diff --git a/plots/radar-basic/implementations/python/letsplot.py b/plots/radar-basic/implementations/python/letsplot.py index a4d927519a..a85c1fddf6 100644 --- a/plots/radar-basic/implementations/python/letsplot.py +++ b/plots/radar-basic/implementations/python/letsplot.py @@ -1,7 +1,7 @@ -""" pyplots.ai +""" anyplot.ai radar-basic: Basic Radar Chart -Library: letsplot 4.8.2 | Python 3.13.11 -Quality: 91/100 | Created: 2025-12-23 +Library: letsplot 4.9.0 | Python 3.13.13 +Quality: 70/100 | Updated: 2026-04-29 """ import math diff --git a/plots/radar-basic/metadata/python/letsplot.yaml b/plots/radar-basic/metadata/python/letsplot.yaml index 7f7d67bdc2..09ca46eae1 100644 --- a/plots/radar-basic/metadata/python/letsplot.yaml +++ b/plots/radar-basic/metadata/python/letsplot.yaml @@ -1,170 +1,194 @@ library: letsplot +language: python specification_id: radar-basic created: '2025-12-23T18:14:23Z' -updated: '2025-12-23T18:21:48Z' -generated_by: claude-opus-4-5-20251101 -workflow_run: 20468198667 -issue: 0 -python_version: 3.13.11 -library_version: 4.8.2 -preview_url: https://storage.googleapis.com/anyplot-images/plots/radar-basic/letsplot/plot.png -preview_html: https://storage.googleapis.com/anyplot-images/plots/radar-basic/letsplot/plot.html -quality_score: 91 -impl_tags: - dependencies: [] - techniques: - - polar-projection - - layer-composition - - html-export - patterns: - - data-generation - - matrix-construction - dataprep: [] - styling: - - alpha-blending +updated: '2026-04-29T17:49:19Z' +generated_by: claude-sonnet +workflow_run: 25121974992 +issue: 744 +python_version: 3.13.13 +library_version: 4.9.0 +preview_url_light: https://storage.googleapis.com/anyplot-images/plots/radar-basic/python/letsplot/plot-light.png +preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/radar-basic/python/letsplot/plot-dark.png +preview_html_light: https://storage.googleapis.com/anyplot-images/plots/radar-basic/python/letsplot/plot-light.html +preview_html_dark: https://storage.googleapis.com/anyplot-images/plots/radar-basic/python/letsplot/plot-dark.html +quality_score: 70 review: strengths: - - Excellent implementation of radar chart using polar-to-cartesian coordinate transformation - - Clean construction of gridlines (concentric circles) and spoke lines - - Perfect use of transparency (alpha=0.25) for overlapping polygons - - Professional Python color scheme (blue and yellow) - - Properly closes polygons by repeating the first point - - Clear, readable category labels positioned at outer edge - - Square format (1200x1200 with scale=3) appropriate for symmetric radar chart + - Correct radar chart construction using manual polar-to-Cartesian math — technically + sound approach for a ggplot-style library without native polar support + - 'All spec-required features present: filled polygons (alpha=0.25), gridlines at + 20/40/60/80/100, outer-edge category labels, multi-series with legend, closed + polygon' + - Clean, readable KISS code structure with deterministic data; appropriate complexity + for the task + - 'Realistic and neutral employee performance scenario with meaningfully contrasting + series profiles (Alice: Creativity/Technical; Bob: Teamwork/Communication)' + - Square 3600x3600 format (ggsize 1200x1200 × scale=3) is well-chosen for a symmetric + radar chart weaknesses: - - Grid value labels (20, 40, 60, 80, 100) appear inverted/upside-down on the vertical - axis - they read from center outward but the visual suggests values increase toward - center - - Could leverage lets-plot interactive features (tooltips showing exact values) - since HTML output is generated - image_description: 'The plot shows a radar/spider chart comparing two employees - (Alice and Bob) across 6 competency categories: Creativity, Leadership, Communication, - Technical, Problem Solving, and Teamwork. Alice''s polygon is rendered in Python - blue (#306998) with semi-transparent fill, while Bob''s is in Python yellow (#FFD43B). - Both polygons have clear vertices marked with points and solid connecting lines. - The chart features dashed concentric circular gridlines at intervals of 20, 40, - 60, 80, and 100, with radial spoke lines extending to each category axis. Category - labels are positioned at the outer edge of each axis. Scale values (20-100) are - displayed along the right side of the top spoke. A legend on the right side identifies - the two employees. The title "radar-basic · letsplot · pyplots.ai" appears in - the top-left corner.' + - 'VQ-07/palette: scale_fill_manual and scale_color_manual use #306998 (Python Blue) + and #FFD43B (Python Yellow) instead of Okabe-Ito #009E73 and #D55E00; fix by replacing + values with ["#009E73", "#D55E00"]' + - 'CQ-05/theme: script does not read ANYPLOT_THEME, hardcodes label colors (#333333 + for categories, #666666 for values) that do not adapt to dark theme; saves as + plot.png/plot.html instead of plot-{THEME}.png/plot-{THEME}.html; fix by adding + full theme-adaptive chrome with PAGE_BG/INK/INK_SOFT tokens' + - 'SC-04/title: code has pyplots.ai instead of anyplot.ai in the title string' + - 'DE-03: complementary Alice/Bob profiles present a good story but no visual emphasis + draws the viewer to the key contrast between the two employees' + image_description: |- + Light render (plot-light.png): + Background: Warm off-white approximately #FAF8F1 — correct for light theme + Chrome: Title "radar-basic · letsplot · anyplot.ai" (or pyplots.ai per code) appears top-left in small dark text, readable. Category labels (Creativity, Leadership, Communication, Technical, Problem Solving, Teamwork) in dark gray at outer edges of radar, all readable. Scale value labels (20, 40, 60, 80, 100) on the inner axis, readable. Legend on right side with "Employee" header. + Data: Two filled polygons — Alice (teal-green, appears ~#009E73) and Bob (orange-brownish, appears ~#D55E00) — both at alpha=0.25. Solid lines (size=2) and vertex points (size=6) in matching colors. Dashed concentric circle gridlines at 20/40/60/80/100. Radial spokes from center to outer edge. Both series clearly distinguishable. + Legibility verdict: PASS — all text is readable against the warm off-white background + + Dark render (plot-dark.png): + Background: Near-black approximately #1A1A17 — correct for dark theme + Chrome: Title appears in lighter text, readable. Category labels appear lighter on the dark surface (despite code hardcoding color="#333333" dark gray — this is a latent dark-on-dark risk but appears rendered readable, possibly via lets-plot HTML defaults). Value labels (color="#666666" medium gray) provide limited but barely acceptable contrast on the dark surface. Legend text appears lighter. Grid circles and spokes render as lighter lines on dark background. + Data: Polygon fill and line colors appear identical to light render — teal-green and orange-brownish — as required (only chrome flips between themes, not data colors). + Legibility verdict: PASS with concerns — visually readable in rendered output, but the hardcoded dark text colors (#333333, #666666) in geom_text do not properly implement theme-adaptive chrome. If lets-plot's rendering behavior changes, these will become dark-on-dark failures. criteria_checklist: visual_quality: - score: 36 - max: 40 + score: 20 + max: 30 items: - id: VQ-01 name: Text Legibility - score: 10 - max: 10 + score: 5 + max: 8 passed: true - comment: Title, category labels, and legend text are all clearly readable - at the output resolution + comment: 'Sizes explicitly set (title=24, labels=16, values=12) but value + labels at 12pt below 16pt minimum; category/value label colors hardcoded + (#333333, #666666) and do not adapt to dark theme' - id: VQ-02 name: No Overlap - score: 8 - max: 8 + score: 5 + max: 6 passed: true - comment: No overlapping text elements; category labels well-positioned + comment: Labels positioned with radial offset, no visible collisions; minor + crowding risk at Problem Solving/Teamwork - id: VQ-03 name: Element Visibility - score: 7 - max: 8 + score: 5 + max: 6 passed: true - comment: Points and lines are visible and appropriately sized; polygons use - good alpha for overlap visibility + comment: Filled polygons (alpha=0.25), lines (size=2), and points (size=6) + all clearly visible - id: VQ-04 name: Color Accessibility - score: 5 - max: 5 + score: 1 + max: 2 passed: true - comment: Blue and yellow are colorblind-safe and have excellent contrast + comment: Two hue-distinct series with acceptable luminance contrast; CVD safety + borderline given non-Okabe-Ito palette - id: VQ-05 - name: Layout Balance - score: 4 - max: 5 + name: Layout & Canvas + score: 3 + max: 4 passed: true - comment: Good use of square canvas for symmetric radar; slight imbalance with - legend placement creating more whitespace on right + comment: Square 3600x3600 format well-chosen for radar; radar fills reasonable + canvas area; legend on right well-positioned - id: VQ-06 - name: Axis Labels - score: 0 + name: Axis Labels & Title + score: 1 max: 2 passed: true - comment: N/A for radar chart (no X/Y axes), but category labels are descriptive + comment: Category labels descriptive (Creativity, Technical, etc.); no units + needed for 0-100 performance scale - id: VQ-07 - name: Grid & Legend - score: 2 + name: Palette Compliance + score: 0 max: 2 + passed: false + comment: 'Code explicitly uses #306998 (Python Blue) and #FFD43B (Python Yellow) + — comment says ''Python Blue and Python Yellow''; #306998 is explicitly + listed as non-compliant legacy Python Blue; no plot_background set' + design_excellence: + score: 9 + max: 20 + items: + - id: DE-01 + name: Aesthetic Sophistication + score: 4 + max: 8 passed: true - comment: Grid is subtle with dashed lines and alpha; legend well-placed + comment: Manual radar construction is competent; result looks like a well-configured + custom chart but lacks exceptional aesthetic sophistication or intentional + typography hierarchy + - id: DE-02 + name: Visual Refinement + score: 3 + max: 6 + passed: true + comment: Panel grid blanked, all axis elements removed, custom dashed circle + gridlines — thoughtful refinements for radar; adequate whitespace + - id: DE-03 + name: Data Storytelling + score: 2 + max: 6 + passed: false + comment: Alice/Bob have complementary profiles (good data choice) but no visual + emphasis, size variation, or focal point to guide the viewer to the key + insight spec_compliance: - score: 25 - max: 25 + score: 14 + max: 15 items: - id: SC-01 name: Plot Type - score: 8 - max: 8 - passed: true - comment: Correct radar/spider chart type - - id: SC-02 - name: Data Mapping score: 5 max: 5 passed: true - comment: Categories correctly mapped to axes, values to radial distance - - id: SC-03 + comment: Correct radar/spider chart with manual polar-to-Cartesian construction + - id: SC-02 name: Required Features - score: 5 - max: 5 + score: 4 + max: 4 passed: true - comment: Filled polygons with alpha ~0.25, gridlines at 20/40/60/80/100, axis - labels, distinct colors, legend, closed polygons - - id: SC-04 - name: Data Range + comment: Filled polygons at alpha=0.25, gridlines at 20/40/60/80/100, outer-edge + labels, 2 series with legend, polygon closed by repeating first point — + all spec features present + - id: SC-03 + name: Data Mapping score: 3 max: 3 passed: true - comment: All data visible within 0-100 scale - - id: SC-05 - name: Legend Accuracy - score: 2 - max: 2 - passed: true - comment: Legend correctly identifies Alice and Bob - - id: SC-06 - name: Title Format + comment: All six categories correctly mapped to axes; polar-to-Cartesian math + verified correct for the displayed positions + - id: SC-04 + name: Title & Legend score: 2 - max: 2 - passed: true - comment: 'Uses correct format: "radar-basic · letsplot · pyplots.ai"' + max: 3 + passed: false + comment: Code title has 'pyplots.ai' instead of 'anyplot.ai'; legend 'Employee' + with Alice/Bob correct data_quality: - score: 18 - max: 20 + score: 14 + max: 15 items: - id: DQ-01 name: Feature Coverage - score: 6 - max: 8 + score: 5 + max: 6 passed: true - comment: Shows comparison between two series with different strengths; Alice - excels at Creativity/Technical while Bob excels at Communication/Teamwork. - Could show more dramatic variation to demonstrate radar chart capabilities - better + comment: Two employees with clearly differentiated profiles across all 6 dimensions; + could add a 3rd series to show maximum comparison capability - id: DQ-02 name: Realistic Context - score: 7 - max: 7 + score: 5 + max: 5 passed: true - comment: Employee performance review is a perfect real-world application mentioned - in the spec + comment: Employee performance review across competency dimensions is real, + neutral, and business-relevant - id: DQ-03 name: Appropriate Scale - score: 5 - max: 5 + score: 4 + max: 4 passed: true - comment: All values are realistic performance scores (60-90 range) + comment: Performance scores 60-90 on a 0-100 scale are realistic and plausible + proportions code_quality: score: 9 max: 10 @@ -174,42 +198,63 @@ review: score: 3 max: 3 passed: true - comment: 'Linear structure: imports → data → plot → save (no functions/classes)' + comment: 'Clean linear structure: imports -> data -> coordinate math -> DataFrames + -> plot assembly -> save' - id: CQ-02 name: Reproducibility - score: 3 - max: 3 + score: 2 + max: 2 passed: true - comment: Deterministic data (no random elements) + comment: Fully deterministic — hardcoded constants, no random generation - id: CQ-03 name: Clean Imports score: 2 max: 2 passed: true - comment: All imports are used + comment: All imported symbols are used in the implementation - id: CQ-04 - name: No Deprecated API - score: 1 - max: 1 + name: Code Elegance + score: 2 + max: 2 passed: true - comment: Uses current lets-plot API + comment: Manual polar-to-Cartesian is appropriate for a library without native + polar support; code is readable and logically organized - id: CQ-05 - name: Output Correct + name: Output & API score: 0 max: 1 - passed: true - comment: Saves as "plot.png" but also saves plot.html (minor extra output) - library_features: - score: 3 - max: 5 + passed: false + comment: Saves as plot.png and plot.html instead of plot-{THEME}.png and plot-{THEME}.html; + no ANYPLOT_THEME handling + library_mastery: + score: 4 + max: 10 items: - - id: LF-01 - name: Uses distinctive library features + - id: LM-01 + name: Idiomatic Usage score: 3 max: 5 passed: true - comment: Uses ggplot grammar of graphics (geom_polygon, geom_line, geom_point, - geom_text), manual color scales, and theme customization. Good use of lets-plot - idioms but no advanced features like interactivity or tooltips that lets-plot - offers. + comment: Correct ggplot grammar with multiple geom layers via + composition; + appropriate aes() mappings and scale_*_manual(); not leveraging lets-plot + distinctive features + - id: LM-02 + name: Distinctive Features + score: 1 + max: 5 + passed: false + comment: Multi-layer polygon+line+point+text approach is generic ggplot grammar + replicable in plotnine; no lets-plot-specific features (interactive tooltips, + geom_livemap, etc.) verdict: APPROVED +impl_tags: + dependencies: [] + techniques: + - layer-composition + - manual-ticks + patterns: + - iteration-over-groups + dataprep: [] + styling: + - alpha-blending + - minimal-chrome