feat(pygal): implement line-parametric

[github-actions]

Implementation: line-parametric - pygal

Implements the pygal version of line-parametric.

File: plots/line-parametric/implementations/pygal.py

Parent Issue: #4424

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays two parametric curves on a white square canvas with range -1.2 to 1.2 on both axes. A Lissajous figure (x=sin(3t), y=sin(2t)) is rendered as 6 color-gradient segments transitioning from deep blue through lighter blue to red-orange and dark red. A spiral curve (x=t·cos(t), y=t·sin(t), normalized) is shown in 6 segments transitioning from dark purple through violet to teal and dark green. Start and end points are marked as small dark dots. The title reads "line-parametric · pygal · pyplots.ai". Axes are labeled "x(t)" and "y(t)". A legend at the bottom contains 13 entries across 3 columns with very small text identifying each segment's parameter range. Grid lines are subtle light gray. The curves are clearly visible with thick strokes.

Score: 76/100

Category	Score	Max
Visual Quality	23	30
Design Excellence	11	20
Spec Compliance	14	15
Data Quality	13	15
Code Quality	10	10
Library Mastery	5	10
Total	76	100

Visual Quality (23/30)

• VQ-01: Text Legibility (6/8) — Font sizes explicitly set (title=56, labels=40, legend=32), title and axis labels readable, but legend text is very small relative to the 4800×4800 canvas
• VQ-02: No Overlap (5/6) — Legend is very dense with 13 entries across 3 columns but no actual text overlap; curves naturally cross
• VQ-03: Element Visibility (5/6) — Lines are thick (stroke_width=12) and clearly visible; start/end points are marked but small and not very prominent
• VQ-04: Color Accessibility (3/4) — Blue→red and purple→teal gradients are distinguishable, but adjacent segments within each gradient are hard to differentiate
• VQ-05: Layout & Canvas (3/4) — Square canvas is appropriate for equal aspect ratio; chart size is 4800×4800 instead of recommended 3600×3600
• VQ-06: Axis Labels & Title (1/2) — "x(t)" and "y(t)" are descriptive but generic mathematical notation without further context

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (5/8) — Intentional dual color gradients (cool→warm, violet→emerald), clean white background, above library defaults
• DE-02: Visual Refinement (3/6) — Subtle grid, white background, custom stroke styles with round linecaps; some refinement visible but not fully polished
• DE-03: Data Storytelling (3/6) — Color gradient effectively communicates direction of parameter traversal; two contrasting curves create comparison; start/end points add context

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct parametric curve plot using pygal.XY
• SC-02: Required Features (4/4) — Equal aspect ratio ✓, color gradient for direction ✓, two curves (Lissajous + spiral) ✓, start/end points marked ✓, sufficient density (800 pts) ✓
• SC-03: Data Mapping (3/3) — X/Y correctly computed from parametric equations, axes show full data range
• SC-04: Title & Legend (2/3) — Title format correct; legend is functional but overwhelming with 13 entries making it hard to parse

Data Quality (13/15)

• DQ-01: Feature Coverage (5/6) — Lissajous shows self-intersection and closed curve; spiral shows expanding open curve; good variety but could show cusps more clearly
• DQ-02: Realistic Context (4/5) — Standard mathematical curves with correct formulas; real-world relevant for physics/signal analysis
• DQ-03: Appropriate Scale (4/4) — Properly normalized to [-1, 1] range with sensible axis bounds

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data → style → chart → segments → save
• CQ-02: Reproducibility (2/2) — Fully deterministic (trigonometric functions, no randomness)
• CQ-03: Clean Imports (2/2) — All imports used: numpy, pygal, Style
• CQ-04: Code Elegance (2/2) — Clean segment loops, appropriate normalization of spiral, no over-engineering
• CQ-05: Output & API (1/1) — Saves as plot.png, uses current pygal API

Library Mastery (5/10)

• LM-01: Idiomatic Usage (3/5) — Correct use of pygal.XY, Style, chart.add with stroke_style; standard patterns
• LM-02: Distinctive Features (2/5) — Uses pygal-specific stroke_style, dots_size, truncate_legend, legend_at_bottom_columns, js=[] to disable JS; somewhat distinctive but mostly standard XY usage

Score Caps Applied

• None

Strengths

• Excellent spec compliance with all required features implemented (two curves, color gradient, start/end points, equal aspect ratio)
• Perfect code quality with clean, deterministic, KISS structure
• Intentional color palette design with dual gradients showing parameter direction
• Proper normalization of spiral to match Lissajous scale

Weaknesses

• Legend is overwhelmed with 13 entries (6+6 segments + start/end) — too granular, hard to read
• Chart dimensions 4800×4800 non-standard (should be 3600×3600 for square format)
• Start/end points are not prominent enough — small dots that blend in
• Adjacent color segments within each gradient are hard to distinguish from each other
• Legend font size (32) is too small for the 4800×4800 canvas

Issues Found

1. VQ-02/SC-04 MODERATE: Legend has 13 entries which is excessive and hard to parse
   • Fix: Reduce segments from 6 to 3-4 per curve to simplify legend, or use a single legend entry per curve
2. VQ-05 MINOR: Non-standard canvas size 4800×4800 instead of 3600×3600
   • Fix: Change to width=3600, height=3600 and adjust font sizes proportionally
3. VQ-03 MINOR: Start/end points are not prominent enough
   • Fix: Increase dots_size and use contrasting marker colors (e.g., bright markers with edge color)
4. LM-02 LOW: Generic pygal usage without leveraging distinctive features
   • Fix: Explore pygal-specific features more creatively

AI Feedback for Next Attempt

Reduce legend clutter by using fewer segments (3-4 per curve instead of 6) with more distinct color steps. Change canvas to 3600×3600. Make start/end points more prominent with larger dots and contrasting colors. Increase legend font size for readability. Consider adding value labels or annotations at key points to improve data storytelling.

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 is a 3600×3600 square chart titled "line-parametric · pygal · pyplots.ai" on a white background with a light gray (#fafafa) plot area. It displays four colored curve segments: a deep blue Lissajous curve (t: 0→π), an amber/orange Lissajous curve (t: π→2π), a purple spiral (t: 0→2π), and a forest green spiral (t: 2π→4π). The Lissajous figure shows the characteristic self-intersecting pattern of sin(3t) vs sin(2t), while the spiral expands outward from the center. Axes are labeled "Horizontal Position x(t)" and "Vertical Position y(t)" with range -1.2 to 1.2. A six-entry legend sits at the bottom in three columns, including entries for start (●) and end (■) markers. The start/end point markers are present but barely visible in the rendered output — they overlap near the origin and are obscured by the curve strokes. Grid lines are subtle light gray.

Score: 81/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	11	20
Spec Compliance	14	15
Data Quality	13	15
Code Quality	10	10
Library Mastery	6	10
Total	81	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title=52, label=36, major_label=32, legend=34). All text clearly readable. Minor: legend entries slightly dense at 34pt with 6 items in 3 columns.
• VQ-02: No Overlap (6/6) — No text overlap. Legend in 3-column layout at bottom fits cleanly.
• VQ-03: Element Visibility (4/6) — Curve strokes are excellent (width 11 and 9), but start/end point markers are barely visible. Both start points are at (0,0) and overlap each other, and the large curve strokes obscure the dots. End points are similarly hard to distinguish.
• VQ-04: Color Accessibility (4/4) — Blue, amber, purple, green palette is colorblind-safe with strong contrast between all series.
• VQ-05: Layout & Canvas (4/4) — Square layout with balanced margins. Plot fills ~65% of canvas area, well-proportioned.
• VQ-06: Axis Labels & Title (2/2) — Descriptive labels with mathematical notation: "Horizontal Position x(t)", "Vertical Position y(t)". Units not applicable for abstract mathematical curves.

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (5/8) — Custom 4-color palette with intentional color semantics (cool→warm for Lissajous, purple→green for spiral). Custom font family, styled stroke widths, light plot background. Above defaults but not publication-level polish.
• DE-02: Visual Refinement (3/6) — Subtle grid (#eeeeee), light plot background (#fafafa), good margins. Some refinement visible, but no spine removal or advanced whitespace management.
• DE-03: Data Storytelling (3/6) — Color-coded halves convey parameter direction. Two contrasting curve types (closed Lissajous vs expanding spiral) create natural comparison. However, the narrative could be stronger — the start/end markers that should anchor the story are nearly invisible.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct: parametric curves plotted as XY data using pygal.XY.
• SC-02: Required Features (3/4) — Equal aspect ratio ✓, two example curves ✓, sufficient point density (1000 pts) ✓, start/end points coded ✓. However, "color gradient along the curve" is only approximated with 2-segment coloring rather than a continuous gradient, and start/end markers are barely visible.
• SC-03: Data Mapping (3/3) — x(t) and y(t) correctly computed and mapped. Axes show full [-1.25, 1.25] range covering all data.
• SC-04: Title & Legend (3/3) — Title follows exact format "line-parametric · pygal · pyplots.ai". Legend labels are descriptive with parameter ranges and color names.

Data Quality (13/15)

• DQ-01: Feature Coverage (5/6) — Shows self-intersecting closed curve (Lissajous) and expanding open spiral. Demonstrates looping, self-intersection, and closed shapes. Missing: no cusp or sharp-feature demonstration.
• DQ-02: Realistic Context (4/5) — Classic mathematical examples (Lissajous with 3:2 ratio, Archimedean spiral). Neutral, educational context matching the spec's exact examples.
• DQ-03: Appropriate Scale (4/4) — Both curves normalized to [-1, 1] range. Sensible for dimensionless mathematical curves.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean flat structure: imports → data generation → style → chart config → add series → save.
• CQ-02: Reproducibility (2/2) — Fully deterministic (trigonometric functions, no randomness).
• CQ-03: Clean Imports (2/2) — All three imports used: numpy, pygal, Style.
• CQ-04: Code Elegance (2/2) — Appropriate complexity. List comprehensions for data conversion to pygal tuple format. No fake functionality.
• CQ-05: Output & API (1/1) — Saves as plot.png via render_to_png. Current pygal 3.1 API.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Good use of pygal.XY with tuple data format, Style class configuration, stroke_style per-series customization, dict-based data points with labels for markers.
• LM-02: Distinctive Features (2/5) — Uses some pygal-specific features (stroke_style, dots_size, legend_at_bottom_columns, truncate_legend=-1, js=[] to disable JS, dual PNG+HTML output). However, the core visualization approach is not uniquely leveraging pygal's strengths like built-in SVG interactivity or custom formatters.

Score Caps Applied

• None

Strengths

• Custom color palette with semantic meaning (cool→warm for time progression) creates coherent design
• Perfect code quality — clean KISS structure, fully deterministic, all imports used
• Excellent curve rendering with 1000 points and well-chosen stroke widths for smooth, visible curves
• Good spec compliance with both required curve types and correct mathematical formulas

Weaknesses

• Start/end point markers are nearly invisible — both start at (0,0) overlapping each other, and curve strokes obscure the dots
• Color gradient along curves is only 2 segments per curve — more segments would better show continuous parameter progression
• Library mastery could leverage more pygal-distinctive features (e.g., custom value formatters for tooltips showing t-parameter values, or more creative use of pygal's SVG capabilities)

Issues Found

1. VQ-03 MEDIUM: Start/end point markers invisible at rendered size — both start points overlap at origin, dots hidden under thick curve strokes
   • Fix: Use more segments (4-6 per curve) to create smoother color gradient AND offset or enlarge start/end markers significantly (dots_size=30+), or place them at non-overlapping positions
2. DE-02 MEDIUM: Visual refinement could be stronger — grid and background are subtle but no advanced polish
   • Fix: Consider reducing grid presence further, adjusting guide_stroke_color to be even lighter
3. LM-02 LOW: Generic library usage — not leveraging pygal's distinctive features
   • Fix: Use custom tooltip formatters to show parameter t values, leverage pygal's built-in SVG interactivity features

AI Feedback for Next Attempt

The main issue is that start/end point markers are nearly invisible — increase their size dramatically (dots_size=30+) or find a way to make them stand out from the thick curve strokes. Consider splitting each curve into more color segments (4-6) to better approximate the spec's "color gradient along the curve" requirement. For design excellence, push the visual refinement further with even subtler grid lines. For library mastery, try leveraging pygal-specific features like custom value formatters or x_value_formatter on the marker series to show meaningful t-parameter information.

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 two parametric curves on a square canvas with a light gray (#f7f7f7) plot background and white outer background. The title "line-parametric · pygal · pyplots.ai" appears at the top in dark text. The x-axis is labeled "Horizontal Position x(t)" and y-axis "Vertical Position y(t)", both ranging from -1.2 to 1.2. The Lissajous figure (x=sin(3t), y=sin(2t)) is drawn in 4 color-gradient segments: dark navy blue (0→π/2), lighter blue (π/2→π), burnt orange (π→3π/2), and warm amber (3π/2→2π), forming a classic figure-eight-like self-intersecting pattern. The spiral curve is drawn in 4 segments: deep purple (0→π, small inner loop), lighter purple (π→2π), teal (2π→3π), and green (3π→4π), spiraling outward from the center. A red dot marks the Lissajous start point near the origin, and a slightly offset red dot marks the spiral start. Black dots mark the endpoints of both curves. The legend at the bottom contains 12 entries in 4 columns showing all segments, start points, and end points. Subtle gray grid lines are visible on the plot area.

Score: 79/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	11	20
Spec Compliance	15	15
Data Quality	13	15
Code Quality	8	10
Library Mastery	7	10
Total	79	100

Visual Quality (25/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title=52, label=36, major_label=32, legend=30, value=28). Text is clearly readable at full size.
• VQ-02: No Overlap (5/6) — Legend with 12 entries in 4 columns is tight but readable. Start markers near origin overlap slightly.
• VQ-03: Element Visibility (5/6) — Thick stroke widths (8-10px) make curves clearly visible. Start/end dots are large and distinct. Minor issue: both curves start near origin so start markers crowd each other.
• VQ-04: Color Accessibility (3/4) — Blue/orange Lissajous segments have good contrast. Purple/teal spiral segments may be harder to distinguish for some colorblind viewers (blue-purple proximity).
• VQ-05: Layout & Canvas (3/4) — Square 3600×3600 canvas is appropriate for geometric curves. Good axis range. Legend area at bottom is spacious but crowded with 12 items.
• VQ-06: Axis Labels & Title (2/2) — Descriptive labels "Horizontal Position x(t)" and "Vertical Position y(t)" with mathematical notation.

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (5/8) — Custom color palette with intentional cool-to-warm gradient for Lissajous and purple-to-green gradient for spiral. Custom style with curated background, grid, and font choices. Above well-configured defaults but not publication-level.
• DE-02: Visual Refinement (3/6) — Light gray plot background, subtle grid styling with custom guide colors, generous margins. Pygal's SVG nature limits spine control.
• DE-03: Data Storytelling (3/6) — Color gradient segments effectively show direction of parameter traversal. Start/end points with distinct markers (red circles vs black circles) create a narrative of curve progression. Two contrasting color families separate the curves visually.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct parametric XY plot showing curves traced by x(t) and y(t).
• SC-02: Required Features (4/4) — Equal aspect ratio via square canvas; color gradient along curves via segmentation; two example curves (Lissajous + spiral as specified); start/end points marked; sufficient point density (1000 points).
• SC-03: Data Mapping (3/3) — X/Y correctly computed from parametric equations. Axes show full data range with appropriate padding.
• SC-04: Title & Legend (3/3) — Title format "line-parametric · pygal · pyplots.ai" is correct. Legend labels clearly describe curve segments with parameter ranges.

Data Quality (13/15)

• DQ-01: Feature Coverage (5/6) — Shows both a self-intersecting closed curve (Lissajous) and an open expanding curve (spiral), demonstrating different parametric behaviors. Could also show a curve with cusps for full coverage.
• DQ-02: Realistic Context (4/5) — Mathematical/scientific context using the exact parametric equations from the spec. Neutral topic. Slightly abstract but appropriate for mathematical visualization.
• DQ-03: Appropriate Scale (4/4) — Values normalized to [-1, 1] range with axis extending to ±1.3 for padding. Appropriate for mathematical curves.

Code Quality (8/10)

• CQ-01: KISS Structure (2/3) — Mostly linear flow (imports → data → style → chart → save), but includes a fmt_tooltip function definition that breaks pure KISS structure.
• CQ-02: Reproducibility (2/2) — Fully deterministic — pure mathematical functions with no random elements.
• CQ-03: Clean Imports (2/2) — All imports used: numpy for math, pygal for charting, Style for styling.
• CQ-04: Code Elegance (1/2) — Segmentation logic is somewhat verbose with repeated loop patterns for Lissajous and spiral. The tooltip formatter function adds unnecessary complexity for a static PNG output.
• CQ-05: Output & API (1/1) — Saves as plot.png via render_to_png. Also saves HTML. Current API usage.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of pygal XY chart, Style class customization, per-series stroke_style, dict-based data points with labels, legend configuration with bottom placement and column control.
• LM-02: Distinctive Features (3/5) — Uses pygal-specific features: XY chart with dict data format for custom tooltips, stroke_style per series, HTML export alongside PNG, truncate_legend=-1, legend_at_bottom_columns.

Score Caps Applied

• None — no score cap conditions triggered

Strengths

• Full spec compliance with both required curves (Lissajous + spiral) and all features (color gradient, start/end markers, equal aspect ratio)
• Effective color segmentation technique to show parameter direction on each curve
• Custom style with intentional color palette progression (cool→warm, purple→green)
• Good use of pygal-specific features (dict data points, per-series stroke_style, HTML export)
• Deterministic, reproducible output with 1000-point resolution for smooth curves

Weaknesses

• Legend is crowded with 12 entries (4 segments × 2 curves + 2 start + 2 end points)
• Start markers overlap near origin since both curves begin at (0,0)
• Tooltip formatter function adds complexity unnecessary for static PNG output
• Blue-purple color proximity may challenge colorblind viewers

Issues Found

1. VQ-02 MINOR: Legend crowding with 12 entries in 4 columns makes it dense to parse
   • Fix: Consolidate start/end markers into fewer legend entries or reduce segment count
2. CQ-01 MINOR: Function definition breaks pure KISS structure
   • Fix: Inline the tooltip formatting or remove it since PNG output doesn't show tooltips
3. DE-01 MODERATE: Good custom palette but not reaching publication quality
   • Fix: Further refine color harmony, consider more distinctive segment transitions

AI Feedback for Next Attempt

Implementation is solid with full spec compliance and good use of pygal features. The color gradient segmentation effectively conveys parameter direction. Main areas for improvement are reducing legend complexity (consolidating 12 entries), removing the tooltip function unnecessary for PNG output, and pushing design further toward publication quality.

Verdict: REJECTED

[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 two parametric curves on a square 3600×3600 canvas with a light gray (#f7f7f7) plot background and white outer background. The title "line-parametric · pygal · pyplots.ai" appears at the top in dark text. The x-axis is labeled "Horizontal Position x(t)" and y-axis "Vertical Position y(t)", both ranging from approximately -1.2 to 1.2. The Lissajous figure (x=sin(3t), y=sin(2t)) is drawn in 4 color-gradient segments: deep navy blue, steel blue, burnt orange, and warm amber, forming the characteristic self-intersecting figure-eight-like pattern spanning the full plot area. The spiral curve is drawn in 4 segments: deep purple (small inner loop), orchid purple, teal, and forest green, expanding outward from the center. Red dots mark the start points near the origin (spiral start slightly offset), and dark/black dots mark the endpoints of both curves. The legend at the bottom contains 12 entries arranged in 4 columns showing all curve segments, start markers, and end markers. Subtle gray grid lines are visible on the light plot background.

Score: 80/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	11	20
Spec Compliance	15	15
Data Quality	13	15
Code Quality	8	10
Library Mastery	7	10
Total	80	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title=52, label=36, major_label=32, legend=30, value=28). Text is clearly readable at full size. Legend text slightly dense with 12 entries.
• VQ-02: No Overlap (5/6) — Legend with 12 entries in 4 columns is tight but readable. Start markers near origin overlap slightly due to both curves starting at (0,0).
• VQ-03: Element Visibility (6/6) — Thick stroke widths (8-10px) make curves highly visible with excellent contrast against the light background. Start/end dots are large (dots_size 26-30) and distinct.
• VQ-04: Color Accessibility (3/4) — Blue-orange Lissajous gradient and purple-green spiral gradient provide good contrast. Minor concern: deep navy and deep purple segments could be challenging for tritanopia.
• VQ-05: Layout & Canvas (3/4) — Square canvas appropriate for geometric curves with equal aspect ratio. Good axis range (±1.3). Bottom legend area is spacious but dense with 12 items.
• VQ-06: Axis Labels & Title (2/2) — Descriptive labels "Horizontal Position x(t)" and "Vertical Position y(t)" with mathematical notation. Units not applicable for dimensionless mathematical curves.

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (5/8) — Custom color palette with intentional cool-to-warm gradient for Lissajous and purple-to-green gradient for spiral. Curated style with custom background, foreground, guide colors, and font choices. Clearly above well-configured defaults but not publication-level polish.
• DE-02: Visual Refinement (3/6) — Light gray plot background (#f7f7f7), subtle grid styling with custom guide stroke colors (#e8e8e8, #d0d0d0), generous margins, rounded line caps/joins. Pygal's SVG nature limits spine control options.
• DE-03: Data Storytelling (3/6) — Color gradient segments effectively convey parameter direction along each curve. Start/end points with distinct markers create a traversal narrative. Two contrasting color families cleanly separate the curves. Lacks a stronger focal point or annotated insight.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct parametric XY plot showing curves traced by x(t) and y(t) as functions of parameter t.
• SC-02: Required Features (4/4) — Equal aspect ratio via square canvas ✓; color gradient along curves via 4-segment coloring ✓; two example curves (Lissajous x=sin(3t),y=sin(2t) + spiral x=t·cos(t),y=t·sin(t)) ✓; start/end points labeled and marked ✓; 1000 points for smooth rendering ✓.
• SC-03: Data Mapping (3/3) — X/Y correctly computed from parametric equations. Axes show full data range with appropriate ±1.3 padding.
• SC-04: Title & Legend (3/3) — Title "line-parametric · pygal · pyplots.ai" matches required format. Legend labels clearly describe curve segments with parameter ranges.

Data Quality (13/15)

• DQ-01: Feature Coverage (5/6) — Shows a self-intersecting closed curve (Lissajous) and an open expanding curve (spiral), demonstrating distinct parametric behaviors. Could include a curve with cusps for fuller coverage.
• DQ-02: Realistic Context (4/5) — Scientific/mathematical context using exact parametric equations from the spec. Neutral topic appropriate for mathematical visualization. Slightly abstract but well-suited to the domain.
• DQ-03: Appropriate Scale (4/4) — Spiral normalized to [-1, 1] for visual comparison with Lissajous. Axis range ±1.3 provides balanced padding.

Code Quality (8/10)

• CQ-01: KISS Structure (2/3) — Mostly linear flow (imports → data → style → chart → save), but includes a fmt_tooltip helper function that breaks pure KISS structure.
• CQ-02: Reproducibility (2/2) — Fully deterministic — pure mathematical functions with no random elements.
• CQ-03: Clean Imports (2/2) — All imports used: numpy for math, pygal for charting, Style for styling.
• CQ-04: Code Elegance (1/2) — Segmentation logic is somewhat verbose with repeated loop patterns. The tooltip formatter adds complexity unnecessary for static PNG output. Spiral start point offset (+0.03) is a minor hack.
• CQ-05: Output & API (1/1) — Saves as plot.png via render_to_png. Also generates HTML. Current API usage.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of pygal XY chart, Style class customization, per-series stroke_style with linecap/linejoin, dict-based data points with labels, legend configuration with bottom placement and column control.
• LM-02: Distinctive Features (3/5) — Leverages pygal-specific features: XY chart with dict data format for custom tooltips/labels, per-series stroke_style, HTML interactive export alongside PNG, truncate_legend=-1, legend_at_bottom_columns.

Score Caps Applied

• None — no score cap conditions triggered

Strengths

• Full spec compliance with both required curves (Lissajous + spiral) and all specified features (color gradient, start/end markers, equal aspect ratio, sufficient density)
• Effective 4-segment color gradient technique to convey parameter direction on each curve
• Custom style with intentional dual color palette (blue-orange + purple-green) that cleanly distinguishes the two curves
• Good use of pygal-specific features (dict data points, per-series stroke_style, HTML export)
• Deterministic output with 1000-point resolution for smooth curve rendering

Weaknesses

• Legend is crowded with 12 entries (4 segments × 2 curves + 2 start + 2 end markers) making it dense to parse
• The fmt_tooltip function and tooltip formatting add complexity not useful in static PNG output
• Design excellence limited by pygal's SVG rendering constraints (no spine removal, limited grid control)
• Start markers near origin overlap since both curves begin at (0,0)

Issues Found

1. CQ-01 MINOR: Helper function fmt_tooltip breaks KISS structure and is unnecessary for PNG output
   • Fix: Inline tooltip text or remove since PNG doesn't render interactive tooltips
2. VQ-02 MINOR: 12 legend entries create visual density at bottom
   • Fix: Could consolidate start/end into fewer entries or reduce segment count to 3
3. DE-03 MODERATE: Color gradients convey direction but no stronger focal point or annotated insight exists
   • Fix: Add emphasis to key features (e.g., self-intersection points of Lissajous, spiral expansion rate)

AI Feedback for Next Attempt

Solid implementation scoring 80/100 with perfect spec compliance (15/15). The dual color gradient technique effectively conveys parameter direction. Main gaps are in Design Excellence (11/20): the implementation is well-crafted but doesn't reach publication-level polish. For future improvement, consolidate the legend, remove the tooltip function unnecessary for PNG, and consider adding visual emphasis at mathematically interesting points (self-intersections, cusps).

Verdict: REJECTED