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chore(plotly): update quality score 84 and review feedback for scatter-lag
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‎plots/scatter-lag/implementations/plotly.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: plotly | Python 3.13
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Quality: pending | Created: 2026-04-12
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Library: plotly 6.7.0 | Python 3.14.3
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Quality: 84/100 | Created: 2026-04-12
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"""
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import numpy as np

‎plots/scatter-lag/metadata/plotly.yaml‎

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# Per-library metadata for plotly implementation of scatter-lag
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# Auto-generated by impl-generate.yml
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library: plotly
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specification_id: scatter-lag
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created: '2026-04-12T18:10:48Z'
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updated: '2026-04-12T18:10:48Z'
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updated: '2026-04-12T18:15:42Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 24313009870
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issue: 5251
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python_version: 3.14.3
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library_version: 6.7.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/plotly/plot.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/plotly/plot.html
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quality_score: null
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quality_score: 84
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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 five required features (lag, reference line, time
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coloring, r annotation, configurable lag) implemented correctly'
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- 'Excellent code quality: clean KISS structure, reproducible, no extraneous imports'
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- Viridis colorscale choice is perceptually correct and colorblind-safe
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- Correlation annotation with styled box adds genuine quantitative value
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- Very subtle grid (rgba(0,0,0,0.08)) is a tasteful refinement above defaults
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weaknesses:
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- 'Design Excellence is the bottleneck: DE-01 at 4/8 means the visual design reads
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as a configured default rather than a crafted visualization'
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- No prominent visual hierarchy beyond the colorbar — the dashed line and annotation
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are helpful but the overall composition lacks a clear focal point
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- Axis labels use abstract mathematical notation (y(t)) rather than the domain variable
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(e.g., Temperature)
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- Marker size slightly large for 500 points causing some crowding in the dense center
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image_description: The plot shows a scatter lag plot for time series autocorrelation
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diagnosis on a white background. The x-axis is labeled "y(t)" and the y-axis is
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labeled "y(t + 1)". Five hundred scatter points are colored using the Viridis
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colorscale — dark purple for early time indices transitioning through teal and
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green to bright yellow for later ones. A gray dashed diagonal reference line (y
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= x) runs from the lower left to the upper right. A boxed annotation in the upper
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left corner reads "r = 0.833" with a white background and thin gray border. A
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colorbar on the right side is labeled "Time Index" with numeric ticks from 0 to
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~450. The points form a tight positive linear cluster along the diagonal, clearly
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demonstrating strong lag-1 autocorrelation. The title reads "scatter-lag · plotly
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· pyplots.ai" centered at the top.
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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 sizes explicitly set: title 28pt, axis labels 22pt, ticks 18pt,
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annotation 20pt, colorbar 18pt'
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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: All text elements clear; expected point overlap at 500 observations
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is acceptable
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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 clearly visible with opacity 0.7; size=10 causes slight crowding
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in the dense center cluster for 500 points
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- id: VQ-04
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name: Color Accessibility
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score: 4
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max: 4
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passed: true
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comment: Viridis is perceptually uniform and colorblind-safe
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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 proportions, 4800x2700px; colorbar creates minor right-side
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imbalance relative to plot width
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- id: VQ-06
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name: Axis Labels & Title
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score: 1
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max: 2
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passed: false
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comment: Conventional y(t)/y(t+1) notation is correct for lag plots but lacks
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domain context (e.g., Temperature (C) at t)
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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: Viridis scatter, subtle grid, styled annotation box are thoughtful
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choices, but overall feels like a well-configured library default rather
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than a crafted design
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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 (rgba(0,0,0,0.08)) and plotly_white template are genuine
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refinements; axis borders remain visible
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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: Time-index coloring plus r annotation quantify and frame the autocorrelation
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story; temporal structure in the color mixing is not dramatic
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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+k)'
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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: Lag=1 default, configurable via variable, diagonal reference line,
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time-index coloring, r annotation all present
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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), all 499 pairs plotted
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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 format correct; colorbar serves as the continuous color legend
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appropriately
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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: AR(1) with phi=0.85 clearly shows strong positive autocorrelation;
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temporal structure visible via coloring; missing contrast with a near-zero
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autocorrelation region
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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) temperature process is plausible and well-motivated;
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axes use generic y(t) notation rather than domain labels
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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 ranging ~15-26C is realistic for sensor monitoring
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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, 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: Only numpy and plotly.graph_objects, both fully 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 and Pythonic; no over-engineering or fake UI elements
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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 plot.png at 4800x2700px and plot.html; current API throughout
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library_features:
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score: 7
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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: Proper go.Figure/go.Scatter usage; hovertemplate and add_annotation
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are 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: Custom hovertemplate, colorbar with full configuration, and write_html
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for interactive export are genuinely Plotly-specific
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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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- colorbar
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- annotations
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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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- matrix-construction
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dataprep: []
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styling:
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- custom-colormap
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- alpha-blending
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- edge-highlighting

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