
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* (* (* (cos phi1) (cos phi2)) t_0) t_0))))
(* R (* 2.0 (atan2 (sqrt t_1) (sqrt (- 1.0 t_1)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0);
return R * (2.0 * atan2(sqrt(t_1), sqrt((1.0 - t_1))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = (sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0)
code = r * (2.0d0 * atan2(sqrt(t_1), sqrt((1.0d0 - t_1))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + (((Math.cos(phi1) * Math.cos(phi2)) * t_0) * t_0);
return R * (2.0 * Math.atan2(Math.sqrt(t_1), Math.sqrt((1.0 - t_1))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + (((math.cos(phi1) * math.cos(phi2)) * t_0) * t_0) return R * (2.0 * math.atan2(math.sqrt(t_1), math.sqrt((1.0 - t_1))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(Float64(Float64(cos(phi1) * cos(phi2)) * t_0) * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(t_1), sqrt(Float64(1.0 - t_1))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = (sin(((phi1 - phi2) / 2.0)) ^ 2.0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0); tmp = R * (2.0 * atan2(sqrt(t_1), sqrt((1.0 - t_1)))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[t$95$1], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$1), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_0\right) \cdot t_0\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1}}{\sqrt{1 - t_1}}\right)
\end{array}
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 21 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* (* (* (cos phi1) (cos phi2)) t_0) t_0))))
(* R (* 2.0 (atan2 (sqrt t_1) (sqrt (- 1.0 t_1)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0);
return R * (2.0 * atan2(sqrt(t_1), sqrt((1.0 - t_1))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = (sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0)
code = r * (2.0d0 * atan2(sqrt(t_1), sqrt((1.0d0 - t_1))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + (((Math.cos(phi1) * Math.cos(phi2)) * t_0) * t_0);
return R * (2.0 * Math.atan2(Math.sqrt(t_1), Math.sqrt((1.0 - t_1))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + (((math.cos(phi1) * math.cos(phi2)) * t_0) * t_0) return R * (2.0 * math.atan2(math.sqrt(t_1), math.sqrt((1.0 - t_1))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(Float64(Float64(cos(phi1) * cos(phi2)) * t_0) * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(t_1), sqrt(Float64(1.0 - t_1))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = (sin(((phi1 - phi2) / 2.0)) ^ 2.0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0); tmp = R * (2.0 * atan2(sqrt(t_1), sqrt((1.0 - t_1)))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[t$95$1], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$1), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_0\right) \cdot t_0\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1}}{\sqrt{1 - t_1}}\right)
\end{array}
\end{array}
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(atan2
(sqrt
(fma
t_0
(* t_1 t_1)
(pow
(fma
(sin (/ phi1 2.0))
(cos (/ phi2 2.0))
(* (sin (/ phi2 2.0)) (- (cos (/ phi1 2.0)))))
2.0)))
(sqrt
(-
1.0
(fma
t_0
(+ 0.5 (* -0.5 (cos (- lambda1 lambda2))))
(pow
(fma
(cos (* phi2 -0.5))
(sin (* phi1 0.5))
(* (cos (* phi1 0.5)) (sin (* phi2 -0.5))))
2.0)))))
(* 2.0 R))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
return atan2(sqrt(fma(t_0, (t_1 * t_1), pow(fma(sin((phi1 / 2.0)), cos((phi2 / 2.0)), (sin((phi2 / 2.0)) * -cos((phi1 / 2.0)))), 2.0))), sqrt((1.0 - fma(t_0, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), pow(fma(cos((phi2 * -0.5)), sin((phi1 * 0.5)), (cos((phi1 * 0.5)) * sin((phi2 * -0.5)))), 2.0))))) * (2.0 * R);
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(atan(sqrt(fma(t_0, Float64(t_1 * t_1), (fma(sin(Float64(phi1 / 2.0)), cos(Float64(phi2 / 2.0)), Float64(sin(Float64(phi2 / 2.0)) * Float64(-cos(Float64(phi1 / 2.0))))) ^ 2.0))), sqrt(Float64(1.0 - fma(t_0, Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2)))), (fma(cos(Float64(phi2 * -0.5)), sin(Float64(phi1 * 0.5)), Float64(cos(Float64(phi1 * 0.5)) * sin(Float64(phi2 * -0.5)))) ^ 2.0))))) * Float64(2.0 * R)) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision] + N[Power[N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] + N[(N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] * (-N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision])), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 * N[(0.5 + N[(-0.5 * N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[(N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] + N[(N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * N[(2.0 * R), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot t_1, {\left(\mathsf{fma}\left(\sin \left(\frac{\phi_1}{2}\right), \cos \left(\frac{\phi_2}{2}\right), \sin \left(\frac{\phi_2}{2}\right) \cdot \left(-\cos \left(\frac{\phi_1}{2}\right)\right)\right)\right)}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(t_0, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), {\left(\mathsf{fma}\left(\cos \left(\phi_2 \cdot -0.5\right), \sin \left(\phi_1 \cdot 0.5\right), \cos \left(\phi_1 \cdot 0.5\right) \cdot \sin \left(\phi_2 \cdot -0.5\right)\right)\right)}^{2}\right)}} \cdot \left(2 \cdot R\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*r*62.1%
*-commutative62.1%
Simplified62.2%
div-sub62.2%
sin-diff63.2%
Applied egg-rr63.2%
div-sub62.2%
sin-diff63.2%
Applied egg-rr75.6%
fma-neg75.6%
*-commutative75.6%
distribute-rgt-neg-in75.6%
Simplified75.6%
Taylor expanded in phi1 around inf 75.6%
*-commutative75.6%
fma-neg75.6%
distribute-rgt-neg-in75.6%
Simplified75.6%
Applied egg-rr75.6%
Simplified75.6%
Final simplification75.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(pow
(fma
(sin (/ phi1 2.0))
(cos (/ phi2 2.0))
(* (sin (/ phi2 2.0)) (- (cos (/ phi1 2.0)))))
2.0))))
(t_2
(pow
(-
(* (sin (* phi1 0.5)) (cos (* phi2 0.5)))
(* (cos (* phi1 0.5)) (sin (* phi2 0.5))))
2.0)))
(if (or (<= lambda1 -2.1) (not (<= lambda1 2.25e-6)))
(*
(* 2.0 R)
(atan2
t_1
(sqrt
(-
1.0
(+
(* (cos phi1) (* (cos phi2) (pow (sin (* lambda1 0.5)) 2.0)))
t_2)))))
(*
(* 2.0 R)
(atan2
t_1
(sqrt
(-
1.0
(+
t_2
(*
(cos phi1)
(* (cos phi2) (pow (sin (* lambda2 -0.5)) 2.0)))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = sqrt(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(fma(sin((phi1 / 2.0)), cos((phi2 / 2.0)), (sin((phi2 / 2.0)) * -cos((phi1 / 2.0)))), 2.0)));
double t_2 = pow(((sin((phi1 * 0.5)) * cos((phi2 * 0.5))) - (cos((phi1 * 0.5)) * sin((phi2 * 0.5)))), 2.0);
double tmp;
if ((lambda1 <= -2.1) || !(lambda1 <= 2.25e-6)) {
tmp = (2.0 * R) * atan2(t_1, sqrt((1.0 - ((cos(phi1) * (cos(phi2) * pow(sin((lambda1 * 0.5)), 2.0))) + t_2))));
} else {
tmp = (2.0 * R) * atan2(t_1, sqrt((1.0 - (t_2 + (cos(phi1) * (cos(phi2) * pow(sin((lambda2 * -0.5)), 2.0)))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = sqrt(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (fma(sin(Float64(phi1 / 2.0)), cos(Float64(phi2 / 2.0)), Float64(sin(Float64(phi2 / 2.0)) * Float64(-cos(Float64(phi1 / 2.0))))) ^ 2.0))) t_2 = Float64(Float64(sin(Float64(phi1 * 0.5)) * cos(Float64(phi2 * 0.5))) - Float64(cos(Float64(phi1 * 0.5)) * sin(Float64(phi2 * 0.5)))) ^ 2.0 tmp = 0.0 if ((lambda1 <= -2.1) || !(lambda1 <= 2.25e-6)) tmp = Float64(Float64(2.0 * R) * atan(t_1, sqrt(Float64(1.0 - Float64(Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(lambda1 * 0.5)) ^ 2.0))) + t_2))))); else tmp = Float64(Float64(2.0 * R) * atan(t_1, sqrt(Float64(1.0 - Float64(t_2 + Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(lambda2 * -0.5)) ^ 2.0)))))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision] + N[Power[N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] + N[(N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] * (-N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision])), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Power[N[(N[(N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, If[Or[LessEqual[lambda1, -2.1], N[Not[LessEqual[lambda1, 2.25e-6]], $MachinePrecision]], N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[t$95$1 / N[Sqrt[N[(1.0 - N[(N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[t$95$1 / N[Sqrt[N[(1.0 - N[(t$95$2 + N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \sqrt{\mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, t_0 \cdot t_0, {\left(\mathsf{fma}\left(\sin \left(\frac{\phi_1}{2}\right), \cos \left(\frac{\phi_2}{2}\right), \sin \left(\frac{\phi_2}{2}\right) \cdot \left(-\cos \left(\frac{\phi_1}{2}\right)\right)\right)\right)}^{2}\right)}\\
t_2 := {\left(\sin \left(\phi_1 \cdot 0.5\right) \cdot \cos \left(\phi_2 \cdot 0.5\right) - \cos \left(\phi_1 \cdot 0.5\right) \cdot \sin \left(\phi_2 \cdot 0.5\right)\right)}^{2}\\
\mathbf{if}\;\lambda_1 \leq -2.1 \lor \neg \left(\lambda_1 \leq 2.25 \cdot 10^{-6}\right):\\
\;\;\;\;\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{t_1}{\sqrt{1 - \left(\cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(\lambda_1 \cdot 0.5\right)}^{2}\right) + t_2\right)}}\\
\mathbf{else}:\\
\;\;\;\;\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{t_1}{\sqrt{1 - \left(t_2 + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}\right)\right)}}\\
\end{array}
\end{array}
if lambda1 < -2.10000000000000009 or 2.25000000000000006e-6 < lambda1 Initial program 44.7%
associate-*r*44.7%
*-commutative44.7%
Simplified44.8%
div-sub44.8%
sin-diff46.2%
Applied egg-rr46.2%
div-sub44.8%
sin-diff46.2%
Applied egg-rr56.1%
fma-neg56.1%
*-commutative56.1%
distribute-rgt-neg-in56.1%
Simplified56.1%
Taylor expanded in lambda2 around 0 55.4%
if -2.10000000000000009 < lambda1 < 2.25000000000000006e-6Initial program 82.1%
associate-*r*82.1%
*-commutative82.1%
Simplified82.2%
div-sub82.2%
sin-diff82.7%
Applied egg-rr82.7%
div-sub82.2%
sin-diff82.7%
Applied egg-rr98.0%
fma-neg98.0%
*-commutative98.0%
distribute-rgt-neg-in98.0%
Simplified98.0%
Taylor expanded in lambda1 around 0 98.0%
Final simplification75.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ phi1 2.0)))
(t_2 (cos (/ phi2 2.0)))
(t_3 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_4 (sin (/ phi2 2.0)))
(t_5 (cos (/ phi1 2.0)))
(t_6 (pow (- (* t_1 t_2) (* t_4 t_5)) 2.0))
(t_7 (sin (/ (- lambda1 lambda2) 2.0)))
(t_8 (* t_7 t_7))
(t_9 (* t_7 (* t_0 t_7))))
(if (<= lambda1 -2.5e-6)
(* R (* 2.0 (atan2 (sqrt (+ t_6 t_9)) (sqrt (- 1.0 (+ t_9 t_3))))))
(if (<= lambda1 0.00016)
(*
(* 2.0 R)
(atan2
(sqrt (fma t_0 t_8 (pow (fma t_1 t_2 (* t_4 (- t_5))) 2.0)))
(sqrt
(-
1.0
(+
(pow
(-
(* (sin (* phi1 0.5)) (cos (* phi2 0.5)))
(* (cos (* phi1 0.5)) (sin (* phi2 0.5))))
2.0)
(* (cos phi1) (* (cos phi2) (pow (sin (* lambda2 -0.5)) 2.0))))))))
(*
(* 2.0 R)
(atan2 (sqrt (fma t_0 t_8 t_3)) (sqrt (- 1.0 (fma t_0 t_8 t_6)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin((phi1 / 2.0));
double t_2 = cos((phi2 / 2.0));
double t_3 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_4 = sin((phi2 / 2.0));
double t_5 = cos((phi1 / 2.0));
double t_6 = pow(((t_1 * t_2) - (t_4 * t_5)), 2.0);
double t_7 = sin(((lambda1 - lambda2) / 2.0));
double t_8 = t_7 * t_7;
double t_9 = t_7 * (t_0 * t_7);
double tmp;
if (lambda1 <= -2.5e-6) {
tmp = R * (2.0 * atan2(sqrt((t_6 + t_9)), sqrt((1.0 - (t_9 + t_3)))));
} else if (lambda1 <= 0.00016) {
tmp = (2.0 * R) * atan2(sqrt(fma(t_0, t_8, pow(fma(t_1, t_2, (t_4 * -t_5)), 2.0))), sqrt((1.0 - (pow(((sin((phi1 * 0.5)) * cos((phi2 * 0.5))) - (cos((phi1 * 0.5)) * sin((phi2 * 0.5)))), 2.0) + (cos(phi1) * (cos(phi2) * pow(sin((lambda2 * -0.5)), 2.0)))))));
} else {
tmp = (2.0 * R) * atan2(sqrt(fma(t_0, t_8, t_3)), sqrt((1.0 - fma(t_0, t_8, t_6))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(phi1 / 2.0)) t_2 = cos(Float64(phi2 / 2.0)) t_3 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_4 = sin(Float64(phi2 / 2.0)) t_5 = cos(Float64(phi1 / 2.0)) t_6 = Float64(Float64(t_1 * t_2) - Float64(t_4 * t_5)) ^ 2.0 t_7 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_8 = Float64(t_7 * t_7) t_9 = Float64(t_7 * Float64(t_0 * t_7)) tmp = 0.0 if (lambda1 <= -2.5e-6) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_6 + t_9)), sqrt(Float64(1.0 - Float64(t_9 + t_3)))))); elseif (lambda1 <= 0.00016) tmp = Float64(Float64(2.0 * R) * atan(sqrt(fma(t_0, t_8, (fma(t_1, t_2, Float64(t_4 * Float64(-t_5))) ^ 2.0))), sqrt(Float64(1.0 - Float64((Float64(Float64(sin(Float64(phi1 * 0.5)) * cos(Float64(phi2 * 0.5))) - Float64(cos(Float64(phi1 * 0.5)) * sin(Float64(phi2 * 0.5)))) ^ 2.0) + Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(lambda2 * -0.5)) ^ 2.0)))))))); else tmp = Float64(Float64(2.0 * R) * atan(sqrt(fma(t_0, t_8, t_3)), sqrt(Float64(1.0 - fma(t_0, t_8, t_6))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$4 = N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$5 = N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$6 = N[Power[N[(N[(t$95$1 * t$95$2), $MachinePrecision] - N[(t$95$4 * t$95$5), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$7 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$8 = N[(t$95$7 * t$95$7), $MachinePrecision]}, Block[{t$95$9 = N[(t$95$7 * N[(t$95$0 * t$95$7), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[lambda1, -2.5e-6], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$6 + t$95$9), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$9 + t$95$3), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[lambda1, 0.00016], N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[N[Sqrt[N[(t$95$0 * t$95$8 + N[Power[N[(t$95$1 * t$95$2 + N[(t$95$4 * (-t$95$5)), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[(N[(N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[N[Sqrt[N[(t$95$0 * t$95$8 + t$95$3), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 * t$95$8 + t$95$6), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\phi_1}{2}\right)\\
t_2 := \cos \left(\frac{\phi_2}{2}\right)\\
t_3 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_4 := \sin \left(\frac{\phi_2}{2}\right)\\
t_5 := \cos \left(\frac{\phi_1}{2}\right)\\
t_6 := {\left(t_1 \cdot t_2 - t_4 \cdot t_5\right)}^{2}\\
t_7 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_8 := t_7 \cdot t_7\\
t_9 := t_7 \cdot \left(t_0 \cdot t_7\right)\\
\mathbf{if}\;\lambda_1 \leq -2.5 \cdot 10^{-6}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_6 + t_9}}{\sqrt{1 - \left(t_9 + t_3\right)}}\right)\\
\mathbf{elif}\;\lambda_1 \leq 0.00016:\\
\;\;\;\;\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_8, {\left(\mathsf{fma}\left(t_1, t_2, t_4 \cdot \left(-t_5\right)\right)\right)}^{2}\right)}}{\sqrt{1 - \left({\left(\sin \left(\phi_1 \cdot 0.5\right) \cdot \cos \left(\phi_2 \cdot 0.5\right) - \cos \left(\phi_1 \cdot 0.5\right) \cdot \sin \left(\phi_2 \cdot 0.5\right)\right)}^{2} + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}\right)\right)}}\\
\mathbf{else}:\\
\;\;\;\;\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_8, t_3\right)}}{\sqrt{1 - \mathsf{fma}\left(t_0, t_8, t_6\right)}}\\
\end{array}
\end{array}
if lambda1 < -2.5000000000000002e-6Initial program 41.5%
div-sub41.6%
sin-diff42.4%
Applied egg-rr43.1%
if -2.5000000000000002e-6 < lambda1 < 1.60000000000000013e-4Initial program 82.1%
associate-*r*82.1%
*-commutative82.1%
Simplified82.2%
div-sub82.2%
sin-diff82.7%
Applied egg-rr82.7%
div-sub82.2%
sin-diff82.7%
Applied egg-rr98.6%
fma-neg98.6%
*-commutative98.6%
distribute-rgt-neg-in98.6%
Simplified98.6%
Taylor expanded in lambda1 around 0 98.5%
if 1.60000000000000013e-4 < lambda1 Initial program 47.6%
associate-*r*47.6%
*-commutative47.6%
Simplified47.6%
div-sub47.6%
sin-diff49.6%
Applied egg-rr49.6%
Final simplification70.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (cos phi1) (cos phi2))))
(*
(* 2.0 R)
(atan2
(sqrt
(fma
t_1
(* t_0 t_0)
(pow
(fma
(sin (/ phi1 2.0))
(cos (/ phi2 2.0))
(* (sin (/ phi2 2.0)) (- (cos (/ phi1 2.0)))))
2.0)))
(sqrt
(-
1.0
(fma
t_1
(+ 0.5 (* -0.5 (cos (- lambda1 lambda2))))
(pow
(-
(* (sin (* phi1 0.5)) (cos (* phi2 0.5)))
(* (cos (* phi1 0.5)) (sin (* phi2 0.5))))
2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = cos(phi1) * cos(phi2);
return (2.0 * R) * atan2(sqrt(fma(t_1, (t_0 * t_0), pow(fma(sin((phi1 / 2.0)), cos((phi2 / 2.0)), (sin((phi2 / 2.0)) * -cos((phi1 / 2.0)))), 2.0))), sqrt((1.0 - fma(t_1, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), pow(((sin((phi1 * 0.5)) * cos((phi2 * 0.5))) - (cos((phi1 * 0.5)) * sin((phi2 * 0.5)))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(cos(phi1) * cos(phi2)) return Float64(Float64(2.0 * R) * atan(sqrt(fma(t_1, Float64(t_0 * t_0), (fma(sin(Float64(phi1 / 2.0)), cos(Float64(phi2 / 2.0)), Float64(sin(Float64(phi2 / 2.0)) * Float64(-cos(Float64(phi1 / 2.0))))) ^ 2.0))), sqrt(Float64(1.0 - fma(t_1, Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2)))), (Float64(Float64(sin(Float64(phi1 * 0.5)) * cos(Float64(phi2 * 0.5))) - Float64(cos(Float64(phi1 * 0.5)) * sin(Float64(phi2 * 0.5)))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[N[Sqrt[N[(t$95$1 * N[(t$95$0 * t$95$0), $MachinePrecision] + N[Power[N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] + N[(N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] * (-N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision])), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$1 * N[(0.5 + N[(-0.5 * N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[(N[(N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \cos \phi_1 \cdot \cos \phi_2\\
\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_1, t_0 \cdot t_0, {\left(\mathsf{fma}\left(\sin \left(\frac{\phi_1}{2}\right), \cos \left(\frac{\phi_2}{2}\right), \sin \left(\frac{\phi_2}{2}\right) \cdot \left(-\cos \left(\frac{\phi_1}{2}\right)\right)\right)\right)}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(t_1, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), {\left(\sin \left(\phi_1 \cdot 0.5\right) \cdot \cos \left(\phi_2 \cdot 0.5\right) - \cos \left(\phi_1 \cdot 0.5\right) \cdot \sin \left(\phi_2 \cdot 0.5\right)\right)}^{2}\right)}}
\end{array}
\end{array}
Initial program 62.1%
associate-*r*62.1%
*-commutative62.1%
Simplified62.2%
div-sub62.2%
sin-diff63.2%
Applied egg-rr63.2%
div-sub62.2%
sin-diff63.2%
Applied egg-rr75.6%
fma-neg75.6%
*-commutative75.6%
distribute-rgt-neg-in75.6%
Simplified75.6%
Taylor expanded in phi1 around inf 75.6%
*-commutative75.6%
fma-neg75.6%
distribute-rgt-neg-in75.6%
Simplified75.6%
Applied egg-rr75.6%
sub-neg75.6%
*-commutative75.6%
cancel-sign-sub-inv75.6%
metadata-eval75.6%
Simplified75.6%
Final simplification75.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 t_0))
(t_2 (* (cos phi1) (cos phi2))))
(*
(* 2.0 R)
(atan2
(sqrt (fma t_2 t_1 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt
(-
1.0
(fma
t_2
t_1
(pow
(-
(* (sin (/ phi1 2.0)) (cos (/ phi2 2.0)))
(* (sin (/ phi2 2.0)) (cos (/ phi1 2.0))))
2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * t_0;
double t_2 = cos(phi1) * cos(phi2);
return (2.0 * R) * atan2(sqrt(fma(t_2, t_1, pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - fma(t_2, t_1, pow(((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (sin((phi2 / 2.0)) * cos((phi1 / 2.0)))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(t_0 * t_0) t_2 = Float64(cos(phi1) * cos(phi2)) return Float64(Float64(2.0 * R) * atan(sqrt(fma(t_2, t_1, (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - fma(t_2, t_1, (Float64(Float64(sin(Float64(phi1 / 2.0)) * cos(Float64(phi2 / 2.0))) - Float64(sin(Float64(phi2 / 2.0)) * cos(Float64(phi1 / 2.0)))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[N[Sqrt[N[(t$95$2 * t$95$1 + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$2 * t$95$1 + N[Power[N[(N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := t_0 \cdot t_0\\
t_2 := \cos \phi_1 \cdot \cos \phi_2\\
\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_2, t_1, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(t_2, t_1, {\left(\sin \left(\frac{\phi_1}{2}\right) \cdot \cos \left(\frac{\phi_2}{2}\right) - \sin \left(\frac{\phi_2}{2}\right) \cdot \cos \left(\frac{\phi_1}{2}\right)\right)}^{2}\right)}}
\end{array}
\end{array}
Initial program 62.1%
associate-*r*62.1%
*-commutative62.1%
Simplified62.2%
div-sub62.2%
sin-diff63.2%
Applied egg-rr63.2%
Final simplification63.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) (* t_0 (* t_1 t_1))))
(sqrt
(-
(-
1.0
(pow
(-
(* (sin (/ phi1 2.0)) (cos (/ phi2 2.0)))
(* (sin (/ phi2 2.0)) (cos (/ phi1 2.0))))
2.0))
(* t_0 (cbrt (pow (sin (* (- lambda1 lambda2) 0.5)) 6.0))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * (t_1 * t_1)))), sqrt(((1.0 - pow(((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (sin((phi2 / 2.0)) * cos((phi1 / 2.0)))), 2.0)) - (t_0 * cbrt(pow(sin(((lambda1 - lambda2) * 0.5)), 6.0)))))));
}
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.cos(phi1) * Math.cos(phi2);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * (t_1 * t_1)))), Math.sqrt(((1.0 - Math.pow(((Math.sin((phi1 / 2.0)) * Math.cos((phi2 / 2.0))) - (Math.sin((phi2 / 2.0)) * Math.cos((phi1 / 2.0)))), 2.0)) - (t_0 * Math.cbrt(Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 6.0)))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(t_0 * Float64(t_1 * t_1)))), sqrt(Float64(Float64(1.0 - (Float64(Float64(sin(Float64(phi1 / 2.0)) * cos(Float64(phi2 / 2.0))) - Float64(sin(Float64(phi2 / 2.0)) * cos(Float64(phi1 / 2.0)))) ^ 2.0)) - Float64(t_0 * cbrt((sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 6.0)))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - N[Power[N[(N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] - N[(t$95$0 * N[Power[N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 6.0], $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_0 \cdot \left(t_1 \cdot t_1\right)}}{\sqrt{\left(1 - {\left(\sin \left(\frac{\phi_1}{2}\right) \cdot \cos \left(\frac{\phi_2}{2}\right) - \sin \left(\frac{\phi_2}{2}\right) \cdot \cos \left(\frac{\phi_1}{2}\right)\right)}^{2}\right) - t_0 \cdot \sqrt[3]{{\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{6}}}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
add-cbrt-cube62.1%
add-cbrt-cube62.0%
cbrt-unprod62.1%
pow362.1%
div-inv62.1%
metadata-eval62.1%
pow362.1%
div-inv62.1%
metadata-eval62.1%
Applied egg-rr62.1%
pow-sqr62.2%
*-commutative62.2%
metadata-eval62.2%
Simplified62.2%
div-sub62.2%
sin-diff63.2%
Applied egg-rr63.2%
Final simplification63.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* (* (cos phi1) (cos phi2)) t_0))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_1 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt
(-
1.0
(+
(pow
(-
(* (sin (/ phi1 2.0)) (cos (/ phi2 2.0)))
(* (sin (/ phi2 2.0)) (cos (/ phi1 2.0))))
2.0)
t_1))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0);
return R * (2.0 * atan2(sqrt((t_1 + pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - (pow(((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (sin((phi2 / 2.0)) * cos((phi1 / 2.0)))), 2.0) + t_1)))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0)
code = r * (2.0d0 * atan2(sqrt((t_1 + (sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0))), sqrt((1.0d0 - ((((sin((phi1 / 2.0d0)) * cos((phi2 / 2.0d0))) - (sin((phi2 / 2.0d0)) * cos((phi1 / 2.0d0)))) ** 2.0d0) + t_1)))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * t_0);
return R * (2.0 * Math.atan2(Math.sqrt((t_1 + Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0))), Math.sqrt((1.0 - (Math.pow(((Math.sin((phi1 / 2.0)) * Math.cos((phi2 / 2.0))) - (Math.sin((phi2 / 2.0)) * Math.cos((phi1 / 2.0)))), 2.0) + t_1)))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = t_0 * ((math.cos(phi1) * math.cos(phi2)) * t_0) return R * (2.0 * math.atan2(math.sqrt((t_1 + math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0))), math.sqrt((1.0 - (math.pow(((math.sin((phi1 / 2.0)) * math.cos((phi2 / 2.0))) - (math.sin((phi2 / 2.0)) * math.cos((phi1 / 2.0)))), 2.0) + t_1)))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64(t_1 + (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - Float64((Float64(Float64(sin(Float64(phi1 / 2.0)) * cos(Float64(phi2 / 2.0))) - Float64(sin(Float64(phi2 / 2.0)) * cos(Float64(phi1 / 2.0)))) ^ 2.0) + t_1)))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0); tmp = R * (2.0 * atan2(sqrt((t_1 + (sin(((phi1 - phi2) / 2.0)) ^ 2.0))), sqrt((1.0 - ((((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (sin((phi2 / 2.0)) * cos((phi1 / 2.0)))) ^ 2.0) + t_1))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[(N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := t_0 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_0\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1 + {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}}}{\sqrt{1 - \left({\left(\sin \left(\frac{\phi_1}{2}\right) \cdot \cos \left(\frac{\phi_2}{2}\right) - \sin \left(\frac{\phi_2}{2}\right) \cdot \cos \left(\frac{\phi_1}{2}\right)\right)}^{2} + t_1\right)}}\right)
\end{array}
\end{array}
Initial program 62.1%
div-sub62.2%
sin-diff63.2%
Applied egg-rr63.1%
Final simplification63.1%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) (* t_0 (* t_1 t_1))))
(sqrt
(exp
(log1p
(-
(* t_0 (- -0.5 (* -0.5 (cos (- lambda1 lambda2)))))
(pow (sin (* 0.5 (- phi1 phi2))) 2.0))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * (t_1 * t_1)))), sqrt(exp(log1p(((t_0 * (-0.5 - (-0.5 * cos((lambda1 - lambda2))))) - pow(sin((0.5 * (phi1 - phi2))), 2.0)))))));
}
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.cos(phi1) * Math.cos(phi2);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * (t_1 * t_1)))), Math.sqrt(Math.exp(Math.log1p(((t_0 * (-0.5 - (-0.5 * Math.cos((lambda1 - lambda2))))) - Math.pow(Math.sin((0.5 * (phi1 - phi2))), 2.0)))))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.cos(phi1) * math.cos(phi2) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * (t_1 * t_1)))), math.sqrt(math.exp(math.log1p(((t_0 * (-0.5 - (-0.5 * math.cos((lambda1 - lambda2))))) - math.pow(math.sin((0.5 * (phi1 - phi2))), 2.0)))))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(t_0 * Float64(t_1 * t_1)))), sqrt(exp(log1p(Float64(Float64(t_0 * Float64(-0.5 - Float64(-0.5 * cos(Float64(lambda1 - lambda2))))) - (sin(Float64(0.5 * Float64(phi1 - phi2))) ^ 2.0)))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[Exp[N[Log[1 + N[(N[(t$95$0 * N[(-0.5 - N[(-0.5 * N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[Power[N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_0 \cdot \left(t_1 \cdot t_1\right)}}{\sqrt{e^{\mathsf{log1p}\left(t_0 \cdot \left(-0.5 - -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right)\right) - {\sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)}^{2}\right)}}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
Applied egg-rr62.2%
neg-sub062.2%
fma-udef62.2%
associate--r+62.2%
Simplified62.2%
Final simplification62.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* (* (cos phi1) (cos phi2)) (* t_0 t_0))))
(sqrt
(fabs
(-
1.0
(fma
(cos phi2)
(* (cos phi1) (+ 0.5 (* -0.5 (cos (- lambda1 lambda2)))))
(pow (sin (* 0.5 (- phi1 phi2))) 2.0))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + ((cos(phi1) * cos(phi2)) * (t_0 * t_0)))), sqrt(fabs((1.0 - fma(cos(phi2), (cos(phi1) * (0.5 + (-0.5 * cos((lambda1 - lambda2))))), pow(sin((0.5 * (phi1 - phi2))), 2.0)))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_0 * t_0)))), sqrt(abs(Float64(1.0 - fma(cos(phi2), Float64(cos(phi1) * Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2))))), (sin(Float64(0.5 * Float64(phi1 - phi2))) ^ 2.0)))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[Abs[N[(1.0 - N[(N[Cos[phi2], $MachinePrecision] * N[(N[Cos[phi1], $MachinePrecision] * N[(0.5 + N[(-0.5 * N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_0 \cdot t_0\right)}}{\sqrt{\left|1 - \mathsf{fma}\left(\cos \phi_2, \cos \phi_1 \cdot \left(0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right)\right), {\sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)}^{2}\right)\right|}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
associate--l-62.2%
+-commutative62.2%
fma-udef62.2%
add-sqr-sqrt62.2%
sqrt-unprod62.8%
Applied egg-rr62.8%
unpow262.8%
rem-sqrt-square62.8%
fma-udef62.8%
associate-*l*62.8%
*-commutative62.8%
associate-*l*62.8%
fma-def62.8%
Simplified62.8%
Final simplification62.8%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* (* (cos phi1) (cos phi2)) t_0))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_1 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (+ 1.0 (- (- (/ (cos (- phi1 phi2)) 2.0) 0.5) t_1))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0);
return R * (2.0 * atan2(sqrt((t_1 + pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 + (((cos((phi1 - phi2)) / 2.0) - 0.5) - t_1)))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0)
code = r * (2.0d0 * atan2(sqrt((t_1 + (sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0))), sqrt((1.0d0 + (((cos((phi1 - phi2)) / 2.0d0) - 0.5d0) - t_1)))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * t_0);
return R * (2.0 * Math.atan2(Math.sqrt((t_1 + Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0))), Math.sqrt((1.0 + (((Math.cos((phi1 - phi2)) / 2.0) - 0.5) - t_1)))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = t_0 * ((math.cos(phi1) * math.cos(phi2)) * t_0) return R * (2.0 * math.atan2(math.sqrt((t_1 + math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0))), math.sqrt((1.0 + (((math.cos((phi1 - phi2)) / 2.0) - 0.5) - t_1)))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64(t_1 + (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 + Float64(Float64(Float64(cos(Float64(phi1 - phi2)) / 2.0) - 0.5) - t_1)))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0); tmp = R * (2.0 * atan2(sqrt((t_1 + (sin(((phi1 - phi2) / 2.0)) ^ 2.0))), sqrt((1.0 + (((cos((phi1 - phi2)) / 2.0) - 0.5) - t_1))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 + N[(N[(N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision] - 0.5), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := t_0 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_0\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1 + {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}}}{\sqrt{1 + \left(\left(\frac{\cos \left(\phi_1 - \phi_2\right)}{2} - 0.5\right) - t_1\right)}}\right)
\end{array}
\end{array}
Initial program 62.1%
unpow233.7%
sin-mult30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
Applied egg-rr62.2%
div-sub30.7%
+-inverses30.7%
cos-030.7%
metadata-eval30.7%
distribute-lft-out30.7%
metadata-eval30.7%
*-rgt-identity30.7%
Simplified62.2%
Final simplification62.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (* (cos phi1) (cos phi2)) (* t_1 t_1)))
(t_3 (sqrt (- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) t_0)))))
(if (<= phi2 -6.7)
(* R (* 2.0 (atan2 (sqrt (+ t_2 (pow (sin (* phi2 -0.5)) 2.0))) t_3)))
(if (<= phi2 0.000122)
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_2))
(sqrt (- (pow (cos (* phi1 0.5)) 2.0) (* (cos phi1) t_0))))))
(*
R
(*
2.0
(atan2 (sqrt (+ t_2 (- 0.5 (/ (cos (- phi1 phi2)) 2.0)))) t_3)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = (cos(phi1) * cos(phi2)) * (t_1 * t_1);
double t_3 = sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * t_0)));
double tmp;
if (phi2 <= -6.7) {
tmp = R * (2.0 * atan2(sqrt((t_2 + pow(sin((phi2 * -0.5)), 2.0))), t_3));
} else if (phi2 <= 0.000122) {
tmp = R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * t_0)))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_2 + (0.5 - (cos((phi1 - phi2)) / 2.0)))), t_3));
}
return tmp;
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: t_3
real(8) :: tmp
t_0 = sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
t_2 = (cos(phi1) * cos(phi2)) * (t_1 * t_1)
t_3 = sqrt(((cos((phi2 * (-0.5d0))) ** 2.0d0) - (cos(phi2) * t_0)))
if (phi2 <= (-6.7d0)) then
tmp = r * (2.0d0 * atan2(sqrt((t_2 + (sin((phi2 * (-0.5d0))) ** 2.0d0))), t_3))
else if (phi2 <= 0.000122d0) then
tmp = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_2)), sqrt(((cos((phi1 * 0.5d0)) ** 2.0d0) - (cos(phi1) * t_0)))))
else
tmp = r * (2.0d0 * atan2(sqrt((t_2 + (0.5d0 - (cos((phi1 - phi2)) / 2.0d0)))), t_3))
end if
code = tmp
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_2 = (Math.cos(phi1) * Math.cos(phi2)) * (t_1 * t_1);
double t_3 = Math.sqrt((Math.pow(Math.cos((phi2 * -0.5)), 2.0) - (Math.cos(phi2) * t_0)));
double tmp;
if (phi2 <= -6.7) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_2 + Math.pow(Math.sin((phi2 * -0.5)), 2.0))), t_3));
} else if (phi2 <= 0.000122) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), Math.sqrt((Math.pow(Math.cos((phi1 * 0.5)), 2.0) - (Math.cos(phi1) * t_0)))));
} else {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_2 + (0.5 - (Math.cos((phi1 - phi2)) / 2.0)))), t_3));
}
return tmp;
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) t_2 = (math.cos(phi1) * math.cos(phi2)) * (t_1 * t_1) t_3 = math.sqrt((math.pow(math.cos((phi2 * -0.5)), 2.0) - (math.cos(phi2) * t_0))) tmp = 0 if phi2 <= -6.7: tmp = R * (2.0 * math.atan2(math.sqrt((t_2 + math.pow(math.sin((phi2 * -0.5)), 2.0))), t_3)) elif phi2 <= 0.000122: tmp = R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), math.sqrt((math.pow(math.cos((phi1 * 0.5)), 2.0) - (math.cos(phi1) * t_0))))) else: tmp = R * (2.0 * math.atan2(math.sqrt((t_2 + (0.5 - (math.cos((phi1 - phi2)) / 2.0)))), t_3)) return tmp
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_1 * t_1)) t_3 = sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * t_0))) tmp = 0.0 if (phi2 <= -6.7) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_2 + (sin(Float64(phi2 * -0.5)) ^ 2.0))), t_3))); elseif (phi2 <= 0.000122) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * t_0)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_2 + Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), t_3))); end return tmp end
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) * 0.5)) ^ 2.0; t_1 = sin(((lambda1 - lambda2) / 2.0)); t_2 = (cos(phi1) * cos(phi2)) * (t_1 * t_1); t_3 = sqrt(((cos((phi2 * -0.5)) ^ 2.0) - (cos(phi2) * t_0))); tmp = 0.0; if (phi2 <= -6.7) tmp = R * (2.0 * atan2(sqrt((t_2 + (sin((phi2 * -0.5)) ^ 2.0))), t_3)); elseif (phi2 <= 0.000122) tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(((cos((phi1 * 0.5)) ^ 2.0) - (cos(phi1) * t_0))))); else tmp = R * (2.0 * atan2(sqrt((t_2 + (0.5 - (cos((phi1 - phi2)) / 2.0)))), t_3)); end tmp_2 = tmp; end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[phi2, -6.7], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$2 + N[Power[N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi2, 0.000122], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$2 + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_1 \cdot t_1\right)\\
t_3 := \sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot t_0}\\
\mathbf{if}\;\phi_2 \leq -6.7:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_2 + {\sin \left(\phi_2 \cdot -0.5\right)}^{2}}}{t_3}\right)\\
\mathbf{elif}\;\phi_2 \leq 0.000122:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_2}}{\sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot t_0}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_2 + \left(0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{t_3}\right)\\
\end{array}
\end{array}
if phi2 < -6.70000000000000018Initial program 53.4%
associate-*l*53.4%
Simplified53.4%
Taylor expanded in phi1 around 0 54.5%
+-commutative54.5%
associate--r+54.6%
unpow254.6%
1-sub-sin54.7%
unpow254.7%
Simplified54.7%
Taylor expanded in phi1 around 0 55.2%
if -6.70000000000000018 < phi2 < 1.21999999999999997e-4Initial program 74.5%
associate-*l*74.5%
Simplified74.5%
Taylor expanded in phi2 around 0 74.5%
+-commutative74.5%
associate--r+74.5%
unpow274.5%
1-sub-sin74.6%
unpow274.6%
*-commutative74.6%
Simplified74.6%
if 1.21999999999999997e-4 < phi2 Initial program 47.9%
associate-*l*47.9%
Simplified47.9%
Taylor expanded in phi1 around 0 49.0%
+-commutative49.0%
associate--r+49.0%
unpow249.0%
1-sub-sin49.0%
unpow249.0%
Simplified49.0%
unpow220.3%
sin-mult20.3%
div-inv20.3%
metadata-eval20.3%
div-inv20.3%
metadata-eval20.3%
div-inv20.3%
metadata-eval20.3%
div-inv20.3%
metadata-eval20.3%
Applied egg-rr49.1%
div-sub20.3%
+-inverses20.3%
cos-020.3%
metadata-eval20.3%
distribute-lft-out20.3%
metadata-eval20.3%
*-rgt-identity20.3%
Simplified49.1%
Final simplification62.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(t_1 (sqrt (- 1.0 t_0)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (* (* (cos phi1) (cos phi2)) (* t_2 t_2))))
(if (<= phi1 -0.00013)
(*
R
(*
2.0
(atan2
(sqrt
(+
t_3
(-
0.5
(/ (fma (cos phi2) (cos phi1) (* (sin phi1) (sin phi2))) 2.0))))
t_1)))
(if (<= phi1 5.6e-13)
(*
R
(*
2.0
(atan2
(sqrt (+ t_3 (pow (sin (* phi2 -0.5)) 2.0)))
(sqrt (- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) t_0))))))
(*
R
(*
2.0
(atan2
(sqrt
(+
t_3
(pow
(+ (sin (* phi1 0.5)) (* -0.5 (* phi2 (cos (* phi1 0.5)))))
2.0)))
t_1)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = sqrt((1.0 - t_0));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = (cos(phi1) * cos(phi2)) * (t_2 * t_2);
double tmp;
if (phi1 <= -0.00013) {
tmp = R * (2.0 * atan2(sqrt((t_3 + (0.5 - (fma(cos(phi2), cos(phi1), (sin(phi1) * sin(phi2))) / 2.0)))), t_1));
} else if (phi1 <= 5.6e-13) {
tmp = R * (2.0 * atan2(sqrt((t_3 + pow(sin((phi2 * -0.5)), 2.0))), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * t_0)))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_3 + pow((sin((phi1 * 0.5)) + (-0.5 * (phi2 * cos((phi1 * 0.5))))), 2.0))), t_1));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0 t_1 = sqrt(Float64(1.0 - t_0)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_2 * t_2)) tmp = 0.0 if (phi1 <= -0.00013) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_3 + Float64(0.5 - Float64(fma(cos(phi2), cos(phi1), Float64(sin(phi1) * sin(phi2))) / 2.0)))), t_1))); elseif (phi1 <= 5.6e-13) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_3 + (sin(Float64(phi2 * -0.5)) ^ 2.0))), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * t_0)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_3 + (Float64(sin(Float64(phi1 * 0.5)) + Float64(-0.5 * Float64(phi2 * cos(Float64(phi1 * 0.5))))) ^ 2.0))), t_1))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$1 = N[Sqrt[N[(1.0 - t$95$0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$2 * t$95$2), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[phi1, -0.00013], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$3 + N[(0.5 - N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision] + N[(N[Sin[phi1], $MachinePrecision] * N[Sin[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi1, 5.6e-13], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$3 + N[Power[N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$3 + N[Power[N[(N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] + N[(-0.5 * N[(phi2 * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\\
t_1 := \sqrt{1 - t_0}\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_2 \cdot t_2\right)\\
\mathbf{if}\;\phi_1 \leq -0.00013:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_3 + \left(0.5 - \frac{\mathsf{fma}\left(\cos \phi_2, \cos \phi_1, \sin \phi_1 \cdot \sin \phi_2\right)}{2}\right)}}{t_1}\right)\\
\mathbf{elif}\;\phi_1 \leq 5.6 \cdot 10^{-13}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_3 + {\sin \left(\phi_2 \cdot -0.5\right)}^{2}}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot t_0}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_3 + {\left(\sin \left(\phi_1 \cdot 0.5\right) + -0.5 \cdot \left(\phi_2 \cdot \cos \left(\phi_1 \cdot 0.5\right)\right)\right)}^{2}}}{t_1}\right)\\
\end{array}
\end{array}
if phi1 < -1.29999999999999989e-4Initial program 41.2%
associate-*l*41.3%
Simplified41.3%
Taylor expanded in phi1 around 0 18.6%
+-commutative18.6%
associate--r+18.6%
unpow218.6%
1-sub-sin18.6%
unpow218.6%
Simplified18.6%
Taylor expanded in phi2 around 0 18.7%
unpow218.7%
sin-mult18.7%
div-inv18.7%
metadata-eval18.7%
div-inv18.7%
metadata-eval18.7%
div-inv18.7%
metadata-eval18.7%
div-inv18.7%
metadata-eval18.7%
Applied egg-rr18.7%
div-sub18.7%
+-inverses18.7%
cos-018.7%
metadata-eval18.7%
distribute-lft-out18.7%
metadata-eval18.7%
*-rgt-identity18.7%
Simplified18.7%
cos-diff21.6%
Applied egg-rr21.6%
*-commutative21.6%
fma-udef21.6%
Simplified21.6%
if -1.29999999999999989e-4 < phi1 < 5.6000000000000004e-13Initial program 76.3%
associate-*l*76.3%
Simplified76.3%
Taylor expanded in phi1 around 0 76.3%
+-commutative76.3%
associate--r+76.3%
unpow276.3%
1-sub-sin76.4%
unpow276.4%
Simplified76.4%
Taylor expanded in phi1 around 0 76.4%
if 5.6000000000000004e-13 < phi1 Initial program 53.5%
associate-*l*53.5%
Simplified53.5%
Taylor expanded in phi1 around 0 26.7%
+-commutative26.7%
associate--r+26.7%
unpow226.7%
1-sub-sin26.7%
unpow226.7%
Simplified26.7%
Taylor expanded in phi2 around 0 26.8%
Taylor expanded in phi2 around 0 27.3%
Final simplification50.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(*
R
(*
2.0
(atan2
(sqrt (+ t_2 (* t_0 (* t_1 t_1))))
(sqrt
(-
(- 1.0 t_2)
(* t_0 (/ (+ (cos (- lambda1 lambda2)) -1.0) -2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
return R * (2.0 * atan2(sqrt((t_2 + (t_0 * (t_1 * t_1)))), sqrt(((1.0 - t_2) - (t_0 * ((cos((lambda1 - lambda2)) + -1.0) / -2.0))))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
t_0 = cos(phi1) * cos(phi2)
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
t_2 = sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0
code = r * (2.0d0 * atan2(sqrt((t_2 + (t_0 * (t_1 * t_1)))), sqrt(((1.0d0 - t_2) - (t_0 * ((cos((lambda1 - lambda2)) + (-1.0d0)) / (-2.0d0)))))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.cos(phi1) * Math.cos(phi2);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_2 = Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0);
return R * (2.0 * Math.atan2(Math.sqrt((t_2 + (t_0 * (t_1 * t_1)))), Math.sqrt(((1.0 - t_2) - (t_0 * ((Math.cos((lambda1 - lambda2)) + -1.0) / -2.0))))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.cos(phi1) * math.cos(phi2) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) t_2 = math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) return R * (2.0 * math.atan2(math.sqrt((t_2 + (t_0 * (t_1 * t_1)))), math.sqrt(((1.0 - t_2) - (t_0 * ((math.cos((lambda1 - lambda2)) + -1.0) / -2.0))))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 return Float64(R * Float64(2.0 * atan(sqrt(Float64(t_2 + Float64(t_0 * Float64(t_1 * t_1)))), sqrt(Float64(Float64(1.0 - t_2) - Float64(t_0 * Float64(Float64(cos(Float64(lambda1 - lambda2)) + -1.0) / -2.0))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(phi1) * cos(phi2); t_1 = sin(((lambda1 - lambda2) / 2.0)); t_2 = sin(((phi1 - phi2) / 2.0)) ^ 2.0; tmp = R * (2.0 * atan2(sqrt((t_2 + (t_0 * (t_1 * t_1)))), sqrt(((1.0 - t_2) - (t_0 * ((cos((lambda1 - lambda2)) + -1.0) / -2.0)))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$2 + N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - t$95$2), $MachinePrecision] - N[(t$95$0 * N[(N[(N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision] + -1.0), $MachinePrecision] / -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_2 + t_0 \cdot \left(t_1 \cdot t_1\right)}}{\sqrt{\left(1 - t_2\right) - t_0 \cdot \frac{\cos \left(\lambda_1 - \lambda_2\right) + -1}{-2}}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
sin-mult28.7%
frac-2neg28.7%
Applied egg-rr62.2%
neg-sub028.7%
sub-neg28.7%
div028.7%
cos-028.7%
sub-neg28.7%
associate--r-28.7%
metadata-eval28.7%
Simplified62.2%
Final simplification62.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* (* (cos phi1) (cos phi2)) (* t_0 t_0))))
(sqrt
(-
(pow (cos (* phi2 -0.5)) 2.0)
(* (cos phi2) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + ((cos(phi1) * cos(phi2)) * (t_0 * t_0)))), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0))))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + ((cos(phi1) * cos(phi2)) * (t_0 * t_0)))), sqrt(((cos((phi2 * (-0.5d0))) ** 2.0d0) - (cos(phi2) * (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0))))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + ((Math.cos(phi1) * Math.cos(phi2)) * (t_0 * t_0)))), Math.sqrt((Math.pow(Math.cos((phi2 * -0.5)), 2.0) - (Math.cos(phi2) * Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0))))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + ((math.cos(phi1) * math.cos(phi2)) * (t_0 * t_0)))), math.sqrt((math.pow(math.cos((phi2 * -0.5)), 2.0) - (math.cos(phi2) * math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0))))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_0 * t_0)))), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + ((cos(phi1) * cos(phi2)) * (t_0 * t_0)))), sqrt(((cos((phi2 * -0.5)) ^ 2.0) - (cos(phi2) * (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0)))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_0 \cdot t_0\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
Taylor expanded in phi1 around 0 49.8%
+-commutative49.8%
associate--r+49.8%
unpow249.8%
1-sub-sin49.9%
unpow249.9%
Simplified49.9%
Final simplification49.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* t_0 (* t_1 t_1))))
(if (or (<= (- lambda1 lambda2) -200000.0)
(not (<= (- lambda1 lambda2) 1e-43)))
(*
R
(*
2.0
(atan2
(sqrt (+ t_2 (- 0.5 (/ (+ t_0 (* (sin phi1) (sin phi2))) 2.0))))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_2))
(sqrt
(-
(pow (cos (* phi2 -0.5)) 2.0)
(* 0.25 (* (cos phi2) (pow lambda1 2.0)))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = t_0 * (t_1 * t_1);
double tmp;
if (((lambda1 - lambda2) <= -200000.0) || !((lambda1 - lambda2) <= 1e-43)) {
tmp = R * (2.0 * atan2(sqrt((t_2 + (0.5 - ((t_0 + (sin(phi1) * sin(phi2))) / 2.0)))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
} else {
tmp = R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (0.25 * (cos(phi2) * pow(lambda1, 2.0)))))));
}
return tmp;
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: tmp
t_0 = cos(phi1) * cos(phi2)
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
t_2 = t_0 * (t_1 * t_1)
if (((lambda1 - lambda2) <= (-200000.0d0)) .or. (.not. ((lambda1 - lambda2) <= 1d-43))) then
tmp = r * (2.0d0 * atan2(sqrt((t_2 + (0.5d0 - ((t_0 + (sin(phi1) * sin(phi2))) / 2.0d0)))), sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))))
else
tmp = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_2)), sqrt(((cos((phi2 * (-0.5d0))) ** 2.0d0) - (0.25d0 * (cos(phi2) * (lambda1 ** 2.0d0)))))))
end if
code = tmp
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.cos(phi1) * Math.cos(phi2);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_2 = t_0 * (t_1 * t_1);
double tmp;
if (((lambda1 - lambda2) <= -200000.0) || !((lambda1 - lambda2) <= 1e-43)) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_2 + (0.5 - ((t_0 + (Math.sin(phi1) * Math.sin(phi2))) / 2.0)))), Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
} else {
tmp = R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), Math.sqrt((Math.pow(Math.cos((phi2 * -0.5)), 2.0) - (0.25 * (Math.cos(phi2) * Math.pow(lambda1, 2.0)))))));
}
return tmp;
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.cos(phi1) * math.cos(phi2) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) t_2 = t_0 * (t_1 * t_1) tmp = 0 if ((lambda1 - lambda2) <= -200000.0) or not ((lambda1 - lambda2) <= 1e-43): tmp = R * (2.0 * math.atan2(math.sqrt((t_2 + (0.5 - ((t_0 + (math.sin(phi1) * math.sin(phi2))) / 2.0)))), math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0))))) else: tmp = R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), math.sqrt((math.pow(math.cos((phi2 * -0.5)), 2.0) - (0.25 * (math.cos(phi2) * math.pow(lambda1, 2.0))))))) return tmp
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(t_0 * Float64(t_1 * t_1)) tmp = 0.0 if ((Float64(lambda1 - lambda2) <= -200000.0) || !(Float64(lambda1 - lambda2) <= 1e-43)) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_2 + Float64(0.5 - Float64(Float64(t_0 + Float64(sin(phi1) * sin(phi2))) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(0.25 * Float64(cos(phi2) * (lambda1 ^ 2.0)))))))); end return tmp end
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(phi1) * cos(phi2); t_1 = sin(((lambda1 - lambda2) / 2.0)); t_2 = t_0 * (t_1 * t_1); tmp = 0.0; if (((lambda1 - lambda2) <= -200000.0) || ~(((lambda1 - lambda2) <= 1e-43))) tmp = R * (2.0 * atan2(sqrt((t_2 + (0.5 - ((t_0 + (sin(phi1) * sin(phi2))) / 2.0)))), sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0))))); else tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(((cos((phi2 * -0.5)) ^ 2.0) - (0.25 * (cos(phi2) * (lambda1 ^ 2.0))))))); end tmp_2 = tmp; end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -200000.0], N[Not[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], 1e-43]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$2 + N[(0.5 - N[(N[(t$95$0 + N[(N[Sin[phi1], $MachinePrecision] * N[Sin[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(0.25 * N[(N[Cos[phi2], $MachinePrecision] * N[Power[lambda1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := t_0 \cdot \left(t_1 \cdot t_1\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -200000 \lor \neg \left(\lambda_1 - \lambda_2 \leq 10^{-43}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_2 + \left(0.5 - \frac{t_0 + \sin \phi_1 \cdot \sin \phi_2}{2}\right)}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_2}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - 0.25 \cdot \left(\cos \phi_2 \cdot {\lambda_1}^{2}\right)}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e5 or 1.00000000000000008e-43 < (-.f64 lambda1 lambda2) Initial program 55.7%
associate-*l*55.7%
Simplified55.8%
Taylor expanded in phi1 around 0 44.4%
+-commutative44.4%
associate--r+44.4%
unpow244.4%
1-sub-sin44.4%
unpow244.4%
Simplified44.4%
Taylor expanded in phi2 around 0 30.7%
unpow230.7%
sin-mult30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
Applied egg-rr30.7%
div-sub30.7%
+-inverses30.7%
cos-030.7%
metadata-eval30.7%
distribute-lft-out30.7%
metadata-eval30.7%
*-rgt-identity30.7%
Simplified30.7%
cos-diff31.7%
Applied egg-rr31.7%
if -2e5 < (-.f64 lambda1 lambda2) < 1.00000000000000008e-43Initial program 85.6%
associate-*l*85.6%
Simplified85.6%
Taylor expanded in phi1 around 0 69.7%
+-commutative69.7%
associate--r+69.7%
unpow269.7%
1-sub-sin69.9%
unpow269.9%
Simplified69.9%
Taylor expanded in lambda1 around 0 69.9%
*-commutative69.9%
*-commutative69.9%
Simplified69.9%
Taylor expanded in lambda2 around 0 69.9%
Final simplification39.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (cos (* phi2 -0.5)))
(t_1 (sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (* (* (cos phi1) (cos phi2)) (* t_2 t_2))))
(if (<= (- lambda1 lambda2) -200000.0)
(*
R
(*
2.0
(atan2
(sqrt (+ t_3 (pow (+ (sin (* phi2 -0.5)) (* 0.5 (* phi1 t_0))) 2.0)))
t_1)))
(if (<= (- lambda1 lambda2) 5e-6)
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_3))
(sqrt (- (pow t_0 2.0) (* 0.25 (* (cos phi2) (pow lambda1 2.0))))))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_3 (- 0.5 (/ (pow (cbrt (cos (- phi1 phi2))) 3.0) 2.0))))
t_1)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((phi2 * -0.5));
double t_1 = sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = (cos(phi1) * cos(phi2)) * (t_2 * t_2);
double tmp;
if ((lambda1 - lambda2) <= -200000.0) {
tmp = R * (2.0 * atan2(sqrt((t_3 + pow((sin((phi2 * -0.5)) + (0.5 * (phi1 * t_0))), 2.0))), t_1));
} else if ((lambda1 - lambda2) <= 5e-6) {
tmp = R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_3)), sqrt((pow(t_0, 2.0) - (0.25 * (cos(phi2) * pow(lambda1, 2.0)))))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_3 + (0.5 - (pow(cbrt(cos((phi1 - phi2))), 3.0) / 2.0)))), t_1));
}
return tmp;
}
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.cos((phi2 * -0.5));
double t_1 = Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)));
double t_2 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_3 = (Math.cos(phi1) * Math.cos(phi2)) * (t_2 * t_2);
double tmp;
if ((lambda1 - lambda2) <= -200000.0) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_3 + Math.pow((Math.sin((phi2 * -0.5)) + (0.5 * (phi1 * t_0))), 2.0))), t_1));
} else if ((lambda1 - lambda2) <= 5e-6) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_3)), Math.sqrt((Math.pow(t_0, 2.0) - (0.25 * (Math.cos(phi2) * Math.pow(lambda1, 2.0)))))));
} else {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_3 + (0.5 - (Math.pow(Math.cbrt(Math.cos((phi1 - phi2))), 3.0) / 2.0)))), t_1));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(Float64(phi2 * -0.5)) t_1 = sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0))) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_2 * t_2)) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -200000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_3 + (Float64(sin(Float64(phi2 * -0.5)) + Float64(0.5 * Float64(phi1 * t_0))) ^ 2.0))), t_1))); elseif (Float64(lambda1 - lambda2) <= 5e-6) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_3)), sqrt(Float64((t_0 ^ 2.0) - Float64(0.25 * Float64(cos(phi2) * (lambda1 ^ 2.0)))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_3 + Float64(0.5 - Float64((cbrt(cos(Float64(phi1 - phi2))) ^ 3.0) / 2.0)))), t_1))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$2 * t$95$2), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -200000.0], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$3 + N[Power[N[(N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision] + N[(0.5 * N[(phi1 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], 5e-6], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[t$95$0, 2.0], $MachinePrecision] - N[(0.25 * N[(N[Cos[phi2], $MachinePrecision] * N[Power[lambda1, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$3 + N[(0.5 - N[(N[Power[N[Power[N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision], 1/3], $MachinePrecision], 3.0], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(\phi_2 \cdot -0.5\right)\\
t_1 := \sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_2 \cdot t_2\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -200000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_3 + {\left(\sin \left(\phi_2 \cdot -0.5\right) + 0.5 \cdot \left(\phi_1 \cdot t_0\right)\right)}^{2}}}{t_1}\right)\\
\mathbf{elif}\;\lambda_1 - \lambda_2 \leq 5 \cdot 10^{-6}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_3}}{\sqrt{{t_0}^{2} - 0.25 \cdot \left(\cos \phi_2 \cdot {\lambda_1}^{2}\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_3 + \left(0.5 - \frac{{\left(\sqrt[3]{\cos \left(\phi_1 - \phi_2\right)}\right)}^{3}}{2}\right)}}{t_1}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e5Initial program 52.7%
associate-*l*52.6%
Simplified52.7%
Taylor expanded in phi1 around 0 46.5%
+-commutative46.5%
associate--r+46.5%
unpow246.5%
1-sub-sin46.5%
unpow246.5%
Simplified46.5%
Taylor expanded in phi2 around 0 31.2%
Taylor expanded in phi1 around 0 31.3%
if -2e5 < (-.f64 lambda1 lambda2) < 5.00000000000000041e-6Initial program 84.1%
associate-*l*84.1%
Simplified84.1%
Taylor expanded in phi1 around 0 65.7%
+-commutative65.7%
associate--r+65.7%
unpow265.7%
1-sub-sin65.9%
unpow265.9%
Simplified65.9%
Taylor expanded in lambda1 around 0 65.9%
*-commutative65.9%
*-commutative65.9%
Simplified65.9%
Taylor expanded in lambda2 around 0 65.9%
if 5.00000000000000041e-6 < (-.f64 lambda1 lambda2) Initial program 58.1%
associate-*l*58.2%
Simplified58.2%
Taylor expanded in phi1 around 0 43.5%
+-commutative43.5%
associate--r+43.5%
unpow243.5%
1-sub-sin43.5%
unpow243.5%
Simplified43.5%
Taylor expanded in phi2 around 0 30.6%
unpow230.6%
sin-mult30.6%
div-inv30.6%
metadata-eval30.6%
div-inv30.6%
metadata-eval30.6%
div-inv30.6%
metadata-eval30.6%
div-inv30.6%
metadata-eval30.6%
Applied egg-rr30.6%
div-sub30.6%
+-inverses30.6%
cos-030.6%
metadata-eval30.6%
distribute-lft-out30.6%
metadata-eval30.6%
*-rgt-identity30.6%
Simplified30.6%
add-cube-cbrt30.6%
pow330.6%
Applied egg-rr30.6%
Final simplification39.1%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (* (cos phi1) (cos phi2)) (* t_0 t_0))
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt
(-
(pow (cos (* phi2 -0.5)) 2.0)
(* (cos phi2) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt((((cos(phi1) * cos(phi2)) * (t_0 * t_0)) + (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0))))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
code = r * (2.0d0 * atan2(sqrt((((cos(phi1) * cos(phi2)) * (t_0 * t_0)) + (0.5d0 - (cos((phi1 - phi2)) / 2.0d0)))), sqrt(((cos((phi2 * (-0.5d0))) ** 2.0d0) - (cos(phi2) * (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0))))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * Math.atan2(Math.sqrt((((Math.cos(phi1) * Math.cos(phi2)) * (t_0 * t_0)) + (0.5 - (Math.cos((phi1 - phi2)) / 2.0)))), Math.sqrt((Math.pow(Math.cos((phi2 * -0.5)), 2.0) - (Math.cos(phi2) * Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0))))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return R * (2.0 * math.atan2(math.sqrt((((math.cos(phi1) * math.cos(phi2)) * (t_0 * t_0)) + (0.5 - (math.cos((phi1 - phi2)) / 2.0)))), math.sqrt((math.pow(math.cos((phi2 * -0.5)), 2.0) - (math.cos(phi2) * math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0))))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_0 * t_0)) + Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); tmp = R * (2.0 * atan2(sqrt((((cos(phi1) * cos(phi2)) * (t_0 * t_0)) + (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt(((cos((phi2 * -0.5)) ^ 2.0) - (cos(phi2) * (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0)))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_0 \cdot t_0\right) + \left(0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
Taylor expanded in phi1 around 0 49.8%
+-commutative49.8%
associate--r+49.8%
unpow249.8%
1-sub-sin49.9%
unpow249.9%
Simplified49.9%
unpow233.7%
sin-mult30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
Applied egg-rr47.0%
div-sub30.7%
+-inverses30.7%
cos-030.7%
metadata-eval30.7%
distribute-lft-out30.7%
metadata-eval30.7%
*-rgt-identity30.7%
Simplified47.0%
Final simplification47.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (* (cos phi1) (cos phi2)) (* t_0 t_0))))
(if (<= (- lambda1 lambda2) 1e-43)
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_1))
(sqrt (pow (cos (* lambda2 -0.5)) 2.0)))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_1 (- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = (cos(phi1) * cos(phi2)) * (t_0 * t_0);
double tmp;
if ((lambda1 - lambda2) <= 1e-43) {
tmp = R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), sqrt(pow(cos((lambda2 * -0.5)), 2.0))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_1 + (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
return tmp;
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: tmp
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = (cos(phi1) * cos(phi2)) * (t_0 * t_0)
if ((lambda1 - lambda2) <= 1d-43) then
tmp = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_1)), sqrt((cos((lambda2 * (-0.5d0))) ** 2.0d0))))
else
tmp = r * (2.0d0 * atan2(sqrt((t_1 + (0.5d0 - (cos((phi1 - phi2)) / 2.0d0)))), sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))))
end if
code = tmp
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = (Math.cos(phi1) * Math.cos(phi2)) * (t_0 * t_0);
double tmp;
if ((lambda1 - lambda2) <= 1e-43) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), Math.sqrt(Math.pow(Math.cos((lambda2 * -0.5)), 2.0))));
} else {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_1 + (0.5 - (Math.cos((phi1 - phi2)) / 2.0)))), Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
return tmp;
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = (math.cos(phi1) * math.cos(phi2)) * (t_0 * t_0) tmp = 0 if (lambda1 - lambda2) <= 1e-43: tmp = R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), math.sqrt(math.pow(math.cos((lambda2 * -0.5)), 2.0)))) else: tmp = R * (2.0 * math.atan2(math.sqrt((t_1 + (0.5 - (math.cos((phi1 - phi2)) / 2.0)))), math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0))))) return tmp
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_0 * t_0)) tmp = 0.0 if (Float64(lambda1 - lambda2) <= 1e-43) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt((cos(Float64(lambda2 * -0.5)) ^ 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_1 + Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))))); end return tmp end
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = (cos(phi1) * cos(phi2)) * (t_0 * t_0); tmp = 0.0; if ((lambda1 - lambda2) <= 1e-43) tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt((cos((lambda2 * -0.5)) ^ 2.0)))); else tmp = R * (2.0 * atan2(sqrt((t_1 + (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0))))); end tmp_2 = tmp; end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], 1e-43], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[Power[N[Cos[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_0 \cdot t_0\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq 10^{-43}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1}}{\sqrt{{\cos \left(\lambda_2 \cdot -0.5\right)}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1 + \left(0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < 1.00000000000000008e-43Initial program 64.6%
associate-*l*64.6%
Simplified64.6%
Taylor expanded in phi1 around 0 54.9%
+-commutative54.9%
associate--r+54.9%
unpow254.9%
1-sub-sin55.0%
unpow255.0%
Simplified55.0%
Taylor expanded in phi2 around 0 36.1%
Taylor expanded in lambda1 around 0 34.6%
unpow234.6%
1-sub-sin34.6%
unpow234.6%
Simplified34.6%
if 1.00000000000000008e-43 < (-.f64 lambda1 lambda2) Initial program 58.6%
associate-*l*58.6%
Simplified58.6%
Taylor expanded in phi1 around 0 42.4%
+-commutative42.4%
associate--r+42.4%
unpow242.4%
1-sub-sin42.4%
unpow242.4%
Simplified42.4%
Taylor expanded in phi2 around 0 30.1%
unpow230.1%
sin-mult30.1%
div-inv30.1%
metadata-eval30.1%
div-inv30.1%
metadata-eval30.1%
div-inv30.1%
metadata-eval30.1%
div-inv30.1%
metadata-eval30.1%
Applied egg-rr30.1%
div-sub30.1%
+-inverses30.1%
cos-030.1%
metadata-eval30.1%
distribute-lft-out30.1%
metadata-eval30.1%
*-rgt-identity30.1%
Simplified30.1%
Final simplification32.8%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* (* (cos phi1) (cos phi2)) (* t_0 t_0))))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + ((cos(phi1) * cos(phi2)) * (t_0 * t_0)))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + ((cos(phi1) * cos(phi2)) * (t_0 * t_0)))), sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + ((Math.cos(phi1) * Math.cos(phi2)) * (t_0 * t_0)))), Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + ((math.cos(phi1) * math.cos(phi2)) * (t_0 * t_0)))), math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_0 * t_0)))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + ((cos(phi1) * cos(phi2)) * (t_0 * t_0)))), sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_0 \cdot t_0\right)}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
Taylor expanded in phi1 around 0 49.8%
+-commutative49.8%
associate--r+49.8%
unpow249.8%
1-sub-sin49.9%
unpow249.9%
Simplified49.9%
Taylor expanded in phi2 around 0 33.7%
Final simplification33.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (* (cos phi1) (cos phi2)) (* t_0 t_0))
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt((((cos(phi1) * cos(phi2)) * (t_0 * t_0)) + (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
code = r * (2.0d0 * atan2(sqrt((((cos(phi1) * cos(phi2)) * (t_0 * t_0)) + (0.5d0 - (cos((phi1 - phi2)) / 2.0d0)))), sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * Math.atan2(Math.sqrt((((Math.cos(phi1) * Math.cos(phi2)) * (t_0 * t_0)) + (0.5 - (Math.cos((phi1 - phi2)) / 2.0)))), Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return R * (2.0 * math.atan2(math.sqrt((((math.cos(phi1) * math.cos(phi2)) * (t_0 * t_0)) + (0.5 - (math.cos((phi1 - phi2)) / 2.0)))), math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(Float64(cos(phi1) * cos(phi2)) * Float64(t_0 * t_0)) + Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); tmp = R * (2.0 * atan2(sqrt((((cos(phi1) * cos(phi2)) * (t_0 * t_0)) + (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t_0 \cdot t_0\right) + \left(0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
Taylor expanded in phi1 around 0 49.8%
+-commutative49.8%
associate--r+49.8%
unpow249.8%
1-sub-sin49.9%
unpow249.9%
Simplified49.9%
Taylor expanded in phi2 around 0 33.7%
unpow233.7%
sin-mult30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
Applied egg-rr30.7%
div-sub30.7%
+-inverses30.7%
cos-030.7%
metadata-eval30.7%
distribute-lft-out30.7%
metadata-eval30.7%
*-rgt-identity30.7%
Simplified30.7%
Final simplification30.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(+
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))
(*
(* (cos phi1) (cos phi2))
(/ (+ (cos (- lambda1 lambda2)) -1.0) -2.0))))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(((0.5 - (cos((phi1 - phi2)) / 2.0)) + ((cos(phi1) * cos(phi2)) * ((cos((lambda1 - lambda2)) + -1.0) / -2.0)))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
code = r * (2.0d0 * atan2(sqrt(((0.5d0 - (cos((phi1 - phi2)) / 2.0d0)) + ((cos(phi1) * cos(phi2)) * ((cos((lambda1 - lambda2)) + (-1.0d0)) / (-2.0d0))))), sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * Math.atan2(Math.sqrt(((0.5 - (Math.cos((phi1 - phi2)) / 2.0)) + ((Math.cos(phi1) * Math.cos(phi2)) * ((Math.cos((lambda1 - lambda2)) + -1.0) / -2.0)))), Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))));
}
def code(R, lambda1, lambda2, phi1, phi2): return R * (2.0 * math.atan2(math.sqrt(((0.5 - (math.cos((phi1 - phi2)) / 2.0)) + ((math.cos(phi1) * math.cos(phi2)) * ((math.cos((lambda1 - lambda2)) + -1.0) / -2.0)))), math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))))
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)) + Float64(Float64(cos(phi1) * cos(phi2)) * Float64(Float64(cos(Float64(lambda1 - lambda2)) + -1.0) / -2.0)))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) tmp = R * (2.0 * atan2(sqrt(((0.5 - (cos((phi1 - phi2)) / 2.0)) + ((cos(phi1) * cos(phi2)) * ((cos((lambda1 - lambda2)) + -1.0) / -2.0)))), sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(N[(N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision] + -1.0), $MachinePrecision] / -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\left(0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right) + \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \frac{\cos \left(\lambda_1 - \lambda_2\right) + -1}{-2}}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)
\end{array}
Initial program 62.1%
associate-*l*62.1%
Simplified62.2%
Taylor expanded in phi1 around 0 49.8%
+-commutative49.8%
associate--r+49.8%
unpow249.8%
1-sub-sin49.9%
unpow249.9%
Simplified49.9%
Taylor expanded in phi2 around 0 33.7%
unpow233.7%
sin-mult30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
div-inv30.7%
metadata-eval30.7%
Applied egg-rr30.7%
div-sub30.7%
+-inverses30.7%
cos-030.7%
metadata-eval30.7%
distribute-lft-out30.7%
metadata-eval30.7%
*-rgt-identity30.7%
Simplified30.7%
sin-mult28.7%
frac-2neg28.7%
Applied egg-rr28.7%
neg-sub028.7%
sub-neg28.7%
div028.7%
cos-028.7%
sub-neg28.7%
associate--r-28.7%
metadata-eval28.7%
Simplified28.7%
Final simplification28.7%
herbie shell --seed 2024024
(FPCore (R lambda1 lambda2 phi1 phi2)
:name "Distance on a great circle"
:precision binary64
(* R (* 2.0 (atan2 (sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) (* (* (* (cos phi1) (cos phi2)) (sin (/ (- lambda1 lambda2) 2.0))) (sin (/ (- lambda1 lambda2) 2.0))))) (sqrt (- 1.0 (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) (* (* (* (cos phi1) (cos phi2)) (sin (/ (- lambda1 lambda2) 2.0))) (sin (/ (- lambda1 lambda2) 2.0))))))))))