grstbx

Scientific code and notebooks to visualize and post-process GRS L2A and L2B images

Example

Documentation

Tutorials and API reference: https://grstbx.readthedocs.io

Installation

grstbx requires Python >= 3.11. Install it from PyPI:

pip install grstbx                 # library and web dashboard (grstbx-viewer)
pip install "grstbx[notebook]"     # + JupyterLab environment for the notebooks

To run the notebooks of the repository or to develop grstbx, clone the repository and create the conda environment (recommended: GDAL, PROJ and xesmf come from conda-forge), which also installs grstbx in editable mode:

git clone https://github.com/Tristanovsk/grstbx.git
cd grstbx
conda env create -f environment.yml
conda activate grstbx

or install the clone with pip in an existing environment:

pip install ".[notebook]"      # library + JupyterLab environment
pip install .                  # library only
pip install -e ".[notebook]"   # editable install, for development

The export of subsets (grstbx.export_l2a, grstbx.crop_and_export) uses the writers of GRS: it needs grs >= 3.0.1 in the same environment.

To register the environment as a Jupyter kernel:

python -m ipykernel install --user --name=grstbx

Web dashboard

Explore GRS L2A and L2B products (Zarr or NetCDF, hGRS NetCDF), single images, time series or datacubes, in the web browser, without notebook:

grstbx-viewer /path/to/product.zarr                  # or: grstbx-viewer --root /data/satellite
grstbx-viewer "/data/L2B/BEFR/2024/*/*/*.nc"         # time series of L2B products

Pan and zoom the images over a basemap, hover them to plot the spectrum (L2A) or the time series (L2B) under the mouse, draw points and polygons, and download their values and statistics. See the dashboard tutorial.

Example of L2A image