[Scipy-svn] r5120 - trunk/doc/releases

scipy-svn@scip... scipy-svn@scip...
Sun Nov 16 00:47:08 CST 2008

Author: jarrod.millman
Date: 2008-11-16 00:47:06 -0600 (Sun, 16 Nov 2008)
New Revision: 5120

merge notes from wiki

Modified: trunk/doc/releases/0.7.0-notes.rst
--- trunk/doc/releases/0.7.0-notes.rst	2008-11-16 06:42:07 UTC (rev 5119)
+++ trunk/doc/releases/0.7.0-notes.rst	2008-11-16 06:47:06 UTC (rev 5120)
@@ -1,3 +1,14 @@
+This is a new stable release.  Please note that unlike previous versions of !SciPy, this release requires Python 2.4 or greater.  This release also requires !NumPy 1.2.0 or greater.
+= Changes =
+== scipy.io ==
+The IO code in both !NumPy and !SciPy is undergoing a major reworking.  !NumPy will be where basic code for reading and writing !NumPy arrays is located, while !SciPy will house file readers and writers for various data formats (data, audio, video, images, matlab, excel, etc.).  This will take place in stages.  The first stage was completed with the release of !NumPy 1.1.0.  As part of this phase:
+ * many of the functions in scipy.io have been deprecated
+mention specific improvements to io, e.g. MATLAB sparse support (TODO)
 Hierarchical Clustering
@@ -38,3 +49,73 @@
 vectors in two sets of vectors. squareform is useful for converting
 a square distance matrix to a condensed matrix and vice versa.
+== scipy.fftpack ==
+FFTW2, FFTW3, MKL and DJBFFT wrappers have been removed. Only (NETLIB) fftpack remains. By focusing on one backend, we hope to add new features - like float32 support - more easily.
+=== scipy.io.matlab ===
+The Matlab (TM) file readers/writers have a number of improvements:
+ * default version 5
+== scipy.sparse ==
+ * added support for integer dtypes such `int8`, `uint32`, etc.
+ * new class `dia_matrix` : the sparse DIAgonal format
+ * new class `bsr_matrix` : sparse Block CSR format
+ * new sparse matrix construction functions
+   * `sparse.kron` : sparse Kronecker product
+   * `sparse.bmat` : sparse version of `numpy.bmat`
+   * `sparse.vstack` : sparse version of `numpy.vstack`
+   * `sparse.hstack` : sparse version of `numpy.hstack`
+ * extraction of submatrices and nonzero values
+   * `sparse.tril` : extract lower triangle
+   * `sparse.kron` : extract upper triangle
+   * `sparse.find` : nonzero values and their indices
+ * `csr_matrix` and `csc_matrix` now support slicing and fancy indexing
+   * e.g. `A[1:3, 4:7]` and `A[[3,2,6,8],:]`
+ * conversions among all sparse formats are now possible
+   * all formats have member functions such as `.tocsr()` and `.tolil()`
+ * sparse constructors now accept dense matrices and other sparse formats
+   * e.g. `A = csr_matrix( rand(3,3) )` and `B = lil_matrix( [[1,2],[3,4]] )`
+ * efficiency improvements to:
+   * format conversions
+   * sparse matrix arithmetic
+ * numerous bugfixes
+== New Spatial package ==
+Collection of spatial algorithms and data structures useful for spatial statistics and clustering applications. Includes fast compiled code for computing exact and approximate nearest neighbors, as well as a pure-python kd-tree with the same interface but that supports annotation and a variety of other algorithms. The API for both modules may change somewhat as user requirements become clearer. Also includes a submodule "distance" containing fast code for many definitions of "distance" between vectors. Distance and dissimilarity functions are provided include Bray-Curtis, Canberra, Chebyshev, City Block, Cosine, Dice, Euclidean, Hamming, Jaccard, Kulsinski, Mahalanobis, Matching, Minkowski, Rogers-Tanimoto, Russell-Rao, Squared Euclidean, Standardized Euclidean, Sokal-Michener, Sokal-Sneath, and Yule. Two functions are provided for computing distances between collections of vectors: pdist and cdist. pdist is similar to the MATLAB(TM) function and computes pairwise distances between a collection of vectors. cdist computes distances between vectors in two sets of vectors. squareform converts between square distance matrices and condensed distance matrices.
+== New Constants package ==
+Collection of physical constants and conversion factors:
+== New Hierarchical Clustering module ==
+This module adds new hierarchical clustering functionality to the
+cluster package. Its interface is similar to the hierarchical
+clustering functions provided in MATLAB(TM)'s Statistics Toolbox to
+facilitate easier migration to the NumPy/SciPy framework. Linkage
+methods implemented include single, complete, average, weighted,
+centroid, median, and ward. Several functions are provided for
+computing statistics on clusters including inconsistency statistics,
+cophenetic distance, and maximum distance of descendants. The fcluster
+and fclusterdata functions take hierarchical tree clusterings
+generated by these algorithms, cuts the tree, and labels the flat
+clusters. The leaders function finds the root of each flat cluster
+given a hierarchical clustering and labellings of its leaves. Finally, a
+matplotlib extension is provided for plotting dendrograms, which
+may be outputted to postscript or any other supported format.
+== New Radial Basis Function module ==
+== Improved documentation ==
+== Running Tests ==
+We are moving away from having our own testing framework and are adopting [http://code.google.com/p/python-nose/ nose].
+== Building !SciPy ==
+Support for !NumScons has been added. numscons is a tentative new build system for numpy/scipy, using scons at its core.

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