# 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, and Module.fn(x) to use the functions.
• Alternatively, using Module will import all exported Module 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.

## Getting Around

Base.exitFunction
exit(code=0)

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

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Base.atexitFunction
atexit(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".)

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

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

Keyword Arguments

• exclude: specifies the types of objects to exclude from the traversal.
• chargeall: specifies the types of objects to always charge the size of all of their fields, even if those fields would normally be excluded.
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Base.requireFunction
require(into::Module, module::Symbol)

This function is part of the implementation of using / import, if a module is not already defined in Main. It can also be called directly to force reloading a module, regardless of whether it has been loaded before (for example, when interactively developing libraries).

Loads a source file, in the context of the Main module, on every active node, searching standard locations for files. require is considered a top-level operation, so it sets the current include path but does not use it to search for files (see help for include). This function is typically used to load library code, and is implicitly called by using to load packages.

When searching for files, require first looks for package code in the global array LOAD_PATH. require is case-sensitive on all platforms, including those with case-insensitive filesystems like macOS and Windows.

For more details regarding code loading, see the manual sections on modules and parallel computing.

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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.

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

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

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

source
Base.MainInclude.includeFunction
include([mapexpr::Function,] path::AbstractString)

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

The 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.

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

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

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

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Base.include_dependencyFunction
include_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.

source
Base.whichMethod
which(f, types)

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

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

source
Base.methodsFunction
methods(f, [types], [module])

Return the method table for f.

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

Julia 1.4

At least Julia 1.4 is required for specifying a module.

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ansKeyword
ans

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

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## 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. Also in and isa are parsed as infix operators. Creation of a variable named where, in or isa is allowed though.

moduleKeyword
module

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

Examples

module Foo
import Base.show
export MyType, foo

struct MyType
x
end

bar(x) = 2x
foo(a::MyType) = bar(a.x) + 1
show(io::IO, a::MyType) = print(io, "MyType $(a.x)") end source importKeyword import import Foo will load the module or package Foo. Names from the imported Foo module can be accessed with dot syntax (e.g. Foo.foo to access the name foo). See the manual section about modules for details. source usingKeyword using using Foo will load the module or package Foo and make its exported names available for direct use. Names can also be used via dot syntax (e.g. Foo.foo to access the name foo), whether they are exported or not. See the manual section about modules for details. source baremoduleKeyword baremodule baremodule declares a module that does not contain using Base or local definitions of eval and include. It does still import Core. In other words, module Mod ... end is equivalent to baremodule Mod using Base eval(x) = Core.eval(Mod, x) include(p) = Base.include(Mod, p) ... end source functionKeyword function Functions are defined with the function keyword: function add(a, b) return a + b end Or the short form notation: add(a, b) = a + b The use of the return keyword is exactly the same as in other languages, but is often optional. A function without an explicit return statement will return the last expression in the function body. source macroKeyword macro macro defines a method for inserting generated code into a program. A macro maps a sequence of argument expressions to a returned expression, and the resulting expression is substituted directly into the program at the point where the macro is invoked. Macros are a way to run generated code without calling eval, since the generated code instead simply becomes part of the surrounding program. Macro arguments may include expressions, literal values, and symbols. Macros can be defined for variable number of arguments (varargs), but do not accept keyword arguments. Every macro also implicitly gets passed the arguments __source__, which contains the line number and file name the macro is called from, and __module__, which is the module the macro is expanded in. 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 source returnKeyword return return x causes the enclosing function to exit early, passing the given value x back to its caller. return by itself with no value is equivalent to return nothing (see nothing). function compare(a, b) a == b && return "equal to" a < b ? "less than" : "greater than" end In general you can place a return statement anywhere within a function body, including within deeply nested loops or conditionals, but be careful with do blocks. For example: function test1(xs) for x in xs iseven(x) && return 2x end end function test2(xs) map(xs) do x iseven(x) && return 2x x end end In the first example, the return breaks out of test1 as soon as it hits an even number, so test1([5,6,7]) returns 12. You might expect the second example to behave the same way, but in fact the return there only breaks out of the inner function (inside the do block) and gives a value back to map. test2([5,6,7]) then returns [5,12,7]. When used in a top-level expression (i.e. outside any function), return causes the entire current top-level expression to terminate early. source doKeyword do Create an anonymous function and pass it as the first argument to a function call. For example: map(1:10) do x 2x end is equivalent to map(x->2x, 1:10). Use multiple arguments like so: map(1:10, 11:20) do x, y x + y end source endKeyword end end marks the conclusion of a block of expressions, for example module, struct, mutable struct, begin, let, for etc. end may also be used when indexing into an array to represent the last index of a dimension. 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 source letKeyword let let statements allocate new variable bindings each time they run. Whereas an assignment modifies an existing value location, let creates new locations. 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. source ifKeyword if/elseif/else if/elseif/else performs conditional evaluation, which allows portions of code to be evaluated or not evaluated depending on the value of a boolean expression. Here is the anatomy of the if/elseif/else conditional syntax: if x < y println("x is less than y") elseif x > y println("x is greater than y") else println("x is equal to y") end If the condition expression x < y is true, then the corresponding block is evaluated; otherwise the condition expression x > y is evaluated, and if it is true, the corresponding block is evaluated; if neither expression is true, the else block is evaluated. The elseif and else blocks are optional, and as many elseif blocks as desired can be used. In contrast to some other languages conditions must be of type Bool. It does not suffice for conditions to be convertible to Bool. julia> if 1 end ERROR: TypeError: non-boolean (Int64) used in boolean context source forKeyword for 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 source whileKeyword while while loops repeatedly evaluate a conditional expression, and continue evaluating the body of the while loop as long as the expression remains true. If the condition expression is false when the while loop is first reached, the body is never evaluated. Examples julia> i = 1 1 julia> while i < 5 println(i) global i += 1 end 1 2 3 4 source breakKeyword break Break out of a loop immediately. Examples julia> i = 0 0 julia> while true global i += 1 i > 5 && break println(i) end 1 2 3 4 5 source continueKeyword continue Skip the rest of the current loop iteration. Examples julia> for i = 1:6 iseven(i) && continue println(i) end 1 3 5 source tryKeyword try/catch A try/catch statement allows intercepting errors (exceptions) thrown by throw so that program execution can continue. For example, the following code attempts to write a file, but warns the user and proceeds instead of terminating execution if the file cannot be written: try open("/danger", "w") do f println(f, "Hello") end catch @warn "Could not write file." end or, when the file cannot be read into a variable: lines = try open("/danger", "r") do f readlines(f) end catch @warn "File not found." end The syntax catch e (where e is any variable) assigns the thrown exception object to the given variable within the catch block. The power of the try/catch construct lies in the ability to unwind a deeply nested computation immediately to a much higher level in the stack of calling functions. source finallyKeyword finally Run some code when a given block of code exits, regardless of how it exits. For example, here is how we can guarantee that an opened file is closed: f = open("file") try operate_on_file(f) finally close(f) end When control leaves the try block (for example, due to a return, or just finishing normally), close(f) will be executed. If the try block exits due to an exception, the exception will continue propagating. A catch block may be combined with try and finally as well. In this case the finally block will run after catch has handled the error. source quoteKeyword quote quote creates multiple expression objects in a block without using the explicit Expr constructor. For example: ex = quote x = 1 y = 2 x + y end Unlike the other means of quoting, :( ... ), this form introduces QuoteNode elements to the expression tree, which must be considered when directly manipulating the tree. For other purposes, :( ... ) and quote .. end blocks are treated identically. source localKeyword local local introduces a new local variable. See the manual section on variable scoping for more information. Examples julia> function foo(n) x = 0 for i = 1:n local x # introduce a loop-local x x = i end x end foo (generic function with 1 method) julia> foo(10) 0 source globalKeyword global global x makes x in the current scope and its inner scopes refer to the global variable of that name. See the manual section on variable scoping for more information. Examples julia> z = 3 3 julia> function foo() global z = 6 # use the z variable defined outside foo end foo (generic function with 1 method) julia> foo() 6 julia> z 6 source constKeyword const const is used to declare global variables whose values will not change. In almost all code (and particularly performance sensitive code) global variables should be declared constant in this way. const x = 5 Multiple variables can be declared within a single const: const y, z = 7, 11 Note that const only applies to one = operation, therefore const x = y = 1 declares x to be constant but not y. On the other hand, const x = const y = 1 declares both x and y constant. Note that "constant-ness" does not extend into mutable containers; only the association between a variable and its value is constant. If x is an array or dictionary (for example) you can still modify, add, or remove elements. In some cases changing the value of a const variable gives a warning instead of an error. However, this can produce unpredictable behavior or corrupt the state of your program, and so should be avoided. This feature is intended only for convenience during interactive use. source structKeyword struct The most commonly used kind of type in Julia is a struct, specified as a name and a set of fields. struct Point x y end Fields can have type restrictions, which may be parameterized: struct Point{X} x::X y::Float64 end A struct can also declare an abstract super type via <: syntax: struct Point <: AbstractPoint x y end structs are immutable by default; an instance of one of these types cannot be modified after construction. Use mutable struct instead to declare a type whose instances can be modified. See the manual section on Composite Types for more details, such as how to define constructors. source abstract typeKeyword abstract type abstract type declares a type that cannot be instantiated, and serves only as a node in the type graph, thereby describing sets of related concrete types: those concrete types which are their descendants. Abstract types form the conceptual hierarchy which makes Julia’s type system more than just a collection of object implementations. For example: abstract type Number end abstract type Real <: Number end Number has no supertype, whereas Real is an abstract subtype of Number. source primitive typeKeyword primitive type primitive type declares a concrete type whose data consists only of a series of bits. Classic examples of primitive types are integers and floating-point values. Some example built-in primitive type declarations: primitive type Char 32 end primitive type Bool <: Integer 8 end The number after the name indicates how many bits of storage the type requires. Currently, only sizes that are multiples of 8 bits are supported. The Bool declaration shows how a primitive type can be optionally declared to be a subtype of some supertype. source whereKeyword where The where keyword creates a type that is an iterated union of other types, over all values of some variable. For example Vector{T} where T<:Real includes all Vectors where the element type is some kind of Real number. The variable bound defaults to Any if it is omitted: Vector{T} where T # short for where T<:Any Variables can also have lower bounds: Vector{T} where T>:Int Vector{T} where Int<:T<:Real There is also a concise syntax for nested where expressions. For example, this: Pair{T, S} where S<:Array{T} where T<:Number can be shortened to: Pair{T, S} where {T<:Number, S<:Array{T}} This form is often found on method signatures. Note that in this form, the variables are listed outermost-first. This matches the order in which variables are substituted when a type is "applied" to parameter values using the syntax T{p1, p2, ...}. source ...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 source ;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!" source =Keyword = = is the assignment operator. • For variable a and expression b, a = b makes a refer to the value of b. • For functions f(x), f(x) = x defines a new function constant f, or adds a new method to f if f is already defined; this usage is equivalent to function f(x); x; end. • a[i] = v calls setindex!(a,v,i). • a.b = c calls setproperty!(a,:b,c). • Inside a function call, f(a=b) passes b as the value of keyword argument a. • Inside parentheses with commas, (a=1,) constructs a NamedTuple. Examples Assigning a to b does not create a copy of b; instead use copy or deepcopy. julia> b = [1]; a = b; b[1] = 2; a 1-element Array{Int64, 1}: 2 julia> b = [1]; a = copy(b); b[1] = 2; a 1-element Array{Int64, 1}: 1  Collections passed to functions are also not copied. Functions can modify (mutate) the contents of the objects their arguments refer to. (The names of functions which do this are conventionally suffixed with '!'.) julia> function f!(x); x[:] .+= 1; end f! (generic function with 1 method) julia> a = [1]; f!(a); a 1-element Array{Int64, 1}: 2  Assignment can operate on multiple variables in parallel, taking values from an iterable: julia> a, b = 4, 5 (4, 5) julia> a, b = 1:3 1:3 julia> a, b (1, 2)  Assignment can operate on multiple variables in series, and will return the value of the right-hand-most expression: julia> a = [1]; b = [2]; c = [3]; a = b = c 1-element Array{Int64, 1}: 3 julia> b[1] = 2; a, b, c ([2], [2], [2])  Assignment at out-of-bounds indices does not grow a collection. If the collection is a Vector it can instead be grown with push! or append!. julia> a = [1, 1]; a[3] = 2 ERROR: BoundsError: attempt to access 2-element Array{Int64, 1} at index [3] [...] julia> push!(a, 2, 3) 4-element Array{Int64, 1}: 1 1 2 3  Assigning [] does not eliminate elements from a collection; instead use filter!. julia> a = collect(1:3); a[a .<= 1] = [] ERROR: DimensionMismatch("tried to assign 0 elements to 1 destinations") [...] julia> filter!(x -> x > 1, a) # in-place & thus more efficient than a = a[a .> 1] 2-element Array{Int64, 1}: 2 3  source ?:Keyword a ? b : c Short form for conditionals; read "if a, evaluate b otherwise evaluate c". Also known as the ternary operator. This syntax is equivalent to if a; b else c end, but is often used to emphasize the value b-or-c which is being used as part of a larger expression, rather than the side effects that evaluating b or c may have. See the manual section on control flow for more details. Examples julia> x = 1; y = 2; julia> println(x > y ? "x is larger" : "y is larger") y is larger source ## Standard Modules MainModule Main Main is the top-level module, and Julia starts with Main set as the current module. Variables defined at the prompt go in Main, and varinfo lists variables in Main. julia> @__MODULE__ Main source CoreModule Core Core is the module that contains all identifiers considered "built in" to the language, i.e. part of the core language and not libraries. Every module implicitly specifies using Core, since you can't do anything without those definitions. source BaseModule Base The base library of Julia. Base is a module that contains basic functionality (the contents of base/). All modules implicitly contain using Base, since this is needed in the vast majority of cases. source ## Base Submodules Base.DocsModule Docs The Docs module provides the @doc macro which can be used to set and retrieve documentation metadata for Julia objects. Please see the manual section on documentation for more information. source ## 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 source Core.isaFunction isa(x, type) -> Bool Determine whether x is of the given type. Can also be used as an infix operator, e.g. x isa type. Examples julia> isa(1, Int) true julia> isa(1, Matrix) false julia> isa(1, Char) false julia> isa(1, Number) true julia> 1 isa Number true source Base.isequalFunction isequal(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. 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. 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 source 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. source Base.islessFunction isless(x, y) Test whether x is less than y, according to a fixed total order. 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 is isless(y, x) and isequal(x, y), and exactly one of those three yields true. • The relation defined by isless is transitive, i.e., isless(x, y) && isless(y, z) implies isless(x, z). Values that are normally unordered, such as NaN, are ordered in an arbitrary but consistent fashion. 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 source Core.ifelseFunction ifelse(condition::Bool, x, y) Return x if condition is true, otherwise return y. This differs from ? or if in that it is an ordinary function, so all the arguments are evaluated first. In some cases, using ifelse instead of an if statement can eliminate the branch in generated code and provide higher performance in tight loops. Examples julia> ifelse(1 > 2, 1, 2) 2 source Core.typeassertFunction typeassert(x, type) Throw a TypeError unless x isa type. The syntax x::type calls this function. Examples julia> typeassert(2.5, Int) ERROR: TypeError: in typeassert, expected Int64, got a value of type Float64 Stacktrace: [...] source Core.typeofFunction typeof(x) Get the concrete type of x. 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}) source Core.tupleFunction tuple(xs...) Construct a tuple of the given objects. Examples julia> tuple(1, 'a', pi) (1, 'a', π) source Base.ntupleFunction ntuple(f::Function, n::Integer) Create a tuple of length n, computing each element as f(i), where i is the index of the element. Examples julia> ntuple(i -> 2*i, 4) (2, 4, 6, 8) source 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) source Base.objectidFunction objectid(x) Get a hash value for x based on object identity. objectid(x)==objectid(y) if x === y. source Base.hashFunction hash(x[, h::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. source Base.finalizerFunction finalizer(f, x) Register a function f(x) to be called when there are no program-accessible references to x, and return x. The type of x must be a mutable struct, otherwise the 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 source Base.copyFunction copy(x) Create a shallow copy of x: the outer structure is copied, but not all internal values. For example, copying an array produces a new array with identically-same elements as the original. source Base.deepcopyFunction deepcopy(x) Create a deep copy of x: everything is copied recursively, resulting in a fully independent object. For example, deep-copying an array produces a new array whose elements are deep copies of the original elements. Calling deepcopy on an object should generally have the same effect as serializing and then deserializing it. While it isn't normally necessary, user-defined types can override the default deepcopy behavior by defining a specialized version of the function deepcopy_internal(x::T, dict::IdDict) (which shouldn't otherwise be used), where T is the type to be specialized for, and dict keeps track of objects copied so far within the recursion. Within the definition, deepcopy_internal should be used in place of deepcopy, and the dict variable should be updated as appropriate before returning. source Base.getpropertyFunction getproperty(value, name::Symbol) The syntax a.b calls getproperty(a, :b). 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 propertynames and setproperty!. source Base.propertynamesFunction propertynames(x, private=false) Get a tuple or a vector of the properties (x.property) of an object x. This is typically the same as fieldnames(typeof(x)), but types that overload getproperty should generally overload propertynames as well to get the properties of an instance of the type. propertynames(x) may return only "public" property names that are part of the documented interface of x. If you want it to also return "private" fieldnames intended for internal use, pass true for the optional second argument. REPL tab completion on x. shows only the private=false properties. source Base.haspropertyFunction hasproperty(x, s::Symbol) Return a boolean indicating whether the object x has s as one of its own properties. Julia 1.2 This function requires at least Julia 1.2. source Core.setfield!Function setfield!(value, name::Symbol, x) 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). 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 source Core.isdefinedFunction isdefined(m::Module, s::Symbol) isdefined(object, s::Symbol) isdefined(object, index::Int) 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. 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 source Base.@isdefinedMacro @isdefined s -> Bool Tests whether variable s is defined in the current scope. See also isdefined. 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 source Base.convertFunction convert(T, x) Convert x to a value of type T. If T is an Integer type, an InexactError will be raised if x is not representable by T, for example if x is not integer-valued, or is outside the range supported by T. Examples julia> convert(Int, 3.0) 3 julia> convert(Int, 3.5) ERROR: InexactError: Int64(3.5) Stacktrace: [...] If T is a AbstractFloat 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 source Base.promoteFunction promote(xs...) Convert all arguments to a common type, and return them all (as a tuple). If no arguments can be converted, an error is raised. Examples julia> promote(Int8(1), Float16(4.5), Float32(4.1)) (1.0f0, 4.5f0, 4.1f0) source Base.oftypeFunction oftype(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 source Base.widenFunction widen(x) If x is a type, return a "larger" type, defined so that arithmetic operations + and - are guaranteed not to overflow nor lose precision for any combination of values that type x can hold. For fixed-size integer types less than 128 bits, widen will return a type with twice the number of bits. If x is a value, it is converted to widen(typeof(x)). Examples julia> widen(Int32) Int64 julia> widen(1.5f0) 1.5 source Base.identityFunction identity(x) The identity function. Returns its argument. Examples julia> identity("Well, what did you expect?") "Well, what did you expect?" source ## Properties of Types ### Type relations Base.supertypeFunction supertype(T::DataType) Return the supertype of DataType T. Examples julia> supertype(Int32) Signed source Core.TypeType Core.Type{T} Core.Type is an abstract type which has all type objects as its instances. The only instance of the singleton type Core.Type{T} is the object T. Examples julia> isa(Type{Float64}, Type) true julia> isa(Float64, Type) true julia> isa(Real, Type{Float64}) false julia> isa(Real, Type{Real}) true source Core.DataTypeType DataType <: Type{T} DataType represents explicitly declared types that have names, explicitly declared supertypes, and, optionally, parameters. Every concrete value in the system is an instance of some DataType. Examples julia> typeof(Real) DataType julia> typeof(Int) DataType julia> struct Point x::Int y end julia> typeof(Point) DataType source 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 source Base.typejoinFunction typejoin(T, S) Return the closest common ancestor of T and S, i.e. the narrowest type from which they both inherit. source Base.typeintersectFunction typeintersect(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. source Base.promote_typeFunction promote_type(type1, type2) Promotion refers to converting values of mixed types to a single common type. promote_type represents the default promotion behavior in Julia when operators (usually mathematical) are given arguments of differing types. promote_type generally tries to return a type which can at least approximate most values of either input type without excessively widening. Some loss is tolerated; for example, promote_type(Int64, Float64) returns Float64 even though strictly, not all Int64 values can be represented exactly as Float64 values. 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 source Base.promote_ruleFunction promote_rule(type1, type2) Specifies what type should be used by promote when given values of types type1 and type2. This function should not be called directly, but should have definitions added to it for new types as appropriate. source Base.isdispatchtupleFunction isdispatchtuple(T) Determine whether type T is a tuple "leaf type", meaning it could appear as a type signature in dispatch and has no subtypes (or supertypes) which could appear in a call. source ### Declared structure Base.ismutableFunction ismutable(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. Examples julia> ismutable(1) false julia> ismutable([1,2]) true Julia 1.5 This function requires at least Julia 1.5. source Base.isimmutableFunction isimmutable(v) -> Bool Warning 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 source Base.isabstracttypeFunction isabstracttype(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 source Base.isprimitivetypeFunction isprimitivetype(T) -> Bool Determine whether type T was declared as a primitive type (i.e. using the primitive keyword). source Base.issingletontypeFunction Base.issingletontype(T) Determine whether type T has exactly one possible instance; for example, a struct type with no fields. source Base.isstructtypeFunction isstructtype(T) -> Bool Determine whether type T was declared as a struct type (i.e. using the struct or mutable struct keyword). source Base.nameofMethod nameof(t::DataType) -> Symbol Get the name of a (potentially UnionAll-wrapped) DataType (without its parent module) as a symbol. Examples julia> module Foo struct S{T} end end Foo julia> nameof(Foo.S{T} where T) :S source Base.fieldnamesFunction fieldnames(x::DataType) Get a tuple with the names of the fields of a DataType. Examples julia> fieldnames(Rational) (:num, :den) source Base.fieldnameFunction fieldname(x::DataType, i::Integer) Get the name of field i of a DataType. Examples julia> fieldname(Rational, 1) :num julia> fieldname(Rational, 2) :den source Base.hasfieldFunction hasfield(T::Type, name::Symbol) Return a boolean indicating whether T has name as one of its own fields. Julia 1.2 This function requires at least Julia 1.2. source ### Memory layout Base.sizeofMethod sizeof(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: [...] source Base.isconcretetypeFunction isconcretetype(T) Determine whether type T is a concrete type, meaning it could have direct instances (values x such that typeof(x) === T). 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 source Base.isbitsFunction isbits(x) Return true if x is an instance of an isbitstype type. source Base.isbitstypeFunction isbitstype(T) Return true if type T is a "plain data" type, meaning it is immutable and contains no references to other values, only primitive types and other isbitstype types. Typical examples are numeric types such as UInt8, Float64, and Complex{Float64}. This category of types is significant since they are valid as type parameters, may not track isdefined / isassigned status, and have a defined layout that is compatible with C. Examples julia> isbitstype(Complex{Float64}) true julia> isbitstype(Complex) false source Core.fieldtypeFunction fieldtype(T, name::Symbol | index::Int) Determine the declared type of a field (specified by name or index) in a composite DataType T. Examples julia> struct Foo x::Int64 y::String end julia> fieldtype(Foo, :x) Int64 julia> fieldtype(Foo, 2) String source Base.fieldtypesFunction fieldtypes(T::Type) The declared types of all fields in a composite DataType T as a tuple. Julia 1.1 This function requires at least Julia 1.1. Examples julia> struct Foo x::Int64 y::String end julia> fieldtypes(Foo) (Int64, String) source Base.fieldcountFunction fieldcount(t::Type) Get the number of fields that an instance of the given type would have. An error is thrown if the type is too abstract to determine this. source Base.fieldoffsetFunction fieldoffset(type, i) The byte offset of field i of a type relative to the data start. For example, we could use it in the following manner to summarize information about a struct: julia> structinfo(T) = [(fieldoffset(T,i), fieldname(T,i), fieldtype(T,i)) for i = 1:fieldcount(T)]; julia> structinfo(Base.Filesystem.StatStruct) 12-element Vector{Tuple{UInt64, Symbol, DataType}}: (0x0000000000000000, :device, UInt64) (0x0000000000000008, :inode, UInt64) (0x0000000000000010, :mode, UInt64) (0x0000000000000018, :nlink, Int64) (0x0000000000000020, :uid, UInt64) (0x0000000000000028, :gid, UInt64) (0x0000000000000030, :rdev, UInt64) (0x0000000000000038, :size, Int64) (0x0000000000000040, :blksize, Int64) (0x0000000000000048, :blocks, Int64) (0x0000000000000050, :mtime, Float64) (0x0000000000000058, :ctime, Float64) source Base.datatype_alignmentFunction Base.datatype_alignment(dt::DataType) -> Int Memory allocation minimum alignment for instances of this type. Can be called on any isconcretetype. source Base.datatype_haspaddingFunction Base.datatype_haspadding(dt::DataType) -> Bool Return whether the fields of instances of this type are packed in memory, with no intervening padding bytes. Can be called on any isconcretetype. source Base.datatype_pointerfreeFunction Base.datatype_pointerfree(dt::DataType) -> Bool Return whether instances of this type can contain references to gc-managed memory. Can be called on any isconcretetype. source ### Special values Base.typeminFunction typemin(T) The lowest value representable by the given (real) numeric DataType T. Examples julia> typemin(Float16) -Inf16 julia> typemin(Float32) -Inf32 source Base.typemaxFunction typemax(T) The highest value representable by the given (real) numeric DataType. Examples julia> typemax(Int8) 127 julia> typemax(UInt32) 0xffffffff source Base.floatminFunction floatmin(T = Float64) Return the smallest positive normal number representable by the floating-point type T. Examples julia> floatmin(Float16) Float16(6.104e-5) julia> floatmin(Float32) 1.1754944f-38 julia> floatmin() 2.2250738585072014e-308 source Base.floatmaxFunction floatmax(T = Float64) Return the largest finite number representable by the floating-point type T. Examples julia> floatmax(Float16) Float16(6.55e4) julia> floatmax(Float32) 3.4028235f38 julia> floatmax() 1.7976931348623157e308 source Base.maxintfloatFunction maxintfloat(T=Float64) The largest consecutive integer-valued floating-point number that is exactly represented in the given floating-point type T (which defaults to Float64). That is, maxintfloat returns the smallest positive integer-valued floating-point number n such that n+1 is not exactly representable in the type T. When an Integer-type value is needed, use Integer(maxintfloat(T)). source 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). source Base.epsMethod eps(::Type{T}) where T<:AbstractFloat eps() Return the machine epsilon of the floating point type T (T = Float64 by default). This is defined as the gap between 1 and the next largest value representable by typeof(one(T)), and is equivalent to eps(one(T)). (Since eps(T) is a bound on the relative error of T, it is a "dimensionless" quantity like one.) Examples julia> eps() 2.220446049250313e-16 julia> eps(Float32) 1.1920929f-7 julia> 1.0 + eps() 1.0000000000000002 julia> 1.0 + eps()/2 1.0 source Base.epsMethod eps(x::AbstractFloat) Return the unit in last place (ulp) of x. This is the distance between consecutive representable floating point values at x. In most cases, if the distance on either side of x is different, then the larger of the two is taken, that is eps(x) == max(x-prevfloat(x), nextfloat(x)-x) The exceptions to this rule are the smallest and largest finite values (e.g. nextfloat(-Inf) and prevfloat(Inf) for Float64), which round to the smaller of the values. The rationale for this behavior is that eps bounds the floating point rounding error. Under the default RoundNearest rounding mode, ify$is a real number and$x$is the nearest floating point number to$y$, then $$$|y-x| \leq \operatorname{eps}(x)/2.$$$ 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 source Base.instancesFunction instances(T::Type) Return a collection of all instances of the given type, if applicable. Mostly used for enumerated types (see @enum). Example julia> @enum Color red blue green julia> instances(Color) (red, blue, green) source ## Special Types Core.AnyType Any::DataType Any is the union of all types. It has the defining property isa(x, Any) == true for any x. Any therefore describes the entire universe of possible values. For example Integer is a subset of Any that includes Int, Int8, and other integer types. source Core.UnionType Union{Types...} A type union is an abstract type which includes all instances of any of its argument types. The empty union Union{} is the bottom type of Julia. Examples julia> IntOrString = Union{Int,AbstractString} Union{Int64, AbstractString} julia> 1 :: IntOrString 1 julia> "Hello!" :: IntOrString "Hello!" julia> 1.0 :: IntOrString ERROR: TypeError: in typeassert, expected Union{Int64, AbstractString}, got a value of type Float64 source Union{}Keyword Union{} Union{}, the empty Union of types, is the type that has no values. That is, it has the defining property isa(x, Union{}) == false for any x. Base.Bottom is defined as its alias and the type of Union{} is Core.TypeofBottom. Examples julia> isa(nothing, Union{}) false source Core.UnionAllType UnionAll A union of types over all values of a type parameter. UnionAll is used to describe parametric types where the values of some parameters are not known. Examples julia> typeof(Vector) UnionAll julia> typeof(Vector{Int}) DataType source Core.TupleType Tuple{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. source Core.NamedTupleType NamedTuple NamedTuples are, as their name suggests, named Tuples. That is, they're a tuple-like collection of values, where each entry has a unique name, represented as a Symbol. Like Tuples, NamedTuples are immutable; neither the names nor the values can be modified in place after construction. 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]. A tuple of the names can be obtained using keys, and a tuple of the values can be obtained using values. Note Iteration over NamedTuples 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> 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,) Julia 1.5 Implicit names from identifiers and dot expressions are available as of Julia 1.5. source Base.@NamedTupleMacro @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}} Julia 1.5 This macro is available as of Julia 1.5. source Base.ValType Val(c) Return Val{c}(), which contains no run-time data. Types like this can be used to pass the information between functions through the value c, which must be an isbits value or a Symbol. The intent of this construct is to be able to dispatch on constants directly (at compile time) without having to test the value of the constant at run time. Examples julia> f(::Val{true}) = "Good" f (generic function with 1 method) julia> f(::Val{false}) = "Bad" f (generic function with 2 methods) julia> f(Val(true)) "Good" source Core.VarargType Vararg{T,N} The last parameter of a tuple type Tuple can be the special type Vararg, which denotes any number of trailing elements. The type Vararg{T,N} corresponds to exactly N elements of type T. 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.) Examples julia> mytupletype = Tuple{AbstractString, Vararg{Int}} Tuple{AbstractString, Vararg{Int64, N} where N} 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 source Base.isnothingFunction isnothing(x) Return true if x === nothing, and return false if not. Julia 1.1 This function requires at least Julia 1.1. source Base.SomeType Some{T} A wrapper type used in Union{Some{T}, Nothing} to distinguish between the absence of a value (nothing) and the presence of a nothing value (i.e. Some(nothing)). Use something to access the value wrapped by a Some object. source Base.somethingFunction something(x, y...) 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. 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 source Base.Enums.@enumMacro @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)
source
Core.ExprType
Expr(head::Symbol, args...)

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

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

Examples

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

julia> dump(:(a ? b : c))
Expr
args: Array{Any}((3,))
1: Symbol a
2: Symbol b
3: Symbol c
source
Core.SymbolType
Symbol

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

julia> :name
:name

julia> typeof(:name)
Symbol

julia> x = 42
42

julia> eval(:x)
42

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

Symbols are immutable and should be compared using ===. The implementation re-uses the same object for all Symbols with the same name, so comparison tends to be efficient (it can just compare pointers).

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

source
Core.SymbolMethod
Symbol(x...) -> Symbol

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

Examples

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

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

## Generic Functions

Core.FunctionType
Function

Abstract type of all functions.

Examples

julia> isa(+, Function)
true

julia> typeof(sin)
typeof(sin)

julia> ans <: Function
true
source
Base.hasmethodFunction
hasmethod(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.

Julia 1.2

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
source
Core.applicableFunction
applicable(f, args...) -> Bool

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

See also hasmethod.

Examples

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

julia> applicable(f, 1)
false

julia> applicable(f, 1, 2)
true
source
Core.invokeFunction
invoke(f, argtypes::Type, args...; kwargs...)

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

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

Examples

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

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

julia> f(2)
5

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

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

julia> f1(1)
Integer

julia> f2(1)
Integer

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

julia> invoke(f2, Tuple{Real}, 1)
Integer
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Base.invokelatestFunction
invokelatest(f, args...; kwargs...)

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

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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
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Base.:∘Function
f ∘ g

Compose functions: i.e. (f ∘ g)(args...) means f(g(args...)). 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.

Julia 1.4

Multiple function composition requires at least Julia 1.4.

Julia 1.5

Composition of one function ∘(f) requires at least Julia 1.5.

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.

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Base.ComposedFunctionType
ComposedFunction{Outer,Inner} <: Function

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

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

The preferred way to construct 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
Julia 1.6

ComposedFunction requires at least Julia 1.6. In earlier versions ∘ returns an anonymous function instead.

See also ∘.

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## Syntax

Core.evalFunction
Core.eval(m::Module, expr)

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

source
Base.MainInclude.evalFunction
eval(expr)

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

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Base.@evalMacro
@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.

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Base.escFunction
esc(e)

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

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Base.@inboundsMacro
@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 = 1:length(A)
@inbounds r += A[i]
end
return r
end
Warning

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.

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Base.@boundscheckMacro
@boundscheck(blk)

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

Note

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]"
Warning

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 size. Therefore, @boundscheck annotations should only be added to a getindex or setindex! implementation after you are certain its behavior is correct.

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Base.@inlineMacro
@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
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Base.@noinlineMacro
@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
Note

If the function is trivial (for example returning a constant) it might get inlined anyway.

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Base.@nospecializeMacro
@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
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Base.@gensymMacro
@gensym

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

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var"name"Keyword
var

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

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

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

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

Julia 1.3

This syntax requires at least Julia 1.3.

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Base.@gotoMacro
@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.

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Base.SimdLoop.@simdMacro
@simd

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

Warning

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 the ifelse function instead of ?: in the loop if it is safe to do so.
• Accesses must have a stride pattern and cannot be "gathers" (random-index reads) or "scatters" (random-index writes).
• The stride should be unit stride.
Note

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.
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Base.@generatedMacro
@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"
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Base.@pureMacro
@pure ex
@pure(ex)

@pure gives the compiler a hint for the definition of a pure function, helping for type inference.

A pure function can only depend on immutable information. This also means a @pure function cannot use any global mutable state, including generic functions. Calls to generic functions depend on method tables which are mutable global state. Use with caution, incorrect @pure annotation of a function may introduce hard to identify bugs. Double check for calls to generic functions. This macro is intended for internal compiler use and may be subject to changes.

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Base.@deprecateMacro
@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.

Julia 1.5

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)
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## Missing Values

Base.coalesceFunction
coalesce(x, y...)

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

See also something.

Examples

julia> coalesce(missing, 1)
1

julia> coalesce(1, missing)
1

julia> coalesce(nothing, 1)  # returns nothing

julia> coalesce(missing, missing)
missing
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Base.skipmissingFunction
skipmissing(itr)

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

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

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
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Base.nonmissingtypeFunction
nonmissingtype(T::Type)

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

Examples

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

julia> nonmissingtype(Any)
Any
Julia 1.3

This function is exported as of Julia 1.3.

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## System

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

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

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.

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Base.devnullConstant
devnull

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

run(pipeline(cat test.txt, devnull))
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Base.successFunction
success(command)

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

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

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

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Base.ignorestatusFunction
ignorestatus(command)

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

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Base.detachFunction
detach(command)

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

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

For any keywords that are not specified, the current settings from cmd are used. Normally, to create a Cmd object in the first place, one uses backticks, e.g.

Cmd(echo "Hello world", ignorestatus=true, detach=false)
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Base.setenvFunction
setenv(command::Cmd, env; dir="")

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

The dir keyword argument can be used to specify a working directory for the command.

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Base.withenvFunction
withenv(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.

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Base.pipelineMethod
pipeline(from, to, ...)

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

Examples:

run(pipeline(ls, grep xyz))
run(pipeline(ls, "out.txt"))
run(pipeline("out.txt", grep xyz))
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Base.pipelineMethod
pipeline(command; stdin, stdout, stderr, append=false)

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

Examples:

run(pipeline(dothings, stdout="out.txt", stderr="errs.txt"))
run(pipeline(update, stdout="log.txt", append=true))
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Base.Libc.getpidFunction
getpid(process) -> Int32

Get the child process ID, if it still exists.

Julia 1.1

This function requires at least Julia 1.1.

source
getpid() -> Int32

Get Julia's process ID.

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Base.Libc.timeMethod
time()

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

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Base.time_nsFunction
time_ns()

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

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Base.@timeMacro
@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 ....

Note

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
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Base.@timevMacro
@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.

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
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Base.@timedMacro
@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 ....

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
Julia 1.5

The return type of this macro was changed from Tuple to NamedTuple in Julia 1.5.

source
Base.@elapsedMacro
@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 ....

julia> @elapsed sleep(0.3)
0.301391426
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Base.@allocatedMacro
@allocated

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

julia> @allocated rand(10^6)
8000080
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Base.EnvDictType
EnvDict() -> EnvDict

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

source
Base.ENVConstant
ENV

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

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

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Base.Sys.isbsdFunction
Sys.isbsd([os])

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

Note

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().

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Base.Sys.isfreebsdFunction
Sys.isfreebsd([os])

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

Note

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.

Julia 1.1

This function requires at least Julia 1.1.

source
Base.Sys.isopenbsdFunction
Sys.isopenbsd([os])

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

Note

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.

Julia 1.1

This function requires at least Julia 1.1.

source
Base.Sys.isnetbsdFunction
Sys.isnetbsd([os])

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

Note

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.

Julia 1.1

This function requires at least Julia 1.1.

source
Base.Sys.isdragonflyFunction
Sys.isdragonfly([os])

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

Note

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.

Julia 1.1

This function requires at least Julia 1.1.

source
Base.Sys.windows_versionFunction
Sys.windows_version()

Return the version number for the Windows NT Kernel as a VersionNumber, i.e. v"major.minor.build", or v"0.0.0" if this is not running on Windows.

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Base.@staticMacro
@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.

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## Versioning

Base.VersionNumberType
VersionNumber

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"
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## Errors

Base.errorFunction
error(message::AbstractString)

Raise an ErrorException with the given message.

source
error(msg...)

Raise an ErrorException with the given message.

source
Base.rethrowFunction
rethrow()

Rethrow the current exception from within a catch block. The rethrown exception will continue propagation as if it had not been caught.

Note

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 catch_stack.

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Base.catch_stackFunction
catch_stack(task=current_task(); [inclue_bt=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 a Vector of (exception,backtrace) pairs, or a Vector of exceptions if include_bt is false.

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.

Julia 1.1

This function is experimental in Julia 1.1 and will likely be renamed in a future release (see https://github.com/JuliaLang/julia/pull/29901).

source
Base.@assertMacro
@assert cond [text]

Throw an AssertionError if cond is false. Preferred syntax for writing assertions. Message text is optionally displayed upon assertion failure.

Warning

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?"
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Base.Experimental.register_error_hintFunction
Experimental.register_error_hint(handler, exceptiontype)

Register a "hinting" function handler(io, exception) that can suggest potential ways for users to circumvent errors. handler should examine exception to see whether the conditions appropriate for a hint are met, and if so generate output to io. Packages should call register_error_hint from within their __init__ function.

For specific exception types, handler is required to accept additional arguments:

• MethodError: provide handler(io, exc::MethodError, argtypes, kwargs), which splits the combined arguments into positional and keyword arguments.

When issuing a hint, the output should typically start with \n.

If you define custom exception types, your showerror method can support hints by calling Experimental.show_error_hints.

Example

julia> module Hinter

only_int(x::Int)      = 1
any_number(x::Number) = 2

function __init__()
Base.Experimental.register_error_hint(MethodError) do io, exc, argtypes, kwargs
if exc.f == only_int
# Color is not necessary, this is just to show it's possible.
print(io, "\nDid you mean to call ")
printstyled(io, "any_number?", color=:cyan)
end
end
end

end

Then if you call Hinter.only_int on something that isn't an Int (thereby triggering a MethodError), it issues the hint:

julia> Hinter.only_int(1.0)
ERROR: MethodError: no method matching only_int(::Float64)
Did you mean to call any_number?
Closest candidates are:
...
Julia 1.5

Custom error hints are available as of Julia 1.5.

Warning

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.

source
Base.Experimental.show_error_hintsFunction
Experimental.show_error_hints(io, ex, args...)

Invoke all handlers from Experimental.register_error_hint for the particular exception type typeof(ex). args must contain any other arguments expected by the handler for that type.

Julia 1.5

Custom error hints are available as of Julia 1.5.

Warning

This interface is experimental and subject to change or removal without notice.

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Core.ArgumentErrorType
ArgumentError(msg)

The parameters to a function call do not match a valid signature. Argument msg is a descriptive error string.

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Core.AssertionErrorType
AssertionError([msg])

The asserted condition did not evaluate to true. Optional argument msg is a descriptive error string.

Examples

julia> @assert false "this is not true"
ERROR: AssertionError: this is not true

AssertionError is usually thrown from @assert.

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Core.BoundsErrorType
BoundsError([a],[i])

An indexing operation into an array, a, tried to access an out-of-bounds element at index i.

Examples

julia> A = fill(1.0, 7);

julia> A[8]
ERROR: BoundsError: attempt to access 7-element Vector{Float64} at index [8]

julia> B = fill(1.0, (2,3));

julia> B[2, 4]
ERROR: BoundsError: attempt to access 2×3 Matrix{Float64} at index [2, 4]

julia> B[9]
ERROR: BoundsError: attempt to access 2×3 Matrix{Float64} at index [9]

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Base.CompositeExceptionType
CompositeException

Wrap a Vector of exceptions thrown by a Task (e.g. generated from a remote worker over a channel or an asynchronously executing local I/O write or a remote worker under pmap) with information about the series of exceptions. For example, if a group of workers are executing several tasks, and multiple workers fail, the resulting CompositeException will contain a "bundle" of information from each worker indicating where and why the exception(s) occurred.

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Base.DimensionMismatchType
DimensionMismatch([msg])

The objects called do not have matching dimensionality. Optional argument msg is a descriptive error string.

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Core.DivideErrorType
DivideError()

Integer division was attempted with a denominator value of 0.

Examples

julia> 2/0
Inf

julia> div(2, 0)
ERROR: DivideError: integer division error
Stacktrace:
[...]
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Core.DomainErrorType
DomainError(val)
DomainError(val, msg)

The argument val to a function or constructor is outside the valid domain.

Examples

julia> sqrt(-1)
ERROR: DomainError with -1.0:
sqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).
Stacktrace:
[...]
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Core.ErrorExceptionType
ErrorException(msg)

Generic error type. The error message, in the .msg field, may provide more specific details.

Examples

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

julia> ex.msg
"I've done a bad thing"
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Core.InexactErrorType
InexactError(name::Symbol, T, val)

Cannot exactly convert val to type T in a method of function name.

Examples

julia> convert(Float64, 1+2im)
ERROR: InexactError: Float64(1 + 2im)
Stacktrace:
[...]
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Core.InterruptExceptionType
InterruptException()

The process was stopped by a terminal interrupt (CTRL+C).

Note that, in Julia script started without -i (interactive) option, InterruptException is not thrown by default. Calling Base.exit_on_sigint(false) in the script can recover the behavior of the REPL. Alternatively, a Julia script can be started with

julia -e "include(popfirst!(ARGS))" script.jl

to let InterruptException be thrown by CTRL+C during the execution.

source
Base.KeyErrorType
KeyError(key)

An indexing operation into an AbstractDict (Dict) or Set like object tried to access or delete a non-existent element.

source
Core.MethodErrorType
MethodError(f, args)

A method with the required type signature does not exist in the given generic function. Alternatively, there is no unique most-specific method.

source
Base.ProcessFailedExceptionType
ProcessFailedException

Indicates problematic exit status of a process. When running commands or pipelines, this is thrown to indicate a nonzero exit code was returned (i.e. that the invoked process failed).

source
Base.SystemErrorType
SystemError(prefix::AbstractString, [errno::Int32])

A system call failed with an error code (in the errno global variable).

source
Core.TypeErrorType
TypeError(func::Symbol, context::AbstractString, expected::Type, got)

A type assertion failure, or calling an intrinsic function with an incorrect argument type.

source
Core.UndefKeywordErrorType
UndefKeywordError(var::Symbol)

The required keyword argument var was not assigned in a function call.

Examples

julia> function my_func(;my_arg)
return my_arg + 1
end
my_func (generic function with 1 method)

julia> my_func()
ERROR: UndefKeywordError: keyword argument my_arg not assigned
Stacktrace:
[1] my_func() at ./REPL[1]:2
[2] top-level scope at REPL[2]:1
source
Core.UndefRefErrorType
UndefRefError()

The item or field is not defined for the given object.

Examples

julia> struct MyType
a::Vector{Int}
MyType() = new()
end

julia> A = MyType()
MyType(#undef)

julia> A.a
Stacktrace:
[...]
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Core.UndefVarErrorType
UndefVarError(var::Symbol)

A symbol in the current scope is not defined.

Examples

julia> a
ERROR: UndefVarError: a not defined

julia> a = 1;

julia> a
1
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Core.InitErrorType
InitError(mod::Symbol, error)

An error occurred when running a module's __init__ function. The actual error thrown is available in the .error field.

source
Base.retryFunction
retry(f;  delays=ExponentialBackOff(), check=nothing) -> Function

Return an anonymous function that calls function f. If an exception arises, f is repeatedly called again, each time check returns true, after waiting the number of seconds specified in delays. check should input delays's current state and the Exception.

Julia 1.2

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)
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Base.ExponentialBackOffType
ExponentialBackOff(; n=1, first_delay=0.05, max_delay=10.0, factor=5.0, jitter=0.1)

A Float64 iterator of length n whose elements exponentially increase at a rate in the interval factor * (1 ± jitter). The first element is first_delay and all elements are clamped to max_delay.

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## Events

Base.TimerMethod
Timer(callback::Function, delay; interval = 0)

Create a timer that wakes up tasks waiting for it (by calling wait on the timer object) and calls the function callback.

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 timer is only triggered once. The function callback is called with a single argument, the timer itself. When the timer is closed (by close) waiting tasks are woken with an error. Use isopen to check whether a timer is still active.

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
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Base.TimerType
Timer(delay; interval = 0)

Create a timer that wakes up tasks waiting for it (by calling wait on the timer object).

Waiting tasks are woken after an initial delay of delay seconds, and then repeating with the given interval in seconds. 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.

source
Base.AsyncConditionMethod
AsyncCondition(callback::Function)

Create a async condition that calls the given callback function. The callback is passed one argument, the async condition object itself.

source

## Reflection

Base.parentmoduleFunction
parentmodule(m::Module) -> Module

Get a module's enclosing Module. Main is its own parent.

Examples

julia> parentmodule(Main)
Main

Base
source
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
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parentmodule(f::Function) -> Module

Determine the module containing the (first) definition of a generic function.

source
parentmodule(f::Function, types) -> Module

Determine the module containing a given definition of a generic function.

source
Base.pathofMethod
pathof(m::Module)

Return the path of the m.jl file that was used to import module m, or nothing if m was not imported from a package.

Use dirname to get the directory part and basename to get the file name part of the path.

source
Base.modulerootFunction
moduleroot(m::Module) -> Module

Find the root module of a given module. This is the first module in the chain of parent modules of m which is either a registered root module or which is its own parent module.

source
Base.@__MODULE__Macro
@__MODULE__ -> Module

Get the Module of the toplevel eval, which is the Module code is currently being read from.

source
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.

source
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>.

source
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.

source
Base.fullnameFunction
fullname(m::Module)

Get the fully-qualified name of a module as a tuple of symbols. For example,

Examples

julia> fullname(Base.Iterators)
(:Base, :Iterators)

julia> fullname(Main)
(:Main,)
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Base.namesFunction
names(x::Module; all::Bool = false, imported::Bool = false)

Get an array of the names exported by a Module, excluding deprecated names. If all is true, then the list also includes non-exported names defined in the module, deprecated names, and compiler-generated names. If imported is true, then names explicitly imported from other modules are also included.

As a special case, all names defined in Main are considered "exported", since it is not idiomatic to explicitly export names from Main.

source
Core.nfieldsFunction
nfields(x) -> Int

Get the number of fields in the given object.

Examples

julia> a = 1//2;

julia> nfields(a)
2

julia> b = 1
1

julia> nfields(b)
0

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

julia> nfields(ex)
1

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

source
Base.isconstFunction
isconst(m::Module, s::Symbol) -> Bool

Determine whether a global is declared const in a given Module.

source
Base.nameofMethod
nameof(f::Function) -> Symbol

Get the name of a generic Function as a symbol. For anonymous functions, this is a compiler-generated name. For explicitly-declared subtypes of Function, it is the name of the function's type.

source
Base.functionlocMethod
functionloc(f::Function, types)

Returns a tuple (filename,line) giving the location of a generic Function definition.

source
Base.functionlocMethod
functionloc(m::Method)

Returns a tuple (filename,line) giving the location of a Method definition.

source

## Internals

Base.GC.gcFunction
GC.gc([full=true])

Perform garbage collection. The argument full determines the kind of collection: A full collection (default) sweeps all objects, which makes the next GC scan much slower, while an incremental collection may only sweep so-called young objects.

Warning

Excessive use will likely lead to poor performance.

source
Base.GC.enableFunction
GC.enable(on::Bool)

Control whether garbage collection is enabled using a boolean argument (true for enabled, false for disabled). Return previous GC state.

Warning

Disabling garbage collection should be used only with caution, as it can cause memory use to grow without bound.

source
Base.GC.@preserveMacro
GC.@preserve x1 x2 ... xn expr

Mark the objects x1, x2, ... as being in use during the evaluation of the expression expr. This is only required in unsafe code where expr implicitly uses memory or other resources owned by one of the xs.

Implicit use of x covers any indirect use of resources logically owned by x which the compiler cannot see. Some examples:

• Accessing memory of an object directly via a Ptr
• Passing a pointer to x to ccall
• Using resources of x which would be cleaned up in the finalizer.

@preserve should generally not have any performance impact in typical use cases where it briefly extends object lifetime. In implementation, @preserve has effects such as protecting dynamically allocated objects from garbage collection.

Examples

When loading from a pointer with unsafe_load, the underlying object is implicitly used, for example x is implicitly used by unsafe_load(p) in the following:

julia> let
x = Ref{Int}(101)
p = Base.unsafe_convert(Ptr{Int}, x)
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
source
Base.GC.safepointFunction
GC.safepoint()

Inserts a point in the program where garbage collection may run. This can be useful in rare cases in multi-threaded programs where some threads are allocating memory (and hence may need to run GC) but other threads are doing only simple operations (no allocation, task switches, or I/O). Calling this function periodically in non-allocating threads allows garbage collection to run.

Julia 1.4

This function is available as of Julia 1.4.

source
Base.Meta.parseMethod
parse(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 index of the first character to start parsing. 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("x = 3, y = 5", 7)
(:(y = 5), 13)

julia> Meta.parse("x = 3, y = 5", 5)
(:((3, y) = 5), 13)
source
Base.Meta.parseMethod
parse(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.\"")))
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Base.macroexpandFunction
macroexpand(m::Module, x; recursive=true)

Take the expression x and return an equivalent expression with all macros removed (expanded) for executing in module m. The recursive keyword controls whether deeper levels of nested macros are also expanded. This is demonstrated in the example below:

julia> module M
macro m1()
42
end
macro m2()
:(@m1())
end
end
M

julia> macroexpand(M, :(@m2()), recursive=true)
42

julia> macroexpand(M, :(@m2()), recursive=false)
:(#= REPL[16]:6 =# M.@m1)
source
Base.@macroexpandMacro
@macroexpand

Return equivalent expression with all macros removed (expanded).

There are differences between @macroexpand and macroexpand.

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.

source
Base.code_loweredFunction
code_lowered(f, types; generated=true, debuginfo=:default)

Return an array of the lowered forms (IR) for the methods matching the given generic function and type signature.

If generated is false, the returned CodeInfo instances will correspond to fallback implementations. An error is thrown if no fallback implementation exists. If generated is true, these CodeInfo instances will correspond to the method bodies yielded by expanding the generators.

The keyword debuginfo controls the amount of code metadata present in the output.

Note that an error will be thrown if types are not leaf types when generated is true and any of the corresponding methods are an @generated method.

source
Base.code_typedFunction
code_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.

source
Base.precompileFunction
precompile(f, args::Tuple{Vararg{Any}})

Compile the given function f for the argument tuple (of types) args, but do not execute it.

source

## Meta

Base.Meta.quotFunction
Meta.quot(ex)::Expr

Quote expression ex to produce an expression with head quote. This can for instance be used to represent objects of type Expr in the AST. See also the manual section about QuoteNode.

Examples

julia> eval(Meta.quot(:x))
:x

julia> dump(Meta.quot(:x))
Expr
args: Array{Any}((1,))
1: Symbol x

julia> eval(Meta.quot(:(1+2)))
:(1 + 2)
source
Base.Meta.isexprFunction
Meta.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 Symbols. 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
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Base.isidentifierFunction
 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 \0s), 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)
source
Base.isoperatorFunction
isoperator(s::Symbol)

Return true if the symbol can be used as an operator, false otherwise.

Examples

julia> Meta.isoperator(:+), Meta.isoperator(:f)
(true, false)
source
Base.isunaryoperatorFunction
isunaryoperator(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)
source
Base.isbinaryoperatorFunction
isbinaryoperator(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)
source
Base.Meta.show_sexprFunction
Meta.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))
source