Graph

Import main classes

from hari_plotter import Graph, Dynamics

Create Graph

H = Graph.strongly_connected_components([10, 10], 10, 3)

Get Graph Info

print(f"{H = }")
print(f"{H.mean_opinion = }")

H = <HariGraph object at 126401107202480: 20 nodes, 190 edges>
H.mean_opinion = 3.039989236795545

Get the information about parameters available for gathering

H.gatherer.node_parameters

['Opinion',
 'Opinion density',
 'Cluster size',
 'Importance',
 'Neighbor mean opinion',
 'Inner opinions',
 'Max opinion',
 'Min opinion',
 'Opinion Standard Deviation',
 'Label',
 'Type']

Get the node parameters

H.gatherer.gather(["Cluster size", "Opinion"])

{'Nodes': [(0,),
  (1,),
  (2,),
  (3,),
  (4,),
  (5,),
  (6,),
  (7,),
  (8,),
  (9,),
  (10,),
  (11,),
  (12,),
  (13,),
  (14,),
  (15,),
  (16,),
  (17,),
  (18,),
  (19,)],
 'Cluster size': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
 'Opinion': [1.874213762484462,
  1.9797692699253586,
  1.9864728476689875,
  2.0039220965041564,
  2.0122373057404292,
  2.0393409858053184,
  2.0595745641582326,
  2.101099281488391,
  2.1243752247086394,
  2.2051583723503527,
  3.8754472569677465,
  3.8934247681969056,
  3.9037741673809077,
  4.029159827368202,
  4.050855128109916,
  4.063263981030825,
  4.080563322149777,
  4.125622363739476,
  4.1870486600547645,
  4.204461550078053]}

Read the graph from Network

H = Graph.read_network("../tests/network.txt", "../tests/opinions_0.txt")
print(f"{H = }")

H = <HariGraph object at 126403507139872: 5 nodes, 6 edges>

Read Dynamics from Network

HD = Dynamics.read_network(
    "../tests/5_ring/network.txt",
    [f"../tests/5_ring/opinions_{i}.txt" for i in range(3)],
)
print(f"{HD = }")

HD = <hari_plotter.dynamics.Dynamics object at 0x72f60afe00d0>