Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, C

Percentage Accurate: 99.9% → 100.0%

Time: 6.4s

Alternatives: 9

Speedup: 1.4×

• 21.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.25\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.25)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.25d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.25)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.25), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.25\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.25)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 1.0d0 + ((4.0d0 * ((x + (y * 0.25d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.25)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.25)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.25), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ 2 + \frac{x - z}{y \cdot 0.25} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ 2.0 (/ (- x z) (* y 0.25))))

C

double code(double x, double y, double z) {
	return 2.0 + ((x - z) / (y * 0.25));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 2.0d0 + ((x - z) / (y * 0.25d0))
end function

Java

public static double code(double x, double y, double z) {
	return 2.0 + ((x - z) / (y * 0.25));
}

Python

def code(x, y, z):
	return 2.0 + ((x - z) / (y * 0.25))

Julia

function code(x, y, z)
	return Float64(2.0 + Float64(Float64(x - z) / Float64(y * 0.25)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 2.0 + ((x - z) / (y * 0.25));
end

Wolfram

code[x_, y_, z_] := N[(2.0 + N[(N[(x - z), $MachinePrecision] / N[(y * 0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 57.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + z \cdot \frac{-4}{y}\\ t_1 := 1 + x \cdot \frac{4}{y}\\ \mathbf{if}\;x \leq -2.4 \cdot 10^{+32}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -0.0195:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1.1 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -2.9 \cdot 10^{-305}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 6 \cdot 10^{-166}:\\ \;\;\;\;2\\ \mathbf{elif}\;x \leq 1.18 \cdot 10^{-136}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 600000:\\ \;\;\;\;2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ 1.0 (* z (/ -4.0 y)))) (t_1 (+ 1.0 (* x (/ 4.0 y)))))
   (if (<= x -2.4e+32)
     t_1
     (if (<= x -0.0195)
       t_0
       (if (<= x -1.1e-47)
         t_1
         (if (<= x -2.9e-305)
           t_0
           (if (<= x 6e-166)
             2.0
             (if (<= x 1.18e-136) t_0 (if (<= x 600000.0) 2.0 t_1)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 + (z * (-4.0 / y));
	double t_1 = 1.0 + (x * (4.0 / y));
	double tmp;
	if (x <= -2.4e+32) {
		tmp = t_1;
	} else if (x <= -0.0195) {
		tmp = t_0;
	} else if (x <= -1.1e-47) {
		tmp = t_1;
	} else if (x <= -2.9e-305) {
		tmp = t_0;
	} else if (x <= 6e-166) {
		tmp = 2.0;
	} else if (x <= 1.18e-136) {
		tmp = t_0;
	} else if (x <= 600000.0) {
		tmp = 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 + (z * ((-4.0d0) / y))
    t_1 = 1.0d0 + (x * (4.0d0 / y))
    if (x <= (-2.4d+32)) then
        tmp = t_1
    else if (x <= (-0.0195d0)) then
        tmp = t_0
    else if (x <= (-1.1d-47)) then
        tmp = t_1
    else if (x <= (-2.9d-305)) then
        tmp = t_0
    else if (x <= 6d-166) then
        tmp = 2.0d0
    else if (x <= 1.18d-136) then
        tmp = t_0
    else if (x <= 600000.0d0) then
        tmp = 2.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 + (z * (-4.0 / y));
	double t_1 = 1.0 + (x * (4.0 / y));
	double tmp;
	if (x <= -2.4e+32) {
		tmp = t_1;
	} else if (x <= -0.0195) {
		tmp = t_0;
	} else if (x <= -1.1e-47) {
		tmp = t_1;
	} else if (x <= -2.9e-305) {
		tmp = t_0;
	} else if (x <= 6e-166) {
		tmp = 2.0;
	} else if (x <= 1.18e-136) {
		tmp = t_0;
	} else if (x <= 600000.0) {
		tmp = 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 + (z * (-4.0 / y))
	t_1 = 1.0 + (x * (4.0 / y))
	tmp = 0
	if x <= -2.4e+32:
		tmp = t_1
	elif x <= -0.0195:
		tmp = t_0
	elif x <= -1.1e-47:
		tmp = t_1
	elif x <= -2.9e-305:
		tmp = t_0
	elif x <= 6e-166:
		tmp = 2.0
	elif x <= 1.18e-136:
		tmp = t_0
	elif x <= 600000.0:
		tmp = 2.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 + Float64(z * Float64(-4.0 / y)))
	t_1 = Float64(1.0 + Float64(x * Float64(4.0 / y)))
	tmp = 0.0
	if (x <= -2.4e+32)
		tmp = t_1;
	elseif (x <= -0.0195)
		tmp = t_0;
	elseif (x <= -1.1e-47)
		tmp = t_1;
	elseif (x <= -2.9e-305)
		tmp = t_0;
	elseif (x <= 6e-166)
		tmp = 2.0;
	elseif (x <= 1.18e-136)
		tmp = t_0;
	elseif (x <= 600000.0)
		tmp = 2.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 + (z * (-4.0 / y));
	t_1 = 1.0 + (x * (4.0 / y));
	tmp = 0.0;
	if (x <= -2.4e+32)
		tmp = t_1;
	elseif (x <= -0.0195)
		tmp = t_0;
	elseif (x <= -1.1e-47)
		tmp = t_1;
	elseif (x <= -2.9e-305)
		tmp = t_0;
	elseif (x <= 6e-166)
		tmp = 2.0;
	elseif (x <= 1.18e-136)
		tmp = t_0;
	elseif (x <= 600000.0)
		tmp = 2.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 + N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.4e+32], t$95$1, If[LessEqual[x, -0.0195], t$95$0, If[LessEqual[x, -1.1e-47], t$95$1, If[LessEqual[x, -2.9e-305], t$95$0, If[LessEqual[x, 6e-166], 2.0, If[LessEqual[x, 1.18e-136], t$95$0, If[LessEqual[x, 600000.0], 2.0, t$95$1]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 57.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + \frac{z \cdot -4}{y}\\ t_1 := 1 + x \cdot \frac{4}{y}\\ \mathbf{if}\;x \leq -1.95 \cdot 10^{+33}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -0.027:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -2 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -3.5 \cdot 10^{-305}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 1.65 \cdot 10^{-165}:\\ \;\;\;\;2\\ \mathbf{elif}\;x \leq 2.75 \cdot 10^{-142}:\\ \;\;\;\;1 + z \cdot \frac{-4}{y}\\ \mathbf{elif}\;x \leq 215000000:\\ \;\;\;\;2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ (* z -4.0) y))) (t_1 (+ 1.0 (* x (/ 4.0 y)))))
   (if (<= x -1.95e+33)
     t_1
     (if (<= x -0.027)
       t_0
       (if (<= x -2e-47)
         t_1
         (if (<= x -3.5e-305)
           t_0
           (if (<= x 1.65e-165)
             2.0
             (if (<= x 2.75e-142)
               (+ 1.0 (* z (/ -4.0 y)))
               (if (<= x 215000000.0) 2.0 t_1)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 + ((z * -4.0) / y);
	double t_1 = 1.0 + (x * (4.0 / y));
	double tmp;
	if (x <= -1.95e+33) {
		tmp = t_1;
	} else if (x <= -0.027) {
		tmp = t_0;
	} else if (x <= -2e-47) {
		tmp = t_1;
	} else if (x <= -3.5e-305) {
		tmp = t_0;
	} else if (x <= 1.65e-165) {
		tmp = 2.0;
	} else if (x <= 2.75e-142) {
		tmp = 1.0 + (z * (-4.0 / y));
	} else if (x <= 215000000.0) {
		tmp = 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 + ((z * (-4.0d0)) / y)
    t_1 = 1.0d0 + (x * (4.0d0 / y))
    if (x <= (-1.95d+33)) then
        tmp = t_1
    else if (x <= (-0.027d0)) then
        tmp = t_0
    else if (x <= (-2d-47)) then
        tmp = t_1
    else if (x <= (-3.5d-305)) then
        tmp = t_0
    else if (x <= 1.65d-165) then
        tmp = 2.0d0
    else if (x <= 2.75d-142) then
        tmp = 1.0d0 + (z * ((-4.0d0) / y))
    else if (x <= 215000000.0d0) then
        tmp = 2.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 + ((z * -4.0) / y);
	double t_1 = 1.0 + (x * (4.0 / y));
	double tmp;
	if (x <= -1.95e+33) {
		tmp = t_1;
	} else if (x <= -0.027) {
		tmp = t_0;
	} else if (x <= -2e-47) {
		tmp = t_1;
	} else if (x <= -3.5e-305) {
		tmp = t_0;
	} else if (x <= 1.65e-165) {
		tmp = 2.0;
	} else if (x <= 2.75e-142) {
		tmp = 1.0 + (z * (-4.0 / y));
	} else if (x <= 215000000.0) {
		tmp = 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 + ((z * -4.0) / y)
	t_1 = 1.0 + (x * (4.0 / y))
	tmp = 0
	if x <= -1.95e+33:
		tmp = t_1
	elif x <= -0.027:
		tmp = t_0
	elif x <= -2e-47:
		tmp = t_1
	elif x <= -3.5e-305:
		tmp = t_0
	elif x <= 1.65e-165:
		tmp = 2.0
	elif x <= 2.75e-142:
		tmp = 1.0 + (z * (-4.0 / y))
	elif x <= 215000000.0:
		tmp = 2.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 + Float64(Float64(z * -4.0) / y))
	t_1 = Float64(1.0 + Float64(x * Float64(4.0 / y)))
	tmp = 0.0
	if (x <= -1.95e+33)
		tmp = t_1;
	elseif (x <= -0.027)
		tmp = t_0;
	elseif (x <= -2e-47)
		tmp = t_1;
	elseif (x <= -3.5e-305)
		tmp = t_0;
	elseif (x <= 1.65e-165)
		tmp = 2.0;
	elseif (x <= 2.75e-142)
		tmp = Float64(1.0 + Float64(z * Float64(-4.0 / y)));
	elseif (x <= 215000000.0)
		tmp = 2.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 + ((z * -4.0) / y);
	t_1 = 1.0 + (x * (4.0 / y));
	tmp = 0.0;
	if (x <= -1.95e+33)
		tmp = t_1;
	elseif (x <= -0.027)
		tmp = t_0;
	elseif (x <= -2e-47)
		tmp = t_1;
	elseif (x <= -3.5e-305)
		tmp = t_0;
	elseif (x <= 1.65e-165)
		tmp = 2.0;
	elseif (x <= 2.75e-142)
		tmp = 1.0 + (z * (-4.0 / y));
	elseif (x <= 215000000.0)
		tmp = 2.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 + N[(N[(z * -4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1.95e+33], t$95$1, If[LessEqual[x, -0.027], t$95$0, If[LessEqual[x, -2e-47], t$95$1, If[LessEqual[x, -3.5e-305], t$95$0, If[LessEqual[x, 1.65e-165], 2.0, If[LessEqual[x, 2.75e-142], N[(1.0 + N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 215000000.0], 2.0, t$95$1]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 57.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 + \frac{z \cdot -4}{y}\\ t_1 := 1 + \frac{x \cdot 4}{y}\\ \mathbf{if}\;x \leq -2.95 \cdot 10^{+32}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -0.017:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1.35 \cdot 10^{-47}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -3.2 \cdot 10^{-305}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{-165}:\\ \;\;\;\;2\\ \mathbf{elif}\;x \leq 2.02 \cdot 10^{-137}:\\ \;\;\;\;1 + z \cdot \frac{-4}{y}\\ \mathbf{elif}\;x \leq 8500000:\\ \;\;\;\;2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ (* z -4.0) y))) (t_1 (+ 1.0 (/ (* x 4.0) y))))
   (if (<= x -2.95e+32)
     t_1
     (if (<= x -0.017)
       t_0
       (if (<= x -1.35e-47)
         t_1
         (if (<= x -3.2e-305)
           t_0
           (if (<= x 1.35e-165)
             2.0
             (if (<= x 2.02e-137)
               (+ 1.0 (* z (/ -4.0 y)))
               (if (<= x 8500000.0) 2.0 t_1)))))))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 + ((z * -4.0) / y);
	double t_1 = 1.0 + ((x * 4.0) / y);
	double tmp;
	if (x <= -2.95e+32) {
		tmp = t_1;
	} else if (x <= -0.017) {
		tmp = t_0;
	} else if (x <= -1.35e-47) {
		tmp = t_1;
	} else if (x <= -3.2e-305) {
		tmp = t_0;
	} else if (x <= 1.35e-165) {
		tmp = 2.0;
	} else if (x <= 2.02e-137) {
		tmp = 1.0 + (z * (-4.0 / y));
	} else if (x <= 8500000.0) {
		tmp = 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 + ((z * (-4.0d0)) / y)
    t_1 = 1.0d0 + ((x * 4.0d0) / y)
    if (x <= (-2.95d+32)) then
        tmp = t_1
    else if (x <= (-0.017d0)) then
        tmp = t_0
    else if (x <= (-1.35d-47)) then
        tmp = t_1
    else if (x <= (-3.2d-305)) then
        tmp = t_0
    else if (x <= 1.35d-165) then
        tmp = 2.0d0
    else if (x <= 2.02d-137) then
        tmp = 1.0d0 + (z * ((-4.0d0) / y))
    else if (x <= 8500000.0d0) then
        tmp = 2.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 + ((z * -4.0) / y);
	double t_1 = 1.0 + ((x * 4.0) / y);
	double tmp;
	if (x <= -2.95e+32) {
		tmp = t_1;
	} else if (x <= -0.017) {
		tmp = t_0;
	} else if (x <= -1.35e-47) {
		tmp = t_1;
	} else if (x <= -3.2e-305) {
		tmp = t_0;
	} else if (x <= 1.35e-165) {
		tmp = 2.0;
	} else if (x <= 2.02e-137) {
		tmp = 1.0 + (z * (-4.0 / y));
	} else if (x <= 8500000.0) {
		tmp = 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 + ((z * -4.0) / y)
	t_1 = 1.0 + ((x * 4.0) / y)
	tmp = 0
	if x <= -2.95e+32:
		tmp = t_1
	elif x <= -0.017:
		tmp = t_0
	elif x <= -1.35e-47:
		tmp = t_1
	elif x <= -3.2e-305:
		tmp = t_0
	elif x <= 1.35e-165:
		tmp = 2.0
	elif x <= 2.02e-137:
		tmp = 1.0 + (z * (-4.0 / y))
	elif x <= 8500000.0:
		tmp = 2.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 + Float64(Float64(z * -4.0) / y))
	t_1 = Float64(1.0 + Float64(Float64(x * 4.0) / y))
	tmp = 0.0
	if (x <= -2.95e+32)
		tmp = t_1;
	elseif (x <= -0.017)
		tmp = t_0;
	elseif (x <= -1.35e-47)
		tmp = t_1;
	elseif (x <= -3.2e-305)
		tmp = t_0;
	elseif (x <= 1.35e-165)
		tmp = 2.0;
	elseif (x <= 2.02e-137)
		tmp = Float64(1.0 + Float64(z * Float64(-4.0 / y)));
	elseif (x <= 8500000.0)
		tmp = 2.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 + ((z * -4.0) / y);
	t_1 = 1.0 + ((x * 4.0) / y);
	tmp = 0.0;
	if (x <= -2.95e+32)
		tmp = t_1;
	elseif (x <= -0.017)
		tmp = t_0;
	elseif (x <= -1.35e-47)
		tmp = t_1;
	elseif (x <= -3.2e-305)
		tmp = t_0;
	elseif (x <= 1.35e-165)
		tmp = 2.0;
	elseif (x <= 2.02e-137)
		tmp = 1.0 + (z * (-4.0 / y));
	elseif (x <= 8500000.0)
		tmp = 2.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 + N[(N[(z * -4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(N[(x * 4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.95e+32], t$95$1, If[LessEqual[x, -0.017], t$95$0, If[LessEqual[x, -1.35e-47], t$95$1, If[LessEqual[x, -3.2e-305], t$95$0, If[LessEqual[x, 1.35e-165], 2.0, If[LessEqual[x, 2.02e-137], N[(1.0 + N[(z * N[(-4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 8500000.0], 2.0, t$95$1]]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 79.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.85 \cdot 10^{+69} \lor \neg \left(x \leq 98000000000000\right) \land \left(x \leq 1.45 \cdot 10^{+108} \lor \neg \left(x \leq 10^{+169}\right)\right):\\ \;\;\;\;1 + \frac{x \cdot 4}{y}\\ \mathbf{else}:\\ \;\;\;\;2 + -4 \cdot \frac{z}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -1.85e+69)
         (and (not (<= x 98000000000000.0))
              (or (<= x 1.45e+108) (not (<= x 1e+169)))))
   (+ 1.0 (/ (* x 4.0) y))
   (+ 2.0 (* -4.0 (/ z y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.85e+69) || (!(x <= 98000000000000.0) && ((x <= 1.45e+108) || !(x <= 1e+169)))) {
		tmp = 1.0 + ((x * 4.0) / y);
	} else {
		tmp = 2.0 + (-4.0 * (z / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-1.85d+69)) .or. (.not. (x <= 98000000000000.0d0)) .and. (x <= 1.45d+108) .or. (.not. (x <= 1d+169))) then
        tmp = 1.0d0 + ((x * 4.0d0) / y)
    else
        tmp = 2.0d0 + ((-4.0d0) * (z / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.85e+69) || (!(x <= 98000000000000.0) && ((x <= 1.45e+108) || !(x <= 1e+169)))) {
		tmp = 1.0 + ((x * 4.0) / y);
	} else {
		tmp = 2.0 + (-4.0 * (z / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -1.85e+69) or (not (x <= 98000000000000.0) and ((x <= 1.45e+108) or not (x <= 1e+169))):
		tmp = 1.0 + ((x * 4.0) / y)
	else:
		tmp = 2.0 + (-4.0 * (z / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -1.85e+69) || (!(x <= 98000000000000.0) && ((x <= 1.45e+108) || !(x <= 1e+169))))
		tmp = Float64(1.0 + Float64(Float64(x * 4.0) / y));
	else
		tmp = Float64(2.0 + Float64(-4.0 * Float64(z / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -1.85e+69) || (~((x <= 98000000000000.0)) && ((x <= 1.45e+108) || ~((x <= 1e+169)))))
		tmp = 1.0 + ((x * 4.0) / y);
	else
		tmp = 2.0 + (-4.0 * (z / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -1.85e+69], And[N[Not[LessEqual[x, 98000000000000.0]], $MachinePrecision], Or[LessEqual[x, 1.45e+108], N[Not[LessEqual[x, 1e+169]], $MachinePrecision]]]], N[(1.0 + N[(N[(x * 4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], N[(2.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 85.2% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.2 \cdot 10^{+33} \lor \neg \left(x \leq -0.021\right) \land \left(x \leq -2 \cdot 10^{-47} \lor \neg \left(x \leq 8.6 \cdot 10^{-51}\right)\right):\\ \;\;\;\;2 + \frac{x}{y \cdot 0.25}\\ \mathbf{else}:\\ \;\;\;\;2 + -4 \cdot \frac{z}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -1.2e+33)
         (and (not (<= x -0.021)) (or (<= x -2e-47) (not (<= x 8.6e-51)))))
   (+ 2.0 (/ x (* y 0.25)))
   (+ 2.0 (* -4.0 (/ z y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.2e+33) || (!(x <= -0.021) && ((x <= -2e-47) || !(x <= 8.6e-51)))) {
		tmp = 2.0 + (x / (y * 0.25));
	} else {
		tmp = 2.0 + (-4.0 * (z / y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-1.2d+33)) .or. (.not. (x <= (-0.021d0))) .and. (x <= (-2d-47)) .or. (.not. (x <= 8.6d-51))) then
        tmp = 2.0d0 + (x / (y * 0.25d0))
    else
        tmp = 2.0d0 + ((-4.0d0) * (z / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.2e+33) || (!(x <= -0.021) && ((x <= -2e-47) || !(x <= 8.6e-51)))) {
		tmp = 2.0 + (x / (y * 0.25));
	} else {
		tmp = 2.0 + (-4.0 * (z / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -1.2e+33) or (not (x <= -0.021) and ((x <= -2e-47) or not (x <= 8.6e-51))):
		tmp = 2.0 + (x / (y * 0.25))
	else:
		tmp = 2.0 + (-4.0 * (z / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -1.2e+33) || (!(x <= -0.021) && ((x <= -2e-47) || !(x <= 8.6e-51))))
		tmp = Float64(2.0 + Float64(x / Float64(y * 0.25)));
	else
		tmp = Float64(2.0 + Float64(-4.0 * Float64(z / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -1.2e+33) || (~((x <= -0.021)) && ((x <= -2e-47) || ~((x <= 8.6e-51)))))
		tmp = 2.0 + (x / (y * 0.25));
	else
		tmp = 2.0 + (-4.0 * (z / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -1.2e+33], And[N[Not[LessEqual[x, -0.021]], $MachinePrecision], Or[LessEqual[x, -2e-47], N[Not[LessEqual[x, 8.6e-51]], $MachinePrecision]]]], N[(2.0 + N[(x / N[(y * 0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 54.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.4 \cdot 10^{+53} \lor \neg \left(x \leq 225000000000\right):\\ \;\;\;\;1 + x \cdot \frac{4}{y}\\ \mathbf{else}:\\ \;\;\;\;2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -7.4e+53) (not (<= x 225000000000.0)))
   (+ 1.0 (* x (/ 4.0 y)))
   2.0))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -7.4e+53) || !(x <= 225000000000.0)) {
		tmp = 1.0 + (x * (4.0 / y));
	} else {
		tmp = 2.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((x <= (-7.4d+53)) .or. (.not. (x <= 225000000000.0d0))) then
        tmp = 1.0d0 + (x * (4.0d0 / y))
    else
        tmp = 2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -7.4e+53) || !(x <= 225000000000.0)) {
		tmp = 1.0 + (x * (4.0 / y));
	} else {
		tmp = 2.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -7.4e+53) or not (x <= 225000000000.0):
		tmp = 1.0 + (x * (4.0 / y))
	else:
		tmp = 2.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -7.4e+53) || !(x <= 225000000000.0))
		tmp = Float64(1.0 + Float64(x * Float64(4.0 / y)));
	else
		tmp = 2.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -7.4e+53) || ~((x <= 225000000000.0)))
		tmp = 1.0 + (x * (4.0 / y));
	else
		tmp = 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -7.4e+53], N[Not[LessEqual[x, 225000000000.0]], $MachinePrecision]], N[(1.0 + N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 99.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ 2 + \left(x - z\right) \cdot \frac{4}{y} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ 2.0 (* (- x z) (/ 4.0 y))))

C

double code(double x, double y, double z) {
	return 2.0 + ((x - z) * (4.0 / y));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 2.0d0 + ((x - z) * (4.0d0 / y))
end function

Java

public static double code(double x, double y, double z) {
	return 2.0 + ((x - z) * (4.0 / y));
}

Python

def code(x, y, z):
	return 2.0 + ((x - z) * (4.0 / y))

Julia

function code(x, y, z)
	return Float64(2.0 + Float64(Float64(x - z) * Float64(4.0 / y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 2.0 + ((x - z) * (4.0 / y));
end

Wolfram

code[x_, y_, z_] := N[(2.0 + N[(N[(x - z), $MachinePrecision] * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 33.6% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 2 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 2.0)

C

double code(double x, double y, double z) {
	return 2.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 2.0d0
end function

Java

public static double code(double x, double y, double z) {
	return 2.0;
}

Python

def code(x, y, z):
	return 2.0

Julia

function code(x, y, z)
	return 2.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 2.0;
end

Wolfram

code[x_, y_, z_] := 2.0

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2023350 
(FPCore (x y z)
  :name "Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, C"
  :precision binary64
  (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.25)) z)) y)))