Arrays

Basic functions

ndims(A) → Integer

Returns the number of dimensions of A

size(A)

Returns a tuple containing the dimensions of A

iseltype(A, T)

Tests whether A or its elements are of type T

length(A) → Integer

Returns the number of elements in A

countnz(A)

Counts the number of nonzero values in array A (dense or sparse). Note that this is not a constant-time operation. For sparse matrices, one should usually use nnz, which returns the number of stored values.

conj!(A)

Convert an array to its complex conjugate in-place

stride(A, k)

Returns the distance in memory (in number of elements) between adjacent elements in dimension k

strides(A)

Returns a tuple of the memory strides in each dimension

ind2sub(dims, index) → subscripts

Returns a tuple of subscripts into an array with dimensions dims, corresponding to the linear index index

Example i, j, ... = ind2sub(size(A), indmax(A)) provides the indices of the maximum element

sub2ind(dims, i, j, k...) → index

The inverse of ind2sub, returns the linear index corresponding to the provided subscripts

Constructors

Array(type, dims)

Construct an uninitialized dense array. dims may be a tuple or a series of integer arguments.

getindex(type[, elements...])

Construct a 1-d array of the specified type. This is usually called with the syntax Type[]. Element values can be specified using Type[a,b,c,...].

cell(dims)

Construct an uninitialized cell array (heterogeneous array). dims can be either a tuple or a series of integer arguments.

zeros(type, dims)

Create an array of all zeros of specified type. The type defaults to Float64 if not specified.

zeros(A)

Create an array of all zeros with the same element type and shape as A.

ones(type, dims)

Create an array of all ones of specified type. The type defaults to Float64 if not specified.

ones(A)

Create an array of all ones with the same element type and shape as A.

trues(dims)

Create a BitArray with all values set to true

falses(dims)

Create a BitArray with all values set to false

fill(x, dims)

Create an array filled with the value x. For example, fill(1.0, (10,10)) returns a 10x10 array of floats, with each element initialized to 1.0.

If x is an object reference, all elements will refer to the same object. fill(Foo(), dims) will return an array filled with the result of evaluating Foo() once.

fill!(A, x)

Fill array A with the value x. If x is an object reference, all elements will refer to the same object. fill!(A, Foo()) will return A filled with the result of evaluating Foo() once.

reshape(A, dims)

Create an array with the same data as the given array, but with different dimensions. An implementation for a particular type of array may choose whether the data is copied or shared.

similar(array, element_type, dims)

Create an uninitialized array of the same type as the given array, but with the specified element type and dimensions. The second and third arguments are both optional. The dims argument may be a tuple or a series of integer arguments.

reinterpret(type, A)

Change the type-interpretation of a block of memory. For example, reinterpret(Float32, uint32(7)) interprets the 4 bytes corresponding to uint32(7) as a Float32. For arrays, this constructs an array with the same binary data as the given array, but with the specified element type.

eye(n)

n-by-n identity matrix

eye(m, n)

m-by-n identity matrix

eye(A)

Constructs an identity matrix of the same dimensions and type as A.

linspace(start, stop, n)

Construct a vector of n linearly-spaced elements from start to stop. See also: linrange() that constructs a range object.

logspace(start, stop, n)

Construct a vector of n logarithmically-spaced numbers from 10^start to 10^stop.

Mathematical operators and functions

All mathematical operations and functions are supported for arrays

broadcast(f, As...)

Broadcasts the arrays As to a common size by expanding singleton dimensions, and returns an array of the results f(as...) for each position.

broadcast!(f, dest, As...)

Like broadcast, but store the result of broadcast(f, As...) in the dest array. Note that dest is only used to store the result, and does not supply arguments to f unless it is also listed in the As, as in broadcast!(f, A, A, B) to perform A[:] = broadcast(f, A, B).

bitbroadcast(f, As...)

Like broadcast, but allocates a BitArray to store the result, rather then an Array.

broadcast_function(f)

Returns a function broadcast_f such that broadcast_function(f)(As...) === broadcast(f, As...). Most useful in the form const broadcast_f = broadcast_function(f).

broadcast!_function(f)

Like broadcast_function, but for broadcast!.

Indexing, Assignment, and Concatenation

getindex(A, inds...)

Returns a subset of array A as specified by inds, where each ind may be an Int, a Range, or a Vector.

sub(A, inds...)

Returns a SubArray, which stores the input A and inds rather than computing the result immediately. Calling getindex on a SubArray computes the indices on the fly.

parent(A)

Returns the “parent array” of an array view type (e.g., SubArray), or the array itself if it is not a view

parentindexes(A)

From an array view A, returns the corresponding indexes in the parent

slicedim(A, d, i)

Return all the data of A where the index for dimension d equals i. Equivalent to A[:,:,...,i,:,:,...] where i is in position d.

slice(A, inds...)

Create a view of the given indexes of array A, dropping dimensions indexed with scalars.

setindex!(A, X, inds...)

Store values from array X within some subset of A as specified by inds.

broadcast_getindex(A, inds...)

Broadcasts the inds arrays to a common size like broadcast, and returns an array of the results A[ks...], where ks goes over the positions in the broadcast.

broadcast_setindex!(A, X, inds...)

Broadcasts the X and inds arrays to a common size and stores the value from each position in X at the indices given by the same positions in inds.

cat(dim, A...)

Concatenate the input arrays along the specified dimension

vcat(A...)

Concatenate along dimension 1

hcat(A...)

Concatenate along dimension 2

hvcat(rows::(Int...), values...)

Horizontal and vertical concatenation in one call. This function is called for block matrix syntax. The first argument specifies the number of arguments to concatenate in each block row. For example, [a b;c d e] calls hvcat((2,3),a,b,c,d,e).

If the first argument is a single integer n, then all block rows are assumed to have n block columns.

flipdim(A, d)

Reverse A in dimension d.

flipud(A)

Equivalent to flipdim(A,1).

fliplr(A)

Equivalent to flipdim(A,2).

circshift(A, shifts)

Circularly shift the data in an array. The second argument is a vector giving the amount to shift in each dimension.

find(A)

Return a vector of the linear indexes of the non-zeros in A (determined by A[i]!=0). A common use of this is to convert a boolean array to an array of indexes of the true elements.

find(f, A)

Return a vector of the linear indexes of A where f returns true.

findn(A)

Return a vector of indexes for each dimension giving the locations of the non-zeros in A (determined by A[i]!=0).

findnz(A)

Return a tuple (I, J, V) where I and J are the row and column indexes of the non-zero values in matrix A, and V is a vector of the non-zero values.

findfirst(A)

Return the index of the first non-zero value in A (determined by A[i]!=0).

findfirst(A, v)

Return the index of the first element equal to v in A.

findfirst(predicate, A)

Return the index of the first element of A for which predicate returns true.

findnext(A, i)

Find the next index >= i of a non-zero element of A, or 0 if not found.

findnext(predicate, A, i)

Find the next index >= i of an element of A for which predicate returns true, or 0 if not found.

findnext(A, v, i)

Find the next index >= i of an element of A equal to v (using ==), or 0 if not found.

permutedims(A, perm)

Permute the dimensions of array A. perm is a vector specifying a permutation of length ndims(A). This is a generalization of transpose for multi-dimensional arrays. Transpose is equivalent to permutedims(A,[2,1]).

ipermutedims(A, perm)

Like permutedims(), except the inverse of the given permutation is applied.

squeeze(A, dims)

Remove the dimensions specified by dims from array A

vec(Array) → Vector

Vectorize an array using column-major convention.

promote_shape(s1, s2)

Check two array shapes for compatibility, allowing trailing singleton dimensions, and return whichever shape has more dimensions.

checkbounds(array, indexes...)

Throw an error if the specified indexes are not in bounds for the given array.

randsubseq(A, p) → Vector

Return a vector consisting of a random subsequence of the given array A, where each element of A is included (in order) with independent probability p. (Complexity is linear in p*length(A), so this function is efficient even if p is small and A is large.) Technically, this process is known as “Bernoulli sampling” of A.

randsubseq!(S, A, p)

Like randsubseq, but the results are stored in S (which is resized as needed).

Array functions

cumprod(A[, dim])

Cumulative product along a dimension.

cumprod!(B, A[, dim])

Cumulative product of A along a dimension, storing the result in B.

cumsum(A[, dim])

Cumulative sum along a dimension.

cumsum!(B, A[, dim])

Cumulative sum of A along a dimension, storing the result in B.

cumsum_kbn(A[, dim])

Cumulative sum along a dimension, using the Kahan-Babuska-Neumaier compensated summation algorithm for additional accuracy.

cummin(A[, dim])

Cumulative minimum along a dimension.

cummax(A[, dim])

Cumulative maximum along a dimension.

diff(A[, dim])

Finite difference operator of matrix or vector.

gradient(F[, h])

Compute differences along vector F, using h as the spacing between points. The default spacing is one.

rot180(A)

Rotate matrix A 180 degrees.

rot180(A, k)

Rotate matrix A 180 degrees an integer k number of times. If k is even, this is equivalent to a copy.

rotl90(A)

Rotate matrix A left 90 degrees.

rotl90(A, k)

Rotate matrix A left 90 degrees an integer k number of times. If k is zero or a multiple of four, this is equivalent to a copy.

rotr90(A)

Rotate matrix A right 90 degrees.

rotr90(A, k)

Rotate matrix A right 90 degrees an integer k number of times. If k is zero or a multiple of four, this is equivalent to a copy.

reducedim(f, A, dims, initial)

Reduce 2-argument function f along dimensions of A. dims is a vector specifying the dimensions to reduce, and initial is the initial value to use in the reductions.

The associativity of the reduction is implementation-dependent; if you need a particular associativity, e.g. left-to-right, you should write your own loop. See documentation for reduce.

mapslices(f, A, dims)

Transform the given dimensions of array A using function f. f is called on each slice of A of the form A[...,:,...,:,...]. dims is an integer vector specifying where the colons go in this expression. The results are concatenated along the remaining dimensions. For example, if dims is [1,2] and A is 4-dimensional, f is called on A[:,:,i,j] for all i and j.

sum_kbn(A)

Returns the sum of all array elements, using the Kahan-Babuska-Neumaier compensated summation algorithm for additional accuracy.

cartesianmap(f, dims)

Given a dims tuple of integers (m, n, ...), call f on all combinations of integers in the ranges 1:m, 1:n, etc.

julia> cartesianmap(println, (2,2))
11
21
12
22

Combinatorics

nthperm(v, k)

Compute the kth lexicographic permutation of a vector.

nthperm(p)

Return the k that generated permutation p. Note that nthperm(nthperm([1:n], k)) == k for 1 <= k <= factorial(n).

nthperm!(v, k)

In-place version of nthperm().

randperm(n)

Construct a random permutation of the given length.

invperm(v)

Return the inverse permutation of v.

isperm(v) → Bool

Returns true if v is a valid permutation.

permute!(v, p)

Permute vector v in-place, according to permutation p. No checking is done to verify that p is a permutation.

To return a new permutation, use v[p]. Note that this is generally faster than permute!(v,p) for large vectors.

ipermute!(v, p)

Like permute!, but the inverse of the given permutation is applied.

randcycle(n)

Construct a random cyclic permutation of the given length.

shuffle(v)

Return a randomly permuted copy of v.

shuffle!(v)

In-place version of shuffle().

reverse(v[, start=1[, stop=length(v)]])

Return a copy of v reversed from start to stop.

reverse!(v[, start=1[, stop=length(v)]]) → v

In-place version of reverse().

combinations(arr, n)

Generate all combinations of n elements from an indexable object. Because the number of combinations can be very large, this function returns an iterator object. Use collect(combinations(a,n)) to get an array of all combinations.

permutations(arr)

Generate all permutations of an indexable object. Because the number of permutations can be very large, this function returns an iterator object. Use collect(permutations(a,n)) to get an array of all permutations.

partitions(n)

Generate all integer arrays that sum to n. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(n)) to get an array of all partitions. The number of partitions to generete can be efficiently computed using length(partitions(n)).

partitions(n, m)

Generate all arrays of m integers that sum to n. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(n,m)) to get an array of all partitions. The number of partitions to generete can be efficiently computed using length(partitions(n,m)).

partitions(array)

Generate all set partitions of the elements of an array, represented as arrays of arrays. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(array)) to get an array of all partitions. The number of partitions to generete can be efficiently computed using length(partitions(array)).

partitions(array, m)

Generate all set partitions of the elements of an array into exactly m subsets, represented as arrays of arrays. Because the number of partitions can be very large, this function returns an iterator object. Use collect(partitions(array,m)) to get an array of all partitions. The number of partitions into m subsets is equal to the Stirling number of the second kind and can be efficiently computed using length(partitions(array,m)).

BitArrays

bitpack(A::AbstractArray{T, N}) → BitArray

Converts a numeric array to a packed boolean array

bitunpack(B::BitArray{N}) → Array{Bool,N}

Converts a packed boolean array to an array of booleans

flipbits!(B::BitArray{N}) → BitArray{N}

Performs a bitwise not operation on B. See ~ operator.

rol(B::BitArray{1}, i::Integer) → BitArray{1}

Left rotation operator.

ror(B::BitArray{1}, i::Integer) → BitArray{1}

Right rotation operator.

Sparse Matrices

Sparse matrices support much of the same set of operations as dense matrices. The following functions are specific to sparse matrices.

sparse(I, J, V[, m, n, combine])

Create a sparse matrix S of dimensions m x n such that S[I[k], J[k]] = V[k]. The combine function is used to combine duplicates. If m and n are not specified, they are set to max(I) and max(J) respectively. If the combine function is not supplied, duplicates are added by default.

sparsevec(I, V[, m, combine])

Create a sparse matrix S of size m x 1 such that S[I[k]] = V[k]. Duplicates are combined using the combine function, which defaults to + if it is not provided. In julia, sparse vectors are really just sparse matrices with one column. Given Julia’s Compressed Sparse Columns (CSC) storage format, a sparse column matrix with one column is sparse, whereas a sparse row matrix with one row ends up being dense.

sparsevec(D::Dict[, m])

Create a sparse matrix of size m x 1 where the row values are keys from the dictionary, and the nonzero values are the values from the dictionary.

issparse(S)

Returns true if S is sparse, and false otherwise.

sparse(A)

Convert a dense matrix A into a sparse matrix.

sparsevec(A)

Convert a dense vector A into a sparse matrix of size m x 1. In julia, sparse vectors are really just sparse matrices with one column.

full(S)

Convert a sparse matrix S into a dense matrix.

nnz(A)

Returns the number of stored (filled) elements in a sparse matrix.

spzeros(m, n)

Create an empty sparse matrix of size m x n.

spones(S)

Create a sparse matrix with the same structure as that of S, but with every nonzero element having the value 1.0.

speye(type, m[, n])

Create a sparse identity matrix of specified type of size m x m. In case n is supplied, create a sparse identity matrix of size m x n.

spdiagm(B, d[, m, n])

Construct a sparse diagonal matrix. B is a tuple of vectors containing the diagonals and d is a tuple containing the positions of the diagonals. In the case the input contains only one diagonaly, B can be a vector (instead of a tuple) and d can be the diagonal position (instead of a tuple), defaulting to 0 (diagonal). Optionally, m and n specify the size of the resulting sparse matrix.

sprand(m, n, p[, rng])

Create a random m by n sparse matrix, in which the probability of any element being nonzero is independently given by p (and hence the mean density of nonzeros is also exactly p). Nonzero values are sampled from the distribution specified by rng. The uniform distribution is used in case rng is not specified.

sprandn(m, n, p)

Create a random m by n sparse matrix with the specified (independent) probability p of any entry being nonzero, where nonzero values are sampled from the normal distribution.

sprandbool(m, n, p)

Create a random m by n sparse boolean matrix with the specified (independent) probability p of any entry being true.

etree(A[, post])

Compute the elimination tree of a symmetric sparse matrix A from triu(A) and, optionally, its post-ordering permutation.

symperm(A, p)

Return the symmetric permutation of A, which is A[p,p]. A should be symmetric and sparse, where only the upper triangular part of the matrix is stored. This algorithm ignores the lower triangular part of the matrix. Only the upper triangular part of the result is returned as well.

nonzeros(A)

Return a vector of the structural nonzero values in sparse matrix A. This includes zeros that are explicitly stored in the sparse matrix. The returned vector points directly to the internal nonzero storage of A, and any modifications to the returned vector will mutate A as well.