`viz`#

Module: `viz.gmem`#

GlobalHorizon()

Module: `viz.horizon`#

Module: `viz.horizon.app`#

`Horizon`([tractograms, images, pams, ...])
`horizon`([tractograms, images, pams, ...])	Interactive medical visualization - Invert the Horizon!

Module: `viz.horizon.tab`#

Module: `viz.horizon.tab.base`#

`HorizonUIElement`(visibility, selected_value, obj)	Dataclass to define properties of horizon ui elements.
`HorizonTab`()	Base for different tabs available in horizon.
`TabManager`(tabs, win_size[, on_tab_changed, ...])	A Manager for tabs of the table panel.
`build_label`(text[, font_size, bold, ...])	Simple utility function to build labels.
`build_slider`(initial_value, max_value[, ...])	Create a horizon theme based disk-knob slider.
`build_checkbox`([labels, checked_labels, ...])	Create horizon theme checkboxes.
`build_switcher`([items, label, ...])	Create horizon theme switcher.
`color_single_slider`(slider)
`color_double_slider`(slider)

Module: `viz.horizon.tab.cluster`#

ClustersTab(clusters_visualizer, threshold)

Module: `viz.horizon.tab.peak`#

PeaksTab(peak_actor)

Module: `viz.horizon.tab.roi`#

ROIsTab(contour_actors)

Module: `viz.horizon.tab.slice`#

SlicesTab(slices_visualizer, tab_name, file_name)

Interaction tab for slice visualization.

Module: `viz.horizon.tab.surface`#

SurfaceTab(visualizer, tab_name, file_name)

Module: `viz.horizon.util`#

`check_img_shapes`(images)	Check if the images have same shapes.
`check_img_dtype`(images)	Check supplied image dtype.
`show_ellipsis`(text, text_size, available_size)	Apply ellipsis to the text.
`unpack_image`(img)	Unpack provided image data.
`unpack_surface`(surface)	Unpack surface data.
`is_binary_image`(data[, unique_points])	Check if an image is binary image.
`check_peak_size`(pams[, ref_img_shape, sync_imgs])	Check shape of peaks.

Module: `viz.horizon.visualizer`#

Module: `viz.horizon.visualizer.cluster`#

ClustersVisualizer(show_manager, scene, ...)

Module: `viz.horizon.visualizer.peak`#

`PeakActor`(directions, indices[, values, ...])	FURY actor for visualizing DWI peaks.
`PeaksVisualizer`(pam, world_coords)
`peak`(peaks_dirs[, peaks_values, mask, ...])	Visualize peak directions as given from `peaks_from_model`.

Module: `viz.horizon.visualizer.slice`#

SlicesVisualizer(interactor, scene, data[, ...])

Module: `viz.horizon.visualizer.surface`#

SurfaceVisualizer(surface, scene, color)

Module: `viz.panel`#

`build_label`(text[, font_size, bold])	Simple utility function to build labels
`slicer_panel`(scene, iren[, data, affine, ...])	Slicer panel with slicer included

Module: `viz.plotting`#

plotting functions

`compare_maps`(fits, maps[, transpose, ...])	Compare one or more scalar maps for different fits or models.
`compare_qti_maps`(gt, fit1, fit2, mask[, ...])	Compare one or more qti derived maps obtained with different fitting routines.
`bundle_shape_profile`(x, shape_profile, std)	Plot bundlewarp bundle shape profile.

Module: `viz.projections`#

Visualization tools for 2D projections of 3D functions on the sphere, such as ODFs.

sph_project(vertices, val[, ax, vmin, vmax, ...])

Draw a signal on a 2D projection of the sphere.

Module: `viz.regtools`#

`simple_plot`(file_name, title, x, y, xlabel, ...)	Saves the simple plot with given x and y values
`overlay_images`(img0, img1[, title0, ...])	Plot two images one on top of the other using red and green channels.
`draw_lattice_2d`(nrows, ncols, delta)	Create a regular lattice of nrows x ncols squares.
`plot_2d_diffeomorphic_map`(mapping[, delta, ...])	Draw the effect of warping a regular lattice by a diffeomorphic map.
`plot_slices`(V[, slice_indices, fname])	Plot 3 slices from the given volume: 1 sagittal, 1 coronal and 1 axial
`overlay_slices`(L, R[, slice_index, ...])	Plot three overlaid slices from the given volumes.

Module: `viz.streamline`#

`show_bundles`(bundles[, interactive, view, ...])	Render bundles to visualize them interactively or save them into a png.
`viz_two_bundles`(b1, b2, fname[, c1, c2, ...])	Render and plot two bundles to visualize them.
`viz_vector_field`(points_aligned, directions, ...)	Render and plot vector field.
`viz_displacement_mag`(bundle, offsets, fname)	Render and plot displacement magnitude over the bundle.

`GlobalHorizon`#

class dipy.viz.gmem.GlobalHorizon#

Bases: object

__init__()#

class dipy.viz.horizon.app.Horizon(tractograms=None, images=None, pams=None, surfaces=None, cluster=False, rgb=False, cluster_thr=15.0, random_colors=None, length_gt=0, length_lt=1000, clusters_gt=0, clusters_lt=10000, world_coords=True, interactive=True, out_png='tmp.png', recorded_events=None, return_showm=False, bg_color=(0, 0, 0), order_transparent=True, buan=False, buan_colors=None, roi_images=False, roi_colors=(1, 0, 0), surface_colors=[(1, 0, 0)])#

Bases: object

__init__(tractograms=None, images=None, pams=None, surfaces=None, cluster=False, rgb=False, cluster_thr=15.0, random_colors=None, length_gt=0, length_lt=1000, clusters_gt=0, clusters_lt=10000, world_coords=True, interactive=True, out_png='tmp.png', recorded_events=None, return_showm=False, bg_color=(0, 0, 0), order_transparent=True, buan=False, buan_colors=None, roi_images=False, roi_colors=(1, 0, 0), surface_colors=[(1, 0, 0)])#

Interactive medical visualization - Invert the Horizon!

Parameters#

tractogramssequence of StatefulTractograms: StatefulTractograms are used for making sure that the coordinate systems are correct
imagessequence of tuples: Each tuple contains data and affine
pamssequence of PeakAndMetrics: Contains peak directions and spherical harmonic coefficients
surfacessequence of tuples: Each tuple contains vertices and faces
clusterbool: Enable QuickBundlesX clustering
rgbbool, optional: Enable the color image (rgb only, alpha channel will be ignored).
cluster_thrfloat: Distance threshold used for clustering. Default value 15.0 for small animal data you may need to use something smaller such as 2.0. The threshold is in mm. For this parameter to be active cluster should be enabled.
random_colorsstring, optional: Given multiple tractograms and/or ROIs then each tractogram and/or ROI will be shown with a different color. If no value is provided, both the tractograms and the ROIs will have a different random color generated from a distinguishable colormap. If the effect should only be applied to one of the 2 types, then use the options ‘tracts’ and ‘rois’ for the tractograms and the ROIs respectively.
length_gtfloat: Clusters with average length greater than length_gt amount in mm will be shown.
length_ltfloat: Clusters with average length less than length_lt amount in mm will be shown.
clusters_gtint: Clusters with size greater than clusters_gt will be shown.
clusters_ltint: Clusters with size less than clusters_lt will be shown.
world_coordsbool: Show data in their world coordinates (not native voxel coordinates) Default True.
interactivebool: Allow user interaction. If False then Horizon goes on stealth mode and just saves pictures.
out_pngstring: Filename of saved picture.
recorded_eventsstring: File path to replay recorded events
return_showmbool: Return ShowManager object. Used only at Python level. Can be used for extending Horizon’s cababilities externally and for testing purposes.
bg_colorndarray or list or tuple: Define the background color of the scene. Default is black (0, 0, 0)
order_transparentbool: Default True. Use depth peeling to sort transparent objects. If True also enables anti-aliasing.
buanbool, optional: Enables BUAN framework visualization. Default is False.
buan_colorslist, optional: List of colors for bundles.
roi_imagesbool, optional: Displays binary images as contours. Default is False.
roi_colorsndarray or list or tuple, optional: Define the colors of the roi images. Default is red (1, 0, 0)

References#

[Horizon_ISMRM19]

Garyfallidis E., M-A. Cote, B.Q. Chandio, S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja, S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and adaptive visualization, Proceedings of: International Society of Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.

build_scene()#

build_show(scene)#

horizon#

dipy.viz.horizon.app.horizon(tractograms=None, images=None, pams=None, surfaces=None, cluster=False, rgb=False, cluster_thr=15.0, random_colors=None, bg_color=(0, 0, 0), order_transparent=True, length_gt=0, length_lt=1000, clusters_gt=0, clusters_lt=10000, world_coords=True, interactive=True, buan=False, buan_colors=None, roi_images=False, roi_colors=(1, 0, 0), out_png='tmp.png', recorded_events=None, return_showm=False)#

Interactive medical visualization - Invert the Horizon!

Parameters#

tractogramssequence of StatefulTractograms: StatefulTractograms are used for making sure that the coordinate systems are correct
imagessequence of tuples: Each tuple contains data and affine
pamssequence of PeakAndMetrics: Contains peak directions and spherical harmonic coefficients
surfacessequence of tuples: Each tuple contains vertices and faces
clusterbool: Enable QuickBundlesX clustering
rgb: bool, optional: Enable the color image.
cluster_thrfloat: Distance threshold used for clustering. Default value 15.0 for small animal data you may need to use something smaller such as 2.0. The threshold is in mm. For this parameter to be active cluster should be enabled.
random_colorsstring: Given multiple tractograms and/or ROIs then each tractogram and/or ROI will be shown with different color. If no value is provided both the tractograms and the ROIs will have a different random color generated from a distinguishable colormap. If the effect should only be applied to one of the 2 objects, then use the options ‘tracts’ and ‘rois’ for the tractograms and the ROIs respectively.
bg_colorndarray or list or tuple: Define the background color of the scene. Default is black (0, 0, 0)
order_transparentbool: Default True. Use depth peeling to sort transparent objects. If True also enables anti-aliasing.
length_gtfloat: Clusters with average length greater than length_gt amount in mm will be shown.
length_ltfloat: Clusters with average length less than length_lt amount in mm will be shown.
clusters_gtint: Clusters with size greater than clusters_gt will be shown.
clusters_ltint: Clusters with size less than clusters_lt will be shown.
world_coordsbool: Show data in their world coordinates (not native voxel coordinates) Default True.
interactivebool: Allow user interaction. If False then Horizon goes on stealth mode and just saves pictures.
buanbool, optional: Enables BUAN framework visualization. Default is False.
buan_colorslist, optional: List of colors for bundles.
roi_imagesbool, optional: Displays binary images as contours. Default is False.
roi_colorsndarray or list or tuple, optional: Define the color of the roi images. Default is red (1, 0, 0)
out_pngstring: Filename of saved picture.
recorded_eventsstring: File path to replay recorded events
return_showmbool: Return ShowManager object. Used only at Python level. Can be used for extending Horizon’s cababilities externally and for testing purposes.

References#

[Horizon_ISMRM19]

`HorizonUIElement`#

class dipy.viz.horizon.tab.base.HorizonUIElement(visibility: bool, selected_value: Any, obj: Any)#

Bases: object

Dataclass to define properties of horizon ui elements.

__init__(visibility: bool, selected_value: Any, obj: Any) → None#

obj: Any#

position = (0, 0)#

selected_value: Any#

size = ('auto', 'auto')#

visibility: bool#

`HorizonTab`#

class dipy.viz.horizon.tab.base.HorizonTab#

Bases: ABC

Base for different tabs available in horizon.

__init__()#

abstract build(tab_id, tab_ui)#

Build all the elements under the tab.

Parameters#

tab_idint: Id of the tab.
tab_uiTabUI: FURY TabUI object for tabs panel.

Notes#

tab_ui will removed once every all tabs adapt new build architecture.

property elements#: list of underlying FURY ui elements in the tab.

abstract property name#: Name of the tab.

on_tab_selected()#: Implement if require to update something while the tab becomes active.

register_elements(*args)#

Parameters#

*argsHorizonUIElement(s): Elements to be register for rendering.

`TabManager`#

class dipy.viz.horizon.tab.base.TabManager(tabs, win_size, on_tab_changed=<function TabManager.<lambda>>, sync_slices=False, sync_volumes=False, sync_peaks=False)#

Bases: object

A Manager for tabs of the table panel.

Attributes#

tab_uiTabUI: Underlying FURY TabUI object.

__init__(tabs, win_size, on_tab_changed=<function TabManager.<lambda>>, sync_slices=False, sync_volumes=False, sync_peaks=False)#

handle_text_overflows()#

reposition(win_size)#

Reposition the tabs panel.

Parameters#

win_size(float, float): size of the horizon window.

synchronize_slices(active_tab_id, x_value, y_value, z_value)#

Synchronize slicers for all the images and peaks.

Parameters#

active_tab_id: int: tab_id of the action performing tab
x_value: float: x-value of the active slicer
y_value: float: y-value of the active slicer
z_value: float: z-value of the active slicer

synchronize_volumes(active_tab_id, value)#

Synchronize volumes for all the images with volumes.

Parameters#

active_tab_idint: tab_id of the action performing tab
valuefloat: volume value of the active volume slider

property tab_ui#: FURY TabUI object.

build_label#

dipy.viz.horizon.tab.base.build_label(text, font_size=16, bold=False, is_horizon_label=False)#: Simple utility function to build labels.

Parameters#

text : str font_size : int, optional bold : bool, optional

Returns#

label : TextBlock2D

build_slider#

dipy.viz.horizon.tab.base.build_slider(initial_value, max_value, min_value=0, length=450, line_width=3, radius=8, font_size=16, text_template='{value:.1f} ({ratio:.0%})', on_moving_slider=<function <lambda>>, on_value_changed=<function <lambda>>, on_change=<function <lambda>>, label='', label_font_size=16, label_style_bold=False, is_double_slider=False)#

Create a horizon theme based disk-knob slider.

Parameters#

initial_valuefloat, (float, float): Initial value(s) of the slider.
max_valuefloat: Maximum value of the slider.
min_valuefloat, optional: Minimum value of the slider.
lengthint, optional: Length of the slider.
line_widthint, optional: Width of the line on which the disk will slide.
radiusint, optional: Radius of the disk handle.
font_sizeint, optional: Size of the text to display alongside the slider (pt).
text_templatestr, callable, optional: If str, text template can contain one or multiple of the replacement fields: {value:}, {ratio:}. If callable, this instance of :class:LineSlider2D will be passed as argument to the text template function.
on_moving_slidercallable, optional: When the slider is interacted by the user.
on_value_changedcallable, optional: When value of the slider changed programmatically.
on_changecallable, optional: When value of the slider changed.
labelstr, optional: Label to ui element for slider
label_font_sizeint, optional: Size of label text to display with slider
label_style_boldbool, optional: Is label should have bold style.

Return#

(label: HorizonUIElement, element(slider): HorizonUIElement)

build_checkbox#

dipy.viz.horizon.tab.base.build_checkbox(labels=None, checked_labels=None, padding=1, font_size=16, on_change=<function <lambda>>)#

Create horizon theme checkboxes.

Parameters#

labelslist(str), optional: List of labels of each option.
checked_labels: list(str), optional: List of labels that are checked on setting up.
paddingfloat, optional: The distance between two adjacent options element
font_sizeint, optional: Size of the text font.
on_changecallback, optional: When checkbox value changed

Returns#

checkbox : HorizonUIElement

build_switcher#

dipy.viz.horizon.tab.base.build_switcher(items=None, label='', initial_selection=0, on_prev_clicked=<function <lambda>>, on_next_clicked=<function <lambda>>, on_value_changed=<function <lambda>>)#

Create horizon theme switcher.

Parameters#

itemslist, optional: dictionaries with keys ‘label’ and ‘value’. Label will be used to show it to user and value will be used for selection.
labelstr, optional: label for the switcher.
initial_selectionint, optional: index of the selected item initially.
on_prev_clickedcallback, optional: method providing a callback when prev value is selected in switcher.
on_next_clickedcallback, optional: method providing a callback when next value is selected in switcher.
on_value_changedcallback, optional: method providing a callback when either prev or next value selected in switcher.

Returns#

HorizonCombineElement(: label: HorizonUIElement, element(switcher): HorizonUIElement)

Notes#

switcher: consists ‘obj’ which is an array providing FURY UI elements used.

color_single_slider#

dipy.viz.horizon.tab.base.color_single_slider(slider)#

color_double_slider#

dipy.viz.horizon.tab.base.color_double_slider(slider)#

`ClustersTab`#

class dipy.viz.horizon.tab.cluster.ClustersTab(clusters_visualizer, threshold)#

Bases: HorizonTab

__init__(clusters_visualizer, threshold)#

build(tab_id, tab_ui)#

Build all the elements under the tab.

Parameters#

tab_idint: Id of the tab.
tab_uiTabUI: FURY TabUI object for tabs panel.

Notes#

tab_ui will removed once every all tabs adapt new build architecture.

property name#: Name of the tab.

`PeaksTab`#

class dipy.viz.horizon.tab.peak.PeaksTab(peak_actor)#

Bases: HorizonTab

__init__(peak_actor)#

property actors#

build(tab_id, tab_ui)#

Build all the elements under the tab.

Parameters#

tab_idint: Id of the tab.
tab_uiTabUI: FURY TabUI object for tabs panel.

Notes#

tab_ui will removed once every all tabs adapt new build architecture.

property name#: Name of the tab.

on_tab_selected()#: Trigger when tab becomes active.

property tab_id#

update_slices(x_slice, y_slice, z_slice)#

Updates slicer positions.

Parameters#

x_slice: float: x-value where the slicer should be placed
y_slice: float: y-value where the slicer should be placed
z_slice: float: z-value where the slicer should be placed

`ROIsTab`#

class dipy.viz.horizon.tab.roi.ROIsTab(contour_actors)#

Bases: HorizonTab

__init__(contour_actors)#

build(tab_id, tab_ui)#

Build all the elements under the tab.

Parameters#

tab_idint: Id of the tab.
tab_uiTabUI: FURY TabUI object for tabs panel.

Notes#

tab_ui will removed once every all tabs adapt new build architecture.

property name#: Name of the tab.

`SlicesTab`#

class dipy.viz.horizon.tab.slice.SlicesTab(slices_visualizer, tab_name, file_name, force_render=<function SlicesTab.<lambda>>)#

Bases: HorizonTab

Interaction tab for slice visualization.

Attributes#

namestr: Name of the tab.

__init__(slices_visualizer, tab_name, file_name, force_render=<function SlicesTab.<lambda>>)#

property actors#: visualization actors controlled by tab.

build(tab_id, _tab_ui)#

Build all the elements under the tab.

Parameters#

tab_idint: Id of the tab.
tab_uiTabUI: FURY TabUI object for tabs panel.

Notes#

tab_ui will removed once every all tabs adapt new build architecture.

property file_name#: Name of the file opened in the tab.

property name#: Name of the tab.

on_tab_selected()#: Trigger when tab becomes active.

property tab_id#: Id of the tab.

update_slices(x_slice, y_slice, z_slice)#

Updates slicer positions.

Parameters#

x_slice: float: x-value where the slicer should be placed
y_slice: float: y-value where the slicer should be placed
z_slice: float: z-value where the slicer should be placed

update_volume(volume)#

Updates volume based on passed volume.

Parameters#

volumefloat: value of where the volume slider should be placed

`SurfaceTab`#

class dipy.viz.horizon.tab.surface.SurfaceTab(visualizer, tab_name, file_name)#

Bases: HorizonTab

__init__(visualizer, tab_name, file_name)#: Surface Tab.

Parameters#

visualizer : SurfaceVisualizer id : int

property actors#: Actors controlled by this tab.

build(tab_id, _tab_ui)#

Build all the elements under the tab.

Parameters#

tab_idint: Id of the tab.
tab_uiTabUI: FURY TabUI object for tabs panel.

Notes#

tab_ui will removed once every all tabs adapt new build architecture.

property name#: Name of the tab.

Returns#

str

property tab_id#: Id of the tab.

check_img_shapes#

dipy.viz.horizon.util.check_img_shapes(images)#

Check if the images have same shapes. It also provides details about the volumes are same or not. If the shapes are not equal it will return False for both shape and volume.

Parameters#

images : list

Returns#

tuple: tuple[0] = True, if shapes are equal. tuple[1] = True, if volumes are equal.

check_img_dtype#

dipy.viz.horizon.util.check_img_dtype(images)#

Check supplied image dtype.

If not supported numerical type, fallback to supported numerical types (either int32 or float 32). If non-numerical type, skip the data.

Parameters#

imageslist: Each image is tuple of (data, affine).

Returns#

list: Valid images from the provided images.

show_ellipsis#

dipy.viz.horizon.util.show_ellipsis(text, text_size, available_size)#

Apply ellipsis to the text.

Parameters#

textstring: Text required to be check for ellipsis.
text_sizefloat: Current size of the text in pixels.
available_sizefloat: Size available to fit the text. This will be used to truncate the text and show ellipsis.

Returns#

string: Text after processing for ellipsis.

unpack_image#

dipy.viz.horizon.util.unpack_image(img)#

Unpack provided image data.

Standard way to handle different value images.

Parameters#

imgtuple: An image can contain either (data, affine) or (data, affine, fname).

Returns#

tuple: If img with (data, affine) it will convert to (data, affine, None). Otherwise it will be passed as it is.

unpack_surface#

dipy.viz.horizon.util.unpack_surface(surface)#

Unpack surface data.

Parameters#

surfacetuple: It either contains (vertices, faces) or (vertices, faces, fname).

Returns#

tuple: If surface with (vertices, faces) it will convert to (vertices, faces, None). Otherwise it will be passed as it is.

is_binary_image#

dipy.viz.horizon.util.is_binary_image(data, unique_points=100)#

Check if an image is binary image.

Parameters#

data : ndarray unique_points : int, optional

number of points to sample the check, by default 100

Returns#

boolean: Whether the image is binary or not

check_peak_size#

dipy.viz.horizon.util.check_peak_size(pams, ref_img_shape=None, sync_imgs=False)#

Check shape of peaks.

Parameters#

pams : PeaksAndMetrics ref_img_shape : tuple, optional

3D shape of the image, by default None.

sync_imgsbool, optional: True if the images are synchronized, by default False.

Returns#

bool: If the peaks are aligned with images and other peaks.

`ClustersVisualizer`#

class dipy.viz.horizon.visualizer.cluster.ClustersVisualizer(show_manager, scene, tractograms, enable_callbacks=True)#

Bases: object

__init__(show_manager, scene, tractograms, enable_callbacks=True)#

add_cluster_actors(tract_idx, streamlines, thr, colors)#

property centroid_actors#

property cluster_actors#

property lengths#

recluster_tractograms(thr)#

property sizes#

property tractogram_clusters#

`PeakActor`#

class dipy.viz.horizon.visualizer.peak.PeakActor(directions, indices, values=None, affine=None, colors=None, lookup_colormap=None, linewidth=1, symmetric=True)#

Bases: vtkActor

FURY actor for visualizing DWI peaks.

Parameters#

directionsndarray: Peak directions. The shape of the array should be (X, Y, Z, D, 3).
indicestuple: Indices given in tuple(x_indices, y_indices, z_indices) format for mapping 2D ODF array to 3D voxel grid.
valuesndarray, optional: Peak values. The shape of the array should be (X, Y, Z, D).
affinearray, optional: 4x4 transformation array from native coordinates to world coordinates.
colorsNone or string (‘rgb_standard’) or tuple (3D or 4D) or: array/ndarray (N, 3 or 4) or array/ndarray (K, 3 or 4) or array/ndarray(N, ) or array/ndarray (K, )

If None a standard orientation colormap is used for every line. If one tuple of color is used. Then all streamlines will have the same color. If an array (N, 3 or 4) is given, where N is equal to the number of points. Then every point is colored with a different RGB(A) color. If an array (K, 3 or 4) is given, where K is equal to the number of lines. Then every line is colored with a different RGB(A) color. If an array (N, ) is given, where N is the number of points then these are considered as the values to be used by the colormap. If an array (K,) is given, where K is the number of lines then these are considered as the values to be used by the colormap.
lookup_colormapvtkLookupTable, optional: Add a default lookup table to the colormap. Default is None which calls fury.actor.colormap_lookup_table().
linewidthfloat, optional: Line thickness. Default is 1.
symmetric: bool, optional: If True, peaks are drawn for both peaks_dirs and -peaks_dirs. Else, peaks are only drawn for directions given by peaks_dirs. Default is True.

__init__(directions, indices, values=None, affine=None, colors=None, lookup_colormap=None, linewidth=1, symmetric=True)#

property cross_section#

display_cross_section(x, y, z)#

display_extent(x1, x2, y1, y2, z1, z2)#

property global_opacity#

property high_ranges#

property is_range#

property linewidth#

property low_ranges#

property max_centers#

property min_centers#

`PeaksVisualizer`#

class dipy.viz.horizon.visualizer.peak.PeaksVisualizer(pam, world_coords)#

Bases: object

__init__(pam, world_coords)#

property actors#

peak#

dipy.viz.horizon.visualizer.peak.peak(peaks_dirs, peaks_values=None, mask=None, affine=None, colors=None, linewidth=1, lookup_colormap=None, symmetric=True)#

Visualize peak directions as given from peaks_from_model.

Parameters#

peaks_dirsndarray: Peak directions. The shape of the array should be (X, Y, Z, D, 3).
peaks_valuesndarray, optional: Peak values. The shape of the array should be (X, Y, Z, D).
affinearray, optional: 4x4 transformation array from native coordinates to world coordinates.
maskndarray, optional: 3D mask
colorstuple or None, optional: Default None. If None then every peak gets an orientation color in similarity to a DEC map.
lookup_colormapvtkLookupTable, optional: Add a default lookup table to the colormap. Default is None which calls fury.actor.colormap_lookup_table().
linewidthfloat, optional: Line thickness. Default is 1.
symmetricbool, optional: If True, peaks are drawn for both peaks_dirs and -peaks_dirs. Else, peaks are only drawn for directions given by peaks_dirs. Default is True.

Returns#

peak_actorPeakActor: Actor or LODActor representing the peaks directions and/or magnitudes.

`SlicesVisualizer`#

class dipy.viz.horizon.visualizer.slice.SlicesVisualizer(interactor, scene, data, affine=None, world_coords=False, percentiles=[2, 98], rgb=False)#

Bases: object

__init__(interactor, scene, data, affine=None, world_coords=False, percentiles=[2, 98], rgb=False)#

change_volume(prev_idx, next_idx, intensities, visible_slices)#

property data_shape#

property intensities_range#

register_picker_callback(callback)#

property rgb#

property selected_slices#

property slice_actors#

property volume_max#

property volume_min#

`SurfaceVisualizer`#

class dipy.viz.horizon.visualizer.surface.SurfaceVisualizer(surface, scene, color)#

Bases: object

__init__(surface, scene, color)#

property actors#

build_label#

dipy.viz.panel.build_label(text, font_size=18, bold=False)#: Simple utility function to build labels

Parameters#

text : str font_size : int bold : bool

Returns#

label : TextBlock2D

slicer_panel#

dipy.viz.panel.slicer_panel(scene, iren, data=None, affine=None, world_coords=False, pam=None, mask=None, mem=<dipy.viz.gmem.GlobalHorizon object>)#

Slicer panel with slicer included

Parameters#

scene : Scene iren : Interactor data : 3d ndarray affine : 4x4 ndarray world_coords : bool

If True then the affine is applied.

peaksPeaksAndMetrics: Default None

mem :

Returns#

panel : Panel

compare_maps#

dipy.viz.plotting.compare_maps(fits, maps, transpose=None, fit_labels=None, map_labels=None, fit_kwargs=None, map_kwargs=None, filename=None)#

Compare one or more scalar maps for different fits or models.

Parameters#

fitslist: List of fits to be compared.
mapslist: Names of attributes to be compared. Default: ‘rtop’.
transposebool, optional: If False, different fits are placed on different rows and different maps on different columns. If True, the order is transposed. If None, the figures are placed such that there are more columns than rows. Default: None.
fit_labelslist, optional: Labels for the different fitting routines. If None the fits are labeled by number. Default: None.
map_labelslist, optional: Labels for the different attributes. If None the attribute names are used. Default: None.
fit_kwargslist or dict, optional: A dict or list of dicts with imshow options for each fitting routine. The dicts are passed to imshow as keyword-argument pairs. Default: {}.
map_kwargslist or dict, optional: A dict or list of dicts with imshow options for each MAP-MRI scalar. The dicts are passed to imshow as keyword-argument pairs. Default: {}.
filenamestring, optional: Filename where the image will be saved. Default: None.

compare_qti_maps#

dipy.viz.plotting.compare_qti_maps(gt, fit1, fit2, mask, maps=('fa', 'ufa'), fitname=('QTI', 'QTI+'), xlimits=([0, 1], [0.4, 1.5]), disprange=([0, 1], [0, 1]), slice=13)#

Compare one or more qti derived maps obtained with different fitting routines.

Parameters#

gtqti fit object: The qti fit to be considered as ground truth
fit1qti fit object: First qti fit to be compared
fit2qti fit object: Second qti fit to be compared
masknp.ndarray: Boolean array indicating which voxels to retain for comparing the values
mapsarray-like, optional: QTI invariants to be compared
fitnamearray-like, optional: Names of the used QTI fitting routines
xlimitsarray-like, optional: X-Axis limits for the histograms visualization
disprangearray-like, optional: Display range for maps
sliceint, optional: Axial brain slice to be visualized

bundle_shape_profile#

dipy.viz.plotting.bundle_shape_profile(x, shape_profile, std)#

Plot bundlewarp bundle shape profile.

Parameters#

xnp.ndarray: Integer array containing x-axis
shape_profilenp.ndarray: Float array containing bundlewarp displacement magnitudes along the length of the bundle
stdnp.ndarray: Float array containing standard deviations

sph_project#

dipy.viz.projections.sph_project(vertices, val, ax=None, vmin=None, vmax=None, cmap=None, cbar=True, tri=False, boundary=False, **basemap_args)#

Draw a signal on a 2D projection of the sphere.

Parameters#

vertices(N,3) ndarray: unit vector points of the sphere
val: (N) ndarray: Function values.
axmpl axis, optional: If specified, draw onto this existing axis instead.
vmin, vmaxfloats: Values to cut the z

cmap : mpl colormap

cbar: Whether to add the color-bar to the figure

triang : Whether to display the plot triangulated as a pseudo-color plot.

boundary : Whether to draw the boundary around the projection in a black line

Returns#

axaxis: Matplotlib figure axis

Examples#

>>> from dipy.data import default_sphere
>>> verts = default_sphere.vertices
>>> ax = sph_project(verts.T, np.random.rand(len(verts.T))) 

simple_plot#

dipy.viz.regtools.simple_plot(file_name, title, x, y, xlabel, ylabel)#

Saves the simple plot with given x and y values

Parameters#

file_namestring: file name for saving the plot
titlestring: title of the plot
xinteger list: x-axis values to be plotted
yinteger list: y-axis values to be plotted
xlabelstring: label for x-axis
ylablestring: label for y-axis

overlay_images#

dipy.viz.regtools.overlay_images(img0, img1, title0='', title_mid='', title1='', fname=None, **fig_kwargs)#

Plot two images one on top of the other using red and green channels.

Creates a figure containing three images: the first image to the left plotted on the red channel of a color image, the second to the right plotted on the green channel of a color image and the two given images on top of each other using the red channel for the first image and the green channel for the second one. It is assumed that both images have the same shape. The intended use of this function is to visually assess the quality of a registration result.

Parameters#

img0array, shape(R, C): the image to be plotted on the red channel, to the left of the figure
img1array, shape(R, C): the image to be plotted on the green channel, to the right of the figure
title0string (optional): the title to be written on top of the image to the left. By default, no title is displayed.
title_midstring (optional): the title to be written on top of the middle image. By default, no title is displayed.
title1string (optional): the title to be written on top of the image to the right. By default, no title is displayed.
fnamestring (optional): the file name to write the resulting figure. If None (default), the image is not saved.

fig_kwargs: extra parameters for saving figure, e.g. dpi=300.

draw_lattice_2d#

dipy.viz.regtools.draw_lattice_2d(nrows, ncols, delta)#

Create a regular lattice of nrows x ncols squares.

Creates an image (2D array) of a regular lattice of nrows x ncols squares. The size of each square is delta x delta pixels (not counting the separation lines). The lines are one pixel width.

Parameters#

nrowsint: the number of squares to be drawn vertically
ncolsint: the number of squares to be drawn horizontally
deltaint: the size of each square of the grid. Each square is delta x delta pixels

Returns#

latticearray, shape (R, C): the image (2D array) of the segular lattice. The shape (R, C) of the array is given by R = 1 + (delta + 1) * nrows C = 1 + (delta + 1) * ncols

plot_2d_diffeomorphic_map#

dipy.viz.regtools.plot_2d_diffeomorphic_map(mapping, delta=10, fname=None, direct_grid_shape=None, direct_grid2world=-1, inverse_grid_shape=None, inverse_grid2world=-1, show_figure=True, **fig_kwargs)#

Draw the effect of warping a regular lattice by a diffeomorphic map.

Draws a diffeomorphic map by showing the effect of the deformation on a regular grid. The resulting figure contains two images: the direct transformation is plotted to the left, and the inverse transformation is plotted to the right.

Parameters#

mappingDiffeomorphicMap object: the diffeomorphic map to be drawn
deltaint, optional: the size (in pixels) of the squares of the regular lattice to be used to plot the warping effects. Each square will be delta x delta pixels. By default, the size will be 10 pixels.
fnamestring, optional: the name of the file the figure will be written to. If None (default), the figure will not be saved to disk.
direct_grid_shapetuple, shape (2,), optional: the shape of the grid image after being deformed by the direct transformation. By default, the shape of the deformed grid is the same as the grid of the displacement field, which is by default equal to the shape of the fixed image. In other words, the resulting deformed grid (deformed by the direct transformation) will normally have the same shape as the fixed image.
direct_grid2worldarray, shape (3, 3), optional: the affine transformation mapping the direct grid’s coordinates to physical space. By default, this transformation will correspond to the image-to-world transformation corresponding to the default direct_grid_shape (in general, if users specify a direct_grid_shape, they should also specify direct_grid2world).
inverse_grid_shapetuple, shape (2,), optional: the shape of the grid image after being deformed by the inverse transformation. By default, the shape of the deformed grid under the inverse transform is the same as the image used as “moving” when the diffeomorphic map was generated by a registration algorithm (so it corresponds to the effect of warping the static image towards the moving).
inverse_grid2worldarray, shape (3, 3), optional: the affine transformation mapping inverse grid’s coordinates to physical space. By default, this transformation will correspond to the image-to-world transformation corresponding to the default inverse_grid_shape (in general, if users specify an inverse_grid_shape, they should also specify inverse_grid2world).
show_figurebool, optional: if True (default), the deformed grids will be plotted using matplotlib, else the grids are just returned

fig_kwargs: extra parameters for saving figure, e.g. dpi=300.

Returns#

warped_forwardarray: Image with the grid showing the effect of transforming the moving image to the static image. The shape will be direct_grid_shape if specified, otherwise the shape of the static image.
warped_backwardarray: Image with the grid showing the effect of transforming the static image to the moving image. Shape will be inverse_grid_shape if specified, otherwise the shape of the moving image.

Notes#

The default value for the affine transformation is “-1” to handle the case in which the user provides “None” as input meaning “identity”. If we used None as default, we wouldn’t know if the user specifically wants to use the identity (specifically passing None) or if it was left unspecified, meaning to use the appropriate default matrix.

plot_slices#

dipy.viz.regtools.plot_slices(V, slice_indices=None, fname=None, **fig_kwargs)#

Plot 3 slices from the given volume: 1 sagittal, 1 coronal and 1 axial

Creates a figure showing the axial, coronal and sagittal slices at the requested positions of the given volume. The requested slices are specified by slice_indices.

Parameters#

Varray, shape (S, R, C): the 3D volume to extract the slices from
slice_indicesarray, shape (3,) (optional): the indices of the sagittal (slice_indices[0]), coronal (slice_indices[1]) and axial (slice_indices[2]) slices to be displayed. If None, the middle slices along each direction are displayed.
fnamestring (optional): the name of the file to save the figure to. If None (default), the figure is not saved to disk.

fig_kwargs: extra parameters for saving figure, e.g. dpi=300.

overlay_slices#

dipy.viz.regtools.overlay_slices(L, R, slice_index=None, slice_type=1, ltitle='Left', rtitle='Right', fname=None, **fig_kwargs)#

Plot three overlaid slices from the given volumes.

Creates a figure containing three images: the gray scale k-th slice of the first volume (L) to the left, where k=slice_index, the k-th slice of the second volume (R) to the right and the k-th slices of the two given images on top of each other using the red channel for the first volume and the green channel for the second one. It is assumed that both volumes have the same shape. The intended use of this function is to visually assess the quality of a registration result.

Parameters#

Larray, shape (S, R, C): the first volume to extract the slice from plotted to the left
Rarray, shape (S, R, C): the second volume to extract the slice from, plotted to the right
slice_indexint (optional): the index of the slices (along the axis given by slice_type) to be overlaid. If None, the slice along the specified axis is used
slice_typeint (optional): the type of slice to be extracted: 0=sagittal, 1=coronal (default), 2=axial.
ltitlestring (optional): the string to be written as the title of the left image. By default, no title is displayed.
rtitlestring (optional): the string to be written as the title of the right image. By default, no title is displayed.
fnamestring (optional): the name of the file to write the image to. If None (default), the figure is not saved to disk.

fig_kwargs: extra parameters for saving figure, e.g. dpi=300.

show_bundles#

dipy.viz.streamline.show_bundles(bundles, interactive=True, view='sagital', colors=None, linewidth=0.3, save_as=None)#

Render bundles to visualize them interactively or save them into a png.

The function allows to just render the bundles in an interactive plot or to export them into a png file.

Parameters#

bundleslist: Bundles to be rendered.
interactiveboolean, optional: If True a 3D interactive rendering is created. Default is True.
viewstr, optional: Viewing angle. Supported options: ‘sagital’,’axial’ and ‘coronal’. Default is ‘sagital’.
colorslist, optional: Colors to be used for each bundle. If None default colors are used.
linewidthfloat, optional: Width of each rendered streamline. Default is 0.3.
save_asstr, optional: If not None rendered scene is stored in a png file with that name. Default is None.

viz_two_bundles#

dipy.viz.streamline.viz_two_bundles(b1, b2, fname, c1=(1, 0, 0), c2=(0, 1, 0), interactive=False)#

Render and plot two bundles to visualize them.

Parameters#

b1Streamlines: Bundle one to be rendered.
b2Streamlines: Bundle two to be rendered.
fname: str: Rendered scene is stored in a png file with that name.
C1tuple, optional: Color to be used for first bundle. Default red.
C2tuple, optional: Color to be used for second bundle. Default green.
interactiveboolean, optional: If True a 3D interactive rendering is created. Default is True.

viz_vector_field#

dipy.viz.streamline.viz_vector_field(points_aligned, directions, colors, offsets, fname, bundle=None, interactive=False)#

Render and plot vector field.

Parameters#

points_alignedList: List containing starting positions of vectors.
directionsList: List containing unitary directions of vectors.
colorsList: List containing colors for each vector.
offsetsList: List containing vector field modules.
fname: str: Rendered scene is stored in a png file with that name.
bundleStreamlines, optional: Bundle to be rendered with vector field (Default None).
interactiveboolean, optional: If True a 3D interactive rendering is created. Default is True.

viz_displacement_mag#

dipy.viz.streamline.viz_displacement_mag(bundle, offsets, fname, interactive=False)#

Render and plot displacement magnitude over the bundle.

Parameters#

bundleStreamlines,: Bundle to be rendered.
offsetsList: List containing displacement magnitdues per point on the bundle.
fname: str: Rendered scene is stored in a png file with that name.
interactiveboolean, optional: If True a 3D interactive rendering is created. Default is True.

viz#

Module: viz.gmem#

Module: viz.horizon#

Module: viz.horizon.app#

Module: viz.horizon.tab#

Module: viz.horizon.tab.base#

Module: viz.horizon.tab.cluster#

Module: viz.horizon.tab.peak#

Module: viz.horizon.tab.roi#

Module: viz.horizon.tab.slice#

Module: viz.horizon.tab.surface#

Module: viz.horizon.util#

Module: viz.horizon.visualizer#

Module: viz.horizon.visualizer.cluster#

Module: viz.horizon.visualizer.peak#

Module: viz.horizon.visualizer.slice#

Module: viz.horizon.visualizer.surface#

Module: viz.panel#

Module: viz.plotting#

Module: viz.projections#

Module: viz.regtools#

Module: viz.streamline#

Parameters#

References#

horizon#

Parameters#

References#

Parameters#

Notes#

Parameters#

Attributes#

Parameters#

Parameters#

Parameters#

build_label#

Parameters#

Returns#

build_slider#

Parameters#

Return#

build_checkbox#

Parameters#

Returns#

build_switcher#

Parameters#

Returns#

Notes#

color_single_slider#

color_double_slider#

Parameters#

Notes#

Parameters#

Notes#

Parameters#

Parameters#

Notes#

Attributes#

Parameters#

Notes#

Parameters#

Parameters#

Parameters#

Parameters#

Notes#

Returns#

check_img_shapes#

Parameters#

Returns#

check_img_dtype#

Parameters#

Returns#

show_ellipsis#

Parameters#

Returns#

unpack_image#

Parameters#

Returns#

unpack_surface#

Parameters#

Returns#

`viz`#

Module: `viz.gmem`#

Module: `viz.horizon`#

Module: `viz.horizon.app`#

Module: `viz.horizon.tab`#

Module: `viz.horizon.tab.base`#

Module: `viz.horizon.tab.cluster`#

Module: `viz.horizon.tab.peak`#

Module: `viz.horizon.tab.roi`#

Module: `viz.horizon.tab.slice`#

Module: `viz.horizon.tab.surface`#

Module: `viz.horizon.util`#

Module: `viz.horizon.visualizer`#

Module: `viz.horizon.visualizer.cluster`#

Module: `viz.horizon.visualizer.peak`#

Module: `viz.horizon.visualizer.slice`#

Module: `viz.horizon.visualizer.surface`#

Module: `viz.panel`#

Module: `viz.plotting`#

Module: `viz.projections`#

Module: `viz.regtools`#

Module: `viz.streamline`#