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This chapter describes functions for performing Discrete Hankel Transforms (DHTs). The functions are declared in the header file ‘gsl_dht.h’.
| 31.1 Definitions | ||
| 31.2 Functions | ||
| 31.3 References and Further Reading |
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The discrete Hankel transform acts on a vector of sampled data, where the samples are assumed to have been taken at points related to the zeroes of a Bessel function of fixed order; compare this to the case of the discrete Fourier transform, where samples are taken at points related to the zeroes of the sine or cosine function.
Specifically, let
be a function on the unit interval.
Then the finite
-Hankel transform of
is defined
to be the set of numbers
given by,
so that,
Suppose that
is band-limited in the sense that
for
. Then we have the following
fundamental sampling theorem.
It is this discrete expression which defines the discrete Hankel
transform. The kernel in the summation above defines the matrix of the
-Hankel transform of size
. The coefficients of
this matrix, being dependent on
and
, must be
precomputed and stored; the gsl_dht object encapsulates this
data. The allocation function gsl_dht_alloc returns a
gsl_dht object which must be properly initialized with
gsl_dht_init before it can be used to perform transforms on data
sample vectors, for fixed
and
, using the
gsl_dht_apply function. The implementation allows a scaling of
the fundamental interval, for convenience, so that one can assume the
function is defined on the interval
, rather than the unit
interval.
Notice that by assumption
vanishes at the endpoints
of the interval, consistent with the inversion formula
and the sampling formula given above. Therefore, this transform
corresponds to an orthogonal expansion in eigenfunctions
of the Dirichlet problem for the Bessel differential equation.
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This function allocates a Discrete Hankel transform object of size size.
This function initializes the transform t for the given values of nu and x.
This function allocates a Discrete Hankel transform object of size size and initializes it for the given values of nu and x.
This function frees the transform t.
This function applies the transform t to the array f_in whose size is equal to the size of the transform. The result is stored in the array f_out which must be of the same length.
This function returns the value of the n-th sample point in the unit interval,
. These are the
points where the function
is assumed to be sampled.
This function returns the value of the n-th sample point in “k-space”,
.
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The algorithms used by these functions are described in the following papers,
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