time-it-profiler 0.0.2

A lightweight Python decorator for measuring execution time and system resource usage.

time_it - Python Function Profiler

🚀 Overview

time_it is a lightweight Python decorator that measures execution time, memory usage, and CPU time of any function. It is designed to be efficient and flexible, allowing you to enable or disable profiling dynamically.

📌 Features

✅ Execution Time Measurement (High-precision timing with perf_counter)
✅ Memory Usage Tracking (Peak memory usage in MB)
✅ CPU Time Monitoring (User & System CPU time)
✅ Zero Overhead When Disabled (measure_time=False bypasses all profiling logic)
✅ Easy to Use as a Decorator (@time_it)
✅ Lightweight & No External Dependencies

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📥 Installation

If using this as a standalone module, simply clone the repository:

git clone https://github.com/brianvess/time_it.git
cd time_it

Or install it as a package (when published to PyPI):

pip install time-it-profiler

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📖 Usage

✅ Basic Example

from timeit_function import time_it

@time_it(measure_time=True)  # Enable profiling
def sample_function():
    total = sum(range(1, 1000000))
    return total

sample_function()

Example Output:

Execution Time: 0.012345 seconds
Memory Usage: 1.23 MB
User CPU Time: 0.002345 seconds
System CPU Time: 0.000678 seconds

✅ Disable Profiling (Zero Overhead)

@time_it(measure_time=False)  # No profiling, function runs normally
def quick_function():
    return sum(range(1, 100))

quick_function()

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🔍 How It Works

time_it Function

import time
import resource
from functools import wraps

def time_it(measure_time=True):
    def decorator(func):
        if not measure_time:
            return func  # Return the function unmodified if profiling is disabled

        @wraps(func)
        def wrapper(*args, **kwargs):
            start_time = time.perf_counter()
            start_resources = resource.getrusage(resource.RUSAGE_SELF)
            
            result = func(*args, **kwargs)
            
            end_time = time.perf_counter()
            end_resources = resource.getrusage(resource.RUSAGE_SELF)

            memory = (end_resources.ru_maxrss - start_resources.ru_maxrss) / 1024  # Convert KB to MB
            user_time = end_resources.ru_utime - start_resources.ru_utime
            system_time = end_resources.ru_stime - start_resources.ru_stime

            print(
                f"Execution Time: {end_time - start_time:.6f} seconds\\n"
                f"Memory Usage: {memory:.2f} MB\\n"
                f"User CPU Time: {user_time:.6f} seconds\\n"
                f"System CPU Time: {system_time:.6f} seconds"
            )

            return result
        return wrapper
    return decorator

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🛠️ Contributing

1. Fork this repository.
2. Create a new branch:

   git checkout -b feature-branch
   

3. Commit your changes:

   git commit -m "Added new feature"
   

4. Push the changes:

   git push origin feature-branch
   

5. Submit a pull request! 🎉

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📜 License

This project is licensed under the MIT License. See LICENSE for details.

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🌟 Credits

Developed by Brian Vess.
If you find this useful, please ⭐ star this repository and contribute! 🚀