cedalion.vis package

Subpackages

• cedalion.vis.anatomy package
  • Submodules
  • cedalion.vis.anatomy.brain_and_scalp module
    • plot_brain_and_scalp()
    • plot_brain_in_axes()
  • cedalion.vis.anatomy.image_recon module
    • image_recon()
    • image_recon_view()
    • image_recon_multi_view()
  • cedalion.vis.anatomy.montage module
    • plot_montage3D()
  • cedalion.vis.anatomy.optode_selector module
    • OptodeSelector
      • OptodeSelector.surface
      • OptodeSelector.points
      • OptodeSelector.normals
      • OptodeSelector.plotter
      • OptodeSelector.labels
      • OptodeSelector.actors
      • OptodeSelector.color
      • OptodeSelector.plot()
      • OptodeSelector.on_pick()
      • OptodeSelector.update_visualization()
      • OptodeSelector.enable_picking()
      • OptodeSelector.plot()
      • OptodeSelector.on_pick()
      • OptodeSelector.update_visualization()
      • OptodeSelector.enable_picking()
      • OptodeSelector.find_surface_normal()
      • OptodeSelector.update_normals()
  • cedalion.vis.anatomy.scalp_plot module
    • scalp_plot()
    • scalp_plot_gif()
  • cedalion.vis.anatomy.sensitivity_matrix module
    • Main
      • Main.plot()
  • Module contents
    • plot_brain_in_axes()
    • plot_brain_and_scalp()
    • image_recon()
    • image_recon_multi_view()
    • image_recon_view()
    • plot_montage3D()
    • OptodeSelector
      • OptodeSelector.surface
      • OptodeSelector.points
      • OptodeSelector.normals
      • OptodeSelector.plotter
      • OptodeSelector.labels
      • OptodeSelector.actors
      • OptodeSelector.color
      • OptodeSelector.plot()
      • OptodeSelector.on_pick()
      • OptodeSelector.update_visualization()
      • OptodeSelector.enable_picking()
      • OptodeSelector.enable_picking()
      • OptodeSelector.find_surface_normal()
      • OptodeSelector.on_pick()
      • OptodeSelector.plot()
      • OptodeSelector.update_normals()
      • OptodeSelector.update_visualization()
    • scalp_plot()
    • scalp_plot_gif()
• cedalion.vis.misc package
  • Submodules
  • cedalion.vis.misc.plot_probe_gui module
    • run_vis()
  • cedalion.vis.misc.time_series_gui module
    • run_vis()
  • Module contents

Submodules

cedalion.vis.blocks module

Blocks of plotting functionality to build visualizations.

cedalion.vis.blocks.plot_stim_markers(

ax,

stim: DataFrame,

fmt: dict[str, dict] | None = None,

y: float = 0.03,

)

Add stimulus indicators to an Axes.

For each trial a Rectangle is plotted in x from onset to onset+duration. The height of the rectangle is specified in axes coordinates. In the default setting a small bar at bottom of the axes is drawn. By setting y to 1. the stimulus marker covers the full height of the axes.

Parameters:

• ax – the matplotlib axes to operate on

• stim – a stimulas data frame

• fmt – for each trial_type a dictioniary of keyword arguments can be provided. These kwargs are passed to matplotlib.patches.Rectangle to format the stimulus indicator.

• y – the height of the Rectangle in axes coordinates.

Initial Contributors:

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

cedalion.vis.blocks.plot_segments(

ax,

segments: list[tuple[float, float]],

fmt: dict | None = None,

y: float = 1.0,

label: str | None = None,

)

Highlight time segments in line plots.

cedalion.vis.blocks.plot_surface(

plotter: pv.Plotter,

surface: cdc.Surface,

color: pv.ColorLike | ArrayLike | None = None,

opacity: float = 1.0,

pick_landmarks: bool = False,

**kwargs,

)

Plots a surface mesh with optional landmark picking in a PyVista plotter.

Parameters:

• plotter – A PyVista plotter instance used for rendering the surface.

• surface – The surface object to be plotted.

• color – Color of the mesh.

• opacity – Opacity of the mesh, ranging from 0 (transparent) to 1 (opaque). Default is 1.0.

• pick_landmarks – If True, enables interactive picking of landmarks on the surface. Default is False.

• **kwargs – Additional keyword arguments are passed to pv.add_mesh.

Returns:

If pick_landmarks is True, returns a function that when called, provides the current picked points and their labels. This function prints warnings if some labels are missing or are repeated.

Return type:

function

Initial Contributors:

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

• Masha Iudina | mashayudi@gmail.com | 2024

cedalion.vis.blocks.plot_labeled_points(

plotter: pv.Plotter,

points: cdt.LabeledPoints,

color: pv.ColorLike | None = None,

show_labels: bool = False,

ppoints: bool = None,

labels: list[str] | None = None,

)

Plots LabeledPoints with optional interaction for picking points.

This function visualizes a point cloud where each point can have a label. Points can be interactively picked if enabled. Picked point is indicated by increased radius.

Parameters:

• plotter – A PyVista plotter instance used for rendering the points.

• points – A labeled point cloud data structure containing points and optional labels.

• color – Override color for all points. If None, colors are assigned based on point types.

• show_labels – If True, labels are displayed next to the points.

• ppoints – A list to store indices of picked points, enables picking if not None.

• labels – List of labels to show if show_labels is True. If None and show_labels is True, the labels from points are used.

Initial Contributors:

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

cedalion.vis.blocks.plot_vector_field(

plotter: Plotter,

points: cdt.LabeledPoints,

vectors: DataArray,

ppoints=None,

)

Plots a vector field on a PyVista plotter.

Parameters:

• plotter (pv.Plotter) – A PyVista plotter instance used for rendering the vector field.

• points (cdt.LabeledPoints) – A labeled point cloud data structure containing point coordinates.

• vectors (xr.DataArray) – A data array containing the vector field.

• ppoints (list, optional) – A list to store indices of picked points, enables picking if not None. Default is None.

cedalion.vis.blocks.camera_at_cog(

plt: Plotter,

surface: Surface,

rpos: tuple[float, float, float],

fp_offset: tuple[float, float, float] = (0, 0, 0),

up: tuple[float, float, float] = (0, 0, 1),

fit_scene: bool = False,

)

Point the camera at the center of gravity of the surface vertices.

Parameters:

• plt – the pyvista plotter

• surface – the surface from which the COG should be calculated

• rpos – positin of the camera relative to the COG

• fp_offset – offset from the COG to calculate the focal point

• up – direction of upwards.

• fit_scene – if True the camera is moved along the position-to-focal-point line to fit all objects in the scene into the view.

cedalion.vis.colors module

Color and colormap definitions.

cedalion.vis.colors.segmented_cmap(

name: str,

vmin: float,

vmax: float,

segments: list[tuple[float, tuple[float, float, float] | str | tuple[float, float, float, float] | tuple[tuple[float, float, float] | str, float] | tuple[tuple[float, float, float, float], float]]],

over: None | tuple[float, float, float] | str | tuple[float, float, float, float] | tuple[tuple[float, float, float] | str, float] | tuple[tuple[float, float, float, float], float] = None,

under: None | tuple[float, float, float] | str | tuple[float, float, float, float] | tuple[tuple[float, float, float] | str, float] | tuple[tuple[float, float, float, float], float] = None,

bad: None | tuple[float, float, float] | str | tuple[float, float, float, float] | tuple[tuple[float, float, float] | str, float] | tuple[tuple[float, float, float, float], float] = None,

) → tuple[Normalize, LinearSegmentedColormap]

Create a linear segmented colormap.

cedalion.vis.colors.p_values_cmap() → tuple[Normalize, LinearSegmentedColormap]

A colormap for log10(p-values).

Gray for p>0.05 and with different colors for common thresholds.

cedalion.vis.colors.threshold_cmap(

name,

vmin,

vmax,

threshold,

higher_is_better=True,

) → tuple[Normalize, LinearSegmentedColormap]

A read and blue colormap with a distinctive break at a threshold value.

cedalion.vis.colors.mask_cmap(

true_is_good=True,

) → tuple[Normalize, LinearSegmentedColormap]

A red and blue colormap to color binary masks.

cedalion.vis.quality module

Tools for visualizing signal quality metrics and masks.

cedalion.vis.quality.plot_quality_mask(

mask,

cb_label: str,

bool_labels=['TAINTED', 'CLEAN'],

true_is_good=True,

figsize=(12, 10),

)

cedalion.vis.timeseries module

Tools for plotting temporal data.

Module contents