Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

Source code for networkx.readwrite.graphml

"""
*******
GraphML
*******
Read and write graphs in GraphML format.

This implementation does not support mixed graphs (directed and unidirected
edges together), hyperedges, nested graphs, or ports.

"GraphML is a comprehensive and easy-to-use file format for graphs. It
consists of a language core to describe the structural properties of a
graph and a flexible extension mechanism to add application-specific
data. Its main features include support of

    * directed, undirected, and mixed graphs,
    * hypergraphs,
    * hierarchical graphs,
    * graphical representations,
    * references to external data,
    * application-specific attribute data, and
    * light-weight parsers.

Unlike many other file formats for graphs, GraphML does not use a
custom syntax. Instead, it is based on XML and hence ideally suited as
a common denominator for all kinds of services generating, archiving,
or processing graphs."

http://graphml.graphdrawing.org/

Format
------
GraphML is an XML format.  See
http://graphml.graphdrawing.org/specification.html for the specification and
http://graphml.graphdrawing.org/primer/graphml-primer.html
for examples.
"""
import warnings
from collections import defaultdict

from xml.etree.ElementTree import Element, ElementTree, tostring, fromstring

try:
    import lxml.etree as lxmletree
except ImportError:
    lxmletree = None

import networkx as nx
from networkx.utils import open_file

__all__ = [
    "write_graphml",
    "read_graphml",
    "generate_graphml",
    "write_graphml_xml",
    "write_graphml_lxml",
    "parse_graphml",
    "GraphMLWriter",
    "GraphMLReader",
]


@open_file(1, mode="wb")
def write_graphml_xml(
    G,
    path,
    encoding="utf-8",
    prettyprint=True,
    infer_numeric_types=False,
    named_key_ids=False,
):
    """Write G in GraphML XML format to path

    Parameters
    ----------
    G : graph
       A networkx graph
    path : file or string
       File or filename to write.
       Filenames ending in .gz or .bz2 will be compressed.
    encoding : string (optional)
       Encoding for text data.
    prettyprint : bool (optional)
       If True use line breaks and indenting in output XML.
    infer_numeric_types : boolean
       Determine if numeric types should be generalized.
       For example, if edges have both int and float 'weight' attributes,
       we infer in GraphML that both are floats.
    named_key_ids : bool (optional)
       If True use attr.name as value for key elements' id attribute.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> nx.write_graphml(G, "test.graphml")

    Notes
    -----
    This implementation does not support mixed graphs (directed
    and unidirected edges together) hyperedges, nested graphs, or ports.
    """
    writer = GraphMLWriter(
        encoding=encoding,
        prettyprint=prettyprint,
        infer_numeric_types=infer_numeric_types,
        named_key_ids=named_key_ids,
    )
    writer.add_graph_element(G)
    writer.dump(path)


@open_file(1, mode="wb")
def write_graphml_lxml(
    G,
    path,
    encoding="utf-8",
    prettyprint=True,
    infer_numeric_types=False,
    named_key_ids=False,
):
    """Write G in GraphML XML format to path

    This function uses the LXML framework and should be faster than
    the version using the xml library.

    Parameters
    ----------
    G : graph
       A networkx graph
    path : file or string
       File or filename to write.
       Filenames ending in .gz or .bz2 will be compressed.
    encoding : string (optional)
       Encoding for text data.
    prettyprint : bool (optional)
       If True use line breaks and indenting in output XML.
    infer_numeric_types : boolean
       Determine if numeric types should be generalized.
       For example, if edges have both int and float 'weight' attributes,
       we infer in GraphML that both are floats.
    named_key_ids : bool (optional)
       If True use attr.name as value for key elements' id attribute.

    Examples
    --------
    >>> G = nx.path_graph(4)
    >>> nx.write_graphml_lxml(G, "fourpath.graphml")  # doctest: +SKIP

    Notes
    -----
    This implementation does not support mixed graphs (directed
    and unidirected edges together) hyperedges, nested graphs, or ports.
    """
    writer = GraphMLWriterLxml(
        path,
        graph=G,
        encoding=encoding,
        prettyprint=prettyprint,
        infer_numeric_types=infer_numeric_types,
        named_key_ids=named_key_ids,
    )
    writer.dump()


[docs]def generate_graphml(G, encoding="utf-8", prettyprint=True, named_key_ids=False): """Generate GraphML lines for G Parameters ---------- G : graph A networkx graph encoding : string (optional) Encoding for text data. prettyprint : bool (optional) If True use line breaks and indenting in output XML. named_key_ids : bool (optional) If True use attr.name as value for key elements' id attribute. Examples -------- >>> G = nx.path_graph(4) >>> linefeed = chr(10) # linefeed = \n >>> s = linefeed.join(nx.generate_graphml(G)) # doctest: +SKIP >>> for line in nx.generate_graphml(G): # doctest: +SKIP ... print(line) Notes ----- This implementation does not support mixed graphs (directed and unidirected edges together) hyperedges, nested graphs, or ports. """ writer = GraphMLWriter( encoding=encoding, prettyprint=prettyprint, named_key_ids=named_key_ids ) writer.add_graph_element(G) yield from str(writer).splitlines()
[docs]@open_file(0, mode="rb") def read_graphml(path, node_type=str, edge_key_type=int, force_multigraph=False): """Read graph in GraphML format from path. 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 will can be read but the graphics information is discarded. 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]
[docs]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", ] ) 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.float_, "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 xml_type = dict(types) 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, } class GraphMLWriter(GraphML): def __init__( self, graph=None, encoding="utf-8", prettyprint=True, infer_numeric_types=False, named_key_ids=False, ): self.myElement = Element self.infer_numeric_types = infer_numeric_types self.prettyprint = prettyprint self.named_key_ids = named_key_ids 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): 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.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: msg = f"GraphML writer does not support {element_type} as data values." raise nx.NetworkXError(msg) keyid = self.get_key(name, self.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(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): 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): 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, ): self.myElement = lxmletree.Element self._encoding = encoding self._prettyprint = prettyprint self.named_key_ids = named_key_ids 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.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.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.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.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): self._graphml.__exit__(None, None, None) self._xml_base.__exit__(None, None, None) # Choose a writer function for default if lxmletree is None: write_graphml = write_graphml_xml else: 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.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): 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 e: raise nx.NetworkXError(f"Bad GraphML data: no key {key}") from e 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 for node_type in ["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") if node_label is not None: data["label"] = node_label.text # check all the different types of edges avaivable in yEd. for e in [ "PolyLineEdge", "SplineEdge", "QuadCurveEdge", "BezierEdge", "ArcEdge", ]: pref = f"{{{self.NS_Y}}}{e}/{{{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 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" subelement of key element default = k.find(f"{{{self.NS_GRAPHML}}}default") if default is not None: graphml_key_defaults[attr_id] = default.text return graphml_keys, graphml_key_defaults