Essentials
Introduction
Julia Base contains a range of functions and macros appropriate for performing scientific and numerical computing, but is also as broad as those of many general purpose programming languages. Additional functionality is available from a growing collection of available packages. Functions are grouped by topic below.
Some general notes:
- To use module functions, use
import Module
to import the module, andModule.fn(x)
to use the functions. - Alternatively,
using Module
will import all exportedModule
functions into the current namespace. - By convention, function names ending with an exclamation point (
!
) modify their arguments. Some functions have both modifying (e.g.,sort!
) and non-modifying (sort
) versions.
The behaviors of Base
and standard libraries are stable as defined in SemVer only if they are documented; i.e., included in the Julia documentation and not marked as unstable. See API FAQ for more information.
Getting Around
Base.exit
— Functionexit(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
.
Base.atexit
— Functionatexit(f)
Register a zero-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.
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".)
Base.isinteractive
— Functionisinteractive() -> Bool
Determine whether Julia is running an interactive session.
Base.summarysize
— FunctionBase.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.
Base.require
— Functionrequire(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
— FunctionBase.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.__precompile__
— Function__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
— FunctionBase.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
.
Julia 1.5 is required for passing the mapexpr
argument.
Base.MainInclude.include
— Functioninclude([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 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.
Julia 1.5 is required for passing the mapexpr
argument.
Base.include_string
— Functioninclude_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
.
Julia 1.5 is required for passing the mapexpr
argument.
Base.include_dependency
— Functioninclude_dependency(path::AbstractString)
In a module, declare that the file specified by path
(relative or absolute) is a dependency for precompilation; that is, the module will need to be recompiled if this file changes.
This is only needed if your module depends on a file that is not used via include
. It has no effect outside of compilation.
Base.which
— Methodwhich(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
.
Base.methods
— Functionmethods(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.
At least Julia 1.4 is required for specifying a module.
See also: which
and @which
.
Base.@show
— Macro@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
— Keywordans
A variable referring to the last computed value, automatically set at the interactive prompt.
Base.active_project
— Functionactive_project()
Return the path of the active Project.toml
file.
Keywords
This is the list of reserved keywords in Julia: baremodule
, begin
, break
, catch
, const
, continue
, do
, else
, elseif
, end
, export
, false
, finally
, for
, function
, global
, if
, import
, let
, local
, macro
, module
, quote
, return
, struct
, true
, try
, using
, while
. Those keywords are not allowed to be used as variable names.
The following two-word sequences are reserved: abstract type
, mutable struct
, primitive type
. However, you can create variables with names: abstract
, mutable
, primitive
and type
.
Finally: where
is parsed as an infix operator for writing parametric method and type definitions; in
and isa
are parsed as infix operators; and outer
is parsed as a keyword when used to modify the scope of a variable in an iteration specification of a for
loop or generator
expression. Creation of variables named where
, in
, isa
or outer
is allowed though.
module
— Keywordmodule
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
— Keywordexport
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
— Keywordimport
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
— Keywordusing
using Foo
will load the module or package Foo
and make its export
ed 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 export
ed or not. See the manual section about modules for details.
baremodule
— Keywordbaremodule
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
— Keywordfunction
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
— Keywordmacro
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.
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
— Keywordreturn
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
— Keyworddo
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
— Keywordbegin
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
— Keywordend
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
— Keywordlet
let
statements create a new hard scope block and introduce new variable bindings each time they run. Whereas assignments might reassign a new value to an existing value location, let
always creates a new location. This difference is only detectable in the case of variables that outlive their scope via closures. The let
syntax accepts a comma-separated series of assignments and variable names:
let var1 = value1, var2, var3 = value3
code
end
The assignments are evaluated in order, with each right-hand side evaluated in the scope before the new variable on the left-hand side has been introduced. Therefore it makes sense to write something like let x = x
, since the two x
variables are distinct and have separate storage.
if
— Keywordif/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
— Keywordfor
for
loops repeatedly evaluate a block of statements while iterating over a sequence of values.
Examples
julia> for i in [1, 4, 0]
println(i)
end
1
4
0
while
— Keywordwhile
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
— Keywordbreak
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
— Keywordcontinue
Skip the rest of the current loop iteration.
Examples
julia> for i = 1:6
iseven(i) && continue
println(i)
end
1
3
5
try
— Keywordtry/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
— Keywordfinally
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
— Keywordquote
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
— Keywordlocal
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
— Keywordglobal
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
const
— Keywordconst
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
— Keywordstruct
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
struct
s 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
— Keywordmutable 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.
abstract type
— Keywordabstract 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
— Keywordprimitive 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
— Keywordwhere
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 Vector
s 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, ...}
.
...
— Keyword...
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
;
— Keyword;
;
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 after new lines, but can be used to separate statements on a single line or to join statements into a single expression. ;
is also used to suppress output printing in the REPL and similar interfaces.
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!"
=
— Keyword=
=
is the assignment operator.
- For variable
a
and expressionb
,a = b
makesa
refer to the value ofb
. - For functions
f(x)
,f(x) = x
defines a new function constantf
, or adds a new method tof
iff
is already defined; this usage is equivalent tofunction f(x); x; end
. a[i] = v
callssetindex!
(a,v,i)
.a.b = c
callssetproperty!
(a,:b,c)
.- Inside a function call,
f(a=b)
passesb
as the value of keyword argumenta
. - Inside parentheses with commas,
(a=1,)
constructs aNamedTuple
.
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
?:
— Keyworda ? 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
Standard Modules
Main
— ModuleMain
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
— ModuleCore
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
— ModuleBase
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 Submodules
Base.Broadcast
— ModuleBase.Broadcast
Module containing the broadcasting implementation.
Base.Docs
— ModuleDocs
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.
Base.Iterators
— ModuleMethods for working with Iterators.
Base.Libc
— ModuleInterface to libc, the C standard library.
Base.Meta
— ModuleConvenience functions for metaprogramming.
Base.StackTraces
— ModuleTools for collecting and manipulating stack traces. Mainly used for building errors.
Base.Sys
— ModuleProvide methods for retrieving information about hardware and the operating system.
Base.Threads
— ModuleMultithreading support.
Base.GC
— ModuleBase.GC
Module with garbage collection utilities.
All Objects
Core.:===
— Function===(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
Core.isa
— Functionisa(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
Base.isequal
— Functionisequal(x, y)
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.
Base.isless
— Functionisless(x, y)
Test whether x
is less than y
, according to a fixed total order (defined together with isequal
). isless
is not defined on all pairs of values (x, y)
. However, if it is defined, it is expected to satisfy the following:
- If
isless(x, y)
is defined, then so isisless(y, x)
andisequal(x, y)
, and exactly one of those three yieldstrue
. - The relation defined by
isless
is transitive, i.e.,isless(x, y) && isless(y, z)
impliesisless(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
Core.ifelse
— Functionifelse(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
Core.typeassert
— Functiontypeassert(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:
[...]
Core.typeof
— Functiontypeof(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})
Core.tuple
— Functiontuple(xs...)
Construct a tuple of the given objects.
See also Tuple
, 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)
Base.ntuple
— Functionntuple(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)
Base.objectid
— Functionobjectid(x) -> UInt
Get a hash value for x
based on object identity. objectid(x)==objectid(y)
if x === y
.
Base.hash
— Functionhash(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 a 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.
Base.finalizer
— Functionfinalizer(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 behavior of this function is unpredictable.
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.
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
Base.finalize
— Functionfinalize(x)
Immediately run finalizers registered for object x
.
Base.copy
— Functioncopy(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.
Base.deepcopy
— Functiondeepcopy(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.
Base.getproperty
— Functiongetproperty(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
x
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
See also getfield
, propertynames
and setproperty!
.
Base.setproperty!
— Functionsetproperty!(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 getproperty(a, :b, :sequentially_consistent)
.
See also setfield!
, propertynames
and getproperty
.
Base.propertynames
— Functionpropertynames(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" fieldnames 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
.
Base.hasproperty
— Functionhasproperty(x, s::Symbol)
Return a boolean indicating whether the object x
has s
as one of its own properties.
This function requires at least Julia 1.2.
See also: propertynames
, hasfield
.
Core.getfield
— Functiongetfield(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
Core.setfield!
— Functionsetfield!(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
Core.isdefined
— Functionisdefined(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
Base.@isdefined
— Macro@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
Base.convert
— Functionconvert(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
or Rational
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(Rational{Int32}, x)
1//3
julia> convert(Rational{Int64}, x)
6004799503160661//18014398509481984
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
.
Base.promote
— Functionpromote(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)
Base.oftype
— Functionoftype(x, y)
Convert y
to the type of x
(convert(typeof(x), y)
).
Examples
julia> x = 4;
julia> y = 3.;
julia> oftype(x, y)
3
julia> oftype(y, x)
4.0
Base.widen
— Functionwiden(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
Base.identity
— Functionidentity(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?"
Properties of Types
Type relations
Base.supertype
— Functionsupertype(T::DataType)
Return the supertype of DataType T
.
Examples
julia> supertype(Int32)
Signed
Core.Type
— TypeCore.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
Core.DataType
— TypeDataType <: 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
Core.:<:
— Function<:(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
Base.:>:
— Function>:(T1, T2)
Supertype operator, equivalent to T2 <: T1
.
Base.typejoin
— Functiontypejoin(T, S)
Return the closest common ancestor of T
and S
, i.e. the narrowest type from which they both inherit.
Base.typeintersect
— Functiontypeintersect(T, S)
Compute a type that contains the intersection of T
and S
. Usually this will be the smallest such type or one close to it.
Base.promote_type
— Functionpromote_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
Base.promote_rule
— Functionpromote_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.
Base.promote_typejoin
— Functionpromote_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
Base.isdispatchtuple
— Functionisdispatchtuple(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.
Declared structure
Base.ismutable
— Functionismutable(v) -> Bool
Return true
iff 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 type, it will tell you that a value of DataType
is mutable.
See also isbits
, isstructtype
.
Examples
julia> ismutable(1)
false
julia> ismutable([1,2])
true
This function requires at least Julia 1.5.
Base.isimmutable
— Functionisimmutable(v) -> Bool
Consider using !ismutable(v)
instead, as isimmutable(v)
will be replaced by !ismutable(v)
in a future release. (Since Julia 1.5)
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
Base.isabstracttype
— Functionisabstracttype(T)
Determine whether type T
was declared as an abstract type (i.e. using the abstract
keyword).
Examples
julia> isabstracttype(AbstractArray)
true
julia> isabstracttype(Vector)
false
Base.isprimitivetype
— Functionisprimitivetype(T) -> Bool
Determine whether type T
was declared as a primitive type (i.e. using the primitive
keyword).
Base.issingletontype
— FunctionBase.issingletontype(T)
Determine whether type T
has exactly one possible instance; for example, a struct type with no fields.
Base.isstructtype
— Functionisstructtype(T) -> Bool
Determine whether type T
was declared as a struct type (i.e. using the struct
or mutable struct
keyword).
Base.nameof
— Methodnameof(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
Base.fieldnames
— Functionfieldnames(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)
Base.fieldname
— Functionfieldname(x::DataType, i::Integer)
Get the name of field i
of a DataType
.
Examples
julia> fieldname(Rational, 1)
:num
julia> fieldname(Rational, 2)
:den
Base.hasfield
— Functionhasfield(T::Type, name::Symbol)
Return a boolean indicating whether T
has name
as one of its own fields.
This function requires at least Julia 1.2.
Memory layout
Base.sizeof
— Methodsizeof(T::DataType)
sizeof(obj)
Size, in bytes, of the canonical binary representation of the given DataType
T
, if any. Size, in bytes, of object obj
if it is not DataType
.
Examples
julia> sizeof(Float32)
4
julia> sizeof(ComplexF64)
16
julia> sizeof(1.0)
8
julia> sizeof([1.0:10.0;])
80
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:
[...]
Base.isconcretetype
— Functionisconcretetype(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
Base.isbits
— Functionisbits(x)
Return true
if x
is an instance of an isbitstype
type.
Base.isbitstype
— Functionisbitstype(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
Core.fieldtype
— Functionfieldtype(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
Base.fieldtypes
— Functionfieldtypes(T::Type)
The declared types of all fields in a composite DataType T
as a tuple.
This function requires at least Julia 1.1.
Examples
julia> struct Foo
x::Int64
y::String
end
julia> fieldtypes(Foo)
(Int64, String)
Base.fieldcount
— Functionfieldcount(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.
Base.fieldoffset
— Functionfieldoffset(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
— FunctionBase.datatype_alignment(dt::DataType) -> Int
Memory allocation minimum alignment for instances of this type. Can be called on any isconcretetype
.
Base.datatype_haspadding
— FunctionBase.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
— FunctionBase.datatype_pointerfree(dt::DataType) -> Bool
Return whether instances of this type can contain references to gc-managed memory. Can be called on any isconcretetype
.
Special values
Base.typemin
— Functiontypemin(T)
The lowest value representable by the given (real) numeric DataType T
.
Examples
julia> typemin(Float16)
-Inf16
julia> typemin(Float32)
-Inf32
Base.typemax
— Functiontypemax(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> floatmax(Float32) # largest finite floating point number
3.4028235f38
Base.floatmin
— Functionfloatmin(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
Base.floatmax
— Functionfloatmax(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
Base.maxintfloat
— Functionmaxintfloat(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)
.
Base.eps
— Methodeps(::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
Base.eps
— Methodeps(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
Base.instances
— Functioninstances(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)
Special Types
Core.Any
— TypeAny::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.
Core.Union
— TypeUnion{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 :: IntOrString
1
julia> "Hello!" :: IntOrString
"Hello!"
julia> 1.0 :: IntOrString
ERROR: TypeError: in typeassert, expected Union{Int64, AbstractString}, got a value of type Float64
Union{}
— KeywordUnion{}
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
Core.UnionAll
— TypeUnionAll
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
Core.Tuple
— TypeTuple{Types...}
Tuples are an abstraction of the arguments of a function – without the function itself. The salient aspects of a function's arguments are their order and their types. Therefore a tuple type is similar to a parameterized immutable type where each parameter is the type of one field. Tuple types may have any number of parameters.
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.
See the manual section on Tuple Types.
See also Vararg
, NTuple
, tuple
, NamedTuple
.
Core.NTuple
— TypeNTuple{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
Core.NamedTuple
— TypeNamedTuple
NamedTuple
s are, as their name suggests, named Tuple
s. That is, they're a tuple-like collection of values, where each entry has a unique name, represented as a Symbol
. Like Tuple
s, NamedTuple
s are immutable; neither the names nor the values can be modified in place after construction.
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
.
Iteration over NamedTuple
s produces the values without the names. (See example below.) To iterate over the name-value pairs, use the pairs
function.
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 a pair name::Symbol => value
or splatting an iterator yielding such pairs after a semicolon inside a tuple literal:
julia> (; :a => 1)
(a = 1,)
julia> keys = (:a, :b, :c); values = (1, 2, 3);
julia> (; zip(keys, values)...)
(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,)
Implicit names from identifiers and dot expressions are available as of Julia 1.5.
Use of getindex
methods with multiple Symbol
s is available as of Julia 1.7.
Base.@NamedTuple
— Macro@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.
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, :b), Tuple{Float64, String}}
julia> @NamedTuple begin
a::Float64
b::String
end
NamedTuple{(:a, :b), Tuple{Float64, String}}
This macro is available as of Julia 1.5.
Base.Val
— TypeVal(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"
Core.Vararg
— ConstantVararg{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
Core.Nothing
— TypeBase.isnothing
— Functionisnothing(x)
Return true
if x === nothing
, and return false
if not.
This function requires at least Julia 1.1.
See also something
, notnothing
, ismissing
.
Base.notnothing
— Functionnotnothing(x)
Throw an error if x === nothing
, and return x
if not.
Base.Some
— TypeSome{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.
Base.something
— Functionsomething(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(missing, nothing)
missing
julia> something(nothing, nothing)
ERROR: ArgumentError: No value arguments present
Base.@something
— Macro@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
This macro is available as of Julia 1.7.
Base.Enums.Enum
— TypeEnum{T<:Integer}
The abstract supertype of all enumerated types defined with @enum
.
Base.Enums.@enum
— Macro@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)
Core.Expr
— TypeExpr(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
Core.Symbol
— TypeSymbol
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). Symbol
s can be entered using the :
quote operator:
julia> :name
:name
julia> typeof(:name)
Symbol
julia> x = 42
42
julia> eval(:x)
42
Symbol
s can also be constructed from strings or other values by calling the constructor Symbol(x...)
.
Symbol
s are immutable and should be compared using ===
. The implementation re-uses the same object for all Symbol
s with the same name, so comparison tends to be efficient (it can just compare pointers).
Unlike strings, Symbol
s are "atomic" or "scalar" entities that do not support iteration over characters.
Core.Symbol
— MethodSymbol(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
Core.Module
— TypeModule
A Module
is a separate global variable workspace. See module
and the manual section about modules for details.
Generic Functions
Core.Function
— TypeFunction
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
Base.hasmethod
— Functionhasmethod(f, t::Type{<:Tuple}[, kwnames]; world=typemax(UInt)) -> 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
.
Providing keyword argument names requires Julia 1.2 or later.
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
Core.applicable
— Functionapplicable(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
— FunctionBase.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
Core.invoke
— Functioninvoke(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
Base.@invoke
— Macro@invoke f(arg::T, ...; kwargs...)
Provides a convenient way to call invoke
; @invoke f(arg1::T1, arg2::T2; kwargs...)
will be expanded into invoke(f, Tuple{T1,T2}, arg1, arg2; kwargs...)
. When an argument's type annotation is omitted, it's specified as Any
argument, e.g. @invoke f(arg1::T, arg2)
will be expanded into invoke(f, Tuple{T,Any}, arg1, arg2)
.
This macro requires Julia 1.7 or later.
Base.invokelatest
— Functioninvokelatest(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.)
Base.@invokelatest
— Macro@invokelatest f(args...; kwargs...)
Provides a convenient way to call Base.invokelatest
. @invokelatest f(args...; kwargs...)
will simply be expanded into Base.invokelatest(f, args...; kwargs...)
.
This macro requires Julia 1.7 or later.
new
— Keywordnew
Special function available to inner constructors which created a new object of the type. See the manual section on Inner Constructor Methods for more information.
Base.:|>
— Function|>(x, f)
Applies a function to the preceding argument. This allows for easy function chaining.
Examples
julia> [1:5;] |> x->x.^2 |> sum |> inv
0.01818181818181818
Base.:∘
— Functionf ∘ 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.
Multiple function composition requires at least Julia 1.4.
Composition of one function ∘(f) requires at least Julia 1.5.
Using keyword arguments requires at least Julia 1.7.
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> fs = [
x -> 2x
x -> x/2
x -> x-1
x -> x+1
];
julia> ∘(fs...)(3)
3.0
See also ComposedFunction
, !f::Function
.
Base.ComposedFunction
— TypeComposedFunction{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 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
ComposedFunction requires at least Julia 1.6. In earlier versions ∘
returns an anonymous function instead.
See also ∘
.
Base.splat
— Functionsplat(f)
Defined as
splat(f) = args->f(args...)
i.e. given a function returns a new function that takes one argument and splats its argument 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(Base.splat(+), zip(1:3,4:6))
3-element Vector{Int64}:
5
7
9
Base.Fix1
— TypeFix1(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
.
Base.Fix2
— TypeFix2(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)
.
Syntax
Core.eval
— FunctionCore.eval(m::Module, expr)
Evaluate an expression in the given module and return the result.
Base.MainInclude.eval
— Functioneval(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.
Base.@eval
— Macro@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.
Base.evalfile
— Functionevalfile(path::AbstractString, args::Vector{String}=String[])
Load the file using include
, evaluate all expressions, and return the value of the last one.
Base.esc
— Functionesc(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.
Base.@inbounds
— Macro@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
Using @inbounds
may return incorrect results/crashes/corruption for out-of-bounds indices. The user is responsible for checking it manually. Only use @inbounds
when it is certain from the information locally available that all accesses are in bounds.
Base.@boundscheck
— Macro@boundscheck(blk)
Annotates the expression blk
as a bounds checking block, allowing it to be elided by @inbounds
.
The function in which @boundscheck
is written must be inlined into its caller in order for @inbounds
to have effect.
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]"
The @boundscheck
annotation allows you, as a library writer, to opt-in to allowing other code to remove your bounds checks with @inbounds
. As noted there, the caller must verify—using information they can access—that their accesses are valid before using @inbounds
. For indexing into your AbstractArray
subclasses, for example, this involves checking the indices against its axes
. Therefore, @boundscheck
annotations should only be added to a getindex
or setindex!
implementation after you are certain its behavior is correct.
Base.@propagate_inbounds
— Macro@propagate_inbounds
Tells the compiler to inline a function while retaining the caller's inbounds context.
Base.@inline
— Macro@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. This is shown in the following example:
@inline function bigfunction(x)
#=
Function Definition
=#
end
Base.@noinline
— Macro@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. This is shown in the following example:
@noinline function smallfunction(x)
#=
Function Definition
=#
end
If the function is trivial (for example returning a constant) it might get inlined anyway.
Base.@nospecialize
— Macro@nospecialize
Applied to a function argument name, hints to the compiler that the method should not be specialized for different types of that argument, but instead to use precisely the declared type for each argument. This is only a hint for avoiding excess code generation. 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. 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
Base.@specialize
— Macro@specialize
Reset the specialization hint for an argument back to the default. For details, see @nospecialize
.
Base.gensym
— Functiongensym([tag])
Generates a symbol which will not conflict with other variable names.
Base.@gensym
— Macro@gensym
Generates a gensym symbol for a variable. For example, @gensym x y
is transformed into x = gensym("x"); y = gensym("y")
.
var"name"
— Keywordvar
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
.
This syntax requires at least Julia 1.3.
Base.@goto
— Macro@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.
Base.@label
— Macro@label name
Labels a statement with the symbolic label name
. The label marks the end-point of an unconditional jump with @goto name
.
Base.SimdLoop.@simd
— Macro@simd
Annotate a for
loop to allow the compiler to take extra liberties to allow loop re-ordering
This feature is experimental and could change or disappear in future versions of Julia. Incorrect use of the @simd
macro may cause unexpected results.
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, 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 theifelse
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.
The @simd
does not assert by default that the loop is completely free of loop-carried memory dependencies, which is an assumption that can easily be violated in generic code. If you are writing non-generic code, you can use @simd ivdep for ... end
to also assert that:
- There exists no loop-carried memory dependencies
- No iteration ever waits on a previous iteration to make forward progress.
Base.@polly
— Macro@polly
Tells the compiler to apply the polyhedral optimizer Polly to a function.
Base.@generated
— Macro@generated f
@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.
Example:
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"
Base.@pure
— Macro@pure ex
@pure(ex)
@pure
gives the compiler a hint for the definition of a pure function, helping for type inference.
This macro is intended for internal compiler use and may be subject to changes.
Base.@deprecate
— Macro@deprecate old new [ex=true]
Deprecate method old
and specify the replacement call new
. Prevent @deprecate
from exporting old
by setting ex
to false
. @deprecate
defines a new method with the same signature as old
.
As of Julia 1.5, functions defined by @deprecate
do not print warning when julia
is run without the --depwarn=yes
flag set, as the default value of --depwarn
option is no
. The warnings are printed from tests run by Pkg.test()
.
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)
Missing Values
Base.Missing
— TypeMissing
A type with no fields whose singleton instance missing
is used to represent missing values.
See also: skipmissing
, nonmissingtype
, Nothing
.
Base.missing
— Constantmissing
The singleton instance of type Missing
representing a missing value.
See also: NaN
, skipmissing
, nonmissingtype
.
Base.coalesce
— Functioncoalesce(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
Base.@coalesce
— Macro@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
[...]
This macro is available as of Julia 1.7.
Base.ismissing
— FunctionBase.skipmissing
— Functionskipmissing(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
Base.nonmissingtype
— Functionnonmissingtype(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
This function is exported as of Julia 1.3.
System
Base.run
— Functionrun(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).
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.
Base.devnull
— Constantdevnull
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))
Base.success
— Functionsuccess(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.
Base.process_running
— Functionprocess_running(p::Process)
Determine whether a process is currently running.
Base.process_exited
— Functionprocess_exited(p::Process)
Determine whether a process has exited.
Base.kill
— Methodkill(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).
Base.Sys.set_process_title
— FunctionSys.set_process_title(title::AbstractString)
Set the process title. No-op on some operating systems.
Base.Sys.get_process_title
— FunctionSys.get_process_title()
Get the process title. On some systems, will always return an empty string.
Base.ignorestatus
— Functionignorestatus(command)
Mark a command object so that running it will not throw an error if the result code is non-zero.
Base.detach
— Functiondetach(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.
Base.Cmd
— TypeCmd(cmd::Cmd; ignorestatus, detach, windows_verbatim, windows_hide, env, dir)
Construct a new Cmd
object, representing an external program and arguments, from cmd
, while changing the settings of the optional keyword arguments:
ignorestatus::Bool
: Iftrue
(defaults tofalse
), then theCmd
will not throw an error if the return code is nonzero.detach::Bool
: Iftrue
(defaults tofalse
), then theCmd
will be run in a new process group, allowing it to outlive thejulia
process and not have Ctrl-C passed to it.windows_verbatim::Bool
: Iftrue
(defaults tofalse
), then on Windows theCmd
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
: Iftrue
(defaults tofalse
), then on Windows no new console window is displayed when theCmd
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 theCmd
.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, initializeenv
withcopy(ENV)
and then setenv["var"]=val
as desired. To add to an environment block within aCmd
object without replacing all elements, useaddenv()
which will return aCmd
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. Normally, to create a Cmd
object in the first place, one uses backticks, e.g.
Cmd(`echo "Hello world"`, ignorestatus=true, detach=false)
Base.setenv
— Functionsetenv(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).
Base.addenv
— Functionaddenv(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
.
This function requires Julia 1.6 or later.
Base.withenv
— Functionwithenv(f::Function, 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.
Base.pipeline
— Methodpipeline(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`))
Base.pipeline
— Methodpipeline(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))
Base.Libc.gethostname
— Functiongethostname() -> AbstractString
Get the local machine's host name.
Base.Libc.getpid
— Functiongetpid() -> Int32
Get Julia's process ID.
getpid(process) -> Int32
Get the child process ID, if it still exists.
This function requires at least Julia 1.1.
Base.Libc.time
— Methodtime()
Get the system time in seconds since the epoch, with fairly high (typically, microsecond) resolution.
Base.time_ns
— Functiontime_ns()
Get the time in nanoseconds. The time corresponding to 0 is undefined, and wraps every 5.8 years.
Base.@time
— Macro@time
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) or compiling is shown as a percentage.
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 @timev
, @timed
, @elapsed
, and @allocated
.
For more serious benchmarking, consider the @btime
macro from the BenchmarkTools.jl package which among other things evaluates the function multiple times in order to reduce noise.
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
Base.@timev
— Macro@timev
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.
See also @time
, @timed
, @elapsed
, and @allocated
.
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
Base.@timed
— Macro@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
, and @allocated
.
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
The return type of this macro was changed from Tuple
to NamedTuple
in Julia 1.5.
Base.@elapsed
— Macro@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
, and @allocated
.
julia> @elapsed sleep(0.3)
0.301391426
Base.@allocated
— Macro@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 @time
, @timev
, @timed
, and @elapsed
.
julia> @allocated rand(10^6)
8000080
Base.EnvDict
— TypeEnvDict() -> EnvDict
A singleton of this type provides a hash table interface to environment variables.
Base.ENV
— ConstantENV
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.)
Base.Sys.isunix
— FunctionSys.isunix([os])
Predicate for testing if the OS provides a Unix-like interface. See documentation in Handling Operating System Variation.
Base.Sys.isapple
— FunctionSys.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.
Base.Sys.islinux
— FunctionSys.islinux([os])
Predicate for testing if the OS is a derivative of Linux. See documentation in Handling Operating System Variation.
Base.Sys.isbsd
— FunctionSys.isbsd([os])
Predicate for testing if the OS is a derivative of BSD. See documentation in Handling Operating System Variation.
The Darwin kernel descends from BSD, which means that Sys.isbsd()
is true
on macOS systems. To exclude macOS from a predicate, use Sys.isbsd() && !Sys.isapple()
.
Base.Sys.isfreebsd
— FunctionSys.isfreebsd([os])
Predicate for testing if the OS is a derivative of FreeBSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on FreeBSD but also on other BSD-based systems. Sys.isfreebsd()
refers only to FreeBSD.
This function requires at least Julia 1.1.
Base.Sys.isopenbsd
— FunctionSys.isopenbsd([os])
Predicate for testing if the OS is a derivative of OpenBSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on OpenBSD but also on other BSD-based systems. Sys.isopenbsd()
refers only to OpenBSD.
This function requires at least Julia 1.1.
Base.Sys.isnetbsd
— FunctionSys.isnetbsd([os])
Predicate for testing if the OS is a derivative of NetBSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on NetBSD but also on other BSD-based systems. Sys.isnetbsd()
refers only to NetBSD.
This function requires at least Julia 1.1.
Base.Sys.isdragonfly
— FunctionSys.isdragonfly([os])
Predicate for testing if the OS is a derivative of DragonFly BSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on DragonFly but also on other BSD-based systems. Sys.isdragonfly()
refers only to DragonFly.
This function requires at least Julia 1.1.
Base.Sys.iswindows
— FunctionSys.iswindows([os])
Predicate for testing if the OS is a derivative of Microsoft Windows NT. See documentation in Handling Operating System Variation.
Base.Sys.windows_version
— FunctionSys.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.
Base.Sys.free_memory
— FunctionSys.free_memory()
Get the total free memory in RAM in bytes.
Base.Sys.total_memory
— FunctionSys.total_memory()
Get the total memory in RAM (including that which is currently used) in bytes.
Base.@static
— Macro@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.
Versioning
Base.VersionNumber
— TypeVersionNumber
Version number type which follow the specifications of semantic versioning, composed of major, minor and patch numeric values, followed by pre-release and build alpha-numeric annotations. See also @v_str
.
Examples
julia> VersionNumber("1.2.3")
v"1.2.3"
julia> VersionNumber("2.0.1-rc1")
v"2.0.1-rc1"
Base.@v_str
— Macro@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"
Errors
Base.error
— Functionerror(message::AbstractString)
Raise an ErrorException
with the given message.
error(msg...)
Raise an ErrorException
with the given message.
Core.throw
— Functionthrow(e)
Throw an object as an exception.
Base.rethrow
— Functionrethrow()
Rethrow the current exception from within a catch
block. The rethrown exception will continue propagation as if it had not been caught.
The alternative form rethrow(e)
allows you to associate an alternative exception object e
with the current backtrace. However this misrepresents the program state at the time of the error so you're encouraged to instead throw a new exception using throw(e)
. In Julia 1.1 and above, using throw(e)
will preserve the root cause exception on the stack, as described in current_exceptions
.
Base.backtrace
— Functionbacktrace()
Get a backtrace object for the current program point.
Base.catch_backtrace
— Functioncatch_backtrace()
Get the backtrace of the current exception, for use within catch
blocks.
Base.current_exceptions
— Functioncurrent_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.
This function went by the experimental name catch_stack()
in Julia 1.1–1.6, and had a plain Vector-of-tuples as a return type.
Base.@assert
— Macro@assert cond [text]
Throw an AssertionError
if cond
is false
. Preferred syntax for writing assertions. Message text
is optionally displayed upon assertion failure.
An assert might be disabled at various optimization levels. Assert should therefore only be used as a debugging tool and not used for authentication verification (e.g., verifying passwords), nor should side effects needed for the function to work correctly be used inside of asserts.
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?"
Base.Experimental.register_error_hint
— FunctionExperimental.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
: providehandler(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:
...
Custom error hints are available as of Julia 1.5.
This interface is experimental and subject to change or removal without notice. To insulate yourself against changes, consider putting any registrations inside an if isdefined(Base.Experimental, :register_error_hint) ... end
block.
Base.Experimental.show_error_hints
— FunctionExperimental.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.
Custom error hints are available as of Julia 1.5.
This interface is experimental and subject to change or removal without notice.
Core.ArgumentError
— TypeArgumentError(msg)
The parameters to a function call do not match a valid signature. Argument msg
is a descriptive error string.
Core.AssertionError
— TypeAssertionError([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
.
Core.BoundsError
— TypeBoundsError([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]
Base.CompositeException
— TypeCompositeException
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.
Base.DimensionMismatch
— TypeDimensionMismatch([msg])
The objects called do not have matching dimensionality. Optional argument msg
is a descriptive error string.
Core.DivideError
— TypeDivideError()
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:
[...]
Core.DomainError
— TypeDomainError(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 will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).
Stacktrace:
[...]
Base.EOFError
— TypeEOFError()
No more data was available to read from a file or stream.
Core.ErrorException
— TypeErrorException(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"
Core.InexactError
— TypeInexactError(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:
[...]
Core.InterruptException
— TypeInterruptException()
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.
Base.KeyError
— TypeKeyError(key)
An indexing operation into an AbstractDict
(Dict
) or Set
like object tried to access or delete a non-existent element.
Core.LoadError
— TypeLoadError(file::AbstractString, line::Int, error)
An error occurred while include
ing, require
ing, or using
a file. The error specifics should be available in the .error
field.
LoadErrors are no longer emitted by @macroexpand
, @macroexpand1
, and macroexpand
as of Julia 1.7.
Core.MethodError
— TypeMethodError(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.
Base.MissingException
— TypeMissingException(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.
Core.OutOfMemoryError
— TypeOutOfMemoryError()
An operation allocated too much memory for either the system or the garbage collector to handle properly.
Core.ReadOnlyMemoryError
— TypeReadOnlyMemoryError()
An operation tried to write to memory that is read-only.
Core.OverflowError
— TypeOverflowError(msg)
The result of an expression is too large for the specified type and will cause a wraparound.
Base.ProcessFailedException
— TypeProcessFailedException
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).
Core.StackOverflowError
— TypeStackOverflowError()
The function call grew beyond the size of the call stack. This usually happens when a call recurses infinitely.
Base.SystemError
— TypeSystemError(prefix::AbstractString, [errno::Int32])
A system call failed with an error code (in the errno
global variable).
Core.TypeError
— TypeTypeError(func::Symbol, context::AbstractString, expected::Type, got)
A type assertion failure, or calling an intrinsic function with an incorrect argument type.
Core.UndefKeywordError
— TypeUndefKeywordError(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
Core.UndefRefError
— TypeUndefRefError()
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:
[...]
Core.UndefVarError
— TypeUndefVarError(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
Base.StringIndexError
— TypeStringIndexError(str, i)
An error occurred when trying to access str
at index i
that is not valid.
Core.InitError
— TypeInitError(mod::Symbol, error)
An error occurred when running a module's __init__
function. The actual error thrown is available in the .error
field.
Base.retry
— Functionretry(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
.
Before Julia 1.2 this signature was restricted to f::Function
.
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)
Base.ExponentialBackOff
— TypeExponentialBackOff(; 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
.
Events
Base.Timer
— MethodTimer(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 cb
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
Base.Timer
— TypeTimer(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.
Note: interval
is subject to accumulating time skew. If you need precise events at a particular absolute time, create a new timer at each expiration with the difference to the next time computed.
Base.AsyncCondition
— TypeAsyncCondition()
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.
Base.AsyncCondition
— MethodAsyncCondition(callback::Function)
Create a async condition that calls the given callback
function. The callback
is passed one argument, the async condition object itself.
Reflection
Base.nameof
— Methodnameof(m::Module) -> Symbol
Get the name of a Module
as a Symbol
.
Examples
julia> nameof(Base.Broadcast)
:Broadcast
Base.parentmodule
— Functionparentmodule(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 a given definition of a generic function.
Base.pathof
— Methodpathof(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.
Base.pkgdir
— Methodpkgdir(m::Module[, paths::String...])
Return the root directory of the package that imported module m
, or nothing
if m
was not imported from a package. Optionally further path component strings can be provided to construct a path within the package root.
julia> pkgdir(Foo)
"/path/to/Foo.jl"
julia> pkgdir(Foo, "src", "file.jl")
"/path/to/Foo.jl/src/file.jl"
The optional argument paths
requires at least Julia 1.7.
Base.moduleroot
— Functionmoduleroot(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__
— Keyword__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__
— Keyword__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.
Base.@__MODULE__
— Macro@__MODULE__ -> Module
Get the Module
of the toplevel eval, which is the Module
code is currently being read from.
Base.@__FILE__
— Macro@__FILE__ -> AbstractString
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
.
Base.@__DIR__
— Macro@__DIR__ -> AbstractString
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>
.
Base.@__LINE__
— Macro@__LINE__ -> Int
Expand to the line number of the location of the macrocall. Return 0
if the line number could not be determined.
Base.fullname
— Functionfullname(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,)
Base.names
— Functionnames(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__
.
Core.nfields
— Functionnfields(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.
Base.isconst
— Functionisconst(m::Module, s::Symbol) -> Bool
Determine whether a global is declared const
in a given Module
.
Base.nameof
— Methodnameof(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.
Base.functionloc
— Methodfunctionloc(f::Function, types)
Returns a tuple (filename,line)
giving the location of a generic Function
definition.
Base.functionloc
— Methodfunctionloc(m::Method)
Returns a tuple (filename,line)
giving the location of a Method
definition.
Base.@locals
— Macro@locals()
Construct a dictionary of the names (as symbols) and values of all local variables defined as of the call site.
This macro requires at least Julia 1.1.
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)
Internals
Base.GC.gc
— FunctionGC.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.
Excessive use will likely lead to poor performance.
Base.GC.enable
— FunctionGC.enable(on::Bool)
Control whether garbage collection is enabled using a boolean argument (true
for enabled, false
for disabled). Return previous GC state.
Disabling garbage collection should be used only with caution, as it can cause memory use to grow without bound.
Base.GC.@preserve
— MacroGC.@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 x
s.
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
toccall
- 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
Base.GC.safepoint
— FunctionGC.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.
This function is available as of Julia 1.4.
Base.Meta.lower
— Functionlower(m, x)
Takes the expression x
and returns an equivalent expression in lowered form for executing in module m
. See also code_lowered
.
Base.Meta.@lower
— Macro@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
.
Base.Meta.parse
— Methodparse(str, start; greedy=true, raise=true, depwarn=true)
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.
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)
Base.Meta.parse
— Methodparse(str; raise=true, depwarn=true)
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.
julia> Meta.parse("x = 3")
:(x = 3)
julia> Meta.parse("x = ")
:($(Expr(:incomplete, "incomplete: premature end of input")))
julia> Meta.parse("1.0.2")
ERROR: Base.Meta.ParseError("invalid numeric constant \"1.0.\"")
Stacktrace:
[...]
julia> Meta.parse("1.0.2"; raise = false)
:($(Expr(:error, "invalid numeric constant \"1.0.\"")))
Base.Meta.ParseError
— TypeParseError(msg)
The expression passed to the parse
function could not be interpreted as a valid Julia expression.
Core.QuoteNode
— TypeQuoteNode
A quoted piece of code, that does not support interpolation. See the manual section about QuoteNodes for details.
Base.macroexpand
— Functionmacroexpand(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)
Base.@macroexpand
— Macro@macroexpand
Return equivalent expression with all macros removed (expanded).
There are differences between @macroexpand
and macroexpand
.
While
macroexpand
takes a keyword argumentrecursive
,@macroexpand
is always recursive. For a non recursive macro version, see@macroexpand1
.While
macroexpand
has an explicitmodule
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.
Base.@macroexpand1
— Macro@macroexpand1
Non recursive version of @macroexpand
.
Base.code_lowered
— Functioncode_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.
Base.code_typed
— Functioncode_typed(f, types; optimize=true, debuginfo=:default)
Returns an array of type-inferred lowered form (IR) for the methods matching the given generic function and type signature. The keyword argument optimize
controls whether additional optimizations, such as inlining, are also applied. The keyword debuginfo
controls the amount of code metadata present in the output, possible options are :source
or :none
.
Base.precompile
— Functionprecompile(f, args::Tuple{Vararg{Any}})
Compile the given function f
for the argument tuple (of types) args
, but do not execute it.
Meta
Base.Meta.quot
— FunctionMeta.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.
Examples
julia> eval(Meta.quot(:x))
:x
julia> dump(Meta.quot(:x))
Expr
head: Symbol quote
args: Array{Any}((1,))
1: Symbol x
julia> eval(Meta.quot(:(1+2)))
:(1 + 2)
Base.isexpr
— FunctionMeta.isexpr(ex, head[, n])::Bool
Return true if ex
is an Expr
with the given type head
and optionally that the argument list is of length n
. head
may be a Symbol
or collection of Symbol
s. For example, to check that a macro was passed a function call expression, you might use isexpr(ex, :call)
.
Examples
julia> ex = :(f(x))
:(f(x))
julia> Meta.isexpr(ex, :block)
false
julia> Meta.isexpr(ex, :call)
true
julia> Meta.isexpr(ex, [:block, :call]) # multiple possible heads
true
julia> Meta.isexpr(ex, :call, 1)
false
julia> Meta.isexpr(ex, :call, 2)
true
Base.isidentifier
— Function isidentifier(s) -> Bool
Return whether the symbol or string s
contains characters that are parsed as a valid identifier in Julia code.
Internally Julia allows any sequence of characters in a Symbol
(except \0
s), and macros automatically use variable names containing #
in order to avoid naming collision with the surrounding code. In order for the parser to recognize a variable, it uses a limited set of characters (greatly extended by Unicode). isidentifier()
makes it possible to query the parser directly whether a symbol contains valid characters.
Examples
julia> Meta.isidentifier(:x), Meta.isidentifier("1x")
(true, false)
Base.isoperator
— Functionisoperator(s::Symbol)
Return true
if the symbol can be used as an operator, false
otherwise.
Examples
julia> Meta.isoperator(:+), Meta.isoperator(:f)
(true, false)
Base.isunaryoperator
— Functionisunaryoperator(s::Symbol)
Return true
if the symbol can be used as a unary (prefix) operator, false
otherwise.
Examples
julia> Meta.isunaryoperator(:-), Meta.isunaryoperator(:√), Meta.isunaryoperator(:f)
(true, true, false)
Base.isbinaryoperator
— Functionisbinaryoperator(s::Symbol)
Return true
if the symbol can be used as a binary (infix) operator, false
otherwise.
Examples
julia> Meta.isbinaryoperator(:-), Meta.isbinaryoperator(:√), Meta.isbinaryoperator(:f)
(true, false, false)
Base.Meta.show_sexpr
— FunctionMeta.show_sexpr([io::IO,], ex)
Show expression ex
as a lisp style S-expression.
Examples
julia> Meta.show_sexpr(:(f(x, g(y,z))))
(:call, :f, :x, (:call, :g, :y, :z))