SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Variables

• gamma

  gamma = scipy.special.gamma
  
  

Function summary

• bspline(x, n)
• c0_P(order)
• cspline1d(signal, lamb = 0.0)
• cspline1d_eval(cj, newx, dx = 1.0, x0 = 0)
• cubic(x)
• factorial(n)
• gauss_spline(x, n)
• qspline1d(signal, lamb = 0.0)
• qspline1d_eval(cj, newx, dx = 1.0, x0 = 0)
• quadratic(x)
• spline_filter(Iin, lmbda = 5.0)

Functions

• bspline(x, n)

  bspline(x,n): B-spline basis function of order n. uses numpy.piecewise and automatic function-generator.

• c0_P(order)
• cspline1d(signal, lamb = 0.0)

  Compute cubic spline coefficients for rank-1 array.

  Description:

  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 .

  Inputs:

  signal -- a rank-1 array representing samples of a signal. lamb -- smoothing coefficient (default = 0.0)

  Output:

  c -- cubic spline coefficients.

• cspline1d_eval(cj, newx, dx = 1.0, x0 = 0)

  Evaluate a spline at the new set of points. dx is the old sample-spacing while x0 was the old origin.

  In other-words the old-sample points (knot-points) for which the cj represent spline coefficients were at equally-spaced points of

  oldx = x0 + j*dx j=0...N-1

  N=len(cj)

  edges are handled using mirror-symmetric boundary conditions.

• cubic(x)

  Special case of bspline. Equivalent to bspline(x,3).

• factorial(n)
• gauss_spline(x, n)

  Gaussian approximation to B-spline basis function of order n.

• qspline1d(signal, lamb = 0.0)

  Compute quadratic spline coefficients for rank-1 array.

  Description:

  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 .

  Inputs:

  signal -- a rank-1 array representing samples of a signal. lamb -- smoothing coefficient (must be zero for now.)

  Output:

  c -- cubic spline coefficients.

• qspline1d_eval(cj, newx, dx = 1.0, x0 = 0)

  Evaluate a quadratic spline at the new set of points. dx is the old sample-spacing while x0 was the old origin.

  In other-words the old-sample points (knot-points) for which the cj represent spline coefficients were at equally-spaced points of

  oldx = x0 + j*dx j=0...N-1

  N=len(cj)

  edges are handled using mirror-symmetric boundary conditions.

• quadratic(x)

  Special case of bspline. Equivalent to bspline(x,2).

• 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.

Imported Names