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This chapter describes functions for creating and manipulating ntuples, sets of values associated with events. The ntuples are stored in files. Their values can be extracted in any combination and booked in a histogram using a selection function.
The values to be stored are held in a user-defined data structure, and an ntuple is created associating this data structure with a file. The values are then written to the file (normally inside a loop) using the ntuple functions described below.
A histogram can be created from ntuple data by providing a selection function and a value function. The selection function specifies whether an event should be included in the subset to be analyzed or not. The value function computes the entry to be added to the histogram for each event.
All the ntuple functions are defined in the header file `gsl_ntuple.h';
gsl_ntuple_write..
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); 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.
Further information on the use of ntuples can be found in the documentation for the CERN packages PAW and HBOOK (available online).
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