Installation

On all platforms, we recommend install pyGIMLi via the conda package manager and the cleanest way is to create a new environment. Besides large installers like Anaconda that come with many (great) packages, you can save space and install a light-weight installer like Miniconda or Miniforge.

Note

After installing Anaconda or Miniconda, make sure that you have at least version 23.10.0 of conda (conda –version).

To avoid conflicts with other packages, we recommend to install pyGIMLi in a separate environment. Here we call this environment pg, but you can give it any name. Note that this environment has to be created only once.

Open a terminal (Linux & Mac) or the Anaconda Prompt (Windows) and type:

conda create -n pg -c gimli -c conda-forge "pygimli>=1.5.0"

If you want to use pyGIMLi from the command line, you have to activate the environment. You can put this line in your ~/.bashrc file so that it is activated automatically if you open a terminal.

conda activate pg

After that you can use pyGIMLi with your text editor of choice and a terminal.

Usage with Spyder or JupyterLab

Depending on your preferences, you can also install third-party software such as the MATLAB-like integrated development environment (https://www.spyder-ide.org):

conda install -c conda-forge spyder

Or alternatively, the web-based IDE JupyterLab (https://jupyterlab.readthedocs.io):

conda install -c conda-forge jupyterlab

If you do one of the above steps in the pg environment, then it will automatically find pyGIMLi. But you may not want to install JupyterLab or Spyder for every different environment. To use your existing JupyterLab installation in the base environment with pyGIMLi in the pg environment, follow these steps:

conda activate pg
conda install ipykernel
conda activate base
conda install -c conda-forge nb_conda_kernels
jupyter lab

pyGIMLi on Google Colab

pyGIMLi can be run on Google Colab without any installation on your own computer. Just create a new Notebook and install pyGIMLi via pip:

!pip install pygimli tetgen

It turns out that there are some packages preinstalled that lead to some incompatibl numpy version, so you might have to uninstall them first.

!pip uninstall -y numba tensorflow pytensor thinc
!pip install pygimli tetgen

Staying up-to-date

Update your pyGIMLi installation from time to time, if want to have the newest functionality.

conda update -c gimli -c conda-forge pygimli

If there something went wrong and you are running an old, not further supported python version, consider a fresh install in a new clean environment. The only drawback of using conda is that you are bound to the rhythm in which we update the conda packages. In order to work with the latest Python codes you should create an environment with the latest pyGIMLi C++ core only,

conda create -n pgcore -c gimli -c conda-forge pgcore

retrieve the source code by git

git clone https://github.com/gimli-org/gimli
cd gimli

and install pygimli as a development package using conda

conda develop .

or using pip

pip install --no-build-isolation --no-deps -e .

Alternatively you could set the PYTHONPATH variable but you would have to care for dependencies by yourself.

Later you can just update the pygimli code by

git pull

Only if you need recent changes to the C++ core, you have to compile pyGIMLi using your systems toolchain as described in https://www.pygimli.org/compilation.html#sec-build

Note

If you want to compile pyGIMLi from source, check out Building from source.