feat(pygal): implement scatter-annotated

[github-actions]

Implementation: scatter-annotated - pygal

Implements the pygal version of scatter-annotated.

File: plots/scatter-annotated/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a scatter chart with 12 tech company data points plotted on a white background. The X-axis shows "Market Cap (Billion $)" ranging from 0 to 160, and the Y-axis shows "Annual Revenue (Billion $)" ranging from 0 to ~65. Each company is represented by a distinctly colored dot with its name annotated directly next to the point. Companies include TechFlow (blue, bottom-left), DataPrime (yellow), ByteLogic (light blue), CyberLink (green), CloudNine (red), SoftEdge (orange), DevStack (teal), DigiTech (purple), NetWave (green), AppForge (dark gray), CodeSphere (purple), and WebCore (gold, top-right). The title "scatter-annotated · pygal · pyplots.ai" appears at the top center. A legend at the bottom lists all 12 companies in 6 columns. Grid lines are visible in light gray. The annotations are clearly readable and well-positioned next to each point.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and annotations are readable; tick labels slightly small
• VQ-02: No Overlap (8/8) - No overlapping text, data points well-spaced, annotations clearly positioned
• VQ-03: Element Visibility (8/8) - Markers appropriately sized for 12 data points, good visibility
• VQ-04: Color Accessibility (4/5) - Good variety of colors, though some similar shades (two purples, two greens)
• VQ-05: Layout Balance (5/5) - Good use of canvas, plot fills appropriate space, balanced margins
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Market Cap (Billion $)", "Annual Revenue (Billion $)"
• VQ-07: Grid & Legend (0/2) - Legend at bottom is very small and hard to read; redundant with annotations

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct scatter plot with annotations
• SC-02: Data Mapping (5/5) - X/Y correctly assigned (market cap vs revenue)
• SC-03: Required Features (5/5) - All spec features present: scatter points, text labels/annotations
• SC-04: Data Range (3/3) - Axes show all data with appropriate padding
• SC-05: Legend Accuracy (2/2) - Legend labels match data series
• SC-06: Title Format (2/2) - Correct format: "scatter-annotated · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows variety of company sizes; could have more outliers
• DQ-02: Realistic Context (7/7) - Tech company market cap vs revenue is a realistic, neutral business scenario
• DQ-03: Appropriate Scale (4/5) - Values are plausible for tech companies; some correlation visible

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Simple imports → data → plot → save structure
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42), though data is actually deterministic
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pygal, Style)
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.html (HTML output not an issue but seed import unused)

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal XY chart with individual series for colors, custom Style, print_values for annotations; could leverage tooltips more

Strengths

• Excellent use of individual series per company to achieve distinct colors for each data point
• Clean annotation placement with company names displayed directly on the chart using print_values and formatter
• Good use of pygal's custom Style for consistent theming with appropriate font sizes
• Well-chosen business context (tech companies market cap vs revenue) that's realistic and neutral
• Proper canvas size (4800x2700) with appropriately scaled fonts

Weaknesses

• Legend at bottom is redundant with on-chart annotations and appears very small/hard to read
• Some color pairs are similar (two purples, two greens) which could cause confusion

Verdict: APPROVED