Initial Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

LLM Generation

Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

Output

To convert the given Game of Life pattern from its RLE (Run Length Encoded) format to a NumPy array, you can use the following Python function. This function takes the RLE string and the desired height and width of the array as inputs, and returns an array filled with 1s for live cells and 0s for dead cells. Here's the code to accomplish this: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize an empty array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the RLE string
    rows = rle_string.strip().split('$')
    y_offset = 0

    for row in rows:
        x_offset = 0
        tokens = row.split()
        
        for token in tokens:
            if token.isdigit():
                count = int(token)
                x_offset += count
            elif token[0].isdigit():
                count = int(token[:-1])
                cell_type = token[-1]
                if cell_type == 'o':  # Live cell
                    for i in range(count):
                        if y_offset < h and x_offset + i < w:
                            arr[y_offset, x_offset + i] = 1
                elif cell_type == 'b':  # Block (dead cell, no need to fill)
                    x_offset += count
            else:
                cell_type = token
                if cell_type == 'o':  # Live cell
                    if y_offset < h and x_offset < w:
                        arr[y_offset, x_offset] = 1
                    x_offset += 1
                elif cell_type == 'b':  # Block (dead cell)
                    x_offset += 1

        y_offset += 1
    
    return arr

# Example usage:
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
height = 75  # Set based on the RLE pattern
width = 75   # Set based on the RLE pattern

result_array = rle_to_arr(rle_string, height, width)
print(result_array)
In this code: - We first create an empty array filled with zeros. - We parse the RLE string, splitting it into rows and then interpreting each symbol. - We fill the array with 1s where the live cells (denoted by 'o') should be, based on the parsed tokens. - The resulting array is returned. Adjust `height` and `width` according to the maximum expected dimensions of the pattern. Make sure to install NumPy before running the code, if you haven't done so already: 
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pip install numpy

Extract Code

I extracted the following code from that output: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize an empty array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the RLE string
    rows = rle_string.strip().split('$')
    y_offset = 0

    for row in rows:
        x_offset = 0
        tokens = row.split()
        
        for token in tokens:
            if token.isdigit():
                count = int(token)
                x_offset += count
            elif token[0].isdigit():
                count = int(token[:-1])
                cell_type = token[-1]
                if cell_type == 'o':  # Live cell
                    for i in range(count):
                        if y_offset < h and x_offset + i < w:
                            arr[y_offset, x_offset + i] = 1
                elif cell_type == 'b':  # Block (dead cell, no need to fill)
                    x_offset += count
            else:
                cell_type = token
                if cell_type == 'o':  # Live cell
                    if y_offset < h and x_offset < w:
                        arr[y_offset, x_offset] = 1
                    x_offset += 1
                elif cell_type == 'b':  # Block (dead cell)
                    x_offset += 1

        y_offset += 1
    
    return arr

Run Code Interpreter

Running the following program: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize an empty array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the RLE string
    rows = rle_string.strip().split('$')
    y_offset = 0

    for row in rows:
        x_offset = 0
        tokens = row.split()
        
        for token in tokens:
            if token.isdigit():
                count = int(token)
                x_offset += count
            elif token[0].isdigit():
                count = int(token[:-1])
                cell_type = token[-1]
                if cell_type == 'o':  # Live cell
                    for i in range(count):
                        if y_offset < h and x_offset + i < w:
                            arr[y_offset, x_offset + i] = 1
                elif cell_type == 'b':  # Block (dead cell, no need to fill)
                    x_offset += count
            else:
                cell_type = token
                if cell_type == 'o':  # Live cell
                    if y_offset < h and x_offset < w:
                        arr[y_offset, x_offset] = 1
                    x_offset += 1
                elif cell_type == 'b':  # Block (dead cell)
                    x_offset += 1

        y_offset += 1
    
    return arr





answer = (np.array(rle_to_arr('''
16b2o3b2o$2o$2o$19b2o$17b5o$21bo$17b3o$o16bo$o17b2o$bo16b2o$b2o$3bo$o
17b2o3b2o$2b2o14bobobobo$11bobo5b5o$11b2o7b3o$2o10bo8bo$b2o$b2o2$2o$bo
2$6bobo$2o3bo$bo3bo$o4bo2bo7bo$5b3o7b2o7bo$15bobo5b3o$22b5o$21b2o3b2o$
22b5o$22b5o$14b2o6bo2bo$14bobo4bo3bo$11b2obobo4bo$11bobobo5bob2o$13bo
8bo$4bobo6b2o$7bo5b3o50bo$7bo5b3o3b2o3b2o40b4o$4bo2bo5b3o3bo5bo41b4o$
5b3o5b2o35bo16bo2bo$13bo6bo3bo24bobo15b4o4b2o$11bobobo5b3o15b2o7bob2o
14b4o5b2o$11b2obobo21bobo6b2ob2o14bo8b2o$14bobo20bo6b3obob2o$14b2o12b
2o7bo2bo2bo2bo2bobo$28b2o7bo6b2o4bo16bobo$38bobo26b2o$39b2o27bo4b2o$
21b2o49bo2bo$21b2o28bobo18b3o$52b2o21b2o$52bo21bo$74b2o$59bo$62bo$59bo
14bo$62bo$61bob3o8bo$59b4o2bo9b2o$64bo$52bo$51b2o$40b2o8b2o4b2o5bo$40b
2o7b3o4b2o3bobo$50b2o4b2o2bobo11b2o$4bobo44b2o6bo2bo11b2o$7bo44bo7bobo
$o6bo53bobo$o3bo2bo55bo$5b3o5$4b4o$3bo3bo$7bo$3bo2bo3$o2b3o2b2o$2b5o2b
o64b3o$9o67bo$9b3o63bo$o7bo2bo$9b2o3$4b2o$o3b3o$o5b2obo5b2o$bo4bo2bo5b
2o$2o4b2obo$o3b3o$4b2o2$59b3o12b2o$61bo12b2o!
''', 101, 77),dtype=np.uint32).flatten() * np.arange(77*101)).sum()
expected = 1563304
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: Traceback (most recent call last): File "/usr/src/app/main.py", line 46, in <module> answer = (np.array(rle_to_arr(''' File "/usr/src/app/main.py", line 21, in rle_to_arr count = int(token[:-1]) ValueError: invalid literal for int() with base 10: '16b2o3b2'

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

LLM Generation

Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

Output

To convert the given Game of Life pattern from RLE (Run Length Encoded) format into a NumPy array, we can define the function `rle_to_arr`. This function will interpret the RLE string, determine the size of the grid, and fill the appropriate cells with 1s (for live cells) and 0s (for dead cells). Here's how you can implement the `rle_to_arr` function: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize the array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into lines
    lines = rle_string.strip().split('$')
    
    # Current position in the array
    y = 0
    for line in lines:
        if y >= h:
            break
        # Initialize the x position for the current line
        x = 0
        
        # Iterate over each character in the line
        i = 0
        while i < len(line):
            # If the character is a digit, it indicates a run length
            if line[i].isdigit():
                num = 0
                while i < len(line) and line[i].isdigit():
                    num = num * 10 + int(line[i])
                    i += 1
                # If the next character is a letter, it's a live cell
                if i < len(line) and line[i] in 'bo':
                    if line[i] == 'b':
                        # 'b' indicates dead cells, just move x forward by num
                        x += num
                    elif line[i] == 'o':
                        # 'o' indicates live cells
                        for _ in range(num):
                            if x < w and y < h:
                                arr[y, x] = 1
                                x += 1
                i += 1  # Move past 'b' or 'o'
            else:
                i += 1  # Move to the next character if no digit is found

        # Move to the next line in the array
        y += 1
    
    return arr

# Example usage
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
h = 70  # Height of the grid
w = 100  # Width of the grid

arr = rle_to_arr(rle_string, h, w)
print(arr)
### Explanation: - `rle_to_arr` initializes a 2D NumPy array filled with zeros, representing dead cells. - It processes the RLE string line by line, interpreting digits as counts of consecutive cells. - Depending on whether the next character is 'b' (which denotes dead cells) or 'o' (which denotes live cells), it fills the array accordingly. - The function returns the resulting NumPy array, which represents the Game of Life pattern. You can run the above script to see the converted pattern in array form. Adjust the height (`h`) and width (`w`) as needed based on the specific dimensions of the RLE input.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize the array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into lines
    lines = rle_string.strip().split('$')
    
    # Current position in the array
    y = 0
    for line in lines:
        if y >= h:
            break
        # Initialize the x position for the current line
        x = 0
        
        # Iterate over each character in the line
        i = 0
        while i < len(line):
            # If the character is a digit, it indicates a run length
            if line[i].isdigit():
                num = 0
                while i < len(line) and line[i].isdigit():
                    num = num * 10 + int(line[i])
                    i += 1
                # If the next character is a letter, it's a live cell
                if i < len(line) and line[i] in 'bo':
                    if line[i] == 'b':
                        # 'b' indicates dead cells, just move x forward by num
                        x += num
                    elif line[i] == 'o':
                        # 'o' indicates live cells
                        for _ in range(num):
                            if x < w and y < h:
                                arr[y, x] = 1
                                x += 1
                i += 1  # Move past 'b' or 'o'
            else:
                i += 1  # Move to the next character if no digit is found

        # Move to the next line in the array
        y += 1
    
    return arr

# Example usage
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
h = 70  # Height of the grid
w = 100  # Width of the grid

arr = rle_to_arr(rle_string, h, w)
print(arr)

Run Code Interpreter

Running the following program: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize the array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into lines
    lines = rle_string.strip().split('$')
    
    # Current position in the array
    y = 0
    for line in lines:
        if y >= h:
            break
        # Initialize the x position for the current line
        x = 0
        
        # Iterate over each character in the line
        i = 0
        while i < len(line):
            # If the character is a digit, it indicates a run length
            if line[i].isdigit():
                num = 0
                while i < len(line) and line[i].isdigit():
                    num = num * 10 + int(line[i])
                    i += 1
                # If the next character is a letter, it's a live cell
                if i < len(line) and line[i] in 'bo':
                    if line[i] == 'b':
                        # 'b' indicates dead cells, just move x forward by num
                        x += num
                    elif line[i] == 'o':
                        # 'o' indicates live cells
                        for _ in range(num):
                            if x < w and y < h:
                                arr[y, x] = 1
                                x += 1
                i += 1  # Move past 'b' or 'o'
            else:
                i += 1  # Move to the next character if no digit is found

        # Move to the next line in the array
        y += 1
    
    return arr

# Example usage
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
h = 70  # Height of the grid
w = 100  # Width of the grid

arr = rle_to_arr(rle_string, h, w)
print(arr)





answer = (np.array(rle_to_arr('''
16b2o3b2o$2o$2o$19b2o$17b5o$21bo$17b3o$o16bo$o17b2o$bo16b2o$b2o$3bo$o
17b2o3b2o$2b2o14bobobobo$11bobo5b5o$11b2o7b3o$2o10bo8bo$b2o$b2o2$2o$bo
2$6bobo$2o3bo$bo3bo$o4bo2bo7bo$5b3o7b2o7bo$15bobo5b3o$22b5o$21b2o3b2o$
22b5o$22b5o$14b2o6bo2bo$14bobo4bo3bo$11b2obobo4bo$11bobobo5bob2o$13bo
8bo$4bobo6b2o$7bo5b3o50bo$7bo5b3o3b2o3b2o40b4o$4bo2bo5b3o3bo5bo41b4o$
5b3o5b2o35bo16bo2bo$13bo6bo3bo24bobo15b4o4b2o$11bobobo5b3o15b2o7bob2o
14b4o5b2o$11b2obobo21bobo6b2ob2o14bo8b2o$14bobo20bo6b3obob2o$14b2o12b
2o7bo2bo2bo2bo2bobo$28b2o7bo6b2o4bo16bobo$38bobo26b2o$39b2o27bo4b2o$
21b2o49bo2bo$21b2o28bobo18b3o$52b2o21b2o$52bo21bo$74b2o$59bo$62bo$59bo
14bo$62bo$61bob3o8bo$59b4o2bo9b2o$64bo$52bo$51b2o$40b2o8b2o4b2o5bo$40b
2o7b3o4b2o3bobo$50b2o4b2o2bobo11b2o$4bobo44b2o6bo2bo11b2o$7bo44bo7bobo
$o6bo53bobo$o3bo2bo55bo$5b3o5$4b4o$3bo3bo$7bo$3bo2bo3$o2b3o2b2o$2b5o2b
o64b3o$9o67bo$9b3o63bo$o7bo2bo$9b2o3$4b2o$o3b3o$o5b2obo5b2o$bo4bo2bo5b
2o$2o4b2obo$o3b3o$4b2o2$59b3o12b2o$61bo12b2o!
''', 101, 77),dtype=np.uint32).flatten() * np.arange(77*101)).sum()
expected = 1563304
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: [[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] [0 0 0 ... 0 0 0]] Traceback (most recent call last): File "/usr/src/app/main.py", line 77, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got 945587 instead of 1563304

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

LLM Generation

Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

Output

To convert the given Game of Life pattern from RLE (run-length encoded) format to a NumPy array, we can implement the `rle_to_arr` function. This function will decode the RLE string and fill a NumPy array accordingly. Here's how you can do that: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Create an empty array of zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into rows
    rows = rle_string.strip().splitlines()
    
    # Initialize the starting point (row, column)
    row = 0
    col = 0
    
    for r in rows:
        # Ignore empty lines
        if not r.strip():
            continue
        
        # Parse the RLE string
        i = 0
        while i < len(r):
            # Determine the count
            count = 0
            while i < len(r) and r[i].isdigit():
                count = count * 10 + int(r[i])
                i += 1
            
            # If there is no count specified, default to 1
            if count == 0:
                count = 1
            
            # Determine the character (either 'o' or 'b')
            if i < len(r):
                char = r[i]
                if char == 'o':  # Alive cell
                    for j in range(count):
                        if row < h and col + j < w:
                            arr[row, col + j] = 1
                elif char == 'b':  # Dead cell
                    pass
                i += 1
            
            # Move the column index forward
            col += count
        
        # After finishing a row, reset column and move to the next row
        row += 1
        col = 0
        
    return arr

# Example usage
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
height = 70  # Specify the height of the array
width = 100  # Specify the width of the array

# Convert the RLE to a NumPy array
result_array = rle_to_arr(rle_string, height, width)

# Print the result
print(result_array)
### Explanation: 1. **Imports**: We import NumPy to create and manipulate the array. 2. **Function Definition**: The `rle_to_arr` function takes a string and the desired height and width of the array. 3. **Array Initialization**: We create a zero-filled NumPy array of shape (h, w). 4. **RLE Parsing**: We iterate over each character in the RLE string, interpreting counts and characters ('o' for alive and 'b' for dead). 5. **Filling the Array**: We fill the array with 1s where the 'o's are found. 6. **Return Value**: The function returns the filled NumPy array. You can adjust the height and width as needed to fit the specific pattern's dimensions.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Create an empty array of zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into rows
    rows = rle_string.strip().splitlines()
    
    # Initialize the starting point (row, column)
    row = 0
    col = 0
    
    for r in rows:
        # Ignore empty lines
        if not r.strip():
            continue
        
        # Parse the RLE string
        i = 0
        while i < len(r):
            # Determine the count
            count = 0
            while i < len(r) and r[i].isdigit():
                count = count * 10 + int(r[i])
                i += 1
            
            # If there is no count specified, default to 1
            if count == 0:
                count = 1
            
            # Determine the character (either 'o' or 'b')
            if i < len(r):
                char = r[i]
                if char == 'o':  # Alive cell
                    for j in range(count):
                        if row < h and col + j < w:
                            arr[row, col + j] = 1
                elif char == 'b':  # Dead cell
                    pass
                i += 1
            
            # Move the column index forward
            col += count
        
        # After finishing a row, reset column and move to the next row
        row += 1
        col = 0
        
    return arr

# Example usage
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
height = 70  # Specify the height of the array
width = 100  # Specify the width of the array

# Convert the RLE to a NumPy array
result_array = rle_to_arr(rle_string, height, width)

# Print the result
print(result_array)

Run Code Interpreter

Running the following program: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Create an empty array of zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into rows
    rows = rle_string.strip().splitlines()
    
    # Initialize the starting point (row, column)
    row = 0
    col = 0
    
    for r in rows:
        # Ignore empty lines
        if not r.strip():
            continue
        
        # Parse the RLE string
        i = 0
        while i < len(r):
            # Determine the count
            count = 0
            while i < len(r) and r[i].isdigit():
                count = count * 10 + int(r[i])
                i += 1
            
            # If there is no count specified, default to 1
            if count == 0:
                count = 1
            
            # Determine the character (either 'o' or 'b')
            if i < len(r):
                char = r[i]
                if char == 'o':  # Alive cell
                    for j in range(count):
                        if row < h and col + j < w:
                            arr[row, col + j] = 1
                elif char == 'b':  # Dead cell
                    pass
                i += 1
            
            # Move the column index forward
            col += count
        
        # After finishing a row, reset column and move to the next row
        row += 1
        col = 0
        
    return arr

# Example usage
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
height = 70  # Specify the height of the array
width = 100  # Specify the width of the array

# Convert the RLE to a NumPy array
result_array = rle_to_arr(rle_string, height, width)

# Print the result
print(result_array)





answer = (np.array(rle_to_arr('''
16b2o3b2o$2o$2o$19b2o$17b5o$21bo$17b3o$o16bo$o17b2o$bo16b2o$b2o$3bo$o
17b2o3b2o$2b2o14bobobobo$11bobo5b5o$11b2o7b3o$2o10bo8bo$b2o$b2o2$2o$bo
2$6bobo$2o3bo$bo3bo$o4bo2bo7bo$5b3o7b2o7bo$15bobo5b3o$22b5o$21b2o3b2o$
22b5o$22b5o$14b2o6bo2bo$14bobo4bo3bo$11b2obobo4bo$11bobobo5bob2o$13bo
8bo$4bobo6b2o$7bo5b3o50bo$7bo5b3o3b2o3b2o40b4o$4bo2bo5b3o3bo5bo41b4o$
5b3o5b2o35bo16bo2bo$13bo6bo3bo24bobo15b4o4b2o$11bobobo5b3o15b2o7bob2o
14b4o5b2o$11b2obobo21bobo6b2ob2o14bo8b2o$14bobo20bo6b3obob2o$14b2o12b
2o7bo2bo2bo2bo2bobo$28b2o7bo6b2o4bo16bobo$38bobo26b2o$39b2o27bo4b2o$
21b2o49bo2bo$21b2o28bobo18b3o$52b2o21b2o$52bo21bo$74b2o$59bo$62bo$59bo
14bo$62bo$61bob3o8bo$59b4o2bo9b2o$64bo$52bo$51b2o$40b2o8b2o4b2o5bo$40b
2o7b3o4b2o3bobo$50b2o4b2o2bobo11b2o$4bobo44b2o6bo2bo11b2o$7bo44bo7bobo
$o6bo53bobo$o3bo2bo55bo$5b3o5$4b4o$3bo3bo$7bo$3bo2bo3$o2b3o2b2o$2b5o2b
o64b3o$9o67bo$9b3o63bo$o7bo2bo$9b2o3$4b2o$o3b3o$o5b2obo5b2o$bo4bo2bo5b
2o$2o4b2obo$o3b3o$4b2o2$59b3o12b2o$61bo12b2o!
''', 101, 77),dtype=np.uint32).flatten() * np.arange(77*101)).sum()
expected = 1563304
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: [[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] [0 0 0 ... 0 0 0]] Traceback (most recent call last): File "/usr/src/app/main.py", line 85, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got 74537 instead of 1563304

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

LLM Generation

Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

Output

To convert the given RLE (Run Length Encoded) representation of a Game of Life pattern into a NumPy array, we can define a function called `rle_to_arr`. This function will read the RLE string, decode it into a 2D array, and fill the array with 1s for live cells and 0s for dead cells. Here's how you can implement the `rle_to_arr` function: 

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import numpy as np

def rle_to_arr(string, h, w):
    # Initialize an empty array of the desired shape filled with 0s
    arr = np.zeros((h, w), dtype=int)

    # Split the RLE string into lines and process each line
    lines = string.strip().split('$')
    current_row = 0

    for line in lines:
        if current_row >= h:  # Prevent going out of bounds
            break
        col = 0  # Reset column index for each new line
        i = 0  # Read through the line

        while i < len(line):
            count = 1  # Default count is 1
            # Check for digits indicating the count
            while i < len(line) and line[i].isdigit():
                count = count * 10 + int(line[i])
                i += 1
            
            if i < len(line):
                char = line[i]  # Get the character (either 'b' or 'o')
                if char == 'o':  # Live cell
                    arr[current_row, col:col + count] = 1
                # Move to the next position
                col += count
            i += 1  # Move past the character

        current_row += 1  # Move to the next row

    return arr

# Example usage:
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
height = 70  # Adjust as needed based on the pattern
width = 80   # Adjust as needed based on the pattern

pattern_array = rle_to_arr(rle_string, height, width)
print(pattern_array)
### Explanation: 1. **Initialization**: The function starts by creating a 2D NumPy array filled with zeros, which will represent dead cells. 2. **Processing the RLE**: The input string is split into lines based on the `$` delimiter. Each line is processed to determine how many cells are alive (`o`) and how many are dead (`b`). 3. **Filling the Array**: The function fills the array with 1s for live cells based on the counts parsed from the RLE string. 4. **Bounds Checking**: Checks are performed to ensure that we do not write outside the boundaries of the array. ### Note: Make sure to adjust the `height` and `width` of the array based on the specific pattern you are working with to ensure that it can accommodate the entire pattern.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def rle_to_arr(string, h, w):
    # Initialize an empty array of the desired shape filled with 0s
    arr = np.zeros((h, w), dtype=int)

    # Split the RLE string into lines and process each line
    lines = string.strip().split('$')
    current_row = 0

    for line in lines:
        if current_row >= h:  # Prevent going out of bounds
            break
        col = 0  # Reset column index for each new line
        i = 0  # Read through the line

        while i < len(line):
            count = 1  # Default count is 1
            # Check for digits indicating the count
            while i < len(line) and line[i].isdigit():
                count = count * 10 + int(line[i])
                i += 1
            
            if i < len(line):
                char = line[i]  # Get the character (either 'b' or 'o')
                if char == 'o':  # Live cell
                    arr[current_row, col:col + count] = 1
                # Move to the next position
                col += count
            i += 1  # Move past the character

        current_row += 1  # Move to the next row

    return arr

# Example usage:
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
height = 70  # Adjust as needed based on the pattern
width = 80   # Adjust as needed based on the pattern

pattern_array = rle_to_arr(rle_string, height, width)
print(pattern_array)

Run Code Interpreter

Running the following program: 

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import numpy as np

def rle_to_arr(string, h, w):
    # Initialize an empty array of the desired shape filled with 0s
    arr = np.zeros((h, w), dtype=int)

    # Split the RLE string into lines and process each line
    lines = string.strip().split('$')
    current_row = 0

    for line in lines:
        if current_row >= h:  # Prevent going out of bounds
            break
        col = 0  # Reset column index for each new line
        i = 0  # Read through the line

        while i < len(line):
            count = 1  # Default count is 1
            # Check for digits indicating the count
            while i < len(line) and line[i].isdigit():
                count = count * 10 + int(line[i])
                i += 1
            
            if i < len(line):
                char = line[i]  # Get the character (either 'b' or 'o')
                if char == 'o':  # Live cell
                    arr[current_row, col:col + count] = 1
                # Move to the next position
                col += count
            i += 1  # Move past the character

        current_row += 1  # Move to the next row

    return arr

# Example usage:
rle_string = "26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"
height = 70  # Adjust as needed based on the pattern
width = 80   # Adjust as needed based on the pattern

pattern_array = rle_to_arr(rle_string, height, width)
print(pattern_array)





answer = (np.array(rle_to_arr('''
16b2o3b2o$2o$2o$19b2o$17b5o$21bo$17b3o$o16bo$o17b2o$bo16b2o$b2o$3bo$o
17b2o3b2o$2b2o14bobobobo$11bobo5b5o$11b2o7b3o$2o10bo8bo$b2o$b2o2$2o$bo
2$6bobo$2o3bo$bo3bo$o4bo2bo7bo$5b3o7b2o7bo$15bobo5b3o$22b5o$21b2o3b2o$
22b5o$22b5o$14b2o6bo2bo$14bobo4bo3bo$11b2obobo4bo$11bobobo5bob2o$13bo
8bo$4bobo6b2o$7bo5b3o50bo$7bo5b3o3b2o3b2o40b4o$4bo2bo5b3o3bo5bo41b4o$
5b3o5b2o35bo16bo2bo$13bo6bo3bo24bobo15b4o4b2o$11bobobo5b3o15b2o7bob2o
14b4o5b2o$11b2obobo21bobo6b2ob2o14bo8b2o$14bobo20bo6b3obob2o$14b2o12b
2o7bo2bo2bo2bo2bobo$28b2o7bo6b2o4bo16bobo$38bobo26b2o$39b2o27bo4b2o$
21b2o49bo2bo$21b2o28bobo18b3o$52b2o21b2o$52bo21bo$74b2o$59bo$62bo$59bo
14bo$62bo$61bob3o8bo$59b4o2bo9b2o$64bo$52bo$51b2o$40b2o8b2o4b2o5bo$40b
2o7b3o4b2o3bobo$50b2o4b2o2bobo11b2o$4bobo44b2o6bo2bo11b2o$7bo44bo7bobo
$o6bo53bobo$o3bo2bo55bo$5b3o5$4b4o$3bo3bo$7bo$3bo2bo3$o2b3o2b2o$2b5o2b
o64b3o$9o67bo$9b3o63bo$o7bo2bo$9b2o3$4b2o$o3b3o$o5b2obo5b2o$bo4bo2bo5b
2o$2o4b2obo$o3b3o$4b2o2$59b3o12b2o$61bo12b2o!
''', 101, 77),dtype=np.uint32).flatten() * np.arange(77*101)).sum()
expected = 1563304
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: [[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] [0 0 0 ... 0 0 0]] Traceback (most recent call last): File "/usr/src/app/main.py", line 66, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got 1977764 instead of 1563304

Substring Evaluation

Testing if the previous output contains the string All tests passed: False

Initial Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

LLM Generation

Query

Convert this game of life pattern 26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b 2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$ 60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5b o15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2b o$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o 3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o! to a numpy array. Call the function `rle_to_arr(string, h, w)` which should return a numpy array with .shape (h,w). Fill the array with 1s, and empty with 0.

Output

To convert the given Game of Life pattern in RLE (Run Length Encoded) format to a NumPy array, we need to implement the function `rle_to_arr(string, h, w)`. This function will parse the RLE string, create an empty array of specified dimensions, and then populate it according to the rules of the Game of Life pattern. Here's how you can implement the function: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize an empty array with zeros (representing dead cells)
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into lines
    lines = rle_string.splitlines()
    
    # Initialize the starting position
    x, y = 0, 0
    
    for line in lines:
        # Ignore comments
        if line.startswith('#'):
            continue
            
        # Parse the line to handle RLE
        i = 0
        while i < len(line):
            if line[i].isdigit():
                # If the character is a digit, keep reading to get the full number
                count = 0
                while i < len(line) and line[i].isdigit():
                    count = count * 10 + int(line[i])
                    i += 1
            else:
                # Default count is 1 if it's not a digit
                count = 1
            
            if line[i] == 'b':
                # 'b' indicates dead cells (0), we just skip them
                i += 1
            elif line[i] == 'o':
                # 'o' indicates live cells (1)
                for _ in range(count):
                    if x < h and y < w:
                        arr[x, y] = 1
                    y += 1
                i += 1
            elif line[i] == '$':
                # New line in the grid
                x += 1
                y = 0
                i += 1
            else:
                # Move to the next character
                i += 1
    
    return arr

# Example usage
rle_string = """26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"""
height = 70  # Adjust as needed
width = 100  # Adjust as needed

pattern_array = rle_to_arr(rle_string, height, width)
print(pattern_array)
### Explanation: 1. **Initialize the Array**: An array of zeros is created with dimensions `(h, w)`. 2. **Read Lines**: The RLE string is split into lines, and comments are disregarded. 3. **Parse the RLE**: Characters are read one by one. The code checks for digits to determine the count of cells, and based on whether the character is `b` (for dead cells) or `o` (for live cells), it fills the array accordingly. A `$` indicates a jump to the next row. 4. **Return the Array**: Finally, the filled array is returned. You can adjust the `height` and `width` parameters based on the specifics of the RLE pattern you are working with.

Extract Code

I extracted the following code from that output: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize an empty array with zeros (representing dead cells)
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into lines
    lines = rle_string.splitlines()
    
    # Initialize the starting position
    x, y = 0, 0
    
    for line in lines:
        # Ignore comments
        if line.startswith('#'):
            continue
            
        # Parse the line to handle RLE
        i = 0
        while i < len(line):
            if line[i].isdigit():
                # If the character is a digit, keep reading to get the full number
                count = 0
                while i < len(line) and line[i].isdigit():
                    count = count * 10 + int(line[i])
                    i += 1
            else:
                # Default count is 1 if it's not a digit
                count = 1
            
            if line[i] == 'b':
                # 'b' indicates dead cells (0), we just skip them
                i += 1
            elif line[i] == 'o':
                # 'o' indicates live cells (1)
                for _ in range(count):
                    if x < h and y < w:
                        arr[x, y] = 1
                    y += 1
                i += 1
            elif line[i] == '$':
                # New line in the grid
                x += 1
                y = 0
                i += 1
            else:
                # Move to the next character
                i += 1
    
    return arr

# Example usage
rle_string = """26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"""
height = 70  # Adjust as needed
width = 100  # Adjust as needed

pattern_array = rle_to_arr(rle_string, height, width)
print(pattern_array)

Run Code Interpreter

Running the following program: 

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize an empty array with zeros (representing dead cells)
    arr = np.zeros((h, w), dtype=int)
    
    # Split the RLE string into lines
    lines = rle_string.splitlines()
    
    # Initialize the starting position
    x, y = 0, 0
    
    for line in lines:
        # Ignore comments
        if line.startswith('#'):
            continue
            
        # Parse the line to handle RLE
        i = 0
        while i < len(line):
            if line[i].isdigit():
                # If the character is a digit, keep reading to get the full number
                count = 0
                while i < len(line) and line[i].isdigit():
                    count = count * 10 + int(line[i])
                    i += 1
            else:
                # Default count is 1 if it's not a digit
                count = 1
            
            if line[i] == 'b':
                # 'b' indicates dead cells (0), we just skip them
                i += 1
            elif line[i] == 'o':
                # 'o' indicates live cells (1)
                for _ in range(count):
                    if x < h and y < w:
                        arr[x, y] = 1
                    y += 1
                i += 1
            elif line[i] == '$':
                # New line in the grid
                x += 1
                y = 0
                i += 1
            else:
                # Move to the next character
                i += 1
    
    return arr

# Example usage
rle_string = """26$13b2o$14bo$14bobo$15b2o14$31b2o$30b2o$32bo13$46b2o$45b2o$47bo13$61b2o$60b2o$62bo4$52b2o$52b2o$71bo$66b2ob3o$66bobobo$68bo$67b2o3$76b2o$60b3o12b2o10bobo$52b3o7bo14bo8bo2bo4bo$51bo3bo5bo10b2o11b2o5b2o$50bo5bo15bobo8b2o3bo8b2o$51bo3bo11b2o6bo9b2o10b2o$52b3o8b2obo2bo2bo2bo10bo2bo$52bo2bo7b2o2b2o6bo11bobo$53b3o16bobo$53b2obo15b2o$54bobo$55bo3$52b2o3b2o$52bobobobo$53b5o$54b3o$55bo7$54b2o$54b2o!"""
height = 70  # Adjust as needed
width = 100  # Adjust as needed

pattern_array = rle_to_arr(rle_string, height, width)
print(pattern_array)





answer = (np.array(rle_to_arr('''
16b2o3b2o$2o$2o$19b2o$17b5o$21bo$17b3o$o16bo$o17b2o$bo16b2o$b2o$3bo$o
17b2o3b2o$2b2o14bobobobo$11bobo5b5o$11b2o7b3o$2o10bo8bo$b2o$b2o2$2o$bo
2$6bobo$2o3bo$bo3bo$o4bo2bo7bo$5b3o7b2o7bo$15bobo5b3o$22b5o$21b2o3b2o$
22b5o$22b5o$14b2o6bo2bo$14bobo4bo3bo$11b2obobo4bo$11bobobo5bob2o$13bo
8bo$4bobo6b2o$7bo5b3o50bo$7bo5b3o3b2o3b2o40b4o$4bo2bo5b3o3bo5bo41b4o$
5b3o5b2o35bo16bo2bo$13bo6bo3bo24bobo15b4o4b2o$11bobobo5b3o15b2o7bob2o
14b4o5b2o$11b2obobo21bobo6b2ob2o14bo8b2o$14bobo20bo6b3obob2o$14b2o12b
2o7bo2bo2bo2bo2bobo$28b2o7bo6b2o4bo16bobo$38bobo26b2o$39b2o27bo4b2o$
21b2o49bo2bo$21b2o28bobo18b3o$52b2o21b2o$52bo21bo$74b2o$59bo$62bo$59bo
14bo$62bo$61bob3o8bo$59b4o2bo9b2o$64bo$52bo$51b2o$40b2o8b2o4b2o5bo$40b
2o7b3o4b2o3bobo$50b2o4b2o2bobo11b2o$4bobo44b2o6bo2bo11b2o$7bo44bo7bobo
$o6bo53bobo$o3bo2bo55bo$5b3o5$4b4o$3bo3bo$7bo$3bo2bo3$o2b3o2b2o$2b5o2b
o64b3o$9o67bo$9b3o63bo$o7bo2bo$9b2o3$4b2o$o3b3o$o5b2obo5b2o$bo4bo2bo5b
2o$2o4b2obo$o3b3o$4b2o2$59b3o12b2o$61bo12b2o!
''', 101, 77),dtype=np.uint32).flatten() * np.arange(77*101)).sum()
expected = 1563304
assert answer == expected, f'Wrong answer; got {answer} instead of {expected}'
print('All tests passed')
And got the output: [[0 0 0 ... 0 0 0] [1 1 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]] Traceback (most recent call last): File "/usr/src/app/main.py", line 82, in <module> assert answer == expected, f'Wrong answer; got {answer} instead of {expected}' AssertionError: Wrong answer; got 1457520 instead of 1563304

Substring Evaluation

Testing if the previous output contains the string All tests passed: False