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Constrained Spectral Approximation

GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI

The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:

  • 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

This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.

Read the documentation here

Requirements

See requirements.txt

NOTE: The Sphinx dependencies can be found in docs/source/conf.py.

Usage

Installation

Install the latest release from PyPI:

pip install pycsa-specappx

The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.

To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:

git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"

Configuration

Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).

Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):

export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs

Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.

Execution

A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:

python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py

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.

Examples

Three self-contained examples ship with bundled data (no download needed):

License

GNU GPL v3 (tentative)

Contributions

Refer to the open issues that require attention.

Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.

About

pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis

ray-chew.github.io/pyCSA/

Topics

data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast

Resources

Readme
GPL-3.0 license
Activity

Stars

1 star

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Used by

Contributors

Languages

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CSA Logo
Constrained Spectral Approximation
GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI
The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:
  • 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
This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.
Read the documentation here
Requirements
See requirements.txt
NOTE: The Sphinx dependencies can be found in docs/source/conf.py.
Usage
Installation
Install the latest release from PyPI:
pip install pycsa-specappx
The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.
To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:
git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"
Configuration
Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).
Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):
export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs
Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.
Execution
A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:
python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py
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.
Examples
Three self-contained examples ship with bundled data (no download needed):
License
GNU GPL v3 (tentative)
Contributions
Refer to the open issues that require attention.
Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.
About
pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
ray-chew.github.io/pyCSA/
Topics
data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Used by
Contributors
Languages

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Skip to content
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CSA Logo
Constrained Spectral Approximation
GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI
The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:
  • 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
This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.
Read the documentation here
Requirements
See requirements.txt
NOTE: The Sphinx dependencies can be found in docs/source/conf.py.
Usage
Installation
Install the latest release from PyPI:
pip install pycsa-specappx
The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.
To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:
git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"
Configuration
Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).
Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):
export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs
Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.
Execution
A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:
python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py
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.
Examples
Three self-contained examples ship with bundled data (no download needed):
License
GNU GPL v3 (tentative)
Contributions
Refer to the open issues that require attention.
Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.
About
pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
ray-chew.github.io/pyCSA/
Topics
data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Used by
Contributors
Languages

, 'i');
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GitHub - ray-chew/pyCSA: pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis · GitHub
Skip to content
BranchesTags
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CSA Logo
Constrained Spectral Approximation
GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI
The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:
  • 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
This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.
Read the documentation here
Requirements
See requirements.txt
NOTE: The Sphinx dependencies can be found in docs/source/conf.py.
Usage
Installation
Install the latest release from PyPI:
pip install pycsa-specappx
The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.
To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:
git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"
Configuration
Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).
Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):
export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs
Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.
Execution
A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:
python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py
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.
Examples
Three self-contained examples ship with bundled data (no download needed):
License
GNU GPL v3 (tentative)
Contributions
Refer to the open issues that require attention.
Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.
About
pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
ray-chew.github.io/pyCSA/
Topics
data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Used by
Contributors
Languages

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CSA Logo
Constrained Spectral Approximation
GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI
The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:
  • 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
This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.
Read the documentation here
Requirements
See requirements.txt
NOTE: The Sphinx dependencies can be found in docs/source/conf.py.
Usage
Installation
Install the latest release from PyPI:
pip install pycsa-specappx
The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.
To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:
git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"
Configuration
Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).
Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):
export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs
Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.
Execution
A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:
python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py
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.
Examples
Three self-contained examples ship with bundled data (no download needed):
License
GNU GPL v3 (tentative)
Contributions
Refer to the open issues that require attention.
Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.
About
pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
ray-chew.github.io/pyCSA/
Topics
data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Used by
Contributors
Languages

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Skip to content
BranchesTags
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CSA Logo
Constrained Spectral Approximation
GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI
The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:
  • 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
This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.
Read the documentation here
Requirements
See requirements.txt
NOTE: The Sphinx dependencies can be found in docs/source/conf.py.
Usage
Installation
Install the latest release from PyPI:
pip install pycsa-specappx
The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.
To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:
git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"
Configuration
Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).
Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):
export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs
Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.
Execution
A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:
python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py
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.
Examples
Three self-contained examples ship with bundled data (no download needed):
License
GNU GPL v3 (tentative)
Contributions
Refer to the open issues that require attention.
Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.
About
pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
ray-chew.github.io/pyCSA/
Topics
data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast
Resources
Readme
GPL-3.0 license
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GitHub - ray-chew/pyCSA: pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis · GitHub
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CSA Logo
Constrained Spectral Approximation
GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI
The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:
  • 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
This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.
Read the documentation here
Requirements
See requirements.txt
NOTE: The Sphinx dependencies can be found in docs/source/conf.py.
Usage
Installation
Install the latest release from PyPI:
pip install pycsa-specappx
The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.
To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:
git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"
Configuration
Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).
Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):
export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs
Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.
Execution
A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:
python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py
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.
Examples
Three self-contained examples ship with bundled data (no download needed):
License
GNU GPL v3 (tentative)
Contributions
Refer to the open issues that require attention.
Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.
About
pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
ray-chew.github.io/pyCSA/
Topics
data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Used by
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GitHub - ray-chew/pyCSA: pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis · GitHub
Skip to content
BranchesTags
Repository files navigation
CSA Logo
Constrained Spectral Approximation
GitHub Actions: CIDocumentationLicense: GPL v3Code style: blackDOI
The Constrained Spectral Approximation (CSA) method is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:
  • 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
This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.
Read the documentation here
Requirements
See requirements.txt
NOTE: The Sphinx dependencies can be found in docs/source/conf.py.
Usage
Installation
Install the latest release from PyPI:
pip install pycsa-specappx
The distribution is named pycsa-specappx (the bare pycsa name was already taken on PyPI by an unrelated project), but the import name is unchanged — import pycsa.
To run the bundled experiment scripts in runs/ / examples/, or to contribute, work from a clone instead:
git clone https://github.com/ray-chew/pyCSA &&cd pyCSA
pip install -e ".[test]"
Configuration
Run parameters are assembled programmatically inside the run scripts using the pycsa.config.params dataclass. Example experiment scripts live in runs/ and examples/; the reusable building blocks are in the pycsa package (pycsa.core, pycsa.wrappers, pycsa.plotting, pycsa.data, pycsa.compute).
Runs that read on-disk data (e.g. the global ICON+ETOPO pipeline) locate it through SPEC_APPX_* environment variables, which are read by pycsa/local_paths.py (copied from local_paths.py.template):
export SPEC_APPX_DATA_DIR=/path/to/data # directory containing the ICON gridexport SPEC_APPX_ETOPO_DIR=/path/to/data/etopo_15s
export SPEC_APPX_MERIT_DIR=/path/to/MERIT # MERIT runs onlyexport SPEC_APPX_REMA_DIR=/path/to/REMA # MERIT runs onlyexport SPEC_APPX_OUTPUT_DIR=/path/to/outputs
Set these directly or with source setup_paths.sh. The bundled examples/ need no such setup — their data ships with the repo.
Execution
A simple setup can be found in runs/idealised_isosceles.py, a fixed-seed idealised benchmark. From a clone, run it directly:
python -m runs.idealised_isoscelespython3 ./runs/idealised_isosceles.py
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.
Examples
Three self-contained examples ship with bundled data (no download needed):
License
GNU GPL v3 (tentative)
Contributions
Refer to the open issues that require attention.
Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.
About
pyCSA is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
ray-chew.github.io/pyCSA/
Topics
data-analysisgmtedicon-modelmerit-demorographicspectral-analysistopographyweather-forecast
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Used by
Contributors
Languages