/* Author: G. Jungman * RCS: $Id: bessel_Jnu.c,v 1.20 1999/08/20 19:12:18 jungman Exp $ */ #include #include #include #include "bessel.h" #include "bessel_olver.h" #include "bessel_temme.h" #include "gsl_sf_bessel.h" /* Evaluate at large enough nu to apply asymptotic * results and apply backward recurrence. */ #if 0 static int bessel_J_recur_asymp(const double nu, const double x, gsl_sf_result * Jnu, gsl_sf_result * Jnup1) { const double nu_cut = 25.0; int n; int steps = ceil(nu_cut - nu) + 1; gsl_sf_result r_Jnp1; gsl_sf_result r_Jn; int stat_O1 = gsl_sf_bessel_Jnu_asymp_Olver_impl(nu + steps + 1.0, x, &r_Jnp1); int stat_O2 = gsl_sf_bessel_Jnu_asymp_Olver_impl(nu + steps, x, &r_Jn); double r_fe = fabs(r_Jnp1.err/r_Jnp1.val) + fabs(r_Jn.err/r_Jn.val); double Jnp1 = r_Jnp1.val; double Jn = r_Jn.val; double Jnm1; double Jnp1_save; for(n=steps; n>0; n--) { Jnm1 = 2.0*(nu+n)/x * Jn - Jnp1; Jnp1 = Jn; Jnp1_save = Jn; Jn = Jnm1; } Jnu->val = Jn; Jnu->err = (r_fe + GSL_DBL_EPSILON * (steps + 1.0)) * fabs(Jn); Jnup1->val = Jnp1_save; Jnup1->err = (r_fe + GSL_DBL_EPSILON * (steps + 1.0)) * fabs(Jnp1_save); return GSL_ERROR_SELECT_2(stat_O1, stat_O2); } #endif /*-*-*-*-*-*-*-*-*-*-*-* (semi)Private Implementations *-*-*-*-*-*-*-*-*-*-*-*/ int gsl_sf_bessel_Jnu_impl(const double nu, const double x, gsl_sf_result * result) { if(result == 0) { return GSL_EFAULT; } else if(x < 0.0 || nu < 0.0) { result->val = 0.0; result->err = 0.0; return GSL_EDOM; } else if(x == 0.0) { if(nu == 0.0) { result->val = 1.0; result->err = 0.0; } else { result->val = 0.0; result->err = 0.0; } return GSL_SUCCESS; } else if(x*x < 10.0*(nu+1.0)) { return gsl_sf_bessel_IJ_taylor_impl(nu, x, -1, 100, GSL_DBL_EPSILON, result); } else if(nu > 50.0) { return gsl_sf_bessel_Jnu_asymp_Olver_impl(nu, x, result); } else { /* -1/2 <= mu <= 1/2 */ int N = (int)(nu + 0.5); double mu = nu - N; /* Determine the J ratio at nu. */ double Jnup1_Jnu; double sgn_Jnu; const int stat_CF1 = gsl_sf_bessel_J_CF1(nu, x, &Jnup1_Jnu, &sgn_Jnu); if(x < 2.0) { /* Determine Y_mu, Y_mup1 directly and recurse forward to nu. * Then use the CF1 information to solve for J_nu and J_nup1. */ gsl_sf_result Y_mu, Y_mup1; const int stat_mu = gsl_sf_bessel_Y_temme(mu, x, &Y_mu, &Y_mup1); double Ynm1 = Y_mu.val; double Yn = Y_mup1.val; double Ynp1; int n; for(n=1; nval = 2.0/(M_PI*x) / (Jnup1_Jnu*Yn - Ynp1); result->err = GSL_DBL_EPSILON * (N + 2.0) * fabs(result->val); return GSL_ERROR_SELECT_2(stat_mu, stat_CF1); } else { /* Recurse backward from nu to mu, determining the J ratio * at mu. Use this together with a Steed method CF2 to * determine the actual J_mu, and thus obtain the normalization. */ double Jmu; double Jmup1_Jmu; double sgn_Jmu; double Jmuprime_Jmu; double P, Q; const int stat_CF2 = gsl_sf_bessel_JY_steed_CF2(mu, x, &P, &Q); double gamma; double Jnp1 = sgn_Jnu * GSL_SQRT_DBL_MIN * Jnup1_Jnu; double Jn = sgn_Jnu * GSL_SQRT_DBL_MIN; double Jnm1; int n; for(n=N; n>0; n--) { Jnm1 = 2.0*(mu+n)/x * Jn - Jnp1; Jnp1 = Jn; Jn = Jnm1; } Jmup1_Jmu = Jnp1/Jn; sgn_Jmu = GSL_SIGN(Jn); Jmuprime_Jmu = mu/x - Jmup1_Jmu; gamma = (P - Jmuprime_Jmu)/Q; Jmu = sgn_Jmu * sqrt(2.0/(M_PI*x) / (Q + gamma*(P-Jmuprime_Jmu))); result->val = Jmu * (sgn_Jnu * GSL_SQRT_DBL_MIN) / Jn; result->err = 2.0 * GSL_DBL_EPSILON * (N + 2.0) * fabs(result->val); return GSL_ERROR_SELECT_2(stat_CF2, stat_CF1); } } } /*-*-*-*-*-*-*-*-*-*-*-* Functions w/ Error Handling *-*-*-*-*-*-*-*-*-*-*-*/ int gsl_sf_bessel_Jnu_e(const double nu, const double x, gsl_sf_result * result) { int status = gsl_sf_bessel_Jnu_impl(nu, x, result); if(status != GSL_SUCCESS) { GSL_ERROR("gsl_sf_bessel_Jnu_e", status); } return status; }