Pkg.jl
PlotDocs.jl
Pkg.jl | PlotDocs.jl | |
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5 | 3 | |
603 | 92 | |
1.0% | - | |
9.0 | 2.4 | |
3 days ago | 3 months ago | |
Julia | ||
GNU General Public License v3.0 or later | MIT License |
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Pkg.jl
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Julia 1.9 Highlights
There was a "bug" (or just unhandled caching case) that effected the Pluto notebook system that required precompilation each time. This is because Pluto notebooks kept a manifest (so they always instantiated with the same packages every time for full reproducibility) and the instantiation of that manifest triggered not just package running but also precompilation. That was fixed in https://github.com/JuliaLang/Pkg.jl/pull/3378, with a larger discussion in https://discourse.julialang.org/t/first-pluto-notebook-launc.... That should largely remove this issue as in included in the v1.9 release (it was first in v1.9-RC2 IIRC).
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Unable to load PDMats package.
The closest thing I got to is this and I don't even understand what they are saying.
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Why Fortran is easy to learn
Julia's compiler is made to be extendable. GPUCompiler.jl which adds the .ptx compilation output for example is a package (https://github.com/JuliaGPU/GPUCompiler.jl). The package manager of Julia itself... is an external package (https://github.com/JuliaLang/Pkg.jl). The built in SuiteSparse usage? That's a package too (https://github.com/JuliaLang/SuiteSparse.jl). It's fairly arbitrary what is "external" and "internal" in a language that allows that kind of extendability. Literally the only thing that makes these packages a standard library is that they are built into and shipped with the standard system image. Do you want to make your own distribution of Julia that changes what the "internal" packages are? Here's a tutorial that shows how to add plotting to the system image (https://julialang.github.io/PackageCompiler.jl/dev/examples/...). You could setup a binary server for that and now the first time to plot is 0.4 seconds.
Julia's arrays system is built so that most arrays that are used are not the simple Base.Array. Instead Julia has an AbstractArray interface definition (https://docs.julialang.org/en/v1/manual/interfaces/#man-inte...) which the Base.Array conforms to, and many effectively standard library packages like StaticArrays.jl, OffsetArrays.jl, etc. conform to, and thus they can be used in any other Julia package, like the differential equation solvers, solving nonlinear systems, optimization libraries, etc. There is a higher chance that packages depend on these packages then that they do not. They are only not part of the Julia distribution because the core idea is to move everything possible out to packages. There's not only a plan to make SuiteSparse and sparse matrix support be a package in 2.0, but also ideas about making the rest of linear algebra and arrays themselves into packages where Julia just defines memory buffer intrinsic (with likely the Arrays.jl package still shipped with the default image). At that point, are arrays not built into the language? I can understand using such a narrow definition for systems like Fortran or C where the standard library is essentially a fixed concept, but that just does not make sense with Julia. It's inherently fuzzy.
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MlJ.jl: A Julia Machine Learning Framework
This is exacerbated by the fact that Julia's Pkg.jl does not yet support conditional/optional dependencies [0]. A lot of these meta packages tend to pull everything but the kitchen sink.
[0]: https://github.com/JuliaLang/Pkg.jl/issues/1285
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Adding packages in Julia extremely painful
The LTS release is over two years old, and Julia has received a lot of developer attention since then, resulting in new features and performance improvements that tutorial authors don't want to do without. You can safely use the latest stable release (v1.5.3), although you may also want to apply the Git registry fix (https://github.com/JuliaLang/Pkg.jl/issues/2014#issuecomment-730676631) for further improvements in download/setup speed.
PlotDocs.jl
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Julia 1.9 Highlights
https://docs.juliaplots.org/stable/
3. See https://juliaacademy.com
Another alternative environment are Pluto notebooks. It's reactive like a spreadsheet, but easy to use in your browser.
https://featured.plutojl.org/
I have several users without much coding experience using Pluto notebooks just to generate plots from CSV files. They are finding the combination of a web based interface, reactive UI, and fast execution easier to use than a MATLAB Live script.
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
Using Plots.jl, you can create a lot of different graphs to analyze your data, similar to Matplotlib or Seaborn in Python. To use it, you have to install the Plots package to your notebook and import it:
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I have custom color palettes and I want to make a nice table like this (from Plots.jl) any simple idea on how to do it?
Did you get that from this page? I'm fairly sure those are just Markdown tables including images created with cgrad or palette as described further up the page. You can check the source to confirm if you want.
What are some alternatives?
Pluto.jl - 🎈 Simple reactive notebooks for Julia
julia_titanic_model - Titanic machine learning model and web service
TriangularSolve.jl - rdiv!(::AbstractMatrix, ::UpperTriangular) and ldiv!(::LowerTriangular, ::AbstractMatrix)
DataScience - Data Science in Julia course for JuliaAcademy.com, taught by Huda Nassar
maptrace - Produce watertight polygonal vector maps by tracing raster images
parca-demo - A collection of languages and frameworks profiled by Parca and Parca agent
AutoMLPipeline.jl - A package that makes it trivial to create and evaluate machine learning pipeline architectures.
JLD2.jl - HDF5-compatible file format in pure Julia
CondaPkg.jl - Add Conda dependencies to your Julia project
Fortran-code-on-GitHub - Directory of Fortran codes on GitHub, arranged by topic
julia - The Julia Programming Language