artist.core.kinematic_reconstructor
===================================

.. py:module:: artist.core.kinematic_reconstructor


Attributes
----------

.. autoapisummary::

   artist.core.kinematic_reconstructor.log


Classes
-------

.. autoapisummary::

   artist.core.kinematic_reconstructor.KinematicReconstructor


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

.. py:data:: log

   A logger for the kinematic reconstructor.


.. py:class:: KinematicReconstructor(ddp_setup: dict[str, Any], scenario: artist.scenario.scenario.Scenario, data: dict[str, artist.data_parser.calibration_data_parser.CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]], optimization_configuration: dict[str, Any], reconstruction_method: str = config_dictionary.kinematic_reconstruction_raytracing)

   Initialize the kinematic optimizer.

   Parameters
   ----------
   ddp_setup : dict[str, Any]
       Information about the distributed environment, process_groups, devices, ranks, world_Size, heliostat group to ranks mapping.
   scenario : Scenario
       The scenario.
   data : dict[str, CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]]
       The data parser and the mapping of heliostat name and calibration data.
   optimization_configuration : dict[str, Any]
       The parameters for the optimizer, learning rate scheduler, regularizers and early stopping.
   reconstruction_method : str
       The reconstruction method. Currently only reconstruction via ray tracing is available (default is ray_tracing).


   .. py:attribute:: ddp_setup


   .. py:attribute:: scenario


   .. py:attribute:: data


   .. py:attribute:: optimization_configuration


   .. py:method:: reconstruct_kinematic(loss_definition: artist.core.loss_functions.Loss, device: torch.device | None = None) -> torch.Tensor

      Reconstruct the kinematic parameters.

      Parameters
      ----------
      loss_definition : Loss
          The definition of the loss function and pre-processing of the prediction.
      device : torch.device | None
          The device on which to perform computations or load tensors and models (default is None).
          If None, ARTIST will automatically select the most appropriate
          device (CUDA or CPU) based on availability and OS.

      Returns
      -------
      torch.Tensor
          The final loss of the kinematic reconstruction for each heliostat in each group.
          Tensor of shape [total_number_of_heliostats_in_scenario].



   .. py:method:: _reconstruct_kinematic_parameters_with_raytracing(loss_definition: artist.core.loss_functions.Loss, device: torch.device | None = None) -> torch.Tensor

      Reconstruct the kinematic parameters using ray tracing.

      This reconstruction method optimizes the kinematic parameters by extracting the focal points
      of calibration images and using heliostat-tracing.

      Parameters
      ----------
      loss_definition : Loss
          The definition of the loss function and pre-processing of the prediction.
      device : torch.device | None
          The device on which to perform computations or load tensors and models (default is None).
          If None, ARTIST will automatically select the most appropriate
          device (CUDA or CPU) based on availability and OS.

      Returns
      -------
      torch.Tensor
          The final loss of the kinematic reconstruction for each heliostat in each group.
          Tensor of shape [total_number_of_heliostats_in_scenario].