In [2]:

    

import nibabel as nib
fp = '/home/grg/upf/Data/RM/rch2.nii'
im = nib.load(fp)
print im.header.keys()
print im.header['pixdim']


    

    




['sizeof_hdr', 'data_type', 'db_name', 'extents', 'session_error', 'regular', 'dim_info', 'dim', 'intent_p1', 'intent_p2', 'intent_p3', 'intent_code', 'datatype', 'bitpix', 'slice_start', 'pixdim', 'vox_offset', 'scl_slope', 'scl_inter', 'slice_end', 'slice_code', 'xyzt_units', 'cal_max', 'cal_min', 'slice_duration', 'toffset', 'glmax', 'glmin', 'descrip', 'aux_file', 'qform_code', 'sform_code', 'quatern_b', 'quatern_c', 'quatern_d', 'qoffset_x', 'qoffset_y', 'qoffset_z', 'srow_x', 'srow_y', 'srow_z', 'intent_name', 'magic']
[-1.  2.  2.  2.  0.  1.  1.  1.]

In [4]:

    

%matplotlib inline
im.orthoview()


    

    
Out[4]:





<OrthoSlicer3D: /home/grg/upf/Data/RM/rch2.nii (79, 95, 68)>






    

In [5]:

    

from matplotlib import pyplot as plt
import numpy as np
d_t1 = np.array(im.dataobj)
d2 = d_t1[35,:,:]
plt.imshow(d2)


    

    
Out[5]:





<matplotlib.image.AxesImage at 0x7f33246a3f90>






    

In [6]:

    

fp2 = '/home/grg/upf/Data/RM/rch2_wm.nii'
wm = nib.load(fp2)
d_wm = np.array(wm.dataobj)
wm.orthoview()


    

    
Out[6]:





<OrthoSlicer3D: /home/grg/upf/Data/RM/rch2_wm.nii (79, 95, 68)>






    

In [7]:

    

fp3 = '/home/grg/upf/Data/Control/norm2ROI2wscan001.nii'
im2 = nib.load(fp3)
#pet1.orthoview()
pet1 = np.array(im2.dataobj)
m = pet1[d_wm>0.8].mean()
print m
plt.hist(pet1[d_wm>0.8])


    

    




174.746






    
Out[7]:





(array([  2724.,   9738.,  11344.,  11497.,  11250.,  11623.,   6368.,
          1982.,    310.,     37.]),
 array([  67.36786652,   95.21324844,  123.05863037,  150.9040123 ,
         178.74939423,  206.59477615,  234.44015808,  262.28554001,
         290.13092194,  317.97630386,  345.82168579]),
 <a list of 10 Patch objects>)






    

In [16]:

    

d_t1[d_wm>0.8] = 0
plt.imshow(d2) # d2 is the sagittal slice
#test = nib.Nifti1Image(d_t1, im.affine)
#test.to_filename('/tmp/test.nii.gz')


    

    
Out[16]:





<matplotlib.image.AxesImage at 0x7f330b505410>






    

In [36]:

    

from nilearn import plotting


    

    




/home/grg/jupyter/local/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

In [46]:

    

plotting.
plotting.plot_glass_brain(im) #, colorbar=True, title='PET exam', black_bg=True, threshold=260)


    

    
Out[46]:





<nilearn.plotting.displays.OrthoProjector at 0x7f26295b8ad0>






    

In [53]:

    

plotting.plot_stat_map(im2, bg_img=im, threshold=260)


    

    
Out[53]:





<nilearn.plotting.displays.OrthoSlicer at 0x7f2626fd8f90>






    

In [55]:

    

plotting.plot_stat_map(im2, bg_img=im, threshold=260, display_mode='z', cut_coords=range(-10,10,2))


    

    
Out[55]:





<nilearn.plotting.displays.ZSlicer at 0x7f262678b210>






    

In [54]:

    

roi_fp = '/home/grg/spm/ROIapoE/ROI_DARTEL/csf5/rois.nii.gz'
plotting.plot_roi(roi_fp)


    

    
Out[54]:





<nilearn.plotting.displays.OrthoSlicer at 0x7f2626c46150>






    

In [19]:

    

from nilearn import image
image.


    

In [57]:

    

import nipype