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

Percentage Accurate: 99.7% → 99.9%

Time: 7.2s

Alternatives: 10

Speedup: 1.4×

• 20.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - 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 * 0.75d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.75)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.75), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 1 + \frac{4 \cdot \left(\left(x + y \cdot 0.75\right) - z\right)}{y} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))

C

double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - 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 * 0.75d0)) - z)) / y)
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
}

Python

def code(x, y, z):
	return 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y)

Julia

function code(x, y, z)
	return Float64(1.0 + Float64(Float64(4.0 * Float64(Float64(x + Float64(y * 0.75)) - z)) / y))
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 + ((4.0 * ((x + (y * 0.75)) - z)) / y);
end

Wolfram

code[x_, y_, z_] := N[(1.0 + N[(N[(4.0 * N[(N[(x + N[(y * 0.75), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.4× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return 4.0 + ((4.0 * (x - 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 = 4.0d0 + ((4.0d0 * (x - z)) / y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 2: 52.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{4}{\frac{y}{x}}\\ t_1 := -4 \cdot \frac{z}{y}\\ \mathbf{if}\;z \leq -2.3 \cdot 10^{+105}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1350000000000:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq -6 \cdot 10^{-103}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -4.2 \cdot 10^{-158}:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq -5.5 \cdot 10^{-302}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 2 \cdot 10^{-200}:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq 6.8 \cdot 10^{-90}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{-36}:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ 4.0 (/ y x))) (t_1 (* -4.0 (/ z y))))
   (if (<= z -2.3e+105)
     t_1
     (if (<= z -1350000000000.0)
       4.0
       (if (<= z -6e-103)
         t_0
         (if (<= z -4.2e-158)
           4.0
           (if (<= z -5.5e-302)
             t_0
             (if (<= z 2e-200)
               4.0
               (if (<= z 6.8e-90) t_0 (if (<= z 1.95e-36) 4.0 t_1))))))))))

C

double code(double x, double y, double z) {
	double t_0 = 4.0 / (y / x);
	double t_1 = -4.0 * (z / y);
	double tmp;
	if (z <= -2.3e+105) {
		tmp = t_1;
	} else if (z <= -1350000000000.0) {
		tmp = 4.0;
	} else if (z <= -6e-103) {
		tmp = t_0;
	} else if (z <= -4.2e-158) {
		tmp = 4.0;
	} else if (z <= -5.5e-302) {
		tmp = t_0;
	} else if (z <= 2e-200) {
		tmp = 4.0;
	} else if (z <= 6.8e-90) {
		tmp = t_0;
	} else if (z <= 1.95e-36) {
		tmp = 4.0;
	} 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 = 4.0d0 / (y / x)
    t_1 = (-4.0d0) * (z / y)
    if (z <= (-2.3d+105)) then
        tmp = t_1
    else if (z <= (-1350000000000.0d0)) then
        tmp = 4.0d0
    else if (z <= (-6d-103)) then
        tmp = t_0
    else if (z <= (-4.2d-158)) then
        tmp = 4.0d0
    else if (z <= (-5.5d-302)) then
        tmp = t_0
    else if (z <= 2d-200) then
        tmp = 4.0d0
    else if (z <= 6.8d-90) then
        tmp = t_0
    else if (z <= 1.95d-36) then
        tmp = 4.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 4.0 / (y / x);
	double t_1 = -4.0 * (z / y);
	double tmp;
	if (z <= -2.3e+105) {
		tmp = t_1;
	} else if (z <= -1350000000000.0) {
		tmp = 4.0;
	} else if (z <= -6e-103) {
		tmp = t_0;
	} else if (z <= -4.2e-158) {
		tmp = 4.0;
	} else if (z <= -5.5e-302) {
		tmp = t_0;
	} else if (z <= 2e-200) {
		tmp = 4.0;
	} else if (z <= 6.8e-90) {
		tmp = t_0;
	} else if (z <= 1.95e-36) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 4.0 / (y / x)
	t_1 = -4.0 * (z / y)
	tmp = 0
	if z <= -2.3e+105:
		tmp = t_1
	elif z <= -1350000000000.0:
		tmp = 4.0
	elif z <= -6e-103:
		tmp = t_0
	elif z <= -4.2e-158:
		tmp = 4.0
	elif z <= -5.5e-302:
		tmp = t_0
	elif z <= 2e-200:
		tmp = 4.0
	elif z <= 6.8e-90:
		tmp = t_0
	elif z <= 1.95e-36:
		tmp = 4.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(4.0 / Float64(y / x))
	t_1 = Float64(-4.0 * Float64(z / y))
	tmp = 0.0
	if (z <= -2.3e+105)
		tmp = t_1;
	elseif (z <= -1350000000000.0)
		tmp = 4.0;
	elseif (z <= -6e-103)
		tmp = t_0;
	elseif (z <= -4.2e-158)
		tmp = 4.0;
	elseif (z <= -5.5e-302)
		tmp = t_0;
	elseif (z <= 2e-200)
		tmp = 4.0;
	elseif (z <= 6.8e-90)
		tmp = t_0;
	elseif (z <= 1.95e-36)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 4.0 / (y / x);
	t_1 = -4.0 * (z / y);
	tmp = 0.0;
	if (z <= -2.3e+105)
		tmp = t_1;
	elseif (z <= -1350000000000.0)
		tmp = 4.0;
	elseif (z <= -6e-103)
		tmp = t_0;
	elseif (z <= -4.2e-158)
		tmp = 4.0;
	elseif (z <= -5.5e-302)
		tmp = t_0;
	elseif (z <= 2e-200)
		tmp = 4.0;
	elseif (z <= 6.8e-90)
		tmp = t_0;
	elseif (z <= 1.95e-36)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(4.0 / N[(y / x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.3e+105], t$95$1, If[LessEqual[z, -1350000000000.0], 4.0, If[LessEqual[z, -6e-103], t$95$0, If[LessEqual[z, -4.2e-158], 4.0, If[LessEqual[z, -5.5e-302], t$95$0, If[LessEqual[z, 2e-200], 4.0, If[LessEqual[z, 6.8e-90], t$95$0, If[LessEqual[z, 1.95e-36], 4.0, t$95$1]]]]]]]]]]

Derivation

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

Alternative 3: 52.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{4 \cdot x}{y}\\ t_1 := -4 \cdot \frac{z}{y}\\ \mathbf{if}\;z \leq -5 \cdot 10^{+105}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -1200000000:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq -3.5 \cdot 10^{-102}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{-156}:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq -3.5 \cdot 10^{-303}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 7.6 \cdot 10^{-200}:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq 2.9 \cdot 10^{-91}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{-36}:\\ \;\;\;\;4\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* 4.0 x) y)) (t_1 (* -4.0 (/ z y))))
   (if (<= z -5e+105)
     t_1
     (if (<= z -1200000000.0)
       4.0
       (if (<= z -3.5e-102)
         t_0
         (if (<= z -2.1e-156)
           4.0
           (if (<= z -3.5e-303)
             t_0
             (if (<= z 7.6e-200)
               4.0
               (if (<= z 2.9e-91) t_0 (if (<= z 1.95e-36) 4.0 t_1))))))))))

C

double code(double x, double y, double z) {
	double t_0 = (4.0 * x) / y;
	double t_1 = -4.0 * (z / y);
	double tmp;
	if (z <= -5e+105) {
		tmp = t_1;
	} else if (z <= -1200000000.0) {
		tmp = 4.0;
	} else if (z <= -3.5e-102) {
		tmp = t_0;
	} else if (z <= -2.1e-156) {
		tmp = 4.0;
	} else if (z <= -3.5e-303) {
		tmp = t_0;
	} else if (z <= 7.6e-200) {
		tmp = 4.0;
	} else if (z <= 2.9e-91) {
		tmp = t_0;
	} else if (z <= 1.95e-36) {
		tmp = 4.0;
	} 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 = (4.0d0 * x) / y
    t_1 = (-4.0d0) * (z / y)
    if (z <= (-5d+105)) then
        tmp = t_1
    else if (z <= (-1200000000.0d0)) then
        tmp = 4.0d0
    else if (z <= (-3.5d-102)) then
        tmp = t_0
    else if (z <= (-2.1d-156)) then
        tmp = 4.0d0
    else if (z <= (-3.5d-303)) then
        tmp = t_0
    else if (z <= 7.6d-200) then
        tmp = 4.0d0
    else if (z <= 2.9d-91) then
        tmp = t_0
    else if (z <= 1.95d-36) then
        tmp = 4.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (4.0 * x) / y;
	double t_1 = -4.0 * (z / y);
	double tmp;
	if (z <= -5e+105) {
		tmp = t_1;
	} else if (z <= -1200000000.0) {
		tmp = 4.0;
	} else if (z <= -3.5e-102) {
		tmp = t_0;
	} else if (z <= -2.1e-156) {
		tmp = 4.0;
	} else if (z <= -3.5e-303) {
		tmp = t_0;
	} else if (z <= 7.6e-200) {
		tmp = 4.0;
	} else if (z <= 2.9e-91) {
		tmp = t_0;
	} else if (z <= 1.95e-36) {
		tmp = 4.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (4.0 * x) / y
	t_1 = -4.0 * (z / y)
	tmp = 0
	if z <= -5e+105:
		tmp = t_1
	elif z <= -1200000000.0:
		tmp = 4.0
	elif z <= -3.5e-102:
		tmp = t_0
	elif z <= -2.1e-156:
		tmp = 4.0
	elif z <= -3.5e-303:
		tmp = t_0
	elif z <= 7.6e-200:
		tmp = 4.0
	elif z <= 2.9e-91:
		tmp = t_0
	elif z <= 1.95e-36:
		tmp = 4.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(4.0 * x) / y)
	t_1 = Float64(-4.0 * Float64(z / y))
	tmp = 0.0
	if (z <= -5e+105)
		tmp = t_1;
	elseif (z <= -1200000000.0)
		tmp = 4.0;
	elseif (z <= -3.5e-102)
		tmp = t_0;
	elseif (z <= -2.1e-156)
		tmp = 4.0;
	elseif (z <= -3.5e-303)
		tmp = t_0;
	elseif (z <= 7.6e-200)
		tmp = 4.0;
	elseif (z <= 2.9e-91)
		tmp = t_0;
	elseif (z <= 1.95e-36)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (4.0 * x) / y;
	t_1 = -4.0 * (z / y);
	tmp = 0.0;
	if (z <= -5e+105)
		tmp = t_1;
	elseif (z <= -1200000000.0)
		tmp = 4.0;
	elseif (z <= -3.5e-102)
		tmp = t_0;
	elseif (z <= -2.1e-156)
		tmp = 4.0;
	elseif (z <= -3.5e-303)
		tmp = t_0;
	elseif (z <= 7.6e-200)
		tmp = 4.0;
	elseif (z <= 2.9e-91)
		tmp = t_0;
	elseif (z <= 1.95e-36)
		tmp = 4.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(4.0 * x), $MachinePrecision] / y), $MachinePrecision]}, Block[{t$95$1 = N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5e+105], t$95$1, If[LessEqual[z, -1200000000.0], 4.0, If[LessEqual[z, -3.5e-102], t$95$0, If[LessEqual[z, -2.1e-156], 4.0, If[LessEqual[z, -3.5e-303], t$95$0, If[LessEqual[z, 7.6e-200], 4.0, If[LessEqual[z, 2.9e-91], t$95$0, If[LessEqual[z, 1.95e-36], 4.0, t$95$1]]]]]]]]]]

Derivation

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

Alternative 4: 55.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := -4 \cdot \frac{z}{y}\\ t_1 := 4 \cdot \frac{x}{y} + 1\\ \mathbf{if}\;z \leq -4.2 \cdot 10^{+144}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -2 \cdot 10^{+131}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -2.3 \cdot 10^{+105}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -185000000000:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq -8 \cdot 10^{-303}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq 1.52 \cdot 10^{-198}:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq 1.45 \cdot 10^{+42}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* -4.0 (/ z y))) (t_1 (+ (* 4.0 (/ x y)) 1.0)))
   (if (<= z -4.2e+144)
     t_0
     (if (<= z -2e+131)
       t_1
       (if (<= z -2.3e+105)
         t_0
         (if (<= z -185000000000.0)
           4.0
           (if (<= z -8e-303)
             t_1
             (if (<= z 1.52e-198) 4.0 (if (<= z 1.45e+42) t_1 t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = -4.0 * (z / y);
	double t_1 = (4.0 * (x / y)) + 1.0;
	double tmp;
	if (z <= -4.2e+144) {
		tmp = t_0;
	} else if (z <= -2e+131) {
		tmp = t_1;
	} else if (z <= -2.3e+105) {
		tmp = t_0;
	} else if (z <= -185000000000.0) {
		tmp = 4.0;
	} else if (z <= -8e-303) {
		tmp = t_1;
	} else if (z <= 1.52e-198) {
		tmp = 4.0;
	} else if (z <= 1.45e+42) {
		tmp = t_1;
	} else {
		tmp = t_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) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = (-4.0d0) * (z / y)
    t_1 = (4.0d0 * (x / y)) + 1.0d0
    if (z <= (-4.2d+144)) then
        tmp = t_0
    else if (z <= (-2d+131)) then
        tmp = t_1
    else if (z <= (-2.3d+105)) then
        tmp = t_0
    else if (z <= (-185000000000.0d0)) then
        tmp = 4.0d0
    else if (z <= (-8d-303)) then
        tmp = t_1
    else if (z <= 1.52d-198) then
        tmp = 4.0d0
    else if (z <= 1.45d+42) then
        tmp = t_1
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = -4.0 * (z / y);
	double t_1 = (4.0 * (x / y)) + 1.0;
	double tmp;
	if (z <= -4.2e+144) {
		tmp = t_0;
	} else if (z <= -2e+131) {
		tmp = t_1;
	} else if (z <= -2.3e+105) {
		tmp = t_0;
	} else if (z <= -185000000000.0) {
		tmp = 4.0;
	} else if (z <= -8e-303) {
		tmp = t_1;
	} else if (z <= 1.52e-198) {
		tmp = 4.0;
	} else if (z <= 1.45e+42) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = -4.0 * (z / y)
	t_1 = (4.0 * (x / y)) + 1.0
	tmp = 0
	if z <= -4.2e+144:
		tmp = t_0
	elif z <= -2e+131:
		tmp = t_1
	elif z <= -2.3e+105:
		tmp = t_0
	elif z <= -185000000000.0:
		tmp = 4.0
	elif z <= -8e-303:
		tmp = t_1
	elif z <= 1.52e-198:
		tmp = 4.0
	elif z <= 1.45e+42:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(-4.0 * Float64(z / y))
	t_1 = Float64(Float64(4.0 * Float64(x / y)) + 1.0)
	tmp = 0.0
	if (z <= -4.2e+144)
		tmp = t_0;
	elseif (z <= -2e+131)
		tmp = t_1;
	elseif (z <= -2.3e+105)
		tmp = t_0;
	elseif (z <= -185000000000.0)
		tmp = 4.0;
	elseif (z <= -8e-303)
		tmp = t_1;
	elseif (z <= 1.52e-198)
		tmp = 4.0;
	elseif (z <= 1.45e+42)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = -4.0 * (z / y);
	t_1 = (4.0 * (x / y)) + 1.0;
	tmp = 0.0;
	if (z <= -4.2e+144)
		tmp = t_0;
	elseif (z <= -2e+131)
		tmp = t_1;
	elseif (z <= -2.3e+105)
		tmp = t_0;
	elseif (z <= -185000000000.0)
		tmp = 4.0;
	elseif (z <= -8e-303)
		tmp = t_1;
	elseif (z <= 1.52e-198)
		tmp = 4.0;
	elseif (z <= 1.45e+42)
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]}, If[LessEqual[z, -4.2e+144], t$95$0, If[LessEqual[z, -2e+131], t$95$1, If[LessEqual[z, -2.3e+105], t$95$0, If[LessEqual[z, -185000000000.0], 4.0, If[LessEqual[z, -8e-303], t$95$1, If[LessEqual[z, 1.52e-198], 4.0, If[LessEqual[z, 1.45e+42], t$95$1, t$95$0]]]]]]]]]

Derivation

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

Alternative 5: 57.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 4 \cdot \frac{x}{y} + 1\\ t_1 := 1 + -4 \cdot \frac{z}{y}\\ \mathbf{if}\;z \leq -2.5 \cdot 10^{+105}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;z \leq -3600000000:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq -3.7 \cdot 10^{-303}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq 1.4 \cdot 10^{-199}:\\ \;\;\;\;4\\ \mathbf{elif}\;z \leq 1.06 \cdot 10^{+37}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (* 4.0 (/ x y)) 1.0)) (t_1 (+ 1.0 (* -4.0 (/ z y)))))
   (if (<= z -2.5e+105)
     t_1
     (if (<= z -3600000000.0)
       4.0
       (if (<= z -3.7e-303)
         t_0
         (if (<= z 1.4e-199) 4.0 (if (<= z 1.06e+37) t_0 t_1)))))))

C

double code(double x, double y, double z) {
	double t_0 = (4.0 * (x / y)) + 1.0;
	double t_1 = 1.0 + (-4.0 * (z / y));
	double tmp;
	if (z <= -2.5e+105) {
		tmp = t_1;
	} else if (z <= -3600000000.0) {
		tmp = 4.0;
	} else if (z <= -3.7e-303) {
		tmp = t_0;
	} else if (z <= 1.4e-199) {
		tmp = 4.0;
	} else if (z <= 1.06e+37) {
		tmp = t_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 = (4.0d0 * (x / y)) + 1.0d0
    t_1 = 1.0d0 + ((-4.0d0) * (z / y))
    if (z <= (-2.5d+105)) then
        tmp = t_1
    else if (z <= (-3600000000.0d0)) then
        tmp = 4.0d0
    else if (z <= (-3.7d-303)) then
        tmp = t_0
    else if (z <= 1.4d-199) then
        tmp = 4.0d0
    else if (z <= 1.06d+37) then
        tmp = t_0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (4.0 * (x / y)) + 1.0;
	double t_1 = 1.0 + (-4.0 * (z / y));
	double tmp;
	if (z <= -2.5e+105) {
		tmp = t_1;
	} else if (z <= -3600000000.0) {
		tmp = 4.0;
	} else if (z <= -3.7e-303) {
		tmp = t_0;
	} else if (z <= 1.4e-199) {
		tmp = 4.0;
	} else if (z <= 1.06e+37) {
		tmp = t_0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (4.0 * (x / y)) + 1.0
	t_1 = 1.0 + (-4.0 * (z / y))
	tmp = 0
	if z <= -2.5e+105:
		tmp = t_1
	elif z <= -3600000000.0:
		tmp = 4.0
	elif z <= -3.7e-303:
		tmp = t_0
	elif z <= 1.4e-199:
		tmp = 4.0
	elif z <= 1.06e+37:
		tmp = t_0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(4.0 * Float64(x / y)) + 1.0)
	t_1 = Float64(1.0 + Float64(-4.0 * Float64(z / y)))
	tmp = 0.0
	if (z <= -2.5e+105)
		tmp = t_1;
	elseif (z <= -3600000000.0)
		tmp = 4.0;
	elseif (z <= -3.7e-303)
		tmp = t_0;
	elseif (z <= 1.4e-199)
		tmp = 4.0;
	elseif (z <= 1.06e+37)
		tmp = t_0;
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (4.0 * (x / y)) + 1.0;
	t_1 = 1.0 + (-4.0 * (z / y));
	tmp = 0.0;
	if (z <= -2.5e+105)
		tmp = t_1;
	elseif (z <= -3600000000.0)
		tmp = 4.0;
	elseif (z <= -3.7e-303)
		tmp = t_0;
	elseif (z <= 1.4e-199)
		tmp = 4.0;
	elseif (z <= 1.06e+37)
		tmp = t_0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]}, Block[{t$95$1 = N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.5e+105], t$95$1, If[LessEqual[z, -3600000000.0], 4.0, If[LessEqual[z, -3.7e-303], t$95$0, If[LessEqual[z, 1.4e-199], 4.0, If[LessEqual[z, 1.06e+37], t$95$0, t$95$1]]]]]]]

Derivation

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

Alternative 6: 79.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.1 \cdot 10^{+177} \lor \neg \left(z \leq 9.5 \cdot 10^{+60}\right):\\ \;\;\;\;1 + -4 \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + 4 \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -2.1e+177) (not (<= z 9.5e+60)))
   (+ 1.0 (* -4.0 (/ z y)))
   (+ 4.0 (* 4.0 (/ x y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.1e+177) || !(z <= 9.5e+60)) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 4.0 + (4.0 * (x / 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 ((z <= (-2.1d+177)) .or. (.not. (z <= 9.5d+60))) then
        tmp = 1.0d0 + ((-4.0d0) * (z / y))
    else
        tmp = 4.0d0 + (4.0d0 * (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2.1e+177) || !(z <= 9.5e+60)) {
		tmp = 1.0 + (-4.0 * (z / y));
	} else {
		tmp = 4.0 + (4.0 * (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -2.1e+177) or not (z <= 9.5e+60):
		tmp = 1.0 + (-4.0 * (z / y))
	else:
		tmp = 4.0 + (4.0 * (x / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -2.1e+177) || !(z <= 9.5e+60))
		tmp = Float64(1.0 + Float64(-4.0 * Float64(z / y)));
	else
		tmp = Float64(4.0 + Float64(4.0 * Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -2.1e+177) || ~((z <= 9.5e+60)))
		tmp = 1.0 + (-4.0 * (z / y));
	else
		tmp = 4.0 + (4.0 * (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -2.1e+177], N[Not[LessEqual[z, 9.5e+60]], $MachinePrecision]], N[(1.0 + N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 7: 85.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.8 \cdot 10^{+46} \lor \neg \left(x \leq 2.35 \cdot 10^{+55}\right):\\ \;\;\;\;4 + 4 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + \frac{z \cdot -4}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -5.8e+46) (not (<= x 2.35e+55)))
   (+ 4.0 (* 4.0 (/ x y)))
   (+ 4.0 (/ (* z -4.0) y))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -5.8e+46) || !(x <= 2.35e+55)) {
		tmp = 4.0 + (4.0 * (x / y));
	} else {
		tmp = 4.0 + ((z * -4.0) / 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 <= (-5.8d+46)) .or. (.not. (x <= 2.35d+55))) then
        tmp = 4.0d0 + (4.0d0 * (x / y))
    else
        tmp = 4.0d0 + ((z * (-4.0d0)) / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -5.8e+46) || !(x <= 2.35e+55)) {
		tmp = 4.0 + (4.0 * (x / y));
	} else {
		tmp = 4.0 + ((z * -4.0) / y);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -5.8e+46) or not (x <= 2.35e+55):
		tmp = 4.0 + (4.0 * (x / y))
	else:
		tmp = 4.0 + ((z * -4.0) / y)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -5.8e+46) || !(x <= 2.35e+55))
		tmp = Float64(4.0 + Float64(4.0 * Float64(x / y)));
	else
		tmp = Float64(4.0 + Float64(Float64(z * -4.0) / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -5.8e+46) || ~((x <= 2.35e+55)))
		tmp = 4.0 + (4.0 * (x / y));
	else
		tmp = 4.0 + ((z * -4.0) / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -5.8e+46], N[Not[LessEqual[x, 2.35e+55]], $MachinePrecision]], N[(4.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(N[(z * -4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 8: 85.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9.2 \cdot 10^{-11}:\\ \;\;\;\;\frac{4 \cdot \left(x - z\right)}{y}\\ \mathbf{elif}\;x \leq 4.8 \cdot 10^{+54}:\\ \;\;\;\;4 + \frac{z \cdot -4}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + 4 \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -9.2e-11)
   (/ (* 4.0 (- x z)) y)
   (if (<= x 4.8e+54) (+ 4.0 (/ (* z -4.0) y)) (+ 4.0 (* 4.0 (/ x y))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -9.2e-11) {
		tmp = (4.0 * (x - z)) / y;
	} else if (x <= 4.8e+54) {
		tmp = 4.0 + ((z * -4.0) / y);
	} else {
		tmp = 4.0 + (4.0 * (x / 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 <= (-9.2d-11)) then
        tmp = (4.0d0 * (x - z)) / y
    else if (x <= 4.8d+54) then
        tmp = 4.0d0 + ((z * (-4.0d0)) / y)
    else
        tmp = 4.0d0 + (4.0d0 * (x / y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -9.2e-11) {
		tmp = (4.0 * (x - z)) / y;
	} else if (x <= 4.8e+54) {
		tmp = 4.0 + ((z * -4.0) / y);
	} else {
		tmp = 4.0 + (4.0 * (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -9.2e-11:
		tmp = (4.0 * (x - z)) / y
	elif x <= 4.8e+54:
		tmp = 4.0 + ((z * -4.0) / y)
	else:
		tmp = 4.0 + (4.0 * (x / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -9.2e-11)
		tmp = Float64(Float64(4.0 * Float64(x - z)) / y);
	elseif (x <= 4.8e+54)
		tmp = Float64(4.0 + Float64(Float64(z * -4.0) / y));
	else
		tmp = Float64(4.0 + Float64(4.0 * Float64(x / y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -9.2e-11)
		tmp = (4.0 * (x - z)) / y;
	elseif (x <= 4.8e+54)
		tmp = 4.0 + ((z * -4.0) / y);
	else
		tmp = 4.0 + (4.0 * (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -9.2e-11], N[(N[(4.0 * N[(x - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], If[LessEqual[x, 4.8e+54], N[(4.0 + N[(N[(z * -4.0), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 9: 51.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.25 \cdot 10^{+144}:\\ \;\;\;\;4\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{+58}:\\ \;\;\;\;-4 \cdot \frac{z}{y}\\ \mathbf{else}:\\ \;\;\;\;4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -2.25e+144) 4.0 (if (<= y 1.3e+58) (* -4.0 (/ z y)) 4.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.25e+144) {
		tmp = 4.0;
	} else if (y <= 1.3e+58) {
		tmp = -4.0 * (z / y);
	} else {
		tmp = 4.0;
	}
	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 (y <= (-2.25d+144)) then
        tmp = 4.0d0
    else if (y <= 1.3d+58) then
        tmp = (-4.0d0) * (z / y)
    else
        tmp = 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (y <= -2.25e+144) {
		tmp = 4.0;
	} else if (y <= 1.3e+58) {
		tmp = -4.0 * (z / y);
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -2.25e+144:
		tmp = 4.0
	elif y <= 1.3e+58:
		tmp = -4.0 * (z / y)
	else:
		tmp = 4.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -2.25e+144)
		tmp = 4.0;
	elseif (y <= 1.3e+58)
		tmp = Float64(-4.0 * Float64(z / y));
	else
		tmp = 4.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -2.25e+144)
		tmp = 4.0;
	elseif (y <= 1.3e+58)
		tmp = -4.0 * (z / y);
	else
		tmp = 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -2.25e+144], 4.0, If[LessEqual[y, 1.3e+58], N[(-4.0 * N[(z / y), $MachinePrecision]), $MachinePrecision], 4.0]]

Derivation

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

Alternative 10: 33.6% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ 4 \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 4.0)

C

double code(double x, double y, double z) {
	return 4.0;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 4.0d0
end function

Java

public static double code(double x, double y, double z) {
	return 4.0;
}

Python

def code(x, y, z):
	return 4.0

Julia

function code(x, y, z)
	return 4.0
end

MATLAB

function tmp = code(x, y, z)
	tmp = 4.0;
end

Wolfram

code[x_, y_, z_] := 4.0

Derivation

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

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z)
  :name "Data.Array.Repa.Algorithms.ColorRamp:rampColorHotToCold from repa-algorithms-3.4.0.1, A"
  :precision binary64
  (+ 1.0 (/ (* 4.0 (- (+ x (* y 0.75)) z)) y)))