feat(matplotlib): implement subplot-grid

[github-actions]

Implementation: subplot-grid - matplotlib

Implements the matplotlib version of subplot-grid.

File: plots/subplot-grid/implementations/matplotlib.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 2x2 grid of financial dashboard-style visualizations with a main title "subplot-grid · matplotlib · pyplots.ai" at the top.

• Top-left: Line chart showing stock price over 100 trading days (blue solid line) with a 20-day moving average (yellow dashed line). Price ranges from ~80 to ~110 dollars.
• Top-right: Bar chart showing trading volume in thousands, with blue bars for positive return days and red/coral bars for negative return days.
• Bottom-left: Histogram showing return distribution from -6% to +4%, with a yellow vertical line at zero and a red dashed line at mean (-0.16%). The distribution appears roughly normal.
• Bottom-right: Scatter plot showing Volume vs Absolute Return magnitude, with points colored by return value using an RdYlGn colormap (red=negative, green=positive), plus a colorbar.
  Colors used: Python blue (#306998), yellow (#FFD43B), red (#D94A4A), RdYlGn colormap. All text is clearly readable with appropriate sizing.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text readable; titles 18pt (should be 24pt per guidelines), labels 16pt, ticks 14pt - slightly smaller than ideal but still clear
• VQ-02: No Overlap (8/8) - No overlapping text anywhere
• VQ-03: Element Visibility (7/8) - Line widths good (2-2.5), scatter markers s=80 appropriate for 99 points, bars clearly visible
• VQ-04: Color Accessibility (5/5) - Blue/yellow/red scheme is distinguishable; RdYlGn colormap works well
• VQ-05: Layout Balance (5/5) - Excellent use of 2x2 grid, proper spacing with tight_layout
• VQ-06: Axis Labels (2/2) - All axes have descriptive labels with units: "Price ($)", "Volume (thousands)", "Daily Return (%)", "Absolute Return (%)"
• VQ-07: Grid & Legend (0/2) - Grid alpha=0.3 is good, but legends are slightly small (fontsize 12-14 vs recommended 16)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correctly implements 2x2 subplot grid with different plot types (line, bar, histogram, scatter)
• SC-02: Data Mapping (5/5) - X/Y correctly mapped in all subplots
• SC-03: Required Features (5/5) - All spec requirements met: configurable grid (2x2), independent axes, different plot types per cell, consistent spacing, clear subplot titles
• SC-04: Data Range (3/3) - All axes show full data range
• SC-05: Legend Accuracy (2/2) - Legends are accurate (Price, 20-day MA, Zero Return, Mean)
• SC-06: Title Format (2/2) - Uses correct format: "subplot-grid · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple plot types (line, bar, histogram, scatter), moving average, color encoding by sign, colorbar - excellent variety
• DQ-02: Realistic Context (7/7) - Financial dashboard is a perfect, neutral real-world use case for subplot grids
• DQ-03: Appropriate Scale (4/5) - Stock prices ~$80-110, returns -6% to +4%, volumes in reasonable range - all realistic, though starting price dropping immediately from 100 is slightly artificial

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported, both used
• CQ-04: No Deprecated API (1/1) - No deprecated functions
• CQ-05: Output Correct (1/1) - Saves as 'plot.png'

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of plt.subplots, colorbar, axes methods - but could showcase more advanced features like gridspec, shared axes, or inset_axes

Strengths

• Excellent financial dashboard example perfectly demonstrating the subplot-grid concept with four distinct but related visualizations
• Creative color encoding: volume bars colored by return sign, scatter colored by return value
• Moving average overlay adds depth to the price chart
• Statistical annotations (mean, zero line) in histogram enhance educational value
• Clean, professional appearance with consistent styling across all subplots

Weaknesses

• Font sizes slightly below recommended guidelines (titles 18pt vs 24pt, labels 16pt vs 20pt)
• Legend fontsize could be larger for better readability (12-14 vs 16)

Verdict: APPROVED