Tutorial básico

Creando animaciones

using Plots 
anim = @animate for n=1:20
    plot( x-> sin(n*x) , label="")
end;

gif(anim, "animation.gif",  fps = 30)

mp4(anim, "video.mp4",  fps = 30)

En un notebook se puede hacer

using Plots
@gif for n=1:20
    plot( x-> sin(n*x) , label="" )
end

Otras librerías de representación

• PyPlot.jl

• Makie.jl

Dictionarios

Para sacar datos a un archivo, es habitual crear un Dictionary

dict = Dict("mivariable" => 1, "b" => 2, "unvector" => [1,2,3], 1=>"mi variable aquí")

Dict{Any, Any} with 4 entries:
  "unvector"   => [1, 2, 3]
  "b"          => 2
  "mivariable" => 1
  1            => "mi variable aquí"

@show dict["unvector"]

dict["unvector"] = [1, 2, 3]

3-element Vector{Int64}:
 1
 2
 3

Variables de fecha y hora

Dates.jl es parte de Standard Library

TimeZones.jl

El problema de zonas horarias es muy difícil Vídeo de Computerphile

FileIO.jl

FileIO aims to provide a common framework for detecting file formats and dispatching to appropriate readers/writers. The two core functions in this package are called load and save, and offer high-level support for formatted files (in contrast with julia’s low-level read and write). To avoid name conflicts, packages that provide support for standard file formats through functions named load and save are encouraged to register with FileIO.

JLD2.jl

JLD2 saves and loads Julia data structures in a format comprising a subset of HDF5, without any dependency on the HDF5 C library. JLD2 is able to read most HDF5 files created by other HDF5 implementations supporting HDF5 File Format Specification Version 3.0 (i.e. libhdf5 1.10 or later) and similarly those should be able to read the files that JLD2 produces. JLD2 provides read-only support for files created with the JLD package.

JSON.jl

Para gestionar archivos JSON.

MAT.jl

Para leer archivos de MATLAB.

DataFrames.jl

DataFrames.jl provides a set of tools for working with tabular data in Julia. Its design and functionality are similar to those of pandas (in Python) and data.frame, data.table and dplyr (in R), making it a great general purpose data science tool.

DataFrames.jl plays a central role in the Julia Data ecosystem, and has tight integrations with a range of different libraries. DataFrames.jl isn’t the only tool for working with tabular data in Julia – as noted below, there are some other great libraries for certain use-cases – but it provides great data wrangling functionality through a familiar interface.

Otros paquetes

Tables.jl

CSV.jl

IndexedTables.jl

RData.jl

Operaciones básicas

using LinearAlgebra
A = [1 2 3; 4 1 6; 7 8 1]

3×3 Matrix{Int64}:
 1  2  3
 4  1  6
 7  8  1

@show tr(A)
@show det(A)
println("inv(A) = ")
inv(A)

tr(A) = 3
det(A) = 104.0
inv(A) = 

3×3 Matrix{Float64}:
 -0.451923   0.211538    0.0865385
  0.365385  -0.192308    0.0576923
  0.240385   0.0576923  -0.0673077

b = [1,2,3]
A \ b 

3-element Vector{Float64}:
 0.2307692307692308
 0.15384615384615383
 0.15384615384615385

eigvals(A) 

3-element Vector{Float64}:
 -6.214612641961068
 -1.554026596484783
 10.768639238445848

Matrices especiales

A = UnitUpperTriangular( [ 1 2 3 ; 1 2 3 ; 1 2 3 ] )

3×3 UnitUpperTriangular{Int64, Matrix{Int64}}:
 1  2  3
 ⋅  1  3
 ⋅  ⋅  1

Más sobre matrices especiales

Distributions.jl

The Distributions package provides a large collection of probabilistic distributions and related functions. Particularly, Distributions implements:

• Sampling from distributions
• Moments (e.g mean, variance, skewness, and kurtosis), entropy, and other properties
• Probability density/mass functions (pdf) and their logarithm (logpdf)
• Moment-generating functions and characteristic functions
• Maximum likelihood estimation
• Distribution composition and derived distributions (Cartesian product of distributions, truncated distributions, censored distributions)

using Random, Distributions
d = Normal()
d + 1.0

Normal{Float64}(μ=1.0, σ=1.0)

@show mean(d)
@show rand(d, 3)

mean(d) = 0.0
rand(d, 3) = 

[-1.1390714777702335, -0.4763905560075741, 2.1951153992912715]

3-element Vector{Float64}:
 -1.1390714777702335
 -0.4763905560075741
  2.1951153992912715

x = [0, 0.4, -0.4] 
fit(Normal, x)

Normal{Float64}(μ=0.0, σ=0.32659863237109044)

Ver también MeasureTheory.jl

Turing.jl

Turing is a general-purpose probabilistic programming language for robust, efficient Bayesian inference and decision making. Current features include:

  * General-purpose probabilistic programming with an intuitive modelling interface;
  * Robust, efficient Hamiltonian Monte Carlo (HMC) sampling for differentiable posterior distributions;
  * Particle MCMC sampling for complex posterior distributions involving discrete variables and stochastic control flow; and
  * Compositional inference via Gibbs sampling that combines particle MCMC, HMC and random-walk MH (RWMH).

Un ejemplo sencillo