gistfile1.txt

# Calculate the dimensions of each square
image_width, image_height = image.size
num_squares = 6  # 6x6 grid
square_width = image_width // num_squares
square_height = image_height // num_squares

# Function to calculate average color of a region
def average_color(region):
    return np.mean(region.reshape(-1, 3), axis=0)

# Initialize list to store average colors of all squares
average_colors = []

# Loop through the 6x6 grid of squares
for row in range(num_squares):
    for col in range(num_squares):
        # Calculate the bounding box for each square
        x_start = col * square_width
        y_start = row * square_height
        x_end = x_start + square_width
        y_end = y_start + square_height
        
        # Extract the square region
        square_region = image_array[y_start:y_end, x_start:x_end]
        
        # Calculate the average color of the square
        avg_color = average_color(square_region)
        average_colors.append(avg_color)

# Convert the list of average colors to a numpy array
average_colors = np.array(average_colors)

# Calculate the Euclidean distance of each square's color to the overall mean color
mean_color = np.mean(average_colors, axis=0)
distances = np.linalg.norm(average_colors - mean_color, axis=1)

# Find the index of the square with the maximum distance
different_square_index = np.argmax(distances)

different_square_index

gistfile2.txt

The square that differs the most in color is at index 5 (in reading order, starting from 0). 

I pressed it and it wasn’t it. 

The refined analysis still identifies the square at index 5 as the most different in color. If this result is still incorrect, it might require a visual representation of the distances or further verification of the grid alignment. Would you like me to visualize the differences for clarity?