time-it-profiler 1.1.1

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

🚀 time-it-profiler – Python Execution & Memory Profiler

time-it-profiler is a lightweight Python decorator and context manager for measuring execution time, memory usage, and CPU time of any function or code block. This updated version adds:

✔ Nanosecond Precision
✔ Function & Code Block Identification (Know exactly what was measured)
✔ Logging Support (log_file or logger)
✔ Context Manager Feature (with time_it():)

---

📌 Features

✅ High-Precision Execution Time (Uses time.perf_counter_ns())
✅ Memory Usage Tracking (Peak memory in MB)
✅ CPU Time Monitoring (User & System CPU time)
✅ Context Manager Support (with time_it(label="Block Name"):)
✅ Function Name in Reports (When used as a decorator)
✅ File & Logging Integration (@time_it(log_file="log.txt") or @time_it(logger=my_logger))
✅ Zero Overhead When Disabled (measure_time=False skips profiling)
✅ Lightweight & No External Dependencies

---

📥 Installation

Install directly from PyPI using:

pip install time-it-profiler

📂 GitHub Repository: https://github.com/brianvess/time_it.git

---

📖 Usage

✅ 1. As a Decorator (Tracks Function Execution)

from time_it import time_it

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

sample_function()

🔹 Example Output:

========================================
Function: sample_function
----------------------------------------
Execution Time: 0.012345 sec
Memory Usage: 1.23 MB
User CPU Time: 0.002345 sec
System CPU Time: 0.000678 sec
========================================

---

✅ 2. As a Context Manager (Tracks Code Blocks)

with time_it(label="Sum Calculation"):
    result = sum(range(1, 1000000))

🔹 Example Output:

========================================
Code Block: Sum Calculation
----------------------------------------
Execution Time: 0.008765 sec
Memory Usage: 0.98 MB
User CPU Time: 0.001987 sec
System CPU Time: 0.000456 sec
========================================

---

✅ 3. With Logging to a File

@time_it(log_file="profile.log")
def test_logging():
    time.sleep(1)

test_logging()

---

✅ 4. Using Python’s logging Module

import logging

logger = logging.getLogger("Profiler")
logging.basicConfig(level=logging.INFO)

@time_it(logger=logger)
def test_logger():
    time.sleep(2)

test_logger()

---

🛠️ Contributing

Contributions are welcome! Fork the repository on GitHub and submit a pull request.

🔗 GitHub Repo: https://github.com/brianvess/time_it.git

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! 🎉

---

📜 License

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

---

🌟 Credits

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

---

What’s New in This Version?

✅ Function & Code Block Identification
✅ Better Output Formatting
✅ Same Lightweight Design – Now Even More Useful!