Search Results for "unit-api"
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Showing 61 open source projects for "unit-api"
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Hecke.jl
Computational algebraic number theory
Hecke is a software package for algebraic number theory maintained by Claus Fieker, Tommy Hofmann and Carlo Sircana. It is written in julia and is based on the computer algebra packages Nemo and AbstractAlgebra. Hecke is part of the OSCAR project and the development is supported by the Deutsche Forschungsgemeinschaft DFG within the Collaborative Research Center TRR 195. -
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OpenCL.jl
OpenCL Julia bindings
Julia interface for the OpenCL parallel computation API. This package aims to be a complete solution for OpenCL programming in Julia, similar in scope to PyOpenCL for Python. It provides a high level API for OpenCL to make programing hardware accelerators, such as GPUs, FPGAs, and DSPs, as well as multicore CPUs much less onerous. -
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CImGui
Julia wrapper for cimgui
This package provides a Julia language wrapper for cimgui: a thin c-api wrapper programmatically generated for the excellent C++ immediate mode gui Dear ImGui. Dear ImGui is mainly for creating content creation tools and visualization / debug tools. You could browse Gallery to get an idea of its use cases. -
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ETH course - Solving PDEs in parallel on GPUs
This course aims to cover state-of-the-art methods in modern parallel Graphical Processing Unit (GPU) computing, supercomputing and code development with applications to natural sciences and engineering. -
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LLVM.jl
Julia wrapper for the LLVM C API
A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language. -
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FEniCS.jl
A scientific machine learning (SciML) wrapper for the FEniCS
...This wrapper includes three parts. Installation and direct access to FEniCS via a Conda installation. Alternatively one may use their current FEniCS installation. A low-level development API and provides some functionality to make directly dealing with the library a little bit easier, but still requires knowledge of FEniCS itself. Interfaces have been provided for the main functions and their attributes, and instructions to add further ones can be found here. A high-level API for usage with DifferentialEquations. An example can be seen in solving the heat equation with high-order adaptive time-stepping. ... -
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ComponentArrays.jl
Arrays with arbitrarily nested named components
The main export of this package is the ComponentArray type. "Components" of ComponentArrays are really just array blocks that can be accessed through a named index. This will create a new ComponentArray whose data is a view into the original, allowing for standalone models to be composed together by simple function composition. In essence, ComponentArrays allow you to do the things you would usually need a modeling language for, but without actually needing a modeling language. The main... -
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Unitful.jl
Physical quantities with arbitrary units
Unitful is a Julia package for physical units. We want to support not only SI units but also any other unit system. We also want to minimize or in some cases eliminate the run-time penalty of units. There should be facilities for dimensional analysis. All of this should integrate easily with the usual mathematical operations and collections that are found in Julia base. -
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Graphs.jl
An optimized graphs package for the Julia programming language
The goal of Graphs.jl is to offer a performant platform for network and graph analysis in Julia, following the example of libraries such as NetworkX in Python. Offers a set of simple, concrete graph implementations – SimpleGraph (for undirected graphs) and SimpleDiGraph (for directed graphs), an API for the development of more sophisticated graph implementations under the AbstractGraph type, and a large collection of graph algorithms with the same requirements as this API. -
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MultivariatePolynomials.jl
Multivariate polynomials interface
MultivariatePolynomials.jl is an implementation-independent library for manipulating multivariate polynomials. It defines abstract types and an API for multivariate monomials, terms, and polynomials and gives default implementation for common operations on them using the API. On the one hand, This packages allows you to implement algorithms on multivariate polynomials that will be independant on the representation of the polynomial that will be chosen by the user. On the other hand, it allows the user to easily switch between different representations of polynomials to see which one is faster for the algorithm that he is using. -
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Vulkan.jl
Using Vulkan from Julia
...It exposes abstractions over the underlying C interface, primarily geared toward developers looking for a more natural way to work with Vulkan with minimal overhead. It builds upon the core API provided by VulkanCore.jl. Because Vulkan is originally a C specification, interfacing with it requires some knowledge before correctly being used from Julia. This package acts as an abstraction layer, so that you don't need to know how to properly call a C library, while still retaining full functionality. The wrapper is generated directly from the Vulkan Specification. -
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Bukdu.jl
Bukdu is a web development framework for Julia
Bukdu.jl is a web development framework for Julia. -
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Interpolations.jl
Fast, continuous interpolation of discrete datasets in Julia
This package implements a variety of interpolation schemes for the Julia language. It has the goals of ease of use, broad algorithmic support, and exceptional performance. Currently, this package supports B-splines and irregular grids. The API has been designed with the intent to support more options. Initial support for Lanczos interpolation was recently added. Pull requests are more than welcome! It should be noted that the API may continue to evolve over time. -
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NLPModels.jl
Data Structures for Optimization Models
This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia. -
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MPI.jl
MPI wrappers for Julia
This is a basic Julia wrapper for the portable message-passing system Message Passing Interface (MPI). Inspiration is taken from mpi4py, although we generally follow the C and not the C++ MPI API. (The C++ MPI API is deprecated.) MPI is based on a single program, multiple data (SPMD) model, where multiple processes are launched running independent programs, which then communicate as necessary via messages. As the main entry point for users, MPI.jl provides a high-level interface which loosely follows the MPI C API and is described in details in the following sections. ... -
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JuliaFormatter.jl
An opinionated code formatter for Julia
...Supports YAS, Blue and SciML style guides. JuliaFormatter.toml configuration file to store options. JuliaFormatter exports format, format_file, format_text, and format_md. format_md has the same API as format_text but differ in that format_md expects the text content to be a Markdown document. See format_text docstring for formatting options at the text level and format_file docstring for formatting options at the file level. JuliaFormatter should work on any valid Julia and Markdown files. If JuliaFormatter cannot parse the code for any reason, it will throw an error pointing to the line that could not be parsed. ... -
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ScientificTypes.jl
An API for dispatching on the "scientific" type of data
This package makes a distinction between machine type and scientific type of a Julia object. The machine type refers to the Julia type being used to represent the object (for instance, Float64). The scientific type is one of the types defined in ScientificTypesBase.jl reflecting how the object should be interpreted (for instance, Continuous or Multiclass). -
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CausalityTools.jl
Algorithms for detecting associations, dynamical influences
...Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can think of. For example, we offer the generic SurrogateTest, which is fully compatible with TimeseriesSurrogates.jl, and the LocalPermutationTest for conditional independence testing. -
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ReverseDiff
Reverse Mode Automatic Differentiation for Julia
ReverseDiff is a fast and compile-able tape-based reverse mode automatic differentiation (AD) that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really). While performance can vary depending on the functions you evaluate, the algorithms implemented by ReverseDiff generally outperform non-AD algorithms in both speed and accuracy. -
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AbstractGPs.jl
Abstract types and methods for Gaussian Processes
AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs. You may want to go through the main API design documentation. -
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
AutoMLPipeline (AMLP) is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for... -
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ODBC.jl
An ODBC interface for the Julia programming language
The ODBC.jl package provides a Julia interface for the ODBC API as implemented by various ODBC driver managers. More specifically, it provides a prebuilt copy of iODBC and unixODBC for OSX/Linux platforms, while still relying on the system-provided libraries on Windows. This means that no extra installation of a driver manager is necessary after installing the ODBC.jl package. -
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MATLAB.jl
Calling MATLAB in Julia through MATLAB Engine
The MATLAB.jl package provides an interface for using MATLAB® from Julia using the MATLAB C api. In other words, this package allows users to call MATLAB functions within Julia, thus making it easy to interoperate with MATLAB from the Julia language. You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB® from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product. -
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Fermi.jl
Fermi quantum chemistry program
...Currently, only restricted references are supported. This is intended as a research code with an ever growing collection of methods implemented in the package itself. However, the Fermi API is designed to make high performance pilot implementations of methods achievable. -
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BlockArrays.jl
BlockArrays for Julia
...Firstly, it defines an interface for an AbstractBlockArray block arrays that can be shared among types representing different types of block arrays. The advantage to this is that it provides a consistent API for block arrays. Secondly, it also implements two different types of block arrays that follow the AbstractBlockArray interface. The type BlockArray stores each block contiguously while the type PseudoBlockArray stores the full matrix contiguously. This means that BlockArray supports fast noncopying extraction and insertion of blocks while PseudoBlockArray supports fast access to the full matrix to use in for example a linear solver.
