contextlab / mind-tools

Docker container prebuilt with python tools for demos and workshops

mind-tools

This is a Docker container pre-built with Python 3 tools you can use for data analysis and workshop demos during the Methods in Neuroscience at Dartmouth summer school 2018.

Free free to contact Eshin (Slack: @ejolly) (email: [email protected]) regarding any issues you run into when setting up or using this container

One time setup

1. Install Docker on your computer using the appropriate guide below:
   • OSX
   • Windows
   • Ubuntu
   • Debian
2. Launch Docker and adjust the preferences to allocate sufficient resources (e.g. > 4GB RAM)
3. Download the Docker image for MIND from docker hub (this download will take a while depending on the speed of your internet connection ~7GB):
   • Open a terminal and enter docker pull ejolly/mind-tools
4. Use the downloaded image to create a new container for the workshop
   • The command below will create a new container that will map your computer's Desktop to /mnt within the container, so that location is shared between your host OS and the container. Feel free to change Desktop to whatever folder you prefer to share instead, but make sure to provide the full path. The command will also share port 9999 with your host computer so any jupyter notebooks launched from within the container will be accessible at localhost:9999 in your web browser
   • docker run -it -p 9999:9999 --name MIND -v ~/Desktop:/mnt ejolly/mind-tools
   • You should now see the root@ prefix in your terminal, if so you've successfully created a container and are running a shell from inside!

Using the container after setup

1. You can always fire up the container by typing the following into a terminal
   • docker start MIND && docker attach MIND
   • When you see the root@ prefix, letting you know you're inside the container
2. For convenience we have setup simple commands to easily open a new jupyter notebook session by typing jp or a new jupyter lab session by typing jl from within the container in the shared folder you specified during setup. Just navigate to localhost:9999 in your web-browser to access them.
3. We have provided Python 2 within the container as well, just select it from the drop down menu in jupyter notebook, or click on the icon in jupyter lab to use it. Note Python 2 is provided purely for compatibility reasons and therefore only includes a few core packages (e.g. numpy). It does not include all the same installed packages that come with Python 3.
4. Whatever folder on your host computer you shared with the container (e.g. Desktop in the above example) is located at /mnt within the container. You can interact with these files using normal unix commands (e.g. cd, mv, etc)
5. To open up a second terminal from within the running container, type the following command in another terminal window docker exec -it MIND bash
6. Close a running container with ctrl + d from the same terminal you used to launch the container, or docker stop MIND from any other terminal

Installed Software

Scientific python stack (Anaconda version; e.g. numpy, pandas)
Brain Imaging Analysis Kit
Neurolearn Toolbox
PyMVPA
Hypertools
MNE toolbox
Deep dish
Nelpy
Dask
Nilearn
SuperEEG HMMLearn