[Scipy-svn] r6613 - trunk/scipy/interpolate
scipy-svn at scipy.org
scipy-svn at scipy.org
Sat Jul 17 02:40:51 EDT 2010
Author: rgommers
Date: 2010-07-17 01:40:51 -0500 (Sat, 17 Jul 2010)
New Revision: 6613
Modified:
trunk/scipy/interpolate/fitpack2.py
Log:
DOC: Fix formatting of BivariateSpline docs. Add note on input lengths, closes #687.
Note, it removes the __init__ docstring, those contents belong in the main
class docstring. This can't be done in the doc wiki, so doing it in svn. No new
content is added except for the above mentioned note.
Modified: trunk/scipy/interpolate/fitpack2.py
===================================================================
--- trunk/scipy/interpolate/fitpack2.py 2010-07-17 04:29:06 UTC (rev 6612)
+++ trunk/scipy/interpolate/fitpack2.py 2010-07-17 06:40:51 UTC (rev 6613)
@@ -468,14 +468,14 @@
[xb,xe] x [yb, ye] calculated from a given set of data points
(x,y,z).
- See also:
-
- bisplrep, bisplev - an older wrapping of FITPACK
- UnivariateSpline - a similar class for univariate spline interpolation
- SmoothUnivariateSpline - to create a BivariateSpline through the
- given points
- LSQUnivariateSpline - to create a BivariateSpline using weighted
- least-squares fitting
+ See Also
+ --------
+ bisplrep, bisplev : an older wrapping of FITPACK
+ UnivariateSpline : a similar class for univariate spline interpolation
+ SmoothUnivariateSpline :
+ to create a BivariateSpline through the given points
+ LSQUnivariateSpline :
+ to create a BivariateSpline using weighted least-squares fitting
"""
def get_residual(self):
@@ -537,37 +537,42 @@
class SmoothBivariateSpline(BivariateSpline):
""" Smooth bivariate spline approximation.
- See also:
+ Parameters
+ ----------
+ x, y, z : array_like
+ 1-D sequences of data points (order is not important).
+ w : array_lie, optional
+ Positive 1-D sequence of weights.
+ bbox : array_like, optional
+ Sequence of length 4 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 : ints, optional
+ Degrees of the bivariate spline. Default is 3.
+ s : float, optional
+ 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 : float, optional
+ A threshold for determining the effective rank of an over-determined
+ linear system of equations. `eps` should have a value between 0 and 1,
+ the default is 1e-16.
- bisplrep, bisplev - an older wrapping of FITPACK
- UnivariateSpline - a similar class for univariate spline interpolation
- LSQUnivariateSpline - to create a BivariateSpline using weighted
- least-squares fitting
+ Notes
+ -----
+ The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
+
+ See Also
+ --------
+ bisplrep, bisplev : an older wrapping of FITPACK
+ UnivariateSpline : a similar class for univariate spline interpolation
+ LSQUnivariateSpline : to create a BivariateSpline using weighted
+
"""
- 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.
- """
+ 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,
@@ -584,35 +589,45 @@
self.degrees = kx,ky
class LSQBivariateSpline(BivariateSpline):
- """ Weighted least-squares spline approximation.
- See also:
+ """ Weighted least-squares bivariate spline approximation.
- bisplrep, bisplev - an older wrapping of FITPACK
- UnivariateSpline - a similar class for univariate spline interpolation
- SmoothUnivariateSpline - to create a BivariateSpline through the
- given points
+ Parameters
+ ----------
+ x, y, z : array_like
+ 1-D sequences of data points (order is not important).
+ tx, ty : array_like
+ Strictly ordered 1-D sequences of knots coordinates.
+ w : array_lie, optional
+ Positive 1-D sequence of weights.
+ bbox : array_like, optional
+ Sequence of length 4 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 : ints, optional
+ Degrees of the bivariate spline. Default is 3.
+ s : float, optional
+ 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 : float, optional
+ A threshold for determining the effective rank of an over-determined
+ linear system of equations. `eps` should have a value between 0 and 1,
+ the default is 1e-16.
+
+ Notes
+ -----
+ The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
+
+ See Also
+ --------
+ bisplrep, bisplev : an older wrapping of FITPACK
+ UnivariateSpline : a similar class for univariate spline interpolation
+ SmoothUnivariateSpline : To create a BivariateSpline through the given points
"""
- 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.
- """
+ 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)
@@ -644,32 +659,33 @@
class RectBivariateSpline(BivariateSpline):
""" Bivariate spline approximation over a rectangular mesh.
- Can be used for both smoothing or interpolating data.
+ Can be used for both smoothing and interpolating data.
- See also:
+ Parameters
+ ----------
+ x,y : array_like
+ 1-D arrays of coordinates in strictly ascending order.
+ z : array_like
+ 2-D array of data with shape (x.size,y.size).
+ bbox : array_like, optional
+ Sequence of length 4 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 : ints, optional
+ Degrees of the bivariate spline. Default is 3.
+ s : float, optional
+ Positive smoothing factor defined for estimation condition:
+ ``sum((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) <= s``
+ Default is s=0, which is for interpolation.
- SmoothBivariateSpline - a smoothing bivariate spline for scattered data
- bisplrep, bisplev - an older wrapping of FITPACK
- UnivariateSpline - a similar class for univariate spline interpolation
+ See Also
+ --------
+ SmoothBivariateSpline : a smoothing bivariate spline for scattered data
+ bisplrep, bisplev : an older wrapping of FITPACK
+ UnivariateSpline : a similar class for univariate spline interpolation
"""
- def __init__(self, x, y, z,
- bbox = [None]*4, kx=3, ky=3, s=0):
- """
- Input:
- x,y - 1-d sequences of coordinates in strictly ascending order
- z - 2-d array of data with shape (x.size,y.size)
- Optional input:
- 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=0 which is for interpolation
- """
+ def __init__(self, x, y, z, bbox = [None]*4, kx=3, ky=3, s=0):
x,y = ravel(x),ravel(y)
if not all(diff(x) > 0.0):
raise TypeError,'x must be strictly increasing'
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