xdas.fft.rfft#

xdas.fft.rfft(da, n=None, dim={'last': 'spectrum'}, norm=None, parallel=None)[source]#

Compute the discrete Fourier Transform for real inputs along a given dimension.

This function computes the one-dimensional n-point discrete Fourier Transform (DFT) or real-valued inputs with the efficient Fast Fourier Transform (FFT) algorithm.

Parameters:

  • da (DataArray) – The data array to process, can be complex.

  • n (int, optional) – Length of transformed dimension of the output. If n is smaller than the length of the input, the input is cropped. If it is larger, the input is padded with zeros. If n is not given, the length of the input along the dimension specified by dim is used.

  • dim ({str: str}, optional) – A mapping indicating as a key the dimension along which to compute the FFT, and as value the new name of the dimension. Default to {“last”: “spectrum”}.

  • norm ({“backward”, “ortho”, “forward”}, optional) – Normalization mode (see numpy.fft). Default is “backward”. Indicates which direction of the forward/backward pair of transforms is scaled and with what normalization factor.

Returns:

The transformed input with an updated dimension name and values. The length of the transformed dimension is (n/2)+1 if n is even or (n+1)/2 if n is odd.

Return type:

DataArray

Notes

To perform a multidimensional Fourier operations, repeat this function on the desired dimensions.

Examples

>>> import xdas as xd
>>> import xdas.fft as xfft
>>> signal = xd.DataArray([0., 1., 0., -1.], coords={"time": [0, 1, 2, 3]})
>>> xfft.rfft(signal, dim={"time": "frequency"})
<xdas.DataArray (frequency: 3)>
[0.+0.j 0.-2.j 0.+0.j]
Coordinates:
* frequency (frequency): [0.  ... 0.5]
On this page
Show Source