artist.optim.kinematics_reconstructor
Attributes
A logger for the kinematic reconstructor. |
Classes
Initialize the kinematics optimizer. |
Module Contents
- artist.optim.kinematics_reconstructor.log
A logger for the kinematic reconstructor.
- class artist.optim.kinematics_reconstructor.KinematicsReconstructor(ddp_setup: artist.util.env.DdpSetup, scenario: artist.scenario.scenario.Scenario, data: dict[str, artist.io.calibration_parser.CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]], optimization_configuration: dict[str, Any], dni: float | None = None, reconstruction_method: str = constants.kinematics_reconstruction_raytracing, bitmap_resolution: torch.Tensor = torch.tensor([256, 256]))
Initialize the kinematics optimizer.
Parameters
- ddp_setupDdpSetup
Information about the distributed environment, process groups, devices, ranks, world size, and heliostat-group-to-ranks mapping.
- scenarioScenario
The scenario.
- datadict[str, CalibrationDataParser | list[tuple[str, list[pathlib.Path], list[pathlib.Path]]]]
The data parser and the mapping of heliostat name and calibration data.
- optimization_configurationdict[str, Any]
Parameters for the optimizer, learning rate scheduler, regularizers, and early stopping.
- dnifloat | None
Direct normal irradiance in W/m^2 (default is None which leads to a ray magnitude of 1.0).
- reconstruction_methodstr
The reconstruction method. Currently, only reconstruction via ray tracing is implemented.
- bitmap_resolutiontorch.Tensor
The resolution of all bitmaps during reconstruction (default is
torch.tensor([256, 256])). Shape is[2].
- ddp_setup
- scenario
- data
- optimizer_dict
- scheduler_dict
- dni = None
- bitmap_resolution
- reconstruct_kinematics(loss_definition: artist.optim.loss.Loss, device: torch.device | None = None) tuple[torch.Tensor, list[Any]]
Reconstruct the kinematic parameters.
Parameters
- loss_definitionLoss
The definition of the loss function and pre-processing of the prediction.
- devicetorch.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 kinematics reconstruction for each heliostat in each group. Shape is
[total_number_of_heliostats_in_scenario].- list[list[dict[str, list[float]]]]
Loss histories over epochs grouped by rank. Outer list: one entry per rank. Inner list: one entry per heliostat group processed on that rank. Each group entry is a dict with key
"total_loss"mapping to a list of per-epoch scalar loss values. In non-distributed mode, this is a single-rank container:[local_group_histories].
- _reconstruct_kinematics_parameters_with_raytracing(loss_definition: artist.optim.loss.Loss, device: torch.device | None = None) tuple[torch.Tensor, list[list[dict[str, list[float]]]]]
Reconstruct the kinematics parameters using ray tracing.
This reconstruction method optimizes the kinematics parameters by extracting the focal points of calibration images and using heliostat-tracing.
Parameters
- loss_definitionLoss
Definition of the loss function and pre-processing of the prediction.
- devicetorch.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 kinematics reconstruction for each heliostat in each group. Shape is
[total_number_of_heliostats_in_scenario].- list[list[dict[str, list[float]]]]
Loss histories over epochs grouped by rank. Outer list: one entry per rank. Inner list: one entry per heliostat group processed on that rank. Each group entry is a dict with key
"total_loss"mapping to a list of per-epoch scalar loss values. In non-distributed mode, this is a single-rank container:[local_group_histories].