feat(highcharts): implement heatmap-rainflow

[github-actions]

Implementation: heatmap-rainflow - highcharts

Implements the highcharts version of heatmap-rainflow.

File: plots/heatmap-rainflow/implementations/highcharts.py

Parent Issue: #4465

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a 20×20 heatmap of rainflow cycle counting results for fatigue analysis. The title reads "heatmap-rainflow · highcharts · pyplots.ai" with a subtitle "Rainflow cycle counting matrix — variable-amplitude fatigue loading". The x-axis shows "Cycle Mean (MPa)" from -50 to 250 with rotated labels, and the y-axis shows "Cycle Amplitude (MPa)" from 10 to 200. A sequential blue colormap ranges from very light blue/white (low counts) to dark navy blue (high counts ~6k), with a logarithmic "Cycle Count" colorbar on the right side. The darkest cells concentrate in the lower-left to center region (low amplitude, mean ~60-120 MPa), showing realistic fatigue loading behavior where low-amplitude cycles near moderate mean stress dominate. White borders separate each cell cleanly. Some cells at high amplitude and extreme mean values appear light, indicating zero or very few cycles. The layout fills the canvas well with the colorbar positioned on the right.

Score: 84/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	6	10
Total	84	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 52px, subtitle 30px, axis titles 34px, tick labels 24px, legend 28px). X-axis rotated labels are slightly small at rendered resolution but still readable.
• VQ-02: No Overlap (6/6) — X-axis labels rotated -45° to prevent overlap, y-axis labels horizontal and well-spaced, no text collisions.
• VQ-03: Element Visibility (5/6) — Heatmap cells clearly visible with white borders. Logarithmic color scale helps distinguish values. Some mid-range cells (200-800 counts) appear similar in shade.
• VQ-04: Color Accessibility (4/4) — Single-hue blue sequential colormap is fully colorblind-safe with good contrast between extremes.
• VQ-05: Layout & Canvas (3/4) — Good canvas utilization overall, but right margin for colorbar is generous (440px) and creates slight imbalance. X-axis labels sit close to the bottom edge.
• VQ-06: Axis Labels & Title (2/2) — Both axes have descriptive labels with units: "Cycle Mean (MPa)" and "Cycle Amplitude (MPa)".

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) — Custom blue sequential palette with 6 color stops, clean sans-serif font stack, subtitle provides context, white cell borders add polish. Clearly above defaults but not publication-quality.
• DE-02: Visual Refinement (4/6) — Grid lines removed, axis lines hidden (lineWidth: 0), white cell borders create clean separation, background is clean white. Good refinement but could benefit from more whitespace tuning.
• DE-03: Data Storytelling (3/6) — The logarithmic scale effectively reveals both high and low frequency regions. The data pattern naturally tells the fatigue story. However, no annotation highlights the peak region or explains the engineering significance. A subtitle helps but the viewer must interpret the pattern themselves.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct 2D heatmap visualization.
• SC-02: Required Features (4/4) — All spec requirements met: sequential colormap ✓, logarithmic color scale ✓, colorbar ✓, axis labels with units ✓, bin center tick labels ✓, zero-count bins visually distinct (nullColor #f0f0f0) ✓.
• SC-03: Data Mapping (3/3) — Mean on x-axis, amplitude on y-axis, color = cycle count — all correctly mapped per spec.
• SC-04: Title & Legend (3/3) — Title follows exact format "heatmap-rainflow · highcharts · pyplots.ai". Colorbar legend titled "Cycle Count".

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows characteristic rainflow pattern: exponential decay with amplitude, Gaussian distribution around mean ~100 MPa, zero-count regions at extremes, wide range of count magnitudes (1 to ~6k). Could show slightly more sparsity at high amplitudes.
• DQ-02: Realistic Context (5/5) — Fatigue analysis is a real engineering application. MPa units are correct. Data represents a plausible variable-amplitude loading scenario typical of mechanical components.
• DQ-03: Appropriate Scale (4/4) — Amplitudes 10-200 MPa and means -50 to 250 MPa are realistic for steel fatigue. Cycle counts up to ~6000 are plausible for measured load spectra.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data generation → chart configuration → HTML rendering → screenshot. No functions or classes.
• CQ-02: Reproducibility (2/2) — Uses np.random.seed(42).
• CQ-03: Clean Imports (2/2) — All imports used: json, tempfile, time, urllib.request, Path, numpy, selenium.
• CQ-04: Code Elegance (2/2) — Well-organized code. JavaScript null-mapping for zero counts is elegant. Chart config is readable and well-structured.
• CQ-05: Output & API (1/1) — Saves as plot.png via Selenium screenshot. Current APIs used.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (3/5) — Builds chart as raw JSON dict rather than using the highcharts-core Python wrapper. While practical for heatmap rendering with Selenium, it bypasses the Python API. The approach works but is more "Highcharts JS via Python" than idiomatic Python wrapper usage.
• LM-02: Distinctive Features (3/5) — Uses Highcharts-specific heatmap module, logarithmic colorAxis with custom stops, nullColor for empty cells, and interactive tooltip formatting. These are distinctly Highcharts features not easily replicated elsewhere.

Score Caps Applied

• None — No score caps triggered.

Strengths

• Perfect spec compliance — all required features implemented correctly (logarithmic color scale, colorbar, units, zero-count distinction)
• Realistic fatigue engineering data with correct physical units and plausible magnitudes
• Clean, well-organized code with good reproducibility
• Custom blue color palette with logarithmic scale effectively reveals data patterns across orders of magnitude

Weaknesses

• Design could be more refined: the colorbar right margin is large, creating visual imbalance
• Mid-range color values (200-800 cycles) are hard to distinguish — the blue-only palette lacks differentiation in the middle range
• No visual emphasis or annotation to highlight the dominant cycle region, which is the key engineering insight
• Bypasses the highcharts-core Python wrapper in favor of raw JSON configuration

Issues Found

1. DE-01 MODERATE: Good custom palette but not publication-quality. The blue-only sequential scheme, while safe, lacks the visual impact of a more varied sequential palette (e.g., viridis-like or warm-to-cool).
   • Fix: Consider a multi-hue sequential palette or add stronger contrast between adjacent stops to better differentiate mid-range values.
2. DE-03 LOW: Data is displayed but the key insight (dominant low-amplitude cycles near mean ~100 MPa) is not visually emphasized.
   • Fix: Add a subtle annotation or contour highlighting the peak region, or use a diverging emphasis to draw the eye to high-count cells.
3. LM-01 MODERATE: Raw JSON configuration bypasses the Python wrapper library.
   • Fix: While functional, consider using highcharts-core Chart and HighchartsOptions objects where feasible.

AI Feedback for Next Attempt

To reach 90+: (1) Enhance color palette differentiation — consider a multi-hue sequential scheme or add more contrast between stops so mid-range values (200-800 cycles) are distinguishable. (2) Add visual storytelling — annotate or emphasize the peak cycle region to immediately communicate the engineering insight. (3) Tighten the layout — reduce right margin to better balance the colorbar placement. (4) Consider using the highcharts-core Python wrapper for more idiomatic library usage.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a 20×20 heatmap of rainflow cycle counting results for fatigue analysis. The title reads "heatmap-rainflow · highcharts · pyplots.ai" in dark blue-gray at the top, followed by a gray subtitle "Rainflow cycle counting matrix — low-amplitude cycles near 100 MPa mean dominate the fatigue spectrum." The x-axis is labeled "Cycle Mean (MPa)" with rotated tick labels ranging from -50 to 250. The y-axis is labeled "Cycle Amplitude (MPa)" with tick labels from 10 to 200. The heatmap uses a sequential yellow-to-dark-red color scheme with a logarithmic scale. A vertical colorbar on the right is labeled "Cycle Count" with values from 1 to 6k. Zero-count bins in the upper-left region appear as light gray (#f0f0f0), clearly distinct from the pale yellow of low-count cells. The darkest red cells (highest cycle counts ~6000+) appear at the bottom rows (low amplitude, 10-30 MPa) centered around 100 MPa mean. An annotation box with a white semi-transparent background sits near the center of the plot, reading "Peak region: low-amplitude cycles near 100 MPa mean stress dominate fatigue damage." The overall background is off-white (#fafafa). Cell borders in matching off-white provide clean separation between heatmap cells.

Score: 88/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	14	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 52px, subtitle 30px, axis titles 34px, ticks 24px, colorbar 26px). All readable at full resolution. Minor: tick labels slightly small relative to the 4800px canvas width.
• VQ-02: No Overlap (6/6) — X-axis labels rotated 315° to prevent collision. No text-on-text overlap anywhere. Annotation box overlaps heatmap cells but this is an intentional design choice, not a text legibility issue.
• VQ-03: Element Visibility (6/6) — All heatmap cells clearly visible with strong color differentiation. Cell borders (#fafafa) provide clean separation. Zero-count bins are visually distinct from low-count cells.
• VQ-04: Color Accessibility (3/4) — The yellow-orange-red sequential palette has good luminance progression but is not optimal for colorblind users. A perceptually uniform palette (e.g., viridis-like stops) would be more accessible.
• VQ-05: Layout & Canvas (3/4) — Good canvas utilization with explicit margins. The colorbar labels and title are slightly cramped against the right edge. Plot fills approximately 55-60% of the canvas.
• VQ-06: Axis Labels & Title (2/2) — Both axes have descriptive labels with units: "Cycle Mean (MPa)" and "Cycle Amplitude (MPa)."

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom 9-stop YlOrRd-like color palette, coordinated text colors (#2c3e50, #34495e, #7f8c8d), custom font stack (Segoe UI/Roboto), off-white background. Clearly above defaults with intentional design choices. To reach 7-8, would need a more refined color palette (perceptually uniform) and additional typographic sophistication.
• DE-02: Visual Refinement (4/6) — Grid lines removed, axis lines removed, tick marks removed, cell borders match background, generous margins. Good refinement but a few details remain (e.g., annotation box styling could be more polished, subtle shadow or refined stroke).
• DE-03: Data Storytelling (4/6) — Subtitle explains the key insight. Annotation box highlights the peak region. Logarithmic color scale naturally creates visual hierarchy. However, the annotation is positioned near the center of the heatmap (~58% down) while the actual peak region (darkest red) is at the bottom rows (low amplitude). Repositioning closer to the dominant region or using a callout line would strengthen the narrative.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct 2D heatmap with amplitude on y-axis and mean on x-axis.
• SC-02: Required Features (4/4) — Sequential colormap ✓, logarithmic color scale ✓, colorbar with cycle count values ✓, axis labels with MPa units ✓, tick labels at bin centers ✓, zero-count bins visually distinct as gray ✓.
• SC-03: Data Mapping (3/3) — X/Y correctly assigned (mean on x, amplitude on y). All data visible within axes.
• SC-04: Title & Legend (3/3) — Title follows exact format "heatmap-rainflow · highcharts · pyplots.ai". Colorbar correctly labeled "Cycle Count".

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows exponential decay with amplitude, Gaussian distribution around mean ~100 MPa, zero-count sparse regions at high amplitude/extreme mean, full range of cycle densities. 20×20 grid is within spec range.
• DQ-02: Realistic Context (5/5) — Fatigue analysis is a genuine engineering application. MPa units correct for stress. Pattern of low-amplitude/moderate-mean dominance is physically realistic for variable-amplitude loading.
• DQ-03: Appropriate Scale (4/4) — Amplitude 10-200 MPa realistic for steel fatigue. Mean -50 to 250 MPa spans compressive to tensile. Cycle counts up to ~6000 plausible for load spectra.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear script: imports → data generation → chart config → HTML → screenshot. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at the start.
• CQ-03: Clean Imports (2/2) — All imports used: tempfile, time, urllib.request, Path, numpy, Chart, HighchartsOptions, webdriver, Options.
• CQ-04: Code Elegance (2/2) — Clean, well-organized code. The renderer.label annotation is a legitimate Highcharts technique. Download retry logic is practical.
• CQ-05: Output & API (1/1) — Saves plot.png and plot.html. Uses current highcharts-core API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Correct use of highcharts-core wrapper: Chart(container="container"), HighchartsOptions.from_dict(), chart.to_js_literal(). Proper heatmap module loading. Inline scripts for headless Chrome.
• LM-02: Distinctive Features (3/5) — Uses Highcharts-specific features: colorAxis with logarithmic type and 9 custom color stops, renderer.label for SVG annotation overlay, heatmap module with nullColor, interactive tooltip with category references via {series.yAxis.categories.(point.y)}.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Perfect spec compliance: all required features present (log color scale, colorbar, units, distinct zero bins)
• Excellent realistic data: physically plausible rainflow matrix with correct exponential-decay-with-amplitude pattern
• Strong design foundation: custom color palette, coordinated typography, clean layout with removed grid/axis lines
• Good data storytelling through subtitle and annotation providing fatigue engineering context
• Clean, well-structured code with proper reproducibility seed

Weaknesses

• Color palette not optimally colorblind-accessible (yellow-orange-red vs perceptually uniform palette)
• Annotation box positioned near center of heatmap rather than adjacent to the actual peak region (low-amplitude rows at bottom)
• Colorbar labels slightly cramped against right canvas edge
• Minor: tick label font sizes could be slightly larger for the 4800px canvas

Issues Found

1. VQ-04 MODERATE: Yellow-orange-red color stops not optimal for colorblind accessibility
   • Fix: Use perceptually uniform color stops (e.g., viridis-inspired: dark purple → blue → teal → yellow) for the colorAxis
2. DE-03 MODERATE: Annotation box placed at center of heatmap, not near the actual dominant region (bottom rows)
   • Fix: Reposition annotation to bottom-left or use a callout line pointing to the high-count cells
3. VQ-05 MINOR: Colorbar labels slightly cramped against right edge
   • Fix: Increase marginRight slightly (e.g., 360px) or reduce symbolWidth

AI Feedback for Next Attempt

To reach 90+, focus on two areas: (1) Replace the YlOrRd-like color stops with a perceptually uniform, colorblind-safe palette (e.g., viridis-inspired stops) for VQ-04. (2) Reposition the annotation label closer to the actual peak region (low-amplitude rows near bottom of heatmap) or add a callout line to better connect the annotation to the data it describes, improving DE-03. These two changes together should gain the needed 2+ points.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a 20×20 heatmap of rainflow cycle counts using a viridis-inspired color gradient (dark purple through teal/green to bright yellow). The y-axis shows "Cycle Amplitude (MPa)" from 10 to 200, the x-axis shows "Cycle Mean (MPa)" from -50 to 250. A vertical logarithmic colorbar on the right is labeled "Cycle Count" with values from 1 to 6k. The background is light gray (#fafafa), and zero-count bins appear as a lighter gray (#f0f0f0) — visible mainly in the upper corners. The highest cycle counts (bright yellow cells) cluster at low amplitudes (10-30 MPa) near a mean of ~100 MPa, showing the expected exponential decay pattern. A subtle annotation box near the bottom-right reads "Peak region: low-amplitude cycles near 100 MPa mean stress dominate fatigue damage." The title "heatmap-rainflow · highcharts · pyplots.ai" appears at the top with a descriptive subtitle below it. X-axis labels are rotated at 315°.

Score: 87/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	14	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	9	10
Library Mastery	8	10
Total	87	100

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 52px, subtitle 30px, axis titles 34px, tick labels 28px, legend 24-28px. All clearly readable.
• VQ-02: No Overlap (5/6) — X-axis labels rotated 315° to avoid collision, but they appear slightly crowded at 20 categories. Minor issue.
• VQ-03: Element Visibility (6/6) — 20×20 grid cells fill the plot area well, colors clearly differentiate values across the logarithmic range.
• VQ-04: Color Accessibility (4/4) — Viridis color stops are colorblind-safe. Good contrast between null cells (#f0f0f0) and the color scale.
• VQ-05: Layout & Canvas (2/4) — The annotation box overlaps some data cells in the lower-right area. The colorbar legend text on the right is somewhat small relative to the canvas. Plot area could use more of the available width.
• VQ-06: Axis Labels & Title (2/2) — Both axes include units: "Cycle Amplitude (MPa)" and "Cycle Mean (MPa)".

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom viridis color stops, #fafafa background, thoughtful font family selection (Segoe UI/Roboto), intentional hierarchy with distinct null cell color. Clearly above defaults.
• DE-02: Visual Refinement (4/6) — Grid lines removed, axis lines and ticks removed (lineWidth: 0, tickLength: 0), cell borders provide structure. Generous margins. Good polish.
• DE-03: Data Storytelling (4/6) — Subtitle explains the key insight, annotation box highlights the dominant fatigue region, logarithmic color scale reveals the full distribution. Visual hierarchy guides the viewer.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct 2D heatmap chart type.
• SC-02: Required Features (4/4) — Sequential colormap with logarithmic scale, colorbar with cycle count values, axis labels with MPa units, bin center tick labels, zero-count bins visually distinct (#f0f0f0).
• SC-03: Data Mapping (3/3) — Amplitude on y-axis, mean on x-axis, as specified.
• SC-04: Title & Legend (3/3) — Title format "heatmap-rainflow · highcharts · pyplots.ai" correct. Colorbar title "Cycle Count" is appropriate.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows exponential amplitude decay, Gaussian mean distribution, sparsity at high amplitudes, zero-count regions. Could show slightly more variation in pattern shape.
• DQ-02: Realistic Context (5/5) — Fatigue analysis with MPa stress units, realistic for mechanical/structural engineering.
• DQ-03: Appropriate Scale (4/4) — Amplitude 10-200 MPa, mean -50 to 250 MPa, counts up to ~6000 — sensible for fatigue analysis.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) — Clean Imports → Data → Plot → Save flow, no functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imports used (numpy, highcharts_core, selenium, tempfile, time, urllib, pathlib).
• CQ-04: Code Elegance (1/2) — The separate DOMContentLoaded script block with renderer.label adds complexity. The retry loop for downloading Highcharts JS is reasonable but slightly verbose.
• CQ-05: Output & API (1/1) — Saves as plot.png via Selenium screenshot. Current API usage.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) — Good use of highcharts-core Python wrapper with from_dict configuration, proper heatmap module inclusion, to_js_literal() for rendering. Could leverage more of the Python API rather than pure dict config.
• LM-02: Distinctive Features (4/5) — Uses Highcharts-specific features: colorAxis with logarithmic type and custom stops, heatmap module, renderer.label() for annotation, nullColor for zero-count bins, structured tooltip format.

Score Caps Applied

• None — no caps triggered

Strengths

• Full spec compliance with all required features (logarithmic colorbar, MPa units, zero-count distinction)
• Strong viridis-inspired color scheme with explicit stops for colorblind accessibility
• Effective data storytelling through subtitle, annotation, and logarithmic scale
• Realistic fatigue engineering context with sensible MPa ranges
• Clean use of Highcharts heatmap module with proper null handling

Weaknesses

• Layout could be tighter: annotation box overlaps data cells, colorbar legend text relatively small
• X-axis labels slightly crowded at 20 categories despite rotation
• Renderer annotation approach adds code complexity that could be simplified

Issues Found

1. VQ-05 LOW: Annotation box overlaps heatmap data cells; colorbar area could be better utilized
   • Fix: Reposition annotation outside the plot area or use subtitle for the insight; adjust margins for better canvas utilization
2. CQ-04 MINOR: DOMContentLoaded listener with renderer.label adds unnecessary complexity
   • Fix: Could use Highcharts annotations API via the options dict instead of post-render JavaScript

AI Feedback for Next Attempt

Score 87/100 on attempt 3/3. The implementation is strong with full spec compliance, good design choices, and effective data storytelling. The main gaps are layout optimization (annotation placement, canvas utilization) and minor code simplification. Overall a solid implementation that demonstrates good Highcharts mastery.

Verdict: REJECTED

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a 20×20 heatmap of rainflow cycle counting results for fatigue analysis. The title reads "heatmap-rainflow · highcharts · pyplots.ai" with a subtitle explaining that low-amplitude cycles near 100 MPa mean dominate the fatigue spectrum. The x-axis shows "Cycle Mean (MPa)" ranging from −50 to 250 with rotated tick labels, and the y-axis shows "Cycle Amplitude (MPa)" ranging from 10 to 200. The color scale uses a viridis-like gradient from dark purple (low counts ~1) through teal/green to bright yellow (high counts ~6k+), mapped logarithmically. A vertical colorbar on the right is labeled "Cycle Count" with logarithmic tick marks. Zero-count cells appear as light gray (#f0f0f0), clearly distinct from data cells. The highest counts (bright yellow) concentrate at low amplitudes near 100 MPa mean stress, forming a clear hotspot. An annotation box in the lower-right area of the plot highlights this peak fatigue region. The background is a subtle off-white (#fafafa).

Score: 90/100

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	8	10
Total	90	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 52px, subtitle 30px, axis titles 34px, tick labels 28px, legend 28px. All readable.
• VQ-02: No Overlap (6/6) — X-axis labels rotated 315° to prevent overlap. No text collisions.
• VQ-03: Element Visibility (6/6) — Heatmap cells clearly visible with good color differentiation. Null cells distinct light gray.
• VQ-04: Color Accessibility (4/4) — Viridis-like colormap with 9 stops is colorblind-safe with good contrast.
• VQ-05: Layout & Canvas (3/4) — Plot fills ~73% of canvas (good). Colorbar labels slightly compressed at rendering resolution.
• VQ-06: Axis Labels & Title (2/2) — "Cycle Mean (MPa)" and "Cycle Amplitude (MPa)" with units.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Custom viridis color stops, custom font family, off-white background, informative subtitle. Above defaults but not publication-tier.
• DE-02: Visual Refinement (4/6) — Grid removed, axis lines removed, tick marks removed, subtle cell borders matching background. Good refinement.
• DE-03: Data Storytelling (4/6) — Subtitle explains data story. Renderer annotation highlights peak fatigue region. Logarithmic scale creates visual hierarchy.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct 2D heatmap.
• SC-02: Required Features (4/4) — Sequential colormap, logarithmic color scale, colorbar, MPa units, bin center tick labels, zero-count bins distinct.
• SC-03: Data Mapping (3/3) — Amplitude on y-axis, mean on x-axis as specified. All data visible.
• SC-04: Title & Legend (3/3) — Title format correct. Colorbar labeled "Cycle Count."

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Full range from 0 to ~6k+ counts. Clear gradient from high-count to zero-count. Sparse regions realistic.
• DQ-02: Realistic Context (5/5) — Fatigue analysis with MPa stress ranges. Real engineering scenario, neutral topic.
• DQ-03: Appropriate Scale (4/4) — Amplitude 10–200 MPa, mean −50–250 MPa. Realistic for mechanical fatigue.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data → chart config → HTML → screenshot.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imports used.
• CQ-04: Code Elegance (2/2) — Clean, appropriate complexity. Retry loop for CDN download is practical.
• CQ-05: Output & API (1/1) — Saves as plot.png. Current API usage.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) — Uses HighchartsOptions.from_dict() efficiently. Proper chart/container pattern.
• LM-02: Distinctive Features (4/5) — colorAxis with logarithmic type, renderer.label() for SVG annotations, heatmap module, nullColor, interactive tooltips.

Score Caps Applied

• None

Strengths

• Perfect spec compliance with all required features (logarithmic colorscale, colorbar, MPa units, zero-count distinction)
• Excellent data quality with realistic fatigue analysis context and appropriate stress ranges
• Good use of Highcharts-specific features: colorAxis, renderer.label(), heatmap module, nullColor
• Clean data storytelling through subtitle, annotation, and logarithmic color scale
• All font sizes explicitly set for consistent readability at 4800×2700

Weaknesses

• Design Excellence could still improve with a more distinctive color palette choice beyond viridis recreation
• Colorbar labels appear slightly compressed at the right edge of the canvas

Issues Found

None critical — implementation meets all requirements.

AI Feedback for Next Attempt

N/A — implementation approved.

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a 20x20 heatmap with the title "heatmap-rainflow · highcharts · pyplots.ai" at the top and a subtitle describing the fatigue spectrum. The x-axis is labeled "Cycle Mean (MPa)" with rotated tick labels ranging from -50 to ~234 MPa. The y-axis is labeled "Cycle Amplitude (MPa)" with tick labels from 10 to 200 MPa. A viridis-derived colormap ranges from dark purple (low counts, ~1) through teal/green to bright yellow (high counts, ~6k). A vertical logarithmic color bar on the right is titled "Cycle Count." The dominant feature is a bright yellow region at low amplitudes (10-40 MPa) near mean ~100 MPa, representing the highest cycle concentrations. High-amplitude and extreme-mean regions are dark purple or light gray (zero-count bins). Cell borders in the background color (#fafafa) provide clean separation. The x-axis tick labels are rotated 315° and appear slightly clipped at the bottom edge. The coded renderer annotation for the peak region is not visible in the rendered image.

Score: 86/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	9	10
Library Mastery	7	10
Total	86	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (52px title, 34px axis titles, 28px/30px labels). Most text clear and readable. Minor clipping on some rotated x-axis tick labels at the bottom edge.
• VQ-02: No Overlap (5/6) — X-axis labels are rotated to prevent overlap; spacing is adequate but slightly cramped for the outermost labels.
• VQ-03: Element Visibility (6/6) — Heatmap cells are well-sized with clear color differentiation. Cell borders provide good separation. Logarithmic color scale effectively spans the range from 1 to ~6000.
• VQ-04: Color Accessibility (4/4) — Viridis colormap with 9 custom stops is colorblind-safe and provides excellent perceptual uniformity.
• VQ-05: Layout & Canvas (3/4) — Good overall layout with the heatmap filling most of the canvas. The right-side legend consumes notable space. X-axis labels are slightly clipped at the bottom despite 200px margin.
• VQ-06: Axis Labels & Title (2/2) — Both axes have descriptive labels with units: "Cycle Mean (MPa)" and "Cycle Amplitude (MPa)."

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom viridis-derived palette with 9 color stops, custom font family (Segoe UI/Roboto), styled title (bold, #2c3e50) and subtitle (lighter #7f8c8d), warm #fafafa background. Clearly above defaults; strong, intentional design.
• DE-02: Visual Refinement (4/6) — Axis lines and grid lines removed (lineWidth: 0, gridLineWidth: 0), tick marks removed, cell borders in background color provide structure without clutter. Credits disabled. Clean and polished.
• DE-03: Data Storytelling (3/6) — The subtitle effectively narrates the key insight ("low-amplitude cycles near 100 MPa mean dominate"). The viridis colormap naturally draws the eye to the yellow hotspot. However, the renderer annotation (lines 199-221) intended to highlight the peak region does not appear in the rendered screenshot, reducing the storytelling impact.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct 2D heatmap visualization of a rainflow counting matrix.
• SC-02: Required Features (4/4) — All spec features present: sequential colormap, logarithmic color scale, colorbar, axis labels with MPa units, bin-center tick labels, zero-count bins visually distinct (#f0f0f0 nullColor).
• SC-03: Data Mapping (3/3) — Amplitude on y-axis, mean on x-axis, color intensity = cycle count. Correct per spec.
• SC-04: Title & Legend (3/3) — Title matches format "heatmap-rainflow · highcharts · pyplots.ai". Legend titled "Cycle Count."

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows full range of the matrix: high-count hotspot, gradual decay with amplitude, sparse high-amplitude regions, zero-count corner bins. Demonstrates all aspects of a rainflow matrix.
• DQ-02: Realistic Context (5/5) — Engineering fatigue analysis with MPa stress units. Exponential amplitude decay with Gaussian mean distribution is physically plausible for variable-amplitude loading.
• DQ-03: Appropriate Scale (4/4) — Amplitude 10-200 MPa and mean -50 to 250 MPa are realistic for structural steel fatigue. Cycle counts from 0 to ~6000 are plausible for measured load histories.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → chart construction → HTML generation → screenshot. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) ensures deterministic output.
• CQ-03: Clean Imports (2/2) — All imports are used (numpy, highcharts_core, selenium, tempfile, time, urllib, Path).
• CQ-04: Code Elegance (1/2) — The DOMContentLoaded renderer annotation (lines 199-221) does not produce visible output in the headless Chrome screenshot, making it dead code that adds complexity without benefit.
• CQ-05: Output & API (1/1) — Saves as plot.png via Selenium screenshot. Current API usage.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Proper use of highcharts-core Python wrapper with HighchartsOptions.from_dict(), Chart(container="container"), to_js_literal(). Correct inline script embedding for headless Chrome. Download retry logic for CDN resources.
• LM-02: Distinctive Features (3/5) — Uses Highcharts-specific features: colorAxis with logarithmic type and custom stops, heatmap module, nullColor for zero-count bins, tooltip.pointFormat with series axis category references, and renderer.label() API (though the annotation doesn't render). These are distinctive Highcharts capabilities.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Perfect spec compliance with all required features (logarithmic colorbar, units, distinct zero-count bins)
• Excellent, realistic engineering data with physically plausible fatigue distributions
• Strong viridis-derived custom colormap with 9 color stops and logarithmic scaling
• Clean visual design with removed grid/spines and warm background
• Informative subtitle that narrates the key data insight

Weaknesses

• The renderer annotation for the peak region (lines 199-221) does not appear in the screenshot — DOMContentLoaded timing issue in headless Chrome
• X-axis tick labels are slightly clipped at the bottom edge despite 200px bottom margin
• Storytelling could be stronger if the annotation rendered or if additional visual emphasis was applied

Issues Found

1. CQ-04 LOW: The renderer.label() annotation block (lines 199-221) is dead code — it doesn't render in the headless Chrome screenshot, adding ~25 lines of non-functional complexity.
   • Fix: Either move the annotation into the main chart options (e.g., using Highcharts annotations API) or remove the block entirely.
2. VQ-05 MINOR: X-axis rotated tick labels clipped at bottom edge.
   • Fix: Increase marginBottom from 200 to 250, or reduce x-axis label font size slightly.
3. DE-03 MODERATE: Data storytelling reduced because the peak-region annotation doesn't render.
   • Fix: Use Highcharts built-in annotations options instead of renderer API post-render.

AI Feedback for Next Attempt

The implementation is strong overall with perfect spec compliance and data quality. The main issues are: (1) the renderer.label() annotation doesn't render in headless Chrome — use Highcharts annotations API in chart options instead, (2) increase marginBottom slightly to prevent x-axis label clipping, and (3) consider adding visual emphasis to the peak region through the annotations API to improve data storytelling.

Verdict: REJECTED