Metadata-Version: 2.1
Name: avtraj
Version: 0.0.6
Summary: A tool to calculate FRET observables for MD trajectories by accessible volume calculations.Here, spatial density of flexible coupled dyes/labels is approximated by the sterically allowedspace for labels modeled by ellipsoids attached by a flexible cylinder.
Home-page: https://github.com/Fluorescence-Tools/avtraj
Author: Thomas-Otavio Peulen
Author-email: thomas.otavio.peulen@gmail.com
License: LGPLv2.1
Description: # avtraj
        MDTrajFPS is tool to calculate FRET observables from MD-trajectories.
        
        MDTrajFPS depends on MDTraj and utilizes the original libraries published together with the "FRET Positioning System" (FPS) in Nature Methods 9, 1218–1225 (2012) "A toolkit and benchmark study for FRET-restrained high-precision structural modeling". 
        
        The implicit model of the label is being improved to support the latest developments 
        [![DOI for Citing COSB](https://img.shields.io/badge/DOI-10.1016/j.sbi.2016.11.012-blue.svg)](https://doi.org/10.1016/j.sbi.2016.11.012). Hence, the library for the calculation of the implicit labels are currently only provided as pre-compiled binaries for (Windows x32, x64, MacOS, and Linux x32, x64). 
        
        With MDTrajFPS, you can
        
        - Process every MD format supported by MDTraj
        - Calculate FRET efficienes using a fast implicit dye model
        - Save calculated positional distributions of dyes for later vizualization
        
        The original FPS-software package is available at http://www.mpc.hhu.de/software/fps.html.
        
        ## Installation
        
        # Anaconda
        
        ```commandline
        conda --add channels tpeulen
        conda install mdtraj_fps
        ```
        
        
        ##  Code Example
        
        ```python
        import mdtraj as md
        from mdtraj_fps import fps
        
        # First load an MD trajectory by mdtraj
        traj = md.load('./examples/hGBP1_out_3.h5')
        
        # Pass a trajectory to fps.AVTrajectory. This creates an object, which can be 
        # accessed as a list. The objects within the "list" are accessible volumes  
        av_traj = fps.AVTrajectory(traj, '18D', attachment_atom_selection='resSeq 7 and name CB')
        # These accessible volumes can be saved as xyz-file
        av_traj[0].save_xyz('test_344.xyz')
        
        # The dye parameters can either be passed explicitly on creation of the object
        av_traj = fps.AVTrajectory(traj, '18D', attachment_atom_selection='resSeq 7 and name CB', linker_length=25., linker_width=1.5, radius_1=6.0)
        
        # or they can be selected from a predefined set of parameters found in the JSON file dye_definition.json located within
        # the package directory 
        av_traj = fps.AVTrajectory(traj, '18D', attachment_atom_selection='resSeq 7 and name CB', dye_parameter_set='D3Alexa488')
        
        # To calculate a trajectory of distances and distance distributions first a labeling file and a "distance file" 
        # needs to be specified. The distance file contains a set of labeling positions and distances and should be compatible
        # to the labeling files used by the software "Olga". By default the 
        av_dist = fps.AvDistanceTrajectory(traj, './examples/hGBP1_distance.json')
        
        ```
        
        ## Dependencies
        The windows dll requires the following redistributables (not included)
        
        MS-visual studio 2005
        	vcredist_x64
        	vcredist_x86
        
        
        ##  Citations 
        * MDTraj - [![DOI for Citing MDTraj](https://img.shields.io/badge/DOI-10.1016%2Fj.bpj.2015.08.015-blue.svg)](http://doi.org/10.1016/j.bpj.2015.08.015)
        * FPS - [![DOI for Citing FPS](https://img.shields.io/badge/DOI-10.1038/nmeth.2222-blue.svg)](http://doi.org/10.1038/nmeth.2222)
        
        
        ## License
        
        GNU LGPL version 2.1, or at your option a later version of the license.
        Various sub-portions of this library may be independently distributed under
        different licenses. See those files for their specific terms.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Other Environment
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
