[Scipy-svn] r6895 - in trunk/scipy: cluster fftpack interpolate io linalg maxentropy misc ndimage odr optimize sparse stats
scipy-svn at scipy.org
scipy-svn at scipy.org
Sun Nov 14 11:18:57 EST 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 at 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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