 # Broadcasting and the dot notation

In some of the previous modules, we have used a notation that looked like function.(arguments), or x .+ y. In this module, we will talk about what the . notation does, and more broadly, what broadcasting is.

Let’s generate two arrays:

x, y = [1, 2, 0], [4, 2, 1]

([1, 2, 0], [4, 2, 1])


We think of them as arrays, but in practice, they are column vectors, and therefore have their own arithmetic. For example, we can add them (element-wise):

x + y

3-element Vector{Int64}:
5
4
1


But the multiplication is not defined. If we want to do an element-wise multiplication, we need to specify that we are applying an operation to each elements:

x .* y

3-element Vector{Int64}:
4
4
0


We can also take the difference between these vectors (element-wise):

x - y

3-element Vector{Int64}:
-3
0
-1


But diving one by the other can give an unexpected result:

x / y

3×3 Matrix{Float64}:
0.190476  0.0952381  0.047619
0.380952  0.190476   0.0952381
0.0       0.0        0.0


What is going on? In simple terms, / is defined as the multiplication of its first argument by the inverse of its second, and because Julia will adhere very strictly to what symbols means mathematically, this operation works.

This behavior can be surprising when coming from other languages, but this is a fact of life. If we want to ensure we work in an element-wise way, we need to use the . notation:

x ./ y

3-element Vector{Float64}:
0.25
1.0
0.0


This (using . to work on the elements instead of the entire object) is a nice little bit of syntactic sugar around what the operation actually is:

broadcast(*, x, y)

3-element Vector{Int64}:
4
4
0


In fact, we can call the .* version with the @lower macro from Meta, and see that it is indeed performing broadcasting:

Meta.@lower x .* y

:(\$(Expr(:thunk, CodeInfo(
@ none within top-level scope
1 ─ %1 = Base.broadcasted(*, x, y)
│   %2 = Base.materialize(%1)
└──      return %2
))))


Using element-wise notation can be extremely important when dealing with higher dimensional objects. For example, if we have two matrices:

U = [1 0; 0 1]
V = [1 2; 1 3]

2×2 Matrix{Int64}:
1  2
1  3


Calling * will perform matrix multiplication, because the * operator has a well accepted behavior when its arguments are matrices:

U * V

2×2 Matrix{Int64}:
1  2
1  3


In order to perform the Hadamard product, we need to call .* instead:

U .* V

2×2 Matrix{Int64}:
1  0
0  3


Again, this is equivalent to

broadcast(*, U, V)

2×2 Matrix{Int64}:
1  0
0  3


The . can also be used betwen a function and its arguments. For example, we can check for the odd elements in a matrix:

isodd.(V)

2×2 BitMatrix:
1  0
1  1


Which is again identical to

broadcast(isodd, V)

2×2 BitMatrix:
1  0
1  1


There is an interesting bit of notation we can use, which is to prefix a line where we only do element-wise operations with @.:

@. (x - y) / (x + y)

3-element Vector{Float64}:
-0.6
0.0
-1.0


This is equivalent to the more lengthy form using a . before each operator:

(x .- y) ./ (x .+ y)

3-element Vector{Float64}:
-0.6
0.0
-1.0


Using this notation will often be a good way to avoid writing a loop, and to apply a function rapidly to any collection. Named tuples and dictionaries are still going to resist this syntax, for reasons that are not necessarilly worth explaining here; but we have seen in previous modules how we can iterate over them regardless.