[Scipy-svn] r6895 - in trunk/scipy: cluster fftpack interpolate io linalg maxentropy misc ndimage odr optimize sparse stats

scipy-svn@scip... scipy-svn@scip...
Sun Nov 14 10:18:57 CST 2010


Author: ptvirtan
Date: 2010-11-14 10:18:57 -0600 (Sun, 14 Nov 2010)
New Revision: 6895

Modified:
   trunk/scipy/cluster/__init__.py
   trunk/scipy/cluster/info.py
   trunk/scipy/fftpack/__init__.py
   trunk/scipy/fftpack/info.py
   trunk/scipy/interpolate/__init__.py
   trunk/scipy/interpolate/info.py
   trunk/scipy/io/__init__.py
   trunk/scipy/io/info.py
   trunk/scipy/linalg/__init__.py
   trunk/scipy/linalg/info.py
   trunk/scipy/maxentropy/__init__.py
   trunk/scipy/maxentropy/info.py
   trunk/scipy/misc/__init__.py
   trunk/scipy/misc/info.py
   trunk/scipy/ndimage/__init__.py
   trunk/scipy/ndimage/info.py
   trunk/scipy/odr/__init__.py
   trunk/scipy/odr/info.py
   trunk/scipy/optimize/__init__.py
   trunk/scipy/optimize/info.py
   trunk/scipy/sparse/__init__.py
   trunk/scipy/sparse/info.py
   trunk/scipy/stats/__init__.py
   trunk/scipy/stats/info.py
Log:
DOC: move docstrings of __init__.py back to info.py

Modified: trunk/scipy/cluster/__init__.py
===================================================================
--- trunk/scipy/cluster/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/cluster/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,20 +1,3 @@
-"""
-Vector Quantization / Kmeans
-============================
-Clustering algorithms are useful in information theory, target detection,
-communications, compression, and other areas.  The `vq` module only
-supports vector quantization and the k-means algorithms.  Development of
-self-organizing maps (SOM) and other approaches is underway.
-
-Hierarchical Clustering
-=======================
-The `hierarchy` module provides functions for hierarchical and
-agglomerative clustering.  Its features include generating hierarchical
-clusters from distance matrices, computing distance matrices from
-observation vectors, calculating statistics on clusters, cutting linkages
-to generate flat clusters, and visualizing clusters with dendrograms.
-
-"""
 #
 # spatial - Distances
 #

Modified: trunk/scipy/cluster/info.py
===================================================================
--- trunk/scipy/cluster/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/cluster/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,25 +1,17 @@
 """
 Vector Quantization / Kmeans
 ============================
+Clustering algorithms are useful in information theory, target detection,
+communications, compression, and other areas.  The `vq` module only
+supports vector quantization and the k-means algorithms.  Development of
+self-organizing maps (SOM) and other approaches is underway.
 
-    Clustering algorithms are useful in information theory, target detection,
-    communications, compression, and other areas. The vq module only
-    supports vector quantization and the k-means algorithms. Development
-    of self-organizing maps (SOM) and other approaches is underway.
-
 Hierarchical Clustering
 =======================
+The `hierarchy` module provides functions for hierarchical and
+agglomerative clustering.  Its features include generating hierarchical
+clusters from distance matrices, computing distance matrices from
+observation vectors, calculating statistics on clusters, cutting linkages
+to generate flat clusters, and visualizing clusters with dendrograms.
 
-    The hierarchy module provides functions for hierarchical and agglomerative
-    clustering. Its features include generating hierarchical clusters from
-    distance matrices, computing distance matrices from observation vectors,
-    calculating statistics on clusters, cutting linkages to generate flat
-    clusters, and visualizing clusters with dendrograms.
-
-Distance Computation
-====================
-
-    The distance module provides functions for computing distances between
-    pairs of vectors from a set of observation vectors.
-
 """

Modified: trunk/scipy/fftpack/__init__.py
===================================================================
--- trunk/scipy/fftpack/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/fftpack/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,57 +1,3 @@
-"""
-Home of discrete Fourier transform algorithms
-
-Modules
-=======
-
-.. autosummary::
-   :toctree: generated/
-
-   basic - Basic discrete Fourier transform operators
-   convolve - Convolution functions
-   helper - TODO
-   pseudo_diffs - Differential and pseudo-differential operators
-   realtransforms - Real spectrum tranforms (DCT, DST, MDCT)
-
-Functions
-=========
-
-Fast Fourier Transforms (FFTs)
-------------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   fft - Fast (discrete) Fourier Transform (FFT)
-   ifft - Inverse FFT
-   fft2 - Two dimensional FFT
-   ifft2 - Two dimensional inverse FFT
-   fftn - n-dimensional FFT
-   ifftn - n-dimensional inverse FFT
-   rfft - FFT of strictly real-valued sequence
-   irfft - Inverse of rfft
-   rfftfreq - DFT sample frequencies (specific to rfft and irfft)
-   dct - Discrete cosine transform
-   idct - Inverse discrete cosine transform
-
-Differential and pseudo-differential operators
-----------------------------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   diff - Differentiation and integration of periodic sequences
-   tilbert - Tilbert transform:         cs_diff(x,h,h)
-   itilbert - Inverse Tilbert transform: sc_diff(x,h,h)
-   hilbert - Hilbert transform:         cs_diff(x,inf,inf)
-   ihilbert - Inverse Hilbert transform: sc_diff(x,inf,inf)
-   cs_diff - cosh/sinh pseudo-derivative of periodic sequences
-   sc_diff - sinh/cosh pseudo-derivative of periodic sequences
-   ss_diff - sinh/sinh pseudo-derivative of periodic sequences
-   cc_diff - cosh/cosh pseudo-derivative of periodic sequences
-   shift - Shift periodic sequences
-
-"""
 #
 # fftpack - Discrete Fourier Transform algorithms.
 #

Modified: trunk/scipy/fftpack/info.py
===================================================================
--- trunk/scipy/fftpack/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/fftpack/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,45 +1,57 @@
 # This file is executed by __init__.py and ppimport hooks.
 """
-Discrete Fourier Transform algorithms
-=====================================
+Discrete Fourier transform algorithms
 
-Fast Fourier Transforms:
+Modules
+=======
 
-  fft       --- FFT of arbitrary type periodic sequences
-  ifft      --- Inverse of fft
-  fftn      --- Multi-dimensional FFT
-  ifftn     --- Inverse of fftn
-  fft2      --- Two-dimensional FFT
-  ifft2     --- Inverse of fft2
-  rfft      --- FFT of real periodic sequences
-  irfft     --- Inverse of rfft
+.. autosummary::
+   :toctree: generated/
 
-Differential and pseudo-differential operators:
+   basic - Basic discrete Fourier transform operators
+   convolve - Convolution functions
+   helper - TODO
+   pseudo_diffs - Differential and pseudo-differential operators
+   realtransforms - Real spectrum tranforms (DCT, DST, MDCT)
 
-  diff      --- Differentiation and integration of periodic sequences
-  tilbert   --- Tilbert transform:         cs_diff(x,h,h)
-  itilbert  --- Inverse Tilbert transform: sc_diff(x,h,h)
-  hilbert   --- Hilbert transform:         cs_diff(x,inf,inf)
-  ihilbert  --- Inverse Hilbert transform: sc_diff(x,inf,inf)
-  cs_diff   --- cosh/sinh pseudo-derivative of periodic sequences
-  sc_diff   --- sinh/cosh pseudo-derivative of periodic sequences
-  ss_diff   --- sinh/sinh pseudo-derivative of periodic sequences
-  cc_diff   --- cosh/cosh pseudo-derivative of periodic sequences
-  shift     --- Shift periodic sequences
+Functions
+=========
 
-Helper functions:
+Fast Fourier Transforms (FFTs)
+------------------------------
 
-  fftshift  --- Shift zero-frequency component to center of spectrum
-  ifftshift --- Inverse of freqshift
-  dftfreq   --- DFT sample frequencies
-  rfftfreq  --- DFT sample frequencies (specific to rfft,irfft)
+.. autosummary::
+   :toctree: generated/
 
-Extension modules:
+   fft - Fast (discrete) Fourier Transform (FFT)
+   ifft - Inverse FFT
+   fft2 - Two dimensional FFT
+   ifft2 - Two dimensional inverse FFT
+   fftn - n-dimensional FFT
+   ifftn - n-dimensional inverse FFT
+   rfft - FFT of strictly real-valued sequence
+   irfft - Inverse of rfft
+   rfftfreq - DFT sample frequencies (specific to rfft and irfft)
+   dct - Discrete cosine transform
+   idct - Inverse discrete cosine transform
 
-  _fftpack   --- Provides functions zfft, drfft, zrfft, zfftnd,
-                destroy_*_cache
-  convolve  --- Provides functions convolve, convolve_z,
-                init_convolution_kernel, destroy_convolve_cache
+Differential and pseudo-differential operators
+----------------------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   diff - Differentiation and integration of periodic sequences
+   tilbert - Tilbert transform:         cs_diff(x,h,h)
+   itilbert - Inverse Tilbert transform: sc_diff(x,h,h)
+   hilbert - Hilbert transform:         cs_diff(x,inf,inf)
+   ihilbert - Inverse Hilbert transform: sc_diff(x,inf,inf)
+   cs_diff - cosh/sinh pseudo-derivative of periodic sequences
+   sc_diff - sinh/cosh pseudo-derivative of periodic sequences
+   ss_diff - sinh/sinh pseudo-derivative of periodic sequences
+   cc_diff - cosh/cosh pseudo-derivative of periodic sequences
+   shift - Shift periodic sequences
+
 """
 
 __all__ = ['fft','ifft','fftn','ifftn','rfft','irfft',

Modified: trunk/scipy/interpolate/__init__.py
===================================================================
--- trunk/scipy/interpolate/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/interpolate/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,133 +1,3 @@
-"""
-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/info.py
===================================================================
--- trunk/scipy/interpolate/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/interpolate/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,146 +1,132 @@
 """
-Interpolation Tools
-===================
+Sub-package for objects used in interpolation.
 
-Wrappers around FITPACK functions
-----------------------------------
+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.
 
-  splrep
-          find smoothing spline given (x,y) points on curve.
-  splprep
-          find smoothing spline given parametrically defined curve.
-  splev
-          evaluate the spline or its derivatives.
-  splint
-          compute definite integral of a spline.
-  sproot
-          find the roots of a cubic spline.
-  spalde
-          compute all derivatives of a spline at given points.
-  bisplrep
-          find bivariate smoothing spline representation.
-  bisplev
-          evaluate bivariate smoothing spline.
+Spline Functions
+----------------
 
-  UnivariateSpline
-          A more recent, object-oriented wrapper; finds a (possibly
-          smoothed) interpolating spline.
+.. autosummary::
+   :toctree: generated/
 
-  InterpolatedUnivariateSpline
+   bisplev
+   bisplrep
+   insert
+   spalde
+   splev
+   spleval
+   splint
+   spline
+   splmake
+   splprep
+   splrep
+   spltopp
+   sproot
 
-  LSQUnivariateSpline
+Spline Classes
+--------------
 
-  BivariateSpline
-           A more recent, object-oriented wrapper; finds a
-           interpolating spline for a bivariate function.
+.. autosummary::
+   :toctree: generated/
 
-  SmoothBivariateSpline
+   UnivariateSpline
+   InterpolatedUnivariateSpline
+   LSQUnivariateSpline
+   BivariateSpline
+   SmoothBivariateSpline
 
-Low-level Piece-wise Spline Tools
------------------------------------
-  splmake
-              Create a spline representation from data-points
-              where the internal knots are the data-points.
-              
-  spleval
-              Evaluate a spline representation on a new set of
-              input data values.
-
-  spline
-              Single-call interface to splmake and spleval
-              
-  spltopp
-              Return piecewise polynomial representation from a
-              spline representation. 
-                
-  
-
 Interpolation Classes (univariate)
------------------------------------
+----------------------------------
 
-  interp1d
-               Create a class whose instances can linearly interpolate
-               to compute unknown values of a univariate function.
+.. autosummary::
+   :toctree: generated/
 
-  BarycentricInterpolator
-               Compute with a numerically-stable version
-               of the Lagrange interpolating polynomial.
+   interp1d
+   BarycentricInterpolator
+   barycentric_interpolate
+   KroghInterpolator
+   krogh_interpolate
+   PiecewisePolynomial
+   piecewise_polynomial_interpolate
+   ppform
 
-  barycentric_interpolate
-               procedural interface to the above
+Interpolation Classes (multivariate)
+------------------------------------
 
-  KroghInterpolator
-               Compute with the Hermite interpolating polynomial
-               (allows the specification of derivatives at some points).
+.. autosummary::
+   :toctree: generated/
 
-  krogh_interpolate
-               procedural interface to the above
+   interp2d
+   Rbf
 
-  PiecewisePolynomial
-               Spline that is specified by giving positions and
-               derivatives at every knot; allows high orders and
-               efficient appending.
+Additional tools
+----------------
 
-  piecewise_polynomial_interpolate
-               procedural interface to the above
+.. autosummary::
+   :toctree: generated/
 
-  ppform
-               Class to create a piecewise polynomial representation of
-               a spline from the coefficients of the polynomial in each
-               section and the break-points
+   lagrange
+   approximate_taylor_polynomial
 
-Interpolation Classes (multivariate)
--------------------------------------
+Wrappers around FITPACK functions
+---------------------------------
 
-  interp2d
-               Create a class whose instances can interpolate
-               to compute unknown values of a bivariate function.
+.. autosummary::
+   :toctree: generated/
 
-  Rbf
-               Apply Radial Basis Functions to interpolate scattered N-D data.
+   fitpack.bisplev
+   fitpack.bisplrep
+   fitpack.insert
+   fitpack.spalde
+   fitpack.splev
+   fitpack.splint
+   fitpack.splprep
+   fitpack.splrep
+   fitpack.sproot
 
-Additional tools
------------------
+Wrappers around DFITPACK functions
+----------------------------------
 
-  lagrange
-               Compute the Lagrange interpolating polynomial.
+   `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`
 
-  approximate_taylor_polynomial
-               compute an approximate Taylor polynomial for
-               a function using polynomial interpolation
-
-
 See Also
-------------
+--------
 
-  ndimage
-     map_coordinates
-               N-d interpolation from evenly-spaced data using
-               fast B-splines. 
+.. autosummary::
+   :toctree: generated/
 
-     spline_filter
-               Method to pre-compute spline coefficients to make
-               map_coordinates efficient for muliple calls on the same
-               set of interpolated points.
+   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
 
-  signal
-     resample
-
-              Perform sinc-interpolation using a Fourier filter.
-              This function can decimate or interpolate to an evenly
-              sampled grid.
-          
-     bspline
-     gauss_spline
-     qspline1d
-     cspline1d
-     qspline1d_eval
-     cspline1d_eval
-     qspline2d
-     cspline2d
-               Low-level spline tools for regularly spaced data using
-               fast B-spline algorithms.
-
 """
 
 postpone_import = 1

Modified: trunk/scipy/io/__init__.py
===================================================================
--- trunk/scipy/io/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/io/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,47 +1,3 @@
-"""
-Data input and output
-
-SciPy has many modules, classes, and functions available to read data
-from and write data to a variety of file formats.
-
-Modules
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   arff - Read ARFF files, the standard data format for WEKA
-   byteordercodes - System byteorder utilities - NumPy byteorder encoding
-   data_store - Load or save values to a file
-   dumbdbm_patched - A dumb and slow but simple dbm clone
-   matlab - Utilities for dealing with MATLAB(R) files
-   mmio - Matrix Market I/O in Python
-   netcdf - NetCDF reader/writer module
-   wavfile - module to read / write wav files using numpy arrays
-
-Classes
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   netcdf_file - A file object for NetCDF data
-   netcdf_variable - A data object for the netcdf module
-
-Functions
----------
-
-.. autosummary::
-   :toctree: generated/
-
-   loadmat - Read a MATLAB style mat file (version 4 through 7.1)
-   savemat - Write a MATLAB style mat file (version 4 through 7.1)
-   mminfo - Query matrix info from Matrix Market formatted file
-   mmread - Read matrix from Matrix Market formatted file
-   mmwrite - Write matrix to Matrix Market formatted file
-   save_as_module - Data saved as module, accessed on load as attirbutes
-
-"""
 #
 # io - Data input and output
 #

Modified: trunk/scipy/io/info.py
===================================================================
--- trunk/scipy/io/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/io/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -2,19 +2,45 @@
 Data input and output
 =====================
 
-  Functions
+SciPy has many modules, classes, and functions available to read data
+from and write data to a variety of file formats.
 
-     loadmat -- read a MATLAB style mat file (version 4 through 7.1)
-     savemat -- write a MATLAB (version through 7.1) style mat file
-     netcdf_file -- read NetCDF files (version of ``pupynere`` package)
-     save_as_module -- simple storing of Python dictionary into module
-               that can then be imported and the data accessed as
-               attributes of the module.
-     mminfo   -- query matrix info from Matrix Market formatted file
-     mmread   -- read matrix from Matrix Market formatted file
-     mmwrite  -- write matrix to Matrix Market formatted file
-     wavfile  -- module to read / write wav files using numpy arrays
-     arrf     -- read files in Arff format
+Modules
+-------
 
+.. autosummary::
+   :toctree: generated/
+
+   arff - Read ARFF files, the standard data format for WEKA
+   byteordercodes - System byteorder utilities - NumPy byteorder encoding
+   data_store - Load or save values to a file
+   dumbdbm_patched - A dumb and slow but simple dbm clone
+   matlab - Utilities for dealing with MATLAB(R) files
+   mmio - Matrix Market I/O in Python
+   netcdf - NetCDF reader/writer module
+   wavfile - module to read / write wav files using numpy arrays
+
+Classes
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   netcdf_file - A file object for NetCDF data
+   netcdf_variable - A data object for the netcdf module
+
+Functions
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   loadmat - Read a MATLAB style mat file (version 4 through 7.1)
+   savemat - Write a MATLAB style mat file (version 4 through 7.1)
+   mminfo - Query matrix info from Matrix Market formatted file
+   mmread - Read matrix from Matrix Market formatted file
+   mmwrite - Write matrix to Matrix Market formatted file
+   save_as_module - Data saved as module, accessed on load as attirbutes
+
 """
 postpone_import = 1

Modified: trunk/scipy/linalg/__init__.py
===================================================================
--- trunk/scipy/linalg/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/linalg/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,98 +1,3 @@
-"""
-Linear Algebra
-==============
-
-Basics
-------
-
-.. autosummary::
-   :toctree: generated/
-
-   inv - Find the inverse of a square matrix
-   solve - Solve a linear system of equations
-   solve_banded - Solve a banded linear system
-   solveh_banded - Solve a Hermitian or symmetric banded system
-   det - Find the determinant of a square matrix
-   norm - Matrix and vector norm
-   lstsq - Solve a linear least-squares problem
-   pinv - Pseudo-inverse (Moore-Penrose) using lstsq
-   pinv2 - Pseudo-inverse using svd
-
-Eigenvalue Problems
--------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   eig - Find the eigenvalues and eigenvectors of a square matrix
-   eigvals - Find just the eigenvalues of a square matrix
-   eigh - Find the e-vals and e-vectors of a Hermitian or symmetric matrix
-   eigvalsh - Find just the eigenvalues of a Hermitian or symmetric matrix
-   eig_banded - Find the eigenvalues and eigenvectors of a banded matrix
-   eigvals_banded - Find just the eigenvalues of a banded matrix
-
-Decompositions
---------------
-
-.. autosummary::
-   :toctree: generated/
-
-   lu - LU decomposition of a matrix
-   lu_factor - LU decomposition returning unordered matrix and pivots
-   lu_solve - Solve Ax=b using back substitution with output of lu_factor
-   svd - Singular value decomposition of a matrix
-   svdvals - Singular values of a matrix
-   diagsvd - Construct matrix of singular values from output of svd
-   orth - Construct orthonormal basis for the range of A using svd
-   cholesky - Cholesky decomposition of a matrix
-   cholesky_banded - Cholesky decomp. of a sym. or Hermitian banded matrix
-   cho_factor - Cholesky decomposition for use in solving a linear system
-   cho_solve - Solve previously factored linear system
-   cho_solve_banded - Solve previously factored banded linear system
-   qr - QR decomposition of a matrix
-   schur - Schur decomposition of a matrix
-   rsf2csf - Real to complex Schur form
-   hessenberg - Hessenberg form of a matrix
-
-Matrix Functions
-----------------
-
-.. autosummary::
-   :toctree: generated/
-
-   expm - Matrix exponential using Pade approximation
-   expm2 - Matrix exponential using eigenvalue decomposition
-   expm3 - Matrix exponential using Taylor-series expansion
-   logm - Matrix logarithm
-   cosm - Matrix cosine
-   sinm - Matrix sine
-   tanm - Matrix tangent
-   coshm - Matrix hyperbolic cosine
-   sinhm - Matrix hyperbolic sine
-   tanhm - Matrix hyperbolic tangent
-   signm - Matrix sign
-   sqrtm - Matrix square root
-   funm - Evaluating an arbitrary matrix function
-
-Special Matrices
-----------------
-
-.. autosummary::
-   :toctree: generated/
-
-   block_diag - Construct a block diagonal matrix from submatrices
-   circulant - Circulant matrix
-   companion - Companion matrix
-   hadamard - Hadamard matrix of order 2**n
-   hankel - Hankel matrix
-   kron - Kronecker product of two arrays
-   leslie - Leslie matrix
-   toeplitz - Toeplitz matrix
-   tri - Construct a matrix filled with ones at and below a given diagonal
-   tril - Construct a lower-triangular matrix from a given matrix
-   triu - Construct an upper-triangular matrix from a given matrix
-
-"""
 #
 # linalg - Dense Linear Algebra routines
 #

Modified: trunk/scipy/linalg/info.py
===================================================================
--- trunk/scipy/linalg/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/linalg/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -2,133 +2,96 @@
 Linear Algebra
 ==============
 
-Linear Algebra Basics:
+Basics
+------
 
-    inv:
-        Find the inverse of a square matrix
-    solve:
-        Solve a linear system of equations
-    solve_banded:
-        Solve a linear system of equations with a banded matrix
-    solveh_banded:
-        Solve a linear system of equations with a Hermitian or symmetric
-        banded matrix
-    det:
-        Find the determinant of a square matrix
-    norm:
-        matrix and vector norm
-    lstsq:
-        Solve linear least-squares problem
-    pinv:
-        Pseudo-inverse (Moore-Penrose) using lstsq
-    pinv2:
-        Pseudo-inverse using svd
+.. autosummary::
+   :toctree: generated/
 
-Eigenvalue Problem:
+   inv - Find the inverse of a square matrix
+   solve - Solve a linear system of equations
+   solve_banded - Solve a banded linear system
+   solveh_banded - Solve a Hermitian or symmetric banded system
+   det - Find the determinant of a square matrix
+   norm - Matrix and vector norm
+   lstsq - Solve a linear least-squares problem
+   pinv - Pseudo-inverse (Moore-Penrose) using lstsq
+   pinv2 - Pseudo-inverse using svd
 
-    eig:
-        Find the eigenvalues and vectors of a square matrix
-    eigvals:
-        Find the eigenvalues of a square matrix
-    eigh:
-        Find the eigenvalues and eigenvectors of a complex Hermitian or
-        real symmetric matrix.
-    eigvalsh:
-        Find the eigenvalues of a complex Hermitian or real symmetric
-        matrix.
-    eig_banded:
-        Find the eigenvalues and vectors of a band matrix
-    eigvals_banded:
-        Find the eigenvalues of a band matrix
+Eigenvalue Problems
+-------------------
 
-Decompositions:
+.. autosummary::
+   :toctree: generated/
 
-    lu:
-        LU decomposition of a matrix
-    lu_factor:
-        LU decomposition returning unordered matrix and pivots
-    lu_solve:
-        solve Ax=b using back substitution with output of lu_factor
-    svd:
-        Singular value decomposition of a matrix
-    svdvals:
-        Singular values of a matrix
-    diagsvd:
-        construct matrix of singular values from output of svd
-    orth:
-        construct orthonormal basis for range of A using svd
-    cholesky:
-        Cholesky decomposition of a matrix
-    cholesky_banded:
-        Cholesky decomposition of a banded symmetric or Hermitian matrix
-    cho_factor:
-        Cholesky decomposition for use in solving linear system
-    cho_solve:
-        Solve previously factored linear system
-    cho_solve_banded:
-        Solve previously factored banded linear system.
-    qr:
-        QR decomposition of a matrix
-    schur:
-        Schur decomposition of a matrix
-    rsf2csf:
-        Real to complex schur form
-    hessenberg:
-        Hessenberg form of a matrix
+   eig - Find the eigenvalues and eigenvectors of a square matrix
+   eigvals - Find just the eigenvalues of a square matrix
+   eigh - Find the e-vals and e-vectors of a Hermitian or symmetric matrix
+   eigvalsh - Find just the eigenvalues of a Hermitian or symmetric matrix
+   eig_banded - Find the eigenvalues and eigenvectors of a banded matrix
+   eigvals_banded - Find just the eigenvalues of a banded matrix
 
-Matrix Functions:
+Decompositions
+--------------
 
-    expm:
-        matrix exponential using Pade approx.
-    expm2:
-        matrix exponential using Eigenvalue decomp.
-    expm3:
-        matrix exponential using Taylor-series expansion
-    logm:
-        matrix logarithm
-    cosm:
-        matrix cosine
-    sinm:
-        matrix sine
-    tanm:
-        matrix tangent
-    coshm:
-        matrix hyperbolic cosine
-    sinhm:
-        matrix hyperbolic sine
-    tanhm:
-        matrix hyperbolic tangent
-    signm:
-        matrix sign
-    sqrtm:
-        matrix square root
-    funm:
-        Evaluating an arbitrary matrix function.
+.. autosummary::
+   :toctree: generated/
 
-Special Matrices:
+   lu - LU decomposition of a matrix
+   lu_factor - LU decomposition returning unordered matrix and pivots
+   lu_solve - Solve Ax=b using back substitution with output of lu_factor
+   svd - Singular value decomposition of a matrix
+   svdvals - Singular values of a matrix
+   diagsvd - Construct matrix of singular values from output of svd
+   orth - Construct orthonormal basis for the range of A using svd
+   cholesky - Cholesky decomposition of a matrix
+   cholesky_banded - Cholesky decomp. of a sym. or Hermitian banded matrix
+   cho_factor - Cholesky decomposition for use in solving a linear system
+   cho_solve - Solve previously factored linear system
+   cho_solve_banded - Solve previously factored banded linear system
+   qr - QR decomposition of a matrix
+   schur - Schur decomposition of a matrix
+   rsf2csf - Real to complex Schur form
+   hessenberg - Hessenberg form of a matrix
 
-    block_diag:
-        Construct a block diagonal matrix from submatrices.
-    circulant:
-        Circulant matrix
-    companion:
-        Companion matrix
-    hadamard:
-        Hadamard matrix of order 2^n
-    hankel:
-        Hankel matrix
-    kron:
-        Kronecker product of two arrays.
-    leslie:
-        Leslie matrix
-    toeplitz:
-        Toeplitz matrix
-    tri:
-        Construct a matrix filled with ones at and below a given diagonal. 
-    tril:
-        Construct a lower-triangular matrix from a given matrix.
-    triu:
-        Construct an upper-triangular matrix from a given matrix.
+Matrix Functions
+----------------
+
+.. autosummary::
+   :toctree: generated/
+
+   expm - Matrix exponential using Pade approximation
+   expm2 - Matrix exponential using eigenvalue decomposition
+   expm3 - Matrix exponential using Taylor-series expansion
+   logm - Matrix logarithm
+   cosm - Matrix cosine
+   sinm - Matrix sine
+   tanm - Matrix tangent
+   coshm - Matrix hyperbolic cosine
+   sinhm - Matrix hyperbolic sine
+   tanhm - Matrix hyperbolic tangent
+   signm - Matrix sign
+   sqrtm - Matrix square root
+   funm - Evaluating an arbitrary matrix function
+
+Special Matrices
+----------------
+
+.. autosummary::
+   :toctree: generated/
+
+   block_diag - Construct a block diagonal matrix from submatrices
+   circulant - Circulant matrix
+   companion - Companion matrix
+   hadamard - Hadamard matrix of order 2**n
+   hankel - Hankel matrix
+   kron - Kronecker product of two arrays
+   leslie - Leslie matrix
+   toeplitz - Toeplitz matrix
+   tri - Construct a matrix filled with ones at and below a given diagonal
+   tril - Construct a lower-triangular matrix from a given matrix
+   triu - Construct an upper-triangular matrix from a given matrix
+
 """
 
 postpone_import = 1

Modified: trunk/scipy/maxentropy/__init__.py
===================================================================
--- trunk/scipy/maxentropy/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/maxentropy/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,91 +1,3 @@
-"""
-Routines for fitting maximum entropy models
-===========================================
-
-Contains two classes for fitting maximum entropy models (also known
-as "exponential family" models) subject to linear constraints on the
-expectations of arbitrary feature statistics.  One class, "model", is
-for small discrete sample spaces, using explicit summation. The other,
-"bigmodel", is for sample spaces that are either continuous (and
-perhaps high-dimensional) or discrete but too large to sum over, and
-uses importance sampling.  conditional Monte Carlo methods.
-
-The maximum entropy model has exponential form
-
-..
-   p(x) = exp(theta^T f(x)) / Z(theta)
-
-.. math::
-   p\\left(x\\right)=\\exp\\left(\\frac{\\theta^{T}f\\left(x\\right)}
-                                  {Z\\left(\\theta\\right)}\\right)
-
-with a real parameter vector theta of the same length as the feature
-statistic f(x), For more background, see, for example, Cover and
-Thomas (1991), *Elements of Information Theory*.
-
-See the file bergerexample.py for a walk-through of how to use these
-routines when the sample space is small enough to be enumerated.
-
-See bergerexamplesimulated.py for a a similar walk-through using
-simulation.
-
-Copyright: Ed Schofield, 2003-2006
-License: BSD-style (see LICENSE.txt in main source directory)
-
-Modules
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   maxentropy -
-   maxentutils -
-
-Classes
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   DivergenceError -
-   basemodel -
-   bigmodel -
-   conditionalmodel -
-   model -
-
-Functions
----------
-
-.. autosummary::
-   :toctree: generated/
-
-   arrayexp -
-   arrayexpcomplex -
-   columnmeans -
-   columnvariances -
-   densefeaturematrix -
-   densefeatures -
-   dotprod -
-   flatten -
-   innerprod -
-   innerprodtranspose -
-   logsumexp -
-   logsumexp_naive -
-   robustlog -
-   rowmeans -
-   sample_wr -
-   sparsefeaturematrix -
-   sparsefeatures -
-
-Objects
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   division -
-
-"""
 from info import __doc__
 from maxentropy import *
 

Modified: trunk/scipy/maxentropy/info.py
===================================================================
--- trunk/scipy/maxentropy/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/maxentropy/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -2,13 +2,13 @@
 Routines for fitting maximum entropy models
 ===========================================
 
-Contains two classes for fitting maximum entropy models (also known as
-"exponential family" models) subject to linear constraints on the expectations
-of arbitrary feature statistics.  One class, "model", is for small discrete sample
-spaces, using explicit summation. The other, "bigmodel", is for sample spaces
-that are either continuous (and perhaps high-dimensional) or discrete but too
-large to sum over, and uses importance sampling.  conditional Monte Carlo
-methods.
+Contains two classes for fitting maximum entropy models (also known
+as "exponential family" models) subject to linear constraints on the
+expectations of arbitrary feature statistics.  One class, "model", is
+for small discrete sample spaces, using explicit summation. The other,
+"bigmodel", is for sample spaces that are either continuous (and
+perhaps high-dimensional) or discrete but too large to sum over, and
+uses importance sampling.  conditional Monte Carlo methods.
 
 The maximum entropy model has exponential form
 
@@ -16,9 +16,8 @@
    p(x) = exp(theta^T f(x)) / Z(theta)
 
 .. math::
-          \\renewcommand{\\v}[1]{\\mathbf{#1}}
-          p( \\v{x} ) = \\exp \\left( {\\v{\\theta}^\\mathsf{T} \\vec{f}( \\v{x} )
-                                                \\over  Z(\\v{\\theta})    }  \\right)
+   p\\left(x\\right)=\\exp\\left(\\frac{\\theta^{T}f\\left(x\\right)}
+                                  {Z\\left(\\theta\\right)}\\right)
 
 with a real parameter vector theta of the same length as the feature
 statistic f(x), For more background, see, for example, Cover and
@@ -33,6 +32,59 @@
 Copyright: Ed Schofield, 2003-2006
 License: BSD-style (see LICENSE.txt in main source directory)
 
+Modules
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   maxentropy -
+   maxentutils -
+
+Classes
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   DivergenceError -
+   basemodel -
+   bigmodel -
+   conditionalmodel -
+   model -
+
+Functions
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   arrayexp -
+   arrayexpcomplex -
+   columnmeans -
+   columnvariances -
+   densefeaturematrix -
+   densefeatures -
+   dotprod -
+   flatten -
+   innerprod -
+   innerprodtranspose -
+   logsumexp -
+   logsumexp_naive -
+   robustlog -
+   rowmeans -
+   sample_wr -
+   sparsefeaturematrix -
+   sparsefeatures -
+
+Objects
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   division -
+
 """
 
 postpone_import = 1

Modified: trunk/scipy/misc/__init__.py
===================================================================
--- trunk/scipy/misc/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/misc/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,51 +1,3 @@
-"""
-Various utilities that don't have another home.
-
-Note that the Python Imaging Library (PIL) is not a dependency
-of SciPy and therefore the `pilutil` module is not available on
-systems that don't have PIL installed.
-
-Modules
--------
-.. autosummary::
-   :toctree: generated/
-
-   common     - Common functions requiring SciPy Base and Level 1 SciPy
-   doccer     - Docstring fragment insertion utilities
-   helpmod    -
-   pexec      -
-   pilutil    - Image utilities using the Python Imaging Library (PIL)
-   ppimport   - Postpone module import to future
-   setup      -
-   setupscons -
-
-Functions
----------
-.. autosummary::
-   :toctree: generated/
-
-   bytescale - Byte scales an array (image)
-   central_diff_weights - Weights for an n-point central m-th derivative
-   comb - Combinations of N things taken k at a time, "N choose k"
-   derivative -\tFind the n-th derivative of a function at a point
-   factorial  - The factorial function, n! = special.gamma(n+1)
-   factorial2 - Double factorial, (n!)!
-   factorialk - (...((n!)!)!...)! where there are k '!'
-   fromimage - Return a copy of a PIL image as a numpy array
-   imfilter - Simple filtering of an image
-   imread - Read an image file from a filename
-   imresize - Resize an image
-   imrotate - Rotate an image counter-clockwise
-   imsave - Save an array to an image file
-   imshow - Simple showing of an image through an external viewer
-   info - Get help information for a function, class, or module
-   lena - Get classic image processing example image Lena
-   pade - Pade approximation to function as the ratio of two polynomials
-   radon -
-   toimage - Takes a numpy array and returns a PIL image
-
-"""
-
 from info import __doc__
 
 __all__ = ['who', 'source', 'info']

Modified: trunk/scipy/misc/info.py
===================================================================
--- trunk/scipy/misc/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/misc/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,6 +1,49 @@
-
 """
 Various utilities that don't have another home.
+
+Note that the Python Imaging Library (PIL) is not a dependency
+of SciPy and therefore the `pilutil` module is not available on
+systems that don't have PIL installed.
+
+Modules
+-------
+.. autosummary::
+   :toctree: generated/
+
+   common     - Common functions requiring SciPy Base and Level 1 SciPy
+   doccer     - Docstring fragment insertion utilities
+   helpmod    -
+   pexec      -
+   pilutil    - Image utilities using the Python Imaging Library (PIL)
+   ppimport   - Postpone module import to future
+   setup      -
+   setupscons -
+
+Functions
+---------
+.. autosummary::
+   :toctree: generated/
+
+   bytescale - Byte scales an array (image)
+   central_diff_weights - Weights for an n-point central m-th derivative
+   comb - Combinations of N things taken k at a time, "N choose k"
+   derivative -\tFind the n-th derivative of a function at a point
+   factorial  - The factorial function, n! = special.gamma(n+1)
+   factorial2 - Double factorial, (n!)!
+   factorialk - (...((n!)!)!...)! where there are k '!'
+   fromimage - Return a copy of a PIL image as a numpy array
+   imfilter - Simple filtering of an image
+   imread - Read an image file from a filename
+   imresize - Resize an image
+   imrotate - Rotate an image counter-clockwise
+   imsave - Save an array to an image file
+   imshow - Simple showing of an image through an external viewer
+   info - Get help information for a function, class, or module
+   lena - Get classic image processing example image Lena
+   pade - Pade approximation to function as the ratio of two polynomials
+   radon -
+   toimage - Takes a numpy array and returns a PIL image
+
 """
 
 global_symbols = ['info','factorial','factorial2','factorialk','comb','who',

Modified: trunk/scipy/ndimage/__init__.py
===================================================================
--- trunk/scipy/ndimage/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/ndimage/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,70 +1,3 @@
-"""
-N-dimensional image package
-===========================
-
-This package contains various functions for multi-dimensional image
-processing.
-
-Modules
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   filters -
-   fourier -
-   interpolation -
-   io -
-   measurements -
-   morphology -
-
-Functions (partial list)
-------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   affine_transform - Apply an affine transformation
-   center_of_mass - The center of mass of the values of an array at labels
-   convolve - Multi-dimensional convolution
-   convolve1d - 1-D convolution along the given axis
-   correlate - Multi-dimensional correlation
-   correlate1d - 1-D correlation along the given axis
-   extrema - Min's and max's of an array at labels, with their positions
-   find_objects - Find objects in a labeled array
-   generic_filter - Multi-dimensional filter using a given function
-   generic_filter1d - 1-D generic filter along the given axis
-   geometric_transform - Apply an arbritrary geometric transform
-   histogram - Histogram of the values of an array, optionally at labels
-   imread - Load an image from a file
-   label - Label features in an array
-   laplace - n-D Laplace filter based on approximate second derivatives
-   map_coordinates - Map input array to new coordinates by interpolation
-   mean - Mean of the values of an array at labels
-   median_filter - Calculates a multi-dimensional median filter
-   percentile_filter - Calculates a multi-dimensional percentile filter
-   rank_filter - Calculates a multi-dimensional rank filter
-   rotate - Rotate an array
-   shift - Shift an array
-   standard_deviation - Standard deviation of an n-D image array
-   sum - Sum of the values of the array
-   uniform_filter - Multi-dimensional uniform filter
-   uniform_filter1d - 1-D uniform filter along the given axis
-   variance - Variance of the values of an n-D image array
-   zoom - Zoom an array
-
-Note: the above is only roughly half the functions available in this
-package
-
-Objects
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   docdict -
-
-"""
 # Copyright (C) 2003-2005 Peter J. Verveer
 #
 # Redistribution and use in source and binary forms, with or without

Modified: trunk/scipy/ndimage/info.py
===================================================================
--- trunk/scipy/ndimage/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/ndimage/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,8 +1,69 @@
-__doc__ = """
-n-dimensional image package
+"""
+N-dimensional image package
 ===========================
 
-This package contains various functions for multi-dimensional image processing.
+This package contains various functions for multi-dimensional image
+processing.
+
+Modules
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   filters -
+   fourier -
+   interpolation -
+   io -
+   measurements -
+   morphology -
+
+Functions (partial list)
+------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   affine_transform - Apply an affine transformation
+   center_of_mass - The center of mass of the values of an array at labels
+   convolve - Multi-dimensional convolution
+   convolve1d - 1-D convolution along the given axis
+   correlate - Multi-dimensional correlation
+   correlate1d - 1-D correlation along the given axis
+   extrema - Min's and max's of an array at labels, with their positions
+   find_objects - Find objects in a labeled array
+   generic_filter - Multi-dimensional filter using a given function
+   generic_filter1d - 1-D generic filter along the given axis
+   geometric_transform - Apply an arbritrary geometric transform
+   histogram - Histogram of the values of an array, optionally at labels
+   imread - Load an image from a file
+   label - Label features in an array
+   laplace - n-D Laplace filter based on approximate second derivatives
+   map_coordinates - Map input array to new coordinates by interpolation
+   mean - Mean of the values of an array at labels
+   median_filter - Calculates a multi-dimensional median filter
+   percentile_filter - Calculates a multi-dimensional percentile filter
+   rank_filter - Calculates a multi-dimensional rank filter
+   rotate - Rotate an array
+   shift - Shift an array
+   standard_deviation - Standard deviation of an n-D image array
+   sum - Sum of the values of the array
+   uniform_filter - Multi-dimensional uniform filter
+   uniform_filter1d - 1-D uniform filter along the given axis
+   variance - Variance of the values of an n-D image array
+   zoom - Zoom an array
+
+Note: the above is only roughly half the functions available in this
+package
+
+Objects
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   docdict -
+
 """
 
 postpone_import = 1

Modified: trunk/scipy/odr/__init__.py
===================================================================
--- trunk/scipy/odr/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/odr/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,90 +1,3 @@
-"""
-Orthogonal Distance Regression (:mod:`scipy.odr`)
-=================================================
-
-Introduction
-------------
-
-Why Orthogonal Distance Regression (ODR)?  Sometimes one has
-measurement errors in the explanatory (a.k.a., "independent")
-variable(s), not just the response (a.k.a., "dependent") variable(s).
-Ordinary Least Squares (OLS) fitting procedures treat the data for
-explanatory variables as fixed, i.e., not subject to error of any kind.
-Furthermore, OLS procedures require that the response variables be an
-explicit function of the explanatory variables; sometimes making the
-equation explicit is impractical and/or introduces errors.  ODR can
-handle both of these cases with ease, and can even reduce to the OLS
-case if that is sufficient for the problem.
-
-ODRPACK is a FORTRAN-77 library for performing ODR with possibly
-non-linear fitting functions.  It uses a modified trust-region
-Levenberg-Marquardt-type algorithm [1]_ to estimate the function
-parameters.  The fitting functions are provided by Python functions
-operating on NumPy arrays.  The required derivatives may be provided
-by Python functions as well, or may be estimated numerically.  ODRPACK
-can do explicit or implicit ODR fits, or it can do OLS.  Input and
-output variables may be multi-dimensional.  Weights can be provided to
-account for different variances of the observations, and even
-covariances between dimensions of the variables.
-
-odr provides two interfaces: a single function, and a set of
-high-level classes that wrap that function; please refer to their
-docstrings for more information.  While the docstring of the function
-odr does not have a full explanation of its arguments, the classes do,
-and arguments of the same name usually have the same requirements.
-Furthermore, the user is urged to at least skim the `ODRPACK User's
-Guide <http://docs.scipy.org/doc/external/odrpack_guide.pdf>`_ -
-"Know Thy Algorithm."
-
-Use
----
-
-See the docstrings of `odr.odrpack` and the functions and classes for
-usage instructions.  The ODRPACK User's Guide (linked above) is also
-quite helpful.
-
-References
-----------
-.. [1] P. T. Boggs and J. E. Rogers, "Orthogonal Distance Regression,"
-   in "Statistical analysis of measurement error models and
-   applications: proceedings of the AMS-IMS-SIAM joint summer research
-   conference held June 10-16, 1989," Contemporary Mathematics,
-   vol. 112, pg. 186, 1990.
-
-.. currentmodule:: scipy.odr
-
-Modules
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   odrpack       Python wrappers for FORTRAN77 ODRPACK.
-   models        Model instances for use with odrpack.
-
-Classes
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   ODR           Gathers all info & manages the main fitting routine.
-   Data          Stores the data to fit.
-   Model         Stores information about the function to be fit.
-   Output
-   RealData      Weights as actual std. dev.s and/or covariances.
-   odr_error
-   odr_stop
-
-Functions
----------
-
-.. autosummary::
-   :toctree: generated/
-
-   odr
-
-"""
 #
 # odr - Orthogonal Distance Regression
 #

Modified: trunk/scipy/odr/info.py
===================================================================
--- trunk/scipy/odr/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/odr/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,44 +1,88 @@
-"""Orthogonal Distance Regression
+"""
+Orthogonal Distance Regression (:mod:`scipy.odr`)
+=================================================
 
 Introduction
-============
+------------
 
-Why Orthogonal Distance Regression (ODR)? Sometimes one has measurement errors
-in the explanatory variable, not just the response variable. Ordinary Least
-Squares (OLS) fitting procedures treat the data for explanatory variables as
-fixed. Furthermore, OLS procedures require that the response variable be an
-explicit function of the explanatory variables; sometimes making the equation
-explicit is unwieldy and introduces errors. ODR can handle both of these cases
-with ease and can even reduce to the OLS case if necessary.
+Why Orthogonal Distance Regression (ODR)?  Sometimes one has
+measurement errors in the explanatory (a.k.a., "independent")
+variable(s), not just the response (a.k.a., "dependent") variable(s).
+Ordinary Least Squares (OLS) fitting procedures treat the data for
+explanatory variables as fixed, i.e., not subject to error of any kind.
+Furthermore, OLS procedures require that the response variables be an
+explicit function of the explanatory variables; sometimes making the
+equation explicit is impractical and/or introduces errors.  ODR can
+handle both of these cases with ease, and can even reduce to the OLS
+case if that is sufficient for the problem.
 
-ODRPACK is a FORTRAN-77 library for performing ODR with possibly non-linear
-fitting functions. It uses a modified trust-region Levenberg-Marquardt-type
-algorithm to estimate the function parameters. The fitting functions are
-provided by Python functions operating on NumPy arrays. The required derivatives
-may be provided by Python functions as well or may be numerically estimated.
-ODRPACK can do explicit or implicit ODR fits or can do OLS. Input and output
-variables may be multi-dimensional. Weights can be provided to account for
-different variances of the observations (even covariances between dimensions of
-the variables).
+ODRPACK is a FORTRAN-77 library for performing ODR with possibly
+non-linear fitting functions.  It uses a modified trust-region
+Levenberg-Marquardt-type algorithm [1]_ to estimate the function
+parameters.  The fitting functions are provided by Python functions
+operating on NumPy arrays.  The required derivatives may be provided
+by Python functions as well, or may be estimated numerically.  ODRPACK
+can do explicit or implicit ODR fits, or it can do OLS.  Input and
+output variables may be multi-dimensional.  Weights can be provided to
+account for different variances of the observations, and even
+covariances between dimensions of the variables.
 
-odr provides two interfaces: a single function and a set of high-level classes
-that wrap that function. Please refer to their docstrings for more information.
-While the docstring of the function, odr, does not have a full explanation of
-its arguments, the classes do, and the arguments with the same name usually have
-the same requirements. Furthermore, it is highly suggested that one at least
-skim the ODRPACK User's Guide.  Know Thy Algorithm.
+odr provides two interfaces: a single function, and a set of
+high-level classes that wrap that function; please refer to their
+docstrings for more information.  While the docstring of the function
+odr does not have a full explanation of its arguments, the classes do,
+and arguments of the same name usually have the same requirements.
+Furthermore, the user is urged to at least skim the `ODRPACK User's
+Guide <http://docs.scipy.org/doc/external/odrpack_guide.pdf>`_ -
+"Know Thy Algorithm."
 
-
 Use
-===
+---
 
-See the docstrings of odr.odrpack and the functions and classes for
-usage instructions. The ODRPACK User's Guide is also quite helpful. It can be
-found on one of the ODRPACK's original author's website:
+See the docstrings of `odr.odrpack` and the functions and classes for
+usage instructions.  The ODRPACK User's Guide (linked above) is also
+quite helpful.
 
-    http://www.boulder.nist.gov/mcsd/Staff/JRogers/odrpack.html
+References
+----------
+.. [1] P. T. Boggs and J. E. Rogers, "Orthogonal Distance Regression,"
+   in "Statistical analysis of measurement error models and
+   applications: proceedings of the AMS-IMS-SIAM joint summer research
+   conference held June 10-16, 1989," Contemporary Mathematics,
+   vol. 112, pg. 186, 1990.
 
-Robert Kern
-robert.kern@gmail.com
+.. currentmodule:: scipy.odr
+
+Modules
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   odrpack       Python wrappers for FORTRAN77 ODRPACK.
+   models        Model instances for use with odrpack.
+
+Classes
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   ODR           Gathers all info & manages the main fitting routine.
+   Data          Stores the data to fit.
+   Model         Stores information about the function to be fit.
+   Output
+   RealData      Weights as actual std. dev.s and/or covariances.
+   odr_error
+   odr_stop
+
+Functions
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   odr
+
 """
 postpone_import = 1

Modified: trunk/scipy/optimize/__init__.py
===================================================================
--- trunk/scipy/optimize/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/optimize/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,137 +1,3 @@
-"""
-Optimization Tools
-==================
-
-General-purpose Optimization Routines
--------------------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   fmin - Nelder-Mead Simplex algorithm #
-   fmin_powell - Powell's (modified) level set method #
-   fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm ##
-   fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno) ##
-   fmin_ncg - Line-search Newton Conjugate Gradient ###
-   leastsq - Minimize the sum of squares of M equations in N unknowns
-
-Constrained Optimizers (Multivariate)
--------------------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer %
-   fmin_tnc - Truncated Newton code %%
-   fmin_cobyla - Constrained optimization by linear approximation
-   fmin_slsqp - Minimization using sequential least-squares programming
-   nnls - Linear least-squares problem with non-negativity constraint
-
-Global Optimizers
------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   anneal - Simulated annealing
-   brute - Brute force searching optimizer
-
-Scalar Function Minimizers
---------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   fminbound - Bounded minimization of a scalar function
-   brent - 1-D function minimization using Brent method
-   golden - 1-D function minimization using Golden Section method
-   bracket - Bracket a minimum, given two starting points
-
-General-purpose Root-finding Routines
--------------------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   fsolve - Non-linear multi-variable equation solver
-
-Scalar Function Solvers
------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   brentq - quadratic interpolation Brent method
-   brenth - Brent method, modified by Harris with hyperbolic extrapolation
-   ridder - Ridder's method
-   bisect - Bisection method
-   newton - Secant method or Newton's method
-   fixed_point - Single-variable fixed-point solver
-
-General-purpose Non-linear Multidimensional Solvers
----------------------------------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   broyden1 - Broyden's first method $
-   broyden2 - Broyden's second method $$
-   broyden3 - Broyden's third method $$$
-   broyden_generalized - Generalized Broyden's method &
-   anderson - Extended Anderson method &&
-   anderson2 - The Anderson method &&&
-
-Utility Functions
------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   line_search - Return a step that satisfies the strong Wolfe conditions
-   check_grad - Check the supplied derivative using finite differences
-
-Related Software
-----------------
-
-OpenOpt - A BSD-licensed optimization framework (see `<http://openopt.org>`_) that includes: a number of constrained and
-unconstrained solvers from and beyond the scipy.optimize module; unified
-text and graphical output of convergence information; and automatic
-differentiation.
-
-Notes
------
-# Uses only function calls
-
-## Can use function and gradient
-
-### Can use function, gradient, and Hessian
-
-% If you use fmin_l_bfgs_b, please cite Zhu, Byrd, and Nocedal's papers; see the function's docstring for references.
-
-%% Originally written by Stephen Nash, adapted to C by Jean-Sebastien Roy.
-
-$ broyden1 is a quasi-Newton-Raphson method for updating an approximate
-Jacobian and then inverting it.
-
-$$ broyden2 is the same as broyden1, but updates the inverse Jacobian
-directly.
-
-$$$ broyden3 is the same as broyden2, but instead of directly computing
-the inverse Jacobian, it remembers how to construct it using vectors, and
-when computing inv(J)*F, it uses those vectors to compute this product,
-thus avoding the expensive NxN matrix multiplication.
-
-& broyden_generalized is the same as broyden2, but instead of
-approximating the full NxN Jacobian, it constructs it at every iteration
-in a way that avoids the NxN matrix multiplication.  This is not as
-precise as broyden3.
-
-&& anderson is the same as broyden_generalized, but (w_0**2)*I is added
-before taking inversion to improve the stability.
-
-&&& anderson2 is the same as anderson, but formulated differently.
-
-"""
 #
 # optimize - Optimization Tools
 #

Modified: trunk/scipy/optimize/info.py
===================================================================
--- trunk/scipy/optimize/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/optimize/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -2,101 +2,120 @@
 Optimization Tools
 ==================
 
-A collection of general-purpose optimization routines.::
+General-purpose Optimization Routines
+-------------------------------------
 
-   fmin        --  Nelder-Mead Simplex algorithm
-                     (uses only function calls)
-   fmin_powell --  Powell's (modified) level set method (uses only
-                     function calls)
-   fmin_cg     --  Non-linear (Polak-Ribiere) conjugate gradient algorithm
-                     (can use function and gradient).
-   fmin_bfgs   --  Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno);
-                     (can use function and gradient)
-   fmin_ncg    --  Line-search Newton Conjugate Gradient (can use
-                     function, gradient and Hessian).
-   leastsq     --  Minimize the sum of squares of M equations in
-                     N unknowns given a starting estimate.
+.. autosummary::
+   :toctree: generated/
 
-Constrained Optimizers (multivariate)::
+   fmin - Nelder-Mead Simplex algorithm
+   fmin_powell - Powell's (modified) level set method
+   fmin_cg - Non-linear (Polak-Ribiere) conjugate gradient algorithm
+   fmin_bfgs - Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)
+   fmin_ncg - Line-search Newton Conjugate Gradient
+   leastsq - Minimize the sum of squares of M equations in N unknowns
 
-   fmin_l_bfgs_b -- Zhu, Byrd, and Nocedal's L-BFGS-B constrained optimizer
-                      (if you use this please quote their papers -- see help)
+Constrained Optimizers (Multivariate)
+-------------------------------------
 
-   fmin_tnc      -- Truncated Newton Code originally written by Stephen Nash and
-                      adapted to C by Jean-Sebastien Roy.
+.. autosummary::
+   :toctree: generated/
 
-   fmin_cobyla   -- Constrained Optimization BY Linear Approximation
+   fmin_l_bfgs_b - Zhu, Byrd, and Nocedal's constrained optimizer
+   fmin_tnc - Truncated Newton code
+   fmin_cobyla - Constrained optimization by linear approximation
+   fmin_slsqp - Minimization using sequential least-squares programming
+   nnls - Linear least-squares problem with non-negativity constraint
 
-   fmin_slsqp    -- Minimize a function using Sequential Least SQuares Programming
+Global Optimizers
+-----------------
 
-   nnls          -- Solve linear least squares problem with non-negativity
-                      constraint
+.. autosummary::
+   :toctree: generated/
 
-Global Optimizers::
+   anneal - Simulated annealing
+   brute - Brute force searching optimizer
 
-   anneal      --  Simulated Annealing
-   brute       --  Brute force searching optimizer
+Scalar Function Minimizers
+--------------------------
 
-Scalar function minimizers::
+.. autosummary::
+   :toctree: generated/
 
-   fminbound   --  Bounded minimization of a scalar function.
-   brent       --  1-D function minimization using Brent method.
-   golden      --  1-D function minimization using Golden Section method
-   bracket     --  Bracket a minimum (given two starting points)
+   fminbound - Bounded minimization of a scalar function
+   brent - 1-D function minimization using Brent method
+   golden - 1-D function minimization using Golden Section method
+   bracket - Bracket a minimum, given two starting points
 
-Also a collection of general-purpose root-finding routines::
+Fitting
+=======
 
-   fsolve      --  Non-linear multi-variable equation solver.
+.. autosummary::
+   :toctree: generated/
 
-Scalar function solvers::
+   curve_fit
 
-   brentq      --  quadratic interpolation Brent method
-   brenth      --  Brent method (modified by Harris with hyperbolic
-                     extrapolation)
-   ridder      --  Ridder's method
-   bisect      --  Bisection method
-   newton      --  Secant method or Newton's method
+Root finding
+============
 
-   fixed_point --  Single-variable fixed-point solver.
+Scalar functions
+----------------
 
-A collection of general-purpose nonlinear multidimensional solvers::
+.. autosummary::
+   :toctree: generated/
 
-   broyden1            --  Broyden's first method - is a quasi-Newton-Raphson
-                           method for updating an approximate Jacobian and then
-                           inverting it
-   broyden2            --  Broyden's second method - the same as broyden1, but
-                           updates the inverse Jacobian directly
-   broyden3            --  Broyden's second method - the same as broyden2, but
-                           instead of directly computing the inverse Jacobian,
-                           it remembers how to construct it using vectors, and
-                           when computing inv(J)*F, it uses those vectors to
-                           compute this product, thus avoding the expensive NxN
-                           matrix multiplication.
-   broyden_generalized --  Generalized Broyden's method, the same as broyden2,
-                           but instead of approximating the full NxN Jacobian,
-                           it construct it at every iteration in a way that
-                           avoids the NxN matrix multiplication.  This is not
-                           as precise as broyden3.
-   anderson            --  extended Anderson method, the same as the
-                           broyden_generalized, but added w_0^2*I to before
-                           taking inversion to improve the stability
-   anderson2           --  the Anderson method, the same as anderson, but
-                           formulated differently
+   brentq - quadratic interpolation Brent method
+   brenth - Brent method, modified by Harris with hyperbolic extrapolation
+   ridder - Ridder's method
+   bisect - Bisection method
+   newton - Secant method or Newton's method
 
-Utility Functions::
+Fixed point finding:
 
-   line_search --  Return a step that satisfies the strong Wolfe conditions.
-   check_grad  --  Check the supplied derivative using finite difference
-                     techniques.
+.. autosummary::
+   :toctree: generated/
 
-Related Software::
+   fixed_point - Single-variable fixed-point solver
 
-   OpenOpt -- A BSD-licensed optimisation framework (see http://openopt.org), 
-              which includes a number of constrained and unconstrained 
-              solvers from and beyond scipy.optimize module, 
-              unified text and graphical output of convergence
-              and automatic differentiation.
 
+Multidimensional
+----------------
+
+General nonlinear solvers:
+
+.. autosummary::
+   :toctree: generated/
+
+   fsolve - Non-linear multi-variable equation solver
+   broyden1 - Broyden's first method
+   broyden2 - Broyden's second method
+
+Large-scale nonlinear solvers:
+
+.. autosummary::
+   :toctree: generated/
+
+   newton_krylov
+   anderson
+
+Simple iterations:
+
+.. autosummary::
+   :toctree: generated/
+
+   excitingmixing
+   linearmixing
+   diagbroyden
+
+Utility Functions
+=================
+
+.. autosummary::
+   :toctree: generated/
+
+   line_search - Return a step that satisfies the strong Wolfe conditions
+   check_grad - Check the supplied derivative using finite differences
+
 """
 
 postpone_import = 1

Modified: trunk/scipy/sparse/__init__.py
===================================================================
--- trunk/scipy/sparse/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/sparse/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,174 +1,3 @@
-"""
-Sparse Matrices
-===============
-
-SciPy 2-D sparse matrix package.
-
-Original code by Travis Oliphant.
-Modified and extended by Ed Schofield, Robert Cimrman, and Nathan Bell.
-
-There are seven available sparse matrix types:
-    1. csc_matrix: Compressed Sparse Column format
-    2. csr_matrix: Compressed Sparse Row format
-    3. bsr_matrix: Block Sparse Row format
-    4. lil_matrix: List of Lists format
-    5. dok_matrix: Dictionary of Keys format
-    6. coo_matrix: COOrdinate format (aka IJV, triplet format)
-    7. dia_matrix: DIAgonal format
-
-To construct a matrix efficiently, use either lil_matrix (recommended) or
-dok_matrix. The lil_matrix class supports basic slicing and fancy
-indexing with a similar syntax to NumPy arrays.  As illustrated below,
-the COO format may also be used to efficiently construct matrices.
-
-To perform manipulations such as multiplication or inversion, first
-convert the matrix to either CSC or CSR format. The lil_matrix format is
-row-based, so conversion to CSR is efficient, whereas conversion to CSC
-is less so.
-
-All conversions among the CSR, CSC, and COO formats are efficient,
-linear-time operations.
-
-Example 1
----------
-Construct a 1000x1000 lil_matrix and add some values to it:
-
->>> from scipy.sparse import lil_matrix
->>> from scipy.sparse.linalg import spsolve
->>> from numpy.linalg import solve, norm
->>> from numpy.random import rand
-
->>> A = lil_matrix((1000, 1000))
->>> A[0, :100] = rand(100)
->>> A[1, 100:200] = A[0, :100]
->>> A.setdiag(rand(1000))
-
-Now convert it to CSR format and solve A x = b for x:
-
->>> A = A.tocsr()
->>> b = rand(1000)
->>> x = spsolve(A, b)
-
-Convert it to a dense matrix and solve, and check that the result
-is the same:
-
->>> x_ = solve(A.todense(), b)
-
-Now we can compute norm of the error with:
-
->>> err = norm(x-x_)
->>> err < 1e-10
-True
-
-It should be small :)
-
-
-Example 2
----------
-
-Construct a matrix in COO format:
-
->>> from scipy import sparse
->>> from numpy import array
->>> I = array([0,3,1,0])
->>> J = array([0,3,1,2])
->>> V = array([4,5,7,9])
->>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))
-
-Notice that the indices do not need to be sorted.
-
-Duplicate (i,j) entries are summed when converting to CSR or CSC.
-
->>> I = array([0,0,1,3,1,0,0])
->>> J = array([0,2,1,3,1,0,0])
->>> V = array([1,1,1,1,1,1,1])
->>> B = sparse.coo_matrix((V,(I,J)),shape=(4,4)).tocsr()
-
-This is useful for constructing finite-element stiffness and mass matrices.
-
-Further Details
----------------
-
-CSR column indices are not necessarily sorted.  Likewise for CSC row
-indices.  Use the .sorted_indices() and .sort_indices() methods when
-sorted indices are required (e.g. when passing data to other libraries).
-
-Package Contents
-================
-
-Modules
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   base - Base class for sparse matrices
-   bsr - Compressed Block Sparse Row matrix format
-   compressed - Sparse matrix base class using compressed storage
-   construct - Functions to construct sparse matrices
-   coo - A sparse matrix in COOrdinate or 'triplet' format
-   csc - Compressed Sparse Column matrix format
-   csgraph - Compressed Sparse graph algorithms
-   csr - Compressed Sparse Row matrix format
-   data - Base class for sparse matrice with a .data attribute
-   dia - Sparse DIAgonal format
-   dok - Dictionary Of Keys based matrix
-   extract - Functions to extract parts of sparse matrices
-   lil - LInked List sparse matrix class
-   linalg -
-   sparsetools - A collection of routines for sparse matrix operations
-   spfuncs - Functions that operate on sparse matrices
-   sputils - Utility functions for sparse matrix module
-
-Classes
--------
-
-.. autosummary::
-   :toctree: generated/
-
-   SparseEfficiencyWarning -
-   SparseWarning -
-   bsr_matrix - Block Sparse Row matrix
-   coo_matrix - A sparse matrix in COOrdinate format
-   csc_matrix - Compressed Sparse Column matrix
-   csr_matrix - Compressed Sparse Row matrix
-   dia_matrix - Sparse matrix with DIAgonal storage
-   dok_matrix - Dictionary Of Keys based sparse matrix
-   lil_matrix - Row-based linked list sparse matrix
-
-Functions
----------
-
-.. autosummary::
-   :toctree: generated/
-
-   bmat - Build a sparse matrix from sparse sub-blocks
-   cs_graph_components -
-   eye - Sparse MxN matrix whose k-th diagonal is all ones
-   find -
-   hstack - Stack sparse matrices horizontally (column wise)
-   identity - Identity matrix in sparse format
-   issparse -
-   isspmatrix -
-   isspmatrix_bsr -
-   isspmatrix_coo -
-   isspmatrix_csc -
-   isspmatrix_csr -
-   isspmatrix_dia -
-   isspmatrix_dok -
-   isspmatrix_lil -
-   kron - kronecker product of two sparse matrices
-   kronsum - kronecker sum of sparse matrices
-   lil_diags - Generate a lil_matrix with the given diagonals
-   lil_eye - RxC lil_matrix whose k-th diagonal set to one
-   rand - Random values in a given shape
-   spdiags - Return a sparse matrix from diagonals
-   tril - Lower triangular portion of a matrix in sparse format
-   triu - Upper triangular portion of a matrix in sparse format
-   vstack - Stack sparse matrices vertically (row wise)
-
-"""
-
 from info import __doc__
 
 from base import *

Modified: trunk/scipy/sparse/info.py
===================================================================
--- trunk/scipy/sparse/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/sparse/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,8 +1,8 @@
 """
 Sparse Matrices
----------------
+===============
 
-Scipy 2D sparse matrix module.
+SciPy 2-D sparse matrix package.
 
 Original code by Travis Oliphant.
 Modified and extended by Ed Schofield, Robert Cimrman, and Nathan Bell.
@@ -93,6 +93,80 @@
 indices.  Use the .sorted_indices() and .sort_indices() methods when
 sorted indices are required (e.g. when passing data to other libraries).
 
+Package Contents
+================
+
+Modules
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   base - Base class for sparse matrices
+   bsr - Compressed Block Sparse Row matrix format
+   compressed - Sparse matrix base class using compressed storage
+   construct - Functions to construct sparse matrices
+   coo - A sparse matrix in COOrdinate or 'triplet' format
+   csc - Compressed Sparse Column matrix format
+   csgraph - Compressed Sparse graph algorithms
+   csr - Compressed Sparse Row matrix format
+   data - Base class for sparse matrice with a .data attribute
+   dia - Sparse DIAgonal format
+   dok - Dictionary Of Keys based matrix
+   extract - Functions to extract parts of sparse matrices
+   lil - LInked List sparse matrix class
+   linalg -
+   sparsetools - A collection of routines for sparse matrix operations
+   spfuncs - Functions that operate on sparse matrices
+   sputils - Utility functions for sparse matrix module
+
+Classes
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   SparseEfficiencyWarning -
+   SparseWarning -
+   bsr_matrix - Block Sparse Row matrix
+   coo_matrix - A sparse matrix in COOrdinate format
+   csc_matrix - Compressed Sparse Column matrix
+   csr_matrix - Compressed Sparse Row matrix
+   dia_matrix - Sparse matrix with DIAgonal storage
+   dok_matrix - Dictionary Of Keys based sparse matrix
+   lil_matrix - Row-based linked list sparse matrix
+
+Functions
+---------
+
+.. autosummary::
+   :toctree: generated/
+
+   bmat - Build a sparse matrix from sparse sub-blocks
+   cs_graph_components -
+   eye - Sparse MxN matrix whose k-th diagonal is all ones
+   find -
+   hstack - Stack sparse matrices horizontally (column wise)
+   identity - Identity matrix in sparse format
+   issparse -
+   isspmatrix -
+   isspmatrix_bsr -
+   isspmatrix_coo -
+   isspmatrix_csc -
+   isspmatrix_csr -
+   isspmatrix_dia -
+   isspmatrix_dok -
+   isspmatrix_lil -
+   kron - kronecker product of two sparse matrices
+   kronsum - kronecker sum of sparse matrices
+   lil_diags - Generate a lil_matrix with the given diagonals
+   lil_eye - RxC lil_matrix whose k-th diagonal set to one
+   rand - Random values in a given shape
+   spdiags - Return a sparse matrix from diagonals
+   tril - Lower triangular portion of a matrix in sparse format
+   triu - Upper triangular portion of a matrix in sparse format
+   vstack - Stack sparse matrices vertically (row wise)
+
 """
 
 __docformat__ = "restructuredtext en"

Modified: trunk/scipy/stats/__init__.py
===================================================================
--- trunk/scipy/stats/__init__.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/stats/__init__.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -1,259 +1,3 @@
-"""
-Statistical Functions
-=====================
-
-This module contains a large number of probability distributions as
-well as a growing library of statistical functions.
-
-Each included distribution is an instance of the class rv_continous.
-For each given name the following methods are available.  See docstring
-for rv_continuous for more information
-
-:rvs:
-   random variates with the distribution
-:pdf:
-   probability density function
-:cdf:
-   cumulative distribution function
-:sf:
-   survival function (1.0 - cdf)
-:ppf:
-   percent-point function (inverse of cdf)
-:isf:
-   inverse survival function
-:stats:
-   mean, variance, and optionally skew and kurtosis
-
-Calling the instance as a function returns a frozen pdf whose shape,
-location, and scale parameters are fixed.
-
-Distributions
----------------
-
-The distributions available with the above methods are:
-
-
-Continuous  (Total == 81 distributions)
----------------------------------------
-
-.. autosummary::
-   :toctree: generated/
-
-   norm              Normal (Gaussian)
-   alpha             Alpha
-   anglit            Anglit
-   arcsine           Arcsine
-   beta              Beta
-   betaprime         Beta Prime
-   bradford          Bradford
-   burr              Burr
-   cauchy            Cauchy
-   chi               Chi
-   chi2              Chi-squared
-   cosine            Cosine
-   dgamma            Double Gamma
-   dweibull          Double Weibull
-   erlang            Erlang
-   expon             Exponential
-   exponweib         Exponentiated Weibull
-   exponpow          Exponential Power
-   f                 F (Snecdor F)
-   fatiguelife       Fatigue Life (Birnbaum-Sanders)
-   fisk              Fisk
-   foldcauchy        Folded Cauchy
-   foldnorm          Folded Normal
-   frechet_r         Frechet Right Sided, Extreme Value Type II (Extreme LB) or weibull_min
-   frechet_l         Frechet Left Sided, Weibull_max
-   genlogistic       Generalized Logistic
-   genpareto         Generalized Pareto
-   genexpon          Generalized Exponential
-   genextreme        Generalized Extreme Value
-   gausshyper        Gauss Hypergeometric
-   gamma             Gamma
-   gengamma          Generalized gamma
-   genhalflogistic   Generalized Half Logistic
-   gompertz          Gompertz (Truncated Gumbel)
-   gumbel_r          Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
-   gumbel_l          Left Sided Gumbel, etc.
-   halfcauchy        Half Cauchy
-   halflogistic      Half Logistic
-   halfnorm          Half Normal
-   hypsecant         Hyperbolic Secant
-   invgamma          Inverse Gamma
-   invnorm           Inverse Normal
-   invweibull        Inverse Weibull
-   johnsonsb         Johnson SB
-   johnsonsu         Johnson SU
-   ksone             Kolmogorov-Smirnov one-sided (no stats)
-   kstwobign         Kolmogorov-Smirnov two-sided test for Large N (no stats)
-   laplace           Laplace
-   logistic          Logistic
-   loggamma          Log-Gamma
-   loglaplace        Log-Laplace (Log Double Exponential)
-   lognorm           Log-Normal
-   gilbrat           Gilbrat
-   lomax             Lomax (Pareto of the second kind)
-   maxwell           Maxwell
-   mielke            Mielke's Beta-Kappa
-   nakagami          Nakagami
-   ncx2              Non-central chi-squared
-   ncf               Non-central F
-   nct               Non-central Student's T
-   pareto            Pareto
-   powerlaw          Power-function
-   powerlognorm      Power log normal
-   powernorm         Power normal
-   rdist             R distribution
-   reciprocal        Reciprocal
-   rayleigh          Rayleigh
-   rice              Rice
-   recipinvgauss     Reciprocal Inverse Gaussian
-   semicircular      Semicircular
-   t                 Student's T
-   triang            Triangular
-   truncexpon        Truncated Exponential
-   truncnorm         Truncated Normal
-   tukeylambda       Tukey-Lambda
-   uniform           Uniform
-   von_mises         Von-Mises (Circular)
-   wald              Wald
-   weibull_min       Minimum Weibull (see Frechet)
-   weibull_max       Maximum Weibull (see Frechet)
-   wrapcauchy        Wrapped Cauchy
-
-
-=============== ==============================================================
-Discrete    (Total == 10 distributions)
-==============================================================================
-binom             Binomial
-bernoulli         Bernoulli
-nbinom            Negative Binomial
-geom              Geometric
-hypergeom         Hypergeometric
-logser            Logarithmic (Log-Series, Series)
-poisson           Poisson
-planck            Planck (Discrete Exponential)
-boltzmann         Boltzmann (Truncated Discrete Exponential)
-randint           Discrete Uniform
-zipf              Zipf
-dlaplace          Discrete Laplacian
-=============== ==============================================================
-
-Statistical Functions (adapted from Gary Strangman)
------------------------------------------------------
-
-================= ==============================================================
-gmean             Geometric mean
-hmean             Harmonic mean
-mean              Arithmetic mean
-cmedian           Computed median
-median            Median
-mode              Modal value
-tmean             Truncated arithmetic mean
-tvar              Truncated variance
-tmin              _
-tmax              _
-tstd              _
-tsem              _
-moment            Central moment
-variation         Coefficient of variation
-skew              Skewness
-kurtosis          Fisher or Pearson kurtosis
-describe          Descriptive statistics
-skewtest          _
-kurtosistest      _
-normaltest        _
-================= ==============================================================
-
-================= ==============================================================
-itemfreq          _
-scoreatpercentile _
-percentileofscore _
-histogram2        _
-histogram         _
-cumfreq           _
-relfreq           _
-================= ==============================================================
-
-================= ==============================================================
-obrientransform   _
-samplevar         _
-samplestd         _
-signaltonoise     _
-bayes_mvs         _
-var               _
-std               _
-stderr            _
-sem               _
-z                 _
-zs                _
-zmap              _
-================= ==============================================================
-
-================= ==============================================================
-threshold         _
-trimboth          _
-trim1             _
-cov               _
-corrcoef          _
-================= ==============================================================
-
-================= ==============================================================
-f_oneway          _
-paired            _
-pearsonr          _
-spearmanr         _
-pointbiserialr    _
-kendalltau        _
-linregress        _
-================= ==============================================================
-
-================= ==============================================================
-ttest_1samp       _
-ttest_ind         _
-ttest_rel         _
-kstest            _
-chisquare         _
-ks_2samp          _
-meanwhitneyu      _
-tiecorrect        _
-ranksums          _
-wilcoxon          _
-kruskal           _
-friedmanchisquare _
-================= ==============================================================
-
-================= ==============================================================
-ansari            _
-bartlett          _
-levene            _
-shapiro           _
-anderson          _
-binom_test        _
-fligner           _
-mood              _
-oneway            _
-================= ==============================================================
-
-================= ==============================================================
-glm               _
-anova             _
-================= ==============================================================
-
-
-================= ==============================================================
-Plot-tests
-================================================================================
-probplot          _
-ppcc_max          _
-ppcc_plot         _
-================= ==============================================================
-
-
-For many more stat related functions install the software R and the
-interface package rpy.
-
-"""
 #
 # stats - Statistical Functions
 #

Modified: trunk/scipy/stats/info.py
===================================================================
--- trunk/scipy/stats/info.py	2010-11-14 10:12:36 UTC (rev 6894)
+++ trunk/scipy/stats/info.py	2010-11-14 16:18:57 UTC (rev 6895)
@@ -6,8 +6,8 @@
 well as a growing library of statistical functions.
 
 Each included distribution is an instance of the class rv_continous.
-For each given name the following methods are available.  See docstring for
-rv_continuous for more information
+For each given name the following methods are available.  See docstring
+for rv_continuous for more information
 
 :rvs:
    random variates with the distribution
@@ -32,93 +32,96 @@
 
 The distributions available with the above methods are:
 
-=============== ==============================================================
+
 Continuous  (Total == 81 distributions)
-==============================================================================
-norm              Normal (Gaussian)
-alpha             Alpha
-anglit            Anglit
-arcsine           Arcsine
-beta              Beta
-betaprime         Beta Prime
-bradford          Bradford
-burr              Burr
-fisk              Fisk
-cauchy            Cauchy
-chi               Chi
-chi2              Chi-squared
-cosine            Cosine
-dgamma            Double Gamma
-dweibull          Double Weibull
-erlang            Erlang
-expon             Exponential
-exponweib         Exponentiated Weibull
-exponpow          Exponential Power
-fatiguelife       Fatigue Life (Birnbaum-Sanders)
-foldcauchy        Folded Cauchy
-f                 F (Snecdor F)
-foldnorm          Folded Normal
-frechet_r         Frechet Right Sided, Extreme Value Type II (Extreme LB) or weibull_min
-frechet_l         Frechet Left Sided, Weibull_max
-genlogistic       Generalized Logistic
-genpareto         Generalized Pareto
-genexpon          Generalized Exponential
-genextreme        Generalized Extreme Value
-gausshyper        Gauss Hypergeometric
-gamma             Gamma
-gengamma          Generalized gamma
-genhalflogistic   Generalized Half Logistic
-gompertz          Gompertz (Truncated Gumbel)
-gumbel_r          Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
-gumbel_l          Left Sided Gumbel, etc.
-halfcauchy        Half Cauchy
-halflogistic      Half Logistic
-halfnorm          Half Normal
-hypsecant         Hyperbolic Secant
-invgamma          Inverse Gamma
-invnorm           Inverse Normal
-invweibull        Inverse Weibull
-johnsonsb         Johnson SB
-johnsonsu         Johnson SU
-laplace           Laplace
-logistic          Logistic
-loggamma          Log-Gamma
-loglaplace        Log-Laplace (Log Double Exponential)
-lognorm           Log-Normal
-gilbrat           Gilbrat
-lomax             Lomax (Pareto of the second kind)
-maxwell           Maxwell
-mielke            Mielke's Beta-Kappa
-nakagami          Nakagami
-ncx2              Non-central chi-squared
-ncf               Non-central F
-t                 Student's T
-nct               Non-central Student's T
-pareto            Pareto
-powerlaw          Power-function
-powerlognorm      Power log normal
-powernorm         Power normal
-rdist             R distribution
-reciprocal        Reciprocal
-rayleigh          Rayleigh
-rice              Rice
-recipinvgauss     Reciprocal Inverse Gaussian
-semicircular      Semicircular
-triang            Triangular
-truncexpon        Truncated Exponential
-truncnorm         Truncated Normal
-tukeylambda       Tukey-Lambda
-uniform           Uniform
-von_mises         Von-Mises (Circular)
-wald              Wald
-weibull_min       Minimum Weibull (see Frechet)
-weibull_max       Maximum Weibull (see Frechet)
-wrapcauchy        Wrapped Cauchy
-ksone             Kolmogorov-Smirnov one-sided (no stats)
-kstwobign         Kolmogorov-Smirnov two-sided test for Large N (no stats)
-=============== ==============================================================
+---------------------------------------
 
+.. autosummary::
+   :toctree: generated/
 
+   norm              Normal (Gaussian)
+   alpha             Alpha
+   anglit            Anglit
+   arcsine           Arcsine
+   beta              Beta
+   betaprime         Beta Prime
+   bradford          Bradford
+   burr              Burr
+   cauchy            Cauchy
+   chi               Chi
+   chi2              Chi-squared
+   cosine            Cosine
+   dgamma            Double Gamma
+   dweibull          Double Weibull
+   erlang            Erlang
+   expon             Exponential
+   exponweib         Exponentiated Weibull
+   exponpow          Exponential Power
+   f                 F (Snecdor F)
+   fatiguelife       Fatigue Life (Birnbaum-Sanders)
+   fisk              Fisk
+   foldcauchy        Folded Cauchy
+   foldnorm          Folded Normal
+   frechet_r         Frechet Right Sided, Extreme Value Type II (Extreme LB) or weibull_min
+   frechet_l         Frechet Left Sided, Weibull_max
+   genlogistic       Generalized Logistic
+   genpareto         Generalized Pareto
+   genexpon          Generalized Exponential
+   genextreme        Generalized Extreme Value
+   gausshyper        Gauss Hypergeometric
+   gamma             Gamma
+   gengamma          Generalized gamma
+   genhalflogistic   Generalized Half Logistic
+   gompertz          Gompertz (Truncated Gumbel)
+   gumbel_r          Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
+   gumbel_l          Left Sided Gumbel, etc.
+   halfcauchy        Half Cauchy
+   halflogistic      Half Logistic
+   halfnorm          Half Normal
+   hypsecant         Hyperbolic Secant
+   invgamma          Inverse Gamma
+   invnorm           Inverse Normal
+   invweibull        Inverse Weibull
+   johnsonsb         Johnson SB
+   johnsonsu         Johnson SU
+   ksone             Kolmogorov-Smirnov one-sided (no stats)
+   kstwobign         Kolmogorov-Smirnov two-sided test for Large N (no stats)
+   laplace           Laplace
+   logistic          Logistic
+   loggamma          Log-Gamma
+   loglaplace        Log-Laplace (Log Double Exponential)
+   lognorm           Log-Normal
+   gilbrat           Gilbrat
+   lomax             Lomax (Pareto of the second kind)
+   maxwell           Maxwell
+   mielke            Mielke's Beta-Kappa
+   nakagami          Nakagami
+   ncx2              Non-central chi-squared
+   ncf               Non-central F
+   nct               Non-central Student's T
+   pareto            Pareto
+   powerlaw          Power-function
+   powerlognorm      Power log normal
+   powernorm         Power normal
+   rdist             R distribution
+   reciprocal        Reciprocal
+   rayleigh          Rayleigh
+   rice              Rice
+   recipinvgauss     Reciprocal Inverse Gaussian
+   semicircular      Semicircular
+   t                 Student's T
+   triang            Triangular
+   truncexpon        Truncated Exponential
+   truncnorm         Truncated Normal
+   tukeylambda       Tukey-Lambda
+   uniform           Uniform
+   von_mises         Von-Mises (Circular)
+   wald              Wald
+   weibull_min       Minimum Weibull (see Frechet)
+   weibull_max       Maximum Weibull (see Frechet)
+   wrapcauchy        Wrapped Cauchy
+
+
 =============== ==============================================================
 Discrete    (Total == 10 distributions)
 ==============================================================================



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