Alexandre B A Villares 2020-08-09 18:17:38 -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/
"""
from random import choice
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 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()
if vertex1 in self.__graph_dict:
self.__graph_dict[vertex1].append(vertex2)
else:
self.__graph_dict[vertex1] = [vertex2]
if vertex2 in self.__graph_dict:
self.__graph_dict[vertex2].append(vertex1)
else:
self.__graph_dict[vertex2] = [vertex1]
else:
# a loop
if vertex1 in self.__graph_dict:
self.__graph_dict[vertex1].append(vertex1)
else:
self.__graph_dict[vertex1] = [vertex1]
def remove_vertex(self, vert):
del self.__graph_dict[vert]
for k in self.__graph_dict.keys():
if vert in self.__graph_dict[k]:
self.__graph_dict[k].remove(vert)
def remove_edge(self, edge):
edge = set(edge)
vertex1 = edge.pop()
if edge:
vertex2 = edge.pop()
self.__graph_dict[vertex1].remove(vertex2)
self.__graph_dict[vertex2].remove(vertex1)
else:
self.__graph_dict[vertex1].remove(vertex1)
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)
def get_random_vertex(self):
return choice(self.vertices())
@staticmethod
def random_graph(names, connect_rate=.9, allow_loops=True):
vertices = set(names)
graph = Graph()
for v in vertices:
graph.add_vertex(v)
if random(1) < connect_rate:
if allow_loops:
names = list(vertices)
else:
names = list(vertices - set((v,)))
graph.add_edge({v, choice(names)})
return graph

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#*- coding: utf-8 -*-
from __future__ import division, print_function
def setup_grid(graph, width, height, margin=None):
margin = margin or width / 40
cols, rows = dim_grid(len(graph))
w, h = (width - margin * 2) / cols, (height - margin * 2) / rows
points = []
for i in range(cols * rows):
c = i % cols
r = i // rows
x = margin + w * 0.5 + c * w - 14 * (r % 2) + 7
y = margin + h * 0.5 + r * h - 14 * (c % 2) + 7
z = 0
points.append((x, y, z))
points = sorted(
points, key=lambda p: dist(p[0], p[1], width / 2, height / 2))
v_list = reversed(sorted(graph.vertices(), key=graph.vertex_degree))
# v_list = sorted(graph.vertices(), key=graph.vertex_degree)
grid = {v: p for v, p in zip(v_list, points)}
return grid
def dim_grid(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 edge_distances(graph, grid):
total = 0
for edge in graph.edges():
if len(edge) == 2:
a, b = edge
d = PVector.dist(PVector(*grid[a]),
PVector(*grid[b]))
total += d
return total
def grid_swap(graph, grid, num=2):
from random import sample
fail = 0
n = m = edge_distances(graph, grid)
while m <= n and fail < len(graph) ** 2:
new_grid= dict(grid)
if num == 2:
a, b = sample(graph.vertices(), 2)
new_grid[a], new_grid[b] = new_grid[b], new_grid[a]
else:
ks = sample(graph.vertices(), num)
vs = [grid[k] for k in sample(ks, num)]
for k, v in zip(ks, vs):
new_grid[k] = v
n = edge_distances(graph, new_grid)
if m > n:
t = "\n{:.2%} at: {} tries of {}v shuffle/swap"
print(t.format((n - m) / m, fail + 1, num), end="")
return new_grid
else:
fail += 1
print(".", end='')
return grid

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from __future__ import print_function, division
from random import choice
from graph import Graph
from grid import setup_grid, grid_swap, edge_distances
thread_count = 0
gx, gy = 0, 100000
def setup():
size(400, 500)
fill(0)
textAlign(CENTER, CENTER)
f = createFont("Source Code Pro Bold", 14)
textFont(f)
setup_graph()
def setup_graph():
global graph, grid
graph = Graph.random_graph(range(36), allow_loops=False)
grid = setup_grid(graph, width=width, height=width, margin=10)
global sel_v
sel_v = graph.get_random_vertex()
global path_walker, t_walker
path_walker = []
t_walker = 0
print(graph)
def draw():
noStroke()
fill(150)
rect(0, 0, width, width)
noFill()
for e in graph.edges():
va = e.pop()
xa, ya, za = grid[va]
if len(e) == 1:
vb = e.pop()
xb, yb, zb = grid[vb]
stroke(150)
strokeWeight(6)
line(xa, ya, xb, yb)
stroke(255)
strokeWeight(3)
line(xa, ya, xb, yb)
else:
circle(20 + xa, ya, 30)
for v in grid.keys():
x, y, z = grid[v]
fill(255)
circle(x, y, 10)
if keyPressed:
fill(0)
text("{}".format(v).upper(), x - 15, y - 3)
walker()
# graph_edge_distances()
stroke(0)
strokeWeight(1)
line(gx, height, gx, height - gy / 1000)
def walker():
global t_walker, path_walker, sel_v
if path_walker and t_walker < 1:
# print(t_walker, path_walker)
p = lerpVectors(t_walker, path_walker)
noFill()
stroke(0, 0, 255)
circle(p.x, p.y, 10)
t_walker += .03 / len(path_walker)
else:
path_walker = []
noStroke()
fill(255, 0, 0)
x, y, _ = grid[sel_v]
circle(x, y, 10)
def lerpVectors(amt, vecs):
""" from Jeremy Douglass """
amt = constrain(amt, 0, 1) # let's play safe
if len(vecs) == 1:
return vecs[0]
cunit = 1.0 / (len(vecs) - 1)
return PVector.lerp(vecs[floor(amt / cunit)],
vecs[ceil(amt / cunit)],
amt % cunit / cunit)
def keyTyped():
global gx, gy
if key == 'r':
setup_graph()
background(200)
gx, gy = 0, 100000
else:
thread("swapping")
def swapping():
global grid, thread_count, gx, gy
thread_count += 1
this_thread, this_key = thread_count, str(key)
m = edge_distances(graph, grid)
print("\nStarting thread:{} key:{}".format(this_thread, key), end="")
len_graph = len(graph)
for _ in range(len_graph):
if this_key == 's':
grid = grid_swap(graph, grid, num = len_graph)
if this_key in '234556789':
grid = grid_swap(graph, grid, num=int(this_key))
n = edge_distances(graph, grid)
gx += 1
if n < m:
gy -= gy * (m - n) / m
m = n
print("\nEnding thread :{}".format(this_thread), end="")
def mousePressed():
global path_walker, t_walker, sel_v
for v in graph.vertices():
x, y, _ = grid[v]
if v != sel_v and dist(x, y, mouseX, mouseY) < 10:
path = graph.find_shortest_path(sel_v, v)
if path:
path_walker = [PVector(*grid[pv]) for pv in path]
t_walker = 0
sel_v = v

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---
![sketch_2020_08_09a](2020/sketch_2020_08_09a/sketch_2020_08_09a.gif)
![sketch_2020_08_09a](2020/sketch_2020_08_09a/sketch_2020_08_09b.gif)
[sketch_2020_08_09a](https://github.com/villares/sketch-a-day/tree/master/2020/sketch_2020_08_09a) [[Py.Processing](https://villares.github.io/como-instalar-o-processing-modo-python/index-EN)]
[sketch_2020_08_09a](https://github.com/villares/sketch-a-day/tree/master/2020/sketch_2020_08_09a) |
[sketch_2020_08_09b](https://github.com/villares/sketch-a-day/tree/master/2020/sketch_2020_08_09b)
[[Py.Processing](https://villares.github.io/como-instalar-o-processing-modo-python/index-EN)]
---