How to use the dipy.viz.actor.streamtube function in dipy

def test_button_and_slider_widgets():
    recording = False
    filename = "test_button_and_slider_widgets.log.gz"
    recording_filename = pjoin(DATA_DIR, filename)
    renderer = window.Renderer()

    # create some minimalistic streamlines
    lines = [np.array([[-1, 0, 0.], [1, 0, 0.]]),
             np.array([[-1, 1, 0.], [1, 1, 0.]])]
    colors = np.array([[1., 0., 0.], [0.3, 0.7, 0.]])
    stream_actor = actor.streamtube(lines, colors)

    states = {'camera_button_count': 0,
              'plus_button_count': 0,
              'minus_button_count': 0,
              'slider_moved_count': 0,
              }

    renderer.add(stream_actor)

    # the show manager allows to break the rendering process
    # in steps so that the widgets can be added properly
    show_manager = window.ShowManager(renderer, size=(800, 800))

    if recording:
        show_manager.initialize()
        show_manager.render()

# create streamtubes of the same lines and shift them a bit
    c2 = actor.streamtube(lines, colors, linewidth=.1)
    c2.SetPosition(2, 0, 0)
    window.add(renderer, c2)

    arr = window.snapshot(renderer)

    report = window.analyze_snapshot(arr,
                                     colors=[(255, 0, 0), (0, 0, 255)],
                                     find_objects=True)

    npt.assert_equal(report.objects, 4)
    npt.assert_equal(report.colors_found, [True, True])

    # as before with splines
    c2 = actor.streamtube(lines, colors, spline_subdiv=5, linewidth=.1)
    c2.SetPosition(2, 0, 0)
    window.add(renderer, c2)

    arr = window.snapshot(renderer)

    report = window.analyze_snapshot(arr,
                                     colors=[(255, 0, 0), (0, 0, 255)],
                                     find_objects=True)

    npt.assert_equal(report.objects, 4)
    npt.assert_equal(report.colors_found, [True, True])

t1 = read_stanford_t1()
t1_data = t1.get_data()
t1_aff = t1.affine
color = colormap.line_colors(streamlines)

"""
To speed up visualization, we will select a random sub-set of streamlines to
display. This is particularly important, if you track from seeds throughout the
entire white matter, generating many streamlines. In this case, for
demonstration purposes, we subselect 900 streamlines.
"""

plot_streamlines = select_random_set_of_streamlines(streamlines, 900)

if has_fury:
    streamlines_actor = actor.streamtube(
        list(transform_streamlines(plot_streamlines, inv(t1_aff))),
        colormap.line_colors(streamlines), linewidth=0.1)

    vol_actor = actor.slicer(t1_data)

    vol_actor.display(40, None, None)
    vol_actor2 = vol_actor.copy()
    vol_actor2.display(None, None, 35)

    ren = window.Renderer()
    ren.add(streamlines_actor)
    ren.add(vol_actor)
    ren.add(vol_actor2)

    window.record(ren, out_path='tractogram_sfm.png', size=(800, 800))
    if interactive:

print(' Number of centroids is {}'.format(len(centroids)))
                sizes = np.array([len(c) for c in clusters])
                linewidths = np.interp(sizes,
                                       [sizes.min(), sizes.max()], [0.1, 2.])
                centroid_lengths = np.array([length(c) for c in centroids])

                print(' Minimum number of streamlines in cluster {}'
                      .format(sizes.min()))

                print(' Maximum number of streamlines in cluster {}'
                      .format(sizes.max()))

                print(' Construct cluster actors')
                for (i, c) in enumerate(centroids):

                    centroid_actor = actor.streamtube([c], colors,
                                                      linewidth=linewidths[i],
                                                      lod=False)
                    scene.add(centroid_actor)
                    self.mem.centroid_actors.append(centroid_actor)

                    cluster_actor = actor.line(clusters[i],
                                               lod=False)
                    cluster_actor.GetProperty().SetRenderLinesAsTubes(1)
                    cluster_actor.GetProperty().SetLineWidth(6)
                    cluster_actor.GetProperty().SetOpacity(1)
                    cluster_actor.VisibilityOff()

                    scene.add(cluster_actor)
                    self.mem.cluster_actors.append(cluster_actor)

                    # Every centroid actor (cea) is paired to a cluster actor

Both, ``set_number_of_points`` and ``approx_polygon_track`` can be thought as 
methods for lossy compression of streamlines.
"""

from dipy.viz import window, actor

# Enables/disables interactive visualization
interactive = False

ren = window.Renderer()
ren.SetBackground(*window.colors.white)
bundle_actor = actor.streamtube(bundle, window.colors.red, linewidth=0.3)

ren.add(bundle_actor)

bundle_actor2 = actor.streamtube(bundle_downsampled, window.colors.red, linewidth=0.3)
bundle_actor2.SetPosition(0, 40, 0)

bundle_actor3 = actor.streamtube(bundle_downsampled2, window.colors.red, linewidth=0.3)
bundle_actor3.SetPosition(0, 80, 0)

ren.add(bundle_actor2)
ren.add(bundle_actor3)

ren.set_camera(position=(0, 0, 0), focal_point=(30, 0, 0))
window.record(ren, out_path='simulated_cosine_bundle.png', size=(900, 900))
if interactive:
    window.show(ren)

"""
.. figure:: simulated_cosine_bundle.png
   :align: center

window.show(ren)

"""
.. figure:: fornix_initial.png
   :align: center

   Initial Fornix dataset.

Show the centroids of the fornix after clustering (with random colors):
"""

colormap = actor.create_colormap(np.arange(len(clusters)))

window.clear(ren)
ren.SetBackground(1, 1, 1)
ren.add(actor.streamtube(streamlines, window.colors.white, opacity=0.05))
ren.add(actor.streamtube(clusters.centroids, colormap, linewidth=0.4))
window.record(ren, out_path='fornix_centroids.png', size=(600, 600))
if interactive:
    window.show(ren)

"""
.. figure:: fornix_centroids.png
   :align: center

   Showing the different QuickBundles centroids with random colors.

Show the labeled fornix (colors from centroids).
"""

colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):

feature = VectorOfEndpointsFeature()
metric = CosineMetric(feature)
qb = QuickBundles(threshold=0.1, metric=metric)
clusters = qb.cluster(streamlines)

# Color each streamline according to the cluster they belong to.
colormap = actor.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
    colormap_full[cluster.indices] = color

# Visualization
ren = window.Renderer()
window.clear(ren)
ren.SetBackground(0, 0, 0)
ren.add(actor.streamtube(streamlines, colormap_full))
window.record(ren, out_path='cosine_metric.png', size=(600, 600))
if interactive:
    window.show(ren)

"""
.. figure:: cosine_metric.png

feature = VectorOfEndpointsFeature()
metric = CosineMetric(feature)
qb = QuickBundles(threshold=0.1, metric=metric)
clusters = qb.cluster(streamlines)

# Color each streamline according to the cluster they belong to.
colormap = actor.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
    colormap_full[cluster.indices] = color

# Visualization
ren = window.Renderer()
window.clear(ren)
ren.SetBackground(0, 0, 0)
ren.add(actor.streamtube(streamlines, colormap_full))
window.record(ren, out_path='vector_of_endpoints_feature.png', size=(600, 600))
if interactive:
    window.show(ren)

"""
.. figure:: vector_of_endpoints_feature.png

"""
.. figure:: fornix_initial.png
   :align: center

   Initial Fornix dataset.

Show the centroids of the fornix after clustering (with random colors):
"""

colormap = actor.create_colormap(np.arange(len(clusters)))

window.clear(ren)
ren.SetBackground(1, 1, 1)
ren.add(actor.streamtube(streamlines, window.colors.white, opacity=0.05))
ren.add(actor.streamtube(clusters.centroids, colormap, linewidth=0.4))
window.record(ren, out_path='fornix_centroids.png', size=(600, 600))
if interactive:
    window.show(ren)

"""
.. figure:: fornix_centroids.png
   :align: center

   Showing the different QuickBundles centroids with random colors.

Show the labeled fornix (colors from centroids).
"""

colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
    colormap_full[cluster.indices] = color

window.show(ren)

"""
.. figure:: fornix_initial.png
   :align: center

   Initial Fornix dataset.

Show the centroids of the fornix after clustering (with random colors):
"""

colormap = actor.create_colormap(np.arange(len(clusters)))

window.clear(ren)
ren.SetBackground(1, 1, 1)
ren.add(actor.streamtube(streamlines, window.colors.white, opacity=0.05))
ren.add(actor.streamtube(clusters.centroids, colormap, linewidth=0.4))
window.record(ren, out_path='fornix_centroids.png', size=(600, 600))
if interactive:
    window.show(ren)

"""
.. figure:: fornix_centroids.png
   :align: center

   Showing the different QuickBundles centroids with random colors.

Show the labeled fornix (colors from centroids).
"""

colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):