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OnEarth Image Analytics

This is a Flask web-server integrated into the OnEarth Docker network for serving on-demand analytics and image resampling from OnEarth imagery. This includes image filters, correlations, time-series data, and more.

Usage

The simplest way to use this library is to install dependencies in using pip install -r app/requirements.txt and then simply run app/example.py using python (e.g. python example.py --layer MODIS_Aqua_L3_SST_MidIR_9km_Night_Monthly --startdate 2012-01-01 --enddate 2014-01-01 --level 2). This should pull data directly from GIBS and plot it using matplotlib. To install the full library, it's advisable to use the provided Dockerfile and run the container with direct access to an OnEarth server.

Installation

Read the docker README for installation advice. The application can be run as a Docker container by simply running ./run.sh, but it is set up to accept requests passed through the OnEarth frontend. To run fully, the simplest system is:

  1. Clone the OnEarth repository from https://github.com/nasa-gibs/onearth
  2. Launch the various docker contains with the start script.
  3. Connect to the onearth-demo container and edit the /etc/httpd/conf.d/oe2_demo.conf by adding the lines
ProxyPass /analytics http://onearth-analytics
ProxyPassReverse /analytics http://onearth-analytics
  1. Restart the Apache server in the onearth-demo container httpd -k restart
  2. Build the onearth-analytics image with ./rebuild.sh if necessary.
  3. Launch the onearth-analytics container with ./run.sh
  4. Launch the frontend from the frontend directory with ./run.sh (may need to run npm install first).

This will work properly, but it will be slow, because it has to pull all of the data remotely from the OnEarth servers, instead of over a local network as part of OnEarth. This also works with OnEarth running locally, and instructions for configuring that can be found in the docs folder. With that running, just change the directories in app/main.py and frontend/index.js to point to the localhost address of OnEarth.

Overview

frontend

This directory hosts a frontend OpenLayers website which allows you to make requests to the WMTS tile service and analytic service hosted in app. This can be setup with npm install and npm start. Basically just follow the basic OpenLayers installation.

app

This directory contains a WMTS tile service capable of transforming data with a variety of filters and performing on-demand analytics on raw image data, including time-series analysis. timeseries.py is the body of the code for doing on-demand analysis. This is not the best code in the world. I was experimenting with async coding in Python, and the quality and speed of the code didn't benefit. This could be vastly improved with a single asynchronous system for making tile requests. The Colormap parsing utility also fails from some colormaps in different formats, so this could be made more robust to work for all products.

docker

This contains the Dockerfile and setup scripts for this application, called onearth-analytics, which hooks into an existing Docker network (oe2) run by the OnEarth service.

docs

This contains documentation for this code, OnEarth in general, and the MRF file format. It also contains benchmarks for MRF generation improvements and the VarnishCache implementation, which could be a good reverse cache for OnEarth/GIBS.