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[![CSAM Logo](https://ray-chew.github.io/spec_appx/_static/logo.png)](https://ray-chew.github.io/spec_appx/index.html) | ||
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<h2 align="center">Constrained Spectral Approximation Method</h2> | ||
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<p align="center"> | ||
<a href="https://github.com/ray-chew/spec_appx/actions/workflows/documentation.yml"> | ||
<img alt="GitHub Actions: docs" src=https://github.com/ray-chew/spec_appx/actions/workflows/documentation.yml/badge.svg> | ||
</a> | ||
<a href="https://www.gnu.org/licenses/agpl-3.0"> | ||
<img alt="License: GNU GPL v3" src=https://img.shields.io/badge/License-AGPL_v3-blue.svg> | ||
</a> | ||
<a href="https://github.com/psf/black"> | ||
<img alt="Code style: black" src=https://img.shields.io/badge/code%20style-black-000000.svg> | ||
</a> | ||
</p> | ||
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The Constrained Spectral Approximation Method (CSAM) is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints: | ||
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* Utilises a limited number of spectral modes (no more than 100) | ||
* Significantly reduces the complexity of physical terrain by over 500 times | ||
* Maintains the integrity of physical information to a large extent | ||
* Compatible with unstructured geodesic grids | ||
* Inherently scale-aware | ||
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This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications. | ||
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--- | ||
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**[Read the documentation here.](https://ray-chew.github.io/spec_appx/index.html)** | ||
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--- | ||
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## Requirements | ||
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See [`requirements.txt`](https://github.com/ray-chew/spec_appx/blob/main/requirements.txt) | ||
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> **NOTE:** The Sphinx dependencies can be found in [`docs/conf.py`](https://github.com/ray-chew/spec_appx/blob/main/docs/source/conf.py). | ||
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## Usage | ||
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### Installation | ||
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Make a fork and clone your remote forked repository. | ||
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### Configuration | ||
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The user-defined input parameters are in the [`inputs`](https://github.com/ray-chew/spec_appx/tree/main/inputs) subpackage. These parameters are imported into the run scripts in [`runs`](https://github.com/ray-chew/spec_appx/tree/main/runs). | ||
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### Execution | ||
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A simple setup can be found in [`runs.idealised_isosceles`](https://github.com/ray-chew/spec_appx/blob/main/runs/idealised_isosceles.py). To execute this run script: | ||
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```console | ||
python3 ./runs/idealised_isosceles.py | ||
``` | ||
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However, the codebase is structured such that the user can easily assemble a run script to define their own experiments. Refer to the documentation for the available APIs. | ||
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## License | ||
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GNU GPL v3 (tentative) | ||
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## Contributions | ||
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Refer to the open issues that require attention. | ||
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Any changes, improvements, or bug fixes can be submitted to upstream via a pull request. | ||
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