Package: libgwas
libGWAS - A GWAS Parser Library for PythonInstallationInstall with PIPManual InstallationSystem RequirementsRunning Unit TestsVirtual EnvMinicondaChangelogInstallationlibGWAS requires python 2.7.x as well as the following libraries:NumPy (version 1.7.2 or later)   www.numpy.orgSciPY (version 0.13.2 or later)  www.scipy.orglibGWAS’s installation will attempt to install these required
components for you, however, it requires that you have write
permission to the installation directory. If you are using a shared
system and lack the necessary privileges to install libraries and
software yourself, you should please see one of the sections,
Miniconda or virtual-env below for instructions on different options
for setting up your own python environement which will exist entirely
under your own control.Installation can be done in two ways:Install with PIPTo install using python’s package manager, pip, simply use the
following command:$pip install libGWASIf you have proper permission to install packages, this will attempt
to download and install all dependencies along with libGWAS itself.Manual InstallationFor users who do not use pip or wish to run the bundled tests as well
as have a local copy of the manuals, manual installation is almost as
easy.For users with Git installed, you can simply clone the sources using
the following command:$git clone https://github.com/edwards-lab/libGWASOr you may visit the website and download the tarball directly from
github:https://github.com/edwards-lab/libGWASOnce you have downloaded the software, simply extract the contents and
run the following command to install it:$python setup.py installIf no errors are reported, it should be installed and ready to use.Regarding PYTHON 3I began the process of updating the code to
work with both python versions 2 and 3, however, there are some real
issues with some library support of version 3 that is discouraging.
So, until those have been resolved, I have no plans to invest further
time toward support for python 3.System RequirementsBecause libGWAS is simply a set of classes and functions, it has no specific
system requirements. However, developers using the library should be aware of
the fact that some parsers, such as the pedigree_parser will require that the
entire dataset be loaded into memory, regardless of the filters that are in
play (i.e. positional filters, such as –from-kb and –to-kb).Running Unit TestslibGWAS comes with a unit test suite which can be run prior to
installation. To run the tests, simply run the following command from
within the root directory of the extracted archive’s contents:$python setup.py testIf no errors are reported, then libGWAS should run correctly on your
system.Virtual EnvVirtual ENV is a powerful too for python programmers and end users
alike as it allows for users to deploy different versions of python
applications without the need for root access to the machine.Because libGWAS requires version 2.7, you’ll need to ensure that your
machine’s python version is in compliance. Virtual Env basically uses
the the system version of python, but creates a user owned environment
wrapper allowing users to install libraries easily without
administrative rights to the machine.For a helpful introduction to VirtualEnv, please have a look at the
tutorial:http://www.simononsoftware.com/virtualenv-tutorial/MinicondaMiniconda is a minimal version of the package manager used by the
Anaconda python distribution. It makes it easy to create local
installations of python with the latest versions of the common
scientific libraries for users who don’t have root access to their
target machines. Basically, when you use miniconda, you’ll be
installing your own version of Python into a directory under your
control which allows you to install anything else you need without
having to submit a helpdesk ticket for administrative assistance.Unlike pip, the folks behind the conda distributions provide binary
downloads of it’s selected library components. As such, only the most
popular libraries, such as pip, NumPY and SciPy, are supported by
conda itself. However, these do not require compilation and may be
easier to get installed than when using pip alone. I have experienced
difficulty installing SciPy through pip and setup tools on our cluster
here at vanderbilt due to non-standard paths for certain required
components, but mini-conda always comes through.Firstly, download and install the appropriate version of miniconda at
the project website. Please be sure to choose the Python 2 version:http://conda.pydata.org/miniconda.htmlWhile it is doing the installation, please allow it to update your
PATH information. If you prefer not to always use this version of
python in the future, simple tell it not to update your .bashrc file
and note the instructions for loading and unloading your new python
environment. Please note that even if you chose to update your .bashrc
file, you will need to follow directions for loading the changes into
your current shell.Once those changes have taken effect, install setuptools and scipy: $conda install pip scipyInstalling SciPy will also force the installation of NumPy, which is
also required for running libGWAS. (setuptools includes easy_install).Once that has been completed successfully, you should be ready to
follow the standard instructions for installing libGWAS.ChangeloglibGWAS.py: 1.0.0 releasedMigrated library out from libGWAS in preparation for release of new analysis program