Getting started with Pastas

On this page you will find all the information to get started with Pastas. A basic knowledge of programming in Python is assumed, but nothing more than that. See the Examples section for some example-code.

Installing Python

To install Pastas, a working version of Python 3.8 or higher has to be installed on your computer. We recommend using the Anaconda Distribution of Python. This Python distribution includes most of the python package dependencies and the Jupyter Lab integrated development environment (IDE) to run the notebooks. However, you are free to install any Python IDE distribution you want.

Installing Pastas

To install Pastas, there are several options available. The easiest is to use the Python Package Index (PyPI), where many official python packages are gathered. To get the latest version of Pastas, open the Anaconda Prompt, a Windows Command Prompt (also called command window) or a Mac/Linux terminal and type:

pip install pastas

Pastas will now be installed on your computer, including the packages necessary for Pastas to work properly (called dependencies in Python language). To install Pastas with the optional dependencies use:

pip install pastas[full]

It sometimes occurs that the automatic installation of the dependencies does not work. A safe method to update another package if you are using Anaconda is to install a package with the follow command line:

conda install package

Updating Pastas

If you have already installed Pastas, it is possible to update Pastas easily. To update, open a Anaconda/Windows command prompt or a Mac terminal and type:

pip install pastas --upgrade

Dependencies

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 following packages are necessary for a minimal function installation of Pastas:

numpy
pandas
scipy
matplotlib
numba  #(significant speed-up)

Other optional, but recommended dependencies include:

jupyter  #(for running notebooks)
lmfit  #(alternative solver)
latexify #(visualising formula's of functions)