cedalion.vis.anatomy package

Submodules

cedalion.vis.anatomy.brain_and_scalp module

cedalion.vis.anatomy.brain_and_scalp.plot_brain_and_scalp( brain_mesh, scalp_mesh, geo3d, timeseries, poly_lines=[], brain_scalars=None, plotter=None, )[source]

Plots a 3D visualization of brain and scalp meshes.

Parameters:

brain_mesh (TrimeshSurface) – The brain mesh as a TrimeshSurface object.
scalp_mesh (TrimeshSurface) – The scalp mesh as a TrimeshSurface object.
geo3d (xarray.Dataset) – Dataset containing 3-dimentional point centers.
timeseries – Time series data array.
poly_lines – List of lists of points to be plotted as polylines.
brain_scalars – Scalars to be used for coloring the brain mesh.
plotter (pv.Plotter, optional) – An existing PyVista plotter instance to use for plotting. If None, a new PyVista plotter instance is created. Default: None.

Initial Contributors:

Eike Middell | middell@tu-berlin.de | 2024

cedalion.vis.anatomy.brain_and_scalp.plot_brain_in_axes( ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')], metric: DataArray | _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], brain_surface: TrimeshSurface, ax: Axes, title: str | None = None, vmin: float | None = None, vmax: float | None = None, cmap: str | Colormap = 'RdBu_r', bad_color: tuple[float, float, float] | str | tuple[float, float, float, float] | tuple[tuple[float, float, float] | str, float] | tuple[tuple[float, float, float, float], float] = [0.7, 0.7, 0.7], cb_label: str = '', camera_pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None, )[source]: Using pyvista render a brain, colored by a metric, and display it in MPL axes.

cedalion.vis.anatomy.image_recon module

Functions to plot image reconstruction results.

cedalion.vis.anatomy.image_recon.image_recon( X: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], head: TwoSurfaceHeadModel, cmap: str | Colormap = 'seismic', clim=None, view_type: str = 'hbo_brain', view_position: str = 'superior', p0=None, title_str: str = None, off_screen: bool = False, plotshape=(1, 1), iax=(0, 0), show_scalar_bar: bool = False, wdw_size: tuple = (1024, 768), )[source]

Render a single frame of brain or scalp activity on a specified view.

This function creates (or reuses) a PyVista plotter, applies a custom colormap, sets the camera view according to the given view_position, adds the surface mesh with the scalar data (extracted from X), and returns the plotter, the mesh, and a text label.

Parameters:

X – cdt.NDTimeSeries (or similar) Scalar data for the current frame. Expected to have a boolean attribute is_brain indicating brain vs. non-brain vertices, and HbO / HbR chromophore dimension
head – TwoSurfaceHeadModel A head model containing attributes such as head.brain and head.scalp.
cmap – str or matplotlib.colors.Colormap, default ‘seismic’ The colormap to use.
clim – tuple, optional Color limits. If None, they are computed from the data.
view_type – str, default ‘hbo_brain’ Indicates whether to plot brain (‘hbo_brain’ or ‘hbr_brain’) or scalp (‘hbo_scalp’ or ‘hbr_scalp’) data.
view_position – str, default ‘superior’ The view direction. Options are: ‘superior’, ‘anterior’, ‘posterior’,’left’, ‘right’, and ‘scale_bar’.
p0 – PyVista Plotter instance, optional If provided the mesh is added to this plotter; else a new plotter is created
title_str – str, optional Title to use on the scalar bar.
off_screen – bool, default False Whether to use off-screen rendering.
plotshape – tuple, default (1, 1) The subplot grid shape.
iax – tuple, default (0, 0) The target subplot index (row, col).
show_scalar_bar – bool, optional Flag to control scalar bar visibility
wdw_size – tuple, default (1024, 768) The window size for the plotter (the plot resolution)

Returns:

p0: the PyVista Plotter instance.
surf: the wrapped surface mesh (a pyvista mesh).
surf_label: a text actor (e.g., the scalar bar label).

Return type:

A tuple (p0, surf, surf_label) where

cedalion.vis.anatomy.image_recon.image_recon_view( X_ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], head: TwoSurfaceHeadModel, cmap: str | Colormap = 'seismic', clim=None, view_type: str = 'hbo_brain', view_position: str = 'superior', title_str: str = None, filename: str = None, SAVE: bool = False, time_range: tuple = None, fps: int = 6, geo3d_plot: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')] | None = None, wdw_size: tuple = (1024, 768), )[source]

Generate a single-view visualization of head activity.

For static data (2D: vertex × channel) the function can display (or save) a single frame. For time series data (3D: vertex × channel × time) the function can create an animated GIF by looping over the specified frame indices.

Parameters:

X_ts – xarray.DataArray or NDTimeSeries Activity data. If 2D, a single static frame is plotted; if 3D, a time series is used. Expected to have a boolean attribute is_brain indicating brain vs. non-brain vertices, and HbO / HbR chromophore dimension
head – TwoSurfaceHeadModel The head mesh data to plot activity on.
cmap – str or matplotlib.colors.Colormap, default ‘seismic’ The colormap to use.
view_position – str, default ‘superior’ The view to render.
clim – tuple, optional Color limits. If None, they are computed from the data.
view_type – str, default ‘hbo_brain’ Indicates whether to plot brain (‘hbo_brain’ or ‘hbr_brain’) or scalp (‘hbo_scalp’ or ‘hbr_scalp’) data.
view_position – str, default ‘superior’ The view direction. Options are: ‘superior’, ‘anterior’, ‘posterior’,’left’, ‘right’, and ‘scale_bar’.
title_str – str, optional Title to use on the scalar bar.
filename – str, optional The output filename (without extension) for saving the image/GIF.
SAVE – bool, default False If True, the resulting still image is saved, otherwise only shown. Rendered gifs are always saved.
time_range – tuple, optional Provides (start_time, stop_time, step_time) in quantity ‘s’ for generating animation.
fps – int, default 6 Frames per second for the GIF.
geo3d_plot – cdt.LabeledPoints, optional For plotting labeled points (e.g. optodes) on the mesh.
wdw_size – tuple, default (1024, 768) The window size for the plotter (the plot resolution)

Returns: Nothing

cedalion.vis.anatomy.image_recon.image_recon_multi_view( X_ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], head: TwoSurfaceHeadModel, cmap: str | Colormap = 'seismic', clim=None, view_type: str = 'hbo_brain', title_str: str = None, filename: str = None, SAVE: bool = True, time_range: tuple = None, fps: int = 6, geo3d_plot: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')] | None = None, wdw_size: tuple = (1024, 768), )[source]

Generate a multi-view (2×3 grid) vis. of head activity across different views.

For static data (2D: vertex × channel) the function can display (or save) a single frame. For time series data (3D: vertex × channel × time) the function creates an animated GIF where each frame updates all views.

Parameters:

X_ts – xarray.DataArray or NDTimeSeries Activity data. If 2D, a single static frame is plotted; if 3D, a time series is used. Expected to have a boolean attribute is_brain indicating brain vs. non-brain vertices, and HbO / HbR chromophore dimension
head – TwoSurfaceHeadModel The head mesh data to plot activity on.
cmap – str or matplotlib.colors.Colormap, default ‘seismic’ The colormap to use.
view_position – str, default ‘superior’ The view to render.
clim – tuple, optional Color limits. If None, they are computed from the data.
view_type – str, default ‘hbo_brain’ Indicates whether to plot brain (‘hbo_brain’ or ‘hbr_brain’) or scalp (‘hbo_scalp’ or ‘hbr_scalp’) data.
title_str – str, optional Title to use on the scalar bar.
filename – str, optional The output filename (without extension) for saving the image/GIF.
SAVE – bool, default False If True, the resulting still image is saved, otherwise only shown. Rendered gifs are always saved.
time_range – tuple, optional Provides (start_time, stop_time, step_time) in quantity ‘s’ for generating animation.
fps – int, default 6 Frames per second for the GIF.
geo3d_plot – cdt.LabeledPoints, optional For plotting labeled points (e.g. optodes) on the mesh.
wdw_size – tuple, default (1024, 768) The window size for the plotter (the plot resolution)

Returns: Nothing

cedalion.vis.anatomy.montage module

cedalion.vis.anatomy.montage.plot_montage3D( amp: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')], )[source]

Plots a 3D visualization of a montage.

Parameters:

amp – Time series data array.
geo3d – Landmark coordinates.

cedalion.vis.anatomy.optode_selector module

class cedalion.vis.anatomy.optode_selector.OptodeSelector(surface, points, normals=None, plotter=None, labels=None)[source]

Bases: object

A class for visualizing point clouds with interactive features in PyVista.

This class provides functionality to visualize and interact with labeled point clouds using a PyVista plotter. It allows points to be dynamically added or removed by picking them directly from the plot interface.

surface[source]

The surface of a head for normals.

Type:: cdc.Surface

points[source]

The point cloud data containing point coordinates.

Type:: cdt.LabeledPoints

normals[source]

Normal vectors to the points.

Type:: xr.DataArray

plotter[source]

A PyVista plotter instance for rendering the point cloud.

Type:: pv.Plotter

labels[source]

Labels corresponding to the points, displayed if provided.

Type:: list of str, optional

actors[source]

List of PyVista actor objects representing the points in the visualization.

Type:: list

color[source]

Default color for points if not specified by point type.

Type:: str or tuple, optional

plot()[source]: Renders the point cloud using the current settings.

on_pick(picked_point)[source]: Callback function for picking points in the visualization

update_visualization()[source]: Clears the existing plot and re-renders the point cloud.

enable_picking()[source]: Enables interactive picking of points on the plot.

Initial Contributors:

Masha Iudina | mashayudi@gmail.com | 2024

plot()[source]

on_pick(picked_point)[source]

update_visualization()[source]

enable_picking()[source]

find_surface_normal(picked_point, radius=6)[source]

update_normals(normal_at_picked_point, label)[source]

cedalion.vis.anatomy.scalp_plot module

Static and animated 2D plots of metrics projected on the scalp.

Creates a 2D plot of the head with channels coloured according to a given metric.

Parameters:

ts – a NDTimeSeries to provide channel definitions
geo3d – a LabeledPoints to provide the probe geometry
metric ((DataArray, (channel,) | ArrayLike)) – the scalar metric to be plotted for each channel. If provided as a DataArray it needs a channel dimension. If provided as a plain array or list it must have the same length as ts.channel and the matching is done by position.
ax – the matplotlib.Axes object into which to draw
title – the axes title
y_title – the y position of the title in axes coordinates
vmin – the minimum value of the metric
vmax – the maximum value of the metric
center – when calculating vmin and vmax, center the value range at this value.
cmap – the name of the colormap
norm – normalization for color map
bad_color – the color to use when the metric contains NaNs
min_dist – if provided channels below this distance threshold are not drawn
min_metric – if provided channels below this metric threshold are toned down
channel_lw – channel line width
optode_size – optode marker size
optode_labels – if True draw optode labels instead of markers
cb_label – colorbar label
cb_ticks_labels – ticks and labels for colorbar
add_colorbar – if true a colorbar is added to the plot
zorder – ‘ascending’ or ‘descending’ or None. Controls whether channels with high or low metric values are plotted on top.

Initial Contributors:

Laura Carlton | lcarlton@bu.edu | 2024
Eike Middell | middell@tu-berlin.de | 2024

cedalion.vis.anatomy.scalp_plot.scalp_plot_gif( data_ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')], filename: str, time_range: tuple = None, cmap: str | Colormap = 'seismic', scl=None, fps: int = 10, optode_size: float = 6, optode_labels: bool = False, str_title: str = '', )[source]

Generate a GIF of scalp topographies over time from time-series data.

Parameters:

data_ts – xarray.DataArray A 2D DataArray with dimensions (channel, time). Must include coordinate labels for ‘source’ and ‘detector’ in the ‘channel’ dimension.
geo3d – 3D geometry defining optode locations for projecting onto the scalp surface.
filename – str Full path to the output GIF file without file extension.
time_range – tuple, optional Provides (start_time, stop_time, step_time) in quantity ‘s’ for generating animation.
cmap – string, optional A matplotlib colormap name or a Colormap object. Default is ‘seismic’.
scl – tuple of (float, float), optional Tuple defining the (vmin, vmax) for the color scale. If None, the color scale is set to ± the maximum absolute value of the data.
fps – int, optional Frames per second for the output GIF. Default is 10.
optode_size – float, optional Size of optode markers on the plot. Default is 6.
optode_labels – bool, optional Whether to show text labels for optodes instead of markers. Default: False.
str_title – str, optional Extra string to append to the title of each frame.

Returns:

None. The function saves a GIF file to the specified location.

Initial Contributors:

David Boas | dboas@bu.edu | 2025
Alexander von Lühmann | vonluehmann@tu-berlin.de | 2025

cedalion.vis.anatomy.sensitivity_matrix module

Plots an fNIRS probe’s sensitivity profile on a 3D brain surface.

Args: sensitivity: brain_surface: head_surface: labeled_points: wavelength: The wavelength of the light source in nm.

Initial Contributors: - Shakiba Moradi | shakiba.moradi@tu-berlin.de | 2024

class cedalion.vis.anatomy.sensitivity_matrix.Main(sensitivity, brain_surface, head_surface=None, labeled_points=None, wavelength=760)[source]

Bases: object

plot(low_th=-3, high_th=0)[source]

Module contents

Tools for visualizing data on brain and scalp surface representations.

cedalion.vis.anatomy.plot_brain_in_axes( ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')], metric: DataArray | _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], brain_surface: TrimeshSurface, ax: Axes, title: str | None = None, vmin: float | None = None, vmax: float | None = None, cmap: str | Colormap = 'RdBu_r', bad_color: tuple[float, float, float] | str | tuple[float, float, float, float] | tuple[tuple[float, float, float] | str, float] | tuple[tuple[float, float, float, float], float] = [0.7, 0.7, 0.7], cb_label: str = '', camera_pos: _Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None, )[source]: Using pyvista render a brain, colored by a metric, and display it in MPL axes.

cedalion.vis.anatomy.plot_brain_and_scalp( brain_mesh, scalp_mesh, geo3d, timeseries, poly_lines=[], brain_scalars=None, plotter=None, )[source]

Plots a 3D visualization of brain and scalp meshes.

Parameters:

brain_mesh (TrimeshSurface) – The brain mesh as a TrimeshSurface object.
scalp_mesh (TrimeshSurface) – The scalp mesh as a TrimeshSurface object.
geo3d (xarray.Dataset) – Dataset containing 3-dimentional point centers.
timeseries – Time series data array.
poly_lines – List of lists of points to be plotted as polylines.
brain_scalars – Scalars to be used for coloring the brain mesh.
plotter (pv.Plotter, optional) – An existing PyVista plotter instance to use for plotting. If None, a new PyVista plotter instance is created. Default: None.

Initial Contributors:

Eike Middell | middell@tu-berlin.de | 2024

cedalion.vis.anatomy.image_recon( X: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], head: TwoSurfaceHeadModel, cmap: str | Colormap = 'seismic', clim=None, view_type: str = 'hbo_brain', view_position: str = 'superior', p0=None, title_str: str = None, off_screen: bool = False, plotshape=(1, 1), iax=(0, 0), show_scalar_bar: bool = False, wdw_size: tuple = (1024, 768), )[source]

Render a single frame of brain or scalp activity on a specified view.

This function creates (or reuses) a PyVista plotter, applies a custom colormap, sets the camera view according to the given view_position, adds the surface mesh with the scalar data (extracted from X), and returns the plotter, the mesh, and a text label.

Parameters:

X – cdt.NDTimeSeries (or similar) Scalar data for the current frame. Expected to have a boolean attribute is_brain indicating brain vs. non-brain vertices, and HbO / HbR chromophore dimension
head – TwoSurfaceHeadModel A head model containing attributes such as head.brain and head.scalp.
cmap – str or matplotlib.colors.Colormap, default ‘seismic’ The colormap to use.
clim – tuple, optional Color limits. If None, they are computed from the data.
view_type – str, default ‘hbo_brain’ Indicates whether to plot brain (‘hbo_brain’ or ‘hbr_brain’) or scalp (‘hbo_scalp’ or ‘hbr_scalp’) data.
view_position – str, default ‘superior’ The view direction. Options are: ‘superior’, ‘anterior’, ‘posterior’,’left’, ‘right’, and ‘scale_bar’.
p0 – PyVista Plotter instance, optional If provided the mesh is added to this plotter; else a new plotter is created
title_str – str, optional Title to use on the scalar bar.
off_screen – bool, default False Whether to use off-screen rendering.
plotshape – tuple, default (1, 1) The subplot grid shape.
iax – tuple, default (0, 0) The target subplot index (row, col).
show_scalar_bar – bool, optional Flag to control scalar bar visibility
wdw_size – tuple, default (1024, 768) The window size for the plotter (the plot resolution)

Returns:

p0: the PyVista Plotter instance.
surf: the wrapped surface mesh (a pyvista mesh).
surf_label: a text actor (e.g., the scalar bar label).

Return type:

A tuple (p0, surf, surf_label) where

cedalion.vis.anatomy.image_recon_multi_view( X_ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], head: TwoSurfaceHeadModel, cmap: str | Colormap = 'seismic', clim=None, view_type: str = 'hbo_brain', title_str: str = None, filename: str = None, SAVE: bool = True, time_range: tuple = None, fps: int = 6, geo3d_plot: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')] | None = None, wdw_size: tuple = (1024, 768), )[source]

Generate a multi-view (2×3 grid) vis. of head activity across different views.

For static data (2D: vertex × channel) the function can display (or save) a single frame. For time series data (3D: vertex × channel × time) the function creates an animated GIF where each frame updates all views.

Parameters:

X_ts – xarray.DataArray or NDTimeSeries Activity data. If 2D, a single static frame is plotted; if 3D, a time series is used. Expected to have a boolean attribute is_brain indicating brain vs. non-brain vertices, and HbO / HbR chromophore dimension
head – TwoSurfaceHeadModel The head mesh data to plot activity on.
cmap – str or matplotlib.colors.Colormap, default ‘seismic’ The colormap to use.
view_position – str, default ‘superior’ The view to render.
clim – tuple, optional Color limits. If None, they are computed from the data.
view_type – str, default ‘hbo_brain’ Indicates whether to plot brain (‘hbo_brain’ or ‘hbr_brain’) or scalp (‘hbo_scalp’ or ‘hbr_scalp’) data.
title_str – str, optional Title to use on the scalar bar.
filename – str, optional The output filename (without extension) for saving the image/GIF.
SAVE – bool, default False If True, the resulting still image is saved, otherwise only shown. Rendered gifs are always saved.
time_range – tuple, optional Provides (start_time, stop_time, step_time) in quantity ‘s’ for generating animation.
fps – int, default 6 Frames per second for the GIF.
geo3d_plot – cdt.LabeledPoints, optional For plotting labeled points (e.g. optodes) on the mesh.
wdw_size – tuple, default (1024, 768) The window size for the plotter (the plot resolution)

Returns: Nothing

cedalion.vis.anatomy.image_recon_view( X_ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], head: TwoSurfaceHeadModel, cmap: str | Colormap = 'seismic', clim=None, view_type: str = 'hbo_brain', view_position: str = 'superior', title_str: str = None, filename: str = None, SAVE: bool = False, time_range: tuple = None, fps: int = 6, geo3d_plot: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')] | None = None, wdw_size: tuple = (1024, 768), )[source]

Generate a single-view visualization of head activity.

For static data (2D: vertex × channel) the function can display (or save) a single frame. For time series data (3D: vertex × channel × time) the function can create an animated GIF by looping over the specified frame indices.

Parameters:

X_ts – xarray.DataArray or NDTimeSeries Activity data. If 2D, a single static frame is plotted; if 3D, a time series is used. Expected to have a boolean attribute is_brain indicating brain vs. non-brain vertices, and HbO / HbR chromophore dimension
head – TwoSurfaceHeadModel The head mesh data to plot activity on.
cmap – str or matplotlib.colors.Colormap, default ‘seismic’ The colormap to use.
view_position – str, default ‘superior’ The view to render.
clim – tuple, optional Color limits. If None, they are computed from the data.
view_type – str, default ‘hbo_brain’ Indicates whether to plot brain (‘hbo_brain’ or ‘hbr_brain’) or scalp (‘hbo_scalp’ or ‘hbr_scalp’) data.
view_position – str, default ‘superior’ The view direction. Options are: ‘superior’, ‘anterior’, ‘posterior’,’left’, ‘right’, and ‘scale_bar’.
title_str – str, optional Title to use on the scalar bar.
filename – str, optional The output filename (without extension) for saving the image/GIF.
SAVE – bool, default False If True, the resulting still image is saved, otherwise only shown. Rendered gifs are always saved.
time_range – tuple, optional Provides (start_time, stop_time, step_time) in quantity ‘s’ for generating animation.
fps – int, default 6 Frames per second for the GIF.
geo3d_plot – cdt.LabeledPoints, optional For plotting labeled points (e.g. optodes) on the mesh.
wdw_size – tuple, default (1024, 768) The window size for the plotter (the plot resolution)

Returns: Nothing

cedalion.vis.anatomy.plot_montage3D( amp: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')], )[source]

Plots a 3D visualization of a montage.

Parameters:

amp – Time series data array.
geo3d – Landmark coordinates.

class cedalion.vis.anatomy.OptodeSelector(surface, points, normals=None, plotter=None, labels=None)[source]

Bases: object

A class for visualizing point clouds with interactive features in PyVista.

This class provides functionality to visualize and interact with labeled point clouds using a PyVista plotter. It allows points to be dynamically added or removed by picking them directly from the plot interface.

surface[source]

The surface of a head for normals.

Type:: cdc.Surface

points[source]

The point cloud data containing point coordinates.

Type:: cdt.LabeledPoints

normals[source]

Normal vectors to the points.

Type:: xr.DataArray

plotter[source]

A PyVista plotter instance for rendering the point cloud.

Type:: pv.Plotter

labels[source]

Labels corresponding to the points, displayed if provided.

Type:: list of str, optional

actors[source]

List of PyVista actor objects representing the points in the visualization.

Type:: list

color[source]

Default color for points if not specified by point type.

Type:: str or tuple, optional

plot()[source]: Renders the point cloud using the current settings.

on_pick(picked_point)[source]: Callback function for picking points in the visualization

update_visualization()[source]: Clears the existing plot and re-renders the point cloud.

enable_picking()[source]: Enables interactive picking of points on the plot.

Initial Contributors:

Masha Iudina | mashayudi@gmail.com | 2024

enable_picking()[source]

find_surface_normal(picked_point, radius=6)[source]

on_pick(picked_point)[source]

plot()[source]

update_normals(normal_at_picked_point, label)[source]

update_visualization()[source]

Creates a 2D plot of the head with channels coloured according to a given metric.

Parameters:

ts – a NDTimeSeries to provide channel definitions
geo3d – a LabeledPoints to provide the probe geometry
metric ((DataArray, (channel,) | ArrayLike)) – the scalar metric to be plotted for each channel. If provided as a DataArray it needs a channel dimension. If provided as a plain array or list it must have the same length as ts.channel and the matching is done by position.
ax – the matplotlib.Axes object into which to draw
title – the axes title
y_title – the y position of the title in axes coordinates
vmin – the minimum value of the metric
vmax – the maximum value of the metric
center – when calculating vmin and vmax, center the value range at this value.
cmap – the name of the colormap
norm – normalization for color map
bad_color – the color to use when the metric contains NaNs
min_dist – if provided channels below this distance threshold are not drawn
min_metric – if provided channels below this metric threshold are toned down
channel_lw – channel line width
optode_size – optode marker size
optode_labels – if True draw optode labels instead of markers
cb_label – colorbar label
cb_ticks_labels – ticks and labels for colorbar
add_colorbar – if true a colorbar is added to the plot
zorder – ‘ascending’ or ‘descending’ or None. Controls whether channels with high or low metric values are plotted on top.

Initial Contributors:

Laura Carlton | lcarlton@bu.edu | 2024
Eike Middell | middell@tu-berlin.de | 2024

cedalion.vis.anatomy.scalp_plot_gif( data_ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')], geo3d: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')], filename: str, time_range: tuple = None, cmap: str | Colormap = 'seismic', scl=None, fps: int = 10, optode_size: float = 6, optode_labels: bool = False, str_title: str = '', )[source]

Generate a GIF of scalp topographies over time from time-series data.

Parameters:

data_ts – xarray.DataArray A 2D DataArray with dimensions (channel, time). Must include coordinate labels for ‘source’ and ‘detector’ in the ‘channel’ dimension.
geo3d – 3D geometry defining optode locations for projecting onto the scalp surface.
filename – str Full path to the output GIF file without file extension.
time_range – tuple, optional Provides (start_time, stop_time, step_time) in quantity ‘s’ for generating animation.
cmap – string, optional A matplotlib colormap name or a Colormap object. Default is ‘seismic’.
scl – tuple of (float, float), optional Tuple defining the (vmin, vmax) for the color scale. If None, the color scale is set to ± the maximum absolute value of the data.
fps – int, optional Frames per second for the output GIF. Default is 10.
optode_size – float, optional Size of optode markers on the plot. Default is 6.
optode_labels – bool, optional Whether to show text labels for optodes instead of markers. Default: False.
str_title – str, optional Extra string to append to the title of each frame.

Returns:

None. The function saves a GIF file to the specified location.

Initial Contributors:

David Boas | dboas@bu.edu | 2025
Alexander von Lühmann | vonluehmann@tu-berlin.de | 2025