CHANGELOG.md

@@ -48,6 +48,8 @@
 - Operations -> Ops
 - Default Buffer and ND algebras are now Ops and lack neutral elements (0, 1) as well as algebra-level shapes.
 - Tensor algebra takes read-only structures as input and inherits AlgebraND
+- `UnivariateDistribution` renamed to `Distribution1D`
+- Rework of histograms.
 
 ### Deprecated
 - Specialized `DoubleBufferAlgebra`

build.gradle.kts

@@ -11,7 +11,7 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.3.0-dev-20"
+    version = "0.3.0-dev-21"
 }
 
 subprojects {

gradle.properties

@@ -6,6 +6,8 @@ kotlin.code.style=official
 kotlin.jupyter.add.scanner=false
 kotlin.mpp.stability.nowarn=true
 kotlin.native.ignoreDisabledTargets=true
+#kotlin.incremental.js.ir=true
+
 org.gradle.configureondemand=true
 org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
 org.gradle.parallel=true

kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Domain1D.kt

@@ -0,0 +1,59 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.domains
+
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.misc.UnstableKMathAPI
+
+@UnstableKMathAPI
+public abstract class Domain1D<T : Comparable<T>>(public val range: ClosedRange<T>) : Domain<T> {
+    override val dimension: Int get() = 1
+
+    public operator fun contains(value: T): Boolean = range.contains(value)
+
+    override operator fun contains(point: Point<T>): Boolean {
+        require(point.size == 0)
+        return contains(point[0])
+    }
+}
+
+@UnstableKMathAPI
+public class DoubleDomain1D(
+    @Suppress("CanBeParameter") public val doubleRange: ClosedFloatingPointRange<Double>,
+) : Domain1D<Double>(doubleRange), DoubleDomain {
+    override fun getLowerBound(num: Int): Double {
+        require(num == 0)
+        return range.start
+    }
+
+    override fun getUpperBound(num: Int): Double {
+        require(num == 0)
+        return range.endInclusive
+    }
+
+    override fun volume(): Double = range.endInclusive - range.start
+
+    override fun equals(other: Any?): Boolean {
+        if (this === other) return true
+        if (other == null || this::class != other::class) return false
+
+        other as DoubleDomain1D
+
+        if (doubleRange != other.doubleRange) return false
+
+        return true
+    }
+
+    override fun hashCode(): Int = doubleRange.hashCode()
+
+    override fun toString(): String = doubleRange.toString()
+
+
+}
+
+@UnstableKMathAPI
+public val Domain1D<Double>.center: Double
+    get() = (range.endInclusive + range.start) / 2

kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/HyperSquareDomain.kt

@@ -7,18 +7,28 @@ package space.kscience.kmath.domains
 import space.kscience.kmath.linear.Point
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.DoubleBuffer
 import space.kscience.kmath.structures.indices
 
 /**
- *
- * HyperSquareDomain class.
- *
- * @author Alexander Nozik
+ * A hyper-square (or hyper-cube) real-space domain. It is formed by a [Buffer] of [lower] boundaries
+ * and a [Buffer] of upper boundaries. Upper should be greater or equals than lower.
  */
 @UnstableKMathAPI
-public class HyperSquareDomain(private val lower: Buffer<Double>, private val upper: Buffer<Double>) : DoubleDomain {
+public class HyperSquareDomain(public val lower: Buffer<Double>, public val upper: Buffer<Double>) : DoubleDomain {
+    init {
+        require(lower.size == upper.size) {
+            "Domain borders size mismatch. Lower borders size is ${lower.size}, but upper borders size is ${upper.size}."
+        }
+        require(lower.indices.all { lower[it] <= upper[it] }) {
+            "Domain borders order mismatch. Lower borders must be less or equals than upper borders."
+        }
+    }
+
     override val dimension: Int get() = lower.size
 
+    public val center: DoubleBuffer get() = DoubleBuffer(dimension) { (lower[it] + upper[it]) / 2.0 }
+
     override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
         point[i] in lower[i]..upper[i]
     }

kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnivariateDomain.kt

@@ -1,33 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.domains
-
-import space.kscience.kmath.linear.Point
-import space.kscience.kmath.misc.UnstableKMathAPI
-
-@UnstableKMathAPI
-public class UnivariateDomain(public val range: ClosedFloatingPointRange<Double>) : DoubleDomain {
-    override val dimension: Int get() = 1
-
-    public operator fun contains(d: Double): Boolean = range.contains(d)
-
-    override operator fun contains(point: Point<Double>): Boolean {
-        require(point.size == 0)
-        return contains(point[0])
-    }
-
-    override fun getLowerBound(num: Int): Double {
-        require(num == 0)
-        return range.start
-    }
-
-    override fun getUpperBound(num: Int): Double {
-        require(num == 0)
-        return range.endInclusive
-    }
-
-    override fun volume(): Double = range.endInclusive - range.start
-}

kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/sorting.kt

@@ -3,43 +3,71 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
  */
 
+
 package space.kscience.kmath.misc
 
-import kotlin.comparisons.*
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.VirtualBuffer
 
 /**
- * Return a new list filled with buffer indices. Indice order is defined by sorting associated buffer value.
- * This feature allows to sort buffer values without reordering its content.
+ * Return a new array filled with buffer indices. Indices order is defined by sorting associated buffer value.
+ * This feature allows sorting buffer values without reordering its content.
  *
- * @return List of buffer indices, sorted by associated value.
+ * @return Buffer indices, sorted by associated value.
  */
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V: Comparable<V>> Buffer<V>.permSort() : IntArray = _permSortWith(compareBy<Int> { get(it) })
+public fun <V : Comparable<V>> Buffer<V>.indicesSorted(): IntArray = permSortIndicesWith(compareBy { get(it) })
+
+/**
+ * Create a zero-copy virtual buffer that contains the same elements but in ascending order
+ */
+@OptIn(UnstableKMathAPI::class)
+public fun <V : Comparable<V>> Buffer<V>.sorted(): Buffer<V> {
+    val permutations = indicesSorted()
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V: Comparable<V>> Buffer<V>.permSortDescending() : IntArray = _permSortWith(compareByDescending<Int> { get(it) })
+public fun <V : Comparable<V>> Buffer<V>.indicesSortedDescending(): IntArray =
+    permSortIndicesWith(compareByDescending { get(it) })
+
+/**
+ * Create a zero-copy virtual buffer that contains the same elements but in descending order
+ */
+@OptIn(UnstableKMathAPI::class)
+public fun <V : Comparable<V>> Buffer<V>.sortedDescending(): Buffer<V> {
+    val permutations = indicesSortedDescending()
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V, C: Comparable<C>> Buffer<V>.permSortBy(selector: (V) -> C) : IntArray = _permSortWith(compareBy<Int> { selector(get(it)) })
+public fun <V, C : Comparable<C>> Buffer<V>.indicesSortedBy(selector: (V) -> C): IntArray =
+    permSortIndicesWith(compareBy { selector(get(it)) })
+
+@OptIn(UnstableKMathAPI::class)
+public fun <V, C : Comparable<C>> Buffer<V>.sortedBy(selector: (V) -> C): Buffer<V> {
+    val permutations = indicesSortedBy(selector)
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V, C: Comparable<C>> Buffer<V>.permSortByDescending(selector: (V) -> C) : IntArray = _permSortWith(compareByDescending<Int> { selector(get(it)) })
+public fun <V, C : Comparable<C>> Buffer<V>.indicesSortedByDescending(selector: (V) -> C): IntArray =
+    permSortIndicesWith(compareByDescending { selector(get(it)) })
+
+@OptIn(UnstableKMathAPI::class)
+public fun <V, C : Comparable<C>> Buffer<V>.sortedByDescending(selector: (V) -> C): Buffer<V> {
+    val permutations = indicesSortedByDescending(selector)
+    return VirtualBuffer(size) { this[permutations[it]] }
+}
 
-@PerformancePitfall
 @UnstableKMathAPI
-public fun <V> Buffer<V>.permSortWith(comparator : Comparator<V>) : IntArray = _permSortWith { i1, i2 -> comparator.compare(get(i1), get(i2)) }
+public fun <V> Buffer<V>.indicesSortedWith(comparator: Comparator<V>): IntArray =
+    permSortIndicesWith { i1, i2 -> comparator.compare(get(i1), get(i2)) }
 
-@PerformancePitfall
-@UnstableKMathAPI
-private fun <V> Buffer<V>._permSortWith(comparator : Comparator<Int>) : IntArray {
-    if (size < 2) return IntArray(size)
+private fun <V> Buffer<V>.permSortIndicesWith(comparator: Comparator<Int>): IntArray {
+    if (size < 2) return IntArray(size) { 0 }
 
-    /* TODO: optimisation : keep a constant big array of indices (Ex: from 0 to 4096), then create indice
+    /* TODO: optimisation : keep a constant big array of indices (Ex: from 0 to 4096), then create indices
      * arrays more efficiently by copying subpart of cached one. For bigger needs, we could copy entire
      * cached array, then fill remaining indices manually. Not done for now, because:
      *  1. doing it right would require some statistics about common used buffer sizes.
@@ -53,3 +81,12 @@ private fun <V> Buffer<V>._permSortWith(comparator : Comparator<Int>) : IntArray
      */
     return packedIndices.sortedWith(comparator).toIntArray()
 }
+
+/**
+ * Checks that the [Buffer] is sorted (ascending) and throws [IllegalArgumentException] if it is not.
+ */
+public fun <T : Comparable<T>> Buffer<T>.requireSorted() {
+    for (i in 0..(size - 2)) {
+        require(get(i + 1) >= get(i)) { "The buffer is not sorted at index $i" }
+    }
+}

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt

@@ -194,7 +194,7 @@ public interface RingOpsND<T, out A : RingOps<T>> : RingOps<StructureND<T>>, Gro
     override fun multiply(left: StructureND<T>, right: StructureND<T>): StructureND<T> =
         zip(left, right) { aValue, bValue -> multiply(aValue, bValue) }
 
-    //TODO move to extensions after KEEP-176
+    //TODO move to extensions with context receivers
 
     /**
      * Multiplies an ND structure by an element of it.

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferND.kt

@@ -32,18 +32,23 @@ public open class BufferND<out T>(
 /**
  * Transform structure to a new structure using provided [BufferFactory] and optimizing if argument is [BufferND]
  */
-public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
-    factory: BufferFactory<R> = Buffer.Companion::auto,
+public inline fun <T, R : Any> StructureND<T>.mapToBuffer(
+    factory: BufferFactory<R>,
     crossinline transform: (T) -> R,
-): BufferND<R> {
-    return if (this is BufferND<T>)
-        BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
-    else {
-        val strides = DefaultStrides(shape)
-        BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
-    }
+): BufferND<R> = if (this is BufferND<T>)
+    BufferND(this.indices, factory.invoke(indices.linearSize) { transform(buffer[it]) })
+else {
+    val strides = DefaultStrides(shape)
+    BufferND(strides, factory.invoke(strides.linearSize) { transform(get(strides.index(it))) })
 }
 
+/**
+ * Transform structure to a new structure using inferred [BufferFactory]
+ */
+public inline fun <T, reified R : Any> StructureND<T>.mapToBuffer(
+    crossinline transform: (T) -> R,
+): BufferND<R> = mapToBuffer(Buffer.Companion::auto, transform)
+
 /**
  * Represents [MutableStructureND] over [MutableBuffer].
  *

kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/Buffer.kt

@@ -105,6 +105,16 @@ public interface Buffer<out T> {
  */
 public val Buffer<*>.indices: IntRange get() = 0 until size
 
+public fun <T> Buffer<T>.first(): T {
+    require(size > 0) { "Can't get the first element of empty buffer" }
+    return get(0)
+}
+
+public fun <T> Buffer<T>.last(): T {
+    require(size > 0) { "Can't get the last element of empty buffer" }
+    return get(size - 1)
+}
+
 /**
  * Immutable wrapper for [MutableBuffer].
  *

kmath-core/src/commonTest/kotlin/space/kscience/kmath/misc/PermSortTest.kt

@@ -6,14 +6,13 @@
 package space.kscience.kmath.misc
 
 import space.kscience.kmath.misc.PermSortTest.Platform.*
-import kotlin.random.Random
-import kotlin.test.Test
-import kotlin.test.assertEquals
-import kotlin.test.assertTrue
-
 import space.kscience.kmath.structures.IntBuffer
 import space.kscience.kmath.structures.asBuffer
+import kotlin.random.Random
+import kotlin.test.Test
 import kotlin.test.assertContentEquals
+import kotlin.test.assertEquals
+import kotlin.test.assertTrue
 
 class PermSortTest {
 
@@ -29,9 +28,9 @@ class PermSortTest {
     @Test
     fun testOnEmptyBuffer() {
         val emptyBuffer = IntBuffer(0) {it}
-        var permutations = emptyBuffer.permSort()
+        var permutations = emptyBuffer.indicesSorted()
         assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result")
-        permutations = emptyBuffer.permSortDescending()
+        permutations = emptyBuffer.indicesSortedDescending()
         assertTrue(permutations.isEmpty(), "permutation on an empty buffer should return an empty result")
     }
 
@@ -47,25 +46,25 @@ class PermSortTest {
 
     @Test
     fun testPermSortBy() {
-        val permutations = platforms.permSortBy { it.name }
+        val permutations = platforms.indicesSortedBy { it.name }
         val expected = listOf(ANDROID, JS, JVM, NATIVE, WASM)
         assertContentEquals(expected, permutations.map { platforms[it] }, "Ascending PermSort by name")
     }
 
     @Test
     fun testPermSortByDescending() {
-        val permutations = platforms.permSortByDescending { it.name }
+        val permutations = platforms.indicesSortedByDescending { it.name }
         val expected = listOf(WASM, NATIVE, JVM, JS, ANDROID)
         assertContentEquals(expected, permutations.map { platforms[it] }, "Descending PermSort by name")
     }
 
     @Test
     fun testPermSortWith() {
-        var permutations = platforms.permSortWith { p1, p2 -> p1.name.length.compareTo(p2.name.length) }
+        var permutations = platforms.indicesSortedWith { p1, p2 -> p1.name.length.compareTo(p2.name.length) }
         val expected = listOf(JS, JVM, WASM, NATIVE, ANDROID)
         assertContentEquals(expected, permutations.map { platforms[it] }, "PermSort using custom ascending comparator")
 
-        permutations = platforms.permSortWith(compareByDescending { it.name.length })
+        permutations = platforms.indicesSortedWith(compareByDescending { it.name.length })
         assertContentEquals(expected.reversed(), permutations.map { platforms[it] }, "PermSort using custom descending comparator")
     }
 
@@ -75,7 +74,7 @@ class PermSortTest {
         println("Test randomization seed: $seed")
 
         val buffer = Random(seed).buffer(bufferSize)
-        val indices = buffer.permSort()
+        val indices = buffer.indicesSorted()
 
         assertEquals(bufferSize, indices.size)
         // Ensure no doublon is present in indices
@@ -87,7 +86,7 @@ class PermSortTest {
             assertTrue(current <= next, "Permutation indices not properly sorted")
         }
 
-        val descIndices = buffer.permSortDescending()
+        val descIndices = buffer.indicesSortedDescending()
         assertEquals(bufferSize, descIndices.size) 
         // Ensure no doublon is present in indices
         assertEquals(descIndices.toSet().size, descIndices.size)

kmath-for-real/build.gradle.kts

@@ -21,19 +21,22 @@ readme {
 
     feature(
         id = "DoubleVector",
-        description = "Numpy-like operations for Buffers/Points",
         ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleVector.kt"
-    )
+    ){
+        "Numpy-like operations for Buffers/Points"
+    }
 
     feature(
         id = "DoubleMatrix",
-        description = "Numpy-like operations for 2d real structures",
         ref = "src/commonMain/kotlin/space/kscience/kmath/real/DoubleMatrix.kt"
-    )
+    ){
+        "Numpy-like operations for 2d real structures"
+    }
 
     feature(
         id = "grids",
-        description = "Uniform grid generators",
         ref = "src/commonMain/kotlin/space/kscience/kmath/structures/grids.kt"
-    )
+    ){
+        "Uniform grid generators"
+    }
 }

kmath-histograms/build.gradle.kts

@@ -17,6 +17,8 @@ kotlin.sourceSets {
     commonTest {
         dependencies {
             implementation(project(":kmath-for-real"))
+            implementation(projects.kmath.kmathStat)
+            implementation("org.jetbrains.kotlinx:kotlinx-coroutines-test:1.6.0")
         }
     }
 }

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Counter.kt

@@ -18,7 +18,8 @@ public interface Counter<T : Any> {
     public val value: T
 
     public companion object {
-        public fun double(): ObjectCounter<Double> = ObjectCounter(DoubleField)
+        public fun ofDouble(): ObjectCounter<Double> = ObjectCounter(DoubleField)
+        public fun <T: Any> of(group: Group<T>): ObjectCounter<T> = ObjectCounter(group)
     }
 }
 

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/DoubleHistogramSpace.kt

@@ -1,130 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.Domain
-import space.kscience.kmath.domains.HyperSquareDomain
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.nd.*
-import space.kscience.kmath.operations.DoubleField
-import space.kscience.kmath.structures.*
-import kotlin.math.floor
-
-public class DoubleHistogramSpace(
-    private val lower: Buffer<Double>,
-    private val upper: Buffer<Double>,
-    private val binNums: IntArray = IntArray(lower.size) { 20 },
-) : IndexedHistogramSpace<Double, Double> {
-
-    init {
-        // argument checks
-        require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
-        require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
-        require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
-    }
-
-    public val dimension: Int get() = lower.size
-
-    override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 }
-    override val histogramValueSpace: DoubleFieldND = DoubleField.ndAlgebra(*shape)
-
-    private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
-
-    /**
-     * Get internal [StructureND] bin index for given axis
-     */
-    private fun getIndex(axis: Int, value: Double): Int = when {
-        value >= upper[axis] -> binNums[axis] + 1 // overflow
-        value < lower[axis] -> 0 // underflow
-        else -> floor((value - lower[axis]) / binSize[axis]).toInt()
-    }
-
-    override fun getIndex(point: Buffer<Double>): IntArray = IntArray(dimension) {
-        getIndex(it, point[it])
-    }
-
-    @OptIn(UnstableKMathAPI::class)
-    override fun getDomain(index: IntArray): Domain<Double> {
-        val lowerBoundary = index.mapIndexed { axis, i ->
-            when (i) {
-                0 -> Double.NEGATIVE_INFINITY
-                shape[axis] - 1 -> upper[axis]
-                else -> lower[axis] + (i.toDouble()) * binSize[axis]
-            }
-        }.asBuffer()
-
-        val upperBoundary = index.mapIndexed { axis, i ->
-            when (i) {
-                0 -> lower[axis]
-                shape[axis] - 1 -> Double.POSITIVE_INFINITY
-                else -> lower[axis] + (i.toDouble() + 1) * binSize[axis]
-            }
-        }.asBuffer()
-
-        return HyperSquareDomain(lowerBoundary, upperBoundary)
-    }
-
-
-    override fun produceBin(index: IntArray, value: Double): Bin<Double> {
-        val domain = getDomain(index)
-        return DomainBin(domain, value)
-    }
-
-    override fun produce(builder: HistogramBuilder<Double>.() -> Unit): IndexedHistogram<Double, Double> {
-        val ndCounter = StructureND.auto(shape) { Counter.double() }
-        val hBuilder = HistogramBuilder<Double> { point, value ->
-            val index = getIndex(point)
-            ndCounter[index].add(value.toDouble())
-        }
-        hBuilder.apply(builder)
-        val values: BufferND<Double> = ndCounter.mapToBuffer { it.value }
-        return IndexedHistogram(this, values)
-    }
-
-    override fun IndexedHistogram<Double, Double>.unaryMinus(): IndexedHistogram<Double, Double> = this * (-1)
-
-    public companion object {
-        /**
-         * Use it like
-         * ```
-         *FastHistogram.fromRanges(
-         *  (-1.0..1.0),
-         *  (-1.0..1.0)
-         *)
-         *```
-         */
-        public fun fromRanges(vararg ranges: ClosedFloatingPointRange<Double>): DoubleHistogramSpace = DoubleHistogramSpace(
-            ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
-            ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer()
-        )
-
-        /**
-         * Use it like
-         * ```
-         *FastHistogram.fromRanges(
-         *  (-1.0..1.0) to 50,
-         *  (-1.0..1.0) to 32
-         *)
-         *```
-         */
-        public fun fromRanges(vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>): DoubleHistogramSpace =
-            DoubleHistogramSpace(
-                ListBuffer(
-                    ranges
-                        .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
-                        .map(ClosedFloatingPointRange<Double>::start)
-                ),
-
-                ListBuffer(
-                    ranges
-                        .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
-                        .map(ClosedFloatingPointRange<Double>::endInclusive)
-                ),
-
-                ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray()
-            )
-    }
-}

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram.kt

@@ -13,14 +13,23 @@ import space.kscience.kmath.structures.asBuffer
 /**
  * The binned data element. Could be a histogram bin with a number of counts or an artificial construct.
  */
-public interface Bin<in T : Any> : Domain<T> {
+public interface Bin<in T : Any, out V> : Domain<T> {
     /**
      * The value of this bin.
      */
-    public val value: Number
+    public val binValue: V
 }
 
-public interface Histogram<in T : Any, out B : Bin<T>> {
+/**
+ * A simple histogram bin based on domain
+ */
+public data class DomainBin<in T : Comparable<T>, D : Domain<T>, out V>(
+    public val domain: D,
+    override val binValue: V,
+) : Bin<T, V>, Domain<T> by domain
+
+
+public interface Histogram<in T : Any, out V, out B : Bin<T, V>> {
     /**
      * Find existing bin, corresponding to given coordinates
      */
@@ -32,29 +41,38 @@ public interface Histogram<in T : Any, out B : Bin<T>> {
     public val dimension: Int
 
     public val bins: Iterable<B>
+
+    public companion object {
+        //A discoverability root
+    }
 }
 
-public fun interface HistogramBuilder<in T : Any> {
+public interface HistogramBuilder<in T : Any, V : Any> {
 
     /**
-     * Increment appropriate bin
+     * The default value increment for a bin
      */
-    public fun putValue(point: Point<out T>, value: Number)
+    public val defaultValue: V
+
+    /**
+     * Increment appropriate bin with given value
+     */
+    public fun putValue(point: Point<out T>, value: V = defaultValue)
 
 }
 
-public fun <T : Any, B : Bin<T>> HistogramBuilder<T>.put(point: Point<out T>): Unit = putValue(point, 1.0)
+public fun <T : Any> HistogramBuilder<T, *>.put(point: Point<out T>): Unit = putValue(point)
 
-public fun <T : Any> HistogramBuilder<T>.put(vararg point: T): Unit = put(point.asBuffer())
+public fun <T : Any> HistogramBuilder<T, *>.put(vararg point: T): Unit = put(point.asBuffer())
 
-public fun HistogramBuilder<Double>.put(vararg point: Number): Unit =
+public fun HistogramBuilder<Double, *>.put(vararg point: Number): Unit =
     put(DoubleBuffer(point.map { it.toDouble() }.toDoubleArray()))
 
-public fun HistogramBuilder<Double>.put(vararg point: Double): Unit = put(DoubleBuffer(point))
-public fun <T : Any> HistogramBuilder<T>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
+public fun HistogramBuilder<Double, *>.put(vararg point: Double): Unit = put(DoubleBuffer(point))
+public fun <T : Any> HistogramBuilder<T, *>.fill(sequence: Iterable<Point<T>>): Unit = sequence.forEach { put(it) }
 
 /**
  * Pass a sequence builder into histogram
  */
-public fun <T : Any> HistogramBuilder<T>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
+public fun <T : Any> HistogramBuilder<T, *>.fill(block: suspend SequenceScope<Point<T>>.() -> Unit): Unit =
     fill(sequence(block).asIterable())

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram1D.kt

@@ -0,0 +1,64 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.Domain1D
+import space.kscience.kmath.domains.center
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.asSequence
+import space.kscience.kmath.structures.Buffer
+
+
+/**
+ * A univariate bin based on a range
+ *
+ * @property binValue The value of histogram including weighting
+ */
+@UnstableKMathAPI
+public data class Bin1D<T : Comparable<T>, out V>(
+    public val domain: Domain1D<T>,
+    override val binValue: V,
+) : Bin<T, V>, ClosedRange<T> by domain.range {
+
+    override val dimension: Int get() = 1
+
+    override fun contains(point: Buffer<T>): Boolean = point.size == 1 && contains(point[0])
+}
+
+@OptIn(UnstableKMathAPI::class)
+public interface Histogram1D<T : Comparable<T>, V> : Histogram<T, V, Bin1D<T, V>> {
+    override val dimension: Int get() = 1
+    public operator fun get(value: T): Bin1D<T, V>?
+    override operator fun get(point: Buffer<T>): Bin1D<T, V>? = get(point[0])
+}
+
+public interface Histogram1DBuilder<in T : Any, V : Any> : HistogramBuilder<T, V> {
+    /**
+     * Thread safe put operation
+     */
+    public fun putValue(at: T, value: V = defaultValue)
+
+    override fun putValue(point: Point<out T>, value: V) {
+        require(point.size == 1) { "Only points with single value could be used in Histogram1D" }
+        putValue(point[0], value)
+    }
+}
+
+@UnstableKMathAPI
+public fun Histogram1DBuilder<Double, *>.fill(items: Iterable<Double>): Unit =
+    items.forEach(this::putValue)
+
+@UnstableKMathAPI
+public fun Histogram1DBuilder<Double, *>.fill(array: DoubleArray): Unit =
+    array.forEach(this::putValue)
+
+@UnstableKMathAPI
+public fun <T : Any> Histogram1DBuilder<T, *>.fill(buffer: Buffer<T>): Unit =
+    buffer.asSequence().forEach(this::putValue)
+
+@OptIn(UnstableKMathAPI::class)
+public val Bin1D<Double, *>.center: Double get() = domain.center

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/HistogramND.kt

@@ -0,0 +1,76 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.Domain
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.nd.DefaultStrides
+import space.kscience.kmath.nd.FieldOpsND
+import space.kscience.kmath.nd.Shape
+import space.kscience.kmath.nd.StructureND
+import space.kscience.kmath.operations.Group
+import space.kscience.kmath.operations.ScaleOperations
+import space.kscience.kmath.operations.invoke
+
+/**
+ * @param T the type of the argument space
+ * @param V the type of bin value
+ */
+public class HistogramND<T : Comparable<T>, D : Domain<T>, V : Any>(
+    public val group: HistogramGroupND<T, D, V>,
+    internal val values: StructureND<V>,
+) : Histogram<T, V, DomainBin<T, D, V>> {
+
+    override fun get(point: Point<T>): DomainBin<T, D, V>? {
+        val index = group.getIndexOrNull(point) ?: return null
+        return group.produceBin(index, values[index])
+    }
+
+    override val dimension: Int get() = group.shape.size
+
+    override val bins: Iterable<DomainBin<T, D, V>>
+        get() = DefaultStrides(group.shape).asSequence().map {
+            group.produceBin(it, values[it])
+        }.asIterable()
+}
+
+/**
+ * A space for producing histograms with values in a NDStructure
+ */
+public interface HistogramGroupND<T : Comparable<T>, D : Domain<T>, V : Any> :
+    Group<HistogramND<T, D, V>>, ScaleOperations<HistogramND<T, D, V>> {
+    public val shape: Shape
+    public val valueAlgebraND: FieldOpsND<V, *> //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape),
+
+    /**
+     * Resolve index of the bin including given [point]. Return null if point is outside histogram area
+     */
+    public fun getIndexOrNull(point: Point<T>): IntArray?
+
+    /**
+     * Get a bin domain represented by given index
+     */
+    public fun getDomain(index: IntArray): Domain<T>?
+
+    public fun produceBin(index: IntArray, value: V): DomainBin<T, D, V>
+
+    public fun produce(builder: HistogramBuilder<T, V>.() -> Unit): HistogramND<T, D, V>
+
+    override fun add(left: HistogramND<T, D, V>, right: HistogramND<T, D, V>): HistogramND<T, D, V> {
+        require(left.group == this && right.group == this) {
+            "A histogram belonging to a different group cannot be operated."
+        }
+        return HistogramND(this, valueAlgebraND { left.values + right.values })
+    }
+
+    override fun scale(a: HistogramND<T, D, V>, value: Double): HistogramND<T, D, V> {
+        require(a.group == this) { "A histogram belonging to a different group cannot be operated." }
+        return HistogramND(this, valueAlgebraND { a.values * value })
+    }
+
+    override val zero: HistogramND<T, D, V> get() = produce { }
+}
+

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/IndexedHistogramSpace.kt

@@ -1,82 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.Domain
-import space.kscience.kmath.linear.Point
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.nd.DefaultStrides
-import space.kscience.kmath.nd.FieldND
-import space.kscience.kmath.nd.Shape
-import space.kscience.kmath.nd.StructureND
-import space.kscience.kmath.operations.Group
-import space.kscience.kmath.operations.ScaleOperations
-import space.kscience.kmath.operations.invoke
-
-/**
- * A simple histogram bin based on domain
- */
-public data class DomainBin<in T : Comparable<T>>(
-    public val domain: Domain<T>,
-    override val value: Number,
-) : Bin<T>, Domain<T> by domain
-
-@OptIn(UnstableKMathAPI::class)
-public class IndexedHistogram<T : Comparable<T>, V : Any>(
-    public val context: IndexedHistogramSpace<T, V>,
-    public val values: StructureND<V>,
-) : Histogram<T, Bin<T>> {
-
-    override fun get(point: Point<T>): Bin<T>? {
-        val index = context.getIndex(point) ?: return null
-        return context.produceBin(index, values[index])
-    }
-
-    override val dimension: Int get() = context.shape.size
-
-    override val bins: Iterable<Bin<T>>
-        get() = DefaultStrides(context.shape).asSequence().map {
-            context.produceBin(it, values[it])
-        }.asIterable()
-}
-
-/**
- * A space for producing histograms with values in a NDStructure
- */
-public interface IndexedHistogramSpace<T : Comparable<T>, V : Any>
-    : Group<IndexedHistogram<T, V>>, ScaleOperations<IndexedHistogram<T, V>> {
-    //public val valueSpace: Space<V>
-    public val shape: Shape
-    public val histogramValueSpace: FieldND<V, *> //= NDAlgebra.space(valueSpace, Buffer.Companion::boxing, *shape),
-
-    /**
-     * Resolve index of the bin including given [point]
-     */
-    public fun getIndex(point: Point<T>): IntArray?
-
-    /**
-     * Get a bin domain represented by given index
-     */
-    public fun getDomain(index: IntArray): Domain<T>?
-
-    public fun produceBin(index: IntArray, value: V): Bin<T>
-
-    public fun produce(builder: HistogramBuilder<T>.() -> Unit): IndexedHistogram<T, V>
-
-    override fun add(left: IndexedHistogram<T, V>, right: IndexedHistogram<T, V>): IndexedHistogram<T, V> {
-        require(left.context == this) { "Can't operate on a histogram produced by external space" }
-        require(right.context == this) { "Can't operate on a histogram produced by external space" }
-        return IndexedHistogram(this, histogramValueSpace { left.values + right.values })
-    }
-
-    override fun scale(a: IndexedHistogram<T, V>, value: Double): IndexedHistogram<T, V> {
-        require(a.context == this) { "Can't operate on a histogram produced by external space" }
-        return IndexedHistogram(this, histogramValueSpace { a.values * value })
-    }
-
-    override val zero: IndexedHistogram<T, V> get() = produce { }
-}
-

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogram1D.kt

@@ -0,0 +1,156 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.DoubleDomain1D
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.Group
+import space.kscience.kmath.operations.Ring
+import space.kscience.kmath.operations.ScaleOperations
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.structures.Buffer
+import kotlin.math.floor
+
+@OptIn(UnstableKMathAPI::class)
+public class UniformHistogram1D<V : Any>(
+    public val group: UniformHistogram1DGroup<V, *>,
+    internal val values: Map<Int, V>,
+) : Histogram1D<Double, V> {
+
+    private val startPoint get() = group.startPoint
+    private val binSize get() = group.binSize
+
+    private fun produceBin(index: Int, value: V): Bin1D<Double, V> {
+        val domain = DoubleDomain1D((startPoint + index * binSize)..(startPoint + (index + 1) * binSize))
+        return Bin1D(domain, value)
+    }
+
+    override val bins: Iterable<Bin1D<Double, V>> get() = values.map { produceBin(it.key, it.value) }
+
+    override fun get(value: Double): Bin1D<Double, V>? {
+        val index: Int = group.getIndex(value)
+        val v = values[index]
+        return v?.let { produceBin(index, it) }
+    }
+}
+
+/**
+ * An algebra for uniform histograms in 1D real space
+ */
+public class UniformHistogram1DGroup<V : Any, A>(
+    public val valueAlgebra: A,
+    public val binSize: Double,
+    public val startPoint: Double = 0.0,
+) : Group<Histogram1D<Double, V>>, ScaleOperations<Histogram1D<Double, V>> where A : Ring<V>, A : ScaleOperations<V> {
+
+    override val zero: UniformHistogram1D<V> = UniformHistogram1D(this, emptyMap())
+
+    /**
+     * Get index of a bin
+     */
+    @PublishedApi
+    internal fun getIndex(at: Double): Int = floor((at - startPoint) / binSize).toInt()
+
+    override fun add(
+        left: Histogram1D<Double, V>,
+        right: Histogram1D<Double, V>,
+    ): UniformHistogram1D<V> = valueAlgebra {
+        val leftUniform = produceFrom(left)
+        val rightUniform = produceFrom(right)
+        val keys = leftUniform.values.keys + rightUniform.values.keys
+        UniformHistogram1D(
+            this@UniformHistogram1DGroup,
+            keys.associateWith {
+                (leftUniform.values[it] ?: valueAlgebra.zero) + (rightUniform.values[it] ?: valueAlgebra.zero)
+            }
+        )
+    }
+
+    override fun Histogram1D<Double, V>.unaryMinus(): UniformHistogram1D<V> = valueAlgebra {
+        UniformHistogram1D(this@UniformHistogram1DGroup, produceFrom(this@unaryMinus).values.mapValues { -it.value })
+    }
+
+    override fun scale(
+        a: Histogram1D<Double, V>,
+        value: Double,
+    ): UniformHistogram1D<V> = UniformHistogram1D(
+        this@UniformHistogram1DGroup,
+        produceFrom(a).values.mapValues { valueAlgebra.scale(it.value, value) }
+    )
+
+    /**
+     * Fill histogram.
+     */
+    public inline fun produce(block: Histogram1DBuilder<Double, V>.() -> Unit): UniformHistogram1D<V> {
+        val map = HashMap<Int, V>()
+        val builder = object : Histogram1DBuilder<Double, V> {
+            override val defaultValue: V get() = valueAlgebra.zero
+
+            override fun putValue(at: Double, value: V) {
+                val index = getIndex(at)
+                map[index] = with(valueAlgebra) { (map[index] ?: zero) + one }
+            }
+        }
+        builder.block()
+        return UniformHistogram1D(this, map)
+    }
+
+    /**
+     * Re-bin given histogram to be compatible if exiting bin fully falls inside existing bin, this bin value
+     * is increased by one. If not, all bins including values from this bin are increased by fraction
+     * (conserving the norming).
+     */
+    @OptIn(UnstableKMathAPI::class)
+    public fun produceFrom(
+        histogram: Histogram1D<Double, V>,
+    ): UniformHistogram1D<V> = if ((histogram as? UniformHistogram1D)?.group == this) {
+        histogram
+    } else {
+        val map = HashMap<Int, V>()
+        histogram.bins.forEach { bin ->
+            val range = bin.domain.range
+            val indexOfLeft = getIndex(range.start)
+            val indexOfRight = getIndex(range.endInclusive)
+            val numBins = indexOfRight - indexOfLeft + 1
+            for (i in indexOfLeft..indexOfRight) {
+                map[indexOfLeft] = with(valueAlgebra) {
+                    (map[indexOfLeft] ?: zero) + bin.binValue / numBins
+                }
+            }
+        }
+        UniformHistogram1D(this, map)
+    }
+}
+
+public fun <V : Any, A> Histogram.Companion.uniform1D(
+    valueAlgebra: A,
+    binSize: Double,
+    startPoint: Double = 0.0,
+): UniformHistogram1DGroup<V, A> where A : Ring<V>, A : ScaleOperations<V> =
+    UniformHistogram1DGroup(valueAlgebra, binSize, startPoint)
+
+@UnstableKMathAPI
+public fun <V : Any> UniformHistogram1DGroup<V, *>.produce(
+    buffer: Buffer<Double>,
+): UniformHistogram1D<V> = produce { fill(buffer) }
+
+/**
+ * Map of bin centers to bin values
+ */
+@OptIn(UnstableKMathAPI::class)
+public val <V : Any> UniformHistogram1D<V>.binValues: Map<Double, V>
+    get() = bins.associate { it.center to it.binValue }
+
+
+//TODO add normalized values inside Field-based histogram spaces with context receivers
+///**
+// * Map of bin centers to normalized bin values (bin size as normalization)
+// */
+//@OptIn(UnstableKMathAPI::class)
+//public val <V : Any> UniformHistogram1D<V>.binValuesNormalized: Map<Double, V>
+//    get() = group.valueAlgebra {
+//        bins.associate { it.center to it.binValue / group.binSize }
+//    }

kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogramGroupND.kt

@@ -0,0 +1,163 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+@file:OptIn(UnstableKMathAPI::class)
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.HyperSquareDomain
+import space.kscience.kmath.linear.Point
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.Field
+import space.kscience.kmath.operations.invoke
+import space.kscience.kmath.structures.*
+import kotlin.math.floor
+
+public typealias HyperSquareBin<V> = DomainBin<Double, HyperSquareDomain, V>
+
+/**
+ * Multivariate histogram space for hyper-square real-field bins.
+ * @param bufferFactory is an optional parameter used to optimize buffer production.
+ */
+public class UniformHistogramGroupND<V : Any, A : Field<V>>(
+    override val valueAlgebraND: FieldOpsND<V, A>,
+    private val lower: Buffer<Double>,
+    private val upper: Buffer<Double>,
+    private val binNums: IntArray = IntArray(lower.size) { 20 },
+    private val bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
+) : HistogramGroupND<Double, HyperSquareDomain, V> {
+
+    init {
+        // argument checks
+        require(lower.size == upper.size) { "Dimension mismatch in histogram lower and upper limits." }
+        require(lower.size == binNums.size) { "Dimension mismatch in bin count." }
+        require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
+    }
+
+    public val dimension: Int get() = lower.size
+
+    override val shape: IntArray = IntArray(binNums.size) { binNums[it] + 2 }
+
+    private val binSize = DoubleBuffer(dimension) { (upper[it] - lower[it]) / binNums[it] }
+
+    /**
+     * Get internal [StructureND] bin index for given axis
+     */
+    private fun getIndex(axis: Int, value: Double): Int = when {
+        value >= upper[axis] -> binNums[axis] + 1 // overflow
+        value < lower[axis] -> 0 // underflow
+        else -> floor((value - lower[axis]) / binSize[axis]).toInt()
+    }
+
+    override fun getIndexOrNull(point: Buffer<Double>): IntArray = IntArray(dimension) {
+        getIndex(it, point[it])
+    }
+
+    override fun getDomain(index: IntArray): HyperSquareDomain {
+        val lowerBoundary = index.mapIndexed { axis, i ->
+            when (i) {
+                0 -> Double.NEGATIVE_INFINITY
+                shape[axis] - 1 -> upper[axis]
+                else -> lower[axis] + (i.toDouble()) * binSize[axis]
+            }
+        }.asBuffer()
+
+        val upperBoundary = index.mapIndexed { axis, i ->
+            when (i) {
+                0 -> lower[axis]
+                shape[axis] - 1 -> Double.POSITIVE_INFINITY
+                else -> lower[axis] + (i.toDouble() + 1) * binSize[axis]
+            }
+        }.asBuffer()
+
+        return HyperSquareDomain(lowerBoundary, upperBoundary)
+    }
+
+    override fun produceBin(index: IntArray, value: V): HyperSquareBin<V> {
+        val domain = getDomain(index)
+        return DomainBin(domain, value)
+    }
+
+
+    override fun produce(builder: HistogramBuilder<Double, V>.() -> Unit): HistogramND<Double, HyperSquareDomain, V> {
+        val ndCounter = StructureND.buffered(shape) { Counter.of(valueAlgebraND.elementAlgebra) }
+        val hBuilder = object : HistogramBuilder<Double, V> {
+            override val defaultValue: V get() = valueAlgebraND.elementAlgebra.one
+
+            override fun putValue(point: Point<out Double>, value: V) = with(valueAlgebraND.elementAlgebra) {
+                val index = getIndexOrNull(point)
+                ndCounter[index].add(value)
+            }
+        }
+        hBuilder.apply(builder)
+        val values: BufferND<V> = ndCounter.mapToBuffer(bufferFactory) { it.value }
+        return HistogramND(this, values)
+    }
+
+    override fun HistogramND<Double, HyperSquareDomain, V>.unaryMinus(): HistogramND<Double, HyperSquareDomain, V> =
+        this * (-1)
+}
+
+/**
+ * Use it like
+ * ```
+ *FastHistogram.fromRanges(
+ *  (-1.0..1.0),
+ *  (-1.0..1.0)
+ *)
+ *```
+ */
+public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
+    valueAlgebraND: FieldOpsND<V, A>,
+    vararg ranges: ClosedFloatingPointRange<Double>,
+    bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
+): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
+    valueAlgebraND,
+    ranges.map(ClosedFloatingPointRange<Double>::start).asBuffer(),
+    ranges.map(ClosedFloatingPointRange<Double>::endInclusive).asBuffer(),
+    bufferFactory = bufferFactory
+)
+
+public fun Histogram.Companion.uniformDoubleNDFromRanges(
+    vararg ranges: ClosedFloatingPointRange<Double>,
+): UniformHistogramGroupND<Double, DoubleField> =
+    uniformNDFromRanges(DoubleFieldOpsND, *ranges, bufferFactory = ::DoubleBuffer)
+
+
+/**
+ * Use it like
+ * ```
+ *FastHistogram.fromRanges(
+ *  (-1.0..1.0) to 50,
+ *  (-1.0..1.0) to 32
+ *)
+ *```
+ */
+public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
+    valueAlgebraND: FieldOpsND<V, A>,
+    vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
+    bufferFactory: BufferFactory<V> = Buffer.Companion::boxing,
+): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
+    valueAlgebraND,
+    ListBuffer(
+        ranges
+            .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
+            .map(ClosedFloatingPointRange<Double>::start)
+    ),
+    ListBuffer(
+        ranges
+            .map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
+            .map(ClosedFloatingPointRange<Double>::endInclusive)
+    ),
+    ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray(),
+    bufferFactory = bufferFactory
+)
+
+public fun Histogram.Companion.uniformDoubleNDFromRanges(
+    vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
+): UniformHistogramGroupND<Double, DoubleField> =
+    uniformNDFromRanges(DoubleFieldOpsND, *ranges, bufferFactory = ::DoubleBuffer)

kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/MultivariateHistogramTest.kt

@@ -3,8 +3,11 @@
  * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
  */
 
+@file:OptIn(UnstableKMathAPI::class)
+
 package space.kscience.kmath.histogram
 
+import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.DefaultStrides
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.real.DoubleVector
@@ -14,14 +17,14 @@ import kotlin.test.*
 internal class MultivariateHistogramTest {
     @Test
     fun testSinglePutHistogram() {
-        val hSpace = DoubleHistogramSpace.fromRanges(
+        val hSpace = Histogram.uniformDoubleNDFromRanges(
             (-1.0..1.0),
             (-1.0..1.0)
         )
         val histogram = hSpace.produce {
             put(0.55, 0.55)
         }
-        val bin = histogram.bins.find { it.value.toInt() > 0 } ?: fail()
+        val bin = histogram.bins.find { it.binValue.toInt() > 0 } ?: fail()
         assertTrue { bin.contains(DoubleVector(0.55, 0.55)) }
         assertTrue { bin.contains(DoubleVector(0.6, 0.5)) }
         assertFalse { bin.contains(DoubleVector(-0.55, 0.55)) }
@@ -29,7 +32,7 @@ internal class MultivariateHistogramTest {
 
     @Test
     fun testSequentialPut() {
-        val hSpace = DoubleHistogramSpace.fromRanges(
+        val hSpace = Histogram.uniformDoubleNDFromRanges(
             (-1.0..1.0),
             (-1.0..1.0),
             (-1.0..1.0)
@@ -44,12 +47,12 @@ internal class MultivariateHistogramTest {
                 put(nextDouble(), nextDouble(), nextDouble())
             }
         }
-        assertEquals(n, histogram.bins.sumOf { it.value.toInt() })
+        assertEquals(n, histogram.bins.sumOf { it.binValue.toInt() })
     }
 
     @Test
     fun testHistogramAlgebra() {
-        DoubleHistogramSpace.fromRanges(
+        Histogram.uniformDoubleNDFromRanges(
             (-1.0..1.0),
             (-1.0..1.0),
             (-1.0..1.0)
@@ -77,7 +80,7 @@ internal class MultivariateHistogramTest {
             assertTrue {
                 res.bins.count() >= histogram1.bins.count()
             }
-            assertEquals(0.0, res.bins.sumOf { it.value.toDouble() })
+            assertEquals(0.0, res.bins.sumOf { it.binValue.toDouble() })
         }
     }
 }

kmath-histograms/src/commonTest/kotlin/space/kscience/kmath/histogram/UniformHistogram1DTest.kt

@@ -0,0 +1,56 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.histogram
+
+import kotlinx.coroutines.ExperimentalCoroutinesApi
+import kotlinx.coroutines.test.runTest
+import space.kscience.kmath.distributions.NormalDistribution
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.stat.RandomGenerator
+import space.kscience.kmath.stat.nextBuffer
+import kotlin.native.concurrent.ThreadLocal
+import kotlin.test.Test
+import kotlin.test.assertEquals
+
+@OptIn(ExperimentalCoroutinesApi::class, UnstableKMathAPI::class)
+internal class UniformHistogram1DTest {
+
+    @Test
+    fun normal() = runTest {
+        val distribution = NormalDistribution(0.0, 1.0)
+        with(Histogram.uniform1D(DoubleField, 0.1)) {
+            val h1 = produce(distribution.nextBuffer(generator, 10000))
+
+            val h2 = produce(distribution.nextBuffer(generator, 50000))
+
+            val h3 = h1 + h2
+
+            assertEquals(60000, h3.bins.sumOf { it.binValue }.toInt())
+        }
+    }
+
+    @Test
+    fun rebinDown() = runTest {
+        val h1 = Histogram.uniform1D(DoubleField, 0.01).produce(generator.nextDoubleBuffer(10000))
+        val h2 = Histogram.uniform1D(DoubleField,0.03).produceFrom(h1)
+
+        assertEquals(10000, h2.bins.sumOf { it.binValue }.toInt())
+    }
+
+    @Test
+    fun rebinUp() = runTest {
+        val h1 = Histogram.uniform1D(DoubleField, 0.03).produce(generator.nextDoubleBuffer(10000))
+        val h2 = Histogram.uniform1D(DoubleField,0.01).produceFrom(h1)
+
+        assertEquals(10000, h2.bins.sumOf { it.binValue }.toInt())
+    }
+
+    @ThreadLocal
+    companion object{
+        private val generator = RandomGenerator.default(123)
+    }
+}

kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramGroup.kt

@@ -0,0 +1,179 @@
+/*
+ * Copyright 2018-2021 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+@file:OptIn(UnstableKMathAPI::class)
+
+package space.kscience.kmath.histogram
+
+import space.kscience.kmath.domains.DoubleDomain1D
+import space.kscience.kmath.domains.center
+import space.kscience.kmath.misc.UnstableKMathAPI
+import space.kscience.kmath.misc.sorted
+import space.kscience.kmath.operations.Group
+import space.kscience.kmath.operations.Ring
+import space.kscience.kmath.operations.ScaleOperations
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.first
+import space.kscience.kmath.structures.indices
+import space.kscience.kmath.structures.last
+import java.util.*
+
+private fun <B : ClosedRange<Double>> TreeMap<Double, B>.getBin(value: Double): B? {
+    // check ceiling entry and return it if it is what needed
+    val ceil = ceilingEntry(value)?.value
+    if (ceil != null && value in ceil) return ceil
+    //check floor entry
+    val floor = floorEntry(value)?.value
+    if (floor != null && value in floor) return floor
+    //neither is valid, not found
+    return null
+}
+
+//public data class ValueAndError(val value: Double, val error: Double)
+//
+//public typealias WeightedBin1D = Bin1D<Double, ValueAndError>
+
+/**
+ * A histogram based on a tree map of values
+ */
+public class TreeHistogram<V : Any>(
+    private val binMap: TreeMap<Double, Bin1D<Double, V>>,
+) : Histogram1D<Double, V> {
+    override fun get(value: Double): Bin1D<Double, V>? = binMap.getBin(value)
+    override val bins: Collection<Bin1D<Double, V>> get() = binMap.values
+}
+
+/**
+ * A space for univariate histograms with variable bin borders based on a tree map
+ */
+public class TreeHistogramGroup<V : Any, A>(
+    public val valueAlgebra: A,
+    @PublishedApi internal val binFactory: (Double) -> DoubleDomain1D,
+) : Group<TreeHistogram<V>>, ScaleOperations<TreeHistogram<V>> where A : Ring<V>, A : ScaleOperations<V> {
+
+    internal inner class DomainCounter(val domain: DoubleDomain1D, val counter: Counter<V> = Counter.of(valueAlgebra)) :
+        ClosedRange<Double> by domain.range
+
+    @PublishedApi
+    internal inner class TreeHistogramBuilder : Histogram1DBuilder<Double, V> {
+
+        override val defaultValue: V get() = valueAlgebra.one
+
+        private val bins: TreeMap<Double, DomainCounter> = TreeMap()
+
+        private fun createBin(value: Double): DomainCounter {
+            val binDefinition: DoubleDomain1D = binFactory(value)
+            val newBin = DomainCounter(binDefinition)
+            synchronized(this) {
+                bins[binDefinition.center] = newBin
+            }
+            return newBin
+        }
+
+        /**
+         * Thread safe put operation
+         */
+        override fun putValue(at: Double, value: V) {
+            (bins.getBin(at) ?: createBin(at)).counter.add(value)
+        }
+
+        fun build(): TreeHistogram<V> {
+            val map = bins.mapValuesTo(TreeMap<Double, Bin1D<Double, V>>()) { (_, binCounter) ->
+                Bin1D(binCounter.domain, binCounter.counter.value)
+            }
+            return TreeHistogram(map)
+        }
+    }
+
+    public inline fun produce(block: Histogram1DBuilder<Double, V>.() -> Unit): TreeHistogram<V> =
+        TreeHistogramBuilder().apply(block).build()
+
+    override fun add(
+        left: TreeHistogram<V>,
+        right: TreeHistogram<V>,
+    ): TreeHistogram<V> {
+        val bins = TreeMap<Double, Bin1D<Double, V>>().apply {
+            (left.bins.map { it.domain } union right.bins.map { it.domain }).forEach { def ->
+                put(
+                    def.center,
+                    Bin1D(
+                        def,
+                        with(valueAlgebra) {
+                            (left[def.center]?.binValue ?: zero) + (right[def.center]?.binValue ?: zero)
+                        }
+                    )
+                )
+            }
+        }
+        return TreeHistogram(bins)
+    }
+
+    override fun scale(a: TreeHistogram<V>, value: Double): TreeHistogram<V> {
+        val bins = TreeMap<Double, Bin1D<Double, V>>().apply {
+            a.bins.forEach { bin ->
+                put(
+                    bin.domain.center,
+                    Bin1D(bin.domain, valueAlgebra.scale(bin.binValue, value))
+                )
+            }
+        }
+
+        return TreeHistogram(bins)
+    }
+
+    override fun TreeHistogram<V>.unaryMinus(): TreeHistogram<V> = this * (-1)
+
+    override val zero: TreeHistogram<V> = produce { }
+}
+
+
+///**
+// * Build and fill a histogram with custom borders. Returns a read-only histogram.
+// */
+//public inline fun Histogram.custom(
+//    borders: DoubleArray,
+//    builder: Histogram1DBuilder<Double, Double>.() -> Unit,
+//): TreeHistogram = custom(borders).fill(builder)
+//
+//
+///**
+// * Build and fill a [DoubleHistogram1D]. Returns a read-only histogram.
+// */
+//public fun uniform(
+//    binSize: Double,
+//    start: Double = 0.0,
+//): TreeHistogramSpace = TreeHistogramSpace { value ->
+//    val center = start + binSize * floor((value - start) / binSize + 0.5)
+//    DoubleDomain1D((center - binSize / 2)..(center + binSize / 2))
+//}
+
+/**
+ * Create a histogram group with custom cell borders
+ */
+public fun <V : Any, A> Histogram.Companion.custom1D(
+    valueAlgebra: A,
+    borders: Buffer<Double>,
+): TreeHistogramGroup<V, A> where A : Ring<V>, A : ScaleOperations<V> {
+    val sorted = borders.sorted()
+
+    return TreeHistogramGroup(valueAlgebra) { value ->
+        when {
+            value <= sorted.first() -> DoubleDomain1D(
+                Double.NEGATIVE_INFINITY..sorted.first()
+            )
+
+            value > sorted.last() -> DoubleDomain1D(
+                sorted.last()..Double.POSITIVE_INFINITY
+            )
+
+            else -> {
+                val index = sorted.indices.first { value <= sorted[it] }
+                val left = sorted[index - 1]
+                val right = sorted[index]
+                DoubleDomain1D(left..right)
+            }
+        }
+    }
+}

kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramSpace.kt

@@ -1,171 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.UnivariateDomain
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.operations.Group
-import space.kscience.kmath.operations.ScaleOperations
-import space.kscience.kmath.structures.Buffer
-import java.util.*
-import kotlin.math.abs
-import kotlin.math.floor
-import kotlin.math.sqrt
-
-private fun <B : ClosedFloatingPointRange<Double>> TreeMap<Double, B>.getBin(value: Double): B? {
-    // check ceiling entry and return it if it is what needed
-    val ceil = ceilingEntry(value)?.value
-    if (ceil != null && value in ceil) return ceil
-    //check floor entry
-    val floor = floorEntry(value)?.value
-    if (floor != null && value in floor) return floor
-    //neither is valid, not found
-    return null
-}
-
-@UnstableKMathAPI
-public class TreeHistogram(
-    private val binMap: TreeMap<Double, out UnivariateBin>,
-) : UnivariateHistogram {
-    override fun get(value: Double): UnivariateBin? = binMap.getBin(value)
-    override val dimension: Int get() = 1
-    override val bins: Collection<UnivariateBin> get() = binMap.values
-}
-
-@OptIn(UnstableKMathAPI::class)
-@PublishedApi
-internal class TreeHistogramBuilder(val binFactory: (Double) -> UnivariateDomain) : UnivariateHistogramBuilder {
-
-    internal class BinCounter(val domain: UnivariateDomain, val counter: Counter<Double> = Counter.double()) :
-        ClosedFloatingPointRange<Double> by domain.range
-
-    private val bins: TreeMap<Double, BinCounter> = TreeMap()
-
-    fun get(value: Double): BinCounter? = bins.getBin(value)
-
-    fun createBin(value: Double): BinCounter {
-        val binDefinition = binFactory(value)
-        val newBin = BinCounter(binDefinition)
-        synchronized(this) { bins[binDefinition.center] = newBin }
-        return newBin
-    }
-
-    /**
-     * Thread safe put operation
-     */
-    override fun putValue(at: Double, value: Double) {
-        (get(at) ?: createBin(at)).apply {
-            counter.add(value)
-        }
-    }
-
-    override fun putValue(point: Buffer<Double>, value: Number) {
-        require(point.size == 1) { "Only points with single value could be used in univariate histogram" }
-        putValue(point[0], value.toDouble())
-    }
-
-    fun build(): TreeHistogram {
-        val map = bins.mapValuesTo(TreeMap<Double, UnivariateBin>()) { (_, binCounter) ->
-            val count = binCounter.counter.value
-            UnivariateBin(binCounter.domain, count, sqrt(count))
-        }
-        return TreeHistogram(map)
-    }
-}
-
-/**
- * A space for univariate histograms with variable bin borders based on a tree map
- */
-@UnstableKMathAPI
-public class TreeHistogramSpace(
-    @PublishedApi internal val binFactory: (Double) -> UnivariateDomain,
-) : Group<UnivariateHistogram>, ScaleOperations<UnivariateHistogram> {
-
-    public inline fun fill(block: UnivariateHistogramBuilder.() -> Unit): UnivariateHistogram =
-        TreeHistogramBuilder(binFactory).apply(block).build()
-
-    override fun add(
-        left: UnivariateHistogram,
-        right: UnivariateHistogram,
-    ): UnivariateHistogram {
-//        require(a.context == this) { "Histogram $a does not belong to this context" }
-//        require(b.context == this) { "Histogram $b does not belong to this context" }
-        val bins = TreeMap<Double, UnivariateBin>().apply {
-            (left.bins.map { it.domain } union right.bins.map { it.domain }).forEach { def ->
-                put(
-                    def.center,
-                    UnivariateBin(
-                        def,
-                        value = (left[def.center]?.value ?: 0.0) + (right[def.center]?.value ?: 0.0),
-                        standardDeviation = (left[def.center]?.standardDeviation
-                            ?: 0.0) + (right[def.center]?.standardDeviation ?: 0.0)
-                    )
-                )
-            }
-        }
-        return TreeHistogram(bins)
-    }
-
-    override fun scale(a: UnivariateHistogram, value: Double): UnivariateHistogram {
-        val bins = TreeMap<Double, UnivariateBin>().apply {
-            a.bins.forEach { bin ->
-                put(
-                    bin.domain.center,
-                    UnivariateBin(
-                        bin.domain,
-                        value = bin.value * value,
-                        standardDeviation = abs(bin.standardDeviation * value)
-                    )
-                )
-            }
-        }
-
-        return TreeHistogram(bins)
-    }
-
-    override fun UnivariateHistogram.unaryMinus(): UnivariateHistogram = this * (-1)
-
-    override val zero: UnivariateHistogram by lazy { fill { } }
-
-    public companion object {
-        /**
-         * Build and fill a [UnivariateHistogram]. Returns a read-only histogram.
-         */
-        public fun uniform(
-            binSize: Double,
-            start: Double = 0.0,
-        ): TreeHistogramSpace = TreeHistogramSpace { value ->
-            val center = start + binSize * floor((value - start) / binSize + 0.5)
-            UnivariateDomain((center - binSize / 2)..(center + binSize / 2))
-        }
-
-        /**
-         * Create a histogram with custom cell borders
-         */
-        public fun custom(borders: DoubleArray): TreeHistogramSpace {
-            val sorted = borders.sortedArray()
-
-            return TreeHistogramSpace { value ->
-                when {
-                    value < sorted.first() -> UnivariateDomain(
-                        Double.NEGATIVE_INFINITY..sorted.first()
-                    )
-
-                    value > sorted.last() -> UnivariateDomain(
-                        sorted.last()..Double.POSITIVE_INFINITY
-                    )
-
-                    else -> {
-                        val index = sorted.indices.first { value > sorted[it] }
-                        val left = sorted[index]
-                        val right = sorted[index + 1]
-                        UnivariateDomain(left..right)
-                    }
-                }
-            }
-        }
-    }
-}

kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/UnivariateHistogram.kt

@@ -1,79 +0,0 @@
-/*
- * Copyright 2018-2021 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.histogram
-
-import space.kscience.kmath.domains.UnivariateDomain
-import space.kscience.kmath.misc.UnstableKMathAPI
-import space.kscience.kmath.operations.asSequence
-import space.kscience.kmath.structures.Buffer
-
-
-@UnstableKMathAPI
-public val UnivariateDomain.center: Double
-    get() = (range.endInclusive + range.start) / 2
-
-/**
- * A univariate bin based on a range
- *
- * @property value The value of histogram including weighting
- * @property standardDeviation Standard deviation of the bin value. Zero or negative if not applicable
- */
-@UnstableKMathAPI
-public class UnivariateBin(
-    public val domain: UnivariateDomain,
-    override val value: Double,
-    public val standardDeviation: Double,
-) : Bin<Double>, ClosedFloatingPointRange<Double> by domain.range {
-
-    override val dimension: Int get() = 1
-
-    override fun contains(point: Buffer<Double>): Boolean = point.size == 1 && contains(point[0])
-}
-
-@OptIn(UnstableKMathAPI::class)
-public interface UnivariateHistogram : Histogram<Double, UnivariateBin> {
-    public operator fun get(value: Double): UnivariateBin?
-    override operator fun get(point: Buffer<Double>): UnivariateBin? = get(point[0])
-
-    public companion object {
-        /**
-         * Build and fill a [UnivariateHistogram]. Returns a read-only histogram.
-         */
-        public inline fun uniform(
-            binSize: Double,
-            start: Double = 0.0,
-            builder: UnivariateHistogramBuilder.() -> Unit,
-        ): UnivariateHistogram = TreeHistogramSpace.uniform(binSize, start).fill(builder)
-
-        /**
-         * Build and fill a histogram with custom borders. Returns a read-only histogram.
-         */
-        public inline fun custom(
-            borders: DoubleArray,
-            builder: UnivariateHistogramBuilder.() -> Unit,
-        ): UnivariateHistogram = TreeHistogramSpace.custom(borders).fill(builder)
-
-    }
-}
-
-@UnstableKMathAPI
-public interface UnivariateHistogramBuilder : HistogramBuilder<Double> {
-    /**
-     * Thread safe put operation
-     */
-    public fun putValue(at: Double, value: Double = 1.0)
-
-    override fun putValue(point: Buffer<Double>, value: Number)
-}
-
-@UnstableKMathAPI
-public fun UnivariateHistogramBuilder.fill(items: Iterable<Double>): Unit = items.forEach(this::putValue)
-
-@UnstableKMathAPI
-public fun UnivariateHistogramBuilder.fill(array: DoubleArray): Unit = array.forEach(this::putValue)
-
-@UnstableKMathAPI
-public fun UnivariateHistogramBuilder.fill(buffer: Buffer<Double>): Unit = buffer.asSequence().forEach(this::putValue)

kmath-histograms/src/jvmTest/kotlin/space/kscience/kmath/histogram/TreeHistogramTest.kt

@@ -6,19 +6,24 @@
 package space.kscience.kmath.histogram
 
 import org.junit.jupiter.api.Test
+import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.real.step
 import kotlin.random.Random
+import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
 class TreeHistogramTest {
 
     @Test
     fun normalFill() {
-        val histogram = UnivariateHistogram.uniform(0.1) {
+        val random  = Random(123)
+        val histogram = Histogram.custom1D(DoubleField, 0.0..1.0 step 0.1).produce {
             repeat(100_000) {
-                putValue(Random.nextDouble())
+                putValue(random.nextDouble())
             }
         }
 
-        assertTrue { histogram.bins.count() > 10 }
+        assertTrue { histogram.bins.count() > 8}
+        assertEquals(100_000, histogram.bins.sumOf { it.binValue }.toInt())
     }
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/Distribution.kt

@@ -27,7 +27,7 @@ public interface Distribution<T : Any> : Sampler<T> {
     public companion object
 }
 
-public interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
+public interface Distribution1D<T : Comparable<T>> : Distribution<T> {
     /**
      * Cumulative distribution for ordered parameter (CDF)
      */
@@ -37,7 +37,7 @@ public interface UnivariateDistribution<T : Comparable<T>> : Distribution<T> {
 /**
  * Compute probability integral in an interval
  */
-public fun <T : Comparable<T>> UnivariateDistribution<T>.integral(from: T, to: T): Double {
+public fun <T : Comparable<T>> Distribution1D<T>.integral(from: T, to: T): Double {
     require(to > from)
     return cumulative(to) - cumulative(from)
 }

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt

@@ -14,14 +14,9 @@ import space.kscience.kmath.stat.RandomGenerator
 import kotlin.math.*
 
 /**
- * Implements [UnivariateDistribution] for the normal (gaussian) distribution.
+ * Implements [Distribution1D] for the normal (gaussian) distribution.
  */
-public class NormalDistribution(public val sampler: GaussianSampler) : UnivariateDistribution<Double> {
-    public constructor(
-        mean: Double,
-        standardDeviation: Double,
-        normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler,
-    ) : this(GaussianSampler(mean, standardDeviation, normalized))
+public class NormalDistribution(public val sampler: GaussianSampler) : Distribution1D<Double> {
 
     override fun probability(arg: Double): Double {
         val x1 = (arg - sampler.mean) / sampler.standardDeviation
@@ -43,3 +38,9 @@ public class NormalDistribution(public val sampler: GaussianSampler) : Univariat
         private val SQRT2 = sqrt(2.0)
     }
 }
+
+public fun NormalDistribution(
+    mean: Double,
+    standardDeviation: Double,
+    normalized: NormalizedGaussianSampler = ZigguratNormalizedGaussianSampler,
+): NormalDistribution = NormalDistribution(GaussianSampler(mean, standardDeviation, normalized))

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/Sampler.kt

@@ -67,3 +67,12 @@ public fun Sampler<Double>.sampleBuffer(generator: RandomGenerator, size: Int):
 @JvmName("sampleIntBuffer")
 public fun Sampler<Int>.sampleBuffer(generator: RandomGenerator, size: Int): Chain<Buffer<Int>> =
     sampleBuffer(generator, size, ::IntBuffer)
+
+
+/**
+ * Samples a [Buffer] of values from this [Sampler].
+ */
+public suspend fun Sampler<Double>.nextBuffer(generator: RandomGenerator, size: Int): Buffer<Double> =
+    sampleBuffer(generator, size).first()
+
+//TODO add `context(RandomGenerator) Sampler.nextBuffer

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/stat/UniformDistribution.kt

@@ -8,9 +8,9 @@ package space.kscience.kmath.stat
 import space.kscience.kmath.chains.Chain
 import space.kscience.kmath.chains.SimpleChain
 import space.kscience.kmath.distributions.Distribution
-import space.kscience.kmath.distributions.UnivariateDistribution
+import space.kscience.kmath.distributions.Distribution1D
 
-public class UniformDistribution(public val range: ClosedFloatingPointRange<Double>) : UnivariateDistribution<Double> {
+public class UniformDistribution(public val range: ClosedFloatingPointRange<Double>) : Distribution1D<Double> {
     private val length: Double = range.endInclusive - range.start
 
     override fun probability(arg: Double): Double = if (arg in range) 1.0 / length else 0.0