--- a/create_streamlines.py	Thu Nov 30 10:10:54 2017 -0500
+++ b/create_streamlines.py	Thu Nov 30 10:11:00 2017 -0500
@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 import argparse
+import os
 import shutil
 
-from dipy.data import fetch_stanford_t1, read_stanford_labels, read_stanford_t1
 from dipy.reconst import peaks, shm
 from dipy.tracking import utils
 from dipy.tracking.eudx import EuDX
@@ -16,20 +16,40 @@
 import numpy as np
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--drmi_dataset', dest='drmi_dataset', help='Input dataset')
+parser.add_argument('--input', dest='input', help='Input dataset')
+parser.add_argument('--input_extra_files_path', dest='input_extra_files_path', help='Input dataset extra files paths')
 parser.add_argument('--output_superiorfrontal_nifti', dest='output_superiorfrontal_nifti', help='Output superiorfrontal nifti1 dataset')
+parser.add_argument('--output_superiorfrontal_nifti_files_path', dest='output_superiorfrontal_nifti_files_path', help='Output superiorfrontal nifti1 extra files path')
 parser.add_argument('--output_trackvis_header', dest='output_trackvis_header', help='Output superiorfrontal track visualization header dataset')
 
 args = parser.parse_args()
 
-hardi_img, gtab, labels_img = read_stanford_labels()
+def move_directory_files(source_dir, destination_dir, copy=False, remove_source_dir=False):
+    source_directory = os.path.abspath(source_dir)
+    destination_directory = os.path.abspath(destination_dir)
+    if not os.path.isdir(destination_directory):
+        os.makedirs(destination_directory)
+    for dir_entry in os.listdir(source_directory):
+        source_entry = os.path.join(source_directory, dir_entry)
+        if copy:
+            shutil.copy(source_entry, destination_directory)
+        else:
+            shutil.move(source_entry, destination_directory)
+    if remove_source_dir:
+        os.rmdir(source_directory)
+
+# Get input data.
+# TODO: do not hard-code 'stanford_hardi'
+input_dir = 'stanford_hardi'
+os.mkdir(input_dir)
+for f in os.listdir(args.input_extra_files_path):
+    shutil.copy(os.path.join(args.input_extra_files_path, f), input_dir)
+hard_img, gtab = read_stanford_hardi()
 data = hardi_img.get_data()
+labels_file = os.path.join(input_dir, "aparc-reduced.nii.gz")
+labels_img = nib.load(labels_file)
 labels = labels_img.get_data()
 
-# For possible later use: if args.drmi_dataset == 'stanford_t1':
-#fetch_stanford_t1()
-#t1 = read_stanford_t1()
-#t1_data = t1.get_data()
 white_matter = (labels == 1) | (labels == 2)
 csamodel = shm.CsaOdfModel(gtab, 6)
 csapeaks = peaks.peaks_from_model(model=csamodel, data=data, sphere=peaks.default_sphere, relative_peak_threshold=.8, min_separation_angle=45, mask=white_matter)
@@ -40,35 +60,7 @@
 cc_slice = labels == 2
 cc_streamlines = utils.target(streamlines, cc_slice, affine=affine)
 cc_streamlines = list(cc_streamlines)
-#other_streamlines = utils.target(streamlines, cc_slice, affine=affine, include=False)
-#other_streamlines = list(other_streamlines)
-#assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
-## Make display objects
-#color = line_colors(cc_streamlines)
-#cc_streamlines_actor = fvtk.line(cc_streamlines, line_colors(cc_streamlines))
-#cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)], opacities=[1.])
-#vol_actor = fvtk.slicer(t1_data)
-#vol_actor.display(40, None, None)
-#vol_actor2 = vol_actor.copy()
-#vol_actor2.display(None, None, 35)
-## Add display objects to canvas
-#r = fvtk.ren()
-#fvtk.add(r, vol_actor)
-#fvtk.add(r, vol_actor2)
-#fvtk.add(r, cc_streamlines_actor)
-#fvtk.add(r, cc_ROI_actor)
-## Save figures
-#fvtk.record(r, n_frames=1, out_path="corpuscallosum_axial.png", size=(800, 800))
-#shutil.move("corpuscallosum_axial.png", args.output_corpuscallosum_axial)
-#fvtk.camera(r, [-1, 0, 0], [0, 0, 0], viewup=[0, 0, 1])
-#fvtk.record(r, n_frames=1, out_path="corpuscallosum_sagittal.png", size=(800, 800))
-#shutil.move("corpuscallosum_sagittal.png", args.output_corpuscallosum_sagittal)
-M, grouping = utils.connectivity_matrix(cc_streamlines, labels, affine=affine, return_mapping=True, mapping_as_streamlines=True)
-#M[:3, :] = 0
-#M[:, :3] = 0
-#plt.imshow(np.log1p(M), interpolation='nearest')
-#plt.savefig("connectivity.png")
-#shutil.move("connectivity.png", args.output_connectivity)
+
 lr_superiorfrontal_track = grouping[11, 54]
 shape = labels.shape
 dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine)
@@ -76,6 +68,8 @@
 dm_img = nib.Nifti1Image(dm.astype("int16"), hardi_img.affine)
 dm_img.to_filename("lr-superiorfrontal-dm.nii")
 shutil.move('lr-superiorfrontal-dm.nii', args.output_superiorfrontal_nifti)
+move_directory_files(input_dir, args.output_superiorfrontal_nifti_files_path)
+
 # Make a trackvis header so we can save streamlines
 voxel_size = labels_img.header.get_zooms()
 trackvis_header = nib.trackvis.empty_header()