[Scipy-svn] r6885 - trunk/scipy/interpolate

[scipy-svn at scipy.org]

Author: rgommers
Date: 2010-11-14 03:59:06 -0600 (Sun, 14 Nov 2010)
New Revision: 6885

Modified:
   trunk/scipy/interpolate/__init__.py
   trunk/scipy/interpolate/interpolate.py
   trunk/scipy/interpolate/rbf.py
Log:
DOC: merge wiki edits for interpolate module.

Modified: trunk/scipy/interpolate/__init__.py
===================================================================
--- trunk/scipy/interpolate/__init__.py	2010-11-14 09:58:41 UTC (rev 6884)
+++ trunk/scipy/interpolate/__init__.py	2010-11-14 09:59:06 UTC (rev 6885)
@@ -1,3 +1,133 @@
+"""
+Sub-package for objects used in interpolation.
+
+As listed below, this sub-package contains spline functions and classes,
+one-dimensional and multi-dimensional (univariate and multivariate)
+interpolation classes, Lagrange and Taylor polynomial interpolators, and
+wrappers for `FITPACK <http://www.cisl.ucar.edu/softlib/FITPACK.html>`_
+and DFITPACK functions.
+
+Spline Functions
+----------------
+
+.. autosummary::
+   :toctree: generated/
+
+   bisplev
+   bisplrep
+   insert
+   spalde
+   splev
+   spleval
+   splint
+   spline
+   splmake
+   splprep
+   splrep
+   spltopp
+   sproot
+
+Spline Classes
+--------------
+
+.. autosummary::
+   :toctree: generated/
+
+   UnivariateSpline
+   InterpolatedUnivariateSpline
+   LSQUnivariateSpline
+   BivariateSpline
+   SmoothBivariateSpline
+
+Interpolation Classes (univariate)
+----------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   interp1d
+   BarycentricInterpolator
+   barycentric_interpolate
+   KroghInterpolator
+   krogh_interpolate
+   PiecewisePolynomial
+   piecewise_polynomial_interpolate
+   ppform
+
+Interpolation Classes (multivariate)
+------------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   interp2d
+   Rbf
+
+Additional tools
+----------------
+
+.. autosummary::
+   :toctree: generated/
+
+   lagrange
+   approximate_taylor_polynomial
+
+Wrappers around FITPACK functions
+---------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   fitpack.bisplev
+   fitpack.bisplrep
+   fitpack.insert
+   fitpack.spalde
+   fitpack.splev
+   fitpack.splint
+   fitpack.splprep
+   fitpack.splrep
+   fitpack.sproot
+
+Wrappers around DFITPACK functions
+----------------------------------
+
+   `dfitpack.bispeu`
+   `dfitpack.bispev`
+   `dfitpack.curfit`
+   `dfitpack.dblint`
+   `dfitpack.fpcurf0`
+   `dfitpack.fpcurf1`
+   `dfitpack.fpcurfm1`
+   `dfitpack.parcur`
+   `dfitpack.percur`
+   `dfitpack.regrid_smth`
+   `dfitpack.spalde`
+   `dfitpack.splder`
+   `dfitpack.splev`
+   `dfitpack.splint`
+   `dfitpack.sproot`
+   `dfitpack.surfit_lsq`
+   `dfitpack.surfit_smth`
+
+See Also
+--------
+
+.. autosummary::
+   :toctree: generated/
+
+   ndimage.map_coordinates
+   ndimage.spline_filter
+   signal.resample
+   signal.bspline
+   signal.gauss_spline
+   signal.qspline1d
+   signal.cspline1d
+   signal.qspline1d_eval
+   signal.cspline1d_eval
+   signal.qspline2d
+   signal.cspline2d
+
+"""
 #
 # interpolate - Interpolation Tools
 #

Modified: trunk/scipy/interpolate/interpolate.py
===================================================================
--- trunk/scipy/interpolate/interpolate.py	2010-11-14 09:58:41 UTC (rev 6884)
+++ trunk/scipy/interpolate/interpolate.py	2010-11-14 09:59:06 UTC (rev 6885)
@@ -23,10 +23,22 @@
     return all
 
 def lagrange(x, w):
-    """Return the Lagrange interpolating polynomial of the data-points (x,w)
+    """
+    Return a Lagrange interpolating polynomial.
 
-    Warning: This implementation is numerically unstable; do not expect to
+    Given two 1-D arrays `x` and `w,` returns the Lagrange interpolating
+    polynomial through the points ``(x, w)``.
+
+    Warning: This implementation is numerically unstable. Do not expect to
     be able to use more than about 20 points even if they are chosen optimally.
+
+    Parameters
+    ----------
+    x : array_like
+        `x` represents the x-coordinates of a set of datapoints.
+    w : array_like
+        `w` represents the y-coordinates of a set of datapoints, i.e. f(`x`).
+
     """
     M = len(x)
     p = poly1d(0.0)
@@ -55,11 +67,11 @@
     x, y : 1D arrays
         Arrays defining the coordinates of a 2D grid.  If the
         points lie on a regular grid, `x` can specify the column coordinates
-        and `y` the row coordinates, e.g.::
+        and `y` the row coordinates, for example:
 
             x = [0,1,2];  y = [0,3,7]
 
-        otherwise x and y must specify the full coordinates, i.e.::
+        otherwise x and y must specify the full coordinates, for example:
 
             x = [0,1,2,0,1,2,0,1,2];  y = [0,0,0,3,3,3,7,7,7]
 

Modified: trunk/scipy/interpolate/rbf.py
===================================================================
--- trunk/scipy/interpolate/rbf.py	2010-11-14 09:58:41 UTC (rev 6884)
+++ trunk/scipy/interpolate/rbf.py	2010-11-14 09:59:06 UTC (rev 6885)
@@ -72,9 +72,9 @@
             'quintic': r**5
             'thin_plate': r**2 * log(r)
 
-        If callable, then it must take 2 arguments (self, r).  The epsilon parameter
-        will be available as self.epsilon.  Other keyword arguments passed in will
-        be available as well.
+        If callable, then it must take 2 arguments (self, r).  The epsilon
+        parameter will be available as self.epsilon.  Other keyword
+        arguments passed in will be available as well.
 
     epsilon : float, optional
         Adjustable constant for gaussian or multiquadrics functions
@@ -93,13 +93,14 @@
                 return sqrt( ((x1 - x2)**2).sum(axis=0) )
 
         which is called with x1=x1[ndims,newaxis,:] and
-        x2=x2[ndims,:,newaxis] such that the result is a matrix of the distances
-        from each point in x1 to each point in x2.
+        x2=x2[ndims,:,newaxis] such that the result is a matrix of the
+        distances from each point in x1 to each point in x2.
 
     Examples
     --------
     >>> rbfi = Rbf(x, y, z, d)  # radial basis function interpolator instance
     >>> di = rbfi(xi, yi, zi)   # interpolated values
+
     """
 
     def _euclidean_norm(self, x1, x2):