
(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 26 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
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0))))
(t_2 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))
(if (<= phi1 -8600000000000.0)
(*
R
(*
2.0
(atan2 t_1 (sqrt (- (pow (cos (* phi1 0.5)) 2.0) (* (cos phi1) t_2))))))
(if (<= phi1 1.75e-8)
(*
R
(*
2.0
(atan2
t_1
(sqrt (- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) t_2))))))
(*
R
(*
2.0
(atan2
t_1
(sqrt
(-
(- 1.0 (pow (sin (* phi1 0.5)) 2.0))
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = sqrt(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0)));
double t_2 = pow(sin((-0.5 * (lambda2 - lambda1))), 2.0);
double tmp;
if (phi1 <= -8600000000000.0) {
tmp = R * (2.0 * atan2(t_1, sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * t_2)))));
} else if (phi1 <= 1.75e-8) {
tmp = R * (2.0 * atan2(t_1, sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * t_2)))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt(((1.0 - pow(sin((phi1 * 0.5)), 2.0)) - (cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = sqrt(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))) t_2 = sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0 tmp = 0.0 if (phi1 <= -8600000000000.0) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * t_2)))))); elseif (phi1 <= 1.75e-8) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * t_2)))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(Float64(1.0 - (sin(Float64(phi1 * 0.5)) ^ 2.0)) - Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - 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]}, Block[{t$95$2 = N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[phi1, -8600000000000.0], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi1, 1.75e-8], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[(1.0 - N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $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]], $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)}\\
t_2 := {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}\\
\mathbf{if}\;\phi_1 \leq -8600000000000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot t_2}}\right)\\
\mathbf{elif}\;\phi_1 \leq 1.75 \cdot 10^{-8}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot t_2}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{\left(1 - {\sin \left(\phi_1 \cdot 0.5\right)}^{2}\right) - \cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if phi1 < -8.6e12Initial program 49.9%
Simplified50.0%
Taylor expanded in phi2 around 0 51.5%
associate--r+51.5%
unpow251.5%
1-sub-sin51.5%
unpow251.5%
*-commutative51.5%
*-commutative51.5%
unpow251.5%
associate-*r*51.5%
Simplified51.5%
if -8.6e12 < phi1 < 1.75000000000000012e-8Initial program 79.7%
Simplified79.7%
Taylor expanded in phi1 around 0 79.7%
associate--r+79.7%
unpow279.7%
1-sub-sin79.8%
unpow279.8%
sub-neg79.8%
mul-1-neg79.8%
+-commutative79.8%
distribute-lft-in79.8%
associate-*r*79.8%
metadata-eval79.8%
metadata-eval79.8%
associate-*r*79.8%
distribute-lft-in79.8%
+-commutative79.8%
Simplified79.8%
if 1.75000000000000012e-8 < phi1 Initial program 38.2%
Simplified38.2%
Taylor expanded in phi2 around 0 39.3%
associate--r+39.3%
*-commutative39.3%
Simplified39.3%
Final simplification62.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (cos phi1) (cos phi2)))
(t_2
(fma
t_1
(pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)
(pow (sin (* (- phi1 phi2) 0.5)) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_1 (* t_0 t_0) (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (/ (- 1.0 (pow t_2 3.0)) (+ 1.0 (* t_2 (+ 1.0 t_2))))))))))
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);
double t_2 = fma(t_1, pow(sin(((lambda1 - lambda2) * 0.5)), 2.0), pow(sin(((phi1 - phi2) * 0.5)), 2.0));
return R * (2.0 * atan2(sqrt(fma(t_1, (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt(((1.0 - pow(t_2, 3.0)) / (1.0 + (t_2 * (1.0 + t_2)))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(cos(phi1) * cos(phi2)) t_2 = fma(t_1, (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0), (sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0)) return Float64(R * Float64(2.0 * atan(sqrt(fma(t_1, Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(Float64(1.0 - (t_2 ^ 3.0)) / Float64(1.0 + Float64(t_2 * Float64(1.0 + t_2)))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 * 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[(1.0 - N[Power[t$95$2, 3.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(t$95$2 * N[(1.0 + t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \cos \phi_1 \cdot \cos \phi_2\\
t_2 := \mathsf{fma}\left(t_1, {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}, {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_1, t_0 \cdot t_0, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{\frac{1 - {t_2}^{3}}{1 + t_2 \cdot \left(1 + t_2\right)}}}\right)
\end{array}
\end{array}
Initial program 61.3%
Simplified61.3%
fma-udef61.3%
associate-*l*61.3%
+-commutative61.3%
flip3--61.3%
Applied egg-rr61.3%
Simplified61.3%
Final simplification61.3%
(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 61.3%
Simplified61.3%
Final simplification61.3%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_1 (* (cos phi1) (cos phi2)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (* t_1 t_2)))
(if (<= lambda1 -5.1e-5)
(*
R
(*
2.0
(atan2
(sqrt (+ t_0 (* t_1 (* t_2 t_2))))
(sqrt (- (- 1.0 t_0) (* t_1 (* t_2 (sin (* lambda1 0.5)))))))))
(*
R
(*
2.0
(atan2
(sqrt (+ t_0 (* t_2 t_3)))
(sqrt (- 1.0 (+ t_0 (* t_3 (sin (* lambda2 -0.5))))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = cos(phi1) * cos(phi2);
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = t_1 * t_2;
double tmp;
if (lambda1 <= -5.1e-5) {
tmp = R * (2.0 * atan2(sqrt((t_0 + (t_1 * (t_2 * t_2)))), sqrt(((1.0 - t_0) - (t_1 * (t_2 * sin((lambda1 * 0.5))))))));
} else {
tmp = R * (2.0 * atan2(sqrt((t_0 + (t_2 * t_3))), sqrt((1.0 - (t_0 + (t_3 * sin((lambda2 * -0.5))))))));
}
return tmp;
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: t_3
real(8) :: tmp
t_0 = sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0
t_1 = cos(phi1) * cos(phi2)
t_2 = sin(((lambda1 - lambda2) / 2.0d0))
t_3 = t_1 * t_2
if (lambda1 <= (-5.1d-5)) then
tmp = r * (2.0d0 * atan2(sqrt((t_0 + (t_1 * (t_2 * t_2)))), sqrt(((1.0d0 - t_0) - (t_1 * (t_2 * sin((lambda1 * 0.5d0))))))))
else
tmp = r * (2.0d0 * atan2(sqrt((t_0 + (t_2 * t_3))), sqrt((1.0d0 - (t_0 + (t_3 * sin((lambda2 * (-0.5d0)))))))))
end if
code = tmp
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = Math.cos(phi1) * Math.cos(phi2);
double t_2 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_3 = t_1 * t_2;
double tmp;
if (lambda1 <= -5.1e-5) {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_0 + (t_1 * (t_2 * t_2)))), Math.sqrt(((1.0 - t_0) - (t_1 * (t_2 * Math.sin((lambda1 * 0.5))))))));
} else {
tmp = R * (2.0 * Math.atan2(Math.sqrt((t_0 + (t_2 * t_3))), Math.sqrt((1.0 - (t_0 + (t_3 * Math.sin((lambda2 * -0.5))))))));
}
return tmp;
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) t_1 = math.cos(phi1) * math.cos(phi2) t_2 = math.sin(((lambda1 - lambda2) / 2.0)) t_3 = t_1 * t_2 tmp = 0 if lambda1 <= -5.1e-5: tmp = R * (2.0 * math.atan2(math.sqrt((t_0 + (t_1 * (t_2 * t_2)))), math.sqrt(((1.0 - t_0) - (t_1 * (t_2 * math.sin((lambda1 * 0.5)))))))) else: tmp = R * (2.0 * math.atan2(math.sqrt((t_0 + (t_2 * t_3))), math.sqrt((1.0 - (t_0 + (t_3 * math.sin((lambda2 * -0.5)))))))) return tmp
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_1 = Float64(cos(phi1) * cos(phi2)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = Float64(t_1 * t_2) tmp = 0.0 if (lambda1 <= -5.1e-5) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_0 + Float64(t_1 * Float64(t_2 * t_2)))), sqrt(Float64(Float64(1.0 - t_0) - Float64(t_1 * Float64(t_2 * sin(Float64(lambda1 * 0.5))))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_0 + Float64(t_2 * t_3))), sqrt(Float64(1.0 - Float64(t_0 + Float64(t_3 * sin(Float64(lambda2 * -0.5))))))))); end return tmp end
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((phi1 - phi2) / 2.0)) ^ 2.0; t_1 = cos(phi1) * cos(phi2); t_2 = sin(((lambda1 - lambda2) / 2.0)); t_3 = t_1 * t_2; tmp = 0.0; if (lambda1 <= -5.1e-5) tmp = R * (2.0 * atan2(sqrt((t_0 + (t_1 * (t_2 * t_2)))), sqrt(((1.0 - t_0) - (t_1 * (t_2 * sin((lambda1 * 0.5)))))))); else tmp = R * (2.0 * atan2(sqrt((t_0 + (t_2 * t_3))), sqrt((1.0 - (t_0 + (t_3 * sin((lambda2 * -0.5)))))))); end tmp_2 = tmp; end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $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$1 * t$95$2), $MachinePrecision]}, If[LessEqual[lambda1, -5.1e-5], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 + N[(t$95$1 * N[(t$95$2 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - t$95$0), $MachinePrecision] - N[(t$95$1 * N[(t$95$2 * N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 + N[(t$95$2 * t$95$3), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 + N[(t$95$3 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \cos \phi_1 \cdot \cos \phi_2\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := t_1 \cdot t_2\\
\mathbf{if}\;\lambda_1 \leq -5.1 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_0 + t_1 \cdot \left(t_2 \cdot t_2\right)}}{\sqrt{\left(1 - t_0\right) - t_1 \cdot \left(t_2 \cdot \sin \left(\lambda_1 \cdot 0.5\right)\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_0 + t_2 \cdot t_3}}{\sqrt{1 - \left(t_0 + t_3 \cdot \sin \left(\lambda_2 \cdot -0.5\right)\right)}}\right)\\
\end{array}
\end{array}
if lambda1 < -5.09999999999999996e-5Initial program 49.5%
associate-*l*49.5%
Simplified49.4%
Taylor expanded in lambda2 around 0 46.9%
if -5.09999999999999996e-5 < lambda1 Initial program 65.0%
Taylor expanded in lambda1 around 0 56.3%
Final simplification54.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (* (cos phi1) (cos phi2)) t_1))
(t_3 (sqrt (+ t_0 (* t_1 t_2)))))
(if (<= lambda1 -7.5e-6)
(*
R
(* 2.0 (atan2 t_3 (sqrt (- 1.0 (+ t_0 (* t_2 (sin (* lambda1 0.5)))))))))
(*
R
(*
2.0
(atan2 t_3 (sqrt (- 1.0 (+ t_0 (* t_2 (sin (* lambda2 -0.5))))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = (cos(phi1) * cos(phi2)) * t_1;
double t_3 = sqrt((t_0 + (t_1 * t_2)));
double tmp;
if (lambda1 <= -7.5e-6) {
tmp = R * (2.0 * atan2(t_3, sqrt((1.0 - (t_0 + (t_2 * sin((lambda1 * 0.5))))))));
} else {
tmp = R * (2.0 * atan2(t_3, sqrt((1.0 - (t_0 + (t_2 * sin((lambda2 * -0.5))))))));
}
return tmp;
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: t_3
real(8) :: tmp
t_0 = sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
t_2 = (cos(phi1) * cos(phi2)) * t_1
t_3 = sqrt((t_0 + (t_1 * t_2)))
if (lambda1 <= (-7.5d-6)) then
tmp = r * (2.0d0 * atan2(t_3, sqrt((1.0d0 - (t_0 + (t_2 * sin((lambda1 * 0.5d0))))))))
else
tmp = r * (2.0d0 * atan2(t_3, sqrt((1.0d0 - (t_0 + (t_2 * sin((lambda2 * (-0.5d0)))))))))
end if
code = tmp
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_2 = (Math.cos(phi1) * Math.cos(phi2)) * t_1;
double t_3 = Math.sqrt((t_0 + (t_1 * t_2)));
double tmp;
if (lambda1 <= -7.5e-6) {
tmp = R * (2.0 * Math.atan2(t_3, Math.sqrt((1.0 - (t_0 + (t_2 * Math.sin((lambda1 * 0.5))))))));
} else {
tmp = R * (2.0 * Math.atan2(t_3, Math.sqrt((1.0 - (t_0 + (t_2 * Math.sin((lambda2 * -0.5))))))));
}
return tmp;
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) t_2 = (math.cos(phi1) * math.cos(phi2)) * t_1 t_3 = math.sqrt((t_0 + (t_1 * t_2))) tmp = 0 if lambda1 <= -7.5e-6: tmp = R * (2.0 * math.atan2(t_3, math.sqrt((1.0 - (t_0 + (t_2 * math.sin((lambda1 * 0.5)))))))) else: tmp = R * (2.0 * math.atan2(t_3, math.sqrt((1.0 - (t_0 + (t_2 * math.sin((lambda2 * -0.5)))))))) return tmp
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(Float64(cos(phi1) * cos(phi2)) * t_1) t_3 = sqrt(Float64(t_0 + Float64(t_1 * t_2))) tmp = 0.0 if (lambda1 <= -7.5e-6) tmp = Float64(R * Float64(2.0 * atan(t_3, sqrt(Float64(1.0 - Float64(t_0 + Float64(t_2 * sin(Float64(lambda1 * 0.5))))))))); else tmp = Float64(R * Float64(2.0 * atan(t_3, sqrt(Float64(1.0 - Float64(t_0 + Float64(t_2 * sin(Float64(lambda2 * -0.5))))))))); end return tmp end
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((phi1 - phi2) / 2.0)) ^ 2.0; t_1 = sin(((lambda1 - lambda2) / 2.0)); t_2 = (cos(phi1) * cos(phi2)) * t_1; t_3 = sqrt((t_0 + (t_1 * t_2))); tmp = 0.0; if (lambda1 <= -7.5e-6) tmp = R * (2.0 * atan2(t_3, sqrt((1.0 - (t_0 + (t_2 * sin((lambda1 * 0.5)))))))); else tmp = R * (2.0 * atan2(t_3, sqrt((1.0 - (t_0 + (t_2 * sin((lambda2 * -0.5)))))))); end tmp_2 = tmp; end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision]}, Block[{t$95$3 = N[Sqrt[N[(t$95$0 + N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda1, -7.5e-6], N[(R * N[(2.0 * N[ArcTan[t$95$3 / N[Sqrt[N[(1.0 - N[(t$95$0 + N[(t$95$2 * N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$3 / N[Sqrt[N[(1.0 - N[(t$95$0 + N[(t$95$2 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_1\\
t_3 := \sqrt{t_0 + t_1 \cdot t_2}\\
\mathbf{if}\;\lambda_1 \leq -7.5 \cdot 10^{-6}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_3}{\sqrt{1 - \left(t_0 + t_2 \cdot \sin \left(\lambda_1 \cdot 0.5\right)\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_3}{\sqrt{1 - \left(t_0 + t_2 \cdot \sin \left(\lambda_2 \cdot -0.5\right)\right)}}\right)\\
\end{array}
\end{array}
if lambda1 < -7.50000000000000019e-6Initial program 49.5%
Taylor expanded in lambda2 around 0 46.9%
if -7.50000000000000019e-6 < lambda1 Initial program 65.0%
Taylor expanded in lambda1 around 0 56.3%
Final simplification54.0%
(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 61.3%
associate-*l*61.3%
Simplified61.3%
Final simplification61.3%
(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 61.3%
Final simplification61.3%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (cos phi1) (cos phi2))))
(if (or (<= phi1 -8600000000000.0) (not (<= phi1 1.75e-8)))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- 1.0 (+ t_0 (* t_1 (* t_2 t_1))))))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_2 (* t_1 t_1) t_0))
(sqrt
(-
(pow (cos (* phi2 -0.5)) 2.0)
(* (cos phi2) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = cos(phi1) * cos(phi2);
double tmp;
if ((phi1 <= -8600000000000.0) || !(phi1 <= 1.75e-8)) {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((1.0 - (t_0 + (t_1 * (t_2 * t_1)))))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(t_2, (t_1 * t_1), t_0)), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(cos(phi1) * cos(phi2)) tmp = 0.0 if ((phi1 <= -8600000000000.0) || !(phi1 <= 1.75e-8)) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_0 + Float64(t_1 * Float64(t_2 * t_1)))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_2, Float64(t_1 * t_1), t_0)), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0))))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[phi1, -8600000000000.0], N[Not[LessEqual[phi1, 1.75e-8]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 + N[(t$95$1 * N[(t$95$2 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$2 * N[(t$95$1 * t$95$1), $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \cos \phi_1 \cdot \cos \phi_2\\
\mathbf{if}\;\phi_1 \leq -8600000000000 \lor \neg \left(\phi_1 \leq 1.75 \cdot 10^{-8}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{1 - \left(t_0 + t_1 \cdot \left(t_2 \cdot t_1\right)\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_2, t_1 \cdot t_1, t_0\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}}}\right)\\
\end{array}
\end{array}
if phi1 < -8.6e12 or 1.75000000000000012e-8 < phi1 Initial program 43.5%
Taylor expanded in phi2 around 0 43.6%
associate-+r+43.6%
+-commutative43.6%
*-commutative43.6%
associate-*r*43.6%
associate-*r*43.6%
distribute-lft1-in43.6%
unpow243.6%
Simplified43.6%
Taylor expanded in phi2 around 0 44.2%
*-commutative44.2%
Simplified44.2%
if -8.6e12 < phi1 < 1.75000000000000012e-8Initial program 79.7%
Simplified79.7%
Taylor expanded in phi1 around 0 79.7%
associate--r+79.7%
unpow279.7%
1-sub-sin79.8%
unpow279.8%
sub-neg79.8%
mul-1-neg79.8%
+-commutative79.8%
distribute-lft-in79.8%
associate-*r*79.8%
metadata-eval79.8%
metadata-eval79.8%
associate-*r*79.8%
distribute-lft-in79.8%
+-commutative79.8%
Simplified79.8%
Final simplification61.7%
(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))))
(t_2 (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))
(if (or (<= phi1 -8600000000000.0) (not (<= phi1 1.75e-8)))
(*
R
(*
2.0
(atan2 t_1 (sqrt (- (pow (cos (* phi1 0.5)) 2.0) (* (cos phi1) t_2))))))
(*
R
(*
2.0
(atan2
t_1
(sqrt (- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) t_2)))))))))
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 t_2 = pow(sin((-0.5 * (lambda2 - lambda1))), 2.0);
double tmp;
if ((phi1 <= -8600000000000.0) || !(phi1 <= 1.75e-8)) {
tmp = R * (2.0 * atan2(t_1, sqrt((pow(cos((phi1 * 0.5)), 2.0) - (cos(phi1) * t_2)))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * t_2)))));
}
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))) t_2 = sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0 tmp = 0.0 if ((phi1 <= -8600000000000.0) || !(phi1 <= 1.75e-8)) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64((cos(Float64(phi1 * 0.5)) ^ 2.0) - Float64(cos(phi1) * t_2)))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * t_2)))))); 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]}, Block[{t$95$2 = N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[Or[LessEqual[phi1, -8600000000000.0], N[Not[LessEqual[phi1, 1.75e-8]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[Power[N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi1], $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \sqrt{\mathsf{fma}\left(\cos \phi_1 \cdot \cos \phi_2, t_0 \cdot t_0, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}\\
t_2 := {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}\\
\mathbf{if}\;\phi_1 \leq -8600000000000 \lor \neg \left(\phi_1 \leq 1.75 \cdot 10^{-8}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{{\cos \left(\phi_1 \cdot 0.5\right)}^{2} - \cos \phi_1 \cdot t_2}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot t_2}}\right)\\
\end{array}
\end{array}
if phi1 < -8.6e12 or 1.75000000000000012e-8 < phi1 Initial program 43.5%
Simplified43.5%
Taylor expanded in phi2 around 0 44.8%
associate--r+44.8%
unpow244.8%
1-sub-sin44.8%
unpow244.8%
*-commutative44.8%
*-commutative44.8%
unpow244.8%
associate-*r*44.8%
Simplified44.8%
if -8.6e12 < phi1 < 1.75000000000000012e-8Initial program 79.7%
Simplified79.7%
Taylor expanded in phi1 around 0 79.7%
associate--r+79.7%
unpow279.7%
1-sub-sin79.8%
unpow279.8%
sub-neg79.8%
mul-1-neg79.8%
+-commutative79.8%
distribute-lft-in79.8%
associate-*r*79.8%
metadata-eval79.8%
metadata-eval79.8%
associate-*r*79.8%
distribute-lft-in79.8%
+-commutative79.8%
Simplified79.8%
Final simplification62.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_1 (* (cos phi1) (cos phi2)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0))))
(if (or (<= phi2 -5.5e+21) (not (<= phi2 0.016)))
(*
R
(*
2.0
(atan2
(sqrt (fma t_1 (* t_2 (sin (* lambda2 -0.5))) t_0))
(sqrt
(-
(pow (cos (* phi2 -0.5)) 2.0)
(* (cos phi2) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- 1.0 (+ t_0 (* t_2 (* t_1 t_2)))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = cos(phi1) * cos(phi2);
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double tmp;
if ((phi2 <= -5.5e+21) || !(phi2 <= 0.016)) {
tmp = R * (2.0 * atan2(sqrt(fma(t_1, (t_2 * sin((lambda2 * -0.5))), t_0)), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0))))));
} else {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((1.0 - (t_0 + (t_2 * (t_1 * t_2)))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_1 = Float64(cos(phi1) * cos(phi2)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) tmp = 0.0 if ((phi2 <= -5.5e+21) || !(phi2 <= 0.016)) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_1, Float64(t_2 * sin(Float64(lambda2 * -0.5))), t_0)), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_0 + Float64(t_2 * Float64(t_1 * t_2)))))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $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]}, If[Or[LessEqual[phi2, -5.5e+21], N[Not[LessEqual[phi2, 0.016]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 * N[(t$95$2 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 + N[(t$95$2 * N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \cos \phi_1 \cdot \cos \phi_2\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\mathbf{if}\;\phi_2 \leq -5.5 \cdot 10^{+21} \lor \neg \left(\phi_2 \leq 0.016\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_1, t_2 \cdot \sin \left(\lambda_2 \cdot -0.5\right), t_0\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {\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{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{1 - \left(t_0 + t_2 \cdot \left(t_1 \cdot t_2\right)\right)}}\right)\\
\end{array}
\end{array}
if phi2 < -5.5e21 or 0.016 < phi2 Initial program 49.5%
Simplified49.4%
Taylor expanded in phi1 around 0 50.7%
associate--r+50.7%
unpow250.7%
1-sub-sin50.8%
unpow250.8%
sub-neg50.8%
mul-1-neg50.8%
+-commutative50.8%
distribute-lft-in50.8%
associate-*r*50.8%
metadata-eval50.8%
metadata-eval50.8%
associate-*r*50.8%
distribute-lft-in50.8%
+-commutative50.8%
Simplified50.8%
Taylor expanded in lambda1 around 0 41.6%
if -5.5e21 < phi2 < 0.016Initial program 73.1%
Taylor expanded in phi2 around 0 73.2%
associate-+r+73.2%
+-commutative73.2%
*-commutative73.2%
associate-*r*73.2%
associate-*r*73.2%
distribute-lft1-in73.2%
unpow273.2%
Simplified73.2%
Taylor expanded in phi2 around 0 72.4%
*-commutative72.4%
Simplified72.4%
Final simplification57.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(if (or (<= phi2 -5.5e+21) (not (<= phi2 1.3e+17)))
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 (* t_1 t_1) (pow (sin (* phi2 -0.5)) 2.0)))
(sqrt
(-
(pow (cos (* phi2 -0.5)) 2.0)
(* (cos phi2) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt
(-
1.0
(+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) (* t_1 (* t_0 t_1)))))))))))
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 tmp;
if ((phi2 <= -5.5e+21) || !(phi2 <= 1.3e+17)) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_1 * t_1), pow(sin((phi2 * -0.5)), 2.0))), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0))))));
} else {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((1.0 - (pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (t_1 * (t_0 * t_1)))))));
}
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)) tmp = 0.0 if ((phi2 <= -5.5e+21) || !(phi2 <= 1.3e+17)) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_1 * t_1), (sin(Float64(phi2 * -0.5)) ^ 2.0))), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64(1.0 - Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(t_1 * Float64(t_0 * 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[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, If[Or[LessEqual[phi2, -5.5e+21], N[Not[LessEqual[phi2, 1.3e+17]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision] + N[Power[N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(t$95$1 * N[(t$95$0 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \phi_1 \cdot \cos \phi_2\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\mathbf{if}\;\phi_2 \leq -5.5 \cdot 10^{+21} \lor \neg \left(\phi_2 \leq 1.3 \cdot 10^{+17}\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot t_1, {\sin \left(\phi_2 \cdot -0.5\right)}^{2}\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {\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{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{1 - \left({\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1 \cdot \left(t_0 \cdot t_1\right)\right)}}\right)\\
\end{array}
\end{array}
if phi2 < -5.5e21 or 1.3e17 < phi2 Initial program 50.1%
Simplified50.1%
Taylor expanded in phi1 around 0 51.3%
associate--r+51.3%
unpow251.3%
1-sub-sin51.4%
unpow251.4%
sub-neg51.4%
mul-1-neg51.4%
+-commutative51.4%
distribute-lft-in51.4%
associate-*r*51.4%
metadata-eval51.4%
metadata-eval51.4%
associate-*r*51.4%
distribute-lft-in51.4%
+-commutative51.4%
Simplified51.4%
Taylor expanded in phi1 around 0 51.1%
if -5.5e21 < phi2 < 1.3e17Initial program 72.0%
Taylor expanded in phi2 around 0 72.1%
associate-+r+72.1%
+-commutative72.1%
*-commutative72.1%
associate-*r*72.1%
associate-*r*72.1%
distribute-lft1-in72.1%
unpow272.1%
Simplified72.1%
Taylor expanded in phi2 around 0 71.3%
*-commutative71.3%
Simplified71.3%
Final simplification61.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1
(sqrt
(-
(pow (cos (* phi2 -0.5)) 2.0)
(* (cos phi2) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(if (<= phi2 -2650000000000.0)
(*
R
(* 2.0 (atan2 (sqrt (fma t_0 (* t_2 (sin (* lambda1 0.5))) t_3)) t_1)))
(if (<= phi2 0.0075)
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- 1.0 (+ t_3 (* t_2 (* t_0 t_2))))))))
(*
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((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0))));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double tmp;
if (phi2 <= -2650000000000.0) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_2 * sin((lambda1 * 0.5))), t_3)), t_1));
} else if (phi2 <= 0.0075) {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((1.0 - (t_3 + (t_2 * (t_0 * t_2)))))));
} 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((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)))) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 tmp = 0.0 if (phi2 <= -2650000000000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_2 * sin(Float64(lambda1 * 0.5))), t_3)), t_1))); elseif (phi2 <= 0.0075) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_3 + Float64(t_2 * Float64(t_0 * t_2)))))))); 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[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[phi2, -2650000000000.0], 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], If[LessEqual[phi2, 0.0075], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$3 + N[(t$95$2 * N[(t$95$0 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $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{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {\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 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
\mathbf{if}\;\phi_2 \leq -2650000000000:\\
\;\;\;\;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{elif}\;\phi_2 \leq 0.0075:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{1 - \left(t_3 + t_2 \cdot \left(t_0 \cdot t_2\right)\right)}}\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 phi2 < -2.65e12Initial program 50.8%
Simplified50.7%
Taylor expanded in phi1 around 0 52.0%
associate--r+52.0%
unpow252.0%
1-sub-sin52.1%
unpow252.1%
sub-neg52.1%
mul-1-neg52.1%
+-commutative52.1%
distribute-lft-in52.1%
associate-*r*52.1%
metadata-eval52.1%
metadata-eval52.1%
associate-*r*52.1%
distribute-lft-in52.1%
+-commutative52.1%
Simplified52.1%
Taylor expanded in lambda2 around 0 42.4%
if -2.65e12 < phi2 < 0.0074999999999999997Initial program 74.1%
Taylor expanded in phi2 around 0 74.1%
associate-+r+74.1%
+-commutative74.1%
*-commutative74.1%
associate-*r*74.1%
associate-*r*74.1%
distribute-lft1-in74.1%
unpow274.1%
Simplified74.1%
Taylor expanded in phi2 around 0 73.2%
*-commutative73.2%
Simplified73.2%
if 0.0074999999999999997 < phi2 Initial program 46.9%
Simplified46.9%
Taylor expanded in phi1 around 0 48.0%
associate--r+48.1%
unpow248.1%
1-sub-sin48.1%
unpow248.1%
sub-neg48.1%
mul-1-neg48.1%
+-commutative48.1%
distribute-lft-in48.1%
associate-*r*48.1%
metadata-eval48.1%
metadata-eval48.1%
associate-*r*48.1%
distribute-lft-in48.1%
+-commutative48.1%
Simplified48.1%
Taylor expanded in lambda1 around 0 39.7%
Final simplification56.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_1 (* (cos phi1) (cos phi2)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (sin (* lambda2 -0.5))))
(if (or (<= phi2 -1.05e+56) (not (<= phi2 0.0018)))
(*
R
(*
2.0
(atan2
(sqrt (fma t_1 (* t_2 t_3) t_0))
(sqrt
(- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) (pow t_3 2.0)))))))
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- 1.0 (+ t_0 (* t_2 (* t_1 t_2)))))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = cos(phi1) * cos(phi2);
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = sin((lambda2 * -0.5));
double tmp;
if ((phi2 <= -1.05e+56) || !(phi2 <= 0.0018)) {
tmp = R * (2.0 * atan2(sqrt(fma(t_1, (t_2 * t_3), t_0)), sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(t_3, 2.0))))));
} else {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((1.0 - (t_0 + (t_2 * (t_1 * t_2)))))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_1 = Float64(cos(phi1) * cos(phi2)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = sin(Float64(lambda2 * -0.5)) tmp = 0.0 if ((phi2 <= -1.05e+56) || !(phi2 <= 0.0018)) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_1, Float64(t_2 * t_3), t_0)), sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (t_3 ^ 2.0))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_0 + Float64(t_2 * Float64(t_1 * t_2)))))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $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[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]}, If[Or[LessEqual[phi2, -1.05e+56], N[Not[LessEqual[phi2, 0.0018]], $MachinePrecision]], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 * N[(t$95$2 * t$95$3), $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[t$95$3, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 + N[(t$95$2 * N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \cos \phi_1 \cdot \cos \phi_2\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := \sin \left(\lambda_2 \cdot -0.5\right)\\
\mathbf{if}\;\phi_2 \leq -1.05 \cdot 10^{+56} \lor \neg \left(\phi_2 \leq 0.0018\right):\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_1, t_2 \cdot t_3, t_0\right)}}{\sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {t_3}^{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{1 - \left(t_0 + t_2 \cdot \left(t_1 \cdot t_2\right)\right)}}\right)\\
\end{array}
\end{array}
if phi2 < -1.05000000000000009e56 or 0.0018 < phi2 Initial program 49.5%
Simplified49.5%
Taylor expanded in phi1 around 0 50.8%
associate--r+50.9%
unpow250.9%
1-sub-sin50.9%
unpow250.9%
sub-neg50.9%
mul-1-neg50.9%
+-commutative50.9%
distribute-lft-in50.9%
associate-*r*50.9%
metadata-eval50.9%
metadata-eval50.9%
associate-*r*50.9%
distribute-lft-in50.9%
+-commutative50.9%
Simplified50.9%
Taylor expanded in lambda1 around 0 42.5%
Taylor expanded in lambda1 around 0 42.0%
if -1.05000000000000009e56 < phi2 < 0.0018Initial program 71.6%
Taylor expanded in phi2 around 0 69.6%
associate-+r+69.6%
+-commutative69.6%
*-commutative69.6%
associate-*r*69.6%
associate-*r*69.6%
distribute-lft1-in69.6%
unpow269.6%
Simplified69.6%
Taylor expanded in phi2 around 0 69.0%
*-commutative69.0%
Simplified69.0%
Final simplification56.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (sin (* lambda2 -0.5)))
(t_3
(sqrt (- (pow (cos (* phi2 -0.5)) 2.0) (* (cos phi2) (pow t_2 2.0)))))
(t_4 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(if (<= phi2 -1.05e+56)
(* R (* 2.0 (atan2 (sqrt (fma t_0 (* t_1 t_2) t_4)) t_3)))
(if (<= phi2 510000000000.0)
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- 1.0 (+ t_4 (* t_1 (* t_0 t_1))))))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 (* t_1 t_1) (pow (sin (* phi2 -0.5)) 2.0)))
t_3)))))))
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 = sin((lambda2 * -0.5));
double t_3 = sqrt((pow(cos((phi2 * -0.5)), 2.0) - (cos(phi2) * pow(t_2, 2.0))));
double t_4 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double tmp;
if (phi2 <= -1.05e+56) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_1 * t_2), t_4)), t_3));
} else if (phi2 <= 510000000000.0) {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((1.0 - (t_4 + (t_1 * (t_0 * t_1)))))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_1 * t_1), pow(sin((phi2 * -0.5)), 2.0))), t_3));
}
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 = sin(Float64(lambda2 * -0.5)) t_3 = sqrt(Float64((cos(Float64(phi2 * -0.5)) ^ 2.0) - Float64(cos(phi2) * (t_2 ^ 2.0)))) t_4 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 tmp = 0.0 if (phi2 <= -1.05e+56) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_1 * t_2), t_4)), t_3))); elseif (phi2 <= 510000000000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_4 + Float64(t_1 * Float64(t_0 * t_1)))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_1 * t_1), (sin(Float64(phi2 * -0.5)) ^ 2.0))), t_3))); 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[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Cos[phi2], $MachinePrecision] * N[Power[t$95$2, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$4 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[phi2, -1.05e+56], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * t$95$2), $MachinePrecision] + t$95$4), $MachinePrecision]], $MachinePrecision] / t$95$3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi2, 510000000000.0], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$4 + N[(t$95$1 * N[(t$95$0 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision] + N[Power[N[Sin[N[(phi2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / t$95$3], $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(\lambda_2 \cdot -0.5\right)\\
t_3 := \sqrt{{\cos \left(\phi_2 \cdot -0.5\right)}^{2} - \cos \phi_2 \cdot {t_2}^{2}}\\
t_4 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
\mathbf{if}\;\phi_2 \leq -1.05 \cdot 10^{+56}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot t_2, t_4\right)}}{t_3}\right)\\
\mathbf{elif}\;\phi_2 \leq 510000000000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{1 - \left(t_4 + t_1 \cdot \left(t_0 \cdot t_1\right)\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot t_1, {\sin \left(\phi_2 \cdot -0.5\right)}^{2}\right)}}{t_3}\right)\\
\end{array}
\end{array}
if phi2 < -1.05000000000000009e56Initial program 52.4%
Simplified52.4%
Taylor expanded in phi1 around 0 54.1%
associate--r+54.1%
unpow254.1%
1-sub-sin54.1%
unpow254.1%
sub-neg54.1%
mul-1-neg54.1%
+-commutative54.1%
distribute-lft-in54.1%
associate-*r*54.1%
metadata-eval54.1%
metadata-eval54.1%
associate-*r*54.1%
distribute-lft-in54.1%
+-commutative54.1%
Simplified54.1%
Taylor expanded in lambda1 around 0 45.8%
Taylor expanded in lambda1 around 0 45.0%
if -1.05000000000000009e56 < phi2 < 5.1e11Initial program 70.6%
Taylor expanded in phi2 around 0 68.6%
associate-+r+68.6%
+-commutative68.6%
*-commutative68.6%
associate-*r*68.6%
associate-*r*68.6%
distribute-lft1-in68.6%
unpow268.6%
Simplified68.6%
Taylor expanded in phi2 around 0 68.0%
*-commutative68.0%
Simplified68.0%
if 5.1e11 < phi2 Initial program 48.1%
Simplified48.1%
Taylor expanded in phi1 around 0 49.2%
associate--r+49.3%
unpow249.3%
1-sub-sin49.3%
unpow249.3%
sub-neg49.3%
mul-1-neg49.3%
+-commutative49.3%
distribute-lft-in49.3%
associate-*r*49.3%
metadata-eval49.3%
metadata-eval49.3%
associate-*r*49.3%
distribute-lft-in49.3%
+-commutative49.3%
Simplified49.3%
Taylor expanded in lambda1 around 0 40.5%
Taylor expanded in phi1 around 0 40.4%
Final simplification56.5%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (cos phi1) (cos phi2))))
(if (<= (- lambda1 lambda2) -2000.0)
(*
R
(*
2.0
(atan2
(sqrt
(+
(* (cos phi1) (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(pow (sin (* phi1 0.5)) 2.0)))
(sqrt (- 1.0 (+ t_0 (* t_1 (* t_2 t_1))))))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_2 (* t_1 t_1) t_0))
(sqrt (- 1.0 (pow (sin (* (- phi1 phi2) 0.5)) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = cos(phi1) * cos(phi2);
double tmp;
if ((lambda1 - lambda2) <= -2000.0) {
tmp = R * (2.0 * atan2(sqrt(((cos(phi1) * pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)) + pow(sin((phi1 * 0.5)), 2.0))), sqrt((1.0 - (t_0 + (t_1 * (t_2 * t_1)))))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(t_2, (t_1 * t_1), t_0)), sqrt((1.0 - pow(sin(((phi1 - phi2) * 0.5)), 2.0)))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(cos(phi1) * cos(phi2)) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(cos(phi1) * (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)) + (sin(Float64(phi1 * 0.5)) ^ 2.0))), sqrt(Float64(1.0 - Float64(t_0 + Float64(t_1 * Float64(t_2 * t_1)))))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_2, Float64(t_1 * t_1), t_0)), sqrt(Float64(1.0 - (sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -2000.0], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(N[Cos[phi1], $MachinePrecision] * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 + N[(t$95$1 * N[(t$95$2 * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$2 * N[(t$95$1 * t$95$1), $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \cos \phi_1 \cdot \cos \phi_2\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -2000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\cos \phi_1 \cdot {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} + {\sin \left(\phi_1 \cdot 0.5\right)}^{2}}}{\sqrt{1 - \left(t_0 + t_1 \cdot \left(t_2 \cdot t_1\right)\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_2, t_1 \cdot t_1, t_0\right)}}{\sqrt{1 - {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e3Initial program 55.5%
Taylor expanded in phi2 around 0 45.8%
associate-+r+45.8%
+-commutative45.8%
*-commutative45.8%
associate-*r*45.8%
associate-*r*45.8%
distribute-lft1-in45.8%
unpow245.8%
Simplified45.8%
Taylor expanded in phi2 around 0 46.8%
*-commutative46.8%
Simplified46.8%
if -2e3 < (-.f64 lambda1 lambda2) Initial program 65.0%
Simplified65.0%
Taylor expanded in lambda1 around 0 54.4%
Taylor expanded in lambda2 around 0 41.4%
Final simplification43.5%
(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 lambda2) -2000.0)
(*
R
(*
2.0
(atan2
t_1
(sqrt
(/
(+
1.0
(+ (* (cos lambda2) (cos lambda1)) (* (sin lambda2) (sin lambda1))))
2.0)))))
(*
R
(*
2.0
(atan2 t_1 (sqrt (- 1.0 (pow (sin (* (- phi1 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(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0)));
double tmp;
if ((lambda1 - lambda2) <= -2000.0) {
tmp = R * (2.0 * atan2(t_1, sqrt(((1.0 + ((cos(lambda2) * cos(lambda1)) + (sin(lambda2) * sin(lambda1)))) / 2.0))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt((1.0 - pow(sin(((phi1 - 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(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2000.0) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(Float64(1.0 + Float64(Float64(cos(lambda2) * cos(lambda1)) + Float64(sin(lambda2) * sin(lambda1)))) / 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(1.0 - (sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[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[N[(lambda1 - lambda2), $MachinePrecision], -2000.0], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[(1.0 + N[(N[(N[Cos[lambda2], $MachinePrecision] * N[Cos[lambda1], $MachinePrecision]), $MachinePrecision] + N[(N[Sin[lambda2], $MachinePrecision] * N[Sin[lambda1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \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 - \lambda_2 \leq -2000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{\frac{1 + \left(\cos \lambda_2 \cdot \cos \lambda_1 + \sin \lambda_2 \cdot \sin \lambda_1\right)}{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{1 - {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e3Initial program 55.5%
Simplified55.5%
Taylor expanded in phi1 around 0 45.7%
associate--r+45.7%
unpow245.7%
1-sub-sin45.7%
unpow245.7%
sub-neg45.7%
mul-1-neg45.7%
+-commutative45.7%
distribute-lft-in45.7%
associate-*r*45.7%
metadata-eval45.7%
metadata-eval45.7%
associate-*r*45.7%
distribute-lft-in45.7%
+-commutative45.7%
Simplified45.7%
Taylor expanded in phi2 around 0 36.5%
unpow236.5%
1-sub-sin36.6%
cos-mult36.6%
cos-sum36.6%
cos-236.6%
Applied egg-rr36.6%
+-commutative36.6%
+-inverses36.6%
cos-036.6%
associate-*r*36.6%
metadata-eval36.6%
mul-1-neg36.6%
cos-neg36.6%
Simplified36.6%
cos-diff37.1%
Applied egg-rr37.1%
if -2e3 < (-.f64 lambda1 lambda2) Initial program 65.0%
Simplified65.0%
Taylor expanded in lambda1 around 0 54.4%
Taylor expanded in lambda2 around 0 41.4%
Final simplification39.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* t_1 t_1))
(t_3 (pow (sin (* (- phi1 phi2) 0.5)) 2.0)))
(if (<= (- lambda1 lambda2) -2000.0)
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 t_2 (log (exp t_3))))
(sqrt (/ (+ 1.0 (cos (- lambda2 lambda1))) 2.0)))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 t_2 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 t_3))))))))
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 t_3 = pow(sin(((phi1 - phi2) * 0.5)), 2.0);
double tmp;
if ((lambda1 - lambda2) <= -2000.0) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, t_2, log(exp(t_3)))), sqrt(((1.0 + cos((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((1.0 - t_3))));
}
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) t_3 = sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0 tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, t_2, log(exp(t_3)))), sqrt(Float64(Float64(1.0 + cos(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(Float64(1.0 - t_3))))); 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]}, Block[{t$95$3 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -2000.0], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * t$95$2 + N[Log[N[Exp[t$95$3], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $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[(1.0 - t$95$3), $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\\
t_3 := {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -2000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_2, \log \left(e^{t_3}\right)\right)}}{\sqrt{\frac{1 + \cos \left(\lambda_2 - \lambda_1\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{1 - t_3}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e3Initial program 55.5%
Simplified55.5%
Taylor expanded in phi1 around 0 45.7%
associate--r+45.7%
unpow245.7%
1-sub-sin45.7%
unpow245.7%
sub-neg45.7%
mul-1-neg45.7%
+-commutative45.7%
distribute-lft-in45.7%
associate-*r*45.7%
metadata-eval45.7%
metadata-eval45.7%
associate-*r*45.7%
distribute-lft-in45.7%
+-commutative45.7%
Simplified45.7%
Taylor expanded in phi2 around 0 36.5%
unpow236.5%
1-sub-sin36.6%
cos-mult36.6%
cos-sum36.6%
cos-236.6%
Applied egg-rr36.6%
+-commutative36.6%
+-inverses36.6%
cos-036.6%
associate-*r*36.6%
metadata-eval36.6%
mul-1-neg36.6%
cos-neg36.6%
Simplified36.6%
add-log-exp36.6%
div-inv36.6%
metadata-eval36.6%
Applied egg-rr36.6%
if -2e3 < (-.f64 lambda1 lambda2) Initial program 65.0%
Simplified65.0%
Taylor expanded in lambda1 around 0 54.4%
Taylor expanded in lambda2 around 0 41.4%
Final simplification39.5%
(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 lambda2) -2000.0)
(*
R
(*
2.0
(atan2
t_1
(sqrt (/ (log (exp (+ 1.0 (cos (- lambda2 lambda1))))) 2.0)))))
(*
R
(*
2.0
(atan2 t_1 (sqrt (- 1.0 (pow (sin (* (- phi1 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(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0)));
double tmp;
if ((lambda1 - lambda2) <= -2000.0) {
tmp = R * (2.0 * atan2(t_1, sqrt((log(exp((1.0 + cos((lambda2 - lambda1))))) / 2.0))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt((1.0 - pow(sin(((phi1 - 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(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2000.0) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(log(exp(Float64(1.0 + cos(Float64(lambda2 - lambda1))))) / 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(1.0 - (sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[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[N[(lambda1 - lambda2), $MachinePrecision], -2000.0], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[Log[N[Exp[N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \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 - \lambda_2 \leq -2000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{\frac{\log \left(e^{1 + \cos \left(\lambda_2 - \lambda_1\right)}\right)}{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{1 - {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e3Initial program 55.5%
Simplified55.5%
Taylor expanded in phi1 around 0 45.7%
associate--r+45.7%
unpow245.7%
1-sub-sin45.7%
unpow245.7%
sub-neg45.7%
mul-1-neg45.7%
+-commutative45.7%
distribute-lft-in45.7%
associate-*r*45.7%
metadata-eval45.7%
metadata-eval45.7%
associate-*r*45.7%
distribute-lft-in45.7%
+-commutative45.7%
Simplified45.7%
Taylor expanded in phi2 around 0 36.5%
unpow236.5%
1-sub-sin36.6%
cos-mult36.6%
cos-sum36.6%
cos-236.6%
Applied egg-rr36.6%
+-commutative36.6%
+-inverses36.6%
cos-036.6%
associate-*r*36.6%
metadata-eval36.6%
mul-1-neg36.6%
cos-neg36.6%
Simplified36.6%
add-log-exp36.6%
Applied egg-rr36.6%
if -2e3 < (-.f64 lambda1 lambda2) Initial program 65.0%
Simplified65.0%
Taylor expanded in lambda1 around 0 54.4%
Taylor expanded in lambda2 around 0 41.4%
Final simplification39.5%
(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 lambda2) -2000.0)
(* R (* 2.0 (atan2 t_1 (sqrt (/ (+ 1.0 (cos (- lambda2 lambda1))) 2.0)))))
(*
R
(*
2.0
(atan2 t_1 (sqrt (- 1.0 (pow (sin (* (- phi1 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(fma((cos(phi1) * cos(phi2)), (t_0 * t_0), pow(sin(((phi1 - phi2) / 2.0)), 2.0)));
double tmp;
if ((lambda1 - lambda2) <= -2000.0) {
tmp = R * (2.0 * atan2(t_1, sqrt(((1.0 + cos((lambda2 - lambda1))) / 2.0))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt((1.0 - pow(sin(((phi1 - 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(fma(Float64(cos(phi1) * cos(phi2)), Float64(t_0 * t_0), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2000.0) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(Float64(1.0 + cos(Float64(lambda2 - lambda1))) / 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(1.0 - (sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[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[N[(lambda1 - lambda2), $MachinePrecision], -2000.0], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \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 - \lambda_2 \leq -2000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{\frac{1 + \cos \left(\lambda_2 - \lambda_1\right)}{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{1 - {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e3Initial program 55.5%
Simplified55.5%
Taylor expanded in phi1 around 0 45.7%
associate--r+45.7%
unpow245.7%
1-sub-sin45.7%
unpow245.7%
sub-neg45.7%
mul-1-neg45.7%
+-commutative45.7%
distribute-lft-in45.7%
associate-*r*45.7%
metadata-eval45.7%
metadata-eval45.7%
associate-*r*45.7%
distribute-lft-in45.7%
+-commutative45.7%
Simplified45.7%
Taylor expanded in phi2 around 0 36.5%
unpow236.5%
1-sub-sin36.6%
cos-mult36.6%
cos-sum36.6%
cos-236.6%
Applied egg-rr36.6%
+-commutative36.6%
+-inverses36.6%
cos-036.6%
associate-*r*36.6%
metadata-eval36.6%
mul-1-neg36.6%
cos-neg36.6%
Simplified36.6%
if -2e3 < (-.f64 lambda1 lambda2) Initial program 65.0%
Simplified65.0%
Taylor expanded in lambda1 around 0 54.4%
Taylor expanded in lambda2 around 0 41.4%
Final simplification39.5%
(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)))
(if (<= (- lambda1 lambda2) -2000.0)
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 (* t_1 t_1) t_2))
(sqrt (/ (+ 1.0 (cos (- lambda2 lambda1))) 2.0)))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 (* t_1 (sin (* lambda2 -0.5))) t_2))
(sqrt (- 1.0 (pow (sin (* (- phi1 phi2) 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 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double tmp;
if ((lambda1 - lambda2) <= -2000.0) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_1 * t_1), t_2)), sqrt(((1.0 + cos((lambda2 - lambda1))) / 2.0))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_1 * sin((lambda2 * -0.5))), t_2)), sqrt((1.0 - pow(sin(((phi1 - phi2) * 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 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2000.0) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_1 * t_1), t_2)), sqrt(Float64(Float64(1.0 + cos(Float64(lambda2 - lambda1))) / 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_1 * sin(Float64(lambda2 * -0.5))), t_2)), sqrt(Float64(1.0 - (sin(Float64(Float64(phi1 - phi2) * 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[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -2000.0], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $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\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -2000:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot t_1, t_2\right)}}{\sqrt{\frac{1 + \cos \left(\lambda_2 - \lambda_1\right)}{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot \sin \left(\lambda_2 \cdot -0.5\right), t_2\right)}}{\sqrt{1 - {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2e3Initial program 55.5%
Simplified55.5%
Taylor expanded in phi1 around 0 45.7%
associate--r+45.7%
unpow245.7%
1-sub-sin45.7%
unpow245.7%
sub-neg45.7%
mul-1-neg45.7%
+-commutative45.7%
distribute-lft-in45.7%
associate-*r*45.7%
metadata-eval45.7%
metadata-eval45.7%
associate-*r*45.7%
distribute-lft-in45.7%
+-commutative45.7%
Simplified45.7%
Taylor expanded in phi2 around 0 36.5%
unpow236.5%
1-sub-sin36.6%
cos-mult36.6%
cos-sum36.6%
cos-236.6%
Applied egg-rr36.6%
+-commutative36.6%
+-inverses36.6%
cos-036.6%
associate-*r*36.6%
metadata-eval36.6%
mul-1-neg36.6%
cos-neg36.6%
Simplified36.6%
if -2e3 < (-.f64 lambda1 lambda2) Initial program 65.0%
Simplified65.0%
Taylor expanded in lambda1 around 0 54.4%
Taylor expanded in lambda1 around 0 52.8%
Taylor expanded in lambda2 around 0 39.7%
Final simplification38.5%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi1) (cos phi2)))
(t_1 (sqrt (/ (+ 1.0 (cos (- lambda2 lambda1))) 2.0)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(if (<= lambda2 2.9e+16)
(*
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 + cos((lambda2 - lambda1))) / 2.0));
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double tmp;
if (lambda2 <= 2.9e+16) {
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(Float64(1.0 + cos(Float64(lambda2 - lambda1))) / 2.0)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 tmp = 0.0 if (lambda2 <= 2.9e+16) 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[(N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[lambda2, 2.9e+16], 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{\frac{1 + \cos \left(\lambda_2 - \lambda_1\right)}{2}}\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
\mathbf{if}\;\lambda_2 \leq 2.9 \cdot 10^{+16}:\\
\;\;\;\;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 < 2.9e16Initial program 67.1%
Simplified67.2%
Taylor expanded in phi1 around 0 54.8%
associate--r+54.8%
unpow254.8%
1-sub-sin54.9%
unpow254.9%
sub-neg54.9%
mul-1-neg54.9%
+-commutative54.9%
distribute-lft-in54.9%
associate-*r*54.9%
metadata-eval54.9%
metadata-eval54.9%
associate-*r*54.9%
distribute-lft-in54.9%
+-commutative54.9%
Simplified54.9%
Taylor expanded in phi2 around 0 36.7%
unpow236.7%
1-sub-sin36.7%
cos-mult36.7%
cos-sum36.7%
cos-236.7%
Applied egg-rr36.7%
+-commutative36.7%
+-inverses36.7%
cos-036.7%
associate-*r*36.7%
metadata-eval36.7%
mul-1-neg36.7%
cos-neg36.7%
Simplified36.7%
Taylor expanded in lambda2 around 0 30.6%
if 2.9e16 < lambda2 Initial program 45.2%
Simplified45.2%
Taylor expanded in phi1 around 0 36.3%
associate--r+36.3%
unpow236.3%
1-sub-sin36.3%
unpow236.3%
sub-neg36.3%
mul-1-neg36.3%
+-commutative36.3%
distribute-lft-in36.3%
associate-*r*36.3%
metadata-eval36.3%
metadata-eval36.3%
associate-*r*36.3%
distribute-lft-in36.3%
+-commutative36.3%
Simplified36.3%
Taylor expanded in phi2 around 0 30.5%
unpow230.5%
1-sub-sin30.5%
cos-mult30.5%
cos-sum30.5%
cos-230.5%
Applied egg-rr30.5%
+-commutative30.5%
+-inverses30.5%
cos-030.5%
associate-*r*30.5%
metadata-eval30.5%
mul-1-neg30.5%
cos-neg30.5%
Simplified30.5%
Taylor expanded in lambda1 around 0 28.4%
Final simplification30.0%
(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)))
(if (<= lambda1 -5.5e-67)
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 (* t_1 t_1) t_2))
(sqrt (/ (+ 1.0 (cos lambda1)) 2.0)))))
(*
R
(*
2.0
(atan2
(sqrt (fma t_0 (* t_1 (sin (* lambda2 -0.5))) t_2))
(sqrt (/ (+ 1.0 (cos (- lambda2 lambda1))) 2.0))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi1) * cos(phi2);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double tmp;
if (lambda1 <= -5.5e-67) {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_1 * t_1), t_2)), sqrt(((1.0 + cos(lambda1)) / 2.0))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(t_0, (t_1 * sin((lambda2 * -0.5))), t_2)), sqrt(((1.0 + cos((lambda2 - lambda1))) / 2.0))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi1) * cos(phi2)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 tmp = 0.0 if (lambda1 <= -5.5e-67) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_1 * t_1), t_2)), sqrt(Float64(Float64(1.0 + cos(lambda1)) / 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(t_0, Float64(t_1 * sin(Float64(lambda2 * -0.5))), t_2)), sqrt(Float64(Float64(1.0 + cos(Float64(lambda2 - lambda1))) / 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[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[lambda1, -5.5e-67], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * t$95$1), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 + N[Cos[lambda1], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$0 * N[(t$95$1 * N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $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 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
\mathbf{if}\;\lambda_1 \leq -5.5 \cdot 10^{-67}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot t_1, t_2\right)}}{\sqrt{\frac{1 + \cos \lambda_1}{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(t_0, t_1 \cdot \sin \left(\lambda_2 \cdot -0.5\right), t_2\right)}}{\sqrt{\frac{1 + \cos \left(\lambda_2 - \lambda_1\right)}{2}}}\right)\\
\end{array}
\end{array}
if lambda1 < -5.5000000000000003e-67Initial program 53.2%
Simplified53.2%
Taylor expanded in phi1 around 0 44.3%
associate--r+44.3%
unpow244.3%
1-sub-sin44.3%
unpow244.3%
sub-neg44.3%
mul-1-neg44.3%
+-commutative44.3%
distribute-lft-in44.3%
associate-*r*44.3%
metadata-eval44.3%
metadata-eval44.3%
associate-*r*44.3%
distribute-lft-in44.3%
+-commutative44.3%
Simplified44.3%
Taylor expanded in phi2 around 0 35.9%
unpow235.9%
1-sub-sin35.9%
cos-mult36.0%
cos-sum36.0%
cos-236.0%
Applied egg-rr36.0%
+-commutative36.0%
+-inverses36.0%
cos-036.0%
associate-*r*36.0%
metadata-eval36.0%
mul-1-neg36.0%
cos-neg36.0%
Simplified36.0%
Taylor expanded in lambda2 around 0 33.7%
cos-neg33.7%
Simplified33.7%
if -5.5000000000000003e-67 < lambda1 Initial program 64.3%
Simplified64.3%
Taylor expanded in phi1 around 0 52.0%
associate--r+52.0%
unpow252.0%
1-sub-sin52.0%
unpow252.0%
sub-neg52.0%
mul-1-neg52.0%
+-commutative52.0%
distribute-lft-in52.0%
associate-*r*52.0%
metadata-eval52.0%
metadata-eval52.0%
associate-*r*52.0%
distribute-lft-in52.0%
+-commutative52.0%
Simplified52.0%
Taylor expanded in phi2 around 0 34.7%
unpow234.7%
1-sub-sin34.8%
cos-mult34.8%
cos-sum34.7%
cos-234.8%
Applied egg-rr34.8%
+-commutative34.8%
+-inverses34.8%
cos-034.8%
associate-*r*34.8%
metadata-eval34.8%
mul-1-neg34.8%
cos-neg34.8%
Simplified34.8%
Taylor expanded in lambda1 around 0 28.8%
Final simplification30.2%
(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 -7.4e-5)
(* R (* 2.0 (atan2 t_1 (sqrt (/ (+ 1.0 (cos lambda1)) 2.0)))))
(* R (* 2.0 (atan2 t_1 (sqrt (/ (+ 1.0 (cos lambda2)) 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 <= -7.4e-5) {
tmp = R * (2.0 * atan2(t_1, sqrt(((1.0 + cos(lambda1)) / 2.0))));
} else {
tmp = R * (2.0 * atan2(t_1, sqrt(((1.0 + cos(lambda2)) / 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 <= -7.4e-5) tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(Float64(1.0 + cos(lambda1)) / 2.0))))); else tmp = Float64(R * Float64(2.0 * atan(t_1, sqrt(Float64(Float64(1.0 + cos(lambda2)) / 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, -7.4e-5], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[(1.0 + N[Cos[lambda1], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[t$95$1 / N[Sqrt[N[(N[(1.0 + N[Cos[lambda2], $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 -7.4 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{\frac{1 + \cos \lambda_1}{2}}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{t_1}{\sqrt{\frac{1 + \cos \lambda_2}{2}}}\right)\\
\end{array}
\end{array}
if lambda1 < -7.39999999999999962e-5Initial program 49.5%
Simplified49.5%
Taylor expanded in phi1 around 0 43.7%
associate--r+43.7%
unpow243.7%
1-sub-sin43.7%
unpow243.7%
sub-neg43.7%
mul-1-neg43.7%
+-commutative43.7%
distribute-lft-in43.7%
associate-*r*43.7%
metadata-eval43.7%
metadata-eval43.7%
associate-*r*43.7%
distribute-lft-in43.7%
+-commutative43.7%
Simplified43.7%
Taylor expanded in phi2 around 0 36.4%
unpow236.4%
1-sub-sin36.4%
cos-mult36.4%
cos-sum36.4%
cos-236.4%
Applied egg-rr36.4%
+-commutative36.4%
+-inverses36.4%
cos-036.4%
associate-*r*36.4%
metadata-eval36.4%
mul-1-neg36.4%
cos-neg36.4%
Simplified36.4%
Taylor expanded in lambda2 around 0 35.4%
cos-neg35.4%
Simplified35.4%
if -7.39999999999999962e-5 < lambda1 Initial program 65.0%
Simplified65.0%
Taylor expanded in phi1 around 0 51.8%
associate--r+51.8%
unpow251.8%
1-sub-sin51.9%
unpow251.9%
sub-neg51.9%
mul-1-neg51.9%
+-commutative51.9%
distribute-lft-in51.9%
associate-*r*51.9%
metadata-eval51.9%
metadata-eval51.9%
associate-*r*51.9%
distribute-lft-in51.9%
+-commutative51.9%
Simplified51.9%
Taylor expanded in phi2 around 0 34.6%
unpow234.6%
1-sub-sin34.7%
cos-mult34.7%
cos-sum34.7%
cos-234.7%
Applied egg-rr34.7%
+-commutative34.7%
+-inverses34.7%
cos-034.7%
associate-*r*34.7%
metadata-eval34.7%
mul-1-neg34.7%
cos-neg34.7%
Simplified34.7%
Taylor expanded in lambda1 around 0 30.3%
Final simplification31.5%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* t_0 t_0)
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (/ (+ 1.0 (cos (- 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 + cos((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(Float64(1.0 + cos(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[(N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $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)\\
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{\frac{1 + \cos \left(\lambda_2 - \lambda_1\right)}{2}}}\right)
\end{array}
\end{array}
Initial program 61.3%
Simplified61.3%
Taylor expanded in phi1 around 0 49.9%
associate--r+49.9%
unpow249.9%
1-sub-sin50.0%
unpow250.0%
sub-neg50.0%
mul-1-neg50.0%
+-commutative50.0%
distribute-lft-in50.0%
associate-*r*50.0%
metadata-eval50.0%
metadata-eval50.0%
associate-*r*50.0%
distribute-lft-in50.0%
+-commutative50.0%
Simplified50.0%
Taylor expanded in phi2 around 0 35.0%
unpow235.0%
1-sub-sin35.1%
cos-mult35.1%
cos-sum35.1%
cos-235.1%
Applied egg-rr35.1%
+-commutative35.1%
+-inverses35.1%
cos-035.1%
associate-*r*35.1%
metadata-eval35.1%
mul-1-neg35.1%
cos-neg35.1%
Simplified35.1%
Final simplification35.1%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(* (cos phi1) (cos phi2))
(* (sin (/ (- lambda1 lambda2) 2.0)) (sin (* lambda2 -0.5)))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (/ (+ 1.0 (cos (- 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))), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt(((1.0 + cos((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))), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(Float64(1.0 + cos(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[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 + N[Cos[N[(lambda2 - lambda1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $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), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{\frac{1 + \cos \left(\lambda_2 - \lambda_1\right)}{2}}}\right)
\end{array}
Initial program 61.3%
Simplified61.3%
Taylor expanded in phi1 around 0 49.9%
associate--r+49.9%
unpow249.9%
1-sub-sin50.0%
unpow250.0%
sub-neg50.0%
mul-1-neg50.0%
+-commutative50.0%
distribute-lft-in50.0%
associate-*r*50.0%
metadata-eval50.0%
metadata-eval50.0%
associate-*r*50.0%
distribute-lft-in50.0%
+-commutative50.0%
Simplified50.0%
Taylor expanded in phi2 around 0 35.0%
unpow235.0%
1-sub-sin35.1%
cos-mult35.1%
cos-sum35.1%
cos-235.1%
Applied egg-rr35.1%
+-commutative35.1%
+-inverses35.1%
cos-035.1%
associate-*r*35.1%
metadata-eval35.1%
mul-1-neg35.1%
cos-neg35.1%
Simplified35.1%
Taylor expanded in lambda1 around 0 25.9%
Final simplification25.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
R
(*
2.0
(atan2
(* (* (sin (* phi1 0.5)) 0.125) (* phi2 phi2))
(sqrt
(-
1.0
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* t_0 (* (* (cos phi1) (cos phi2)) t_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(((sin((phi1 * 0.5)) * 0.125) * (phi2 * phi2)), sqrt((1.0 - (pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * ((cos(phi1) * cos(phi2)) * t_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(((sin((phi1 * 0.5d0)) * 0.125d0) * (phi2 * phi2)), sqrt((1.0d0 - ((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + (t_0 * ((cos(phi1) * cos(phi2)) * t_0)))))))
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.sin((phi1 * 0.5)) * 0.125) * (phi2 * phi2)), Math.sqrt((1.0 - (Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * t_0)))))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return R * (2.0 * math.atan2(((math.sin((phi1 * 0.5)) * 0.125) * (phi2 * phi2)), math.sqrt((1.0 - (math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + (t_0 * ((math.cos(phi1) * math.cos(phi2)) * t_0)))))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(R * Float64(2.0 * atan(Float64(Float64(sin(Float64(phi1 * 0.5)) * 0.125) * Float64(phi2 * phi2)), sqrt(Float64(1.0 - Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); tmp = R * (2.0 * atan2(((sin((phi1 * 0.5)) * 0.125) * (phi2 * phi2)), sqrt((1.0 - ((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + (t_0 * ((cos(phi1) * cos(phi2)) * t_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[(N[(N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision] * 0.125), $MachinePrecision] * N[(phi2 * phi2), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(1.0 - 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]), $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{\left(\sin \left(\phi_1 \cdot 0.5\right) \cdot 0.125\right) \cdot \left(\phi_2 \cdot \phi_2\right)}{\sqrt{1 - \left({\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)\right)}}\right)
\end{array}
\end{array}
Initial program 61.3%
Taylor expanded in phi2 around 0 45.5%
associate-+r+45.5%
+-commutative45.5%
*-commutative45.5%
associate-*r*45.5%
associate-*r*45.5%
distribute-lft1-in45.5%
unpow245.5%
Simplified45.5%
Taylor expanded in phi2 around inf 6.8%
*-commutative6.8%
associate-*r*6.8%
*-commutative6.8%
unpow26.8%
Simplified6.8%
Final simplification6.8%
herbie shell --seed 2023238
(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))))))))))