Metadata-Version: 1.1
Name: pyopenms
Version: 2.1.0
Summary: Python wrapper for C++ LCMS library OpenMS
Home-page: http://open-ms.de
Author: Uwe Schmitt
Author-email: uschmitt@mineway.de
License: http://opensource.org/licenses/BSD-3-Clause
Description: 
        .. contents:: **Table of Contents**
        
        ------------
        Introduction
        ------------
        
        This package contains Python bindings for a large part of the OpenMS library
        (http://www.open-ms.de) for mass spectrometry based proteomics.  It thus
        provides providing facile access to a feature-rich, open-source algorithm
        library for mass-spectrometry based proteomics analysis. These Python bindings
        allow raw access to the data-structures and algorithms implemented in OpenMS,
        specifically those for file access (mzXML, mzML, TraML, mzIdentML among
        others), basic signal processing (smoothing, filtering, de-isotoping and
        peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ and
        SWATH analysis tools).
        
        The pyOpenMS package runs - like OpenMS - on Windows, Linux and OSX.
        
        ------------
        Installation
        ------------
        
        We provide binary packages for Python 2.6 and 2.7 on Windows (64 bit) 
        and Linux 64 bit which makes the installation very straightforward with pip.
        For other platforms, please refer to the compilation instructions.
        
        Binary installation
        ===================
        
        The current binaries require numpy **1.7.x**.
        As we distribute the package as binary eggs, you have to use *easy_install*,
        installing with *pip* does not work::
        
            $ easy_install pyopenms
        
        
        Source installation
        ===================
        
        Download the latest OpenMS source from SVN (following `the OpenMS documentation`_), configure and build.
        
        Install Qt and then start with the dependencies of OpenMS itself::
        
            $ git clone https://github.com/OpenMS/contrib.git
            $ cmake .
        
        Now you have to install the dependencies of pyOpenMS:
        
        - Install Python (2.6 or 2.7)
        - Install numpy (On OSX, numpy should already be installed. On GNU/Linux there
          should be packages for numpy (e.g. python-numpy for Ubuntu/Debian). On
          Windows, you can install it from `Christoph Gohlkes webpage`_).
        - Install setuptools, see the `setuptools PyPI page`_ .
        - Use setuptools to install pip, autowrap and nose::
        
           $ easy_install pip
           $ pip install autowrap
           $ pip install nose
        
        - Configure and build pyOpenMS::
        
            $ git clone https://github.com/OpenMS/OpenMS.git
            $ cmake -DPYOPENMS=ON .
            $ make pyopenms
        
        This should build a file like *pyopenms-1.10.1-py2.7-linux-x86_64.egg* the
        folder *./pyOpenMS/dist* of your build directory which you can distribute
        or install it from there::
        
            $ cd pyOpenMS/dist
            $ easy_install pyopenms-1.10.1-py-2.7-linux-x86_64.egg
        
        ------------
        Testing
        ------------
        
        pyOpenMS provides unittests, they are found under ./pyOpenMS/tests/ and can be
        executed using nosetests::
        
            $ python run_nose.py
        
        ------------
        License
        ------------
        
        pyOpenMS is published under the 3-clause BSD licence, see ./pyOpenMS/License.txt
        
        -------------
        Documentation
        -------------
        
        pyOpenMS follows the `OpenMS
        documentation <http://ftp.mi.fu-berlin.de/pub/OpenMS/release-documentation/html/classes.html>`_ very closely. Additionally, there is also a `pyOpenMS
        Manual <http://proteomics.ethz.ch/pyOpenMS_Manual.pdf>`_ available. The online
        manual contains a complete record of every wrapped class and function while the
        documentation of the corresponding class or function can be inferred from the
        OpenMS online documentation.
        
        
        
        .. _the OpenMS documentation: http://ftp.mi.fu-berlin.de/pub/OpenMS/release-documentation/html/classes.html
        .. _Christoph Gohlkes webpage: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
        .. _setuptools PyPI page: https://pypi.python.org/pypi/setuptools
        
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
