/disp-s1

OPERA Displacement workflows

Primary LanguagePythonOtherNOASSERTION

DISP-S1

Pytest and build docker image pre-commit.ci status

Surface Displacement workflows for OPERA DISP-S1 products.

Creates the science application software (SAS) using the dolphin library.

Development setup

Prerequisite installs

  1. Download source code:
git clone https://github.com/isce-framework/dolphin.git
git clone https://github.com/isce-framework/tophu.git
git clone https://github.com/opera-adt/disp-s1.git
  1. Install dependencies, either to a new environment:
mamba env create --name my-disp-env --file disp-s1/conda-env.yml
conda activate my-disp-env

or install within your existing env with mamba.

  1. Install tophu, dolphin and disp-s1 via pip in editable mode
python -m pip install --no-deps -e dolphin/ tophu/ disp-s1/

Setup for contributing

We use pre-commit to automatically run linting, formatting, and mypy type checking. Additionally, we follow numpydoc conventions for docstrings. To install pre-commit locally, run:

pre-commit install

This adds a pre-commit hooks so that linting/formatting is done automatically. If code does not pass the checks, you will be prompted to fix it before committing. Remember to re-add any files you want to commit which have been altered by pre-commit. You can do this by re-running git add on the files.

Since we use black for formatting and flake8 for linting, it can be helpful to install these plugins into your editor so that code gets formatted and linted as you save.

Running the unit tests

After making functional changes and/or have added new tests, you should run pytest to check that everything is working as expected.

First, install the extra test dependencies:

python -m pip install --no-deps -e .[test]

Then run the tests:

pytest

Optional GPU setup

To enable GPU support (on aurora with CUDA 11.6 installed), install the following extra packages:

mamba install -c conda-forge "cudatoolkit=11.6" cupy "pynvml>=11.0"

Building the docker image

To build the docker image, run:

./docker/build-docker-image.sh --tag my-tag

which will print out instructions for running the image.