
(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 14 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1
(+
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)
(* (* (* (cos phi1) (cos phi2)) t_0) t_0))))
(* R (* 2.0 (atan2 (sqrt t_1) (sqrt (- 1.0 t_1)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = pow(sin(((phi1 - phi2) / 2.0)), 2.0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0);
return R * (2.0 * atan2(sqrt(t_1), sqrt((1.0 - t_1))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = (sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0)
code = r * (2.0d0 * atan2(sqrt(t_1), sqrt((1.0d0 - t_1))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + (((Math.cos(phi1) * Math.cos(phi2)) * t_0) * t_0);
return R * (2.0 * Math.atan2(Math.sqrt(t_1), Math.sqrt((1.0 - t_1))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + (((math.cos(phi1) * math.cos(phi2)) * t_0) * t_0) return R * (2.0 * math.atan2(math.sqrt(t_1), math.sqrt((1.0 - t_1))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(Float64(Float64(cos(phi1) * cos(phi2)) * t_0) * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(t_1), sqrt(Float64(1.0 - t_1))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = (sin(((phi1 - phi2) / 2.0)) ^ 2.0) + (((cos(phi1) * cos(phi2)) * t_0) * t_0); tmp = R * (2.0 * atan2(sqrt(t_1), sqrt((1.0 - t_1)))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[t$95$1], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$1), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_0\right) \cdot t_0\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1}}{\sqrt{1 - t_1}}\right)
\end{array}
\end{array}
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (cos (* phi1 0.5)))
(t_1 (cos (* phi2 0.5)))
(t_2 (* (cos phi2) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0)))
(t_3 (sin (* phi1 0.5)))
(t_4 (sin (* phi2 0.5))))
(*
(atan2
(sqrt (fma (cos phi1) t_2 (pow (- (* t_1 t_3) (* t_4 t_0)) 2.0)))
(sqrt
(-
1.0
(fma (cos phi1) t_2 (cbrt (pow (fma t_1 t_3 (* t_4 (- t_0))) 6.0))))))
(* 2.0 R))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((phi1 * 0.5));
double t_1 = cos((phi2 * 0.5));
double t_2 = cos(phi2) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0);
double t_3 = sin((phi1 * 0.5));
double t_4 = sin((phi2 * 0.5));
return atan2(sqrt(fma(cos(phi1), t_2, pow(((t_1 * t_3) - (t_4 * t_0)), 2.0))), sqrt((1.0 - fma(cos(phi1), t_2, cbrt(pow(fma(t_1, t_3, (t_4 * -t_0)), 6.0)))))) * (2.0 * R);
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(Float64(phi1 * 0.5)) t_1 = cos(Float64(phi2 * 0.5)) t_2 = Float64(cos(phi2) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)) t_3 = sin(Float64(phi1 * 0.5)) t_4 = sin(Float64(phi2 * 0.5)) return Float64(atan(sqrt(fma(cos(phi1), t_2, (Float64(Float64(t_1 * t_3) - Float64(t_4 * t_0)) ^ 2.0))), sqrt(Float64(1.0 - fma(cos(phi1), t_2, cbrt((fma(t_1, t_3, Float64(t_4 * Float64(-t_0))) ^ 6.0)))))) * Float64(2.0 * R)) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$4 = N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision]}, N[(N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$2 + N[Power[N[(N[(t$95$1 * t$95$3), $MachinePrecision] - N[(t$95$4 * t$95$0), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Cos[phi1], $MachinePrecision] * t$95$2 + N[Power[N[Power[N[(t$95$1 * t$95$3 + N[(t$95$4 * (-t$95$0)), $MachinePrecision]), $MachinePrecision], 6.0], $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * N[(2.0 * R), $MachinePrecision]), $MachinePrecision]]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(\phi_1 \cdot 0.5\right)\\
t_1 := \cos \left(\phi_2 \cdot 0.5\right)\\
t_2 := \cos \phi_2 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}\\
t_3 := \sin \left(\phi_1 \cdot 0.5\right)\\
t_4 := \sin \left(\phi_2 \cdot 0.5\right)\\
\tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_2, {\left(t_1 \cdot t_3 - t_4 \cdot t_0\right)}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(\cos \phi_1, t_2, \sqrt[3]{{\left(\mathsf{fma}\left(t_1, t_3, t_4 \cdot \left(-t_0\right)\right)\right)}^{6}}\right)}} \cdot \left(2 \cdot R\right)
\end{array}
\end{array}
Initial program 68.1%
div-sub68.1%
sin-diff69.0%
Applied egg-rr69.0%
div-sub68.1%
sin-diff69.0%
Applied egg-rr83.4%
Taylor expanded in R around 0 83.4%
Simplified83.4%
*-commutative83.4%
add-cbrt-cube83.4%
pow383.3%
pow-pow83.4%
Applied egg-rr83.4%
Final simplification83.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0
(-
(* (cos (* phi2 0.5)) (sin (* phi1 0.5)))
(* (sin (* phi2 0.5)) (cos (* phi1 0.5)))))
(t_1 (* (cos phi2) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))))
(*
(* 2.0 R)
(atan2
(sqrt (fma (cos phi1) t_1 (pow t_0 2.0)))
(sqrt (- 1.0 (fma (cos phi1) t_1 (cbrt (pow t_0 6.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = (cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)));
double t_1 = cos(phi2) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0);
return (2.0 * R) * atan2(sqrt(fma(cos(phi1), t_1, pow(t_0, 2.0))), sqrt((1.0 - fma(cos(phi1), t_1, cbrt(pow(t_0, 6.0))))));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5)))) t_1 = Float64(cos(phi2) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)) return Float64(Float64(2.0 * R) * atan(sqrt(fma(cos(phi1), t_1, (t_0 ^ 2.0))), sqrt(Float64(1.0 - fma(cos(phi1), t_1, cbrt((t_0 ^ 6.0))))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]}, N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[Power[t$95$0, 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Cos[phi1], $MachinePrecision] * t$95$1 + N[Power[N[Power[t$95$0, 6.0], $MachinePrecision], 1/3], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\\
t_1 := \cos \phi_2 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}\\
\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, t_1, {t_0}^{2}\right)}}{\sqrt{1 - \mathsf{fma}\left(\cos \phi_1, t_1, \sqrt[3]{{t_0}^{6}}\right)}}
\end{array}
\end{array}
Initial program 68.1%
div-sub68.1%
sin-diff69.0%
Applied egg-rr69.0%
div-sub68.1%
sin-diff69.0%
Applied egg-rr83.4%
Taylor expanded in R around 0 83.4%
Simplified83.4%
*-commutative83.4%
add-cbrt-cube83.4%
pow383.3%
pow-pow83.4%
Applied egg-rr83.4%
fma-udef83.4%
*-commutative83.4%
*-commutative83.4%
distribute-lft-neg-in83.4%
*-commutative83.4%
*-commutative83.4%
cancel-sign-sub-inv83.4%
Simplified83.4%
Final simplification83.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0
(fma
(cos phi1)
(* (cos phi2) (pow (sin (* -0.5 (- lambda2 lambda1))) 2.0))
(pow
(-
(* (cos (* phi2 0.5)) (sin (* phi1 0.5)))
(* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
2.0))))
(* (* 2.0 R) (atan2 (sqrt t_0) (sqrt (- 1.0 t_0))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = fma(cos(phi1), (cos(phi2) * pow(sin((-0.5 * (lambda2 - lambda1))), 2.0)), pow(((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))), 2.0));
return (2.0 * R) * atan2(sqrt(t_0), sqrt((1.0 - t_0)));
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = fma(cos(phi1), Float64(cos(phi2) * (sin(Float64(-0.5 * Float64(lambda2 - lambda1))) ^ 2.0)), (Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5)))) ^ 2.0)) return Float64(Float64(2.0 * R) * atan(sqrt(t_0), sqrt(Float64(1.0 - t_0)))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * N[(lambda2 - lambda1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]}, N[(N[(2.0 * R), $MachinePrecision] * N[ArcTan[N[Sqrt[t$95$0], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot {\sin \left(-0.5 \cdot \left(\lambda_2 - \lambda_1\right)\right)}^{2}, {\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\right)}^{2}\right)\\
\left(2 \cdot R\right) \cdot \tan^{-1}_* \frac{\sqrt{t_0}}{\sqrt{1 - t_0}}
\end{array}
\end{array}
Initial program 68.1%
div-sub68.1%
sin-diff69.0%
Applied egg-rr69.0%
div-sub68.1%
sin-diff69.0%
Applied egg-rr83.4%
Taylor expanded in R around 0 83.4%
Simplified83.4%
Final simplification83.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0
(+
(pow
(-
(* (cos (* phi2 0.5)) (sin (* phi1 0.5)))
(* (sin (* phi2 0.5)) (cos (* phi1 0.5))))
2.0)
(*
(cos phi1)
(* (cos phi2) (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0))))))
(* 2.0 (* R (atan2 (sqrt t_0) (sqrt (- 1.0 t_0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))), 2.0) + (cos(phi1) * (cos(phi2) * pow(sin((0.5 * (lambda1 - lambda2))), 2.0)));
return 2.0 * (R * atan2(sqrt(t_0), sqrt((1.0 - 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 = (((cos((phi2 * 0.5d0)) * sin((phi1 * 0.5d0))) - (sin((phi2 * 0.5d0)) * cos((phi1 * 0.5d0)))) ** 2.0d0) + (cos(phi1) * (cos(phi2) * (sin((0.5d0 * (lambda1 - lambda2))) ** 2.0d0)))
code = 2.0d0 * (r * atan2(sqrt(t_0), sqrt((1.0d0 - t_0))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.pow(((Math.cos((phi2 * 0.5)) * Math.sin((phi1 * 0.5))) - (Math.sin((phi2 * 0.5)) * Math.cos((phi1 * 0.5)))), 2.0) + (Math.cos(phi1) * (Math.cos(phi2) * Math.pow(Math.sin((0.5 * (lambda1 - lambda2))), 2.0)));
return 2.0 * (R * Math.atan2(Math.sqrt(t_0), Math.sqrt((1.0 - t_0))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.pow(((math.cos((phi2 * 0.5)) * math.sin((phi1 * 0.5))) - (math.sin((phi2 * 0.5)) * math.cos((phi1 * 0.5)))), 2.0) + (math.cos(phi1) * (math.cos(phi2) * math.pow(math.sin((0.5 * (lambda1 - lambda2))), 2.0))) return 2.0 * (R * math.atan2(math.sqrt(t_0), math.sqrt((1.0 - t_0))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(phi1 * 0.5))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(phi1 * 0.5)))) ^ 2.0) + Float64(cos(phi1) * Float64(cos(phi2) * (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0)))) return Float64(2.0 * Float64(R * atan(sqrt(t_0), sqrt(Float64(1.0 - t_0))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = (((cos((phi2 * 0.5)) * sin((phi1 * 0.5))) - (sin((phi2 * 0.5)) * cos((phi1 * 0.5)))) ^ 2.0) + (cos(phi1) * (cos(phi2) * (sin((0.5 * (lambda1 - lambda2))) ^ 2.0))); tmp = 2.0 * (R * atan2(sqrt(t_0), sqrt((1.0 - t_0)))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi1 * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(2.0 * N[(R * N[ArcTan[N[Sqrt[t$95$0], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(\phi_1 \cdot 0.5\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(\phi_1 \cdot 0.5\right)\right)}^{2} + \cos \phi_1 \cdot \left(\cos \phi_2 \cdot {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}\right)\\
2 \cdot \left(R \cdot \tan^{-1}_* \frac{\sqrt{t_0}}{\sqrt{1 - t_0}}\right)
\end{array}
\end{array}
Initial program 68.1%
div-sub68.1%
sin-diff69.0%
Applied egg-rr69.0%
div-sub68.1%
sin-diff69.0%
Applied egg-rr83.4%
Taylor expanded in R around 0 83.4%
Final simplification83.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* (* (cos phi1) (cos phi2)) t_0))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_1))
(sqrt
(-
1.0
(+
t_1
(pow
(-
(* (sin (/ phi1 2.0)) (cos (/ phi2 2.0)))
(* (cos (/ phi1 2.0)) (sin (/ phi2 2.0))))
2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0);
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), sqrt((1.0 - (t_1 + pow(((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (cos((phi1 / 2.0)) * sin((phi2 / 2.0)))), 2.0))))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0)
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_1)), sqrt((1.0d0 - (t_1 + (((sin((phi1 / 2.0d0)) * cos((phi2 / 2.0d0))) - (cos((phi1 / 2.0d0)) * sin((phi2 / 2.0d0)))) ** 2.0d0))))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * t_0);
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), Math.sqrt((1.0 - (t_1 + Math.pow(((Math.sin((phi1 / 2.0)) * Math.cos((phi2 / 2.0))) - (Math.cos((phi1 / 2.0)) * Math.sin((phi2 / 2.0)))), 2.0))))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = t_0 * ((math.cos(phi1) * math.cos(phi2)) * t_0) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), math.sqrt((1.0 - (t_1 + math.pow(((math.sin((phi1 / 2.0)) * math.cos((phi2 / 2.0))) - (math.cos((phi1 / 2.0)) * math.sin((phi2 / 2.0)))), 2.0))))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(Float64(1.0 - Float64(t_1 + (Float64(Float64(sin(Float64(phi1 / 2.0)) * cos(Float64(phi2 / 2.0))) - Float64(cos(Float64(phi1 / 2.0)) * sin(Float64(phi2 / 2.0)))) ^ 2.0))))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt((1.0 - (t_1 + (((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (cos((phi1 / 2.0)) * sin((phi2 / 2.0)))) ^ 2.0)))))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$1 + N[Power[N[(N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := 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{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1}}{\sqrt{1 - \left(t_1 + {\left(\sin \left(\frac{\phi_1}{2}\right) \cdot \cos \left(\frac{\phi_2}{2}\right) - \cos \left(\frac{\phi_1}{2}\right) \cdot \sin \left(\frac{\phi_2}{2}\right)\right)}^{2}\right)}}\right)
\end{array}
\end{array}
Initial program 68.1%
div-sub68.1%
sin-diff69.0%
Applied egg-rr69.0%
Final simplification69.0%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* (* (cos phi1) (cos phi2)) t_0))))
(*
R
(*
2.0
(atan2
(sqrt
(+
t_1
(pow
(-
(* (sin (/ phi1 2.0)) (cos (/ phi2 2.0)))
(* (cos (/ phi1 2.0)) (sin (/ phi2 2.0))))
2.0)))
(sqrt (+ (- (- (/ (cos (- phi1 phi2)) 2.0) 0.5) t_1) 1.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0);
return R * (2.0 * atan2(sqrt((t_1 + pow(((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (cos((phi1 / 2.0)) * sin((phi2 / 2.0)))), 2.0))), sqrt(((((cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0)
code = r * (2.0d0 * atan2(sqrt((t_1 + (((sin((phi1 / 2.0d0)) * cos((phi2 / 2.0d0))) - (cos((phi1 / 2.0d0)) * sin((phi2 / 2.0d0)))) ** 2.0d0))), sqrt(((((cos((phi1 - phi2)) / 2.0d0) - 0.5d0) - t_1) + 1.0d0))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((Math.cos(phi1) * Math.cos(phi2)) * t_0);
return R * (2.0 * Math.atan2(Math.sqrt((t_1 + Math.pow(((Math.sin((phi1 / 2.0)) * Math.cos((phi2 / 2.0))) - (Math.cos((phi1 / 2.0)) * Math.sin((phi2 / 2.0)))), 2.0))), Math.sqrt(((((Math.cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = t_0 * ((math.cos(phi1) * math.cos(phi2)) * t_0) return R * (2.0 * math.atan2(math.sqrt((t_1 + math.pow(((math.sin((phi1 / 2.0)) * math.cos((phi2 / 2.0))) - (math.cos((phi1 / 2.0)) * math.sin((phi2 / 2.0)))), 2.0))), math.sqrt(((((math.cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)) return Float64(R * Float64(2.0 * atan(sqrt(Float64(t_1 + (Float64(Float64(sin(Float64(phi1 / 2.0)) * cos(Float64(phi2 / 2.0))) - Float64(cos(Float64(phi1 / 2.0)) * sin(Float64(phi2 / 2.0)))) ^ 2.0))), sqrt(Float64(Float64(Float64(Float64(cos(Float64(phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0); tmp = R * (2.0 * atan2(sqrt((t_1 + (((sin((phi1 / 2.0)) * cos((phi2 / 2.0))) - (cos((phi1 / 2.0)) * sin((phi2 / 2.0)))) ^ 2.0))), sqrt(((((cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0)))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 + N[Power[N[(N[(N[Sin[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Cos[N[(phi1 / 2.0), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(phi2 / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(N[(N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision] - 0.5), $MachinePrecision] - t$95$1), $MachinePrecision] + 1.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := 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 + {\left(\sin \left(\frac{\phi_1}{2}\right) \cdot \cos \left(\frac{\phi_2}{2}\right) - \cos \left(\frac{\phi_1}{2}\right) \cdot \sin \left(\frac{\phi_2}{2}\right)\right)}^{2}}}{\sqrt{\left(\left(\frac{\cos \left(\phi_1 - \phi_2\right)}{2} - 0.5\right) - t_1\right) + 1}}\right)
\end{array}
\end{array}
Initial program 68.1%
div-sub68.1%
sin-diff69.0%
Applied egg-rr69.0%
div-sub68.1%
sin-diff69.0%
Applied egg-rr83.4%
sin-diff68.8%
div-sub68.8%
unpow268.8%
sin-mult68.9%
Applied egg-rr68.9%
div-sub68.9%
+-inverses68.9%
cos-068.9%
metadata-eval68.9%
distribute-lft-out68.9%
metadata-eval68.9%
*-rgt-identity68.9%
Simplified68.9%
Final simplification68.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* (cos phi1) (* (cos phi2) t_0)))))
(*
R
(*
2.0
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_1))
(sqrt (+ (- (- (/ (cos (- phi1 phi2)) 2.0) 0.5) t_1) 1.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * (cos(phi1) * (cos(phi2) * t_0));
return R * (2.0 * atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), sqrt(((((cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))));
}
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = t_0 * (cos(phi1) * (cos(phi2) * t_0))
code = r * (2.0d0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_1)), sqrt(((((cos((phi1 - phi2)) / 2.0d0) - 0.5d0) - t_1) + 1.0d0))))
end function
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * (Math.cos(phi1) * (Math.cos(phi2) * t_0));
return R * (2.0 * Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), Math.sqrt(((((Math.cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))));
}
def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = t_0 * (math.cos(phi1) * (math.cos(phi2) * t_0)) return R * (2.0 * math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_1)), math.sqrt(((((math.cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))))
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(t_0 * Float64(cos(phi1) * Float64(cos(phi2) * t_0))) return Float64(R * Float64(2.0 * atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(Float64(Float64(Float64(Float64(cos(Float64(phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0))))) end
function tmp = code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(((lambda1 - lambda2) / 2.0)); t_1 = t_0 * (cos(phi1) * (cos(phi2) * t_0)); tmp = R * (2.0 * atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_1)), sqrt(((((cos((phi1 - phi2)) / 2.0) - 0.5) - t_1) + 1.0)))); end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(N[(N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision] - 0.5), $MachinePrecision] - t$95$1), $MachinePrecision] + 1.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := t_0 \cdot \left(\cos \phi_1 \cdot \left(\cos \phi_2 \cdot t_0\right)\right)\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t_1}}{\sqrt{\left(\left(\frac{\cos \left(\phi_1 - \phi_2\right)}{2} - 0.5\right) - t_1\right) + 1}}\right)
\end{array}
\end{array}
Initial program 68.1%
Simplified68.1%
unpow268.1%
sin-mult65.9%
div-inv65.9%
metadata-eval65.9%
div-inv65.9%
metadata-eval65.9%
div-inv65.9%
metadata-eval65.9%
div-inv65.9%
metadata-eval65.9%
Applied egg-rr68.2%
div-sub65.9%
+-inverses65.9%
cos-065.9%
metadata-eval65.9%
distribute-lft-out65.9%
metadata-eval65.9%
*-rgt-identity65.9%
Simplified68.2%
Final simplification68.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* t_0 (* (* (cos phi1) (cos phi2)) t_0)))
(t_2 (/ (cos (- phi1 phi2)) 2.0)))
(if (<= (- lambda1 lambda2) -2e-5)
(*
R
(*
2.0
(atan2 (sqrt (+ t_1 (- 0.5 t_2))) (sqrt (+ (- (- t_2 0.5) t_1) 1.0)))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = t_0 * ((cos(phi1) * cos(phi2)) * t_0);
double t_2 = cos((phi1 - phi2)) / 2.0;
double tmp;
if ((lambda1 - lambda2) <= -2e-5) {
tmp = R * (2.0 * atan2(sqrt((t_1 + (0.5 - t_2))), sqrt((((t_2 - 0.5) - t_1) + 1.0))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * pow(sin((0.5 * (lambda1 - lambda2))), 2.0)), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(t_0 * Float64(Float64(cos(phi1) * cos(phi2)) * t_0)) t_2 = Float64(cos(Float64(phi1 - phi2)) / 2.0) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -2e-5) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(t_1 + Float64(0.5 - t_2))), sqrt(Float64(Float64(Float64(t_2 - 0.5) - t_1) + 1.0))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0)), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -2e-5], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(t$95$1 + N[(0.5 - t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(N[(t$95$2 - 0.5), $MachinePrecision] - t$95$1), $MachinePrecision] + 1.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := t_0 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_0\right)\\
t_2 := \frac{\cos \left(\phi_1 - \phi_2\right)}{2}\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -2 \cdot 10^{-5}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{t_1 + \left(0.5 - t_2\right)}}{\sqrt{\left(\left(t_2 - 0.5\right) - t_1\right) + 1}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -2.00000000000000016e-5Initial program 61.2%
unpow261.2%
sin-mult61.4%
div-inv61.4%
metadata-eval61.4%
div-inv61.4%
metadata-eval61.4%
div-inv61.4%
metadata-eval61.4%
div-inv61.4%
metadata-eval61.4%
Applied egg-rr61.4%
div-sub61.4%
+-inverses61.4%
cos-061.4%
metadata-eval61.4%
distribute-lft-out61.4%
metadata-eval61.4%
*-rgt-identity61.4%
Simplified61.4%
unpow261.2%
sin-mult61.4%
div-inv61.4%
metadata-eval61.4%
div-inv61.4%
metadata-eval61.4%
div-inv61.4%
metadata-eval61.4%
div-inv61.4%
metadata-eval61.4%
Applied egg-rr61.4%
div-sub61.4%
+-inverses61.4%
cos-061.4%
metadata-eval61.4%
distribute-lft-out61.4%
metadata-eval61.4%
*-rgt-identity61.4%
Simplified61.4%
if -2.00000000000000016e-5 < (-.f64 lambda1 lambda2) Initial program 71.5%
Simplified71.5%
Taylor expanded in lambda1 around 0 57.6%
Taylor expanded in lambda2 around 0 44.6%
pow244.6%
div-inv44.6%
metadata-eval44.6%
Applied egg-rr44.6%
Final simplification50.2%
(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 (* t_1 (* (* (cos phi1) (cos phi2)) t_1))))
(if (<= (- lambda1 lambda2) -5e+29)
(*
R
(*
2.0
(atan2
(sqrt (+ (* phi2 (* phi1 -0.5)) t_2))
(sqrt (- 1.0 (+ t_0 t_2))))))
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0))
t_0))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((phi1 - phi2) / 2.0)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = t_1 * ((cos(phi1) * cos(phi2)) * t_1);
double tmp;
if ((lambda1 - lambda2) <= -5e+29) {
tmp = R * (2.0 * atan2(sqrt(((phi2 * (phi1 * -0.5)) + t_2)), sqrt((1.0 - (t_0 + t_2)))));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * pow(sin((0.5 * (lambda1 - lambda2))), 2.0)), t_0)), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 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(t_1 * Float64(Float64(cos(phi1) * cos(phi2)) * t_1)) tmp = 0.0 if (Float64(lambda1 - lambda2) <= -5e+29) tmp = Float64(R * Float64(2.0 * atan(sqrt(Float64(Float64(phi2 * Float64(phi1 * -0.5)) + t_2)), sqrt(Float64(1.0 - Float64(t_0 + t_2)))))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0)), t_0)), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))); end return tmp end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[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[(t$95$1 * N[(N[(N[Cos[phi1], $MachinePrecision] * N[Cos[phi2], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(lambda1 - lambda2), $MachinePrecision], -5e+29], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[(phi2 * N[(phi1 * -0.5), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$0 + t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := t_1 \cdot \left(\left(\cos \phi_1 \cdot \cos \phi_2\right) \cdot t_1\right)\\
\mathbf{if}\;\lambda_1 - \lambda_2 \leq -5 \cdot 10^{+29}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\phi_2 \cdot \left(\phi_1 \cdot -0.5\right) + t_2}}{\sqrt{1 - \left(t_0 + t_2\right)}}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}, t_0\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)\\
\end{array}
\end{array}
if (-.f64 lambda1 lambda2) < -5.0000000000000001e29Initial program 59.7%
unpow259.7%
sin-mult59.9%
div-inv59.9%
metadata-eval59.9%
div-inv59.9%
metadata-eval59.9%
div-inv59.9%
metadata-eval59.9%
div-inv59.9%
metadata-eval59.9%
Applied egg-rr59.9%
div-sub59.9%
+-inverses59.9%
cos-059.9%
metadata-eval59.9%
distribute-lft-out59.9%
metadata-eval59.9%
*-rgt-identity59.9%
Simplified59.9%
Taylor expanded in phi2 around 0 43.6%
Taylor expanded in phi1 around 0 24.1%
*-commutative24.1%
*-commutative24.1%
associate-*l*24.1%
Simplified24.1%
if -5.0000000000000001e29 < (-.f64 lambda1 lambda2) Initial program 71.9%
Simplified71.9%
Taylor expanded in lambda1 around 0 57.9%
Taylor expanded in lambda2 around 0 43.9%
pow243.9%
div-inv43.9%
metadata-eval43.9%
Applied egg-rr43.9%
Final simplification37.7%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (/ (- phi1 phi2) 2.0)) 2.0))
(t_1 (sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))
(if (<= lambda2 4.5e-147)
(*
R
(*
2.0
(atan2
(sqrt
(fma (cos phi1) (* (cos phi2) (pow (sin (* lambda1 0.5)) 2.0)) t_0))
t_1)))
(*
R
(*
2.0
(atan2
(sqrt
(fma (cos phi1) (* (cos phi2) (pow (sin (* -0.5 lambda2)) 2.0)) t_0))
t_1))))))
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 = sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)));
double tmp;
if (lambda2 <= 4.5e-147) {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * pow(sin((lambda1 * 0.5)), 2.0)), t_0)), t_1));
} else {
tmp = R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * pow(sin((-0.5 * lambda2)), 2.0)), t_0)), t_1));
}
return tmp;
}
function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0 t_1 = sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0))) tmp = 0.0 if (lambda2 <= 4.5e-147) tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * (sin(Float64(lambda1 * 0.5)) ^ 2.0)), t_0)), t_1))); else tmp = Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * (sin(Float64(-0.5 * lambda2)) ^ 2.0)), t_0)), t_1))); 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[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda2, 4.5e-147], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(-0.5 * lambda2), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision] / t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\\
t_1 := \sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}\\
\mathbf{if}\;\lambda_2 \leq 4.5 \cdot 10^{-147}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot {\sin \left(\lambda_1 \cdot 0.5\right)}^{2}, t_0\right)}}{t_1}\right)\\
\mathbf{else}:\\
\;\;\;\;R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot {\sin \left(-0.5 \cdot \lambda_2\right)}^{2}, t_0\right)}}{t_1}\right)\\
\end{array}
\end{array}
if lambda2 < 4.49999999999999973e-147Initial program 68.5%
Simplified68.5%
Taylor expanded in lambda1 around 0 51.4%
Taylor expanded in lambda2 around 0 38.1%
Taylor expanded in lambda2 around 0 35.5%
if 4.49999999999999973e-147 < lambda2 Initial program 67.2%
Simplified67.2%
Taylor expanded in lambda1 around 0 54.6%
Taylor expanded in lambda2 around 0 34.8%
Taylor expanded in lambda1 around 0 32.2%
*-commutative32.2%
Simplified32.2%
Final simplification34.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (pow (sin (* 0.5 (- lambda1 lambda2))) 2.0))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * pow(sin((0.5 * (lambda1 - lambda2))), 2.0)), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * (sin(Float64(0.5 * Float64(lambda1 - lambda2))) ^ 2.0)), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(0.5 * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot {\sin \left(0.5 \cdot \left(\lambda_1 - \lambda_2\right)\right)}^{2}, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 68.1%
Simplified68.1%
Taylor expanded in lambda1 around 0 52.5%
Taylor expanded in lambda2 around 0 36.9%
pow236.9%
div-inv36.9%
metadata-eval36.9%
Applied egg-rr36.9%
Final simplification36.9%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (pow (sin (* lambda1 0.5)) 2.0))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * pow(sin((lambda1 * 0.5)), 2.0)), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * (sin(Float64(lambda1 * 0.5)) ^ 2.0)), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[Power[N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot {\sin \left(\lambda_1 \cdot 0.5\right)}^{2}, {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 68.1%
Simplified68.1%
Taylor expanded in lambda1 around 0 52.5%
Taylor expanded in lambda2 around 0 36.9%
Taylor expanded in lambda2 around 0 33.2%
Final simplification33.2%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (- 0.5 (/ (cos (- lambda1 lambda2)) 2.0)))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (0.5 - (cos((lambda1 - lambda2)) / 2.0))), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(0.5 - Float64(cos(Float64(lambda1 - lambda2)) / 2.0))), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(0.5 - N[(N[Cos[N[(lambda1 - lambda2), $MachinePrecision]], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(0.5 - \frac{\cos \left(\lambda_1 - \lambda_2\right)}{2}\right), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 68.1%
Simplified68.1%
Taylor expanded in lambda1 around 0 52.5%
Taylor expanded in lambda2 around 0 36.9%
sin-mult33.4%
div-inv33.4%
metadata-eval33.4%
div-inv33.4%
metadata-eval33.4%
cos-sum33.4%
cos-233.4%
div-inv33.4%
metadata-eval33.4%
Applied egg-rr33.4%
div-sub33.4%
+-inverses33.4%
+-inverses33.4%
+-inverses33.4%
cos-033.4%
metadata-eval33.4%
*-commutative33.4%
associate-*l*33.4%
metadata-eval33.4%
Simplified33.4%
Final simplification33.4%
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(*
R
(*
2.0
(atan2
(sqrt
(fma
(cos phi1)
(* (cos phi2) (- 0.5 (/ (cos lambda1) 2.0)))
(pow (sin (/ (- phi1 phi2) 2.0)) 2.0)))
(sqrt (- 1.0 (pow (sin (* 0.5 (- phi2 phi1))) 2.0)))))))
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
return R * (2.0 * atan2(sqrt(fma(cos(phi1), (cos(phi2) * (0.5 - (cos(lambda1) / 2.0))), pow(sin(((phi1 - phi2) / 2.0)), 2.0))), sqrt((1.0 - pow(sin((0.5 * (phi2 - phi1))), 2.0)))));
}
function code(R, lambda1, lambda2, phi1, phi2) return Float64(R * Float64(2.0 * atan(sqrt(fma(cos(phi1), Float64(cos(phi2) * Float64(0.5 - Float64(cos(lambda1) / 2.0))), (sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0))), sqrt(Float64(1.0 - (sin(Float64(0.5 * Float64(phi2 - phi1))) ^ 2.0)))))) end
code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * N[(2.0 * N[ArcTan[N[Sqrt[N[(N[Cos[phi1], $MachinePrecision] * N[(N[Cos[phi2], $MachinePrecision] * N[(0.5 - N[(N[Cos[lambda1], $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(0.5 * N[(phi2 - phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
R \cdot \left(2 \cdot \tan^{-1}_* \frac{\sqrt{\mathsf{fma}\left(\cos \phi_1, \cos \phi_2 \cdot \left(0.5 - \frac{\cos \lambda_1}{2}\right), {\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2}\right)}}{\sqrt{1 - {\sin \left(0.5 \cdot \left(\phi_2 - \phi_1\right)\right)}^{2}}}\right)
\end{array}
Initial program 68.1%
Simplified68.1%
Taylor expanded in lambda1 around 0 52.5%
Taylor expanded in lambda2 around 0 36.9%
sin-mult33.4%
div-inv33.4%
metadata-eval33.4%
div-inv33.4%
metadata-eval33.4%
cos-sum33.4%
cos-233.4%
div-inv33.4%
metadata-eval33.4%
Applied egg-rr33.4%
div-sub33.4%
+-inverses33.4%
+-inverses33.4%
+-inverses33.4%
cos-033.4%
metadata-eval33.4%
*-commutative33.4%
associate-*l*33.4%
metadata-eval33.4%
Simplified33.4%
Taylor expanded in lambda2 around 0 32.3%
Final simplification32.3%
herbie shell --seed 2023333
(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))))))))))