def __init__(self,
x,
y,
z,
w=None,
bbox=[None] * 4,
kx=3,
ky=3,
s=None,
eps=None):
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth(x,
y,
z,
w,
xb,
xe,
yb,
ye,
kx,
ky,
s=s,
eps=eps,
lwrk2=1)
if ier in [0, -1, -2]: # normal return
pass
else:
message = _surfit_messages.get(ier, 'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky
def __init__(self, x, y, z, w=None, bbox=[None] * 4, kx=3, ky=3, s=None, eps=None):
"""
Input:
x,y,z - 1-d sequences of data points (order is not
important)
Optional input:
w - positive 1-d sequence of weights
bbox - 4-sequence specifying the boundary of
the rectangular approximation domain.
By default, bbox=[min(x,tx),max(x,tx),
min(y,ty),max(y,ty)]
kx,ky=3,3 - degrees of the bivariate spline.
s - positive smoothing factor defined for
estimation condition:
sum((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) <= s
Default s=len(w) which should be a good value
if 1/w[i] is an estimate of the standard
deviation of z[i].
eps - a threshold for determining the effective rank
of an over-determined linear system of
equations. 0 < eps < 1, default is 1e-16.
"""
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth(
x, y, z, w, xb, xe, yb, ye, kx, ky, s=s, eps=eps, lwrk2=1
)
if ier in [0, -1, -2]: # normal return
pass
else:
message = _surfit_messages.get(ier, "ier=%s" % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[: (nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky
def __init__(self,
x,
y,
z,
w=None,
bbox=[None] * 4,
kx=3,
ky=3,
s=None,
eps=None):
"""
Input:
x,y,z - 1-d sequences of data points (order is not
important)
Optional input:
w - positive 1-d sequence of weights
bbox - 4-sequence specifying the boundary of
the rectangular approximation domain.
By default, bbox=[min(x,tx),max(x,tx),
min(y,ty),max(y,ty)]
kx,ky=3,3 - degrees of the bivariate spline.
s - positive smoothing factor defined for
estimation condition:
sum((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) <= s
Default s=len(w) which should be a good value
if 1/w[i] is an estimate of the standard
deviation of z[i].
eps - a threshold for determining the effective rank
of an over-determined linear system of
equations. 0 < eps < 1, default is 1e-16.
"""
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth(x,
y,
z,
w,
xb,
xe,
yb,
ye,
kx,
ky,
s=s,
eps=eps,
lwrk2=1)
if ier in [0, -1, -2]: # normal return
pass
else:
message = _surfit_messages.get(ier, 'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[:(nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky
def __init__(self, x, y, z, w=None, bbox=[None] * 4, kx=3, ky=3, s=None, eps=None):
xb, xe, yb, ye = bbox
nx, tx, ny, ty, c, fp, wrk1, ier = dfitpack.surfit_smth(
x, y, z, w, xb, xe, yb, ye, kx, ky, s=s, eps=eps, lwrk2=1
)
if ier in [0, -1, -2]: # normal return
pass
else:
message = _surfit_messages.get(ier, "ier=%s" % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx[:nx], ty[:ny], c[: (nx - kx - 1) * (ny - ky - 1)]
self.degrees = kx, ky