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Did you know that you can try OGGM in your browser before installing it on your computer? Visit Try OGGM online for more information.
OGGM itself is a pure Python package, but it has several dependencies which are not trivial to install. The instructions below provide all the required details and should work on Linux and Mac OS. See Installation troubleshooting if something goes wrong.
OGGM is fully tested with Python version 3.7, 3.8 and 3.9 on Linux. OGGM does not work with Python 2. We do not test OGGM automatically on Mac OSX, but it should probably run fine there as well.
OGGM does not work on Windows. If you are using Windows 10, we recommend to install the free Windows subsystem for Linux and install and run OGGM from there.
For most users we recommend to install Python and the package dependencies with the conda package manager. Linux users with experience with pip can follow these instructions to install OGGM in a pyenv environment with pip.
Here is a list of all dependencies of the OGGM model. If you want to use OGGM’s numerical models only (i.e. no GIS or preprocessing tools), refer to Install a minimal OGGM environment below.
- Standard SciPy stack:
- Configuration file parsing tool:
- GIS tools:
pytest-mpl (OGGM fork required)
- Other libraries:
progressbar2 (displays the download progress)
bottleneck (might speed up some xarray operations)
Install with conda (all platforms)#
This is the recommended way to install OGGM for most users.
You should have a recent version of the conda package manager. Our
recommendation is to install mambaforge. If you are completely
new to these things, check out
which explains how to install
for installing packages.
We recommend to use mamba over conda as an
installation command. Mamba is a drop-in
replacement for all conda commands. If you feel like it, install mamba in your conda
conda install -c conda-forge mamba)
and replace all occurrences of
mamba in the instructions below.
Note 2022: soon, conda will use mamba per default. See this post for more info.
The simplest way: with an environment file#
Download (right-click -> “save as”) or copy the content of
into a file called
environment.yml on your system.
From the location of the file, run
mamba env create -f environment.yml.
This will create a new environment called
oggm_env in your conda installation.
For more information about conda environments, visit the
conda documentation on the topic. Similarly,
visit conda documentation on environment files
for more information about how to create an environment from a
Don’t forget to Test OGGM before using it!
Install OGGM itself#
First, choose which version of OGGM you would like to install:
stable: this is the latest version officially released and has a fixed version number (e.g. v1.4).
dev: this is the development version. It might contain new features and bug fixes, but is also likely to continue to change until a new release is made. This is the recommended way if you want to use the latest changes to the code.
dev+code: this is the recommended way if you plan to explore the OGGM codebase, contribute to the model, and/or if you want to use the most recent model updates.
‣ install the stable version:
If you installed OGGM with the environment file above, OGGM will be installed already in the latest stable version.
In your conda environment, use pip:
pip install oggm
‣ install the dev version:
For this to work you’ll need to have the git software installed on your system. In your conda environment, simply do:
pip install --upgrade git+https://github.com/OGGM/oggm.git
With this command you can also update an already installed OGGM version to the latest version.
‣ install the dev version + get access to the OGGM code:
For this to work you’ll need to have the git software installed on your system. Then, clone the latest repository version:
git clone https://github.com/OGGM/oggm.git
Then go to the project root directory:
And install OGGM in development mode (this is valid for both pip and conda environments):
pip install -e .
Installing OGGM in development mode means that subsequent changes to this
code repository will be taken into account the next time you will
import oggm. You can also update OGGM with a simple git pull from
the root of the cloned repository.
Don’t forget to Test OGGM before using it!
You can test your OGGM installation by running the following command from anywhere (don’t forget to activate your environment first):
The tests can run for about 10 minutes (we are trying to reduce this). If everything worked fine, you should see something like:
================================ test session starts ================================ platform linux -- Python 3.8.5, pytest-6.0.2, py-1.9.0, pluggy-0.13.1 Matplotlib: 3.3.2 Freetype: 2.6.1 rootdir: /home/mowglie/disk/Dropbox/HomeDocs/git/oggm-fork, configfile: pytest.ini plugins: mpl-0.122 collected 297 items oggm/tests/test_benchmarks.py ....... [ 2%] oggm/tests/test_graphics.py ...................X [ 9%] oggm/tests/test_minimal.py ... [ 10%] oggm/tests/test_models.py ..........................sss.......ssss..s.ss..sss [ 27%] sss..sss [ 29%] oggm/tests/test_numerics.py .sssssssssss.ssss...s..ss.s [ 39%] oggm/tests/test_prepro.py .................s........................s........ [ 56%] ........s....s............ [ 64%] oggm/tests/test_shop.py ....... [ 67%] oggm/tests/test_utils.py .................................................... [ 84%] ss.ss..sssss.ssssss..sss...s.ss.ss.ss.. [ 97%] oggm/tests/test_workflow.py ssssss [100%] ================================= warnings summary ================================== (warnings are mostly ok) ======== 223 passed, 73 skipped, 1 xpassed, 9 warnings in 771.11s (0:12:51) =========
You can safely ignore deprecation warnings and other messages (if any), as long as the tests end without errors.
The tests (without the
--run-slow option) should run in 5 to 10 minutes.
If this takes too long, this may be an indiv
This runs a minimal suite of tests. If you want to run the entire test suite (including graphics and slow running tests), type:
pytest.oggm --run-slow --mpl
Congrats, you are now set-up for the Getting started section!
We try to do our best to avoid issues, but experience shows that the installation of the necessary packages can be difficult. Typical errors are often related to the pyproj, fiona and GDAL packages, which are heavy and (for pyproj) have changed a lot in the recent past and are prone to platform specific errors.
If the tests don’t pass, a diagnostic of which package creates the errors
might be necessary. Errors like
segmentation fault or
are frequent and point to errors in upstream packages, rarely in OGGM itself.
If you encounter issues, please get in touch with us on github.
Install with pyenv (Linux)#
We recommend our users to use
conda instead of
of the ease of installation with
conda. If you are familiar with
pyenv, the instructions below work as well: as of Sept 2022 (and thanks
to pip wheels), a pyenv
installation is possible without major issue on Debian/Ubuntu/Mint
Run the following commands to install the required linux packages.
For building python and stuff:
$ sudo apt-get install --no-install-recommends make build-essential git \ libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget \ curl llvm libncurses5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev \ libffi-dev liblzma-dev
For NetCDF and HDF:
$ sudo apt-get install netcdf-bin ncview hdf5-tools libhdf5-dev
Pyenv and pyenv-virtualenv#
If you are not familiar with pyenv, you can visit their documentation (especially the installing pyenv section).
Be sure to be on the working environment:
$ pyenv activate oggm_env
Update pip (important!):
$ pip install --upgrade pip
Install some packages one by one:
$ pip install numpy scipy pandas shapely matplotlib pyproj \ rasterio Pillow geopandas netcdf4 scikit-image configobj joblib \ xarray progressbar2 pytest motionless dask bottleneck toolz \ tables rioxarray
A pinning of the NetCDF4 package to 1.3.1 might be necessary on some systems (related issue).
Finally, install the pytest-mpl OGGM fork and salem libraries:
$ pip install git+https://github.com/OGGM/pytest-mpl.git $ pip install salem
Install OGGM and run the tests#
Refer to Install OGGM itself above.
Install a minimal OGGM environment#
If you plan to use only the numerical core of OGGM (that is, for idealized simulations or teaching), you can skip many dependencies and only install this shorter list:
name: oggm_minimal channels: - conda-forge dependencies: - numpy - scipy - pandas - matplotlib - shapely - requests - configobj - netcdf4 - xarray - pytables - pytest # For oggm-edu - seaborn pip: - oggm
Installing them with pip or conda should be much easier. Install OGGM itself then as above.
Running the tests in this minimal environment works the same. Simply run from a terminal:
The number of tests will be much smaller!