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 detail and should work on any platform.
OGGM is fully tested with Python version 3.6 on Linux, and all versions above 3.4 should work as well. OGGM doesn’t work with Python 2.7.
OGGM itself should also work on Mac OS and Windows platforms, but we make no guarantee that our dependencies do.
Complete beginners should get familiar with Python and its packaging ecosystem before trying to install and run OGGM.
For most users we recommend following the steps here to install Python and the package dependencies with the conda package manager. Linux or Debian users and people with experience with pip can follow the specific instructions here to install with virtualenv.
- Standard SciPy stack:
- Configuration file parsing tool:
- GIS tools:
- Other libraries:
- progressbar2 (displays the download progress)
- bottleneck (speeds up xarray operations)
- dask (works nicely with xarray)
- python-colorspace (applies HCL-based color palettes to some graphics)
Install with conda (all platforms)¶
This is the recommended way to install OGGM.
You should have a recent version of the conda package manager. You can get conda by installing miniconda (the package manager alone - recommended) or anaconda (the full suite - with many packages you won’t need).
We recommend to create a specific environment for OGGM. In a terminal window, type:
conda create --name oggm_env python=3.X
3.X is the Python version shipped with conda (currently 3.6).
You can of course use any other name for your environment.
Don’t forget to activate it before going on:
source activate oggm_env
Install all OGGM dependencies from the
oggm conda channels:
conda install -c oggm -c conda-forge oggm-deps
oggm-deps package is a “meta package”. It does not contain any code but
will install all the packages OGGM needs automatically.
The conda-forge channel ensures that the complex package dependencies are handled correctly. Subsequent installations or upgrades from the default conda channel might brake the chain. We strongly recommend to always use the the conda-forge channel for your installation.
You might consider setting conda-forge (and
oggm) as your
conda config --add channels conda-forge conda config --add channels oggm
conda install -c conda-forge ipython jupyter
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. v1.1).
- 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 plan to contribute to the model, and/or if you want to use the most recent model updates.
‣ install the stable version:
If you are using conda, you can install stable OGGM as a normal conda package:
conda install -c oggm oggm
If you are using pip, you can install OGGM from PyPI:
pip install oggm
‣ install the dev version:
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.
You can test your OGGM installation by running the following command from anywhere (don’t forget to activate your environment first):
pytest --pyargs oggm
The tests can run for a couple of minutes. If everything worked fine, you should see something like:
=============================== test session starts =============================== platform linux -- Python 3.5.2, pytest-3.3.1, py-1.5.2, pluggy-0.6.0 Matplotlib: 2.1.1 Freetype: 2.6.1 rootdir: plugins: mpl-0.9 collected 164 items oggm/tests/test_benchmarks.py ... [ 1%] oggm/tests/test_graphics.py ................... [ 13%] oggm/tests/test_models.py ................sss.ss.....sssssss [ 34%] oggm/tests/test_numerics.py .ssssssssssssssss [ 44%] oggm/tests/test_prepro.py .......s........................s..s....... [ 70%] oggm/tests/test_utils.py .....................sss.s.sss.sssss..ss. [ 95%] oggm/tests/test_workflow.py sssssss [100%] ==================== 112 passed, 52 skipped in 187.35 seconds =====================
You can safely ignore deprecation warnings and other messages (if any), as long as the tests end without errors.
This runs a minimal suite of tests. If you want to run the entire test suite (including graphics and slow running tests), type:
pytest --pyargs oggm --run-slow --mpl
Congrats, you are now set-up for the Getting started section!
Install with virtualenv (Linux/Debian)¶
The installation with virtualenv and pip requires a few more steps than with conda. Unless you have a good reason to install by this route, installing with conda is probably what you want to do.
The instructions below have been tested on Debian / Ubuntu / Mint systems only!
Run the following commands to install required packages.
For the build:
$ sudo apt-get install build-essential python-pip liblapack-dev gfortran libproj-dev python-setuptools
$ sudo apt-get install tk-dev python3-tk python3-dev
$ sudo apt-get install gdal-bin libgdal-dev python-gdal
$ sudo apt-get install netcdf-bin ncview python-netcdf4
Next follow these steps to set up a virtual environment.
Install extensions to virtualenv:
$ sudo apt-get install virtualenvwrapper
Reload your profile:
$ source /etc/profile
Make a new environment, for example called
oggm_env, with Python 3:
$ mkvirtualenv oggm_env -p /usr/bin/python3
(further details can be found for example in this tutorial)
Be sure to be on the working environment:
$ workon oggm_env
Update pip (important!):
$ pip install --upgrade pip
Install some packages one by one:
$ pip install numpy scipy pandas shapely matplotlib
Installing GDAL is not so straightforward. First, check which version of GDAL is installed on your Linux system:
$ gdal-config --version
The package version (e.g.
2.3.1, …) should match
that of the Python package you want to install. For example, if the Linux
GDAL version is
2.2.0, install the latest corresponding Python version.
The following command works on any system and automatically gets the right version:
$ pip install gdal=="$(gdal-config --version)" --install-option="build_ext" --install-option="$(gdal-config --cflags | sed 's/-I/--include-dirs=/')"
Fiona also builds upon GDAL, so let’s compile it the same way:
$ pip install fiona --install-option="build_ext" --install-option="$(gdal-config --cflags | sed 's/-I/--include-dirs=/')"
(Details can be found in this blog post.)
Now install further dependencies:
$ pip install pyproj rasterio Pillow geopandas netcdf4 scikit-image configobj joblib xarray boto3 progressbar2 pytest motionless dask bottleneck
Finally, install the salem and python-colorspace libraries:
$ pip install git+https://github.com/fmaussion/salem.git $ pip install git+https://github.com/retostauffer/python-colorspace.git