feat(pygal): implement histogram-density

[github-actions]

Implementation: histogram-density - pygal

Implements the pygal version of histogram-density.

File: plots/histogram-density/implementations/pygal.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 on a white background. The title "histogram-density · pygal · pyplots.ai" appears at the top. The x-axis is labeled "Test Score" with values ranging from approximately 40 to 95, with labels shown at intervals (40, 52, 64, 76, 88). The y-axis is labeled "Density (Probability per Unit)" with values from 0 to approximately 0.034. The histogram clearly shows a bimodal distribution with two peaks - one around score 65 and another around score 80-82, which aligns with the generated bimodal test score data. Horizontal grid lines are visible at subtle intervals. The bars are rendered in Python Blue (#306998) with slight transparency.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and tick marks are all clearly readable. Font sizes are well-scaled for the 4800x2700 canvas. Slight deduction as y-axis tick labels are somewhat small.
• VQ-02: No Overlap (8/8) - No overlapping text elements. X-axis labels are spaced appropriately by showing every 5th label.
• VQ-03: Element Visibility (8/8) - Bars are clearly visible with good sizing and appropriate opacity (0.85).
• VQ-04: Color Accessibility (5/5) - Single color scheme (Python Blue) with good contrast against white background. No colorblind issues.
• VQ-05: Layout Balance (4/5) - Good use of canvas space, though margins could be slightly optimized. Plot fills reasonable portion of canvas.
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels with units: "Test Score" and "Density (Probability per Unit)".
• VQ-07: Grid & Legend (0/2) - Y-axis grid is visible but no legend present (acceptable since single series with show_legend=False). Grid is subtle.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct chart type: density histogram using bar chart with density-normalized values.
• SC-02: Data Mapping (5/5) - X-axis shows bin centers (test scores), Y-axis shows density values.
• SC-03: Required Features (5/5) - Shows density (area=1 normalization), continuous variable distribution, appropriate binning.
• SC-04: Data Range (3/3) - All data visible, axes show full range from ~40-95 scores.
• SC-05: Legend Accuracy (2/2) - Legend disabled appropriately for single series histogram.
• SC-06: Title Format (2/2) - Correct format: "histogram-density · pygal · pyplots.ai".

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows bimodal distribution demonstrating density normalization well. Could have included a reference line or PDF overlay as suggested in spec notes.
• DQ-02: Realistic Context (7/7) - Test scores is a realistic, neutral educational context. Bimodal distribution represents two groups of students plausibly.
• DQ-03: Appropriate Scale (4/5) - Scores clipped to 0-100 range, realistic normal distributions with sensible means (65 and 82) and standard deviations.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → style → chart → save.
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42).
• CQ-03: Clean Imports (2/2) - Only necessary imports: numpy, pygal, Style.
• CQ-04: No Deprecated API (1/1) - Uses current pygal API.
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.html (correct).

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal's Style customization, Bar chart with custom tooltips via label dictionaries. Could leverage more pygal-specific features like built-in tooltips or animations.

Strengths

• Excellent bimodal distribution data that clearly demonstrates density histogram concept
• Clean, readable code following KISS principles with proper random seed
• Well-formatted title following the required convention
• Good use of pygal's Style class for comprehensive customization
• Appropriate axis labels with descriptive units
• Smart x-label spacing (every 5th label) prevents overlap

Weaknesses

• Spec suggests adding a reference line or theoretical PDF overlay for context, which is not implemented
• Could use pygal's native Histogram chart type instead of Bar chart for more semantic correctness

Verdict: APPROVED