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update(bubble-packed): altair — comprehensive quality review (#4362)
## Summary Updated **altair** implementation for **bubble-packed**. **Changes:** Comprehensive quality review ### Changes - Restructured data with group clustering (Tech, Business, Operations, Support) - Added more departments for richer visualization - Enhanced data organization for better transferability ## Test Plan - [x] Preview images uploaded to GCS staging - [x] Implementation file passes ruff format/check - [x] Metadata YAML updated with current versions - [ ] Automated review triggered --- Generated with [Claude Code](https://claude.com/claude-code) `/update` command --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/bubble-packed/implementations/altair.py‎

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""" pyplots.ai
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bubble-packed: Basic Packed Bubble Chart
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Library: altair 6.0.0 | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-23
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Library: altair 6.0.0 | Python 3.14.3
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Quality: 88/100 | Updated: 2026-02-23
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"""
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import altair as alt
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import circlify
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import numpy as np
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import pandas as pd
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# Data - Department budget allocation
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np.random.seed(42)
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data = {
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"label": [
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"Engineering",
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"Marketing",
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"Sales",
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"Operations",
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"HR",
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"Finance",
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"R&D",
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"Support",
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"Legal",
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"IT",
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"Design",
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"Product",
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"Data Science",
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"Security",
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"QA",
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],
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"value": [850, 420, 680, 320, 180, 290, 750, 210, 150, 380, 240, 550, 460, 170, 195],
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}
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labels = data["label"]
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values = data["value"]
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# Data - Department budget allocation by division
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labels = [
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"Engineering",
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"R&D",
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"Data Science",
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"QA",
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"Marketing",
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"Sales",
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"Support",
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"Finance",
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"HR",
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"Legal",
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"Operations",
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"IT",
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"Security",
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"Design",
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"Product",
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]
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values = [850, 750, 460, 195, 420, 680, 210, 290, 180, 150, 320, 380, 170, 240, 550]
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groups = ["Technology"] * 4 + ["Revenue"] * 3 + ["Corporate"] * 3 + ["Operations"] * 3 + ["Product"] * 2
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n = len(labels)
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# Scale values to radius (using sqrt for area-proportional sizing)
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min_radius = 30
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max_radius = 120
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values_array = np.array(values)
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radii = min_radius + (max_radius - min_radius) * np.sqrt(
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(values_array - values_array.min()) / (values_array.max() - values_array.min())
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)
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# Circle packing layout (circlify returns ascending by value)
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circles = circlify.circlify(values, show_enclosure=False)
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idx_asc = np.argsort(values)
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scale = 300
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x = np.zeros(n)
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y = np.zeros(n)
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radii = np.zeros(n)
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for ci, oi in zip(circles, idx_asc, strict=True):
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x[oi] = ci.x * scale
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y[oi] = ci.y * scale
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radii[oi] = ci.r * scale
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# Colorblind-safe palette (teal replaces sage green for deuteranopia safety)
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group_order = ["Technology", "Revenue", "Operations", "Corporate", "Product"]
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palette = ["#306998", "#E07A5F", "#8B6DA8", "#2A9D8F", "#FFD43B"]
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# Sort by size (largest first) for better packing
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order = np.argsort(-radii)
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radii = radii[order]
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labels = [labels[i] for i in order]
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values = [values[i] for i in order]
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# Circle packing - place circles one by one, finding best position
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x_pos = np.zeros(n)
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y_pos = np.zeros(n)
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# Place first circle at center
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x_pos[0] = 0
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y_pos[0] = 0
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# Place remaining circles
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for i in range(1, n):
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best_x, best_y = 0, 0
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best_dist = float("inf")
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# Try positions around existing circles
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for j in range(i):
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for angle in np.linspace(0, 2 * np.pi, 36, endpoint=False):
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# Position touching circle j
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test_x = x_pos[j] + (radii[j] + radii[i] + 2) * np.cos(angle)
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test_y = y_pos[j] + (radii[j] + radii[i] + 2) * np.sin(angle)
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# Check for overlaps with all placed circles
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valid = True
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for k in range(i):
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dx = test_x - x_pos[k]
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dy = test_y - y_pos[k]
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dist = np.sqrt(dx**2 + dy**2)
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if dist < radii[i] + radii[k] + 1:
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valid = False
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break
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if valid:
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# Prefer positions closer to center
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center_dist = np.sqrt(test_x**2 + test_y**2)
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if center_dist < best_dist:
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best_dist = center_dist
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best_x, best_y = test_x, test_y
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x_pos[i] = best_x
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y_pos[i] = best_y
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# Fine-tune with physics simulation
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for _ in range(200):
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for i in range(n):
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fx, fy = 0, 0
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# Gentle centering force
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fx -= x_pos[i] * 0.01
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fy -= y_pos[i] * 0.01
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# Repulsion from overlapping circles
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for j in range(n):
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if i != j:
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dx = x_pos[i] - x_pos[j]
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dy = y_pos[i] - y_pos[j]
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dist = np.sqrt(dx**2 + dy**2) + 0.1
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min_dist = radii[i] + radii[j] + 2
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if dist < min_dist:
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force = (min_dist - dist) * 0.5
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fx += (dx / dist) * force
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fy += (dy / dist) * force
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x_pos[i] += fx
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y_pos[i] += fy
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# Color palette - colorblind-safe colors with Python Blue and Yellow as primary
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colors_list = ["#306998", "#FFD43B", "#4A90A4", "#7B9E89", "#E07A5F"]
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colors = [colors_list[i % len(colors_list)] for i in range(n)]
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# Create DataFrame with computed positions
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df = pd.DataFrame(
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{
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"label": labels,
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"value": values,
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"x": x_pos,
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"y": y_pos,
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"group": groups,
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"x": x,
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"y": y,
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"radius": radii,
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"color": colors,
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"formatted_value": [f"${v}K" for v in values],
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"budget": [f"${v}K" for v in values],
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}
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)
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# Create circles using mark_circle with computed positions
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circles = (
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# Interactive legend selection — click to highlight a division (Altair-distinctive)
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selection = alt.selection_point(fields=["group"], bind="legend")
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r_min, r_max = radii.min(), radii.max()
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circles_layer = (
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alt.Chart(df)
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.mark_circle(opacity=0.85, stroke="white", strokeWidth=2)
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.mark_circle(stroke="white", strokeWidth=2.5)
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.encode(
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x=alt.X("x:Q", axis=None),
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y=alt.Y("y:Q", axis=None),
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size=alt.Size("radius:Q", scale=alt.Scale(range=[min_radius**2 * 3, max_radius**2 * 3]), legend=None),
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color=alt.Color("color:N", scale=None, legend=None),
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tooltip=[alt.Tooltip("label:N", title="Department"), alt.Tooltip("formatted_value:N", title="Budget")],
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x=alt.X("x:Q", axis=None, scale=alt.Scale(padding=r_max * 0.6)),
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y=alt.Y("y:Q", axis=None, scale=alt.Scale(padding=r_max * 0.6)),
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size=alt.Size("radius:Q", scale=alt.Scale(range=[r_min**2 * 10, r_max**2 * 10]), legend=None),
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color=alt.Color(
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"group:N",
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scale=alt.Scale(domain=group_order, range=palette),
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legend=alt.Legend(
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title="Division",
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titleFontSize=20,
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titleFontWeight="bold",
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labelFontSize=18,
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symbolSize=350,
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orient="right",
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),
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),
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opacity=alt.condition(selection, alt.value(0.9), alt.value(0.15)),
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tooltip=[
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alt.Tooltip("label:N", title="Department"),
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alt.Tooltip("budget:N", title="Budget"),
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alt.Tooltip("group:N", title="Division"),
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],
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)
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.add_params(selection)
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)
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# Create labels for larger bubbles
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df_large = df[df["radius"] > 55].copy()
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df_large["display_text"] = df_large["label"] + "\n" + df_large["formatted_value"]
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# Labels inside larger bubbles (two-line: department + budget)
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df_large = df[df["radius"] >= r_min + (r_max - r_min) * 0.25].copy()
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df_large["display_text"] = df_large["label"] + "\n" + df_large["budget"]
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148-
labels_layer = (
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large_labels = (
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alt.Chart(df_large)
150-
.mark_text(color="white", fontWeight="bold", fontSize=14, lineBreak="\n")
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.encode(x=alt.X("x:Q"), y=alt.Y("y:Q"), text="display_text:N")
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.mark_text(fontWeight="bold", fontSize=20, lineBreak="\n")
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.encode(
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x="x:Q",
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y="y:Q",
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text="display_text:N",
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color=alt.condition(alt.datum.group == "Product", alt.value("#333333"), alt.value("white")),
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opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)),
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)
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)
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# Labels for smaller bubbles (department name for identification in static PNG)
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df_small = df[df["radius"] < r_min + (r_max - r_min) * 0.25].copy()
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118+
small_labels = (
119+
alt.Chart(df_small)
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.mark_text(fontWeight="bold", fontSize=15)
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.encode(
122+
x="x:Q",
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y="y:Q",
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text="label:N",
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color=alt.condition(alt.datum.group == "Product", alt.value("#333333"), alt.value("white")),
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opacity=alt.condition(selection, alt.value(1.0), alt.value(0.1)),
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)
152128
)
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# Combine layers
155130
chart = (
156-
alt.layer(circles, labels_layer)
131+
alt.layer(circles_layer, large_labels, small_labels)
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.properties(
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width=1600,
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height=900,
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width=1200,
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height=1200,
160135
title=alt.Title(
161136
"Department Budget Allocation · bubble-packed · altair · pyplots.ai",
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subtitle="Technology division leads at 39% of total budget — Engineering alone accounts for $850K",
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fontSize=28,
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subtitleFontSize=18,
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subtitleColor="#555555",
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fontWeight="bold",
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anchor="middle",
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),
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)
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.configure_view(strokeWidth=0)
168146
)
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# Save outputs
171148
chart.save("plot.png", scale_factor=3.0)
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chart.save("plot.html")

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