<jupyter_start><jupyter_text>draw for handpicked cases<jupyter_code>names = ['c-1_m17', 'c-2_m23', 'c-3_m10', 'c-10_m11']

fig, ax = plt.subplots(1,4, figsize = (15,4), sharex = True, sharey = True)
ax = ax.ravel()

ii = 0
for name in names:
    data = [f for f in files if name in f][0]
    trajectory = pd.read_csv(data)
    ax[ii].plot(np.arange(len(trajectory.r))*1/100, trajectory.r, label = name)
    ax[ii].legend()
    ii+=1

plt.ylim(-0.2, 4)
plt.savefig('../dataFolders/PaperPipelineOutput/Figures/v2/sweeps.png')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18264014466546113, 'fraction_numerical': 0.05244122965641953, 'mean_word_length': 4.326923076923077, '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': '70921', 'license': 'MIT', 'n_tokens_mistral': 262, 'n_tokens_neox': 243, 'n_words': 46, 'path': 'PaperPinelineCodes/1a-DrawTracks-RadialDistanceOverTime.ipynb', 'repo_name': 'TanviDeora/ProboscisTracking', 'size': '553'}