diff --git a/task3/08_visualize.py b/task3/08_visualize.py new file mode 100644 index 0000000..f113482 --- /dev/null +++ b/task3/08_visualize.py @@ -0,0 +1,294 @@ +""" +Task 3 - Step 8: 可视化（Fig.1/2/4/5） +===================================== + +输入: + - 01_distance.xlsx (sites: 经纬度、mu/sigma) + - 02_pairing.xlsx (selected_pairs: 34对配对连线) + - 03_allocation.xlsx (allocation: q*, E_total) + - 05_calendar.xlsx (calendar: 365天×2槽位排程) + - 06_evaluate.xlsx (pair_risk: 缺口概率分布) + +输出: + - figures/fig1_pairing_map.png + - figures/fig2_allocation_scatter.png + - figures/fig4_calendar_heatmap.png + - figures/fig5_risk_distribution.png +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +import os +import tempfile + +import numpy as np +import pandas as pd + +_mpl_config_dir = Path(tempfile.gettempdir()) / "mm_task3_mplconfig" +_xdg_cache_dir = Path(tempfile.gettempdir()) / "mm_task3_xdg_cache" +_mpl_config_dir.mkdir(parents=True, exist_ok=True) +_xdg_cache_dir.mkdir(parents=True, exist_ok=True) +os.environ.setdefault("MPLCONFIGDIR", str(_mpl_config_dir)) +os.environ.setdefault("XDG_CACHE_HOME", str(_xdg_cache_dir)) + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt + + +BASE_DIR = Path(__file__).resolve().parent + + +@dataclass(frozen=True) +class Paths: + distance_xlsx: Path + pairing_xlsx: Path + allocation_xlsx: Path + calendar_xlsx: Path + evaluate_xlsx: Path + figures_dir: Path + + +def _default_paths() -> Paths: + return Paths( + distance_xlsx=BASE_DIR / "01_distance.xlsx", + pairing_xlsx=BASE_DIR / "02_pairing.xlsx", + allocation_xlsx=BASE_DIR / "03_allocation.xlsx", + calendar_xlsx=BASE_DIR / "05_calendar.xlsx", + evaluate_xlsx=BASE_DIR / "06_evaluate.xlsx", + figures_dir=BASE_DIR / "figures", + ) + + +def _require_file(path: Path) -> None: + if not path.exists(): + raise FileNotFoundError( + f"Missing required file: {path}. Run the earlier steps first (01~06)." + ) + + +def _pair_key(a: int, b: int) -> tuple[int, int]: + return (a, b) if a <= b else (b, a) + + +def fig1_pairing_map(paths: Paths) -> Path: + sites = pd.read_excel(paths.distance_xlsx, sheet_name="sites") + pairs = pd.read_excel(paths.pairing_xlsx, sheet_name="selected_pairs") + + sites = sites.copy() + sites["site_id"] = sites["site_id"].astype(int) + + paired_ids = set(pairs["site_i_id"].astype(int)).union( + set(pairs["site_j_id"].astype(int)) + ) + sites["is_paired"] = sites["site_id"].isin(paired_ids) + + fig, ax = plt.subplots(figsize=(8.5, 6.5), dpi=200) + + for _, row in pairs.iterrows(): + i = int(row["site_i_id"]) + j = int(row["site_j_id"]) + si = sites.loc[sites["site_id"] == i].iloc[0] + sj = sites.loc[sites["site_id"] == j].iloc[0] + ax.plot( + [si["lon"], sj["lon"]], + [si["lat"], sj["lat"]], + color="#2c7fb8", + alpha=0.35, + linewidth=1.0, + zorder=1, + ) + + paired = sites[sites["is_paired"]] + unpaired = sites[~sites["is_paired"]] + + ax.scatter( + paired["lon"], + paired["lat"], + s=18, + color="#d95f0e", + alpha=0.85, + label=f"Paired sites (n={len(paired)})", + zorder=2, + ) + if len(unpaired) > 0: + ax.scatter( + unpaired["lon"], + unpaired["lat"], + s=22, + color="#636363", + alpha=0.9, + marker="x", + label=f"Unpaired sites (n={len(unpaired)})", + zorder=3, + ) + + ax.set_title("Fig.1 Pairing map (sites + selected 34 links)") + ax.set_xlabel("Longitude") + ax.set_ylabel("Latitude") + ax.grid(True, alpha=0.2) + ax.legend(loc="best", frameon=True) + + out = paths.figures_dir / "fig1_pairing_map.png" + fig.tight_layout() + fig.savefig(out) + plt.close(fig) + return out + + +def fig2_allocation_scatter(paths: Paths) -> Path: + df = pd.read_excel(paths.allocation_xlsx, sheet_name="allocation") + + q_ratio = df["q_ratio"].to_numpy(dtype=float) + mu_share = (df["mu_i"] / (df["mu_i"] + df["mu_j"])).to_numpy(dtype=float) + sigma_share = (df["sigma_i"] / (df["sigma_i"] + df["sigma_j"])).to_numpy(dtype=float) + sigma_j_share = 1.0 - sigma_share + + fig, axes = plt.subplots(1, 3, figsize=(12, 3.8), dpi=200, sharey=True) + + panels = [ + ("$q^*/Q$ vs $\\mu_i/(\\mu_i+\\mu_j)$", mu_share), + ("$q^*/Q$ vs $\\sigma_i/(\\sigma_i+\\sigma_j)$", sigma_share), + ("$q^*/Q$ vs $\\sigma_j/(\\sigma_i+\\sigma_j)$", sigma_j_share), + ] + + for ax, (title, x) in zip(axes, panels): + ax.scatter(x, q_ratio, s=22, alpha=0.75, color="#3182bd", edgecolor="none") + if np.isfinite(x).all() and np.isfinite(q_ratio).all() and len(x) >= 2: + coef = np.polyfit(x, q_ratio, 1) + xx = np.linspace(float(np.min(x)), float(np.max(x)), 100) + yy = coef[0] * xx + coef[1] + ax.plot(xx, yy, color="#de2d26", linewidth=2.0, alpha=0.9) + ax.set_title(title) + ax.set_xlabel("x") + ax.grid(True, alpha=0.2) + + axes[0].set_ylabel("$q^*/Q$") + fig.suptitle("Fig.2 Allocation strategy scatter (34 pairs)", y=1.02) + fig.tight_layout() + + out = paths.figures_dir / "fig2_allocation_scatter.png" + fig.savefig(out, bbox_inches="tight") + plt.close(fig) + return out + + +def fig4_calendar_heatmap(paths: Paths) -> Path: + sites = pd.read_excel(paths.distance_xlsx, sheet_name="sites")[["site_id", "mu"]] + sites["site_id"] = sites["site_id"].astype(int) + mu_by_id = dict(zip(sites["site_id"], sites["mu"])) + + alloc = pd.read_excel(paths.allocation_xlsx, sheet_name="allocation")[ + ["site_i_id", "site_j_id", "E_total"] + ].copy() + alloc["site_i_id"] = alloc["site_i_id"].astype(int) + alloc["site_j_id"] = alloc["site_j_id"].astype(int) + pair_E = {_pair_key(i, j): float(e) for i, j, e in alloc.to_numpy()} + + cal = pd.read_excel(paths.calendar_xlsx, sheet_name="calendar") + + expected = np.full((365, 2), np.nan, dtype=float) + + for idx, row in cal.iterrows(): + day = int(row["day"]) + for slot in (1, 2): + typ = str(row[f"slot_{slot}_type"]).strip().lower() + i = row[f"slot_{slot}_site_i"] + j = row.get(f"slot_{slot}_site_j", np.nan) + + if pd.isna(i): + continue + i_id = int(i) + if typ == "single": + expected[day - 1, slot - 1] = float(mu_by_id.get(i_id, np.nan)) + elif typ == "dual": + if pd.isna(j): + continue + j_id = int(j) + expected[day - 1, slot - 1] = pair_E.get(_pair_key(i_id, j_id), np.nan) + + fig, ax = plt.subplots(figsize=(12, 2.6), dpi=200) + im = ax.imshow(expected.T, aspect="auto", cmap="viridis") + ax.set_yticks([0, 1], labels=["Slot 1", "Slot 2"]) + ax.set_xlabel("Day of year") + ax.set_title("Fig.4 Calendar heatmap (expected service per slot)") + + month_days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + month_starts = np.cumsum([0] + month_days[:-1]) # 0-based + month_labels = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] + ax.set_xticks(month_starts, labels=month_labels) + + cbar = fig.colorbar(im, ax=ax, fraction=0.03, pad=0.02) + cbar.set_label("Expected service") + + fig.tight_layout() + out = paths.figures_dir / "fig4_calendar_heatmap.png" + fig.savefig(out, bbox_inches="tight") + plt.close(fig) + return out + + +def fig5_risk_distribution(paths: Paths) -> Path: + risk = pd.read_excel(paths.evaluate_xlsx, sheet_name="pair_risk").copy() + p = risk["shortfall_prob_either"].to_numpy(dtype=float) + p = p[np.isfinite(p)] + + fig, axes = plt.subplots(1, 2, figsize=(11, 3.8), dpi=200) + + ax = axes[0] + ax.hist(p, bins=10, color="#3182bd", alpha=0.85, edgecolor="white") + ax.axvline(float(np.mean(p)), color="#de2d26", linewidth=2.0, label=f"mean={np.mean(p):.3f}") + ax.set_title("Histogram") + ax.set_xlabel("Shortfall probability (either site)") + ax.set_ylabel("Count") + ax.grid(True, alpha=0.2) + ax.legend(loc="best", frameon=True) + + ax = axes[1] + p_sorted = np.sort(p)[::-1] + ax.plot(np.arange(1, len(p_sorted) + 1), p_sorted, marker="o", markersize=3.5, linewidth=1.5) + ax.set_title("Sorted by risk (descending)") + ax.set_xlabel("Pair rank") + ax.set_ylabel("Shortfall probability") + ax.grid(True, alpha=0.2) + + fig.suptitle("Fig.5 Risk distribution across 34 pairs", y=1.02) + fig.tight_layout() + out = paths.figures_dir / "fig5_risk_distribution.png" + fig.savefig(out, bbox_inches="tight") + plt.close(fig) + return out + + +def main() -> None: + paths = _default_paths() + for p in [ + paths.distance_xlsx, + paths.pairing_xlsx, + paths.allocation_xlsx, + paths.calendar_xlsx, + paths.evaluate_xlsx, + ]: + _require_file(p) + paths.figures_dir.mkdir(parents=True, exist_ok=True) + + print("=" * 60) + print("Task 3 - Step 8: Visualization") + print("=" * 60) + + out1 = fig1_pairing_map(paths) + print(f"Saved: {out1.relative_to(BASE_DIR)}") + out2 = fig2_allocation_scatter(paths) + print(f"Saved: {out2.relative_to(BASE_DIR)}") + out4 = fig4_calendar_heatmap(paths) + print(f"Saved: {out4.relative_to(BASE_DIR)}") + out5 = fig5_risk_distribution(paths) + print(f"Saved: {out5.relative_to(BASE_DIR)}") + + +if __name__ == "__main__": + main() diff --git a/task3/README.md b/task3/README.md index 5ae0c58..a2f1ada 100644 --- a/task3/README.md +++ b/task3/README.md @@ -63,22 +63,22 @@ flowchart TB subgraph SENSITIVITY["敏感性分析 ✅ 已完成"] S1[07_sensitivity.py
4参数扫描] - S2[合并比例: 1/3,1/2,2/3] - S3[距离阈值: 30-70mi] - S4[容量上限: 400-500] - S5[CV阈值: 0.3-0.6] + S2[合并比例: 0.10-0.90] + S3[距离阈值: 10-100mi] + S4[容量上限: 350-550] + S5[CV阈值: 0.10-1.00] S1 --> S2 S1 --> S3 S1 --> S4 S1 --> S5 end - subgraph VISUAL["可视化 ⏳ 待实现"] - P1[Fig.1 站点配对地图] - P2[Fig.2 分配策略散点图] - P3[Fig.3 敏感性曲线] - P4[Fig.4 日历热力图] - P5[Fig.5 风险分布图] + subgraph VISUAL["可视化 ✅ 已完成"] + P1[Fig.1 站点配对地图 ✅] + P2[Fig.2 分配策略散点图 ✅] + P3[Fig.3 敏感性曲线 ✅] + P4[Fig.4 日历热力图 ✅] + P5[Fig.5 风险分布图 ✅] end CORE --> VALIDATE @@ -141,16 +141,16 @@ flowchart TB │ │ │ │ │ │ │ ▼ ▼ ▼ ▼ │ │ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │ -│ │ 结果验证 [✓] │ │ 敏感性分析 [✓] │ │ 可视化 [待实现] │ │ 输出文件 │ │ +│ │ 结果验证 [✓] │ │ 敏感性分析 [✓] │ │ 可视化 [✓] │ │ 输出文件 │ │ │ │ │ │ │ │ │ │ │ │ -│ │ ✓ 每日2事件 │ │ 07_sensitivity │ │ □ 配对地图 │ │ 01_distance.xlsx │ │ -│ │ ✓ 总730次访问 │ │ ┌──────────────┐ │ │ □ 分配散点图 │ │ 02_pairing.xlsx │ │ +│ │ ✓ 每日2事件 │ │ 07_sensitivity │ │ ✓ 配对地图 │ │ 01_distance.xlsx │ │ +│ │ ✓ 总730次访问 │ │ ┌──────────────┐ │ │ ✓ 分配散点图 │ │ 02_pairing.xlsx │ │ │ │ ✓ q*边界检查 │ │ │合并比例 │ │ │ ✓ 敏感性曲线 │ │ 03_allocation.xlsx│ │ -│ │ ✓ Task 1对比 │ │ │ 0.10-0.90 │ │ │ □ 日历热力图 │ │ 04_reschedule.xlsx│ │ -│ │ │ │ ├──────────────┤ │ │ □ 风险分布图 │ │ 05_calendar.xlsx │ │ +│ │ ✓ Task 1对比 │ │ │ 0.10-0.90 │ │ │ ✓ 日历热力图 │ │ 04_reschedule.xlsx│ │ +│ │ │ │ ├──────────────┤ │ │ ✓ 风险分布图 │ │ 05_calendar.xlsx │ │ │ │ 结论: │ │ │距离阈值 │ │ │ │ │ 06_evaluate.xlsx │ │ │ │ E1↑16.9% │ │ │ 10-100 mi │ │ │ 图表清单: │ │ 07_sensitivity.xlsx│ │ -│ │ E2↑5.3% │ │ ├──────────────┤ │ │ Fig.1-5 │ │ │ │ +│ │ E2↑5.3% │ │ ├──────────────┤ │ │ Fig.1-5 ✅ │ │ │ │ │ │ RS=19.5% │ │ │容量上限 │ │ │ │ │ │ │ │ │ R1=17.1% │ │ │ 350-550 │ │ │ │ │ │ │ │ │ │ │ ├──────────────┤ │ │ │ │ │ │ @@ -625,21 +625,21 @@ $$R_1 = P(S_i / D_i < 0.8 \text{ 或 } S_j / D_j < 0.8)$$ --- -## 9. 可视化图表（Fig.3 已完成） +## 9. 可视化图表（Fig.1-5 已完成） ### 9.1 图表清单 | 图编号 | 图名 | 内容 | 目的 | |--------|------|------|------| -| Fig.1 | 站点配对地图 | 70站点+34条配对连线 | 展示空间分布 | -| Fig.2 | 分配策略散点图 | q* vs (μ_i, σ_i, σ_j) | 验证分配逻辑 | +| Fig.1 ✅ | 站点配对地图 | 70站点+34条配对连线 | 展示空间分布 | +| Fig.2 ✅ | 分配策略散点图 | q* vs (μ_i, σ_i, σ_j) | 验证分配逻辑 | | Fig.3 ✅ | 敏感性曲线 | 4参数对E1,E2,R1的影响 | 参数选择依据 | -| Fig.4 | 日历热力图 | 365天×2槽位 | 排程可视化 | -| Fig.5 | 风险分布图 | 34对的缺口概率分布 | 风险识别 | +| Fig.4 ✅ | 日历热力图 | 365天×2槽位 | 排程可视化 | +| Fig.5 ✅ | 风险分布图 | 34对的缺口概率分布 | 风险识别 | -> Fig.3 已由 `07_sensitivity.py` 生成：`figures/fig3_sensitivity.png`。 +> Fig.1/2/4/5 已由 `08_visualize.py` 生成；Fig.3 已由 `07_sensitivity.py` 生成。 -### 9.2 可视化脚本（待实现：Fig.1/2/4/5） +### 9.2 可视化脚本（已实现） ``` task3/ @@ -652,6 +652,16 @@ task3/ └── fig5_risk_distribution.png ``` +### 9.3 可视化结果 + +![Fig.1 站点配对地图](figures/fig1_pairing_map.png) + +![Fig.2 分配策略散点图](figures/fig2_allocation_scatter.png) + +![Fig.4 日历热力图](figures/fig4_calendar_heatmap.png) + +![Fig.5 风险分布图](figures/fig5_risk_distribution.png) + --- ## 10. 结论与政策建议 @@ -712,7 +722,7 @@ task3/ │ └── 06_evaluate.xlsx (指标对比) ├── 07_sensitivity.py ✅ 敏感性分析 │ └── 07_sensitivity.xlsx (4参数) -├── 08_visualize.py ⏳ 可视化（待实现） +├── 08_visualize.py ✅ 可视化（Fig.1/2/4/5） │ └── figures/ (5张图) └── README.md ✅ 本文档 ``` @@ -732,9 +742,11 @@ python 04_reschedule.py python 05_calendar.py python 06_evaluate.py python 07_sensitivity.py +python 08_visualize.py # 一键运行（可选） for i in 01 02 03 04 05 06 07; do python ${i}_*.py; done +python 08_visualize.py ``` --- diff --git a/task3/__pycache__/07_sensitivity.cpython-313.pyc b/task3/__pycache__/07_sensitivity.cpython-313.pyc new file mode 100644 index 0000000..17fa2fa Binary files /dev/null and b/task3/__pycache__/07_sensitivity.cpython-313.pyc differ diff --git a/task3/__pycache__/08_visualize.cpython-313.pyc b/task3/__pycache__/08_visualize.cpython-313.pyc new file mode 100644 index 0000000..d623973 Binary files /dev/null and b/task3/__pycache__/08_visualize.cpython-313.pyc differ diff --git a/task3/figures/fig1_pairing_map.png b/task3/figures/fig1_pairing_map.png new file mode 100644 index 0000000..a014b1b Binary files /dev/null and b/task3/figures/fig1_pairing_map.png differ diff --git a/task3/figures/fig2_allocation_scatter.png b/task3/figures/fig2_allocation_scatter.png new file mode 100644 index 0000000..a5ee808 Binary files /dev/null and b/task3/figures/fig2_allocation_scatter.png differ diff --git a/task3/figures/fig4_calendar_heatmap.png b/task3/figures/fig4_calendar_heatmap.png new file mode 100644 index 0000000..e43ed3e Binary files /dev/null and b/task3/figures/fig4_calendar_heatmap.png differ diff --git a/task3/figures/fig5_risk_distribution.png b/task3/figures/fig5_risk_distribution.png new file mode 100644 index 0000000..3d5186a Binary files /dev/null and b/task3/figures/fig5_risk_distribution.png differ