scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
gauss_spline(x, n)
Gaussian approximation to B-spline basis function of order n.
cspline1d(signal, lamb=0.0)
Compute cubic spline coefficients for rank-1 array.
Description:
Inputs:
Output:
Find the cubic spline coefficients for a 1-D signal assuming mirror-symmetric boundary conditions.
To obtain the signal back from the spline representation mirror-symmetric-convolve these
coefficients with a length 3 FIR window [1.0, 4.0, 1.0]/ 6.0 .
signal – a rank-1 array representing samples of a signal. lamb – smoothing coefficient (default =
0.0)
c – cubic spline coefficients.
qspline1d(signal, lamb=0.0)
Compute quadratic spline coefficients for rank-1 array.
Description:
Inputs:
Output:
Find the quadratic spline coefficients for a 1-D signal assuming mirror-symmetric boundary conditions.
To obtain the signal back from the spline representation mirror-symmetric-convolve these
coefficients with a length 3 FIR window [1.0, 6.0, 1.0]/ 8.0 .
signal – a rank-1 array representing samples of a signal. lamb – smoothing coefficient (must be zero
for now.)
c – cubic spline coefficients.
cspline2d()
cspline2d(input {, lambda, precision}) -> ck
Description:
Return the third-order B-spline coefficients over a regularly spacedi input grid for the twodimensional
input image. The lambda argument specifies the amount of smoothing. The precision
argument allows specifying the precision used when computing the infinite sum needed to apply
mirror- symmetric boundary conditions.
qspline2d()
qspline2d(input {, lambda, precision}) -> qk
Description:
Return the second-order B-spline coefficients over a regularly spaced input grid for the twodimensional
input image. The lambda argument specifies the amount of smoothing. The precision
argument allows specifying the precision used when computing the infinite sum needed to apply
mirror- symmetric boundary conditions.
spline_filter(Iin, lmbda=5.0)
Smoothing spline (cubic) filtering of a rank-2 array.
Filter an input data set, Iin, using a (cubic) smoothing spline of fall-off lmbda.
328 Chapter 3. Reference