#!/usr/bin/env python3
"""
FAF File Tools - Data Analysis Module
Demonstrates complex Python file operations with data processing
"""
import pandas as pd
import numpy as np
from typing import List, Dict, Tuple, Optional
import json
import csv
from datetime import datetime, timedelta
import random
import os
class FAFDataAnalyzer:
"""Analyze file operations and performance metrics"""
def __init__(self):
self.performance_data = []
self.file_metrics = {}
def generate_sample_data(self, num_operations: int = 100) -> pd.DataFrame:
"""Generate sample performance data for testing"""
operations = ['faf_read', 'faf_write', 'faf_init', 'faf_sync', 'faf_enhance']
file_types = ['.py', '.ts', '.json', '.md', '.txt', '.yaml', '.html']
data = []
base_time = datetime.now() - timedelta(hours=24)
for i in range(num_operations):
timestamp = base_time + timedelta(minutes=i*15)
operation = random.choice(operations)
file_type = random.choice(file_types)
# Simulate realistic performance metrics
if operation == 'faf_read':
duration_ms = random.gauss(40, 10) # Average 40ms
size_bytes = random.randint(100, 50000)
elif operation == 'faf_write':
duration_ms = random.gauss(94, 20) # Average 94ms
size_bytes = random.randint(500, 100000)
else:
duration_ms = random.gauss(150, 50)
size_bytes = random.randint(1000, 25000)
data.append({
'timestamp': timestamp,
'operation': operation,
'file_type': file_type,
'duration_ms': max(1, duration_ms), # Ensure positive
'size_bytes': size_bytes,
'success': random.random() > 0.02, # 98% success rate
'path': f'/test/path/file_{i}{file_type}'
})
return pd.DataFrame(data)
def analyze_performance(self, df: pd.DataFrame) -> Dict:
"""Analyze performance metrics from operations data"""
analysis = {
'summary': {
'total_operations': len(df),
'unique_files': df['path'].nunique(),
'time_range': {
'start': str(df['timestamp'].min()),
'end': str(df['timestamp'].max())
},
'success_rate': (df['success'].sum() / len(df) * 100)
},
'performance': {
'avg_duration_ms': df['duration_ms'].mean(),
'median_duration_ms': df['duration_ms'].median(),
'p95_duration_ms': df['duration_ms'].quantile(0.95),
'p99_duration_ms': df['duration_ms'].quantile(0.99),
'total_bytes_processed': df['size_bytes'].sum(),
'avg_file_size_bytes': df['size_bytes'].mean()
},
'by_operation': {},
'by_file_type': {}
}
# Analysis by operation type
for op in df['operation'].unique():
op_data = df[df['operation'] == op]
analysis['by_operation'][op] = {
'count': len(op_data),
'avg_duration_ms': op_data['duration_ms'].mean(),
'avg_size_bytes': op_data['size_bytes'].mean(),
'success_rate': (op_data['success'].sum() / len(op_data) * 100)
}
# Analysis by file type
for ft in df['file_type'].unique():
ft_data = df[df['file_type'] == ft]
analysis['by_file_type'][ft] = {
'count': len(ft_data),
'avg_duration_ms': ft_data['duration_ms'].mean(),
'avg_size_bytes': ft_data['size_bytes'].mean()
}
return analysis
def generate_performance_report(self, analysis: Dict) -> str:
"""Generate a formatted performance report"""
report = []
report.append("=" * 60)
report.append("FAF FILE TOOLS - PERFORMANCE ANALYSIS REPORT")
report.append("=" * 60)
report.append("")
# Summary section
report.append("š SUMMARY")
report.append("-" * 40)
summary = analysis['summary']
report.append(f"Total Operations: {summary['total_operations']:,}")
report.append(f"Unique Files: {summary['unique_files']:,}")
report.append(f"Success Rate: {summary['success_rate']:.2f}%")
report.append(f"Time Range: {summary['time_range']['start'][:19]} to")
report.append(f" {summary['time_range']['end'][:19]}")
report.append("")
# Performance metrics
report.append("ā” PERFORMANCE METRICS")
report.append("-" * 40)
perf = analysis['performance']
report.append(f"Average Duration: {perf['avg_duration_ms']:.2f}ms")
report.append(f"Median Duration: {perf['median_duration_ms']:.2f}ms")
report.append(f"95th Percentile: {perf['p95_duration_ms']:.2f}ms")
report.append(f"99th Percentile: {perf['p99_duration_ms']:.2f}ms")
report.append(f"Total Bytes: {perf['total_bytes_processed']:,}")
report.append(f"Avg File Size: {perf['avg_file_size_bytes']:.0f} bytes")
report.append("")
# By operation type
report.append("š§ PERFORMANCE BY OPERATION")
report.append("-" * 40)
for op, metrics in analysis['by_operation'].items():
report.append(f"\n{op}:")
report.append(f" Count: {metrics['count']}")
report.append(f" Avg Duration: {metrics['avg_duration_ms']:.2f}ms")
report.append(f" Avg Size: {metrics['avg_size_bytes']:.0f} bytes")
report.append(f" Success Rate: {metrics['success_rate']:.2f}%")
report.append("")
# By file type
report.append("š PERFORMANCE BY FILE TYPE")
report.append("-" * 40)
for ft, metrics in analysis['by_file_type'].items():
report.append(f"{ft}: {metrics['count']} ops, "
f"{metrics['avg_duration_ms']:.1f}ms avg, "
f"{metrics['avg_size_bytes']:.0f} bytes avg")
report.append("")
report.append("=" * 60)
report.append("Report generated by FAF Data Analyzer v2.0.0")
report.append("šļø Built for speed. Designed for excellence.")
return "\n".join(report)
def export_metrics_csv(self, df: pd.DataFrame, filepath: str):
"""Export metrics to CSV for further analysis"""
df.to_csv(filepath, index=False)
return f"Exported {len(df)} records to {filepath}"
def create_benchmark_suite(self) -> str:
"""Create a Python benchmark suite for FAF tools"""
benchmark_code = '''#!/usr/bin/env python3
"""
FAF File Tools - Benchmark Suite
Measures performance of file operations
"""
import time
import os
import json
import random
import string
from pathlib import Path
from typing import List, Dict, Callable
import statistics
class FAFBenchmark:
"""Benchmark FAF file operations"""
def __init__(self, iterations: int = 100):
self.iterations = iterations
self.results = {}
self.test_dir = Path("benchmark_tests")
self.test_dir.mkdir(exist_ok=True)
def benchmark_operation(self, name: str, operation: Callable, *args, **kwargs) -> Dict:
"""Benchmark a single operation"""
timings = []
errors = 0
for i in range(self.iterations):
try:
start = time.perf_counter()
operation(*args, **kwargs)
duration = (time.perf_counter() - start) * 1000 # Convert to ms
timings.append(duration)
except Exception as e:
errors += 1
print(f"Error in {name}: {e}")
if timings:
return {
'name': name,
'iterations': self.iterations,
'errors': errors,
'min_ms': min(timings),
'max_ms': max(timings),
'mean_ms': statistics.mean(timings),
'median_ms': statistics.median(timings),
'stdev_ms': statistics.stdev(timings) if len(timings) > 1 else 0,
'success_rate': ((self.iterations - errors) / self.iterations * 100)
}
else:
return {'name': name, 'errors': errors, 'success_rate': 0}
def generate_test_file(self, size_kb: int) -> str:
"""Generate a test file of specified size"""
content = ''.join(random.choices(string.ascii_letters + string.digits,
k=size_kb * 1024))
filepath = self.test_dir / f"test_{size_kb}kb.txt"
filepath.write_text(content)
return str(filepath)
def benchmark_read_operations(self):
"""Benchmark file read operations"""
# Create test files of different sizes
test_files = {
'1KB': self.generate_test_file(1),
'10KB': self.generate_test_file(10),
'100KB': self.generate_test_file(100),
'1MB': self.generate_test_file(1024)
}
results = {}
for size, filepath in test_files.items():
def read_op():
with open(filepath, 'r') as f:
_ = f.read()
results[f'read_{size}'] = self.benchmark_operation(f'Read {size}', read_op)
return results
def benchmark_write_operations(self):
"""Benchmark file write operations"""
sizes = {'1KB': 1, '10KB': 10, '100KB': 100, '1MB': 1024}
results = {}
for size_name, size_kb in sizes.items():
content = ''.join(random.choices(string.ascii_letters, k=size_kb * 1024))
def write_op():
filepath = self.test_dir / f"write_test_{random.randint(1, 10000)}.txt"
filepath.write_text(content)
filepath.unlink() # Clean up immediately
results[f'write_{size_name}'] = self.benchmark_operation(
f'Write {size_name}', write_op
)
return results
def benchmark_json_operations(self):
"""Benchmark JSON read/write operations"""
# Create test JSON data
test_data = {
'small': {'key': 'value'} * 10,
'medium': {'key': 'value'} * 100,
'large': {'key': 'value'} * 1000
}
results = {}
for size, data in test_data.items():
# Write benchmark
def json_write():
filepath = self.test_dir / f"json_test_{random.randint(1, 10000)}.json"
with open(filepath, 'w') as f:
json.dump(data, f)
filepath.unlink()
results[f'json_write_{size}'] = self.benchmark_operation(
f'JSON Write {size}', json_write
)
# Prepare file for read benchmark
test_file = self.test_dir / f"json_read_{size}.json"
with open(test_file, 'w') as f:
json.dump(data, f)
# Read benchmark
def json_read():
with open(test_file, 'r') as f:
_ = json.load(f)
results[f'json_read_{size}'] = self.benchmark_operation(
f'JSON Read {size}', json_read
)
return results
def run_full_benchmark(self) -> Dict:
"""Run complete benchmark suite"""
print("šļø Starting FAF Benchmark Suite...")
print("-" * 50)
all_results = {}
print("š Benchmarking read operations...")
all_results.update(self.benchmark_read_operations())
print("āļø Benchmarking write operations...")
all_results.update(self.benchmark_write_operations())
print("š§ Benchmarking JSON operations...")
all_results.update(self.benchmark_json_operations())
return all_results
def generate_report(self, results: Dict) -> str:
"""Generate benchmark report"""
report = []
report.append("=" * 60)
report.append("FAF FILE TOOLS - BENCHMARK RESULTS")
report.append("=" * 60)
report.append("")
# Group results by operation type
read_ops = {k: v for k, v in results.items() if 'read' in k}
write_ops = {k: v for k, v in results.items() if 'write' in k}
# Read operations
report.append("š READ OPERATIONS")
report.append("-" * 40)
for name, metrics in read_ops.items():
report.append(f"{name}:")
report.append(f" Mean: {metrics['mean_ms']:.2f}ms")
report.append(f" Median: {metrics['median_ms']:.2f}ms")
report.append(f" Min/Max: {metrics['min_ms']:.2f}ms / {metrics['max_ms']:.2f}ms")
report.append(f" Success: {metrics['success_rate']:.1f}%")
report.append("")
# Write operations
report.append("āļø WRITE OPERATIONS")
report.append("-" * 40)
for name, metrics in write_ops.items():
report.append(f"{name}:")
report.append(f" Mean: {metrics['mean_ms']:.2f}ms")
report.append(f" Median: {metrics['median_ms']:.2f}ms")
report.append(f" Min/Max: {metrics['min_ms']:.2f}ms / {metrics['max_ms']:.2f}ms")
report.append(f" Success: {metrics['success_rate']:.1f}%")
report.append("")
# Overall statistics
all_means = [v['mean_ms'] for v in results.values() if 'mean_ms' in v]
report.append("š OVERALL STATISTICS")
report.append("-" * 40)
report.append(f"Total Operations: {sum(v.get('iterations', 0) for v in results.values())}")
report.append(f"Average Duration: {statistics.mean(all_means):.2f}ms")
report.append(f"Operations Tested: {len(results)}")
report.append("")
report.append("=" * 60)
report.append("š Benchmark Complete!")
return "\\n".join(report)
if __name__ == "__main__":
# Run benchmark
benchmark = FAFBenchmark(iterations=50)
results = benchmark.run_full_benchmark()
# Generate and print report
report = benchmark.generate_report(results)
print(report)
# Save results to JSON
with open('benchmark_results.json', 'w') as f:
json.dump(results, f, indent=2, default=str)
print("\\nā
Results saved to benchmark_results.json")
'''
return benchmark_code
def main():
"""Run data analysis demonstration"""
print("š FAF File Tools - Python Data Analysis")
print("=" * 50)
analyzer = FAFDataAnalyzer()
# Generate sample data
print("\nš Generating sample performance data...")
df = analyzer.generate_sample_data(100)
print(f"ā
Generated {len(df)} operation records")
# Analyze performance
print("\nš Analyzing performance metrics...")
analysis = analyzer.analyze_performance(df)
# Generate report
report = analyzer.generate_performance_report(analysis)
print("\n" + report)
# Export to CSV
csv_path = "faf_metrics.csv"
result = analyzer.export_metrics_csv(df, csv_path)
print(f"\nā
{result}")
# Save benchmark suite
print("\nšļø Generating benchmark suite...")
benchmark_code = analyzer.create_benchmark_suite()
with open("faf_benchmark.py", "w") as f:
f.write(benchmark_code)
print("ā
Created faf_benchmark.py")
# Save analysis results
with open("faf_analysis_results.json", "w") as f:
json.dump(analysis, f, indent=2, default=str)
print("ā
Saved analysis results to faf_analysis_results.json")
if __name__ == "__main__":
main()
