artist.core

Bundle all classes responsible for the core functions in ARTIST.

Submodules

Classes

`DistortionsDataset`	Initialize the dataset.
`HeliostatRayTracer`	Initialize the heliostat ray tracer.
`RestrictedDistributedSampler`	Set up a custom distributed sampler to assign data to each rank or leave them idle.
`KinematicReconstructor`	Initialize the kinematic optimizer.
`MotorPositionsOptimizer`	Initialize the motor positions optimizer.
`SurfaceReconstructor`	Initialize the surface reconstructor.

Package Contents

class artist.core.DistortionsDataset(light_source: artist.scene.LightSource, number_of_points_per_heliostat: int, number_of_heliostats: int, random_seed: int = 7)

Bases: torch.utils.data.Dataset

Initialize the dataset.

This class implements a custom dataset according to the torch interface. The content of this dataset are the distortions. The distortions are used in our version of “heliostat”-tracing to indicate how each incoming ray must be multiplied and scattered on the heliostat. According to torch, this dataset must implement a function to return the length of the dataset and one function to retrieve an element through an index.

Parameters

light_sourceLightSource: The light source used to model the distortions.
number_of_points_per_heliostatint: The number of points on the heliostats for which distortions are created.
number_of_heliostatsint: The number of heliostats in the scenario.
random_seedint: The random seed used for generating the distortions (default is 7).

__len__() → int

Calculate the length of the dataset, i.e., the number of items contained.

Returns

int: The length of the dataset.

__getitem__(idx: int) → tuple[torch.Tensor, torch.Tensor]

Select an item from the dataset.

Parameters

idxint: The index of the item to select.

Returns

torch.Tensor: The distortions in the up direction for the given index.
torch.Tensor: The distortions in the east direction for the given index.

class artist.core.HeliostatRayTracer(scenario: artist.scenario.scenario.Scenario, heliostat_group: artist.field.heliostat_group.HeliostatGroup, world_size: int = 1, rank: int = 0, batch_size: int = 100, random_seed: int = 7, bitmap_resolution: torch.Tensor = torch.tensor([artist.util.index_mapping.bitmap_resolution, artist.util.index_mapping.bitmap_resolution]))

Initialize the heliostat ray tracer.

“Heliostat”-tracing is one kind of ray tracing applied in ARTIST. For this kind of ray tracing, the rays are initialized on the heliostats. The rays originate in the discrete surface points. There they are multiplied, distorted, and scattered, and then they are sent to the aim points. Letting the rays originate on the heliostats, drastically reduces the number of rays that need to be traced.

Parameters

scenarioScenario: The scenario used to perform ray tracing.
heliostat_groupHeliostatGroup: The selected heliostat group containing active heliostats.
world_sizeint: The world size i.e., the overall number of processes (default is 1).
rankint: The rank, i.e., individual process ID (default is 0).
batch_sizeint: The amount of samples (heliostats) processed in parallel within a single rank (default is 100).
random_seedint: The random seed used for generating the distortions (default is 7).
bitmap_resolutiontorch.Tensor: The resolution of the bitmap in both directions. (default is torch.tensor([256,256])). Tensor of shape [2].

scenario

heliostat_group

world_size = 1

rank = 0

batch_size = 100

light_source

distortions_dataset

distortions_sampler

distortions_loader

bitmap_resolution

trace_rays(incident_ray_directions: torch.Tensor, active_heliostats_mask: torch.Tensor, target_area_mask: torch.Tensor, device: torch.device | None = None) → torch.Tensor

Perform heliostat ray tracing.

Scatter the rays according to the distortions, calculate the intersections with the target planes, and sample the resulting bitmaps on the target areas. The bitmaps are generated separately for each active heliostat and can be accessed individually or they can be combined to get the total flux density distribution for all heliostats on all target areas.

Parameters

incident_ray_directionstorch.Tensor: The direction of the incident rays as seen from the heliostats. Tensor of shape [number_of_active_heliostats, 4].
active_heliostats_masktorch.Tensor: A mask where 0 indicates a deactivated heliostat and 1 an activated one. An integer greater than 1 indicates that this heliostat is regarded multiple times. Tensor of shape [number_of_heliostats].
target_area_masktorch.Tensor: The indices of the target areas for each active heliostat. Tensor of shape [number_of_active_heliostats].
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.

Raises

ValueError: If not all heliostats used for ray tracing have been aligned.

Returns

torch.Tensor: The resulting bitmaps per heliostat. Tensor of shape [number_of_active_heliostats, bitmap_resolution_e, bitmap_resolution_u].

scatter_rays(distortion_u: torch.Tensor, distortion_e: torch.Tensor, original_ray_direction: torch.Tensor, device: torch.device | None = None) → artist.scene.rays.Rays

Scatter the reflected rays around the preferred ray directions for each heliostat.

Parameters

distortion_utorch.Tensor: The distortions in up direction (angles for scattering). Tensor of shape [number_of_active_heliostats, number_of_rays, number_of_combined_surface_normals_all_facets].
distortion_etorch.Tensor: The distortions in east direction (angles for scattering). Tensor of shape [number_of_active_heliostats, number_of_rays, number_of_combined_surface_normals_all_facets].
original_ray_directiontorch.Tensor: The ray direction around which to scatter. Tensor of shape [number_of_active_heliostats, number_of_combined_surface_normals_all_facets, 4].
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

Rays: Scattered rays around the preferred reflection directions.

sample_bitmaps(intersections: torch.Tensor, absolute_intensities: torch.Tensor, active_heliostats_mask: torch.Tensor, target_area_mask: torch.Tensor, device: torch.device | None = None) → torch.Tensor

Sample bitmaps (flux density distributions) of the reflected rays on the target areas.

The bitmaps are saved for each active heliostat separately.

Parameters

intersectionstorch.Tensor: The intersections of rays on the target area planes for each heliostat. Tensor of shape [number_of_active_heliostats, number_of_rays, number_of_combined_surface_points_all_facets, 4].
absolute_intensitiestorch.Tensor: The absolute intensities of the rays hitting the target planes for each heliostat. Tensor of shape [number_of_active_heliostats, number_of_rays, number_of_combined_surface_points_all_facets].
active_heliostats_masktorch.Tensor: Used to map bitmaps per heliostat to correct index. Tensor of shape [number_of_heliostats].
target_area_masktorch.Tensor: The indices of target areas on which each heliostat is raytraced. Tensor of shape [number_of_active_heliostats].
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 flux density distributions of the reflected rays on the target areas for each active heliostat. Tensor of shape [number_of_active_heliostats, bitmap_resolution_e, bitmap_resolution_u].

get_bitmaps_per_target(bitmaps_per_heliostat: torch.Tensor, target_area_mask: torch.Tensor, device: torch.device | None = None) → torch.Tensor

Transform bitmaps per heliostat to bitmaps per target area.

Parameters

bitmaps_per_heliostattorch.Tensor: Bitmaps per heliostat. Tensor of shape [number_of_active_heliostats, bitmap_resolution_e, bitmap_resolution_u].
target_area_masktorch.Tensor: The mapping from heliostat to target area. Tensor of shape [number_of_active_heliostats].
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: Bitmaps per target area. Tensor of shape [number_of_target_areas, bitmap_resolution_e, bitmap_resolution_u].

class artist.core.RestrictedDistributedSampler(number_of_samples: int, world_size: int = 1, rank: int = 0)

Bases: torch.utils.data.Sampler

Set up a custom distributed sampler to assign data to each rank or leave them idle.

Parameters

number_of_samplesint: The length of the dataset or total number of samples.
world_sizeint: The world size or total number of processes (default is 1).
rankint: The rank of the current process (default is 0).

number_of_samples

world_size = 1

rank = 0

number_of_active_ranks

number_of_samples_per_rank

__iter__() → Iterator[int]

Generate a sequence of indices for the current rank’s portion of the dataset.

Returns

Iterator[int]: An iterator over indices for the current rank.

class artist.core.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_setupdict[str, Any]: Information about the distributed environment, process_groups, devices, ranks, world_Size, 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]: The parameters for the optimizer, learning rate scheduler, regularizers and early stopping.
reconstruction_methodstr: The reconstruction method. Currently only reconstruction via ray tracing is available (default is ray_tracing).

ddp_setup

scenario

data

optimization_configuration

reconstruct_kinematic(loss_definition: artist.core.loss_functions.Loss, device: torch.device | None = None) → torch.Tensor

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 kinematic reconstruction for each heliostat in each group. Tensor of shape [total_number_of_heliostats_in_scenario].

_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_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 kinematic reconstruction for each heliostat in each group. Tensor of shape [total_number_of_heliostats_in_scenario].

class artist.core.MotorPositionsOptimizer(ddp_setup: dict[str, Any], scenario: artist.scenario.scenario.Scenario, optimization_configuration: dict[str, Any], incident_ray_direction: torch.Tensor, target_area_index: int, ground_truth: torch.Tensor, bitmap_resolution: torch.Tensor = torch.tensor([256, 256]), device: torch.device | None = None)

Initialize the motor positions optimizer.

Parameters

ddp_setupdict[str, Any]: Information about the distributed environment, process_groups, devices, ranks, world_Size, heliostat group to ranks mapping.
scenarioScenario: The scenario.
optimization_configurationdict[str, Any]: The parameters for the optimizer, learning rate scheduler, regularizers and early stopping.
incident_ray_directiontorch.Tensor: The incident ray direction during the optimization. Tensor of shape [4].
target_area_indexint: The index of the target used for the optimization.
ground_truthtorch.Tensor: The desired focal spot or distribution. Tensor of shape [4] or tensor of shape [bitmap_resolution_e, bitmap_resolution_u].
bitmap_resolutiontorch.Tensor: The resolution of all bitmaps during optimization (default is torch.tensor([256,256])). Tensor of shape [2].
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.

ddp_setup

scenario

optimization_configuration

incident_ray_direction

target_area_index

ground_truth

bitmap_resolution

optimize(loss_definition: artist.core.loss_functions.Loss, device: torch.device | None = None) → torch.Tensor

Optimize the motor positions.

The motor positions are optimized through a reparameterization to ensure stable training across different heliostats with widely varying initial motor positions and ranges. Motor positions can range from 0 to up to ~80000. Instead of directly optimizing the absolute motor positions, which can differ in magnitudes, an unconstrained parameter is optimized. Directly optimizing the absolute motor positions, would have very different effects depending on the scale of the motors. For small initial motor positions (e.g. ~100), a gradient update of size 10 may cause a ~10% relative change, drastically altering the motor positions of this heliostat. For large initial motor positions (e.g. ~50000), the same optimizer step would correspond to only a 0.02% relative change in motor positions, effectively freezing the optimization of this heliostat. This mismatch makes it impossible to choose a single learning rate that works robustly across all heliostats. The reparametrization of the optimizable parameter (motor positions) defines the optimizable parameter as:

\[\text{motor\_positions\_optimized} = \tanh( \text{torch.nn.Parameter(optimizable\_parameter)} )\]

The true motor positions can be reconstructed by:

\[\text{motor\_positions} = \text{initial\_motor\_positions} + \text{motor\_positions\_normalized} \cdot \text{scale}\]

where scale defines the range (e.g. up to ~80000) for adjustments. By optimizing as explained above instead of raw motor positions, every heliostat sees updates of comparable relative magnitude, regardless of the absolute size of its motors positions.

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 motor position optimization.

class artist.core.SurfaceReconstructor(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], number_of_surface_points: torch.Tensor = torch.tensor([50, 50]), bitmap_resolution: torch.Tensor = torch.tensor([256, 256]), device: torch.device | None = None)

Initialize the surface reconstructor.

Parameters

ddp_setupdict[str, Any]: Information about the distributed environment, process_groups, devices, ranks, world_Size, 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]: The parameters for the optimizer, learning rate scheduler and early stopping.
number_of_surface_pointstorch.Tensor: The number of surface points of the reconstructed surfaces (default is torch.tensor([50,50])). Tensor of shape [2].
bitmap_resolutiontorch.Tensor: The resolution of all bitmaps during reconstruction (default is torch.tensor([256,256])). Tensor of shape [2].
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.

ddp_setup

scenario

data

optimization_configuration

number_of_surface_points

bitmap_resolution

reconstruct_surfaces(loss_definition: artist.core.loss_functions.Loss, device: torch.device | None = None) → torch.Tensor

Reconstruct NURBS surfaces from bitmaps.

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 surface reconstruction for each heliostat in each group. Tensor of shape [total_number_of_heliostats_in_scenario].

static lock_control_points_on_outer_edges(gradients: torch.Tensor, device: torch.device | None = None) → torch.Tensor

Lock the u and v values of the control points on the outer edges of each facet.

As the knots of the first and last knots on each facet have full multiplicity, the NURBS surfaces all start and end in control points. If the outer control points are not fixed in their u and v values, the reconstructed surfaces may not be rectangular anymore. To keep them rectangular, this function zeros the gradients of the u and v coordinates of all outer control points.

Parameters

gradientstorch.Tensor: The gradients of the outer control points. Tensor of shape [number_of_active_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].
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 updated gradients. Tensor of shape [number_of_active_heliostats, number_of_facets_per_surface, number_of_control_points_u_direction, number_of_control_points_v_direction, 3].