Reduce memory usage in FFTs

[djps]

Is your feature request related to a problem? Please describe.
Post-processing currently always auto-casts to double precision for methods that use scipy.fftpack, e.g. extract_amp_and_phase. This can lead to OOM needless errors e.g. there have been cases where the simulation has run, but I have been unable to take the Fourier transform as I run out of memory.

Describe the solution you'd like

• use scipy.fft instead of scipy.fftpack or numpy.fft as this does not automatically cast to double precision
• ensure that get_win and extract_amp_phase keep the same precision as the data which is supplied.

Describe alternatives you've considered
The new numpy 2.0 will have this capability, as it also upcasts, see issue.

[waltsims]

For context: https://stackoverflow.com/questions/30826224/how-to-get-complex64-output-from-numpy-fft