Diagrams.TwoD.Layout.CirclePacking:approxRadius from diagrams-contrib-1.3.0.5

Percentage Accurate: 43.3% → 57.1%

Time: 13.2s

Alternatives: 2

Speedup: 244.0×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y \cdot 2}\\ \frac{\tan t\_0}{\sin t\_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (* y 2.0)))) (/ (tan t_0) (sin t_0))))

C

double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return tan(t_0) / sin(t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = x / (y * 2.0d0)
    code = tan(t_0) / sin(t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return Math.tan(t_0) / Math.sin(t_0);
}

Python

def code(x, y):
	t_0 = x / (y * 2.0)
	return math.tan(t_0) / math.sin(t_0)

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y * 2.0))
	return Float64(tan(t_0) / sin(t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = x / (y * 2.0);
	tmp = tan(t_0) / sin(t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y * 2.0), $MachinePrecision]), $MachinePrecision]}, N[(N[Tan[t$95$0], $MachinePrecision] / N[Sin[t$95$0], $MachinePrecision]), $MachinePrecision]]

Initial Program: 43.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y \cdot 2}\\ \frac{\tan t\_0}{\sin t\_0} \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (* y 2.0)))) (/ (tan t_0) (sin t_0))))

C

double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return tan(t_0) / sin(t_0);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    t_0 = x / (y * 2.0d0)
    code = tan(t_0) / sin(t_0)
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	return Math.tan(t_0) / Math.sin(t_0);
}

Python

def code(x, y):
	t_0 = x / (y * 2.0)
	return math.tan(t_0) / math.sin(t_0)

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y * 2.0))
	return Float64(tan(t_0) / sin(t_0))
end

MATLAB

function tmp = code(x, y)
	t_0 = x / (y * 2.0);
	tmp = tan(t_0) / sin(t_0);
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y * 2.0), $MachinePrecision]), $MachinePrecision]}, N[(N[Tan[t$95$0], $MachinePrecision] / N[Sin[t$95$0], $MachinePrecision]), $MachinePrecision]]

Alternative 1: 57.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ \begin{array}{l} t_0 := e^{\left(-0.5\right) \cdot \left(\log y\_m \cdot 0.5\right)}\\ t_1 := \frac{x\_m}{2 \cdot y\_m}\\ \mathbf{if}\;\frac{\tan t\_1}{\sin t\_1} \leq 9:\\ \;\;\;\;\frac{1}{\cos \left(\frac{\left(t\_0 \cdot {\left(\frac{1}{y\_m}\right)}^{0.5}\right) \cdot t\_0}{{\left(e^{-1}\right)}^{\log \left(0.5 \cdot x\_m\right)}}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m)
 :precision binary64
 (let* ((t_0 (exp (* (- 0.5) (* (log y_m) 0.5)))) (t_1 (/ x_m (* 2.0 y_m))))
   (if (<= (/ (tan t_1) (sin t_1)) 9.0)
     (/
      1.0
      (cos
       (/
        (* (* t_0 (pow (/ 1.0 y_m) 0.5)) t_0)
        (pow (exp -1.0) (log (* 0.5 x_m))))))
     1.0)))

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	double t_0 = exp((-0.5 * (log(y_m) * 0.5)));
	double t_1 = x_m / (2.0 * y_m);
	double tmp;
	if ((tan(t_1) / sin(t_1)) <= 9.0) {
		tmp = 1.0 / cos((((t_0 * pow((1.0 / y_m), 0.5)) * t_0) / pow(exp(-1.0), log((0.5 * x_m)))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = exp((-0.5d0 * (log(y_m) * 0.5d0)))
    t_1 = x_m / (2.0d0 * y_m)
    if ((tan(t_1) / sin(t_1)) <= 9.0d0) then
        tmp = 1.0d0 / cos((((t_0 * ((1.0d0 / y_m) ** 0.5d0)) * t_0) / (exp((-1.0d0)) ** log((0.5d0 * x_m)))))
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	double t_0 = Math.exp((-0.5 * (Math.log(y_m) * 0.5)));
	double t_1 = x_m / (2.0 * y_m);
	double tmp;
	if ((Math.tan(t_1) / Math.sin(t_1)) <= 9.0) {
		tmp = 1.0 / Math.cos((((t_0 * Math.pow((1.0 / y_m), 0.5)) * t_0) / Math.pow(Math.exp(-1.0), Math.log((0.5 * x_m)))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	t_0 = math.exp((-0.5 * (math.log(y_m) * 0.5)))
	t_1 = x_m / (2.0 * y_m)
	tmp = 0
	if (math.tan(t_1) / math.sin(t_1)) <= 9.0:
		tmp = 1.0 / math.cos((((t_0 * math.pow((1.0 / y_m), 0.5)) * t_0) / math.pow(math.exp(-1.0), math.log((0.5 * x_m)))))
	else:
		tmp = 1.0
	return tmp

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	t_0 = exp(Float64(Float64(-0.5) * Float64(log(y_m) * 0.5)))
	t_1 = Float64(x_m / Float64(2.0 * y_m))
	tmp = 0.0
	if (Float64(tan(t_1) / sin(t_1)) <= 9.0)
		tmp = Float64(1.0 / cos(Float64(Float64(Float64(t_0 * (Float64(1.0 / y_m) ^ 0.5)) * t_0) / (exp(-1.0) ^ log(Float64(0.5 * x_m))))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp_2 = code(x_m, y_m)
	t_0 = exp((-0.5 * (log(y_m) * 0.5)));
	t_1 = x_m / (2.0 * y_m);
	tmp = 0.0;
	if ((tan(t_1) / sin(t_1)) <= 9.0)
		tmp = 1.0 / cos((((t_0 * ((1.0 / y_m) ^ 0.5)) * t_0) / (exp(-1.0) ^ log((0.5 * x_m)))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := Block[{t$95$0 = N[Exp[N[((-0.5) * N[(N[Log[y$95$m], $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(x$95$m / N[(2.0 * y$95$m), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[Tan[t$95$1], $MachinePrecision] / N[Sin[t$95$1], $MachinePrecision]), $MachinePrecision], 9.0], N[(1.0 / N[Cos[N[(N[(N[(t$95$0 * N[Power[N[(1.0 / y$95$m), $MachinePrecision], 0.5], $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision] / N[Power[N[Exp[-1.0], $MachinePrecision], N[Log[N[(0.5 * x$95$m), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 1.0]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (tan.f64 (/.f64 x (*.f64 y #s(literal 2 binary64)))) (sin.f64 (/.f64 x (*.f64 y #s(literal 2 binary64))))) < 9

   1. Initial program 57.2%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \color{blue}{\left(\frac{x}{y \cdot 2}\right)}} \]

      2. clear-num N/A

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \color{blue}{\left(\frac{1}{\frac{y \cdot 2}{x}}\right)}} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{1}{\frac{\color{blue}{y \cdot 2}}{x}}\right)} \]

      4. associate-/l* N/A

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{1}{\color{blue}{y \cdot \frac{2}{x}}}\right)} \]

      5. associate-/r* N/A

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \color{blue}{\left(\frac{\frac{1}{y}}{\frac{2}{x}}\right)}} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \color{blue}{\left(\frac{\frac{1}{y}}{\frac{2}{x}}\right)}} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{\color{blue}{\frac{1}{y}}}{\frac{2}{x}}\right)} \]

      8. lower-/.f64 56.6

         \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{\frac{1}{y}}{\color{blue}{\frac{2}{x}}}\right)} \]

   4. Applied rewrites 56.6%

      \[\leadsto \frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \color{blue}{\left(\frac{\frac{1}{y}}{\frac{2}{x}}\right)}} \]

   6. Applied rewrites 57.3%

      \[\leadsto \color{blue}{\frac{1}{\cos \left(\frac{-0.5}{y} \cdot x\right)}} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{\frac{-1}{2}}{y} \cdot x\right)}} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{1}{\cos \left(\color{blue}{\frac{\frac{-1}{2}}{y}} \cdot x\right)} \]

      3. clear-num N/A

         \[\leadsto \frac{1}{\cos \left(\color{blue}{\frac{1}{\frac{y}{\frac{-1}{2}}}} \cdot x\right)} \]

      4. inv-pow N/A

         \[\leadsto \frac{1}{\cos \left(\color{blue}{{\left(\frac{y}{\frac{-1}{2}}\right)}^{-1}} \cdot x\right)} \]

      5. div-inv N/A

         \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(y \cdot \frac{1}{\frac{-1}{2}}\right)}}^{-1} \cdot x\right)} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left({\left(y \cdot \color{blue}{-2}\right)}^{-1} \cdot x\right)} \]

      7. metadata-eval N/A

         \[\leadsto \frac{1}{\cos \left({\left(y \cdot \color{blue}{\left(\mathsf{neg}\left(2\right)\right)}\right)}^{-1} \cdot x\right)} \]

      8. distribute-rgt-neg-in N/A

         \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\mathsf{neg}\left(y \cdot 2\right)\right)}}^{-1} \cdot x\right)} \]

      9. *-commutative N/A

         \[\leadsto \frac{1}{\cos \left({\left(\mathsf{neg}\left(\color{blue}{2 \cdot y}\right)\right)}^{-1} \cdot x\right)} \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{1}{\cos \left({\left(\mathsf{neg}\left(\color{blue}{2 \cdot y}\right)\right)}^{-1} \cdot x\right)} \]

      11. metadata-eval N/A

          \[\leadsto \frac{1}{\cos \left({\left(\mathsf{neg}\left(2 \cdot y\right)\right)}^{\color{blue}{\left(2 \cdot \frac{-1}{2}\right)}} \cdot x\right)} \]

      12. pow-pow N/A

          \[\leadsto \frac{1}{\cos \left(\color{blue}{{\left({\left(\mathsf{neg}\left(2 \cdot y\right)\right)}^{2}\right)}^{\frac{-1}{2}}} \cdot x\right)} \]

      13. pow2 N/A

          \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\left(\mathsf{neg}\left(2 \cdot y\right)\right) \cdot \left(\mathsf{neg}\left(2 \cdot y\right)\right)\right)}}^{\frac{-1}{2}} \cdot x\right)} \]

      14. sqr-neg N/A

          \[\leadsto \frac{1}{\cos \left({\color{blue}{\left(\left(2 \cdot y\right) \cdot \left(2 \cdot y\right)\right)}}^{\frac{-1}{2}} \cdot x\right)} \]

      15. pow2 N/A

          \[\leadsto \frac{1}{\cos \left({\color{blue}{\left({\left(2 \cdot y\right)}^{2}\right)}}^{\frac{-1}{2}} \cdot x\right)} \]

      16. pow-pow N/A

          \[\leadsto \frac{1}{\cos \left(\color{blue}{{\left(2 \cdot y\right)}^{\left(2 \cdot \frac{-1}{2}\right)}} \cdot x\right)} \]

      17. metadata-eval N/A

          \[\leadsto \frac{1}{\cos \left({\left(2 \cdot y\right)}^{\color{blue}{-1}} \cdot x\right)} \]

      18. inv-pow N/A

          \[\leadsto \frac{1}{\cos \left(\color{blue}{\frac{1}{2 \cdot y}} \cdot x\right)} \]

      19. associate-/r/ N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{1}{\frac{2 \cdot y}{x}}\right)}} \]

      20. lift-/.f64 N/A

          \[\leadsto \frac{1}{\cos \left(\frac{1}{\color{blue}{\frac{2 \cdot y}{x}}}\right)} \]

      21. inv-pow N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(\frac{2 \cdot y}{x}\right)}^{-1}\right)}} \]

      22. exp-to-pow N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left(e^{\log \left(\frac{2 \cdot y}{x}\right) \cdot -1}\right)}} \]

      23. lift-log.f64 N/A

          \[\leadsto \frac{1}{\cos \left(e^{\color{blue}{\log \left(\frac{2 \cdot y}{x}\right)} \cdot -1}\right)} \]

      24. *-commutative N/A

          \[\leadsto \frac{1}{\cos \left(e^{\color{blue}{-1 \cdot \log \left(\frac{2 \cdot y}{x}\right)}}\right)} \]

      25. exp-prod N/A

          \[\leadsto \frac{1}{\cos \color{blue}{\left({\left(e^{-1}\right)}^{\log \left(\frac{2 \cdot y}{x}\right)}\right)}} \]

   8. Applied rewrites 10.1%

      \[\leadsto \frac{1}{\cos \color{blue}{\left(\frac{{\left(e^{-1}\right)}^{\log y}}{{\left(e^{-1}\right)}^{\log \left(0.5 \cdot x\right)}}\right)}} \]
   9. Step-by-step derivation

      1. lift-pow.f64 N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{{\left(e^{-1}\right)}^{\log y}}}{{\left(e^{-1}\right)}^{\log \left(\frac{1}{2} \cdot x\right)}}\right)} \]

      2. sqr-pow N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{{\left(e^{-1}\right)}^{\left(\frac{\log y}{2}\right)} \cdot {\left(e^{-1}\right)}^{\left(\frac{\log y}{2}\right)}}}{{\left(e^{-1}\right)}^{\log \left(\frac{1}{2} \cdot x\right)}}\right)} \]

      3. sqr-pow N/A

         \[\leadsto \frac{1}{\cos \left(\frac{{\left(e^{-1}\right)}^{\left(\frac{\log y}{2}\right)} \cdot \color{blue}{\left({\left(e^{-1}\right)}^{\left(\frac{\frac{\log y}{2}}{2}\right)} \cdot {\left(e^{-1}\right)}^{\left(\frac{\frac{\log y}{2}}{2}\right)}\right)}}{{\left(e^{-1}\right)}^{\log \left(\frac{1}{2} \cdot x\right)}}\right)} \]

      4. associate-*r* N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\left({\left(e^{-1}\right)}^{\left(\frac{\log y}{2}\right)} \cdot {\left(e^{-1}\right)}^{\left(\frac{\frac{\log y}{2}}{2}\right)}\right) \cdot {\left(e^{-1}\right)}^{\left(\frac{\frac{\log y}{2}}{2}\right)}}}{{\left(e^{-1}\right)}^{\log \left(\frac{1}{2} \cdot x\right)}}\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\left({\left(e^{-1}\right)}^{\left(\frac{\log y}{2}\right)} \cdot {\left(e^{-1}\right)}^{\left(\frac{\frac{\log y}{2}}{2}\right)}\right) \cdot {\left(e^{-1}\right)}^{\left(\frac{\frac{\log y}{2}}{2}\right)}}}{{\left(e^{-1}\right)}^{\log \left(\frac{1}{2} \cdot x\right)}}\right)} \]

   10. Applied rewrites 10.0%

       \[\leadsto \frac{1}{\cos \left(\frac{\color{blue}{\left({\left(\frac{1}{y}\right)}^{0.5} \cdot e^{-1 \cdot \left(\left(\log y \cdot 0.5\right) \cdot 0.5\right)}\right) \cdot e^{-1 \cdot \left(\left(\log y \cdot 0.5\right) \cdot 0.5\right)}}}{{\left(e^{-1}\right)}^{\log \left(0.5 \cdot x\right)}}\right)} \]

   if 9 < (/.f64 (tan.f64 (/.f64 x (*.f64 y #s(literal 2 binary64)))) (sin.f64 (/.f64 x (*.f64 y #s(literal 2 binary64)))))

   1. Initial program 1.0%

      \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in y around inf

      \[\leadsto \color{blue}{1} \]
   4. Step-by-step derivation

   5. Applied rewrites 47.1%

      \[\leadsto \color{blue}{1} \]
3. Recombined 2 regimes into one program.

4. Final simplification 21.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{\tan \left(\frac{x}{2 \cdot y}\right)}{\sin \left(\frac{x}{2 \cdot y}\right)} \leq 9:\\ \;\;\;\;\frac{1}{\cos \left(\frac{\left(e^{\left(-0.5\right) \cdot \left(\log y \cdot 0.5\right)} \cdot {\left(\frac{1}{y}\right)}^{0.5}\right) \cdot e^{\left(-0.5\right) \cdot \left(\log y \cdot 0.5\right)}}{{\left(e^{-1}\right)}^{\log \left(0.5 \cdot x\right)}}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 55.9% accurate, 244.0× speedup

Math

\[\begin{array}{l} y_m = \left|y\right| \\ x_m = \left|x\right| \\ 1 \end{array} \]

FPCore

y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
(FPCore (x_m y_m) :precision binary64 1.0)

C

y_m = fabs(y);
x_m = fabs(x);
double code(double x_m, double y_m) {
	return 1.0;
}

Fortran

y_m = abs(y)
x_m = abs(x)
real(8) function code(x_m, y_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    code = 1.0d0
end function

Java

y_m = Math.abs(y);
x_m = Math.abs(x);
public static double code(double x_m, double y_m) {
	return 1.0;
}

Python

y_m = math.fabs(y)
x_m = math.fabs(x)
def code(x_m, y_m):
	return 1.0

Julia

y_m = abs(y)
x_m = abs(x)
function code(x_m, y_m)
	return 1.0
end

MATLAB

y_m = abs(y);
x_m = abs(x);
function tmp = code(x_m, y_m)
	tmp = 1.0;
end

Wolfram

y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y$95$m_] := 1.0

Derivation

1. Initial program 39.6%

   \[\frac{\tan \left(\frac{x}{y \cdot 2}\right)}{\sin \left(\frac{x}{y \cdot 2}\right)} \]
2. Add Preprocessing

3. Taylor expanded in y around inf

   \[\leadsto \color{blue}{1} \]
4. Step-by-step derivation

5. Applied rewrites 52.8%

   \[\leadsto \color{blue}{1} \]
6. Add Preprocessing

Developer Target 1: 55.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{y \cdot 2}\\ t_1 := \sin t\_0\\ \mathbf{if}\;y < -1.2303690911306994 \cdot 10^{+114}:\\ \;\;\;\;1\\ \mathbf{elif}\;y < -9.102852406811914 \cdot 10^{-222}:\\ \;\;\;\;\frac{t\_1}{t\_1 \cdot \log \left(e^{\cos t\_0}\right)}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ x (* y 2.0))) (t_1 (sin t_0)))
   (if (< y -1.2303690911306994e+114)
     1.0
     (if (< y -9.102852406811914e-222)
       (/ t_1 (* t_1 (log (exp (cos t_0)))))
       1.0))))

C

double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	double t_1 = sin(t_0);
	double tmp;
	if (y < -1.2303690911306994e+114) {
		tmp = 1.0;
	} else if (y < -9.102852406811914e-222) {
		tmp = t_1 / (t_1 * log(exp(cos(t_0))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = x / (y * 2.0d0)
    t_1 = sin(t_0)
    if (y < (-1.2303690911306994d+114)) then
        tmp = 1.0d0
    else if (y < (-9.102852406811914d-222)) then
        tmp = t_1 / (t_1 * log(exp(cos(t_0))))
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = x / (y * 2.0);
	double t_1 = Math.sin(t_0);
	double tmp;
	if (y < -1.2303690911306994e+114) {
		tmp = 1.0;
	} else if (y < -9.102852406811914e-222) {
		tmp = t_1 / (t_1 * Math.log(Math.exp(Math.cos(t_0))));
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = x / (y * 2.0)
	t_1 = math.sin(t_0)
	tmp = 0
	if y < -1.2303690911306994e+114:
		tmp = 1.0
	elif y < -9.102852406811914e-222:
		tmp = t_1 / (t_1 * math.log(math.exp(math.cos(t_0))))
	else:
		tmp = 1.0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(x / Float64(y * 2.0))
	t_1 = sin(t_0)
	tmp = 0.0
	if (y < -1.2303690911306994e+114)
		tmp = 1.0;
	elseif (y < -9.102852406811914e-222)
		tmp = Float64(t_1 / Float64(t_1 * log(exp(cos(t_0)))));
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = x / (y * 2.0);
	t_1 = sin(t_0);
	tmp = 0.0;
	if (y < -1.2303690911306994e+114)
		tmp = 1.0;
	elseif (y < -9.102852406811914e-222)
		tmp = t_1 / (t_1 * log(exp(cos(t_0))));
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x / N[(y * 2.0), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[Sin[t$95$0], $MachinePrecision]}, If[Less[y, -1.2303690911306994e+114], 1.0, If[Less[y, -9.102852406811914e-222], N[(t$95$1 / N[(t$95$1 * N[Log[N[Exp[N[Cos[t$95$0], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1.0]]]]

Reproduce

herbie shell --seed 2024235 
(FPCore (x y)
  :name "Diagrams.TwoD.Layout.CirclePacking:approxRadius from diagrams-contrib-1.3.0.5"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< y -1230369091130699400000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) 1 (if (< y -4551426203405957/500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (/ (sin (/ x (* y 2))) (* (sin (/ x (* y 2))) (log (exp (cos (/ x (* y 2))))))) 1)))

  (/ (tan (/ x (* y 2.0))) (sin (/ x (* y 2.0)))))