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Silverpieces

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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.

License

MIT-derived (see License.txt)

Installation

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 install

If 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

Documentation

Example

Notebooks provide some examples of usage. You can use:

  • Docker, running docker-compose up will create a docker container running on jupyter port 8199 containing the examples.
  • or manually set up a conda environment in the notebook readme

Related work

Silverpieces started to cater for operations on data cubes that go beyond xarray current built-in options. Possibly related work includes:

silverpieces's People

Contributors

benjaminleighton avatar fareedmirza avatar fre171csiro avatar jmp75 avatar umbersar avatar

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silverpieces's Issues

Cookie cutting mask generation

Integrate https://github.com/CSIRO-enviro-informatics/c3dis-2019-pangeo/blob/master/All_Catchment_Polygon_Mask_Averages.ipynb and (preferably) salem base approaches to cookie cutting / producing masks.

I suggest the first iteration on this might be to add new functions to functions.py that accept geojson or files, a target xarray template (or maybe another kind of description of spatial dimensions) and generates a mask xarray. Additional functions might save the mask to disk and load and apply the mask to real datasets.

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