update(scatter-basic): plotly — comprehensive quality review

[MarkusNeusinger]

Summary

Updated LIB_PLACEHOLDER implementation for scatter-basic.

Changes: Comprehensive quality review — improved variable naming, realistic data context, white marker edges for point definition, subtler grid styling, and explicit font sizing for large canvas.

Changes

• Descriptive variable names with realistic data context
• White marker edges for better point definition
• Subtler grid styling (opacity ~0.15-0.2)
• Explicit font sizing for 4800x2700 canvas
• Library-specific improvements for idiomatic usage

Test Plan

• Preview images uploaded to GCS staging
• Implementation file passes ruff format/check
• Metadata YAML updated with current versions
• Automated review triggered

---

Generated with Claude Code /update command

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a scatter plot of 120 data points representing student study hours (x-axis, 1–10 h) versus exam scores (y-axis, ~44–100%). Markers are steel-blue (#306998) circles with white edge lines and moderate transparency (alpha 0.7). The title "scatter-basic · plotly · pyplots.ai" is centered at the top in a large font. Both axes have descriptive labels with units — "Study Hours (h)" and "Exam Score (%)". Light gray grid lines are present on a white background (plotly_white template). The data shows a clear positive linear correlation with natural scatter and a few visible outliers (e.g., high study hours with low scores, and low study hours with high scores). The layout is clean with balanced margins and good canvas utilization.

Quality Score: 76/100

Criteria Checklist

Visual Quality (25/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 28pt, axis labels 22pt, tick labels 18pt. All perfectly readable at 4800×2700.
• VQ-02: No Overlap (6/6) - No overlapping text or data elements. All labels and ticks are well-spaced.
• VQ-03: Element Visibility (5/6) - Markers at size 14 with alpha 0.7 for 120 points. Per the guidelines, 100-300 points should use marker size 50-100 (matplotlib s= equivalent). Plotly's size=14 with scale=3 renders reasonably, but markers could be slightly larger for optimal visibility at this density.
• VQ-04: Color Accessibility (4/4) - Single-series using Python Blue (#306998), no colorblind concerns.
• VQ-05: Layout & Canvas (0/4) - The plot area fills a reasonable portion of the canvas, but the data is somewhat concentrated in the lower-left to upper-right diagonal, leaving significant empty space in the upper-left and lower-right corners. However, the primary issue: the y-axis range starts at ~44 and the x-axis at ~0.5, with substantial whitespace above and to the right of the data cloud. The margins are well-balanced though. Score: 2/4.
• VQ-06: Axis Labels & Title (2/2) - Descriptive labels with units: "Study Hours (h)" and "Exam Score (%)".

Correction on VQ-05: 2/4 — Layout is acceptable but the plot area could use the canvas more efficiently. Total Visual Quality: 27/30.

Design Excellence (8/20)

• DE-01: Aesthetic Sophistication (4/8) - Clean plotly_white template, Python Blue color, white marker edges add polish. However, this looks like a well-configured default rather than exceptional design. No custom palette, no typographic hierarchy beyond size.
• DE-02: Visual Refinement (4/6) - Subtle grid lines with reduced opacity (0.15), clean template with spines removed, good whitespace. Some refinement visible.
• DE-03: Data Storytelling (0/6) - No annotations, no trend line, no callouts for the outliers that were specifically injected. The outliers at (8.5, 52), (2.0, 78), and (9.2, 55) are present in the data but the viewer has no guidance to notice them. Data is displayed but not interpreted.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) - Correct scatter plot type.
• SC-02: Required Features (3/4) - Spec calls for transparency (present), axis labels (present), title (present), grid lines (present). However, spec notes "Consider point size that balances visibility with overlap clarity" — markers are a touch large for the density, causing some overlap where clusters are tight.
• SC-03: Data Mapping (3/3) - X (study hours) and Y (exam scores) correctly assigned. All data visible.
• SC-04: Title & Legend (3/3) - Title uses exact format "scatter-basic · plotly · pyplots.ai". No legend needed for single-series.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows positive correlation, scatter/noise, and injected outliers. Good variety. Missing: no negative correlation or non-linear pattern, but for a basic scatter this is adequate. Could show more diversity in the scatter pattern.
• DQ-02: Realistic Context (5/5) - Study hours vs exam scores is a real, comprehensible, neutral educational scenario.
• DQ-03: Appropriate Scale (4/4) - Study hours 1-10, exam scores 0-100, perfectly realistic values.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean Import → Data → Plot → Save flow, no functions or classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) is set.
• CQ-03: Clean Imports (2/2) - Only numpy and plotly.graph_objects imported, both used.
• CQ-04: Code Elegance (2/2) - Clean, Pythonic, appropriate complexity. Dict-style parameters are clear.
• CQ-05: Output & API (1/1) - Saves as plot.png at 4800×2700 via write_image() with correct dimensions.

Library Mastery (5/10)

• LM-01: Idiomatic Usage (3/5) - Uses go.Figure() + add_trace() which is correct but verbose for a simple scatter. Could use plotly.express for a more idiomatic single-scatter plot. The graph_objects approach is more appropriate for complex plots.
• LM-02: Distinctive Features (2/5) - Uses Plotly-specific hovertemplate for interactive hover customization, and write_html() for interactive output alongside PNG. These are somewhat distinctive but the hover template won't be visible in the static PNG output.

Strengths

• Excellent text legibility with all font sizes explicitly set to appropriate values
• Clean, realistic educational dataset with intentional outliers showing scatter plot utility
• Proper use of plotly_white template creating a professional, clean appearance
• Good code structure following KISS principle with reproducible seed
• Interactive HTML export alongside PNG is a nice Plotly-specific touch

Weaknesses

• No data storytelling: the injected outliers (high-effort/low-result students, low-effort/high-result students) are present but not annotated or highlighted — this is a missed opportunity to demonstrate scatter plot's outlier-detection value as mentioned in the code comments
• No trend line or regression line to visualize the correlation, which would add analytical value
• Design is clean but generic — looks like a configured default rather than publication-quality design
• Graph Objects API is more verbose than needed; Plotly Express would be more idiomatic for this simple use case

Verdict: REJECTED

[Copilot]

Pull request overview

This pull request updates the plotly implementation for the scatter-basic plot specification with comprehensive quality improvements. The changes focus on enhanced visual styling, better variable naming, and more descriptive data context to create a more professional and informative visualization.

Changes:

• Improved variable naming with realistic educational context (study hours vs exam scores)
• Enhanced visual elements including white marker edges and subtle grid styling
• Added intentional outliers to demonstrate scatter plot's analytical value
• Updated library versions (plotly 6.5.0 → 6.5.2, Python 3.13.11 → 3.14)

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 4 comments.

File	Description
plots/scatter-basic/metadata/plotly.yaml	Updated metadata with new library versions, timestamp, and reset quality_score to null
plots/scatter-basic/implementations/plotly.py	Enhanced implementation with better naming, outlier injection, improved styling, and richer hover interactivity

[Copilot]

The docstring header format is inconsistent with the established convention in the codebase. The majority of implementations use a space after the opening triple quotes. The header should be """ pyplots.ai (with a space) rather than """pyplots.ai (without a space) to match the convention seen in most other plot implementations like matplotlib, seaborn, bokeh, altair, etc.

Suggested change

	"""pyplots.ai
	""" pyplots.ai

[Copilot]

The date "2026-02-14" is in the future. The current date according to the system context is February 14, 2026, which means this is today's date. However, the timestamp format is inconsistent with the original format "2025-12-22T23:38:17Z" which included time information. The new format "2026-02-14T14:28:20+00:00" has a different timezone indicator (+00:00 vs Z) which is semantically equivalent but stylistically inconsistent.

[Copilot]

The quality_score is set to null, but the header comment in the implementation file shows "Quality: /100 | Updated: 2026-02-14". This inconsistency suggests the quality score should either be populated in the metadata file or the header should indicate it's pending (e.g., "Quality: Pending" or "Quality: TBD"). Setting it to null when an update is made is unusual - typically updates either maintain the previous score or get a new score from automated review.

Suggested change

	quality_score: null
	quality_score: 92

[Copilot]

The PR description mentions "LIB_PLACEHOLDER" which appears to be a placeholder that was not replaced with the actual library name "plotly". This is a discrepancy between the PR description and the actual code changes - the description template was not fully filled in.

[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 scatter plot of "Study Hours (h)" (x-axis, range 0–11) vs "Exam Score (%)" (y-axis, range 35–105) for ~120 students. Blue circle markers (#306998) with white edges and ~65% opacity form the main data cloud, showing a clear positive correlation. A dashed blue trend line runs from lower-left to upper-right. Two orange diamond markers (#D4782F) highlight outliers: one at (~2.0, 78) annotated "Low effort, high score" and two at (~8.5, 52) and (~9.2, 55) with an annotation "High effort, low score". The Pearson correlation coefficient (r = 0.79) is displayed near the upper-right portion of the trend line. The title "scatter-basic · plotly · pyplots.ai" is centered at the top in dark bold font. The background is off-white (#FAFBFC) with very subtle grid lines. Layout is clean with balanced margins.

Quality Score: 88/100

Criteria Checklist

Visual Quality (26/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 28pt, axis labels 22pt, ticks 18pt, annotations 16pt
• VQ-02: No Overlap (5/6) - Mostly clean; "High effort, low score" annotation placed below both orange diamonds rather than clearly pointing to one specific outlier
• VQ-03: Element Visibility (5/6) - Marker size 15 with opacity 0.65 for 120 points is reasonable but slightly large, causing some visual congestion in the lower-left cluster
• VQ-04: Color Accessibility (4/4) - Blue/orange palette is colorblind-safe with strong contrast
• VQ-05: Layout Balance (3/4) - Good proportions (~60% canvas utilization), some unused space in upper-left
• VQ-06: Axis Labels & Title (2/2) - "Study Hours (h)" and "Exam Score (%)" — descriptive with units

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) - Strong design: custom Python blue/orange palette, white marker edges, custom background, plotly_white template — clearly above defaults
• DE-02: Visual Refinement (5/6) - Subtle grid (rgba 0.08 opacity), zeroline removed, custom dtick spacing, near-polished
• DE-03: Data Storytelling (5/6) - Annotated outliers tell a compelling story, correlation coefficient displayed, orange diamonds guide the eye to insights

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct 2D scatter plot
• SC-02: Required Features (4/4) - Transparency, axis labels, title, grid lines, appropriate point sizes — all present
• SC-03: Data Mapping (3/3) - X/Y correctly assigned, all data within axis range
• SC-04: Title & Legend (3/3) - "scatter-basic · plotly · pyplots.ai" format correct, no legend needed for single-series

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows correlation, scatter pattern, outliers, and range; could add density variation
• DQ-02: Realistic Context (5/5) - Study hours vs exam scores — neutral educational scenario
• DQ-03: Appropriate Scale (4/4) - 120 students, 1-10 hours, 0-100% scores, plausible values

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Imports → Data → Plot → Save, no functions/classes
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only numpy and plotly.graph_objects
• CQ-04: Code Elegance (1/2) - Annotation loop is somewhat verbose; dict-building could be more concise
• CQ-05: Output & API (1/1) - Saves plot.png at 4800×2700, plus interactive HTML

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) - Good go.Figure/go.Scatter usage, dict-based config, effective hovertemplate
• LM-02: Distinctive Features (4/5) - Plotly-specific: hovertemplate, hoverlabel styling, diamond symbol, HTML export with interactivity

Strengths

• Excellent data storytelling with annotated outliers and correlation coefficient — the annotations add narrative value beyond raw data display
• Strong color design with Python blue (#306998) and accent orange (#D4782F) creating a cohesive, professional palette with white marker edges
• Good use of Plotly-distinctive features: interactive hover templates, HTML export, hoverlabel customization
• Clean visual refinement: subtle grid (0.08 opacity), plotly_white template, custom background, dtick spacing
• Realistic educational dataset with intentional outliers demonstrating the scatter plot's outlier-detection purpose

Weaknesses

• Marker size 15 is slightly too large for 120 data points, causing visual congestion in the dense lower-left region (1-2h study hours, 44-58% scores) — reducing to ~11-12 would improve readability
• The "High effort, low score" annotation arrow points between the two orange diamonds at (8.5, 52) and (9.2, 55) rather than clearly connecting to one; repositioning to annotate both distinctly or adjusting arrow target would be clearer
• Annotation loop (lines 86-107) is more verbose than needed — the dict-building with append could be simplified

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 scatter plot of "Study Hours (h)" (x-axis, range 0–11) vs "Exam Score (%)" (y-axis, range ~35–105) with 120 blue circular markers (#306998) at opacity 0.6 with white edge outlines on a white background with very subtle gray grid lines. A dashed blue trend line shows the positive correlation. Three orange (#D4782F) diamond markers highlight outliers: "Low effort, high score" at (~2h, 78%), "High effort, low score" at (~8.5h, 52%), and "Underperformer" at (~9.2h, 55%). Each outlier has a bordered orange annotation box with an arrow pointing to the marker. The correlation coefficient "r = 0.79" is displayed in blue near the upper portion of the trend line. The title "scatter-basic · plotly · pyplots.ai" is centered at the top in dark bold text. The paper background is a light off-white (#FAFBFC). Overall the plot has a clean, professional appearance with clear data storytelling.

Quality Score: 92/100

Criteria Checklist

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 28pt, axis labels 22pt, ticks 18pt, annotations 16-17pt
• VQ-02: No Overlap (5/6) - Minor congestion between "High effort, low score" and "Underperformer" annotations in bottom-right
• VQ-03: Element Visibility (6/6) - 120 points at size 11, opacity 0.6 — well adapted; outlier diamonds larger at size 15
• VQ-04: Color Accessibility (4/4) - Blue/orange pair is colorblind-safe with good contrast
• VQ-05: Layout Balance (2/4) - Asymmetric margins (L=80, R=40); some wasted space on left side of the plot area
• VQ-06: Axis Labels & Title (2/2) - "Study Hours (h)" and "Exam Score (%)" — descriptive with units

Design Excellence (17/20)

• DE-01: Aesthetic Sophistication (7/8) - Custom palette, intentional typography hierarchy, white marker edges, subtle grid, off-white paper — near publication quality
• DE-02: Visual Refinement (5/6) - plotly_white template, 0.06 opacity grid, zeroline removed, generous standoff — well polished
• DE-03: Data Storytelling (5/6) - Three annotated outliers with descriptive labels, trend line with r=0.79 guides interpretation effectively

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct 2D scatter plot
• SC-02: Required Features (4/4) - Transparency, axis labels, title, grid lines, appropriate point size all present
• SC-03: Data Mapping (3/3) - X=study hours, Y=exam scores correctly assigned
• SC-04: Title Format (3/3) - "scatter-basic · plotly · pyplots.ai" correct; no legend needed for single-series

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows correlation, scatter distribution, outliers, and trend — all aspects of scatter plots
• DQ-02: Realistic Context (5/5) - Study hours vs exam scores — neutral educational scenario
• DQ-03: Appropriate Scale (4/4) - Hours 1-10, scores 0-100% — perfectly realistic ranges

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Imports → Data → Plot → Save, no functions/classes
• CQ-02: Reproducibility (2/2) - np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only numpy and plotly.graph_objects
• CQ-04: Code Elegance (2/2) - Clean dict-spread pattern for annotations, appropriate complexity
• CQ-05: Output & API (1/1) - Saves plot.png at 4800×2700 via write_image

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) - Good Graph Objects API usage with proper modes, hovertemplate, and update_layout
• LM-02: Distinctive Features (4/5) - hovertemplate, write_html for interactivity, hoverlabel styling — Plotly-distinctive

Strengths

• Excellent data storytelling with three annotated outliers and correlation coefficient — goes well beyond just plotting points
• Professional aesthetic: custom blue/orange palette, white marker edges, subtle grid, off-white paper background
• All font sizes explicitly set and well-calibrated for 4800×2700 output
• Plotly-distinctive features used effectively (hovertemplate, HTML export, hoverlabel styling)
• Clean, well-structured code with elegant annotation dict-spread pattern

Weaknesses

• Minor annotation congestion in the bottom-right between "High effort, low score" (8.5, 52) and "Underperformer" (9.2, 55) — arrows nearly overlap
• Layout margins asymmetric (L=80 vs R=40) leading to slightly unbalanced whitespace distribution

Verdict: APPROVED