cedalion.vis package

Subpackages

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, )[source]

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, )[source]: 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: list[str] | bool = False,

**kwargs,

)[source]

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 (‘Nz’, ‘Iz’, ‘Cz’, ‘Lpa’, ‘Rpa’) on the surface. If a list of strings is provided, these are used as the landmark labels instead. 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, meas_list: xr.DataArray | None = None, )[source]

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.
meas_list – A DataArray containing channel information for plotting channels as lines between sources and detectors.

Initial Contributors:

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

cedalion.vis.blocks.plot_vector_field( plotter: Plotter, points: cdt.LabeledPoints, vectors: DataArray, ppoints=None, )[source]

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, )[source]

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.

Create a linear segmented colormap from (value, color) breakpoints.

Parameters:

name – Name for the colormap (used by matplotlib internally).
vmin – Data value corresponding to the bottom of the colormap.
vmax – Data value corresponding to the top of the colormap.
segments – List of (value, color) pairs defining the colour breakpoints. Values are normalised by vmin/vmax before being passed to LinearSegmentedColormap.
over – Color for out-of-range values above vmax (optional).
under – Color for out-of-range values below vmin (optional).
bad – Color for masked/NaN values (optional).

Returns:

Tuple (norm, cmap) — a Normalize instance and the constructed LinearSegmentedColormap.

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

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, colors=['#000000', '#DC3220', '#5D3A9B', '#0C7BDC'], ) → tuple[Normalize, LinearSegmentedColormap][source]

Create a red-and-blue colormap with a sharp break at a quality threshold.

Parameters:

name – Colormap name.
vmin – Minimum data value.
vmax – Maximum data value.
threshold – Value at which the colour transitions sharply.
higher_is_better – If True (default), colours below the threshold are “bad” (warm) and colours above are “good” (cool). Reversed when False.
colors – Four colours [vmin, threshold-, threshold+, vmax].

Returns:

Tuple (norm, cmap) — a Normalize and the constructed LinearSegmentedColormap.

cedalion.vis.colors.mask_cmap( true_is_good=True, colors=['#DC3220', '#DC3220', '#0C7BDC', '#0C7BDC'], ) → tuple[Normalize, LinearSegmentedColormap][source]

Create a binary red/blue colormap for boolean quality masks.

Parameters:

true_is_good – If True (default), True values are shown in blue (good) and False values in red (bad). Reversed when False.
colors – Four colours defining the two-level step ([0, 0.5, 0.5, 1] breakpoints).

Returns:

Tuple (norm, cmap) — a Normalize for [0, 1] and the constructed LinearSegmentedColormap.

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), )[source]

Plot a boolean quality mask as a colour-coded channel × time heatmap.

Parameters:

mask – Boolean DataArray with "channel" and "time" dimensions.
cb_label – Label for the colorbar.
bool_labels – Two-element list giving the colorbar tick labels for False and True values respectively (default: ["TAINTED", "CLEAN"]).
true_is_good – Passed to mask_cmap(); if True (default), True is rendered in blue (good).
figsize – Matplotlib figure size tuple.

cedalion.vis.timeseries module

Tools for plotting temporal data.

cedalion.vis package

Subpackages

Submodules

cedalion.vis.blocks module

cedalion.vis.colors module

cedalion.vis.quality module

cedalion.vis.timeseries module

Module contents