This provides a collection of functions and routines for standard data processing and analyses used in the lab. It is intended as a starting point for your own project.
Download the zip file of mimo_pack using the green button above.
Unzip the package and place the contents of the folder 'mimo_pack-main' inside a folder with the name of your project.
In the command, navigate to that folder with your project name. Then, create a conda environment for working with mimo_pack.
conda env create -n PROJECT_NAME -f environment.yml`
where PROJECT_NAME is the name you want to give to the environment. You should create a new environment for each project.
Upon completion, a conda environment named PROJECT_NAME is created that can be used as starting point for your own project. Additional packages can be added to it as needed (e.g. see here).
Once the environment is created, activate it with the command:
conda activate PROJECT_NAME
and then run the following command to make the code base accesible:
pip install -e .
To start working the the code base immediately, in the same terminal run:
jupyter notebook
From there, you can load the tutorials to familiarize yourself with types of analyses you can do.
mimo_pack is intended to provide an environment and suite of functions that facilitate analysis of your own data. To add your own code, create a folder in the mimo_pack directory called 'project' and add your scripts to it. Inside that directory, create a folder called 'results' that will contain files resulting from your analyses. Since these can get quite large, they should be excluded from being updated on the github page. To do that, the .gitignore file has been setup to exclude anything placed in the 'results' folder. From the project directory, you can access the functions in mimo_pack using import statements. If you want to do something very similar to what is carried out in the tutorials, you can copy a tutorial into your 'project' directory and then modify it to fit your needs.