Usage tutorial

This is a quick run-through of how you might analyze a dataset. First, we need to import the library:

import padre as p

Data organization

Here’s the organization of our sample data:

Name of experiment:  Exp1 Types of datasets:  anatomy, flair, task1, task2

We’ll process all the data in a subdirectory of our home directory ~/Exp1. The datasets are named:

raw data:[subject].run[#].nii.gz anatomy:[subject].anat.nii.gz stim files:[subject].[task].1D

Get the subject list

The easiest way to get the list of subjects is to organize them by experiment, and get them by calling padre.subjects():

# Return all subjects who have a session labeled with experiment "Exp1"
subjects = p.subjects('Exp1')
# The same thing, using keyword arguments
subjects = p.subjects(experiment='Exp1')

If you want to get any subject with who has any runs of the task task1, you could use this instead:

subjects = p.subjects(label='task1')

Or, you could keep a list of subject numbers the old fashion way, and load them individually:

subjects = []
with open('subjects.txt') as f:
        for line in f.readlines():
                subj = p.load(line.strip())
                if subj:
                        subjects.append(subj)

Preprocess data

Now that we have our list of subjects, we can loop through the data and run some preprocessing functions on them:

import neural as nl

for subj in subjects:
        # The dsets function will return the datasets we're interested in.
        # The first argument is the dset label, but you could also specify
        # session parameters like tags or session name
        # The "[0]" needs to be there because dsets() always returns a list
        anatomy = subj.dsets('anatomy')[0]
        # epis now is a list of all of the task1 and task2 datasets
        epis = subj.dsets('task1') + subj.dsets('task2')
        # run the "align_epi_anat.py" script (using the neural library)
        nl.afni.align_epi_anat(anatomy,epis)