def kosaraju(G: Graph) -> list:
"""
Finds strongly connected components in directed graphs
using Kosaraju algorithm.
"""
G.to_adjacency_list()
visited = [False for _ in range(len(G.repr))]
stack = []
for i in range(len(G.repr)):
if visited[i] == False:
dfs(G.repr, i, visited, stack)
T = transpose(G)
stack.reverse()
visited = [False for _ in range(len(G.repr))]
groups = [0 for _ in range(len(G.repr))]
group = []
index = 0
for i in stack:
if visited[i] == False:
dfs(T.repr, i, visited, group)
for x in group:
groups[x] = index
# print_group(group, index)
group = []
index += 1
return groups
def present_graph_from_file(G: Graph) -> None:
operations_choice = ''
v = len(G.repr)
while operations_choice != 'b':
display_submenu()
operations_choice = input("Pick the option:\n")
try:
if operations_choice == '1':
cycle = hamilton(G)
if cycle == '[]':
print('Graph is not Hamiltonian')
else:
print('Graph is Hamiltonian')
print(cycle)
if operations_choice == '2':
cycle = euler_cycle(G)
if cycle == '[]':
print('Graph is not Eulerian')
else:
print('Graph is Eulerian')
print(euler_cycle(G))
if operations_choice == '3':
G.to_adjacency_matrix()
for k in range(1, v):
if G.is_k_regular(k):
print('Graph is ' + str(k) + '-regular')
break
if k == v - 1:
print('Graph is not k-regular')
if operations_choice == '4':
G.to_adjacency_matrix()
randomizations = int(input('Number of randomizations: '))
randomize(G, randomizations)
if operations_choice == '5':
G.to_adjacency_list()
groups = get_components(G)
print_sorted_components(G, groups)
GraphPlotter.plot_graph(G, False, False, groups)
if operations_choice == '6':
GraphPlotter.plot_graph(G)
except:
print("Something went wrong. Try again!")
def present_components_finding() -> None:
"""
Presents finding graph components for graph
generated by 'get_graph_with_probability'
with parameters given by user.
"""
vertices = int(input('\nNumber of vertices: '))
probability = float(input("Put probability (0;1):\n"))
G = Graph()
G.load_data(get_graph_with_probability(vertices, probability), RepresentationType.ADJACENCY_MATRIX)
G.to_adjacency_list()
groups = get_components(G)
print_sorted_components(G, groups)
GraphPlotter.plot_graph(G, False, False, groups)
def handle_convert(G: Graph) -> None:
"""
Graph representation type convert handler.
"""
convert_repr_type = ''
print("\nPick representation type:")
print("[1] Adjancency matrix")
print("[2] Adjacency list")
print("[3] Incidence matrix")
convert_repr_type = input()
if convert_repr_type == '1':
G.to_adjacency_matrix()
print_graph(G)
elif convert_repr_type == '2':
G.to_adjacency_list()
print_graph(G)
elif convert_repr_type == '3':
G.to_incidence_matrix()
print_graph(G)
else:
print("\nPut a valid option.")
import os, sys
currentdir = os.path.dirname(os.path.realpath(__file__))
parentdir = os.path.dirname(currentdir)
sys.path.append(parentdir)
from utils.Graph import Graph, RepresentationType
from utils.graph_plotter import GraphPlotter
from algorithms.components import get_components, print_sorted_components
if __name__ == '__main__':
G = Graph()
G.load_data([2, 2, 2, 1, 3, 1, 2, 1, 4, 2, 2, 1, 3, 1, 1],
RepresentationType.GRAPH_SEQUENCE)
G.to_adjacency_list()
groups = get_components(G)
print_sorted_components(G, groups)
G.to_adjacency_matrix()
GraphPlotter.plot_graph(G, False, False, groups)
def plot_graph(graph: Graph, draw_wages: bool = False, draw_arrows: bool = False, nodes_color_modes: list = None) -> None:
"""
Plots graph using Matplotlib.
If color modes are not passed, the color of each node will be the same.
"""
is_digraph = isinstance(graph, DirectedGraph)
current_repr_type = graph.repr_type
if current_repr_type == RepresentationType.ADJACENCY_LIST \
or current_repr_type == RepresentationType.ADJACENCY_MATRIX \
or current_repr_type == RepresentationType.INCIDENCE_MATRIX \
or current_repr_type == RepresentationType.GRAPH_SEQUENCE \
or current_repr_type == RepresentationType.ADJACENCY_MATRIX_WITH_WEIGHTS:
if current_repr_type != RepresentationType.ADJACENCY_MATRIX_WITH_WEIGHTS:
graph.to_adjacency_matrix()
source_matrix = graph.repr
num_of_nodes = len(source_matrix)
if nodes_color_modes is None:
nodes_color_modes = [0] * num_of_nodes
for i in range(len(nodes_color_modes)):
if nodes_color_modes[i] > len(GraphPlotter.node_color_modes) - 1:
nodes_color_modes[i] = 0
ax = GraphPlotter.__prepare_plot()
node_positions = GraphPlotter.__get_node_positions(num_of_nodes)
GraphPlotter.__draw_edges(num_of_nodes=num_of_nodes,
source_matrix=source_matrix,
node_positions=node_positions,
digraph=is_digraph)
if draw_wages:
GraphPlotter.__draw_wages(num_of_nodes=num_of_nodes,
source_matrix=source_matrix,
node_positions=node_positions,
label_offset=0.25,
digraph=is_digraph)
if draw_arrows:
GraphPlotter.__draw_arrows(num_of_nodes=num_of_nodes,
source_matrix=source_matrix,
node_positions=node_positions,
arrow_size=0.05,
digraph=is_digraph)
GraphPlotter.__draw_nodes(num_of_nodes=num_of_nodes,
ax=ax,
groups=nodes_color_modes,
node_positions=node_positions)
plot.show()
if current_repr_type == RepresentationType.INCIDENCE_MATRIX:
graph.to_incidence_matrix()
elif current_repr_type == RepresentationType.ADJACENCY_LIST:
graph.to_adjacency_list()
else:
print("Incorrect graph type.")
