eigenstrapping.fit.volumetric_fit

eigenstrapping.fit.volumetric_fit(x, volume, nsurrs=10, num_modes=100, return_data=False, extra_diags=False, **params)[source]

Evaluate variogram fits for :class: eigenstrapping.VolumetricEigenstrapping to determine how many modes to decompose volumetric map with.

Parameters:

  • mask (str to mask file) – Target ROI volume with 1 inside region-of-interest, and 0 elsewhere

  • x (str to data inside mask) – Target brain map

  • D ((N,N) np.ndarray or np.memmap) – Pairwise distance matrix between elements of x

  • index ((N,N) np.ndarray or np.memmap) – See :method:`variogram.variogram`

  • nsurr (int, default 20) – Number of simulated surrogate maps from which to compute variograms

  • return_data (bool, default False) – if True, return: 1, the smoothed variogram values for the target brain map; 2, the distances at which the smoothed variograms values were computed; and 3, the surrogate maps’ smoothed variogram values

  • params – Keyword arguments for :class: eigenstrapping.VolumetricEigenstrapping or :method: variogram.variogram. If eigenmodes and eigenvalues have been precomputed for the surface that x exists on, then pass them as evals and emodes. If they’re not given, then a surface must be given. See :class: eigenstrapping.VolumetricEigenstrapping for more details.

Returns:

  • if and only if return_data is True

  • surrs ((N,) np.ndarray) – Eigenstrapped surrogates in volume space