chore(matplotlib): update quality score 96 and review feedback for facet-grid

Author: github-actions[bot]

plots/facet-grid/implementations/python/matplotlib.py

@@ -1,7 +1,7 @@
-"""anyplot.ai
+""" anyplot.ai
 facet-grid: Faceted Grid Plot
 Library: matplotlib 3.10.9 | Python 3.13.13
-Quality: 87/100 | Updated: 2026-05-13
+Quality: 96/100 | Updated: 2026-05-13
 """
 
 import os

plots/facet-grid/metadata/python/matplotlib.yaml

@@ -2,7 +2,7 @@ library: matplotlib
 language: python
 specification_id: facet-grid
 created: '2025-12-30T16:30:15Z'
-updated: '2026-05-13T03:26:12Z'
+updated: '2026-05-13T03:32:20Z'
 generated_by: claude-haiku
 workflow_run: 25776150899
 issue: 2696
@@ -12,37 +12,33 @@ preview_url_light: https://storage.googleapis.com/anyplot-images/plots/facet-gri
 preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/facet-grid/python/matplotlib/plot-dark.png
 preview_html_light: null
 preview_html_dark: null
-quality_score: 87
+quality_score: 96
 review:
   strengths:
-  - Perfect visual quality with excellent readability in both light and dark themes
-  - Flawless palette compliance using Okabe-Ito colors in canonical order with theme-correct
-    chrome
-  - 'All specification requirements met: row/column faceting, shared axes, descriptive
-    labels with units'
-  - Clean, reproducible code following KISS structure with explicit theme-aware styling
-  - Well-chosen realistic data showing variation across regions and seasons with clear
-    pattern demonstration
-  weaknesses:
-  - Design Excellence score relies on standard library defaults with custom colors
-    but minimal visual refinement beyond that; could benefit from more intentional
-    typographic hierarchy or subtle visual touches
-  - Data storytelling is functional but lacks additional visual emphasis or focal
-    points that would elevate insight communication
-  - Library Mastery is generic; no distinctive matplotlib features (GridSpec, inset
-    axes, transforms) are leveraged for specialized control or visual enhancement
+  - Excellent text legibility with all font sizes explicitly set and readable in both
+    themes
+  - Perfect palette compliance — Okabe-Ito colors used correctly with region-to-color
+    mapping
+  - Sophisticated faceted grid layout with proper spacing and clear region/season
+    labels
+  - Polished visual design with theme-adaptive chrome that works flawlessly in both
+    light and dark
+  - Clean code structure with GridSpec for advanced layout control and scipy trend
+    line analysis
+  - Well-executed data storytelling through visual hierarchy and trend line emphasis
+  weaknesses: []
   image_description: |-
     Light render (plot-light.png):
-      Background: Warm off-white (#FAF8F1) - correct light theme surface
-      Chrome: Title, column headers, row labels, axis labels all in dark INK color (#1A1A17) - fully readable against light background
-      Data: Green (#009E73) points for North region, orange (#D55E00) for South, blue (#0072B2) for East - Okabe-Ito canonical order maintained
-      Legibility verdict: PASS - all text explicitly sized (title=24pt, headers=18pt, labels=20pt, ticks=14pt) with perfect contrast
+      Background: Warm off-white (#FAF8F1), appropriate and never pure white
+      Chrome: Title "facet-grid · matplotlib · anyplot.ai" (26pt, semibold) is bold and prominent. Region labels (North, South, East) and season headers (Spring, Summer, Fall, Winter) are clearly visible at 20pt. X-axis label "Temperature (°C)" and Y-axis label "Energy Consumption (kWh)" are centered below and to the left at 22pt. All tick labels (14pt) are readable. Text colors are INK (#1A1A17) for primary, INK_SOFT (#4A4A44) for secondary
+      Data: Each of 12 facets shows scatter points with white edges (s=120, alpha=0.7) colored by region: North = #009E73 (Okabe-Ito green), South = #D55E00 (vermillion), East = #0072B2 (blue). Subtle trend lines (alpha=0.4) show temperature-energy relationships. Grid lines very subtle (alpha=0.10 y-axis, 0.08 x-axis)
+      Legibility verdict: PASS - all text is dark, high-contrast against light background
 
     Dark render (plot-dark.png):
-      Background: Warm near-black (#1A1A17) - correct dark theme surface
-      Chrome: All text elements (title, headers, axis labels, ticks) render in light INK color (#F0EFE8) - fully readable, no dark-on-dark failures
-      Data: Green, orange, and blue point colors identical to light render - theme-consistent data representation
-      Legibility verdict: PASS - theme adaptation seamless; all chrome light-colored with perfect contrast against dark background
+      Background: Warm near-black (#1A1A17), never pure black, provides excellent contrast
+      Chrome: Title, labels, and tick labels are all light (INK #F0EFE8 for primary, INK_SOFT #B8B7B0 for secondary) and clearly readable against dark background. No dark-on-dark failures. Spine colors are appropriately adapted (INK_SOFT)
+      Data: Scatter colors are identical to light render — North #009E73, South #D55E00, East #0072B2. White edges on markers become even more prominent and well-defined on dark background. Trend lines remain subtle. Grid lines visible but appropriately dim
+      Legibility verdict: PASS - all text is light, high-contrast against dark background; perfect theme adaptation
   criteria_checklist:
     visual_quality:
       score: 30
@@ -53,67 +49,75 @@ review:
         score: 8
         max: 8
         passed: true
-        comment: All fonts explicitly sized; perfectly readable in both themes
+        comment: All font sizes explicitly set (title 26pt, labels 20-22pt, ticks
+          14pt); readable at full resolution in both themes
       - id: VQ-02
         name: No Overlap
         score: 6
         max: 6
         passed: true
-        comment: No overlapping text; clean layout
+        comment: No overlapping text; x-labels only on bottom row, y-labels only on
+          left column
       - id: VQ-03
         name: Element Visibility
         score: 6
         max: 6
         passed: true
-        comment: Marker size (s=120) optimal for data density
+        comment: Marker size (s=120) and alpha (0.7) optimal for ~25 points per facet;
+          trend lines clearly visible
       - id: VQ-04
         name: Color Accessibility
         score: 2
         max: 2
         passed: true
-        comment: CVD-safe Okabe-Ito palette with good contrast
+        comment: Okabe-Ito palette with white edges for definition; colorblind-safe
+          and high contrast
       - id: VQ-05
         name: Layout & Canvas
         score: 4
         max: 4
         passed: true
-        comment: Perfect layout with balanced margins
+        comment: 16x9 figure at 300dpi = 4800x2700px; plot fills 60-70% with balanced
+          whitespace; GridSpec spacing optimal
       - id: VQ-06
         name: Axis Labels & Title
         score: 2
         max: 2
         passed: true
-        comment: Descriptive labels with units
+        comment: 'Labels include units: Temperature (°C), Energy Consumption (kWh);
+          title format correct'
       - id: VQ-07
         name: Palette Compliance
         score: 2
         max: 2
         passed: true
-        comment: Okabe-Ito first series (#009E73); theme-correct chrome in both renders
+        comment: 'First series #009E73 (Okabe-Ito pos 1), backgrounds #FAF8F1/#1A1A17,
+          chrome theme-correct in both renders'
     design_excellence:
-      score: 12
+      score: 17
       max: 20
       items:
       - id: DE-01
         name: Aesthetic Sophistication
-        score: 5
+        score: 6
         max: 8
-        passed: false
-        comment: Custom color assignment is thoughtful but relies on defaults for
-          refinement
+        passed: true
+        comment: Strong design with theme-adaptive colors, white edges on markers,
+          refined trend line styling. More than configured defaults but not cutting-edge
       - id: DE-02
         name: Visual Refinement
-        score: 4
+        score: 6
         max: 6
-        passed: false
-        comment: Subtle grid and spine removal present; good but could be more polished
+        passed: true
+        comment: Spines removed (L-shape), grid very subtle, whitespace generous,
+          all details polished
       - id: DE-03
         name: Data Storytelling
-        score: 3
+        score: 5
         max: 6
-        passed: false
-        comment: Faceting shows patterns; lacks additional visual emphasis or focal
-          points
+        passed: true
+        comment: Faceted layout guides viewer through regions and seasons; trend lines
+          highlight patterns; color consistency aids narrative
     spec_compliance:
       score: 15
       max: 15
@@ -123,25 +127,27 @@ review:
         score: 5
         max: 5
         passed: true
-        comment: Correct faceted scatter grid
+        comment: Perfect 3x4 faceted scatter grid with trend lines
       - id: SC-02
         name: Required Features
         score: 4
         max: 4
         passed: true
-        comment: 'All features present: row/column facets, shared axes, labels'
+        comment: 'All spec features: shared axes, row/column faceting, labels identifying
+          categories'
       - id: SC-03
         name: Data Mapping
         score: 3
         max: 3
         passed: true
-        comment: X/Y correctly assigned; all data visible
+        comment: X/Y correctly mapped; all data visible; axes appropriate
       - id: SC-04
         name: Title & Legend
         score: 3
         max: 3
         passed: true
-        comment: Title format correct; row labels serve as identifiers
+        comment: Title format correct; legend not needed (region colors identified
+          by row labels)
     data_quality:
       score: 15
       max: 15
@@ -151,20 +157,21 @@ review:
         score: 6
         max: 6
         passed: true
-        comment: 'Shows all aspects: temperature variation, energy patterns, seasonal
-          effects'
+        comment: 'Shows all dimensions: region variation, seasonal variation, temperature-energy
+          relationship'
       - id: DQ-02
         name: Realistic Context
         score: 5
         max: 5
         passed: true
-        comment: Realistic scenario; neutral topic
+        comment: HVAC/energy scenario is realistic, neutral, and comprehensible
       - id: DQ-03
         name: Appropriate Scale
         score: 4
         max: 4
         passed: true
-        comment: Factually correct proportions; realistic values
+        comment: Temperature ranges plausible (0-30°C), energy consumption realistic
+          (100-180 kWh), U-shaped curve physically accurate
     code_quality:
       score: 10
       max: 10
@@ -174,58 +181,62 @@ review:
         score: 3
         max: 3
         passed: true
-        comment: Clean Imports → Data → Plot → Save structure
+        comment: 'Linear flow: imports → tokens → data → plot → save; no functions/classes'
       - id: CQ-02
         name: Reproducibility
         score: 2
         max: 2
         passed: true
-        comment: np.random.seed(42) set
+        comment: np.random.seed(42) set; deterministic data generation
       - id: CQ-03
         name: Clean Imports
         score: 2
         max: 2
         passed: true
-        comment: Only used imports
+        comment: Only used imports; scipy.stats used for linregress
       - id: CQ-04
         name: Code Elegance
         score: 2
         max: 2
         passed: true
-        comment: Clean, Pythonic, no fake functionality
+        comment: Clean, appropriate complexity, no over-engineering or fake functionality
       - id: CQ-05
         name: Output & API
         score: 1
         max: 1
         passed: true
-        comment: Correct plot-{THEME}.png naming
+        comment: Saves as plot-{THEME}.png; current API; no deprecated functions
     library_mastery:
-      score: 5
+      score: 9
       max: 10
       items:
       - id: LM-01
         name: Idiomatic Usage
-        score: 4
+        score: 5
         max: 5
         passed: true
-        comment: Correct plt.subplots() and Axes methods; good patterns
+        comment: Expert use of GridSpec, ax.scatter(), ax.plot(), ax.spines, fig.text(),
+          fig.suptitle(); all idiomatic
       - id: LM-02
         name: Distinctive Features
-        score: 1
+        score: 4
         max: 5
-        passed: false
-        comment: Generic faceted grid; no distinctive matplotlib features
+        passed: true
+        comment: Uses GridSpec for sophisticated layout, scipy.stats for trend lines,
+          color mapping dictionary; more than generic but not exceptionally creative
   verdict: REJECTED
 impl_tags:
-  dependencies: []
+  dependencies:
+  - scipy
   techniques:
   - subplots
-  - manual-ticks
+  - faceting
   patterns:
   - data-generation
   - iteration-over-groups
-  - explicit-figure
-  dataprep: []
+  dataprep:
+  - regression
   styling:
+  - publication-ready
+  - edge-highlighting
   - grid-styling
-  - alpha-blending