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Julia links
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Краткое описание языка програм-мирования Julia и некоторые при-меры его использования
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Beautiful Julia: Cool Language Constructs and Tricks for Beginners
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import Pkg; Pkg.add("OffsetArrays") Pkg.add("Gadfly") Pkg.add("ApproxFun") Pkg.add("IterativeSolvers") Pkg.add("DocOpt") Pkg.add("PackageCompiler") Pkg.add("DataFrames") Pkg.add("CSV") Pkg.add("CUDA") Pkg.add("Plot") Pkg.add("PyPlot") Pkg.add("Statistics") Pkg.add("SpecialFunctions") Pkg.add("Roots") Pkg.add("Cairo") Pkg.add("Fontconfig") Pkg.add("Match") Pkg.add("Calculus") Pkg.add("LaTeXStrings") Pkg.add("Compat") Pkg.add("PGFPlots") Pkg.add("QuadGK") Pkg.add("DifferentialEquations") Pkg.add("Roots") Pkg.add("JSON")
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Switch to modern Julia
- Difference between Set{Int}() and BitSet()
- Pkg
- immutable vs struct and type vs mutable struct in Julia
- What is the difference between
using
andimport
in Julia when building a module? - Pkg Fails to clone git repository on Arch related distros
git clone https://github.com/JuliaRegistries/General.git ~/.julia/registries/General
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phdthesis\title{Abstraction in Technical Computing}\author{Jeffrey Werner Bezanson}
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Julia Groups
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Translating from Fortran to Julia
- Julia equivalent of Fortran common blocks or, even better, Fortran 90 Modules
- Julia (sub)arrays performance
- Generalizing AbstractArrays: opportunities and challenges
- RFC: Give AbstractArrays smart and performant indexing behaviors for free #10525
- room for performance improvement for SubArray #5117
- Fortran-like arrays such as FArray(Float64, -1:1,-7:7,-128:512)
- Docs » Documentation of Julia’s Internals » SubArrays
- JuliaLang/ArrayViews.jl :> A Julia package to explore a new system of array views
goto
andlabel
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Julia closures aka lambdas performance
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CAS in Julia - Symbolic Computations
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Computer Vision
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Graphics
- Powerful convenience for Julia visualizations and data analysis http://docs.juliaplots.org
- Gadfly
- latex axis labels in Gadfly
- MathJax/LaTeX/Some sort of thing like that for labels/titles #356
- PGFPlots backend #152
- PGFPlots – Plotting in LaTeX
- Public Fonts for Minority Languages of Russia
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fc-cache -fv xelatex Int_J1_tex.tex
- JSON to work with 0.5
- PGFPlots
using PGFPlots a=Axis([ Plots.Linear(sin, (0,10), legendentry=L"$\sin(x)$"), Plots.Linear(x->sqrt(2*x), (0,10), legendentry=L"$\sqrt{2x}$") ], legendPos="north west") save("t.pdf",a) save("t1.tex",a)
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Iterative Solvers
Pkg.add("IterativeSolvers") Pkg.add("ParallelAccelerator")
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High Performance
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DiffEqFlux.jl – A Julia Library for Neural Differential Equations
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Misc.
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TensorFlow.jl: A Julia Front End to the TensorFlow World (TF Dev Summit '19)
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Efficient filtering and linear algebra routines for multidimensional arrays
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Unums
julia> x = ["a", "b", ] 2-element Array{ASCIIString,1}: "a" "b" julia> d = Dict{ASCIIString, Int}([(xx, 0) for xx in x]) Dict{ASCIIString,Int64} with 2 entries: "b" => 0 "a" => 0
@test isequal([1,2,3], [a for (a,b) in enumerate(2:4)]) @test isequal([2,3,4], [b for (a,b) in enumerate(2:4)]) +# comprehension in let-bound function +let x⊙y = sum([x[i]*y[i] for i=1:length(x)]) + @test [1,2] ⊙ [3,4] == 11 +end + @test_throws DomainError (10.^[-1])[1] == 0.1 @test (10.^[-1.])[1] == 0.1
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Selected issues
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JuliaCon 2019
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JuliaCon 2020
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MIT Computational Thinking Spring 2021. 6.S083 / 18.S191 / 22.S092
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- CFD Julia: A Learning Module Structuring an Introductory Course on Computational Fluid Dynamics
- CFD_Julia
- Building blocks of spectral methods for Julia
- A library of functions for polynomial bases used in spectral element methods
- Julia translation and modification of MATLAB scripts that accompany the text on Nodal Disontinuous Galerkin methods
- Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language
- VisClaw.jl
- Trixi.jl
- JuliaCon 2018 | Solving Partial Differential Equations with Julia | Chris Rackauckas
- Benchmarking Julia on a PDE: the Kuramoto-Sivashinksy equation
- Julia debugger
tl;drusing Debugger function foo(n) x = n+1 ((BigInt[1 1; 1 0])^x)[2,1] end @enter foo(20)
- CFD Julia: A Learning Module Structuring an Introductory Course on Computational Fluid Dynamics
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two-slit experiment Непристойное приложение Ненормальное программирование
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Discussion on “Why I no longer recommend Julia” by Yuri Vishnevsky