https://github.com/equinor/dass
Data Assimilation in Python for teaching purposes
https://github.com/equinor/dass
data-assimilation history-matching
Last synced: 3 months ago
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Data Assimilation in Python for teaching purposes
- Host: GitHub
- URL: https://github.com/equinor/dass
- Owner: equinor
- Created: 2022-05-16T07:28:03.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-07T07:50:36.000Z (over 1 year ago)
- Last Synced: 2024-08-07T11:28:15.832Z (over 1 year ago)
- Topics: data-assimilation, history-matching
- Language: Jupyter Notebook
- Homepage:
- Size: 13.2 MB
- Stars: 10
- Watchers: 2
- Forks: 11
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Data Assimilation
`dass` is tool for learning about data assimilation / history matching created by the developers of [ERT](https://github.com/equinor/ert).
It is inspired by [DAPPER](https://github.com/nansencenter/DAPPER) and [HistoryMatching](https://github.com/patnr/HistoryMatching).
It includes implementations of Ensemble Smoother (ES) as given in [1], see [dass/analysis.py](dass/analysis.py).
The implementation of ES can easily be extended to the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) as described in [2].
For notebooks with examples and tutorials see the `notebooks/` folder.
**NB!** notice that there are no `.ipynb` files in the `notebooks/` folder.
This is because we use [Jupytext](https://github.com/mwouts/jupytext) to sync `.py` and `.ipynb` files,
which means that we only need to keep the `.py` files in source control.
## Installation
```bash
git clone https://github.com/equinor/dass.git
cd dass
# dass supports Python 3.8 and above.
python3.9 -m venv .venvdass
source .venvdass/bin/activate
# Add -e if you want to make changes.
pip install -e .
# Install additional requirements for developers.
pip install -r dev-requirements.txt
# Start jupyter notebook
jupyter notebook
# To make sure everything works, run on the of the notebooks in the notebooks/ folder.
```
## References
[1] - [Data Assimilation
The Ensemble Kalman Filter](https://link.springer.com/book/10.1007/978-3-642-03711-5)
[2] - [Ensemble smoother with multiple data assimilation](https://www.sciencedirect.com/science/article/pii/S0098300412000994)