AtomSelectionConfigurator.py

#MDANSE : Molecular Dynamics Analysis for Neutron Scattering Experiments
#------------------------------------------------------------------------------------------
#Copyright (C)
#2015- Eric C. Pellegrini Institut Laue-Langevin
#BP 156
#6, rue Jules Horowitz
#38042 Grenoble Cedex 9
#France
#pellegrini[at]ill.fr
#goret[at]ill.fr
#aoun[at]ill.fr
#
#This library is free software; you can redistribute it and/or
#modify it under the terms of the GNU Lesser General Public
#License as published by the Free Software Foundation; either
#version 2.1 of the License, or (at your option) any later version.
#
#This library is distributed in the hope that it will be useful,
#but WITHOUT ANY WARRANTY; without even the implied warranty of
#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
#Lesser General Public License for more details.
#
#You should have received a copy of the GNU Lesser General Public
#License along with this library; if not, write to the Free Software
#Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA

''' 
Created on Mar 30, 2015

@author: Eric C. Pellegrini
'''

import collections
import operator

import numpy

from MDANSE.Framework.UserDefinitions.IUserDefinition import UD_STORE
from MDANSE.Framework.Configurators.IConfigurator import IConfigurator, ConfiguratorError
from MDANSE.Framework.AtomSelectionParser import AtomSelectionParser

# The granularities at which the selection will be performed
LEVELS = collections.OrderedDict()
LEVELS["atom"]     = {"atom" : 0, "atomcluster" : 0, "molecule" : 0, "nucleotidechain" : 0, "peptidechain" : 0, "protein" : 0}
LEVELS["group"]    = {"atom" : 0, "atomcluster" : 1, "molecule" : 1, "nucleotidechain" : 1, "peptidechain" : 1, "protein" : 1}
LEVELS["residue"]  = {"atom" : 0, "atomcluster" : 1, "molecule" : 1, "nucleotidechain" : 2, "peptidechain" : 2, "protein" : 2}
LEVELS["chain"]    = {"atom" : 0, "atomcluster" : 1, "molecule" : 1, "nucleotidechain" : 3, "peptidechain" : 3, "protein" : 3}
LEVELS["molecule"] = {"atom" : 0, "atomcluster" : 1, "molecule" : 1, "nucleotidechain" : 3, "peptidechain" : 3, "protein" : 4}

class AtomSelectionConfigurator(IConfigurator):    
    '''
    This configurator allows the selection of a specific set of atoms on which the analysis will be performed.

    Without any selection, all the atoms stored into the trajectory file will be selected.
    
    To Build an atom selection from the GUI you have to :
    #. Create a workspace based on a MMTK trajectory data
    #. Drag a molecular viewer on it
    #. Drag into the Molecular Viewer the Atom selection plugin
    
    :note: this configurator depends on 'trajectory' configurator to be configured
    '''

    type = 'atom_selection'
    
    _default = "all"
                    
    def configure(self, configuration, value):
        '''
        Configure an input value. 
        
        The value must be a string that can be either an atom selection string or a valid user 
        definition.
        
        :param configuration: the current configuration
        :type configuration: a MDANSE.Framework.Configurable.Configurable object
        :param value: the input value
        :type value: str
        '''
                          
        trajConfig = configuration[self._dependencies['trajectory']]
                
        if not isinstance(value,basestring):
            raise ConfiguratorError("invalid type for atom selection. Must be a string", self)
        
        self["value"] = value
        
        ud = UD_STORE[trajConfig["basename"],"atom_selection",value]        
        # The input value is a user definition: get it and update the configuration
        if ud is not None:
            self.update(ud)
        # The input value is an atom selection string: parse it and update the configuration
        else:
            parser = AtomSelectionParser(trajConfig["instance"])
            self["indexes"] = parser.parse(value)
            self["expression"] = value

        self["n_selected_atoms"] = len(self["indexes"])
        atoms = sorted(trajConfig["universe"].atomList(), key = operator.attrgetter('index'))
        selectedAtoms = [atoms[idx] for idx in self["indexes"]]
        self["elements"] = [[at.symbol] for at in selectedAtoms]

        if self._dependencies.has_key("grouping_level"):
            self.group(selectedAtoms, configuration[self._dependencies['grouping_level']]['value'])
        else:
            self.group(selectedAtoms)
                                 
        self.set_contents()
            
    @staticmethod                                                                                                                        
    def find_parent(atom, level):
        '''
        Retrieve recursively the parent of a given MMTK atom at a given level.
        For example, a level of 1 will return the direct parent of the atom. 
        
        :note: this is a static method
        
        :param atom: the atom for which the parent is searched for
        :type atom: MMTK.Atom object
        :param level: the level of the parent
        :type level: int
        '''
        
        for _ in range(level):
            atom = atom.parent
            
        return atom
    
    def group(self, atoms, level="atom"):
        '''
        Group the selected atoms according to a given granularity and update
        the configurator with the grouping results.
        
        :param atoms: the atoms for 
        :type atoms: list of MMTK.Atom
        :param level: the level of granularity at which the atoms should be grouped
        :type level: str
        '''
                                        
        groups = collections.OrderedDict()
        
        for at in atoms:
            lvl = LEVELS[level][at.topLevelChemicalObject().__class__.__name__.lower()]
            parent = self.find_parent(at,lvl)        
            groups.setdefault(parent,[]).append(at.index)
        
        self["groups"] = groups.values()
            
        self["n_groups"] = len(self["groups"])
        
        if level != "atom":
            self["elements"] = [["dummy"]]*self["n_groups"]
                                        
        self["level"] = level
                
        self.set_contents()
                        
    def set_contents(self):
        '''
        Sets the contents of the atom selection.
        '''
                    
        self["contents"] = collections.OrderedDict()
        self['index_to_symbol'] = collections.OrderedDict()
        for i, group in enumerate(self["elements"]):
            for j, el in enumerate(group):
                self["contents"].setdefault(el,[]).append(self["groups"][i][j])
                self['index_to_symbol'][self["groups"][i][j]] = el
                 
        for k,v in self["contents"].items():
            self["contents"][k] = numpy.array(v,dtype=numpy.int32)
            
        self["n_atoms_per_element"] = dict([(k,len(v)) for k,v in self["contents"].items()])              
        self['n_selected_elements'] = len(self["contents"])
                        
    def get_information(self):
        '''
        Returns some informations about this configurator.
        
        :return: the information about this configurator
        :rtype: str
        '''
        
        info = []
        info.append("Number of selected atoms:%d\n" % self["n_selected_atoms"])
        info.append("Level of selection:%s\n" % self["level"])
        info.append("Number of selected groups:%d\n" % self["n_groups"])
        info.append("Selected elements:%s\n" % self["contents"].keys())
        
        return "".join(info)