kopia lustrzana https://github.com/villares/sketch-a-day
main
rodzic
7cbf07a8b8
commit
2f02fd4ff7
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#*- coding: utf-8 -*-
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"""
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A simple Python graph class, demonstrating the essential facts and functionalities of graphs
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based on https://www.python-course.eu/graphs_python.php and https://www.python.org/doc/essays/graphs/
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"""
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from random import choice
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class Graph(object):
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def __init__(self, graph_dict=None):
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"""
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Initialize a graph object with dictionary provided,
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if none provided, create an empty one.
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"""
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if graph_dict is None:
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graph_dict = {}
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self.__graph_dict = graph_dict
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def __len__(self):
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return len(self.__graph_dict)
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def vertices(self):
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"""Return the vertices of graph."""
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return list(self.__graph_dict.keys())
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def edges(self):
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"""Return the edges of graph """
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return self.__generate_edges()
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def add_vertex(self, vertex):
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"""
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If the vertex "vertex" is not in self.__graph_dict,
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add key "vertex" with an empty list as a value,
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otherwise, do nothing.
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"""
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if vertex not in self.__graph_dict:
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self.__graph_dict[vertex] = []
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def add_edge(self, edge):
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"""
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Assuming that edge is of type set, tuple or list;
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add edge between vertices. Can add multiple edges!
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"""
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edge = set(edge)
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vertex1 = edge.pop()
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if edge:
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# not a loop
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vertex2 = edge.pop()
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if vertex1 in self.__graph_dict:
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self.__graph_dict[vertex1].append(vertex2)
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else:
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self.__graph_dict[vertex1] = [vertex2]
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if vertex2 in self.__graph_dict:
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self.__graph_dict[vertex2].append(vertex1)
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else:
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self.__graph_dict[vertex2] = [vertex1]
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else:
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# a loop
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if vertex1 in self.__graph_dict:
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self.__graph_dict[vertex1].append(vertex1)
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else:
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self.__graph_dict[vertex1] = [vertex1]
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def remove_vertex(self, vert):
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del self.__graph_dict[vert]
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for k in self.__graph_dict.keys():
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if vert in self.__graph_dict[k]:
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self.__graph_dict[k].remove(vert)
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def remove_edge(self, edge):
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edge = set(edge)
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vertex1 = edge.pop()
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if edge:
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vertex2 = edge.pop()
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self.__graph_dict[vertex1].remove(vertex2)
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self.__graph_dict[vertex2].remove(vertex1)
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else:
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self.__graph_dict[vertex1].remove(vertex1)
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def __generate_edges(self):
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"""
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Generate the edges, represented as sets with one
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(a loop back to the vertex) or two vertices.
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"""
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edges = []
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for vertex in self.__graph_dict:
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for neighbour in self.__graph_dict[vertex]:
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if {neighbour, vertex} not in edges:
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edges.append({vertex, neighbour})
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return edges
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def __str__(self):
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res = "vertices: "
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for k in self.__graph_dict:
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res += str(k) + " "
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res += "\nedges: "
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for edge in self.__generate_edges():
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res += str(edge) + " "
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return res
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def find_isolated_vertices(self):
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"""
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Return a list of isolated vertices.
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"""
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graph = self.__graph_dict
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isolated = []
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for vertex in graph:
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print(isolated, vertex)
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if not graph[vertex]:
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isolated += [vertex]
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return isolated
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def find_path(self, start_vertex, end_vertex, path=[]):
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"""
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Find a path from start_vertex to end_vertex in graph.
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"""
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graph = self.__graph_dict
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path = path + [start_vertex]
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if start_vertex == end_vertex:
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return path
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if start_vertex not in graph:
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return None
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for vertex in graph[start_vertex]:
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if vertex not in path:
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extended_path = self.find_path(vertex,
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end_vertex,
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path)
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if extended_path:
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return extended_path
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return None
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def find_all_paths(self, start_vertex, end_vertex, path=[]):
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"""
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Find all paths from start_vertex to end_vertex.
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"""
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graph = self.__graph_dict
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path = path + [start_vertex]
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if start_vertex == end_vertex:
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return [path]
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if start_vertex not in graph:
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return []
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paths = []
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for vertex in graph[start_vertex]:
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if vertex not in path:
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extended_paths = self.find_all_paths(vertex,
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end_vertex,
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path)
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for p in extended_paths:
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paths.append(p)
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return paths
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def is_connected(self,
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vertices_encountered=None,
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start_vertex=None):
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"""Find if the graph is connected."""
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if vertices_encountered is None:
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vertices_encountered = set()
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gdict = self.__graph_dict
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vertices = list(gdict.keys()) # "list" necessary in Python 3
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if not start_vertex:
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# chosse a vertex from graph as a starting point
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start_vertex = vertices[0]
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vertices_encountered.add(start_vertex)
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if len(vertices_encountered) != len(vertices):
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for vertex in gdict[start_vertex]:
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if vertex not in vertices_encountered:
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if self.is_connected(vertices_encountered, vertex):
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return True
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else:
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return True
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return False
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def vertex_degree(self, vertex):
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"""
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Return the number of edges connecting to a vertex (the number of adjacent vertices).
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Loops are counted double, i.e. every occurence of vertex in the list of adjacent vertices.
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"""
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adj_vertices = self.__graph_dict[vertex]
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degree = len(adj_vertices) + adj_vertices.count(vertex)
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return degree
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def degree_sequence(self):
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"""Calculates the degree sequence."""
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seq = []
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for vertex in self.__graph_dict:
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seq.append(self.vertex_degree(vertex))
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seq.sort(reverse=True)
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return tuple(seq)
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@staticmethod
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def is_degree_sequence(sequence):
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"""
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Return True, if the sequence is a degree sequence (non-increasing),
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otherwise return False.
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"""
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return all(x >= y for x, y in zip(sequence, sequence[1:]))
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def delta(self):
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"""Find minimum degree of vertices."""
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min = 100000000
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for vertex in self.__graph_dict:
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vertex_degree = self.vertex_degree(vertex)
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if vertex_degree < min:
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min = vertex_degree
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return min
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def Delta(self):
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"""Finde maximum degree of vertices."""
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max = 0
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for vertex in self.__graph_dict:
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vertex_degree = self.vertex_degree(vertex)
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if vertex_degree > max:
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max = vertex_degree
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return max
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def density(self):
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"""Calculate the graph density."""
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g = self.__graph_dict
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V = len(g.keys())
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E = len(self.edges())
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return 2.0 * E / (V * (V - 1))
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def diameter(self):
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"""Calculates the graph diameter."""
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v = self.vertices()
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pairs = [
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(v[i],
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v[j]) for i in range(
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len(v)) for j in range(
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i + 1,
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len(v) - 1)]
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smallest_paths = []
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for (s, e) in pairs:
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paths = self.find_all_paths(s, e)
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smallest = sorted(paths, key=len)[0]
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smallest_paths.append(smallest)
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smallest_paths.sort(key=len)
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# longest path is at the end of list,
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# i.e. diameter corresponds to the length of this path
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diameter = len(smallest_paths[-1]) - 1
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return diameter
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@staticmethod
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def erdoes_gallai(dsequence):
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"""
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Check if Erdoes-Gallai inequality condition is fullfilled.
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"""
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if sum(dsequence) % 2:
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# sum of sequence is odd
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return False
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if Graph.is_degree_sequence(dsequence):
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for k in range(1, len(dsequence) + 1):
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left = sum(dsequence[:k])
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right = k * (k - 1) + sum([min(x, k) for x in dsequence[k:]])
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if left > right:
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return False
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else:
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# sequence is increasing
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return False
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return True
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# Code by Eryk Kopczyński
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def find_shortest_path(self, start, end):
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from collections import deque
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graph = self.__graph_dict
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dist = {start: [start]}
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q = deque((start,))
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while len(q):
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at = q.popleft()
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for next in graph[at]:
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if next not in dist:
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#dist[next] = [dist[at], next]
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dist[next] = dist[at]+[next] # less efficient but nicer output
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q.append(next)
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return dist.get(end)
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def get_random_vertex(self):
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return choice(self.vertices())
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@staticmethod
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def random_graph(names, connect_rate=.9, allow_loops=True):
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vertices = set(names)
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graph = Graph()
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for v in vertices:
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graph.add_vertex(v)
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if random(1) < connect_rate:
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if allow_loops:
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names = list(vertices)
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else:
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names = list(vertices - set((v,)))
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graph.add_edge({v, choice(names)})
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return graph
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#*- coding: utf-8 -*-
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from __future__ import division, print_function
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def setup_grid(graph, width, height, margin=None):
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margin = margin or width / 40
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cols, rows = dim_grid(len(graph))
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w, h = (width - margin * 2) / cols, (height - margin * 2) / rows
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points = []
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for i in range(cols * rows):
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c = i % cols
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r = i // rows
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x = margin + w * 0.5 + c * w - 14 * (r % 2) + 7
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y = margin + h * 0.5 + r * h - 14 * (c % 2) + 7
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z = 0
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points.append((x, y, z))
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points = sorted(
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points, key=lambda p: dist(p[0], p[1], width / 2, height / 2))
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v_list = reversed(sorted(graph.vertices(), key=graph.vertex_degree))
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# v_list = sorted(graph.vertices(), key=graph.vertex_degree)
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grid = {v: p for v, p in zip(v_list, points)}
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return grid
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def dim_grid(n):
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a = int(sqrt(n))
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b = n // a
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if a * b < n:
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b += 1
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print(u'{}: {} × {} ({})'.format(n, a, b, a * b))
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return a, b
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def edge_distances(graph, grid):
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total = 0
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for edge in graph.edges():
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if len(edge) == 2:
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a, b = edge
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d = PVector.dist(PVector(*grid[a]),
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PVector(*grid[b]))
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total += d
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return total
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def grid_swap(graph, grid, num=2):
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from random import sample
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fail = 0
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n = m = edge_distances(graph, grid)
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while m <= n and fail < len(graph) ** 2:
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new_grid= dict(grid)
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if num == 2:
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a, b = sample(graph.vertices(), 2)
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new_grid[a], new_grid[b] = new_grid[b], new_grid[a]
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else:
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ks = sample(graph.vertices(), num)
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vs = [grid[k] for k in sample(ks, num)]
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for k, v in zip(ks, vs):
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new_grid[k] = v
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n = edge_distances(graph, new_grid)
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if m > n:
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t = "\n{:.2%} at: {} tries of {}v shuffle/swap"
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print(t.format((n - m) / m, fail + 1, num), end="")
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return new_grid
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else:
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fail += 1
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print(".", end='')
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return grid
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Plik binarny nie jest wyświetlany.
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Po Szerokość: | Wysokość: | Rozmiar: 391 KiB |
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@ -0,0 +1,128 @@
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from __future__ import print_function, division
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from random import choice
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from graph import Graph
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from grid import setup_grid, grid_swap, edge_distances
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thread_count = 0
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gx, gy = 0, 100000
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def setup():
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size(400, 500)
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fill(0)
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textAlign(CENTER, CENTER)
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f = createFont("Source Code Pro Bold", 14)
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textFont(f)
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setup_graph()
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def setup_graph():
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global graph, grid
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graph = Graph.random_graph(range(36), allow_loops=False)
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grid = setup_grid(graph, width=width, height=width, margin=10)
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global sel_v
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sel_v = graph.get_random_vertex()
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global path_walker, t_walker
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path_walker = []
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t_walker = 0
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print(graph)
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def draw():
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noStroke()
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fill(150)
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rect(0, 0, width, width)
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noFill()
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for e in graph.edges():
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va = e.pop()
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xa, ya, za = grid[va]
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if len(e) == 1:
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vb = e.pop()
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xb, yb, zb = grid[vb]
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stroke(150)
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strokeWeight(6)
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line(xa, ya, xb, yb)
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stroke(255)
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strokeWeight(3)
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line(xa, ya, xb, yb)
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else:
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circle(20 + xa, ya, 30)
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for v in grid.keys():
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x, y, z = grid[v]
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fill(255)
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circle(x, y, 10)
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if keyPressed:
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fill(0)
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text("{}".format(v).upper(), x - 15, y - 3)
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walker()
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# graph_edge_distances()
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stroke(0)
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strokeWeight(1)
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line(gx, height, gx, height - gy / 1000)
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def walker():
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global t_walker, path_walker, sel_v
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if path_walker and t_walker < 1:
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# print(t_walker, path_walker)
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p = lerpVectors(t_walker, path_walker)
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noFill()
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stroke(0, 0, 255)
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circle(p.x, p.y, 10)
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t_walker += .03 / len(path_walker)
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else:
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path_walker = []
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noStroke()
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fill(255, 0, 0)
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x, y, _ = grid[sel_v]
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circle(x, y, 10)
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def lerpVectors(amt, vecs):
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""" from Jeremy Douglass """
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amt = constrain(amt, 0, 1) # let's play safe
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if len(vecs) == 1:
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return vecs[0]
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cunit = 1.0 / (len(vecs) - 1)
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return PVector.lerp(vecs[floor(amt / cunit)],
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vecs[ceil(amt / cunit)],
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amt % cunit / cunit)
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def keyTyped():
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global gx, gy
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if key == 'r':
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setup_graph()
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background(200)
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gx, gy = 0, 100000
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else:
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thread("swapping")
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def swapping():
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global grid, thread_count, gx, gy
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thread_count += 1
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this_thread, this_key = thread_count, str(key)
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m = edge_distances(graph, grid)
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print("\nStarting thread:{} key:{}".format(this_thread, key), end="")
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len_graph = len(graph)
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for _ in range(len_graph):
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if this_key == 's':
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grid = grid_swap(graph, grid, num = len_graph)
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if this_key in '234556789':
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grid = grid_swap(graph, grid, num=int(this_key))
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n = edge_distances(graph, grid)
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gx += 1
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if n < m:
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gy -= gy * (m - n) / m
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m = n
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print("\nEnding thread :{}".format(this_thread), end="")
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def mousePressed():
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global path_walker, t_walker, sel_v
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for v in graph.vertices():
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x, y, _ = grid[v]
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if v != sel_v and dist(x, y, mouseX, mouseY) < 10:
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path = graph.find_shortest_path(sel_v, v)
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if path:
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path_walker = [PVector(*grid[pv]) for pv in path]
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t_walker = 0
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sel_v = v
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@ -23,8 +23,12 @@
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---
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[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)]
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[sketch_2020_08_09a](https://github.com/villares/sketch-a-day/tree/master/2020/sketch_2020_08_09a) |
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[sketch_2020_08_09b](https://github.com/villares/sketch-a-day/tree/master/2020/sketch_2020_08_09b)
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[[Py.Processing](https://villares.github.io/como-instalar-o-processing-modo-python/index-EN)]
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---
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Ładowanie…
Reference in New Issue