def to_grid(self) -> Grid:
# noinspection PyTypeChecker
empty_grid_array: List[Row] = np.full([
self.number_of_rows,
self.number_of_columns,
], self.background_colour).tolist()
# find lowest depth, then colour all objects that have that depth, then all objects for depth above, etc.
# noinspection PyTypeChecker
objects_by_depth: Dict[int, List[Object]] = defaultdict(list)
for obj in self.objects.values():
objects_by_depth[obj.depth].append(obj)
ascending_depths = list(objects_by_depth.keys())
ascending_depths.sort()
grid = Grid(empty_grid_array)
for lowest_depth in ascending_depths:
objects_for_this_depth = objects_by_depth[lowest_depth]
for obj in objects_for_this_depth:
absolute_positions = obj.convert_to_absolute_positions()
for absolute_position in absolute_positions:
grid.set_colour(absolute_position[0], absolute_position[1],
obj.colour)
return grid
def test_making_frame_model_from_grid_with_one_object(self):
g = [
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 0, 0, 0],
[0, 3, 0, 3, 0, 0],
[0, 0, 3, 0, 3, 0],
[0, 0, 0, 3, 0, 0],
]
grid = Grid(g)
frame_model = FrameModel.create_from_grid(grid)
self.assertEqual(
6,
frame_model.number_of_columns
)
self.assertEqual(
5,
frame_model.number_of_rows
)
self.assertEqual(
0,
frame_model.background_colour
)
self.assertEqual(
1,
len(frame_model.objects)
)
def test_identical_frames(self):
frame_model_a = FrameModel.create_from_grid(
Grid([
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
]))
frame_model_b = FrameModel.create_from_grid(
Grid([
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
]))
self.assertEqual(
1, ARCDriver.get_frame_similarity(frame_model_a, frame_model_b))
def test_frames_of_different_sizes(self):
frame_model_a = FrameModel.create_from_grid(
Grid([
[1, 1, 3],
[3, 2, 2],
[3, 2, 2],
]))
frame_model_b = FrameModel.create_from_grid(
Grid([
[0, 0, 0],
[0, 2, 2],
[0, 2, 2],
[0, 2, 2],
]))
self.assertEqual(
0, ARCDriver.get_frame_similarity(frame_model_a, frame_model_b))
def make_frames_from_task(
self, train_or_test: str) -> List[Dict[str, FrameModel]]:
frames: List[Dict[str, FrameModel]] = []
pairs = self.task[train_or_test]
number_of_pairs = len(pairs)
for pair_number in range(number_of_pairs):
input_matrix = pairs[pair_number]['input']
input_grid = Grid(input_matrix)
input_frame_model = FrameModel.create_from_grid(input_grid)
output_matrix = pairs[pair_number]['output']
output_grid = Grid(output_matrix)
output_frame_model = FrameModel.create_from_grid(output_grid)
frames.append({
"input": input_frame_model,
"output": output_frame_model,
})
return frames
def test_making_grid_from_frame_model_with_one_object(self):
g = [
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 0, 0, 0],
[0, 3, 0, 3, 0, 0],
[0, 0, 3, 0, 3, 0],
[0, 0, 0, 3, 0, 0],
]
grid = Grid(g)
frame_model = FrameModel.create_from_grid(grid)
grid2 = frame_model.to_grid()
self.assertSequenceEqual(grid.grid_array, grid2.grid_array)
def create_from_grid(grid: Grid) -> "FrameModel":
return FrameModel(grid.number_of_rows, grid.number_of_columns,
grid.background_colour, grid.parse_objects(), [])
def plot_task(task):
"""
Plots the first train and test pairs of a specified task,
using same color scheme as the ARC app
"""
number_of_training_examples = len(task['train'])
fig, axes_array = plt.subplots(2,
number_of_training_examples,
figsize=(3 * number_of_training_examples,
3 * 2))
for task_number in range(number_of_training_examples):
input_axis = axes_array[0, task_number]
input_matrix = task['train'][task_number]['input']
input_grid = Grid(input_matrix)
input_grid.plot(input_axis, 'train input')
output_axis = axes_array[1, task_number]
output_matrix = task['train'][task_number]['output']
input_grid = Grid(output_matrix)
input_grid.plot(output_axis, 'train output')
plt.tight_layout()
plt.show()
number_of_test_examples = len(task['test'])
fig, axes_array = plt.subplots(2,
number_of_test_examples,
figsize=(3 * number_of_test_examples,
3 * 2))
if number_of_test_examples == 1:
input_axis = axes_array[0]
input_matrix = task['test'][0]['input']
test_input_grid = Grid(input_matrix)
test_input_grid.plot(input_axis, 'test input')
output_axis = axes_array[1]
output_matrix = task['test'][0]['output']
test_input_grid = Grid(output_matrix)
test_input_grid.plot(output_axis, 'test output')
else:
for task_number in range(number_of_test_examples):
input_axis = axes_array[0, task_number]
input_matrix = task['test'][task_number]['input']
test_input_grid = Grid(input_matrix)
test_input_grid.plot(input_axis, 'test input')
output_axis = axes_array[1, task_number]
output_matrix = task['test'][task_number]['output']
test_input_grid = Grid(output_matrix)
test_input_grid.plot(output_axis, 'test output')
plt.tight_layout()
plt.show()