# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import networkx as nx
import numpy as np
from .defined_histogram import DefinedHistogram
from typing import List, Union
from .make_cuts import MakeCuts, filter_function_for_make_cuts
bin_directive: Union[int, List[Union[float, int]], np.ndarray, str] = 10
) -> DefinedHistogram:
Generates a histogram of the vertex degree centrality of the provided graph.
Histogram function is fundamentally proxied through to numpy's `histogram` function, and bin selection
follows `numpy.histogram` processes.
:param networkx.Graph graph: the graph. No changes will be made to it.
:param bin_directive: Is passed directly through to numpy's
"histogram" (and thus, "histogram_bin_edges") functions.
In short description: if an int is provided, we use `bin_directive` number of equal range bins.
If a sequence is provided, these bin edges will be used and can be sized to whatever size you prefer
Note that the np.ndarray should be ndim=1 and the values should be float or int.
:type bin_directive: Union[int, List[Union[float, int]], numpy.ndarray, str]
:return: A named tuple that contains the histogram and the bin_edges used in the histogram
""" # noqa:501
degree_centrality_dict = nx.degree_centrality(graph)
histogram, bin_edges = np.histogram(
return DefinedHistogram(histogram=histogram, bin_edges=bin_edges)
cut_threshold: Union[int, float],
) -> nx.Graph:
Given a graph and a cut_threshold and a cut_process, return a copy of the graph with the vertices outside of the
:param networkx.Graph graph: The graph that will be copied and pruned.
:param cut_threshold: The threshold for making cuts based on degree centrality.
:type cut_threshold: Union[int, float]
:param MakeCuts cut_process: Describes how we should make the cut; cut all edges larger or smaller than the
cut_threshold, and whether exclusive or inclusive.
:return: Pruned copy of the graph
graph_copy = graph.copy()
degree_centrality_dict = nx.degree_centrality(graph_copy)
filter_by = filter_function_for_make_cuts(cut_threshold, cut_process)
vertices_to_cut = list(filter(filter_by, degree_centrality_dict.items()))
for vertex, degree_centrality in vertices_to_cut: