Compiling gives different results than windows compiler - c

I tried to compile and run some old C programs that I developed years back on windows with codeblocks.
I get no error from gcc and program executes correctly. Only in some cases (some initial conditions), I do not get the same numerical results as before! I did not change anything in my program (except sed the ^M characters).
I first thought it was because of the two scanf functions. But no. I removed them and get the same erroneous results.
Did anyone encounter that kind of strange behavior before when using a Windows C code to linux?
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include "differentialeqsolve.h"
#define TINY 1.0e-30
/*----------------------------------------------------------------------------------------------*/
/*main*/
/*----------------------------------------------------------------------------------------------*/
int main(void){
/*miscellaneous variables*/
int i,k,dummy;
int N=4; /*nb equa diff +1 (we don't want to use f[0])*/
/*output files*/
char namefile[20]={0}; /*file for couplings*/
FILE * file[N];
/*Runge Kutta variables*/
double accuracy=1e-7; /*minimal precision required for values of f obtained by rk*/
double h=1.0e-7; /*initial step for RG (for the first iteration)*/
double htry,hdid,hnext; /*initial/effective/final step for rk5adapt*/
double t=0.; /*RG time*/
double limite=1.0e12; /*cutoff limit for the strong coupling*/
double U,J; /*lattice parameters for initial conditions*/
int NN; /*number of spin components*/
double thetamin,thetamax,dtheta;
double theta;
double R=0.1;
int n=100; /*number of points in the calculation around the ring*/
double f[N];
double df[N];
double fscal[N];
double g[N]; /*renormalized (by f1) couplings*/
/*opening output files*/
for (k = 1 ; k < N ; k++ )
{sprintf(namefile,"g%d.dat",k);
file[k]=fopen(namefile, "w");}
/*initialization of couplings f[]*/
NN=3;
thetamin = -0.1;
thetamax = 0.1;
dtheta=(thetamax-thetamin)/((float) n);
printf("dtheta=%.10lf\n",dtheta);
for(i=0;i<=n;i++){
theta=thetamin+i*dtheta;
U=R*cos(theta);
J=R*sin(theta);
/*initialization of table df[]*/
for(k=1;k<N;k++) df[k]=0;
f[1]=-2*NN*U-3*NN*J;
f[2]=-2*NN*U+NN*J+2*NN*NN*J;
f[3]=-2*NN*U+NN*J;
t=0.; /*initialization of RG time*/
/*initialization of RG time step for rk5adapt*/
htry=h;
/*calculation of the scaling to calculate precision*/
for(k=1;k<N;k++)
fscal[k]=fabs(f[k])+fabs(df[k]*htry)+TINY;
/* ********************************************************/
/*iteration of RG until at least one of the couplings reaches the cutoff "limite"*/
/* ********************************************************/
for(;;){
/*calculation of f(t+h) with rk5adapt*/
rk5adapt(f,df,N,NN,&t,htry,accuracy,fscal,&hdid,&hnext,dfunctions);
/*new time is readily set by rk5adapt*/
/*new step (set as hnext by rk5adapt)*/
htry=hnext;
/*new scaling fscal*/
for(k=1;k<N;k++)
fscal[k]=fabs(f[k])+fabs(df[k]*htry)+TINY;
/*Stop RG iteration when at least one of the couplings reaches the cutoff limit*/
for(k=1;k<N;k++)
{if(fabs(f[k])>=limite) {goto RGstop;}}
}
RGstop : if(f[1]>0) for(k=1;k<N;k++)
{
g[k]=f[k]/f[1];
fprintf(file[k],"%lf %lf\n",theta,g[k]);
}
else printf("%lf, g[1]<0\n",theta);
/*end theta*/
}
for ( k = 1 ; k < N ; k++ )
{
fclose(file[k]);
}
return 0;
}

Ok, I solved the problem by setting the TINY parameter (see Runge Kutta in Numerical Recipes) to 1e-25. Before it was set at 1e-30.
Apparently, my compiler does not treat float and float operations the same way my old compiler did.
Following chux remarks, I checked the value of FLT_EVAL_METHOD which was 0. Setting it to 2 changed a little bit the results of my differential equations but it's only when I set TINY to a different value that I finally got the expected results.

Related

Parallelization for Monte Carlo pi approximation

I am writing a c script to parallelize pi approximation with OpenMp. I think my code works fine with a convincing output. I am running it with 4 threads now. What I am not sure is that if this code is vulnerable to race condition? and if it is, how do I coordinate the thread action in this code ?
the code looks as follows:
#include <stdlib.h>
#include <stdio.h>
#include <time.h>
#include <math.h>
#include <omp.h>
double sample_interval(double a, double b) {
double x = ((double) rand())/((double) RAND_MAX);
return (b-a)*x + a;
}
int main (int argc, char **argv) {
int N = atoi( argv[1] ); // convert command-line input to N = number of points
int i;
int NumThreads = 4;
const double pi = 3.141592653589793;
double x, y, z;
double counter = 0;
#pragma omp parallel firstprivate(x, y, z, i) reduction(+:counter) num_threads(NumThreads)
{
srand(time(NULL));
for (int i=0; i < N; ++i)
{
x = sample_interval(-1.,1.);
y = sample_interval(-1.,1.);
z = ((x*x)+(y*y));
if (z<= 1)
{
counter++;
}
}
}
double approx_pi = 4.0 * counter/ (double)N;
printf("%i %1.6e %1.6e\n ", N, 4.0 * counter/ (double)N, fabs(4.0 * counter/ (double)N - pi) / pi);
return 0;
}
Also I was wondering if the seed for random number should be declared inside or outside parallelization. my output looks like this:
10 3.600000e+00 1.459156e-01
100 3.160000e+00 5.859240e-03
1000 3.108000e+00 1.069287e-02
10000 3.142400e+00 2.569863e-04
100000 3.144120e+00 8.044793e-04
1000000 3.142628e+00 3.295610e-04
10000000 3.141379e+00 6.794439e-05
100000000 3.141467e+00 3.994585e-05
1000000000 3.141686e+00 2.971945e-05
Which looks OK for now. your suggestion for race condition and seed placement is most welcome.
There are a few problems in your code that I can see. The main one is from my standpoint that it isn't parallelized. Or more precisely, you didn't enable the parallelism you introduced with OpenMP while compiling it. Here is the way one can see that:
The way the code is parallelized, the main for loop should be executed in full by all the threads (there is no worksharing here, no #pragma omp parallel for, only a #pragma omp parallel). Therefore, considering you set the number of threads to be 4, the global number of iterations should be 4*N. Thus, your output should slowly converge towards 4*Pi, not towards Pi.
Indeed, I tried your code on my laptop, compiled it with OpenMP support, and that is pretty-much what I get. However, when I don't enable OpenMP, I get an output similar to yours. So in conclusion, you need to:
Enable OpenMP at compilation time for getting a parallel version of your code.
Divide your result by NumThreads to get a "valid" approximation of Pi (or distribute your loop over N with a #pragma omp for for example)
But that is if / when your code is correct elsewhere, which it isn't yet.
As BitTickler already hinted, rand() isn't thread-safe. So you have to go for another random number generator, which will allow you to privatize it's state. That could be rand_r() for example. That said, this still has quite a few issues:
rand() / rand_r() is a terrible RNG in term of randomness and periodicity. While increasing your number of tries, you'll rapidly go over the period of the RNG and repeat over and over again the same sequence. You need something more robust to do anything remotely serious.
Even with a "good" RNG, the parallelism aspect can be an issue in the sense that you want your sequences in parallel to be uncorrelated between each-other. And just using a different seed value per thread doesn't guaranty that to you (although with a wide-enough RNG, you have a bit of headroom for that)
Anyway, bottom line is:
Use a better thread-safe RNG (I find drand48_r() or random_r() to be OK for toy codes on Linux)
Initialize its state per-thread based on the thread id for example, while keeping in mind that this won't ensure a proper decorrelation of the random series in some circumstances (and the larger the number of times you call the functions, the more likely you are to finally have overlapping series).
This done (along with a few minor fixes), your code becomes for example as follows:
#include <stdlib.h>
#include <stdio.h>
#include <time.h>
#include <math.h>
#include <omp.h>
typedef struct drand48_data RNGstate;
double sample_interval(double a, double b, RNGstate *state) {
double x;
drand48_r(state, &x);
return (b-a)*x + a;
}
int main (int argc, char **argv) {
int N = atoi( argv[1] ); // convert command-line input to N = number of points
int NumThreads = 4;
const double pi = 3.141592653589793;
double x, y, z;
double counter = 0;
time_t ctime = time(NULL);
#pragma omp parallel private(x, y, z) reduction(+:counter) num_threads(NumThreads)
{
RNGstate state;
srand48_r(ctime+omp_get_thread_num(), &state);
for (int i=0; i < N; ++i) {
x = sample_interval(-1, 1, &state);
y = sample_interval(-1, 1, &state);
z = ((x*x)+(y*y));
if (z<= 1) {
counter++;
}
}
}
double approx_pi = 4.0 * counter / (NumThreads * N);
printf("%i %1.6e %1.6e\n ", N, approx_pi, fabs(approx_pi - pi) / pi);
return 0;
}
Which I compile like this:
gcc -std=gnu99 -fopenmp -O3 -Wall pi.c -o pi_omp

Why does the following code run out of memory?

The following code describes 2 harmonic oscillators. They are initially uncoupled and independent and am only looking at one of them, which is a mechanical oscillator. The other oscillator's variables have been declared and forced to 0. I first do 300,000 time iterations and do this for 80 different frequencies of wdm (mechanical drive w).
//find frequency response
int index_A;
double wdm_1;
wdm_1=wm-2*3.142*2e5;
double wdm_2;
wdm_2=wm+2*3.142*2e5;
double wdm_prec=2*3.142*5e3;
index_A=(wdm_2-wdm_1)/wdm_prec;
printf("%d \n", index_A);
However, my code runs till 58 frequencies and gives this error:
Strangely, if I run the code in 2 parts of 40 frequencies each and append the files together, it works fine.
Resonance peak in frequency domain
Also, when I reduce the size of v0 to 3, the code works properly. However, I will need the other variables later.
int j=0;
for (j=0; j<= index_A ; j++){
wdm=wdm_1+j*wdm_prec;
printf("%d \n",j);
v0[0] = 0;
v0[1] = 0;
v0[2] = 0;
v0[3] = 0;
v0[4] = 0;
v0[5] = 0;
v0[6]= wdm;
for (i=0; i< n ; i++){
if (cabs(xa)>=1){
printf("Breaking Loop \n");
break;
}
v1 = rk4vec_ameya_complex_1 ( tau, 7, v0, dtau, rk4vec_f_ameya_complex_1 );
memcpy(v0, v1, 4 * sizeof ( double complex ) );
tau=tau+dtau;
}
fprintf(f1, "%g, %g \n", wdm/(2*3.142), cabs(v1[2]) );
}
printf("Completed");
fclose(f1);
At the end of time iterations, I save the value of the frequency wdm and last value of displacement x=v1[2] in a file and move on to do time iterations with another frequency. Hence, my file contains the frequency response.
fprintf(f1, "%g, %g \n", wdm/(2*3.142), cabs(v1[2]) );
I have used runge kutta (rk4.c) from people.sc.fsu.edu/~jburkardt/c_src/rk4/rk4.html and modified it for complex datatype using
#include <complex.h>
Following is the function runge-kutta-4 has to solve:
/******************************************************************************/
double complex *rk4vec_f_ameya_complex_1 ( double t, int n, double complex u[] )
/******************************************************************************/
{
double complex drive_m;
double complex drive_c;
double x;
double xrf0_1;
double complex *uprime;
uprime = ( double * ) malloc ( 7 * sizeof ( double complex ) );
//Check if memory unavailable
if(uprime==NULL){
printf("No memory available \n");
return 0;
}
///////////////////Second Order////////////////////////
xrf0_1=xrf0*(1-exp(-0.2*t));
drive_m=(xrf0*cexp(I*((u[6]-wm)/gammac)*t)/(2*wm*gammac));
uprime[2]=u[3];
uprime[3]=(wm/gammac)*(drive_m-u[3]*(2*I+2*gammam/wm)-u[2]*(2*I*gammam/gammac));
return uprime;
free(uprime);
}
Kindly suggest any solutions if my usage of malloc() is responsible for running out of memory.
One obvious problem - you free uprime after you have returned from rk4vec_f_ameya_complex_1, I'd expect you to be getting an unreachable code warning. Also as uprime is always the same size why do you use malloc at all? If you just make it an array of size 7 then you won't have issues with malloc and your code will probably run faster.

How to make gaussian package move in numerical simulation of a square barrier in C

I am trying to use Gaussian packages to study the transmission probability via Trotter-Suzuki formula and fast Fourier transform (FFT) when confronted with a square barrier, just as done in this Quantum Python article. But I need to realize it using C. In principle, the wave function will remain its shape before the collision with the square barrier. But I found that the wave function becomes flat dramatically with time before colliding with the square barrier. Anybody finds problems in the following codes?
Here, two files - result and psi.txt - are created to store the initial and evolved wave-function. The first two data for each are x coordinates, the probability of the wave function at that x. The third data for each line in file result is the square barrier distribution. The FFT I use is shown in this C program.
#include <stdio.h>
#include <math.h>
#define h_bar 1.0
#define pi 3.1415926535897932385E0
#define m0 1.0
typedef double real;
typedef struct { real Re; real Im; } complex;
extern void fft(complex x[], int N, int flag);
complex complex_product(complex x, real y_power, real y_scale)
{//x*exp(i*y_power)*y_scale
real Re, Im;
Re = (x.Re*cos(y_power)-x.Im*sin(y_power))*y_scale;
Im = (x.Re*sin(y_power)+x.Im*cos(y_power))*y_scale;
x.Re = Re; x.Im = Im;
return x;
}
real potential(real x, real a)
{
return (x<0 || x>=a) ? 0 : 1;
}
void main()
{
int t_steps=20, i, N=pow(2,10), m, n;
complex psi[N];
real x0=-2, p0=1, k0=p0/h_bar, x[N], k[N], V[N];
real sigma=0.5, a=0.1, x_lower=-5, x_upper=5;
real dt=1, dx=(x_upper-x_lower)/N, dk=2*pi/(dx*N);
FILE *file;
file = fopen("result", "w");
//initialize
for (n=0; n<N; n++)
{
x[n] = x_lower+n*dx;
k[n] = k0+(n-N*0.5)*dk;
V[n] = potential(x[n], a);
psi[n].Re = exp(-pow((x[n]-x0)/sigma, 2)/2)*cos(p0*(x[n]-x0)/h_bar);
psi[n].Im = exp(-pow((x[n]-x0)/sigma, 2)/2)*sin(p0*(x[n]-x0)/h_bar);
}
for (m=0; m<N; m++)
fprintf(file, "%g %g %g\n", x[m], psi[m].Re*psi[m].Re+psi[m].Im*psi[m].Im, V[m]);
fclose(file);
for (i=0; i<t_steps; i++)
{
printf("t_steps=%d\n", i);
for (n=0; n<N; n++)
{
psi[n]=complex_product(psi[n], -V[n]*dt/h_bar, 1);
psi[n]=complex_product(psi[n], -k[0]*x[n], dx/sqrt(2*pi));//x--->x_mod
}
fft(psi, N, 1);//psi: x_mod--->k_mod
for (m=0; m<N; m++)
{
psi[m]=complex_product(psi[m], -m*dk*x[0], 1);//k_mod--->k
psi[m]=complex_product(psi[m], -h_bar*k[m]*k[m]*dt/(2*m0), 1./N);
psi[m]=complex_product(psi[m], m*dk*x[0], 1);//k--->k_mod
}
fft(psi, N, -1);
for (n=0; n<N; n++)
psi[n] = complex_product(psi[n], k[0]*x[n], sqrt(2*pi)/dx);//x_mod--->x
}
file = fopen("psi.txt", "w");
for (m=0; m<N; m++)
fprintf(file, "%g %g 0\n", x[m], pow((psi[m]).Re, 2)+pow((psi[m]).Im, 2));
fclose(file);
}
I use the following Python code to plot the initial and final evolved wave functions:
call: `>>> python plot.py result psi.txt`
import matplotlib.pyplot as plt
from sys import argv
for filename in argv[1:]:
print filename
f = open(filename, 'r')
lines = [line.strip(" \n").split(" ") for line in f]
x = [float(line[0]) for line in lines]
y = [float(line[2]) for line in lines]
psi = [float(line[1]) for line in lines]
print "x=%g, max=%g" % (x[psi.index(max(psi))], max(psi))
plt.plot(x, y, x, psi)
#plt.xlim([-1.0e-10, 1.0e-10])
plt.ylim([0, 3])
plt.show()
Your code is almost correct, sans the fact that you are missing the initial/final half-step in the real domain and some unnecessary operations (kmod -> k and back), but the main problem is that your initial conditions are really chosen badly. The time evolution of a Gaussian wavepacket results in the uncertainty spreading out quadratically in time:
Given your choice of particle mass and initial wavepacket width, the term in the braces equals 1 + 4 t2. After one timestep, the wavepacket is already significantly wider than initially and after another timestep becomes wider than the entire simulation box. The periodicity implied by the use of FFT results in spatial and frequency aliasing, which together with the overly large timestep is why your final wavefunction looks that strange.
I would advise that you try to replicate exactly the conditions of the Python program, including the fact that the entire system is in a deep potential well (Vborder -> +oo).
The variable i is uninitialised here:
k[n] = k0+(i-N*0.5)*dk;

Calculating the Power spectral density

I am trying to get the PSD of a real data set by making use of fftw3 library
To test I wrote a small program as shown below ,that generates the a signal which follows sinusoidal function
#include <stdio.h>
#include <math.h>
#define PI 3.14
int main (){
double value= 0.0;
float frequency = 5;
int i = 0 ;
double time = 0.0;
FILE* outputFile = NULL;
outputFile = fopen("sinvalues","wb+");
if(outputFile==NULL){
printf(" couldn't open the file \n");
return -1;
}
for (i = 0; i<=5000;i++){
value = sin(2*PI*frequency*zeit);
fwrite(&value,sizeof(double),1,outputFile);
zeit += (1.0/frequency);
}
fclose(outputFile);
return 0;
}
Now I'm reading the output file of above program and trying to calculate its PSD like as shown below
#include <stdio.h>
#include <fftw3.h>
#include <complex.h>
#include <stdlib.h>
#include <math.h>
#define PI 3.14
int main (){
FILE* inp = NULL;
FILE* oup = NULL;
double* value;// = 0.0;
double* result;
double spectr = 0.0 ;
int windowsSize =512;
double power_spectrum = 0.0;
fftw_plan plan;
int index=0,i ,k;
double multiplier =0.0;
inp = fopen("1","rb");
oup = fopen("psd","wb+");
value=(double*)malloc(sizeof(double)*windowsSize);
result = (double*)malloc(sizeof(double)*(windowsSize)); // what is the length that I have to choose here ?
plan =fftw_plan_r2r_1d(windowsSize,value,result,FFTW_R2HC,FFTW_ESTIMATE);
while(!feof(inp)){
index =fread(value,sizeof(double),windowsSize,inp);
// zero padding
if( index != windowsSize){
for(i=index;i<windowsSize;i++){
value[i] = 0.0;
}
}
// windowing Hann
for (i=0; i<windowsSize; i++){
multiplier = 0.5*(1-cos(2*PI*i/(windowsSize-1)));
value[i] *= multiplier;
}
fftw_execute(plan);
for(i = 0;i<(windowsSize/2 +1) ;i++){ //why only tell the half size of the window
power_spectrum = result[i]*result[i] +result[windowsSize/2 +1 -i]*result[windowsSize/2 +1 -i];
printf("%lf \t\t\t %d \n",power_spectrum,i);
fprintf(oup," %lf \n ",power_spectrum);
}
}
fclose(oup);
fclose(inp);
return 0;
}
Iam not sure about the correctness of the way I am doing this, but below are the results i have obtained:
Can any one help me in tracing the errors of the above approach
Thanks in advance
*UPDATE
after hartmut answer I'vve edited the code but still got the same result :
and the input data look like :
UPDATE
after increasing the sample frequencyand a windows size of 2048 here is what I've got :
UPDATE
after using the ADD-ON here how the result looks like using the window :
You combine the wrong output values to power spectrum lines. There are windowsSize / 2 + 1 real values at the beginning of result and windowsSize / 2 - 1 imaginary values at the end in reverse order. This is because the imaginary components of the first (0Hz) and last (Nyquist frequency) spectral lines are 0.
int spectrum_lines = windowsSize / 2 + 1;
power_spectrum = (double *)malloc( sizeof(double) * spectrum_lines );
power_spectrum[0] = result[0] * result[0];
for ( i = 1 ; i < windowsSize / 2 ; i++ )
power_spectrum[i] = result[i]*result[i] + result[windowsSize-i]*result[windowsSize-i];
power_spectrum[i] = result[i] * result[i];
And there is a minor mistake: You should apply the window function only to the input signal and not to the zero-padding part.
ADD-ON:
Your test program generates 5001 samples of a sinusoid signal and then you read and analyse the first 512 samples of this signal. The result of this is that you analyse only a fraction of a period. Due to the hard cut-off of the signal it contains a wide spectrum of energy with almost unpredictable energy levels, because you not even use PI but only 3.41 which is not precise enough to do any predictable calculation.
You need to guarantee that an integer number of periods is exactly fitting into your analysis window of 512 samples. Therefore, you should change this in your test signal creation program to have exactly numberOfPeriods periods in your test signal (e.g. numberOfPeriods=1 means that one period of the sinoid has a period of exactly 512 samples, 2 => 256, 3 => 512/3, 4 => 128, ...). This way, you are able to generate energy at a specific spectral line. Keep in mind that windowSize must have the same value in both programs because different sizes make this effort useless.
#define PI 3.141592653589793 // This has to be absolutely exact!
int windowSize = 512; // Total number of created samples in the test signal
int numberOfPeriods = 64; // Total number of sinoid periods in the test signal
for ( n = 0 ; n < windowSize ; ++n ) {
value = sin( (2 * PI * numberOfPeriods * n) / windowSize );
fwrite( &value, sizeof(double), 1, outputFile );
}
Some remarks to your expected output function.
Your input is a function with pure real values.
The result of a DFT has complex values.
So you have to declare the variable out not as double but as fftw_complex *out.
In general the number of dft input values is the same as the number of output values.
However, the output spectrum of a dft contains the complex amplitudes for positive
frequencies as well as for negative frequencies.
In the special case for pure real input, the amplitudes of the positive frequencies are
conjugated complex values of the amplitudes of the negative frequencies.
For that, only the frequencies of the positive spectrum are calculated,
which means that the number of the complex output values is the half of
the number of real input values.
If your input is a simple sinewave, the spectrum contains only a single frequency component.
This is true for 10, 100, 1000 or even more input samples.
All other values are zero. So it doesn't make any sense to work with a huge number of input values.
If the input data set contains a single period, the complex output value is
contained in out[1].
If the If the input data set contains M complete periods, in your case 5,
so the result is stored in out[5]
I did some modifications on your code. To make some facts more clear.
#include <iostream>
#include <stdio.h>
#include <math.h>
#include <complex.h>
#include "fftw3.h"
int performDFT(int nbrOfInputSamples, char *fileName)
{
int nbrOfOutputSamples;
double *in;
fftw_complex *out;
fftw_plan p;
// In the case of pure real input data,
// the output values of the positive frequencies and the negative frequencies
// are conjugated complex values.
// This means, that there no need for calculating both.
// If you have the complex values for the positive frequencies,
// you can calculate the values of the negative frequencies just by
// changing the sign of the value's imaginary part
// So the number of complex output values ( amplitudes of frequency components)
// are the half of the number of the real input values ( amplitutes in time domain):
nbrOfOutputSamples = ceil(nbrOfInputSamples/2.0);
// Create a plan for a 1D DFT with real input and complex output
in = (double*) fftw_malloc(sizeof(double) * nbrOfInputSamples);
out = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * nbrOfOutputSamples);
p = fftw_plan_dft_r2c_1d(nbrOfInputSamples, in, out, FFTW_ESTIMATE);
// Read data from input file to input array
FILE* inputFile = NULL;
inputFile = fopen(fileName,"r");
if(inputFile==NULL){
fprintf(stdout,"couldn't open the file %s\n", fileName);
return -1;
}
double value;
int idx = 0;
while(!feof(inputFile)){
fscanf(inputFile, "%lf", &value);
in[idx++] = value;
}
fclose(inputFile);
// Perform the dft
fftw_execute(p);
// Print output results
char outputFileName[] = "dftvalues.txt";
FILE* outputFile = NULL;
outputFile = fopen(outputFileName,"w+");
if(outputFile==NULL){
fprintf(stdout,"couldn't open the file %s\n", outputFileName);
return -1;
}
double realVal;
double imagVal;
double powVal;
double absVal;
fprintf(stdout, " Frequency Real Imag Abs Power\n");
for (idx=0; idx<nbrOfOutputSamples; idx++) {
realVal = out[idx][0]/nbrOfInputSamples; // Ideed nbrOfInputSamples is correct!
imagVal = out[idx][1]/nbrOfInputSamples; // Ideed nbrOfInputSamples is correct!
powVal = 2*(realVal*realVal + imagVal*imagVal);
absVal = sqrt(powVal/2);
if (idx == 0) {
powVal /=2;
}
fprintf(outputFile, "%10i %10.4lf %10.4lf %10.4lf %10.4lf\n", idx, realVal, imagVal, absVal, powVal);
fprintf(stdout, "%10i %10.4lf %10.4lf %10.4lf %10.4lf\n", idx, realVal, imagVal, absVal, powVal);
// The total signal power of a frequency is the sum of the power of the posive and the negative frequency line.
// Because only the positive spectrum is calculated, the power is multiplied by two.
// However, there is only one single line in the prectrum for DC.
// This means, the DC value must not be doubled.
}
fclose(outputFile);
// Clean up
fftw_destroy_plan(p);
fftw_free(in); fftw_free(out);
return 0;
}
int main(int argc, const char * argv[]) {
// Set basic parameters
float timeIntervall = 1.0; // in seconds
int nbrOfSamples = 50; // number of Samples per time intervall, so the unit is S/s
double timeStep = timeIntervall/nbrOfSamples; // in seconds
float frequency = 5; // frequency in Hz
// The period time of the signal is 1/5Hz = 0.2s
// The number of samples per period is: nbrOfSamples/frequency = (50S/s)/5Hz = 10S
// The number of periods per time intervall is: frequency*timeIntervall = 5Hz*1.0s = (5/s)*1.0s = 5
// Open file for writing signal values
char fileName[] = "sinvalues.txt";
FILE* outputFile = NULL;
outputFile = fopen(fileName,"w+");
if(outputFile==NULL){
fprintf(stdout,"couldn't open the file %s\n", fileName);
return -1;
}
// Calculate signal values and write them to file
double time;
double value;
double dcValue = 0.2;
int idx = 0;
fprintf(stdout, " SampleNbr Signal value\n");
for (time = 0; time<=timeIntervall; time += timeStep){
value = sin(2*M_PI*frequency*time) + dcValue;
fprintf(outputFile, "%lf\n",value);
fprintf(stdout, "%10i %15.5f\n",idx++, value);
}
fclose(outputFile);
performDFT(nbrOfSamples, fileName);
return 0;
}
If the input of a dft is pure real, the output is complex in any case.
So you have to use the plan r2c (RealToComplex).
If the signal is sin(2*pi*f*t), starting at t=0, the spectrum contains a single frequency line
at f, which is pure imaginary.
If the sign has an offset in phase, like sin(2*pi*f*t+phi) the single line's value is complex.
If your sampling frequency is fs, the range of the output spectrum is -fs/2 ... +fs/2.
The real parts of the positive and negative frequencies are the same.
The imaginary parts of the positive and negative frequencies have opposite signs.
This is called conjugated complex.
If you have the complex values of the positive spectrum you can calculate the values of the
negative spectrum by changing the sign of the imaginary parts.
For this reason there is no need to compute both, the positive and the negative sprectrum.
One sideband holds all information.
Therefore the number of output samples in the plan r2c is the half+1 of the number
of input samples.
To get the power of a frequency, you have to consider the positive frequency as well
as the negative frequency. However, the plan r2c delivers only the right positive half
of the spectrum. So you have to double the power of the positive side to get the total power.
By the way, the documentation of the fftw3 package describes the usage of plans quite well.
You should invest the time to go over the manual.
I'm not sure what your question is. Your results seem reasonable, with the information provided.
As you must know, the PSD is the Fourier transform of the autocorrelation function. With sine wave inputs, your AC function will be periodic, therefore the PSD will have tones, like you've plotted.
My 'answer' is really some thought starters on debugging. It would be easier for all involved if we could post equations. You probably know that there's a signal processing section on SE these days.
First, you should give us a plot of your AC function. The inverse FT of the PSD you've shown will be a linear combination of periodic tones.
Second, try removing the window, just make it a box or skip the step if you can.
Third, try replacing the DFT with the FFT (I only skimmed the fftw3 library docs, maybe this is an option).
Lastly, trying inputting white noise. You can use a Bernoulli dist, or just a Gaussian dist. The AC will be a delta function, although the sample AC will not. This should give you a (sample) white PSD distribution.
I hope these suggestions help.

newton raphson in C

I have implemented the newton raphson algorithm for finding roots in C. I want to print out the most accurate approximation of the root as possible without going into nan land. My strategy for this is while (!(isnan(x0)) { dostuff(); } But this continues to print out the result multiple times. Ideally I would like to setup a range so that the difference between each computed x intercept approximation would stop when the previous - current is less than some range .000001 in my case. I have a possible implementation below. When I input 2.999 It takes only one step, but when I input 3.0 it takes 20 steps, this seems incorrect to me.
(When I input 3.0)
λ newton_raphson 3
2.500000
2.250000
2.125000
2.062500
2.031250
2.015625
2.007812
2.003906
2.001953
2.000977
2.000488
2.000244
2.000122
2.000061
2.000031
2.000015
2.000008
2.000004
2.000002
2.000001
Took 20 operation(s) to approximate a proper root of 2.000002
within a range of 0.000001
(When I input 2.999)
λ newton_raphson 2.999
Took 1 operation(s) to approximate a proper root of 2.000000
within a range of 0.000001
My code:
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define RANGE 0.000001
double absolute(double number)
{
if (number < 0) return -number;
else return number;
}
double newton_raphson(double (*func)(double), double (*derivative)(double), double x0){
int count;
double temp;
count = 0;
while (!isnan(x0)) {
temp = x0;
x0 = (x0 - (func(x0)/derivative(x0)));
if (!isnan(x0))
printf("%f\n", x0);
count++;
if (absolute(temp - x0) < RANGE && count > 1)
break;
}
printf("Took %d operation(s) to approximate a proper root of %6f\nwithin a range of 0.000001\n", count, temp);
return x0;
}
/* (x-2)^2 */
double func(double x){ return pow(x-2.0, 2.0); }
/* 2x-4 */
double derivative(double x){ return 2.0*x - 4.0; }
int main(int argc, char ** argv)
{
double x0 = atof(argv[1]);
double (*funcPtr)(double) = &func; /* this is a user defined function */
double (*derivativePtr)(double) = &derivative; /* this is the derivative of that function */
double result = newton_raphson(funcPtr, derivativePtr, x0);
return 0;
}
You call trunc(x0) which turns 2.999 into 2.0. Naturally, when you start at the right answer, no iteration is needed! In other words, although you intended to use 2.999 as your starting value, you actually used 2.0.
Simply remove the call to trunc().
Worth pointing out: taking 20 steps to converge is also anomalous; because you are converging to a multiple root, the convergence is only linear instead of the typical quadratic convergence that Newton-Raphson gives in the general case. You can see this in the fact that your error is halved with each iteration (with the usual quadratic convergence, you would get twice as many correct digits on each iteration, and converge much, much faster).

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