tedana.model
.fitmodels_direct¶
-
fitmodels_direct
(catd, mmix, mask, t2s, t2s_full, tes, combmode, ref_img, reindex=False, mmixN=None, full_sel=True, label=None, out_dir='.', verbose=False)[source]¶ Fit TE-dependence and -independence models to components.
Parameters: - catd ((S x E x T) array_like) – Input data, where S is samples, E is echos, 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 catd
- mask ((S [x E]) array_like) – Boolean mask array
- t2s ((S [x T]) array_like) – Limited T2* map or timeseries.
- t2s_full ((S [x T]) array_like) – Full T2* map or timeseries. For voxels with good signal in only one echo, which are zeros in the limited T2* map, this map uses the T2* estimate using the first two echoes.
- tes (list) – List of echo times associated with catd, in milliseconds
- combmode ({'t2s', 'ste'} str) – How optimal combination of echos should be made, where ‘t2s’ indicates using the method of Posse 1999 and ‘ste’ indicates using the method of Poser 2006
- ref_img (str or img_like) – Reference image to dictate how outputs are saved to disk
- reindex (bool, optional) – Default: False
- mmixN (array_like, optional) – Default: None
- full_sel (bool, optional) – Whether to perform selection of components based on Rho/Kappa scores. Default: True
Returns: - seldict (dict)
- comptab ((N x 5)
pandas.DataFrame
) – Array with columns denoting (1) index of component, (2) Kappa score of component, (3) Rho score of component, (4) variance explained by component, and (5) normalized variance explained by component - betas (
numpy.ndarray
) - mmix_new (
numpy.ndarray
)