This is the workflow that generates OPERA's DIST-S1 product. This workflow is designed to delineate generic disturbance from a time-series of OPERA Radiometric and Terrain Corrected Sentinel-1 (OPERA RTC-S1) products. The output DIST-S1 product is resampled to a 30 meter Military Grid Reference System (MGRS) tile.
Currently, this workflow is just scaffolding. It is not ready for use!
We recommend using the mamba/conda package manager to install the DIST-S1 workflow, manage the environment, and install the dependencies.
mamba update -f environment.yml
pip install dist-s1 # update to conda when it is ready on conda-forge
conda activate dist-s1-env
python -m ipykernel install --user --name dist-s1-env
The last command is optional, but will allow this project to be imported into a Jupyter notebook.
As above, we recommend using the mamba/conda package manager to install the DIST-S1 workflow, manage the environment, and install the dependencies.
mamba update -f environment.yml
pip install -e .
conda activate dist-s1-env
python -m ipykernel install --user --name dist-s1-env
There are two entrypoints for the DIST-S1 workflow:
dist-s1 run_sas- This is the primary entrypoint for Science Data System (SDS) operations in which this library is viewed as the Science Application Software (SAS) for DIST-S1 within JPL's Hybrid Science Data System (HySDS).dist-s1 run(not implemented yet) - This is the simplified entrypoint for scientific and research users (non-SDS), allowing for the localization of data from publicly available data sources with more human readable inputs.
It is worth noting that the SDS workflow (dist-s1 run_sas) is not user friendly requiring the explicit specification of the numerous input RTC-S1 datasets (nominally there are 100s of these files required for the generation of a single DIST-S1 product over an MGRS tile). The dist-s1 run entrypoint has far fewer inputs and is designed to be human-operable. Specifically, the dist-s1 run takes care of the localization and accounting of the all the necessary input RTC-S1 datasets.
dist-s1 run_sas --runconfig_yml_path <path/to/runconfig.yml>
See tests/test_main.py for an example of how to use the CLI with sample data.
This is not yet implemented.
docker pull ghcr.io/asf-hyp3/dist-s1:<tag>
Where <tag> is the semantic version of the release you want to download.
Notes:
- our image does not currently support the
arm64(i.e. Mac M1) architecture. Therefore, you will need to build the image from the Dockerfile yourself. - Currently, the image is still under development and we will likely update it to ensure compatibility with GPU processing.
Make sure you have Docker installed for Mac or Windows. We call the docker image dist_s1_img for the remainder of this README.
docker build -f Dockerfile -t dist-s1 .
To run the container interactively:
docker run -ti dist-s1
Within the container, you can run the CLI commands and the test suite.
There are certain releases associated with OPERA project deliveries. Here we provide instructions for how to run and verify the DIST-S1 workflow.
We have included sample input data, associated a Docker image via the Github registry, and run tests via github actions all within this repository.
docker pull ghcr.io/opera-adt/dist-s1
If a specific version is required (or assumed for a delivery), then you use docker pull ghcr.io/opera-adt/dist-s1:<version> e.g.
docker pull ghcr.io/opera-adt/dist-s1:0.0.4
The command will pull the latest released version of the Docker image. To run the test suite, run:
docker run ghcr.io/opera-adt/dist-s1 bash -l -c 'cd dist-s1 && pytest tests'