scripts/dsbuild.py

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)