def mode_solve(algo):
filepath = ask_open_filepath()
if len(filepath) == 0:
return
play = Page("play")
play.appendElement(image)
play.appendElement(btn_quitter)
play.appendElement(btn_retour)
app.push_page(play)
maze, err = utils.loadcsv(filepath)
if err == None:
chemin = algo(maze)
play.appendElement(
Maze(
*CENTER,
#Taille du maze pour ecran Imerir
window_width / 1.5,
window_height / 1.5,
#Taille du maze pour ecran Alexandre
#window_width / 2,
#window_height / 2,
maze,
chemin,
time_sleep=.5))
else:
play.appendElement(
Text(*CENTER,
f"Une erreur c'est produite en ouvrant le ficher:",
color=WHITE))
play.appendElement(
Text(CENTER[0], CENTER[1] + BTN_HEIGHT + 10, err, color=WHITE))
def load_data(output, select, norm=False, phi=None, tau=None, tol=1e-5):
phi = phi if phi is not None else list(iter_phi())
tau = tau if tau is not None else list(iter_tau())
dname = fname_array(output, select, norm=norm)
if True: # not os.path.exists(dname): # TODO: whats going on here?
array = [[
load_element(output, select, p, t, norm, tol=tol) for p in phi
] for t in tau]
utils.savecsv(dname, array, append=False)
else:
array = utils.loadcsv(dname, asdict=False)
array = np.array(array, dtype=np.float)
return array
def _populate_reference_sets(self, refsets=None):
for chromosome in self.chromosomes:
refsetgen = utils.loadcsv(
os.path.join(
self.reference_set_base_dir,
"reference_set_{}_sorted.txt".format(
string.strip(chromosome,
"chr"))), self.min_microsatellite_length,
self.max_microsatellite_length, self.repeat_units)
refsets = [x for x in refsetgen]
self.reference_set_dict[chromosome] = refsets
# get the index positions
refset_ini_end = [x[1] for x in refsets]
self.reference_set_ini_end_dict[chromosome] = refset_ini_end
self.reference_sets = refsets
def __init__(self, length=5, train=True):
super(my_dataset, self).__init__()
self.database = []
self.length = length
self.train = train
if self.train:
self.size = (60 - self.length) * 2
else:
self.size = (15 - self.length) * 2
s = ['j', 'k', 's', 'y']
for s1 in range(4):
for s2 in range(4):
data = loadcsv('szy_double/%s%s.csv' % (s[s1], s[s2]),
[2, 3, 8, 13, 17, 18],
begining=10,
ending=85)
_, _, data = to_DFS(data, 100, 100, cut=[-6, 6])
data = aggregate(data, [2, 3, 8, 13, 17, 18])
data = torch.tensor(data, dtype=torch.float32)
self.database.append((data, s1 * 10 + s2))
from resolveur import *
from utils import loadcsv
grille = loadcsv("exemple/exemple1.csv")
print("ligne", len(grille), "colonne", len(grille[0]))
def print_maze(grille):
for i in range(len(grille[0])):
print(f"{i:2d}", end="")
print("")
for i, ligne in enumerate(grille):
for colonne in ligne:
if colonne == -1:
print("\u2588\u2588", end="")
else:
print(f"{colonne:2d}", end="")
print(f" {i:}")
find_short_path(grille, [len(grille) - 2, len(grille[0]) - 2], [len(grille) - 2, len(grille[0]) - 2], 1)
print_maze(grille)