Timeout for inverse-phi-iter

Specification

\[\left(\left(k > 0 \land \alpha > 0\right) \land evar \geq 0\right) \land evar < 1\]

Math

\[\begin{array}{l} t_0 := evar \cdot \sin fi\\ 2 \cdot \left(\tan^{-1} \left({k}^{\left(\frac{-1}{\alpha}\right)} \cdot \left({\tan \left(0.5 \cdot u + \frac{\pi}{4}\right)}^{\left(\frac{1}{\alpha}\right)} \cdot {\left(\frac{1 + t\_0}{1 - t\_0}\right)}^{\left(0.5 \cdot evar\right)}\right)\right) - \frac{\pi}{4}\right) \end{array} \]

FPCore

(FPCore (u k alpha evar fi)
  :precision binary64
  :pre (and (and (and (> k 0.0) (> alpha 0.0)) (>= evar 0.0))
     (< evar 1.0))
  (let* ((t_0 (* evar (sin fi))))
  (*
   2.0
   (-
    (atan
     (*
      (pow k (/ -1.0 alpha))
      (*
       (pow (tan (+ (* 0.5 u) (/ PI 4.0))) (/ 1.0 alpha))
       (pow (/ (+ 1.0 t_0) (- 1.0 t_0)) (* 0.5 evar)))))
    (/ PI 4.0)))))

C

double code(double u, double k, double alpha, double evar, double fi) {
	double t_0 = evar * sin(fi);
	return 2.0 * (atan((pow(k, (-1.0 / alpha)) * (pow(tan(((0.5 * u) + (((double) M_PI) / 4.0))), (1.0 / alpha)) * pow(((1.0 + t_0) / (1.0 - t_0)), (0.5 * evar))))) - (((double) M_PI) / 4.0));
}

Java

public static double code(double u, double k, double alpha, double evar, double fi) {
	double t_0 = evar * Math.sin(fi);
	return 2.0 * (Math.atan((Math.pow(k, (-1.0 / alpha)) * (Math.pow(Math.tan(((0.5 * u) + (Math.PI / 4.0))), (1.0 / alpha)) * Math.pow(((1.0 + t_0) / (1.0 - t_0)), (0.5 * evar))))) - (Math.PI / 4.0));
}

Python

def code(u, k, alpha, evar, fi):
	t_0 = evar * math.sin(fi)
	return 2.0 * (math.atan((math.pow(k, (-1.0 / alpha)) * (math.pow(math.tan(((0.5 * u) + (math.pi / 4.0))), (1.0 / alpha)) * math.pow(((1.0 + t_0) / (1.0 - t_0)), (0.5 * evar))))) - (math.pi / 4.0))

Julia

function code(u, k, alpha, evar, fi)
	t_0 = Float64(evar * sin(fi))
	return Float64(2.0 * Float64(atan(Float64((k ^ Float64(-1.0 / alpha)) * Float64((tan(Float64(Float64(0.5 * u) + Float64(pi / 4.0))) ^ Float64(1.0 / alpha)) * (Float64(Float64(1.0 + t_0) / Float64(1.0 - t_0)) ^ Float64(0.5 * evar))))) - Float64(pi / 4.0)))
end

MATLAB

function tmp = code(u, k, alpha, evar, fi)
	t_0 = evar * sin(fi);
	tmp = 2.0 * (atan(((k ^ (-1.0 / alpha)) * ((tan(((0.5 * u) + (pi / 4.0))) ^ (1.0 / alpha)) * (((1.0 + t_0) / (1.0 - t_0)) ^ (0.5 * evar))))) - (pi / 4.0));
end

Wolfram

code[u_, k_, alpha_, evar_, fi_] := Block[{t$95$0 = N[(evar * N[Sin[fi], $MachinePrecision]), $MachinePrecision]}, N[(2.0 * N[(N[ArcTan[N[(N[Power[k, N[(-1.0 / alpha), $MachinePrecision]], $MachinePrecision] * N[(N[Power[N[Tan[N[(N[(0.5 * u), $MachinePrecision] + N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[(1.0 / alpha), $MachinePrecision]], $MachinePrecision] * N[Power[N[(N[(1.0 + t$95$0), $MachinePrecision] / N[(1.0 - t$95$0), $MachinePrecision]), $MachinePrecision], N[(0.5 * evar), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

ReFlow

f(u, k, alpha, evar, fi):
	u in [-inf, +inf],
	k in [0, +inf],
	alpha in [0, +inf],
	evar in [0, 1],
	fi in [-inf, +inf]
code: THEORY
BEGIN
f(u, k, alpha, evar, fi: real): real =
	LET t_0 = (evar * (sin(fi))) IN
	(2) * ((atan(((k ^ ((-1) / alpha)) * (((tan((((5e-1) * u) + ((4 * atan(1)) / (4))))) ^ ((1) / alpha)) * ((((1) + t_0) / ((1) - t_0)) ^ ((5e-1) * evar)))))) - ((4 * atan(1)) / (4)))
END code