Metadata-Version: 1.1
Name: anncolvar
Version: 0.2
Summary: Coding collective variables by artificial neural networks
Home-page: https://github.com/spiwokv/anncolvar
Author: Vojtech Spiwok, 
Author-email: spiwokv@vscht.cz
License: MIT
Description: [![PyPI](https://img.shields.io/pypi/v/anncolvar.svg)](https://pypi.org/project/anncolvar/)
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        # anncolvar
        Collective variables by artificial neural networks
        
        ```
        usage: anncolvar [-h] [-i INFILE] [-p INTOP] [-c COLVAR] [-col COL]
                         [-boxx BOXX] [-boxy BOXY] [-boxz BOXZ] [-nofit NOFIT]
                         [-testset TESTSET] [-shuffle SHUFFLE] [-layers LAYERS]
                         [-layer1 LAYER1] [-layer2 LAYER2] [-layer3 LAYER3]
                         [-actfun1 ACTFUN1] [-actfun2 ACTFUN2] [-actfun3 ACTFUN3]
                         [-optim OPTIM] [-loss LOSS] [-epochs EPOCHS] [-batch BATCH]
                         [-o OFILE] [-model MODELFILE] [-plumed PLUMEDFILE]
        
        Artificial neural network learning of collective variables of molecular
        systems, requires numpy, keras and mdtraj
        
        optional arguments:
          -h, --help          show this help message and exit
          -i INFILE           Input trajectory in pdb, xtc, trr, dcd, netcdf or mdcrd,
                              WARNING: the trajectory must be 1. centered in the PBC
                              box, 2. fitted to a reference structure and 3. must
                              contain only atoms to be analysed!
          -p INTOP            Input topology in pdb, WARNING: the structure must be 1.
                              centered in the PBC box and 2. must contain only atoms
                              to be analysed!
          -c COLVAR           Input collective variable file in text formate, must
                              contain the same number of lines as frames in the
                              trajectory
          -col COL            The index of the column containing collective variables
                              in the input collective variable file
          -boxx BOXX          Size of x coordinate of PBC box (from 0 to set value in
                              nm)
          -boxy BOXY          Size of y coordinate of PBC box (from 0 to set value in
                              nm)
          -boxz BOXZ          Size of z coordinate of PBC box (from 0 to set value in
                              nm)
          -nofit NOFIT        Disable fitting, the trajectory must be properly fited
                              (default False)
          -testset TESTSET    Size of test set (fraction of the trajectory, default =
                              0.1)
          -shuffle SHUFFLE    Shuffle trajectory frames to obtain training and test
                              set (default True)
          -layers LAYERS      Number of hidden layers (allowed values 1-3, default =
                              1)
          -layer1 LAYER1      Number of neurons in the first encoding layer (default =
                              256)
          -layer2 LAYER2      Number of neurons in the second encoding layer (default
                              = 256)
          -layer3 LAYER3      Number of neurons in the third encoding layer (default =
                              256)
          -actfun1 ACTFUN1    Activation function of the first layer (default =
                              sigmoid, for options see keras documentation)
          -actfun2 ACTFUN2    Activation function of the second layer (default =
                              linear, for options see keras documentation)
          -actfun3 ACTFUN3    Activation function of the third layer (default =
                              linear, for options see keras documentation)
          -optim OPTIM        Optimizer (default = adam, for options see keras
                              documentation)
          -loss LOSS          Loss function (default = mean_squared_error, for options
                              see keras documentation)
          -epochs EPOCHS      Number of epochs (default = 100, >1000 may be necessary
                              for real life applications)
          -batch BATCH        Batch size (0 = no batches, default = 256)
          -o OFILE            Output file with original and approximated collective
                              variables (txt, default = no output)
          -model MODELFILE    Prefix for output model files (experimental, default =
                              no output)
          -plumed PLUMEDFILE  Output file for Plumed (default = plumed.dat)
        
        ```
         
        
Keywords: artificial neural networks molecular dynamics simulation
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Chemistry
