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Julia 多维数组

# Multi-dimensional Arrays

Julia, like most technical computing languages, provides a first-class array implementation. Most
technical computing languages pay a lot of attention to their array implementation at the expense
of other containers. Julia does not treat arrays in any special way. The array library is implemented
almost completely in Julia itself, and derives its performance from the compiler, just like any
other code written in Julia. As such, it's also possible to define custom array types by inheriting
from AbstractArray. See the [manual section on the AbstractArray interface](@ref man-interface-array) for more details
on implementing a custom array type.

An array is a collection of objects stored in a multi-dimensional grid. In the most general case,
an array may contain objects of type Any. For most computational purposes, arrays should contain
objects of a more specific type, such as Float64 or Int32.

In general, unlike many other technical computing languages, Julia does not expect programs to
be written in a vectorized style for performance. Julia's compiler uses type inference and generates
optimized code for scalar array indexing, allowing programs to be written in a style that is convenient
and readable, without sacrificing performance, and using less memory at times.

In Julia, all arguments to functions are passed by

(i.e. by pointers). Some technical computing languages pass arrays by value, and
while this prevents accidental modification by callees of a value in the caller,
it makes avoiding unwanted copying of arrays difficult. By convention, a
function name ending with a ! indicates that it will mutate or destroy the
value of one or more of its arguments (compare, for example, sort and sort!).
Callees must make explicit copies to ensure that they don't modify inputs that
they don't intend to change. Many non- mutating functions are implemented by
calling a function of the same name with an added ! at the end on an explicit
copy of the input, and returning that copy.

# Basic Functions

eltype(A)the type of the elements contained in A
length(A)the number of elements in A
ndims(A)the number of dimensions of A
size(A)a tuple containing the dimensions of A
size(A,n)the size of A along dimension n
axes(A)a tuple containing the valid indices of A
axes(A,n)a range expressing the valid indices along dimension n
eachindex(A)an efficient iterator for visiting each position in A
stride(A,k)the stride (linear index distance between adjacent elements) along dimension k
strides(A)a tuple of the strides in each dimension

# Construction and Initialization

Many functions for constructing and initializing arrays are provided. In the following list of
such functions, calls with a dims... argument can either take a single tuple of dimension sizes
or a series of dimension sizes passed as a variable number of arguments. Most of these functions
also accept a first input T, which is the element type of the array. If the type T is
omitted it will default to Float64.

Array{T}(undef, dims...)an uninitialized dense Array
zeros(T, dims...)an Array of all zeros
ones(T, dims...)an Array of all ones
trues(dims...)a BitArray with all values true
falses(dims...)a BitArray with all values false
reshape(A, dims...)an array containing the same data as A, but with different dimensions
copy(A)copy A
deepcopy(A)copy A, recursively copying its elements
similar(A, T, dims...)an uninitialized array of the same type as A (dense, sparse, etc.), but with the specified element type and dimensions. The second and third arguments are both optional, defaulting to the element type and dimensions of A if omitted.
reinterpret(T, A)an array with the same binary data as A, but with element type T
rand(T, dims...)an Array with random, iid [^1] and uniformly distributed values in the half-open interval [0, 1)
randn(T, dims...)an Array with random, iid and standard normally distributed values
Matrix{T}(I, m, n)m-by-n identity matrix
range(start, stop=stop, length=n)range of n linearly spaced elements from start to stop
fill!(A, x)fill the array A with the value x
fill(x, dims...)an Array filled with the value x

[^1]: iid, independently and identically distributed.

The syntax [A, B, C, ...] constructs a 1-d array (i.e., a vector) of its arguments. If all
arguments have a common [promotion type](@ref conversion-and-promotion) then they get
converted to that type using convert.

To see the various ways we can pass dimensions to these constructors, consider the following examples:

julia> zeros(Int8, 2, 3)
2×3 Array{Int8,2}:
 0  0  0
 0  0  0

julia> zeros(Int8, (2, 3))
2×3 Array{Int8,2}:
 0  0  0
 0  0  0

julia> zeros((2, 3))
2×3 Array{Float64,2}:
 0.0  0.0  0.0
 0.0  0.0  0.0

Here, (2, 3) is a Tuple.

# Concatenation

Arrays can be constructed and also concatenated using the following functions:

cat(A...; dims=k)concatenate input arrays along dimension(s) k
vcat(A...)shorthand for cat(A...; dims=1)
hcat(A...)shorthand for cat(A...; dims=2)

Scalar values passed to these functions are treated as 1-element arrays. For example,

julia> vcat([1, 2], 3)
3-element Array{Int64,1}:

julia> hcat([1 2], 3)
1×3 Array{Int64,2}:
 1  2  3

The concatenation functions are used so often that they have special syntax:

[A; B; C; ...]vcat
[A B C ...]hcat
[A B; C D; ...]hvcat

hvcat concatenates in both dimension 1 (with semicolons) and dimension 2 (with spaces).
Consider these examples of this syntax:

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

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

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

# Typed array initializers

An array with a specific element type can be constructed using the syntax T[A, B, C, ...]. This
will construct a 1-d array with element type T, initialized to contain elements A, B, C,
etc. For example, Any[x, y, z] constructs a heterogeneous array that can contain any values.

Concatenation syntax can similarly be prefixed with a type to specify the element type of the

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

julia> Int8[[1 2] [3 4]]
1×4 Array{Int8,2}:
 1  2  3  4

# Comprehensions

Comprehensions provide a general and powerful way to construct arrays. Comprehension syntax is
similar to set construction notation in mathematics:

A = [ F(x,y,...) for x=rx, y=ry, ... ]

The meaning of this form is that F(x,y,...) is evaluated with the variables x, y, etc. taking
on each value in their given list of values. Values can be specified as any iterable object, but
will commonly be ranges like 1:n or 2:(n-1), or explicit arrays of values like [1.2, 3.4, 5.7].
The result is an N-d dense array with dimensions that are the concatenation of the dimensions
of the variable ranges rx, ry, etc. and each F(x,y,...) evaluation returns a scalar.

The following example computes a weighted average of the current element and its left and right
neighbor along a 1-d grid. :

julia> x = rand(8)
8-element Array{Float64,1}:

julia> [ 0.25*x[i-1] + 0.5*x[i] + 0.25*x[i+1] for i=2:length(x)-1 ]
6-element Array{Float64,1}:

The resulting array type depends on the types of the computed elements. In order to control the
type explicitly, a type can be prepended to the comprehension. For example, we could have requested
the result in single precision by writing:

Float32[ 0.25x[i-1] + 0.5x[i] + 0.25*x[i+1] for i=2:length(x)-1 ]

## Generator Expressions

Comprehensions can also be written without the enclosing square brackets, producing an object
known as a generator. This object can be iterated to produce values on demand, instead of allocating
an array and storing them in advance (see [Iteration](@ref)). For example, the following expression
sums a series without allocating memory:

julia> sum(1/n^2 for n=1:1000)

When writing a generator expression with multiple dimensions inside an argument list, parentheses
are needed to separate the generator from subsequent arguments:

julia> map(tuple, 1/(i+j) for i=1:2, j=1:2, [1:4;])
ERROR: syntax: invalid iteration specification

All comma-separated expressions after `for` are interpreted as ranges. Adding parentheses lets
us add a third argument to [`map`](@ref):

julia> map(tuple, (1/(i+j) for i=1:2, j=1:2), [1 3; 2 4])
2×2 Array{Tuple{Float64,Int64},2}:
(0.5, 1) (0.333333, 3)
(0.333333, 2) (0.25, 4)

Generators are implemented via inner functions. Just like
inner functions used elsewhere in the language, variables from the enclosing scope can be
"captured" in the inner function.  For example, `sum(p[i] - q[i] for i=1:n)`
captures the three variables `p`, `q` and `n` from the enclosing scope.
Captured variables can present performance challenges; see
[performance tips](@ref man-performance-tips).

Ranges in generators and comprehensions can depend on previous ranges by writing multiple `for`

julia> [(i,j) for i=1:3 for j=1:i]
6-element Array{Tuple{Int64,Int64},1}:
(1, 1)
(2, 1)
(2, 2)
(3, 1)
(3, 2)
(3, 3)

In such cases, the result is always 1-d.

Generated values can be filtered using the `if` keyword:

julia> [(i,j) for i=1:3 for j=1:i if i+j == 4]
2-element Array{Tuple{Int64,Int64},1}:
(2, 2)
(3, 1)

## [Indexing](@id man-array-indexing)

The general syntax for indexing into an n-dimensional array A is:

X = A[I_1, I_2, ..., I_n]

where each `I_k` may be a scalar integer, an array of integers, or any other
[supported index](@ref man-supported-index-types). This includes
[`Colon`](@ref) (`:`) to select all indices within the entire dimension,
ranges of the form `a:c` or `a:b:c` to select contiguous or strided
subsections, and arrays of booleans to select elements at their `true` indices.

If all the indices are scalars, then the result `X` is a single element from the array `A`. Otherwise,
`X` is an array with the same number of dimensions as the sum of the dimensionalities of all the

If all indices are vectors, for example, then the shape of `X` would be `(length(I_1), length(I_2), ..., length(I_n))`,
with location `(i_1, i_2, ..., i_n)` of `X` containing the value `A[I_1[i_1], I_2[i_2], ..., I_n[i_n]]`.


julia> A = reshape(collect(1:16), (2, 2, 2, 2))
2×2×2×2 Array{Int64,4}:
[:, :, 1, 1] =
1 3
2 4

[:, :, 2, 1] =
5 7
6 8

[:, :, 1, 2] =
9 11
10 12

[:, :, 2, 2] =
13 15
14 16

julia> A[1, 2, 1, 1] # all scalar indices

julia> A[[1, 2], [1], [1, 2], [1]] # all vector indices
2×1×2×1 Array{Int64,4}:
[:, :, 1, 1] =

[:, :, 2, 1] =

julia> A[[1, 2], [1], [1, 2], 1] # a mix of index types
2×1×2 Array{Int64,3}:
[:, :, 1] =

[:, :, 2] =

Note how the size of the resulting array is different in the last two cases.

If `I_1` is changed to a two-dimensional matrix, then `X` becomes an `n+1`-dimensional array of
shape `(size(I_1, 1), size(I_1, 2), length(I_2), ..., length(I_n))`. The matrix adds a dimension.


julia> A = reshape(collect(1:16), (2, 2, 2, 2));

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

julia> A[[1 2; 1 2], 1, 2, 1]
2×2 Array{Int64,2}:
5 6
5 6

The location `(i_1, i_2, i_3, ..., i_{n+1})` contains the value at `A[I_1[i_1, i_2], I_2[i_3], ..., I_n[i_{n+1}]]`.
All dimensions indexed with scalars are dropped. For example, the result of `A[2, I, 3]` is an
array with size `size(I)`. Its `i`th element is populated by `A[2, I[i], 3]`.

As a special part of this syntax, the `end` keyword may be used to represent the last index of
each dimension within the indexing brackets, as determined by the size of the innermost array
being indexed. Indexing syntax without the `end` keyword is equivalent to a call to [`getindex`](@ref):

X = getindex(A, I_1, I_2, ..., I_n)


julia> x = reshape(1:16, 4, 4)
4×4 reshape(::UnitRange{Int64}, 4, 4) with eltype Int64:
1 5 9 13
2 6 10 14
3 7 11 15
4 8 12 16

julia> x[2:3, 2:end-1]
2×2 Array{Int64,2}:
6 10
7 11

julia> x[1, [2 3; 4 1]]
2×2 Array{Int64,2}:
5 9
13 1

Empty ranges of the form `n:n-1` are sometimes used to indicate the inter-index location between
`n-1` and `n`. For example, the [`searchsorted`](@ref) function uses this convention to indicate
the insertion point of a value not found in a sorted array:

julia> a = [1,2,5,6,7];

julia> searchsorted(a, 4)

## Assignment

The general syntax for assigning values in an n-dimensional array A is:

A[I_1, I_2, ..., I_n] = X

where each `I_k` may be a scalar integer, an array of integers, or any other
[supported index](@ref man-supported-index-types). This includes
[`Colon`](@ref) (`:`) to select all indices within the entire dimension,
ranges of the form `a:c` or `a:b:c` to select contiguous or strided
subsections, and arrays of booleans to select elements at their `true` indices.

If `X` is an array, it must have the same number of elements as the product of the lengths of
the indices: `prod(length(I_1), length(I_2), ..., length(I_n))`. The value in location `I_1[i_1], I_2[i_2], ..., I_n[i_n]`
of `A` is overwritten with the value `X[i_1, i_2, ..., i_n]`. If `X` is not an array, its value
is written to all referenced locations of `A`.

Just as in [Indexing](@ref man-array-indexing), the `end` keyword may be used
to represent the last index of each dimension within the indexing brackets, as
determined by the size of the array being assigned into. Indexed assignment
syntax without the `end` keyword is equivalent to a call to

setindex!(A, X, I_1, I_2, ..., I_n)


julia> x = collect(reshape(1:9, 3, 3))
3×3 Array{Int64,2}:
1 4 7
2 5 8
3 6 9

julia> x[3, 3] = -9;

julia> x[1:2, 1:2] = [-1 -4; -2 -5];

julia> x
3×3 Array{Int64,2}:
-1 -4 7
-2 -5 8
3 6 -9

## [Supported index types](@id man-supported-index-types)

In the expression `A[I_1, I_2, ..., I_n]`, each `I_k` may be a scalar index, an
array of scalar indices, or an object that represents an array of scalar
indices and can be converted to such by [`to_indices`](@ref):

1. A scalar index. By default this includes:
    * Non-boolean integers
    * [`CartesianIndex{N}`](@ref)s, which behave like an `N`-tuple of integers spanning multiple dimensions (see below for more details)
2. An array of scalar indices. This includes:
    * Vectors and multidimensional arrays of integers
    * Empty arrays like `[]`, which select no elements
    * Ranges like `a:c` or `a:b:c`, which select contiguous or strided subsections from `a` to `c` (inclusive)
    * Any custom array of scalar indices that is a subtype of `AbstractArray`
    * Arrays of `CartesianIndex{N}` (see below for more details)
3. An object that represents an array of scalar indices and can be converted to such by [`to_indices`](@ref). By default this includes:
    * [`Colon()`](@ref) (`:`), which represents all indices within an entire dimension or across the entire array
    * Arrays of booleans, which select elements at their `true` indices (see below for more details)

Some examples:

julia> A = reshape(collect(1:2:18), (3, 3))
3×3 Array{Int64,2}:
1 7 13
3 9 15
5 11 17

julia> A[4]

julia> A[[2, 5, 8]]
3-element Array{Int64,1}:

julia> A[[1 4; 3 8]]
2×2 Array{Int64,2}:
1 7
5 15

julia> A[[]]
0-element Array{Int64,1}

julia> A[1:2:5]
3-element Array{Int64,1}:

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

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

### Cartesian indices

The special `CartesianIndex{N}` object represents a scalar index that behaves
like an `N`-tuple of integers spanning multiple dimensions.  For example:

``` cartesianindex
julia> A = reshape(1:32, 4, 4, 2);

julia> A[3, 2, 1]

julia> A[CartesianIndex(3, 2, 1)] == A[3, 2, 1] == 7

Considered alone, this may seem relatively trivial; CartesianIndex simply
gathers multiple integers together into one object that represents a single
multidimensional index. When combined with other indexing forms and iterators
that yield CartesianIndexes, however, this can produce very elegant
and efficient code. See Iteration below, and for some more advanced
examples, see this blog post on multidimensional algorithms and

Arrays of CartesianIndex{N} are also supported. They represent a collection
of scalar indices that each span N dimensions, enabling a form of indexing
that is sometimes referred to as pointwise indexing. For example, it enables
accessing the diagonal elements from the first "page" of A from above:

julia> page = A[:,:,1]
4×4 Array{Int64,2}:
 1  5   9  13
 2  6  10  14
 3  7  11  15
 4  8  12  16

julia> page[[CartesianIndex(1,1),
4-element Array{Int64,1}:

This can be expressed much more simply with [dot broadcasting](@ref man-vectorized)
and by combining it with a normal integer index (instead of extracting the
first page from A as a separate step). It can even be combined with a :
to extract both diagonals from the two pages at the same time:

julia> A[CartesianIndex.(axes(A, 1), axes(A, 2)), 1]
4-element Array{Int64,1}:

julia> A[CartesianIndex.(axes(A, 1), axes(A, 2)), :]
4×2 Array{Int64,2}:
  1  17
  6  22
 11  27
 16  32

!!! warning

`CartesianIndex` and arrays of `CartesianIndex` are not compatible with the
`end` keyword to represent the last index of a dimension. Do not use `end`
in indexing expressions that may contain either `CartesianIndex` or arrays thereof.

# Logical indexing

Often referred to as logical indexing or indexing with a logical mask, indexing
by a boolean array selects elements at the indices where its values are true.
Indexing by a boolean vector B is effectively the same as indexing by the
vector of integers that is returned by findall(B). Similarly, indexing
by a N-dimensional boolean array is effectively the same as indexing by the
vector of CartesianIndex{N}s where its values are true. A logical index
must be a vector of the same length as the dimension it indexes into, or it
must be the only index provided and match the size and dimensionality of the
array it indexes into. It is generally more efficient to use boolean arrays as
indices directly instead of first calling findall.

julia> x = reshape(1:16, 4, 4)
4×4 reshape(::UnitRange{Int64}, 4, 4) with eltype Int64:
 1  5   9  13
 2  6  10  14
 3  7  11  15
 4  8  12  16

julia> x[[false, true, true, false], :]
2×4 Array{Int64,2}:
 2  6  10  14
 3  7  11  15

julia> mask = map(ispow2, x)
4×4 Array{Bool,2}:
  true  false  false  false
  true  false  false  false
 false  false  false  false
  true   true  false   true

julia> x[mask]
5-element Array{Int64,1}:

# Iteration

The recommended ways to iterate over a whole array are

for a in A

# Do something with the element a


for i in eachindex(A)

# Do something with i and/or A[i]


The first construct is used when you need the value, but not index, of each element. In the second
construct, `i` will be an `Int` if `A` is an array type with fast linear indexing; otherwise,
it will be a `CartesianIndex`:

julia> A = rand(4,3);

julia> B = view(A, 1:3, 2:3);

julia> for i in eachindex(B)
@show i
i = CartesianIndex(1, 1)
i = CartesianIndex(2, 1)
i = CartesianIndex(3, 1)
i = CartesianIndex(1, 2)
i = CartesianIndex(2, 2)
i = CartesianIndex(3, 2)

In contrast with `for i = 1:length(A)`, iterating with [`eachindex`](@ref) provides an efficient way to
iterate over any array type.

## Array traits

If you write a custom [`AbstractArray`](@ref) type, you can specify that it has fast linear indexing using

Base.IndexStyle(::Type{<:MyArray}) = IndexLinear()

This setting will cause eachindex iteration over a MyArray to use integers. If you don't
specify this trait, the default value IndexCartesian() is used.

# Array and Vectorized Operators and Functions

The following operators are supported for arrays:

  1. Unary arithmetic -- -, +
  2. Binary arithmetic -- -, +, *, /, \, ^
  3. Comparison -- ==, !=, (isapprox),

To enable convenient vectorization of mathematical and other operations,
Julia [provides the dot syntax](@ref man-vectorized) f.(args...), e.g. sin.(x)
or min.(x,y), for elementwise operations over arrays or mixtures of arrays and
scalars (a Broadcasting operation); these have the additional advantage of
"fusing" into a single loop when combined with other dot calls, e.g. sin.(cos.(x)).

Also, every binary operator supports a [dot version](@ref man-dot-operators)
that can be applied to arrays (and combinations of arrays and scalars) in such
[fused broadcasting operations](@ref man-vectorized), e.g. z .== sin.(x .* y).

Note that comparisons such as == operate on whole arrays, giving a single boolean
answer. Use dot operators like .== for elementwise comparisons. (For comparison
operations like <, only the elementwise .< version is applicable to arrays.)

Also notice the difference between max.(a,b), which broadcasts max
elementwise over a and b, and maximum(a), which finds the largest value within
a. The same relationship holds for min.(a,b) and minimum(a).

# Broadcasting

It is sometimes useful to perform element-by-element binary operations on arrays of different
sizes, such as adding a vector to each column of a matrix. An inefficient way to do this would
be to replicate the vector to the size of the matrix:

julia> a = rand(2,1); A = rand(2,3);

julia> repeat(a,1,3)+A
2×3 Array{Float64,2}:
 1.20813  1.82068  1.25387
 1.56851  1.86401  1.67846

This is wasteful when dimensions get large, so Julia provides broadcast, which expands
singleton dimensions in array arguments to match the corresponding dimension in the other array
without using extra memory, and applies the given function elementwise:

julia> broadcast(+, a, A)
2×3 Array{Float64,2}:
 1.20813  1.82068  1.25387
 1.56851  1.86401  1.67846

julia> b = rand(1,2)
1×2 Array{Float64,2}:
 0.867535  0.00457906

julia> broadcast(+, a, b)
2×2 Array{Float64,2}:
 1.71056  0.847604
 1.73659  0.873631

[Dotted operators](@ref man-dot-operators) such as .+ and .* are equivalent
to broadcast calls (except that they fuse, as described below). There is also a
broadcast! function to specify an explicit destination (which can also
be accessed in a fusing fashion by .= assignment). In fact, f.(args...)
is equivalent to broadcast(f, args...), providing a convenient syntax to broadcast any function
([dot syntax](@ref man-vectorized)). Nested "dot calls" f.(...) (including calls to .+ etcetera)
[automatically fuse](@ref man-dot-operators) into a single broadcast call.

Additionally, broadcast is not limited to arrays (see the function documentation),
it also handles tuples and treats any argument that is not an array, tuple or Ref
(except for Ptr) as a "scalar".

julia> convert.(Float32, [1, 2])
2-element Array{Float32,1}:

julia> ceil.((UInt8,), [1.2 3.4; 5.6 6.7])
2×2 Array{UInt8,2}:
 0x02  0x04
 0x06  0x07

julia> string.(1:3, ". ", ["First", "Second", "Third"])
3-element Array{String,1}:
 "1. First"
 "2. Second"
 "3. Third"

# Implementation

The base array type in Julia is the abstract type AbstractArray{T,N}. It is parameterized by
the number of dimensions N and the element type T. AbstractVector and AbstractMatrix are
aliases for the 1-d and 2-d cases. Operations on AbstractArray objects are defined using higher
level operators and functions, in a way that is independent of the underlying storage. These operations
generally work correctly as a fallback for any specific array implementation.

The AbstractArray type includes anything vaguely array-like, and implementations of it might
be quite different from conventional arrays. For example, elements might be computed on request
rather than stored. However, any concrete AbstractArray{T,N} type should generally implement
at least size(A) (returning an Int tuple), getindex(A,i) and getindex(A,i1,...,iN)](@ref getindex);
mutable arrays should also implement
setindex!. It is recommended that these operations
have nearly constant time complexity, or technically Õ(1) complexity, as otherwise some array
functions may be unexpectedly slow. Concrete types should also typically provide a similar(A,T=eltype(A),dims=size(A))
method, which is used to allocate a similar array for copy and other out-of-place
operations. No matter how an AbstractArray{T,N} is represented internally, T is the type of
object returned by integer indexing (A[1, ..., 1], when A is not empty) and N should be
the length of the tuple returned by [size. For more details on defining custom
AbstractArray implementations, see the [array interface guide in the interfaces chapter](@ref man-interface-array).

DenseArray is an abstract subtype of AbstractArray intended to include all arrays where
elements are stored contiguously in column-major order (see additional notes in
[Performance Tips](@ref man-performance-tips)). The Array type is a specific instance
of DenseArray; Vector and Matrix are aliases for the 1-d and 2-d cases.
Very few operations are implemented specifically for Array beyond those that are required
for all AbstractArrays; much of the array library is implemented in a generic
manner that allows all custom arrays to behave similarly.

SubArray is a specialization of AbstractArray that performs indexing by
sharing memory with the original array rather than by copying it. A SubArray
is created with the view function, which is called the same way as
getindex (with an array and a series of index arguments). The result
of view looks the same as the result of getindex, except the
data is left in place. view stores the input index vectors in a
SubArray object, which can later be used to index the original array
indirectly. By putting the @views macro in front of an expression or
block of code, any array[...] slice in that expression will be converted to
create a SubArray view instead.

BitArrays are space-efficient "packed" boolean arrays, which store one bit per boolean value.
They can be used similarly to Array{Bool} arrays (which store one byte per boolean value),
and can be converted to/from the latter via Array(bitarray) and BitArray(array), respectively.

A "strided" array is stored in memory with elements laid out in regular offsets such that
an instance with a supported isbits element type can be passed to
external C and Fortran functions that expect this memory layout. Strided arrays
must define a strides(A) method that returns a tuple of "strides" for each dimension; a
provided stride(A,k) method accesses the kth element within this tuple. Increasing the
index of dimension k by 1 should increase the index i of getindex(A,i) by
stride(A,k). If a pointer conversion method Base.unsafe_convert(Ptr{T}, A) is
provided, the memory layout must correspond in the same way to these strides. DenseArray is a
very specific example of a strided array where the elements are arranged contiguously, thus it
provides its subtypes with the appropriate definition of strides. More concrete examples
can be found within the [interface guide for strided arrays](@ref man-interface-strided-arrays).
StridedVector and StridedMatrix are convenient aliases for many of the builtin array types that
are considered strided arrays, allowing them to dispatch to select specialized implementations that
call highly tuned and optimized BLAS and LAPACK functions using just the pointer and strides.

The following example computes the QR decomposition of a small section of a larger array, without
creating any temporaries, and by calling the appropriate LAPACK function with the right leading
dimension size and stride parameters.

julia> a = rand(10, 10)
10×10 Array{Float64,2}:
 0.517515  0.0348206  0.749042   0.0979679  …  0.75984     0.950481   0.579513
 0.901092  0.873479   0.134533   0.0697848     0.0586695   0.193254   0.726898
 0.976808  0.0901881  0.208332   0.920358      0.288535    0.705941   0.337137
 0.657127  0.0317896  0.772837   0.534457      0.0966037   0.700694   0.675999
 0.471777  0.144969   0.0718405  0.0827916     0.527233    0.173132   0.694304
 0.160872  0.455168   0.489254   0.827851   …  0.62226     0.0995456  0.946522
 0.291857  0.769492   0.68043    0.629461      0.727558    0.910796   0.834837
 0.775774  0.700731   0.700177   0.0126213     0.00822304  0.327502   0.955181
 0.9715    0.64354    0.848441   0.241474      0.591611    0.792573   0.194357
 0.646596  0.575456   0.0995212  0.038517      0.709233    0.477657   0.0507231

julia> b = view(a, 2:2:8,2:2:4)
4×2 view(::Array{Float64,2}, 2:2:8, 2:2:4) with eltype Float64:
 0.873479   0.0697848
 0.0317896  0.534457
 0.455168   0.827851
 0.700731   0.0126213

julia> (q, r) = qr(b);

julia> q
4×4 LinearAlgebra.QRCompactWYQ{Float64,Array{Float64,2}}:
 -0.722358    0.227524  -0.247784    -0.604181
 -0.0262896  -0.575919  -0.804227     0.144377
 -0.376419   -0.75072    0.540177    -0.0541979
 -0.579497    0.230151  -0.00552346   0.781782

julia> r
2×2 Array{Float64,2}:
 -1.20921  -0.383393
  0.0      -0.910506