ifht(A, dln, mu, offset=0.0, bias=0.0)
Computes the discrete inverse Hankel transform of a logarithmically spaced periodic sequence. This is the inverse operation to fht
.
This function computes a discrete version of the Hankel transform
$$a(r) = \int_{0}^{\infty} \! A(k) \, J_\mu(kr) \, r \, dk \;,$$where $J_\mu$ is the Bessel function of order $\mu$ . The index $\mu$ may be any real number, positive or negative.
See fht
for further details.
Real periodic input array, uniformly logarithmically spaced. For multidimensional input, the transform is performed over the last axis.
Uniform logarithmic spacing of the input array.
Order of the Hankel transform, any positive or negative real number.
Offset of the uniform logarithmic spacing of the output array.
Exponent of power law bias, any positive or negative real number.
The transformed output array, which is real, periodic, uniformly logarithmically spaced, and of the same shape as the input array.
Compute the inverse fast Hankel transform.
fht
Definition of the fast Hankel transform.
fhtoffset
Return an optimal offset for :None:None:`ifht`.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.fft._fftlog.fht
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