update(bubble-packed): plotnine — comprehensive quality review

Comprehensive review improving code quality, data choice, visual design,
spec compliance, and library feature usage.

Author: MarkusNeusinger

plots/bubble-packed/implementations/plotnine.py

@@ -1,19 +1,20 @@
-""" pyplots.ai
+"""pyplots.ai
 bubble-packed: Basic Packed Bubble Chart
-Library: plotnine 0.15.2 | Python 3.13.11
-Quality: 90/100 | Created: 2025-12-23
+Library: plotnine 0.15.3 | Python 3.14.3
+Quality: /100 | Updated: 2026-02-23
 """
 
 import numpy as np
 import pandas as pd
 from plotnine import (
     aes,
     coord_fixed,
-    element_blank,
+    element_rect,
     element_text,
     geom_polygon,
     geom_text,
     ggplot,
+    guides,
     labs,
     scale_fill_manual,
     theme,
@@ -22,8 +23,7 @@
 
 
 # Data - department budgets (in millions)
-np.random.seed(42)
-data = {
+departments = {
     "label": [
         "Engineering",
         "Marketing",
@@ -34,7 +34,7 @@
         "R&D",
         "IT Support",
         "Legal",
-        "Customer Service",
+        "Customer Svc",
         "Design",
         "Logistics",
         "Quality",
@@ -61,41 +61,36 @@
     ],
 }
 
-df = pd.DataFrame(data)
+df = pd.DataFrame(departments)
 
 # Scale values to radii (area-based scaling for accurate perception)
 max_radius = 1.0
-min_radius = 0.3
+min_radius = 0.25
 df["radius"] = min_radius + (max_radius - min_radius) * np.sqrt(df["value"] / df["value"].max())
 
-# Circle packing using force simulation (inline, KISS style)
+# Circle packing - greedy placement + force simulation
 n = len(df)
 radii = df["radius"].values
-
-# Sort by size (largest first) for better packing
 idx = np.argsort(-radii)
 sorted_radii = radii[idx]
+gap = 0.03
 
-# Initialize positions
 x = np.zeros(n)
 y = np.zeros(n)
 
-# Place circles using greedy algorithm
 for i in range(1, n):
     best_dist = float("inf")
     best_x, best_y = 0.0, 0.0
 
-    for angle in np.linspace(0, 2 * np.pi, 36):
+    for angle in np.linspace(0, 2 * np.pi, 72, endpoint=False):
         for ref in range(i):
-            # Try placing next to reference circle
-            test_x = x[ref] + (sorted_radii[ref] + sorted_radii[i] + 0.05) * np.cos(angle)
-            test_y = y[ref] + (sorted_radii[ref] + sorted_radii[i] + 0.05) * np.sin(angle)
+            test_x = x[ref] + (sorted_radii[ref] + sorted_radii[i] + gap) * np.cos(angle)
+            test_y = y[ref] + (sorted_radii[ref] + sorted_radii[i] + gap) * np.sin(angle)
 
-            # Check for collisions
             valid = True
             for j in range(i):
                 dist = np.sqrt((test_x - x[j]) ** 2 + (test_y - y[j]) ** 2)
-                if dist < sorted_radii[i] + sorted_radii[j] + 0.03:
+                if dist < sorted_radii[i] + sorted_radii[j] + gap:
                     valid = False
                     break
 
@@ -109,94 +104,77 @@
     y[i] = best_y
 
 # Force simulation to tighten packing
-for _ in range(1000):
-    # Move toward center
-    x -= x * 0.001
-    y -= y * 0.001
+for _ in range(2000):
+    x -= x * 0.003
+    y -= y * 0.003
 
-    # Separate overlapping circles
     for i in range(n):
         for j in range(i + 1, n):
             dx = x[j] - x[i]
             dy = y[j] - y[i]
             dist = np.sqrt(dx * dx + dy * dy)
-            min_dist = sorted_radii[i] + sorted_radii[j] + 0.03
+            min_dist = sorted_radii[i] + sorted_radii[j] + gap
 
             if dist < min_dist and dist > 0.001:
                 overlap = (min_dist - dist) / 2
                 dx_norm = dx / dist
                 dy_norm = dy / dist
-                x[i] -= overlap * dx_norm * 0.5
-                y[i] -= overlap * dy_norm * 0.5
-                x[j] += overlap * dx_norm * 0.5
-                y[j] += overlap * dy_norm * 0.5
+                x[i] -= overlap * dx_norm
+                y[i] -= overlap * dy_norm
+                x[j] += overlap * dx_norm
+                y[j] += overlap * dy_norm
 
 # Restore original order
-x_out = np.zeros(n)
-y_out = np.zeros(n)
+x_final = np.zeros(n)
+y_final = np.zeros(n)
 for i, orig_idx in enumerate(idx):
-    x_out[orig_idx] = x[i]
-    y_out[orig_idx] = y[i]
+    x_final[orig_idx] = x[i]
+    y_final[orig_idx] = y[i]
 
-df["x"] = x_out
-df["y"] = y_out
+df["x"] = x_final
+df["y"] = y_final
 
-# Create circle polygons for geom_polygon
+# Build circle polygons for geom_polygon
 circle_dfs = []
+angles = np.linspace(0, 2 * np.pi, 64)
 for i, row in df.iterrows():
-    angles = np.linspace(0, 2 * np.pi, 64)
     cx = row["x"] + row["radius"] * np.cos(angles)
     cy = row["y"] + row["radius"] * np.sin(angles)
-    circle_df = pd.DataFrame({"x": cx, "y": cy, "label": row["label"], "group": row["group"], "circle_id": i})
-    circle_dfs.append(circle_df)
-
+    circle_dfs.append(pd.DataFrame({"x": cx, "y": cy, "label": row["label"], "group": row["group"], "circle_id": i}))
 circles_df = pd.concat(circle_dfs, ignore_index=True)
+circles_df["group"] = pd.Categorical(circles_df["group"], categories=["Tech", "Business", "Operations", "Support"])
 
-# Color palette for groups - colorblind-safe (Okabe-Ito palette)
-group_colors = {
-    "Tech": "#0072B2",  # Blue
-    "Business": "#E69F00",  # Orange
-    "Operations": "#009E73",  # Bluish Green
-    "Support": "#CC79A7",  # Reddish Purple
-}
-
-# Create label dataframe (centers) - show full labels for circles large enough
-labels_df = df[["x", "y", "label", "radius"]].copy()
-
-# Show full label for large circles, abbreviated for medium, none for small
+# Labels - full name for large, first word for medium, none for small
+labels_df = df.copy()
 labels_df["display_label"] = labels_df.apply(
-    lambda row: (
-        row["label"]
-        if row["radius"] >= 0.85
-        else (
-            (row["label"][:8] if len(row["label"]) > 8 else row["label"])
-            if row["radius"] >= 0.6
-            else ((row["label"][:5] if len(row["label"]) > 5 else row["label"]) if row["radius"] >= 0.45 else "")
-        )
-    ),
-    axis=1,
+    lambda row: row["label"] if row["value"] >= 22 else (row["label"].split()[0] if row["value"] >= 12 else ""), axis=1
 )
 
-# Create plot
+# Color palette - Okabe-Ito colorblind-safe
+group_colors = {"Tech": "#0072B2", "Business": "#E69F00", "Operations": "#009E73", "Support": "#CC79A7"}
+
+# Plot
 plot = (
     ggplot()
     + geom_polygon(
-        data=circles_df, mapping=aes(x="x", y="y", fill="group", group="circle_id"), color="white", size=0.5, alpha=0.85
+        data=circles_df, mapping=aes(x="x", y="y", fill="group", group="circle_id"), color="white", size=0.6, alpha=0.85
     )
     + geom_text(
         data=labels_df, mapping=aes(x="x", y="y", label="display_label"), size=9, color="white", fontweight="bold"
     )
-    + scale_fill_manual(values=group_colors)
+    + scale_fill_manual(values=group_colors, name="Department Group")
+    + guides(fill="legend")
     + coord_fixed()
-    + labs(title="bubble-packed · plotnine · pyplots.ai", fill="Department Group")
+    + labs(title="bubble-packed · plotnine · pyplots.ai")
     + theme_void()
     + theme(
         figure_size=(16, 9),
-        plot_title=element_text(size=24, ha="center", weight="bold"),
-        legend_title=element_text(size=18),
-        legend_text=element_text(size=14),
+        plot_title=element_text(size=24, ha="center", weight="bold", margin={"b": 12}),
+        legend_title=element_text(size=18, weight="bold"),
+        legend_text=element_text(size=16),
         legend_position="right",
-        plot_background=element_blank(),
+        legend_key=element_rect(fill="white", color="none"),
+        plot_background=element_rect(fill="white", color="none"),
     )
 )
 

plots/bubble-packed/metadata/plotnine.yaml

@@ -1,16 +1,16 @@
 library: plotnine
 specification_id: bubble-packed
 created: '2025-12-23T09:16:24Z'
-updated: '2025-12-23T09:27:41Z'
-generated_by: claude-opus-4-5-20251101
+updated: 2026-02-23T15:35:00+00:00
+generated_by: claude-opus-4-6
 workflow_run: 20456560252
 issue: 0
-python_version: 3.13.11
-library_version: 0.15.2
+python_version: "3.14.3"
+library_version: "0.15.3"
 preview_url: https://storage.googleapis.com/pyplots-images/plots/bubble-packed/plotnine/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bubble-packed/plotnine/plot_thumb.png
 preview_html: null
-quality_score: 90
+quality_score: null
 impl_tags:
   dependencies: []
   techniques: