Functions for reading/building graphs/digraphs.¶
This module gathers functions needed to build a graph from any other data.
Note
This is an internal module of Sage. All features implemented here are
made available to end-users through the constructors of Graph and
DiGraph.
Note that because they are called by the constructors of Graph and
DiGraph, most of these functions modify a graph inplace.
from_adjacency_matrix() |
Fill G with the data of an adjacency matrix. |
from_dict_of_dicts() |
Fill G with the data of a dictionary of dictionaries. |
from_dict_of_lists() |
Fill G with the data of a dictionary of lists. |
from_dig6() |
Fill G with the data of a dig6 string. |
from_graph6() |
Fill G with the data of a graph6 string. |
from_incidence_matrix() |
Fill G with the data of an incidence matrix. |
from_oriented_incidence_matrix() |
Fill G with the data of an oriented incidence matrix. |
from_seidel_adjacency_matrix() |
Fill G with the data of a Seidel adjacency matrix. |
from_sparse6() |
Fill G with the data of a sparse6 string. |
Functions¶
-
sage.graphs.graph_input.from_adjacency_matrix(G, M, loops=False, multiedges=False, weighted=False)¶ Fill
Gwith the data of an adjacency matrix.INPUT:
G– aGraphorDiGraphM– an adjacency matrixloops,multiedges,weighted– booleans (default:False); whether to consider the graph as having loops, multiple edges, or weights
EXAMPLES:
sage: from sage.graphs.graph_input import from_adjacency_matrix sage: g = Graph() sage: from_adjacency_matrix(g, graphs.PetersenGraph().adjacency_matrix()) sage: g.is_isomorphic(graphs.PetersenGraph()) True
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sage.graphs.graph_input.from_dict_of_dicts(G, M, loops=False, multiedges=False, weighted=False, convert_empty_dict_labels_to_None=False)¶ Fill
Gwith the data of a dictionary of dictionaries.INPUT:
G– a graphM– a dictionary of dictionariesloops,multiedges,weighted– booleans (default:False); whether to consider the graph as having loops, multiple edges, or weightsconvert_empty_dict_labels_to_None– booleans (default:False); whether to adjust for empty dicts instead ofNonein NetworkX default edge labels
EXAMPLES:
sage: from sage.graphs.graph_input import from_dict_of_dicts sage: g = Graph() sage: from_dict_of_dicts(g, graphs.PetersenGraph().to_dictionary(edge_labels=True)) sage: g.is_isomorphic(graphs.PetersenGraph()) True
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sage.graphs.graph_input.from_dict_of_lists(G, D, loops=False, multiedges=False, weighted=False)¶ Fill
Gwith the data of a dictionary of lists.INPUT:
G– aGraphorDiGraphD– a dictionary of listsloops,multiedges,weighted– booleans (default:False); whether to consider the graph as having loops, multiple edges, or weights
EXAMPLES:
sage: from sage.graphs.graph_input import from_dict_of_lists sage: g = Graph() sage: from_dict_of_lists(g, graphs.PetersenGraph().to_dictionary()) sage: g.is_isomorphic(graphs.PetersenGraph()) True
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sage.graphs.graph_input.from_dig6(G, dig6_string)¶ Fill
Gwith the data of a dig6 string.INPUT:
G– a graphdig6_string– a dig6 string
EXAMPLES:
sage: from sage.graphs.graph_input import from_dig6 sage: g = DiGraph() sage: from_dig6(g, digraphs.Circuit(10).dig6_string()) sage: g.is_isomorphic(digraphs.Circuit(10)) True
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sage.graphs.graph_input.from_graph6(G, g6_string)¶ Fill
Gwith the data of a graph6 string.INPUT:
G– a graphg6_string– a graph6 string
EXAMPLES:
sage: from sage.graphs.graph_input import from_graph6 sage: g = Graph() sage: from_graph6(g, 'IheA@GUAo') sage: g.is_isomorphic(graphs.PetersenGraph()) True
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sage.graphs.graph_input.from_incidence_matrix(G, M, loops=False, multiedges=False, weighted=False)¶ Fill
Gwith the data of an incidence matrix.INPUT:
G– a graphM– an incidence matrixloops,multiedges,weighted– booleans (default:False); whether to consider the graph as having loops, multiple edges, or weights
EXAMPLES:
sage: from sage.graphs.graph_input import from_incidence_matrix sage: g = Graph() sage: from_incidence_matrix(g, graphs.PetersenGraph().incidence_matrix()) sage: g.is_isomorphic(graphs.PetersenGraph()) True
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sage.graphs.graph_input.from_oriented_incidence_matrix(G, M, loops=False, multiedges=False, weighted=False)¶ Fill
Gwith the data of an oriented incidence matrix.An oriented incidence matrix is the incidence matrix of a directed graph, in which each non-loop edge corresponds to a \(+1\) and a \(-1\), indicating its source and destination.
INPUT:
G– aDiGraphM– an incidence matrixloops,multiedges,weighted– booleans (default:False); whether to consider the graph as having loops, multiple edges, or weights
EXAMPLES:
sage: from sage.graphs.graph_input import from_oriented_incidence_matrix sage: g = DiGraph() sage: from_oriented_incidence_matrix(g, digraphs.Circuit(10).incidence_matrix()) sage: g.is_isomorphic(digraphs.Circuit(10)) True
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sage.graphs.graph_input.from_seidel_adjacency_matrix(G, M)¶ Fill
Gwith the data of a Seidel adjacency matrix.INPUT:
G– a graphM– a Seidel adjacency matrix
EXAMPLES:
sage: from sage.graphs.graph_input import from_seidel_adjacency_matrix sage: g = Graph() sage: from_seidel_adjacency_matrix(g, graphs.PetersenGraph().seidel_adjacency_matrix()) sage: g.is_isomorphic(graphs.PetersenGraph()) True
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sage.graphs.graph_input.from_sparse6(G, g6_string)¶ Fill
Gwith the data of a sparse6 string.INPUT:
G– a graphg6_string– a sparse6 string
EXAMPLES:
sage: from sage.graphs.graph_input import from_sparse6 sage: g = Graph() sage: from_sparse6(g, ':I`ES@obGkqegW~') sage: g.is_isomorphic(graphs.PetersenGraph()) True