# Deriving equations in Python

/Last week I wrote about the elastic moduli, and showed the latest version of my table of equations. Here it is; click on it for a large version:

Making this grid was a bit of an exercise in itself. One could spend some happy hours rearranging things by hand; instead, I spent some (mostly) happy hours learning to use SymPy, a symbolic maths library for Python. For what it's worth, you can see my flailing in this Jupyter Notebook. Warning: it's pretty untidy.

### Wrangling equations

Fortunately, SymPy is easy to get started with. Let's look at getting an expression for \(V_\mathrm{P}\) in terms of \(E\) and \(K\), given that I already have an expression in terms of \(E\) and \(\mu\), plus an expression for \(\mu\) in terms of \(E\) and \(K\).

First we import the SymPy library, set it up for nice math display in the Notebook, and initialize some parameter names:

```
>>> import sympy
>>> sympy.init_printing(use_latex='mathjax')
>>> lamda, mu, nu, E, K, rho = sympy.symbols("lamda, mu, nu, E, K, rho")
```

`lamda`

is not a typo: `lambda`

means something else in Python — it's a sort of unnamed function.

Now we're ready to define an expression. First, I'll import SymPy's own square root function for convenience. Then I define an expression for \(V_\mathrm{P}\) in terms of \(E\) and \(\mu\):

```
>>> vp_expr = sympy.sqrt((mu * (E - 4*mu)) / (rho * (E - 3*mu)))
>>> vp_expr
```

$$ \sqrt{\frac{\mu \left(E - 4 \mu\right)}{\rho \left(E - 3 \mu\right)}} $$

Now we can give SymPy the expression for \(\mu\) in terms of \(E\) and \(K\) and substitute:

```
>>> mu_expr = (3 * K * E) / (9 * K - E)
>>> vp_new = vp_expr.subs(mu, mu_expr)
>>> vp_new
```

$$\sqrt{3} \sqrt{\frac{E K \left(- \frac{12 E K}{- E + 9 K} + E\right)}{\rho \left(- E + 9 K\right) \left(- \frac{9 E K}{- E + 9 K} + E\right)}}$$

Argh, what is that?? Luckily, it's easy to simplify:

`>>> sympy.simplify(vp_new)`

$$\sqrt{3} \sqrt{\frac{K \left(E + 3 K\right)}{\rho \left(- E + 9 K\right)}}$$

That's more like it! What's really cool is that SymPy can even generate the \(\LaTeX\) code for your favourite math renderer:

```
>>> print(sympy.latex(sympy.simplify(vp_new)))
\sqrt{3} \sqrt{\frac{K \left(E + 3 K\right)}{\rho \left(- E + 9 K\right)}}
```

That's all there is to it!

### What is the mystery X?

Have a look at the expression for \(V_\mathrm{P}\) in terms of \(E\) and \(\lambda\):

$$\frac{\sqrt{2}}{2} \sqrt{\frac{1}{\rho} \left(E - \lambda + \sqrt{E^{2} + 2 E \lambda + 9 \lambda^{2}}\right)}$$

I find this quantity — I call it \(X\) in the big table of equations — really curious:

$$ X = \sqrt{9\lambda^2 + 2E\lambda + E^2} $$

As you can see from the similar table on Wikipedia, a similar quantity appears in expressions in terms of \(E\) and \(M\). These quantities look like elastic moduli, and even have the right units and order of magnitude as the others. If anyone has thoughts on what significance it might have, if any, or on why expressions in terms of \(E\) and \(\lambda\) or \(M\) should be so uncommonly clunky, I'm all ears.

**One last thing... I've mentioned Melvyn Bragg's wonderful BBC radio programme In Our Time before. If you like listening to the radio, try this recent episode on the life and work of Robert Hooke. Not only did he invent the study of elasticity with his eponymous law, he was also big in microscopy, describing things like the cellular structure of cork in detail (right).**