brainspace.null_models.spin.SpinPermutations

class brainspace.null_models.spin.SpinPermutations(unique=False, n_rep=100, random_state=None, surface_algorithm='FreeSurfer')[source]

Spin permutations.

Parameters:
  • unique (bool, optional) – Whether to enforce a one-to-one correspondence between original points and rotated ones. If True, the Hungarian algorithm is used. Default is False.
  • n_rep (int, optional) – Number of randomizations. Default is 100.
  • random_state (int or None, optional) – Random state. Default is None.
  • surface_algorithm ({'FreeSurfer', 'CIVET'}) – For ‘CIVET’, no flip is required to generate the spins for the right hemisphere. Only used when points_rh is not None. Default is ‘FreeSurfer’.
Variables:
  • spin_lh (ndarray, shape (n_rep, n_lh)) – Spin indices for points in left hemisphere.
  • spin_rh (ndarray, shape (n_rep, n_rh)) – Spin indices for points in right hemisphere. Only if user provides right hemisphere points. None, otherwise.

See also

spin_permutations, MoranRandomization

Notes

Right hemisphere permutations are generated by reflecting the rotation matrix used for the left hemisphere.

__init__(unique=False, n_rep=100, random_state=None, surface_algorithm='FreeSurfer')[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([unique, n_rep, random_state, …]) Initialize self.
fit(points_lh[, points_rh]) Compute spin indices by random rotation.
get_params([deep]) Get parameters for this estimator.
randomize(x_lh[, x_rh]) Generate random samples from x_lh and x_rh.
randomize_gen(x_lh[, x_rh]) Generate random samples from x_lh and x_rh.
set_params(**params) Set the parameters of this estimator.
fit(points_lh, points_rh=None)[source]

Compute spin indices by random rotation.

Parameters:
  • points_lh (BSPolyData or ndarray, shape = (n_lh, 3)) – Sphere for the left hemisphere. If ndarray, each row must represent a vertex in the sphere.
  • points_rh (BSPolyData or ndarray, shape = (n_rh, 3), optional) – Sphere for the right hemisphere. If ndarray, row must represent a vertex in the sphere. Default is None.
Returns:

self (object) – Returns self.
randomize(x_lh, x_rh=None)[source]

Generate random samples from x_lh and x_rh.

Parameters:
  • x_lh (ndarray, shape = (n_lh,) or (n_lh, n_feat)) – Array of variables arranged in columns, where n_feat is the number of variables.
  • x_rh (ndarray, shape = (n_rh,) or (n_rh, n_feat), optional) – Array of variables arranged in columns for the right hemisphere. Default is None.
Returns:

  • rand_lh (ndarray, shape = (n_rep, n_lh, n_feat)) – Permutations of x_lh. If n_feat == 1, shape = (n_rep, n_lh).
  • rand_lh (ndarray, shape = (n_rep, n_rh, n_feat)) – Permutations of x_rh. If n_feat == 1, shape = (n_rep, n_rh). None if x_rh is None. Only if spin_rh_ is not None.

See also

SpinPermutations.randomize_gen

randomize_gen(x_lh, x_rh=None)[source]

Generate random samples from x_lh and x_rh.

This is the generator version of SpinPermutations.randomize.

Parameters:
  • x_lh (ndarray, shape = (n_lh,) or (n_lh, n_feat)) – Array of variables arranged in columns, where n_feat is the number of variables.
  • x_rh (ndarray, shape = (n_rh,) or (n_rh, n_feat), optional) – Array of variables arranged in columns for the right hemisphere. Default is None.
Yields:
  • rand_lh (ndarray, shape = (n_lh, n_feat)) – Permutation of x_lh. If n_feat == 1, shape = (n_lh,).
  • rand_lh (ndarray, shape = (n_rh, n_feat)) – Permutation of x_rh. If n_feat == 1, shape = (n_rh,). None if x_rh is None. Only if spin_rh_ is not None.

See also

SpinPermutations.randomize