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1 #!/usr/bin/env python
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2 import argparse
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3 import os
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4 import nibabel
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5 import shutil
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6
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7 from dipy.data import fetch_sherbrooke_3shell
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8
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9 parser = argparse.ArgumentParser()
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10 parser.add_argument('--input', dest='input', help='Input dataset')
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11 parser.add_argument('--output', dest='output', help='Output dataset')
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12
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13 args = parser.parse_args()
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14
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15 def move_directory_files(source_dir, destination_dir, copy=False, remove_source_dir=False):
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16 source_directory = os.path.abspath(source_dir)
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17 destination_directory = os.path.abspath(destination_dir)
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18 if not os.path.isdir(destination_directory):
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19 os.makedirs(destination_directory)
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20 for dir_entry in os.listdir(source_directory):
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21 source_entry = os.path.join(source_directory, dir_entry)
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22 if copy:
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23 shutil.copy(source_entry, destination_directory)
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24 else:
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25 shutil.move(source_entry, destination_directory)
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26 if remove_source_dir:
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27 os.rmdir(source_directory)
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28
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29 input_dir = 'input_dir'
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30 # Get input data.
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31 fetch_sherbrooke_3shell()
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32 # MNove inputs to working directory.
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33 move_directory_files('/home/greg/.dipy', input_dir)
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5
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34 fdwi = os.path.join(input_dir, 'HARDI193.nii.gz')
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35 fbval = os.path.join(input_dir, 'HARDI193.bval')
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36 fbvec = os.path.join(input_dir, 'HARDI193.bvec')
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0
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37 # Load the dMRI datasets.
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38 img = nibabel.load(fdwi)
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39 data = img.get_data()
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40 # data is a 4D array where the first 3 dimensions are the i, j,
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41 # k voxel coordinates and the last dimension is the number of
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42 # non-weighted (S0s) and diffusion-weighted volumes.
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43 # Visualize the results using matplotlib.
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44 axial_middle = data.shape[2] // 2
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45 plt.figure('Showing the datasets')
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46 plt.subplot(1, 2, 1).set_axis_off()
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47 plt.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
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48 plt.subplot(1, 2, 2).set_axis_off()
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49 plt.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
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50 plt.show()
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51 plt.savefig(args.output, bbox_inches='tight')
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