<jupyter_start><jupyter_text>Errors happened on ObsIDs:<jupyter_code>error_IDs = [18375, 1476, 5584, 19838, 4327]
error_files = []
for item in error_IDs:
    # On Symmetry
    # path = f'/Users/grant/Science/HRC_Database/EVT1_Files/{item}/secondary/' 
    # On Monolith
    path = f'/home/grant/Temp/{item}/secondary/'
    fitsfile = glob.glob(path + '*evt1*')[0]
    error_files.append(fitsfile)
error_files
observations = {}
for obsid, item in zip(error_IDs, error_files):
    observations[f'{obsid}'] = HRCevt1(item)
    
for item in observations: 
    print(observations[item].detector, round(observations[item].exptime/1000,2))
obs18375 = observations['18375']
obs18375.boomerang()
obs18375.image(detcoords=True)
obs18375.hyperscreen()
results = []
for item in observations:
    result = observations[item].hyperscreen()
    results.append(result)
results[3]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13885778275475924, 'fraction_numerical': 0.06382978723404255, 'mean_word_length': 5.208333333333333, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, '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': '638491', 'license': 'MIT', 'n_tokens_mistral': 365, 'n_tokens_neox': 319, 'n_words': 56, 'path': 'notebooks/Fixing_TapLength_Issue.ipynb', 'repo_name': 'granttremblay/hyperscreen', 'size': '893'}