<jupyter_start><jupyter_text>Gaussian assumption<jupyter_code>test_gaussian = pd.read_csv('./results/test_gaussian.csv')
plt.figure(figsize=(5, 5), constrained_layout=True)
bplot = sns.boxplot(data=test_gaussian, orient='h')
bplot.set_yticklabels([])
for i in range(5):
    mybox = bplot.artists[i]
    mybox.set_facecolor(COLORS[i])
plt.xlabel('$p$-values', size=16)
plt.xlim((0, 1))
plt.axvline(x=0.05, color='red', linestyle='-.')
plt.savefig('./figures/test_gaussian.eps', format='eps')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19038461538461537, 'fraction_numerical': 0.019230769230769232, 'mean_word_length': 7.830508474576271, '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': '651526', 'license': 'MIT', 'n_tokens_mistral': 222, 'n_tokens_neox': 210, 'n_words': 26, 'path': 'scenario_4/03-results.ipynb', 'repo_name': 'StevenGolovkine/fcubt', 'size': '520'}