Added
- Base functionality.
- Translate Python functions describing CA logic into CUDA C.
- Evolve CA step by step, using resulting GPU kernels.
- Render the current CA state into 2D NumPy array for subsequent
visualization.
- Save and load current CA state.
- Multi-dimensional CA topologies.
- N-dimensional orthogonal lattice.
- N-dimensional cases of Moore and Von Neumann neighborhoods.
- Wrap the board into N-torus.
- Surround the board by static cells.
- CA inner logic.
- Multiple integer 'properties' per each cell state.
- Buffered interaction between neighbors.
- Lazy read access and deferred write access to GPU memory.
- Packing and unpacking cell's state into raw bit sequence.
- Mixed values in expressions with variables/properties.
- Rendering.
- Computed colors per each cell.
- 'Moving average' color effect, for smoother visualization.
- Projection into 2D plain from higher dimensions.
- Experiments.
- Experiment classes describing initial state and meta-parameters.
- Independent deterministic RNG stream per each CA.
- 'Big Bang' and 'Primordial Soup' initial random patterns.
- Bridge with Moire GUI.
- Tests.
- 100% code coverage by unittests.
- Performance check script.
- Documentation.
- Throughout API documentation via docstrings.
- Sphinx configuration to automatically build and publish at Read
The Docs.
- Project overview.
- Brief installation instructions.
- Placeholder for tutorial.
- Miscellaneous.
- MIT License.
- Readme.
- Contributing Guidelines.
- Code of Conduct
- Changelog.
[Unreleased]: https://github.com/a5kin/xentica/compare/v0.2.0...HEAD