def splprep(x,w=None,u=None,ub=None,ue=None,k=3,task=0,s=None,t=None,
full_output=0,nest=None,per=0,quiet=1):
"""Find the B-spline representation of an N-dimensional curve.
Description:
Given a list of N rank-1 arrays, x, which represent a curve in N-dimensional
space parametrized by u, find a smooth approximating spline curve g(u).
Uses the FORTRAN routine parcur from FITPACK
Inputs:
x -- A list of sample vector arrays representing the curve.
u -- An array of parameter values. If not given, these values are
calculated automatically as (M = len(x[0])):
v[0] = 0
v[i] = v[i-1] + distance(x[i],x[i-1])
u[i] = v[i] / v[M-1]
ub, ue -- The end-points of the parameters interval. Defaults to
u[0] and u[-1].
k -- Degree of the spline. Cubic splines are recommended. Even values of
k should be avoided especially with a small s-value.
1 <= k <= 5.
task -- If task==0 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
knots, t.
s -- 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 tradeoff 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 datapoints in x, y, and w.
t -- The knots needed for task=-1.
full_output -- If non-zero, then return optional outputs.
nest -- 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 -- If non-zero, data points are considered periodic with period
x[m-1] - x[0] and a smooth periodic spline approximation is returned.
Values of y[m-1] and w[m-1] are not used.
quiet -- Non-zero to suppress messages.
Outputs: (tck, u, {fp, ier, msg})
tck -- (t,c,k) a tuple containing the vector of knots, the B-spline
coefficients, and the degree of the spline.
u -- An array of the values of the parameter.
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 splev for evaluation of the spline and its derivatives.
See also:
splrep, splev, sproot, spalde, splint - evaluation, roots, integral
bisplrep, bisplev - bivariate splines
UnivariateSpline, BivariateSpline - an alternative wrapping
of the FITPACK functions
"""
if task<=0:
_parcur_cache = {'t': array([],float), 'wrk': array([],float),
'iwrk':array([],int32),'u': array([],float),
'ub':0,'ue':1}
x=myasarray(x)
idim,m=x.shape
if per:
for i in range(idim):
if x[i][0]!=x[i][-1]:
if quiet<2:print 'Warning: Setting x[%d][%d]=x[%d][0]'%(i,m,i)
x[i][-1]=x[i][0]
if not 0<idim<11: raise TypeError,'0<idim<11 must hold'
if w is None: w=ones(m,float)
else: w=myasarray(w)
ipar=(u is not None)
if ipar:
_parcur_cache['u']=u
if ub is None: _parcur_cache['ub']=u[0]
else: _parcur_cache['ub']=ub
if ue is None: _parcur_cache['ue']=u[-1]
else: _parcur_cache['ue']=ue
else: _parcur_cache['u']=zeros(m,float)
if not (1<=k<=5): raise TypeError, '1<=k=%d<=5 must hold'%(k)
if not (-1<=task<=1): raise TypeError, 'task must be either -1,0, or 1'
if (not len(w)==m) or (ipar==1 and (not len(u)==m)):
raise TypeError,'Mismatch of input dimensions'
if s is None: s=m-sqrt(2*m)
if t is None and task==-1: raise TypeError, 'Knots must be given for task=-1'
if t is not None:
_parcur_cache['t']=myasarray(t)
n=len(_parcur_cache['t'])
if task==-1 and n<2*k+2:
raise TypeError, 'There must be at least 2*k+2 knots for task=-1'
if m<=k: raise TypeError, 'm>k must hold'
if nest is None: nest=m+2*k
if (task>=0 and s==0) or (nest<0):
if per: nest=m+2*k
else: nest=m+k+1
nest=max(nest,2*k+3)
u=_parcur_cache['u']
ub=_parcur_cache['ub']
ue=_parcur_cache['ue']
t=_parcur_cache['t']
wrk=_parcur_cache['wrk']
iwrk=_parcur_cache['iwrk']
t,c,o=_fitpack._parcur(ravel(transpose(x)),w,u,ub,ue,k,task,ipar,s,t,
nest,wrk,iwrk,per)
_parcur_cache['u']=o['u']
_parcur_cache['ub']=o['ub']
_parcur_cache['ue']=o['ue']
_parcur_cache['t']=t
_parcur_cache['wrk']=o['wrk']
_parcur_cache['iwrk']=o['iwrk']
ier,fp,n=o['ier'],o['fp'],len(t)
u=o['u']
c.shape=idim,n-k-1
tcku = [t,list(c),k],u
if ier<=0 and not quiet:
print _iermess[ier][0]
print "\tk=%d n=%d m=%d fp=%f s=%f"%(k,len(t),m,fp,s)
if ier>0 and not full_output:
if ier in [1,2,3]:
print "Warning: "+_iermess[ier][0]
else:
try:
raise _iermess[ier][1],_iermess[ier][0]
except KeyError:
raise _iermess['unknown'][1],_iermess['unknown'][0]
if full_output:
try:
return tcku,fp,ier,_iermess[ier][0]
except KeyError:
return tcku,fp,ier,_iermess['unknown'][0]
else:
return tcku
def splprep(x,w=None,u=None,ub=None,ue=None,k=3,task=0,s=None,t=None,
full_output=0,nest=None,per=0,quiet=1):
"""
Find the B-spline representation of an N-dimensional curve.
Given a list of N rank-1 arrays, x, which represent a curve in
N-dimensional space parametrized by u, find a smooth approximating
spline curve g(u). Uses the FORTRAN routine parcur from FITPACK.
Parameters
----------
x : array_like
A list of sample vector arrays representing the curve.
u : array_like, optional
An array of parameter values. If not given, these values are
calculated automatically as ``M = len(x[0])``:
v[0] = 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[0] 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
knots, t.
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[0] 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.
Returns
-------
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.
See Also
--------
splrep, splev, sproot, spalde, splint,
bisplrep, bisplev
UnivariateSpline, BivariateSpline
Notes
-----
See `splev` for evaluation of the spline and its derivatives.
References
----------
.. [1] P. Dierckx, "Algorithms for smoothing data with periodic and
parametric splines, Computer Graphics and Image Processing",
20 (1982) 171-184.
.. [2] P. Dierckx, "Algorithms for smoothing data with periodic and
parametric splines", report tw55, Dept. Computer Science,
K.U.Leuven, 1981.
.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs on
Numerical Analysis, Oxford University Press, 1993.
"""
if task<=0:
_parcur_cache = {'t': array([],float), 'wrk': array([],float),
'iwrk':array([],int32),'u': array([],float),
'ub':0,'ue':1}
x=myasarray(x)
idim,m=x.shape
if per:
for i in range(idim):
if x[i][0]!=x[i][-1]:
if quiet<2:print 'Warning: Setting x[%d][%d]=x[%d][0]'%(i,m,i)
x[i][-1]=x[i][0]
if not 0<idim<11: raise TypeError,'0<idim<11 must hold'
if w is None: w=ones(m,float)
else: w=myasarray(w)
ipar=(u is not None)
if ipar:
_parcur_cache['u']=u
if ub is None: _parcur_cache['ub']=u[0]
else: _parcur_cache['ub']=ub
if ue is None: _parcur_cache['ue']=u[-1]
else: _parcur_cache['ue']=ue
else: _parcur_cache['u']=zeros(m,float)
if not (1<=k<=5): raise TypeError, '1<=k=%d<=5 must hold'%(k)
if not (-1<=task<=1): raise TypeError, 'task must be either -1,0, or 1'
if (not len(w)==m) or (ipar==1 and (not len(u)==m)):
raise TypeError,'Mismatch of input dimensions'
if s is None: s=m-sqrt(2*m)
if t is None and task==-1: raise TypeError, 'Knots must be given for task=-1'
if t is not None:
_parcur_cache['t']=myasarray(t)
n=len(_parcur_cache['t'])
if task==-1 and n<2*k+2:
raise TypeError, 'There must be at least 2*k+2 knots for task=-1'
if m<=k: raise TypeError, 'm>k must hold'
if nest is None: nest=m+2*k
if (task>=0 and s==0) or (nest<0):
if per: nest=m+2*k
else: nest=m+k+1
nest=max(nest,2*k+3)
u=_parcur_cache['u']
ub=_parcur_cache['ub']
ue=_parcur_cache['ue']
t=_parcur_cache['t']
wrk=_parcur_cache['wrk']
iwrk=_parcur_cache['iwrk']
t,c,o=_fitpack._parcur(ravel(transpose(x)),w,u,ub,ue,k,task,ipar,s,t,
nest,wrk,iwrk,per)
_parcur_cache['u']=o['u']
_parcur_cache['ub']=o['ub']
_parcur_cache['ue']=o['ue']
_parcur_cache['t']=t
_parcur_cache['wrk']=o['wrk']
_parcur_cache['iwrk']=o['iwrk']
ier,fp,n=o['ier'],o['fp'],len(t)
u=o['u']
c.shape=idim,n-k-1
tcku = [t,list(c),k],u
if ier<=0 and not quiet:
print _iermess[ier][0]
print "\tk=%d n=%d m=%d fp=%f s=%f"%(k,len(t),m,fp,s)
if ier>0 and not full_output:
if ier in [1,2,3]:
print "Warning: "+_iermess[ier][0]
else:
try:
raise _iermess[ier][1],_iermess[ier][0]
except KeyError:
raise _iermess['unknown'][1],_iermess['unknown'][0]
if full_output:
try:
return tcku,fp,ier,_iermess[ier][0]
except KeyError:
return tcku,fp,ier,_iermess['unknown'][0]
else:
return tcku
def splprep(x,
w=None,
u=None,
ub=None,
ue=None,
k=3,
task=0,
s=None,
t=None,
full_output=0,
nest=None,
per=0,
quiet=1):
"""Find the B-spline representation of an N-dimensional curve.
Description:
Given a list of N rank-1 arrays, x, which represent a curve in N-dimensional
space parametrized by u, find a smooth approximating spline curve g(u).
Uses the FORTRAN routine parcur from FITPACK
Inputs:
x -- A list of sample vector arrays representing the curve.
u -- An array of parameter values. If not given, these values are
calculated automatically as (M = len(x[0])):
v[0] = 0
v[i] = v[i-1] + distance(x[i],x[i-1])
u[i] = v[i] / v[M-1]
ub, ue -- The end-points of the parameters interval. Defaults to
u[0] and u[-1].
k -- Degree of the spline. Cubic splines are recommended. Even values of
k should be avoided especially with a small s-value.
1 <= k <= 5.
task -- If task==0 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
knots, t.
s -- 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 tradeoff 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 datapoints in x, y, and w.
t -- The knots needed for task=-1.
full_output -- If non-zero, then return optional outputs.
nest -- 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 -- If non-zero, data points are considered periodic with period
x[m-1] - x[0] and a smooth periodic spline approximation is returned.
Values of y[m-1] and w[m-1] are not used.
quiet -- Non-zero to suppress messages.
Outputs: (tck, u, {fp, ier, msg})
tck -- (t,c,k) a tuple containing the vector of knots, the B-spline
coefficients, and the degree of the spline.
u -- An array of the values of the parameter.
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 splev for evaluation of the spline and its derivatives.
See also:
splrep, splev, sproot, spalde, splint - evaluation, roots, integral
bisplrep, bisplev - bivariate splines
UnivariateSpline, BivariateSpline - an alternative wrapping
of the FITPACK functions
Notes:
Dierckx P. : Algorithms for smoothing data with periodic and
parametric splines, Computer Graphics and Image
Processing 20 (1982) 171-184.
Dierckx P. : Algorithms for smoothing data with periodic and param-
etric splines, report tw55, Dept. Computer Science,
K.U.Leuven, 1981.
Dierckx P. : Curve and surface fitting with splines, Monographs on
Numerical Analysis, Oxford University Press, 1993.
"""
if task <= 0:
_parcur_cache = {
't': array([], float),
'wrk': array([], float),
'iwrk': array([], int32),
'u': array([], float),
'ub': 0,
'ue': 1
}
x = myasarray(x)
idim, m = x.shape
if per:
for i in range(idim):
if x[i][0] != x[i][-1]:
if quiet < 2:
print 'Warning: Setting x[%d][%d]=x[%d][0]' % (i, m, i)
x[i][-1] = x[i][0]
if not 0 < idim < 11: raise TypeError, '0<idim<11 must hold'
if w is None: w = ones(m, float)
else: w = myasarray(w)
ipar = (u is not None)
if ipar:
_parcur_cache['u'] = u
if ub is None: _parcur_cache['ub'] = u[0]
else: _parcur_cache['ub'] = ub
if ue is None: _parcur_cache['ue'] = u[-1]
else: _parcur_cache['ue'] = ue
else: _parcur_cache['u'] = zeros(m, float)
if not (1 <= k <= 5): raise TypeError, '1<=k=%d<=5 must hold' % (k)
if not (-1 <= task <= 1): raise TypeError, 'task must be either -1,0, or 1'
if (not len(w) == m) or (ipar == 1 and (not len(u) == m)):
raise TypeError, 'Mismatch of input dimensions'
if s is None: s = m - sqrt(2 * m)
if t is None and task == -1:
raise TypeError, 'Knots must be given for task=-1'
if t is not None:
_parcur_cache['t'] = myasarray(t)
n = len(_parcur_cache['t'])
if task == -1 and n < 2 * k + 2:
raise TypeError, 'There must be at least 2*k+2 knots for task=-1'
if m <= k: raise TypeError, 'm>k must hold'
if nest is None: nest = m + 2 * k
if (task >= 0 and s == 0) or (nest < 0):
if per: nest = m + 2 * k
else: nest = m + k + 1
nest = max(nest, 2 * k + 3)
u = _parcur_cache['u']
ub = _parcur_cache['ub']
ue = _parcur_cache['ue']
t = _parcur_cache['t']
wrk = _parcur_cache['wrk']
iwrk = _parcur_cache['iwrk']
t, c, o = _fitpack._parcur(ravel(transpose(x)), w, u, ub, ue, k, task,
ipar, s, t, nest, wrk, iwrk, per)
_parcur_cache['u'] = o['u']
_parcur_cache['ub'] = o['ub']
_parcur_cache['ue'] = o['ue']
_parcur_cache['t'] = t
_parcur_cache['wrk'] = o['wrk']
_parcur_cache['iwrk'] = o['iwrk']
ier, fp, n = o['ier'], o['fp'], len(t)
u = o['u']
c.shape = idim, n - k - 1
tcku = [t, list(c), k], u
if ier <= 0 and not quiet:
print _iermess[ier][0]
print "\tk=%d n=%d m=%d fp=%f s=%f" % (k, len(t), m, fp, s)
if ier > 0 and not full_output:
if ier in [1, 2, 3]:
print "Warning: " + _iermess[ier][0]
else:
try:
raise _iermess[ier][1], _iermess[ier][0]
except KeyError:
raise _iermess['unknown'][1], _iermess['unknown'][0]
if full_output:
try:
return tcku, fp, ier, _iermess[ier][0]
except KeyError:
return tcku, fp, ier, _iermess['unknown'][0]
else:
return tcku
def splprep(x,
w=None,
u=None,
ub=None,
ue=None,
k=3,
task=0,
s=None,
t=None,
full_output=0,
nest=None,
per=0,
quiet=1):
"""
Find the B-spline representation of an N-dimensional curve.
Given a list of N rank-1 arrays, x, which represent a curve in
N-dimensional space parametrized by u, find a smooth approximating
spline curve g(u). Uses the FORTRAN routine parcur from FITPACK.
Parameters
----------
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[0].
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[0])).
u : array_like, optional
An array of parameter values. If not given, these values are
calculated automatically as ``M = len(x[0])``::
v[0] = 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[0] 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
knots, t.
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[0]`` 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.
Returns
-------
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.
See Also
--------
splrep, splev, sproot, spalde, splint,
bisplrep, bisplev
UnivariateSpline, BivariateSpline
Notes
-----
See `splev` for evaluation of the spline and its derivatives.
References
----------
.. [1] P. Dierckx, "Algorithms for smoothing data with periodic and
parametric splines, Computer Graphics and Image Processing",
20 (1982) 171-184.
.. [2] P. Dierckx, "Algorithms for smoothing data with periodic and
parametric splines", report tw55, Dept. Computer Science,
K.U.Leuven, 1981.
.. [3] P. Dierckx, "Curve and surface fitting with splines", Monographs on
Numerical Analysis, Oxford University Press, 1993.
"""
if task <= 0:
_parcur_cache = {
't': array([], float),
'wrk': array([], float),
'iwrk': array([], int32),
'u': array([], float),
'ub': 0,
'ue': 1
}
x = myasarray(x)
idim, m = x.shape
if per:
for i in range(idim):
if x[i][0] != x[i][-1]:
if quiet < 2:
print 'Warning: Setting x[%d][%d]=x[%d][0]' % (i, m, i)
x[i][-1] = x[i][0]
if not 0 < idim < 11:
raise TypeError('0 < idim < 11 must hold')
if w is None:
w = ones(m, float)
else:
w = myasarray(w)
ipar = (u is not None)
if ipar:
_parcur_cache['u'] = u
if ub is None: _parcur_cache['ub'] = u[0]
else: _parcur_cache['ub'] = ub
if ue is None: _parcur_cache['ue'] = u[-1]
else: _parcur_cache['ue'] = ue
else: _parcur_cache['u'] = zeros(m, float)
if not (1 <= k <= 5):
raise TypeError('1 <= k= %d <=5 must hold' % k)
if not (-1 <= task <= 1):
raise TypeError('task must be -1, 0 or 1')
if (not len(w) == m) or (ipar == 1 and (not len(u) == m)):
raise TypeError('Mismatch of input dimensions')
if s is None: s = m - sqrt(2 * m)
if t is None and task == -1:
raise TypeError('Knots must be given for task=-1')
if t is not None:
_parcur_cache['t'] = myasarray(t)
n = len(_parcur_cache['t'])
if task == -1 and n < 2 * k + 2:
raise TypeError('There must be at least 2*k+2 knots for task=-1')
if m <= k:
raise TypeError('m > k must hold')
if nest is None: nest = m + 2 * k
if (task >= 0 and s == 0) or (nest < 0):
if per: nest = m + 2 * k
else: nest = m + k + 1
nest = max(nest, 2 * k + 3)
u = _parcur_cache['u']
ub = _parcur_cache['ub']
ue = _parcur_cache['ue']
t = _parcur_cache['t']
wrk = _parcur_cache['wrk']
iwrk = _parcur_cache['iwrk']
t, c, o = _fitpack._parcur(ravel(transpose(x)), w, u, ub, ue, k, task,
ipar, s, t, nest, wrk, iwrk, per)
_parcur_cache['u'] = o['u']
_parcur_cache['ub'] = o['ub']
_parcur_cache['ue'] = o['ue']
_parcur_cache['t'] = t
_parcur_cache['wrk'] = o['wrk']
_parcur_cache['iwrk'] = o['iwrk']
ier, fp, n = o['ier'], o['fp'], len(t)
u = o['u']
c.shape = idim, n - k - 1
tcku = [t, list(c), k], u
if ier <= 0 and not quiet:
print _iermess[ier][0]
print "\tk=%d n=%d m=%d fp=%f s=%f" % (k, len(t), m, fp, s)
if ier > 0 and not full_output:
if ier in [1, 2, 3]:
print "Warning: " + _iermess[ier][0]
else:
try:
raise _iermess[ier][1](_iermess[ier][0])
except KeyError:
raise _iermess['unknown'][1](_iermess['unknown'][0])
if full_output:
try:
return tcku, fp, ier, _iermess[ier][0]
except KeyError:
return tcku, fp, ier, _iermess['unknown'][0]
else:
return tcku
