:py:mod:`cars.applications.dem_generation.dichotomic_generation`
================================================================

.. py:module:: cars.applications.dem_generation.dichotomic_generation

.. autoapi-nested-parse::

   this module contains the dichotomic dem generation application class.



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

Classes
~~~~~~~

.. autoapisummary::

   cars.applications.dem_generation.dichotomic_generation.DichotomicGeneration



Functions
~~~~~~~~~

.. autoapisummary::

   cars.applications.dem_generation.dichotomic_generation.generate_grid
   cars.applications.dem_generation.dichotomic_generation.multi_res_rec



.. py:class:: DichotomicGeneration(conf=None)


   Bases: :py:obj:`cars.applications.dem_generation.dem_generation.DemGeneration`

   DichotomicGeneration

   .. py:method:: check_conf(conf)

      Check configuration

      :param conf: configuration to check
      :type conf: dict

      :return: overloaded configuration
      :rtype: dict



   .. py:method:: run(triangulated_matches_list, output_dir, geoid_path, dem_roi_to_use=None, initial_elevation=None, cars_orchestrator=None)

      Run dichotomic dem generation using matches

      :param triangulated_matches_list: list of triangulated matches
          positions must be in a metric system
      :type triangulated_matches_list: list(pandas.Dataframe)
      :param output_dir: directory to save dem
      :type output_dir: str
      :param geoid_path: geoid path
      :param cars_orchrestrator: the main cars orchestrator
      :param dem_roi_to_use: dem roi polygon to use as roi
      :param initial_elevation: the path to the initial elevation file

      :return: dem data computed with mean, min and max, and new initial
               elevation file. dem is also saved in disk, and paths are
               available in attributes.
               (DEM_MEDIAN_PATH, DEM_MIN_PATH, DEM_MAX_PATH)
      :rtype: Tuple(CarsDataset, str | None)



.. py:function:: generate_grid(pd_pc, resolution, xmin=None, xmax=None, ymin=None, ymax=None)

   Generate regular grid

   :param pd_pc: point cloud
   :type pd_pc: Pandas Dataframe
   :param resolution: resolution in meter
   :type resolution: float
   :param xmin: x min position in metric system
   :type xmin: float
   :param xmax: x max position in metric system
   :type xmax: float
   :param ymin: y min position in metric system
   :type ymin: float
   :param ymax: y max position in metric system
   :type ymax: float

   :return: regular grid
   :rtype: numpy array



.. py:function:: multi_res_rec(pd_pc, list_fun, x_grid, y_grid, list_z_grid, row_min, row_max, col_min, col_max, min_number_matches, overlap)

   Recursive function to fill grid with results of given functions

   :param pd_pc: point cloud
   :type pd_pc: Pandas Dataframe
   :param list_fun: list of functions
   :type list_fun: list(function)
   :param x_grid: x grid
   :type x_grid: numpy array
   :param y_grid: y grid
   :type y_grid: numpy array
   :param list_z_grid: list of z grid computed with functions
   :type list_z_grid: list(numpy array)
   :param row_min: row min
   :type row_min: int
   :param row_max: row max
   :type row_max: int
   :param col_min: col min
   :type col_min: int
   :param col_max: col max
   :type col_max: int
   :param min_number_matches: minimum of matches: stop condition
   :type min_number_matches: int
   :param overlap: overlap to use for include condition
   :type overlap: float