def challenge_2(file: str, vertex_a: str, vertex_b: str) -> Graph:
graph, verticies, edges_str = graph_from_file(file)
# fill graph instance with edges and verticies
fill(graph, verticies, edges_str)
# getting parent dict
dict_ = graph.breadth_first_search(vertex_a, vertex_b)
parent = dict_[vertex_b]
output = list()
# append vertex_b
output.append(vertex_b)
# walking backwards in "parent" dict
while parent != vertex_a:
output.append(parent)
parent = dict_[parent]
# prepending vertex_a
output.append(vertex_a)
output = output[::-1]
print(f"Vertices in shortest path: {output}")
print(f"Number of edges in shortest path: {len(output)-1}")
def challenge_5(file: str) -> Graph:
graph, verticies, edges_str = graph_from_file(file)
# fill graph instance with edges and verticies
fill(graph, verticies, edges_str)
bool_ = graph.is_eulerian()
print(f"This graph is Eulerian: {bool_}")
def challenge_3(file: str, vertex_a: str, vertex_b: str) -> Graph:
graph, verticies, edges_str = graph_from_file(file)
# fill graph instance with edges and verticies
fill(graph, verticies, edges_str)
# get distance and previous dicts
distance, previous = graph.min_weight_path(vertex_a, vertex_b)
print(
f"The weight of the minimum weight path between vertex {vertex_a} and {vertex_b} is: {distance[vertex_b]}"
)
def challenge_3(file: str, vertex_a: str, vertex_b: str) -> Graph:
graph, verticies, edges_str = graph_from_file(file)
# fill graph instance with edges and verticies
fill(graph, verticies, edges_str)
# using recursion, set used for dfs
set_ = set()
visited_list = list()
tuple_ = graph.dfs_recursive(vertex_a, vertex_b, visited_list, set_, print)
print(
f"There exists a path between vertex {vertex_a} and {vertex_b}: {tuple_[0]}"
)
print(f"Vertices in the path: {tuple_[1]}")
def challenge_1(file: str) -> Graph:
graph, verticies, edges_str = graph_from_file(file)
# fill graph instance with edges and verticies
fill(graph, verticies, edges_str)
# print vertices
print(f"Verticies: {len(graph.get_vertices())}")
# print edges
print(f"Edges: {graph.num_edges}")
# print edge list
for vertex in graph.vert_dict:
vert = graph.get_vertex(vertex)
for neighbour, weight in vert.neighbours.items():
print(f"({vert.id}, {neighbour.id}, {weight})")
def chapter_1(filename: str) -> Graph:
"""
Builds graph according to data text file
and returns graph to be manipulated
"""
graph, verticies, edges_str = graph_from_file(filename)
# fill graph instance with edges and verticies
return fill(graph, verticies, edges_str)
def airplane_modelling(file_name: str, city_1: str, city_2: str) -> Graph:
graph, verticies, edges_str = graph_from_file(file_name)
# fill graph instance with edges and verticies
fill(graph, verticies, edges_str)
most_connected_city = graph.most_connected_vertex()
least_connected_city = graph.least_connected_vertex()
distance, previous = graph.min_weight_path(city_1, city_2)
is_eulerian = graph.is_eulerian()
print(f"The most connected city is: {most_connected_city.id}")
print(f"The least connected city is: {least_connected_city.id}")
print(
f"Can you travel to every airport from any airport in the graph?: {is_eulerian}"
)
print(
f"The shortest path for a message between {city_1} & {city_2} is {distance[city_2]}"
)