/* output_filter_power()
*
* Used for testing whether the filters (MSV scores, Viterbi scores)
* have the power they're supposed to have: for example, if MSV filter
* is set at a P-value threshold of 0.02, ~2% of sequences should get
* through, regardless of things like model and target sequence
* length.
*
* Output a file suitable for constructing histograms over many HMMs,
* for a particular choice of hmmsim'ed L and N targets:
* <hmm name> <# of seqs passing threshold> <fraction of seqs passing threshold>
*
* SRE, Thu Apr 9 08:57:32 2009 [Janelia] xref J4/133
*/
static int
output_filter_power(ESL_GETOPTS *go, struct cfg_s *cfg, char *errbuf, P7_HMM *hmm, double *scores)
{
double pthresh = esl_opt_GetReal(go, "--pthresh"); /* P-value threshold set for the filter score */
double P; /* calculated P-value (using HMM's own calibration) */
int npass = 0; /* number of scores that pass the P threshold */
double fpass; /* fraction of scores that pass the P threshold */
int i; /* counter over scores */
int do_gumbel; /* flag for how to determine P values */
double pmu, plambda;
if (esl_opt_GetBoolean(go, "--vit")) { pmu = hmm->evparam[p7_VMU]; plambda = hmm->evparam[p7_VLAMBDA]; do_gumbel = TRUE; }
else if (esl_opt_GetBoolean(go, "--msv")) { pmu = hmm->evparam[p7_MMU]; plambda = hmm->evparam[p7_MLAMBDA]; do_gumbel = TRUE; }
else if (esl_opt_GetBoolean(go, "--fwd")) { pmu = hmm->evparam[p7_FTAU]; plambda = hmm->evparam[p7_FLAMBDA]; do_gumbel = FALSE; }
else ESL_FAIL(eslEINVAL, errbuf, "can only use --ffile with viterbi, msv, or fwd scores");
for (i = 0; i < cfg->N; i++)
{
P = (do_gumbel ? esl_gumbel_surv(scores[i], pmu, plambda) :
esl_exp_surv (scores[i], pmu, plambda));
if (P <= pthresh) npass++;
}
fpass = (double) npass / (double) cfg->N;
fprintf(cfg->ffp, "%s\t%d\t%.4f\n", hmm->name, npass, fpass);
return eslOK;
}
/* tevd_grad()
*
* Called by the optimizer: evaluate the gradient of the objective
* function (the negative posterior log probability of the parameters
* mu and w, where w = log(lambda), at a particular choice of mu and
* lambda.
*/
static void
tevd_grad(double *p, int nparam, void *dptr, double *dp)
{
double mu, lambda, w;
struct tevd_data *data;
double *x;
int n;
double phi;
double dmu, dw;
double coeff;
int i;
/* unpack what the optimizer gave us; nparam==2 always
*/
mu = p[0];
w = p[1];
lambda = exp(w);
data = (struct tevd_data *) dptr;
x = data->x;
n = data->n;
phi = data->phi;
/* Both partials include a coefficient that
* basically looks like P(S=phi) / P(S>=phi); pre-calculate it.
* Watch out when phi >> mu, which'll give us 0/0; instead,
* recognize that for phi >> mu, coeff converges to \lambda.
*/
if (lambda*(phi-mu) > 50.) /* arbitrary crossover. */
coeff = lambda;
else
coeff = esl_gumbel_pdf(phi, mu, lambda) / esl_gumbel_surv(phi, mu, lambda);
/* Partial derivative w.r.t. mu.
*/
dmu = n * lambda;
for (i = 0; i < n; i++)
dmu -= lambda * exp(-1. * lambda * (x[i] - mu));
dmu -= n * coeff;
/* Partial derivative w.r.t. w=log(lambda).
*/
dw = n;
for (i = 0; i < n; i++) dw -= (x[i] - mu) * lambda;
for (i = 0; i < n; i++) dw += (x[i] - mu) * lambda * exp(-1. * lambda * (x[i] - mu));
dw += n * (phi - mu) * coeff;
/* Return the negative, because we're minimizing NLP, not maximizing.
*/
dp[0] = -1. * dmu; /* negative because we're minimizing NLP, not maximizing */
dp[1] = -1. * dw;
return;
}
int
main(int argc, char **argv)
{
ESL_GETOPTS *go = esl_getopts_CreateDefaultApp(options, 2, argc, argv, banner, usage);
char *hmmfile = esl_opt_GetArg(go, 1);
char *seqfile = esl_opt_GetArg(go, 2);
ESL_ALPHABET *abc = NULL;
P7_HMMFILE *hfp = NULL;
P7_HMM *hmm = NULL;
P7_BG *bg = NULL;
P7_PROFILE *gm = NULL;
P7_OPROFILE *om = NULL;
P7_OMX *ox = NULL;
P7_GMX *gx = NULL;
ESL_SQ *sq = NULL;
ESL_SQFILE *sqfp = NULL;
int format = eslSQFILE_UNKNOWN;
float vfraw, nullsc, vfscore;
float graw, gscore;
double P, gP;
int status;
/* Read in one HMM */
if (p7_hmmfile_Open(hmmfile, NULL, &hfp) != eslOK) p7_Fail("Failed to open HMM file %s", hmmfile);
if (p7_hmmfile_Read(hfp, &abc, &hmm) != eslOK) p7_Fail("Failed to read HMM");
/* Read in one sequence */
sq = esl_sq_CreateDigital(abc);
status = esl_sqfile_Open(seqfile, format, NULL, &sqfp);
if (status == eslENOTFOUND) p7_Fail("No such file.");
else if (status == eslEFORMAT) p7_Fail("Format unrecognized.");
else if (status == eslEINVAL) p7_Fail("Can't autodetect stdin or .gz.");
else if (status != eslOK) p7_Fail("Open failed, code %d.", status);
/* create default null model, then create and optimize profile */
bg = p7_bg_Create(abc);
p7_bg_SetLength(bg, sq->n);
gm = p7_profile_Create(hmm->M, abc);
p7_ProfileConfig(hmm, bg, gm, sq->n, p7_LOCAL);
om = p7_oprofile_Create(gm->M, abc);
p7_oprofile_Convert(gm, om);
/* allocate DP matrices, both a generic and an optimized one */
ox = p7_omx_Create(gm->M, 0, sq->n);
gx = p7_gmx_Create(gm->M, sq->n);
/* Useful to place and compile in for debugging:
p7_oprofile_Dump(stdout, om); dumps the optimized profile
p7_omx_SetDumpMode(ox, TRUE); makes the fast DP algorithms dump their matrices
p7_gmx_Dump(stdout, gx); dumps a generic DP matrix
*/
while ((status = esl_sqio_Read(sqfp, sq)) == eslOK)
{
p7_oprofile_ReconfigLength(om, sq->n);
p7_ReconfigLength(gm, sq->n);
p7_bg_SetLength(bg, sq->n);
p7_omx_GrowTo(ox, om->M, 0, sq->n);
p7_gmx_GrowTo(gx, gm->M, sq->n);
p7_ViterbiFilter (sq->dsq, sq->n, om, ox, &vfraw);
p7_bg_NullOne (bg, sq->dsq, sq->n, &nullsc);
vfscore = (vfraw - nullsc) / eslCONST_LOG2;
P = esl_gumbel_surv(vfscore, om->evparam[p7_VMU], om->evparam[p7_VLAMBDA]);
p7_GViterbi (sq->dsq, sq->n, gm, gx, &graw);
gscore = (graw - nullsc) / eslCONST_LOG2;
gP = esl_gumbel_surv(gscore, gm->evparam[p7_VMU], gm->evparam[p7_VLAMBDA]);
if (esl_opt_GetBoolean(go, "-1"))
{
printf("%-30s\t%-20s\t%9.2g\t%7.2f\t%9.2g\t%7.2f\n", sq->name, hmm->name, P, vfscore, gP, gscore);
}
else if (esl_opt_GetBoolean(go, "-P"))
{ /* output suitable for direct use in profmark benchmark postprocessors: */
printf("%g\t%.2f\t%s\t%s\n", P, vfscore, sq->name, hmm->name);
}
else
{
printf("target sequence: %s\n", sq->name);
printf("vit filter raw score: %.2f nats\n", vfraw);
printf("null score: %.2f nats\n", nullsc);
printf("per-seq score: %.2f bits\n", vfscore);
printf("P-value: %g\n", P);
printf("GViterbi raw score: %.2f nats\n", graw);
printf("GViterbi seq score: %.2f bits\n", gscore);
printf("GViterbi P-value: %g\n", gP);
}
esl_sq_Reuse(sq);
}
/* cleanup */
esl_sq_Destroy(sq);
esl_sqfile_Close(sqfp);
p7_omx_Destroy(ox);
p7_gmx_Destroy(gx);
p7_oprofile_Destroy(om);
p7_profile_Destroy(gm);
p7_bg_Destroy(bg);
p7_hmm_Destroy(hmm);
p7_hmmfile_Close(hfp);
esl_alphabet_Destroy(abc);
esl_getopts_Destroy(go);
return 0;
}
static int
output_result(ESL_GETOPTS *go, struct cfg_s *cfg, char *errbuf, P7_HMM *hmm, double *scores, int *alilens)
{
ESL_HISTOGRAM *h = NULL;
int i;
double tailp;
double x10;
double mu, lambda, E10;
double mufix, E10fix;
double mufix2, E10fix2;
double E10p;
double almean, alvar; /* alignment length mean and variance (optional output) */
double pmu, plambda;
int status;
/* fetch statistical params from HMM for expected distribution */
if (esl_opt_GetBoolean(go, "--vit")) { pmu = hmm->evparam[p7_VMU]; plambda = hmm->evparam[p7_VLAMBDA]; }
else if (esl_opt_GetBoolean(go, "--msv")) { pmu = hmm->evparam[p7_MMU]; plambda = hmm->evparam[p7_MLAMBDA]; }
else if (esl_opt_GetBoolean(go, "--fwd")) { pmu = hmm->evparam[p7_FTAU]; plambda = hmm->evparam[p7_FLAMBDA]; }
/* Optional output of scores/alignment lengths: */
if (cfg->xfp) fwrite(scores, sizeof(double), cfg->N, cfg->xfp);
if (cfg->alfp) for (i = 0; i < cfg->N; i++) fprintf(cfg->alfp, "%d %.3f\n", alilens[i], scores[i]);
if (esl_opt_GetBoolean(go, "-v")) for (i = 0; i < cfg->N; i++) printf("%.3f\n", scores[i]);
/* optional "filter power" data file: <hmm name> <# seqs <= P threshold> <fraction of seqs <= P threshold> */
if (cfg->ffp) output_filter_power(go, cfg, errbuf, hmm, scores);
/* Count the scores into a histogram object. */
if ((h = esl_histogram_CreateFull(-50., 50., 0.2)) == NULL) ESL_XFAIL(eslEMEM, errbuf, "allocation failed");
for (i = 0; i < cfg->N; i++) esl_histogram_Add(h, scores[i]);
/* For viterbi, MSV, and hybrid, fit data to a Gumbel, either with known lambda or estimated lambda. */
if (esl_opt_GetBoolean(go, "--vit") || esl_opt_GetBoolean(go, "--msv"))
{
esl_histogram_GetRank(h, 10, &x10);
tailp = 1.0;
/* mu, lambda, E10 fields are for ML Gumbel fit to the observed data */
if (esl_gumbel_FitComplete(scores, cfg->N, &mu, &lambda) != eslOK) esl_fatal("gumbel complete data fit failed");
E10 = cfg->N * esl_gumbel_surv(x10, mu, lambda);
/* mufix, E10fix fields: assume lambda = log2; fit an ML mu to the data */
if (esl_gumbel_FitCompleteLoc(scores, cfg->N, 0.693147, &mufix) != eslOK) esl_fatal("gumbel mu- (location-)only data fit failed for lambda = log2");
E10fix = cfg->N * esl_gumbel_surv(x10, mufix, 0.693147);
/* mufix2, E10fix2 fields: assume H3's own lambda estimate; fit ML mu */
if (esl_gumbel_FitCompleteLoc(scores, cfg->N, plambda, &mufix2) != eslOK) esl_fatal("gumbel mu- (location-)only data fit failed for fitted lambda");
E10fix2 = cfg->N * esl_gumbel_surv(x10, mufix2, plambda);
/* pmu, plambda, E10p: use H3 expectation estimates (pmu, plambda) */
E10p = cfg->N * esl_gumbel_surv(x10, pmu, plambda);
fprintf(cfg->ofp, "%-20s %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f",
hmm->name, tailp, mu, lambda, E10, mufix, E10fix, mufix2, E10fix2, pmu, plambda, E10p);
if (esl_opt_GetBoolean(go, "-a")) {
esl_stats_IMean(alilens, cfg->N, &almean, &alvar);
fprintf(cfg->ofp, " %8.4f %8.4f\n", almean, sqrt(alvar));
} else
fprintf(cfg->ofp, "\n");
if (cfg->survfp != NULL) {
double xmax = esl_opt_IsOn(go, "--xmax") ? esl_opt_GetReal(go, "--xmax") : h->xmax + 5.;
esl_histogram_PlotSurvival(cfg->survfp, h);
esl_gumbel_Plot(cfg->survfp, pmu, plambda, esl_gumbel_surv, h->xmin - 5., xmax, 0.1);
esl_gumbel_Plot(cfg->survfp, mu, lambda, esl_gumbel_surv, h->xmin - 5., xmax, 0.1);
esl_gumbel_Plot(cfg->survfp, mufix, 0.693147, esl_gumbel_surv, h->xmin - 5., xmax, 0.1);
}
if (cfg->efp != NULL) {
double x;
fprintf(cfg->efp, "# %s\n", hmm->name);
for (i = 1; i <= 1000 && i <= cfg->N; i++) {
esl_histogram_GetRank(h, i, &x);
fprintf(cfg->efp, "%d %g\n", i, cfg->N * esl_gumbel_surv(x, pmu, plambda));
}
fprintf(cfg->efp, "&\n");
}
}
/* For Forward, fit tail to exponential tails, for a range of tail mass choices. */
else if (esl_opt_GetBoolean(go, "--fwd"))
{
double tmin = esl_opt_GetReal(go, "--tmin");
double tmax = esl_opt_GetReal(go, "--tmax");
double tpoints = (double) esl_opt_GetInteger(go, "--tpoints");
int do_linear = esl_opt_GetBoolean(go, "--tlinear");
double *xv;
double tau;
int n;
esl_histogram_GetRank(h, 10, &x10);
tailp = tmin;
do {
if (tailp > 1.0) tailp = 1.0;
esl_histogram_GetTailByMass(h, tailp, &xv, &n, NULL);
if (esl_exp_FitComplete(xv, n, &mu, &lambda) != eslOK) esl_fatal("exponential fit failed");
E10 = cfg->N * tailp * esl_exp_surv(x10, mu, lambda);
mufix = mu;
E10fix = cfg->N * tailp * esl_exp_surv(x10, mu, 0.693147);
E10p = cfg->N * esl_exp_surv(x10, pmu, plambda); /* the pmu is relative to a P=1.0 tail origin. */
tau = mu + log(tailp) / lambda;
fprintf(cfg->ofp, "%-20s %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\n",
hmm->name, tailp, mu, lambda, E10, mufix, E10fix, pmu, plambda, E10p);
if (tpoints == 1) break;
else if (do_linear) tailp += (tmax-tmin) / (tpoints-1);
else tailp *= exp(log(tmax/tmin) / (tpoints-1));
} while (tailp <= tmax+1e-7);
if (cfg->survfp)
{
double xmax = esl_opt_IsOn(go, "--xmax") ? esl_opt_GetReal(go, "--xmax") : h->xmax + 5.;
esl_histogram_PlotSurvival(cfg->survfp, h);
esl_exp_Plot(cfg->survfp, pmu, plambda, esl_exp_surv, pmu, xmax, 0.1);
esl_exp_Plot(cfg->survfp, tau, lambda, esl_exp_surv, tau, xmax, 0.1);
esl_exp_Plot(cfg->survfp, tau, 0.693147, esl_exp_surv, tau, xmax, 0.1);
}
if (cfg->efp != NULL) {
double x;
fprintf(cfg->efp, "# %s\n", hmm->name);
for (i = 1; i <= 1000 && i <= cfg->N; i++) {
esl_histogram_GetRank(h, i, &x);
fprintf(cfg->efp, "%d %g\n", i, cfg->N * esl_exp_surv(x, pmu, plambda));
}
fprintf(cfg->efp, "&\n");
}
}
/* fallthrough: both normal, error cases execute same cleanup code */
status = eslOK;
ERROR:
esl_histogram_Destroy(h);
return status;
}
void run_hmmer_pipeline(const char* seq) {
int index, i, status;
ESL_SQ* sq = esl_sq_CreateFrom(NULL, seq, NULL, NULL, NULL);
P7_OPROFILE *om = NULL;
P7_PROFILE *gm = NULL;
float usc, vfsc, fwdsc; /* filter scores */
float filtersc; /* HMM null filter score */
float nullsc; /* null model score */
float seqbias;
float seq_score; /* the corrected per-seq bit score */
double P;
WRAPPER_RESULT* result;
num_results = 0;
if(sq->n == 0) {
esl_sq_Destroy(sq);
return;
}
esl_sq_Digitize(abc, sq);
int n = 0;
float oasc;
for(index = 0;index < num_models;index++) {
om = models[index];
p7_omx_Reuse(oxf);
p7_omx_Reuse(oxb);
p7_omx_GrowTo(oxf, om->M, sq->n, sq->n);
p7_omx_GrowTo(oxb, om->M, sq->n, sq->n);
p7_oprofile_ReconfigLength(om, sq->n);
p7_bg_SetFilter(bg, om->M, om->compo);
p7_bg_SetLength(bg, sq->n);
//Calibrate null model
p7_bg_NullOne(bg, sq->dsq, sq->n, &nullsc);
//MSV Filter
p7_MSVFilter(sq->dsq, sq->n, om, oxf, &usc);
seq_score = (usc - nullsc) / eslCONST_LOG2;
P = esl_gumbel_surv(seq_score, om->evparam[p7_MMU], om->evparam[p7_MLAMBDA]);
if (P > f1) continue;
//Bias filter (model compo)
p7_bg_FilterScore(bg, sq->dsq, sq->n, &filtersc);
seq_score = (usc - filtersc) / eslCONST_LOG2;
P = esl_gumbel_surv(seq_score, om->evparam[p7_MMU], om->evparam[p7_MLAMBDA]);
if (P > f1) continue;
//Viterbi filter (Only do if P value from Bias is high)
if(P > f2) {
p7_ViterbiFilter(sq->dsq, sq->n, om, oxf, &vfsc);
seq_score = (vfsc - filtersc) / eslCONST_LOG2;
P = esl_gumbel_surv(seq_score, om->evparam[p7_VMU], om->evparam[p7_VLAMBDA]);
if (P > f2) continue;
}
//Get the real probability (forward)
p7_Forward(sq->dsq, sq->n, om, oxf, &fwdsc);
seq_score = (fwdsc - filtersc) / eslCONST_LOG2;
P = esl_exp_surv(seq_score, om->evparam[p7_FTAU], om->evparam[p7_FLAMBDA]);
if(hmmer_error) {
fprintf(stderr, "HMM: %s, seq: %s", om->name, seq);
hmmer_error = 0;
continue;
}
if (P > f3) continue;
//Real hit, go in to posterior decoding and alignment
p7_omx_Reuse(oxb);
p7_trace_Reuse(tr);
p7_Backward(sq->dsq, sq->n, om, oxf, oxb, NULL);
status = p7_Decoding(om, oxf, oxb, oxb);
if(status == eslOK) {
//And then trace the result
p7_OptimalAccuracy(om, oxb, oxf, &oasc);
p7_OATrace(om, oxb, oxf, tr);
} else if(status == eslERANGE) {
fprintf(stderr, "Decoding overflow on model %s\n", om->name);
gm = gmodels[index];
if(gxf == NULL) {
gxf = p7_gmx_Create(gm->M, sq->n);
gxb = p7_gmx_Create(gm->M, sq->n);
} else {
p7_gmx_GrowTo(gxf, gm->M, sq->n);
p7_gmx_GrowTo(gxb, gm->M, sq->n);
}
p7_ReconfigLength(gm, sq->n);
p7_GForward (sq->dsq, sq->n, gm, gxf, &fwdsc);
p7_GBackward(sq->dsq, sq->n, gm, gxb, NULL);
p7_GDecoding(gm, gxf, gxb, gxb);
p7_GOptimalAccuracy(gm, gxb, gxf, &oasc);
p7_GOATrace (gm, gxb, gxf, tr);
p7_gmx_Reuse(gxf);
p7_gmx_Reuse(gxb);
}
if(hmmer_error) {
fprintf(stderr, "HMM: %s, seq: %s", om->name, seq);
hmmer_error = 0;
continue;
}
result = wrapper_results[num_results];
reuse_result(result, tr->N + om->M, om->name); //We're way overallocating here, but it's hard to know at this point how much space we'll need for the alignment (plus leading and trailing gaps)
trace_into(tr, result, sq, abc, om->M);
result->bits = seq_score;
num_results++;
}
esl_sq_Destroy(sq);
}
int
main(int argc, char **argv)
{
ESL_GETOPTS *go = esl_getopts_CreateDefaultApp(options, 2, argc, argv, banner, usage);
char *hmmfile = esl_opt_GetArg(go, 1);
char *seqfile = esl_opt_GetArg(go, 2);
float nu = esl_opt_GetReal(go, "--nu");
ESL_ALPHABET *abc = NULL;
P7_HMMFILE *hfp = NULL;
P7_HMM *hmm = NULL;
P7_BG *bg = NULL;
P7_PROFILE *gm = NULL;
P7_GMX *fwd = NULL;
ESL_SQ *sq = NULL;
ESL_SQFILE *sqfp = NULL;
P7_TRACE *tr = NULL;
int format = eslSQFILE_UNKNOWN;
float sc, nullsc, seqscore, P;
int status;
/* Read in one HMM */
if (p7_hmmfile_Open(hmmfile, NULL, &hfp) != eslOK) p7_Fail("Failed to open HMM file %s", hmmfile);
if (p7_hmmfile_Read(hfp, &abc, &hmm) != eslOK) p7_Fail("Failed to read HMM");
p7_hmmfile_Close(hfp);
/* Open sequence file */
sq = esl_sq_CreateDigital(abc);
status = esl_sqfile_Open(seqfile, format, NULL, &sqfp);
if (status == eslENOTFOUND) p7_Fail("No such file.");
else if (status == eslEFORMAT) p7_Fail("Format unrecognized.");
else if (status == eslEINVAL) p7_Fail("Can't autodetect stdin or .gz.");
else if (status != eslOK) p7_Fail("Open failed, code %d.", status);
/* Configure a profile from the HMM */
bg = p7_bg_Create(abc);
gm = p7_profile_Create(hmm->M, abc);
p7_ProfileConfig(hmm, bg, gm, sq->n, p7_LOCAL);
/* Allocate matrix */
fwd = p7_gmx_Create(gm->M, sq->n);
while ((status = esl_sqio_Read(sqfp, sq)) == eslOK)
{
p7_ReconfigLength(gm, sq->n);
p7_bg_SetLength(bg, sq->n);
p7_gmx_GrowTo(fwd, gm->M, sq->n);
/* Run MSV */
p7_GMSV(sq->dsq, sq->n, gm, fwd, nu, &sc);
/* Calculate bit score and P-value using standard null1 model*/
p7_bg_NullOne (bg, sq->dsq, sq->n, &nullsc);
seqscore = (sc - nullsc) / eslCONST_LOG2;
P = esl_gumbel_surv(seqscore, gm->evparam[p7_MMU], gm->evparam[p7_MLAMBDA]);
/* output suitable for direct use in profmark benchmark postprocessors:
* <Pvalue> <bitscore> <target name> <query name>
*/
printf("%g\t%.2f\t%s\t%s\n", P, seqscore, sq->name, hmm->name);
esl_sq_Reuse(sq);
}
if (status == eslEFORMAT) esl_fatal("Parse failed (sequence file %s):\n%s\n", sqfp->filename, esl_sqfile_GetErrorBuf(sqfp));
else if (status != eslEOF) esl_fatal("Unexpected error %d reading sequence file %s", status, sqfp->filename);
/* Cleanup */
esl_sqfile_Close(sqfp);
esl_sq_Destroy(sq);
p7_trace_Destroy(tr);
p7_gmx_Destroy(fwd);
p7_profile_Destroy(gm);
p7_bg_Destroy(bg);
p7_hmm_Destroy(hmm);
esl_alphabet_Destroy(abc);
esl_getopts_Destroy(go);
return 0;
}
/* Function: esl_gumbel_generic_surv()
* Incept: SRE, Thu Aug 25 07:56:04 2005 [St. Louis]
*
* Purpose: Generic-API version of survival function.
*/
double
esl_gumbel_generic_surv(double p, void *params)
{
double *v = (double *) params;
return esl_gumbel_surv(p, v[0], v[1]);
}
