feat(pygal): implement scatter-lag

[github-actions]

Implementation: scatter-lag - pygal

Implements the pygal version of scatter-lag.

File: plots/scatter-lag/implementations/pygal.py

Parent Issue: #5251

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot is a scatter lag plot rendered using pygal's XY chart type. The title reads "Lag Plot (k=1, r=0.75) · scatter-lag · pygal · pyplots.ai". The x-axis is labeled "y(t)" and the y-axis "y(t+1)", with tick values ranging from approximately 16.5 to 24.5 on both axes. Four series of dots represent temporal quartiles, colored from dark navy blue (Days 1–100) through medium blue (Days 101–200) and steel blue (Days 201–300) to very light blue (Days 301–399). A dashed gray diagonal reference line (y = x) runs from the bottom-left to the top-right of the plot. The background is very light gray (#fafafa) with subtle dotted grid lines. The legend is positioned at the bottom in a single row with 5 items. The scatter points cluster along the diagonal in an elongated ellipse, clearly showing positive autocorrelation (r=0.75) consistent with an AR(1) process.

Score: 82/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	10	20
Spec Compliance	15	15
Data Quality	13	15
Code Quality	10	10
Library Mastery	6	10
Total	82	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set (title=56, labels=42, ticks=38, legend=32); all text clearly readable
• VQ-02: No Overlap (6/6) — No text collisions; legend, tick labels, and axis titles all clear
• VQ-03: Element Visibility (5/6) — Dots are appropriately sized with opacity=0.55; slight over-density in the 19–21 range where quartile groups overlap, but overall acceptable
• VQ-04: Color Accessibility (3/4) — Monochromatic blue palette is colorblind-safe (no red-green), but the 4 shades are close in hue and may be hard to distinguish; sequential lightness helps somewhat
• VQ-05: Layout & Canvas (4/4) — Plot fills the canvas well with appropriate margins; bottom legend is clean
• VQ-06: Axis Labels & Title (2/2) — "y(t)" and "y(t+1)" are standard lag-plot notation; title includes k and r parameters

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) — Custom monochromatic blue palette with intentional temporal progression is coherent, but still reads as a well-configured default rather than a distinctive design
• DE-02: Visual Refinement (3/6) — Subtle dotted grid (guide_stroke_color=#e0e0e0) and slightly off-white background (#fafafa) show some refinement; no spine removal (N/A for pygal SVG) but minimal additional polish
• DE-03: Data Storytelling (3/6) — Temporal quartile coloring creates a visual narrative about time evolution; r=0.75 in the title quantifies the pattern; reference line is effective as a guide — but no within-plot annotation or additional emphasis highlights the autocorrelation insight

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct lag plot as XY scatter with y(t) vs y(t+lag)
• SC-02: Required Features (4/4) — Configurable lag (k=1), diagonal reference line (y=x), temporal color coding, r value included in title
• SC-03: Data Mapping (3/3) — X=y(t), Y=y(t+1) correctly assigned; full data range displayed
• SC-04: Title & Legend (3/3) — Title contains "scatter-lag · pygal · pyplots.ai"; legend labels match the 4 quartile series + reference line

Data Quality (13/15)

• DQ-01: Feature Coverage (5/6) — Shows positive autocorrelation pattern clearly; temporal structure via quartile coloring; but only one autocorrelation regime shown (all strongly positive)
• DQ-02: Realistic Context (4/5) — Synthetic AR(1) process with values in plausible range (~16–25); "Days 1–100" labels are clear temporal framing; neutral scenario
• DQ-03: Appropriate Scale (4/4) — Values 16–25 with phi=0.78 produce realistic temperature-like autocorrelation

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data → style → chart → save; no functions or classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set
• CQ-03: Clean Imports (2/2) — Only numpy, pygal, and pygal.style.Style; all used
• CQ-04: Code Elegance (2/2) — Clean list comprehensions for quartile splitting; no over-engineering or fake UI
• CQ-05: Output & API (1/1) — Saves as plot.png; current pygal API used

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Uses pygal.XY() with stroke=False for scatter, stroke_style for the reference line, legend_at_bottom_columns, and value formatters; good pygal patterns throughout
• LM-02: Distinctive Features (2/5) — SVG stroke_style with dasharray for the reference line is pygal-specific; but overall usage is fairly standard; pygal's distinctive SVG/tooltip capabilities are disabled (js=[]) without substituting other unique features

Score Caps Applied

• None applied (DE-01=4 > 2, DE-02=3 > 2; no other caps triggered)

Strengths

• Perfect spec compliance: all required features (reference line, temporal coloring, r value, configurable lag) are present and correct
• All font sizes are explicitly set and well-proportioned for the 4800×2700 canvas
• Coherent monochromatic blue palette with temporal quartile progression tells a clear time-evolution story
• Clean, reproducible KISS code with proper seed and linear structure
• Subtle dotted grid lines and light background improve readability without visual clutter

Weaknesses

• Design excellence is the main gap (10/20): the blue palette is intentional but not sophisticated enough to feel publication-ready
• LM-02 is low: pygal's distinctive SVG-based stroke and styling capabilities aren't exploited beyond the dashed reference line; the js=[] disables interactivity without adding compensating visual distinctiveness
• Only one autocorrelation regime shown: data doesn't contrast positive-autocorrelation behavior against near-random noise

Issues Found

1. DE-01 MODERATE: Palette is all-blue sequential — visually coherent but not distinctive. Fix: introduce a complementary accent color (e.g., use a warm color for the reference line instead of gray), or use a more varied but still harmonious palette for the quartile groups (e.g., blue→teal→green→amber)
2. DE-02 MODERATE: Beyond the subtle grid, there is little additional polish. Fix: tighten margins, add a more distinctive plot border treatment, or increase contrast between plot background and outer background
3. DE-03 MODERATE: The r=0.75 value is only in the title. Fix: add a text annotation inside the plot area (e.g., near the reference line) noting the autocorrelation strength, or vary dot size slightly by quartile to create additional emphasis
4. LM-02 LOW: js=[] disables pygal's interactive SVG tooltips. Fix: leverage pygal's unique stroke_style options more creatively (e.g., custom stroke-dasharray patterns, or use pygal's native opacity_hover for the HTML export), or add pygal-specific configuration that no other library would offer

AI Feedback for Next Attempt

Improve design excellence: introduce a second accent color (warm tone) for the reference line or reference annotation to break the all-blue monochrome; add an in-plot text annotation for the r value near the y=x line rather than only in the title; tighten margins for a more polished feel (reduce margin_right and margin_top slightly, increase left margin to give y-axis label more breathing room). For library mastery, leverage pygal's stroke_style more creatively on multiple elements, or use the HTML export as a genuine feature rather than a side effect. Consider using a more varied palette (e.g., blue→teal→amber→coral) so the temporal quartiles are immediately distinguishable even without the legend.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

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

The plot shows a lag scatter plot on a light beige (#f8f7f5) background. The title reads "Lag Plot (k=1, r=0.75) · scatter-lag · pygal · pyplots.ai" centered at the top. The X-axis is labeled "y(t)" and the Y-axis "y(t+1)". Four temporal quartile series are plotted: Days 1–100 in terracotta (brick-red), Days 101–200 in amber/golden-yellow, Days 201–300 in teal/seafoam, and Days 301–399 in deep navy. A dashed gray diagonal reference line (y = x ±1σ) runs from lower-left to upper-right. Subtle dotted grid lines are visible across the plot. The legend sits at the bottom with five labeled entries. A clear positive linear autocorrelation pattern is evident — data points cluster diagonally, confirming the AR(1) process (r=0.75).

Score: 87/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	8	10
Total	87	100

Visual Quality (25/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set (title=56, labels=42, major=38, legend=32, value=28); fully readable at 4800×2700 px
• VQ-02: No Overlap (5/6) — Text elements are clear; minor dot overlap in the dense central cluster is expected for 400 points
• VQ-03: Element Visibility (4/6) — dots_size=8–9 for 400 points causes noticeable overplotting in the center; guidelines suggest size 20–50 with alpha 0.3–0.5 for 300+ points
• VQ-04: Color Accessibility (3/4) — Terracotta (#c25a3c) and amber (#d4a028) may be confused by deuteranopic users; rest of palette is good
• VQ-05: Layout & Canvas (3/4) — Good proportions; margins tuned; legend well-anchored at bottom
• VQ-06: Axis Labels & Title (2/2) — y(t) / y(t+1) are standard lag-plot notation; title is descriptive and complete

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Intentional warm-to-cool temporal palette (terracotta → amber → teal → navy) with custom beige background; clearly above defaults, but not quite publication-ready
• DE-02: Visual Refinement (4/6) — Subtle guide_stroke_color, beige plot background, legend columns, and margin tuning show real effort; limited further by pygal's API (no spine removal)
• DE-03: Data Storytelling (5/6) — Temporal color gradient is an effective storytelling device; correlation coefficient embedded in title; ±1σ envelope contextualises autocorrelation spread — viewer immediately grasps the structure

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct XY scatter for lag plot
• SC-02: Required Features (4/4) — Diagonal y=x reference line, temporal point coloring, r-value annotation in title, configurable lag; bonus ±1σ envelope
• SC-03: Data Mapping (3/3) — y(t) on X, y(t+lag) on Y, all data visible
• SC-04: Title & Legend (3/3) — Title includes spec-id, library, pyplots.ai, plus informative k and r values; legend labels match temporal quartiles

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Clear positive autocorrelation pattern demonstrated; temporal coloring reveals consistent structure across time; could include a mix of stronger/weaker autocorrelation for richer demonstration
• DQ-02: Realistic Context (5/5) — Daily temperature readings with AR(1) mean reversion (μ=20°C) is a natural, neutral scenario
• DQ-03: Appropriate Scale (4/4) — Values 16–25°C and phi=0.78 are realistic for daily temperatures

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean imports → data → plot → save pattern; no functions or classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set
• CQ-03: Clean Imports (2/2) — numpy, pygal, Style; all used
• CQ-04: Code Elegance (2/2) — Four separate list comprehensions for quartiles are verbose but clear; no fake functionality
• CQ-05: Output & API (1/1) — render_to_png('plot.png') is correct; HTML export is a pygal bonus

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) — dict-format data points with 'label' keys for SVG tooltips, stroke_style for line customisation, legend_at_bottom_columns — all idiomatic pygal
• LM-02: Distinctive Features (3/5) — Pygal-specific dict tooltip data and stroke_style dasharray are library-distinctive; could leverage pygal's interactive SVG rendering more explicitly (e.g., x_labels_major, custom dots_size per series)

Score Caps Applied

• None — no caps triggered

Strengths

• Intentional warm-to-cool temporal palette creates a compelling visual narrative of time progression within the autocorrelation structure
• All required spec features fully implemented plus value-adding ±1σ envelope
• Comprehensive explicit font sizing; perfect spec compliance (15/15) and code quality (10/10)

Weaknesses

• dots_size=8–9 causes overplotting for 400 data points; smaller dots with lower opacity would improve legibility
• Terracotta and amber colors risk confusion for red-green colorblind viewers

Issues Found

1. VQ-03 LOW: 400 points at dots_size=8–9 causes dense overplotting in the center cluster
   • Fix: Reduce dots_size to 5–6 and opacity to 0.40–0.50 to reduce overlap while maintaining temporal readability
2. VQ-04 MINOR: Terracotta (#c25a3c) and amber (#d4a028) may be confused by deuteranopic users
   • Fix: Shift the early-time color toward coral-pink or orange-red to maximise separation from the amber mid-tone, or use a sequential palette (e.g., light-orange → dark-teal) that is fully colorblind-safe

AI Feedback for Next Attempt

Improve marker sizing for density: reduce dots_size to 5–6 and opacity to ~0.45 to reduce central overplotting while preserving the temporal color story. Adjust the terracotta/amber palette to be more colorblind-safe (e.g., shift Q1 toward a coral-pink or use a diverging sequential scheme). The design and storytelling are strong; these targeted tweaks to VQ-03/VQ-04 are the clearest path to 90+.

Verdict: REJECTED