<jupyter_start><jupyter_text>DWTExplorando aplicação de _discrete wavelet transform_ semelhante ao exemplo da documentação da biblioteca [pywt](https://pywavelets.readthedocs.io/en/latest/index.html) Importando bibliotecas<jupyter_code>import pywt
import numpy as np
mock = np.random.randint(0, 100, 4)
print(f'''mock: {mock}

mean: {mock.mean()}; std: {mock.std()};''')
coef_aprox1, coef_detail1 = pywt.dwt(data=mock, wavelet='db4')

print(f'''coef_aprox1: {coef_aprox1}

mean: {coef_aprox1.mean()}; std: {coef_aprox1.std()};\n''')

print(f'''coef_detail1: {coef_detail1}

mean: {coef_detail1.mean()}; std: {coef_detail1.std()};''')
rebuild_mock = pywt.idwt(cA=coef_aprox1, cD=coef_detail1, wavelet='db4')
print(f'mock:           {mock}')
print(f'rebuild_mock:   {rebuild_mock}')<jupyter_output>mock:           [ 5 36  9  2]
rebuild_mock:   [ 5. 36.  9.  2.]
{'fraction_non_alphanumeric': 0.17345750873108265, 'fraction_numerical': 0.033760186263096625, 'mean_word_length': 5.88, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 1, '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': '194076', 'license': 'MIT', 'n_tokens_mistral': 391, 'n_tokens_neox': 367, 'n_words': 64, 'path': 'ML/f00-sklearn_explore/06-wavelets/00-dwt.ipynb', 'repo_name': 'newtmagalhaes/Machine-Learning', 'size': '859'}