feat(letsplot): implement histogram-stepwise

[github-actions]

Implementation: histogram-stepwise - letsplot

Implements the letsplot version of histogram-stepwise.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays two step histogram distributions comparing morning and afternoon temperature readings. The morning distribution (dark blue/navy line) is centered around 18°C with a peak frequency near 58, while the afternoon distribution (golden yellow line) is centered around 26°C with a peak frequency near 52. Both distributions are rendered as connected step lines (outline only, no fill) creating a classic step function appearance. The x-axis shows "Temperature (°C)" ranging from 4 to 40, and the y-axis shows "Frequency" ranging from 0 to 60. A legend on the right identifies "Morning" and "Afternoon" under "Time Period". The title reads "histogram-stepwise · letsplot · pyplots.ai". The background uses a minimal theme with subtle gray gridlines.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title, axis labels, tick marks, and legend text are all clearly readable at the output resolution
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere in the plot
• VQ-03: Element Visibility (7/8) - Step lines are well-sized (size=2.5) and clearly visible; minor deduction as line width could be slightly thicker for even better visibility
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) provide excellent contrast and are colorblind-safe (not red-green)
• VQ-05: Layout Balance (5/5) - Good use of canvas space, plot fills appropriate area with balanced margins
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels with units: "Temperature (°C)" and "Frequency"
• VQ-07: Grid & Legend (0/2) - Grid is subtle and appropriate, but legend title shows "Time Period" which is slightly generic; legend placement is good

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct step histogram with outline-only lines (no fill)
• SC-02: Data Mapping (5/5) - Continuous temperature values correctly binned and displayed
• SC-03: Required Features (5/5) - Step lines, no fill, multiple overlaid distributions for comparison
• SC-04: Data Range (3/3) - Axes show complete data range from ~4°C to ~40°C
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies Morning and Afternoon distributions
• SC-06: Title Format (2/2) - Uses correct format: "histogram-stepwise · letsplot · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows two overlaid distributions demonstrating the key use case; distributions overlap in the middle showing comparison benefit; minor deduction for not showing more extreme distribution shapes
• DQ-02: Realistic Context (7/7) - Temperature readings comparing morning vs afternoon is a real, neutral, comprehensible scientific scenario
• DQ-03: Appropriate Scale (4/5) - Temperature values are realistic (morning ~18°C, afternoon ~26°C); minor deduction as the spread could show more realistic weather variation

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Simple script structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pandas, lets_plot)
• CQ-04: No Deprecated API (1/1) - Uses current lets-plot API
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.html correctly, but uses path="." parameter style

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ggplot grammar correctly with geom_line, scale_color_manual, theme_minimal, and custom theme elements; however, doesn't leverage lets-plot's interactive tooltips or other distinctive features beyond basic ggplot grammar

Strengths

• Excellent implementation of step histogram concept using manual bin calculation and geom_line
• Two overlaid distributions clearly demonstrate the key use case of comparing distributions without visual overlap
• Strong color contrast between blue and yellow that is colorblind-accessible
• Proper use of ggplot grammar with clean, readable code structure
• Realistic temperature comparison scenario that makes immediate sense
• Text sizing and layout are publication-quality

Weaknesses

• Could leverage lets-plot's interactive tooltip features for the HTML output
• Legend title "Time Period" is slightly generic compared to a more descriptive label

Verdict: APPROVED