<jupyter_start><jupyter_text>Testing PyDoA<jupyter_code>from DoaProcessor import DoaProcessor
d = DoaProcessor('./samples/sample.csv',4)
d.setDegreeForSpeaker(d.getHighestNdegrees(sep=60))
d.getPeakDegree(group='group-1',sigma=1.0)
d.plotDegreeDistribution(group='group-1')
d.getPeakDegree(1.5)
d.getHighestNdegrees(sep=50)
d = {104: 939, 315: 775, 194: 636, 300: 597, 30: 470, 45: 432, 14: 335, 120: 285, 210: 245, 284: 65, 358: 31, 88: 23, 225: 20, 241: 18, 61: 10, 178: 8, 135: 7, 82: 4, 331: 2, 151: 2, 268: 2, 337: 1}
d.getHighestNdegrees(sep=60)
d.setDegreeForSpeaker([30, 104, 194, 315])
a = d.assignUserLabel()
a.groupby('users').count()
d.getSpeakingTime(plot=True,time='min')
xlabels = []
for i in range(4):
    xlabels.append('user-%d'%(i+1))
d.drawNetwork('group-1')
plt.title('Group-1 Network')
d.generateEdgeFile()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1888111888111888, 'fraction_numerical': 0.1561771561771562, 'mean_word_length': 6.669642857142857, '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': '285727', 'license': 'MIT', 'n_tokens_mistral': 492, 'n_tokens_neox': 364, 'n_words': 76, 'path': 'Test place.ipynb', 'repo_name': 'pankajchejara23/pydoa', 'size': '858'}