xdas.signal.hilbert#

xdas.signal.hilbert(da, N=None, dim='last', parallel=None)[source]#

Compute the analytic signal, using the Hilbert transform.

The transformation is done along the last axis by default.

Parameters:

  • da (DataArray) – Signal data. Must be real.

  • N (int, optional) – Number of Fourier components. Default: da.sizes[dim].

  • dim (str, optional) – The dimension along which to transform. Default: last.

  • parallel (bool or int, optional) – Number of threads to use. True uses all cores, False uses one, an int uses that many, None defers to the global xdas configuration. Default is None.

Returns:

Analytic signal of da along dim.

Return type:

DataArray

Notes

Splits on data discontinuities along dim.

Examples

In this example we use the Hilbert transform to determine the analytic signal.

>>> import xdas.signal as xs
>>> from xdas.synthetics import wavelet_wavefronts

>>> da = wavelet_wavefronts()
>>> xs.hilbert(da, dim="time")
<xdas.DataArray (time: 300, distance: 401)>
[[ 0.0497+0.1632j -0.0635+0.0125j ...  0.1352-0.3107j -0.2832-0.0126j]
 [-0.1096-0.0335j  0.124 +0.0257j ... -0.0444+0.2409j  0.1378-0.2702j]
 ...
 [-0.1977+0.0545j -0.0533-0.1947j ...  0.3722+0.125j  -0.0127+0.1723j]
 [ 0.1221-0.1808j  0.1888+0.0368j ... -0.4517+0.1581j  0.0411+0.1512j]]
Coordinates:
  * time (time): 2023-01-01T00:00:00.000 to 2023-01-01T00:00:05.980
  * distance (distance): 0.000 to 10000.000