/* G01EA_A1W_F C++ Header Example Program.
 *
 * Copyright 2019 Numerical Algorithms Group.
 * Mark 27, 2019.
 */

#include <nag.h>
#include <nagad.h>
#include <stdio.h>
#include <nag_stdlib.h>
#include <string>
#include <iostream>
using namespace std;

int main(void)
{
  int exit_status = 0;

  // Input and output variables
  nagad_a1w_w_rtype x, p;

  cout << "G01EA_A1W_F C++ Header Example Program Results\n\n";
  // Skip heading in data file
  string mystr;
  getline (cin, mystr);

  // Read number of x values
  Integer n;
  cin >> n;

  // Create AD tape
  nagad_a1w_ir_create();

  // Create AD configuration data object
  Integer ifail = -1;
  void    *ad_handle = 0;
  x10aa_a1w_f_(ad_handle,ifail);
  if (ifail != 0) {
    exit_status = 1;
    goto END;
  }

  cout << "Two tail significance levels and derivatives\n\n";
  cout << "       x           p         dp/dx\n";
  cout.setf(ios::scientific,ios::floatfield);
  cout.setf(ios::right);
  cout.precision(4);
    
  // Loop over x values
  for (Integer i = 0; i < n; ++i) {
    // Read next x
    double xr;
    cin >> xr;
    x.value = xr;
    x.id = 0;
    // Register x, i.e. differentiate w.r.t. x
    nagad_a1w_ir_register_variable(&x);

    // Call NAG AD Routine
    ifail = -1;
    g01ea_a1w_f_(ad_handle,"S",x,p,ifail,1);
    if (ifail != 0) {
      exit_status = 2;
      goto END;
    }
    
    // Reset adjoints, increment y, and evaluate adjoint of y w.r.t. x
    nagad_a1w_ir_zero_adjoints();
    double inc = 1.0;
    nagad_a1w_inc_derivative(&p,inc);
    ifail = -1;
    nagad_a1w_ir_interpret_adjoint(ifail);
    if (ifail != 0) {
      exit_status = 3;
      goto END;
    }

    // Get derivative, dydx and output values
    double dpdx;
    dpdx = nagad_a1w_get_derivative(x);

    cout.width(12);  cout << x.value;
    cout.width(12);  cout << p.value;
    cout.width(12);  cout << dpdx << endl;
  }

  // Remove computational data object and tape
  x10ab_a1w_f_(ad_handle,ifail);
  nagad_a1w_ir_remove();

 END:
  return exit_status;
}