asubb

import io.wavebeans.daw.SineVoice
import io.wavebeans.daw.Standard6StringTuning
import io.wavebeans.daw.synthesize
import io.wavebeans.daw.tab
import io.wavebeans.execution.SingleThreadedOverseer
import io.wavebeans.lib.io.toMono24bitWav

val stream =
    """
        e|-------5-7-----7-|-8-----8-2-----2-|-0---------0-----|-----------------|

asubb

docker rm -f $(docker container ls -q -a)
docker volume rm $(docker volume ls -q)
docker rmi $(docker images -q)

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val df1 = fftData("fft-triangular", freqCutOff = 10 to 3800)
lets_plot(df1) {x = "time"; y = "freq"; fill = "value"} + 
    ggsize(1000, 600) + 
    geom_tile() + 
    scale_fill_gradient(low = "light_green", high = "red")

val df2 = fftData("fft-hamming", freqCutOff = 10 to 3800)
lets_plot(df2) {x = "time"; y = "freq"; fill = "value"} + 
    ggsize(1000, 600) + 
    geom_tile() + 

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fun fftData(table: String, freqCutOff: Pair<Int, Int>): Map<String, List<Any>> {
    val k = Klaxon()
    val server = "http://localhost:6800"

    // 1. read and deserializae the data
    val data = URL("$server/table/$table/last?interval=10s").openStream().reader().readLines()
    .map { k.parse<Result>(it)!! }
    
    // 2. Calculate the time value to shift values to 0 by x-axis
    val timeShift = data.asSequence().map { it.value.time }.min() ?: 0

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data class FftSample(
    val index: Long, 
    val binCount: Int, 
    val samplesCount: Int, 
    val sampleRate: Float, 
    val magnitude: List<Double>,
    val phase: List<Double>,
    val frequency: List<Double>,
    val time: Long
)

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%use klaxon
%use lets-plot

import java.net.*
import jetbrains.letsPlot.scale.*

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val overseer = LocalDistributedOverseer(
    listOf(triangularFft, hammingFft), 
    threadsCount = 2,
    partitionsCount = 2
)

val errors = overseer.eval(44100.0f)
    .map { it.get().exception }
    .filterNotNull()
    .toList()

asubb

//Specify the parameters of future calculations:
val windowSize = 801
val stepSize = 256
val fftSize = 1024

// Define a signal, windowed sum of sinusoids:
val signal = (880.sine() + 440.sine() + 220.sine())
            .window(windowSize, stepSize)

// The first FFT applying the triangular window function:

asubb

%use lets-plot

@file:Repository("https://dl.bintray.com/wavebeans/wavebeans")
@file:DependsOn("io.wavebeans:lib:0.0.3")
@file:DependsOn("io.wavebeans:exe:0.0.3")
@file:DependsOn("io.wavebeans:http:0.0.3")

import io.wavebeans.lib.*
import io.wavebeans.lib.io.*
import io.wavebeans.lib.math.*

asubb

val o = (880.sine() + 440.sine() + 220.sine())
    .trim(1000, MILLISECONDS)

val values = o.asSequence(44100.0f)
    .drop(500)
    .take(400)
    .map { it.asDouble() }
    .toList()
    
val values2 = o.asSequence(44100.0f)