def compat_position_extrinsic(o0, p0, n0, v0, o1, p1, n1, v1, scale):
""" Find compatible versions of two representative positions (with specified normals and orientations) """
t0, t1, middle = np.cross(n0, o0), np.cross(n1, o1), intermediate_pos(v0, n0, v1, n1)
p0, p1 = lattice_op(p0, o0, n0, middle, scale), lattice_op(p1, o1, n1, middle, scale)
x = min(all_combinations([0, 1], [0, 1], [0, 1], [0, 1]),
key = lambda x : np.linalg.norm((p0 + scale * (o0 * x[0] + t0 * x[1])) - (p1 + scale * (o1 * x[2] + t1 * x[3]))))
result = (p0 + scale * (o0 * x[0] + t0 * x[1]), p1 + scale * (o1 * x[2] + t1 * x[3]))
return result
def compat_position_extrinsic(o0, p0, n0, v0, o1, p1, n1, v1, scale):
""" Find compatible versions of two representative positions (with specified normals and orientations) """
t0, t1, middle = np.cross(n0, o0), np.cross(n1, o1), intermediate_pos(
v0, n0, v1, n1)
p0, p1 = lattice_op(p0, o0, n0, middle,
scale), lattice_op(p1, o1, n1, middle, scale)
x = min(all_combinations([0, 1], [0, 1], [0, 1], [0, 1]),
key=lambda x: np.linalg.norm(
(p0 + scale * (o0 * x[0] + t0 * x[1])) -
(p1 + scale * (o1 * x[2] + t1 * x[3]))))
result = (p0 + scale * (o0 * x[0] + t0 * x[1]),
p1 + scale * (o1 * x[2] + t1 * x[3]))
return result
def compat_orientation_extrinsic(o0, n0, o1, n1):
""" Find compatible versions of two representative orientations (with specified normals) """
return max(
all_combinations([o0, np.cross(n0, o0), -o0, -np.cross(n0, o0)],
[o1, np.cross(n1, o1)]),
key=lambda x: np.dot(x[0], x[1]))
if property_tally[p] > 0:
score *= property_tally[p]
else:
return (0, False)
return score, calorie_target_met
ingredients_list = list()
recipes_list = list()
best_ignoring_calories = 0
best_calorie_target = 0
with open(puzzleinput) as f:
for ingredients_text in f:
i = list(re.match(regex, ingredients_text).groups())
i = [i[0], int(i[1]), int(i[2]), int(i[3]), int(i[4]), int(i[5])]
ingredient = dict(zip(['name'] + properties, i))
ingredients_list.append(ingredient)
ingredient_names = [i['name'] for i in ingredients_list]
all_ingredient_combinations = all_combinations(ingredient_names, teaspoons)
for combination in all_ingredient_combinations:
recipes_list.append(Counter(combination).items())
for recipe in recipes_list:
current_score, calorie_target_met = score(recipe)
best_ignoring_calories = max(current_score, best_ignoring_calories)
if calorie_target_met:
best_calorie_target = max(current_score, best_calorie_target)
print('part 1: %d' % best_ignoring_calories)
print('part 2: %d' % best_calorie_target)
def compat_orientation_extrinsic(o0, n0, o1, n1):
""" Find compatible versions of two representative orientations (with specified normals) """
return max(all_combinations([o0, np.cross(n0, o0), -o0, -np.cross(n0, o0)], [o1, np.cross(n1, o1)]),
key = lambda x: np.dot(x[0], x[1]))