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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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14
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7 from dipy.core.gradients import gradient_table
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8 from dipy.data import fetch_sherbrooke_3shell
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9 from dipy.io import read_bvals_bvecs
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11
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10 from matplotlib import pyplot
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11
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12 parser = argparse.ArgumentParser()
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13 parser.add_argument('--input', dest='input', help='Input dataset')
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14
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14 parser.add_argument('--output_gtab', dest='output_gtab', help='Output gradient table dataset')
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15 parser.add_argument('--output_png', dest='output_png', help='Output dataset')
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16
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17 args = parser.parse_args()
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18
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19 input_dir = 'sherbrooke_3shell'
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9
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20 # Get input data.
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21 fetch_sherbrooke_3shell()
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22 fdwi = os.path.join(input_dir, 'HARDI193.nii.gz')
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23 fbval = os.path.join(input_dir, 'HARDI193.bval')
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24 fbvec = os.path.join(input_dir, 'HARDI193.bvec')
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25 # Load the dMRI datasets.
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26 img = nibabel.load(fdwi)
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27 data = img.get_data()
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28 # data is a 4D array where the first 3 dimensions are the i, j,
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29 # k voxel coordinates and the last dimension is the number of
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30 # non-weighted (S0s) and diffusion-weighted volumes.
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31 # Visualize the results using matplotlib.
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32 axial_middle = data.shape[2] // 2
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33 pyplot.figure('Showing the datasets')
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34 pyplot.subplot(1, 2, 1).set_axis_off()
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35 pyplot.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
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36 pyplot.subplot(1, 2, 2).set_axis_off()
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37 pyplot.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
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38 pyplot.savefig('data.png', bbox_inches='tight')
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39 shutil.move('data.png', args.output_png)
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40 # Load the b-values and b-vectors.
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41 bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
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42 gtab = gradient_table(bvals, bvecs)
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43 with open(args.output_gtab) as fh:
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44 fh.write(gtab.info) |
