Metadata-Version: 2.1
Name: simframe-gpu
Version: 1.0.6.1
Summary: Framework for Scientific Simulations
Keywords: numerical,simulation,integration,science,mathematics,physics
Author-Email: Rixin Li <rixin.li.astro@gmail.com>, Sebastian Stammler <sebastian.stammler@gmail.com>, Til Birnstiel <til.birnstiel@lmu.de>
Maintainer-Email: Rixin Li <rixin.li.astro@gmail.com>
License: BSD 3-Clause License
         
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Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Project-URL: Repository, https://github.com/astroboylrx/simframe-gpu/
Project-URL: Documentation, https://simframe.readthedocs.io/
Requires-Dist: dill
Requires-Dist: h5py
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: scipy
Description-Content-Type: text/markdown

# Simframe GPU

`simframe-gpu` is a backend-enabled fork of [Simframe](https://github.com/stammler/simframe) maintained to support [`dustpy-gpu`](https://github.com/astroboylrx/dustpy-gpu). It preserves the `simframe` import name and adds the NumPy/CuPy backend infrastructure required by that fork.

Users normally do not need to install this package directly: installing `dustpy-gpu` from GitHub installs `simframe-gpu` automatically.

## Backend-enabled fields

Upstream `simframe` implements `Field` as a NumPy array subclass. In `simframe-gpu`, `Field` is a stable array-like wrapper whose internal storage is native to the selected backend: a NumPy array for the NumPy backend and a CuPy array for the CuPy backend. This allows simulation objects using different backends to coexist in one process when executed sequentially and keeps the design extensible to tensor backends that do not inherit from NumPy arrays.

Standard arithmetic, indexing, assignment, and common array attributes remain available through the `Field` interface. Backend-specific low-level code can explicitly access the native array when an actual `numpy.ndarray` or `cupy.ndarray` is required.

Use `simframe.frame.field_data(field)` when low-level code needs the mutable backend-native storage directly. This helper does not copy or convert the array.

For direct development use, install it in a dedicated virtual environment:

```bash
python -m pip install git+https://github.com/astroboylrx/simframe-gpu.git
```

Refer to the [upstream Simframe documentation](https://simframe.readthedocs.io/) for the underlying framework API.
