--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/fast_fiber_tracking.py	Tue Nov 07 13:48:48 2017 -0500
@@ -0,0 +1,70 @@
+#!/usr/bin/env python
+import argparse
+import shutil
+
+from dipy.data import fetch_sherbrooke_3shell
+from dipy.data import fetch_stanford_hardi
+from dipy.data import get_sphere
+from dipy.data import read_sherbrooke_3shell
+from dipy.data import read_stanford_hardi
+from dipy.direction import peaks_from_model
+from dipy.io.image import save_nifti
+from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel, auto_response)
+from dipy.reconst.dti import TensorModel
+from dipy.segment.mask import median_otsu
+from dipy.tracking.local import LocalTracking, ThresholdTissueClassifier
+from dipy.tracking.streamline import Streamlines
+from dipy.tracking.utils import random_seeds_from_mask
+from dipy.viz import actor, window
+
+import numpy as np
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--drmi_dataset', dest='drmi_dataset', help='Input dataset')
+parser.add_argument('--output_csd_direction_field', dest='output_csd_direction_field', help='Output csd direction field dataset')
+parser.add_argument('--output_det_streamlines', dest='output_det_streamlines', help='Output det streamlines dataset')
+parser.add_argument('--output_fa_map', dest='output_fa_map', help='Output fa map dataset')
+
+args = parser.parse_args()
+
+interactive = False
+
+# Get input data.
+input_dir = args.drmi_dataset
+if input_dir == 'sherbrooke_3shell':
+    fetch_sherbrooke_3shell()
+    img, gtab = read_sherbrooke_3shell()
+elif input_dir == 'stanford_hardi':
+    fetch_stanford_hardi()
+    img, gtab = read_stanford_hardi()
+
+data = img.get_data()
+maskdata, mask = median_otsu(data, 3, 1, False, vol_idx=range(10, 50), dilate=2)
+
+response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
+csd_model = ConstrainedSphericalDeconvModel(gtab, response)
+sphere = get_sphere('symmetric724')
+csd_peaks = peaks_from_model(model=csd_model, data=data, sphere=sphere, mask=mask, relative_peak_threshold=.5, min_separation_angle=25, parallel=True)
+
+tensor_model = TensorModel(gtab, fit_method='WLS')
+tensor_fit = tensor_model.fit(data, mask)
+fa = tensor_fit.fa
+
+tissue_classifier = ThresholdTissueClassifier(fa, 0.1)
+seeds = random_seeds_from_mask(fa > 0.3, seeds_count=1)
+
+ren = window.Renderer()
+ren.add(actor.peak_slicer(csd_peaks.peak_dirs, csd_peaks.peak_values, colors=None))
+window.record(ren, out_path='csd_direction_field.png', size=(900, 900))
+shutil.move('csd_direction_field.png', args.output_csd_direction_field)
+
+streamline_generator = LocalTracking(csd_peaks, tissue_classifier, seeds, affine=np.eye(4), step_size=0.5)
+streamlines = Streamlines(streamline_generator)
+
+ren.clear()
+ren.add(actor.line(streamlines))
+window.record(ren, out_path='det_streamlines.png', size=(900, 900))
+shutil.move('det_streamlines.png', args.output_det_streamlines)
+
+save_nifti('fa_map.nii', fa, img.affine)
+shutil.move('fa_map.nii', args.output_fa_map)