Processor

This module provides tools for processing and analyzing device matrices in nanofabrication prediction tasks. It includes functionality for image binarization, contour generation, and prediction uncertainty computation.

binarize

binarize(device, eta=0.5, beta=np.inf)

Applies soft binarization to a device image using a sigmoid function.

The binarization process can be controlled by adjusting the thresholding level (eta) and the steepness of the sigmoid function (beta). eta influences the threshold level for binarization, simulating under-etching for smaller values and over-etching for larger values. beta controls the steepness of the sigmoid function, thereby determining the degree of binarization.

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binarize_hard

binarize_hard(device, eta=0.5)

Applies hard binarization to a device image using a step function.

The binarization process depends solely on the threshold level (eta), which controls the demarcation point for determining the binary values in the output image. Smaller eta values simulate under-etching (more pixels are turned off), while larger eta values simulate over-etching (more pixels are turned on). Compared to the sigmoid binarization function, this hard binarization method is less likely to produce NaN values and may sometimes yield better results.

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ternarize

ternarize(device, eta1=0.33, eta2=0.66)

Applies ternarization to a device image using two thresholds.

This function performs a ternarization process on a given device image, dividing it into three distinct regions based on two threshold values (eta1 and eta2). It assigns three different values (0, 1, or 2) to each pixel based on its intensity in relation to the thresholds. Pixels with intensity less than eta1 are assigned 0, pixels with intensity greater than or equal to eta2 are assigned 2, and pixels with intensity between eta1 and eta2 are assigned 1. This function can be useful for categorizing different regions in a device image.

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remove_padding

remove_padding(device)

Removes the empty padding from the edges of a device.

This function eliminates rows and columns from the edges of the device matrix that are entirely zeros, effectively removing any unnecessary padding present in the device representation.

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zero_boundary

zero_boundary(device, margin)

Sets the boundaries of a device matrix to zero up to a specified margin.

This function zeroes the outermost rows and columns of the device matrix up to a distance (margin) from the boundaries, effectively creating a "zeroed" frame around the device representation.

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generate_device_contour

generate_device_contour(device, linewidth=None)

Generates a contour of a device for visualization purposes.

This function generates a binary contour of a device's shape which can be overlaid on top of the device's image for better visualization. The thickness of the contour line can be specified, with a default value calculated as 1% of the device's height.

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calculate_prediction_uncertainty

calculate_prediction_uncertainty(prediction)

Computes the uncertainty profile of a non-binary prediction matrix.

This function quantifies the level of uncertainty in a given prediction matrix by identifying the areas between the core (value 1) and cladding (value 0). These regions often correspond to the boundaries of the predicted structure and are represented by pixel values ranging between 0 and 1 in the prediction matrix. The function calculates the uncertainty as the distance from the pixel value to the nearest extreme (0 or 1), highlighting regions of maximum uncertainty.

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mse

mse(prediction, device)

Computes the mean squared error (MSE) between a prediction and a device matrix.

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