A library for the iterative ensemble smoother algorithm.
Project description
Iterative Ensemble Smoother
About
iterative_ensemble_smoother is a Python package that implements the subspace iterative ensemble smoother as described in evensen2019. This algorithm is particularly effective for problems with a large number of parameters (e.g., millions) and a few realizations or samples (e.g., hundreds).
Installation
iterative_ensemble_smoother is on PyPi and can be installed using pip:
pip install iterative_ensemble_smoother
If you want to do development, then run:
git clone https://github.com/equinor/iterative_ensemble_smoother.git
cd iterative_ensemble_smoother
<create environment>
pip install --editable '.[doc,dev]'
Usage
iterative_ensemble_smoother mainly implements the two classes SIES
and ESMDA
.
Check out the examples section to see how to use them.
Building the documentation
apt install pandoc # Pandoc is required to build the documentation.
pip install .[doc]
sphinx-build -c docs/source/ -b html docs/source/ docs/build/html/
Releasing a new version
- Create a tag, e.g.
git tag -a v1.0.0 -m "A short note" cf2c87270d3
locally on the commit. - Push the tag, e.g.
git push upstream v1.0.0
. - Create a release on the GitHub GUI.
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
Built Distribution
Close
Hashes for iterative_ensemble_smoother-0.2.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 116979411ae2f73b825756e5314c9ce466d47245d45790d005c42d3ff36bb25b |
|
MD5 | 6eaaf9446e45453c79dd25d913f5ea4f |
|
BLAKE2b-256 | 37e75d040fdd2d4e34b765936d9e7bb67dbe88fa346ed46e11f9ca7dde82293a |
Close
Hashes for iterative_ensemble_smoother-0.2.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 270121f64a5cef5e658087d1ad953964cd77516f2d9c4f940d2847e9138ac10b |
|
MD5 | 38f8f3f571df72a96faf810634e629ca |
|
BLAKE2b-256 | 091a44bf09fd25cdb477e2fa0fb0453b4ad019569ad4398cd31da944510186b2 |