agent-runner
a173fbaf76
- 新增 3.5 成本效益量化分析与表 5 结论-证据映射表（8 条结论可核验） - 结论 5.2 为五类受众补充执行周期、量化目标与证据关联 - 创建 notebooks/pilot_analysis.md（26 项数据一致性核验通过） - 创建 plots/ 三张图表 + CSV 数据 + 生成脚本 - 创建 processing/validate_data.py（27 项校验通过） - 更新 data/paper_loop/round_notes.md 与 sync_summary.md
93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""生成试点数据分析图表。
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输出：
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plots/pilot_run_data.png — 各地市预警数据柱状图
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plots/vendor_effort.png — 供应商人天投入饼图
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plots/access_cost.png — 接入链路耗时对比图
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"""
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import csv
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import os
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import sys
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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PLOT_DIR = os.path.dirname(os.path.abspath(__file__))
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def load_csv(name):
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path = os.path.join(PLOT_DIR, name)
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with open(path, encoding="utf-8") as f:
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return list(csv.DictReader(f))
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def plot_run_data():
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rows = load_csv("pilot_run_data.csv")
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cities = [r["city"] for r in rows]
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alerts = [int(r["alert_count"]) if r["alert_count"] else 0 for r in rows]
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colors = ["#2196F3" if a > 0 else "#BDBDBD" for a in alerts]
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fig, ax = plt.subplots(figsize=(10, 5))
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bars = ax.bar(cities, alerts, color=colors)
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ax.set_ylabel("累计预警数据（条）")
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ax.set_title("各地市电量突增能力应用预警数据（截至 2026-06-25）")
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for bar, val in zip(bars, alerts):
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if val > 0:
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ax.text(bar.get_x() + bar.get_width() / 2, val + 200,
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f"{val:,}", ha="center", fontsize=9)
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plt.xticks(rotation=30, ha="right")
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plt.tight_layout()
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plt.savefig(os.path.join(PLOT_DIR, "pilot_run_data.png"), dpi=150)
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plt.close()
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def plot_vendor_effort():
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rows = load_csv("vendor_effort.csv")
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labels = [r["category"] for r in rows]
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sizes = [int(r["person_days"]) for r in rows]
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fig, ax = plt.subplots(figsize=(8, 6))
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wedges, texts, autotexts = ax.pie(
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sizes, labels=labels, autopct="%1.0f%%", startangle=140,
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textprops={"fontsize": 9},
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)
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ax.set_title("供应商人天投入结构（合计 100 人天）")
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plt.tight_layout()
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plt.savefig(os.path.join(PLOT_DIR, "vendor_effort.png"), dpi=150)
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plt.close()
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def plot_access_cost():
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rows = load_csv("access_cost.csv")
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steps = [r["step"] for r in rows if r["step"] != "端到端合计"]
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ideal = [int(r["ideal_days"]) for r in rows if r["step"] != "端到端合计"]
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actual = [int(r["actual_days"]) for r in rows if r["step"] != "端到端合计"]
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x = range(len(steps))
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width = 0.35
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fig, ax = plt.subplots(figsize=(10, 5))
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ax.bar([i - width / 2 for i in x], ideal, width, label="理想耗时", color="#4CAF50")
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ax.bar([i + width / 2 for i in x], actual, width, label="实际耗时", color="#FF5722")
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ax.set_ylabel("耗时（工作日）")
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ax.set_title("单地市接入链路耗时：理想 vs 实际")
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ax.set_xticks(x)
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ax.set_xticklabels(steps, rotation=20, ha="right")
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ax.legend()
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plt.tight_layout()
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plt.savefig(os.path.join(PLOT_DIR, "access_cost.png"), dpi=150)
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plt.close()
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def main():
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plot_run_data()
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plot_vendor_effort()
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plot_access_cost()
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print("图表已生成：pilot_run_data.png, vendor_effort.png, access_cost.png")
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if __name__ == "__main__":
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main()
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