MatecDev

This is an expanding series of tutorials of numerical computing in Julia.

This post explains how to access the elements of a multidimensional array in a few simple ways.

A Dictionary in Julia is a collection of key-value pairs, which provide much more flexibility than arrays or named tuples.

Learning best programming practices is important and difficult.

We compare the performance of Julia and Numba, for a minimal benchmark that enables to see some fundamental difference between Julia and accelerated-Python.

A great way to contribute to the open-source community is to share you fixes and enhacements to existing packages.

Let's address some frequently asked questios about pure and applied mathematics.

FFT basics, properties, libraries, and all the nitty gritty

Definitions and basic properties of the FFT

While open-source offers great long-term value for end-users, there are challenges in making a high-quality open-source scientific software project viable beyond academic use

Compiling a custom sysimage can be a convenient way of reducing precompilation time, and for example to reduce the well known long-time-to-first-plot issue.

Julia has three closely related terms which are "environments", "projects" and "packages".

Organizing our code with simple loops, nested for loops, and parallel for loops.

Perform basic operations with multidimensional arrays, like matrix multiplication and dot product.

Initialize empty multidimensional arrays of various types.

Apply a scalar function to an array by resorting to the broadcasting dot operator.

Get started handling multidimensional arrays in Julia with this simple guide.

I'm super enthusiastic about Julia after running this comparison of Julia vs Numpy vs Fortran, for performance and code simplicity.

Fortran is a language that is specialized for high-performance computing.

This post presents a quick tour of numerical quadrature, with example code in Julia that relies on different packages.

We describe some basic approaches for polynomial approximation, and how to implement them in Julia.

This introductory tutorial about Julia focuses on its use in Science and Engineering.

Start using Julia interactively with VSCode, edit your code efficiently, and find about some cool things you can do.

Write your own Python script to automatically download of data from a password-protected https directory

How to bulk download data with existing open-source download scripts, or your with your own one.

Fortran has been the optimal tool for numerical heavy-lifting for such a long time, that I tend to be quite skeptical about the idea of replacing it.

Encapsulate your Julia code into functions, for convenience and performance.

A tutorial for performing fast point-in-polygon tests with Python's Shapeley and Geopandas.