feat(matplotlib): implement scatter-regression-linear (#10132)

## Implementation: `scatter-regression-linear` - python/matplotlib

Implements the **python/matplotlib** version of
`scatter-regression-linear`.

**File:**
`plots/scatter-regression-linear/implementations/python/matplotlib.py`

**Parent Issue:** #1821

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/31008270400)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/scatter-regression-linear/implementations/python/matplotlib.py

@@ -1,25 +1,26 @@
 """ anyplot.ai
 scatter-regression-linear: Scatter Plot with Linear Regression
-Library: matplotlib 3.10.9 | Python 3.13.13
-Quality: 93/100 | Updated: 2026-05-06
+Library: matplotlib 3.11.1 | Python 3.13.14
+Quality: 92/100 | Updated: 2026-08-05
 """
 
 import os
 
 import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib.transforms import blended_transform_factory
 
 
-# Theme tokens
+# Theme tokens (Imprint)
 THEME = os.getenv("ANYPLOT_THEME", "light")
 PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
 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
-SECONDARY = "#C475FD"  # Okabe-Ito position 2 for regression line
+BRAND = "#009E73"  # Imprint palette position 1
+SECONDARY = "#C475FD"  # Imprint palette position 2, for regression line
 
-# Data: Study hours vs exam scores (realistic educational context)
+# Data: study hours vs exam scores (realistic educational context)
 np.random.seed(42)
 n_points = 80
 x = np.random.uniform(1, 10, n_points)  # Study hours
@@ -31,12 +32,15 @@
 coefficients = np.polyfit(x, y, 1)
 slope, intercept = coefficients[0], coefficients[1]
 
-# Calculate R-squared
+# Coefficient of determination
 y_pred = np.polyval(coefficients, x)
 ss_res = np.sum((y - y_pred) ** 2)
 ss_tot = np.sum((y - np.mean(y)) ** 2)
 r_squared = 1 - (ss_res / ss_tot)
 
+# Largest residual: notable outlier worth calling out
+outlier_idx = np.argmax(np.abs(y - y_pred))
+
 # Regression line and 95% confidence interval
 x_line = np.linspace(x.min() - 0.5, x.max() + 0.5, 100)
 y_line = np.polyval(coefficients, x_line)
@@ -45,45 +49,63 @@
 ss_xx = np.sum((x - x_mean) ** 2)
 se_y = np.sqrt(ss_res / (n_points - 2))
 se_line = se_y * np.sqrt(1 / n_points + (x_line - x_mean) ** 2 / ss_xx)
-t_val = 1.99  # 95% CI
+t_val = 1.99  # 95% CI, df ~ 78
 ci_upper = y_line + t_val * se_line
 ci_lower = y_line - t_val * se_line
 
 # 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)
 
-# Confidence interval band (using SECONDARY color with reduced alpha)
-ax.fill_between(x_line, ci_lower, ci_upper, alpha=0.2, color=SECONDARY, label="95% CI")
+# Confidence interval band (SECONDARY color, low alpha)
+ax.fill_between(x_line, ci_lower, ci_upper, alpha=0.2, color=SECONDARY, label="95% CI", zorder=1)
 
-# Scatter points (BRAND green as first series)
-ax.scatter(x, y, s=200, alpha=0.7, color=BRAND, edgecolors=PAGE_BG, linewidth=0.5, zorder=3)
+# Rug plot: marginal x-distribution along the bottom, drawn in a blended
+# transform (data x, axes y) so it hugs the axis regardless of y-range.
+trans_x = blended_transform_factory(ax.transData, ax.transAxes)
+ax.plot(x, np.full_like(x, 0.015), "|", transform=trans_x, color=BRAND, alpha=0.5, markersize=7, zorder=1)
 
-# Regression line (using SECONDARY color)
-ax.plot(x_line, y_line, color=SECONDARY, linewidth=3, label="Regression Line", zorder=2)
+# Scatter points (BRAND green as first series)
+ax.scatter(x, y, s=130, alpha=0.7, color=BRAND, edgecolors=PAGE_BG, linewidth=0.5, zorder=3)
+
+# Regression line (SECONDARY color)
+ax.plot(x_line, y_line, color=SECONDARY, linewidth=2.5, label="Regression Line", zorder=2)
+
+# Callout for the largest residual, showing how far the point strays from the fit
+ax.annotate(
+    "Largest residual",
+    xy=(x[outlier_idx], y[outlier_idx]),
+    xytext=(x[outlier_idx] + 1.4, y[outlier_idx] + 10),
+    fontsize=8,
+    color=INK_SOFT,
+    arrowprops={"arrowstyle": "->", "color": INK_SOFT, "linewidth": 1},
+    zorder=4,
+)
 
-# Annotations with theme-adaptive styling
+# Annotation: equation and R-squared
 equation = f"y = {slope:.2f}x + {intercept:.2f}"
 r_text = f"R² = {r_squared:.3f}"
 ax.text(
-    0.05,
-    0.95,
+    0.04,
+    0.94,
     f"{equation}\n{r_text}",
     transform=ax.transAxes,
-    fontsize=18,
+    fontsize=9,
     verticalalignment="top",
     color=INK,
     bbox={"boxstyle": "round,pad=0.4", "facecolor": ELEVATED_BG, "edgecolor": INK_SOFT, "alpha": 0.95},
 )
 
-# Styling with theme-adaptive chrome
-ax.set_xlabel("Study Hours (hrs)", fontsize=20, color=INK)
-ax.set_ylabel("Exam Score (points)", fontsize=20, color=INK)
-ax.set_title("scatter-regression-linear · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
-ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
+# Title and axis labels (title short enough to use the default 12pt)
+title = "scatter-regression-linear · python · matplotlib · anyplot.ai"
+ax.set_title(title, fontsize=12, fontweight="medium", color=INK)
+ax.set_xlabel("Study Hours (hrs)", fontsize=10, color=INK)
+ax.set_ylabel("Exam Score (points)", fontsize=10, color=INK)
+ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT)
 
-# Grid styling (subtle, y-axis preferred)
-ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
+# Grid: both axes, subtle (scatter convention)
+ax.grid(True, alpha=0.15, linewidth=0.8, color=INK)
+ax.set_axisbelow(True)
 
 # Spine styling
 ax.spines["top"].set_visible(False)
@@ -92,7 +114,7 @@
     ax.spines[spine].set_color(INK_SOFT)
 
 # Legend styling
-leg = ax.legend(fontsize=16, loc="lower right")
+leg = ax.legend(fontsize=8, loc="lower right")
 if leg:
     leg.get_frame().set_facecolor(ELEVATED_BG)
     leg.get_frame().set_edgecolor(INK_SOFT)
@@ -101,4 +123,4 @@
         text.set_color(INK_SOFT)
 
 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)