170 lines
4.2 KiB
Python
170 lines
4.2 KiB
Python
from contextlib import contextmanager
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from timeit import default_timer
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from pathlib import Path
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import cProfile
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import functools
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import pstats
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def profile(func):
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@functools.wraps(func)
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def inner(*args, **kwargs):
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profiler = cProfile.Profile()
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profiler.enable()
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try:
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retval = func(*args, **kwargs)
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finally:
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profiler.disable()
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with open("profile.out", "w") as profile_file:
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stats = pstats.Stats(profiler, stream=profile_file)
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stats.print_stats()
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return retval
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return inner
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spl = lambda y: [int(w) for w in y]
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def minmax(l):
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return min(l), max(l)
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def load_rows(day, part2=False):
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return [row for row in load(day, part2)]
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def load(day, part2=False):
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if part2:
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path = Path(get_fname(day) + ".part2")
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try:
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return path.read_text().rstrip().split("\n")
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except FileNotFoundError:
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# No part 2 file, use first file
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pass
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path = Path(get_fname(day))
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return path.read_text().rstrip().split("\n")
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def get_fname(day: int) -> str:
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import sys
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if sys.argv[-1] == "--sample":
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return f"samples/day{day:02}.txt"
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else:
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return f"full/day{day:02}.txt"
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#############
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def load_char_matrix(f):
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my_file = []
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for line in f:
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my_file.append(line.rstrip())
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return [list(x) for x in my_file]
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def load_file_char_matrix(name):
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with open(name, "r") as f:
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return load_char_matrix(f)
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def load_int_matrix(f):
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my_file = []
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for line in f:
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my_file.append(line.rstrip())
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return [list(map(int, x)) for x in my_file]
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def load_file_int_matrix(name):
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with open(name, "r") as f:
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return load_int_matrix(f)
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def load_word_matrix(f):
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my_file = []
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for line in f:
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my_file.append(line.rstrip())
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return [x.split(" ") for x in my_file]
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def load_file_word_matrix(name):
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with open(name, "r") as f:
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return load_word_matrix(f)
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#############
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def rotate(WHAT, times=1):
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what = WHAT
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for x in range(times):
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what = list(zip(*what[::-1]))
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return what
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@contextmanager
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def elapsed_timer():
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start = default_timer()
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elapser = lambda: default_timer() - start
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yield lambda: elapser()
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end = default_timer()
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elapser = lambda: end - start
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def render_cubes(maxX, maxY, maxZ, my_cubes):
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d.art3d import Poly3DCollection
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def cuboid_data(o, size=(1, 1, 1)):
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X = [
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[[0, 1, 0], [0, 0, 0], [1, 0, 0], [1, 1, 0]],
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[[0, 0, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0]],
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[[1, 0, 1], [1, 0, 0], [1, 1, 0], [1, 1, 1]],
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[[0, 0, 1], [0, 0, 0], [0, 1, 0], [0, 1, 1]],
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[[0, 1, 0], [0, 1, 1], [1, 1, 1], [1, 1, 0]],
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[[0, 1, 1], [0, 0, 1], [1, 0, 1], [1, 1, 1]],
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]
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X = np.array(X).astype(float)
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for i in range(3):
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X[:, :, i] *= size[i]
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X += np.array(o)
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return X
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def plotCubeAt(positions, sizes=None, colors=None, **kwargs):
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if not isinstance(colors, (list, np.ndarray)):
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colors = ["C0"] * len(positions)
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if not isinstance(sizes, (list, np.ndarray)):
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sizes = [(1, 1, 1)] * len(positions)
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g = []
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for p, s, c in zip(positions, sizes, colors):
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g.append(cuboid_data(p, size=s))
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return Poly3DCollection(
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np.concatenate(g), facecolors=np.repeat(colors, 6, axis=0), **kwargs
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)
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N1 = maxX
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N2 = maxY
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N3 = maxZ
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ma = np.random.choice([0, 1], size=(N1, N2, N3), p=[0.99, 0.01])
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x, y, z = np.indices((N1, N2, N3)) - 0.5
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# positions = np.c_[x[ma==1],y[ma==1],z[ma==1]]
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positions = np.c_[my_cubes]
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colors = np.random.rand(len(positions), 3)
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fig = plt.figure()
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ax = fig.add_subplot(projection="3d")
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ax.set_aspect("equal")
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pc = plotCubeAt(positions, colors=colors, edgecolor="k")
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ax.add_collection3d(pc)
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ax.set_xlim([0, maxX])
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ax.set_ylim([0, maxY])
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ax.set_zlim([0, maxZ])
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# plotMatrix(ax, ma)
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# ax.voxels(ma, edgecolor="k")
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plt.show()
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