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Introduction

This is a collection of tools that are helpful for gait analysis. Some are specific to the needs of the Human Motion and Control Lab at Cleveland State University but other portions may have potential for general use. It is relatively modular so you can use what you want.

https://travis-ci.org/csu-hmc/Gait-Analysis-Toolkit.png?branch=master

Modules

gait
Tools for working with gait data.
motek
Tools for processing data from Motek Medical's products, primarily the D-Flow software outputs.
controlid
Tools for identifying control systems in human locomotion.

Octave/Matlab Librarys

2D Lower Body Inverse Dynamics
Implements joint angle and moment computations of a 2D lower body human.
Inertial Compensation
Compensates force plate forces and moments for inertial effects and re-expresses the forces and moments in the camera reference frame.

Installation

You will need Python 2.7 and setuptools to install the packages. Its best to install the dependencies first (NumPy, SciPy, matplotlib, Pandas). The SciPy Stack instructions are helpful for this: http://www.scipy.org/stackspec.html.

We recommend installing Anaconda_ for users in our lab to get all of the dependencies.

We also utilize Octave/Matlab code, so an install of Octave with the toolkits is also required. See http://octave.sourceforge.net/index.html for installation instructions.

You can install using pip (or easy_install). Pip will theoretically [1] get the dependencies for you (or at least check if you have them):

$ pip install https://github.com/csu-hmc/Gait-Analysis-Toolkit/zipball/master

Or download the source with your preferred method and install manually.

Using Git:

$ git clone git@github.com:csu-hmc/Gait-Analysis-Toolkit.git
$ cd Gait-Analysis-Toolkit

Or wget:

$ wget https://github.com/csu-hmc/Gait-Analysis-Toolkit/archive/master.zip
$ unzip master.zip
$ cd Gait-Analysis-Toolkit-master

Then for basic installation:

$ python setup.py install

Or install for development purposes:

$ python setup.py develop
[1]You will need all build dependencies and also note that matplotlib doesn't play nice with pip.

Tests

Run the tests with nose:

$ nosetests

Vagrant

A vagrant file and provisioning script are included to test the code on both a Ubuntu 12.04 and Ubuntu 13.10 box. To load the box and run the tests simply type:

$ vagrant up

See VagrantFile and the *bootstrap.sh files to see what's going on.

Documentation

The documentation is hosted at ReadTheDocs:

http://gait-analysis-toolkit.readthedocs.org

You can build the documentation (currently sparse) if you have Sphinx and numpydoc:

$ cd docs
$ make html
$ firefox _build/html/index.html

Contributing

The recommended procedure for contributing code to this repository is detailed here. It is the standard method of contributing to Github based repositories (https://help.github.com/articles/fork-a-repo).

Fork the repository on Github using the Github UI and clone the fork that you just made to your machine:

git clone git@github.com:<your-username>/Gait-Analysis-Toolkit.git

Change into the directory:

cd Gait-Analysis-Toolkit

Now, setup a remote called upstream that points to the main repository so that you can keep your local repository up-to-date:

git remote add upstream git@github.com:csu-hmc/Gait-Analysis-Toolkit.git

Now you have a remote called origin (the default) which points to your Github account's copy and a remote called upstream that points to the main repository on the csu-hmc organization Github account.

It's best to keep your local master branch up-to-date with the upstream master branch and then branch locally to create new features. To update your local master branch simply:

git checkout master
git pull upstream master

Now to contribute a change to the repository you should create a new branch off of the local master branch (which is up-to-date with upstream):

git checkout -b my-branch

Now make changes to the software and be sure to always include tests! Make sure all tests pass on your machine with:

nosetests

Once tests pass, add any new files you created:

git add my_new_file.py

Now commit your changes:

git commit -am "Added an amazing new feature

Push your commits to a mirrored branch on your Github repository:

git push origin my-branch

Now visit teh repository on your Github account and you should see a "compare and pull button" to make a pull request against the main repository. Github and Travis-CI will check to merge conflicts and run the tests again on a cloud machine. You can ask others to review your code at this point and if all is well, press the "merge" button on the pull request. Finally, delete the branches on your local machine and on your Github repo:

git branch -d my-branch && git push origin :my-branch

Git Notes

  • The master branch on main repository on Github should always pass all tests and we should strive to keep it in a stable state. It is best to not merge contributions into master unless tests are passing, and preferably if someone else approved your code.
  • In general, do not commit changes to your local master branch, always pull in the latest changes from the master branch with git pull upstream master then checkout a new branch for your changes. This way you keep your local master branch up-to-date with the main master branch on Github.
  • In general, do not commit binary files, files generated from source, or large data files to the repository. See https://help.github.com/articles/working-with-large-files for some reasons.

Release Notes

0.1.0

  • Included Octave/Matlab source for inertial compensation.
  • Included Octave/Matlab source to compute inverse 2D dynamics.
  • Copied the walk module from DynamicistToolKit @ eecaebd31940179fe25e99a68c91b75d8b8f191f