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.is_string_like('spam')
True
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(). |
is_list_of_ints (intlist) |
Return True if list is a list of ints. |
make_str (x) |
Return 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. |
Data Structures and Algorithms¶
Union-find data structure.
UnionFind.union (*objects) |
Find the sets containing the objects and merge them all. |
Random Sequence Generators¶
Utilities for generating random numbers, random sequences, and random selections.
powerlaw_sequence (n[, exponent]) |
Return sample sequence of length n from a power law distribution. |
cumulative_distribution (distribution) |
Return 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]) |
Return a random value chosen from the Zipf distribution. |
random_weighted_sample (mapping, k) |
Return k items without replacement from a weighted sample. |
weighted_choice (mapping) |
Return a single element from a weighted sample. |
Cuthill-Mckee Ordering¶
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. |