AtomsListConfigurator.py 6.48 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
#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 numpy

from MDANSE.Framework.UserDefinitionsStore import UD_STORE
from MDANSE.Framework.Configurators.IConfigurator import IConfigurator
from MDANSE.MolecularDynamics.Trajectory import find_atoms_in_molecule

class AtomsListConfigurator(IConfigurator):    
    '''
    This configurator allows of a given list of atom names.

    The atoms has to belong to the same molecule.
        
    :note: this configurator depends on 'trajectory'
    '''

    type = 'atoms_list'
    
    _default = None
                    
    def __init__(self, name, nAtoms=2, **kwargs):
        '''
        Initializes the configurator.

        :param name: the name of the configurator as it will appear in the configuration.
        :type name: str
        :param nAtoms: the (exact) number of atoms of the list.
        :type nAtoms: int
        '''
        
        IConfigurator.__init__(self, name, **kwargs)
        
        self._nAtoms = nAtoms
        
    @property
    def nAtoms(self):
        
        return self._nAtoms
    
    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 UD_STORE.has_definition(trajConfig["basename"],"%d_atoms_list" % self._nAtoms,value): 
            molecule,atoms = UD_STORE.get_definition(trajConfig["basename"],"%d_atoms_list" % self._nAtoms,value)
        else:
            molecule,atoms=value

        self["value"] = value

        indexes = [find_atoms_in_molecule(trajConfig['instance'].universe,molecule, at, True) for at in atoms]
          
        self['atoms'] = zip(*indexes)      
        
        self['n_values'] = len(self['atoms'])
            
    @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 the atom selection.
        
        :return: the information about the atom selection.
        :rtype: str
        '''

        if not self.has_key("n_selected_atoms"):
            return "No configured yet"
        
        info = []
        info.append("Number of selected atoms:%d" % self["n_selected_atoms"])
        info.append("Level of selection:%s" % self["level"])
        info.append("Number of selected groups:%d" % self["n_groups"])
        info.append("Selected elements:%s" % self["contents"].keys())
        
        return "\n".join(info)