<jupyter_start><jupyter_text>Initialise<jupyter_code>filter.initialise(img, bbox)<jupyter_output><empty_output><jupyter_text>Predict<jupyter_code>center = ((w)/2, (h)/2)
window = cv.getRectSubPix(img, (w,h), center)
translate_transform = np.array([[1., 0, -25.3], [0., 1., 10.]])
transformed_win = cv.warpAffine(window, M=translate_transform, dsize=(int(w), int(h)))
plt.imshow(transformed_win)
plt.show()
filter.predict(transformed_win, bbox)
H = filter.H
G = filter.G
filter_h = np.real(np.fft.ifft2(H))
plt.imshow(filter_h, cmap="gray")
g = np.real(np.fft.ifft2(G))
plt.imshow(g, cmap="gray")<jupyter_output><empty_output><jupyter_text>Update<jupyter_code>filter.update(transformed_win, bbox)
filter_h = np.real(np.fft.ifft2(H))
plt.imshow(filter_h, cmap="gray")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1861635220125786, 'fraction_numerical': 0.01761006289308176, 'mean_word_length': 8.95, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '320326', 'license': 'Apache-2.0', 'n_tokens_mistral': 360, 'n_tokens_neox': 343, 'n_words': 41, 'path': 'src/testers/mosse_tester.ipynb', 'repo_name': 'lleon95/NanoSciTracker-Python', 'size': '795'}