artist.data_parser.calibration_data_parser
==========================================

.. py:module:: artist.data_parser.calibration_data_parser


Attributes
----------

.. autoapisummary::

   artist.data_parser.calibration_data_parser.log


Classes
-------

.. autoapisummary::

   artist.data_parser.calibration_data_parser.CalibrationDataParser


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

.. py:data:: log

   A logger for the calibration data parser.


.. py:class:: CalibrationDataParser(sample_limit: int | None = None)

   Initialize the the base data parser.

   Parameters
   ----------
   sample_limit : int | None
       The number specifying the maximum number of samples to be loaded (default is None).


   .. py:attribute:: sample_limit
      :value: None



   .. py:method:: parse_data_for_reconstruction(heliostat_data_mapping: list[tuple[str, list[pathlib.Path], list[pathlib.Path]]], heliostat_group: artist.field.heliostat_group.HeliostatGroup, scenario: artist.scenario.scenario.Scenario, bitmap_resolution: torch.Tensor = torch.tensor([artist.util.index_mapping.bitmap_resolution, artist.util.index_mapping.bitmap_resolution]), device: torch.device | None = None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
      :abstractmethod:


      Extract measured fluxes and their respective calibration properties data.

      Parameters
      ----------
      heliostat_data_mapping : list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]
          The mapping from heliostat to calibration data files.
      heliostat_group : HeliostatGroup
          The heliostat group.
      scenario : Scenario
          The scenario.
      bitmap_resolution : torch.Tensor
          The resolution of all bitmaps during reconstruction (default is torch.tensor([256,256])).
          Tensor of shape [2].
      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
      -------
      The measured flux density distributions.
          Tensor of shape [number_of_active_heliostats, bitmap_resolution_e, bitmap_resolution_u].
      torch.Tensor
          The calibration focal spots.
          Tensor of shape [number_of_calibration_data_points, 4].
      torch.Tensor
          The incident ray directions.
          Tensor of shape [number_of_calibration_data_points, 4].
      torch.Tensor
          The motor positions.
          Tensor of shape [number_of_calibration_data_points, 2].
      torch.Tensor
          A mask with active heliostats and their replications.
          Tensor of shape [number_of_heliostats].
      torch.Tensor
          The target area mapping for the heliostats.
          Tensor of shape [number_of_active_heliostats].



   .. py:method:: load_flux_from_png(heliostat_flux_path_mapping: list[tuple[str, list[pathlib.Path]]], heliostat_names: list[str], resolution: torch.Tensor = torch.tensor([artist.util.index_mapping.bitmap_resolution, artist.util.index_mapping.bitmap_resolution]), device: torch.device | None = None) -> torch.Tensor

      Load flux density distributions as tensors from png images.

      Note that the order of width and height are reversed in ``PIL`` and ``torch``.
      ``PIL`` takes (width, height), while ``torch`` tensors are [height, width].

      Parameters
      ----------
      heliostat_flux_path_mapping : list[tuple[str, list[pathlib.Path]]]
          The mapping of heliostats and their measured flux density distributions.
      heliostat_names : list[str]
          All possible heliostat names.
      resolution : torch.Tensor
          The resolution of the loaded png files (default is torch.tensor([256,256])).
          Tensor of shape [2].
      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 measured flux density distributions.
          Tensor of shape [number_of_active_heliostats, bitmap_resolution_e, bitmap_resolution_u].