cortecs
A Python package for decreasing the memory footprint of opacity functions. The primary functionality is compressing opacity functions with varying flexibility. Current methods include
- polynomial fitting
- PCA-based fitting
- neural network fitting
All fits are currently made in along the temperature and pressure axes.
Additionally, cortecs can chunk up opacity functions. The radiative transfer problem can often be cast as embarassingly parallel, so each chunk can be sent to a different CPU.
Installation instructions
cortecs can be installed via pip:
pip install cortecs
or conda:
conda install -c conda-forge cortecs
or from source:
git clone
cd cortecs
pip install -e .
To install from source with optional neural network support:
pip install -e .[neural_networks]
1 maintainer
Metadata
1 maintainer
Release files for cortecs 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| cortecs-1.0.1.tar.gz | 25.5 MB | Without provenance | Details for cortecs-1.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| cortecs-1.0.1-py3-none-any.whl | Python 3 | none | any | Details for cortecs-1.0.1-py3-none-any.whl |
Total release size: 35.0 MB
Release files / cortecs-1.0.1.tar.gz
| Download URL | cortecs-1.0.1.tar.gz |
|---|---|
| Size | 25.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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twine/5.1.1 CPython/3.12.4
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Release files / cortecs-1.0.1-py3-none-any.whl
| Download URL | cortecs-1.0.1-py3-none-any.whl |
|---|---|
| Size | 9.6 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.12.4
|
Security for cortecs 1.0.1
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None of the files in this release were uploaded with provenance metadata.
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