feat(letsplot): implement bland-altman-basic

[github-actions]

Implementation: bland-altman-basic - python/letsplot

Implements the python/letsplot version of bland-altman-basic.

File: plots/bland-altman-basic/implementations/python/letsplot.py

Parent Issue: #2032

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (): The plot displays a warm off-white background (#FAF8F1) with 80 teal data points showing blood pressure measurement differences. The title 'bland-altman-basic · letsplot · anyplot.ai' is rendered in dark text at the top. Axis labels ('Mean of Two Methods (mmHg)' on X, 'Difference (Method 1 - Method 2) (mmHg)' on Y) are in dark text and clearly readable. A solid teal line represents the mean bias at -2.55 mmHg, with two dashed orange lines indicating the ±1.96 SD limits (+7.84 and -12.93 mmHg). Annotations in orange text are positioned on the left with elevated background boxes. The subtle gray grid aids readability without overwhelming the data. All text is clearly readable against the light background.

Dark render (): The same plot rendered on a warm near-black background (#1A1A17). Title and axis labels are now in light text (#F0EFE8), with tick labels in soft light gray (#B8B7B0). Critically, the data colors remain identical: teal points and orange limit lines match the light render exactly—only the chrome (background, text, and grid) has flipped. Annotations use orange text on an elevated dark background (#242420). All text is clearly visible against the dark surface. No dark-on-dark failures detected. Theme adaptation is correct and complete.

Score: 89/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	89	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All text readable in both themes; font sizes properly set
• VQ-02: No Overlap (6/6) - No overlapping elements; annotations well-positioned
• VQ-03: Element Visibility (6/6) - All markers, lines, annotations clearly visible
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette, colorblind-safe, good contrast
• VQ-05: Layout & Canvas (4/4) - Proper 4800×2700 px rendering; good proportions
• VQ-06: Axis Labels & Title (2/2) - Descriptive with units (mmHg)
• VQ-07: Palette Compliance (2/2) - Brand #009E73 first, Okabe-Ito Add Claude Code GitHub Workflow #2 for limits, correct backgrounds

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Professional theme token usage, but design is competent rather than exceptional
• DE-02: Visual Refinement (4/6) - theme_minimal base, subtle grid, good whitespace
• DE-03: Data Storytelling (4/6) - Clear visual hierarchy (solid vs dashed lines), color-coded interpretation guides

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct Bland-Altman chart
• SC-02: Required Features (4/4) - Bias line, limits, annotations, transparency all present
• SC-03: Data Mapping (3/3) - X = mean, Y = difference; all data shown
• SC-04: Title & Legend (3/3) - Correct title format; single-series plot needs no legend

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - All plot aspects shown
• DQ-02: Realistic Context (5/5) - Blood pressure readings (100–160 mmHg); neutral medical context
• DQ-03: Appropriate Scale (4/4) - 80 observations within 30–200 recommended range

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No functions/classes
• CQ-02: Reproducibility (2/2) - Random seed set
• CQ-03: Clean Imports (2/2) - Only used imports
• CQ-04: Code Elegance (2/2) - No fake UI
• CQ-05: Output & API (1/1) - Correct filenames and format

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) - Proper grammar of graphics; theme tokens applied idiomatically
• LM-02: Distinctive Features (2/5) - HTML export used, but limited exploration of letsplot capabilities

Score Caps Applied

• None

Strengths

• Perfect theme adaptation: all tokens (PAGE_BG, ELEVATED_BG, INK, INK_SOFT) applied correctly in both renders
• All Bland-Altman requirements implemented with precision: bias line, limits of agreement, annotations, transparency
• Clean, idiomatic letsplot code with reproducible seed (42) and correct file naming
• Correct palette compliance: brand #009E73 for data, Okabe-Ito position 2 (#D55E00) for limits
• Realistic, properly-scaled blood pressure data (80 observations in recommended 30–200 range)

Weaknesses

• Design Excellence above default but not exceptional—could benefit from more sophisticated visual refinement
• Limited exploration of distinctive letsplot features beyond HTML export

Issues Found

None critical. Minor opportunity: LM-02 could be raised with additional letsplot-specific techniques.

AI Feedback for Next Attempt

This is a strong, solid implementation. Theme adaptation is exemplary. To reach higher design excellence: consider custom annotation positioning strategies, explore interactive letsplot features (hover tooltips, or layer composition patterns), and refine grid/spine styling for greater visual polish.

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 1/4

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (): The plot displays a Bland-Altman scatter diagram with a warm off-white background (#FAF8F1). Eighty data points in teal/green (#009E73) are scattered across the canvas, each representing the difference between two measurement methods versus their mean. A solid teal horizontal line marks the mean bias (-2.55 mmHg), while two dashed orange lines (#D55E00) indicate the ±1.96 SD limits of agreement (+7.84 and -12.93 mmHg). Each reference line has a clearly readable annotation label positioned on the left with an elevated background box. The x-axis shows "Mean of Two Methods (mmHg)" and y-axis shows "Difference (Method 1 - Method 2) (mmHg)". All text (title, axis labels, tick labels, and annotation values) is dark and clearly readable against the light background. The grid is subtle but visible, and the overall layout is clean with generous whitespace.

Dark render (): The same plot rendered on a warm near-black background (#1A1A17). All data point colors remain identical teal/green (#009E73), ensuring the same categorical identity. The reference lines maintain their colors (solid teal for bias, dashed orange for LOA). The title and axis labels are now light-colored text (#F0EFE8) against the dark background, while tick labels use a lighter gray (#B8B7B0). All annotation boxes have an elevated dark background (#242420) for contrast. The grid styling remains subtle but visible. Every text element is clearly readable with no dark-on-dark contrast failures. The brand green data points remain prominently visible and distinguished. Both renders demonstrate perfect theme adaptation with only chromatic elements (backgrounds, text colors) flipping while data colors remain constant.

Score: 92/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	14	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	92	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All text properly sized (title 24, labels 20, ticks 16, annotations 12) and readable in both themes
• VQ-02: No Overlap (6/6) - No collisions; annotations positioned with offset (y_offset=0.8) on left margin
• VQ-03: Element Visibility (6/6) - Points with size=6 and alpha=0.7 reveal overlaps; lines clearly visible
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette (#009E73, #D55E00) is CVD-safe
• VQ-05: Layout & Canvas (4/4) - 1600×900 (scaled 3×) with generous whitespace and no clipping
• VQ-06: Axis Labels & Title (2/2) - Labels include units (mmHg); title follows spec format
• VQ-07: Palette Compliance (2/2) - Brand #009E73 for primary data; LOA in #D55E00 (position 2); backgrounds #FAF8F1/#1A1A17; both themes correct

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) - Professional polish with intentional color hierarchy (green for bias, orange for LOA); annotation styling with elevated backgrounds is refined
• DE-02: Visual Refinement (4/6) - Uses theme_minimal() with custom theme; spines implicit; subtle grid; stroke=0.5 on points adds definition
• DE-03: Data Storytelling (4/6) - Clear visual hierarchy through color (green = primary finding, orange = secondary bounds); annotations guide viewer through interpretation

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct Bland-Altman scatter plot
• SC-02: Required Features (4/4) - Scatter points, mean bias line, ±1.96 SD limits, value annotations all present
• SC-03: Data Mapping (3/3) - X = mean of methods, Y = difference; all data visible
• SC-04: Title & Legend (3/3) - Title "bland-altman-basic · letsplot · anyplot.ai"; descriptive axes with units

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Complete Bland-Altman plot with all aspects shown
• DQ-02: Realistic Context (5/5) - Blood pressure device validation scenario; realistic normal-distribution data with appropriate noise
• DQ-03: Appropriate Scale (4/4) - BP values (100-160 mmHg) realistic; method differences (2-4 mmHg range) plausible

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Simple linear script, no unnecessary abstractions
• CQ-02: Reproducibility (2/2) - Seed set (np.random.seed(42))
• CQ-03: Clean Imports (2/2) - Only used imports (os, numpy, pandas, lets_plot)
• CQ-04: Code Elegance (2/2) - No fake interactivity; Bland-Altman calculations clearly expressed
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png and plot-{THEME}.html

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) - Canonical ggplot2-style grammar with proper layer composition; theme_minimal() base
• LM-02: Distinctive Features (3/5) - Uses letsplot basics (ggsize, ggsave, theme elements) but generic beyond ggplot2 patterns

Score Caps Applied

• None

Strengths

• Excellent theme-adaptive implementation with proper color tokens threaded through all elements
• Perfect legibility in both light and dark renders; no contrast failures
• Correct Bland-Altman implementation with all required statistical features
• Realistic medical device validation scenario with plausible data
• Clean, reproducible code with clear calculations
• Proper use of transparency and visual hierarchy through color

Weaknesses

• Design Excellence below potential: Could feature more refined annotation styling or margin-aligned labels
• Library usage somewhat generic: Demonstrates idiomatic letsplot but doesn't showcase unique features beyond standard ggplot2

Issues Found

None — implementation is strong and meets all requirements.

AI Feedback for Next Attempt

This implementation is excellent and approved. To reach 95+, consider: enhanced annotation styling (e.g., distinct background treatment or shadow effects), margin-aligned reference line labels, or more sophisticated visual hierarchy through size/weight variation.

Verdict: APPROVED