<jupyter_start><jupyter_text>Load customize joint profiling data<jupyter_code>data_path = "demo_data/"
# provide your own files
data_name_dict = {
        "demo_gene_barcode.txt":"gene_barcodes",\
        "demo_gene_count.txt": "gene_expression",\
        "demo_gene_name.txt": "gene_names",\
        "demo_peak_barcode.txt":"atac_barcodes",\
        "demo_peak_expression.txt":"atac_expression",\
        "demo_peaks.txt":"atac_names"}

dataset = scMVP_dataloader(data_name_dict, save_path = data_path)
print("ok!")<jupyter_output>ok!
{'fraction_non_alphanumeric': 0.13644859813084112, 'fraction_numerical': 0.0, 'mean_word_length': 4.8901098901098905, 'pattern_counts': {'":': 6, '<': 4, '<?xml version=': 0, '>': 4, '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': '506601', 'license': 'MIT', 'n_tokens_mistral': 208, 'n_tokens_neox': 196, 'n_words': 24, 'path': 'demos/scMVP_dataloader.ipynb', 'repo_name': 'lgyzngc/sctest', 'size': '535'}