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