feat(letsplot): implement dashboard-metrics-tiles

[github-actions]

Implementation: dashboard-metrics-tiles - python/letsplot

Implements the python/letsplot version of dashboard-metrics-tiles.

File: plots/dashboard-metrics-tiles/implementations/python/letsplot.py

Parent Issue: #3791

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): Warm off-white (#FAF8F1) background with six metric tiles arranged in a 3×2 facet grid. Each tile has an elevated cream (#FFFDF6) panel background with a subtle border. Strip headers show metric names in bold semi-muted dark text: "Active Users", "CPU Usage", "Error Rate", "Response Time", "Throughput", and "⚠ Memory" (warning badge prefix). Sparklines run across each tile as green (#009E73) area-fills with a line and an end-point dot; the Memory/warning tile uses amber (#F59E0B). Prominent bold values are displayed in large dark ink (e.g., 1,284, 45%, 0.8%, 120ms, 847req/s, 72%). Change indicators show colored arrows: green ▲/▼ for favorable, red ▲/▼ for unfavorable changes. All title and strip text is clearly readable. Minor tightness in the Error Rate tile — the large "0.8%" value text is positioned very close to the sparkline area, creating slight visual crowding though still readable. Title "dashboard-metrics-tiles · python · letsplot · anyplot.ai" is bold, centered, and clearly visible. Legibility verdict: PASS.

Dark render (plot-dark.png): Near-black (#1A1A17) outer background with dark-elevated (#242420) tile panels. The title text renders in off-white against the near-black — clearly readable. Strip headers in #B8B7B0 (secondary text token) on the dark elevated panel — readable contrast. Value labels (1,284; 45%; etc.) appear in bright off-white/cream on dark panel background — highly visible. Sparkline data colors are identical to light render: green (#009E73) lines for good-status tiles, amber (#F59E0B) for Memory/warning — colors unchanged, only chrome flipped. Change arrows remain green (favorable) and red (unfavorable). No dark-on-dark text failures detected. The minor crowding in the Error Rate tile persists but text remains readable. Legibility verdict: PASS.

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 (7/8) — Value labels are large and bold. Change indicators are readable in both themes. Title and strip headers clear. Minor: Error Rate tile is tight with the large "0.8%" value text close to the sparkline area.
• VQ-02: No Overlap (5/6) — Generally clean layout. Minor proximity/crowding between value text and sparkline fill in the Error Rate tile where text sits close to the area chart top edge.
• VQ-03: Element Visibility (6/6) — All sparklines clearly visible with appropriate line weight (size=2). End-point markers prominent (size=5). Area fills have good alpha (0.25) for transparency.
• VQ-04: Color Accessibility (2/2) — Green/amber/red status colors are distinct from each other and accessible. No red-green sole signal issues.
• VQ-05: Layout & Canvas (4/4) — Clean 3×2 facet grid. Proper canvas at 3200×1800. All elements fully within frame. Panel spacing is generous. No overflow or clipping.
• VQ-06: Axis Labels & Title (2/2) — Title correctly formatted. No axis labels needed or shown (appropriate for a dashboard tile layout).
• VQ-07: Palette Compliance (2/2) — Backgrounds correct (#FAF8F1 light, #1A1A17 dark). Brand green #009E73 correctly used for "good" status (first categorical value). Warning/critical use semantic amber/red as mandated by spec. Both themes render correctly.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Status-aware color coding creates intentional semantic design. Warning badge emoji (⚠) in strip header is a clever visual signal. Clean elevated panel backgrounds distinguish tile content from page. Rises clearly above defaults, though overall feel is still template-like.
• DE-02: Visual Refinement (4/6) — Axis text, ticks, lines, and grid are all blanked out. Panel borders add definition without clutter. Elevated background for panels vs. page background adds depth. Good minimalism.
• DE-03: Data Storytelling (4/6) — Warning badge on Memory tile clearly signals the problematic metric. Color coding (green=good, amber=warning, red=unfavorable change) creates immediate visual hierarchy. End-point markers on sparklines anchor the current value in time.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct: dashboard grid of KPI tiles with embedded sparklines.
• SC-02: Required Features (4/4) — All features present: prominent value display, metric label (strip header), sparkline (area+line+dot), change indicator with arrow and color coding, status color coding.
• SC-03: Data Mapping (3/3) — All 6 metrics shown with historical sparkline data, current values, and change percentages. 3×2 grid layout for 6 tiles per spec.
• SC-04: Title & Legend (3/3) — Title: "dashboard-metrics-tiles · python · letsplot · anyplot.ai" — correct format. No legend (appropriate).

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Sparklines, prominent values, change indicators with directional arrows, semantic status colors, warning badge — comprehensive.
• DQ-02: Realistic Context (5/5) — Classic DevOps/SRE metrics: CPU Usage (45%), Memory (72%), Response Time (120ms), Active Users (1,284), Error Rate (0.8%), Throughput (847 req/s) — all realistic and neutral.
• DQ-03: Appropriate Scale (4/4) — All values plausible for their domains. Sparkline history generated with realistic noise around base values.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — No functions or classes. Clean top-level script.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set before data generation.
• CQ-03: Clean Imports (2/2) — All imported symbols are used.
• CQ-04: Code Elegance (2/2) — Appropriate complexity. Clean loop for sparkline generation with trend anchoring. Facet label engineering (warning badge) is a nice touch.
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html correctly.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of lets-plot grammar: layered geoms (area+line+point+text), scale_color/fill_manual, facet_wrap with free_y, comprehensive theme() customization including strip_text, strip_background, panel_background, plot_background.
• LM-02: Distinctive Features (3/5) — facet_wrap with scales="free_y" is well-used. Multi-layer geom composition is idiomatic. Could leverage more lets-plot-specific features (e.g., tooltip configuration for HTML export).

Score Caps Applied

• None

Strengths

• All 6 required dashboard features implemented: value display, metric label, sparkline, change indicator with directional arrow, status color coding, and warning badge
• Theme adaptation is correct and complete — both light and dark renders are readable with proper background tokens throughout
• Warning badge emoji (⚠) in strip header for Memory tile is an elegant way to signal elevated status
• Data choices are realistic DevOps metrics with semantically meaningful change directions
• Canvas dimensions exactly correct at 3200×1800 via ggsize(800,450)+scale=4

Weaknesses

• Error Rate tile shows visual crowding: "0.8%" value text and the sparkline area chart are too close — increase scale_y_continuous expand or increase the label y-offset to push value labels further above the sparkline top
• Change indicator labels land inside or near the sparkline area in narrow tiles — the y_change position (y_min - 25% of range) is too close to the data; increase the bottom multiplier from 0.25 to 0.40 and expand to [0.55, 0.55]
• LM-02 relatively low — HTML output does not configure tooltips, missing an opportunity to showcase lets-plot's interactive capabilities

Issues Found

1. VQ-01/VQ-02 MINOR: Value text and sparkline crowding in Error Rate tile
   • Fix: Increase scale_y_continuous expand to [0.55, 0.55] and push y_value offset to +0.55 of range, y_change to -0.40 of range for more breathing room
2. LM-02 MODERATE: Tooltip opportunity missed in HTML output
   • Fix: Add tooltip configuration to the HTML export for interactive value display

AI Feedback for Next Attempt

The implementation is strong — all spec requirements met, both themes correct, clean code. Focus improvements on two areas: (1) layout breathing room — increase scale_y_continuous expand from [0.45, 0.45] to [0.55, 0.55] and adjust label y-offsets so value text and change text never overlap the sparkline area; (2) leverage lets-plot's interactive capabilities in the HTML export by adding tooltip configuration, which would raise LM-02.

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): Six metric tiles arranged in a 3×2 grid on the warm off-white #FAF8F1 background. Each tile has an elevated card background (#FFFDF6) with a subtle #4A4A44 border. Five tiles show green (#009E73) sparklines (area fill + line + endpoint dot) for good-status metrics — Active Users (1,284), CPU Usage (45%), Error Rate (0.8%), Response Time (120ms), Throughput (847req/s). The Memory tile has an amber (#F59E0B) sparkline with a ⚠ prefix in its strip label, clearly distinguishing it as a warning state. Each tile shows a large bold dark value at center, and a smaller change indicator below with an arrow (▲/▼) colored green for favorable or red for unfavorable changes. Title dashboard-metrics-tiles · python · letsplot · anyplot.ai is centered in bold dark text. All text is readable against the light background with no legibility failures.

Dark render (plot-dark.png): Same layout on the warm near-black #1A1A17 background. Tile panels use the dark-elevated #242420 fill with #B8B7B0 borders, creating clear visual separation. All data colors are identical to the light render — green sparklines remain #009E73, amber Memory sparkline remains #F59E0B. Large bold metric values now appear in light cream (#F0EFE8) — no dark-on-dark failure. Strip metric labels render in #B8B7B0 (INK_SOFT) against the elevated dark tile background, clearly legible. Title is visible in light text. Change indicators retain their green/red semantic colors. Legibility verdict: PASS — no chrome inversion issues found.

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	14	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	88	100

Visual Quality (28/30)

• VQ-01: Text Legibility (6/8) — Large value labels (size=22) are very prominent. Change labels (size=14) in a 3-column layout become borderline small at mobile width (~400px) — each tile would be ~130px wide, leaving ~7 effective pixels for change text. Strip text and title are clear in both themes.
• VQ-02: No Overlap (6/6) — Value labels and change labels are positioned at distinct y offsets (y_value above sparkline peak, y_change below trough); no collisions observed.
• VQ-03: Element Visibility (6/6) — Sparklines (area alpha=0.25 + line size=2 + endpoint dot size=5) are clearly visible. Free-y scaling ensures each tile's sparkline fills available vertical space optimally.
• VQ-04: Color Accessibility (2/2) — Green/red change colors are supplemented by directional arrows (▲/▼), providing a second encoding channel beyond color alone. Amber warning separates clearly from both green and red.
• VQ-05: Layout & Canvas (4/4) — Canvas gate passed (3200×1800). 3×2 grid with panel_spacing_x/y=30 provides generous tile breathing room. Good proportions throughout.
• VQ-06: Axis Labels & Title (2/2) — No axis labels (appropriate for sparkline tiles). Title follows the required format exactly.
• VQ-07: Palette Compliance (2/2) — Primary/good status uses #009E73 (Okabe-Ito Add workflow diagram for new prototype discovery #1). Backgrounds: #FAF8F1 light / #1A1A17 dark. Warning amber and critical red are semantically justified deviations.

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (5/8) — Status-semantic color coding, elevated card backgrounds creating visual depth, ⚠ badge for warning tiles, and the value+sparkline+change combination deliver a genuine dashboard aesthetic. Above the default but not exceptional — falls short of outstanding typography hierarchy within each tile.
• DE-02: Visual Refinement (4/6) — All axis decorations (text, ticks, titles, lines, grid) removed cleanly; elevated panel backgrounds and INK_SOFT borders define tiles well. Panel spacing generous. Could further refine by differentiating strip font size from change label size more distinctly.
• DE-03: Data Storytelling (5/6) — Clear visual hierarchy: large value → change indicator → sparkline. Warning tile stands out immediately via amber color + ⚠ badge. Each tile provides a self-contained metric story. Excellent at-a-glance readability.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Dashboard metric tiles with embedded sparklines; 3×2 grid for 6 tiles matches spec requirement.
• SC-02: Required Features (4/4) — All four required elements present per tile: prominent KPI value, metric label (strip text), mini sparkline, change indicator with directional arrow. Status color coding implemented.
• SC-03: Data Mapping (3/3) — Historical trend mapped to sparklines, current value as prominent label, change percent as arrow+color indicator. Semantically correct favorable/unfavorable color logic per metric type.
• SC-04: Title & Legend (3/3) — Title is dashboard-metrics-tiles · python · letsplot · anyplot.ai — correct. No legend (appropriate; tiles are self-labelled).

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Covers current value, trend history, directional change, percentage change, and status classification — all aspects of the dashboard tile pattern.
• DQ-02: Realistic Context (5/5) — Real-world server/operations metrics: CPU Usage (45%), Memory (72%), Response Time (120ms), Active Users (1,284), Error Rate (0.8%), Throughput (847 req/s). All values are realistic for a mid-size web application.
• DQ-03: Appropriate Scale (4/4) — Metric values, units, and change percentages are domain-appropriate. Seed=42 ensures deterministic data generation.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Flat script, no functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imported names are used; no unused imports.
• CQ-04: Code Elegance (2/2) — Clean, readable structure. Data preparation loop is appropriate complexity for the task.
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html via ggsave. Correct path handling.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Correct ggplot grammar: facet_wrap with scales='free_y', scale_color_manual/scale_fill_manual, theme() with element_* functions, ggsize + ggsave with scale=4. layer_tooltips() used idiomatically.
• LM-02: Distinctive Features (2/5) — layer_tooltips() is letsplot-distinctive and used on both line and point geoms. HTML interactive export adds value. However, more letsplot-specific features (e.g., ggbunch() for true independent-layout tiles, ggtb(), or letsplot theme presets) could have been leveraged for higher distinction.

Score Caps Applied

• None

Strengths

• Full spec compliance: all four required tile elements (value, sparkline, change indicator, metric label) present across all 6 tiles
• Fully theme-adaptive chrome in both renders — correct backgrounds, INK/INK_SOFT token usage, no dark-on-dark failures
• Semantically correct change indicator logic: favorable/unfavorable determined per metric context, not just raw positive/negative
• Warning badge (⚠) in Memory strip text provides instant at-a-glance differentiation beyond color alone
• Clean dashboard aesthetic with elevated card backgrounds, removed axis decorations, generous tile spacing
• Correct canvas (3200×1800), title format, and dual HTML+PNG output; layer_tooltips() enhances interactive HTML

Weaknesses

• Change labels (size=14) in a 3-column tile layout become borderline small at mobile width (~400px) — consider increasing to size=16 or reducing column count to ncol=2 for 4 tiles when fewer are shown, or bump size
• Strip text and change label are similar size (both 14), flattening the typographic hierarchy within each tile — strip could be larger (16-18) to anchor metric identity more strongly
• LM-02 underutilizes letsplot-distinctive features: layer_tooltips is present but tooltip content could be richer; ggbunch() or plot composition could create more independent tile layouts instead of shared facet axes

Issues Found

1. VQ-01 MINOR: Change percentage labels (size=14) may be too small at mobile rendering (~400px viewport in 3-column layout). Fix: increase to size=16-17 or add a facet_wrap ncol=2 option for fewer tiles.
2. DE-01/DE-02 MODERATE: Strip text at size=14 equals change label size — missing typographic hierarchy. Fix: increase strip_text to size=16-18 for stronger metric-name anchoring within each tile.
3. LM-02 MINOR: HTML tooltips show raw y values — could enrich with metric name, formatted value, and units via layer_tooltips().line('@metric').line('value|@y{,.1f}') (unit already implicit in value_label).

AI Feedback for Next Attempt

Strong implementation with excellent spec coverage and proper theme adaptation. To push score higher: (1) increase change label size to 16-17 for mobile legibility; (2) differentiate strip_text fontsize (16-18) from change label (14) to create clearer typographic hierarchy within tiles; (3) enrich layer_tooltips content to better leverage letsplot's interactive distinctive feature.

Verdict: APPROVED