<jupyter_start><jupyter_text>Do some simple, illustrative calculations:<jupyter_code>mean_depth = 0.5

bc, dep_dist = tools.depth_distribution_from_depth(mean_depth,
                                                   dep_bin_edges=np.linspace(0,2.5,200),
                                                   statistics=statistics['all'])

sigma = 0.417 * mean_depth

print(f'sigma = {sigma}')

bin_width = np.diff(bc)[0]

plt.plot(bc, dep_dist/bin_width)



print('Probability >1m')
print(np.sum(dep_dist[bc>1.0]))
print('Probability <0.3m')
print(np.sum(dep_dist[bc<0.3]))
md = 0.5

bc, dep_dist = tools.depth_distribution_from_depth(md,dep_bin_edges=np.linspace(0,2.5,200),statistics=statistics['all'])

bin_width = np.diff(bc)[0]

plt.plot(bc, dep_dist/bin_width)


print(np.sum(dep_dist[bc<0.15]))
bc, dep_dist = tools.depth_distribution_from_depth(0.25,dep_bin_edges=np.linspace(0,2.5,200),statistics=statistics['all'])

bin_width = np.diff(bc)[0]

plt.plot(bc, dep_dist/bin_width)


print(np.sum(dep_dist[bc<0.15]))<jupyter_output>0.16600815553828668
{'fraction_non_alphanumeric': 0.15464895635673626, 'fraction_numerical': 0.05977229601518026, 'mean_word_length': 4.121359223300971, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '189688', 'license': 'MIT', 'n_tokens_mistral': 447, 'n_tokens_neox': 412, 'n_words': 44, 'path': 'notebooks/Make_fit.ipynb', 'repo_name': 'robbiemallett/sub_km', 'size': '1054'}