def hidden_image_maze(fname, style='jittery'):
""" Supported styles: jittery, smooth, sketch"""
H = models.image_grid_graph(fname) # get a subgraph of the grid corresponding to edges between black pixels
G = H.base_graph
# for every edge in H, make the corresponding edge in H have weight 0
for u,v in H.edges():
G[u][v]['weight'] = 0
# find a minimum spanning tree on G (which will include the maze solution)
T = nx.minimum_spanning_tree(G)
# find the maze solution in the spanning tree
P = models.my_path_graph(nx.shortest_path(T, (0,0), max(H.nodes())))
# generate the dual graph, including edges not crossed by the spanning tree
D = models.dual_grid(G, T)
views.add_maze_boundary(D, max(G.nodes()))
views.make_entry_and_exit(D, max(G.nodes()))
pos = views.layout_maze(D, fast=(style == 'jittery'))
views.plot_maze(D, pos, P, G.pos)
# make it stylish if requested
if style == 'sketch':
plt.figure(1)
D_pos = views.layout_maze(D, fast=True)
nx.draw_networkx_edges(D, D_pos, width=1, edge_color='k')
D_pos = views.layout_maze(D, fast=True)
nx.draw_networkx_edges(D, D_pos, width=1, edge_color='k')
# show the pixel colors loaded from the file, for "debugging"
plt.figure(2)
for v in G:
plt.plot([G.pos[v][0]], [G.pos[v][1]], '.', alpha=.5, color=G.node[v]['color'])
def random_maze(n=25):
G = models.my_grid_graph([n,n])
T = nx.minimum_spanning_tree(G)
P = models.my_path_graph(nx.shortest_path(T, (0,0), (n-1, n-1)))
D = models.dual_grid(G, T)
views.add_maze_boundary(D, [n, n])
views.make_entry_and_exit(D, [n, n])
pos = views.layout_maze(D, fast=True)
views.plot_maze(D, pos, P, G.pos)
def random_maze(n=25):
G = models.my_grid_graph([n, n])
T = nx.minimum_spanning_tree(G)
P = models.my_path_graph(nx.shortest_path(T, (0, 0), (n - 1, n - 1)))
D = models.dual_grid(G, T)
views.add_maze_boundary(D, [n, n])
views.make_entry_and_exit(D, [n, n])
pos = views.layout_maze(D, fast=True)
views.plot_maze(D, pos, P, G.pos)
def test_graph_utils(self):
P = model.my_path_graph(model.nx.shortest_path(self.G, (0,0), (4,4)))
H = model.image_grid_graph('test.png')
d = model.dual_grid_edge((0,0), (0,1))
assert d == ((-0.5, 0.5), (0.5, 0.5)), 'dual of integer lattice should be offset by .5s'
D = model.dual_grid(H.base_graph, H)
graphics.add_maze_boundary(D, [5,5])
graphics.make_entry_and_exit(D, [5,5])
HH = graphics.split_edges(H)
def test_graph_utils(self):
P = model.my_path_graph(model.nx.shortest_path(self.G, (0, 0), (4, 4)))
H = model.image_grid_graph('test.png')
d = model.dual_grid_edge((0, 0), (0, 1))
assert d == ((-0.5, 0.5),
(0.5,
0.5)), 'dual of integer lattice should be offset by .5s'
D = model.dual_grid(H.base_graph, H)
graphics.add_maze_boundary(D, [5, 5])
graphics.make_entry_and_exit(D, [5, 5])
HH = graphics.split_edges(H)
def hidden_image_maze(fname, style='jittery'):
""" Supported styles: jittery, smooth, sketch"""
H = models.image_grid_graph(
fname
) # get a subgraph of the grid corresponding to edges between black pixels
G = H.base_graph
# for every edge in H, make the corresponding edge in H have weight 0
for u, v in H.edges():
G[u][v]['weight'] = 0
# find a minimum spanning tree on G (which will include the maze solution)
T = nx.minimum_spanning_tree(G)
# find the maze solution in the spanning tree
P = models.my_path_graph(nx.shortest_path(T, (0, 0), max(H.nodes())))
# generate the dual graph, including edges not crossed by the spanning tree
D = models.dual_grid(G, T)
views.add_maze_boundary(D, max(G.nodes()))
views.make_entry_and_exit(D, max(G.nodes()))
pos = views.layout_maze(D, fast=(style == 'jittery'))
views.plot_maze(D, pos, P, G.pos)
# make it stylish if requested
if style == 'sketch':
plt.figure(1)
D_pos = views.layout_maze(D, fast=True)
nx.draw_networkx_edges(D, D_pos, width=1, edge_color='k')
D_pos = views.layout_maze(D, fast=True)
nx.draw_networkx_edges(D, D_pos, width=1, edge_color='k')
# show the pixel colors loaded from the file, for "debugging"
plt.figure(2)
for v in G:
plt.plot([G.pos[v][0]], [G.pos[v][1]],
'.',
alpha=.5,
color=G.node[v]['color'])
def ld_maze(n=25):
""" having many low-degree vertices makes for hard mazes
unfortunately, finding them is slow"""
# start with an nxn square grid
G = models.my_grid_graph([n,n])
# make a pymc model of a low-degree spanning tree on this
T = models.LDST(G, beta=10)
mod_mc = mc.MCMC([T])
mod_mc.use_step_method(models.STMetropolis, T)
mod_mc.sample(100, burn=99)
T = T.value
P = models.my_path_graph(nx.shortest_path(T, (0,0), (n-1, n-1)))
D = models.dual_grid(G, T)
views.add_maze_boundary(D, [n,n])
views.make_entry_and_exit(D, [n,n])
D = views.split_edges(D)
D = views.split_edges(D)
D_pos = views.layout_maze(D, fast=False)
views.plot_maze(D, D_pos, P, G.pos)
def ld_maze(n=25):
""" having many low-degree vertices makes for hard mazes
unfortunately, finding them is slow"""
# start with an nxn square grid
G = models.my_grid_graph([n, n])
# make a pymc model of a low-degree spanning tree on this
T = models.LDST(G, beta=10)
mod_mc = mc.MCMC([T])
mod_mc.use_step_method(models.STMetropolis, T)
mod_mc.sample(100, burn=99)
T = T.value
P = models.my_path_graph(nx.shortest_path(T, (0, 0), (n - 1, n - 1)))
D = models.dual_grid(G, T)
views.add_maze_boundary(D, [n, n])
views.make_entry_and_exit(D, [n, n])
D = views.split_edges(D)
D = views.split_edges(D)
D_pos = views.layout_maze(D, fast=False)
views.plot_maze(D, D_pos, P, G.pos)
