def __init__(self, x, y, t, w=None, bbox=[None] * 2, k=3):
"""
Input:
x,y - 1-d sequences of data points (x must be
in strictly ascending order)
t - 1-d sequence of the positions of user-defined
interior knots of the spline (t must be in strictly
ascending order and bbox[0]<t[0]<...<t[-1]<bbox[-1])
Optional input:
w - positive 1-d sequence of weights
bbox - 2-sequence specifying the boundary of
the approximation interval.
By default, bbox=[x[0],x[-1]]
k=3 - degree of the univariate spline.
"""
#_data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
xb = bbox[0]
xe = bbox[1]
if xb is None: xb = x[0]
if xe is None: xe = x[-1]
t = concatenate(([xb] * (k + 1), t, [xe] * (k + 1)))
n = len(t)
if not alltrue(t[k + 1:n - k] - t[k:n - k - 1] > 0, axis=0):
raise ValueError('Interior knots t must satisfy '
'Schoenberg-Whitney conditions')
data = dfitpack.fpcurfm1(x, y, k, t, w=w, xb=xb, xe=xe)
self._data = data[:-3] + (None, None, data[-1])
self._reset_class()
def __init__(self, x, y, t, w=None, bbox = [None]*2, k=3):
"""
Input:
x,y - 1-d sequences of data points (x must be
in strictly ascending order)
t - 1-d sequence of the positions of user-defined
interior knots of the spline (t must be in strictly
ascending order and bbox[0]<t[0]<...<t[-1]<bbox[-1])
Optional input:
w - positive 1-d sequence of weights
bbox - 2-sequence specifying the boundary of
the approximation interval.
By default, bbox=[x[0],x[-1]]
k=3 - degree of the univariate spline.
"""
#_data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
xb=bbox[0]
xe=bbox[1]
if xb is None: xb = x[0]
if xe is None: xe = x[-1]
t = concatenate(([xb]*(k+1),t,[xe]*(k+1)))
n = len(t)
if not alltrue(t[k+1:n-k]-t[k:n-k-1] > 0,axis=0):
raise ValueError,\
'Interior knots t must satisfy Schoenberg-Whitney conditions'
data = dfitpack.fpcurfm1(x,y,k,t,w=w,xb=xb,xe=xe)
self._data = data[:-3] + (None,None,data[-1])
self._reset_class()