Recap and Recommended libraries

Last updated on 2025-02-09 | Edit this page

Overview

Questions

  • Is there a recommended set of standard libraries?
  • What is the equivalent of SciPy in Julia.

Objectives

  • Get street-wise.

This is a nice closing episode.

Notebooks

Use Pluto.jl!

Reactive programming

At several points in this lesson we saw the use of Observables.jl to create interactive demos with Makie.jl. If you want to build more complicated interfaces, you may want to take a look at GtkObservables.jl.

JULIA 

using Observables
x = Observable(3.0)
y = map(sqrt, x)

x[] = 25.0
y[]

Logging and Progress bars

Logging is done through the Logging library that is part of the standard library.

JULIA 

using Logging

@warn "Hello, World!"

While there is a package called ProgressBars.jl that seems quite popular, it is better to use ProgressLogging.jl to log progress of a computation. This utility hooks into the logging system and integrates well with Pluto and VS Code.

JULIA 

using ProgressLogging

@progress for i in 1:100
    sleep(0.02)
end

Parallel pipelines

We’ve seen that we can use IterTools.jl to extend the functionality of standard Julia Iterators, at only a marginal performance hit.

The Transducers.jl package is a fundamental when building pipelines for parallel processing. Transducers are an interesting concept from functional programming, developed by the great Rich Hickey of Clojure fame. Several packages build on top of Transducers.jl with a more imperative interface, FLoops.jl for instance.

SciML-verse

There is a large group of libraries managed by the same group under the nomer SciML. These libraries are focussed on scientific modelling and machine learning. Some of these libraries are incredibly useful on their own:

  • Symbolics.jl for symbolic computation
  • Integrals.jl for numeric integration
  • DifferentialEquations.jl for solving differential equations
  • MethodOfLines.jl for solving PDEs

etc.

These libraries are very powerful and offer highly abstracted interfaces to problems. The downside is that they pull in a massive amount of dependencies (While this is also true for a package like Makie.jl, plotting is usually not a core buisness for a package, so Makie will usually be an optional dependency. The same can’t be said of numerical algorithms). Also, the high level of abstraction means that code can be hard to debug.

Key Points

  • SciML toolkit is dominant but not always the best choice.
  • Search for libraries that specify your needs.