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github-actions merged 7 commits into from
Feb 23, 2026
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update(bubble-packed): plotnine — comprehensive quality review #4363

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6ff3779
update(bubble-packed): plotnine — comprehensive quality review
MarkusNeusinger Feb 23, 2026
7d87b64
chore(plotnine): update quality score 85 and review feedback for bubb…
github-actions[bot] Feb 23, 2026
bcdc360
fix(plotnine): address review feedback for bubble-packed
github-actions[bot] Feb 23, 2026
786a22a
chore(plotnine): update quality score 88 and review feedback for bubb…
github-actions[bot] Feb 23, 2026
ed4e7ca
fix(plotnine): address review feedback for bubble-packed
github-actions[bot] Feb 23, 2026
6826a76
chore(plotnine): update quality score 90 and review feedback for bubb…
github-actions[bot] Feb 23, 2026
1ee71de
Merge branch 'main' into implementation/bubble-packed/plotnine
github-actions[bot] Feb 23, 2026
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120 changes: 49 additions & 71 deletions plots/bubble-packed/implementations/plotnine.py
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Original file line number Diff line number Diff line change
@@ -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
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Copilot AI Feb 23, 2026

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The docstring header now includes Quality: /100 without a numeric value, which looks like a formatting regression and can break any tooling that parses these headers. Populate the quality score (e.g., Quality: 90/100) or remove the Quality: field if it’s intentionally unknown.

Suggested change
Quality: /100 | Updated: 2026-02-23
Quality: 90/100 | Updated: 2026-02-23

Copilot uses AI. Check for mistakes.
"""

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,
Expand All @@ -22,8 +23,7 @@


# Data - department budgets (in millions)
np.random.seed(42)
data = {
departments = {
"label": [
"Engineering",
"Marketing",
Expand All @@ -34,7 +34,7 @@
"R&D",
"IT Support",
"Legal",
"Customer Service",
"Customer Svc",
"Design",
"Logistics",
"Quality",
Expand All @@ -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

Expand All @@ -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"),
)
)

Expand Down
10 changes: 5 additions & 5 deletions plots/bubble-packed/metadata/plotnine.yaml
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Original file line number Diff line number Diff line change
@@ -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
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quality_score changed from a numeric value to null, which is likely to break any downstream consumers expecting an integer/float score (and it also conflicts with the implementation header still showing a /100 format). If the pipeline requires a score, keep it numeric; if unknown is allowed, ensure all other places (like the implementation header and any validators) agree with null.

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Timestamp formatting is inconsistent: created is a quoted string with Z, while updated is unquoted with an offset. For consistency and to avoid YAML parsers auto-typing timestamps differently across fields, consider quoting updated and using the same ISO-8601 style (Z vs +00:00) as created.

Suggested change
updated: 2026-02-23T15:35:00+00:00
updated: '2026-02-23T15:35:00Z'

Copilot uses AI. Check for mistakes.
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"
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quality_score changed from a numeric value to null, which is likely to break any downstream consumers expecting an integer/float score (and it also conflicts with the implementation header still showing a /100 format). If the pipeline requires a score, keep it numeric; if unknown is allowed, ensure all other places (like the implementation header and any validators) agree with null.

Copilot uses AI. Check for mistakes.
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
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quality_score changed from a numeric value to null, which is likely to break any downstream consumers expecting an integer/float score (and it also conflicts with the implementation header still showing a /100 format). If the pipeline requires a score, keep it numeric; if unknown is allowed, ensure all other places (like the implementation header and any validators) agree with null.

Suggested change
quality_score: null
quality_score: 95

Copilot uses AI. Check for mistakes.
impl_tags:
dependencies: []
techniques:
Expand Down
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