feat(pygal): implement strip-basic (#5673)

## Implementation: `strip-basic` - python/pygal

Implements the **python/pygal** version of `strip-basic`.

**File:** `plots/strip-basic/implementations/python/pygal.py`

**Parent Issue:** #975

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25342549595)*

---------

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/strip-basic/implementations/python/pygal.py

@@ -1,79 +1,96 @@
-""" pyplots.ai
+""" anyplot.ai
 strip-basic: Basic Strip Plot
-Library: pygal 3.1.0 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: pygal 3.1.0 | Python 3.13.13
+Quality: 87/100 | Updated: 2026-05-04
 """
 
-import random
+import os
 
+import numpy as np
 import pygal
 from pygal.style import Style
 
 
-# Set seed for reproducibility
-random.seed(42)
+# Theme tokens
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 
-# Data - Survey response scores by department
+OKABE_ITO = ("#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442")
+
+# Data — employee satisfaction scores by department (1–10 scale)
+np.random.seed(42)
 categories = ["Engineering", "Marketing", "Sales", "Support"]
 n_per_category = 40
 
-# Generate realistic survey scores (1-10 scale) with different distributions
-data = {
-    "Engineering": [random.gauss(7.5, 1.2) for _ in range(n_per_category)],
-    "Marketing": [random.gauss(6.8, 1.5) for _ in range(n_per_category)],
-    "Sales": [random.gauss(7.2, 1.0) for _ in range(n_per_category)],
-    "Support": [random.gauss(6.5, 1.8) for _ in range(n_per_category)],
+scores = {
+    "Engineering": np.clip(np.random.normal(7.5, 1.2, n_per_category), 1, 10),
+    "Marketing": np.clip(np.random.normal(6.8, 1.5, n_per_category), 1, 10),
+    "Sales": np.clip(np.random.normal(7.2, 1.0, n_per_category), 1, 10),
+    "Support": np.clip(np.random.normal(6.5, 1.8, n_per_category), 1, 10),
 }
 
-# Clip values to realistic 1-10 range
-for cat in data:
-    data[cat] = [max(1, min(10, v)) for v in data[cat]]
-
-# Custom style for 4800x2700 output
+# Style
 custom_style = Style(
-    background="white",
-    plot_background="white",
-    foreground="#333333",
-    foreground_strong="#333333",
-    foreground_subtle="#666666",
-    colors=("#306998", "#FFD43B", "#4B8BBE", "#FFE873"),
+    background=PAGE_BG,
+    plot_background=PAGE_BG,
+    foreground=INK,
+    foreground_strong=INK,
+    foreground_subtle=INK_MUTED,
+    colors=OKABE_ITO,
     title_font_size=72,
     label_font_size=48,
     major_label_font_size=42,
     legend_font_size=42,
     value_font_size=36,
-    opacity=0.65,
+    opacity=0.60,
     stroke_width=0,
 )
 
-# Create XY chart for strip plot
+# Chart
 chart = pygal.XY(
     width=4800,
     height=2700,
     style=custom_style,
-    title="strip-basic · pygal · pyplots.ai",
+    title="strip-basic · pygal · anyplot.ai",
     x_title="Department",
-    y_title="Survey Score",
+    y_title="Satisfaction Score (1–10)",
     show_legend=True,
     legend_at_bottom=True,
-    legend_at_bottom_columns=4,
+    legend_at_bottom_columns=5,
     show_x_guides=False,
     show_y_guides=True,
     stroke=False,
-    dots_size=12,
+    dots_size=17,
     x_label_rotation=0,
 )
 
-# Set x-axis range and labels
+# Pygal-native tooltip formatting: value_formatter applies to all hover labels
+chart.value_formatter = lambda y: f"{y:.1f}"
+
+# X-axis labels aligned to integer positions
 chart.x_labels = ["", "Engineering", "Marketing", "Sales", "Support", ""]
 chart.xrange = (0, 5)
 
-# Add jittered points for each category
+# Add jittered points per category; per-point dicts enrich HTML tooltips
 for i, cat in enumerate(categories, start=1):
-    # Create jittered x-positions around the category index
-    points = [(i + random.uniform(-0.25, 0.25), val) for val in data[cat]]
+    jitter = np.random.uniform(-0.25, 0.25, n_per_category)
+    points = [
+        {"value": (float(i + j), float(v)), "label": f"{cat}: {v:.1f}"}
+        for j, v in zip(jitter, scores[cat], strict=True)
+    ]
     chart.add(cat, points)
 
-# Save outputs
-chart.render_to_file("plot.html")
-chart.render_to_png("plot.png")
+# Mean reference markers — one dot per category at the mean position (5th Okabe color: #E69F00)
+# Positioned at exact integer x (no jitter) so they stand apart from the scattered data cloud
+mean_points = [
+    {"value": (float(i), float(np.mean(scores[cat]))), "label": f"Mean {cat}: {np.mean(scores[cat]):.2f}"}
+    for i, cat in enumerate(categories, start=1)
+]
+chart.add("─ Mean", mean_points)
+
+# Save
+chart.render_to_png(f"plot-{THEME}.png")
+with open(f"plot-{THEME}.html", "wb") as f:
+    f.write(chart.render())