In [18]:

    

import numpy as np
# Set numpy to print only 2 decimal digits for neatness
np.set_printoptions(precision=2, suppress=True)
import os
import nibabel as nb
from nibabel.testing import data_path


    

In [2]:

    

from nilearn import image


    

In [3]:

    

img1 = nib.load('/home/sophie/Documents/100411ss2on.nii.gz')
img2 = nib.load('/home/sophie/Documents/100412ss2on.nii.gz')


    

In [4]:

    

img2


    

    
Out[4]:





<nibabel.nifti1.Nifti1Image at 0x7fc8e5a3b4d0>

In [13]:

    

img1c = image.crop_img(img2, rtol=1e-01, copy=True)


    

In [14]:

    

img1c


    

    
Out[14]:





<nibabel.nifti1.Nifti1Image at 0x7fc8e5a2be10>

In [15]:

    

nib.save(img1c, '/home/sophie/Documents/100412ss2onc.nii.gz')


    

In [ ]:

    

filename1 = '/home/sophie/Documents/100411ss2on.nii.gz'
img1 = nb.load(filename1)
data = img1.get_data()
S=data.shape


    

In [ ]:

    




    

In [ ]:

    

MeanImage=np.sum(data(:,:,:,1),2)
for x in 1:100
    for y in 1:200
    F(x,y)=sum(sum(MeanImage(x:x+180,y:y+140)))

[Id,M]=max(F)