ebsdtorch 0.1.1

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ebsdtorch

PyTorch-only Python library for analyzing electron backscatter diffraction (EBSD) patterns. It is designed to be fast and easy to use.

Installation

To install ebsdtorch, first install PyTorch, then run this command in your terminal:

pip install ebsdtorch

Features (and TODOs)

• :white_check_mark: Uniform orientation sampling on the sphere and SO(3)

• :white_check_mark: Laue group operations on the sphere and SO(3)

• :white_check_mark: Modified square Lambert projection and inverse

• :white_check_mark: EBSD dictionary indexing (conventional pixel space)

• :white_check_mark: EBSD dictionary indexing (covariance matrix PCA)

• :white_large_square: EBSD dictionary indexing (Halko randomized PCA)

• :white_check_mark: 8-bit Quantization on CPU for fast indexing

• :white_large_square: 8-bit Quantization on GPU for (very) fast indexing

• :white_large_square: Further reduced bit depth quantization (CPU or GPU)

• :white_check_mark: EBSD master pattern direct space convolution with detector annulus

• :white_check_mark: Spherical covariance matrix calculation

• :white_large_square: Spherical covariance matrix interpolation onto detector

• :white_check_mark: pattern projection with average projection center

• :white_check_mark: pattern projection with individual projection centers

• :white_large_square: pattern projection with single camera matrix

• :white_large_square: pattern center fitting (conventional)

• :white_large_square: geometry fitting (single camera matrix)

• :white_check_mark: Wigner D matrices

• :white_large_square: spherical harmonics

• :white_large_square: SO3 FFT for cross correlation / convolution

• :white_large_square: EBSD master pattern blur via SO3 FFT (for BSE image simulation)

• :white_large_square: Support for generic crystal unit cells

• :white_large_square: Monte Carlo backscatter electron simulation

• :white_large_square: Dynamical scattering simulation