tedana.io.split_ts

split_ts(data, mmix, mask, comptable)[source]

Split data time series into accepted component time series and remainder.

Parameters:

  • data ((S x T) array_like) – Input data, where S is samples and T is time

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

  • 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”.

Returns:

  • hikts ((S x T) numpy.ndarray) – Time series reconstructed using only components in acc

  • resid ((S x T) numpy.ndarray) – Original data with hikts removed