cars.applications.triangulation.triangulation_wrappers
======================================================

.. py:module:: cars.applications.triangulation.triangulation_wrappers

.. autoapi-nested-parse::

   Preprocessing module:
   contains functions used for triangulation



Functions
---------

.. autoapisummary::

   cars.applications.triangulation.triangulation_wrappers.add_layer
   cars.applications.triangulation.triangulation_wrappers.interpolate_geoid_height
   cars.applications.triangulation.triangulation_wrappers.geoid_offset
   cars.applications.triangulation.triangulation_wrappers.generate_point_cloud_file_names
   cars.applications.triangulation.triangulation_wrappers.compute_performance_map


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

.. py:function:: add_layer(dataset, layer_name, layer_coords, point_cloud)

   Add layer point cloud to point cloud dataset

   :param dataset: input disparity map dataset
   :param layer_name: layer key in disparity dataset
   :param layer_coords: layer axis name in disparity dataset
   :param point_cloud: output point cloud dataset


.. py:function:: interpolate_geoid_height(geoid_filename, positions, interpolation_method='linear')

   terrain to index conversion
   retrieve geoid height above ellispoid
   This is a modified version of the Shareloc interpolate_geoid_height
   function that supports Nan positions (return Nan)

   :param geoid_filename: geoid_filename
   :type geoid_filename: str
   :param positions: geodetic coordinates
   :type positions: 2D numpy array: (number of points,[long coord, lat coord])
   :param interpolation_method: default is 'linear' (interpn parameter)
   :type interpolation_method: str
   :return: geoid height
   :rtype: 1 numpy array (number of points)


.. py:function:: geoid_offset(points, geoid_path)

   Compute the point cloud height offset from geoid.

   :param points: point cloud data in lat/lon/alt WGS84 (EPSG 4326)
       coordinates.
   :type points: xarray.Dataset or pandas.DataFrame
   :param geoid_path: path to input geoid file on disk
   :type geoid_path: string
   :return: the same point cloud but using geoid as altimetric reference.
   :rtype: xarray.Dataset or pandas.DataFrame


.. py:function:: generate_point_cloud_file_names(csv_dir: str, laz_dir: str, row: int, col: int, index: dict = None, pair_key: str = 'PAIR_0')

   generate the point cloud CSV and LAZ filenames of a given tile from its
   corresponding row and col. Optionally update the index, if provided.

   :param csv_dir: target directory for csv files, If None no csv filenames
       will be generated
   :type csv_dir: str
   :param laz_dir: target directory for laz files, If None no laz filenames
       will be generated
   :type laz_dir: str
   :param row: row index of the tile
   :type row: int
   :param col: col index of the tile
   :type col: int
   :param index: product index to update with the filename
   :type index: dict
   :param pair_key: current product key (used in index), if a list is given
       a filename will be added to the index for each element of the list
   :type pair_key: str


.. py:function:: compute_performance_map(alti_ref, z_inf, z_sup, ambiguity_map=None, perf_ambiguity_threshold=None)

   Compute performance map

   :param alti_ref: z
   :type alti_ref: xarray Dataarray
   :param z_inf: z inf map
   :type z_inf: xarray Dataarray
   :param z_sup: z sup map
   :type z_sup: xarray Dataarray
   :param ambiguity_map: None or ambiguity map
   :type ambiguity_map: xarray Dataarray
   :param perf_ambiguity_threshold: ambiguity threshold to use
   :type perf_ambiguity_threshold: None or float