<jupyter_start><jupyter_text>Compute optimal filter in R<jupyter_code>#Then reimport
prespike = np.load('./optimal_filter_prespike.npy')
postspike = np.load('./optimal_filter_postspike.npy')
plt.plot(prespike[:,0], prespike[:,1], color=(0,0,0))
plt.plot(postspike[:,0], postspike[:,1], color=(0,0,0))
plt.axvline(x = 1, linestyle = '--', color = (0,0,0))
plt.xlim([0, 1.5])
plt.savefig('./fig_3.pdf')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.22325581395348837, 'fraction_numerical': 0.04186046511627907, 'mean_word_length': 7.795918367346939, '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': '8153', 'license': 'MIT', 'n_tokens_mistral': 194, 'n_tokens_neox': 185, 'n_words': 24, 'path': 'make_optimal_filter_data.ipynb', 'repo_name': 'prashanth-prakash/Spiking-rdd', 'size': '430'}