"""
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')
True
"""
# Authors: Aric Hagberg (hagberg@lanl.gov),
# Dan Schult(dschult@colgate.edu),
# Ben Edwards(bedwards@cs.unm.edu)
# Copyright (C) 2004-2016 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
from collections import defaultdict
from collections import deque
import sys
import uuid
from itertools import tee, chain
# itertools.accumulate is only available on Python 3.2 or later.
#
# Once support for Python versions less than 3.2 is dropped, this code should
# be removed.
try:
from itertools import accumulate
except ImportError:
import operator
# The code for this function is from the Python 3.5 documentation,
# distributed under the PSF license:
# <https://docs.python.org/3.5/library/itertools.html#itertools.accumulate>
def accumulate(iterable, func=operator.add):
it = iter(iterable)
try:
total = next(it)
except StopIteration:
return
yield total
for element in it:
total = func(total, element)
yield total
### 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 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.
"""
from subprocess import call
cmds = {'darwin': ['open'],
'linux2': ['xdg-open'],
'win32': ['cmd.exe', '/C', 'start', '']}
cmd = cmds[sys.platform] + [filename]
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
def is_iterator(obj):
"""Returns True if and only if the given object is an iterator
object.
"""
has_next_attr = hasattr(obj, '__next__') or hasattr(obj, 'next')
return iter(obj) is obj and has_next_attr
def arbitrary_element(iterable):
"""Returns an arbitrary element of `iterable` without removing it.
This is most useful for "peeking" at an arbitrary element of a set,
but can be used for any list, dictionary, etc., as well::
>>> arbitrary_element({3, 2, 1})
1
>>> arbitrary_element('hello')
'h'
This function raises a :exc:`ValueError` if `iterable` is an
iterator (because the current implementation of this function would
consume an element from the iterator)::
>>> iterator = iter([1, 2, 3])
>>> arbitrary_element(iterator)
Traceback (most recent call last):
...
ValueError: cannot return an arbitrary item from an iterator
"""
if is_iterator(iterable):
raise ValueError('cannot return an arbitrary item from an iterator')
# Another possible implementation is ``for x in iterable: return x``.
return next(iter(iterable))
# Recipe from the itertools documentation.
def consume(iterator):
"Consume the iterator entirely."
# Feed the entire iterator into a zero-length deque.
deque(iterator, maxlen=0)
# Recipe from the itertools documentation.
[docs]def pairwise(iterable, cyclic=False):
"s -> (s0, s1), (s1, s2), (s2, s3), ..."
a, b = tee(iterable)
first = next(b, None)
if cyclic is True:
return zip(a, chain(b, (first,)))
return zip(a, b)
[docs]def groups(many_to_one):
"""Converts a many-to-one mapping into a one-to-many mapping.
`many_to_one` must be a dictionary whose keys and values are all
:term:`hashable`.
The return value is a dictionary mapping values from `many_to_one`
to sets of keys from `many_to_one` that have that value.
For example::
>>> from networkx.utils import groups
>>> many_to_one = {'a': 1, 'b': 1, 'c': 2, 'd': 3, 'e': 3}
>>> groups(many_to_one) # doctest: +SKIP
{1: {'a', 'b'}, 2: {'c'}, 3: {'d', 'e'}}
"""
one_to_many = defaultdict(set)
for v, k in many_to_one.items():
one_to_many[k].add(v)
return dict(one_to_many)
def to_tuple(x):
"""Converts lists to tuples.
For example::
>>> from networkx.utils import to_tuple
>>> a_list = [1, 2, [1, 4]]
>>> to_tuple(a_list)
(1, 2, (1, 4))
"""
if not isinstance(x, (tuple, list)):
return x
return tuple(map(to_tuple, x))