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chore(letsplot): update quality score 87 and review feedback for scatter-lag
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‎plots/scatter-lag/implementations/letsplot.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: letsplot 4.9.0 | Python 3.14
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Quality: pending | Created: 2026-04-12
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Library: letsplot 4.9.0 | Python 3.14.3
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Quality: 87/100 | Created: 2026-04-12
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"""
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import numpy as np

‎plots/scatter-lag/metadata/letsplot.yaml‎

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# Per-library metadata for letsplot implementation of scatter-lag
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# Auto-generated by impl-generate.yml
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library: letsplot
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specification_id: scatter-lag
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created: '2026-04-12T18:09:58Z'
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updated: '2026-04-12T18:09:58Z'
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updated: '2026-04-12T18:15:02Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 24313009924
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issue: 5251
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python_version: 3.14.3
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library_version: 4.9.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/letsplot/plot.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/letsplot/plot.html
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quality_score: null
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quality_score: 87
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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: diagonal reference line, temporal color encoding, r
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annotation, and configurable lag all implemented correctly'
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- 'Strong data quality: realistic AR(1) temperature simulation with phi=0.85 shows
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clear autocorrelation pattern in plausible range'
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- 'Excellent code quality: clean KISS structure, reproducible, fully idiomatic letsplot
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grammar-of-graphics pattern'
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- 'Good data storytelling: temporal gradient plus r annotation effectively communicates
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autocorrelation structure to the viewer'
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weaknesses:
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- 'DE-01: Design is clean but not publication-quality; needs more intentional visual
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hierarchy beyond temporal gradient'
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- 'VQ-04: Custom blue-orange gradient is not a standard colorblind-safe palette;
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viridis or plasma preferred for perceptual uniformity'
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- 'VQ-03: Alpha=0.6 slightly high for 399 points; central cluster has visual density;
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reduce to 0.4-0.5'
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- 'LM-02: layer_tooltips() underutilized; could leverage more letsplot-distinctive
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capabilities like geom_smooth'
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image_description: The plot shows a lag-1 scatter plot of an AR(1) simulated daily
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temperature time series (~399 points). Points are colored with a blue (#306998)
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to orange (#E3882D) gradient encoding temporal order — early days are dark blue,
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later days warm orange. The x-axis is labeled "y(t)" and the y-axis "y(t + 1)".
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A dashed light-gray diagonal reference line (y = x) spans the entire plot area.
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A large monospace "r = 0.83" annotation appears in the lower-right corner. The
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title reads "scatter-lag · letsplot · pyplots.ai" in bold. A "Day" gradient legend
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is displayed on the right. The plot uses a minimal theme with very subtle gray
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grid lines (major only), no minor grid. An italic gray caption at the bottom reads
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"AR(1) simulated daily temperature · dashed line = y(t+1) = y(t)". The overall
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layout is 16:9, clean, and professional.
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criteria_checklist:
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visual_quality:
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score: 27
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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=24, axis titles=20, axis text=16,
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legend=14'
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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 element overlap; r annotation well-placed in lower right
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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: Points visible but size=4 slightly large for 399 points; alpha=0.6
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slightly high causing density in center
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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: Blue-orange gradient not red-green so accessible, but not a standard
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perceptually-uniform colormap like viridis
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- id: VQ-05
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name: Layout & Canvas
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score: 3
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max: 4
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passed: true
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comment: Good 16:9 proportions with balanced legend placement; minor whitespace
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on margins
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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 descriptive standard lag plot notation
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design_excellence:
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score: 12
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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 blue-orange gradient and monospace annotation show design
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intent; fundamentally a well-configured minimal default, not publication-ready
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- id: DE-02
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name: Visual Refinement
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score: 4
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max: 6
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passed: true
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comment: Subtle grid (E8E8E8, size=0.35), minor grid removed, custom tick
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colors and margins; good attention to detail
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- id: DE-03
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name: Data Storytelling
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score: 4
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max: 6
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passed: true
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comment: Temporal color encoding reveals time series movement through scatter;
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r=0.83 annotation immediately communicates autocorrelation strength
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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: scatter of 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: Diagonal reference line, color by time index, r correlation annotation,
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configurable lag all implemented
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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=value_t (y(t)), y=value_t_lag (y(t+1)); correct mapping
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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: scatter-lag · letsplot · pyplots.ai exact format; Day gradient
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legend correctly labeled'
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data_quality:
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score: 15
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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: 6
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max: 6
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passed: true
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comment: Shows strong positive autocorrelation, temporal structure via color,
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reference diagonal, quantified r value
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: AR(1) simulated daily temperature; plausible real-world scenario;
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non-controversial
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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: Temperature range ~12-32°C realistic for daily temp; phi=0.85 gives
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realistic 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: 'Clean linear flow: imports, data, lag construction, r calc, ref
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line, plot, save; no functions 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: All imports used; noqa comments appropriate for star import pattern
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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: Pythonic, appropriate complexity; AR(1) loop is clearest way to generate
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the process
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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 via export_ggsave with scale=3
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library_mastery:
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score: 8
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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: 5
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max: 5
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passed: true
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comment: 'Expert grammar-of-graphics usage: ggplot/geom/aes/labs/theme composition,
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ggsize for sizing, scale_color_gradient — fully idiomatic'
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- id: LM-02
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name: Distinctive Features
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score: 3
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max: 5
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passed: true
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comment: Uses layer_tooltips() (letsplot-specific interactive tooltips) and
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HTML export; good but could leverage more letsplot-specific capabilities
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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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- annotations
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- layer-composition
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- hover-tooltips
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- html-export
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patterns:
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- data-generation
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dataprep: []
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styling:
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- alpha-blending
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- custom-colormap

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