Utilities#

Helper Functions#

Miscellaneous Helpers for NetworkX.

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

>>> import networkx
>>> networkx.utils.make_list_of_ints({1, 2, 3})
[1, 2, 3]
>>> networkx.utils.arbitrary_element({5, 1, 7})  
1

`arbitrary_element`(iterable)	Returns an arbitrary element of `iterable` without removing it.
`is_string_like`(obj)	Check if obj is string.
`flatten`(obj[, result])	Return flattened version of (possibly nested) iterable object.
`iterable`(obj)	Return True if obj is iterable with a well-defined len().
`make_list_of_ints`(sequence)	Return list of ints from sequence of integral numbers.
`make_str`(x)	Returns the string representation of t.
`generate_unique_node`()	Generate a unique node label.
`default_opener`(filename)	Opens `filename` using system's default program.
`pairwise`(iterable[, cyclic])	s -> (s0, s1), (s1, s2), (s2, s3), ...
`groups`(many_to_one)	Converts a many-to-one mapping into a one-to-many mapping.
`create_random_state`([random_state])	Returns a numpy.random.RandomState or numpy.random.Generator instance depending on input.
`nodes_equal`(nodes1, nodes2)	Check if nodes are equal.
`edges_equal`(edges1, edges2)	Check if edges are equal.
`graphs_equal`(graph1, graph2)	Check if graphs are equal.

Union-find data structure.

Find the sets containing the objects and merge them all.

Utilities for generating random numbers, random sequences, and random selections.

`powerlaw_sequence`(n[, exponent, seed])	Return sample sequence of length n from a power law distribution.
`cumulative_distribution`(distribution)	Returns normalized cumulative distribution from discrete distribution.
`discrete_sequence`(n[, distribution, ...])	Return sample sequence of length n from a given discrete distribution or discrete cumulative distribution.
`zipf_rv`(alpha[, xmin, seed])	Returns a random value chosen from the Zipf distribution.
`random_weighted_sample`(mapping, k[, seed])	Returns k items without replacement from a weighted sample.
`weighted_choice`(mapping[, seed])	Returns a single element from a weighted sample.

`open_file`(path_arg[, mode])	Decorator to ensure clean opening and closing of files.
`not_implemented_for`(*graph_types)	Decorator to mark algorithms as not implemented
`nodes_or_number`(which_args)	Decorator to allow number of nodes or container of nodes.
`np_random_state`(random_state_argument)	Decorator to generate a `numpy.random.RandomState` instance.
`py_random_state`(random_state_argument)	Decorator to generate a random.Random instance (or equiv).
`argmap`(func, *args[, try_finally])	A decorator to apply a map to arguments before calling the function

Cuthill-McKee ordering of graph nodes to produce sparse matrices

`cuthill_mckee_ordering`(G[, heuristic])	Generate an ordering (permutation) of the graph nodes to make a sparse matrix.
`reverse_cuthill_mckee_ordering`(G[, heuristic])	Generate an ordering (permutation) of the graph nodes to make a sparse matrix.