# 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
#<function>
```

This creates an unnamed function taking one argument and returning the
value of the polynomial *x*^2 + 2*x* - 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])
3-element Float64 Array:
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])
3-element Int64 Array:
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]
2-element Int64 Array:
3
4
julia> bar(1,2,x...)
(1,2,(3,4))
julia> x = [1,2,3,4]
4-element Int64 Array:
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]
2-element Int64 Array:
1
2
julia> baz(args...)
3
julia> args = [1,2,3]
3-element Int64 Array:
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.

## Optional Arguments¶

In many cases, function arguments have sensible default values and therefore
might not need to be passed explicitly in every call. For example, the
library function `parseint(num,base)`

interprets a string as a number
in some base. The `base`

argument defaults to `10`

. This behavior can be
expressed concisely as:

```
function parseint(num, base=10)
###
end
```

With this definition, the function can be called with either one or two
arguments, and `10`

is automatically passed when a second argument is not
specified:

```
julia> parseint("12",10)
12
julia> parseint("12",3)
5
julia> parseint("12")
12
```

Optional arguments are actually just a convenient syntax for writing multiple method definitions with different numbers of arguments (see Methods).

## Named Arguments¶

Some functions need a large number of arguments, or have a large number of behaviors. Remembering how to call such functions can be difficult. Named arguments, also called keyword arguments, can make these complex interfaces easier to use and extend by allowing arguments to be identified by name instead of only by position.

For example, consider a function `plot`

that
plots a line. This function might have many options, for controlling line
style, width, color, and so on. If it accepts named arguments, a possible
call might look like `plot(x, y, width=2)`

, where we have chosen to
specify only line width. Notice that this serves two purposes. The call is
easier to read, since we can label an argument with its meaning. It also
becomes possible to pass any subset of a large number of arguments, in
any order.

Functions with named arguments are defined using a semicolon in the signature:

```
function plot(x, y; style="solid", width=1, color="black")
###
end
```

Extra named arguments can be collected using `...`

, as in varargs
functions:

```
function f(x; args...)
###
end
```

Inside `f`

, `args`

will be a collection of `(key,value)`

tuples,
where each `key`

is a symbol. Such collections can be passed as named
arguments using a semicolon in a call, `f(x; k...)`

. Dictionaries
can be used for this purpose.

## Block Syntax for Function Arguments¶

Passing functions as arguments to other functions is a powerful technique,
but the syntax for it is not always convenient. Such calls are especially
awkward to write when the function argument requires multiple lines. As
an example, consider calling `map`

on a function with several cases:

```
map(x->begin
if x < 0 && iseven(x)
return 0
elseif x == 0
return 1
else
return x
end
end,
[A, B, C])
```

Julia provides a reserved word `do`

for rewriting this code more clearly:

```
map([A, B, C]) do x
if x < 0 && iseven(x)
return 0
elseif x == 0
return 1
else
return x
end
end
```

The `do x`

syntax creates an anonymous function with argument `x`

and
passes it as the first argument to `map`

. This syntax makes it easier to
use functions to effectively extend the language, since calls look like
normal code blocks. There are many possible uses quite different from `map`

,
such as managing system state. For example, the standard library provides
a function `cd`

for running code in a given directory, and switching back
to the previous directory when the code finishes or aborts. There is also
a definition of `open`

that runs code ensuring that the opened file is
eventually closed. We can combine these functions to safely write a file
in a certain directory:

```
cd("data") do
open("outfile", "w") do f
write(f, data)
end
end
```

The function argument to `cd`

takes no arguments; it is just a block of
code. The function argument to `open`

receives a handle to the opened
file.

## 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.