def save_mask(data, out_path):
'''Save mask of data.
Args:
data (numpy.array): Data to mask
out_path (str): Output path for mask.
'''
print 'Getting mask'
s, n, x, y, z = data.shape
mask = np.zeros((x, y, z))
_data = data.reshape((s * n, x, y, z))
mask[np.where(_data.mean(axis=0) > _data.mean())] = 1
print 'Masked out %d out of %d voxels' % ((mask == 0).sum(), reduce(
lambda x_, y_: x_ * y_, mask.shape))
np.save(out_path, mask)
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