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<title>Example ntuple programs - GNU Scientific Library -- Reference Manual</title>
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<p>
<a name="Example-ntuple-programs"></a>
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Previous: <a rel="previous" accesskey="p" href="Histogramming-ntuple-values.html">Histogramming ntuple values</a>,
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<h3 class="section">22.8 Examples</h3>
<p>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 <samp><span class="file">test.dat</span></samp>.
<pre class="example"><pre class="verbatim"> #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;
}
</pre></pre>
<p class="noindent">The next program analyses the ntuple data in the file <samp><span class="file">test.dat</span></samp>.
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.
<pre class="example"><pre class="verbatim"> #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;
}
</pre></pre>
<p>The following plot shows the distribution of the selected events.
Note the cut-off at the lower bound.
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