
(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 (sin (* phi1 0.5)))
(t_1 (cos (* phi1 0.5)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (cos (* 0.5 phi2)))
(t_4 (pow (fma t_3 t_0 (* t_1 (sin (* phi2 -0.5)))) 2.0))
(t_5 (* (cos phi1) (cos phi2)))
(t_6
(sqrt
(+
(pow (- (* t_3 t_0) (* t_1 (sin (* 0.5 phi2)))) 2.0)
(* t_2 (* t_5 t_2))))))
(if (or (<= lambda2 -4.3e-7) (not (<= lambda2 2.1e-29)))
(*
R
(*
2.0
(atan2
t_6
(sqrt (- 1.0 (+ (* t_5 (pow (sin (* lambda2 -0.5)) 2.0)) t_4))))))
(*
R
(*
2.0
(atan2
t_6
(sqrt
(-
1.0
(+
t_4
(*
(cos phi1)
(* (cos phi2) (pow (sin (* 0.5 lambda1)) 2.0))))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((phi1 * 0.5));
double t_1 = cos((phi1 * 0.5));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = cos((0.5 * phi2));
double t_4 = pow(fma(t_3, t_0, (t_1 * sin((phi2 * -0.5)))), 2.0);
double t_5 = cos(phi1) * cos(phi2);
double t_6 = sqrt((pow(((t_3 * t_0) - (t_1 * sin((0.5 * phi2)))), 2.0) + (t_2 * (t_5 * t_2))));
double tmp;
if ((lambda2 <= -4.3e-7) || !(lambda2 <= 2.1e-29)) {
tmp = R * (2.0 * atan2(t_6, sqrt((1.0 - ((t_5 * pow(sin((lambda2 * -0.5)), 2.0)) + t_4)))));
} else {
tmp = R * (2.0 * atan2(t_6, sqrt((1.0 - (t_4 + (cos(phi1) * (cos(phi2) * pow(sin((0.5 * lambda1)), 2.0))))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(phi1 * 0.5)) t_1 = cos(Float64(phi1 * 0.5)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = cos(Float64(0.5 * phi2)) t_4 = fma(t_3, t_0, Float64(t_1 * sin(Float64(phi2 * -0.5)))) ^ 2.0 t_5 = Float64(cos(phi1) * cos(phi2)) t_6 = sqrt(Float64((Float64(Float64(t_3 * t_0) - Float64(t_1 * sin(Float64(0.5 * phi2)))) ^ 2.0) + Float64(t_2 * Float64(t_5 * t_2)))) tmp = 0.0 if ((lambda2 <= -4.3e-7) || !(lambda2 <= 2.1e-29)) tmp = Float64(R * Float64(2.0 * atan(t_6, sqrt(Float64(1.0 - Float64(Float64(t_5 * (sin(Float64(lambda2 * -0.5)) ^ 2.0)) + t_4)))))); else tmp = Float64(R * Float64(2.0 * atan(t_6, sqrt(Float64(1.0 - Float64(t_4 + Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(0.5 * lambda1)) ^ 2.0))))))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$4 = N[Power[N[(t$95$3 * t$95$0 + N[(t$95$1 * N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$5 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$6 = N[Sqrt[N[(N[Power[N[(N[(t$95$3 * t$95$0), $MachinePrecision] - N[(t$95$1 * N[Sin[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(t$95$2 * N[(t$95$5 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[Or[LessEqual[lambda2, -4.3e-7], N[Not[LessEqual[lambda2, 2.1e-29]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[t$95$6 / N[Sqrt[N[(1.0 - N[(N[(t$95$5 * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + t$95$4), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$6 / N[Sqrt[N[(1.0 - N[(t$95$4 + N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * lambda1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_1 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := \cos \left(0.5 \cdot \phi_2\right)\\
t_4 := {\left(\mathsf{fma}\left(t\_3, t\_0, t\_1 \cdot \sin \left(\phi_2 \cdot -0.5\right)\right)\right)}^{2}\\
t_5 := \cos \phi_1 \cdot \cos \phi_2\\
t_6 := \sqrt{{\left(t\_3 \cdot t\_0 - t\_1 \cdot \sin \left(0.5 \cdot \phi_2\right)\right)}^{2} + t\_2 \cdot \left(t\_5 \cdot t\_2\right)}\\
\mathbf{if}\;\lambda_2 \leq -4.3 \cdot 10^{-7} \lor \neg \left(\lambda_2 \leq 2.1 \cdot 10^{-29}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t\_6}{\sqrt{1 - \left(t\_5 \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2} + t\_4\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t\_6}{\sqrt{1 - \left(t\_4 + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(0.5 \cdot \lambda_1\right)}^{2}\right)\right)}}\right)\\
\end{array}
\end{array}
if lambda2 < -4.3000000000000001e-7 or 2.09999999999999989e-29 < lambda2 Initial program 52.8%
div-sub52.8%
sin-diff53.9%
div-inv53.9%
metadata-eval53.9%
div-inv53.9%
metadata-eval53.9%
div-inv53.9%
metadata-eval53.9%
div-inv53.9%
metadata-eval53.9%
Applied egg-rr53.9%
*-commutative53.9%
*-commutative53.9%
fmm-def53.9%
*-commutative53.9%
*-commutative53.9%
*-commutative53.9%
distribute-lft-neg-in53.9%
sin-neg53.9%
distribute-rgt-neg-in53.9%
metadata-eval53.9%
*-commutative53.9%
*-commutative53.9%
Simplified53.9%
div-sub52.8%
sin-diff53.9%
div-inv53.9%
metadata-eval53.9%
div-inv53.9%
metadata-eval53.9%
div-inv53.9%
metadata-eval53.9%
div-inv53.9%
metadata-eval53.9%
Applied egg-rr67.5%
log1p-expm1-u67.5%
Applied egg-rr67.5%
Taylor expanded in lambda1 around 0 67.5%
associate-*r*67.5%
*-commutative67.5%
*-commutative67.5%
Simplified67.5%
if -4.3000000000000001e-7 < lambda2 < 2.09999999999999989e-29Initial program 75.2%
div-sub75.2%
sin-diff76.6%
div-inv76.6%
metadata-eval76.6%
div-inv76.6%
metadata-eval76.6%
div-inv76.6%
metadata-eval76.6%
div-inv76.6%
metadata-eval76.6%
Applied egg-rr76.6%
*-commutative76.6%
*-commutative76.6%
fmm-def76.6%
*-commutative76.6%
*-commutative76.6%
*-commutative76.6%
distribute-lft-neg-in76.6%
sin-neg76.6%
distribute-rgt-neg-in76.6%
metadata-eval76.6%
*-commutative76.6%
*-commutative76.6%
Simplified76.6%
div-sub75.2%
sin-diff76.6%
div-inv76.6%
metadata-eval76.6%
div-inv76.6%
metadata-eval76.6%
div-inv76.6%
metadata-eval76.6%
div-inv76.6%
metadata-eval76.6%
Applied egg-rr99.0%
log1p-expm1-u99.0%
Applied egg-rr99.0%
Taylor expanded in lambda2 around 0 99.0%
Final simplification82.8%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (* phi1 0.5)))
(t_1 (cos (* phi1 0.5)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (cos (* 0.5 phi2))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (- (* t_3 t_0) (* t_1 (sin (* 0.5 phi2)))) 2.0)
(* t_2 (* (* (cos phi1) (cos phi2)) t_2))))
(sqrt
(-
1.0
(+
(* t_2 (* t_2 (* (cos phi1) (log1p (expm1 (cos phi2))))))
(pow (fma t_3 t_0 (* t_1 (sin (* phi2 -0.5)))) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((phi1 * 0.5));
double t_1 = cos((phi1 * 0.5));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = cos((0.5 * phi2));
return R * (2.0 * atan2(sqrt((pow(((t_3 * t_0) - (t_1 * sin((0.5 * phi2)))), 2.0) + (t_2 * ((cos(phi1) * cos(phi2)) * t_2)))), sqrt((1.0 - ((t_2 * (t_2 * (cos(phi1) * log1p(expm1(cos(phi2)))))) + pow(fma(t_3, t_0, (t_1 * sin((phi2 * -0.5)))), 2.0))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(phi1 * 0.5)) t_1 = cos(Float64(phi1 * 0.5)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = cos(Float64(0.5 * phi2)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((Float64(Float64(t_3 * t_0) - Float64(t_1 * sin(Float64(0.5 * phi2)))) ^ 2.0) + Float64(t_2 * Float64(Float64(cos(phi1) * cos(phi2)) * t_2)))), sqrt(Float64(1.0 - Float64(Float64(t_2 * Float64(t_2 * Float64(cos(phi1) * log1p(expm1(cos(phi2)))))) + (fma(t_3, t_0, Float64(t_1 * sin(Float64(phi2 * -0.5)))) ^ 2.0))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[(N[(t$95$3 * t$95$0), $MachinePrecision] - N[(t$95$1 * N[Sin[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(t$95$2 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[(t$95$2 * N[(t$95$2 * N[(N[Cos[phi1], $MachinePrecision] * N[Log[1 + N[(Exp[N[Cos[phi2], $MachinePrecision]] - 1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[(t$95$3 * t$95$0 + N[(t$95$1 * N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_1 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := \cos \left(0.5 \cdot \phi_2\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\left(t\_3 \cdot t\_0 - t\_1 \cdot \sin \left(0.5 \cdot \phi_2\right)\right)}^{2} + t\_2 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t\_2\right)}}{\sqrt{1 - \left(t\_2 \cdot \left(t\_2 \cdot \left(\cos \phi_1 \cdot \mathsf{log1p}\left(\mathsf{expm1}\left(\cos \phi_2\right)\right)\right)\right) + {\left(\mathsf{fma}\left(t\_3, t\_0, t\_1 \cdot \sin \left(\phi_2 \cdot -0.5\right)\right)\right)}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 63.6%
div-sub63.6%
sin-diff64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
Applied egg-rr64.9%
*-commutative64.9%
*-commutative64.9%
fmm-def64.9%
*-commutative64.9%
*-commutative64.9%
*-commutative64.9%
distribute-lft-neg-in64.9%
sin-neg64.9%
distribute-rgt-neg-in64.9%
metadata-eval64.9%
*-commutative64.9%
*-commutative64.9%
Simplified64.9%
div-sub63.6%
sin-diff64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
Applied egg-rr82.7%
log1p-expm1-u82.7%
Applied egg-rr82.7%
Final simplification82.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (cos (* phi1 0.5)))
(t_1 (* (cos phi1) (cos phi2)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (* t_2 (* t_1 t_2)))
(t_4 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_5 (cos (* 0.5 phi2)))
(t_6 (sin (* phi1 0.5)))
(t_7 (pow (fma t_5 t_6 (* t_0 (sin (* phi2 -0.5)))) 2.0))
(t_8 (pow (- (* t_5 t_6) (* t_0 (sin (* 0.5 phi2)))) 2.0))
(t_9 (* t_1 (* t_2 t_2))))
(if (<= lambda2 -1.65e-5)
(*
R
(*
2.0
(atan2
(sqrt (+ (* t_1 (pow (sin (* lambda2 -0.5)) 2.0)) t_4))
(sqrt (- 1.0 (+ t_3 t_7))))))
(if (<= lambda2 2.9e-8)
(*
R
(*
2.0
(atan2
(sqrt (+ t_8 t_3))
(sqrt
(-
1.0
(+
t_7
(*
(cos phi1)
(* (cos phi2) (pow (sin (* 0.5 lambda1)) 2.0)))))))))
(* R (* 2.0 (atan2 (sqrt (+ t_8 t_9)) (sqrt (- (- 1.0 t_4) t_9)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((phi1 * 0.5));
double t_1 = cos(phi1) * cos(phi2);
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = t_2 * (t_1 * t_2);
double t_4 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_5 = cos((0.5 * phi2));
double t_6 = sin((phi1 * 0.5));
double t_7 = pow(fma(t_5, t_6, (t_0 * sin((phi2 * -0.5)))), 2.0);
double t_8 = pow(((t_5 * t_6) - (t_0 * sin((0.5 * phi2)))), 2.0);
double t_9 = t_1 * (t_2 * t_2);
double tmp;
if (lambda2 <= -1.65e-5) {
tmp = R * (2.0 * atan2(sqrt(((t_1 * pow(sin((lambda2 * -0.5)), 2.0)) + t_4)), sqrt((1.0 - (t_3 + t_7)))));
} else if (lambda2 <= 2.9e-8) {
tmp = R * (2.0 * atan2(sqrt((t_8 + t_3)), sqrt((1.0 - (t_7 + (cos(phi1) * (cos(phi2) * pow(sin((0.5 * lambda1)), 2.0))))))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_8 + t_9)), sqrt(((1.0 - t_4) - t_9))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(Float64(phi1 * 0.5)) t_1 = Float64(cos(phi1) * cos(phi2)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = Float64(t_2 * Float64(t_1 * t_2)) t_4 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_5 = cos(Float64(0.5 * phi2)) t_6 = sin(Float64(phi1 * 0.5)) t_7 = fma(t_5, t_6, Float64(t_0 * sin(Float64(phi2 * -0.5)))) ^ 2.0 t_8 = Float64(Float64(t_5 * t_6) - Float64(t_0 * sin(Float64(0.5 * phi2)))) ^ 2.0 t_9 = Float64(t_1 * Float64(t_2 * t_2)) tmp = 0.0 if (lambda2 <= -1.65e-5) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(t_1 * (sin(Float64(lambda2 * -0.5)) ^ 2.0)) + t_4)), sqrt(Float64(1.0 - Float64(t_3 + t_7)))))); elseif (lambda2 <= 2.9e-8) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_8 + t_3)), sqrt(Float64(1.0 - Float64(t_7 + Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(0.5 * lambda1)) ^ 2.0))))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_8 + t_9)), sqrt(Float64(Float64(1.0 - t_4) - t_9))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 * N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$5 = N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$6 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$7 = N[Power[N[(t$95$5 * t$95$6 + N[(t$95$0 * N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$8 = N[Power[N[(N[(t$95$5 * t$95$6), $MachinePrecision] - N[(t$95$0 * N[Sin[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$9 = N[(t$95$1 * N[(t$95$2 * t$95$2), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[lambda2, -1.65e-5], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(t$95$1 * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + t$95$4), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$3 + t$95$7), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[lambda2, 2.9e-8], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$8 + t$95$3), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$7 + N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * lambda1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$8 + t$95$9), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - t$95$4), $MachinePrecision] - t$95$9), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_1 := \cos \phi_1 \cdot \cos \phi_2\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := t\_2 \cdot \left(t\_1 \cdot t\_2\right)\\
t_4 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_5 := \cos \left(0.5 \cdot \phi_2\right)\\
t_6 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_7 := {\left(\mathsf{fma}\left(t\_5, t\_6, t\_0 \cdot \sin \left(\phi_2 \cdot -0.5\right)\right)\right)}^{2}\\
t_8 := {\left(t\_5 \cdot t\_6 - t\_0 \cdot \sin \left(0.5 \cdot \phi_2\right)\right)}^{2}\\
t_9 := t\_1 \cdot \left(t\_2 \cdot t\_2\right)\\
\mathbf{if}\;\lambda_2 \leq -1.65 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_1 \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2} + t\_4}}{\sqrt{1 - \left(t\_3 + t\_7\right)}}\right)\\
\mathbf{elif}\;\lambda_2 \leq 2.9 \cdot 10^{-8}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_8 + t\_3}}{\sqrt{1 - \left(t\_7 + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(0.5 \cdot \lambda_1\right)}^{2}\right)\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_8 + t\_9}}{\sqrt{\left(1 - t\_4\right) - t\_9}}\right)\\
\end{array}
\end{array}
if lambda2 < -1.6500000000000001e-5Initial program 52.0%
div-sub52.0%
sin-diff53.4%
div-inv53.4%
metadata-eval53.4%
div-inv53.4%
metadata-eval53.4%
div-inv53.4%
metadata-eval53.4%
div-inv53.4%
metadata-eval53.4%
Applied egg-rr53.4%
*-commutative53.4%
*-commutative53.4%
fmm-def53.4%
*-commutative53.4%
*-commutative53.4%
*-commutative53.4%
distribute-lft-neg-in53.4%
sin-neg53.4%
distribute-rgt-neg-in53.4%
metadata-eval53.4%
*-commutative53.4%
*-commutative53.4%
Simplified53.4%
Taylor expanded in lambda1 around 0 53.7%
associate-*r*53.7%
*-commutative53.7%
*-commutative53.7%
Simplified53.7%
if -1.6500000000000001e-5 < lambda2 < 2.9000000000000002e-8Initial program 74.9%
div-sub74.9%
sin-diff76.3%
div-inv76.3%
metadata-eval76.3%
div-inv76.3%
metadata-eval76.3%
div-inv76.3%
metadata-eval76.3%
div-inv76.3%
metadata-eval76.3%
Applied egg-rr76.3%
*-commutative76.3%
*-commutative76.3%
fmm-def76.3%
*-commutative76.3%
*-commutative76.3%
*-commutative76.3%
distribute-lft-neg-in76.3%
sin-neg76.3%
distribute-rgt-neg-in76.3%
metadata-eval76.3%
*-commutative76.3%
*-commutative76.3%
Simplified76.3%
div-sub74.9%
sin-diff76.3%
div-inv76.3%
metadata-eval76.3%
div-inv76.3%
metadata-eval76.3%
div-inv76.3%
metadata-eval76.3%
div-inv76.3%
metadata-eval76.3%
Applied egg-rr99.0%
log1p-expm1-u99.0%
Applied egg-rr99.0%
Taylor expanded in lambda2 around 0 99.0%
if 2.9000000000000002e-8 < lambda2 Initial program 53.4%
associate-*l*53.4%
Simplified53.4%
div-sub53.4%
sin-diff54.3%
div-inv54.3%
metadata-eval54.3%
div-inv54.3%
metadata-eval54.3%
div-inv54.3%
metadata-eval54.3%
div-inv54.3%
metadata-eval54.3%
Applied egg-rr54.6%
Final simplification76.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (* phi1 0.5)))
(t_1 (cos (* phi1 0.5)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (* t_2 (* (* (cos phi1) (cos phi2)) t_2)))
(t_4 (cos (* 0.5 phi2))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (- (* t_4 t_0) (* t_1 (sin (* 0.5 phi2)))) 2.0) t_3))
(sqrt
(-
1.0
(+ t_3 (pow (fma t_4 t_0 (* t_1 (sin (* phi2 -0.5)))) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((phi1 * 0.5));
double t_1 = cos((phi1 * 0.5));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = t_2 * ((cos(phi1) * cos(phi2)) * t_2);
double t_4 = cos((0.5 * phi2));
return R * (2.0 * atan2(sqrt((pow(((t_4 * t_0) - (t_1 * sin((0.5 * phi2)))), 2.0) + t_3)), sqrt((1.0 - (t_3 + pow(fma(t_4, t_0, (t_1 * sin((phi2 * -0.5)))), 2.0))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(phi1 * 0.5)) t_1 = cos(Float64(phi1 * 0.5)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = Float64(t_2 * Float64(Float64(cos(phi1) * cos(phi2)) * t_2)) t_4 = cos(Float64(0.5 * phi2)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((Float64(Float64(t_4 * t_0) - Float64(t_1 * sin(Float64(0.5 * phi2)))) ^ 2.0) + t_3)), sqrt(Float64(1.0 - Float64(t_3 + (fma(t_4, t_0, Float64(t_1 * sin(Float64(phi2 * -0.5)))) ^ 2.0))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[(N[(t$95$4 * t$95$0), $MachinePrecision] - N[(t$95$1 * N[Sin[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$3 + N[Power[N[(t$95$4 * t$95$0 + N[(t$95$1 * N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_1 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := t\_2 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t\_2\right)\\
t_4 := \cos \left(0.5 \cdot \phi_2\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\left(t\_4 \cdot t\_0 - t\_1 \cdot \sin \left(0.5 \cdot \phi_2\right)\right)}^{2} + t\_3}}{\sqrt{1 - \left(t\_3 + {\left(\mathsf{fma}\left(t\_4, t\_0, t\_1 \cdot \sin \left(\phi_2 \cdot -0.5\right)\right)\right)}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 63.6%
div-sub63.6%
sin-diff64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
Applied egg-rr64.9%
*-commutative64.9%
*-commutative64.9%
fmm-def64.9%
*-commutative64.9%
*-commutative64.9%
*-commutative64.9%
distribute-lft-neg-in64.9%
sin-neg64.9%
distribute-rgt-neg-in64.9%
metadata-eval64.9%
*-commutative64.9%
*-commutative64.9%
Simplified64.9%
div-sub63.6%
sin-diff64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
Applied egg-rr82.7%
Final simplification82.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (cos (* 0.5 phi2)))
(t_2 (sin (* phi1 0.5)))
(t_3 (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0))
(t_4 (* (cos phi1) (* (cos phi2) t_3)))
(t_5 (cos (* phi1 0.5)))
(t_6 (* t_5 (sin (* phi2 -0.5))))
(t_7 (* t_1 t_2))
(t_8 (pow (- t_7 (* t_5 (sin (* 0.5 phi2)))) 2.0))
(t_9 (sin (/ (- lambda1 lambda2) 2.0)))
(t_10 (* t_0 (* t_9 t_9))))
(if (<= (- lambda1 lambda2) -4000000.0)
(*
R
(*
2.0
(atan2
(sqrt (+ t_8 t_10))
(sqrt (- (- 1.0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)) t_10)))))
(if (<= (- lambda1 lambda2) 5e-40)
(*
R
(*
2.0
(atan2
(sqrt (+ t_8 (* t_9 (* t_0 t_9))))
(sqrt (- 1.0 (+ (* (cos phi1) t_3) (pow (fma t_1 t_2 t_6) 2.0)))))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_4 (pow (sin (* 0.5 (- phi1 phi2))) 2.0)))
(sqrt (- 1.0 (+ t_4 (pow (+ t_7 t_6) 2.0)))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = cos((0.5 * phi2));
double t_2 = sin((phi1 * 0.5));
double t_3 = pow(sin((0.5 * (lambda1 - lambda2))), 2.0);
double t_4 = cos(phi1) * (cos(phi2) * t_3);
double t_5 = cos((phi1 * 0.5));
double t_6 = t_5 * sin((phi2 * -0.5));
double t_7 = t_1 * t_2;
double t_8 = pow((t_7 - (t_5 * sin((0.5 * phi2)))), 2.0);
double t_9 = sin(((lambda1 - lambda2) / 2.0));
double t_10 = t_0 * (t_9 * t_9);
double tmp;
if ((lambda1 - lambda2) <= -4000000.0) {
tmp = R * (2.0 * atan2(sqrt((t_8 + t_10)), sqrt(((1.0 - pow(sin(((phi1 - phi2) / 2.0)), 2.0)) - t_10))));
} else if ((lambda1 - lambda2) <= 5e-40) {
tmp = R * (2.0 * atan2(sqrt((t_8 + (t_9 * (t_0 * t_9)))), sqrt((1.0 - ((cos(phi1) * t_3) + pow(fma(t_1, t_2, t_6), 2.0))))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_4 + pow(sin((0.5 * (phi1 - phi2))), 2.0))), sqrt((1.0 - (t_4 + pow((t_7 + t_6), 2.0))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = cos(Float64(0.5 * phi2)) t_2 = sin(Float64(phi1 * 0.5)) t_3 = sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0 t_4 = Float64(cos(phi1) * Float64(cos(phi2) * t_3)) t_5 = cos(Float64(phi1 * 0.5)) t_6 = Float64(t_5 * sin(Float64(phi2 * -0.5))) t_7 = Float64(t_1 * t_2) t_8 = Float64(t_7 - Float64(t_5 * sin(Float64(0.5 * phi2)))) ^ 2.0 t_9 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_10 = Float64(t_0 * Float64(t_9 * t_9)) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -4000000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_8 + t_10)), sqrt(Float64(Float64(1.0 - (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0)) - t_10))))); elseif (Float64(lambda1 - lambda2) <= 5e-40) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_8 + Float64(t_9 * Float64(t_0 * t_9)))), sqrt(Float64(1.0 - Float64(Float64(cos(phi1) * t_3) + (fma(t_1, t_2, t_6) ^ 2.0))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_4 + (sin(Float64(0.5 * Float64(phi1 - phi2))) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_4 + (Float64(t_7 + t_6) ^ 2.0))))))); 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[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$4 = N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * t$95$3), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$6 = N[(t$95$5 * N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(t$95$1 * t$95$2), $MachinePrecision]}, Block[{t$95$8 = N[Power[N[(t$95$7 - N[(t$95$5 * N[Sin[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$9 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$10 = N[(t$95$0 * N[(t$95$9 * t$95$9), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -4000000.0], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$8 + t$95$10), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] - t$95$10), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], 5e-40], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$8 + N[(t$95$9 * N[(t$95$0 * t$95$9), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[(N[Cos[phi1], $MachinePrecision] * t$95$3), $MachinePrecision] + N[Power[N[(t$95$1 * t$95$2 + t$95$6), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$4 + N[Power[N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$4 + N[Power[N[(t$95$7 + t$95$6), $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 := \cos \left(0.5 \cdot \phi_2\right)\\
t_2 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_3 := {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}\\
t_4 := \cos \phi_1 \cdot \left(\cos \phi_2 \cdot t\_3\right)\\
t_5 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_6 := t\_5 \cdot \sin \left(\phi_2 \cdot -0.5\right)\\
t_7 := t\_1 \cdot t\_2\\
t_8 := {\left(t\_7 - t\_5 \cdot \sin \left(0.5 \cdot \phi_2\right)\right)}^{2}\\
t_9 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_10 := t\_0 \cdot \left(t\_9 \cdot t\_9\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -4000000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_8 + t\_10}}{\sqrt{\left(1 - {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right) - t\_10}}\right)\\
\mathbf{elif}\;\lambda_1 - \lambda_2 \leq 5 \cdot 10^{-40}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_8 + t\_9 \cdot \left(t\_0 \cdot t\_9\right)}}{\sqrt{1 - \left(\cos \phi_1 \cdot t\_3 + {\left(\mathsf{fma}\left(t\_1, t\_2, t\_6\right)\right)}^{2}\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_4 + {\sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)}^{2}}}{\sqrt{1 - \left(t\_4 + {\left(t\_7 + t\_6\right)}^{2}\right)}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -4e6Initial program 55.2%
associate-*l*55.2%
Simplified55.2%
div-sub55.2%
sin-diff56.2%
div-inv56.2%
metadata-eval56.2%
div-inv56.2%
metadata-eval56.2%
div-inv56.2%
metadata-eval56.2%
div-inv56.2%
metadata-eval56.2%
Applied egg-rr56.3%
if -4e6 < (-.f64 lambda1 lambda2) < 4.99999999999999965e-40Initial program 74.6%
div-sub74.6%
sin-diff75.6%
div-inv75.6%
metadata-eval75.6%
div-inv75.6%
metadata-eval75.6%
div-inv75.6%
metadata-eval75.6%
div-inv75.6%
metadata-eval75.6%
Applied egg-rr75.6%
*-commutative75.6%
*-commutative75.6%
fmm-def75.6%
*-commutative75.6%
*-commutative75.6%
*-commutative75.6%
distribute-lft-neg-in75.6%
sin-neg75.6%
distribute-rgt-neg-in75.6%
metadata-eval75.6%
*-commutative75.6%
*-commutative75.6%
Simplified75.6%
div-sub74.6%
sin-diff75.6%
div-inv75.6%
metadata-eval75.6%
div-inv75.6%
metadata-eval75.6%
div-inv75.6%
metadata-eval75.6%
div-inv75.6%
metadata-eval75.6%
Applied egg-rr99.3%
log1p-expm1-u99.3%
Applied egg-rr99.3%
Taylor expanded in phi2 around 0 98.2%
*-commutative98.2%
Simplified98.2%
if 4.99999999999999965e-40 < (-.f64 lambda1 lambda2) Initial program 64.9%
div-sub64.9%
sin-diff66.6%
div-inv66.6%
metadata-eval66.6%
div-inv66.6%
metadata-eval66.6%
div-inv66.6%
metadata-eval66.6%
div-inv66.6%
metadata-eval66.6%
Applied egg-rr66.6%
*-commutative66.6%
*-commutative66.6%
fmm-def66.6%
*-commutative66.6%
*-commutative66.6%
*-commutative66.6%
distribute-lft-neg-in66.6%
sin-neg66.6%
distribute-rgt-neg-in66.6%
metadata-eval66.6%
*-commutative66.6%
*-commutative66.6%
Simplified66.6%
Taylor expanded in phi1 around 0 66.6%
Final simplification70.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (cos (* 0.5 phi2)))
(t_2 (sin (* phi1 0.5)))
(t_3 (* (cos phi2) (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0)))
(t_4 (* (cos phi1) t_3))
(t_5 (cos (* phi1 0.5)))
(t_6 (* t_5 (sin (* phi2 -0.5))))
(t_7 (* t_1 t_2))
(t_8 (pow (- t_7 (* t_5 (sin (* 0.5 phi2)))) 2.0))
(t_9 (sin (/ (- lambda1 lambda2) 2.0)))
(t_10 (* t_0 (* t_9 t_9))))
(if (<= (- lambda1 lambda2) -2e-5)
(*
R
(*
2.0
(atan2
(sqrt (+ t_8 t_10))
(sqrt (- (- 1.0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)) t_10)))))
(if (<= (- lambda1 lambda2) 5e-40)
(*
R
(*
2.0
(atan2
(sqrt (+ t_8 (* t_9 (* t_0 t_9))))
(sqrt (- 1.0 (+ t_3 (pow (fma t_1 t_2 t_6) 2.0)))))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_4 (pow (sin (* 0.5 (- phi1 phi2))) 2.0)))
(sqrt (- 1.0 (+ t_4 (pow (+ t_7 t_6) 2.0)))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = cos((0.5 * phi2));
double t_2 = sin((phi1 * 0.5));
double t_3 = cos(phi2) * pow(sin((0.5 * (lambda1 - lambda2))), 2.0);
double t_4 = cos(phi1) * t_3;
double t_5 = cos((phi1 * 0.5));
double t_6 = t_5 * sin((phi2 * -0.5));
double t_7 = t_1 * t_2;
double t_8 = pow((t_7 - (t_5 * sin((0.5 * phi2)))), 2.0);
double t_9 = sin(((lambda1 - lambda2) / 2.0));
double t_10 = t_0 * (t_9 * t_9);
double tmp;
if ((lambda1 - lambda2) <= -2e-5) {
tmp = R * (2.0 * atan2(sqrt((t_8 + t_10)), sqrt(((1.0 - pow(sin(((phi1 - phi2) / 2.0)), 2.0)) - t_10))));
} else if ((lambda1 - lambda2) <= 5e-40) {
tmp = R * (2.0 * atan2(sqrt((t_8 + (t_9 * (t_0 * t_9)))), sqrt((1.0 - (t_3 + pow(fma(t_1, t_2, t_6), 2.0))))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_4 + pow(sin((0.5 * (phi1 - phi2))), 2.0))), sqrt((1.0 - (t_4 + pow((t_7 + t_6), 2.0))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = cos(Float64(0.5 * phi2)) t_2 = sin(Float64(phi1 * 0.5)) t_3 = Float64(cos(phi2) * (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0)) t_4 = Float64(cos(phi1) * t_3) t_5 = cos(Float64(phi1 * 0.5)) t_6 = Float64(t_5 * sin(Float64(phi2 * -0.5))) t_7 = Float64(t_1 * t_2) t_8 = Float64(t_7 - Float64(t_5 * sin(Float64(0.5 * phi2)))) ^ 2.0 t_9 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_10 = Float64(t_0 * Float64(t_9 * t_9)) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2e-5) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_8 + t_10)), sqrt(Float64(Float64(1.0 - (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0)) - t_10))))); elseif (Float64(lambda1 - lambda2) <= 5e-40) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_8 + Float64(t_9 * Float64(t_0 * t_9)))), sqrt(Float64(1.0 - Float64(t_3 + (fma(t_1, t_2, t_6) ^ 2.0))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_4 + (sin(Float64(0.5 * Float64(phi1 - phi2))) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_4 + (Float64(t_7 + t_6) ^ 2.0))))))); 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[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$4 = N[(N[Cos[phi1], $MachinePrecision] * t$95$3), $MachinePrecision]}, Block[{t$95$5 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$6 = N[(t$95$5 * N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$7 = N[(t$95$1 * t$95$2), $MachinePrecision]}, Block[{t$95$8 = N[Power[N[(t$95$7 - N[(t$95$5 * N[Sin[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$9 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$10 = N[(t$95$0 * N[(t$95$9 * t$95$9), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -2e-5], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$8 + t$95$10), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] - t$95$10), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], 5e-40], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$8 + N[(t$95$9 * N[(t$95$0 * t$95$9), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$3 + N[Power[N[(t$95$1 * t$95$2 + t$95$6), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$4 + N[Power[N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$4 + N[Power[N[(t$95$7 + t$95$6), $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 := \cos \left(0.5 \cdot \phi_2\right)\\
t_2 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_3 := \cos \phi_2 \cdot {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}\\
t_4 := \cos \phi_1 \cdot t\_3\\
t_5 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_6 := t\_5 \cdot \sin \left(\phi_2 \cdot -0.5\right)\\
t_7 := t\_1 \cdot t\_2\\
t_8 := {\left(t\_7 - t\_5 \cdot \sin \left(0.5 \cdot \phi_2\right)\right)}^{2}\\
t_9 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_10 := t\_0 \cdot \left(t\_9 \cdot t\_9\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -2 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_8 + t\_10}}{\sqrt{\left(1 - {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right) - t\_10}}\right)\\
\mathbf{elif}\;\lambda_1 - \lambda_2 \leq 5 \cdot 10^{-40}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_8 + t\_9 \cdot \left(t\_0 \cdot t\_9\right)}}{\sqrt{1 - \left(t\_3 + {\left(\mathsf{fma}\left(t\_1, t\_2, t\_6\right)\right)}^{2}\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_4 + {\sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)}^{2}}}{\sqrt{1 - \left(t\_4 + {\left(t\_7 + t\_6\right)}^{2}\right)}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2.00000000000000016e-5Initial program 56.6%
associate-*l*56.6%
Simplified56.6%
div-sub56.6%
sin-diff57.6%
div-inv57.6%
metadata-eval57.6%
div-inv57.6%
metadata-eval57.6%
div-inv57.6%
metadata-eval57.6%
div-inv57.6%
metadata-eval57.6%
Applied egg-rr57.7%
if -2.00000000000000016e-5 < (-.f64 lambda1 lambda2) < 4.99999999999999965e-40Initial program 73.8%
div-sub73.8%
sin-diff74.9%
div-inv74.9%
metadata-eval74.9%
div-inv74.9%
metadata-eval74.9%
div-inv74.9%
metadata-eval74.9%
div-inv74.9%
metadata-eval74.9%
Applied egg-rr74.9%
*-commutative74.9%
*-commutative74.9%
fmm-def74.9%
*-commutative74.9%
*-commutative74.9%
*-commutative74.9%
distribute-lft-neg-in74.9%
sin-neg74.9%
distribute-rgt-neg-in74.9%
metadata-eval74.9%
*-commutative74.9%
*-commutative74.9%
Simplified74.9%
div-sub73.8%
sin-diff74.9%
div-inv74.9%
metadata-eval74.9%
div-inv74.9%
metadata-eval74.9%
div-inv74.9%
metadata-eval74.9%
div-inv74.9%
metadata-eval74.9%
Applied egg-rr99.3%
log1p-expm1-u99.3%
Applied egg-rr99.3%
Taylor expanded in phi1 around 0 99.3%
if 4.99999999999999965e-40 < (-.f64 lambda1 lambda2) Initial program 64.9%
div-sub64.9%
sin-diff66.6%
div-inv66.6%
metadata-eval66.6%
div-inv66.6%
metadata-eval66.6%
div-inv66.6%
metadata-eval66.6%
div-inv66.6%
metadata-eval66.6%
Applied egg-rr66.6%
*-commutative66.6%
*-commutative66.6%
fmm-def66.6%
*-commutative66.6%
*-commutative66.6%
*-commutative66.6%
distribute-lft-neg-in66.6%
sin-neg66.6%
distribute-rgt-neg-in66.6%
metadata-eval66.6%
*-commutative66.6%
*-commutative66.6%
Simplified66.6%
Taylor expanded in phi1 around 0 66.6%
Final simplification70.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0
(*
(cos phi1)
(* (cos phi2) (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0)))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_0 (pow (sin (* 0.5 (- phi1 phi2))) 2.0)))
(sqrt
(-
1.0
(+
t_0
(pow
(+
(* (cos (* 0.5 phi2)) (sin (* phi1 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 = cos(phi1) * (cos(phi2) * pow(sin((0.5 * (lambda1 - lambda2))), 2.0));
return R * (2.0 * atan2(sqrt((t_0 + pow(sin((0.5 * (phi1 - phi2))), 2.0))), sqrt((1.0 - (t_0 + pow(((cos((0.5 * phi2)) * sin((phi1 * 0.5))) + (cos((phi1 * 0.5)) * sin((phi2 * -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 = cos(phi1) * (cos(phi2) * (sin((0.5d0 * (lambda1 - lambda2))) ** 2.0d0))
code = r * (2.0d0 * atan2(sqrt((t_0 + (sin((0.5d0 * (phi1 - phi2))) ** 2.0d0))), sqrt((1.0d0 - (t_0 + (((cos((0.5d0 * phi2)) * sin((phi1 * 0.5d0))) + (cos((phi1 * 0.5d0)) * sin((phi2 * (-0.5d0))))) ** 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) * Math.pow(Math.sin((0.5 * (lambda1 - lambda2))), 2.0));
return R * (2.0 * Math.atan2(Math.sqrt((t_0 + Math.pow(Math.sin((0.5 * (phi1 - phi2))), 2.0))), Math.sqrt((1.0 - (t_0 + Math.pow(((Math.cos((0.5 * phi2)) * Math.sin((phi1 * 0.5))) + (Math.cos((phi1 * 0.5)) * Math.sin((phi2 * -0.5)))), 2.0))))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.cos(phi1) * (math.cos(phi2) * math.pow(math.sin((0.5 * (lambda1 - lambda2))), 2.0)) return R * (2.0 * math.atan2(math.sqrt((t_0 + math.pow(math.sin((0.5 * (phi1 - phi2))), 2.0))), math.sqrt((1.0 - (t_0 + math.pow(((math.cos((0.5 * phi2)) * math.sin((phi1 * 0.5))) + (math.cos((phi1 * 0.5)) * math.sin((phi2 * -0.5)))), 2.0))))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0))) return Float64(R * Float64(2.0 * atan(sqrt(Float64(t_0 + (sin(Float64(0.5 * Float64(phi1 - phi2))) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_0 + (Float64(Float64(cos(Float64(0.5 * phi2)) * sin(Float64(phi1 * 0.5))) + Float64(cos(Float64(phi1 * 0.5)) * sin(Float64(phi2 * -0.5)))) ^ 2.0))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(phi1) * (cos(phi2) * (sin((0.5 * (lambda1 - lambda2))) ^ 2.0)); tmp = R * (2.0 * atan2(sqrt((t_0 + (sin((0.5 * (phi1 - phi2))) ^ 2.0))), sqrt((1.0 - (t_0 + (((cos((0.5 * phi2)) * sin((phi1 * 0.5))) + (cos((phi1 * 0.5)) * sin((phi2 * -0.5)))) ^ 2.0)))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 + N[Power[N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 + N[Power[N[(N[(N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 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]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_0 + {\sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)}^{2}}}{\sqrt{1 - \left(t\_0 + {\left(\cos \left(0.5 \cdot \phi_2\right) \cdot \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)}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 63.6%
div-sub63.6%
sin-diff64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
div-inv64.9%
metadata-eval64.9%
Applied egg-rr64.9%
*-commutative64.9%
*-commutative64.9%
fmm-def64.9%
*-commutative64.9%
*-commutative64.9%
*-commutative64.9%
distribute-lft-neg-in64.9%
sin-neg64.9%
distribute-rgt-neg-in64.9%
metadata-eval64.9%
*-commutative64.9%
*-commutative64.9%
Simplified64.9%
Taylor expanded in phi1 around 0 64.9%
Final simplification64.9%
(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)))
(t_2 (cos (* 0.5 (- phi1 phi2)))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_1))
(sqrt (- (* t_2 t_2) 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 = (cos(phi1) * cos(phi2)) * (t_0 * t_0);
double t_2 = cos((0.5 * (phi1 - phi2)));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), sqrt(((t_2 * t_2) - 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
real(8) :: t_2
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = (cos(phi1) * cos(phi2)) * (t_0 * t_0)
t_2 = cos((0.5d0 * (phi1 - phi2)))
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_1)), sqrt(((t_2 * t_2) - 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.cos(phi1) * Math.cos(phi2)) * (t_0 * t_0);
double t_2 = Math.cos((0.5 * (phi1 - phi2)));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), Math.sqrt(((t_2 * t_2) - t_1))));
}
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) t_2 = math.cos((0.5 * (phi1 - phi2))) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), math.sqrt(((t_2 * t_2) - t_1))))
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)) t_2 = cos(Float64(0.5 * Float64(phi1 - phi2))) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(Float64(Float64(t_2 * t_2) - t_1))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = (cos(phi1) * cos(phi2)) * (t_0 * t_0); t_2 = cos((0.5 * (phi1 - phi2))); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(((t_2 * t_2) - 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[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[Cos[N[(0.5 * N[(phi1 - phi2), $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[(N[(t$95$2 * t$95$2), $MachinePrecision] - 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 := \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t\_0 \cdot t\_0\right)\\
t_2 := \cos \left(0.5 \cdot \left(\phi_1 - \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{t\_2 \cdot t\_2 - t\_1}}\right)
\end{array}
\end{array}
Initial program 63.6%
associate-*l*63.6%
Simplified63.6%
unpow263.6%
1-sub-sin63.7%
div-inv63.7%
metadata-eval63.7%
div-inv63.7%
metadata-eval63.7%
Applied egg-rr63.7%
Final simplification63.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1
(sqrt
(+
(* t_0 (* (* (cos phi1) (cos phi2)) t_0))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0))))
(t_2 (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0)))
(if (or (<= phi1 -2.1e-6) (not (<= phi1 4.1e-6)))
(*
R
(*
2.0
(atan2
t_1
(sqrt (- 1.0 (+ (* (cos phi1) t_2) (pow (sin (* phi1 0.5)) 2.0)))))))
(*
R
(*
2.0
(atan2
t_1
(sqrt
(- 1.0 (+ (* (cos phi2) t_2) (pow (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 = sqrt(((t_0 * ((cos(phi1) * cos(phi2)) * t_0)) + pow(sin(((phi1 - phi2) / 2.0)), 2.0)));
double t_2 = pow(sin((0.5 * (lambda1 - lambda2))), 2.0);
double tmp;
if ((phi1 <= -2.1e-6) || !(phi1 <= 4.1e-6)) {
tmp = R * (2.0 * atan2(t_1, sqrt((1.0 - ((cos(phi1) * t_2) + pow(sin((phi1 * 0.5)), 2.0))))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt((1.0 - ((cos(phi2) * t_2) + pow(sin((phi2 * -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) :: t_2
real(8) :: tmp
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = sqrt(((t_0 * ((cos(phi1) * cos(phi2)) * t_0)) + (sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0)))
t_2 = sin((0.5d0 * (lambda1 - lambda2))) ** 2.0d0
if ((phi1 <= (-2.1d-6)) .or. (.not. (phi1 <= 4.1d-6))) then
tmp = r * (2.0d0 * atan2(t_1, sqrt((1.0d0 - ((cos(phi1) * t_2) + (sin((phi1 * 0.5d0)) ** 2.0d0))))))
else
tmp = r * (2.0d0 * atan2(t_1, sqrt((1.0d0 - ((cos(phi2) * t_2) + (sin((phi2 * (-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.sqrt(((t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * t_0)) + Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0)));
double t_2 = Math.pow(Math.sin((0.5 * (lambda1 - lambda2))), 2.0);
double tmp;
if ((phi1 <= -2.1e-6) || !(phi1 <= 4.1e-6)) {
tmp = R * (2.0 * Math.atan2(t_1, Math.sqrt((1.0 - ((Math.cos(phi1) * t_2) + Math.pow(Math.sin((phi1 * 0.5)), 2.0))))));
} else {
tmp = R * (2.0 * Math.atan2(t_1, Math.sqrt((1.0 - ((Math.cos(phi2) * t_2) + Math.pow(Math.sin((phi2 * -0.5)), 2.0))))));
}
return tmp;
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = math.sqrt(((t_0 * ((math.cos(phi1) * math.cos(phi2)) * t_0)) + math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0))) t_2 = math.pow(math.sin((0.5 * (lambda1 - lambda2))), 2.0) tmp = 0 if (phi1 <= -2.1e-6) or not (phi1 <= 4.1e-6): tmp = R * (2.0 * math.atan2(t_1, math.sqrt((1.0 - ((math.cos(phi1) * t_2) + math.pow(math.sin((phi1 * 0.5)), 2.0)))))) else: tmp = R * (2.0 * math.atan2(t_1, math.sqrt((1.0 - ((math.cos(phi2) * t_2) + math.pow(math.sin((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 = sqrt(Float64(Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)) + (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))) t_2 = sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0 tmp = 0.0 if ((phi1 <= -2.1e-6) || !(phi1 <= 4.1e-6)) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(1.0 - Float64(Float64(cos(phi1) * t_2) + (sin(Float64(phi1 * 0.5)) ^ 2.0))))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(1.0 - Float64(Float64(cos(phi2) * t_2) + (sin(Float64(phi2 * -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 = sqrt(((t_0 * ((cos(phi1) * cos(phi2)) * t_0)) + (sin(((phi1 - phi2) / 2.0)) ^ 2.0))); t_2 = sin((0.5 * (lambda1 - lambda2))) ^ 2.0; tmp = 0.0; if ((phi1 <= -2.1e-6) || ~((phi1 <= 4.1e-6))) tmp = R * (2.0 * atan2(t_1, sqrt((1.0 - ((cos(phi1) * t_2) + (sin((phi1 * 0.5)) ^ 2.0)))))); else tmp = R * (2.0 * atan2(t_1, sqrt((1.0 - ((cos(phi2) * t_2) + (sin((phi2 * -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[Sqrt[N[(N[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[Or[LessEqual[phi1, -2.1e-6], N[Not[LessEqual[phi1, 4.1e-6]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(1.0 - N[(N[(N[Cos[phi1], $MachinePrecision] * t$95$2), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(1.0 - N[(N[(N[Cos[phi2], $MachinePrecision] * t$95$2), $MachinePrecision] + N[Power[N[Sin[N[(phi2 * -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)\\
t_1 := \sqrt{t\_0 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t\_0\right) + {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}}\\
t_2 := {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}\\
\mathbf{if}\;\phi_1 \leq -2.1 \cdot 10^{-6} \lor \neg \left(\phi_1 \leq 4.1 \cdot 10^{-6}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t\_1}{\sqrt{1 - \left(\cos \phi_1 \cdot t\_2 + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t\_1}{\sqrt{1 - \left(\cos \phi_2 \cdot t\_2 + {\sin \left(\phi_2 \cdot -0.5\right)}^{2}\right)}}\right)\\
\end{array}
\end{array}
if phi1 < -2.0999999999999998e-6 or 4.0999999999999997e-6 < phi1 Initial program 49.9%
Taylor expanded in phi2 around 0 50.1%
if -2.0999999999999998e-6 < phi1 < 4.0999999999999997e-6Initial program 82.7%
Taylor expanded in phi1 around 0 82.6%
Final simplification63.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (sin (* 0.5 (- lambda1 lambda2))))
(t_2 (* (cos phi1) (cos phi2))))
(if (or (<= phi1 -5.1e-19) (not (<= phi1 2.05e-23)))
(*
R
(*
2.0
(atan2
(sqrt (+ (* t_0 (* t_2 t_0)) (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt
(-
1.0
(+ (* (cos phi1) (pow t_1 2.0)) (pow (sin (* phi1 0.5)) 2.0)))))))
(*
(atan2
(hypot (sin (* 0.5 (- phi1 phi2))) (* t_1 (sqrt t_2)))
(sqrt
(-
1.0
(fma
t_2
(+ 0.5 (* -0.5 (cos (- lambda1 lambda2))))
(pow (sin (* phi2 -0.5)) 2.0)))))
(* R 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 = sin((0.5 * (lambda1 - lambda2)));
double t_2 = cos(phi1) * cos(phi2);
double tmp;
if ((phi1 <= -5.1e-19) || !(phi1 <= 2.05e-23)) {
tmp = R * (2.0 * atan2(sqrt(((t_0 * (t_2 * t_0)) + pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - ((cos(phi1) * pow(t_1, 2.0)) + pow(sin((phi1 * 0.5)), 2.0))))));
} else {
tmp = atan2(hypot(sin((0.5 * (phi1 - phi2))), (t_1 * sqrt(t_2))), sqrt((1.0 - fma(t_2, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), pow(sin((phi2 * -0.5)), 2.0))))) * (R * 2.0);
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = sin(Float64(0.5 * Float64(lambda1 - lambda2))) t_2 = Float64(cos(phi1) * cos(phi2)) tmp = 0.0 if ((phi1 <= -5.1e-19) || !(phi1 <= 2.05e-23)) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(t_0 * Float64(t_2 * t_0)) + (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - Float64(Float64(cos(phi1) * (t_1 ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))))))); else tmp = Float64(atan(hypot(sin(Float64(0.5 * Float64(phi1 - phi2))), Float64(t_1 * sqrt(t_2))), sqrt(Float64(1.0 - fma(t_2, Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2)))), (sin(Float64(phi2 * -0.5)) ^ 2.0))))) * Float64(R * 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[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[phi1, -5.1e-19], N[Not[LessEqual[phi1, 2.05e-23]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(t$95$0 * N[(t$95$2 * 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[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[t$95$1, 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[ArcTan[N[Sqrt[N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] ^ 2 + N[(t$95$1 * N[Sqrt[t$95$2], $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$2 * N[(0.5 + N[(-0.5 * N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * N[(R * 2.0), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)\\
t_2 := \cos \phi_1 \cdot \cos \phi_2\\
\mathbf{if}\;\phi_1 \leq -5.1 \cdot 10^{-19} \lor \neg \left(\phi_1 \leq 2.05 \cdot 10^{-23}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t\_0 \cdot \left(t\_2 \cdot t\_0\right) + {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}}}{\sqrt{1 - \left(\cos \phi_1 \cdot {t\_1}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;\tan^{-1}_* \frac{\mathsf{hypot}\left(\sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right), t\_1 \cdot \sqrt{t\_2}\right)}{\sqrt{1 - \mathsf{fma}\left(t\_2, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), {\sin \left(\phi_2 \cdot -0.5\right)}^{2}\right)}} \cdot \left(R \cdot 2\right)\\
\end{array}
\end{array}
if phi1 < -5.0999999999999998e-19 or 2.05000000000000015e-23 < phi1 Initial program 50.4%
Taylor expanded in phi2 around 0 49.3%
if -5.0999999999999998e-19 < phi1 < 2.05000000000000015e-23Initial program 84.2%
associate-*r*84.2%
*-commutative84.2%
Simplified84.2%
Applied egg-rr73.4%
*-lft-identity73.4%
*-commutative73.4%
*-commutative73.4%
cancel-sign-sub-inv73.4%
metadata-eval73.4%
Simplified73.4%
Taylor expanded in phi1 around 0 73.4%
*-commutative73.4%
Simplified73.4%
Final simplification58.7%
(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))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_1))
(sqrt (- (+ (- (/ (cos (- phi1 phi2)) 2.0) 0.5) 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 = (cos(phi1) * cos(phi2)) * (t_0 * t_0);
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), sqrt(((((cos((phi1 - phi2)) / 2.0) - 0.5) + 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 = (cos(phi1) * cos(phi2)) * (t_0 * t_0)
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_1)), sqrt(((((cos((phi1 - phi2)) / 2.0d0) - 0.5d0) + 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.cos(phi1) * Math.cos(phi2)) * (t_0 * t_0);
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), Math.sqrt(((((Math.cos((phi1 - phi2)) / 2.0) - 0.5) + 1.0) - t_1))));
}
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) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), math.sqrt(((((math.cos((phi1 - phi2)) / 2.0) - 0.5) + 1.0) - t_1))))
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)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(Float64(Float64(Float64(Float64(cos(Float64(phi1 - phi2)) / 2.0) - 0.5) + 1.0) - t_1))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = (cos(phi1) * cos(phi2)) * (t_0 * t_0); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(((((cos((phi1 - phi2)) / 2.0) - 0.5) + 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[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$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] + t$95$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(N[(N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision] - 0.5), $MachinePrecision] + 1.0), $MachinePrecision] - 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 := \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot \left(t\_0 \cdot t\_0\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t\_1}}{\sqrt{\left(\left(\frac{\cos \left(\phi_1 - \phi_2\right)}{2} - 0.5\right) + 1\right) - t\_1}}\right)
\end{array}
\end{array}
Initial program 63.6%
associate-*l*63.6%
Simplified63.6%
unpow263.6%
sin-mult63.7%
div-inv63.7%
metadata-eval63.7%
div-inv63.7%
metadata-eval63.7%
div-inv63.7%
metadata-eval63.7%
div-inv63.7%
metadata-eval63.7%
Applied egg-rr63.7%
div-sub63.7%
+-inverses63.7%
cos-063.7%
metadata-eval63.7%
distribute-lft-out63.7%
metadata-eval63.7%
*-rgt-identity63.7%
Simplified63.7%
Final simplification63.7%
(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 63.6%
associate-*l*63.6%
Simplified63.6%
sin-mult63.6%
cos-sum63.7%
cos-263.6%
div-sub63.6%
+-inverses63.6%
Applied egg-rr63.6%
cos-063.6%
div-sub63.6%
Simplified63.6%
Final simplification63.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (* 0.5 (- phi1 phi2))))
(t_1 (pow t_0 2.0))
(t_2 (* (cos phi1) (cos phi2)))
(t_3 (sqrt t_2)))
(if (or (<= lambda2 -3.7e-6) (not (<= lambda2 3.8e-38)))
(*
(* R 2.0)
(atan2
(hypot t_0 (* (sin (* lambda2 -0.5)) t_3))
(sqrt
(- 1.0 (fma t_2 (+ 0.5 (* -0.5 (cos (- lambda1 lambda2)))) t_1)))))
(*
(* R 2.0)
(atan2
(hypot t_0 (* (sin (* 0.5 (- lambda1 lambda2))) t_3))
(sqrt (- 1.0 (fma t_2 (+ 0.5 (* -0.5 (cos lambda1))) t_1))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((0.5 * (phi1 - phi2)));
double t_1 = pow(t_0, 2.0);
double t_2 = cos(phi1) * cos(phi2);
double t_3 = sqrt(t_2);
double tmp;
if ((lambda2 <= -3.7e-6) || !(lambda2 <= 3.8e-38)) {
tmp = (R * 2.0) * atan2(hypot(t_0, (sin((lambda2 * -0.5)) * t_3)), sqrt((1.0 - fma(t_2, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), t_1))));
} else {
tmp = (R * 2.0) * atan2(hypot(t_0, (sin((0.5 * (lambda1 - lambda2))) * t_3)), sqrt((1.0 - fma(t_2, (0.5 + (-0.5 * cos(lambda1))), t_1))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(0.5 * Float64(phi1 - phi2))) t_1 = t_0 ^ 2.0 t_2 = Float64(cos(phi1) * cos(phi2)) t_3 = sqrt(t_2) tmp = 0.0 if ((lambda2 <= -3.7e-6) || !(lambda2 <= 3.8e-38)) tmp = Float64(Float64(R * 2.0) * atan(hypot(t_0, Float64(sin(Float64(lambda2 * -0.5)) * t_3)), sqrt(Float64(1.0 - fma(t_2, Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2)))), t_1))))); else tmp = Float64(Float64(R * 2.0) * atan(hypot(t_0, Float64(sin(Float64(0.5 * Float64(lambda1 - lambda2))) * t_3)), sqrt(Float64(1.0 - fma(t_2, Float64(0.5 + Float64(-0.5 * cos(lambda1))), t_1))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Power[t$95$0, 2.0], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[Sqrt[t$95$2], $MachinePrecision]}, If[Or[LessEqual[lambda2, -3.7e-6], N[Not[LessEqual[lambda2, 3.8e-38]], $MachinePrecision]], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[t$95$0 ^ 2 + N[(N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision] * t$95$3), $MachinePrecision] ^ 2], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$2 * N[(0.5 + N[(-0.5 * N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[t$95$0 ^ 2 + N[(N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * t$95$3), $MachinePrecision] ^ 2], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$2 * N[(0.5 + N[(-0.5 * N[Cos[lambda1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)\\
t_1 := {t\_0}^{2}\\
t_2 := \cos \phi_1 \cdot \cos \phi_2\\
t_3 := \sqrt{t\_2}\\
\mathbf{if}\;\lambda_2 \leq -3.7 \cdot 10^{-6} \lor \neg \left(\lambda_2 \leq 3.8 \cdot 10^{-38}\right):\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(t\_0, \sin \left(\lambda_2 \cdot -0.5\right) \cdot t\_3\right)}{\sqrt{1 - \mathsf{fma}\left(t\_2, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), t\_1\right)}}\\
\mathbf{else}:\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(t\_0, \sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot t\_3\right)}{\sqrt{1 - \mathsf{fma}\left(t\_2, 0.5 + -0.5 \cdot \cos \lambda_1, t\_1\right)}}\\
\end{array}
\end{array}
if lambda2 < -3.7000000000000002e-6 or 3.8e-38 < lambda2 Initial program 52.9%
associate-*r*52.9%
*-commutative52.9%
Simplified53.0%
Applied egg-rr36.0%
*-lft-identity36.0%
*-commutative36.0%
*-commutative36.0%
cancel-sign-sub-inv36.0%
metadata-eval36.0%
Simplified36.0%
Taylor expanded in lambda1 around 0 36.1%
*-commutative36.1%
Simplified36.1%
if -3.7000000000000002e-6 < lambda2 < 3.8e-38Initial program 75.4%
associate-*r*75.4%
*-commutative75.4%
Simplified75.4%
Applied egg-rr53.4%
*-lft-identity53.4%
*-commutative53.4%
*-commutative53.4%
cancel-sign-sub-inv53.4%
metadata-eval53.4%
Simplified53.4%
Taylor expanded in lambda2 around 0 53.4%
Final simplification44.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (* 0.5 (- phi1 phi2))))
(t_1 (pow t_0 2.0))
(t_2 (* (cos phi1) (cos phi2)))
(t_3 (hypot t_0 (* (sin (* 0.5 (- lambda1 lambda2))) (sqrt t_2)))))
(if (or (<= lambda2 -3.8e-6) (not (<= lambda2 3.8e-38)))
(*
(* R 2.0)
(atan2 t_3 (sqrt (- 1.0 (fma t_2 (+ 0.5 (* -0.5 (cos lambda2))) t_1)))))
(*
(* R 2.0)
(atan2
t_3
(sqrt (- 1.0 (fma t_2 (+ 0.5 (* -0.5 (cos lambda1))) t_1))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((0.5 * (phi1 - phi2)));
double t_1 = pow(t_0, 2.0);
double t_2 = cos(phi1) * cos(phi2);
double t_3 = hypot(t_0, (sin((0.5 * (lambda1 - lambda2))) * sqrt(t_2)));
double tmp;
if ((lambda2 <= -3.8e-6) || !(lambda2 <= 3.8e-38)) {
tmp = (R * 2.0) * atan2(t_3, sqrt((1.0 - fma(t_2, (0.5 + (-0.5 * cos(lambda2))), t_1))));
} else {
tmp = (R * 2.0) * atan2(t_3, sqrt((1.0 - fma(t_2, (0.5 + (-0.5 * cos(lambda1))), t_1))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(0.5 * Float64(phi1 - phi2))) t_1 = t_0 ^ 2.0 t_2 = Float64(cos(phi1) * cos(phi2)) t_3 = hypot(t_0, Float64(sin(Float64(0.5 * Float64(lambda1 - lambda2))) * sqrt(t_2))) tmp = 0.0 if ((lambda2 <= -3.8e-6) || !(lambda2 <= 3.8e-38)) tmp = Float64(Float64(R * 2.0) * atan(t_3, sqrt(Float64(1.0 - fma(t_2, Float64(0.5 + Float64(-0.5 * cos(lambda2))), t_1))))); else tmp = Float64(Float64(R * 2.0) * atan(t_3, sqrt(Float64(1.0 - fma(t_2, Float64(0.5 + Float64(-0.5 * cos(lambda1))), t_1))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Power[t$95$0, 2.0], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[Sqrt[t$95$0 ^ 2 + N[(N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[Sqrt[t$95$2], $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision]}, If[Or[LessEqual[lambda2, -3.8e-6], N[Not[LessEqual[lambda2, 3.8e-38]], $MachinePrecision]], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[t$95$3 / N[Sqrt[N[(1.0 - N[(t$95$2 * N[(0.5 + N[(-0.5 * N[Cos[lambda2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[t$95$3 / N[Sqrt[N[(1.0 - N[(t$95$2 * N[(0.5 + N[(-0.5 * N[Cos[lambda1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)\\
t_1 := {t\_0}^{2}\\
t_2 := \cos \phi_1 \cdot \cos \phi_2\\
t_3 := \mathsf{hypot}\left(t\_0, \sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot \sqrt{t\_2}\right)\\
\mathbf{if}\;\lambda_2 \leq -3.8 \cdot 10^{-6} \lor \neg \left(\lambda_2 \leq 3.8 \cdot 10^{-38}\right):\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{t\_3}{\sqrt{1 - \mathsf{fma}\left(t\_2, 0.5 + -0.5 \cdot \cos \lambda_2, t\_1\right)}}\\
\mathbf{else}:\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{t\_3}{\sqrt{1 - \mathsf{fma}\left(t\_2, 0.5 + -0.5 \cdot \cos \lambda_1, t\_1\right)}}\\
\end{array}
\end{array}
if lambda2 < -3.8e-6 or 3.8e-38 < lambda2 Initial program 52.9%
associate-*r*52.9%
*-commutative52.9%
Simplified53.0%
Applied egg-rr36.0%
*-lft-identity36.0%
*-commutative36.0%
*-commutative36.0%
cancel-sign-sub-inv36.0%
metadata-eval36.0%
Simplified36.0%
Taylor expanded in lambda1 around 0 36.1%
cos-neg36.1%
Simplified36.1%
if -3.8e-6 < lambda2 < 3.8e-38Initial program 75.4%
associate-*r*75.4%
*-commutative75.4%
Simplified75.4%
Applied egg-rr53.4%
*-lft-identity53.4%
*-commutative53.4%
*-commutative53.4%
cancel-sign-sub-inv53.4%
metadata-eval53.4%
Simplified53.4%
Taylor expanded in lambda2 around 0 53.4%
Final simplification44.3%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (* 0.5 (- phi1 phi2))))
(t_1 (pow t_0 2.0))
(t_2 (* (cos phi1) (cos phi2)))
(t_3 (* (sin (* 0.5 (- lambda1 lambda2))) (sqrt t_2))))
(if (or (<= lambda2 -6.5e+56) (not (<= lambda2 0.00034)))
(*
(* R 2.0)
(atan2
(hypot (sin (* phi1 0.5)) t_3)
(sqrt
(- 1.0 (fma t_2 (+ 0.5 (* -0.5 (cos (- lambda1 lambda2)))) t_1)))))
(*
(* R 2.0)
(atan2
(hypot t_0 t_3)
(sqrt (- 1.0 (fma t_2 (+ 0.5 (* -0.5 (cos lambda1))) t_1))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((0.5 * (phi1 - phi2)));
double t_1 = pow(t_0, 2.0);
double t_2 = cos(phi1) * cos(phi2);
double t_3 = sin((0.5 * (lambda1 - lambda2))) * sqrt(t_2);
double tmp;
if ((lambda2 <= -6.5e+56) || !(lambda2 <= 0.00034)) {
tmp = (R * 2.0) * atan2(hypot(sin((phi1 * 0.5)), t_3), sqrt((1.0 - fma(t_2, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), t_1))));
} else {
tmp = (R * 2.0) * atan2(hypot(t_0, t_3), sqrt((1.0 - fma(t_2, (0.5 + (-0.5 * cos(lambda1))), t_1))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(0.5 * Float64(phi1 - phi2))) t_1 = t_0 ^ 2.0 t_2 = Float64(cos(phi1) * cos(phi2)) t_3 = Float64(sin(Float64(0.5 * Float64(lambda1 - lambda2))) * sqrt(t_2)) tmp = 0.0 if ((lambda2 <= -6.5e+56) || !(lambda2 <= 0.00034)) tmp = Float64(Float64(R * 2.0) * atan(hypot(sin(Float64(phi1 * 0.5)), t_3), sqrt(Float64(1.0 - fma(t_2, Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2)))), t_1))))); else tmp = Float64(Float64(R * 2.0) * atan(hypot(t_0, t_3), sqrt(Float64(1.0 - fma(t_2, Float64(0.5 + Float64(-0.5 * cos(lambda1))), t_1))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Power[t$95$0, 2.0], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[Sqrt[t$95$2], $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[lambda2, -6.5e+56], N[Not[LessEqual[lambda2, 0.00034]], $MachinePrecision]], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] ^ 2 + t$95$3 ^ 2], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$2 * N[(0.5 + N[(-0.5 * N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[t$95$0 ^ 2 + t$95$3 ^ 2], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$2 * N[(0.5 + N[(-0.5 * N[Cos[lambda1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)\\
t_1 := {t\_0}^{2}\\
t_2 := \cos \phi_1 \cdot \cos \phi_2\\
t_3 := \sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot \sqrt{t\_2}\\
\mathbf{if}\;\lambda_2 \leq -6.5 \cdot 10^{+56} \lor \neg \left(\lambda_2 \leq 0.00034\right):\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(\sin \left(\phi_1 \cdot 0.5\right), t\_3\right)}{\sqrt{1 - \mathsf{fma}\left(t\_2, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), t\_1\right)}}\\
\mathbf{else}:\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(t\_0, t\_3\right)}{\sqrt{1 - \mathsf{fma}\left(t\_2, 0.5 + -0.5 \cdot \cos \lambda_1, t\_1\right)}}\\
\end{array}
\end{array}
if lambda2 < -6.5000000000000001e56 or 3.4e-4 < lambda2 Initial program 53.9%
associate-*r*53.9%
*-commutative53.9%
Simplified54.0%
Applied egg-rr37.4%
*-lft-identity37.4%
*-commutative37.4%
*-commutative37.4%
cancel-sign-sub-inv37.4%
metadata-eval37.4%
Simplified37.4%
Taylor expanded in phi2 around 0 30.0%
if -6.5000000000000001e56 < lambda2 < 3.4e-4Initial program 71.6%
associate-*r*71.6%
*-commutative71.6%
Simplified71.5%
Applied egg-rr50.0%
*-lft-identity50.0%
*-commutative50.0%
*-commutative50.0%
cancel-sign-sub-inv50.0%
metadata-eval50.0%
Simplified50.0%
Taylor expanded in lambda2 around 0 49.1%
Final simplification40.5%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (* 0.5 (- phi1 phi2))))
(t_1 (* (cos phi1) (cos phi2)))
(t_2 (sqrt t_1))
(t_3
(sqrt
(-
1.0
(fma
t_1
(+ 0.5 (* -0.5 (cos (- lambda1 lambda2))))
(pow t_0 2.0))))))
(if (<= phi2 0.00156)
(*
(* R 2.0)
(atan2
(hypot (sin (* phi1 0.5)) (* (sin (* 0.5 (- lambda1 lambda2))) t_2))
t_3))
(* (* R 2.0) (atan2 (hypot t_0 (* (sin (* 0.5 lambda1)) t_2)) t_3)))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((0.5 * (phi1 - phi2)));
double t_1 = cos(phi1) * cos(phi2);
double t_2 = sqrt(t_1);
double t_3 = sqrt((1.0 - fma(t_1, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), pow(t_0, 2.0))));
double tmp;
if (phi2 <= 0.00156) {
tmp = (R * 2.0) * atan2(hypot(sin((phi1 * 0.5)), (sin((0.5 * (lambda1 - lambda2))) * t_2)), t_3);
} else {
tmp = (R * 2.0) * atan2(hypot(t_0, (sin((0.5 * lambda1)) * t_2)), t_3);
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(0.5 * Float64(phi1 - phi2))) t_1 = Float64(cos(phi1) * cos(phi2)) t_2 = sqrt(t_1) t_3 = sqrt(Float64(1.0 - fma(t_1, Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2)))), (t_0 ^ 2.0)))) tmp = 0.0 if (phi2 <= 0.00156) tmp = Float64(Float64(R * 2.0) * atan(hypot(sin(Float64(phi1 * 0.5)), Float64(sin(Float64(0.5 * Float64(lambda1 - lambda2))) * t_2)), t_3)); else tmp = Float64(Float64(R * 2.0) * atan(hypot(t_0, Float64(sin(Float64(0.5 * lambda1)) * t_2)), t_3)); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[Sqrt[t$95$1], $MachinePrecision]}, Block[{t$95$3 = 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[t$95$0, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[phi2, 0.00156], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] ^ 2 + N[(N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * t$95$2), $MachinePrecision] ^ 2], $MachinePrecision] / t$95$3], $MachinePrecision]), $MachinePrecision], N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[t$95$0 ^ 2 + N[(N[Sin[N[(0.5 * lambda1), $MachinePrecision]], $MachinePrecision] * t$95$2), $MachinePrecision] ^ 2], $MachinePrecision] / t$95$3], $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)\\
t_1 := \cos \phi_1 \cdot \cos \phi_2\\
t_2 := \sqrt{t\_1}\\
t_3 := \sqrt{1 - \mathsf{fma}\left(t\_1, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), {t\_0}^{2}\right)}\\
\mathbf{if}\;\phi_2 \leq 0.00156:\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(\sin \left(\phi_1 \cdot 0.5\right), \sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot t\_2\right)}{t\_3}\\
\mathbf{else}:\\
\;\;\;\;\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(t\_0, \sin \left(0.5 \cdot \lambda_1\right) \cdot t\_2\right)}{t\_3}\\
\end{array}
\end{array}
if phi2 < 0.00155999999999999997Initial program 70.4%
associate-*r*70.4%
*-commutative70.4%
Simplified70.5%
Applied egg-rr51.3%
*-lft-identity51.3%
*-commutative51.3%
*-commutative51.3%
cancel-sign-sub-inv51.3%
metadata-eval51.3%
Simplified51.3%
Taylor expanded in phi2 around 0 44.2%
if 0.00155999999999999997 < phi2 Initial program 41.4%
associate-*r*41.4%
*-commutative41.4%
Simplified41.4%
Applied egg-rr21.5%
*-lft-identity21.5%
*-commutative21.5%
*-commutative21.5%
cancel-sign-sub-inv21.5%
metadata-eval21.5%
Simplified21.5%
Taylor expanded in lambda2 around 0 17.8%
Final simplification38.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2))) (t_1 (sin (* 0.5 (- phi1 phi2)))))
(*
(* R 2.0)
(atan2
(hypot t_1 (* (sin (* 0.5 (- lambda1 lambda2))) (sqrt t_0)))
(sqrt
(-
1.0
(fma t_0 (+ 0.5 (* -0.5 (cos (- lambda1 lambda2)))) (pow t_1 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((0.5 * (phi1 - phi2)));
return (R * 2.0) * atan2(hypot(t_1, (sin((0.5 * (lambda1 - lambda2))) * sqrt(t_0))), sqrt((1.0 - fma(t_0, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), pow(t_1, 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(0.5 * Float64(phi1 - phi2))) return Float64(Float64(R * 2.0) * atan(hypot(t_1, Float64(sin(Float64(0.5 * Float64(lambda1 - lambda2))) * sqrt(t_0))), sqrt(Float64(1.0 - fma(t_0, Float64(0.5 + Float64(-0.5 * cos(Float64(lambda1 - lambda2)))), (t_1 ^ 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[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[t$95$1 ^ 2 + N[(N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[Sqrt[t$95$0], $MachinePrecision]), $MachinePrecision] ^ 2], $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[t$95$1, 2.0], $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(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)\\
\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(t\_1, \sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot \sqrt{t\_0}\right)}{\sqrt{1 - \mathsf{fma}\left(t\_0, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), {t\_1}^{2}\right)}}
\end{array}
\end{array}
Initial program 63.6%
associate-*r*63.6%
*-commutative63.6%
Simplified63.7%
Applied egg-rr44.3%
*-lft-identity44.3%
*-commutative44.3%
*-commutative44.3%
cancel-sign-sub-inv44.3%
metadata-eval44.3%
Simplified44.3%
Final simplification44.3%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2))))
(*
(* R 2.0)
(atan2
(hypot
(sin (* phi1 0.5))
(* (sin (* 0.5 (- lambda1 lambda2))) (sqrt t_0)))
(sqrt
(-
1.0
(fma
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);
return (R * 2.0) * atan2(hypot(sin((phi1 * 0.5)), (sin((0.5 * (lambda1 - lambda2))) * sqrt(t_0))), sqrt((1.0 - fma(t_0, (0.5 + (-0.5 * cos((lambda1 - lambda2)))), pow(sin((0.5 * (phi1 - phi2))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) return Float64(Float64(R * 2.0) * atan(hypot(sin(Float64(phi1 * 0.5)), Float64(sin(Float64(0.5 * Float64(lambda1 - lambda2))) * sqrt(t_0))), sqrt(Float64(1.0 - fma(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]}, N[(N[(R * 2.0), $MachinePrecision] * N[ArcTan[N[Sqrt[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] ^ 2 + N[(N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[Sqrt[t$95$0], $MachinePrecision]), $MachinePrecision] ^ 2], $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[Sin[N[(0.5 * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
\left(R \cdot 2\right) \cdot \tan^{-1}_* \frac{\mathsf{hypot}\left(\sin \left(\phi_1 \cdot 0.5\right), \sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot \sqrt{t\_0}\right)}{\sqrt{1 - \mathsf{fma}\left(t\_0, 0.5 + -0.5 \cdot \cos \left(\lambda_1 - \lambda_2\right), {\sin \left(0.5 \cdot \left(\phi_1 - \phi_2\right)\right)}^{2}\right)}}
\end{array}
\end{array}
Initial program 63.6%
associate-*r*63.6%
*-commutative63.6%
Simplified63.7%
Applied egg-rr44.3%
*-lft-identity44.3%
*-commutative44.3%
*-commutative44.3%
cancel-sign-sub-inv44.3%
metadata-eval44.3%
Simplified44.3%
Taylor expanded in phi2 around 0 35.7%
Final simplification35.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* t_0 (* (* (cos phi1) (cos phi2)) t_0))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt
(-
1.0
(+
(* (cos phi1) (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0))
(* 0.25 (pow 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(((t_0 * ((cos(phi1) * cos(phi2)) * t_0)) + pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - ((cos(phi1) * pow(sin((0.5 * (lambda1 - lambda2))), 2.0)) + (0.25 * pow(phi1, 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(((t_0 * ((cos(phi1) * cos(phi2)) * t_0)) + (sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0))), sqrt((1.0d0 - ((cos(phi1) * (sin((0.5d0 * (lambda1 - lambda2))) ** 2.0d0)) + (0.25d0 * (phi1 ** 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(((t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * t_0)) + Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0))), Math.sqrt((1.0 - ((Math.cos(phi1) * Math.pow(Math.sin((0.5 * (lambda1 - lambda2))), 2.0)) + (0.25 * Math.pow(phi1, 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(((t_0 * ((math.cos(phi1) * math.cos(phi2)) * t_0)) + math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0))), math.sqrt((1.0 - ((math.cos(phi1) * math.pow(math.sin((0.5 * (lambda1 - lambda2))), 2.0)) + (0.25 * math.pow(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(Float64(Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)) + (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - Float64(Float64(cos(phi1) * (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0)) + Float64(0.25 * (phi1 ^ 2.0)))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); tmp = R * (2.0 * atan2(sqrt(((t_0 * ((cos(phi1) * cos(phi2)) * t_0)) + (sin(((phi1 - phi2) / 2.0)) ^ 2.0))), sqrt((1.0 - ((cos(phi1) * (sin((0.5 * (lambda1 - lambda2))) ^ 2.0)) + (0.25 * (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[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * 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[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[(0.25 * N[Power[phi1, 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{t\_0 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t\_0\right) + {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}}}{\sqrt{1 - \left(\cos \phi_1 \cdot {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2} + 0.25 \cdot {\phi_1}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 63.6%
Taylor expanded in phi1 around 0 35.3%
associate-*r*35.3%
*-commutative35.3%
*-commutative35.3%
metadata-eval35.3%
distribute-rgt-neg-in35.3%
cos-neg35.3%
Simplified35.3%
Taylor expanded in phi2 around 0 24.6%
Final simplification24.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(pow (sin (* 0.5 (- lambda1 lambda2))) 2.0)))
(sqrt (* -0.25 (* (pow phi1 2.0) (pow (cos (* 0.5 phi2)) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + pow(sin((0.5 * (lambda1 - lambda2))), 2.0))), sqrt((-0.25 * (pow(phi1, 2.0) * pow(cos((0.5 * phi2)), 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(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + (sin((0.5d0 * (lambda1 - lambda2))) ** 2.0d0))), sqrt(((-0.25d0) * ((phi1 ** 2.0d0) * (cos((0.5d0 * phi2)) ** 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((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + Math.pow(Math.sin((0.5 * (lambda1 - lambda2))), 2.0))), Math.sqrt((-0.25 * (Math.pow(phi1, 2.0) * Math.pow(Math.cos((0.5 * phi2)), 2.0))))));
}
def code(R, lambda1, lambda2, phi1, phi2): return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + math.pow(math.sin((0.5 * (lambda1 - lambda2))), 2.0))), math.sqrt((-0.25 * (math.pow(phi1, 2.0) * math.pow(math.cos((0.5 * phi2)), 2.0))))))
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0))), sqrt(Float64(-0.25 * Float64((phi1 ^ 2.0) * (cos(Float64(0.5 * phi2)) ^ 2.0))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + (sin((0.5 * (lambda1 - lambda2))) ^ 2.0))), sqrt((-0.25 * ((phi1 ^ 2.0) * (cos((0.5 * phi2)) ^ 2.0)))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := 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[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(-0.25 * N[(N[Power[phi1, 2.0], $MachinePrecision] * N[Power[N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}}}{\sqrt{-0.25 \cdot \left({\phi_1}^{2} \cdot {\cos \left(0.5 \cdot \phi_2\right)}^{2}\right)}}\right)
\end{array}
Initial program 63.6%
Taylor expanded in phi1 around 0 35.3%
associate-*r*35.3%
*-commutative35.3%
*-commutative35.3%
metadata-eval35.3%
distribute-rgt-neg-in35.3%
cos-neg35.3%
Simplified35.3%
Taylor expanded in phi1 around inf 3.0%
*-commutative3.0%
*-commutative3.0%
Simplified3.0%
Taylor expanded in phi1 around 0 3.0%
Taylor expanded in phi2 around 0 3.0%
Final simplification3.0%
herbie shell --seed 2024155
(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))))))))))