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

plots/bubble-packed/implementations/matplotlib.py

@@ -1,16 +1,17 @@
 """ pyplots.ai
 bubble-packed: Basic Packed Bubble Chart
-Library: matplotlib 3.10.8 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-23
+Library: matplotlib 3.10.8 | Python 3.14.3
+Quality: 87/100 | Updated: 2026-02-23
 """
 
+import matplotlib.collections as mcoll
 import matplotlib.patches as mpatches
+import matplotlib.patheffects as pe
 import matplotlib.pyplot as plt
 import numpy as np
 
 
 # Data - Department budget allocation (in thousands)
-np.random.seed(42)
 labels = [
     "Engineering",
     "Marketing",
@@ -28,136 +29,235 @@
     "Security",
     "QA",
 ]
-values = [850, 420, 680, 320, 180, 290, 750, 210, 150, 380, 240, 550, 460, 170, 195]
-
-# Colors by group (Python Blue primary, Yellow secondary, others colorblind-safe)
-colors = [
-    "#306998",  # Engineering - Blue (Tech)
-    "#FFD43B",  # Marketing - Yellow (Business)
-    "#306998",  # Sales - Blue (Revenue)
-    "#4A90A4",  # Operations - Teal (Support)
-    "#4A90A4",  # HR - Teal (Support)
-    "#4A90A4",  # Finance - Teal (Support)
-    "#FFD43B",  # R&D - Yellow (Innovation)
-    "#4A90A4",  # Customer Support - Teal (Support)
-    "#7B9E89",  # Legal - Sage (Compliance)
-    "#306998",  # IT - Blue (Tech)
-    "#FFD43B",  # Design - Yellow (Creative)
-    "#306998",  # Product - Blue (Tech)
-    "#FFD43B",  # Data Science - Yellow (Analytics)
-    "#7B9E89",  # Security - Sage (Compliance)
-    "#7B9E89",  # QA - Sage (Quality)
-]
+values = [950, 420, 680, 310, 160, 280, 820, 200, 130, 370, 230, 580, 470, 145, 175]
+
+# Group assignments - realistic organizational structure
+group_map = {
+    "Engineering": "Engineering",
+    "IT": "Engineering",
+    "Data Science": "Engineering",
+    "R&D": "Engineering",
+    "Marketing": "Business",
+    "Sales": "Business",
+    "Product": "Business",
+    "Design": "Business",
+    "Operations": "Operations",
+    "HR": "Operations",
+    "Finance": "Operations",
+    "Customer Support": "Operations",
+    "Legal": "Compliance",
+    "Security": "Compliance",
+    "QA": "Compliance",
+}
+
+# Colorblind-safe palette with high hue separation
+group_colors = {"Engineering": "#306998", "Business": "#E8C33A", "Operations": "#D4654A", "Compliance": "#8B6DB0"}
+colors = [group_colors[group_map[label]] for label in labels]
 
 # Scale values to radius (sqrt for area-proportional sizing)
-min_radius = 0.35
-max_radius = 1.9
-values_array = np.array(values)
+min_radius = 0.30
+max_radius = 2.0
+values_array = np.array(values, dtype=float)
 radii = min_radius + (max_radius - min_radius) * np.sqrt(
     (values_array - values_array.min()) / (values_array.max() - values_array.min())
 )
 
-# Circle packing using physics simulation
-n = len(labels)
-
-# Initial positions in grid
-grid_size = int(np.ceil(np.sqrt(n)))
-positions = np.zeros((n, 2))
-for i in range(n):
-    positions[i] = [(i % grid_size) * 4 - grid_size * 2, (i // grid_size) * 4 - grid_size * 2]
-
 # Sort by size (largest first) for better packing
+n = len(labels)
 order = np.argsort(-radii)
-positions = positions[order]
 radii_sorted = radii[order]
 labels_sorted = [labels[i] for i in order]
 values_sorted = [values[i] for i in order]
 colors_sorted = [colors[i] for i in order]
+groups_sorted = [group_map[labels[i]] for i in order]
 
-# Physics simulation for packing
-for iteration in range(350):
-    # Pull toward center with decreasing strength
-    pull_strength = 0.06 * (1 - iteration / 400)
+# Assign group IDs for clustering
+unique_groups = list(group_colors.keys())
+group_ids = np.array([unique_groups.index(g) for g in groups_sorted])
+
+# Initial positions in spiral pattern for tighter convergence
+angles = np.linspace(0, 4 * np.pi, n)
+spiral_r = np.linspace(0, 3, n)
+positions = np.column_stack([spiral_r * np.cos(angles), spiral_r * np.sin(angles)])
+
+# Physics simulation with group-aware clustering
+for iteration in range(500):
+    progress = iteration / 500
+    pull_strength = 0.06 * (1 - progress * 0.8)
+    group_pull = 0.04 * (1 - progress * 0.5)
+
+    # Compute group centers of mass
+    group_centers = {}
+    for gid in range(len(unique_groups)):
+        mask = group_ids == gid
+        if np.any(mask):
+            group_centers[gid] = positions[mask].mean(axis=0)
+
+    # Pull toward center + pull toward own group center
     for i in range(n):
-        dist = np.sqrt(positions[i, 0] ** 2 + positions[i, 1] ** 2)
+        dist = np.linalg.norm(positions[i])
         if dist > 0.01:
             positions[i] -= pull_strength * positions[i] / dist
 
+        gc = group_centers[group_ids[i]]
+        to_group = gc - positions[i]
+        gd = np.linalg.norm(to_group)
+        if gd > 0.01:
+            positions[i] += group_pull * to_group / gd
+
     # Push apart overlapping circles
     for i in range(n):
         for j in range(i + 1, n):
-            dx = positions[j, 0] - positions[i, 0]
-            dy = positions[j, 1] - positions[i, 1]
-            dist = np.sqrt(dx**2 + dy**2)
-            min_dist = radii_sorted[i] + radii_sorted[j] + 0.05  # Small gap between circles
+            delta = positions[j] - positions[i]
+            dist = np.linalg.norm(delta)
+            same_group = group_ids[i] == group_ids[j]
+            gap = 0.04 if same_group else 0.15
+            min_dist = radii_sorted[i] + radii_sorted[j] + gap
 
             if dist < min_dist and dist > 0.001:
                 overlap = (min_dist - dist) / 2
-                dx_norm = dx / dist
-                dy_norm = dy / dist
-                positions[i, 0] -= overlap * dx_norm
-                positions[i, 1] -= overlap * dy_norm
-                positions[j, 0] += overlap * dx_norm
-                positions[j, 1] += overlap * dy_norm
-
-# Create plot (4800x2700 px at 300 dpi)
+                direction = delta / dist
+                positions[i] -= overlap * direction
+                positions[j] += overlap * direction
+
+# Center the layout: shift all positions so the bounding box is centered at origin
+bbox_min = positions.min(axis=0) - radii_sorted.max()
+bbox_max = positions.max(axis=0) + radii_sorted.max()
+positions -= (bbox_min + bbox_max) / 2
+
+# Plot (4800x2700 px at 300 dpi)
 fig, ax = plt.subplots(figsize=(16, 9))
 
-# Draw circles
+# Draw circles using PatchCollection for efficient rendering
+circles = []
+face_colors = []
 for i in range(n):
-    circle = mpatches.Circle(
-        (positions[i, 0], positions[i, 1]),
-        radii_sorted[i],
-        facecolor=colors_sorted[i],
-        edgecolor="white",
-        linewidth=2.5,
-        alpha=0.88,
-    )
-    ax.add_patch(circle)
+    circle = mpatches.Circle((positions[i, 0], positions[i, 1]), radii_sorted[i])
+    circles.append(circle)
+    face_colors.append(colors_sorted[i])
 
-    # Add labels inside larger circles
-    label_len = len(labels_sorted[i])
-    min_radius_for_label = 0.55 + label_len * 0.025
-    if radii_sorted[i] > min_radius_for_label:
+collection = mcoll.PatchCollection(
+    circles, facecolors=face_colors, edgecolors="white", linewidths=2.5, alpha=0.90, zorder=2
+)
+ax.add_collection(collection)
+
+# Add labels inside circles that are large enough, external labels for small ones
+small_circles = []
+for i in range(n):
+    label_chars = len(labels_sorted[i])
+    min_r_for_label = 0.48 + label_chars * 0.018
+    if radii_sorted[i] > min_r_for_label:
         font_scale = min(1.0, radii_sorted[i] / 1.4)
-        label_fontsize = max(9, int(15 * font_scale))
-        value_fontsize = max(8, int(13 * font_scale))
+        label_fontsize = max(12, int(15 * font_scale))
+        value_fontsize = max(12, int(13 * font_scale))
+
+        # Determine text color based on background luminance (WCAG relative luminance)
+        bg_color = colors_sorted[i]
+        rgb = [int(bg_color[j : j + 2], 16) / 255 for j in (1, 3, 5)]
+        luminance = 0.2126 * rgb[0] + 0.7152 * rgb[1] + 0.0722 * rgb[2]
+        text_color = "#1a1a2e" if luminance > 0.45 else "white"
+        text_outline = (
+            pe.withStroke(linewidth=3, foreground="#00000033")
+            if luminance <= 0.45
+            else pe.withStroke(linewidth=3, foreground="#ffffff33")
+        )
+
+        # Wrap long labels for smaller circles
+        display_label = labels_sorted[i]
+        is_wrapped = False
+        if " " in display_label and radii_sorted[i] < 1.0:
+            display_label = display_label.replace(" ", "\n")
+            is_wrapped = True
+
+        # Adjust vertical offsets for wrapped vs single-line labels
+        label_y_offset = 0.05 if is_wrapped else 0.12
+        value_y_offset = -0.35 if is_wrapped else -0.22
+
         ax.text(
             positions[i, 0],
-            positions[i, 1] + radii_sorted[i] * 0.1,
-            labels_sorted[i],
+            positions[i, 1] + radii_sorted[i] * label_y_offset,
+            display_label,
             ha="center",
             va="center",
             fontsize=label_fontsize,
             fontweight="bold",
-            color="white",
+            color=text_color,
+            path_effects=[text_outline],
+            zorder=3,
         )
         ax.text(
             positions[i, 0],
-            positions[i, 1] - radii_sorted[i] * 0.22,
+            positions[i, 1] + radii_sorted[i] * value_y_offset,
             f"${values_sorted[i]}K",
             ha="center",
             va="center",
             fontsize=value_fontsize,
-            color="white",
-            alpha=0.95,
+            color=text_color,
+            alpha=0.85,
+            path_effects=[text_outline],
+            zorder=3,
         )
+    else:
+        small_circles.append(i)
 
-# Set axis limits with padding
+# External labels with leader lines for small circles
+for i in small_circles:
+    cx, cy = positions[i, 0], positions[i, 1]
+    r = radii_sorted[i]
+
+    # Find direction away from center for label placement
+    angle = np.arctan2(cy, cx)
+    offset_dist = r + 0.6
+    lx = cx + offset_dist * np.cos(angle)
+    ly = cy + offset_dist * np.sin(angle)
+
+    ax.annotate(
+        f"{labels_sorted[i]}\n${values_sorted[i]}K",
+        xy=(cx, cy),
+        xytext=(lx, ly),
+        fontsize=12,
+        fontweight="bold",
+        color="#333333",
+        ha="center",
+        va="center",
+        arrowprops={"arrowstyle": "-", "color": "#666666", "lw": 1.2, "shrinkA": 0, "shrinkB": 2},
+        zorder=4,
+    )
+
+# Axis limits with padding
 all_x = positions[:, 0]
 all_y = positions[:, 1]
 max_r = radii_sorted.max()
-padding = 0.6
+padding = 0.9
 ax.set_xlim(all_x.min() - max_r - padding, all_x.max() + max_r + padding)
 ax.set_ylim(all_y.min() - max_r - padding, all_y.max() + max_r + padding)
 ax.set_aspect("equal")
-
-# Remove axes for clean visualization
 ax.axis("off")
 
-# Title
+# Title with total budget subtitle for context
+total_budget = sum(values)
 ax.set_title(
-    "Department Budget Allocation · bubble-packed · matplotlib · pyplots.ai", fontsize=24, fontweight="bold", pad=20
+    f"Department Budget Allocation (${total_budget / 1000:.1f}M Total)\nbubble-packed · matplotlib · pyplots.ai",
+    fontsize=24,
+    fontweight="bold",
+    pad=20,
+)
+
+# Legend for group colors
+legend_handles = [
+    mpatches.Patch(facecolor=color, edgecolor="white", linewidth=1.5, label=group)
+    for group, color in group_colors.items()
+]
+ax.legend(
+    handles=legend_handles,
+    loc="lower right",
+    fontsize=16,
+    framealpha=0.9,
+    edgecolor="#cccccc",
+    fancybox=True,
+    borderpad=0.8,
+    handlelength=1.5,
+    handleheight=1.2,
 )
 
 plt.tight_layout()