Metadata-Version: 2.1
Name: tltsne
Version: 0.0.1
Summary: Time-lagged t-SNE of molecular trajectories
Home-page: https://github.com/spiwokv/tltsne
Author: Vojtech Spiwok
Author-email: spiwokv@vscht.cz
License: UNKNOWN
Description: [![Total alerts](https://img.shields.io/lgtm/alerts/g/spiwokv/tltsne.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/spiwokv/tltsne/alerts/)
        
        # tltsne
        
        Time-lagged t-SNE of molecular trajectories.
        
        Trajectory of molecular simulation is dimensionally reduced by t-distributed stochastic embedding (t-SNE)
        [[1](#References)] and by a version of t-SNE that focuses on slow motions via analysis inspired by time-lagged
        independent component analysis (TICA) [[2,3](#References)].
        
        The input is a trajectory in pdb, xtc, trr, dcd, netcdf or mdcrd (only atoms intended for analysis).
        The second input file is a topology (pdb file with same atoms as in trajectory). Output contains
        frame ID, PCA, TICA, t-SNE and time-lagged t-SNE coordinates.
        
        ## Usage
        
        ```
        usage: tltsne [-h] [-i INFILE] [-p INTOP] [-o OUTFILE] [-nofit NOFIT]
                      [-lagtime LAGTIME] [-pcadim PCADIM] [-ticadim TICADIM]
                      [-maxpcs MAXPCS] [-ncomp NCOMP] [-perplex1 PERPLEX1]
                      [-perplex2 PERPLEX2] [-rate RATE] [-niter NITER] [-exag EXAG]
        
        Time-lagged t-SNE of simulation trajectories, requires scimpy, pyemma, sklearn
        and mdtraj
        
        optional arguments:
          -h, --help          show this help message and exit
          -i INFILE           Input trajectory in pdb, xtc, trr, dcd, netcdf or mdcrd
                              of atoms to be analyzed. No jumps in PBC allowed.
          -p INTOP            Input topology in pdb with atoms to be analyzed.
          -o OUTFILE          Output file.
          -nofit NOFIT        Structure is NOT fit to reference topology if nofit is
                              set to 1 (default 0).
          -lagtime LAGTIME    Lag time in number of frames (default 1).
          -pcadim PCADIM      Number o PCA coordinates to be printed (defaut 2).
          -ticadim TICADIM    Number o TICA coordinates to be printed (defaut 2).
          -maxpcs MAXPCS      Number of TICA coordinates to be passed to t-SNE
                              (default 50).
          -ncomp NCOMP        Number of t-SNE and time-lagged t-SNE coordinates to be
                              printed (defaut 2).
          -perplex1 PERPLEX1  Perplexity of t-SNE (default 10.0
          -perplex2 PERPLEX2  Perplexity of time-lagged t-SNE (default 10.0
          -rate RATE          Learnning rate of t-SNE and time-lagged t-SNE (default
                              200.0).
          -niter NITER        Number of iterations of t-SNE and time-lagged t-SNE
                              (default 1000).
          -exag EXAG          Early exaggeration of t-SNE and time-lagged t-SNE
        ```
        
        ## Install
        
        Install via PIP:
        ```
        pip3 install tltsne
        ```
        (or with `sudo`).
        
        Install from GitHub:
        ```
        git clone https://github.com/spiwokv/tltsne.git
        cd tltsne
        pip3 install .
        ```
        (or with `sudo`).
        
        ## Thanks
        
        * pyemma [[4](#References)]
        * mdtraj [[5](#References)]
        * scipy [[6](#References)]
        * sklearn [[7](#References)]
        
        ## References
        
        1. L.J.P. van der Maaten, G.E. Hinton. [Visualizing High-Dimensional Data Using t-SNE.](https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf) J. Mach. Learn. Res. 2008, 9, 2579-2605.
        
        2. G. Perez-Hernandez, F. Paul, T. Giorgino, G. de Fabritiis, F. Noé: [Identification of slow molecular order parameters for Markov model construction.](https://doi.org/10.1063/1.4811489) J. Chem. Phys. 2013, 139, 015102.
        
        3. C. R. Schwantes and V. S. Pande: [Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9.](https://doi.org/10.1021/ct300878a) J. Chem. Theory Comput. 2013, 9, 2000-2009.
        
        4. http://emma-project.org/
        
        5. http://mdtraj.org/1.9.3/
        
        6. https://www.scipy.org/
        
        7. https://scikit-learn.org/
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Scientific/Engineering :: Chemistry
Description-Content-Type: text/markdown
