The SlideRule Python client is most easily installed using the Conda Python package manager.
conda install -c conda-forge sliderule
To get the latest unreleased version of the Python client, the contents of the sliderule-python repository can be cloned or download as a zipped file. If cloning, please consider forking into your own account before cloning onto your system. To clone the repository:
git clone https://github.com/ICESat2-SlideRule/sliderule-python.git
You can then install the sliderule-python client using setuptools:
cd sliderule-python python3 setup.py install
For developer installs using conda, you can use the provided environment file to create an initial conda environment that has the sliderule-python client installed along with all the dependencies necessary to run the included sliderule utilities and examples.
cd sliderule-python conda env create -f environment.yml
To install and setup JupyterLab to run the provided example notebooks, you must first install JupyterLab.
conda install -c conda-forge jupyterlab
Then make sure the conda environment with the sliderule-python client installed in it is available to use as one of the Python kernels. To gaurantee that JuypterLab is using the correct Python kernel, you can start JupyterLab from the conda environment with sliderule-python installed.
conda activate sliderule jupyter lab
If you start JupyterLab from the base conda environment, then it will be necessary to select the correct kernel by using the kernel selection widget in the upper-right hand corner of the Jupyter notebook you are running. If you used the provided environment.yml file to create your conda environment then the correct kernel will likely be something like Python [conda env:sliderule].
If your conda environment does not show up as an available kernel for your Jupyter Notebooks then install the nb_conda_kernels package in your base conda environment and then make sure your conda environment has the ipykernel package installed. All environments with that package installed will automatically show up as available kernels. Alternatively, see JupyterLab documentation for how to register and unregister individual conda environments.