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final preps for a PR
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Adam G Nelson
parent
a6e75c3509
commit
d7b20fb51e
4 changed files with 23 additions and 87 deletions
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@ -1,39 +1,38 @@
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from ctypes import (c_int, c_double, POINTER, CFUNCTYPE)
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from ctypes import (c_int, c_double, POINTER)
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import numpy as np
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from numpy.ctypeslib import ndpointer
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from . import _dll
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# _dll.normal_percentile.restype = c_double
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# _dll.normal_percentile.argtypes = [POINTER(c_double)]
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_dll.t_percentile.restype = c_double
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_dll.t_percentile.argtypes = [POINTER(c_double), POINTER(c_int)]
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_dll.calc_pn.restype = None
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_dll.calc_pn.argtypes = [POINTER(c_int), POINTER(c_double),
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ndpointer(c_double)]
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_dll.evaluate_legendre.restype = c_double
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_dll.evaluate_legendre.argtypes = [POINTER(c_int), POINTER(c_double),
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POINTER(c_double)]
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_dll.calc_rn.restype = None
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_dll.calc_rn.argtypes = [POINTER(c_int), ndpointer(c_double),
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ndpointer(c_double)]
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_dll.calc_zn.restype = None
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_dll.calc_zn.argtypes = [POINTER(c_int), POINTER(c_double), POINTER(c_double),
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ndpointer(c_double)]
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_dll.rotate_angle.restype = None
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_dll.rotate_angle.argtypes = [ndpointer(c_double), POINTER(c_double),
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ndpointer(c_double), POINTER(c_double)]
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_dll.maxwell_spectrum.restype = c_double
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_dll.maxwell_spectrum.argtypes = [POINTER(c_double)]
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_dll.watt_spectrum.restype = c_double
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_dll.watt_spectrum.argtypes = [POINTER(c_double), POINTER(c_double)]
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# COMPLEX DATA TYPES?
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# _dll.faddeeva.restype = c_double
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# _dll.faddeeva.argtypes = [POINTER(c_double)]
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# _dll.w_derivative.restype = c_double
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# _dll.w_derivative.argtypes = [POINTER(c_double)]
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_dll.broaden_wmp_polynomials.restype = None
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_dll.broaden_wmp_polynomials.argtypes = [POINTER(c_double), POINTER(c_double),
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POINTER(c_int), ndpointer(c_double)]
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@ -2,6 +2,16 @@
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namespace openmc {
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//==============================================================================
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// Module constants.
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//==============================================================================
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// TODO: cmath::M_PI has 3 more digits precision than the Fortran constant we
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// use so for now we will reuse the Fortran constant until we are OK with
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// modifying test results
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extern "C" const double PI {3.1415926535898};
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extern "C" const double SQRT_PI {std::sqrt(PI)};
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//==============================================================================
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// NORMAL_PERCENTILE calculates the percentile of the standard normal
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@ -748,59 +758,6 @@ double watt_spectrum_c(const double a, const double b) {
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return E_out;
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}
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//==============================================================================
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// FADDEEVA the Faddeeva function, using Stephen Johnson's implementation
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//==============================================================================
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// double complex __attribute__ ((const)) faddeeva_c(double complex z) {
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// double complex wv; // The resultant w(z) value
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// double relerr; // Target relative error in the inner loop of MIT Faddeeva
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// // Technically, the value we want is given by the equation:
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// // w(z) = I/Pi * Integrate[Exp[-t^2]/(z-t), {t, -Infinity, Infinity}]
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// // as shown in Equation 63 from Hwang, R. N. "A rigorous pole
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// // representation of multilevel cross sections and its practical
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// // applications." Nuclear Science and Engineering 96.3 (1987): 192-209.
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// //
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// // The MIT Faddeeva function evaluates w(z) = exp(-z^2)erfc(-iz). These
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// // two forms of the Faddeeva function are related by a transformation.
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// //
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// // If we call the integral form w_int, and the function form w_fun:
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// // For imag(z) > 0, w_int(z) = w_fun(z)
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// // For imag(z) < 0, w_int(z) = -conjg(w_fun(conjg(z)))
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// // Note that faddeeva_w will interpret zero as machine epsilon
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// relerr = 0.;
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// if (cimag(z) > 0.) {
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// wv = Faddeeva_w(z, relerr);
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// } else {
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// wv = -conj(Faddeeva_w(conj(z), relerr));
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// }
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// return wv;
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// }
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// double complex w_derivative_c(double complex z, int order){
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// double complex wv; // The resultant w(z) value
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// const double complex twoi_sqrtpi = 2.0 / SQRT_PI * I;
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// switch(order) {
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// case 0:
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// wv = faddeeva_c(z);
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// break;
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// case 1:
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// wv = -2. * z * faddeeva_c(z) + twoi_sqrtpi;
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// break;
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// default:
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// wv = -2. * z * w_derivative_c(z, order - 1) - 2. * (order - 1) *
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// w_derivative_c(z, order - 2);
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// }
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// return wv;
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// }
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//==============================================================================
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// BROADEN_WMP_POLYNOMIALS Doppler broadens the windowed multipole curvefit.
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// The curvefit is a polynomial of the form a/E + b/sqrt(E) + c + d sqrt(E) ...
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@ -2,21 +2,22 @@
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#define MATH_FUNCTIONS_H
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#include <cmath>
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#include <complex.h>
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#include <iostream>
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#include "random_lcg.h"
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// #include "faddeeva/Faddeeva.h"
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namespace openmc {
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//==============================================================================
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// Module constants.
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//==============================================================================
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// TODO: cmath::M_PI has 3 more digits precision than the Fortran constant we
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// use so for now we will reuse the Fortran constant until we are OK with
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// modifying test results
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const double PI = 3.1415926535898;
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extern "C" const double PI;
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const double SQRT_PI = std::sqrt(PI);
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extern "C" const double SQRT_PI;
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//==============================================================================
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// NORMAL_PERCENTILE calculates the percentile of the standard normal
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@ -94,14 +95,6 @@ extern "C" double maxwell_spectrum_c(const double T);
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extern "C" double watt_spectrum_c(const double a, const double b);
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//==============================================================================
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// FADDEEVA the Faddeeva function, using Stephen Johnson's implementation
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//==============================================================================
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// extern "C" double complex faddeeva_c(double complex z) __attribute__ ((const));
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// extern "C" double complex w_derivative_c(double complex z, int order);
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//==============================================================================
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// BROADEN_WMP_POLYNOMIALS Doppler broadens the windowed multipole curvefit.
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// The curvefit is a polynomial of the form a/E + b/sqrt(E) + c + d sqrt(E) ...
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@ -5,19 +5,6 @@ import openmc
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import openmc.capi
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# def test_normal_percentile():
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# # normal_percentile has three branches to consider:
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# # p < 0.02425; 0.02425 <= p <= 0.97575; and p > 0.97575
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# test_ps = [0.02, 0.4, 0.5, 0.6, 0.98]
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# # The reference solutions come from Scipy
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# ref_zs = [sp.stats.norm.ppf(p) for p in test_ps]
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# test_zs = [openmc.capi.math.normal_percentile(p) for p in test_ps]
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# assert np.allclose(ref_zs, test_zs)
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def test_t_percentile():
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# Permutations include 1 DoF, 2 DoF, and > 2 DoF
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# We will test 5 p-values at 3-DoF values
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