feat(matplotlib): implement facet-grid

[github-actions]

Implementation: facet-grid - matplotlib

Implements the matplotlib version of facet-grid.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 3×4 grid of scatter plots showing Energy Consumption (kWh) vs Temperature (°C). The columns represent seasons (Spring, Summer, Fall, Winter) and rows represent regions (North, South, East). Each region uses a distinct color: North is blue (#306998), South is yellow (#FFD43B), and East is green (#4DAF4A). The main title "facet-grid · matplotlib · pyplots.ai" appears at the top. Row labels (North, South, East) are positioned on the right side of each row. All subplots share the same axes scales, with temperature ranging from approximately -10 to 50°C and energy consumption from ~100 to 450 kWh. Subtle dashed grid lines are present in each subplot. The relationship between temperature and energy consumption varies by season and region, showing realistic patterns where extreme temperatures lead to higher energy consumption.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, facet headers at 18pt, tick labels at 12pt - all clearly readable
• VQ-02: No Overlap (8/8) - No overlapping text or elements; facet labels well separated
• VQ-03: Element Visibility (7/8) - Markers sized at s=120 with alpha=0.7, appropriate for 25 points per facet; white edges provide good contrast
• VQ-04: Color Accessibility (5/5) - Blue, yellow, green palette is colorblind-safe with good contrast
• VQ-05: Layout Balance (4/5) - Good use of canvas space; slight crowding of row labels against right edge
• VQ-06: Axis Labels (2/2) - "Temperature (°C)" and "Energy Consumption (kWh)" include units
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3, dashed); no legend needed as colors identify rows

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct faceted grid with scatter plots
• SC-02: Data Mapping (5/5) - X=Temperature, Y=Energy correctly mapped; faceted by Region (rows) and Season (columns)
• SC-03: Required Features (5/5) - Two-way faceting, shared axes, scatter base plots, category labels all present
• SC-04: Data Range (3/3) - All data visible within axes
• SC-05: Legend Accuracy (2/2) - Row and column labels correctly identify facets
• SC-06: Title Format (2/2) - Uses exact format "facet-grid · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows variation across regions and seasons; different patterns visible (U-shaped energy vs temp relationship)
• DQ-02: Realistic Context (7/7) - Temperature vs Energy consumption is a real, comprehensible scenario with region/season variation
• DQ-03: Appropriate Scale (4/5) - Most values realistic; some South summer temperatures reaching ~50°C are quite high

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib, numpy, pandas - all used
• CQ-04: No Deprecated API (1/1) - Modern matplotlib API used
• CQ-05: Output Correct (0/1) - Saves as "plot.png" but library guidelines suggest absolute path or consistent naming

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Good use of plt.subplots with sharex/sharey, fig.supxlabel/supylabel, axes array indexing; could leverage matplotlib's GridSpec for more advanced layout control

Strengths

• Excellent two-way faceting implementation with clear row and column structure
• Realistic temperature vs energy consumption scenario that varies meaningfully across regions and seasons
• Clean KISS code structure with appropriate use of pandas for data management
• Good color palette that distinguishes regions while remaining colorblind-accessible
• Proper use of shared axes (sharex=True, sharey=True) for easy comparison across facets
• Effective use of supxlabel/supylabel for shared axis labels
• White edge on markers improves visibility when points overlap

Weaknesses

• Grid and legend score could be improved by adding a small legend explaining region colors (VQ-07: 1/2)
• Row labels positioned with xy=(1.05, 0.5) are functional but tight_layout rect may clip them slightly
• South summer temperatures reaching ~50°C are on the extreme end for realistic data

Verdict: APPROVED