fftfreq(n, d=1.0)
The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cycles/second.
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
Window length.
Sample spacing (inverse of the sampling rate). Defaults to 1.
Return the Discrete Fourier Transform sample frequencies.
>>> signal = np.array([-2, 8, 6, 4, 1, 0, 3, 5], dtype=float)See :
... fourier = np.fft.fft(signal)
... n = signal.size
... timestep = 0.1
... freq = np.fft.fftfreq(n, d=timestep)
... freq array([ 0. , 1.25, 2.5 , ..., -3.75, -2.5 , -1.25])
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.fft.ifftshift
numpy.fft.fft
numpy.fft.rfftfreq
dask.array.fft.fftfreq
dask.array.fft.fftshift
dask.array.fft.rfftfreq
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them