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Plotting

function plot_ate

plot_ate(
    ate_results: Union[trajectopy.core.evaluation.ate_result.ATEResult, List[trajectopy.core.evaluation.ate_result.ATEResult]],
    plot_settings: trajectopy.core.settings.mpl_settings.MPLPlotSettings = MPLPlotSettings(scatter_cbar_show_zero=True, scatter_cbar_steps=4, scatter_no_axis=False, scatter_max_std=3.0, ate_unit_is_mm=False, hist_as_stairs=False, directed_ate=False, scatter_pos_dim=2)
) → Figure

Plots ATE for the given ATEResult(s) as a line plot using matplotlib. If available, the plot contains the position and rotation deviations. The x-axis depends on the sorting of the trajectory.

Args:

  • ate_results (Union[ATEResult, List[ATEResult]]): ATE result(s) to plot.
  • plot_settings (MPLPlotSettings, optional): Plot settings. Defaults to MPLPlotSettings().

Returns:

  • Figure: Figure containing the plot.

function plot_ate_bars

plot_ate_bars(
    ate_results: List[trajectopy.core.evaluation.ate_result.ATEResult],
    plot_settings: trajectopy.core.settings.mpl_settings.MPLPlotSettings = MPLPlotSettings(scatter_cbar_show_zero=True, scatter_cbar_steps=4, scatter_no_axis=False, scatter_max_std=3.0, ate_unit_is_mm=False, hist_as_stairs=False, directed_ate=False, scatter_pos_dim=2),
    mode: str = 'positions'
) → Figure

Plots multiple ATE results as bars for different characteristics (min, max, mean, median, rms, std) using matplotlib.

Args:

  • ate_result (List[ATEResult]): List of ATE results.
  • plot_settings (MPLPlotSettings, optional): Plot settings. Defaults to MPLPlotSettings().
  • mode (str, optional): Mode to plot. Either 'positions' or 'rotations'. Defaults to 'positions'.

Returns:

  • Figure: Bar plot figure.

function plot_ate_edf

plot_ate_edf(
    ate_results: Union[trajectopy.core.evaluation.ate_result.ATEResult, List[trajectopy.core.evaluation.ate_result.ATEResult]],
    plot_settings: trajectopy.core.settings.mpl_settings.MPLPlotSettings = MPLPlotSettings(scatter_cbar_show_zero=True, scatter_cbar_steps=4, scatter_no_axis=False, scatter_max_std=3.0, ate_unit_is_mm=False, hist_as_stairs=False, directed_ate=False, scatter_pos_dim=2)
) → Figure

Plots ATE EDF for the given ATEResult(s) as a line plot using matplotlib. The EDF (Empirical Distribution Function) shows the cummulative probability of the deviations. Using this plot, one can easily see how many percent of the deviations are below a certain value.

Args:

  • ate_results (Union[ATEResult, List[ATEResult]]): ATE result to plot.
  • plot_settings (MPLPlotSettings, optional): Plot settings. Defaults to MPLPlotSettings().

Returns:

  • Figure: Figure containing the plot.

function plot_compact_ate_hist

plot_compact_ate_hist(
    ate_result: trajectopy.core.evaluation.ate_result.ATEResult,
    plot_settings: trajectopy.core.settings.mpl_settings.MPLPlotSettings = MPLPlotSettings(scatter_cbar_show_zero=True, scatter_cbar_steps=4, scatter_no_axis=False, scatter_max_std=3.0, ate_unit_is_mm=False, hist_as_stairs=False, directed_ate=False, scatter_pos_dim=2)
) → Figure

Plots compact ATE histograms for the given ATEResult. The plot contains histograms for the position deviations and, if available, the rotation deviations.

Args:

  • ate_result (ATEResult): ATE result to plot.
  • plot_settings (MPLPlotSettings, optional): Plot settings. Defaults to MPLPlotSettings().

Returns:

  • Figure: Figure containing the plot.

function plot_correlation_heatmap

plot_correlation_heatmap(
    estimated_parameters: trajectopy.core.alignment.parameters.AlignmentParameters,
    enabled_only: bool = True
) → Figure

Plots the correlation heatmap of the alignment parameters using matplotlib.

Args:

  • estimated_parameters (AlignmentParameters): Estimated parameters.
  • enabled_only (bool, optional): Whether to consider only enabled parameters. Defaults to True.

Returns:

  • plt.Figure: Correlation heatmap figure.

function plot_covariance_heatmap

plot_covariance_heatmap(
    estimated_parameters: trajectopy.core.alignment.parameters.AlignmentParameters,
    enabled_only: bool = True
) → Figure

Plots the covariance heatmap of the alignment parameters using matplotlib.

Args:

  • estimated_parameters (AlignmentParameters): Estimated parameters.
  • enabled_only (bool, optional): Whether to consider only enabled parameters. Defaults to True.

Returns:

  • plt.Figure: Covariance heatmap figure.

function plot_rpe

plot_rpe(
    rpe_results: List[trajectopy.core.evaluation.rpe_result.RPEResult]
) → Tuple[matplotlib.figure.Figure, matplotlib.figure.Figure]

Plots the RPE results as a line plot with violin plots for the position and rotation deviations.

Depending on the pair distance unit, the unit of the position deviations is either in meters/meters (%) or meters/seconds. The unit of the rotation deviations is respectively in degrees/m or degrees/second.

Args:

  • rpe_results (list[RelativeTrajectoryDeviations]): list of RelativeTrajectoryDeviations

Returns:

  • Tuple[Figure, Figure]: metric and time RPE plots

function plot_trajectories

plot_trajectories(
    trajectories: List[trajectopy.core.trajectory.Trajectory],
    dim: int = 2
) → Tuple[matplotlib.figure.Figure, matplotlib.figure.Figure, Optional[matplotlib.figure.Figure]]

Plots the trajectories in 2d or 3d using matplotlib.

Args:

  • trajectories (List[Trajectory]): List of trajectories to plot.
  • dim (int, optional): Dimension of the plot. Defaults to 2.

This function creates one 2D or 3D plot for the xy(z) coordinates of the trajectories, one subplot for the xyz coordinates and one subplot for the rpy angles.

Returns:

  • Tuple[Figure, Figure, Union[Figure, None]]: Figures for the position, xyz and rpy plots.

function scatter_ate

scatter_ate(
    ate_result: trajectopy.core.evaluation.ate_result.ATEResult,
    plot_settings: trajectopy.core.settings.mpl_settings.MPLPlotSettings = MPLPlotSettings(scatter_cbar_show_zero=True, scatter_cbar_steps=4, scatter_no_axis=False, scatter_max_std=3.0, ate_unit_is_mm=False, hist_as_stairs=False, directed_ate=False, scatter_pos_dim=2)
) → Tuple[matplotlib.figure.Figure, matplotlib.figure.Figure]

Plots the ATE results as a scatter plot with color-coded deviations.

Args:

  • ate_result (ATEResult): ATE result to plot.
  • plot_settings (MPLPlotSettings, optional): Plot settings. Defaults to MPLPlotSettings().