A declaration like this one is legal:
julia> function foo end
foo (generic function with 0 methods)
However, besides a placeholder to centralize documentation (and I am not even sure):
module Foo
"""
foo(...) functions
- foo(n::Int) : do something
- foo(n::Int,m::Int) : do other thing
"""
function foo end
I can not see the role/goal of such declaration.
Question: is there an illustrative use case?
The reason is explained in the Julia Manual in Empty generic functions section. There are two main uses:
simplifying documentation or improvement of code readability;
separation of interface definition from implementations.
A typical use case can be seen for definitions of randn! and randexp!. First empty generic functions are defined and are coupled with documentation string. Next methods for those functions are dynamically defined.
Another similar example to study is definition randstring.
Finally look at strides function. It is defined in abstractarray.jl as empty generic function and then used in the same file. But no methods are defined for this function in this file, other files e.g. dense.jl or subarray.jl define such methods.
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)
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.
The problem is the following:
I have an abstract type MyAbstract and derived composite types MyType1 and MyType2:
abstract type MyAbstract end
struct MyType1 <: MyAbstract
somestuff
end
struct MyType2 <: MyAbstract
someotherstuff
end
I want to specify some general behaviour for objects of type MyAbstract, so I have a function
function dosth(x::MyAbstract)
println(1) # instead of something useful
end
This general behaviour suffices for MyType1 but when dosth is called with an argument of type MyType2, I want some additional things to happen that are specific for MyType2 and, of course, I want to reuse the existing code, so I tried the following, but it did not work:
function dosth(x::MyType2)
dosth(x::MyAbstract)
println(2)
end
x = MyType2("")
dosth(x) # StackOverflowError
This means Julia did not recognize my attempt to treat x like its "supertype" for some time.
Is it possible to call an overloaded function from the overwriting function in Julia? How can I elegantly solve this problem?
You can use the invoke function
function dosth(x::MyType2)
invoke(dosth, Tuple{MyAbstract}, x)
println(2)
end
With the same setup, this gives the follow output instead of a stack overflow:
julia> dosth(x)
1
2
There's a currently internal and experimental macro version of invoke which can be called like this:
function dosth(x::MyType2)
Base.#invoke dosth(x::MyAbstract)
println(2)
end
This makes the calling syntax quite close to what you wrote.
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).
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.