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spectrochempy.ifft

ifft(dataset, size=None, **kwargs)[source]

Apply a inverse fast fourier transform.

For multidimensional NDDataset, the apodization is by default performed on the last dimension.

The data in the last dimension MUST be in frequency (or without dimension) or an error is raised.

To make direct Fourier transform, i.e., from frequency to time domain, use the fft transform.

Parameters:

dataset (NDDataset) – The dataset on which to apply the fft transformation.
size (int, optional) – Size of the transformed dataset dimension; the shorter alias is si. By default, the input size is used. If size is larger or smaller than the input, the unshifted frequency data are padded with zeros or truncated, respectively, before the inverse transform.
**kwargs – Optional keyword parameters (see Other Parameters).

Returns:

out – Transformed NDDataset .

Other Parameters:

dim (str or int, optional, default=’x’.) – Specify on which dimension to apply this method. If dim is specified as an integer it is equivalent to the usual axis numpy parameter.
inplace (bool, optional, default=False.) – True if we make the transform inplace. If False, the function return a new object