Goal: create a visualization of streaming OpenBCI data.
- Ensure that you have write access to the project's GitHub repository
- Join the Brain Haxorz Slack group
- Subscribe to the project's Trello board. This is used for brainstorming and prioritising ideas.
- Also check out the GitHub project board. This is used to keep track of implementation details and notes.
- Install Enthought Canopy or another Python 2.x distribution.
- Run the install stript (sh install.sh). This will download all dependencies and set up your Python path to include the relevant toolbox packages.
- Install PyCharm CE or another Python IDE of your choice. Set up your environment to use Enthought Canopy as your project's Python interpreter.
The project is divided into two primary tasks (proposed API):
- Write streaming data to a file, using OpenBCI_Python (PROJECT BOARD; SLACK CHANNEL; branch: streamdata)
- Read streaming data from file and plot it using hyper-plot tools. (PROJECT BOARD; SLACK CHANNEL; branch: plotdata)
If time, we will try to implement a third task:
- Read a series of samples from the data stream and return a feature vector comprising spectral freatures at each electrode. (PROJECT BOARD; SLACK CHANNEL; branch: xformdata)
Choose your project team (these can be fluid, but you should start somewhere). Make sure everyone on your team has checked out the appropriate branch and is added to the Slack channel. Decide as a team how you want to organize yourselves. For example, you might want to code in pairs, as a group, separately, or something else. Divide your sub-project into a series of sub-tasks.
As you finish a task or get stuck, update the main project board. Also add any notes and/or usage examples to the API. Then create a pull request to merge your branch into the main channel so that everyone can use your function(s).