feat(letsplot): implement streamgraph-basic

[github-actions]

Implementation: streamgraph-basic - python/letsplot

Implements the python/letsplot version of streamgraph-basic.

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

Parent Issue: #856

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): Streamgraph on warm off-white (#FAF8F1) background. Title "streamgraph-basic · letsplot · anyplot.ai" appears in dark ink at the top left — clearly readable. Five music genre ribbons flow smoothly across 24 months (Jan '23 → Dec '24): Pop (#009E73 brand green, bottom), Rock (#D55E00 orange, second), Hip-Hop (#0072B2 blue, third), Electronic (#CC79A7 pink, fourth), Jazz (#E69F00 yellow, top). Symmetric baseline creates the organic river-like appearance. X-axis tick labels ("Jan '23", "Jul '23", "Jan '24", "Jul '24", "Dec '24") are readable. Y-axis labels removed (appropriate). Legend on the right side clearly identifies all genres. Subtle vertical dashed grid lines provide time reference. At the top-right, the Jazz (yellow) band reaches close to the top boundary of the plot area and may be very slightly clipped. All text is readable against the light background — PASS.

Dark render (plot-dark.png): Same streamgraph on near-black (#1A1A17) background. Title renders in light (#F0EFE8) ink — clearly visible. All five data colors are identical to the light render (same green, orange, blue, pink, yellow — Okabe-Ito positions 1–5 unchanged). X-axis tick labels appear in muted light color (#B8B7B0) — readable. Legend background switches to elevated dark (#242420) with light-colored text — legible. No dark-on-dark text failures observed. Grid lines remain subtle. All text is readable against the dark background — PASS.

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

Score: 88/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title=24, axis_title=20, axis_text=16, legend=16/18. Readable in both themes.
• VQ-02: No Overlap (6/6) — No overlapping text in either render; tick labels well-spaced.
• VQ-03: Element Visibility (6/6) — Ribbon bands are clearly visible with alpha=0.9; smooth spline curves render cleanly.
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette is colorblind-safe; adjacent bands have adequate contrast.
• VQ-05: Layout & Canvas (3/4) — Plot fills canvas well, but the Jazz (yellow) top band approaches or touches the upper boundary at the right side, risking slight clipping.
• VQ-06: Axis Labels & Title (2/2) — "Month" on x-axis; y-axis intentionally blank (appropriate for streamgraph); title format correct.
• VQ-07: Palette Compliance (2/2) — First series (Pop) = #009E73 ✓; Okabe-Ito order followed for all 5 series; #FAF8F1 light background, #1A1A17 dark background; text/grid/legend colors theme-adaptive in both renders.

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) — Above defaults: correct palette, smooth spline curves, clean theme adaptation. But not exceptional — no visual layering, no depth effects, no typography hierarchy beyond sizing.
• DE-02: Visual Refinement (4/6) — Y-axis and ticks removed (appropriate for streamgraph); only subtle dashed x-axis grid lines retained; panel/plot background unified; legend frame included. Good refinement but spines not fully removed (axis_line visible).
• DE-03: Data Storytelling (3/6) — Music genre popularity over two years with alternating growth/decline trends creates inherent narrative. But no visual emphasis differentiates dominant genres or highlights trend inflections — all series receive identical alpha and no focal point is established.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Streamgraph correctly implemented: symmetric baseline (−total/2), smooth cubic spline interpolation via scipy, stacked ribbons with organic curves.
• SC-02: Required Features (4/4) — All spec requirements met: smooth interpolation ✓, centered symmetric baseline ✓, distinct harmonious colors ✓, legend ✓.
• SC-03: Data Mapping (3/3) — Time (month) on x, stacked value bands on y, genre category on fill color.
• SC-04: Title & Legend (3/3) — Title: "streamgraph-basic · letsplot · anyplot.ai" ✓; legend title "Genre" with labels Pop, Rock, Hip-Hop, Electronic, Jazz ✓.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows 5 genres with distinct sinusoidal patterns + linear trends (alternating growth/decline), seasonal variation, and relative magnitude differences — all aspects of streamgraph demonstrated.
• DQ-02: Realistic Context (5/5) — Monthly streaming hours by music genre over two years — real-world, neutral, comprehensible scenario.
• DQ-03: Appropriate Scale (4/4) — Values clipped to ≥25 hours, ranging ~50–250 hours/month per genre — plausible streaming magnitudes with sensible proportions.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear 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: os, numpy, pandas, lets_plot, ggsave, scipy.interpolate.
• CQ-04: Code Elegance (2/2) — Clean, Pythonic; # noqa suppressions justified for wildcard import convention; no fake functionality.
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html with correct naming and scale=3.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (5/5) — Expertly uses lets-plot grammar: ggplot() + geom_ribbon() + scale_fill_manual() + theme_minimal() + anyplot_theme + ggsize() + ggsave(scale=3) — all idiomatic patterns.
• LM-02: Distinctive Features (2/5) — HTML export produced (lets-plot distinctive); otherwise the implementation doesn't leverage lets-plot's interactive tooltip system or any other features unique to this library. Smoothing is done via external scipy rather than using lets-plot's built-in geom_smooth or stat capabilities.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Perfect spec compliance: symmetric baseline, cubic spline interpolation, and Okabe-Ito palette all correctly implemented
• Excellent code quality: clean linear structure, seed set, all imports used, correct output filenames with scale=3
• Full theme adaptation: both renders use correct background colors (#FAF8F1 / #1A1A17) and all chrome tokens (INK, INK_SOFT, ELEVATED_BG) applied consistently
• Realistic, neutral music genre data with plausible variation and good feature coverage
• Y-axis decluttered appropriately for streamgraph (no y labels/ticks needed)

Weaknesses

• Jazz (yellow) top band approaches the upper plot boundary at the right — increase vertical canvas space or reduce the ggsize height to add breathing room above the topmost band
• Design Excellence limited: no visual hierarchy (all bands same alpha), no emphasis on dominant or trending genres, no depth cues between adjacent layers
• LM-02 underutilizes lets-plot: smoothing is done externally (scipy) rather than through lets-plot's grammar; HTML output lacks tooltips that could leverage lets-plot's interactive layer

Issues Found

1. VQ-05 MINOR: Jazz (yellow) band clips at top-right boundary
   • Fix: Increase ggsize height (e.g., 1600×950 or 1600×1000) or reduce the value scale to leave vertical margin above the top band
2. DE-01/DE-03 LOW: No visual hierarchy or emphasis across the 5 genres
   • Fix: Vary alpha between layers (e.g., center layers at 0.95, outer layers at 0.85) or slightly darken/lighten the bottom/top bands to create depth; optionally add a subtle peak annotation for the highest-traffic genre
3. LM-02 LOW: External scipy for smoothing bypasses lets-plot capabilities
   • Fix: Use geom_ribbon with stat='smooth' or keep scipy but add interactive tooltips via lets-plot's tooltips parameter for the HTML output — e.g., geom_ribbon(..., tooltips=layer_tooltips().line('@genre: @ymax'))

AI Feedback for Next Attempt

Fix the top-boundary clipping by increasing canvas height (ggsize 1600×1000). Improve design excellence by varying alpha across layers for visual depth (outer bands slightly more transparent), and add lets-plot interactive tooltips to the HTML output using the tooltips parameter on geom_ribbon. These two changes address VQ-05, DE-01, and LM-02 simultaneously.

Verdict: REJECTED

[github-actions]

🔧 Repair Workflow Crashed (Attempt 1/4) — Auto-Retrying

The repair workflow failed (probably a transient Claude Code Action issue). Automatically re-triggering this attempt...

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🤖 impl-repair

[github-actions]

❌ Repair Workflow Crashed (Attempt 1/4, retry exhausted)

The repair workflow itself failed twice for this attempt — likely a persistent Claude Code Action issue.

Manual restart:

gh workflow run impl-repair.yml -f pr_number=5705 -f specification_id=streamgraph-basic -f library=letsplot -f attempt=1

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🤖 impl-repair

[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 — correct, not pure white. Five ribbon streams flow smoothly across a 24-month time axis (Jan '23 to Dec '24). First stream (Pop) is #009E73 teal/green at the bottom, followed by Rock (#D55E00 orange), Hip-Hop (#0072B2 blue), Electronic (#CC79A7 pink), Jazz (#E69F00 yellow) at the top. The symmetric baseline creates an organic river-like silhouette. Subtle dashed vertical grid lines provide time orientation without cluttering. Title "streamgraph-basic · letsplot · anyplot.ai" is dark ink at the top. X-axis label "Month" and tick labels (Jan '23, Jul '23, Jan '24, Jul '24, Dec '24) are in INK_SOFT (#4A4A44) — clearly readable. Legend "Genre" sits to the right with an elevated background. All text is readable against the light background. Legibility verdict: PASS.

Dark render (plot-dark.png): Warm near-black (#1A1A17) background — correct, not pure black. Data colors are identical to the light render (theme-independent Okabe-Ito positions 1–5). Title renders in light ink (#F0EFE8) — clearly visible. X-axis tick labels appear in INK_SOFT (#B8B7B0 light gray) — legible against the dark background. Legend uses elevated dark background (#242420) with light text. The lower corners of the plot (where no streams reach the extremes) show the #1A1A17 background correctly. No dark-on-dark failures observed. Legibility verdict: PASS.

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

Score: 87/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	11	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	87	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title=24, axis_title=20, axis_text=16, legend_text=16, legend_title=18. Readable in both themes with correct INK/INK_SOFT tokens.
• VQ-02: No Overlap (6/6) — 5 well-spaced tick labels, legend positioned right without overlapping data.
• VQ-03: Element Visibility (6/6) — All 5 ribbon streams clearly visible with alpha=0.9 and distinct Okabe-Ito colors.
• VQ-04: Color Accessibility (2/2) — Okabe-Ito is CVD-safe; sufficient luminance contrast between all adjacent streams.
• VQ-05: Layout & Canvas (4/4) — 1600×900 with scale=3 → 4800×2700. Plot fills canvas well with balanced margins.
• VQ-06: Axis Labels & Title (2/2) — X: "Month" appropriate; Y: blank (correct — symmetric-baseline values are meaningless to readers). Title in correct format.
• VQ-07: Palette Compliance (2/2) — First series #009E73, all 5 in canonical Okabe-Ito order. Light bg #FAF8F1, dark bg #1A1A17. Both renders theme-correct.

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (5/8) — Above the default: clean minimal look, deliberate y-axis hiding, warm theme backgrounds, smooth flowing curves. But no distinctive design touches elevate this above a well-configured default.
• DE-02: Visual Refinement (4/6) — theme_minimal removes spines, y-axis fully hidden, only subtle dashed x-grid retained. Good, deliberate refinement choices.
• DE-03: Data Storytelling (2/6) — Data is displayed but not interpreted. All genres have equal visual weight; no focal point, emphasis technique, or annotation guides the viewer toward an insight.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct streamgraph with symmetric baseline computation and smooth ribbons via cubic spline.
• SC-02: Required Features (4/4) — Smooth interpolation (scipy spline), symmetric baseline, distinct colors, legend — all present.
• SC-03: Data Mapping (3/3) — X=months (Jan '23–Dec '24), Y=symmetric stacked values. All 24 months represented.
• SC-04: Title & Legend (3/3) — Title: "streamgraph-basic · letsplot · anyplot.ai" exact match. Legend "Genre" with all 5 categories.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows all streamgraph aspects: multiple categories, temporal evolution, varying magnitude trends (some rising, some falling).
• DQ-02: Realistic Context (5/5) — Monthly streaming hours by music genre (Pop, Rock, Hip-Hop, Electronic, Jazz) 2023–2024. Neutral, plausible, non-controversial.
• DQ-03: Appropriate Scale (4/4) — Base ~100 hours/month per genre with realistic noise and trend. Proportions are plausible for streaming data.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Procedural imports→data→plot→save with no functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imports used. noqa annotations acceptable for lets_plot star import.
• CQ-04: Code Elegance (2/2) — Clean, readable. No fake UI elements. Appropriate complexity for streamgraph baseline computation.
• CQ-05: Output & API (1/1) — Saves plot-{THEME}.png and plot-{THEME}.html. No bare plot.png. Current API.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Correct use of ggplot() + geom_ribbon(), scale_fill_manual, scale_x_continuous with explicit breaks/labels, labs, ggsize, and full theme() element system.
• LM-02: Distinctive Features (2/5) — HTML export is a lets-plot strength but is expected by the library guide. No interactive tooltip configuration or other feature that clearly distinguishes lets-plot from plotnine.

Score Caps Applied

• None

Strengths

• Correct streamgraph: symmetric baseline + cubic spline interpolation deliver smooth, flowing curves exactly as the spec requires
• Perfect Okabe-Ito compliance — first series #009E73, all 5 in canonical order, identical across both renders
• Thorough theme-adaptive chrome: PAGE_BG, ELEVATED_BG, INK, INK_SOFT applied to every element; both renders pass legibility checks
• Y-axis blank is a deliberate, correct design choice for a symmetric-baseline chart
• All text sizes explicitly set for 4800×2700 resolution

Weaknesses

• DE-03 LOW: No data storytelling or visual emphasis — all genres rendered with equal visual weight; no focal point or annotation highlights an interesting trend or peak
• DE-01 MODERATE: Aesthetic sophistication is functional but not distinctive — no design touches elevate this above a clean default (e.g., subtle edge strokes on ribbons, alpha gradient, typographic emphasis in title)
• LM-02 LOW: lets-plot distinctive features under-exploited — interactive tooltip configuration in the HTML output would showcase what makes lets-plot different

Issues Found

1. DE-03 LOW: No visual hierarchy or data storytelling — all streams have equal prominence
   • Fix: Apply slight alpha variation (0.85–0.95) to create depth, or emphasize one dominant stream with a brighter color or slightly increased size
2. DE-01 MODERATE: Missing small polish details
   • Fix: Add a thin white/INK_SOFT border stroke to the ribbons for visual separation, or add a subtle alpha to streams at edges for a softer appearance

AI Feedback for Next Attempt

Improve data storytelling and aesthetic sophistication: add a slight alpha gradient or thin ribbon border strokes to create visual depth, and choose or highlight one genre as a focal point to give the viewer an immediate insight to take away. Consider leveraging lets-plot's HTML tooltips to demonstrate the library's interactive strengths.

Verdict: APPROVED