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Pastas is an open-source Python framework for the analysis of groundwater time series.
[!IMPORTANT] As of Pastas 1.5, noisemodels are not added to the Pastas models by default anymore. Read more about this change here.
Pastas is an open source python package for processing, simulating and analyzing groundwater time series. The object oriented structure allows for the quick implementation of new model components. Time series models can be created, calibrated, and analysed with just a few lines of python code with the built-in optimization, visualisation, and statistical analysis tools.
To install Pastas, a working version of Python 3.9, 3.10, 3.11, or 3.12 has to be installed on your computer. We recommend using the Anaconda Distribution as it includes most of the python package dependencies and the Jupyter Notebook software to run the notebooks. However, you are free to install any Python distribution you want.
To get the latest stable version, use:
pip install pastas
To update pastas, use:
pip install pastas --upgrade
To get the latest development version, use:
pip install git+https://github.com/pastas/pastas.git@dev#egg=pastas
Pastas depends on a number of Python packages, of which all of the necessary are automatically installed when using the pip install manager. To summarize, the dependencies necessary for a minimal function installation of Pastas
To install the most important optional dependencies (solver LmFit and function visualisation Latexify) at the same time with Pastas use:
pip install pastas[full]
or for the development version use:
pip install git+https://github.com/pastas/pastas.git@dev#egg=pastas[full]
If you use Pastas in one of your studies, please cite the Pastas article in Groundwater:
To cite a specific version of Pastas, you can use the DOI provided for each official release (>0.9.7) through Zenodo. Click on the link to get a specific version and DOI, depending on the Pastas version.
FAQs
Pastas is an open-source Python framework for the analysis of groundwater time series.
We found that pastas demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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