# Python | Inverse Fast Fourier Transform

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This transformation is a translation from the configuration space to the frequency space, and this is very important from the point of view of studying both transformations of certain tasks for more efficient computation, and studying the signal power spectrum. This translation can be from xn to Xk. It is a transformation of spatial or temporal data into frequency domain data.

## sympy.discrete.transforms.ifft ():

It can perform Inverse Discrete Fourier Transform (DFT) in complex domain ...
The sequence is automatically padded with zeros to the right, because the radix-2 FFT requires the number of sampling points as a power of 2. For short sequences, use this method only with the default arguments, since the complexity of the expressions increases with the size of the sequence,

`  Parameters:  - & gt;  seq:  [iterable] sequence on which Inverse DFT is to be applied. - & gt;  dps:  [Integer] number of decimal digits for precision.  Returns:  Fast Fourier Transform `

Example # 1:

` `

` # import sympy from sympy import ifft # sequence seq = [ 15 , 21 , 13 , 44 ] #fft transform = ifft (seq) print ( "Inverse FFT:" , transform) `

Output:

` Inverse FFT: [93/4, 1/2 + 23 * I / 4, -37/4, 1/2 - 23 * I / 4] `

Example 2:

 ` # import sympy ` ` from ` ` sympy ` ` import ` ` ifft ` ` # sequence ` ` seq ` ` = ` ` [` ` 15 ` `, ` ` 21 ` `, ` ` 13 ` `, ` ` 44 ` `] ` ` ` ` decimal_point ` ` = ` ` 4 ` ` #fft ` ` transform ` ` = ` ` ifft (seq, decimal_point) ` ` print ` ` (` ` "Inverse FFT:" ` `, transform) `

Output:

` Inverse FFT: [23.25, 0.5 + 5.75 * I, -9.250, 0.5 - 5.75 * I] `

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