Tools for basic array manipulation and help dealing with the different flavors of arrays in Julia
Author emmt
10 Stars
Updated Last
1 Year Ago
Started In
October 2019

Utilities for coding with Julia arrays

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This Julia package provides a number of methods and types to deal with the variety of array types (sub-types of AbstractArray) that exist in Julia and to help building custom array-like types without sacrificing performances.

These are useful to implement methods to process arrays in a generic way.

Rubber indices

The constants .. and (type \dots and hit the tab key) can be used in array indexation to left or right justify the other indices. For instance, assuming A is a 3×4×5×6 array, then all the following equalities hold:

A[..]          === A # the two are the same object
A[..,3]         == A[:,:,:,3]
A[2,..]         == A[2,:,:,:]
A[..,2:4,5]     == A[:,:,2:4,5]
A[:,2:3,..]     == A[:,2:3,:,:]
A[2:3,..,1,2:4] == A[2:3,:,1,2:4]

As can be seen, the advantage of the rubber index .. is that it automatically expands as the list of colons needed to have the correct number of indices. The expressions are also more readable. The idea comes from the Yorick language by Dave Munro.

The rubber index may also be used for setting values. For instance:

A[..] .= 1         # to fill A with ones
A[..,3] = A[..,2]  # to copy A[:,:,:,2] in A[:,:,:,3]
A[..,3] .= A[..,2] # idem but faster
A[2,..] = A[3,..]  # to copy A[3,:,:,:] in A[2,:,:,:]
A[..,2:4,5] .= 7   # to set all elements in A[:,:,2:4,5] to 7

Leading/trailing indices may also be specified as Cartesian indices (of type CartesianIndex).

Technically, the constant .. is defined as RubberIndex() where RubberIndex is the singleton type that represents any number of indices.

Call colons(n) if you need a n-tuple of colons :. When n is known at compile time, it is faster to call colons(Val(n)).

⚠️ Warning. A current limitation of the rubber index is that it will confuse the interpretation of the end token appearing in the same index list after the rubber index. This is beacuse the parser wrongly assumes that the rubber index counts for a single dimension. The end token may however appears before the rubber index. Example:

A = rand(5,10,4,3);
A[:,5:end,..] == A[:,5:end,:,:] # ok
A[..,5:end,:] == A[:,:,5:end,:] # throws a BoundsError

Array-like objects

Defining custom array-like objects

Julia array interface is very powerful and flexible, it is therefore tempting to define custom array-like types, that is Julia types that behave like arrays, without sacrificing efficiency. The ArrayTools package provides simple means to define such array-like types if the values to be accessed as if in an array are stored in an array (of any concrete type) embedded in the object instance.

This is as simple as:

  1. Make your type inherit from LinearArray{T,N} or CartesianArray{T,N} depending whether the index style of the embedded array is IndexLinear() or IndexCartesian().

  2. Extend the Base.parent(A) method for your custom type so that it returns the embedded array of an instance A.

For instance (of course replacing the ellipsis ...):

using ArrayTools.PseudoArrays
struct CustomArray{T,N,...} <: LinearArray{T,N}
    arr::Array{T,N} # can be any array type with linear index style
    ...             # another member
    ...             # yet another member
    ...             # etc.

@inline Base.parent(A::CustomArray) = A.arr

As a result, instances of your CustomArray{T,N} will be also seen as instances of AbstractArray{T,N} and will behave as if they implement linear indexing. Apart from the needs to extend the Base.parent method, the interface to LinearArray{T,N} should provide any necessary methods for indexation, getting the dimensions, the element type, etc. for the derived custom type. You may however override these definitions by more optimized or more suitable methods specialized for your custom array-like type.

If your custom array-like type is based on an array whose index style is IndexCartesian() (instead of IndexLinear() in the above example), just make your custom type derived from CartesianArray{T,N} (instead of LinearArray{T,N}). For such array-like object, index checking requires an efficient implementation of the Base.axes() method which you may have to specialize. The default implementation is:

@inline Base.axes(A::CartesianArray) = axes(parent(A))

Array-like objects with properties

As a working example of custom array-like objects, the ArrayTools package provides AnnotatedArray{T,N,P} objects which store values like arrays but also have properties stored in a dictionary or a named tuple (of type P). Here the parameters are the element type T of the values in the array part, the number N of dimensions of the array part and the type P of the object storing the properties.

Building annotated arrays is easy:

using ArrayTools.AnnotatedArrays
dims = (100, 50)
T = Float32
A = AnnotatedArray(zeros(T, dims), units = "photons", Δx = 0.20, Δy = 0.15)
B = AnnotatedArray{T}(undef, dims, units = "µm", Δx = 0.10, Δy = 0.20)

Here the initial properties of A and B are specified by the keywords in the call to the constructor; their properties will have symbolic names with any kind of value. The array contents of A is an array of zeros, while the array contents of B is created by the constructor with undefined values. Indexing A or B with integers of Cartesian indices is the same as accessing the values of their array contents while indexing A or B by symbols is the same as accessing their properties. For example:

A.Δx             # yields 0.2
A[:Δx]           # idem
A.units          # yields "photons"
A[:units]        # idem
A[:,3] .= 3.14   # set some values in the array contents of A
sum(A)           # yields the sum of the values of A
A[:gizmo] = π    # set a property
A.gizmo = π      # idem
pop!(A, :gizmo)  # yields property value and delete it

Creating an annotated array is summarized by:

using ArrayTools.AnnotatedArrays
A = AnnotatedArray(arr, prop)
B = AnnotatedArray{T}(init, dims, prop)

where arr is an existing array or an expression whose result is an array, prop specifies the initial properties (more on this below), T is the type of array element, init is usually undef and dims is a tuple of array dimensions. If arr is an existing array, the object A created above will reference this array and hence share its contents with the caller (call copy(arr) to avoid that). The same applies if the initial properties are specified by a dictionary.

The properties prop can be specified by keywords, by key-value pairs, as a dictionary or as a named tuple. To avoid ambiguities, these different styles cannot be mixed. Below are a few examples:

using ArrayTools.AnnotatedArrays
arr = zeros(3,4,5)
A = AnnotatedArray(arr,  units  =  "µm",  Δx  =  0.1,  Δy  =  0.2)
B = AnnotatedArray(arr, :units  => "µm", :Δx  => 0.1, :Δy  => 0.2)
C = AnnotatedArray(arr, "units" => "µm", "Δx" => 0.1, "Δy" => 0.2)
D = AnnotatedArray(arr, (units  =  "µm",  Δx  =  0.1,  Δy  =  0.2))

The two first examples (A and B) both yield an annotated array whose properties have symbolic keys and can have any type of value. The third example (C) yields an annotated array whose properties have string keys and can have any type of value. The properties of A, B and C are dynamic: they can be modified, deleted and new properties can be inserted. The fourth example (D) yields an annotated array whose properties are stored by a named tuple, they are immutable and have symbolic keys.

Accessing a property is possible via the syntax obj[key] or, for symbolic and textual keys, via the syntax obj.key. Accessing immutable properties is the fastest while accessing textual properties as obj.key is the slowest (because it involves converting a symbol into a string).

When initially specified by keywords or as key-value pairs, the properties are stored in a dictionary whose key type is specialized if possible (for efficiency) but with value type Any (for flexibility). If one wants specific properties key and value types, it is always possible to explicitly specify a dictionary in the call to AnnotatedArray. For instance:

E = AnnotatedArray(arr, Dict{Symbol,Int32}(:a => 1, :b => 2))

yields an annotated array whose properties have symbolic keys and integer values of type Int32.

Property key types are not limited to Symbol or String, but, to avoid ambiguities, key types must be more specialized than Any and must not inherit from types like Integer or CartesianIndex which are reserved for indexing the array contents of annotated arrays.

If the dictionary is unspecified, the properties are stored in a, initially empty, dictionary with symbolic keys and value of any type, i.e. Dict{Symbol,Any}().

Iterating on an annotated array is iterating on its array values. To iterate on its properties, call the properties method which returns the object storing the properties:

dims = (100, 50)
T = Float32
N = length(dims)
A = AnnotatedArray(zeros(T, dims), units = "µm", Δx = 0.2, Δy = 0.1)
for (k,v) in properties(A)
    println(k, " => ", v)

Similar types are provided by MetaArrays, MetadataArrays and ImageMetadata.

General tools

Array indexing

The all_indices method takes any number of array arguments and yields an efficient iterator for visiting all indices each index of the arguments. Its behavior is similar to that of eachindex method except that all_indices throws a DimensionMismatch exception if the arrays have different axes. It is always safe to specify @inbounds (and @simd) for a loop like:

for i in all_indices(A, B, C, D)
   A[i] = B[i]*C[i] + D[i]

The eachindex and all_indices methods are very useful when writing loops over array elements so as to be agnostic to which specfic indexing rule is the most suitable. Some algorithms are however more efficient or easier to write if all involved arrays are indexed by a single 1-based index. In that case, using ArrayTools provides:


which checks whether array A is suitable for fast indexing (by a single integer starting at 1); if it does, A is returned to the caller; otherwise, the contents of A is converted to a suitable array type implementing fast indexing and is returned to the caller.

To just check whether array A is suitable for fast indexing, call:

is_fast_array(A) -> bool

Multiple arguments can be checked at the same time: is_fast_array(A,B,...) is the same as is_fast_array(A) && is_fast_array(B) && is_flat_array(...).

Array storage

When calling (with ccall) a compiled function coded in another language (C, FORTRAN, etc.), you have to make sure that array arguments have the same storage as assumed by these languages so that it is safe to pass the pointer of the array to the compiled function.

Typically, you want to ensure that the elements are stored in memory contiguously and in column-major order. This can be ckecked by calling:

is_flat_array(A) -> bool

or, with several arguments:

is_flat_array(A, B, C, ...)  -> bool

In order to get an array with such flat storage and possibly with a given element type T, call:

to_flat_array([T = eltype(A),] A)

which just returns A if the requirements hold or converts A to a suitable array form.


  • What is the difference between IndexStyle (defined in base Julia) and IndexingTrait (defined in ArrayTools)?

    If IndexStyle(A) === IndexLinear(), then array A can be efficiently indexed by one integer (even if A is multidimensional) and column-major ordering is used to access the elements of A. The only (known) other possibility is IndexStyle(A) === IndexCartesian().

    If IndexingTrait(A) === FastIndexing(), then IndexStyle(A) === IndexLinear() also holds (see above) and array A has standard 1-based indices.

  • What is the difference between Base.has_offset_axes (provided by Julia) and has_standard_indexing (provided by ArrayTools)?

    For the caller, has_standard_indexing(args...) yields the opposite result as Base.has_offset_axes(args...). Furthermore, has_standard_indexing is a bit faster.


ArrayTools is an official Julia package and is easy to install. In Julia, hit the ] key to switch to the package manager REPL (you should get a ... pkg> prompt) and type:

... pkg> add ArrayTools

Required Packages

No packages found.