tedana.io.writeresults

writeresults(ts, mask, comptable, mmix, n_vols, ref_img)[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

Notes

This function writes out several files:

Filename Content
ts_OC.nii Optimally combined 4D time series.
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().
comp_table.txt Component table. Generated by tedana.utils.io.writect().