feat(letsplot): implement facet-grid

[github-actions]

Implementation: facet-grid - python/letsplot

Implements the python/letsplot version of facet-grid.

File: plots/facet-grid/implementations/python/letsplot.py

Parent Issue: #2696

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (): A 3×4 faceted grid showing scatter plots with linear trend lines, faceted by product category (rows: Clothing, Electronics, Home) and region (columns: East, North, South, West). The background is warm off-white (#FAF8F1). Data points use the Okabe-Ito palette: green (#009E73) for Electronics, orange (#D55E00) for Clothing, and blue (#0072B2) for Home. The title "facet-grid · letsplot · anyplot.ai" is in dark text at the top. Axis labels ("Unit Price ($)" and "Units Sold") are clearly visible in dark ink. Tick labels are readable in secondary-text color. The legend shows category labels with colors. Subtle horizontal grid lines aid readability. All text is readable against the light surface with no overlap or collision issues.

Dark render (): The same faceted grid layout rendered on a warm near-black surface (#1A1A17). The title and all text elements are now in light colors (INK and INK_SOFT tokens), maintaining full readability. The data colors—green, orange, blue—are identical to the light render; only the chrome (background, text, grid) has flipped to dark-theme colors. Tick labels are in the correct light tone with no dark-on-dark failures. Grid lines remain subtle and visible. The legend maintains proper contrast. Both renders demonstrate proper theme-adaptive styling with no legibility issues in either mode.

Score: 95/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	9	10
Total	95	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All text properly sized and readable in both themes
• VQ-02: No Overlap (6/6) - Clean layout with no collisions
• VQ-03: Element Visibility (6/6) - Points and trend lines clearly visible with appropriate alpha=0.7
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette is colorblind-safe with good contrast
• VQ-05: Layout & Canvas (4/4) - Well-proportioned 3×4 grid, nothing cut off
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels with units ("Unit Price ($)", "Units Sold")
• VQ-07: Palette Compliance (2/2) - First series (#009E73) correct, multi-series follows Okabe-Ito order, backgrounds theme-correct (#FAF8F1 light / #1A1A17 dark), both renders theme-adaptive

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) - Professional theme-adaptive styling with custom color tokens; Okabe-Ito palette applied intentionally; slight room for additional visual flourishes
• DE-02: Visual Refinement (5/6) - Minimal theme applied, subtle grid with RULE color at 10% opacity, generous whitespace
• DE-03: Data Storytelling (5/6) - Clear faceted comparison across regions and categories; trend lines provide analytical insight; systematic visual hierarchy

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct facet-grid with scatter + trend lines
• SC-02: Required Features (4/4) - Row faceting (Category), column faceting (Region), scatter plot, trend lines all present
• SC-03: Data Mapping (3/3) - X=Price, Y=Units Sold, Row=Category, Col=Region, Color=Category; all axes show full data range
• SC-04: Title & Legend (3/3) - Title "facet-grid · letsplot · anyplot.ai" correct; legend shows Category with all three values

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Full facet-grid capabilities demonstrated: row facets, column facets, scatter + trend, color encoding
• DQ-02: Realistic Context (5/5) - Sales analysis by category and region is plausible and neutral; no controversial aspects
• DQ-03: Appropriate Scale (4/4) - Price range (0–00) and Units Sold (50–250) are sensible for product categories

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No functions or classes; straightforward data generation and plotting
• CQ-02: Reproducibility (2/2) - Uses np.random.seed(42) for deterministic output
• CQ-03: Clean Imports (2/2) - Only necessary imports from lets_plot, numpy, pandas, os
• CQ-04: Code Elegance (2/2) - No fake UI; appropriate complexity; well-structured
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png and plot-{THEME}.html; uses current ggsave API with scale=3

Library Mastery (9/10)

• LM-01: Idiomatic Usage (5/5) - Excellent ggplot2-style grammar with + operator chaining; proper aes() mapping; correct facet_grid() usage; idiomatic theme() customization; scale_color_manual() for Okabe-Ito
• LM-02: Distinctive Features (4/5) - Effective use of facet_grid for multi-dimensional comparison; geom_smooth for trend analysis; theme-adaptive styling demonstrates library mastery

Score Caps Applied

• None

Strengths

• Perfect visual quality with all text legible in both light and dark themes
• Correct implementation of row+column faceting with proper category and region separation
• Excellent theme-adaptive styling with proper color token mapping for light/dark modes
• Clean, reproducible code with seed management
• Proper application of Okabe-Ito palette with first series in brand green (#009E73)
• Comprehensive feature coverage demonstrating full facet-grid capabilities

Weaknesses

• Design excellence could be slightly enhanced with additional visual refinement techniques (e.g., custom legend styling, spine removal customization)
• Library mastery in distinctive features is solid but could explore more letsplot-specific interactive capabilities

Issues Found

None. This is a high-quality, well-executed implementation.

AI Feedback for Next Attempt

The implementation is exceptionally strong and ready for approval. All quality metrics are met or exceeded. The theme-adaptive styling is particularly well-executed with proper token mapping. The facet-grid layout effectively demonstrates the plot type's capability for multi-dimensional data exploration. No fixes required.

Verdict: APPROVED