use id as edge key.
|
||
|
|
Non-multigraphs add to edge attribute dict with name "id".
|
||
|
|
|
||
|
|
force_multigraph : bool (default: False)
|
||
|
|
If True, return a multigraph with edge keys. If False (the default)
|
||
|
|
return a multigraph when multiedges are in the graph.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
graph: NetworkX graph
|
||
|
|
If parallel edges are present or `force_multigraph=True` then
|
||
|
|
a MultiGraph or MultiDiGraph is returned. Otherwise a Graph/DiGraph.
|
||
|
|
The returned graph is directed if the file indicates it should be.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Default node and edge attributes are not propagated to each node and edge.
|
||
|
|
They can be obtained from `G.graph` and applied to node and edge attributes
|
||
|
|
if desired using something like this:
|
||
|
|
|
||
|
|
>>> default_color = G.graph["node_default"]["color"] # doctest: +SKIP
|
||
|
|
>>> for node, data in G.nodes(data=True): # doctest: +SKIP
|
||
|
|
... if "color" not in data:
|
||
|
|
... data["color"] = default_color
|
||
|
|
>>> default_color = G.graph["edge_default"]["color"] # doctest: +SKIP
|
||
|
|
>>> for u, v, data in G.edges(data=True): # doctest: +SKIP
|
||
|
|
... if "color" not in data:
|
||
|
|
... data["color"] = default_color
|
||
|
|
|
||
|
|
This implementation does not support mixed graphs (directed and unidirected
|
||
|
|
edges together), hypergraphs, nested graphs, or ports.
|
||
|
|
|
||
|
|
For multigraphs the GraphML edge "id" will be used as the edge
|
||
|
|
key. If not specified then they "key" attribute will be used. If
|
||
|
|
there is no "key" attribute a default NetworkX multigraph edge key
|
||
|
|
will be provided.
|
||
|
|
|
||
|
|
Files with the yEd "yfiles" extension can be read. The type of the node's
|
||
|
|
shape is preserved in the `shape_type` node attribute.
|
||
|
|
|
||
|
|
yEd compressed files ("file.graphmlz" extension) can be read by renaming
|
||
|
|
the file to "file.graphml.gz".
|
||
|
|
|
||
|
|
"""
|
||
|
|
reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
|
||
|
|
# need to check for multiple graphs
|
||
|
|
glist = list(reader(path=path))
|
||
|
|
if len(glist) == 0:
|
||
|
|
# If no graph comes back, try looking for an incomplete header
|
||
|
|
header = b'<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
|
||
|
|
path.seek(0)
|
||
|
|
old_bytes = path.read()
|
||
|
|
new_bytes = old_bytes.replace(b"<graphml>", header)
|
||
|
|
glist = list(reader(string=new_bytes))
|
||
|
|
if len(glist) == 0:
|
||
|
|
raise nx.NetworkXError("file not successfully read as graphml")
|
||
|
|
return glist[0]
|
||
|
|
|
||
|
|
|
||
|
|
@nx._dispatchable(graphs=None, returns_graph=True)
|
||
|
|
def parse_graphml(
|
||
|
|
graphml_string, node_type=str, edge_key_type=int, force_multigraph=False
|
||
|
|
):
|
||
|
|
"""Read graph in GraphML format from string.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
graphml_string : string
|
||
|
|
String containing graphml information
|
||
|
|
(e.g., contents of a graphml file).
|
||
|
|
|
||
|
|
node_type: Python type (default: str)
|
||
|
|
Convert node ids to this type
|
||
|
|
|
||
|
|
edge_key_type: Python type (default: int)
|
||
|
|
Convert graphml edge ids to this type. Multigraphs use id as edge key.
|
||
|
|
Non-multigraphs add to edge attribute dict with name "id".
|
||
|
|
|
||
|
|
force_multigraph : bool (default: False)
|
||
|
|
If True, return a multigraph with edge keys. If False (the default)
|
||
|
|
return a multigraph when multiedges are in the graph.
|
||
|
|
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
graph: NetworkX graph
|
||
|
|
If no parallel edges are found a Graph or DiGraph is returned.
|
||
|
|
Otherwise a MultiGraph or MultiDiGraph is returned.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> G = nx.path_graph(4)
|
||
|
|
>>> linefeed = chr(10) # linefeed = \n
|
||
|
|
>>> s = linefeed.join(nx.generate_graphml(G))
|
||
|
|
>>> H = nx.parse_graphml(s)
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
Default node and edge attributes are not propagated to each node and edge.
|
||
|
|
They can be obtained from `G.graph` and applied to node and edge attributes
|
||
|
|
if desired using something like this:
|
||
|
|
|
||
|
|
>>> default_color = G.graph["node_default"]["color"] # doctest: +SKIP
|
||
|
|
>>> for node, data in G.nodes(data=True): # doctest: +SKIP
|
||
|
|
... if "color" not in data:
|
||
|
|
... data["color"] = default_color
|
||
|
|
>>> default_color = G.graph["edge_default"]["color"] # doctest: +SKIP
|
||
|
|
>>> for u, v, data in G.edges(data=True): # doctest: +SKIP
|
||
|
|
... if "color" not in data:
|
||
|
|
... data["color"] = default_color
|
||
|
|
|
||
|
|
This implementation does not support mixed graphs (directed and unidirected
|
||
|
|
edges together), hypergraphs, nested graphs, or ports.
|
||
|
|
|
||
|
|
For multigraphs the GraphML edge "id" will be used as the edge
|
||
|
|
key. If not specified then they "key" attribute will be used. If
|
||
|
|
there is no "key" attribute a default NetworkX multigraph edge key
|
||
|
|
will be provided.
|
||
|
|
|
||
|
|
"""
|
||
|
|
reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
|
||
|
|
# need to check for multiple graphs
|
||
|
|
glist = list(reader(string=graphml_string))
|
||
|
|
if len(glist) == 0:
|
||
|
|
# If no graph comes back, try looking for an incomplete header
|
||
|
|
header = '<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
|
||
|
|
new_string = graphml_string.replace("<graphml>", header)
|
||
|
|
glist = list(reader(string=new_string))
|
||
|
|
if len(glist) == 0:
|
||
|
|
raise nx.NetworkXError("file not successfully read as graphml")
|
||
|
|
return glist[0]
|
||
|
|
|
||
|
|
|
||
|
|
class GraphML:
|
||
|
|
NS_GRAPHML = "http://graphml.graphdrawing.org/xmlns"
|
||
|
|
NS_XSI = "http://www.w3.org/2001/XMLSchema-instance"
|
||
|
|
# xmlns:y="http://www.yworks.com/xml/graphml"
|
||
|
|
NS_Y = "http://www.yworks.com/xml/graphml"
|
||
|
|
SCHEMALOCATION = " ".join(
|
||
|
|
[
|
||
|
|
"http://graphml.graphdrawing.org/xmlns",
|
||
|
|
"http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd",
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
def construct_types(self):
|
||
|
|
types = [
|
||
|
|
(int, "integer"), # for Gephi GraphML bug
|
||
|
|
(str, "yfiles"),
|
||
|
|
(str, "string"),
|
||
|
|
(int, "int"),
|
||
|
|
(int, "long"),
|
||
|
|
(float, "float"),
|
||
|
|
(float, "double"),
|
||
|
|
(bool, "boolean"),
|
||
|
|
]
|
||
|
|
|
||
|
|
# These additions to types allow writing numpy types
|
||
|
|
try:
|
||
|
|
import numpy as np
|
||
|
|
except:
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
# prepend so that python types are created upon read (last entry wins)
|
||
|
|
types = [
|
||
|
|
(np.float64, "float"),
|
||
|
|
(np.float32, "float"),
|
||
|
|
(np.float16, "float"),
|
||
|
|
(np.int_, "int"),
|
||
|
|
(np.int8, "int"),
|
||
|
|
(np.int16, "int"),
|
||
|
|
(np.int32, "int"),
|
||
|
|
(np.int64, "int"),
|
||
|
|
(np.uint8, "int"),
|
||
|
|
(np.uint16, "int"),
|
||
|
|
(np.uint32, "int"),
|
||
|
|
(np.uint64, "int"),
|
||
|
|
(np.int_, "int"),
|
||
|
|
(np.intc, "int"),
|
||
|
|
(np.intp, "int"),
|
||
|
|
] + types
|
||
|
|
|
||
|
|
self.xml_type = dict(types)
|
||
|
|
self.python_type = dict(reversed(a) for a in types)
|
||
|
|
|
||
|
|
# This page says that data types in GraphML follow Java(TM).
|
||
|
|
# http://graphml.graphdrawing.org/primer/graphml-primer.html#AttributesDefinition
|
||
|
|
# true and false are the only boolean literals:
|
||
|
|
# http://en.wikibooks.org/wiki/Java_Programming/Literals#Boolean_Literals
|
||
|
|
convert_bool = {
|
||
|
|
# We use data.lower() in actual use.
|
||
|
|
"true": True,
|
||
|
|
"false": False,
|
||
|
|
# Include integer strings for convenience.
|
||
|
|
"0": False,
|
||
|
|
0: False,
|
||
|
|
"1": True,
|
||
|
|
1: True,
|
||
|
|
}
|
||
|
|
|
||
|
|
def get_xml_type(self, key):
|
||
|
|
"""Wrapper around the xml_type dict that raises a more informative
|
||
|
|
exception message when a user attempts to use data of a type not
|
||
|
|
supported by GraphML."""
|
||
|
|
try:
|
||
|
|
return self.xml_type[key]
|
||
|
|
except KeyError as err:
|
||
|
|
raise TypeError(
|
||
|
|
f"GraphML does not support type {key} as data values."
|
||
|
|
) from err
|
||
|
|
|
||
|
|
|
||
|
|
class GraphMLWriter(GraphML):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
graph=None,
|
||
|
|
encoding="utf-8",
|
||
|
|
prettyprint=True,
|
||
|
|
infer_numeric_types=False,
|
||
|
|
named_key_ids=False,
|
||
|
|
edge_id_from_attribute=None,
|
||
|
|
):
|
||
|
|
self.construct_types()
|
||
|
|
from xml.etree.ElementTree import Element
|
||
|
|
|
||
|
|
self.myElement = Element
|
||
|
|
|
||
|
|
self.infer_numeric_types = infer_numeric_types
|
||
|
|
self.prettyprint = prettyprint
|
||
|
|
self.named_key_ids = named_key_ids
|
||
|
|
self.edge_id_from_attribute = edge_id_from_attribute
|
||
|
|
self.encoding = encoding
|
||
|
|
self.xml = self.myElement(
|
||
|
|
"graphml",
|
||
|
|
{
|
||
|
|
"xmlns": self.NS_GRAPHML,
|
||
|
|
"xmlns:xsi": self.NS_XSI,
|
||
|
|
"xsi:schemaLocation": self.SCHEMALOCATION,
|
||
|
|
},
|
||
|
|
)
|
||
|
|
self.keys = {}
|
||
|
|
self.attributes = defaultdict(list)
|
||
|
|
self.attribute_types = defaultdict(set)
|
||
|
|
|
||
|
|
if graph is not None:
|
||
|
|
self.add_graph_element(graph)
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
from xml.etree.ElementTree import tostring
|
||
|
|
|
||
|
|
if self.prettyprint:
|
||
|
|
self.indent(self.xml)
|
||
|
|
s = tostring(self.xml).decode(self.encoding)
|
||
|
|
return s
|
||
|
|
|
||
|
|
def attr_type(self, name, scope, value):
|
||
|
|
"""Infer the attribute type of data named name. Currently this only
|
||
|
|
supports inference of numeric types.
|
||
|
|
|
||
|
|
If self.infer_numeric_types is false, type is used. Otherwise, pick the
|
||
|
|
most general of types found across all values with name and scope. This
|
||
|
|
means edges with data named 'weight' are treated separately from nodes
|
||
|
|
with data named 'weight'.
|
||
|
|
"""
|
||
|
|
if self.infer_numeric_types:
|
||
|
|
types = self.attribute_types[(name, scope)]
|
||
|
|
|
||
|
|
if len(types) > 1:
|
||
|
|
types = {self.get_xml_type(t) for t in types}
|
||
|
|
if "string" in types:
|
||
|
|
return str
|
||
|
|
elif "float" in types or "double" in types:
|
||
|
|
return float
|
||
|
|
else:
|
||
|
|
return int
|
||
|
|
else:
|
||
|
|
return list(types)[0]
|
||
|
|
else:
|
||
|
|
return type(value)
|
||
|
|
|
||
|
|
def get_key(self, name, attr_type, scope, default):
|
||
|
|
keys_key = (name, attr_type, scope)
|
||
|
|
try:
|
||
|
|
return self.keys[keys_key]
|
||
|
|
except KeyError:
|
||
|
|
if self.named_key_ids:
|
||
|
|
new_id = name
|
||
|
|
else:
|
||
|
|
new_id = f"d{len(list(self.keys))}"
|
||
|
|
|
||
|
|
self.keys[keys_key] = new_id
|
||
|
|
key_kwargs = {
|
||
|
|
"id": new_id,
|
||
|
|
"for": scope,
|
||
|
|
"attr.name": name,
|
||
|
|
"attr.type": attr_type,
|
||
|
|
}
|
||
|
|
key_element = self.myElement("key", **key_kwargs)
|
||
|
|
# add subelement for data default value if present
|
||
|
|
if default is not None:
|
||
|
|
default_element = self.myElement("default")
|
||
|
|
default_element.text = str(default)
|
||
|
|
key_element.append(default_element)
|
||
|
|
self.xml.insert(0, key_element)
|
||
|
|
return new_id
|
||
|
|
|
||
|
|
def add_data(self, name, element_type, value, scope="all", default=None):
|
||
|
|
"""
|
||
|
|
Make a data element for an edge or a node. Keep a log of the
|
||
|
|
type in the keys table.
|
||
|
|
"""
|
||
|
|
if element_type not in self.xml_type:
|
||
|
|
raise nx.NetworkXError(
|
||
|
|
f"GraphML writer does not support {element_type} as data values."
|
||
|
|
)
|
||
|
|
keyid = self.get_key(name, self.get_xml_type(element_type), scope, default)
|
||
|
|
data_element = self.myElement("data", key=keyid)
|
||
|
|
data_element.text = str(value)
|
||
|
|
return data_element
|
||
|
|
|
||
|
|
def add_attributes(self, scope, xml_obj, data, default):
|
||
|
|
"""Appends attribute data to edges or nodes, and stores type information
|
||
|
|
to be added later. See add_graph_element.
|
||
|
|
"""
|
||
|
|
for k, v in data.items():
|
||
|
|
self.attribute_types[(str(k), scope)].add(type(v))
|
||
|
|
self.attributes[xml_obj].append([k, v, scope, default.get(k)])
|
||
|
|
|
||
|
|
def add_nodes(self, G, graph_element):
|
||
|
|
default = G.graph.get("node_default", {})
|
||
|
|
for node, data in G.nodes(data=True):
|
||
|
|
node_element = self.myElement("node", id=str(node))
|
||
|
|
self.add_attributes("node", node_element, data, default)
|
||
|
|
graph_element.append(node_element)
|
||
|
|
|
||
|
|
def add_edges(self, G, graph_element):
|
||
|
|
if G.is_multigraph():
|
||
|
|
for u, v, key, data in G.edges(data=True, keys=True):
|
||
|
|
edge_element = self.myElement(
|
||
|
|
"edge",
|
||
|
|
source=str(u),
|
||
|
|
target=str(v),
|
||
|
|
id=str(data.get(self.edge_id_from_attribute))
|
||
|
|
if self.edge_id_from_attribute
|
||
|
|
and self.edge_id_from_attribute in data
|
||
|
|
else str(key),
|
||
|
|
)
|
||
|
|
default = G.graph.get("edge_default", {})
|
||
|
|
self.add_attributes("edge", edge_element, data, default)
|
||
|
|
graph_element.append(edge_element)
|
||
|
|
else:
|
||
|
|
for u, v, data in G.edges(data=True):
|
||
|
|
if self.edge_id_from_attribute and self.edge_id_from_attribute in data:
|
||
|
|
# select attribute to be edge id
|
||
|
|
edge_element = self.myElement(
|
||
|
|
"edge",
|
||
|
|
source=str(u),
|
||
|
|
target=str(v),
|
||
|
|
id=str(data.get(self.edge_id_from_attribute)),
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# default: no edge id
|
||
|
|
edge_element = self.myElement("edge", source=str(u), target=str(v))
|
||
|
|
default = G.graph.get("edge_default", {})
|
||
|
|
self.add_attributes("edge", edge_element, data, default)
|
||
|
|
graph_element.append(edge_element)
|
||
|
|
|
||
|
|
def add_graph_element(self, G):
|
||
|
|
"""
|
||
|
|
Serialize graph G in GraphML to the stream.
|
||
|
|
"""
|
||
|
|
if G.is_directed():
|
||
|
|
default_edge_type = "directed"
|
||
|
|
else:
|
||
|
|
default_edge_type = "undirected"
|
||
|
|
|
||
|
|
graphid = G.graph.pop("id", None)
|
||
|
|
if graphid is None:
|
||
|
|
graph_element = self.myElement("graph", edgedefault=default_edge_type)
|
||
|
|
else:
|
||
|
|
graph_element = self.myElement(
|
||
|
|
"graph", edgedefault=default_edge_type, id=graphid
|
||
|
|
)
|
||
|
|
default = {}
|
||
|
|
data = {
|
||
|
|
k: v
|
||
|
|
for (k, v) in G.graph.items()
|
||
|
|
if k not in ["node_default", "edge_default"]
|
||
|
|
}
|
||
|
|
self.add_attributes("graph", graph_element, data, default)
|
||
|
|
self.add_nodes(G, graph_element)
|
||
|
|
self.add_edges(G, graph_element)
|
||
|
|
|
||
|
|
# self.attributes contains a mapping from XML Objects to a list of
|
||
|
|
# data that needs to be added to them.
|
||
|
|
# We postpone processing in order to do type inference/generalization.
|
||
|
|
# See self.attr_type
|
||
|
|
for xml_obj, data in self.attributes.items():
|
||
|
|
for k, v, scope, default in data:
|
||
|
|
xml_obj.append(
|
||
|
|
self.add_data(
|
||
|
|
str(k), self.attr_type(k, scope, v), str(v), scope, default
|
||
|
|
)
|
||
|
|
)
|
||
|
|
self.xml.append(graph_element)
|
||
|
|
|
||
|
|
def add_graphs(self, graph_list):
|
||
|
|
"""Add many graphs to this GraphML document."""
|
||
|
|
for G in graph_list:
|
||
|
|
self.add_graph_element(G)
|
||
|
|
|
||
|
|
def dump(self, stream):
|
||
|
|
from xml.etree.ElementTree import ElementTree
|
||
|
|
|
||
|
|
if self.prettyprint:
|
||
|
|
self.indent(self.xml)
|
||
|
|
document = ElementTree(self.xml)
|
||
|
|
document.write(stream, encoding=self.encoding, xml_declaration=True)
|
||
|
|
|
||
|
|
def indent(self, elem, level=0):
|
||
|
|
# in-place prettyprint formatter
|
||
|
|
i = "\n" + level * " "
|
||
|
|
if len(elem):
|
||
|
|
if not elem.text or not elem.text.strip():
|
||
|
|
elem.text = i + " "
|
||
|
|
if not elem.tail or not elem.tail.strip():
|
||
|
|
elem.tail = i
|
||
|
|
for elem in elem:
|
||
|
|
self.indent(elem, level + 1)
|
||
|
|
if not elem.tail or not elem.tail.strip():
|
||
|
|
elem.tail = i
|
||
|
|
else:
|
||
|
|
if level and (not elem.tail or not elem.tail.strip()):
|
||
|
|
elem.tail = i
|
||
|
|
|
||
|
|
|
||
|
|
class IncrementalElement:
|
||
|
|
"""Wrapper for _IncrementalWriter providing an Element like interface.
|
||
|
|
|
||
|
|
This wrapper does not intend to be a complete implementation but rather to
|
||
|
|
deal with those calls used in GraphMLWriter.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, xml, prettyprint):
|
||
|
|
self.xml = xml
|
||
|
|
self.prettyprint = prettyprint
|
||
|
|
|
||
|
|
def append(self, element):
|
||
|
|
self.xml.write(element, pretty_print=self.prettyprint)
|
||
|
|
|
||
|
|
|
||
|
|
class GraphMLWriterLxml(GraphMLWriter):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
path,
|
||
|
|
graph=None,
|
||
|
|
encoding="utf-8",
|
||
|
|
prettyprint=True,
|
||
|
|
infer_numeric_types=False,
|
||
|
|
named_key_ids=False,
|
||
|
|
edge_id_from_attribute=None,
|
||
|
|
):
|
||
|
|
self.construct_types()
|
||
|
|
import lxml.etree as lxmletree
|
||
|
|
|
||
|
|
self.myElement = lxmletree.Element
|
||
|
|
|
||
|
|
self._encoding = encoding
|
||
|
|
self._prettyprint = prettyprint
|
||
|
|
self.named_key_ids = named_key_ids
|
||
|
|
self.edge_id_from_attribute = edge_id_from_attribute
|
||
|
|
self.infer_numeric_types = infer_numeric_types
|
||
|
|
|
||
|
|
self._xml_base = lxmletree.xmlfile(path, encoding=encoding)
|
||
|
|
self._xml = self._xml_base.__enter__()
|
||
|
|
self._xml.write_declaration()
|
||
|
|
|
||
|
|
# We need to have a xml variable that support insertion. This call is
|
||
|
|
# used for adding the keys to the document.
|
||
|
|
# We will store those keys in a plain list, and then after the graph
|
||
|
|
# element is closed we will add them to the main graphml element.
|
||
|
|
self.xml = []
|
||
|
|
self._keys = self.xml
|
||
|
|
self._graphml = self._xml.element(
|
||
|
|
"graphml",
|
||
|
|
{
|
||
|
|
"xmlns": self.NS_GRAPHML,
|
||
|
|
"xmlns:xsi": self.NS_XSI,
|
||
|
|
"xsi:schemaLocation": self.SCHEMALOCATION,
|
||
|
|
},
|
||
|
|
)
|
||
|
|
self._graphml.__enter__()
|
||
|
|
self.keys = {}
|
||
|
|
self.attribute_types = defaultdict(set)
|
||
|
|
|
||
|
|
if graph is not None:
|
||
|
|
self.add_graph_element(graph)
|
||
|
|
|
||
|
|
def add_graph_element(self, G):
|
||
|
|
"""
|
||
|
|
Serialize graph G in GraphML to the stream.
|
||
|
|
"""
|
||
|
|
if G.is_directed():
|
||
|
|
default_edge_type = "directed"
|
||
|
|
else:
|
||
|
|
default_edge_type = "undirected"
|
||
|
|
|
||
|
|
graphid = G.graph.pop("id", None)
|
||
|
|
if graphid is None:
|
||
|
|
graph_element = self._xml.element("graph", edgedefault=default_edge_type)
|
||
|
|
else:
|
||
|
|
graph_element = self._xml.element(
|
||
|
|
"graph", edgedefault=default_edge_type, id=graphid
|
||
|
|
)
|
||
|
|
|
||
|
|
# gather attributes types for the whole graph
|
||
|
|
# to find the most general numeric format needed.
|
||
|
|
# Then pass through attributes to create key_id for each.
|
||
|
|
graphdata = {
|
||
|
|
k: v
|
||
|
|
for k, v in G.graph.items()
|
||
|
|
if k not in ("node_default", "edge_default")
|
||
|
|
}
|
||
|
|
node_default = G.graph.get("node_default", {})
|
||
|
|
edge_default = G.graph.get("edge_default", {})
|
||
|
|
# Graph attributes
|
||
|
|
for k, v in graphdata.items():
|
||
|
|
self.attribute_types[(str(k), "graph")].add(type(v))
|
||
|
|
for k, v in graphdata.items():
|
||
|
|
element_type = self.get_xml_type(self.attr_type(k, "graph", v))
|
||
|
|
self.get_key(str(k), element_type, "graph", None)
|
||
|
|
# Nodes and data
|
||
|
|
for node, d in G.nodes(data=True):
|
||
|
|
for k, v in d.items():
|
||
|
|
self.attribute_types[(str(k), "node")].add(type(v))
|
||
|
|
for node, d in G.nodes(data=True):
|
||
|
|
for k, v in d.items():
|
||
|
|
T = self.get_xml_type(self.attr_type(k, "node", v))
|
||
|
|
self.get_key(str(k), T, "node", node_default.get(k))
|
||
|
|
# Edges and data
|
||
|
|
if G.is_multigraph():
|
||
|
|
for u, v, ekey, d in G.edges(keys=True, data=True):
|
||
|
|
for k, v in d.items():
|
||
|
|
self.attribute_types[(str(k), "edge")].add(type(v))
|
||
|
|
for u, v, ekey, d in G.edges(keys=True, data=True):
|
||
|
|
for k, v in d.items():
|
||
|
|
T = self.get_xml_type(self.attr_type(k, "edge", v))
|
||
|
|
self.get_key(str(k), T, "edge", edge_default.get(k))
|
||
|
|
else:
|
||
|
|
for u, v, d in G.edges(data=True):
|
||
|
|
for k, v in d.items():
|
||
|
|
self.attribute_types[(str(k), "edge")].add(type(v))
|
||
|
|
for u, v, d in G.edges(data=True):
|
||
|
|
for k, v in d.items():
|
||
|
|
T = self.get_xml_type(self.attr_type(k, "edge", v))
|
||
|
|
self.get_key(str(k), T, "edge", edge_default.get(k))
|
||
|
|
|
||
|
|
# Now add attribute keys to the xml file
|
||
|
|
for key in self.xml:
|
||
|
|
self._xml.write(key, pretty_print=self._prettyprint)
|
||
|
|
|
||
|
|
# The incremental_writer writes each node/edge as it is created
|
||
|
|
incremental_writer = IncrementalElement(self._xml, self._prettyprint)
|
||
|
|
with graph_element:
|
||
|
|
self.add_attributes("graph", incremental_writer, graphdata, {})
|
||
|
|
self.add_nodes(G, incremental_writer) # adds attributes too
|
||
|
|
self.add_edges(G, incremental_writer) # adds attributes too
|
||
|
|
|
||
|
|
def add_attributes(self, scope, xml_obj, data, default):
|
||
|
|
"""Appends attribute data."""
|
||
|
|
for k, v in data.items():
|
||
|
|
data_element = self.add_data(
|
||
|
|
str(k), self.attr_type(str(k), scope, v), str(v), scope, default.get(k)
|
||
|
|
)
|
||
|
|
xml_obj.append(data_element)
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
return object.__str__(self)
|
||
|
|
|
||
|
|
def dump(self, stream=None):
|
||
|
|
self._graphml.__exit__(None, None, None)
|
||
|
|
self._xml_base.__exit__(None, None, None)
|
||
|
|
|
||
|
|
|
||
|
|
# default is lxml is present.
|
||
|
|
write_graphml = write_graphml_lxml
|
||
|
|
|
||
|
|
|
||
|
|
class GraphMLReader(GraphML):
|
||
|
|
"""Read a GraphML document. Produces NetworkX graph objects."""
|
||
|
|
|
||
|
|
def __init__(self, node_type=str, edge_key_type=int, force_multigraph=False):
|
||
|
|
self.construct_types()
|
||
|
|
self.node_type = node_type
|
||
|
|
self.edge_key_type = edge_key_type
|
||
|
|
self.multigraph = force_multigraph # If False, test for multiedges
|
||
|
|
self.edge_ids = {} # dict mapping (u,v) tuples to edge id attributes
|
||
|
|
|
||
|
|
def __call__(self, path=None, string=None):
|
||
|
|
from xml.etree.ElementTree import ElementTree, fromstring
|
||
|
|
|
||
|
|
if path is not None:
|
||
|
|
self.xml = ElementTree(file=path)
|
||
|
|
elif string is not None:
|
||
|
|
self.xml = fromstring(string)
|
||
|
|
else:
|
||
|
|
raise ValueError("Must specify either 'path' or 'string' as kwarg")
|
||
|
|
(keys, defaults) = self.find_graphml_keys(self.xml)
|
||
|
|
for g in self.xml.findall(f"{{{self.NS_GRAPHML}}}graph"):
|
||
|
|
yield self.make_graph(g, keys, defaults)
|
||
|
|
|
||
|
|
def make_graph(self, graph_xml, graphml_keys, defaults, G=None):
|
||
|
|
# set default graph type
|
||
|
|
edgedefault = graph_xml.get("edgedefault", None)
|
||
|
|
if G is None:
|
||
|
|
if edgedefault == "directed":
|
||
|
|
G = nx.MultiDiGraph()
|
||
|
|
else:
|
||
|
|
G = nx.MultiGraph()
|
||
|
|
# set defaults for graph attributes
|
||
|
|
G.graph["node_default"] = {}
|
||
|
|
G.graph["edge_default"] = {}
|
||
|
|
for key_id, value in defaults.items():
|
||
|
|
key_for = graphml_keys[key_id]["for"]
|
||
|
|
name = graphml_keys[key_id]["name"]
|
||
|
|
python_type = graphml_keys[key_id]["type"]
|
||
|
|
if key_for == "node":
|
||
|
|
G.graph["node_default"].update({name: python_type(value)})
|
||
|
|
if key_for == "edge":
|
||
|
|
G.graph["edge_default"].update({name: python_type(value)})
|
||
|
|
# hyperedges are not supported
|
||
|
|
hyperedge = graph_xml.find(f"{{{self.NS_GRAPHML}}}hyperedge")
|
||
|
|
if hyperedge is not None:
|
||
|
|
raise nx.NetworkXError("GraphML reader doesn't support hyperedges")
|
||
|
|
# add nodes
|
||
|
|
for node_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}node"):
|
||
|
|
self.add_node(G, node_xml, graphml_keys, defaults)
|
||
|
|
# add edges
|
||
|
|
for edge_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}edge"):
|
||
|
|
self.add_edge(G, edge_xml, graphml_keys)
|
||
|
|
# add graph data
|
||
|
|
data = self.decode_data_elements(graphml_keys, graph_xml)
|
||
|
|
G.graph.update(data)
|
||
|
|
|
||
|
|
# switch to Graph or DiGraph if no parallel edges were found
|
||
|
|
if self.multigraph:
|
||
|
|
return G
|
||
|
|
|
||
|
|
G = nx.DiGraph(G) if G.is_directed() else nx.Graph(G)
|
||
|
|
# add explicit edge "id" from file as attribute in NX graph.
|
||
|
|
nx.set_edge_attributes(G, values=self.edge_ids, name="id")
|
||
|
|
return G
|
||
|
|
|
||
|
|
def add_node(self, G, node_xml, graphml_keys, defaults):
|
||
|
|
"""Add a node to the graph."""
|
||
|
|
# warn on finding unsupported ports tag
|
||
|
|
ports = node_xml.find(f"{{{self.NS_GRAPHML}}}port")
|
||
|
|
if ports is not None:
|
||
|
|
warnings.warn("GraphML port tag not supported.")
|
||
|
|
# find the node by id and cast it to the appropriate type
|
||
|
|
node_id = self.node_type(node_xml.get("id"))
|
||
|
|
# get data/attributes for node
|
||
|
|
data = self.decode_data_elements(graphml_keys, node_xml)
|
||
|
|
G.add_node(node_id, **data)
|
||
|
|
# get child nodes
|
||
|
|
if node_xml.attrib.get("yfiles.foldertype") == "group":
|
||
|
|
graph_xml = node_xml.find(f"{{{self.NS_GRAPHML}}}graph")
|
||
|
|
self.make_graph(graph_xml, graphml_keys, defaults, G)
|
||
|
|
|
||
|
|
def add_edge(self, G, edge_element, graphml_keys):
|
||
|
|
"""Add an edge to the graph."""
|
||
|
|
# warn on finding unsupported ports tag
|
||
|
|
ports = edge_element.find(f"{{{self.NS_GRAPHML}}}port")
|
||
|
|
if ports is not None:
|
||
|
|
warnings.warn("GraphML port tag not supported.")
|
||
|
|
|
||
|
|
# raise error if we find mixed directed and undirected edges
|
||
|
|
directed = edge_element.get("directed")
|
||
|
|
if G.is_directed() and directed == "false":
|
||
|
|
msg = "directed=false edge found in directed graph."
|
||
|
|
raise nx.NetworkXError(msg)
|
||
|
|
if (not G.is_directed()) and directed == "true":
|
||
|
|
msg = "directed=true edge found in undirected graph."
|
||
|
|
raise nx.NetworkXError(msg)
|
||
|
|
|
||
|
|
source = self.node_type(edge_element.get("source"))
|
||
|
|
target = self.node_type(edge_element.get("target"))
|
||
|
|
data = self.decode_data_elements(graphml_keys, edge_element)
|
||
|
|
# GraphML stores edge ids as an attribute
|
||
|
|
# NetworkX uses them as keys in multigraphs too if no key
|
||
|
|
# attribute is specified
|
||
|
|
edge_id = edge_element.get("id")
|
||
|
|
if edge_id:
|
||
|
|
# self.edge_ids is used by `make_graph` method for non-multigraphs
|
||
|
|
self.edge_ids[source, target] = edge_id
|
||
|
|
try:
|
||
|
|
edge_id = self.edge_key_type(edge_id)
|
||
|
|
except ValueError: # Could not convert.
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
edge_id = data.get("key")
|
||
|
|
|
||
|
|
if G.has_edge(source, target):
|
||
|
|
# mark this as a multigraph
|
||
|
|
self.multigraph = True
|
||
|
|
|
||
|
|
# Use add_edges_from to avoid error with add_edge when `'key' in data`
|
||
|
|
# Note there is only one edge here...
|
||
|
|
G.add_edges_from([(source, target, edge_id, data)])
|
||
|
|
|
||
|
|
def decode_data_elements(self, graphml_keys, obj_xml):
|
||
|
|
"""Use the key information to decode the data XML if present."""
|
||
|
|
data = {}
|
||
|
|
for data_element in obj_xml.findall(f"{{{self.NS_GRAPHML}}}data"):
|
||
|
|
key = data_element.get("key")
|
||
|
|
try:
|
||
|
|
data_name = graphml_keys[key]["name"]
|
||
|
|
data_type = graphml_keys[key]["type"]
|
||
|
|
except KeyError as err:
|
||
|
|
raise nx.NetworkXError(f"Bad GraphML data: no key {key}") from err
|
||
|
|
text = data_element.text
|
||
|
|
# assume anything with subelements is a yfiles extension
|
||
|
|
if text is not None and len(list(data_element)) == 0:
|
||
|
|
if data_type == bool:
|
||
|
|
# Ignore cases.
|
||
|
|
# http://docs.oracle.com/javase/6/docs/api/java/lang/
|
||
|
|
# Boolean.html#parseBoolean%28java.lang.String%29
|
||
|
|
data[data_name] = self.convert_bool[text.lower()]
|
||
|
|
else:
|
||
|
|
data[data_name] = data_type(text)
|
||
|
|
elif len(list(data_element)) > 0:
|
||
|
|
# Assume yfiles as subelements, try to extract node_label
|
||
|
|
node_label = None
|
||
|
|
# set GenericNode's configuration as shape type
|
||
|
|
gn = data_element.find(f"{{{self.NS_Y}}}GenericNode")
|
||
|
|
if gn is not None:
|
||
|
|
data["shape_type"] = gn.get("configuration")
|
||
|
|
for node_type in ["GenericNode", "ShapeNode", "SVGNode", "ImageNode"]:
|
||
|
|
pref = f"{{{self.NS_Y}}}{node_type}/{{{self.NS_Y}}}"
|
||
|
|
geometry = data_element.find(f"{pref}Geometry")
|
||
|
|
if geometry is not None:
|
||
|
|
data["x"] = geometry.get("x")
|
||
|
|
data["y"] = geometry.get("y")
|
||
|
|
if node_label is None:
|
||
|
|
node_label = data_element.find(f"{pref}NodeLabel")
|
||
|
|
shape = data_element.find(f"{pref}Shape")
|
||
|
|
if shape is not None:
|
||
|
|
data["shape_type"] = shape.get("type")
|
||
|
|
if node_label is not None:
|
||
|
|
data["label"] = node_label.text
|
||
|
|
|
||
|
|
# check all the different types of edges available in yEd.
|
||
|
|
for edge_type in [
|
||
|
|
"PolyLineEdge",
|
||
|
|
"SplineEdge",
|
||
|
|
"QuadCurveEdge",
|
||
|
|
"BezierEdge",
|
||
|
|
"ArcEdge",
|
||
|
|
]:
|
||
|
|
pref = f"{{{self.NS_Y}}}{edge_type}/{{{self.NS_Y}}}"
|
||
|
|
edge_label = data_element.find(f"{pref}EdgeLabel")
|
||
|
|
if edge_label is not None:
|
||
|
|
break
|
||
|
|
if edge_label is not None:
|
||
|
|
data["label"] = edge_label.text
|
||
|
|
elif text is None:
|
||
|
|
data[data_name] = ""
|
||
|
|
return data
|
||
|
|
|
||
|
|
def find_graphml_keys(self, graph_element):
|
||
|
|
"""Extracts all the keys and key defaults from the xml."""
|
||
|
|
graphml_keys = {}
|
||
|
|
graphml_key_defaults = {}
|
||
|
|
for k in graph_element.findall(f"{{{self.NS_GRAPHML}}}key"):
|
||
|
|
attr_id = k.get("id")
|
||
|
|
attr_type = k.get("attr.type")
|
||
|
|
attr_name = k.get("attr.name")
|
||
|
|
yfiles_type = k.get("yfiles.type")
|
||
|
|
if yfiles_type is not None:
|
||
|
|
attr_name = yfiles_type
|
||
|
|
attr_type = "yfiles"
|
||
|
|
if attr_type is None:
|
||
|
|
attr_type = "string"
|
||
|
|
warnings.warn(f"No key type for id {attr_id}. Using string")
|
||
|
|
if attr_name is None:
|
||
|
|
raise nx.NetworkXError(f"Unknown key for id {attr_id}.")
|
||
|
|
graphml_keys[attr_id] = {
|
||
|
|
"name": attr_name,
|
||
|
|
"type": self.python_type[attr_type],
|
||
|
|
"for": k.get("for"),
|
||
|
|
}
|
||
|
|
# check for "default" sub-element of key element
|
||
|
|
default = k.find(f"{{{self.NS_GRAPHML}}}default")
|
||
|
|
if default is not None:
|
||
|
|
# Handle default values identically to data element values
|
||
|
|
python_type = graphml_keys[attr_id]["type"]
|
||
|
|
if python_type == bool:
|
||
|
|
graphml_key_defaults[attr_id] = self.convert_bool[
|
||
|
|
default.text.lower()
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
graphml_key_defaults[attr_id] = python_type(default.text)
|
||
|
|
return graphml_keys, graphml_key_defaults
|