[Scipy-svn] r5458 - trunk/doc/release
scipy-svn@scip...
scipy-svn@scip...
Tue Jan 13 02:12:56 CST 2009
Author: stefan
Date: 2009-01-13 02:12:45 -0600 (Tue, 13 Jan 2009)
New Revision: 5458
Modified:
trunk/doc/release/0.7.0-notes.rst
Log:
Wrap release notes to 78.
Modified: trunk/doc/release/0.7.0-notes.rst
===================================================================
--- trunk/doc/release/0.7.0-notes.rst 2009-01-13 08:12:08 UTC (rev 5457)
+++ trunk/doc/release/0.7.0-notes.rst 2009-01-13 08:12:45 UTC (rev 5458)
@@ -4,66 +4,69 @@
.. contents::
-SciPy 0.7.0 is the culmination of 16 months of hard work. It contains many new features, numerous bug-fixes, improved test
-coverage and better documentation. There have been a number of
-deprecations and API changes in this release, which are documented
-below. All users are encouraged to upgrade to this release, as
-there are a large number of bug-fixes and optimizations. Moreover,
-our development attention will now shift to bug-fix releases on the
-0.7.x branch, and on adding new features on the development trunk. This release
-requires Python 2.4 or 2.5 and NumPy 1.2 or greater.
+SciPy 0.7.0 is the culmination of 16 months of hard work. It contains
+many new features, numerous bug-fixes, improved test coverage and
+better documentation. There have been a number of deprecations and
+API changes in this release, which are documented below. All users
+are encouraged to upgrade to this release, as there are a large number
+of bug-fixes and optimizations. Moreover, our development attention
+will now shift to bug-fix releases on the 0.7.x branch, and on adding
+new features on the development trunk. This release requires Python
+2.4 or 2.5 and NumPy 1.2 or greater.
-Please note that SciPy is still considered to have "Beta" status, as we work
-toward a SciPy 1.0.0 release. The 1.0.0 release will mark a major
-milestone in the development of SciPy, after which changing the package
-structure or API will be much more difficult. Whilst these pre-1.0
-releases are considered to have "Beta" status, we are committed to making
-them as bug-free 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 SciPy is still considered to have "Beta" status, as
+we work toward a SciPy 1.0.0 release. The 1.0.0 release will mark a
+major milestone in the development of SciPy, after which changing the
+package structure or API will be much more difficult. Whilst these
+pre-1.0 releases are considered to have "Beta" status, we are
+committed to making them as bug-free 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.
However, until the 1.0 release, we are aggressively reviewing and
refining the functionality, organization, and interface. This is being
-done 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 regarding all aspects of the project - everything - from which
-algorithms we implement, to details about our function's call signatures.
+done 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 regarding all aspects of the
+project - everything - from which algorithms we implement, to details
+about our function's call signatures.
-Over the last year, we have seen a 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.
+Over the last year, we have seen a 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 problems. Any remaining issues with 2.6 support for SciPy 0.7 will
-be addressed in a bug-fix release.
+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 problems. Any remaining issues with 2.6
+support for SciPy 0.7 will be addressed in a bug-fix release.
Python 3.0 is not supported at all; it requires NumPy to be ported to
-Python 3.0. This requires immense effort, since a lot of C code has to be
-ported. The transition to 3.0 is still under consideration; currently, we don't have any timeline or roadmap for this transition.
+Python 3.0. This requires immense effort, since a lot of C code has
+to be ported. The transition to 3.0 is still under consideration;
+currently, we don't have any timeline or roadmap for this transition.
Major documentation improvements
--------------------------------
-SciPy documentation is greatly improved; you can
-view a HTML reference manual `online <http://docs.scipy.org/>`__ or
-download it as a PDF file. The new reference guide was built using
-the popular `Sphinx tool <http://sphinx.pocoo.org/>`__.
+SciPy documentation is greatly improved; you can view a HTML reference
+manual `online <http://docs.scipy.org/>`__ or download it as a PDF
+file. The new reference guide was built using the popular `Sphinx tool
+<http://sphinx.pocoo.org/>`__.
This release also includes an updated tutorial, which hadn't been
available since SciPy was ported to NumPy in 2005. Though not
-comprehensive, the tutorial shows how to use several essential
-parts of Scipy. It also includes the ``ndimage`` documentation
-from the ``numarray`` manual.
+comprehensive, the tutorial shows how to use several essential parts
+of Scipy. It also includes the ``ndimage`` documentation from the
+``numarray`` manual.
Nevertheless, more effort is needed on the documentation front.
Luckily, contributing to Scipy documentation is now easier than
@@ -74,13 +77,13 @@
Running Tests
-------------
-NumPy 1.2 introduced a new testing framework based on `nose
-<http://code.google.com/p/python-nose/>`__. Starting with this release, SciPy
-now uses the new NumPy test framework as well. Taking advantage of the new
-testing framework requires ``nose`` version 0.10, or later. One major advantage
-of the new framework is that it greatly simplifies writing unit
-tests - which has all ready paid off, given the rapid increase in tests. To run
-the full test suite::
+NumPy 1.2 introduced a new testing framework based on `nose
+<http://code.google.com/p/python-nose/>`__. Starting with this
+release, SciPy now uses the new NumPy test framework as well. Taking
+advantage of the new testing framework requires ``nose`` version 0.10,
+or later. One major advantage of the new framework is that it greatly
+simplifies 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')
@@ -88,40 +91,45 @@
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 over
-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
--------------
-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.
+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 next-generation build system, intended 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 next-generation build system, intended 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.
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, but was starting to create confusion. Thus ``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.
+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, but was starting to create
+confusion. Thus ``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.
Sparse Matrices
---------------
-Sparse matrices have seen extensive improvements. There is now support for integer
-dtypes such ``int8``, ``uint32``, etc. Two new sparse formats were added:
+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
@@ -139,65 +147,69 @@
* ``sparse.triu`` : 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:
+``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:
* 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.
+For example:
* ``A = csr_matrix( rand(3,3) )`` and ``B = lil_matrix( [[1,2],[3,4]] )``
-Numerous efficiency improvements to format conversions and sparse matrix
-arithmetic have been made. Finally, this release contains numerous bugfixes.
+Numerous efficiency improvements to format conversions and sparse
+matrix arithmetic have been made. Finally, this release contains
+numerous bugfixes.
Statistics package
------------------
-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
-and algorithms.
+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 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.
+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 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 out-of-the-box 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 out-of-the-box 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.
Reworking of IO package
-----------------------
-The IO code in both NumPy and SciPy is being extensively reworked. 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, etc.).
+The IO code in both NumPy and SciPy is being extensively
+reworked. 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, 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, memory-mapping capabilities,
-or array methods. Others have been moved from SciPy to NumPy, since basic
-array reading and writing capability is now handled by NumPy.
+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, memory-mapping 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:
@@ -235,12 +247,13 @@
New Spatial package
-------------------
-The new spatial package contains a collection of spatial algorithms and data structures, useful for spatial
-statistics and clustering applications. It includes rapidly 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.
+The new spatial package contains a collection of spatial algorithms
+and data structures, useful for spatial statistics and clustering
+applications. It includes rapidly 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.
It also includes a ``distance`` module, containing a collection of
distance and dissimilarity functions for computing distances between
@@ -272,41 +285,40 @@
conversion factors. These constants are taken from CODATA Recommended
Values of the Fundamental Physical Constants: 2002. They may be found
at physics.nist.gov/constants. The values are stored in the dictionary
-physical_constants as a tuple containing the value, the units, and
-the relative precision - in that order. All constants are in SI units,
+physical_constants as a tuple containing the value, the units, and the
+relative precision - in that order. All constants are in SI units,
unless otherwise stated. Several helper functions are provided.
New Radial Basis Function module
--------------------------------
``scipy.interpolate`` now contains a Radial Basis Function module.
-Radial basis functions can be used for smoothing/interpolating scattered
-data in n-dimensions, but should be used with caution for extrapolation
-outside of the observed data range.
+Radial basis functions can be used for smoothing/interpolating
+scattered data in n-dimensions, but should be used with caution for
+extrapolation outside of the observed data range.
New complex ODE integrator
--------------------------
``scipy.integrate.ode`` now contains a wrapper for the ZVODE
-complex-valued ordinary differential equation solver
-(by Peter N. Brown, Alan C. Hindmarsh, and George D. Byrne).
+complex-valued ordinary differential equation solver (by Peter
+N. Brown, Alan C. Hindmarsh, and George D. Byrne).
New generalized symmetric and hermitian eigenvalue problem solver
-----------------------------------------------------------------
-``scipy.linalg.eigh`` now contains wrappers for more LAPACK
-symmetric and hermitian eigenvalue problem solvers. Users
-can now solve generalized problems, select a range of
-eigenvalues only, and choose to use a faster algorithm at the expense
-of increased memory usage. The signature of the ``scipy.linalg.eigh``
-changed accordingly.
+``scipy.linalg.eigh`` now contains wrappers for more LAPACK symmetric
+and hermitian eigenvalue problem solvers. Users can now solve
+generalized problems, select a range of eigenvalues only, and choose
+to use a faster algorithm at the expense of increased memory
+usage. The signature of the ``scipy.linalg.eigh`` changed accordingly.
Bug fixes in the interpolation package
--------------------------------------
-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 non-default value. This has been fixed.
+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 non-default value. This has been fixed.
Users of ``scipy.interpolate.interp1d`` may need to revise their code
if it relies on the incorrect behavior.
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