Multivariate Pattern Analysis in Python

# mvpa2.support.nibabel.afni_niml_dset

AFNI NIML dataset I/O support. Usually this type of datasets are used for functional data (timeseries, preprocessed data), statistical maps or searchlight results.

Created on Feb 19, 2012

@author: Nikolaas. N. Oosterhof (nikolaas.oosterhof@unitn.it)

Files that are read with the afni_niml_dset.read function contain a dict with the following fields:

> .data PxN data for P nodes and N columns (values per node). .node_indices P indices of P nodes that data refers to (base 0) .history String with history information .stats list with statistical information for each column. .labels list with labels of the data columns .dset_type String with the data set type

Similarly, such a dict can be saved to a .niml.dset file using the afni_niml_dset.write function

Functions

`dset2rawniml`(s) |
`from_any`(s[, itemifsingletonlist]) |
`label2index`(dset, label) |
`rawniml2dset`(p) |
`read`(fn[, itemifsingletonlist]) |
`sparse2full`(dset[, pad_to_ico_ld, ...]) | Creates a ‘full’ dataset which has values associated with all nodes
`ttest`(dsets[, sa_labels, return_values, ...]) | Runs a one-sample t-test across datasets
`write`(fnout, dset[, form]) |