def __init__(self, x, y, z, tx, ty, w=None, bbox=[None]*4, kx=3, ky=3,
eps=None):
nx = 2*kx+2+len(tx)
ny = 2*ky+2+len(ty)
tx1 = zeros((nx,),float)
ty1 = zeros((ny,),float)
tx1[kx+1:nx-kx-1] = tx
ty1[ky+1:ny-ky-1] = ty
xb,xe,yb,ye = bbox
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=1)
if ier>10:
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
if ier<-2:
deficiency = (nx-kx-1)*(ny-ky-1)+ier
message = _surfit_messages.get(-3) % (deficiency)
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx1,ty1,c
self.degrees = kx,ky
def __init__(self, x, y, z, tx, ty, w=None, bbox=[None]*4, kx=3, ky=3,
eps=None):
nx = 2*kx+2+len(tx)
ny = 2*ky+2+len(ty)
tx1 = zeros((nx,),float)
ty1 = zeros((ny,),float)
tx1[kx+1:nx-kx-1] = tx
ty1[ky+1:ny-ky-1] = ty
xb,xe,yb,ye = bbox
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=1)
if ier>10:
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
if ier<-2:
deficiency = (nx-kx-1)*(ny-ky-1)+ier
message = _surfit_messages.get(-3) % (deficiency)
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx1,ty1,c
self.degrees = kx,ky
def __init__(self,
x,
y,
z,
tx,
ty,
w=None,
bbox=[None] * 4,
kx=3,
ky=3,
eps=None):
"""
Input:
x,y,z - 1-d sequences of data points (order is not
important)
tx,ty - strictly ordered 1-d sequences of knots
coordinates.
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.
eps - a threshold for determining the effective rank
of an over-determined linear system of
equations. 0 < eps < 1, default is 1e-16.
"""
nx = 2 * kx + 2 + len(tx)
ny = 2 * ky + 2 + len(ty)
tx1 = zeros((nx, ), float)
ty1 = zeros((ny, ), float)
tx1[kx + 1:nx - kx - 1] = tx
ty1[ky + 1:ny - ky - 1] = ty
xb, xe, yb, ye = bbox
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=1)
if ier > 10:
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=ier)
if ier in [0, -1, -2]: # normal return
pass
else:
if ier < -2:
deficiency = (nx - kx - 1) * (ny - ky - 1) + ier
message = _surfit_messages.get(-3) % (deficiency)
else:
message = _surfit_messages.get(ier, 'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx1, ty1, c
self.degrees = kx, ky
def __init__(self, x, y, z, tx, ty, w=None,
bbox = [None]*4,
kx=3, ky=3, eps=None):
"""
Input:
x,y,z - 1-d sequences of data points (order is not
important)
tx,ty - strictly ordered 1-d sequences of knots
coordinates.
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.
eps - a threshold for determining the effective rank
of an over-determined linear system of
equations. 0 < eps < 1, default is 1e-16.
"""
nx = 2*kx+2+len(tx)
ny = 2*ky+2+len(ty)
tx1 = zeros((nx,),float)
ty1 = zeros((ny,),float)
tx1[kx+1:nx-kx-1] = tx
ty1[ky+1:ny-ky-1] = ty
xb,xe,yb,ye = bbox
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=1)
if ier>10:
tx1,ty1,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx1,ty1,w,\
xb,xe,yb,ye,\
kx,ky,eps,lwrk2=ier)
if ier in [0,-1,-2]: # normal return
pass
else:
if ier<-2:
deficiency = (nx-kx-1)*(ny-ky-1)+ier
message = _surfit_messages.get(-3) % (deficiency)
else:
message = _surfit_messages.get(ier,'ier=%s' % (ier))
warnings.warn(message)
self.fp = fp
self.tck = tx1,ty1,c
self.degrees = kx,ky
def bisplrep(x,y,z,w=None,xb=None,xe=None,yb=None,ye=None,kx=3,ky=3,task=0,
s=None,eps=1e-16,tx=None,ty=None,full_output=0,
nxest=None,nyest=None,quiet=1):
"""Find a bivariate B-spline representation of a surface.
Description:
Given a set of data points (x[i], y[i], z[i]) representing a surface
z=f(x,y), compute a B-spline representation of the surface.
Inputs:
x, y, z -- Rank-1 arrays of data points.
w -- Rank-1 array of weights. By default w=ones(len(x)).
xb, xe -- End points of approximation interval in x.
yb, ye -- End points of approximation interval in y.
By default xb, xe, yb, ye = x.min(), x.max(), y.min(), y.max()
kx, ky -- The degrees of the spline (1 <= kx, ky <= 5). Third order
(kx=ky=3) is recommended.
task -- If task=0, find knots in x and y and coefficients for a given
smoothing factor, s.
If task=1, find knots and coefficients for another value of the
smoothing factor, s. bisplrep must have been previously called
with task=0 or task=1.
If task=-1, find coefficients for a given set of knots tx, ty.
s -- A non-negative smoothing factor. If weights correspond
to the inverse of the standard-deviation of the errors in z,
then a good s-value should be found in the range
(m-sqrt(2*m),m+sqrt(2*m)) where m=len(x)
eps -- A threshold for determining the effective rank of an
over-determined linear system of equations (0 < eps < 1)
--- not likely to need changing.
tx, ty -- Rank-1 arrays of the knots of the spline for task=-1
full_output -- Non-zero to return optional outputs.
nxest, nyest -- Over-estimates of the total number of knots.
If None then nxest = max(kx+sqrt(m/2),2*kx+3),
nyest = max(ky+sqrt(m/2),2*ky+3)
quiet -- Non-zero to suppress printing of messages.
Outputs: (tck, {fp, ier, msg})
tck -- A list [tx, ty, c, kx, ky] containing the knots (tx, ty) and
coefficients (c) of the bivariate B-spline representation of the
surface along with the degree of the spline.
fp -- The weighted sum of squared residuals of the spline approximation.
ier -- 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 -- A message corresponding to the integer flag, ier.
Remarks:
SEE bisplev to evaluate the value of the B-spline given its tck
representation.
See also:
splprep, splrep, splint, sproot, splev - evaluation, roots, integral
UnivariateSpline, BivariateSpline - an alternative wrapping
of the FITPACK functions
"""
x,y,z=map(myasarray,[x,y,z])
x,y,z=map(ravel,[x,y,z]) # ensure 1-d arrays.
m=len(x)
if not (m==len(y)==len(z)):
raise TypeError, 'len(x)==len(y)==len(z) must hold.'
if w is None: w=ones(m,float)
else: w=myasarray(w)
if not len(w) == m:
raise TypeError,' len(w)=%d is not equal to m=%d'%(len(w),m)
if xb is None: xb=x.min()
if xe is None: xe=x.max()
if yb is None: yb=y.min()
if ye is None: ye=y.max()
if not (-1<=task<=1): raise TypeError, 'task must be either -1,0, or 1'
if s is None: s=m-sqrt(2*m)
if task==-1:
if tx is None: raise TypeError, 'Knots_x must be given for task=-1'
_surfit_cache['tx']=myasarray(tx)
if ty is None: raise TypeError, 'Knots_y must be given for task=-1'
_surfit_cache['ty']=myasarray(ty)
if nx<2*kx+2:
raise TypeError,'There must be at least 2*kx+2 knots_x for task=-1'
if ny<2*ky+2:
raise TypeError,'There must be at least 2*ky+2 knots_y for task=-1'
if not ((1<=kx<=5) and (1<=ky<=5)):
raise TypeError, 'Given degree of the spline (kx,ky=%d,%d) is not \
supported. (1<=k<=5)'%(kx,ky)
if m<(kx+1)*(ky+1): raise TypeError, 'm>=(kx+1)(ky+1) must hold'
if nxest is None: nxest=kx+sqrt(m/2)
if nyest is None: nyest=ky+sqrt(m/2)
nxest,nyest=max(nxest,2*kx+3),max(nyest,2*ky+3)
if task>=0 and s==0:
nxest=int(kx+sqrt(3*m))
nyest=int(ky+sqrt(3*m))
if task == 0:
nx,tx,ny,ty,c,fp,wrk1,ier = dfitpack.surfit_smth0(x,y,z,w,xb,xe,yb,ye,
kx,ky,s=s,eps=eps,
nxest=nxest,nyest=nyest)
elif task==1:
try:
tx,ty=_surfit_cache['tx'],_surfit_cache['ty']
nx,ny= _surfit_cache['nx'],_surfit_cache['ny']
wrk1 = _surfit_cache['wrk1']
except KeyError:
raise ValueError, 'task=1 can only be called after task=0'
nx,tx,ny,ty,c,fp,wrk1,ier=dfitpack.surfit_smth1(x,y,z,nx,tx,ny,ty,wrk1,
w,xb,xe,yb,ye,kx,ky,s=s,eps=eps,
nxest=nxest,nyest=nyest)
elif task==-1:
tx = myasarray(tx)
ty = myasarray(ty)
tx,ty,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx,ty,w,
xb,xe,yb,ye,
kx,ky,eps,
nxest=nxest,nyest=nyest)
if ier>10:
tx,ty,c,fp,ier = dfitpack.surfit_lsq(x,y,z,tx,ty,w,\
xb,xe,yb,ye,\
kx,ky,eps,\
nxest=nxest,nyest=nyest)
if task>=0:
_surfit_cache['tx']=tx
_surfit_cache['ty']=ty
_surfit_cache['nx']=nx
_surfit_cache['ny']=ny
_surfit_cache['wrk1']=wrk1
tck=[tx[:nx],ty[:ny],c[:(nx-kx-1)*(ny-ky-1)],kx,ky]
if ier<=0 and not quiet:
print _iermess2[ier][0]
print "\tkx,ky=%d,%d nx,ny=%d,%d m=%d fp=%f s=%f"%(kx,ky,len(tx),
len(ty),m,fp,s)
ierm=min(11,max(-3,ier))
if ierm>0 and not full_output:
if ier in [1,2,3,4,5]:
print "Warning: "+_iermess2[ierm][0]
print "\tkx,ky=%d,%d nx,ny=%d,%d m=%d fp=%f s=%f"%(kx,ky,len(tx),
len(ty),m,fp,s)
else:
try:
raise _iermess2[ierm][1],_iermess2[ierm][0]
except KeyError:
raise _iermess2['unknown'][1],_iermess2['unknown'][0]
if full_output:
try:
return tck,fp,ier,_iermess2[ierm][0]
except KeyError:
return tck,fp,ier,_iermess2['unknown'][0]
else:
return tck
