Discover and read the best of Twitter Threads about #julialang

Most recents (24)

New version of the @pumas_ai manuscript is online!

biorxiv.org/content/10.110…

"real applications like NLME fitting see a median 81x acceleration while retaining the same accuracy"

For example, take a look at these SAEM benchmarks!

#julialang and #sciml in #pharmacometrics! Image
We see similar improvements in FOCE and LaplaceI maximum likelihood fitting. Image
We even see such runtime improvements to maximum likelihood estimation on larger nonlinear models, even when including the compile time (CT)! Image
Read 5 tweets
The #JuliaLang REPL is amazing.

Here are 3 things I love about it:
1/ The Built-in Package Manager

With the Julia REPL open, press the right square bracket ] to drop into the 𝙿𝚔𝚐 REPL where you can install packages and manage Julia environments.

There's no need to install a separate package manager. It comes with every Julia install.
The support for Julia environments is one of my favorite parts. Inside the 𝙿𝚔𝚐 REPL, type:

𝚊𝚌𝚝𝚒𝚟𝚊𝚝𝚎 /𝚙𝚊𝚝𝚑/𝚝𝚘/𝚎𝚗𝚟𝚒𝚛𝚘𝚗𝚖𝚎𝚗𝚝

to activate the environment at the path, or create a new one if it doesn't exist.
Read 14 tweets
My turn to learn the new #DiD literature.

I estimated tried as many relevant methods as I could find on two (simple) DGPs.
All #STATA code here: github.com/azev77/Compare…

#EconTwitter what did I miss? (1/n) Image
Thanks to @AsjadNaqvi @borusyak @XJaravel @jannspiess @kylefbutts @pedrohcgs @CdeChaisemartin @arindube I missed a bunch (sorry)

1: asjadnaqvi.github.io/DiD/ is maybe the best resource to learn these methods
2: the world would be much better off if these methods were implemented in a modern/fast language like #JuliaLang
github.com/JuliaDiffinDif…
by @JunyuanChenEcon

3: why doesn't anyone compare the staggered DiD methods to #SCM?
I think I'll do that...
Read 3 tweets
Today's #JuliaLang doodle with Javis.jl is recreating a classic...
Look closely at the circles. Are the curving at all?

Here's the code:

(I'm sure there's a better way to do this, I just haven't completely figured out Javis yet!)
I think disks work better than circles for this:
Read 8 tweets
Bayesian Generalized Linear Models with Julia! 🚀🚀🚀

TuringGLM is a new Julia package for GLM models with Bayesian flavor ❤️. As its name implies, the package uses the Turing package on the backend for the regression engine. 🧵 👇🏼

#JuliaLang #TuringLang #DataScience #Stats Image
It enables to specify Bayesian Generalized Linear Models using the formula syntax and returns an instantiated Turing model.
The package is inspired by the R's brms and Python's bambi packages (see links 👇🏼).

#rstats #Python
The package supports the following Bayesian GLM models:
✅ Linear regression
✅ Logistic regression
✅ Poisson regression
✅ Negative binomial regression
✅ Robust Regression
✅ Hierarchical models Image
Read 6 tweets
Grammar of graphic with Julia (part 2)! 🌈

Gadfly is another Julia package that follows the grammar of graphics. Similar to the Algebra of Graphics package, the Gadfly is also inspired by the R's #ggplot2 package and The Grammar of Graphics book 🧵👇🏼

#julialang #dataviz #RStats
Like ggplot2, the Gadfly uses geometries (or geom) to draw the input data with representation (e.g., point, line, bar, etc.).

In addition to the default static plot, the package supports interactive mode with #JavaScript code.
The package has the following features (1/2)
✅ Renders publication-quality graphics to SVG, PNG, Postscript, and PDF
✅ Intuitive and consistent plotting interface
✅ Works with Jupyter notebooks via IJulia out of the box
Read 6 tweets
Grammar of graphic with Julia!

The Algebra of Graphics is an extension of the Makie - a Julia package for data visualization. This library supports the grammar of graphic plotting style inspired by R's ggplot2 package. 🧵 👇🏼

#julialang #dataviz #rstats #ggplot2 #DataScience Image
Like the ggplot2 package, the AlgebraofGraphics uses the '+' and '*' symbols to add different layers to the plot.

Package philosophy ➡️ juliaplots.org/AlgebraOfGraph… Image
The package has four key layers:
✨ Data - encoding and dataset
✨ Mapping - associating variables to the plot attributes
✨ Visual - encoding data independent plot information
✨ Analyses - encoding transformations that are applied to the data before plotting. Image
Read 5 tweets
Data visualization with Julia! ❤️❤️❤️

This week I learned about Makie, a data visualization ecosystem for the Julia programming language. 🧵👇

License: MIT 🌈

Animation credit: @LazarusAlon
#julialang #dataviz #DataScience
This ecosystem includes multiple packages providing a variety of 2D and 3D plotting tools 🌈, supporting GPU for both interactive and noninteractive, animation, and other data visualization applications 🚀.
✅ 𝐆𝐋𝐌𝐚𝐤𝐢𝐞 - a GPU-powered package for 2D and 3D plotting in standalone GLFW.jl windows
makie.juliaplots.org/stable/documen…

✅ 𝐂𝐚𝐢𝐫𝐨𝐌𝐚𝐤𝐢𝐞 - based on the Cairo.jl package supports noninteractive 2D backend for publication-quality vector graphics.
makie.juliaplots.org/stable/documen…
Read 6 tweets
Got the #julialang #python #rstats Sundials and Hairer small ODE solver benchmark updated. That one is a pain since you need a ton of wrappers to all work and make sure no overhead creeped in. It's a beauty.

benchmarks.sciml.ai/html/MultiLang…

New addition: timings with static Julia #sciml
This is all part of the SciMLBenchmarks, so feel free to reproduce it on your machine. Note you may have to do funky things to fix Python package versions on Windows (see )

github.com/SciML/SciMLBen…
And because someone always asks (even though it's stated at the top), these results are with JIT compiling the function sent to Python, and is about 3x faster than #Numba+SciPy direct. See measurements at github.com/SciML/SciPyDif…. So normal #Python usage would expect a larger gap
Read 4 tweets
Did you know that An Introduction to Statistical Learning (ISLR) book has an online course? 🎥 🌈❤️
@edXOnline is offering an online course by @Stanford University, following the book curriculum: 🧵 👇🏼

#rstats #Statistics #ML #datascience
The course instructors are two of the book authors - Prof. Trevor Hastie and Prof. @robtibshirani. While the book is based on #R some awesome people translate it to #python, #julialang, and other #Rstats flavors (see links on the comments below 👇🏼).
The course covers the following topics (aligned with the book curriculum):
Read 9 tweets
In 2022, the difference between symbolic computing and compiler optimizations will be erased in #julialang. Anyone who can come up with a set of symbolic mathematical rules will automatically receive an optimized compiler pass to build better code. Thread on how it will be done.
What happens when you build a composable ecosystem? Serendipitous synergies. Nothing showcases this as well as the automated code optimization with e-graphs where this technique is now being used to accelerate simulations in #julialang #sciml

arxiv.org/abs/2112.14714
"Hey, I only need 11 digits of accuracy so please optimize my model according to this tolerance" and boom it's able to simplify the code and accelerate it by almost an order of magnitude! This is for a system of differential-algebraic equations in rigid body dynamics / robotics.
Read 9 tweets
Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and #Julialang. This post is about the differences in the tools and how the differences match the target audiences.

stochasticlifestyle.com/engineering-tr…
And for a more specific discussion about how AD in #julialang for #sciml applications is evolving to use multiple AD systems together, see the discussion on the Julia discourse about Diffractor and Enzyme.

discourse.julialang.org/t/open-discuss…
No automatic differentiation system or approach is "the best", and understanding the trade-offs will be required in order to most effectively achieve full-language differentiable programming.
Read 6 tweets
I just saw @j_v_66's recent paper on type-stability in #julialang and its relationship to compiler optimizations. This is quite a good read for anyone interested in why #julialang compilers can optimize so much in comparison to say #python or #Rlang.

arxiv.org/abs/2109.01950
The core idea: type-groundedness allows for full devirtualization, i.e. all dynamic dispatches can become static in a way that is provably correct. A lot Julia programmers have used this idea for a long time, so knowing it's a provable fact is a nice addition to the community.
Let me dive into a tangent that could be a follow up analysis. Here's something the Julia compiler does not seem to analyze well that could use some more theory behind it. It's related to why the % groundedness for DifferentialEquations.jl is low, but the package is still fast.
Read 15 tweets
#COVID19mx 2021-11-30 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,887,873 || nuevos: 3,307 (+0.09%)
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información).
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 40,630 || variación diaria: +2,572 (+6.76%)
Read 12 tweets
#COVID19mx 2021-11-29 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,884,566 || nuevos: 724 (+0.02%)
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información).
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 38,058 || variación diaria: -51 (-0.13%)
Read 12 tweets
#COVID19mx 2021-11-28 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,883,842 || nuevos: 1,050 (+0.03%) Image
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información). Image
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 38,109 || variación diaria: +108 (+0.28%) Image
Read 12 tweets
#COVID19mx 2021-11-27 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,882,792 || nuevos: 2,956 (+0.08%) Image
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información). Image
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 38,001 || variación diaria: -174 (-0.46%) Image
Read 12 tweets
Learning a new programming language can be difficult.

Our goal with juliaacademy.com is to make that a little easier. Here is a quick thread highlighting some of my favorite (free) #JuliaLang courses 🧵
For those with programming experience, @JaneHerriman 's course on an Intro to Julia is extremely popular. Jane is a great teacher and this course helps you build a solid #JuliaLang foundation: juliaacademy.com/p/intro-to-jul…
For those with perhaps less programming experience / comfort, Dr Henri Laurie's "Julia for Nervous Beginners" course is an excellent option: juliaacademy.com/p/julia-progra…
Read 8 tweets
#COVID19mx 2021-11-16 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,847,243 || nuevos: 735 (+0.02%)
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información).
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 35,450 || variación diaria: -2,853 (-7.45%)
Read 12 tweets
Someone shared with me something really cool today: learning orbital dynamics from gravitational waves using #julialang #sciml universal differential equations. Details in the thread:

arxiv.org/abs/2102.12695
Universal differential equations? Just short hand for universal approximators embedded within differential equations. You write the prior known physics/chemistry/biology and add in universal approximators, train them, and then can regress to discover models extensions. #sciml
You can do a whole lot of other things with them, like implement discrete physics-informed neural networks (PINNs) and such, so check out arxiv.org/abs/2001.04385 for details. It's the underlying principle to how all of the @SciML_Org software connects.
Read 11 tweets
#COVID19mx 2021-11-15 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,846,508 || nuevos: 775 (+0.02%)
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información).
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 38,303 || variación diaria: -219 (-0.57%)
Read 12 tweets
New #sciml method out of the #julialang lab: using coarse-grained models as a foundation for a physics-informed surrogate to predict solutions of partial differential equations. IT'S SUPER FAST! Shown on Maxwell's equations. Thread below.

arxiv.org/abs/2111.05841
Classic way to speedup simulation: course-grained methods. A course grained method uses perturbation theory to develop a simplified version of a physics model that captures some of the core larger-scale features. Instead of all atoms, groups of atoms? Etc. Random picture.
But that of course modeling a higher level feature will loose some of the accuracy. But it is a form of a physics-informed "pretty accurate" model. Could we use a neural network to capture the terms dropped off from the perturbation to build better coarse models? This is PEDS.
Read 6 tweets
#COVID19mx 2021-11-14 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,845,733 || nuevos: 942 (+0.02%) Image
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información). Image
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 38,522 || variación diaria: -504 (-1.29%) Image
Read 12 tweets
#COVID19mx 2021-10-08 [hilo] #YoTengoOtrasGráficas
Casos #CONFIRMADOS (por fecha de reporte)
Acumulados: 3,714,392 || nuevos: 7,158 (+0.19%)
Promedio móvil de 7 días de nuevos casos confirmados: por fecha de #reporte (curva roja) versus fecha de inicio de #síntomas (curva azul, se omiten últimas 2 semanas por rezago en información).
Casos #ACTIVOS (confirmados los últimos 14 días)
Acumulados: 95,277 || variación diaria: -2,981 (-3.03%)
Read 12 tweets

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