Tutorials: Getting started#
These tutorials provide a walk-through of basic pyAFQ usage and some more advanced use-cases. Each one of these pages contains code that users can run from top to bottom. The code will download the data necessary to run the tutorial, and will save results as it goes along.
- Getting started with pyAFQ - GroupAFQ
- Example data
- Set tractography parameters (optional)
- Define PVE images (optional)
- Brain Mask Definition (optional)
- Initialize a GroupAFQ object:
- Calculating DTI FA (Diffusion Tensor Imaging Fractional Anisotropy)
- Visualize the result with Matplotlib
- Recognizing the bundles and calculating tract profiles:
- Visualizing the bundles and calculating tract profiles:
- Exporting citations
- Alternative way to initialize GroupAFQ when using QSIPrep data
- References
- Getting started with pyAFQ - ParticipantAFQ
- Example data
- Defining data files
- Set tractography parameters (optional)
- Define PVE images (optional)
- Brain Mask Definition (optional)
- Initialize a ParticipantAFQ object:
- Calculating DTI FA (Diffusion Tensor Imaging Fractional Anisotropy)
- Visualize the result with Matplotlib
- Recognizing the bundles and calculating tract profiles:
- Visualizing the bundles and calculating tract profiles:
- Exporting citations
- References
- Re-running pyAFQ
- Exporting pyAFQ Results
- Visualizing AFQ derivatives
- Get some data from HBN POD2
- Read data into memory
- Transform into the T1w reference frame
- Visualizing bundles with principal direction coloring
- Slicer actors
- Making a
scene - Showing the visualization
- Record the visualization
- Setting bundle colors
- Adding core bundles with tract profiles
- Adding ROIs
- Visualizing tracts and tract profiles with a “glass brain”
- Making a Figure out of many fury panels
- References
- How pyAFQ uses BIDS
- Plotting Default Regions of Interest (ROIs) to Understand the Tracts