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import gzip |
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import json |
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import os |
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from matplotlib import pyplot as plt |
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def load_stats(path: str) -> dict: |
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stats: dict = {} |
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for filename in os.listdir(path): |
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file_path: str = os.path.join(path, filename) |
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if os.path.isdir(filename): |
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continue |
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data: dict |
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if filename.endswith('.gz'): |
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with gzip.open(file_path, mode='rt') as file: |
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data = json.loads(file.read()) |
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else: |
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with open(file_path, mode='rt') as file: |
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data = json.loads(file.read()) |
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print(f'Loaded stats from {file_path}') |
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stats.update(**data) |
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return stats |
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def stat_filter(stats: dict, deviation_cutoff=(1.0, 0.0), clamp=(200.0, 2048.0), min_messages=4) -> list[dict]: |
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cutoff_threshold: (float, float) = ( |
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stats['wordsStdDev'] * deviation_cutoff[0], stats['wordsStdDev'] * deviation_cutoff[1]) |
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if cutoff_threshold[1] <= 0: |
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cutoff_threshold = (cutoff_threshold[0], stats['wordsMax']) |
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cutoff_min: float = max(max(clamp[0], cutoff_threshold[0]), stats['wordsMean'] - cutoff_threshold[0]) |
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cutoff_max: float = stats['wordsMean'] + cutoff_threshold[1] |
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if clamp[1] > 0: |
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cutoff_max = min(clamp[1], cutoff_max) |
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conversations: list[dict] = [v for k, v in stats['conversations'].items() if |
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v['wordsMax'] <= cutoff_max and v['wordsMin'] >= cutoff_min and v[ |
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'messagesCount'] >= min_messages] |
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print( |
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f'Min: {cutoff_min:0.0f}\tMax: {cutoff_max:0.0f}\n' |
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f'Clamped from {cutoff_threshold[0]:0.0f}, {cutoff_threshold[1]:0.0f}') |
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print(f'{len(conversations)} conversations') |
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return conversations |
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def build_mean_word_plot(conv_stats: list[float], title: str = 'Conversation Message Mean Words', xlabel: str = 'Mean', |
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ylabel: str = 'Conversations', text: str = '', |
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**kwargs): |
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fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained') |
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n, bins, patches = ax.hist(conv_stats, density=True, |
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facecolor='C0', alpha=0.75, **kwargs) |
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ax.set_title(title) |
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ax.set_xlabel(xlabel) |
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ax.set_ylabel(ylabel) |
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plt.figtext(0, 0.95, text) |
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