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

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import numpy as np

def rle_to_arr(rle_string, h, w):
    # Initialize array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the pattern
    y, x = 0, 0  # Current position in the array
    run_count = ""
    
    for char in rle_string:
        if char.isdigit():
            run_count += char
        else:
            # Convert run_count to int, default to 1 if empty
            count = int(run_count) if run_count else 1
            run_count = ""
            
            if char == 'b':  # Dead cell
                x += count
            elif char == 'o':  # Live cell
                for _ in range(count):
                    if 0 <= y < h and 0 <= x < w:
                        arr[y, x] = 1
                    x += 1
            elif char == '$':  # End of line
                y += count
                x = 0
            elif char == '!':  # End of pattern
                break
    
    return arr
This function reads the RLE-encoded Conway's Game of Life pattern and converts it to a numpy array where: - 1 represents live cells - 0 represents dead cells The function handles: - Run lengths (numbers before characters) - Live cells ('o') - Dead cells ('b') - Line endings ('$') - Multiple line skips (numbers before '$') - Pattern termination ('!') The h and w parameters define the dimensions of the output array.

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 array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the pattern
    y, x = 0, 0  # Current position in the array
    run_count = ""
    
    for char in rle_string:
        if char.isdigit():
            run_count += char
        else:
            # Convert run_count to int, default to 1 if empty
            count = int(run_count) if run_count else 1
            run_count = ""
            
            if char == 'b':  # Dead cell
                x += count
            elif char == 'o':  # Live cell
                for _ in range(count):
                    if 0 <= y < h and 0 <= x < w:
                        arr[y, x] = 1
                    x += 1
            elif char == '$':  # End of line
                y += count
                x = 0
            elif char == '!':  # End of pattern
                break
    
    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 array with zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the pattern
    y, x = 0, 0  # Current position in the array
    run_count = ""
    
    for char in rle_string:
        if char.isdigit():
            run_count += char
        else:
            # Convert run_count to int, default to 1 if empty
            count = int(run_count) if run_count else 1
            run_count = ""
            
            if char == 'b':  # Dead cell
                x += count
            elif char == 'o':  # Live cell
                for _ in range(count):
                    if 0 <= y < h and 0 <= x < w:
                        arr[y, x] = 1
                    x += 1
            elif char == '$':  # End of line
                y += count
                x = 0
            elif char == '!':  # End of pattern
                break
    
    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: All tests passed

Substring Evaluation

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

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 RLE (Run Length Encoding) format from Conway's Game of Life to a numpy array, I'll write a function that parses the pattern and fills an array with 1s for live cells and 0s for dead cells. 

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import numpy as np

def rle_to_arr(string, h, w):
    # Initialize an array of zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the RLE
    row = 0
    col = 0
    count_str = ''
    
    for char in string:
        if char.isdigit():
            count_str += char
        elif char in 'bo$!':
            count = int(count_str) if count_str else 1
            count_str = ''
            
            if char == 'b':
                col += count
            elif char == 'o':
                for i in range(count):
                    if row < h and col < w:
                        arr[row, col] = 1
                    col += 1
            elif char == '$':
                row += count
                col = 0
            elif char == '!':
                break
    
    return arr
This function works by: 1. Creating a zero-filled numpy array of the specified size 2. Processing the RLE string character by character 3. Collecting digits to form the count 4. When a command character is encountered: - 'b': Skip `count` dead cells - 'o': Place `count` live cells (1's) - '$': Move down `count` rows and reset column position - '!': End the pattern The function handles the pattern correctly, placing live cells according to the RLE encoding while respecting the specified dimensions.

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 array of zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the RLE
    row = 0
    col = 0
    count_str = ''
    
    for char in string:
        if char.isdigit():
            count_str += char
        elif char in 'bo$!':
            count = int(count_str) if count_str else 1
            count_str = ''
            
            if char == 'b':
                col += count
            elif char == 'o':
                for i in range(count):
                    if row < h and col < w:
                        arr[row, col] = 1
                    col += 1
            elif char == '$':
                row += count
                col = 0
            elif char == '!':
                break
    
    return arr

Run Code Interpreter

Running the following program: 

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import numpy as np

def rle_to_arr(string, h, w):
    # Initialize an array of zeros
    arr = np.zeros((h, w), dtype=int)
    
    # Parse the RLE
    row = 0
    col = 0
    count_str = ''
    
    for char in string:
        if char.isdigit():
            count_str += char
        elif char in 'bo$!':
            count = int(count_str) if count_str else 1
            count_str = ''
            
            if char == 'b':
                col += count
            elif char == 'o':
                for i in range(count):
                    if row < h and col < w:
                        arr[row, col] = 1
                    col += 1
            elif char == '$':
                row += count
                col = 0
            elif char == '!':
                break
    
    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: All tests passed

Substring Evaluation

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