# Introducing Julia/Controlling the flow

## Contents

- 1 Different ways to control the flow
- 2 Ternary expressions
- 3 Boolean switching expressions
- 4 If and Else
- 5 For loops and iteration
- 6 Making your own iterable objects
- 7 While loops
- 8 Exceptions
- 9 Do block

### Different ways to control the flow[edit]

Typically each line of a Julia program is evaluated in turn. There are various ways to control and modify the flow of evaluation. These correspond with the constructs used in other languages:

**ternary**and**compound**expressions**Boolean**switching expressions**if elseif else end**- conditional evaluation**for end**- iterative evaluation**while end**- iterative conditional evaluation**try catch error throw**exception handling**do**blocks

### Ternary expressions[edit]

Often you'll want to do job A (or call function A) if some condition is true, or job B (function B) if it isn't. The quickest way to write this is using the ternary operator ("?" and ":"):

```
julia> x = 1
1
julia> x > 3 ? "yes" : "no"
"no"
julia> x = 5
5
julia> x > 3 ? "yes" : "no"
"yes"
```

Here's another example:

```
julia> x = 0.3
0.3
julia> x < 0.5 ? sin(x) : cos(x)
0.29552020666133955
```

and Julia returned the value of `sin(x)`

, because x was less than 0.5. `cos(x)`

wasn't evaluated at all.

### Boolean switching expressions[edit]

Boolean operators let you evaluate an expression if a condition is true. You can combine the condition and expression using `&&`

or `||`

. `&&`

means "and", and `||`

means "or". Since Julia evaluates expressions one by one, you can easily arrange for an expression to be evaluated only if a previous condition is true or false.

The following example uses a Julia function that returns true or false depending on whether the number is odd: `isodd(n)`

.

With `&&`

, both parts have to be true, so we can write this:

```
julia> isodd(1000003) && @warn("That's odd!")
WARNING: That's odd!
julia> isodd(1000004) && @warn("That's odd!")
false
```

If the first condition (number is odd) is true, the second expression is evaluated. If the first isn't true, the expression isn't evaluated, and just the condition is returned.

With the `||`

operator, on the other hand:

```
julia> isodd(1000003) || @warn("That's odd!")
true
julia> isodd(1000004) || @warn("That's odd!")
WARNING: That's odd!
```

If the first condition is true, there's no need to evaluate the second expression, since we already have the one truth value we need for "or", and it returns the value true. If the first condition is false, the second expression is evaluated, because that one might turn out to be true.

This type of evaluation is also called "short-circuit evaluation".

### If and Else[edit]

For a more general — and traditional — approach to conditional execution, you can use `if`

, `elseif`

, and `else`

. If you're used to other languages, don't worry about white space, braces, indentation, brackets, semicolons, or anything like that, but remember to finish the conditional construction with ` end`

.

```
name = "Julia"
if name == "Julia"
println("I like Julia")
elseif name == "Python"
println("I like Python.")
println("But I prefer Julia.")
else
println("I don't know what I like")
end
```

The `elseif`

and `else`

parts are optional too:

```
name = "Julia"
if name == "Julia"
println("I like Julia")
end
```

Just don't forget the `end`

!

How about 'switch' and 'case' statements? Well, you don't have to learn the syntax for those, because they don't exist!

#### ifelse[edit]

There's an `ifelse`

function, too. It looks like this in action:

julia>s = ifelse(false, "hello", "goodbye") * " world"

`ifelse`

is an ordinary function, which evaluates all the arguments, and returns the second or third, depending on the value of the first. With the conditional `if`

or `? ... :`

, only the expressions in the chosen route are evaluated.

### For loops and iteration[edit]

Working through a list or a set of values or from a start value to a finish value are all examples of iteration, and the `for`

... `end`

construction can let you iterate through a number of different types of object, including ranges, arrays, sets, dictionaries, and strings.

Here's the standard syntax for a simple iteration through a range of values:

julia>for i in 0:10:100println(i)end0 10 20 30 40 50 60 70 80 90 100

The variable `i`

takes the value of each element in the array (which is built from a range object) in turn — here stepping from 0 to 100 in steps of 10.

julia>for color in ["red", "green", "blue"]# an arrayprint(color, " ")endred green blue

julia>for letter in "julia"# a stringprint(letter, " ")endj u l i a

julia>for element in (1, 2, 4, 8, 16, 32)# a tupleprint(element, " ")end1 2 4 8 16 32

julia>for i in Dict("A"=>1, "B"=>2)# a dictionaryprintln(i)end"B"=>2 "A"=>1

julia>for i in Set(["a", "e", "a", "e", "i", "o", "i", "o", "u"])println(i)ende o u a i

We haven't yet met sets and dictionaries, but iterating through them is exactly the same.

You can iterate through a 2D array, stepping "down" through column 1 from top to bottom, then through column 2, and so on:

julia>a = reshape(1:100, (10, 10))10x10 Array{Int64,2}: 1 11 21 31 41 51 61 71 81 91 2 12 22 32 42 52 62 72 82 92 3 13 23 33 43 53 63 73 83 93 4 14 24 34 44 54 64 74 84 94 5 15 25 35 45 55 65 75 85 95 6 16 26 36 46 56 66 76 86 96 7 17 27 37 47 57 67 77 87 97 8 18 28 38 48 58 68 78 88 98 9 19 29 39 49 59 69 79 89 99 10 20 30 40 50 60 70 80 90 100

julia>for n in aprint(n, " ")end1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

You can use `=`

instead of `in`

.

##### Iterating over an array and updating it[edit]

When you're iterating over an array, the array is checked each time through the loop, in case it's changed. A mistake you should avoid making is to use `push!`

to make an array grow in the middle of a loop. Run the following text carefully, and be ready to `Ctrl-C`

when you've seen enough (otherwise your computer will eventually crash):

julia>c = [1]1-element Array{Int64,1}: 1 julia>for i in cpush!(c, i)@show csleep(1)endc = [1,1] c = [1,1,1] c = [1,1,1,1] ...

#### Loop variables and scope[edit]

The variable that steps through each item—the 'loop variable'—exists only inside the loop, and disappears as soon as the loop finishes.

julia>for i in 1:10@show iendi = 1 i = 2 i = 3 i = 4 i = 5 i = 6 i = 7 i = 8 i = 9 i = 10 julia>iERROR: UndefVarError: i not defined

If you want to remember the value of the loop variable outside the loop (eg if you had to exit the loop and needed to know the value you'd reached), use the `global`

keyword to define a variable that outlasts the loop.

julia>for i in 1:10global howfarif i % 4 == 0howfar = iendend

julia>howfar8

Here, `howfar`

didn't exist before the loop, but it survived to tell its story when the looping was over. If `howfar`

existed before the loop started, you can change its value only if you use `global`

in the loop.

Working in the REPL is slightly different to how you write code inside functions. In a function, you would write this:

```
function f()
howfar = 0
for i in 1:10
if i % 4 == 0
howfar = i
end
end
return howfar
end
@show f()
```

`8`

#### Variables declared inside a loop[edit]

In a similar way, if you declare a new variable inside a loop, it won't exist once the loop finishes. In this example, `k`

is created inside:

julia>for i in 1:5k = i^2println("$(i) squared is $(k)")end

1 squared is 1 2 squared is 4 3 squared is 9 4 squared is 16 5 squared is 25

so it doesn't exist after the loop has finished:

julia>kERROR: UndefVarError: k not defined

Variables created inside one iteration of a loop are forgotten at the end of each iteration. In this loop:

```
for i in 1:10
z = i
println("z is $z")
end
```

```
z is 1
z is 2
z is 3
z is 4
z is 5
z is 6
z is 7
z is 8
z is 9
z is 10
```

`z`

is created afresh each time. If you want a variable to persist from iteration to iteration, it has to be global:

julia>counter = 00julia>for i in 1:10global countercounter += iendjulia>counter55

To see this in more detail, consider the following code.

```
for i in 1:10
if ! @isdefined z
println("z isn't defined")
end
z = i
println("z is $z")
end
```

Perhaps you expected only the first loop to produce the "z isn't defined error"? In fact, even if `z`

is created in the body of the loop, it is undefined at the start of the next iteration.

```
z isn't defined
z is 1
z isn't defined
z is 2
z isn't defined
z is 3
z isn't defined
z is 4
z isn't defined
z is 5
z isn't defined
z is 6
z isn't defined
z is 7
z isn't defined
z is 8
z isn't defined
z is 9
z isn't defined
z is 10
```

Again, use the `global`

keyword to force `z`

to be available outside the loop once it's been created:

```
for i in 1:10
global z
if ! @isdefined z
println("z isn't defined")
else
println("z was $z")
end
z = i
println("z is $z")
end
```

```
z isn't defined
z is 1
z was 1
z is 2
z was 2
...
z is 9
z was 9
z is 10
```

although, if you're working in global scope, `z`

is now available everywhere, with the value 10.

This behaviour is because we're working in the REPL. It's generally better practice to put your code inside functions, where you don't need to mark variables inherited from outside the loop as global:

```
function f()
counter = 0
for i in 1:10
counter += i
end
return counter
end
```

julia>f()55

#### Fine tuning the loop: Continue[edit]

Sometimes on a particular iteration you might want to skip to the next value. You can use `continue`

to skip the rest of the code inside the loop and start the loop again with the next value.

```
for i in 1:10
if i % 3 == 0
continue
end
println(i) # this and subsequent lines are
# skipped if i is a multiple of 3
end
1
2
4
5
7
8
10
```

#### Comprehensions[edit]

This oddly-named concept is simply a way of generating and collecting items. In mathematical circles you would say something like this:

"Let S be the set of all elements n where n is greater than or equal to 1 and less than or equal to 10".

In Julia, you can write this as:

julia>S = Set([n for n in 1:10])Set([7,4,9,10,2,3,5,8,6,1])

and the `[n for n in 1:10]`

construction is called **array comprehension** or **list comprehension** ('comprehension' in the sense of 'getting everything' rather than 'understanding'). The outer brackets collect together the elements generated by evaluating the expression placed before the `for`

iteration. Instead of `end`

, use a square bracket to finish.

julia>[i^2 for i in 1:10]10-element Array{Int64,1}: 1 4 9 16 25 36 49 64 81 100

The type of elements can be specified:

julia>Complex[i^2 for i in 1:10]10-element Array{Complex,1}: 1.0+0.0im 4.0+0.0im 9.0+0.0im 16.0+0.0im 25.0+0.0im 36.0+0.0im 49.0+0.0im 64.0+0.0im 81.0+0.0im 100.0+0.0im

But Julia can work out the types of the results you're producing:

julia>[(i, sqrt(i)) for i in 1:10]10-element Array{Tuple{Int64,Float64},1}: (1,1.0) (2,1.41421) (3,1.73205) (4,2.0) (5,2.23607) (6,2.44949) (7,2.64575) (8,2.82843) (9,3.0) (10,3.16228)

Here's how to make a dictionary via comprehension:

julia>Dict(string(Char(i + 64)) => i for i in 1:26)Dict{String,Int64} with 26 entries: "Z" => 26 "Q" => 17 "W" => 23 "T" => 20 "C" => 3 "P" => 16 "V" => 22 "L" => 12 "O" => 15 "B" => 2 "M" => 13 "N" => 14 "H" => 8 "A" => 1 "X" => 24 "D" => 4 "G" => 7 "E" => 5 "Y" => 25 "I" => 9 "J" => 10 "S" => 19 "U" => 21 "K" => 11 "R" => 18 "F" => 6

Next, here are two iterators in a comprehension, separated with a comma, which makes generating tables very easy. Here we're making a tuple-table:

julia>[(r,c) for r in 1:5, c in 1:2]5×2 Array{Tuple{Int64,Int64},2}: (1,1) (1,2) (2,1) (2,2) (3,1) (3,2) (4,1) (4,2) (5,1) (5,2)

`r`

goes through five cycles, one cycle for every value of `c`

. Nested loops work in the opposite manner. Here the column-major order is respected, as shown when the array is filled with nanosecond time values:

julia>[Int(time_ns()) for r in 1:5, c in 1:2]5×2 Array{Int64,2}: 1223184391741562 1223184391742642 1223184391741885 1223184391742817 1223184391742067 1223184391743009 1223184391742256 1223184391743184 1223184391742443 1223184391743372

You can supply a test expression as well to filter the production. For example, produce all the integers between 1 and 100 that are exactly divisible by 7:

julia>[x for x in 1:100 if x % 7 == 0]14-element Array{Int64,1}: 7 14 21 28 35 42 49 56 63 70 77 84 91 98

##### Generator expressions[edit]

Like comprehensions, generator expressions can be used to produce values from iterating a variable, but, unlike comprehensions, the values are produced on demand.

julia>sum(x^2 for x in 1:10)385

julia>collect(x for x in 1:100 if x % 7 == 0)14-element Array{Int64,1}: 7 14 21 28 35 42 49 56 63 70 77 84 91 98

#### Enumerating arrays[edit]

Often you want to go through an array element by element while also keeping track of the index number of each element. The `enumerate()`

function gives you an **iterable** version of something, producing both an index number and the value at each index number:

julia>m = rand(0:9, 3, 3)3×3 Array{Int64,2}: 6 5 3 4 0 7 1 7 4 julia>[i for i in enumerate(m)]3×3 Array{Tuple{Int64,Int64},2}: (1, 6) (4, 5) (7, 3) (2, 4) (5, 0) (8, 7) (3, 1) (6, 7) (9, 4)

The array is checked for possible changes at each iteration of the loop.

#### Zipping arrays[edit]

Sometimes you want to work through two or more arrays at the same time, taking the first element of each array first, then the second, and so on. This is possible using the well-named `zip()`

function:

julia>for i in zip(0:10, 100:110, 200:210)println(i)end

(0,100,200) (1,101,201) (2,102,202) (3,103,203) (4,104,204) (5,105,205) (6,106,206) (7,107,207) (8,108,208) (9,109,209) (10,110,210)

You'd think it would all go wrong if the arrays were different sizes. What if the third array is too big, or too small?

julia>for i in zip(0:10, 100:110, 200:215)println(i)end(0,100,200) (1,101,201) (2,102,202) (3,103,203) (4,104,204) (5,105,205) (6,106,206) (7,107,207) (8,108,208) (9,109,209) (10,110,210)

but Julia isn't fooled — any oversupply or undersupply in any one of the arrays is handled gracefully.

julia>for i in zip(0:15, 100:110, 200:210)println(i)end(0,100,200) (1,101,201) (2,102,202) (3,103,203) (4,104,204) (5,105,205) (6,106,206) (7,107,207) (8,108,208) (9,109,209) (10,110,210)

This however does not work in case of filling of arrays, in this case dimensions must match:

(v1.0) julia>[i for i in zip(0:4, 100:102, 200:202)]ERROR: DimensionMismatch("dimensions must match") Stacktrace: [1] promote_shape at ./indices.jl:129 [inlined] [2] axes(::Base.Iterators.Zip{UnitRange{Int64},Base.Iterators.Zip2{UnitRange{Int64},UnitRange{Int64}}}) at ./iterators.jl:371 [3] _array_for at ./array.jl:611 [inlined] [4] collect(::Base.Generator{Base.Iterators.Zip{UnitRange{Int64},Base.Iterators.Zip2{UnitRange{Int64},UnitRange{Int64}}},getfield(Main, Symbol("##5#6"))}) at ./array.jl:624 [5] top-level scope at none:0

(v1.0) julia>[i for i in zip(0:2, 100:102, 200:202)]3-element Array{Tuple{Int64,Int64,Int64},1}: (0, 100, 200) (1, 101, 201) (2, 102, 202)

#### Iterable objects[edit]

The "for something in something" construction is the same for everything that you can iterate through: arrays, dictionaries, strings, sets, ranges, and so on. In Julia this is a general principle: there are a number of ways in which you can create an "iterable object", an object that is designed to be used as part of the iteration process that provides the elements one at a time.

The most obvious example we've already met is the range object. It doesn't look much when you type it into the REPL:

julia>ro = 0:2:1000:2:100

But it gives you the numbers when you start iterating through it:

julia>[i for i in ro]51-element Array{Int64,1}: 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 ⋮ 74 76 78 80 82 84 86 88 90 92 94 96 98 100

Should you want the numbers from a range (or other iterable object) in an array, you can use `collect()`

to collect them up:

julia>collect(0:25:100)5-element Array{Int64,1}: 0 25 50 75 100

You don't have to collect every element of an iterable object, you can just iterate through it. This can be particularly helpful when you have iterable objects created by other Julia functions. For example, `permutations()`

creates an iterable object containing all the permutations of an array. You could of course use `collect()`

to grab them and make a new array:

```
julia> collect(permutations(1:4))
24-element Array{Array{Int64,1},1}:
[1,2,3,4]
[1,2,4,3]
…
[4,3,2,1]
```

but on anything large there are going to hundreds or thousands of permutations. That's the reason why iterator objects don't produce all the values from the iteration at the same time: memory and performance. A range object doesn't take up much room, even if iterating over it might take ages, depending on how big the range is. If you generate all the numbers at once, rather than only producing them when they're needed, they would all have to be stored somewhere until you need them…

Julia provides iterable objects for working with other types of data. For example, when you're working with files, you can treat an open file as an iterable object:

```
filehandle = "/Users/me/.julia/logs/repl_history.jl"
for line in eachline(filehandle)
println(length(line), line)
end
```

##### Use eachindex()[edit]

A common pattern when iterating through arrays is to perform some task for each value of `i`

, where `i`

is the index number of each element, not the element:

```
for i = 1:length(A)
# do something with i or A[i]
end
```

There is a more efficient (i.e. faster in some situations) way to do this:

```
for i in eachindex(A)
# do something with i or A[i]
end
```

#### Even more iterators[edit]

There's a Julia package called IterTools.jl that provides some advanced iterator functions.

(v1.0) pkg>] add IterToolsjulia>using IterTools

For example, `partition()`

groups the objects in the iterator into easily-handled chunks:

julia>collect(partition(1:10, 3, 1))8-element Array{Tuple{Int64,Int64,Int64},1}: (1, 2, 3) (2, 3, 4) (3, 4, 5) (4, 5, 6) (5, 6, 7) (6, 7, 8) (7, 8, 9) (8, 9, 10)

`chain()`

works through all the iterators one after the other:

```
for i in chain(1:3, ['a', 'b', 'c'])
@show i
end
i = 1
i = 2
i = 3
i = 'a'
i = 'b'
i = 'c'
```

`subsets()`

works through all subsets of an object. You can specify a size:

```
for i in subsets(collect(1:6), 3)
@show i
end
i = [1,2,3]
i = [1,2,4]
i = [1,2,5]
i = [1,2,6]
i = [1,3,4]
i = [1,3,5]
i = [1,3,6]
i = [1,4,5]
i = [1,4,6]
i = [1,5,6]
i = [2,3,4]
i = [2,3,5]
i = [2,3,6]
i = [2,4,5]
i = [2,4,6]
i = [2,5,6]
i = [3,4,5]
i = [3,4,6]
i = [3,5,6]
i = [4,5,6]
```

#### Nested loops[edit]

If you want to nest one loop inside another, you don't have to duplicate the ` for`

and ` end`

keywords. Just use a comma:

```
julia> for x in 1:10, y in 1:10
@show (x, y)
end
(x,y) = (1,1)
(x,y) = (1,2)
(x,y) = (1,3)
(x,y) = (1,4)
(x,y) = (1,5)
(x,y) = (1,6)
(x,y) = (1,7)
(x,y) = (1,8)
(x,y) = (1,9)
(x,y) = (1,10)
(x,y) = (2,1)
(x,y) = (2,2)
(x,y) = (2,3)
(x,y) = (2,4)
(x,y) = (2,5)
(x,y) = (2,6)
(x,y) = (2,7)
(x,y) = (2,8)
(x,y) = (2,9)
(x,y) = (2,10)
(x,y) = (3,1)
(x,y) = (3,2)
...
(x,y) = (9,9)
(x,y) = (9,10)
(x,y) = (10,1)
(x,y) = (10,2)
(x,y) = (10,3)
(x,y) = (10,4)
(x,y) = (10,5)
(x,y) = (10,6)
(x,y) = (10,7)
(x,y) = (10,8)
(x,y) = (10,9)
(x,y) = (10,10)
```

(The useful `@show`

macro prints out the names of things and their values.)

One difference between the shorter and longer forms of nesting loops is the behaviour of `break`

:

```
julia> for x in 1:10
for y in 1:10
@show (x, y)
if y % 3 == 0
break
end
end
end
(x,y) = (1,1)
(x,y) = (1,2)
(x,y) = (1,3)
(x,y) = (2,1)
(x,y) = (2,2)
(x,y) = (2,3)
(x,y) = (3,1)
(x,y) = (3,2)
(x,y) = (3,3)
(x,y) = (4,1)
(x,y) = (4,2)
(x,y) = (4,3)
(x,y) = (5,1)
(x,y) = (5,2)
(x,y) = (5,3)
(x,y) = (6,1)
(x,y) = (6,2)
(x,y) = (6,3)
(x,y) = (7,1)
(x,y) = (7,2)
(x,y) = (7,3)
(x,y) = (8,1)
(x,y) = (8,2)
(x,y) = (8,3)
(x,y) = (9,1)
(x,y) = (9,2)
(x,y) = (9,3)
(x,y) = (10,1)
(x,y) = (10,2)
(x,y) = (10,3)
julia> for x in 1:10, y in 1:10
@show (x, y)
if y % 3 == 0
break
end
end
(x,y) = (1,1)
(x,y) = (1,2)
(x,y) = (1,3)
```

Notice that ` break`

breaks out of both inner and outer loops in the shorter form, but only out of the inner loop in the longer form.

#### Optimizing nested loops[edit]

With Julia, inner loops should concern rows rather than columns. This is due to how arrays are stored in memory. In this Julia array, for example, cells 1, 2, 3, and 4 are stored next to each other in memory (the 'column-major' format). So moving down the columns from 1 to 2 to 3 is faster than moving along rows, because jumping across from column to column, from 1 to 5 to 9, requires an extra calculation:

+-----+-----+-----+--+ | 1 | 5 | 9 | | | | | +--------------------+ | 2 | 6 | 10 | | | | | +--------------------+ | 3 | 7 | 11 | | | | | +--------------------+ | 4 | 8 | 12 | | | | | +-----+-----+-----+--+

The following examples consist of simple loops, but the way the rows and columns are iterated differ. The "bad" version looks along the first row column by column, then moves down to the next row, and so on.

```
function laplacian_bad(lap_x::Array{Float64,2}, x::Array{Float64,2})
nr, nc = size(x)
for ir = 2:nr-1, ic = 2:nc-1 # bad loop nesting order
lap_x[ir, ic] =
(x[ir+1, ic] + x[ir-1, ic] +
x[ir, ic+1] + x[ir, ic-1]) - 4*x[ir, ic]
end
end
```

In the "good" version, the two loops are nested properly, so that the inner loop moves down through the rows, following the memory layout of the array:

```
function laplacian_good(lap_x::Array{Float64,2}, x::Array{Float64,2})
nr,nc = size(x)
for ic = 2:nc-1, ir = 2:nr-1 # good loop nesting order
lap_x[ir,ic] =
(x[ir+1,ic] + x[ir-1,ic] +
x[ir,ic+1] + x[ir,ic-1]) - 4*x[ir,ic]
end
end
```

Another way to increase the speed is to remove the array bounds checking, using the macro `@inbounds`

:

```
function laplacian_good_nocheck(lap_x::Array{Float64,2}, x::Array{Float64,2})
nr,nc = size(x)
for ic = 2:nc-1, ir = 2:nr-1 # good loop nesting order
@inbounds begin lap_x[ir,ic] = # no array bounds checking
(x[ir+1,ic] + x[ir-1,ic] +
x[ir,ic+1] + x[ir,ic-1]) - 4*x[ir,ic]
end
end
end
```

Here's the test function:

```
function main_test(nr, nc)
field = zeros(nr, nc)
for ic = 1:nc, ir = 1:nr
if ir == 1 || ic == 1 || ir == nr || ic == nc
field[ir,ic] = 1.0
end
end
lap_field = zeros(size(field))
t = @elapsed laplacian_bad(lap_field, field)
println(rpad("laplacian_bad", 30), t)
t = @elapsed laplacian_good(lap_field, field)
println(rpad("laplacian_good", 30), t)
t = @elapsed laplacian_good_nocheck(lap_field, field)
println(rpad("laplacian_good no check", 30), t)
end
```

and the results show the difference in performance just based on the row/column scanning order. The "no check" version is even faster....

julia> main_test(10000,10000) laplacian_bad 1.947936034 laplacian_good 0.190697149 laplacian_good no check 0.092164871

### Making your own iterable objects[edit]

It's possible to design your own iterable objects. When you're defining your type, you add a couple of methods to Julia's `iterate()`

function. Then you can use something like `for`

.. `end`

loop to work through the components of your object, and these `iterate()`

methods are called automatically as necessary.

The following example shows how you can create an iterable object that generates the sequence of strings combining an uppercase letter with a number from 1 to 9. So the first item in our sequence is "A1", followed by "A2", "A3", up to "A9", then "B1", "B2", and so on, finishing at "Z9".

First, we'll define a new type called SN (StringNumber):

```
mutable struct SN
str::String
num::Int64
end
```

Later we'll create an iterable object of this type using something like this:

```
sn = SN("A", 1)
```

and the iterator will yield all the strings up to "Z9".

We must now add two methods to the `iterate()`

function. This function already exists in Julia (that's why you can iterate over all the basic data objects), so the `Base`

prefix is required: we're adding a new method to the existing `iterate()`

function, one which is designed to handle these special objects.

The first method takes no arguments, except for the type, and is for starting the iteration process off.

```
function Base.iterate(sn::SN)
str = sn.str
num = sn.num
if num == 9
nextnum = 1
nextstr = string(Char(Int(str[1])) + 1)
else
nextnum = num + 1
nextstr = str
end
return (sn, SN(nextstr, nextnum))
end
```

This returns a tuple: the first value, and the future value of the iterator, which we've calculated (just in case we ever want to start the iterator at a point other than "A1").

The second method of `iterate()`

takes two arguments: an iterable object and the current state. It again returns a tuple of two values, the next item and the next state. But first, if there are no more values available, the `iterate()`

function should return nothing.

```
function Base.iterate(sn::SN, state)
# check if we've finished?
if state.str == "[" # when Z changes to [ we're done
return
end
# we haven't finished, so we'll use the incoming one immediately
str = state.str
num = state.num
# and prepare the one after that, to be saved for later
if num == 9
nextnum = 1
nextstr = string(Char(Int(str[1])) + 1)
else
nextnum = num + 1
nextstr = state.str
end
# return: the one to use next, the one after that
return (SN(str, num), SN(nextstr, nextnum))
end
```

Telling the iterator when it's finished is easy, because as soon as the incoming state contains a "[" we've finished, because the code for "[" (91) is immediately after the code for "Z" (90).

With these two methods added to handle the SN type, it's now possible to iterate through them. It's also useful to add methods for a few other Base functions, such as `show()`

and `length()`

. The `length()`

method works out how many more SN strings are available starting at `sn`

.

```
Base.show(io::IO, sn::SN) = print(io, string(sn.str, sn.num))
function Base.length(sn::SN)
cn1 = Char(Int(Char(sn.str[1]) + 1))
cnz = Char(Int(Char('Z')))
(length(cn1:cnz) * 9) + (10 - sn.num)
end
```

The iterator is now ready for use:

julia>sn = SN("A", 1)A1 julia>for i in sn@show iend

```
i = A1
i = A2
i = A3
i = A4
i = A5
i = A6
i = A7
i = A8
...
i = Z6
i = Z7
i = Z8
i = Z9
```

julia>for sn in SN("K", 9)print(sn, " ")end

```
K9 L1 L2 L3 L4 L5 L6 L7 L8 L9 M1 M2 M3 M4 M5 M6 M7 M8 M9 N1 N2 N3 N4 N5 N6 N7 N8
N9 O1 O2 O3 O4 O5 O6 O7 O8 O9 P1 P2 P3 P4 P5 P6 P7 P8 P9 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8
Q9 R1 R2 R3 R4 R5 R6 R7 R8 R9 S1 S2 S3 S4 S5 S6 S7 S8 S9 T1 T2 T3 T4 T5 T6 T7 T8
T9 U1 U2 U3 U4 U5 U6 U7 U8 U9 V1 V2 V3 V4 V5 V6 V7 V8 V9 W1 W2 W3 W4 W5 W6 W7 W8
W9 X1 X2 X3 X4 X5 X6 X7 X8 X9 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Z1 Z2 Z3 Z4 Z5 Z6 Z7 Z8
Z9
```

julia>collect(SN("Q", 7)),(Any[Q7, Q8, Q9, R1, R2, R3, R4, R5, R6, R7 … Y9, Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9],)

### While loops[edit]

To repeat some expressions while a condition is true, use the `while`

... `end`

construction.

julia>x = 00 julia>while x < 4println(x)global x += 1end0 1 2 3

If you're working outside a function, you'll need the `global`

declaration of `x`

before you can change its value. Inside a function, you don't need `global`

.

If you want the condition to be tested after the statements, rather than before, producing a "do .. until" form, use the following construction:

```
while true
println(x)
x += 1
x >= 4 && break
end
0
1
2
3
```

Here we're using a Boolean switch rather than an `if`

... `end`

statement.

Using Julia's macros, you can create your own control structures. See Metaprogramming.

### Exceptions[edit]

If you want to write code that checks for errors and handles them gracefully, use the `try`

... `catch`

construction.

With a `catch`

phrase, you can handle problems that occur in your code, possibly allowing the program to continue rather than grind to a halt.

In the next example, our code attempts to change the first character of a string directly (which isn't allowed, because strings in Julia can't be modified in place):

```
julia> s = "string";
julia> try
s[1] = "p"
catch e
println("caught an error: $e")
println("but we can continue with execution...")
end
caught an error: MethodError(setindex!,("string","p",1)) but we can continue with execution...
```

The `error()`

function raises an error exception with a given message.

### Do block[edit]

Finally, let's look at a `do`

block, which is another syntax form that, like the list comprehension, looks at first sight to be a bit backwards (i.e. it can perhaps be better understood by starting at the end and working towards the beginning).

Remember the `find()`

example from earlier?

julia>smallprimes = [1,2,3,5,7,11,13,17,19,23];

julia>findall(x -> isequal(13, x), smallprimes)1-element Array{Int64,1}: 7

The anonymous function (`x -> isequal(13, x)`

) is the first argument of `find()`

, and it operates on the second. But with a `do`

block, you can lift the function out and put it in between a `do ... end`

block construction:

julia>findall(smallprimes) do xisequal(x, 13)end1-element Array{Int64,1}: 7

You just lose the arrow and change the order, putting the `find()`

function and its target argument first, then adding the anonymous function's arguments and body after the `do`

.

The idea is that it's easier to write a longer anonymous function on multiple lines at the end of the form, rather than wedged in as the first argument.