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update(bubble-packed): plotly — comprehensive quality review (#4360)
## Summary Updated **plotly** implementation for **bubble-packed**. **Changes:** Comprehensive quality review ### Changes - Renamed data dict to budgets for better transferability - Converted values to numpy array for vectorized radius calculation - Improved radius scaling formula - Removed unnecessary random seed ## 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/plotly.py‎

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""" pyplots.ai
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bubble-packed: Basic Packed Bubble Chart
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Library: plotly 6.5.0 | Python 3.13.11
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Quality: 93/100 | Created: 2025-12-23
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Library: plotly 6.5.2 | Python 3.14.3
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Quality: 91/100 | Updated: 2026-02-23
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"""
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import numpy as np
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import plotly.graph_objects as go
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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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"Marketing": 2800000,
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"Engineering": 4500000,
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"Sales": 3200000,
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"Operations": 1800000,
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"HR": 950000,
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"Finance": 1200000,
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"R&D": 3800000,
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"Support": 1100000,
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"Legal": 650000,
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"IT": 2100000,
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"Product": 1500000,
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"QA": 880000,
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"Data Science": 1650000,
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"Design": 720000,
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"Admin": 450000,
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}
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labels = list(data.keys())
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values = list(data.values())
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# Circle packing simulation using force-directed approach
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# Data — department budgets with functional groupings
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departments = [
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("Engineering", 4500000, "Technology"),
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("R&D", 3800000, "Technology"),
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("IT", 2100000, "Technology"),
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("Data Science", 1650000, "Technology"),
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("QA", 880000, "Technology"),
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("Sales", 3200000, "Revenue"),
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("Marketing", 2800000, "Revenue"),
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("Operations", 1800000, "Operations"),
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("Finance", 1200000, "Operations"),
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("Support", 1100000, "Operations"),
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("Admin", 450000, "Operations"),
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("HR", 950000, "Corporate"),
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("Legal", 650000, "Corporate"),
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("Product", 1500000, "Corporate"),
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("Design", 720000, "Corporate"),
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]
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labels = [d[0] for d in departments]
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values = np.array([d[1] for d in departments])
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groups = [d[2] for d in departments]
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n = len(labels)
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# Group colors — colorblind-safe palette starting with Python Blue
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group_colors = {"Technology": "#306998", "Revenue": "#E69F00", "Operations": "#009E73", "Corporate": "#CC79A7"}
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# Scale radii by area (sqrt) for accurate visual perception
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radii_scale = np.sqrt(np.array(values)) / np.sqrt(max(values)) * 100
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radii = np.sqrt(values / values.max()) * 110
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# Initial positions - spread in a circle
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# Circle packing via force simulation
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np.random.seed(42)
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angles = np.linspace(0, 2 * np.pi, n, endpoint=False)
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x_pos = np.cos(angles) * 200 + np.random.randn(n) * 50
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y_pos = np.sin(angles) * 200 + np.random.randn(n) * 50
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x_pos = np.cos(angles) * 150 + np.random.randn(n) * 30
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y_pos = np.sin(angles) * 150 + np.random.randn(n) * 30
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# Force simulation for circle packing
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for _ in range(500):
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for _ in range(600):
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for i in range(n):
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fx, fy = 0, 0
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# 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 between circles
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fx, fy = -x_pos[i] * 0.01, -y_pos[i] * 0.01
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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_scale[i] + radii_scale[j] + 5
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min_dist = radii[i] + radii[j] + 4
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if dist < min_dist:
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force = (min_dist - dist) * 0.3
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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 - Python colors first, then colorblind-safe
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colors = [
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"#306998", # Python Blue
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"#FFD43B", # Python Yellow
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"#4E79A7",
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"#F28E2B",
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"#E15759",
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"#76B7B2",
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"#59A14F",
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"#EDC948",
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"#B07AA1",
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"#FF9DA7",
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"#9C755F",
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"#BAB0AC",
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"#5778A4",
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"#E49444",
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"#85B6B2",
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]
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# Weight-based centering for better visual balance (larger bubbles pull center)
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area_weights = radii**2
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x_pos -= np.average(x_pos, weights=area_weights)
66+
y_pos -= np.average(y_pos, weights=area_weights)
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# Format values for display (inline)
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formatted_values = [f"${v / 1000000:.1f}M" if v >= 1000000 else f"${v / 1000:.0f}K" for v in values]
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# Format values for display
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formatted = [f"${v / 1e6:.1f}M" if v >= 1e6 else f"${v / 1e3:.0f}K" for v in values]
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shares = [f"{v / values.sum() * 100:.1f}" for v in values]
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total = f"${values.sum() / 1e6:.1f}M"
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# Create bubble chart
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# Tight axis ranges for better canvas utilization
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pad = 15
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x_lo = (x_pos - radii).min() - pad
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x_hi = (x_pos + radii).max() + pad
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y_lo = (y_pos - radii).min() - pad
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y_hi = (y_pos + radii).max() + pad
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# Convert data-coordinate radii to pixel marker diameters
81+
fig_w, fig_h = 1600, 900
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m_l, m_r, m_t, m_b = 35, 35, 85, 85
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plot_w, plot_h = fig_w - m_l - m_r, fig_h - m_t - m_b
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px_per_unit = min(plot_w / (x_hi - x_lo), plot_h / (y_hi - y_lo))
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marker_diameters = 2 * radii * px_per_unit
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# Text colors for contrast against group backgrounds
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text_colors = []
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for g in groups:
90+
c = group_colors[g]
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lum = 0.299 * int(c[1:3], 16) + 0.587 * int(c[3:5], 16) + 0.114 * int(c[5:7], 16)
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text_colors.append("white" if lum < 160 else "#333")
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# Build figure — one trace per group for idiomatic Plotly legend
8895
fig = go.Figure()
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# Add markers
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fig.add_trace(
92-
go.Scatter(
93-
x=x_pos,
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y=y_pos,
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mode="markers",
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marker=dict(size=radii_scale * 2, color=colors[:n], line=dict(color="white", width=2), opacity=0.85),
97-
hovertemplate=[
98-
f"<b>{lbl}</b><br>{fval}<extra></extra>" for lbl, fval in zip(labels, formatted_values, strict=True)
99-
],
97+
for group_name, group_color in group_colors.items():
98+
idx = [i for i in range(n) if groups[i] == group_name]
99+
fig.add_trace(
100+
go.Scatter(
101+
x=x_pos[idx],
102+
y=y_pos[idx],
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mode="markers",
104+
name=group_name,
105+
marker={
106+
"size": marker_diameters[idx],
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"sizemode": "diameter",
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"color": group_color,
109+
"opacity": 0.9,
110+
"line": {"color": "white", "width": 2.5},
111+
},
112+
text=[labels[i] for i in idx],
113+
customdata=np.column_stack(
114+
[[formatted[i] for i in idx], [shares[i] for i in idx], [groups[i] for i in idx]]
115+
),
116+
hovertemplate="<b>%{text}</b> (%{customdata[2]})<br>Budget: %{customdata[0]}<br>Share: %{customdata[1]}%<extra></extra>",
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)
100118
)
101-
)
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103-
# Add text annotations with size based on bubble radius
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# Text labels inside bubbles — minimum 14pt for readability
104121
for i in range(n):
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font_size = max(10, min(18, int(radii_scale[i] * 0.2)))
122+
font_size = max(14, min(20, int(radii[i] * 0.22)))
123+
label_text = f"<b>{labels[i]}</b><br>{formatted[i]}" if radii[i] > 35 else f"<b>{labels[i]}</b>"
106124
fig.add_annotation(
107125
x=x_pos[i],
108126
y=y_pos[i],
109-
text=f"<b>{labels[i]}</b><br>{formatted_values[i]}",
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text=label_text,
110128
showarrow=False,
111-
font=dict(size=font_size, color="white", family="Arial"),
129+
font={"size": font_size, "color": text_colors[i], "family": "Arial"},
112130
)
113131

114132
# Layout
115133
fig.update_layout(
116-
title=dict(
117-
text="Department Budget Allocation · bubble-packed · plotly · pyplots.ai",
118-
font=dict(size=32, color="#333"),
119-
x=0.5,
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xanchor="center",
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),
122-
xaxis=dict(
123-
showgrid=False, zeroline=False, showticklabels=False, title="", range=[min(x_pos) - 150, max(x_pos) + 150]
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),
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yaxis=dict(
126-
showgrid=False,
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zeroline=False,
128-
showticklabels=False,
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title="",
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scaleanchor="x",
131-
scaleratio=1,
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range=[min(y_pos) - 150, max(y_pos) + 150],
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),
134+
title={
135+
"text": "Department Budget Allocation · bubble-packed · plotly · pyplots.ai",
136+
"font": {"size": 32, "color": "#333"},
137+
"x": 0.5,
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"xanchor": "center",
139+
},
140+
xaxis={"showgrid": False, "zeroline": False, "showticklabels": False, "title": "", "range": [x_lo, x_hi]},
141+
yaxis={
142+
"showgrid": False,
143+
"zeroline": False,
144+
"showticklabels": False,
145+
"title": "",
146+
"scaleanchor": "x",
147+
"scaleratio": 1,
148+
"range": [y_lo, y_hi],
149+
},
134150
template="plotly_white",
135-
showlegend=False,
136-
margin=dict(l=50, r=50, t=100, b=50),
151+
legend={
152+
"font": {"size": 16, "family": "Arial"},
153+
"orientation": "h",
154+
"yanchor": "top",
155+
"y": -0.04,
156+
"xanchor": "center",
157+
"x": 0.5,
158+
"itemsizing": "constant",
159+
},
160+
margin={"l": m_l, "r": m_r, "t": m_t, "b": m_b},
137161
paper_bgcolor="white",
138162
plot_bgcolor="white",
139163
)
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141-
# Save outputs
142-
fig.write_image("plot.png", width=1600, height=900, scale=3)
165+
# Total budget annotation below the cluster
166+
fig.add_annotation(
167+
text=f"Total: {total}",
168+
xref="paper",
169+
yref="paper",
170+
x=0.5,
171+
y=-0.01,
172+
showarrow=False,
173+
font={"size": 18, "color": "#666", "family": "Arial"},
174+
)
175+
176+
# Save
177+
fig.write_image("plot.png", width=fig_w, height=fig_h, scale=3)
143178
fig.write_html("plot.html", include_plotlyjs=True, full_html=True)

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