93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mticker
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import numpy as np
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# Data - monthly distribution
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months = ['2026-03', '2026-04', '2026-05']
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categories = ['2次', '3次', '4次', '5次+']
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# Monthly data: [2次, 3次, 4次, 5次+]
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data = {
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'2026-03': [474, 299, 42, 42],
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'2026-04': [1274, 232, 60, 64],
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'2026-05': [931, 174, 58, 46],
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}
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colors = ['#4472C4', '#ED7D31', '#A5A5A5', '#FFC000']
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plt.rcParams['font.family'] = 'sans-serif'
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for f in ['WenQuanYi Micro Hei', 'Noto Sans CJK SC', 'SimHei', 'DejaVu Sans']:
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try:
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plt.rcParams['font.sans-serif'] = [f]
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break
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except:
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continue
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plt.rcParams['axes.unicode_minus'] = False
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 7))
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# === Left: Monthly stacked bar ===
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x = np.arange(len(months))
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width = 0.55
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bottom = np.zeros(3)
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for i, cat in enumerate(categories):
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vals = [data[m][i] for m in months]
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bars = ax1.bar(x, vals, width, bottom=bottom, color=colors[i], label=cat, edgecolor='white', linewidth=0.5)
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bottom += vals
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# Data labels for significant segments
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for j, v in enumerate(vals):
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if v > 40:
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ax1.text(x[j], bottom[j] - v/2, str(v), ha='center', va='center', fontsize=9, fontweight='bold', color='white')
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ax1.set_xticks(x)
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ax1.set_xticklabels(months, fontsize=12)
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ax1.set_ylabel('重复学习组合数', fontsize=13)
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ax1.set_title('各月重复学习次数分布（user×chapter组合）', fontsize=14, fontweight='bold')
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ax1.legend(loc='upper right', fontsize=10, title='完成次数')
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ax1.set_ylim(0, max(bottom) * 1.15)
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ax1.grid(axis='y', alpha=0.2, linestyle='--')
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# Annotations
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totals = [sum(data[m]) for m in months]
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for j, (xi, t) in enumerate(zip(x, totals)):
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ax1.text(xi, bottom[j] + max(bottom)*0.02, f'合计 {t}', ha='center', fontsize=10, fontweight='bold', color='#333')
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# === Right: Overall pie ===
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overall_2 = 4136 # 2 times
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overall_3 = 653 # 3 times
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overall_4 = 130 # 4 times
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overall_5plus = sum(c for c in [51,14,17,5,5,2,2,2,2,1,1]) # 5+
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# Actually let me use the exact numbers from the query
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all_counts = [2]*4136 + [3]*653 + [4]*130 + [5]*51 + [6]*14 + [7]*17 + [8]*5 + [9]*5 + [10]*2 + [11]*2 + [12]*2 + [13]*2 + [14]*1 + [19]*1
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# Grouped for clarity
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labels = ['2次', '3次', '4次', '5次', '6-19次']
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sizes = [4136, 653, 130, 51, 14+17+5+5+2+2+2+2+1+1] # = 51
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explode = (0, 0, 0, 0, 0.1)
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wedges, texts, autotexts = ax2.pie(sizes, explode=explode, labels=None,
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colors=['#4472C4','#ED7D31','#A5A5A5','#FFC000','#C00000'],
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autopct='%1.1f%%', startangle=90,
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textprops={'fontsize': 11})
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ax2.set_title('整体分布占比', fontsize=14, fontweight='bold')
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# Custom legend
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legend_labels = [f'{l}: {s}个组合 ({s/5021*100:.1f}%)' if s < 5000 else f'{l}: {s}个组合'
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for l, s in zip(labels, sizes)]
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ax2.legend(wedges, legend_labels, loc='lower center', fontsize=10, ncol=1)
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# Summary text
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fig.suptitle('最近3个月（2026.03-05）重复学习次数分布\n（同一角色×同一课时完成≥2次）',
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fontsize=16, fontweight='bold', y=1.02)
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fig.text(0.5, 0.02,
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f'活跃角色: 10,409人 | 重复学习角色: 1,317人 (12.7%) | 重复学习组合: 5,021个 | 平均重复: 2.3次',
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ha='center', fontsize=11, color='#555')
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plt.tight_layout(rect=[0, 0.05, 1, 0.95])
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plt.savefig('/root/.openclaw/workspace/output/repeat_distribution_3m.png', dpi=150, bbox_inches='tight')
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print('Saved.')
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