static int
magcal_init(const satdata_mag *data, magcal_workspace *w)
{
int s = 0;
size_t i;
size_t n = 0;
for (i = 0; i < data->n; ++i)
{
/* don't store flagged data */
if (data->flags[i])
continue;
/* don't process high latitude data */
if (fabs(data->latitude[i]) > MAGCAL_MAX_LATITUDE)
continue;
w->Ex[n] = SATDATA_VEC_X(data->B_VFM, i);
w->Ey[n] = SATDATA_VEC_Y(data->B_VFM, i);
w->Ez[n] = SATDATA_VEC_Z(data->B_VFM, i);
w->F[n] = data->F[i];
++n;
}
if (n < 200)
{
fprintf(stderr, "magcal_init: insufficient data points for calibration: %zu\n",
n);
return -1;
}
if (n != w->n)
{
gsl_multifit_fdfsolver_free(w->fdf_s);
gsl_multifit_fdfridge_free(w->fdf_ridge);
w->fdf_s = gsl_multifit_fdfsolver_alloc(w->fdf_type, n, w->p);
w->fdf_ridge = gsl_multifit_fdfridge_alloc(w->fdf_type, n, w->p);
w->n = n;
}
#if MAGCAL_SCALE
w->B_s = GSL_MAX(gsl_stats_sd(w->Ex, 1, n),
GSL_MAX(gsl_stats_sd(w->Ey, 1, n),
gsl_stats_sd(w->Ez, 1, n)));
#endif
/* center and scale data arrays */
for (i = 0; i < n; ++i)
{
w->Ex[i] /= w->B_s;
w->Ey[i] /= w->B_s;
w->Ez[i] /= w->B_s;
w->F[i] /= w->B_s;
}
return s;
} /* magcal_init() */
void MultiPeakFit::guessInitialValues()
{
if (!d_n || d_peaks > 1)
return;
size_t imin, imax;
gsl_stats_minmax_index(&imin, &imax, d_y, 1, d_n);
double min_out = d_y[imin];
double max_out = d_y[imax];
QVarLengthArray<double> temp(d_n);//double temp[d_n];
for (int i = 0; i < d_n; i++)
temp[i] = fabs(d_y[i]);
size_t imax_temp = gsl_stats_max_index(temp.data(), 1, d_n);//size_t imax_temp = gsl_stats_max_index(temp, 1, d_n);
double offset, area;
if (imax_temp == imax)
offset = min_out;
else //reversed bell
offset = max_out;
double xc = d_x[imax_temp];
double width = 2*gsl_stats_sd(d_x, 1, d_n);
if (d_profile == Lorentz)
area = M_PI_2*width*fabs(max_out - min_out);
else
area = sqrt(M_PI_2)*width*fabs(max_out - min_out);
gsl_vector_set(d_param_init, 0, area);
gsl_vector_set(d_param_init, 1, xc);
gsl_vector_set(d_param_init, 2, width);
gsl_vector_set(d_param_init, 3, offset);
}
void GaussAmpFit::guessInitialValues()
{
size_t imin, imax;
gsl_stats_minmax_index(&imin, &imax, d_y, 1, d_n);
double min_out = d_y[imin];
double max_out = d_y[imax];
gsl_vector_set(d_param_init, 1, fabs(max_out - min_out));
#ifdef Q_CC_MSVC
QVarLengthArray<double> temp(d_n);
#else
double temp[d_n];
#endif
for (int i = 0; i < d_n; i++)
temp[i] = fabs(d_y[i]);
#ifdef Q_CC_MSVC
size_t imax_temp = gsl_stats_max_index(temp.data(), 1, d_n);
#else
size_t imax_temp = gsl_stats_max_index(temp, 1, d_n);
#endif
gsl_vector_set(d_param_init, 2, d_x[imax_temp]);
gsl_vector_set(d_param_init, 3, gsl_stats_sd(d_x, 1, d_n));
if (imax_temp == imax)
gsl_vector_set(d_param_init, 0, min_out);
else //reversed bell
gsl_vector_set(d_param_init, 0, max_out);
}
/**
* Get a summary statistic for the orbital elements; for instance,
* the median value calculated over all the elements of the list.
* @param kl List
* @param what Can be one of: STAT_MEAN, STAT_MEDIAN, STAT_STDDEV, STAT_MAD.
* Summary statistic is calculated correctly for angle parameters.
* @return A matrix whose entries are the summary statistic for the
* corresponding orbital element.
*/
gsl_matrix* KL_getElementsStats(const ok_list* kl, const int what) {
int npl = MROWS(kl->kernels[0]->elements);
if (npl == 0)
return NULL;
gsl_vector* v = gsl_vector_alloc(kl->size);
gsl_matrix* m = gsl_matrix_alloc(npl, ALL_ELEMENTS_SIZE);
gsl_matrix_set_all(m, 0.);
for (int i = 0; i < npl; i++)
for (int j = 0; j < ALL_ELEMENTS_SIZE; j++) {
for (int n = 0; n < kl->size; n++) {
VSET(v, n, MGET(kl->kernels[n]->elements, i, j));
}
switch (what) {
case STAT_MEAN:
if (j == MA || j == LOP || j == INC || j == NODE || j == TRUEANOMALY)
MSET(m, i, j, ok_average_angle(v->data, v->size, false));
else
MSET(m, i, j, gsl_stats_mean(v->data, 1, v->size));
break;
case STAT_STDDEV:
if (j == MA || j == LOP || j == INC || j == NODE || j == TRUEANOMALY) {
MSET(m, i, j, ok_stddev_angle(v->data, v->size, false));
}
else
MSET(m, i, j, gsl_stats_sd(v->data, 1, v->size));
break;
case STAT_MEDIAN:
if (j == MA || j == LOP || j == INC || j == NODE || j == TRUEANOMALY)
MSET(m, i, j, ok_median_angle(v->data, v->size, false));
else {
gsl_sort_vector(v);
MSET(m, i, j, gsl_stats_median_from_sorted_data(v->data, 1, v->size));
}
break;
case STAT_MAD:
if (j == MA || j == LOP || j == INC || j == NODE || j == TRUEANOMALY) {
double med = ok_median_angle(v->data, v->size, false);
MSET(m, i, j, 1.4826 * ok_mad_angle(v->data, v->size, med, false));
} else {
gsl_sort_vector(v);
double med = gsl_stats_median_from_sorted_data(v->data, 1, v->size);
MSET(m, i, j, 1.4826 * ok_mad(v->data, v->size, med));
}
break;
default:
// percentiles
gsl_sort_vector(v);
MSET(m, i, j, gsl_stats_quantile_from_sorted_data(v->data, 1, v->size, (double)(what)/100.));
};
}
gsl_vector_free(v);
return m;
}
bool myHistogram::Convert(vector<double> &x)
{
gsl_histogram *r;
size_t n=x.size();
if(n<=2) return false;
double *res;
res=new double[n];
size_t i;
for(i=0;i<n;i++) res[i]=x[i];
double std=gsl_stats_sd(res,1,n);
double bin=3.49*std/pow(n*1.0,1.0/3);//Scott's ruler
if(bin<=0)
{
delete []res;
return false;
}
double a=gsl_stats_min(res,1,n);
double b=gsl_stats_max(res,1,n);
int num=(int)((b-a)/bin);
r=gsl_histogram_alloc(num);
gsl_histogram_set_ranges_uniform(r,a,b);
for(i=0;i<n;i++)
{
gsl_histogram_increment(r,res[i]);
}
Convert(r,n);
gsl_histogram_free(r);
delete []res;
return true;
}
double compute_cov_mean(long nreps, double* runtimes_sec,
pred_method_info_t prediction_info) {
int my_rank, i,j;
double* mean_list = NULL;
int nmeans;
long current_nreps;
double* tmp_runtimes;
double cov_mean = COEF_ERROR_VALUE;
MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
if (my_rank == OUTPUT_ROOT_PROC) {
double mean_of_means = 0, q1,q3, sd;
long start_index, end_index;
double* runtimes;
nmeans = prediction_info.method_win;
if (nmeans > nreps) {
return COEF_ERROR_VALUE;
}
mean_list = (double*)malloc(prediction_info.method_win * sizeof(double));
tmp_runtimes = (double*)malloc(nreps * sizeof(double));
for (i = 0; i < nmeans; i++) {
current_nreps = nreps - i;
for (j=0; j < current_nreps; j++) {
tmp_runtimes[j] = runtimes_sec[j];
}
runtimes = tmp_runtimes;
gsl_sort(tmp_runtimes, 1, current_nreps);
if (current_nreps > OUTLIER_FILTER_MIN_MEAS) {
q1 = gsl_stats_quantile_from_sorted_data (tmp_runtimes, 1, current_nreps, 0.25);
q3 = gsl_stats_quantile_from_sorted_data (tmp_runtimes, 1, current_nreps, 0.75);
filter_outliers_from_sorted(tmp_runtimes, current_nreps, q1, q3, OUTLIER_FILTER_THRES, &start_index, &end_index);
runtimes = runtimes + start_index;
current_nreps = end_index - start_index + 1;
}
mean_list[i] = gsl_stats_mean(runtimes, 1, current_nreps);
}
mean_of_means = gsl_stats_mean(mean_list, 1, nmeans);
sd = gsl_stats_sd(mean_list, 1, nmeans);
cov_mean = sd/(mean_of_means);
//printf("cov_mean=%lf, nreps = %ld, thres=%lf (mean_of_means=%.10f)\n", cov_mean, nreps, prediction_info.method_thres, mean_of_means);
free(tmp_runtimes);
free(mean_list);
}
return cov_mean;
}
// feature 11: aspect ratio1: GEOMETRY_MAX(std_x,std_y) / GEOMETRY_MIN(std_x,std_y)
int LSL_lfeatures_class :: feature_11(LSL_Point3D_container *laserfeat_cluster, Real *out)
{
char ret = 1;
double GEOMETRY_MIN_v, GEOMETRY_MAX_v, std1, std2;
std::vector <double> pts_coord;
laserfeat_cluster -> get_coords(pts_coord, GEOMETRY_COORD_X);
std1 = gsl_stats_sd (&pts_coord[0], 1, laserfeat_cluster->pts.size() -1) ;
laserfeat_cluster -> get_coords(pts_coord, GEOMETRY_COORD_Y);
std2 = gsl_stats_sd (&pts_coord[0], 1, laserfeat_cluster->pts.size() -1) ;
GEOMETRY_MAX_v = GEOMETRY_MAX(std1, std2);
GEOMETRY_MIN_v = GEOMETRY_MIN(std1, std2);
*out = (1.0 + GEOMETRY_MIN_v) / ( 1.0 + GEOMETRY_MAX_v) ;
return(ret);
}
/********************************************************************************
* clusterupdatebatch: Runs clusterupdate multiple times and gets physics as well
* as error estimates.
*******************************************************************************/
int
clusterupdatebatch(lattice_site * lattice, settings conf, double beta, datapoint * data )
{
int i,j;
double * e_block, * m_block, * e_block_avg, * m_block_avg, \
* e_block_error, * m_block_error, * c_block , * chi_block;
gsl_vector * mag_vector;
e_block = (double *) malloc(conf.block_size*sizeof(double));
m_block = (double *) malloc(conf.block_size*sizeof(double));
e_block_avg = (double *) malloc(conf.blocks*sizeof(double));
m_block_avg = (double *) malloc(conf.blocks*sizeof(double));
c_block = (double *) malloc(conf.blocks*sizeof(double));
chi_block = (double *) malloc(conf.blocks*sizeof(double));
e_block_error = (double *) malloc(conf.blocks*sizeof(double));
m_block_error = (double *) malloc(conf.blocks*sizeof(double));
mag_vector = gsl_vector_alloc(conf.spindims);
//Settle first
for(i = 0 ; i < conf.max_settle ; i++)
{
clusterupdate(lattice,conf,beta);
}
//Get averages and stdev for messurements
for(i = 0 ; i < conf.blocks ; i++)
{
for(j = 0 ; j < conf.block_size ; j++)
{
clusterupdate(lattice,conf,beta);
e_block[j] = total_energy(lattice,conf);
m_block[j] = magnetization(lattice,conf,mag_vector);
}
e_block_avg[i] = gsl_stats_mean(e_block,1,conf.block_size);
e_block_error[i] = gsl_stats_sd(e_block,1,conf.block_size);
m_block_avg[i] = gsl_stats_mean(m_block,1,conf.block_size);
m_block_error[i] = gsl_stats_sd(m_block,1,conf.block_size);
c_block[i] = gsl_pow_2(beta)*gsl_pow_2(e_block_error[i]);
chi_block[i] = beta*gsl_pow_2(m_block_error[i]);
}
(*data).beta = beta;
(*data).erg = gsl_stats_mean(e_block_avg,1,conf.blocks);
(*data).erg_error = gsl_stats_sd(e_block_avg,1,conf.blocks);
(*data).mag = gsl_stats_mean(m_block_avg,1,conf.blocks);
(*data).mag_error = gsl_stats_sd(m_block_avg,1,conf.blocks);
(*data).c = gsl_stats_mean(c_block,1,conf.blocks);
(*data).c_error = gsl_stats_sd(c_block,1,conf.blocks);
(*data).chi = gsl_stats_mean(chi_block,1,conf.blocks);
(*data).chi_error = gsl_stats_sd(chi_block,1,conf.blocks);
free(e_block);
free(m_block);
free(e_block_avg);
free(m_block_avg);
free(e_block_error);
free(m_block_error);
gsl_vector_free(mag_vector);
return(0);
}
/**
* Compute the standard deviation of a REAL4Vector
* \param [out] sigma Pointer to the output standard deviation value
* \param [in] vector Pointer to a REAL4Vector of values
* \return Status value
*/
INT4 calcStddev(REAL4 *sigma, REAL4Vector *vector)
{
double *gslarray = NULL;
XLAL_CHECK( (gslarray = XLALMalloc(sizeof(double)*vector->length)) != NULL, XLAL_ENOMEM );
for (INT4 ii=0; ii<(INT4)vector->length; ii++) gslarray[ii] = (double)vector->data[ii];
*sigma = (REAL4)gsl_stats_sd(gslarray, 1, vector->length);
XLALFree((double*)gslarray);
return XLAL_SUCCESS;
} /* calcStddev() */
void
test_basic(const size_t n, const double data[], const double tol)
{
gsl_rstat_workspace *rstat_workspace_p = gsl_rstat_alloc();
const double expected_mean = gsl_stats_mean(data, 1, n);
const double expected_var = gsl_stats_variance(data, 1, n);
const double expected_sd = gsl_stats_sd(data, 1, n);
const double expected_sd_mean = expected_sd / sqrt((double) n);
const double expected_skew = gsl_stats_skew(data, 1, n);
const double expected_kurtosis = gsl_stats_kurtosis(data, 1, n);
double expected_rms = 0.0;
double mean, var, sd, sd_mean, rms, skew, kurtosis;
size_t i, num;
int status;
/* compute expected rms */
for (i = 0; i < n; ++i)
expected_rms += data[i] * data[i];
expected_rms = sqrt(expected_rms / n);
/* add data to rstat workspace */
for (i = 0; i < n; ++i)
gsl_rstat_add(data[i], rstat_workspace_p);
mean = gsl_rstat_mean(rstat_workspace_p);
var = gsl_rstat_variance(rstat_workspace_p);
sd = gsl_rstat_sd(rstat_workspace_p);
sd_mean = gsl_rstat_sd_mean(rstat_workspace_p);
rms = gsl_rstat_rms(rstat_workspace_p);
skew = gsl_rstat_skew(rstat_workspace_p);
kurtosis = gsl_rstat_kurtosis(rstat_workspace_p);
num = gsl_rstat_n(rstat_workspace_p);
gsl_test_int(num, n, "n n=%zu" , n);
gsl_test_rel(mean, expected_mean, tol, "mean n=%zu", n);
gsl_test_rel(var, expected_var, tol, "variance n=%zu", n);
gsl_test_rel(sd, expected_sd, tol, "stddev n=%zu", n);
gsl_test_rel(sd_mean, expected_sd_mean, tol, "stddev_mean n=%zu", n);
gsl_test_rel(rms, expected_rms, tol, "rms n=%zu", n);
gsl_test_rel(skew, expected_skew, tol, "skew n=%zu", n);
gsl_test_rel(kurtosis, expected_kurtosis, tol, "kurtosis n=%zu", n);
status = gsl_rstat_reset(rstat_workspace_p);
gsl_test_int(status, GSL_SUCCESS, "rstat returned success");
num = gsl_rstat_n(rstat_workspace_p);
gsl_test_int(num, 0, "n n=%zu" , n);
gsl_rstat_free(rstat_workspace_p);
}
StatBox2D::BoxWhiskerData Layout2D::generateBoxWhiskerData(Column *colData,
int from, int to,
int key) {
size_t size = static_cast<size_t>((to - from) + 1);
double *data = new double[size];
for (int i = 0, j = from; j < to + 1; i++, j++) {
data[i] = colData->valueAt(i);
}
// sort the data
gsl_sort(data, 1, size - 1);
StatBox2D::BoxWhiskerData statBoxData;
statBoxData.key = key;
// basic stats
statBoxData.mean = gsl_stats_mean(data, 1, size);
statBoxData.median = gsl_stats_median_from_sorted_data(data, 1, size);
statBoxData.sd = gsl_stats_sd(data, 1, size);
statBoxData.se = statBoxData.sd / sqrt(static_cast<double>(size));
// data bounds
statBoxData.boxWhiskerDataBounds.sd_lower = statBoxData.mean - statBoxData.sd;
statBoxData.boxWhiskerDataBounds.sd_upper = statBoxData.mean + statBoxData.sd;
statBoxData.boxWhiskerDataBounds.se_lower = statBoxData.mean - statBoxData.se;
statBoxData.boxWhiskerDataBounds.se_upper = statBoxData.mean + statBoxData.se;
statBoxData.boxWhiskerDataBounds.perc_1 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.01);
statBoxData.boxWhiskerDataBounds.perc_5 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.05);
statBoxData.boxWhiskerDataBounds.perc_10 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.10);
statBoxData.boxWhiskerDataBounds.perc_25 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.25);
statBoxData.boxWhiskerDataBounds.perc_75 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.75);
statBoxData.boxWhiskerDataBounds.perc_90 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.90);
statBoxData.boxWhiskerDataBounds.perc_95 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.95);
statBoxData.boxWhiskerDataBounds.perc_99 =
gsl_stats_quantile_from_sorted_data(data, 1, size, 0.99);
statBoxData.boxWhiskerDataBounds.max = data[size - 1];
statBoxData.boxWhiskerDataBounds.min = data[0];
// delete the double data pointer
delete[] data;
return statBoxData;
}
/* Statistics code
* Make only one call to reading for a particular time range and compute all our stats
*/
void _compute_statistics(cdb_range_t *range, uint64_t *num_recs, cdb_record_t *records) {
uint64_t i = 0;
uint64_t valid = 0;
double sum = 0.0;
double *values = calloc(*num_recs, sizeof(double));
for (i = 0; i < *num_recs; i++) {
if (!isnan(records[i].value)) {
sum += values[valid] = records[i].value;
valid++;
}
}
range->num_recs = valid;
range->mean = gsl_stats_mean(values, 1, valid);
range->max = gsl_stats_max(values, 1, valid);
range->min = gsl_stats_min(values, 1, valid);
range->sum = sum;
range->stddev = gsl_stats_sd(values, 1, valid);
range->absdev = gsl_stats_absdev(values, 1, valid);
/* The rest need sorted data */
gsl_sort(values, 1, valid);
range->median = gsl_stats_median_from_sorted_data(values, 1, valid);
range->pct95th = gsl_stats_quantile_from_sorted_data(values, 1, valid, 0.95);
range->pct75th = gsl_stats_quantile_from_sorted_data(values, 1, valid, 0.75);
range->pct50th = gsl_stats_quantile_from_sorted_data(values, 1, valid, 0.50);
range->pct25th = gsl_stats_quantile_from_sorted_data(values, 1, valid, 0.25);
/* MAD must come last because it alters the values array
* http://en.wikipedia.org/wiki/Median_absolute_deviation */
for (i = 0; i < valid; i++) {
values[i] = fabs(values[i] - range->median);
if (values[i] < 0.0) {
values[i] *= -1.0;
}
}
/* Final sort is required MAD */
gsl_sort(values, 1, valid);
range->mad = gsl_stats_median_from_sorted_data(values, 1, valid);
free(values);
}
void single_sort(const char *dist_name, function_ptr dist_func, const char *sort_name, function_ptr sort_func, int runs, int side)
{
double cmean, csdev, tmean, tsdev;
struct timespec t0, t1;
int progress = (runs <= 10) ? 1 : runs / 10;
std::cout << std::setw(4) << side << " ";
std::cout << dist_name << " " << sort_name << " " << std::flush;
for (int i = 1; i <= runs; i++) {
random_seed(i);
tcomp = tread = twrit = 0;
(*dist_func)();
if ((i % progress) == 0) {
std::cout << '.' << std::flush;
}
clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &t0);
(*sort_func)();
clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &t1);
comp[i - 1] = tcomp;
msec[i - 1] = (t1.tv_sec - t0.tv_sec) * 1000.0 + (t1.tv_nsec - t0.tv_nsec) * 0.000001;
if (! is_spatially_sorted()) {
std::cout << "result is not sorted!\n";
}
}
cmean = gsl_stats_mean(comp, 1, runs);
csdev = gsl_stats_sd(comp, 1, runs);
tmean = gsl_stats_mean(msec, 1, runs);
tsdev = gsl_stats_sd(msec, 1, runs);
std::cout << std::setprecision(0) << std::fixed;
std::cout << std::setw(12) << cmean << ' ' << std::setw(12) << csdev << ' ';
std::cout << std::setprecision(2) << std::fixed;
std::cout << std::setw(12) << tmean << ' ' << std::setw(12) << tsdev << '\n';
}
/**
* Get a summary statistic for the parameters; for instance,
* the median value calculated over all the elements of the list.
* @param kl List
* @param what Can be one of: STAT_MEAN, STAT_MEDIAN, STAT_STDDEV, STAT_MAD.
* @return A vector whose entries are the summary statistic for the
* corresponding orbital parameter.
*/
gsl_vector* KL_getParsStats(const ok_list* kl, const int what) {
gsl_vector* v = gsl_vector_alloc(kl->size);
gsl_vector* ret = gsl_vector_calloc(PARAMS_SIZE + 1);
for (int j = 0; j < PARAMS_SIZE + 1; j++) {
if (j == PARAMS_SIZE)
for (int n = 0; n < kl->size; n++) {
VSET(v, n, kl->kernels[n]->merit);
}
else
for (int n = 0; n < kl->size; n++) {
VSET(v, n, VGET(kl->kernels[n]->params, j));
}
switch (what) {
case STAT_MEAN:
VSET(ret, j, gsl_stats_mean(v->data, 1, v->size));
break;
case STAT_STDDEV:
VSET(ret, j, gsl_stats_sd(v->data, 1, v->size));
break;
case STAT_MEDIAN:
gsl_sort_vector(v);
VSET(ret, j, gsl_stats_median_from_sorted_data(v->data, 1, v->size));
break;
case STAT_MAD:
gsl_sort_vector(v);
double med = gsl_stats_median_from_sorted_data(v->data, 1, v->size);
VSET(ret, j, 1.4826 * ok_mad(v->data, v->size, med));
break;
default:
// percentiles
gsl_sort_vector(v);
VSET(v , j, gsl_stats_quantile_from_sorted_data(v->data, 1, v->size, (double)(what)/100.));
};
};
gsl_vector_free(v);
return ret;
}
bool myHistogram::Convert(double *res,int n)
{
gsl_histogram *r;
if(n<=2) return false;
double std=gsl_stats_sd(res,1,n);
double bin=3.49*std/pow(n*1.0,1.0/3);//Scott's ruler
if(bin<=0) return false;
double a=gsl_stats_min(res,1,n);
double b=gsl_stats_max(res,1,n);
int num=(int)((b-a)/bin);
r=gsl_histogram_alloc(num);
gsl_histogram_set_ranges_uniform(r,a,b);
for(int i=0;i<n;i++)
{
gsl_histogram_increment(r,res[i]);
}
Convert(r,n);
gsl_histogram_free(r);
return true;
}
// feature 08: countour regularity (in 2D plane xy)
int LSL_lfeatures_class :: feature_08(LSL_Point3D_container *laserfeat_cluster, Real *out)
{
char ret=1;
if(laserfeat_cluster->pts.size() > 2)
{
std::vector <double> dist ( laserfeat_cluster->pts.size() - 1);
int c = 0;
for(unsigned int i = 0; i <laserfeat_cluster->pts.size() - 1; i++,c++)
dist[c] = distance_L2_XY (&laserfeat_cluster->pts[i] , &laserfeat_cluster->pts[i + 1]);
*out = gsl_stats_sd (&dist[0], 1, dist.size());
}
else
ret = 0;
return(ret);
}
bool HistList::AddHist(double *res,int n,double m,double sd,CString raw)
{
gsl_histogram *r;
if(n<=0) return false;
double std=gsl_stats_sd(res,1,n);
double bin=3.49*std/pow(n*1.0,1.0/3);//Scott's ruler
if(bin<=0) return false;
double a=gsl_stats_min(res,1,n);
double b=gsl_stats_max(res,1,n);
int num=(int)((b-a)/bin);
r=gsl_histogram_alloc(num);
gsl_histogram_set_ranges_uniform(r,a,b);
for(int i=0;i<n;i++)
{
gsl_histogram_increment(r,res[i]);
}
bool bResult=AddHist(r,m,sd,raw);
gsl_histogram_free(r);
return bResult;
}
void
test_basic(const size_t n, const double data[], const double tol)
{
gsl_rstat_workspace *rstat_workspace_p = gsl_rstat_alloc();
const double expected_mean = gsl_stats_mean(data, 1, n);
const double expected_var = gsl_stats_variance(data, 1, n);
const double expected_sd = gsl_stats_sd(data, 1, n);
const double expected_skew = gsl_stats_skew(data, 1, n);
const double expected_kurtosis = gsl_stats_kurtosis(data, 1, n);
double expected_rms = 0.0;
double mean, var, sd, rms, skew, kurtosis;
size_t i;
/* compute expected rms */
for (i = 0; i < n; ++i)
expected_rms += data[i] * data[i];
expected_rms = sqrt(expected_rms / n);
/* add data to rstat workspace */
for (i = 0; i < n; ++i)
gsl_rstat_add(data[i], rstat_workspace_p);
mean = gsl_rstat_mean(rstat_workspace_p);
var = gsl_rstat_variance(rstat_workspace_p);
sd = gsl_rstat_sd(rstat_workspace_p);
rms = gsl_rstat_rms(rstat_workspace_p);
skew = gsl_rstat_skew(rstat_workspace_p);
kurtosis = gsl_rstat_kurtosis(rstat_workspace_p);
gsl_test_rel(mean, expected_mean, tol, "mean n=%zu", n);
gsl_test_rel(var, expected_var, tol, "variance n=%zu", n);
gsl_test_rel(sd, expected_sd, tol, "stddev n=%zu", n);
gsl_test_rel(rms, expected_rms, tol, "rms n=%zu", n);
gsl_test_rel(skew, expected_skew, tol, "skew n=%zu", n);
gsl_test_rel(kurtosis, expected_kurtosis, tol, "kurtosis n=%zu", n);
gsl_rstat_free(rstat_workspace_p);
}
CAMLprim value ml_gsl_stats_sd(value ow, value omean, value data)
{
size_t len = Double_array_length(data);
double result;
if(ow == Val_none)
if(omean == Val_none)
result = gsl_stats_sd(Double_array_val(data), 1, len);
else
result = gsl_stats_sd_m(Double_array_val(data), 1, len,
Double_val(Unoption(omean)));
else {
value w = Unoption(ow);
check_array_size(data, w);
if(omean == Val_none)
result = gsl_stats_wsd(Double_array_val(w), 1,
Double_array_val(data), 1, len);
else
result = gsl_stats_wsd_m(Double_array_val(w), 1,
Double_array_val(data), 1, len,
Double_val(Unoption(omean)));
}
return copy_double(result);
}
void GaussAmpFit::guessInitialValues()
{
size_t imin, imax;
gsl_stats_minmax_index(&imin, &imax, d_y, 1, d_n);
double min_out = d_y[imin];
double max_out = d_y[imax];
gsl_vector_set(d_param_init, 1, fabs(max_out - min_out));
double temp[d_n];
for (int i = 0; i < d_n; i++)
temp[i] = fabs(d_y[i]);
size_t imax_temp = gsl_stats_max_index(temp, 1, d_n);
gsl_vector_set(d_param_init, 2, d_x[imax_temp]);
gsl_vector_set(d_param_init, 3, gsl_stats_sd(d_x, 1, d_n));
if (imax_temp == imax)
gsl_vector_set(d_param_init, 0, min_out);
else //reversed bell
gsl_vector_set(d_param_init, 0, max_out);
}
int
test_nist (void)
{
size_t i ;
const size_t nlew = 200 ;
const double lew[200] = {
-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, 194,
-220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, -175,
162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, -541,
-224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, 131,
-507, -254, 199, -311, -495, 143, -46, -579, -90, 136, -472, -338,
202, -287, -477, 169, -124, -568, 17, 48, -568, -135, 162, -430,
-422, 172, -74, -577, -13, 92, -534, -243, 194, -355, -465, 156,
-81, -578, -64, 139, -449, -384, 193, -198, -538, 110, -44, -577,
-6, 66, -552, -164, 161, -460, -344, 205, -281, -504, 134, -28,
-576, -118, 156, -437, -381, 200, -220, -540, 83, 11, -568, -160,
172, -414, -408, 188, -125, -572, -32, 139, -492, -321, 205, -262,
-504, 142, -83, -574, 0, 48, -571, -106, 137, -501, -266, 190,
-391, -406, 194, -186, -553, 83, -13, -577, -49, 103, -515, -280,
201, 300, -506, 131, -45, -578, -80, 138, -462, -361, 201, -211,
-554, 32, 74, -533, -235, 187, -372, -442, 182, -147, -566, 25,
68, -535, -244, 194, -351, -463, 174, -125, -570, 15, 72, -550,
-190, 172, -424, -385, 198, -218, -536, 96 } ;
const size_t nlottery = 218 ;
const double lottery[218] = {
162, 671, 933, 414, 788, 730, 817, 33, 536, 875, 670, 236, 473, 167,
877, 980, 316, 950, 456, 92, 517, 557, 956, 954, 104, 178, 794, 278,
147, 773, 437, 435, 502, 610, 582, 780, 689, 562, 964, 791, 28, 97,
848, 281, 858, 538, 660, 972, 671, 613, 867, 448, 738, 966, 139, 636,
847, 659, 754, 243, 122, 455, 195, 968, 793, 59, 730, 361, 574, 522,
97, 762, 431, 158, 429, 414, 22, 629, 788, 999, 187, 215, 810, 782,
47, 34, 108, 986, 25, 644, 829, 630, 315, 567, 919, 331, 207, 412,
242, 607, 668, 944, 749, 168, 864, 442, 533, 805, 372, 63, 458, 777,
416, 340, 436, 140, 919, 350, 510, 572, 905, 900, 85, 389, 473, 758,
444, 169, 625, 692, 140, 897, 672, 288, 312, 860, 724, 226, 884, 508,
976, 741, 476, 417, 831, 15, 318, 432, 241, 114, 799, 955, 833, 358,
935, 146, 630, 830, 440, 642, 356, 373, 271, 715, 367, 393, 190, 669,
8, 861, 108, 795, 269, 590, 326, 866, 64, 523, 862, 840, 219, 382,
998, 4, 628, 305, 747, 247, 34, 747, 729, 645, 856, 974, 24, 568, 24,
694, 608, 480, 410, 729, 947, 293, 53, 930, 223, 203, 677, 227, 62,
455, 387, 318, 562, 242, 428, 968 } ;
const size_t nmavro = 50 ;
const double mavro[50] = {
2.00180, 2.00170, 2.00180, 2.00190, 2.00180, 2.00170, 2.00150,
2.00140, 2.00150, 2.00150, 2.00170, 2.00180, 2.00180, 2.00190,
2.00190, 2.00210, 2.00200, 2.00160, 2.00140, 2.00130, 2.00130,
2.00150, 2.00150, 2.00160, 2.00150, 2.00140, 2.00130, 2.00140,
2.00150, 2.00140, 2.00150, 2.00160, 2.00150, 2.00160, 2.00190,
2.00200, 2.00200, 2.00210, 2.00220, 2.00230, 2.00240, 2.00250,
2.00270, 2.00260, 2.00260, 2.00260, 2.00270, 2.00260, 2.00250,
2.00240 } ;
const size_t nmichelson = 100 ;
const double michelson [100] = {
299.85, 299.74, 299.90, 300.07, 299.93, 299.85, 299.95, 299.98,
299.98, 299.88, 300.00, 299.98, 299.93, 299.65, 299.76, 299.81,
300.00, 300.00, 299.96, 299.96, 299.96, 299.94, 299.96, 299.94,
299.88, 299.80, 299.85, 299.88, 299.90, 299.84, 299.83, 299.79,
299.81, 299.88, 299.88, 299.83, 299.80, 299.79, 299.76, 299.80,
299.88, 299.88, 299.88, 299.86, 299.72, 299.72, 299.62, 299.86,
299.97, 299.95, 299.88, 299.91, 299.85, 299.87, 299.84, 299.84,
299.85, 299.84, 299.84, 299.84, 299.89, 299.81, 299.81, 299.82,
299.80, 299.77, 299.76, 299.74, 299.75, 299.76, 299.91, 299.92,
299.89, 299.86, 299.88, 299.72, 299.84, 299.85, 299.85, 299.78,
299.89, 299.84, 299.78, 299.81, 299.76, 299.81, 299.79, 299.81,
299.82, 299.85, 299.87, 299.87, 299.81, 299.74, 299.81, 299.94,
299.95, 299.80, 299.81, 299.87 } ;
const size_t npidigits = 5000 ;
const double pidigits [5000] = { 3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8,
9, 7, 9, 3, 2, 3, 8, 4, 6, 2, 6, 4, 3, 3, 8, 3, 2, 7, 9, 5, 0, 2,
8, 8, 4, 1, 9, 7, 1, 6, 9, 3, 9, 9, 3, 7, 5, 1, 0, 5, 8, 2, 0, 9,
7, 4, 9, 4, 4, 5, 9, 2, 3, 0, 7, 8, 1, 6, 4, 0, 6, 2, 8, 6, 2, 0,
8, 9, 9, 8, 6, 2, 8, 0, 3, 4, 8, 2, 5, 3, 4, 2, 1, 1, 7, 0, 6, 7,
9, 8, 2, 1, 4, 8, 0, 8, 6, 5, 1, 3, 2, 8, 2, 3, 0, 6, 6, 4, 7, 0,
9, 3, 8, 4, 4, 6, 0, 9, 5, 5, 0, 5, 8, 2, 2, 3, 1, 7, 2, 5, 3, 5,
9, 4, 0, 8, 1, 2, 8, 4, 8, 1, 1, 1, 7, 4, 5, 0, 2, 8, 4, 1, 0, 2,
7, 0, 1, 9, 3, 8, 5, 2, 1, 1, 0, 5, 5, 5, 9, 6, 4, 4, 6, 2, 2, 9,
4, 8, 9, 5, 4, 9, 3, 0, 3, 8, 1, 9, 6, 4, 4, 2, 8, 8, 1, 0, 9, 7,
5, 6, 6, 5, 9, 3, 3, 4, 4, 6, 1, 2, 8, 4, 7, 5, 6, 4, 8, 2, 3, 3,
7, 8, 6, 7, 8, 3, 1, 6, 5, 2, 7, 1, 2, 0, 1, 9, 0, 9, 1, 4, 5, 6,
4, 8, 5, 6, 6, 9, 2, 3, 4, 6, 0, 3, 4, 8, 6, 1, 0, 4, 5, 4, 3, 2,
6, 6, 4, 8, 2, 1, 3, 3, 9, 3, 6, 0, 7, 2, 6, 0, 2, 4, 9, 1, 4, 1,
2, 7, 3, 7, 2, 4, 5, 8, 7, 0, 0, 6, 6, 0, 6, 3, 1, 5, 5, 8, 8, 1,
7, 4, 8, 8, 1, 5, 2, 0, 9, 2, 0, 9, 6, 2, 8, 2, 9, 2, 5, 4, 0, 9,
1, 7, 1, 5, 3, 6, 4, 3, 6, 7, 8, 9, 2, 5, 9, 0, 3, 6, 0, 0, 1, 1,
3, 3, 0, 5, 3, 0, 5, 4, 8, 8, 2, 0, 4, 6, 6, 5, 2, 1, 3, 8, 4, 1,
4, 6, 9, 5, 1, 9, 4, 1, 5, 1, 1, 6, 0, 9, 4, 3, 3, 0, 5, 7, 2, 7,
0, 3, 6, 5, 7, 5, 9, 5, 9, 1, 9, 5, 3, 0, 9, 2, 1, 8, 6, 1, 1, 7,
3, 8, 1, 9, 3, 2, 6, 1, 1, 7, 9, 3, 1, 0, 5, 1, 1, 8, 5, 4, 8, 0,
7, 4, 4, 6, 2, 3, 7, 9, 9, 6, 2, 7, 4, 9, 5, 6, 7, 3, 5, 1, 8, 8,
5, 7, 5, 2, 7, 2, 4, 8, 9, 1, 2, 2, 7, 9, 3, 8, 1, 8, 3, 0, 1, 1,
9, 4, 9, 1, 2, 9, 8, 3, 3, 6, 7, 3, 3, 6, 2, 4, 4, 0, 6, 5, 6, 6,
4, 3, 0, 8, 6, 0, 2, 1, 3, 9, 4, 9, 4, 6, 3, 9, 5, 2, 2, 4, 7, 3,
7, 1, 9, 0, 7, 0, 2, 1, 7, 9, 8, 6, 0, 9, 4, 3, 7, 0, 2, 7, 7, 0,
5, 3, 9, 2, 1, 7, 1, 7, 6, 2, 9, 3, 1, 7, 6, 7, 5, 2, 3, 8, 4, 6,
7, 4, 8, 1, 8, 4, 6, 7, 6, 6, 9, 4, 0, 5, 1, 3, 2, 0, 0, 0, 5, 6,
8, 1, 2, 7, 1, 4, 5, 2, 6, 3, 5, 6, 0, 8, 2, 7, 7, 8, 5, 7, 7, 1,
3, 4, 2, 7, 5, 7, 7, 8, 9, 6, 0, 9, 1, 7, 3, 6, 3, 7, 1, 7, 8, 7,
2, 1, 4, 6, 8, 4, 4, 0, 9, 0, 1, 2, 2, 4, 9, 5, 3, 4, 3, 0, 1, 4,
6, 5, 4, 9, 5, 8, 5, 3, 7, 1, 0, 5, 0, 7, 9, 2, 2, 7, 9, 6, 8, 9,
2, 5, 8, 9, 2, 3, 5, 4, 2, 0, 1, 9, 9, 5, 6, 1, 1, 2, 1, 2, 9, 0,
2, 1, 9, 6, 0, 8, 6, 4, 0, 3, 4, 4, 1, 8, 1, 5, 9, 8, 1, 3, 6, 2,
9, 7, 7, 4, 7, 7, 1, 3, 0, 9, 9, 6, 0, 5, 1, 8, 7, 0, 7, 2, 1, 1,
3, 4, 9, 9, 9, 9, 9, 9, 8, 3, 7, 2, 9, 7, 8, 0, 4, 9, 9, 5, 1, 0,
5, 9, 7, 3, 1, 7, 3, 2, 8, 1, 6, 0, 9, 6, 3, 1, 8, 5, 9, 5, 0, 2,
4, 4, 5, 9, 4, 5, 5, 3, 4, 6, 9, 0, 8, 3, 0, 2, 6, 4, 2, 5, 2, 2,
3, 0, 8, 2, 5, 3, 3, 4, 4, 6, 8, 5, 0, 3, 5, 2, 6, 1, 9, 3, 1, 1,
8, 8, 1, 7, 1, 0, 1, 0, 0, 0, 3, 1, 3, 7, 8, 3, 8, 7, 5, 2, 8, 8,
6, 5, 8, 7, 5, 3, 3, 2, 0, 8, 3, 8, 1, 4, 2, 0, 6, 1, 7, 1, 7, 7,
6, 6, 9, 1, 4, 7, 3, 0, 3, 5, 9, 8, 2, 5, 3, 4, 9, 0, 4, 2, 8, 7,
5, 5, 4, 6, 8, 7, 3, 1, 1, 5, 9, 5, 6, 2, 8, 6, 3, 8, 8, 2, 3, 5,
3, 7, 8, 7, 5, 9, 3, 7, 5, 1, 9, 5, 7, 7, 8, 1, 8, 5, 7, 7, 3, 0,
5, 3, 2, 1, 7, 1, 2, 2, 6, 8, 0, 6, 6, 1, 3, 0, 0, 1, 9, 2, 7, 8,
7, 6, 6, 1, 1, 1, 9, 5, 9, 0, 9, 2, 1, 6, 4, 2, 0, 1, 9, 8, 9, 3,
8, 0, 9, 5, 2, 5, 7, 2, 0, 1, 0, 6, 5, 4, 8, 5, 8, 6, 3, 2, 7, 8,
8, 6, 5, 9, 3, 6, 1, 5, 3, 3, 8, 1, 8, 2, 7, 9, 6, 8, 2, 3, 0, 3,
0, 1, 9, 5, 2, 0, 3, 5, 3, 0, 1, 8, 5, 2, 9, 6, 8, 9, 9, 5, 7, 7,
3, 6, 2, 2, 5, 9, 9, 4, 1, 3, 8, 9, 1, 2, 4, 9, 7, 2, 1, 7, 7, 5,
2, 8, 3, 4, 7, 9, 1, 3, 1, 5, 1, 5, 5, 7, 4, 8, 5, 7, 2, 4, 2, 4,
5, 4, 1, 5, 0, 6, 9, 5, 9, 5, 0, 8, 2, 9, 5, 3, 3, 1, 1, 6, 8, 6,
1, 7, 2, 7, 8, 5, 5, 8, 8, 9, 0, 7, 5, 0, 9, 8, 3, 8, 1, 7, 5, 4,
6, 3, 7, 4, 6, 4, 9, 3, 9, 3, 1, 9, 2, 5, 5, 0, 6, 0, 4, 0, 0, 9,
2, 7, 7, 0, 1, 6, 7, 1, 1, 3, 9, 0, 0, 9, 8, 4, 8, 8, 2, 4, 0, 1,
2, 8, 5, 8, 3, 6, 1, 6, 0, 3, 5, 6, 3, 7, 0, 7, 6, 6, 0, 1, 0, 4,
7, 1, 0, 1, 8, 1, 9, 4, 2, 9, 5, 5, 5, 9, 6, 1, 9, 8, 9, 4, 6, 7,
6, 7, 8, 3, 7, 4, 4, 9, 4, 4, 8, 2, 5, 5, 3, 7, 9, 7, 7, 4, 7, 2,
6, 8, 4, 7, 1, 0, 4, 0, 4, 7, 5, 3, 4, 6, 4, 6, 2, 0, 8, 0, 4, 6,
6, 8, 4, 2, 5, 9, 0, 6, 9, 4, 9, 1, 2, 9, 3, 3, 1, 3, 6, 7, 7, 0,
2, 8, 9, 8, 9, 1, 5, 2, 1, 0, 4, 7, 5, 2, 1, 6, 2, 0, 5, 6, 9, 6,
6, 0, 2, 4, 0, 5, 8, 0, 3, 8, 1, 5, 0, 1, 9, 3, 5, 1, 1, 2, 5, 3,
3, 8, 2, 4, 3, 0, 0, 3, 5, 5, 8, 7, 6, 4, 0, 2, 4, 7, 4, 9, 6, 4,
7, 3, 2, 6, 3, 9, 1, 4, 1, 9, 9, 2, 7, 2, 6, 0, 4, 2, 6, 9, 9, 2,
2, 7, 9, 6, 7, 8, 2, 3, 5, 4, 7, 8, 1, 6, 3, 6, 0, 0, 9, 3, 4, 1,
7, 2, 1, 6, 4, 1, 2, 1, 9, 9, 2, 4, 5, 8, 6, 3, 1, 5, 0, 3, 0, 2,
8, 6, 1, 8, 2, 9, 7, 4, 5, 5, 5, 7, 0, 6, 7, 4, 9, 8, 3, 8, 5, 0,
5, 4, 9, 4, 5, 8, 8, 5, 8, 6, 9, 2, 6, 9, 9, 5, 6, 9, 0, 9, 2, 7,
2, 1, 0, 7, 9, 7, 5, 0, 9, 3, 0, 2, 9, 5, 5, 3, 2, 1, 1, 6, 5, 3,
4, 4, 9, 8, 7, 2, 0, 2, 7, 5, 5, 9, 6, 0, 2, 3, 6, 4, 8, 0, 6, 6,
5, 4, 9, 9, 1, 1, 9, 8, 8, 1, 8, 3, 4, 7, 9, 7, 7, 5, 3, 5, 6, 6,
3, 6, 9, 8, 0, 7, 4, 2, 6, 5, 4, 2, 5, 2, 7, 8, 6, 2, 5, 5, 1, 8,
1, 8, 4, 1, 7, 5, 7, 4, 6, 7, 2, 8, 9, 0, 9, 7, 7, 7, 7, 2, 7, 9,
3, 8, 0, 0, 0, 8, 1, 6, 4, 7, 0, 6, 0, 0, 1, 6, 1, 4, 5, 2, 4, 9,
1, 9, 2, 1, 7, 3, 2, 1, 7, 2, 1, 4, 7, 7, 2, 3, 5, 0, 1, 4, 1, 4,
4, 1, 9, 7, 3, 5, 6, 8, 5, 4, 8, 1, 6, 1, 3, 6, 1, 1, 5, 7, 3, 5,
2, 5, 5, 2, 1, 3, 3, 4, 7, 5, 7, 4, 1, 8, 4, 9, 4, 6, 8, 4, 3, 8,
5, 2, 3, 3, 2, 3, 9, 0, 7, 3, 9, 4, 1, 4, 3, 3, 3, 4, 5, 4, 7, 7,
6, 2, 4, 1, 6, 8, 6, 2, 5, 1, 8, 9, 8, 3, 5, 6, 9, 4, 8, 5, 5, 6,
2, 0, 9, 9, 2, 1, 9, 2, 2, 2, 1, 8, 4, 2, 7, 2, 5, 5, 0, 2, 5, 4,
2, 5, 6, 8, 8, 7, 6, 7, 1, 7, 9, 0, 4, 9, 4, 6, 0, 1, 6, 5, 3, 4,
6, 6, 8, 0, 4, 9, 8, 8, 6, 2, 7, 2, 3, 2, 7, 9, 1, 7, 8, 6, 0, 8,
5, 7, 8, 4, 3, 8, 3, 8, 2, 7, 9, 6, 7, 9, 7, 6, 6, 8, 1, 4, 5, 4,
1, 0, 0, 9, 5, 3, 8, 8, 3, 7, 8, 6, 3, 6, 0, 9, 5, 0, 6, 8, 0, 0,
6, 4, 2, 2, 5, 1, 2, 5, 2, 0, 5, 1, 1, 7, 3, 9, 2, 9, 8, 4, 8, 9,
6, 0, 8, 4, 1, 2, 8, 4, 8, 8, 6, 2, 6, 9, 4, 5, 6, 0, 4, 2, 4, 1,
9, 6, 5, 2, 8, 5, 0, 2, 2, 2, 1, 0, 6, 6, 1, 1, 8, 6, 3, 0, 6, 7,
4, 4, 2, 7, 8, 6, 2, 2, 0, 3, 9, 1, 9, 4, 9, 4, 5, 0, 4, 7, 1, 2,
3, 7, 1, 3, 7, 8, 6, 9, 6, 0, 9, 5, 6, 3, 6, 4, 3, 7, 1, 9, 1, 7,
2, 8, 7, 4, 6, 7, 7, 6, 4, 6, 5, 7, 5, 7, 3, 9, 6, 2, 4, 1, 3, 8,
9, 0, 8, 6, 5, 8, 3, 2, 6, 4, 5, 9, 9, 5, 8, 1, 3, 3, 9, 0, 4, 7,
8, 0, 2, 7, 5, 9, 0, 0, 9, 9, 4, 6, 5, 7, 6, 4, 0, 7, 8, 9, 5, 1,
2, 6, 9, 4, 6, 8, 3, 9, 8, 3, 5, 2, 5, 9, 5, 7, 0, 9, 8, 2, 5, 8,
2, 2, 6, 2, 0, 5, 2, 2, 4, 8, 9, 4, 0, 7, 7, 2, 6, 7, 1, 9, 4, 7,
8, 2, 6, 8, 4, 8, 2, 6, 0, 1, 4, 7, 6, 9, 9, 0, 9, 0, 2, 6, 4, 0,
1, 3, 6, 3, 9, 4, 4, 3, 7, 4, 5, 5, 3, 0, 5, 0, 6, 8, 2, 0, 3, 4,
9, 6, 2, 5, 2, 4, 5, 1, 7, 4, 9, 3, 9, 9, 6, 5, 1, 4, 3, 1, 4, 2,
9, 8, 0, 9, 1, 9, 0, 6, 5, 9, 2, 5, 0, 9, 3, 7, 2, 2, 1, 6, 9, 6,
4, 6, 1, 5, 1, 5, 7, 0, 9, 8, 5, 8, 3, 8, 7, 4, 1, 0, 5, 9, 7, 8,
8, 5, 9, 5, 9, 7, 7, 2, 9, 7, 5, 4, 9, 8, 9, 3, 0, 1, 6, 1, 7, 5,
3, 9, 2, 8, 4, 6, 8, 1, 3, 8, 2, 6, 8, 6, 8, 3, 8, 6, 8, 9, 4, 2,
7, 7, 4, 1, 5, 5, 9, 9, 1, 8, 5, 5, 9, 2, 5, 2, 4, 5, 9, 5, 3, 9,
5, 9, 4, 3, 1, 0, 4, 9, 9, 7, 2, 5, 2, 4, 6, 8, 0, 8, 4, 5, 9, 8,
7, 2, 7, 3, 6, 4, 4, 6, 9, 5, 8, 4, 8, 6, 5, 3, 8, 3, 6, 7, 3, 6,
2, 2, 2, 6, 2, 6, 0, 9, 9, 1, 2, 4, 6, 0, 8, 0, 5, 1, 2, 4, 3, 8,
8, 4, 3, 9, 0, 4, 5, 1, 2, 4, 4, 1, 3, 6, 5, 4, 9, 7, 6, 2, 7, 8,
0, 7, 9, 7, 7, 1, 5, 6, 9, 1, 4, 3, 5, 9, 9, 7, 7, 0, 0, 1, 2, 9,
6, 1, 6, 0, 8, 9, 4, 4, 1, 6, 9, 4, 8, 6, 8, 5, 5, 5, 8, 4, 8, 4,
0, 6, 3, 5, 3, 4, 2, 2, 0, 7, 2, 2, 2, 5, 8, 2, 8, 4, 8, 8, 6, 4,
8, 1, 5, 8, 4, 5, 6, 0, 2, 8, 5, 0, 6, 0, 1, 6, 8, 4, 2, 7, 3, 9,
4, 5, 2, 2, 6, 7, 4, 6, 7, 6, 7, 8, 8, 9, 5, 2, 5, 2, 1, 3, 8, 5,
2, 2, 5, 4, 9, 9, 5, 4, 6, 6, 6, 7, 2, 7, 8, 2, 3, 9, 8, 6, 4, 5,
6, 5, 9, 6, 1, 1, 6, 3, 5, 4, 8, 8, 6, 2, 3, 0, 5, 7, 7, 4, 5, 6,
4, 9, 8, 0, 3, 5, 5, 9, 3, 6, 3, 4, 5, 6, 8, 1, 7, 4, 3, 2, 4, 1,
1, 2, 5, 1, 5, 0, 7, 6, 0, 6, 9, 4, 7, 9, 4, 5, 1, 0, 9, 6, 5, 9,
6, 0, 9, 4, 0, 2, 5, 2, 2, 8, 8, 7, 9, 7, 1, 0, 8, 9, 3, 1, 4, 5,
6, 6, 9, 1, 3, 6, 8, 6, 7, 2, 2, 8, 7, 4, 8, 9, 4, 0, 5, 6, 0, 1,
0, 1, 5, 0, 3, 3, 0, 8, 6, 1, 7, 9, 2, 8, 6, 8, 0, 9, 2, 0, 8, 7,
4, 7, 6, 0, 9, 1, 7, 8, 2, 4, 9, 3, 8, 5, 8, 9, 0, 0, 9, 7, 1, 4,
9, 0, 9, 6, 7, 5, 9, 8, 5, 2, 6, 1, 3, 6, 5, 5, 4, 9, 7, 8, 1, 8,
9, 3, 1, 2, 9, 7, 8, 4, 8, 2, 1, 6, 8, 2, 9, 9, 8, 9, 4, 8, 7, 2,
2, 6, 5, 8, 8, 0, 4, 8, 5, 7, 5, 6, 4, 0, 1, 4, 2, 7, 0, 4, 7, 7,
5, 5, 5, 1, 3, 2, 3, 7, 9, 6, 4, 1, 4, 5, 1, 5, 2, 3, 7, 4, 6, 2,
3, 4, 3, 6, 4, 5, 4, 2, 8, 5, 8, 4, 4, 4, 7, 9, 5, 2, 6, 5, 8, 6,
7, 8, 2, 1, 0, 5, 1, 1, 4, 1, 3, 5, 4, 7, 3, 5, 7, 3, 9, 5, 2, 3,
1, 1, 3, 4, 2, 7, 1, 6, 6, 1, 0, 2, 1, 3, 5, 9, 6, 9, 5, 3, 6, 2,
3, 1, 4, 4, 2, 9, 5, 2, 4, 8, 4, 9, 3, 7, 1, 8, 7, 1, 1, 0, 1, 4,
5, 7, 6, 5, 4, 0, 3, 5, 9, 0, 2, 7, 9, 9, 3, 4, 4, 0, 3, 7, 4, 2,
0, 0, 7, 3, 1, 0, 5, 7, 8, 5, 3, 9, 0, 6, 2, 1, 9, 8, 3, 8, 7, 4,
4, 7, 8, 0, 8, 4, 7, 8, 4, 8, 9, 6, 8, 3, 3, 2, 1, 4, 4, 5, 7, 1,
3, 8, 6, 8, 7, 5, 1, 9, 4, 3, 5, 0, 6, 4, 3, 0, 2, 1, 8, 4, 5, 3,
1, 9, 1, 0, 4, 8, 4, 8, 1, 0, 0, 5, 3, 7, 0, 6, 1, 4, 6, 8, 0, 6,
7, 4, 9, 1, 9, 2, 7, 8, 1, 9, 1, 1, 9, 7, 9, 3, 9, 9, 5, 2, 0, 6,
1, 4, 1, 9, 6, 6, 3, 4, 2, 8, 7, 5, 4, 4, 4, 0, 6, 4, 3, 7, 4, 5,
1, 2, 3, 7, 1, 8, 1, 9, 2, 1, 7, 9, 9, 9, 8, 3, 9, 1, 0, 1, 5, 9,
1, 9, 5, 6, 1, 8, 1, 4, 6, 7, 5, 1, 4, 2, 6, 9, 1, 2, 3, 9, 7, 4,
8, 9, 4, 0, 9, 0, 7, 1, 8, 6, 4, 9, 4, 2, 3, 1, 9, 6, 1, 5, 6, 7,
9, 4, 5, 2, 0, 8, 0, 9, 5, 1, 4, 6, 5, 5, 0, 2, 2, 5, 2, 3, 1, 6,
0, 3, 8, 8, 1, 9, 3, 0, 1, 4, 2, 0, 9, 3, 7, 6, 2, 1, 3, 7, 8, 5,
5, 9, 5, 6, 6, 3, 8, 9, 3, 7, 7, 8, 7, 0, 8, 3, 0, 3, 9, 0, 6, 9,
7, 9, 2, 0, 7, 7, 3, 4, 6, 7, 2, 2, 1, 8, 2, 5, 6, 2, 5, 9, 9, 6,
6, 1, 5, 0, 1, 4, 2, 1, 5, 0, 3, 0, 6, 8, 0, 3, 8, 4, 4, 7, 7, 3,
4, 5, 4, 9, 2, 0, 2, 6, 0, 5, 4, 1, 4, 6, 6, 5, 9, 2, 5, 2, 0, 1,
4, 9, 7, 4, 4, 2, 8, 5, 0, 7, 3, 2, 5, 1, 8, 6, 6, 6, 0, 0, 2, 1,
3, 2, 4, 3, 4, 0, 8, 8, 1, 9, 0, 7, 1, 0, 4, 8, 6, 3, 3, 1, 7, 3,
4, 6, 4, 9, 6, 5, 1, 4, 5, 3, 9, 0, 5, 7, 9, 6, 2, 6, 8, 5, 6, 1,
0, 0, 5, 5, 0, 8, 1, 0, 6, 6, 5, 8, 7, 9, 6, 9, 9, 8, 1, 6, 3, 5,
7, 4, 7, 3, 6, 3, 8, 4, 0, 5, 2, 5, 7, 1, 4, 5, 9, 1, 0, 2, 8, 9,
7, 0, 6, 4, 1, 4, 0, 1, 1, 0, 9, 7, 1, 2, 0, 6, 2, 8, 0, 4, 3, 9,
0, 3, 9, 7, 5, 9, 5, 1, 5, 6, 7, 7, 1, 5, 7, 7, 0, 0, 4, 2, 0, 3,
3, 7, 8, 6, 9, 9, 3, 6, 0, 0, 7, 2, 3, 0, 5, 5, 8, 7, 6, 3, 1, 7,
6, 3, 5, 9, 4, 2, 1, 8, 7, 3, 1, 2, 5, 1, 4, 7, 1, 2, 0, 5, 3, 2,
9, 2, 8, 1, 9, 1, 8, 2, 6, 1, 8, 6, 1, 2, 5, 8, 6, 7, 3, 2, 1, 5,
7, 9, 1, 9, 8, 4, 1, 4, 8, 4, 8, 8, 2, 9, 1, 6, 4, 4, 7, 0, 6, 0,
9, 5, 7, 5, 2, 7, 0, 6, 9, 5, 7, 2, 2, 0, 9, 1, 7, 5, 6, 7, 1, 1,
6, 7, 2, 2, 9, 1, 0, 9, 8, 1, 6, 9, 0, 9, 1, 5, 2, 8, 0, 1, 7, 3,
5, 0, 6, 7, 1, 2, 7, 4, 8, 5, 8, 3, 2, 2, 2, 8, 7, 1, 8, 3, 5, 2,
0, 9, 3, 5, 3, 9, 6, 5, 7, 2, 5, 1, 2, 1, 0, 8, 3, 5, 7, 9, 1, 5,
1, 3, 6, 9, 8, 8, 2, 0, 9, 1, 4, 4, 4, 2, 1, 0, 0, 6, 7, 5, 1, 0,
3, 3, 4, 6, 7, 1, 1, 0, 3, 1, 4, 1, 2, 6, 7, 1, 1, 1, 3, 6, 9, 9,
0, 8, 6, 5, 8, 5, 1, 6, 3, 9, 8, 3, 1, 5, 0, 1, 9, 7, 0, 1, 6, 5,
1, 5, 1, 1, 6, 8, 5, 1, 7, 1, 4, 3, 7, 6, 5, 7, 6, 1, 8, 3, 5, 1,
5, 5, 6, 5, 0, 8, 8, 4, 9, 0, 9, 9, 8, 9, 8, 5, 9, 9, 8, 2, 3, 8,
7, 3, 4, 5, 5, 2, 8, 3, 3, 1, 6, 3, 5, 5, 0, 7, 6, 4, 7, 9, 1, 8,
5, 3, 5, 8, 9, 3, 2, 2, 6, 1, 8, 5, 4, 8, 9, 6, 3, 2, 1, 3, 2, 9,
3, 3, 0, 8, 9, 8, 5, 7, 0, 6, 4, 2, 0, 4, 6, 7, 5, 2, 5, 9, 0, 7,
0, 9, 1, 5, 4, 8, 1, 4, 1, 6, 5, 4, 9, 8, 5, 9, 4, 6, 1, 6, 3, 7,
1, 8, 0, 2, 7, 0, 9, 8, 1, 9, 9, 4, 3, 0, 9, 9, 2, 4, 4, 8, 8, 9,
5, 7, 5, 7, 1, 2, 8, 2, 8, 9, 0, 5, 9, 2, 3, 2, 3, 3, 2, 6, 0, 9,
7, 2, 9, 9, 7, 1, 2, 0, 8, 4, 4, 3, 3, 5, 7, 3, 2, 6, 5, 4, 8, 9,
3, 8, 2, 3, 9, 1, 1, 9, 3, 2, 5, 9, 7, 4, 6, 3, 6, 6, 7, 3, 0, 5,
8, 3, 6, 0, 4, 1, 4, 2, 8, 1, 3, 8, 8, 3, 0, 3, 2, 0, 3, 8, 2, 4,
9, 0, 3, 7, 5, 8, 9, 8, 5, 2, 4, 3, 7, 4, 4, 1, 7, 0, 2, 9, 1, 3,
2, 7, 6, 5, 6, 1, 8, 0, 9, 3, 7, 7, 3, 4, 4, 4, 0, 3, 0, 7, 0, 7,
4, 6, 9, 2, 1, 1, 2, 0, 1, 9, 1, 3, 0, 2, 0, 3, 3, 0, 3, 8, 0, 1,
9, 7, 6, 2, 1, 1, 0, 1, 1, 0, 0, 4, 4, 9, 2, 9, 3, 2, 1, 5, 1, 6,
0, 8, 4, 2, 4, 4, 4, 8, 5, 9, 6, 3, 7, 6, 6, 9, 8, 3, 8, 9, 5, 2,
2, 8, 6, 8, 4, 7, 8, 3, 1, 2, 3, 5, 5, 2, 6, 5, 8, 2, 1, 3, 1, 4,
4, 9, 5, 7, 6, 8, 5, 7, 2, 6, 2, 4, 3, 3, 4, 4, 1, 8, 9, 3, 0, 3,
9, 6, 8, 6, 4, 2, 6, 2, 4, 3, 4, 1, 0, 7, 7, 3, 2, 2, 6, 9, 7, 8,
0, 2, 8, 0, 7, 3, 1, 8, 9, 1, 5, 4, 4, 1, 1, 0, 1, 0, 4, 4, 6, 8,
2, 3, 2, 5, 2, 7, 1, 6, 2, 0, 1, 0, 5, 2, 6, 5, 2, 2, 7, 2, 1, 1,
1, 6, 6, 0, 3, 9, 6, 6, 6, 5, 5, 7, 3, 0, 9, 2, 5, 4, 7, 1, 1, 0,
5, 5, 7, 8, 5, 3, 7, 6, 3, 4, 6, 6, 8, 2, 0, 6, 5, 3, 1, 0, 9, 8,
9, 6, 5, 2, 6, 9, 1, 8, 6, 2, 0, 5, 6, 4, 7, 6, 9, 3, 1, 2, 5, 7,
0, 5, 8, 6, 3, 5, 6, 6, 2, 0, 1, 8, 5, 5, 8, 1, 0, 0, 7, 2, 9, 3,
6, 0, 6, 5, 9, 8, 7, 6, 4, 8, 6, 1, 1, 7, 9, 1, 0, 4, 5, 3, 3, 4,
8, 8, 5, 0, 3, 4, 6, 1, 1, 3, 6, 5, 7, 6, 8, 6, 7, 5, 3, 2, 4, 9,
4, 4, 1, 6, 6, 8, 0, 3, 9, 6, 2, 6, 5, 7, 9, 7, 8, 7, 7, 1, 8, 5,
5, 6, 0, 8, 4, 5, 5, 2, 9, 6, 5, 4, 1, 2, 6, 6, 5, 4, 0, 8, 5, 3,
0, 6, 1, 4, 3, 4, 4, 4, 3, 1, 8, 5, 8, 6, 7, 6, 9, 7, 5, 1, 4, 5,
6, 6, 1, 4, 0, 6, 8, 0, 0, 7, 0, 0, 2, 3, 7, 8, 7, 7, 6, 5, 9, 1,
3, 4, 4, 0, 1, 7, 1, 2, 7, 4, 9, 4, 7, 0, 4, 2, 0, 5, 6, 2, 2, 3,
0, 5, 3, 8, 9, 9, 4, 5, 6, 1, 3, 1, 4, 0, 7, 1, 1, 2, 7, 0, 0, 0,
4, 0, 7, 8, 5, 4, 7, 3, 3, 2, 6, 9, 9, 3, 9, 0, 8, 1, 4, 5, 4, 6,
6, 4, 6, 4, 5, 8, 8, 0, 7, 9, 7, 2, 7, 0, 8, 2, 6, 6, 8, 3, 0, 6,
3, 4, 3, 2, 8, 5, 8, 7, 8, 5, 6, 9, 8, 3, 0, 5, 2, 3, 5, 8, 0, 8,
9, 3, 3, 0, 6, 5, 7, 5, 7, 4, 0, 6, 7, 9, 5, 4, 5, 7, 1, 6, 3, 7,
7, 5, 2, 5, 4, 2, 0, 2, 1, 1, 4, 9, 5, 5, 7, 6, 1, 5, 8, 1, 4, 0,
0, 2, 5, 0, 1, 2, 6, 2, 2, 8, 5, 9, 4, 1, 3, 0, 2, 1, 6, 4, 7, 1,
5, 5, 0, 9, 7, 9, 2, 5, 9, 2, 3, 0, 9, 9, 0, 7, 9, 6, 5, 4, 7, 3,
7, 6, 1, 2, 5, 5, 1, 7, 6, 5, 6, 7, 5, 1, 3, 5, 7, 5, 1, 7, 8, 2,
9, 6, 6, 6, 4, 5, 4, 7, 7, 9, 1, 7, 4, 5, 0, 1, 1, 2, 9, 9, 6, 1,
4, 8, 9, 0, 3, 0, 4, 6, 3, 9, 9, 4, 7, 1, 3, 2, 9, 6, 2, 1, 0, 7,
3, 4, 0, 4, 3, 7, 5, 1, 8, 9, 5, 7, 3, 5, 9, 6, 1, 4, 5, 8, 9, 0,
1, 9, 3, 8, 9, 7, 1, 3, 1, 1, 1, 7, 9, 0, 4, 2, 9, 7, 8, 2, 8, 5,
6, 4, 7, 5, 0, 3, 2, 0, 3, 1, 9, 8, 6, 9, 1, 5, 1, 4, 0, 2, 8, 7,
0, 8, 0, 8, 5, 9, 9, 0, 4, 8, 0, 1, 0, 9, 4, 1, 2, 1, 4, 7, 2, 2,
1, 3, 1, 7, 9, 4, 7, 6, 4, 7, 7, 7, 2, 6, 2, 2, 4, 1, 4, 2, 5, 4,
8, 5, 4, 5, 4, 0, 3, 3, 2, 1, 5, 7, 1, 8, 5, 3, 0, 6, 1, 4, 2, 2,
8, 8, 1, 3, 7, 5, 8, 5, 0, 4, 3, 0, 6, 3, 3, 2, 1, 7, 5, 1, 8, 2,
9, 7, 9, 8, 6, 6, 2, 2, 3, 7, 1, 7, 2, 1, 5, 9, 1, 6, 0, 7, 7, 1,
6, 6, 9, 2, 5, 4, 7, 4, 8, 7, 3, 8, 9, 8, 6, 6, 5, 4, 9, 4, 9, 4,
5, 0, 1, 1, 4, 6, 5, 4, 0, 6, 2, 8, 4, 3, 3, 6, 6, 3, 9, 3, 7, 9,
0, 0, 3, 9, 7, 6, 9, 2, 6, 5, 6, 7, 2, 1, 4, 6, 3, 8, 5, 3, 0, 6,
7, 3, 6, 0, 9, 6, 5, 7, 1, 2, 0, 9, 1, 8, 0, 7, 6, 3, 8, 3, 2, 7,
1, 6, 6, 4, 1, 6, 2, 7, 4, 8, 8, 8, 8, 0, 0, 7, 8, 6, 9, 2, 5, 6,
0, 2, 9, 0, 2, 2, 8, 4, 7, 2, 1, 0, 4, 0, 3, 1, 7, 2, 1, 1, 8, 6,
0, 8, 2, 0, 4, 1, 9, 0, 0, 0, 4, 2, 2, 9, 6, 6, 1, 7, 1, 1, 9, 6,
3, 7, 7, 9, 2, 1, 3, 3, 7, 5, 7, 5, 1, 1, 4, 9, 5, 9, 5, 0, 1, 5,
6, 6, 0, 4, 9, 6, 3, 1, 8, 6, 2, 9, 4, 7, 2, 6, 5, 4, 7, 3, 6, 4,
2, 5, 2, 3, 0, 8, 1, 7, 7, 0, 3, 6, 7, 5, 1, 5, 9, 0, 6, 7, 3, 5,
0, 2, 3, 5, 0, 7, 2, 8, 3, 5, 4, 0, 5, 6, 7, 0, 4, 0, 3, 8, 6, 7,
4, 3, 5, 1, 3, 6, 2, 2, 2, 2, 4, 7, 7, 1, 5, 8, 9, 1, 5, 0, 4, 9,
5, 3, 0, 9, 8, 4, 4, 4, 8, 9, 3, 3, 3, 0, 9, 6, 3, 4, 0, 8, 7, 8,
0, 7, 6, 9, 3, 2, 5, 9, 9, 3, 9, 7, 8, 0, 5, 4, 1, 9, 3, 4, 1, 4,
4, 7, 3, 7, 7, 4, 4, 1, 8, 4, 2, 6, 3, 1, 2, 9, 8, 6, 0, 8, 0, 9,
9, 8, 8, 8, 6, 8, 7, 4, 1, 3, 2, 6, 0, 4, 7, 2 } ;
const size_t nacc1 = 3 ;
const double numacc1[3] = { 10000001, 10000003, 10000002 } ;
const size_t nacc2 = 1001 ;
double numacc2[1001] ;
const size_t nacc3 = 1001 ;
double numacc3[1001] ;
const size_t nacc4 = 1001 ;
double numacc4[1001] ;
numacc2[0] = 1.2 ;
numacc3[0] = 1000000.2 ;
numacc4[0] = 10000000.2 ;
for (i = 1 ; i < 1000 ; i += 2)
{
numacc2[i] = 1.1 ;
numacc2[i+1] = 1.3 ;
numacc3[i] = 1000000.1 ;
numacc3[i+1] = 1000000.3 ;
numacc4[i] = 10000000.1 ;
numacc4[i+1] = 10000000.3 ;
}
gsl_ieee_env_setup ();
{
double mean = gsl_stats_mean (lew, 1, nlew);
double sd = gsl_stats_sd (lew, 1, nlew);
double lag1 = gsl_stats_lag1_autocorrelation (lew, 1, nlew);
double expected_mean = -177.435000000000;
double expected_sd = 277.332168044316;
double expected_lag1 = -0.307304800605679;
gsl_test_rel (mean, expected_mean, 1e-15, "lew gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-15, "lew gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-14, "lew autocorrelation") ;
}
{
double mean = gsl_stats_mean (lottery, 1, nlottery);
double sd = gsl_stats_sd (lottery, 1, nlottery);
double lag1 = gsl_stats_lag1_autocorrelation (lottery, 1, nlottery);
double expected_mean = 518.958715596330;
double expected_sd = 291.699727470969;
double expected_lag1 = -0.120948622967393;
gsl_test_rel (mean, expected_mean, 1e-15, "lottery gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-15, "lottery gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-14, "lottery autocorrelation") ;
}
{
double mean = gsl_stats_mean (mavro, 1, nmavro);
double sd = gsl_stats_sd (mavro, 1, nmavro);
double lag1 = gsl_stats_lag1_autocorrelation (mavro, 1, nmavro);
double expected_mean = 2.00185600000000;
double expected_sd = 0.000429123454003053;
double expected_lag1 = 0.937989183438248;
gsl_test_rel (mean, expected_mean, 1e-15, "mavro gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-13, "mavro gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-13, "mavro autocorrelation") ;
}
{
double mean = gsl_stats_mean (michelson, 1, nmichelson);
double sd = gsl_stats_sd (michelson, 1, nmichelson);
double lag1 = gsl_stats_lag1_autocorrelation (michelson, 1, nmichelson);
double expected_mean = 299.852400000000;
double expected_sd = 0.0790105478190518;
double expected_lag1 = 0.535199668621283;
gsl_test_rel (mean, expected_mean, 1e-15, "michelson gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-13, "michelson gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-13, "michelson autocorrelation") ;
}
{
double mean = gsl_stats_mean (pidigits, 1, npidigits);
double sd = gsl_stats_sd (pidigits, 1, npidigits);
double lag1 = gsl_stats_lag1_autocorrelation (pidigits, 1, npidigits);
double expected_mean = 4.53480000000000;
double expected_sd = 2.86733906028871;
double expected_lag1 = -0.00355099287237972;
gsl_test_rel (mean, expected_mean, 1e-14, "pidigits gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-15, "pidigits gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-14, "pidigits autocorrelation") ;
}
{
double mean = gsl_stats_mean (numacc1, 1, nacc1);
double sd = gsl_stats_sd (numacc1, 1, nacc1);
double lag1 = gsl_stats_lag1_autocorrelation (numacc1, 1, nacc1);
double expected_mean = 10000002;
double expected_sd = 1;
double expected_lag1 = -0.5;
gsl_test_rel (mean, expected_mean, 1e-15, "acc1 gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-15, "acc1 gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-15, "acc1 autocorrelation") ;
}
{
double mean = gsl_stats_mean (numacc2, 1, nacc2);
double sd = gsl_stats_sd (numacc2, 1, nacc2);
double lag1 = gsl_stats_lag1_autocorrelation (numacc2, 1, nacc2);
double expected_mean = 1.2;
double expected_sd = 0.1;
double expected_lag1 = -0.999;
gsl_test_rel (mean, expected_mean, 1e-15, "acc2 gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-15, "acc2 gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-10, "acc2 autocorrelation") ;
}
{
double mean = gsl_stats_mean (numacc3, 1, nacc3);
double sd = gsl_stats_sd (numacc3, 1, nacc3);
double lag1 = gsl_stats_lag1_autocorrelation (numacc3, 1, nacc3);
double expected_mean = 1000000.2;
double expected_sd = 0.1;
double expected_lag1 = -0.999;
gsl_test_rel (mean, expected_mean, 1e-15, "acc3 gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-9, "acc3 gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-10, "acc3 autocorrelation") ;
}
{
double mean = gsl_stats_mean (numacc4, 1, nacc4);
double sd = gsl_stats_sd (numacc4, 1, nacc4);
double lag1 = gsl_stats_lag1_autocorrelation (numacc4, 1, nacc4);
double expected_mean = 10000000.2;
double expected_sd = 0.1;
double expected_lag1 = -0.999;
gsl_test_rel (mean, expected_mean, 1e-15, "acc4 gsl_stats_mean") ;
gsl_test_rel (sd, expected_sd, 1e-7, "acc4 gsl_stats_sd") ;
gsl_test_rel (lag1, expected_lag1, 1e-10, "acc4 autocorrelation") ;
}
return 0;
}
FrequencyCountDialog::FrequencyCountDialog(Table *t, QWidget* parent, Qt::WFlags fl )
: QDialog( parent, fl ),
d_source_table(t),
d_result_table(NULL),
d_col_name(""),
d_col_values(NULL),
d_bins(10)
{
setObjectName( "FrequencyCountDialog" );
setWindowTitle(tr("QtiPlot - Frequency count"));
setSizeGripEnabled( true );
setAttribute(Qt::WA_DeleteOnClose);
QGroupBox *gb1 = new QGroupBox();
QGridLayout *gl1 = new QGridLayout(gb1);
ApplicationWindow *app = (ApplicationWindow *)parent;
double min = 0.0, max = 0.0, step = 0.0;
if (t){
int col = -1;
int sr = 0;
int er = t->numRows();
int ts = t->table()->currentSelection();
if (ts >= 0){
Q3TableSelection sel = t->table()->selection(ts);
sr = sel.topRow();
er = sel.bottomRow() + 1;
col = sel.leftCol();
d_col_name = t->colName(col);
}
int size = 0;
for (int i = sr; i < er; i++){
if (!t->text(i, col).isEmpty())
size++;
}
if (size > 1)
d_col_values = gsl_vector_alloc(size);
if (d_col_values){
int aux = 0;
for (int i = sr; i < er; i++){
if (!t->text(i, col).isEmpty()){
gsl_vector_set(d_col_values, aux, t->cell(i, col));
aux++;
}
}
gsl_sort_vector(d_col_values);
min = floor(gsl_vector_get(d_col_values, 0));
max = ceil(gsl_vector_get(d_col_values, size - 1));
step = (max - min)/(double)d_bins;
int p = app->d_decimal_digits;
double *data = d_col_values->data;
QLocale l = app->locale();
QString s = "[" + QDateTime::currentDateTime().toString(Qt::LocalDate)+ " \"" + t->objectName() + "\"]\n";
s += tr("Statistics on %1").arg(d_col_name) + ":\n";
s += tr("Mean") + " = " + l.toString(gsl_stats_mean (data, 1, size), 'f', p) + "\n";
s += tr("Standard Deviation") + " = " + l.toString(gsl_stats_sd(data, 1, size), 'f', p) + "\n";
s += tr("Median") + " = " + l.toString(gsl_stats_median_from_sorted_data(data, 1, size), 'f', p) + "\n";
s += tr("Size") + " = " + QString::number(size) + "\n";
s += "--------------------------------------------------------------------------------------\n";
app->updateLog(s);
}
}
gl1->addWidget(new QLabel(tr("From Minimum")), 0, 0);
boxStart = new DoubleSpinBox();
boxStart->setLocale(app->locale());
boxStart->setValue(min);
boxStart->setDecimals(app->d_decimal_digits);
gl1->addWidget(boxStart, 0, 1);
gl1->addWidget(new QLabel(tr("To Maximum")), 1, 0);
boxEnd = new DoubleSpinBox();
boxEnd->setLocale(app->locale());
boxEnd->setValue(max);
boxEnd->setDecimals(app->d_decimal_digits);
gl1->addWidget(boxEnd, 1, 1);
gl1->addWidget(new QLabel(tr("Step Size")), 2, 0);
boxStep = new DoubleSpinBox();
boxStep->setLocale(app->locale());
boxStep->setValue(step);
boxStep->setDecimals(app->d_decimal_digits);
gl1->addWidget(boxStep, 2, 1);
gl1->setRowStretch(3, 1);
gl1->setColumnStretch(1, 1);
buttonApply = new QPushButton(tr( "&Apply" ));
buttonApply->setDefault( true );
buttonCancel = new QPushButton(tr( "&Cancel" ));
buttonOk = new QPushButton(tr( "&Ok" ));
QVBoxLayout *vl = new QVBoxLayout();
vl->addWidget(buttonApply);
vl->addWidget(buttonOk);
vl->addWidget(buttonCancel);
vl->addStretch();
QHBoxLayout *hb = new QHBoxLayout(this);
hb->addWidget(gb1, 1);
hb->addLayout(vl);
connect( buttonApply, SIGNAL( clicked() ), this, SLOT( apply() ) );
connect( buttonCancel, SIGNAL( clicked() ), this, SLOT( reject() ) );
connect( buttonOk, SIGNAL( clicked() ), this, SLOT( accept() ) );
}
void BoxCurve::drawBox(QPainter *painter, const QwtDiMap &xMap, const QwtDiMap &yMap, double *dat, int size)
{
const int px = xMap.transform(x(0));
const int px_min = xMap.transform(x(0) - 0.5);
const int px_max = xMap.transform(x(0) + 0.5);
const int box_width = 1+(px_max - px_min)*b_width/100;
const int hbw = box_width/2;
const int median = yMap.transform(gsl_stats_median_from_sorted_data (dat, 1, size));
int b_lowerq, b_upperq;
double sd, se, mean;
if(w_range == SD || w_range == SE || b_range == SD || b_range == SE)
{
sd = gsl_stats_sd(dat, 1, size);
se = sd/sqrt((double)size);
mean = gsl_stats_mean(dat, 1, size);
}
if(b_range == SD)
{
b_lowerq = yMap.transform(mean - sd*b_coeff);
b_upperq = yMap.transform(mean + sd*b_coeff);
}
else if(b_range == SE)
{
b_lowerq = yMap.transform(mean - se*b_coeff);
b_upperq = yMap.transform(mean + se*b_coeff);
}
else
{
b_lowerq = yMap.transform(gsl_stats_quantile_from_sorted_data (dat, 1, size, 1-0.01*b_coeff));
b_upperq = yMap.transform(gsl_stats_quantile_from_sorted_data (dat, 1, size, 0.01*b_coeff));
}
//draw box
if (b_style == Rect)
{
const QRect r = QRect(px - hbw, b_upperq, box_width, b_lowerq - b_upperq + 1);
painter->fillRect(r, QwtPlotCurve::brush());
painter->drawRect(r);
}
else if (b_style == Diamond)
{
const QPointArray pa(4);
pa[0] = QPoint(px, b_upperq);
pa[1] = QPoint(px + hbw, median);
pa[2] = QPoint(px, b_lowerq);
pa[3] = QPoint(px - hbw, median);
painter->setBrush(QwtPlotCurve::brush());
painter->drawPolygon(pa);
}
else if (b_style == WindBox)
{
const int lowerq = yMap.transform(gsl_stats_quantile_from_sorted_data (dat, 1, size, 0.25));
const int upperq = yMap.transform(gsl_stats_quantile_from_sorted_data (dat, 1, size, 0.75));
const QPointArray pa(8);
pa[0] = QPoint(px + hbw, b_upperq);
pa[1] = QPoint(int(px + 0.4*box_width), upperq);
pa[2] = QPoint(int(px + 0.4*box_width), lowerq);
pa[3] = QPoint(px + hbw, b_lowerq);
pa[4] = QPoint(px - hbw, b_lowerq);
pa[5] = QPoint(int(px - 0.4*box_width), lowerq);
pa[6] = QPoint(int(px - 0.4*box_width), upperq);
pa[7] = QPoint(px - hbw, b_upperq);
painter->setBrush(QwtPlotCurve::brush());
painter->drawPolygon(pa);
}
else if (b_style == Notch)
{
int j = (int)ceil(0.5*(size - 1.96*sqrt((double)size)));
int k = (int)ceil(0.5*(size + 1.96*sqrt((double)size)));
const int lowerCI = yMap.transform(dat[j]);
const int upperCI = yMap.transform(dat[k]);
const QPointArray pa(10);
pa[0] = QPoint(px + hbw, b_upperq);
pa[1] = QPoint(px + hbw, upperCI);
pa[2] = QPoint(int(px + 0.25*hbw), median);
pa[3] = QPoint(px + hbw, lowerCI);
pa[4] = QPoint(px + hbw, b_lowerq);
pa[5] = QPoint(px - hbw, b_lowerq);
pa[6] = QPoint(px - hbw, lowerCI);
pa[7] = QPoint(int(px - 0.25*hbw), median);
pa[8] = QPoint(px - hbw, upperCI);
pa[9] = QPoint(px - hbw, b_upperq);
painter->setBrush(QwtPlotCurve::brush());
painter->drawPolygon(pa);
}
if (w_range)
{//draw whiskers
const int l = int(0.1*box_width);
int w_upperq, w_lowerq;
if(w_range == SD)
{
w_lowerq = yMap.transform(mean - sd*w_coeff);
w_upperq = yMap.transform(mean + sd*w_coeff);
}
else if(w_range == SE)
{
w_lowerq = yMap.transform(mean - se*w_coeff);
w_upperq = yMap.transform(mean + se*w_coeff);
}
else
{
w_lowerq = yMap.transform(gsl_stats_quantile_from_sorted_data (dat, 1, size, 1-0.01*w_coeff));
w_upperq = yMap.transform(gsl_stats_quantile_from_sorted_data (dat, 1, size, 0.01*w_coeff));
}
painter->drawLine(px - l, w_lowerq, px + l, w_lowerq);
painter->drawLine(px - l, w_upperq, px + l, w_upperq);
if (b_style)
{
if (w_upperq != b_upperq)
painter->drawLine(px, w_upperq, px, b_upperq);
if (w_lowerq != b_lowerq)
painter->drawLine(px, w_lowerq, px, b_lowerq);
}
else
painter->drawLine(px, w_upperq, px, w_lowerq);
}
//draw median line
if (b_style == Notch || b_style == NoBox)
return;
if (b_style == WindBox)
painter->drawLine(int(px - 0.4*box_width), median, int(px + 0.4*box_width), median);
else
painter->drawLine(px - hbw, median, px + hbw, median);
}
int
main()
{
gsl_rng *r = gsl_rng_alloc(gsl_rng_default);
const double tol1 = 1.0e-8;
const double tol2 = 1.0e-3;
gsl_ieee_env_setup();
{
const size_t N = 2000000;
double *data = random_data(N, r);
double data2[] = { 4.0, 7.0, 13.0, 16.0 };
size_t i;
test_basic(2, data, tol1);
test_basic(100, data, tol1);
test_basic(1000, data, tol1);
test_basic(10000, data, tol1);
test_basic(50000, data, tol1);
test_basic(80000, data, tol1);
test_basic(1500000, data, tol1);
test_basic(2000000, data, tol1);
for (i = 0; i < 4; ++i)
data2[i] += 1.0e9;
test_basic(4, data2, tol1);
free(data);
}
{
/* dataset from Jain and Chlamtac paper */
const size_t n_jain = 20;
const double data_jain[] = { 0.02, 0.15, 0.74, 3.39, 0.83,
22.37, 10.15, 15.43, 38.62, 15.92,
34.60, 10.28, 1.47, 0.40, 0.05,
11.39, 0.27, 0.42, 0.09, 11.37 };
double expected_jain = 4.44063435326;
test_quantile(0.5, data_jain, n_jain, expected_jain, tol1, "jain");
}
{
size_t n = 1000000;
double *data = malloc(n * sizeof(double));
double *sorted_data = malloc(n * sizeof(double));
gsl_rstat_workspace *rstat_workspace_p = gsl_rstat_alloc();
double p;
size_t i;
for (i = 0; i < n; ++i)
{
data[i] = gsl_ran_gaussian_tail(r, 1.3, 1.0);
gsl_rstat_add(data[i], rstat_workspace_p);
}
memcpy(sorted_data, data, n * sizeof(double));
gsl_sort(sorted_data, 1, n);
/* test quantile calculation */
for (p = 0.1; p <= 0.9; p += 0.1)
{
double expected = gsl_stats_quantile_from_sorted_data(sorted_data, 1, n, p);
test_quantile(p, data, n, expected, tol2, "gauss");
}
/* test mean, variance */
{
const double expected_mean = gsl_stats_mean(data, 1, n);
const double expected_var = gsl_stats_variance(data, 1, n);
const double expected_sd = gsl_stats_sd(data, 1, n);
const double expected_skew = gsl_stats_skew(data, 1, n);
const double expected_kurtosis = gsl_stats_kurtosis(data, 1, n);
const double expected_median = gsl_stats_quantile_from_sorted_data(sorted_data, 1, n, 0.5);
const double mean = gsl_rstat_mean(rstat_workspace_p);
const double var = gsl_rstat_variance(rstat_workspace_p);
const double sd = gsl_rstat_sd(rstat_workspace_p);
const double skew = gsl_rstat_skew(rstat_workspace_p);
const double kurtosis = gsl_rstat_kurtosis(rstat_workspace_p);
const double median = gsl_rstat_median(rstat_workspace_p);
gsl_test_rel(mean, expected_mean, tol1, "mean");
gsl_test_rel(var, expected_var, tol1, "variance");
gsl_test_rel(sd, expected_sd, tol1, "stddev");
gsl_test_rel(skew, expected_skew, tol1, "skew");
gsl_test_rel(kurtosis, expected_kurtosis, tol1, "kurtosis");
gsl_test_abs(median, expected_median, tol2, "median");
}
free(data);
free(sorted_data);
gsl_rstat_free(rstat_workspace_p);
}
gsl_rng_free(r);
exit (gsl_test_summary());
}
/**
* Computes the Lomb-Scargle periodogram of the matrix "data". "data" should contain at least three
* columns: time, measurement and measurement error. The periodogram is calculated in "samples" intervals
* between "Pmin" and "Pmax", spaced logarithmically.
*
* The function returns a matrix of "samples" rows and several columns, including period, power (z) and
* an estimation of the upper bound for the false alarm probability. The estimation is calculated using
* the method of Baluev, 2008 (Baluev08). The column PS_Z_LS contains the unnormalized LS periodogram
* (z = 1/2 * (Chi^2_0 - Chi^2_SC)), while the column PS_Z contains z_1 = 1/2 * N_H * z / Chi^2_0 (z_1 in Baluev08).
* The FAP upper bound is estimated as ~ tau(z_1). (Another estimate of the FAP can be calculated by
* estimating the indep. frequencies through your own algorithm, or using the ok_periodogram_boot routine.)
*
* @param data Input data containing the data; each row containing (t_i, x_i, sigma_i)
* @param samples Number of frequencies sampled
* @param Pmin Minimum period sampled
* @param Pmax Maximum period sampled
* @param method Method to compute periodogram (ignored)
* @param timecol Time column (e.g. 0) in the matrix data
* @param valcol Value column (e.g. 1) in the matrix data
* @param sigmacol Sigma column (e.g. 2) in the matrix data
* @param p If not NULL, it is used to return additional info for the periodogram and reuse matrices to save space/speed. If you pass
* a value different than NULL, you are responsible for deallocating the workspace and its fields. p->buf is an array of
* gsl_matrix*, sized the same as the value of omp_get_max_threads().
* @return A matrix containing: {PS_TIME, PS_Z, PS_FAP, PS_Z_LS} (period, power, FAP upper limit, unnormalized
* LS power). You are responsible for deallocating it.
*/
gsl_matrix* ok_periodogram_ls(const gsl_matrix* data, const unsigned int samples, const double Pmin, const double Pmax, const int method,
unsigned int timecol, unsigned int valcol, unsigned int sigcol, ok_periodogram_workspace* p) {
gsl_matrix* ret = NULL;
gsl_matrix* buf = NULL;
gsl_vector* bufv = gsl_vector_alloc(data->size1);
int ndata = data->size1;
// If no pre-allocated buffers are passed through p, or p is null,
// allocate those buffers.
if (p != NULL) {
if (p->per != NULL && MROWS(p->per) == samples && MCOLS(p->per) == PS_SIZE)
ret = p->per;
if (p->buf != NULL && MROWS(p->buf) == ndata && MCOLS(p->per) == 5)
ret = p->buf;
}
ret = (ret != NULL ? ret : gsl_matrix_alloc(samples, PS_SIZE));
buf = (buf != NULL ? buf : gsl_matrix_alloc(ndata, 5));
double fmin = 1. / Pmax;
double fmax = 1. / Pmin;
double df = (fmax - fmin) / (double) samples;
gsl_matrix_get_col(bufv, data, timecol);
double W = 2. * M_PI * gsl_stats_sd(bufv->data, 1, ndata) / Pmin;
gsl_matrix_get_col(bufv, data, valcol);
double avg = gsl_stats_mean(bufv->data, 1, ndata);
double z1_max = 0.;
double xa[ndata];
// pre-calculate cdf, sdf
for (int i = 0; i < ndata; i++) {
double t = MGET(data, i, timecol) - MGET(data, 0, timecol);
MSET(buf, i, BUF_CDF, cos(2 * M_PI * df * t));
MSET(buf, i, BUF_SDF, sin(2 * M_PI * df * t));
MSET(buf, i, BUF_C, cos(2 * M_PI * fmin * t));
MSET(buf, i, BUF_S, sin(2 * M_PI * fmin * t));
MSET(buf, i, BUF_SIG, 1. / (MGET(data, i, sigcol) * MGET(data, i, sigcol)));
xa[i] = MGET(data, i, valcol) - avg;
}
// Calculate periodogram by looping over all angular frequencies
for (int i = 0; i < samples; i++) {
// Current frequency
double f = fmin + df * i;
double w = 2 * M_PI*f;
// Calculate tau(w)
double s_2wt = 0.;
double c_2wt = 0.;
for (int j = 0; j < ndata; j++) {
double cos_wt = C(j);
double sin_wt = S(j);
c_2wt += (1. - 2. * sin_wt * sin_wt) * SIG(j);
s_2wt += (2. * sin_wt * cos_wt) * SIG(j);
}
double tau = atan2(s_2wt, c_2wt) / (2. * w);
double numa = 0.;
double numb = 0.;
double dena = 0.;
double denb = 0.;
double numa_w = 0.;
double numb_w = 0.;
double dena_w = 0.;
double denb_w = 0.;
double coswtau = cos(w * tau);
double sinwtau = sin(w * tau);
double chi2_h = 0.;
double chi2_h_w = 0;
for (int j = 0; j < ndata; j++) {
double sig = SIG(j);
const double cos_wt = C(j);
const double sin_wt = S(j);
double cos_wdf = CDF(j);
double sin_wdf = SDF(j);
double c = cos_wt * coswtau + sin_wt * sinwtau;
double s = sin_wt * coswtau - cos_wt * sinwtau;
double x = xa[j];
MSET(buf, j, BUF_C, cos_wt * cos_wdf - sin_wt * sin_wdf);
MSET(buf, j, BUF_S, sin_wt * cos_wdf + cos_wt * sin_wdf);
numa += x * c * sig;
numb += x * s * sig;
dena += c * c * sig;
denb += s * s * sig;
chi2_h += x * x * sig;
numa_w += c;
numb_w += s;
dena_w += c*c;
denb_w += s*s;
chi2_h_w += 1;
}
double z = 0.5 * (numa * numa / dena + numb * numb / denb);
double z_1 = z * ndata / chi2_h;
double w_1 = 0.5 * (numa_w * numa_w / dena_w + numb_w * numb_w / denb_w) * ndata / chi2_h_w;
double fap_single = pow(1. - 2. * z_1 / (double) ndata, 0.5 * (double) (ndata - 3.));
double tau_z = W * fap_single * sqrt(z_1);
MSET(ret, samples - i - 1, PS_TIME, 1. / f);
MSET(ret, samples - i - 1, PS_Z, z_1);
MSET(ret, samples - i - 1, PS_Z_LS, z);
MSET(ret, samples - i - 1, PS_FAP, MIN(fap_single + tau_z, 1.));
MSET(ret, samples - i - 1, PS_TAU, tau);
MSET(ret, samples - i - 1, PS_WIN, w_1);
z1_max = MAX(z1_max, z_1);
}
if (p != NULL && p->calc_z_fap) {
gsl_root_fsolver * s = gsl_root_fsolver_alloc(gsl_root_fsolver_brent);
double pars[3];
pars[0] = ndata;
pars[1] = W;
pars[2] = 0.;
gsl_function F;
F.function = _baluev_tau;
F.params = pars;
double zz = z1_max;
while (_baluev_tau(zz, pars) > 1e-3)
zz *= 2;
p->z_fap_3 = _find_z(s, &F, 1e-3, 0.1, zz);
p->z_fap_2 = _find_z(s, &F, 1e-2, 0.1, p->z_fap_3);
p->z_fap_1 = _find_z(s, &F, 1e-1, 0.1, p->z_fap_2);
gsl_root_fsolver_free(s);
p->calc_z_fap = false;
}
if (p == NULL) {
gsl_matrix_free(buf);
} else {
p->per = ret;
p->buf = buf;
p->zmax = z1_max;
};
gsl_vector_free(bufv);
return ret;
}
void ErrDialog::add()
{
ApplicationWindow *app = qobject_cast<ApplicationWindow *>(parent());
if (!app)
return;
MultiLayer *plot = (MultiLayer *)app->activeWindow(ApplicationWindow::MultiLayerWindow);
if (!plot)
return;
Graph* g = plot->activeLayer();
if (!g)
return;
QString name = nameLabel->currentText();
DataCurve *curve = g->dataCurve(name);
if (!curve){
QMessageBox::critical(app, tr("QtiPlot - Error"),
tr("This feature is not available for user defined function curves!"));
return;
}
int direction = xErrBox->isChecked() ? 0 : 1;
ErrorBarsCurve *er = NULL;
if (columnBox->isChecked()){
QString errColumnName = tableNamesBox->currentText() + "_" + colNamesBox->currentText();
Table *errTable = app->table(errColumnName);
if (!errTable)
return;
/*if (w->numRows() != errTable->numRows()){
QMessageBox::critical(app, tr("QtiPlot - Error"), tr("The selected columns have different numbers of rows!"));
return;
}*/
if (errTable->isEmptyColumn(errTable->colIndex(errColumnName))){
QMessageBox::critical(app, tr("QtiPlot - Error"), tr("The selected error column is empty!"));
return;
}
er = g->addErrorBars(curve, errTable, errColumnName, direction);
} else {
Table *t = curve->table();
if (!t)
return;
if (direction == ErrorBarsCurve::Horizontal)
t->addCol(Table::xErr);
else
t->addCol(Table::yErr);
int r = curve->dataSize();
int rows = t->numRows();
int col = t->numCols() - 1;
int ycol = t->colIndex(curve->title().text());
if (!direction)
ycol = t->colIndex(curve->xColumnName());
QVarLengthArray<double> Y(r);
if (direction == ErrorBarsCurve::Horizontal){
for (int i = 0; i < r; i++)
Y[i] = curve->x(i);
} else {
for (int i = 0; i < r; i++)
Y[i] = curve->y(i);
}
if (percentBox->isChecked()){
double prc = 0.01*valueBox->value();
int aux = 0;
for (int i = curve->startRow(); i <= curve->endRow(); i++){
if (!t->text(i, ycol).isEmpty() && aux < r){
t->setCell(i, col, Y[aux]*prc);
aux++;
}
}
} else if (standardBox->isChecked() || standardErrorBox->isChecked()){
double sd = gsl_stats_sd(Y.data(), 1, r);
if (standardErrorBox->isChecked())
sd /= sqrt(r);
for (int i = 0; i < rows; i++){
if (!t->text(i, ycol).isEmpty())
t->setCell(i, col, sd);
}
}
er = g->addErrorBars(curve, t, t->colName(col), direction);
}
if (er){
er->setColor(curve->pen().color());
g->replot();
plot->notifyChanges();
}
}
int main(int argc, char *argv []) {
if(populate_env_variable(REF_ERROR_CODES_FILE, "L2_ERROR_CODES_FILE")) {
printf("\nUnable to populate [REF_ERROR_CODES_FILE] variable with corresponding environment variable. Routine will proceed without error handling\n");
}
if (argc != 15) {
if(populate_env_variable(SPF_BLURB_FILE, "L2_SPF_BLURB_FILE")) {
RETURN_FLAG = 1;
} else {
print_file(SPF_BLURB_FILE);
}
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -1, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
return 1;
} else {
// ***********************************************************************
// Redefine routine input parameters
char *target_f = strdup(argv[1]);
int bin_size_px = strtol(argv[2], NULL, 0);
double bg_percentile = strtod(argv[3], NULL);
double clip_sigma = strtod(argv[4], NULL);
int median_filter_width_px = strtol(argv[5], NULL, 0);
double min_SNR = strtod(argv[6], NULL);
int min_spatial_width_px = strtol(argv[7], NULL, 0);
int finding_window_lo_px = strtol(argv[8], NULL, 0);
int finding_window_hi_px = strtol(argv[9], NULL, 0);
int max_centering_num_px = strtol(argv[10], NULL, 0);
int centroid_half_window_size_px = strtol(argv[11], NULL, 0);
int min_used_bins = strtol(argv[12], NULL, 0);
int window_x_lo = strtol(argv[13], NULL, 0);
int window_x_hi = strtol(argv[14], NULL, 0);
// ***********************************************************************
// Open target file (ARG 1), get parameters and perform any data format
// checks
fitsfile *target_f_ptr;
int target_f_maxdim = 2, target_f_status = 0, target_f_bitpix, target_f_naxis;
long target_f_naxes [2] = {1,1};
if(!fits_open_file(&target_f_ptr, target_f, IMG_READ_ACCURACY, &target_f_status)) {
if(!populate_img_parameters(target_f, target_f_ptr, target_f_maxdim, &target_f_bitpix, &target_f_naxis, target_f_naxes, &target_f_status, "TARGET FRAME")) {
if (target_f_naxis != 2) { // any data format checks here
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -2, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
free(target_f);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
} else {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -3, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
fits_report_error(stdout, target_f_status);
free(target_f);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
} else {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -4, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
fits_report_error(stdout, target_f_status);
free(target_f);
return 1;
}
// ***********************************************************************
// Set the range limits
int cut_x [2] = {window_x_lo, window_x_hi};
int cut_y [2] = {1, target_f_naxes[1]};
// ***********************************************************************
// Set parameters used when reading data from target file (ARG 1)
long fpixel [2] = {cut_x[0], cut_y[0]};
long nxelements = (cut_x[1] - cut_x[0]) + 1;
long nyelements = (cut_y[1] - cut_y[0]) + 1;
// ***********************************************************************
// Create arrays to store pixel values from target fits file (ARG 1)
double target_f_pixels [nxelements];
// ***********************************************************************
// Get target fits file (ARG 1) values and store in 2D array
int ii;
double target_frame_values [nyelements][nxelements];
memset(target_frame_values, 0, sizeof(double)*nxelements*nyelements);
for (fpixel[1] = cut_y[0]; fpixel[1] <= cut_y[1]; fpixel[1]++) {
memset(target_f_pixels, 0, sizeof(double)*nxelements);
if(!fits_read_pix(target_f_ptr, TDOUBLE, fpixel, nxelements, NULL, target_f_pixels, NULL, &target_f_status)) {
for (ii=0; ii<nxelements; ii++) {
target_frame_values[fpixel[1]-1][ii] = target_f_pixels[ii];
}
} else {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -5, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
fits_report_error(stdout, target_f_status);
free(target_f);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
}
// FIND VALUES OF PEAK CENTROID ALONG DISPERSION AXIS
// ***********************************************************************
// 1. Bin array according to bin width given by [bin_size_px]
int disp_nelements = nxelements, spat_nelements = nyelements;
int disp_nelements_binned = (int)floor(disp_nelements/bin_size_px);
double this_frame_values_binned[spat_nelements][disp_nelements_binned];
memset(this_frame_values_binned, 0, sizeof(double)*spat_nelements*disp_nelements_binned);
double this_bin_value;
int bin_number = 0;
int jj;
for (jj=0; jj<spat_nelements; jj++) {
this_bin_value = 0;
bin_number = 0;
for (ii=0; ii<disp_nelements; ii++) {
if (ii % bin_size_px == 0 && ii != 0) {
this_frame_values_binned[jj][bin_number] = this_bin_value;
bin_number++;
this_bin_value = 0;
}
this_bin_value += target_frame_values[jj][ii];
}
}
printf("\nFinding peaks");
printf("\n-------------------------------------\n");
double peaks[disp_nelements_binned];
int num_bins_used = 0;
for (ii=0; ii<disp_nelements_binned; ii++) {
// 1a. Establish if any target flux is in this bin
// First find the mean/sd of the [bg_percentile]th lowest valued pixels as an initial parameters for sigma clip
double this_spat_values[spat_nelements];
double this_spat_values_sorted[spat_nelements];
for (jj=0; jj<spat_nelements; jj++) {
this_spat_values[jj] = this_frame_values_binned[jj][ii];
}
memcpy(this_spat_values_sorted, this_spat_values, sizeof(double)*spat_nelements);
gsl_sort(this_spat_values_sorted, 1, spat_nelements);
int bg_nelements = (int)floor(spat_nelements*bg_percentile);
double bg_values [bg_nelements];
int idx = 0;
for (jj=0; jj<spat_nelements; jj++) {
if (this_spat_values_sorted[jj] != 0) { // avoid 0s set from median filter edges
bg_values[idx] = this_spat_values_sorted[jj];
idx++;
if (idx == bg_nelements)
break;
}
}
if (idx != bg_nelements) {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -6, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
free(target_f);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
double start_mean = gsl_stats_mean(bg_values, 1, bg_nelements);
double start_sd = gsl_stats_sd(bg_values, 1, bg_nelements);
// 1b. Iterative sigma clip around dataset with the initial guess
int retain_indexes[spat_nelements];
double final_mean, final_sd;
int final_num_retained_indexes;
printf("\nBin:\t\t\t\t%d", ii);
printf("\nStart mean:\t\t\t%f", start_mean);
printf("\nStart SD:\t\t\t%f", start_sd);
iterative_sigma_clip(this_spat_values, spat_nelements, clip_sigma, retain_indexes, start_mean, start_sd, &final_mean, &final_sd, &final_num_retained_indexes, FALSE);
printf("\nFinal mean:\t\t\t%f", final_mean);
printf("\nFinal SD:\t\t\t%f", final_sd);
// 2. Smooth array with median filter
double this_spat_values_smoothed[spat_nelements];
memset(this_spat_values_smoothed, 0, sizeof(double)*spat_nelements);
median_filter(this_spat_values, this_spat_values_smoothed, spat_nelements, median_filter_width_px);
// 3. Ascertain if this bin contains target flux
int num_pixels_contain_target_flux = 0;
for (jj=0; jj<spat_nelements-1; jj++) {
if (this_spat_values_smoothed[jj] > final_mean + final_sd*min_SNR) {
num_pixels_contain_target_flux++;
}
}
printf("\nNum pixels (target):\t\t%d", num_pixels_contain_target_flux);
printf("\nDoes bin contain target flux?\t");
if (num_pixels_contain_target_flux >= min_spatial_width_px) {
printf("Yes\n");
} else {
printf("No\n");
peaks[ii] = -1;
continue;
}
// 3. Take derivatives
double this_spat_values_der[spat_nelements-1];
memset(this_spat_values_der, 0, sizeof(double)*spat_nelements-1);
for (jj=1; jj<spat_nelements; jj++) {
this_spat_values_der[jj-1] = this_frame_values_binned[jj][ii] - this_frame_values_binned[jj-1][ii];
}
// 4. Smooth derivatives
double this_spat_values_der_smoothed[spat_nelements-1];
memcpy(this_spat_values_der_smoothed, this_spat_values_der, sizeof(double)*spat_nelements-1);
median_filter(this_spat_values_der, this_spat_values_der_smoothed, spat_nelements-1, median_filter_width_px);
// 5. Pick most positive gradient from window, retaining proper index
double this_spat_values_der_smoothed_windowed[spat_nelements-1];
memcpy(this_spat_values_der_smoothed_windowed, this_spat_values_der_smoothed, sizeof(double)*spat_nelements-1);
for (jj=0; jj<spat_nelements; jj++) {
if (jj >= finding_window_lo_px && jj <= finding_window_hi_px) {
this_spat_values_der_smoothed_windowed[jj] = this_spat_values_der_smoothed_windowed[jj];
} else {
this_spat_values_der_smoothed_windowed[jj] = -1;
}
}
size_t this_pk_idx = gsl_stats_max_index(this_spat_values_der_smoothed_windowed, 1, spat_nelements-1);
printf("Start peak index:\t\t%d\n", this_pk_idx);
// 6. Using this index, walk through centering window [max_centering_num_px] and find derivative turnover point
printf("Found turnover:\t\t\t");
bool found_turnover = FALSE;
for (jj=this_pk_idx; jj<this_pk_idx+max_centering_num_px; jj++) {
if (this_spat_values_der_smoothed[jj] < 0.) {
this_pk_idx = jj;
found_turnover = TRUE;
break;
}
}
if (found_turnover) {
printf("Yes\n");
printf("End peak index:\t\t\t%d\n", this_pk_idx);
} else {
printf("No\n");
peaks[ii] = -1;
continue;
}
// 7. Get parabolic centroid using the full centroid window [centroid_half_window_size_px]
double this_pk_window_idxs[1 + (2*centroid_half_window_size_px)];
double this_pk_window_vals[1 + (2*centroid_half_window_size_px)];
memset(this_pk_window_idxs, 0, sizeof(double)*(1 + (2*centroid_half_window_size_px)));
memset(this_pk_window_vals, 0, sizeof(double)*(1 + (2*centroid_half_window_size_px)));
idx = 0;
for (jj=this_pk_idx-centroid_half_window_size_px; jj<=this_pk_idx+centroid_half_window_size_px; jj++) {
this_pk_window_idxs[idx] = jj;
this_pk_window_vals[idx] = this_frame_values_binned[jj][ii];
idx++;
}
int order = 2;
double coeffs [order+1];
memset(coeffs, 0, sizeof(double)*order+1);
double chi_squared;
if (calc_least_sq_fit(2, 1 + (2*centroid_half_window_size_px), this_pk_window_idxs, this_pk_window_vals, coeffs, &chi_squared)) {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -7, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
free(target_f);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
double fitted_peak_idx = -coeffs[1]/(2*coeffs[2]);
printf("Fitted peak index:\t\t%f\n", fitted_peak_idx);
// 8. Ensure fitted peak location is within finding window
printf("Is fitted peak within window?\t");
if (fitted_peak_idx > finding_window_lo_px && fitted_peak_idx < finding_window_hi_px) {
printf("Yes\n");
num_bins_used++;
} else {
printf("No\n");
peaks[ii] = -1;
continue;
}
peaks[ii] = fitted_peak_idx;
/*for (jj=0; jj<spat_nelements-1; jj++) {
printf("%d\t%f\t%f\n", jj, this_spat_values_der_smoothed[jj], this_spat_values_der[jj]);
}*/ // DEBUG
}
printf("\nNum bins used:\t%d\n", num_bins_used);
if (num_bins_used < min_used_bins) {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -8, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
free(target_f);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
// ***********************************************************************
// Create [FRFIND_OUTPUTF_PEAKS_FILE] output file and print a few
// parameters
FILE *outputfile;
outputfile = fopen(SPFIND_OUTPUTF_PEAKS_FILE, FILE_WRITE_ACCESS);
if (!outputfile) {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -9, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
free(target_f);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
char timestr [80];
memset(timestr, '\0', sizeof(char)*80);
find_time(timestr);
fprintf(outputfile, "#### %s ####\n\n", SPFIND_OUTPUTF_PEAKS_FILE);
fprintf(outputfile, "# Lists the coordinates of the peaks found using the spfind routine.\n\n");
fprintf(outputfile, "# Run filename:\t%s\n", target_f);
fprintf(outputfile, "# Run datetime:\t%s\n\n", timestr);
for (ii=0; ii<disp_nelements_binned; ii++) {
if (peaks[ii] == -1)
continue;
fprintf(outputfile, "%f\t%f\n", cut_x[0]+(ii*bin_size_px) + (double)bin_size_px/2., peaks[ii]);
}
fprintf(outputfile, "%d", EOF);
// ***********************************************************************
// Clean up heap memory
free(target_f);
// ***********************************************************************
// Close [FRFIND_OUTPUTF_PEAKS_FILE] output file and target file (ARG 1)
if (fclose(outputfile)) {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -10, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
if(fits_close_file(target_f_ptr, &target_f_status)) fits_report_error (stdout, target_f_status);
return 1;
}
if(fits_close_file(target_f_ptr, &target_f_status)) {
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", -11, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
fits_report_error (stdout, target_f_status);
return 1;
}
// ***********************************************************************
// Write success to [ERROR_CODES_FILE]
write_key_to_file(ERROR_CODES_FILE, REF_ERROR_CODES_FILE, "L2STATFI", RETURN_FLAG, "Status flag for L2 spfind routine", ERROR_CODES_FILE_WRITE_ACCESS);
return 0;
}
}
int
gsl_multifit_robust(const gsl_matrix * X,
const gsl_vector * y,
gsl_vector * c,
gsl_matrix * cov,
gsl_multifit_robust_workspace *w)
{
/* check matrix and vector sizes */
if (X->size1 != y->size)
{
GSL_ERROR
("number of observations in y does not match rows of matrix X",
GSL_EBADLEN);
}
else if (X->size2 != c->size)
{
GSL_ERROR ("number of parameters c does not match columns of matrix X",
GSL_EBADLEN);
}
else if (cov->size1 != cov->size2)
{
GSL_ERROR ("covariance matrix is not square", GSL_ENOTSQR);
}
else if (c->size != cov->size1)
{
GSL_ERROR
("number of parameters does not match size of covariance matrix",
GSL_EBADLEN);
}
else if (X->size1 != w->n || X->size2 != w->p)
{
GSL_ERROR
("size of workspace does not match size of observation matrix",
GSL_EBADLEN);
}
else
{
int s;
double chisq;
const double tol = GSL_SQRT_DBL_EPSILON;
int converged = 0;
size_t numit = 0;
const size_t n = y->size;
double sigy = gsl_stats_sd(y->data, y->stride, n);
double sig_lower;
size_t i;
/*
* if the initial fit is very good, then finding outliers by comparing
* them to the residual standard deviation is difficult. Therefore we
* set a lower bound on the standard deviation estimate that is a small
* fraction of the standard deviation of the data values
*/
sig_lower = 1.0e-6 * sigy;
if (sig_lower == 0.0)
sig_lower = 1.0;
/* compute initial estimates using ordinary least squares */
s = gsl_multifit_linear(X, y, c, cov, &chisq, w->multifit_p);
if (s)
return s;
/* save Q S^{-1} of original matrix */
gsl_matrix_memcpy(w->QSI, w->multifit_p->QSI);
gsl_vector_memcpy(w->D, w->multifit_p->D);
/* compute statistical leverage of each data point */
s = gsl_linalg_SV_leverage(w->multifit_p->A, w->resfac);
if (s)
return s;
/* correct residuals with factor 1 / sqrt(1 - h) */
for (i = 0; i < n; ++i)
{
double h = gsl_vector_get(w->resfac, i);
if (h > 0.9999)
h = 0.9999;
gsl_vector_set(w->resfac, i, 1.0 / sqrt(1.0 - h));
}
/* compute residuals from OLS fit r = y - X c */
s = gsl_multifit_linear_residuals(X, y, c, w->r);
if (s)
return s;
/* compute estimate of sigma from ordinary least squares */
w->stats.sigma_ols = gsl_blas_dnrm2(w->r) / sqrt((double) w->stats.dof);
while (!converged && ++numit <= w->maxiter)
{
double sig;
/* adjust residuals by statistical leverage (see DuMouchel and O'Brien) */
s = gsl_vector_mul(w->r, w->resfac);
if (s)
return s;
/* compute estimate of standard deviation using MAD */
sig = robust_madsigma(w->r, w);
/* scale residuals by standard deviation and tuning parameter */
gsl_vector_scale(w->r, 1.0 / (GSL_MAX(sig, sig_lower) * w->tune));
/* compute weights using these residuals */
s = w->type->wfun(w->r, w->weights);
if (s)
return s;
gsl_vector_memcpy(w->c_prev, c);
/* solve weighted least squares with new weights */
s = gsl_multifit_wlinear(X, w->weights, y, c, cov, &chisq, w->multifit_p);
if (s)
return s;
/* compute new residuals r = y - X c */
s = gsl_multifit_linear_residuals(X, y, c, w->r);
if (s)
return s;
converged = robust_test_convergence(w->c_prev, c, tol);
}
/* compute final MAD sigma */
w->stats.sigma_mad = robust_madsigma(w->r, w);
/* compute robust estimate of sigma */
w->stats.sigma_rob = robust_robsigma(w->r, w->stats.sigma_mad, w->tune, w);
/* compute final estimate of sigma */
w->stats.sigma = robust_sigma(w->stats.sigma_ols, w->stats.sigma_rob, w);
/* store number of iterations */
w->stats.numit = numit;
{
double dof = (double) w->stats.dof;
double rnorm = w->stats.sigma * sqrt(dof); /* see DuMouchel, sec 4.2 */
double ss_err = rnorm * rnorm;
double ss_tot = gsl_stats_tss(y->data, y->stride, n);
/* compute R^2 */
w->stats.Rsq = 1.0 - ss_err / ss_tot;
/* compute adjusted R^2 */
w->stats.adj_Rsq = 1.0 - (1.0 - w->stats.Rsq) * (n - 1.0) / dof;
/* compute rmse */
w->stats.rmse = sqrt(ss_err / dof);
/* store SSE */
w->stats.sse = ss_err;
}
/* calculate covariance matrix = sigma^2 (X^T X)^{-1} */
s = robust_covariance(w->stats.sigma, cov, w);
if (s)
return s;
/* raise an error if not converged */
if (numit > w->maxiter)
{
GSL_ERROR("maximum iterations exceeded", GSL_EMAXITER);
}
return s;
}
} /* gsl_multifit_robust() */
int main (void)
{
int i, j, warn, counter;
double lim[15];
lim[0]=0.01;
//lim[1]=0.02;
lim[1]=0.03;
//lim[3]=0.04;
lim[2]=0.05;
//lim[5]=0.09;
lim[3]=0.11;
//lim[7]=0.14;
lim[4]=0.17;
//lim[9]=0.208;
lim[5]=0.26;
//lim[11]=0.32;
lim[6]=0.4;
//lim[13]=0.51;
lim[7]=0.64;
//lim[15]=0.8;
lim[8]=1.0;
//lim[17]=1.25;
lim[9]=1.56;
//lim[19]=1.95;
lim[10]=2.44;
//lim[21]=3.05;
lim[11]=3.81;
//lim[23]=4.76;
lim[12]=5.96;
//lim[25]=7.45;
lim[13]=9.31;
//lim[27]=11.64;
lim[14]=14.55;
//lim[29]=18.18;
counter = 0.0;
double pos[N],vel[N],err[N],vel_sq, sum;
double mean, median, skew, sd, kurtosis, error, sum1, sum2;
FILE *velo, *sig, *script;
velo = fopen("organized.dat", "r");
sig = fopen("sigma.dat","w");
for(i=0.0; i<N; i++)
{
fscanf(velo, "%lf %lf %lf", &pos[i], &vel[i], &err[i]);
}
//printf ("The sample mean is %g\n", mean);
fclose(velo);
for(j=0.0; j<14.0; j=j+1.0)
{
counter = 0.0;
for(i = 0.0;(i)<=N; i++)
{
if( lim[j] <= pos[i] && pos[i] < lim[(j)+1] )
{
counter++;
gsl_sort (vel, 1, counter);
mean = gsl_stats_mean(vel, 1, counter);
median = gsl_stats_median_from_sorted_data (vel, 1, counter);
sd = gsl_stats_sd (vel, 1, counter);
skew = gsl_stats_skew(vel, 1, counter);
kurtosis = gsl_stats_kurtosis (vel, 1, counter);
sum1 = (vel[i]-mean)*(vel[i]-mean);
sum1++;
sum2 = (vel[i]-mean)*err[i];
sum2++;
error = 1.0/(sqrt(counter)) * pow(sum1,-0.5) * sum2;
}
}
printf("Particulas entre %lf y %lf es %d\n",lim[j],lim[j+1], counter);
printf("Error %lf\n", error);
printf ("The sample mean is %g\n", mean);
printf ("The median is %g\n", median);
printf ("Standard deviation is %g\n", sd);
printf ("Skewness is %g\n", skew);
printf ("kurtosis is %g\n\n", kurtosis);
//printf("Sigma in the bin is: %.2f\n\n", sig);
fprintf(sig, "%lf\t %lf\t %lf\t %d\n", (lim[j]+lim[j+1])*0.5, sd, error, counter);
}
fclose(sig);
script = fopen( "script1.gpl", "w" );
fprintf(script, "set grid\nset terminal png\nset output 'Sigma_vs_rad.png'\nset nokey\n");
fprintf( script, "set title 'Sigma vs Radius'\n" );
fprintf( script, "set logscale x\n" );
fprintf( script, "set xlabel 'Radius in Arcmin'\n" );
fprintf( script, "set xrange [0.01:20]\n" );
//fprintf( script, "set logscale x 10\n" );
fprintf( script, "set ylabel 'Sigma (km/s)'\n" );
fprintf( script, "plot 'sigma.dat' u 1:2:3 pt 7 ps 1 with errorbars\n");
fclose(script);
warn = system("gnuplot script1.gpl");
return(warn);
}
/**
* Compute the standard deviation of a REAL8Vector
* \param [in] vector Pointer to a REAL8Vector of values
* \return Standard deviation value
*/
REAL8 calcStddevD(REAL8Vector *vector)
{
REAL8 stddev = gsl_stats_sd((double*)vector->data, 1, vector->length);
return stddev;
} /* calcStddevD() */
