chore(pygal): update quality score 80 and review feedback for line-retention-cohort

Author: github-actions[bot]

plots/line-retention-cohort/implementations/pygal.py

@@ -1,7 +1,7 @@
-"""pyplots.ai
+""" pyplots.ai
 line-retention-cohort: User Retention Curve by Cohort
-Library: pygal | Python 3.13
-Quality: pending | Created: 2026-03-16
+Library: pygal 3.1.0 | Python 3.14.3
+Quality: 80/100 | Created: 2026-03-16
 """
 
 import numpy as np

plots/line-retention-cohort/metadata/pygal.yaml

@@ -1,10 +1,7 @@
-# Per-library metadata for pygal implementation of line-retention-cohort
-# Auto-generated by impl-generate.yml
-
 library: pygal
 specification_id: line-retention-cohort
 created: '2026-03-16T20:43:49Z'
-updated: '2026-03-16T20:43:49Z'
+updated: '2026-03-16T20:48:08Z'
 generated_by: claude-opus-4-5-20251101
 workflow_run: 23164943848
 issue: 4572
@@ -13,7 +10,210 @@ library_version: 3.1.0
 preview_url: https://storage.googleapis.com/pyplots-images/plots/line-retention-cohort/pygal/plot.png
 preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-retention-cohort/pygal/plot_thumb.png
 preview_html: https://storage.googleapis.com/pyplots-images/plots/line-retention-cohort/pygal/plot.html
-quality_score: null
+quality_score: 80
 review:
-  strengths: []
-  weaknesses: []
+  strengths:
+  - Excellent data quality with realistic retention scenario and improving cohort
+    trend
+  - Perfect code quality — clean KISS structure with seed for reproducibility
+  - Correct title format and legend labels with cohort sizes
+  - Custom color palette with good accessibility
+  weaknesses:
+  - No visual differentiation between older and newer cohorts (opacity/line thickness)
+    as suggested by spec
+  - No reference/threshold line to provide benchmark context
+  - Lines converge in later weeks without visual emphasis to guide the viewer
+  - Lower chart area (0-20%) is mostly wasted space
+  image_description: The plot displays a line chart with 5 cohort curves (Jan–May
+    2025) on a white background. Each line starts at 100% retention at week 0 and
+    decays over 12 weeks. Colors used are steel blue (Jan), coral/salmon (Feb), muted
+    green (Mar), gold/amber (Apr), and muted purple (May). The Y-axis is labeled "Retained
+    Users (%)" ranging 0–100 with horizontal gridlines every 10%. The X-axis is labeled
+    "Weeks Since Signup" with values 0–12. The title reads "line-retention-cohort
+    · pygal · pyplots.ai" at top center. A legend at the bottom shows each cohort
+    with its sample size (e.g., "Jan 2025 (n=1,245)"). Dots mark each data point on
+    the lines. Older cohorts (Jan) decay fastest to ~21%, while newer cohorts (May)
+    retain ~40% by week 12, showing an improvement trend.
+  criteria_checklist:
+    visual_quality:
+      score: 26
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 7
+        max: 8
+        passed: true
+        comment: All font sizes explicitly set and readable; slightly oversized relative
+          to library guide but effective
+      - id: VQ-02
+        name: No Overlap
+        score: 6
+        max: 6
+        passed: true
+        comment: No overlapping text anywhere; legend well-separated from data
+      - id: VQ-03
+        name: Element Visibility
+        score: 5
+        max: 6
+        passed: true
+        comment: Lines and dots visible but converge in later weeks making series
+          harder to distinguish
+      - id: VQ-04
+        name: Color Accessibility
+        score: 4
+        max: 4
+        passed: true
+        comment: Five distinct hues with good contrast; no pure red-green pairing
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 2
+        max: 4
+        passed: true
+        comment: Decent canvas usage but significant empty space in lower portion
+          (0-20% range mostly unused)
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: Descriptive labels with units on both axes
+    design_excellence:
+      score: 10
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 5
+        max: 8
+        passed: true
+        comment: Custom palette, white background, explicit font sizing — above defaults
+          but not publication-level
+      - id: DE-02
+        name: Visual Refinement
+        score: 3
+        max: 6
+        passed: true
+        comment: Subtle gray grid, X-guides hidden, legend at bottom; some refinement
+          visible
+      - id: DE-03
+        name: Data Storytelling
+        score: 2
+        max: 6
+        passed: false
+        comment: Data shows trend but no visual emphasis — identical line weight/opacity,
+          no reference line
+    spec_compliance:
+      score: 14
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: Correct line chart with multiple cohorts
+      - id: SC-02
+        name: Required Features
+        score: 3
+        max: 4
+        passed: true
+        comment: Missing opacity/thickness differentiation and reference line
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: X = weeks, Y = retention %, cohort = series — all correct
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Correct title format and legend labels with cohort sizes
+    data_quality:
+      score: 14
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 5
+        max: 6
+        passed: true
+        comment: 5 cohorts with different decay rates showing improving retention
+          trend
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: Realistic product analytics scenario with plausible cohort sizes
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: Retention 5-100%, cohort sizes ~1000-1600, 12-week tracking — all
+          realistic
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: Clean imports → data → style → chart → save structure
+      - id: CQ-02
+        name: Reproducibility
+        score: 2
+        max: 2
+        passed: true
+        comment: np.random.seed(42) set
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: All imports used
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Clean, appropriate complexity with realistic decay model
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png, current API
+    library_mastery:
+      score: 6
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 4
+        max: 5
+        passed: true
+        comment: Good use of pygal.Line, Style, legend_at_bottom, truncate_legend,
+          range
+      - id: LM-02
+        name: Distinctive Features
+        score: 2
+        max: 5
+        passed: false
+        comment: Uses truncate_legend=-1 and stroke_style but doesn't leverage pygal's
+          interactive SVG or other distinctive features
+  verdict: REJECTED
+impl_tags:
+  dependencies: []
+  techniques: []
+  patterns:
+  - data-generation
+  - iteration-over-groups
+  dataprep: []
+  styling:
+  - grid-styling