tedana.metrics.dependence.calculate_f_maps

calculate_f_maps(*, data_cat: ndarray, mixing: ndarray, adaptive_mask: ndarray, tes: ndarray, n_independent_echos=None, f_max: float = 500) → Tuple[ndarray, ndarray, ndarray, ndarray][source]

Calculate pseudo-F-statistic maps for TE-dependence and -independence models.

Parameters:

  • data_cat ((Mb x E x T) array_like) – Multi-echo data, already masked, where Mb is samples in base mask, E is echos, and T is time.

  • mixing ((T x C) array_like) – Mixing matrix

  • adaptive_mask ((Mb) array_like) – Adaptive mask, where each voxel’s value is the number of echoes with “good signal”. Limited to masked voxels.

  • tes ((E) array_like) – Echo times in milliseconds, in the same order as the echoes in data_cat.

  • n_independent_echos (int) – Number of independent echoes to use in goodness of fit metrics (fstat). Primarily used for EPTI acquisitions. If None, number of echoes will be used. Default is None.

  • f_max (float, optional) – Maximum F-statistic, used to crop extreme values. Values in the F-statistic maps greater than this value are set to it.

Returns:

f_t2_maps, f_s0_maps, pred_t2_maps, pred_s0_maps ((Mb x C) array_like) – Pseudo-F-statistic maps for TE-dependence and -independence models, respectively.