Showing 17 open source projects for "macro for mac"

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  • 1
    PlutoUI.jl

    PlutoUI.jl

    A tiny package to make html"input" a bit more Julian

    A tiny package to make HTML "input" a bit more Julian. Use it with the @bind macro in Pluto.
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  • 2
    Tullio.jl

    Tullio.jl

    Tullio is a very flexible einsum macro

    Tullio is a very flexible einsum macro. It understands many array operations written in index notation -- not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting. Used by itself the macro writes ordinary nested loops much like Einsum.@einsum. One difference is that it can parse more expressions, and infer ranges for their indices. Another is that it will use multi-threading (via Threads.@spawn) and recursive tiling, on large enough...
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  • 3
    Comonicon

    Comonicon

    Your best CLI generator in JuliaLang

    Roger's magic book for command line interfaces.
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  • 4
    CBinding.jl

    CBinding.jl

    Automatic C interfacing for Julia

    Use CBinding.jl to automatically create C library bindings with Julia at runtime. In order to support the fully automatic conversion and avoid name collisions, the names of C types or functions are mangled a bit to work in Julia. Therefore everything generated by CBinding.jl can be accessed with the c"..." string macro to indicate that it lives in C-land. As an example, the function func above is available in Julia as c"func". It is possible to store the generated bindings to more user-friendly...
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  • 5
    StatProfilerHTML.jl

    StatProfilerHTML.jl

    Show Julia profiling data in an explorable HTML page

    This module formats the output from Julia's Profile module into an html rendering of the source function lines and functions, allowing for interactive exploration of any bottlenecks that may exist in your code.
    Downloads: 0 This Week
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  • 6
    Chain.jl

    Chain.jl

    A Julia package for piping a value through transformation expressions

    A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
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  • 7
    WhereTraits.jl

    WhereTraits.jl

    Traits for julia: dispatch on whatever you want using where syntax

    Welcome to WhereTraits.jl. This package exports one powerful macro @traits with which you can extend Julia's where syntax in order to support traits definitions. In addition, WhereTraits comes with a standardized way how to resolve ambiguities among traits, by defining an order among the traits with @traits_order. Under the hood @traits uses normal function dispatch to achieve the speed and flexibility, however, julia function dispatch can lead to ambiguities. With traits these can easily...
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  • 8
    DynamicQuantities.jl

    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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  • 9
    Compat.jl

    Compat.jl

    Compatibility across Julia versions

    The Compat package is designed to ease interoperability between older and newer versions of the Julia language. In particular, in cases where it is impossible to write code that works with both the latest Julia master branch and older Julia versions, or impossible to write code that doesn't generate a deprecation warning in some Julia version, the Compat package provides a macro that lets you use the latest syntax in a backward-compatible way. This is primarily intended for use by other Julia...
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  • 10
    Infiltrator.jl

    Infiltrator.jl

    No-overhead breakpoints in Julia

    This package provides the @infiltrate macro, which acts as a breakpoint with negligible runtime performance overhead. Note that you cannot access other function scopes or step into further calls. Use an actual debugger if you need that level of flexibility. Running code that ends up triggering the @infiltrate REPL mode via inline evaluation in VS Code or Juno can cause issues, so it's recommended to always use the REPL directly. When the infiltration point is hit, it will drop you...
    Downloads: 0 This Week
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  • 11
    ParallelStencil.jl

    ParallelStencil.jl

    Package for writing high-level code for parallel stencil computations

    ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array...
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  • 12
    AutoMLPipeline.jl

    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 ica...
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  • 13
    FLoops.jl

    FLoops.jl

    Fast sequential, threaded, and distributed for-loops for Julia

    Fast sequential, threaded, and distributed for-loops for Julia, fold for humans.FLoops.jl provides a macro @floop. It can be used to generate a fast generic sequential and parallel iteration over complex collections. Furthermore, the loop written in @floop can be executed with any compatible executors. See FoldsThreads.jl for various thread-based executors that are optimized for different kinds of loops. FoldsCUDA.jl provides an executor for GPU. FLoops.jl also provides a simple distributed...
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  • 14
    FromFile.jl

    FromFile.jl

    Julia enhancement proposal (Julep) for implicit per file module

    This package exports a macro @from, which can be used to import objects from files. The hope is that you will never have to write include again. FromFile is a Julia Language package. To install FromFile, please open Julia's interactive session (known as REPL) and press ] key in the REPL to use the package mode.
    Downloads: 0 This Week
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  • 15
    Reexport.jl

    Reexport.jl

    Julia macro for re-exporting one module from another

    Maybe you have a module X that depends on module Y and you want using X to pull in all of the symbols from Y. Maybe you have an outer module A with an inner module B, and you want to export all of the symbols in B from A. It would be nice to have this functionality built into Julia, but we have yet to reach an agreement on what it should look like. This short macro is a stopgap we have a better solution.
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  • 16
    SimpleTraits.jl

    SimpleTraits.jl

    Simple Traits for Julia

    This package provides a macro-based implementation of traits, using Tim Holy's trait trick. The main idea behind traits is to group types outside the type-hierarchy and to make dispatch work with that grouping. The difference to Union-types is that types can be added to a trait after the creation of the trait, whereas Union types are fixed after creation. The cool thing about Tim's trick is that there is no performance impact compared to using ordinary dispatch. For a bit of background...
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  • 17
    ParallelAccelerator.jl

    ParallelAccelerator.jl

    ParallelAccelerator package, part of the High Performance Scripting

    ParallelAccelerator is a Julia package for speeding up compute-intensive Julia programs. In particular, Julia code that makes heavy use of high-level array operations is a good candidate for speeding up with ParallelAccelerator. With the @acc macro that ParallelAccelerator provides, users may specify parts of a program to accelerate. ParallelAccelerator compiles these parts of the program to fast native code. It automatically eliminates overheads such as array bounds checking when it is safe...
    Downloads: 0 This Week
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