<jupyter_start><jupyter_text>Generates "sensor data" for lightning talk barcode<jupyter_code>import random
ground_truth = ''.join([
    '11010010000',  # code 128 b start
    '11101110110',  # P
    '11000101000',  # H
    '11101110110',  # P
    '10000101100',  # checksum -> c
    '11000111010',  # stop
    '11',  # final bar
])
lengths = random.choices([3,4,5,6], [0.1, 1.0, 0.2, 0.05], k=len(ground_truth))
clean = ''.join([ch * ln for ch, ln in zip(ground_truth, lengths)])
print(clean)
def noisemaker(clean):
    return ''.join([
        ch if random.random() < 0.95 else random.choice(['0', '1']) 
        for ch in clean
    ])

print('\n'.join(['0000000000' + noisemaker(clean) + '000000000000' for _ in range(3)]))<jupyter_output>0000000000111111110000111110000000011100000000001000000111111111110000111111111111100001111111111100000011101011100000000000000111100001111000000000000011111111111000011111111111100001111111110000011110000000000000000011101001111111110000000011111111100000000000001111111111111000011011000011111111000000000000
0000000000111111110000111110000000011100000000000000000111111111110000111011111111101001111111110100000111111111100000000000000111100001111000000000000011101111111000011111111110100001111111111000001110000000000000000111100001111111110000000101111111100000100000001101111111111000011111000011111111000000000000
0000000000111111010000111110000000011100000000000000000111111111110000111111111111100001111111111100000011111111100000000000000111100001111000000000000011111111111000001111111111100101111111110000011110000000000000000111100001111111110000000111111111101000000000001111111111111000011111000011111111000000000000
{'fraction_non_alphanumeric': 0.07830245068738792, 'fraction_numerical': 0.6228332337118948, 'mean_word_length': 9.333333333333334, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 63, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '649910', 'license': 'MIT', 'n_tokens_mistral': 1295, 'n_tokens_neox': 475, 'n_words': 69, 'path': 'notebooks/Zebra Sequence Generator.ipynb', 'repo_name': 'jonemo/zebras-and-lasers', 'size': '1673'}