def to_maze(level, title):
maze = Vmaze_NHT(level['canvas'],
start=level['start'],
goal=level['goal'],
nodes_pos=level['nodes_pos'])
maze.title = title
return maze
def to_maze(level, title):
maze = Vmaze_NHT(level['canvas'],
start= level['start'],
goal= level['goal'],
nodes_pos = level['nodes_pos'])
maze.title = title
return maze
# This script shows how to make a canvas with the editor,
# save it, and load it later ::
from vmfactory import Vmaze
from vmfactory.canvas import canvas_editor
canvas = canvas_editor() # will open an interactive session.
# For when the session has ended:
maze = Vmaze(canvas)
maze.to_file('mycanvas.can')
# Later:
from vmfactory import Vmaze_NHT
maze_nht = Vmaze_NHT.from_file('mycanvas.can')
maze_nht.draw_quick()
# This is a simple script to automatically design a maze:
from vmfactory import Vmaze_NHT
from vmfactory.canvas import squares_grid
canvas = squares_grid(4,4) # nodes will be numbered 0..15
# NHT means no half-turns (can't pass a light twice in a row)
maze = Vmaze_NHT(canvas, start = 0, goal = 15)
maze.colorize( maze.random_colors() )
maze.anneal(400,20) # optimize the maze
# maze.make_report().savefig('myreport.png')
maze.to_file('my_nice_maze.vm')
# This is a simple script to automatically design a maze:
from vmfactory import Vmaze_NHT
from vmfactory.canvas import squares_grid
canvas = squares_grid(4, 4) # nodes will be numbered 0..15
# NHT means no half-turns (can't pass a light twice in a row)
maze = Vmaze_NHT(canvas, start=0, goal=15)
maze.colorize(maze.random_colors())
maze.anneal(400, 20) # optimize the maze
# maze.make_report().savefig('myreport.png')
maze.to_file('my_nice_maze.vm')
def animate(nframe):
G = Vmaze_NHT.from_file(input_name_format % nframe)
fig = G.make_report(axes=axes)
fig.savefig("_tmp_%02d.jpeg" % nframe)
# The next script shows how to make an animation of a
# maze being optimized (requires a recent Matplotlib
# and ImageMagick): ::
import os
import networkx as nx
from matplotlib import animation
import matplotlib.pyplot as plt
from vmfactory import Vmaze_NHT
from vmfactory.canvas import stacked_cubes
input_name_format = "_tmp_evograph_%03d.vm"
G = Vmaze_NHT(**stacked_cubes())
G.start, G.goal = 0, max(G.nodes())
G.colorize(G.random_colors())
counter = 0
score = G.score
for i in range(100):
G.improve(100, max(.1, 0.3 * (1 - 1.0 * i / 400.0)))
if G.score != score:
score = G.score
G.to_file(input_name_format % counter)
counter += 1
fig, axes = plt.subplots(1, 3, figsize=(10, 3))
def animate(nframe):
G = Vmaze_NHT.from_file(input_name_format % nframe)
def animate(nframe):
G = Vmaze_NHT.from_file(input_name_format % nframe)
fig = G.make_report(axes=axes)
fig.savefig("_tmp_%02d.jpeg"%nframe)
# The next script shows how to make an animation of a
# maze being optimized (requires a recent Matplotlib
# and ImageMagick): ::
import os
import networkx as nx
from matplotlib import animation
import matplotlib.pyplot as plt
from vmfactory import Vmaze_NHT
from vmfactory.canvas import stacked_cubes
input_name_format = "_tmp_evograph_%03d.vm"
G = Vmaze_NHT(** stacked_cubes())
G.start, G.goal = 0, max(G.nodes())
G.colorize( G.random_colors() )
counter = 0
score = G.score
for i in range(100):
G.improve(100,max(.1,0.3*(1-1.0*i/400.0)))
if G.score != score:
score = G.score
G.to_file(input_name_format%counter)
counter += 1
fig, axes = plt.subplots(1,3, figsize=(10,3))
def animate(nframe):
