• Interactive Scripting: Designed for interactive use via Jupyter Notebooks or terminal environments, SlothPy offers a user-friendly, scripting-like experience. SlothPy harnesses advanced threading and multiprocessing techniques to ensure efficient performance, even with complex simulations. For guidance on getting started with interactive scripting, see the How to Start section.

• Customizable Workflows: The software’s flexible architecture allows users to craft their own pipelines and automated processes within Python environment. Thanks to Just-In-Time (JIT) compilation, SlothPy ensures that all workflows are inherently fast and efficient, facilitating rapid data processing and analysis. Refer to the How to Start section for more on setting up your workflows.

• Autotune Module: Unlock the power of your hardware with a unique autotune feature that optimizes the performance of your machine. It automatically tunes and selects the optimal number of threads and processes for your CPU, providing access to the full performance potential. This module also estimates job completion times under various settings, allowing for more efficient workflow planning.

• HDF5 File Utilization: Emphasizing the use of HDF5 files, SlothPy offers powerful data management capabilities. HDF5’s speed, portability, and ease of integration make it an ideal choice for handling complex data structures efficiently in computational workflows.

• Enhanced User Experience: SlothPy enhances the user experience with custom error handling, an improved printout system, a comprehensive user manual, and extensive documentation. These features make it easier for users to understand, troubleshoot, and effectively utilize the software. For detailed guidance and best practices, users are encouraged to consult the Reference Manual section, which offers in-depth insights into SlothPy’s capabilities.

• Extensibility: Easily integrate results from popular chemistry programs like MOLCAS and ORCA. We are constantly expanding compatibility – reach out to include your preferred software in future releases. SlothPy utilizes HDF5 files as a robust framework for data management, enhancing the scalability and accessibility of computational data.

• Highly Visual: SlothPy provides built-in functions for convenient data visualization, enabling users to easily interpret complex molecular data. The software includes a variety of visualization tools tailored to the needs of molecular magnetism, facilitating insightful and intuitive analysis.

• Licensing & Access: SlothPy is distributed under the GPL-3.0 license. Explore and contribute to the source code on GitHub. Download the latest version from the PyPi repository.

SlothPy is built using a robust stack of technologies and libraries, ensuring high performance, flexibility, and a wide range of features:

• NumPy: Fundamental package for scientific computing in Python, used for efficient array data structures. Visit NumPy.

• Numba: An open-source JIT compiler that translates Python and NumPy code into fast machine code. Explore Numba.

• h5py: Interface to the HDF5 binary data format, utilized for managing complex data structures. Learn more about h5py.

• Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python. Check out Matplotlib.

• ThreadPoolCtl: Used for controlling the number of threads used in native libraries. More on ThreadPoolCtl.

• SciPy: Python-based ecosystem of open-source software for mathematics, science, and engineering, specifically for linear algebra algorithms. Visit SciPy.

• PyQt5: A comprehensive set of Python bindings for Qt application framework, used for developing the GUI elements in the plotting modules. Learn more about PyQt5.

• Pandas: A powerful data analysis and manipulation library, used for integrating pipelines and exporting data efficiently. Explore Pandas.

• Multiprocessing & Multithreaded Linear Algebra Libraries: Utilizes Python’s multiprocessing for parallel processing and OpenMP (OMP) for multithreaded linear algebra operations.

By leveraging these technologies, SlothPy provides a powerful and versatile platform for molecular magnetism research.

Support and Feedback

Encounter an issue or have a suggestion? File a report on our GitHub Issues page or contact Mikołaj Żychowicz directly at mikolaj.zychowicz@uj.edu.pl.

We hope that SlothPy will become a useful tool for the community dedicated to advancing molecular magnetism research. Join us in shaping its future by sending us everything from bug reports, requests, and suggestions, to feedback and comments.