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Alexandre B A Villares 2020-07-14 20:38:22 -03:00
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#*- coding: utf-8 -*-
"""
A simple Python graph class, demonstrating the essential facts and functionalities of graphs
based on https://www.python-course.eu/graphs_python.php and https://www.python.org/doc/essays/graphs/
"""
class Graph(object):
def __init__(self, graph_dict=None):
"""
Initialize a graph object with dictionary provided,
if none provided, create an empty one.
"""
if graph_dict is None:
graph_dict = {}
self.__graph_dict = graph_dict
def __len__(self):
return len(self.__graph_dict)
def __iter__(self):
return iter(self.__graph_dict.keys())
def __getitem__(self, i):
return self.__graph_dict[i]
def vertices(self):
"""Return the vertices of graph."""
return list(self.__graph_dict.keys())
def edges(self):
"""Return the edges of graph """
return self.__generate_edges()
def add_vertex(self, vertex):
"""
If the vertex "vertex" is not in self.__graph_dict,
add key "vertex" with an empty list as a value,
otherwise, do nothing.
"""
if vertex not in self.__graph_dict:
self.__graph_dict[vertex] = []
def add_edge(self, edge):
"""
Assuming that edge is of type set, tuple or list;
add edge between vertices. Can add multiple edges!
"""
edge = set(edge)
vertex1 = edge.pop()
if edge:
# not a loop
vertex2 = edge.pop()
else:
# a loop
vertex2 = vertex1
if vertex1 in self.__graph_dict:
self.__graph_dict[vertex1].append(vertex2)
else:
self.__graph_dict[vertex1] = [vertex2]
def __generate_edges(self):
"""
Generate the edges, represented as sets with one
(a loop back to the vertex) or two vertices.
"""
edges = []
for vertex in self.__graph_dict:
for neighbour in self.__graph_dict[vertex]:
if {neighbour, vertex} not in edges:
edges.append({vertex, neighbour})
return edges
def __str__(self):
res = "vertices: "
for k in self.__graph_dict:
res += str(k) + " "
res += "\nedges: "
for edge in self.__generate_edges():
res += str(edge) + " "
return res
def find_isolated_vertices(self):
"""
Return a list of isolated vertices.
"""
graph = self.__graph_dict
isolated = []
for vertex in graph:
print(isolated, vertex)
if not graph[vertex]:
isolated += [vertex]
return isolated
def find_path(self, start_vertex, end_vertex, path=[]):
"""
Find a path from start_vertex to end_vertex in graph.
"""
graph = self.__graph_dict
path = path + [start_vertex]
if start_vertex == end_vertex:
return path
if start_vertex not in graph:
return None
for vertex in graph[start_vertex]:
if vertex not in path:
extended_path = self.find_path(vertex,
end_vertex,
path)
if extended_path:
return extended_path
return None
def find_all_paths(self, start_vertex, end_vertex, path=[]):
"""
Find all paths from start_vertex to end_vertex.
"""
graph = self.__graph_dict
path = path + [start_vertex]
if start_vertex == end_vertex:
return [path]
if start_vertex not in graph:
return []
paths = []
for vertex in graph[start_vertex]:
if vertex not in path:
extended_paths = self.find_all_paths(vertex,
end_vertex,
path)
for p in extended_paths:
paths.append(p)
return paths
def is_connected(self,
vertices_encountered=None,
start_vertex=None):
"""Find if the graph is connected."""
if vertices_encountered is None:
vertices_encountered = set()
gdict = self.__graph_dict
vertices = list(gdict.keys()) # "list" necessary in Python 3
if not start_vertex:
# chosse a vertex from graph as a starting point
start_vertex = vertices[0]
vertices_encountered.add(start_vertex)
if len(vertices_encountered) != len(vertices):
for vertex in gdict[start_vertex]:
if vertex not in vertices_encountered:
if self.is_connected(vertices_encountered, vertex):
return True
else:
return True
return False
def vertex_degree(self, vertex):
"""
Return the number of edges connecting to a vertex (the number of adjacent vertices).
Loops are counted double, i.e. every occurence of vertex in the list of adjacent vertices.
"""
adj_vertices = self.__graph_dict[vertex]
degree = len(adj_vertices) + adj_vertices.count(vertex)
return degree
def degree_sequence(self):
"""Calculates the degree sequence."""
seq = []
for vertex in self.__graph_dict:
seq.append(self.vertex_degree(vertex))
seq.sort(reverse=True)
return tuple(seq)
@staticmethod
def is_degree_sequence(sequence):
"""
Return True, if the sequence is a degree sequence (non-increasing),
otherwise return False.
"""
return all(x >= y for x, y in zip(sequence, sequence[1:]))
def delta(self):
"""Find minimum degree of vertices."""
min = 100000000
for vertex in self.__graph_dict:
vertex_degree = self.vertex_degree(vertex)
if vertex_degree < min:
min = vertex_degree
return min
def Delta(self):
"""Finde maximum degree of vertices."""
max = 0
for vertex in self.__graph_dict:
vertex_degree = self.vertex_degree(vertex)
if vertex_degree > max:
max = vertex_degree
return max
def density(self):
"""Calculate the graph density."""
g = self.__graph_dict
V = len(g.keys())
E = len(self.edges())
return 2.0 * E / (V * (V - 1))
def diameter(self):
"""Calculates the graph diameter."""
v = self.vertices()
pairs = [
(v[i],
v[j]) for i in range(
len(v)) for j in range(
i + 1,
len(v) - 1)]
smallest_paths = []
for (s, e) in pairs:
paths = self.find_all_paths(s, e)
smallest = sorted(paths, key=len)[0]
smallest_paths.append(smallest)
smallest_paths.sort(key=len)
# longest path is at the end of list,
# i.e. diameter corresponds to the length of this path
diameter = len(smallest_paths[-1]) - 1
return diameter
@staticmethod
def erdoes_gallai(dsequence):
"""
Check if Erdoes-Gallai inequality condition is fullfilled.
"""
if sum(dsequence) % 2:
# sum of sequence is odd
return False
if Graph.is_degree_sequence(dsequence):
for k in range(1, len(dsequence) + 1):
left = sum(dsequence[:k])
right = k * (k - 1) + sum([min(x, k) for x in dsequence[k:]])
if left > right:
return False
else:
# sequence is increasing
return False
return True
# Code by Eryk Kopczyński
def find_shortest_path(self, start, end):
from collections import deque
graph = self.__graph_dict
dist = {start: [start]}
q = deque(start)
while len(q):
at = q.popleft()
for next in graph[at]:
if next not in dist:
#dist[next] = [dist[at], next]
dist[next] = dist[at]+[next] # less efficient but nicer output
q.append(next)
return dist.get(end)

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from graph import Graph
graph = Graph({"g": ["d", "f"],
"i": ["c"],
"c": ["i", "c", "d", "e"],
"d": ["f", "c"],
"e": ["c"],
"f": ["d", "h"],
"a": ["b"],
"h": ["f"],
"b": ["a"]
})
def setup():
size(500, 500)
noLoop()
setup_graph(graph)
fill(0)
textSize(18)
textAlign(CENTER, CENTER)
stroke(255)
strokeWeight(3)
def draw():
for v in grid.keys():
xa, ya = grid[v]
for e in graph[v]:
xb, yb = grid[e]
line(xa, ya, xb, yb)
for v in grid.keys():
x, y = grid[v]
text(v, x, y)
saveFrame("sketch_2020_07_14a.png")
def setup_graph(g):
global cols, rows, grid
cols, rows = dimensionar_grade(len(g))
w, h = width / cols, height / rows
grid = {}
v_list = g.vertices()
for c in range(cols):
for r in range(rows):
if v_list:
v = v_list.pop()
grid[v] = (w /2 + c * w,
h /2 + r * h)
def dimensionar_grade(n):
a = int(sqrt(n))
b = n / a
if a * b < n:
b += 1
print(u'{}: {} × {} ({})'.format(n, a, b, a * b))
return a, b
def keyPressed():
global n
redraw()
if str(key) in '+=':
n += 1
if key == '-' and n > 2:
n -= 1