基本功能

一些通用的提示：

可以通过导入想要使用的模块，并利用 Module.fn(x) 语句来实现对模块内函数的调用。
此外，using Module 语句会将名为 Module 的模块中的所有可调函数引入当前的命名空间。
按照约定，名字以感叹号（!）结尾的函数会改变其输入参数的内容。一些函数同时拥有改变参数（例如 sort!）和不改变参数（sort）的版本

概览

— Function.

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.

source

— 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.

source

— Function.

isinteractive() -> Bool

Determine whether Julia is running an interactive session.

source

— 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.

source

— 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.

source

— 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 to evaluate a file into another module.

source

— Function.

include_string(m::Module, code::AbstractString, filename::AbstractString="string")

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

source

— 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.

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— 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.

source

— Function.

methods(f, [types])

Returns the method table for f.

If types is specified, returns an array of methods whose types match.

source

— Macro.

@show

Show an expression and result, returning the result.

source

— Keyword.

ans

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

source

关键字

— 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 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 for details.

source

— Keyword.

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 for details.

source

— Keyword.

baremodule

baremodule declares a module that does not contain using Base or a definition of eval. It does still import Core.

source

— 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 to include generated code in the final body of a program. A macro maps a tuple of arguments to a returned expression, and the resulting expression is compiled directly rather than requiring a runtime eval call. Macro arguments may include expressions, literal values, and symbols. For example:

Examples

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

return — Keyword.

return

return can be used in function bodies to exit early and return a given value, e.g.

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 its enclosing function 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].

do — Keyword.

do

Create an anonymous function. 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.

begin

begin…end denotes a block of code.

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

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

source

— Keyword.

end

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

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— 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.

source

— 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.

source

— Keyword.

for

for loops repeatedly evaluate the body of the loop by iterating over a sequence of values.

Examples

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

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— Keyword.

while

while loops repeatedly evaluate a conditional expression, and continues evaluating the body of the while loop so 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

— 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

source

— 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

source

— Keyword.

try/catch

A try/catch statement allows for Exceptions to be tested for. For example, a customized square root function can be written to automatically call either the real or complex square root method on demand using Exceptions:

f(x) = try
    sqrt(x)
catch
    sqrt(complex(x, 0))
end

try/catch statements also allow the Exception to be saved in a variable, e.g. catch y.

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

— 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 , 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 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

— 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

structs are immutable by default; an instance of one of these types cannot be modified after construction. Use 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 , 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

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 declaration shows how a primitive type can be optionally declared to be a subtype of some supertype.

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
6
julia> add(7, 1:100..., 1000:1100...)
111107

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— 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

Base 模块

— Keyword.

The base library of Julia.

source

— Module.

Base.Broadcast

Module containing the broadcasting implementation.

source

— 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.

source

— Module.

Methods for working with Iterators.

source

— Module.

Interface to libc, the C standard library.

source

— Module.

Convenience functions for metaprogramming.

source

— Module.

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

source

— Module.

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

source

— Module.

Experimental multithreading support.

source

— Module.

Base.GC

Module with garbage collection utilities.

source

所有对象

— 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

— 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

source

— 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 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 , 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

— 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 .

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 , which is required to hash values inside heterogeneous arrays.

source

— 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.

source

— Function.

finalize(x)

Immediately run finalizers registered for object x.

source

— 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.

source

— 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.

source

— Function.

getproperty(value, name::Symbol)

The syntax a.b calls getproperty(a, :b).

特殊类型

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 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

source

— 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 .

source

— Type.

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 , e.g. x[:a]. A tuple of the names can be obtained using keys, and a tuple of the values can be obtained using .

Note

Iteration over NamedTuples 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 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 .

source

— Type.

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 to access the value wrapped by a Some object.

source

— 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 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

source

— 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)

泛型

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.

语法

— Function.

Core.eval(m::Module, expr)

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

source

— 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.

source

— 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.

source

— 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 section of the Metaprogramming chapter of the manual for more details and examples.

source

— 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

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.

source

— Macro.

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

source

— 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

source

— 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

source

— 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 .

source

— Function.

gensym([tag])

Generates a symbol which will not conflict with other variable names.

source

— Macro.

@gensym

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

source

— 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 .

source

— Macro.

@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 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.

source

— Macro.

@polly

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

source

— 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.

缺失值

Base.Missing — Type.

Missing

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

source

— 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 , 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

source

— Function.

ismissing(x)

Indicate whether x is missing.

Base.skipmissing — Function.

skipmissing(itr)

Return an iterator over the elements in itr skipping 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

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 to control I/O redirection.

source

— 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))

source

— 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.

source

— Function.

process_running(p::Process)

Determine whether a process is currently running.

source

— Function.

process_exited(p::Process)

Determine whether a process has exited.

source

— Method.

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).

source

— Function.

Sys.set_process_title(title::AbstractString)

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

source

— 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: 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, or nothing. In order to modify (rather than replace) the existing environment, create env by copy(ENV) and then set env["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.

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 , @timed, , 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 , @timed, , 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 , @timev, , 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 , @timev, , 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.

版本控制

— 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 .

Examples

julia> VersionNumber("1.2.3")
v"1.2.3"

julia> VersionNumber("2.0.1-rc1")
v"2.0.1-rc1"

source

— 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"

错误

Base.error — Function.

error(message::AbstractString)

Raise an ErrorException with the given message.

error(msg...)

Raise an ErrorException with the given message.

source

— Function.

throw(e)

Throw an object as an exception.

source

— 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).

source

— Function.

backtrace()

Get a backtrace object for the current program point.

source

— Function.

catch_backtrace()

Get the backtrace of the current exception, for use within catch blocks.

source

— Macro.

@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?"

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 .

source

— 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

source

— 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(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 includeing, requireing, or a file. The error specifics should be available in the .error field.

source

— 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.

source

— 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 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.

source

事件

— 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 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 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 to check whether a timer is still active.

source

— Type.

AsyncCondition()

Create a async condition that wakes up tasks waiting for it (by calling wait on the object) when notified from C by a call to uv_async_send. Waiting tasks are woken with an error when the object is closed (by . Use isopen to check whether it is still active.

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.

反射

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

source

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.

source

— Method.

pathof(m::Module)

Return the path of 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 to get the file name part of the path.

source

— 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.

source

— Macro.

@__MODULE__ -> Module

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

source

— 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,)

source

— 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.

source

— 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 , which has one field.

source

— Function.

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

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

source

— Method.

nameof(f::Function) -> Symbol

Get the name of a generic Function as a symbol, or :anonymous.

source

— Method.

functionloc(f::Function, types)

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

source

— Method.

functionloc(m::Method)

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

source

— Function.

GC.gc()

Perform garbage collection.

Warning

Excessive use will likely lead to poor performance.

source

— 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.

Warning

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

source

— 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.

source

— 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 .

source

— 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)

source

— 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.\"")))

source

— 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 .

While macroexpand takes a keyword argument recursive, @macroexpandis always recursive. For a non recursive macro version, see .

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

— Macro.

@macroexpand1

Non recursive version of @macroexpand.

Base.code_lowered — Function.

code_lowered(f, types; generated = true)

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.

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

Base.code_typed — Function.

code_typed(f, types; optimize=true)

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.

Base.precompile — Function.

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