Using Packages

It is entirely likely that in the course of writing a package providing a custom data transformer, one would come across packages that may be needed.

Every possibly desired package could be shoved into the list of dependences, but this is a somewhat crude approach. A more granular approach is enabled with two macros, @addpkg and @import.

Letting DataToolkitBase know about extra packages

DataToolkitBase.@addpkg — Macro

@addpkg name::Symbol uuid::String

All @addpkg statements should lie within a module's __init__ function.

Example

@addpkg CSV "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"

Using extra packages

DataToolkitBase.@import — Macro

@import pkg1, pkg2...
@import pkg1 as name1, pkg2 as name2...
@import pkg: foo, bar...
@import pkg: foo as bar, bar as baz...

Fetch modules previously registered with @addpkg, and import them into the current namespace. This macro tries to largely mirror the syntax of using.

For the sake of type inference, it is assumed that all bindings that start with a lower case letter are functions, and bindings that start with an upper case letter are types. Exceptions must be manually annotated with type assertions.

If a required package had to be loaded for the @import statement, a PkgRequiredRerunNeeded singleton will be returned.

Example

@import pkg
pkg.dothing(...)
# Alternative form
@import pkg: dothing
dothing(...)

Example

module DataToolkitExample

using DataToolkitBase
using DataFrame

function __init__()
    @addpkg CSV "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
    @addpkg DelimitedFiles "8bb1440f-4735-579b-a4ab-409b98df4dab"
end

function load(::DataLoader{:csv}, from::IOStream, ::Type{DataFrame})
    @import CSV
    result = CSV.read(from, DataFrame)
    close(from)
    result
end

function load(::DataLoader{:delimcsv}, from::IOStream, ::Type{DataFrame})
    @import DelimitedFiles
    result = DelimitedFiles.readdlm(from, ',', DataFrame)
    close(from)
    result
end

end