feat(letsplot): implement line-timeseries

[github-actions]

Implementation: line-timeseries - python/letsplot

Implements the python/letsplot version of line-timeseries.

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

Parent Issue: #2006

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): A time series line plot on warm off-white background (#FAF8F1) showing 365 days of temperature data with a clear seasonal pattern. The line in teal/green (#009E73) rises through mid-year (June/July peak ~25°C) and declines toward year-end. Title "line-timeseries · letsplot · anyplot.ai" in dark text is prominent. Axis labels "Date" (rotated 45°) and "Temperature (°C)" are clearly readable. Small circular markers trace the line. Grid lines in subtle gray provide readability aids. All text is sharp and perfectly readable against the light background. No dark-on-light or light-on-light conflicts.

Dark render (plot-dark.png): Identical plot rendered on warm near-black background (#1A1A17). The data line and markers remain exactly #009E73 (teal/green) — unchanged from light render, confirming correct theme separation. Title and axis labels now display in light text (#F0EFE8 or similar), providing excellent contrast. Grid lines visible in light gray. All text is clearly readable against the dark background — no dark-on-dark failures. The brand green stands out well against both surfaces. Chrome (background, text, grid) has flipped to match the dark theme, while data colors remained constant.

Theme Compliance Summary: Both renders pass all legibility checks. Data colors are identical; only chrome adapted. No readable-text failures in either theme.

Score: 87/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	9	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	87	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set (title=24pt, labels=20pt, ticks=16pt). Perfect readability in both themes.
• VQ-02: No Overlap (6/6) - X-axis labels rotated 45° to prevent collision. All text fully readable.
• VQ-03: Element Visibility (6/6) - Line and markers perfectly adapted to 365-point density. Markers size=1.5, alpha=0.6 optimal.
• VQ-04: Color Accessibility (2/2) - Okabe-Ito #009E73 colorblind-safe with excellent contrast in both themes.
• VQ-05: Layout & Canvas (4/4) - Plot utilizes 60-70% of canvas with balanced margins. Excellent layout.
• VQ-06: Axis Labels & Title (2/2) - "Temperature (°C)" with units. Title in correct format.
• VQ-07: Palette Compliance (2/2) - First series #009E73 ✓. Backgrounds #FAF8F1/#1A1A17 ✓. Theme-adaptive chrome perfect in both renders.

Design Excellence (9/20)

• DE-01: Aesthetic Sophistication (4/8) - Well-configured library defaults with proper theme tokens. Professional but relies on built-in aesthetics; not exceptional or custom.
• DE-02: Visual Refinement (3/6) - Minimal customization: grid opacity adjusted, x-axis rotation thoughtful. Lacking spine refinement or whitespace enhancements.
• DE-03: Data Storytelling (2/6) - Seasonal pattern clearly visible but no visual hierarchy, emphasis, or focal points created.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct time series line plot with datetime x-axis.
• SC-02: Required Features (4/4) - Datetime parsing, smart date formatting ("%b %Y"), grid on both axes.
• SC-03: Data Mapping (3/3) - X/Y correct. All 365 points visible.
• SC-04: Title & Legend (3/3) - Title format correct. Single-series, no legend needed.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Full seasonal variation demonstrated with baseline, pattern, and noise.
• DQ-02: Realistic Context (5/5) - Real-world temperature scenario, plausible values (0–25°C), neutral context.
• DQ-03: Appropriate Scale (4/4) - Factually correct proportions for seasonal temperatures.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → theme → plot → save.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) ensures deterministic output.
• CQ-03: Clean Imports (2/2) - Only used imports, no waste.
• CQ-04: Code Elegance (2/2) - Pythonic, appropriate complexity. No over-engineering or fake functionality.
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png/html. Current API.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) - Expert use of ggplot() + grammar of graphics. Proper datetime handling with scale_x_datetime(). Excellent theme integration.
• LM-02: Distinctive Features (3/5) - Uses HTML export and theme system, but these are standard ggplot features. Not particularly distinctive to letsplot.

Score Caps Applied

• None — all criteria above caps.

Strengths

• Flawless visual quality across all technical criteria
• Perfect theme implementation: both light and dark renders are excellently executed with no readability failures
• Complete spec compliance with intelligent datetime formatting and layout
• Idiomatic letsplot usage with proper grammar of graphics
• Clean, reproducible code with explicit font sizing
• Realistic, plausible temperature data showing clear seasonal patterns

Weaknesses

• Design excellence is generic: relies on library defaults rather than custom sophistication
• No visual hierarchy or emphasis — plot displays data without interpretation or focal points
• Limited visual refinement beyond grid opacity adjustment; could use spine customization or whitespace enhancement
• Library mastery is solid but not distinctive — uses standard ggplot patterns, not letsplot-specific innovations

Issues Found

1. DE-01 LOW (4/8): Generic styling with library defaults
   • Fix: Add custom visual sophistication — consider refined color accents, deliberate typography hierarchy, or custom elements that go beyond library presets
2. DE-02 LOW (3/6): Minimal visual refinement beyond grid opacity
   • Fix: Consider spine removal/adjustment, enhanced whitespace, or subtle visual elements that signal polish
3. DE-03 LOW (2/6): No data storytelling or visual hierarchy
   • Fix: Create visual emphasis through color contrast, size variation, or strategic data annotation that guides the viewer to the key insight (e.g., highlight peak temperature or seasonal transition)

AI Feedback for Next Attempt

Excellent technical execution — visual quality, spec compliance, and code are flawless. The opportunity lies in aesthetic design. To reach publication quality: (1) add visual sophistication beyond library defaults (custom color palette or intentional design elements), (2) enhance visual refinement through spine/whitespace/grid customization, (3) create visual storytelling by adding emphasis to the seasonal insight through color contrast, annotations, or focal points. The plot is correct; make it beautiful.

Verdict: REJECTED

Threshold for Attempt 1: ≥90 points. Score: 87/100.

This implementation excels in technical execution but falls short on design excellence. It requires a repair loop focusing on aesthetic sophistication and visual storytelling.

[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 a full year of daily temperature data on a warm off-white background (#FAF8F1). The title "line-timeseries · letsplot · anyplot.ai" is clearly visible at the top. The x-axis shows rotated date labels (month/year format) preventing overlap, and the y-axis shows "Temperature (°C)" with appropriate scale. The visualization includes a primary teal-green line (#009E73) showing individual daily readings with small circular markers, a brownish-tan LOESS smoothing curve overlay showing the underlying trend, and two larger points highlighting the peak and trough temperatures. A subtle grid is visible on both axes. All text (title, axis labels, tick labels) is dark and clearly readable against the light background.

Dark render (plot-dark.png): The plot renders identically on a warm near-black background (#1A1A17), with all text converted to light colors for theme-adaptive readability. The data colors remain identical to the light render—the primary line stays teal-green (#009E73), and the smoothing curve remains the same brownish tan, with the larger extreme-point markers matching. The grid lines remain subtle and visible. All text is light-colored and clearly readable against the dark background. No dark-on-dark legibility issues are present. The plot successfully demonstrates theme-adaptive chrome while preserving data color identity across both renders.

Score: 89/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	89	100

Visual Quality (29/30)

• VQ-01: Text Legibility (7/8) - Font sizes explicitly set; all readable across themes
• VQ-02: No Overlap (6/6) - X-axis labels rotated 45° preventing overlap
• VQ-03: Element Visibility (6/6) - All data elements clearly visible and well-adapted to density
• VQ-04: Color Accessibility (2/2) - Okabe-Ito colorblind-safe palette with strong contrast
• VQ-05: Layout & Canvas (4/4) - Balanced margins; plot fills canvas appropriately
• VQ-06: Axis Labels & Title (2/2) - Title follows spec format; Y-axis includes units
• VQ-07: Palette Compliance (2/2) - First series #009E73; theme-correct backgrounds and chrome

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Thoughtful design with LOESS overlay and themed colors
• DE-02: Visual Refinement (4/6) - Subtle grid; generous whitespace; polished presentation
• DE-03: Data Storytelling (4/6) - Clear seasonal narrative with visual hierarchy

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct time series line plot
• SC-02: Required Features (4/4) - Intelligent date formatting, grid lines, datetime handling
• SC-03: Data Mapping (3/3) - X-axis dates, Y-axis temperature; all data visible
• SC-04: Title & Legend (3/3) - Title format correct; no legend needed (single series)

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows seasonal pattern, daily variation, extremes
• DQ-02: Realistic Context (5/5) - Year-long temperature readings; realistic scenario
• DQ-03: Appropriate Scale (4/4) - Plausible values; correct proportions

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only used imports
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, no over-engineering
• CQ-05: Output & API (1/1) - Correct PNG and HTML output

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - ggplot2-style grammar with proper datetime handling
• LM-02: Distinctive Features (3/5) - Uses geom_smooth (LOESS) and theme-adaptive styling

Score Caps Applied

• None

Strengths

• Excellent theme-adaptive chrome implementation with proper use of theme tokens across both light and dark renders
• Clean visual hierarchy through layered geoms (line → smooth → extremes)
• Proper handling of datetime data with intelligent date formatting
• All text elements explicitly sized and fully readable in both themes
• Realistic, well-chosen example data that meaningfully demonstrates the plot type
• Solid color palette compliance with Okabe-Ito standards

Weaknesses

• Design excellence could be elevated through additional visual refinement (e.g., more sophisticated grid styling)
• DE-02 and DE-03 leave room for enhanced visual polish and emphasis techniques

AI Feedback for Next Attempt

This is a strong submission meeting the Attempt 2 threshold. No repairs needed.

Verdict: APPROVED