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Python is available on all Sterrewacht and Lorentz Institute GNU/Linux workstations. In most cases, both python v2 and python v3 are available. Please note that python v2 has reached its end-of-life on 01-01-2020 and therefore is no longer actively supported.
Many common python packages, such as numpy, scipy, and astropy are available to any users regardless of the workstation/server. These are installed either locally to the workstation – usually via the OS package manager – or remotely and exposed to the users by means of the
For local python installations, you can list all installed packages via
python3 -m pip list # or python2 -m pip list
For remote python installations – installations on our software disk – you must first load a python module and then list all packages in that module
module load Python/3.6.6-foss-2018b which python /easybuild/easybuild/fc31/software/Python/3.6.6-foss-2018b/bin/python python -m pip list
If the package you would like to use is not installed at all you have two options:
The two options are described in detail in the sections below.
If you believe that the required package could be useful to other researchers in the Observatory or Lorentz Institute, then you can request its installation via our helpdesk https://helpdesk.strw.leidenuniv.nl/ (STRW) or https://helpdesk.lorentz.leidenuniv.nl/ (Lorentz) giving motivations and detailed instructions on where to find the requested package and its license information. We will notify you when the installation is complete.
There are instances in which you would like to install a python package that is not useful to other researchers in your department and/or you are a developer who wants to try and modify development versions of installed packages or new packages. In other words, if
we advise you follow one of the methods below to install the package yourself. Rest assured though that we can always assist you during the process.
Python 2.6 introduced the possibility of package installations via a “user scheme”. According to this scheme, Python distributions support an alternative install location that is specific to a user. A user's default install location is defined through the `site' module with the variable
site.USER_BASE. In the GNU/Linux OS
site.USER_BASE defaults to
This mode of installation can be turned on by specifying the `–user' option to pip install, for instance
pip install --user SomePackage
To display the value of `site.USER_BASE', type
python[2,3] -m site --user-base
and to show the path to your site-packages directory
python[2,3] -m site --user-site
In the STRW and IL environments the site.USER_BASE variable defaults to a user's
~/.local directory nonetheless it can be customised/updated by modifying the environment variable PYTHONUSERBASE:
# in bash export PYTHONUSERBASE=/somewhere/I/can/write/to pip install --user SomePackage
will install `SomePackage' in
When using the `user' scheme to install packages, it is important to note
–system-site-packages. Nonetheless, pip will never install a package that conflicts with a package in the virtualenv site-packages.
Unfortunately, python's 'user' directory is independent of the operating system version, but most of the compute nodes including the LOFAR cluster, run RedHat Enterprise Linux, which is sufficiently different to cause packages installed on the desktop not to work all the time.
In cases like this, it might be necessary to create a separate python user directory structure for those machines:
Add to your .bashrc something like this:
if [ ! -f /etc/fedora-release ]; then export PYTHONUSERBASE=$HOME/.local-rhel7 fi
For users of the
tcsh shell, add this to your .cshrc in stead:
if (! -f /etc/fedora-release) then setenv PYTHONUSERBASE $HOME/.local-rhel7 endif
And make sure to create that directory ~/.local-rhel7. Now the pip –user commands on RHEL7 machines will install into that newly created directory in stead of the default one used by the desktop systems.
This guide refers to virtualenv version 12.0.7.
virtualenv is a tool that creates isolated Python environments. A python environment is essentially a folder which contains copies of all necessary files needed for a Python project to run. In addition each virtual environment will contain a copy of the utility pip to manage packages. For example, let us suppose you would like to install
pymatlab which is not installed on the departmental workstations, then you could do
$ mkdir python_virt_envs && cd python_virt_envs $ virtualenv --system-site-packages pymatlab
to create a virtual environment (folder) called pymatlab.
Please note that your newly created virtual environment will be a `python2' one if you used `virtualenv' or a
`python3' one if using `virtualenv-3'. Note that if the command `virtualenv-3' is missing you could use
virtualenv -p /usr/bin/python3.
The last step before starting to use the newly generated environment is to activate it, that is to prepend its /bin folder to your $PATH environment variable. This is done by issuing
if you are using csh! To acknowledge the activation of pymatlab, virtualenv will change the terminal prompt ($PS1) to
to emphasize that you are operating in a virtual environment. To install pymatlab (or any other package) locally (in your virtual environment) run
pip install pymatlab
Your virtual environment should now have the same core python packages defined globally for all the Observatory or Lorentz Institute users plus any packages installed in the virtual environment.
In any cases, it is advisable to keep a backup of your virtual environment configuration by creating a list of installed packages
pip freeze > packages.dat
This can help collaborators and fellow developers to reproduce your environment with
pip install -r packages.dat
When you are done working in a virtual environment deactivate it running
At any time, any virtual environment can be destroyed by removing the corresponding folder from the file system by executing
rm -rf ~/python_virt_envs/pymatlab
so do not panic if things do not work, just delete your virtual environment and start all over again.
Finally, it possible to choose which python interpreter to use in your virtual environment and that is done by running virtualenv with the `p' option
virtualenv -p /usr/bin/python3.4 pymatlab
Note: System administrators will not be responsible and/or manage users virtual environments. You are strongly advised to consult the documentation:
Easy Install is a python module (easy_install) that lets you automatically download, build, install, and manage Python packages. By default, easy_install installs python packages into Python’s main site-packages directory, and manages them using a custom .pth file in that same directory. Very often though, a user or developer wants easy_install to install and manage python packages in an alternative location, so to install a package locally type
easy_install -N --user pymatlab
This will install pymatlab in ~/.local/lib ready to be imported in your next python session. Note: If you want to install your package in a different location than ~/.local, then set the environment variable $PYTHONUSERBASE to a custom location, e.g,
Please consult the docs to know more:
python -m easy_install --help
by appending the path of your choice, for instance
echo "/my/home/sweet/home/library" >> ~/.local/lib/python2.7/site-packages/my-super-library.pth
.pth files will be sourced by python provided they are in the right location.
First enable the module package to search also private module directories
module load use.own
the line above will create a $HOME/privatemodules if it does not exist and its path will be searched for the presence of environment modules files.
Let us now install some packages to an arbitrary location and upgrade (only in $PYTHONUSERBASE) an already system-wide installed package
export PYTHONUSERBASE=/somewhere/I/can/write/to pip install --user SomePackage pip install -I --user SomePackageThatWASInstalledSystemwide
Create a file, say `$HOME/privatemodules/super-module' with the following contents
#%Module 1.0 # # # prepend-path PATH /somewhere/I/can/write/to/bin # if executables were installed prepend-path PYTHONPATH /somewhere/I/can/write/to/lib/python2.7/site-packages
module load super-module
and you are ready to use your newly created python environment. Note that is similar procedure can be repeated using python3.
In this example we create a python2 virtual environment in which we will install the latest version of numpy that will use the openBLAS library.
The procedure and paths below will work on any maris node and on the para cluster.
virtualenv py2_numpy_openBLAS source py2_numpy_openBLAS/bin/activate cd py2_numpy_openBLAS mkdir numpy pip install -d numpy numpy && cd numpy tar xzf numpy-X.Y.z.tar.gz cd numpy-X.Y.Z/ cp site.cfg.example site.cfg
site.cfg with your favorite editor such that
[openblas] libraries = openblas library_dirs = /usr/lib64 include_dirs = /usr/include/openblas/ runtime_library_dirs = /usr/lib64
then install numpy
python setup.py install
If the installation is going smoothly you should see
.... openblas_info: FOUND: libraries = ['openblas', 'openblas'] library_dirs = ['/usr/lib64'] language = c define_macros = [('HAVE_CBLAS', None)] runtime_library_dirs = ['/usr/lib64'] .... Installed /some/where/py2_numpy_openBLAS/lib/python2.7/site-packages/numpy-X.Y.Z-py2.7-linux-x86_64.egg
Now that numpy is installed you could also install scipy, for instance
pip install scipy
openBLAS will automatically use multithreading on the basis of the computer resources and the executable. If you wanted more control on multithreading you could either build openBLAS from source by specifying the number of threads or specify the number of threads in your application. If none of the above methods satisfies you, then it is possible to set the environment variable OPENBLAS_NUM_THREADS.
Be careful! Choose the number of threads with care or your application will run slower than a single-threaded one!
If your application is parallelized please build OpenBLAS with USE_OPENMP=1.
If your application is already multi-threaded, it will conflict with OpenBLAS multi-threading. You must
In any cases, please READ the docs.
Occasionally, something in the systemwide directories (e.g
/software/local/lib64/python2.7/site-packages) interferes with your python application. Perhaps you have a code that requires a specific, older, version of numpy or matplotlib. Just installing that version is not always sufficient. The trick is, to set the PYTHONPATH to point first to a directory where you place a private
sitecustomize.py which then overrides the one we have placed in /usr/lib64/python2.7/site-packages (which is where we add the /software directories to the path for everyone). Here is how:
mkdir /some/location/python_custom_dir setenv PYTHONPATH /some/location/python_custom_dir:/usr/lib64/python2.7/site-packages
sitecustomize.py could be something like this:
import sys import site mypath='/usr/lib64/python%s/site-packages' % sys.version[:3] # We want this directory at the start of the path, to enforce the original defaults sys.path.insert(1,mypath) # In order to find also eggs and subdirectories, addsitedir seems necessary: site.addsitedir(mypath, known_paths=None)
Another way of using a private python install (separate versions etc), is to install and use Anaconda/Miniconda. Since these environments can encompass much more than just python, they deserve their own page (especially since they come with their own share of pitfalls).