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chore(pygal): update quality score 82 and review feedback for scatter-lag
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‎plots/scatter-lag/implementations/pygal.py‎

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"""pyplots.ai
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""" pyplots.ai
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scatter-lag: Lag Plot for Time Series Autocorrelation Diagnosis
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Library: pygal | Python 3.13
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Quality: pending | Created: 2026-04-12
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Library: pygal 3.1.0 | Python 3.14.3
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Quality: 82/100 | Created: 2026-04-12
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"""
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import numpy as np

‎plots/scatter-lag/metadata/pygal.yaml‎

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# Per-library metadata for pygal implementation of scatter-lag
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# Auto-generated by impl-generate.yml
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library: pygal
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specification_id: scatter-lag
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created: '2026-04-12T18:12:36Z'
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updated: '2026-04-12T18:12:36Z'
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updated: '2026-04-12T18:18:29Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 24313009875
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issue: 5251
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python_version: 3.14.3
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library_version: 3.1.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/pygal/plot.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/pygal/plot.html
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quality_score: null
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quality_score: 82
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review:
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strengths: []
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weaknesses: []
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strengths:
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- 'Perfect spec compliance: all required features (reference line, temporal coloring,
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r value, configurable lag) are present and correct'
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- All font sizes explicitly set and well-proportioned for the 4800x2700 canvas
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- Coherent monochromatic blue palette with temporal quartile progression tells a
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clear time-evolution story
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- Clean reproducible KISS code with proper seed and linear structure
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- Subtle dotted grid lines and light background improve readability without visual
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clutter
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weaknesses:
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- 'Design excellence is the main gap (10/20): the all-blue sequential palette is
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intentional but not sophisticated enough for publication quality'
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- 'LM-02 is low: pygal SVG-specific capabilities are underutilized; js=[] disables
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interactivity without adding compensating visual distinctiveness'
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- 'Only one autocorrelation regime shown: data does not contrast positive-autocorrelation
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against near-random noise to demonstrate the diagnostic value of lag plots'
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image_description: The plot is a scatter lag plot rendered using pygal's XY chart
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type. The title reads "Lag Plot (k=1, r=0.75) · scatter-lag · pygal · pyplots.ai".
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The x-axis is labeled "y(t)" and the y-axis "y(t+1)", with tick values ranging
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from approximately 16.5 to 24.5 on both axes. Four series of dots represent temporal
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quartiles, colored from dark navy blue (Days 1-100) through medium blue (Days
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101-200) and steel blue (Days 201-300) to very light blue (Days 301-399). A dashed
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gray diagonal reference line (y = x) runs from the bottom-left to the top-right
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of the plot. The background is very light gray (#fafafa) with subtle dotted grid
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lines. The legend is positioned at the bottom in a single row with 5 items. The
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scatter points cluster along the diagonal in an elongated ellipse, clearly showing
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positive autocorrelation (r=0.75) consistent with an AR(1) process. The four quartile
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groups overlap substantially in the 19-21 range of both axes, which is expected
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for this data distribution.
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criteria_checklist:
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visual_quality:
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score: 28
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 8
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max: 8
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passed: true
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comment: 'All font sizes explicitly set: title=56, labels=42, major_label=38,
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legend=32; all text clearly readable at 4800x2700'
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- id: VQ-02
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name: No Overlap
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score: 6
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max: 6
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passed: true
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comment: No text collisions; legend, tick labels, and axis titles all clear
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- id: VQ-03
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name: Element Visibility
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score: 5
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max: 6
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passed: true
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comment: Dots appropriately sized with opacity=0.55; slight over-density in
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19-21 range where quartile groups overlap
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- id: VQ-04
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name: Color Accessibility
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score: 3
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max: 4
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passed: true
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comment: Monochromatic blue palette is colorblind-safe (no red-green), but
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4 shades are close in hue and may be hard to distinguish
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- id: VQ-05
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name: Layout & Canvas
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score: 4
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max: 4
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passed: true
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comment: Plot fills canvas well with appropriate margins; bottom legend is
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clean
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: y(t) and y(t+1) are standard lag-plot notation; title includes k
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and r parameters
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design_excellence:
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score: 10
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 4
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max: 8
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passed: false
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comment: Custom monochromatic blue palette with temporal progression is coherent
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but reads as a well-configured default rather than distinctive design
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- id: DE-02
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name: Visual Refinement
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score: 3
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max: 6
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passed: false
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comment: Subtle dotted grid and slightly off-white background show some refinement;
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minimal additional polish beyond that
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- id: DE-03
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name: Data Storytelling
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score: 3
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max: 6
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passed: false
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comment: Temporal quartile coloring creates a narrative; r value in title
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quantifies the pattern; but no in-plot annotation or additional emphasis
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on the autocorrelation insight
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: Correct lag plot as XY scatter with y(t) vs y(t+lag)
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: Configurable lag (k=1), diagonal reference line (y=x), temporal color
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coding by quartile, r value in title
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: X=y(t), Y=y(t+1) correctly assigned; full data range displayed
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: Title contains scatter-lag · pygal · pyplots.ai; legend labels match
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4 quartile series + reference line
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data_quality:
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score: 13
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 5
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max: 6
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passed: true
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comment: Shows positive autocorrelation pattern clearly with temporal structure;
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only one autocorrelation regime shown
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- id: DQ-02
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name: Realistic Context
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score: 4
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max: 5
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passed: true
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comment: Synthetic AR(1) process with plausible range (~16-25); Days 1-100
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labels are clear temporal framing; neutral scenario
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: Values 16-25 with phi=0.78 produce realistic temperature-like autocorrelation
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: 'Linear flow: imports -> data -> style -> chart -> save; no functions
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or classes'
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: np.random.seed(42) set
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: Only numpy, pygal, and pygal.style.Style; all used
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Clean list comprehensions for quartile splitting; no over-engineering
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or fake UI
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: Saves as plot.png; current pygal API used
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library_mastery:
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score: 6
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 4
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max: 5
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passed: true
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comment: Uses pygal.XY() with stroke=False for scatter, stroke_style for reference
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line, legend_at_bottom_columns, and value formatters; good pygal patterns
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- id: LM-02
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name: Distinctive Features
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score: 2
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max: 5
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passed: false
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comment: SVG stroke_style with dasharray is pygal-specific; but overall usage
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is standard; interactive SVG capabilities disabled (js=[]) without substituting
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other unique features
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verdict: REJECTED
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impl_tags:
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dependencies: []
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techniques:
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- html-export
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patterns:
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- data-generation
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- iteration-over-groups
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dataprep:
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- time-series
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styling:
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- alpha-blending

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