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chore(bokeh): update quality score 84 and review feedback for scatter-lag
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‎plots/scatter-lag/implementations/bokeh.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: bokeh | Python 3.13
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Quality: pending | Created: 2026-04-12
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Library: bokeh 3.9.0 | 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/bokeh.yaml‎

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# Per-library metadata for bokeh implementation of scatter-lag
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# Auto-generated by impl-generate.yml
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library: bokeh
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specification_id: scatter-lag
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created: '2026-04-12T18:12:37Z'
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updated: '2026-04-12T18:12:37Z'
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updated: '2026-04-12T18:17:04Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 24313009887
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issue: 5251
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python_version: 3.14.3
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library_version: 3.9.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/bokeh/plot.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-lag/bokeh/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: diagonal reference line, time-indexed color with Viridis,
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correlation coefficient annotation, and configurable lag=1 all implemented correctly'
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- Colorblind-safe Viridis colormap with proper ColorBar integration and ColumnDataSource
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— idiomatic Bokeh usage
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- 'Good visual refinement: subtle grid (alpha=0.2), outline removed, tick marks
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removed, white background'
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- All font sizes explicitly set and meet or exceed minimums (title 32pt, axis labels
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24pt, tick labels 20pt)
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- Clean KISS code structure with np.random.seed(42), no over-engineering, all imports
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used
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- HTML export with CDN resources adds interactive Bokeh output alongside the PNG
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weaknesses:
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- 'DE-01 (4/8): Aesthetic sophistication is limited to Viridis defaults — no custom
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typographic hierarchy, no distinctive color treatment for non-data elements, still
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looks like a configured library default rather than publication-ready'
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- 'DE-02 (4/6): Visual refinement is good but incomplete — the Bokeh frame/border
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area and padding could be tightened; a single-axis grid or no grid at all would
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be more refined'
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- 'VQ-03 (4/6): Marker size=20 is at the lower bound for 500 points; slightly larger
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markers (25-30) with lower alpha (0.5-0.6) would reduce crowding in the center
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and improve density rendering'
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- 'DE-03 (4/6): The correlation annotation and temporal color together tell a decent
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story, but there is no emphasis drawing the viewer''s eye to the core insight
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— a subtle regression trend line or shaded confidence band would strengthen the
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narrative'
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- 'DQ-02 (4/5): The AR(1) scenario is plausible but labels are generic (y(t), y(t+1))
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with no real-world context (e.g., ''Daily Returns'', ''Temperature''); adding
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a subtitle or data label would improve realism'
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image_description: The plot shows a lag-1 scatter plot (scatter-lag) of a synthetic
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AR(1) time series against its lagged version. The x-axis is labeled "y(t)" and
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the y-axis "y(t + 1)", with the title "scatter-lag · bokeh · pyplots.ai" displayed
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in the top-left corner. The 499 scatter points are colored using the Viridis colormap,
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transitioning from dark purple (early time index, near 0) through teal/green to
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bright yellow-green (late time index, near 499). A dashed gray diagonal reference
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line (y = x) runs through the center of the plot from lower-left to upper-right.
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A text annotation "r = 0.834" appears in the upper-left area of the data region.
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A vertical color bar labeled "Time Index" is positioned on the right side of the
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plot, showing the purple-to-yellow gradient with tick labels at 0, 100, 200, 300,
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400, 499. The scatter points form a strong diagonal linear cluster consistent
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with the high positive autocorrelation (φ=0.85) of the AR(1) process. The background
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is white, the grid lines are very subtle (light gray, low opacity), and tick marks
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are absent. The overall plot occupies roughly 60-65% of the canvas width, with
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the color bar taking some right-side space.
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criteria_checklist:
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visual_quality:
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score: 26
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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=32pt, axis labels=24pt, tick
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labels=20pt, colorbar title=22pt, correlation label=24pt — all meet or exceed
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minimums'
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- id: VQ-02
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name: No Overlap
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score: 5
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max: 6
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passed: true
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comment: Some point overlap in center of distribution with 500 points, but
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text elements don't overlap and the pattern remains readable
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- id: VQ-03
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name: Element Visibility
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score: 4
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max: 6
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passed: true
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comment: 'size=20 is at the lower bound for 300+ points (guideline: 20-50);
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dots visible but slightly small for the 4800x2700 canvas'
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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: Viridis256 is perceptually uniform and colorblind-safe; excellent
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choice
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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 layout with plot filling ~60-65% of canvas; colorbar on right
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is well-integrated but some top whitespace could be reduced
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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 and unambiguous for a lag plot;
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title follows required format
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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: Well-configured default using Viridis palette; clean but no distinctive
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design language, typographic hierarchy, or publication-level polish beyond
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good color choice
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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: Grid alpha set to 0.2 (subtle), outline removed, tick marks removed
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— good but frame padding and border could be tightened further
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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 gradient + diagonal reference line + r-value annotation
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together communicate autocorrelation effectively, but no emphasis element
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guides the eye to the core 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 scatter plot: y(t) on x-axis vs y(t+1) on y-axis'
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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: 'All spec features present: diagonal reference line, time-indexed
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color (Viridis), correlation coefficient annotation (r=0.834), configurable
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lag variable'
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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 (current values), Y=y_t_lag (lagged values) — correctly mapped
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per spec
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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 · bokeh · pyplots.ai' matches required format;
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ColorBar labeled 'Time Index' correctly represents the color encoding
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data_quality:
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score: 14
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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: AR(1) with phi=0.85 shows strong positive autocorrelation, clear
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diagonal cluster, temporal evolution visible through color — covers all
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diagnostic aspects of a lag plot
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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: AR(1) process is a plausible time series; however axis labels are
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generic (y(t)/y(t+1)) with no real-world context (e.g., financial returns,
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temperature)
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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 range ~-5 to 6 for standardized AR(1) process — appropriate
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and realistic
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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: 'Clear linear structure: imports → data generation → plot setup →
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styling → save'
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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 at the start
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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 7 import groups are actually used in the code
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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, Pythonic code; appropriate complexity for the visualization;
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no 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 via export_png; current Bokeh API used throughout
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library_mastery:
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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: 'Good idiomatic Bokeh: ColumnDataSource, linear_cmap transform, figure()
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API, model-based ColorBar and Label — slightly below expert level'
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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 Bokeh-distinctive features: linear_cmap with ColumnDataSource
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for continuous color mapping, ColorBar model, HTML export with CDN — above
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generic but not fully leveraging Bokeh''s interactive 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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- colorbar
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- annotations
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- html-export
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patterns:
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- data-generation
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- columndatasource
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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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- grid-styling

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