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Name	Size	Permission	Action
__pycache__	[ DIR ]	drwxr-xr-x
json_graph	[ DIR ]	drwxr-xr-x
tests	[ DIR ]	drwxr-xr-x
__init__.py	644 B	-rw-r--r--
adjlist.py	8.22 KB	-rw-r--r--
edgelist.py	13.87 KB	-rw-r--r--
gexf.py	39.66 KB	-rw-r--r--
gml.py	28.2 KB	-rw-r--r--
gpickle.py	2.75 KB	-rw-r--r--
graph6.py	11.3 KB	-rw-r--r--
graphml.py	34.68 KB	-rw-r--r--
leda.py	2.83 KB	-rw-r--r--
multiline_adjlist.py	11.5 KB	-rw-r--r--
nx_shp.py	11.21 KB	-rw-r--r--
nx_yaml.py	2.55 KB	-rw-r--r--
p2g.py	3.27 KB	-rw-r--r--
pajek.py	8.63 KB	-rw-r--r--
sparse6.py	10.43 KB	-rw-r--r--

Code Editor : leda.py

"""
Read graphs in LEDA format.

LEDA is a C++ class library for efficient data types and algorithms.

Format
------
See http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html

"""
# Original author: D. Eppstein, UC Irvine, August 12, 2003.
# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
__author__ = """Aric Hagberg (hagberg@lanl.gov)"""
#    Copyright (C) 2004-2019 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.

__all__ = ['read_leda', 'parse_leda']

import networkx as nx
from networkx.exception import NetworkXError
from networkx.utils import open_file, is_string_like

@open_file(0, mode='rb')
def read_leda(path, encoding='UTF-8'):
    """Read graph in LEDA format from path.

Parameters
    ----------
    path : file or string
       File or filename to read.  Filenames ending in .gz or .bz2  will be
       uncompressed.

Returns
    -------
    G : NetworkX graph

Examples
    --------
    G=nx.read_leda('file.leda')

References
    ----------
    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
    """
    lines = (line.decode(encoding) for line in path)
    G = parse_leda(lines)
    return G

def parse_leda(lines):
    """Read graph in LEDA format from string or iterable.

Parameters
    ----------
    lines : string or iterable
       Data in LEDA format.

Returns
    -------
    G : NetworkX graph

Examples
    --------
    G=nx.parse_leda(string)

References
    ----------
    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
    """
    if is_string_like(lines):
        lines = iter(lines.split('\n'))
    lines = iter([line.rstrip('\n') for line in lines
                  if not (line.startswith('#') or line.startswith('\n') or line == '')])
    for i in range(3):
        next(lines)
    # Graph
    du = int(next(lines))  # -1=directed, -2=undirected
    if du == -1:
        G = nx.DiGraph()
    else:
        G = nx.Graph()

# Nodes
    n = int(next(lines))  # number of nodes
    node = {}
    for i in range(1, n + 1):  # LEDA counts from 1 to n
        symbol = next(lines).rstrip().strip('|{}|  ')
        if symbol == "":
            symbol = str(i)  # use int if no label - could be trouble
        node[i] = symbol

G.add_nodes_from([s for i, s in node.items()])

# Edges
    m = int(next(lines))  # number of edges
    for i in range(m):
        try:
            s, t, reversal, label = next(lines).split()
        except:
            raise NetworkXError('Too few fields in LEDA.GRAPH edge %d' % (i + 1))
        # BEWARE: no handling of reversal edges
        G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
    return G

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Urban Research Lab

The Urban Research Lab (URL) at IIIT-Delhi was established in 2020, during a time when the pandemic brought the complexities of urban life more visible than ever. What started as an initiative by faculty and students has grown into a collaborative space for critically exploring and reimagining the urban questions through research, dialogue, and interdisciplinary engagement.