<jupyter_start><jupyter_text>FIR Filters Produced Locally<jupyter_code>for fp in glob(os.path.join(ROOT_DIR, 'my_results', 'HD 600 FIR', '*.wav')):
    plot_fir_fr(fp)
    plt.show()<jupyter_output><empty_output><jupyter_text>FIR Filters Provided by the User<jupyter_code>fig, ax = plt.subplots()
fig.set_size_inches(18, 9)
legend = []
for fp in glob(os.path.join(DIR_PATH, '*.wav')):
    plot_fir_fr(fp, ax=ax)
    legend.append(os.path.split(fp)[1])
ax.legend(legend)
fig.savefig('user_filters.png')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16605166051660517, 'fraction_numerical': 0.012915129151291513, 'mean_word_length': 6.757142857142857, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '305039', 'license': 'MIT', 'n_tokens_mistral': 243, 'n_tokens_neox': 232, 'n_words': 35, 'path': 'research/fir_fr/fir_fr.ipynb', 'repo_name': 'vinzmc/AutoEq', 'size': '542'}