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%matplotlib inline

import nilearn
from nilearn import plotting
from nilearn.masking import compute_background_mask
from nilearn.masking import new_img_like, apply_mask, unmask
import nibabel as nib
import numpy as np
from wand.image import Image as WImage

mniT1 =  'MNI152_T1_2mm_brain.nii.gz'
mask_img = compute_background_mask('mask_avg.nii.gz')


    

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# Brainmask

masked_data = apply_mask('mask_avg.nii.gz', mask_img)
masked_nifti = unmask(masked_data, mask_img, order='F')

plotting.plot_stat_map(masked_nifti, bg_img = mniT1, 
                       cut_coords=range(-80,60,20), draw_cross=False, vmax=np.max(masked_data), threshold=np.min(masked_data),
                       black_bg=False, cmap='jet', alpha=0.7, display_mode='y', output_file="mask_y.pdf")
    
plotting.plot_stat_map(masked_nifti, bg_img = mniT1, 
                       cut_coords=range(-40,41,20), draw_cross=False, vmax=np.max(masked_data), threshold=np.min(masked_data),
                       black_bg=False, cmap='jet', alpha=0.7, display_mode='x', output_file="mask_x.pdf")

!pdfnup mask_y.pdf mask_x.pdf --nup 1x2 --outfile mask.pdf -q
!rm mask_y.pdf mask_x.pdf
WImage(filename='mask.pdf')


    

    
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# TSNR

masked_data = apply_mask('tsnr_avg.nii.gz', mask_img)
masked_nifti = unmask(masked_data, mask_img, order='F')

plotting.plot_stat_map(masked_nifti, bg_img = mniT1, 
                       cut_coords=range(-80,60,20), draw_cross=False, vmax=np.max(masked_data), threshold=np.min(masked_data),
                       black_bg=False, cmap='jet', alpha=0.7, display_mode='y', output_file="tsnr_y.pdf")
    
plotting.plot_stat_map(masked_nifti, bg_img = mniT1, 
                       cut_coords=range(-40,41,20), draw_cross=False, vmax=np.max(masked_data), threshold=np.min(masked_data),
                       black_bg=False, cmap='jet', alpha=0.7, display_mode='x', output_file="tsnr_x.pdf")

plotting.plot_stat_map(masked_nifti, bg_img = mniT1, 
                       cut_coords=range(-40,61,20), draw_cross=False, vmax=np.max(masked_data), threshold=np.min(masked_data),
                       black_bg=False, cmap='jet', alpha=0.7, display_mode='z', output_file="tsnr_z.pdf")

!pdfnup tsnr_y.pdf tsnr_x.pdf tsnr_z.pdf --nup 1x3 --outfile tsnr.pdf -q
!rm tsnr_y.pdf tsnr_x.pdf tsnr_z.pdf
WImage(filename='tsnr.pdf')


    

    
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