Functions in Julia programming labguage

Functions

In Julia, a function is an object that maps a tuple of argument values to a return value. Julia functions are not pure mathematical functions, in the sense that functions can alter and be affected by the global state of the program. The basic syntax for defining functions in Julia is:

function f(x,y)
  x + y
end

This syntax is similar to MATLAB®, but there are some significant differences:

• In MATLAB®, this definition must be saved in a file, named f.m, whereas in Julia, this expression can appear anywhere, including in an interactive session.
• In MATLAB®, the closing end is optional, being implied by the end of the file. In Julia, the terminating end is required.
• In MATLAB®, this function would print the value x + y but would not return any value, whereas in Julia, the last expression evaluated is a function’s return value.
• Expression values are never printed automatically except in interactive sessions. Semicolons are only required to separate expressions on the same line.

In general, while the function definition syntax is reminiscent of MATLAB®, the similarity is largely superficial. Therefore, rather than continually comparing the two, in what follows, we will simply describe the behavior of functions in Julia directly.
There is a second, more terse syntax for defining a function in Julia. The traditional function declaration syntax demonstrated above is equivalent to the following compact “assignment form”:

f(x,y) = x + y

In the assignment form, the body of the function must be a single expression, although it can be a compound expression (see Compound Expressions). Short, simple function definitions are common in Julia. The short function syntax is accordingly quite idiomatic, considerably reducing both typing and visual noise.
A function is called using the traditional parenthesis syntax:

julia> f(2,3)
5

Without parentheses, the expression f refers to the function object, and can be passed around like any value:

julia> g = f;

julia> g(2,3)
5

There are two other ways that functions can be applied: using special operator syntax for certain function names (see Operators Are Functions below), or with the apply function:

julia> apply(f,2,3)
5

The apply function applies its first argument — a function object — to its remaining arguments.

The “return” Keyword

The value returned by a function is the value of the last expression evaluated, which, by default, is the last expression in the body of the function definition. In the example function, f, from the previous section this is the value of the expression x + y. As in C and most other imperative or functional languages, the return keyword causes a function to return immediately, providing an expression whose value is returned:

function g(x,y)
  return x * y
  x + y
end

Since functions definitions can be entered into interactive sessions, it is easy to compare these definitions:

f(x,y) = x + y

function g(x,y)
  return x * y
  x + y
end

julia> f(2,3)
5

julia> g(2,3)
6

Of course, in a purely linear function body like g, the usage of return is pointless since the expression x + y is never evaluated and we could simply make x * y the last expression in the function and omit the return. In conjunction with other control flow, however, return is of real use. Here, for example, is a function that computes the hypotenuse length of a right triangle with sides of length x and y, avoiding overflow:

function hypot(x,y)
  x = abs(x)
  y = abs(y)
  if x > y
    r = y/x
    return x*sqrt(1+r*r)
  end
  if y == 0
    return zero(x)
  end
  r = x/y
  return y*sqrt(1+r*r)
end

There are three possible points of return from this function, returning the values of three different expressions, depending on the values of x and y. The return on the last line could be omitted since it is the last expression.

Operators Are Functions

In Julia, most operators are just functions with support for special syntax. The exceptions are operators with special evaluation semantics like && and ||. These operators cannot be functions since short-circuit evaluation (see Short-Circuit Evaluation) requires that their operands are not evaluated before evaluation of the operator. Accordingly, you can also apply them using parenthesized argument lists, just as you would any other function:

julia> 1 + 2 + 3
6

julia> +(1,2,3)
6

The infix form is exactly equivalent to the function application form — in fact the former is parsed to produce the function call internally. This also means that you can assign and pass around operators such as + and * just like you would with other function values:

julia> f = +;

julia> f(1,2,3)
6

Under the name f, the function does not support infix notation, however.

Anonymous Functions

Functions in Julia are first-class objects: they can be assigned to variables, called using the standard function call syntax from the variable they have been assigned to. They can be used as arguments, and they can be returned as values. They can also be created anonymously, without giving them a name:

julia> x -> x^2 + 2x - 1
#

This creates an unnamed function taking one argument and returning the value of the polynomial x^2 + 2x - 1 at that value. The primary use for anonymous functions is passing them to functions which take other functions as arguments. A classic example is the map function, which applies a function to each value of an array and returns a new array containing the resulting values:

julia> map(round, [1.2,3.5,1.7])
[1.0,4.0,2.0]

This is fine if a named function effecting the transform one wants already exists to pass as the first argument to map. Often, however, a ready-to-use, named function does not exist. In these situations, the anonymous function construct allows easy creation of a single-use function object without needing a name:

julia> map(x -> x^2 + 2x - 1, [1,3,-1])
[2,14,-2]

An anonymous function accepting multiple arguments can be written using the syntax (x,y,z)->2x+y-z. A zero-argument anonymous function is written as ()->3. The idea of a function with no arguments may seem strange, but is useful for “delaying” a computation. In this usage, a block of code is wrapped in a zero-argument function, which is later invoked by calling it as f().

Multiple Return Values

In Julia, one returns a tuple of values to simulate returning multiple values. However, tuples can be created and destructured without needing parentheses, thereby providing an illusion that multiple values are being returned, rather than a single tuple value. For example, the following function returns a pair of values:

function foo(a,b)
  a+b, a*b
end

If you call it in an interactive session without assigning the return value anywhere, you will see the tuple returned:

julia> foo(2,3)
(5,6)

A typical usage of such a pair of return values, however, extracts each value into a variable. Julia supports simple tuple “destructuring” that facilitates this:

julia> x, y = foo(2,3);

julia> x
5

julia> y
6

You can also return multiple values via an explicit usage of the return keyword:

function foo(a,b)
  return a+b, a*b
end

This has the exact same effect as the previous definition of foo.

Varargs Functions

It is often convenient to be able to write functions taking an arbitrary number of arguments. Such functions are traditionally known as “varargs” functions, which is short for “variable number of arguments”. You can define a varargs function by following the last argument with an ellipsis:

bar(a,b,x...) = (a,b,x)

The variables a and b are bound to the first two argument values as usual, and the variable x is bound to an iterable collection of the zero or more values passed to bar after its first two arguments:

julia> bar(1,2)
(1,2,())

julia> bar(1,2,3)
(1,2,(3,))

julia> bar(1,2,3,4)
(1,2,(3,4))

julia> bar(1,2,3,4,5,6)
(1,2,(3,4,5,6))

In all these cases, x is bound to a tuple of the trailing values passed to bar.
On the flip side, it is often handy to “splice” the values contained in an iterable collection into a function call as individual arguments. To do this, one also uses ... but in the function call instead:

julia> x = (3,4)
(3,4)

julia> bar(1,2,x...)
(1,2,(3,4))

In this case a tuple of values is spliced into a varargs call precisely where the variable number of arguments go. This need not be the case, however:

julia> x = (2,3,4)
(2,3,4)

julia> bar(1,x...)
(1,2,(3,4))

julia> x = (1,2,3,4)
(1,2,3,4)

julia> bar(x...)
(1,2,(3,4))

Furthermore, the iterable object spliced into a function call need not be a tuple:

julia> x = [3,4]
[3,4]

julia> bar(1,2,x...)
(1,2,(3,4))

julia> x = [1,2,3,4]
[1,2,3,4]

julia> bar(x...)
(1,2,(3,4))

Also, the function that arguments are spliced into need not be a varargs function (although it often is):

baz(a,b) = a + b

julia> args = [1,2]
[1,2]

julia> baz(args...)
3

julia> args = [1,2,3]
[1,2,3]

julia> baz(args...)
no method baz(Int64,Int64,Int64)

As you can see, if the wrong number of elements are in the spliced container, then the function call will fail, just as it would if too many arguments were given explicitly.

Further Reading

We should mention here that this is far from a complete picture of defining functions. Julia has a sophisticated type system and allows multiple dispatch on argument types. None of the examples given here provide any type annotations on their arguments, meaning that they are applicable to all types of arguments. The type system is described in Types and defining a function in terms of methods chosen by multiple dispatch on run-time argument types is described in Methods.