feat(seaborn): implement line-stress-strain

[github-actions]

Implementation: line-stress-strain - seaborn

Implements the seaborn version of line-stress-strain.

File: plots/line-stress-strain/implementations/seaborn.py

Parent Issue: #4413

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a mild steel engineering stress-strain curve as a thick blue line on a 16:9 canvas. The x-axis is labeled "Engineering Strain" and the y-axis "Engineering Stress (MPa)". The title reads "Mild Steel Tensile Test · line-stress-strain · seaborn · pyplots.ai". Three shaded background regions delineate the elastic (light blue), strain hardening (light orange), and necking (light pink) phases, with colored region labels at the bottom. An orange circle marks the yield point with a "Yield Point (0.2% offset)" annotation and arrow. A red circle marks UTS at 400 MPa with annotation. A dark X marker denotes fracture with annotation. A dashed gray 0.2% offset line is drawn parallel to the elastic region. An italic "E = 200 GPa" annotation with arrow points to the elastic slope. A legend on the center-right lists all regions and critical points. Top and right spines are removed; a subtle y-axis grid is present.

Score: 84/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	16	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	3	10
Total	84	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — All major text explicitly sized (title 24, labels 20, ticks 16). Region labels at 12pt and 0.2% label at 11pt are slightly small but still readable.
• VQ-02: No Overlap (4/6) — The elastic region is very narrow on the x-axis, causing the "Elastic" label, "0.2%" text, yield point annotation, offset line, and E annotation to crowd the left portion. Minor visual clutter.
• VQ-03: Element Visibility (6/6) — Line thickness 3.5, markers 12-14px with white edge highlighting, annotations with clear arrows. All elements well-visible.
• VQ-04: Color Accessibility (3/4) — Blue, orange, red, dark palette. Orange (#E07B39) and red (#C0392B) are somewhat similar for deuteranopia, though mitigated by different marker shapes (circle vs X) and text labels.
• VQ-05: Layout & Canvas (4/4) — Good 16:9 proportions, plot fills canvas well, balanced margins, legend positioned appropriately.
• VQ-06: Axis Labels & Title (2/2) — "Engineering Stress (MPa)" with units, "Engineering Strain" correctly unitless (dimensionless quantity).

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom color palette anchored on Python Blue, intentional color-coding per region, bold annotations with styled arrows, shaded region backgrounds. Clearly above defaults — professional and cohesive.
• DE-02: Visual Refinement (5/6) — Top/right spines removed, subtle y-grid (alpha=0.2), white marker edges, generous whitespace. Well-polished.
• DE-03: Data Storytelling (5/6) — Excellent engineering narrative: three distinct phases are visually delineated with shading and color-coded annotations. Critical points (yield, UTS, fracture) are annotated with values. The 0.2% offset method is visualized. The viewer immediately grasps the material behavior story.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct line plot showing stress-strain curve.
• SC-02: Required Features (4/4) — All required features present: labeled regions (elastic, strain hardening, necking), marked critical points (yield via 0.2% offset, UTS, fracture), elastic modulus annotation, 0.2% offset line drawn.
• SC-03: Data Mapping (3/3) — X=strain, Y=stress correctly mapped. Axes show full data range.
• SC-04: Title & Legend (2/3) — Title includes spec-id, library, and pyplots.ai but adds a "Mild Steel Tensile Test" prefix deviating from the exact {spec-id} · {library} · pyplots.ai format. Legend labels are clear and match data.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows all stress-strain curve phases: elastic region with linear slope, strain hardening with gradual curve, necking with stress drop to fracture. All critical points represented.
• DQ-02: Realistic Context (5/5) — Mild steel tensile test is a classic, neutral engineering scenario.
• DQ-03: Appropriate Scale (4/4) — Young's modulus 200 GPa, yield stress 250 MPa, UTS 400 MPa, fracture strain 0.35 — all realistic values for mild steel.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean imports → data → plot → save structure. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imports used: matplotlib.patches, pyplot, numpy, seaborn.
• CQ-04: Code Elegance (2/2) — Well-structured, appropriate complexity for a heavily annotated engineering plot. No fake UI elements.
• CQ-05: Output & API (1/1) — Saves as plot.png, dpi=300, current API.

Library Mastery (3/10)

• LM-01: Idiomatic Usage (2/5) — Only uses sns.lineplot for the main curve and sns.set_context for sizing. The vast majority of the plot (annotations, markers, shaded regions, offset line, legend) is built with raw matplotlib. The seaborn usage is minimal.
• LM-02: Distinctive Features (1/5) — No distinctive seaborn features used. sns.lineplot adds little over ax.plot. The implementation could be trivially rewritten as pure matplotlib.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Excellent data storytelling with color-coded regions, shaded backgrounds, and annotated critical points
• Professional design with custom palette, spine removal, subtle grid, and white marker edges
• Perfect data quality — realistic mild steel values with comprehensive feature coverage
• Clean, well-structured code with appropriate complexity

Weaknesses

• Very low seaborn utilization — the implementation is essentially matplotlib with a single sns.lineplot call
• Left side of plot is visually cluttered where elastic region, offset line, yield annotation, and E annotation compete for space
• Title format deviates from spec (adds descriptive prefix)

Issues Found

1. LM-01 LOW: Minimal seaborn usage — only sns.lineplot and sns.set_context
   • Fix: Consider using seaborn's DataFrame-based approach with pd.DataFrame, use sns.lineplot with hue for multi-segment styling, or leverage seaborn's statistical/styling capabilities more deeply
2. LM-02 LOW: No distinctive seaborn features
   • Fix: Use seaborn's built-in styling themes, color palette functions (sns.color_palette), or DataFrame-oriented plotting patterns that distinguish it from raw matplotlib
3. VQ-02 MINOR: Left-side crowding in elastic region
   • Fix: Adjust annotation positions — move E annotation further right or use different xytext coordinates to reduce overlap in the narrow elastic region
4. SC-04 MINOR: Title format has extra prefix
   • Fix: Use exact format line-stress-strain · seaborn · pyplots.ai

AI Feedback for Next Attempt

Focus on improving library mastery: use a pandas DataFrame to structure the data and leverage seaborn's DataFrame-oriented API. Use sns.color_palette() or seaborn styling functions to demonstrate library-specific capabilities. Fix the title to match exact spec format. Reduce visual clutter in the elastic region by repositioning annotations. Keep the excellent data storytelling, design polish, and comprehensive feature coverage.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

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

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays an engineering stress-strain curve for mild steel with the title "line-stress-strain · seaborn · pyplots.ai". The x-axis shows "Engineering Strain" (0 to ~0.38) and the y-axis shows "Engineering Stress (MPa)" (0 to ~450). Three distinct regions are rendered as colored line segments: Elastic (blue, #306998), Strain Hardening (orange, #E07B39), and Necking (dark red, #C0392B). Subtle background shading in matching colors distinguishes each region. Critical points are marked with distinct markers: Yield Point (orange circle), UTS (dark red circle at 400 MPa), and Fracture (dark X marker). Annotated arrows point to each critical point with bold colored text. A dashed gray line shows the 0.2% offset method. An italic annotation reads "E = 200 GPa" with an arrow to the elastic region midpoint. The legend on the center-right shows both region lines and critical point markers. Top and right spines are removed, x-grid is off, y-grid is subtle.

Score: 90/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	90	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set via sns.set_context with rc overrides: title 24pt, labels 20pt, ticks 16pt, legend 13pt, annotations 14pt
• VQ-02: No Overlap (5/6) — Minor crowding near the yield point area where the elastic line, 0.2% offset line, and yield marker converge in a very narrow x-range; annotation text wisely offset to x=0.06
• VQ-03: Element Visibility (5/6) — Lines thick (3.5), markers large (s=200), all clearly visible; however the elastic region is extremely compressed on the x-axis (~0.3% of total range), making the 0.2% offset line hard to distinguish from the elastic line
• VQ-04: Color Accessibility (4/4) — Blue/orange/dark-red palette is colorblind-distinguishable, no red-green ambiguity
• VQ-05: Layout & Canvas (4/4) — 16:9 canvas well-utilized, plot fills majority of area, balanced margins
• VQ-06: Axis Labels & Title (2/2) — Descriptive labels with units: "Engineering Strain", "Engineering Stress (MPa)"

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom coordinated palette where annotation colors match their respective region colors, region shading ties the visual language together, professional appearance clearly above defaults
• DE-02: Visual Refinement (5/6) — Spines removed via sns.despine(), x-grid disabled, y-grid subtle (alpha 0.15), region shading at alpha 0.06, generous whitespace
• DE-03: Data Storytelling (5/6) — Annotations guide the viewer through the material behavior narrative (elastic → yield → hardening → UTS → necking → fracture), region shading creates visual hierarchy, color-coded annotations reinforce the story

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct line plot showing stress-strain curve
• SC-02: Required Features (4/4) — All spec features present: region labels via color-coded segments, critical points marked (yield, UTS, fracture), elastic modulus annotation, 0.2% offset line
• SC-03: Data Mapping (3/3) — X=strain, Y=stress, correctly mapped, all data visible
• SC-04: Title & Legend (3/3) — Title "line-stress-strain · seaborn · pyplots.ai" correct format, legend shows both regions and critical points with clear labels

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows all key regions: elastic deformation, strain hardening, necking, and fracture point
• DQ-02: Realistic Context (5/5) — Mild steel tensile test with realistic parameters, neutral engineering topic
• DQ-03: Appropriate Scale (4/4) — Young's modulus 200 GPa, yield stress 250 MPa, UTS 400 MPa, fracture strain 0.35 — all realistic for mild steel

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → plot → save, no functions/classes
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set
• CQ-03: Clean Imports (2/2) — All four imports (matplotlib, numpy, pandas, seaborn) are used
• CQ-04: Code Elegance (2/2) — Clean, well-organized, appropriate complexity for the visualization
• CQ-05: Output & API (1/1) — Saves as plot.png with dpi=300, no deprecated API

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Good use of sns.lineplot with hue for region coloring, sns.scatterplot with hue/style for critical points, sns.set_style, sns.set_context, sns.despine; some matplotlib fallback for annotations and axvspan
• LM-02: Distinctive Features (2/5) — Hue-based line segmentation by region and scatterplot with style mapping are seaborn patterns, but most visual impact (annotations, region shading, offset line) comes from matplotlib calls

Score Caps Applied

• None

Strengths

• Excellent color coordination: annotation colors match their respective region colors throughout
• Complete spec compliance with all required features (regions, critical points, offset line, modulus annotation)
• Realistic mild steel data with accurate material properties
• Clean data storytelling through annotations and region shading that guide the viewer through the mechanical behavior narrative
• Well-structured code with clear linear flow

Weaknesses

• Elastic region is extremely compressed on the x-axis, making the 0.2% offset line nearly indistinguishable from the elastic line at this scale
• Heavy reliance on matplotlib for annotations and shading rather than seaborn-specific features

Issues Found

1. LM-02 LOW: Most visual impact comes from matplotlib calls (annotations, axvspan) rather than seaborn-distinctive features
   • Fix: Consider using seaborn's FacetGrid or other high-level API features where possible

AI Feedback for Next Attempt

Strong implementation that meets all spec requirements with good design. The main area for improvement is leveraging more seaborn-distinctive features rather than falling back to matplotlib for annotations and shading. The elastic region compression is inherent to the data (yield strain is tiny compared to fracture strain) but could be addressed with an inset axes showing the elastic region detail.

Verdict: APPROVED