I'd like to start that I've not been able to recreate my problem in a stripped down version of the code. The code below works as intended, so this post is perhaps not well posed. The extended code, which is too long to post here, fails. I'll describe what I'm trying to do as maybe it'll help someone else out there.
I create three types: Bar, Baz, and Qux, which contains the method foo on Bar and Baz. I create a qux and query it's foo
qux = Wubble.Qux()
qux.foo
I get the following two methods, as expected:
foo(bar::Bar)
foo(baz::Baz)
Then when I try to actually use qux.foo with a bar or a baz, it gives me an error 'foo' has no method matching foo(::Bar).
Sadly, I can't recreate this error with stripped down code and the real code is unattractively long. What are the various ways of getting this error in this scenario that I've missed? It may be related to method extension and function shadowing like in this post, but I couldn't work a fix.
module Wibble
type Bar
data::Number
function Bar(num::Number=0)
this = new(num)
return this
end
end
end
module Wobble
type Baz
data::String
function Baz(vec::String="a")
this = new(vec)
return this
end
end
end
module Wubble
using Wibble
using Wobble
typealias Bar Wibble.Bar
typealias Baz Wobble.Baz
type Qux
foo::Function
function Methods(this::Qux)
function foo(bar::Bar)
println("foo with bar says ", bar.data)
end
function foo(baz::Baz)
println("foo with baz says ", baz.data)
end
this.foo = foo
return this
end
function Qux()
this = new()
this = Methods(this)
return this
end
end
end
I'm not really sure what's going wrong, but a couple of points which might help
You almost never want to have Function field in a type: this is a common idiomatic mistake made by people coming from "dot-based" OOP languages. Julia methods are generic, and don't belong to a particular type. There is no advantage to doing this, and not only is it more confusing (you have a lot of nested levels to write something that could be written in 2 lines), but it can make it harder for the compiler to reason about types, impacting performance.
You should use import Wibble.Bar instead of a typealias. If you use this, you don't need using.
Outer constructors are easier to use for specifying default arguments.
So in short, my version would be:
module Wibble
type Bar
data::Number
end
Bar() = Bar(0)
end
module Wobble
type Baz
data::String
end
Baz() = Baz("a")
end
module Wubble
import Wibble.Bar
import Wobble.Baz
qux(bar::Bar) = println("foo with bar says ", bar.data)
qux(baz::Baz) = println("foo with baz says ", baz.data)
end
Related
I want to import a function present in a julia file somewhere during runtime
just like in python we have importlib.import_module to import module is there something present in julia
I'm new to julia and I'm not sure how to do that.
I have to import a function main from another julia file and want to run it but I have to also check a condition before that if the condition is true then I want to import the function.
EDIT
I have a file
main.jl
function myMain()
s1 = "Hello"
s2 = "World!"
include("functions/hello.jl")
say(s1, s2)
end
myMain()
hello.jl
function say(s1, s2)
print(s1, s2)
end
Error
ERROR: LoadError: MethodError: no method matching say(::String, ::String)
The applicable method may be too new: running in world age 32378, while current world is 32379.
Closest candidates are:
say(::Any, ::Any) at ~/Desktop/julia_including/functions/hello.jl:1 (method too new to be called from this world context.)
Stacktrace:
[1] myMain()
# Main ~/Desktop/julia_including/main.jl:5
[2] top-level scope
# ~/Desktop/julia_including/main.jl:8
in expression starting at /home/shivansh/Desktop/julia_including/main.jl:8
It works fine when I don't use include inside the myMain() function in main.jl
Julia contrary to Python is a compiled language.
Hence for maximum performance all functions should be known at the compilation time so efficient assembly code can be generated.
Hence generally you want to avoid syntax like the one you are proposing and have the inlude outside of the function.
However, if you really know what you are doing, why you are doing this and need such functionality then comes the world of Julia metaprogramming.
The code is the following:
function myMain()
s1 = "Hello"
s2 = "World!"
include("say.jl")
Base.invokelatest(say, s1, s2)
end
Now you can do:
julia> myMain()
HelloWorld!
Honestly, it just sounds like you wanna do this
if some_condition
include("/path/to/some/file/that/you/need.jl")
else
include("/path/to/some/OTHER/file/that/you/need.jl")
end
EDIT
I think though, after seeing the error that you added, what you wanna do is define your function multiple times instead, and with different argument types:
function say(x::Any, y::Any)
println("Default")
end
function say(x::String, y::Any)
println("String in arg1: $x")
end
function say(x::Any, y::String)
println("String in arg2: $y")
end
function say(x::String, y::String)
println("String in args 1 and 2: $x $y")
end
That way using say differs based on the datatype of the arguments:
julia> say("Foo", "Bar")
String in args 1 and 2: Foo Bar
julia> say("Foo", 2)
String in arg1: Foo
julia> say(0, "Bar")
String in arg2: Bar
and if you have two different functions that act on 2 strings you can do this instead:
struct MySayFunctionType{Symbol}
end
function say(x::String, y::String, ::MySayFunctionType{:Type1})
println("Type1 function is being called: $x $y")
end
function say(x::String, y::String, ::MySayFunctionType{:Type2})
println("Type2 function is being called: $x $y")
end
and then use these like this
if condition
say("Foo", "Bar", MySayFunctionType{Symbol("Type1")}() )
else
say("Foo", "Bar", MySayFunctionType{Symbol("Type2")}() )
end
and if you want a default version of this function to be called -- say Type2 -- you can just add a default value for the third argument like this:
function say(x::String, y::String, z::MySayFunctionType{:Type2}=MySayFunctionType{Symbol("Type2")}())
println("Type2 function is being called: $x $y")
end
The reason this works is because "MySayFunctionType{:Type1}" is considered to be a different type than "MySayFunctionType{:Type2}" so multiple dispatch would consider that as well.
What is the correct way to import local modules in Pluto v0.18.0 notebooks with Julia 1.7.2?
The using keyword adds exported members to the main namespace in Julia. Given a sample module, in foo.jl,
module Foo
export bar
function bar()
return "baz"
end
end
The following code cell works in a Pluto Notebook:
# "baz"
begin
include("./foo.jl")
using .Foo
bar()
end
However, if I attempt to call bar from another cell, I get the following error:
# UndefVarError: bar not defined
bar()
Though I notice that Foo.bar() does work.
How do I add my own modules in a way that I get access to their exported members directly in the notebook's namespace?
This discussion gives a possible solution. It describes a better way of getting the module reference than what I have in my question.
If the module is not the last definition in the file, the import function has to be redefined. A suggested name is to have a variant called "ingredients"
#ingredients (generic function with 1 method)
function ingredients(path::String)
# this is from the Julia source code (evalfile in base/loading.jl)
# but with the modification that it returns the module instead of the last object
name = Symbol(basename(path))
m = Module(name)
Core.eval(m,
Expr(:toplevel,
:(eval(x) = $(Expr(:core, :eval))($name, x)),
:(include(x) = $(Expr(:top, :include))($name, x)),
:(include(mapexpr::Function, x) = $(Expr(:top, :include))(mapexpr, $name, x)),
:(include($path))))
m
end
Then the module can be loaded in like so (I don't know the reason why I have to say ingredients().Foo)
#Main.Foo.jl.Foo
Foo = ingredients("My Library/Foo.jl").Foo
and then with the module reference, you have to manually bring all exported variables into the global scope:
#bar = bar (generic function with 1 method)
bar = Foo.bar
Some of the parameters to a simulation I am writing are functions. When the output is generated, I want to put the definition of these functional parameters in the output. I have in mind a macro that somehow saves the definition as a string, and then defines it. For example, here's what I do now:
borda_score_fn(p) = exp(1/p)
global g_borda_score_fn_string = "exp(1/p)"
And then I write g_borda_score_fn_string to my output. But this is really ugly!
What I would like to do is something like this:
#paramfn borda_score_fn(p) = exp(1/p)
And later be able to both call borda_score_fn(p), and have the form (i.e., "exp(1/p)") available for writing to my output log. (The string form might get stashed in a global dict, actually, they both could.)
I have tried many version of this, but can't get the right set of parses and calls to get it to work. Any help would be appreciated.
This may be a bit different than what you have in mind, but one perhaps "Julian" approach might be to have the function itself return the form string via multiple dispatch, rather than defining a whole new global variable just for that. For example, say we have a type
struct Form end
that we can use for dispatch, then we can write
borda_score_fn(p) = exp(1/p)
borda_score_fn(::Form) = "exp(1/p)"
which can then be retrieved just by calling the function with our type
julia> borda_score_fn(2)
1.6487212707001282
julia> borda_score_fn(Form())
"exp(1/p)"
That might actually be not bad on its own. But, if you want a macro to do both parts at once, then something along the lines of
macro paramfn(e)
name = esc(e.args[1].args[1])
str = string(e.args[2].args[2])
f = esc(e)
quote
$name(::Form) = $str
$f
end
end
would let you write
julia> #paramfn borda_score_fn(p) = exp(1/p)
borda_score_fn (generic function with 2 methods)
julia> borda_score_fn(1)
2.718281828459045
julia> borda_score_fn(Form())
"exp(1 / p)"
For completeness, here's how you can do it in a way more similar to your original approach, but more idiomatically than with a global variable:
julia> module FormOf
export formof, #paramfn
function formof end
macro paramfn(expr)
name = esc(expr.args[1].args[1])
form_str = string(expr.args[2].args[2])
quote
$(esc(expr))
$FormOf.formof(::typeof($name)) = $form_str
$name
end
end
end
Main.FormOf
julia> FormOf.#paramfn borda_score_fn(p) = exp(1/p)
borda_score_fn (generic function with 1 method)
julia> FormOf.formof(borda_score_fn)
"exp(1 / p)"
However, since it defines a new method of FormOf.formof, this only works in global scope:
julia> function bla()
FormOf.#paramfn fn(p) = exp(1/p)
fn(10) + 1
end
ERROR: syntax: Global method definition around REPL[45]:10 needs to be placed at the top level, or use "eval".
Stacktrace:
[1] top-level scope
# REPL[50]:1
#cbk's solution does not have this limitation.
I am using the code below and it seems Julia should be able to infer the type parameters by itself, however this is not the case. Any ideas, maybe I'm doing something wrong?
abstract type ABS{A,B} end
struct MyStruct{A,B,K<:ABS{A,B}}
a::A
b::B
MyStruct{A,B,K}(a::A,b::B) where {A,B,K<:ABS{A,B}} = new(a,b)
end
MyStruct{Int64,Float64,ABS{Int64,Float64}}(1,2.1) # <<-- works
MyStruct(1,2.1) # <<-- doesn't work
I forgot the outer constructor, as #gnimuc pointed out. This code works:
abstract type ABS{A,B} end
struct Myk <: ABS{Int64,Float64} end
struct MyStruct{A,B,K<:ABS{A,B}}
a::A
b::B
MyStruct{A,B,K}(a::A,b::B) where {A,B,K<:ABS{A,B}}= new(a,b)
end
# this is the outer constructor:
MyStruct(a::A, b::B, ::K) where {A,B,K<:ABS{A,B}} = MyStruct{A,B,K}(a,b)
# now this works:
MyStruct(1,2.1,Myk())
How to check that a type implements an interface in Julia?
For exemple iteration interface is implemented by the functions start, next, done.
I need is to have a specialization of a function depending on wether the argument type implements a given interface or not.
EDIT
Here is an example of what I would like to do.
Consider the following code:
a = [7,8,9]
f = 1.0
s = Set()
push!(s,30)
push!(s,40)
function getsummary(obj)
println("Object of type ", typeof(obj))
end
function getsummary{T<:AbstractArray}(obj::T)
println("Iterable Object starting with ", next(obj, start(obj))[1])
end
getsummary(a)
getsummary(f)
getsummary(s)
The output is:
Iterable Object starting with 7
Object of type Float64
Object of type Set{Any}
Which is what we would expect since Set is not an AbstractArray. But clearly my second method only requires the type T to implement the iteration interface.
my issue isn't only related to the iteration interface but to all interfaces defined by a set of functions.
EDIT-2
I think my question is related to
https://github.com/JuliaLang/julia/issues/5
Since we could have imagined something like T<:Iterable
Typically, this is done with traits. See Traits.jl for one implementation; a similar approach is used in Base to dispatch on Base.iteratorsize, Base.linearindexing, etc. For instance, this is how Base implements collect using the iteratorsize trait:
"""
collect(element_type, collection)
Return an `Array` with the given element type of all items in a collection or iterable.
The result has the same shape and number of dimensions as `collection`.
"""
collect{T}(::Type{T}, itr) = _collect(T, itr, iteratorsize(itr))
_collect{T}(::Type{T}, itr, isz::HasLength) = copy!(Array{T,1}(Int(length(itr)::Integer)), itr)
_collect{T}(::Type{T}, itr, isz::HasShape) = copy!(similar(Array{T}, indices(itr)), itr)
function _collect{T}(::Type{T}, itr, isz::SizeUnknown)
a = Array{T,1}(0)
for x in itr
push!(a,x)
end
return a
end
See also Mauro Werder's talk on traits.
I would define a iterability(::T) trait as follows:
immutable Iterable end
immutable NotIterable end
iterability(T) =
if method_exists(length, (T,)) || !isa(Base.iteratorsize(T), Base.HasLength)
Iterable()
else
NotIterable()
end
which seems to work:
julia> iterability(Set)
Iterable()
julia> iterability(Number)
Iterable()
julia> iterability(Symbol)
NotIterable()
you can check whether a type implements an interface via methodswith as follows:
foo(a_type::Type, an_interface::Symbol) = an_interface ∈ [i.name for i in methodswith(a_type, true)]
julia> foo(EachLine, :done)
true
but I don't quite understand the dynamic dispatch approach you mentioned in the comment, what does the generic function looks like? what's the input & output of the function? I guess you want something like this?
function foo(a_type::Type, an_interface::Symbol)
# assume bar baz are predefined
if an_interface ∈ [i.name for i in methodswith(a_type, true)]
# call function bar
else
# call function baz
end
end
or some metaprogramming stuff to generate those functions respectively at compile time?