Author: Robert Langlois



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.

	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 <>`_ or later.

For more information, see ` <>`_.

Alternatively, HTML documentation can be built locally using 
`python 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 <>`_ for more information and downloadable citations.

Important links

- Official source code repo:
- HTML documentation (stable release):
- Download releases:
- Issue tracker:
- Mailing list:
- Cite:


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`_.


The prefered method of installation is to use Anaconda::
	# If you do not have Anaconda then run the following (assumes bash shell)
	sh -b -p $PWD/anaconda
	export PATH=$PWD/anaconda/bin:$PATH
	# If you have anaconda or just installed it, then run
	conda install -c arachnid


	# Install from downloaded source
	$ python install --prefix=$HOME
	# Using Setup tools
	$ easy_install arachnid
	# Using PIP
	$ pip install arachnid
	# Using Anaconda
	$ conda install -c arachnid


You can check out the latest source with the command::
	git clone

.. _`Frank Lab`:
.. _`Anaconda`: