Essentials

exit(code=0)

Stop the program with an exit code. The default exit code is zero, indicating that the program completed successfully. In an interactive session, exit() can be called with the keyboard shortcut ^D.

atexit(f)

Register a zero- or one-argument function f() to be called at process exit. atexit() hooks are called in last in first out (LIFO) order and run before object finalizers.

If f has a method defined for one integer argument, it will be called as f(n::Int32), where n is the current exit code, otherwise it will be called as f().

Exit hooks are allowed to call exit(n), in which case Julia will exit with exit code n (instead of the original exit code). If more than one exit hook calls exit(n), then Julia will exit with the exit code corresponding to the last called exit hook that calls exit(n). (Because exit hooks are called in LIFO order, "last called" is equivalent to "first registered".)

Note: Once all exit hooks have been called, no more exit hooks can be registered, and any call to atexit(f) after all hooks have completed will throw an exception. This situation may occur if you are registering exit hooks from background Tasks that may still be executing concurrently during shutdown.

isinteractive() -> Bool

Determine whether Julia is running an interactive session.

Base.summarysize(obj; exclude=Union{...}, chargeall=Union{...}) -> Int

Compute the amount of memory, in bytes, used by all unique objects reachable from the argument.

Keyword Arguments

• exclude: specifies the types of objects to exclude from the traversal.
• chargeall: specifies the types of objects to always charge the size of all of their fields, even if those fields would normally be excluded.

See also sizeof.

Examples

julia> Base.summarysize(1.0)
8

julia> Base.summarysize(Ref(rand(100)))
848

julia> sizeof(Ref(rand(100)))
8

__precompile__(isprecompilable::Bool)

Specify whether the file calling this function is precompilable, defaulting to true. If a module or file is not safely precompilable, it should call __precompile__(false) in order to throw an error if Julia attempts to precompile it.

Base.include([mapexpr::Function,] m::Module, path::AbstractString)

Evaluate the contents of the input source file in the global scope of module m. Every module (except those defined with baremodule) has its own definition of include omitting the m argument, which evaluates the file in that module. Returns the result of the last evaluated expression of the input file. During including, a task-local include path is set to the directory containing the file. Nested calls to include will search relative to that path. This function is typically used to load source interactively, or to combine files in packages that are broken into multiple source files.

The optional first argument mapexpr can be used to transform the included code before it is evaluated: for each parsed expression expr in path, the include function actually evaluates mapexpr(expr). If it is omitted, mapexpr defaults to identity.

include([mapexpr::Function,] path::AbstractString)

Evaluate the contents of the input source file in the global scope of the containing module. Every module (except those defined with baremodule) has its own definition of include, which evaluates the file in that module. Returns the result of the last evaluated expression of the input file. During including, a task-local include path is set to the directory containing the file. Nested calls to include will search relative to that path. This function is typically used to load source interactively, or to combine files in packages that are broken into multiple source files. The argument path is normalized using normpath which will resolve relative path tokens such as .. and convert / to the appropriate path separator.

The optional first argument mapexpr can be used to transform the included code before it is evaluated: for each parsed expression expr in path, the include function actually evaluates mapexpr(expr). If it is omitted, mapexpr defaults to identity.

Use Base.include to evaluate a file into another module.

include_string([mapexpr::Function,] m::Module, code::AbstractString, filename::AbstractString="string")

Like include, except reads code from the given string rather than from a file.

The optional first argument mapexpr can be used to transform the included code before it is evaluated: for each parsed expression expr in code, the include_string function actually evaluates mapexpr(expr). If it is omitted, mapexpr defaults to identity.

include_dependency(path::AbstractString)

In a module, declare that the file, directory, or symbolic link specified by path (relative or absolute) is a dependency for precompilation; that is, the module will need to be recompiled if the modification time of path changes.

This is only needed if your module depends on a path that is not used via include. It has no effect outside of compilation.

__init__

The __init__() function in a module executes immediately after the module is loaded at runtime for the first time. It is called once, after all other statements in the module have been executed. Because it is called after fully importing the module, __init__ functions of submodules will be executed first. Two typical uses of __init__ are calling runtime initialization functions of external C libraries and initializing global constants that involve pointers returned by external libraries. See the manual section about modules for more details.

Examples

const foo_data_ptr = Ref{Ptr{Cvoid}}(0)
function __init__()
    ccall((:foo_init, :libfoo), Cvoid, ())
    foo_data_ptr[] = ccall((:foo_data, :libfoo), Ptr{Cvoid}, ())
    nothing
end

which(f, types)

Returns the method of f (a Method object) that would be called for arguments of the given types.

If types is an abstract type, then the method that would be called by invoke is returned.

See also: parentmodule, and @which and @edit in InteractiveUtils.

methods(f, [types], [module])

Return the method table for f.

If types is specified, return an array of methods whose types match. If module is specified, return an array of methods defined in that module. A list of modules can also be specified as an array.

See also: which and @which.

@show exs...

Prints one or more expressions, and their results, to stdout, and returns the last result.

See also: show, @info, println.

Examples

julia> x = @show 1+2
1 + 2 = 3
3

julia> @show x^2 x/2;
x ^ 2 = 9
x / 2 = 1.5

ans

A variable referring to the last computed value, automatically imported to the interactive prompt.

err

A variable referring to the last thrown errors, automatically imported to the interactive prompt. The thrown errors are collected in a stack of exceptions.

active_project()

Return the path of the active Project.toml file. See also Base.set_active_project.

set_active_project(projfile::Union{AbstractString,Nothing})

Set the active Project.toml file to projfile. See also Base.active_project.

module

module declares a Module, which is a separate global variable workspace. Within a module, you can control which names from other modules are visible (via importing), and specify which of your names are intended to be public (via exporting). Modules allow you to create top-level definitions without worrying about name conflicts when your code is used together with somebody else’s. See the manual section about modules for more details.

Examples

module Foo
import Base.show
export MyType, foo

struct MyType
    x
end

bar(x) = 2x
foo(a::MyType) = bar(a.x) + 1
show(io::IO, a::MyType) = print(io, "MyType $(a.x)")
end

export

export is used within modules to tell Julia which functions should be made available to the user. For example: export foo makes the name foo available when using the module. See the manual section about modules for details.

import

import Foo will load the module or package Foo. Names from the imported Foo module can be accessed with dot syntax (e.g. Foo.foo to access the name foo). See the manual section about modules for details.

using

using Foo will load the module or package Foo and make its exported names available for direct use. Names can also be used via dot syntax (e.g. Foo.foo to access the name foo), whether they are exported or not. See the manual section about modules for details.

as

as is used as a keyword to rename an identifier brought into scope by import or using, for the purpose of working around name conflicts as well as for shortening names. (Outside of import or using statements, as is not a keyword and can be used as an ordinary identifier.)

import LinearAlgebra as LA brings the imported LinearAlgebra standard library into scope as LA.

import LinearAlgebra: eigen as eig, cholesky as chol brings the eigen and cholesky methods from LinearAlgebra into scope as eig and chol respectively.

as works with using only when individual identifiers are brought into scope. For example, using LinearAlgebra: eigen as eig or using LinearAlgebra: eigen as eig, cholesky as chol works, but using LinearAlgebra as LA is invalid syntax, since it is nonsensical to rename all exported names from LinearAlgebra to LA.

baremodule

baremodule declares a module that does not contain using Base or local definitions of eval and include. It does still import Core. In other words,

module Mod

...

end

is equivalent to

baremodule Mod

using Base

eval(x) = Core.eval(Mod, x)
include(p) = Base.include(Mod, p)

...

end

function

Functions are defined with the function keyword:

function add(a, b)
    return a + b
end

Or the short form notation:

add(a, b) = a + b

The use of the return keyword is exactly the same as in other languages, but is often optional. A function without an explicit return statement will return the last expression in the function body.

macro

macro defines a method for inserting generated code into a program. A macro maps a sequence of argument expressions to a returned expression, and the resulting expression is substituted directly into the program at the point where the macro is invoked. Macros are a way to run generated code without calling eval, since the generated code instead simply becomes part of the surrounding program. Macro arguments may include expressions, literal values, and symbols. Macros can be defined for variable number of arguments (varargs), but do not accept keyword arguments. Every macro also implicitly gets passed the arguments __source__, which contains the line number and file name the macro is called from, and __module__, which is the module the macro is expanded in.

See the manual section on Metaprogramming for more information about how to write a macro.

Examples

julia> macro sayhello(name)
           return :( println("Hello, ", $name, "!") )
       end
@sayhello (macro with 1 method)

julia> @sayhello "Charlie"
Hello, Charlie!

julia> macro saylots(x...)
           return :( println("Say: ", $(x...)) )
       end
@saylots (macro with 1 method)

julia> @saylots "hey " "there " "friend"
Say: hey there friend

return

return x causes the enclosing function to exit early, passing the given value x back to its caller. return by itself with no value is equivalent to return nothing (see nothing).

function compare(a, b)
    a == b && return "equal to"
    a < b ? "less than" : "greater than"
end

In general you can place a return statement anywhere within a function body, including within deeply nested loops or conditionals, but be careful with do blocks. For example:

function test1(xs)
    for x in xs
        iseven(x) && return 2x
    end
end

function test2(xs)
    map(xs) do x
        iseven(x) && return 2x
        x
    end
end

In the first example, the return breaks out of test1 as soon as it hits an even number, so test1([5,6,7]) returns 12.

You might expect the second example to behave the same way, but in fact the return there only breaks out of the inner function (inside the do block) and gives a value back to map. test2([5,6,7]) then returns [5,12,7].

When used in a top-level expression (i.e. outside any function), return causes the entire current top-level expression to terminate early.

do

Create an anonymous function and pass it as the first argument to a function call. For example:

map(1:10) do x
    2x
end

is equivalent to map(x->2x, 1:10).

Use multiple arguments like so:

map(1:10, 11:20) do x, y
    x + y
end

begin

begin...end denotes a block of code.

begin
    println("Hello, ")
    println("World!")
end

Usually begin will not be necessary, since keywords such as function and let implicitly begin blocks of code. See also ;.

begin may also be used when indexing to represent the first index of a collection or the first index of a dimension of an array.

Examples

julia> A = [1 2; 3 4]
2×2 Array{Int64,2}:
 1  2
 3  4

julia> A[begin, :]
2-element Array{Int64,1}:
 1
 2

end

end marks the conclusion of a block of expressions, for example module, struct, mutable struct, begin, let, for etc.

end may also be used when indexing to represent the last index of a collection or the last index of a dimension of an array.

Examples

julia> A = [1 2; 3 4]
2×2 Array{Int64, 2}:
 1  2
 3  4

julia> A[end, :]
2-element Array{Int64, 1}:
 3
 4

let

let blocks create a new hard scope and optionally introduce new local bindings.

Just like the other scope constructs, let blocks define the block of code where newly introduced local variables are accessible. Additionally, the syntax has a special meaning for comma-separated assignments and variable names that may optionally appear on the same line as the let:

let var1 = value1, var2, var3 = value3
    code
end

The variables introduced on this line are local to the let block and the assignments are evaluated in order, with each right-hand side evaluated in the scope without considering the name on the left-hand side. Therefore it makes sense to write something like let x = x, since the two x variables are distinct with the left-hand side locally shadowing the x from the outer scope. This can even be a useful idiom as new local variables are freshly created each time local scopes are entered, but this is only observable in the case of variables that outlive their scope via closures. A let variable without an assignment, such as var2 in the example above, declares a new local variable that is not yet bound to a value.

By contrast, begin blocks also group multiple expressions together but do not introduce scope or have the special assignment syntax.

Examples

In the function below, there is a single x that is iteratively updated three times by the map. The closures returned all reference that one x at its final value:

julia> function test_outer_x()
           x = 0
           map(1:3) do _
               x += 1
               return ()->x
           end
       end
test_outer_x (generic function with 1 method)

julia> [f() for f in test_outer_x()]
3-element Vector{Int64}:
 3
 3
 3

If, however, we add a let block that introduces a new local variable we will end up with three distinct variables being captured (one at each iteration) even though we chose to use (shadow) the same name.

julia> function test_let_x()
           x = 0
           map(1:3) do _
               x += 1
               let x = x
                   return ()->x
               end
           end
       end
test_let_x (generic function with 1 method)

julia> [f() for f in test_let_x()]
3-element Vector{Int64}:
 1
 2
 3

All scope constructs that introduce new local variables behave this way when repeatedly run; the distinctive feature of let is its ability to succinctly declare new locals that may shadow outer variables of the same name. For example, directly using the argument of the do function similarly captures three distinct variables:

julia> function test_do_x()
           map(1:3) do x
               return ()->x
           end
       end
test_do_x (generic function with 1 method)

julia> [f() for f in test_do_x()]
3-element Vector{Int64}:
 1
 2
 3

if/elseif/else

if/elseif/else performs conditional evaluation, which allows portions of code to be evaluated or not evaluated depending on the value of a boolean expression. Here is the anatomy of the if/elseif/else conditional syntax:

if x < y
    println("x is less than y")
elseif x > y
    println("x is greater than y")
else
    println("x is equal to y")
end

If the condition expression x < y is true, then the corresponding block is evaluated; otherwise the condition expression x > y is evaluated, and if it is true, the corresponding block is evaluated; if neither expression is true, the else block is evaluated. The elseif and else blocks are optional, and as many elseif blocks as desired can be used.

In contrast to some other languages conditions must be of type Bool. It does not suffice for conditions to be convertible to Bool.

julia> if 1 end
ERROR: TypeError: non-boolean (Int64) used in boolean context

for

for loops repeatedly evaluate a block of statements while iterating over a sequence of values.

The iteration variable is always a new variable, even if a variable of the same name exists in the enclosing scope. Use outer to reuse an existing local variable for iteration.

Examples

julia> for i in [1, 4, 0]
           println(i)
       end
1
4
0

while

while loops repeatedly evaluate a conditional expression, and continue evaluating the body of the while loop as long as the expression remains true. If the condition expression is false when the while loop is first reached, the body is never evaluated.

Examples

julia> i = 1
1

julia> while i < 5
           println(i)
           global i += 1
       end
1
2
3
4

break

Break out of a loop immediately.

Examples

julia> i = 0
0

julia> while true
           global i += 1
           i > 5 && break
           println(i)
       end
1
2
3
4
5

continue

Skip the rest of the current loop iteration.

Examples

julia> for i = 1:6
           iseven(i) && continue
           println(i)
       end
1
3
5

try/catch

A try/catch statement allows intercepting errors (exceptions) thrown by throw so that program execution can continue. For example, the following code attempts to write a file, but warns the user and proceeds instead of terminating execution if the file cannot be written:

try
    open("/danger", "w") do f
        println(f, "Hello")
    end
catch
    @warn "Could not write file."
end

or, when the file cannot be read into a variable:

lines = try
    open("/danger", "r") do f
        readlines(f)
    end
catch
    @warn "File not found."
end

The syntax catch e (where e is any variable) assigns the thrown exception object to the given variable within the catch block.

The power of the try/catch construct lies in the ability to unwind a deeply nested computation immediately to a much higher level in the stack of calling functions.

finally

Run some code when a given block of code exits, regardless of how it exits. For example, here is how we can guarantee that an opened file is closed:

f = open("file")
try
    operate_on_file(f)
finally
    close(f)
end

When control leaves the try block (for example, due to a return, or just finishing normally), close(f) will be executed. If the try block exits due to an exception, the exception will continue propagating. A catch block may be combined with try and finally as well. In this case the finally block will run after catch has handled the error.

quote

quote creates multiple expression objects in a block without using the explicit Expr constructor. For example:

ex = quote
    x = 1
    y = 2
    x + y
end

Unlike the other means of quoting, :( ... ), this form introduces QuoteNode elements to the expression tree, which must be considered when directly manipulating the tree. For other purposes, :( ... ) and quote .. end blocks are treated identically.

local

local introduces a new local variable. See the manual section on variable scoping for more information.

Examples

julia> function foo(n)
           x = 0
           for i = 1:n
               local x # introduce a loop-local x
               x = i
           end
           x
       end
foo (generic function with 1 method)

julia> foo(10)
0

global

global x makes x in the current scope and its inner scopes refer to the global variable of that name. See the manual section on variable scoping for more information.

Examples

julia> z = 3
3

julia> function foo()
           global z = 6 # use the z variable defined outside foo
       end
foo (generic function with 1 method)

julia> foo()
6

julia> z
6

for outer

Reuse an existing local variable for iteration in a for loop.

See the manual section on variable scoping for more information.

See also for.

Examples

julia> function f()
           i = 0
           for i = 1:3
               # empty
           end
           return i
       end;

julia> f()
0

julia> function f()
           i = 0
           for outer i = 1:3
               # empty
           end
           return i
       end;

julia> f()
3

julia> i = 0 # global variable
       for outer i = 1:3
       end
ERROR: syntax: no outer local variable declaration exists for "for outer"
[...]

const

const is used to declare global variables whose values will not change. In almost all code (and particularly performance sensitive code) global variables should be declared constant in this way.

const x = 5

Multiple variables can be declared within a single const:

const y, z = 7, 11

Note that const only applies to one = operation, therefore const x = y = 1 declares x to be constant but not y. On the other hand, const x = const y = 1 declares both x and y constant.

Note that "constant-ness" does not extend into mutable containers; only the association between a variable and its value is constant. If x is an array or dictionary (for example) you can still modify, add, or remove elements.

In some cases changing the value of a const variable gives a warning instead of an error. However, this can produce unpredictable behavior or corrupt the state of your program, and so should be avoided. This feature is intended only for convenience during interactive use.

struct

The most commonly used kind of type in Julia is a struct, specified as a name and a set of fields.

struct Point
    x
    y
end

Fields can have type restrictions, which may be parameterized:

struct Point{X}
    x::X
    y::Float64
end

A struct can also declare an abstract super type via <: syntax:

struct Point <: AbstractPoint
    x
    y
end

structs are immutable by default; an instance of one of these types cannot be modified after construction. Use mutable struct instead to declare a type whose instances can be modified.

See the manual section on Composite Types for more details, such as how to define constructors.

mutable struct

mutable struct is similar to struct, but additionally allows the fields of the type to be set after construction. See the manual section on Composite Types for more information.

@kwdef typedef

This is a helper macro that automatically defines a keyword-based constructor for the type declared in the expression typedef, which must be a struct or mutable struct expression. The default argument is supplied by declaring fields of the form field::T = default or field = default. If no default is provided then the keyword argument becomes a required keyword argument in the resulting type constructor.

Inner constructors can still be defined, but at least one should accept arguments in the same form as the default inner constructor (i.e. one positional argument per field) in order to function correctly with the keyword outer constructor.

Examples

julia> @kwdef struct Foo
           a::Int = 1         # specified default
           b::String          # required keyword
       end
Foo

julia> Foo(b="hi")
Foo(1, "hi")

julia> Foo()
ERROR: UndefKeywordError: keyword argument `b` not assigned
Stacktrace:
[...]

abstract type

abstract type declares a type that cannot be instantiated, and serves only as a node in the type graph, thereby describing sets of related concrete types: those concrete types which are their descendants. Abstract types form the conceptual hierarchy which makes Julia’s type system more than just a collection of object implementations. For example:

abstract type Number end
abstract type Real <: Number end

Number has no supertype, whereas Real is an abstract subtype of Number.

primitive type

primitive type declares a concrete type whose data consists only of a series of bits. Classic examples of primitive types are integers and floating-point values. Some example built-in primitive type declarations:

primitive type Char 32 end
primitive type Bool <: Integer 8 end

The number after the name indicates how many bits of storage the type requires. Currently, only sizes that are multiples of 8 bits are supported. The Bool declaration shows how a primitive type can be optionally declared to be a subtype of some supertype.

where

The where keyword creates a type that is an iterated union of other types, over all values of some variable. For example Vector{T} where T<:Real includes all Vectors where the element type is some kind of Real number.

The variable bound defaults to Any if it is omitted:

Vector{T} where T    # short for `where T<:Any`

Variables can also have lower bounds:

Vector{T} where T>:Int
Vector{T} where Int<:T<:Real

There is also a concise syntax for nested where expressions. For example, this:

Pair{T, S} where S<:Array{T} where T<:Number

can be shortened to:

Pair{T, S} where {T<:Number, S<:Array{T}}

This form is often found on method signatures.

Note that in this form, the variables are listed outermost-first. This matches the order in which variables are substituted when a type is "applied" to parameter values using the syntax T{p1, p2, ...}.

...

The "splat" operator, ..., represents a sequence of arguments. ... can be used in function definitions, to indicate that the function accepts an arbitrary number of arguments. ... can also be used to apply a function to a sequence of arguments.

Examples

julia> add(xs...) = reduce(+, xs)
add (generic function with 1 method)

julia> add(1, 2, 3, 4, 5)
15

julia> add([1, 2, 3]...)
6

julia> add(7, 1:100..., 1000:1100...)
111107

;

; has a similar role in Julia as in many C-like languages, and is used to delimit the end of the previous statement.

; is not necessary at the end of a line, but can be used to separate statements on a single line or to join statements into a single expression.

Adding ; at the end of a line in the REPL will suppress printing the result of that expression.

In function declarations, and optionally in calls, ; separates regular arguments from keywords.

In array literals, arguments separated by semicolons have their contents concatenated together. A separator made of a single ; concatenates vertically (i.e. along the first dimension), ;; concatenates horizontally (second dimension), ;;; concatenates along the third dimension, etc. Such a separator can also be used in last position in the square brackets to add trailing dimensions of length 1.

A ; in first position inside of parentheses can be used to construct a named tuple. The same (; ...) syntax on the left side of an assignment allows for property destructuring.

In the standard REPL, typing ; on an empty line will switch to shell mode.

Examples

julia> function foo()
           x = "Hello, "; x *= "World!"
           return x
       end
foo (generic function with 1 method)

julia> bar() = (x = "Hello, Mars!"; return x)
bar (generic function with 1 method)

julia> foo();

julia> bar()
"Hello, Mars!"

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

julia> A = [1 2; 3 4]
2×2 Matrix{Int64}:
 1  2
 3  4

julia> [1; 3;; 2; 4;;; 10*A]
2×2×2 Array{Int64, 3}:
[:, :, 1] =
 1  2
 3  4

[:, :, 2] =
 10  20
 30  40

julia> [2; 3;;;]
2×1×1 Array{Int64, 3}:
[:, :, 1] =
 2
 3

julia> nt = (; x=1) # without the ; or a trailing comma this would assign to x
(x = 1,)

julia> key = :a; c = 3;

julia> nt2 = (; key => 1, b=2, c, nt.x)
(a = 1, b = 2, c = 3, x = 1)

julia> (; b, x) = nt2; # set variables b and x using property destructuring

julia> b, x
(2, 1)

julia> ; # upon typing ;, the prompt changes (in place) to: shell>
shell> echo hello
hello

=

= is the assignment operator.

• For variable a and expression b, a = b makes a refer to the value of b.
• For functions f(x), f(x) = x defines a new function constant f, or adds a new method to f if f is already defined; this usage is equivalent to function f(x); x; end.
• a[i] = v calls setindex!(a,v,i).
• a.b = c calls setproperty!(a,:b,c).
• Inside a function call, f(a=b) passes b as the value of keyword argument a.
• Inside parentheses with commas, (a=1,) constructs a NamedTuple.

Examples

Assigning a to b does not create a copy of b; instead use copy or deepcopy.

julia> b = [1]; a = b; b[1] = 2; a
1-element Array{Int64, 1}:
 2

julia> b = [1]; a = copy(b); b[1] = 2; a
1-element Array{Int64, 1}:
 1

Collections passed to functions are also not copied. Functions can modify (mutate) the contents of the objects their arguments refer to. (The names of functions which do this are conventionally suffixed with '!'.)

julia> function f!(x); x[:] .+= 1; end
f! (generic function with 1 method)

julia> a = [1]; f!(a); a
1-element Array{Int64, 1}:
 2

Assignment can operate on multiple variables in parallel, taking values from an iterable:

julia> a, b = 4, 5
(4, 5)

julia> a, b = 1:3
1:3

julia> a, b
(1, 2)

Assignment can operate on multiple variables in series, and will return the value of the right-hand-most expression:

julia> a = [1]; b = [2]; c = [3]; a = b = c
1-element Array{Int64, 1}:
 3

julia> b[1] = 2; a, b, c
([2], [2], [2])

Assignment at out-of-bounds indices does not grow a collection. If the collection is a Vector it can instead be grown with push! or append!.

julia> a = [1, 1]; a[3] = 2
ERROR: BoundsError: attempt to access 2-element Array{Int64, 1} at index [3]
[...]

julia> push!(a, 2, 3)
4-element Array{Int64, 1}:
 1
 1
 2
 3

Assigning [] does not eliminate elements from a collection; instead use filter!.

julia> a = collect(1:3); a[a .<= 1] = []
ERROR: DimensionMismatch: tried to assign 0 elements to 1 destinations
[...]

julia> filter!(x -> x > 1, a) # in-place & thus more efficient than a = a[a .> 1]
2-element Array{Int64, 1}:
 2
 3

a ? b : c

Short form for conditionals; read "if a, evaluate b otherwise evaluate c". Also known as the ternary operator.

This syntax is equivalent to if a; b else c end, but is often used to emphasize the value b-or-c which is being used as part of a larger expression, rather than the side effects that evaluating b or c may have.

See the manual section on control flow for more details.

Examples

julia> x = 1; y = 2;

julia> x > y ? println("x is larger") : println("y is larger")
y is larger

Main

Main is the top-level module, and Julia starts with Main set as the current module. Variables defined at the prompt go in Main, and varinfo lists variables in Main.

julia> @__MODULE__
Main

Core

Core is the module that contains all identifiers considered "built in" to the language, i.e. part of the core language and not libraries. Every module implicitly specifies using Core, since you can't do anything without those definitions.

Base

The base library of Julia. Base is a module that contains basic functionality (the contents of base/). All modules implicitly contain using Base, since this is needed in the vast majority of cases.

Base.Broadcast

Module containing the broadcasting implementation.

Docs

The Docs module provides the @doc macro which can be used to set and retrieve documentation metadata for Julia objects.

Please see the manual section on documentation for more information.

Methods for working with Iterators.

Interface to libc, the C standard library.

Convenience functions for metaprogramming.

Tools for collecting and manipulating stack traces. Mainly used for building errors.

Provide methods for retrieving information about hardware and the operating system.

Multithreading support.

Base.GC

Module with garbage collection utilities.

===(x,y) -> Bool
≡(x,y) -> Bool

Determine whether x and y are identical, in the sense that no program could distinguish them. First the types of x and y are compared. If those are identical, mutable objects are compared by address in memory and immutable objects (such as numbers) are compared by contents at the bit level. This function is sometimes called "egal". It always returns a Bool value.

Examples

julia> a = [1, 2]; b = [1, 2];

julia> a == b
true

julia> a === b
false

julia> a === a
true

isa(x, type) -> Bool

Determine whether x is of the given type. Can also be used as an infix operator, e.g. x isa type.

Examples

julia> isa(1, Int)
true

julia> isa(1, Matrix)
false

julia> isa(1, Char)
false

julia> isa(1, Number)
true

julia> 1 isa Number
true

isequal(x, y) -> Bool

Similar to ==, except for the treatment of floating point numbers and of missing values. isequal treats all floating-point NaN values as equal to each other, treats -0.0 as unequal to 0.0, and missing as equal to missing. Always returns a Bool value.

isequal is an equivalence relation - it is reflexive (=== implies isequal), symmetric (isequal(a, b) implies isequal(b, a)) and transitive (isequal(a, b) and isequal(b, c) implies isequal(a, c)).

Implementation

The default implementation of isequal calls ==, so a type that does not involve floating-point values generally only needs to define ==.

isequal is the comparison function used by hash tables (Dict). isequal(x,y) must imply that hash(x) == hash(y).

This typically means that types for which a custom == or isequal method exists must implement a corresponding hash method (and vice versa). Collections typically implement isequal by calling isequal recursively on all contents.

Furthermore, isequal is linked with isless, and they work together to define a fixed total ordering, where exactly one of isequal(x, y), isless(x, y), or isless(y, x) must be true (and the other two false).

Scalar types generally do not need to implement isequal separate from ==, unless they represent floating-point numbers amenable to a more efficient implementation than that provided as a generic fallback (based on isnan, signbit, and ==).

Examples

julia> isequal([1., NaN], [1., NaN])
true

julia> [1., NaN] == [1., NaN]
false

julia> 0.0 == -0.0
true

julia> isequal(0.0, -0.0)
false

julia> missing == missing
missing

julia> isequal(missing, missing)
true

isequal(x)

Create a function that compares its argument to x using isequal, i.e. a function equivalent to y -> isequal(y, x).

The returned function is of type Base.Fix2{typeof(isequal)}, which can be used to implement specialized methods.

isless(x, y)

Test whether x is less than y, according to a fixed total order (defined together with isequal). isless is not defined for pairs (x, y) of all types. However, if it is defined, it is expected to satisfy the following:

• If isless(x, y) is defined, then so is isless(y, x) and isequal(x, y), and exactly one of those three yields true.
• The relation defined by isless is transitive, i.e., isless(x, y) && isless(y, z) implies isless(x, z).

Values that are normally unordered, such as NaN, are ordered after regular values. missing values are ordered last.

This is the default comparison used by sort!.

Implementation

Non-numeric types with a total order should implement this function. Numeric types only need to implement it if they have special values such as NaN. Types with a partial order should implement <. See the documentation on Alternate Orderings for how to define alternate ordering methods that can be used in sorting and related functions.

Examples

julia> isless(1, 3)
true

julia> isless("Red", "Blue")
false

isunordered(x)

Return true if x is a value that is not orderable according to <, such as NaN or missing.

The values that evaluate to true with this predicate may be orderable with respect to other orderings such as isless.

ifelse(condition::Bool, x, y)

Return x if condition is true, otherwise return y. This differs from ? or if in that it is an ordinary function, so all the arguments are evaluated first. In some cases, using ifelse instead of an if statement can eliminate the branch in generated code and provide higher performance in tight loops.

Examples

julia> ifelse(1 > 2, 1, 2)
2

typeassert(x, type)

Throw a TypeError unless x isa type. The syntax x::type calls this function.

Examples

julia> typeassert(2.5, Int)
ERROR: TypeError: in typeassert, expected Int64, got a value of type Float64
Stacktrace:
[...]

typeof(x)

Get the concrete type of x.

See also eltype.

Examples

julia> a = 1//2;

julia> typeof(a)
Rational{Int64}

julia> M = [1 2; 3.5 4];

julia> typeof(M)
Matrix{Float64} (alias for Array{Float64, 2})

tuple(xs...)

Construct a tuple of the given objects.

See also Tuple, ntuple, NamedTuple.

Examples

julia> tuple(1, 'b', pi)
(1, 'b', π)

julia> ans === (1, 'b', π)
true

julia> Tuple(Real[1, 2, pi])  # takes a collection
(1, 2, π)

ntuple(f::Function, n::Integer)

Create a tuple of length n, computing each element as f(i), where i is the index of the element.

Examples

julia> ntuple(i -> 2*i, 4)
(2, 4, 6, 8)

ntuple(f, ::Val{N})

Create a tuple of length N, computing each element as f(i), where i is the index of the element. By taking a Val(N) argument, it is possible that this version of ntuple may generate more efficient code than the version taking the length as an integer. But ntuple(f, N) is preferable to ntuple(f, Val(N)) in cases where N cannot be determined at compile time.

Examples

julia> ntuple(i -> 2*i, Val(4))
(2, 4, 6, 8)

objectid(x) -> UInt

Get a hash value for x based on object identity.

If x === y then objectid(x) == objectid(y), and usually when x !== y, objectid(x) != objectid(y).

See also hash, IdDict.

hash(x[, h::UInt]) -> UInt

Compute an integer hash code such that isequal(x,y) implies hash(x)==hash(y). The optional second argument h is another hash code to be mixed with the result.

New types should implement the 2-argument form, typically by calling the 2-argument hash method recursively in order to mix hashes of the contents with each other (and with h). Typically, any type that implements hash should also implement its own == (hence isequal) to guarantee the property mentioned above. Types supporting subtraction (operator -) should also implement widen, which is required to hash values inside heterogeneous arrays.

The hash value may change when a new Julia process is started.

julia> a = hash(10)
0x95ea2955abd45275

julia> hash(10, a) # only use the output of another hash function as the second argument
0xd42bad54a8575b16

See also: objectid, Dict, Set.

finalizer(f, x)

Register a function f(x) to be called when there are no program-accessible references to x, and return x. The type of x must be a mutable struct, otherwise the function will throw.

f must not cause a task switch, which excludes most I/O operations such as println. Using the @async macro (to defer context switching to outside of the finalizer) or ccall to directly invoke IO functions in C may be helpful for debugging purposes.

Note that there is no guaranteed world age for the execution of f. It may be called in the world age in which the finalizer was registered or any later world age.

Examples

finalizer(my_mutable_struct) do x
    @async println("Finalizing $x.")
end

finalizer(my_mutable_struct) do x
    ccall(:jl_safe_printf, Cvoid, (Cstring, Cstring), "Finalizing %s.", repr(x))
end

A finalizer may be registered at object construction. In the following example note that we implicitly rely on the finalizer returning the newly created mutable struct x.

Example

mutable struct MyMutableStruct
    bar
    function MyMutableStruct(bar)
        x = new(bar)
        f(t) = @async println("Finalizing $t.")
        finalizer(f, x)
    end
end

finalize(x)

Immediately run finalizers registered for object x.

copy(x)

Create a shallow copy of x: the outer structure is copied, but not all internal values. For example, copying an array produces a new array with identically-same elements as the original.

See also copy!, copyto!, deepcopy.

deepcopy(x)

Create a deep copy of x: everything is copied recursively, resulting in a fully independent object. For example, deep-copying an array produces a new array whose elements are deep copies of the original elements. Calling deepcopy on an object should generally have the same effect as serializing and then deserializing it.

While it isn't normally necessary, user-defined types can override the default deepcopy behavior by defining a specialized version of the function deepcopy_internal(x::T, dict::IdDict) (which shouldn't otherwise be used), where T is the type to be specialized for, and dict keeps track of objects copied so far within the recursion. Within the definition, deepcopy_internal should be used in place of deepcopy, and the dict variable should be updated as appropriate before returning.

getproperty(value, name::Symbol)
getproperty(value, name::Symbol, order::Symbol)

The syntax a.b calls getproperty(a, :b). The syntax @atomic order a.b calls getproperty(a, :b, :order) and the syntax @atomic a.b calls getproperty(a, :b, :sequentially_consistent).

Examples

julia> struct MyType{T <: Number}
           x::T
       end

julia> function Base.getproperty(obj::MyType, sym::Symbol)
           if sym === :special
               return obj.x + 1
           else # fallback to getfield
               return getfield(obj, sym)
           end
       end

julia> obj = MyType(1);

julia> obj.special
2

julia> obj.x
1

One should overload getproperty only when necessary, as it can be confusing if the behavior of the syntax obj.f is unusual. Also note that using methods is often preferable. See also this style guide documentation for more information: Prefer exported methods over direct field access.

See also getfield, propertynames and setproperty!.

setproperty!(value, name::Symbol, x)
setproperty!(value, name::Symbol, x, order::Symbol)

The syntax a.b = c calls setproperty!(a, :b, c). The syntax @atomic order a.b = c calls setproperty!(a, :b, c, :order) and the syntax @atomic a.b = c calls setproperty!(a, :b, c, :sequentially_consistent).

See also setfield!, propertynames and getproperty.

replaceproperty!(x, f::Symbol, expected, desired, success_order::Symbol=:not_atomic, fail_order::Symbol=success_order)

Perform a compare-and-swap operation on x.f from expected to desired, per egal. The syntax @atomic_replace! x.f expected => desired can be used instead of the function call form.

See also replacefield! and setproperty!.

swapproperty!(x, f::Symbol, v, order::Symbol=:not_atomic)

The syntax @atomic a.b, _ = c, a.b returns (c, swapproperty!(a, :b, c, :sequentially_consistent)), where there must be one getproperty expression common to both sides.

See also swapfield! and setproperty!.

modifyproperty!(x, f::Symbol, op, v, order::Symbol=:not_atomic)

The syntax @atomic op(x.f, v) (and its equivalent @atomic x.f op v) returns modifyproperty!(x, :f, op, v, :sequentially_consistent), where the first argument must be a getproperty expression and is modified atomically.

Invocation of op(getproperty(x, f), v) must return a value that can be stored in the field f of the object x by default. In particular, unlike the default behavior of setproperty!, the convert function is not called automatically.

See also modifyfield! and setproperty!.

propertynames(x, private=false)

Get a tuple or a vector of the properties (x.property) of an object x. This is typically the same as fieldnames(typeof(x)), but types that overload getproperty should generally overload propertynames as well to get the properties of an instance of the type.

propertynames(x) may return only "public" property names that are part of the documented interface of x. If you want it to also return "private" property names intended for internal use, pass true for the optional second argument. REPL tab completion on x. shows only the private=false properties.

See also: hasproperty, hasfield.

hasproperty(x, s::Symbol)

Return a boolean indicating whether the object x has s as one of its own properties.

See also: propertynames, hasfield.

getfield(value, name::Symbol, [order::Symbol])
getfield(value, i::Int, [order::Symbol])

Extract a field from a composite value by name or position. Optionally, an ordering can be defined for the operation. If the field was declared @atomic, the specification is strongly recommended to be compatible with the stores to that location. Otherwise, if not declared as @atomic, this parameter must be :not_atomic if specified. See also getproperty and fieldnames.

Examples

julia> a = 1//2
1//2

julia> getfield(a, :num)
1

julia> a.num
1

julia> getfield(a, 1)
1

setfield!(value, name::Symbol, x, [order::Symbol])
setfield!(value, i::Int, x, [order::Symbol])

Assign x to a named field in value of composite type. The value must be mutable and x must be a subtype of fieldtype(typeof(value), name). Additionally, an ordering can be specified for this operation. If the field was declared @atomic, this specification is mandatory. Otherwise, if not declared as @atomic, it must be :not_atomic if specified. See also setproperty!.

Examples

julia> mutable struct MyMutableStruct
           field::Int
       end

julia> a = MyMutableStruct(1);

julia> setfield!(a, :field, 2);

julia> getfield(a, :field)
2

julia> a = 1//2
1//2

julia> setfield!(a, :num, 3);
ERROR: setfield!: immutable struct of type Rational cannot be changed

modifyfield!(value, name::Symbol, op, x, [order::Symbol]) -> Pair
modifyfield!(value, i::Int, op, x, [order::Symbol]) -> Pair

These atomically perform the operations to get and set a field after applying the function op.

y = getfield(value, name)
z = op(y, x)
setfield!(value, name, z)
return y => z

If supported by the hardware (for example, atomic increment), this may be optimized to the appropriate hardware instruction, otherwise it'll use a loop.

replacefield!(value, name::Symbol, expected, desired,
              [success_order::Symbol, [fail_order::Symbol=success_order]) -> (; old, success::Bool)
replacefield!(value, i::Int, expected, desired,
              [success_order::Symbol, [fail_order::Symbol=success_order]) -> (; old, success::Bool)

These atomically perform the operations to get and conditionally set a field to a given value.

y = getfield(value, name, fail_order)
ok = y === expected
if ok
    setfield!(value, name, desired, success_order)
end
return (; old = y, success = ok)

If supported by the hardware, this may be optimized to the appropriate hardware instruction, otherwise it'll use a loop.

swapfield!(value, name::Symbol, x, [order::Symbol])
swapfield!(value, i::Int, x, [order::Symbol])

These atomically perform the operations to simultaneously get and set a field:

y = getfield(value, name)
setfield!(value, name, x)
return y

isdefined(m::Module, s::Symbol, [order::Symbol])
isdefined(object, s::Symbol, [order::Symbol])
isdefined(object, index::Int, [order::Symbol])

Tests whether a global variable or object field is defined. The arguments can be a module and a symbol or a composite object and field name (as a symbol) or index. Optionally, an ordering can be defined for the operation. If the field was declared @atomic, the specification is strongly recommended to be compatible with the stores to that location. Otherwise, if not declared as @atomic, this parameter must be :not_atomic if specified.

To test whether an array element is defined, use isassigned instead.

See also @isdefined.

Examples

julia> isdefined(Base, :sum)
true

julia> isdefined(Base, :NonExistentMethod)
false

julia> a = 1//2;

julia> isdefined(a, 2)
true

julia> isdefined(a, 3)
false

julia> isdefined(a, :num)
true

julia> isdefined(a, :numerator)
false

getglobal(module::Module, name::Symbol, [order::Symbol=:monotonic])

Retrieve the value of the binding name from the module module. Optionally, an atomic ordering can be defined for the operation, otherwise it defaults to monotonic.

While accessing module bindings using getfield is still supported to maintain compatibility, using getglobal should always be preferred since getglobal allows for control over atomic ordering (getfield is always monotonic) and better signifies the code's intent both to the user as well as the compiler.

Most users should not have to call this function directly – The getproperty function or corresponding syntax (i.e. module.name) should be preferred in all but few very specific use cases.

See also getproperty and setglobal!.

Examples

julia> a = 1
1

julia> module M
       a = 2
       end;

julia> getglobal(@__MODULE__, :a)
1

julia> getglobal(M, :a)
2

setglobal!(module::Module, name::Symbol, x, [order::Symbol=:monotonic])

Set or change the value of the binding name in the module module to x. No type conversion is performed, so if a type has already been declared for the binding, x must be of appropriate type or an error is thrown.

Additionally, an atomic ordering can be specified for this operation, otherwise it defaults to monotonic.

Users will typically access this functionality through the setproperty! function or corresponding syntax (i.e. module.name = x) instead, so this is intended only for very specific use cases.

See also setproperty! and getglobal

Examples

julia> module M end;

julia> M.a  # same as `getglobal(M, :a)`
ERROR: UndefVarError: `a` not defined

julia> setglobal!(M, :a, 1)
1

julia> M.a
1

@isdefined s -> Bool

Tests whether variable s is defined in the current scope.

See also isdefined for field properties and isassigned for array indexes or haskey for other mappings.

Examples

julia> @isdefined newvar
false

julia> newvar = 1
1

julia> @isdefined newvar
true

julia> function f()
           println(@isdefined x)
           x = 3
           println(@isdefined x)
       end
f (generic function with 1 method)

julia> f()
false
true

convert(T, x)

Convert x to a value of type T.

If T is an Integer type, an InexactError will be raised if x is not representable by T, for example if x is not integer-valued, or is outside the range supported by T.

Examples

julia> convert(Int, 3.0)
3

julia> convert(Int, 3.5)
ERROR: InexactError: Int64(3.5)
Stacktrace:
[...]

If T is a AbstractFloat type, then it will return the closest value to x representable by T.

julia> x = 1/3
0.3333333333333333

julia> convert(Float32, x)
0.33333334f0

julia> convert(BigFloat, x)
0.333333333333333314829616256247390992939472198486328125

If T is a collection type and x a collection, the result of convert(T, x) may alias all or part of x.

julia> x = Int[1, 2, 3];

julia> y = convert(Vector{Int}, x);

julia> y === x
true

See also: round, trunc, oftype, reinterpret.

promote(xs...)

Convert all arguments to a common type, and return them all (as a tuple). If no arguments can be converted, an error is raised.

See also: promote_type, promote_rule.

Examples

julia> promote(Int8(1), Float16(4.5), Float32(4.1))
(1.0f0, 4.5f0, 4.1f0)

julia> promote_type(Int8, Float16, Float32)
Float32

julia> reduce(Base.promote_typejoin, (Int8, Float16, Float32))
Real

julia> promote(1, "x")
ERROR: promotion of types Int64 and String failed to change any arguments
[...]

julia> promote_type(Int, String)
Any

oftype(x, y)

Convert y to the type of x i.e. convert(typeof(x), y).

Examples

julia> x = 4;

julia> y = 3.;

julia> oftype(x, y)
3

julia> oftype(y, x)
4.0

widen(x)

If x is a type, return a "larger" type, defined so that arithmetic operations + and - are guaranteed not to overflow nor lose precision for any combination of values that type x can hold.

For fixed-size integer types less than 128 bits, widen will return a type with twice the number of bits.

If x is a value, it is converted to widen(typeof(x)).

Examples

julia> widen(Int32)
Int64

julia> widen(1.5f0)
1.5

identity(x)

The identity function. Returns its argument.

See also: one, oneunit, and LinearAlgebra's I.

Examples

julia> identity("Well, what did you expect?")
"Well, what did you expect?"

WeakRef(x)

w = WeakRef(x) constructs a weak reference to the Julia value x: although w contains a reference to x, it does not prevent x from being garbage collected. w.value is either x (if x has not been garbage-collected yet) or nothing (if x has been garbage-collected).

julia> x = "a string"
"a string"

julia> w = WeakRef(x)
WeakRef("a string")

julia> GC.gc()

julia> w           # a reference is maintained via `x`
WeakRef("a string")

julia> x = nothing # clear reference

julia> GC.gc()

julia> w
WeakRef(nothing)

supertype(T::DataType)

Return the supertype of DataType T.

Examples

julia> supertype(Int32)
Signed

Core.Type{T}

Core.Type is an abstract type which has all type objects as its instances. The only instance of the singleton type Core.Type{T} is the object T.

Examples

julia> isa(Type{Float64}, Type)
true

julia> isa(Float64, Type)
true

julia> isa(Real, Type{Float64})
false

julia> isa(Real, Type{Real})
true

DataType <: Type{T}

DataType represents explicitly declared types that have names, explicitly declared supertypes, and, optionally, parameters. Every concrete value in the system is an instance of some DataType.

Examples

julia> typeof(Real)
DataType

julia> typeof(Int)
DataType

julia> struct Point
           x::Int
           y
       end

julia> typeof(Point)
DataType

<:(T1, T2)

Subtype operator: returns true if and only if all values of type T1 are also of type T2.

Examples

julia> Float64 <: AbstractFloat
true

julia> Vector{Int} <: AbstractArray
true

julia> Matrix{Float64} <: Matrix{AbstractFloat}
false

>:(T1, T2)

Supertype operator, equivalent to T2 <: T1.

typejoin(T, S, ...)

Return the closest common ancestor of types T and S, i.e. the narrowest type from which they both inherit. Recurses on additional varargs.

Examples

julia> typejoin(Int, Float64)
Real

julia> typejoin(Int, Float64, ComplexF32)
Number

typeintersect(T::Type, S::Type)

Compute a type that contains the intersection of T and S. Usually this will be the smallest such type or one close to it.

promote_type(type1, type2, ...)

Promotion refers to converting values of mixed types to a single common type. promote_type represents the default promotion behavior in Julia when operators (usually mathematical) are given arguments of differing types. promote_type generally tries to return a type which can at least approximate most values of either input type without excessively widening. Some loss is tolerated; for example, promote_type(Int64, Float64) returns Float64 even though strictly, not all Int64 values can be represented exactly as Float64 values.

See also: promote, promote_typejoin, promote_rule.

Examples

julia> promote_type(Int64, Float64)
Float64

julia> promote_type(Int32, Int64)
Int64

julia> promote_type(Float32, BigInt)
BigFloat

julia> promote_type(Int16, Float16)
Float16

julia> promote_type(Int64, Float16)
Float16

julia> promote_type(Int8, UInt16)
UInt16

promote_rule(type1, type2)

Specifies what type should be used by promote when given values of types type1 and type2. This function should not be called directly, but should have definitions added to it for new types as appropriate.

promote_typejoin(T, S)

Compute a type that contains both T and S, which could be either a parent of both types, or a Union if appropriate. Falls back to typejoin.

See instead promote, promote_type.

Examples

julia> Base.promote_typejoin(Int, Float64)
Real

julia> Base.promote_type(Int, Float64)
Float64

isdispatchtuple(T)

Determine whether type T is a tuple "leaf type", meaning it could appear as a type signature in dispatch and has no subtypes (or supertypes) which could appear in a call.

ismutable(v) -> Bool

Return true if and only if value v is mutable. See Mutable Composite Types for a discussion of immutability. Note that this function works on values, so if you give it a DataType, it will tell you that a value of the type is mutable.

See also isbits, isstructtype.

Examples

julia> ismutable(1)
false

julia> ismutable([1,2])
true

isimmutable(v) -> Bool

Return true iff value v is immutable. See Mutable Composite Types for a discussion of immutability. Note that this function works on values, so if you give it a type, it will tell you that a value of DataType is mutable.

Examples

julia> isimmutable(1)
true

julia> isimmutable([1,2])
false

ismutabletype(T) -> Bool

Determine whether type T was declared as a mutable type (i.e. using mutable struct keyword).

isabstracttype(T)

Determine whether type T was declared as an abstract type (i.e. using the abstract type syntax).

Examples

julia> isabstracttype(AbstractArray)
true

julia> isabstracttype(Vector)
false

isprimitivetype(T) -> Bool

Determine whether type T was declared as a primitive type (i.e. using the primitive type syntax).

Base.issingletontype(T)

Determine whether type T has exactly one possible instance; for example, a struct type with no fields.

isstructtype(T) -> Bool

Determine whether type T was declared as a struct type (i.e. using the struct or mutable struct keyword).

nameof(t::DataType) -> Symbol

Get the name of a (potentially UnionAll-wrapped) DataType (without its parent module) as a symbol.

Examples

julia> module Foo
           struct S{T}
           end
       end
Foo

julia> nameof(Foo.S{T} where T)
:S

fieldnames(x::DataType)

Get a tuple with the names of the fields of a DataType.

See also propertynames, hasfield.

Examples

julia> fieldnames(Rational)
(:num, :den)

julia> fieldnames(typeof(1+im))
(:re, :im)

fieldname(x::DataType, i::Integer)

Get the name of field i of a DataType.

Examples

julia> fieldname(Rational, 1)
:num

julia> fieldname(Rational, 2)
:den

fieldtype(T, name::Symbol | index::Int)

Determine the declared type of a field (specified by name or index) in a composite DataType T.

Examples

julia> struct Foo
           x::Int64
           y::String
       end

julia> fieldtype(Foo, :x)
Int64

julia> fieldtype(Foo, 2)
String

fieldtypes(T::Type)

The declared types of all fields in a composite DataType T as a tuple.

Examples

julia> struct Foo
           x::Int64
           y::String
       end

julia> fieldtypes(Foo)
(Int64, String)

fieldcount(t::Type)

Get the number of fields that an instance of the given type would have. An error is thrown if the type is too abstract to determine this.

hasfield(T::Type, name::Symbol)

Return a boolean indicating whether T has name as one of its own fields.

See also fieldnames, fieldcount, hasproperty.

Examples

julia> struct Foo
            bar::Int
       end

julia> hasfield(Foo, :bar)
true

julia> hasfield(Foo, :x)
false

nfields(x) -> Int

Get the number of fields in the given object.

Examples

julia> a = 1//2;

julia> nfields(a)
2

julia> b = 1
1

julia> nfields(b)
0

julia> ex = ErrorException("I've done a bad thing");

julia> nfields(ex)
1

In these examples, a is a Rational, which has two fields. b is an Int, which is a primitive bitstype with no fields at all. ex is an ErrorException, which has one field.

isconst(m::Module, s::Symbol) -> Bool

Determine whether a global is declared const in a given module m.

isconst(t::DataType, s::Union{Int,Symbol}) -> Bool

Determine whether a field s is declared const in a given type t.

isfieldatomic(t::DataType, s::Union{Int,Symbol}) -> Bool

Determine whether a field s is declared @atomic in a given type t.

sizeof(T::DataType)
sizeof(obj)

Size, in bytes, of the canonical binary representation of the given DataType T, if any. Or the size, in bytes, of object obj if it is not a DataType.

See also Base.summarysize.

Examples

julia> sizeof(Float32)
4

julia> sizeof(ComplexF64)
16

julia> sizeof(1.0)
8

julia> sizeof(collect(1.0:10.0))
80

julia> struct StructWithPadding
           x::Int64
           flag::Bool
       end

julia> sizeof(StructWithPadding) # not the sum of `sizeof` of fields due to padding
16

julia> sizeof(Int64) + sizeof(Bool) # different from above
9

If DataType T does not have a specific size, an error is thrown.

julia> sizeof(AbstractArray)
ERROR: Abstract type AbstractArray does not have a definite size.
Stacktrace:
[...]

isconcretetype(T)

Determine whether type T is a concrete type, meaning it could have direct instances (values x such that typeof(x) === T).

See also: isbits, isabstracttype, issingletontype.

Examples

julia> isconcretetype(Complex)
false

julia> isconcretetype(Complex{Float32})
true

julia> isconcretetype(Vector{Complex})
true

julia> isconcretetype(Vector{Complex{Float32}})
true

julia> isconcretetype(Union{})
false

julia> isconcretetype(Union{Int,String})
false

isbits(x)

Return true if x is an instance of an isbitstype type.

isbitstype(T)

Return true if type T is a "plain data" type, meaning it is immutable and contains no references to other values, only primitive types and other isbitstype types. Typical examples are numeric types such as UInt8, Float64, and Complex{Float64}. This category of types is significant since they are valid as type parameters, may not track isdefined / isassigned status, and have a defined layout that is compatible with C.

See also isbits, isprimitivetype, ismutable.

Examples

julia> isbitstype(Complex{Float64})
true

julia> isbitstype(Complex)
false

fieldoffset(type, i)

The byte offset of field i of a type relative to the data start. For example, we could use it in the following manner to summarize information about a struct:

julia> structinfo(T) = [(fieldoffset(T,i), fieldname(T,i), fieldtype(T,i)) for i = 1:fieldcount(T)];

julia> structinfo(Base.Filesystem.StatStruct)
13-element Vector{Tuple{UInt64, Symbol, Type}}:
 (0x0000000000000000, :desc, Union{RawFD, String})
 (0x0000000000000008, :device, UInt64)
 (0x0000000000000010, :inode, UInt64)
 (0x0000000000000018, :mode, UInt64)
 (0x0000000000000020, :nlink, Int64)
 (0x0000000000000028, :uid, UInt64)
 (0x0000000000000030, :gid, UInt64)
 (0x0000000000000038, :rdev, UInt64)
 (0x0000000000000040, :size, Int64)
 (0x0000000000000048, :blksize, Int64)
 (0x0000000000000050, :blocks, Int64)
 (0x0000000000000058, :mtime, Float64)
 (0x0000000000000060, :ctime, Float64)

Base.datatype_alignment(dt::DataType) -> Int

Memory allocation minimum alignment for instances of this type. Can be called on any isconcretetype.

Base.datatype_haspadding(dt::DataType) -> Bool

Return whether the fields of instances of this type are packed in memory, with no intervening padding bytes. Can be called on any isconcretetype.

Base.datatype_pointerfree(dt::DataType) -> Bool

Return whether instances of this type can contain references to gc-managed memory. Can be called on any isconcretetype.

typemin(T)

The lowest value representable by the given (real) numeric DataType T.

See also: floatmin, typemax, eps.

Examples

julia> typemin(Int8)
-128

julia> typemin(UInt32)
0x00000000

julia> typemin(Float16)
-Inf16

julia> typemin(Float32)
-Inf32

julia> nextfloat(-Inf32)  # smallest finite Float32 floating point number
-3.4028235f38

typemax(T)

The highest value representable by the given (real) numeric DataType.

See also: floatmax, typemin, eps.

Examples

julia> typemax(Int8)
127

julia> typemax(UInt32)
0xffffffff

julia> typemax(Float64)
Inf

julia> typemax(Float32)
Inf32

julia> floatmax(Float32)  # largest finite Float32 floating point number
3.4028235f38

floatmin(T = Float64)

Return the smallest positive normal number representable by the floating-point type T.

Examples

julia> floatmin(Float16)
Float16(6.104e-5)

julia> floatmin(Float32)
1.1754944f-38

julia> floatmin()
2.2250738585072014e-308

floatmax(T = Float64)

Return the largest finite number representable by the floating-point type T.

See also: typemax, floatmin, eps.

Examples

julia> floatmax(Float16)
Float16(6.55e4)

julia> floatmax(Float32)
3.4028235f38

julia> floatmax()
1.7976931348623157e308

julia> typemax(Float64)
Inf

maxintfloat(T=Float64)

The largest consecutive integer-valued floating-point number that is exactly represented in the given floating-point type T (which defaults to Float64).

That is, maxintfloat returns the smallest positive integer-valued floating-point number n such that n+1 is not exactly representable in the type T.

When an Integer-type value is needed, use Integer(maxintfloat(T)).

maxintfloat(T, S)

The largest consecutive integer representable in the given floating-point type T that also does not exceed the maximum integer representable by the integer type S. Equivalently, it is the minimum of maxintfloat(T) and typemax(S).

eps(::Type{T}) where T<:AbstractFloat
eps()

Return the machine epsilon of the floating point type T (T = Float64 by default). This is defined as the gap between 1 and the next largest value representable by typeof(one(T)), and is equivalent to eps(one(T)). (Since eps(T) is a bound on the relative error of T, it is a "dimensionless" quantity like one.)

Examples

julia> eps()
2.220446049250313e-16

julia> eps(Float32)
1.1920929f-7

julia> 1.0 + eps()
1.0000000000000002

julia> 1.0 + eps()/2
1.0

eps(x::AbstractFloat)

Return the unit in last place (ulp) of x. This is the distance between consecutive representable floating point values at x. In most cases, if the distance on either side of x is different, then the larger of the two is taken, that is

eps(x) == max(x-prevfloat(x), nextfloat(x)-x)

The exceptions to this rule are the smallest and largest finite values (e.g. nextfloat(-Inf) and prevfloat(Inf) for Float64), which round to the smaller of the values.

The rationale for this behavior is that eps bounds the floating point rounding error. Under the default RoundNearest rounding mode, if $y$ is a real number and $x$ is the nearest floating point number to $y$, then

\[|y-x| \leq \operatorname{eps}(x)/2.\]

See also: nextfloat, issubnormal, floatmax.

Examples

julia> eps(1.0)
2.220446049250313e-16

julia> eps(prevfloat(2.0))
2.220446049250313e-16

julia> eps(2.0)
4.440892098500626e-16

julia> x = prevfloat(Inf)      # largest finite Float64
1.7976931348623157e308

julia> x + eps(x)/2            # rounds up
Inf

julia> x + prevfloat(eps(x)/2) # rounds down
1.7976931348623157e308

instances(T::Type)

Return a collection of all instances of the given type, if applicable. Mostly used for enumerated types (see @enum).

Example

julia> @enum Color red blue green

julia> instances(Color)
(red, blue, green)

Any::DataType

Any is the union of all types. It has the defining property isa(x, Any) == true for any x. Any therefore describes the entire universe of possible values. For example Integer is a subset of Any that includes Int, Int8, and other integer types.

Union{Types...}

A type union is an abstract type which includes all instances of any of its argument types. The empty union Union{} is the bottom type of Julia.

Examples

julia> IntOrString = Union{Int,AbstractString}
Union{Int64, AbstractString}

julia> 1 isa IntOrString
true

julia> "Hello!" isa IntOrString
true

julia> 1.0 isa IntOrString
false

Union{}

Union{}, the empty Union of types, is the type that has no values. That is, it has the defining property isa(x, Union{}) == false for any x. Base.Bottom is defined as its alias and the type of Union{} is Core.TypeofBottom.

Examples

julia> isa(nothing, Union{})
false

UnionAll

A union of types over all values of a type parameter. UnionAll is used to describe parametric types where the values of some parameters are not known.

Examples

julia> typeof(Vector)
UnionAll

julia> typeof(Vector{Int})
DataType

Tuple{Types...}

A tuple is a fixed-length container that can hold any values of different types, but cannot be modified (it is immutable). The values can be accessed via indexing. Tuple literals are written with commas and parentheses:

julia> (1, 1+1)
(1, 2)

julia> (1,)
(1,)

julia> x = (0.0, "hello", 6*7)
(0.0, "hello", 42)

julia> x[2]
"hello"

julia> typeof(x)
Tuple{Float64, String, Int64}

A length-1 tuple must be written with a comma, (1,), since (1) would just be a parenthesized value. () represents the empty (length-0) tuple.

A tuple can be constructed from an iterator by using a Tuple type as constructor:

julia> Tuple(["a", 1])
("a", 1)

julia> Tuple{String, Float64}(["a", 1])
("a", 1.0)

Tuple types are covariant in their parameters: Tuple{Int} is a subtype of Tuple{Any}. Therefore Tuple{Any} is considered an abstract type, and tuple types are only concrete if their parameters are. Tuples do not have field names; fields are only accessed by index. Tuple types may have any number of parameters.

See the manual section on Tuple Types.

See also Vararg, NTuple, ntuple, tuple, NamedTuple.

NTuple{N, T}

A compact way of representing the type for a tuple of length N where all elements are of type T.

Examples

julia> isa((1, 2, 3, 4, 5, 6), NTuple{6, Int})
true

See also ntuple.

NamedTuple

NamedTuples are, as their name suggests, named Tuples. That is, they're a tuple-like collection of values, where each entry has a unique name, represented as a Symbol. Like Tuples, NamedTuples are immutable; neither the names nor the values can be modified in place after construction.

A named tuple can be created as a tuple literal with keys, e.g. (a=1, b=2), or as a tuple literal with semicolon after the opening parenthesis, e.g. (; a=1, b=2) (this form also accepts programmatically generated names as described below), or using a NamedTuple type as constructor, e.g. NamedTuple{(:a, :b)}((1,2)).

Accessing the value associated with a name in a named tuple can be done using field access syntax, e.g. x.a, or using getindex, e.g. x[:a] or x[(:a, :b)]. A tuple of the names can be obtained using keys, and a tuple of the values can be obtained using values.

The @NamedTuple macro can be used for conveniently declaring NamedTuple types.

Examples

julia> x = (a=1, b=2)
(a = 1, b = 2)

julia> x.a
1

julia> x[:a]
1

julia> x[(:a,)]
(a = 1,)

julia> keys(x)
(:a, :b)

julia> values(x)
(1, 2)

julia> collect(x)
2-element Vector{Int64}:
 1
 2

julia> collect(pairs(x))
2-element Vector{Pair{Symbol, Int64}}:
 :a => 1
 :b => 2

In a similar fashion as to how one can define keyword arguments programmatically, a named tuple can be created by giving pairs name::Symbol => value after a semicolon inside a tuple literal. This and the name=value syntax can be mixed:

julia> (; :a => 1, :b => 2, c=3)
(a = 1, b = 2, c = 3)

The name-value pairs can also be provided by splatting a named tuple or any iterator that yields two-value collections holding each a symbol as first value:

julia> keys = (:a, :b, :c); values = (1, 2, 3);

julia> NamedTuple{keys}(values)
(a = 1, b = 2, c = 3)

julia> (; (keys .=> values)...)
(a = 1, b = 2, c = 3)

julia> nt1 = (a=1, b=2);

julia> nt2 = (c=3, d=4);

julia> (; nt1..., nt2..., b=20) # the final b overwrites the value from nt1
(a = 1, b = 20, c = 3, d = 4)

julia> (; zip(keys, values)...) # zip yields tuples such as (:a, 1)
(a = 1, b = 2, c = 3)

As in keyword arguments, identifiers and dot expressions imply names:

julia> x = 0
0

julia> t = (; x)
(x = 0,)

julia> (; t.x)
(x = 0,)

@NamedTuple{key1::Type1, key2::Type2, ...}
@NamedTuple begin key1::Type1; key2::Type2; ...; end

This macro gives a more convenient syntax for declaring NamedTuple types. It returns a NamedTuple type with the given keys and types, equivalent to NamedTuple{(:key1, :key2, ...), Tuple{Type1,Type2,...}}. If the ::Type declaration is omitted, it is taken to be Any. The begin ... end form allows the declarations to be split across multiple lines (similar to a struct declaration), but is otherwise equivalent. The NamedTuple macro is used when printing NamedTuple types to e.g. the REPL.

For example, the tuple (a=3.1, b="hello") has a type NamedTuple{(:a, :b), Tuple{Float64, String}}, which can also be declared via @NamedTuple as:

julia> @NamedTuple{a::Float64, b::String}
@NamedTuple{a::Float64, b::String}

julia> @NamedTuple begin
           a::Float64
           b::String
       end
@NamedTuple{a::Float64, b::String}

@Kwargs{key1::Type1, key2::Type2, ...}

This macro gives a convenient way to construct the type representation of keyword arguments from the same syntax as @NamedTuple. For example, when we have a function call like func([positional arguments]; kw1=1.0, kw2="2"), we can use this macro to construct the internal type representation of the keyword arguments as @Kwargs{kw1::Float64, kw2::String}. The macro syntax is specifically designed to simplify the signature type of a keyword method when it is printed in the stack trace view.

julia> @Kwargs{init::Int} # the internal representation of keyword arguments
Base.Pairs{Symbol, Int64, Tuple{Symbol}, @NamedTuple{init::Int64}}

julia> sum("julia"; init=1)
ERROR: MethodError: no method matching +(::Char, ::Char)

Closest candidates are:
  +(::Any, ::Any, ::Any, ::Any...)
   @ Base operators.jl:585
  +(::Integer, ::AbstractChar)
   @ Base char.jl:247
  +(::T, ::Integer) where T<:AbstractChar
   @ Base char.jl:237

Stacktrace:
  [1] add_sum(x::Char, y::Char)
    @ Base ./reduce.jl:24
  [2] BottomRF
    @ Base ./reduce.jl:86 [inlined]
  [3] _foldl_impl(op::Base.BottomRF{typeof(Base.add_sum)}, init::Int64, itr::String)
    @ Base ./reduce.jl:62
  [4] foldl_impl(op::Base.BottomRF{typeof(Base.add_sum)}, nt::Int64, itr::String)
    @ Base ./reduce.jl:48 [inlined]
  [5] mapfoldl_impl(f::typeof(identity), op::typeof(Base.add_sum), nt::Int64, itr::String)
    @ Base ./reduce.jl:44 [inlined]
  [6] mapfoldl(f::typeof(identity), op::typeof(Base.add_sum), itr::String; init::Int64)
    @ Base ./reduce.jl:175 [inlined]
  [7] mapreduce(f::typeof(identity), op::typeof(Base.add_sum), itr::String; kw::@Kwargs{init::Int64})
    @ Base ./reduce.jl:307 [inlined]
  [8] sum(f::typeof(identity), a::String; kw::@Kwargs{init::Int64})
    @ Base ./reduce.jl:535 [inlined]
  [9] sum(a::String; kw::@Kwargs{init::Int64})
    @ Base ./reduce.jl:564 [inlined]
 [10] top-level scope
    @ REPL[12]:1

Val(c)

Return Val{c}(), which contains no run-time data. Types like this can be used to pass the information between functions through the value c, which must be an isbits value or a Symbol. The intent of this construct is to be able to dispatch on constants directly (at compile time) without having to test the value of the constant at run time.

Examples

julia> f(::Val{true}) = "Good"
f (generic function with 1 method)

julia> f(::Val{false}) = "Bad"
f (generic function with 2 methods)

julia> f(Val(true))
"Good"

Vararg{T,N}

The last parameter of a tuple type Tuple can be the special value Vararg, which denotes any number of trailing elements. Vararg{T,N} corresponds to exactly N elements of type T. Finally Vararg{T} corresponds to zero or more elements of type T. Vararg tuple types are used to represent the arguments accepted by varargs methods (see the section on Varargs Functions in the manual.)

See also NTuple.

Examples

julia> mytupletype = Tuple{AbstractString, Vararg{Int}}
Tuple{AbstractString, Vararg{Int64}}

julia> isa(("1",), mytupletype)
true

julia> isa(("1",1), mytupletype)
true

julia> isa(("1",1,2), mytupletype)
true

julia> isa(("1",1,2,3.0), mytupletype)
false

Nothing

A type with no fields that is the type of nothing.

See also: isnothing, Some, Missing.

isnothing(x)

Return true if x === nothing, and return false if not.

See also something, Base.notnothing, ismissing.

notnothing(x)

Throw an error if x === nothing, and return x if not.

Some{T}

A wrapper type used in Union{Some{T}, Nothing} to distinguish between the absence of a value (nothing) and the presence of a nothing value (i.e. Some(nothing)).

Use something to access the value wrapped by a Some object.

something(x...)

Return the first value in the arguments which is not equal to nothing, if any. Otherwise throw an error. Arguments of type Some are unwrapped.

See also coalesce, skipmissing, @something.

Examples

julia> something(nothing, 1)
1

julia> something(Some(1), nothing)
1

julia> something(Some(nothing), 2) === nothing
true

julia> something(missing, nothing)
missing

julia> something(nothing, nothing)
ERROR: ArgumentError: No value arguments present

@something(x...)

Short-circuiting version of something.

Examples

julia> f(x) = (println("f($x)"); nothing);

julia> a = 1;

julia> a = @something a f(2) f(3) error("Unable to find default for `a`")
1

julia> b = nothing;

julia> b = @something b f(2) f(3) error("Unable to find default for `b`")
f(2)
f(3)
ERROR: Unable to find default for `b`
[...]

julia> b = @something b f(2) f(3) Some(nothing)
f(2)
f(3)

julia> b === nothing
true

Enum{T<:Integer}

The abstract supertype of all enumerated types defined with @enum.

@enum EnumName[::BaseType] value1[=x] value2[=y]

Create an Enum{BaseType} subtype with name EnumName and enum member values of value1 and value2 with optional assigned values of x and y, respectively. EnumName can be used just like other types and enum member values as regular values, such as

Examples

julia> @enum Fruit apple=1 orange=2 kiwi=3

julia> f(x::Fruit) = "I'm a Fruit with value: $(Int(x))"
f (generic function with 1 method)

julia> f(apple)
"I'm a Fruit with value: 1"

julia> Fruit(1)
apple::Fruit = 1

Values can also be specified inside a begin block, e.g.

@enum EnumName begin
    value1
    value2
end

BaseType, which defaults to Int32, must be a primitive subtype of Integer. Member values can be converted between the enum type and BaseType. read and write perform these conversions automatically. In case the enum is created with a non-default BaseType, Integer(value1) will return the integer value1 with the type BaseType.

To list all the instances of an enum use instances, e.g.

julia> instances(Fruit)
(apple, orange, kiwi)

It is possible to construct a symbol from an enum instance:

julia> Symbol(apple)
:apple

Expr(head::Symbol, args...)

A type representing compound expressions in parsed julia code (ASTs). Each expression consists of a head Symbol identifying which kind of expression it is (e.g. a call, for loop, conditional statement, etc.), and subexpressions (e.g. the arguments of a call). The subexpressions are stored in a Vector{Any} field called args.

See the manual chapter on Metaprogramming and the developer documentation Julia ASTs.

Examples

julia> Expr(:call, :+, 1, 2)
:(1 + 2)

julia> dump(:(a ? b : c))
Expr
  head: Symbol if
  args: Array{Any}((3,))
    1: Symbol a
    2: Symbol b
    3: Symbol c

Symbol

The type of object used to represent identifiers in parsed julia code (ASTs). Also often used as a name or label to identify an entity (e.g. as a dictionary key). Symbols can be entered using the : quote operator:

julia> :name
:name

julia> typeof(:name)
Symbol

julia> x = 42
42

julia> eval(:x)
42

Symbols can also be constructed from strings or other values by calling the constructor Symbol(x...).

Symbols are immutable and their implementation re-uses the same object for all Symbols with the same name.

Unlike strings, Symbols are "atomic" or "scalar" entities that do not support iteration over characters.

Symbol(x...) -> Symbol

Create a Symbol by concatenating the string representations of the arguments together.

Examples

julia> Symbol("my", "name")
:myname

julia> Symbol("day", 4)
:day4

Module

A Module is a separate global variable workspace. See module and the manual section about modules for details.

Module(name::Symbol=:anonymous, std_imports=true, default_names=true)

Return a module with the specified name. A baremodule corresponds to Module(:ModuleName, false)

An empty module containing no names at all can be created with Module(:ModuleName, false, false). This module will not import Base or Core and does not contain a reference to itself.

Function

Abstract type of all functions.

Examples

julia> isa(+, Function)
true

julia> typeof(sin)
typeof(sin) (singleton type of function sin, subtype of Function)

julia> ans <: Function
true

hasmethod(f, t::Type{<:Tuple}[, kwnames]; world=get_world_counter()) -> Bool

Determine whether the given generic function has a method matching the given Tuple of argument types with the upper bound of world age given by world.

If a tuple of keyword argument names kwnames is provided, this also checks whether the method of f matching t has the given keyword argument names. If the matching method accepts a variable number of keyword arguments, e.g. with kwargs..., any names given in kwnames are considered valid. Otherwise the provided names must be a subset of the method's keyword arguments.

See also applicable.

Examples

julia> hasmethod(length, Tuple{Array})
true

julia> f(; oranges=0) = oranges;

julia> hasmethod(f, Tuple{}, (:oranges,))
true

julia> hasmethod(f, Tuple{}, (:apples, :bananas))
false

julia> g(; xs...) = 4;

julia> hasmethod(g, Tuple{}, (:a, :b, :c, :d))  # g accepts arbitrary kwargs
true

applicable(f, args...) -> Bool

Determine whether the given generic function has a method applicable to the given arguments.

See also hasmethod.

Examples

julia> function f(x, y)
           x + y
       end;

julia> applicable(f, 1)
false

julia> applicable(f, 1, 2)
true

Base.isambiguous(m1, m2; ambiguous_bottom=false) -> Bool

Determine whether two methods m1 and m2 may be ambiguous for some call signature. This test is performed in the context of other methods of the same function; in isolation, m1 and m2 might be ambiguous, but if a third method resolving the ambiguity has been defined, this returns false. Alternatively, in isolation m1 and m2 might be ordered, but if a third method cannot be sorted with them, they may cause an ambiguity together.

For parametric types, the ambiguous_bottom keyword argument controls whether Union{} counts as an ambiguous intersection of type parameters – when true, it is considered ambiguous, when false it is not.

Examples

julia> foo(x::Complex{<:Integer}) = 1
foo (generic function with 1 method)

julia> foo(x::Complex{<:Rational}) = 2
foo (generic function with 2 methods)

julia> m1, m2 = collect(methods(foo));

julia> typeintersect(m1.sig, m2.sig)
Tuple{typeof(foo), Complex{Union{}}}

julia> Base.isambiguous(m1, m2, ambiguous_bottom=true)
true

julia> Base.isambiguous(m1, m2, ambiguous_bottom=false)
false

invoke(f, argtypes::Type, args...; kwargs...)

Invoke a method for the given generic function f matching the specified types argtypes on the specified arguments args and passing the keyword arguments kwargs. The arguments args must conform with the specified types in argtypes, i.e. conversion is not automatically performed. This method allows invoking a method other than the most specific matching method, which is useful when the behavior of a more general definition is explicitly needed (often as part of the implementation of a more specific method of the same function).

Be careful when using invoke for functions that you don't write. What definition is used for given argtypes is an implementation detail unless the function is explicitly states that calling with certain argtypes is a part of public API. For example, the change between f1 and f2 in the example below is usually considered compatible because the change is invisible by the caller with a normal (non-invoke) call. However, the change is visible if you use invoke.

Examples

julia> f(x::Real) = x^2;

julia> f(x::Integer) = 1 + invoke(f, Tuple{Real}, x);

julia> f(2)
5

julia> f1(::Integer) = Integer
       f1(::Real) = Real;

julia> f2(x::Real) = _f2(x)
       _f2(::Integer) = Integer
       _f2(_) = Real;

julia> f1(1)
Integer

julia> f2(1)
Integer

julia> invoke(f1, Tuple{Real}, 1)
Real

julia> invoke(f2, Tuple{Real}, 1)
Integer

@invoke f(arg::T, ...; kwargs...)

Provides a convenient way to call invoke by expanding @invoke f(arg1::T1, arg2::T2; kwargs...) to invoke(f, Tuple{T1,T2}, arg1, arg2; kwargs...). When an argument's type annotation is omitted, it's replaced with Core.Typeof that argument. To invoke a method where an argument is untyped or explicitly typed as Any, annotate the argument with ::Any.

It also supports the following syntax:

• @invoke (x::X).f expands to invoke(getproperty, Tuple{X,Symbol}, x, :f)
• @invoke (x::X).f = v::V expands to invoke(setproperty!, Tuple{X,Symbol,V}, x, :f, v)
• @invoke (xs::Xs)[i::I] expands to invoke(getindex, Tuple{Xs,I}, xs, i)
• @invoke (xs::Xs)[i::I] = v::V expands to invoke(setindex!, Tuple{Xs,V,I}, xs, v, i)

Examples

julia> @macroexpand @invoke f(x::T, y)
:(Core.invoke(f, Tuple{T, Core.Typeof(y)}, x, y))

julia> @invoke 420::Integer % Unsigned
0x00000000000001a4

julia> @macroexpand @invoke (x::X).f
:(Core.invoke(Base.getproperty, Tuple{X, Core.Typeof(:f)}, x, :f))

julia> @macroexpand @invoke (x::X).f = v::V
:(Core.invoke(Base.setproperty!, Tuple{X, Core.Typeof(:f), V}, x, :f, v))

julia> @macroexpand @invoke (xs::Xs)[i::I]
:(Core.invoke(Base.getindex, Tuple{Xs, I}, xs, i))

julia> @macroexpand @invoke (xs::Xs)[i::I] = v::V
:(Core.invoke(Base.setindex!, Tuple{Xs, V, I}, xs, v, i))

invokelatest(f, args...; kwargs...)

Calls f(args...; kwargs...), but guarantees that the most recent method of f will be executed. This is useful in specialized circumstances, e.g. long-running event loops or callback functions that may call obsolete versions of a function f. (The drawback is that invokelatest is somewhat slower than calling f directly, and the type of the result cannot be inferred by the compiler.)

@invokelatest f(args...; kwargs...)

Provides a convenient way to call invokelatest. @invokelatest f(args...; kwargs...) will simply be expanded into Base.invokelatest(f, args...; kwargs...).

It also supports the following syntax:

• @invokelatest x.f expands to Base.invokelatest(getproperty, x, :f)
• @invokelatest x.f = v expands to Base.invokelatest(setproperty!, x, :f, v)
• @invokelatest xs[i] expands to Base.invokelatest(getindex, xs, i)
• @invokelatest xs[i] = v expands to Base.invokelatest(setindex!, xs, v, i)

julia> @macroexpand @invokelatest f(x; kw=kwv)
:(Base.invokelatest(f, x; kw = kwv))

julia> @macroexpand @invokelatest x.f
:(Base.invokelatest(Base.getproperty, x, :f))

julia> @macroexpand @invokelatest x.f = v
:(Base.invokelatest(Base.setproperty!, x, :f, v))

julia> @macroexpand @invokelatest xs[i]
:(Base.invokelatest(Base.getindex, xs, i))

julia> @macroexpand @invokelatest xs[i] = v
:(Base.invokelatest(Base.setindex!, xs, v, i))

new, or new{A,B,...}

Special function available to inner constructors which creates a new object of the type. The form new{A,B,...} explicitly specifies values of parameters for parametric types. See the manual section on Inner Constructor Methods for more information.

|>(x, f)

Infix operator which applies function f to the argument x. This allows f(g(x)) to be written x |> g |> f. When used with anonymous functions, parentheses are typically required around the definition to get the intended chain.

Examples

julia> 4 |> inv
0.25

julia> [2, 3, 5] |> sum |> inv
0.1

julia> [0 1; 2 3] .|> (x -> x^2) |> sum
14

f ∘ g

Compose functions: i.e. (f ∘ g)(args...; kwargs...) means f(g(args...; kwargs...)). The ∘ symbol can be entered in the Julia REPL (and most editors, appropriately configured) by typing \circ<tab>.

Function composition also works in prefix form: ∘(f, g) is the same as f ∘ g. The prefix form supports composition of multiple functions: ∘(f, g, h) = f ∘ g ∘ h and splatting ∘(fs...) for composing an iterable collection of functions. The last argument to ∘ execute first.

Examples

julia> map(uppercase∘first, ["apple", "banana", "carrot"])
3-element Vector{Char}:
 'A': ASCII/Unicode U+0041 (category Lu: Letter, uppercase)
 'B': ASCII/Unicode U+0042 (category Lu: Letter, uppercase)
 'C': ASCII/Unicode U+0043 (category Lu: Letter, uppercase)

julia> (==(6)∘length).(["apple", "banana", "carrot"])
3-element BitVector:
 0
 1
 1

julia> fs = [
           x -> 2x
           x -> x-1
           x -> x/2
           x -> x+1
       ];

julia> ∘(fs...)(3)
2.0

See also ComposedFunction, !f::Function.

ComposedFunction{Outer,Inner} <: Function

Represents the composition of two callable objects outer::Outer and inner::Inner. That is

ComposedFunction(outer, inner)(args...; kw...) === outer(inner(args...; kw...))

The preferred way to construct an instance of ComposedFunction is to use the composition operator ∘:

julia> sin ∘ cos === ComposedFunction(sin, cos)
true

julia> typeof(sin∘cos)
ComposedFunction{typeof(sin), typeof(cos)}

The composed pieces are stored in the fields of ComposedFunction and can be retrieved as follows:

julia> composition = sin ∘ cos
sin ∘ cos

julia> composition.outer === sin
true

julia> composition.inner === cos
true

See also ∘.

splat(f)

Equivalent to

    my_splat(f) = args->f(args...)

i.e. given a function returns a new function that takes one argument and splats it into the original function. This is useful as an adaptor to pass a multi-argument function in a context that expects a single argument, but passes a tuple as that single argument.

Example usage:

julia> map(splat(+), zip(1:3,4:6))
3-element Vector{Int64}:
 5
 7
 9

julia> my_add = splat(+)
splat(+)

julia> my_add((1,2,3))
6

Fix1(f, x)

A type representing a partially-applied version of the two-argument function f, with the first argument fixed to the value "x". In other words, Fix1(f, x) behaves similarly to y->f(x, y).

See also Fix2.

Fix2(f, x)

A type representing a partially-applied version of the two-argument function f, with the second argument fixed to the value "x". In other words, Fix2(f, x) behaves similarly to y->f(y, x).

Core.eval(m::Module, expr)

Evaluate an expression in the given module and return the result.

eval(expr)

Evaluate an expression in the global scope of the containing module. Every Module (except those defined with baremodule) has its own 1-argument definition of eval, which evaluates expressions in that module.

@eval [mod,] ex

Evaluate an expression with values interpolated into it using eval. If two arguments are provided, the first is the module to evaluate in.

evalfile(path::AbstractString, args::Vector{String}=String[])

Load the file into an anonymous module using include, evaluate all expressions, and return the value of the last expression. The optional args argument can be used to set the input arguments of the script (i.e. the global ARGS variable). Note that definitions (e.g. methods, globals) are evaluated in the anonymous module and do not affect the current module.

Example

julia> write("testfile.jl", """
           @show ARGS
           1 + 1
       """);

julia> x = evalfile("testfile.jl", ["ARG1", "ARG2"]);
ARGS = ["ARG1", "ARG2"]

julia> x
2

julia> rm("testfile.jl")

esc(e)

Only valid in the context of an Expr returned from a macro. Prevents the macro hygiene pass from turning embedded variables into gensym variables. See the Macros section of the Metaprogramming chapter of the manual for more details and examples.

@inbounds(blk)

Eliminates array bounds checking within expressions.

In the example below the in-range check for referencing element i of array A is skipped to improve performance.

function sum(A::AbstractArray)
    r = zero(eltype(A))
    for i in eachindex(A)
        @inbounds r += A[i]
    end
    return r
end

@boundscheck(blk)

Annotates the expression blk as a bounds checking block, allowing it to be elided by @inbounds.

Examples

julia> @inline function g(A, i)
           @boundscheck checkbounds(A, i)
           return "accessing ($A)[$i]"
       end;

julia> f1() = return g(1:2, -1);

julia> f2() = @inbounds return g(1:2, -1);

julia> f1()
ERROR: BoundsError: attempt to access 2-element UnitRange{Int64} at index [-1]
Stacktrace:
 [1] throw_boundserror(::UnitRange{Int64}, ::Tuple{Int64}) at ./abstractarray.jl:455
 [2] checkbounds at ./abstractarray.jl:420 [inlined]
 [3] g at ./none:2 [inlined]
 [4] f1() at ./none:1
 [5] top-level scope

julia> f2()
"accessing (1:2)[-1]"

@propagate_inbounds

Tells the compiler to inline a function while retaining the caller's inbounds context.

@inline

Give a hint to the compiler that this function is worth inlining.

Small functions typically do not need the @inline annotation, as the compiler does it automatically. By using @inline on bigger functions, an extra nudge can be given to the compiler to inline it.

@inline can be applied immediately before a function definition or within a function body.

# annotate long-form definition
@inline function longdef(x)
    ...
end

# annotate short-form definition
@inline shortdef(x) = ...

# annotate anonymous function that a `do` block creates
f() do
    @inline
    ...
end

@inline block

Give a hint to the compiler that calls within block are worth inlining.

# The compiler will try to inline `f`
@inline f(...)

# The compiler will try to inline `f`, `g` and `+`
@inline f(...) + g(...)

@noinline function explicit_noinline(args...)
    # body
end

let
    @inline explicit_noinline(args...) # will be inlined
end

@noinline let a0, b0 = ...
    a = @inline f(a0)  # the compiler will try to inline this call
    b = f(b0)          # the compiler will NOT try to inline this call
    return a, b
end

@noinline

Give a hint to the compiler that it should not inline a function.

Small functions are typically inlined automatically. By using @noinline on small functions, auto-inlining can be prevented.

@noinline can be applied immediately before a function definition or within a function body.

# annotate long-form definition
@noinline function longdef(x)
    ...
end

# annotate short-form definition
@noinline shortdef(x) = ...

# annotate anonymous function that a `do` block creates
f() do
    @noinline
    ...
end

@noinline block

Give a hint to the compiler that it should not inline the calls within block.

# The compiler will try to not inline `f`
@noinline f(...)

# The compiler will try to not inline `f`, `g` and `+`
@noinline f(...) + g(...)

@inline function explicit_inline(args...)
    # body
end

let
    @noinline explicit_inline(args...) # will not be inlined
end

@inline let a0, b0 = ...
    a = @noinline f(a0)  # the compiler will NOT try to inline this call
    b = f(b0)            # the compiler will try to inline this call
    return a, b
end

@nospecialize

Applied to a function argument name, hints to the compiler that the method implementation should not be specialized for different types of that argument, but instead use the declared type for that argument. It can be applied to an argument within a formal argument list, or in the function body. When applied to an argument, the macro must wrap the entire argument expression, e.g., @nospecialize(x::Real) or @nospecialize(i::Integer...) rather than wrapping just the argument name. When used in a function body, the macro must occur in statement position and before any code.

When used without arguments, it applies to all arguments of the parent scope. In local scope, this means all arguments of the containing function. In global (top-level) scope, this means all methods subsequently defined in the current module.

Specialization can reset back to the default by using @specialize.

function example_function(@nospecialize x)
    ...
end

function example_function(x, @nospecialize(y = 1))
    ...
end

function example_function(x, y, z)
    @nospecialize x y
    ...
end

@nospecialize
f(y) = [x for x in y]
@specialize

Example

julia> f(A::AbstractArray) = g(A)
f (generic function with 1 method)

julia> @noinline g(@nospecialize(A::AbstractArray)) = A[1]
g (generic function with 1 method)

julia> @code_typed f([1.0])
CodeInfo(
1 ─ %1 = invoke Main.g(_2::AbstractArray)::Float64
└──      return %1
) => Float64

Here, the @nospecialize annotation results in the equivalent of

f(A::AbstractArray) = invoke(g, Tuple{AbstractArray}, A)

ensuring that only one version of native code will be generated for g, one that is generic for any AbstractArray. However, the specific return type is still inferred for both g and f, and this is still used in optimizing the callers of f and g.

@specialize

Reset the specialization hint for an argument back to the default. For details, see @nospecialize.

Base.@nospecializeinfer function f(args...)
    @nospecialize ...
    ...
end
Base.@nospecializeinfer f(@nospecialize args...) = ...

Tells the compiler to infer f using the declared types of @nospecialized arguments. This can be used to limit the number of compiler-generated specializations during inference.

Example

julia> f(A::AbstractArray) = g(A)
f (generic function with 1 method)

julia> @noinline Base.@nospecializeinfer g(@nospecialize(A::AbstractArray)) = A[1]
g (generic function with 1 method)

julia> @code_typed f([1.0])
CodeInfo(
1 ─ %1 = invoke Main.g(_2::AbstractArray)::Any
└──      return %1
) => Any

In this example, f will be inferred for each specific type of A, but g will only be inferred once with the declared argument type A::AbstractArray, meaning that the compiler will not likely see the excessive inference time on it while it can not infer the concrete return type of it. Without the @nospecializeinfer, f([1.0]) would infer the return type of g as Float64, indicating that inference ran for g(::Vector{Float64}) despite the prohibition on specialized code generation.

@constprop setting [ex]

Control the mode of interprocedural constant propagation for the annotated function.

Two settings are supported:

• @constprop :aggressive [ex]: apply constant propagation aggressively. For a method where the return type depends on the value of the arguments, this can yield improved inference results at the cost of additional compile time.
• @constprop :none [ex]: disable constant propagation. This can reduce compile times for functions that Julia might otherwise deem worthy of constant-propagation. Common cases are for functions with Bool- or Symbol-valued arguments or keyword arguments.

@constprop can be applied immediately before a function definition or within a function body.

# annotate long-form definition
@constprop :aggressive function longdef(x)
  ...
end

# annotate short-form definition
@constprop :aggressive shortdef(x) = ...

# annotate anonymous function that a `do` block creates
f() do
    @constprop :aggressive
    ...
end

The usage within a function body requires at least Julia 1.10.

gensym([tag])

Generates a symbol which will not conflict with other variable names (in the same module).

@gensym

Generates a gensym symbol for a variable. For example, @gensym x y is transformed into x = gensym("x"); y = gensym("y").

var

The syntax var"#example#" refers to a variable named Symbol("#example#"), even though #example# is not a valid Julia identifier name.

This can be useful for interoperability with programming languages which have different rules for the construction of valid identifiers. For example, to refer to the R variable draw.segments, you can use var"draw.segments" in your Julia code.

It is also used to show julia source code which has gone through macro hygiene or otherwise contains variable names which can't be parsed normally.

Note that this syntax requires parser support so it is expanded directly by the parser rather than being implemented as a normal string macro @var_str.

@goto name

@goto name unconditionally jumps to the statement at the location @label name.

@label and @goto cannot create jumps to different top-level statements. Attempts cause an error. To still use @goto, enclose the @label and @goto in a block.

@label name

Labels a statement with the symbolic label name. The label marks the end-point of an unconditional jump with @goto name.

@simd

Annotate a for loop to allow the compiler to take extra liberties to allow loop re-ordering

The object iterated over in a @simd for loop should be a one-dimensional range. By using @simd, you are asserting several properties of the loop:

• It is safe to execute iterations in arbitrary or overlapping order, with special consideration for reduction variables.
• Floating-point operations on reduction variables can be reordered or contracted, possibly causing different results than without @simd.

In many cases, Julia is able to automatically vectorize inner for loops without the use of @simd. Using @simd gives the compiler a little extra leeway to make it possible in more situations. In either case, your inner loop should have the following properties to allow vectorization:

• The loop must be an innermost loop
• The loop body must be straight-line code. Therefore, @inbounds is currently needed for all array accesses. The compiler can sometimes turn short &&, ||, and ?: expressions into straight-line code if it is safe to evaluate all operands unconditionally. Consider using the ifelse function instead of ?: in the loop if it is safe to do so.
• Accesses must have a stride pattern and cannot be "gathers" (random-index reads) or "scatters" (random-index writes).
• The stride should be unit stride.

• There exists no loop-carried memory dependencies
• No iteration ever waits on a previous iteration to make forward progress.

@polly

Tells the compiler to apply the polyhedral optimizer Polly to a function.

@generated f

@generated is used to annotate a function which will be generated. In the body of the generated function, only types of arguments can be read (not the values). The function returns a quoted expression evaluated when the function is called. The @generated macro should not be used on functions mutating the global scope or depending on mutable elements.

See Metaprogramming for further details.

Examples

julia> @generated function bar(x)
           if x <: Integer
               return :(x ^ 2)
           else
               return :(x)
           end
       end
bar (generic function with 1 method)

julia> bar(4)
16

julia> bar("baz")
"baz"

@assume_effects setting... [ex]

Override the compiler's effect modeling for the given method or foreign call. @assume_effects can be applied immediately before a function definition or within a function body. It can also be applied immediately before a @ccall expression.

Examples

julia> Base.@assume_effects :terminates_locally function pow(x)
           # this :terminates_locally allows `pow` to be constant-folded
           res = 1
           1 < x < 20 || error("bad pow")
           while x > 1
               res *= x
               x -= 1
           end
           return res
       end
pow (generic function with 1 method)

julia> code_typed() do
           pow(12)
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     return 479001600
) => Int64

julia> code_typed() do
           map((2,3,4)) do x
               # this :terminates_locally allows this anonymous function to be constant-folded
               Base.@assume_effects :terminates_locally
               res = 1
               1 < x < 20 || error("bad pow")
               while x > 1
                   res *= x
                   x -= 1
               end
               return res
           end
       end
1-element Vector{Any}:
 CodeInfo(
1 ─     return (2, 6, 24)
) => Tuple{Int64, Int64, Int64}

julia> Base.@assume_effects :total !:nothrow @ccall jl_type_intersection(Vector{Int}::Any, Vector{<:Integer}::Any)::Any
Vector{Int64} (alias for Array{Int64, 1})

The usage within a function body requires at least Julia 1.10.

In general, each setting value makes an assertion about the behavior of the function, without requiring the compiler to prove that this behavior is indeed true. These assertions are made for all world ages. It is thus advisable to limit the use of generic functions that may later be extended to invalidate the assumption (which would cause undefined behavior).

The following settings are supported.

• :consistent
• :effect_free
• :nothrow
• :terminates_globally
• :terminates_locally
• :notaskstate
• :inaccessiblememonly
• :foldable
• :removable
• :total

Extended help

:consistent

The :consistent setting asserts that for egal (===) inputs:

• The manner of termination (return value, exception, non-termination) will always be the same.
• If the method returns, the results will always be egal.

:effect_free

The :effect_free setting asserts that the method is free of externally semantically visible side effects. The following is an incomplete list of externally semantically visible side effects:

• Changing the value of a global variable.
• Mutating the heap (e.g. an array or mutable value), except as noted below
• Changing the method table (e.g. through calls to eval)
• File/Network/etc. I/O
• Task switching

However, the following are explicitly not semantically visible, even if they may be observable:

• Memory allocations (both mutable and immutable)
• Elapsed time
• Garbage collection
• Heap mutations of objects whose lifetime does not exceed the method (i.e. were allocated in the method and do not escape).
• The returned value (which is externally visible, but not a side effect)

The rule of thumb here is that an externally visible side effect is anything that would affect the execution of the remainder of the program if the function were not executed.

:nothrow

The :nothrow settings asserts that this method does not terminate abnormally (i.e. will either always return a value or never return).

:terminates_globally

The :terminates_globally settings asserts that this method will eventually terminate (either normally or abnormally), i.e. does not loop indefinitely.

:terminates_locally

The :terminates_locally setting is like :terminates_globally, except that it only applies to syntactic control flow within the annotated method. It is thus a much weaker (and thus safer) assertion that allows for the possibility of non-termination if the method calls some other method that does not terminate.

:notaskstate

The :notaskstate setting asserts that the method does not use or modify the local task state (task local storage, RNG state, etc.) and may thus be safely moved between tasks without observable results.

:inaccessiblememonly

The :inaccessiblememonly setting asserts that the method does not access or modify externally accessible mutable memory. This means the method can access or modify mutable memory for newly allocated objects that is not accessible by other methods or top-level execution before return from the method, but it can not access or modify any mutable global state or mutable memory pointed to by its arguments.

:foldable

This setting is a convenient shortcut for the set of effects that the compiler requires to be guaranteed to constant fold a call at compile time. It is currently equivalent to the following settings:

• :consistent
• :effect_free
• :terminates_globally

:removable

This setting is a convenient shortcut for the set of effects that the compiler requires to be guaranteed to delete a call whose result is unused at compile time. It is currently equivalent to the following settings:

• :effect_free
• :nothrow
• :terminates_globally

:total

This setting is the maximum possible set of effects. It currently implies the following other settings:

• :consistent
• :effect_free
• :nothrow
• :terminates_globally
• :notaskstate
• :inaccessiblememonly

Negated effects

Effect names may be prefixed by ! to indicate that the effect should be removed from an earlier meta effect. For example, :total !:nothrow indicates that while the call is generally total, it may however throw.

@deprecate old new [export_old=true]

Deprecate method old and specify the replacement call new, defining a new method old with the specified signature in the process.

To prevent old from being exported, set export_old to false.

Examples

julia> @deprecate old(x) new(x)
old (generic function with 1 method)

julia> @deprecate old(x) new(x) false
old (generic function with 1 method)

Calls to @deprecate without explicit type-annotations will define deprecated methods accepting any number of positional and keyword arguments of type Any.

To restrict deprecation to a specific signature, annotate the arguments of old. For example,

julia> new(x::Int) = x;

julia> new(x::Float64) = 2x;

julia> @deprecate old(x::Int) new(x);

julia> methods(old)
# 1 method for generic function "old" from Main:
 [1] old(x::Int64)
     @ deprecated.jl:94

will define and deprecate a method old(x::Int) that mirrors new(x::Int) but will not define nor deprecate the method old(x::Float64).

Missing

A type with no fields whose singleton instance missing is used to represent missing values.

See also: skipmissing, nonmissingtype, Nothing.

missing

The singleton instance of type Missing representing a missing value.

See also: NaN, skipmissing, nonmissingtype.

coalesce(x...)

Return the first value in the arguments which is not equal to missing, if any. Otherwise return missing.

See also skipmissing, something, @coalesce.

Examples

julia> coalesce(missing, 1)
1

julia> coalesce(1, missing)
1

julia> coalesce(nothing, 1)  # returns `nothing`

julia> coalesce(missing, missing)
missing

@coalesce(x...)

Short-circuiting version of coalesce.

Examples

julia> f(x) = (println("f($x)"); missing);

julia> a = 1;

julia> a = @coalesce a f(2) f(3) error("`a` is still missing")
1

julia> b = missing;

julia> b = @coalesce b f(2) f(3) error("`b` is still missing")
f(2)
f(3)
ERROR: `b` is still missing
[...]

ismissing(x)

Indicate whether x is missing.

See also: skipmissing, isnothing, isnan.

skipmissing(itr)

Return an iterator over the elements in itr skipping missing values. The returned object can be indexed using indices of itr if the latter is indexable. Indices corresponding to missing values are not valid: they are skipped by keys and eachindex, and a MissingException is thrown when trying to use them.

Use collect to obtain an Array containing the non-missing values in itr. Note that even if itr is a multidimensional array, the result will always be a Vector since it is not possible to remove missings while preserving dimensions of the input.

See also coalesce, ismissing, something.

Examples

julia> x = skipmissing([1, missing, 2])
skipmissing(Union{Missing, Int64}[1, missing, 2])

julia> sum(x)
3

julia> x[1]
1

julia> x[2]
ERROR: MissingException: the value at index (2,) is missing
[...]

julia> argmax(x)
3

julia> collect(keys(x))
2-element Vector{Int64}:
 1
 3

julia> collect(skipmissing([1, missing, 2]))
2-element Vector{Int64}:
 1
 2

julia> collect(skipmissing([1 missing; 2 missing]))
2-element Vector{Int64}:
 1
 2

nonmissingtype(T::Type)

If T is a union of types containing Missing, return a new type with Missing removed.

Examples

julia> nonmissingtype(Union{Int64,Missing})
Int64

julia> nonmissingtype(Any)
Any

run(command, args...; wait::Bool = true)

Run a command object, constructed with backticks (see the Running External Programs section in the manual). Throws an error if anything goes wrong, including the process exiting with a non-zero status (when wait is true).

The args... allow you to pass through file descriptors to the command, and are ordered like regular unix file descriptors (eg stdin, stdout, stderr, FD(3), FD(4)...).

If wait is false, the process runs asynchronously. You can later wait for it and check its exit status by calling success on the returned process object.

When wait is false, the process' I/O streams are directed to devnull. When wait is true, I/O streams are shared with the parent process. Use pipeline to control I/O redirection.

devnull

Used in a stream redirect to discard all data written to it. Essentially equivalent to /dev/null on Unix or NUL on Windows. Usage:

run(pipeline(`cat test.txt`, devnull))

success(command)

Run a command object, constructed with backticks (see the Running External Programs section in the manual), and tell whether it was successful (exited with a code of 0). An exception is raised if the process cannot be started.

process_running(p::Process)

Determine whether a process is currently running.

process_exited(p::Process)

Determine whether a process has exited.

kill(p::Process, signum=Base.SIGTERM)

Send a signal to a process. The default is to terminate the process. Returns successfully if the process has already exited, but throws an error if killing the process failed for other reasons (e.g. insufficient permissions).

Sys.set_process_title(title::AbstractString)

Set the process title. No-op on some operating systems.

Sys.get_process_title()

Get the process title. On some systems, will always return an empty string.

ignorestatus(command)

Mark a command object so that running it will not throw an error if the result code is non-zero.

detach(command)

Mark a command object so that it will be run in a new process group, allowing it to outlive the julia process, and not have Ctrl-C interrupts passed to it.

Cmd(cmd::Cmd; ignorestatus, detach, windows_verbatim, windows_hide, env, dir)
Cmd(exec::Vector{String})

Construct a new Cmd object, representing an external program and arguments, from cmd, while changing the settings of the optional keyword arguments:

• ignorestatus::Bool: If true (defaults to false), then the Cmd will not throw an error if the return code is nonzero.
• detach::Bool: If true (defaults to false), then the Cmd will be run in a new process group, allowing it to outlive the julia process and not have Ctrl-C passed to it.
• windows_verbatim::Bool: If true (defaults to false), then on Windows the Cmd will send a command-line string to the process with no quoting or escaping of arguments, even arguments containing spaces. (On Windows, arguments are sent to a program as a single "command-line" string, and programs are responsible for parsing it into arguments. By default, empty arguments and arguments with spaces or tabs are quoted with double quotes " in the command line, and \ or " are preceded by backslashes. windows_verbatim=true is useful for launching programs that parse their command line in nonstandard ways.) Has no effect on non-Windows systems.
• windows_hide::Bool: If true (defaults to false), then on Windows no new console window is displayed when the Cmd is executed. This has no effect if a console is already open or on non-Windows systems.
• env: Set environment variables to use when running the Cmd. env is either a dictionary mapping strings to strings, an array of strings of the form "var=val", an array or tuple of "var"=>val pairs. In order to modify (rather than replace) the existing environment, initialize env with copy(ENV) and then set env["var"]=val as desired. To add to an environment block within a Cmd object without replacing all elements, use addenv() which will return a Cmd object with the updated environment.
• dir::AbstractString: Specify a working directory for the command (instead of the current directory).

For any keywords that are not specified, the current settings from cmd are used.

Note that the Cmd(exec) constructor does not create a copy of exec. Any subsequent changes to exec will be reflected in the Cmd object.

The most common way to construct a Cmd object is with command literals (backticks), e.g.

`ls -l`

This can then be passed to the Cmd constructor to modify its settings, e.g.

Cmd(`echo "Hello world"`, ignorestatus=true, detach=false)

setenv(command::Cmd, env; dir)

Set environment variables to use when running the given command. env is either a dictionary mapping strings to strings, an array of strings of the form "var=val", or zero or more "var"=>val pair arguments. In order to modify (rather than replace) the existing environment, create env through copy(ENV) and then setting env["var"]=val as desired, or use addenv.

The dir keyword argument can be used to specify a working directory for the command. dir defaults to the currently set dir for command (which is the current working directory if not specified already).

See also Cmd, addenv, ENV, pwd.

addenv(command::Cmd, env...; inherit::Bool = true)

Merge new environment mappings into the given Cmd object, returning a new Cmd object. Duplicate keys are replaced. If command does not contain any environment values set already, it inherits the current environment at time of addenv() call if inherit is true. Keys with value nothing are deleted from the env.

See also Cmd, setenv, ENV.

withenv(f, kv::Pair...)

Execute f in an environment that is temporarily modified (not replaced as in setenv) by zero or more "var"=>val arguments kv. withenv is generally used via the withenv(kv...) do ... end syntax. A value of nothing can be used to temporarily unset an environment variable (if it is set). When withenv returns, the original environment has been restored.

setcpuaffinity(original_command::Cmd, cpus) -> command::Cmd

Set the CPU affinity of the command by a list of CPU IDs (1-based) cpus. Passing cpus = nothing means to unset the CPU affinity if the original_command has any.

This function is supported only in Linux and Windows. It is not supported in macOS because libuv does not support affinity setting.

Examples

In Linux, the taskset command line program can be used to see how setcpuaffinity works.

julia> run(setcpuaffinity(`sh -c 'taskset -p $$'`, [1, 2, 5]));
pid 2273's current affinity mask: 13

Note that the mask value 13 reflects that the first, second, and the fifth bits (counting from the least significant position) are turned on:

julia> 0b010011
0x13

pipeline(from, to, ...)

Create a pipeline from a data source to a destination. The source and destination can be commands, I/O streams, strings, or results of other pipeline calls. At least one argument must be a command. Strings refer to filenames. When called with more than two arguments, they are chained together from left to right. For example, pipeline(a,b,c) is equivalent to pipeline(pipeline(a,b),c). This provides a more concise way to specify multi-stage pipelines.

Examples:

run(pipeline(`ls`, `grep xyz`))
run(pipeline(`ls`, "out.txt"))
run(pipeline("out.txt", `grep xyz`))

pipeline(command; stdin, stdout, stderr, append=false)

Redirect I/O to or from the given command. Keyword arguments specify which of the command's streams should be redirected. append controls whether file output appends to the file. This is a more general version of the 2-argument pipeline function. pipeline(from, to) is equivalent to pipeline(from, stdout=to) when from is a command, and to pipeline(to, stdin=from) when from is another kind of data source.

Examples:

run(pipeline(`dothings`, stdout="out.txt", stderr="errs.txt"))
run(pipeline(`update`, stdout="log.txt", append=true))

gethostname() -> String

Get the local machine's host name.

getpid() -> Int32

Get Julia's process ID.

getpid(process) -> Int32

Get the child process ID, if it still exists.

time() -> Float64

Get the system time in seconds since the epoch, with fairly high (typically, microsecond) resolution.

time_ns() -> UInt64

Get the time in nanoseconds. The time corresponding to 0 is undefined, and wraps every 5.8 years.

@time expr
@time "description" expr

A macro to execute an expression, printing the time it took to execute, the number of allocations, and the total number of bytes its execution caused to be allocated, before returning the value of the expression. Any time spent garbage collecting (gc), compiling new code, or recompiling invalidated code is shown as a percentage.

Optionally provide a description string to print before the time report.

In some cases the system will look inside the @time expression and compile some of the called code before execution of the top-level expression begins. When that happens, some compilation time will not be counted. To include this time you can run @time @eval ....

See also @showtime, @timev, @timed, @elapsed, @allocated, and @allocations.

julia> x = rand(10,10);

julia> @time x * x;
  0.606588 seconds (2.19 M allocations: 116.555 MiB, 3.75% gc time, 99.94% compilation time)

julia> @time x * x;
  0.000009 seconds (1 allocation: 896 bytes)

julia> @time begin
           sleep(0.3)
           1+1
       end
  0.301395 seconds (8 allocations: 336 bytes)
2

julia> @time "A one second sleep" sleep(1)
A one second sleep: 1.005750 seconds (5 allocations: 144 bytes)

julia> for loop in 1:3
            @time loop sleep(1)
        end
1: 1.006760 seconds (5 allocations: 144 bytes)
2: 1.001263 seconds (5 allocations: 144 bytes)
3: 1.003676 seconds (5 allocations: 144 bytes)

@showtime expr

Like @time but also prints the expression being evaluated for reference.

See also @time.

julia> @showtime sleep(1)
sleep(1): 1.002164 seconds (4 allocations: 128 bytes)

@timev expr
@timev "description" expr

This is a verbose version of the @time macro. It first prints the same information as @time, then any non-zero memory allocation counters, and then returns the value of the expression.

Optionally provide a description string to print before the time report.

See also @time, @timed, @elapsed, @allocated, and @allocations.

julia> x = rand(10,10);

julia> @timev x * x;
  0.546770 seconds (2.20 M allocations: 116.632 MiB, 4.23% gc time, 99.94% compilation time)
elapsed time (ns): 546769547
gc time (ns):      23115606
bytes allocated:   122297811
pool allocs:       2197930
non-pool GC allocs:1327
malloc() calls:    36
realloc() calls:   5
GC pauses:         3

julia> @timev x * x;
  0.000010 seconds (1 allocation: 896 bytes)
elapsed time (ns): 9848
bytes allocated:   896
pool allocs:       1

@timed

A macro to execute an expression, and return the value of the expression, elapsed time, total bytes allocated, garbage collection time, and an object with various memory allocation counters.

In some cases the system will look inside the @timed expression and compile some of the called code before execution of the top-level expression begins. When that happens, some compilation time will not be counted. To include this time you can run @timed @eval ....

See also @time, @timev, @elapsed, @allocated, and @allocations.

julia> stats = @timed rand(10^6);

julia> stats.time
0.006634834

julia> stats.bytes
8000256

julia> stats.gctime
0.0055765

julia> propertynames(stats.gcstats)
(:allocd, :malloc, :realloc, :poolalloc, :bigalloc, :freecall, :total_time, :pause, :full_sweep)

julia> stats.gcstats.total_time
5576500

@elapsed

A macro to evaluate an expression, discarding the resulting value, instead returning the number of seconds it took to execute as a floating-point number.

In some cases the system will look inside the @elapsed expression and compile some of the called code before execution of the top-level expression begins. When that happens, some compilation time will not be counted. To include this time you can run @elapsed @eval ....

See also @time, @timev, @timed, @allocated, and @allocations.

julia> @elapsed sleep(0.3)
0.301391426

@allocated

A macro to evaluate an expression, discarding the resulting value, instead returning the total number of bytes allocated during evaluation of the expression.

See also @allocations, @time, @timev, @timed, and @elapsed.

julia> @allocated rand(10^6)
8000080

@allocations

A macro to evaluate an expression, discard the resulting value, and instead return the total number of allocations during evaluation of the expression.

See also @allocated, @time, @timev, @timed, and @elapsed.

julia> @allocations rand(10^6)
2

EnvDict() -> EnvDict

A singleton of this type provides a hash table interface to environment variables.

ENV

Reference to the singleton EnvDict, providing a dictionary interface to system environment variables.

(On Windows, system environment variables are case-insensitive, and ENV correspondingly converts all keys to uppercase for display, iteration, and copying. Portable code should not rely on the ability to distinguish variables by case, and should beware that setting an ostensibly lowercase variable may result in an uppercase ENV key.)

Examples

julia> ENV
Base.EnvDict with "50" entries:
  "SECURITYSESSIONID"            => "123"
  "USER"                         => "username"
  "MallocNanoZone"               => "0"
  ⋮                              => ⋮

julia> ENV["JULIA_EDITOR"] = "vim"
"vim"

julia> ENV["JULIA_EDITOR"]
"vim"

See also: withenv, addenv.

Sys.STDLIB::String

A string containing the full path to the directory containing the stdlib packages.

Sys.isunix([os])

Predicate for testing if the OS provides a Unix-like interface. See documentation in Handling Operating System Variation.

Sys.isapple([os])

Predicate for testing if the OS is a derivative of Apple Macintosh OS X or Darwin. See documentation in Handling Operating System Variation.

Sys.islinux([os])

Predicate for testing if the OS is a derivative of Linux. See documentation in Handling Operating System Variation.

Sys.isbsd([os])

Predicate for testing if the OS is a derivative of BSD. See documentation in Handling Operating System Variation.

Sys.isfreebsd([os])

Predicate for testing if the OS is a derivative of FreeBSD. See documentation in Handling Operating System Variation.

Sys.isopenbsd([os])

Predicate for testing if the OS is a derivative of OpenBSD. See documentation in Handling Operating System Variation.

Sys.isnetbsd([os])

Predicate for testing if the OS is a derivative of NetBSD. See documentation in Handling Operating System Variation.

Sys.isdragonfly([os])

Predicate for testing if the OS is a derivative of DragonFly BSD. See documentation in Handling Operating System Variation.

Sys.iswindows([os])

Predicate for testing if the OS is a derivative of Microsoft Windows NT. See documentation in Handling Operating System Variation.

Sys.windows_version()

Return the version number for the Windows NT Kernel as a VersionNumber, i.e. v"major.minor.build", or v"0.0.0" if this is not running on Windows.

Sys.free_memory()

Get the total free memory in RAM in bytes.

Sys.total_memory()

Get the total memory in RAM (including that which is currently used) in bytes. This amount may be constrained, e.g., by Linux control groups. For the unconstrained amount, see Sys.physical_memory().

Sys.free_physical_memory()

Get the free memory of the system in bytes. The entire amount may not be available to the current process; use Sys.free_memory() for the actually available amount.

Sys.total_physical_memory()

Get the total memory in RAM (including that which is currently used) in bytes. The entire amount may not be available to the current process; see Sys.total_memory().

Sys.uptime()

Gets the current system uptime in seconds.

Sys.isjsvm([os])

Predicate for testing if Julia is running in a JavaScript VM (JSVM), including e.g. a WebAssembly JavaScript embedding in a web browser.

Sys.loadavg()

Get the load average. See: https://en.wikipedia.org/wiki/Load_(computing).

Sys.isexecutable(path::String)

Return true if the given path has executable permissions.

@static

Partially evaluate an expression at parse time.

For example, @static Sys.iswindows() ? foo : bar will evaluate Sys.iswindows() and insert either foo or bar into the expression. This is useful in cases where a construct would be invalid on other platforms, such as a ccall to a non-existent function. @static if Sys.isapple() foo end and @static foo <&&,||> bar are also valid syntax.

VersionNumber

Version number type which follows the specifications of semantic versioning (semver), composed of major, minor and patch numeric values, followed by pre-release and build alpha-numeric annotations.

VersionNumber objects can be compared with all of the standard comparison operators (==, <, <=, etc.), with the result following semver rules.

See also @v_str to efficiently construct VersionNumber objects from semver-format literal strings, VERSION for the VersionNumber of Julia itself, and Version Number Literals in the manual.

Examples

julia> a = VersionNumber(1, 2, 3)
v"1.2.3"

julia> a >= v"1.2"
true

julia> b = VersionNumber("2.0.1-rc1")
v"2.0.1-rc1"

julia> b >= v"2.0.1"
false

@v_str

String macro used to parse a string to a VersionNumber.

Examples

julia> v"1.2.3"
v"1.2.3"

julia> v"2.0.1-rc1"
v"2.0.1-rc1"

error(message::AbstractString)

Raise an ErrorException with the given message.

error(msg...)

Raise an ErrorException with the given message.

throw(e)

Throw an object as an exception.

See also: rethrow, error.

rethrow()

Rethrow the current exception from within a catch block. The rethrown exception will continue propagation as if it had not been caught.

backtrace()

Get a backtrace object for the current program point.

catch_backtrace()

Get the backtrace of the current exception, for use within catch blocks.

current_exceptions(task::Task=current_task(); [backtrace::Bool=true])

Get the stack of exceptions currently being handled. For nested catch blocks there may be more than one current exception in which case the most recently thrown exception is last in the stack. The stack is returned as an ExceptionStack which is an AbstractVector of named tuples (exception,backtrace). If backtrace is false, the backtrace in each pair will be set to nothing.

Explicitly passing task will return the current exception stack on an arbitrary task. This is useful for inspecting tasks which have failed due to uncaught exceptions.

@assert cond [text]

Throw an AssertionError if cond is false. Preferred syntax for writing assertions. Message text is optionally displayed upon assertion failure.

Examples

julia> @assert iseven(3) "3 is an odd number!"
ERROR: AssertionError: 3 is an odd number!

julia> @assert isodd(3) "What even are numbers?"

Experimental.register_error_hint(handler, exceptiontype)

Register a "hinting" function handler(io, exception) that can suggest potential ways for users to circumvent errors. handler should examine exception to see whether the conditions appropriate for a hint are met, and if so generate output to io. Packages should call register_error_hint from within their __init__ function.

For specific exception types, handler is required to accept additional arguments:

• MethodError: provide handler(io, exc::MethodError, argtypes, kwargs), which splits the combined arguments into positional and keyword arguments.

When issuing a hint, the output should typically start with \n.

If you define custom exception types, your showerror method can support hints by calling Experimental.show_error_hints.

Example

julia> module Hinter

       only_int(x::Int)      = 1
       any_number(x::Number) = 2

       function __init__()
           Base.Experimental.register_error_hint(MethodError) do io, exc, argtypes, kwargs
               if exc.f == only_int
                    # Color is not necessary, this is just to show it's possible.
                    print(io, "\nDid you mean to call ")
                    printstyled(io, "`any_number`?", color=:cyan)
               end
           end
       end

       end

Then if you call Hinter.only_int on something that isn't an Int (thereby triggering a MethodError), it issues the hint:

julia> Hinter.only_int(1.0)
ERROR: MethodError: no method matching only_int(::Float64)
Did you mean to call `any_number`?
Closest candidates are:
    ...

Experimental.show_error_hints(io, ex, args...)

Invoke all handlers from Experimental.register_error_hint for the particular exception type typeof(ex). args must contain any other arguments expected by the handler for that type.

ArgumentError(msg)

The arguments passed to a function are invalid. msg is a descriptive error message.

AssertionError([msg])

The asserted condition did not evaluate to true. Optional argument msg is a descriptive error string.

Examples

julia> @assert false "this is not true"
ERROR: AssertionError: this is not true

AssertionError is usually thrown from @assert.

BoundsError([a],[i])

An indexing operation into an array, a, tried to access an out-of-bounds element at index i.

Examples

julia> A = fill(1.0, 7);

julia> A[8]
ERROR: BoundsError: attempt to access 7-element Vector{Float64} at index [8]


julia> B = fill(1.0, (2,3));

julia> B[2, 4]
ERROR: BoundsError: attempt to access 2×3 Matrix{Float64} at index [2, 4]


julia> B[9]
ERROR: BoundsError: attempt to access 2×3 Matrix{Float64} at index [9]

CompositeException

Wrap a Vector of exceptions thrown by a Task (e.g. generated from a remote worker over a channel or an asynchronously executing local I/O write or a remote worker under pmap) with information about the series of exceptions. For example, if a group of workers are executing several tasks, and multiple workers fail, the resulting CompositeException will contain a "bundle" of information from each worker indicating where and why the exception(s) occurred.

DimensionMismatch([msg])

The objects called do not have matching dimensionality. Optional argument msg is a descriptive error string.

DivideError()

Integer division was attempted with a denominator value of 0.

Examples

julia> 2/0
Inf

julia> div(2, 0)
ERROR: DivideError: integer division error
Stacktrace:
[...]

DomainError(val)
DomainError(val, msg)

The argument val to a function or constructor is outside the valid domain.

Examples

julia> sqrt(-1)
ERROR: DomainError with -1.0:
sqrt was called with a negative real argument but will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).
Stacktrace:
[...]

EOFError()

No more data was available to read from a file or stream.

ErrorException(msg)

Generic error type. The error message, in the .msg field, may provide more specific details.

Examples

julia> ex = ErrorException("I've done a bad thing");

julia> ex.msg
"I've done a bad thing"

InexactError(name::Symbol, T, val)

Cannot exactly convert val to type T in a method of function name.

Examples

julia> convert(Float64, 1+2im)
ERROR: InexactError: Float64(1 + 2im)
Stacktrace:
[...]

InterruptException()

The process was stopped by a terminal interrupt (CTRL+C).

Note that, in Julia script started without -i (interactive) option, InterruptException is not thrown by default. Calling Base.exit_on_sigint(false) in the script can recover the behavior of the REPL. Alternatively, a Julia script can be started with

julia -e "include(popfirst!(ARGS))" script.jl

to let InterruptException be thrown by CTRL+C during the execution.

KeyError(key)

An indexing operation into an AbstractDict (Dict) or Set like object tried to access or delete a non-existent element.

LoadError(file::AbstractString, line::Int, error)

An error occurred while includeing, requireing, or using a file. The error specifics should be available in the .error field.

MethodError(f, args)

A method with the required type signature does not exist in the given generic function. Alternatively, there is no unique most-specific method.

MissingException(msg)

Exception thrown when a missing value is encountered in a situation where it is not supported. The error message, in the msg field may provide more specific details.

OutOfMemoryError()

An operation allocated too much memory for either the system or the garbage collector to handle properly.

ReadOnlyMemoryError()

An operation tried to write to memory that is read-only.

OverflowError(msg)

The result of an expression is too large for the specified type and will cause a wraparound.

ProcessFailedException

Indicates problematic exit status of a process. When running commands or pipelines, this is thrown to indicate a nonzero exit code was returned (i.e. that the invoked process failed).

TaskFailedException

This exception is thrown by a wait(t) call when task t fails. TaskFailedException wraps the failed task t.

StackOverflowError()

The function call grew beyond the size of the call stack. This usually happens when a call recurses infinitely.

SystemError(prefix::AbstractString, [errno::Int32])

A system call failed with an error code (in the errno global variable).

TypeError(func::Symbol, context::AbstractString, expected::Type, got)

A type assertion failure, or calling an intrinsic function with an incorrect argument type.

UndefKeywordError(var::Symbol)

The required keyword argument var was not assigned in a function call.

Examples

julia> function my_func(;my_arg)
           return my_arg + 1
       end
my_func (generic function with 1 method)

julia> my_func()
ERROR: UndefKeywordError: keyword argument `my_arg` not assigned
Stacktrace:
 [1] my_func() at ./REPL[1]:2
 [2] top-level scope at REPL[2]:1

UndefRefError()

The item or field is not defined for the given object.

Examples

julia> struct MyType
           a::Vector{Int}
           MyType() = new()
       end

julia> A = MyType()
MyType(#undef)

julia> A.a
ERROR: UndefRefError: access to undefined reference
Stacktrace:
[...]

UndefVarError(var::Symbol)

A symbol in the current scope is not defined.

Examples

julia> a
ERROR: UndefVarError: `a` not defined

julia> a = 1;

julia> a
1

StringIndexError(str, i)

An error occurred when trying to access str at index i that is not valid.

InitError(mod::Symbol, error)

An error occurred when running a module's __init__ function. The actual error thrown is available in the .error field.

retry(f;  delays=ExponentialBackOff(), check=nothing) -> Function

Return an anonymous function that calls function f. If an exception arises, f is repeatedly called again, each time check returns true, after waiting the number of seconds specified in delays. check should input delays's current state and the Exception.

Examples

retry(f, delays=fill(5.0, 3))
retry(f, delays=rand(5:10, 2))
retry(f, delays=Base.ExponentialBackOff(n=3, first_delay=5, max_delay=1000))
retry(http_get, check=(s,e)->e.status == "503")(url)
retry(read, check=(s,e)->isa(e, IOError))(io, 128; all=false)

ExponentialBackOff(; n=1, first_delay=0.05, max_delay=10.0, factor=5.0, jitter=0.1)

A Float64 iterator of length n whose elements exponentially increase at a rate in the interval factor * (1 ± jitter). The first element is first_delay and all elements are clamped to max_delay.

Timer(callback::Function, delay; interval = 0)

Create a timer that runs the function callback at each timer expiration.

Waiting tasks are woken and the function callback is called after an initial delay of delay seconds, and then repeating with the given interval in seconds. If interval is equal to 0, the callback is only run once. The function callback is called with a single argument, the timer itself. Stop a timer by calling close. The callback may still be run one final time, if the timer has already expired.

Examples

Here the first number is printed after a delay of two seconds, then the following numbers are printed quickly.

julia> begin
           i = 0
           cb(timer) = (global i += 1; println(i))
           t = Timer(cb, 2, interval=0.2)
           wait(t)
           sleep(0.5)
           close(t)
       end
1
2
3

Timer(delay; interval = 0)

Create a timer that wakes up tasks waiting for it (by calling wait on the timer object).

Waiting tasks are woken after an initial delay of at least delay seconds, and then repeating after at least interval seconds again elapse. If interval is equal to 0, the timer is only triggered once. When the timer is closed (by close) waiting tasks are woken with an error. Use isopen to check whether a timer is still active.

AsyncCondition()

Create a async condition that wakes up tasks waiting for it (by calling wait on the object) when notified from C by a call to uv_async_send. Waiting tasks are woken with an error when the object is closed (by close). Use isopen to check whether it is still active.

This provides an implicit acquire & release memory ordering between the sending and waiting threads.

AsyncCondition(callback::Function)

Create a async condition that calls the given callback function. The callback is passed one argument, the async condition object itself.

nameof(m::Module) -> Symbol

Get the name of a Module as a Symbol.

Examples

julia> nameof(Base.Broadcast)
:Broadcast

parentmodule(m::Module) -> Module

Get a module's enclosing Module. Main is its own parent.

See also: names, nameof, fullname, @__MODULE__.

Examples

julia> parentmodule(Main)
Main

julia> parentmodule(Base.Broadcast)
Base

parentmodule(t::DataType) -> Module

Determine the module containing the definition of a (potentially UnionAll-wrapped) DataType.

Examples

julia> module Foo
           struct Int end
       end
Foo

julia> parentmodule(Int)
Core

julia> parentmodule(Foo.Int)
Foo

parentmodule(f::Function) -> Module

Determine the module containing the (first) definition of a generic function.

parentmodule(f::Function, types) -> Module

Determine the module containing the first method of a generic function f matching the specified types.

parentmodule(m::Method) -> Module

Return the module in which the given method m is defined.

pathof(m::Module)

Return the path of the m.jl file that was used to import module m, or nothing if m was not imported from a package.

Use dirname to get the directory part and basename to get the file name part of the path.

pkgdir(m::Module[, paths::String...])

Return the root directory of the package that declared module m, or nothing if m was not declared in a package. Optionally further path component strings can be provided to construct a path within the package root.

To get the root directory of the package that implements the current module the form pkgdir(@__MODULE__) can be used.

julia> pkgdir(Foo)
"/path/to/Foo.jl"

julia> pkgdir(Foo, "src", "file.jl")
"/path/to/Foo.jl/src/file.jl"

pkgversion(m::Module)

Return the version of the package that imported module m, or nothing if m was not imported from a package, or imported from a package without a version field set.

The version is read from the package's Project.toml during package load.

To get the version of the package that imported the current module the form pkgversion(@__MODULE__) can be used.

moduleroot(m::Module) -> Module

Find the root module of a given module. This is the first module in the chain of parent modules of m which is either a registered root module or which is its own parent module.

__module__

The argument __module__ is only visible inside the macro, and it provides information (in the form of a Module object) about the expansion context of the macro invocation. See the manual section on Macro invocation for more information.

__source__

The argument __source__ is only visible inside the macro, and it provides information (in the form of a LineNumberNode object) about the parser location of the @ sign from the macro invocation. See the manual section on Macro invocation for more information.

@__MODULE__ -> Module

Get the Module of the toplevel eval, which is the Module code is currently being read from.

@__FILE__ -> String

Expand to a string with the path to the file containing the macrocall, or an empty string if evaluated by julia -e <expr>. Return nothing if the macro was missing parser source information. Alternatively see PROGRAM_FILE.

@__DIR__ -> String

Expand to a string with the absolute path to the directory of the file containing the macrocall. Return the current working directory if run from a REPL or if evaluated by julia -e <expr>.

@__LINE__ -> Int

Expand to the line number of the location of the macrocall. Return 0 if the line number could not be determined.

fullname(m::Module)

Get the fully-qualified name of a module as a tuple of symbols. For example,

Examples

julia> fullname(Base.Iterators)
(:Base, :Iterators)

julia> fullname(Main)
(:Main,)

names(x::Module; all::Bool = false, imported::Bool = false)

Get an array of the names exported by a Module, excluding deprecated names. If all is true, then the list also includes non-exported names defined in the module, deprecated names, and compiler-generated names. If imported is true, then names explicitly imported from other modules are also included.

As a special case, all names defined in Main are considered "exported", since it is not idiomatic to explicitly export names from Main.

See also: @locals, @__MODULE__.

nameof(f::Function) -> Symbol

Get the name of a generic Function as a symbol. For anonymous functions, this is a compiler-generated name. For explicitly-declared subtypes of Function, it is the name of the function's type.

functionloc(f::Function, types)

Return a tuple (filename,line) giving the location of a generic Function definition.

functionloc(m::Method)

Return a tuple (filename,line) giving the location of a Method definition.

@locals()

Construct a dictionary of the names (as symbols) and values of all local variables defined as of the call site.

Examples

julia> let x = 1, y = 2
           Base.@locals
       end
Dict{Symbol, Any} with 2 entries:
  :y => 2
  :x => 1

julia> function f(x)
           local y
           show(Base.@locals); println()
           for i = 1:1
               show(Base.@locals); println()
           end
           y = 2
           show(Base.@locals); println()
           nothing
       end;

julia> f(42)
Dict{Symbol, Any}(:x => 42)
Dict{Symbol, Any}(:i => 1, :x => 42)
Dict{Symbol, Any}(:y => 2, :x => 42)

Base.identify_package(name::String)::Union{PkgId, Nothing}
Base.identify_package(where::Union{Module,PkgId}, name::String)::Union{PkgId, Nothing}

Identify the package by its name from the current environment stack, returning its PkgId, or nothing if it cannot be found.

If only the name argument is provided, it searches each environment in the stack and its named direct dependencies.

There where argument provides the context from where to search for the package: in this case it first checks if the name matches the context itself, otherwise it searches all recursive dependencies (from the resolved manifest of each environment) until it locates the context where, and from there identifies the dependency with the corresponding name.

julia> Base.identify_package("Pkg") # Pkg is a dependency of the default environment
Pkg [44cfe95a-1eb2-52ea-b672-e2afdf69b78f]

julia> using LinearAlgebra

julia> Base.identify_package(LinearAlgebra, "Pkg") # Pkg is not a dependency of LinearAlgebra

Base.locate_package(pkg::PkgId)::Union{String, Nothing}

The path to the entry-point file for the package corresponding to the identifier pkg, or nothing if not found. See also identify_package.

julia> pkg = Base.identify_package("Pkg")
Pkg [44cfe95a-1eb2-52ea-b672-e2afdf69b78f]

julia> Base.locate_package(pkg)
"/path/to/julia/stdlib/v1.10/Pkg/src/Pkg.jl"

require(into::Module, module::Symbol)

This function is part of the implementation of using / import, if a module is not already defined in Main. It can also be called directly to force reloading a module, regardless of whether it has been loaded before (for example, when interactively developing libraries).

Loads a source file, in the context of the Main module, on every active node, searching standard locations for files. require is considered a top-level operation, so it sets the current include path but does not use it to search for files (see help for include). This function is typically used to load library code, and is implicitly called by using to load packages.

When searching for files, require first looks for package code in the global array LOAD_PATH. require is case-sensitive on all platforms, including those with case-insensitive filesystems like macOS and Windows.

For more details regarding code loading, see the manual sections on modules and parallel computing.

Base.compilecache(module::PkgId)

Creates a precompiled cache file for a module and all of its dependencies. This can be used to reduce package load times. Cache files are stored in DEPOT_PATH[1]/compiled. See Module initialization and precompilation for important notes.

Base.isprecompiled(pkg::PkgId; ignore_loaded::Bool=false)

Returns whether a given PkgId within the active project is precompiled.

By default this check observes the same approach that code loading takes with respect to when different versions of dependencies are currently loaded to that which is expected. To ignore loaded modules and answer as if in a fresh julia session specify ignore_loaded=true.

get_extension(parent::Module, extension::Symbol)

Return the module for extension of parent or return nothing if the extension is not loaded.

GC.gc([full=true])

Perform garbage collection. The argument full determines the kind of collection: A full collection (default) sweeps all objects, which makes the next GC scan much slower, while an incremental collection may only sweep so-called young objects.

GC.enable(on::Bool)

Control whether garbage collection is enabled using a boolean argument (true for enabled, false for disabled). Return previous GC state.

GC.@preserve x1 x2 ... xn expr

Mark the objects x1, x2, ... as being in use during the evaluation of the expression expr. This is only required in unsafe code where expr implicitly uses memory or other resources owned by one of the xs.

Implicit use of x covers any indirect use of resources logically owned by x which the compiler cannot see. Some examples:

• Accessing memory of an object directly via a Ptr
• Passing a pointer to x to ccall
• Using resources of x which would be cleaned up in the finalizer.

@preserve should generally not have any performance impact in typical use cases where it briefly extends object lifetime. In implementation, @preserve has effects such as protecting dynamically allocated objects from garbage collection.

Examples

When loading from a pointer with unsafe_load, the underlying object is implicitly used, for example x is implicitly used by unsafe_load(p) in the following:

julia> let
           x = Ref{Int}(101)
           p = Base.unsafe_convert(Ptr{Int}, x)
           GC.@preserve x unsafe_load(p)
       end
101

When passing pointers to ccall, the pointed-to object is implicitly used and should be preserved. (Note however that you should normally just pass x directly to ccall which counts as an explicit use.)

julia> let
           x = "Hello"
           p = pointer(x)
           Int(GC.@preserve x @ccall strlen(p::Cstring)::Csize_t)
           # Preferred alternative
           Int(@ccall strlen(x::Cstring)::Csize_t)
       end
5

GC.safepoint()

Inserts a point in the program where garbage collection may run. This can be useful in rare cases in multi-threaded programs where some threads are allocating memory (and hence may need to run GC) but other threads are doing only simple operations (no allocation, task switches, or I/O). Calling this function periodically in non-allocating threads allows garbage collection to run.

GC.enable_logging(on::Bool)

When turned on, print statistics about each GC to stderr.

lower(m, x)

Takes the expression x and returns an equivalent expression in lowered form for executing in module m. See also code_lowered.

@lower [m] x

Return lowered form of the expression x in module m. By default m is the module in which the macro is called. See also lower.

parse(str, start; greedy=true, raise=true, depwarn=true, filename="none")

Parse the expression string and return an expression (which could later be passed to eval for execution). start is the code unit index into str of the first character to start parsing at (as with all string indexing, these are not character indices). If greedy is true (default), parse will try to consume as much input as it can; otherwise, it will stop as soon as it has parsed a valid expression. Incomplete but otherwise syntactically valid expressions will return Expr(:incomplete, "(error message)"). If raise is true (default), syntax errors other than incomplete expressions will raise an error. If raise is false, parse will return an expression that will raise an error upon evaluation. If depwarn is false, deprecation warnings will be suppressed. The filename argument is used to display diagnostics when an error is raised.

julia> Meta.parse("(α, β) = 3, 5", 1) # start of string
(:((α, β) = (3, 5)), 16)

julia> Meta.parse("(α, β) = 3, 5", 1, greedy=false)
(:((α, β)), 9)

julia> Meta.parse("(α, β) = 3, 5", 16) # end of string
(nothing, 16)

julia> Meta.parse("(α, β) = 3, 5", 11) # index of 3
(:((3, 5)), 16)

julia> Meta.parse("(α, β) = 3, 5", 11, greedy=false)
(3, 13)

parse(str; raise=true, depwarn=true, filename="none")

Parse the expression string greedily, returning a single expression. An error is thrown if there are additional characters after the first expression. If raise is true (default), syntax errors will raise an error; otherwise, parse will return an expression that will raise an error upon evaluation. If depwarn is false, deprecation warnings will be suppressed. The filename argument is used to display diagnostics when an error is raised.

julia> Meta.parse("x = 3")
:(x = 3)

julia> Meta.parse("1.0.2")
ERROR: ParseError:
# Error @ none:1:1
1.0.2
└──┘ ── invalid numeric constant
[...]

julia> Meta.parse("1.0.2"; raise = false)
:($(Expr(:error, "invalid numeric constant "1.0."")))

julia> Meta.parse("x = ")
:($(Expr(:incomplete, "incomplete: premature end of input")))

ParseError(msg)

The expression passed to the parse function could not be interpreted as a valid Julia expression.

QuoteNode

A quoted piece of code, that does not support interpolation. See the manual section about QuoteNodes for details.

macroexpand(m::Module, x; recursive=true)

Take the expression x and return an equivalent expression with all macros removed (expanded) for executing in module m. The recursive keyword controls whether deeper levels of nested macros are also expanded. This is demonstrated in the example below:

julia> module M
           macro m1()
               42
           end
           macro m2()
               :(@m1())
           end
       end
M

julia> macroexpand(M, :(@m2()), recursive=true)
42

julia> macroexpand(M, :(@m2()), recursive=false)
:(#= REPL[16]:6 =# M.@m1)

@macroexpand

Return equivalent expression with all macros removed (expanded).

There are differences between @macroexpand and macroexpand.

• While macroexpand takes a keyword argument recursive, @macroexpand is always recursive. For a non recursive macro version, see @macroexpand1.

• While macroexpand has an explicit module argument, @macroexpand always expands with respect to the module in which it is called.

This is best seen in the following example:

julia> module M
           macro m()
               1
           end
           function f()
               (@macroexpand(@m),
                macroexpand(M, :(@m)),
                macroexpand(Main, :(@m))
               )
           end
       end
M

julia> macro m()
           2
       end
@m (macro with 1 method)

julia> M.f()
(1, 1, 2)

With @macroexpand the expression expands where @macroexpand appears in the code (module M in the example). With macroexpand the expression expands in the module given as the first argument.

@macroexpand1

Non recursive version of @macroexpand.

code_lowered(f, types; generated=true, debuginfo=:default)

Return an array of the lowered forms (IR) for the methods matching the given generic function and type signature.

If generated is false, the returned CodeInfo instances will correspond to fallback implementations. An error is thrown if no fallback implementation exists. If generated is true, these CodeInfo instances will correspond to the method bodies yielded by expanding the generators.

The keyword debuginfo controls the amount of code metadata present in the output.

Note that an error will be thrown if types are not leaf types when generated is true and any of the corresponding methods are an @generated method.

code_typed(f, types; kw...)

Returns an array of type-inferred lowered form (IR) for the methods matching the given generic function and type signature.

Keyword Arguments

• optimize::Bool = true: optional, controls whether additional optimizations, such as inlining, are also applied.
• debuginfo::Symbol = :default: optional, controls the amount of code metadata present in the output, possible options are :source or :none.

Internal Keyword Arguments

This section should be considered internal, and is only for who understands Julia compiler internals.

• world::UInt = Base.get_world_counter(): optional, controls the world age to use when looking up methods, use current world age if not specified.
• interp::Core.Compiler.AbstractInterpreter = Core.Compiler.NativeInterpreter(world): optional, controls the abstract interpreter to use, use the native interpreter if not specified.

Example

One can put the argument types in a tuple to get the corresponding code_typed.

julia> code_typed(+, (Float64, Float64))
1-element Vector{Any}:
 CodeInfo(
1 ─ %1 = Base.add_float(x, y)::Float64
└──      return %1
) => Float64

precompile(f, argtypes::Tuple{Vararg{Any}})

Compile the given function f for the argument tuple (of types) argtypes, but do not execute it.

precompile(f, argtypes::Tuple{Vararg{Any}}, m::Method)

Precompile a specific method for the given argument types. This may be used to precompile a different method than the one that would ordinarily be chosen by dispatch, thus mimicking invoke.

Base.jit_total_bytes()

Return the total amount (in bytes) allocated by the just-in-time compiler for e.g. native code and data.

Meta.quot(ex)::Expr

Quote expression ex to produce an expression with head quote. This can for instance be used to represent objects of type Expr in the AST. See also the manual section about QuoteNode.