analistica/ex-4/main.c

#include "lib.h"
#include <math.h>
#include <stdlib.h>
#include <string.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_min.h>
#include <gsl/gsl_deriv.h>


// Process CLI arguments.
//
int parser(size_t *N, size_t *n, double *p_max, char argc, char **argv)
  {
  for (size_t i = 1; i < argc; i++)
    {
         if (!strcmp(argv[i], "-N")) *N     = atol(argv[++i]);
    else if (!strcmp(argv[i], "-n")) *n     = atol(argv[++i]);
    else if (!strcmp(argv[i], "-p")) *p_max = atof(argv[++i]);
    else
      {
      fprintf(stderr, "Usage: %s -[hiIntp]\n", argv[0]);
      fprintf(stderr, "\t-h\tShow this message.\n");
      fprintf(stderr, "\t-N integer\tThe number of events to generate.\n");
      fprintf(stderr, "\t-n integer\tThe number of bins of the histogram.\n");
      fprintf(stderr, "\t-p float\tThe maximum value of momentum.\n");
      return EXIT_FAILURE;
      }
    }
  return EXIT_SUCCESS;
  }


int main(int argc, char **argv)
  {

  // Set default options.
  //
  size_t N = 50000;          // number of events.
  size_t n = 50;             // number of bins.
  double p_max = 10;         // maximum value of momentum module.
  int res = parser(&N, &n, &p_max, argc, argv);
  if (res == 0) printf("\nGenerating histogram with:\n"
                       "%ld points\n"
                       "%ld bins\n"
                       "p_max = %.3f\n\n", N, n, p_max);

  // printf("step: \t%.5f\n", step);

  // Initialize an RNG.
  //
  gsl_rng_env_setup();
  gsl_rng *r = gsl_rng_alloc(gsl_rng_default);

  // Generate the angle θ uniformly distributed on a sphere using the
  // inverse transform:
  //
  //   θ = acos(1 - 2X)
  //
  // where X is a random uniform variable in [0,1), and the module p of
  // the vector:
  //
  //   p² = p_v² + p_h²
  // 
  // uniformly distributed between 0 and p_max. The two components are
  // then computed as:
  //
  //   p_v = p⋅cos(θ)
  //   p_h = p⋅sin(θ)
  //
  // The histogram is updated this way.
  // The j-th bin where p_h goes in is given by:
  //
  //   step = p_max / n
  //   j = floor(p_h / step)
  //
  // Thus an histogram was created and a structure containing the number of
  // entries in each bin and the sum of |p_v| in each of them is created and
  // filled while generating the events (struct bin).
  //
  struct bin *histo = calloc(n, sizeof(struct bin));

  // Some useful variables.
  //
  double step = p_max / n;
  struct bin *b;
  double theta;
  double p;
  double p_v;
  double p_h;
  size_t j;

  for (size_t i = 0; i < N; i++)
    {
    // Generate the event.
    // 
    theta = acos(1 - 2*gsl_rng_uniform(r));
    p = p_max * gsl_rng_uniform(r);

    // Compute the components.
    //
    p_v = p * cos(theta);
    p_h = p * sin(theta);

    // Update the histogram.
    //
    j = floor(p_h / step);
    b = &histo[j];
    b -> amo++;
    b -> sum += fabs(p_v);
    }

  // Compute the mean value of each bin and print it to stodut
  // together with other useful things to make the histogram.
  //
  // printf("bins: \t%ld\n", n);
  // printf("step: \t%.5f\n", step);
  for (size_t i = 0; i < n; i++)
    {
    histo[i].sum = histo[i].sum / histo[i].amo;    // Average P_v
    //printf("\n%.5f", histo[i].sum);
    };
  
  // Compare the histigram with the expected function:
  //
  //   x * log(p_max/x)/arctan(sqrt(p_max^2/x^2 - 1))
  //
  // using the χ² test.
  //
  struct parameters params;
  params.histo = histo;
  params.n     = n;
  params.step  = step;

  gsl_function func;
  func.function = &chi2;
  func.params = &params;

  double min_p = 5;
  double max_p = 15;

  // Initialize minimization.
  //
  double x = 10;
  int    max_iter = 100;
  double prec = 1e-7;
  int    status;
  const gsl_min_fminimizer_type *T = gsl_min_fminimizer_brent;
  gsl_min_fminimizer *s = gsl_min_fminimizer_alloc(T);
  gsl_min_fminimizer_set(s, &func, x, min_p, max_p);

  // Minimization.
  //
  for (int iter = 0; status == GSL_CONTINUE && iter < max_iter; iter++)
    {  
    status = gsl_min_fminimizer_iterate(s);
    x      = gsl_min_fminimizer_x_minimum(s);
    min_p  = gsl_min_fminimizer_x_lower(s);
    max_p  = gsl_min_fminimizer_x_upper(s);
    status = gsl_min_test_interval(min_p, max_p, 0, prec);
    }

  double result = x;
  printf("p_max: %.7f\n", result);

  // Compute the second derivative of χ² in its minimum for the result error.
  //   
  //   p_max = α
  //
  //           (Ei - Oi)²
  //   χ² = Σi ----------
  //               Ei
  //
  //              /    Oi² \
  //   ∂αχ² = Σi | 1 - ---  | ∂αE
  //              \    Ei² /
  //
  //               /         Oi²         /    Oi² \  \
  //   ∂²αχ² = Σi | (∂αE)² 2 --- + ∂²αE | 1 - ---  | |
  //               \         Ei³         \    Ei² /  /
  //
  double expecto, A, B;
  double error = 0;
  for (size_t i = 0; i < n; i++)
    {
    x = (i + 0.5) * step;
    expecto = expected(x, result);
    A = 2 * pow(exp1d(x, result) * histo[i].sum / expecto, 2);
    B = exp2d(x, result) * (1 - pow((histo[i].sum / expecto), 2));
    error = error + A + B;
    };
    error = 1/error;
  printf("ΔP_max: %.7f\n\n", error);


  // Free memory.
  //
  gsl_min_fminimizer_free(s);
  gsl_rng_free(r);
  free(histo);

  return EXIT_SUCCESS;
  }