feat(bokeh): implement timeseries-decomposition

[github-actions]

Implementation: timeseries-decomposition - python/bokeh

Implements the python/bokeh version of timeseries-decomposition.

File: plots/timeseries-decomposition/implementations/python/bokeh.py

Parent Issue: #2992

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The image shows four vertically stacked panels on a warm off-white (#FAF8F1) background with the title "timeseries-decomposition · matplotlib · anyplot.ai" (NOTE: Should be bokeh, not matplotlib). Each panel displays a component of time series decomposition with distinct y-axis labels. The Original Series panel (top) shows fluctuating data in green (#009E73), the Trend panel shows an upward-sloping orange line, the Seasonal panel shows oscillating blue pattern, and the Residual panel (bottom) shows pink/magenta noise. All text is readable. However, the colors do not match the code specification: bokeh.py lines 113, 117, 121, 127 all specify color=BRAND (#009E73 green), yet the render shows different colors for each component. This discrepancy suggests either the bokeh code did not execute or the wrong images were provided.

Dark render (plot-dark.png): Shows the same four panels on a warm near-black (#1A1A17) background. Again titled "timeseries-decomposition · matplotlib" (wrong library). The data colors (green, orange, blue, magenta) are identical to the light render (correct for theme consistency). Text is light and readable against the dark background. However, the same color mismatch issue persists: code specifies all lines should be the same green (#009E73), but the render shows different colors for each component. Both renders have readable chrome and appropriate theme colors, but the library name and color rendering are fundamentally incorrect.

Score: 55/100

Category	Score	Max
Visual Quality	24	30
Design Excellence	9	20
Spec Compliance	8	15
Data Quality	15	15
Code Quality	8	10
Library Mastery	5	10
Total	55	100

Visual Quality (24/30)

• VQ-01: Text Legibility (7/8) - All text readable, though bokeh font sizing could be more explicit
• VQ-02: No Overlap (6/6) - No overlapping text elements
• VQ-03: Element Visibility (6/6) - Lines clearly visible
• VQ-04: Color Accessibility (2/2) - Adequate contrast between elements
• VQ-05: Layout & Canvas (3/4) - Good panel stacking, minor whitespace improvements possible
• VQ-06: Axis Labels & Title (2/2) - Descriptive component labels with units
• VQ-07: Palette Compliance (0/2) - CRITICAL FAILURE: Title says "matplotlib" not "bokeh"; bokeh.py specifies all lines use BRAND (#009E73) but render shows green, orange, blue, magenta for respective panels. This is either a code bug or wrong images provided.

Design Excellence (9/20)

• DE-01: Aesthetic Sophistication (4/8) - Generic library defaults, no custom design choices
• DE-02: Visual Refinement (3/6) - Minimal customization beyond setup; grid and spacing adequate but not refined
• DE-03: Data Storytelling (2/6) - Data displayed but no visual hierarchy or emphasis; each component treated equally

Spec Compliance (8/15)

• SC-01: Plot Type (5/5) - Correct four-panel vertical decomposition structure
• SC-02: Required Features (3/4) - All four components present; color scheme doesn't match code
• SC-03: Data Mapping (3/3) - Dates and values correctly mapped
• SC-04: Title & Legend (0/3) - CRITICAL FAILURE: Title format is incorrect. Should be "timeseries-decomposition · bokeh · anyplot.ai" but images show "... · matplotlib · ...". Spec requires exact format {spec-id} · {library} · anyplot.ai

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows all four decomposition components clearly
• DQ-02: Realistic Context (5/5) - Monthly airline passenger data is realistic and well-contextualized
• DQ-03: Appropriate Scale (4/4) - Passenger counts, trend, seasonality, and residuals all have sensible ranges

Code Quality (8/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → figures → save
• CQ-02: Reproducibility (2/2) - np.random.seed(42) ensures deterministic output
• CQ-03: Clean Imports (2/2) - All imports are used (numpy, pandas, bokeh, statsmodels, selenium)
• CQ-04: Code Elegance (1/2) - Issue: Code specifies colors that don't appear in output; implementation doesn't produce expected result
• CQ-05: Output & API (1/1) - Correct file naming (plot-{THEME}.png, plot-{THEME}.html)

Library Mastery (5/10)

• LM-01: Idiomatic Usage (3/5) - Uses bokeh API correctly (figure, ColumnDataSource, column layout, Selenium export)
• LM-02: Distinctive Features (2/5) - Generic bokeh usage; could leverage interactive tooltips, crossfilter, or theme system more explicitly

Score Caps Applied

• SC-04 = 0 (wrong library name in title) - Reduces credibility
• VQ-07 = 0 (colors don't match code specification) - Either code bug or wrong images

Strengths

• Time series decomposition structure is correct (four stacked panels with shared x-axis)
• Data is realistic and demonstrates the decomposition concept well
• Theme tokens are properly configured for both light and dark modes
• Code is clean, deterministic, and uses appropriate libraries

Weaknesses

• CRITICAL: Title says "matplotlib" not "bokeh" — rendering output doesn't match bokeh implementation
• CRITICAL: Bokeh.py code specifies color=BRAND (#009E73) for all four line components, but rendered image shows different colors (green, orange, blue, magenta). This is a code-output mismatch indicating either:
  1. Bokeh code did not execute properly
  2. Wrong images were provided (matplotlib render instead)
  3. Color parameter not being honored by bokeh rendering
• Lacks design polish: generic defaults, no custom color palette beyond brand color
• No visual hierarchy or emphasis to highlight patterns
• Text sizing relies on library defaults rather than explicit configuration

Issues Found

1. SC-04 CRITICAL: Wrong library name in output — shows "matplotlib" instead of "bokeh"
   • Fix: Ensure bokeh.py is executed and produces output with correct title format
2. VQ-07 CRITICAL: Color mismatch between code and rendered output
   • Code specifies all lines use BRAND (#009E73)
   • Output shows different colors per panel: green, orange, blue, magenta
   • Fix: Verify bokeh code execution; investigate why color parameter doesn't apply; consider if matplotlib output was used instead
3. DE-01 LOW: Generic aesthetic — no design sophistication beyond configured defaults
   • Fix: Consider intentional color palette per component (if design intent), refined typography, or visual emphasis

AI Feedback for Next Attempt

CRITICAL BLOCKER: The output appears to be from the matplotlib implementation, not bokeh. The title explicitly says "matplotlib" and the rendered colors don't match the bokeh.py code specification (all lines should be green #009E73, not varied colors).

Before repair: Verify that bokeh.py is being executed correctly. If the bokeh implementation cannot render properly, regenerate. If matplotlib output was incorrectly labeled as bokeh, ensure the correct bokeh render is used. Once bokeh output is confirmed (title says "bokeh" and colors match code), improve design excellence through intentional color choices, refined typography, and visual storytelling.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/4

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (): The plot displays four vertically stacked subplots on a warm off-white background (#FAF8F1). The Original Series shows airline passenger data with #009E73 (brand green), the Trend Component shows upward movement in #D55E00 (vermillion), the Seasonal Component shows regular oscillation in #0072B2 (blue), and the Residual Component shows noise patterns in #CC79A7 (reddish purple). All axis labels, tick marks, and subplot titles are clearly visible in dark text (#1A1A17 for titles, #4A4A44 for ticks). Grid lines are subtle and don't compete with data. The layout provides generous spacing between panels and clear Y-axis labels for each component. Legibility verdict: PASS — all text readable, proper contrast, colors match Okabe-Ito palette exactly.

Dark render (): The same four subplots render on a warm near-black background (#1A1A17). All data colors are identical to the light render (green, vermillion, blue, reddish purple in the same positions), confirming proper theme separation. Chrome elements (titles, labels, ticks, grid) use light text (#F0EFE8 for titles, #B8B7B0 for ticks) against the dark background. All axis labels and tick marks are clearly visible — no dark-on-dark text problems. Grid lines are appropriately subtle at reduced alpha. Legibility verdict: PASS — excellent theme adaptation with light text readable on dark surface, brand green (#009E73) maintains visibility on both surfaces.

Score: 86/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	10	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	86	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set; 26pt title, 20pt axis labels, 16pt ticks; fully readable in both themes with proper INK tokens
• VQ-02: No Overlap (6/6) — No text collisions; proper spacing between subplots; Y-axis labels clearly separated; all elements have breathing room
• VQ-03: Element Visibility (6/6) — Lines sized at 3px width; all data clearly distinguishable from backgrounds; density-appropriate rendering
• VQ-04: Color Accessibility (2/2) — Okabe-Ito palette used throughout (colorblind-safe); adequate contrast on both light and dark surfaces
• VQ-05: Layout & Canvas (4/4) — Good proportions (4800×2700 total, 4×650px panels); nothing cut off; proper min_border settings
• VQ-06: Axis Labels & Title (2/2) — Subplot titles descriptive; Y-axis labels include units; X-axis labeled on bottom panel
• VQ-07: Palette Compliance (2/2) — First series #009E73 ✓; Okabe-Ito order (1-4) ✓; backgrounds #FAF8F1/#1A1A17 ✓; both renders theme-correct ✓

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) - Uses Okabe-Ito palette appropriately and theme tokens correctly, but styling is functional rather than sophisticated. No custom visual flourishes beyond defaults.
• DE-02: Visual Refinement (3/6) - Subtle outline color (INK_SOFT), low alpha grids (0.12 y-axis, 0.08 x-axis), generous margins. Could be more refined with accent styling or visual hierarchy.
• DE-03: Data Storytelling (3/6) - Logical vertical arrangement guides decomposition flow, but no emphasis, annotations, or focal points to draw attention to key patterns.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct decomposition chart with 4 stacked subplots
• SC-02: Required Features (4/4) — Original, Trend, Seasonal, Residual all shown
• SC-03: Data Mapping (3/3) — Date on shared X-axis; component values on Y; all data visible in ranges
• SC-04: Title & Legend (3/3) — Clear subplot titles; no legend needed (single series per subplot); consistent labeling

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — All 4 components shown; complete decomposition via statsmodels
• DQ-02: Realistic Context (5/5) — Monthly airline passengers (classic, real-world); 10 years of data; realistic trend and seasonality
• DQ-03: Appropriate Scale (4/4) — 2018-2028 timeframe; ~100-250k passengers; ~30-unit seasonal amplitude; sensible domain values

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Helper function justified (used 4 times); no unnecessary classes or functions
• CQ-02: Reproducibility (2/2) — Seed set (np.random.seed(42)); deterministic generation
• CQ-03: Clean Imports (2/2) — All imports used: os, time, Path, numpy, pandas, bokeh, selenium, statsmodels
• CQ-04: Code Elegance (2/2) — No fake UI; appropriate complexity; clean patterns
• CQ-05: Output & API (1/1) — Saves as plot-{THEME}.png and .html; current API (Bokeh 3.9.0)

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Uses recommended bokeh patterns: figure(), ColumnDataSource, column() layout, datetime axis type, shared x_range for linked panning; high-level API
• LM-02: Distinctive Features (2/5) — Theme-adaptive styling via env vars, Selenium integration, shared x_range linking. Could explore HoverTool for interactive value inspection.

Score Caps Applied

• None — all thresholds passed

Strengths

• Perfect visual quality: all text readable in both themes with proper Okabe-Ito palette colors and excellent contrast
• Flawless spec compliance: all 4 decomposition components shown with correct data mapping
• Clean, reproducible code with proper theme token separation (light/dark)
• Effective use of shared x_range for linked axis panning across subplots
• Proper Okabe-Ito palette order (positions 1-4) with consistent application

Weaknesses

• Generic design: uses default styling without custom visual refinement or sophistication
• No visual hierarchy: all components treated equally without emphasis on key patterns
• No data annotations or callouts: missing opportunities to guide interpretation (e.g., highlighting notable peaks, trend slope changes, residual bounds)
• Missing interactive features: could use HoverTool to explore exact values at each timestamp

Issues Found

1. DE-01 LOW: Generic styling with default visual treatment. The plot is functionally correct but lacks design sophistication. Uniform line widths and colors don't create visual hierarchy.

   • Fix: Add subtle visual emphasis to key patterns (e.g., annotations for major peaks, trend inflection points); consider slightly different line weights for different components

2. DE-03 LOW: No visual storytelling or focal point. Subplots arranged logically but no visual guidance through the decomposition narrative.

   • Fix: Add subtle background shading for noteworthy regions, confidence bands for residuals, or use color intensity variation to create visual hierarchy among components

AI Feedback for Next Attempt

The bokeh implementation correctly renders the time series decomposition with proper theme handling and clean code. To improve the design excellence score: add subtle visual annotations or emphasis to highlight interesting patterns (trend inflection points, seasonal peaks, residual outliers); consider using slightly different visual weights or styles to create hierarchy; explore adding a HoverTool for interactive value exploration to leverage bokeh's interactive capabilities.

Verdict: APPROVED