feat(bokeh): implement bifurcation-basic

[github-actions]

Implementation: bifurcation-basic - bokeh

Implements the bokeh version of bifurcation-basic.

File: plots/bifurcation-basic/implementations/bokeh.py

Parent Issue: #4415

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a bifurcation diagram of the logistic map with the title "bifurcation-basic · bokeh · pyplots.ai" at the top in dark gray. The x-axis is labeled "Growth Rate (r)" ranging from 2.5 to 4.0, and the y-axis is "Steady-State Population (x)" ranging from 0 to 1. Data points are rendered in a muted blue (#306998) with very low alpha (0.12). The stable fixed-point branch is visible from r=2.5 to ~3.0 as a thin line, followed by period-doubling splits. Three red annotations mark key bifurcation points: "r ≈ 3.0", "r ≈ 3.449", and "r ≈ 3.57 (chaos)". The background is light gray (#FAFAFA) with very subtle grid lines. Axis lines and ticks are removed for a clean look. However, the chaotic region (r > 3.57) appears extremely sparse and faint — the characteristic dense fill of the chaotic band is barely visible, making the diagram look incomplete in its most important region.

Score: 83/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	6	10
Total	83	100

Visual Quality (26/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: 72pt title, 48pt axis labels, 36pt tick labels, 30pt annotations. All clearly readable.
• VQ-02: No Overlap (6/6) - No text overlap anywhere in the plot.
• VQ-03: Element Visibility (3/6) - The stable branch and early period-doubling are visible but thin. The chaotic region (r > 3.57) is extremely faint and sparse — the dense chaotic bands that are the hallmark of this diagram are barely perceptible. With only 2000 r-values and alpha=0.12, there isn't enough density to render the chaotic region properly.
• VQ-04: Color Accessibility (4/4) - Single blue color with red annotations; no colorblind issues.
• VQ-05: Layout & Canvas (3/4) - Good proportions overall, but some wasted vertical space below the data (y goes to 0 but most data is above 0.3).
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels with variable notation: "Growth Rate (r)" and "Steady-State Population (x)".

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) - Clean design with custom Python Blue, removed axis lines/ticks, subtle grid, light background. Above defaults but not publication-level polish.
• DE-02: Visual Refinement (4/6) - Good refinement: axis lines removed, ticks removed, grid at alpha=0.15, background customized, outline removed. Thoughtful attention to detail.
• DE-03: Data Storytelling (3/6) - Annotations guide the viewer through the period-doubling route, which is good. However, the "r ≈ 3.57 (chaos)" label is placed at y=0.10, far from the actual chaos onset, reducing its storytelling value. The faintness of the chaotic region undermines the narrative.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct bifurcation diagram using scatter plot.
• SC-02: Required Features (4/4) - Small point size with low alpha ✓, full period-doubling cascade ✓, logistic map ✓, labeled bifurcation points ✓, transient discard (200 iterations) ✓, parameter range 2.5–4.0 ✓.
• SC-03: Data Mapping (3/3) - X = growth rate (r), Y = steady-state population (x). Correct mapping.
• SC-04: Title & Legend (3/3) - Title follows exact format. No legend needed for single series.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows stable fixed point, period-2, period-4, and chaos onset. Periodic windows within chaos are structurally present but hard to see due to point sparsity.
• DQ-02: Realistic Context (5/5) - Logistic map is the canonical example for bifurcation diagrams. Perfect scientific context.
• DQ-03: Appropriate Scale (4/4) - r from 2.5 to 4.0, x from 0 to 1 — correct for the logistic map.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data generation → plot → styling → save. No functions or classes.
• CQ-02: Reproducibility (2/2) - Fully deterministic: logistic map with fixed x₀=0.5, no randomness.
• CQ-03: Clean Imports (2/2) - All imports used: numpy, bokeh.io, bokeh.models, bokeh.plotting, bokeh.resources.
• CQ-04: Code Elegance (2/2) - Clean, straightforward implementation appropriate for the task.
• CQ-05: Output & API (1/1) - Saves plot.png and plot.html with current Bokeh API.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) - Uses ColumnDataSource, figure, scatter, Label — all idiomatic Bokeh patterns. Could vectorize the computation more.
• LM-02: Distinctive Features (2/5) - Generates HTML export with CDN resources, which is Bokeh-distinctive. However, doesn't leverage Bokeh's interactive strengths like HoverTool for the HTML version, which would be the natural reason to use Bokeh for this plot.

Score Caps Applied

• None

Strengths

• Perfect spec compliance — all required features implemented correctly
• Clean, refined visual design with removed chrome and subtle grid
• Deterministic, well-structured code with proper transient discarding
• Correct title format and descriptive axis labels
• Dual output (PNG + HTML) leverages Bokeh's web-native nature

Weaknesses

• Chaotic region (r > 3.57) is too faint/sparse — the most visually striking part of a bifurcation diagram is nearly invisible
• "r ≈ 3.57 (chaos)" annotation placed at y=0.10, disconnected from the actual chaos onset region
• No HoverTool added for the HTML version — missed opportunity for Bokeh's signature interactivity
• Could increase n_r (e.g., 4000+) and/or adjust alpha to make the chaotic bands denser and more visible

Issues Found

1. VQ-03 LOW: Chaotic region barely visible due to insufficient point density and very low alpha
   • Fix: Increase n_r to 4000+ and/or raise alpha slightly (e.g., 0.18-0.25) to make the chaotic bands prominent
2. DE-03 MODERATE: Chaos annotation misplaced at y=0.10, disconnected from the actual data
   • Fix: Move "r ≈ 3.57 (chaos)" annotation closer to the chaotic band (y ≈ 0.5-0.7) or use an arrow pointing to the onset
3. LM-02 LOW: Bokeh's interactive features not leveraged
   • Fix: Add HoverTool to show r and x values on hover in the HTML version

AI Feedback for Next Attempt

Critical fix: increase point density in the chaotic region — raise n_r to 4000+ and increase alpha to ~0.20 so the dense chaotic bands become the visual focal point. Move the chaos annotation to where the chaos actually begins visually (y ≈ 0.5-0.7). Add a HoverTool for the HTML export to leverage Bokeh's interactive strengths. These changes will significantly improve element visibility, data storytelling, and library mastery.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays a bifurcation diagram of the logistic map with "Growth Rate (r)" on the x-axis ranging from 2.5 to 4.0 and "Steady-State Population (x)" on the y-axis from 0 to 1. The title reads "bifurcation-basic · bokeh · pyplots.ai" in dark gray at the top. Blue (#306998) scatter points with very low alpha (0.12) create a density-based visualization showing the classic period-doubling cascade: a single stable branch from r=2.5 to ~3.0, period-2 splitting around r=3.0, further doublings, and full chaos beyond r≈3.57. Three red annotation labels mark key bifurcation points: "r ≈ 3.0", "r ≈ 3.449", and "r ≈ 3.57 (chaos)". The background is light gray (#FAFAFA) with subtle grid lines (alpha=0.15). Axis lines and tick marks are removed for a clean look. Windows of periodicity within the chaotic regime are visible near r≈3.83.

Score: 86/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	5	10
Total	86	100

Visual Quality (28/30)

• VQ-01: Text Legibility (7/8) - Font sizes explicitly set (title 72pt, labels 48pt, ticks 36pt, annotations 30pt). Annotations slightly small relative to canvas.
• VQ-02: No Overlap (5/6) - The "r ≈ 3.449" and "r ≈ 3.57 (chaos)" annotations are positioned close together near the top-right, creating minor proximity issues.
• VQ-03: Element Visibility (6/6) - Excellent density adaptation with size=1 and alpha=0.12 for 200K points. The bifurcation structure is perfectly visible.
• VQ-04: Color Accessibility (4/4) - Single blue color with red annotations provides strong contrast. Colorblind-safe.
• VQ-05: Layout & Canvas (4/4) - Good canvas utilization, plot fills the space well with balanced margins.
• VQ-06: Axis Labels & Title (2/2) - "Growth Rate (r)" and "Steady-State Population (x)" are descriptive with parameter notation.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Above defaults: removed axis lines/ticks, custom background, Python Blue, red annotations for emphasis. Thoughtful but not yet publication-level polish.
• DE-02: Visual Refinement (4/6) - Good refinement with axis lines removed, ticks removed, subtle grid at alpha=0.15, light background. Could benefit from more typographic refinement and whitespace tuning.
• DE-03: Data Storytelling (4/6) - Annotations guide the viewer through the period-doubling cascade to chaos, creating a clear narrative flow through the diagram.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct bifurcation diagram using scatter points
• SC-02: Required Features (4/4) - All features present: logistic map, small points with low alpha, period-doubling cascade visible, key bifurcation points labeled, transient iterations discarded (200), subsequent values recorded (100)
• SC-03: Data Mapping (3/3) - r parameter on x-axis (2.5-4.0), state values on y-axis (0-1), correct mapping
• SC-04: Title & Legend (3/3) - Title format "bifurcation-basic · bokeh · pyplots.ai" correct. No legend needed for single-series.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows all aspects: period-1 stability, period-2 bifurcation, period-4, chaos, and windows of periodicity within chaotic regime
• DQ-02: Realistic Context (5/5) - Logistic map is the canonical example for bifurcation diagrams, real scientific context
• DQ-03: Appropriate Scale (4/4) - r from 2.5 to 4.0, x from 0 to 1, correct parameter ranges for the logistic map

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data generation → plot → styling → save
• CQ-02: Reproducibility (2/2) - Fully deterministic: x=0.5 start value, no random components
• CQ-03: Clean Imports (2/2) - All imports used (ColumnDataSource, Label, figure, export_png, save, Resources)
• CQ-04: Code Elegance (2/2) - Appropriate complexity for the logistic map iteration, clean and Pythonic
• CQ-05: Output & API (1/1) - Saves as plot.png and plot.html, current API usage

Library Mastery (5/10)

• LM-01: Idiomatic Usage (3/5) - Uses ColumnDataSource, figure, scatter, and Label correctly. Standard Bokeh patterns but nothing beyond the basics.
• LM-02: Distinctive Features (2/5) - HTML export with CDN resources is Bokeh-distinctive. However, Bokeh's key interactive features (HoverTool for showing r/x values, zoom tools) are not leveraged.

Score Caps Applied

• None

Strengths

• Excellent data quality showing the complete period-doubling cascade with windows of periodicity clearly visible
• Clean, minimal visual design with removed axis lines/ticks and subtle grid
• Annotations effectively label key bifurcation points, guiding the viewer
• Fully deterministic, clean code with appropriate density visualization parameters
• Perfect spec compliance — all required features implemented

Weaknesses

• Library mastery is limited — Bokeh's interactive capabilities (HoverTool, zoom, linked brushing) are untapped
• Annotation font size (30pt) could be larger for better readability on the 4800×2700 canvas
• The two upper annotations ("r ≈ 3.449" and "r ≈ 3.57 (chaos)") are positioned too close together
• Design polish could be elevated further with font family customization or a more refined color treatment

Issues Found

1. LM-02 LOW: Bokeh's interactive features are not utilized
   • Fix: Add HoverTool to show r and x values on hover, consider enabling wheel_zoom or box_zoom in toolbar
2. DE-01 MODERATE: Design is above defaults but not publication-quality
   • Fix: Consider a more refined annotation style (e.g., arrows pointing to bifurcation points), explore font family options
3. VQ-02 MINOR: Upper annotations too close together
   • Fix: Reposition "r ≈ 3.57 (chaos)" label to avoid proximity with "r ≈ 3.449"

AI Feedback for Next Attempt

To reach 90+: (1) Add Bokeh HoverTool to show r and x values — this is the most impactful improvement for library mastery. (2) Increase annotation font size to 36-40pt and reposition the upper annotations to avoid proximity issues. (3) Consider enabling some toolbar tools (wheel_zoom, box_zoom) that showcase Bokeh's interactive strengths. (4) For design polish, explore arrow annotations or vertical line markers at bifurcation points for a more refined look.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a bifurcation diagram of the logistic map with the x-axis labeled "Growth Rate (r)" spanning 2.5 to 4.0 and y-axis labeled "Steady-State Population (x)" spanning 0 to 1. The title reads "bifurcation-basic · bokeh · pyplots.ai" in dark text at top-left. Blue scatter points (#306998) with low alpha form the characteristic period-doubling cascade: a single stable branch from r=2.5 to ~3.0, splitting into period-2 branches, then period-4, and eventually into chaotic bands beyond r≈3.57. Three red (#AA3939) annotations mark key bifurcation points: "r ≈ 3.0 Period-2", "r ≈ 3.449", and "r ≈ 3.57 Onset of chaos". Faint red dashed vertical lines mark these transitions. The background is light gray (#FAFAFA) with subtle gridlines, no axis lines or tick marks, giving a clean minimal aesthetic. The chaotic region shows density-based structure with visible periodic windows.

Score: 88/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	13	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	88	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 72pt, axis labels 48pt, tick labels 36pt, annotations 36pt. All perfectly readable.
• VQ-02: No Overlap (6/6) — Annotations positioned at different y-levels (0.68, 0.92, 0.05) to avoid collision. No text overlap anywhere.
• VQ-03: Element Visibility (5/6) — Scatter points with size=1 and alpha=0.12 create effective density visualization for 200k points. The chaotic region could be slightly denser/more visible, but the period-doubling structure is clear.
• VQ-04: Color Accessibility (4/4) — Single blue series with red annotations. No colorblind issues.
• VQ-05: Layout & Canvas (3/4) — Good proportions at 4800×2700. Plot fills canvas well but the rightmost data near r=4.0 is slightly cramped against the edge.
• VQ-06: Axis Labels & Title (2/2) — "Growth Rate (r)" and "Steady-State Population (x)" are descriptive with parameter notation.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Thoughtful color choices (Python Blue + muted red annotations), Helvetica typography, custom background. Above configured defaults but not yet publication-level polish — the annotation at r≈3.449 lacks context text, and the overall palette could have more intentional hierarchy.
• DE-02: Visual Refinement (4/6) — Good refinement: axis lines removed, tick marks removed, subtle grid (alpha=0.12), clean #FAFAFA background, white border fill. Grid could be further refined (e.g., y-only or dotted style).
• DE-03: Data Storytelling (4/6) — Annotations and vertical dashed lines guide the viewer through the route to chaos. The period-doubling narrative is supported visually. Could be stronger with more context on the r≈3.449 annotation and perhaps color-coding different regimes.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct bifurcation diagram showing period-doubling cascade to chaos.
• SC-02: Required Features (3/4) — Small point size ✓, low alpha ✓, full cascade ✓, logistic map ✓, transient discard (200 iterations) ✓, r range 2.5–4.0 ✓. Minor: annotation at r≈3.449 doesn't label the bifurcation type (should indicate "Period-4"), and code internally mislabels it as "Period-8".
• SC-03: Data Mapping (3/3) — X=growth rate parameter r, Y=steady-state values x. Correct mapping, axes show full data range.
• SC-04: Title & Legend (3/3) — Title format "bifurcation-basic · bokeh · pyplots.ai" is correct. No legend needed for single-series scatter.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows complete period-doubling route: stable fixed point → period-2 → period-4 → chaos, with visible periodic windows in the chaotic regime.
• DQ-02: Realistic Context (5/5) — Logistic map is the canonical example for bifurcation diagrams, directly from nonlinear dynamics / chaos theory. Neutral scientific context.
• DQ-03: Appropriate Scale (4/4) — r from 2.5 to 4.0, x from 0 to 1. Mathematically correct range for the logistic map.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → plot → styling → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic: logistic map with fixed x₀=0.5, no random components.
• CQ-03: Clean Imports (2/2) — All imports used: numpy, bokeh.io (export_png, save), bokeh.models (ColumnDataSource, HoverTool, Label, Range1d, Span), bokeh.plotting (figure), bokeh.resources (Resources).
• CQ-04: Code Elegance (2/2) — Clean, appropriate complexity. Iteration loop is necessary for the logistic map computation. No over-engineering.
• CQ-05: Output & API (1/1) — Saves as plot.png via export_png() and plot.html via save(). Current Bokeh API.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) — Uses ColumnDataSource, figure(), scatter(), Label, Span — idiomatic Bokeh patterns. Could leverage more advanced features like color mapping or transforms.
• LM-02: Distinctive Features (4/5) — HoverTool with snap-to-data tooltips, HTML export with CDN resources, active_scroll="wheel_zoom" — these are distinctly Bokeh interactive features that wouldn't exist in static libraries.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Excellent data quality: logistic map correctly computed with proper transient discard, full cascade visible
• Clean visual design with removed axis lines, subtle grid, and well-placed annotations
• Good use of Bokeh-distinctive features (HoverTool, HTML export, interactive tools)
• Perfect code structure: deterministic, clean imports, KISS layout

Weaknesses

• Annotation at r≈3.449 lacks bifurcation type label (should say "Period-4"); code internally mislabels as "Period-8"
• Chaotic region could benefit from slightly higher point density or alpha to make the band structure more visually impactful
• Design excellence could be elevated with regime-based color coding or more intentional visual hierarchy

Issues Found

1. SC-02 MINOR: r≈3.449 annotation missing "Period-4" label; code comment says "Period-8" which is incorrect
   • Fix: Change annotation text to "r ≈ 3.449\nPeriod-4" and fix span tuple label
2. DE-01 MODERATE: Design is clean but could push further toward publication quality
   • Fix: Consider color-coding different dynamical regimes or adding a subtle gradient to distinguish stable vs chaotic regions

AI Feedback for Next Attempt

Solid implementation at 88/100. The main gap to 90+ is design excellence: consider color-coding different dynamical regimes (stable, periodic, chaotic) to create stronger visual storytelling, and fix the r≈3.449 annotation to label it as "Period-4". The chaotic band structure could also be slightly more visually prominent.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a classic logistic map bifurcation diagram with the title "bifurcation-basic · bokeh · pyplots.ai". The x-axis shows "Growth Rate (r)" from 2.5 to 4.0, and the y-axis shows "Steady-State Population (x)" from 0 to 1. From r=2.5 to ~3.0, a single dark blue stable fixed point line curves downward slightly then rises. At r≈3.0 the line splits into two branches (period-2, labeled in red). The branches continue in lighter blue, splitting again at r≈3.449 (period-4, labeled in red at top). Beyond r≈3.57 (labeled "Onset of chaos" at bottom), the diagram explodes into the characteristic chaotic fractal structure rendered in faint red/pink points. Dashed red vertical lines mark each bifurcation point. The background is light gray (#FAFAFA) with subtle grid lines. Typography uses Helvetica with axis lines and ticks removed for a clean look. The regime-based color transition from blue (stable) → purple (higher period) → red (chaotic) provides effective visual storytelling.

Score: 87/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	14	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	9	10
Library Mastery	7	10
Total	87	100

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: 72pt title, 48pt axis labels, 36pt tick labels, 36pt annotations. All perfectly readable.
• VQ-02: No Overlap (6/6) — Annotations well-spaced at different y-positions (0.68, 0.92, 0.05). No text collisions.
• VQ-03: Element Visibility (5/6) — Point size 1.5 appropriate for 450K points. Chaotic region (alpha=0.15) is slightly faint in places but structure remains visible.
• VQ-04: Color Accessibility (3/4) — Blue→purple→red scheme avoids red-green issue, but the very light red/pink (alpha=0.15) has low contrast against the light gray background.
• VQ-05: Layout & Canvas (3/4) — Good proportions overall. Data points at the far right edge (r≈4.0) appear slightly compressed near the boundary.
• VQ-06: Axis Labels & Title (2/2) — "Growth Rate (r)" and "Steady-State Population (x)" are descriptive with parameter notation.

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Regime-based color palette (blue→purple→red) is thoughtful and intentional. Helvetica typography, removed axis/tick lines, custom background. Clearly above defaults but not quite publication-perfect.
• DE-02: Visual Refinement (4/6) — Axis lines removed, tick lines removed, subtle grid (alpha=0.12), clean background. Good refinement but grid could be even more subtle or y-axis only.
• DE-03: Data Storytelling (4/6) — Regime coloring creates visual hierarchy showing transition from stability to chaos. Annotations mark key bifurcation points. The "Onset of chaos" annotation at y=0.05 feels disconnected from the visual action happening above.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct bifurcation diagram showing logistic map behavior.
• SC-02: Required Features (4/4) — Small point size ✓, low alpha ✓, full period-doubling cascade ✓, logistic map ✓, labeled bifurcation points ✓, transients discarded (300) ✓, range 2.5–4.0 ✓.
• SC-03: Data Mapping (3/3) — X=parameter r, Y=steady-state x. Correct mapping with proper ranges.
• SC-04: Title & Legend (3/3) — Title follows exact format "bifurcation-basic · bokeh · pyplots.ai". No legend needed for this continuous color-encoded single-variable plot.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows stable fixed point, period-2, period-4, chaotic regime, and windows of periodicity within chaos. Complete coverage.
• DQ-02: Realistic Context (5/5) — Logistic map is the canonical example for bifurcation diagrams. Appropriate scientific context with proper terminology.
• DQ-03: Appropriate Scale (4/4) — r ∈ [2.5, 4.0] and x ∈ [0, 1] are the mathematically correct ranges for the logistic map.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → plot → styling → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic: logistic map with fixed x₀=0.5, no random elements.
• CQ-03: Clean Imports (2/2) — All imports used: numpy, bokeh.io (export_png, save), bokeh.models (ColumnDataSource, HoverTool, Label, Range1d, Span), bokeh.plotting, bokeh.resources.
• CQ-04: Code Elegance (1/2) — Element-by-element loop for color/alpha assignment (lines 38-51) could be vectorized with np.where/np.select. Slightly verbose.
• CQ-05: Output & API (1/1) — Saves as plot.png via export_png. Also saves HTML. Current API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Proper use of ColumnDataSource, figure API, Label/Span models. Color passed via source column. Good Bokeh patterns but the color/alpha arrays could use Bokeh transforms.
• LM-02: Distinctive Features (3/5) — HoverTool with snap_to_data, HTML export with CDN resources, Span for vertical reference lines, active_scroll. These leverage Bokeh's interactive capabilities, though hover is only visible in HTML output.

Score Caps Applied

• None — No score caps triggered.

Strengths

• Perfect spec compliance with all required features (bifurcation points labeled, transients discarded, correct parameter range)
• Regime-based color encoding (blue→purple→red) effectively tells the chaos story
• Clean visual refinement with removed axis lines, subtle grid, and Helvetica typography
• Dual output (PNG + interactive HTML) leverages Bokeh's strengths
• Fully deterministic computation with appropriate resolution (3000 r-values × 150 states)

Weaknesses

• Chaotic region (red, alpha=0.15) has low contrast against light background — increasing to 0.20-0.25 would improve visibility
• "Onset of chaos" annotation placed at y=0.05 feels visually disconnected from the chaotic structure above
• Element-by-element color/alpha loop could be vectorized for cleaner code

Issues Found

1. VQ-04 LOW: Chaotic regime points too faint (alpha=0.15 on light background)
   • Fix: Increase chaotic alpha to 0.20-0.25 for better contrast
2. DE-03 MODERATE: "Onset of chaos" annotation placement at y=0.05 disconnected from visual action
   • Fix: Reposition to y≈0.50 or use an arrow pointing to the bifurcation region
3. CQ-04 LOW: Verbose per-element color assignment loop
   • Fix: Use np.select() or np.where() for vectorized color/alpha assignment

AI Feedback for Next Attempt

Solid implementation at 87/100. To reach 90+: (1) increase alpha for chaotic regime to 0.20-0.25 for better contrast, (2) reposition "Onset of chaos" annotation closer to the visual action or add an arrow, (3) vectorize color/alpha assignment with np.select for cleaner code. The regime-based coloring and clean styling are strong — these are minor refinements.

Verdict: REJECTED