<jupyter_start><jupyter_text>get domains<jupyter_code>#from sabueso.tools.string_uniprot import to_uniprotkb_XMLDict
#from sabueso.tools.uniprotkb_XMLDict import get_domains
#item = to_uniprotkb_XMLDict('uniprot:P19367')
#item = to_uniprotkb_XMLDict('uniprot:P52789')
#item = to_uniprotkb_XMLDict('uniprot:P52790')
#item = to_uniprotkb_XMLDict('uniprot:P35557')
#domains = get_domains(item)
#domains
#domains = get_domains(item, as_cards=True)
#domains
#domains[0].to_pandas_DataFrame().T<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12162162162162163, 'fraction_numerical': 0.04054054054054054, 'mean_word_length': 13.027027027027026, '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': '259650', 'license': 'MIT', 'n_tokens_mistral': 237, 'n_tokens_neox': 216, 'n_words': 25, 'path': 'docs/contents/tools/sabueso_UniProtKB_XMLDict/get_domains.ipynb', 'repo_name': 'dprada/sabueso', 'size': '518'}