December 2017
Intermediate to advanced
386 pages
10h 42m
English
Content preview from SciPy Recipes
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How to do it…
To follow with the example, we need to continue with the following steps:
- The basic routines in the scipy.fftpack module compute the DFT and its inverse, for discrete signals in any dimension—fft, ifft (one dimension), fft2, ifft2 (two dimensions), and fftn, ifftn (any number of dimensions).
- Verify all these routines assume that the data is complex valued. If we know beforehand that a particular dataset is actually real-valued, and should offer real-valued frequencies, we use rfft and irfft instead, for a faster algorithm.
- In order to complete with this, these routines are designed so that composition with their inverses always yields the identity.
- The syntax is the same in all cases, as follows:
fft(x[, n, axis, overwrite_x]) ...
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