A while back I wrote about a simple equation for the shape of an egg. That equation is useful for producing egg-like images, but it’s not based on extensive research into actual eggs.

I recently ran across a more realistic, but also more complicated, equation for modeling the shape of real eggs [1]. The equation models the shape of an egg by rotating the graph of a function of the form

*y* = (B/2) *f*(*x*; *L*, *w*, *D*)

around the *x*-axis. The parametersĀ *B* and *L* are simple to describe, but *w* and *D* take a little more explanation.

## Parameters

Imagine the egg positioned so the *x*-axis runs along the center of the egg, with the fat end on the left at –*L*/2 and the pointy end on the right at *L*/2. So *L* is the length of the egg.

The parameter *B* is the maximum breadth of the egg, the maximum diameter perpendicular to the central axis. If you plot *y* above as a function of *x*, the maximum value of *y* is *B*/2.

The authors describe *w* as “the parameter that shows the distance between two vertical axes corresponding to the maximum breadth and the half length of the egg.” I find that description confusing. I believe they’re saying that –*w* is the location of the maximum of *y*. So *B*/2 is the maximum value of *y*, and –*w* is where that maximum occurs.

The authors define a parameter *D*_{L/4} that I’m calling *D* for simplicity. They define this parameter as the “egg diameter at the point of *L*/4 from the pointed end.” That is, *D* is twice the value of *y* at *x* = *L*/4. Recall that the egg runs from –*L*/2 to *L*/2, so we’re talking about the point 3/4 of the way between the fat end and the pointed end.

To summarize, *L*, *B*, *w*, and *D* are the length, maximum breadth, the negative of the *location* of the maximum breadth, and the breadth three quarters of the way between the fat and pointed ends.

## Equation

With all that out of the way, here’s the equation. I’ll write it up in Python, but I wanted to include a screen shot from the paper in case I made an error in coding it up.

## Python implementation

Here’s Python code that implements this function, if I haven’t made any errors.

from numpy import sqrt def egg_radius(x, L, B, w, D): # Note that k does not involve x k = sqrt(5.5*L**2 + 11*L*w + 4*w**2) k *= sqrt(3)*B*L - 2*D*sqrt(L**2 + 2*w*L + 4*w**2) k /= sqrt(3)*B*L k /= sqrt(5.5*L**2 + 11*L*w + 4*w*w) - 2*sqrt(L**2 + 2*w*L + 4*w**2) # coefficients of a quadratic in x a = 2*(L-2*w) b = L**2 + 8*L*w - 4*w**2 c = 2*L*w**2 + L**2*w + L**3 t = L*(L**2 + 8*w*x + 4*w**2) t /= a*x**2 + b*x + c y = 0.5*B*sqrt(L**2 - 4*x**2)/sqrt(L**2 + 8*w*x + 4*w**2) y *= (1 - k*(1 - sqrt(t))) return y

Here’s a plot using this code:

And here’s the code that was used to make the plot:

from numpy import linspace import matplotlib.pyplot as plt L = 2 B = 1.3 w = 0.2 D = 0.9 plt.plot([-w, -w], [-B/2, B/2], 'g--') z = egg_radius(L/4, L, B, w, D) plt.plot([L/4, L/4], [-z, z], "r-.") x = linspace(-L/2, L/2, 200) y = egg_radius(x, L, B, w, D) plt.plot(x, y, 'b') plt.plot(x, -y, 'b') plt.legend(["B", "D"]) plt.xlabel("$x$") plt.ylabel("$y$") plt.show()

## Error

Something does add up. It appears that *y*(-*w*) = *B*, as it should, but the maximum of *y* is not at –*w*.

from scipy.optimize import minimize_scalar print(-w, egg_radius(-w, L, B, w, D)) res = minimize_scalar(lambda x: -egg_radius(x, L, B, w, D), bracket=[-1,0,1], method='brent') print(res.x, egg_radius(res.x, L, B, w, D))

This shows the maximum is near -0.3, not at -0.2.

Maybe I’ve misunderstood something or made a coding error. Or maybe there’s an error in the paper.

Maybe the breadth you specify is a *target* and not something that is always exactly achieved?

***

[1] Egg and math: introducing a universal formula for egg shape. Valeriy G. Narushin, Michael N. Romanov, Darren K. Griffin. Annals of the New York Academy of Sciences. Published online August 23, 2021.

I can’t help with the question about w, but I will suggest that when you’re plotting physical measurements, it’s good to force the x and y axes to have the same scale (even though yours aren’t too far off). Add these lines:

ax = plt.gca()

ax.set_aspect(‘equal’)

The sad truth is that the paper is wrong as x=-w is only the maximum for the original, unmodified, formula but not for the one they found.

You are right about y(-w) and B not always equal

but i think this is a limitation in the formula