--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/create_streamlines.py	Tue Nov 28 14:15:52 2017 -0500
@@ -0,0 +1,97 @@
+#!/usr/bin/env python
+import argparse
+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
+from dipy.viz import fvtk
+from dipy.viz.colormap import line_colors
+
+import matplotlib.pyplot as plt
+
+import nibabel as nib
+
+import numpy as np
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--drmi_dataset', dest='drmi_dataset', help='Input dataset')
+parser.add_argument('--output_corpuscallosum_axial', dest='output_corpuscallosum_axial', help='Output corpuscallosum axial dataset')
+parser.add_argument('--output_corpuscallosum_sagittal', dest='output_corpuscallosum_sagittal', help='Output corpuscallosum sagittal dataset')
+parser.add_argument('--output_connectivity', dest='output_connectivity', help='Output connectivity dataset')
+parser.add_argument('--output_superiorfrontal_nifti', dest='output_superiorfrontal_nifti', help='Output superiorfrontal nifti1 dataset')
+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()
+data = hardi_img.get_data()
+labels = labels_img.get_data()
+
+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)
+seeds = utils.seeds_from_mask(white_matter, density=2)
+streamline_generator = EuDX(csapeaks.peak_values, csapeaks.peak_indices, odf_vertices=peaks.default_sphere.vertices, a_low=.05, step_sz=.5, seeds=seeds)
+affine = streamline_generator.affine
+streamlines = list(streamline_generator)
+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=args.output_corpuscallosum_axial, size=(800, 800))
+fvtk.camera(r, [-1, 0, 0], [0, 0, 0], viewup=[0, 0, 1])
+fvtk.record(r, n_frames=1, out_path=args.output_corpuscallosum_sagittal, size=(800, 800))
+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(args.output_connectivity)
+lr_superiorfrontal_track = grouping[11, 54]
+shape = labels.shape
+dm = utils.density_map(lr_superiorfrontal_track, shape, affine=affine)
+# Save density map
+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)
+
+# Make a trackvis header so we can save streamlines
+voxel_size = labels_img.header.get_zooms()
+trackvis_header = nib.trackvis.empty_header()
+trackvis_header['voxel_size'] = voxel_size
+trackvis_header['dim'] = shape
+trackvis_header['voxel_order'] = "RAS"
+
+# Move streamlines to "trackvis space"
+trackvis_point_space = utils.affine_for_trackvis(voxel_size)
+lr_sf_trk = utils.move_streamlines(lr_superiorfrontal_track, trackvis_point_space, input_space=affine)
+lr_sf_trk = list(lr_sf_trk)
+
+# Save streamlines
+for_save = [(sl, None, None) for sl in lr_sf_trk]
+nib.trackvis.write("lr-superiorfrontal.trk", for_save, trackvis_header)
+shutil.move('lr-superiorfrontal.trk', args.output_trackvis_header)
+dm_trackvis = utils.density_map(lr_sf_trk, shape, affine=trackvis_point_space)
+assert np.all(dm == dm_trackvis)

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/create_streamlines.xml	Tue Nov 28 14:15:52 2017 -0500
@@ -0,0 +1,46 @@
+<tool id="create_streamlines" name="Create streamlines" version="0.13.0">
+    <description>within brain images</description>
+    <requirements>
+        <requirement type="package" version="0.13.0">dipy</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+python '$__tool_directory__/linear_fascile_evaluation.py'
+--candidates '$candidates'
+--output_trackvis_header '$output_trackvis_header'
+--output_superiorfrontal_nifti '$output_superiorfrontal_nifti'
+--output_connectivity '$output_connectivity'
+--output_corpuscallosum_sagittal '$output_corpuscallosum_sagittal'
+--output_corpuscallosum_axial 'output_corpuscallosum_axial'
+    ]]></command>
+    <inputs>
+        <param name="drmi_dataset" type="select" label="dRMI dataset">
+            <option value="stanford_t1" selected="true">stanford_t1</option>
+        </param>
+    </inputs>
+    <outputs>
+        <data name="output_trackvis_header" format="txt" label="${tool.name}: Superiorfrontal Track Visualization Header" />
+        <data name="output_superiorfrontal_nifti" format="nifti1" label="${tool.name}: Superiorfrontal" />
+        <data name="output_connectivity" format="png" label="${tool.name}: Connectivity" />
+        <data name="output_corpuscallosum_sagittal" format="png" label="${tool.name}: Corpuscallosum Sagittal" />
+        <data name="output_corpuscallosum_axial" format="png" label="${tool.name}: Corpuscallosum Axial" />
+    </outputs>
+    <tests>
+        <test>
+        </test>
+    </tests>
+    <help>
+**What it does**
+
+Uses the target function to target streamlines that either pass through or do
+not pass through some region of the brain, the connectivity_matrix groups function
+to count streamlines based on where in the brain they begin and end and the density
+map function to count the number of streamlines that pass though every voxel of some image.
+
+-----
+
+**Options**
+
+    </help>
+    <citations>
+    </citations>
+</tool>