org.scalacheck.Gen.scala Maven / Gradle / Ivy
/*-------------------------------------------------------------------------*\
** ScalaCheck **
** Copyright (c) 2007-2014 Rickard Nilsson. All rights reserved. **
** http://www.scalacheck.org **
** **
** This software is released under the terms of the Revised BSD License. **
** There is NO WARRANTY. See the file LICENSE for the full text. **
\*------------------------------------------------------------------------ */
package org.scalacheck
import util.{Buildable, Buildable2}
import scala.collection.immutable.TreeMap
sealed trait Gen[+T] {
//// Private interface ////
import Gen.{R, r, gen}
/** Just an alias */
private type P = Gen.Parameters
/** Should be a copy of R.sieve. Used internally in Gen when some generators
* with suchThat-claues are created (when R is not available). This method
* actually breaks covariance, but since this method will only ever be
* called with a value of exactly type T, it is OK. */
protected def sieveCopy(x: Any): Boolean = true
private[scalacheck] def doApply(p: P): R[T]
//// Public interface ////
/** A class supporting filtered operations. */
final class WithFilter(p: T => Boolean) {
def map[U](f: T => U): Gen[U] = Gen.this.suchThat(p).map(f)
def flatMap[U](f: T => Gen[U]): Gen[U] = Gen.this.suchThat(p).flatMap(f)
def withFilter(q: T => Boolean): WithFilter = Gen.this.withFilter(x => p(x) && q(x))
}
/** Evaluate this generator with the given parameters */
def apply(p: Gen.Parameters): Option[T] = doApply(p).retrieve
/** Create a new generator by mapping the result of this generator */
def map[U](f: T => U): Gen[U] = gen { p => doApply(p).map(f) }
/** Create a new generator by flat-mapping the result of this generator */
def flatMap[U](f: T => Gen[U]): Gen[U] = gen { p =>
doApply(p).flatMap(t => f(t).doApply(p))
}
/** Create a new generator that uses this generator to produce a value
* that fulfills the given condition. If the condition is not fulfilled,
* the generator fails (returns None). */
def filter(p: T => Boolean): Gen[T] = suchThat(p)
/** Creates a non-strict filtered version of this generator. */
def withFilter(p: T => Boolean): WithFilter = new WithFilter(p)
/** Create a new generator that uses this generator to produce a value
* that fulfills the given condition. If the condition is not fulfilled,
* the generator fails (returns None). This method is identical to
* [Gen.filter]. */
def suchThat(f: T => Boolean): Gen[T] = new Gen[T] {
def doApply(p: P) = Gen.this.doApply(p).copy(s = f)
override def sieveCopy(x: Any) =
try f(x.asInstanceOf[T])
catch { case _: java.lang.ClassCastException => false }
}
/** Create a generator that calls this generator repeatedly until
* the given condition is fulfilled. The generated value is then
* returned. Use this combinator with care, since it may result
* in infinite loops. */
def retryUntil(p: T => Boolean): Gen[T] = flatMap { t =>
if (p(t)) Gen.const(t).suchThat(p) else retryUntil(p)
}
def sample: Option[T] = doApply(Gen.Parameters.default).retrieve
/** Returns a new property that holds if and only if both this
* and the given generator generates the same result, or both
* generators generate no result. */
def ==[U](g: Gen[U]) = Prop { prms =>
(doApply(prms).retrieve, g.doApply(prms).retrieve) match {
case (None,None) => Prop.proved(prms)
case (Some(r1),Some(r2)) if r1 == r2 => Prop.proved(prms)
case _ => Prop.falsified(prms)
}
}
def !=[U](g: Gen[U]) = Prop.forAll(this)(r => Prop.forAll(g)(_ != r))
def !==[U](g: Gen[U]) = Prop { prms =>
(doApply(prms).retrieve, g.doApply(prms).retrieve) match {
case (None,None) => Prop.falsified(prms)
case (Some(r1),Some(r2)) if r1 == r2 => Prop.falsified(prms)
case _ => Prop.proved(prms)
}
}
/** Put a label on the generator to make test reports clearer */
def label(l: String) = new Gen[T] {
def doApply(p: P) = {
val r = Gen.this.doApply(p)
r.copy(l = r.labels + l)
}
override def sieveCopy(x: Any) = Gen.this.sieveCopy(x)
}
/** Put a label on the generator to make test reports clearer */
def :|(l: String) = label(l)
/** Put a label on the generator to make test reports clearer */
def |:(l: String) = label(l)
/** Put a label on the generator to make test reports clearer */
def :|(l: Symbol) = label(l.toString.drop(1))
/** Put a label on the generator to make test reports clearer */
def |:(l: Symbol) = label(l.toString.drop(1))
}
object Gen {
//// Private interface ////
import Arbitrary.arbitrary
/** Just an alias */
private type P = Parameters
private[scalacheck] trait R[+T] {
def labels: Set[String] = Set()
def sieve[U >: T]: U => Boolean = _ => true
protected def result: Option[T]
def retrieve = result.filter(sieve)
def copy[U >: T](
l: Set[String] = this.labels,
s: U => Boolean = this.sieve,
r: Option[U] = this.result
): R[U] = new R[U] {
override val labels = l
override def sieve[V >: U] = { x:Any =>
try s(x.asInstanceOf[U])
catch { case _: java.lang.ClassCastException => false }
}
val result = r
}
def map[U](f: T => U): R[U] = r(retrieve.map(f)).copy(l = labels)
def flatMap[U](f: T => R[U]): R[U] = retrieve match {
case None => r(None).copy(l = labels)
case Some(t) =>
val r = f(t)
r.copy(l = labels ++ r.labels)
}
}
private[scalacheck] def r[T](r: Option[T]): R[T] = new R[T] {
val result = r
}
/** Generator factory method */
private[scalacheck] def gen[T](f: P => R[T]): Gen[T] = new Gen[T] {
def doApply(p: P) = f(p)
}
//// Public interface ////
/** Generator parameters, used by [[org.scalacheck.Gen.apply]] */
trait Parameters {
/** The size of the generated value. Generator implementations are allowed
* to freely interpret (or ignore) this value. During test execution, the
* value of this parameter is controlled by [[Test.Parameters.minSize]] and
* [[Test.Parameters.maxSize]]. */
val size: Int
/** Create a copy of this [[Parameters]] instance with [[size]]
* set to the specified value. */
def withSize(size: Int): Parameters = cp(size = size)
/** The random number generator used. */
val rng: scala.util.Random
/** Create a copy of this [[Parameters]] instance with [[rng]]
* set to the specified value. */
def withRng(rng: scala.util.Random): Parameters = cp(rng = rng)
/** Change the size parameter.
* @deprecated Use [[withSize]] instead. */
@deprecated("Use withSize instead.", "1.11.2")
def resize(newSize: Int): Parameters = withSize(newSize)
// private since we can't guarantee binary compatibility for this one
private case class cp(
size: Int = size,
rng: scala.util.Random = rng
) extends Parameters
}
/** Provides methods for creating [[org.scalacheck.Gen.Parameters]] values */
object Parameters {
/** Default generator parameters trait. This can be overriden if you
* need to tweak the parameters. */
trait Default extends Parameters {
val size: Int = 100
val rng: scala.util.Random = scala.util.Random
}
/** Default generator parameters instance. */
val default: Parameters = new Default {}
}
/** A wrapper type for range types */
trait Choose[T] {
/** Creates a generator that returns a value in the given inclusive range */
def choose(min: T, max: T): Gen[T]
}
/** Provides implicit [[org.scalacheck.Gen.Choose]] instances */
object Choose {
private def chLng(l: Long, h: Long)(p: P): R[Long] = {
if (h < l) r(None) else {
val d = h - l + 1
if (d <= 0) {
var n = p.rng.nextLong
while (n < l || n > h) {
n = p.rng.nextLong
}
r(Some(n))
} else {
r(Some(l + math.abs(p.rng.nextLong % d)))
}
}
}
private def chDbl(l: Double, h: Double)(p: P): R[Double] = {
val d = h-l
if (d < 0 || d > Double.MaxValue) r(None)
else if (d == 0) r(Some(l))
else r(Some(p.rng.nextDouble * (h-l) + l))
}
implicit val chooseLong: Choose[Long] = new Choose[Long] {
def choose(low: Long, high: Long) =
gen(chLng(low,high)).suchThat(x => x >= low && x <= high)
}
implicit val chooseInt: Choose[Int] = new Choose[Int] {
def choose(low: Int, high: Int) =
gen(chLng(low,high)).map(_.toInt).suchThat(x => x >= low && x <= high)
}
implicit val chooseByte: Choose[Byte] = new Choose[Byte] {
def choose(low: Byte, high: Byte) =
gen(chLng(low,high)).map(_.toByte).suchThat(x => x >= low && x <= high)
}
implicit val chooseShort: Choose[Short] = new Choose[Short] {
def choose(low: Short, high: Short) =
gen(chLng(low,high)).map(_.toShort).suchThat(x => x >= low && x <= high)
}
implicit val chooseChar: Choose[Char] = new Choose[Char] {
def choose(low: Char, high: Char) =
gen(chLng(low,high)).map(_.toChar).suchThat(x => x >= low && x <= high)
}
implicit val chooseDouble: Choose[Double] = new Choose[Double] {
def choose(low: Double, high: Double) =
gen(chDbl(low,high)).suchThat(x => x >= low && x <= high)
}
implicit val chooseFloat: Choose[Float] = new Choose[Float] {
def choose(low: Float, high: Float) =
gen(chDbl(low,high)).map(_.toFloat).suchThat(x => x >= low && x <= high)
}
}
//// Various Generator Combinators ////
/** A generator that always generates the given value */
@deprecated("Use Gen.const instead", "1.11.0")
def value[T](x: T): Gen[T] = const(x)
/** A generator that always generates the given value */
implicit def const[T](x: T): Gen[T] = gen(_ => r(Some(x))).suchThat(_ == x)
/** A generator that never generates a value */
def fail[T]: Gen[T] = gen(_ => r(None)).suchThat(_ => false)
/** A generator that generates a random value in the given (inclusive)
* range. If the range is invalid, the generator will not generate
* any value. */
def choose[T](min: T, max: T)(implicit c: Choose[T]): Gen[T] =
c.choose(min, max)
/** Sequences generators. If any of the given generators fails, the
* resulting generator will also fail. */
def sequence[C[_],T](gs: Traversable[Gen[T]])(implicit b: Buildable[T,C]): Gen[C[T]] = {
val g = gen { p =>
gs.foldLeft(r(Some(collection.immutable.Vector.empty[T]))) {
case (rs,g) => g.doApply(p).flatMap(r => rs.map(_ :+ r))
}
}
g.map(b.fromIterable)
}
/** Sequences generators. If any of the given generators fails, the
* resulting generator will also fail. */
def sequence[C[_,_],T,U](gs: Traversable[Gen[(T,U)]])(implicit b: Buildable2[T,U,C]): Gen[C[T,U]] = {
val g = gen { p =>
gs.foldLeft(r(Some(collection.immutable.Vector.empty[(T,U)]))) {
case (rs,g) => g.doApply(p).flatMap(r => rs.map(_ :+ r))
}
}
g.map(b.fromIterable)
}
/** Wraps a generator lazily. The given parameter is only evaluated once,
* and not until the wrapper generator is evaluated. */
def lzy[T](g: => Gen[T]): Gen[T] = {
lazy val h = g
gen { p => h.doApply(p) }
}
/** Wraps a generator for later evaluation. The given parameter is
* evaluated each time the wrapper generator is evaluated. */
def wrap[T](g: => Gen[T]) = gen { p => g.doApply(p) }
/** Creates a generator that can access its generation parameters */
def parameterized[T](f: Parameters => Gen[T]) = gen { p => f(p).doApply(p) }
/** Creates a generator that can access its generation size */
def sized[T](f: Int => Gen[T]) = gen { p => f(p.size).doApply(p) }
/** A generator that returns the current generation size */
lazy val size: Gen[Int] = sized { sz => sz }
/** Creates a resized version of a generator */
def resize[T](s: Int, g: Gen[T]) = gen(p => g.doApply(p.withSize(s)))
/** Picks a random value from a list */
def oneOf[T](xs: Seq[T]): Gen[T] =
choose(0, xs.size-1).map(xs(_)).suchThat(xs.contains)
/** Picks a random value from a list */
def oneOf[T](t0: T, t1: T, tn: T*): Gen[T] = oneOf(t0 +: t1 +: tn)
/** Picks a random generator from a list */
def oneOf[T](g0: Gen[T], g1: Gen[T], gn: Gen[T]*): Gen[T] = {
val gs = g0 +: g1 +: gn
choose(0,gs.size-1).flatMap(gs(_)).suchThat(x => gs.exists(_.sieveCopy(x)))
}
/** Makes a generator result optional. Either `Some(T)` or `None` will be provided. */
def option[T](g: Gen[T]): Gen[Option[T]] =
oneOf[Option[T]](g.map(Some.apply), None)
/** Chooses one of the given generators with a weighted random distribution */
def frequency[T](gs: (Int,Gen[T])*): Gen[T] = {
gs.filter(_._1 > 0) match {
case Nil => fail
case filtered =>
var tot = 0l
val tree: TreeMap[Long, Gen[T]] = {
val builder = TreeMap.newBuilder[Long, Gen[T]]
filtered.foreach {
case (f, v) =>
tot += f
builder.+=((tot, v))
}
builder.result()
}
choose(1L, tot).flatMap(r => tree.from(r).head._2).suchThat { x =>
gs.exists(_._2.sieveCopy(x))
}
}
}
/** Implicit convenience method for using the `frequency` method
* like this:
* {{{
* frequency((1, "foo"), (3, "bar"))
* }}}
*/
implicit def freqTuple[T](t: (Int,T)): (Int,Gen[T]) = (t._1, const(t._2))
//// List Generators ////
/** Generates a container of any Traversable type for which there exists an implicit
* [[org.scalacheck.util.Buildable]] instance. The elements in the container will
* be generated by the given generator. The size of the generated container
* is given by `n`. If the given generator fails generating a value, the
* complete container generator will also fail. */
def containerOfN[C[_],T](n: Int, g: Gen[T])(implicit
evb: Buildable[T,C], evt: C[T] => Traversable[T]
): Gen[C[T]] =
sequence[C,T](Traversable.fill(n)(g)) suchThat { c =>
c.size == n && c.forall(g.sieveCopy)
}
/** Generates a container of any Traversable type for which there exists an
* implicit [[org.scalacheck.util.Buildable]] instance. The elements in the
* container will be generated by the given generator. The size of the
* container is bounded by the size parameter used when generating values. */
def containerOf[C[_],T](g: Gen[T])(implicit
evb: Buildable[T,C], evt: C[T] => Traversable[T]
): Gen[C[T]] =
sized(s => choose(0,s).flatMap(containerOfN[C,T](_,g))) suchThat { c =>
c.forall(g.sieveCopy)
}
/** Generates a non-empty container of any Traversable type for which there
* exists an implicit [[org.scalacheck.util.Buildable]] instance. The
* elements in the container will be generated by the given generator. The
* size of the container is bounded by the size parameter used when
* generating values. */
def nonEmptyContainerOf[C[_],T](g: Gen[T])(implicit
evb: Buildable[T,C], evt: C[T] => Traversable[T]
): Gen[C[T]] =
sized(s => choose(1,s).flatMap(containerOfN[C,T](_,g))) suchThat { c =>
c.size > 0 && c.forall(g.sieveCopy)
}
/** Generates a non-empty container of any Traversable type for which there
* exists an implicit [[org.scalacheck.util.Buildable]] instance. The
* elements in the container will be generated by the given generator. The
* size of the container is bounded by the size parameter used when
* generating values. */
@deprecated("Use Gen.nonEmptyContainerOf instead", "1.11.0")
def containerOf1[C[_],T](g: Gen[T])(implicit
evb: Buildable[T,C], evt: C[T] => Traversable[T]
): Gen[C[T]] = nonEmptyContainerOf[C,T](g)
/** Generates a container of any Traversable type for which there exists an
* implicit [[org.scalacheck.util.Buildable2]] instance. The elements in
* the container will be generated by the given generator. The size of the
* generated container is given by `n`. If the given generator fails
* generating a value, the complete container generator will also fail. */
def containerOfN[C[_,_],T,U](n: Int, g: Gen[(T,U)])(implicit
evb: Buildable2[T,U,C], evt: C[T,U] => Traversable[(T,U)]
): Gen[C[T,U]] =
sequence[C,T,U](Traversable.fill(n)(g)).suchThat { c =>
c.size == n && c.forall(g.sieveCopy)
}
/** Generates a container of any Traversable type for which there exists
* an implicit Buildable2 instance. The elements in the
* container will be generated by the given generator. The size of the
* container is bounded by the size parameter used when generating values. */
def containerOf[C[_,_],T,U](g: Gen[(T,U)])(implicit
evb: Buildable2[T,U,C], evt: C[T,U] => Traversable[(T,U)]
): Gen[C[T,U]] =
sized(s => choose(0,s).flatMap(containerOfN[C,T,U](_,g))) suchThat { c =>
c.forall(g.sieveCopy)
}
/** Generates a non-empty container of any type for which there exists an
* implicit Buildable2 instance. The elements in the container
* will be generated by the given generator. The size of the container is
* bounded by the size parameter used when generating values. */
def nonEmptyContainerOf[C[_,_],T,U](g: Gen[(T,U)])(implicit
evb: Buildable2[T,U,C], evt: C[T,U] => Traversable[(T,U)]
): Gen[C[T,U]] =
sized(s => choose(1,s).flatMap(containerOfN[C,T,U](_,g))) suchThat { c =>
c.size > 0 && c.forall(g.sieveCopy)
}
/** Generates a list of random length. The maximum length depends on the
* size parameter. This method is equal to calling
* `containerOf[List,T](g)`. */
def listOf[T](g: => Gen[T]) = containerOf[List,T](g)
/** Generates a non-empty list of random length. The maximum length depends
* on the size parameter. This method is equal to calling
* `nonEmptyContainerOf[List,T](g)`. */
def nonEmptyListOf[T](g: => Gen[T]) = nonEmptyContainerOf[List,T](g)
/** Generates a non-empty list of random length. The maximum length depends
* on the size parameter. This method is equal to calling
* `nonEmptyContainerOf[List,T](g)`. */
@deprecated("Use Gen.nonEmptyListOf instead", "1.11.0")
def listOf1[T](g: => Gen[T]) = nonEmptyListOf[T](g)
/** Generates a list of the given length. This method is equal to calling
* `containerOfN[List,T](n,g)`. */
def listOfN[T](n: Int, g: Gen[T]) = containerOfN[List,T](n,g)
/** Generates a map of random length. The maximum length depends on the
* size parameter. This method is equal to calling
* containerOf[Map,T,U](g). */
def mapOf[T,U](g: => Gen[(T,U)]) = containerOf[Map,T,U](g)
/** Generates a non-empty map of random length. The maximum length depends
* on the size parameter. This method is equal to calling
* nonEmptyContainerOf[Map,T,U](g). */
def nonEmptyMap[T,U](g: => Gen[(T,U)]) = nonEmptyContainerOf[Map,T,U](g)
/** Generates a map of the given length. This method is equal to calling
* containerOfN[List,T](n,g). */
def mapOfN[T,U](n: Int, g: Gen[(T,U)]) = containerOfN[Map,T,U](n,g)
/** A generator that picks a random number of elements from a list */
def someOf[T](l: Iterable[T]) = choose(0,l.size).flatMap(pick(_,l))
/** A generator that picks a random number of elements from a list */
def someOf[T](g1: Gen[T], g2: Gen[T], gs: Gen[T]*) =
choose(0, gs.length+2).flatMap(pick(_, g1, g2, gs: _*))
/** A generator that picks a given number of elements from a list, randomly */
def pick[T](n: Int, l: Iterable[T]): Gen[Seq[T]] =
if(n > l.size || n < 0) fail
else (gen { p =>
val b = new collection.mutable.ListBuffer[T]
b ++= l
while(b.length > n) b.remove(choose(0, b.length-1).doApply(p).retrieve.get)
r(Some(b))
}).suchThat(_.forall(x => l.exists(x == _)))
/** A generator that picks a given number of elements from a list, randomly */
def pick[T](n: Int, g1: Gen[T], g2: Gen[T], gn: Gen[T]*): Gen[Seq[T]] = {
val gs = g1 +: g2 +: gn
pick(n, 0 until gs.size).flatMap(idxs =>
sequence[List,T](idxs.toList.map(gs(_)))
).suchThat(_.forall(x => gs.exists(_.sieveCopy(x))))
}
//// Character Generators ////
/** Generates a numerical character */
def numChar: Gen[Char] = choose(48.toChar, 57.toChar)
/** Generates an upper-case alpha character */
def alphaUpperChar: Gen[Char] = choose(65.toChar, 90.toChar)
/** Generates a lower-case alpha character */
def alphaLowerChar: Gen[Char] = choose(97.toChar, 122.toChar)
/** Generates an alpha character */
def alphaChar = frequency((1,alphaUpperChar), (9,alphaLowerChar))
/** Generates an alphanumerical character */
def alphaNumChar = frequency((1,numChar), (9,alphaChar))
//// String Generators ////
/** Generates a string that starts with a lower-case alpha character,
* and only contains alphanumerical characters */
def identifier: Gen[String] = (for {
c <- alphaLowerChar
cs <- listOf(alphaNumChar)
} yield (c::cs).mkString).suchThat(_.forall(c => c.isLetter || c.isDigit))
/** Generates a string of alpha characters */
def alphaStr: Gen[String] =
listOf(alphaChar).map(_.mkString).suchThat(_.forall(_.isLetter))
/** Generates a string of digits */
def numStr: Gen[String] =
listOf(numChar).map(_.mkString).suchThat(_.forall(_.isDigit))
//// Number Generators ////
/** Generates positive numbers of uniform distribution, with an
* upper bound of the generation size parameter. */
def posNum[T](implicit num: Numeric[T], c: Choose[T]): Gen[T] = {
import num._
sized(max => c.choose(one, fromInt(max)))
}
/** Generates negative numbers of uniform distribution, with an
* lower bound of the negated generation size parameter. */
def negNum[T](implicit num: Numeric[T], c: Choose[T]): Gen[T] = {
import num._
sized(max => c.choose(-fromInt(max), -one))
}
/** Generates numbers within the given inclusive range, with
* extra weight on zero, +/- unity, both extremities, and any special
* numbers provided. The special numbers must lie within the given range,
* otherwise they won't be included. */
def chooseNum[T](minT: T, maxT: T, specials: T*)(
implicit num: Numeric[T], c: Choose[T]
): Gen[T] = {
import num._
val basics = List(minT, maxT, zero, one, -one)
val basicsAndSpecials = for {
t <- specials ++ basics if t >= minT && t <= maxT
} yield (1, const(t))
val allGens = basicsAndSpecials ++ List(
(basicsAndSpecials.length, c.choose(minT, maxT))
)
frequency(allGens: _*)
}
/** Generates a version 4 (random) UUID. */
lazy val uuid: Gen[java.util.UUID] = for {
l1 <- Gen.choose(Long.MinValue, Long.MaxValue)
l2 <- Gen.choose(Long.MinValue, Long.MaxValue)
y <- Gen.oneOf('8', '9', 'a', 'b')
} yield java.util.UUID.fromString(
new java.util.UUID(l1,l2).toString.updated(14, '4').updated(19, y)
)
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2](g1: Gen[T1], g2: Gen[T2]): Gen[(T1,T2)] = {
val g = for {
t1 <- g1; t2 <- g2
} yield (t1,t2)
g.suchThat { case (t1,t2) => g1.sieveCopy(t1) && g2.sieveCopy(t2) }
}
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2,T3](g1: Gen[T1], g2: Gen[T2], g3: Gen[T3]): Gen[(T1,T2,T3)] = {
val g0 = zip(g1,g2)
val g = for {
(t1,t2) <- g0; t3 <- g3
} yield (t1,t2,t3)
g.suchThat { case (t1,t2,t3) => g0.sieveCopy(t1,t2) && g3.sieveCopy(t3) }
}
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2,T3,T4](g1: Gen[T1], g2: Gen[T2], g3: Gen[T3], g4: Gen[T4]
): Gen[(T1,T2,T3,T4)] = {
val g0 = zip(g1,g2,g3)
val g = for {
(t1,t2,t3) <- g0; t4 <- g4
} yield (t1,t2,t3,t4)
g.suchThat { case (t1,t2,t3,t4) => g0.sieveCopy(t1,t2,t3) && g4.sieveCopy(t4) }
}
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2,T3,T4,T5](g1: Gen[T1], g2: Gen[T2], g3: Gen[T3], g4: Gen[T4],
g5: Gen[T5]
): Gen[(T1,T2,T3,T4,T5)] = {
val g0 = zip(g1,g2,g3,g4)
val g = for {
(t1,t2,t3,t4) <- g0; t5 <- g5
} yield (t1,t2,t3,t4,t5)
g.suchThat { case (t1,t2,t3,t4,t5) =>
g0.sieveCopy(t1,t2,t3,t4) && g5.sieveCopy(t5)
}
}
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2,T3,T4,T5,T6](g1: Gen[T1], g2: Gen[T2], g3: Gen[T3], g4: Gen[T4],
g5: Gen[T5], g6: Gen[T6]
): Gen[(T1,T2,T3,T4,T5,T6)] = {
val g0 = zip(g1,g2,g3,g4,g5)
val g = for {
(t1,t2,t3,t4,t5) <- g0; t6 <- g6
} yield (t1,t2,t3,t4,t5,t6)
g.suchThat { case (t1,t2,t3,t4,t5,t6) =>
g0.sieveCopy(t1,t2,t3,t4,t5) && g6.sieveCopy(t6)
}
}
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2,T3,T4,T5,T6,T7](g1: Gen[T1], g2: Gen[T2], g3: Gen[T3],
g4: Gen[T4], g5: Gen[T5], g6: Gen[T6], g7: Gen[T7]
): Gen[(T1,T2,T3,T4,T5,T6,T7)] = {
val g0 = zip(g1,g2,g3,g4,g5,g6)
val g = for {
(t1,t2,t3,t4,t5,t6) <- g0; t7 <- g7
} yield (t1,t2,t3,t4,t5,t6,t7)
g.suchThat { case (t1,t2,t3,t4,t5,t6,t7) =>
g0.sieveCopy(t1,t2,t3,t4,t5,t6) && g7.sieveCopy(t7)
}
}
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2,T3,T4,T5,T6,T7,T8](g1: Gen[T1], g2: Gen[T2], g3: Gen[T3],
g4: Gen[T4], g5: Gen[T5], g6: Gen[T6], g7: Gen[T7], g8: Gen[T8]
): Gen[(T1,T2,T3,T4,T5,T6,T7,T8)] = {
val g0 = zip(g1,g2,g3,g4,g5,g6,g7)
val g = for {
(t1,t2,t3,t4,t5,t6,t7) <- g0; t8 <- g8
} yield (t1,t2,t3,t4,t5,t6,t7,t8)
g.suchThat { case (t1,t2,t3,t4,t5,t6,t7,t8) =>
g0.sieveCopy(t1,t2,t3,t4,t5,t6,t7) && g8.sieveCopy(t8)
}
}
/** Combines the given generators into one generator that produces a
* tuple of their generated values. */
def zip[T1,T2,T3,T4,T5,T6,T7,T8,T9](g1: Gen[T1], g2: Gen[T2], g3: Gen[T3],
g4: Gen[T4], g5: Gen[T5], g6: Gen[T6], g7: Gen[T7], g8: Gen[T8], g9: Gen[T9]
): Gen[(T1,T2,T3,T4,T5,T6,T7,T8,T9)] = {
val g0 = zip(g1,g2,g3,g4,g5,g6,g7,g8)
val g = for {
(t1,t2,t3,t4,t5,t6,t7,t8) <- g0; t9 <- g9
} yield (t1,t2,t3,t4,t5,t6,t7,t8,t9)
g.suchThat { case (t1,t2,t3,t4,t5,t6,t7,t8,t9) =>
g0.sieveCopy(t1,t2,t3,t4,t5,t6,t7,t8) && g9.sieveCopy(t9)
}
}
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T,R](f: T => R)(implicit a: Arbitrary[T]): Gen[R] =
arbitrary[T] map f
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,R](f: (T1,T2) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2]
): Gen[R] = arbitrary[T1] flatMap { t => resultOf(f(t, _:T2)) }
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,T3,R](f: (T1,T2,T3) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2], a3: Arbitrary[T3]
): Gen[R] = arbitrary[T1] flatMap { t => resultOf(f(t, _:T2, _:T3)) }
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,T3,T4,R](f: (T1,T2,T3,T4) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2], a3: Arbitrary[T3], a4: Arbitrary[T4]
): Gen[R] = arbitrary[T1] flatMap {
t => resultOf(f(t, _:T2, _:T3, _:T4))
}
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,T3,T4,T5,R](f: (T1,T2,T3,T4,T5) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2], a3: Arbitrary[T3], a4: Arbitrary[T4],
a5: Arbitrary[T5]
): Gen[R] = arbitrary[T1] flatMap {
t => resultOf(f(t, _:T2, _:T3, _:T4, _:T5))
}
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,T3,T4,T5,T6,R](
f: (T1,T2,T3,T4,T5,T6) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2], a3: Arbitrary[T3],
a4: Arbitrary[T4], a5: Arbitrary[T5], a6: Arbitrary[T6]
): Gen[R] = arbitrary[T1] flatMap {
t => resultOf(f(t, _:T2, _:T3, _:T4, _:T5, _:T6))
}
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,T3,T4,T5,T6,T7,R](
f: (T1,T2,T3,T4,T5,T6,T7) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2], a3: Arbitrary[T3],
a4: Arbitrary[T4], a5: Arbitrary[T5], a6: Arbitrary[T6], a7: Arbitrary[T7]
): Gen[R] = arbitrary[T1] flatMap {
t => resultOf(f(t, _:T2, _:T3, _:T4, _:T5, _:T6, _:T7))
}
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,T3,T4,T5,T6,T7,T8,R](
f: (T1,T2,T3,T4,T5,T6,T7,T8) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2], a3: Arbitrary[T3], a4: Arbitrary[T4],
a5: Arbitrary[T5], a6: Arbitrary[T6], a7: Arbitrary[T7], a8: Arbitrary[T8]
): Gen[R] = arbitrary[T1] flatMap {
t => resultOf(f(t, _:T2, _:T3, _:T4, _:T5, _:T6, _:T7, _:T8))
}
/** Takes a function and returns a generator that generates arbitrary
* results of that function by feeding it with arbitrarily generated input
* parameters. */
def resultOf[T1,T2,T3,T4,T5,T6,T7,T8,T9,R](
f: (T1,T2,T3,T4,T5,T6,T7,T8,T9) => R)(implicit
a1: Arbitrary[T1], a2: Arbitrary[T2], a3: Arbitrary[T3], a4: Arbitrary[T4],
a5: Arbitrary[T5], a6: Arbitrary[T6], a7: Arbitrary[T7], a8: Arbitrary[T8],
a9: Arbitrary[T9]
): Gen[R] = arbitrary[T1] flatMap {
t => resultOf(f(t, _:T2, _:T3, _:T4, _:T5, _:T6, _:T7, _:T8, _:T9))
}
}
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