def classic_calc_mdmrs(D, regressor, cols, iter, strata=None):
nVoxels = D.shape[0]
nSubjects = D.shape[1]
F_set = np.zeros(nVoxels)
p_set = np.zeros(nVoxels)
for i in range(nVoxels):
p_set[i], F_set[i], _, _ = mdmr(D[i].reshape(nSubjects**2,1), regressor, cols, iter, strata)
return F_set, p_set
def calc_mdmrs(D, regressor, cols, iter, strata=None):
nVoxels = D.shape[0]
nSubjects = D.shape[1]
F_set = np.zeros(nVoxels)
p_set = np.zeros(nVoxels)
for i in range(nVoxels):
p_set[i], F_set[i], _, _ = mdmr(D[i].reshape(nSubjects**2, 1),
regressor, cols, iter, strata)
return F_set, p_set
def calc_mdmrs(D, regressor, cols, perms, strata=None, voxel_block=1):
nVoxels = D.shape[0]
nSubjects = D.shape[1]
vox_inds = split_list_into_groups(range(nVoxels), voxel_block)
perms, H2perms, IHperms = gen_perm_mats(regressor, cols, perms, strata)
nperms = perms.shape[0]
Fs = np.zeros(nVoxels)
ps = np.zeros(nVoxels)
Fperms = np.zeros((nperms, nVoxels))
for i,inds in enumerate(vox_inds):
ps[inds], Fs[inds], Fperms[:,inds], _ = mdmr(D[inds], regressor, cols,
perms, strata, H2perms,
IHperms)
return Fs, ps, Fperms
