Common functional iterator patterns.

Iterators.jl has been deprecated in favour of IterTools.jl. Please update your package dependencies: Iterators 0.3.1 maps to IterTools 0.1.0.

See #104 for more information.

Install this package with `Pkg.add("Iterators")`

**takestrict**(xs, n)

Equivalent to `take`

, but will throw an exception if fewer than `n`

items
are encountered in `xs`

.

**repeatedly**(f, [n])Call a function

`n`

times, or infinitely if`n`

is omitted.Example:

`for t in repeatedly(time_ns, 3) @show t end`

```
t = 0x0000592ff83caf87
t = 0x0000592ff83d8cf4
t = 0x0000592ff83dd11e
```

**chain**(xs...)Iterate through any number of iterators in sequence.

Example:

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

```
i = 1
i = 2
i = 3
i = 'a'
i = 'b'
i = 'c'
```

**product**(xs...)Iterate over all combinations in the cartesian product of the inputs.

Example:

`for p in product(1:3,1:2) @show p end`

yields

`p = (1,1) p = (2,1) p = (3,1) p = (1,2) p = (2,2) p = (3,2)`

**distinct**(xs)Iterate through values skipping over those already encountered.

Example:

`for i in distinct([1,1,2,1,2,4,1,2,3,4]) @show i end`

```
i = 1
i = 2
i = 4
i = 3
```

**nth**(xs, n)Return the n'th element of

`xs`

. Mostly useful for non indexable collections.Example:

`nth(1:3, 3)`

```
3
```

**takenth**(xs, n)Iterate through every n'th element of

`xs`

Example:

`collect(takenth(5:15,3))`

```
3-element Array{Int32,1}:
7
10
13
```

**partition**(xs, n, [step])Group values into

`n`

-tuples.Example:

`for i in partition(1:9, 3) @show i end`

```
i = (1,2,3)
i = (4,5,6)
i = (7,8,9)
```

```
If the `step` parameter is set, each tuple is separated by `step` values.
Example:
```

```
for i in partition(1:9, 3, 2)
@show i
end
```

```
i = (1,2,3)
i = (3,4,5)
i = (5,6,7)
i = (7,8,9)
```

**groupby**(f, xs)Group consecutive values that share the same result of applying

`f`

.Example:

`for i in groupby(x -> x[1], ["face", "foo", "bar", "book", "baz", "zzz"]) @show i end`

```
i = ASCIIString["face","foo"]
i = ASCIIString["bar","book","baz"]
i = ASCIIString["zzz"]
```

**imap**(f, xs1, [xs2, ...])Iterate over values of a function applied to successive values from one or more iterators.

Example:

`for i in imap(+, [1,2,3], [4,5,6]) @show i end`

```
i = 5
i = 7
i = 9
```

**subsets**(xs)Iterate over every subset of a collection

`xs`

.Example:

`for i in subsets([1,2,3]) @show i end`

```
i = []
i = [1]
i = [2]
i = [1,2]
i = [3]
i = [1,3]
i = [2,3]
i = [1,2,3]
```

**subsets**(xs, k)Iterate over every subset of size

`k`

from a collection`xs`

.Example:

`for i in subsets([1,2,3],2) @show i end`

```
i = [1,2]
i = [1,3]
i = [2,3]
```

**peekiter**(xs)Add possibility to peek head element of an iterator without updating the state.

Example:

`it = peekiter(["face", "foo", "bar", "book", "baz", "zzz"]) s = start(it) @show peek(it, s) @show peek(it, s) x, s = next(it, s) @show x @show peek(it, s)`

```
peek(it,s) = Nullable("face")
peek(it,s) = Nullable("face") # no change
x = "face"
peek(it,s) = Nullable("foo")
```

**ncycle**(xs,n)Cycles through an iterator

`n`

timesExample:

`for i in ncycle(1:3, 2) @show i end`

```
i = 1
i = 2
i = 3
i = 1
i = 2
i = 3
```

**iterate**(f, x)Iterate over successive applications of

`f`

, as in`f(x), f(f(x)), f(f(f(x))), ...`

.Example:

`for i in take(iterate(x -> 2x, 1), 5) @show i end`

```
i = 1
i = 2
i = 4
i = 8
i = 16
```

`@itr`

macro for automatic inlining in `for`

loopsUsing functional iterators is powerful and concise, but may incur in some
overhead, and manually inlining the operations can typically improve
performance in critical parts of the code. The `@itr`

macro is provided to do
that automatically in some cases. Its usage is trivial: for example, given this code:

```
for (x,y) in zip(a,b)
@show x,y
end
```

the automatically inlined version can be obtained by simply doing:

```
@itr for (x,y) in zip(a,b)
@show x,y
end
```

This typically results in faster code, but its applicability has limitations:

- it only works with
`for`

loops; - if multiple nested iterators are used, only the outermost is affected by the transformation;
- explicit expressions are required (i.e. when a
`Tuple`

is expected, an explicit tuple must be provided, a tuple variable won't be accepted); - splicing is not supported;
- multidimensional loops (i.e. expressions such as
`for x in a, y in b`

) are not supported

The `@itr`

macro can be used with the following supported iterators:

- zip
- enumerate
- take
- takestrict
- drop
- chain

11/13/2012

6 months ago

120 commits