Re: An idea for an entirely new effect -- warning, long post

Nyquist does have FFT and IFFT, but they are quite tricky to use.
The Nyquist manual documentation is here:
http://www.cs.cmu.edu/~rbd/doc/nyquist/part11.html#index912
but if you intend using these functions, then I would highly recommend
reading the "demos/fft_tutorial.htm", which is part of the standard
Nyquist release.

Steve

On Sun, 16 May 2021 at 19:10, freddyjohn via audacity-devel
<[email protected]> wrote:
>
> I’ve only wrote a few time domain effects and have only recently started messing around with the frequency domain so take my word with a grain of salt. It’s trivial to start playing with your idea in Python with numpy, scipy, and matplotlib. Then when you have working implementation you can extract the core logic into a language of your choice.
>
> Numpy can help you generate waveforms
>
> import numpy as np
> t=np.linspace(0,5,5*48000)
> y=np.sin(44*t)
>
> And then turn 5 second 44hz sampled at 48000 sps to 16 bit pcm
>
> pcm=y.astype(np.short).tobytes()
>
> want to go into frequency domain and back again?
>
> from scipy.fft import fft, ifft
> frequency_domain = fft(y)
> time_domain = ifft( frequency_domain)
>
> You can visualize what is happening in either domain with matplotlib
>
> from matplotlib import pyplot as plt
>
> plt.plot(y)
> plt.show()
>
>
>
> Sent from ProtonMail for iOS
>
>
> On Sun, May 16, 2021 at 2:05 AM, Petr Pařízek via audacity-devel <[email protected]> wrote:
>
> I wrote:
>
> > - C) we sum the two products to get the frequency-shifted signal,
>
> Oops, my fault. After summing them, we obviously need to skip the first
> n*2 or n*2-1 samples because that's by how much our IP and Q signals
> were delayed.
> (Gosh, I knew I would forget something.)
>
> Petr
>
>
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