Metadata-Version: 1.1
Name: arachnid
Version: 0.1.7
Summary: Single Particle Data Analysis Suite
Home-page: http://www.arachnid.us
Author: Robert Langlois
Author-email: rl2528@columbia.edu
License: GPL
Description: 
        Arachnid
        ========
        
        Arachnid is an open source software package written primarily in Python that processes
        images of macromolecules captured by cryo-electron microscopy (cryo-EM). Arachnid is
        focused on automating the single-particle reconstruction workflow and can be thought 
        of as two subpackages:
        	
        #. Arachnid Prime
        	A SciPy Toolkit (SciKit) that focuses on every step of the single-particle
        	reconstruction workflow up to orientation assignment and classification. This
        	toolkit also includes a set of application scripts and a workflow manager.
        
        #. pySPIDER
        	This subpackage functions as an interface to the SPIDER package. It includes
        	both a library of SPIDER commands and a set of application scripts to run
        	a set of procedures for every step of single-particle reconstruction including
        	orientation assignment but not classification.
        
        Arachnid Prime currently focuses on automating the pre-processing of the image 
        data captured by cryo-EM. For example, Arachnid has the following highlighted applications 
        handle the particle-picking problem:
        
        - AutoPicker: Automated reference-free particle selection
        
        - ViCer: Automated unsupervised particle verification
        
        This software is under development by the `Frank Lab`_ and is licensed under 
        `GPL 2.0 <http://www.arachnid.us/license.html>`_ or later.
        
        For more information, see `http://www.arachnid.us <http://www.arachnid.us>`_.
        
        Alternatively, HTML documentation can be built locally using 
        `python setup.py build_sphinx`, which assumes you have the prerequisite 
        Python libraries. The documents can be found in `build/sphinx/html/`.
        
        How to cite
        ===========
        
        The main reference to cite is:
        
        
        	Langlois, R. E., Ho D. N., Frank, J., 2014. Arachnid: Automated 
        	Image-processing for Electron Microscopy. In Preparation.
        
        See `CITE <http://www.arachnid.us/CITE.html>`_ for more information and downloadable citations.
        
        Important links
        ===============
        
        - Official source code repo: https://github.com/ezralanglois/arachnid
        - HTML documentation (stable release): http://www.arachnid.us/
        - Download releases: https://binstar.org/
        - Issue tracker: https://github.com/ezralanglois/arachnid/issues
        - Mailing list: http://groups.google.com/group/arachnid-general
        - Cite: http://www.arachnid.us/CITE.html
        
        Dependencies
        ============
        
        The required dependencies to build the software are Python >= 2.6,
        setuptools, Numpy >= 1.3, SciPy >= 0.7, matplotlib>=1.1.0, mpi4py>=1.2.2, 
        scikit-learn, scikit-image, psutil, sqlalchemy, mysql-python, PIL, basemap,
        FFTW3 or MKL, and both C/C++ and Fortran compilers.
        
        It is also recommended you install NumPy and SciPy with an optimized Blas
        library such as MKL, ACML, ATLAS or GOTOBlas.
        
        To build the documentation, Sphinx>=1.0.4 is required.
        
        All of these dependencies can be found in a single free binary 
        package: `Anaconda`_.
        
        Install
        =======
        
        The prefered method of installation is to use Anaconda::
        	
        	# If you do not have Anaconda then run the following (assumes bash shell)
        	
        	wget http://repo.continuum.io/miniconda/Miniconda-3.0.0-Linux-x86_64.sh
        	sh Miniconda-3.0.0-Linux-x86_64.sh -b -p $PWD/anaconda
        	export PATH=$PWD/anaconda/bin:$PATH
        	
        	# If you have anaconda or just installed it, then run
        	
        	conda install -c https://conda.binstar.org/ezralanglois arachnid
        
        Alternatives:
        
        	# Install from downloaded source
        	
        	$ python setup.py install --prefix=$HOME
        	
        	# Using Setup tools
        	
        	$ easy_install arachnid
        	
        	# Using PIP
        	
        	$ pip install arachnid
        	
        	# Using Anaconda
        	
        	$ conda install -c https://conda.binstar.org/ezralanglois arachnid
        
        Development
        ===========
        
        You can check out the latest source with the command::
        	
        	git clone https://github.com/ezralanglois/arachnid/arachnid.git
        
        .. _`Frank Lab`: http://franklab.cpmc.columbia.edu/franklab/
        .. _`Anaconda`: https://store.continuum.io/
        
Keywords: cryo-EM particle picking image-processing single-particle reconstruction machine learning
Platform: linux
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Environment :: X11 Applications
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: C
Classifier: Programming Language :: C++
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
