This package is largely unnecessary on julia v1.8, as DelimitedFiles is comparably fast now. The performance comparison below was carried out on julia v1.7, where this package still has a comparative edge. If you're using julia v1.8 and above, you may choose DelimitedFiles instead, which will be better maintained.
This is a very basic package meant to read in numerical matrices from files.
This package defines its own readdlm function that is not exported.
For more advanced usage consider using DelimitedFiles.
Examples of usage:
julia> using DelimitedFiles, SimpleDelimitedFiles
julia> A = rand(2,2)
2×2 Matrix{Float64}:
0.0630187 0.351596
0.257851 0.601259
julia> f = tempname();
julia> writedlm(f, A) # from DelimitedFiles
julia> SimpleDelimitedFiles.readdlm(f)
2×2 Matrix{Float64}:
0.0630187 0.351596
0.257851 0.601259
julia> A = rand(100, 100); # A larger matrix for performance benchmarks
julia> f = tempname();
julia> writedlm(f, A)
julia> readdlm(f) == SimpleDelimitedFiles.readdlm(f)
true
julia> @time readdlm(f);
0.084439 seconds (597.56 k allocations: 15.646 MiB)
julia> @time SimpleDelimitedFiles.readdlm(f);
0.005872 seconds (1.33 k allocations: 1.076 MiB)The function defined in this package uses less memory, and is often more performant. However this function may not support some functionality from DelimitedFiles. This package is useful if you want to quickly read in files where the data is known to be a numerical matrix, and whose element type is either known, or may be parsed as Float64.
For even more performant IO, consider using CSV.jl. This package provides a middle ground with lower runtimes than DelimitedFiles and lower compile times than CSV.jl.