iterative-ensemble-smoother
Advanced tools
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).
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]'
iterative_ensemble_smoother mainly implements the two classes SIES and ESMDA.
Check out the examples section to see how to use them.
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/
git tag -a v1.0.0 -m "A short note" cf2c87270d3 locally on the commit.git push upstream v1.0.0.FAQs
A library for the iterative ensemble smoother algorithm.
We found that iterative-ensemble-smoother demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Malicious Go packages are impersonating popular libraries to install hidden loader malware on Linux and macOS, targeting developers with obfuscated payloads.
Security News
Bybit's $1.46B hack by North Korea's Lazarus Group pushes 2025 crypto losses to $1.6B in just two months, already surpassing all of 2024's $1.49B total.
Security News
OpenSSF has published OSPS Baseline, an initiative designed to establish a minimum set of security-related best practices for open source software projects.