GLACIES INDICIUM

Welcome to the code repository for Team Glacies Indicium at NAU.

You can visit our Team Website to learn more about our project.

HOW TO GET SET UP

1. Install Anaconda and the git tool on your machine.

• On Windows: Use the Start Menu to find and open the Anaconda/Miniconda command prompt/terminal.

• On Linux: Use the normal command prompt (start up a fresh command prompt after installing Anaconda).

2. Navigate to wherever you want to keep our repository on your hard disk.

3. Clone this repo with the command git clone https://github.com/mlgarrett/glacies-indicium.git

4. Change into the repo directory

5. Run the command conda env create -f environment.yml. This will install and set up the Terracotta tool in an Anaconda virtual environment.

6. Activate the virtual environment with conda activate terracotta. You should now be able to run terracotta commands, like terracotta --help.

ATTRIBUTION

This project is based on the open-source project Terracotta, a Python-based GeoTIFF data exploration tool. The original Terracotta readme information can be found below.

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Terracotta is a pure Python tile server that runs as a WSGI app on a dedicated webserver or as a serverless app on AWS Lambda. It is built on a modern Python 3.6 stack, powered by awesome open-source software such as Flask, Zappa, and Rasterio.

Read the docs | Explore the API | Satlas, powered by Terracotta

Why Terracotta?

• It is trivial to get going. Got a folder full of cloud-optimized GeoTiffs in different projections you want to have a look at in your browser? terracotta serve -r {name}.tif and terracotta connect localhost:5000 get you there.
• We make minimal assumptions about your data, so you stay in charge. Keep using the tools you know and love to create and organize your data, Terracotta serves it exactly as it is.
• Serverless deployment is a first-priority use case, so you don’t have to worry about maintaining or scaling your architecture.
• Terracotta instances are self-documenting. Everything the frontend needs to know about your data is accessible from only a handful of API endpoints.

The Terracotta workflow

1. Optimize raster files

$ ls -lh
total 1.4G
-rw-r--r-- 1 dimh 1049089 231M Aug 29 16:45 S2A_20160724_135032_27XVB_B02.tif
-rw-r--r-- 1 dimh 1049089 231M Aug 29 16:45 S2A_20160724_135032_27XVB_B03.tif
-rw-r--r-- 1 dimh 1049089 231M Aug 29 16:46 S2A_20160724_135032_27XVB_B04.tif
-rw-r--r-- 1 dimh 1049089 231M Aug 29 16:56 S2A_20170831_171901_25XEL_B02.tif
-rw-r--r-- 1 dimh 1049089 231M Aug 29 16:57 S2A_20170831_171901_25XEL_B03.tif
-rw-r--r-- 1 dimh 1049089 231M Aug 29 16:57 S2A_20170831_171901_25XEL_B04.tif

$ terracotta optimize-rasters *.tif -o optimized/

Optimizing rasters: 100%|██████████████████████████| [05:16<00:00, file=S2A_20170831_...25XEL_B04.tif]

2. Create a database from file name pattern

$ terracotta ingest optimized/S2A_{date}_{}_{tile}_{band}.tif -o greenland.sqlite
Ingesting raster files: 100%|███████████████████████████████████████████| 6/6 [00:49<00:00,  8.54s/it]

3. Serve it up

$ terracotta serve -d greenland.sqlite
 * Serving Flask app "terracotta.server" (lazy loading)
 * Environment: production
   WARNING: Do not use the development server in a production environment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://localhost:5000/ (Press CTRL+C to quit)

4. Explore the running server

Manually

You can use any HTTP-capable client, such as curl.

$ curl localhost:5000/datasets?tile=25XEL
{"page":0,"limit":100,"datasets":[{"date":"20170831","tile":"25XEL","band":"B02"},{"date":"20170831","tile":"25XEL","band":"B03"},{"date":"20170831","tile":"25XEL","band":"B04"}]}

Modern browsers (e.g. Chrome or Firefox) will render the JSON as a tree.

Interactively

Terracotta also includes a web client. You can start the client (assuming the server is running at http://localhost:5000) using

$ terracotta connect localhost:5000
 * Serving Flask app "terracotta.client" (lazy loading)
 * Environment: production
   WARNING: Do not use the development server in a production environment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://127.0.0.1:5100/ (Press CTRL+C to quit)

Then open the client page (http://127.0.0.1:5100/ in this case) in your browser.