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828
return dbi"
4628,"def _load_neighbors_from_external_source(self) -> None:
""""""
Loads the neighbors of the node from the igraph `Graph` instance that is
wrapped by the graph that has this node.
""""""
graph: IGraphWrapper = self._graph
ig_vertex: IGraphVertex = graph.wrapped_graph.vs[self._igraph_index]
ig_neighbors: List[IGraphVertex] = ig_vertex.neighbors()
for ig_neighbor in ig_neighbors:
try:
name: str = ig_neighbor[""name""]
except KeyError:
name: str = str(ig_neighbor.index)
try:
external_id: Optional[str] = ig_neighbor[""external_id""]
except KeyError:
external_id: Optional[str] = None
neighbor: IGraphNode = graph.nodes.get_node_by_name(name,
can_validate_and_load=True,
external_id=external_id)
graph.add_edge(self, neighbor)"
4629,"def _create_node(self, index: int, name: str, external_id: Optional[str] = None) -> IGraphNode:
""""""
Returns a new `IGraphNode` instance with the given index and name.
Arguments:
index (int): The index of the node to create.
name (str): The name of the node to create.
external_id (Optional[str]): The external ID of the node.
""""""
return IGraphNode(graph=self._graph, index=index, name=name, external_id=external_id)"
4630,"def parse(self):
""""""Parse the table data string into records.""""""
self.parse_fields()
records = []
for line in self.t['data'].split('\n'):
if EMPTY_ROW.match(line):
continue
row = [self.autoconvert(line[start_field:end_field+1])
for start_field, end_field in self.fields]
records.append(tuple(row))
self.records = records"
4631,"def recarray(self):
""""""Return a recarray from the (parsed) string.""""""
if self.records is None:
self.parse()
try:
# simple (should this also be subjected to convert.to_int64() ?)
return numpy.rec.fromrecords(self.records, names=self.names)
except ValueError:
# complicated because fromrecords cannot deal with records of lists
# Quick hack: use objects for lists etc (instead of building the proper
# data types (see docs for numpy.dtype , eg dtype('coord', (float, 3)) )
D = numpy.empty(len(self.records[0]), dtype=object) # number of fields from first record
types = numpy.array([map(type, r) for r in self.records]) # types of all fields
for icol, isSame in enumerate([numpy.all(col) for col in types.T]):
if isSame:
D[icol] = types[0][icol]
else:
D[icol] = object
dtype = numpy.dtype(zip(self.names, D))
# from numpy.rec.records
# TODO: this is not working properly yet; for instance, text fields
# are reduced to length 0 (<U0) and the final convert.to_int64 dies
# with '<U0'*** TypeError: TypeError('data type not understood',)
retval = numpy.array(self.records, dtype=dtype)
res = retval.view(numpy.recarray)
## res.dtype = numpy.dtype((numpy.rec.record, res.dtype)) # fails -- ARGH, this makes it a recarray
return convert.to_int64(res)"
4632,"def parse_fields(self):
""""""Determine the start and end columns and names of the fields.""""""
rule = self.t['toprule'].rstrip() # keep leading space for correct columns!!
if not (rule == self.t['midrule'].rstrip() and rule == self.t['botrule'].rstrip()):
raise ParseError(""Table rules differ from each other (check white space)."")
names = self.t['fields'].split()
nfields = len(rule.split())
if nfields != len(names):
raise ParseError(""number of field names (%d) does not match number of fields (%d)""
% (nfields, len(names)))
fields = [] # list of tuples (first,last) column of the field
ifield = 0
is_field = rule.startswith('=') # state
len_rule = len(rule)
start_field = 0
end_field = 0
for c in xrange(len_rule):
char = rule[c]
if not is_field and char == '=':
start_field = c
is_field = True
if is_field and (char == ' ' or c == len_rule-1):
# finished field