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The following example programs demonstrate the use of ntuples in managing a large dataset. The first program creates a set of 10,000 simulated “events”, each with 3 associated values (x,y,z). These are generated from a Gaussian distribution with unit variance, for demonstration purposes, and written to the ntuple file test.dat.
#include <gsl/gsl_ntuple.h> #include <gsl/gsl_rng.h> #include <gsl/gsl_randist.h> struct data { double x; double y; double z; }; int main (void) { const gsl_rng_type * T; gsl_rng * r; struct data ntuple_row; int i; gsl_ntuple *ntuple = gsl_ntuple_create ("test.dat", &ntuple_row, sizeof (ntuple_row)); gsl_rng_env_setup (); T = gsl_rng_default; r = gsl_rng_alloc (T); for (i = 0; i < 10000; i++) { ntuple_row.x = gsl_ran_ugaussian (r); ntuple_row.y = gsl_ran_ugaussian (r); ntuple_row.z = gsl_ran_ugaussian (r); gsl_ntuple_write (ntuple); } gsl_ntuple_close (ntuple); gsl_rng_free (r); return 0; }
The next program analyses the ntuple data in the file test.dat. The analysis procedure is to compute the squared-magnitude of each event, E^2=x^2+y^2+z^2, and select only those which exceed a lower limit of 1.5. The selected events are then histogrammed using their E^2 values.
#include <math.h> #include <gsl/gsl_ntuple.h> #include <gsl/gsl_histogram.h> struct data { double x; double y; double z; }; int sel_func (void *ntuple_data, void *params); double val_func (void *ntuple_data, void *params); int main (void) { struct data ntuple_row; gsl_ntuple *ntuple = gsl_ntuple_open ("test.dat", &ntuple_row, sizeof (ntuple_row)); double lower = 1.5; gsl_ntuple_select_fn S; gsl_ntuple_value_fn V; gsl_histogram *h = gsl_histogram_alloc (100); gsl_histogram_set_ranges_uniform(h, 0.0, 10.0); S.function = &sel_func; S.params = &lower; V.function = &val_func; V.params = 0; gsl_ntuple_project (h, ntuple, &V, &S); gsl_histogram_fprintf (stdout, h, "%f", "%f"); gsl_histogram_free (h); gsl_ntuple_close (ntuple); return 0; } int sel_func (void *ntuple_data, void *params) { struct data * data = (struct data *) ntuple_data; double x, y, z, E2, scale; scale = *(double *) params; x = data->x; y = data->y; z = data->z; E2 = x * x + y * y + z * z; return E2 > scale; } double val_func (void *ntuple_data, void *params) { (void)(params); /* avoid unused parameter warning */ struct data * data = (struct data *) ntuple_data; double x, y, z; x = data->x; y = data->y; z = data->z; return x * x + y * y + z * z; }
The following plot shows the distribution of the selected events. Note the cut-off at the lower bound.
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