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 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.

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 * This code is free software; you can redistribute it and/or modify it

 * under the terms of the GNU General Public License version 2 only, as

 * published by the Free Software Foundation.  Oracle designates this

 * particular file as subject to the "Classpath" exception as provided

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 *

 * This code is distributed in the hope that it will be useful, but WITHOUT

 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or

 * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License

 * version 2 for more details (a copy is included in the LICENSE file that

 * accompanied this code).

 *

 * You should have received a copy of the GNU General Public License version

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 *

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package java.util.stream;

import java.util.Arrays;

import java.util.DoubleSummaryStatistics;

import java.util.Objects;

import java.util.OptionalDouble;

import java.util.PrimitiveIterator;

import java.util.Spliterator;

import java.util.Spliterators;

import java.util.function.BiConsumer;

import java.util.function.DoubleBinaryOperator;

import java.util.function.DoubleConsumer;

import java.util.function.DoubleFunction;

import java.util.function.DoublePredicate;

import java.util.function.DoubleSupplier;

import java.util.function.DoubleToIntFunction;

import java.util.function.DoubleToLongFunction;

import java.util.function.DoubleUnaryOperator;

import java.util.function.Function;

import java.util.function.ObjDoubleConsumer;

import java.util.function.Supplier;

/**

 * A sequence of primitive double-valued elements supporting sequential and parallel

 * aggregate operations.  This is the {@code double} primitive specialization of

 * {@link Stream}.

 *

 * <p>The following example illustrates an aggregate operation using

 * {@link Stream} and {@link DoubleStream}, computing the sum of the weights of the

 * red widgets:

 *

 * <pre>{@code

 *     double sum = widgets.stream()

 *                         .filter(w -> w.getColor() == RED)

 *                         .mapToDouble(w -> w.getWeight())

 *                         .sum();

 * }</pre>

 *

 * See the class documentation for {@link Stream} and the package documentation

 * for <a href="package-summary.html">java.util.stream</a> for additional

 * specification of streams, stream operations, stream pipelines, and

 * parallelism.

 *

 * @since 1.8

 * @see Stream

 * @see <a href="package-summary.html">java.util.stream</a>

 */

public interface DoubleStream extends BaseStream<Double, DoubleStream> {

    /**

     * Returns a stream consisting of the elements of this stream that match

     * the given predicate.

     *

     * <p>This is an <a href="package-summary.html#StreamOps">intermediate

     * operation</a>.

     *

     * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *                  <a href="package-summary.html#Statelessness">stateless</a>

     *                  predicate to apply to each element to determine if it

     *                  should be included

     * @return the new stream

     */

    DoubleStream filter(DoublePredicate predicate);

    /**

     * Returns a stream consisting of the results of applying the given

     * function to the elements of this stream.

     *

     * <p>This is an <a href="package-summary.html#StreamOps">intermediate

     * operation</a>.

     *

     * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *               <a href="package-summary.html#Statelessness">stateless</a>

     *               function to apply to each element

     * @return the new stream

     */

    DoubleStream map(DoubleUnaryOperator mapper);

    /**

     * Returns an object-valued {@code Stream} consisting of the results of

     * applying the given function to the elements of this stream.

     *

     * <p>This is an <a href="package-summary.html#StreamOps">

     *     intermediate operation</a>.

     *

     * @param <U> the element type of the new stream

     * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *               <a href="package-summary.html#Statelessness">stateless</a>

     *               function to apply to each element

     * @return the new stream

     */

    <U> Stream<U> mapToObj(DoubleFunction<? extends U> mapper);

    /**

     * Returns an {@code IntStream} consisting of the results of applying the

     * given function to the elements of this stream.

     *

     * <p>This is an <a href="package-summary.html#StreamOps">intermediate

     * operation</a>.

     *

     * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *               <a href="package-summary.html#Statelessness">stateless</a>

     *               function to apply to each element

     * @return the new stream

     */

    IntStream mapToInt(DoubleToIntFunction mapper);

    /**

     * Returns a {@code LongStream} consisting of the results of applying the

     * given function to the elements of this stream.

     *

     * <p>This is an <a href="package-summary.html#StreamOps">intermediate

     * operation</a>.

     *

     * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *               <a href="package-summary.html#Statelessness">stateless</a>

     *               function to apply to each element

     * @return the new stream

     */

    LongStream mapToLong(DoubleToLongFunction mapper);

    /**

     * Returns a stream consisting of the results of replacing each element of

     * this stream with the contents of a mapped stream produced by applying

     * the provided mapping function to each element.  Each mapped stream is

     * {@link java.util.stream.BaseStream#close() closed} after its contents

     * have been placed into this stream.  (If a mapped stream is {@code null}

     * an empty stream is used, instead.)

     *

     * <p>This is an <a href="package-summary.html#StreamOps">intermediate

     * operation</a>.

     *

     * @param mapper a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *               <a href="package-summary.html#Statelessness">stateless</a>

     *               function to apply to each element which produces a

     *               {@code DoubleStream} of new values

     * @return the new stream

     * @see Stream#flatMap(Function)

     */

    DoubleStream flatMap(DoubleFunction<? extends DoubleStream> mapper);

    /**

     * Returns a stream consisting of the distinct elements of this stream. The

     * elements are compared for equality according to

     * {@link java.lang.Double#compare(double, double)}.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">stateful

     * intermediate operation</a>.

     *

     * @return the result stream

     */

    DoubleStream distinct();

    /**

     * Returns a stream consisting of the elements of this stream in sorted

     * order. The elements are compared for equality according to

     * {@link java.lang.Double#compare(double, double)}.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">stateful

     * intermediate operation</a>.

     *

     * @return the result stream

     */

    DoubleStream sorted();

    /**

     * Returns a stream consisting of the elements of this stream, additionally

     * performing the provided action on each element as elements are consumed

     * from the resulting stream.

     *

     * <p>This is an <a href="package-summary.html#StreamOps">intermediate

     * operation</a>.

     *

     * <p>For parallel stream pipelines, the action may be called at

     * whatever time and in whatever thread the element is made available by the

     * upstream operation.  If the action modifies shared state,

     * it is responsible for providing the required synchronization.

     *

     * @apiNote This method exists mainly to support debugging, where you want

     * to see the elements as they flow past a certain point in a pipeline:

     * <pre>{@code

     *     DoubleStream.of(1, 2, 3, 4)

     *         .filter(e -> e > 2)

     *         .peek(e -> System.out.println("Filtered value: " + e))

     *         .map(e -> e * e)

     *         .peek(e -> System.out.println("Mapped value: " + e))

     *         .sum();

     * }</pre>

     *

     * @param action a <a href="package-summary.html#NonInterference">

     *               non-interfering</a> action to perform on the elements as

     *               they are consumed from the stream

     * @return the new stream

     */

    DoubleStream peek(DoubleConsumer action);

    /**

     * Returns a stream consisting of the elements of this stream, truncated

     * to be no longer than {@code maxSize} in length.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting

     * stateful intermediate operation</a>.

     *

     * @apiNote

     * While {@code limit()} is generally a cheap operation on sequential

     * stream pipelines, it can be quite expensive on ordered parallel pipelines,

     * especially for large values of {@code maxSize}, since {@code limit(n)}

     * is constrained to return not just any <em>n</em> elements, but the

     * <em>first n</em> elements in the encounter order.  Using an unordered

     * stream source (such as {@link #generate(DoubleSupplier)}) or removing the

     * ordering constraint with {@link #unordered()} may result in significant

     * speedups of {@code limit()} in parallel pipelines, if the semantics of

     * your situation permit.  If consistency with encounter order is required,

     * and you are experiencing poor performance or memory utilization with

     * {@code limit()} in parallel pipelines, switching to sequential execution

     * with {@link #sequential()} may improve performance.

     *

     * @param maxSize the number of elements the stream should be limited to

     * @return the new stream

     * @throws IllegalArgumentException if {@code maxSize} is negative

     */

    DoubleStream limit(long maxSize);

    /**

     * Returns a stream consisting of the remaining elements of this stream

     * after discarding the first {@code n} elements of the stream.

     * If this stream contains fewer than {@code n} elements then an

     * empty stream will be returned.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">stateful

     * intermediate operation</a>.

     *

     * @apiNote

     * While {@code skip()} is generally a cheap operation on sequential

     * stream pipelines, it can be quite expensive on ordered parallel pipelines,

     * especially for large values of {@code n}, since {@code skip(n)}

     * is constrained to skip not just any <em>n</em> elements, but the

     * <em>first n</em> elements in the encounter order.  Using an unordered

     * stream source (such as {@link #generate(DoubleSupplier)}) or removing the

     * ordering constraint with {@link #unordered()} may result in significant

     * speedups of {@code skip()} in parallel pipelines, if the semantics of

     * your situation permit.  If consistency with encounter order is required,

     * and you are experiencing poor performance or memory utilization with

     * {@code skip()} in parallel pipelines, switching to sequential execution

     * with {@link #sequential()} may improve performance.

     *

     * @param n the number of leading elements to skip

     * @return the new stream

     * @throws IllegalArgumentException if {@code n} is negative

     */

    DoubleStream skip(long n);

    /**

     * Returns, if this stream is ordered, a stream consisting of the longest

     * prefix of elements taken from this stream that match the given predicate.

     * Otherwise returns, if this stream is unordered, a stream consisting of a

     * subset of elements taken from this stream that match the given predicate.

     *

     * <p>If this stream is ordered then the longest prefix is a contiguous

     * sequence of elements of this stream that match the given predicate.  The

     * first element of the sequence is the first element of this stream, and

     * the element immediately following the last element of the sequence does

     * not match the given predicate.

     *

     * <p>If this stream is unordered, and some (but not all) elements of this

     * stream match the given predicate, then the behavior of this operation is

     * nondeterministic; it is free to take any subset of matching elements

     * (which includes the empty set).

     *

     * <p>Independent of whether this stream is ordered or unordered if all

     * elements of this stream match the given predicate then this operation

     * takes all elements (the result is the same as the input), or if no

     * elements of the stream match the given predicate then no elements are

     * taken (the result is an empty stream).

     *

     * <p>This is a <a href="package-summary.html#StreamOps">short-circuiting

     * stateful intermediate operation</a>.

     *

     * @implSpec

     * The default implementation obtains the {@link #spliterator() spliterator}

     * of this stream, wraps that spliterator so as to support the semantics

     * of this operation on traversal, and returns a new stream associated with

     * the wrapped spliterator.  The returned stream preserves the execution

     * characteristics of this stream (namely parallel or sequential execution

     * as per {@link #isParallel()}) but the wrapped spliterator may choose to

     * not support splitting.  When the returned stream is closed, the close

     * handlers for both the returned and this stream are invoked.

     *

     * @apiNote

     * While {@code takeWhile()} is generally a cheap operation on sequential

     * stream pipelines, it can be quite expensive on ordered parallel

     * pipelines, since the operation is constrained to return not just any

     * valid prefix, but the longest prefix of elements in the encounter order.

     * Using an unordered stream source (such as

     * {@link #generate(DoubleSupplier)}) or removing the ordering constraint

     * with {@link #unordered()} may result in significant speedups of

     * {@code takeWhile()} in parallel pipelines, if the semantics of your

     * situation permit.  If consistency with encounter order is required, and

     * you are experiencing poor performance or memory utilization with

     * {@code takeWhile()} in parallel pipelines, switching to sequential

     * execution with {@link #sequential()} may improve performance.

     *

     * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *                  <a href="package-summary.html#Statelessness">stateless</a>

     *                  predicate to apply to elements to determine the longest

     *                  prefix of elements.

     * @return the new stream

     * @since 9

     */

    default DoubleStream takeWhile(DoublePredicate predicate) {

        Objects.requireNonNull(predicate);

        // Reuses the unordered spliterator, which, when encounter is present,

        // is safe to use as long as it configured not to split

        return StreamSupport.doubleStream(

                new WhileOps.UnorderedWhileSpliterator.OfDouble.Taking(spliterator(), true, predicate),

                isParallel()).onClose(this::close);

    }

    /**

     * Returns, if this stream is ordered, a stream consisting of the remaining

     * elements of this stream after dropping the longest prefix of elements

     * that match the given predicate.  Otherwise returns, if this stream is

     * unordered, a stream consisting of the remaining elements of this stream

     * after dropping a subset of elements that match the given predicate.

     *

     * <p>If this stream is ordered then the longest prefix is a contiguous

     * sequence of elements of this stream that match the given predicate.  The

     * first element of the sequence is the first element of this stream, and

     * the element immediately following the last element of the sequence does

     * not match the given predicate.

     *

     * <p>If this stream is unordered, and some (but not all) elements of this

     * stream match the given predicate, then the behavior of this operation is

     * nondeterministic; it is free to drop any subset of matching elements

     * (which includes the empty set).

     *

     * <p>Independent of whether this stream is ordered or unordered if all

     * elements of this stream match the given predicate then this operation

     * drops all elements (the result is an empty stream), or if no elements of

     * the stream match the given predicate then no elements are dropped (the

     * result is the same as the input).

     *

     * <p>This is a <a href="package-summary.html#StreamOps">stateful

     * intermediate operation</a>.

     *

     * @implSpec

     * The default implementation obtains the {@link #spliterator() spliterator}

     * of this stream, wraps that spliterator so as to support the semantics

     * of this operation on traversal, and returns a new stream associated with

     * the wrapped spliterator.  The returned stream preserves the execution

     * characteristics of this stream (namely parallel or sequential execution

     * as per {@link #isParallel()}) but the wrapped spliterator may choose to

     * not support splitting.  When the returned stream is closed, the close

     * handlers for both the returned and this stream are invoked.

     *

     * @apiNote

     * While {@code dropWhile()} is generally a cheap operation on sequential

     * stream pipelines, it can be quite expensive on ordered parallel

     * pipelines, since the operation is constrained to return not just any

     * valid prefix, but the longest prefix of elements in the encounter order.

     * Using an unordered stream source (such as

     * {@link #generate(DoubleSupplier)}) or removing the ordering constraint

     * with {@link #unordered()} may result in significant speedups of

     * {@code dropWhile()} in parallel pipelines, if the semantics of your

     * situation permit.  If consistency with encounter order is required, and

     * you are experiencing poor performance or memory utilization with

     * {@code dropWhile()} in parallel pipelines, switching to sequential

     * execution with {@link #sequential()} may improve performance.

     *

     * @param predicate a <a href="package-summary.html#NonInterference">non-interfering</a>,

     *                  <a href="package-summary.html#Statelessness">stateless</a>

     *                  predicate to apply to elements to determine the longest

     *                  prefix of elements.

     * @return the new stream

     * @since 9

     */

    default DoubleStream dropWhile(DoublePredicate predicate) {

        Objects.requireNonNull(predicate);

        // Reuses the unordered spliterator, which, when encounter is present,

        // is safe to use as long as it configured not to split

        return StreamSupport.doubleStream(

                new WhileOps.UnorderedWhileSpliterator.OfDouble.Dropping(spliterator(), true, predicate),

                isParallel()).onClose(this::close);

    }

    /**

     * Performs an action for each element of this stream.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">terminal

     * operation</a>.

     *

     * <p>For parallel stream pipelines, this operation does <em>not</em>

     * guarantee to respect the encounter order of the stream, as doing so

     * would sacrifice the benefit of parallelism.  For any given element, the

     * action may be performed at whatever time and in whatever thread the

     * library chooses.  If the action accesses shared state, it is

     * responsible for providing the required synchronization.

     *

     * @param action a <a href="package-summary.html#NonInterference">

     *               non-interfering</a> action to perform on the elements

     */

    void forEach(DoubleConsumer action);

    /**

     * Performs an action for each element of this stream, guaranteeing that

     * each element is processed in encounter order for streams that have a

     * defined encounter order.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">terminal

     * operation</a>.

     *

     * @param action a <a href="package-summary.html#NonInterference">

     *               non-interfering</a> action to perform on the elements

     * @see #forEach(DoubleConsumer)

     */

    void forEachOrdered(DoubleConsumer action);

    /**

     * Returns an array containing the elements of this stream.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">terminal

     * operation</a>.

     *

     * @return an array containing the elements of this stream

     */

    double[] toArray();

    /**

     * Performs a <a href="package-summary.html#Reduction">reduction</a> on the

     * elements of this stream, using the provided identity value and an

     * <a href="package-summary.html#Associativity">associative</a>

     * accumulation function, and returns the reduced value.  This is equivalent

     * to:

     * <pre>{@code

     *     double result = identity;

     *     for (double element : this stream)

     *         result = accumulator.applyAsDouble(result, element)

     *     return result;

     * }</pre>

     *

     * but is not constrained to execute sequentially.

     *

     * <p>The {@code identity} value must be an identity for the accumulator

     * function. This means that for all {@code x},

     * {@code accumulator.apply(identity, x)} is equal to {@code x}.

     * The {@code accumulator} function must be an

     * <a href="package-summary.html#Associativity">associative</a> function.

     *

     * <p>This is a <a href="package-summary.html#StreamOps">terminal

     * operation</a>.

     *

     * @apiNote Sum, min, max, and average are all special cases of reduction.

     * Summing a stream of numbers can be expressed as:

     * <pre>{@code

     *     double sum = numbers.reduce(0, (a, b) -> a+b);

     * }</pre>

     *

     * or more compactly:

     *

     * <pre>{@code

     *     double sum = numbers.reduce(0, Double::sum);

     * }</pre>

     *

     * <p>While this may seem a more roundabout way to perform an aggregation

     * compared to simply mutating a running total in a loop, reduction

     * operations parallelize more gracefully, without needing additional

     * synchronization and with greatly reduced risk of data races.

     *

     * @param identity the identity value for the accumulating function

     * @param op an <a href="package-summary.html#Associativity">associative</a>,

     *           <a href="package-summary.html#NonInterference">non-interfering</a>,

     *           <a href="package-summary.html#Statelessness">stateless</a>

     *           function for combining two values

     * @return the result of the reduction

     * @see #sum()

     * @see #min()

     * @see #max()

     * @see #average()

     */