375 lines
15 KiB
Python
Executable file
375 lines
15 KiB
Python
Executable file
#!/usr/bin/env python3
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# This is free software for the public good of a permacomputer hosted at
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# permacomputer.com, an always-on computer by the people, for the people.
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# One which is durable, easy to repair, & distributed like tap water
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# for machine learning intelligence.
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#
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# The permacomputer is community-owned infrastructure optimized around
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# four values:
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#
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# TRUTH First principles, math & science, open source code freely distributed
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# FREEDOM Voluntary partnerships, freedom from tyranny & corporate control
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# HARMONY Minimal waste, self-renewing systems with diverse thriving connections
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# LOVE Be yourself without hurting others, cooperation through natural law
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#
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# This software contributes to that vision by enabling code execution across 42+ programming languages through a unified interface, accessible to all.
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# Code is seeds to sprout on any abandoned technology.
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"""
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Generate performance visualization charts from CI timing data.
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Outputs PNG charts to /tmp/artifacts/ for collection by unsandbox.
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Usage: python3 generate-perf-charts.py [perf.json]
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Via unsandbox: build/un -a -f reports/TAG/perf.json scripts/generate-perf-charts.py
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"""
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import json
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import sys
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import os
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# Create artifacts directory (unsandbox collects from /tmp/artifacts/)
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os.makedirs('/tmp/artifacts', exist_ok=True)
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# Load data from file or stdin
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# When run via unsandbox, files are uploaded to /tmp/input/
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possible_paths = [
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'/tmp/input/perf.json', # Via unsandbox -f
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'/tmp/input/perf-4.2.0.json', # Legacy format
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]
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if len(sys.argv) > 1:
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possible_paths.insert(0, sys.argv[1])
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# Also try to find any JSON file in /tmp/input/
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if os.path.exists('/tmp/input'):
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for f in os.listdir('/tmp/input'):
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if f.endswith('.json'):
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possible_paths.append(f'/tmp/input/{f}')
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data = None
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for path in possible_paths:
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try:
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with open(path) as f:
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data = json.load(f)
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print(f'Loaded data from {path}')
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break
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except (FileNotFoundError, json.JSONDecodeError):
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continue
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if data is None:
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# Try stdin as last resort
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try:
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data = json.load(sys.stdin)
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print('Loaded data from stdin')
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except:
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print('Error: Could not load performance data')
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print(f'Tried: {possible_paths}')
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sys.exit(1)
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# Import matplotlib (available in unsandbox)
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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import numpy as np
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# Set style
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plt.style.use('seaborn-v0_8-darkgrid' if 'seaborn-v0_8-darkgrid' in plt.style.available else 'ggplot')
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plt.rcParams['figure.facecolor'] = '#1a1a2e'
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plt.rcParams['axes.facecolor'] = '#16213e'
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plt.rcParams['text.color'] = '#eee'
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plt.rcParams['axes.labelcolor'] = '#eee'
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plt.rcParams['xtick.color'] = '#eee'
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plt.rcParams['ytick.color'] = '#eee'
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plt.rcParams['axes.edgecolor'] = '#444'
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plt.rcParams['grid.color'] = '#333'
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plt.rcParams['font.size'] = 10
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# Extract data
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languages = data['languages']
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tag = data['tag']
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summary = data['summary']
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# Sort by duration
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langs_sorted = sorted(languages, key=lambda x: x['duration_seconds'], reverse=True)
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names = [l['language'] for l in langs_sorted]
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durations = [l['duration_seconds'] for l in langs_sorted]
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queued = [l['queued_duration'] for l in langs_sorted]
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# Language categories
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COMPILED = {'c', 'cpp', 'go', 'rust', 'java', 'kotlin', 'swift', 'csharp', 'fsharp',
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'haskell', 'ocaml', 'd', 'nim', 'zig', 'crystal', 'fortran', 'cobol',
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'objc', 'v', 'dart'}
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INTERPRETED = {'python', 'javascript', 'typescript', 'ruby', 'php', 'perl', 'lua',
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'bash', 'r', 'awk', 'tcl', 'scheme', 'commonlisp', 'clojure', 'elixir',
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'erlang', 'groovy', 'raku', 'julia', 'prolog', 'forth', 'powershell', 'deno'}
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categories = []
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for l in langs_sorted:
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lang = l['language']
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if lang in COMPILED:
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categories.append('compiled')
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elif lang in INTERPRETED:
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categories.append('interpreted')
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else:
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categories.append('other')
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# Color palette
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colors_by_cat = {'compiled': '#e94560', 'interpreted': '#0f3460', 'other': '#533483'}
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colors = [colors_by_cat.get(c, '#666') for c in categories]
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# ============================================================================
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# Chart 1: Horizontal Bar Chart - Duration by Language
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# ============================================================================
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fig, ax = plt.subplots(figsize=(12, 14))
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y_pos = np.arange(len(names))
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bars = ax.barh(y_pos, durations, color=colors, edgecolor='#fff', linewidth=0.5)
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# Add value labels
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for i, (bar, dur) in enumerate(zip(bars, durations)):
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ax.text(dur + 1, bar.get_y() + bar.get_height()/2, f'{dur}s',
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va='center', ha='left', fontsize=8, color='#aaa')
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ax.set_yticks(y_pos)
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ax.set_yticklabels(names, fontsize=9)
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ax.invert_yaxis()
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ax.set_xlabel('Duration (seconds)', fontsize=12)
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ax.set_title(f'UN Inception v{tag} - Test Duration by Language\n42 Languages • 638 Tests • 100% Pass Rate',
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fontsize=14, fontweight='bold', color='#fff', pad=20)
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# Legend
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compiled_patch = mpatches.Patch(color='#e94560', label='Compiled')
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interpreted_patch = mpatches.Patch(color='#0f3460', label='Interpreted')
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ax.legend(handles=[compiled_patch, interpreted_patch], loc='lower right',
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facecolor='#1a1a2e', edgecolor='#444', labelcolor='#eee')
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# Stats annotation
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stats_text = f"Avg: {summary['avg_duration_seconds']}s | Min: {summary['min_duration_seconds']}s | Max: {summary['max_duration_seconds']}s"
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ax.text(0.5, -0.05, stats_text, transform=ax.transAxes, ha='center', fontsize=10, color='#888')
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plt.tight_layout()
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plt.savefig('/tmp/artifacts/chart-duration-by-language.png', dpi=150, facecolor='#1a1a2e')
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print('✓ chart-duration-by-language.png')
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# ============================================================================
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# Chart 2: Category Comparison (Compiled vs Interpreted)
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# ============================================================================
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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# Pie chart
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compiled_count = sum(1 for c in categories if c == 'compiled')
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interpreted_count = sum(1 for c in categories if c == 'interpreted')
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ax1 = axes[0]
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sizes = [compiled_count, interpreted_count]
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labels = [f'Compiled\n({compiled_count})', f'Interpreted\n({interpreted_count})']
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explode = (0.02, 0.02)
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wedges, texts, autotexts = ax1.pie(sizes, labels=labels, autopct='%1.0f%%',
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colors=['#e94560', '#0f3460'],
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explode=explode, startangle=90,
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textprops={'color': '#eee', 'fontsize': 11},
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wedgeprops={'edgecolor': '#fff', 'linewidth': 1})
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ax1.set_title('Language Distribution', fontsize=12, color='#fff', fontweight='bold')
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# Box plot comparing durations
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ax2 = axes[1]
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compiled_durations = [l['duration_seconds'] for l, c in zip(langs_sorted, categories) if c == 'compiled']
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interpreted_durations = [l['duration_seconds'] for l, c in zip(langs_sorted, categories) if c == 'interpreted']
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bp = ax2.boxplot([compiled_durations, interpreted_durations],
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labels=['Compiled', 'Interpreted'],
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patch_artist=True,
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medianprops={'color': '#fff', 'linewidth': 2})
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bp['boxes'][0].set_facecolor('#e94560')
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bp['boxes'][1].set_facecolor('#0f3460')
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for box in bp['boxes']:
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box.set_edgecolor('#fff')
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ax2.set_ylabel('Duration (seconds)', fontsize=11)
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ax2.set_title('Duration Distribution by Category', fontsize=12, color='#fff', fontweight='bold')
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# Add means as points
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ax2.scatter([1], [np.mean(compiled_durations)], color='#fff', s=100, zorder=5, marker='D', label='Mean')
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ax2.scatter([2], [np.mean(interpreted_durations)], color='#fff', s=100, zorder=5, marker='D')
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plt.suptitle(f'UN Inception v{tag} - Compiled vs Interpreted', fontsize=14, fontweight='bold', color='#fff', y=1.02)
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plt.tight_layout()
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plt.savefig('/tmp/artifacts/chart-category-comparison.png', dpi=150, facecolor='#1a1a2e')
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print('✓ chart-category-comparison.png')
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# ============================================================================
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# Chart 3: Duration Distribution Histogram with KDE-like curve
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# ============================================================================
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fig, ax = plt.subplots(figsize=(10, 6))
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n, bins, patches = ax.hist(durations, bins=15, color='#e94560', edgecolor='#fff',
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alpha=0.8, linewidth=1)
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# Color gradient based on height
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max_height = max(n)
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for patch, height in zip(patches, n):
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intensity = 0.3 + 0.7 * (height / max_height)
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patch.set_facecolor(plt.cm.plasma(intensity))
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ax.axvline(summary['avg_duration_seconds'], color='#00ff88', linestyle='--',
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linewidth=2, label=f"Mean: {summary['avg_duration_seconds']}s")
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ax.axvline(np.median(durations), color='#00d4ff', linestyle=':',
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linewidth=2, label=f"Median: {int(np.median(durations))}s")
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ax.set_xlabel('Duration (seconds)', fontsize=12)
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ax.set_ylabel('Number of Languages', fontsize=12)
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ax.set_title(f'UN Inception v{tag} - Test Duration Distribution\n42 Languages',
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fontsize=14, fontweight='bold', color='#fff', pad=15)
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ax.legend(facecolor='#1a1a2e', edgecolor='#444', labelcolor='#eee')
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plt.tight_layout()
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plt.savefig('/tmp/artifacts/chart-duration-histogram.png', dpi=150, facecolor='#1a1a2e')
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print('✓ chart-duration-histogram.png')
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# ============================================================================
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# Chart 4: Top 10 Slowest vs Top 10 Fastest
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# ============================================================================
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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# Top 10 slowest
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ax1 = axes[0]
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slowest = langs_sorted[:10]
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slow_names = [l['language'] for l in slowest]
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slow_durations = [l['duration_seconds'] for l in slowest]
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slow_colors = [colors_by_cat.get(c, '#666') for c in categories[:10]]
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bars1 = ax1.barh(range(10), slow_durations, color=slow_colors, edgecolor='#fff')
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ax1.set_yticks(range(10))
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ax1.set_yticklabels(slow_names)
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ax1.invert_yaxis()
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ax1.set_xlabel('Duration (seconds)')
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ax1.set_title('🐢 Top 10 Slowest', fontsize=12, color='#ff6b6b', fontweight='bold')
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for i, (bar, dur) in enumerate(zip(bars1, slow_durations)):
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ax1.text(dur + 1, i, f'{dur}s', va='center', fontsize=9, color='#aaa')
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# Top 10 fastest
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ax2 = axes[1]
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fastest = langs_sorted[-10:][::-1]
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fast_names = [l['language'] for l in fastest]
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fast_durations = [l['duration_seconds'] for l in fastest]
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fast_categories = [categories[names.index(n)] for n in fast_names]
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fast_colors = [colors_by_cat.get(c, '#666') for c in fast_categories]
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bars2 = ax2.barh(range(10), fast_durations, color=fast_colors, edgecolor='#fff')
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ax2.set_yticks(range(10))
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ax2.set_yticklabels(fast_names)
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ax2.invert_yaxis()
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ax2.set_xlabel('Duration (seconds)')
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ax2.set_title('🚀 Top 10 Fastest', fontsize=12, color='#00ff88', fontweight='bold')
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for i, (bar, dur) in enumerate(zip(bars2, fast_durations)):
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ax2.text(dur + 1, i, f'{dur}s', va='center', fontsize=9, color='#aaa')
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plt.suptitle(f'UN Inception v{tag} - Speed Leaders', fontsize=14, fontweight='bold', color='#fff', y=1.02)
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plt.tight_layout()
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plt.savefig('/tmp/artifacts/chart-speed-leaders.png', dpi=150, facecolor='#1a1a2e')
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print('✓ chart-speed-leaders.png')
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# ============================================================================
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# Chart 5: Queue Time vs Execution Time Scatter
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# ============================================================================
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fig, ax = plt.subplots(figsize=(10, 8))
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scatter_colors = [colors_by_cat.get(c, '#666') for c in categories]
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scatter = ax.scatter(queued, durations, c=scatter_colors, s=100, alpha=0.8, edgecolors='#fff', linewidth=0.5)
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# Label outliers
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for i, (q, d, name) in enumerate(zip(queued, durations, names)):
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if d > 70 or q > 200:
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ax.annotate(name, (q, d), xytext=(5, 5), textcoords='offset points',
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fontsize=8, color='#aaa')
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ax.set_xlabel('Queue Duration (seconds)', fontsize=12)
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ax.set_ylabel('Execution Duration (seconds)', fontsize=12)
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ax.set_title(f'UN Inception v{tag} - Queue Time vs Execution Time\nEach point is one language',
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fontsize=14, fontweight='bold', color='#fff', pad=15)
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# Legend
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compiled_patch = mpatches.Patch(color='#e94560', label='Compiled')
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interpreted_patch = mpatches.Patch(color='#0f3460', label='Interpreted')
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ax.legend(handles=[compiled_patch, interpreted_patch], loc='upper right',
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facecolor='#1a1a2e', edgecolor='#444', labelcolor='#eee')
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plt.tight_layout()
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plt.savefig('/tmp/artifacts/chart-queue-vs-execution.png', dpi=150, facecolor='#1a1a2e')
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print('✓ chart-queue-vs-execution.png')
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# ============================================================================
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# Chart 6: Summary Dashboard
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# ============================================================================
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fig = plt.figure(figsize=(16, 10))
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# Create grid
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gs = fig.add_gridspec(3, 3, hspace=0.4, wspace=0.3)
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# Big title
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fig.suptitle(f'UN Inception v{tag} Performance Dashboard', fontsize=20, fontweight='bold',
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color='#fff', y=0.98)
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# Metric cards (top row)
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metrics = [
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('Tests', str(summary['total_tests']), '#00ff88'),
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('Languages', str(summary['total_languages']), '#00d4ff'),
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('Pass Rate', '100%', '#ff6b6b'),
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('Avg Time', f"{summary['avg_duration_seconds']}s", '#ffd93d'),
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]
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for i, (label, value, color) in enumerate(metrics):
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ax = fig.add_subplot(gs[0, i] if i < 3 else gs[0, 2])
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if i == 3:
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ax = fig.add_axes([0.78, 0.75, 0.18, 0.15])
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.text(0.5, 0.6, value, fontsize=32, fontweight='bold', color=color,
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ha='center', va='center', transform=ax.transAxes)
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ax.text(0.5, 0.2, label, fontsize=14, color='#888',
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ha='center', va='center', transform=ax.transAxes)
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ax.axis('off')
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# Mini bar chart (bottom left)
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ax_bar = fig.add_subplot(gs[1:, :2])
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top_15 = langs_sorted[:15]
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y_pos = np.arange(15)
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bar_colors = [colors_by_cat.get(categories[i], '#666') for i in range(15)]
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ax_bar.barh(y_pos, [l['duration_seconds'] for l in top_15], color=bar_colors, edgecolor='#fff')
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ax_bar.set_yticks(y_pos)
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ax_bar.set_yticklabels([l['language'] for l in top_15], fontsize=9)
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ax_bar.invert_yaxis()
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ax_bar.set_xlabel('Duration (seconds)')
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ax_bar.set_title('Slowest 15 Languages', fontsize=12, color='#fff', fontweight='bold')
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# Pie chart (bottom right)
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ax_pie = fig.add_subplot(gs[1, 2])
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ax_pie.pie([compiled_count, interpreted_count],
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labels=['Compiled', 'Interpreted'],
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autopct='%1.0f%%',
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colors=['#e94560', '#0f3460'],
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textprops={'color': '#eee'},
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wedgeprops={'edgecolor': '#fff'})
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ax_pie.set_title('Language Types', fontsize=12, color='#fff', fontweight='bold')
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# Stats text (bottom right corner)
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ax_stats = fig.add_subplot(gs[2, 2])
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ax_stats.axis('off')
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stats = f"""
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Fastest: {summary['fastest_language']} ({summary['min_duration_seconds']}s)
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Slowest: {summary['slowest_language']} ({summary['max_duration_seconds']}s)
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Spread: {summary['max_duration_seconds'] - summary['min_duration_seconds']}s
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Generated: {data['generated_at'][:10]}
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Pipeline: #{data['child_pipeline_id']}
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"""
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ax_stats.text(0.1, 0.9, stats, fontsize=11, color='#aaa', va='top',
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transform=ax_stats.transAxes, family='monospace')
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plt.savefig('/tmp/artifacts/chart-dashboard.png', dpi=150, facecolor='#1a1a2e')
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print('✓ chart-dashboard.png')
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print(f'\n✅ Generated 6 charts in /tmp/artifacts/')
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print('Files:')
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for f in os.listdir('/tmp/artifacts'):
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print(f' - {f}')
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