004 • History
- puzzle_id:
2-0ca9ddb6
- description:
example_summary
- timestamp:
24.329.064813
- call_count:
4
Begin puzzle: 2-0ca9ddb6
example_1
input
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
output
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 0 0 0 0 7 0 0]
[0 0 0 0 0 7 1 7 0]
[0 0 0 0 0 0 7 0 0]
[0 0 0 0 0 0 0 0 0]]
images
observations
The input grid is a 9x9 grid containing mostly white pixels (0). There is a single red (2) pixel at (3,2) and a single blue (1) pixel at (7,6).
The output grid expands on the input. The red pixel remains unchanged. The blue pixel is surrounded by orange (7) pixels, forming a 3x3 square with the blue pixel in the center. Additionally, yellow (4) pixels appear in a cross pattern around the red pixel.
code_execution:
import numpy as np
input_grid = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
output_grid = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 4, 0, 4, 0, 0, 0, 0, 0],
[0, 0, 2, 0, 0, 0, 0, 0, 0],
[0, 4, 0, 4, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 0, 0],
[0, 0, 0, 0, 0, 7, 1, 7, 0],
[0, 0, 0, 0, 0, 0, 7, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
print("Input Grid:\n", input_grid)
print("\nOutput Grid:\n", output_grid)
red_indices = np.where(input_grid == 2)
blue_indices = np.where(input_grid == 1)
print("\nRed Pixel Coordinates:", red_indices)
print("Blue Pixel Coordinates:", blue_indices)
code_execution_result: 1
Input Grid:
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
Output Grid:
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 0 0 0 0 7 0 0]
[0 0 0 0 0 7 1 7 0]
[0 0 0 0 0 0 7 0 0]
[0 0 0 0 0 0 0 0 0]]
Red Pixel Coordinates: (array([3]), array([2]))
Blue Pixel Coordinates: (array([6]), array([6]))
input:
width: 9
height: 9
colors:
- 0: 71
- 1: 1
- 2: 1
objects:
- size: 1x1, position: (3,2), color: red
- size: 1x1, position: (6,6), color: blue
output:
width: 9
height: 9
colors:
- 0: 64
- 2: 1
- 4: 4
- 7: 4
objects:
- size: 1x1, position: (3,2), color: red
- size: 3x3, position: (6,5), color: blue surrounded by orange
- size: 1x1, position: (2,1), color: yellow
- size: 1x1, position: (2,3), color: yellow
- size: 1x1, position: (4,1), color: yellow
- size: 1x1, position: (4,3), color: yellow
differences:
cells_changed: 13
colors_changed: added yellow and orange colors around the blue pixel and added yellow pixels in a cross pattern around the red pixel.
transformation:
- A cross pattern of yellow pixels appears around the red pixel.
- The blue pixel is surrounded by an orange 3x3 square.
The transformation seems to involve the addition of new colored pixels around the existing red and blue pixels, forming distinct patterns. The red pixel gets a yellow cross, and the blue gets a 3x3 orange square. There’s no obvious mathematical relationship or positional rule beyond proximity to the original pixels.
example_2
input
[[0 0 0 8 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 2 0 0]
[0 0 1 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0]
[0 2 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
output
[[0 0 0 8 0 0 0 0 0]
[0 0 0 0 0 4 0 4 0]
[0 0 7 0 0 0 2 0 0]
[0 7 1 7 0 4 0 4 0]
[0 0 7 0 0 0 0 0 0]
[0 0 0 0 0 0 7 0 0]
[4 0 4 0 0 7 1 7 0]
[0 2 0 0 0 0 7 0 0]
[4 0 4 0 0 0 0 0 0]]
images
observations
The input grid is a 9x9 grid. It contains a few colored pixels against a white background. There’s an azure (8) pixel, two red (2) pixels, and two blue (1) pixels. The output grid shows the same colored pixels as the input, but with added yellow (4) and orange (7) pixels surrounding the blue and red pixels in specific patterns. The patterns are consistent with the previous example: yellow crosses around red pixels, and 3x3 orange squares surrounding blue pixels.
code_execution:
import numpy as np
input_grid = np.array([[0, 0, 0, 8, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 2, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
output_grid = np.array([[0, 0, 0, 8, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 4, 0, 4, 0],
[0, 0, 7, 0, 0, 0, 2, 0, 0],
[0, 7, 1, 7, 0, 4, 0, 4, 0],
[0, 0, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 0, 0],
[4, 0, 4, 0, 0, 7, 1, 7, 0],
[0, 2, 0, 0, 0, 0, 7, 0, 0],
[4, 0, 4, 0, 0, 0, 0, 0, 0]])
print("Input Grid:\n", input_grid)
print("\nOutput Grid:\n", output_grid)
red_indices = np.where(input_grid == 2)
blue_indices = np.where(input_grid == 1)
print("\nRed Pixel Coordinates:", red_indices)
print("Blue Pixel Coordinates:", blue_indices)
code_execution_result: 1
Input Grid:
[[0 0 0 8 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 2 0 0]
[0 0 1 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 1 0 0]
[0 2 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
Output Grid:
[[0 0 0 8 0 0 0 0 0]
[0 0 0 0 0 4 0 4 0]
[0 0 7 0 0 0 2 0 0]
[0 7 1 7 0 4 0 4 0]
[0 0 7 0 0 0 0 0 0]
[0 0 0 0 0 0 7 0 0]
[4 0 4 0 0 7 1 7 0]
[0 2 0 0 0 0 7 0 0]
[4 0 4 0 0 0 0 0 0]]
Red Pixel Coordinates: (array([2, 7]), array([6, 1]))
Blue Pixel Coordinates: (array([3, 6]), array([2, 6]))
input:
width: 9
height: 9
colors:
- 0: 71
- 1: 2
- 2: 2
- 8: 1
objects:
- size: 1x1, position: (3,2), color: blue
- size: 1x1, position: (6,6), color: blue
- size: 1x1, position: (2,6), color: red
- size: 1x1, position: (7,1), color: red
- size: 1x1, position: (0,3), color: azure
output:
width: 9
height: 9
colors:
- 0: 60
- 1: 2
- 2: 2
- 4: 6
- 7: 12
- 8: 1
objects:
- size: 1x1, position: (0,3), color: azure
- size: 1x1, position: (2,6), color: red
- size: 1x1, position: (7,1), color: red
- size: 3x3, position: (2,2), color: blue surrounded by orange and yellow
- size: 3x3, position: (5,5), color: blue surrounded by orange and yellow
differences:
cells_changed: 22
colors_changed: added yellow and orange pixels around blue pixels; yellow cross pattern around red pixels
transformation:
- Blue pixels are surrounded by a 3x3 square of orange pixels, with yellow pixels in the corners.
- Red pixels have a cross pattern of yellow pixels around them.
The transformation in this example is consistent with the previous one. Each blue pixel is surrounded by a 3x3 square of orange (7) pixels, and the yellow (4) pixels are added to form a cross pattern around each red (2) pixel. The azure (8) pixel remains unchanged.
example_3
input
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 6 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
output
[[0 0 0 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 6 0 0]
[0 0 0 7 0 0 0 0 0]
[0 0 7 1 7 0 0 0 0]
[0 0 0 7 0 0 0 0 0]]
images
observations
The input grid is a 9x9 grid with a sparse distribution of colored pixels. There’s a red (2) pixel, a blue (1) pixel, and a magenta (6) pixel. The output grid shows the same colored pixels, but now the red pixel is surrounded by a yellow (4) cross, and the blue pixel is embedded within a 3x3 square of orange (7) pixels. The magenta pixel remains unchanged.
code_execution:
import numpy as np
input_grid = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 2, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 6, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
output_grid = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 4, 0, 4, 0, 0, 0, 0, 0],
[0, 0, 2, 0, 0, 0, 0, 0, 0],
[0, 4, 0, 4, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 6, 0, 0],
[0, 0, 0, 7, 0, 0, 0, 0, 0],
[0, 0, 7, 1, 7, 0, 0, 0, 0],
[0, 0, 0, 7, 0, 0, 0, 0, 0]])
print("Input Grid:\n", input_grid)
print("\nOutput Grid:\n", output_grid)
red_indices = np.where(input_grid == 2)
blue_indices = np.where(input_grid == 1)
magenta_indices = np.where(input_grid ==6)
print("\nRed Pixel Coordinates:", red_indices)
print("Blue Pixel Coordinates:", blue_indices)
print("Magenta Pixel Coordinates:", magenta_indices)
code_execution_result: 1
Input Grid:
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 6 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
Output Grid:
[[0 0 0 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 2 0 0 0 0 0 0]
[0 4 0 4 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 6 0 0]
[0 0 0 7 0 0 0 0 0]
[0 0 7 1 7 0 0 0 0]
[0 0 0 7 0 0 0 0 0]]
Red Pixel Coordinates: (array([2]), array([2]))
Blue Pixel Coordinates: (array([7]), array([3]))
Magenta Pixel Coordinates: (array([5]), array([6]))
input:
width: 9
height: 9
colors:
- 0: 71
- 1: 1
- 2: 1
- 6: 1
objects:
- size: 1x1, position: (2,2), color: red
- size: 1x1, position: (7,3), color: blue
- size: 1x1, position: (5,6), color: magenta
output:
width: 9
height: 9
colors:
- 0: 64
- 1: 1
- 2: 1
- 4: 4
- 6: 1
- 7: 4
objects:
- size: 1x1, position: (2,2), color: red
- size: 3x3, position: (7,2), color: blue surrounded by orange
- size: 1x1, position: (5,6), color: magenta
- size: 1x1, position: (1,1), color: yellow
- size: 1x1, position: (1,3), color: yellow
- size: 1x1, position: (3,1), color: yellow
- size: 1x1, position: (3,3), color: yellow
differences:
cells_changed: 8
colors_changed: added yellow cross around red, orange 3x3 around blue
transformation:
- A yellow cross pattern is added around the red pixel.
- A 3x3 square of orange pixels is added around the blue pixel.
- The magenta pixel remains unchanged.
The transformation pattern remains consistent across all three examples. Red pixels gain a yellow cross pattern, blue pixels are surrounded by a 3x3 orange square, and other colors are unaffected. There is no apparent relationship between the positions of the original pixels and the pattern placement. The only consistent factor is the color of the original pixel and its immediate surroundings.
examples summary
INSTRUCTIONS:
summarize your observations to explain the transformation of the input to output
use code_execution to investigate properties, patterns and differences in the grids
See also