Source code for MDAnalysis.core.topology

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# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,
# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.
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"""\
Core Topology object --- :mod:`MDAnalysis.core.topology`
========================================================

.. versionadded:: 0.16.0

:class:`Topology` is the core object that holds all topology information.

TODO: Add in-depth discussion.

Notes
-----
For developers: In MDAnalysis 0.16.0 this new topology system was
introduced and discussed as issue `#363`_; this issue contains key
information and discussions on the new system. The issue number *363*
is also being used as a short-hand in discussions to refer to the new
topology system.


.. _`#363`: https://github.com/MDAnalysis/mdanalysis/issues/363

Classes
-------

.. autoclass:: Topology
   :members:
.. autoclass:: TransTable
   :members:

Helper functions
----------------

.. autofunction:: make_downshift_arrays

"""
from __future__ import absolute_import

from six.moves import zip
import numpy as np

from .topologyattrs import Atomindices, Resindices, Segindices
from ..exceptions import NoDataError


# TODO Notes:
#   Could make downshift tables lazily built! This would
#     a) Make these not get built when not used
#     b) Optimise moving multiple atoms between residues as only built once
#     afterwards

#   Could optimise moves by only updating the two parent tables rather than
#   rebuilding everything!


[docs]def make_downshift_arrays(upshift, nparents): """From an upwards translation table, create the opposite direction Turns a many to one mapping (eg atoms to residues) to a one to many mapping (residues to atoms) Parameters ---------- upshift : array_like Array of integers describing which parent each item belongs to nparents : integer Total number of parents that exist. Returns ------- downshift : array_like (dtype object) An array of arrays, each containing the indices of the children of each parent. Length `nparents` + 1 Examples -------- To find the residue to atom mappings for a given atom to residue mapping: >>> atom2res = np.array([0, 1, 0, 2, 2, 0, 2]) >>> make_downshift_arrays(atom2res) array([array([0, 2, 5]), array([1]), array([3, 4, 6]), None], dtype=object) Entry 0 corresponds to residue 0 and says that this contains atoms 0, 2 & 5 Notes ----- The final entry in the return array will be ``None`` to ensure that the dtype of the array is :class:`object`. .. warning:: This means negative indexing should **never** be used with these arrays. """ if not len(upshift): return np.array([], dtype=object) order = np.argsort(upshift) upshift_sorted = upshift[order] borders = [None] + list(np.nonzero(np.diff(upshift_sorted))[0] + 1) + [None] # returns an array of arrays downshift = [] counter = -1 # don't use enumerate, we modify counter in place for x, y in zip(borders[:-1], borders[1:]): counter += 1 # If parent is skipped, eg (0, 0, 2, 2, etc) while counter != upshift[order[x:y][0]]: downshift.append(np.array([], dtype=np.intp)) counter += 1 downshift.append(np.sort(np.array(order[x:y], copy=True, dtype=np.intp))) # Add entries for childless parents at end of range while counter < (nparents - 1): downshift.append(np.array([], dtype=np.intp)) counter += 1 # Add None to end of array to force it to be of type Object # Without this, a rectangular array gets squashed into a single array downshift.append(None) return np.array(downshift, dtype=object)
[docs]class TransTable(object): """Membership tables with methods to translate indices across levels. There are three levels; Atom, Residue and Segment. Each Atom **must** belong in a Residue, each Residue **must** belong to a Segment. When translating upwards, eg finding which Segment a Residue belongs in, a single numpy array is returned. When translating downwards, two options are available; a concatenated result (suffix `_1`) or a list for each parent object (suffix `_2d`). Parameters ---------- n_atoms : int number of atoms in topology n_residues : int number of residues in topology n_segments : int number of segments in topology atom_resindex : 1-D array resindex for each atom in the topology; the number of unique values in this array must be <= `n_residues`, and the array must be length `n_atoms`; giving None defaults to placing all atoms in residue 0 residue_segindex : 1-D array segindex for each residue in the topology; the number of unique values in this array must be <= `n_segments`, and the array must be length `n_residues`; giving None defaults to placing all residues in segment 0 Attributes ---------- n_atoms : int number of atoms in topology n_residues : int number of residues in topology n_segments : int number of segments in topology size tuple describing the shape of the TransTable Methods ------- atoms2residues(aix) Returns the residue index for many atom indices residues2atoms_1d(rix) All atoms in the residues represented by *rix* residues2atoms_2d(rix) List of atom indices for each residue in *rix* residues2segments(rix) Segment indices for each residue in *rix* segments2residues_1d(six) Similar to `residues2atoms_1d` segments2residues_2d(six) Similar to `residues2atoms_2d` atoms2segments(aix) Segment indices for each atom in *aix* segments2atoms_1d(six) Similar to `residues2atoms_1d` segments2atoms_2d(six) Similar to `residues2atoms_2d` """ def __init__(self, n_atoms, n_residues, n_segments, # Size of tables atom_resindex=None, residue_segindex=None, # Contents of tables ): self.n_atoms = n_atoms self.n_residues = n_residues self.n_segments = n_segments # built atom-to-residue mapping, and vice-versa if atom_resindex is None: self._AR = np.zeros(n_atoms, dtype=np.intp) else: self._AR = np.asarray(atom_resindex, dtype=np.intp).copy() if not len(self._AR) == n_atoms: raise ValueError("atom_resindex must be len n_atoms") self._RA = make_downshift_arrays(self._AR, n_residues) # built residue-to-segment mapping, and vice-versa if residue_segindex is None: self._RS = np.zeros(n_residues, dtype=np.intp) else: self._RS = np.asarray(residue_segindex, dtype=np.intp).copy() if not len(self._RS) == n_residues: raise ValueError("residue_segindex must be len n_residues") self._SR = make_downshift_arrays(self._RS, n_segments)
[docs] def copy(self): """Return a deepcopy of this Transtable""" return self.__class__(self.n_atoms, self.n_residues, self.n_segments, atom_resindex=self._AR, residue_segindex=self._RS)
@property def size(self): """The shape of the table, (n_atoms, n_residues, n_segments)""" return (self.n_atoms, self.n_residues, self.n_segments)
[docs] def atoms2residues(self, aix): """Get residue indices for each atom. Parameters ---------- aix : array atom indices Returns ------- rix : array residue index for each atom """ return self._AR[aix]
[docs] def residues2atoms_1d(self, rix): """Get atom indices collectively represented by given residue indices. Parameters ---------- rix : array residue indices Returns ------- aix : array indices of atoms present in residues, collectively """ try: return np.concatenate(self._RA[rix]) except ValueError: # rix is not iterable or empty # don't accidentally return a view! return self._RA[rix].astype(np.intp, copy=True)
[docs] def residues2atoms_2d(self, rix): """Get atom indices represented by each residue index. Parameters ---------- rix : array residue indices Returns ------- raix : list each element corresponds to a residue index, in order given in `rix`, with each element being an array of the atom indices present in that residue """ try: return [self._RA[r].copy() for r in rix] except TypeError: return [self._RA[rix].copy()] # why would this be singular for 2d?
[docs] def residues2segments(self, rix): """Get segment indices for each residue. Parameters ---------- rix : array residue indices Returns ------- six : array segment index for each residue """ return self._RS[rix]
[docs] def segments2residues_1d(self, six): """Get residue indices collectively represented by given segment indices Parameters ---------- six : array segment indices Returns ------- rix : array sorted indices of residues present in segments, collectively """ try: return np.concatenate(self._SR[six]) except ValueError: # six is not iterable or empty # don't accidentally return a view! return self._SR[six].astype(np.intp, copy=True)
[docs] def segments2residues_2d(self, six): """Get residue indices represented by each segment index. Parameters ---------- six : array residue indices Returns ------- srix : list each element corresponds to a segment index, in order given in `six`, with each element being an array of the residue indices present in that segment """ try: return [self._SR[s].copy() for s in six] except TypeError: return [self._SR[six].copy()]
# Compound moves, does 2 translations
[docs] def atoms2segments(self, aix): """Get segment indices for each atom. Parameters ---------- aix : array atom indices Returns ------- rix : array segment index for each atom """ rix = self.atoms2residues(aix) return self.residues2segments(rix)
[docs] def segments2atoms_1d(self, six): """Get atom indices collectively represented by given segment indices. Parameters ---------- six : array segment indices Returns ------- aix : array sorted indices of atoms present in segments, collectively """ rix = self.segments2residues_1d(six) return self.residues2atoms_1d(rix)
[docs] def segments2atoms_2d(self, six): """Get atom indices represented by each segment index. Parameters ---------- six : array residue indices Returns ------- saix : list each element corresponds to a segment index, in order given in `six`, with each element being an array of the atom indices present in that segment """ # residues in EACH rixs = self.segments2residues_2d(six) return [self.residues2atoms_1d(rix) for rix in rixs]
# Move between different groups.
[docs] def move_atom(self, aix, rix): """Move aix to be in rix""" self._AR[aix] = rix self._RA = make_downshift_arrays(self._AR, self.n_residues)
[docs] def move_residue(self, rix, six): """Move rix to be in six""" self._RS[rix] = six self._SR = make_downshift_arrays(self._RS, self.n_segments)
def add_Residue(self, segidx): # segidx - index of parent self.n_residues += 1 self._RA = make_downshift_arrays(self._AR, self.n_residues) self._RS = np.concatenate([self._RS, np.array([segidx])]) self._SR = make_downshift_arrays(self._RS, self.n_segments) return self.n_residues - 1 def add_Segment(self): self.n_segments += 1 # self._RS remains the same, no residues point to the new segment yet self._SR = make_downshift_arrays(self._RS, self.n_segments) return self.n_segments - 1
[docs]class Topology(object): """In-memory, array-based topology database. The topology model of MDanalysis features atoms, which must each be a member of one residue. Each residue, in turn, must be a member of one segment. The details of maintaining this heirarchy, and mappings of atoms to residues, residues to segments, and vice-versa, are handled internally by this object. """ def __init__(self, n_atoms=1, n_res=1, n_seg=1, attrs=None, atom_resindex=None, residue_segindex=None): """ Parameters ---------- n_atoms : int number of atoms in topology. Must be larger then 1 at each level n_residues : int number of residues in topology. Must be larger then 1 at each level n_segments : int number of segments in topology. Must be larger then 1 at each level attrs : TopologyAttr objects components of the topology to be included atom_resindex : array 1-D array giving the resindex of each atom in the system residue_segindex : array 1-D array giving the segindex of each residue in the system """ self.tt = TransTable(n_atoms, n_res, n_seg, atom_resindex=atom_resindex, residue_segindex=residue_segindex) if attrs is None: attrs = [] # add core TopologyAttrs that give access to indices attrs.extend((Atomindices(), Resindices(), Segindices())) # attach the TopologyAttrs self.attrs = [] for topologyattr in attrs: self.add_TopologyAttr(topologyattr)
[docs] def copy(self): """Return a deepcopy of this Topology""" new = self.__class__(1, 1, 1) # copy the tt new.tt = self.tt.copy() # remove indices for attr in self.attrs: if isinstance(attr, (Atomindices, Resindices, Segindices)): continue new.add_TopologyAttr(attr.copy()) return new
@property def n_atoms(self): return self.tt.n_atoms @property def n_residues(self): return self.tt.n_residues @property def n_segments(self): return self.tt.n_segments
[docs] def add_TopologyAttr(self, topologyattr): """Add a new TopologyAttr to the Topology. Parameters ---------- topologyattr : TopologyAttr """ self.attrs.append(topologyattr) topologyattr.top = self self.__setattr__(topologyattr.attrname, topologyattr)
@property def guessed_attributes(self): """A list of the guessed attributes in this topology""" return filter(lambda x: x.is_guessed, self.attrs) @property def read_attributes(self): """A list of the attributes read from the topology""" return filter(lambda x: not x.is_guessed, self.attrs)
[docs] def add_Residue(self, segment, **new_attrs): """ Returns ------- residx of the new Residue Raises ------ NoDataError If not all data was provided. This error is raised before any """ # Check that all data is here before making any changes for attr in self.attrs: if not attr.per_object == 'residue': continue if attr.singular not in new_attrs: missing = (attr.singular for attr in self.attrs if (attr.per_object == 'residue' and attr.singular not in new_attrs)) raise NoDataError("Missing the following attributes for the new" " Residue: {}".format(', '.join(missing))) # Resize topology table residx = self.tt.add_Residue(segment.segindex) # Add new value to each attribute for attr in self.attrs: if not attr.per_object == 'residue': continue newval = new_attrs[attr.singular] attr.values = np.concatenate([attr.values, np.array([newval])]) return residx
def add_Segment(self, **new_attrs): for attr in self.attrs: if attr.per_object == 'segment': if attr.singular not in new_attrs: missing = (attr.singular for attr in self.attrs if (attr.per_object == 'segment' and attr.singular not in new_attrs)) raise NoDataError("Missing the following attributes for the" " new Segment: {}" "".format(', '.join(missing))) segidx = self.tt.add_Segment() for attr in self.attrs: if not attr.per_object == 'segment': continue newval = new_attrs[attr.singular] attr.values = np.concatenate([attr.values, np.array([newval])]) return segidx