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WIP: feature/emd #521

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teldufalsari wants to merge 11 commits from teldufalsari/kmath:feature/emd into dev
pull from: teldufalsari/kmath:feature/emd
merge into: kscience:dev
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4 changed files with 192 additions and 2 deletions
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85
examples/src/main/kotlin/space/kscience/kmath/series/peakFinding.kt Normal file
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@ -0,0 +1,85 @@
/*
* 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)
}

4
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/EmpiricalModeDecomposition.kt
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@ -174,7 +174,7 @@ where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = EmpiricalModeDecomposit
/**
* Brute force count all zeros in the series.
*/
private fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.countZeros(
internal fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.countZeros(
signal: Series<T>
): Int where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> {
require(signal.size >= 2) { "Expected series with at least 2 elements, but got ${signal.size} elements" }
@ -203,7 +203,7 @@ private fun <T, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.relativeDifference(
/**
* Brute force count all extrema of a series.
*/
private fun <T: Comparable<T>> Series<T>.countExtrema(): Int {
internal fun <T: Comparable<T>> Series<T>.countExtrema(): Int {
lounres commented 2024-04-25 16:53:28 +03:00
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private fun <T : Comparable<T>> Series<T>.maxima(): List<Int> {
```kotlin private fun <T : Comparable<T>> Series<T>.maxima(): List<Int> { ```
require(size >= 3) { "Expected series with at least 3 elements, but got $size elements" }
return peaks().size + troughs().size
}
lounres commented 2024-04-25 17:09:32 +03:00
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I would recommend rewriting it with old good plain loop on indices:

    for (index in 1 .. size - 2) {
        val left = this[index-1]
        val middle = this[index]
        val right = this[index+1]
        if (middle > left && middle > right) maxima.add(index)
    }

or using

    return (1 .. size - 2).filter { (this[it] > this[it-1] && this[it] > this[it+1]) || it == 0 || it == size - 1 }
I would recommend rewriting it with old good plain loop on indices: ```kotlin for (index in 1 .. size - 2) { val left = this[index-1] val middle = this[index] val right = this[index+1] if (middle > left && middle > right) maxima.add(index) } ``` or using ```kotlin return (1 .. size - 2).filter { (this[it] > this[it-1] && this[it] > this[it+1]) || it == 0 || it == size - 1 } ```
lounres commented 2024-04-25 17:17:55 +03:00
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Also, is it intended that the spline will ignore double extrema?

I mean for series [0.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 0.0] there will be no maxima and minima points but the end points.

Also, is it intended that the spline will ignore double extrema? I mean for series `[0.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 0.0]` there will be no maxima and minima points but the end points.
teldufalsari commented 2024-04-25 21:15:05 +03:00
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Also, is it intended that the spline will ignore double extrema?

No, I'm planning on improving this function and making it public placed in SeriesExtensions.kt

> Also, is it intended that the spline will ignore double extrema? No, I'm planning on improving this function and making it `public` placed in `SeriesExtensions.kt`
👍 1

66
kmath-stat/src/commonTest/kotlin/space/kscience/kmath/series/TestEmd.kt Normal file
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/*
* 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)
}
}

39
kmath-stat/src/commonTest/kotlin/space/kscience/kmath/series/TestPeakFinding.kt Normal file
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/*
* 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)
}
}