agent-runner
ef901e2eb9
- 新增 3.7 地市间对比分析（表 6，8 地市分三梯队，含梯度分析与跨地市验证） - 新增 5.4 实施路线图与风险缓解（表 7 三阶段路线图 + 表 8 六类风险矩阵） - 更新 notebooks/pilot_analysis.md（新增第 9-10 节） - 新增 plots/city_comparison.png + city_comparison.csv + roadmap_data.csv - 更新 processing/validate_data.py（校验扩展至 46 项，全部通过）
122 lines
4.2 KiB
Python
122 lines
4.2 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 plot_city_comparison():
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rows = load_csv("city_comparison.csv")
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cities = [r["city"] for r in rows]
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alerts = [int(r["alert_count"]) for r in rows]
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teams = [r["team"] for r in rows]
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colors = {"第一梯队": "#4CAF50", "第二梯队": "#2196F3", "第三梯队": "#BDBDBD"}
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bar_colors = [colors.get(t, "#999") for t in teams]
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fig, ax = plt.subplots(figsize=(10, 5))
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bars = ax.bar(cities, alerts, color=bar_colors)
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ax.set_ylabel("累计预警数据（条）")
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ax.set_title("各地市接入阶段与产出对比（截至 2026-06-25）")
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for bar, val, team in zip(bars, alerts, teams):
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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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else:
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ax.text(bar.get_x() + bar.get_width() / 2, 200,
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team, ha="center", fontsize=8, color="#666")
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from matplotlib.patches import Patch
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legend = [Patch(facecolor=colors[t], label=t) for t in ["第一梯队", "第二梯队", "第三梯队"]]
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ax.legend(handles=legend, loc="upper right")
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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, "city_comparison.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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plot_city_comparison()
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print("图表已生成：pilot_run_data.png, vendor_effort.png, access_cost.png, city_comparison.png")
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if __name__ == "__main__":
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main()
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