Silverpieces is the codename for a general purpose library for processing N-dimensional arrays of data. The primary use case as of 2019-07 is to extract statistical information from multivariate spatial-temporal grids (lat/lon/time), building on top of xarray. Silverpieces subscribes to the goals of the Pangeo community.
MIT-derived (see License.txt)
DRAFT
In line with the stated intent of major Python scientific libraries, Silverpieces will only aim to run on Python 3.
Set up using conda, or
If using pip:
pip install -r requirements.txt
python setup.py installIf using manual method:
Pull latest version from the repository:
https://github.com/jmp75/silverpieces.git
in \silverpieces, run:
>conda env create -f=./environment.yml
Activate the environment:
>conda activate sv
(where is ‘sv’ is the name configured in ‘environment.yml’)
Then create the wheel:
python setup.py sdist bdist_wheel
The change to the ‘dist’ directory, where the file ‘silverpieces-0.2.0-py2.py3-none-any.whl’ should now be ready:
>pip install silverpieces-0.2.0-py2.py3-none-any.whl
Drop back to ‘silverpieces’ and run:
>jupyter-labextension install @jupyter-widgets/jupyterlab-manager
An error will occur if ‘Node.js’ is not installed.
If so, run:
>conda install -c conda-forge nodejs
(see ‘https://anaconda.org/conda-forge/nodejs’)
Install the manager:
>jupyter-labextension install @jupyter-widgets/jupyterlab-manager
Install the kernel:
>python -m ipykernel install --user --name sv --display-name "Py3 Silverpieces"
And to run notebooks:
>jupyter lab
Notebooks provide some examples of usage. You can use:
- Docker, running
docker-compose upwill create a docker container running on jupyter port 8199 containing the examples. - or manually set up a conda environment in the notebook readme
Silverpieces started to cater for operations on data cubes that go beyond xarray current built-in options. Possibly related work includes: