feat(highcharts): implement subplot-grid-custom

[github-actions]

Implementation: subplot-grid-custom - highcharts

Implements the highcharts version of subplot-grid-custom.

File: plots/subplot-grid-custom/implementations/highcharts.py

Parent Issue: #2856

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a custom subplot grid dashboard with the title "subplot-grid-custom · highcharts · pyplots.ai" at the top. The layout contains 3 subplots arranged in a custom grid:

• Left (spanning 2 rows): A large blue (#306998) line chart showing "Stock Price (90 Days)" with price values ranging from ~123 to 161 on the Y-axis (labeled "Price ($)") and trading days 0-90 on the X-axis. The line shows a declining trend from ~155 to ~130 with some volatility.
• Top Right: A yellow (#FFD43B) column chart showing "Trading Volume" with volume in millions (0-5M) against day numbers, displaying uniform bar distribution.
• Bottom Right: A cyan (#17BECF) scatter plot titled "Risk vs Return by Sector" showing 5 labeled data points (Tech, Health, Finance, Energy, Consumer) plotting volatility (%) vs return (%), with clear data labels above each point.
  All charts have white backgrounds with subtle gray borders and grid lines. Text is generally legible but somewhat small at full resolution.

Quality Score: 82/100

Criteria Checklist

Visual Quality (32/40 pts)

• VQ-01: Text Legibility (7/10) - Text is readable but titles could be larger for 4800x2700; axis labels on smaller charts are slightly small
• VQ-02: No Overlap (8/8) - No overlapping text; all labels clear and well-positioned
• VQ-03: Element Visibility (6/8) - Main chart markers visible but small; scatter markers well-sized with labels
• VQ-04: Color Accessibility (5/5) - Colorblind-safe palette (blue, yellow, purple, cyan) with no red-green conflicts
• VQ-05: Layout Balance (4/5) - Good grid layout with main chart spanning 2 rows; slight imbalance with right column being narrow
• VQ-06: Axis Labels (1/2) - Labels present with units (Price $, Volume M, Volatility %, Return %) but some are hard to read
• VQ-07: Grid & Legend (1/2) - Grid subtle and appropriate; legends disabled (appropriate for this dashboard)

Spec Compliance (22/25 pts)

• SC-01: Plot Type (8/8) - Correctly implements custom subplot grid with multiple chart types (line, column, scatter)
• SC-02: Data Mapping (5/5) - X/Y correctly assigned for all three subplots
• SC-03: Required Features (4/5) - Has colspan/rowspan layout, multiple chart types, but missing the histogram mentioned in spec (returns distribution replaced with scatter)
• SC-04: Data Range (3/3) - All axes show complete data ranges
• SC-05: Legend Accuracy (0/2) - Legends disabled; no legend to evaluate (acceptable for dashboard but loses points)
• SC-06: Title Format (2/2) - Uses correct format: "subplot-grid-custom · highcharts · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple chart types in custom grid; demonstrates colspan; lacks rowspan example
• DQ-02: Realistic Context (7/7) - Excellent financial dashboard scenario with stock price, volume, and sector analysis
• DQ-03: Appropriate Scale (4/5) - Realistic stock prices (~$130-160), volumes (1-5M), and return/volatility percentages

Code Quality (7/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure with imports → data → configs → HTML → screenshot
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (0/2) - histogram_data is generated but never used in the final charts (dead code)
• CQ-04: No Deprecated API (1/1) - Modern Highcharts usage
• CQ-05: Output Correct (0/1) - Saves as plot.png but code generates histogram data that's not rendered

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses CSS grid for custom layout and multiple Highcharts instances; could leverage Highcharts dashboard module or synchronized charts

Strengths

• Excellent custom grid layout using CSS grid with main chart spanning 2 rows (colspan equivalent)
• Colorblind-safe color palette using Python-themed blue/yellow and complementary cyan/purple
• Realistic financial dashboard scenario with coherent related data (stock price, volume, sector analysis)
• Proper title format following pyplots.ai conventions
• Clean inline Highcharts JS approach for reliable headless rendering
• Good use of data labels on scatter plot for sector identification

Weaknesses

• Missing histogram chart that was defined in code (histogram_chart_config created but not rendered, scatter used instead)
• Text sizes slightly small for 4800x2700 resolution - titles should be 40px+ for optimal readability
• No legend on any chart (acceptable for dashboard but spec shows supporting "detail views")
• Only 3 subplots when spec example suggests 5+ cells (one 2x2 main + four 1x1 detail views)

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a financial dashboard with a custom grid layout containing 5 charts. The main chart (spanning 2 columns on the left) shows "Stock Price (90 Days)" as a blue line chart with markers, displaying price values from ~$123 to $161 over 90 trading days. The Y-axis shows "Price ($)" and X-axis shows "Trading Day". On the right side, there are 4 smaller detail charts arranged in a 2x2 grid:

• Top-right 1: "Trading Volume" bar chart in yellow showing volume in millions
• Top-right 2: "Returns Distribution" histogram in purple showing daily return percentages
• Bottom-right 1: "Risk vs Return by Sector" scatter plot in cyan with labeled data points for Tech, Health, Finance, Energy, and Consumer sectors
• Bottom-right 2: "20-Day Moving Average" line chart in brown showing smoothed price data

The dashboard title "subplot-grid-custom · highcharts · pyplots.ai" appears at the top. All charts have subtle gray gridlines, proper axis labels with units, and legends. The color palette uses blue (#306998), yellow (#FFD43B), purple (#9467BD), cyan (#17BECF), and brown (#8C564B) - a colorblind-safe combination.

Quality Score: 91/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - All titles, axis labels, and tick marks are clearly readable at the output size
• VQ-02: No Overlap (8/8) - No overlapping text or elements anywhere in the dashboard
• VQ-03: Element Visibility (7/8) - Line widths, bar sizes, and scatter markers are well-sized; main chart markers could be slightly larger
• VQ-04: Color Accessibility (5/5) - Excellent colorblind-safe palette with distinct colors for each chart
• VQ-05: Layout Balance (5/5) - Custom grid layout excellently demonstrates colspan/rowspan with main chart spanning 2x2 and 4 detail charts
• VQ-06: Axis Labels (2/2) - All axes have descriptive labels with units: "Price ($)", "Volume (M)", "Daily Return (%)", "Volatility (%)", "Return (%)", "MA Price ($)"
• VQ-07: Grid & Legend (0/2) - Legends are present but very small compared to the chart sizes; legend text appears tiny relative to titles

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correctly implements custom subplot grid with mixed chart types (line, column, scatter)
• SC-02: Data Mapping (5/5) - X/Y correctly assigned for all 5 charts
• SC-03: Required Features (5/5) - All spec features present: colspan/rowspan, multiple chart types, dashboard layout
• SC-04: Data Range (3/3) - All axes show complete data ranges
• SC-05: Legend Accuracy (2/2) - Legend labels correctly describe the data series
• SC-06: Title Format (2/2) - Correct format: "subplot-grid-custom · highcharts · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (8/8) - Demonstrates all aspects: time series, volume bars, histogram distribution, scatter with labels, moving average
• DQ-02: Realistic Context (6/7) - Financial dashboard is a realistic use case; data values are plausible but generic
• DQ-03: Appropriate Scale (5/5) - Stock prices ($123-$161), volumes in millions, returns in percentages - all realistic

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear script: imports → data → configs → HTML → screenshot
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports used
• CQ-04: No Deprecated API (1/1) - Current Highcharts and Selenium APIs
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - While the implementation works, it uses raw JavaScript configuration objects instead of the highcharts-core Python library. The code constructs Highcharts charts directly via JavaScript literals embedded in HTML rather than using the Chart class with HighchartsOptions as demonstrated in the library guidelines.

Strengths

• Excellent custom grid layout with CSS Grid demonstrating colspan/rowspan effectively
• Dashboard-style visualization with a large main chart (2x2) and 4 detail views (1x1 each)
• Perfect colorblind-safe palette with 5 distinct colors
• All text is highly legible with appropriately sized fonts for the 4800x2700 canvas
• Realistic financial data scenario with coherent relationships between charts
• Clean KISS code structure with proper random seed

Weaknesses

• Legend text sizes are disproportionately small compared to the chart titles and should be increased for better readability
• Does not use the highcharts-core Python library API (Chart, HighchartsOptions classes) as recommended in the library guidelines; instead manually constructs JavaScript configurations

Verdict: APPROVED