feat(seaborn): implement nyquist-basic

[github-actions]

Implementation: nyquist-basic - seaborn

Implements the seaborn version of nyquist-basic.

File: plots/nyquist-basic/implementations/seaborn.py

Parent Issue: #4412

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a Nyquist diagram on a square canvas with 1:1 aspect ratio. A solid blue curve (#306998) traces the positive-frequency response from approximately (10, -0.2) sweeping down and left through a large arc, passing near the critical point and approaching the origin. A dashed, semi-transparent blue curve mirrors the positive-frequency contour for negative frequencies in the upper half-plane. A small dotted gray unit circle is centered at the origin. The critical point (-1, 0) is marked with a bold red "x" and annotated with a red arrow. Five frequency labels (ω=0.1, 0.5, 1.0, 3.0, 10.0) are placed along the curve with small arrow connectors. Three direction arrows on the solid curve indicate increasing frequency. Axes are labeled "Real" (x) and "Imaginary" (y). Title reads "nyquist-basic · seaborn · pyplots.ai". Top and right spines are removed. A subtle grid is visible. Axis reference lines at x=0 and y=0 are drawn in light gray.

Score: 81/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	4	10
Total	81	100

Visual Quality (25/30)

• VQ-01: Text Legibility (7/8) — Font sizes explicitly set (title 24, labels 20, ticks 16). Frequency annotations at 12pt are slightly small for a high-res canvas.
• VQ-02: No Overlap (5/6) — Minor crowding near the origin where ω=3.0, ω=10.0, and the (-1, 0) annotation are close together, but all remain readable.
• VQ-03: Element Visibility (5/6) — Lines and markers are clear. The unit circle is very small relative to the full curve range (curve extends to ~10 on real axis), making it hard to see its relationship to the curve near (-1, 0).
• VQ-04: Color Accessibility (4/4) — Single blue color for data with red for the critical point. Colorblind-safe.
• VQ-05: Layout & Canvas (3/4) — Square 10×10 inch figure at 300 dpi = 3000×3000 px (spec expects 3600×3600). Good use of space with 1:1 aspect ratio but slightly undersized.
• VQ-06: Axis Labels & Title (1/2) — "Real" and "Imaginary" are descriptive but lack units or context (e.g., "Real Part of G(jω)" or "Imaginary Part of G(jω)").

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Custom Python Blue, bold red critical point marker, removed spines, subtle grid. Clearly above library defaults with intentional color choices and annotation styling.
• DE-02: Visual Refinement (4/6) — Top/right spines removed, subtle grid (alpha=0.15), light gray axis reference lines, clean annotation arrows. Good refinement.
• DE-03: Data Storytelling (4/6) — The critical point is prominently highlighted with color contrast. Frequency annotations guide the viewer along the curve. Direction arrows show the flow of increasing frequency. Clear focal point at (-1, 0).

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct Nyquist plot mapping frequency response on the complex plane.
• SC-02: Required Features (4/4) — All spec requirements met: critical point (-1, 0) with distinct marker, unit circle, frequency annotations at key points, direction arrows, 1:1 aspect ratio, "Real"/"Imaginary" axis labels.
• SC-03: Data Mapping (3/3) — Real part on X, imaginary part on Y, correct mapping.
• SC-04: Title & Legend (3/3) — Title "nyquist-basic · seaborn · pyplots.ai" in correct format. No legend needed for single-series Nyquist plot.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Good transfer function G(s) = 10/((s+1)(0.5s+1)(0.2s+1)) showing a curve that encircles and approaches the critical point. Mirror contour for negative frequencies included. Could show gain/phase margin annotations for full coverage.
• DQ-02: Realistic Context (5/5) — Third-order control system transfer function — a realistic engineering scenario commonly found in control theory textbooks.
• DQ-03: Appropriate Scale (4/4) — 800 logarithmically-spaced frequency points from 0.01 to 100 rad/s. Realistic values for this transfer function.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → transfer function data → plot → annotations → styling → save.
• CQ-02: Reproducibility (2/2) — Fully deterministic (uses scipy.signal.freqresp on a fixed transfer function, no randomness).
• CQ-03: Clean Imports (2/2) — All imports used: matplotlib, numpy, seaborn, scipy.signal.
• CQ-04: Code Elegance (2/2) — Clean and appropriate. Good use of loops for frequency annotations and direction arrows.
• CQ-05: Output & API (1/1) — Saves as plot.png at 300 dpi with bbox_inches="tight".

Library Mastery (4/10)

• LM-01: Idiomatic Usage (3/5) — Uses sns.lineplot with sort=False and estimator=None for the main curves, and sns.set_context for global font scaling. However, most of the plot construction (annotations, markers, unit circle, critical point) is pure matplotlib.
• LM-02: Distinctive Features (1/5) — Generic usage. The plot could be reproduced almost identically with pure matplotlib. No distinctive seaborn features leveraged (no statistical estimation, no palette integration, no seaborn-specific styling).

Score Caps Applied

• None

Strengths

• Full spec compliance — all required Nyquist features present (critical point, unit circle, frequency annotations, direction arrows, 1:1 aspect)
• Clean, deterministic code using scipy.signal for proper transfer function computation
• Good data storytelling with highlighted critical point and frequency progression
• Professional annotation styling with consistent arrow properties

Weaknesses

• Seaborn is barely utilized — the implementation is essentially a matplotlib plot with sns.lineplot as a thin wrapper
• Figure size is 3000×3000 px instead of the expected 3600×3600 px (use figsize=(12, 12))
• Frequency annotation labels near the origin are crowded (ω=3.0, ω=10.0, (-1,0))
• Axis labels could be more descriptive (e.g., "Real Part of G(jω)")
• Unit circle is barely visible at the current scale since the curve extends far (to ~10 on real axis)

Issues Found

1. LM-01/LM-02 LOW: Seaborn used only as a thin wrapper around matplotlib — sns.lineplot with sort=False is the only seaborn plotting call, everything else is pure matplotlib
   • Fix: Consider using seaborn's styling integration more deeply (e.g., sns.set_style, sns.color_palette for consistent theming), or use seaborn's scatterplot for frequency markers
2. VQ-05 MINOR: Canvas is 3000×3000 instead of 3600×3600
   • Fix: Change figsize to (12, 12) for 3600×3600 at 300 dpi
3. VQ-02 MINOR: Label crowding near the origin
   • Fix: Adjust annotation offsets for ω=3.0 and ω=10.0 to avoid proximity to (-1, 0) label
4. VQ-06 LOW: Axis labels lack descriptive context
   • Fix: Use "Real Part of G(jω)" and "Imaginary Part of G(jω)" or similar

AI Feedback for Next Attempt

Improve library mastery: use more seaborn features (sns.set_style, sns.color_palette, sns.scatterplot for frequency markers). Fix canvas size to 3600×3600 (figsize=(12,12)). Spread out frequency annotations near the origin to reduce crowding. Make axis labels more descriptive. Consider zooming in or adding an inset near the critical point to better show the unit circle relationship.

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 a Nyquist diagram on the complex plane. A solid blue curve represents the positive-frequency response of a third-order transfer function G(s) = 10/((s+1)(0.5s+1)(0.2s+1)), starting near (10, 0) at low frequencies and spiraling inward toward the origin at high frequencies. A dashed, semi-transparent blue curve mirrors the positive-frequency contour for negative frequencies. A small dashed gray unit circle is drawn at the origin. The critical point (-1, 0) is prominently marked with a large red "X" and a red annotation arrow labeled "Critical point (-1, 0)". Five frequency markers (ω = 0.1, 0.5, 1.0, 3.0, 10.0 rad/s) are annotated along the curve with small arrows. Three directional arrows on the solid curve indicate increasing frequency. Axes are labeled "Real Part of G(jω)" and "Imaginary Part of G(jω)". Top and right spines are removed, a subtle grid is visible, and thin gray reference lines mark the real and imaginary axes. A legend in the bottom-left identifies the three visual elements. Title reads "nyquist-basic · seaborn · pyplots.ai".

Score: 82/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	3	10
Total	82	100

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 24pt, axis labels 20pt, ticks 16pt, annotations 12–14pt. All text clearly readable.
• VQ-02: No Overlap (5/6) — Frequency annotations near the origin (ω=3.0, ω=10.0) are slightly crowded; all still readable but not perfectly spaced.
• VQ-03: Element Visibility (5/6) — Lines and markers are clearly visible. Direction arrows are effective. Frequency dot markers (size 7) could be slightly larger for emphasis.
• VQ-04: Color Accessibility (4/4) — Blue/red/gray palette is colorblind-safe with strong contrast.
• VQ-05: Layout & Canvas (3/4) — Square 10×10 canvas is appropriate for 1:1 aspect ratio. Some wasted space in the upper-right quadrant due to the mirror curve extending beyond the main data region.
• VQ-06: Axis Labels & Title (2/2) — Descriptive labels with proper mathematical notation "Real Part of G(jω)" and "Imaginary Part of G(jω)".

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Thoughtful color scheme with Python Blue primary, red accent for the critical point, and muted grays for reference elements. Good typography hierarchy. Above defaults but not publication-flagship level.
• DE-02: Visual Refinement (4/6) — Top/right spines removed, subtle grid (alpha=0.15), thin axis reference lines at zero. Good polish, but grid could be even more refined (y-axis only or removed).
• DE-03: Data Storytelling (4/6) — Red critical point creates a clear focal point. Alpha differentiation between positive and negative frequencies guides the eye. Frequency labels along the curve help readers trace the response. Clear visual hierarchy.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct Nyquist plot on the complex plane.
• SC-02: Required Features (4/4) — All spec requirements met: critical point (-1,0) marked, unit circle drawn, frequency annotations present, direction arrows included, 1:1 aspect ratio, axes labeled.
• SC-03: Data Mapping (3/3) — Real part on x-axis, imaginary part on y-axis. All data visible.
• SC-04: Title & Legend (3/3) — Title format "nyquist-basic · seaborn · pyplots.ai" correct. Legend clearly identifies positive frequencies, negative frequencies, and unit circle.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Uses a realistic 3rd-order transfer function showing characteristic Nyquist curve behavior: the curve passes near the critical point demonstrating gain/phase margin concepts. Includes both positive and negative frequency branches. Could show a system closer to instability for more dramatic demonstration.
• DQ-02: Realistic Context (5/5) — Standard control systems transfer function G(s) = 10/((s+1)(0.5s+1)(0.2s+1)), commonly found in textbooks and engineering practice. Neutral, scientific context.
• DQ-03: Appropriate Scale (4/4) — Frequency range 0.01–100 rad/s covers multiple decades appropriately. 800 logarithmically-spaced points provide smooth curves. Gain values are realistic for this system.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean Imports → Data → Plot → Style → Save flow. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic: transfer function and frequency response are computed analytically with no randomness.
• CQ-03: Clean Imports (2/2) — All imports used: matplotlib.pyplot, numpy, seaborn, scipy.signal.
• CQ-04: Code Elegance (2/2) — Appropriate complexity for the visualization. Clean loop for frequency annotations. No over-engineering.
• CQ-05: Output & API (1/1) — Saves as plot.png at 300 DPI with bbox_inches="tight". Current API usage.

Library Mastery (3/10)

• LM-01: Idiomatic Usage (2/5) — Uses sns.lineplot for the two main curves and sns.set_context for global font scaling, but the vast majority of the plot is built with matplotlib directly (annotations, unit circle, markers, axis formatting, legend). Seaborn's contribution is minimal.
• LM-02: Distinctive Features (1/5) — No seaborn-distinctive features are leveraged. sns.lineplot with sort=False is functional but not a showcase of seaborn's strengths. This plot could be written almost identically in pure matplotlib.

Score Caps Applied

• None

Strengths

• Complete spec compliance — all six required features are implemented correctly
• Strong data storytelling with the red critical point as focal and alpha-differentiated mirror curve
• Clean, well-structured code with deterministic data from scipy
• Professional axis labels with proper mathematical notation G(jω)
• Good visual refinement with removed spines and subtle grid

Weaknesses

• Very low seaborn utilization — most of the plot is matplotlib; seaborn is used only for two lineplot calls and set_context
• Frequency annotations near the origin are slightly crowded
• Could leverage seaborn's styling capabilities more (e.g., sns.set_style, sns.despine, color_palette)

Issues Found

1. LM-01 LOW (2/5): Seaborn is barely used — only sns.lineplot and sns.set_context. The bulk of the visualization is pure matplotlib.
   • Fix: Use sns.despine() instead of manual spine removal, leverage sns.set_style("whitegrid") or sns.set_theme(), use sns.color_palette() for color management
2. LM-02 LOW (1/5): No seaborn-distinctive features demonstrated.
   • Fix: Use seaborn's built-in theming, context scaling, and palette functions to showcase library-specific capabilities
3. VQ-02 MINOR (5/6): Slight crowding of frequency annotations near the origin area.
   • Fix: Adjust annotation offsets for ω=3.0 and ω=10.0 to provide more spacing, or use adjustText-style logic

AI Feedback for Next Attempt

Focus primarily on improving Library Mastery: replace manual spine removal with sns.despine(), use sns.set_style() or sns.set_theme() for base styling, and leverage sns.color_palette() for color management. These changes will demonstrate idiomatic seaborn usage. Also slightly adjust annotation offsets near the origin to reduce crowding.

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 Nyquist diagram on the complex plane with a solid blue curve representing G(jω) for ω ≥ 0 and a dashed, semi-transparent blue curve for the mirror branch (ω < 0). A red "X" marker at (-1, 0) is annotated as "Critical point (-1, 0)" with a red arrow. A dotted gray unit circle is centered at the origin. Five frequency points (ω = 0.1, 0.5, 1.0, 3.0, 10.0 rad/s) are marked with filled blue dots and labeled with gray annotation arrows. Three direction arrows along the main curve indicate increasing frequency. The title reads "nyquist-basic · seaborn · pyplots.ai". Axes are labeled "Real Part of G(jω)" and "Imaginary Part of G(jω)". The plot uses a 1:1 aspect ratio with top/right spines removed and a subtle grid. The legend in the upper right distinguishes the two branches.

Score: 88/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	14	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	6	10
Total	88	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt, annotations 12-14pt
• VQ-02: No Overlap (5/6) — Minor crowding near the origin where ω=3.0, ω=10.0, and critical point annotations converge, but all remain readable
• VQ-03: Element Visibility (6/6) — Line widths (2.5), scatter markers (s=120), and arrow sizes well-adapted; mirror branch appropriately subdued at alpha=0.4
• VQ-04: Color Accessibility (4/4) — Blue/red palette is colorblind-safe with strong contrast
• VQ-05: Layout & Canvas (4/4) — Square 10×10 figure with 1:1 aspect ratio; plot fills canvas well with balanced margins
• VQ-06: Axis Labels & Title (2/2) — Descriptive labels "Real Part of G(jω)" and "Imaginary Part of G(jω)"

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) — Thoughtful color hierarchy: bold blue primary curve, subdued dashed mirror, red critical point emphasis, gray reference circle. Custom palette, intentional visual weight distribution
• DE-02: Visual Refinement (4/6) — Spines removed via despine, subtle grid (alpha=0.15), light axis reference lines, generous whitespace. Could further refine with thinner grid or no grid
• DE-03: Data Storytelling (4/6) — Clear visual hierarchy: critical point highlighted in bold red, mirror branch subordinated, frequency annotations guide the reader through the curve. Direction arrows reinforce the narrative of increasing frequency

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct Nyquist plot on complex plane
• SC-02: Required Features (4/4) — All spec features present: critical point marker, unit circle, frequency annotations, direction arrows, 1:1 aspect ratio, proper axis labels
• SC-03: Data Mapping (3/3) — Real part on X-axis, Imaginary on Y-axis, correct mapping
• SC-04: Title & Legend (3/3) — Title "nyquist-basic · seaborn · pyplots.ai" correct; legend labels clearly distinguish branches

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Third-order transfer function produces a characteristic Nyquist curve with interesting shape; both positive and negative frequency branches shown. Could show a system closer to instability for more dramatic demonstration
• DQ-02: Realistic Context (5/5) — Control systems transfer function G(s) = 10/((s+1)(0.5s+1)(0.2s+1)) is a realistic engineering scenario
• DQ-03: Appropriate Scale (4/4) — 800 logarithmically spaced points from 0.01 to 100 rad/s; appropriate frequency range for this system

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean imports → data → plot → save structure with no functions/classes
• CQ-02: Reproducibility (2/2) — Deterministic data from scipy.signal transfer function, no randomness
• CQ-03: Clean Imports (2/2) — All imports used: matplotlib, numpy, pandas, seaborn, scipy
• CQ-04: Code Elegance (2/2) — Appropriate complexity, well-organized with clear sections
• CQ-05: Output & API (1/1) — Saves as plot.png with dpi=300, current API usage

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Uses seaborn lineplot with hue/style differentiation, scatterplot with DataFrame, set_theme with context, despine. Good seaborn-idiomatic approach but many elements (annotations, unit circle, axis lines) necessarily use matplotlib directly
• LM-02: Distinctive Features (2/5) — Uses set_theme context scaling, despine, hue+style in lineplot, but these are relatively basic seaborn features. The plot type inherently requires significant matplotlib for annotations and geometric shapes

Score Caps Applied

• None — no caps triggered

Strengths

• Complete spec compliance with all six required features implemented correctly
• Strong visual hierarchy with intentional color and opacity choices that guide the viewer
• Deterministic, realistic engineering data using scipy.signal transfer function
• Clean, well-structured code with explicit font sizes throughout
• Effective use of seaborn's hue/style system to differentiate positive and negative frequency branches

Weaknesses

• Annotation crowding near origin where multiple labels converge (ω=3.0, ω=10.0, critical point)
• Library mastery limited by the inherently matplotlib-heavy nature of Nyquist plots — seaborn adds value mainly through theming and the main line/scatter plots
• Unit circle could be slightly more visible while remaining a background reference element

Issues Found

1. VQ-02 MINOR: Slight annotation crowding near the origin area where three labels (ω=3.0, ω=10.0, critical point) are in proximity
   • Fix: Adjust offsets for ω=3.0 and ω=10.0 annotations to spread them further apart
2. LM-02 LOW: Seaborn's distinctive features are underutilized — much of the visual complexity is handled by matplotlib
   • Fix: This is inherent to the plot type; limited room for improvement

AI Feedback for Next Attempt

Solid implementation at 88/100. The main areas holding it back from 90+ are the minor annotation crowding near the origin and the limited opportunity for seaborn-distinctive features given the plot type. To reach 90+: spread the near-origin frequency annotations further apart, and consider whether seaborn's FacetGrid or additional statistical overlays could add value. The design excellence is strong with good visual hierarchy.

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 Nyquist diagram on the complex plane. The positive-frequency branch (solid dark blue line, labeled "G(jω), ω ≥ 0") sweeps from approximately (10, 0) at low frequencies downward through the lower half-plane, curving left and back toward the origin at high frequencies. The mirror (negative-frequency) branch (dashed lighter blue, labeled "G(jω), ω < 0") reflects symmetrically through the upper half-plane with reduced opacity. A dotted gray unit circle is centered at the origin for reference. The critical point at (-1, 0) is marked with a bold red X and annotated with a red arrow and "Critical point (-1, 0)" label. Five frequency points (ω = 0.1, 0.5, 1.0, 3.0, 10.0 rad/s) are marked with teal dots and labeled via gray annotation arrows. Three blue directional arrows along the positive branch indicate increasing frequency. Title reads "nyquist-basic · seaborn · pyplots.ai". Axes are labeled "Real Part of G(jω)" and "Imaginary Part of G(jω)". Legend in upper right with both branch labels. Top and right spines removed. Subtle whitegrid with low-alpha gridlines. Square aspect ratio maintained.

Score: 90/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	6	10
Total	90	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 24pt, axis labels 20pt, ticks 16pt, annotations 12-14pt. All text clearly readable at output resolution.
• VQ-02: No Overlap (5/6) — Manual annotation offsets prevent most collisions. Minor crowding near the origin where ω = 3.0 and ω = 10.0 labels are in proximity, but all remain readable.
• VQ-03: Element Visibility (6/6) — Lines at 2.5 width are clearly visible, frequency markers at s=120 with white edge highlighting stand out well, directional arrows are prominent.
• VQ-04: Color Accessibility (4/4) — Blue (#306998) primary with red accent for critical point and gray for reference elements. No red-green ambiguity. Colorblind-safe.
• VQ-05: Layout & Canvas (4/4) — Square 10×10 figure with equal aspect ratio. Plot fills canvas well with balanced margins. Nothing cut off.
• VQ-06: Axis Labels & Title (2/2) — Descriptive labels "Real Part of G(jω)" and "Imaginary Part of G(jω)" with mathematical notation.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Cohesive color scheme with Python Blue primary, red accent for the critical point, gray for reference geometry. Intentional visual hierarchy with alpha-reduced mirror branch. Professional typography. Clearly above library defaults.
• DE-02: Visual Refinement (5/6) — Spines removed via sns.despine(), grid alpha set to 0.15 for subtlety, axis reference lines at origin in light gray, generous whitespace. Well-polished details.
• DE-03: Data Storytelling (4/6) — Good visual hierarchy: critical point emphasized with bold red marker and annotation arrow, mirror branch visually de-emphasized with dashing and lower alpha, directional arrows guide the viewer along the frequency sweep. The viewer can follow the stability analysis narrative.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct Nyquist plot: imaginary vs. real part of open-loop frequency response on complex plane.
• SC-02: Required Features (4/4) — All spec features present: critical point (-1, 0) marked with red X, unit circle drawn, frequency annotations at 5 key points, direction arrows on curve, 1:1 aspect ratio, proper axis labeling.
• SC-03: Data Mapping (3/3) — Real part on X-axis, imaginary part on Y-axis. Both branches correctly computed and displayed.
• SC-04: Title & Legend (3/3) — Title "nyquist-basic · seaborn · pyplots.ai" matches required format. Legend correctly labels both frequency branches.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows all aspects of a Nyquist plot: full frequency sweep (0.01–100 rad/s), both positive and negative branches, curve behavior near the critical point, gain/phase characteristics visible from curve shape, 800 log-spaced points for smooth curve.
• DQ-02: Realistic Context (5/5) — Uses a realistic 3rd-order control system transfer function G(s) = 10/((s+1)(0.5s+1)(0.2s+1)), standard in control theory textbooks. Neutral engineering context.
• DQ-03: Appropriate Scale (4/4) — Frequency range 0.01–100 rad/s is appropriate for this system. Transfer function gains and phases produce sensible curve geometry.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → transfer function definition → frequency response → DataFrame construction → plotting → styling → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic: data computed from scipy.signal.freqresp with fixed transfer function coefficients. No randomness.
• CQ-03: Clean Imports (2/2) — All five imports used: matplotlib.pyplot, numpy, pandas, seaborn, scipy.signal.
• CQ-04: Code Elegance (2/2) — Clean and well-organized. Manual annotation offsets are the right approach for this plot. No over-engineering or fake functionality.
• CQ-05: Output & API (1/1) — Saves as plot.png at dpi=300 with bbox_inches="tight". Current API usage throughout.

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) — Uses seaborn's axes-level API well: lineplot with hue and style/dashes for branch differentiation, scatterplot for frequency markers, set_theme with context and rc params for global styling, despine for spine removal, color_palette for color management. DataFrames used as data source.
• LM-02: Distinctive Features (2/5) — Leverages seaborn's hue-based line styling with custom dash patterns, theme system, and despine. However, the bulk of the plot (unit circle, annotations, arrows, critical point) uses matplotlib directly, which is unavoidable for this specialized plot type.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• All specification requirements fully implemented with careful attention to detail
• Excellent visual hierarchy: critical point prominently marked, mirror branch appropriately de-emphasized, directional arrows guide the viewer
• Realistic and well-chosen transfer function that produces a pedagogically clear Nyquist curve
• Clean, deterministic code with proper use of scipy.signal for frequency response computation
• Cohesive color scheme and professional polish with subtle grid, removed spines, and balanced layout

Weaknesses

• Minor annotation crowding near the origin where multiple frequency labels converge
• Seaborn's role is somewhat limited since Nyquist plots require specialized matplotlib features for annotations, arrows, and geometric references

Issues Found

No critical issues found.

AI Feedback for Next Attempt

Implementation meets publication quality standards. All spec requirements are satisfied with good visual design, clean code, and appropriate data. The seaborn integration is well-executed given the specialized nature of Nyquist plots.

Verdict: APPROVED