// compose transducer
xform = comp(
    filter((x) => (x & 1) > 0), // odd numbers only
    distinct(),                 // distinct numbers only
    map((x) => x * 3)           // times 3
);

// collect into array (push)
transduce(xform, push(), [1, 2, 3, 4, 5, 4, 3, 2, 1]);
// [ 3, 9, 15 ]

// re-use same xform, but collect into ES6 Set
transduce(xform, conj(), [1, 2, 3, 4, 5, 4, 3, 2, 1]);
// Set { 3, 9, 15 }

// or apply as transforming iterator
// no reduction, only transformations
[...iterator(xform, [1, 2, 3, 4, 5])]
// [ 3, 9, 15]

// alternatively provide an input iterable and
// use xform as transforming iterator
[...filter((x) => /[A-Z]/.test(x), "Hello World!")]
// ["H", "W"]

// single step execution
// returns undefined if transducer returned no result for this input
// returns array if transducer step produced multiple results
f = step(xform);
f(1) // 3
f(2) // undefined
f(3) // 9
f(4) // undefined

f = step(take)

import { asSvg, svgDoc, circle, polyline } from "@thi.ng/geom";

// source values
const values = [5, 10, 4, 8, 20, 2, 11, 7];

// interpolate values and transform into 2D points
const vertices = [...iterator(
    comp(
        interpolateHermite(10),
        mapIndexed((x, y) => [x, y])
    ),
    // duplicate first & last vals (1x LHS / 2x RHS)
    // this is only needed for hermite interpolation
    // (see doc string for `interpolateHermite`)
    extendSides(values, 1, 2)
)];

// generate SVG
asSvg(
    svgDoc(
        { width: 800, height: 200, "stroke-width": 0.1 },
        // interpolated points as polyline
        polyline(vertices, { stroke: "red" }),
        // original values as dots
        ...values.map((y, x) => circle([x * 10, y], 0.2))
    )
)

[...filterFuzzy("ho", ["hello", "hallo", "hey", "heyoka"])]
// ["hello", "hallo", "heyoka"]
[...filterFuzzy("hlo", ["hello", "hallo", "hey", "heyoka"])]
// ["hello", "hallo"]

// works with any array-like values & supports custom key extractors
[...filterFuzzy(
    [1, 3],
    { key: (x) => x.tags },
    [
        { tags: [1, 2, 3] },
        { tags: [2, 3, 4] },
        { tags: [4, 5, 6] },
        { tags: [1, 3, 6] }
    ]
)]
// [ { tags: [ 1, 2, 3 ] }, { tags: [ 1, 3, 6 ] } ]

// use the `frequencies` reducer to create
// a map counting occurrence of each value
transduce(map((x) => x.toUpperCase()), frequencies(), "hello world");
// Map { 'H' => 1, 'E' => 1, 'L' => 3, 'O' => 2, ' ' => 1, 'W' => 1, 'R' => 1, 'D' => 1 }

// reduction only (no transform)
reduce(frequencies(), [1, 1, 1, 2, 3, 4, 4]);
// Map { 1 => 3, 2 => 1, 3 => 1, 4 => 2 }

// direct reduction if input is given
frequencies([1, 1, 1, 2, 3, 4, 4]);
// Map { 1 => 3, 2 => 1, 3 => 1, 4 => 2 }

// with optional key function, here to bin by word length
frequencies(
    (x) => x.length,
    "my camel is collapsing and needs some water".split(" ")
);
// Map { 2 => 2, 5 => 3, 10 => 1, 3 => 1, 4 => 1 }

// actual grouping (here: by word length)
groupByMap(
    { key: (x) => x.length },
    "my camel is collapsing and needs some water".split(" ")
);
// Map {
//   2 => [ 'my', 'is' ],
//   3 => [ 'and' ],
//   4 => [ 'some' ],
//   5 => [ 'camel', 'needs', 'water' ],
//   10 => [ 'collapsing' ]
// }

// extract only items for given page id & page length
[...page(0, 5, range(12))]
// [ 0, 1, 2, 3, 4 ]

// when composing with other transducers
// it's most efficient to place `page()` early on in the chain
// that way only the page items will be further processed
[...iterator(comp(page(1, 5), map(x => x * 10)), range(12))]
// [ 50, 60, 70, 80, 90 ]

// use `padLast()` to fill up missing values
[...iterator(comp(page(2, 5), padLast(5, "n/a")), range(12))]
// [ 10, 11, 'n/a', 'n/a', 'n/a' ]

// no values produced for invalid pages
[...page(3, 5, range(12))]
// []

transduce(
    multiplex(
        map((x) => x.charAt(0)),
        map((x) => x.toUpperCase()),
        map((x) => x.length)
    ),
    push(),
    ["Alice", "Bob", "Charlie"]
);
// [ [ "A", "ALICE", 5 ], [ "B", "BOB", 3 ], [ "C", "CHARLIE", 7 ] ]

transduce(
    multiplexObj({
        initial: map((x) => x.charAt(0)),
        name: map((x) => x.toUpperCase()),
        len: map((x) => x.length)
    }),
    push(),
    ["Alice", "Bob", "Charlie"]
);
// [ { len: 5, name: 'ALICE', initial: 'A' },
//   { len: 3, name: 'BOB', initial: 'B' },
//   { len: 7, name: 'CHARLIE', initial: 'C' } ]

// use nested reduce to compute window averages
transduce(
    comp(
        partition(5, 1),
        map(x => reduce(mean(), x))
    ),
    push(),
    [1, 2, 3, 3, 4, 5, 5, 6, 7, 8, 8, 9, 10]
)
// [ 2.6, 3.4, 4, 4.6, 5.4, 6.2, 6.8, 7.6, 8.4 ]

// this combined transducer is also directly
// available as: `movingAverage(n)`
[...movingAverage(5, [1, 2, 3, 3, 4, 5, 5, 6, 7, 8, 8, 9, 10])]
// [ 2.6, 3.4, 4, 4.6, 5.4, 6.2, 6.8, 7.6, 8.4 ]

// function to test
fn = () => {
    let x;
    for (i = 0; i < 1e6; i++) {
        x = Math.cos(i);
    }
    return x;
};

// compute the mean of 100 runs
transduce(benchmark(), mean(), repeatedly(fn, 100));
// 1.93 (milliseconds)

// alternatively, use sideEffect() for any side fx
transduce(
    comp(
        trace("orig"),
        map((x) => x + 1),
        trace("mapped"),
        filter((x) => (x & 1) > 0)
    ),
    push(),
    [1, 2, 3, 4]
);
// orig 1
// mapped 2
// orig 2
// mapped 3
// orig 3
// mapped 4
// orig 4
// mapped 5
// [ 3, 5 ]

// Higher-order transducer to convert linear input into structured objects
// using given field specs and ordering. A single field spec is an array of
// 2 or 3 items: `[name, size, transform?]`. If `transform` is given, it will
// be used to produce the final value for this field. In the example below,
// it is used to unwrap the ID field values, e.g. from `[0] => 0`
[
    ...struct(
        [["id", 1, (id) => id[0]], ["pos", 2], ["vel", 2], ["color", 4]],
        [0, 100, 200, -1, 0, 1, 0.5, 0, 1, 1, 0, 0, 5, 4, 0, 0, 1, 1]
    )
];
// [ { color: [ 1, 0.5, 0, 1 ],
//     vel: [ -1, 0 ],
//     pos: [ 100, 200 ],
//     id: 0 },
//   { color: [ 0, 0, 1, 1 ],
//     vel: [ 5, 4 ],
//     pos: [ 0, 0 ],
//     id: 1 } ]

transduce(
    comp(
        // split into rows
        mapcat((x) => x.split("\n")),
        // split each row
        map((x) => x.split(",")),
        // convert each row into object, rename array indices
        rename({ id: 0, name: 1, alias: 2, num: "length" })
    ),
    push(),
    ["100,typescript\n101,clojure,clj\n110,rust,rs"]
);
// [ { num: 2, name: 'typescript', id: '100' },
//   { num: 3, alias: 'clj', name: 'clojure', id: '101' },
//   { num: 3, alias: 'rs', name: 'rust', id: '110' } ]

// result is realized after max. 7 values, irrespective of nesting
transduce(comp(flatten(), take(7)), push(), [
    1,
    [2, [3, 4, [5, 6, [7, 8], 9, [10]]]]
]);
// [1, 2, 3, 4, 5, 6, 7]

// this transducer uses 2 scans (a scan = inner reducer per item)
// 1) counts incoming values
// 2) forms an array of the current counter value `x` & repeated `x` times
// 3) emits results as series of reductions in the outer array produced
//    by the main reducer
// IMPORTANT: since arrays are mutable we use `pushCopy` as the inner reducer
// instead of `push` (the outer reducer)
xform = comp(
    scan(count()),
    map(x => [...repeat(x,x)]),
    scan(pushCopy())
)

[...iterator(xform, [1, 1, 1, 1])]
// [ [ [ 1 ] ],
//   [ [ 1 ], [ 2, 2 ] ],
//   [ [ 1 ], [ 2, 2 ], [ 3, 3, 3 ] ],
//   [ [ 1 ], [ 2, 2 ], [ 3, 3, 3 ], [ 4, 4, 4, 4 ] ] ]

// more simple & similar to previous, but without the 2nd xform step
transduce(comp(scan(count()), scan(pushCopy())), push(), [1,1,1,1])
// [ [ 1 ], [ 1, 2 ], [ 1, 2, 3 ], [ 1, 2, 3, 4 ] ]

[...take(10, choices("abcd", [1, 0.5, 0.25, 0.125]))]
// [ 'a', 'a', 'b', 'a', 'a', 'b', 'a', 'c', 'd', 'b' ]

transduce(
    take(1000),
    frequencies(),
    choices("abcd", [1, 0.5, 0.25, 0.125])
);
// Map { 'c' => 132, 'a' => 545, 'b' => 251, 'd' => 72 }

[
    ...tween(
        10,
        0,
        100,
        (a, b) => [a, b],
        ([a, b], t) => Math.floor(a + (b - a) * t),
        [20, 100],
        [50, 200],
        [80, 0]
    )
];
// [ 100, 100, 100, 133, 166, 200, 133, 66, 0, 0, 0 ]

interface Reducer<A, B> extends Array<any> {
    /**
     * Initialization, e.g. to provide a suitable accumulator value,
     * only called when no initial accumulator has been provided by user.
     */
    [0]: () => A;
    /**
     * Completion. When called usually just returns `acc`, but stateful
     * transformers should flush/apply their outstanding results.
     */
    [1]: (acc: A) => A;
    /**
     * Reduction step. Combines new input with accumulator.
     * If reduction should terminate early, wrap result via `reduced()`
     */
    [2]: (acc: A, x: B) => A | Reduced<A>;
}

// A concrete example:
const push: Reducer<any[], any> = [
    // init
    () => [],
    // completion (nothing to do in this case)
    (acc) => acc,
    // step
    (acc, x) => (acc.push(x), acc)
];

class Reduced<T> implements IDeref<T> {
    protected value: T;
    constructor(val: T);
    deref(): T;
}

type Transducer<A, B> = (rfn: Reducer<any, B>) => Reducer<any, A>;

// concrete example of stateless transducer (expanded for clarity)
function map<A, B>(fn: (x: A) => B): Transducer<A, B> {
    return (rfn: Reducer<any, B>) => {
        return [
            () => rfn[0](),
            (acc) => rfn[1](acc),
            (acc, x: A) => rfn[2](acc, fn(x))
        ];
    };
}

// stateful transducer
// removes successive value repetitions
function dedupe<T>(): Transducer<T, T> {
    return (rfn: Reducer<any, T>) => {
        // state initialization
        let prev = {};
        return [
            () => rfn[0](),
            (acc) => rfn[1](acc),
            (acc, x) => {
                acc = prev === x ? acc : rfn[2](acc, x);
                prev = x;
                return acc;
            }
        ];
    };
}

class Mul implements IXform<number, number> {

    constructor(public factor = 10) {}

    xform() {
        return map((x) => this.factor * x);
    }
}

transduce(new Mul(11), push(), range(4))
// [0, 11, 22, 33, 44]

// also usable w/ comp(), iterator(), step(), run() etc.
transduce(
    comp(drop(1), new Mul(11), takeNth(2)),
    push(),
    range(4)
)
// [11, 33]

// as transducer
transduce(map((x) => x*10), push(), range(4))
// [ 0, 10, 20, 30 ]

// as transforming iterator
[...map((x) => x*10, range(4))]
// [ 0, 10, 20, 30 ]