planner.core.graph_visuals#

Module Contents#

planner.core.graph_visuals.rgba_palette(index: int, transparency: float = 1.0) → pydrake.geometry.Rgba#
Parameters:

  • index (int) –

  • transparency (float) –

Return type:

pydrake.geometry.Rgba

planner.core.graph_visuals.color_gradient(color: pydrake.geometry.Rgba, steps: int) → torch.FloatTensor#
Parameters:

  • color (pydrake.geometry.Rgba) –

  • steps (int) –

Return type:

torch.FloatTensor

planner.core.graph_visuals.display_point_cloud(meshcat_vis: pydrake.geometry.Meshcat, points: numpy.ndarray | torch.Tensor, path: str = '/points', point_size: float = 0.01, color: pydrake.geometry.Rgba = None) → None#
Parameters:

  • meshcat_vis (pydrake.geometry.Meshcat) –

  • points (numpy.ndarray | torch.Tensor) –

  • path (str) –

  • point_size (float) –

  • color (pydrake.geometry.Rgba) –

Return type:

None

planner.core.graph_visuals.display_segments(meshcat_vis: pydrake.geometry.Meshcat, start: numpy.ndarray | torch.Tensor, end: numpy.ndarray | torch.Tensor, path: str = '/segments', line_width: float = 0.01, color: pydrake.geometry.Rgba = None) → None#
Parameters:

  • meshcat_vis (pydrake.geometry.Meshcat) –

  • start (numpy.ndarray | torch.Tensor) –

  • end (numpy.ndarray | torch.Tensor) –

  • path (str) –

  • line_width (float) –

  • color (pydrake.geometry.Rgba) –

Return type:

None

planner.core.graph_visuals.display_colormap_point_cloud(meshcat_vis: pydrake.geometry.Meshcat, points: numpy.ndarray | torch.Tensor, rewards: numpy.ndarray | torch.Tensor, path: str = '/colormap_points', point_size: float = 0.01, num_color_bins: int = 12, transparency: float = 0.7) → None#
Parameters:

  • meshcat_vis (pydrake.geometry.Meshcat) –

  • points (numpy.ndarray | torch.Tensor) –

  • rewards (numpy.ndarray | torch.Tensor) –

  • path (str) –

  • point_size (float) –

  • num_color_bins (int) –

  • transparency (float) –

Return type:

None

planner.core.graph_visuals.display_edges_by_category(meshcat_vis: pydrake.geometry.Meshcat, starts: numpy.ndarray | torch.Tensor, ends: numpy.ndarray | torch.Tensor, categories: List, edge_size: int = 1, path: str = '/3d_graph/categories') → None#
Parameters:

  • meshcat_vis (pydrake.geometry.Meshcat) –

  • starts (numpy.ndarray | torch.Tensor) –

  • ends (numpy.ndarray | torch.Tensor) –

  • categories (List) –

  • edge_size (int) –

  • path (str) –

Return type:

None

planner.core.graph_visuals.display_3d_graph(graph: jacta.planner.core.graph.Graph, logger: jacta.planner.core.logger.Logger, meshcat_vis: pydrake.geometry.Meshcat, vis_scale: torch.FloatTensor | None = None, vis_indices: List | None = None, node_size: float = 0.01, start_goal_size: float = 0.08, edge_size: int = 1, best_path_edge_size: int = 4, segment_color: pydrake.geometry.Rgba | None = None, best_path_color: pydrake.geometry.Rgba | None = None, node_transparency: float = 0.7, display_segment: bool = True, display_best_path: bool = True, display_reward_colormap: bool = True, node_cap: int | None = None, reset_visualizer: bool = True, search_index: int = 0) → None#
Parameters:

  • graph (jacta.planner.core.graph.Graph) –

  • logger (jacta.planner.core.logger.Logger) –

  • meshcat_vis (pydrake.geometry.Meshcat) –

  • vis_scale (Optional[torch.FloatTensor]) –

  • vis_indices (Optional[List]) –

  • node_size (float) –

  • start_goal_size (float) –

  • edge_size (int) –

  • best_path_edge_size (int) –

  • segment_color (Optional[pydrake.geometry.Rgba]) –

  • best_path_color (Optional[pydrake.geometry.Rgba]) –

  • node_transparency (float) –

  • display_segment (bool) –

  • display_best_path (bool) –

  • display_reward_colormap (bool) –

  • node_cap (Optional[int]) –

  • reset_visualizer (bool) –

  • search_index (int) –

Return type:

None