def __call__(self, x, nu=None):
""" Evaluate spline (or its nu-th derivative) at positions x.
Note: x can be unordered but the evaluation is more efficient
if x is (partially) ordered.
"""
if nu is None:
return dfitpack.splev(*(self._eval_args+(x,)))
return dfitpack.splder(nu=nu,*(self._eval_args+(x,)))
def __call__(self, x, nu=None):
""" Evaluate spline (or its nu-th derivative) at positions x.
Note: x can be unordered but the evaluation is more efficient
if x is (partially) ordered.
"""
if nu is None:
return dfitpack.splev(*(self._eval_args+(x,)))
return dfitpack.splder(nu=nu,*(self._eval_args+(x,)))
def splev(x,tck,der=0):
"""Evaulate a B-spline and its derivatives.
Description:
Given the knots and coefficients of a B-spline representation, evaluate
the value of the smoothing polynomial and it's derivatives.
This is a wrapper around the FORTRAN routines splev and splder of FITPACK.
Inputs:
x (u) -- a 1-D array of points at which to return the value of the
smoothed spline or its derivatives. If tck was returned from
splprep, then the parameter values, u should be given.
tck -- A sequence of length 3 returned by splrep or splprep containg the
knots, coefficients, and degree of the spline.
der -- The order of derivative of the spline to compute (must be less than
or equal to k).
Outputs: (y, )
y -- an array of values representing the spline function or curve.
If tck was returned from splrep, then this is a list of arrays
representing the curve in N-dimensional space.
See also:
splprep, splrep, sproot, spalde, splint - evaluation, roots, integral
bisplrep, bisplev - bivariate splines
UnivariateSpline, BivariateSpline - an alternative wrapping
of the FITPACK functions
"""
t,c,k=tck
try:
c[0][0] # check if c is >1-d
parametric = True # it is
except TypeError: parametric = False # c is 1-d
if parametric:
return map(lambda c,x=x,t=t,k=k,der=der:splev(x,[t,c,k],der),c)
else:
if not (0<=der<=k):
raise ValueError,"0<=der=%d<=k=%d must hold"%(der,k)
x=myasarray(x)
c = c.copy()
c.resize(len(t))
if (der>0):
y,ier=dfitpack.splder(t,c,k,x,der)
else:
y,ier=dfitpack.splev(t,c,k,x)
if ier==10: raise ValueError,"Invalid input data"
if ier: raise TypeError,"An unkown error occurred"
if len(y)>1: return y
return y[0]
