examples/src/main/kotlin/space/kscience/kmath/series/emdDemo.kt

@@ -5,24 +5,20 @@
 
 package space.kscience.kmath.series
 
-import space.kscience.kmath.operations.algebra
-import space.kscience.kmath.operations.bufferAlgebra
 import space.kscience.kmath.structures.*
 import space.kscience.kmath.operations.invoke
 import space.kscience.plotly.*
 import space.kscience.plotly.models.Scatter
 import kotlin.math.sin
 
-private val customAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it.toDouble() } }
+fun main(): Unit = (Double.seriesAlgebra()) {
-
-fun main(): Unit = (customAlgebra) {
     val signal = DoubleArray(800) {
         sin(it.toDouble() / 10.0) + 3.5 * sin(it.toDouble() / 60.0)
     }.asBuffer().moveTo(0)
+
     val emd = empiricalModeDecomposition(
         sConditionThreshold = 1,
         maxSiftIterations = 15,
-        siftingDelta = 1e-2,
         nModes = 4
     ).decompose(signal)
     println("EMD: ${emd.modes.size} modes extracted, terminated because ${emd.terminatedBecause}")
@@ -30,7 +26,7 @@ fun main(): Unit = (customAlgebra) {
     fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
         this.scatter {
             this.name = name
-            this.x.numbers = buffer.labels
+            this.x.numbers = buffer.offsetIndices
             this.y.doubles = buffer.toDoubleArray()
             block()
         }

examples/src/main/kotlin/space/kscience/kmath/series/peakFinding.kt

@@ -1,85 +0,0 @@
-/*
- * Copyright 2018-2024 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.series
-
-import space.kscience.kmath.operations.*
-import space.kscience.kmath.structures.*
-import space.kscience.plotly.*
-import space.kscience.plotly.models.Scatter
-import space.kscience.plotly.models.ScatterMode
-import kotlin.math.sin
-
-private val customAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it * 50.0 / 599.0 } }
-
-fun main(): Unit = (customAlgebra) {
-    /*
-    val signal = DoubleArray(600) {
-        val x = it * 50.0 / 599
-        (3.0 * sin(x) + 0.5 * cos(7.0 * x)).coerceIn(-3.0 .. 3.0)
-    }.asBuffer().moveTo(0)
-    val peaks = signal.peaks()
-    val troughs = signal.troughs()
-    println(peaks)
-    println(troughs)
-
-    fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
-        scatter {
-            this.name = name
-            this.x.numbers = buffer.labels
-            this.y.doubles = buffer.toDoubleArray()
-            block()
-        }
-    }
-    Plotly.plot {
-        series("Signal", signal)
-        scatter {
-            name = "Peaks"
-            mode = ScatterMode.markers
-            x.doubles = peaks.map { signal.labels[it] }.toDoubleArray()
-            y.doubles = peaks.map { signal[it] }.toDoubleArray()
-        }
-        scatter {
-            name = "Troughs"
-            mode = ScatterMode.markers
-            x.doubles = troughs.map {  signal.labels[it] }.toDoubleArray()
-            y.doubles = troughs.map { signal[it] }.toDoubleArray()
-        }
-    }.makeFile(resourceLocation = ResourceLocation.REMOTE)
-     */
-    val nSamples = 600
-    val signal = DoubleArray(nSamples) {
-        val x = it * 12.0 / (nSamples - 1)
-        (3.5 * sin(x)).coerceIn(-3.0 .. 3.0)
-    }.asBuffer().moveTo(0)
-    val peaks = signal.peaks(PlateauEdgePolicy.KEEP_ALL_EDGES)
-    val troughs = signal.troughs(PlateauEdgePolicy.KEEP_ALL_EDGES)
-    println(peaks)
-    println(troughs)
-
-    fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
-        scatter {
-            this.name = name
-            this.x.numbers = buffer.labels
-            this.y.doubles = buffer.toDoubleArray()
-            block()
-        }
-    }
-    Plotly.plot {
-        series("Signal", signal)
-        scatter {
-            name = "Peaks"
-            mode = ScatterMode.markers
-            x.doubles = peaks.map { signal.labels[it] }.toDoubleArray()
-            y.doubles = peaks.map { signal[it] }.toDoubleArray()
-        }
-        scatter {
-            name = "Troughs"
-            mode = ScatterMode.markers
-            x.doubles = troughs.map {  signal.labels[it] }.toDoubleArray()
-            y.doubles = troughs.map { signal[it] }.toDoubleArray()
-        }
-    }.makeFile(resourceLocation = ResourceLocation.REMOTE)
-}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/EmpiricalModeDecomposition.kt

@@ -8,8 +8,11 @@ package space.kscience.kmath.series
 import space.kscience.kmath.interpolation.SplineInterpolator
 import space.kscience.kmath.interpolation.interpolate
 import space.kscience.kmath.operations.*
+import space.kscience.kmath.operations.Float64BufferOps.Companion.div
+import space.kscience.kmath.operations.Float64BufferOps.Companion.pow
 import space.kscience.kmath.structures.Buffer
-import space.kscience.kmath.structures.last
+import space.kscience.kmath.structures.asBuffer
+import kotlin.math.sign
 
 /**
  * Empirical mode decomposition of a signal represented as a [Series].
@@ -26,13 +29,13 @@ import space.kscience.kmath.structures.last
  * @param nModes how many modes should be extracted at most. The algorithm may return fewer modes if it was not
  * possible to extract more modes from the signal.
  */
-public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T> (
+public class EmpiricalModeDecomposition<BA, L: Number> (
-    private val seriesAlgebra: SeriesAlgebra<T, A, BA, L>,
+    private val seriesAlgebra: SeriesAlgebra<Double, *, BA, L>,
     private val sConditionThreshold: Int = 15,
     private val maxSiftIterations: Int = 20,
-    private val siftingDelta: T,
+    private val siftingDelta: Double = 1e-2,
     private val nModes: Int = 6
-) where BA: BufferAlgebra<T, A>,  BA: FieldOps<Buffer<T>> {
+) where BA: BufferAlgebra<Double, *>,  BA: RingOps<Buffer<Double>> {
 
     /**
      * Take a signal, construct an upper and a lower envelopes, find the mean value of two,
@@ -42,38 +45,37 @@ public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T>
      * @return mean [Series] or `null`. `null` is returned in case
      * the signal does not have enough extrema to construct envelopes.
      */
-    private fun findMean(signal: Series<T>): Series<T>? = (seriesAlgebra) {
+    private fun findMean(signal: Series<Double>): Series<Double>? = (seriesAlgebra) {
-        val interpolator = SplineInterpolator(elementAlgebra)
+        val interpolator = SplineInterpolator(Float64Field)
-        val makeBuffer = elementAlgebra.bufferFactory
+        fun generateEnvelope(extrema: List<Int>, paddedExtremeValues: DoubleArray): Series<Double> {
-        fun generateEnvelope(extrema: List<Int>, paddedExtremeValues: Buffer<T>): Series<T> {
             val envelopeFunction = interpolator.interpolate(
-                makeBuffer(extrema.size) { signal.labels[extrema[it]] },
+                Buffer(extrema.size) { signal.labels[extrema[it]].toDouble() },
-                paddedExtremeValues
+                paddedExtremeValues.asBuffer()
             )
             return signal.mapWithLabel { _, label ->
                 // For some reason PolynomialInterpolator is exclusive and the right boundary
                 // TODO Notify interpolator authors
-                envelopeFunction(label) ?: paddedExtremeValues.last()
+                envelopeFunction(label.toDouble()) ?: paddedExtremeValues.last()
                 // need to make the interpolator yield values outside boundaries?
             }
         }
         // Extrema padding (experimental) TODO padding needs a dedicated function
         val maxima = listOf(0) + signal.peaks() + (signal.size - 1)
-        val maxValues = makeBuffer(maxima.size) { signal[maxima[it]] }
+        val maxValues = DoubleArray(maxima.size) { signal[maxima[it]] }
         if (maxValues[0] < maxValues[1]) {
             maxValues[0] = maxValues[1]
         }
-        if (maxValues.last() < maxValues[maxValues.size - 2]) {
+        if (maxValues.last() < maxValues[maxValues.lastIndex - 1]) {
-            maxValues[maxValues.size - 1] = maxValues[maxValues.size - 2]
+            maxValues[maxValues.lastIndex] = maxValues[maxValues.lastIndex - 1]
         }
 
         val minima = listOf(0) + signal.troughs() + (signal.size - 1)
-        val minValues = makeBuffer(minima.size) { signal[minima[it]] }
+        val minValues = DoubleArray(minima.size) { signal[minima[it]] }
         if (minValues[0] > minValues[1]) {
             minValues[0] = minValues[1]
         }
-        if (minValues.last() > minValues[minValues.size - 2]) {
+        if (minValues.last() > minValues[minValues.lastIndex - 1]) {
-            minValues[minValues.size - 1] = minValues[minValues.size - 2]
+            minValues[minValues.lastIndex] = minValues[minValues.lastIndex - 1]
         }
          return if (maxima.size < 3 || minima.size < 3) null else { // maybe make an early return?
             val upperEnvelope = generateEnvelope(maxima, maxValues)
@@ -90,13 +92,13 @@ public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T>
      * @return [SiftingResult.NotEnoughExtrema] is returned if the signal has too few extrema to extract a mode.
      * Success of an appropriate type (See [SiftingResult.Success] class) is returned otherwise.
      */
-    private fun sift(signal: Series<T>): SiftingResult = siftInner(signal, 1, 0)
+    private fun sift(signal: Series<Double>): SiftingResult = siftInner(signal, 1, 0)
 
     /**
      * Compute a single iteration of the sifting process.
      */
     private tailrec fun siftInner(
-        prevMode: Series<T>,
+        prevMode: Series<Double>,
         iterationNumber: Int,
         sNumber: Int
     ): SiftingResult = (seriesAlgebra) {
@@ -104,12 +106,11 @@ public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T>
             return if (iterationNumber == 1) SiftingResult.NotEnoughExtrema
             else SiftingResult.SignalFlattened(prevMode)
         val mode = prevMode.zip(mean) { p, m -> p - m }
-        val newSNumber = if (sCondition(mode)) sNumber + 1 else sNumber
+        val newSNumber = if (mode.sCondition()) sNumber + 1 else sNumber
         return when {
             iterationNumber >= maxSiftIterations -> SiftingResult.MaxIterationsReached(mode)
             sNumber >= sConditionThreshold -> SiftingResult.SNumberReached(mode)
-            relativeDifference(mode, prevMode) < (elementAlgebra) { siftingDelta * mode.size } ->
+            relativeDifference(mode, prevMode) < siftingDelta * mode.size -> SiftingResult.DeltaReached(mode)
-                SiftingResult.DeltaReached(mode)
             else -> siftInner(mode, iterationNumber + 1, newSNumber)
         }
     }
@@ -122,8 +123,8 @@ public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T>
      * Modes returned in a list which contains as many modes as it was possible
      * to extract before triggering one of the termination conditions.
      */
-     public fun decompose(signal: Series<T>): EMDecompositionResult<T> = (seriesAlgebra) {
+     public fun decompose(signal: Series<Double>): EMDecompositionResult = (seriesAlgebra) {
-        val modes = mutableListOf<Series<T>>()
+        val modes = mutableListOf<Series<Double>>()
         var residual = signal
         repeat(nModes) {
             val nextMode = when(val r = sift(residual)) {
@@ -131,15 +132,14 @@ public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T>
                     return EMDecompositionResult(
                         if (it == 0) EMDTerminationReason.SIGNAL_TOO_FLAT
                         else EMDTerminationReason.ALL_POSSIBLE_MODES_EXTRACTED,
-                        modes,
+                        modes
-                        residual
                     )
-                is SiftingResult.Success<*> -> r.result
+                is SiftingResult.Success -> r.result
             }
-            modes.add(nextMode as Series<T>) // TODO remove unchecked cast
+            modes.add(nextMode)
             residual = residual.zip(nextMode) { l, r -> l - r }
         }
-        return EMDecompositionResult(EMDTerminationReason.MAX_MODES_REACHED, modes, residual)
+        return EMDecompositionResult(EMDTerminationReason.MAX_MODES_REACHED, modes)
     }
 }
 
@@ -157,13 +157,13 @@ public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T>
  * @param nModes how many modes should be extracted at most. The algorithm may return fewer modes if it was not
  * possible to extract more modes from the signal.
  */
-public fun <T: Comparable<T>, L: T, A: Field<T>, BA> SeriesAlgebra<T, A, BA, L>.empiricalModeDecomposition(
+public fun <L: Number, BA> SeriesAlgebra<Double, *, BA, L>.empiricalModeDecomposition(
     sConditionThreshold: Int = 15,
     maxSiftIterations: Int = 20,
-    siftingDelta: T,
+    siftingDelta: Double = 1e-2,
     nModes: Int = 3
-): EmpiricalModeDecomposition<T, A, BA, L>
+): EmpiricalModeDecomposition<BA, L>
-where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = EmpiricalModeDecomposition(
+where BA: BufferAlgebra<Double, *>, BA: RingOps<Buffer<Double>> = EmpiricalModeDecomposition(
     seriesAlgebra = this,
     sConditionThreshold = sConditionThreshold,
     maxSiftIterations = maxSiftIterations,
@@ -174,15 +174,12 @@ where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = EmpiricalModeDecomposit
 /**
  * Brute force count all zeros in the series.
  */
-internal fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.countZeros(
+private fun Series<Double>.countZeros(): Int {
-    signal: Series<T>
+    require(size >= 2) { "Expected series with at least 2 elements, but got $size elements" }
-): Int where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> {
+    data class SignCounter(val prevSign: Double, val zeroCount: Int)
-    require(signal.size >= 2) { "Expected series with at least 2 elements, but got ${signal.size} elements" }
-    data class SignCounter(val prevSign: Int, val zeroCount: Int)
-    fun strictSign(arg: T): Int = if (arg > elementAlgebra.zero) 1 else -1
 
-    return signal.fold(SignCounter(strictSign(signal[0]), 0)) { acc, it ->
+    return fold(SignCounter(sign(get(0)), 0)) { acc: SignCounter, it: Double ->
-        val currentSign = strictSign(it)
+        val currentSign = sign(it)
         if (acc.prevSign != currentSign) SignCounter(currentSign, acc.zeroCount + 1)
         else SignCounter(currentSign, acc.zeroCount)
     }.zeroCount
@@ -191,19 +188,18 @@ internal fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.count
 /**
  * Compute relative difference of two series.
  */
-private fun <T, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.relativeDifference(
+private fun <BA> SeriesAlgebra<Double, *, BA, *>.relativeDifference(
-    current: Series<T>,
+    current: Series<Double>,
-    previous: Series<T>
+    previous: Series<Double>
-): T where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = (bufferAlgebra) {
+):Double where BA: BufferAlgebra<Double, *>, BA: RingOps<Buffer<Double>> =
-    ((current - previous) * (current - previous))
+    (current - previous).pow(2)
-        .div(previous * previous)
+        .div(previous pow 2)
-        .fold(elementAlgebra.zero) { acc, it -> acc + it}
+        .fold(0.0) { acc, d -> acc + d } // TODO replace with Series<>.sum() method when it's implemented
-}
 
 /**
  * Brute force count all extrema of a series.
  */
-internal fun <T: Comparable<T>> Series<T>.countExtrema(): Int {
+private fun Series<Double>.countExtrema(): Int {
     require(size >= 3) { "Expected series with at least 3 elements, but got $size elements" }
     return peaks().size + troughs().size
 }
@@ -212,10 +208,7 @@ internal fun <T: Comparable<T>> Series<T>.countExtrema(): Int {
  * Check whether the numbers of zeroes and extrema of a series differ by no more than 1.
  * This is a necessary condition of an empirical mode.
  */
-private fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.sCondition(
+private fun Series<Double>.sCondition(): Boolean = (countExtrema() - countZeros()) in -1..1
-    signal: Series<T>
-): Boolean where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> =
-    (signal.countExtrema() - countZeros(signal)) in -1..1
 
 internal sealed interface SiftingResult {
 
@@ -223,33 +216,33 @@ internal sealed interface SiftingResult {
      * Represents a condition when a mode has been successfully
      * extracted in a sifting process.
      */
-    open class Success<T>(val result: Series<T>): SiftingResult
+    open class Success(val result: Series<Double>): SiftingResult
 
     /**
      * Returned when no termination condition was reached and the proto-mode
      * has become too flat (with not enough extrema to build envelopes)
      * after several sifting iterations.
      */
-    class SignalFlattened<T>(result: Series<T>) : Success<T>(result)
+    class SignalFlattened(result: Series<Double>) : Success(result)
 
     /**
      * Returned when sifting process has been terminated due to the
      * S-number condition being reached.
      */
-    class SNumberReached<T>(result: Series<T>) : Success<T>(result)
+    class SNumberReached(result: Series<Double>) : Success(result)
 
     /**
      * Returned when sifting process has been terminated due to the
      * delta condition (Cauchy criterion) being reached.
      */
-    class DeltaReached<T>(result: Series<T>) : Success<T>(result)
+    class DeltaReached(result: Series<Double>) : Success(result)
 
     /**
      * Returned when sifting process has been terminated after
      * executing the maximum number of iterations (specified when creating an instance
      * of [EmpiricalModeDecomposition]).
      */
-    class MaxIterationsReached<T>(result: Series<T>): Success<T>(result)
+    class MaxIterationsReached(result: Series<Double>): Success(result)
 
     /**
      * Returned when the submitted signal has not enough extrema to build envelopes,
@@ -281,8 +274,7 @@ public enum class EMDTerminationReason {
     ALL_POSSIBLE_MODES_EXTRACTED
 }
 
-public data class EMDecompositionResult<T>(
+public data class EMDecompositionResult(
     val terminatedBecause: EMDTerminationReason,
-    val modes: List<Series<T>>,
+    val modes: List<Series<Double>>
-    val residual: Series<T>
 )

kmath-stat/src/commonTest/kotlin/space/kscience/kmath/series/TestEmd.kt

@@ -1,66 +0,0 @@
-/*
- * Copyright 2018-2024 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.series
-
-import space.kscience.kmath.operations.algebra
-import space.kscience.kmath.operations.bufferAlgebra
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.asBuffer
-import kotlin.math.sin
-import kotlin.test.Test
-import kotlin.test.assertEquals
-import kotlin.test.assertTrue
-import kotlin.random.Random
-
-
-class TestEmd {
-    companion object{
-        val testAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it.toDouble() } }
-    }
-
-    @Test
-    fun testBasic() = (testAlgebra) {
-        val signal = DoubleArray(800) {
-            sin(it.toDouble() / 10.0) + 3.5 * sin(it.toDouble() / 60.0)
-        }.asBuffer().moveTo(0)
-        val emd = empiricalModeDecomposition(
-            sConditionThreshold = 1,
-            maxSiftIterations = 15,
-            siftingDelta = 1e-2,
-            nModes = 4
-        ).decompose(signal)
-
-        assertEquals(emd.modes.size, 3)
-        emd.modes.forEach { imf ->
-            assertTrue(imf.peaks().size - imf.troughs().size in -1..1)
-        }
-    }
-
-    @Test
-    fun testNoiseFiltering() = (testAlgebra) {
-        val signal = DoubleArray(800) {
-            sin(it.toDouble() / 30.0) + 2.0 * (Random.nextDouble() - 0.5)
-        }.asBuffer().moveTo(0)
-        val emd = empiricalModeDecomposition(
-            sConditionThreshold = 10,
-            maxSiftIterations = 15,
-            siftingDelta = 1e-2,
-            nModes = 10
-        ).decompose(signal)
-        // Check whether the signal with the expected frequency is present
-        assertEquals(emd.modes.count { it.countExtrema() in 7..9 }, 1)
-    }
-
-    @Test
-    fun testZeros() = (testAlgebra) {
-        val nSamples = 200
-        // sin(10*x) where x in [0, 1)
-        val signal = DoubleArray(nSamples) {
-            sin(it * 10.0 / (nSamples - 1))
-        }.asBuffer().moveTo(0)
-        assertEquals(countZeros(signal), 4)
-    }
-}

kmath-stat/src/commonTest/kotlin/space/kscience/kmath/series/TestPeakFinding.kt

@@ -1,39 +0,0 @@
-/*
- * Copyright 2018-2024 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.series
-
-import space.kscience.kmath.operations.algebra
-import space.kscience.kmath.operations.bufferAlgebra
-import space.kscience.kmath.operations.invoke
-import space.kscience.kmath.structures.asBuffer
-import kotlin.math.sin
-import kotlin.test.Test
-import kotlin.test.assertEquals
-
-class TestPeakFinding {
-    companion object {
-        val testAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it.toDouble() } }
-    }
-
-    @Test
-    fun testPeakFinding() = (testAlgebra) {
-        val nSamples = 600
-        val signal = DoubleArray(nSamples) {
-            val x = it * 12.0 / (nSamples - 1)
-            (3.5 * sin(x)).coerceIn(-3.0 .. 3.0)
-        }.asBuffer().moveTo(0)
-
-        val peaksAvg = signal.peaks(PlateauEdgePolicy.AVERAGE)
-        val troughsAvg = signal.troughs(PlateauEdgePolicy.AVERAGE)
-        assertEquals(peaksAvg.size, 2)
-        assertEquals(troughsAvg.size, 2)
-
-        val peaksBoth = signal.peaks(PlateauEdgePolicy.KEEP_ALL_EDGES)
-        val troughsBoth = signal.peaks(PlateauEdgePolicy.KEEP_ALL_EDGES)
-        assertEquals(peaksBoth.size, 4)
-        assertEquals(troughsBoth.size, 4)
-    }
-}