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Until now, the Scipy citation was only one paper and not the correct one
(it was the online manual).
With this commit, Scipy is properly cited using the two papers. Also
some modifications in the `tex/src/references.tex' have been done
(remove last page number).
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Until now, name and version of all Python packages were indicated in the
final paper, but not the main paper of them (if it exists).
With this commit, some Python packages (Cython, Matplotlib, Numpy and
Scipy) are now properly acknoledged by citating the source paper.
`mpi4py' is also cited although this package is not yet included into
the pipeline.
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Since we mixed the installation of Python packages with all other
software, it may occur that some Python packages start to be installed
before having installed `unzip'. As a consecuence, they could not be
decompressed and the installation will fail. In particular, tarballs of
Numpy and Setuptools are .zip files.
With this commit, we fix this issue by setting `unzip' as a prerequisit
of Numpy and Setuptools.
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The step where we check the possibility of using `sys/cdefs.h' was still
using `$$' for shell variables (in Make), not `$' (for the shell). This was
corrected.
Also, since Astropy needs two citations, the `,' in the citation command
would conflict with Make's parsing. So we just used an `echo' command to
re-write the version info.
In Astroquery, the prerequisite list was just reordered by length to be
more clear to the eye.
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On some GNU/Linux distros, the compiler is separated into `multilib' mode
(for 32-bit and 64-bit support) and by default the extra component of the
compiler is not installed! In such systems for now, we are just creating
symbolic links to the host's compiler (similar to Mac).
While testing, I noticed that we weren't passing a "$downloader" option to
the downloading script of `dependencies.mk' and `dependencies-python.mk'.
Also, I noticed that the Cython and Python-pkg-config packages didn't have
setuptools as a dependency! Both have now been fixed. Also, Cython's
tarball name is now all small-caps (as in all the other tarballs).
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This work is now merged, I just added the new argument to the `pybuild'
function.
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Until now, management of the software names and versions in the paper was
done manually (a macro had to be defined in `initialize.mk', then used in
`paper.tex', so they had to be manually set in two places). Managing this
was not easy.
To fix this, with this commit, each software building rule's target is a
text file that contains its human-readable name and its version. In the
end, the configure script sorts them by their name and writes them into a
LaTeX input file that we can easily import as a file into the main paper.
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After trying to set the pipeline from scratch with no internet conection
(but all tarballs already downladed), `h5py' Python package complained
about not having access to download `pkgconfig'. After solving this
dependency, it also complained about not having `cython'.
With this commit, we add `pkgconfig' (Python) and `cython' to the
pipeline in order to be able to install `h5py' properly.
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After trying the build a system with no Python library, I noticed that
Python's HDF5 module (`h5py') needs the HDF5 library and OpenMPI (to work
in parallel). So they were added. Finally `h5py' uses the `mpi4py' module
to communicate with OpenMPI, so it was also added. However, for some
reason, mpi4py doesn't work with this version of OpenMPI (as described in
the comments above).
So for now, h5py doesn't use it and can only work on a single thread, while
the HDF5 C library links with OpenMPI with no problem.
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Until this commit, the installation of all Python packages were
done in a separate Makefile.
With this commit, the pipeline install Python packages as part of the
hight level software. All Python packages rules them remain in a
separate Makefile, but this Makefile is included in the high level
dependency `reproduce/src/make/dependencies.mk'.
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We could not get ATLAS shared libraries on Mac (while the static ATLAS
libraries are built and can be used successfully on Mac). So, the
pipeline now builds OpenBLAS, which both Numpy and Scipy can use on Mac
and GNU/Linux.
We also added FFTW as a dependency of Numpy. Altough Numpy is not linking to
FFTW for some reason. However, since FFTW is a low level library used by
many programs, we have kept it as a dependency of Numpy anyway for now.
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The Makefile that build the shared libraries comes from Arch Linux so it
does not work easily on Mac. But the full ATLAS build goes successfully
for static libraries. For now we are disabling shared libraries on Mac.
Python was built explicity with `clang' on Mac.
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Until now, we were simply using the host's GCC for Mac systems. But we
found that except for a single step (to fixing `rpath'), it works on
Mac!!! So, GCC is now part of the Mac build as well.
However, we are still having some problems in building ATLAS on Mac. It
works on GNU/Linux, but not in Mac. So for the time being (just
temporarily), we are avoiding ATLAS (and thus Scipy) on Mac systems. We
just filed an issue on the ATLAS discussion list to hopefully fix the
problem soon.
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In this commit we add `h5py' Python package.
We also include `setuptools' as a main dependency of Python because with the
previous commit it (as well as `pip') is no longer installed with Python.
Numpy version also has been incremented.
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Numpy needs ATLAS as shared libraries. So we also need to build Python with
shared libraries. We also need to define site.cfg for numpy and scipy so we
define a master template:
`reproduce/config/pipeline/dependency-numpy-scipy.cfg'
Also `Openssl' did not have rpath so we added with this commit.
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An initial installation of atlas is now included in the pipeline,
but we are still trying to make it compile and build smoothly. In
the process, we found that GCC also needs some modifications
(for example rpath issues).
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To ensure that we have all the necessary Python dependencies, I done an
offline build and noticed that several packages were also necessary for the
`./configure' step to finish (`libffi', `asn1crypto', `cffi', `jeepney',
`pycparser' and `secretstorage'). With this commit they are added.
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Until now, we were only resetting the Python environment variables in the
actual processing Makefiles, not in the Makefile that build Python and its
modules. They are now added there also.
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Until this commit, we had some of the python packages intalled
but they did not work properly because of the `PYTHONPATH' variables.
That is, the pipeline's `python' was the `python' of the system
instead of the pipeline's `python'.
With this commit this issue has been fixed by setting the correct
`PYTHONPATH'. In this commit we also modify the installation of
`bzip2' because `CMake' was complaining about some libraries built
statically.
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Until now the installation of Python and its packages (numpy, astropy,
astroquery, etc.) were done in the same `makefile'.
With this commit the installation of Python and its packages have been
split and now it is independent of the other programs. The installation
of all Python packages needs to be written explicitely because pip is
not used anymore.
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As matplotlib is a general package for plotting and it is widely
used in science, we have added it to the pipeline.
When installing a dependency of matplotlib `python-dateutil', we
found a conflict in the download of the tarball. This is because
the name has a dash (-) in the middle. In addition, the name starts
with 'python', so it is the same as the python itself. Now it is
possible to install any package with any name, just adding an elif
in before the URL direction.
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All dependencies for building astroquery package have been done.
Until nowthe Python dependencies were built in the same Makefile
as the high level libraries and programs. But, because astroquery
has many dependencies we split the Python and Python packages
installation in a new Makefile.
The installation of differents packages are done using Python and
not pip, because we found some problems when doing it with pip.
Apparently there are some interferences between the packages
installed by the pip of the system and the pip installed as part
of Python in the pipeline.
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