
(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 22 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}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (cos (* -0.5 phi1)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (sin (* -0.5 phi2)))
(t_3 (cos (* -0.5 phi2)))
(t_4 (* (* (* (cos phi2) (cos phi1)) t_1) t_1))
(t_5 (sin (* 0.5 phi1))))
(*
(*
(atan2
(sqrt (+ t_4 (pow (fma t_3 t_5 (* t_0 t_2)) 2.0)))
(sqrt (- 1.0 (+ (pow (fma t_2 t_0 (* t_5 t_3)) 2.0) t_4))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((-0.5 * phi1));
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = sin((-0.5 * phi2));
double t_3 = cos((-0.5 * phi2));
double t_4 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
double t_5 = sin((0.5 * phi1));
return (atan2(sqrt((t_4 + pow(fma(t_3, t_5, (t_0 * t_2)), 2.0))), sqrt((1.0 - (pow(fma(t_2, t_0, (t_5 * t_3)), 2.0) + t_4)))) * 2.0) * R;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(Float64(-0.5 * phi1)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = sin(Float64(-0.5 * phi2)) t_3 = cos(Float64(-0.5 * phi2)) t_4 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_1) * t_1) t_5 = sin(Float64(0.5 * phi1)) return Float64(Float64(atan(sqrt(Float64(t_4 + (fma(t_3, t_5, Float64(t_0 * t_2)) ^ 2.0))), sqrt(Float64(1.0 - Float64((fma(t_2, t_0, Float64(t_5 * t_3)) ^ 2.0) + t_4)))) * 2.0) * R) end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Cos[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$4 = N[(N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]}, Block[{t$95$5 = N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(t$95$4 + N[Power[N[(t$95$3 * t$95$5 + N[(t$95$0 * t$95$2), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[(t$95$2 * t$95$0 + N[(t$95$5 * t$95$3), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$4), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \cos \left(-0.5 \cdot \phi_1\right)\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \sin \left(-0.5 \cdot \phi_2\right)\\
t_3 := \cos \left(-0.5 \cdot \phi_2\right)\\
t_4 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_1\right) \cdot t\_1\\
t_5 := \sin \left(0.5 \cdot \phi_1\right)\\
\left(\tan^{-1}_* \frac{\sqrt{t\_4 + {\left(\mathsf{fma}\left(t\_3, t\_5, t\_0 \cdot t\_2\right)\right)}^{2}}}{\sqrt{1 - \left({\left(\mathsf{fma}\left(t\_2, t\_0, t\_5 \cdot t\_3\right)\right)}^{2} + t\_4\right)}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval63.9
Applied rewrites63.9%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites81.8%
Taylor expanded in phi1 around inf
*-commutativeN/A
distribute-lft-neg-inN/A
metadata-evalN/A
*-commutativeN/A
lower-pow.f64N/A
Applied rewrites81.8%
Taylor expanded in phi1 around inf
*-commutativeN/A
cancel-sub-sign-invN/A
sin-negN/A
distribute-lft-neg-inN/A
metadata-evalN/A
*-commutativeN/A
+-commutativeN/A
lower-pow.f64N/A
Applied rewrites81.8%
Final simplification81.8%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (sin (* 0.5 phi1)))
(t_2 (* (cos phi2) (cos phi1)))
(t_3
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) t_1)
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
(* (* t_2 t_0) t_0))))
(t_4
(pow
(fma
(sin (* -0.5 phi2))
(cos (* -0.5 phi1))
(* t_1 (cos (* -0.5 phi2))))
2.0)))
(if (<= lambda2 4.2e-6)
(*
(*
(atan2 t_3 (sqrt (- 1.0 (fma t_2 (pow (sin (* lambda1 0.5)) 2.0) t_4))))
2.0)
R)
(*
(*
(atan2
t_3
(sqrt (- 1.0 (fma t_2 (pow (sin (* lambda2 -0.5)) 2.0) t_4))))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = sin((0.5 * phi1));
double t_2 = cos(phi2) * cos(phi1);
double t_3 = sqrt((pow(((cos((phi2 * 0.5)) * t_1) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + ((t_2 * t_0) * t_0)));
double t_4 = pow(fma(sin((-0.5 * phi2)), cos((-0.5 * phi1)), (t_1 * cos((-0.5 * phi2)))), 2.0);
double tmp;
if (lambda2 <= 4.2e-6) {
tmp = (atan2(t_3, sqrt((1.0 - fma(t_2, pow(sin((lambda1 * 0.5)), 2.0), t_4)))) * 2.0) * R;
} else {
tmp = (atan2(t_3, sqrt((1.0 - fma(t_2, pow(sin((lambda2 * -0.5)), 2.0), t_4)))) * 2.0) * R;
}
return tmp;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = sin(Float64(0.5 * phi1)) t_2 = Float64(cos(phi2) * cos(phi1)) t_3 = sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * t_1) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + Float64(Float64(t_2 * t_0) * t_0))) t_4 = fma(sin(Float64(-0.5 * phi2)), cos(Float64(-0.5 * phi1)), Float64(t_1 * cos(Float64(-0.5 * phi2)))) ^ 2.0 tmp = 0.0 if (lambda2 <= 4.2e-6) tmp = Float64(Float64(atan(t_3, sqrt(Float64(1.0 - fma(t_2, (sin(Float64(lambda1 * 0.5)) ^ 2.0), t_4)))) * 2.0) * R); else tmp = Float64(Float64(atan(t_3, sqrt(Float64(1.0 - fma(t_2, (sin(Float64(lambda2 * -0.5)) ^ 2.0), t_4)))) * 2.0) * R); end return tmp end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * t$95$1), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(t$95$2 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$4 = N[Power[N[(N[Sin[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision] + N[(t$95$1 * N[Cos[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[lambda2, 4.2e-6], N[(N[(N[ArcTan[t$95$3 / N[Sqrt[N[(1.0 - N[(t$95$2 * N[Power[N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$4), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[t$95$3 / N[Sqrt[N[(1.0 - N[(t$95$2 * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$4), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \sin \left(0.5 \cdot \phi_1\right)\\
t_2 := \cos \phi_2 \cdot \cos \phi_1\\
t_3 := \sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot t\_1 - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + \left(t\_2 \cdot t\_0\right) \cdot t\_0}\\
t_4 := {\left(\mathsf{fma}\left(\sin \left(-0.5 \cdot \phi_2\right), \cos \left(-0.5 \cdot \phi_1\right), t\_1 \cdot \cos \left(-0.5 \cdot \phi_2\right)\right)\right)}^{2}\\
\mathbf{if}\;\lambda_2 \leq 4.2 \cdot 10^{-6}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_3}{\sqrt{1 - \mathsf{fma}\left(t\_2, {\sin \left(\lambda_1 \cdot 0.5\right)}^{2}, t\_4\right)}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_3}{\sqrt{1 - \mathsf{fma}\left(t\_2, {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}, t\_4\right)}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if lambda2 < 4.1999999999999996e-6Initial program 68.5%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval69.3
Applied rewrites69.3%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites85.4%
Taylor expanded in lambda2 around 0
lower--.f64N/A
associate-*r*N/A
lower-fma.f64N/A
*-commutativeN/A
lower-*.f64N/A
lower-cos.f64N/A
lower-cos.f64N/A
lower-pow.f64N/A
lower-sin.f64N/A
lower-*.f64N/A
Applied rewrites69.1%
if 4.1999999999999996e-6 < lambda2 Initial program 46.4%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval47.7
Applied rewrites47.7%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites71.1%
Taylor expanded in lambda1 around 0
lower--.f64N/A
associate-*r*N/A
lower-fma.f64N/A
*-commutativeN/A
lower-*.f64N/A
lower-cos.f64N/A
lower-cos.f64N/A
lower-pow.f64N/A
lower-sin.f64N/A
lower-*.f64N/A
Applied rewrites71.2%
Final simplification69.6%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (cos (* -0.5 phi1)))
(t_2 (sin (* -0.5 phi2)))
(t_3 (sin (* 0.5 phi1)))
(t_4 (* (cos phi2) (cos phi1)))
(t_5
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) t_3)
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
(* (* t_4 t_0) t_0))))
(t_6 (cos (* -0.5 phi2))))
(if (<= lambda2 4.2e-6)
(*
(*
(atan2
t_5
(sqrt
(-
1.0
(fma
t_4
(pow (sin (* lambda1 0.5)) 2.0)
(pow (fma t_6 t_3 (* t_1 t_2)) 2.0)))))
2.0)
R)
(*
(*
(atan2
t_5
(sqrt
(-
1.0
(fma
t_4
(pow (sin (* lambda2 -0.5)) 2.0)
(pow (fma t_2 t_1 (* t_3 t_6)) 2.0)))))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = cos((-0.5 * phi1));
double t_2 = sin((-0.5 * phi2));
double t_3 = sin((0.5 * phi1));
double t_4 = cos(phi2) * cos(phi1);
double t_5 = sqrt((pow(((cos((phi2 * 0.5)) * t_3) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + ((t_4 * t_0) * t_0)));
double t_6 = cos((-0.5 * phi2));
double tmp;
if (lambda2 <= 4.2e-6) {
tmp = (atan2(t_5, sqrt((1.0 - fma(t_4, pow(sin((lambda1 * 0.5)), 2.0), pow(fma(t_6, t_3, (t_1 * t_2)), 2.0))))) * 2.0) * R;
} else {
tmp = (atan2(t_5, sqrt((1.0 - fma(t_4, pow(sin((lambda2 * -0.5)), 2.0), pow(fma(t_2, t_1, (t_3 * t_6)), 2.0))))) * 2.0) * R;
}
return tmp;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = cos(Float64(-0.5 * phi1)) t_2 = sin(Float64(-0.5 * phi2)) t_3 = sin(Float64(0.5 * phi1)) t_4 = Float64(cos(phi2) * cos(phi1)) t_5 = sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * t_3) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + Float64(Float64(t_4 * t_0) * t_0))) t_6 = cos(Float64(-0.5 * phi2)) tmp = 0.0 if (lambda2 <= 4.2e-6) tmp = Float64(Float64(atan(t_5, sqrt(Float64(1.0 - fma(t_4, (sin(Float64(lambda1 * 0.5)) ^ 2.0), (fma(t_6, t_3, Float64(t_1 * t_2)) ^ 2.0))))) * 2.0) * R); else tmp = Float64(Float64(atan(t_5, sqrt(Float64(1.0 - fma(t_4, (sin(Float64(lambda2 * -0.5)) ^ 2.0), (fma(t_2, t_1, Float64(t_3 * t_6)) ^ 2.0))))) * 2.0) * R); end return tmp end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$4 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$5 = N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * t$95$3), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(t$95$4 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$6 = N[Cos[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda2, 4.2e-6], N[(N[(N[ArcTan[t$95$5 / N[Sqrt[N[(1.0 - N[(t$95$4 * N[Power[N[Sin[N[(lambda1 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(t$95$6 * t$95$3 + N[(t$95$1 * t$95$2), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[t$95$5 / N[Sqrt[N[(1.0 - N[(t$95$4 * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(t$95$2 * t$95$1 + N[(t$95$3 * t$95$6), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \cos \left(-0.5 \cdot \phi_1\right)\\
t_2 := \sin \left(-0.5 \cdot \phi_2\right)\\
t_3 := \sin \left(0.5 \cdot \phi_1\right)\\
t_4 := \cos \phi_2 \cdot \cos \phi_1\\
t_5 := \sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot t\_3 - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + \left(t\_4 \cdot t\_0\right) \cdot t\_0}\\
t_6 := \cos \left(-0.5 \cdot \phi_2\right)\\
\mathbf{if}\;\lambda_2 \leq 4.2 \cdot 10^{-6}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_5}{\sqrt{1 - \mathsf{fma}\left(t\_4, {\sin \left(\lambda_1 \cdot 0.5\right)}^{2}, {\left(\mathsf{fma}\left(t\_6, t\_3, t\_1 \cdot t\_2\right)\right)}^{2}\right)}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_5}{\sqrt{1 - \mathsf{fma}\left(t\_4, {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}, {\left(\mathsf{fma}\left(t\_2, t\_1, t\_3 \cdot t\_6\right)\right)}^{2}\right)}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if lambda2 < 4.1999999999999996e-6Initial program 68.5%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval69.3
Applied rewrites69.3%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites85.4%
Taylor expanded in phi1 around inf
*-commutativeN/A
distribute-lft-neg-inN/A
metadata-evalN/A
*-commutativeN/A
lower-pow.f64N/A
Applied rewrites85.4%
Taylor expanded in lambda2 around 0
lower--.f64N/A
associate-*r*N/A
lower-fma.f64N/A
*-commutativeN/A
lower-*.f64N/A
lower-cos.f64N/A
lower-cos.f64N/A
lower-pow.f64N/A
lower-sin.f64N/A
lower-*.f64N/A
Applied rewrites69.1%
if 4.1999999999999996e-6 < lambda2 Initial program 46.4%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval47.7
Applied rewrites47.7%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites71.1%
Taylor expanded in lambda1 around 0
lower--.f64N/A
associate-*r*N/A
lower-fma.f64N/A
*-commutativeN/A
lower-*.f64N/A
lower-cos.f64N/A
lower-cos.f64N/A
lower-pow.f64N/A
lower-sin.f64N/A
lower-*.f64N/A
Applied rewrites71.2%
Final simplification69.6%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (sin (* 0.5 phi1)))
(t_2 (* (cos phi2) (cos phi1)))
(t_3 (* (* t_2 t_0) t_0))
(t_4
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) t_1)
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
t_3))))
(if (<= lambda1 -0.021)
(*
(*
(atan2
t_4
(sqrt
(- 1.0 (+ (- 0.5 (* (cos (* (* (- phi1 phi2) 0.5) 2.0)) 0.5)) t_3))))
2.0)
R)
(*
(*
(atan2
t_4
(sqrt
(-
1.0
(fma
t_2
(pow (sin (* lambda2 -0.5)) 2.0)
(pow
(fma
(sin (* -0.5 phi2))
(cos (* -0.5 phi1))
(* t_1 (cos (* -0.5 phi2))))
2.0)))))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = sin((0.5 * phi1));
double t_2 = cos(phi2) * cos(phi1);
double t_3 = (t_2 * t_0) * t_0;
double t_4 = sqrt((pow(((cos((phi2 * 0.5)) * t_1) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + t_3));
double tmp;
if (lambda1 <= -0.021) {
tmp = (atan2(t_4, sqrt((1.0 - ((0.5 - (cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_3)))) * 2.0) * R;
} else {
tmp = (atan2(t_4, sqrt((1.0 - fma(t_2, pow(sin((lambda2 * -0.5)), 2.0), pow(fma(sin((-0.5 * phi2)), cos((-0.5 * phi1)), (t_1 * cos((-0.5 * phi2)))), 2.0))))) * 2.0) * R;
}
return tmp;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = sin(Float64(0.5 * phi1)) t_2 = Float64(cos(phi2) * cos(phi1)) t_3 = Float64(Float64(t_2 * t_0) * t_0) t_4 = sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * t_1) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + t_3)) tmp = 0.0 if (lambda1 <= -0.021) tmp = Float64(Float64(atan(t_4, sqrt(Float64(1.0 - Float64(Float64(0.5 - Float64(cos(Float64(Float64(Float64(phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_3)))) * 2.0) * R); else tmp = Float64(Float64(atan(t_4, sqrt(Float64(1.0 - fma(t_2, (sin(Float64(lambda2 * -0.5)) ^ 2.0), (fma(sin(Float64(-0.5 * phi2)), cos(Float64(-0.5 * phi1)), Float64(t_1 * cos(Float64(-0.5 * phi2)))) ^ 2.0))))) * 2.0) * R); end return tmp end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(t$95$2 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, Block[{t$95$4 = N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * t$95$1), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda1, -0.021], N[(N[(N[ArcTan[t$95$4 / N[Sqrt[N[(1.0 - N[(N[(0.5 - N[(N[Cos[N[(N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision] * 2.0), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$3), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[t$95$4 / N[Sqrt[N[(1.0 - N[(t$95$2 * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(N[Sin[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision] + N[(t$95$1 * N[Cos[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \sin \left(0.5 \cdot \phi_1\right)\\
t_2 := \cos \phi_2 \cdot \cos \phi_1\\
t_3 := \left(t\_2 \cdot t\_0\right) \cdot t\_0\\
t_4 := \sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot t\_1 - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + t\_3}\\
\mathbf{if}\;\lambda_1 \leq -0.021:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_4}{\sqrt{1 - \left(\left(0.5 - \cos \left(\left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right) \cdot 2\right) \cdot 0.5\right) + t\_3\right)}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_4}{\sqrt{1 - \mathsf{fma}\left(t\_2, {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}, {\left(\mathsf{fma}\left(\sin \left(-0.5 \cdot \phi_2\right), \cos \left(-0.5 \cdot \phi_1\right), t\_1 \cdot \cos \left(-0.5 \cdot \phi_2\right)\right)\right)}^{2}\right)}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if lambda1 < -0.0210000000000000013Initial program 33.1%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval34.7
Applied rewrites34.7%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6434.7
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6434.7
Applied rewrites34.7%
if -0.0210000000000000013 < lambda1 Initial program 69.3%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval70.1
Applied rewrites70.1%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites89.9%
Taylor expanded in lambda1 around 0
lower--.f64N/A
associate-*r*N/A
lower-fma.f64N/A
*-commutativeN/A
lower-*.f64N/A
lower-cos.f64N/A
lower-cos.f64N/A
lower-pow.f64N/A
lower-sin.f64N/A
lower-*.f64N/A
Applied rewrites78.2%
Final simplification70.6%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (sin (* 0.5 phi1)))
(t_2 (* (cos phi2) (cos phi1)))
(t_3 (* (* t_2 t_0) t_0))
(t_4
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) t_1)
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
t_3))))
(if (<= lambda1 -0.021)
(*
(*
(atan2
t_4
(sqrt
(- 1.0 (+ (- 0.5 (* (cos (* (* (- phi1 phi2) 0.5) 2.0)) 0.5)) t_3))))
2.0)
R)
(*
(*
(atan2
t_4
(sqrt
(-
1.0
(fma
t_2
(pow (sin (* lambda2 -0.5)) 2.0)
(pow
(fma
(cos (* -0.5 phi2))
t_1
(* (cos (* -0.5 phi1)) (sin (* -0.5 phi2))))
2.0)))))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = sin((0.5 * phi1));
double t_2 = cos(phi2) * cos(phi1);
double t_3 = (t_2 * t_0) * t_0;
double t_4 = sqrt((pow(((cos((phi2 * 0.5)) * t_1) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + t_3));
double tmp;
if (lambda1 <= -0.021) {
tmp = (atan2(t_4, sqrt((1.0 - ((0.5 - (cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_3)))) * 2.0) * R;
} else {
tmp = (atan2(t_4, sqrt((1.0 - fma(t_2, pow(sin((lambda2 * -0.5)), 2.0), pow(fma(cos((-0.5 * phi2)), t_1, (cos((-0.5 * phi1)) * sin((-0.5 * phi2)))), 2.0))))) * 2.0) * R;
}
return tmp;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = sin(Float64(0.5 * phi1)) t_2 = Float64(cos(phi2) * cos(phi1)) t_3 = Float64(Float64(t_2 * t_0) * t_0) t_4 = sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * t_1) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + t_3)) tmp = 0.0 if (lambda1 <= -0.021) tmp = Float64(Float64(atan(t_4, sqrt(Float64(1.0 - Float64(Float64(0.5 - Float64(cos(Float64(Float64(Float64(phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_3)))) * 2.0) * R); else tmp = Float64(Float64(atan(t_4, sqrt(Float64(1.0 - fma(t_2, (sin(Float64(lambda2 * -0.5)) ^ 2.0), (fma(cos(Float64(-0.5 * phi2)), t_1, Float64(cos(Float64(-0.5 * phi1)) * sin(Float64(-0.5 * phi2)))) ^ 2.0))))) * 2.0) * R); end return tmp end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(t$95$2 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, Block[{t$95$4 = N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * t$95$1), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[lambda1, -0.021], N[(N[(N[ArcTan[t$95$4 / N[Sqrt[N[(1.0 - N[(N[(0.5 - N[(N[Cos[N[(N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision] * 2.0), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$3), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[t$95$4 / N[Sqrt[N[(1.0 - N[(t$95$2 * N[Power[N[Sin[N[(lambda2 * -0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(N[Cos[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision] * t$95$1 + N[(N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \sin \left(0.5 \cdot \phi_1\right)\\
t_2 := \cos \phi_2 \cdot \cos \phi_1\\
t_3 := \left(t\_2 \cdot t\_0\right) \cdot t\_0\\
t_4 := \sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot t\_1 - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + t\_3}\\
\mathbf{if}\;\lambda_1 \leq -0.021:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_4}{\sqrt{1 - \left(\left(0.5 - \cos \left(\left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right) \cdot 2\right) \cdot 0.5\right) + t\_3\right)}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{t\_4}{\sqrt{1 - \mathsf{fma}\left(t\_2, {\sin \left(\lambda_2 \cdot -0.5\right)}^{2}, {\left(\mathsf{fma}\left(\cos \left(-0.5 \cdot \phi_2\right), t\_1, \cos \left(-0.5 \cdot \phi_1\right) \cdot \sin \left(-0.5 \cdot \phi_2\right)\right)\right)}^{2}\right)}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if lambda1 < -0.0210000000000000013Initial program 33.1%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval34.7
Applied rewrites34.7%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6434.7
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6434.7
Applied rewrites34.7%
if -0.0210000000000000013 < lambda1 Initial program 69.3%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval70.1
Applied rewrites70.1%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites89.9%
Taylor expanded in phi1 around inf
*-commutativeN/A
distribute-lft-neg-inN/A
metadata-evalN/A
*-commutativeN/A
lower-pow.f64N/A
Applied rewrites89.9%
Taylor expanded in lambda1 around 0
lower--.f64N/A
associate-*r*N/A
lower-fma.f64N/A
*-commutativeN/A
lower-*.f64N/A
lower-cos.f64N/A
lower-cos.f64N/A
lower-pow.f64N/A
lower-sin.f64N/A
lower-*.f64N/A
Applied rewrites78.2%
Final simplification70.6%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (* 0.5 phi1)))
(t_1 (* (cos phi2) (cos phi1)))
(t_2 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) t_0)
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
(* (* t_1 t_2) t_2)))
(sqrt
(-
1.0
(fma
t_1
(pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)
(pow
(fma
(sin (* -0.5 phi2))
(cos (* -0.5 phi1))
(* t_0 (cos (* -0.5 phi2))))
2.0)))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin((0.5 * phi1));
double t_1 = cos(phi2) * cos(phi1);
double t_2 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt((pow(((cos((phi2 * 0.5)) * t_0) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + ((t_1 * t_2) * t_2))), sqrt((1.0 - fma(t_1, pow(sin(((lambda1 - lambda2) * 0.5)), 2.0), pow(fma(sin((-0.5 * phi2)), cos((-0.5 * phi1)), (t_0 * cos((-0.5 * phi2)))), 2.0))))) * 2.0) * R;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(0.5 * phi1)) t_1 = Float64(cos(phi2) * cos(phi1)) t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * t_0) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + Float64(Float64(t_1 * t_2) * t_2))), sqrt(Float64(1.0 - fma(t_1, (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0), (fma(sin(Float64(-0.5 * phi2)), cos(Float64(-0.5 * phi1)), Float64(t_0 * cos(Float64(-0.5 * phi2)))) ^ 2.0))))) * 2.0) * R) end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * t$95$0), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(t$95$1 * t$95$2), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(t$95$1 * N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(N[Sin[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision] + N[(t$95$0 * N[Cos[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(0.5 \cdot \phi_1\right)\\
t_1 := \cos \phi_2 \cdot \cos \phi_1\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot t\_0 - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + \left(t\_1 \cdot t\_2\right) \cdot t\_2}}{\sqrt{1 - \mathsf{fma}\left(t\_1, {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}, {\left(\mathsf{fma}\left(\sin \left(-0.5 \cdot \phi_2\right), \cos \left(-0.5 \cdot \phi_1\right), t\_0 \cdot \cos \left(-0.5 \cdot \phi_2\right)\right)\right)}^{2}\right)}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval63.9
Applied rewrites63.9%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites81.8%
Taylor expanded in lambda1 around inf
Applied rewrites81.8%
Final simplification81.8%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (* (* (cos phi2) (cos phi1)) t_0) t_0)))
(*
(*
(atan2
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) (sin (* 0.5 phi1)))
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
t_1))
(sqrt
(- 1.0 (+ (- 0.5 (* (cos (* (* (- phi1 phi2) 0.5) 2.0)) 0.5)) t_1))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = ((cos(phi2) * cos(phi1)) * t_0) * t_0;
return (atan2(sqrt((pow(((cos((phi2 * 0.5)) * sin((0.5 * phi1))) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + t_1)), sqrt((1.0 - ((0.5 - (cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_1)))) * 2.0) * R;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = ((cos(phi2) * cos(phi1)) * t_0) * t_0
code = (atan2(sqrt(((((cos((phi2 * 0.5d0)) * sin((0.5d0 * phi1))) - (sin((phi2 * 0.5d0)) * cos((0.5d0 * phi1)))) ** 2.0d0) + t_1)), sqrt((1.0d0 - ((0.5d0 - (cos((((phi1 - phi2) * 0.5d0) * 2.0d0)) * 0.5d0)) + t_1)))) * 2.0d0) * r
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
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(phi2) * Math.cos(phi1)) * t_0) * t_0;
return (Math.atan2(Math.sqrt((Math.pow(((Math.cos((phi2 * 0.5)) * Math.sin((0.5 * phi1))) - (Math.sin((phi2 * 0.5)) * Math.cos((0.5 * phi1)))), 2.0) + t_1)), Math.sqrt((1.0 - ((0.5 - (Math.cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_1)))) * 2.0) * R;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = ((math.cos(phi2) * math.cos(phi1)) * t_0) * t_0 return (math.atan2(math.sqrt((math.pow(((math.cos((phi2 * 0.5)) * math.sin((0.5 * phi1))) - (math.sin((phi2 * 0.5)) * math.cos((0.5 * phi1)))), 2.0) + t_1)), math.sqrt((1.0 - ((0.5 - (math.cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_1)))) * 2.0) * R
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_0) * t_0) return Float64(Float64(atan(sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(0.5 * phi1))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + t_1)), sqrt(Float64(1.0 - Float64(Float64(0.5 - Float64(cos(Float64(Float64(Float64(phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_1)))) * 2.0) * R) end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) / 2.0));
t_1 = ((cos(phi2) * cos(phi1)) * t_0) * t_0;
tmp = (atan2(sqrt(((((cos((phi2 * 0.5)) * sin((0.5 * phi1))) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))) ^ 2.0) + t_1)), sqrt((1.0 - ((0.5 - (cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_1)))) * 2.0) * R;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$1), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[(0.5 - N[(N[Cos[N[(N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision] * 2.0), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_0\right) \cdot t\_0\\
\left(\tan^{-1}_* \frac{\sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(0.5 \cdot \phi_1\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + t\_1}}{\sqrt{1 - \left(\left(0.5 - \cos \left(\left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right) \cdot 2\right) \cdot 0.5\right) + t\_1\right)}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval63.9
Applied rewrites63.9%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6464.0
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6464.0
Applied rewrites64.0%
Final simplification64.0%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi2) (cos phi1)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) (sin (* 0.5 phi1)))
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
(* (* t_0 t_1) t_1)))
(sqrt
(-
(- 1.0 (fma (cos (- phi2 phi1)) -0.5 0.5))
(* (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0) t_0))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi2) * cos(phi1);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt((pow(((cos((phi2 * 0.5)) * sin((0.5 * phi1))) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + ((t_0 * t_1) * t_1))), sqrt(((1.0 - fma(cos((phi2 - phi1)), -0.5, 0.5)) - (pow(sin(((lambda1 - lambda2) * 0.5)), 2.0) * t_0)))) * 2.0) * R;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi2) * cos(phi1)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(0.5 * phi1))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + Float64(Float64(t_0 * t_1) * t_1))), sqrt(Float64(Float64(1.0 - fma(cos(Float64(phi2 - phi1)), -0.5, 0.5)) - Float64((sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0) * t_0)))) * 2.0) * R) end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(t$95$0 * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - N[(N[Cos[N[(phi2 - phi1), $MachinePrecision]], $MachinePrecision] * -0.5 + 0.5), $MachinePrecision]), $MachinePrecision] - N[(N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \cos \phi_2 \cdot \cos \phi_1\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(0.5 \cdot \phi_1\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + \left(t\_0 \cdot t\_1\right) \cdot t\_1}}{\sqrt{\left(1 - \mathsf{fma}\left(\cos \left(\phi_2 - \phi_1\right), -0.5, 0.5\right)\right) - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} \cdot t\_0}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval63.9
Applied rewrites63.9%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites81.8%
Taylor expanded in phi1 around inf
*-commutativeN/A
distribute-lft-neg-inN/A
metadata-evalN/A
*-commutativeN/A
lower-pow.f64N/A
Applied rewrites81.8%
Applied rewrites64.0%
Final simplification64.0%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi2) (cos phi1)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt
(+
(pow
(-
(* (cos (* phi2 0.5)) (sin (* 0.5 phi1)))
(* (sin (* phi2 0.5)) (cos (* 0.5 phi1))))
2.0)
(* (* t_0 t_1) t_1)))
(sqrt
(-
1.0
(fma
(pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)
t_0
(fma (cos (- phi2 phi1)) -0.5 0.5)))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi2) * cos(phi1);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt((pow(((cos((phi2 * 0.5)) * sin((0.5 * phi1))) - (sin((phi2 * 0.5)) * cos((0.5 * phi1)))), 2.0) + ((t_0 * t_1) * t_1))), sqrt((1.0 - fma(pow(sin(((lambda1 - lambda2) * 0.5)), 2.0), t_0, fma(cos((phi2 - phi1)), -0.5, 0.5))))) * 2.0) * R;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi2) * cos(phi1)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64((Float64(Float64(cos(Float64(phi2 * 0.5)) * sin(Float64(0.5 * phi1))) - Float64(sin(Float64(phi2 * 0.5)) * cos(Float64(0.5 * phi1)))) ^ 2.0) + Float64(Float64(t_0 * t_1) * t_1))), sqrt(Float64(1.0 - fma((sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0), t_0, fma(cos(Float64(phi2 - phi1)), -0.5, 0.5))))) * 2.0) * R) end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[(N[(N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] - N[(N[Sin[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision] * N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(t$95$0 * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[(N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] * t$95$0 + N[(N[Cos[N[(phi2 - phi1), $MachinePrecision]], $MachinePrecision] * -0.5 + 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \cos \phi_2 \cdot \cos \phi_1\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{{\left(\cos \left(\phi_2 \cdot 0.5\right) \cdot \sin \left(0.5 \cdot \phi_1\right) - \sin \left(\phi_2 \cdot 0.5\right) \cdot \cos \left(0.5 \cdot \phi_1\right)\right)}^{2} + \left(t\_0 \cdot t\_1\right) \cdot t\_1}}{\sqrt{1 - \mathsf{fma}\left({\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}, t\_0, \mathsf{fma}\left(\cos \left(\phi_2 - \phi_1\right), -0.5, 0.5\right)\right)}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval63.9
Applied rewrites63.9%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites81.8%
Taylor expanded in phi1 around inf
*-commutativeN/A
distribute-lft-neg-inN/A
metadata-evalN/A
*-commutativeN/A
lower-pow.f64N/A
Applied rewrites81.8%
Applied rewrites64.0%
Final simplification64.0%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(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 phi2) (cos phi1)) t_0) t_0))))
(* (* (atan2 (sqrt t_1) (sqrt (- 1.0 t_1))) 2.0) R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
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(phi2) * cos(phi1)) * t_0) * t_0);
return (atan2(sqrt(t_1), sqrt((1.0 - t_1))) * 2.0) * R;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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(phi2) * cos(phi1)) * t_0) * t_0)
code = (atan2(sqrt(t_1), sqrt((1.0d0 - t_1))) * 2.0d0) * r
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
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(phi2) * Math.cos(phi1)) * t_0) * t_0);
return (Math.atan2(Math.sqrt(t_1), Math.sqrt((1.0 - t_1))) * 2.0) * R;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) 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(phi2) * math.cos(phi1)) * t_0) * t_0) return (math.atan2(math.sqrt(t_1), math.sqrt((1.0 - t_1))) * 2.0) * R
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) 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(phi2) * cos(phi1)) * t_0) * t_0)) return Float64(Float64(atan(sqrt(t_1), sqrt(Float64(1.0 - t_1))) * 2.0) * R) end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
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(phi2) * cos(phi1)) * t_0) * t_0);
tmp = (atan2(sqrt(t_1), sqrt((1.0 - t_1))) * 2.0) * R;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[t$95$1], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$1), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\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_2 \cdot \cos \phi_1\right) \cdot t\_0\right) \cdot t\_0\\
\left(\tan^{-1}_* \frac{\sqrt{t\_1}}{\sqrt{1 - t\_1}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
Final simplification63.0%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (cos (* -0.5 phi1)))
(t_1 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(t_2 (sin (/ (- lambda1 lambda2) 2.0)))
(t_3 (* (* (* (cos phi2) (cos phi1)) t_2) t_2)))
(if (<= phi1 -2.8e-5)
(*
(*
(atan2
(sqrt (+ (pow (fma t_0 (* -0.5 phi2) (sin (* 0.5 phi1))) 2.0) t_3))
(sqrt (- (pow t_0 2.0) (* t_1 (cos phi1)))))
2.0)
R)
(*
(*
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_3))
(sqrt (- (pow (cos (* phi2 0.5)) 2.0) (* t_1 (cos phi2)))))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos((-0.5 * phi1));
double t_1 = pow(sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_2 = sin(((lambda1 - lambda2) / 2.0));
double t_3 = ((cos(phi2) * cos(phi1)) * t_2) * t_2;
double tmp;
if (phi1 <= -2.8e-5) {
tmp = (atan2(sqrt((pow(fma(t_0, (-0.5 * phi2), sin((0.5 * phi1))), 2.0) + t_3)), sqrt((pow(t_0, 2.0) - (t_1 * cos(phi1))))) * 2.0) * R;
} else {
tmp = (atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_3)), sqrt((pow(cos((phi2 * 0.5)), 2.0) - (t_1 * cos(phi2))))) * 2.0) * R;
}
return tmp;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = cos(Float64(-0.5 * phi1)) t_1 = sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0 t_2 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_3 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_2) * t_2) tmp = 0.0 if (phi1 <= -2.8e-5) tmp = Float64(Float64(atan(sqrt(Float64((fma(t_0, Float64(-0.5 * phi2), sin(Float64(0.5 * phi1))) ^ 2.0) + t_3)), sqrt(Float64((t_0 ^ 2.0) - Float64(t_1 * cos(phi1))))) * 2.0) * R); else tmp = Float64(Float64(atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_3)), sqrt(Float64((cos(Float64(phi2 * 0.5)) ^ 2.0) - Float64(t_1 * cos(phi2))))) * 2.0) * R); end return tmp end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$2 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$2), $MachinePrecision] * t$95$2), $MachinePrecision]}, If[LessEqual[phi1, -2.8e-5], N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[(t$95$0 * N[(-0.5 * phi2), $MachinePrecision] + N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[t$95$0, 2.0], $MachinePrecision] - N[(t$95$1 * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$3), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(t$95$1 * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \cos \left(-0.5 \cdot \phi_1\right)\\
t_1 := {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\\
t_2 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_3 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_2\right) \cdot t\_2\\
\mathbf{if}\;\phi_1 \leq -2.8 \cdot 10^{-5}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{{\left(\mathsf{fma}\left(t\_0, -0.5 \cdot \phi_2, \sin \left(0.5 \cdot \phi_1\right)\right)\right)}^{2} + t\_3}}{\sqrt{{t\_0}^{2} - t\_1 \cdot \cos \phi_1}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t\_3}}{\sqrt{{\cos \left(\phi_2 \cdot 0.5\right)}^{2} - t\_1 \cdot \cos \phi_2}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if phi1 < -2.79999999999999996e-5Initial program 45.3%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites45.5%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate-*r*N/A
*-commutativeN/A
lower-fma.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
lower-*.f6445.8
Applied rewrites45.8%
if -2.79999999999999996e-5 < phi1 Initial program 69.7%
Taylor expanded in phi1 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites61.7%
Final simplification57.3%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi2) (cos phi1)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) (* (* t_0 t_1) t_1)))
(sqrt
(-
(- 1.0 (* (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0) t_0))
(pow (sin (* (- phi1 phi2) 0.5)) 2.0))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi2) * cos(phi1);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + ((t_0 * t_1) * t_1))), sqrt(((1.0 - (pow(sin(((lambda1 - lambda2) * 0.5)), 2.0) * t_0)) - pow(sin(((phi1 - phi2) * 0.5)), 2.0)))) * 2.0) * R;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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 = cos(phi2) * cos(phi1)
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
code = (atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + ((t_0 * t_1) * t_1))), sqrt(((1.0d0 - ((sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0) * t_0)) - (sin(((phi1 - phi2) * 0.5d0)) ** 2.0d0)))) * 2.0d0) * r
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.cos(phi2) * Math.cos(phi1);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
return (Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + ((t_0 * t_1) * t_1))), Math.sqrt(((1.0 - (Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0) * t_0)) - Math.pow(Math.sin(((phi1 - phi2) * 0.5)), 2.0)))) * 2.0) * R;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.cos(phi2) * math.cos(phi1) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) return (math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + ((t_0 * t_1) * t_1))), math.sqrt(((1.0 - (math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0) * t_0)) - math.pow(math.sin(((phi1 - phi2) * 0.5)), 2.0)))) * 2.0) * R
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi2) * cos(phi1)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + Float64(Float64(t_0 * t_1) * t_1))), sqrt(Float64(Float64(1.0 - Float64((sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0) * t_0)) - (sin(Float64(Float64(phi1 - phi2) * 0.5)) ^ 2.0)))) * 2.0) * R) end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp = code(R, lambda1, lambda2, phi1, phi2)
t_0 = cos(phi2) * cos(phi1);
t_1 = sin(((lambda1 - lambda2) / 2.0));
tmp = (atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + ((t_0 * t_1) * t_1))), sqrt(((1.0 - ((sin(((lambda1 - lambda2) * 0.5)) ^ 2.0) * t_0)) - (sin(((phi1 - phi2) * 0.5)) ^ 2.0)))) * 2.0) * R;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + N[(N[(t$95$0 * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(1.0 - N[(N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision] - N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \cos \phi_2 \cdot \cos \phi_1\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + \left(t\_0 \cdot t\_1\right) \cdot t\_1}}{\sqrt{\left(1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} \cdot t\_0\right) - {\sin \left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right)}^{2}}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
lift--.f64N/A
lift-+.f64N/A
+-commutativeN/A
associate--r+N/A
lower--.f64N/A
Applied rewrites63.0%
Final simplification63.0%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (* (* (cos phi2) (cos phi1)) t_1) t_1)))
(if (<= phi1 -245000.0)
(*
(*
(atan2
(sqrt (+ (pow (sin (* 0.5 phi1)) 2.0) t_2))
(sqrt (- (pow (cos (* -0.5 phi1)) 2.0) (* t_0 (cos phi1)))))
2.0)
R)
(*
(*
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_2))
(sqrt (- (pow (cos (* phi2 0.5)) 2.0) (* t_0 (cos phi2)))))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
double tmp;
if (phi1 <= -245000.0) {
tmp = (atan2(sqrt((pow(sin((0.5 * phi1)), 2.0) + t_2)), sqrt((pow(cos((-0.5 * phi1)), 2.0) - (t_0 * cos(phi1))))) * 2.0) * R;
} else {
tmp = (atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), sqrt((pow(cos((phi2 * 0.5)), 2.0) - (t_0 * cos(phi2))))) * 2.0) * R;
}
return tmp;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: tmp
t_0 = sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1
if (phi1 <= (-245000.0d0)) then
tmp = (atan2(sqrt(((sin((0.5d0 * phi1)) ** 2.0d0) + t_2)), sqrt(((cos(((-0.5d0) * phi1)) ** 2.0d0) - (t_0 * cos(phi1))))) * 2.0d0) * r
else
tmp = (atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_2)), sqrt(((cos((phi2 * 0.5d0)) ** 2.0d0) - (t_0 * cos(phi2))))) * 2.0d0) * r
end if
code = tmp
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_2 = ((Math.cos(phi2) * Math.cos(phi1)) * t_1) * t_1;
double tmp;
if (phi1 <= -245000.0) {
tmp = (Math.atan2(Math.sqrt((Math.pow(Math.sin((0.5 * phi1)), 2.0) + t_2)), Math.sqrt((Math.pow(Math.cos((-0.5 * phi1)), 2.0) - (t_0 * Math.cos(phi1))))) * 2.0) * R;
} else {
tmp = (Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), Math.sqrt((Math.pow(Math.cos((phi2 * 0.5)), 2.0) - (t_0 * Math.cos(phi2))))) * 2.0) * R;
}
return tmp;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) t_2 = ((math.cos(phi2) * math.cos(phi1)) * t_1) * t_1 tmp = 0 if phi1 <= -245000.0: tmp = (math.atan2(math.sqrt((math.pow(math.sin((0.5 * phi1)), 2.0) + t_2)), math.sqrt((math.pow(math.cos((-0.5 * phi1)), 2.0) - (t_0 * math.cos(phi1))))) * 2.0) * R else: tmp = (math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), math.sqrt((math.pow(math.cos((phi2 * 0.5)), 2.0) - (t_0 * math.cos(phi2))))) * 2.0) * R return tmp
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_1) * t_1) tmp = 0.0 if (phi1 <= -245000.0) tmp = Float64(Float64(atan(sqrt(Float64((sin(Float64(0.5 * phi1)) ^ 2.0) + t_2)), sqrt(Float64((cos(Float64(-0.5 * phi1)) ^ 2.0) - Float64(t_0 * cos(phi1))))) * 2.0) * R); else tmp = Float64(Float64(atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(Float64((cos(Float64(phi2 * 0.5)) ^ 2.0) - Float64(t_0 * cos(phi2))))) * 2.0) * R); end return tmp end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) * 0.5)) ^ 2.0;
t_1 = sin(((lambda1 - lambda2) / 2.0));
t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
tmp = 0.0;
if (phi1 <= -245000.0)
tmp = (atan2(sqrt(((sin((0.5 * phi1)) ^ 2.0) + t_2)), sqrt(((cos((-0.5 * phi1)) ^ 2.0) - (t_0 * cos(phi1))))) * 2.0) * R;
else
tmp = (atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(((cos((phi2 * 0.5)) ^ 2.0) - (t_0 * cos(phi2))))) * 2.0) * R;
end
tmp_2 = tmp;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $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[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]}, If[LessEqual[phi1, -245000.0], N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(t$95$0 * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(phi2 * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(t$95$0 * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_1\right) \cdot t\_1\\
\mathbf{if}\;\phi_1 \leq -245000:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{{\sin \left(0.5 \cdot \phi_1\right)}^{2} + t\_2}}{\sqrt{{\cos \left(-0.5 \cdot \phi_1\right)}^{2} - t\_0 \cdot \cos \phi_1}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t\_2}}{\sqrt{{\cos \left(\phi_2 \cdot 0.5\right)}^{2} - t\_0 \cdot \cos \phi_2}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if phi1 < -245000Initial program 45.7%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites46.3%
Taylor expanded in phi2 around 0
lower-sin.f64N/A
lower-*.f6445.7
Applied rewrites45.7%
if -245000 < phi1 Initial program 69.3%
Taylor expanded in phi1 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites61.3%
Final simplification57.1%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (* (* (cos phi2) (cos phi1)) t_1) t_1)))
(if (<= phi2 1.05e-26)
(*
(*
(atan2
(sqrt (+ (pow (sin (/ (- phi1 phi2) 2.0)) 2.0) t_2))
(sqrt (- (pow (cos (* -0.5 phi1)) 2.0) (* t_0 (cos phi1)))))
2.0)
R)
(*
(*
(atan2
(sqrt (+ (- 0.5 (* (cos (* (* (- phi1 phi2) 0.5) 2.0)) 0.5)) t_2))
(sqrt (- (pow (cos (* -0.5 phi2)) 2.0) (* t_0 (cos phi2)))))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
double tmp;
if (phi2 <= 1.05e-26) {
tmp = (atan2(sqrt((pow(sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), sqrt((pow(cos((-0.5 * phi1)), 2.0) - (t_0 * cos(phi1))))) * 2.0) * R;
} else {
tmp = (atan2(sqrt(((0.5 - (cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_2)), sqrt((pow(cos((-0.5 * phi2)), 2.0) - (t_0 * cos(phi2))))) * 2.0) * R;
}
return tmp;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: tmp
t_0 = sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1
if (phi2 <= 1.05d-26) then
tmp = (atan2(sqrt(((sin(((phi1 - phi2) / 2.0d0)) ** 2.0d0) + t_2)), sqrt(((cos(((-0.5d0) * phi1)) ** 2.0d0) - (t_0 * cos(phi1))))) * 2.0d0) * r
else
tmp = (atan2(sqrt(((0.5d0 - (cos((((phi1 - phi2) * 0.5d0) * 2.0d0)) * 0.5d0)) + t_2)), sqrt(((cos(((-0.5d0) * phi2)) ** 2.0d0) - (t_0 * cos(phi2))))) * 2.0d0) * r
end if
code = tmp
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_2 = ((Math.cos(phi2) * Math.cos(phi1)) * t_1) * t_1;
double tmp;
if (phi2 <= 1.05e-26) {
tmp = (Math.atan2(Math.sqrt((Math.pow(Math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), Math.sqrt((Math.pow(Math.cos((-0.5 * phi1)), 2.0) - (t_0 * Math.cos(phi1))))) * 2.0) * R;
} else {
tmp = (Math.atan2(Math.sqrt(((0.5 - (Math.cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_2)), Math.sqrt((Math.pow(Math.cos((-0.5 * phi2)), 2.0) - (t_0 * Math.cos(phi2))))) * 2.0) * R;
}
return tmp;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) t_2 = ((math.cos(phi2) * math.cos(phi1)) * t_1) * t_1 tmp = 0 if phi2 <= 1.05e-26: tmp = (math.atan2(math.sqrt((math.pow(math.sin(((phi1 - phi2) / 2.0)), 2.0) + t_2)), math.sqrt((math.pow(math.cos((-0.5 * phi1)), 2.0) - (t_0 * math.cos(phi1))))) * 2.0) * R else: tmp = (math.atan2(math.sqrt(((0.5 - (math.cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_2)), math.sqrt((math.pow(math.cos((-0.5 * phi2)), 2.0) - (t_0 * math.cos(phi2))))) * 2.0) * R return tmp
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_1) * t_1) tmp = 0.0 if (phi2 <= 1.05e-26) tmp = Float64(Float64(atan(sqrt(Float64((sin(Float64(Float64(phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(Float64((cos(Float64(-0.5 * phi1)) ^ 2.0) - Float64(t_0 * cos(phi1))))) * 2.0) * R); else tmp = Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(Float64(Float64(Float64(phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_2)), sqrt(Float64((cos(Float64(-0.5 * phi2)) ^ 2.0) - Float64(t_0 * cos(phi2))))) * 2.0) * R); end return tmp end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) * 0.5)) ^ 2.0;
t_1 = sin(((lambda1 - lambda2) / 2.0));
t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
tmp = 0.0;
if (phi2 <= 1.05e-26)
tmp = (atan2(sqrt(((sin(((phi1 - phi2) / 2.0)) ^ 2.0) + t_2)), sqrt(((cos((-0.5 * phi1)) ^ 2.0) - (t_0 * cos(phi1))))) * 2.0) * R;
else
tmp = (atan2(sqrt(((0.5 - (cos((((phi1 - phi2) * 0.5) * 2.0)) * 0.5)) + t_2)), sqrt(((cos((-0.5 * phi2)) ^ 2.0) - (t_0 * cos(phi2))))) * 2.0) * R;
end
tmp_2 = tmp;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $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[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]}, If[LessEqual[phi2, 1.05e-26], N[(N[(N[ArcTan[N[Sqrt[N[(N[Power[N[Sin[N[(N[(phi1 - phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(t$95$0 * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[N[(N[(N[(phi1 - phi2), $MachinePrecision] * 0.5), $MachinePrecision] * 2.0), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(-0.5 * phi2), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(t$95$0 * N[Cos[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_1\right) \cdot t\_1\\
\mathbf{if}\;\phi_2 \leq 1.05 \cdot 10^{-26}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{{\sin \left(\frac{\phi_1 - \phi_2}{2}\right)}^{2} + t\_2}}{\sqrt{{\cos \left(-0.5 \cdot \phi_1\right)}^{2} - t\_0 \cdot \cos \phi_1}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \left(\left(\left(\phi_1 - \phi_2\right) \cdot 0.5\right) \cdot 2\right) \cdot 0.5\right) + t\_2}}{\sqrt{{\cos \left(-0.5 \cdot \phi_2\right)}^{2} - t\_0 \cdot \cos \phi_2}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if phi2 < 1.05000000000000004e-26Initial program 66.9%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites57.1%
if 1.05000000000000004e-26 < phi2 Initial program 54.5%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites21.8%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6421.8
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6421.8
Applied rewrites21.8%
Taylor expanded in phi1 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
metadata-evalN/A
distribute-lft-neg-inN/A
cos-neg-revN/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-cos.f64N/A
lower-*.f64N/A
*-commutativeN/A
Applied rewrites54.5%
Final simplification56.3%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (* (cos phi2) (cos phi1)))
(t_1 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt (+ (- 0.5 (* (cos (- phi1 phi2)) 0.5)) (* (* t_0 t_1) t_1)))
(sqrt
(-
(fma (cos (- phi2 phi1)) 0.5 0.5)
(* (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0) t_0))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = cos(phi2) * cos(phi1);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt(((0.5 - (cos((phi1 - phi2)) * 0.5)) + ((t_0 * t_1) * t_1))), sqrt((fma(cos((phi2 - phi1)), 0.5, 0.5) - (pow(sin(((lambda1 - lambda2) * 0.5)), 2.0) * t_0)))) * 2.0) * R;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = Float64(cos(phi2) * cos(phi1)) t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) * 0.5)) + Float64(Float64(t_0 * t_1) * t_1))), sqrt(Float64(fma(cos(Float64(phi2 - phi1)), 0.5, 0.5) - Float64((sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0) * t_0)))) * 2.0) * R) end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$0 * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[(N[Cos[N[(phi2 - phi1), $MachinePrecision]], $MachinePrecision] * 0.5 + 0.5), $MachinePrecision] - N[(N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \cos \phi_2 \cdot \cos \phi_1\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \left(\phi_1 - \phi_2\right) \cdot 0.5\right) + \left(t\_0 \cdot t\_1\right) \cdot t\_1}}{\sqrt{\mathsf{fma}\left(\cos \left(\phi_2 - \phi_1\right), 0.5, 0.5\right) - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} \cdot t\_0}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sin-diffN/A
lower--.f64N/A
lower-*.f64N/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-*.f64N/A
lower-cos.f64N/A
div-invN/A
lower-*.f64N/A
metadata-evalN/A
lower-sin.f64N/A
div-invN/A
lower-*.f64N/A
metadata-eval63.9
Applied rewrites63.9%
lift-sin.f64N/A
lift-/.f64N/A
lift--.f64N/A
div-subN/A
sub-negN/A
sin-sumN/A
lower-sin.f64N/A
div-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
cos-neg-revN/A
lower-fma.f64N/A
lower-cos.f64N/A
div-invN/A
metadata-evalN/A
lower-*.f64N/A
Applied rewrites81.8%
lift-pow.f64N/A
Applied rewrites62.2%
Applied rewrites61.3%
Final simplification61.3%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (* (* (cos phi2) (cos phi1)) t_1) t_1)))
(if (<= phi2 3.7e-34)
(*
(*
(atan2
(sqrt (+ (- 0.5 (* (cos phi1) 0.5)) t_2))
(sqrt (- (pow (cos (* -0.5 phi1)) 2.0) (* t_0 (cos phi1)))))
2.0)
R)
(*
(*
(atan2
(sqrt
(+
(- 0.5 (* (fma (cos phi2) (cos phi1) (* (sin phi1) (sin phi2))) 0.5))
t_2))
(sqrt (- 1.0 t_0)))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
double tmp;
if (phi2 <= 3.7e-34) {
tmp = (atan2(sqrt(((0.5 - (cos(phi1) * 0.5)) + t_2)), sqrt((pow(cos((-0.5 * phi1)), 2.0) - (t_0 * cos(phi1))))) * 2.0) * R;
} else {
tmp = (atan2(sqrt(((0.5 - (fma(cos(phi2), cos(phi1), (sin(phi1) * sin(phi2))) * 0.5)) + t_2)), sqrt((1.0 - t_0))) * 2.0) * R;
}
return tmp;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_1) * t_1) tmp = 0.0 if (phi2 <= 3.7e-34) tmp = Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(phi1) * 0.5)) + t_2)), sqrt(Float64((cos(Float64(-0.5 * phi1)) ^ 2.0) - Float64(t_0 * cos(phi1))))) * 2.0) * R); else tmp = Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(fma(cos(phi2), cos(phi1), Float64(sin(phi1) * sin(phi2))) * 0.5)) + t_2)), sqrt(Float64(1.0 - t_0))) * 2.0) * R); end return tmp end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $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[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]}, If[LessEqual[phi2, 3.7e-34], N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[phi1], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(t$95$0 * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision] + N[(N[Sin[phi1], $MachinePrecision] * N[Sin[phi2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_1\right) \cdot t\_1\\
\mathbf{if}\;\phi_2 \leq 3.7 \cdot 10^{-34}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \phi_1 \cdot 0.5\right) + t\_2}}{\sqrt{{\cos \left(-0.5 \cdot \phi_1\right)}^{2} - t\_0 \cdot \cos \phi_1}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \mathsf{fma}\left(\cos \phi_2, \cos \phi_1, \sin \phi_1 \cdot \sin \phi_2\right) \cdot 0.5\right) + t\_2}}{\sqrt{1 - t\_0}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if phi2 < 3.69999999999999988e-34Initial program 66.7%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites56.9%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6455.2
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6455.2
Applied rewrites55.2%
Taylor expanded in phi2 around 0
lower-cos.f6452.1
Applied rewrites52.1%
if 3.69999999999999988e-34 < phi2 Initial program 55.1%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites22.8%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6421.6
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6421.6
Applied rewrites21.6%
Taylor expanded in phi1 around 0
Applied rewrites21.6%
lift-cos.f64N/A
lift-*.f64N/A
lift-*.f64N/A
associate-*r*N/A
metadata-evalN/A
*-lft-identityN/A
lift--.f64N/A
cos-diffN/A
lift-cos.f64N/A
lift-cos.f64N/A
*-commutativeN/A
lower-fma.f64N/A
*-commutativeN/A
lower-*.f64N/A
lower-sin.f64N/A
lower-sin.f6423.8
Applied rewrites23.8%
Final simplification43.1%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))
(t_1 (sin (/ (- lambda1 lambda2) 2.0)))
(t_2 (* (* (* (cos phi2) (cos phi1)) t_1) t_1)))
(if (<= phi1 -6.4e-6)
(*
(*
(atan2
(sqrt (+ (- 0.5 (* (cos phi1) 0.5)) t_2))
(sqrt (- (pow (cos (* -0.5 phi1)) 2.0) (* t_0 (cos phi1)))))
2.0)
R)
(*
(*
(atan2
(sqrt (+ (- 0.5 (* (cos (- phi2 phi1)) 0.5)) t_2))
(sqrt (- 1.0 t_0)))
2.0)
R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = pow(sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = sin(((lambda1 - lambda2) / 2.0));
double t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
double tmp;
if (phi1 <= -6.4e-6) {
tmp = (atan2(sqrt(((0.5 - (cos(phi1) * 0.5)) + t_2)), sqrt((pow(cos((-0.5 * phi1)), 2.0) - (t_0 * cos(phi1))))) * 2.0) * R;
} else {
tmp = (atan2(sqrt(((0.5 - (cos((phi2 - phi1)) * 0.5)) + t_2)), sqrt((1.0 - t_0))) * 2.0) * R;
}
return tmp;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: tmp
t_0 = sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0
t_1 = sin(((lambda1 - lambda2) / 2.0d0))
t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1
if (phi1 <= (-6.4d-6)) then
tmp = (atan2(sqrt(((0.5d0 - (cos(phi1) * 0.5d0)) + t_2)), sqrt(((cos(((-0.5d0) * phi1)) ** 2.0d0) - (t_0 * cos(phi1))))) * 2.0d0) * r
else
tmp = (atan2(sqrt(((0.5d0 - (cos((phi2 - phi1)) * 0.5d0)) + t_2)), sqrt((1.0d0 - t_0))) * 2.0d0) * r
end if
code = tmp
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0);
double t_1 = Math.sin(((lambda1 - lambda2) / 2.0));
double t_2 = ((Math.cos(phi2) * Math.cos(phi1)) * t_1) * t_1;
double tmp;
if (phi1 <= -6.4e-6) {
tmp = (Math.atan2(Math.sqrt(((0.5 - (Math.cos(phi1) * 0.5)) + t_2)), Math.sqrt((Math.pow(Math.cos((-0.5 * phi1)), 2.0) - (t_0 * Math.cos(phi1))))) * 2.0) * R;
} else {
tmp = (Math.atan2(Math.sqrt(((0.5 - (Math.cos((phi2 - phi1)) * 0.5)) + t_2)), Math.sqrt((1.0 - t_0))) * 2.0) * R;
}
return tmp;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0) t_1 = math.sin(((lambda1 - lambda2) / 2.0)) t_2 = ((math.cos(phi2) * math.cos(phi1)) * t_1) * t_1 tmp = 0 if phi1 <= -6.4e-6: tmp = (math.atan2(math.sqrt(((0.5 - (math.cos(phi1) * 0.5)) + t_2)), math.sqrt((math.pow(math.cos((-0.5 * phi1)), 2.0) - (t_0 * math.cos(phi1))))) * 2.0) * R else: tmp = (math.atan2(math.sqrt(((0.5 - (math.cos((phi2 - phi1)) * 0.5)) + t_2)), math.sqrt((1.0 - t_0))) * 2.0) * R return tmp
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0 t_1 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_2 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_1) * t_1) tmp = 0.0 if (phi1 <= -6.4e-6) tmp = Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(phi1) * 0.5)) + t_2)), sqrt(Float64((cos(Float64(-0.5 * phi1)) ^ 2.0) - Float64(t_0 * cos(phi1))))) * 2.0) * R); else tmp = Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(Float64(phi2 - phi1)) * 0.5)) + t_2)), sqrt(Float64(1.0 - t_0))) * 2.0) * R); end return tmp end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) * 0.5)) ^ 2.0;
t_1 = sin(((lambda1 - lambda2) / 2.0));
t_2 = ((cos(phi2) * cos(phi1)) * t_1) * t_1;
tmp = 0.0;
if (phi1 <= -6.4e-6)
tmp = (atan2(sqrt(((0.5 - (cos(phi1) * 0.5)) + t_2)), sqrt(((cos((-0.5 * phi1)) ^ 2.0) - (t_0 * cos(phi1))))) * 2.0) * R;
else
tmp = (atan2(sqrt(((0.5 - (cos((phi2 - phi1)) * 0.5)) + t_2)), sqrt((1.0 - t_0))) * 2.0) * R;
end
tmp_2 = tmp;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $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[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$1), $MachinePrecision]}, If[LessEqual[phi1, -6.4e-6], N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[phi1], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(t$95$0 * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[N[(phi2 - phi1), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$2), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - t$95$0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}\\
t_1 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_2 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_1\right) \cdot t\_1\\
\mathbf{if}\;\phi_1 \leq -6.4 \cdot 10^{-6}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \phi_1 \cdot 0.5\right) + t\_2}}{\sqrt{{\cos \left(-0.5 \cdot \phi_1\right)}^{2} - t\_0 \cdot \cos \phi_1}} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \left(\phi_2 - \phi_1\right) \cdot 0.5\right) + t\_2}}{\sqrt{1 - t\_0}} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if phi1 < -6.3999999999999997e-6Initial program 45.3%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites45.5%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6445.5
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6445.5
Applied rewrites45.5%
Taylor expanded in phi2 around 0
lower-cos.f6444.6
Applied rewrites44.6%
if -6.3999999999999997e-6 < phi1 Initial program 69.7%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites46.1%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6444.1
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6444.1
Applied rewrites44.1%
Taylor expanded in phi1 around 0
Applied rewrites35.6%
lift-cos.f64N/A
lift-*.f64N/A
lift-*.f64N/A
associate-*r*N/A
metadata-evalN/A
*-lft-identityN/A
lift--.f64N/A
cos-diffN/A
lift-cos.f64N/A
lift-cos.f64N/A
*-commutativeN/A
lift-cos.f64N/A
lift-cos.f64N/A
*-commutativeN/A
cos-diff-revN/A
lower-cos.f64N/A
lower--.f6435.6
Applied rewrites35.6%
Final simplification38.1%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt
(+
(- 0.5 (* (cos (- phi1 phi2)) 0.5))
(* (* (* (cos phi2) (cos phi1)) t_0) t_0)))
(sqrt
(-
(pow (cos (* -0.5 phi1)) 2.0)
(* (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0) (cos phi1)))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt(((0.5 - (cos((phi1 - phi2)) * 0.5)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((pow(cos((-0.5 * phi1)), 2.0) - (pow(sin(((lambda1 - lambda2) * 0.5)), 2.0) * cos(phi1))))) * 2.0) * R;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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 = (atan2(sqrt(((0.5d0 - (cos((phi1 - phi2)) * 0.5d0)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt(((cos(((-0.5d0) * phi1)) ** 2.0d0) - ((sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0) * cos(phi1))))) * 2.0d0) * r
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
return (Math.atan2(Math.sqrt(((0.5 - (Math.cos((phi1 - phi2)) * 0.5)) + (((Math.cos(phi2) * Math.cos(phi1)) * t_0) * t_0))), Math.sqrt((Math.pow(Math.cos((-0.5 * phi1)), 2.0) - (Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0) * Math.cos(phi1))))) * 2.0) * R;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return (math.atan2(math.sqrt(((0.5 - (math.cos((phi1 - phi2)) * 0.5)) + (((math.cos(phi2) * math.cos(phi1)) * t_0) * t_0))), math.sqrt((math.pow(math.cos((-0.5 * phi1)), 2.0) - (math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0) * math.cos(phi1))))) * 2.0) * R
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(Float64(phi1 - phi2)) * 0.5)) + Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt(Float64((cos(Float64(-0.5 * phi1)) ^ 2.0) - Float64((sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0) * cos(phi1))))) * 2.0) * R) end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) / 2.0));
tmp = (atan2(sqrt(((0.5 - (cos((phi1 - phi2)) * 0.5)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt(((cos((-0.5 * phi1)) ^ 2.0) - ((sin(((lambda1 - lambda2) * 0.5)) ^ 2.0) * cos(phi1))))) * 2.0) * R;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[N[(phi1 - phi2), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Cos[N[(-0.5 * phi1), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] - N[(N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \left(\phi_1 - \phi_2\right) \cdot 0.5\right) + \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_0\right) \cdot t\_0}}{\sqrt{{\cos \left(-0.5 \cdot \phi_1\right)}^{2} - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2} \cdot \cos \phi_1}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites46.0%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6444.5
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6444.5
Applied rewrites44.5%
lift-*.f64N/A
lift-*.f64N/A
associate-*r*N/A
metadata-evalN/A
*-lft-identity44.5
Applied rewrites44.5%
Final simplification44.5%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))) (t_1 (cos (- phi2 phi1))))
(*
(*
(atan2
(sqrt
(+
(- 0.5 (* (- (fma t_1 0.5 0.5) (fma t_1 -0.5 0.5)) 0.5))
(* (* (* (cos phi2) (cos phi1)) t_0) t_0)))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = cos((phi2 - phi1));
return (atan2(sqrt(((0.5 - ((fma(t_1, 0.5, 0.5) - fma(t_1, -0.5, 0.5)) * 0.5)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))) * 2.0) * R;
}
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = cos(Float64(phi2 - phi1)) return Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(Float64(fma(t_1, 0.5, 0.5) - fma(t_1, -0.5, 0.5)) * 0.5)) + Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))) * 2.0) * R) end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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[Cos[N[(phi2 - phi1), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[(N[(t$95$1 * 0.5 + 0.5), $MachinePrecision] - N[(t$95$1 * -0.5 + 0.5), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \cos \left(\phi_2 - \phi_1\right)\\
\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \left(\mathsf{fma}\left(t\_1, 0.5, 0.5\right) - \mathsf{fma}\left(t\_1, -0.5, 0.5\right)\right) \cdot 0.5\right) + \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_0\right) \cdot t\_0}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites46.0%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6444.5
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6444.5
Applied rewrites44.5%
Taylor expanded in phi1 around 0
Applied rewrites31.1%
Applied rewrites31.1%
Final simplification31.1%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0)))
(t_1 (* (* (* (cos phi2) (cos phi1)) t_0) t_0))
(t_2 (sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0)))))
(if (<= phi1 -245000.0)
(* (* (atan2 (sqrt (+ (- 0.5 (* (cos phi1) 0.5)) t_1)) t_2) 2.0) R)
(* (* (atan2 (sqrt (+ (- 0.5 (* (cos phi2) 0.5)) t_1)) t_2) 2.0) R))))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
double t_1 = ((cos(phi2) * cos(phi1)) * t_0) * t_0;
double t_2 = sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)));
double tmp;
if (phi1 <= -245000.0) {
tmp = (atan2(sqrt(((0.5 - (cos(phi1) * 0.5)) + t_1)), t_2) * 2.0) * R;
} else {
tmp = (atan2(sqrt(((0.5 - (cos(phi2) * 0.5)) + t_1)), t_2) * 2.0) * R;
}
return tmp;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
real(8) function code(r, lambda1, lambda2, phi1, phi2)
real(8), intent (in) :: r
real(8), intent (in) :: lambda1
real(8), intent (in) :: lambda2
real(8), intent (in) :: phi1
real(8), intent (in) :: phi2
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
real(8) :: tmp
t_0 = sin(((lambda1 - lambda2) / 2.0d0))
t_1 = ((cos(phi2) * cos(phi1)) * t_0) * t_0
t_2 = sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))
if (phi1 <= (-245000.0d0)) then
tmp = (atan2(sqrt(((0.5d0 - (cos(phi1) * 0.5d0)) + t_1)), t_2) * 2.0d0) * r
else
tmp = (atan2(sqrt(((0.5d0 - (cos(phi2) * 0.5d0)) + t_1)), t_2) * 2.0d0) * r
end if
code = tmp
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
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(phi2) * Math.cos(phi1)) * t_0) * t_0;
double t_2 = Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)));
double tmp;
if (phi1 <= -245000.0) {
tmp = (Math.atan2(Math.sqrt(((0.5 - (Math.cos(phi1) * 0.5)) + t_1)), t_2) * 2.0) * R;
} else {
tmp = (Math.atan2(Math.sqrt(((0.5 - (Math.cos(phi2) * 0.5)) + t_1)), t_2) * 2.0) * R;
}
return tmp;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) t_1 = ((math.cos(phi2) * math.cos(phi1)) * t_0) * t_0 t_2 = math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0))) tmp = 0 if phi1 <= -245000.0: tmp = (math.atan2(math.sqrt(((0.5 - (math.cos(phi1) * 0.5)) + t_1)), t_2) * 2.0) * R else: tmp = (math.atan2(math.sqrt(((0.5 - (math.cos(phi2) * 0.5)) + t_1)), t_2) * 2.0) * R return tmp
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) t_1 = Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_0) * t_0) t_2 = sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0))) tmp = 0.0 if (phi1 <= -245000.0) tmp = Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(phi1) * 0.5)) + t_1)), t_2) * 2.0) * R); else tmp = Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(phi2) * 0.5)) + t_1)), t_2) * 2.0) * R); end return tmp end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) / 2.0));
t_1 = ((cos(phi2) * cos(phi1)) * t_0) * t_0;
t_2 = sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0)));
tmp = 0.0;
if (phi1 <= -245000.0)
tmp = (atan2(sqrt(((0.5 - (cos(phi1) * 0.5)) + t_1)), t_2) * 2.0) * R;
else
tmp = (atan2(sqrt(((0.5 - (cos(phi2) * 0.5)) + t_1)), t_2) * 2.0) * R;
end
tmp_2 = tmp;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[phi1, -245000.0], N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[phi1], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]], $MachinePrecision] / t$95$2], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision], N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[phi2], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]], $MachinePrecision] / t$95$2], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]]]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
t_1 := \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_0\right) \cdot t\_0\\
t_2 := \sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}\\
\mathbf{if}\;\phi_1 \leq -245000:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \phi_1 \cdot 0.5\right) + t\_1}}{t\_2} \cdot 2\right) \cdot R\\
\mathbf{else}:\\
\;\;\;\;\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \phi_2 \cdot 0.5\right) + t\_1}}{t\_2} \cdot 2\right) \cdot R\\
\end{array}
\end{array}
if phi1 < -245000Initial program 45.7%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites46.3%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6446.3
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6446.3
Applied rewrites46.3%
Taylor expanded in phi1 around 0
Applied rewrites19.5%
Taylor expanded in phi2 around 0
lower-cos.f6418.9
Applied rewrites18.9%
if -245000 < phi1 Initial program 69.3%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites45.8%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6443.8
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6443.8
Applied rewrites43.8%
Taylor expanded in phi1 around 0
Applied rewrites35.4%
Taylor expanded in phi1 around 0
cos-neg-revN/A
lower-cos.f6433.6
Applied rewrites33.6%
Final simplification29.6%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt
(+
(- 0.5 (* (cos (- phi2 phi1)) 0.5))
(* (* (* (cos phi2) (cos phi1)) t_0) t_0)))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt(((0.5 - (cos((phi2 - phi1)) * 0.5)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))) * 2.0) * R;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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 = (atan2(sqrt(((0.5d0 - (cos((phi2 - phi1)) * 0.5d0)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))) * 2.0d0) * r
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
return (Math.atan2(Math.sqrt(((0.5 - (Math.cos((phi2 - phi1)) * 0.5)) + (((Math.cos(phi2) * Math.cos(phi1)) * t_0) * t_0))), Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))) * 2.0) * R;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return (math.atan2(math.sqrt(((0.5 - (math.cos((phi2 - phi1)) * 0.5)) + (((math.cos(phi2) * math.cos(phi1)) * t_0) * t_0))), math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))) * 2.0) * R
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(Float64(phi2 - phi1)) * 0.5)) + Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))) * 2.0) * R) end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) / 2.0));
tmp = (atan2(sqrt(((0.5 - (cos((phi2 - phi1)) * 0.5)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0)))) * 2.0) * R;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[N[(phi2 - phi1), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \left(\phi_2 - \phi_1\right) \cdot 0.5\right) + \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_0\right) \cdot t\_0}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites46.0%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6444.5
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6444.5
Applied rewrites44.5%
Taylor expanded in phi1 around 0
Applied rewrites31.1%
lift-cos.f64N/A
lift-*.f64N/A
lift-*.f64N/A
associate-*r*N/A
metadata-evalN/A
*-lft-identityN/A
lift--.f64N/A
cos-diffN/A
lift-cos.f64N/A
lift-cos.f64N/A
*-commutativeN/A
lift-cos.f64N/A
lift-cos.f64N/A
*-commutativeN/A
cos-diff-revN/A
lower-cos.f64N/A
lower--.f6431.1
Applied rewrites31.1%
Final simplification31.1%
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
(FPCore (R lambda1 lambda2 phi1 phi2)
:precision binary64
(let* ((t_0 (sin (/ (- lambda1 lambda2) 2.0))))
(*
(*
(atan2
(sqrt
(+
(- 0.5 (* (cos phi1) 0.5))
(* (* (* (cos phi2) (cos phi1)) t_0) t_0)))
(sqrt (- 1.0 (pow (sin (* (- lambda1 lambda2) 0.5)) 2.0))))
2.0)
R)))assert(R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2);
double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = sin(((lambda1 - lambda2) / 2.0));
return (atan2(sqrt(((0.5 - (cos(phi1) * 0.5)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((1.0 - pow(sin(((lambda1 - lambda2) * 0.5)), 2.0)))) * 2.0) * R;
}
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
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 = (atan2(sqrt(((0.5d0 - (cos(phi1) * 0.5d0)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((1.0d0 - (sin(((lambda1 - lambda2) * 0.5d0)) ** 2.0d0)))) * 2.0d0) * r
end function
assert R < lambda1 && lambda1 < lambda2 && lambda2 < phi1 && phi1 < phi2;
public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
double t_0 = Math.sin(((lambda1 - lambda2) / 2.0));
return (Math.atan2(Math.sqrt(((0.5 - (Math.cos(phi1) * 0.5)) + (((Math.cos(phi2) * Math.cos(phi1)) * t_0) * t_0))), Math.sqrt((1.0 - Math.pow(Math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))) * 2.0) * R;
}
[R, lambda1, lambda2, phi1, phi2] = sort([R, lambda1, lambda2, phi1, phi2]) def code(R, lambda1, lambda2, phi1, phi2): t_0 = math.sin(((lambda1 - lambda2) / 2.0)) return (math.atan2(math.sqrt(((0.5 - (math.cos(phi1) * 0.5)) + (((math.cos(phi2) * math.cos(phi1)) * t_0) * t_0))), math.sqrt((1.0 - math.pow(math.sin(((lambda1 - lambda2) * 0.5)), 2.0)))) * 2.0) * R
R, lambda1, lambda2, phi1, phi2 = sort([R, lambda1, lambda2, phi1, phi2]) function code(R, lambda1, lambda2, phi1, phi2) t_0 = sin(Float64(Float64(lambda1 - lambda2) / 2.0)) return Float64(Float64(atan(sqrt(Float64(Float64(0.5 - Float64(cos(phi1) * 0.5)) + Float64(Float64(Float64(cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt(Float64(1.0 - (sin(Float64(Float64(lambda1 - lambda2) * 0.5)) ^ 2.0)))) * 2.0) * R) end
R, lambda1, lambda2, phi1, phi2 = num2cell(sort([R, lambda1, lambda2, phi1, phi2])){:}
function tmp = code(R, lambda1, lambda2, phi1, phi2)
t_0 = sin(((lambda1 - lambda2) / 2.0));
tmp = (atan2(sqrt(((0.5 - (cos(phi1) * 0.5)) + (((cos(phi2) * cos(phi1)) * t_0) * t_0))), sqrt((1.0 - (sin(((lambda1 - lambda2) * 0.5)) ^ 2.0)))) * 2.0) * R;
end
NOTE: R, lambda1, lambda2, phi1, and phi2 should be sorted in increasing order before calling this function.
code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(N[(N[ArcTan[N[Sqrt[N[(N[(0.5 - N[(N[Cos[phi1], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision] + N[(N[(N[(N[Cos[phi2], $MachinePrecision] * N[Cos[phi1], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Sqrt[N[(1.0 - N[Power[N[Sin[N[(N[(lambda1 - lambda2), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * 2.0), $MachinePrecision] * R), $MachinePrecision]]
\begin{array}{l}
[R, lambda1, lambda2, phi1, phi2] = \mathsf{sort}([R, lambda1, lambda2, phi1, phi2])\\
\\
\begin{array}{l}
t_0 := \sin \left(\frac{\lambda_1 - \lambda_2}{2}\right)\\
\left(\tan^{-1}_* \frac{\sqrt{\left(0.5 - \cos \phi_1 \cdot 0.5\right) + \left(\left(\cos \phi_2 \cdot \cos \phi_1\right) \cdot t\_0\right) \cdot t\_0}}{\sqrt{1 - {\sin \left(\left(\lambda_1 - \lambda_2\right) \cdot 0.5\right)}^{2}}} \cdot 2\right) \cdot R
\end{array}
\end{array}
Initial program 63.0%
Taylor expanded in phi2 around 0
+-commutativeN/A
associate--r+N/A
unpow2N/A
1-sub-sin-revN/A
unpow2N/A
lower--.f64N/A
lower-pow.f64N/A
cos-neg-revN/A
lower-cos.f64N/A
distribute-lft-neg-inN/A
metadata-evalN/A
lower-*.f64N/A
*-commutativeN/A
lower-*.f64N/A
Applied rewrites46.0%
lift-pow.f64N/A
unpow2N/A
lift-sin.f64N/A
lift-sin.f64N/A
sqr-sin-aN/A
lower--.f64N/A
cos-2N/A
cos-sumN/A
lower-*.f64N/A
cos-sumN/A
cos-2N/A
lower-cos.f64N/A
lower-*.f6444.5
lift-/.f64N/A
clear-numN/A
associate-/r/N/A
metadata-evalN/A
lower-*.f6444.5
Applied rewrites44.5%
Taylor expanded in phi1 around 0
Applied rewrites31.1%
Taylor expanded in phi2 around 0
lower-cos.f6426.4
Applied rewrites26.4%
Final simplification26.4%
herbie shell --seed 2024312
(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))))))))))