functionmonitor 0.1.4

A tool for monitoring and managing asynchronous function execution.

functionmonitor

Python's standard function calls are synchronous and blocking, executing each function sequentially and waiting for each to complete before moving on. This design can be a limitation, especially with I/O-bound or long-running operations.

functionmonitor is designed specifically for use in Jupyter notebooks to enable easy, asynchronous function execution. By running functions in separate threads, you can manage multiple tasks concurrently within the interactive Jupyter environment, allowing for:

• Concurrent function execution, freeing up the main thread to continue processing other tasks.
• Real-time monitoring of each function's progress.
• Automatic result assignment to variables in the global namespace for easy access as soon as functions complete.

Key Features

• Execute Functions Concurrently: Run functions in separate threads, improving efficiency by enabling asynchronous execution.
• Easy Result Access: Results are assigned to global variables with the same name as the task key if the create_variables parameter is enabled.
• Supports Any Callable: Works with any callable (function, lambda, etc.), allowing for flexible function management.

Usage Overview

1. Basic Structure
   functionmonitor behaves similarly to a dictionary, where each task is stored with a task name as the key and the result as the value. Once a task completes, its result is directly accessible.

2. Using Callables
   Any callable can be assigned to functionmonitor, allowing functions, lambda expressions, and more to run asynchronously. Prefixing the callable with lambda prevents immediate execution, enabling background processing.

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This setup lets you add tasks asynchronously with minimal effort, simplifying concurrent execution and real-time progress tracking in Python projects.