A collection of Python decorators designed for enhancing functions with concurrency, error handling, and performance measurement capabilities.
This module provides decorators for running functions concurrently, utilizing threads and processes.
@threaded: Runs a function in a separate thread, suitable for I/O-bound tasks to avoid blocking the main thread.@process: Runs a function in a separate process, ideal for CPU-bound tasks to bypass the Global Interpreter Lock (GIL).
This module contains decorators to elegantly handle exceptions and perform retries.
@suppress_errors: Suppresses specified exceptions and optionally returns a predefined default value.@retry: Retries a function call with a specified delay between attempts if exceptions occur.
Includes decorators for detailed monitoring of function execution, resource consumption, and call frequency.
@timeit: Times the function execution and prints the duration in seconds.@timeit_detailed: Provides a detailed timing of function execution in milliseconds usingtime.perf_counter.@resource_usage: Monitors and reports the memory usage of the decorated function.@cpu_timeit: Measures the CPU time taken by the function, excluding any sleep time.@cpu_timeit_detailed: Similar to@cpu_timeitbut provides time in detailed milliseconds.@count_calls: Counts and prints the number of times a function is called.
To use these decorators, clone the repository and import the desired decorators into your Python project.
git clone https://github.com/JoshCap20/PythonDecoratorUtils.gitContributions to enhance the utility and efficiency of these decorators are welcome. Please feel free to submit pull requests or raise issues to improve the functionality of PythonDecoratorUtils.
This project is open-sourced and available under the MIT License.