artist.flux.bitmap

Functions

`get_center_of_mass`(→ torch.Tensor)	Calculate the coordinates of the flux density center of mass.
`trapezoid_distribution`(→ torch.Tensor)	Create a one-dimensional trapezoid distribution.
`crop_flux_distributions_around_center`(→ torch.Tensor)	Crop a centered rectangular region from grayscale intensity images based on physical dimensions.

Module Contents

artist.flux.bitmap.get_center_of_mass(bitmaps: torch.Tensor, device: torch.device | None = None) → torch.Tensor

Calculate the coordinates of the flux density center of mass.

Bitmaps and the resolution are conceptually defined as: [W, H] # width, height Tensor memory layout follows PyTorch convention: [H, W] # height, width

First determine the indices of the bitmap center of mass. Next determine the position (coordinates) of the center of mass on the target.

Returns (0.0, 0.0) for empty fluxes.

Parameters

bitmapstorch.Tensor: Flux densities in form of bitmaps. Shape is [number_of_active_heliostats, bitmap_resolution_e, bitmap_resolution_u].
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: Bitmap coordinates of the flux density centers of mass (e pixel, u pixel). Shape is [number_of_active_heliostats, 2].

artist.flux.bitmap.trapezoid_distribution(total_width: int, slope_width: int, plateau_width: int, device: torch.device | None = None) → torch.Tensor

Create a one-dimensional trapezoid distribution.

If the total width is less than 2 * slope_width + plateau_width, the slope is cut off. If the total width is greater than 2 * slope_width + plateau_width, the trapezoid is padded with zeros on both sides.

Parameters

total_widthint: The total width of the trapezoid. Must be > 0.
slope_widthint: The width of the slope of the trapezoid.
plateau_widthint: The width of the plateau.
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 one dimensional trapezoid distribution. Shape is [total_width].

artist.flux.bitmap.crop_flux_distributions_around_center(flux_distributions: torch.Tensor, solar_tower: artist.field.solar_tower.SolarTower, target_area_indices: torch.Tensor, crop_width: float = constants.utis_crop_width, crop_height: float = constants.utis_crop_height, device: torch.device | None = None) → torch.Tensor

Crop a centered rectangular region from grayscale intensity images based on physical dimensions.

This function identifies the center of mass in each image and then crops a region centered around this point with the specified physical width and height (in meters). The cropping is applied via affine transformation, which accounts for the desired crop size relative to the target’s physical plane dimensions.

Parameters

flux_distributionstorch.Tensor: Flux density bitmaps, one per heliostat. Shape is [number_of_bitmaps, bitmap_height, bitmap_width].
solar_towerSolarTower: Solar tower containing the physical target area dimensions.
target_area_indicestorch.Tensor: Global target area index for each bitmap (planar indices first, cylindrical second). Shape is [number_of_bitmaps].
crop_widthfloat: Desired width of the cropped region in meters (default is constants.utis_crop_width).
crop_heightfloat: Desired height of the cropped region in meters (default is constants.utis_crop_height).
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: Cropped and centered image regions. Shape is [number_of_bitmaps, bitmap_height, bitmap_width].