feat(seaborn): implement errorbar-basic (#9517)

## Implementation: `errorbar-basic` - python/seaborn

Implements the **python/seaborn** version of `errorbar-basic`.

**File:** `plots/errorbar-basic/implementations/python/seaborn.py`

**Parent Issue:** #973

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/28472936805)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: github-actions[bot]

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

@@ -1,11 +1,12 @@
 """ anyplot.ai
 errorbar-basic: Basic Error Bar Plot
-Library: seaborn 0.13.2 | Python 3.14.4
-Quality: 89/100 | Updated: 2026-04-25
+Library: seaborn 0.13.2 | Python 3.13.14
+Quality: 88/100 | Updated: 2026-06-30
 """
 
 import os
 
+import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -17,20 +18,22 @@
 ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
 INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
 INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
+# Imprint palette
 IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477"]
 BRAND = IMPRINT[0]
 ACCENT = IMPRINT[1]
 
-# Clinical trial: symptom reduction (%) measured for each treatment group
+# Clinical trial: symptom reduction (%) by dose group (n=30 per group)
 np.random.seed(42)
-categories = ["Control", "Treatment A", "Treatment B", "Treatment C", "Treatment D", "Treatment E"]
-means = [45.2, 52.8, 61.3, 48.7, 57.4, 43.1]
-stds = [4.5, 6.2, 5.8, 7.1, 4.9, 5.5]
+categories = ["Control", "Placebo", "10 mg", "25 mg", "50 mg", "100 mg"]
+means = [45.2, 46.8, 52.3, 57.4, 61.3, 58.9]
+stds = [4.5, 5.1, 6.2, 4.9, 5.8, 7.1]
 n_per_group = 30
 
 records = [
-    {"Treatment": cat, "Symptom Reduction (%)": value}
+    {"Dose": cat, "Symptom Reduction (%)": value}
     for cat, mu, sigma in zip(categories, means, stds, strict=True)
     for value in np.random.normal(mu, sigma, n_per_group)
 ]
@@ -50,38 +53,63 @@
         "xtick.color": INK_SOFT,
         "ytick.color": INK_SOFT,
         "grid.color": INK,
-        "grid.alpha": 0.10,
+        "grid.alpha": 0.15,
         "legend.facecolor": ELEVATED_BG,
         "legend.edgecolor": INK_SOFT,
     },
 )
 
-fig, ax = plt.subplots(figsize=(16, 9))
+fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400)
 
 sns.barplot(
     data=df,
-    x="Treatment",
+    x="Dose",
     y="Symptom Reduction (%)",
-    hue="Treatment",
+    hue="Dose",
     palette=palette,
     legend=False,
     errorbar="sd",
     capsize=0.25,
-    err_kws={"color": INK, "linewidth": 2.5},
+    err_kws={"color": INK, "linewidth": 1.5},
     edgecolor=INK_SOFT,
-    linewidth=1.0,
+    linewidth=0.6,
     ax=ax,
 )
 
-ax.set_xlabel("Treatment Group", fontsize=20)
-ax.set_ylabel("Symptom Reduction (%)", fontsize=20)
-ax.set_title("errorbar-basic · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
-ax.tick_params(axis="both", labelsize=16)
-ax.yaxis.grid(True)
+ax.set_xlabel("Dose Group", fontsize=10)
+ax.set_ylabel("Symptom Reduction (%)", fontsize=10)
+ax.tick_params(axis="both", labelsize=8)
+ax.yaxis.grid(True, alpha=0.15, linewidth=0.8)
 ax.xaxis.grid(False)
 ax.set_axisbelow(True)
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
 
-plt.tight_layout()
-plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
+# Legend explaining the two-color strategy: all groups vs. top performer
+brand_patch = mpatches.Patch(color=BRAND, label="Dose group")
+accent_patch = mpatches.Patch(color=ACCENT, label=f"Top performer ({top_performer})")
+ax.legend(handles=[brand_patch, accent_patch], fontsize=8, framealpha=0.9, loc="lower right")
+
+# Title and subtitle placed in figure coordinates to control vertical spacing precisely
+fig.text(
+    0.5,
+    0.96,
+    "errorbar-basic · python · seaborn · anyplot.ai",
+    ha="center",
+    va="top",
+    fontsize=12,
+    fontweight="medium",
+    color=INK,
+)
+fig.text(
+    0.5,
+    0.90,
+    f"Bars show mean ± 1 SD — {top_performer} achieves the highest mean symptom reduction",
+    ha="center",
+    va="top",
+    fontsize=9,
+    color=INK_MUTED,
+)
+
+fig.subplots_adjust(top=0.81, bottom=0.13, left=0.11, right=0.97)
+plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)