[Scipy-svn] r5455 - trunk/doc/release
Tue Jan 13 02:11:47 CST 2009
Date: 2009-01-13 02:11:37 -0600 (Tue, 13 Jan 2009)
New Revision: 5455
Language review of release notes.
--- trunk/doc/release/0.7.0-notes.rst 2009-01-13 08:07:27 UTC (rev 5454)
+++ trunk/doc/release/0.7.0-notes.rst 2009-01-13 08:11:37 UTC (rev 5455)
@@ -4,33 +4,33 @@
-SciPy 0.7.0 is the culmination of 16 months of hard work and
-and contains many new features, numerous bug-fixes, improved test
-coverage, and better documentation. There have been a number of
+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
+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 new feature on the development trunk. This release
+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 "Beta" status as we work
+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
-step in the development of SciPy after which changing the package
-structure or API will be much more difficult. While these pre-1.0
-releases are considered "Beta" status, we are committed to making
+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 in an effort
+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 about all aspects of the project--everything from which
-algorithms we implement to details about our function's call signatures.
+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 an rapid increase in community involvement
+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
@@ -44,13 +44,12 @@
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
+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, which is a massive effort, since a lot of C code has to be
-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.
+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.
Major documentation improvements
@@ -61,12 +60,12 @@
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. While not
+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.
-Nevertheless, more effort is still needed on the documentation front.
+Nevertheless, more effort is needed on the documentation front.
Luckily, contributing to Scipy documentation is now easier than
before: if you find that a part of it requires improvements, and want
to help us out, please register a user name in our web-based
@@ -76,11 +75,11 @@
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. 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
+<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
@@ -90,7 +89,7 @@
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
+tests in the 0.6.0 release; this release nearly doubles that number, with just over
4,000 unit tests.
@@ -99,11 +98,11 @@
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 meant to replace the venerable ``Make``
+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 advanced functionality, such as ``autoconf``, improved fortran support,
more tools, and support for ``numpy.distutils``/``scons`` cooperation.
@@ -113,9 +112,9 @@
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.
+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
+``scikit``, and the remaining code was just deleted, as the functionality had
been replaced by other code.
@@ -154,12 +153,12 @@
* ``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.
+arithmetic have been made. Finally, this release contains numerous bugfixes.
-Statistical functions for masked arrays have been added and are accessible
+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
@@ -176,7 +175,7 @@
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
+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.
@@ -186,7 +185,7 @@
Reworking of IO package
-The IO code in both NumPy and SciPy is undergoing a major reworking. NumPy
+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.).
@@ -224,26 +223,26 @@
between descendants. The ``fcluster`` and ``fclusterdata`` functions
transform a hierarchical clustering into a set of flat clusters. Since
these flat clusters are generated by cutting the tree into a forest of
-trees, the ``leaders`` function takes a linkage and a flat clustering
+trees, the ``leaders`` function takes a linkage and a flat clustering,
and finds the root of each tree in the forest. The ``ClusterNode``
class represents a hierarchical clusterings as a field-navigable tree
object. ``to_tree`` converts a matrix-encoded hierarchical clustering
to a ``ClusterNode`` object. Routines for converting between MATLAB
and SciPy linkage encodings are provided. Finally, a ``dendrogram``
-function plots hierarchical clusterings as a dendrogram using
+function plots hierarchical clusterings as a dendrogram, using
New Spatial package
-Collection of spatial algorithms and data structures useful for spatial
-statistics and clustering applications. Includes fast compiled code for
+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
+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 ``distance`` module containing a collection of
+It also includes a ``distance`` module, containing a collection of
distance and dissimilarity functions for computing distances between
vectors, which is useful for spatial statistics, clustering, and
kd-trees. Distance and dissimilarity functions provided include
@@ -256,7 +255,7 @@
unordered pairs of vectors in a set of vectors. The ``cdist`` computes
the distance on all pairs of vectors in the Cartesian product of two
sets of vectors. Pairwise distance matrices are stored in condensed
-form, only the upper triangular is stored. ``squareform`` converts
+form; only the upper triangular is stored. ``squareform`` converts
distance matrices between square and condensed forms.
Reworked fftpack package
@@ -264,7 +263,7 @@
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.
+features - like float32 support - more easily.
New Constants package
@@ -274,7 +273,7 @@
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
+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
@@ -297,8 +296,8 @@
``scipy.linalg.eigh`` now contains wrappers for more LAPACK
symmetric and hermitian eigenvalue problem solvers. Users
-can now solve generalized problems, select just a range of
-eigenvalues, and choose to use a faster algorithm at the expense
+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``
@@ -306,7 +305,7 @@
The shape of return values from ``scipy.interpolate.interp1d`` used
-to be incorrect if interpolated data had more than 2 dimensions and
+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.
@@ -323,8 +322,7 @@
Here are known problems with scipy 0.7.0:
-* weave test failures on windows: those are known, and are being worked
+* weave test failures on windows: those are known, and are being revised.
* weave test failure with gcc 4.3 (std::labs): this is a gcc 4.3 bug. A
workaround is to add #include <cstdlib> in
scipy/weave/blitz/blitz/funcs.h (line 27). You can make the change in
More information about the Scipy-svn