feat(pygal): implement swarm-basic (#9939)

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

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

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

**Parent Issue:** #974

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/30192192064)*

---------

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

@@ -1,7 +1,7 @@
 """ anyplot.ai
 swarm-basic: Basic Swarm Plot
-Library: pygal 3.1.0 | Python 3.13.13
-Quality: 80/100 | Updated: 2026-05-05
+Library: pygal 3.1.3 | Python 3.13.14
+Quality: 88/100 | Updated: 2026-07-26
 """
 
 import os
@@ -11,65 +11,86 @@
 from pygal.style import Style
 
 
-# Theme tokens
+# Theme tokens (see prompts/default-style-guide.md "Theme-adaptive Chrome")
 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"
 
-IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")
+IMPRINT_PALETTE = ("#009E73", "#C475FD", "#4467A3", "#BD8233")
 
-# Data - Employee performance scores by department
+# Data - employee performance scores by department (clamped to a plausible 0-100 scale)
 np.random.seed(42)
 categories = ["Engineering", "Marketing", "Sales", "Operations"]
 data = {
-    "Engineering": np.random.normal(82, 8, 45),
-    "Marketing": np.random.normal(75, 12, 50),
-    "Sales": np.random.normal(78, 15, 40),
-    "Operations": np.random.normal(70, 10, 55),
+    "Engineering": np.clip(np.random.normal(82, 7, 45), 0, 100),
+    "Marketing": np.clip(np.random.normal(75, 9, 50), 0, 100),
+    "Sales": np.clip(np.random.normal(78, 10, 40), 0, 100),
+    "Operations": np.clip(np.random.normal(70, 8, 55), 0, 100),
 }
 
-# Style
+all_values = np.concatenate(list(data.values()))
+Y_MIN = 10 * np.floor(all_values.min() / 10)
+Y_MAX = 10 * np.ceil(all_values.max() / 10)
+
+# Style - source-pixel sizes for a 3200x1800 canvas (see prompts/library/pygal.md)
 custom_style = Style(
     background=PAGE_BG,
     plot_background=PAGE_BG,
     foreground=INK,
     foreground_strong=INK,
     foreground_subtle=INK_MUTED,
-    colors=IMPRINT,
-    title_font_size=72,
-    label_font_size=48,
-    major_label_font_size=42,
-    legend_font_size=42,
-    tooltip_font_size=36,
-    opacity=0.85,
+    colors=IMPRINT_PALETTE + (INK,),  # last color reserved for the Group Mean marker
+    title_font_size=66,
+    label_font_size=56,
+    major_label_font_size=44,
+    legend_font_size=44,
+    value_font_size=36,
+    opacity=0.75,
     opacity_hover=1.0,
+    stroke_width=2.5,
 )
 
 # Plot
 chart = pygal.XY(
-    width=4800,
-    height=2700,
+    width=3200,
+    height=1800,
     style=custom_style,
-    title="swarm-basic · pygal · anyplot.ai",
+    title="swarm-basic · python · pygal · anyplot.ai",
     x_title="Department",
     y_title="Performance Score",
     show_legend=True,
     legend_at_bottom=True,
     stroke=False,
-    dots_size=12,
+    dots_size=10,
     show_x_guides=False,
     show_y_guides=True,
     xrange=(0, 5),
-    range=(40, 115),
-    margin=50,
+    range=(Y_MIN, Y_MAX),
+    margin=40,
+    margin_right=20,
 )
 
-# Beeswarm algorithm - spreads points horizontally to avoid overlap
+# Beeswarm algorithm - spreads points horizontally to avoid overlap.
+# Collision thresholds are derived per-axis from the actual rendered dot
+# footprint (dots_size in px) against each axis's own data-unit-per-pixel
+# scale, so a 10px dot compares correctly whether it's 0.03 x-units wide
+# (category axis spans 5 units over ~2900 plot px) or ~0.6 y-units tall
+# (value axis spans Y_MAX-Y_MIN over ~1270 plot px) - not one flat number
+# for both axes.
+PLOT_WIDTH_PX = 2900
+PLOT_HEIGHT_PX = 1270
+DOT_RADIUS_PX = 10
+SPACING_PX = 4
+
+x_unit_per_px = 5 / PLOT_WIDTH_PX
+y_unit_per_px = (Y_MAX - Y_MIN) / PLOT_HEIGHT_PX
+min_dist_x = 2 * DOT_RADIUS_PX * x_unit_per_px + SPACING_PX * x_unit_per_px
+min_dist_y = 2 * DOT_RADIUS_PX * y_unit_per_px + SPACING_PX * y_unit_per_px
+step_x = DOT_RADIUS_PX * x_unit_per_px + SPACING_PX * x_unit_per_px / 2
+
 for cat_idx, (category, values) in enumerate(data.items()):
     center_x = cat_idx + 1
-    point_radius = 0.1
-    spacing = 0.05
 
     sorted_indices = np.argsort(values)
     placed = []
@@ -87,8 +108,7 @@
             for px, py in placed:
                 dist_y = abs(y - py)
                 dist_x = abs(test_x - px)
-                min_dist = 2 * point_radius + spacing
-                if dist_y < min_dist and dist_x < min_dist:
+                if dist_y < min_dist_y and dist_x < min_dist_x:
                     overlap = True
                     break
             if not overlap:
@@ -98,13 +118,17 @@
                 direction = -1
             else:
                 direction = 1
-                offset += point_radius + spacing / 2
+                offset += step_x
 
         placed.append((x, y))
         swarm_points.append((x, y))
 
     chart.add(category, swarm_points)
 
+# Group mean markers - subtle reference points per category (neutral anchor color)
+mean_points = [(cat_idx + 1, float(np.mean(values))) for cat_idx, (_, values) in enumerate(data.items())]
+chart.add("Group Mean", mean_points, dots_size=20)
+
 # x-axis category labels
 chart.x_labels = ["", "Engineering", "Marketing", "Sales", "Operations", ""]