feat(seaborn): implement line-timeseries

[github-actions]

Implementation: line-timeseries - python/seaborn

Implements the python/seaborn version of line-timeseries.

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

Parent Issue: #2006

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot displays a clean time series line in Okabe-Ito green (#009E73) on a warm off-white background (#FAF8F1). The title 'line-timeseries · seaborn · anyplot.ai' is clearly visible in dark text. The x-axis shows month labels ('Jan 2024', 'Feb 2024', etc.) rotated 45° to prevent overlap. The y-axis displays temperature values (2.5–20.0°C) with clear tick labels. Axis labels 'Date' and 'Temperature (°C)' are readable in dark text. Grid lines are subtle but visible on both axes. The line is thick enough to see clearly. All elements have proper contrast against the light background with no unreadable text.

Dark render (plot-dark.png): The same plot rendered on a warm near-black background (#1A1A17) with all text (title, axis labels, tick labels) adapted to light colors for proper contrast. The data line remains the same green (#009E73) and is clearly visible against the dark background. Grid lines are properly adapted with light colors at low alpha. No dark-on-dark text contrast issues present. The overall appearance is professionally adapted between themes, with only the 'chrome' (background, text, grid) changing while data colors remain constant. Both renders are theme-correct and fully readable.

Score: 86/100

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	6	10
Total	86	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set. Readable in both themes.
• VQ-02: No Overlap (6/6) - Date labels rotated 45° prevent overlaps.
• VQ-03: Element Visibility (6/6) - Thick line clearly visible throughout range.
• VQ-04: Color Accessibility (2/2) - Okabe-Ito #009E73, CVD-safe, adequate contrast.
• VQ-05: Layout & Canvas (4/4) - Good proportions, nothing cut off.
• VQ-06: Axis Labels & Title (2/2) - Descriptive with units, correct format.
• VQ-07: Palette Compliance (2/2) - First series #009E73, backgrounds #FAF8F1/#1A1A17, both renders theme-correct.

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (5/8) - Clean seaborn styling, professional but not distinctive. Default baseline, +1 for proper implementation.
• DE-02: Visual Refinement (3/6) - Removes top/right spines, subtle grid. Could benefit from more refined visual hierarchy.
• DE-03: Data Storytelling (2/6) - Clear temporal progression but no special emphasis on key insights.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct line plot with datetime x-axis.
• SC-02: Required Features (4/4) - Smart date formatting, intelligent tick spacing, rotated labels, grid on both axes.
• SC-03: Data Mapping (3/3) - X-axis dates, Y-axis temperature values, all data visible.
• SC-04: Title & Legend (3/3) - Correct format, no legend (single series).

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Complete 90-day time series with clear patterns.
• DQ-02: Realistic Context (5/5) - Realistic temperature range (5–20°C), plausible data.
• DQ-03: Appropriate Scale (4/4) - 90 points at daily frequency appropriate for months time scale.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No unnecessary functions, straightforward script.
• CQ-02: Reproducibility (2/2) - Uses np.random.seed(42).
• CQ-03: Clean Imports (2/2) - Only necessary imports.
• CQ-04: Code Elegance (2/2) - Appropriate complexity, proper API usage.
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) - High-level seaborn API, proper theme configuration, matplotlib date tools. Default=3, +1 for date locator sophistication.
• LM-02: Distinctive Features (2/5) - Uses MonthLocator/WeekdayLocator showing time series expertise. Otherwise standard patterns.

Score Caps Applied

• None

Strengths

• Perfect visual quality with both themes correctly styled and all text clearly readable
• Full spec compliance with all required features (smart date formatting, grid on both axes, rotated labels)
• Clean, reproducible code with proper theme tokens and idiomatic seaborn usage
• Professional appearance with appropriate visual hierarchy

Weaknesses

• Design excellence moderate: uses default seaborn styling without distinctive visual enhancements
• Could benefit from more refined visual hierarchy or emphasis on key temporal patterns

Issues Found

None — implementation meets all requirements.

AI Feedback for Next Attempt

Implementation is solid and meets all specifications. No fixes required. Consider enhancing visual storytelling through color emphasis or annotation to improve design excellence score.

Verdict: APPROVED

[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 daily temperature readings over 90 days (Jan-Apr 2024) on a warm off-white background (#FAF8F1). The title 'line-timeseries · seaborn · anyplot.ai' is clearly visible in dark text at the top. The Y-axis is labeled 'Temperature (°C)' with dark text, and the X-axis is labeled 'Date' with dark rotated tick labels showing month/year (Jan 2024, Feb 2024, Mar 2024, Apr 2024). The data line in brand green (#009E73) shows a sinusoidal seasonal trend with daily noise variation, with a confidence interval band around it. Grid lines in subtle gray are visible on both axes. All text is clearly readable against the light background with no overlap or contrast issues.

Dark render (plot-dark.png): The plot renders identically on a warm near-black background (#1A1A17), with all chrome elements flipped to light colors. Title and axis labels are now in light text (#F0EFE8) with excellent contrast against the dark surface. Tick labels are in soft light gray (#B8B7B0). Critically, the data line remains the same brand green (#009E73) — proving the Okabe-Ito palette is theme-independent. Grid lines are subtly visible in light gray at the same alpha. All text remains perfectly readable. No dark-on-dark failures are present.

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) — Title 24pt, Labels 20pt, Ticks 16pt all explicitly set and perfectly readable in both themes
• VQ-02: No Overlap (6/6) — X-axis labels rotated 45° with ha="right", no collisions
• VQ-03: Element Visibility (6/6) — Linewidth=3 with 90 points provides optimal visibility; confidence bands clearly visible
• VQ-04: Color Accessibility (2/2) — Brand green has excellent contrast on both light and dark; CVD-safe palette
• VQ-05: Layout & Canvas (4/4) — Plot fills ~60% of canvas with balanced margins and generous whitespace
• VQ-06: Axis Labels & Title (2/2) — 'Temperature (°C)' with units; title matches spec format exactly
• VQ-07: Palette Compliance (2/2) — Data series #009E73 (Okabe-Ito position 1); backgrounds #FAF8F1 (light) and #1A1A17 (dark); chrome properly adapted

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) — Well-configured defaults with thoughtful theme tokens; lacks custom visual hierarchy
• DE-02: Visual Refinement (4/6) — Top/right spines removed, grid at alpha=0.15; could be more polished
• DE-03: Data Storytelling (2/6) — Data shows seasonal patterns but no visual emphasis techniques

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct time series line plot
• SC-02: Required Features (4/4) — Smart date formatting, rotated labels, grid on both axes
• SC-03: Data Mapping (3/3) — X=Date, Y=Temperature; full range visible
• SC-04: Title & Legend (3/3) — Title format correct; no legend needed for single series

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows daily variation, weekly oscillation, seasonal trend, confidence intervals
• DQ-02: Realistic Context (5/5) — Real-world, neutral, scientifically plausible
• DQ-03: Appropriate Scale (4/4) — Realistic temperature range for 3-month period

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow, no over-engineering
• CQ-02: Reproducibility (2/2) — np.random.seed(42) ensures reproducibility
• CQ-03: Clean Imports (2/2) — All imports used
• CQ-04: Code Elegance (2/2) — Pythonic, appropriate complexity
• CQ-05: Output & API (1/1) — Saves as plot-{THEME}.png correctly

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) — sns.lineplot(), sns.set_theme(), idiomatic matplotlib patterns
• LM-02: Distinctive Features (3/5) — Uses matplotlib date tools and errorbar/CI; could explore more

Score Caps Applied

• None

Strengths

• Perfect theme adaptation with no legibility issues in either render
• Complete spec compliance with smart date formatting using MonthLocator and DateFormatter
• Clean, reproducible code following KISS principles
• Realistic example data showing seasonal patterns with daily noise
• Proper use of matplotlib's datetime handling for professional time series

Weaknesses

• Design aesthetics are functional but generic—no custom visual hierarchy or emphasis techniques
• Data storytelling relies on data choice rather than visual design
• Limited exploration of seaborn-specific features beyond basic lineplot

Verdict: APPROVED

Score of 88/100 exceeds the Review 2 threshold of 80. The implementation meets all spec requirements with excellent theme handling and legibility in both renders.