update task3
This commit is contained in:
@@ -1,10 +1,10 @@
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"""
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Task 3 - Step 8: 可视化（Fig.1/2/4/5）
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Task 3 - Step 8: 可视化（Fig.1/2/4/5/6）
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=====================================
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输入:
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- 01_distance.xlsx (sites: 经纬度、mu/sigma)
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- 02_pairing.xlsx (selected_pairs: 34对配对连线)
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- 02_pairing.xlsx (selected_pairs: 配对连线)
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- 03_allocation.xlsx (allocation: q*, E_total)
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- 05_calendar.xlsx (calendar: 365天×2槽位排程)
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- 06_evaluate.xlsx (pair_risk: 缺口概率分布)
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@@ -12,6 +12,7 @@ Task 3 - Step 8: 可视化（Fig.1/2/4/5）
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输出:
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- figures/fig1_pairing_map.png
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- figures/fig2_allocation_scatter.png
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- figures/fig6_fairness_satisfaction.png
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- figures/fig4_calendar_heatmap.png
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- figures/fig5_risk_distribution.png
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"""
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@@ -47,7 +48,6 @@ BASE_DIR = Path(__file__).resolve().parent
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MORANDI = {
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"bg": "#F5F2ED",
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"grid": "#D7D1C7",
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"text": "#3F3F3F",
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"muted_blue": "#8FA3A7",
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@@ -59,19 +59,22 @@ MORANDI = {
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"terracotta": "#B07A6A",
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}
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FIG_BG = "#FFFFFF"
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FIG_GRID = "#DADDE1"
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def _apply_morandi_style() -> None:
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plt.rcParams.update(
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{
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"figure.facecolor": MORANDI["bg"],
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"axes.facecolor": MORANDI["bg"],
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"savefig.facecolor": MORANDI["bg"],
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"axes.edgecolor": MORANDI["grid"],
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"figure.facecolor": FIG_BG,
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"axes.facecolor": FIG_BG,
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"savefig.facecolor": FIG_BG,
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"axes.edgecolor": FIG_GRID,
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"axes.labelcolor": MORANDI["text"],
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"xtick.color": MORANDI["text"],
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"ytick.color": MORANDI["text"],
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"text.color": MORANDI["text"],
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"grid.color": MORANDI["grid"],
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"grid.color": FIG_GRID,
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"grid.alpha": 0.55,
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"axes.grid": True,
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"axes.titleweight": "semibold",
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@@ -79,8 +82,8 @@ def _apply_morandi_style() -> None:
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"axes.labelsize": 10,
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"legend.frameon": True,
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"legend.framealpha": 0.9,
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"legend.facecolor": MORANDI["bg"],
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"legend.edgecolor": MORANDI["grid"],
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"legend.facecolor": FIG_BG,
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"legend.edgecolor": FIG_GRID,
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"axes.prop_cycle": cycler(
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"color",
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[
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@@ -188,7 +191,7 @@ def fig1_pairing_map(paths: Paths) -> Path:
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zorder=3,
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)
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ax.set_title("Fig.1 Pairing map (sites + selected 34 links)")
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ax.set_title(f"Fig.1 Pairing map (sites + selected n={len(pairs)} links)")
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ax.set_xlabel("Longitude")
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ax.set_ylabel("Latitude")
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ax.grid(True, alpha=0.55)
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@@ -196,7 +199,7 @@ def fig1_pairing_map(paths: Paths) -> Path:
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out = paths.figures_dir / "fig1_pairing_map.png"
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fig.tight_layout()
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fig.savefig(out)
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fig.savefig(out, facecolor=FIG_BG)
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plt.close(fig)
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return out
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@@ -287,11 +290,11 @@ def fig2_allocation_scatter(paths: Paths) -> Path:
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ax.grid(True, alpha=0.55)
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axes[0].set_ylabel("$q^*/Q$")
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fig.suptitle("Fig.2 Allocation strategy scatter (34 pairs)", y=1.02)
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fig.suptitle(f"Fig.2 Allocation strategy scatter (n={len(df)} pairs)", y=1.02)
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fig.tight_layout()
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out = paths.figures_dir / "fig2_allocation_scatter.png"
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fig.savefig(out, bbox_inches="tight")
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fig.savefig(out, bbox_inches="tight", facecolor=FIG_BG)
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plt.close(fig)
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return out
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@@ -336,7 +339,7 @@ def fig4_calendar_heatmap(paths: Paths) -> Path:
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cmap = mcolors.LinearSegmentedColormap.from_list(
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"morandi",
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[
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MORANDI["bg"],
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FIG_BG,
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"#E7E1D7",
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MORANDI["sage"],
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MORANDI["muted_blue"],
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@@ -358,7 +361,7 @@ def fig4_calendar_heatmap(paths: Paths) -> Path:
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fig.tight_layout()
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out = paths.figures_dir / "fig4_calendar_heatmap.png"
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fig.savefig(out, bbox_inches="tight")
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fig.savefig(out, bbox_inches="tight", facecolor=FIG_BG)
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plt.close(fig)
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return out
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@@ -373,7 +376,7 @@ def fig5_risk_distribution(paths: Paths) -> Path:
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fig, axes = plt.subplots(1, 2, figsize=(11, 3.8), dpi=200)
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ax = axes[0]
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ax.hist(p, bins=10, color=MORANDI["sage"], alpha=0.9, edgecolor=MORANDI["bg"])
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ax.hist(p, bins=10, color=MORANDI["sage"], alpha=0.9, edgecolor=FIG_BG)
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ax.axvline(
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float(np.mean(p)),
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color=MORANDI["terracotta"],
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@@ -396,7 +399,7 @@ def fig5_risk_distribution(paths: Paths) -> Path:
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linewidth=1.6,
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color=MORANDI["muted_blue_dark"],
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markerfacecolor=MORANDI["muted_blue"],
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markeredgecolor=MORANDI["bg"],
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markeredgecolor=FIG_BG,
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markeredgewidth=0.6,
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)
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ax.set_title("Sorted by risk (descending)")
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@@ -404,10 +407,61 @@ def fig5_risk_distribution(paths: Paths) -> Path:
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ax.set_ylabel("Shortfall probability")
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ax.grid(True, alpha=0.55)
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fig.suptitle("Fig.5 Risk distribution across 34 pairs", y=1.02)
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fig.suptitle(f"Fig.5 Risk distribution across n={len(p)} pairs", y=1.02)
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fig.tight_layout()
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out = paths.figures_dir / "fig5_risk_distribution.png"
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fig.savefig(out, bbox_inches="tight")
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fig.savefig(out, bbox_inches="tight", facecolor=FIG_BG)
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plt.close(fig)
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return out
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def fig6_fairness_satisfaction(paths: Paths) -> Path:
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_apply_morandi_style()
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df = pd.read_excel(paths.evaluate_xlsx, sheet_name="site_satisfaction").copy()
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r = df["satisfaction_rate_r"].to_numpy(dtype=float)
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r = r[np.isfinite(r)]
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r_sorted = np.sort(r)
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# Lorenz curve (site share vs satisfaction share)
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n = len(r_sorted)
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if n == 0 or np.sum(r_sorted) <= 0:
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x = np.array([0.0, 1.0])
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y = np.array([0.0, 1.0])
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gini = 0.0
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else:
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cum = np.cumsum(r_sorted)
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x = np.concatenate([[0.0], np.arange(1, n + 1) / n])
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y = np.concatenate([[0.0], cum / cum[-1]])
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# Gini via Lorenz area
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gini = 1.0 - 2.0 * float(np.trapezoid(y, x))
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fig, axes = plt.subplots(1, 2, figsize=(11, 3.8), dpi=200)
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ax = axes[0]
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ax.hist(r, bins=12, color=MORANDI["muted_blue"], alpha=0.9, edgecolor=FIG_BG)
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ax.axvline(float(np.mean(r)), color=MORANDI["terracotta"], linewidth=2.2, label=f"mean={np.mean(r):.2f}")
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ax.set_title("Satisfaction rate distribution")
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ax.set_xlabel("Satisfaction rate $r_i$")
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ax.set_ylabel("Count")
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ax.grid(True, alpha=0.55)
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ax.legend(loc="best", frameon=True)
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ax = axes[1]
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ax.plot(x, y, color=MORANDI["sage"], linewidth=2.2, label="Lorenz curve")
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ax.plot([0, 1], [0, 1], color=MORANDI["warm_gray"], linestyle="--", linewidth=1.4, label="Equality line")
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ax.set_title(f"Lorenz curve (Gini={gini:.3f})")
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ax.set_xlabel("Cumulative share of sites")
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ax.set_ylabel("Cumulative share of satisfaction")
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.grid(True, alpha=0.55)
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ax.legend(loc="lower right", frameon=True)
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fig.suptitle(f"Fig.6 Fairness diagnostics (n={len(r)} sites)", y=1.02)
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fig.tight_layout()
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out = paths.figures_dir / "fig6_fairness_satisfaction.png"
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fig.savefig(out, bbox_inches="tight", facecolor=FIG_BG)
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plt.close(fig)
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return out
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@@ -434,6 +488,8 @@ def main() -> None:
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print(f"Saved: {out1_js.relative_to(BASE_DIR)}")
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out2 = fig2_allocation_scatter(paths)
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print(f"Saved: {out2.relative_to(BASE_DIR)}")
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out6 = fig6_fairness_satisfaction(paths)
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print(f"Saved: {out6.relative_to(BASE_DIR)}")
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out4 = fig4_calendar_heatmap(paths)
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print(f"Saved: {out4.relative_to(BASE_DIR)}")
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out5 = fig5_risk_distribution(paths)
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