Modules.UncertaintyTube package

Submodules

Modules.UncertaintyTube.uncertainty_tubes module

Modules.UncertaintyTube.uncertainty_tubes.uncertainty_tubes(trajectories, colormap='viridis', plotter=None, resolution=20, e_proj=1, sym=False, clim=None, n_jobs=1)[source]

Generate and visualize 3D uncertainty tubes from trajectories.

Parameters:

  • trajectories (np.ndarray) – Array of shape (n_steps, n_starting_locations, n_ensemble_members, 3) representing the 3D trajectories.

  • (str (colormap) – Colormap to use for rendering the tube. Defaults to “viridis”.

  • optional) – Colormap to use for rendering the tube. Defaults to “viridis”.

  • pyvista.Plotter (plotter (matplotlib.axes.Axes or) – The plotting object to use. If None, a new Matplotlib figure/axis is created.

  • optional) – The plotting object to use. If None, a new Matplotlib figure/axis is created.

  • (int (n_jobs) – Number of points to sample on each cross-section boundary. Defaults to 20.

  • optional) – Number of points to sample on each cross-section boundary. Defaults to 20.

  • (float (e_proj) – Exponent controlling the superellipse shape. e_proj=1 creates a standard ellipse. Defaults to 1.

  • optional) – Exponent controlling the superellipse shape. e_proj=1 creates a standard ellipse. Defaults to 1.

  • (bool (sym) – If True, forces the superellipse to be symmetric. Defaults to False.

  • optional) – If True, forces the superellipse to be symmetric. Defaults to False.

  • (int – Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

  • optional) – Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

Returns:

matplotlib.axes.Axes or pyvista.Plotter

Return type:

The plotting object with the visualization.

Modules.UncertaintyTube.uncertainty_tubes_mesh module

Modules.UncertaintyTube.uncertainty_tubes_mesh.align_cross_sections(ellipsoids, seed_idx, n_steps, resolution)[source]

Align cross-sections to minimize twisting for a given seed.

Modules.UncertaintyTube.uncertainty_tubes_mesh.apply_circular_alignment(points, best_shift, best_is_reversed)[source]

Apply circular alignment transformation to a set of points based on the calculated shift and orientation.

Parameters:

points (np.ndarray):

Points to transform (shape: n_points × num_dims)

best_shift (int):

Number of positions to shift

best_is_reversed (bool):

Whether to reverse point order

Returns:

aligned (np.ndarray):

Transformed points

point_correspondence_map (np.ndarray):

Index mapping from original to transformed points

Modules.UncertaintyTube.uncertainty_tubes_mesh.calculate_mesh_dimensions(n_steps, n_seeds, resolution)[source]

Calculate dimensions needed for mesh arrays.

Modules.UncertaintyTube.uncertainty_tubes_mesh.circular_align_min_twist(points_ref, points_target, stride=1)[source]

Aligns two sets of points forming closed loops with minimal twist.

Parameters:

points_ref (np.ndarray):

Reference point set (shape: n_points × num_dims)

points_target (np.ndarray):

Target point set to align (shape: n_points × num_dims)

stride (int):

Stride for sampling subset of points (for efficiency)

Returns:

aligned_points (np.ndarray):

Aligned target points

correspondence_map (np.ndarray):

Index mapping from original to aligned points

Modules.UncertaintyTube.uncertainty_tubes_mesh.compute_alignment_scores(reference, target, shifts)[source]

Compute alignment scores for all possible shifts of target relative to reference.

Parameters:

  • reference (np.ndarray) – Reference point set (shape: n_points × num_dims)

  • target (np.ndarray) – Target point set to align (shape: n_points × num_dims)

  • shifts (np.ndarray) – Array of shift values to try

Returns:

Scores for each shift (lower is better)

Return type:

np.ndarray

Modules.UncertaintyTube.uncertainty_tubes_mesh.expcos(x, e)[source]
Modules.UncertaintyTube.uncertainty_tubes_mesh.expsin(x, e)[source]
Modules.UncertaintyTube.uncertainty_tubes_mesh.uncertainty_tubes_mesh(summary_statistics, resolution=20, e_proj=1, sym=False, n_jobs=1)[source]

Generate the 3D mesh for uncertainty tubes.

Parameters:

summary_statistics (dict):

Dictionary containing “mean_trajectory”, “eigen_values”, and “eigen_vectors”.

resolution (int, optional):

Number of points to sample on each cross-section boundary. Defaults to 20.

e_proj (float, optional):

Exponent controlling the superellipse shape. e_proj=1 creates a standard ellipse. Defaults to 1.

sym (bool, optional):

If True, forces the superellipse to be symmetric. Defaults to False.

n_jobs (int, optional):

Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

Returns:

dict:

  • “vertices” (np.ndarray): Tube mesh vertices.

  • “faces” (np.ndarray): Triangle indices.

  • “uv_coords” (np.ndarray): UV coordinates for texture mapping.

Modules.UncertaintyTube.uncertainty_tubes_stats module

Modules.UncertaintyTube.uncertainty_tubes_stats.compute_eigen_2d(points)[source]
Modules.UncertaintyTube.uncertainty_tubes_stats.expcos(x, e)[source]
Modules.UncertaintyTube.uncertainty_tubes_stats.expsin(x, e)[source]
Modules.UncertaintyTube.uncertainty_tubes_stats.project_points_to_plane(point0, point1, points)[source]

Projects points onto a plane perpendicular to the line segment from point0 to point1.

Parameters:

  • point0 (np.ndarray) – Define the line segment direction

  • point1 (np.ndarray) – Define the line segment direction

  • points (np.ndarray) – Points to project (shape: (n_points, num_dims))

Returns:

Projected points on the orthogonal plane

Return type:

np.ndarray

Modules.UncertaintyTube.uncertainty_tubes_stats.uncertainty_tubes_summary_statistics(trajectories, n_jobs=1)[source]

Compute 3D uncertainty summary statistics along trajectory paths.

Parameters:

  • trajectories (np.ndarray) – Ensemble trajectories with shape (n_steps, n_starting_locations, n_samples, num_dims).

  • n_jobs (int, optional) – Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

Returns:

  • “mean_trajectory” (np.ndarray): The mean trajectory with shape (n_steps, n_starting_locations, num_dims).

  • ”eigen_values” (np.ndarray): Eigenvalues for each cross-section with shape (n_steps, n_starting_locations, 2).

  • ”eigen_vectors” (np.ndarray): Eigenvectors for each cross-section with shape (n_steps, n_starting_locations, num_dims, 2).

Return type:

dict

Modules.UncertaintyTube.uncertainty_tubes_vis module

Modules.UncertaintyTube.uncertainty_tubes_vis.visualize_uncertainty_tubes(mesh_data, colormap='viridis', clim=None, plotter=None)[source]

Visualize 3D uncertainty tubes using either Matplotlib or PyVista.

Parameters:

mesh_data (dict):

Dictionary containing “vertices”, “faces”, and “uv_coords” from uncertainty_tube_mesh.

colormap (str, optional):

Colormap to use for rendering the tube. Defaults to “viridis”.

plotter (matplotlib.axes.Axes or pyvista.Plotter, optional):

The plotting object to use. If None, a new Matplotlib figure/axis is created. If a Matplotlib Axes3D object, it plots on that. If a PyVista Plotter object, it adds the mesh to it.

Returns:

matplotlib.axes.Axes or pyvista.Plotter: The plotting object with the visualization.

Module contents

UncertaintyTube Module

This module provides uncertainty tube functionality for uncertainty visualization.

Modules.UncertaintyTube.uncertainty_tubes(trajectories, colormap='viridis', plotter=None, resolution=20, e_proj=1, sym=False, clim=None, n_jobs=1)[source]

Generate and visualize 3D uncertainty tubes from trajectories.

Parameters:

  • trajectories (np.ndarray) – Array of shape (n_steps, n_starting_locations, n_ensemble_members, 3) representing the 3D trajectories.

  • (str (colormap) – Colormap to use for rendering the tube. Defaults to “viridis”.

  • optional) – Colormap to use for rendering the tube. Defaults to “viridis”.

  • pyvista.Plotter (plotter (matplotlib.axes.Axes or) – The plotting object to use. If None, a new Matplotlib figure/axis is created.

  • optional) – The plotting object to use. If None, a new Matplotlib figure/axis is created.

  • (int (n_jobs) – Number of points to sample on each cross-section boundary. Defaults to 20.

  • optional) – Number of points to sample on each cross-section boundary. Defaults to 20.

  • (float (e_proj) – Exponent controlling the superellipse shape. e_proj=1 creates a standard ellipse. Defaults to 1.

  • optional) – Exponent controlling the superellipse shape. e_proj=1 creates a standard ellipse. Defaults to 1.

  • (bool (sym) – If True, forces the superellipse to be symmetric. Defaults to False.

  • optional) – If True, forces the superellipse to be symmetric. Defaults to False.

  • (int – Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

  • optional) – Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

Returns:

matplotlib.axes.Axes or pyvista.Plotter

Return type:

The plotting object with the visualization.

Modules.UncertaintyTube.uncertainty_tubes_mesh(summary_statistics, resolution=20, e_proj=1, sym=False, n_jobs=1)[source]

Generate the 3D mesh for uncertainty tubes.

Parameters:

summary_statistics (dict):

Dictionary containing “mean_trajectory”, “eigen_values”, and “eigen_vectors”.

resolution (int, optional):

Number of points to sample on each cross-section boundary. Defaults to 20.

e_proj (float, optional):

Exponent controlling the superellipse shape. e_proj=1 creates a standard ellipse. Defaults to 1.

sym (bool, optional):

If True, forces the superellipse to be symmetric. Defaults to False.

n_jobs (int, optional):

Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

Returns:

dict:

  • “vertices” (np.ndarray): Tube mesh vertices.

  • “faces” (np.ndarray): Triangle indices.

  • “uv_coords” (np.ndarray): UV coordinates for texture mapping.

Modules.UncertaintyTube.uncertainty_tubes_summary_statistics(trajectories, n_jobs=1)[source]

Compute 3D uncertainty summary statistics along trajectory paths.

Parameters:

  • trajectories (np.ndarray) – Ensemble trajectories with shape (n_steps, n_starting_locations, n_samples, num_dims).

  • n_jobs (int, optional) – Number of parallel jobs to use. If n_jobs=1, uses sequential processing. Defaults to 1.

Returns:

  • “mean_trajectory” (np.ndarray): The mean trajectory with shape (n_steps, n_starting_locations, num_dims).

  • ”eigen_values” (np.ndarray): Eigenvalues for each cross-section with shape (n_steps, n_starting_locations, 2).

  • ”eigen_vectors” (np.ndarray): Eigenvectors for each cross-section with shape (n_steps, n_starting_locations, num_dims, 2).

Return type:

dict

Modules.UncertaintyTube.visualize_uncertainty_tubes(mesh_data, colormap='viridis', clim=None, plotter=None)[source]

Visualize 3D uncertainty tubes using either Matplotlib or PyVista.

Parameters:

mesh_data (dict):

Dictionary containing “vertices”, “faces”, and “uv_coords” from uncertainty_tube_mesh.

colormap (str, optional):

Colormap to use for rendering the tube. Defaults to “viridis”.

plotter (matplotlib.axes.Axes or pyvista.Plotter, optional):

The plotting object to use. If None, a new Matplotlib figure/axis is created. If a Matplotlib Axes3D object, it plots on that. If a PyVista Plotter object, it adds the mesh to it.

Returns:

matplotlib.axes.Axes or pyvista.Plotter: The plotting object with the visualization.