diff --git a/task3/01_distance.xlsx b/task3/01_distance.xlsx index 64afdc6..d1b6a81 100644 Binary files a/task3/01_distance.xlsx and b/task3/01_distance.xlsx differ diff --git a/task3/02_pairing.py b/task3/02_pairing.py index f5100e2..ae66bee 100644 --- a/task3/02_pairing.py +++ b/task3/02_pairing.py @@ -26,10 +26,10 @@ INPUT_FILE = '01_distance.xlsx' OUTPUT_FILE = '02_pairing.xlsx' # 约束参数 -L_MAX = 50 # 距离阈值 (英里) -MU_SUM_MAX = 450 # 需求和上限 -CV_MAX = 0.5 # 变异系数上限 -Q = 400 # 卡车容量 +L_MAX = 30 # 距离阈值 (英里) +MU_SUM_MAX = 350 # 需求和上限 +CV_MAX = 0.3 # 变异系数上限 +Q = 350 # 卡车容量 # 价值函数权重 ALPHA = 1.0 # 容量利用率权重 diff --git a/task3/02_pairing.xlsx b/task3/02_pairing.xlsx index e8bd422..bea037a 100644 Binary files a/task3/02_pairing.xlsx and b/task3/02_pairing.xlsx differ diff --git a/task3/03_allocation.xlsx b/task3/03_allocation.xlsx index 216b5ea..acf68cc 100644 Binary files a/task3/03_allocation.xlsx and b/task3/03_allocation.xlsx differ diff --git a/task3/04_reschedule.xlsx b/task3/04_reschedule.xlsx index 4f15d64..c1708d7 100644 Binary files a/task3/04_reschedule.xlsx and b/task3/04_reschedule.xlsx differ diff --git a/task3/05_calendar.xlsx b/task3/05_calendar.xlsx index ca57067..23fa505 100644 Binary files a/task3/05_calendar.xlsx and b/task3/05_calendar.xlsx differ diff --git a/task3/06_evaluate.xlsx b/task3/06_evaluate.xlsx index 9528082..1a9057b 100644 Binary files a/task3/06_evaluate.xlsx and b/task3/06_evaluate.xlsx differ diff --git a/task3/07_sensitivity.py b/task3/07_sensitivity.py index b96f00e..cb524db 100644 --- a/task3/07_sensitivity.py +++ b/task3/07_sensitivity.py @@ -16,17 +16,66 @@ Task 3 - Step 7: 敏感性分析 (Refined) import pandas as pd import numpy as np from scipy import stats -import matplotlib.pyplot as plt import warnings import os +import tempfile from pathlib import Path warnings.filterwarnings('ignore') -# 设置绘图风格 -plt.style.use('seaborn-v0_8-whitegrid') -plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans'] # 适配中文 -plt.rcParams['axes.unicode_minus'] = False +# Matplotlib cache dirs（避免 home 目录不可写导致的警告） +_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.pyplot as plt +from cycler import cycler + +# 设置绘图风格（低饱和度 Morandi 色系） +MORANDI = { + "bg": "#F5F2ED", + "grid": "#D7D1C7", + "text": "#3F3F3F", + "muted_blue": "#8FA3A7", + "muted_blue_dark": "#6E858A", + "sage": "#AEB8A6", + "terracotta": "#B07A6A", + "warm_gray": "#8C857A", +} + +plt.rcParams.update( + { + "figure.facecolor": MORANDI["bg"], + "axes.facecolor": MORANDI["bg"], + "savefig.facecolor": MORANDI["bg"], + "axes.edgecolor": MORANDI["grid"], + "axes.labelcolor": MORANDI["text"], + "xtick.color": MORANDI["text"], + "ytick.color": MORANDI["text"], + "text.color": MORANDI["text"], + "grid.color": MORANDI["grid"], + "grid.alpha": 0.55, + "axes.grid": True, + "axes.prop_cycle": cycler( + "color", + [ + MORANDI["muted_blue"], + MORANDI["sage"], + MORANDI["terracotta"], + MORANDI["warm_gray"], + ], + ), + "legend.frameon": True, + "legend.framealpha": 0.9, + "legend.facecolor": MORANDI["bg"], + "legend.edgecolor": MORANDI["grid"], + "font.sans-serif": ["Arial Unicode MS", "SimHei", "DejaVu Sans"], + "axes.unicode_minus": False, + } +) # ============================================ # 基础参数和函数 (保持不变) @@ -333,16 +382,38 @@ for i, param in enumerate(params_list): data = df_results[df_results['param'] == param].sort_values('value') # 绘制 E1/E2 - 左轴 - line1, = ax.plot(data['value'], data['E1'], 'b-', linewidth=1.6, label='Expected Service (E1)') - line2, = ax.plot(data['value'], data['E2'], 'g-', linewidth=1.6, alpha=0.9, label='Quality-weighted (E2)') + line1, = ax.plot( + data['value'], + data['E1'], + linestyle='-', + color=MORANDI["muted_blue_dark"], + linewidth=1.8, + label='Expected Service (E1)', + ) + line2, = ax.plot( + data['value'], + data['E2'], + linestyle='-', + color=MORANDI["sage"], + linewidth=1.8, + alpha=0.95, + label='Quality-weighted (E2)', + ) ax.set_xlabel(param_labels[param][0]) ax.set_ylabel('Service (E1/E2)') # 绘制 R1 (风险) - 右轴 ax2 = ax.twinx() - line3, = ax2.plot(data['value'], data['R1'], 'r--', linewidth=1.6, label='Shortfall Risk (R1)') - ax2.set_ylabel('Risk Probability (R1)', color='r') - ax2.tick_params(axis='y', labelcolor='r') + line3, = ax2.plot( + data['value'], + data['R1'], + linestyle='--', + color=MORANDI["terracotta"], + linewidth=1.8, + label='Shortfall Risk (R1)', + ) + ax2.set_ylabel('Risk Probability (R1)', color=MORANDI["terracotta"]) + ax2.tick_params(axis='y', labelcolor=MORANDI["terracotta"]) # 添加基准线 if param == 'merge_ratio': @@ -354,7 +425,7 @@ for i, param in enumerate(params_list): elif param == 'cv_max': base_val = BASE_CV_MAX - ax.axvline(x=base_val, color='gray', linestyle=':', alpha=0.5) + ax.axvline(x=base_val, color=MORANDI["warm_gray"], linestyle=':', alpha=0.65) # 图例 lines = [line1, line2, line3] @@ -362,7 +433,7 @@ for i, param in enumerate(params_list): ax.legend(lines, labels, loc='best', frameon=True) ax.set_title(f'Effect of {param_labels[param][0]}') - ax.grid(True, alpha=0.3) + ax.grid(True, alpha=0.55) plt.tight_layout(rect=[0, 0.03, 1, 0.95]) plt.savefig(OUTPUT_FIG, dpi=300) diff --git a/task3/07_sensitivity.xlsx b/task3/07_sensitivity.xlsx index ad10fdd..463faba 100644 Binary files a/task3/07_sensitivity.xlsx and b/task3/07_sensitivity.xlsx differ diff --git a/task3/08_visualize.py b/task3/08_visualize.py index f113482..6a44d58 100644 --- a/task3/08_visualize.py +++ b/task3/08_visualize.py @@ -19,6 +19,7 @@ Task 3 - Step 8: 可视化（Fig.1/2/4/5） from __future__ import annotations from dataclasses import dataclass +import json from pathlib import Path import os @@ -38,11 +39,62 @@ import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt +from matplotlib import colors as mcolors +from cycler import cycler BASE_DIR = Path(__file__).resolve().parent +MORANDI = { + "bg": "#F5F2ED", + "grid": "#D7D1C7", + "text": "#3F3F3F", + "muted_blue": "#8FA3A7", + "muted_blue_dark": "#6E858A", + "sage": "#AEB8A6", + "clay": "#C6AA9B", + "rose": "#D2B8B3", + "warm_gray": "#8C857A", + "terracotta": "#B07A6A", +} + + +def _apply_morandi_style() -> None: + plt.rcParams.update( + { + "figure.facecolor": MORANDI["bg"], + "axes.facecolor": MORANDI["bg"], + "savefig.facecolor": MORANDI["bg"], + "axes.edgecolor": MORANDI["grid"], + "axes.labelcolor": MORANDI["text"], + "xtick.color": MORANDI["text"], + "ytick.color": MORANDI["text"], + "text.color": MORANDI["text"], + "grid.color": MORANDI["grid"], + "grid.alpha": 0.55, + "axes.grid": True, + "axes.titleweight": "semibold", + "axes.titlesize": 11, + "axes.labelsize": 10, + "legend.frameon": True, + "legend.framealpha": 0.9, + "legend.facecolor": MORANDI["bg"], + "legend.edgecolor": MORANDI["grid"], + "axes.prop_cycle": cycler( + "color", + [ + MORANDI["muted_blue"], + MORANDI["sage"], + MORANDI["clay"], + MORANDI["rose"], + MORANDI["warm_gray"], + ], + ), + } + ) + + @dataclass(frozen=True) class Paths: distance_xlsx: Path @@ -76,6 +128,15 @@ def _pair_key(a: int, b: int) -> tuple[int, int]: def fig1_pairing_map(paths: Paths) -> Path: + _apply_morandi_style() + + # Warmer + higher contrast for Fig.1 specifically (clearer on paper/screens) + fig1_colors = { + "paired": "#e76f51", + "unpaired": "#6c757d", + "link": "#bc6c25", + } + sites = pd.read_excel(paths.distance_xlsx, sheet_name="sites") pairs = pd.read_excel(paths.pairing_xlsx, sheet_name="selected_pairs") @@ -97,9 +158,9 @@ def fig1_pairing_map(paths: Paths) -> Path: ax.plot( [si["lon"], sj["lon"]], [si["lat"], sj["lat"]], - color="#2c7fb8", - alpha=0.35, - linewidth=1.0, + color=fig1_colors["link"], + alpha=0.5, + linewidth=1.2, zorder=1, ) @@ -110,8 +171,8 @@ def fig1_pairing_map(paths: Paths) -> Path: paired["lon"], paired["lat"], s=18, - color="#d95f0e", - alpha=0.85, + color=fig1_colors["paired"], + alpha=0.9, label=f"Paired sites (n={len(paired)})", zorder=2, ) @@ -120,7 +181,7 @@ def fig1_pairing_map(paths: Paths) -> Path: unpaired["lon"], unpaired["lat"], s=22, - color="#636363", + color=fig1_colors["unpaired"], alpha=0.9, marker="x", label=f"Unpaired sites (n={len(unpaired)})", @@ -130,7 +191,7 @@ def fig1_pairing_map(paths: Paths) -> Path: 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.grid(True, alpha=0.55) ax.legend(loc="best", frameon=True) out = paths.figures_dir / "fig1_pairing_map.png" @@ -140,7 +201,58 @@ def fig1_pairing_map(paths: Paths) -> Path: return out +def export_fig1_points_js(paths: Paths) -> Path: + sites = pd.read_excel(paths.distance_xlsx, sheet_name="sites").copy() + pairs = pd.read_excel(paths.pairing_xlsx, sheet_name="selected_pairs").copy() + + sites["site_id"] = sites["site_id"].astype(int) + sites["k"] = sites["k"].astype(int) + + paired_ids = set(pairs["site_i_id"].astype(int)).union( + set(pairs["site_j_id"].astype(int)) + ) + + points = [] + for _, r in sites.iterrows(): + points.append( + { + "site_id": int(r["site_id"]), + "site_name": str(r["site_name"]), + "lat": float(r["lat"]), + "lng": float(r["lon"]), + "mu": float(r["mu"]), + "sigma": float(r["sigma"]), + "k": int(r["k"]), + "is_paired": int(r["site_id"]) in paired_ids, + } + ) + + links = [] + for _, r in pairs.iterrows(): + links.append( + { + "site_i_id": int(r["site_i_id"]), + "site_j_id": int(r["site_j_id"]), + "site_i_name": str(r["site_i_name"]), + "site_j_name": str(r["site_j_name"]), + "distance": float(r["distance"]), + } + ) + + out = BASE_DIR / "fig1_points.js" + payload = ( + "// Auto-generated from `task3/01_distance.xlsx` (sites) + `task3/02_pairing.xlsx` (selected_pairs)\n" + "// Usage: open `task3/fig1_carto.html` in a browser (same directory must contain this file).\n" + f"window.FIG1_POINTS = {json.dumps(points, ensure_ascii=False, separators=(',', ':'))};\n" + f"window.FIG1_LINKS = {json.dumps(links, ensure_ascii=False, separators=(',', ':'))};\n" + ) + out.write_text(payload, encoding="utf-8") + return out + + def fig2_allocation_scatter(paths: Paths) -> Path: + _apply_morandi_style() + df = pd.read_excel(paths.allocation_xlsx, sheet_name="allocation") q_ratio = df["q_ratio"].to_numpy(dtype=float) @@ -157,15 +269,22 @@ def fig2_allocation_scatter(paths: Paths) -> Path: ] for ax, (title, x) in zip(axes, panels): - ax.scatter(x, q_ratio, s=22, alpha=0.75, color="#3182bd", edgecolor="none") + ax.scatter( + x, + q_ratio, + s=22, + alpha=0.8, + color=MORANDI["muted_blue"], + 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.plot(xx, yy, color=MORANDI["terracotta"], linewidth=2.0, alpha=0.95) ax.set_title(title) ax.set_xlabel("x") - ax.grid(True, alpha=0.2) + ax.grid(True, alpha=0.55) axes[0].set_ylabel("$q^*/Q$") fig.suptitle("Fig.2 Allocation strategy scatter (34 pairs)", y=1.02) @@ -178,6 +297,8 @@ def fig2_allocation_scatter(paths: Paths) -> Path: def fig4_calendar_heatmap(paths: Paths) -> Path: + _apply_morandi_style() + 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"])) @@ -212,7 +333,17 @@ def fig4_calendar_heatmap(paths: Paths) -> Path: 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") + cmap = mcolors.LinearSegmentedColormap.from_list( + "morandi", + [ + MORANDI["bg"], + "#E7E1D7", + MORANDI["sage"], + MORANDI["muted_blue"], + "#5F7478", + ], + ) + im = ax.imshow(expected.T, aspect="auto", cmap=cmap) 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)") @@ -233,6 +364,8 @@ def fig4_calendar_heatmap(paths: Paths) -> Path: def fig5_risk_distribution(paths: Paths) -> Path: + _apply_morandi_style() + 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)] @@ -240,21 +373,36 @@ def fig5_risk_distribution(paths: Paths) -> Path: 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.hist(p, bins=10, color=MORANDI["sage"], alpha=0.9, edgecolor=MORANDI["bg"]) + ax.axvline( + float(np.mean(p)), + color=MORANDI["terracotta"], + linewidth=2.2, + 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.grid(True, alpha=0.55) 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.plot( + np.arange(1, len(p_sorted) + 1), + p_sorted, + marker="o", + markersize=3.2, + linewidth=1.6, + color=MORANDI["muted_blue_dark"], + markerfacecolor=MORANDI["muted_blue"], + markeredgecolor=MORANDI["bg"], + markeredgewidth=0.6, + ) ax.set_title("Sorted by risk (descending)") ax.set_xlabel("Pair rank") ax.set_ylabel("Shortfall probability") - ax.grid(True, alpha=0.2) + ax.grid(True, alpha=0.55) fig.suptitle("Fig.5 Risk distribution across 34 pairs", y=1.02) fig.tight_layout() @@ -282,6 +430,8 @@ def main() -> None: out1 = fig1_pairing_map(paths) print(f"Saved: {out1.relative_to(BASE_DIR)}") + out1_js = export_fig1_points_js(paths) + print(f"Saved: {out1_js.relative_to(BASE_DIR)}") out2 = fig2_allocation_scatter(paths) print(f"Saved: {out2.relative_to(BASE_DIR)}") out4 = fig4_calendar_heatmap(paths) diff --git a/task3/README.md b/task3/README.md index a2f1ada..7072d58 100644 --- a/task3/README.md +++ b/task3/README.md @@ -643,6 +643,8 @@ $$R_1 = P(S_i / D_i < 0.8 \text{ 或 } S_j / D_j < 0.8)$$ ``` task3/ +├── fig1_carto.html # 交互地图（CartoDB 底图） +├── fig1_points.js # ✅ 自动生成（08_visualize.py） ├── 08_visualize.py # 可视化脚本 └── figures/ ├── fig1_pairing_map.png @@ -656,6 +658,8 @@ task3/ ![Fig.1 站点配对地图](figures/fig1_pairing_map.png) +交互版：用浏览器打开 `task3/fig1_carto.html`（同目录需有 `task3/fig1_points.js`；需联网加载 CartoDB 底图）。 + ![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) diff --git a/task3/__pycache__/07_sensitivity.cpython-313.pyc b/task3/__pycache__/07_sensitivity.cpython-313.pyc index 17fa2fa..d7ab62d 100644 Binary files a/task3/__pycache__/07_sensitivity.cpython-313.pyc 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 index d623973..e1e59f0 100644 Binary files a/task3/__pycache__/08_visualize.cpython-313.pyc and b/task3/__pycache__/08_visualize.cpython-313.pyc differ diff --git a/task3/fig1_carto.html b/task3/fig1_carto.html new file mode 100644 index 0000000..e462d4a --- /dev/null +++ b/task3/fig1_carto.html @@ -0,0 +1,175 @@ + + + + + + Task 3 Fig.1 (CartoDB + Leaflet) + + + + +
+ + + + + + diff --git a/task3/fig1_points.js b/task3/fig1_points.js new file mode 100644 index 0000000..175f19c --- /dev/null +++ b/task3/fig1_points.js @@ -0,0 +1,4 @@ +// Auto-generated from `task3/01_distance.xlsx` (sites) + `task3/02_pairing.xlsx` (selected_pairs) +// Usage: open `task3/fig1_carto.html` in a browser (same directory must contain this file). +window.FIG1_POINTS = [{"site_id":1,"site_name":"MFP American Legion - Binghamton","lat":42.108036,"lng":-75.887779,"mu":200.1818181818182,"sigma":46.0669473306365,"k":14,"is_paired":true},{"site_id":2,"site_name":"MFP Avoca","lat":42.4174468,"lng":-77.4358433,"mu":314.5833333333333,"sigma":57.3497377870416,"k":24,"is_paired":true},{"site_id":3,"site_name":"MFP Bath","lat":42.3362172,"lng":-77.3175047,"mu":279.4545454545454,"sigma":45.2534277958336,"k":19,"is_paired":true},{"site_id":4,"site_name":"MFP Beaver Dams","lat":42.2606415,"lng":-76.9559856,"mu":170.7,"sigma":28.7906234736241,"k":12,"is_paired":true},{"site_id":5,"site_name":"MFP Birnie Transportation Services","lat":42.172309,"lng":-77.1101217,"mu":213.375,"sigma":53.4039525021991,"k":15,"is_paired":true},{"site_id":6,"site_name":"MFP Boys and Girls Club","lat":42.1053841,"lng":-75.9213736,"mu":210.8,"sigma":52.3551971313896,"k":15,"is_paired":true},{"site_id":7,"site_name":"MFP Bradford","lat":42.3691309,"lng":-77.1081064,"mu":122.2727272727273,"sigma":29.7425315300864,"k":9,"is_paired":true},{"site_id":8,"site_name":"MFP Campbell","lat":42.2314129,"lng":-77.1948265,"mu":168.5,"sigma":40.968946111104,"k":12,"is_paired":true},{"site_id":9,"site_name":"MFP Canisteo","lat":42.272779,"lng":-77.606849,"mu":177.0,"sigma":51.3051870888531,"k":13,"is_paired":true},{"site_id":10,"site_name":"MFP 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