Simplification of discriminant from scale-rotated-ellipse

Percentage Accurate: 25.1% → 94.0%

Time: 2.0min

Alternatives: 6

Speedup: 2485.0×

• 33.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{angle}{180} \cdot \pi\\ t_1 := \sin t_0\\ t_2 := \cos t_0\\ t_3 := \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot t_1\right) \cdot t_2}{x-scale}}{y-scale}\\ t_3 \cdot t_3 - \left(4 \cdot \frac{\frac{{\left(a \cdot t_1\right)}^{2} + {\left(b \cdot t_2\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot t_2\right)}^{2} + {\left(b \cdot t_1\right)}^{2}}{y-scale}}{y-scale} \end{array} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (let* ((t_0 (* (/ angle 180.0) PI))
        (t_1 (sin t_0))
        (t_2 (cos t_0))
        (t_3
         (/
          (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) t_1) t_2) x-scale)
          y-scale)))
   (-
    (* t_3 t_3)
    (*
     (*
      4.0
      (/ (/ (+ (pow (* a t_1) 2.0) (pow (* b t_2) 2.0)) x-scale) x-scale))
     (/ (/ (+ (pow (* a t_2) 2.0) (pow (* b t_1) 2.0)) y-scale) y-scale)))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * ((double) M_PI);
	double t_1 = sin(t_0);
	double t_2 = cos(t_0);
	double t_3 = ((((2.0 * (pow(b, 2.0) - pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((pow((a * t_1), 2.0) + pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((pow((a * t_2), 2.0) + pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * Math.PI;
	double t_1 = Math.sin(t_0);
	double t_2 = Math.cos(t_0);
	double t_3 = ((((2.0 * (Math.pow(b, 2.0) - Math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((Math.pow((a * t_1), 2.0) + Math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((Math.pow((a * t_2), 2.0) + Math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	t_0 = (angle / 180.0) * math.pi
	t_1 = math.sin(t_0)
	t_2 = math.cos(t_0)
	t_3 = ((((2.0 * (math.pow(b, 2.0) - math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale
	return (t_3 * t_3) - ((4.0 * (((math.pow((a * t_1), 2.0) + math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((math.pow((a * t_2), 2.0) + math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = Float64(Float64(angle / 180.0) * pi)
	t_1 = sin(t_0)
	t_2 = cos(t_0)
	t_3 = Float64(Float64(Float64(Float64(Float64(2.0 * Float64((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale)
	return Float64(Float64(t_3 * t_3) - Float64(Float64(4.0 * Float64(Float64(Float64((Float64(a * t_1) ^ 2.0) + (Float64(b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * Float64(Float64(Float64((Float64(a * t_2) ^ 2.0) + (Float64(b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale)))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = (angle / 180.0) * pi;
	t_1 = sin(t_0);
	t_2 = cos(t_0);
	t_3 = ((((2.0 * ((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	tmp = (t_3 * t_3) - ((4.0 * (((((a * t_1) ^ 2.0) + ((b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * (((((a * t_2) ^ 2.0) + ((b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := Block[{t$95$0 = N[(N[(angle / 180.0), $MachinePrecision] * Pi), $MachinePrecision]}, Block[{t$95$1 = N[Sin[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Cos[t$95$0], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(N[(2.0 * N[(N[Power[b, 2.0], $MachinePrecision] - N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$2), $MachinePrecision] / x$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]}, N[(N[(t$95$3 * t$95$3), $MachinePrecision] - N[(N[(4.0 * N[(N[(N[(N[Power[N[(a * t$95$1), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$2), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / x$45$scale), $MachinePrecision] / x$45$scale), $MachinePrecision]), $MachinePrecision] * N[(N[(N[(N[Power[N[(a * t$95$2), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$1), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / y$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Initial Program: 25.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{angle}{180} \cdot \pi\\ t_1 := \sin t_0\\ t_2 := \cos t_0\\ t_3 := \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot t_1\right) \cdot t_2}{x-scale}}{y-scale}\\ t_3 \cdot t_3 - \left(4 \cdot \frac{\frac{{\left(a \cdot t_1\right)}^{2} + {\left(b \cdot t_2\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot t_2\right)}^{2} + {\left(b \cdot t_1\right)}^{2}}{y-scale}}{y-scale} \end{array} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (let* ((t_0 (* (/ angle 180.0) PI))
        (t_1 (sin t_0))
        (t_2 (cos t_0))
        (t_3
         (/
          (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) t_1) t_2) x-scale)
          y-scale)))
   (-
    (* t_3 t_3)
    (*
     (*
      4.0
      (/ (/ (+ (pow (* a t_1) 2.0) (pow (* b t_2) 2.0)) x-scale) x-scale))
     (/ (/ (+ (pow (* a t_2) 2.0) (pow (* b t_1) 2.0)) y-scale) y-scale)))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * ((double) M_PI);
	double t_1 = sin(t_0);
	double t_2 = cos(t_0);
	double t_3 = ((((2.0 * (pow(b, 2.0) - pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((pow((a * t_1), 2.0) + pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((pow((a * t_2), 2.0) + pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * Math.PI;
	double t_1 = Math.sin(t_0);
	double t_2 = Math.cos(t_0);
	double t_3 = ((((2.0 * (Math.pow(b, 2.0) - Math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((Math.pow((a * t_1), 2.0) + Math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((Math.pow((a * t_2), 2.0) + Math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	t_0 = (angle / 180.0) * math.pi
	t_1 = math.sin(t_0)
	t_2 = math.cos(t_0)
	t_3 = ((((2.0 * (math.pow(b, 2.0) - math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale
	return (t_3 * t_3) - ((4.0 * (((math.pow((a * t_1), 2.0) + math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((math.pow((a * t_2), 2.0) + math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = Float64(Float64(angle / 180.0) * pi)
	t_1 = sin(t_0)
	t_2 = cos(t_0)
	t_3 = Float64(Float64(Float64(Float64(Float64(2.0 * Float64((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale)
	return Float64(Float64(t_3 * t_3) - Float64(Float64(4.0 * Float64(Float64(Float64((Float64(a * t_1) ^ 2.0) + (Float64(b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * Float64(Float64(Float64((Float64(a * t_2) ^ 2.0) + (Float64(b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale)))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = (angle / 180.0) * pi;
	t_1 = sin(t_0);
	t_2 = cos(t_0);
	t_3 = ((((2.0 * ((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	tmp = (t_3 * t_3) - ((4.0 * (((((a * t_1) ^ 2.0) + ((b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * (((((a * t_2) ^ 2.0) + ((b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := Block[{t$95$0 = N[(N[(angle / 180.0), $MachinePrecision] * Pi), $MachinePrecision]}, Block[{t$95$1 = N[Sin[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Cos[t$95$0], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(N[(2.0 * N[(N[Power[b, 2.0], $MachinePrecision] - N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$2), $MachinePrecision] / x$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]}, N[(N[(t$95$3 * t$95$3), $MachinePrecision] - N[(N[(4.0 * N[(N[(N[(N[Power[N[(a * t$95$1), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$2), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / x$45$scale), $MachinePrecision] / x$45$scale), $MachinePrecision]), $MachinePrecision] * N[(N[(N[(N[Power[N[(a * t$95$2), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$1), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / y$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Alternative 1: 94.0% accurate, 22.6× speedup

Math

\[\begin{array}{l} \\ -4 \cdot {\left(\frac{a}{y-scale} \cdot \frac{b}{x-scale}\right)}^{2} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* -4.0 (pow (* (/ a y-scale) (/ b x-scale)) 2.0)))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * pow(((a / y_45_scale) * (b / x_45_scale)), 2.0);
}

Fortran

real(8) function code(a, b, angle, x_45scale, y_45scale)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (-4.0d0) * (((a / y_45scale) * (b / x_45scale)) ** 2.0d0)
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * Math.pow(((a / y_45_scale) * (b / x_45_scale)), 2.0);
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return -4.0 * math.pow(((a / y_45_scale) * (b / x_45_scale)), 2.0)

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(-4.0 * (Float64(Float64(a / y_45_scale) * Float64(b / x_45_scale)) ^ 2.0))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = -4.0 * (((a / y_45_scale) * (b / x_45_scale)) ^ 2.0);
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(-4.0 * N[Power[N[(N[(a / y$45$scale), $MachinePrecision] * N[(b / x$45$scale), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 24.0%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0 42.8%

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{y-scale}^{2} \cdot {x-scale}^{2}}} \]
3. Step-by-step derivation

   1. times-frac 42.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{{a}^{2}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right)} \]

   2. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{\color{blue}{a \cdot a}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   3. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{\color{blue}{y-scale \cdot y-scale}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   4. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{\color{blue}{b \cdot b}}{{x-scale}^{2}}\right) \]

   5. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{\color{blue}{x-scale \cdot x-scale}}\right) \]

4. Simplified 42.8%

   \[\leadsto \color{blue}{-4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)} \]
5. Step-by-step derivation

   1. pow1 42.8%

      \[\leadsto -4 \cdot \color{blue}{{\left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1}} \]

   2. times-frac 58.0%

      \[\leadsto -4 \cdot {\left(\color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1} \]

   3. associate-/l* 67.8%

      \[\leadsto -4 \cdot {\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \color{blue}{\frac{b}{\frac{x-scale \cdot x-scale}{b}}}\right)}^{1} \]

6. Applied egg-rr 67.8%

   \[\leadsto -4 \cdot \color{blue}{{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)}^{1}} \]
7. Step-by-step derivation

   1. unpow1 67.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)} \]

   2. associate-*l* 71.6%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)\right)} \]

   3. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{\color{blue}{{x-scale}^{2}}}{b}}\right)\right) \]

   4. associate-/r/ 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{{x-scale}^{2}} \cdot b\right)}\right)\right) \]

   5. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{\color{blue}{x-scale \cdot x-scale}} \cdot b\right)\right)\right) \]

8. Simplified 71.6%

   \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{x-scale \cdot x-scale} \cdot b\right)\right)\right)} \]
9. Step-by-step derivation

   1. associate-*l/ 61.0%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{b \cdot b}{x-scale \cdot x-scale}}\right)\right) \]

   2. frac-times 84.1%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right)\right) \]

   3. associate-*r/ 81.8%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]

10. Applied egg-rr 81.8%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]

11. Taylor expanded in a around 0 42.8%

    \[\leadsto -4 \cdot \color{blue}{\frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
12. Step-by-step derivation

    1. unpow2 42.8%

       \[\leadsto -4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{\color{blue}{\left(x-scale \cdot x-scale\right)} \cdot {y-scale}^{2}} \]

    2. unpow2 42.8%

       \[\leadsto -4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \color{blue}{\left(y-scale \cdot y-scale\right)}} \]

    3. *-commutative 42.8%

       \[\leadsto -4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{\color{blue}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}} \]

    4. unpow2 42.8%

       \[\leadsto -4 \cdot \frac{\color{blue}{\left(a \cdot a\right)} \cdot {b}^{2}}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)} \]

    5. unpow2 42.8%

       \[\leadsto -4 \cdot \frac{\left(a \cdot a\right) \cdot \color{blue}{\left(b \cdot b\right)}}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)} \]

    6. times-frac 42.8%

       \[\leadsto -4 \cdot \color{blue}{\left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)} \]

    7. times-frac 58.0%

       \[\leadsto -4 \cdot \left(\color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right) \]

    8. times-frac 79.7%

       \[\leadsto -4 \cdot \left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right) \]

    9. swap-sqr 93.9%

       \[\leadsto -4 \cdot \color{blue}{\left(\left(\frac{a}{y-scale} \cdot \frac{b}{x-scale}\right) \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{x-scale}\right)\right)} \]

    10. unpow2 93.9%

        \[\leadsto -4 \cdot \color{blue}{{\left(\frac{a}{y-scale} \cdot \frac{b}{x-scale}\right)}^{2}} \]

13. Simplified 93.9%

    \[\leadsto -4 \cdot \color{blue}{{\left(\frac{a}{y-scale} \cdot \frac{b}{x-scale}\right)}^{2}} \]

14. Final simplification 93.9%

    \[\leadsto -4 \cdot {\left(\frac{a}{y-scale} \cdot \frac{b}{x-scale}\right)}^{2} \]

Alternative 2: 77.6% accurate, 146.2× speedup

Math

\[\begin{array}{l} \\ -4 \cdot \left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)\right) \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* -4.0 (* (* (/ a y-scale) (/ a y-scale)) (* (/ b x-scale) (/ b x-scale)))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * (((a / y_45_scale) * (a / y_45_scale)) * ((b / x_45_scale) * (b / x_45_scale)));
}

Fortran

real(8) function code(a, b, angle, x_45scale, y_45scale)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (-4.0d0) * (((a / y_45scale) * (a / y_45scale)) * ((b / x_45scale) * (b / x_45scale)))
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * (((a / y_45_scale) * (a / y_45_scale)) * ((b / x_45_scale) * (b / x_45_scale)));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return -4.0 * (((a / y_45_scale) * (a / y_45_scale)) * ((b / x_45_scale) * (b / x_45_scale)))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(-4.0 * Float64(Float64(Float64(a / y_45_scale) * Float64(a / y_45_scale)) * Float64(Float64(b / x_45_scale) * Float64(b / x_45_scale))))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = -4.0 * (((a / y_45_scale) * (a / y_45_scale)) * ((b / x_45_scale) * (b / x_45_scale)));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(-4.0 * N[(N[(N[(a / y$45$scale), $MachinePrecision] * N[(a / y$45$scale), $MachinePrecision]), $MachinePrecision] * N[(N[(b / x$45$scale), $MachinePrecision] * N[(b / x$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 24.0%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0 42.8%

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{y-scale}^{2} \cdot {x-scale}^{2}}} \]
3. Step-by-step derivation

   1. times-frac 42.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{{a}^{2}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right)} \]

   2. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{\color{blue}{a \cdot a}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   3. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{\color{blue}{y-scale \cdot y-scale}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   4. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{\color{blue}{b \cdot b}}{{x-scale}^{2}}\right) \]

   5. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{\color{blue}{x-scale \cdot x-scale}}\right) \]

4. Simplified 42.8%

   \[\leadsto \color{blue}{-4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)} \]
5. Step-by-step derivation

   1. times-frac 60.4%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right) \]

6. Applied egg-rr 60.4%

   \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right) \]
7. Step-by-step derivation

   1. *-un-lft-identity 60.4%

      \[\leadsto -4 \cdot \left(\color{blue}{\left(1 \cdot \frac{a \cdot a}{y-scale \cdot y-scale}\right)} \cdot \left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)\right) \]

   2. times-frac 79.7%

      \[\leadsto -4 \cdot \left(\left(1 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)}\right) \cdot \left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)\right) \]

8. Applied egg-rr 79.7%

   \[\leadsto -4 \cdot \left(\color{blue}{\left(1 \cdot \left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)\right)} \cdot \left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)\right) \]
9. Step-by-step derivation

   1. *-lft-identity 79.7%

      \[\leadsto -4 \cdot \left(\color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)} \cdot \left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)\right) \]

10. Simplified 79.7%

    \[\leadsto -4 \cdot \left(\color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)} \cdot \left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)\right) \]

11. Final simplification 79.7%

    \[\leadsto -4 \cdot \left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)\right) \]

Alternative 3: 80.6% accurate, 146.2× speedup

Math

\[\begin{array}{l} \\ -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b \cdot \frac{b}{x-scale}}{x-scale}\right)\right) \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* -4.0 (* (/ a y-scale) (* (/ a y-scale) (/ (* b (/ b x-scale)) x-scale)))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * ((a / y_45_scale) * ((a / y_45_scale) * ((b * (b / x_45_scale)) / x_45_scale)));
}

Fortran

real(8) function code(a, b, angle, x_45scale, y_45scale)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (-4.0d0) * ((a / y_45scale) * ((a / y_45scale) * ((b * (b / x_45scale)) / x_45scale)))
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * ((a / y_45_scale) * ((a / y_45_scale) * ((b * (b / x_45_scale)) / x_45_scale)));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return -4.0 * ((a / y_45_scale) * ((a / y_45_scale) * ((b * (b / x_45_scale)) / x_45_scale)))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(-4.0 * Float64(Float64(a / y_45_scale) * Float64(Float64(a / y_45_scale) * Float64(Float64(b * Float64(b / x_45_scale)) / x_45_scale))))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = -4.0 * ((a / y_45_scale) * ((a / y_45_scale) * ((b * (b / x_45_scale)) / x_45_scale)));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(-4.0 * N[(N[(a / y$45$scale), $MachinePrecision] * N[(N[(a / y$45$scale), $MachinePrecision] * N[(N[(b * N[(b / x$45$scale), $MachinePrecision]), $MachinePrecision] / x$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 24.0%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0 42.8%

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{y-scale}^{2} \cdot {x-scale}^{2}}} \]
3. Step-by-step derivation

   1. times-frac 42.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{{a}^{2}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right)} \]

   2. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{\color{blue}{a \cdot a}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   3. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{\color{blue}{y-scale \cdot y-scale}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   4. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{\color{blue}{b \cdot b}}{{x-scale}^{2}}\right) \]

   5. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{\color{blue}{x-scale \cdot x-scale}}\right) \]

4. Simplified 42.8%

   \[\leadsto \color{blue}{-4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)} \]
5. Step-by-step derivation

   1. pow1 42.8%

      \[\leadsto -4 \cdot \color{blue}{{\left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1}} \]

   2. times-frac 58.0%

      \[\leadsto -4 \cdot {\left(\color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1} \]

   3. associate-/l* 67.8%

      \[\leadsto -4 \cdot {\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \color{blue}{\frac{b}{\frac{x-scale \cdot x-scale}{b}}}\right)}^{1} \]

6. Applied egg-rr 67.8%

   \[\leadsto -4 \cdot \color{blue}{{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)}^{1}} \]
7. Step-by-step derivation

   1. unpow1 67.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)} \]

   2. associate-*l* 71.6%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)\right)} \]

   3. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{\color{blue}{{x-scale}^{2}}}{b}}\right)\right) \]

   4. associate-/r/ 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{{x-scale}^{2}} \cdot b\right)}\right)\right) \]

   5. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{\color{blue}{x-scale \cdot x-scale}} \cdot b\right)\right)\right) \]

8. Simplified 71.6%

   \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{x-scale \cdot x-scale} \cdot b\right)\right)\right)} \]
9. Step-by-step derivation

   1. associate-*l/ 61.0%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{b \cdot b}{x-scale \cdot x-scale}}\right)\right) \]

   2. frac-times 84.1%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right)\right) \]

   3. associate-*r/ 81.8%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]

10. Applied egg-rr 81.8%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]

11. Final simplification 81.8%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b \cdot \frac{b}{x-scale}}{x-scale}\right)\right) \]

Alternative 4: 89.2% accurate, 146.2× speedup

Math

\[\begin{array}{l} \\ -4 \cdot \left(\frac{a}{y-scale} \cdot \frac{a \cdot \frac{b}{x-scale}}{y-scale \cdot \frac{x-scale}{b}}\right) \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* -4.0 (* (/ a y-scale) (/ (* a (/ b x-scale)) (* y-scale (/ x-scale b))))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * ((a / y_45_scale) * ((a * (b / x_45_scale)) / (y_45_scale * (x_45_scale / b))));
}

Fortran

real(8) function code(a, b, angle, x_45scale, y_45scale)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (-4.0d0) * ((a / y_45scale) * ((a * (b / x_45scale)) / (y_45scale * (x_45scale / b))))
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * ((a / y_45_scale) * ((a * (b / x_45_scale)) / (y_45_scale * (x_45_scale / b))));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return -4.0 * ((a / y_45_scale) * ((a * (b / x_45_scale)) / (y_45_scale * (x_45_scale / b))))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(-4.0 * Float64(Float64(a / y_45_scale) * Float64(Float64(a * Float64(b / x_45_scale)) / Float64(y_45_scale * Float64(x_45_scale / b)))))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = -4.0 * ((a / y_45_scale) * ((a * (b / x_45_scale)) / (y_45_scale * (x_45_scale / b))));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(-4.0 * N[(N[(a / y$45$scale), $MachinePrecision] * N[(N[(a * N[(b / x$45$scale), $MachinePrecision]), $MachinePrecision] / N[(y$45$scale * N[(x$45$scale / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 24.0%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0 42.8%

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{y-scale}^{2} \cdot {x-scale}^{2}}} \]
3. Step-by-step derivation

   1. times-frac 42.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{{a}^{2}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right)} \]

   2. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{\color{blue}{a \cdot a}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   3. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{\color{blue}{y-scale \cdot y-scale}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   4. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{\color{blue}{b \cdot b}}{{x-scale}^{2}}\right) \]

   5. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{\color{blue}{x-scale \cdot x-scale}}\right) \]

4. Simplified 42.8%

   \[\leadsto \color{blue}{-4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)} \]
5. Step-by-step derivation

   1. pow1 42.8%

      \[\leadsto -4 \cdot \color{blue}{{\left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1}} \]

   2. times-frac 58.0%

      \[\leadsto -4 \cdot {\left(\color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1} \]

   3. associate-/l* 67.8%

      \[\leadsto -4 \cdot {\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \color{blue}{\frac{b}{\frac{x-scale \cdot x-scale}{b}}}\right)}^{1} \]

6. Applied egg-rr 67.8%

   \[\leadsto -4 \cdot \color{blue}{{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)}^{1}} \]
7. Step-by-step derivation

   1. unpow1 67.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)} \]

   2. associate-*l* 71.6%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)\right)} \]

   3. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{\color{blue}{{x-scale}^{2}}}{b}}\right)\right) \]

   4. associate-/r/ 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{{x-scale}^{2}} \cdot b\right)}\right)\right) \]

   5. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{\color{blue}{x-scale \cdot x-scale}} \cdot b\right)\right)\right) \]

8. Simplified 71.6%

   \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{x-scale \cdot x-scale} \cdot b\right)\right)\right)} \]
9. Step-by-step derivation

   1. associate-*l/ 61.0%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{b \cdot b}{x-scale \cdot x-scale}}\right)\right) \]

   2. frac-times 84.1%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right)\right) \]

   3. associate-*r/ 81.8%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]

10. Applied egg-rr 81.8%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]
11. Step-by-step derivation

    1. associate-*r/ 84.1%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right)\right) \]

    2. *-commutative 84.1%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right) \cdot \frac{a}{y-scale}\right)}\right) \]

    3. associate-*r/ 81.8%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}} \cdot \frac{a}{y-scale}\right)\right) \]

    4. associate-/l* 84.1%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\color{blue}{\frac{\frac{b}{x-scale}}{\frac{x-scale}{b}}} \cdot \frac{a}{y-scale}\right)\right) \]

    5. frac-times 86.2%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot a}{\frac{x-scale}{b} \cdot y-scale}}\right) \]

12. Applied egg-rr 86.2%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot a}{\frac{x-scale}{b} \cdot y-scale}}\right) \]

13. Final simplification 86.2%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \frac{a \cdot \frac{b}{x-scale}}{y-scale \cdot \frac{x-scale}{b}}\right) \]

Alternative 5: 91.4% accurate, 146.2× speedup

Math

\[\begin{array}{l} \\ -4 \cdot \left(\frac{a}{y-scale} \cdot \frac{\frac{b}{x-scale}}{\frac{y-scale}{a} \cdot \frac{x-scale}{b}}\right) \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* -4.0 (* (/ a y-scale) (/ (/ b x-scale) (* (/ y-scale a) (/ x-scale b))))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * ((a / y_45_scale) * ((b / x_45_scale) / ((y_45_scale / a) * (x_45_scale / b))));
}

Fortran

real(8) function code(a, b, angle, x_45scale, y_45scale)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (-4.0d0) * ((a / y_45scale) * ((b / x_45scale) / ((y_45scale / a) * (x_45scale / b))))
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return -4.0 * ((a / y_45_scale) * ((b / x_45_scale) / ((y_45_scale / a) * (x_45_scale / b))));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return -4.0 * ((a / y_45_scale) * ((b / x_45_scale) / ((y_45_scale / a) * (x_45_scale / b))))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(-4.0 * Float64(Float64(a / y_45_scale) * Float64(Float64(b / x_45_scale) / Float64(Float64(y_45_scale / a) * Float64(x_45_scale / b)))))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = -4.0 * ((a / y_45_scale) * ((b / x_45_scale) / ((y_45_scale / a) * (x_45_scale / b))));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(-4.0 * N[(N[(a / y$45$scale), $MachinePrecision] * N[(N[(b / x$45$scale), $MachinePrecision] / N[(N[(y$45$scale / a), $MachinePrecision] * N[(x$45$scale / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 24.0%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0 42.8%

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{y-scale}^{2} \cdot {x-scale}^{2}}} \]
3. Step-by-step derivation

   1. times-frac 42.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{{a}^{2}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right)} \]

   2. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{\color{blue}{a \cdot a}}{{y-scale}^{2}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   3. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{\color{blue}{y-scale \cdot y-scale}} \cdot \frac{{b}^{2}}{{x-scale}^{2}}\right) \]

   4. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{\color{blue}{b \cdot b}}{{x-scale}^{2}}\right) \]

   5. unpow2 42.8%

      \[\leadsto -4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{\color{blue}{x-scale \cdot x-scale}}\right) \]

4. Simplified 42.8%

   \[\leadsto \color{blue}{-4 \cdot \left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)} \]
5. Step-by-step derivation

   1. pow1 42.8%

      \[\leadsto -4 \cdot \color{blue}{{\left(\frac{a \cdot a}{y-scale \cdot y-scale} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1}} \]

   2. times-frac 58.0%

      \[\leadsto -4 \cdot {\left(\color{blue}{\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right)} \cdot \frac{b \cdot b}{x-scale \cdot x-scale}\right)}^{1} \]

   3. associate-/l* 67.8%

      \[\leadsto -4 \cdot {\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \color{blue}{\frac{b}{\frac{x-scale \cdot x-scale}{b}}}\right)}^{1} \]

6. Applied egg-rr 67.8%

   \[\leadsto -4 \cdot \color{blue}{{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)}^{1}} \]
7. Step-by-step derivation

   1. unpow1 67.8%

      \[\leadsto -4 \cdot \color{blue}{\left(\left(\frac{a}{y-scale} \cdot \frac{a}{y-scale}\right) \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)} \]

   2. associate-*l* 71.6%

      \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{x-scale \cdot x-scale}{b}}\right)\right)} \]

   3. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \frac{b}{\frac{\color{blue}{{x-scale}^{2}}}{b}}\right)\right) \]

   4. associate-/r/ 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{{x-scale}^{2}} \cdot b\right)}\right)\right) \]

   5. unpow2 71.6%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{\color{blue}{x-scale \cdot x-scale}} \cdot b\right)\right)\right) \]

8. Simplified 71.6%

   \[\leadsto -4 \cdot \color{blue}{\left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{b}{x-scale \cdot x-scale} \cdot b\right)\right)\right)} \]
9. Step-by-step derivation

   1. associate-*l/ 61.0%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{b \cdot b}{x-scale \cdot x-scale}}\right)\right) \]

   2. frac-times 84.1%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\left(\frac{b}{x-scale} \cdot \frac{b}{x-scale}\right)}\right)\right) \]

   3. associate-*r/ 81.8%

      \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]

10. Applied egg-rr 81.8%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale} \cdot b}{x-scale}}\right)\right) \]
11. Step-by-step derivation

    1. clear-num 81.4%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\color{blue}{\frac{1}{\frac{y-scale}{a}}} \cdot \frac{\frac{b}{x-scale} \cdot b}{x-scale}\right)\right) \]

    2. associate-/l* 83.7%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \left(\frac{1}{\frac{y-scale}{a}} \cdot \color{blue}{\frac{\frac{b}{x-scale}}{\frac{x-scale}{b}}}\right)\right) \]

    3. frac-times 89.5%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{1 \cdot \frac{b}{x-scale}}{\frac{y-scale}{a} \cdot \frac{x-scale}{b}}}\right) \]

    4. *-un-lft-identity 89.5%

       \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \frac{\color{blue}{\frac{b}{x-scale}}}{\frac{y-scale}{a} \cdot \frac{x-scale}{b}}\right) \]

12. Applied egg-rr 89.5%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \color{blue}{\frac{\frac{b}{x-scale}}{\frac{y-scale}{a} \cdot \frac{x-scale}{b}}}\right) \]

13. Final simplification 89.5%

    \[\leadsto -4 \cdot \left(\frac{a}{y-scale} \cdot \frac{\frac{b}{x-scale}}{\frac{y-scale}{a} \cdot \frac{x-scale}{b}}\right) \]

Alternative 6: 35.4% accurate, 2485.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale) :precision binary64 0.0)

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return 0.0;
}

Fortran

real(8) function code(a, b, angle, x_45scale, y_45scale)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = 0.0d0
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return 0.0;
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return 0.0

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return 0.0
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = 0.0;
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := 0.0

Derivation

1. Initial program 24.0%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]
2. Step-by-step derivation

   1. fma-neg 24.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale}, \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale}, -\left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale}\right)} \]

3. Simplified 17.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\left(2 \cdot \left(b \cdot b - a \cdot a\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)}{y-scale} \cdot \frac{\cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}, \frac{\left(2 \cdot \left(b \cdot b - a \cdot a\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)}{y-scale} \cdot \frac{\cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}, \frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale \cdot y-scale} \cdot \left(\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale \cdot x-scale} \cdot -4\right)\right)} \]

4. Taylor expanded in b around 0 24.9%

   \[\leadsto \color{blue}{4 \cdot \frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{y-scale}^{2} \cdot {x-scale}^{2}} + -4 \cdot \frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
5. Step-by-step derivation

   1. *-commutative 24.9%

      \[\leadsto \color{blue}{\frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{y-scale}^{2} \cdot {x-scale}^{2}} \cdot 4} + -4 \cdot \frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \]

   2. *-commutative 24.9%

      \[\leadsto \frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{y-scale}^{2} \cdot {x-scale}^{2}} \cdot 4 + \color{blue}{\frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4} \]

   3. *-commutative 24.9%

      \[\leadsto \frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{y-scale}^{2} \cdot {x-scale}^{2}} \cdot 4 + \frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{\color{blue}{{y-scale}^{2} \cdot {x-scale}^{2}}} \cdot -4 \]

   4. distribute-lft-out 24.9%

      \[\leadsto \color{blue}{\frac{{a}^{4} \cdot \left({\cos \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2} \cdot {\sin \left(0.005555555555555556 \cdot \left(angle \cdot \pi\right)\right)}^{2}\right)}{{y-scale}^{2} \cdot {x-scale}^{2}} \cdot \left(4 + -4\right)} \]

6. Simplified 38.3%

   \[\leadsto \color{blue}{0} \]

7. Final simplification 38.3%

   \[\leadsto 0 \]

Reproduce

herbie shell --seed 2023174 
(FPCore (a b angle x-scale y-scale)
  :name "Simplification of discriminant from scale-rotated-ellipse"
  :precision binary64
  (- (* (/ (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) (sin (* (/ angle 180.0) PI))) (cos (* (/ angle 180.0) PI))) x-scale) y-scale) (/ (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) (sin (* (/ angle 180.0) PI))) (cos (* (/ angle 180.0) PI))) x-scale) y-scale)) (* (* 4.0 (/ (/ (+ (pow (* a (sin (* (/ angle 180.0) PI))) 2.0) (pow (* b (cos (* (/ angle 180.0) PI))) 2.0)) x-scale) x-scale)) (/ (/ (+ (pow (* a (cos (* (/ angle 180.0) PI))) 2.0) (pow (* b (sin (* (/ angle 180.0) PI))) 2.0)) y-scale) y-scale))))