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Revert "Added power spectral density curves at the spectrum chart" #824

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Merged
merged 1 commit into from
May 5, 2025
Merged

Revert "Added power spectral density curves at the spectrum chart" #824

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1 change: 0 additions & 1 deletion index.html
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Original file line number Diff line number Diff line change
Expand Up @@ -459,7 +459,6 @@ <h4>Workspace</h4>
<option value="1">Freq. vs Throttle</option>
<option value="3">Freq. vs RPM</option>
<option value="2">Error vs Setpoint</option>
<option value="4">Power spectral density</option>
</select>
</div>

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9 changes: 0 additions & 9 deletions src/graph_spectrum.js
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Expand Up @@ -116,10 +116,6 @@ export function FlightLogAnalyser(flightLog, canvas, analyserCanvas) {
fftData = GraphSpectrumCalc.dataLoadPidErrorVsSetpoint();
break;

case SPECTRUM_TYPE.POWER_SPECTRAL_DENSITY:
fftData = GraphSpectrumCalc.dataLoadPSD(analyserZoomY);
break;

case SPECTRUM_TYPE.FREQUENCY:
default:
fftData = GraphSpectrumCalc.dataLoadFrequency();
Expand Down Expand Up @@ -191,11 +187,6 @@ export function FlightLogAnalyser(flightLog, canvas, analyserCanvas) {
debounce(100, function () {
analyserZoomY = 1 / (analyserZoomYElem.val() / 100);
GraphSpectrumPlot.setZoom(analyserZoomX, analyserZoomY);
// Recalculate PSD with updated samples per segment count
if (userSettings.spectrumType == SPECTRUM_TYPE.POWER_SPECTRAL_DENSITY) {
dataLoad();
GraphSpectrumPlot.setData(fftData, userSettings.spectrumType);
}
that.refresh();
})
)
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148 changes: 2 additions & 146 deletions src/graph_spectrum_calc.js
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Original file line number Diff line number Diff line change
Expand Up @@ -106,33 +106,6 @@ GraphSpectrumCalc.dataLoadFrequency = function() {
return fftData;
};

GraphSpectrumCalc.dataLoadPSD = function(analyserZoomY) {
const flightSamples = this._getFlightSamplesFreq(false);

let pointsPerSegment = 512;
const multiplier = Math.floor(1 / analyserZoomY); // 0. ... 10
if (multipiler == 0) {
pointsPerSegment = 256;
} else if (multiplier > 1) {
pointsPerSegment *= 2 ** Math.floor(multiplier / 2);
}
pointsPerSegment = Math.min(pointsPerSegment, flightSamples.samples.length);
const overlapCount = Math.floor(pointsPerSegment / 2);

const psd = this._psd(flightSamples.samples, pointsPerSegment, overlapCount);

const psdData = {
fieldIndex : this._dataBuffer.fieldIndex,
fieldName : this._dataBuffer.fieldName,
psdLength : psd.psdOutput.length,
psdOutput : psd.psdOutput,
blackBoxRate : this._blackBoxRate,
minimum: psd.min,
maximum: psd.max,
maxNoiseIdx: psd.maxNoiseIdx,
};
return psdData;
};

GraphSpectrumCalc._dataLoadFrequencyVsX = function(vsFieldNames, minValue = Infinity, maxValue = -Infinity) {

Expand Down Expand Up @@ -310,7 +283,7 @@ GraphSpectrumCalc._getFlightChunks = function() {
return allChunks;
};

GraphSpectrumCalc._getFlightSamplesFreq = function(scaled = true) {
GraphSpectrumCalc._getFlightSamplesFreq = function() {

const allChunks = this._getFlightChunks();

Expand All @@ -320,11 +293,7 @@ GraphSpectrumCalc._getFlightSamplesFreq = function(scaled = true) {
let samplesCount = 0;
for (const chunk of allChunks) {
for (const frame of chunk.frames) {
if (scaled) {
samples[samplesCount] = this._dataBuffer.curve.lookupRaw(frame[this._dataBuffer.fieldIndex]);
} else {
samples[samplesCount] = frame[this._dataBuffer.fieldIndex];
}
samples[samplesCount] = (this._dataBuffer.curve.lookupRaw(frame[this._dataBuffer.fieldIndex]));
samplesCount++;
}
}
Expand Down Expand Up @@ -516,116 +485,3 @@ GraphSpectrumCalc._normalizeFft = function(fftOutput, fftLength) {

return fftData;
};

/**
* Compute PSD for data samples by Welch method follow Python code
*/
GraphSpectrumCalc._psd = function(samples, pointsPerSegment, overlapCount, scaling = 'density') {
// Compute FFT for samples segments
const fftOutput = this._fft_segmented(samples, pointsPerSegment, overlapCount);

const dataCount = fftOutput[0].length;
const segmentsCount = fftOutput.length;
const psdOutput = new Float64Array(dataCount);

// Compute power scale coef
let scale = 1;
if (userSettings.analyserHanning) {
const window = Array(pointsPerSegment).fill(1);
this._hanningWindow(window, pointsPerSegment);
if (scaling == 'density') {
let skSum = 0;
for (const value of window) {
skSum += value ** 2;
}
scale = 1 / (this._blackBoxRate * skSum);
} else if (scaling == 'spectrum') {
let sum = 0;
for (const value of window) {
sum += value;
}
scale = 1 / sum ** 2;
}
} else if (scaling == 'density') {
scale = 1 / pointsPerSegment;
} else if (scaling == 'spectrum') {
scale = 1 / pointsPerSegment ** 2;
}

// Compute average for scaled power
let min = 1e6,
max = -1e6;
// Early exit if no segments were processed
if (segmentsCount === 0) {
return {
psdOutput: new Float64Array(0),
min: 0,
max: 0,
maxNoiseIdx: 0
};
}
const maxFrequency = (this._blackBoxRate / 2.0);
const noise50HzIdx = 50 / maxFrequency * dataCount;
const noise3HzIdx = 3 / maxFrequency * dataCount;
let maxNoiseIdx = 0;
let maxNoise = -100;
for (let i = 0; i < dataCount; i++) {
psdOutput[i] = 0.0;
for (let j = 0; j < segmentsCount; j++) {
let p = scale * fftOutput[j][i] ** 2;
if (i != dataCount - 1) {
p *= 2;
}
psdOutput[i] += p;
}

const min_avg = 1e-7; // limit min value for -70db
let avg = psdOutput[i] / segmentsCount;
avg = Math.max(avg, min_avg);
psdOutput[i] = 10 * Math.log10(avg);
if (i > noise3HzIdx) { // Miss big zero freq magnitude
min = Math.min(psdOutput[i], min);
max = Math.max(psdOutput[i], max);
}
if (i > noise50HzIdx && psdOutput[i] > maxNoise) {
maxNoise = psdOutput[i];
maxNoiseIdx = i;
}
}

const maxNoiseFrequency = maxNoiseIdx / dataCount * maxFrequency;

return {
psdOutput: psdOutput,
min: min,
max: max,
maxNoiseIdx: maxNoiseFrequency,
};
};


/**
* Compute FFT for samples segments by lenghts as n_per_seg with n_overlap overlap points count
*/
GraphSpectrumCalc._fft_segmented = function(samples, n_per_seg, n_overlap) {
const samplesCount = samples.length;
let output = [];
for (let i = 0; i < samplesCount - n_per_seg; i += n_per_seg - n_overlap) {
const fftInput = samples.slice(i, i + n_per_seg);

if (userSettings.analyserHanning) {
this._hanningWindow(fftInput, n_per_seg);
}

const fftComplex = this._fft(fftInput);
const magnitudes = new Float64Array(n_per_seg / 2);
for (let i = 0; i < n_per_seg / 2; i++) {
const re = fftComplex[2 * i];
const im = fftComplex[2 * i + 1];
magnitudes[i] = Math.hypot(re, im);
}
output.push(magnitudes);
}

return output;
};
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