wave-blog/using-jupyter/long.signal.query.func.kt

wave-blog:using-jupyter:long.signal.query.func.kt

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
    
    // 3. remap the FftSample to list of tuples
    val table = data.asSequence().map {v -> 
            // magnitudes and frequencies are making pairs like (10.0dB, 100hz), (20.0dB, 150Hz), etc.
            v.value.magnitude.asSequence()
                .zip(v.value.frequency.asSequence())
                // filtering out frequencies out of the provided range
                .filter { it.second >= freqCutOff.first && it.second <= freqCutOff.second}
                // map values to tuples
                .map { arrayOf(
                        (v.value.time - timeShift) / 1e+6, // to milliseconds
                        it.second, 
                        it.first
                )}
        }
        // convert list of lists to plain list
        .flatten()
        .toList() // this gives a list of tuples [ (time, freq, value), ... , (time, freq, value)]

    // 4. remap tuples to columnar structure by index
    val dataFrame = mapOf(
        "time" to table.map { it[0] },
        "freq" to table.map { it[1] },
        "value" to table.map { it[2] }
    )    

    return dataFrame    
}