Gaussian Smoother

diffinytrace.gaussian_smoother.gaussian_func_1D(eval_points: Tensor, x_range, num_gauss_points: int, sigma: float, include_boundary=False) → Tensor

Gaussian function for 1D convolution.

Parameters:

• eval_points (torch.Tensor) – Points where the Gaussian function is evaluated.

• x_range (tuple) – Range of the target plane.

• num_gauss_points (int) – Number of Gaussian points.

• sigma (float) – Standard deviation of the Gaussian function.

• include_boundary (bool) – Whether to include the boundary points.

Returns:

Evaluated Gaussian function.

Return type:

torch.Tensor

diffinytrace.gaussian_smoother.gaussian_func_2D(eval_points: Tensor, x_range, y_range, x_grid_size: int, y_grid_size: int, sigma: float | Tensor, val_multi: Tensor | None = None, summed: bool = True, include_boundary=False) → Tensor

Gaussian function for 2D convolution.

Parameters:

• eval_points (torch.Tensor) – Points where the Gaussian function is evaluated.

• y_range (tuple) – Range of the target plane in the vertical direction.

• x_range (tuple) – Range of the target plane in the horizontal direction.

• y_grid_size (int) – Number of Gaussian points in the vertical direction.

• x_grid_size (int) – Number of Gaussian points in the horizontal direction.

• sigma (float) – Standard deviation of the Gaussian function.

• val_multi (torch.Tensor|None) – Optional multiplier for the Gaussian function.

• summed (bool) – Whether to sum the Gaussian function.

• include_boundary (bool) – Whether to include the boundary points.

Returns:

Evaluated Gaussian function.

Return type:

torch.Tensor

diffinytrace.gaussian_smoother.calc_smooth_desired_irradiance(desired_irradiance_fun: Callable, x_range: List[float], y_range: List[float], x_grid_size: int, y_grid_size: int, sigma: float, num_integration_points: int, num_splits=5, dtype=torch.float64, device=device(type='cpu')) → Tensor

Calculates the smoothed desired irradiance using Gaussian convolution.

Parameters:

• desired_irradiance_fun (Callable) – Function that computes the desired irradiance at given points.

• x_range (List[float]) – Range of the target plane in the x direction [min, max].

• y_range (List[float]) – Range of the target plane in the y direction [min, max].

• x_grid_size (int) – Number of pixels in the x direction.

• y_grid_size (int) – Number of pixels in the y direction.

• sigma (float) – Standard deviation of the Gaussian kernel.

• num_integration_points (int) – Number of integration points for numerical integration.

• num_splits (int, optional) – Number of splits for integration to reduce memory usage. Defaults to 5.

• dtype (torch.dtype, optional) – Data type for tensors. Defaults to torch.get_default_dtype().

• device (torch.device, optional) – Device for computation. Defaults to torch.get_default_device().

Returns:

Smoothed desired irradiance map.

Return type:

torch.Tensor

class diffinytrace.gaussian_smoother.GaussianSmoother(x_range: list, y_range: list, x_grid_size: int, y_grid_size: int, sigma: float, desired_irradiance_fun: Callable, smoothed_num_integration_points: int, smoothed_num_splits: int, dtype=torch.float64, device=device(type='cpu'))

Bases: object

The GaussianSmoother class implements gaussian measurement functions but also computes smoothed desired irradiance distributions. For more information on this class please refer to the examples.

Parameters:

• x_range (list) – Range of the target plane in the x direction [min, max].

• y_range (list) – Range of the target plane in the y direction [min, max].

• x_grid_size (int) – Number of pixels in the x direction.

• y_grid_size (int) – Number of pixels in the y direction.

• sigma (float) – Standard deviation of the Gaussian kernel.

• desired_irradiance_fun (Callable) – Function that computes the desired irradiance at given points.

• smoothed_num_integration_points (int) – Number of integration points for smoothing.

• smoothed_num_splits (int) – Number of splits for integration to reduce memory usage.

• dtype (torch.dtype, optional) – Data type for tensors. Defaults to torch.get_default_dtype().

• device (torch.device, optional) – Device for computation. Defaults to torch.get_default_device().

x_grid_size

Number of pixels in the x direction.

Type:

int

y_grid_size

Number of pixels in the y direction.

Type:

int

sigma

Standard deviation of the Gaussian kernel.

Type:

float

include_boundary

Whether to include boundary points in the grid.

Type:

bool

x_range

Range of the target plane in the x direction.

Type:

list

y_range

Range of the target plane in the y direction.

Type:

list

grid

Grid object for pixel centers.

Type:

Grid

discrete_desired_irradiance

Desired irradiance at pixel centers.

Type:

torch.Tensor

smoothed_desired_irradiance

Smoothed desired irradiance map.

Type:

torch.Tensor

smoothed_irradiance(points: Tensor, ray_multi: Tensor, x_range=None, y_range=None) → Tensor

Computes the smoothed irradiance at given points using a Gaussian kernel.

Parameters:

• points (torch.Tensor) – Array of points where the irradiance is evaluated, shape [N, 2].

• ray_multi (torch.Tensor) – Multiplicative weights for each point, e.g., ray flux.

• x_range (tuple, optional) – Range of the target plane in the x direction. Defaults to None.

• y_range (tuple, optional) – Range of the target plane in the y direction. Defaults to None.

Returns:

Smoothed irradiance values at the specified points.

Return type:

torch.Tensor

none_smoothed_irradiance(points: Tensor, ray_multi: Tensor) → Tensor

Computes the non-smoothed irradiance at given points by summing ray contributions in each grid cell.

Parameters:

• points (torch.Tensor) – Array of points where the irradiance is evaluated, shape [N, 2].

• ray_multi (torch.Tensor) – Multiplicative weights for each point, e.g., ray flux.

Returns:

Non-smoothed irradiance values at the specified grid cells.

Return type:

torch.Tensor

integrate_values(vals: Tensor, x_range=None, y_range=None) → Tensor

Integrates the provided values over the grid using midpoint rule.

Parameters:

vals (torch.Tensor) – Values to integrate, typically irradiance or residuals, shape matching the grid.

Returns:

The integrated sum over the grid.

Return type:

torch.Tensor

diffinytrace.gaussian_smoother.make_evaluation_function(optical_system: SequentialOpticalSystem, sequence: List, source: LightSource, detector, smoother: GaussianSmoother, num_splits: int = 10, num_rays_per_split: int = 100000, method_ray_tracing='monte_carlo', device=device(type='cpu')) → Callable

Creates an evaluation function for comparing simulated and desired irradiance.

Parameters:

• optical_system (SequentialOpticalSystem) – The optical system to be used for ray tracing.

• sequence – The sequence of optical elements.

• source (LightSource) – The light source for the simulation.

• detector – The detector object.

• smoother (GaussianSmoother) – Smoother object for irradiance comparison.

• num_splits (int, optional) – Number of splits for ray tracing to reduce memory usage. Defaults to 10.

• num_rays_per_split (int, optional) – Number of rays per split. Defaults to 1,000,000.

• method_ray_tracing (str, optional) – Ray tracing method (‘monte_carlo’, etc.). Defaults to “monte_carlo”.

• device (torch.device, optional) – Device for computation. Defaults to torch.get_default_device().

Returns:

A function that computes the L2 error between simulated and desired irradiance.

Return type:

Callable

diffinytrace.gaussian_smoother.make_merit_function(optical_system: SequentialOpticalSystem, sequence: List, source: LightSource, detector, smoother: GaussianSmoother, num_rays: int, method_ray_tracing='sobol_pow2', use_desired_irradiance_smoothing=True, device=device(type='cpu'), T_margin=None) → Callable

Creates a merit function to obtain a desired irradiance distribution for the given optical system, source, and detector.

Parameters:

• optical_system (SequentialOpticalSystem) – The optical system to be used.

• sequence – The sequence of elements in the optical system.

• source (LightSource) – The light source to be used.

• detector – The detector to be used.

• num_rays (int) – Number of rays to be traced.

• smoother (Smoother) – The smoother object for merit function calculation.

• device – The device to be used for calculations.

• method_ray_tracing (str) – Method for ray tracing (‘sobol’ or ‘midpoint’).

• use_desired_irradiance_smoothing (bool) – Whether to use desired irradiance smoothing.

• use_power_correction (bool) – Whether to use power correction.

• save_last_eval (bool) – Whether to save the last evaluation.

• T_margin (float|None) – Optional margin for integration domain if it is None the integration domain will not be adjusted on the fly.

Returns:

A function that computes the merit value.

Return type:

Callable

class diffinytrace.gaussian_smoother.GaussianSmootherSquare(aperture_radius: list, grid_size: int, sigma: float, desired_irradiance_fun: Callable, smoothed_num_integration_points: int, smoothed_num_splits: int, dtype=torch.float64, device=device(type='cpu'))

Bases: GaussianSmoother

This class is a specialized version of GaussianSmoother for cases where the x and y ranges are identical, and the grid is square (same number of pixels in both directions).

Parameters:

• x_range (list) – Range of the target plane in both x and y directions [min, max].

• x_grid_size (int) – Number of pixels in both x and y directions.

• sigma (float) – Standard deviation of the Gaussian kernel.

• desired_irradiance_fun (Callable) – Function that computes the desired irradiance at given points.

• smoothed_num_integration_points (int) – Number of integration points for smoothing.

• smoothed_num_splits (int) – Number of splits for integration to reduce memory usage.

• dtype (torch.dtype, optional) – Data type for tensors. Defaults to torch.get_default_dtype().

• device (torch.device, optional) – Device for computation. Defaults to torch.get_default_device().