feat(pygal): implement histogram-stepwise

[github-actions]

Implementation: histogram-stepwise - pygal

Implements the pygal version of histogram-stepwise.

File: plots/histogram-stepwise/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a step histogram showing a bimodal distribution on a white background. A blue step line (Python blue #306998) outlines the histogram without any fill, creating a clean step-function appearance. The title "histogram-stepwise · pygal · pyplots.ai" appears at the top in dark gray text. The X-axis is labeled "Value" (ranging approximately 20-110) and the Y-axis is labeled "Frequency" (ranging 0-38). The bimodal distribution shows two distinct peaks: one centered around value 50 with frequency ~38, and another around value 85 with frequency ~27. Horizontal grid lines are visible at regular intervals. The step lines correctly connect horizontal segments (bin counts) with vertical segments between adjacent bins.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title, axis labels, and tick marks are all clearly readable at the 4800x2700 resolution with appropriately sized fonts
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere in the plot
• VQ-03: Element Visibility (8/8) - Step lines are clearly visible with appropriate stroke width (4px)
• VQ-04: Color Accessibility (5/5) - Single blue color on white background, excellent contrast
• VQ-05: Layout Balance (3/5) - Good use of canvas but some wasted space at the top; plot could be slightly larger
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("Value", "Frequency") but lack units
• VQ-07: Grid & Legend (1/2) - Horizontal grid is subtle and helpful; no legend needed for single series

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct step histogram with outline only, no fill
• SC-02: Data Mapping (5/5) - X-axis shows value bins, Y-axis shows frequency counts
• SC-03: Required Features (5/5) - Step lines present, no fill, horizontal segments at count levels, vertical connections between bins
• SC-04: Data Range (3/3) - All data visible, axes show complete distribution
• SC-05: Legend Accuracy (2/2) - No legend needed, correctly omitted with show_legend=False
• SC-06: Title Format (2/2) - Correct format: "histogram-stepwise · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Bimodal distribution excellently demonstrates the step histogram's ability to show complex distributions
• DQ-02: Realistic Context (5/7) - Generic numeric data; could represent measurement distributions but context is abstract
• DQ-03: Appropriate Scale (5/5) - Values in 20-110 range with frequencies up to ~38 are sensible

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → histogram computation → chart creation → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) ensures reproducible results
• 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, but should save to plot.png (minor: both are saved)

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of pygal.XY with show_dots=False and fill=False to create step effect; custom Style for sizing; however, pygal has a native Histogram chart type that wasn't used

Strengths

• Excellent implementation of step histogram using XY chart with carefully constructed step coordinates
• Clean, readable code with proper KISS structure and reproducible seed
• Bimodal distribution data effectively showcases the plot type's ability to reveal distribution shapes
• Proper title format and clear axis labels
• Good font sizing for the 4800x2700 canvas
• Baseline at 0 properly added at start and end of step data

Weaknesses

• Axis labels lack units (e.g., "Value (units)" or "Frequency (count)")
• Could use more realistic/contextual data scenario instead of abstract values
• Minor layout issue: some unused space at top of canvas

Verdict: APPROVED