Gaussian hypergeometric function 2F1(a,b;c;z)

Hi,
this is a somewhat specific question
I was very happy when I realized that the Advanced Analysis Library of CVI provides the Gaussian or ordinary hypergeometric function 2F1(a,b;c;z), GaussHG.
Unfortunately I have not been able to get the expected results, so I have to assume that I am doing something wrong and I would be grateful for assistance.
Here is the simple code trying to calculate the function
    for ( index = 0;
          index <= 1000;
          index ++ )
        x [ index ] = -10.0 + index * 0.0108;
        y_1 [ index ] = GaussHG ( x [ index ], 0.5, 2.0, 1.5 );
The first 833 elements of the y_1 array yield NaNs, the following numbers are oscillating in the range from +0.57 ... +3.29
Here is what I would have expected, using the online function plotter from Wolfram Research:
Help is very much appreciated!
Thanks,
Wolfgang
Solved!
Go to Solution.

Hi Wolfgang,
We have reported this issue as a bug. For a quick workaround, I suggest you take advantage of the following transform:
F(a,b,c,x) =  F(a, c - b, c, x/(x-1)) / (1-x)^a, which could map x from (-Inf, 0] to [0,1).
You could revise the original code a bit if performance is not a concern:
for ( index = 0;index <= 1000;index ++ )
   x [ index ] = -10.0 + index * 0.0108;
   if(x[ index ]>=0)
   y_1 [ index ] = GaussHG ( x [ index ], 0.5, 2.0, 1.5 );
   else //use the transform to map negative x value to [0,1)
   y_1[ index ] = GaussHG ( x [ index ] / (x [ index ] - 1.0), 0.5, -0.5, 1.5 ) / pow(1.0-x [ index ], 0.5);
for ( index = 0;index <= 1000;index ++ )
   x [ index ] = -10.0 + index * 0.0108;
   if(x[ index ]>=0)
   y_1 [ index ] = GaussHG ( x [ index ], 0.5, 2.0, 1.5 );
   else //use the transform to map negative x value to [0,1)
   y_1[ index ] = GaussHG ( x [ index ] / (x [ index ] - 1.0), 0.5, -0.5, 1.5 ) / pow(1.0-x [ index ], 0.5);
Hope this workaround could help. Please feel free to let us know if you have any more questions, thanks!
Arthur

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Ok,
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* Spectral Analysis Module
* @author Denis Mishin ([email protected])
package jpitsa.core;
import java.util.LinkedList;
public final class Methods implements Params {
    private Methods() { }
     * Discrete Fourier Transform. <BR>
     * This method can be applied to data with any data points, not only a power of 2,
     * but works significantly slower then DFFT algorithm
     * @param source the data to transform
     * @param direct true if transform is direct, false for inverse
     * @param fullSpectrum as fourier transform is symmetrical, it is possible to use only one part of it.\
     *      then the spectrum is created for 1/2 N points. reverse transform will work pretty well.
     * @see jpitsa.core.ComplexVector
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        ComplexVector result;
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        srcRe = source.getDataRealPart();
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        float mf = (direct == true) ? 1f : 1f/(float)N;
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        resRe = new float[N];
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            resIm[N-1-n] = resIm[n];
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     * This method computes Discrete Fast Fourier Transform from a given
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     * Objectives: only data of 2^n points can be processed. Thus providing number of aliasing effects.
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        ndat = (data.length-1)/2;
        four1 (data, ndat, isign);
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        // too big values .... [mar-99]
        if(!direct) {
            for(int i=0; i < ndat*2-1; i++)
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     * Computes a spectrum of TimeSeries
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        ComplexVector spec = spectrum(source);
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        sPart[0] = sPart[1];
        sPart = smooth(sPart, smooth_win);
        return new FrequencySeries(sPart, source.getSamplingFrequency()/((float)(2*sPart.length)));
public static FrequencySeries power_spectrum(TimeSeries src)
    float tr_wrk[];              /* pntr to work trace                   */
    float tr_tap[];              /* pntr to taper                        */
    float tr_ans[];              /* pntr to answer                       */
//    float *tr_src[];                /* pntr to source trace               */
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    float delta_f;          /* delta f of resulting power spec     */
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    float sumsq;          /* sum of the input trace squared     */
    float avesq;          /* ave of the input trace squared     */
    float pow_sum;          /* PSD Sum                    */
    int i1, i2;                 /* index of the zoom in spr_zoom        */
    int n1, n2, n3, n4;         /* taper window size                    */
    int i,j;                    /* loop vars                            */
    int index;                  /* index into data from cha_list        */
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//     dt_head_access(buffer[2],RCD_NDAT,(void *)&n);
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     //tr_tap = // taper_it
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      calculate the mean squared amplitude
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      get the taper values. Buffer 0 gets tapered even thought there is nothing there
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      calculate fft's until we need to pad with zeros
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          load work array and taper
         for (j=0; j<n_fft; j++) {
          if ((i_st + j) > i2) {
              tr_wrk[j] = 0f;
              zero_pad = true;
          } else {
              tr_wrk[j] = tr_src[i_st+j] * tr_tap[j];
          calculate fft
         n++;
         realft(tr_wrk,n_fft/2,1);
          accum results
         tr_ans[0] += tr_wrk[0]*tr_wrk[0];
         for (j=2; j<n_fft; j+=2) {
          tr_ans[j/2] += 2.0 * (tr_wrk[j]*tr_wrk[j] + tr_wrk[j+1]*tr_wrk[j+1]);
         tr_ans[n_fft/2] += tr_wrk[1]*tr_wrk[1];
         i_st += n_fft/2;
      normalize
     for (j=0; j<=n_fft/2; j++) {
         tr_ans[j] /= ((float)n*wss);
      integrate result
     delta_f = t_samp*n_fft;
     pow_sum = tr_ans[0];
     for (j=1; j<n_fft/2; j++) {
         pow_sum += tr_ans[j];
     pow_sum += tr_ans[n_fft/2];
      convert to units^2/Hz
     for (j=0; j<n_fft/2; j++) {
         tr_ans[j] = tr_ans[j]/delta_f;
     //delta_f = (t_samp*n_fft);
    return new FrequencySeries(tr_ans, src.getSamplingFrequency()/ (float) tr_ans.length);
    //source.getSamplingFrequency()/((float)(2*sPart.length))
    public static FrequencySeries powspec1(TimeSeries src, int m) {
        float[] rawdata = src.getData();
        float[] res = PowSpec.spctrm(rawdata, m, rawdata.length/2, true);
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     * returns coherence spectrum of two traces
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    public static ComplexVector coherenceSpectrum (TimeSeries trace1, TimeSeries trace2) {
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        throw new IllegalArgumentException("Sampling freqs do not match");
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        throw new IllegalArgumentException("vectors length do not match");
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     * returns a given part of coherence spectrum of two traces
    public static FrequencySeries coherenceSpectrum (TimeSeries trace1, TimeSeries trace2, int part) {
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      return new FrequencySeries(res, trace1.getSamplingFrequency()/(float)(2*res.length));
     * XCorellation of two vectors
     * @param v1 first vector
     * @param v2 second vector
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      ComplexVector sp1, sp2, res;
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      float[] data2 = new float[data.length/2];
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     * Computes a sonogram for a trace
     * This is an optimized algorithm,
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      win.startOffset = currentWindow.startOffset;
      win.length      = Math.round(window*sampling);
      boolean computing = true;
      float[] tgt = null;
      int leng = 0;
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        step = 1;
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        tgt = new float[ftlen];
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        tgt = src;
        // --- THIS CODE SHOULD BE REMOVED
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//tgt = smooth(tgt, Math.round(.05f*tgt.length));
// --- THIS CODE SHOULD BE REMOVED
//System.err.println("Step: "+win.startOffset+":"+win.length);
frq.add(tgt);
win.startOffset += step;
if (win.getEnd() > currentWindow.getEnd())
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data.setMessagesAllowed(true);
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* Differentiate a trace
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int i;
float[] tr;
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* Integrate a trace
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* @return integrated trace
public static TimeSeries integrate(TimeSeries source, int method) {
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else if (method == Params.TICK)
return Integrate.tickRule(source);
else
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* Integrates a trace using Tick's rule
* @param source source trace
* @return integrated trace
public static TimeSeries integrate(TimeSeries source) {
return integrate(source, Params.TICK);
* Trace resampling
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* Generic moving average window smoothing. <BR>
* @param src source array
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* Generic moving average window smoothing
* @param src source array
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int ndat = src.length;
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float sum1, sum2;
int i, j, k;
//System.err.println("NX2 = "+nx2);
jpitsa.Out.cerr.println("data = "+src.length+"NX2 = "+nx2);
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sum1=0f; sum2=0f;
for(j=1;j<=nx2;j++) {
sum1 = sum1 + src[i-j];
sum2 = sum2 + src[i+j];
res[i] = (sum1+sum2+src[i])/(float)(2*nx2+1);
for(i=1;i<=nx2-1;i++) {
sum1=0f;
sum2=0f;
k = 0;
for(j=1;j<=i;j++) {
sum1 = sum1+ src[i-j];
sum2 = sum2+ src[i+j];
k = j;
res[i] = (sum1+sum2+src[i])/(float)(2*k+1);
for(i=1;i<=nx2-1;i++) {
sum1 = sum2 = 0f;
k = 0;
for(j=1;j<=i;j++) {
sum1 = sum1+ src[ndat-1-i-j];
sum2 = sum2+ src[ndat-1-i+j];
k = j;
res[ndat-1-i] = (sum1+sum2+src[ndat-1-i])/(float)(2*k+1);
res[ndat-1] = src[ndat-1];
res[0] = src[0];
return res;
* Generic 4-point spline interpolation method.
* @param data source data
* @param oldSampFreq source sampling frequency (1/sampling interval)
* @param newSampFreq target sampling frequency
public static float[] interpolate(float[] data, // source data
float oldSampFreq, // old sampling freq. [Hz]
float newSampFreq) // new samp. freq. [Hz]
int n_int = 4; /* degree of polynomial for interpoltion */
float xa[]= new float[n_int+1]; /* interpolation buffer */
float ya[]= new float[n_int+1]; /* interpolation buffer */
int closest_sample=-1, int_start_sample=-1;
float delta_value = 0f; /* error estimate for interpolated value */
// this is because PITSA and JPITSA use different schemes for sampling freq. storing
float t_samp_old = 1f / oldSampFreq;
float t_samp_new = 1f / newSampFreq;
int ndat = data.length;
float x1=t_samp_old/t_samp_new;
x1 *= (float)ndat;
int ndat2 = Math.round(x1+5e-1f); // Number of data in new arrary
float[] tr1 = data;
float[] b1 = new float[ndat2];
float[] prom_res= new float[2]; // temp. buffer for polint() procedure
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float new_x;
for (int j = 0; j < ndat2;j++){
new_x = (float)j*t_samp_new;
/* determine index of closest sample in trace */
closest_sample = (int)(new_x/t_samp_old);
if ((closest_sample >= (int)((float)n_int/2.)) &&
(closest_sample <= ndat - (int)((float)n_int/2.) - 1))
int_start_sample = closest_sample - (int)((float)n_int/2.) - 1;
}else if (closest_sample < (int)((float)n_int/2.)) {
int_start_sample = -1;
}else if (closest_sample > ndat - (int)((float)n_int/2.) - 1) {
int_start_sample = ndat -1 - n_int;
for (int jj = 1;jj <= n_int; jj++) {
ya[jj] = tr1[int_start_sample + jj];
xa[jj] = (int_start_sample + jj)*t_samp_old;
prom_res[0] = x1; prom_res[1] = delta_value;
polint(xa,ya,n_int,new_x, prom_res);
b1[j] = x1 = prom_res[0]; delta_value = prom_res[1];
return b1;
* Generic 4-point spline interpolation method.
* @param data source data
* @param newLength number of points in a new array
public static float[] interpolate(float[] data, int newLength) {
int n_int = 4; /* degree of polynomial for interpoltion */
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float t_samp_old = 1f;
float t_samp_new = ((float)ndat)/((float)ndat2);
float x1 = (float)ndat2;
float[] tr1 = data;
float[] b1 = new float[ndat2];
float[] prom_res= new float[2]; // temp. buffer for polint() procedure
// 4-point Spline interpolation procedure
float new_x;
for (int j = 0; j < ndat2;j++){
new_x = (float)j*t_samp_new;
/* determine index of closest sample in trace */
closest_sample = (int)(new_x/t_samp_old);
if ((closest_sample >= (int)((float)n_int/2.)) &&
(closest_sample <= ndat - (int)((float)n_int/2.) - 1))
int_start_sample = closest_sample - (int)((float)n_int/2.) - 1;
}else if (closest_sample < (int)((float)n_int/2.)) {
int_start_sample = -1;
}else if (closest_sample > ndat - (int)((float)n_int/2.) - 1) {
int_start_sample = ndat -1 - n_int;
for (int jj = 1;jj <= n_int; jj++) {
ya[jj] = tr1[int_start_sample + jj];
xa[jj] = (int_start_sample + jj)*t_samp_old;
prom_res[0] = x1; prom_res[1] = delta_value;
polint(xa,ya,n_int,new_x, prom_res);
b1[j] = x1 = prom_res[0]; delta_value = prom_res[1];
return b1;
* Butterworth Band Pass Filter
* implements Band, Low and High Pass filters
* All three Butterworth filters are recursive time domain filters
* using the bilinear z-transform design after Stearns (1984)
* They are applied in section of 30 dB/decade or 12 Db/octave for
* the slope of the transition band. They may be given zero phase
* characteristic by filterin the reversed filtered time seris
* again (forwards-backwards filtering)
* Finally, the trace is reversed to its original orientation
* based on spr_bp_bworth (spr/spr_bpbt.c)
* @author Denis Mishin ([email protected]) - Java code
* @author PITSA team - original C code
* @param trace input trace
* @param flo low cut corner frequency [Hz]
* @param fhi high cut corner frequency [Hz]
* @param ns number of filter passes [Hz]
* @param is_zph TRUE -> zero phase filter
* @return new filtered trace
public static TimeSeries bandpassFilter(TimeSeries trace,
float flo,
float fhi,
int ns,
boolean is_zph)
final int MAX_SEC = 10;
int i, k; /* index */
int n,m,mm;
int ndat; /* number of points in trace */
double tsa;
double a[] = new double[MAX_SEC+1];
double b[] = new double[MAX_SEC+1];
double c[] = new double[MAX_SEC+1];
double d[] = new double[MAX_SEC+1];
double e[] = new double[MAX_SEC+1];
double f[][] = new double[MAX_SEC+1][6];
double temp;
double c1,c2,c3;
double w1,w2,wc,q,p,r,s,cs,x;
float tr[];
ndat = trace.getDataLength();
tsa = 1f / trace.getSamplingFrequency();
tr = trace.getData();
// remove mean
float mean = mean(tr);
for (i=0; i < tr.length; i++)
tr[i] -= mean;
/* design filter weights */
/* bandpass */
w1 = java.lang.Math.sin(flo*java.lang.Math.PI*tsa)/java.lang.Math.cos(flo*java.lang.Math.PI*tsa);
w2 = java.lang.Math.sin(fhi*java.lang.Math.PI*tsa)/java.lang.Math.cos(fhi*java.lang.Math.PI*tsa);
wc=w2-w1;
q=wc*wc +2.0*w1*w2;
s=w1*w1*w2*w2;
for (k=1;k<=ns;k++)
c1 = (float)(k+ns);
c2 = (float)(4*ns);
c3 = (2.0*c1-1.0)*java.lang.Math.PI/c2;
cs = java.lang.Math.cos(c3);
p = -2.0*wc*cs;
r = p*w1*w2;
x = 1.0+p+q+r+s;
a[k]= wc*wc/x;
b[k]= (-4.0 -2.0*p+ 2.0*r+4.0*s)/x;
c[k]= (6.0 - 2.0*q +6.0*s)/x;
d[k]= (-4.0 +2.0*p -2.0*r +4.0*s)/x;
e[k]= (1.0 - p q-r s)/x;
/* set initial values to 0 */
for(n=0;n<=MAX_SEC;n++)
for(m=0;m<=5;m++)
f[n][m]=0.0;
/* filtering */
for (m=1;m<=ndat;m++) {
f[1][5]= tr[m-1];
/* go thru ns filter sections */
for(n=1;n<=ns;n++)
temp=a[n]*(f[n][5]-2.0*f[n][3] +f[n][1]);
temp=temp-b[n]*f[n+1][4]-c[n]*f[n+1][3];
f[n+1][5]=temp-d[n]*f[n+1][2]-e[n]*f[n+1][1];
/* update past values */
for(n=1;n<=ns+1;n++)
for(mm=1;mm<=4;mm++)
f[n][mm]=f[n][mm+1];
/* set present data value and continue */
tr[m-1] = (float)f[ns+1][5];
// System.out.println("tr ["+(m-1)+"] = "+tr[m-1]);
if (is_zph == true) {
/* filtering reverse signal*/
for (m=ndat;m>=1;m--) {
f[1][5]= (double)tr[m-1];
/* go thru ns filter sections */
for(n=1;n<=ns;n++)
temp=a[n]*(f[n][5]-2.0*f[n][3] +f[n][1]);
temp=temp-b[n]*f[n+1][4]-c[n]*f[n+1][3];
f[n+1][5]=temp-d[n]*f[n+1][2]-e[n]*f[n+1][1];
/* update past values */
for(n=1;n<=ns+1;n++)
for(mm=1;mm<=4;mm++)
f[n][mm]=f[n][mm+1];
/* set present data value and continue */
tr[m-1] = (float)f[ns+1][5];
// restore mean
for (i=0; i < tr.length; i++)
tr[i] += mean;
return (TimeSeries)trace.newInstance(tr);
* Gaussian filter. <BR>
* Removes mean by default
public static TimeSeries gaussianFilter(TimeSeries src, float fCent, float alpha) {
return gaussianFilter(src, fCent, alpha, true);
* Gaussian bandpass filter
* @param src source trace
* @param fCent central frequency value
* @param alpha bandwidth
* @param rmean remove mean first flag
* @return new filtered vector
public static TimeSeries gaussianFilter(TimeSeries src, float fCent, float alpha, boolean rmean) {
float[] fc = new float[1]; fc[0] = fCent;
float[] al = new float[1]; al[0] = alpha;
return gaussianFilter(src, fc, al, rmean);
* Gaussian bandpass filter
* @param src source trace
* @param fCent central frequency values
* @param alpha bandwidths
public static TimeSeries gaussianFilter(TimeSeries src, float fCent[], float alpha[], boolean rmean) {
float[] sourceData = src.getData();
float mean = 0f;
int i;
if (rmean) {
mean = mean(sourceData);
for (i=0; i < sourceData.length; i++)
sourceData[i] -= mean;
ComplexVector spec = DFFT(new ComplexVector(src.getData(),null), true);
int npoints = spec.getDataPointsNumber();
float nyq = src.getSamplingFrequency()/2f;
// ---- computing the Gaussian distribution function
float[] gaus = new float[npoints];
float freqIncrement = (2f*nyq)/((float)npoints);
int cp = 0; float f = 0f;
while (f <= nyq && cp < (npoints/2) ) {
gaus[cp] = Filter.gaussianWindow(f,fCent,alpha);
cp++; f += freqIncrement;
int last_index = gaus.length-1;
for (cp=0; cp < (npoints/2); cp++)
     gaus[last_index - 1] = gaus[cp];
float[] real = extractPart(spec,Params.REAL);
float[] imag = extractPart(spec,Params.IMAGINARY);
//System.out.println("Gaus: real.length="+real.length+" spec.length="+npoints+
//     " stop at "+f+" nyq="+nyq);
real = multiplicate(real,gaus);
imag = multiplicate(imag,gaus);
//real = glue(real, invert(real));
//imag = glue(imag, invert(imag));
ComplexVector inv = DFFT(new ComplexVector(real,imag), Params.INVERSE);
real = extractPart(inv, Params.REAL);
float[] data = new float[real.length/2];
System.arraycopy(real,0,data,0,data.length);
TimeSeries result = new TimeSeries(data, nyq*2f);
float[] data = sourceData;
System.arraycopy(real,0,data,0,data.length);
if (rmean) {
for (i=0; i < data.length; i++)
data[i] += mean;
TimeSeries result = (TimeSeries)src.newInstance(data);
return result;
* Discretisation of source trace data
* Discretization will treat the input trace as a pseudo-continuous trace from which

How can I extract a gaussian function out of a spectrum which has five gaussain functions....

I have a spectrum which has five gaussian functions. but I have to extract this Gaussian functions from the spectrum. There are diferent gaussian functions. What are the possible methods for extracting the gauss functions using labview. I have attached a picture to give you a picture. The solid line is the one I have and the dotted lines are the individual Gaussian function which will be extracted.
Note: the Gaussian function center number and width are known.
Attachments:
Picture1.png ‏27 KB

Let me get this straight.
You have:
   The experimental data set (spectrum)
   The number of Gaussian curves you want to use for your curve fit
   The center positions and variances for all of your Gaussians
You need:
   The height of the Gaussian curves
Is that it? If so, all you need to do is set up an optimizing routine, using some measure to optimize (i.e., sum of abs(residuals) or residuals squared for each data point) and the acceptable final error for your solution.
A modified Simplex routine should work well, there may be others which would be more efficient, but you're talking about a trivial amount of comkputer time unless you have a gazillion of these spectra to fit.
If that is not your problem, please describe exactly what data you have and what you need to calculate more fully.
LabVIEW can use essentially any method for solving this problem that any other computer language can, but it is not likely to be a simple canned plug-in (at least it won't be one which you get in the vanilla version of LabVIEW), you'll have to put it together yourself.
Cameron
To err is human, but to really foul it up requires a computer.
The optimist believes we are in the best of all possible worlds - the pessimist fears this is true.
Profanity is the one language all programmers know best.
An expert is someone who has made all the possible mistakes.
To learn something about LabVIEW at no extra cost, work the online LabVIEW tutorial(s):
LabVIEW Unit 1 - Getting Started
Learn to Use LabVIEW with MyDAQ

How can I measure the full width half max pulse-width of a gaussian function?

Hi I am measuring a laser pulse using a Tektronix scope and feeding that data into my labview (v 7.1) based data system on a Mac OS X computer. I want to measure the full width pulse at half the maximum peak. The included measurement VIs that come with labview and the tek scope dont seem to do the job (even though the scope itself can do the measurement). I've attached my example VI. If anyone has any suggestions that would be great.
Thanks,
Paul
Attachments:
test scope.vi ‏255 KB

If you can display a trace on the screen, you can get the full width at half maximum. LabVIEW gives you several options:
Use the Pulse Parameters VI to determine the width directly. This will be a width based solely on the data with no assumption as to an underlying model.
Use the Levenberg Marquardt VI to fit a gaussian to your curve, then calculate the full width at half maximum from the coefficients. Levenberg Marquardt is a least-squares/gaussian algorithm, so is somewhat noise sensitive.
Use one of the minimizer functions (e.g. Downhill Simplex) to construct your own fit using a different assumed error distribution. If you are unsure how to do this, check out a copy of Numerical Recipes in C by Press et. al. and read the section on data fitting.
Do your own data based calculation. Find the peak with Peak Detector. This will give you the magnitude, but you may want to verify it with a polynomial fit (parabola is usually good enough) across the top of the peak to avoid noise problems. Then find the positions of the half-maxima on either side of the peak. The Interpolate 1D Array will probably prove useful for this.
Choose your method depending on your pain threshold and how accurate and robust you need the result to be. Good luck!
This account is no longer active. Contact ShadesOfGray for current posts and information.

Firewire does not function after uninstalling macfuse and an out of date version of NTFS32 and then installed latest version of Tuxera NTFS for Mac.

I purchased a 3TB HDD (WD USB 3.0 $100) to replace the 2TB Seagate for a new Time Machine HDD. The 2B was full and backups were deleting older files. The 3TB was formatted NTFS and I want to "recover" the included SW before I reformatted the HDD for the iMac. I discovered that MacFuse was out of date and unsupported and my NTFS-32 was also out of date so I uninstalled both and then installed the most recent version of Tuxera NTFS for Mac. I rebooted and discovered that the FW800 port was unresponsive. Below is the EtreCheck report:
Problem description:
Firewire does not function after uninstalling macfuse and an out of date version of NTFS32 and then installed Tuxera NTFS for Mac.
EtreCheck version: 2.0.8 (95)
Report generated October 30, 2014 7:11:17 PM EDT
Hardware Information: ℹ️
iMac (24-inch Mid 2007) (Verified)
iMac - model: iMac7,1
1 2.8 GHz Intel Core 2 Duo CPU: 2-core
4 GB RAM
BANK 0/DIMM0
2 GB DDR2 SDRAM 667 MHz ok
BANK 1/DIMM1
2 GB DDR2 SDRAM 667 MHz ok
Bluetooth: Old - Handoff/Airdrop2 not supported
Wireless: en1: 802.11 a/b/g/n
Video Information: ℹ️
ATI,RadeonHD2600 - VRAM: 256 MB
iMac 1920 x 1200
System Software: ℹ️
OS X 10.8.5 (12F45) - Uptime: 0:17:34
Disk Information: ℹ️
WDC WD5000AAKS-40TMA0 disk0 : (500.11 GB)
S.M.A.R.T. Status: Verified
disk0s1 (disk0s1) <not mounted> : 210 MB
ML 10.8 (disk0s2) / [Startup]: 499.25 GB (20.92 GB free)
Recovery HD (disk0s3) <not mounted> [Recovery]: 650 MB
USB Information: ℹ️
Apple Inc. Built-in iSight
Apple Computer, Inc. IR Receiver
Logitech USB Receiver
Apple Inc. Bluetooth USB Host Controller
Gatekeeper: ℹ️
Mac App Store and identified developers
Kernel Extensions: ℹ️
/Applications/Hardware Monitor ƒ/HardwareMonitor.app
[loaded] com.bresink.driver.BRESINKx86Monitoring (7.0) Support
/Applications/Parallels Desktop.app
[not loaded] com.parallels.kext.hidhook (8.0 18615.948847) Support
[not loaded] com.parallels.kext.hypervisor (8.0 18615.948847) Support
[not loaded] com.parallels.kext.netbridge (8.0 18615.948847) Support
[not loaded] com.parallels.kext.usbconnect (8.0 18615.948847) Support
[not loaded] com.parallels.kext.vnic (8.0 18615.948847) Support
/Applications/Toast Titanium v11.1/Spin Doctor.app
[not loaded] com.hzsystems.terminus.driver (4) Support
/Applications/Toast Titanium/Toast Titanium v11.1 (1072).app
[not loaded] com.roxio.TDIXController (2.0) Support
/Library/Application Support/Symantec/AntiVirus
[loaded] com.symantec.kext.SymAPComm (12.2f1 - SDK 10.6) Support
/System/Library/Extensions
[loaded] com.Logitech.Control Center.HID Driver (3.9.0 - SDK 10.6) Support
[loaded] com.eltima.ElmediaPlayer.kext (1.58 - SDK 10.4) Support
[not loaded] com.hzsystems.driver.CDSDAudioCaptureSupport (1.5) Support
[not loaded] com.increw.kext.speedit (0.32) Support
[loaded] com.logitech.manager.kernel.driver (4.10.0 - SDK 10.8) Support
[not loaded] com.olympus.CamBlockCommandsDevice (2.0.0) Support
[not loaded] com.olympus.CamBlockCommandsDeviceUP (2.0.0) Support
[not loaded] com.palm.ClassicNotSeizeDriver (3.2.1) Support
[not loaded] com.roxio.BluRaySupport (1.1.6) Support
[not loaded] com.seagate.driver.PowSecDriverCore (5.1.1) Support
[loaded] com.symantec.kext.internetSecurity (5.2f1 - SDK 10.6) Support
[loaded] com.symantec.kext.ips (3.5f1 - SDK 10.6) Support
[loaded] com.symantec.kext.ndcengine (1.0f1 - SDK 10.6) Support
[loaded] com.taoeffect.ispy.kext (2.0.2 - SDK 10.2) Support
/System/Library/Extensions/OlympusDSCDriver.kext/Contents/PlugIns
[not loaded] com.olympus.CamFWSerialBusProtocolTransport (2.0.0) Support
[not loaded] com.olympus.CamUSBMassStorageClass (2.0.0) Support
/System/Library/Extensions/Seagate Storage Driver.kext/Contents/PlugIns
[not loaded] com.seagate.driver.PowSecLeafDriver_10_4 (5.1.1) Support
[not loaded] com.seagate.driver.PowSecLeafDriver_10_5 (5.1.1) Support
[not loaded] com.seagate.driver.SeagateDriveIcons (5.1.1) Support
/Users/[redacted]/Downloads/LCC Installer.app
[not loaded] com.Logitech.Unifying.HID Driver (1.3.1 - SDK 10.8) Support
Startup Items: ℹ️
TuxeraNTFSUnmountHelper: Path: /Library/StartupItems/TuxeraNTFSUnmountHelper
Startup items are obsolete and will not work in future versions of OS X
Launch Agents: ℹ️
[running] com.canon.MFManager.plist Support
[not loaded] com.cooliris.SetLaunchArch.plist Support
[loaded] com.google.keystone.agent.plist Support
[running] com.Logitech.Control Center.Daemon.plist Support
[running] com.logitech.manager.daemon.plist Support
[running] com.micromat.TechToolProAgent.plist Support
[loaded] com.oracle.java.Java-Updater.plist Support
[running] com.seagate.SeagateStorageGauge.plist Support
[running] com.symantec.uiagent.application.plist Support
Launch Daemons: ℹ️
[loaded] com.adobe.fpsaud.plist Support
[running] com.cleverfiles.cfbackd.plist Support
[running] com.eltima.ElmediaPlayer.daemon.plist Support
[loaded] com.embraceware.awaken.plist Support
[loaded] com.google.keystone.daemon.plist Support
[loaded] com.macpaw.CleanMyMac2.Agent.plist Support
[running] com.micromat.TechToolProDaemon.plist Support
[loaded] com.microsoft.office.licensing.helper.plist Support
[loaded] com.oracle.java.Helper-Tool.plist Support
[loaded] com.oracle.java.JavaUpdateHelper.plist Support
[loaded] com.prosofteng.DriveGenius.locum.plist Support
[running] com.smithmicro.schedulerdaemon.plist Support
[loaded] com.symantec.liveupdate.daemon.ondemand.plist Support
[failed] com.symantec.liveupdate.daemon.plist Support
[not loaded] com.symantec.sep.migratesettings.plist Support
[running] com.symantec.sharedsettings.plist Support
[running] com.symantec.symdaemon.plist Support
[running] com.taoeffect.ispyd.plist Support
[running] com.wdc.drivemanagerservice.plist Support
User Launch Agents: ℹ️
[loaded] com.adobe.ARM.[...].plist Support
[loaded] com.macpaw.CleanMyMac2Helper.diskSpaceWatcher.plist Support
[loaded] com.macpaw.CleanMyMac2Helper.scheduledScan.plist Support
[loaded] com.macpaw.CleanMyMac2Helper.trashWatcher.plist Support
[running] com.prosofteng.DGMonitor.plist Support
[running] com.smithmicro.cleaning.schedulermailer.plist Support
[running] com.taoeffect.EspionageHelper.plist Support
User Login Items: ℹ️
Speedy Mac Application (/Applications/Speedy Mac/Speedy Mac.app)
AwakenHelper Application (/Users/[redacted]/Library/Application Support/Awaken/AwakenHelper.app)
GetBackupAgent Application (/Users/[redacted]/Library/Application Support/BeLight Software/Get Backup 2/GetBackupAgent.app)
WDDriveUtilityHelper Application (/Users/[redacted]/Downloads/WD/WD Drive Utilities.app/Contents/Resources/WDDriveUtilityHelper.app)
ClustersHelper Application (/Library/PreferencePanes/Clusters.prefPane/Contents/Resources/ClustersHelper.a pp)
RealPlayer Downloader Agent Application (/Users/[redacted]/Library/Application Support/RealNetworks/RealPlayer Downloader Agent.app)
SAVDiskMountNotify UNKNOWN (missing value)
ScanNotification UNKNOWN (missing value)
Launch Nikon Message Center 2 Application (/Applications/Nikon Software/Nikon Message Center 2/Nikon Message Center 2.app/Contents/SharedSupport/Launch Nikon Message Center 2.app)
Internet Plug-ins: ℹ️
EPPEX Plugin: Version: 10.0 Support
Flash Player: Version: 15.0.0.189 - SDK 10.6 Support
AdobePDFViewer: Version: 9.4.6 Support
Unity Web Player: Version: UnityPlayer version 3.5.3f3 - SDK 10.6 Support
NPVirtools: Version: 4.0 Support
iPhotoPhotocast: Version: 7.0 - SDK 10.8
RealPlayer Plugin: Version: (null) Support
DirectorShockwave: Version: 11.6.8r638 Support
QuickTime Plugin: Version: 7.7.1
FlashPlayer-10.6: Version: 15.0.0.189 - SDK 10.6 Support
GarminGpsControl: Version: 2.9.3.0 Release Support
ToontownBundleManager: Version: (null) Support
CANONiMAGEGATEWAYDL: Version: 3.1.0.2 Support
Silverlight: Version: 5.1.10411.0 - SDK 10.6 Support
OVSHelper: Version: 1.0 Support
CoolirisWebKitPlugin: Version: (null) Support
Google Earth Web Plug-in: Version: 6.1 Support
Flip4Mac WMV Plugin: Version: 3.2.0.16   - SDK 10.8 Support
SharePointBrowserPlugin: Version: 14.4.4 - SDK 10.6 Support
JavaAppletPlugin: Version: Java 7 Update 67 Check version
OfficeLiveBrowserPlugin: Version: 12.3.6 Support
User Internet Plug-ins: ℹ️
BrowserPlus_2.9.2: Version: 2.9.2 Support
Picasa: Version: 1.0 Support
Safari Extensions: ℹ️
DivX Plus Web Player HTML5 <video>
Open in Internet Explorer
DivX HiQ
Aimersoft Video Converter
3rd Party Preference Panes: ℹ️
BrowserPlus Support
Clusters Support
Déjà Vu Support
Flash Player Support
Flip4Mac WMV Support
Java Support
Logitech Control Center Support
Logi Preference Manager Support
MenuMeters Support
Perian Support
Symantec QuickMenu Support
TechTool Protection Support
Tuxera NTFS Support
Time Machine: ℹ️
Skip System Files: NO
Auto backup: YES
Destinations:
iMac TimeMachine [Local]
Total size: 2 TB
Total number of backups: 80
Oldest backup: 2011-09-18 05:21:33 +0000
Last backup: 2014-10-05 20:18:55 +0000
Size of backup disk: Excellent
Backup size 2 TB > (Disk size 0 B X 3)
/sbin excluded from backup!
/usr excluded from backup!
/System excluded from backup!
/bin excluded from backup!
/private excluded from backup!
/Library excluded from backup!
/Applications excluded from backup!
Top Processes by CPU: ℹ️
      2% WindowServer
      0% Speedy
      0% SystemUIServer
      0% RealPlayer Downloader Agent
      0% ps
Top Processes by Memory: ℹ️
288 MB SymDaemon
150 MB Speedy
137 MB Safari
82 MB WebProcess
82 MB coreservicesd
Virtual Memory Information: ℹ️
2.17 GB Free RAM
1.46 GB Active RAM
148 MB Inactive RAM
518 MB Wired RAM
388 MB Page-ins
0 B Page-outs
I hope someone has some ideas! TIA.

Do you have another cable you can test with? Do you have access to another computer with Firewire you can test with?
FireWire Device not Working
FireWire/USB - Quick Assist

Creating a random noise (Gaussian) using Matlab Script

Hello,
Since I only have Labview 6.1 base, I can't use the white-noise generator in Labview. I'm thinking of using the Matlab Script. I have Matlab 2006R installed in the same computer. However, I tried running the program and nothing happened (I used the help example "a=rand(50);surf(a)" in the Matlab Script.
Why it's not calling the Matlab, or is there a better way to create a Gaussian distribution in Labview 6.1 Base?
Thanks a lot!

Hello,
Why are you unable to use the Gaussian White Noise VI in LabVIEW 6.1? It is under the Analyze >> Signal Processing >> Signal Generation palette. I have attached a simple VI that illustrates creating a matrix.
If you wish to use the MATLAB® script node, let's first make sure it is working correctly. Try opening one of the LabVIEW shipping examples. Browse to <LabVIEW>\examples\scriptnode and open "HiQMATLAB_Fractal.llb." Then open "MATLAB Fractal.vi." Run the VI. Do you get any error windows that pop up? If not and you see a picture of a fractal appear, then the connection to the MATLAB software is working correctly. In your VI, try creating an error out indicator from the MATLAB script node and see if any errors appear.
If you do see an error in the shipping example, it means there is a problem with the software connection. Make sure that you have launched the MATLAB software at least once after you installed it. This will set up the necessary connection information. Then launch LabVIEW. If this isn't the issue, we could try manually restoring the server commands. Make sure LabVIEW and the MATLAB software are not running. Go to a command prompt, browse to the MATLAB bin directory (e.g. MATLAB\R2006b\bin), and type
matlab -regserver
Quit the instance of the MATLAB software that appears and launch LabVIEW. If this also fails, please post again with any error information you receive.
Also, the rand function in your script will not generate a Gaussian distribution. You will need to use the randn function instead.
MATLAB® is a registered trademark of The MathWorks, Inc.
Grant M.
Staff Software Engineer | LabVIEW Math & Signal Processing | National Instruments
Edit: For some reason, I am unable to attach the VI. I have included a picture instead.
Message Edited by GrantM on 08-08-2008 02:50 PM
Attachments:
Gaussian Matrix.png ‏35 KB

Bessel function best fit

Hi all,
Quite new to LabVIEW. I am capturing diffraction images that are described by Bessel J functions. The maxima of the diffraction correspond to the maxima of the Bessel J functions ( or zeros of 1st derivative). I wish to fit the diffraction image intensities to a Bessel J function , pixel value -> best fit to x in J(x). Has anyone previously done this before? I know the order n of the Bessel function but not necessarilly the pixel where x=0.
Similarly, I hope to fit the diffraction image of a pinhole which is an Airy Disk to use as calibration ie pixel -> m^-1.
Any help or suggestions would be gratefully accepted.
Regards,
Leeser

Leeser,
Considering you are relatively new to labVIEW, your program was very well structured and seems to approach the problem in the right manner. As you correctly guessed right from the beginning, no-one it seems has attempted fitting a Bessel function to their data. The 'best-fit' VI's found in the 'Fitting Pallette' are more generally concerned with parametric, powers and exponential type functions, rather than more complex differentials such as the Bessel. In cases similar to yours, it seems that people haave tried adopting the Levenberg-Marquardt algorithm which is a Non-Linear Fitting VI found in the Fitting Pallette. This is the Lev-Mar VI which Kristie Elam reccomended you using for the Airy disc. This function allows one to define the equation with which they wish to fit their data, but due to the complexity of the Bessel function, this may be quite tricky.
I have been working on an example VI which provides the basic structure of what you need to implement in to your program. The Bestfit VI converts a picture using the IMAQ functions and then plots the converted data. You can manipulate the row control to choose which row of pixels you wish to analyse. The VI then calls the Lev-Mar Best Fit function which in itself calls the Bessel Formulae VI which is a template for the Bessel Formulae that the Lev-Mar function will use in its best-fit approximation. It is within this VI that you will need to enter the neccessary formulae to produce your theoretical Bessel function. The Lev-Mar Bestfit function can then use this data to plot a Least Squares Best Fit approximation against your spectrum.
Aswell as the Bestfit and Bessel Formulae VI's, I have attached a Gaussian Example VI which gives a complete example of a Bestfit approximation using this method. Furthermore, I have attached a couple of VI's that I found which I hope may help you to develop the Bessel formulae. These include a table of Bessel roots, which you may be able to use to produce a table of the derivative roots, those found on your schematic.
I really hope this information and the files I have attached will be of use to you, I'm just sorry there isn't a direct tool with which you can produce the bestfit approximation. Keep me posted on your progress as I've been working on this for a few days now and would like to see how it goes!
With regards to the Airy disc, I would keep on with the files that you received from Kristie Elam, hopefully using the same method with both the Bessel functions and Airy disc might make things easier for you.
I hope this informtaion is of use to you, best wishes and good luck!
Rob
Rob L
NI Applications Engineer
UK & Ireland
It only takes a click to rate this message ;-)
Attachments:
Bessel Bestfit.zip ‏98 KB

Wire: Function Conflict, Static VI Reference

Hello Community,
Sorry if this has already been answered before, but I have spent an hour searching. I am trying to learn a little about using subVIs to clean up my code.
I'm trying to connect a static VI reference of a Gaussian function that I made to the Nonlinear Curve Fit.vi, but I get a broken wire which says "function conflict".
When I create constants for the output of my Gaussian VI and the input of the Nonlinear Curve Fit.vi, they are clearly different classes.
Could someone show me how to edit my Gaussian VI to match the class properly? I'd greatly appreciate it.
Thanks,
-Patrick
Solved!
Go to Solution.
Attachments:
pat_gaussian_fit.vi ‏24 KB
pat_gaussian_fit.vi ‏24 KB

Your model subvi needs a connector pattern that is identical to the template. Start with the template mentioned in the help and only modify the code, not the existing controls and indicators. Simply leave unused terminals disconnect, don't delete them.
LabVIEW Champion . Do more with less code and in less time .

Gaussian blur results in banding

Here's a piece of wall I try to gaussian blur (> 60 radius) and always results in bandings. I tried adding noise but it doesn't help, unless I add so much noise the image becomes unrecognizable.
Here's the same image in case the one above is re-compressed
blur
In case you wonder why blur this image, there is an electrical outlet near this wall (not shown) that I edited out and need to blur the edited area, and realize the resulting banding is caused by the wall and photoshop.
Are there other solutions?
I'm using photoshop CS4 in 16 bit mode.

Yes, I know what you're talking about. It's as though you don't want to accept the explanation.
With 24 bit color you will see the divisions between regions of identical RGB values in the 24 bit color space where they change by one level. It's most easily seen in gray or near gray gradients of dim luminosity (which is what you have here).
There is no uneven banding in the data in the image you posted. It's a 16 bit PNG file and the data is perfectly clean and smooth, which is what you'd expect from a large radius Gaussian Blur.
If you're seeing uneven or more severe banding on your display only, it's possible something in your system setup is amiss (e.g. a poorly constructed monitor profile, or a malfunctioning display driver), causing you to see multi-level jumps between adjacent regions of color.
On my very well tuned 24 bit color setup I can see the even (but very subtle) banding of your 16 bit image. Doing the following hides it completely:
If your display setup is functioning optimally you won't sense any banding in the above image (after having clicked it to make it show at full size of course).
-Noel

Urxvt's Gaussian Blur

I cannot seem to get urxvt's Gaussian Blur working. It should be enabled with the switch -blr HxV, where H and V are numbers, but this errors with:
"unknown or malformed option"
Also, the resource blurRadius, doesn't seem to work either. These functions are documented in the manpages, but is does not seem to work. Does anyone have the same problem? I'm using version v9.06 - released: 2008-06-15.
Last edited by OutOfPhase (2009-01-08 23:19:30)

Are you sure it's compiled with afterimage support?

Are there functions to call Adobe's built-in filters?

Like say I wanted to call Gaussian Blur that photoshop already has, are there functions to do this? Or would I have to create my own Gaussian Blur?
Thanks

Yes, from automation plugins or scripts.
From other plugins: no, you'd have to create your own.

Can someone verify my code for calculating a Gaussian kernel?

I've attached my VI for calculating a Gausssian kernel function of selectable width and resolution. I am not an expert in mathmatics and I was hoping someone on here could confirm the validity of my code. If anyone has any suggestions please let me know.
Eric
Attachments:
Old Gaussian Kernel.vi ‏24 KB

To better show what it is I'm trying to accomplish, I've attached
the top level VI that's using the kernel function, entitled "Pattern
Analysis of IPIs 2," . The zip folder contains the top level VI, a
folder with all the subVI's and two data files. Open the "Pattern
Analysis of IPIs 2" VI and go to the "Files" selector tab toward the
bottom right of the front panel. Here you'll be able to select the
folder containing the data files. You'll need to unzip the attached
folder and save it somewhere, then select that folder as the "current
folder" to be opened (you don't need to specify which data file to
open, it will open both). Once the folder is selected you can run the
VI and view its results.
When the program begins
running a boolean indicator located in the bottom left corner will
appear red and the slider bars along the bottom will be disabled. When
everything has been calculated the boolean will turn green and the
slider bars will become enabled. Choose the selector tab entitled "SPI,
SDF & SDD" to view the spike density function. The spike density
function (SDF) is what's being created with the Gaussian kernel. The
enabled slider bars allow you to adjust the x-axis values of the plot.
The cursors on the plot indicate indicate the start, peak and end of
high rate bursts. It may be easier to visualize this by viewing the
raw data located at the "Wfm Absolute and Relative Time" selector tab.
To reduce file sizes the raw data has areas of "quite" removed, so you
will only see the voltage change events, represented by what appear to
be vertical lines.
To move to the next data file, click
the button entitled "Save and Go to Next" located in the bottom right
hand corner. A prompt will appear asking if you want to save the data,
click "No" and the next file will automatically be loaded. To stop the
VI, wait until the boolean is green and the slider bars are enabled,
then click the "Stop" bottom located at the bottom right. It may take
a few seconds for the VI to stop.
Moving to the
block diagram, you'll see a yellow subVI with black writing that says
"SDF," this is where the SDF is calculated. Open the front panel and
block diagram of this subVI. On the block diagram you'll see the subVI
entitled "Gaussian Kernel," this is the subVI you saw yesterday. The
generated Gaussian kernel is alligned at peak pulse times (can be seen
in the graph entitled "Gaussian Kernel Alligned at Peak Times") and
summed to create the SDF. I only choose this method of generating the
SDF because it seemed intuitive to me, it may be wrong. If it is,
could you please demonstrate how to program the convolution.
Ultimately, I'm trying to achieve the plot described on the third page
of the attached paper.
Attachments:
Pattern Analysis.zip ‏2916 KB
Stereotyped temporal patterns in electrical communication.pdf ‏380 KB

Gaussian hypergeometric function 2F1(a,b;c;z)

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