Colouring Noise – Generating Noise with a Specific Power Spectral Density (opens in new tab)

The "integration to zero" effect the author observes is a consequence of the fact that the Fourier Transform is meant only for periods signals. Practically, we all often toss any time series data into an FFT to get a look at it in frequency space, but the math is only exactly correct with the time series samples used from one cycle of a period signal that repeats forever.

By generating the frequency domain Fourier coefficients and then transforming them to the time domain, the author is guaranteed to get a signal that starts and ends at the same value, because the transformation assumes it's a periodic signal and so the first and last values of the period must be the same.

If you like this, you may find the allantools library useful.

https://github.com/aewallin/allantools

We use a similar method to simulate 2D atmospheric turbulence for adaptive optics simulations in astronomy. The structure of turbulence is described by an analytical power spectral density function (classically Kolmogorov spectrum). We generate random coefficients in the Fourier space and then weight them by the PSD. After an inverse Fourier transform we obtain a beautiful draw of turbulence with the desired spatial structure.

Time domain methods to make pink noise:

https://www.firstpr.com.au/dsp/pink-noise/ http://stenzel.waldorfmusic.de/post/a-new-shade-of-pink/

In cosmology we use a similar procedure to generate initial conditions of sample universes. Just then the noise is in 3D. The article raises a question about the Gaussian nature of the generated noise. To generate non-gaussian noise, you need a way to correlate the phases, otherwise the central-limit theorem will always kick in.

The 0 freq component is just the general, time independent bias of the signal. If it is 0 then of course the integral of something which is neither positively nor negatively biased overall will average out to null.

The apparent convergence to zero happens because the amount of sample points is exactly the amount of data that can be represented with the number of frequency components you are calculating in your FT. Obviously net error in electronic readings don't really go to zero when you have exactly 1 million of them. Use fewer frequencies in your fft, see the convergence sooner. Use more, see it later.

The libraries aren't that impressive. The FFT is simple enough to write in any coding language with basic trig and arithmetic. It could certainly fit into a similar amount of code to what the author has written.