Essentials
Introduction
Julia Base contains a range of functions and macros appropriate for performing scientific and numerical computing, but is also as broad as those of many general purpose programming languages. Additional functionality is available from a growing collection of available packages. Functions are grouped by topic below.
Some general notes:
- To use module functions, use
import Module
to import the module, andModule.fn(x)
to use the functions. - Alternatively,
using Module
will import all exportedModule
functions into the current namespace. - By convention, function names ending with an exclamation point (
!
) modify their arguments. Some functions have both modifying (e.g.,sort!
) and non-modifying (sort
) versions.
Getting Around
Base.exit
— Function.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
.
Base.atexit
— Function.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.
Base.isinteractive
— Function.isinteractive() -> Bool
Determine whether Julia is running an interactive session.
Base.summarysize
— Function.Base.summarysize(obj; exclude=Union{...}, chargeall=Union{...}) -> Int
Compute the amount of memory used by all unique objects reachable from the argument.
Keyword Arguments
exclude
: specifies the types of objects to exclude from the traversal.chargeall
: specifies the types of objects to always charge the size of all of their fields, even if those fields would normally be excluded.
Base.require
— Function.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.
Base.compilecache
— Function.Base.compilecache(module::PkgId)
Creates a precompiled cache file for a module and all of its dependencies. This can be used to reduce package load times. Cache files are stored in DEPOT_PATH[1]/compiled
. See Module initialization and precompilation for important notes.
Base.__precompile__
— Function.__precompile__(isprecompilable::Bool)
Specify whether the file calling this function is precompilable, defaulting to true
. If a module or file is not safely precompilable, it should call __precompile__(false)
in order to throw an error if Julia attempts to precompile it.
Base.include
— Function.Base.include([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 1-argument 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.
Base.MainInclude.include
— Function.include(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 1-argument 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.
Use Base.include
to evaluate a file into another module.
Base.include_string
— Function.include_string(m::Module, code::AbstractString, filename::AbstractString="string")
Like include
, except reads code from the given string rather than from a file.
Base.include_dependency
— Function.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.
Base.which
— Method.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.
Base.methods
— Function.methods(f, [types])
Returns the method table for f
.
If types
is specified, returns an array of methods whose types match.
Base.@show
— Macro.@show
Show an expression and result, returning the result.
ans
— Keyword.ans
A variable referring to the last computed value, automatically set at the interactive prompt.
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.
module
— Keyword.module
module
declares a Module
, which is a separate global variable workspace. Within a module, you can control which names from other modules are visible (via importing), and specify which of your names are intended to be public (via exporting). Modules allow you to create top-level definitions without worrying about name conflicts when your code is used together with somebody else’s. See the manual section about modules for more details.
Examples
module Foo
import Base.show
export MyType, foo
struct MyType
x
end
bar(x) = 2x
foo(a::MyType) = bar(a.x) + 1
show(io::IO, a::MyType) = print(io, "MyType $(a.x)")
end
export
— Keyword.export
export
is used within modules to tell Julia which functions should be made available to the user. For example: export foo
makes the name foo
available when using
the module. See the manual section about modules for details.
import
— Keyword.import
import Foo
will load the module or package Foo
. Names from the imported Foo
module can be accessed with dot syntax (e.g. Foo.foo
to access the name foo
). See the manual section about modules for details.
using
— Keyword.using
using Foo
will load the module or package Foo
and make its export
ed names available for direct use. Names can also be used via dot syntax (e.g. Foo.foo
to access the name foo
), whether they are export
ed or not. See the manual section about modules for details.
baremodule
— Keyword.baremodule
baremodule
declares a module that does not contain using Base
or a definition of eval
. It does still import Core
.
function
— Keyword.function
Functions are defined with the function
keyword:
function add(a, b)
return a + b
end
Or the short form notation:
add(a, b) = a + b
The use of the return
keyword is exactly the same as in other languages, but is often optional. A function without an explicit return
statement will return the last expression in the function body.
macro
— Keyword.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.
Examples
julia> macro sayhello(name)
return :( println("Hello, ", $name, "!") )
end
@sayhello (macro with 1 method)
julia> @sayhello "Charlie"
Hello, Charlie!
return
— Keyword.return
return x
causes the enclosing function to exit early, passing the given value x
back to its caller. return
by itself with no value is equivalent to return nothing
(see nothing
).
function compare(a, b)
a == b && return "equal to"
a < b ? "less than" : "greater than"
end
In general you can place a return
statement anywhere within a function body, including within deeply nested loops or conditionals, but be careful with do
blocks. For example:
function test1(xs)
for x in xs
iseven(x) && return 2x
end
end
function test2(xs)
map(xs) do x
iseven(x) && return 2x
x
end
end
In the first example, the return breaks out of test1
as soon as it hits an even number, so test1([5,6,7])
returns 12
.
You might expect the second example to behave the same way, but in fact the return
there only breaks out of the inner function (inside the do
block) and gives a value back to map
. test2([5,6,7])
then returns [5,12,7]
.
When used in a top-level expression (i.e. outside any function), return
causes the entire current top-level expression to terminate early.
do
— Keyword.do
Create an anonymous function and pass it as the first argument to a function call. For example:
map(1:10) do x
2x
end
is equivalent to map(x->2x, 1:10)
.
Use multiple arguments like so:
map(1:10, 11:20) do x, y
x + y
end
begin
— Keyword.end
— Keyword.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
let
— Keyword.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.
if
— Keyword.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.
for
— Keyword.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
while
— Keyword.while
while
loops repeatedly evaluate a conditional expression, and continue evaluating the body of the while loop as long as the expression remains true. If the condition expression is false when the while loop is first reached, the body is never evaluated.
Examples
julia> i = 1
1
julia> while i < 5
println(i)
global i += 1
end
1
2
3
4
break
— Keyword.break
Break out of a loop immediately.
Examples
julia> i = 0
0
julia> while true
global i += 1
i > 5 && break
println(i)
end
1
2
3
4
5
continue
— Keyword.continue
Skip the rest of the current loop iteration.
Examples
julia> for i = 1:6
iseven(i) && continue
println(i)
end
1
3
5
try
— Keyword.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
The syntax catch e
(where e
is any variable) assigns the thrown exception object to the given variable within the catch
block.
The power of the try
/catch
construct lies in the ability to unwind a deeply nested computation immediately to a much higher level in the stack of calling functions.
finally
— Keyword.finally
Run some code when a given block of code exits, regardless of how it exits. For example, here is how we can guarantee that an opened file is closed:
f = open("file")
try
operate_on_file(f)
finally
close(f)
end
When control leaves the try
block (for example, due to a return
, or just finishing normally), close(f)
will be executed. If the try
block exits due to an exception, the exception will continue propagating. A catch
block may be combined with try
and finally
as well. In this case the finally
block will run after catch
has handled the error.
quote
— Keyword.quote
quote
creates multiple expression objects in a block without using the explicit Expr
constructor. For example:
ex = quote
x = 1
y = 2
x + y
end
Unlike the other means of quoting, :( ... )
, this form introduces QuoteNode
elements to the expression tree, which must be considered when directly manipulating the tree. For other purposes, :( ... )
and quote .. end
blocks are treated identically.
local
— Keyword.local
local
introduces a new local variable. See the manual section on variable scoping for more information.
Examples
julia> function foo(n)
x = 0
for i = 1:n
local x # introduce a loop-local x
x = i
end
x
end
foo (generic function with 1 method)
julia> foo(10)
0
global
— Keyword.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
const
— Keyword.const
const
is used to declare global variables whose values will not change. In almost all code (and particularly performance sensitive code) global variables should be declared constant in this way.
const x = 5
Multiple variables can be declared within a single const
:
const y, z = 7, 11
Note that const
only applies to one =
operation, therefore const x = y = 1
declares x
to be constant but not y
. On the other hand, const x = const y = 1
declares both x
and y
constant.
Note that "constant-ness" does not extend into mutable containers; only the association between a variable and its value is constant. If x
is an array or dictionary (for example) you can still modify, add, or remove elements.
In some cases changing the value of a const
variable gives a warning instead of an error. However, this can produce unpredictable behavior or corrupt the state of your program, and so should be avoided. This feature is intended only for convenience during interactive use.
struct
— Keyword.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
struct
s are immutable by default; an instance of one of these types cannot be modified after construction. Use mutable struct
instead to declare a type whose instances can be modified.
See the manual section on Composite Types for more details, such as how to define constructors.
mutable struct
— Keyword.mutable struct
mutable struct
is similar to struct
, but additionally allows the fields of the type to be set after construction. See the manual section on Composite Types for more information.
abstract type
— Keyword.abstract type
abstract type
declares a type that cannot be instantiated, and serves only as a node in the type graph, thereby describing sets of related concrete types: those concrete types which are their descendants. Abstract types form the conceptual hierarchy which makes Julia’s type system more than just a collection of object implementations. For example:
abstract type Number end
abstract type Real <: Number end
Number
has no supertype, whereas Real
is an abstract subtype of Number
.
primitive type
— Keyword.primitive type
primitive type
declares a concrete type whose data consists only of a series of bits. Classic examples of primitive types are integers and floating-point values. Some example built-in primitive type declarations:
primitive type Char 32 end
primitive type Bool <: Integer 8 end
The number after the name indicates how many bits of storage the type requires. Currently, only sizes that are multiples of 8 bits are supported. The Bool
declaration shows how a primitive type can be optionally declared to be a subtype of some supertype.
where
— Keyword.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 Vector
s where the element type is some kind of Real
number.
The variable bound defaults to Any
if it is omitted:
Vector{T} where T # short for `where T<:Any`
Variables can also have lower bounds:
Vector{T} where T>:Int
Vector{T} where Int<:T<:Real
There is also a concise syntax for nested where
expressions. For example, this:
Pair{T, S} where S<:Array{T} where T<:Number
can be shortened to:
Pair{T, S} where {T<:Number, S<:Array{T}}
This form is often found on method signatures.
Note that in this form, the variables are listed outermost-first. This matches the order in which variables are substituted when a type is "applied" to parameter values using the syntax T{p1, p2, ...}
.
...
— Keyword....
The "splat" operator, ...
, represents a sequence of arguments. ...
can be used in function definitions, to indicate that the function accepts an arbitrary number of arguments. ...
can also be used to apply a function to a sequence of arguments.
Examples
julia> add(xs...) = reduce(+, xs)
add (generic function with 1 method)
julia> add(1, 2, 3, 4, 5)
15
julia> add([1, 2, 3]...)
6
julia> add(7, 1:100..., 1000:1100...)
111107
;
— Keyword.;
;
has a similar role in Julia as in many C-like languages, and is used to delimit the end of the previous statement. ;
is not necessary after new lines, but can be used to separate statements on a single line or to join statements into a single expression. ;
is also used to suppress output printing in the REPL and similar interfaces.
Examples
julia> function foo()
x = "Hello, "; x *= "World!"
return x
end
foo (generic function with 1 method)
julia> bar() = (x = "Hello, Mars!"; return x)
bar (generic function with 1 method)
julia> foo();
julia> bar()
"Hello, Mars!"
Base Modules
Base
— Keyword.The base library of Julia.
Base.Broadcast
— Module.Base.Broadcast
Module containing the broadcasting implementation.
Base.Docs
— Module.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.
Base.Iterators
— Module.Methods for working with Iterators.
Base.Libc
— Module.Interface to libc, the C standard library.
Base.Meta
— Module.Convenience functions for metaprogramming.
Base.StackTraces
— Module.Tools for collecting and manipulating stack traces. Mainly used for building errors.
Base.Sys
— Module.Provide methods for retrieving information about hardware and the operating system.
Base.Threads
— Module.Experimental multithreading support.
Base.GC
— Module.Base.GC
Module with garbage collection utilities.
All Objects
Core.:===
— Function.===(x,y) -> Bool
≡(x,y) -> Bool
Determine whether x
and y
are identical, in the sense that no program could distinguish them. First the types of x
and y
are compared. If those are identical, mutable objects are compared by address in memory and immutable objects (such as numbers) are compared by contents at the bit level. This function is sometimes called "egal". It always returns a Bool
value.
Examples
julia> a = [1, 2]; b = [1, 2];
julia> a == b
true
julia> a === b
false
julia> a === a
true
Core.isa
— Function.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
Base.isequal
— Function.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
isequal(x)
Create a function that compares its argument to x
using isequal
, i.e. a function equivalent to y -> isequal(y, x)
.
The returned function is of type Base.Fix2{typeof(isequal)}
, which can be used to implement specialized methods.
Base.isless
— Function.isless(x, y)
Test whether x
is less than y
, according to a canonical total order. 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 canonical total order should implement this function. Numeric types only need to implement it if they have special values such as NaN
. Types with a canonical partial order should implement <
.
Core.ifelse
— Function.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
Core.typeassert
— Function.typeassert(x, type)
Throw a TypeError unless x isa type
. The syntax x::type
calls this function.
Core.typeof
— Function.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)
Array{Float64,2}
Core.tuple
— Function.tuple(xs...)
Construct a tuple of the given objects.
Examples
julia> tuple(1, 'a', pi)
(1, 'a', π = 3.1415926535897...)
Base.ntuple
— Function.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)
Base.objectid
— Function.objectid(x)
Get a hash value for x
based on object identity. objectid(x)==objectid(y)
if x === y
.
Base.hash
— Function.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.
Base.finalizer
— Function.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.
Base.finalize
— Function.finalize(x)
Immediately run finalizers registered for object x
.
Base.copy
— Function.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.
Base.deepcopy
— Function.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.
As a special case, functions can only be actually deep-copied if they are anonymous, otherwise they are just copied. The difference is only relevant in the case of closures, i.e. functions which may contain hidden internal references.
While it isn't normally necessary, user-defined types can override the default deepcopy
behavior by defining a specialized version of the function deepcopy_internal(x::T, dict::IdDict)
(which shouldn't otherwise be used), where T
is the type to be specialized for, and dict
keeps track of objects copied so far within the recursion. Within the definition, deepcopy_internal
should be used in place of deepcopy
, and the dict
variable should be updated as appropriate before returning.
Base.getproperty
— Function.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!
.
Base.setproperty!
— Function.setproperty!(value, name::Symbol, x)
The syntax a.b = c
calls setproperty!(a, :b, c)
.
See also propertynames
and getproperty
.
Base.propertynames
— Function.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.
Core.getfield
— Function.getfield(value, name::Symbol)
Extract a named field from a value
of composite type. See also getproperty
.
Examples
julia> a = 1//2
1//2
julia> getfield(a, :num)
1
julia> a.num
1
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
Core.isdefined
— Function.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.
Examples
julia> isdefined(Base, :sum)
true
julia> isdefined(Base, :NonExistentMethod)
false
julia> a = 1//2;
julia> isdefined(a, 2)
true
julia> isdefined(a, 3)
false
julia> isdefined(a, :num)
true
julia> isdefined(a, :numerator)
false
Base.@isdefined
— Macro.@isdefined s -> Bool
Tests whether variable s
is defined in the current scope.
Examples
julia> function f()
println(@isdefined x)
x = 3
println(@isdefined x)
end
f (generic function with 1 method)
julia> f()
false
true
Base.convert
— Function.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
Base.promote
— Function.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)
Base.oftype
— Function.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
Base.widen
— Function.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
Base.identity
— Function.identity(x)
The identity function. Returns its argument.
Examples
julia> identity("Well, what did you expect?")
"Well, what did you expect?"
Properties of Types
Type relations
Base.supertype
— Function.supertype(T::DataType)
Return the supertype of DataType T
.
Examples
julia> supertype(Int32)
Signed
Core.:<:
— Function.<:(T1, T2)
Subtype operator: returns true
if and only if all values of type T1
are also of type T2
.
Examples
julia> Float64 <: AbstractFloat
true
julia> Vector{Int} <: AbstractArray
true
julia> Matrix{Float64} <: Matrix{AbstractFloat}
false
Base.:>:
— Function.>:(T1, T2)
Supertype operator, equivalent to T2 <: T1
.
Base.typejoin
— Function.typejoin(T, S)
Return the closest common ancestor of T
and S
, i.e. the narrowest type from which they both inherit.
Base.typeintersect
— Function.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.
Base.promote_type
— Function.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
Base.promote_rule
— Function.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.
Base.isdispatchtuple
— Function.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.
Declared structure
Base.isimmutable
— Function.isimmutable(v) -> Bool
Return true
iff value v
is immutable. See Mutable Composite Types for a discussion of immutability. Note that this function works on values, so if you give it a type, it will tell you that a value of DataType
is mutable.
Examples
julia> isimmutable(1)
true
julia> isimmutable([1,2])
false
Base.isabstracttype
— Function.Base.isabstracttype(T)
Determine whether type T
was declared as an abstract type (i.e. using the abstract
keyword).
Examples
julia> Base.isabstracttype(AbstractArray)
true
julia> Base.isabstracttype(Vector)
false
Base.isprimitivetype
— Function.Base.isprimitivetype(T) -> Bool
Determine whether type T
was declared as a primitive type (i.e. using the primitive
keyword).
Base.isstructtype
— Function.Base.isstructtype(T) -> Bool
Determine whether type T
was declared as a struct type (i.e. using the struct
or mutable struct
keyword).
Base.nameof
— Method.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
Base.fieldnames
— Function.fieldnames(x::DataType)
Get a tuple with the names of the fields of a DataType
.
Examples
julia> fieldnames(Rational)
(:num, :den)
Base.fieldname
— Function.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
Memory layout
Base.sizeof
— Method.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: argument is an abstract type; size is indeterminate
Stacktrace:
[...]
Base.isconcretetype
— Function.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
Base.isbits
— Function.isbits(x)
Return true
if x
is an instance of an isbitstype
type.
Base.isbitstype
— Function.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
Core.fieldtype
— Function.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
Base.fieldtypes
— Function.fieldtypes(T::Type)
The declared types of all fields in a composite DataType T
as a tuple.
This function requires at least Julia 1.1.
Examples
julia> struct Foo
x::Int64
y::String
end
julia> fieldtypes(Foo)
(Int64, String)
Base.fieldcount
— Function.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.
Base.fieldoffset
— Function.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 Array{Tuple{UInt64,Symbol,DataType},1}:
(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)
Base.datatype_alignment
— Function.Base.datatype_alignment(dt::DataType) -> Int
Memory allocation minimum alignment for instances of this type. Can be called on any isconcretetype
.
Base.datatype_haspadding
— Function.Base.datatype_haspadding(dt::DataType) -> Bool
Return whether the fields of instances of this type are packed in memory, with no intervening padding bytes. Can be called on any isconcretetype
.
Base.datatype_pointerfree
— Function.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
.
Special values
Base.typemin
— Function.typemin(T)
The lowest value representable by the given (real) numeric DataType T
.
Examples
julia> typemin(Float16)
-Inf16
julia> typemin(Float32)
-Inf32
Base.typemax
— Function.typemax(T)
The highest value representable by the given (real) numeric DataType
.
Examples
julia> typemax(Int8)
127
julia> typemax(UInt32)
0xffffffff
Base.floatmin
— Function.floatmin(T)
The smallest in absolute value non-subnormal value representable by the given floating-point DataType T
.
Base.floatmax
— Function.floatmax(T)
The highest finite value representable by the given floating-point DataType T
.
Examples
julia> floatmax(Float16)
Float16(6.55e4)
julia> floatmax(Float32)
3.4028235f38
Base.maxintfloat
— Function.maxintfloat(T=Float64)
The largest consecutive integer-valued floating-point number that is exactly represented in the given floating-point type T
(which defaults to Float64
).
That is, maxintfloat
returns the smallest positive integer-valued floating-point number n
such that n+1
is not exactly representable in the type T
.
When an Integer
-type value is needed, use Integer(maxintfloat(T))
.
maxintfloat(T, S)
The largest consecutive integer representable in the given floating-point type T
that also does not exceed the maximum integer representable by the integer type S
. Equivalently, it is the minimum of maxintfloat(T)
and typemax(S)
.
Base.eps
— Method.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
Base.eps
— Method.eps(x::AbstractFloat)
Return the unit in last place (ulp) of x
. This is the distance between consecutive representable floating point values at x
. In most cases, if the distance on either side of x
is different, then the larger of the two is taken, that is
eps(x) == max(x-prevfloat(x), nextfloat(x)-x)
The exceptions to this rule are the smallest and largest finite values (e.g. nextfloat(-Inf)
and prevfloat(Inf)
for Float64
), which round to the smaller of the values.
The rationale for this behavior is that eps
bounds the floating point rounding error. Under the default RoundNearest
rounding mode, if $y$ is a real number and $x$ is the nearest floating point number to $y$, then
Examples
julia> eps(1.0)
2.220446049250313e-16
julia> eps(prevfloat(2.0))
2.220446049250313e-16
julia> eps(2.0)
4.440892098500626e-16
julia> x = prevfloat(Inf) # largest finite Float64
1.7976931348623157e308
julia> x + eps(x)/2 # rounds up
Inf
julia> x + prevfloat(eps(x)/2) # rounds down
1.7976931348623157e308
Base.instances
— Function.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::Color = 0, blue::Color = 1, green::Color = 2)
Special Types
Core.Any
— Type.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.
Core.Union
— Type.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 Float64
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
Core.UnionAll
— Type.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
Core.Tuple
— Type.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.
Core.NamedTuple
— Type.NamedTuple
NamedTuple
s are, as their name suggests, named Tuple
s. That is, they're a tuple-like collection of values, where each entry has a unique name, represented as a Symbol
. Like Tuple
s, NamedTuple
s are immutable; neither the names nor the values can be modified in place after construction.
Accessing the value associated with a name in a named tuple can be done using field access syntax, e.g. x.a
, or using getindex
, e.g. x[:a]
. A tuple of the names can be obtained using keys
, and a tuple of the values can be obtained using values
.
Iteration over NamedTuple
s produces the values without the names. (See example below.) To iterate over the name-value pairs, use the pairs
function.
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 Array{Int64,1}:
1
2
julia> collect(pairs(x))
2-element Array{Pair{Symbol,Int64},1}:
:a => 1
:b => 2
Base.Val
— Type.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. The intent of this construct is to be able to dispatch on constants directly (at compile time) without having to test the value of the constant at run time.
Examples
julia> f(::Val{true}) = "Good"
f (generic function with 1 method)
julia> f(::Val{false}) = "Bad"
f (generic function with 2 methods)
julia> f(Val(true))
"Good"
Core.Vararg
— Type.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
Core.Nothing
— Type.Nothing
A type with no fields that is the type of nothing
.
Base.isnothing
— Function.isnothing(x)
Return true
if x === nothing
, and return false
if not.
This function requires at least Julia 1.1.
Base.Some
— Type.Base.something
— Function.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.
Examples
julia> something(nothing, 1)
1
julia> something(Some(1), nothing)
1
julia> something(missing, nothing)
missing
julia> something(nothing, nothing)
ERROR: ArgumentError: No value arguments present
Base.Enums.@enum
— Macro.@enum EnumName[::BaseType] value1[=x] value2[=y]
Create an Enum{BaseType}
subtype with name EnumName
and enum member values of value1
and value2
with optional assigned values of x
and y
, respectively. EnumName
can be used just like other types and enum member values as regular values, such as
Examples
julia> @enum Fruit apple=1 orange=2 kiwi=3
julia> f(x::Fruit) = "I'm a Fruit with value: $(Int(x))"
f (generic function with 1 method)
julia> f(apple)
"I'm a Fruit with value: 1"
julia> Fruit(1)
apple::Fruit = 1
Values can also be specified inside a begin
block, e.g.
@enum EnumName begin
value1
value2
end
BaseType
, which defaults to Int32
, must be a primitive subtype of Integer
. Member values can be converted between the enum type and BaseType
. read
and write
perform these conversions automatically.
To list all the instances of an enum use instances
, e.g.
julia> instances(Fruit)
(apple::Fruit = 1, orange::Fruit = 2, kiwi::Fruit = 3)
Generic Functions
Core.Function
— Type.Function
Abstract type of all functions.
julia> isa(+, Function)
true
julia> typeof(sin)
typeof(sin)
julia> ans <: Function
true
Base.hasmethod
— Function.hasmethod(f, Tuple type; 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
.
See also applicable
.
Examples
julia> hasmethod(length, Tuple{Array})
true
Core.applicable
— Function.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
Core.invoke
— Function.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).
Examples
julia> f(x::Real) = x^2;
julia> f(x::Integer) = 1 + invoke(f, Tuple{Real}, x);
julia> f(2)
5
Base.invokelatest
— Function.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.)
new
— Keyword.new
Special function available to inner constructors which created a new object of the type. See the manual section on Inner Constructor Methods for more information.
Base.:|>
— Function.|>(x, f)
Applies a function to the preceding argument. This allows for easy function chaining.
Examples
julia> [1:5;] |> x->x.^2 |> sum |> inv
0.01818181818181818
Base.:∘
— 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>
.
Examples
julia> map(uppercase∘first, ["apple", "banana", "carrot"])
3-element Array{Char,1}:
'A'
'B'
'C'
Syntax
Core.eval
— Function.Core.eval(m::Module, expr)
Evaluate an expression in the given module and return the result.
Base.MainInclude.eval
— Function.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.
Base.@eval
— Macro.@eval [mod,] ex
Evaluate an expression with values interpolated into it using eval
. If two arguments are provided, the first is the module to evaluate in.
Base.evalfile
— Function.evalfile(path::AbstractString, args::Vector{String}=String[])
Load the file using Base.include
, evaluate all expressions, and return the value of the last one.
Base.esc
— Function.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.
Base.@inbounds
— Macro.@inbounds(blk)
Eliminates array bounds checking within expressions.
In the example below the in-range check for referencing element i
of array A
is skipped to improve performance.
function sum(A::AbstractArray)
r = zero(eltype(A))
for i = 1:length(A)
@inbounds r += A[i]
end
return r
end
Using @inbounds
may return incorrect results/crashes/corruption for out-of-bounds indices. The user is responsible for checking it manually. Only use @inbounds
when it is certain from the information locally available that all accesses are in bounds.
Base.@boundscheck
— Macro.@boundscheck(blk)
Annotates the expression blk
as a bounds checking block, allowing it to be elided by @inbounds
.
The function in which @boundscheck
is written must be inlined into its caller in order for @inbounds
to have effect.
Examples
julia> @inline function g(A, i)
@boundscheck checkbounds(A, i)
return "accessing ($A)[$i]"
end;
julia> f1() = return g(1:2, -1);
julia> f2() = @inbounds return g(1:2, -1);
julia> f1()
ERROR: BoundsError: attempt to access 2-element UnitRange{Int64} at index [-1]
Stacktrace:
[1] throw_boundserror(::UnitRange{Int64}, ::Tuple{Int64}) at ./abstractarray.jl:455
[2] checkbounds at ./abstractarray.jl:420 [inlined]
[3] g at ./none:2 [inlined]
[4] f1() at ./none:1
[5] top-level scope
julia> f2()
"accessing (1:2)[-1]"
The @boundscheck
annotation allows you, as a library writer, to opt-in to allowing other code to remove your bounds checks with @inbounds
. As noted there, the caller must verify—using information they can access—that their accesses are valid before using @inbounds
. For indexing into your AbstractArray
subclasses, for example, this involves checking the indices against its size
. Therefore, @boundscheck
annotations should only be added to a getindex
or setindex!
implementation after you are certain its behavior is correct.
Base.@inline
— Macro.@inline
Give a hint to the compiler that this function is worth inlining.
Small functions typically do not need the @inline
annotation, as the compiler does it automatically. By using @inline
on bigger functions, an extra nudge can be given to the compiler to inline it. This is shown in the following example:
@inline function bigfunction(x)
#=
Function Definition
=#
end
Base.@noinline
— Macro.@noinline
Prevents the compiler from inlining 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
Base.@nospecialize
— Macro.@nospecialize
Applied to a function argument name, hints to the compiler that the method should not be specialized for different types of that argument, but instead to use precisely the declared type for each argument. This is only a hint for avoiding excess code generation. Can be applied to an argument within a formal argument list, or in the function body. When applied to an argument, the macro must wrap the entire argument expression. When used in a function body, the macro must occur in statement position and before any code.
When used without arguments, it applies to all arguments of the parent scope. In local scope, this means all arguments of the containing function. In global (top-level) scope, this means all methods subsequently defined in the current module.
Specialization can reset back to the default by using @specialize
.
function example_function(@nospecialize x)
...
end
function example_function(@nospecialize(x = 1), y)
...
end
function example_function(x, y, z)
@nospecialize x y
...
end
@nospecialize
f(y) = [x for x in y]
@specialize
Base.@specialize
— Macro.@specialize
Reset the specialization hint for an argument back to the default. For details, see @nospecialize
.
Base.gensym
— Function.gensym([tag])
Generates a symbol which will not conflict with other variable names.
Base.@gensym
— Macro.@gensym
Generates a gensym symbol for a variable. For example, @gensym x y
is transformed into x = gensym("x"); y = gensym("y")
.
Base.@goto
— Macro.@goto name
@goto name
unconditionally jumps to the statement at the location @label name
.
@label
and @goto
cannot create jumps to different top-level statements. Attempts cause an error. To still use @goto
, enclose the @label
and @goto
in a block.
Base.@label
— Macro.@label name
Labels a statement with the symbolic label name
. The label marks the end-point of an unconditional jump with @goto name
.
Base.SimdLoop.@simd
— Macro.@simd
Annotate a for
loop to allow the compiler to take extra liberties to allow loop re-ordering
This feature is experimental and could change or disappear in future versions of Julia. Incorrect use of the @simd
macro may cause unexpected results.
The object iterated over in a @simd for
loop should be a one-dimensional range. By using @simd
, you are asserting several properties of the loop:
- It is safe to execute iterations in arbitrary or overlapping order, with special consideration for reduction variables.
- Floating-point operations on reduction variables can be reordered, possibly causing different results than without
@simd
.
In many cases, Julia is able to automatically vectorize inner for loops without the use of @simd
. Using @simd
gives the compiler a little extra leeway to make it possible in more situations. In either case, your inner loop should have the following properties to allow vectorization:
- The loop must be an innermost loop
- The loop body must be straight-line code. Therefore,
@inbounds
is currently needed for all array accesses. The compiler can sometimes turn short&&
,||
, and?:
expressions into straight-line code if it is safe to evaluate all operands unconditionally. Consider using theifelse
function instead of?:
in the loop if it is safe to do so. - Accesses must have a stride pattern and cannot be "gathers" (random-index reads) or "scatters" (random-index writes).
- The stride should be unit stride.
The @simd
does not assert by default that the loop is completely free of loop-carried memory dependencies, which is an assumption that can easily be violated in generic code. If you are writing non-generic code, you can use @simd ivdep for ... end
to also assert that:
- There exists no loop-carried memory dependencies
- No iteration ever waits on a previous iteration to make forward progress.
Base.@polly
— Macro.@polly
Tells the compiler to apply the polyhedral optimizer Polly to a function.
Base.@generated
— Macro.@generated f
@generated(f)
@generated
is used to annotate a function which will be generated. In the body of the generated function, only types of arguments can be read (not the values). The function returns a quoted expression evaluated when the function is called. The @generated
macro should not be used on functions mutating the global scope or depending on mutable elements.
See Metaprogramming for further details.
Example:
julia> @generated function bar(x)
if x <: Integer
return :(x ^ 2)
else
return :(x)
end
end
bar (generic function with 1 method)
julia> bar(4)
16
julia> bar("baz")
"baz"
Base.@pure
— Macro.@pure ex
@pure(ex)
@pure
gives the compiler a hint for the definition of a pure function, helping for type inference.
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.
Missing Values
Base.Missing
— Type.Missing
A type with no fields whose singleton instance missing
is used to represent missing values.
Base.missing
— Constant.missing
The singleton instance of type Missing
representing a missing value.
Base.coalesce
— Function.coalesce(x, y...)
Return the first value in the arguments which is not equal to missing
, if any. Otherwise return missing
.
Examples
julia> coalesce(missing, 1)
1
julia> coalesce(1, missing)
1
julia> coalesce(nothing, 1) # returns `nothing`
julia> coalesce(missing, missing)
missing
Base.ismissing
— Function.ismissing(x)
Indicate whether x
is missing
.
Base.skipmissing
— Function.skipmissing(itr)
Return an iterator over the elements in itr
skipping missing
values.
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> sum(skipmissing([1, missing, 2]))
3
julia> collect(skipmissing([1, missing, 2]))
2-element Array{Int64,1}:
1
2
julia> collect(skipmissing([1 missing; 2 missing]))
2-element Array{Int64,1}:
1
2
System
Base.run
— Function.run(command, args...; wait::Bool = true)
Run a command object, constructed with backticks. Throws an error if anything goes wrong, including the process exiting with a non-zero status (when wait
is true).
If wait
is false, the process runs asynchronously. You can later wait for it and check its exit status by calling success
on the returned process object.
When wait
is false, the process' I/O streams are directed to devnull
. When wait
is true, I/O streams are shared with the parent process. Use pipeline
to control I/O redirection.
Base.devnull
— Constant.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))
Base.success
— Function.success(command)
Run a command object, constructed with backticks, and tell whether it was successful (exited with a code of 0). An exception is raised if the process cannot be started.
Base.process_running
— Function.process_running(p::Process)
Determine whether a process is currently running.
Base.process_exited
— Function.process_exited(p::Process)
Determine whether a process has exited.
Base.kill
— Method.kill(p::Process, signum=SIGTERM)
Send a signal to a process. The default is to terminate the process. Returns successfully if the process has already exited, but throws an error if killing the process failed for other reasons (e.g. insufficient permissions).
Base.Sys.set_process_title
— Function.Sys.set_process_title(title::AbstractString)
Set the process title. No-op on some operating systems.
Base.Sys.get_process_title
— Function.Sys.get_process_title()
Get the process title. On some systems, will always return an empty string.
Base.ignorestatus
— Function.ignorestatus(command)
Mark a command object so that running it will not throw an error if the result code is non-zero.
Base.detach
— Function.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.
Base.Cmd
— Type.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
: Iftrue
(defaults tofalse
), then theCmd
will not throw an error if the return code is nonzero.detach::Bool
: Iftrue
(defaults tofalse
), then theCmd
will be run in a new process group, allowing it to outlive thejulia
process and not have Ctrl-C passed to it.windows_verbatim::Bool
: Iftrue
(defaults tofalse
), then on Windows theCmd
will send a command-line string to the process with no quoting or escaping of arguments, even arguments containing spaces. (On Windows, arguments are sent to a program as a single "command-line" string, and programs are responsible for parsing it into arguments. By default, empty arguments and arguments with spaces or tabs are quoted with double quotes"
in the command line, and\
or"
are preceded by backslashes.windows_verbatim=true
is useful for launching programs that parse their command line in nonstandard ways.) Has no effect on non-Windows systems.windows_hide::Bool
: Iftrue
(defaults tofalse
), then on Windows no new console window is displayed when theCmd
is executed. This has no effect if a console is already open or on non-Windows systems.env
: Set environment variables to use when running theCmd
.env
is either a dictionary mapping strings to strings, an array of strings of the form"var=val"
, an array or tuple of"var"=>val
pairs, ornothing
. In order to modify (rather than replace) the existing environment, createenv
bycopy(ENV)
and then setenv["var"]=val
as desired.dir::AbstractString
: Specify a working directory for the command (instead of the current directory).
For any keywords that are not specified, the current settings from cmd
are used. Normally, to create a Cmd
object in the first place, one uses backticks, e.g.
Cmd(`echo "Hello world"`, ignorestatus=true, detach=false)
Base.setenv
— Function.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
by copy(ENV)
and then setting env["var"]=val
as desired, or use withenv
.
The dir
keyword argument can be used to specify a working directory for the command.
Base.withenv
— Function.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.
Base.pipeline
— Method.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`))
Base.pipeline
— Method.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))
Base.Libc.gethostname
— Function.gethostname() -> AbstractString
Get the local machine's host name.
Base.Libc.getpid
— Function.getpid() -> Int32
Get Julia's process ID.
getpid(process) -> Int32
Get the child process ID, if it still exists.
This function requires at least Julia 1.1.
Base.Libc.time
— Method.time()
Get the system time in seconds since the epoch, with fairly high (typically, microsecond) resolution.
Base.time_ns
— Function.time_ns()
Get the time in nanoseconds. The time corresponding to 0 is undefined, and wraps every 5.8 years.
Base.@time
— Macro.@time
A macro to execute an expression, printing the time it took to execute, the number of allocations, and the total number of bytes its execution caused to be allocated, before returning the value of the expression.
See also @timev
, @timed
, @elapsed
, and @allocated
.
julia> @time rand(10^6);
0.001525 seconds (7 allocations: 7.630 MiB)
julia> @time begin
sleep(0.3)
1+1
end
0.301395 seconds (8 allocations: 336 bytes)
2
Base.@timev
— Macro.@timev
This is a verbose version of the @time
macro. It first prints the same information as @time
, then any non-zero memory allocation counters, and then returns the value of the expression.
See also @time
, @timed
, @elapsed
, and @allocated
.
julia> @timev rand(10^6);
0.001006 seconds (7 allocations: 7.630 MiB)
elapsed time (ns): 1005567
bytes allocated: 8000256
pool allocs: 6
malloc() calls: 1
Base.@timed
— Macro.@timed
A macro to execute an expression, and return the value of the expression, elapsed time, total bytes allocated, garbage collection time, and an object with various memory allocation counters.
See also @time
, @timev
, @elapsed
, and @allocated
.
julia> val, t, bytes, gctime, memallocs = @timed rand(10^6);
julia> t
0.006634834
julia> bytes
8000256
julia> gctime
0.0055765
julia> fieldnames(typeof(memallocs))
(:allocd, :malloc, :realloc, :poolalloc, :bigalloc, :freecall, :total_time, :pause, :full_sweep)
julia> memallocs.total_time
5576500
Base.@elapsed
— Macro.@elapsed
A macro to evaluate an expression, discarding the resulting value, instead returning the number of seconds it took to execute as a floating-point number.
See also @time
, @timev
, @timed
, and @allocated
.
julia> @elapsed sleep(0.3)
0.301391426
Base.@allocated
— Macro.@allocated
A macro to evaluate an expression, discarding the resulting value, instead returning the total number of bytes allocated during evaluation of the expression. Note: the expression is evaluated inside a local function, instead of the current context, in order to eliminate the effects of compilation, however, there still may be some allocations due to JIT compilation. This also makes the results inconsistent with the @time
macros, which do not try to adjust for the effects of compilation.
See also @time
, @timev
, @timed
, and @elapsed
.
julia> @allocated rand(10^6)
8000080
Base.EnvDict
— Type.EnvDict() -> EnvDict
A singleton of this type provides a hash table interface to environment variables.
Base.ENV
— Constant.ENV
Reference to the singleton EnvDict
, providing a dictionary interface to system environment variables.
Base.Sys.isunix
— Function.Sys.isunix([os])
Predicate for testing if the OS provides a Unix-like interface. See documentation in Handling Operating System Variation.
Base.Sys.isapple
— Function.Sys.isapple([os])
Predicate for testing if the OS is a derivative of Apple Macintosh OS X or Darwin. See documentation in Handling Operating System Variation.
Base.Sys.islinux
— Function.Sys.islinux([os])
Predicate for testing if the OS is a derivative of Linux. See documentation in Handling Operating System Variation.
Base.Sys.isbsd
— Function.Sys.isbsd([os])
Predicate for testing if the OS is a derivative of BSD. See documentation in Handling Operating System Variation.
The Darwin kernel descends from BSD, which means that Sys.isbsd()
is true
on macOS systems. To exclude macOS from a predicate, use Sys.isbsd() && !Sys.isapple()
.
Base.Sys.isfreebsd
— Function.Sys.isfreebsd([os])
Predicate for testing if the OS is a derivative of FreeBSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on FreeBSD but also on other BSD-based systems. Sys.isfreebsd()
refers only to FreeBSD.
This function requires at least Julia 1.1.
Base.Sys.isopenbsd
— Function.Sys.isopenbsd([os])
Predicate for testing if the OS is a derivative of OpenBSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on OpenBSD but also on other BSD-based systems. Sys.isopenbsd()
refers only to OpenBSD.
This function requires at least Julia 1.1.
Base.Sys.isnetbsd
— Function.Sys.isnetbsd([os])
Predicate for testing if the OS is a derivative of NetBSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on NetBSD but also on other BSD-based systems. Sys.isnetbsd()
refers only to NetBSD.
This function requires at least Julia 1.1.
Base.Sys.isdragonfly
— Function.Sys.isdragonfly([os])
Predicate for testing if the OS is a derivative of DragonFly BSD. See documentation in Handling Operating System Variation.
Not to be confused with Sys.isbsd()
, which is true
on DragonFly but also on other BSD-based systems. Sys.isdragonfly()
refers only to DragonFly.
This function requires at least Julia 1.1.
Base.Sys.iswindows
— Function.Sys.iswindows([os])
Predicate for testing if the OS is a derivative of Microsoft Windows NT. See documentation in Handling Operating System Variation.
Base.Sys.windows_version
— Function.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.
Base.@static
— Macro.@static
Partially evaluate an expression at parse time.
For example, @static Sys.iswindows() ? foo : bar
will evaluate Sys.iswindows()
and insert either foo
or bar
into the expression. This is useful in cases where a construct would be invalid on other platforms, such as a ccall
to a non-existent function. @static if Sys.isapple() foo end
and @static foo <&&,||> bar
are also valid syntax.
Versioning
Base.VersionNumber
— Type.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"
Base.@v_str
— Macro.@v_str
String macro used to parse a string to a VersionNumber
.
Examples
julia> v"1.2.3"
v"1.2.3"
julia> v"2.0.1-rc1"
v"2.0.1-rc1"
Errors
Base.error
— Function.error(message::AbstractString)
Raise an ErrorException
with the given message.
error(msg...)
Raise an ErrorException
with the given message.
Core.throw
— Function.throw(e)
Throw an object as an exception.
Base.rethrow
— Function.rethrow([e])
Throw an object without changing the current exception backtrace. The default argument is the current exception (if called within a catch
block).
Base.backtrace
— Function.backtrace()
Get a backtrace object for the current program point.
Base.catch_backtrace
— Function.catch_backtrace()
Get the backtrace of the current exception, for use within catch
blocks.
Base.catch_stack
— Function.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.
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).
Base.@assert
— Macro.@assert cond [text]
Throw an AssertionError
if cond
is false
. Preferred syntax for writing assertions. Message text
is optionally displayed upon assertion failure.
An assert might be disabled at various optimization levels. Assert should therefore only be used as a debugging tool and not used for authentication verification (e.g., verifying passwords), nor should side effects needed for the function to work correctly be used inside of asserts.
Examples
julia> @assert iseven(3) "3 is an odd number!"
ERROR: AssertionError: 3 is an odd number!
julia> @assert isodd(3) "What even are numbers?"
Core.ArgumentError
— Type.ArgumentError(msg)
The parameters to a function call do not match a valid signature. Argument msg
is a descriptive error string.
Core.AssertionError
— Type.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
.
Core.BoundsError
— Type.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 Array{Float64,1} at index [8]
Stacktrace:
[1] getindex(::Array{Float64,1}, ::Int64) at ./array.jl:660
[2] top-level scope
julia> B = fill(1.0, (2,3));
julia> B[2, 4]
ERROR: BoundsError: attempt to access 2×3 Array{Float64,2} at index [2, 4]
Stacktrace:
[1] getindex(::Array{Float64,2}, ::Int64, ::Int64) at ./array.jl:661
[2] top-level scope
julia> B[9]
ERROR: BoundsError: attempt to access 2×3 Array{Float64,2} at index [9]
Stacktrace:
[1] getindex(::Array{Float64,2}, ::Int64) at ./array.jl:660
[2] top-level scope
Base.CompositeException
— Type.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.
Base.DimensionMismatch
— Type.DimensionMismatch([msg])
The objects called do not have matching dimensionality. Optional argument msg
is a descriptive error string.
Core.DivideError
— Type.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:
[...]
Core.DomainError
— Type.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:
[...]
Base.EOFError
— Type.EOFError()
No more data was available to read from a file or stream.
Core.ErrorException
— Type.ErrorException(msg)
Generic error type. The error message, in the .msg
field, may provide more specific details.
Example
julia> ex = ErrorException("I've done a bad thing");
julia> ex.msg
"I've done a bad thing"
Core.InexactError
— Type.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:
[...]
Core.InterruptException
— Type.InterruptException()
The process was stopped by a terminal interrupt (CTRL+C).
Base.KeyError
— Type.KeyError(key)
An indexing operation into an AbstractDict
(Dict
) or Set
like object tried to access or delete a non-existent element.
Core.LoadError
— Type.LoadError(file::AbstractString, line::Int, error)
An error occurred while include
ing, require
ing, or using
a file. The error specifics should be available in the .error
field.
Core.MethodError
— Type.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.
Base.MissingException
— Type.MissingException(msg)
Exception thrown when a missing
value is encountered in a situation where it is not supported. The error message, in the msg
field may provide more specific details.
Core.OutOfMemoryError
— Type.OutOfMemoryError()
An operation allocated too much memory for either the system or the garbage collector to handle properly.
Core.ReadOnlyMemoryError
— Type.ReadOnlyMemoryError()
An operation tried to write to memory that is read-only.
Core.OverflowError
— Type.OverflowError(msg)
The result of an expression is too large for the specified type and will cause a wraparound.
Core.StackOverflowError
— Type.StackOverflowError()
The function call grew beyond the size of the call stack. This usually happens when a call recurses infinitely.
Base.SystemError
— Type.SystemError(prefix::AbstractString, [errno::Int32])
A system call failed with an error code (in the errno
global variable).
Core.TypeError
— Type.TypeError(func::Symbol, context::AbstractString, expected::Type, got)
A type assertion failure, or calling an intrinsic function with an incorrect argument type.
Core.UndefKeywordError
— Type.UndefKeywordError(var::Symbol)
The required keyword argument var
was not assigned in a function call.
Core.UndefRefError
— Type.UndefRefError()
The item or field is not defined for the given object.
Core.UndefVarError
— Type.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
Base.StringIndexError
— Type.StringIndexError(str, i)
An error occurred when trying to access str
at index i
that is not valid.
Core.InitError
— Type.InitError(mod::Symbol, error)
An error occurred when running a module's __init__
function. The actual error thrown is available in the .error
field.
Base.retry
— Function.retry(f::Function; delays=ExponentialBackOff(), check=nothing) -> Function
Return an anonymous function that calls function f
. If an exception arises, f
is repeatedly called again, each time check
returns true
, after waiting the number of seconds specified in delays
. check
should input delays
's current state and the Exception
.
Examples
retry(f, delays=fill(5.0, 3))
retry(f, delays=rand(5:10, 2))
retry(f, delays=Base.ExponentialBackOff(n=3, first_delay=5, max_delay=1000))
retry(http_get, check=(s,e)->e.status == "503")(url)
retry(read, check=(s,e)->isa(e, IOError))(io, 128; all=false)
Base.ExponentialBackOff
— Type.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
.
Events
Base.Timer
— Method.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
Base.Timer
— Type.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.
Base.AsyncCondition
— Type.Base.AsyncCondition
— Method.AsyncCondition(callback::Function)
Create a async condition that calls the given callback
function. The callback
is passed one argument, the async condition object itself.
Reflection
Base.nameof
— Method.nameof(m::Module) -> Symbol
Get the name of a Module
as a Symbol
.
Examples
julia> nameof(Base.Broadcast)
:Broadcast
Base.parentmodule
— Function.parentmodule(m::Module) -> Module
Get a module's enclosing Module
. Main
is its own parent.
Examples
julia> parentmodule(Main)
Main
julia> parentmodule(Base.Broadcast)
Base
parentmodule(t::DataType) -> Module
Determine the module containing the definition of a (potentially UnionAll
-wrapped) DataType
.
Examples
julia> module Foo
struct Int end
end
Foo
julia> parentmodule(Int)
Core
julia> parentmodule(Foo.Int)
Foo
parentmodule(f::Function) -> Module
Determine the module containing the (first) definition of a generic function.
parentmodule(f::Function, types) -> Module
Determine the module containing a given definition of a generic function.
Base.pathof
— Method.Base.moduleroot
— Function.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.
Base.@__MODULE__
— Macro.@__MODULE__ -> Module
Get the Module
of the toplevel eval, which is the Module
code is currently being read from.
Base.fullname
— Function.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,)
Base.names
— Function.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
.
Core.nfields
— Function.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.
Base.isconst
— Function.isconst(m::Module, s::Symbol) -> Bool
Determine whether a global is declared const
in a given Module
.
Base.nameof
— Method.nameof(f::Function) -> Symbol
Get the name of a generic Function
as a symbol, or :anonymous
.
Base.functionloc
— Method.functionloc(f::Function, types)
Returns a tuple (filename,line)
giving the location of a generic Function
definition.
Base.functionloc
— Method.functionloc(m::Method)
Returns a tuple (filename,line)
giving the location of a Method
definition.
Internals
Base.GC.gc
— Function.GC.gc()
Perform garbage collection.
Excessive use will likely lead to poor performance.
Base.GC.enable
— Function.GC.enable(on::Bool)
Control whether garbage collection is enabled using a boolean argument (true
for enabled, false
for disabled). Return previous GC state.
Disabling garbage collection should be used only with caution, as it can cause memory use to grow without bound.
Base.GC.@preserve
— Macro.GC.@preserve x1 x2 ... xn expr
Temporarily protect the given objects from being garbage collected, even if they would otherwise be unreferenced.
The last argument is the expression during which the object(s) will be preserved. The previous arguments are the objects to preserve.
Base.Meta.lower
— Function.lower(m, x)
Takes the expression x
and returns an equivalent expression in lowered form for executing in module m
. See also code_lowered
.
Base.Meta.@lower
— Macro.@lower [m] x
Return lowered form of the expression x
in module m
. By default m
is the module in which the macro is called. See also lower
.
Base.Meta.parse
— Method.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)
Base.Meta.parse
— Method.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.\"")))
Base.Meta.ParseError
— Type.ParseError(msg)
The expression passed to the parse
function could not be interpreted as a valid Julia expression.
Base.macroexpand
— Function.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)
Base.@macroexpand
— Macro.@macroexpand
Return equivalent expression with all macros removed (expanded).
There are differences between @macroexpand
and macroexpand
.
- While
macroexpand
takes a keyword argumentrecursive
,@macroexpand
is always recursive. For a non recursive macro version, see @macroexpand1
.
- While
macroexpand
has an explicitmodule
argument,@macroexpand
always
expands with respect to the module in which it is called. This is best seen in the following example:
julia> module M
macro m()
1
end
function f()
(@macroexpand(@m),
macroexpand(M, :(@m)),
macroexpand(Main, :(@m))
)
end
end
M
julia> macro m()
2
end
@m (macro with 1 method)
julia> M.f()
(1, 1, 2)
With @macroexpand
the expression expands where @macroexpand
appears in the code (module M
in the example). With macroexpand
the expression expands in the module given as the first argument.
Base.@macroexpand1
— Macro.@macroexpand1
Non recursive version of @macroexpand
.
Base.code_lowered
— Function.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.
Base.code_typed
— Function.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.
Base.precompile
— Function.precompile(f, args::Tuple{Vararg{Any}})
Compile the given function f
for the argument tuple (of types) args
, but do not execute it.