Loading autocnet/graph/network.py +16 −0 Original line number Diff line number Diff line from collections import Hashable import networkx as nx from autocnet.control.control import C from autocnet.fileio import io_json import numpy as np from scipy import misc misc.bytescale(np.arange(100).reshape(10,10)) class CandidateGraph(nx.DiGraph): """ A NetworkX derived directed graph to store candidate overlap images. Loading Loading @@ -44,6 +49,17 @@ class CandidateGraph(nx.DiGraph): adjacency_dict[n] = self.neighbors(n) io_json.write_json(adjacency_dict, outputfile) def cnet_from_graph(self): """ Create a control network from a graph or subgraph Returns ------- cnet : object A control network object """ pass @classmethod def from_adjacency(cls, inputfile): """ Loading Loading
autocnet/graph/network.py +16 −0 Original line number Diff line number Diff line from collections import Hashable import networkx as nx from autocnet.control.control import C from autocnet.fileio import io_json import numpy as np from scipy import misc misc.bytescale(np.arange(100).reshape(10,10)) class CandidateGraph(nx.DiGraph): """ A NetworkX derived directed graph to store candidate overlap images. Loading Loading @@ -44,6 +49,17 @@ class CandidateGraph(nx.DiGraph): adjacency_dict[n] = self.neighbors(n) io_json.write_json(adjacency_dict, outputfile) def cnet_from_graph(self): """ Create a control network from a graph or subgraph Returns ------- cnet : object A control network object """ pass @classmethod def from_adjacency(cls, inputfile): """ Loading