Ein Anderssono
43870e5920
- Create reports/ directory with detailed performance reports - Simplify README.md to provide overview and summary - Add links to detailed reports for each decimal level (1, 2, 5, 10, 100, 1000, 2000) - Keep summary table and key findings in main README - Remove verbose charts from main README to reduce noise
116 lines
3.1 KiB
Python
116 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""Create separate report files for each decimal level."""
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import re
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from pathlib import Path
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# Read README
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with open('README.md', 'r') as f:
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content = f.read()
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# Decimal levels to extract
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decimal_levels = [1, 2, 5, 10, 100, 1000, 2000]
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# Create reports directory
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Path('reports').mkdir(exist_ok=True)
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# Extract sections for each decimal level
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for level in decimal_levels:
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# Find the section for this decimal level
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pattern = rf'#### {level} Decimal[s]?\n\n(.*?)(?=#### \d+ Decimal|### |$)'
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match = re.search(pattern, content, re.DOTALL)
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if match:
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section = match.group(1)
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# Create report file
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report_content = f"""# Performance Report: {level} Decimal{'s' if level > 1 else ''}
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## Test Environment
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**Hardware:**
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- **Model:** MacBook Neo (Mac17,5)
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- **Processor:** Apple A18 Pro (6 cores: 2 performance + 4 efficiency)
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- **Memory:** 8 GB RAM
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- **Operating System:** macOS (Darwin)
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## Performance Charts
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{section}
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---
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*Generated from Pi Calculation Benchmark - {level} decimal{'s' if level > 1 else ''} precision*
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"""
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# Write report file
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filename = f'reports/{level}_decimals.md'
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with open(filename, 'w') as f:
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f.write(report_content)
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print(f"Created {filename}")
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else:
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print(f"No section found for {level} decimals")
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# Create main summary report
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summary_content = """# Pi Calculation Benchmark: Performance Summary
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## Overview
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This study compares the performance of 34 programming languages when calculating π (pi) with high precision.
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## Test Environment
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**Hardware:**
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- **Model:** MacBook Neo (Mac17,5)
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- **Processor:** Apple A18 Pro (6 cores: 2 performance + 4 efficiency)
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- **Memory:** 8 GB RAM
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- **Operating System:** macOS (Darwin)
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## Method: Machin's Formula
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All implementations use Machin's formula for π calculation:
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```
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π/4 = 4·arctan(1/5) - arctan(1/239)
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```
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## Performance Reports by Decimal Level
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- [1 Decimal](reports/1_decimals.md) - Minimal precision
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- [2 Decimals](reports/2_decimals.md) - Low precision
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- [5 Decimals](reports/5_decimals.md) - Medium precision
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- [10 Decimals](reports/10_decimals.md) - Standard precision
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- [100 Decimals](reports/100_decimals.md) - High precision
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- [1000 Decimals](reports/1000_decimals.md) - Very high precision
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- [2000 Decimals](reports/2000_decimals.md) - Extreme precision
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## Key Findings
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### Fastest Languages (100 decimals)
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1. **C** - 9ms (compiled)
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2. **Assembly** - 9ms (compiled)
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3. **Rust** - 9ms (compiled)
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4. **Go** - 9ms (compiled)
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5. **Nim** - 9ms (compiled)
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### Memory Efficiency (100 decimals)
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- **C, Rust, Assembly**: < 1 MB
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- **JIT Languages**: ~2 MB
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- **Interpreted Languages**: ~2 MB
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### Performance Scaling
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- Compiled languages maintain consistent performance across all decimal levels
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- JIT languages show startup overhead but good performance
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- Interpreted languages scale poorly with increased precision
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---
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*Generated from Pi Calculation Benchmark*
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"""
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with open('reports/summary.md', 'w') as f:
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f.write(summary_content)
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print("Created reports/summary.md")
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print("\nAll reports created successfully!") |