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()
.