

In [3]:

    
%matplotlib inline

Simple Downsampling technique for Clarity brains



In [10]:

    
import os
os.chdir('/Users/albert/ndreg')



In [11]:

    
from ndreg import *
import matplotlib
import ndio.remote.neurodata as neurodata
import nibabel as nb



In [13]:

    
inImg = imgRead("../atlasfull.nii")
imgShow(inImg, vmax=500)



In [15]:

    
print(inImg.GetSpacing())









    



(0.018719999119639397, 0.004999999888241291, 0.018719999119639397)



In [16]:

    
inImg = imgResample(inImg, spacing=(1.8719999119639397, .04999999888241291, 1.8719999119639397))
imgShow(inImg, vmax=500)



In [21]:

    
imgWrite(inImg, "../seelviz/miniatlas.nii")



In [22]:

    
inImg = imgRead("../seelviz/miniatlas.nii")
imgShow(inImg, vmax=500)



In [28]:

    
inImg = imgResample(inImg, spacing=(3.6719999119639397, .16999999888241291, 3.6719999119639397))
imgShow(inImg, vmax=500)



In [29]:

    
imgWrite(inImg, "../seelviz/miniatlas.nii")



In [27]:

    
inImg = imgRead("../seelviz/miniatlas.nii")
imgShow(inImg, vmax=500)



In [31]:

    
inImg = imgResample(inImg, spacing=(1.8719999119639397, .04999999888241291, 1.8719999119639397))
imgShow(inImg, vmax=500)

As shown above, a huge limitation to downsampling is a loss of data such that reconversions to a higher quality are not advised.