Introduction ============ Pastas is an open source Python package to analyse hydro(geo)logical time series. The objective of Pastas is twofold: to provide a scientific framework to develop and test new methods, and to provide a reliable ready‐to‐use software tool for groundwater practitioners. All code is available from the `Pastas GitHub `_. Want to contribute to the project? Check out the :doc:`Developers ` section. .. grid:: .. grid-item-card:: User Guide :link: userguide/index :link-type: doc User guide on the basic concepts of Pastas. .. grid-item-card:: Examples :link: examples/index :link-type: doc Examples of Pastas usage. .. grid-item-card:: Code Reference :link: api/index :link-type: doc Pastas code reference. .. grid:: .. grid-item-card:: Contribute :link: developers/index :link-type: doc Want to contribute to Pastas? Find resources and guides for developers here. .. grid-item-card:: Publications :link: about/publications :link-type: doc Find an overview of scientific peer-reviewed studies that used Pastas. .. grid-item-card:: More Pastas :link: https://github.com/pastas/ Find out more useful resources developed by the Pastas community! Quick Example ------------- .. tab-set:: .. tab-item:: Python In this example a head time series is modelled in just a few lines of Python code. .. code-block:: python # Import python packages import pandas as pd import pastas as ps # Read head and stress data obs = pd.read_csv("head.csv", index_col=0, parse_dates=True).squeeze("columns") rain = pd.read_csv("rain.csv", index_col=0, parse_dates=True).squeeze("columns") evap = pd.read_csv("evap.csv", index_col=0, parse_dates=True).squeeze("columns") # Create and calibrate model ml = ps.Model(obs, name="head") sm = ps.RechargeModel(prec=rain, evap=evap, rfunc=ps.Exponential(), name="recharge") ml.add_stressmodel(sm) ml.solve() ml.plots.results() .. tab-item:: Result .. figure:: _static/example_output.png :figwidth: 500px Using Pastas? Please cite us! ----------------------------- If you find Pastas useful and use it in your research or project, we kindly ask you to cite the Pastas article published in Groundwater journal as follows: - Collenteur, R.A., Bakker, M., Caljé, R., Klop, S.A., Schaars, F. (2019) `Pastas: open source software for the analysis of groundwater time series. Groundwater `_. doi: 10.1111/gwat.12925. .. toctree:: :maxdepth: 2 :hidden: User Guide Examples API Docs Benchmarks Developers About