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/* Copyright: Holger Vogt, 2008
Generates 1/f noise values according to:
"Discrete simulation of colored noise and stochastic
processes and 1/fa power law noise generation"
Kasdin, N.J.;
Proceedings of the IEEE
Volume 83, Issue 5, May 1995 Page(s):802 - 827
*/
#include <math.h>
#include <stdio.h>
#include <stddef.h>
#include <stdlib.h>
#include <stdarg.h> // var. argumente
#include "ngspice.h"
#include "cpextern.h"
#include "cktdefs.h"
#include "1-f-code.h"
#include "fftext.h"
#include "wallace.h"
void f_alpha(int n_pts, int n_exp, float X[], float Q_d,
float alpha)
{
int i;
float *hfa, *wfa;
float ha;
ha = alpha/2.0f ;
// Q_d = sqrt(Q_d); /* find the deviation of the noise */
hfa = TMALLOC(float,n_pts);
wfa = TMALLOC(float,n_pts);
hfa[0] = 1.0f;
wfa[0] = Q_d * (float)GaussWa;
/* generate the coefficients hk */
for (i=1 ; i < n_pts; i++) {
/* generate the coefficients hk */
hfa[i] = hfa[i-1] * (ha + (float)(i-1)) / ( (float)(i) );
/* fill the sequence wk with white noise */
wfa[i] = Q_d * (float)GaussWa;
}
// for (i=0 ; i < n_pts; i++)
// printf("rnd %e, hk %e\n", wfa[i], hfa[i]);
/* perform the discrete Fourier transform */
fftInit(n_exp);
rffts(hfa, n_exp, 1);
rffts(wfa, n_exp, 1) ;
/* multiply the two complex vectors */
rspectprod(hfa, wfa, X, n_pts);
/* inverse transform */
riffts(X, n_exp, 1);
free(hfa) ;
free(wfa);
/* fft tables will be freed in vsrcaccept.c and isrcaccept.c
fftFree(); */
fprintf(stdout,"%d (2e%d) one over f values created\n", n_pts, n_exp);
}
/*-----------------------------------------------------------------------------*/
void
trnoise_state_gen(struct trnoise_state *this, CKTcircuit *ckt)
{
if(this->top == 0) {
if(cp_getvar("notrnoise", CP_BOOL, NULL))
this -> NA = this -> TS = this -> NALPHA = this -> NAMP = 0.0;
if((this->NALPHA > 0.0) && (this->NAMP > 0.0)) {
// add 10 steps for start up sequence
size_t nosteps = (size_t) (ckt->CKTfinalTime / this->TS) + 10;
size_t newsteps = 1;
long int newexp = 0;
// generate number of steps as power of 2
while(newsteps < nosteps) {
newsteps <<= 1;
newexp++;
}
this->oneof = TMALLOC(float, newsteps);
this->oneof_length = newsteps;
f_alpha((int) newsteps, newexp,
this -> oneof,
(float) this -> NAMP,
(float) this -> NALPHA);
}
trnoise_state_push(this, 0.0); /* first is deterministic */
return;
}
// make use of two random variables per call to rgauss()
{
double ra1, ra2;
double NA = this -> NA;
if(NA != 0.0) {
#ifdef FastRand
// use FastNorm3
ra1 = NA * FastNorm;
ra2 = NA * FastNorm;
#elif defined (WaGauss)
// use WallaceHV
ra1 = NA * GaussWa;
ra2 = NA * GaussWa;
#else
rgauss(&ra1, &ra2);
ra1 *= NA;
ra2 *= NA;
#endif
} else {
ra1 = 0.0;
ra2 = 0.0;
}
if(this -> oneof) {
if(this->top + 1 >= this->oneof_length) {
fprintf(stderr,"ouch, noise data exhausted\n");
exit(1);
}
ra1 += this->oneof[this->top] - this->oneof[0];
ra2 += this->oneof[this->top + 1] - this->oneof[0];
}
trnoise_state_push(this, ra1);
trnoise_state_push(this, ra2);
}
}
struct trnoise_state *
trnoise_state_init(double NA, double TS, double NALPHA, double NAMP)
{
struct trnoise_state *this = TMALLOC(struct trnoise_state, 1);
this->NA = NA;
this->TS = TS;
this->NALPHA = NALPHA;
this->NAMP = NAMP;
this -> top = 0;
this -> oneof = NULL;
return this;
}