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DOC: more updates pending the release
Update release instructions, installation page.
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doc/devel/guidelines/make_release.rst

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@@ -59,7 +59,7 @@ Release checklist
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because this will be the output used by pypi_
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* Check the dependencies listed in ``nipy/info.py`` (e.g.
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``NUMPY_MIN_VERSION``) and in ``doc/installation.rst``. They should
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``NUMPY_MIN_VERSION``) and in ``doc/users/installation.rst``. They should
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at least match. Do they still hold? Make sure ``.travis.yml`` is testing
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these minimum dependencies specifically.
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* Do a final check on the `nipy buildbot`_
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* If you have travis-ci_ building set up you might want to push the code in its
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current state to a branch that will build, e.g::
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* If you have travis-ci_ building set up on your own fork of nipy you might
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want to push the code in its current state to a branch that will build,
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e.g::
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git branch -D pre-release-test # in case branch already exists
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git co -b pre-release-test
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git push origin pre-release-test
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git push your-github-user pre-release-test
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* Make sure all the ``.c`` generated files are up to date with Cython sources
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with::
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Fix ``setup.py`` to carry across any files that should be in the distribution.
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* Check the documentation doctests pass from
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http://nipy.bic.berkeley.edu/builders/nipy-doc-builder
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* You may have virtualenvs for different python versions. Check the tests
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pass for different configurations. If you have pytox_ and a network
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connection, and lots of pythons installed, you might be able to do::
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tox
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and get tests for python 2.5, 2.6, 2.7, 3.2. I (MB) have my own set of
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virtualenvs installed and I've set them up to run with::
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tox -e python25,python26,python27,python32,np-1.2.1
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The trick was only to define these ``testenv`` sections in ``tox.ini``.
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These two above run with::
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make tox-fresh
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make tox-stale
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respectively.
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The long-hand not-tox way looks like this::
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workon python26
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make sdist-tests
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deactivate
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etc for the different virtualenvs.
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Doing the release
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=================
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builds will appear in http://nipy.bic.berkeley.edu/nipy-dist . Download the
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builds and upload to pypi.
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* Trigger binary builds for OSX from the buildbots ``nipy-bdist-mpkg-2.6``,
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``nipy-bdist-mpkg-2.7``, ``nipy-bdist-mpkg-3.3``. ``egg`` and ``mpkg`` builds
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will appear in http://nipy.bic.berkeley.edu/nipy-dist . Download the eggs and
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upload to pypi.
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* Download the ``mpkg`` builds, maybe with::
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scp -r buildbot@nipy.bic.berkeley.edu:nibotmi/public_html/nipy-dist/*.mpkg .
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Make sure you have `github bdist_mpkg`_ installed, for the root user. For
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each ``mpkg`` directory, run::
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* Trigger binary builds for OSX from travis-ci:
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sudo reown_mpkg nipy-0.3.0.dev-py2.6-macosx10.6.mpkg root admin
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zip -r nipy-0.3.0.dev-py2.6-macosx10.6.mpkg.zip nipy-0.3.0.dev-py2.6-macosx10.6.mpkg
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* https://travis-ci.org/MacPython/nipy-wheels
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* https://github.com/MacPython/nipy-wheels
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Upload the ``mpkg.zip`` files. (At the moment, these don't seem to store the
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scripts - needs more work)
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Upload the resulting wheels to pypi from http://wheels.scipy.org;
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* Tag the release with tag of form ``0.3.0``::
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* Tag the release with tag of form ``0.5.0``::
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git tag -am 'Second main release' 0.3.0
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git tag -am 'Second main release' 0.5.0
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* Now the version number is OK, push the docs to github pages with::
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* Branch to maintenance::
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git co -b maint/0.2.x
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git co -b maint/0.5.x
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Set ``_version_extra`` back to ``.dev`` and bump ``_version_micro`` by 1.
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Thus the maintenance series will have version numbers like - say - '0.2.1.dev'
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until the next maintenance release - say '0.2.1'. Commit. Don't forget to
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Thus the maintenance series will have version numbers like - say - '0.5.1.dev'
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until the next maintenance release - say '0.5.1'. Commit. Don't forget to
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push upstream with something like::
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git push upstream maint/0.2.x --set-upstream
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* Announce to the mailing lists.
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.. _pytox: http://codespeak.net/tox
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.. _setuptools intro: http://packages.python.org/an_example_pypi_project/setuptools.html
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.. _travis-ci: http://travis-ci.org
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doc/users/installation.rst

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=========
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* Python_ 2.6 or later
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* NumPy_ 1.2 or later: Numpy is an array library for Python
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* SciPy_ 0.7 or later: Scipy contains scientific computing libraries based on
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* NumPy_ 1.6 or later: Numpy is an array library for Python
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* SciPy_ 0.9 or later: Scipy contains scientific computing libraries based on
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numpy
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* Sympy_ 0.6.6 or later: Sympy is a symbolic mathematics library for Python. We
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use it for statistical formulae.
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* Sympy_ 0.7.0 or later: Sympy is a symbolic mathematics library for Python.
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We use it for statistical formulae.
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* Nibabel_ 1.2.0 or later. Nibabel loads and saves images in neuroimaging
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formats.
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