chore(seaborn): add metadata for radar-basic

Author: github-actions[bot]

plots/radar-basic/implementations/python/seaborn.py

@@ -1,75 +1,96 @@
-""" pyplots.ai
+"""anyplot.ai
 radar-basic: Basic Radar Chart
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: seaborn | Python 3.13
+Quality: 91/100 | Updated: 2026-04-29
 """
 
+import os
+
 import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 import seaborn as sns
 
 
+# Theme tokens
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+
+OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+
+sns.set_theme(
+    style="ticks",
+    rc={
+        "figure.facecolor": PAGE_BG,
+        "axes.facecolor": PAGE_BG,
+        "axes.edgecolor": INK_SOFT,
+        "axes.labelcolor": INK,
+        "text.color": INK,
+        "xtick.color": INK_SOFT,
+        "ytick.color": INK_SOFT,
+        "grid.color": INK_SOFT,
+        "grid.alpha": 0.15,
+        "legend.facecolor": ELEVATED_BG,
+        "legend.edgecolor": INK_SOFT,
+    },
+)
+
 # Data - Employee performance comparison across competencies
 categories = ["Communication", "Technical Skills", "Teamwork", "Leadership", "Problem Solving", "Creativity"]
 employee_a_values = [85, 90, 75, 70, 88, 82]  # Senior Developer
 employee_b_values = [78, 65, 92, 85, 72, 75]  # Team Lead
 
-# Setup for radar chart
 n_vars = len(categories)
 angles = np.linspace(0, 2 * np.pi, n_vars, endpoint=False).tolist()
 angles += angles[:1]  # Close the polygon
 
-# Close the polygons
 employee_a = employee_a_values + employee_a_values[:1]
 employee_b = employee_b_values + employee_b_values[:1]
 
-# Create DataFrame for seaborn plotting
-df = pd.DataFrame(
-    {
-        "Category": categories * 2,
-        "Score": employee_a_values + employee_b_values,
-        "Employee": ["Senior Developer"] * n_vars + ["Team Lead"] * n_vars,
-        "angle": (angles[:-1] * 2),
-    }
-)
-
-# Apply seaborn styling with context for proper scaling
-sns.set_theme(style="whitegrid", context="poster", font_scale=1.2)
-palette = sns.color_palette("colorblind", 2)
-
-# Create square figure for radar chart (3600x3600 at 300 dpi = 12x12 inches)
-fig, ax = plt.subplots(figsize=(12, 12), subplot_kw={"projection": "polar"})
+# Plot - square canvas for symmetric radar chart (3600x3600 at 300 dpi)
+fig, ax = plt.subplots(figsize=(12, 12), subplot_kw={"projection": "polar"}, facecolor=PAGE_BG)
+ax.set_facecolor(PAGE_BG)
 
-# Use seaborn scatterplot for data points on the polar axes
-sns.scatterplot(data=df, x="angle", y="Score", hue="Employee", palette=palette, s=400, ax=ax, legend=False, zorder=5)
+color_a = OKABE_ITO[0]  # #009E73 - Senior Developer (first series)
+color_b = OKABE_ITO[1]  # #D55E00 - Team Lead
 
-# Draw filled polygons and lines (matplotlib needed for fill and closed polygon)
-color_senior = palette[0]
-color_lead = palette[1]
+ax.fill(angles, employee_a, alpha=0.25, color=color_a)
+ax.plot(angles, employee_a, color=color_a, linewidth=3.5, label="Senior Developer")
+ax.scatter(angles[:-1], employee_a_values, color=color_a, s=150, zorder=5)
 
-ax.fill(angles, employee_a, alpha=0.25, color=color_senior)
-ax.plot(angles, employee_a, color=color_senior, linewidth=4, label="Senior Developer")
+ax.fill(angles, employee_b, alpha=0.25, color=color_b)
+ax.plot(angles, employee_b, color=color_b, linewidth=3.5, label="Team Lead")
+ax.scatter(angles[:-1], employee_b_values, color=color_b, s=150, zorder=5)
 
-ax.fill(angles, employee_b, alpha=0.25, color=color_lead)
-ax.plot(angles, employee_b, color=color_lead, linewidth=4, label="Team Lead")
-
-# Configure axes with larger tick labels
+# Style axes
 ax.set_xticks(angles[:-1])
-ax.set_xticklabels(categories, fontsize=22, fontweight="medium")
+ax.set_xticklabels(categories, fontsize=20, color=INK, fontweight="medium")
 ax.set_ylim(0, 100)
 ax.set_yticks([20, 40, 60, 80, 100])
-ax.set_yticklabels(["20", "40", "60", "80", "100"], fontsize=18, color="gray")
-
-# Style grid
-ax.grid(True, alpha=0.3, linestyle="-", linewidth=1.5)
-ax.spines["polar"].set_visible(False)
-
-# Title with proper padding
-ax.set_title("radar-basic · seaborn · pyplots.ai", fontsize=28, fontweight="bold", pad=35)
-
-# Legend positioned inside the plot area for better balance
-ax.legend(loc="upper right", fontsize=18, framealpha=0.95, edgecolor="lightgray", fancybox=True)
+ax.set_yticklabels(["20", "40", "60", "80", "100"], fontsize=16, color=INK_SOFT)
+
+# Grid and spines
+grid_alpha = 0.20 if THEME == "light" else 0.25
+ax.grid(True, alpha=grid_alpha, linestyle="-", linewidth=1.2, color=INK_SOFT)
+ax.spines["polar"].set_color(INK_SOFT)
+ax.spines["polar"].set_alpha(0.4)
+
+# Title
+ax.set_title("radar-basic · seaborn · anyplot.ai", fontsize=26, fontweight="medium", color=INK, pad=40)
+
+# Legend
+legend = ax.legend(
+    loc="upper right",
+    bbox_to_anchor=(1.35, 1.15),
+    fontsize=18,
+    framealpha=0.95,
+    facecolor=ELEVATED_BG,
+    edgecolor=INK_SOFT,
+)
+for text in legend.get_texts():
+    text.set_color(INK)
 
-plt.tight_layout()
-plt.savefig("plot.png", dpi=300, bbox_inches="tight")
+# Save
+plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

plots/radar-basic/metadata/python/seaborn.yaml

@@ -1,217 +1,21 @@
+# Per-library metadata for seaborn implementation of radar-basic
+# Auto-generated by impl-generate.yml
+
 library: seaborn
+language: python
 specification_id: radar-basic
 created: '2025-12-23T18:32:28Z'
-updated: '2025-12-23T18:44:53Z'
-generated_by: claude-opus-4-5-20251101
-workflow_run: 20468591792
-issue: 0
-python_version: 3.13.11
+updated: '2026-04-29T16:39:41Z'
+generated_by: claude-sonnet
+workflow_run: 25121287681
+issue: 744
+python_version: 3.13.13
 library_version: 0.13.2
-preview_url: https://storage.googleapis.com/anyplot-images/plots/radar-basic/seaborn/plot.png
-preview_html: null
-quality_score: 91
-impl_tags:
-  dependencies: []
-  techniques:
-  - polar-projection
-  - manual-ticks
-  patterns:
-  - data-generation
-  - explicit-figure
-  dataprep: []
-  styling:
-  - alpha-blending
+preview_url_light: https://storage.googleapis.com/anyplot-images/plots/radar-basic/python/seaborn/plot-light.png
+preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/radar-basic/python/seaborn/plot-dark.png
+preview_html_light: null
+preview_html_dark: null
+quality_score: null
 review:
-  strengths:
-  - Excellent visual clarity with properly sized text elements and appropriate use
-    of the colorblind palette
-  - 'Perfect implementation of all spec requirements: filled polygons with alpha 0.25,
-    gridlines at correct intervals, closed polygons connecting back to first point'
-  - Realistic employee performance comparison scenario that matches spec applications
-  - Good use of seaborn styling (set_theme with poster context) to achieve proper
-    scaling
-  - Square aspect ratio is appropriate for radar charts, canvas well-utilized
-  weaknesses:
-  - Radar charts lack units notation - could add "(Score 0-100)" in subtitle or annotation
-  - The two employee profiles could show more contrasting values to better demonstrate
-    radar chart comparison capability
-  image_description: 'The plot displays a radar/spider chart comparing two employees
-    (Senior Developer in blue, Team Lead in yellow/gold) across 6 competency dimensions:
-    Technical Skills, Communication, Creativity, Problem Solving, Leadership, and
-    Teamwork. The chart uses a polar coordinate system with concentric gridlines at
-    20, 40, 60, 80, and 100. Both series are shown as filled polygons with transparency
-    (~0.25 alpha), allowing overlap visibility. Data points are marked with circular
-    markers at each vertex. The title "radar-basic · seaborn · pyplots.ai" appears
-    at the top in bold black text. A legend in the upper right corner identifies the
-    two series. The chart uses a square 1:1 aspect ratio with good canvas utilization.
-    Category labels are positioned at the outer edge of each axis and are clearly
-    readable.'
-  criteria_checklist:
-    visual_quality:
-      score: 37
-      max: 40
-      items:
-      - id: VQ-01
-        name: Text Legibility
-        score: 10
-        max: 10
-        passed: true
-        comment: Title at 28pt, category labels at 22pt, tick labels at 18pt - all
-          clearly readable
-      - id: VQ-02
-        name: No Overlap
-        score: 8
-        max: 8
-        passed: true
-        comment: No overlapping text elements, all labels well-spaced
-      - id: VQ-03
-        name: Element Visibility
-        score: 8
-        max: 8
-        passed: true
-        comment: Markers sized at s=400 with linewidth=4, perfect for 6 data points
-          per series
-      - id: VQ-04
-        name: Color Accessibility
-        score: 5
-        max: 5
-        passed: true
-        comment: Uses seaborn's colorblind palette, blue and gold/yellow are easily
-          distinguishable
-      - id: VQ-05
-        name: Layout Balance
-        score: 5
-        max: 5
-        passed: true
-        comment: Square format appropriate for radar, plot fills canvas well, balanced
-          margins
-      - id: VQ-06
-        name: Axis Labels
-        score: 0
-        max: 2
-        passed: false
-        comment: No axis labels with units (radar charts typically don't have traditional
-          axis labels, but score units could be noted)
-      - id: VQ-07
-        name: Grid & Legend
-        score: 2
-        max: 2
-        passed: true
-        comment: Grid at alpha=0.3, legend well-placed in upper right with good styling
-    spec_compliance:
-      score: 25
-      max: 25
-      items:
-      - id: SC-01
-        name: Plot Type
-        score: 8
-        max: 8
-        passed: true
-        comment: Correct radar/spider chart
-      - id: SC-02
-        name: Data Mapping
-        score: 5
-        max: 5
-        passed: true
-        comment: Categories on angular axis, values on radial axis correctly mapped
-      - id: SC-03
-        name: Required Features
-        score: 5
-        max: 5
-        passed: true
-        comment: Filled polygons with alpha 0.25, gridlines at 20/40/60/80/100, axis
-          labels at outer edge, distinct colors with legend, closed polygons
-      - id: SC-04
-        name: Data Range
-        score: 3
-        max: 3
-        passed: true
-        comment: 0-100 scale as recommended, all data visible
-      - id: SC-05
-        name: Legend Accuracy
-        score: 2
-        max: 2
-        passed: true
-        comment: Legend correctly identifies Senior Developer and Team Lead
-      - id: SC-06
-        name: Title Format
-        score: 2
-        max: 2
-        passed: true
-        comment: 'Correct format: "radar-basic · seaborn · pyplots.ai"'
-    data_quality:
-      score: 17
-      max: 20
-      items:
-      - id: DQ-01
-        name: Feature Coverage
-        score: 6
-        max: 8
-        passed: true
-        comment: Shows comparison between two profiles with contrasting strengths,
-          but profiles could show more dramatic differences to better demonstrate
-          radar chart utility
-      - id: DQ-02
-        name: Realistic Context
-        score: 7
-        max: 7
-        passed: true
-        comment: Employee performance review is a perfect, relatable use case from
-          spec applications
-      - id: DQ-03
-        name: Appropriate Scale
-        score: 4
-        max: 5
-        passed: true
-        comment: Values are realistic (65-92 range), though slightly compressed -
-          could use fuller range
-    code_quality:
-      score: 9
-      max: 10
-      items:
-      - id: CQ-01
-        name: KISS Structure
-        score: 3
-        max: 3
-        passed: true
-        comment: 'Linear flow: imports → data → setup → plot → save'
-      - id: CQ-02
-        name: Reproducibility
-        score: 2
-        max: 3
-        passed: false
-        comment: No random seed, but data is deterministic (hardcoded values), minor
-          deduction for not including np.random.seed(42) even though not strictly
-          needed
-      - id: CQ-03
-        name: Clean Imports
-        score: 2
-        max: 2
-        passed: true
-        comment: 'All imports used: matplotlib, numpy, pandas, seaborn'
-      - id: CQ-04
-        name: No Deprecated API
-        score: 1
-        max: 1
-        passed: true
-        comment: Uses current seaborn 0.13+ API
-      - id: CQ-05
-        name: Output Correct
-        score: 1
-        max: 1
-        passed: true
-        comment: Saves as plot.png with dpi=300
-    library_features:
-      score: 3
-      max: 5
-      items:
-      - id: LF-01
-        name: Uses distinctive library features
-        score: 3
-        max: 5
-        passed: false
-        comment: Uses sns.set_theme() and sns.scatterplot() for data points, plus
-          colorblind palette. However, radar charts are not a seaborn specialty -
-          the implementation correctly falls back to matplotlib polar projection for
-          the core chart while leveraging seaborn for styling and the data point markers
-  verdict: APPROVED
+  strengths: []
+  weaknesses: []