Introduction

spinor-gpe is high-level, object-oriented Python package for numerically solving the quasi-2D, psuedospinor (two component) Gross-Piteavskii equation (GPE), for both ground state solutions and real-time dynamics. This project grew out of a desire to make high-performance simulations of the GPE more accessible to the entering researcher.

While this package is primarily built on NumPy, the main computational heavy-lifting is performed using PyTorch, a deep neural network library commonly used in machine learning applications. PyTorch has a NumPy-like interface, but a backend that can run either on a conventional processor or a CUDA-enabled NVIDIA(R) graphics card. Accessing a CUDA device will provide a significant hardware acceleration of the simulations.

This package has been tested on Windows, Mac, and Linux systems.

View the documentation on ReadTheDocs

Contents:

• Introduction
• Installation
  • Dependencies
    • Primary packages:
    • Other packages:
  • Installing Dependencies
  • CUDA Installation
  • Getting Started
• Basic Operation
  • 1. Setup: Data path and PSpinor object
  • 2. Run: Begin Propagation
  • 3. Analyze: Plot the results
  • 4. Repeat
• Examples
• Benchmarks
  • Physical Parameters
  • Numerical Parameters
• pspinor package
  • Submodules
  • pspinor.pspinor module
  • pspinor.tensor_propagator module
  • pspinor.prop_result module
  • pspinor.tensor_tools module
  • pspinor.plotting_tools module
  • Module contents

Indices and tables

• Index

• Module Index

• Search Page