/// <summary>
/// Runs a complex FFT on the interleaved time domain IQ samples and return the FFT's magnitude in dBFS
/// </summary>
/// <param name="timeDataInterleavedIq">Signed 16bit array interleaved with I and Q data. First sample is I, second sample Q.</param>
/// <param name="sampleRate_MHz">The sampling frequency, used to scale the analysis data.</param>
/// <param name="runAnalysis">If true, will return a FftAnalysis structure with information about the FFT</param>
/// <param name="sampleBitWidth"> Bit width of the converter sample (example: 16bit sample)</param>
/// <param name="analysisData">FftAnalysis struture that is returned when runAnalysis = true</param>
/// <returns></returns>
public static double[] complexfftAndScale(short[] timeDataInterleavedIq, double sampleRate_MHz, byte sampleBitWidth, bool runAnalysis, out FftAnalysis analysisData)
{
int numSamples = timeDataInterleavedIq.Length / 2;
double[] fftReal;
double[] fftImag;
double[] fftMagnitude_dB = new double[numSamples];
analysisData = new FftAnalysis();
double AdcFullScaleCode = System.Math.Pow(2, (sampleBitWidth - 1)); //2^(16-1) for 16bit converter ...assumming two's complement
//ScaledWindowType window = ScaledWindowType.Hanning;
ScaledWindow window = ScaledWindow.CreateHanningWindow();
ComplexDouble[] complexSignal = new ComplexDouble[numSamples];
int j = 0;
//determine scaling value to normalize the time domain data.
ComplexDouble scaleValue = new ComplexDouble(AdcFullScaleCode * (numSamples) * Math.Sqrt(window.EquivalentNoiseBandwidth), 0);
//deinterleave IQ data array into complex number data type and normalize data.
for (int i = 0; i < complexSignal.Length; i++)
{
complexSignal[i] = new ComplexDouble(timeDataInterleavedIq[j], timeDataInterleavedIq[j + 1]);
complexSignal[i] = complexSignal[i].Divide(scaleValue);
j = j + 2;
}
//Apply the Window (Default hanning)
window.Apply(complexSignal);
ComplexDouble[] fftData = Transforms.Fft(complexSignal, true);
NationalInstruments.ComplexDouble.DecomposeArray(fftData, out fftReal, out fftImag);
if (runAnalysis == true)
{
analysisData = analyzeFft(fftReal, fftImag, sampleRate_MHz);
}
else
{
analysisData = new FftAnalysis();
}
for (int i = 0; i < numSamples; i++)
{
fftMagnitude_dB[i] = 10.0 * System.Math.Log10(fftReal[i] * fftReal[i] + fftImag[i] * fftImag[i]);
}
return(fftMagnitude_dB);
}
void formFFTSource_FormClosing(object sender, FormClosingEventArgs e)
{
if (formFFTSource.isOKButton)
{
string selectedObject = formFFTSource.listBoxControlSource.SelectedValue.ToString();
for (int i = 0; i < tbControlPanelAssignment.Rows.Count; i++)
{
if (((ObjectSignal)tbControlPanelAssignment.Rows[i]["control"]).ControlName == selectedObject)
{
OSequence = (ObjectSequence)tbControlPanelAssignment.Rows[i]["control"];
break;
}
}
windowType = GetSelectedWindow((ScaledWindowType)Enum.Parse(typeof(ScaledWindowType), formFFTSource.comboBoxEditWindow.SelectedItem.ToString()));
outputType = formFFTSource.comboBoxEditOutput.SelectedText;
SequenceSource = OSequence;
}
}
public override void ToDo(object Arg)
{
b = new BoxController();
b.Init("USB0::0x0957::0x1718::TW54334510::INSTR");
var _ch = new AI_ChannelConfig[4]
{
new AI_ChannelConfig()
{
ChannelName = AnalogInChannelsEnum.AIn1, Enabled = true, Mode = ChannelModeEnum.DC, Polarity = PolarityEnum.Polarity_Bipolar, Range = RangesEnum.Range_1_25
},
new AI_ChannelConfig()
{
ChannelName = AnalogInChannelsEnum.AIn2, Enabled = false, Mode = ChannelModeEnum.DC, Polarity = PolarityEnum.Polarity_Bipolar, Range = RangesEnum.Range_1_25
},
new AI_ChannelConfig()
{
ChannelName = AnalogInChannelsEnum.AIn3, Enabled = false, Mode = ChannelModeEnum.DC, Polarity = PolarityEnum.Polarity_Bipolar, Range = RangesEnum.Range_1_25
},
new AI_ChannelConfig()
{
ChannelName = AnalogInChannelsEnum.AIn4, Enabled = false, Mode = ChannelModeEnum.DC, Polarity = PolarityEnum.Polarity_Bipolar, Range = RangesEnum.Range_1_25
}
};
b.ConfigureAI_Channels(_ch);
var freq = 500000;
var updNumber = 1;
var avgNumber = 1000;
double[] autoPSDLowFreq;
double[] autoPSDHighFreq;
Point[] noisePSD = new Point[] { };
if (freq % 2 != 0)
{
throw new ArgumentException("The frequency should be an even number!");
}
b.AcquisitionInProgress = true;
b.AI_ChannelCollection[AnalogInChannelsEnum.AIn1].DataReady += DefResistanceNoise_DataReady;
var sb = new StringBuilder();
double dtLowFreq = 0.0, dtHighFreq = 0.0;
double dfLowFreq = 1.0, dfHighFreq = 0.0;
double equivalentNoiseBandwidthLowFreq, equivalentNoiseBandwidthHighFreq;
double coherentGainLowFreq, coherentGainHighFreq;
Parallel.Invoke(
() =>
{
b.StartAnalogAcquisition(freq);
IsRunning = false;
},
() =>
{
while (true)
{
if (!IsRunning)
{
b.AcquisitionInProgress = false;
break;
}
if (averagingCounter >= avgNumber)
{
b.AcquisitionInProgress = false;
break;
}
Point[] timeTrace;
var dataReadingSuccess = b.AI_ChannelCollection[AnalogInChannelsEnum.AIn1].ChannelData.TryDequeue(out timeTrace);
if (dataReadingSuccess)
{
var query = from val in timeTrace
select val.Y;
var counter = 0;
var traceData = new double[timeTrace.Length];
foreach (var item in query)
{
traceData[counter] = item;
++counter;
}
var unit = new System.Text.StringBuilder("V", 256);
var sw = ScaledWindow.CreateRectangularWindow();
// Calculation of the low-frequency part of the spectrum
sw.Apply(traceData, out equivalentNoiseBandwidthLowFreq, out coherentGainLowFreq);
dtLowFreq = 1.0 / (double)freq;
autoPSDLowFreq = Measurements.AutoPowerSpectrum(traceData, dtLowFreq, out dfLowFreq);
var singlePSDLowFreq = Measurements.SpectrumUnitConversion(autoPSDLowFreq, SpectrumType.Power, ScalingMode.Linear, DisplayUnits.VoltsPeakSquaredPerHZ, dfLowFreq, equivalentNoiseBandwidthLowFreq, coherentGainLowFreq, unit);
// Calculation of the hugh-frequency part of the spectrum
var selection64Hz = PointSelector.SelectPoints(ref traceData, 64);
sw.Apply(selection64Hz, out equivalentNoiseBandwidthHighFreq, out coherentGainHighFreq);
dtHighFreq = 64.0 * 1.0 / (double)freq;
autoPSDHighFreq = Measurements.AutoPowerSpectrum(selection64Hz, dtHighFreq, out dfHighFreq);
var singlePSDHighFreq = Measurements.SpectrumUnitConversion(autoPSDHighFreq, SpectrumType.Power, ScalingMode.Linear, DisplayUnits.VoltsPeakSquaredPerHZ, dfHighFreq, equivalentNoiseBandwidthHighFreq, coherentGainHighFreq, unit);
var lowFreqSpectrum = singlePSDLowFreq.Select((value, index) => new Point((index + 1) * dfLowFreq, value)).Where(value => value.X <= 1064);
var highFreqSpectrum = singlePSDLowFreq.Select((value, index) => new Point((index + 1) * dfHighFreq, value)).Where(value => value.X > 1064);
noisePSD = new Point[lowFreqSpectrum.Count() + highFreqSpectrum.Count()];
counter = 0;
foreach (var item in lowFreqSpectrum)
{
noisePSD[counter].X = item.X;
noisePSD[counter].Y += item.Y;
++counter;
}
foreach (var item in highFreqSpectrum)
{
noisePSD[counter].X = item.X;
noisePSD[counter].Y += item.Y;
++counter;
}
//for (int i = 0; i < singlePSDLowFreq.Length; i++)
// noisePSD[i] += singlePSDLowFreq[i];
if (averagingCounter % updNumber == 0)
{
sb = new StringBuilder();
for (int i = 0; i < noisePSD.Length; i++)
{
sb.AppendFormat("{0}\t{1}\r\n", (noisePSD[i].X).ToString(NumberFormatInfo.InvariantInfo), (noisePSD[i].Y / (double)averagingCounter).ToString(NumberFormatInfo.InvariantInfo));
}
onDataArrived(new ExpDataArrivedEventArgs(sb.ToString()));
}
}
}
//sb = new StringBuilder();
//for (int i = 0; i < noisePSD.Length; i++)
// sb.AppendFormat("{0}\t{1}\r\n", (noisePSD[i].X).ToString(NumberFormatInfo.InvariantInfo), (noisePSD[i].Y / (double)averagingCounter).ToString(NumberFormatInfo.InvariantInfo));
//onDataArrived(new ExpDataArrivedEventArgs(sb.ToString()));
});
b.Close();
}
public Point[] GetTwoPartsFFT(double[] timeTrace, int samplingFrequency = 500000, int nDataSamples = 1, double kAmpl = 1.0, double lowFreqStartFreq = 1.0, double cutOffLowFreq = 1600, double cutOffHighFreq = 102400, int filterOrder = 8, double filterFrequency = 6400, int lowFreqPeriod = 10, int highFreqPeriod = 10)
{
Point[] autoPSDLowFreq = new Point[] { };
Point[] autoPSDHighFreq = new Point[] { };
if (filterFrequency == -1)
{
filterFrequency = cutOffLowFreq;
}
var sb = new StringBuilder();
double dtLowFreq = 0.0, dtHighFreq = 0.0;
double dfLowFreq = 0.0, dfHighFreq = 0.0;
double equivalentNoiseBandwidthLowFreq, equivalentNoiseBandwidthHighFreq;
double coherentGainLowFreq, coherentGainHighFreq;
// Subsetting samples from the entire trace
var timeTraceSelectionList = new LinkedList <double[]>();
var range = (int)((timeTrace.Length) / nDataSamples);
for (int i = 0; i != nDataSamples;)
{
var selection = timeTrace.Where((value, index) => index >= i * range && index < (i + 1) * range).Select(val => val);
timeTraceSelectionList.AddLast(selection.ToArray());
++i;
}
var filter = new NationalInstruments.Analysis.Dsp.Filters.EllipticLowpassFilter(filterOrder, samplingFrequency, filterFrequency, 0.1, 100.0);
var unit = new System.Text.StringBuilder("V", 256);
var noisePSD = new Point[] { };
// Calculating FFT in each sample
foreach (var trace in timeTraceSelectionList)
{
// Calculation of the LOW-FREQUENCY part of the spectrum
// Filtering data for low frequency selection
var filteredData = filter.FilterData(trace);
var sw = ScaledWindow.CreateRectangularWindow();
// Selecting lower amount of data points to reduce the FFT noise
var selectionLowFreq = filteredData.Where((value, index) => (index) % lowFreqPeriod == 0).ToArray();
sw.Apply(selectionLowFreq, out equivalentNoiseBandwidthLowFreq, out coherentGainLowFreq);
dtLowFreq = lowFreqPeriod * 1.0 / (double)samplingFrequency;
var singlePSD_LOW_Freq = Measurements.AutoPowerSpectrum(selectionLowFreq, dtLowFreq, out dfLowFreq);
autoPSDLowFreq = (singlePSD_LOW_Freq.Select((value, index) => new Point(index * dfLowFreq, value)).Where(p => p.X >= 1 && p.X <= cutOffLowFreq)).ToArray();
// Calculation of the HIGH-FREQUENCY part of the spectrum
dtHighFreq = 1.0 / (double)samplingFrequency;
var highFreqSelectionRange = (int)((timeTrace.Length) / highFreqPeriod);
LinkedList <double[]> selectionList = new LinkedList <double[]>();
for (int i = 0; i != highFreqPeriod;)
{
var arr = new double[highFreqSelectionRange];
Array.Copy(timeTrace, i * highFreqSelectionRange, arr, 0, highFreqSelectionRange);
selectionList.AddLast(arr);
++i;
}
var cumulativePSD_HIGH_Freq = new double[] { };
foreach (var selection in selectionList)
{
sw.Apply(selection, out equivalentNoiseBandwidthHighFreq, out coherentGainHighFreq);
var singlePSD_HIGH_Freq = Measurements.AutoPowerSpectrum(selection, dtHighFreq, out dfHighFreq);
if (cumulativePSD_HIGH_Freq.Length == 0)
{
cumulativePSD_HIGH_Freq = Enumerable.Repeat(0.0, singlePSD_HIGH_Freq.Length).ToArray();
}
for (int i = 0; i != singlePSD_HIGH_Freq.Length;)
{
cumulativePSD_HIGH_Freq[i] += singlePSD_HIGH_Freq[i];
++i;
}
}
autoPSDHighFreq = (cumulativePSD_HIGH_Freq
.Select((value, index) => new Point(index * dfHighFreq, value)))
.Where(p => p.X > cutOffLowFreq && p.X <= cutOffHighFreq).ToArray();
if (noisePSD == null || noisePSD.Length == 0)
{
noisePSD = new Point[autoPSDLowFreq.Length + autoPSDHighFreq.Length];
}
var counter = 0;
for (int i = 0; i != autoPSDLowFreq.Length;)
{
var item = autoPSDLowFreq[i];
noisePSD[counter].X = item.X;
noisePSD[counter].Y += item.Y;
++counter;
++i;
}
for (int i = 0; i != autoPSDHighFreq.Length;)
{
var item = autoPSDHighFreq[i];
noisePSD[counter].X = item.X;
noisePSD[counter].Y += item.Y / ((double)(highFreqPeriod * highFreqPeriod));
++counter;
++i;
}
}
return((from item in noisePSD
select new Point(item.X, item.Y / (kAmpl * kAmpl))).ToArray());
}
