feat(altair): implement scatter-connected-temporal

[github-actions]

Implementation: scatter-connected-temporal - altair

Implements the altair version of scatter-connected-temporal.

File: plots/scatter-connected-temporal/implementations/altair.py

Parent Issue: #4675

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a connected scatter plot titled "scatter-connected-temporal · altair · pyplots.ai" with subtitle "Unemployment vs. Inflation — tracing the Phillips curve path (1994–2023)". The x-axis shows "Unemployment Rate (%)" ranging from ~2.5 to 8.5, and the y-axis shows "Inflation Rate (%)" from -1.5 to 7.0. Data points are connected by a semi-transparent blue line (#306998) in temporal order. Points are colored using a viridis gradient from dark purple (1994) to yellow (~2023), with a vertical color legend labeled "Year" on the right. Key years are annotated in bold text: 1994, 2000, 2008, 2010, 2015, and 2023. The path traces from higher unemployment/inflation in the 1990s through a recession excursion (2008-2010 cluster at high unemployment/low inflation) toward lower unemployment/inflation in recent years. The lower-left area is crowded with recent-year data points. Grid is subtle dashed lines, axis domains are removed, and the view border is removed.

Score: 84/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	13	20
Spec Compliance	14	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	7	10
Total	84	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — Font sizes explicitly set throughout (title=28, subtitle=16, axis labels=22, ticks=18, annotations=16, legend title=16). Legend tick labels at 14pt are slightly small but still readable.
• VQ-02: No Overlap (5/6) — Minor crowding in the lower-left cluster where recent years (2015-2023) bunch together. The "2023" and "2015" labels are near each other and close to overlapping data points, but remain readable.
• VQ-03: Element Visibility (5/6) — Point size=180 is appropriate for 30 data points. White stroke on markers aids visibility. However, the connecting line at opacity=0.5 with a single blue color is quite subtle and visually disconnected from the viridis-colored points.
• VQ-04: Color Accessibility (4/4) — Viridis colormap is colorblind-safe and perceptually uniform.
• VQ-05: Layout & Canvas (3/4) — Good overall layout. The y-axis extends to 7.0 but data peaks around 5.5, leaving unused vertical space. The lower portion of the chart area is underutilized.
• VQ-06: Axis Labels & Title (2/2) — "Unemployment Rate (%)" and "Inflation Rate (%)" are descriptive with units.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Above defaults: viridis gradient, custom axis colors (#333333/#555555), dashed grid, removed domains, white-stroked markers, informative subtitle. However, the single-color connecting line clashes with the multi-color points, preventing a fully cohesive design.
• DE-02: Visual Refinement (4/6) — Good refinements: removed domain lines, subtle dashed grid at 0.15 opacity, removed view border, custom tick/label colors. Generous whitespace. Some excess vertical space above data.
• DE-03: Data Storytelling (4/6) — Clear narrative through year annotations at key economic moments (1994 start, 2000 boom, 2008-2010 recession, 2015, 2023 end). The viridis gradient on points conveys temporal progression. Subtitle adds context. The 2008 recession excursion is visually apparent. Could strengthen emphasis on the recession cluster.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct connected scatter plot with temporal path ordering.
• SC-02: Required Features (3/4) — Points connected chronologically ✓, key time points annotated ✓, color gradient for temporal progression ✓, point markers visible ✓. However, the connecting line itself lacks color gradient encoding — only points show temporal color. Spec suggests "color gradient along the path" which is only partially achieved.
• SC-03: Data Mapping (3/3) — X=unemployment, Y=inflation, axes show all data with appropriate ranges.
• SC-04: Title & Legend (3/3) — Title follows exact format "scatter-connected-temporal · altair · pyplots.ai". Year legend with gradient bar is appropriate.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows Phillips curve dynamics with a clear recession spike (2008-2010), different economic regimes, cyclical movement, and directional trends. The lower-left cluster demonstrates convergence in recent years. Minor: all years are synthetic random walks rather than showing distinct economic phases.
• DQ-02: Realistic Context (5/5) — US unemployment vs. inflation 1994-2023 is a classic, neutral, real-world economic scenario. Perfect for demonstrating connected scatter temporal patterns.
• DQ-03: Appropriate Scale (4/4) — Unemployment 3-10.5% and inflation -0.5-6% are realistic ranges for the US economy over this period.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data generation → label filtering → layer construction → composition → save.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at the beginning.
• CQ-03: Clean Imports (2/2) — All three imports (altair, numpy, pandas) are used.
• CQ-04: Code Elegance (2/2) — Clean, well-organized layer composition. Appropriate complexity for the visualization. No over-engineering.
• CQ-05: Output & API (1/1) — Saves as plot.png with scale_factor=3.0 and plot.html. Current API usage.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of Altair's declarative grammar: layered composition with + operator, proper encoding types (Q), Scale/Axis configuration, configure_axis/configure_view chains. Uses alt.Order for path ordering. Could use more advanced encoding patterns.
• LM-02: Distinctive Features (3/5) — Leverages .interactive() for zoom/pan, rich tooltips with formatting, HTML export, and alt.Order for path control. These are Altair-distinctive features that wouldn't easily port to matplotlib.

Score Caps Applied

• None — no caps triggered (DE-01=5 > 2, no other cap conditions met)

Strengths

• Excellent data context: Phillips curve dynamics with recession modeling is a compelling, real-world narrative
• Clean code structure with perfect reproducibility and KISS pattern
• Good visual refinement: subtle grid, removed domains/borders, custom text colors
• Effective use of year annotations at economically meaningful time points
• Proper use of Altair's interactive features (tooltips, zoom/pan) and HTML export

Weaknesses

• The connecting line uses a single static color (#306998 at 0.5 opacity) instead of encoding temporal progression, creating a visual disconnect with the viridis-colored points
• Lower-left region is crowded with recent years (2015-2023) clustering together
• Y-axis extends to 7.0 but data peaks around 5.5, wasting vertical space
• The line's low opacity (0.5) makes the path hard to follow in dense areas

Issues Found

1. SC-02 PARTIAL: Connecting line lacks temporal color encoding — only points show viridis gradient
   • Fix: Use alt.Chart(df).mark_trail() or segment the line with color encoding to show temporal progression along the path itself
2. VQ-05 MINOR: Excess vertical space above data
   • Fix: Tighten y-axis domain to [-1.0, 5.5] or similar to better frame the actual data
3. VQ-02 MINOR: Crowding in lower-left cluster
   • Fix: Consider adjusting annotation offsets (dx/dy) for crowded labels, or use alt.condition to selectively position labels
4. VQ-03 MINOR: Connecting line too subtle
   • Fix: Increase line opacity or use mark_trail with size encoding to make the path more prominent

AI Feedback for Next Attempt

The main improvement needed is encoding temporal progression on the connecting path itself, not just the points. Consider using mark_trail() which supports both color and size encoding along the path — this would unify the visual language. Tighten the y-axis domain to reduce wasted vertical space. Adjust annotation positioning in the crowded lower-left region to prevent near-overlaps. The code quality and data context are excellent — focus improvements on the visual coherence between line and point colors.

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 connected scatter plot titled "scatter-connected-temporal · altair · pyplots.ai" with subtitle "Unemployment vs. Inflation — tracing the Phillips curve path (1994–2023)". The X-axis shows "Unemployment Rate (%)" ranging from ~2.6 to 8.4, and the Y-axis shows "Inflation Rate (%)" ranging from ~-1.5 to 5.5. Data points are connected by line segments in temporal order, colored with a viridis gradient (dark purple for early 1990s, teal/cyan for mid-2000s, yellow-green for recent years). Six key years are annotated in bold text: 1994, 2000, 2008, 2010, 2015, and 2023. The 2008-2010 recession is visible as a dramatic rightward swing to high unemployment (~7-8%) with low inflation. The path then curves back left toward lower unemployment in recent years. Grid lines are subtle dashed lines at 0.15 opacity. The domain/frame is removed for a clean look. However, the Year color legend defined in the code does not appear in the rendered PNG output.

Score: 85/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	15	20
Spec Compliance	13	15
Data Quality	14	15
Code Quality	9	10
Library Mastery	8	10
Total	85	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) - Font sizes explicitly set: title 28pt, subtitle 16pt, axis titles 22pt, tick labels 18pt, annotations 16pt. All readable, though subtitle is on the small side.
• VQ-02: No Overlap (5/6) - Minor crowding in the lower-left cluster (2015-2023 region) where annotations and points are close together, but still readable.
• VQ-03: Element Visibility (6/6) - Points at size=180 with white stroke are well-adapted for 30 data points. Line segments at strokeWidth=2.5 are clearly visible.
• VQ-04: Color Accessibility (4/4) - Viridis colormap is perceptually uniform and colorblind-safe.
• VQ-05: Layout & Canvas (2/4) - Plot fills the canvas well at 1600x900, but the Year color legend is missing from the rendered PNG despite being defined in code. This is a significant layout issue.
• VQ-06: Axis Labels & Title (2/2) - "Unemployment Rate (%)" and "Inflation Rate (%)" are descriptive with units.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Viridis gradient, white-stroked points, custom axis colors ([area-basic] plotnine implementation #333/feat(plotly): implement donut-labeled #555), dashed grid, domain removed, clean frame. Clearly above defaults with thoughtful color and typography choices.
• DE-02: Visual Refinement (5/6) - Domain/spines removed, grid at 0.15 opacity with dashes, configure_view strokeWidth=0, generous whitespace. Well-polished.
• DE-03: Data Storytelling (4/6) - The recession spike (2008-2010) creates a natural focal point. Key year annotations guide the viewer through the narrative. Subtitle provides context about the Phillips curve. Viridis gradient encodes temporal progression effectively.

Spec Compliance (13/15)

• SC-01: Plot Type (5/5) - Connected scatter plot with temporal path — correct chart type.
• SC-02: Required Features (4/4) - Points connected in chronological order, key time points annotated, color gradient indicates time direction, point markers visible at each position.
• SC-03: Data Mapping (3/3) - X=unemployment, Y=inflation correctly mapped. Axes show full data range with appropriate domains.
• SC-04: Title & Legend (1/3) - Title format "scatter-connected-temporal · altair · pyplots.ai" is correct. However, the Year color legend is not visible in the rendered output, making the color encoding unexplained to viewers.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows multiple regimes: stable 1990s, recession spike 2008-2010, recovery, recent low-unemployment era. Could show slightly more variation in mid-period.
• DQ-02: Realistic Context (5/5) - US unemployment vs. inflation (Phillips curve dynamics) is a classic, neutral economic dataset with real-world plausibility.
• DQ-03: Appropriate Scale (4/4) - Unemployment 3-10.5%, inflation -0.5 to 6% — realistic for US economic data over 30 years.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data generation → segments → chart layers → composition → save. No functions or classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports used: altair, numpy, pandas.
• CQ-04: Code Elegance (1/2) - Axis scale domains are duplicated between the lines layer (x/y with inline Scale) and points layer (x_enc/y_enc with Scale). Could be consolidated.
• CQ-05: Output & API (1/1) - Saves as plot.png with scale_factor=3.0 and plot.html. Current Altair 6.0 API.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) - Good use of Altair's declarative grammar: layer composition with +, encoding types, alt.Title with subtitle, configure_* methods, .interactive(). The segment approach using mark_rule with x2/y2 is pragmatic but not the most idiomatic Altair pattern.
• LM-02: Distinctive Features (4/5) - Leverages Altair-specific features: interactive pan/zoom, tooltips with formatted fields, HTML export, declarative layer composition, mark_rule for directed segments. These are distinctly Altair capabilities.

Score Caps Applied

• None — no score cap conditions triggered.

Strengths

• Excellent color gradient encoding with viridis showing temporal progression clearly
• Well-chosen annotations at key historical moments (1994, 2000, 2008, 2010, 2015, 2023)
• Strong data storytelling with the recession spike creating a natural focal point
• Clean visual refinement: no domain lines, subtle dashed grid, white-stroked points
• Realistic Phillips curve data with plausible economic values

Weaknesses

• Year color legend is not visible in the rendered PNG output — viewers cannot decode the color mapping
• Axis scale definitions are duplicated between the lines and points layers
• Minor annotation crowding in the lower-left cluster (2015-2023 area)

Issues Found

1. SC-04 LOW: Year color legend missing from rendered PNG output
   • Fix: Ensure the legend renders correctly — may need to adjust chart width/padding or legend positioning so it's not clipped during PNG export
2. CQ-04 LOW: Duplicated scale domain definitions
   • Fix: Share scale configuration between lines and points layers to avoid inconsistency risk
3. VQ-02 MINOR: Slight annotation crowding in lower-left
   • Fix: Add directional offsets (dx/dy) to labels in the 2015-2023 cluster to prevent overlap

AI Feedback for Next Attempt

Fix the missing Year legend in the PNG render — this is the most impactful issue. Consider adjusting chart padding or legend position to ensure it appears. Also consolidate duplicated axis scale definitions between the lines and points layers. Minor: spread out the year annotations in the crowded lower-left area with directional offsets.

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 connected scatter plot titled "scatter-connected-temporal · altair · pyplots.ai" with subtitle "Unemployment vs. Inflation — tracing the Phillips curve path (1994–2023)". The X-axis shows "Unemployment Rate (%)" ranging from approximately 2.6 to 8.4, and the Y-axis shows "Inflation Rate (%)" from about -1.5 to 5.5. Data points are connected by line segments in temporal order, with both points and connecting segments colored using the viridis colormap — dark purple for 1994, transitioning through teal/cyan for mid-2000s, to yellow-green for the 2020s. Six key years are annotated in bold dark text: 1994 (upper right area, ~6.1% unemployment, 2.6% inflation), 2000 (right side, ~6.8%, 3.7%), 2008 and 2010 (far right, showing the recession spike with high unemployment ~7-8% and low inflation), 2015 (lower left, ~3.2%, 0.9%), and 2023 (lower left cluster, ~3.3%, 0.4%). The recession excursion of 2008-2010 is the most visually dramatic feature, with the path swinging far right. The lower-left region is dense with recent-year data points clustered tightly. Grid lines are subtle dashed lines at low opacity. Axis domain lines and the view border are removed. Notably, the Year color legend defined in the code is NOT rendered in the PNG output — viewers cannot decode the viridis color mapping without it.

Score: 85/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	15	20
Spec Compliance	13	15
Data Quality	14	15
Code Quality	9	10
Library Mastery	8	10
Total	85	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — Font sizes explicitly set: title=28pt, subtitle=16pt, axis titles=22pt, tick labels=18pt, annotations=16pt, legend title=16pt, legend labels=15pt. All text is readable at full resolution. Subtitle is slightly small but adequate.
• VQ-02: No Overlap (5/6) — Minor crowding in the lower-left cluster where the 2015 and 2023 annotations sit close together among densely packed recent-year data points. All labels remain readable but proximity is not ideal.
• VQ-03: Element Visibility (6/6) — Point size=180 with white stroke (1.2px) is well-adapted for 30 data points. Line segments at strokeWidth=2.5 and opacity=0.75 are clearly visible with good color differentiation along the path.
• VQ-04: Color Accessibility (4/4) — Viridis colormap is perceptually uniform and colorblind-safe. Excellent choice.
• VQ-05: Layout & Canvas (2/4) — Plot area fills the canvas reasonably at 1600×900. However, the Year color legend is missing from the rendered PNG output despite being defined in code — this is a significant layout/rendering failure that leaves the color encoding unexplained.
• VQ-06: Axis Labels & Title (2/2) — "Unemployment Rate (%)" and "Inflation Rate (%)" are descriptive with units.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Clearly above defaults: viridis gradient on both segments and points creates visual coherence, custom axis text colors ([area-basic] plotnine implementation #333/feat(plotly): implement donut-labeled #555), dashed grid styling, removed domain lines, white-stroked markers, informative subtitle. Professional appearance.
• DE-02: Visual Refinement (5/6) — Domains removed, grid at 0.15 opacity with 3px dashes, view border removed (strokeWidth=0), custom tick/label colors, generous whitespace. Well-polished details.
• DE-03: Data Storytelling (4/6) — The 2008-2010 recession spike creates a natural focal point. Year annotations at economically meaningful moments guide the viewer through the Phillips curve narrative. Subtitle adds context. The viridis gradient effectively encodes temporal progression. The story is clear without being heavy-handed.

Spec Compliance (13/15)

• SC-01: Plot Type (5/5) — Correct connected scatter plot with temporal path ordering.
• SC-02: Required Features (4/4) — Points connected in chronological order ✓, key time points annotated with text labels ✓, color gradient along the path indicates time direction ✓ (now on both segments and points), point markers visible at each data position ✓.
• SC-03: Data Mapping (3/3) — X=unemployment, Y=inflation correctly mapped. Axes show full data range with manually set domains.
• SC-04: Title & Legend (1/3) — Title format "scatter-connected-temporal · altair · pyplots.ai" is correct. However, the Year color legend is not visible in the rendered PNG output, so viewers cannot decode what the viridis colors represent. This was flagged in attempt 2 and remains unfixed.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows multiple economic regimes: stable 1990s with higher unemployment, recession spike 2008-2010, recovery period, and recent low-unemployment convergence. The synthetic recession modeling adds realistic drama. Minor: mid-period transitions could show more distinct phases.
• DQ-02: Realistic Context (5/5) — US unemployment vs. inflation (Phillips curve dynamics) is a classic, neutral, real-world economic scenario. Perfect domain for demonstrating connected scatter temporal patterns.
• DQ-03: Appropriate Scale (4/4) — Unemployment 3-10.5%, inflation -0.5 to 6% are realistic ranges for US economic data over this period.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → data generation → segment construction → label filtering → shared encodings → layer construction → composition → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at the beginning.
• CQ-03: Clean Imports (2/2) — All three imports (altair, numpy, pandas) are used.
• CQ-04: Code Elegance (1/2) — Axis scale domains are duplicated between the lines layer (inline alt.X("x:Q", scale=...) / alt.Y("y:Q", scale=...)) and the shared x_enc/y_enc encodings used by the points layer. This redundancy could lead to inconsistencies.
• CQ-05: Output & API (1/1) — Saves as plot.png with scale_factor=3.0 and plot.html. Current Altair 6.0 API.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) — Good use of Altair's declarative grammar: layer composition with + operator, proper encoding types (:Q), alt.Scale/alt.Axis configuration, configure_axis/configure_view chains, alt.Title with subtitle. The mark_rule with x2/y2 for directed segments is a pragmatic approach.
• LM-02: Distinctive Features (4/5) — Leverages .interactive() for zoom/pan, rich tooltips with formatted fields, HTML export, and declarative layer composition. These are distinctly Altair capabilities not easily replicated elsewhere.

Score Caps Applied

• None — no score cap conditions triggered (DE-01=6 > 2, DE-02=5 > 2)

Strengths

• Viridis gradient now applied to both line segments AND points, creating visual coherence (improved from attempt 1)
• Compelling Phillips curve narrative with realistic recession modeling at 2008-2010
• Clean visual refinement: removed domains/borders, subtle dashed grid, custom axis colors
• Effective year annotations at economically meaningful time points
• Good use of Altair-specific features: interactive zoom, tooltips, HTML export

Weaknesses

• Year color legend is still missing from PNG output — unfixed from attempt 2
• Duplicated scale domain definitions between lines and points layers
• Lower-left annotation crowding (2015/2023 proximity)

Issues Found

1. SC-04 LOW: Year color legend not rendered in PNG output — viewers cannot decode the viridis color mapping
   • Fix: Investigate why the legend defined on the points layer is not appearing in the PNG export; may need to adjust chart padding or ensure legend is not clipped by the save dimensions
2. CQ-04 LOW: Duplicated scale domain specifications across layers
   • Fix: Consolidate the x/y scale definitions so the lines layer references the same shared encodings as the points layer
3. VQ-02 MINOR: Annotation crowding in lower-left cluster (2015, 2023)
   • Fix: Add directional dx offsets to separate the labels or use conditional positioning

AI Feedback for Next Attempt

The missing Year legend in the PNG render remains the most impactful unfixed issue from attempt 2 — the color encoding is unexplained to viewers. Consolidate the duplicated axis scale definitions between the lines (mark_rule) and points layers. Consider directional offsets for annotations in the crowded lower-left cluster.

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 connected scatter plot tracing the path of unemployment rate (%) vs. inflation rate (%) from 1994 to 2023. Points are colored using a viridis gradient from dark purple (1994) to yellow (2023), connected by a subtle gray line (opacity ~0.35) following temporal order. Six key years are annotated in bold text: 1994, 2000, 2008, 2010, 2015, and 2023. The x-axis spans approximately 2.6–8.4% unemployment and the y-axis from -1.5% to 5.5% inflation. A vertical viridis color legend labeled "Year" appears on the right. The title reads "scatter-connected-temporal · altair · pyplots.ai" with a subtitle "Unemployment vs. Inflation — tracing the Phillips curve path (1994–2023)". The view has no spines, a subtle dashed grid, and a clean white background. The 2008–2010 recession spike creates a dramatic rightward excursion, while later years cluster in the lower-left with low unemployment and low inflation.

Score: 87/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	7	10
Total	87	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 28, subtitle 16, axis titles 22, tick labels 18, annotations 16, legend 15–16). All readable; legend labels slightly smaller than ideal.
• VQ-02: No Overlap (5/6) — Lower-left cluster (2015–2023 era) is dense with labels and points close together. Nudging helps but the area is still somewhat crowded.
• VQ-03: Element Visibility (5/6) — Points (size=180) are well-sized for 30 data points with white stroke separation. Connecting path at opacity 0.35 is quite subtle and hard to follow in places, especially through the dense lower-left cluster.
• VQ-04: Color Accessibility (4/4) — Viridis colormap is perceptually uniform and colorblind-safe. Good contrast against white background.
• VQ-05: Layout & Canvas (3/4) — 4800×2700 px output. Chart fills canvas well. Y-axis range extends to -1.5 but data only reaches about -0.5, creating some unused space at the bottom.
• VQ-06: Axis Labels & Title (2/2) — "Unemployment Rate (%)" and "Inflation Rate (%)" — descriptive with units.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Strong design: viridis palette for temporal encoding, white stroke on points for visual separation, gray connecting path avoids color conflict, intentional title/subtitle hierarchy. Clearly above defaults but not quite FiveThirtyEight-level polish.
• DE-02: Visual Refinement (5/6) — Spines removed (strokeWidth=0, domain=False), grid subtle (opacity 0.15, dashed), tick colors customized, generous whitespace. Most details are polished.
• DE-03: Data Storytelling (4/6) — Key years annotated to create temporal narrative, recession spike (2008–2010) is visually dramatic, subtitle provides analytical context ("Phillips curve path"). Color gradient reinforces temporal progression. The crowded lower-left somewhat weakens the story's ending.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct connected scatter plot with temporal path.
• SC-02: Required Features (4/4) — Points connected in chronological order via order encoding. Key time points annotated. Color gradient indicates time direction. Point markers visible at each position with white stroke separation.
• SC-03: Data Mapping (3/3) — X=unemployment, Y=inflation, temporal ordering correct. Axes display full data range.
• SC-04: Title & Legend (3/3) — Title format "scatter-connected-temporal · altair · pyplots.ai" correct. Year legend with viridis gradient is clear and properly formatted.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows cyclical patterns, regime changes (2008 recession spike), directional trends, and temporal clustering. The dense lower-left cluster demonstrates convergence but makes individual paths harder to trace.
• DQ-02: Realistic Context (5/5) — US-style unemployment vs. inflation over 30 years is a well-known, neutral economic concept (Phillips curve dynamics). Excellent real-world framing.
• DQ-03: Appropriate Scale (4/4) — Unemployment 3–10.5%, inflation -0.5–6% are realistic ranges. Starting values (6.1% unemployment, 2.6% inflation in 1994) and the 2008 recession spike are plausible.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean Imports → Data → Plot → Save flow. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — altair, numpy, pandas — all used.
• CQ-04: Code Elegance (2/2) — Well-organized code. Nudge dictionary for label positioning is a clean approach. Shared axis config avoids duplication.
• CQ-05: Output & API (1/1) — Saves as plot.png (scale_factor=3.0) and plot.html. Current Altair 6.0 API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Proper declarative encoding, layer composition with + operator, order encoding for temporal path, configure_axis/configure_view for global styling. Good but could leverage Altair's selection API or condition encoding for more advanced patterns.
• LM-02: Distinctive Features (3/5) — Interactive tooltips with formatted fields, .interactive() for zoom/pan, HTML export alongside PNG, declarative grammar of graphics approach. These are distinctively Altair features.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Excellent spec compliance — all required features present with proper temporal ordering, annotations, and color gradient
• Strong design with viridis color encoding, white point strokes, subtle gray path, and removed spines
• Realistic and neutral Phillips curve data with convincing recession dynamics
• Clean, well-organized code following KISS principles with proper reproducibility
• Good use of Altair-specific features: interactive tooltips, HTML export, declarative layer composition

Weaknesses

• Connecting path opacity (0.35) is too subtle — temporal trajectory is hard to follow visually
• Lower-left cluster (recent years) is dense, making individual year progression difficult to trace
• Y-axis domain extends well below the data range, wasting vertical space

Issues Found

1. VQ-03 MINOR: Path line opacity at 0.35 makes the temporal connections hard to follow
   • The connecting path could benefit from slightly higher opacity (0.5–0.6) for better visibility
2. VQ-05 MINOR: Y-axis domain extends to -1.5 but data minimum is around -0.5
   • Tightening the y-axis domain would improve canvas utilization

AI Feedback for Next Attempt

This is a solid implementation at 87/100 on attempt 3. The design is well-crafted with good spec compliance and data quality. To reach 90+, increase path line opacity for better temporal flow visibility, tighten the y-axis domain to reduce unused space, and consider using Altair's selection/condition API for more distinctive library usage.

Verdict: APPROVED