feat(letsplot): implement timeseries-decomposition

[github-actions]

Implementation: timeseries-decomposition - python/letsplot

Implements the python/letsplot version of timeseries-decomposition.

File: plots/timeseries-decomposition/implementations/python/letsplot.py

Parent Issue: #2992

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot displays four vertically stacked subplots on a warm off-white (#FAF8F1) background showing the time series decomposition of monthly temperature data over 5 years. The top plot shows the Original Series in green (#009E73), displaying clear seasonal oscillations around an upward trend. Below it, the Trend subplot (orange, #D55E00) shows a gradual warming from ~15°C to ~18°C. The Seasonal subplot (blue, #0072B2) isolates the repeating annual cycle with amplitude of ~12°C. The bottom Residual subplot (pink, #CC79A7) shows the remaining noise after removing trend and seasonality. The overall title "timeseries-decomposition · letsplot · anyplot.ai" is prominently displayed in bold at the top. All text including axis labels "Date" and "Temperature (°C)", subplot titles, and tick labels are clearly readable against the light background. Grid lines are subtle and aid value reading without competing with data.

Dark render (plot-dark.png): The same four-component layout is displayed on a warm near-black (#1A1A17) background with all text adapted to light colors for readability against the dark surface. The Original Series, Trend, Seasonal, and Residual components retain their identical Okabe-Ito colors (green, orange, blue, pink respectively) — only the chrome (background, text, grid) has flipped to dark-mode equivalents. The title is clearly visible in light text. All axis labels and tick labels are rendered in appropriate light tones (#F0EFE8 for primary, #B8B7B0 for secondary text). The grid lines remain subtle and visible. There are no "dark-on-dark" legibility issues — all text is clearly distinguishable from the dark background, and the brand green (#009E73) remains visible and prominent.

Score: 88/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	10	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	88	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set; title 24pt bold, labels 16pt, tick text 14pt; perfectly readable in both light and dark renders
• VQ-02: No Overlap (6/6) - No overlapping text; component labels well-separated; date labels angled on final subplot to prevent collision
• VQ-03: Element Visibility (6/6) - Lines sized at 1.2 (geom_line); optimal for 60 monthly data points; clearly distinguishable from background in both themes
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette (green, orange, blue, pink) is colorblind-safe; all colors clearly distinguishable; no red-green-only signal
• VQ-05: Layout & Canvas (4/4) - Four subplots fill canvas well with balanced margins; title at top provides clear hierarchy; proportions are perfect
• VQ-06: Axis Labels & Title (2/2) - "Date" on x-axis, "Temperature (°C)" on y-axes (with units); descriptive subplot titles
• VQ-07: Palette Compliance (2/2) - Okabe-Ito colors correct: Original=#009E73 (brand), Trend=#D55E00, Seasonal=#0072B2, Residual=#CC79A7; backgrounds are #FAF8F1 (light) and #1A1A17 (dark); text colors properly theme-adapted in both renders

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) - Well-configured letsplot defaults with proper Okabe-Ito palette and theme tokens; lacks distinctive design flourishes or custom touches
• DE-02: Visual Refinement (4/6) - Some refinement visible: axis_text_x hidden on first three plots to avoid redundancy; grid subtle (size=0.5); x-axis labels angled on residual plot; overall layout is clean but not extensively customized
• DE-03: Data Storytelling (2/6) - Data displayed clearly but without visual hierarchy or emphasis; each component given equal treatment; viewer must extract insight from structure rather than guided visualization

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct: four vertically stacked subplots showing Original, Trend, Seasonal, Residual components
• SC-02: Required Features (4/4) - All features present: consistent time axis across subplots, clear component labels, statsmodels seasonal_decompose used for decomposition, light grid lines present
• SC-03: Data Mapping (3/3) - X-axis correctly maps dates, Y-axis maps temperature values; all data visible across 60-month span; decomposition correctly separates components
• SC-04: Title & Legend (3/3) - Title format correct: "timeseries-decomposition · letsplot · anyplot.ai"; component labels clearly identify each subplot

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Synthetic data demonstrates all aspects: original series with realistic variation, clear trend component, regular seasonal pattern, and residual noise
• DQ-02: Realistic Context (5/5) - Real-world scenario: monthly temperature readings over 5 years; neutral, scientific domain; no controversial elements
• DQ-03: Appropriate Scale (4/4) - Temperatures 15-18°C realistic; seasonal amplitude ±12°C realistic for monthly data; trend of 0.3°C/year warming plausible; noise level (std 1.5°C) appropriate

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → constants → data generation → decomposition → plots → save; no unnecessary functions or classes
• CQ-02: Reproducibility (2/2) - np.random.seed(42) ensures deterministic output
• CQ-03: Clean Imports (2/2) - All imports used: os (environment), shutil (file management), numpy/pandas (data), statsmodels (decomposition), lets_plot (visualization)
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, appropriate complexity; no fake functionality; proper use of pandas DataFrames and statsmodels API
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png (PNG) and plot-{THEME}.html (interactive HTML); correct file naming and API usage

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) - Expert use of letsplot's grammar of graphics: ggplot() + geom_line() + theme() pattern; proper aes() mapping; ggsave() with scale=3 for 4800×2700px export; element_* functions for theming
• LM-02: Distinctive Features (3/5) - Uses gggrid() for subplot arrangement (somewhat distinctive to letsplot vs. other ggplot-style libraries); HTML export is standard; overall approach could translate to plotnine with syntax changes

Score Caps Applied

• None

Strengths

• Perfect visual and technical quality: all text legible in both themes, no overlaps, proper palette compliance, and correct theme-adaptive chrome
• Excellent spec compliance: all requirements met with proper data, decomposition method, and layout
• Clean code structure: straightforward flow, deterministic output, proper use of statsmodels for decomposition
• Strong data quality: realistic temperature scenario, clear demonstration of trend/seasonal/residual separation, appropriate time scale
• Proper theme implementation: both light and dark renders are fully functional with identical data colors and properly adapted text/backgrounds

Weaknesses

• Generic design sophistication: relies on library defaults rather than intentional design choices; lacks distinctive visual touches or custom palette refinement
• Limited visual storytelling: the four components are displayed equally without visual hierarchy or emphasis; no focal point to guide viewer's interpretation of which insight matters most
• Subtle visual refinement: while present (hidden axes, angled labels), customization is minimal; grid and styling follow library defaults rather than intentional polish

Issues Found

1. DE-01 LOW: Generic styling with well-configured defaults but no distinctive design
   • Fix: Consider adding intentional design choices — perhaps highlight the trend component through line weight variation, use subtle color intensity changes, or add subtle annotations that guide the story
2. DE-03 LOW: Plot displays decomposition clearly but lacks visual hierarchy
   • Fix: Create emphasis through visual techniques: make the trend component slightly bolder/more prominent, use subtle color intensity or saturation changes, or arrange components with visual weight to guide reading (e.g., original at top, smaller supporting components below)

AI Feedback for Next Attempt

Strengthen design excellence by adding visual hierarchy: emphasize the trend component as the primary insight (bolder line or color saturation) while supporting components provide context. Consider subtle refinements like removing unnecessary gridlines on residual plot (mostly noise), or using slight transparency variations to create depth. The foundation is solid — now add intentional design sophistication.

Verdict: REJECTED

Rationale: Score of 88/100 does not meet the ≥90 threshold for Review 1 (publication quality). The implementation is technically excellent and meets all specifications, but Design Excellence is below publication standard. Rejection allows opportunity to add visual sophistication and data storytelling that would elevate this to exceptional quality.

[github-actions]

🔧 Repair Attempt 1/4

Applied fixes based on AI review feedback.

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

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The plot displays four vertically stacked time series subplots on a warm off-white background (#FAF8F1). The Original Series (top) shows temperature data in teal (#009E73), Trend displays gradual warming in orange (#D55E00), Seasonal shows regular oscillations in blue (#0072B2), and Residual shows scattered noise in reddish-purple (#CC79A7). All text is crisp and readable: bold titles, dark gray labels and ticks, with subtle visible grid lines. X-axis shows no labels except on final subplot where dates are rotated 45° to avoid collision. All chrome (titles, axis labels, tick marks) is clearly readable against the light surface with no color conflicts.

Dark render (plot-dark.png): The same four components render identically on warm near-black background (#1A1A17). Text has flipped to light colors: white/cream for titles and axis labels, lighter gray for ticks. Critically, all data colors remain identical to the light render (teal, orange, blue, reddish-purple). No dark-on-dark failures appear—all text is clearly readable with no near-black text on near-black background. Grid lines remain subtle and visible. Layout and spacing are preserved perfectly.

Theme-readability verdict: Both renders PASS. Light render: background is warm off-white, all text is dark and readable, grid is subtle. Dark render: background is warm near-black, all text is light and readable, no dark-on-dark issues detected. Both renders successfully thread theme tokens through to every element.

Score: 88/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	88	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicit and readable in both themes
• VQ-02: No Overlap (6/6) - Subplots well-separated, date labels rotated to prevent collision
• VQ-03: Element Visibility (6/6) - All lines clearly visible at size=1.2
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette is colorblind-safe
• VQ-05: Layout & Canvas (4/4) - Balanced subplots in 4800×2700 landscape format
• VQ-06: Axis Labels & Title (2/2) - Spec-compliant title with descriptive labels and units
• VQ-07: Palette Compliance (2/2) - First series #009E73, Okabe-Ito order followed, correct backgrounds

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) - Clean design with proper theme handling, though limited custom refinement
• DE-02: Visual Refinement (3/6) - X-axis cleanup, subtle grid, generous whitespace
• DE-03: Data Storytelling (4/6) - Vertical stacking guides viewer through decomposition hierarchy

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct time series decomposition implementation
• SC-02: Required Features (4/4) - All requirements present (4 subplots, shared axis, labels, grid)
• SC-03: Data Mapping (3/3) - X/Y mapping correct, all 60 months displayed
• SC-04: Title & Legend (3/3) - Spec-format title, component labels present

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows original, trend, seasonal, and residual
• DQ-02: Realistic Context (5/5) - Temperature data is plausible and neutral
• DQ-03: Appropriate Scale (4/4) - Realistic values for monthly temperature decomposition

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Straightforward script with no unnecessary abstractions
• CQ-02: Reproducibility (2/2) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: Code Elegance (2/2) - Clean implementation with proper API usage
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png and plot-{THEME}.html

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) - Grammar of graphics style with proper geom/theme usage
• LM-02: Distinctive Features (2/5) - Uses gggrid but limited exploration of letsplot-specific features

Score Caps Applied

• None

Strengths

• Perfect theme adaptation: backgrounds, text colors, and grid all correctly switch between light and dark without compromising readability
• Compliant palette: all data colors follow Okabe-Ito order with first series correctly #009E73 (brand green)
• Clear decomposition storytelling: stacked subplot layout intuitively guides viewer through components
• Proper data handling: uses statsmodels seasonal_decompose for scientifically sound decomposition
• Clean code: minimal structure, deterministic seed, no fake functionality

Weaknesses

• Design could be more sophisticated with distinctive visual refinements beyond defaults
• Limited exploration of letsplot-specific features beyond basic ggplot syntax

Verdict: APPROVED