feat(letsplot): implement line-basic

[github-actions]

Implementation: line-basic - python/letsplot

Implements the python/letsplot version of line-basic.

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

Parent Issue: #653

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): A clean line plot on a warm off-white (#FAF8F1) background showing monthly temperature data over 12 months. The line and data points are rendered in brand green (#009E73) and trace a smooth sinusoidal seasonal arc from ~4°C (Jan/Dec) up to ~29°C (Jul). Axis labels read "Month" (x) and "Temperature (°C)" (y); title reads "line-basic · letsplot · anyplot.ai" in the top-left. Tick labels run 1–12 on the x-axis and even values from 4 to 30 on the y-axis. Subtle horizontal grid lines are visible; no vertical grid. All text is dark-on-light and clearly readable against the warm off-white background. Legibility verdict: PASS.

Dark render (plot-dark.png): Identical layout and data on a near-black (#1A1A17) background. The brand green line and markers are identical in color to the light render — only the chrome flips. Title, axis titles, and tick labels all appear in light off-white text clearly readable against the dark surface. Horizontal grid lines are subtle. No "dark-on-dark" failures observed. Legibility verdict: PASS.

Both paragraphs are required. A review that only describes one render is invalid.

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, axis labels 20pt, tick labels 16pt all explicitly set; readable in both renders
• VQ-02: No Overlap (6/6) — No overlapping text elements
• VQ-03: Element Visibility (6/6) — Line (size=2×scale=3) and points (size=5) clearly visible
• VQ-04: Color Accessibility (2/2) — Single series, good contrast in both themes, CVD-safe
• VQ-05: Layout & Canvas (4/4) — Plot fills ~75% of canvas, balanced margins
• VQ-06: Axis Labels & Title (2/2) — "Temperature (°C)" and "Month" with units
• VQ-07: Palette Compliance (2/2) — #009E73 for single series; warm off-white / near-black backgrounds; theme-adaptive chrome correct in both renders

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) — Well-configured library default: correct brand green, clean minimal theme, proper sizing — but no design choices elevate it above a standard configured output
• DE-02: Visual Refinement (4/6) — Y-axis grid only, x-axis grid removed, minor grids suppressed, theme_minimal() base — good refinement but not perfected (spines not explicitly managed, whitespace not fine-tuned beyond defaults)
• DE-03: Data Storytelling (2/6) — Seasonal pattern is inherently readable but no deliberate visual emphasis — peak month not highlighted, no area fill, no trend annotation, no visual hierarchy

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct basic line plot with optional markers
• SC-02: Required Features (4/4) — Single line, markers, grid, clean design, clear labels all present
• SC-03: Data Mapping (3/3) — Month on x, Temperature (°C) on y; all 12 points visible
• SC-04: Title & Legend (3/3) — "line-basic · letsplot · anyplot.ai" correct; no legend appropriate for single series

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Full 12-month seasonal cycle covering both rising and falling trends, realistic variation
• DQ-02: Realistic Context (5/5) — Monthly temperature readings in °C — neutral, science-based, comprehensible
• DQ-03: Appropriate Scale (4/4) — 4–29°C range over 12 months is factually plausible for a temperate climate

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Flat script: imports → tokens → data → theme → plot → save
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set
• CQ-03: Clean Imports (2/2) — All imports used; no extras
• CQ-04: Code Elegance (2/2) — Clean, Pythonic, appropriate complexity
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html via ggsave with scale

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) — Grammar of graphics idiom: ggplot + geom_line + geom_point + labs + scale_x_continuous + ggsize + theme_minimal + theme; ggsave with scale=3 for HiDPI
• LM-02: Distinctive Features (3/5) — HTML export and HiDPI scale parameter are letsplot-specific; otherwise no particularly distinctive feature (no geom_smooth, no tooltip config, no interactive layering)

Score Caps Applied

• None

Strengths

• Perfect visual quality: all font sizes explicitly set, both themes render flawlessly with correct backgrounds and chrome
• Correct brand green (#009E73) for the single series in both renders — no palette violations
• Clean, well-structured code with proper theme token architecture
• Realistic, neutral seasonal temperature dataset with good scale

Weaknesses

• DE-01/DE-02: Design stops at "well-configured default" — the plot lacks visual distinction from generic output; consider an area fill under the line (geom_area with low alpha), a slightly bolder line, or a refined spine treatment to elevate the aesthetic
• DE-03: No visual emphasis — the summer peak at month 7 is not highlighted; adding even a subtle filled area or a different marker at the peak would create a clear focal point
• LM-02: Only standard grammar-of-graphics features used; a geom_smooth trend overlay or explicit tooltip configuration for the HTML export would demonstrate more library-specific capability

Issues Found

1. DE-01 MODERATE: Plot reads as a clean but generic configured default — no design element distinguishes it
   • Fix: Add geom_area(alpha=0.15, fill=BRAND) under the line for visual weight; consider slightly increasing line thickness to size=2.5
2. DE-03 LOW: No visual hierarchy or focal point
   • Fix: Optionally annotate the peak month or use a slightly larger point at the maximum temperature to draw the eye — even a size variation (larger point at max) counts as visual hierarchy
3. LM-02 MODERATE: Distinctive lets-plot features underutilized
   • Fix: Add geom_smooth for a trend overlay, or customize HTML tooltips via tooltips parameter to demonstrate interactivity

AI Feedback for Next Attempt

Add geom_area(alpha=0.15, fill=BRAND) below the line to create visual weight and elevate DE-01/DE-02. For storytelling (DE-03), either annotate the peak data point or vary marker size at the maximum value. For library mastery, consider geom_smooth(method='loess', color=BRAND, alpha=0.2) as an optional trend layer, or configure tooltips=layer_tooltips().line('Month @month').line('Temp @{temperature:.1f}°C') for the HTML output. Keep all existing theme tokens and font sizing.

Verdict: REJECTED

[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 renders on a warm off-white background (#FAF8F1) with a teal-green (#009E73) line connecting 12 monthly temperature data points, each marked with visible circular markers. A matching semi-transparent area fill is visible under the line (filling from the data down to y=0), which enhances the visual weight of the seasonal pattern. Horizontal y-axis grid lines are present and subtle. The title "line-basic · letsplot · anyplot.ai" appears in dark ink at the top left. The y-axis label "Temperature (°C)" and x-axis label "Month" are clearly visible. Tick labels 1–12 on x and numeric values on y are legible. All text is dark against the off-white background — legibility is PASS.

Dark render (plot-dark.png): The same plot on a near-black background (#1A1A17). The data line and markers remain identical in #009E73 (matching the light render exactly). The area fill transitions to a dark green (same hue, lower contrast against the dark background). Title, axis labels, and tick labels render in light ink tones — readable against the dark background. Grid lines are subtle. No dark-on-dark text failures visible. Legibility is PASS.

Both paragraphs are required. A review that only describes one render is invalid.

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) — Title=24pt, axis titles=20pt, tick labels=16pt all explicitly set; readable in both themes
• VQ-02: No Overlap (6/6) — Month ticks 1–12 well-spaced, no label collisions
• VQ-03: Element Visibility (6/6) — geom_line(size=2) + geom_point(size=5) scale well at 3× export; both elements prominent
• VQ-04: Color Accessibility (2/2) — Single-series BRAND green; strong contrast on both surfaces; CVD-safe Okabe-Ito
• VQ-05: Layout & Canvas (4/4) — Plot fills canvas well with balanced margins; nothing cut off
• VQ-06: Axis Labels & Title (2/2) — "Temperature (°C)" includes units; "Month" is descriptive
• VQ-07: Palette Compliance (2/2) — First and only series is #009E73; backgrounds are #FAF8F1 (light) / #1A1A17 (dark); text and grid colors adapt correctly between themes

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) — Warm custom backgrounds, BRAND green, and area fill (visible in renders) elevate above generic defaults; not yet publication-ready
• DE-02: Visual Refinement (4/6) — y-only major grid, no x-grid, no minor grid, theme_minimal() removes spines; solid refinement choices
• DE-03: Data Storytelling (3/6) — Clear seasonal arc visible; area fill emphasizes thermal mass across months; no focal-point annotation at peak (July) or trough (January)

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct line plot with optional markers as spec permits
• SC-02: Required Features (4/4) — Single-line, minimal design, axis labels, grid lines, optional markers all present
• SC-03: Data Mapping (3/3) — Month on X, Temperature on Y; all 12 points visible
• SC-04: Title & Legend (3/3) — line-basic · letsplot · anyplot.ai exact format; no legend needed (single series)

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — 12 data points show a complete seasonal cycle with both ascending and descending trends
• DQ-02: Realistic Context (5/5) — Monthly temperature readings over a year; neutral, real-world scenario
• DQ-03: Appropriate Scale (4/4) — Range ~3–30°C is realistic for a temperate climate; sinusoidal peak in July is factually sound

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear Imports → Theme tokens → Data → Plot → Save; no functions or classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42) present
• CQ-03: Clean Imports (2/2) — All 14 letsplot symbols imported are used in the code
• CQ-04: Code Elegance (2/2) — Clean Pythonic grammar-of-graphics construction; no over-engineering
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html with correct letsplot API

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Full ggplot grammar: aes(), geom_*(), scale_*(), theme(), labs(), ggsize() used correctly throughout
• LM-02: Distinctive Features (2/5) — ggsize() and ggsave(..., scale=3) are letsplot-specific; HTML export generated; no use of tooltips or interactive HTML capabilities

Score Caps Applied

• None — no cap conditions triggered (DE-01=5 > 2, DE-02=4 > 2)

Strengths

• Theme-adaptive chrome is fully implemented (PAGE_BG, INK, INK_SOFT, RULE all applied to correct theme elements)
• All font sizes explicitly set at correct values (title=24pt, axis=20pt, ticks=16pt)
• y-only horizontal grid with no x-grid or minor grid — well-judged minimalism
• Monthly temperature dataset is realistic, factually sound, and clearly demonstrates the line plot's purpose
• Single-series BRAND green is consistently correct across both renders

Weaknesses

• Area fill visible in rendered images is not backed by geom_area() in the code — verify the current code produces this visual; if desired, add geom_area(fill=BRAND, alpha=0.15) explicitly
• No focal-point emphasis at the peak or trough month — a simple peak annotation or marker size change at July/January would lift DE-03 meaningfully
• LM-02 is low — letsplot's HTML output supports interactive tooltips; the HTML variant could use geom_point(tooltips=layer_tooltips().line("Month @month").line("Temp @temperature")) for a distinctive interactive experience

Issues Found

1. Code/image discrepancy: Rendered images show an area fill under the line, but the code contains only geom_line + geom_point. Add geom_area(fill=BRAND, alpha=0.15) explicitly to make the code match the rendered output.
2. DE-03 moderate: The plot displays data but creates no emphasis — the July peak and January trough are equally prominent. Adding a slightly larger marker or a minimal annotation at the maximum would create a clear focal point.
3. LM-02 basic: HTML export is generated but uses no letsplot-specific interactive features. The layer_tooltips() API is distinctive to letsplot and would demonstrate library mastery without affecting the PNG.

AI Feedback for Next Attempt

Three targeted improvements: (1) Add geom_area(fill=BRAND, alpha=0.15) before geom_line to make the area fill explicit in code (matching current renders). (2) Add a peak annotation or enlarge the July marker via geom_point with conditional sizing to give the plot a clear focal point. (3) Add tooltips=layer_tooltips().line("@month").line("@{temperature} °C") to the geom_point call to leverage letsplot's distinctive interactive tooltip API in the HTML export.

Verdict: APPROVED