feat(pygal): implement scatter-regression-lowess (#2878)

## Implementation: `scatter-regression-lowess` - pygal

Implements the **pygal** version of `scatter-regression-lowess`.

**File:** `plots/scatter-regression-lowess/implementations/pygal.py`

**Parent Issue:** #2855

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608464403)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/scatter-regression-lowess/implementations/pygal.py

@@ -0,0 +1,77 @@
+""" pyplots.ai
+scatter-regression-lowess: Scatter Plot with LOWESS Regression
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+from statsmodels.nonparametric.smoothers_lowess import lowess
+
+
+# Data - Drug dose-response relationship with non-linear effect
+# Simulates enzyme activity response to varying drug concentrations
+np.random.seed(42)
+n_points = 150
+
+# Drug concentration in mg/L (log-spaced for pharmacological realism)
+concentration = np.linspace(0.1, 50, n_points)
+
+# Enzyme activity response: sigmoidal with saturation and hormesis effect
+# Low doses show slight stimulation, mid-range shows increase, high doses plateau
+base_response = 25 + 55 * (1 - np.exp(-concentration / 8)) - 10 * np.exp(-concentration / 3)
+noise = np.random.normal(0, 4, n_points)
+activity = base_response + noise
+
+# Calculate LOWESS smoothed curve
+lowess_result = lowess(activity, concentration, frac=0.35, return_sorted=True)
+conc_smooth = lowess_result[:, 0]
+activity_smooth = lowess_result[:, 1]
+
+# Custom style for large canvas with increased font sizes
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#306998", "#FFD43B", "#E74C3C"),
+    title_font_size=56,
+    label_font_size=42,
+    major_label_font_size=32,
+    legend_font_size=32,
+    value_font_size=28,
+    stroke_width=5,
+    opacity=0.6,
+    opacity_hover=0.9,
+)
+
+# Create XY chart for scatter plot
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="scatter-regression-lowess · pygal · pyplots.ai",
+    x_title="Drug Concentration (mg/L)",
+    y_title="Enzyme Activity (%)",
+    show_dots=True,
+    dots_size=8,
+    stroke=False,
+    show_x_guides=True,
+    show_y_guides=True,
+    legend_at_bottom=True,
+    legend_at_bottom_columns=2,
+)
+
+# Add scatter points (as XY data with no stroke)
+scatter_data = list(zip(concentration, activity, strict=True))
+chart.add("Observed Response", scatter_data, stroke=False, dots_size=10)
+
+# Add LOWESS curve (as line with no dots)
+lowess_data = list(zip(conc_smooth, activity_smooth, strict=True))
+chart.add("LOWESS Fit (frac=0.35)", lowess_data, stroke=True, show_dots=False, stroke_style={"width": 6})
+
+# Save as PNG and SVG/HTML
+chart.render_to_png("plot.png")
+chart.render_to_file("plot.html")

plots/scatter-regression-lowess/metadata/pygal.yaml

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+library: pygal
+specification_id: scatter-regression-lowess
+created: '2025-12-30T23:54:43Z'
+updated: '2025-12-31T00:10:44Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608464403
+issue: 2855
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/pygal/plot.html
+quality_score: 90
+review:
+  strengths:
+  - LOWESS curve is visually distinct from scatter points with good color contrast
+    (blue vs yellow)
+  - Clean code structure following KISS principles with proper use of statsmodels
+    for LOWESS calculation
+  - Custom pygal Style properly configured for large canvas with appropriate font
+    sizes
+  - Correct title format following spec-id · library · pyplots.ai convention
+  - Good use of transparency (opacity=0.6) for scatter points to show density
+  - Appropriate LOWESS smoothing parameter (frac=0.35) within recommended range
+  weaknesses:
+  - Axis labels in rendered output are generic (X Value, Y Value) rather than the
+    domain-specific labels defined in the code
+  - Legend entries in output differ from code-defined labels