Ein Anderssono
3ef7736b1d
- Added performance tables for 1, 2, 5, 10, 100, 1000, 2000 decimals - Shows how each language scales with increasing precision - Includes memory usage data for all decimal counts - Added key observations about scaling behavior - Documented performance leaders and memory efficiency patterns
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
#!/usr/bin/env python3
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import csv
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# Read the performance data
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data = {}
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with open('performance_data.csv', 'r') as f:
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reader = csv.reader(f)
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next(reader) # Skip header
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for row in reader:
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if len(row) >= 5:
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decimals = row[0]
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# Skip rows where language name is missing or is just "SUCCESS"
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if row[1] and row[1] not in ['SUCCESS', 'Brainfuck', 'Ruby'] and not row[1].startswith('3.'):
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language = row[1]
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time = row[2]
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memory = row[3]
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status = row[4]
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if decimals not in data:
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data[decimals] = []
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data[decimals].append({
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'language': language,
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'time': time,
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'memory': memory,
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'status': status
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})
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# Print summary
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for dec in ['1', '2', '5', '10', '100', '1000', '2000']:
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if dec in data:
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print(f"\n=== {dec} decimals ===")
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for entry in data[dec][:10]: # Show first 10
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print(f"{entry['language']}: {entry['time']}ms, {entry['memory']}") |