Mathematically, the Fourier series works like this. Suppose the periodic function you start with is $f(t)$, repeating with a period $T$. Then it can be expressed by

$$f(t)=a_0+\sum_{n=1}^\infty \left( a_n \cos \dfrac{2n\pi t}{T} + b_n \sin \dfrac{2n\pi t}{T} \right) \tag{1}$$

where

$$a_0=\dfrac{1}{T}\int_0^T f(t) dt, a_n=\dfrac{2}{T}\int_0^T f(t)\cos \dfrac{2n\pi t}{T} dt \tag{2}$$

and

$$b_n=\dfrac{2}{T}\int_0^T f(t)\sin \dfrac{2n\pi t}{T} dt. \tag{3}$$

Alternatively, there is a neater form in terms of complex numbers:

$$f(t)=\sum_{n=-\infty}^\infty c_n e^{2in\pi t/T} \tag{4}$$

where

$$c_n=\dfrac{2}{T}\int_0^T f(t)e^{-2in\pi t/T} dt. \tag{5}$$

If the function $f$ is not periodic, like the hammer pulse discussed in the main text, then instead of a sum over discrete harmonic frequency components we need an integral:

$$f(t)=\int_{-\infty}^\infty F(\omega) e^{i \omega t} dt \tag{6}$$

where

$$F(\omega)=\dfrac{1}{2\pi}\int_{-\infty}^\infty f(t) e^{-i \omega t} d\omega. \tag{7}$$

$F(\omega)$ is called the Fourier transform of $f(t)$.

The FFT algorithm gives a way to calculate a Fourier series for a sampled function, where we only know the values of the function at times $t_n=nh$ where $h$ is the chosen time step, the inverse of the sampling rate. So for example a typical audio file with a sampling rate of 44.1 kHz has an interval $h = 1/44100$ s = 22.7~$\mu$s. Although it is called the Fast Fourier *Transform*, this is misleading: a computer can only ever handle a finite number of sampled values, and what the FFT gives you is the set of (complex) Fourier *series* coefficients based on harmonics of the frequency which is the inverse of the total length of the sampled segment in seconds. An important result, the Shannon sampling theorem [], tells us that the FFT contains frequency components only up to the *Nyquist frequency*, which is half the sampling frequency (e.g. 22.05 kHz for the audio file). If the original signal contained frequency content higher than this frequency it will be represented in a misleading way in the FFT result, a phenomenon known as *aliasing*.