feat(matplotlib): implement histogram-density

[github-actions]

Implementation: histogram-density - matplotlib

Implements the matplotlib version of histogram-density.

File: plots/histogram-density/implementations/matplotlib.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a density histogram with blue bars representing test score distribution (labeled "Observed Distribution"). The x-axis shows "Test Score (points)" ranging from 30 to 110, while the y-axis shows "Probability Density" ranging from 0.000 to 0.040. A yellow/gold curve overlays the histogram showing the theoretical Normal PDF (μ=75, σ=12). The histogram bars have white edges and semi-transparent fill (alpha=0.7). The legend is positioned in the upper left corner. A subtle grid with dashed lines is visible. The title follows the required format: "histogram-density · matplotlib · pyplots.ai".

Quality Score: 93/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Histogram bars clearly visible, PDF line has good linewidth (3)
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) provide excellent contrast and are colorblind-safe
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins, tight_layout applied
• VQ-06: Axis Labels (2/2) - X-axis has units "Test Score (points)", Y-axis is descriptive "Probability Density"
• VQ-07: Grid & Legend (0/2) - Grid alpha is 0.3 (acceptable), but legend fontsize should match other text proportionally

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct density histogram with density=True
• SC-02: Data Mapping (5/5) - Values correctly mapped to histogram
• SC-03: Required Features (5/5) - Density histogram with PDF overlay as suggested in spec notes
• SC-04: Data Range (3/3) - All data visible within axis limits
• SC-05: Legend Accuracy (2/2) - Legend correctly describes both elements
• SC-06: Title Format (2/2) - Uses correct format "histogram-density · matplotlib · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows normal distribution shape well, but could demonstrate more distributional features (e.g., slight asymmetry from clipping)
• DQ-02: Realistic Context (7/7) - Test scores are a perfect, neutral, realistic scenario
• DQ-03: Appropriate Scale (5/5) - Scores in 0-100 range with mean of 75 and SD of 12 are realistic

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) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib API
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses basic matplotlib features (hist with density, plot), but could leverage more distinctive features like fill_between for PDF area, or axvline for mean indicator

Strengths

• Excellent specification compliance with density histogram and theoretical PDF overlay
• Clean, readable code following KISS principles
• Great color contrast between histogram bars and PDF curve
• Perfect title format and axis labeling with units
• Realistic test score data scenario with appropriate sample size (n=500)

Weaknesses

• Could use more distinctive matplotlib features like fill_between for PDF shading or axvline for mean
• Legend positioned in upper left slightly overlaps with potential data area (minor)

Verdict: APPROVED