ifft#
- ivy.ifft(x, dim, *, norm='backward', n=None, out=None)[source]#
Compute the one dimensional discrete Fourier transform given input at least 1-D input x.
- Parameters:
x (
Union
[Array
,NativeArray
]) – Input volume […,d_in,…], where d_in indicates the dimension that needs IFFT.dim (
int
) – The dimension along which to take the one dimensional IFFT.norm (
str
, default:'backward'
) – Optional argument, “backward”, “ortho” or “forward”. Defaults to be “backward”. “backward” indicates no normalization. “ortho” indicates normalization by $frac{1}{sqrt{n}}$. “forward” indicates normalization by $frac{1}{n}$.n (
Optional
[Union
[int
,Tuple
[int
]]], default:None
) – Optional argument indicating the sequence length, if given, the input would be padded with zero or truncated to length n before performing IFFT. Should be a integer greater than 1.out (
Optional
[Array
], default:None
) – Optional output array, for writing the result to. It must have a shape that the inputs broadcast to.
- Return type:
- Returns:
ret – The result of the IFFT operation.
Examples
>>> ivy.ifft(np.exp(2j * np.pi * np.arange(8) / 8), 0) ivy.array([-4.30636606e-17+1.43029718e-18j, 0.00000000e+00+1.53080850e-17j, 1.43029718e-18+1.53080850e-17j, 0.00000000e+00+9.58689626e-18j, 1.24474906e-17+2.91858728e-17j, 0.00000000e+00+1.53080850e-17j, 2.91858728e-17+1.53080850e-17j, 1.00000000e+00-1.01435406e-16j]) >>> ivy.ifft(np.exp(2j * np.pi * np.arange(8) / 8), 0, n=16) ivy.array([-2.15318303e-17+7.15148591e-19j, 6.25000000e-02+9.35378602e-02j, 0.00000000e+00+7.65404249e-18j, 6.25000000e-02+4.17611649e-02j, 7.15148591e-19+7.65404249e-18j, 6.25000000e-02+1.24320230e-02j, 0.00000000e+00+4.79344813e-18j, 6.25000000e-02-1.24320230e-02j, 6.22374531e-18+1.45929364e-17j, 6.25000000e-02-4.17611649e-02j, 0.00000000e+00+7.65404249e-18j, 6.25000000e-02-9.35378602e-02j, 1.45929364e-17+7.65404249e-18j, 6.25000000e-02-3.14208718e-01j, 5.00000000e-01-5.07177031e-17j, 6.25000000e-02+3.14208718e-01j]) >>> ivy.ifft(np.exp(2j * np.pi * np.arange(8) / 8), 0, norm="ortho") ivy.array([-1.21802426e-16+4.04549134e-18j, 0.00000000e+00+4.32978028e-17j, 4.04549134e-18+4.32978028e-17j, 0.00000000e+00+2.71158374e-17j, 3.52068201e-17+8.25501143e-17j, 0.00000000e+00+4.32978028e-17j, 8.25501143e-17+4.32978028e-17j, 2.82842712e+00-2.86902654e-16j])
- Array.ifft(self, dim, *, norm='backward', n=None, out=None)[source]#
ivy.Array instance method variant of ivy.ifft. This method simply wraps the function, and so the docstring for ivy.ifft also applies to this method with minimal changes.
- Parameters:
self (
Array
) – Input volume […,d_in,…], where d_in indicates the dimension that needs IFFT.dim (
int
) – The dimension along which to take the one dimensional IFFT.norm (
str
, default:'backward'
) – Optional argument, “backward”, “ortho” or “forward”. Defaults to be “backward”. “backward” indicates no normalization. “ortho” indicates normalization by 1/sqrt(n). “forward” indicates normalization by 1/n.n (
Optional
[Union
[int
,Tuple
[int
]]], default:None
) – Optional argument indicating the sequence length, if given, the input would be padded with zero or truncated to length n before performing IFFT. Should be a integer greater than 1.out (
Optional
[Array
], default:None
) – Optional output array, for writing the result to. It must have a shape that the inputs broadcast to.
- Return type:
Array
- Returns:
ret – Array containing the transformed input.
Examples
>>> a = ivy.array((np.exp(2j * np.pi * np.arange(8) / 8))) >>> a.ifft(0) ivy.array([-4.30636606e-17+1.43029718e-18j, 0.00000000e+00+1.53080850e-17j, 1.43029718e-18+1.53080850e-17j, 0.00000000e+00+9.58689626e-18j, 1.24474906e-17+2.91858728e-17j, 0.00000000e+00+1.53080850e-17j, 2.91858728e-17+1.53080850e-17j, 1.00000000e+00-1.01435406e-16j])
- Container.ifft(self, dim, *, norm='backward', n=None, out=None)[source]#
ivy.Container instance method variant of ivy.ifft. This method simply wraps the function, and so the docstring for ivy.ifft also applies to this method with minimal changes.
- Parameters:
self (
Container
) – Container containing input volumes […,d_in,…], where d_in indicates the dimension that needs IFFT.dim (
Union
[int
,Container
]) – The dimension along which to take the one dimensional IFFT.norm (
Union
[str
,Container
], default:'backward'
) – Optional argument, “backward”, “ortho” or “forward”. Defaults to be “backward”. “backward” indicates no normalization. “ortho” indicates normalization by 1/sqrt(n). “forward” indicates normalization by 1/n.n (
Optional
[Union
[int
,Tuple
[int
],Container
]], default:None
) – Optional argument indicating the sequence length, if given, the input would be padded with zero or truncated to length n before performing IFFT. Should be a integer greater than 1.out (
Optional
[Union
[Array
,Container
]], default:None
) – Optional output array, for writing the result to. It must have a shape that the inputs broadcast to.
- Returns:
ret – Container containing the transformed inputs.
Examples
>>> a = ivy.array(np.array([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])) >>> b = ivy.array(np.exp(2j * np.pi * np.arange(8) / 8)) >>> c = ivy.Container(a=a, b=b) >>> dims = ivy.Container(a=0, b=0) >>> c.ifft(dims) { a: ivy.array([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]), b: ivy.array([-4.30636606e-17+1.43029718e-18j, 0.00000000e+00+1.53080850e-17j, 1.43029718e-18+1.53080850e-17j, 0.00000000e+00+9.58689626e-18j, 1.24474906e-17+2.91858728e-17j, 0.00000000e+00+1.53080850e-17j, 2.91858728e-17+1.53080850e-17j, 1.00000000e+00-1.01435406e-16j]) }