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SciPy Reference Guide, Release 0.8.dev
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.
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.
2.1.1 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.
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.
2.1.2 Major documentation improvements
SciPy documentation is greatly improved; you can view a HTML reference manual online or download it as a PDF
file. The new reference guide was built using the popular Sphinx tool.
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.
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 documentation editor at http://docs.scipy.org/ and correct the issues.
2.1.3 Running Tests
NumPy 1.2 introduced a new testing framework based on 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’)
For more information, please see The NumPy/SciPy Testing Guide.
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.
124 Chapter 2. Release Notes