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- fftfreq(n, d=1.0)
- Discrete Fourier Transform sample frequencies.
The returned float array contains the frequency bins in
cycles/unit (with zero at the start) given a window length `n` and a
sample spacing `d`.
::
f = [0,1,...,n/2-1,-n/2,...,-1]/(d*n) if n is even
f = [0,1,...,(n-1)/2,-(n-1)/2,...,-1]/(d*n) if n is odd
Parameters
----------
n : int
Window length.
d : scalar
Sample spacing.
Returns
-------
out : ndarray, shape(`n`,)
Sample frequencies.
Examples
--------
>>> signal = np.array([-2., 8., -6., 4., 1., 0., 3., 5.])
>>> fourier = np.fft.fft(signal)
>>> n = len(signal)
>>> timestep = 0.1
>>> freq = np.fft.fftfreq(n, d=timestep)
>>> freq
array([ 0. , 1.25, 2.5 , 3.75, -5. , -3.75, -2.5 , -1.25])
- fftshift(x, axes=None)
- Shift zero-frequency component to center of spectrum.
This function swaps half-spaces for all axes listed (defaults to all).
If len(x) is even then the Nyquist component is y[0].
Parameters
----------
x : array_like
Input array.
axes : int or shape tuple, optional
Axes over which to shift. Default is None which shifts all axes.
See Also
--------
ifftshift
- ifftshift(x, axes=None)
- Inverse of fftshift.
Parameters
----------
x : array_like
Input array.
axes : int or shape tuple, optional
Axes over which to calculate. Defaults to None which is over all axes.
See Also
--------
fftshift
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