/* Function: PrintXMGRHistogram()
* Date: SRE, Wed Nov 12 11:02:00 1997 [St. Louis]
*
* Purpose: Print an XMGR data file that contains two data sets:
* - xy data for the observed histogram
* - xy data for the theoretical histogram
*/
void
PrintXMGRHistogram(FILE *fp, struct histogram_s *h)
{
int sc; /* integer score in histogram structure */
double val;
/* First data set is the observed histogram
*/
for (sc = h->lowscore; sc <= h->highscore; sc++)
if (h->histogram[sc - h->min] > 0)
fprintf(fp, "%-6d %f\n", sc,
(float) h->histogram[sc - h->min]/ (float) h->total);
fprintf(fp, "&\n");
/* Second data set is the theoretical histogram
*/
if (h->fit_type != HISTFIT_NONE)
{
for (sc = h->lowscore; sc <= h->highscore; sc++)
{
val =
(1. - ExtremeValueP((float)sc+1, h->param[EVD_MU], h->param[EVD_LAMBDA]))-
(1. - ExtremeValueP((float)sc, h->param[EVD_MU], h->param[EVD_LAMBDA]));
fprintf(fp, "%-6d %f\n", sc, val);
}
fprintf(fp, "&\n");
}
}
/* Function: PrintXMGRRegressionLine()
* Date: SRE, Wed Nov 12 11:02:19 1997 [St. Louis]
*
* Purpose: Print an XMGR data file that contains two data sets:
* - xy data for log log transform of observed distribution P(S<x)
* - xy data for log log transform of theoretical distribution P(S<x)
*/
void
PrintXMGRRegressionLine(FILE *fp, struct histogram_s *h)
{
int sc; /* integer score in histogram structure */
int cum;
double val; /* log log transform */
/* First data set is the observed distribution;
* histogram bin x contains # of scores between x and x+1,
* hence the sc+1 offset.
*/
for (cum = 0, sc = h->lowscore; sc <= h->highscore; sc++)
{
cum += h->histogram[sc - h->min];
val = log (-1. * log((double) cum / (double) h->total));
if (cum < h->total)
fprintf(fp, "%-6d %f\n", sc + 1, val);
}
fprintf(fp, "&\n");
/* Second data set is the theoretical histogram
*/
if (h->fit_type != HISTFIT_NONE)
{
for (sc = h->lowscore; sc <= h->highscore; sc++)
{
val = log(-1. * log(1. - ExtremeValueP((float) sc, h->param[EVD_MU],
h->param[EVD_LAMBDA])));
fprintf(fp, "%-6d %f\n", sc, val);
}
fprintf(fp, "&\n");
}
}
double ExtremeValueP2(float x, float mu, float lambda, int N)
{
double y;
y = N * ExtremeValueP(x,mu,lambda);
if (y < 1e-7) return y;
else return (1.0 - exp(-1. * y));
}
/* Function: ExtremeValueE()
*
* Purpose: Calculate E(S>x) in a database of size N,
* using P(S>x) for a single sequence: simply np.
*
* Args: x = score
* mu = characteristic value of extreme value distribution
* lambda = decay constant of extreme value distribution
* N = number of trials (number of sequences)
*
* Return: E(S>x) for database of size N
*/
double
ExtremeValueE(float x, float mu, float lambda, long N)
{
return (double)N * ExtremeValueP(x,mu,lambda);
}
