[Scipysvn] r5416  trunk/doc/release
scipysvn@scip...
scipysvn@scip...
Sat Jan 10 03:07:34 CST 2009
Author: jarrod.millman
Date: 20090110 03:07:33 0600 (Sat, 10 Jan 2009)
New Revision: 5416
Modified:
trunk/doc/release/0.7.0notes.rst
Log:
updated release notes
Modified: trunk/doc/release/0.7.0notes.rst
===================================================================
 trunk/doc/release/0.7.0notes.rst 20090110 06:58:47 UTC (rev 5415)
+++ trunk/doc/release/0.7.0notes.rst 20090110 09:07:33 UTC (rev 5416)
@@ 4,107 +4,118 @@
.. contents::
This new minor release comes almost one year after the 0.6.0 release
+SciPy 0.7.0 is the culmination of 16 months of hard work and
and contains many new features, numerous bugfixes, improved test
coverage, and better documentation. There have been a number of
deprecations and API changes in this release, which are documented
below. While NumPy is an extremely stable, productionready package,
SciPy is still under rapid development. Every effort is made to ensure
that our code is as stable and bugfree as possible (e.g., this release
almost doubles the number of unit tests compared to 0.6.0). However, as
we work toward the first stable, productionready release (1.0.0), we
will be adding new modules and packages and we reserve the right to modify
SciPy's API and move code around to make the overall code organization
as intuitive as possible for new users.
+below. All users are encouraged to upgrade to this release as
+there are a large number of bugfixes and optimizations. Moreover,
+our development attention will now shift to bugfix releases on the
+0.7.x branch and new feature on the development trunk. This release
+requires Python 2.4 or 2.5 and NumPy 1.2 or greater.
All users are encouraged to upgrade to this release as there are a large
number of bugfixes and optimizations. Moreover, our development attention
will now shift to bugfix releases on the 0.7.x branch and new feature on
the development trunk.
+Please note that SciPy is still considered "Beta" status as we work
+toward a SciPy 1.0.0 release. The 1.0.0 release will mark a major
+step in the development of SciPy after which changing the package
+structure or API will be much more difficult. While these pre1.0
+releases are considered "Beta" status, we are committed to making
+them as bugfree as possible. For example, in addition to fixing
+numerous bugs in this release, we have also doubled the number
+of unit tests since the last release.
Please note that unlike previous versions of SciPy, this release
requires Python 2.4 or 2.5. This release also requires NumPy 1.2.0
or greater.
+However, until the 1.0 release we are aggressively reviewing and
+refining the functionality, organization, and interface in an effort
+to make the package as coherent, intuitive, and useful as possible.
+To achieve this, we need help from the community of users. Specifically,
+we need feedback about all aspects of the projecteverything from which
+algorithms we implement to details about our function's call signatures.
+Over the last year, we have seen an rapid increase in community involvement
+and numerous infrastructure improvements to lower the barrier to contributions
+(e.g., more explicit coding standards, improved testing infrastructure, better
+documentation tools). Over the next year, we hope to see this trend continue
+and invite everyone to become more involved.
+
Python 2.6 and 3.0

+A significant amount of work has gone into making SciPy compatible with
+Python 2.6; however, there are still some issues in this regard.
+The main issue with 2.6 support is NumPy. On UNIX (including Mac OS X),
+NumPy 1.2.1 mostly works, with a few caveats. On Windows, there are problems
+related to the compilation process. The upcoming NumPy 1.3 release will fix
+these issues. Any remaining issues with 2.6 support for SciPy 0.7 will
+be addressed in a bugfix release.
+
Python 3.0 is not supported at all: it requires NumPy to be ported to
Python 3.0, which is a massive effort, since a lot of C code has to be
ported. It will happen, but not anytime soon.
+ported. We are still considering how to make the transition to 3.0, but we
+currently don't have any timeline or roadmap for this transition.
The main issue with 2.6 support is NumPy. On UNIX (including Mac OS X), NumPy
1.2.1 mostly works, with a few caveats. On Windows, there are some problems
related to the compilation process. The upcoming 1.3 version of NumPy will fix
those.

Sparse Matrices

* added support for integer dtypes such ``int8``, ``uint32``, etc.
+Sparse matrices have seen extensive improvements. There is now support for integer
+dtypes such ``int8``, ``uint32``, etc. Two new sparse formats were added:
+
* new class ``dia_matrix`` : the sparse DIAgonal format
* new class ``bsr_matrix`` : the 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``
+Several new sparse matrix construction functions were added:
* extraction of submatrices and nonzero values
+* ``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``
 * ``sparse.tril`` : extract lower triangle
 * ``sparse.triu`` : extract upper triangle
 * ``sparse.find`` : nonzero values and their indices
+Extraction of submatrices and nonzero values have been added:
* ``csr_matrix`` and ``csc_matrix`` now support slicing and fancy indexing
+* ``sparse.tril`` : extract lower triangle
+* ``sparse.triu`` : extract upper triangle
+* ``sparse.find`` : nonzero values and their indices
 * e.g. ``A[1:3, 4:7]`` and ``A[[3,2,6,8],:]``
+``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:
* conversions among all sparse formats are now possible
+* using member functions such as ``.tocsr()`` and ``.tolil()``
+* using the ``.asformat()`` member function, e.g. ``A.asformat('csr')``
+* using constructors ``A = lil_matrix([[1,2]]); B = csr_matrix(A)``
 * using member functions such as ``.tocsr()`` and ``.tolil()``
 * using the ``.asformat()`` member function, e.g. ``A.asformat('csr')``
 * using constructors ``A = lil_matrix([[1,2]]); B = csr_matrix(A)``
+All sparse constructors now accept dense matrices and lists of lists. For
+example:
* all sparse constructors now accept dense matrices and lists of lists
+* ``A = csr_matrix( rand(3,3) )`` and ``B = lil_matrix( [[1,2],[3,4]] )``
 * e.g. ``A = csr_matrix( rand(3,3) )`` and ``B = lil_matrix( [[1,2],[3,4]] )``
+Numerous efficiency improvements to format conversions and sparse matrix
+arithmetic. Finally, this release contains numerous bugfixes.
* efficiency improvements to:

 * format conversions
 * sparse matrix arithmetic

* numerous bugfixes

Reworking of IO package

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 reworking started
NumPy 1.1.0 and will take place over many release. SciPy 0.7.0 has several
changes including:
+(data, audio, video, images, matlab, etc.).
* Several functions in ``scipy.io`` have been deprecated and will be removed
 in the 0.8.0 release including ``npfile``, ``save``, ``load``, ``create_module``,
 ``create_shelf``, ``objload``, ``objsave``, ``fopen``, ``read_array``,
 ``write_array``, ``fread``, ``fwrite``, ``bswap``, ``packbits``, ``unpackbits``,
 and ``convert_objectarray``. Some of these functions have been replaced by
 NumPy's raw reading and writing capabilities, memorymapping capabilities,
 or array methods. Others have been moved from SciPy to NumPy, since basic
 array reading and writing capability is now handled by NumPy.
* the Matlab (TM) file readers/writers have a number of improvements:
+Several functions in ``scipy.io`` have been deprecated and will be removed
+in the 0.8.0 release including ``npfile``, ``save``, ``load``, ``create_module``,
+``create_shelf``, ``objload``, ``objsave``, ``fopen``, ``read_array``,
+``write_array``, ``fread``, ``fwrite``, ``bswap``, ``packbits``, ``unpackbits``,
+and ``convert_objectarray``. Some of these functions have been replaced by
+NumPy's raw reading and writing capabilities, memorymapping capabilities,
+or array methods. Others have been moved from SciPy to NumPy, since basic
+array reading and writing capability is now handled by NumPy.
 * default version 5
 * v5 writers for structures, cell arrays, and objects
 * v5 readers/writers for function handles and 64bit integers
 * new struct_as_record keyword argument to ``loadmat``, which loads
 struct arrays in matlab as record arrays in numpy
 * string arrays have ``dtype='U...'`` instead of ``dtype=object``
+The Matlab (TM) file readers/writers have a number of improvements:
+* default version 5
+* v5 writers for structures, cell arrays, and objects
+* v5 readers/writers for function handles and 64bit integers
+* new struct_as_record keyword argument to ``loadmat``, which loads
+ struct arrays in matlab as record arrays in numpy
+* string arrays have ``dtype='U...'`` instead of ``dtype=object``
+
New Hierarchical Clustering module

@@ 173,9 +184,6 @@
the relative precision, in that order. All constants are in SI units
unless otherwise stated. Several helper functions are provided.
The list is not meant to be comprehensive, but just a convenient list
for everyday use.

New Radial Basis Function module

@@ 226,11 +234,7 @@
The shape of return values from ``scipy.interpolate.interp1d`` used
to be incorrect if interpolated data had more than 2 dimensions and
the axis keyword was set to a nondefault value. This is fixed in 0.7.0:

  http://projects.scipy.org/scipy/scipy/ticket/289
  http://projects.scipy.org/scipy/scipy/ticket/660

+the axis keyword was set to a nondefault value. This has been fixed.
Users of ``scipy.interpolate.interp1d`` may need to revise their code
if it relies on the incorrect behavior.
@@ 238,36 +242,39 @@

Statistical functions for masked arrays have been added and are accessible
through scipy.stats.mstats. The functions are similar to their counterparts
in scipy.stats but they have not yet been verified for identical interfaces
+through ``scipy.stats.mstats``. The functions are similar to their counterparts
+in ``scipy.stats`` but they have not yet been verified for identical interfaces
and algorithms.
Several bugs were fixed for statistical functions, of those, kstest and percentileofscore
gained new keyword arguments.
+Several bugs were fixed for statistical functions, of those, ``kstest`` and
+``percentileofscore`` gained new keyword arguments.
Added deprecation warning for mean, median, var, std, cov and corrcoef. These functions
should be replaced by their numpy counterparts. Note, however, that some of the default
options differ between the scipy.stats and numpy versions of these functions.
+Added deprecation warning for ``mean``, ``median``, ``var``, ``std``,
+``cov``, and ``corrcoef``. These functions should be replaced by their
+numpy counterparts. Note, however, that some of the default options differ
+between the ``scipy.stats`` and numpy versions of these functions.
Numerous bug fixes to stats.distributions: all generic methods work now correctly, several
methods in individual distributions were corrected. However, a few issues remain with
higher moments (skew, kurtosis) and entropy. The maximum likelihood estimator, fit, does not
work outofthebox for some distributions, in some cases, starting values have to be
carefully chosen, in other cases, the generic implementation of the maximum likelihood
method might not be the numerically appropriate estimation method.
+Numerous bug fixes to ``stats.distributions``: all generic methods now work
+correctly, several methods in individual distributions were corrected. However,
+a few issues remain with higher moments (``skew``, ``kurtosis``) and entropy.
+The maximum likelihood estimator, ``fit``, does not work outofthebox for
+some distributions, in some cases, starting values have to be
+carefully chosen, in other cases, the generic implementation of the maximum
+likelihood method might not be the numerically appropriate estimation method.
We expect more bugfixes, increases in numerical precision and enhancements in the next
release of scipy.
+We expect more bugfixes, increases in numerical precision and enhancements in
+the next release of scipy.
Running Tests

NumPy 1.2 introduced a new testing framework based on `nose
<http://code.google.com/p/pythonnose/>`__. Starting with this release SciPy now
uses the new NumPy test framework as well. To take advantage of the new testing framework
requires nose version 0.10 or later. One major advantage of the new framework is that
it greatly reduces the difficulty of writing unit tests, which has all ready paid off given
the rapid increase in tests. To run the full test suite::
+<http://code.google.com/p/pythonnose/>`__. Starting with this release SciPy
+now uses the new NumPy test framework as well. To take advantage of the new
+testing framework requires ``nose`` version 0.10 or later. One major advantage
+of the new framework is that it greatly reduces the difficulty of writing unit
+tests, which has all ready paid off given the rapid increase in tests. To run
+the full test suite::
>>> import scipy
>>> scipy.test('full')
@@ 275,8 +282,9 @@
For more information, please see `The NumPy/SciPy Testing Guide
<http://projects.scipy.org/scipy/numpy/wiki/TestingGuidelines>`__.
We have also greatly improved our test coverage. There were just over 2,000 unit tests in
the 0.6.0 release; this release nearly doubles that number with just under 4,000 unit tests.
+We have also greatly improved our test coverage. There were just over 2,000 unit
+tests in the 0.6.0 release; this release nearly doubles that number with just over
+4,000 unit tests.
Building SciPy

@@ 284,20 +292,28 @@
Support for NumScons has been added. NumScons is a tentative new
build system for NumPy/SciPy, using `SCons <http://www.scons.org/>`__ at its core.
SCons is a nextgeneration build system meant to replace the venerable ``Make`` with
the integrated functionality of ``autoconf``/``automake`` and ``ccache``. Scons is
written in Python and its configuration files are Python scripts. NumScons is meant
to replace NumPy's custom version of ``distutils`` providing more advanced functionality
such as autoconf, improved fortran support, more tools, and support for
``numpy.distutils``/``scons`` cooperation.
+SCons is a nextgeneration build system meant to replace the venerable ``Make``
+with the integrated functionality of ``autoconf``/``automake`` and ``ccache``.
+Scons is written in Python and its configuration files are Python scripts.
+NumScons is meant to replace NumPy's custom version of ``distutils`` providing
+more advanced functionality such as ``autoconf``, improved fortran support,
+more tools, and support for ``numpy.distutils``/``scons`` cooperation.
+Weave clean up
+
+
+There were numerous improvements to ``scipy.weave``. ``blitz++`` was
+relicensed by the author to be compatible with the SciPy license.
+``wx_spec.py`` was removed.
+
Sandbox Removed

While porting SciPy to NumPy in 2005, several packages and modules were moved into
``scipy.sandbox``. The sandbox was a staging ground for packages that were undergoing
rapid development and whose APIs were in flux. It was also a place where broken code
could live. The sandbox has served its purpose well and was starting to create confusion,
so ``scipy.sandbox`` was removed. Most of the code was moved into ``scipy``, some code was
made into a ``scikit``, and the remaining code was just deleted as the functionality had
+While porting SciPy to NumPy in 2005, several packages and modules were
+moved into ``scipy.sandbox``. The sandbox was a staging ground for packages
+that were undergoing rapid development and whose APIs were in flux. It was
+also a place where broken code could live. The sandbox has served its purpose
+well and was starting to create confusion, so ``scipy.sandbox`` was removed.
+Most of the code was moved into ``scipy``, some code was made into a
+``scikit``, and the remaining code was just deleted as the functionality had
been replaced by other code.
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