Scientific and statistical methods from Python exposed through Docker.
List methods available with JSON descriptions of inputs and outputs:
python -m pysciencedock
Print help information for a method:
python -m pysciencedock <method> --help
Run one of the methods:
python -m pysciencedock <method> <arg1> ...
List the methods available through Docker:
docker run kitware/pysciencedock
Run a method through Docker:
docker run <docker_options> kitware/pysciencedock <method> <arg1> ...
In order to send data files to Docker, mount a volume and use the mounted
volume prefix for the input and output paths. For example, if myinput.csv
is in your current directory, the following will produce myoutput.csv in
the current directory:
docker run -v $PWD:/data kitware/pysciencedock normalize --data=/data/myinput.csv --output=/data/myoutput.csv
Build the Docker image:
git clone https://github.com/Kitware/pysciencedock.git
cd pysciencedock
docker build -t pysciencedock .
Now use pysciencedock instead of kitware/pysciencedock in the commands
above to use your local version.
- Install Girder.
- Enable the "Item tasks" plugin.
- Start a
girder_workerCelery worker and point it and the Worker plugin settings to use the same task queue. - From any folder you have write access to (you must be admin), select "Add tasks" from the actions menu.
- Enter "kitware/pysciencedock" as the image name and click Run.
- When that task completes, navigate to a created task item and select "Run task" from the actions menu.
- Fill in the task parameters and click Run.
To run the unit tests:
python -m unittest tests