feat(seaborn): implement sequence-logo-basic

[github-actions]

Implementation: sequence-logo-basic - seaborn

Implements the seaborn version of sequence-logo-basic.

File: plots/sequence-logo-basic/implementations/seaborn.py

Parent Issue: #4421

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a DNA sequence logo for a 10-position transcription factor binding site motif. Letters (A, C, G, T) are stacked vertically at each position, with heights proportional to information content in bits. Colors follow standard DNA convention: A=green, C=blue, G=orange, T=red. Position 8 shows the highest conservation (tall green A reaching ~1.4 bits), while position 5 is completely flat (uniform distribution, no preference). The x-axis shows positions 1-10 labeled "Position", and the y-axis shows "Information content (bits)" ranging from 0 to 2.0. The title reads "sequence-logo-basic · seaborn · pyplots.ai". Top and right spines are removed, with a subtle y-axis grid. The background is white and the layout is clean with good use of canvas space.

Score: 82/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	2	10
Total	82	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt
• VQ-02: No Overlap (6/6) - All letters stack cleanly, no text collisions
• VQ-03: Element Visibility (5/6) - Letters well-scaled; smallest low-frequency bases are tiny but correctly proportioned
• VQ-04: Color Accessibility (3/4) - Standard DNA colors used; green/red pair may cause issues for some colorblind viewers
• VQ-05: Layout & Canvas (4/4) - Good proportions, plot fills canvas well, nothing cut off
• VQ-06: Axis Labels & Title (2/2) - "Position" and "Information content (bits)" — descriptive with units

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) - Professional sequence logo with standard bioinformatics colors, clean execution of scaled glyphs, spines removed
• DE-02: Visual Refinement (4/6) - Top/right spines removed, subtle y-grid (alpha=0.2), white style, axisbelow enabled
• DE-03: Data Storytelling (3/6) - Conservation patterns visible through IC scaling; position 5 vs position 8 contrast tells a story, but no additional emphasis or annotation

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct sequence logo with vertically stacked letters scaled by information content
• SC-02: Required Features (4/4) - All spec features: stacked letters ordered by frequency, IC-scaled heights, standard DNA colors, scaled glyphs (not plain text)
• SC-03: Data Mapping (3/3) - X-axis positions 1-10, Y-axis information content 0-2 bits
• SC-04: Title & Legend (3/3) - Correct title format, no legend needed for standard DNA color convention

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows highly conserved (pos 8), moderately conserved (pos 6), and unconserved (pos 5) positions; all 4 bases represented
• DQ-02: Realistic Context (5/5) - DNA transcription factor binding site motif — real bioinformatics domain
• DQ-03: Appropriate Scale (4/4) - IC values in valid 0-2 bit range for DNA, frequencies sum to 1 per position

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → IC calculation → plot → style → save
• CQ-02: Reproducibility (2/2) - Deterministic hardcoded frequency matrix
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: Code Elegance (2/2) - Clean TextPath/PathPatch approach, appropriate complexity for the visualization
• CQ-05: Output & API (1/1) - Saves as plot.png with dpi=300, bbox_inches="tight"

Library Mastery (2/10)

• LM-01: Idiomatic Usage (1/5) - Only seaborn usage is sns.set_style("white"). All plotting is done with matplotlib primitives (TextPath, PathPatch, Affine2D). This is essentially a matplotlib implementation with seaborn styling.
• LM-02: Distinctive Features (1/5) - No distinctive seaborn features leveraged. Seaborn lacks a native sequence logo, but the implementation could use seaborn for auxiliary elements (color palettes, context settings, etc.)

Score Caps Applied

• None

Strengths

• Excellent glyph rendering using TextPath/PathPatch with proper affine transforms — letters are properly scaled and stretched
• Perfect spec compliance — all required features implemented correctly
• High-quality, realistic bioinformatics data with good variation across positions
• Clean code structure with correct information content calculation
• Professional visual styling with spine removal and subtle grid

Weaknesses

• Minimal seaborn usage — only sns.set_style("white") is used, making this essentially a matplotlib implementation
• Could enhance data storytelling with color intensity variation or annotations highlighting key conserved positions
• Green/red color pair in the standard DNA scheme may challenge some colorblind viewers

Issues Found

1. LM-01 LOW: Seaborn is used only for set_style("white") — no seaborn plotting functions or distinctive features
   • Fix: Use sns.set_context("talk") for font scaling, sns.color_palette() for color management, or other seaborn utilities to demonstrate library integration
2. LM-02 LOW: No distinctive seaborn features used
   • Fix: Leverage seaborn's palette system, context management, or despine utility (sns.despine()) instead of manual spine removal
3. DE-03 MODERATE: Data storytelling could be stronger
   • Fix: Add subtle annotations or visual cues highlighting the most/least conserved positions

AI Feedback for Next Attempt

The sequence logo rendering is excellent and spec compliance is perfect. The main weakness is library mastery — this is essentially a matplotlib implementation with only sns.set_style("white") from seaborn. To improve: (1) Use sns.despine() instead of manual spine removal, (2) Use sns.set_context("talk", font_scale=1.2) for font management, (3) Use sns.color_palette() for color handling. Also consider enhancing data storytelling by using visual emphasis on the most conserved positions.

[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 DNA sequence logo for a 10-position transcription factor binding site motif. At each position, letters (A, C, G, T) are stacked vertically with heights proportional to information content in bits. The colors use seaborn's colorblind palette: A=blue, C=orange, G=green, T=red-brown. Semi-transparent colored bars (alpha≈0.18) sit behind the letter glyphs at each position. Position 8 has the tallest stack (~1.4 bits, a large blue "A") and is highlighted with a subtle yellow vertical band and an italic annotation reading "Most conserved (1.4 bits)" with an arrow. Position 5 is essentially empty (equal base frequencies = 0 bits IC). The x-axis is labeled "Position" (1-10), the y-axis "Information content (bits)" (0-2.0). The title reads "sequence-logo-basic · seaborn · pyplots.ai" at 24pt. Top and right spines are removed via sns.despine(). A subtle whitegrid is visible (alpha=0.15). The overall layout is clean with good canvas utilization on a 16×9 figure.

Score: 82/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	13	20
Spec Compliance	13	15
Data Quality	15	15
Code Quality	9	10
Library Mastery	4	10
Total	82	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt, annotation 13pt
• VQ-02: No Overlap (6/6) - All letters stack cleanly with no text collisions
• VQ-03: Element Visibility (5/6) - Letter glyphs are well-scaled; however the semi-transparent background bars (alpha=0.18) add visual noise behind the letters, slightly muddying the smallest glyphs
• VQ-04: Color Accessibility (3/4) - Uses seaborn colorblind palette which is accessibility-friendly, but deviates from standard DNA color convention specified in the spec
• VQ-05: Layout & Canvas (4/4) - Good proportions, 16×9 figure fills canvas well, nothing cut off
• VQ-06: Axis Labels & Title (2/2) - "Position" and "Information content (bits)" — descriptive with units

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Colorblind palette, spines removed, annotation with arrow, yellow highlight on most conserved position — clearly above defaults but the background bars create visual clutter inconsistent with traditional sequence logo aesthetics
• DE-02: Visual Refinement (4/6) - sns.despine() removes top/right spines, subtle whitegrid (alpha=0.15, linewidth=0.6), set_axisbelow, tight_layout — good refinement but background bars detract
• DE-03: Data Storytelling (4/6) - Yellow highlight and annotation on most conserved position create a clear focal point; IC scaling naturally communicates conservation patterns; viewer can quickly identify the key position

Spec Compliance (13/15)

• SC-01: Plot Type (5/5) - Correct sequence logo with vertically stacked letters scaled by information content
• SC-02: Required Features (2/4) - Letters are stacked ordered by frequency, IC-scaled heights, rendered as scaled glyphs (not plain text). However, the spec explicitly requires "A=green, C=blue, G=orange/yellow, T=red" but implementation uses seaborn colorblind palette giving A=blue, C=orange, G=green — three bases have wrong colors
• SC-03: Data Mapping (3/3) - X-axis positions 1-10, Y-axis information content 0-2 bits, correct mapping
• SC-04: Title & Legend (3/3) - Correct title format "sequence-logo-basic · seaborn · pyplots.ai"; no legend needed for this plot type

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows highly conserved (pos 8, ~1.4 bits), moderately conserved (pos 6, 9), and unconserved (pos 5, 0 bits) positions; all 4 bases well-represented
• DQ-02: Realistic Context (5/5) - DNA transcription factor binding site motif — genuine bioinformatics domain
• DQ-03: Appropriate Scale (4/4) - IC values in valid 0-2 bit range for DNA, frequencies sum to 1 per position

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → IC calculation → DataFrame → plot → style → save
• CQ-02: Reproducibility (2/2) - Deterministic hardcoded frequency matrix
• CQ-03: Clean Imports (2/2) - All imports used (matplotlib.transforms, FontProperties, PathPatch, TextPath, seaborn, numpy, pandas)
• CQ-04: Code Elegance (1/2) - Calling sns.barplot() individually per data point in a loop is inefficient and un-idiomatic; the bars serve only as faint backgrounds and add code complexity
• CQ-05: Output & API (1/1) - Saves as plot.png with dpi=300, bbox_inches="tight"

Library Mastery (4/10)

• LM-01: Idiomatic Usage (2/5) - Uses sns.set_context(), sns.set_style(), sns.color_palette(), sns.barplot(), sns.despine() — improved from attempt 1, but barplot is called per individual data point in a loop (anti-pattern). The actual visualization is still built with matplotlib primitives (TextPath/PathPatch/Affine2D)
• LM-02: Distinctive Features (2/5) - Uses seaborn's context management, style system, despine utility, and colorblind palette — these are genuine seaborn features but not deeply distinctive. Seaborn lacks native sequence logo support, so heavy matplotlib usage is understandable

Score Caps Applied

• None

Strengths

• Excellent glyph rendering using TextPath/PathPatch with proper affine transforms — letters are properly scaled and stretched as the spec requires
• Perfect data quality — realistic bioinformatics domain with good variation showing all aspects of a sequence logo
• Good data storytelling with the yellow highlight and annotation on the most conserved position
• Clean code structure with correct information content calculation
• Improved seaborn integration (set_context, set_style, color_palette, despine) compared to attempt 1

Weaknesses

• Color scheme does not match spec — spec requires standard DNA colors (A=green, C=blue, G=orange, T=red) but implementation uses seaborn colorblind palette with different assignments
• Background bars (sns.barplot with alpha=0.18) add visual clutter and don't contribute to the standard sequence logo appearance
• Library mastery still limited because seaborn has no native sequence logo — the core visualization necessarily relies on matplotlib primitives

Issues Found

1. SC-02 LOW: Color scheme deviates from spec's standard DNA colors (A=green, C=blue, G=orange/yellow, T=red)
   • Fix: Define colors explicitly as {"A": "#3AA655", "C": "#4169E1", "G": "#F5A623", "T": "#E74C3C"} or similar green/blue/orange/red mapping matching the spec
2. CQ-04 MODERATE: sns.barplot() called per data point in a loop creates visual clutter and is anti-idiomatic
   • Fix: Remove the background barplot entirely — the scaled letter glyphs ARE the visualization; background bars are redundant in a sequence logo
3. LM-01 MODERATE: barplot usage is contrived; core visualization is matplotlib
   • Fix: Accept that seaborn lacks native sequence logo support. Focus seaborn usage on styling (set_context, set_style, despine, color_palette) which is legitimate. Remove the forced barplot calls

AI Feedback for Next Attempt

Two key fixes needed: (1) Use standard DNA colors as specified — A=green, C=blue, G=orange/yellow, T=red. The colorblind palette is good in general but the spec explicitly requires standard bioinformatics colors. (2) Remove the background barplot bars — they add visual clutter and the contrived per-row sns.barplot() calls don't improve library mastery score. The letter glyphs alone are the correct visualization for a sequence logo. Keep all other improvements (set_context, set_style, despine, color_palette for the actual letter colors, annotation). These two changes would significantly improve both spec compliance and visual quality.

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 DNA sequence logo for a 10-position transcription factor binding site motif. At each position, nucleotide letters (A, C, G, T) are stacked vertically with heights proportional to their information content in bits. The colors follow the standard DNA scheme specified: A=green (#3AA655), C=blue (#4169E1), G=orange (#F5A623), T=red (#E74C3C). Position 8 has the tallest stack (~1.4 bits, dominated by a large green "A") and is highlighted with a subtle yellow vertical band and an italic annotation "Most conserved (1.4 bits)" with an arrow. Position 5 is essentially empty (uniform base frequencies = 0 bits). The x-axis is labeled "Position" (1-10), the y-axis "Information content (bits)" (0-2.0). The title reads "sequence-logo-basic · seaborn · pyplots.ai" at 24pt. Top and right spines are removed. A subtle whitegrid is visible. The layout is clean with good canvas utilization on a 16x9 figure. No background bars or visual clutter — just clean letter glyphs.

Score: 84/100

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	3	10
Total	84	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt, annotation 13pt
• VQ-02: No Overlap (6/6) - All letters stack cleanly with no text collisions; annotation positioned clearly
• VQ-03: Element Visibility (5/6) - Letter glyphs are well-scaled and clearly visible; the smallest bases at low-frequency positions are tiny but this correctly represents the data — minor deduction for some glyphs being barely distinguishable at the base
• VQ-04: Color Accessibility (3/4) - Standard DNA colors (green, blue, orange, red) are distinguishable, but green (A) and red (T) could pose mild difficulty for red-green colorblind viewers. Mitigated by blue and orange providing additional contrast
• VQ-05: Layout & Canvas (4/4) - 16x9 figure with good proportions, plot fills canvas well, nothing cut off
• VQ-06: Axis Labels & Title (2/2) - "Position" and "Information content (bits)" — descriptive with units

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Custom DNA color scheme, clean glyph rendering, annotation with arrow, yellow highlight on most conserved position — clearly above defaults and visually appealing
• DE-02: Visual Refinement (4/6) - sns.despine() removes top/right spines, subtle whitegrid (alpha=0.15, linewidth=0.6), set_axisbelow, tight_layout — good refinement overall
• DE-03: Data Storytelling (4/6) - Yellow highlight and annotation on most conserved position create a clear focal point; IC scaling naturally communicates conservation; viewer immediately grasps which positions are conserved

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct sequence logo with vertically stacked letters scaled by information content
• SC-02: Required Features (4/4) - Letters stacked by frequency, IC-scaled heights, standard DNA color scheme (A=green, C=blue, G=orange, T=red), rendered as scaled glyphs, axis labels present
• SC-03: Data Mapping (3/3) - X-axis positions 1-10, Y-axis information content 0-2 bits, correct mapping
• SC-04: Title & Legend (3/3) - Correct title format "sequence-logo-basic · seaborn · pyplots.ai"; no legend needed

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows highly conserved (pos 8, ~1.4 bits), moderately conserved (pos 2, 6, 9), strongly conserved (pos 1, 3, 4, 7, 10), and unconserved (pos 5, 0 bits) — full range of conservation levels
• DQ-02: Realistic Context (5/5) - DNA transcription factor binding site motif — genuine bioinformatics domain
• DQ-03: Appropriate Scale (4/4) - IC values in valid 0-2 bit range for DNA, frequencies sum to 1 per position

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → IC calculation → plot → style → save
• CQ-02: Reproducibility (2/2) - Deterministic hardcoded frequency matrix
• CQ-03: Clean Imports (2/2) - All imports used (matplotlib.transforms, FontProperties, PathPatch, TextPath, seaborn, numpy)
• CQ-04: Code Elegance (2/2) - Clean rendering loop with proper affine transforms; no unnecessary barplot or visual clutter; appropriate complexity for the task
• CQ-05: Output & API (1/1) - Saves as plot.png with dpi=300, bbox_inches="tight"

Library Mastery (3/10)

• LM-01: Idiomatic Usage (2/5) - Uses sns.set_context("talk"), sns.set_style("whitegrid"), sns.despine() — legitimate seaborn styling usage, but the core visualization is built entirely with matplotlib primitives (TextPath, PathPatch, Affine2D). Seaborn lacks native sequence logo support so this is understandable
• LM-02: Distinctive Features (1/5) - Seaborn's context management and despine are used but these are convenience wrappers. No deeply distinctive seaborn feature is leveraged — the implementation would work nearly identically without seaborn

Score Caps Applied

• None

Strengths

• Excellent glyph rendering using TextPath/PathPatch with proper affine transforms — letters are properly scaled and stretched as the spec requires
• Perfect spec compliance — all required features implemented including correct standard DNA colors (fixed from attempt 2)
• Perfect data quality with realistic bioinformatics domain and full range of conservation levels
• Good data storytelling with yellow highlight and annotation on most conserved position
• Clean code with no unnecessary barplot clutter (fixed from attempt 2)

Weaknesses

• Library mastery inherently limited — seaborn has no native sequence logo support, so the core visualization must use matplotlib primitives
• Minor color accessibility concern with standard DNA green/red, though mitigated by blue and orange
• Design excellence could benefit from more typographic polish or additional visual hierarchy elements

Issues Found

1. LM-01 LOW: Core visualization relies on matplotlib primitives rather than seaborn plotting functions
   • This is inherent to the plot type — seaborn lacks a sequence logo function. The styling usage (set_context, set_style, despine) is the best possible seaborn integration for this visualization type
2. DE-01 MODERATE: While above defaults, the design doesn't reach publication-ready sophistication
   • Could benefit from a more refined grid style, custom font choices, or additional typographic detail

AI Feedback for Next Attempt

The implementation has addressed both issues from the previous review: colors now match the spec's standard DNA scheme and the unnecessary background barplot has been removed. The main remaining gap is library mastery (3/10), which is inherently limited since seaborn lacks a native sequence logo function. The design excellence (13/20) could be improved with more typographic refinement or a more sophisticated visual treatment. Overall this is a solid implementation at 84/100.

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 features a two-panel layout on a white background. The top panel is a sequence logo showing 10 positions of a DNA transcription factor binding site. Letters (A in green, C in blue, G in orange, T in red) are stacked vertically at each position, with height proportional to information content in bits (y-axis, 0–2). Position 8 has the tallest stack (~1.4 bits, dominated by a large green "A") and is highlighted with a pale yellow vertical band and an italic annotation "Most conserved (1.4 bits)" with a curved arrow. Position 5 is nearly empty (uniform distribution). The title reads "sequence-logo-basic · seaborn · pyplots.ai" in medium weight at the top. Top and right spines are removed; a very subtle horizontal grid is visible. The bottom panel is a seaborn heatmap showing the raw frequency matrix (4 rows: A, C, G, T × 10 columns) with annotated values (.2f format), colored from white to dark blue. Y-axis labels are colored to match their respective base colors (green A, blue C, orange G, red T). A "Frequency" colorbar appears on the right. Both panels share "Position" as the x-axis label.

Score: 92/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	92	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt, heatmap annotations 12pt
• VQ-02: No Overlap (6/6) — No overlapping text or elements; annotation well-positioned
• VQ-03: Element Visibility (6/6) — Letter glyphs are properly scaled and clearly visible at all positions
• VQ-04: Color Accessibility (3/4) — Standard DNA color scheme (green/blue/orange/red); green-red pairing is a mild colorblind concern but is the established bioinformatics convention
• VQ-05: Layout & Canvas (4/4) — Two-panel layout with 3.5:1 ratio gives proper emphasis to the logo; good use of 16×9 canvas
• VQ-06: Axis Labels & Title (2/2) — "Position" and "Information content (bits)" with units

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom DNA color palette, two-panel complementary layout, colored heatmap y-labels matching logo colors, annotation with curved arrow. Clearly above defaults, approaching publication quality
• DE-02: Visual Refinement (5/6) — Spines removed, subtle grid (alpha=0.15), white linecolor in heatmap, generous spacing between panels
• DE-03: Data Storytelling (5/6) — Yellow highlight on most conserved position with annotation arrow creates clear focal point; heatmap provides complementary quantitative detail; data variation (from uniform at pos 5 to highly conserved at pos 8) guides the viewer

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct sequence logo with vertically stacked letters scaled by information content
• SC-02: Required Features (4/4) — Letters stacked by frequency (most frequent on top), height scaled by IC, standard color scheme, rendered as scaled glyphs via TextPath/PathPatch
• SC-03: Data Mapping (3/3) — X-axis shows positions 1–10, Y-axis shows IC in bits (0–2 range for DNA)
• SC-04: Title & Legend (3/3) — Title format correct; no traditional legend needed as letters self-identify by color+shape

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows full range: strong conservation (pos 3, 7, 8), moderate (pos 2, 6, 9), and no conservation (pos 5)
• DQ-02: Realistic Context (5/5) — DNA transcription factor binding site motif — real bioinformatics context, neutral scientific topic
• DQ-03: Appropriate Scale (4/4) — IC values 0–1.4 bits realistic for DNA; frequencies sum to 1 per position

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data → IC calculation → logo rendering → heatmap → save
• CQ-02: Reproducibility (2/2) — Fully deterministic hardcoded frequency data
• CQ-03: Clean Imports (2/2) — All imports used (matplotlib.pyplot, transforms, numpy, pandas, seaborn, FontProperties, PathPatch, TextPath)
• CQ-04: Code Elegance (2/2) — Clean and well-structured; TextPath/PathPatch approach is the correct technique for scaled letter glyphs
• CQ-05: Output & API (1/1) — Saves as plot.png, dpi=300, bbox_inches='tight', no deprecated API

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of sns.set_context(), sns.set_style(), sns.despine(), sns.heatmap(), sns.color_palette(), sns.light_palette(). The logo itself necessarily uses matplotlib primitives since seaborn has no sequence logo function
• LM-02: Distinctive Features (3/5) — sns.heatmap() with annotations as complementary panel, sns.light_palette() for custom colormap, sns.despine() for clean styling — these are distinctively seaborn features

Score Caps Applied

• None

Strengths

• Excellent two-panel design combining sequence logo with frequency heatmap for complementary views
• Properly rendered letter glyphs using TextPath/PathPatch (correct technique for sequence logos)
• Strong data storytelling with highlighted most-conserved position and annotation arrow
• Colored heatmap y-axis labels matching logo colors — thoughtful design touch
• Well-chosen data showing full range of conservation levels including uniform position

Weaknesses

• Minor colorblind concern with green-red pairing (though this is the standard bioinformatics convention)

Issues Found

None — implementation meets all requirements.

AI Feedback for Next Attempt

N/A — Implementation approved.

Verdict: APPROVED