23.8 Examples

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)
     {
       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.