I am new to Julia, so this might be trivial.
I have a function definition within a module that looks like (using URIParser):
function add!(graph::Graph,
subject::URI,
predicate::URI,
object::URI)
...
end
Outside of the module, I call:
add!(g, URIParser.URI("http://test.org/1"), URIParser.URI("http://test.org/2"), URIParser.URI("http://test.org/1"))
Which gives me this error:
ERROR: no method add!(Graph,URI,URI,URI)
in include at boot.jl:238
in include_from_node1 at loading.jl:114
at /Users/jbaran/src/RDF/src/RDF.jl:79
Weird. Because when I can see a matching signature:
julia> methods(RDF.add!)
# 4 methods for generic function "add!":
add!(graph::Graph,subject::URI,predicate::URI,object::Number) at /Users/jbaran/src/RDF/src/RDF.jl:29
add!(graph::Graph,subject::URI,predicate::URI,object::String) at /Users/jbaran/src/RDF/src/RDF.jl:36
add!(graph::Graph,subject::URI,predicate::URI,object::URI) at /Users/jbaran/src/RDF/src/RDF.jl:43
add!(graph::Graph,statement::Statement) at /Users/jbaran/src/RDF/src/RDF.jl:68
At first I thought it was my use of object::Union(...), but even when I define three functions with Number, String, and URI, I get this error.
Is there something obvious that I am missing? I am using Julia 0.2.1 x86_64-apple-darwin12.5.0, by the way.
Thanks,
Kim
This looks like you may be getting bit by the very slight difference between method extension and function shadowing.
Here's the short of it. When you write function add!(::Graph, ...); …; end;, Julia looks at just your local scope and sees if add! is defined. If it is, then it will extend that function with this new method signature. But if it's not already defined locally, then Julia creates a new local variable add! for that function.
As JMW's comment suggests, I bet that you have two independent add! functions. Base.add! and RDF.add!. In your RDF module, you're shadowing the definition of Base.add!. This is similar to how you can name a local variable pi = 3 without affecting the real Base.pi in other scopes. But in this case, you want to merge your methods with the Base.add! function and let multiple dispatch take care of the resolution.
There are two ways to get the method extension behavior:
Within your module RDF scope, say import Base: add!. This explicitly brings Base.add! into your local scope as add!, allowing method extension.
Explicitly define your methods as function Base.add!(graph::Graph, …). I like this form as it more explicitly documents your intentions to extend the Base function at the definition site.
This could definitely be better documented. There's a short reference to this in the Modules section, and there's currently a pull request that should be merged soon that will help.
Related
In Julia, how do I create custom types MyOrderedDictA and MyOrderedDictB such that:
Each has all the functionality of an OrderdDict, and can be passed to any function that accepts AbstractDicts
They are distinct from each other, so that I can take advantage of multiple dispatch.
I suspect\hope this is straightforward, but haven’t been able to figure it out.
Basically, what you have to do is to define your type MyOrderedDictA, wrapping a regular OrderedDict, and forward all functions that one can apply to an OrderedDict to this wrapped dict.
Unfortunately, the AbstractDict interface is (to my knowledge) currently not documented (cf. AbstractArray). You could look at their definition and check which functions are defined for them. Alternatively, there is the more practical approach to just use your MyOrderedDictA and whenever you get an error message, because a function is not defined, you forward this function "on-the-fly".
In any case, using the macro #forward from Lazy.jl you can do something along the lines of the following.
using Lazy
struct MyOrderedDictA{T,S} <: AbstractDict{T,S}
dict::OrderedDict{T,S}
end
MyOrderedDictA{T,S}(args...; kwargs...) where {T,S} = new{T,S}(OrderedDict{T,S}(args...; kwargs...))
function MyOrderedDictA(args...; kwargs...)
d = OrderedDict(args...; kwargs...)
MyOrderedDictA{keytype(d),valtype(d)}(d)
end
#forward MyOrderedDictA.dict (Base.length, Base.iterate, Base.getindex, Base.setindex!)
d = MyOrderedDictA(2=>1, 1=>2)
Others will be better placed to answer this, but a quick take:
For this you will need to look at the OrderedDict implementation, and specifically which methods are defined for OrderedDicts. If you want to be able to pass it to methods accepting AbstractDicts you need to subtype it like struct MyDictA{T, S} <: AbstractDict{T, S}
If you define two structs they will automatically be discting from each other!? (I might be misunderstanding the question here)
What is the sane way to go from a Module object to a path to the file in which it was declared?
To be precise, I am looking for the file where the keyword module occurs.
The indirect method is to find the location of the automatically defined eval method in each module.
moduleloc(mm::Module) = first(functionloc(mm.eval, (Symbol,)))
for example
moduleloc(mm::Module) = first(functionloc(mm.eval, (Symbol,)))
using DataStructures
moduleloc(DataStructures)
Outputs:
/home/oxinabox/.julia/v0.6/DataStructures/src/DataStructures.jl
This indirect method works, but it feels like a bit of a kludge.
Have I missed some inbuilt function to do this?
I will remind answered that Modules are not the same thing as packages.
Consider the existence of submodules, or even modules that are being loaded via includeing some abolute path that is outside the package directory or loadpath.
Modules simply do not store the file location where they were defined. You can see that for yourself in their definition in C. Your only hope is to look through the bindings they hold.
Methods, on the other hand, do store their file location. And eval is the one function that is defined in every single module (although not baremodules). Slightly more correct might be:
moduleloc(mm::Module) = first(functionloc(mm.eval, (Any,)))
as that more precisely mirrors the auto-defined eval method.
If you aren't looking for a programmatic way of doing it you can use the methods function.
using DataFrames
locations = methods(DataFrames.readtable).ms
It's for all methods but it's hardly difficult to find the right one unless you have an enormous number of methods that differ only in small ways.
There is now pathof:
using DataStructures
pathof(DataStructures)
"/home/ederag/.julia/packages/DataStructures/59MD0/src/DataStructures.jl"
See also: pkgdir.
pkgdir(DataStructures)
"/home/ederag/.julia/packages/DataStructures/59MD0"
Tested with julia-1.7.3
require obviously needs to perform that operation. Looking into loading.jl, I found that finding the module path has changed a bit recently: in v0.6.0, there is a function
load_hook(prefix::String, name::String, ::Void)
which you can call "manually":
julia> Base.load_hook(Pkg.dir(), "DataFrames", nothing)
"/home/philipp/.julia/v0.6/DataFrames/src/DataFrames.jl"
However, this has changed to the better in the current master; there's now a function find_package, which we can copy:
macro return_if_file(path)
quote
path = $(esc(path))
isfile(path) && return path
end
end
function find_package(name::String)
endswith(name, ".jl") && (name = chop(name, 0, 3))
for dir in [Pkg.dir(); LOAD_PATH]
dir = abspath(dir)
#return_if_file joinpath(dir, "$name.jl")
#return_if_file joinpath(dir, "$name.jl", "src", "$name.jl")
#return_if_file joinpath(dir, name, "src", "$name.jl")
end
return nothing
end
and add a little helper:
find_package(m::Module) = find_package(string(module_name(m)))
Basically, this takes Pkg.dir() and looks in the "usual locations".
Additionally, chop in v0.6.0 doesn't take these additional arguments, which we can fix by adding
chop(s::AbstractString, m, n) = SubString(s, m, endof(s)-n)
Also, if you're not on Unix, you might want to care about the definitions of isfile_casesensitive above the linked code.
And if you're not so concerned about corner cases, maybe this is enough or can serve as a basis:
function modulepath(m::Module)
name = string(module_name(m))
Pkg.dir(name, "src", "$name.jl")
end
julia> Pkg.dir("DataStructures")
"/home/liso/.julia/v0.7/DataStructures"
Edit: I now realized that you want to use Module object!
julia> m = DataStructures
julia> Pkg.dir(repr(m))
"/home/liso/.julia/v0.7/DataStructures"
Edit2: I am not sure if you are trying to find path to module or to object defined in module (I hope that parsing path from next result is easy):
julia> repr(which(DataStructures.eval, (String,)))
"eval(x) in DataStructures at /home/liso/.julia/v0.7/DataStructures/src/DataStructures.jl:3"
I wrote an importer for an obscure TSV format, which I want to package and document: https://github.com/katrinleinweber/MWX-import/commits/package
The importer function passes a renamed skip_lines parameter to utils::read.table so I would like to "pass" the latter's documentation of skip into my .Rd. However, trying a few notations like #inheritParams utils::read.table skip always results in Warning: Failed to find topic […].
Whether it's actually possible to inherit a single, specific parameter from another package's function is not clear to me after reading http://r-pkgs.had.co.nz/man.html and https://blog.rstudio.org/2017/02/01/roxygen2-6-0-0/.
Is it possible? If yes, thanks for any hint!
If you use #inheritParams utils::read.table, then any parameters in your function which match those in utils::read.table will be inherited (provided they aren't already explicitly documented). So this would cover your use case if you used skip instead of skip_lines.
I don't think it's possible to inherit documentation for a parameter when your parameter name doesn't match though.
I saw this example in the Julia language documentation. It uses something called Base. What is this Base?
immutable Squares
count::Int
end
Base.start(::Squares) = 1
Base.next(S::Squares, state) = (state*state, state+1)
Base.done(S::Squares, s) = s > S.count;
Base.eltype(::Type{Squares}) = Int # Note that this is defined for the type
Base.length(S::Squares) = S.count;
Base is a module which defines many of the functions, types and macros used in the Julia language. You can view the files for everything it contains here or call whos(Base) to print a list.
In fact, these functions and types (which include things like sum and Int) are so fundamental to the language that they are included in Julia's top-level scope by default.
This means that we can just use sum instead of Base.sum every time we want to use that particular function. Both names refer to the same thing:
Julia> sum === Base.sum
true
Julia> #which sum # show where the name is defined
Base
So why, you might ask, is it necessary is write things like Base.start instead of simply start?
The point is that start is just a name. We are free to rebind names in the top-level scope to anything we like. For instance start = 0 will rebind the name 'start' to the integer 0 (so that it no longer refers to Base.start).
Concentrating now on the specific example in docs, if we simply wrote start(::Squares) = 1, then we find that we have created a new function with 1 method:
Julia> start
start (generic function with 1 method)
But Julia's iterator interface (invoked using the for loop) requires us to add the new method to Base.start! We haven't done this and so we get an error if we try to iterate:
julia> for i in Squares(7)
println(i)
end
ERROR: MethodError: no method matching start(::Squares)
By updating the Base.start function instead by writing Base.start(::Squares) = 1, the iterator interface can use the method for the Squares type and iteration will work as we expect (as long as Base.done and Base.next are also extended for this type).
I'll grant that for something so fundamental, the explanation is buried a bit far down in the documentation, but http://docs.julialang.org/en/release-0.4/manual/modules/#standard-modules describes this:
There are three important standard modules: Main, Core, and Base.
Base is the standard library (the contents of base/). All modules
implicitly contain using Base, since this is needed in the vast
majority of cases.
In Julia, is there any way to get the name of a passed to a function?
x = 10
function myfunc(a)
# do something here
end
assert(myfunc(x) == "x")
Do I need to use macros or is there a native method that provides introspection?
You can grab the variable name with a macro:
julia> macro mymacro(arg)
string(arg)
end
julia> #mymacro(x)
"x"
julia> #assert(#mymacro(x) == "x")
but as others have said, I'm not sure why you'd need that.
Macros operate on the AST (code tree) during compile time, and the x is passed into the macro as the Symbol :x. You can turn a Symbol into a string and vice versa. Macros replace code with code, so the #mymacro(x) is simply pulled out and replaced with string(:x).
Ok, contradicting myself: technically this is possible in a very hacky way, under one (fairly limiting) condition: the function name must have only one method signature. The idea is very similar the answers to such questions for Python. Before the demo, I must emphasize that these are internal compiler details and are subject to change. Briefly:
julia> function foo(x)
bt = backtrace()
fobj = eval(current_module(), symbol(Profile.lookup(bt[3]).func))
Base.arg_decl_parts(fobj.env.defs)[2][1][1]
end
foo (generic function with 1 method)
julia> foo(1)
"x"
Let me re-emphasize that this is a bad idea, and should not be used for anything! (well, except for backtrace display). This is basically "stupid compiler tricks", but I'm showing it because it can be kind of educational to play with these objects, and the explanation does lead to a more useful answer to the clarifying comment by #ejang.
Explanation:
bt = backtrace() generates a ... backtrace ... from the current position. bt is an array of pointers, where each pointer is the address of a frame in the current call stack.
Profile.lookup(bt[3]) returns a LineInfo object with the function name (and several other details about each frame). Note that bt[1] and bt[2] are in the backtrace-generation function itself, so we need to go further up the stack to get the caller.
Profile.lookup(...).func returns the function name (the symbol :foo)
eval(current_module(), Profile.lookup(...)) returns the function object associated with the name :foo in the current_module(). If we modify the definition of function foo to return fobj, then note the equivalence to the foo object in the REPL:
julia> function foo(x)
bt = backtrace()
fobj = eval(current_module(), symbol(Profile.lookup(bt[3]).func))
end
foo (generic function with 1 method)
julia> foo(1) == foo
true
fobj.env.defs returns the first Method entry from the MethodTable for foo/fobj
Base.decl_arg_parts is a helper function (defined in methodshow.jl) that extracts argument information from a given Method.
the rest of the indexing drills down to the name of the argument.
Regarding the restriction that the function have only one method signature, the reason is that multiple signatures will all be listed (see defs.next) in the MethodTable. As far as I know there is no currently exposed interface to get the specific method associated with a given frame address. (as an exercise for the advanced reader: one way to do this would be to modify the address lookup functionality in jl_getFunctionInfo to also return the mangled function name, which could then be re-associated with the specific method invocation; however, I don't think we currently store a reverse mapping from mangled name -> Method).
Note also that (1) backtraces are slow (2) there is no notion of "function-local" eval in Julia, so even if one has the variable name, I believe it would be impossible to actually access the variable (and the compiler may completely elide local variables, unused or otherwise, put them in a register, etc.)
As for the IDE-style introspection use mentioned in the comments: foo.env.defs as shown above is one place to start for "object introspection". From the debugging side, Gallium.jl can inspect DWARF local variable info in a given frame. Finally, JuliaParser.jl is a pure-Julia implementation of the Julia parser that is actively used in several IDEs to introspect code blocks at a high level.
Another method is to use the function's vinfo. Here is an example:
function test(argx::Int64)
vinfo = code_lowered(test,(Int64,))
string(vinfo[1].args[1][1])
end
test (generic function with 1 method)
julia> test(10)
"argx"
The above depends on knowing the signature of the function, but this is a non-issue if it is coded within the function itself (otherwise some macro magic could be needed).