feat(highcharts): implement scatter-annotated

[github-actions]

Implementation: scatter-annotated - highcharts

Implements the highcharts version of scatter-annotated.

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

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a scatter chart with 15 data points representing tech companies. Each point is a semi-transparent blue circle (rgba blue ~#306998 with alpha 0.7) with company name labels positioned above each point. The title "scatter-annotated · highcharts · pyplots.ai" appears at the top in bold. The X-axis shows "Annual Revenue ($ millions)" ranging from 0 to 500+, and the Y-axis shows "Year-over-Year Growth (%)" ranging from approximately -14 to 96. Companies like PrimeData (high growth ~89%), SyncLabs (~67%), and QuantumIO (negative growth ~-8%) are clearly labeled. The background is white with subtle gray grid lines. Labels have white text outlines for readability.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and tick marks are clearly readable. Data labels are legible with good text outline. Slightly smaller than optimal for 4800px canvas.
• VQ-02: No Overlap (8/8) - No overlapping text thanks to allowOverlap: false setting. All labels are fully readable.
• VQ-03: Element Visibility (7/8) - Markers are well-sized (radius 20) with good alpha (0.7). Could be slightly larger for this data density (15 points).
• VQ-04: Color Accessibility (5/5) - Single blue color (#306998) is colorblind-safe. No problematic color combinations.
• VQ-05: Layout Balance (4/5) - Good margins and plot fills canvas well. Minor asymmetry with some empty space in upper-left quadrant.
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Annual Revenue ($ millions)" and "Year-over-Year Growth (%)"
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha 0.15). Legend disabled which is appropriate for single series, but grid could be slightly more visible.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct scatter plot type
• SC-02: Data Mapping (5/5) - X/Y correctly mapped (revenue vs growth)
• SC-03: Required Features (5/5) - Text annotations present via dataLabels, points have alpha transparency
• SC-04: Data Range (3/3) - Axes show all data points with appropriate padding
• SC-05: Legend Accuracy (2/2) - Legend appropriately disabled for single series
• SC-06: Title Format (2/2) - Correct format: "scatter-annotated · highcharts · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows variety: high performers (PrimeData), low performers (QuantumIO with negative growth), mid-range companies. Good spread across revenue range.
• DQ-02: Realistic Context (7/7) - Tech companies with revenue and growth metrics is a realistic, neutral business scenario
• DQ-03: Appropriate Scale (4/5) - Revenue 50-500M and growth -10% to 90% are plausible. Some growth values (89%) are on the high end but acceptable for tech.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → config → render → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (0/1) - Uses raw dict config instead of highcharts-core Python library (not deprecated, but doesn't follow library rules pattern)
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Uses Highcharts dataLabels feature for annotations with text outline. Could leverage more Highcharts-specific features like hover tooltips configuration or animation settings.

Strengths

• Excellent use of Highcharts dataLabels with allowOverlap: false to prevent label collisions
• Clean, professional appearance with appropriate color choice and transparency
• Good text outline (textOutline: "3px white") ensures label readability over grid lines
• Realistic tech company business context that demonstrates the annotation use case well
• Proper title format following spec requirements

Weaknesses

• Uses raw JSON dict configuration instead of highcharts-core Python classes as shown in library rules
• Grid lines could be slightly more visible (alpha 0.15 is quite faint)
• Some labels could benefit from smart positioning (e.g., labels near axis edges like "DataFlow" appear slightly cramped)

Verdict: APPROVED