# Working with delimited files

A lot of files we use in scientific computing are very simple, and organized as tables. There are a lot of packages in Julia to handle these files, including the full-featured DataFrames and DataFramesMeta. But in this module, we will focus on the standard library package DelimitedFiles, which allows to store files where fields are separated by a specified character.

In order to demonstrate how DelimitedFiles works, we will come up with a simple example of something we might want to save: the Singular Value Decomposition of a matrix. In order to have access to SVD, we will load the LinearAlgebra package.

import LinearAlgebra

A = round.(rand(5, 5); digits = 2)
U, Σ, V = LinearAlgebra.svd(A);


We have three pieces of data here: the $\mathbf{U}$ and $\mathbf{V}^\intercal$ matrices, and the vector of eigenvalues $\mathbf{\Sigma}$. If we save these three pieces of information, we can reproduce our matrix $\mathbf{A}$.

In order to start saving the data, we need access to functionalities within DelimitedFiles:

using DelimitedFiles


Specifically, we need to use the writedlm function. Before we continue, let’s make sure we put our matrices in the same place:

destination = tempname()
mkdir(destination)

"/tmp/jl_QBiPwjYBOB"


We can save, for example, the matrix U:

writedlm(joinpath(destination, "U.mat"), U)


Checking that it has been written is easy, as we can can simply read the content of the destination directory:

readdir(destination)

1-element Vector{String}:
"U.mat"


Perfect! One thing that we can tweak with writedlm is the separator, which is \t (a tabulation) by default. We can change this to turn U.mat into a csv file:

writedlm(joinpath(destination, "U.mat"), U, ';')


Note that the separator is given as a character, not as a string! That’s right, no one can stop you from writing the following line:

writedlm(joinpath(destination, "U.mat"), U, '🙂')

Someone, preferably yourself, should definitely stop you from writing this line.

Let’s reset the U.mat file to something sensible (tab-separated):

writedlm(joinpath(destination, "U.mat"), U)


We can now write the V matrix. In addition to the form we have seen here, there is another way to call writedlm, from within an open/end statement:

open(joinpath(destination, "V.mat"), "w") do io
return writedlm(io, V)
end


Why would we ever pick the more verbose, more complex way? Well, it’s because of the "w" character. It stands for write, and is a way to specify what Julia is allowed to do with the file. For now, it can only write in it. Alternative permissions are "r" (read only), "r+" (read and write), "w+" (write and read), “"a" (append to the file), and "a+" (essentially all of the above).

This is very useful when you couple this with a lock, as is detailed in the documentation for open, as it can prevent multiple threads or parallel processes from inadvertently over-writing one another. Although we will not go into this topic for this material, this is an important piece of information to keep in mind when you start dealing with distributed computing.

We can similarly check that the file has been correctly written:

readdir(destination)

2-element Vector{String}:
"U.mat"
"V.mat"


Let’s write the array of eigenvalues now:

writedlm(joinpath(destination, "eigenvalues.vec"), Σ)


All done. And now, we are going to run things in reverse, and read these files to re-assemble our original matrix. But we will apply a little twist! The readdlm function (like writedlm) allows specifying the type of the data to read. This is a good idea if you want to save on memory and load, for example, data as Float16:

u = readdlm(joinpath(destination, "U.mat"), Float16)
typeof(σ)

Matrix{Float16} (alias for Array{Float16, 2})


Notice that the type of σ is Matrix{Float16}, whereas the type of Σ was Vector{Float64}. This is because, by default, readdlm assumes that things are matrices, and therefore will return matrices. Thankfully this is easy to correct:

σ = reshape(σ, length(σ))

5-element Vector{Float16}:
2.88
1.096
0.5493
0.4922
0.1449

This assumption that things are a matrix is likely to cause issues depending on how you dispatch on arguments, or on the type of operations you apply. As always, check the output of functions and ensure that the computer representation and the mathematical representation match!

And there we go. We have used DelimitedFiles to store tabular data using a custom separator, loaded these data back with a floating point precision, and corrected a little reshape incident. We can finally check that the decomposition/recomposition worked:

B = u * LinearAlgebra.Diagonal(σ) * v'
B ≈ A

true


This concludes the module on DelimitedFiles. At a later point in this material, we will use CSV to read more structured data, but in a broad variety of situations, having access to simple features will get you a long way.