27d10570af678e1059920c4ae895860dda11433c,doc/examples/tracking_quick_start.py,,,#,33

Before Change

slice_no = data.shape[2] // 2

fvtk.add(ren, fvtk.peaks(csd_peaks.peak_dirs[:, :, slice_no:slice_no + 1],
                         stopping_values[:, :, slice_no:slice_no + 1]))

print("Saving illustration as csd_direction_field.png")
fvtk.record(ren, out_path="csd_direction_field.png", size=(900, 900))

After Change

which we will use as the basis to perform the tracking.


ren = window.Renderer()
ren.add(actor.peak_slicer(csd_peaks.peak_dirs, csd_peaks.peak_values))

if interactive:
    window.show(ren, size=(900, 900))
else:
    window.record(ren, out_path="csd_direction_field.png", size=(900, 900))


.. figure:: csd_direction_field.png
 :align: center

 **Direction Field (peaks)**

``EuDX`` [Garyfallidis12]_ is a fast algorithm that we use here to generate
streamlines. This algorithm is what is used here and the default option
when providing the output of peaks directly in LocalTracking.


streamline_generator = LocalTracking(csd_peaks, tissue_classifier,
                                     seeds, affine=np.eye(4),
                                     step_size=0.5)

from dipy.tracking.streamline import Streamlines

streamlines = Streamlines(streamline_generator)


We can visualize the streamlines using ``actor.line`` or ``actor.streamtube``.


ren.clear()
ren.add(actor.line(streamlines))

if interactive:
    window.show(ren, size=(900, 900))
else:
    print("Saving illustration as det_streamlines.png")
    window.record(ren, out_path="det_streamlines.png", size=(900, 900))


.. figure:: det_streamlines.png
 :align: center

Instances