static void horizontal()
{
gmx_rng_t rng;
gmx_stats_t straight;
int i, ok, n = 1000;
real y, a, b, da, db, aver, sigma, error, chi2, R, *xh, *yh;
FILE *fp;
rng = gmx_rng_init(13);
straight = gmx_stats_init();
for (i = 0; (i < n); i++)
{
y = gmx_rng_uniform_real(rng);
if ((ok = gmx_stats_add_point(straight, i, y, 0, 0)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
}
/* Horizontal test */
if ((ok = gmx_stats_get_ase(straight, &aver, &sigma, &error)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
fp = fopen("straight.xvg", "w");
if ((ok = gmx_stats_dump_xy(straight, fp)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
fclose(fp);
printf("Horizontal line: average %g, sigma %g, error %g\n", aver, sigma, error);
if ((ok = gmx_stats_done(straight)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
}
static void line()
{
gmx_rng_t rng;
gmx_stats_t line;
int i, dy, ok, n = 1000;
real y, a, b, da, db, aver, sigma, error, chi2, R, rfit;
const real a0 = 0.23, b0 = 2.7;
FILE *fp;
for (dy = 0; (dy < 2); dy++)
{
rng = gmx_rng_init(13);
line = gmx_stats_init();
for (i = 0; (i < n); i++)
{
y = a0*i+b0+50*(gmx_rng_uniform_real(rng)-0.5);
if ((ok = gmx_stats_add_point(line, i, y, 0, dy*0.1)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
}
/* Line with slope test */
if ((ok = gmx_stats_get_ab(line, elsqWEIGHT_NONE, &a, &b, &da, &db, &chi2, &rfit)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
if ((ok = gmx_stats_get_corr_coeff(line, &R)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
if (dy == 0)
{
fp = fopen("line0.xvg", "w");
}
else
{
fp = fopen("line1.xvg", "w");
}
if ((ok = gmx_stats_dump_xy(line, fp)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
fclose(fp);
printf("Line with eqn. y = %gx + %g with noise%s\n", a0, b0,
(dy == 0) ? "" : " and uncertainties");
printf("Found: a = %g +/- %g, b = %g +/- %g\n", a, da, b, db);
if ((ok = gmx_stats_done(line)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
gmx_rng_destroy(rng);
}
}
static void histogram()
{
gmx_rng_t rng;
gmx_stats_t camel;
int i, ok, n = 1000, norm;
real y, a, b, da, db, aver, sigma, error, chi2, R, *xh, *yh;
const real a0 = 0.23, b0 = 2.7;
FILE *fp;
char fn[256];
for (norm = 0; (norm < 2); norm++)
{
rng = gmx_rng_init(13);
camel = gmx_stats_init();
for (i = 0; (i < n); i++)
{
y = sqr(gmx_rng_uniform_real(rng));
if ((ok = gmx_stats_add_point(camel, i, y+1, 0, 0)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
y = sqr(gmx_rng_uniform_real(rng));
if ((ok = gmx_stats_add_point(camel, i+0.5, y+2, 0, 0)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
}
/* Histogram test */
if ((ok = gmx_stats_make_histogram(camel, 0, 101, norm, &xh, &yh)) != estatsOK)
{
fprintf(stderr, "%s\n", gmx_stats_message(ok));
}
sprintf(fn, "histo%d-data.xvg", norm);
fp = fopen(fn, "w");
gmx_stats_dump_xy(camel, fp);
fclose(fp);
sprintf(fn, "histo%d.xvg", norm);
fp = fopen(fn, "w");
for (i = 0; (i < 101); i++)
{
fprintf(fp, "%12g %12g\n", xh[i], yh[i]);
}
fclose(fp);
sfree(xh);
sfree(yh);
}
}
static void regression_analysis(int n, gmx_bool bXYdy,
real *x, int nset, real **val)
{
real S, chi2, a, b, da, db, r = 0;
int ok;
if (bXYdy || (nset == 1))
{
printf("Fitting data to a function f(x) = ax + b\n");
printf("Minimizing residual chi2 = Sum_i w_i [f(x_i) - y_i]2\n");
printf("Error estimates will be given if w_i (sigma) values are given\n");
printf("(use option -xydy).\n\n");
if (bXYdy)
{
if ((ok = lsq_y_ax_b_error(n, x, val[0], val[1], &a, &b, &da, &db, &r, &S)) != estatsOK)
{
gmx_fatal(FARGS, "Error fitting the data: %s",
gmx_stats_message(ok));
}
}
else
{
if ((ok = lsq_y_ax_b(n, x, val[0], &a, &b, &r, &S)) != estatsOK)
{
gmx_fatal(FARGS, "Error fitting the data: %s",
gmx_stats_message(ok));
}
}
chi2 = sqr((n-2)*S);
printf("Chi2 = %g\n", chi2);
printf("S (Sqrt(Chi2/(n-2)) = %g\n", S);
printf("Correlation coefficient = %.1f%%\n", 100*r);
printf("\n");
if (bXYdy)
{
printf("a = %g +/- %g\n", a, da);
printf("b = %g +/- %g\n", b, db);
}
else
{
printf("a = %g\n", a);
printf("b = %g\n", b);
}
}
else
{
double chi2, *a, **xx, *y;
int i, j;
snew(y, n);
snew(xx, nset-1);
for (j = 0; (j < nset-1); j++)
{
snew(xx[j], n);
}
for (i = 0; (i < n); i++)
{
y[i] = val[0][i];
for (j = 1; (j < nset); j++)
{
xx[j-1][i] = val[j][i];
}
}
snew(a, nset-1);
chi2 = multi_regression(NULL, n, y, nset-1, xx, a);
printf("Fitting %d data points in %d sets\n", n, nset-1);
printf("chi2 = %g\n", chi2);
printf("A =");
for (i = 0; (i < nset-1); i++)
{
printf(" %g", a[i]);
sfree(xx[i]);
}
printf("\n");
sfree(xx);
sfree(y);
sfree(a);
}
}
