7. Extending junifer

While we aim to provide as many datasets and markers as possible, we are also interested in allowing users to extend the functionality with their own DataGrabbers, Preprocessors, Markers, etc., .

It’s not necessary to have the new functionality included in junifer before the user can use them. The user can simply create a new Python file, code the desired functionality and use it with junifer. This is the first step towards including the new functionality in the junifer pipeline.

In this section we will show how to extend junifer, by creating new DataGrabbers, Preprocessors, Markers, etc., following the junifer way.

Contents:

• 7.1. Creating a junifer extension
• 7.2. Creating Data Grabbers
  • 7.2.1. Step 1: Think about the element
  • 7.2.2. Step 2: Think about the dataset’s structure
  • 7.2.3. Step 3: Create a Data Grabber
  • 7.2.4. Step 4: Optional: Adding BOLD confounds
• 7.3. Creating Markers
  • 7.3.1. Step 1: Configure input and output
  • 7.3.2. Step 2: Initialise the Marker
  • 7.3.3. Step 3: Compute the Marker
  • 7.3.4. Step 4: Finalise the Marker
  • 7.3.5. Template for a custom Marker
• 7.4. Creating Preprocessors
  • 7.4.1. Step 1: Configure input and output
  • 7.4.2. Step 2: Initialise the Preprocessor
  • 7.4.3. Step 3: Preprocess the data
  • 7.4.4. Step 4: Finalise the Preprocessor
  • 7.4.5. Template for a custom Preprocessor
• 7.5. Specifying Dependencies
  • 7.5.1. Handling dependencies that come as Python packages
  • 7.5.2. Handling external dependencies from toolboxes
  • 7.5.3. Handling conditional dependencies
• 7.6. Adding Parcellations
  • 7.6.1. Step 1: Prepare code to register a parcellation
  • 7.6.2. Step 2: Add parcellation registration to the YAML file
• 7.7. Adding Coordinates
  • 7.7.1. Step 1: Prepare code to register a set of coordinates
  • 7.7.2. Step 2: Add coordinate registration to the YAML file
• 7.8. Adding Masks
  • 7.8.1. Step 1: Prepare code to register a mask
  • 7.8.2. Step 2: Configure a YAML file for registration of a mask