colocate

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

Preview/Run:

ERDDAP colocate.ipynb:

ERDDAP colocate-dev.ipyb:

Installation:

Install with conda:

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

Run via command line:

erddap-co-locate

Run in Jupyter notebook:

jupyter notebook &

Run in JupyterLab:

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 &

Run with voila:

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

Local Development:

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

OceanHackWeek 2019 'co-locators' Project

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

The problem

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.

Application example

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.

Collaborators:

Name	Year
Mathew Biddle	2019
Sophie Chu	2019
Yeray Santana Falcon	2019
Molly James	2019
Pedro Magaña	2019
Jazlyn Natalie	2019
Laura Gomez Navarro	2019
Shikhar Rai	2019
Micah Wengren	2019
Jacqueline Tay	2020
Mike Morley	2020
Yuta Norden	2020

Specific tasks

• 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?

Existing methods

• 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

Proposed methods/tools

• Jupyter
• Python
  • erddapy
  • Pandas

Background reading

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