Easy access to oceanographic data co-located in space and time via Python

git clone https://github.com/ioos/colocate.git
cd colocate
conda env create -f environment.yml
conda activate colocate

erddap-co-locate

jupyter notebook &

This step may be necessary for ipyleaflet and HoloViz to run correctly in JupyterLab. Run the following on the command line with the 'colocate' conda environment active:

jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install jupyter-leaflet
jupyter labextension install @pyviz/jupyterlab_pyviz

Then, start JupyterLab:

jupyter-lab &

voila colocate.ipynb --enable_nbextensions=True --VoilaConfiguration.file_whitelist="['.*']"

If you want to develop locally, clone from GitHub, cd to the cloned repository root directory, and run pip install as follows:

git clone https://github.com/ioos/colocate.git
cd colocate
pip install -e . --no-deps --force-reinstall

Description of the original OceanHackWeek 2019 project that led to the development of this module.

Co-locate oceanographic data (served via ERDDAP) by establishing constraints to use as data server query filters.

Submit the identical filter criteria to all ERDDAP servers indexed by the awesome-erddap project and visualize the results.

A user is interested in all the available oceanographic data in a region where an eddy just formed. They provide the geospatial bounds of the region and a temporal range and get an aggregated response of all available data.

• Collect temporal bounds.
• Collect spatial bounds.
• Collect keywords?
• Build query url.
• Do the search.
• Evaluate the response.
• Manage response.
• Geospatial plotting.
• Temporal plotting.
• Link back to dataset on erddap server.
• Aggregated download?

• The Irish Marine Institute has developed a keyword search across existing ERDDAP servers
  • Here is the list of all ERDDAP's they use.
• OHW18 built a search interface for one ERDDAP server
• yodapy: https://github.com/cormorack/yodapy

• Jupyter
• Python
  • erddapy
  • Pandas

Optional: links to manuscripts or technical documents for more in-depth analysis.