Showing 24 open source projects for "python package"

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  • 1
    PyCall.jl

    PyCall.jl

    Package to call Python functions from the Julia language

    Package to call Python functions from the Julia language. This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them.
    Downloads: 0 This Week
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  • 2
    Conda.jl

    Conda.jl

    https://github.com/JuliaPy/Conda.jl

    This package allows one to use conda as a cross-platform binary provider for Julia for other Julia packages, especially to install binaries that have complicated dependencies like Python. conda is a package manager that started as the binary package manager for the Anaconda Python distribution, but it also provides arbitrary packages. Instead of the full Anaconda distribution, Conda.jl uses the miniconda Python environment, which only includes conda and its dependencies.
    Downloads: 0 This Week
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  • 3
    PairPlots.jl

    PairPlots.jl

    Beautiful and flexible vizualizations of high dimensional data

    Beautiful and flexible visualizations of high-dimensional data. This package produces pair plots, otherwise known as corner plots or scatter plot matrices: grids of 1D and 2D histograms that allow you to visualize high-dimensional data. Pair plots are an excellent way to visualize the results of MCMC simulations, but are also a useful way to visualize correlations in general data tables. The default styles of this package roughly reproduce the output of the Python library corner.py for a single series and chainconsumer.py for multiple series. ...
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  • 4
    Braket.jl

    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.
    Downloads: 0 This Week
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    Smart Business Texting that Generates Pipeline

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  • 5
    The PyPlot module for Julia

    The PyPlot module for Julia

    Plotting for Julia based on matplotlib.pyplot

    This module provides a Julia interface to the Matplotlib plotting library from Python, and specifically to the matplotlib.pyplot module. PyPlot uses the Julia PyCall package to call Matplotlib directly from Julia with little or no overhead (arrays are passed without making a copy). (See also PythonPlot.jl for a version of PyPlot.jl using the alternative PythonCall.jl package.) This package takes advantage of Julia's multimedia I/O API to display plots in any Julia graphical backend, including as inline graphics in IJulia. ...
    Downloads: 0 This Week
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  • 6
    OptimalTransport.jl

    OptimalTransport.jl

    Optimal transport algorithms for Julia

    This package provides some Julia implementations of algorithms for computational optimal transport, including the Earth-Mover's (Wasserstein) distance, Sinkhorn algorithm for entropically regularized optimal transport as well as some variants or extensions. Notably, OptimalTransport.jl provides GPU acceleration through CUDA.jl and NNlibCUDA.jl.
    Downloads: 0 This Week
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  • 7
    ArviZ.jl

    ArviZ.jl

    Exploratory analysis of Bayesian models with Julia

    ArviZ.jl (pronounced "AR-vees") is a Julia package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, model checking, comparison and diagnostics.
    Downloads: 0 This Week
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  • 8
    CounterfactualExplanations.jl

    CounterfactualExplanations.jl

    A package for Counterfactual Explanations and Algorithmic Recourse

    CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R.
    Downloads: 0 This Week
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  • 9
    ConcurrentSim.jl

    ConcurrentSim.jl

    Discrete event process oriented simulation framework written in Julia

    A discrete event process-oriented simulation framework written in Julia inspired by the Python library SimPy. One of the longest-lived Julia packages (originally under the name SimJulia).
    Downloads: 1 This Week
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  • 10
    CxxWrap

    CxxWrap

    Package to make C++ libraries available in Julia

    This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library.
    Downloads: 0 This Week
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  • 11
    BetaML.jl

    BetaML.jl

    Beta Machine Learning Toolkit

    The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its implementation. ...
    Downloads: 0 This Week
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  • 12
    InteractiveViz.jl

    InteractiveViz.jl

    Interactive visualization tools for Julia

    ...To allow generation of data points on demand through a graphics pipeline, requiring computation only at a level of detail appropriate for display at the viewing resolution. Additional data points can be generated on demand when zooming or panning. This package was partly inspired by the excellent Datashader package available in the Python ecosystem.
    Downloads: 0 This Week
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  • 13
    DataFrames.jl

    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.
    Downloads: 0 This Week
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  • 14
    NBInclude.jl

    NBInclude.jl

    import code from IJulia Jupyter notebooks into Julia programs

    NBInclude is a package for the Julia language that allows you to include and execute IJulia (Julia-language Jupyter) notebook files just as you would include an ordinary Julia file. The goal of this package is to make notebook files just as easy to incorporate into Julia programs as ordinary Julia (.jl) files, giving you the advantages of a notebook (integrated code, formatted text, equations, graphics, and other results) while retaining the modularity and re-usability of .jl files.
    Downloads: 0 This Week
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  • 15
    UnROOT.jl

    UnROOT.jl

    Native Julia I/O package to work with CERN ROOT files objects

    UnROOT.jl is a reader for the CERN ROOT file format written entirely in Julia, without any dependence on ROOT or Python.
    Downloads: 0 This Week
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  • 16
    Sundials.jl

    Sundials.jl

    Julia interface to Sundials, including a nonlinear solver

    This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations.
    Downloads: 0 This Week
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  • 17
    OpenCL.jl

    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.
    Downloads: 0 This Week
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  • 18
    Graphs.jl

    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.
    Downloads: 0 This Week
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  • 19
    FiniteDifferences.jl

    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.
    Downloads: 0 This Week
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  • 20
    JUDI.jl

    JUDI.jl

    Julia Devito inversion

    JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation.
    Downloads: 0 This Week
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  • 21
    Tokenize.jl

    Tokenize.jl

    Tokenization for Julia source code

    Tokenize is a Julia package that serves a similar purpose and API as the tokenize module in Python but for Julia. This is to take a string or buffer containing Julia code, perform lexical analysis and return a stream of tokens.
    Downloads: 0 This Week
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  • 22
    DifferentialEquations.jl

    DifferentialEquations.jl

    Multi-language suite for high-performance solvers of equations

    This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. The well-optimized DifferentialEquations solvers benchmark as some of the fastest implementations, using classic algorithms and ones from recent research which routinely outperform the “standard” C/Fortran methods, and include algorithms optimized for high-precision and HPC applications. ...
    Downloads: 0 This Week
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  • 23
    OrdinaryDiffEq.jl

    OrdinaryDiffEq.jl

    High performance ordinary differential equation (ODE)

    This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. The well-optimized DifferentialEquations solvers benchmark as some of the fastest implementations, using classic algorithms and ones from recent research that routinely outperform the “standard” C/Fortran methods, and include algorithms optimized for high-precision and HPC applications. ...
    Downloads: 0 This Week
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  • 24
    Indicators.jl

    Indicators.jl

    Financial market technical analysis & indicators in Julia

    Indicators is a Julia package offering efficient implementations of many technical analysis indicators and algorithms. This work is inspired by the TTR package in R and the Python implementation of TA-Lib, and the ultimate goal is to implement all of the functionality of these offerings (and more) in Julia. This package has been written to be able to interface with both native Julia Array types, as well as the TS time series type from the Temporal package. ...
    Downloads: 0 This Week
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