/// <summary>
/// Frequency filter.
/// </summary>
/// <param name="source">Input stream</param>
/// <param name="period">Period of input stream</param>
/// <param name="window">Window size</param>
/// <param name="filter">Filter function to select in frequencies</param>
/// <returns>Signal after frequency filter pass.</returns>
public static IStreamable <Empty, Signal> BandPassFilter(
this IStreamable <Empty, Signal> source,
long period,
long window,
double low,
double high
)
{
var bp = OnlineFilter.CreateBandpass(ImpulseResponse.Finite, period, low, high);
return(source
.Multicast(s => s
.ShiftEventLifetime(window)
.Join(s
.TumblingWindowLifetime(window, window)
.Aggregate(w => new BatchAggregate <Signal>())
.Select(input => FreqFilter(input, bp))
.SelectMany(e => e),
l => l.ts, r => r.ts,
(l, r) => r
)
)
.ShiftEventLifetime(-window)
);
}
public double[] Filter(double[] inputArray, MathNet.Filtering.ImpulseResponse responseType, int samplingFreq, int frequencyLow, int frequencyHigh, int q)//ResponseType: IIR or FIR, samplingFreq: Sampling frequency in Hz, frequencyLow: Low cuttoff for filter.
{
OnlineFilter bandpassFast = OnlineFilter.CreateBandpass(ImpulseResponse.Infinite, samplingFreq, frequencyLow, frequencyHigh, q);
bandpassFast.ProcessSamples(inputArray);
return(inputArray);//Double check to see if we really need to return, if this is working on a referenced array, then the input array should already be altered, which would be this classes data frame.
}
public ClientAudioProvider()
{
_filters = new OnlineFilter[2];
_filters[0] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.INPUT_SAMPLE_RATE, 560, 3900);
_filters[1] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.INPUT_SAMPLE_RATE, 100, 4500);
JitterBufferProviderInterface =
new JitterBufferProviderInterface(new WaveFormat(AudioManager.INPUT_SAMPLE_RATE, 2));
SampleProvider = new Pcm16BitToSampleProvider(JitterBufferProviderInterface);
_decoder = OpusDecoder.Create(AudioManager.INPUT_SAMPLE_RATE, 1);
_decoder.ForwardErrorCorrection = false;
_highPassFilter = BiQuadFilter.HighPassFilter(AudioManager.INPUT_SAMPLE_RATE, 520, 0.97f);
_lowPassFilter = BiQuadFilter.LowPassFilter(AudioManager.INPUT_SAMPLE_RATE, 4130, 2.0f);
var effect = new CachedAudioEffect(CachedAudioEffect.AudioEffectTypes.NATO_TONE);
if (effect.AudioEffectBytes.Length > 0)
{
natoTone = ConversionHelpers.ByteArrayToShortArray(effect.AudioEffectBytes);
var vol = Settings.GlobalSettingsStore.Instance.GetClientSetting(GlobalSettingsKeys.NATOToneVolume)
.FloatValue;
for (int i = 0; i < natoTone.Length; i++)
{
natoTone[i] = (short)(natoTone[i] * vol);
}
}
}
private void DistanceFromTime(Stopwatch time, OnlineFilter filter, out double distance)
{
// Formula: uS / 58 == centimeters
double microsecondsPerTick = (1000.0 * 1000.0) / (double)Stopwatch.Frequency;
double deltaInMicroseconds = (double)time.ElapsedTicks * microsecondsPerTick;
double measuredDistance = deltaInMicroseconds / 58.0;
distance = filter.ProcessSample(measuredDistance);
}
public void notch_interference(double low_value, double high_value)
{
OnlineFilter bandstop = OnlineFirFilter.CreateBandstop(ImpulseResponse.Finite, fs_hz, low_value, high_value);
for (int column_id = first_eeg_channel; column_id <= last_eeg_channel; column_id++)
{
double[] filtered = bandstop.ProcessSamples(data.GetColumn(column_id));
data = data.SetColumn(column_id, filtered);
}
}
public void lowpass(double value)
{
OnlineFilter lowpass = OnlineFirFilter.CreateLowpass(ImpulseResponse.Finite, fs_hz, value);
for (int column_id = first_eeg_channel; column_id <= last_eeg_channel; column_id++)
{
double[] filtered = lowpass.ProcessSamples(data.GetColumn(column_id));
data = data.SetColumn(column_id, filtered);
}
}
// median
public void denoise(int order)
{
OnlineFilter denoise = OnlineFirFilter.CreateDenoise(order);
for (int column_id = first_eeg_channel; column_id <= last_eeg_channel; column_id++)
{
double[] filtered = denoise.ProcessSamples(data.GetColumn(column_id));
data = data.SetColumn(column_id, filtered);
}
}
public async Task initialize()
{
var controller = await GpioController.GetDefaultAsync();
if (controller == null)
{
// Rogin?
return;
}
frontFilter = OnlineFilter.CreateDenoise();
backFilter = OnlineFilter.CreateDenoise();
rightFilter = OnlineFilter.CreateDenoise();
leftFilter = OnlineFilter.CreateDenoise();
triggerFrontPin = controller.OpenPin(kTriggerFront);
triggerBackPin = controller.OpenPin(kTriggerBack);
triggerLeftPin = controller.OpenPin(kTriggerLeft);
triggerRightPin = controller.OpenPin(kTriggerRight);
echoFrontPin = controller.OpenPin(kEchoFront);
echoBackPin = controller.OpenPin(kEchoBack);
echoLeftPin = controller.OpenPin(kEchoLeft);
echoRightPin = controller.OpenPin(kEchoRight);
triggerFrontPin.SetDriveMode(GpioPinDriveMode.Output);
triggerBackPin.SetDriveMode(GpioPinDriveMode.Output);
triggerLeftPin.SetDriveMode(GpioPinDriveMode.Output);
triggerRightPin.SetDriveMode(GpioPinDriveMode.Output);
echoFrontPin.SetDriveMode(GpioPinDriveMode.Input);
echoBackPin.SetDriveMode(GpioPinDriveMode.Input);
echoLeftPin.SetDriveMode(GpioPinDriveMode.Input);
echoRightPin.SetDriveMode(GpioPinDriveMode.Input);
long debounceTicks = Stopwatch.Frequency / (1000 * 1000 * 50);
echoFrontPin.DebounceTimeout = TimeSpan.FromTicks(debounceTicks);
echoBackPin.DebounceTimeout = TimeSpan.FromTicks(debounceTicks);
echoLeftPin.DebounceTimeout = TimeSpan.FromTicks(debounceTicks);
echoRightPin.DebounceTimeout = TimeSpan.FromTicks(debounceTicks);
echoFrontPin.ValueChanged += EchoFrontPin_ValueChanged;
echoBackPin.ValueChanged += EchoBackPin_ValueChanged;
echoLeftPin.ValueChanged += EchoLeftPin_ValueChanged;
echoRightPin.ValueChanged += EchoRightPin_ValueChanged;
// Every greater than 60 ms per datasheet due to echo.
// Round robin, so servicing each sonar every 120ms, but
// allowing for echo by servicing opposite sides.
timer.Interval = TimeSpan.FromMilliseconds(30);
timer.Tick += Timer_Tick;
timer.Start();
}
public ClientPassThroughAudioProvider()
{
profileSettings = Settings.GlobalSettingsStore.Instance.ProfileSettingsStore;
_filters = new OnlineFilter[2];
_filters[0] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.OUTPUT_SAMPLE_RATE, 560, 3900);
_filters[1] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.OUTPUT_SAMPLE_RATE, 100, 4500);
_highPassFilter = BiQuadFilter.HighPassFilter(AudioManager.OUTPUT_SAMPLE_RATE, 520, 0.97f);
_lowPassFilter = BiQuadFilter.LowPassFilter(AudioManager.OUTPUT_SAMPLE_RATE, 4130, 2.0f);
}
/// <summary>
/// Samples the input stream and applies a high-pass filter (using MathNet.Filtering) over the sampled input stream. Removes low frequencies.
/// </summary>
/// <param name="input">The input source of doubles.</param>
/// <param name="sampleRate">The desired sample rate for the filter.</param>
/// <param name="cutoffRate">The desired cutoff for the filter.</param>
/// <param name="impulseResponse">Specifies how the filter will respond to an impulse input (Finite or Infinite). Default is Finite.</param>
/// <param name="sampleInterpolator">Optional sampling interpolator over the input. Default is a reproducible linear interpolation.</param>
/// <param name="alignmentDateTime">If non-null, this parameter specifies a time to align the sampled messages with, and the sampled messages
/// will have originating times that align with (i.e., are an integral number of intervals away from) the specified alignment time.</param>
/// <param name="deliveryPolicy">An optional delivery policy for the input stream.</param>
/// <returns>A high-pass filtered version of the input.</returns>
public static IProducer <double> HighpassFilter(
this IProducer <double> input,
double sampleRate,
double cutoffRate,
ImpulseResponse impulseResponse = ImpulseResponse.Finite,
Interpolator <double, double> sampleInterpolator = null,
DateTime?alignmentDateTime = null,
DeliveryPolicy <double> deliveryPolicy = null)
{
var filter = OnlineFilter.CreateHighpass(impulseResponse, sampleRate, cutoffRate);
return(MathNetFilter(input, sampleRate, sampleInterpolator ?? Reproducible.Linear(), alignmentDateTime, filter, deliveryPolicy));
}
public ClientAudioProvider()
{
_filters = new OnlineFilter[2];
_filters[0] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.INPUT_SAMPLE_RATE, 560, 3900);
_filters[1] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.INPUT_SAMPLE_RATE, 100, 4500);
JitterBufferProviderInterface =
new JitterBufferProviderInterface(new WaveFormat(AudioManager.INPUT_SAMPLE_RATE, 2));
SampleProvider = new Pcm16BitToSampleProvider(JitterBufferProviderInterface);
}
private void timer1_Tick(object sender, EventArgs e)
{
LineItem kurvaMagnitude = zedGraphControl1.GraphPane.CurveList[0] as LineItem;
IPointListEdit listMagnitude = kurvaMagnitude.Points as IPointListEdit;
dataMagnitude = new double[1000];
try
{
for (int i = 0; i < 1000; i++)
{
dataMagnitude[i] = double.Parse(uno.ReadLine(), CultureInfo.InvariantCulture.NumberFormat);
}
}
catch (Exception)
{
//uno.Close();
return;
}
OnlineFilter bandpass = OnlineFilter.CreateBandpass(MathNet.Filtering.ImpulseResponse.Finite, 1000, 20, 500);
OnlineFilter bandstop = OnlineFilter.CreateBandstop(MathNet.Filtering.ImpulseResponse.Finite, 1000, 48.5, 51.5);
OnlineFilter denoise = OnlineFilter.CreateDenoise();
dataFilter = new double[1000];
dataFilter = bandpass.ProcessSamples(dataMagnitude);
dataFilter = bandstop.ProcessSamples(dataFilter);
dataFilter = denoise.ProcessSamples(dataFilter);
var N = Enumerable.Range(1, dataMagnitude.Length).ToArray();
waktu = Array.ConvertAll <int, double>(N, Convert.ToDouble);
// double waktu = (Environment.TickCount - waktustart) / 1000.0;
listMagnitude.Clear();
for (int i = 0; i < 1000; i++)
{
listMagnitude.Add(waktu[i], dataFilter[i]);
}
label1.Text = Convert.ToString(dataFilter[500]);
//uno.Close();
zedGraphControl1.AxisChange();
zedGraphControl1.Refresh();
}
public ClientAudioProvider()
{
_filters = new OnlineFilter[2];
_filters[0] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.INPUT_SAMPLE_RATE, 560, 3900);
_filters[1] =
OnlineFilter.CreateBandpass(ImpulseResponse.Finite, AudioManager.INPUT_SAMPLE_RATE, 100, 4500);
JitterBufferProviderInterface =
new JitterBufferProviderInterface(new WaveFormat(AudioManager.INPUT_SAMPLE_RATE, 2));
SampleProvider = new Pcm16BitToSampleProvider(JitterBufferProviderInterface);
_decoder = OpusDecoder.Create(AudioManager.INPUT_SAMPLE_RATE, 1);
_decoder.ForwardErrorCorrection = false;
_highPassFilter = BiQuadFilter.HighPassFilter(AudioManager.INPUT_SAMPLE_RATE, 520, 0.97f);
_lowPassFilter = BiQuadFilter.LowPassFilter(AudioManager.INPUT_SAMPLE_RATE, 4130, 2.0f);
}
public MainWindow()
{
try
{
InitializeComponent();
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
//Graphing Add In Code-Behind
SoundData = new XyDataSeries <double, short>();
SoundData.FifoCapacity = this.Capacity * 16000;
//View Model Update
Sthetho = new StethoViewModel(Init, Stop, Clear, Retrieve);
this.DataContext = Sthetho;
Sthetho.IsStreaming = false;
Sthetho.FilterHeart = false;
//Open File (Remove for serial Stream)
reader = OpenFile(inputfile);
//Audio Class
StethoPlayer = new AudioPlayer(15200, 16, 1);
//Open Serial Port
SerialDataIn = new SerialCom(921600, "COM8");
//Open File For Writing
Sthetho.OutFileName = "StethoSound"; //Deprecated
Sthetho.WriteToFile = true;
VertAnnotate.CoordinateMode = SciChart.Charting.Visuals.Annotations.AnnotationCoordinateMode.Absolute;
VertAnnotate.Y1 = -32000;
VertAnnotate.VerticalAlignment = VerticalAlignment.Top;
Sthetho.AnnotationX = 0;
//Create filter objects
BandPassfilter = OnlineFilter.CreateHighpass(ImpulseResponse.Finite, samplerate, fc1);
//Denoiser = OnlineFilter.CreateDenoise(25);
}
private static List <Signal> FreqFilter(List <Signal> input, OnlineFilter bp)
{
var len = input.Count;
var ival = new double[len];
var output = new List <Signal>(len);
for (int i = 0; i < len; i++)
{
ival[i] = input[i].val;
}
var new_val = bp.ProcessSamples(ival);
for (int k = 0; k < new_val.Length; k++)
{
output.Add(new Signal(input[k].ts, (float)new_val[k]));
}
return(output);
}
private void button4_Click(object sender, EventArgs e)
{
openFileDialog1.Multiselect = true;
openFileDialog1.Title = "Open Files...";
openFileDialog1.FileName = "";
openFileDialog1.Filter = "CSV (comma delimited)|*.csv|All Files|*.*";
if (openFileDialog1.ShowDialog() == DialogResult.Cancel)
{
MessageBox.Show("Choice Cancelled");
}
else
{
folderBrowserDialog1.Description = "Choose Folder to Save...";
if (folderBrowserDialog1.ShowDialog() == DialogResult.Cancel)
{
MessageBox.Show("Choice Cancelled");
}
else
{
var saveto = folderBrowserDialog1.SelectedPath;
for (int i = 0; i < openFileDialog1.FileNames.Length; i++)
{
data = File.ReadAllLines(openFileDialog1.FileNames[i]);
dataMagnitude = data.Select(x => double.Parse(x)).ToArray();
dataFilter = new double[dataMagnitude.Length];
OnlineFilter bandstop = OnlineFilter.CreateBandstop(MathNet.Filtering.ImpulseResponse.Finite, 80, 48.5, 51.5);
// OnlineFilter denoise = OnlineFilter.CreateDenoise();
dataFilter = bandstop.ProcessSamples(dataMagnitude);
//dataFilter = denoise.ProcessSamples(dataFilter);
var path = Path.Combine(saveto, "Datafilter" + Convert.ToString(i) + ".csv");
File.WriteAllLines(path, Array.ConvertAll <double, string>(dataFilter, Convert.ToString), Encoding.Default);
}
MessageBox.Show("Done");
}
}
}
public RadioFilter(ISampleProvider sampleProvider)
{
_source = sampleProvider;
_filters = new OnlineFilter[2];
/**
* From Coug4r
* Apart from adding noise i'm only doing 3 things in this order:
* - Custom clipping (which basicly creates the overmodulation effect)
* - Run the audio through bandpass filter 1
* - Run the audio through bandpass filter 2
*
* These are the values i use for the bandpass filters:
* 1 - low 560, high 3900
* 2 - low 100, high 4500
*
*/
_filters[0] = OnlineFilter.CreateBandpass(ImpulseResponse.Finite, sampleProvider.WaveFormat.SampleRate, 560,
3900);
_filters[1] = OnlineFilter.CreateBandpass(ImpulseResponse.Finite, sampleProvider.WaveFormat.SampleRate, 100,
4500);
}
private void button2_Click(object sender, EventArgs e)
{
dataFilter = new double[dataMagnitude.Length];
OnlineFilter bandpass = OnlineFilter.CreateBandpass(ImpulseResponse.Finite, 1000, 20, 500);
OnlineFilter bandstop = OnlineFilter.CreateBandstop(MathNet.Filtering.ImpulseResponse.Finite, 1000, 49, 51);
OnlineFilter denoise = OnlineFilter.CreateDenoise();
dataFilter = bandpass.ProcessSamples(dataMagnitude);
dataFilter = bandstop.ProcessSamples(dataFilter);
dataFilter = denoise.ProcessSamples(dataFilter);
LineItem kurvaMagnitude = zedGraphControl1.GraphPane.CurveList[1] as LineItem;
IPointListEdit listFilter = kurvaMagnitude.Points as IPointListEdit;
listFilter.Clear();
for (int i = 1; i < dataFilter.Length; i++)
{
listFilter.Add(dataN[i], dataFilter[i]);
}
zedGraphControl1.AxisChange();
zedGraphControl1.Invalidate();
button2.Enabled = false;
button3.Enabled = true;
}
public RealtimeEngine()
{
nsamples = new int[MainClass.devices.Length];
bool usehpf = MainClass.win._handles.useHPF.Active;
bool uselpf = MainClass.win._handles.useLPF.Active;
double hpf = 0.016;
try
{
hpf = Convert.ToDouble(MainClass.win._handles.editHPF.Text);
}
catch
{
usehpf = false;
}
double lpf = 0.5;
try
{
lpf = Convert.ToDouble(MainClass.win._handles.editLPF.Text);
}
catch
{
uselpf = false;
}
fs = new double[MainClass.devices.Length];
OnlineFIRFiltersHPF = new OnlineFilter[MainClass.devices.Length][];
OnlineFIRFiltersHPF2 = new OnlineFilter[MainClass.devices.Length][];
OnlineFIRFiltersLPF = new OnlineFilter[MainClass.devices.Length][];
MocoKalman = new DiscreteKalmanFilter[MainClass.devices.Length][];
mBLLmappings = new MBLLmapping[MainClass.devices.Length];
for (int i = 0; i < MainClass.devices.Length; i++)
{
int nSDpairs = MainClass.win.nirsdata[i].probe.numChannels / MainClass.win.nirsdata[i].probe.numWavelengths;
mBLLmappings[i] = new MBLLmapping();
mBLLmappings[i].distances = new double[nSDpairs];
mBLLmappings[i].hboIndex = new int[nSDpairs][];
mBLLmappings[i].measurementPairs = new int[nSDpairs][];
mBLLmappings[i].ExtCoefHbO = new double[nSDpairs][];
mBLLmappings[i].ExtCoefHbR = new double[nSDpairs][];
int cnt = 0;
int[] cnt2 = new int[nSDpairs];
for (int ii = 0; ii < nSDpairs; ii++)
{
cnt2[ii] = 0;
mBLLmappings[i].hboIndex[ii] = new int[2];
mBLLmappings[i].measurementPairs[ii] = new int[MainClass.win.nirsdata[i].probe.numWavelengths];
mBLLmappings[i].ExtCoefHbO[ii] = new double[MainClass.win.nirsdata[i].probe.numWavelengths];
mBLLmappings[i].ExtCoefHbR[ii] = new double[MainClass.win.nirsdata[i].probe.numWavelengths];
}
nirs.Media media = new nirs.Media();
for (int sI = 0; sI < MainClass.win.nirsdata[i].probe.numSrc; sI++)
{
for (int dI = 0; dI < MainClass.win.nirsdata[i].probe.numDet; dI++)
{
bool found = false;;
for (int mI = 0; mI < MainClass.win.nirsdata[i].probe.numChannels; mI++)
{
if (MainClass.win.nirsdata[i].probe.ChannelMap[mI].sourceindex == sI &
MainClass.win.nirsdata[i].probe.ChannelMap[mI].detectorindex == dI)
{
mBLLmappings[i].measurementPairs[cnt][cnt2[cnt]] = mI + MainClass.win.nirsdata[i].probe.numChannels;
if (mBLLmappings[i].hboIndex[cnt][0] == 0)
{
mBLLmappings[i].hboIndex[cnt][0] = mI + MainClass.win.nirsdata[i].probe.numChannels * 2;
mBLLmappings[i].hboIndex[cnt][1] = mI + nSDpairs + MainClass.win.nirsdata[i].probe.numChannels * 2;
}
mBLLmappings[i].distances[cnt] = 0;
media.GetSpectrum(MainClass.win.nirsdata[i].probe.ChannelMap[mI].wavelength,
out mBLLmappings[i].ExtCoefHbO[cnt][cnt2[cnt]], out mBLLmappings[i].ExtCoefHbR[cnt][cnt2[cnt]]);
mBLLmappings[i].distances[cnt] = Math.Sqrt((MainClass.win.nirsdata[i].probe.SrcPos[sI, 0] -
MainClass.win.nirsdata[i].probe.DetPos[dI, 0]) *
(MainClass.win.nirsdata[i].probe.SrcPos[sI, 0] -
MainClass.win.nirsdata[i].probe.DetPos[dI, 0]) +
(MainClass.win.nirsdata[i].probe.SrcPos[sI, 1] -
MainClass.win.nirsdata[i].probe.DetPos[dI, 1]) *
(MainClass.win.nirsdata[i].probe.SrcPos[sI, 1] -
MainClass.win.nirsdata[i].probe.DetPos[dI, 1]));
cnt2[cnt]++;
found = true;
}
}
if (found)
{
cnt++;
}
}
}
nsamples[i] = 0;
NIRSDAQ.info info = MainClass.devices[i].GetInfo();
fs[i] = info.sample_rate;
OnlineFIRFiltersHPF[i] = new OnlineFilter[MainClass.win.nirsdata[i].probe.numChannels];
OnlineFIRFiltersLPF[i] = new OnlineFilter[MainClass.win.nirsdata[i].probe.numChannels];
OnlineFIRFiltersHPF2[i] = new OnlineFilter[MainClass.win.nirsdata[i].probe.numChannels];
MocoKalman[i] = new DiscreteKalmanFilter[MainClass.win.nirsdata[i].probe.numChannels];
for (int j = 0; j < MainClass.win.nirsdata[i].probe.numChannels; j++)
{
OnlineFIRFiltersLPF[i][j] = OnlineFilter.CreateLowpass(ImpulseResponse.Finite, fs[i], lpf);
OnlineFIRFiltersHPF[i][j] = OnlineFilter.CreateHighpass(ImpulseResponse.Finite, fs[i], hpf);
OnlineFIRFiltersHPF2[i][j] = OnlineFilter.CreateHighpass(ImpulseResponse.Finite, fs[i], 0.001);
OnlineFIRFiltersHPF[i][j].Reset();
OnlineFIRFiltersHPF2[i][j].Reset();
OnlineFIRFiltersLPF[i][j].Reset();
int order = 5;
var x0 = MathNet.Numerics.LinearAlgebra.Matrix <double> .Build.Dense(order + 1, 1, 0);
var P0 = MathNet.Numerics.LinearAlgebra.Matrix <double> .Build.DenseIdentity(order + 1);
MocoKalman[i][j] = new DiscreteKalmanFilter(x0, P0);
}
}
}
public FeatureFilterSelection(FeaturesSelc aFeaturesSelc, OnlineFilter aOnlineFilter)
{
feature = aFeaturesSelc;
bandpass = aOnlineFilter;
}
private void InitPlots(CallModel callModel)
{
BatCall call = callModel.Call;
PlotModel pmPower = new PlotModel();
LineSeries pwrLineSeries = new LineSeries();
pwrLineSeries.LineJoin = LineJoin.Round;
pmPower.Series.Add(pwrLineSeries);
pmPower.Axes.Add(new LinearAxis {
Maximum = 260, Minimum = 0, Position = AxisPosition.Left, Title = "Intensität"
});
pmPower.Axes.Add(new LinearAxis {
Position = AxisPosition.Bottom, Title = "Dauer [ms]"
});
if (call.PowerData != null)
{
pwrLineSeries.Points.AddRange(call.PowerData.Select((b, i) => new DataPoint(i * 0.251, b)));
}
//if (call.OriginalCalls.Count > 1)
//{
// int leftSum = call.OriginalCalls[0].PowerData.Length;
// for (int i = 0; i < call.OriginalCalls.Count-1; i++)
// {
// int width = (int)Math.Round((call.OriginalCalls[i + 1].StartTimeMs - call.OriginalCalls[i].EndTimeMs) / 0.251);
// double left = leftSum* 0.251;
// double right = left + (width* 0.251);
// RectangleAnnotation annotation = new RectangleAnnotation { Fill = OxyColors.LightGray, Layer = AnnotationLayer.BelowAxes, MinimumX = left, MaximumX = right, MinimumY = 0, MaximumY = 260 };
// //LineAnnotation lineAnnotation = new LineAnnotation { Color = OxyColors.Red, StrokeThickness = 2, LineStyle = LineStyle.Dash, Type = LineAnnotationType.Vertical, X = linePos };
// pmPower.Annotations.Add(annotation);
// leftSum = leftSum + width + call.OriginalCalls[i].PowerData.Length;
// }
//}
OnlineFilter filter = OnlineFilter.CreateBandstop(ImpulseResponse.Infinite, 20, 20, 150);
LineSeries pwrLineSeriesAvg = new LineSeries();
pwrLineSeriesAvg.LineJoin = LineJoin.Round;
pwrLineSeriesAvg.Color = OxyColors.Blue;
pmPower.Series.Add(pwrLineSeriesAvg);
//double[] input = call.PowerData.Select(d => (double)d).ToArray();
//double[] data = filter.ProcessSamples(input);
//pwrLineSeriesAvg.Points.AddRange(data.Select((d, i) => new DataPoint(i * 0.251, d)));
int[] window = new int[] { 2, 4, 8, 4, 2 };
int divisor = window.Sum();
byte[] input = call.PowerData;
for (int i = 0; i < input.Length; i++)
{
double sum = 0;
for (int j = 0; j < 5; j++)
{
int x = i + (j - 2);
double q;
if (x < 0)
{
q = input[0];
}
else if (x >= input.Length)
{
q = input[input.Length - 1];
}
else
{
q = input[x];
}
sum += q * window[j];
}
pwrLineSeriesAvg.Points.Add(new DataPoint(i * 0.251, sum / divisor));
}
LineSeries pwrLineSeriesAvg2 = new LineSeries();
pwrLineSeriesAvg2.LineJoin = LineJoin.Round;
pwrLineSeriesAvg2.Color = OxyColors.Crimson;
pmPower.Series.Add(pwrLineSeriesAvg2);
int avgCount = 7;
int[] ringBuffer = Enumerable.Repeat(-1, avgCount).ToArray();
int bufferIndex = 0;
for (int i = 0; i < call.PowerData.Length; i++)
{
ringBuffer[bufferIndex++] = call.PowerData[i];
if (bufferIndex >= ringBuffer.Length)
{
bufferIndex = 0;
}
if (i > 4)
{
int c = 0;
double mAvg = 0;
for (int j = 0; j < ringBuffer.Length; j++)
{
if (ringBuffer[j] >= 0)
{
c++;
mAvg += ringBuffer[j];
}
}
pwrLineSeriesAvg2.Points.Add(new DataPoint((i - 4) * 0.251, mAvg / c));
}
}
LineAnnotation lineAnnotation = new LineAnnotation {
Color = OxyColors.Red, StrokeThickness = 2, LineStyle = LineStyle.Dash, Type = LineAnnotationType.Horizontal, Y = call.AveragePower
};
pmPower.Annotations.Add(lineAnnotation);
Power = pmPower.AddStyles();
PlotModel pmFreq = new PlotModel();
ColumnSeries freqSeries = new ColumnSeries();
pmFreq.Series.Add(freqSeries);
CategoryAxis item = new CategoryAxis();
item.Position = AxisPosition.Bottom;
item.Title = "Frequenz [kHz]";
item.GapWidth = 0.1;
item.IsTickCentered = true;
item.LabelFormatter = i => LogAnalyzer.GetFrequencyFromFftBin((int)i).ToString(CultureInfo.CurrentCulture);
item.MajorGridlineThickness = 0;
pmFreq.Axes.Add(item);
if (call.FftData != null)
{
freqSeries.Items.AddRange(call.FftData.Select((f, i) =>
{
ColumnItem columnItem = new ColumnItem(f, i);
columnItem.Color = i == call.MaxFrequencyBin ? OxyColors.Red : OxyColors.Green;
return(columnItem);
}));
}
Frequency = pmFreq.AddStyles();
}
public void StorePeakAcceleration(DataItemVehicle itemVehicle, DataItemRun itemRun)
{
if (samples.Count == 0)
{
Debug.LogToFileMethod("peak-a: no samlpes");
return;
}
if (isPeakAStored == true)
{
// refuse to process peakA again
Debug.LogToFileEventText("peak-a: already found");
return;
}
double peakA = 0;
double speed = 0;
double magnitude = 0;
int count = samples.Count;
// user calibrated profile before run -> get accelerometer offsets
// to eleminate earth gravitation and slope of the device in the car mount
double offsetX = itemVehicle.AxisOffsetX;
double offsetY = itemVehicle.AxisOffsetY;
double offsetZ = itemVehicle.AxisOffsetZ;
// from collection to array with offset compensation ("normalize" to axis origin)
ToArray(offsetX, offsetY, offsetZ);
// apply low pass filter to each "normalized" axis
OnlineFilter filter = OnlineFilter.CreateLowpass(ImpulseResponse.Finite,
FILTER_SAMPLE_RATE,
FILTER_CUT_OFF_RATE,
FILTER_ORDER);
double[] filteredX = filter.ProcessSamples(samplesX);
filter.Reset();
double[] filteredY = filter.ProcessSamples(samplesY);
filter.Reset();
double[] filteredZ = filter.ProcessSamples(samplesZ);
filter.Reset();
// search max value of magnitude of all filtered axes
for (int i = 0; i < count; i++)
{
// magnitude of all components
magnitude = Math.Sqrt(filteredX[i] * filteredX[i] +
filteredY[i] * filteredY[i] +
filteredZ[i] * filteredZ[i]);
// store if new max
if (magnitude > peakA)
{
peakA = magnitude;
speed = samplesS[i];
}
}
// store to given run
itemRun.PeakAcceleration = (float)peakA;
Debug.LogToFileEventText("found peak-a [m/s^2]: " + peakA.ToString("0.00") +
" at speed [m/s]: " + speed.ToString("0.00"));
if ((count >= SIZE_MAX) && (peakA > 0))
{
isPeakAStored = true;
}
}
/// <summary>
/// Applies a de-noising filter (using MathNet.Filtering) over the input stream. Implemented as an unweighted median filter.
/// </summary>
/// <param name="input">The input source of doubles.</param>
/// <param name="order">Window Size, should be odd. A larger number results in a smoother response but also in a longer delay.</param>
/// <param name="deliveryPolicy">An optional delivery policy for the input stream.</param>
/// <returns>A high-pass filtered version of the input.</returns>
public static IProducer <double> DenoiseFilter(this IProducer <double> input, int order, DeliveryPolicy <double> deliveryPolicy = null)
{
var filter = OnlineFilter.CreateDenoise(order);
return(input.Select(s => filter.ProcessSample(s), deliveryPolicy));
}
protected void ChangeBPF(object sender, EventArgs e)
{
double[] fs = new double[MainClass.devices.Length];
for (int i = 0; i < MainClass.devices.Length; i++)
{
NIRSDAQ.info info = MainClass.devices[i].GetInfo();
fs[i] = info.sample_rate;
}
double hpf = 0.016;
double lpf = 0.5;
try
{
lpf = Convert.ToDouble(MainClass.win._handles.editLPF.Text);
if (lpf > fs[0] / 2)
{
lpf = fs[0] * 5 / 11;
MainClass.win._handles.editLPF.Text = string.Format("{0}", lpf);
}
}
catch
{
MainClass.win._handles.editLPF.Text = string.Format("{0}", 0.5);
lpf = 0.5;
}
try
{
hpf = Convert.ToDouble(MainClass.win._handles.editHPF.Text);
if (hpf < 0)
{
hpf = 0;
MainClass.win._handles.editHPF.Text = string.Format("{0}", 0);
}
if (hpf > lpf)
{
hpf = lpf / 2;
MainClass.win._handles.editHPF.Text = string.Format("{0}", hpf);
}
}
catch
{
MainClass.win._handles.editHPF.Text = string.Format("{0}", 0.016);
hpf = 0.016;
}
if (maindisplaythread.IsAlive)
{
for (int i = 0; i < MainClass.devices.Length; i++)
{
for (int j = 0; j < MainClass.win.nirsdata[i].probe.numChannels; j++)
{
realtimeEngine.OnlineFIRFiltersLPF[i][j] = OnlineFilter.CreateLowpass(ImpulseResponse.Finite, fs[i], lpf);
realtimeEngine.OnlineFIRFiltersHPF[i][j] = OnlineFilter.CreateHighpass(ImpulseResponse.Finite, fs[i], hpf);
}
}
}
}
static void Main(string[] args)
{
try
{
//Load data
data_label_train = loadTrainingData("train.csv");
data_label_test = loadTestData("test_data.csv", "result.csv");
//Normalize train data
foreach (Data reading in data_label_train)
{
reading.Normalize();
}
//Scaling test data and Normalize it
Random rnd = new System.Random();
foreach (LabeledData reading in data_label_test)
{
double a = rnd.NextDouble() + 1.0;
double b = rnd.NextDouble();
reading.Scaling(a, b);
reading.Normalize();
}
double fs = 8000.0;
//Create bandpass filters
OnlineFilter bandpass0 = OnlineFilter.CreateLowpass(ImpulseResponse.Finite, fs, 200.0);
OnlineFilter bandpass1 = OnlineFilter.CreateBandpass(ImpulseResponse.Finite, fs, 200.0, 2000.0);
OnlineFilter bandpass2 = OnlineFilter.CreateBandpass(ImpulseResponse.Finite, fs, 2000.0, 3000.0);
OnlineFilter bandpass3 = OnlineFilter.CreateBandpass(ImpulseResponse.Finite, fs, 3000.0, 3400.0);
OnlineFilter bandpass4 = OnlineFilter.CreateHighpass(ImpulseResponse.Finite, fs, 3400.0);
//Pre process: extract features from raw data
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.crestFactor, bandpass0));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.skew, bandpass0));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.kuri, bandpass0));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.std, bandpass0));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.var, bandpass0));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.crestFactor, bandpass1));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.skew, bandpass1));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.kuri, bandpass1));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.std, bandpass1));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.var, bandpass1));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.crestFactor, bandpass2));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.skew, bandpass2));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.kuri, bandpass2));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.std, bandpass2));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.var, bandpass2));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.crestFactor, bandpass3));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.skew, bandpass3));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.kuri, bandpass3));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.std, bandpass3));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.var, bandpass3));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.crestFactor, bandpass4));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.skew, bandpass4));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.kuri, bandpass4));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.std, bandpass4));
selectedFeat.Add(new FeatureFilterSelection(FeaturesSelc.var, bandpass4));
// learn the model
var learner = new ClassificationRandomForestLearner(trees: 50);
var model = learner.Learn(FeatureExtract(data_label_train, selectedFeat), data_label_train.Select(x => x.label).ToArray());
// Test: use the model for predicting new observations
List <double> p = new List <double>();
foreach (LabeledData reading in data_label_test)
{
var prediction = model.Predict(reading.FeatureExtract(selectedFeat));
p.Add(prediction);
}
double accuracy = 0.0;
for (int i = 0; i < data_label_test.Count; i++)
{
if (data_label_test[i].label == p[i])
{
accuracy += 1.0 / data_label_test.Count * 100;
}
Console.WriteLine("Prediction result: {0} Ideal result: {1}", p[i], data_label_test[i].label);
}
Console.WriteLine("Accuracy {0}", accuracy.ToString());
Console.ReadKey();
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
Console.ReadKey();
}
