= Installation procedures for the imminent numpy ANUGA release = This page describes the procedures used to install ANUGA with the support packages which allow you to run numpy ANUGA on Ubuntu 8.10 (32 or 64 bit) and 32 bit Windows (XP or Vista). This page will exist until the numpy release is finalised, at which time these procedures will be moved to the ANUGA installation guide. The procedures below use various support packages such as '''numpy'''. The files containing the packages may be downloaded from [https://sourceforge.net/projects/anuga/ SourceForge] in the [https://sourceforge.net/project/showfiles.php?group_id=172848&package_id=326049 numpy_support_software] package. Source package installations require you to unpack a *.tgz file. It doesn't matter ''where'' you do this, as you don't need the unpacked directory after a successful installation. You could use /tmp, for instance. These procedures were written before the numpy trunk became the main branch in the repository. Any paths to numpy code mentioned here will be those in the old system. After numpy code goes into the main branch this document will be amended, but you should check the ANUGA wiki [https://datamining.anu.edu.au/anuga front page] for announcements. As always, the ANUGA user's list can be used to get help: mailto:anuga-user@lists.sourceforge.net === Ubuntu 8.10 - 32 bit === Using the Ubuntu Synaptic package manager, install the following packages: {{{ subversion python-dev python-profiler g++ python-psyco python-numpy }}} Of course, if you prefer using the console, you could do: {{{ sudo install subversion }}} and so on. Next, you need to install NetCDF 4.0.1 from the source package: {{{ tar xzf netcdf.tar.gz cd netcdf-4.0.1 ./configure make check sudo make install }}} Now install !ScientificPython 2.9.0: {{{ tar xzf ScientificPython-2.9.0.tar.gz cd ScientificPython-2.9.0 python setup.py build --numpy sudo python setup.py install }}} And finally, install matplotlib through Synaptic: {{{ python-matplotlib }}} === Ubuntu 8.10 - 64 bit === Using the Ubuntu Synaptic package manager, install the following packages: {{{ subversion python-dev python-profiler g++ python-numpy }}} Of course, if you prefer using the console, you could do: {{{ sudo install subversion }}} and so on. You need to specify a special flag for source package builds: {{{ export CFLAGS=-fPIC }}} Note: you must execute the above console command in any terminal in which you do the NetCDF and !ScientificPython source builds (below). Next, you need to install NetCDF 4.0.1 from the source package: {{{ tar xzf netcdf.tar.gz cd netcdf-4.0.1 ./configure make check sudo make install }}} Now install !ScientificPython 2.9.0: {{{ tar xzf ScientificPython-2.9.0.tar.gz cd ScientificPython-2.9.0 python setup.py build --numpy sudo python setup.py install }}} And finally, install matplotlib through Synaptic: {{{ python-matplotlib }}} === Windows === Install python 2.5: {{{ execute python-2.5.4.msi }}} and add ';C:\python25' to the end of the PATH environmental variable. Install the MinGW package (requires an internet connection) by: {{{ execute MinGW-5.1.4.exe . install MinGW base tools . g++ compiler . MinGW Make }}} and add ';C:\MinGW\bin' to the end of the PATH environmental variable. Install numpy: {{{ execute numpy-1.3.0-win32-superpack-python2.5.exe }}} install NetCDF4: {{{ execute netCDF_binary_4.0.exe }}} and add ';C:\netcdf4\bin' to the end of the PATH environmental variable Install !ScientificPython: {{{ execute ScientificPython-2.9.0.win32-py2.5.exe }}} install psyco: {{{ execute psyco-1.6.win32-py25.exe }}} This is not strictly required, but it's small and speeds up your ANUGA system. Install matplotlib: {{{ execute matplotlib-0.98.5.2.win32-py2.5.exe }}} = Testing the installation = === Ubuntu === Before testing, you must have installed the ANUGA system. How to get the source code is documented elsewhere, but to be brief, you can either download the latest '''numpy''' release package and unpack it into the suggested place ({{{/usr/lib/python2.5/site-packages}}}), or you can get the numpy source tree through subversion. If you get the numpy ANUGA system through subversion, you will probably download the {{{branches/numpy}}} source tree to a place of your choice, say {{{~/ANUGA}}}. In this case you need to set the PYTHONPATH environment variable to run ANUGA: {{{ cd ~/ANUGA svn co https://datamining.anu.edu.au/svn/ga/branches/numpy svn co https://datamining.anu.edu.au/svn/ga/branches/numpy_anuga_validation # need to validate ANUGA export PYTHONPATH=~/ANUGA/numpy }}} Note that the PYTHONPATH environment variable needs to be persistent, so you might consider putting the above command in the appropriate file (~/.bashrc, etc). To test your ANUGA install, do: {{{ cd ~/ANUGA/numpy/anuga python compile_all.py python test_all.py }}} To validate ANUGA, do: {{{ cd ~/ANUGA/numpy_anuga_validation/automated_validation_tests python validate_all.py }}} === Windows === Before testing, you must have installed the ANUGA system. How to get the source code is documented elsewhere, but to be brief, you can either download the latest '''numpy''' release package and unpack it into the suggested place ({{{C:\Python25\Lib\site-packages}}}), or you can get the numpy source tree through subversion. Follow the test and validation precodures in the current installation guide if you go this way. If you get the numpy ANUGA system through subversion, you will probably download the {{{branches/numpy}}} source tree to a place of your choice, say {{{C:\ANUGA}}}. In this case you need to set the PYTHONPATH environment variable to run ANUGA: {{{ cd C:\ANUGA checkout https://datamining.anu.edu.au/svn/ga/branches/numpy checkout https://datamining.anu.edu.au/svn/ga/branches/numpy_anuga_validation # needed to validate ANUGA set PYTHONPATH environment variable to C:\ANUGA\numpy }}} The Windows Tortoise SVN client can be used to access the repository. To test your ANUGA install, do: {{{ cd C:\ANUGA\numpy\anuga python compile_all.py python test_all.py }}} To validate ANUGA, do: {{{ cd C:\ANUGA\numpy_anuga_validation\automated_validation_tests python validate_all.py }}}