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

Percentage Accurate: 99.8% → 100.0%

Time: 7.4s

Alternatives: 9

Speedup: 1.4×

• 20.8% 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: 99.8% 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: 100.0% accurate, 1.4× speedup

Math

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

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(4.0 * Float64(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[(4.0 * N[(N[(x - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 52.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \frac{4}{y}\\ t_1 := \frac{z}{y} \cdot -4\\ \mathbf{if}\;x \leq -1.2 \cdot 10^{+52}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1.36 \cdot 10^{-142}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.1 \cdot 10^{-189}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -4.6 \cdot 10^{-266}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{-220}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 5 \cdot 10^{-70}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (/ 4.0 y))) (t_1 (* (/ z y) -4.0)))
   (if (<= x -1.2e+52)
     t_0
     (if (<= x -1.36e-142)
       t_1
       (if (<= x -1.1e-189)
         4.0
         (if (<= x -4.6e-266)
           t_1
           (if (<= x 1.35e-220) 4.0 (if (<= x 5e-70) t_1 t_0))))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (4.0 / y);
	double t_1 = (z / y) * -4.0;
	double tmp;
	if (x <= -1.2e+52) {
		tmp = t_0;
	} else if (x <= -1.36e-142) {
		tmp = t_1;
	} else if (x <= -1.1e-189) {
		tmp = 4.0;
	} else if (x <= -4.6e-266) {
		tmp = t_1;
	} else if (x <= 1.35e-220) {
		tmp = 4.0;
	} else if (x <= 5e-70) {
		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 = x * (4.0d0 / y)
    t_1 = (z / y) * (-4.0d0)
    if (x <= (-1.2d+52)) then
        tmp = t_0
    else if (x <= (-1.36d-142)) then
        tmp = t_1
    else if (x <= (-1.1d-189)) then
        tmp = 4.0d0
    else if (x <= (-4.6d-266)) then
        tmp = t_1
    else if (x <= 1.35d-220) then
        tmp = 4.0d0
    else if (x <= 5d-70) 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 = x * (4.0 / y);
	double t_1 = (z / y) * -4.0;
	double tmp;
	if (x <= -1.2e+52) {
		tmp = t_0;
	} else if (x <= -1.36e-142) {
		tmp = t_1;
	} else if (x <= -1.1e-189) {
		tmp = 4.0;
	} else if (x <= -4.6e-266) {
		tmp = t_1;
	} else if (x <= 1.35e-220) {
		tmp = 4.0;
	} else if (x <= 5e-70) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (4.0 / y)
	t_1 = (z / y) * -4.0
	tmp = 0
	if x <= -1.2e+52:
		tmp = t_0
	elif x <= -1.36e-142:
		tmp = t_1
	elif x <= -1.1e-189:
		tmp = 4.0
	elif x <= -4.6e-266:
		tmp = t_1
	elif x <= 1.35e-220:
		tmp = 4.0
	elif x <= 5e-70:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(4.0 / y))
	t_1 = Float64(Float64(z / y) * -4.0)
	tmp = 0.0
	if (x <= -1.2e+52)
		tmp = t_0;
	elseif (x <= -1.36e-142)
		tmp = t_1;
	elseif (x <= -1.1e-189)
		tmp = 4.0;
	elseif (x <= -4.6e-266)
		tmp = t_1;
	elseif (x <= 1.35e-220)
		tmp = 4.0;
	elseif (x <= 5e-70)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (4.0 / y);
	t_1 = (z / y) * -4.0;
	tmp = 0.0;
	if (x <= -1.2e+52)
		tmp = t_0;
	elseif (x <= -1.36e-142)
		tmp = t_1;
	elseif (x <= -1.1e-189)
		tmp = 4.0;
	elseif (x <= -4.6e-266)
		tmp = t_1;
	elseif (x <= 1.35e-220)
		tmp = 4.0;
	elseif (x <= 5e-70)
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(4.0 / y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(z / y), $MachinePrecision] * -4.0), $MachinePrecision]}, If[LessEqual[x, -1.2e+52], t$95$0, If[LessEqual[x, -1.36e-142], t$95$1, If[LessEqual[x, -1.1e-189], 4.0, If[LessEqual[x, -4.6e-266], t$95$1, If[LessEqual[x, 1.35e-220], 4.0, If[LessEqual[x, 5e-70], t$95$1, t$95$0]]]]]]]]

Derivation

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

Alternative 3: 52.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{4 \cdot x}{y}\\ t_1 := \frac{z}{y} \cdot -4\\ \mathbf{if}\;x \leq -1.46 \cdot 10^{+52}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1.2 \cdot 10^{-142}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.9 \cdot 10^{-187}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq -3.7 \cdot 10^{-267}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 2.25 \cdot 10^{-220}:\\ \;\;\;\;4\\ \mathbf{elif}\;x \leq 2.7 \cdot 10^{-70}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* 4.0 x) y)) (t_1 (* (/ z y) -4.0)))
   (if (<= x -1.46e+52)
     t_0
     (if (<= x -1.2e-142)
       t_1
       (if (<= x -1.9e-187)
         4.0
         (if (<= x -3.7e-267)
           t_1
           (if (<= x 2.25e-220) 4.0 (if (<= x 2.7e-70) t_1 t_0))))))))

C

double code(double x, double y, double z) {
	double t_0 = (4.0 * x) / y;
	double t_1 = (z / y) * -4.0;
	double tmp;
	if (x <= -1.46e+52) {
		tmp = t_0;
	} else if (x <= -1.2e-142) {
		tmp = t_1;
	} else if (x <= -1.9e-187) {
		tmp = 4.0;
	} else if (x <= -3.7e-267) {
		tmp = t_1;
	} else if (x <= 2.25e-220) {
		tmp = 4.0;
	} else if (x <= 2.7e-70) {
		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 * x) / y
    t_1 = (z / y) * (-4.0d0)
    if (x <= (-1.46d+52)) then
        tmp = t_0
    else if (x <= (-1.2d-142)) then
        tmp = t_1
    else if (x <= (-1.9d-187)) then
        tmp = 4.0d0
    else if (x <= (-3.7d-267)) then
        tmp = t_1
    else if (x <= 2.25d-220) then
        tmp = 4.0d0
    else if (x <= 2.7d-70) 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 * x) / y;
	double t_1 = (z / y) * -4.0;
	double tmp;
	if (x <= -1.46e+52) {
		tmp = t_0;
	} else if (x <= -1.2e-142) {
		tmp = t_1;
	} else if (x <= -1.9e-187) {
		tmp = 4.0;
	} else if (x <= -3.7e-267) {
		tmp = t_1;
	} else if (x <= 2.25e-220) {
		tmp = 4.0;
	} else if (x <= 2.7e-70) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (4.0 * x) / y
	t_1 = (z / y) * -4.0
	tmp = 0
	if x <= -1.46e+52:
		tmp = t_0
	elif x <= -1.2e-142:
		tmp = t_1
	elif x <= -1.9e-187:
		tmp = 4.0
	elif x <= -3.7e-267:
		tmp = t_1
	elif x <= 2.25e-220:
		tmp = 4.0
	elif x <= 2.7e-70:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(4.0 * x) / y)
	t_1 = Float64(Float64(z / y) * -4.0)
	tmp = 0.0
	if (x <= -1.46e+52)
		tmp = t_0;
	elseif (x <= -1.2e-142)
		tmp = t_1;
	elseif (x <= -1.9e-187)
		tmp = 4.0;
	elseif (x <= -3.7e-267)
		tmp = t_1;
	elseif (x <= 2.25e-220)
		tmp = 4.0;
	elseif (x <= 2.7e-70)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (4.0 * x) / y;
	t_1 = (z / y) * -4.0;
	tmp = 0.0;
	if (x <= -1.46e+52)
		tmp = t_0;
	elseif (x <= -1.2e-142)
		tmp = t_1;
	elseif (x <= -1.9e-187)
		tmp = 4.0;
	elseif (x <= -3.7e-267)
		tmp = t_1;
	elseif (x <= 2.25e-220)
		tmp = 4.0;
	elseif (x <= 2.7e-70)
		tmp = t_1;
	else
		tmp = t_0;
	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[(N[(z / y), $MachinePrecision] * -4.0), $MachinePrecision]}, If[LessEqual[x, -1.46e+52], t$95$0, If[LessEqual[x, -1.2e-142], t$95$1, If[LessEqual[x, -1.9e-187], 4.0, If[LessEqual[x, -3.7e-267], t$95$1, If[LessEqual[x, 2.25e-220], 4.0, If[LessEqual[x, 2.7e-70], t$95$1, t$95$0]]]]]]]]

Derivation

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

Alternative 4: 86.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7.2 \cdot 10^{+34} \lor \neg \left(z \leq 1.06 \cdot 10^{-23}\right):\\ \;\;\;\;1 + \frac{4}{\frac{y}{x - z}}\\ \mathbf{else}:\\ \;\;\;\;4 + 4 \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -7.2e+34) (not (<= z 1.06e-23)))
   (+ 1.0 (/ 4.0 (/ y (- x z))))
   (+ 4.0 (* 4.0 (/ x y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -7.2e+34) || !(z <= 1.06e-23)) {
		tmp = 1.0 + (4.0 / (y / (x - z)));
	} 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 <= (-7.2d+34)) .or. (.not. (z <= 1.06d-23))) then
        tmp = 1.0d0 + (4.0d0 / (y / (x - z)))
    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 <= -7.2e+34) || !(z <= 1.06e-23)) {
		tmp = 1.0 + (4.0 / (y / (x - z)));
	} else {
		tmp = 4.0 + (4.0 * (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -7.2e+34) or not (z <= 1.06e-23):
		tmp = 1.0 + (4.0 / (y / (x - z)))
	else:
		tmp = 4.0 + (4.0 * (x / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -7.2e+34) || !(z <= 1.06e-23))
		tmp = Float64(1.0 + Float64(4.0 / Float64(y / Float64(x - z))));
	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 <= -7.2e+34) || ~((z <= 1.06e-23)))
		tmp = 1.0 + (4.0 / (y / (x - z)));
	else
		tmp = 4.0 + (4.0 * (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -7.2e+34], N[Not[LessEqual[z, 1.06e-23]], $MachinePrecision]], N[(1.0 + N[(4.0 / N[(y / N[(x - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 85.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.45 \cdot 10^{+34} \lor \neg \left(z \leq 1.4 \cdot 10^{+51}\right):\\ \;\;\;\;4 \cdot \frac{x - z}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + 4 \cdot \frac{x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -3.45e+34) (not (<= z 1.4e+51)))
   (* 4.0 (/ (- x z) y))
   (+ 4.0 (* 4.0 (/ x y)))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -3.45e+34) || !(z <= 1.4e+51)) {
		tmp = 4.0 * ((x - 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 <= (-3.45d+34)) .or. (.not. (z <= 1.4d+51))) then
        tmp = 4.0d0 * ((x - 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 <= -3.45e+34) || !(z <= 1.4e+51)) {
		tmp = 4.0 * ((x - z) / y);
	} else {
		tmp = 4.0 + (4.0 * (x / y));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -3.45e+34) or not (z <= 1.4e+51):
		tmp = 4.0 * ((x - z) / y)
	else:
		tmp = 4.0 + (4.0 * (x / y))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -3.45e+34) || !(z <= 1.4e+51))
		tmp = Float64(4.0 * Float64(Float64(x - 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 <= -3.45e+34) || ~((z <= 1.4e+51)))
		tmp = 4.0 * ((x - z) / y);
	else
		tmp = 4.0 + (4.0 * (x / y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -3.45e+34], N[Not[LessEqual[z, 1.4e+51]], $MachinePrecision]], N[(4.0 * N[(N[(x - z), $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 6: 79.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+143}:\\ \;\;\;\;4\\ \mathbf{elif}\;y \leq 3.6 \cdot 10^{+198}:\\ \;\;\;\;4 \cdot \frac{x - z}{y}\\ \mathbf{else}:\\ \;\;\;\;4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -1.9e+143) 4.0 (if (<= y 3.6e+198) (* 4.0 (/ (- x z) y)) 4.0)))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -1.9e+143) {
		tmp = 4.0;
	} else if (y <= 3.6e+198) {
		tmp = 4.0 * ((x - 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 <= (-1.9d+143)) then
        tmp = 4.0d0
    else if (y <= 3.6d+198) then
        tmp = 4.0d0 * ((x - 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 <= -1.9e+143) {
		tmp = 4.0;
	} else if (y <= 3.6e+198) {
		tmp = 4.0 * ((x - z) / y);
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if y <= -1.9e+143:
		tmp = 4.0
	elif y <= 3.6e+198:
		tmp = 4.0 * ((x - z) / y)
	else:
		tmp = 4.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -1.9e+143)
		tmp = 4.0;
	elseif (y <= 3.6e+198)
		tmp = Float64(4.0 * Float64(Float64(x - z) / y));
	else
		tmp = 4.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (y <= -1.9e+143)
		tmp = 4.0;
	elseif (y <= 3.6e+198)
		tmp = 4.0 * ((x - z) / y);
	else
		tmp = 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -1.9e+143], 4.0, If[LessEqual[y, 3.6e+198], N[(4.0 * N[(N[(x - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], 4.0]]

Derivation

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

Alternative 7: 86.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.5 \cdot 10^{+34}:\\ \;\;\;\;4 \cdot \frac{x - z}{y}\\ \mathbf{elif}\;z \leq 1.46 \cdot 10^{+54}:\\ \;\;\;\;4 + 4 \cdot \frac{x}{y}\\ \mathbf{else}:\\ \;\;\;\;4 + \frac{z}{y} \cdot -4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -4.5e+34)
   (* 4.0 (/ (- x z) y))
   (if (<= z 1.46e+54) (+ 4.0 (* 4.0 (/ x y))) (+ 4.0 (* (/ z y) -4.0)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -4.5e+34) {
		tmp = 4.0 * ((x - z) / y);
	} else if (z <= 1.46e+54) {
		tmp = 4.0 + (4.0 * (x / y));
	} else {
		tmp = 4.0 + ((z / y) * -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 (z <= (-4.5d+34)) then
        tmp = 4.0d0 * ((x - z) / y)
    else if (z <= 1.46d+54) then
        tmp = 4.0d0 + (4.0d0 * (x / y))
    else
        tmp = 4.0d0 + ((z / y) * (-4.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -4.5e+34) {
		tmp = 4.0 * ((x - z) / y);
	} else if (z <= 1.46e+54) {
		tmp = 4.0 + (4.0 * (x / y));
	} else {
		tmp = 4.0 + ((z / y) * -4.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -4.5e+34:
		tmp = 4.0 * ((x - z) / y)
	elif z <= 1.46e+54:
		tmp = 4.0 + (4.0 * (x / y))
	else:
		tmp = 4.0 + ((z / y) * -4.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -4.5e+34)
		tmp = Float64(4.0 * Float64(Float64(x - z) / y));
	elseif (z <= 1.46e+54)
		tmp = Float64(4.0 + Float64(4.0 * Float64(x / y)));
	else
		tmp = Float64(4.0 + Float64(Float64(z / y) * -4.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -4.5e+34)
		tmp = 4.0 * ((x - z) / y);
	elseif (z <= 1.46e+54)
		tmp = 4.0 + (4.0 * (x / y));
	else
		tmp = 4.0 + ((z / y) * -4.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -4.5e+34], N[(4.0 * N[(N[(x - z), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.46e+54], N[(4.0 + N[(4.0 * N[(x / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(4.0 + N[(N[(z / y), $MachinePrecision] * -4.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

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

Alternative 8: 54.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.7 \cdot 10^{+34} \lor \neg \left(z \leq 1.8 \cdot 10^{+46}\right):\\ \;\;\;\;\frac{z}{y} \cdot -4\\ \mathbf{else}:\\ \;\;\;\;4\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -3.7e+34) (not (<= z 1.8e+46))) (* (/ z y) -4.0) 4.0))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -3.7e+34) || !(z <= 1.8e+46)) {
		tmp = (z / y) * -4.0;
	} 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 ((z <= (-3.7d+34)) .or. (.not. (z <= 1.8d+46))) then
        tmp = (z / y) * (-4.0d0)
    else
        tmp = 4.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -3.7e+34) || !(z <= 1.8e+46)) {
		tmp = (z / y) * -4.0;
	} else {
		tmp = 4.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -3.7e+34) or not (z <= 1.8e+46):
		tmp = (z / y) * -4.0
	else:
		tmp = 4.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -3.7e+34) || !(z <= 1.8e+46))
		tmp = Float64(Float64(z / y) * -4.0);
	else
		tmp = 4.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -3.7e+34) || ~((z <= 1.8e+46)))
		tmp = (z / y) * -4.0;
	else
		tmp = 4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -3.7e+34], N[Not[LessEqual[z, 1.8e+46]], $MachinePrecision]], N[(N[(z / y), $MachinePrecision] * -4.0), $MachinePrecision], 4.0]

Derivation

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

Alternative 9: 34.2% 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 2024008 
(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)))