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41 #include "visibility.h"
42 #include "types/simple.h"
50 #define M_PI 3.14159265358979323846
54 #define M_PI_2 1.57079632679489661923
58 #define M_2PI 6.28318530717958647692
62 #define M_SQRT2 sqrt(2.0)
66 #define M_1_PI 0.31830988618379067154
69 #ifndef M_FLOAT_1_SQRTPI /* used in CUDA kernels */
70 /* 1.0 / sqrt(M_PI) */
71 #define M_FLOAT_1_SQRTPI 0.564189583547756f
75 /* 1.0 / sqrt(M_PI) */
76 #define M_1_SQRTPI 0.564189583547756
80 /* 2.0 / sqrt(M_PI) */
81 #define M_2_SQRTPI 1.128379167095513
84 /* Suzuki-Yoshida Constants, for n=3 and n=5, for symplectic integration */
86 /* for n=3, w0 = w2 = 1/(2-2^-(1/3)), w1 = 1-2*w0 */
87 /* for n=5, w0 = w1 = w3 = w4 = 1/(4-4^-(1/3)), w1 = 1-4*w0 */
89 #define MAX_SUZUKI_YOSHIDA_NUM 5
90 #define SUZUKI_YOSHIDA_NUM 5
92 static const double sy_const_1
[] = { 1. };
93 static const double sy_const_3
[] = { 0.828981543588751,-0.657963087177502,0.828981543588751 };
94 static const double sy_const_5
[] = { 0.2967324292201065,0.2967324292201065,-0.186929716880426,0.2967324292201065,0.2967324292201065 };
96 static const double* sy_const
[] = {
106 static const double sy_const[MAX_SUZUKI_YOSHIDA_NUM+1][MAX_SUZUKI_YOSHIDA_NUM+1] = {
110 {0.828981543588751,-0.657963087177502,0.828981543588751},
112 {0.2967324292201065,0.2967324292201065,-0.186929716880426,0.2967324292201065,0.2967324292201065}
116 int gmx_nint(real a
);
117 real
sign(real x
,real y
);
119 real
cuberoot (real a
);
121 double gmx_erfd(double x
);
122 double gmx_erfcd(double x
);
124 float gmx_erff(float x
);
126 float gmx_erfcf(float x
);
128 #define gmx_erf(x) gmx_erfd(x)
129 #define gmx_erfc(x) gmx_erfcd(x)
131 #define gmx_erf(x) gmx_erff(x)
132 #define gmx_erfc(x) gmx_erfcf(x)
136 gmx_bool
gmx_isfinite(real x
);
138 /*! \brief Check if two numbers are within a tolerance
140 * This routine checks if the relative difference between two numbers is
141 * approximately within the given tolerance, defined as
142 * fabs(f1-f2)<=tolerance*fabs(f1+f2).
144 * To check if two floating-point numbers are almost identical, use this routine
145 * with the tolerance GMX_REAL_EPS, or GMX_DOUBLE_EPS if the check should be
146 * done in double regardless of Gromacs precision.
148 * To check if two algorithms produce similar results you will normally need
149 * to relax the tolerance significantly since many operations (e.g. summation)
150 * accumulate floating point errors.
152 * \param f1 First number to compare
153 * \param f2 Second number to compare
154 * \param tol Tolerance to use
156 * \return 1 if the relative difference is within tolerance, 0 if not.
159 gmx_within_tol(double f1
,
163 /* The or-equal is important - otherwise we return false if f1==f2==0 */
164 if( fabs(f1
-f2
) <= tol
*0.5*(fabs(f1
)+fabs(f2
)) )
177 * Check if a number is smaller than some preset safe minimum
178 * value, currently defined as GMX_REAL_MIN/GMX_REAL_EPS.
180 * If a number is smaller than this value we risk numerical overflow
181 * if any number larger than 1.0/GMX_REAL_EPS is divided by it.
183 * \return 1 if 'almost' numerically zero, 0 otherwise.
186 gmx_numzero(double a
)
188 return gmx_within_tol(a
,0.0,GMX_REAL_MIN
/GMX_REAL_EPS
);
195 const real iclog2
= 1.0/log( 2.0 );
197 return log( x
) * iclog2
;
200 /*! /brief Multiply two large ints
202 * Returns true when overflow did not occur.
206 check_int_multiply_for_overflow(gmx_large_int_t a
,
208 gmx_large_int_t
*result
);
214 #endif /* _maths_h */