Search Results for "numerics"

Swift Numerics is a foundational library that extends the Swift standard library with essential numerical protocols, types, and functions needed for scientific and systems programming. It defines generic abstractions over real and complex numbers so algorithms can be written once and work across concrete floating-point types. The package includes RealModule utilities and a full Complex type with the expected arithmetic and transcendental functions, bridging a long-standing gap for numerics in Swift. ...

fvcore is a lightweight utility library that factors out common performance-minded components used across Facebook/Meta computer-vision codebases. It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A standout capability is FLOP and activation counting, which analyzes arbitrary PyTorch graphs to report cost by operator and by module for precise profiling. ...

Dynamical Systems and Homology Software. The CAPD library is a collection of flexible C++ modules which are mainly designed to computation of homology of sets and maps and nonrigorous and validated numerics for dynamical systems.

Matplotlib is a python library for making publication quality plots using a syntax familiar to MATLAB users. Matplotlib uses numpy for numerics. Output formats include PDF, Postscript, SVG, and PNG, as well as screen display. As of matplotlib version 1.5, we are no longer making file releases available on SourceForge. Please visit http://matplotlib.org/users/installing.html for help obtaining matplotlib.

Fluid2D allows to study a wide variety of 2D flows. It is written entirely in Python. It is both a teaching code and a research code, capable of running from one core to thousands. Its numerics has been chosen to yield to as small dissipation as possible allowing to simulate easily high Reynolds flows, much higher than any of its concurrents. High performances are achieved by writting most of the operations as matrix-vector multiplications, handled by numpy that itself relies on the highly optimized BLAS.

Pex adds to Pyrex, or Cython, the ability to write C fast numerics using numpy.ndarray, frees you from Makefiles and header files, and makes your Pyrex classes serializable, through both pickling and a much faster scheme.