Bin and back project

[rb643]

Binning the principal gradient into 10 percentile block and create nifti's for each. Then compare each bin against the same neurosynth keywords as were used in the original paper.

[rb643]

This is the bit of code that plots the entire principal gradient:

def rebuild_nii_individ(num):

onlyfiles = [f for f in listdir_nohidden('./data/Outputs/Regs/') if isfile(join('./data/Outputs/Regs/', f))]

for sub in onlyfiles:

    data = np.load('./data/Outputs/Regs/%s' % sub)
    a = data[:,num].copy()
    nim = nib.load('cc400_roi_atlas.nii')
    imdat=nim.get_data()
    imdat_new = imdat.copy()

    for n, i in enumerate(np.unique(imdat)):
        if i != 0:
            imdat_new[imdat == i] = a[n-1] * 10 # scaling factor. Could also try to get float values in nifti...

    nim_out = nib.Nifti1Image(imdat_new, nim.get_affine(), nim.get_header())
    nim_out.set_data_dtype('float32')
    # to save:
    nim_out.to_filename('res.nii')
    nilearn.plotting.plot_epi(nim_out, cut_coords=(0,0,0))

[rb643]

num == gradient number