Post-Processing

`sigmoid_falloff_jax(u, amplitude, width)`

Applies a sigmoid to the scalar (Signed Distance Function) field.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `amplitude` (`scalar_like_type`) – Maximum value of the transformed scalar field. `width` (`scalar_like_type`) – Width of the sigmoid.

Returns:	`ndarray` – Transformed scalar field.

`positive_sigmoid_falloff_jax(u, amplitude, width)`

Applies a sigmoid, shifted to the positive values by the value of the width parameter, to the scalar (Signed Distance Function) field.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `amplitude` (`scalar_like_type`) – Maximum value of the transformed scalar field. `width` (`scalar_like_type`) – Width of the sigmoid.

Returns:	`ndarray` – Transformed scalar field.

`capped_exponential_jax(u, amplitude, width)`

Applies a decreasing exponential functon to the scalar (Signed Distance Function) field.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `amplitude` (`scalar_like_type`) – Maximum value of the transformed scalar field. `width` (`scalar_like_type`) – Range at which the value of the transformed scalar field drops to almost zero.

Returns:	`ndarray` – Transformed scalar field.

`hard_binarization_jax(u, threshold)`

Binarizes the Signed Distance field/pattern based on a threshold. Values below the threshold are 1 and values above are 0.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `threshold` (`scalar_like_type`) – Binarization threshold.

Returns:	`ndarray` – Binarized scalar field.

`linear_falloff_jax(u, amplitude, width)`

Applies a decreasing linear function to the scalar (Signed Distance Function) field.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `amplitude` (`scalar_like_type`) – Maximum value of the transformed scalar field. `width` (`scalar_like_type`) – Range at which the value of the transformed scalar field drops to zero.

Returns:	`ndarray` – Transformed scalar field.

`relu_jax(u, width=1)`

Applies the ReLU function to the scalar (Signed Distance Function) field.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `width` (`scalar_like_type`, default: `1` ) – Range at which the value of the transformed field reaches one.

Returns:	`ndarray` – Transformed scalar field.

`smooth_relu_jax(u, smooth_width, width=1, threshold=0.01)`

Applies the "squareplus" function to the scalar (Signed Distance Function) field. https://en.wikipedia.org/wiki/Rectifier_(neural_networks)

Parameters:

u (array_like_type) –

Signed Distance field or any scalar field.
smooth_width (scalar_like_type) –

Distance from the origin at which the Smooth ReLU function is greater than ReLU for less than the value of the threshold parameter.
width (scalar_like_type, default: 1 ) –

Range at which the value of the transformed field reaches one.
threshold (scalar_like_type, default: 0.01 ) –

At smooth_width distance from the origin the value of the Smooth ReLU function is greater than ReLU for the value of the threshold parameter. at smooth_width distance from the origin.

Returns:	`ndarray` – Transformed scalar field.

`slowstart_jax(u, smooth_width, width=1, threshold=0.01, ground=True)`

Applies the SlowStart function to the scalar (Signed Distance Function) field.

Parameters:

u (array_like_type) –

Signed Distance field or any scalar field.
smooth_width (scalar_like_type) –

Distance from the origin at which the SlowStart function is greater than ReLU for less than the value of the threshold parameter.
width (scalar_like_type, default: 1 ) –

Range at which the value of the transformed field reaches one.
threshold (scalar_like_type, default: 0.01 ) –

At smooth_width distance from the origin the value of the SlowStart function is greater than ReLU for the value of the threshold parameter.
ground (bool, default: True ) –

if True the value of the function is zero at zero.

Returns:	`ndarray` – Transformed scalar field.

`gaussian_boundary_jax(u, amplitude, width)`

Applies the Gaussian to the scalar (Signed Distance Function) field.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `amplitude` (`scalar_like_type`) – Maximum value of the transformed scalar field. `width` (`scalar_like_type`) – Range at which the value of the transformed scalar field drops to almost zero.

Returns:	`ndarray` – Transformed scalar field.

`gaussian_falloff_jax(u, amplitude, width)`

Applies the Gaussian to the positive values of the scalar (Signed Distance Function) field.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `amplitude` (`scalar_like_type`) – Maximum value of the transformed scalar field (and points at which the scalar field was < 0). `width` (`scalar_like_type`) – Range at which the value of the transformed scalar field drops to almost zero.

Returns:	`ndarray` – Transformed scalar field.

`conv_multiple_jax(u, filter_kernel, iterations)`

Averages the field using an averaging convolutional kernel of the specified size.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field. `filter_kernel` (`array_like_type`) – Convolutional kernel of appropriate dimensions. `iterations` (`int`) – Number of times the convolutional averaging is applied to the input scalar field.

Returns:	`ndarray` – Transformed scalar field.

`conv_edge_detection_jax(u)`

Edge detection with a 3x3 convolutional kernel.

Parameters:	`u` (`array_like_type`) – Signed Distance field or any scalar field.

Returns:	`ndarray` – Transformed scalar field.