
(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 15 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 (/ (- lambda1 lambda2) 2.0)))
(t_1 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))
(if (or (<= phi1 -2.6e-5) (not (<= phi1 0.49)))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- (pow (cos (* phi1 0.5)) 2.0) (* (cos phi1) t_1))))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) 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((-0.5 * (lambda2 - lambda1))), 2.0);
double tmp;
if ((phi1 <= -2.6e-5) || !(phi1 <= 0.49)) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), pow(sin(((lambda1 - lambda2) * 0.5)), 2.0), pow(sin((phi1 * 0.5)), 2.0))), sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * t_1)))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * t_1)))));
}
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(lambda2 - lambda1))) ^ 2.0 tmp = 0.0 if ((phi1 <= -2.6e-5) || !(phi1 <= 0.49)) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0), (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * t_1)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * t_1)))))); 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[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[Or[LessEqual[phi1, -2.6e-5], N[Not[LessEqual[phi1, 0.49]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}\\
\mathbf{if}\;\phi_1 \leq -2.6 \cdot 10^{-5} \lor \neg \left(\phi_1 \leq 0.49\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}, {\sin \left(\phi_1 \cdot 0.5\right)}^{2}\right)}}{\sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot t_1}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, t_0 \cdot t_0, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot t_1}}\right)\\
\end{array}
\end{array}
if phi1 < -2.59999999999999984e-5 or 0.48999999999999999 < phi1 Initial program 45.7%
expm1-log1p-u45.7%
div-inv45.7%
metadata-eval45.7%
Applied egg-rr45.7%
Taylor expanded in phi2 around 0 46.8%
associate--r+46.8%
unpow246.8%
1-sub-sin46.9%
unpow246.9%
*-commutative46.9%
unpow246.9%
associate-*r*46.9%
Simplified46.9%
Taylor expanded in phi2 around 0 48.9%
+-commutative48.9%
fma-def48.9%
Simplified48.9%
if -2.59999999999999984e-5 < phi1 < 0.48999999999999999Initial program 74.5%
Simplified74.5%
Taylor expanded in phi1 around 0 74.5%
associate--r+74.5%
unpow274.5%
1-sub-sin74.5%
unpow274.5%
*-commutative74.5%
*-commutative74.5%
unpow274.5%
associate-*l*74.5%
Simplified74.5%
Final simplification61.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0))))
(* 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 = fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0));
return R * (2.0 * atan2(sqrt(t_1), sqrt((1.0 - t_1))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(t_1), sqrt(Float64(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] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $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 := \mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, t_0 \cdot t_0, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1}}{\sqrt{1 - t_1}}\right)
\end{array}
\end{array}
Initial program 60.0%
Simplified60.0%
Final simplification60.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (* (cos phi1) (cos phi2)) (* t_0 t_0)))
(t_2 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(* R (* 2.0 (atan2 (sqrt (+ t_2 t_1)) (sqrt (- (- 1.0 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 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
return R * (2.0 * atan2(sqrt((t_2 + t_1)), sqrt(((1.0 - 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 = sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0
code = r * (2.0d0 * atan2(sqrt((t_2 + t_1)), sqrt(((1.0d0 - 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.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0);
return R * (2.0 * Math.atan2(Math.sqrt((t_2 + t_1)), Math.sqrt(((1.0 - 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.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) return R * (2.0 * math.atan2(math.sqrt((t_2 + t_1)), math.sqrt(((1.0 - 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 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 return Float64(R * Float64(2.0 * atan(sqrt(Float64(t_2 + t_1)), sqrt(Float64(Float64(1.0 - 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 = sin(((phi1 - phi2) / 2.0)) ^ 2.0; tmp = R * (2.0 * atan2(sqrt((t_2 + t_1)), sqrt(((1.0 - 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[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 + t$95$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - 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 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_2 + t_1}}{\sqrt{\left(1 - t_2\right) - t_1}}\right)
\end{array}
\end{array}
Initial program 60.0%
associate-*l*60.0%
Simplified60.0%
Final simplification60.0%
(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)
(* t_0 (* (* (cos phi1) (cos phi2)) 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) + (t_0 * ((cos(phi1) * cos(phi2)) * 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) + (t_0 * ((cos(phi1) * cos(phi2)) * 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) + (t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * 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) + (t_0 * ((math.cos(phi1) * math.cos(phi2)) * 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(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * 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) + (t_0 * ((cos(phi1) * cos(phi2)) * 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[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $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} + t_0 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_0\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1}}{\sqrt{1 - t_1}}\right)
\end{array}
\end{array}
Initial program 60.0%
Final simplification60.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(if (or (<= phi1 -2.55e-5) (not (<= phi1 0.49)))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- (pow (cos (* phi1 0.5)) 2.0) (* (cos phi1) t_0))))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* t_1 (* (* (cos phi1) (cos phi2)) t_1))))
(sqrt (- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) t_0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin((-0.5 * (lambda2 - lambda1))), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double tmp;
if ((phi1 <= -2.55e-5) || !(phi1 <= 0.49)) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), pow(sin(((lambda1 - lambda2) * 0.5)), 2.0), pow(sin((phi1 * 0.5)), 2.0))), sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * t_0)))));
} else {
tmp = R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (t_1 * ((cos(phi1) * cos(phi2)) * t_1)))), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * t_0)))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) tmp = 0.0 if ((phi1 <= -2.55e-5) || !(phi1 <= 0.49)) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0), (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * t_0)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(t_1 * Float64(Float64(cos(phi1) * cos(phi2)) * t_1)))), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * t_0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, If[Or[LessEqual[phi1, -2.55e-5], N[Not[LessEqual[phi1, 0.49]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(t$95$1 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\mathbf{if}\;\phi_1 \leq -2.55 \cdot 10^{-5} \lor \neg \left(\phi_1 \leq 0.49\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}, {\sin \left(\phi_1 \cdot 0.5\right)}^{2}\right)}}{\sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot t_0}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_1\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot t_0}}\right)\\
\end{array}
\end{array}
if phi1 < -2.54999999999999998e-5 or 0.48999999999999999 < phi1 Initial program 45.7%
expm1-log1p-u45.7%
div-inv45.7%
metadata-eval45.7%
Applied egg-rr45.7%
Taylor expanded in phi2 around 0 46.8%
associate--r+46.8%
unpow246.8%
1-sub-sin46.9%
unpow246.9%
*-commutative46.9%
unpow246.9%
associate-*r*46.9%
Simplified46.9%
Taylor expanded in phi2 around 0 48.9%
+-commutative48.9%
fma-def48.9%
Simplified48.9%
if -2.54999999999999998e-5 < phi1 < 0.48999999999999999Initial program 74.5%
expm1-log1p-u74.5%
div-inv74.5%
metadata-eval74.5%
Applied egg-rr74.5%
Taylor expanded in phi1 around 0 74.5%
associate--r+74.5%
unpow274.5%
1-sub-sin74.5%
unpow274.5%
sub-neg74.5%
mul-1-neg74.5%
distribute-lft-in74.5%
associate-*r*74.5%
metadata-eval74.5%
metadata-eval74.5%
associate-*r*74.5%
distribute-lft-in74.5%
+-commutative74.5%
mul-1-neg74.5%
sub-neg74.5%
Simplified74.5%
Final simplification61.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0
(sqrt
(-
(pow (cos (* phi1 0.5)) 2.0)
(* (cos phi1) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))
(if (<= lambda2 3.2e-64)
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (* (- phi1 phi2) 0.5)) 2.0)
(* (cos phi2) (* (cos phi1) (pow (sin (* lambda1 0.5)) 2.0)))))
t_0)))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (* phi1 0.5)) 2.0)
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))
t_0))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0))));
double tmp;
if (lambda2 <= 3.2e-64) {
tmp = R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) * 0.5)), 2.0) + (cos(phi2) * (cos(phi1) * pow(sin((lambda1 * 0.5)), 2.0))))), t_0));
} else {
tmp = R * (2.0 * atan2(sqrt((pow(sin((phi1 * 0.5)), 2.0) + (cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))), t_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) :: tmp
t_0 = sqrt(((cos((phi1 * 0.5d0)) ** 2.0d0) - (cos(phi1) * (sin(((-0.5d0) * (lambda2 - lambda1))) ** 2.0d0))))
if (lambda2 <= 3.2d-64) then
tmp = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) * 0.5d0)) ** 2.0d0) + (cos(phi2) * (cos(phi1) * (sin((lambda1 * 0.5d0)) ** 2.0d0))))), t_0))
else
tmp = r * (2.0d0 * atan2(sqrt(((sin((phi1 * 0.5d0)) ** 2.0d0) + (cos(phi1) * (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))), t_0))
end if
code = tmp
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sqrt((Math.pow(Math.cos((phi1 * 0.5)), 2.0) - (Math.cos(phi1) * Math.pow(Math.sin((-0.5 * (lambda2 - lambda1))), 2.0))));
double tmp;
if (lambda2 <= 3.2e-64) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) * 0.5)), 2.0) + (Math.cos(phi2) * (Math.cos(phi1) * Math.pow(Math.sin((lambda1 * 0.5)), 2.0))))), t_0));
} else {
tmp = R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin((phi1 * 0.5)), 2.0) + (Math.cos(phi1) * Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))), t_0));
}
return tmp;
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sqrt((math.pow(math.cos((phi1 * 0.5)), 2.0) - (math.cos(phi1) * math.pow(math.sin((-0.5 * (lambda2 - lambda1))), 2.0)))) tmp = 0 if lambda2 <= 3.2e-64: tmp = R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) * 0.5)), 2.0) + (math.cos(phi2) * (math.cos(phi1) * math.pow(math.sin((lambda1 * 0.5)), 2.0))))), t_0)) else: tmp = R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin((phi1 * 0.5)), 2.0) + (math.cos(phi1) * math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))), t_0)) return tmp
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)))) tmp = 0.0 if (lambda2 <= 3.2e-64) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0) + Float64(cos(phi2) * Float64(cos(phi1) * (sin(Float64(lambda1 * 0.5)) ^ 2.0))))), t_0))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(phi1 * 0.5)) ^ 2.0) + Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))), t_0))); end return tmp end
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2) t_0 = sqrt(((cos((phi1 * 0.5)) ^ 2.0) - (cos(phi1) * (sin((-0.5 * (lambda2 - lambda1))) ^ 2.0)))); tmp = 0.0; if (lambda2 <= 3.2e-64) tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) * 0.5)) ^ 2.0) + (cos(phi2) * (cos(phi1) * (sin((lambda1 * 0.5)) ^ 2.0))))), t_0)); else tmp = R * (2.0 * atan2(sqrt(((sin((phi1 * 0.5)) ^ 2.0) + (cos(phi1) * (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0)))), t_0)); end tmp_2 = tmp; end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda2, 3.2e-64], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[Cos[phi2], $MachinePrecision] * N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}\\
\mathbf{if}\;\lambda_2 \leq 3.2 \cdot 10^{-64}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2} + \cos \phi_2 \cdot \left(\cos \phi_1 \cdot {\sin \left(\lambda_1 \cdot 0.5\right)}^{2}\right)}}{t_0}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\phi_1 \cdot 0.5\right)}^{2} + \cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}{t_0}\right)\\
\end{array}
\end{array}
if lambda2 < 3.19999999999999975e-64Initial program 61.6%
expm1-log1p-u61.6%
div-inv61.6%
metadata-eval61.6%
Applied egg-rr61.6%
Taylor expanded in phi2 around 0 45.5%
associate--r+45.5%
unpow245.5%
1-sub-sin45.5%
unpow245.5%
*-commutative45.5%
unpow245.5%
associate-*r*45.5%
Simplified45.5%
Taylor expanded in lambda2 around 0 40.5%
if 3.19999999999999975e-64 < lambda2 Initial program 55.4%
expm1-log1p-u55.4%
div-inv55.4%
metadata-eval55.4%
Applied egg-rr55.4%
Taylor expanded in phi2 around 0 41.1%
associate--r+41.1%
unpow241.1%
1-sub-sin41.2%
unpow241.2%
*-commutative41.2%
unpow241.2%
associate-*r*41.1%
Simplified41.2%
Taylor expanded in phi2 around 0 41.6%
Final simplification40.8%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0
(sqrt
(-
(pow (cos (* phi1 0.5)) 2.0)
(* (cos phi1) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))
(if (<= lambda1 -4.5e-36)
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)
(pow (sin (* phi1 0.5)) 2.0)))
t_0)))
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (* (- phi1 phi2) 0.5)) 2.0)
(* (* (cos phi1) (cos phi2)) (pow (sin (* lambda2 -0.5)) 2.0))))
t_0))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0))));
double tmp;
if (lambda1 <= -4.5e-36) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), pow(sin(((lambda1 - lambda2) * 0.5)), 2.0), pow(sin((phi1 * 0.5)), 2.0))), t_0));
} else {
tmp = R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) * 0.5)), 2.0) + ((cos(phi1) * cos(phi2)) * pow(sin((lambda2 * -0.5)), 2.0)))), t_0));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)))) tmp = 0.0 if (lambda1 <= -4.5e-36) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0), (sin(Float64(phi1 * 0.5)) ^ 2.0))), t_0))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0) + Float64(Float64(cos(phi1) * cos(phi2)) * (sin(Float64(lambda2 * -0.5)) ^ 2.0)))), t_0))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda1, -4.5e-36], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}\\
\mathbf{if}\;\lambda_1 \leq -4.5 \cdot 10^{-36}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}, {\sin \left(\phi_1 \cdot 0.5\right)}^{2}\right)}}{t_0}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2} + \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}}}{t_0}\right)\\
\end{array}
\end{array}
if lambda1 < -4.50000000000000024e-36Initial program 45.5%
expm1-log1p-u45.5%
div-inv45.5%
metadata-eval45.5%
Applied egg-rr45.5%
Taylor expanded in phi2 around 0 36.6%
associate--r+36.6%
unpow236.6%
1-sub-sin36.7%
unpow236.7%
*-commutative36.7%
unpow236.7%
associate-*r*36.6%
Simplified36.7%
Taylor expanded in phi2 around 0 37.7%
+-commutative37.7%
fma-def37.7%
Simplified37.7%
if -4.50000000000000024e-36 < lambda1 Initial program 65.8%
expm1-log1p-u65.8%
div-inv65.8%
metadata-eval65.8%
Applied egg-rr65.8%
Taylor expanded in phi2 around 0 47.4%
associate--r+47.4%
unpow247.4%
1-sub-sin47.5%
unpow247.5%
*-commutative47.5%
unpow247.5%
associate-*r*47.4%
Simplified47.5%
Taylor expanded in lambda1 around 0 40.2%
Final simplification39.5%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt
(-
(pow (cos (* phi1 0.5)) 2.0)
(* (cos phi1) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(fma(cos(phi1), pow(sin(((lambda1 - lambda2) * 0.5)), 2.0), pow(sin((phi1 * 0.5)), 2.0))), sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0))))));
}
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0), (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}, {\sin \left(\phi_1 \cdot 0.5\right)}^{2}\right)}}{\sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 60.0%
expm1-log1p-u60.0%
div-inv60.0%
metadata-eval60.0%
Applied egg-rr60.0%
Taylor expanded in phi2 around 0 44.3%
associate--r+44.3%
unpow244.3%
1-sub-sin44.4%
unpow244.4%
*-commutative44.4%
unpow244.4%
associate-*r*44.4%
Simplified44.4%
Taylor expanded in phi2 around 0 42.6%
+-commutative42.6%
fma-def42.6%
Simplified42.6%
Final simplification42.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(+
(pow (sin (* phi1 0.5)) 2.0)
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))
(sqrt
(-
(pow (cos (* phi1 0.5)) 2.0)
(* (cos phi1) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt((pow(sin((phi1 * 0.5)), 2.0) + (cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))), sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * pow(sin((-0.5 * (lambda2 - lambda1))), 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 * 0.5d0)) ** 2.0d0) + (cos(phi1) * (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))), sqrt(((cos((phi1 * 0.5d0)) ** 2.0d0) - (cos(phi1) * (sin(((-0.5d0) * (lambda2 - lambda1))) ** 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 * 0.5)), 2.0) + (Math.cos(phi1) * Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))), Math.sqrt((Math.pow(Math.cos((phi1 * 0.5)), 2.0) - (Math.cos(phi1) * Math.pow(Math.sin((-0.5 * (lambda2 - lambda1))), 2.0))))));
}
def code(R, lambda1, lambda2, phi1, phi2): return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin((phi1 * 0.5)), 2.0) + (math.cos(phi1) * math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))), math.sqrt((math.pow(math.cos((phi1 * 0.5)), 2.0) - (math.cos(phi1) * math.pow(math.sin((-0.5 * (lambda2 - lambda1))), 2.0))))))
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(phi1 * 0.5)) ^ 2.0) + Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))), sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) tmp = R * (2.0 * atan2(sqrt(((sin((phi1 * 0.5)) ^ 2.0) + (cos(phi1) * (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0)))), sqrt(((cos((phi1 * 0.5)) ^ 2.0) - (cos(phi1) * (sin((-0.5 * (lambda2 - lambda1))) ^ 2.0)))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $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(\phi_1 \cdot 0.5\right)}^{2} + \cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}{\sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 60.0%
expm1-log1p-u60.0%
div-inv60.0%
metadata-eval60.0%
Applied egg-rr60.0%
Taylor expanded in phi2 around 0 44.3%
associate--r+44.3%
unpow244.3%
1-sub-sin44.4%
unpow244.4%
*-commutative44.4%
unpow244.4%
associate-*r*44.4%
Simplified44.4%
Taylor expanded in phi2 around 0 42.6%
Final simplification42.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((-0.5 * (lambda2 - lambda1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, t_0 \cdot t_0, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 60.0%
Simplified60.0%
Taylor expanded in phi1 around 0 46.7%
associate--r+46.7%
unpow246.7%
1-sub-sin46.7%
unpow246.7%
*-commutative46.7%
*-commutative46.7%
unpow246.7%
associate-*l*46.8%
Simplified46.7%
Taylor expanded in phi2 around 0 30.6%
Final simplification30.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* t_1 t_1)))
(if (<= lambda1 -9e-9)
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 t_2 (- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt (- 1.0 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 t_2 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (pow (cos (* lambda2 -0.5)) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = t_1 * t_1;
double tmp;
if (lambda1 <= -9e-9) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, t_2, (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - pow(sin((-0.5 * (lambda2 - lambda1))), 2.0)))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, t_2, pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt(pow(cos((lambda2 * -0.5)), 2.0))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(t_1 * t_1) tmp = 0.0 if (lambda1 <= -9e-9) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, t_2, Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, t_2, (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt((cos(Float64(lambda2 * -0.5)) ^ 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[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * t$95$1), $MachinePrecision]}, If[LessEqual[lambda1, -9e-9], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * t$95$2 + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * t$95$2 + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[Power[N[Cos[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := t_1 \cdot t_1\\
\mathbf{if}\;\lambda_1 \leq -9 \cdot 10^{-9}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_2, 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{1 - {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_2, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{{\cos \left(\lambda_2 \cdot -0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if lambda1 < -8.99999999999999953e-9Initial program 43.8%
Simplified44.0%
Taylor expanded in phi1 around 0 31.5%
associate--r+31.5%
unpow231.5%
1-sub-sin31.4%
unpow231.4%
*-commutative31.4%
*-commutative31.4%
unpow231.4%
associate-*l*31.4%
Simplified31.4%
Taylor expanded in phi2 around 0 21.3%
unpow221.3%
sin-mult21.3%
div-inv21.3%
metadata-eval21.3%
div-inv21.3%
metadata-eval21.3%
div-inv21.3%
metadata-eval21.3%
div-inv21.3%
metadata-eval21.3%
Applied egg-rr21.3%
div-sub21.3%
+-inverses21.3%
+-inverses21.3%
+-inverses21.3%
cos-021.3%
metadata-eval21.3%
distribute-lft-out21.3%
metadata-eval21.3%
*-rgt-identity21.3%
Simplified21.3%
if -8.99999999999999953e-9 < lambda1 Initial program 65.3%
Simplified65.2%
Taylor expanded in phi1 around 0 51.7%
associate--r+51.7%
unpow251.7%
1-sub-sin51.7%
unpow251.7%
*-commutative51.7%
*-commutative51.7%
unpow251.7%
associate-*l*51.8%
Simplified51.7%
Taylor expanded in phi2 around 0 33.6%
Taylor expanded in lambda1 around 0 31.0%
unpow231.0%
1-sub-sin31.0%
unpow231.0%
Simplified31.0%
Final simplification28.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))))
(if (<= lambda1 -0.00015)
(* R (* 2.0 (atan2 t_1 (sqrt (pow (cos (* lambda1 0.5)) 2.0)))))
(* R (* 2.0 (atan2 t_1 (sqrt (pow (cos (* lambda2 -0.5)) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = sqrt(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0)));
double tmp;
if (lambda1 <= -0.00015) {
tmp = R * (2.0 * atan2(t_1, sqrt(pow(cos((lambda1 * 0.5)), 2.0))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt(pow(cos((lambda2 * -0.5)), 2.0))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = sqrt(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))) tmp = 0.0 if (lambda1 <= -0.00015) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt((cos(Float64(lambda1 * 0.5)) ^ 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt((cos(Float64(lambda2 * -0.5)) ^ 2.0))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda1, -0.00015], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[Power[N[Cos[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[Power[N[Cos[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \sqrt{\mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, t_0 \cdot t_0, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}\\
\mathbf{if}\;\lambda_1 \leq -0.00015:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{{\cos \left(\lambda_1 \cdot 0.5\right)}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{{\cos \left(\lambda_2 \cdot -0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if lambda1 < -1.49999999999999987e-4Initial program 43.8%
Simplified44.0%
Taylor expanded in phi1 around 0 31.5%
associate--r+31.5%
unpow231.5%
1-sub-sin31.4%
unpow231.4%
*-commutative31.4%
*-commutative31.4%
unpow231.4%
associate-*l*31.4%
Simplified31.4%
Taylor expanded in phi2 around 0 21.3%
Taylor expanded in lambda2 around 0 21.3%
unpow221.3%
1-sub-sin21.3%
unpow221.3%
Simplified21.3%
if -1.49999999999999987e-4 < lambda1 Initial program 65.3%
Simplified65.2%
Taylor expanded in phi1 around 0 51.7%
associate--r+51.7%
unpow251.7%
1-sub-sin51.7%
unpow251.7%
*-commutative51.7%
*-commutative51.7%
unpow251.7%
associate-*l*51.8%
Simplified51.7%
Taylor expanded in phi2 around 0 33.6%
Taylor expanded in lambda1 around 0 31.0%
unpow231.0%
1-sub-sin31.0%
unpow231.0%
Simplified31.0%
Final simplification28.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sqrt (- 1.0 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(if (<= lambda2 4.5e-40)
(*
R
(* 2.0 (atan2 (sqrt (fma t_0 (* t_2 (sin (* lambda1 0.5))) t_3)) t_1)))
(*
R
(*
2.0
(atan2 (sqrt (fma t_0 (* t_2 (sin (* lambda2 -0.5))) t_3)) t_1))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sqrt((1.0 - pow(sin((-0.5 * (lambda2 - lambda1))), 2.0)));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = 0.5 - (cos((phi1 - phi2)) / 2.0);
double tmp;
if (lambda2 <= 4.5e-40) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_2 * sin((lambda1 * 0.5))), t_3)), t_1));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_2 * sin((lambda2 * -0.5))), t_3)), t_1));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sqrt(Float64(1.0 - (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0))) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)) tmp = 0.0 if (lambda2 <= 4.5e-40) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_2 * sin(Float64(lambda1 * 0.5))), t_3)), t_1))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_2 * sin(Float64(lambda2 * -0.5))), t_3)), t_1))); 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[Sqrt[N[(1.0 - N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[lambda2, 4.5e-40], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$2 * N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$2 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sqrt{1 - {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\\
\mathbf{if}\;\lambda_2 \leq 4.5 \cdot 10^{-40}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_2 \cdot \sin \left(\lambda_1 \cdot 0.5\right), t_3\right)}}{t_1}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_2 \cdot \sin \left(\lambda_2 \cdot -0.5\right), t_3\right)}}{t_1}\right)\\
\end{array}
\end{array}
if lambda2 < 4.5000000000000001e-40Initial program 62.0%
Simplified62.0%
Taylor expanded in phi1 around 0 46.9%
associate--r+46.9%
unpow246.9%
1-sub-sin46.9%
unpow246.9%
*-commutative46.9%
*-commutative46.9%
unpow246.9%
associate-*l*46.9%
Simplified46.9%
Taylor expanded in phi2 around 0 30.6%
unpow230.6%
sin-mult27.1%
div-inv27.1%
metadata-eval27.1%
div-inv27.1%
metadata-eval27.1%
div-inv27.1%
metadata-eval27.1%
div-inv27.1%
metadata-eval27.1%
Applied egg-rr27.1%
div-sub27.1%
+-inverses27.1%
+-inverses27.1%
+-inverses27.1%
cos-027.1%
metadata-eval27.1%
distribute-lft-out27.1%
metadata-eval27.1%
*-rgt-identity27.1%
Simplified27.1%
Taylor expanded in lambda2 around 0 23.1%
if 4.5000000000000001e-40 < lambda2 Initial program 54.1%
Simplified54.0%
Taylor expanded in phi1 around 0 46.2%
associate--r+46.2%
unpow246.2%
1-sub-sin46.2%
unpow246.2%
*-commutative46.2%
*-commutative46.2%
unpow246.2%
associate-*l*46.2%
Simplified46.2%
Taylor expanded in phi2 around 0 30.4%
unpow230.4%
sin-mult29.5%
div-inv29.5%
metadata-eval29.5%
div-inv29.5%
metadata-eval29.5%
div-inv29.5%
metadata-eval29.5%
div-inv29.5%
metadata-eval29.5%
Applied egg-rr29.5%
div-sub29.5%
+-inverses29.5%
+-inverses29.5%
+-inverses29.5%
cos-029.5%
metadata-eval29.5%
distribute-lft-out29.5%
metadata-eval29.5%
*-rgt-identity29.5%
Simplified29.5%
Taylor expanded in lambda1 around 0 29.2%
Final simplification24.6%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt (- 1.0 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return R * (2.0 * atan2(sqrt(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - pow(sin((-0.5 * (lambda2 - lambda1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 * t$95$0), $MachinePrecision] + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, t_0 \cdot t_0, 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{1 - {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}}\right)
\end{array}
\end{array}
Initial program 60.0%
Simplified60.0%
Taylor expanded in phi1 around 0 46.7%
associate--r+46.7%
unpow246.7%
1-sub-sin46.7%
unpow246.7%
*-commutative46.7%
*-commutative46.7%
unpow246.7%
associate-*l*46.8%
Simplified46.7%
Taylor expanded in phi2 around 0 30.6%
unpow230.6%
sin-mult27.7%
div-inv27.7%
metadata-eval27.7%
div-inv27.7%
metadata-eval27.7%
div-inv27.7%
metadata-eval27.7%
div-inv27.7%
metadata-eval27.7%
Applied egg-rr27.7%
div-sub27.7%
+-inverses27.7%
+-inverses27.7%
+-inverses27.7%
cos-027.7%
metadata-eval27.7%
distribute-lft-out27.7%
metadata-eval27.7%
*-rgt-identity27.7%
Simplified27.7%
Final simplification27.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* (sin (/ (- lambda1 lambda2) 2.0)) (sin (* lambda2 -0.5)))
(- 0.5 (/ (cos (- phi1 phi2)) 2.0))))
(sqrt (- 1.0 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(fma((cos(phi1) * cos(phi2)), (sin(((lambda1 - lambda2) / 2.0)) * sin((lambda2 * -0.5))), (0.5 - (cos((phi1 - phi2)) / 2.0)))), sqrt((1.0 - pow(sin((-0.5 * (lambda2 - lambda1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(fma(Float64(cos(phi1) * cos(phi2)), Float64(sin(Float64(Float64(lambda1 - lambda2) / 2.0)) * sin(Float64(lambda2 * -0.5))), Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) / 2.0)))), sqrt(Float64(1.0 - (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * N[(N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right) \cdot \sin \left(\lambda_2 \cdot -0.5\right), 0.5 - \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\right)}}{\sqrt{1 - {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 60.0%
Simplified60.0%
Taylor expanded in phi1 around 0 46.7%
associate--r+46.7%
unpow246.7%
1-sub-sin46.7%
unpow246.7%
*-commutative46.7%
*-commutative46.7%
unpow246.7%
associate-*l*46.8%
Simplified46.7%
Taylor expanded in phi2 around 0 30.6%
unpow230.6%
sin-mult27.7%
div-inv27.7%
metadata-eval27.7%
div-inv27.7%
metadata-eval27.7%
div-inv27.7%
metadata-eval27.7%
div-inv27.7%
metadata-eval27.7%
Applied egg-rr27.7%
div-sub27.7%
+-inverses27.7%
+-inverses27.7%
+-inverses27.7%
cos-027.7%
metadata-eval27.7%
distribute-lft-out27.7%
metadata-eval27.7%
*-rgt-identity27.7%
Simplified27.7%
Taylor expanded in lambda1 around 0 23.3%
Final simplification23.3%
herbie shell --seed 2023217
(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))))))))))