70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib
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import os
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from collections import Counter
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matplotlib.rcParams['font.sans-serif'] = ['DejaVu Sans'] # 或系统支持的中文字体
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matplotlib.rcParams['axes.unicode_minus'] = False
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OUT_DIR = "output"
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os.makedirs(OUT_DIR, exist_ok=True)
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# 读取数据
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stats = pd.read_csv("data/processed/code_stats.csv")
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langs = pd.read_csv("data/processed/language_distribution.csv", index_col=0)
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contrib = pd.read_csv("data/processed/contributor_network.csv")
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# ========== 1. 代码量对比柱状图 ==========
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fig, ax = plt.subplots(figsize=(12, 6))
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x = range(len(stats))
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width = 0.35
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ax.bar(x, stats['total_additions']/1000, width, label='Additions (k lines)', color='steelblue')
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ax.bar([i+width for i in x], stats['total_deletions']/1000, width, label='Deletions (k lines)', color='lightcoral')
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ax.set_xticks([i+width/2 for i in x])
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ax.set_xticklabels(stats['repo'], rotation=15, ha='right')
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ax.set_ylabel('Lines (thousands)')
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ax.set_title('Code Additions vs Deletions per Repository')
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ax.legend()
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plt.tight_layout()
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plt.savefig(os.path.join(OUT_DIR, "code_volume_comparison.png"), dpi=150)
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plt.close()
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# ========== 2. 提交次数与贡献者人数气泡图 ==========
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.scatter(stats['author_count'], stats['commit_count'], s=stats['total_additions']/500, alpha=0.6, c='darkgreen')
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for i, row in stats.iterrows():
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ax.annotate(row['repo'], (row['author_count'], row['commit_count']), fontsize=8)
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ax.set_xlabel('Number of Authors')
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ax.set_ylabel('Commit Count')
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ax.set_title('Repository Activity: Commits vs Authors (bubble size = additions)')
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plt.tight_layout()
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plt.savefig(os.path.join(OUT_DIR, "activity_bubble.png"), dpi=150)
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plt.close()
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# ========== 3. 语言分布堆叠条形图 ==========
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# 将百分比字符串转为数值
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lang_perc = langs.apply(lambda col: col.str.rstrip('%').astype(float))
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ax = lang_perc.plot(kind='bar', stacked=True, figsize=(12, 6), colormap='Set3')
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ax.set_ylabel('Percentage')
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ax.set_title('Language Distribution Across Repositories')
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ax.legend(loc='upper right', bbox_to_anchor=(1.15, 1))
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plt.tight_layout()
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plt.savefig(os.path.join(OUT_DIR, "language_distribution.png"), dpi=150)
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plt.close()
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# ========== 4. 贡献者网络图（前20位跨仓库贡献者） ==========
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# 简单条形图展示贡献仓库数最多的贡献者
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top_contrib = contrib[contrib['repo_count'] > 1].sort_values('repo_count', ascending=False).head(15)
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if not top_contrib.empty:
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fig, ax = plt.subplots(figsize=(12, 6))
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ax.barh(top_contrib['email'], top_contrib['repo_count'], color='mediumpurple')
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ax.set_xlabel('Number of Repositories')
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ax.set_title('Top Contributors Across Multiple Repositories')
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plt.tight_layout()
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plt.savefig(os.path.join(OUT_DIR, "cross_repo_contributors.png"), dpi=150)
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plt.close()
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else:
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print("No cross-repo contributors found.")
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print("可视化图表已保存到 output/") |