feat(highcharts): implement strip-basic

[github-actions]

Implementation: strip-basic - python/highcharts

Implements the python/highcharts version of strip-basic.

File: plots/strip-basic/implementations/python/highcharts.py

Parent Issue: #975

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot shows a strip chart on a warm off-white #FAF8F1 background with four subject categories along the x-axis (Mathematics, Science, Literature, History). Each category's data points are rendered as semi-transparent circles (alpha ~0.65) with horizontal jitter, colored with Okabe-Ito: Mathematics green #009E73, Science orange #D55E00, Literature blue #0072B2, History pink #CC79A7. Mean markers appear as dark diamonds for each group. Y-axis spans 40–106 labeled "Test Score (%)", x-axis labeled "Subject". Title "strip-basic · highcharts · anyplot.ai" appears bold at top, with subtitle "Student Test Scores by Subject" in a lighter shade. Legend floats in the upper-right corner. All text is clearly readable against the light background; dark text on warm off-white passes legibility.

Dark render (plot-dark.png): Same chart rendered on a warm near-black #1A1A17 background. Data colors are identical to the light render — green, orange, blue, and pink points unchanged. Mean markers appear as light/white diamonds. Title and all axis labels render in #F0EFE8-range light text, fully readable against the dark background. Tick labels are light-colored. Legend box shows elevated dark background with light text and colored markers. No "dark-on-dark" failures detected. Legibility verdict: PASS for both renders.

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

Score: 88/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 64px, subtitle/axes 40px, ticks 32px, legend 30px; both renders fully readable
• VQ-02: No Overlap (6/6) — No text collisions; jitter spreads points without crowding category labels
• VQ-03: Element Visibility (5/6) — Radius 12 with fillOpacity 0.65 works well; slight density overlap in core regions of Math/Science
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette CVD-safe; appropriate alpha for density
• VQ-05: Layout & Canvas (3/4) — Chart uses canvas well but ~15–20% unused space in upper chart area; y-axis minimum at 40 leaves empty bandwidth below lowest point
• VQ-06: Axis Labels & Title (2/2) — Y-axis "Test Score (%)" has units; x-axis "Subject" is descriptive
• VQ-07: Palette Compliance (2/2) — First series #009E73; full Okabe-Ito order; backgrounds #FAF8F1 / #1A1A17; chrome tokens fully theme-adaptive in both renders

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Above defaults: explicit color palette, typography hierarchy, edge-highlighted markers, mean diamond overlays, credits disabled. Not yet publication-ready polish
• DE-02: Visual Refinement (4/6) — Y-axis subtle grid (GRID token), x-axis tick marks removed (tickWidth=0), legend framed with ELEVATED_BG, marginBottom=200 for breathing room; no explicit spine treatment
• DE-03: Data Storytelling (4/6) — Bimodal distributions for Math and Literature create genuine visual interest; mean markers provide summary reference; different density patterns across groups tell a story with visual hierarchy

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct strip plot: individual scatter points per category with random jitter
• SC-02: Required Features (4/4) — Jitter applied (width 0.25, within 0.1–0.3 spec range), transparency (fillOpacity 0.65, within 0.5–0.7 range), mean reference markers included
• SC-03: Data Mapping (3/3) — Categorical x-axis correctly mapped; numeric y-axis "Test Score (%)" spans full data range
• SC-04: Title & Legend (3/3) — Title matches strip-basic · highcharts · anyplot.ai; legend labels match series names with Mean marker

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows bimodal distributions (Math high-performer cluster, Literature two-cluster), different spreads, varying sample sizes, and mean markers
• DQ-02: Realistic Context (5/5) — Neutral real-world scenario: student test scores by academic subject (Mathematics, Science, Literature, History)
• DQ-03: Appropriate Scale (4/4) — Scores clipped to 30–100, means in 72–78 range, SDs 5–12 — all factually realistic for student performance data

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Flat structure: imports → tokens → data → chart config → series → export
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set
• CQ-03: Clean Imports (2/2) — All imports used; no extraneous packages
• CQ-04: Code Elegance (2/2) — Clean Pythonic code; uses zip(..., strict=True); for-loop over categories is clean; no over-engineering
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png via container screenshot + plot-{THEME}.html (CDN variant for site)

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Proper highcharts_core patterns: Chart/HighchartsOptions, ScatterSeries with attribute-level config, category axis with tickPositions, container screenshot
• LM-02: Distinctive Features (3/5) — HTML export with CDN optimization (dual HTML/PNG output), interactive tooltip configuration, element-level screenshot for precise framing

Score Caps Applied

• None — DE-01=5 > 2 and DE-02=4 > 2, so "generic + no visual refinement" cap does not apply

Strengths

• Perfect spec compliance: jitter, alpha, and mean reference markers all match spec requirements exactly
• Perfect data quality: realistic academic context, bimodal distributions show feature coverage, appropriate scale
• Full theme adaptability: all chrome tokens correctly flip between light and dark; both renders pass legibility
• Perfect code quality: clean KISS structure, reproducible, dual HTML/PNG output with CDN optimization
• Okabe-Ito palette applied correctly with brand green #009E73 as first series and adaptive neutral for mean markers

Weaknesses

• DE-01 not yet at publication-ready level: design is thoughtful but lacks the extra polish that would elevate it (e.g., refined spine treatment, tighter layout with legend inside chart, stronger visual focal point)
• VQ-05: y-axis min of 40 leaves unused space below the actual data minimum (~48); tightening to ~45 or using auto-min would better utilize the canvas
• Legend position (upper-right, floating outside chart area) creates slight visual imbalance; placing it inside or repositioning to upper-left within chart bounds would improve layout
• LM-02: could push further into Highcharts-distinctive territory (e.g., plotLines for mean markers instead of a separate series, or custom renderer elements)

Issues Found

1. DE-01 BELOW TARGET (5/8): Design is above defaults but not at publication quality
   • Fix: Tighten y-axis range (min ~45), move legend inside chart bounds, consider plotLines for mean references to reduce legend noise
2. VQ-05 MINOR (3/4): Canvas utilization slightly suboptimal due to wide y-axis range minimum
   • Fix: Remove "min": 40 or set it to ~44 to match actual data range; data minimum is ~48

AI Feedback for Next Attempt

The implementation is strong (88/100) and very close to approval. Two targeted improvements will push it over 90: (1) Tighten the y-axis — remove the fixed "min": 40 or set it to 44 so the data fills the chart area better (VQ-05); (2) Elevate design polish — place the legend inside the plot area rather than floating outside, and consider using plotLines for the mean markers to keep the legend cleaner (the Mean legend entry adds noise). These changes require minimal code edits and should bring the score to 90+.

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): Warm off-white #FAF8F1 background. Title "strip-basic · highcharts · anyplot.ai" is bold at 64px in dark ink and clearly readable. Subtitle "Student Test Scores by Subject" renders at 40px in INK_SOFT. X-axis label "Subject" and Y-axis label "Test Score (%)" at 40px — both dark and readable. Tick labels at 32px in INK_SOFT. Each category column has a light-tinted background band matching its series hue. Mathematics plots in #009E73 (brand green, correct first series), Science in #D55E00, Literature in #0072B2, History in #CC79A7. Mean diamonds at radius 20 in near-black neutral. Semi-transparent markers (fillOpacity=0.65) with PAGE_BG edge color. Legend top-right on elevated #FFFDF6 background with border. Grid lines at 10% opacity — subtle and non-competing. All text is dark and clearly readable against the warm off-white background. Legibility verdict: PASS.

Dark render (plot-dark.png): Warm near-black #1A1A17 background. Title and axis labels render in light #F0EFE8 — clearly readable. Tick labels in #B8B7B0 — readable against dark surface with no dark-on-dark failures. Legend text in INK_SOFT on elevated #242420 background. Data colors are identical to the light render: Mathematics #009E73, Science #D55E00, Literature #0072B2, History #CC79A7. Mean diamonds flip to white #E8E8E0 (adaptive neutral, correct). Category background bands render as dark-tinted zones. Grid at rgba(240,239,232,0.10) — appropriately subtle. Brand green #009E73 is clearly visible on the dark surface. No dark-on-dark issues. Legibility verdict: PASS.

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

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 (8/8) — All sizes explicitly set: title 64px, axis titles 40px, ticks 32px, legend 30px. Readable in both themes.
• VQ-02: No Overlap (6/6) — No text collisions. Jitter width 0.25 spreads points cleanly within categories.
• VQ-03: Element Visibility (5/6) — Markers at radius 12 with fillOpacity 0.65 appropriate for ~40 pts/category. Mean diamonds at radius 20 clearly distinguishable. Slight deduction: markers are on the smaller side for a 4800×2700 canvas.
• VQ-04: Color Accessibility (2/2) — Okabe-Ito is CVD-safe. Semi-transparency aids density reading. Adaptive neutral for mean series doesn't compete with data.
• VQ-05: Layout & Canvas (4/4) — Plot fills canvas well; balanced margins; marginBottom: 200 prevents label clipping; legend well-positioned.
• VQ-06: Axis Labels & Title (2/2) — "Test Score (%)" with units, "Subject" descriptive.
• VQ-07: Palette Compliance (2/2) — First series #009E73 ✓. Okabe-Ito order for all 4 series ✓. Backgrounds #FAF8F1/#1A1A17 ✓. Data colors identical across themes ✓. Chrome fully theme-adaptive ✓.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Above default: subtitle, mean diamond markers, category background bands, bordered legend. Not yet at publication-ready level — category bands appear to be a Highcharts default behavior for category-axis scatter rather than explicitly coded plotBands.
• DE-02: Visual Refinement (4/6) — Subtle 10%-opacity grid, elevated legend background, generous marginBottom. Category bands provide visual separation. No explicit top/right spine removal.
• DE-03: Data Storytelling (4/6) — Bimodal distributions for Mathematics (high-performer cluster at 90) and Literature (lower/upper split at 65/82) create distributional variety. Mean diamonds guide cross-group comparison. Viewer can see story but no single focal point is emphasized.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct strip plot: scatter series with random x-jitter per category.
• SC-02: Required Features (4/4) — Jitter width 0.25 (within 0.1-0.3), fillOpacity 0.65 (within 0.5-0.7), mean markers as reference. All spec features present.
• SC-03: Data Mapping (3/3) — Categories on x-axis, continuous score on y-axis. All 4 groups fully visible.
• SC-04: Title & Legend (3/3) — Title "strip-basic · highcharts · anyplot.ai" correct. Legend labels match category names. Mean series labeled.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — All strip plot aspects: individual points, jitter, density variation, different distributions (unimodal vs bimodal), mean reference.
• DQ-02: Realistic Context (5/5) — Student test scores by subject — realistic, neutral academic context.
• DQ-03: Appropriate Scale (4/4) — Scores 30-100%, means ~72-78%. Clipping to [30,100]. Realistic academic distribution.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → theme tokens → data → chart config → export. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) before all random calls.
• CQ-03: Clean Imports (2/2) — All imports used. None unused.
• CQ-04: Code Elegance (2/2) — Clean Pythonic code. Dual HTML output (inline for headless Chrome, CDN for site) is a thoughtful pattern.
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png + plot-{THEME}.html. Element-level container.screenshot() for precise capture.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Chart+HighchartsOptions pattern, ScatterSeries, add_series(), category x-axis with tickPositions+min+max. Good idiomatic usage with thoughtful axis configuration.
• LM-02: Distinctive Features (3/5) — Dual HTML output mode (inline for rendering, CDN for serving) is Highcharts-specific. Category-axis scatter with manual jitter is a distinctive Highcharts pattern. Could further distinguish with explicit plotBands or plotOptions.scatter configuration.

Score Caps Applied

• None — all cap conditions clear (VQ-02=6, VQ-03=5, SC-01=5, DQ-02=5, DE-01=5>2 so boring-cap doesn't trigger, CQ-04=2).

Strengths

• Perfect Okabe-Ito palette with correct first series #009E73 and complete theme-adaptive chrome (both renders fully correct)
• Strong spec compliance: strip plot with random x-jitter (width 0.25), semi-transparent markers (fillOpacity 0.65), and mean diamond markers per group
• High data quality: bimodal distributions for Mathematics and Literature make the educational scenario credible and interesting
• Clean KISS code with reproducible seed, dual HTML output (inline/CDN), and element-level screenshot
• Refined presentation: subtle 10%-opacity grid, bordered legend with elevated background, well-scaled fonts across all text hierarchy

Weaknesses

• Category background bands appear to be Highcharts default behavior for category-axis scatter rather than intentionally coded plotBands — would be stronger with explicit control
• No explicit removal of top/right spines — chart frame could be cleaner with chart.options.chart = {'plotBorderWidth': 0} and axis line removal
• Library mastery could be elevated by using more Highcharts-distinctive features such as explicit plotBands for category zone shading or plotOptions.scatter for series-level configuration

Issues Found

1. DE-01 (5/8): Design is above default due to subtitle, mean markers, and background bands, but background bands are likely automatic Highcharts behavior. Score reflects this uncertainty.
   • Fix: Add explicit plotBands with alpha-tinted series colors to intentionally control category zone shading
2. LM-02 (3/5): HTML export pattern is distinctive but more Highcharts-specific features could be leveraged.
   • Fix: Use plotOptions for series-level scatter configuration; consider Highcharts' built-in jitter option if available in the API version

AI Feedback for Next Attempt

Implementation is strong and approved at this attempt. If a further iteration were needed: explicitly code plotBands for category zone backgrounds (removes dependency on Highcharts defaults), remove top/right chart borders for cleaner framing, and consider using Highcharts plotOptions.scatter for series-level marker configuration as a more idiomatic pattern.

Verdict: APPROVED