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This project provides an easy way for researchers and developers to develop and share algorithms related to geospatial data and imagery.

Project description

Welcome to the Algorithm Toolkit!

We built the Algorithm Toolkit (ATK) to provide researchers and data anaysts the tools they need to process data quickly and efficiently, so they can focus on actually doing scientific or other analysis work.

Please see this project's official documentation on Read The Docs. Below are instructions for how to install the ATK on your own system.


Super Quick Start

Python projects should be installed in virtual environments in order to keep versions of various packages in sync with the project code. However, if you want to get up and running immediately you can do the following in a Terminal window (assumes Python and pip are installed):

pip install algorithm_toolkit
alg cp myproject
cd myproject
alg run

Point your browser to http://localhost:5000/. You should see the development environment welcome page.

See the next section for a more detailed install process.

Slightly More Involved But Still Pretty Quick Start

A word about virtual environments

As noted above, it's a best practice to start new Python projects in virtual environments. As you work on code, you rely more and more on external libraries. As time passes, changes to those libraries will eventually break your code unless you are constantly updating it. So when you install a new version of a library for another project, suddenly you find that your earlier project no longer works.

Virtual environments solve this problem. Each environment contains a discreet set of the libraries used by your code, at the versions you determine.

Thankfully, creating virtual environments in Python is easy.

Python 3

Linux and Mac:

python3 -m venv myenvironment
source myenvironment/bin/activate

On some Linux systems, python3-venv is not installed by default. If you get an error with the command above, try:

# Debian/Ubuntu
sudo apt install python3-venv

# RedHat/CentOS
sudo yum install python3-venv

On Windows:

py -3 -m venv myenvironment

Python 2

Python 2 does not come with a built in virtual environment creator. We recommend using virtualenvwrapper. Their install docs are excellent.

Once you've installed and configured virtualenvwrapper, create your virtual environment:

mkvirtualenv myenvironment

Setting up your ATK project

pip install algorithm_toolkit
alg cp myproject
cd myproject
alg run

Point your browser to http://localhost:5000/. You should see the development environment welcome page.

What just happened?

Let's walk through this line by line.

pip install algorithm_toolkit

The ATK lives on PyPi, so this line downloads and installs the ATK in your virtual environment. Several dependencies will be installed as well.

alg cp myproject

This line uses the ATK's Command Line Interface (CLI) called alg. The cp command stands for "create project". "myproject" is the name of your project, which will also be the name of the folder created for the project (feel free to use a more original name).

cd myproject

Puts you in the project folder.

alg run

This command also uses the CLI. The run command starts a development web server. As we discuss elsewhere in the docs (see TODO: create page), setting up your algorithms and processing chains is accomplished using a web-based interface.

Installing the example project

The ATK comes with an example project to help you understand how it works.


The example project requires NumPy in order to work. If you're on a Linux machine, you can install the example project and it will handle this dependency for you.

However, if you're on a Mac or Windows machine, installing NumPy is more complicated.


For these operating systems, we highly recommend using Anaconda or it's smaller cousin Miniconda. The only difference between these two is that Anaconda installs over 150 packages (including SciPy and NumPy) out of the box whereas with Miniconda you need to install everything separately. Either way is fine.

Once Anaconda is installed, you can set up the example project right away. If you decide to use Miniconda, first do the following:

conda install numpy

Install the example project

To set up the example project, just use the --example flag when setting up a new project:

alg cp myproject --example

Installing documentation locally

If you want these docs to be installed locally, use the --with-docs flag when creating a project. You need to have Sphinx installed for this to work.

pip install sphinx sphinx_rtd_theme
alg cp myproject --with-docs

Troubleshooting Install Issues

On some systems, additional libraries may be needed to install the Algorithm Toolkit. Try these packages if your ATK install fails:

Debian/Ubuntu Linux

# python 3
sudo apt install python3-dev build-essential

# python 2
sudo apt install python-dev build-essential

RedHat/CentOS Linux

# python 3
sudo yum install python3-devel

# python 2
sudo yum install python-devel


This repository contains 2 Docker files

Once built and run, Docker containers are generated, which gives users access to easy to use development/deployment tools.

Check out our official Docker documentation to learn more


Thanks for your interest in contributing to the Algorithm Toolkit codebase! You should know a few things.

Code of Conduct

First of all: this project has a code of conduct. Please read the CODE_OF_CONDUCT file in the project root folder and stick to its principles.


The MIT license (see LICENSE file) applies to all contributions.

Contributing to Docs

We also welcome contributions to the documentation. You should follow the same workflow for contributing code as for contributing documentation. Also, please follow the reSructuredText format used by the existing documents (guidelines here).

You will need to install Sphinx and the RTD theme to build docs locally (which you should do to make sure they look OK).

$ pip install sphinx sphinx_rtd_theme
$ sphinx-build docs docs/html -a

More Info

Please see the Contributing page on Read The Docs for more detailed information.

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