x : array_like
A list of sample vector arrays representing the curve.
w : array_like
Strictly positive rank-1 array of weights the same length as x.
The weights are used in computing the weighted least-squares spline
fit. If the errors in the x values have standard-deviation given by
the vector d, then w should be 1/d. Default is ones(len(x)).
u : array_like, optional
An array of parameter values. If not given, these values are
calculated automatically as M = len(x), where
v = 0
v[i] = v[i-1] + distance(x[i], x[i-1])
u[i] = v[i] / v[M-1]
ub, ue : int, optional
The end-points of the parameters interval. Defaults to
u and u[-1].
k : int, optional
Degree of the spline. Cubic splines are recommended.
Even values of k should be avoided especially with a small s-value.
1 <= k <= 5, default is 3.
task : int, optional
If task==0 (default), find t and c for a given smoothing factor, s.
If task==1, find t and c for another value of the smoothing factor, s.
There must have been a previous call with task=0 or task=1
for the same set of data.
If task=-1 find the weighted least square spline for a given set of
s : float, optional
A smoothing condition. The amount of smoothness is determined by
satisfying the conditions: sum((w * (y - g))**2,axis=0) <= s,
where g(x) is the smoothed interpolation of (x,y). The user can
use s to control the trade-off between closeness and smoothness
of fit. Larger s means more smoothing while smaller values of s
indicate less smoothing. Recommended values of s depend on the
weights, w. If the weights represent the inverse of the
standard-deviation of y, then a good s value should be found in
the range (m-sqrt(2*m),m+sqrt(2*m)), where m is the number of
data points in x, y, and w.
t : int, optional
The knots needed for task=-1.
full_output : int, optional
If non-zero, then return optional outputs.
nest : int, optional
An over-estimate of the total number of knots of the spline to
help in determining the storage space. By default nest=m/2.
Always large enough is nest=m+k+1.
per : int, optional
If non-zero, data points are considered periodic with period
x[m-1] - x and a smooth periodic spline approximation is
returned. Values of y[m-1] and w[m-1] are not used.
quiet : int, optional
Non-zero to suppress messages.
tck : tuple
A tuple (t,c,k) containing the vector of knots, the B-spline
coefficients, and the degree of the spline.
u : array
An array of the values of the parameter.
fp : float
The weighted sum of squared residuals of the spline approximation.
ier : int
An integer flag about splrep success. Success is indicated
if ier<=0. If ier in [1,2,3] an error occurred but was not raised.
Otherwise an error is raised.
msg : str
A message corresponding to the integer flag, ier.