How to install#

Installing with conda (recommended)#

conda install -c conda-forge --strict-channel-priority xdem

Notes

The --strict-channel-priority flag seems essential for Windows installs to function correctly, and is recommended for UNIX-based systems as well.
Solving dependencies can take a long time with conda. To speed up this, consider installing mamba:
```
conda install mamba -n base -c conda-forge
```
Once installed, the same commands can be run by simply replacing conda by mamba. More details available through the mamba project.
If running into the sklearn error ImportError: dlopen: cannot load any more object with static TLS, your system needs to update its glibc (see details here). If you have no administrator right on the system, you might be able to circumvent this issue by installing a working environment with specific downgraded versions of scikit-learn and numpy:
```
conda create -n xdem-env -c conda-forge xdem scikit-learn==0.20.3 numpy==1.19.*
```
On very old systems, if the above install results in segmentation faults, try setting more specifically numpy==1.19.2=py37h54aff64_0 (worked with Debian 8.11, GLIBC 2.19).

pip install xdem

NOTE: Setting up GDAL and PROJ may need some extra steps, depending on your operating system and configuration.

Recommended: Use conda for dependency solving.

git clone https://github.com/GlacioHack/xdem.git
cd ./xdem
conda env create -f dev-environment.yml
conda activate xdem
pip install -e .

After installing, we recommend to check that everything is working by running the tests:

pytest -rA