This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.utils.misc

Miscellaneous Helpers for NetworkX.

These are not imported into the base networkx namespace but
can be accessed, for example, as

>>> import networkx
>>> networkx.utils.is_string_like('spam')
#    Copyright (C) 2004-2011 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
import sys
import subprocess
import uuid

import networkx as nx

__author__ = '\n'.join(['Aric Hagberg (hagberg@lanl.gov)',
                        'Dan Schult(dschult@colgate.edu)',
                        'Ben Edwards(bedwards@cs.unm.edu)'])
### some cookbook stuff
# used in deciding whether something is a bunch of nodes, edges, etc.
# see G.add_nodes and others in Graph Class in networkx/base.py

[docs]def is_string_like(obj): # from John Hunter, types-free version """Check if obj is string.""" try: obj + '' except (TypeError, ValueError): return False return True
[docs]def iterable(obj): """ Return True if obj is iterable with a well-defined len().""" if hasattr(obj,"__iter__"): return True try: len(obj) except: return False return True
[docs]def flatten(obj, result=None): """ Return flattened version of (possibly nested) iterable object. """ if not iterable(obj) or is_string_like(obj): return obj if result is None: result = [] for item in obj: if not iterable(item) or is_string_like(item): result.append(item) else: flatten(item, result) return obj.__class__(result)
[docs]def is_list_of_ints( intlist ): """ Return True if list is a list of ints. """ if not isinstance(intlist,list): return False for i in intlist: if not isinstance(i,int): return False return True
PY2 = sys.version_info[0] == 2 if PY2: def make_str(x): """Return the string representation of t.""" if isinstance(x, unicode): return x else: # Note, this will not work unless x is ascii-encoded. # That is good, since we should be working with unicode anyway. # Essentially, unless we are reading a file, we demand that users # convert any encoded strings to unicode before using the library. # # Also, the str() is necessary to convert integers, etc. # unicode(3) works, but unicode(3, 'unicode-escape') wants a buffer. # return unicode(str(x), 'unicode-escape') else:
[docs] def make_str(x): """Return the string representation of t.""" return str(x)
[docs]def cumulative_sum(numbers): """Yield cumulative sum of numbers. >>> import networkx.utils as utils >>> list(utils.cumulative_sum([1,2,3,4])) [1, 3, 6, 10] """ csum = 0 for n in numbers: csum += n yield csum
[docs]def generate_unique_node(): """ Generate a unique node label.""" return str(uuid.uuid1())
[docs]def default_opener(filename): """Opens `filename` using system's default program. Parameters ---------- filename : str The path of the file to be opened. """ cmds = {'darwin': ['open'], 'linux2': ['xdg-open'], 'win32': ['cmd.exe', '/C', 'start', '']} cmd = cmds[sys.platform] + [filename] subprocess.call(cmd)
def dict_to_numpy_array(d,mapping=None): """Convert a dictionary of dictionaries to a numpy array with optional mapping.""" try: return dict_to_numpy_array2(d, mapping) except (AttributeError, TypeError): # AttributeError is when no mapping was provided and v.keys() fails. # TypeError is when a mapping was provided and d[k1][k2] fails. return dict_to_numpy_array1(d,mapping) def dict_to_numpy_array2(d,mapping=None): """Convert a dictionary of dictionaries to a 2d numpy array with optional mapping. """ import numpy if mapping is None: s=set(d.keys()) for k,v in d.items(): s.update(v.keys()) mapping=dict(zip(s,range(len(s)))) n=len(mapping) a = numpy.zeros((n, n)) for k1, i in mapping.items(): for k2, j in mapping.items(): try: a[i,j]=d[k1][k2] except KeyError: pass return a def dict_to_numpy_array1(d,mapping=None): """Convert a dictionary of numbers to a 1d numpy array with optional mapping. """ import numpy if mapping is None: s = set(d.keys()) mapping = dict(zip(s,range(len(s)))) n = len(mapping) a = numpy.zeros(n) for k1,i in mapping.items(): i = mapping[k1] a[i] = d[k1] return a