iterative-ensemble-smoother
A library for the iterative ensemble smoother algorithm.
Decision gist · record as of 2026-08-14
Yes, if you work on inverse problems or data assimilation in scientific computing. The package is actively maintained, has low install friction, and fills a specific niche in ensemble-based parameter estimation. The GPL-3.0 copyleft license is suitable for research and open-source projects but rules out proprietary closed-source use. The 'Development Status :: 1 - Planning' classifier warrants checking whether all advertised localization methods are production-ready.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later (supports 3.12, 3.13, 3.14); numpy, scipy, scikit-learn, joblib, and networkx must be available.
- Low friction install with standard scientific Python dependencies (numpy, scipy, scikit-learn, joblib, networkx).
- Active maintenance with a release 9 days ago; repository shows recent commits and is not archived.
License · maintenance · safety
GPL-3.0 (copyleft) — GPL-3.0 copyleft license requires that derivative works and distributions remain under GPL-3.0 or compatible terms; suitable for research and open-source projects but incompatible with proprietary closed-source use.
last release 2026-08-05 (9 days) · last repo commit 2026-08-12 · 30 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,340 downloads/mo, #12,946 on PyPI
Alternatives
Verify before relying
pip install iterative_ensemble_smoother
from iterative_ensemble_smoother import ESMDA
# Initialize and run ESMDA on your ensemble and observations
esmda = ESMDA()
esmda.update(ensemble, observations, std)- Whether AdaptiveESMDA and DistanceESMDA are fully implemented or still in development given 'Development Status :: 1 - Planning' classifier
- Performance characteristics and scalability limits for the 'millions of parameters' use case mentioned in description
- Availability and completeness of examples referenced in the usage section
What it is and what it does
iterative_ensemble_smoother is a Python library for data assimilation and history matching using ensemble-based methods. It focuses on inverse problems where you have many parameters (potentially millions) but relatively few ensemble realizations (hundreds). The library implements ESMDA (Ensemble Smoother with Multiple Data Assimilation), a non-iterative algorithm that performs multiple sequential data assimilation steps to update an ensemble of model realizations toward observations.
The package depends on numpy, scipy, scikit-learn, joblib, and networkx to handle the numerical computations and ensemble operations. It provides both correlation-based (AdaptiveESMDA) and distance-based (DistanceESMDA) localization methods to improve results in high-dimensional problems. The library is actively maintained and supports modern Python versions (3.12–3.14).
Use it for
- Calibrate reservoir simulation models using production data in petroleum engineering workflows
- Update weather or climate model ensembles with observational data for forecasting
- Inverse problems in geophysics where you estimate subsurface properties from seismic or well data
- History matching in hydrogeology to constrain groundwater flow parameters
- Parameter estimation in computational models with expensive forward simulations
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work on inverse problems or data assimilation in scientific computing.
The package is actively maintained, has low install friction, and fills a specific niche in ensemble-based parameter estimation. The GPL-3.0 copyleft license is suitable for research and open-source projects but rules out proprietary closed-source use. The 'Development Status :: 1 - Planning' classifier warrants checking whether all advertised localization methods are production-ready.
Install
iterative-ensemble-smoother on PyPI
Before you install
Low friction install with standard scientific Python dependencies (numpy, scipy, scikit-learn, joblib, networkx). Active maintenance with a release 9 days ago; repository shows recent commits and is not archived.
Requires Python 3.12 or later (supports 3.12, 3.13, 3.14); numpy, scipy, scikit-learn, joblib, and networkx must be available.
License in practice
GPL-3.0 copyleft license requires that derivative works and distributions remain under GPL-3.0 or compatible terms; suitable for research and open-source projects but incompatible with proprietary closed-source use.
Quickstart
pip install iterative_ensemble_smoother
from iterative_ensemble_smoother import ESMDA
# Initialize and run ESMDA on your ensemble and observations
esmda = ESMDA()
esmda.update(ensemble, observations, std)Verify before relying
- Whether AdaptiveESMDA and DistanceESMDA are fully implemented or still in development given 'Development Status :: 1 - Planning' classifier
- Performance characteristics and scalability limits for the 'millions of parameters' use case mentioned in description
- Availability and completeness of examples referenced in the usage section
Package facts
| License | GPL-3.0 copyleft |
| Python support | Supports the current Python release <3.15,>=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpyscipyjoblibnetworkxscikit-learn |
| Maintenance | Actively maintained 9 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 101,340 / month, #12,946 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 1 - PlanningLicense :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)Programming Language :: PythonProgramming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: iterative_ensemble_smoother-1.3.0-py3-none-any.whl
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