feat(seaborn): implement qq-basic

plots/qq-basic/implementations/python/seaborn.py

@@ -1,13 +1,14 @@
 """ anyplot.ai
 qq-basic: Basic Q-Q Plot
-Library: seaborn 0.13.2 | Python 3.14.4
-Quality: 88/100 | Updated: 2026-04-27
+Library: seaborn 0.13.2 | Python 3.13.14
+Quality: 92/100 | Updated: 2026-07-24
 """
 
 import os
 
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 import seaborn as sns
 
 
@@ -17,7 +18,7 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
-BRAND = "#009E73"  # Okabe-Ito position 1
+BRAND = "#009E73"  # Imprint palette position 1
 
 sns.set_theme(
     style="ticks",
@@ -37,8 +38,8 @@
 )
 
 # Data - mixture distribution with right-skewed tail to demonstrate Q-Q deviation
-np.random.seed(42)
-sample = np.concatenate([np.random.normal(loc=50, scale=10, size=180), np.random.normal(loc=75, scale=5, size=20)])
+rng = np.random.default_rng(42)
+sample = np.concatenate([rng.normal(loc=50, scale=10, size=180), rng.normal(loc=75, scale=5, size=20)])
 n = len(sample)
 
 # Theoretical quantiles via Abramowitz & Stegun 26.2.17 rational approximation
@@ -49,30 +50,57 @@
 theoretical_q = np.where(p < 0.5, -(t - num / den), t - num / den)
 sample_q = np.sort((sample - sample.mean()) / sample.std(ddof=1))
 
+# Simulation envelope: draw many perfectly-normal replicates of size n and let
+# seaborn's lineplot bootstrap a 95% percentile interval around their order
+# statistics. This gives the reference guide real statistical depth (the
+# natural sampling variability a truly normal sample would show) instead of a
+# bare y=x line, and leverages seaborn's own error-bar estimation.
+n_replicates = 300
+replicates = np.sort(rng.standard_normal(size=(n_replicates, n)), axis=1)
+envelope_df = pd.DataFrame({"theoretical": np.tile(theoretical_q, n_replicates), "simulated": replicates.ravel()})
+
 # Plot
-fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
+fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
 ax.set_facecolor(PAGE_BG)
 
-sns.scatterplot(x=theoretical_q, y=sample_q, ax=ax, s=200, color=BRAND, alpha=0.7, edgecolor=PAGE_BG, linewidth=0.5)
+sns.lineplot(
+    data=envelope_df,
+    x="theoretical",
+    y="simulated",
+    ax=ax,
+    estimator="mean",
+    errorbar=("pi", 95),
+    color=INK_SOFT,
+    linewidth=1.5,
+    linestyle="--",
+    label="Normal reference (95% envelope)",
+    zorder=1,
+)
 
-# Reference line (y=x for perfect normal distribution)
-q_min = min(theoretical_q.min(), sample_q.min())
-q_max = max(theoretical_q.max(), sample_q.max())
-ax.plot(
-    [q_min, q_max], [q_min, q_max], color=INK_SOFT, linewidth=2.5, linestyle="--", label="Reference (y=x)", zorder=1
+sns.scatterplot(
+    x=theoretical_q,
+    y=sample_q,
+    ax=ax,
+    s=90,
+    color=BRAND,
+    alpha=0.75,
+    edgecolor=PAGE_BG,
+    linewidth=0.8,
+    label="Sample quantiles",
+    zorder=2,
 )
 
 # Style
-ax.set_xlabel("Theoretical Quantiles", fontsize=20, color=INK)
-ax.set_ylabel("Sample Quantiles", fontsize=20, color=INK)
-ax.set_title("qq-basic · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
-ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
-ax.legend(fontsize=16, loc="upper left")
+ax.set_xlabel("Theoretical Quantiles", fontsize=10, color=INK)
+ax.set_ylabel("Sample Quantiles", fontsize=10, color=INK)
+ax.set_title("qq-basic · python · seaborn · anyplot.ai", fontsize=12, fontweight="medium", color=INK)
+ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT)
+ax.legend(fontsize=8, loc="upper left")
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
 for s in ("left", "bottom"):
     ax.spines[s].set_color(INK_SOFT)
 ax.grid(True, alpha=0.10, linewidth=0.8, color=INK)
 
 plt.tight_layout()
-plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
+plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)