Showing 33 open source projects for "linux operating systems"

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    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: 8 This Week
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  • 2
    InteractiveDynamics.jl

    InteractiveDynamics.jl

    Fast, general-purpose interactive applications for complex systems

    Fast, general-purpose interactive applications for dynamical systems of all kinds, including ODEs, maps, billiards, and agent-based-models.
    Downloads: 6 This Week
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  • 3
    MonteCarlo.jl

    MonteCarlo.jl

    Classical and quantum Monte Carlo simulations in Julia

    MonteCarlo.jl is a package implementing classical and quantum Monte Carlo simulations primarily for solid state physics. Currently the focus is on finding a overall design for the package and verifying that determinant Quantum Monte Carlo (DQMC) is working correctly. As such the package may still go through significant changes and the documentation may be outdated and incomplete. Note that classical Monte Carlo is also not a focus at this point. It is probably usable, but a lot of the...
    Downloads: 3 This Week
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  • 4
    ThreadsX.jl

    ThreadsX.jl

    Parallelized Base functions

    Add prefix ThreadsX. to functions from Base to get some speedup, if supported. The reduce-based functions support any collections that implement SplittablesBase.jl interface including arrays, Dict, Set, and iterator transformations. In particular, these functions support iterator comprehension. ThreadsX.jl is aiming at providing API compatible with Base functions to easily parallelize Julia programs. All functions that exist directly under ThreadsX namespace are public API and they implement...
    Downloads: 5 This Week
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  • 5
    Soss

    Soss

    Probabilistic programming via source rewriting

    Soss is a library for probabilistic programming. Soss and DynamicPPL are both maturing and becoming more complete, so the above will change over time. It's also worth noting that we (the Turing team and I) hope to move toward a natural way of using these systems together to arrive at the best of both.
    Downloads: 6 This Week
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  • 6
    OpticSim.jl

    OpticSim.jl

    Optical Simulation software

    OpticSim.jl is a Julia package for geometric optics (ray tracing) simulation and optimization of complex optical systems developed by the Microsoft Research Interactive Media Group and the Microsoft Hardware Architecture Incubation Team (HART). It is designed to allow optical engineers to create optical systems procedurally and then to simulate and optimize them. Unlike Zemax, Code V, or other interactive optical design systems OpticSim.jl has limited support for interactivity, primarily in...
    Downloads: 7 This Week
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  • 7
    Geodesy.jl

    Geodesy.jl

    Work with points defined in various coordinate systems

    Geodesy is a Julia package for working with points in various world and local coordinate systems. The primary feature of Geodesy is to define and perform coordinate transformations in a convenient and safe framework, leveraging the CoordinateTransformations package. Transformations are accurate and efficient and implemented in native Julia code (with many functions being ported from Charles Karney's GeographicLib C++ library), and some common geodetic datums are provided for convenience.
    Downloads: 5 This Week
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  • 8
    RigidBodyDynamics.jl

    RigidBodyDynamics.jl

    Julia implementation of various rigid body dynamics

    RigidBodyDynamics.jl is a rigid body dynamics library in pure Julia. It aims to be user friendly and performant, but also generic in the sense that the algorithms can be called with inputs of any (suitable) scalar types. This means that if fast numeric dynamics evaluations are required, a user can supply Float64 or Float32 inputs. However, if symbolic quantities are desired for analysis purposes, they can be obtained by calling the algorithms with e.g. SymPy.Sym inputs. If gradients are...
    Downloads: 7 This Week
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