def visualize_bundles(sft,
affine=None,
n_points=None,
bundle_dict=None,
bundle=None,
colors=None,
color_by_volume=None,
cbv_lims=[None, None],
figure=None,
background=(1, 1, 1),
interact=False,
inline=False,
flip_axes=None):
"""
Visualize bundles in 3D using VTK
Parameters
----------
sft : Stateful Tractogram, str
A Stateful Tractogram containing streamline information
or a path to a trk file
In order to visualize individual bundles, the Stateful Tractogram
must contain a bundle key in it's data_per_streamline which is a list
of bundle `'uid'`.
affine : ndarray, optional
An affine transformation to apply to the streamlines before
visualization. Default: no transform.
n_points : int or None
n_points to resample streamlines to before plotting. If None, no
resampling is done.
bundle_dict : dict, optional
Keys are names of bundles and values are dicts that should include
a key `'uid'` with values as integers for selection from the sft
metadata. Default: bundles are either not identified, or identified
only as unique integers in the metadata.
bundle : str or int, optional
The name of a bundle to select from among the keys in `bundle_dict`
or an integer for selection from the sft metadata.
colors : dict or list
If this is a dict, keys are bundle names and values are RGB tuples.
If this is a list, each item is an RGB tuple. Defaults to a list
with Tableau 20 RGB values if bundle_dict is None, or dict from
bundles to Tableau 20 RGB values if bundle_dict is not None.
color_by_volume : ndarray or str, optional
3d volume use to shade the bundles. If None, no shading
is performed. Only works when using the plotly backend.
Default: None
cbv_lims : ndarray
Of the form (lower bound, upper bound). Shading based on
color_by_volume will only differentiate values within these bounds.
If lower bound is None, will default to 0.
If upper bound is None, will default to the maximum value in
color_by_volume.
Default: [None, None]
background : tuple, optional
RGB values for the background. Default: (1, 1, 1), which is white
background.
figure : fury Scene object, optional
If provided, the visualization will be added to this Scene. Default:
Initialize a new Scene.
interact : bool
Whether to provide an interactive VTK window for interaction.
Default: False
inline : bool
Whether to embed the visualization inline in a notebook. Only works
in the notebook context. Default: False.
flip_axes : None
This parameter is to conform fury and plotly APIs.
Returns
-------
Fury Scene object
"""
if figure is None:
figure = window.Scene()
figure.SetBackground(background[0], background[1], background[2])
for (sls, color, name, _) in vut.tract_generator(sft, affine, bundle,
bundle_dict, colors,
n_points):
sls = list(sls)
if name == "all_bundles":
color = line_colors(sls)
sl_actor = actor.line(sls, color)
figure.add(sl_actor)
sl_actor.GetProperty().SetRenderLinesAsTubes(1)
sl_actor.GetProperty().SetLineWidth(6)
return _inline_interact(figure, inline, interact)
def visualize_bundles(sft,
affine=None,
n_points=None,
bundle_dict=None,
bundle=None,
colors=None,
color_by_volume=None,
cbv_lims=[None, None],
flip_axes=[False, False, False],
figure=None,
background=(1, 1, 1),
interact=False,
inline=False):
"""
Visualize bundles in 3D
Parameters
----------
sft : Stateful Tractogram, str
A Stateful Tractogram containing streamline information
or a path to a trk file.
In order to visualize individual bundles, the Stateful Tractogram
must contain a bundle key in it's data_per_streamline which is a list
of bundle `'uid'`.
affine : ndarray, optional
An affine transformation to apply to the streamlines before
visualization. Default: no transform.
n_points : int or None
n_points to resample streamlines to before plotting. If None, no
resampling is done.
bundle_dict : dict, optional
Keys are names of bundles and values are dicts that should include
a key `'uid'` with values as integers for selection from the sft
metadata. Default: bundles are either not identified, or identified
only as unique integers in the metadata.
bundle : str or int, optional
The name of a bundle to select from among the keys in `bundle_dict`
or an integer for selection from the sft metadata.
colors : dict or list
If this is a dict, keys are bundle names and values are RGB tuples.
If this is a list, each item is an RGB tuple. Defaults to a list
with Tableau 20 RGB values if bundle_dict is None, or dict from
bundles to Tableau 20 RGB values if bundle_dict is not None.
color_by_volume : ndarray or str, optional
3d volume use to shade the bundles. If None, no shading
is performed. Only works when using the plotly backend.
Default: None
cbv_lims : ndarray
Of the form (lower bound, upper bound). Shading based on
color_by_volume will only differentiate values within these bounds.
If lower bound is None, will default to 0.
If upper bound is None, will default to the maximum value in
color_by_volume.
Default: [None, None]
flip_axes : ndarray
Which axes to flip, to orient the image as RAS, which is how we
visualize.
For example, if the input image is LAS, use [True, False, False].
Default: [False, False, False]
background : tuple, optional
RGB values for the background. Default: (1, 1, 1), which is white
background.
figure : Plotly Figure object, optional
If provided, the visualization will be added to this Figure. Default:
Initialize a new Figure.
interact : bool
Whether to open the visualization in an interactive window.
Default: False
inline : bool
Whether to embed the interactivevisualization inline in a notebook.
Only works in the notebook context. Default: False.
Returns
-------
Plotly Figure object
"""
if color_by_volume is not None:
color_by_volume = vut.load_volume(color_by_volume)
if figure is None:
figure = go.Figure()
set_layout(figure, color=_color_arr2str(background))
for (sls, color, name,
dimensions) in vut.tract_generator(sft, affine, bundle, bundle_dict,
colors, n_points):
_draw_streamlines(figure,
sls,
dimensions,
color,
name,
cbv=color_by_volume,
cbv_lims=cbv_lims,
flip_axes=flip_axes)
figure.update_layout(legend=dict(itemsizing="constant"))
return _inline_interact(figure, interact, inline)