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The RHT (Rolling Hough Transform) measures linear intensity as a function of orientation in images. This machine vision algorithm works on any image-space (2D) data, and quantifies the presence of linear structure as a function of orientation. The RHT can be used to identify linear features in images, to quantify the orientation of structure in images, and to map image intensity from 2D x-y space to 3D x-y-orientation space. An option in the code allows the user to quantify intensity as a function of direction (modulo 2pi) rather than orientation (modulo pi). The RHT was first used to discover that filamentary structures in neutral hydrogen emission are aligned with the ambient magnetic field.
Eigentools is a set of tools for studying linear eigenvalue problems. The underlying eigenproblems are solved using Dedalus (ascl:1603.015), which provides a domain-specific language for partial differential equations. Eigentools extends Dedalus's EigenvalueProblem object and provides automatic rejection of unresolved eigenvalues, simple plotting of specified eigenmodes and of spectra, and computation of $\epsilon$-pseudospectra for any Differential-Algebraic Equations with user-specifiable norms. It includes tools to find critical parameters for linear stability analysis and is able to project eigenmode onto 2- or 3-D domain for visualization. It can also output projected eigenmodes as Dedalus-formatted HDF5 file to be used as initial conditions for Initial Value Problems, and provides simple plotting of drift ratios (both ordinal and nearest) to evaluate tolerance for eigenvalue rejection.