torchdrivesim.lanelet2
======================

.. py:module:: torchdrivesim.lanelet2

.. autoapi-nested-parse::

   All utilities for working with maps in Lanelet2 format.
   This module imports correctly and exports all the usual signatures even
   when the lanelet2 package is not installed, but in that case all calls
   to its functions raise the Lanelet2NotFound exception.



Attributes
----------

.. autoapisummary::

   torchdrivesim.lanelet2.is_available


Exceptions
----------

.. autoapisummary::

   torchdrivesim.lanelet2.Lanelet2NotFound
   torchdrivesim.lanelet2.LaneletError


Classes
-------

.. autoapisummary::

   torchdrivesim.lanelet2.LaneFeatures


Functions
---------

.. autoapisummary::

   torchdrivesim.lanelet2.load_lanelet_map
   torchdrivesim.lanelet2.find_lanelet_directions
   torchdrivesim.lanelet2.find_direction
   torchdrivesim.lanelet2.pick_random_point_and_orientation
   torchdrivesim.lanelet2.road_mesh_from_lanelet_map
   torchdrivesim.lanelet2.line_segments_to_mesh
   torchdrivesim.lanelet2.lanelet_map_to_lane_mesh


Module Contents
---------------

.. py:data:: is_available
   :value: True


.. py:class:: LaneFeatures

   .. py:attribute:: dense_lane_features
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:attribute:: dense_lane_features_mask
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:attribute:: sparse_lane_features
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:attribute:: sparse_lane_features_mask
      :type:  Optional[torch.Tensor]
      :value: None



   .. py:method:: to(device) -> typing_extensions.Self


   .. py:method:: copy() -> typing_extensions.Self


   .. py:method:: extend(n: int) -> typing_extensions.Self


   .. py:method:: select_batch_elements(idx) -> typing_extensions.Self


.. py:exception:: Lanelet2NotFound

   Bases: :py:obj:`ImportError`


   Python bindings for Lanelet2 are not installed.


.. py:exception:: LaneletError

   Bases: :py:obj:`RuntimeError`


   Some function related to Lanelet2 failed.


.. py:function:: load_lanelet_map(map_path: str, origin: Tuple[float, float] = (0, 0)) -> LaneletMap

   Load a Lanelet2 map from an OSM file on disk.

   :param map_path: local path to OSM file containing the map
   :param origin: latitude and longitude of the origin to use with UTM projector

   :raises Lanelet2NotFound: if lanelet2 package is not available
   :raises FileNotFoundError: if specified file doesn't exist


.. py:function:: find_lanelet_directions(lanelet_map: LaneletMap, x: float, y: float, tags_to_exclude: Optional[List[str]] = None, lanelet_dist_tolerance: float = 1.0) -> List[float]

   For a given point, find local orientations of all lanelets that contain it.

   :param lanelet_map: the map with a lanelet layer
   :param x: first coordinate of the point in the map's coordinate frame
   :param y: second coordinate of the point in the map's coordinate frame
   :param tags_to_exclude: lanelets tagged with any of those tags will be omitted as if they didn't exist
   :param lanelet_dist_tolerance: how far out of a lanelet a car can be for the lanelet to still be containing it

   :returns: for each lanelet, the angle representing its local orientation in radians

   :raises Lanelet2NotFound: if lanelet2 package is not available


.. py:function:: find_direction(linestring, location3d) -> float

   For a given linestring and a point near it, finds the local orientation of the linestring.

   :returns: the linestring's local orientation in radians

   :raises Lanelet2NotFound: if lanelet2 package is not available
   :raises LaneletError: when the method fails, usually because the linestring has a weird shape


.. py:function:: pick_random_point_and_orientation(lanelet_map: LaneletMap) -> Tuple[float, float, float]

   Picks a point on the map by randomly selecting a lanelet and then randomly selecting a point
   along its centerline, both using `random` package.

   :returns: tuple (x, y, orientation), using the map's coordinate frame and radians

   :raises Lanelet2NotFound: if lanelet2 package is not available


.. py:function:: road_mesh_from_lanelet_map(lanelet_map: LaneletMap, lanelets: Optional[List[int]] = None) -> torchdrivesim.mesh.BaseMesh

   Creates a road mesh by triangulating all lanelets in a given map.

   :param lanelet_map: map to triangulate
   :param lanelets: if specified, only use lanelets with those ids


.. py:function:: line_segments_to_mesh(points: torch.Tensor, line_width: float = 0.3, eps: float = 1e-06) -> torchdrivesim.mesh.BaseMesh

   Converts a given collection of line segments to a mesh visualizing them.

   :param points: BxNx2x2 tensor specifying N line segments, each consisting of a pair of points
   :param line_width: width of the line in meters
   :param eps: small value to add for avoiding division by zero

   :returns: mesh visualizing the line, with 6*N verts and 4*N faces


.. py:function:: lanelet_map_to_lane_mesh(lanelet_map: LaneletMap, left_handed: bool = False, batch_size: int = 50000, left_right_marking_join_threshold: float = 0.1, lanelets: Optional[List[int]] = None, lane_boundary_width: float = 0.275) -> torchdrivesim.mesh.BirdviewMesh

   Creates a lane marking mesh from a given map.

   :param lanelet_map: map to use
   :param left_handed: whether the map's coordinate system is left-handed (flips the left and right boundary designations)
   :param batch_size: controls the amount of points processed in parallel
   :param left_right_marking_join_threshold: if left and right markings are this close, they will be treated as joint
   :param lane_boundary_width: in meters, the thickness of the lane markings