A First Example

Here is a simple example of a pipeline made of two pipes: transform and filter:

auto const source = std::vector<int>{0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
auto destination = std::vector<int>{};

source >>= pipes::filter([](int i){ return i % 2 == 0; })
       >>= pipes::transform([](int i){ return i * 2; })
       >>= pipes::push_back(destination);

// destination contains {0, 4, 8, 12, 16};

What's going on here:

Each elements of source is sent to filter.
Every time filter receives a piece of data, it sends its to the next pipe (here, transform) only if that piece of data satisfies filter's' predicate.
transform then applies its function on the data its gets and sends the result to the next pipe (here, pipes::push_back).
pipes::push_back push_backs the data it receives to its vector (here, destination).

A Second Example

Here is a more elaborate example with a pipeline that branches out in several directions:

A >>= pipes::transform(f)
  >>= pipes::filter(p)
  >>= pipes::unzip(pipes::push_back(B),
                   pipes::demux(pipes::push_back(C),
                                pipes::filter(q) >>= pipes::push_back(D),
                                pipes::filter(r) >>= pipes::push_back(E));

Here, unzip takes the std::pairs or std::tuples it receives and breaks them down into individual elements. It sends each element to the pipes it takes (here pipes::push_back and demux).

demux takes any number of pipes and sends the data it receives to each of them.

Since data circulates through pipes, real life pipes and plumbing provide a nice analogy (which gave its names to the library). For example, the above pipeline can be graphically represented like this:

Doesn't it look like ranges?

Pipes sort of look like ranges adaptors from afar, but those two libraries have very different designs.

Range views are about adapting ranges with view layers, and reading through those layers in lazy mode. Ranges are "pull based", in that components ask for the next value. Pipes are about sending pieces of data as they come along in a collection through a pipeline, and let them land in a destination. Pipes are "push based", in that components wait for the next value.

Ranges and pipes have overlapping components such as transform and filter. But pipes do things like ranges can't do, such as pipes::mux, pipes::demux and pipes:unzip, and ranges do things that pipes can't do, like infinite ranges.

It is possible to use ranges and pipes in the same expression though:

ranges::view::zip(dadChromosome, momChromosome)
    >>= pipes::transform(crossover) // crossover takes and returns a tuple of 2 elements
    >>= pipes::unzip(pipes::push_back(gameteChromosome1),
                     pipes::push_back(gameteChromosome2));

Operating on several collections

The pipes library allows to manipulate several collections at the same time, with the pipes::mux helper. Note that contrary to range::view::zip, pipes::mux doesn't require to use tuples:

auto const input1 = std::vector<int>{1, 2, 3, 4, 5};
auto const input2 = std::vector<int>{10, 20, 30, 40, 50};

auto results = std::vector<int>{};

pipes::mux(input1, input2) >>= pipes::filter   ([](int a, int b){ return a + b < 40; })
                           >>= pipes::transform([](int a, int b) { return a * b; })
                           >>= pipes::push_back(results);

// results contains {10, 40, 90}

Operating on all the possible combinations between several collections

pipes::cartesian_product takes any number of collections, and generates all the possible combinations between the elements of those collections. It sends each combination successively to the next pipe after it.

Like pipes::mux, pipes::cartesian_product doesn't use tuples but sends the values directly to the next pipe:

auto const inputs1 = std::vector<int>{1, 2, 3};
auto const inputs2 = std::vector<std::string>{"up", "down"};

auto results = std::vector<std::string>{};

pipes::cartesian_product(inputs1, inputs2)
    >>= pipes::transform([](int i, std::string const& s){ return std::to_string(i) + '-' + s; })
    >>= pipes::push_back(results);

// results contains {"1-up", "1-down", "2-up", "2-down", "3-up", "3-down"}

Operating on adjacent elements of a collection

pipes::adjacent allows to send adjacent pairs of element from a range to a pipeline:

auto const input = std::vector<int>{1, 2, 4, 7, 11, 16};

auto results = std::vector<int>{};

pipes::adjacent(input)
    >>= pipes::transform([](int a, int b){ return b - a; })
    >>= pipes::push_back(results);

// result contains {1, 2, 3, 4, 5};

End pipes

This library also provides end pipes, which are components that send data to a collection in an elaborate way. For example, the map_aggregate pipe receives std::pair<Key, Value>s and adds them to a map with the following rule:

if its key is not already in the map, insert the incoming pair in the map,
otherwise, aggregate the value of the incoming pair with the existing one in the map.

Example:

std::map<int, std::string> entries = { {1, "a"}, {2, "b"}, {3, "c"}, {4, "d"} };
std::map<int, std::string> entries2 = { {2, "b"}, {3, "c"}, {4, "d"}, {5, "e"} };
std::map<int, std::string> results;

// results is empty

entries >>= pipes::map_aggregator(results, concatenateStrings);

// the elements of entries have been inserted into results

entries2 >>= pipes::map_aggregator(results, concatenateStrings);

// the new elements of entries2 have been inserter into results, the existing ones have been concatenated with the new values 
// results contains { {1, "a"}, {2, "bb"}, {3, "cc"}, {4, "dd"}, {5, "e"} }

All components are located in the namespace pipes.

Easy integration with STL algorithms

All pipes can be used as output iterators of STL algorithms:

std::set_difference(begin(setA), end(setA),
                    begin(setB), end(setB),
                    transform(f) >>= filter(p) >>= map_aggregator(results, addValues));

Streams support

The contents of an input stream can be sent to a pipe by using read_in_stream. The end pipe to_out_stream sends data to an output stream.

The following example reads strings from the standard input, transforms them to upper case, and sends them to the standard output:

std::cin >>= pipes::read_in_stream<std::string>{}
         >>= pipes::transform(toUpper)
         >>= pipes::to_out_stream(std::cout);

List of available pipes

General pipes
- demux
- dev_null
- drop
- drop_while
- filter
- partition
- read_in_stream
- switch
- stride
- take
- tee
- transform
- unzip
End pipes

General pipes

`demux`

demux is a pipe that takes any number of pipes, and sends a copy of the values it receives to each of those pipes.

std::vector<int> input = {1, 2, 3, 4, 5};
std::vector<int> results1;
std::vector<int> results2;
std::vector<int> results3;

input >>= pipes::demux(pipes::push_back(results1),
                       pipes::push_back(results2),
                       pipes::push_back(results3));

// results1 contains {1, 2, 3, 4, 5}
// results2 contains {1, 2, 3, 4, 5}
// results3 contains {1, 2, 3, 4, 5}

`dev_null`

dev_null is a pipe that doesn't do anything with the value it receives. It is useful for selecting only some data coming out of an algorithm that has several outputs. An example of such algorithm is set_segregate:

std::set<int> setA = {1, 2, 3, 4, 5};
std::set<int> setB = {3, 4, 5, 6, 7};

std::vector<int> inAOnly;
std::vector<int> inBoth;

sets::set_seggregate(setA, setB,
                     pipes::push_back(inAOnly),
                     pipes::push_back(inBoth),
                     dev_null{});

// inAOnly contains {1, 2}
// inBoth contains {3, 4, 5}

`drop`

drop is a pipe that ignores the first N incoming values, and sends on the values after them to the next pipe:

auto const input = std::vector<int>{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

auto result = std::vector<int>{};

input >>= pipes::drop(5)
      >>= pipes::push_back(result);

// result contains { 6, 7, 8, 9, 10 }

`drop_while`

drop is a pipe that ignores the incoming values until they stop satisfying a predicate, and sends on the values after them to the next pipe:

auto const input = std::vector<int>{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

auto result = std::vector<int>{};

input >>= pipes::drop_while([](int i){ return i != 6; })
      >>= pipes::push_back(result);

// result contains { 6, 7, 8, 9, 10 }

`filter`

filter is a pipe that takes a predicate p and, when it receives a value x, sends the result on to the next pipe iif p(x) is true.

std::vector<int> input = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
std::vector<int> results;

input >>= pipes::filter([](int i){ return i % 2 == 0; })
      >>= pipes::push_back(results);

// results contains {2, 4, 6, 8, 10}

`join`

The join pipe receives collection and sends each element of each of those collections to the next pipe:

auto const input = std::vector<std::vector<int>>{ {1, 2}, {3, 4}, {5, 6} };
auto results = std::vector<int>{};

input >>= pipes::join >>= pipes::push_back(results);

// results contain {1, 2, 3, 4, 5, 6}

`partition`

partition is a pipe that takes a predicate p and two other pipes. When it receives a value x, sends the result on to the first pipe iif p(x) is true, and to the second pipe if p(x) is false.

std::vector<int> input = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
std::vector<int> evens;
std::vector<int> odds;

input >>= pipes::partition([](int n){ return n % 2 == 0; },
                           pipes::push_back(evens),
                           pipes::push_back(odds));

// evens contains {2, 4, 6, 8, 10}
// odds contains {1, 3, 5, 7, 9}

`read_in_stream`

read_in_stream is a template pipe that reads from an input stream. The template parameter indicates what type of data to request from the stream:

auto const input = std::string{"1.1 2.2 3.3"};

std::istringstream(input) >>= pipes::read_in_stream<double>{}
                          >>= pipes::transform([](double d){ return d * 10; })
                          >>= pipes::push_back(results);

// results contain {11, 22, 33};

`switch`

switch_ is a pipe that takes several case_ branches. Each branch contains a predicate and a pipe. When it receives a value, it tries it successively on the predicates of each branch, and sends the value on to the pipe of the first branch where the predicate returns true. The default_ branch is equivalent to one that takes a predicate that returns always true. Having a default_ branch is not mandatory.

std::vector<int> numbers = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
std::vector<int> multiplesOf4;
std::vector<int> multiplesOf3;
std::vector<int> rest;

numbers >>= pipes::switch_(pipes::case_([](int n){ return n % 4 == 0; }) >>= pipes::push_back(multiplesOf4),
                           pipes::case_([](int n){ return n % 3 == 0; }) >>= pipes::push_back(multiplesOf3),
                           pipes::default_ >>= pipes::push_back(rest) ));

// multiplesOf4 contains {4, 8};
// multiplesOf3 contains {3, 6, 9};
// rest contains {1, 2, 5, 7, 10};

`stride`

stride is a pipe that sends every N^th element starting from the first one. Hence N-1 elements after every N^th element are ignored

auto const input = std::vector<int>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

auto result = std::vector<int>{};

input >>= pipes::stride(3)
      >>= pipes::push_back(result);
      
// result contains {1, 4, 7, 10}

`take`

take takes a number N and sends to the next pipe the first N element that it receives. The elements after it are ignored:

auto const input = std::vector<int>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

auto result = std::vector<int>{};

input >>= pipes::take(6)
      >>= pipes::push_back(result);
      
// result contains {1, 2, 3, 4, 5, 6}

`take_while`

take_while takes a predicate and sends to the next pipe the first values it receives. It stops when one of them doesn't satisfy the predicate:

auto const input = std::vector<int>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

auto result = std::vector<int>{};

input >>= pipes::take_while([](int i){ return i != 7; })
      >>= pipes::push_back(result);

// result contains {1, 2, 3, 4, 5, 6}

`tee`

tee is a pipe that takes one other pipe, and sends a copy of the values it receives to each of these pipes before sending them on to the next pipe. Like the tee command on UNIX, this pipe is useful to take a peek at intermediary results.

auto const inputs = std::vector<int>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
auto intermediaryResults = std::vector<int>{};
auto results = std::vector<int>{};

inputs
    >>= pipes::transform([](int i) { return i * 2; })
    >>= pipes::tee(pipes::push_back(intermediaryResults))
    >>= pipes::filter([](int i){ return i >= 12; })
    >>= pipes::push_back(results);

// intermediaryResults contains {2, 4, 6, 8, 10, 12, 14, 16, 18, 20}
// results contains {12, 14, 16, 18, 20}

`transform`

transform is a pipe that takes a function f and, when it receives a value, applies f on it and sends the result on to the next pipe.

std::vector<int> input = {1, 2, 3, 4, 5};
std::vector<int> results;

input >>= pipes::transform([](int i) { return i*2; })
      >>= pipes::push_back(results);

// results contains {2, 4, 6, 8, 10}

`unzip`

unzip is a pipe that takes N other pipes. When it receives a std::pair or std::tuple of size N (for std::pair N is 2), it sends each of its components to the corresponding output pipe:

std::map<int, std::string> entries = { {1, "one"}, {2, "two"}, {3, "three"}, {4, "four"}, {5, "five"} };
std::vector<int> keys;
std::vector<std::string> values;

entries >>= pipes::unzip(pipes::push_back(keys),
                         pipes::push_back(values)));

// keys contains {1, 2, 3, 4, 5};
// values contains {"one", "two", "three", "four", "five"};

End pipes

`custom`

custom takes a function (or function object) that sends to the data it receives to that function. One of its usages is to give legacy code that does not use STL containers access to STL algorithms:

std::vector<int> input = {1, 2, 3, 4, 5, 6, 7 ,8, 9, 10};

void legacyInsert(int number, DarkLegacyStructure const& thing); // this function inserts into the old non-STL container

DarkLegacyStructure legacyStructure = // ...

std::copy(begin(input), end(input), custom([&legacyStructure](int number){ legacyInsert(number, legacyStructure); });

Read the full story about making legacy code compatible with the STL.

Note that custom goes along with a helper function object, do_, that allows to perfom several actions sequentially on the output of the algorithm:

std::copy(begin(input), end(input), pipes::custom(pipes::do_([&](int i){ results1.push_back(i*2);}).
                                                         then_([&](int i){ results2.push_back(i+1);}).
                                                         then_([&](int i){ results3.push_back(-i);})));

`map_aggregator`

map_aggregator provides the possibility to embark an aggregator function in the inserter iterator, so that new elements whose key is already present in the map can be merged with the existent (e.g. have their values added together).

std::vector<std::pair<int, std::string>> entries = { {1, "a"}, {2, "b"}, {3, "c"}, {4, "d"} };
std::vector<std::pair<int, std::string>> entries2 = { {2, "b"}, {3, "c"}, {4, "d"}, {5, "e"} };
std::map<int, std::string> results;

std::copy(entries.begin(), entries.end(), map_aggregator(results, concatenateStrings));
std::copy(entries2.begin(), entries2.end(), map_aggregator(results, concatenateStrings));

// results contains { {1, "a"}, {2, "bb"}, {3, "cc"}, {4, "dd"}, {5, "e"} }

set_aggreagator provides a similar functionality for aggregating elements into sets.

Read the full story about map_aggregator and set_aggregator.

`override`

override is the pipe equivalent to calling begin on an existing collection. The data that override receives overrides the first element of the container, then the next, and so on:

std::vector<int> input = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
std::vector<int> results = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0};

input >>= pipes::filter([](int i){ return i % 2 == 0; })
      >>= pipes::override(results);

// results contains {2, 4, 6, 8, 10, 0, 0, 0, 0, 0};

`push_back`

push_back is a pipe that is equivalent to std::back_inserter. It takes a collection that has a push_back member function, such as a std::vector, and push_backs the values it receives into that collection.

`set_aggregator`

Like map_aggregator, but inserting/aggregating into std::sets. Since std::set values are const, this pipe erases the element and re-inserts the aggregated value into the std::set.

struct Value
{
    int i;
    std::string s;
};

bool operator==(Value const& value1, Value const& value2)
{
    return value1.i == value2.i && value1.s == value2.s;
}

bool operator<(Value const& value1, Value const& value2)
{
    if (value1.i < value2.i) return true;
    if (value2.i < value1.i) return false;
    return value1.s < value2.s;
}

Value concatenateValues(Value const& value1, Value const& value2)
{
    if (value1.i != value2.i) throw std::runtime_error("Incompatible values");
    return { value1.i, value1.s + value2.s };
}

int main()
{
    std::vector<Value> entries = { Value{1, "a"}, Value{2, "b"}, Value{3, "c"}, Value{4, "d"} };
    std::vector<Value> entries2 = { Value{2, "b"}, Value{3, "c"}, Value{4, "d"}, Value{5, "e"} };
    std::set<Value> results;

    std::copy(entries.begin(), entries.end(), pipes::set_aggregator(results, concatenateValues));
    std::copy(entries2.begin(), entries2.end(), pipes::set_aggregator(results, concatenateValues));

    // results contain { Value{1, "a"}, Value{2, "bb"}, Value{3, "cc"}, Value{4, "dd"}, Value{5, "e"} }
}

`sorted_inserter`

In the majority of cases where it is used in algoritms, std::inserter forces its user to provide a position. It makes sense for un-sorted containers such as std::vector, but for sorted containers such as std::set we end up choosing begin or end by default, which doesn't make sense:

std::vector<int> v = {1, 3, -4, 2, 7, 10, 8};
std::set<int> results;
std::copy(begin(v), end(v), std::inserter(results, end(results)));

sorted_inserter removes this constraint by making the position optional. If no hint is passed, the containers is left to determine the correct position to insert:

std::vector<int> v = {1, 3, -4, 2, 7, 10, 8};
std::set<int> results;
std::copy(begin(v), end(v), sorted_inserter(results));

//results contains { -4, 1, 2, 3, 7, 8, 10 }

Read the full story about sorted_inserter.

`to_out_stream`

to_out_stream takes an output stream and sends incoming to it:

auto const input = std::vector<std::string>{"word1", "word2", "word3"};

input >>= pipes::transform(toUpper)
      >>= pipes::to_out_stream(std::cout);

// sends "WORD1WORD2WORD3" to the standard output

Name		Name	Last commit message	Last commit date
Latest commit History 231 Commits
cmake		cmake
docs		docs
include/pipes		include/pipes
tests		tests
.gitignore		.gitignore
.travis.yml		.travis.yml
CMakeLists.txt		CMakeLists.txt
LICENSE		LICENSE
README.md		README.md

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Contents

A First Example

What's going on here:

A Second Example

Doesn't it look like ranges?

Operating on several collections

Operating on all the possible combinations between several collections

Operating on adjacent elements of a collection

End pipes

Easy integration with STL algorithms

Streams support

List of available pipes

General pipes

demux

dev_null

drop

drop_while

filter

join

partition

read_in_stream

switch

stride

take

take_while

tee

transform

unzip

End pipes

custom

map_aggregator

override

push_back

set_aggregator

sorted_inserter

to_out_stream

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`demux`

`dev_null`

`drop`

`drop_while`

`filter`

`join`

`partition`

`read_in_stream`

`switch`

`stride`

`take`

`take_while`

`tee`

`transform`

`unzip`

`custom`

`map_aggregator`

`override`

`push_back`

`set_aggregator`

`sorted_inserter`

`to_out_stream`

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