Search Results for "vx-linux" - Page 11
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Showing 1987 open source projects for "vx-linux"
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DynamicQuantities.jl
Lightweight + fast physical quantities in Julia
DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods. -
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PhysicalConstants.jl
Collection of fundamental physical constants with uncertainties
PhysicalConstants.jl provides common physical constants. They are defined as instances of the new Constant type, which is a subtype of AbstractQuantity (from Unitful.jl package) and can also be turned into Measurement objects (from Measurements.jl package) at request. Constants are grouped into different submodules so that the user can choose different datasets as needed. Currently, 2014 and 2018 editions of CODATA recommended values of the fundamental physical constants are provided. -
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DynamicalBilliards.jl
An easy-to-use, modular, extendable and absurdly fast Julia package
A Julia package for dynamical billiard systems in two dimensions. The goals of the package is to provide a flexible and intuitive framework for fast implementation of billiard systems of arbitrary construction. -
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ImageInTerminal.jl
Julia package for displaying images in the terminal using ANSI colors
ImageInTerminal is a drop-in package that once imported changes how a single Colorant and whole Colorant arrays (regular images) are displayed in the interactive REPL. The displayed images will be downscaled to fit into the size of your active terminal session. By default, this package will detect if your running terminal supports 24-bit colors (true colors). If it does, the image will be displayed in 24-bit colors, otherwise, it falls back to 8-bit (256 colors). -
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Circuitscape.jl
Algorithms from circuit theory to predict connectivity
Circuitscape is an open-source program that uses circuit theory to model connectivity in heterogeneous landscapes. Its most common applications include modeling the movement and gene flow of plants and animals, as well as identifying areas important for connectivity conservation. The new Circuitscape is built entirely in the Julia language, a new programming language for technical computing. Julia is built from the ground up to be fast. As such, this offers a number of advantages over the... -
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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... -
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FunSQL.jl
Julia library for compositional construction of SQL queries
FunSQL is a Julia library for the compositional construction of SQL queries. Julia programmers sometimes need to interrogate data with the Structured Query Language (SQL). But SQL is notoriously hard to write in a modular fashion. FunSQL exposes full expressive power of SQL with compositional semantics. FunSQL allows you to build queries incrementally from small independent fragments. This approach is particularly useful for building applications that programmatically construct SQL... -
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ColorSchemes.jl
colorschemes, colormaps, gradients, and palettes
Color schemes, colormaps, gradients, and palettes. Choose ColorSchemes with care. Refer to Peter Kovesi's PerceptualColourMaps package, or to Fabio Crameri's Scientific Colour Maps for more information. If you want to make more advanced ColorSchemes, use linear-segment dictionaries or indexed lists, and use functions to generate color values, see the make_colorscheme() function in the ColorSchemeTools.jl package. -
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ReachabilityAnalysis.jl
Compute reachable states of dynamical systems
Reachability analysis is concerned with computing rigorous approximations of the set of states reachable by a dynamical system. In the scope of this package are systems modeled by continuous or hybrid dynamical systems, where the dynamics change with discrete events. Systems are modeled by ordinary differential equations (ODEs) or semi-discrete partial differential equations (PDEs), with uncertain initial states, uncertain parameters or non-deterministic inputs. -
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Stan.jl
Stan.jl illustrates the usage of the 'single method' packages
A collection of example Stan Language programs demonstrating all methods available in Stan's cmdstan executable (as an external program) from Julia. For most applications one of the "single method" packages, e.g. StanSample.jl, StanDiagnose.jl, etc., is a better choice for day-to-day use. To execute the most important method in Stan ("sample"), use StanSample.jl. Some Pluto notebook examples can be found in the repository. -
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IntervalArithmetic.jl
Library for validated numerics using interval arithmetic
IntervalArithmetic.jl is a Julia package for validated numerics in Julia. All calculations are carried out using interval arithmetic where quantities are treated as intervals. The final result is a rigorous enclosure of the true value. We are working towards having the IntervalArithmetic library be conformant with the IEEE 1788-2015 Standard for Interval Arithmetic. To do so, we have incorporated tests from the ITF1788 test suite. -
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FiniteDifferences.jl
High accuracy derivatives, estimated via numerical finite differences
FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other. -
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PGFPlotsX.jl
Plots in Julia using the PGFPlots LaTeX package
PGFPlotsX is a Julia package to generate publication quality figures using the LaTeX library PGFPlots. It is similar in spirit to the package PGFPlots.jl but it tries to have a very close mapping to the PGFPlots API as well as minimize the number of dependencies. The fact that the syntax is similar to the TeX version means that examples from Stack Overflow and the PGFPlots manual can easily be incorporated in the Julia code. -
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JET.jl
An experimental code analyzer for Julia
JET employs Julia's type inference system to detect potential bugs and type instabilities. JET is tightly coupled to the Julia compiler, and so each JET release supports a limited range of Julia versions. See the Project.toml file for the range of supported Julia versions. The Julia package manager should install a version of JET compatible with the Julia version you are running. If you want to use JET on unreleased version of Julia where compatibility with JET is yet unknown, clone this git... -
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DataFrames.jl
In-memory tabular data in Julia
DataFrames.jl is a powerful Julia package for working with in-memory tabular data. It provides a familiar, flexible, and efficient interface for handling datasets, making it easy to load, manipulate, join, and analyze structured data. With syntax inspired by data frames in R and pandas in Python, it offers intuitive tools while taking advantage of Julia’s speed and type system. The package is actively maintained by the JuliaData community, with contributions from over 200 developers... -
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SPX
A simple & straight-to-the-point PHP profiling extension
SPX, which stands for Simple Profiling eXtension, is just another profiling extension for PHP. It differentiates itself from other similar extensions as being totally free and confined to your infrastructure (i.e. no data leaks to a SaaS). Very simple to use: just set an environment variable (command line) or switch on a radio button (web request) to profile your script. Thus, you are free of manually instrumenting your code (Ctrl-C a long running command line script is even supported).... -
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Chokidar
Minimal and efficient cross-platform file watching library
...Node.js fs.watch often reports events twice, emits most changes as rename, and it does not provide an easy way to recursively watch file trees nor supports recursive watching on Linux. Same as with Node.js fs.watchFile. Therefore, Chokidar resolves these problems. Initially made for Brunch (an ultra-swift web app build tool), it is now used in Microsoft's Visual Studio Code, gulp, karma, PM2, browserify, webpack, BrowserSync, and many others. It has proven itself in production environments. Chokidar does still rely on the Node.js core fs module, but when using fs.watch and fs.watchFile for watching, it normalizes the events it receives, often checking for truth by getting file stats and/or dir contents. -
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miepython
Mie scattering of light by perfect spheres
miepython is a pure Python module to calculate light scattering for non-absorbing, partially-absorbing, or perfectly-conducting spheres. Mie theory is used, following the procedure described by Wiscombe. This code has been validated against his results. This code provides functions for calculating the extinction efficiency, scattering efficiency, backscattering, and scattering asymmetry. Moreover, a set of angles can be given to calculate the scattering for a sphere at each of those angles. -
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ExponentialUtilities.jl
Fast and differentiable implementations of matrix exponentials
ExponentialUtilities is a package of utility functions for matrix functions of exponential type, including functionality for the matrix exponential and phi-functions. These methods are more numerically stable, generic (thus support a wider range of number types), and faster than the matrix exponentiation tools in Julia's Base. The tools are used by the exponential integrators in OrdinaryDiffEq. The package has no external dependencies, so it can also be used independently. -
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AppleAccelerate.jl
Julia interface to the macOS Accelerate framework
Julia interface to the macOS Accelerate framework. This provides a Julia interface to some of the macOS Accelerate frameworks. At the moment, this package provides access to Accelerate BLAS and LAPACK using the libblastrampoline framework, an interface to the array-oriented functions, which provide a vectorized form for many common mathematical functions. The performance is significantly better than using standard libm functions in some cases, though there does appear to be some reduced... -
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Braket.jl
Experimental Julia implementation of the Amazon Braket SDK
Braket.jl is not an officially supported AWS product. This package is a Julia implementation of the Amazon Braket SDK allowing customers to access Quantum Hardware and Simulators. This is experimental software, and support may be discontinued in the future. For a fully supported SDK, please use the Python SDK. We may change, remove, or deprecate parts of the API when making new releases. Please review the CHANGELOG for information about changes in each release. -
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GDAL.jl
Thin Julia wrapper for GDAL - Geospatial Data Abstraction Library
Julia wrapper for GDAL - Geospatial Data Abstraction Library. This package is a binding to the C API of GDAL/OGR. It provides only a C style usage, where resources must be closed manually, and datasets are pointers. Other packages can build on top of this to provide a more Julian user experience. See for example ArchGDAL.jl. Most users will want to use ArchGDAL.jl instead of using GDAL.jl directly. -
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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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ReTest.jl
Testing framework for Julia
ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests. Filtering run testsets with a Regex, which is matched against the descriptions of testsets. This is useful for... -
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ConformalPrediction.jl
Predictive Uncertainty Quantification through Conformal Prediction
ConformalPrediction.jl is a package for Predictive Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in MLJ (Blaom et al. 2020). Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions. Intuitively, CP works under the premise of turning heuristic notions of uncertainty into rigorous uncertainty estimates through repeated sampling or the use...
