--- a/linear_fascile_evaluation.py	Thu Nov 30 11:28:15 2017 -0500
+++ b/linear_fascile_evaluation.py	Thu Nov 30 11:28:22 2017 -0500
@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 import argparse
+import os
 import shutil
 
 import dipy.core.optimize as opt
@@ -18,22 +19,36 @@
 import numpy as np
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--input', dest='input', help='Track Visualization Header dataset')
+parser.add_argument('--input_nifti1', dest='input_nifti1', help='Input nifti1 dataset')
+parser.add_argument('--input_nifti1_files_path', dest='input_nifti1_files_path', help='Input nifti1 extra files path')
+parser.add_argument('--input_nifti2', dest='input_nifti2', help='Input nifti2 dataset')
+parser.add_argument('--input_nifti2_files_path', dest='input_nifti2_files_path', help='Input nifti2 extra files path')
+parser.add_argument('--input_trackvis', dest='input_trackvis', help='Track Visualization Header dataset')
 parser.add_argument('--output_life_candidates', dest='output_life_candidates', help='Output life candidates')
 
 args = parser.parse_args()
 
+# Get input data.
+# TODO: do not hard-code 'stanford_hardi'
+input_dir = 'stanford_hardi'
+os.mkdir(input_dir)
+# Copy the dRMI dataset (stanford_t1) files.
+for f in os.listdir(args.input_nifti1_files_path):
+    shutil.copy(os.path.join(args.input_nifti1_files_path, f), input_dir)
+# Copy the dRMI dataset and label map (stanford_hardi) files.
+for f in os.listdir(args.input_nifti2_files_path):
+    shutil.copy(os.path.join(args.input_nifti2_files_path, f), input_dir)
+
 # We'll need to know where the corpus callosum is from these variables.
 hardi_img, gtab, labels_img = read_stanford_labels()
 labels = labels_img.get_data()
 cc_slice = labels == 2
-fetch_stanford_t1()
 t1 = read_stanford_t1()
 t1_data = t1.get_data()
 data = hardi_img.get_data()
 
 # Read the candidates from file in voxel space:
-candidate_sl = [s[0] for s in nib.trackvis.read(args.input, points_space='voxel')[0]]
+candidate_sl = [s[0] for s in nib.trackvis.read(args.input_trackvis, points_space='voxel')[0]]
 # Visualize the initial candidate group of streamlines
 # in 3D, relative to the anatomical structure of this brain.
 candidate_streamlines_actor = fvtk.streamtube(candidate_sl, line_colors(candidate_sl))