
(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 20 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 0.5)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (cos (* phi2 0.5)))
(t_3 (sin (* phi1 0.5)))
(t_4 (* (cos phi2) (* t_1 t_1)))
(t_5 (sin (* phi2 0.5))))
(*
R
(*
2.0
(atan2
(sqrt (fma (cos phi1) t_4 (pow (fma t_2 t_3 (* t_5 (- t_0))) 2.0)))
(sqrt
(- 1.0 (fma (cos phi1) t_4 (pow (- (* t_5 t_0) (* t_2 t_3)) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((phi1 * 0.5));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = cos((phi2 * 0.5));
double t_3 = sin((phi1 * 0.5));
double t_4 = cos(phi2) * (t_1 * t_1);
double t_5 = sin((phi2 * 0.5));
return R * (2.0 * atan2(sqrt(fma(cos(phi1), t_4, pow(fma(t_2, t_3, (t_5 * -t_0)), 2.0))), sqrt((1.0 - fma(cos(phi1), t_4, pow(((t_5 * t_0) - (t_2 * t_3)), 2.0))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(Float64(phi1 * 0.5)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = cos(Float64(phi2 * 0.5)) t_3 = sin(Float64(phi1 * 0.5)) t_4 = Float64(cos(phi2) * Float64(t_1 * t_1)) t_5 = sin(Float64(phi2 * 0.5)) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), t_4, (fma(t_2, t_3, Float64(t_5 * Float64(-t_0))) ^ 2.0))), sqrt(Float64(1.0 - fma(cos(phi1), t_4, (Float64(Float64(t_5 * t_0) - Float64(t_2 * t_3)) ^ 2.0))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$4 = N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$4 + N[Power[N[(t$95$2 * t$95$3 + N[(t$95$5 * (-t$95$0)), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Cos[phi1], $MachinePrecision] * t$95$4 + N[Power[N[(N[(t$95$5 * t$95$0), $MachinePrecision] - N[(t$95$2 * t$95$3), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \cos \left(\phi_2 \cdot 0.5\right)\\
t_3 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_4 := \cos \phi_2 \cdot \left(t_1 \cdot t_1\right)\\
t_5 := \sin \left(\phi_2 \cdot 0.5\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_4, {\left(\mathsf{fma}\left(t_2, t_3, t_5 \cdot \left(-t_0\right)\right)\right)}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(\cos \phi_1, t_4, {\left(t_5 \cdot t_0 - t_2 \cdot t_3\right)}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 66.2%
Simplified66.2%
div-sub66.2%
sin-diff67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
Applied egg-rr67.2%
div-sub67.2%
sin-diff82.3%
div-inv82.3%
metadata-eval82.3%
div-inv82.3%
metadata-eval82.3%
div-inv82.3%
metadata-eval82.3%
div-inv82.3%
metadata-eval82.3%
Applied egg-rr82.3%
Taylor expanded in phi1 around inf 82.3%
fma-neg82.3%
*-commutative82.3%
*-commutative82.3%
distribute-rgt-neg-in82.3%
*-commutative82.3%
Simplified82.3%
Final simplification82.3%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos (* phi2 0.5)) (sin (* phi1 0.5))))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* t_1 (* t_1 (* (cos phi1) (cos phi2)))))
(t_3 (* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
(t_4 (pow (- t_0 t_3) 2.0)))
(if (<= lambda1 -1.2e-5)
(*
R
(*
2.0
(atan2
(sqrt (+ (- 0.5 (/ (cos (- phi1 phi2)) 2.0)) t_2))
(sqrt (- 1.0 (+ t_4 t_2))))))
(*
R
(*
2.0
(atan2
(sqrt (fma (cos phi1) (* (cos phi2) (* t_1 t_1)) t_4))
(sqrt
(-
1.0
(+
(pow (- t_3 t_0) 2.0)
(*
(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 = cos((phi2 * 0.5)) * sin((phi1 * 0.5));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = t_1 * (t_1 * (cos(phi1) * cos(phi2)));
double t_3 = sin((phi2 * 0.5)) * cos((phi1 * 0.5));
double t_4 = pow((t_0 - t_3), 2.0);
double tmp;
if (lambda1 <= -1.2e-5) {
tmp = R * (2.0 * atan2(sqrt(((0.5 - (cos((phi1 - phi2)) / 2.0)) + t_2)), sqrt((1.0 - (t_4 + t_2)))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * t_1)), t_4)), sqrt((1.0 - (pow((t_3 - t_0), 2.0) + (cos(phi1) * (cos(phi2) * pow(sin((lambda2 * -0.5)), 2.0))))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(t_1 * Float64(t_1 * Float64(cos(phi1) * cos(phi2)))) t_3 = Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5))) t_4 = Float64(t_0 - t_3) ^ 2.0 tmp = 0.0 if (lambda1 <= -1.2e-5) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)) + t_2)), sqrt(Float64(1.0 - Float64(t_4 + t_2)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * t_1)), t_4)), sqrt(Float64(1.0 - Float64((Float64(t_3 - t_0) ^ 2.0) + 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[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * N[(t$95$1 * N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[Power[N[(t$95$0 - t$95$3), $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[lambda1, -1.2e-5], 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] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$4 + t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision] + t$95$4), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[(t$95$3 - t$95$0), $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]), $MachinePrecision]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right)\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := t_1 \cdot \left(t_1 \cdot \left(\cos \phi_1 \cdot \cos \phi_2\right)\right)\\
t_3 := \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\\
t_4 := {\left(t_0 - t_3\right)}^{2}\\
\mathbf{if}\;\lambda_1 \leq -1.2 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\left(0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right) + t_2}}{\sqrt{1 - \left(t_4 + t_2\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot t_1\right), t_4\right)}}{\sqrt{1 - \left({\left(t_3 - t_0\right)}^{2} + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}\right)\right)}}\right)\\
\end{array}
\end{array}
if lambda1 < -1.2e-5Initial program 60.8%
unpow222.1%
sin-mult22.1%
div-inv22.1%
metadata-eval22.1%
div-inv22.1%
metadata-eval22.1%
div-inv22.1%
metadata-eval22.1%
div-inv22.1%
metadata-eval22.1%
Applied egg-rr60.8%
div-sub22.1%
+-inverses22.1%
cos-022.1%
metadata-eval22.1%
distribute-lft-out22.1%
metadata-eval22.1%
*-rgt-identity22.1%
Simplified60.8%
div-sub60.8%
sin-diff61.3%
div-inv61.3%
metadata-eval61.3%
div-inv61.3%
metadata-eval61.3%
div-inv61.3%
metadata-eval61.3%
div-inv61.3%
metadata-eval61.3%
Applied egg-rr61.3%
if -1.2e-5 < lambda1 Initial program 68.0%
Simplified68.0%
div-sub68.0%
sin-diff69.2%
div-inv69.2%
metadata-eval69.2%
div-inv69.2%
metadata-eval69.2%
div-inv69.2%
metadata-eval69.2%
div-inv69.2%
metadata-eval69.2%
Applied egg-rr69.2%
div-sub69.2%
sin-diff87.5%
div-inv87.5%
metadata-eval87.5%
div-inv87.5%
metadata-eval87.5%
div-inv87.5%
metadata-eval87.5%
div-inv87.5%
metadata-eval87.5%
Applied egg-rr87.5%
Taylor expanded in lambda1 around 0 75.1%
Final simplification71.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (cos (* phi2 0.5)) (sin (* phi1 0.5))))
(t_3 (pow (- t_0 t_2) 2.0))
(t_4
(sqrt
(fma (cos phi1) (* (cos phi2) (* t_1 t_1)) (pow (- t_2 t_0) 2.0)))))
(if (<= lambda1 -9e-6)
(*
R
(*
2.0
(atan2
t_4
(sqrt
(-
1.0
(+
t_3
(* (cos phi1) (* (cos phi2) (pow (sin (* lambda1 0.5)) 2.0)))))))))
(*
R
(*
2.0
(atan2
t_4
(sqrt
(-
1.0
(+
t_3
(*
(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((phi2 * 0.5)) * cos((phi1 * 0.5));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = cos((phi2 * 0.5)) * sin((phi1 * 0.5));
double t_3 = pow((t_0 - t_2), 2.0);
double t_4 = sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * t_1)), pow((t_2 - t_0), 2.0)));
double tmp;
if (lambda1 <= -9e-6) {
tmp = R * (2.0 * atan2(t_4, sqrt((1.0 - (t_3 + (cos(phi1) * (cos(phi2) * pow(sin((lambda1 * 0.5)), 2.0))))))));
} else {
tmp = R * (2.0 * atan2(t_4, sqrt((1.0 - (t_3 + (cos(phi1) * (cos(phi2) * pow(sin((lambda2 * -0.5)), 2.0))))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5))) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) t_3 = Float64(t_0 - t_2) ^ 2.0 t_4 = sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * t_1)), (Float64(t_2 - t_0) ^ 2.0))) tmp = 0.0 if (lambda1 <= -9e-6) tmp = Float64(R * Float64(2.0 * atan(t_4, sqrt(Float64(1.0 - Float64(t_3 + Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(lambda1 * 0.5)) ^ 2.0))))))))); else tmp = Float64(R * Float64(2.0 * atan(t_4, sqrt(Float64(1.0 - Float64(t_3 + 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[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[Power[N[(t$95$0 - t$95$2), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$4 = N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[Power[N[(t$95$2 - t$95$0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda1, -9e-6], N[(R * N[(2.0 * N[ArcTan[t$95$4 / N[Sqrt[N[(1.0 - N[(t$95$3 + 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]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$4 / N[Sqrt[N[(1.0 - N[(t$95$3 + 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]), $MachinePrecision]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right)\\
t_3 := {\left(t_0 - t_2\right)}^{2}\\
t_4 := \sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot t_1\right), {\left(t_2 - t_0\right)}^{2}\right)}\\
\mathbf{if}\;\lambda_1 \leq -9 \cdot 10^{-6}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_4}{\sqrt{1 - \left(t_3 + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(\lambda_1 \cdot 0.5\right)}^{2}\right)\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_4}{\sqrt{1 - \left(t_3 + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}\right)\right)}}\right)\\
\end{array}
\end{array}
if lambda1 < -9.00000000000000023e-6Initial program 60.8%
Simplified60.8%
div-sub60.8%
sin-diff61.3%
div-inv61.3%
metadata-eval61.3%
div-inv61.3%
metadata-eval61.3%
div-inv61.3%
metadata-eval61.3%
div-inv61.3%
metadata-eval61.3%
Applied egg-rr61.3%
div-sub61.3%
sin-diff66.7%
div-inv66.7%
metadata-eval66.7%
div-inv66.7%
metadata-eval66.7%
div-inv66.7%
metadata-eval66.7%
div-inv66.7%
metadata-eval66.7%
Applied egg-rr66.7%
Taylor expanded in lambda2 around 0 66.8%
if -9.00000000000000023e-6 < lambda1 Initial program 68.0%
Simplified68.0%
div-sub68.0%
sin-diff69.2%
div-inv69.2%
metadata-eval69.2%
div-inv69.2%
metadata-eval69.2%
div-inv69.2%
metadata-eval69.2%
div-inv69.2%
metadata-eval69.2%
Applied egg-rr69.2%
div-sub69.2%
sin-diff87.5%
div-inv87.5%
metadata-eval87.5%
div-inv87.5%
metadata-eval87.5%
div-inv87.5%
metadata-eval87.5%
div-inv87.5%
metadata-eval87.5%
Applied egg-rr87.5%
Taylor expanded in lambda1 around 0 75.1%
Final simplification73.1%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (cos (* phi2 0.5)) (sin (* phi1 0.5))))
(t_2 (* (sin (* phi2 0.5)) (cos (* phi1 0.5)))))
(*
R
(*
2.0
(atan2
(sqrt (fma (cos phi1) (* (cos phi2) (* t_0 t_0)) (pow (- t_1 t_2) 2.0)))
(sqrt
(-
1.0
(+
(pow (- t_2 t_1) 2.0)
(*
(cos phi1)
(* (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));
double t_1 = cos((phi2 * 0.5)) * sin((phi1 * 0.5));
double t_2 = sin((phi2 * 0.5)) * cos((phi1 * 0.5));
return R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_0 * t_0)), pow((t_1 - t_2), 2.0))), sqrt((1.0 - (pow((t_2 - t_1), 2.0) + (cos(phi1) * (cos(phi2) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0))))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) t_2 = Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5))) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_0 * t_0)), (Float64(t_1 - t_2) ^ 2.0))), sqrt(Float64(1.0 - Float64((Float64(t_2 - t_1) ^ 2.0) + Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(Float64(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]}, Block[{t$95$1 = N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[Power[N[(t$95$1 - t$95$2), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[(t$95$2 - t$95$1), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[Cos[phi1], $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]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right)\\
t_2 := \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_0 \cdot t_0\right), {\left(t_1 - t_2\right)}^{2}\right)}}{\sqrt{1 - \left({\left(t_2 - t_1\right)}^{2} + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\right)\right)}}\right)
\end{array}
\end{array}
Initial program 66.2%
Simplified66.2%
div-sub66.2%
sin-diff67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
Applied egg-rr67.2%
div-sub67.2%
sin-diff82.3%
div-inv82.3%
metadata-eval82.3%
div-inv82.3%
metadata-eval82.3%
div-inv82.3%
metadata-eval82.3%
div-inv82.3%
metadata-eval82.3%
Applied egg-rr82.3%
Taylor expanded in phi1 around inf 82.3%
Final simplification82.3%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (cos phi2) (* t_0 t_0))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
t_1
(pow
(-
(* (cos (* phi2 0.5)) (sin (* phi1 0.5)))
(* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
2.0)))
(sqrt
(-
1.0
(fma
(cos phi1)
t_1
(pow (log (exp (sin (* 0.5 (- phi2 phi1))))) 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(phi2) * (t_0 * t_0);
return R * (2.0 * atan2(sqrt(fma(cos(phi1), t_1, pow(((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))), 2.0))), sqrt((1.0 - fma(cos(phi1), t_1, pow(log(exp(sin((0.5 * (phi2 - phi1))))), 2.0))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(cos(phi2) * Float64(t_0 * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), t_1, (Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5)))) ^ 2.0))), sqrt(Float64(1.0 - fma(cos(phi1), t_1, (log(exp(sin(Float64(0.5 * Float64(phi2 - phi1))))) ^ 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[phi2], $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[Power[N[Log[N[Exp[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $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)\\
t_1 := \cos \phi_2 \cdot \left(t_0 \cdot t_0\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_1, {\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\right)}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(\cos \phi_1, t_1, {\log \left(e^{\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}\right)}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 66.2%
Simplified66.2%
div-sub66.2%
sin-diff67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
Applied egg-rr67.2%
add-sqr-sqrt33.1%
sqrt-prod67.2%
unpow267.2%
add-log-exp67.2%
unpow267.2%
sqrt-prod33.1%
add-sqr-sqrt67.2%
div-inv67.2%
metadata-eval67.2%
Applied egg-rr67.2%
Final simplification67.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (cos phi2) (* t_0 t_0))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
t_1
(pow
(-
(* (cos (* phi2 0.5)) (sin (* phi1 0.5)))
(* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
2.0)))
(sqrt
(- 1.0 (fma (cos phi1) t_1 (pow (sin (/ (- phi2 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 = cos(phi2) * (t_0 * t_0);
return R * (2.0 * atan2(sqrt(fma(cos(phi1), t_1, pow(((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))), 2.0))), sqrt((1.0 - fma(cos(phi1), t_1, pow(sin(((phi2 - phi1) / 2.0)), 2.0))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(cos(phi2) * Float64(t_0 * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), t_1, (Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5)))) ^ 2.0))), sqrt(Float64(1.0 - fma(cos(phi1), t_1, (sin(Float64(Float64(phi2 - 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[(N[Cos[phi2], $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[Power[N[Sin[N[(N[(phi2 - phi1), $MachinePrecision] / 2.0), $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)\\
t_1 := \cos \phi_2 \cdot \left(t_0 \cdot t_0\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_1, {\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\right)}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(\cos \phi_1, t_1, {\sin \left(\frac{\phi_2 - \phi_1}{2}\right)}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 66.2%
Simplified66.2%
div-sub66.2%
sin-diff67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
Applied egg-rr67.2%
Final simplification67.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* t_0 (* (cos phi1) (cos phi2))))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_1))
(sqrt
(-
1.0
(+
(pow
(-
(* (cos (* phi2 0.5)) (sin (* phi1 0.5)))
(* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
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 * (t_0 * (cos(phi1) * cos(phi2)));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), sqrt((1.0 - (pow(((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))), 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 * (t_0 * (cos(phi1) * cos(phi2)))
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_1)), sqrt((1.0d0 - ((((cos((phi2 * 0.5d0)) * sin((phi1 * 0.5d0))) - (sin((phi2 * 0.5d0)) * cos((phi1 * 0.5d0)))) ** 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 * (t_0 * (Math.cos(phi1) * Math.cos(phi2)));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), Math.sqrt((1.0 - (Math.pow(((Math.cos((phi2 * 0.5)) * Math.sin((phi1 * 0.5))) - (Math.sin((phi2 * 0.5)) * Math.cos((phi1 * 0.5)))), 2.0) + t_1)))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = t_0 * (t_0 * (math.cos(phi1) * math.cos(phi2))) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), math.sqrt((1.0 - (math.pow(((math.cos((phi2 * 0.5)) * math.sin((phi1 * 0.5))) - (math.sin((phi2 * 0.5)) * math.cos((phi1 * 0.5)))), 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(t_0 * Float64(cos(phi1) * cos(phi2)))) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(Float64(1.0 - Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5)))) ^ 2.0) + t_1)))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = t_0 * (t_0 * (cos(phi1) * cos(phi2))); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt((1.0 - ((((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))) ^ 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[(t$95$0 * N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $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$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $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(t_0 \cdot \left(\cos \phi_1 \cdot \cos \phi_2\right)\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1}}{\sqrt{1 - \left({\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\right)}^{2} + t_1\right)}}\right)
\end{array}
\end{array}
Initial program 66.2%
div-sub66.2%
sin-diff67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
Applied egg-rr66.9%
Final simplification66.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* t_0 (* (cos phi1) (cos phi2))))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) (sin (* phi1 0.5)))
(* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
2.0)
t_1))
(sqrt (- 1.0 (+ (pow (sin (/ (- phi1 phi2) 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 * (t_0 * (cos(phi1) * cos(phi2)));
return R * (2.0 * atan2(sqrt((pow(((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))), 2.0) + t_1)), sqrt((1.0 - (pow(sin(((phi1 - phi2) / 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 * (t_0 * (cos(phi1) * cos(phi2)))
code = r * (2.0d0 * atan2(sqrt(((((cos((phi2 * 0.5d0)) * sin((phi1 * 0.5d0))) - (sin((phi2 * 0.5d0)) * cos((phi1 * 0.5d0)))) ** 2.0d0) + t_1)), sqrt((1.0d0 - ((sin(((phi1 - phi2) / 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 * (t_0 * (Math.cos(phi1) * Math.cos(phi2)));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(((Math.cos((phi2 * 0.5)) * Math.sin((phi1 * 0.5))) - (Math.sin((phi2 * 0.5)) * Math.cos((phi1 * 0.5)))), 2.0) + t_1)), Math.sqrt((1.0 - (Math.pow(Math.sin(((phi1 - phi2) / 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 * (t_0 * (math.cos(phi1) * math.cos(phi2))) return R * (2.0 * math.atan2(math.sqrt((math.pow(((math.cos((phi2 * 0.5)) * math.sin((phi1 * 0.5))) - (math.sin((phi2 * 0.5)) * math.cos((phi1 * 0.5)))), 2.0) + t_1)), math.sqrt((1.0 - (math.pow(math.sin(((phi1 - phi2) / 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(t_0 * Float64(cos(phi1) * cos(phi2)))) return Float64(R * Float64(2.0 * atan(sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5)))) ^ 2.0) + t_1)), sqrt(Float64(1.0 - Float64((sin(Float64(Float64(phi1 - phi2) / 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 * (t_0 * (cos(phi1) * cos(phi2))); tmp = R * (2.0 * atan2(sqrt(((((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))) ^ 2.0) + t_1)), sqrt((1.0 - ((sin(((phi1 - phi2) / 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[(t$95$0 * N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $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(t_0 \cdot \left(\cos \phi_1 \cdot \cos \phi_2\right)\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\right)}^{2} + t_1}}{\sqrt{1 - \left({\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1\right)}}\right)
\end{array}
\end{array}
Initial program 66.2%
div-sub66.2%
sin-diff67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
div-inv67.2%
metadata-eval67.2%
Applied egg-rr67.2%
Final simplification67.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* t_0 (* (cos phi1) (cos phi2))))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_1))
(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 * (t_0 * (cos(phi1) * cos(phi2)));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), 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 * (t_0 * (cos(phi1) * cos(phi2)))
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_1)), 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 * (t_0 * (Math.cos(phi1) * Math.cos(phi2)));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), 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 * (t_0 * (math.cos(phi1) * math.cos(phi2))) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), 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(t_0 * Float64(cos(phi1) * cos(phi2)))) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), 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 * (t_0 * (cos(phi1) * cos(phi2))); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), 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[(t$95$0 * N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $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$1), $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(t_0 \cdot \left(\cos \phi_1 \cdot \cos \phi_2\right)\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1}}{\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 66.2%
unpow236.9%
sin-mult33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
Applied egg-rr66.3%
div-sub33.1%
+-inverses33.1%
cos-033.1%
metadata-eval33.1%
distribute-lft-out33.1%
metadata-eval33.1%
*-rgt-identity33.1%
Simplified66.3%
Final simplification66.3%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (/ (cos (- phi1 phi2)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* t_1 (* t_1 (* (cos phi1) (cos phi2))))))
(if (<= (- lambda1 lambda2) -1e-5)
(*
R
(*
2.0
(atan2 (sqrt (+ (- 0.5 t_0) t_2)) (sqrt (+ 1.0 (- (- t_0 0.5) t_2))))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_1 t_1))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (log (exp (sin (* 0.5 (- phi2 phi1))))) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((phi1 - phi2)) / 2.0;
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = t_1 * (t_1 * (cos(phi1) * cos(phi2)));
double tmp;
if ((lambda1 - lambda2) <= -1e-5) {
tmp = R * (2.0 * atan2(sqrt(((0.5 - t_0) + t_2)), sqrt((1.0 + ((t_0 - 0.5) - t_2)))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * t_1)), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(log(exp(sin((0.5 * (phi2 - phi1))))), 2.0)))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(Float64(phi1 - phi2)) / 2.0) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(t_1 * Float64(t_1 * Float64(cos(phi1) * cos(phi2)))) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -1e-5) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(0.5 - t_0) + t_2)), sqrt(Float64(1.0 + Float64(Float64(t_0 - 0.5) - t_2)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * t_1)), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (log(exp(sin(Float64(0.5 * Float64(phi2 - phi1))))) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[N[(phi1 - phi2), $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[(t$95$1 * N[(t$95$1 * N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -1e-5], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(0.5 - t$95$0), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 + N[(N[(t$95$0 - 0.5), $MachinePrecision] - t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Log[N[Exp[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := t_1 \cdot \left(t_1 \cdot \left(\cos \phi_1 \cdot \cos \phi_2\right)\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -1 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\left(0.5 - t_0\right) + t_2}}{\sqrt{1 + \left(\left(t_0 - 0.5\right) - t_2\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot t_1\right), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\log \left(e^{\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -1.00000000000000008e-5Initial program 65.5%
unpow222.0%
sin-mult22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
Applied egg-rr65.5%
div-sub22.0%
+-inverses22.0%
cos-022.0%
metadata-eval22.0%
distribute-lft-out22.0%
metadata-eval22.0%
*-rgt-identity22.0%
Simplified65.5%
unpow222.0%
sin-mult22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
Applied egg-rr65.6%
div-sub22.0%
+-inverses22.0%
cos-022.0%
metadata-eval22.0%
distribute-lft-out22.0%
metadata-eval22.0%
*-rgt-identity22.0%
Simplified65.6%
if -1.00000000000000008e-5 < (-.f64 lambda1 lambda2) Initial program 66.7%
Simplified66.7%
Taylor expanded in lambda1 around 0 56.4%
Taylor expanded in lambda2 around 0 46.2%
add-log-exp46.2%
Applied egg-rr46.2%
Final simplification53.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (/ (cos (- phi1 phi2)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* t_1 (* t_1 (* (cos phi1) (cos phi2))))))
(if (<= (- lambda1 lambda2) -1e-5)
(*
R
(*
2.0
(atan2 (sqrt (+ (- 0.5 t_0) t_2)) (sqrt (+ 1.0 (- (- t_0 0.5) t_2))))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_1 t_1))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((phi1 - phi2)) / 2.0;
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = t_1 * (t_1 * (cos(phi1) * cos(phi2)));
double tmp;
if ((lambda1 - lambda2) <= -1e-5) {
tmp = R * (2.0 * atan2(sqrt(((0.5 - t_0) + t_2)), sqrt((1.0 + ((t_0 - 0.5) - t_2)))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * t_1)), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(Float64(phi1 - phi2)) / 2.0) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(t_1 * Float64(t_1 * Float64(cos(phi1) * cos(phi2)))) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -1e-5) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(0.5 - t_0) + t_2)), sqrt(Float64(1.0 + Float64(Float64(t_0 - 0.5) - t_2)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * t_1)), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[N[(phi1 - phi2), $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[(t$95$1 * N[(t$95$1 * N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -1e-5], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(0.5 - t$95$0), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 + N[(N[(t$95$0 - 0.5), $MachinePrecision] - t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := t_1 \cdot \left(t_1 \cdot \left(\cos \phi_1 \cdot \cos \phi_2\right)\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -1 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\left(0.5 - t_0\right) + t_2}}{\sqrt{1 + \left(\left(t_0 - 0.5\right) - t_2\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot t_1\right), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -1.00000000000000008e-5Initial program 65.5%
unpow222.0%
sin-mult22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
Applied egg-rr65.5%
div-sub22.0%
+-inverses22.0%
cos-022.0%
metadata-eval22.0%
distribute-lft-out22.0%
metadata-eval22.0%
*-rgt-identity22.0%
Simplified65.5%
unpow222.0%
sin-mult22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
div-inv22.0%
metadata-eval22.0%
Applied egg-rr65.6%
div-sub22.0%
+-inverses22.0%
cos-022.0%
metadata-eval22.0%
distribute-lft-out22.0%
metadata-eval22.0%
*-rgt-identity22.0%
Simplified65.6%
if -1.00000000000000008e-5 < (-.f64 lambda1 lambda2) Initial program 66.7%
Simplified66.7%
Taylor expanded in lambda1 around 0 56.4%
Taylor expanded in lambda2 around 0 46.2%
Final simplification53.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(if (<= lambda2 8.4e-32)
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_0 t_0))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0))))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt
(-
(pow (cos (* phi1 -0.5)) 2.0)
(* (cos phi1) (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 tmp;
if (lambda2 <= 8.4e-32) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_0 * t_0)), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
} else {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((pow(cos((phi1 * -0.5)), 2.0) - (cos(phi1) * 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)) tmp = 0.0 if (lambda2 <= 8.4e-32) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_0 * t_0)), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64((cos(Float64(phi1 * -0.5)) ^ 2.0) - Float64(cos(phi1) * (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]}, If[LessEqual[lambda2, 8.4e-32], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(lambda2 * -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)\\
\mathbf{if}\;\lambda_2 \leq 8.4 \cdot 10^{-32}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_0 \cdot t_0\right), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{{\cos \left(\phi_1 \cdot -0.5\right)}^{2} - \cos \phi_1 \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if lambda2 < 8.3999999999999996e-32Initial program 72.4%
Simplified72.4%
Taylor expanded in lambda1 around 0 51.0%
Taylor expanded in lambda2 around 0 42.4%
if 8.3999999999999996e-32 < lambda2 Initial program 50.1%
Simplified50.1%
Taylor expanded in lambda1 around 0 48.0%
Taylor expanded in phi2 around 0 42.9%
+-commutative42.9%
associate--r+43.0%
unpow243.0%
1-sub-sin43.0%
unpow243.0%
*-commutative43.0%
Simplified43.0%
Taylor expanded in phi2 around 0 44.4%
Final simplification42.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (cos phi2) (* t_0 t_0))))
(if (or (<= phi1 -6500.0) (not (<= phi1 1.15e+38)))
(*
R
(*
2.0
(atan2
(sqrt (fma (cos phi1) t_1 (- 0.5 (/ (cos phi1) 2.0))))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0))))))
(*
R
(*
2.0
(atan2
(sqrt (fma (cos phi1) t_1 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (pow (cos (* 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(phi2) * (t_0 * t_0);
double tmp;
if ((phi1 <= -6500.0) || !(phi1 <= 1.15e+38)) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), t_1, (0.5 - (cos(phi1) / 2.0)))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), t_1, pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt(pow(cos((phi2 * 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(cos(phi2) * Float64(t_0 * t_0)) tmp = 0.0 if ((phi1 <= -6500.0) || !(phi1 <= 1.15e+38)) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), t_1, Float64(0.5 - Float64(cos(phi1) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), t_1, (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt((cos(Float64(phi2 * 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[(N[Cos[phi2], $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[phi1, -6500.0], N[Not[LessEqual[phi1, 1.15e+38]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[(0.5 - N[(N[Cos[phi1], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[Power[N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $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_2 \cdot \left(t_0 \cdot t_0\right)\\
\mathbf{if}\;\phi_1 \leq -6500 \lor \neg \left(\phi_1 \leq 1.15 \cdot 10^{+38}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_1, 0.5 - \frac{\cos \phi_1}{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_1, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{{\cos \left(\phi_2 \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if phi1 < -6500 or 1.1500000000000001e38 < phi1 Initial program 51.2%
Simplified51.2%
Taylor expanded in lambda1 around 0 40.8%
Taylor expanded in lambda2 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%
Taylor expanded in phi2 around 0 31.3%
if -6500 < phi1 < 1.1500000000000001e38Initial program 79.2%
Simplified79.2%
Taylor expanded in lambda1 around 0 58.4%
Taylor expanded in lambda2 around 0 42.4%
Taylor expanded in phi1 around 0 42.3%
unpow242.3%
*-commutative42.3%
*-commutative42.3%
1-sub-sin42.3%
*-commutative42.3%
*-commutative42.3%
unpow242.3%
*-commutative42.3%
Simplified42.3%
Final simplification37.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0))))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (cos phi2) (* t_1 t_1))))
(if (<= phi2 -0.008)
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_1 (sin (* lambda1 0.5))))
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
t_0)))
(if (<= phi2 26000000000000.0)
(*
R
(*
2.0
(atan2
(sqrt (fma (cos phi1) t_2 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (pow (cos (* phi1 -0.5)) 2.0)))))
(*
R
(*
2.0
(atan2
(sqrt (fma (cos phi1) t_2 (- 0.5 (/ (cos phi2) 2.0))))
t_0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = cos(phi2) * (t_1 * t_1);
double tmp;
if (phi2 <= -0.008) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * sin((lambda1 * 0.5)))), (0.5 - (cos((phi1 - phi2)) / 2.0)))), t_0));
} else if (phi2 <= 26000000000000.0) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), t_2, pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt(pow(cos((phi1 * -0.5)), 2.0))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), t_2, (0.5 - (cos(phi2) / 2.0)))), t_0));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0))) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(cos(phi2) * Float64(t_1 * t_1)) tmp = 0.0 if (phi2 <= -0.008) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * sin(Float64(lambda1 * 0.5)))), Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), t_0))); elseif (phi2 <= 26000000000000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), t_2, (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt((cos(Float64(phi1 * -0.5)) ^ 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), t_2, Float64(0.5 - Float64(cos(phi2) / 2.0)))), t_0))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[phi2, -0.008], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi2, 26000000000000.0], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$2 + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[Power[N[Cos[N[(phi1 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$2 + N[(0.5 - N[(N[Cos[phi2], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \cos \phi_2 \cdot \left(t_1 \cdot t_1\right)\\
\mathbf{if}\;\phi_2 \leq -0.008:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot \sin \left(\lambda_1 \cdot 0.5\right)\right), 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{t_0}\right)\\
\mathbf{elif}\;\phi_2 \leq 26000000000000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_2, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{{\cos \left(\phi_1 \cdot -0.5\right)}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_2, 0.5 - \frac{\cos \phi_2}{2}\right)}}{t_0}\right)\\
\end{array}
\end{array}
if phi2 < -0.0080000000000000002Initial program 48.6%
Simplified48.6%
Taylor expanded in lambda1 around 0 36.6%
Taylor expanded in lambda2 around 0 25.8%
unpow225.8%
sin-mult25.8%
div-inv25.8%
metadata-eval25.8%
div-inv25.8%
metadata-eval25.8%
div-inv25.8%
metadata-eval25.8%
div-inv25.8%
metadata-eval25.8%
Applied egg-rr25.8%
div-sub25.8%
+-inverses25.8%
cos-025.8%
metadata-eval25.8%
distribute-lft-out25.8%
metadata-eval25.8%
*-rgt-identity25.8%
Simplified25.8%
Taylor expanded in lambda2 around 0 25.8%
if -0.0080000000000000002 < phi2 < 2.6e13Initial program 79.7%
Simplified79.7%
Taylor expanded in lambda1 around 0 60.5%
Taylor expanded in lambda2 around 0 42.8%
Taylor expanded in phi2 around 0 42.8%
unpow242.8%
1-sub-sin42.9%
unpow242.9%
*-commutative42.9%
Simplified42.9%
if 2.6e13 < phi2 Initial program 50.3%
Simplified50.3%
Taylor expanded in lambda1 around 0 38.3%
Taylor expanded in lambda2 around 0 33.9%
unpow233.9%
sin-mult33.9%
div-inv33.9%
metadata-eval33.9%
div-inv33.9%
metadata-eval33.9%
div-inv33.9%
metadata-eval33.9%
div-inv33.9%
metadata-eval33.9%
Applied egg-rr33.9%
div-sub33.9%
+-inverses33.9%
cos-033.9%
metadata-eval33.9%
distribute-lft-out33.9%
metadata-eval33.9%
*-rgt-identity33.9%
Simplified33.9%
Taylor expanded in phi1 around 0 34.4%
cos-neg34.4%
Simplified34.4%
Final simplification37.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0))))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(if (<= lambda1 -2.95e-83)
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_1 (sin (* lambda1 0.5))))
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
t_0)))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_1 (sin (* lambda2 -0.5))))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
t_0))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double tmp;
if (lambda1 <= -2.95e-83) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * sin((lambda1 * 0.5)))), (0.5 - (cos((phi1 - phi2)) / 2.0)))), t_0));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * sin((lambda2 * -0.5)))), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), t_0));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0))) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) tmp = 0.0 if (lambda1 <= -2.95e-83) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * sin(Float64(lambda1 * 0.5)))), Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), t_0))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * sin(Float64(lambda2 * -0.5)))), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), t_0))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda1, -2.95e-83], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\mathbf{if}\;\lambda_1 \leq -2.95 \cdot 10^{-83}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot \sin \left(\lambda_1 \cdot 0.5\right)\right), 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{t_0}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot \sin \left(\lambda_2 \cdot -0.5\right)\right), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{t_0}\right)\\
\end{array}
\end{array}
if lambda1 < -2.9499999999999998e-83Initial program 64.3%
Simplified64.3%
Taylor expanded in lambda1 around 0 32.5%
Taylor expanded in lambda2 around 0 29.7%
unpow229.7%
sin-mult29.7%
div-inv29.7%
metadata-eval29.7%
div-inv29.7%
metadata-eval29.7%
div-inv29.7%
metadata-eval29.7%
div-inv29.7%
metadata-eval29.7%
Applied egg-rr29.7%
div-sub29.7%
+-inverses29.7%
cos-029.7%
metadata-eval29.7%
distribute-lft-out29.7%
metadata-eval29.7%
*-rgt-identity29.7%
Simplified29.7%
Taylor expanded in lambda2 around 0 28.9%
if -2.9499999999999998e-83 < lambda1 Initial program 67.1%
Simplified67.1%
Taylor expanded in lambda1 around 0 57.9%
Taylor expanded in lambda2 around 0 40.1%
Taylor expanded in lambda1 around 0 39.7%
Final simplification36.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0))))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(if (<= lambda1 -2.4e-130)
(*
R
(*
2.0
(atan2
(sqrt
(fma (cos phi1) (* (cos phi2) (* t_1 (sin (* lambda1 0.5)))) t_2))
t_0)))
(*
R
(*
2.0
(atan2
(sqrt
(fma (cos phi1) (* (cos phi2) (* t_1 (sin (* lambda2 -0.5)))) t_2))
t_0))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double tmp;
if (lambda1 <= -2.4e-130) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * sin((lambda1 * 0.5)))), t_2)), t_0));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * sin((lambda2 * -0.5)))), t_2)), t_0));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0))) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 tmp = 0.0 if (lambda1 <= -2.4e-130) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * sin(Float64(lambda1 * 0.5)))), t_2)), t_0))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * sin(Float64(lambda2 * -0.5)))), t_2)), t_0))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $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]}, If[LessEqual[lambda1, -2.4e-130], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
\mathbf{if}\;\lambda_1 \leq -2.4 \cdot 10^{-130}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot \sin \left(\lambda_1 \cdot 0.5\right)\right), t_2\right)}}{t_0}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot \sin \left(\lambda_2 \cdot -0.5\right)\right), t_2\right)}}{t_0}\right)\\
\end{array}
\end{array}
if lambda1 < -2.39999999999999997e-130Initial program 65.6%
Simplified65.7%
Taylor expanded in lambda1 around 0 36.9%
Taylor expanded in lambda2 around 0 31.6%
Taylor expanded in lambda2 around 0 30.8%
if -2.39999999999999997e-130 < lambda1 Initial program 66.5%
Simplified66.5%
Taylor expanded in lambda1 around 0 56.9%
Taylor expanded in lambda2 around 0 39.6%
Taylor expanded in lambda1 around 0 39.3%
Final simplification36.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_0 t_0))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 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(fma(cos(phi1), (cos(phi2) * (t_0 * t_0)), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 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(fma(cos(phi1), Float64(cos(phi2) * Float64(t_0 * t_0)), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 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[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $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)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_0 \cdot t_0\right), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 66.2%
Simplified66.2%
Taylor expanded in lambda1 around 0 50.2%
Taylor expanded in lambda2 around 0 36.9%
Final simplification36.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0))))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(if (<= lambda1 -3.5e-140)
(*
R
(*
2.0
(atan2
(sqrt
(fma (cos phi1) (* (cos phi2) (* t_1 (sin (* lambda1 0.5)))) t_2))
t_0)))
(*
R
(*
2.0
(atan2
(sqrt
(fma (cos phi1) (* (cos phi2) (* t_1 (sin (* lambda2 -0.5)))) t_2))
t_0))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = 0.5 - (cos((phi1 - phi2)) / 2.0);
double tmp;
if (lambda1 <= -3.5e-140) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * sin((lambda1 * 0.5)))), t_2)), t_0));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (t_1 * sin((lambda2 * -0.5)))), t_2)), t_0));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0))) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)) tmp = 0.0 if (lambda1 <= -3.5e-140) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * sin(Float64(lambda1 * 0.5)))), t_2)), t_0))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(t_1 * sin(Float64(lambda2 * -0.5)))), t_2)), t_0))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[lambda1, -3.5e-140], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(t$95$1 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\\
\mathbf{if}\;\lambda_1 \leq -3.5 \cdot 10^{-140}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot \sin \left(\lambda_1 \cdot 0.5\right)\right), t_2\right)}}{t_0}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_1 \cdot \sin \left(\lambda_2 \cdot -0.5\right)\right), t_2\right)}}{t_0}\right)\\
\end{array}
\end{array}
if lambda1 < -3.4999999999999998e-140Initial program 66.4%
Simplified66.4%
Taylor expanded in lambda1 around 0 38.3%
Taylor expanded in lambda2 around 0 31.4%
unpow231.4%
sin-mult30.2%
div-inv30.2%
metadata-eval30.2%
div-inv30.2%
metadata-eval30.2%
div-inv30.2%
metadata-eval30.2%
div-inv30.2%
metadata-eval30.2%
Applied egg-rr30.2%
div-sub30.2%
+-inverses30.2%
cos-030.2%
metadata-eval30.2%
distribute-lft-out30.2%
metadata-eval30.2%
*-rgt-identity30.2%
Simplified30.2%
Taylor expanded in lambda2 around 0 29.2%
if -3.4999999999999998e-140 < lambda1 Initial program 66.1%
Simplified66.1%
Taylor expanded in lambda1 around 0 56.4%
Taylor expanded in lambda2 around 0 39.8%
unpow239.8%
sin-mult34.6%
div-inv34.6%
metadata-eval34.6%
div-inv34.6%
metadata-eval34.6%
div-inv34.6%
metadata-eval34.6%
div-inv34.6%
metadata-eval34.6%
Applied egg-rr34.6%
div-sub34.6%
+-inverses34.6%
cos-034.6%
metadata-eval34.6%
distribute-lft-out34.6%
metadata-eval34.6%
*-rgt-identity34.6%
Simplified34.6%
Taylor expanded in lambda1 around 0 34.3%
Final simplification32.5%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (* t_0 t_0))
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 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(fma(cos(phi1), (cos(phi2) * (t_0 * t_0)), (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 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(fma(cos(phi1), Float64(cos(phi2) * Float64(t_0 * t_0)), Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 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[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $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[(0.5 * N[(phi2 - phi1), $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)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(t_0 \cdot t_0\right), 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 66.2%
Simplified66.2%
Taylor expanded in lambda1 around 0 50.2%
Taylor expanded in lambda2 around 0 36.9%
unpow236.9%
sin-mult33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
Applied egg-rr33.1%
div-sub33.1%
+-inverses33.1%
cos-033.1%
metadata-eval33.1%
distribute-lft-out33.1%
metadata-eval33.1%
*-rgt-identity33.1%
Simplified33.1%
Final simplification33.1%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(*
(cos phi2)
(* (sin (/ (- lambda1 lambda2) 2.0)) (sin (* lambda2 -0.5))))
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (sin(((lambda1 - lambda2) / 2.0)) * sin((lambda2 * -0.5)))), (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(sin(Float64(Float64(lambda1 - lambda2) / 2.0)) * sin(Float64(lambda2 * -0.5)))), Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $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[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(\sin \left(\frac{\lambda_1 - \lambda_2}{2}\right) \cdot \sin \left(\lambda_2 \cdot -0.5\right)\right), 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 66.2%
Simplified66.2%
Taylor expanded in lambda1 around 0 50.2%
Taylor expanded in lambda2 around 0 36.9%
unpow236.9%
sin-mult33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
div-inv33.1%
metadata-eval33.1%
Applied egg-rr33.1%
div-sub33.1%
+-inverses33.1%
cos-033.1%
metadata-eval33.1%
distribute-lft-out33.1%
metadata-eval33.1%
*-rgt-identity33.1%
Simplified33.1%
Taylor expanded in lambda1 around 0 29.9%
Final simplification29.9%
herbie shell --seed 2023311
(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))))))))))