tedana.io.writeresults

writeresults(ts, mask, comptable, mmix, n_vols, ref_img, out_dir='.')[source]

Denoises ts and saves all resulting files to disk

Parameters

  • ts ((S x T) array_like) – Time series to denoise and save to disk

  • mask ((S,) array_like) – Boolean mask array

  • comptable ((C x X) pandas.DataFrame) – Component metric table. One row for each component, with a column for each metric. Requires at least two columns: “component” and “classification”.

  • mmix ((C x T) array_like) – Mixing matrix for converting input data to component space, where C is components and T is the same as in data

  • n_vols (int) – Number of volumes in original time series

  • ref_img (str or img_like) – Reference image to dictate how outputs are saved to disk

  • out_dir (str, optional) – Output directory.

Notes

This function writes out several files:

Filename

Content

hik_ts_OC.nii

High-Kappa time series. Generated by tedana.utils.io.write_split_ts().

midk_ts_OC.nii

Mid-Kappa time series. Generated by tedana.utils.io.write_split_ts().

low_ts_OC.nii

Low-Kappa time series. Generated by tedana.utils.io.write_split_ts().

dn_ts_OC.nii

Denoised time series. Generated by tedana.utils.io.write_split_ts().

betas_OC.nii

Full ICA coefficient feature set.

betas_hik_OC.nii

Denoised ICA coefficient feature set.

feats_OC2.nii

Z-normalized spatial component maps. Generated by tedana.utils.io.writefeats().

ts_OC.nii

Optimally combined 4D time series.