feat(seaborn): implement subplot-grid-custom

[github-actions]

Implementation: subplot-grid-custom - seaborn

Implements the seaborn version of subplot-grid-custom.

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

Parent Issue: #2856

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a dashboard-style layout with 6 subplots arranged in a custom grid. The main plot (top-left, spanning 2x2 cells) displays a "Daily Price Trend" line chart with a blue line (#306998) and light blue fill beneath, showing price ranging from ~100 to ~80 over time from Dec 29 to Apr 27. Top-right shows a yellow scatter plot (#FFD43B) titled "Correlation Analysis" with Variable X vs Y. Middle-right displays a "Quarterly Performance" boxplot with Q1-Q4 in Set2 colors (teal, coral, olive, pink). Bottom row contains three smaller plots: "Monthly Volume" bar chart with viridis palette (Jan-Dec), "Returns Distribution" histogram with KDE curve and yellow zero-reference line, and a "Correlation Matrix" heatmap in RdBu_r diverging colormap showing Price/Volume/Returns correlations. The main title "subplot-grid-custom · seaborn · pyplots.ai" appears at the top in bold.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - All text readable, main title 24pt, subplot titles 16-20pt, labels 14-16pt, ticks 10-12pt. Slightly smaller than optimal but still clear.
• VQ-02: No Overlap (8/8) - No overlapping text. Month labels rotated 45° to avoid overlap.
• VQ-03: Element Visibility (7/8) - Elements well-sized. Scatter markers s=120 appropriate for 80 points. Line width 3 is good. Minor: boxplot could be slightly larger.
• VQ-04: Color Accessibility (5/5) - Uses viridis, Set2, RdBu_r - all colorblind-safe palettes.
• VQ-05: Layout Balance (4/5) - Good use of GridSpec with main plot spanning 2x2. Minor: heatmap and scatter feel slightly cramped.
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Price ($)", "Volume (Units)", "Daily Returns (%)", "Performance Score".
• VQ-07: Grid & Legend (1/2) - Grid alpha=0.3 is good. No legends needed (colors self-explanatory). Heatmap colorbar slightly small.

Spec Compliance (24/25 pts)

• SC-01: Plot Type (8/8) - Correctly implements custom subplot grid with GridSpec.
• SC-02: Data Mapping (5/5) - X/Y correctly assigned in all subplots.
• SC-03: Required Features (4/5) - Has colspan (main plot 2 cols), but no rowspan example shown (spec mentions tall sidebar plot).
• SC-04: Data Range (3/3) - All data visible within axes.
• SC-05: Legend Accuracy (2/2) - Heatmap annotations accurate, boxplot categories clear.
• SC-06: Title Format (2/2) - Correct format: "subplot-grid-custom · seaborn · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows 6 different plot types (line, scatter, boxplot, bar, histogram, heatmap). Good variety but could show rowspan feature.
• DQ-02: Realistic Context (7/7) - Financial dashboard scenario: price trend, volume, returns, correlations. Very realistic.
• DQ-03: Appropriate Scale (5/5) - Values realistic: price ~80-110, returns ±5%, volumes 1000-5000, correlations 0.4-1.0.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → plot → save. No functions/classes.
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42).
• CQ-03: Clean Imports (2/2) - All imports used: matplotlib.dates, gridspec, plt, np, pd, sns.
• CQ-04: No Deprecated API (0/1) - Uses hue parameter correctly for seaborn 0.14+, but legend=False pattern is verbose.
• CQ-05: Output Correct (1/1) - Saves as plot.png.

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Good use of seaborn functions: lineplot, scatterplot, boxplot, barplot, histplot with kde, heatmap. Uses axes-level API correctly. Could leverage FacetGrid or jointplot for more distinctive seaborn features.

Strengths

• Excellent dashboard layout demonstrating GridSpec with main plot spanning 2x2 cells
• Good variety of seaborn plot types (6 different visualizations) showcasing library breadth
• Financial dashboard scenario is realistic and coherent with interconnected data (price, volume, returns, correlations)
• Proper use of seaborn 0.14+ API with hue parameter for palettes
• Clean color scheme with colorblind-safe palettes (viridis, Set2, RdBu_r)

Weaknesses

• Missing rowspan example - spec mentions "cells spanning multiple rows (e.g., tall sidebar plot)" but all cells only span columns
• Heatmap y-axis label "ReturnsVolume Price" appears concatenated/overlapping on the image (likely tight spacing issue)

Verdict: APPROVED