OGGM itself is a pure python package, but it has several dependencies wich are not trivial to install. The instructions below are self-explanatory and should work on any platform.
Complete beginners should get familiar with python and its packaging ecosystem before trying to install and run OGGM.
For most users we recommend to install python and the package dependencies with the conda package manager: Install with conda (all platforms). Linux users and people with experience with pip can follow the specific instructions Install with virtualenv (linux/debian).
If you are using a Linux Mint distribution you may want to test if you are affected by the pyproj bug described here
- Standard SciPy track:
- Configuration file parsing tool:
- GIS tools:
- Other libraries:
- progressbar2 (displays the download progress)
- bottleneck (speeds up xarray operations)
- dask (works nicely with xarray)
Install with conda (all platforms)¶
This is the recommended way to install OGGM.
You should have a recent version of git and 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 wont need).
Linux users should install a couple of linux packages (not all of them are required but it’s good to have them anyway):
$ sudo apt-get install build-essential liblapack-dev gfortran libproj-dev git gdal-bin libgdal-dev netcdf-bin ncview python-netcdf4 ttf-bitstream-vera
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 the packages from the conda-forge and oggm channels:
conda install -c oggm -c conda-forge oggm-deps
The oggm-deps package is a “meta package”. It does not contain any code but will insall 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 (see an example here). We strongly recommend to always use the the conda-forge channel for your installation.
You might consider setting conda-forge (and oggm) per default, as suggested on their documentation page:
conda config --add channels conda-forge conda config --add channels oggm conda install <package-name>
conda install -c conda-forge ipython jupyter
If you are using conda, you can install OGGM as a normal conda package:
conda install -c oggm -c conda-forge oggm
If you are using pip, you can install OGGM from PyPI:
pip install oggm
In this case you will be able to use the model but you cannot change its code. If you want to explore the code or participate to its development, we recommend to clone the git repository (or your own fork , see also Contributing to OGGM):
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 pip or 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 are almost there! The last step is to check if everything works as expected. From the oggm directory, type:
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 DLL messages as long as the tests end without errors.
Congrats, you are now set-up for the Getting started section!
Install with virtualenv (linux/debian)¶
The installation with pip requires a few more steps than with conda. Unless you have a good reason to be here, Install with conda (all platforms) is probably what you want do do.
The instructions below are for Debian / Ubuntu / Mint systems only!
For building stuffs:
$ 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
$ sudo pip install virtualenvwrapper
Create the directory where the virtual environments will be created:
$ mkdir ~/.pyvirtualenvs
Add these three lines to the files: ~/.profile and ~/.bashrc:
# Virtual environment options export WORKON_HOME=$HOME/.pyvirtualenvs source /usr/local/bin/virtualenvwrapper_lazy.sh
Reset your profile:
$ . ~/.profile
Make a new environment with python 3:
$ mkvirtualenv oggm_env -p /usr/bin/python3
Be sure to be on the working environment:
$ workon oggm_env
Update pip (important!):
$ pip install --upgrade pip
Install one by one the easy stuff:
$ pip install numpy scipy pandas shapely matplotlib
For GDAL, it’s not as straight forward. First, check which version of GDAL is installed:
$ dpkg -s libgdal-dev | grep '^Version:'
The major and minor package version (e.g.
1.11, …) should match
that of the python package you want to install. For example, if the linux
GDAL version is
1.11.3, install the latest corresponding python version
(in this case,
$ pip install gdal==1.11.2 --install-option="build_ext" --install-option="--include-dirs=/usr/include/gdal"
Fiona also builds upon GDAL, so let’s compile it the same way:
$ pip install fiona --install-option="build_ext" --install-option="--include-dirs=/usr/include/gdal"
Install further stuffs:
$ pip install pyproj rasterio Pillow geopandas netcdf4 scikit-image configobj joblib xarray boto3 progressbar2 pytest motionless dask bottleneck
And the salem library:
$ pip install git+https://github.com/fmaussion/salem.git