How can I convert a Julia Int/Bool Array/Vector to a Fortran LOGICAL array for use within Julia's ccall?
I tried passing it as Array{Bool} in https://gist.github.com/axsk/28f297e2207365a7f4e8/, but the code is not working correctly and I am quite confident the problem is the Bool-Logical conversion.
I don't know too much about calling Fortran code, but according to this
The Fortran standard does not specify how variables of LOGICAL type
are represented, beyond requiring that LOGICAL variables of default
kind have the same storage size as default INTEGER and REAL variables.
The GNU Fortran internal representation is as follows.
A LOGICAL(KIND=N) variable is represented as an INTEGER(KIND=N)
variable, however, with only two permissible values: 1 for .TRUE. and
0 for .FALSE.. Any other integer value results in undefined behavior.
So I'd do something like the following
julia_array = rand(Bool, 1:10)
fort_array = Int[x?1:0 for x in julia_array]
Then use fort_array as the input. Which Fortran compiler are you using?
EDIT: It turns out the Julia developers already define a type that will work with the linked BLAS/LAPACK, Base.BLAS.BlasInt, that will use the correct Int variant for the system.
As iaindunning posted before, Fortran represents Logical variables as Integers.
Unfortunately the representation of the type Integer varies from platform to platform.
While I had success using Int on Windows and Cint on Linux/MacOS, in the end I used BlasInt, which adopts depending on the platform.
Related
I am new to Julia. Have a quick question.
In Fortran, we can do
implicit none
Therefore whenever we use a variable, we need to define it first. Otherwise it will give error.
In Julia, I want to do the same time thing. I want to define the type of each variable first, like Float64, Int64, etc. So that I wish that Julia no longer need to automatically do type conversion, which may slow down the code.
Because I know if Julia code looks like Fortran it usually runs fast.
So, in short, is there a way in Julia such that, I can force it to only use variables whose types has been defined, and otherwise give me an error message? I just want to be strict.
I just wanted to define all the variables first, then use them. Just like Fortran does.
Thanks!
[Edit]
As suggested by the experts who answers the questions, thank you very much! I know that perhaps in Julia there is no need to manually define each variables one by one. Because for one thing, if I do that, the code will become just like Fortran, and it can be very very long especially if I have many variables.
But if I do not define the type of the each variables, is Julia smart enough to know the type? Will it do some conversions which may slow down the code?
Also, even if there is no need to define variables one by one, is there in some situations we may have to manually define the type manually?
No, this is not possible* as such. You can, however, add type annotations at any point in your code, which will raise an error if the variable is not of the expected type:
julia> i = 1
1
julia> i::Int64
1
julia> i = 1.0
1.0
julia> i::Int64
ERROR: TypeError: in typeassert, expected Int64, got a value of type Float64
Stacktrace:
[1] top-level scope
# REPL[4]:1
julia> i=0x01::UInt8
0x01
*Julia is a dynamically-typed language, though there are packages such as https://github.com/aviatesk/JET.jl for static type-checking (i.e., no type annotations required) and https://github.com/JuliaDebug/Cthulhu.jl for exploring Julia's type inference system.
Strict type declaration is not how you achieve performance in Julia.
You achieve the performance by type stability.
Moreover declaring the types is usually not recommended because it decreases interoperability.
Consider the following function:
f(x::Int) = rand() < 4+x ? 1 : "0"
The types of everything seem to be known. However, this is a terrible (from performance point of view) function because the type of output can not be calcuated by looking types of input. And this is exactly how you write the performant code with regard to types.
So how to check your code? There is a special macro #code_warntype to detect such cases so you can correct your code:
Another type related issue are the containers that should not be specified by abstract elements. So you never want to have neither Vector{Any} nor Vector{Real} - rather than that you want Vector{Int} or Vector{Float64}.
See also https://docs.julialang.org/en/v1/manual/performance-tips/ for further discussion.
I would like to check if a variable is scalar in julia, such as Integer, String, Number, but not AstractArray, Tuple, type, struct, etc. Is there a simple method to do this (i.e. isscalar(x))
The notion of what is, or is not a scalar is under-defined without more context.
Mathematically, a scalar is defined; (Wikipedia)
A scalar is an element of a field which is used to define a vector space.
That is to say, you need to define a vector space, based on a field, before you can determine if something is, or is not a scalar (relative to that vector space.).
For the right vector space, tuples could be a scalar.
Of-course we are not looking for a mathematically rigorous definition.
Just a pragmatic one.
Base it off what Broadcasting considers to be scalar
I suggest that the only meaningful way in which a scalar can be defined in julia, is of the behavior of broadcast.
As of Julia 1:
using Base.Broadcast
isscalar(x::T) where T = isscalar(T)
isscalar(::Type{T}) where T = BroadcastStyle(T) isa Broadcast.DefaultArrayStyle{0}
See the docs for Broadcast.
In julia 0.7, Scalar is the default. So it is basically anything that doesn't have specific broadcasting behavior, i.e. it knocks out things like array and tuples etc.:
using Base.Broadcast
isscalar(x::T) where T = isscalar(T)
isscalar(::Type{T}) where T = BroadcastStyle(T) isa Broadcast.Scalar
In julia 0.6 this is a bit more messy, but similar:
isscalar(x::T) where T = isscalar(T)
isscalar(::Type{T}) where T = Base.Broadcast._containertype(T)===Any
The advantage of using the methods for Broadcast to determine if something is scalar, over using your own methods, is that anyone making a new type that is going to act in a scalar way must make sure it works with those methods correctly
(or actually nonscalar since scalar is the default.)
Structs are not not scalar
That is to say: sometimes structs are scalar and sometimes they are not and it depends on the struct.
Note however that these methods do not consider struct to be non-scalar.
I think you are mistaken in your desire to.
Julia structs are not (necessarily or usually) a collection type.
Consider that: BigInteger, BigFloat, Complex128 etc etc
are all defined using structs
I was tempted to say that having a start method makes a type nonscalar, but that would be incorrect as start(::Number) is defined.
(This has been debated a few times)
For completeness, I am copying Tasos Papastylianou's answer from the comments to here. If all you want to do is distinguish scalars from arrays you can use:
isa(x, Number)
This will output true if x is a Number (like a float or an int), and output false if x is an Array (vector, matrix, etc.)
I found myself needing to capture the notion of if something was scalar or not recently in MultiResolutionIterators.jl.
I found the boardcasting based rules from the other answer,
did not meet my needs.
In particular I wanted to consider strings as nonscalar.
I defined a trait,
bases on method_exists(start, (T,)),
with some exceptions as mentioned e.g. for Number.
abstract type Scalarness end
struct Scalar <: Scalarness end
struct NotScalar <: Scalarness end
isscalar(::Type{Any}) = NotScalar() # if we don't know the type we can't really know if scalar or not
isscalar(::Type{<:AbstractString}) = NotScalar() # We consider strings to be nonscalar
isscalar(::Type{<:Number}) = Scalar() # We consider Numbers to be scalar
isscalar(::Type{Char}) = Scalar() # We consider Sharacter to be scalar
isscalar(::Type{T}) where T = method_exists(start, (T,)) ? NotScalar() : Scalar()
Something similar is also done by AbstractTrees.jl
isscalar(x) == applicable(start, x) && !isa(x, Integer) && !isa(x, Char) && !isa(x, Task)
I'm trying to pass a three-dimensional data structure to Stan (in RStan) where the entries must be integers, because a function down-stream requires that. However I'm having trouble declaring it.
I tried the straight-forward approach:
int x[n,n,k];
But that gave me the error
mismatch in number dimensions declared and found in context; ... dims declared=(n,n,k); dims found=(n*n*k)
meaning, clearly, the input array is getting flattened, for some reason (that I don't understand). I'm giving it a simple 3d array, no NAs, the dimensions look right before I pass it. And in fact, the same things is happening for 2d arrays, as well, meaning I can't even declare a set of 2d matrices, as a workaround.
Then I tried
row_vector[K] x[N,N];
but that gives back real, not int. And when I do something like
int row_vector[K] x[N,N];
that's just not proper syntax.
I also tried passing logical values, hoping they'd be re-cast as ints, but no. I passed arrays, I passed them cast with as.matrix, I checked their dimension both before and after being put into the data list.
This is with R version 3.4.1 on OSX 10.11.6, using the most recent version of stan, that was just compiled from source, today.
What am I missing? OR, how might I cast a single real to an integer, so that the integer-requiring function doesn't break?
(And, WHERE is the documentation? The best I can find is long-dead comment threads.)
This is similar to my previous question, but a bit more complicated.
Before I was defining a type with an associated integer as a parameter, Intp{p}. Now I would like to define a type using a vector as a parameter.
The following is the closest I can write to what I want:
type Extp{g::Vector{T}}
c::Vector{T}
end
In other words, Extp should be defined with respect to a Vector, g, and I want the contents, c, to be another Vector, whose entries should be the of the same type as the entries of g.
Well, this does not work.
Problem 1: I don't think I can use :: in the type parameter.
Problem 2: I could work around that by making the types of g and c arbitary and just making sure the types in the vectors match up in the constructor. But, even if I completely take everything out and use
type Extp{g}
c
end
it still doesn't seem to like this. When I try to use it the way I want to,
julia> Extp{[1,1,1]}([0,0,1])
ERROR: type: apply_type: in Extp, expected Type{T<:Top}, got Array{Int64,1}
So, does Julia just not like particular Vectors being associated with types? Does what I'm trying to do only work with integers, like in my Intp question?
EDIT: In the documentation I see that type parameters "can be any type at all (or an integer, actually, although here it’s clearly used as a type)." Does that mean that what I'm asking is impossible, and that that only types and integers work for Type parameters? If so, why? (what makes integers special over other types in Julia in this way?)
In Julia 0.4, you can use any "bitstype" as a parameter of a type. However, a vector is not a bitstype, so this is not going to work. The closest analog is to use a tuple: for example, (3.2, 1.5) is a perfectly valid type parameter.
In a sense vectors (or any mutable object) are antithetical to types, which cannot change at runtime.
Here is the relevant quote:
Both abstract and concrete types can be parameterized by other types
and by certain other values (currently integers, symbols, bools, and
tuples thereof).
So, your EDIT is correct. Widening this has come up on the Julia issues page (e.g., #5102 and #6081 were two related issues I found with some discussion), so this may change in the future - I'm guessing not in v0.4 though. It'd have to be an immutable type really to make any sense, so not Vector. I'm not sure I understand your application, but would a Tuple work?
Recently I started using Armadillo C++ library. Given my C++ coding skills are not that great, I found this very friendly for linear algebra. I am also using that along with my matlab to speed things up for many of reconstruction algorithm.
I do need to create a vector of boolean and I would prefer using this library rather than . However, I could not figure out how to do it. I tried using uvec; but, documentation seems to indicate that it can not be used with boolean.
Any help would be appreciated.
Regards,
Dushyant
Consider using a matrix uchar_mat which is a typdef for Mat<unsigned char>, it should consume the same amount of memory as a matrix of boolean values.
The Armadillo documentation of version 7.8 states that a matrix Mat<type>, can be of the following types:
float, double, std::complex<float>, std::complex<double>, short, int, long, and unsigned versions of short, int, and long. The code on GitHub however contains typedef Mat <unsigned char> uchar_mat; in the file include/armadillo_bits/typedef_mat.hpp so you should also be able to use uchar_mat.
You will not save any memory by creating a matrix of bool values compared to a matrix of unsigned char values (a bool type consumes 8 bits). This is because in C++ every data type must be addressable; it must be at least 1 byte long so that it is possible to create a pointer pointing to it.