A library to detect fronts using an heterogeneity index
Project description
Heterogeneity-Index
Python library to compute the Heterogeneity Index from SST, as defined in Haëck et al. (2023) and Liu & Levine (2016), and some associated diagnostics (front detection for instance).
Documentation: https://biofronts.pages.in2p3.fr/heterogeneity-index
Installation
git clone https://gitlab.in2p3.fr/biofronts/heterogeneity-index
cd heterogeneity-index
pip install .
Will be available on PyPI.
Requirements
- Python >= 3.10
- Numpy >= 1.24
- Numba >= 0.57
Optional:
- Xarray
- Dask
References
- Haëck, C., Lévy, M., Mangolte, I., and Bopp, L.: “Satellite data reveal earlier and stronger phytoplankton blooms over fronts in the Gulf Stream region”, Biogeosciences 20, 1741–1758, https://doi.org/10.5194/bg-20-1741-2023, 2023.
- Liu, X. and Levine, N. M.: “Enhancement of phytoplankton chlorophyll by submesoscale frontal dynamics in the North Pacific Subtropical Gyre”, Geophys. Res. Lett. 43, 1651–1659, https://doi.org/10.1002/2015gl066996, 2016.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
heterogeneity_index-0.0.2.tar.gz
(12.6 kB
view details)
Built Distribution
File details
Details for the file heterogeneity_index-0.0.2.tar.gz
.
File metadata
- Download URL: heterogeneity_index-0.0.2.tar.gz
- Upload date:
- Size: 12.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.10.15
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | d551c17758026d71b1874655825630d0ad42a43e402281c895e8a9205059fea6 |
|
MD5 | 69c81dcaf8ba1760f74f11b54f0410c1 |
|
BLAKE2b-256 | 57dc69f4aac6cffaca3d7beb9c068a23f74545b8779473954b8d0455204b4151 |
File details
Details for the file heterogeneity_index-0.0.2-py3-none-any.whl
.
File metadata
- Download URL: heterogeneity_index-0.0.2-py3-none-any.whl
- Upload date:
- Size: 12.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.10.15
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1e9034a792ce3e285b08bb38a370ded4083e2bd1bbc7a735aa7a4459ec8ab9ec |
|
MD5 | 7b3f5a20606eaa0939918d7608e69e0e |
|
BLAKE2b-256 | 3e1c94cb17243a29bedb974e60e3171ba2a0a46739604dcb97d2ca09c25b9d78 |