Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4

Percentage Accurate: 99.9% → 100.0%

Time: 5.0s

Alternatives: 8

Speedup: 1.2×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, y, z)
	return Float64(Float64(Float64(Float64(Float64(x + y) + y) + x) + z) + x)
end

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, y, z)
	return Float64(Float64(Float64(Float64(Float64(x + y) + y) + x) + z) + x)
end

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ z - \mathsf{fma}\left(x, -3, y \cdot -2\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- z (fma x -3.0 (* y -2.0))))

C

double code(double x, double y, double z) {
	return z - fma(x, -3.0, (y * -2.0));
}

Julia

function code(x, y, z)
	return Float64(z - fma(x, -3.0, Float64(y * -2.0)))
end

Wolfram

code[x_, y_, z_] := N[(z - N[(x * -3.0 + N[(y * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

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

Alternative 2: 52.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -9.5 \cdot 10^{+90}:\\ \;\;\;\;x \cdot 3\\ \mathbf{elif}\;x \leq -1.42 \cdot 10^{-5}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;x \leq -4.8 \cdot 10^{-94}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq -5.2 \cdot 10^{-231}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;x \leq -1.3 \cdot 10^{-282}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq 9 \cdot 10^{-280}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;x \leq 9 \cdot 10^{-210}:\\ \;\;\;\;z\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{-87}:\\ \;\;\;\;y \cdot 2\\ \mathbf{elif}\;x \leq 5000000:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;x \cdot 3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -9.5e+90)
   (* x 3.0)
   (if (<= x -1.42e-5)
     (* y 2.0)
     (if (<= x -4.8e-94)
       z
       (if (<= x -5.2e-231)
         (* y 2.0)
         (if (<= x -1.3e-282)
           z
           (if (<= x 9e-280)
             (* y 2.0)
             (if (<= x 9e-210)
               z
               (if (<= x 2.6e-87)
                 (* y 2.0)
                 (if (<= x 5000000.0) z (* x 3.0)))))))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -9.5e+90) {
		tmp = x * 3.0;
	} else if (x <= -1.42e-5) {
		tmp = y * 2.0;
	} else if (x <= -4.8e-94) {
		tmp = z;
	} else if (x <= -5.2e-231) {
		tmp = y * 2.0;
	} else if (x <= -1.3e-282) {
		tmp = z;
	} else if (x <= 9e-280) {
		tmp = y * 2.0;
	} else if (x <= 9e-210) {
		tmp = z;
	} else if (x <= 2.6e-87) {
		tmp = y * 2.0;
	} else if (x <= 5000000.0) {
		tmp = z;
	} else {
		tmp = x * 3.0;
	}
	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 <= (-9.5d+90)) then
        tmp = x * 3.0d0
    else if (x <= (-1.42d-5)) then
        tmp = y * 2.0d0
    else if (x <= (-4.8d-94)) then
        tmp = z
    else if (x <= (-5.2d-231)) then
        tmp = y * 2.0d0
    else if (x <= (-1.3d-282)) then
        tmp = z
    else if (x <= 9d-280) then
        tmp = y * 2.0d0
    else if (x <= 9d-210) then
        tmp = z
    else if (x <= 2.6d-87) then
        tmp = y * 2.0d0
    else if (x <= 5000000.0d0) then
        tmp = z
    else
        tmp = x * 3.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -9.5e+90) {
		tmp = x * 3.0;
	} else if (x <= -1.42e-5) {
		tmp = y * 2.0;
	} else if (x <= -4.8e-94) {
		tmp = z;
	} else if (x <= -5.2e-231) {
		tmp = y * 2.0;
	} else if (x <= -1.3e-282) {
		tmp = z;
	} else if (x <= 9e-280) {
		tmp = y * 2.0;
	} else if (x <= 9e-210) {
		tmp = z;
	} else if (x <= 2.6e-87) {
		tmp = y * 2.0;
	} else if (x <= 5000000.0) {
		tmp = z;
	} else {
		tmp = x * 3.0;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -9.5e+90:
		tmp = x * 3.0
	elif x <= -1.42e-5:
		tmp = y * 2.0
	elif x <= -4.8e-94:
		tmp = z
	elif x <= -5.2e-231:
		tmp = y * 2.0
	elif x <= -1.3e-282:
		tmp = z
	elif x <= 9e-280:
		tmp = y * 2.0
	elif x <= 9e-210:
		tmp = z
	elif x <= 2.6e-87:
		tmp = y * 2.0
	elif x <= 5000000.0:
		tmp = z
	else:
		tmp = x * 3.0
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -9.5e+90)
		tmp = Float64(x * 3.0);
	elseif (x <= -1.42e-5)
		tmp = Float64(y * 2.0);
	elseif (x <= -4.8e-94)
		tmp = z;
	elseif (x <= -5.2e-231)
		tmp = Float64(y * 2.0);
	elseif (x <= -1.3e-282)
		tmp = z;
	elseif (x <= 9e-280)
		tmp = Float64(y * 2.0);
	elseif (x <= 9e-210)
		tmp = z;
	elseif (x <= 2.6e-87)
		tmp = Float64(y * 2.0);
	elseif (x <= 5000000.0)
		tmp = z;
	else
		tmp = Float64(x * 3.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -9.5e+90)
		tmp = x * 3.0;
	elseif (x <= -1.42e-5)
		tmp = y * 2.0;
	elseif (x <= -4.8e-94)
		tmp = z;
	elseif (x <= -5.2e-231)
		tmp = y * 2.0;
	elseif (x <= -1.3e-282)
		tmp = z;
	elseif (x <= 9e-280)
		tmp = y * 2.0;
	elseif (x <= 9e-210)
		tmp = z;
	elseif (x <= 2.6e-87)
		tmp = y * 2.0;
	elseif (x <= 5000000.0)
		tmp = z;
	else
		tmp = x * 3.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -9.5e+90], N[(x * 3.0), $MachinePrecision], If[LessEqual[x, -1.42e-5], N[(y * 2.0), $MachinePrecision], If[LessEqual[x, -4.8e-94], z, If[LessEqual[x, -5.2e-231], N[(y * 2.0), $MachinePrecision], If[LessEqual[x, -1.3e-282], z, If[LessEqual[x, 9e-280], N[(y * 2.0), $MachinePrecision], If[LessEqual[x, 9e-210], z, If[LessEqual[x, 2.6e-87], N[(y * 2.0), $MachinePrecision], If[LessEqual[x, 5000000.0], z, N[(x * 3.0), $MachinePrecision]]]]]]]]]]

Derivation

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

Alternative 3: 83.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x + 2 \cdot \left(x + y\right)\\ \mathbf{if}\;x \leq -49:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 1.9 \cdot 10^{-85}:\\ \;\;\;\;z - y \cdot -2\\ \mathbf{elif}\;x \leq 3.7 \cdot 10^{+96}:\\ \;\;\;\;z - x \cdot -3\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ x (* 2.0 (+ x y)))))
   (if (<= x -49.0)
     t_0
     (if (<= x 1.9e-85)
       (- z (* y -2.0))
       (if (<= x 3.7e+96) (- z (* x -3.0)) t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = x + (2.0 * (x + y));
	double tmp;
	if (x <= -49.0) {
		tmp = t_0;
	} else if (x <= 1.9e-85) {
		tmp = z - (y * -2.0);
	} else if (x <= 3.7e+96) {
		tmp = z - (x * -3.0);
	} 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) :: tmp
    t_0 = x + (2.0d0 * (x + y))
    if (x <= (-49.0d0)) then
        tmp = t_0
    else if (x <= 1.9d-85) then
        tmp = z - (y * (-2.0d0))
    else if (x <= 3.7d+96) then
        tmp = z - (x * (-3.0d0))
    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 + (2.0 * (x + y));
	double tmp;
	if (x <= -49.0) {
		tmp = t_0;
	} else if (x <= 1.9e-85) {
		tmp = z - (y * -2.0);
	} else if (x <= 3.7e+96) {
		tmp = z - (x * -3.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x + (2.0 * (x + y))
	tmp = 0
	if x <= -49.0:
		tmp = t_0
	elif x <= 1.9e-85:
		tmp = z - (y * -2.0)
	elif x <= 3.7e+96:
		tmp = z - (x * -3.0)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x + Float64(2.0 * Float64(x + y)))
	tmp = 0.0
	if (x <= -49.0)
		tmp = t_0;
	elseif (x <= 1.9e-85)
		tmp = Float64(z - Float64(y * -2.0));
	elseif (x <= 3.7e+96)
		tmp = Float64(z - Float64(x * -3.0));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x + (2.0 * (x + y));
	tmp = 0.0;
	if (x <= -49.0)
		tmp = t_0;
	elseif (x <= 1.9e-85)
		tmp = z - (y * -2.0);
	elseif (x <= 3.7e+96)
		tmp = z - (x * -3.0);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x + N[(2.0 * N[(x + y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -49.0], t$95$0, If[LessEqual[x, 1.9e-85], N[(z - N[(y * -2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.7e+96], N[(z - N[(x * -3.0), $MachinePrecision]), $MachinePrecision], t$95$0]]]]

Derivation

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

Alternative 4: 78.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -6 \cdot 10^{+205} \lor \neg \left(y \leq 7 \cdot 10^{+85}\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;z - x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -6e+205) (not (<= y 7e+85))) (* y 2.0) (- z (* x -3.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -6e+205) || !(y <= 7e+85)) {
		tmp = y * 2.0;
	} else {
		tmp = z - (x * -3.0);
	}
	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 <= (-6d+205)) .or. (.not. (y <= 7d+85))) then
        tmp = y * 2.0d0
    else
        tmp = z - (x * (-3.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -6e+205) || !(y <= 7e+85)) {
		tmp = y * 2.0;
	} else {
		tmp = z - (x * -3.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -6e+205) or not (y <= 7e+85):
		tmp = y * 2.0
	else:
		tmp = z - (x * -3.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -6e+205) || !(y <= 7e+85))
		tmp = Float64(y * 2.0);
	else
		tmp = Float64(z - Float64(x * -3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -6e+205) || ~((y <= 7e+85)))
		tmp = y * 2.0;
	else
		tmp = z - (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -6e+205], N[Not[LessEqual[y, 7e+85]], $MachinePrecision]], N[(y * 2.0), $MachinePrecision], N[(z - N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 5: 85.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.8 \cdot 10^{+101} \lor \neg \left(y \leq 2.45 \cdot 10^{+27}\right):\\ \;\;\;\;z - y \cdot -2\\ \mathbf{else}:\\ \;\;\;\;z - x \cdot -3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -2.8e+101) (not (<= y 2.45e+27)))
   (- z (* y -2.0))
   (- z (* x -3.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.8e+101) || !(y <= 2.45e+27)) {
		tmp = z - (y * -2.0);
	} else {
		tmp = z - (x * -3.0);
	}
	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 <= (-2.8d+101)) .or. (.not. (y <= 2.45d+27))) then
        tmp = z - (y * (-2.0d0))
    else
        tmp = z - (x * (-3.0d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -2.8e+101) || !(y <= 2.45e+27)) {
		tmp = z - (y * -2.0);
	} else {
		tmp = z - (x * -3.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -2.8e+101) or not (y <= 2.45e+27):
		tmp = z - (y * -2.0)
	else:
		tmp = z - (x * -3.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -2.8e+101) || !(y <= 2.45e+27))
		tmp = Float64(z - Float64(y * -2.0));
	else
		tmp = Float64(z - Float64(x * -3.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -2.8e+101) || ~((y <= 2.45e+27)))
		tmp = z - (y * -2.0);
	else
		tmp = z - (x * -3.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -2.8e+101], N[Not[LessEqual[y, 2.45e+27]], $MachinePrecision]], N[(z - N[(y * -2.0), $MachinePrecision]), $MachinePrecision], N[(z - N[(x * -3.0), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 6: 99.9% accurate, 1.2× speedup

Math

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

FPCore

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

C

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

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 + y)) + (z + x)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 7: 52.9% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.9 \cdot 10^{+57} \lor \neg \left(y \leq 2.9 \cdot 10^{+35}\right):\\ \;\;\;\;y \cdot 2\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= y -1.9e+57) (not (<= y 2.9e+35))) (* y 2.0) z))

C

double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.9e+57) || !(y <= 2.9e+35)) {
		tmp = y * 2.0;
	} else {
		tmp = z;
	}
	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+57)) .or. (.not. (y <= 2.9d+35))) then
        tmp = y * 2.0d0
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((y <= -1.9e+57) || !(y <= 2.9e+35)) {
		tmp = y * 2.0;
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (y <= -1.9e+57) or not (y <= 2.9e+35):
		tmp = y * 2.0
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((y <= -1.9e+57) || !(y <= 2.9e+35))
		tmp = Float64(y * 2.0);
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((y <= -1.9e+57) || ~((y <= 2.9e+35)))
		tmp = y * 2.0;
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[y, -1.9e+57], N[Not[LessEqual[y, 2.9e+35]], $MachinePrecision]], N[(y * 2.0), $MachinePrecision], z]

Derivation

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

Alternative 8: 34.9% accurate, 11.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

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

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4"
  :precision binary64
  (+ (+ (+ (+ (+ x y) y) x) z) x))