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 times

  Example:

  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
  

Using 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