feat(letsplot): implement scatter-lag

[github-actions]

Implementation: scatter-lag - letsplot

Implements the letsplot version of scatter-lag.

File: plots/scatter-lag/implementations/letsplot.py

Parent Issue: #5251

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a lag-1 scatter plot of an AR(1) simulated daily temperature time series (~399 points). Points are colored with a blue (#306998) to orange (#E3882D) gradient encoding temporal order — early days are dark blue, later days warm orange. The x-axis is labeled "y(t)" and the y-axis "y(t + 1)". A dashed light-gray diagonal reference line (y = x) spans the entire plot area. A large monospace "r = 0.83" annotation appears in the lower-right corner. The title reads "scatter-lag · letsplot · pyplots.ai" in bold. A "Day" gradient legend is displayed on the right. The plot uses a minimal theme with very subtle gray grid lines (major only), no minor grid. An italic gray caption at the bottom reads "AR(1) simulated daily temperature · dashed line = y(t+1) = y(t)". The overall layout is 16:9, clean, and professional.

Score: 87/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	87	100

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title=24, axis titles=20, axis text=16, legend=14; all clearly readable
• VQ-02: No Overlap (6/6) — No text element overlap; r annotation is well-placed in lower right
• VQ-03: Element Visibility (5/6) — Points visible with alpha=0.6, but size=4 is slightly large for 399 points; some density in center cluster
• VQ-04: Color Accessibility (3/4) — Blue-orange gradient is not red-green; acceptable but not a standard perceptually-uniform colormap (viridis preferred)
• VQ-05: Layout & Canvas (3/4) — Good 16:9 proportions, balanced legend placement; minor whitespace on left/right margins
• VQ-06: Axis Labels & Title (2/2) — "y(t)" and "y(t + 1)" are descriptive and use standard lag plot notation

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (4/8) — Custom blue-orange gradient palette and monospace r annotation show design intent; fundamentally a well-configured minimal default, not publication-ready
• DE-02: Visual Refinement (4/6) — Subtle grid (color=#E8E8E8, size=0.35), minor grid removed, custom tick colors and margins; good attention to detail
• DE-03: Data Storytelling (4/6) — Temporal color encoding reveals how the time series moves through the scatter space; r=0.83 annotation immediately communicates autocorrelation strength; dashed diagonal creates clear benchmark

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct lag plot: scatter of y(t) vs y(t+lag)
• SC-02: Required Features (4/4) — Diagonal reference line ✓, color by time index ✓, r correlation annotation ✓, configurable lag ✓
• SC-03: Data Mapping (3/3) — x=value_t (y(t)), y=value_t_lag (y(t+1)); correct mapping
• SC-04: Title & Legend (3/3) — Title: "scatter-lag · letsplot · pyplots.ai" exact format ✓; Day gradient legend correctly labeled ✓

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows strong positive autocorrelation (linear cluster along diagonal), temporal structure via color, reference diagonal, and quantified r value — all key aspects of a lag plot
• DQ-02: Realistic Context (5/5) — AR(1) simulated daily temperature; plausible real-world scenario; non-controversial
• DQ-03: Appropriate Scale (4/4) — Temperature range ~12–32°C is realistic for a daily temperature series; phi=0.85 gives realistic autocorrelation

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data → lag construction → r calc → ref line → plot → save; no functions or classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set
• CQ-03: Clean Imports (2/2) — All imports used; noqa comments appropriate for star import pattern
• CQ-04: Code Elegance (2/2) — Pythonic, appropriate complexity; AR(1) loop is the clearest way to generate the process
• CQ-05: Output & API (1/1) — Saves as plot.png via export_ggsave with scale=3

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) — Expert grammar-of-graphics usage: ggplot/geom/aes/labs/theme composition, ggsize for sizing, scale_color_gradient — fully idiomatic
• LM-02: Distinctive Features (3/5) — Uses layer_tooltips() (letsplot-specific interactive tooltips) and generates HTML export alongside PNG; good distinctive usage but could go further with letsplot-specific capabilities

Score Caps Applied

• None — no caps triggered (DE-01=4, DE-02=4; both > 2, so no 75-cap)

Strengths

• Perfect spec compliance: diagonal reference line, temporal color encoding, r annotation, and configurable lag are all implemented correctly
• Strong data quality: realistic AR(1) temperature simulation with phi=0.85 shows clear autocorrelation pattern, all values in plausible range
• Excellent code quality: clean KISS structure, reproducible, idiomatic letsplot grammar-of-graphics pattern
• Good data storytelling: temporal gradient + r annotation effectively communicates autocorrelation structure to the viewer

Weaknesses

• DE-01 needs stronger aesthetic differentiation: the design is clean but not publication-quality; needs more intentional visual hierarchy beyond the temporal gradient
• VQ-04: Blue-orange custom gradient is not a standard colorblind-safe palette — viridis or plasma would provide better perceptual uniformity and accessibility
• VQ-03: Alpha=0.6 is slightly high for 399 points; could reduce to 0.4–0.5 for better density visualization in the central cluster
• LM-02: layer_tooltips() is underutilized — could leverage more letsplot-distinctive capabilities (e.g., geom_smooth for regression line, or theme flavor customization)

Issues Found

1. VQ-04 MINOR: Custom blue-orange gradient lacks perceptual uniformity and colorblind certification
   • Fix: Replace scale_color_gradient(low="#306998", high="#E3882D") with scale_color_viridis or scale_color_gradient(low="#440154", high="#FDE725") for better accessibility
2. DE-01 LOW: Well-configured default but not exceptional; missing publication-ready polish
   • Fix: Add subtitle with data description, refine legend positioning, consider adding a geom_smooth trend line to reinforce the linear autocorrelation pattern
3. VQ-03 MINOR: Alpha=0.6 too high for 399 points causing visual density in central cluster
   • Fix: Reduce alpha to 0.4 and possibly reduce point size to 3

AI Feedback for Next Attempt

Improve design excellence and visual accessibility: (1) Replace the blue-orange gradient with viridis or a perceptually-uniform colormap for better colorblind safety; (2) Reduce point alpha to 0.4 and size to 3 for better density visualization with 399 points; (3) Enhance aesthetic sophistication by adding a geom_smooth(method="lm", ...) regression line to reinforce the autocorrelation pattern and create clearer visual hierarchy; (4) Consider adding a subtitle or refining caption placement for publication-ready polish.

Verdict: REJECTED