Search Results for "ray"

...These books are formatted for printing directly from your browser, where you can also (on most browsers) save them as PDF. In Ray Tracing in One Weekend, you will build a simple brute-force path tracer. Continuing with Ray Tracing: The Next Week, you will add textures, volumes (like fog), rectangles, instances, lights, and support for lots of objects using a bounding volume hierarchy (BVH). Finally, with Ray Tracing: The Rest Of Your Life, we'll dive into the math of creating a very serious ray tracer.

A tool to enforce Swift style and conventions, loosely based on the now archived GitHub Swift Style Guide. SwiftLint enforces the style guide rules that are generally accepted by the Swift community. These rules are well described in popular style guides like Ray Wenderlich’s Swift Style Guide. SwiftLint hooks into Clang and SourceKit to use the AST representation of your source files for more accurate results. You might want to move your SwiftLint phase directly before ‘Compile Sources’ step, to detect errors quickly before compiling. However, SwiftLint is designed to run on valid Swift code that cleanly completes the compiler’s parsing stage. ...

xrayutilities is a python package used to analyze x-ray diffraction data. It can support with performing diffraction experiments and used for common steps in the data analysis. It can read experimental data from several data formats (spec, edf, xrdml, ...); convert them to reciprocal space for arbitrary goniometer geometries and different detector systems (point, linear as well as area detectors); for further processing the data can be gridded (transformed to a regular grid). ...

Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. The repository may also contain domain-specific modules: loss functions like Dice, focal loss, metrics such as sensitivity/recall/IoU, and visualization utilities for overlaying segmentation masks.