<jupyter_start><jupyter_text>pull<jupyter_code>nu_no_di = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])

nu_po_no = np.array([[1, 0, 0, 0], [1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 1]])

nu_po_di = kwat.point.pull(nu_no_di, nu_po_no)

plot_point(*nu_no_di.T, *nu_po_di.T)<jupyter_output><empty_output><jupyter_text>scale<jupyter_code>di_po_po = np.array(
    [
        [0.0, 0.5, 1.0, 2.0],
        [0.5, 0.0, 0.5, 0.0],
        [1.0, 0.5, 0.0, 0.5],
        [2.0, 0.0, 0.5, 0.0],
    ]
)

nu_po_di = kwat.point.scale(di_po_po, 2)

plot_point(None, None, *nu_po_di.T)
nu_po_di = np.array([[-8, -8, -8], [0, 0, 0], [2, 2, 2], [4, 4, 4], [8, 8, 8]])

print(nu_po_di)
di_po_po = squareform(pdist(nu_po_di))

for n_di in [1, 2]:

    nu_po_di = kwat.point.scale(di_po_po, n_di)

    if n_di == 1:

        plot_point(None, None, nu_po_di.ravel(), [0] * nu_po_di.shape[0])

    elif n_di == 2:

        plot_point(None, None, *nu_po_di.T)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.22466039707419017, 'fraction_numerical': 0.08150470219435736, 'mean_word_length': 3.2767857142857144, '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': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '564463', 'license': 'MIT', 'n_tokens_mistral': 558, 'n_tokens_neox': 512, 'n_words': 95, 'path': 'nb/point.ipynb', 'repo_name': 'KwatME/ccal', 'size': '957'}