Metadata-Version: 2.1
Name: pyesmda
Version: 0.3.0
Summary: Python Ensemble Smoother with Multiple Data Assimilations
Home-page: https://gitlab.com/antoinecollet5/pyesmda
Author: Antoine Collet
Author-email: antoine.collet5@gmail.com
License: MIT license
Keywords: esmda,es-mda,inversion,inverse problem,parameter estimation,stochastic-optimization,ensemble smoother
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.6
License-File: LICENSE
License-File: AUTHORS.rst

=======
pyESMDA
=======


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Python Ensemble Smoother with Multiple Data Assimilations

This package is an object-oriented python implementation of the ES-MDA
algorithm based on the work of Emerick and Reynolds [1-2].

Thanks to its simple formulation, ES-MDA of Emerick and Reynolds (2012) is perhaps the 
most used iterative form of the ensemble smoother in geoscience applications.

* Free software: MIT license
* Documentation: https://pyesmda.readthedocs.io.

The original python implementation was by Muhammad Iffan Hannanu
(https://github.com/iffanh/Playground).



References
----------

* [1] Emerick, A. A. and A. C. Reynolds, Ensemble smoother with multiple
  data assimilation, Computers & Geosciences, 2012.
* [2] Emerick, A. A. and A. C. Reynolds. (2013). History-Matching
  Production and Seismic Data in a Real Field Case Using the Ensemble
  Smoother With Multiple Data Assimilation. Society of Petroleum
  Engineers - SPE Reservoir Simulation Symposium
  1.    2. 10.2118/163675-MS.

==============
Changelog
==============

0.3.0 (2022-08-12)
------------------

* `!PR15 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/15>`_ Implement ESMDA-RS (restricted step) which provides
  an automatic estimation of the inflation parameter and determines when to stop (number of assimilations) on the fly.
* `!PR14 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/14>`_ Add keyword **is_forecast_for_last_assimilation** to choose whether to 
  compute the predictions for the ensemble obtained at the last assimilation step. The default is True.
* `!PR13 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/13>`_ Implementation: Faster analyse step by avoiding matrix inversion.
* `!PR12 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/12>`_ Add a seed parameter for the random 
  number generation **seed** in the prediction perturbation step.
  To avoid confusion , **cov_d** has been renamed **cov_obs**.
* `!PR11 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/11>`_ Implement the covariance localization. Introduces the 
  correlation matrices **dd_correlation_matrix** and **md_correlation_matrix**.
  To avoid confusion , **cov_d** has been renamed **cov_obs**.
* `!PR10 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/10>`_ Implement the parameters auto-covariance inflation.
  Add the estimation of the parameters auto-covariance matrix. The parameter **alpha** becomes **cov_obs_inflation_factors**.


0.2.0 (2022-07-23)
------------------

* `!PR6 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/6>`_ The parameter **stdev_d** becomes **cov_d**.
* `!PR5 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/5>`_ The parameter **n_assimilation** becomes **n_assimilations**.
* `!PR4 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/4>`_ The parameter **stdev_m** is removed.
* `!PR3 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/3>`_ Type hints are now used in the library.
* `!PR2 <https://gitlab.com/antoinecollet5/pyesmda/-/merge_requests/2>`_ Add the possibility to save the history of m and d. This introduces a new knew
  keyword (boolean) for the constructor **save_ensembles_history**. 
  Note that the **m_mean** attribute is depreciated and two new attributes are 
  introduced: **m_history**, **d_history** respectively to access the successive
  parameter and predictions ensemble. 


0.1.0 (2021-11-28)
------------------

* First release on PyPI.


