forked from kscience/kmath
Tests and peak finding examples
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/*
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* Copyright 2018-2024 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.series
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import space.kscience.kmath.operations.*
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import space.kscience.kmath.structures.*
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import space.kscience.plotly.*
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import space.kscience.plotly.models.Scatter
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import space.kscience.plotly.models.ScatterMode
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import kotlin.math.sin
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private val customAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it * 50.0 / 599.0 } }
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fun main(): Unit = (customAlgebra) {
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/*
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val signal = DoubleArray(600) {
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val x = it * 50.0 / 599
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(3.0 * sin(x) + 0.5 * cos(7.0 * x)).coerceIn(-3.0 .. 3.0)
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}.asBuffer().moveTo(0)
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val peaks = signal.peaks()
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val troughs = signal.troughs()
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println(peaks)
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println(troughs)
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fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
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scatter {
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this.name = name
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this.x.numbers = buffer.labels
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this.y.doubles = buffer.toDoubleArray()
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block()
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}
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}
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Plotly.plot {
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series("Signal", signal)
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scatter {
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name = "Peaks"
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mode = ScatterMode.markers
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x.doubles = peaks.map { signal.labels[it] }.toDoubleArray()
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y.doubles = peaks.map { signal[it] }.toDoubleArray()
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}
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scatter {
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name = "Troughs"
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mode = ScatterMode.markers
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x.doubles = troughs.map { signal.labels[it] }.toDoubleArray()
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y.doubles = troughs.map { signal[it] }.toDoubleArray()
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}
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}.makeFile(resourceLocation = ResourceLocation.REMOTE)
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*/
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val nSamples = 600
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val signal = DoubleArray(nSamples) {
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val x = it * 12.0 / (nSamples - 1)
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(3.5 * sin(x)).coerceIn(-3.0 .. 3.0)
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}.asBuffer().moveTo(0)
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val peaks = signal.peaks(PlateauEdgePolicy.KEEP_ALL_EDGES)
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val troughs = signal.troughs(PlateauEdgePolicy.KEEP_ALL_EDGES)
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println(peaks)
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println(troughs)
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fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
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scatter {
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this.name = name
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this.x.numbers = buffer.labels
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this.y.doubles = buffer.toDoubleArray()
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block()
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}
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}
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Plotly.plot {
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series("Signal", signal)
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scatter {
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name = "Peaks"
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mode = ScatterMode.markers
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x.doubles = peaks.map { signal.labels[it] }.toDoubleArray()
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y.doubles = peaks.map { signal[it] }.toDoubleArray()
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}
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scatter {
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name = "Troughs"
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mode = ScatterMode.markers
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x.doubles = troughs.map { signal.labels[it] }.toDoubleArray()
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y.doubles = troughs.map { signal[it] }.toDoubleArray()
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}
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}.makeFile(resourceLocation = ResourceLocation.REMOTE)
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}
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@ -174,7 +174,7 @@ where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = EmpiricalModeDecomposit
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/**
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/**
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* Brute force count all zeros in the series.
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* Brute force count all zeros in the series.
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*/
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*/
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private fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.countZeros(
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internal fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.countZeros(
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signal: Series<T>
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signal: Series<T>
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): Int where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> {
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): Int where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> {
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require(signal.size >= 2) { "Expected series with at least 2 elements, but got ${signal.size} elements" }
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require(signal.size >= 2) { "Expected series with at least 2 elements, but got ${signal.size} elements" }
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@ -203,7 +203,7 @@ private fun <T, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.relativeDifference(
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/**
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/**
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* Brute force count all extrema of a series.
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* Brute force count all extrema of a series.
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*/
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*/
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private fun <T: Comparable<T>> Series<T>.countExtrema(): Int {
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internal fun <T: Comparable<T>> Series<T>.countExtrema(): Int {
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require(size >= 3) { "Expected series with at least 3 elements, but got $size elements" }
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require(size >= 3) { "Expected series with at least 3 elements, but got $size elements" }
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return peaks().size + troughs().size
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return peaks().size + troughs().size
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}
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}
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/*
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* Copyright 2018-2024 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.series
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import space.kscience.kmath.operations.algebra
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import space.kscience.kmath.operations.bufferAlgebra
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.asBuffer
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import kotlin.math.sin
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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertTrue
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import kotlin.random.Random
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class TestEmd {
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companion object{
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val testAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it.toDouble() } }
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}
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@Test
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fun testBasic() = (testAlgebra) {
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val signal = DoubleArray(800) {
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sin(it.toDouble() / 10.0) + 3.5 * sin(it.toDouble() / 60.0)
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}.asBuffer().moveTo(0)
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val emd = empiricalModeDecomposition(
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sConditionThreshold = 1,
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maxSiftIterations = 15,
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siftingDelta = 1e-2,
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nModes = 4
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).decompose(signal)
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assertEquals(emd.modes.size, 3)
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emd.modes.forEach { imf ->
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assertTrue(imf.peaks().size - imf.troughs().size in -1..1)
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}
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}
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@Test
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fun testNoiseFiltering() = (testAlgebra) {
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val signal = DoubleArray(800) {
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sin(it.toDouble() / 30.0) + 2.0 * (Random.nextDouble() - 0.5)
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}.asBuffer().moveTo(0)
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val emd = empiricalModeDecomposition(
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sConditionThreshold = 10,
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maxSiftIterations = 15,
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siftingDelta = 1e-2,
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nModes = 10
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).decompose(signal)
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// Check whether the signal with the expected frequency is present
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assertEquals(emd.modes.count { it.countExtrema() in 7..9 }, 1)
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}
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@Test
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fun testZeros() = (testAlgebra) {
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val nSamples = 200
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// sin(10*x) where x in [0, 1)
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val signal = DoubleArray(nSamples) {
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sin(it * 10.0 / (nSamples - 1))
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}.asBuffer().moveTo(0)
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assertEquals(countZeros(signal), 4)
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}
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}
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@ -0,0 +1,39 @@
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/*
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* Copyright 2018-2024 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.series
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import space.kscience.kmath.operations.algebra
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import space.kscience.kmath.operations.bufferAlgebra
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.structures.asBuffer
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import kotlin.math.sin
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import kotlin.test.Test
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import kotlin.test.assertEquals
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class TestPeakFinding {
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companion object {
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val testAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it.toDouble() } }
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}
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@Test
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fun testPeakFinding() = (testAlgebra) {
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val nSamples = 600
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val signal = DoubleArray(nSamples) {
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val x = it * 12.0 / (nSamples - 1)
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(3.5 * sin(x)).coerceIn(-3.0 .. 3.0)
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}.asBuffer().moveTo(0)
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val peaksAvg = signal.peaks(PlateauEdgePolicy.AVERAGE)
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val troughsAvg = signal.troughs(PlateauEdgePolicy.AVERAGE)
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assertEquals(peaksAvg.size, 2)
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assertEquals(troughsAvg.size, 2)
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val peaksBoth = signal.peaks(PlateauEdgePolicy.KEEP_ALL_EDGES)
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val troughsBoth = signal.peaks(PlateauEdgePolicy.KEEP_ALL_EDGES)
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assertEquals(peaksBoth.size, 4)
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assertEquals(troughsBoth.size, 4)
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}
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}
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