@ewatercycle/jupyterlab_thredds v0.5.0
jupyterlab_thredds
JupyterLab dataset browser for THREDDS catalog
Can inject iris/xarray/leaflet code cells into a Python notebook of a selected dataset to further process/visualize the dataset.
Prerequisites
- JupyterLab,
pip install jupyterlab
- ipywidgets,
jupyter labextension install @jupyter-widgets/jupyterlab-manager
, requirement for ipyleaflet - ipyleaflet,
jupyter labextension install jupyter-leaflet
, to load a WMS layer - iris,
conda install -c conda-forge iris
Installation
pip install jupyterlab_thredds
jupyter labextension install @ewatercycle/jupyterlab_thredds
Usage
- Start Jupyter lab with
jupyter lab
- In Jupyter lab open a notebook
- Open the
THREDDS
tab on the left side. - Fill the catalog url
- Press search button
- Select how you would like to open the dataset, by default it uses iris Python package.
- Press a dataset to insert code into a notebook
Development
For a development install, do the following in the repository directory:
pip install -r requirements.txt
jlpm
jlpm build
jupyter labextension link .
jupyter serverextension enable --sys-prefix jupyterlab_thredds
(jlpm
command is JupyterLab's pinned version of yarn that is installed with JupyterLab.)
To rebuild the package and the JupyterLab app:
jlpm build
jupyter lab build
Watch mode
# shell 1
jlpm watch
# shell 2
jupyter lab --ip=0.0.0.0 --no-browser --watch
Release
To make a new release perform the following steps:
1. Update version in package.json
and jupyterlab_thredds/version.py
2. Record changes in CHANGELOG.md
3. Make sure tests pass by running jlpm test
and pytest
5. Commit and push all changes
6. Publish lab extension to npmjs with jlpm build
and jlpm publish --access=public
7. Publish server extension to pypi with python setup.py sdist bdist_wheel
and twine upload dist/*
8. Create GitHub release
9. Update DOI in README.md
and CITATION.cff